diff --git a/.agents/skills/db-crud-patterns/SKILL.md b/.agents/skills/db-crud-patterns/SKILL.md new file mode 100644 index 000000000..992859d49 --- /dev/null +++ b/.agents/skills/db-crud-patterns/SKILL.md @@ -0,0 +1,247 @@ +--- +name: db-crud-patterns +description: Guides DB model design, CRUD classes, and manager loading for persisted pluggable config in ktem — vendor column separation, spec as kwargs-only JSON, BaseCRUD context manager, and migrating away from __type__ in spec. Use when adding DB tables, writing CRUD for model pools, refactoring managers, or migrating legacy deserialize/__type__ query logic. +--- + +# DB & CRUD Patterns + +Follow the LLM and embedding stack as the reference when persisting +pluggable modules (model pools) in `libs/ktem/`. + +Reference implementations: + +- `ktem/db/models/llm.py` + `ktem/db/cruds/llm.py` + `ktem/llms/manager.py` +- `ktem/db/models/embedding.py` + `ktem/db/cruds/embedding.py` + `ktem/embeddings/manager.py` +- `ktem/rerankings/manager.py` — same load pattern + +Pair with: [pluggable-modules-creating](../pluggable-modules-creating/SKILL.md) +for the kotaemon-side factory/registry. + +## Core principle — separate type from data + +``` +vendor column → which class to instantiate (factory key) +spec column → constructor kwargs only (plain JSON) +``` + +Never embed `"__type__"` in `spec`. Never `deepcopy` + `del params["__type__"]`. + +```python +# Load — one expression, no key surgery +model = LLMFactory.get_cls(item.vendor)(**item.spec) +``` + +## Checklist — new persisted pool + +``` +- [ ] 1. Abstract DB model: name, vendor, spec, default +- [ ] 2. Concrete table in ktem/db/models/tables.py +- [ ] 3. CRUD class extending BaseCRUD +- [ ] 4. Manager: load via Factory.get_cls(item.vendor)(**item.spec) +- [ ] 5. UI: pass vendor separately on create/update +- [ ] 6. Migration script if replacing __type__-in-spec rows +``` + +## 1. DB model + +```python +from typing import Any + +from sqlalchemy import JSON, Boolean, String +from sqlalchemy.orm import Mapped, mapped_column + +from kotaemon.llms.chats.factory import LLMVendor + +from .base import Base + + +class BaseLLM(Base): + __abstract__ = True + + name: Mapped[str] = mapped_column(String, primary_key=True) + vendor: Mapped[LLMVendor] = mapped_column(String) + spec: Mapped[dict[str, Any]] = mapped_column(JSON, default=dict) + default: Mapped[bool] = mapped_column(Boolean, default=False) + + @property + def ui(self) -> dict[str, Any]: + return {"name": self.name, "vendor": self.vendor, "default": self.default} +``` + +Rules: + +- `vendor` typed as the `str, Enum` from kotaemon factory +- `spec` is kwargs-only — no framework metadata, no `__type__` +- `ui` property exposes what Gradio needs; keep spec out of it + +## 2. CRUD class + +Extend `BaseCRUD` — always use as a context manager: + +```python +with LLMCRUD(engine) as crud: + item = crud.get("my-model") + crud.update("my-model", spec={"api_key": "..."}) +``` + +Standard methods for model pools: + +| Method | Purpose | +| ------------------------------------------------------- | ------------------------------------- | +| `create(name, vendor, spec, *, default=False)` | insert; demote others if default | +| `get(name)` | primary-key lookup | +| `list_all()` | `select(Table)` → list | +| `update(name, *, vendor=None, spec=None, default=None)` | partial update | +| `delete(name)` | remove row | +| `clear_defaults()` | `update(Table).values(default=False)` | + +Signature pattern — `vendor` is always separate from `spec`: + +```python +def create( + self, + name: str, + vendor: LLMVendor, + spec: dict[str, Any], + *, + default: bool = False, +) -> LLMTable: + if not name: + raise ValueError("Name must not be empty") + if self.get(name) is not None: + raise ValueError(f"LLM '{name}' already exists") + if default: + self.clear_defaults() + item = LLMTable(name=name, vendor=vendor, spec=spec, default=default) + self.session.add(item) + self.commit() + self.session.refresh(item) + return item +``` + +CRUD rules: + +- Validate early (`ValueError` on empty name, duplicate, not found) +- `default=True` → call `clear_defaults()` in the same transaction +- Use `self.commit()` (rolls back on failure) — not raw session commits +- Keyword-only booleans (`*, default: bool = False`) + +## 3. Manager loading + +Manager owns the in-memory pool; CRUD owns DB access. + +```python +class LLMManager: + def load(self) -> None: + self._models, self._info, self._default = {}, {}, "" + with LLMCRUD(engine) as crud: + for item in crud.list_all(): + cls = LLMFactory.get_cls(item.vendor) + self._models[item.name] = cls(**item.spec) + self._info[item.name] = item + if item.default: + self._default = item.name + + def add(self, name: str, vendor: LLMVendor, spec: dict, default: bool) -> None: + with LLMCRUD(engine) as crud: + crud.create(name=name, vendor=vendor, spec=spec, default=default) + self.load() + + def update(self, name: str, vendor: LLMVendor, spec: dict, default: bool, + new_name: str = "") -> None: + # rename = delete + add; otherwise crud.update(...) + self.load() +``` + +Manager rules: + +- Reload pool after every mutating operation (`self.load()`) +- `add` / `update` take `vendor` and `spec` as separate args +- No `deserialize`, no spec key surgery in the manager + +## 4. UI integration + +```python +# Dropdown — enum values as plain strings +choices=[v.value for v in LLMFactory.supported_vendors()] + +# Create — coerce UI string to enum +vendor = LLMVendor(selected_vendor) +llms.add(name, vendor=vendor, spec=spec, default=default) + +# Edit — spec is already clean +edit_spec = yaml.dump(item.spec) # no spec.pop("__type__") + +# Save — vendor from row, not from spec +llms.update(name, vendor=item.vendor, spec=spec, default=default) + +# Test connection — merge edited params, construct via factory +params = {**item.spec, **yaml.load(edited_yaml)} +llm = LLMFactory.get_cls(item.vendor)(**params) +``` + +## 5. Migrating legacy **type**-in-spec rows + +When refactoring an existing table: + +```sql +ALTER TABLE llm_table ADD COLUMN vendor VARCHAR; +UPDATE llm_table SET vendor = json_extract(spec, '$.__type__'); +-- then strip __type__ from spec JSON in application code or migration script +``` + +Before (remove this pattern): + +```python +params = deepcopy(item.spec) +del params["__type__"] +model = deserialize(item.spec, safe=False) +``` + +After: + +```python +model = LLMFactory.get_cls(item.vendor)(**item.spec) +``` + +Symptom of wrong schema: any `deepcopy` + `del` on spec keys. Fix the +schema, not the call site. + +## 6. Query patterns + +Use SQLAlchemy 2.0 style via the session on `BaseCRUD`: + +```python +from sqlalchemy import select, update + +# list all +list(self.session.scalars(select(LLMTable)).all()) + +# primary key +self.session.get(LLMTable, name) + +# bulk update +self.session.execute(update(LLMTable).values(default=False)) +``` + +Keep queries inside CRUD — managers call CRUD methods, not raw SQL. + +## Anti-patterns + +| Don't | Do instead | +| ------------------------------------- | ------------------------------------------- | +| `spec["__type__"]` in JSON | `vendor` column | +| `deserialize(spec, safe=False)` | `Factory.get_cls(item.vendor)(**item.spec)` | +| `deepcopy` + `del params["__type__"]` | clean schema with separate vendor | +| CRUD `create(name, spec)` only | `create(name, vendor, spec)` | +| Manager constructs from spec alone | manager uses vendor + spec | +| Raw session outside context manager | `with FooCRUD(engine) as crud:` | + +## Verify + +```bash +python app.py +``` + +Confirm pool loads, UI create/edit/save works, and no `__type__` appears +in new rows. diff --git a/.agents/skills/gradio-app-debugging/SKILL.md b/.agents/skills/gradio-app-debugging/SKILL.md new file mode 100644 index 000000000..9da9a96ec --- /dev/null +++ b/.agents/skills/gradio-app-debugging/SKILL.md @@ -0,0 +1,232 @@ +--- +name: gradio-app-debugging +description: >- + Debug Kotaemon Gradio app by running python app.py, fixing Traceback errors + from terminal logs, and iterating with the user on basic UI smoke tests. + Appends a session log of bugs and fixes. Use when the user asks to debug the + app, test UI after code changes, watch terminal logs, or run an interactive + debug session. +--- + +# Debugging Gradio App + +Interactive debug loop for Kotaemon (`app.py` + Gradio UI). + +## Prerequisites + +- Run from **repo root** (`kotaemon/`). +- Use project venv when available: `.venv/bin/python app.py`. +- **Debug mode must be on** so errors surface in the terminal: + - `KH_GRADIO_DEBUG=true` (project default), or + - `debug=True` passed to `demo.launch()` in `app.py`. +- `debug=True` shows errors in the terminal; it does **not** hot-reload code. + After every code fix, **restart** the server (kill old process → run again). +- Optional faster iteration: `GRADIO_WATCH_DIRS="libs/kotaemon,libs/ktem" gradio app.py` + (requires `app.py` compatible with Gradio reload mode). + +## What to fix vs ignore + +| Signal | Action | +| ------------------------------------------------------------ | ---------------------------------------- | +| `Traceback (most recent call last)` | **Fix** — only this counts as a bug | +| Startup crash before server is up (caused by traceback) | **Fix** | +| `Warning`, `WARN`, `SAWarning`, `DeprecationWarning` | **Ignore** | +| `ERROR` / `Exception` without a traceback | **Ignore** unless user reports broken UI | +| HTTP 500 without traceback in terminal | **Ignore** unless user reports broken UI | +| Browser console noise | **Ignore** (user must paste if relevant) | +| Benign info (model download, Gradio startup, LanceDB create) | **Ignore** | + +**Rule:** Only fix when terminal shows a Python **Traceback**. Do not chase +warnings or noisy log lines. + +## Session log (required) + +At the **start** of each debug session, create a log file: + +``` +.cursor/debug-sessions/YYYY-MM-DD_HH-MM-SS.md +``` + +Use local time or UTC consistently within one session. Also write/overwrite: + +``` +.cursor/debug-sessions/latest.md +``` + +(symlink not required — copy or same content is fine.) + +### Log header (once per session) + +```markdown +# Debug session — YYYY-MM-DD HH:MM:SS + +- **Command:** `.venv/bin/python app.py` (or equivalent) +- **URL:** (fill when server is up) + +## Fixes + +(none yet) +``` + +### Append one block per fix + +After each traceback fix, **append** to the session log: + +```markdown +### Fix N — short title + +- **Trigger:** startup | UI — describe user action if UI +- **Error:** one-line summary (exception type + message) +- **Traceback (excerpt):** +``` + +last 5–10 relevant lines + +``` +- **Root cause:** one sentence +- **Files changed:** `path/to/file.py` +- **Fix:** what changed and why (1–3 sentences) +- **Verified:** restart OK / user retried action OK +``` + +If the session ends with **no** fixes, write `## Fixes\n\nNo tracebacks in this session.` + +The user uses this file later to summarize bugs and fixes — keep entries factual +and concise. + +## Workflow + +``` +- [ ] 0. Create session log file +- [ ] 1. Startup — run app, fix tracebacks until server is healthy +- [ ] 2. UI smoke — user clicks through UI, agent watches logs, fix tracebacks & loop +- [ ] 3. Report — point user to session log + brief summary +``` + +--- + +## Bước 1: Startup + +- Bước 1: hãy chạy python app.py tử terminal. Nếu có bugs thì hãy fix (phải bật chế độ debug để có thể reflect những gì đã sửa). + +### Run + +Start in a **background** terminal (keep it alive for Step 2): + +```bash +cd /path/to/kotaemon +.venv/bin/python app.py +``` + +If a server is already running on the same port, **stop it first** (kill the +old shell/process) before restarting after a fix. + +### Poll until ready or failed + +Use `Await` on the terminal file. Success signals: + +- `Running on local URL: http://127.0.0.1:7860` (port may differ) +- Server listening on 7860, no traceback after startup + +Failure — fix only if traceback present: + +- `Traceback (most recent call last)` (+ following exception lines) + +### Fix loop + +1. Read the **full** traceback from terminal output. +2. Fix the root cause (minimal diff; match project style). +3. **Append fix entry** to session log. +4. Kill the crashed/running server. +5. Re-run `python app.py`. +6. Repeat until startup has no traceback. + +**Do not** claim Step 1 is done while the server is down or still traceback-ing. + +--- + +## Bước 2: UI smoke test with log watch + +- Bước 2: hãy yêu cầu người dùng thao tác với UI các thao tác cơ bản, bạn ngồi canh logs từ terminal, nếu có thì fix và lặp lại. cho tới khi người dùng xong + +### Tell the user + +Share the local URL from Step 1 logs, then ask them to perform **basic +operations** (adapt to visible tabs): + +| Area | Actions | +| ----------- | ------------------------------------------------------------------------- | +| Login | Sign in (default dev creds: `admin` / `admin` when user management is on) | +| Chat | Open Chat tab, send a short message, wait for response | +| Collections | Open file/index tab, list or upload a small test file if available | +| Settings | Open Settings, change a harmless option or open model pools | +| Resources | Open Resources / Users tab if present | +| Help | Open Help tab | + +Ask the user to **report what they clicked** and **what looked wrong** in the +UI, not only errors. + +### Watch logs + +Keep polling the **same** background terminal. + +- **Fix:** `Traceback (most recent call last)` during or after a user action +- **Ignore:** warnings, deprecation notices, SAWarning, benign INFO lines + +### Fix loop + +When logs show a **traceback**: + +1. Note **user action → error** mapping. +2. Fix code (minimal scope). +3. **Append fix entry** to session log. +4. Restart `python app.py` (debug still on). +5. Ask user to **retry the same action**. +6. Confirm no new traceback for that path before moving on. + +Continue until the user says they are **done** with UI testing. + +--- + +## Commands reference + +```bash +# Start (background — preferred for Step 2 log watching) +.venv/bin/python app.py + +# Quick import smoke test (no browser; catches many startup errors faster) +.venv/bin/python -c "from ktem.main import App; App().make(); print('OK')" + +# After touching Python files (optional, before restart) +pre-commit run --files path/to/changed.py +``` + +## Scope rules + +- Fix **only** tracebacks found in this session. +- Do **not** fix warnings unless the user explicitly asks. +- No drive-by refactors. +- Prefer root-cause fixes over silencing errors. +- If the fix needs env/API keys the user lacks, log as **blocked** in session + file and skip that UI path. + +## When stuck + +Report back with: + +- Exact terminal traceback +- User action that triggered it +- Session log path +- Files changed and what was tried +- Whether the server is currently running and on which URL + +Ask the user before: disabling features globally, weakening error handling, or +large architectural changes just to pass smoke tests. + +## Step 3: Report + +When the user is done: + +1. Point to **`.cursor/debug-sessions/latest.md`** (and timestamped copy). +2. Brief summary: count of fixes, server URL, still running or not. +3. **Untested paths** (missing credentials, features disabled, etc.). diff --git a/.agents/skills/pluggable-modules-creating/SKILL.md b/.agents/skills/pluggable-modules-creating/SKILL.md new file mode 100644 index 000000000..7c9d4d3e3 --- /dev/null +++ b/.agents/skills/pluggable-modules-creating/SKILL.md @@ -0,0 +1,173 @@ +--- +name: pluggable-modules-creating +description: Guides creation of new pluggable kotaemon modules (LLM, embedding, reranking, vector store) using dataclass implementations, vendor enums, and factory registries. Use when adding a new provider class, creating factory.py, refactoring away from theflow Param/Node/BaseComponent, or mirroring the llms/embeddings module layout. +--- + +# Creating Pluggable Modules + +Follow the LLM and embedding modules as the reference layout when adding a +new pluggable component type to `libs/kotaemon/`. + +Reference implementations: + +- `kotaemon/llms/chats/` — LLM vendors +- `kotaemon/embeddings/` — embedding vendors +- `kotaemon/rerankings/` — reranking vendors (same pattern) + +## Module layout + +``` +kotaemon// +├── base.py # abstract base + shared behaviour +├── factory.py # Vendor enum + MP_VENDOR_CLS + Factory +├── openai.py # one file per vendor (or group) +└── ... +``` + +## Checklist — new pluggable type + +``` +- [ ] 1. Define base class +- [ ] 2. Implement vendor classes with typed fields +- [ ] 3. Create factory.py (enum + registry + Factory) +``` + +## 1. Base class + +Use `@dataclass(kw_only=True)`. Python native, do not use any extra imports from 3rd packages such as theflow (Param, Node,...). + +```python +from dataclasses import dataclass + +from kotaemon.base.describe import DataclassDescribe, describe_dataclass + + +@dataclass(kw_only=True) +class BaseEmbeddings: + @classmethod + def describe(cls) -> DataclassDescribe: + return describe_dataclass(cls) + + def run(self, text: str) -> list[float]: + raise NotImplementedError +``` + +- Shared logic across unrelated bases → extract to a module-level function + (see `kotaemon/base/describe.py`). + +## 2. Vendor implementation + +Each vendor is a plain `@dataclass` subclass. Fields replace `Param`: + +```python +from dataclasses import dataclass, field + + +@dataclass(kw_only=True) +class OpenAIEmbeddings(BaseEmbeddings): + api_key: str = field(metadata={"description": "API key"}) + model: str = field( + default="text-embedding-3-large", + metadata={"description": "Model name"}, + ) + timeout: float | None = field( + default=None, + metadata={"description": "Request timeout"}, + ) +``` + +Rules: + +- `field(metadata={"description": ...})` feeds UI via `describe_dataclass` +- Dependencies injected via constructor — not `Node(...)` +- Lazy sub-components → `@cached_property`, not `@Node.auto` +- Object construction → `functools.partial` or `make_*()` factory, not `.withx()` + +## 3. Factory registry + +Every pluggable set gets a `factory.py`: + +```python +from enum import Enum + +from .base import BaseEmbeddings +from .openai import OpenAIEmbeddings + + +class EmbeddingVendor(str, Enum): + OPENAI = "OpenAIEmbeddings" # value = class __qualname__ + + +MP_VENDOR_CLS: dict[EmbeddingVendor, type[BaseEmbeddings]] = { + EmbeddingVendor.OPENAI: OpenAIEmbeddings, +} + + +class EmbeddingFactory: + @staticmethod + def get_cls(vendor: EmbeddingVendor | str) -> type[BaseEmbeddings]: + key = EmbeddingVendor(vendor) + if key not in MP_VENDOR_CLS: + raise ValueError(f"Invalid embedding vendor: {vendor!r}") + return MP_VENDOR_CLS[key] + + @staticmethod + def supported_vendors() -> list[EmbeddingVendor]: + return list(MP_VENDOR_CLS.keys()) +``` + +Naming conventions: + +| Item | Convention | +| ---------- | -------------------------------------------------------- | +| Enum | `class FooVendor(str, Enum)` | +| Enum value | class `__qualname__` (e.g. `"OpenAIEmbeddings"`) | +| Registry | `MP_VENDOR_CLS: dict[FooVendor, type[BaseFoo]]` — public | +| Factory | `get_cls(vendor)` + `supported_vendors()` | + +`get_cls` must accept `VendorEnum | str` so legacy DB rows keep working. + +## 4. Adding a new vendor to an existing type + +``` +- [ ] 1. Implement the class in its own file +- [ ] 2. Add enum member: NEW_VENDOR = "NewClassName" +- [ ] 3. Register in MP_VENDOR_CLS +- [ ] 4. Export if needed from package __init__.py +- [ ] 5. UI dropdown picks it up via Factory.supported_vendors() +``` + +No `deserialize`, `import_dotted_string`, or `__type__` in spec dicts. + +## 5. Typed return shapes + +Use `TypedDict` for fixed-key dicts returned from public functions: + +```python +class DataclassDescribe(TypedDict): + type: str + params: dict[str, DataclassParamDesc] +``` + +Avoid `dict[str, Any]` on public APIs when the key schema is known. + +## Anti-patterns + +| Don't | Do instead | +| ---------------------------- | ---------------------------------------------------- | +| `Param(help=...)` | `@dataclass` field + `metadata={"description": ...}` | +| `Node(...)` | constructor argument | +| `@Node.auto(...)` | `@cached_property` | +| `BaseComponent` / `Function` | plain class + narrow Protocol | +| `deserialize(spec)` | `Factory.get_cls(vendor)(**spec)` | +| `serialize(obj)` | plain kwargs dict; type in `vendor` column | +| `.withx()` | `partial()` or named factory | +| duplicate classmethod bodies | module-level function | + +## Verify + +After changes, confirm the app starts and the pool loads: + +```bash +python app.py +``` diff --git a/.agents/skills/python-guidelines/SKILL.md b/.agents/skills/python-guidelines/SKILL.md new file mode 100644 index 000000000..937741fe4 --- /dev/null +++ b/.agents/skills/python-guidelines/SKILL.md @@ -0,0 +1,73 @@ +--- +name: python-guidelines +description: Use when writing, reviewing, or refactoring Python code in this workspace. Applies local Python standards for typing, testing, data modeling, API boundaries, logging, and maintainable module design. +--- + +# Python + +Apply these guidelines when the task materially involves Python code. + +## Priorities + +1. Keep changes scoped to the request. +2. Prefer the simplest implementation that matches the surrounding codebase. +3. Use modern Python features where they improve clarity, not novelty. +4. Make behavior easy to verify with focused tests. + +## Coding Standards + +- Follow Python and PEP guidance unless the codebase already uses a different local convention. +- Keep production code clear and direct; avoid speculative abstractions and single-use indirection. +- Use guard clauses and fail fast when preconditions are invalid. +- Use type hints on public functions, non-trivial internal helpers, and tests. +- Limit the use of global variables to reduce side effects. +- Use comprehensions when they improve readability; do not compress complex logic into one expression. +- Handle expected failure modes explicitly, typically with narrow `try`/`except` blocks. Do not add defensive exception handling for impossible paths. +- Prefer `pathlib` over `os.path` for filesystem paths. + +## Modular Design + +- Prefer small, cohesive modules with clear ownership boundaries. +- Prefer modular design with clear separation of responsibilities such as models, services, controllers, and utilities, unless the codebase already uses a different structure. +- When a module has distinct failure cases, define focused exceptions where they improve call-site clarity. + +## Data and Modeling + +- Separate data containers from behavioral services when that makes the code easier to reason about. +- Prefer placing behavior on the type it naturally belongs to when that avoids duplicated logic across call sites. +- Prefer explicit types and named fields over loosely typed dictionaries for stable internal or external shapes. +- Prefer passing and returning typed objects for non-trivial workflows when that makes interfaces clearer and more stable. +- Prefer explicit modes (Enum, strategy object, registry) over many interacting boolean flags at workflow boundaries. + - Use `enum.StrEnum` (or equivalent) for wire-backed labels, with serialize or display helpers on the enum instead of parallel lookup dicts scattered at call sites. + - Use `dataclasses.dataclass` for simple structured data. + - Use `pydantic.dataclasses.dataclass` when dataclass ergonomics are preferred but validation is still required. + - Use `pydantic.BaseModel` only where validation, serialization, or wire-shape guarantees are actually needed. + +## External APIs and SDKs + +- Type SDK and HTTP boundaries with library-provided or locally defined models where practical. +- Build request payloads from typed objects when that keeps call sites simpler and more reliable. +- Validate shared configuration once, close to settings or initialization, rather than repeating partial checks in consumers. +- Prefer explicit variant handling such as `isinstance` or `match` over `getattr` chains on opaque objects. + +## Logging + +- Use `structlog` for non-trivial workflows when the project already depends on it or structured logs are materially useful. +- Do not replace straightforward local feedback with heavy logging scaffolding unless the task needs it. + +## Testing + +- Add or update tests for behavior changes, bug fixes, and non-trivial refactors. +- Keep tests in `./tests` unless the codebase clearly uses another layout. +- Use `pytest`; do not introduce `unittest`. +- Use `pytest.mark.parametrize` for meaningful input matrices; pass parameter names as tuples. +- Use fixtures to isolate third-party dependencies and prefer `autospec` when mocking. +- Use `pytest.mark.asyncio` for async tests when required by the test stack. +- Type-annotate tests where the project already does so or where it improves clarity. +- Add `__init__.py` files only if the codebase's test/package layout requires them. + +## Documentation + +- Write docstrings for non-obvious public functions, classes, and modules. +- Keep docstrings concise and focused on behavior or contract. +- Update nearby documentation or README content only when the user-facing behavior or setup actually changed. diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index cb4424803..650f70bdf 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -30,11 +30,10 @@ repos: hooks: - id: flake8 args: ["--max-line-length", "88", "--extend-ignore", "E203"] - - repo: https://github.com/myint/autoflake - rev: v1.4 + - repo: https://github.com/PyCQA/autoflake + rev: v2.3.1 hooks: - id: autoflake - additional_dependencies: [setuptools] args: [ "--in-place", diff --git a/app.py b/app.py index 393abb323..e6d87ce87 100644 --- a/app.py +++ b/app.py @@ -1,9 +1,9 @@ import os -from theflow.settings import settings as flowsettings +from ktem.settings_config import app_settings as flowsettings -KH_APP_DATA_DIR = getattr(flowsettings, "KH_APP_DATA_DIR", ".") -KH_GRADIO_SHARE = getattr(flowsettings, "KH_GRADIO_SHARE", False) +KH_APP_DATA_DIR = flowsettings.KH_APP_DATA_DIR +KH_GRADIO_SHARE = flowsettings.KH_GRADIO_SHARE GRADIO_TEMP_DIR = os.getenv("GRADIO_TEMP_DIR", None) # override GRADIO_TEMP_DIR if it's not set if GRADIO_TEMP_DIR is None: @@ -23,4 +23,5 @@ GRADIO_TEMP_DIR, ], share=KH_GRADIO_SHARE, + debug=flowsettings.KH_GRADIO_DEBUG, ) diff --git a/flowsettings.py b/flowsettings.py deleted file mode 100644 index 8bd6db314..000000000 --- a/flowsettings.py +++ /dev/null @@ -1,404 +0,0 @@ -import os -from importlib.metadata import version -from inspect import currentframe, getframeinfo -from pathlib import Path - -from decouple import config -from ktem.utils.lang import SUPPORTED_LANGUAGE_MAP -from theflow.settings.default import * # noqa - -cur_frame = currentframe() -if cur_frame is None: - raise ValueError("Cannot get the current frame.") -this_file = getframeinfo(cur_frame).filename -this_dir = Path(this_file).parent - -# change this if your app use a different name -KH_PACKAGE_NAME = "kotaemon_app" - -KH_APP_VERSION = config("KH_APP_VERSION", None) -if not KH_APP_VERSION: - try: - # Caution: This might produce the wrong version - # https://stackoverflow.com/a/59533071 - KH_APP_VERSION = version(KH_PACKAGE_NAME) - except Exception: - KH_APP_VERSION = "local" - -KH_GRADIO_SHARE = config("KH_GRADIO_SHARE", default=False, cast=bool) -KH_ENABLE_FIRST_SETUP = config("KH_ENABLE_FIRST_SETUP", default=True, cast=bool) -KH_DEMO_MODE = config("KH_DEMO_MODE", default=False, cast=bool) -KH_OLLAMA_URL = config("KH_OLLAMA_URL", default="http://localhost:11434/v1/") - -# App can be ran from anywhere and it's not trivial to decide where to store app data. -# So let's use the same directory as the flowsetting.py file. -KH_APP_DATA_DIR = this_dir / "ktem_app_data" -KH_APP_DATA_EXISTS = KH_APP_DATA_DIR.exists() -KH_APP_DATA_DIR.mkdir(parents=True, exist_ok=True) - -# User data directory -KH_USER_DATA_DIR = KH_APP_DATA_DIR / "user_data" -KH_USER_DATA_DIR.mkdir(parents=True, exist_ok=True) - -# markdown output directory -KH_MARKDOWN_OUTPUT_DIR = KH_APP_DATA_DIR / "markdown_cache_dir" -KH_MARKDOWN_OUTPUT_DIR.mkdir(parents=True, exist_ok=True) - -# chunks output directory -KH_CHUNKS_OUTPUT_DIR = KH_APP_DATA_DIR / "chunks_cache_dir" -KH_CHUNKS_OUTPUT_DIR.mkdir(parents=True, exist_ok=True) - -# zip output directory -KH_ZIP_OUTPUT_DIR = KH_APP_DATA_DIR / "zip_cache_dir" -KH_ZIP_OUTPUT_DIR.mkdir(parents=True, exist_ok=True) - -# zip input directory -KH_ZIP_INPUT_DIR = KH_APP_DATA_DIR / "zip_cache_dir_in" -KH_ZIP_INPUT_DIR.mkdir(parents=True, exist_ok=True) - -# HF models can be big, let's store them in the app data directory so that it's easier -# for users to manage their storage. -# ref: https://huggingface.co/docs/huggingface_hub/en/guides/manage-cache -os.environ["HF_HOME"] = str(KH_APP_DATA_DIR / "huggingface") -os.environ["HF_HUB_CACHE"] = str(KH_APP_DATA_DIR / "huggingface") - -# doc directory -KH_DOC_DIR = this_dir / "docs" - -KH_MODE = "dev" -KH_SSO_ENABLED = config("KH_SSO_ENABLED", default=False, cast=bool) - -KH_FEATURE_CHAT_SUGGESTION = config( - "KH_FEATURE_CHAT_SUGGESTION", default=False, cast=bool -) -KH_FEATURE_USER_MANAGEMENT = config( - "KH_FEATURE_USER_MANAGEMENT", default=True, cast=bool -) -KH_USER_CAN_SEE_PUBLIC = None -KH_FEATURE_USER_MANAGEMENT_ADMIN = str( - config("KH_FEATURE_USER_MANAGEMENT_ADMIN", default="admin") -) -KH_FEATURE_USER_MANAGEMENT_PASSWORD = str( - config("KH_FEATURE_USER_MANAGEMENT_PASSWORD", default="admin") -) -KH_ENABLE_ALEMBIC = False -KH_DATABASE = f"sqlite:///{KH_USER_DATA_DIR / 'sql.db'}" -KH_FILESTORAGE_PATH = str(KH_USER_DATA_DIR / "files") -KH_WEB_SEARCH_BACKEND = ( - "kotaemon.indices.retrievers.tavily_web_search.WebSearch" - # "kotaemon.indices.retrievers.jina_web_search.WebSearch" -) - -KH_DOCSTORE = { - # "__type__": "kotaemon.storages.ElasticsearchDocumentStore", - # "__type__": "kotaemon.storages.SimpleFileDocumentStore", - "__type__": "kotaemon.storages.LanceDBDocumentStore", - "path": str(KH_USER_DATA_DIR / "docstore"), -} -KH_VECTORSTORE = { - # "__type__": "kotaemon.storages.LanceDBVectorStore", - "__type__": "kotaemon.storages.ChromaVectorStore", - # "__type__": "kotaemon.storages.MilvusVectorStore", - # "__type__": "kotaemon.storages.QdrantVectorStore", - "path": str(KH_USER_DATA_DIR / "vectorstore"), -} -KH_LLMS = {} -KH_EMBEDDINGS = {} -KH_RERANKINGS = {} - -# populate options from config -if config("AZURE_OPENAI_API_KEY", default="") and config( - "AZURE_OPENAI_ENDPOINT", default="" -): - if config("AZURE_OPENAI_CHAT_DEPLOYMENT", default=""): - KH_LLMS["azure"] = { - "spec": { - "__type__": "kotaemon.llms.AzureChatOpenAI", - "temperature": 0, - "azure_endpoint": config("AZURE_OPENAI_ENDPOINT", default=""), - "api_key": config("AZURE_OPENAI_API_KEY", default=""), - "api_version": config("OPENAI_API_VERSION", default="") - or "2024-02-15-preview", - "azure_deployment": config("AZURE_OPENAI_CHAT_DEPLOYMENT", default=""), - "timeout": 20, - }, - "default": False, - } - if config("AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT", default=""): - KH_EMBEDDINGS["azure"] = { - "spec": { - "__type__": "kotaemon.embeddings.AzureOpenAIEmbeddings", - "azure_endpoint": config("AZURE_OPENAI_ENDPOINT", default=""), - "api_key": config("AZURE_OPENAI_API_KEY", default=""), - "api_version": config("OPENAI_API_VERSION", default="") - or "2024-02-15-preview", - "azure_deployment": config( - "AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT", default="" - ), - "timeout": 10, - }, - "default": False, - } - -OPENAI_DEFAULT = "" -OPENAI_API_KEY = config("OPENAI_API_KEY", default=OPENAI_DEFAULT) -GOOGLE_API_KEY = config("GOOGLE_API_KEY", default="your-key") -IS_OPENAI_DEFAULT = len(OPENAI_API_KEY) > 0 and OPENAI_API_KEY != OPENAI_DEFAULT - -if OPENAI_API_KEY: - KH_LLMS["openai"] = { - "spec": { - "__type__": "kotaemon.llms.ChatOpenAI", - "temperature": 0, - "base_url": config("OPENAI_API_BASE", default="") - or "https://api.openai.com/v1", - "api_key": OPENAI_API_KEY, - "model": config("OPENAI_CHAT_MODEL", default="gpt-4o-mini"), - "timeout": 20, - }, - "default": IS_OPENAI_DEFAULT, - } - KH_EMBEDDINGS["openai"] = { - "spec": { - "__type__": "kotaemon.embeddings.OpenAIEmbeddings", - "base_url": config("OPENAI_API_BASE", default="https://api.openai.com/v1"), - "api_key": OPENAI_API_KEY, - "model": config( - "OPENAI_EMBEDDINGS_MODEL", default="text-embedding-3-large" - ), - "timeout": 10, - "context_length": 8191, - }, - "default": IS_OPENAI_DEFAULT, - } - -VOYAGE_API_KEY = config("VOYAGE_API_KEY", default="") -if VOYAGE_API_KEY: - KH_EMBEDDINGS["voyageai"] = { - "spec": { - "__type__": "kotaemon.embeddings.VoyageAIEmbeddings", - "api_key": VOYAGE_API_KEY, - "model": config("VOYAGE_EMBEDDINGS_MODEL", default="voyage-3-large"), - }, - "default": False, - } - KH_RERANKINGS["voyageai"] = { - "spec": { - "__type__": "kotaemon.rerankings.VoyageAIReranking", - "model_name": "rerank-2", - "api_key": VOYAGE_API_KEY, - }, - "default": False, - } - -if config("LOCAL_MODEL", default=""): - KH_LLMS["ollama"] = { - "spec": { - "__type__": "kotaemon.llms.ChatOpenAI", - "base_url": KH_OLLAMA_URL, - "model": config("LOCAL_MODEL", default="qwen2.5:7b"), - "api_key": "ollama", - }, - "default": False, - } - KH_LLMS["ollama-long-context"] = { - "spec": { - "__type__": "kotaemon.llms.LCOllamaChat", - "base_url": KH_OLLAMA_URL.replace("v1/", ""), - "model": config("LOCAL_MODEL", default="qwen2.5:7b"), - "num_ctx": 8192, - }, - "default": False, - } - - KH_EMBEDDINGS["ollama"] = { - "spec": { - "__type__": "kotaemon.embeddings.OpenAIEmbeddings", - "base_url": KH_OLLAMA_URL, - "model": config("LOCAL_MODEL_EMBEDDINGS", default="nomic-embed-text"), - "api_key": "ollama", - }, - "default": False, - } - KH_EMBEDDINGS["fast_embed"] = { - "spec": { - "__type__": "kotaemon.embeddings.FastEmbedEmbeddings", - "model_name": "BAAI/bge-base-en-v1.5", - }, - "default": False, - } - -# additional LLM configurations -KH_LLMS["claude"] = { - "spec": { - "__type__": "kotaemon.llms.chats.LCAnthropicChat", - "model_name": "claude-3-5-sonnet-20240620", - "api_key": "your-key", - }, - "default": False, -} -KH_LLMS["google"] = { - "spec": { - "__type__": "kotaemon.llms.chats.LCGeminiChat", - "model_name": "gemini-1.5-flash", - "api_key": GOOGLE_API_KEY, - }, - "default": not IS_OPENAI_DEFAULT, -} -KH_LLMS["groq"] = { - "spec": { - "__type__": "kotaemon.llms.ChatOpenAI", - "base_url": "https://api.groq.com/openai/v1", - "model": "llama-3.1-8b-instant", - "api_key": "your-key", - }, - "default": False, -} -KH_LLMS["cohere"] = { - "spec": { - "__type__": "kotaemon.llms.chats.LCCohereChat", - "model_name": "command-r-plus-08-2024", - "api_key": config("COHERE_API_KEY", default="your-key"), - }, - "default": False, -} -KH_LLMS["mistral"] = { - "spec": { - "__type__": "kotaemon.llms.ChatOpenAI", - "base_url": "https://api.mistral.ai/v1", - "model": "ministral-8b-latest", - "api_key": config("MISTRAL_API_KEY", default="your-key"), - }, - "default": False, -} - -# additional embeddings configurations -KH_EMBEDDINGS["cohere"] = { - "spec": { - "__type__": "kotaemon.embeddings.LCCohereEmbeddings", - "model": "embed-multilingual-v3.0", - "cohere_api_key": config("COHERE_API_KEY", default="your-key"), - "user_agent": "default", - }, - "default": False, -} -KH_EMBEDDINGS["google"] = { - "spec": { - "__type__": "kotaemon.embeddings.LCGoogleEmbeddings", - "model": "models/text-embedding-004", - "google_api_key": GOOGLE_API_KEY, - }, - "default": not IS_OPENAI_DEFAULT, -} -KH_EMBEDDINGS["mistral"] = { - "spec": { - "__type__": "kotaemon.embeddings.LCMistralEmbeddings", - "model": "mistral-embed", - "api_key": config("MISTRAL_API_KEY", default="your-key"), - }, - "default": False, -} -# KH_EMBEDDINGS["huggingface"] = { -# "spec": { -# "__type__": "kotaemon.embeddings.LCHuggingFaceEmbeddings", -# "model_name": "sentence-transformers/all-mpnet-base-v2", -# }, -# "default": False, -# } - -# default reranking models -KH_RERANKINGS["cohere"] = { - "spec": { - "__type__": "kotaemon.rerankings.CohereReranking", - "model_name": "rerank-v4.0-fast", - "cohere_api_key": config("COHERE_API_KEY", default=""), - }, - "default": True, -} - -KH_REASONINGS = [ - "ktem.reasoning.simple.FullQAPipeline", - "ktem.reasoning.simple.FullDecomposeQAPipeline", - "ktem.reasoning.react.ReactAgentPipeline", - "ktem.reasoning.rewoo.RewooAgentPipeline", -] -KH_REASONINGS_USE_MULTIMODAL = config("USE_MULTIMODAL", default=False, cast=bool) -KH_VLM_ENDPOINT = "{0}/openai/deployments/{1}/chat/completions?api-version={2}".format( - config("AZURE_OPENAI_ENDPOINT", default=""), - config("OPENAI_VISION_DEPLOYMENT_NAME", default="gpt-4o"), - config("OPENAI_API_VERSION", default=""), -) - - -SETTINGS_APP: dict[str, dict] = {} - - -SETTINGS_REASONING = { - "use": { - "name": "Reasoning options", - "value": None, - "choices": [], - "component": "radio", - }, - "lang": { - "name": "Language", - "value": "en", - "choices": [(lang, code) for code, lang in SUPPORTED_LANGUAGE_MAP.items()], - "component": "dropdown", - }, - "max_context_length": { - "name": "Max context length (LLM)", - "value": 32000, - "component": "number", - }, -} - -USE_GLOBAL_GRAPHRAG = config("USE_GLOBAL_GRAPHRAG", default=True, cast=bool) -USE_NANO_GRAPHRAG = config("USE_NANO_GRAPHRAG", default=False, cast=bool) -USE_LIGHTRAG = config("USE_LIGHTRAG", default=True, cast=bool) -USE_MS_GRAPHRAG = config("USE_MS_GRAPHRAG", default=True, cast=bool) - -GRAPHRAG_INDEX_TYPES = [] - -if USE_MS_GRAPHRAG: - GRAPHRAG_INDEX_TYPES.append("ktem.index.file.graph.GraphRAGIndex") -if USE_NANO_GRAPHRAG: - GRAPHRAG_INDEX_TYPES.append("ktem.index.file.graph.NanoGraphRAGIndex") -if USE_LIGHTRAG: - GRAPHRAG_INDEX_TYPES.append("ktem.index.file.graph.LightRAGIndex") - -KH_INDEX_TYPES = [ - "ktem.index.file.FileIndex", - *GRAPHRAG_INDEX_TYPES, -] - -GRAPHRAG_INDICES = [ - { - "name": graph_type.split(".")[-1].replace("Index", "") - + " Collection", # get last name - "config": { - "supported_file_types": ( - ".png, .jpeg, .jpg, .tiff, .tif, .pdf, .xls, .xlsx, .doc, .docx, " - ".pptx, .csv, .html, .mhtml, .txt, .md, .zip" - ), - "private": True, - }, - "index_type": graph_type, - } - for graph_type in GRAPHRAG_INDEX_TYPES -] - -KH_INDICES = [ - { - "name": "File Collection", - "config": { - "supported_file_types": ( - ".png, .jpeg, .jpg, .tiff, .tif, .pdf, .xls, .xlsx, .doc, .docx, " - ".pptx, .csv, .html, .mhtml, .txt, .md, .zip" - ), - "private": True, - }, - "index_type": "ktem.index.file.FileIndex", - }, - *GRAPHRAG_INDICES, -] diff --git a/libs/kotaemon/kotaemon/contribs/__init__.py b/libs/kotaemon/__init__.py similarity index 100% rename from libs/kotaemon/kotaemon/contribs/__init__.py rename to libs/kotaemon/__init__.py diff --git a/libs/kotaemon/kotaemon/agents/base.py b/libs/kotaemon/kotaemon/agents/base.py index 131466c9f..7d5ac6ae3 100644 --- a/libs/kotaemon/kotaemon/agents/base.py +++ b/libs/kotaemon/kotaemon/agents/base.py @@ -1,37 +1,22 @@ +from __future__ import annotations + +from dataclasses import dataclass, field from typing import Optional, Union -from kotaemon.base import BaseComponent, Node, Param from kotaemon.llms import BaseLLM, PromptTemplate from .io import AgentOutput, AgentType from .tools import BaseTool -class BaseAgent(BaseComponent): - """Define base agent interface""" - - name: str = Param(help="Name of the agent.") - agent_type: AgentType = Param(help="Agent type, must be one of AgentType") - description: str = Param( - help=( - "Description used to tell the model how/when/why to use the agent. You can" - " provide few-shot examples as a part of the description. This will be" - " input to the prompt of LLM." - ) - ) - llm: Optional[BaseLLM] = Node( - help=( - "LLM to be used for the agent (optional). LLM must implement BaseLLM" - " interface." - ) - ) - prompt_template: Optional[Union[PromptTemplate, dict[str, PromptTemplate]]] = Param( - help="A prompt template or a dict to supply different prompt to the agent" - ) - plugins: list[BaseTool] = Param( - default_callback=lambda _: [], - help="List of plugins / tools to be used in the agent", - ) +@dataclass(kw_only=True) +class BaseAgent: + name: str + agent_type: AgentType + description: str + llm: Optional[BaseLLM] = None + prompt_template: Optional[Union[PromptTemplate, dict[str, PromptTemplate]]] = None + plugins: list[BaseTool] = field(default_factory=list) @staticmethod def safeguard_run(run_func, *args, **kwargs): @@ -49,9 +34,7 @@ def wrapper(self, *args, **kwargs): return wrapper def add_tools(self, tools: list[BaseTool]) -> None: - """Helper method to add tools and update agent state if needed""" self.plugins.extend(tools) def run(self, *args, **kwargs) -> AgentOutput | list[AgentOutput]: - """Run the component.""" raise NotImplementedError() diff --git a/libs/kotaemon/kotaemon/agents/langchain_based.py b/libs/kotaemon/kotaemon/agents/langchain_based.py index 3083b704e..805cccdd3 100644 --- a/libs/kotaemon/kotaemon/agents/langchain_based.py +++ b/libs/kotaemon/kotaemon/agents/langchain_based.py @@ -30,7 +30,7 @@ def __init__(self, *args, **kwargs): if self.agent_type not in self.AGENT_TYPE_MAP: raise NotImplementedError( - f"AgentType {self.agent_type } not supported by Langchain wrapper" + f"AgentType {self.agent_type} not supported by Langchain wrapper" ) self.update_agent_tools() diff --git a/libs/kotaemon/kotaemon/agents/react/agent.py b/libs/kotaemon/kotaemon/agents/react/agent.py index e49a35f66..03c23ab5a 100644 --- a/libs/kotaemon/kotaemon/agents/react/agent.py +++ b/libs/kotaemon/kotaemon/agents/react/agent.py @@ -1,5 +1,6 @@ import logging import re +from dataclasses import dataclass, field from functools import partial from typing import Optional @@ -8,41 +9,29 @@ from kotaemon.agents.base import BaseAgent, BaseLLM from kotaemon.agents.io import AgentAction, AgentFinish, AgentOutput, AgentType from kotaemon.agents.tools import BaseTool -from kotaemon.base import Document, Param +from kotaemon.base import Document from kotaemon.indices.splitters import TokenSplitter from kotaemon.llms import PromptTemplate FINAL_ANSWER_ACTION = "Final Answer:" +@dataclass(kw_only=True) class ReactAgent(BaseAgent): - """ - Sequential ReactAgent class inherited from BaseAgent. - Implementing ReAct agent paradigm https://arxiv.org/pdf/2210.03629.pdf - """ - name: str = "ReactAgent" agent_type: AgentType = AgentType.react description: str = "ReactAgent for answering multi-step reasoning questions" - llm: BaseLLM + llm: BaseLLM | None = None prompt_template: Optional[PromptTemplate] = None output_lang: str = "English" - plugins: list[BaseTool] = Param( - default_callback=lambda _: [], help="List of tools to be used in the agent. " - ) - examples: dict[str, str | list[str]] = Param( - default_callback=lambda _: {}, help="Examples to be used in the agent. " - ) - intermediate_steps: list[tuple[AgentAction | AgentFinish, str]] = Param( - default_callback=lambda _: [], - help="List of AgentAction and observation (tool) output", + plugins: list[BaseTool] = field(default_factory=list) + examples: dict[str, str | list[str]] = field(default_factory=dict) + intermediate_steps: list[tuple[AgentAction | AgentFinish, str]] = field( + default_factory=list ) max_iterations: int = 5 strict_decode: bool = False - max_context_length: int = Param( - default=3000, - help="Max context length for each tool output.", - ) + max_context_length: int = 3000 trim_func: TokenSplitter | None = None def _compose_plugin_description(self) -> str: @@ -204,6 +193,7 @@ def run(self, instruction, max_iterations=None) -> AgentOutput: for step_count in range(1, max_iterations + 1): prompt = self._compose_prompt(instruction) logging.info(f"Prompt: {prompt}") + assert self.llm is not None response = self.llm( prompt, stop=["Observation:"] ) # could cause bugs if llm doesn't have `stop` as a parameter @@ -283,6 +273,7 @@ def stream(self, instruction, max_iterations=None): prompt = self._compose_prompt(instruction) logging.info(f"Prompt: {prompt}") print(f"Prompt: {prompt}") + assert self.llm is not None response = self.llm( prompt, stop=["Observation:"] ) # TODO: could cause bugs if llm doesn't have `stop` as a parameter diff --git a/libs/kotaemon/kotaemon/agents/rewoo/agent.py b/libs/kotaemon/kotaemon/agents/rewoo/agent.py index 076595be1..b20900552 100644 --- a/libs/kotaemon/kotaemon/agents/rewoo/agent.py +++ b/libs/kotaemon/kotaemon/agents/rewoo/agent.py @@ -1,7 +1,8 @@ import logging import re from concurrent.futures import ThreadPoolExecutor -from functools import partial +from dataclasses import dataclass, field +from functools import cached_property, partial from typing import Any import tiktoken @@ -10,7 +11,7 @@ from kotaemon.agents.io import AgentOutput, AgentType, BaseScratchPad from kotaemon.agents.tools import BaseTool from kotaemon.agents.utils import get_plugin_response_content -from kotaemon.base import Document, Node, Param +from kotaemon.base import Document from kotaemon.indices.qa.citation import CitationPipeline from kotaemon.indices.splitters import TokenSplitter from kotaemon.llms import BaseLLM, PromptTemplate @@ -19,33 +20,21 @@ from .solver import Solver +@dataclass(kw_only=True) class RewooAgent(BaseAgent): - """Distributive RewooAgent class inherited from BaseAgent. - Implementing ReWOO paradigm https://arxiv.org/pdf/2305.18323.pdf""" - name: str = "RewooAgent" agent_type: AgentType = AgentType.rewoo description: str = "RewooAgent for answering multi-step reasoning questions" output_lang: str = "English" - planner_llm: BaseLLM - solver_llm: BaseLLM - prompt_template: dict[str, PromptTemplate] = Param( - default_callback=lambda _: {}, - help="A dict to supply different prompt to the agent.", - ) - plugins: list[BaseTool] = Param( - default_callback=lambda _: [], help="A list of plugins to be used in the model." - ) - examples: dict[str, str | list[str]] = Param( - default_callback=lambda _: {}, help="Examples to be used in the agent." - ) - max_context_length: int = Param( - default=3000, - help="Max context length for each tool output.", - ) + planner_llm: BaseLLM | None = None + solver_llm: BaseLLM | None = None + prompt_template: dict[str, PromptTemplate] = field(default_factory=dict) + plugins: list[BaseTool] = field(default_factory=list) + examples: dict[str, str | list[str]] = field(default_factory=dict) + max_context_length: int = 3000 trim_func: TokenSplitter | None = None - @Node.auto(depends_on=["planner_llm", "plugins", "prompt_template", "examples"]) + @cached_property def planner(self): return Planner( model=self.planner_llm, @@ -54,7 +43,7 @@ def planner(self): examples=self.examples.get("Planner", None), ) - @Node.auto(depends_on=["solver_llm", "prompt_template", "examples"]) + @cached_property def solver(self): return Solver( model=self.solver_llm, @@ -273,7 +262,7 @@ def run(self, instruction: str, use_citation: bool = False) -> AgentOutput: total_token = 0 # Plan - planner_output = self.planner(instruction) + planner_output = self.planner.run(instruction) planner_text_output = planner_output.text plan_to_es, plans = self._parse_plan_map(planner_text_output) planner_evidences, evidence_level = self._parse_planner_evidences( @@ -291,7 +280,7 @@ def run(self, instruction: str, use_citation: bool = False) -> AgentOutput: worker_log += f"{e}: {worker_evidences[e]}\n" # Solve - solver_output = self.solver(instruction, worker_log) + solver_output = self.solver.run(instruction, worker_log) solver_output_text = solver_output.text if use_citation: citation_pipeline = CitationPipeline(llm=self.solver_llm) @@ -318,7 +307,7 @@ def stream(self, instruction: str, use_citation: bool = False): total_token = 0 # Plan - planner_output = self.planner(instruction) + planner_output = self.planner.run(instruction) planner_text_output = planner_output.text plan_to_es, plans = self._parse_plan_map(planner_text_output) planner_evidences, evidence_level = self._parse_planner_evidences( diff --git a/libs/kotaemon/kotaemon/agents/rewoo/planner.py b/libs/kotaemon/kotaemon/agents/rewoo/planner.py index cf2a779df..271272058 100644 --- a/libs/kotaemon/kotaemon/agents/rewoo/planner.py +++ b/libs/kotaemon/kotaemon/agents/rewoo/planner.py @@ -1,47 +1,33 @@ +from dataclasses import dataclass from typing import Any, List, Optional, Union from kotaemon.agents.base import BaseLLM, BaseTool from kotaemon.agents.io import BaseScratchPad -from kotaemon.base import BaseComponent from kotaemon.llms import PromptTemplate from .prompt import few_shot_planner_prompt, zero_shot_planner_prompt -class Planner(BaseComponent): +@dataclass(kw_only=True) +class Planner: model: BaseLLM + plugins: List[BaseTool] prompt_template: Optional[PromptTemplate] = None examples: Optional[Union[str, List[str]]] = None - plugins: List[BaseTool] def _compose_worker_description(self) -> str: - """ - Compose the worker prompt from the workers. - - Example: - toolname1[input]: tool1 description - toolname2[input]: tool2 description - """ - prompt = "" - try: - for worker in self.plugins: - prompt += f"{worker.name}[input]: {worker.description}\n" - except Exception: - raise ValueError("Worker must have a name and description.") - return prompt + return "".join( + f"{worker.name}[input]: {worker.description}\n" for worker in self.plugins + ) def _compose_fewshot_prompt(self) -> str: if self.examples is None: return "" if isinstance(self.examples, str): return self.examples - else: - return "\n\n".join([e.strip("\n") for e in self.examples]) + return "\n\n".join(e.strip("\n") for e in self.examples) def _compose_prompt(self, instruction) -> str: - """ - Compose the prompt from template, worker description, examples and instruction. - """ worker_desctription = self._compose_worker_description() fewshot = self._compose_fewshot_prompt() if self.prompt_template is not None: @@ -51,56 +37,28 @@ def _compose_prompt(self, instruction) -> str: fewshot=fewshot, task=instruction, ) - else: - return self.prompt_template.populate( - tool_description=worker_desctription, task=instruction - ) - else: - if self.examples is not None: - return few_shot_planner_prompt.populate( - tool_description=worker_desctription, - fewshot=fewshot, - task=instruction, - ) - else: - return zero_shot_planner_prompt.populate( - tool_description=worker_desctription, task=instruction - ) + return self.prompt_template.populate( + tool_description=worker_desctription, task=instruction + ) + if self.examples is not None: + return few_shot_planner_prompt.populate( + tool_description=worker_desctription, + fewshot=fewshot, + task=instruction, + ) + return zero_shot_planner_prompt.populate( + tool_description=worker_desctription, task=instruction + ) def run(self, instruction: str, output: BaseScratchPad = BaseScratchPad()) -> Any: - response = None output.info("Running Planner") prompt = self._compose_prompt(instruction) - output.debug(f"Prompt: {prompt}") - try: - response = self.model(prompt) - self.log_progress(".planner", response=response) - output.info("Planner run successful.") - except ValueError as e: - output.error("Planner failed to retrieve response from LLM") - raise ValueError("Planner failed to retrieve response from LLM") from e - - return response + return self.model(prompt) def stream(self, instruction: str, output: BaseScratchPad = BaseScratchPad()): - response = None - output.info("Running Planner") prompt = self._compose_prompt(instruction) - output.debug(f"Prompt: {prompt}") - - response = "" try: for text in self.model.stream(prompt): - response += text yield text - self.log_progress(".planner", response=response) - output.info("Planner run successful.") except NotImplementedError: - print("Streaming is not supported, falling back to normal run") - response = self.model(prompt) - yield response - except ValueError as e: - output.error("Planner failed to retrieve response from LLM") - raise ValueError("Planner failed to retrieve response from LLM") from e - - return response + yield self.model(prompt) diff --git a/libs/kotaemon/kotaemon/agents/rewoo/solver.py b/libs/kotaemon/kotaemon/agents/rewoo/solver.py index dbd4c6fe0..05f317484 100644 --- a/libs/kotaemon/kotaemon/agents/rewoo/solver.py +++ b/libs/kotaemon/kotaemon/agents/rewoo/solver.py @@ -1,13 +1,14 @@ +from dataclasses import dataclass from typing import Any, List, Optional, Union from kotaemon.agents.io import BaseScratchPad -from kotaemon.base import BaseComponent from kotaemon.llms import BaseLLM, PromptTemplate from .prompt import few_shot_solver_prompt, zero_shot_solver_prompt -class Solver(BaseComponent): +@dataclass(kw_only=True) +class Solver: model: BaseLLM prompt_template: Optional[PromptTemplate] = None examples: Optional[Union[str, List[str]]] = None @@ -18,13 +19,9 @@ def _compose_fewshot_prompt(self) -> str: return "" if isinstance(self.examples, str): return self.examples - else: - return "\n\n".join([e.strip("\n") for e in self.examples]) + return "\n\n".join(e.strip("\n") for e in self.examples) def _compose_prompt(self, instruction, plan_evidence, output_lang) -> str: - """ - Compose the prompt from template, plan&evidence, examples and instruction. - """ fewshot = self._compose_fewshot_prompt() if self.prompt_template is not None: if "fewshot" in self.prompt_template.placeholders: @@ -34,24 +31,19 @@ def _compose_prompt(self, instruction, plan_evidence, output_lang) -> str: task=instruction, lang=output_lang, ) - else: - return self.prompt_template.populate( - plan_evidence=plan_evidence, task=instruction, lang=output_lang - ) - else: - if self.examples is not None: - return few_shot_solver_prompt.populate( - plan_evidence=plan_evidence, - fewshot=fewshot, - task=instruction, - lang=output_lang, - ) - else: - return zero_shot_solver_prompt.populate( - plan_evidence=plan_evidence, - task=instruction, - lang=output_lang, - ) + return self.prompt_template.populate( + plan_evidence=plan_evidence, task=instruction, lang=output_lang + ) + if self.examples is not None: + return few_shot_solver_prompt.populate( + plan_evidence=plan_evidence, + fewshot=fewshot, + task=instruction, + lang=output_lang, + ) + return zero_shot_solver_prompt.populate( + plan_evidence=plan_evidence, task=instruction, lang=output_lang + ) def run( self, @@ -59,19 +51,8 @@ def run( plan_evidence: str, output: BaseScratchPad = BaseScratchPad(), ) -> Any: - response = None - output.info("Running Solver") - output.debug(f"Instruction: {instruction}") - output.debug(f"Plan Evidence: {plan_evidence}") prompt = self._compose_prompt(instruction, plan_evidence, self.output_lang) - output.debug(f"Prompt: {prompt}") - try: - response = self.model(prompt) - output.info("Solver run successful.") - except ValueError: - output.error("Solver failed to retrieve response from LLM") - - return response + return self.model(prompt) def stream( self, @@ -79,21 +60,9 @@ def stream( plan_evidence: str, output: BaseScratchPad = BaseScratchPad(), ) -> Any: - response = "" - output.info("Running Solver") - output.debug(f"Instruction: {instruction}") - output.debug(f"Plan Evidence: {plan_evidence}") prompt = self._compose_prompt(instruction, plan_evidence, self.output_lang) - output.debug(f"Prompt: {prompt}") try: for text in self.model.stream(prompt): - response += text.text yield text - output.info("Planner run successful.") except NotImplementedError: - response = self.model(prompt).text - output.info("Solver run successful.") - except ValueError: - output.error("Solver failed to retrieve response from LLM") - - return response + yield self.model(prompt) diff --git a/libs/kotaemon/kotaemon/agents/tools/base.py b/libs/kotaemon/kotaemon/agents/tools/base.py index 1caf3d2db..4c4be7a73 100644 --- a/libs/kotaemon/kotaemon/agents/tools/base.py +++ b/libs/kotaemon/kotaemon/agents/tools/base.py @@ -1,9 +1,10 @@ +from dataclasses import dataclass from typing import Any, Callable, Dict, Optional, Tuple, Type, Union from langchain.agents import Tool as LCTool from pydantic import BaseModel -from kotaemon.base import BaseComponent +from kotaemon.base import Runnable class ToolException(Exception): @@ -16,7 +17,8 @@ class ToolException(Exception): """ -class BaseTool(BaseComponent): +@dataclass(kw_only=True) +class BaseTool: name: str """The unique name of the tool that clearly communicates its purpose.""" description: str @@ -120,15 +122,11 @@ def from_langchain_format(cls, langchain_tool: LCTool) -> "BaseTool": return new_tool +@dataclass(kw_only=True) class ComponentTool(BaseTool): - """Wrapper around other BaseComponent to use it as a tool + """Wrapper around a Runnable pipeline used as a tool.""" - Args: - component: BaseComponent-based component to wrap - postprocessor: Optional postprocessor for the component output - """ - - component: BaseComponent + component: Runnable postprocessor: Optional[Callable] = None def _run_tool(self, *args: Any, **kwargs: Any) -> Any: diff --git a/libs/kotaemon/kotaemon/agents/tools/google.py b/libs/kotaemon/kotaemon/agents/tools/google.py index 98c5eaf40..3286a3d6c 100644 --- a/libs/kotaemon/kotaemon/agents/tools/google.py +++ b/libs/kotaemon/kotaemon/agents/tools/google.py @@ -1,3 +1,4 @@ +from dataclasses import dataclass from typing import AnyStr, Optional, Type from urllib.error import HTTPError @@ -11,6 +12,7 @@ class GoogleSearchArgs(BaseModel): query: str = Field(..., description="a search query") +@dataclass(kw_only=True) class GoogleSearchTool(BaseTool): name: str = "google_search" description: str = ( @@ -42,9 +44,10 @@ def _run_tool(self, query: AnyStr) -> str: return output +@dataclass(kw_only=True) class SerpTool(BaseTool): - name = "google_search" - description = ( + name: str = "google_search" + description: str = ( "Worker that searches results from Google. Useful when you need to find short " "and succinct answers about a specific topic. Input should be a search query." ) diff --git a/libs/kotaemon/kotaemon/agents/tools/llm.py b/libs/kotaemon/kotaemon/agents/tools/llm.py index d7cf7485c..13a3d5427 100644 --- a/libs/kotaemon/kotaemon/agents/tools/llm.py +++ b/libs/kotaemon/kotaemon/agents/tools/llm.py @@ -1,3 +1,4 @@ +from dataclasses import dataclass from typing import AnyStr, Optional, Type from pydantic import BaseModel, Field @@ -12,6 +13,7 @@ class LLMArgs(BaseModel): query: str = Field(..., description="a search question or prompt") +@dataclass(kw_only=True) class LLMTool(BaseTool): name: str = "llm" description: str = ( @@ -20,7 +22,7 @@ class LLMTool(BaseTool): "are confident in solving the problem " "yourself. Input can be any instruction." ) - llm: BaseLLM + llm: BaseLLM | None = None args_schema: Optional[Type[BaseModel]] = LLMArgs dummy_mode: bool = True @@ -28,6 +30,7 @@ def _run_tool(self, query: AnyStr) -> str: output = None try: if not self.dummy_mode: + assert self.llm is not None response = self.llm(query) else: response = None diff --git a/libs/kotaemon/kotaemon/agents/tools/mcp.py b/libs/kotaemon/kotaemon/agents/tools/mcp.py index e0bce4cca..ce4feec64 100644 --- a/libs/kotaemon/kotaemon/agents/tools/mcp.py +++ b/libs/kotaemon/kotaemon/agents/tools/mcp.py @@ -13,6 +13,7 @@ import json import logging import shlex +from dataclasses import dataclass, field from typing import Any, Optional, Type from pydantic import BaseModel, Field, create_model @@ -279,6 +280,7 @@ def format_tool_list( # --------------------------------------------------------------------------- +@dataclass(kw_only=True) class MCPTool(BaseTool): """A kotaemon BaseTool wrapper around a single MCP server tool. @@ -305,8 +307,8 @@ class MCPTool(BaseTool): # MCP server connection details server_transport: str = "stdio" server_command: str = "" - server_args: list[str] = [] - server_env: dict[str, str] = {} + server_args: list[str] = field(default_factory=list) + server_env: dict[str, str] = field(default_factory=dict) # The original MCP tool name (on the server) mcp_tool_name: str = "" diff --git a/libs/kotaemon/kotaemon/agents/tools/wikipedia.py b/libs/kotaemon/kotaemon/agents/tools/wikipedia.py index 9e6a362c8..30fc32f4c 100644 --- a/libs/kotaemon/kotaemon/agents/tools/wikipedia.py +++ b/libs/kotaemon/kotaemon/agents/tools/wikipedia.py @@ -1,3 +1,4 @@ +from dataclasses import dataclass from typing import Any, AnyStr, Optional, Type, Union from pydantic import BaseModel, Field @@ -45,6 +46,7 @@ class WikipediaArgs(BaseModel): query: str = Field(..., description="a search query as input to wkipedia") +@dataclass(kw_only=True) class WikipediaTool(BaseTool): """Tool that adds the capability to query the Wikipedia API.""" diff --git a/libs/kotaemon/kotaemon/base/__init__.py b/libs/kotaemon/kotaemon/base/__init__.py index 1f7871023..0aff1e6c4 100644 --- a/libs/kotaemon/kotaemon/base/__init__.py +++ b/libs/kotaemon/kotaemon/base/__init__.py @@ -1,4 +1,5 @@ -from .component import BaseComponent, Node, Param, lazy +from .contracts import Runnable, Serializable +from .describe import DataclassDescribe, DataclassParamDesc, describe_dataclass from .schema import ( AIMessage, BaseMessage, @@ -13,7 +14,6 @@ ) __all__ = [ - "BaseComponent", "Document", "DocumentWithEmbedding", "BaseMessage", @@ -24,7 +24,9 @@ "LLMInterface", "StructuredOutputLLMInterface", "ExtractorOutput", - "Param", - "Node", - "lazy", + "Runnable", + "Serializable", + "describe_dataclass", + "DataclassDescribe", + "DataclassParamDesc", ] diff --git a/libs/kotaemon/kotaemon/base/component.py b/libs/kotaemon/kotaemon/base/component.py deleted file mode 100644 index 5a38747f1..000000000 --- a/libs/kotaemon/kotaemon/base/component.py +++ /dev/null @@ -1,66 +0,0 @@ -from abc import abstractmethod -from typing import Any, AsyncGenerator, Iterator, Optional - -from theflow import Function, Node, Param, lazy - -from kotaemon.base.schema import Document - - -class BaseComponent(Function): - """A component is a class that can be used to compose a pipeline. - - !!! tip "Benefits of component" - - Auto caching, logging - - Allow deployment - - !!! tip "For each component, the spirit is" - - Tolerate multiple input types, e.g. str, Document, List[str], List[Document] - - Enforce single output type. Hence, the output type of a component should be - as generic as possible. - """ - - inflow = None - - def flow(self): - if self.inflow is None: - raise ValueError("No inflow provided.") - - if not isinstance(self.inflow, BaseComponent): - raise ValueError( - f"inflow must be a BaseComponent, found {type(self.inflow)}" - ) - - return self.__call__(self.inflow.flow()) - - def set_output_queue(self, queue): - self._queue = queue - for name in self._ff_nodes: - node = getattr(self, name) - if isinstance(node, BaseComponent): - node.set_output_queue(queue) - - def report_output(self, output: Optional[Document]): - if self._queue is not None: - self._queue.put_nowait(output) - - def invoke(self, *args, **kwargs) -> Document | list[Document] | None: - ... - - async def ainvoke(self, *args, **kwargs) -> Document | list[Document] | None: - ... - - def stream(self, *args, **kwargs) -> Iterator[Document] | None: - ... - - def astream(self, *args, **kwargs) -> AsyncGenerator[Document, None] | None: - ... - - @abstractmethod - def run( - self, *args, **kwargs - ) -> Document | list[Document] | Iterator[Document] | None | Any: - """Run the component.""" - ... - - -__all__ = ["BaseComponent", "Param", "Node", "lazy"] diff --git a/libs/kotaemon/kotaemon/base/contracts.py b/libs/kotaemon/kotaemon/base/contracts.py new file mode 100644 index 000000000..c14b038da --- /dev/null +++ b/libs/kotaemon/kotaemon/base/contracts.py @@ -0,0 +1,15 @@ +from typing import Any, Protocol + + +class Runnable(Protocol): + """Minimal execution contract for pipelines/components.""" + + def run(self, *args: Any, **kwargs: Any) -> Any: + """Run component logic.""" + + +class Serializable(Protocol): + """Contract for objects that can be stored as runtime specs.""" + + def to_spec(self) -> dict[str, Any]: + """Return a JSON-serializable runtime spec.""" diff --git a/libs/kotaemon/kotaemon/base/describe.py b/libs/kotaemon/kotaemon/base/describe.py new file mode 100644 index 000000000..77567897d --- /dev/null +++ b/libs/kotaemon/kotaemon/base/describe.py @@ -0,0 +1,36 @@ +from __future__ import annotations + +from dataclasses import MISSING, fields +from typing import TypedDict + + +class DataclassParamDesc(TypedDict): + """Metadata for one dataclass field in a describe spec.""" + + required: bool + help: str + type: str + + +class DataclassDescribe(TypedDict): + """Spec description returned by ``describe_dataclass``.""" + + type: str + params: dict[str, DataclassParamDesc] + + +def describe_dataclass(cls: type) -> DataclassDescribe: + """Build a spec description from dataclass fields on ``cls``.""" + return { + "type": f"{cls.__module__}.{cls.__qualname__}", + "params": { + field.name: { + "required": ( + field.default is MISSING and field.default_factory is MISSING + ), + "help": field.metadata.get("description", ""), + "type": repr(field.type), + } + for field in fields(cls) + }, + } diff --git a/libs/kotaemon/kotaemon/base/spec.py b/libs/kotaemon/kotaemon/base/spec.py new file mode 100644 index 000000000..e1e3f8804 --- /dev/null +++ b/libs/kotaemon/kotaemon/base/spec.py @@ -0,0 +1,14 @@ +from __future__ import annotations + +from typing import Any + + +def spec_value(value: Any) -> Any: + """Return a JSON-friendly value for runtime spec dicts.""" + if isinstance(value, (str, int, float, bool, type(None))): + return value + if isinstance(value, dict): + return {str(k): spec_value(v) for k, v in value.items()} + if isinstance(value, (list, tuple)): + return [spec_value(v) for v in value] + raise TypeError(f"Cannot serialize value of type {type(value)!r}") diff --git a/libs/kotaemon/kotaemon/chatbot/__init__.py b/libs/kotaemon/kotaemon/chatbot/__init__.py deleted file mode 100644 index 3668d531b..000000000 --- a/libs/kotaemon/kotaemon/chatbot/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from .base import BaseChatBot, ChatConversation -from .simple_respondent import SimpleRespondentChatbot - -__all__ = ["BaseChatBot", "SimpleRespondentChatbot", "ChatConversation"] diff --git a/libs/kotaemon/kotaemon/chatbot/base.py b/libs/kotaemon/kotaemon/chatbot/base.py deleted file mode 100644 index b6a3baf4f..000000000 --- a/libs/kotaemon/kotaemon/chatbot/base.py +++ /dev/null @@ -1,115 +0,0 @@ -from abc import abstractmethod -from typing import List, Optional - -from theflow import SessionFunction - -from kotaemon.base import BaseComponent, LLMInterface -from kotaemon.base.schema import AIMessage, BaseMessage, HumanMessage, SystemMessage - - -class BaseChatBot(BaseComponent): - @abstractmethod - def run(self, messages: List[BaseMessage]) -> LLMInterface: - ... - - -def session_chat_storage(obj): - """Store using the bot location rather than the session location""" - return obj._store_result - - -class ChatConversation(SessionFunction): - """Base implementation of a chat bot component - - A chatbot component should: - - handle internal state, including history messages - - return output for a given input - """ - - class Config: - store_result = session_chat_storage - - system_message: str = "" - bot: BaseChatBot - - def __init__(self, *args, **kwargs): - self._history: List[BaseMessage] = [] - self._store_result = ( - f"{self.__module__}.{self.__class__.__name__},uninitiated_bot" - ) - super().__init__(*args, **kwargs) - - def run(self, message: HumanMessage) -> Optional[BaseMessage]: - """Chat, given a message, return a response - - Args: - message: The message to respond to - - Returns: - The response to the message. If None, no response is sent. - """ - user_message = ( - HumanMessage(content=message) if isinstance(message, str) else message - ) - self.history.append(user_message) - - output = self.bot(self.history).text - output_message = None - if output is not None: - output_message = AIMessage(content=output) - self.history.append(output_message) - - return output_message - - def start_session(self): - self._store_result = self.bot.config.store_result - super().start_session() - if not self.history and self.system_message: - system_message = SystemMessage(content=self.system_message) - self.history.append(system_message) - - def end_session(self): - super().end_session() - self._history = [] - - def check_end( - self, - history: Optional[List[BaseMessage]] = None, - user_message: Optional[HumanMessage] = None, - bot_message: Optional[AIMessage] = None, - ) -> bool: - """Check if a conversation should end""" - if user_message is not None and user_message.content == "": - return True - - return False - - def terminal_session(self): - """Create a terminal session""" - self.start_session() - print(">> Start chat:") - - while True: - human = HumanMessage(content=input("Human: ")) - if self.check_end(history=self.history, user_message=human): - break - - output = self(human) - if output is None: - print("AI: ") - else: - print("AI:", output.content) - - if self.check_end(history=self.history, bot_message=output): - break - - self.end_session() - - @property - def history(self): - return self._history - - @history.setter - def history(self, value): - self._history = value - self._variablex() diff --git a/libs/kotaemon/kotaemon/chatbot/simple_respondent.py b/libs/kotaemon/kotaemon/chatbot/simple_respondent.py deleted file mode 100644 index 6a14a632a..000000000 --- a/libs/kotaemon/kotaemon/chatbot/simple_respondent.py +++ /dev/null @@ -1,11 +0,0 @@ -from ..llms import ChatLLM -from .base import BaseChatBot - - -class SimpleRespondentChatbot(BaseChatBot): - """Simple text respondent chatbot that essentially wraps around a chat LLM""" - - llm: ChatLLM - - def _get_message(self) -> str: - return self.llm(self.history).text diff --git a/libs/kotaemon/kotaemon/cli.py b/libs/kotaemon/kotaemon/cli.py deleted file mode 100644 index 75101ff6c..000000000 --- a/libs/kotaemon/kotaemon/cli.py +++ /dev/null @@ -1,190 +0,0 @@ -import os - -import click -import yaml -from trogon import tui - - -# check if the output is not a .yml file -> raise error -def check_config_format(config): - if os.path.exists(config): - if isinstance(config, str): - with open(config) as f: - yaml.safe_load(f) - else: - raise ValueError("config must be yaml format.") - - -@tui(command="ui", help="Open the terminal UI") # generate the terminal UI -@click.group() -def main(): - pass - - -@click.group() -def promptui(): - pass - - -main.add_command(promptui) - - -@promptui.command() -@click.argument("export_path", nargs=1) -@click.option("--output", default="promptui.yml", show_default=True, required=False) -def export(export_path, output): - """Export a pipeline to a config file""" - import sys - - from theflow.utils.modules import import_dotted_string - - from kotaemon.contribs.promptui.config import export_pipeline_to_config - - sys.path.append(os.getcwd()) - cls = import_dotted_string(export_path, safe=False) - export_pipeline_to_config(cls, output) - check_config_format(output) - - -@promptui.command() -@click.argument("run_path", required=False, default="promptui.yml") -@click.option( - "--share", - is_flag=True, - show_default=True, - default=False, - help="Share the app through Gradio. Requires --username to enable authentication.", -) -@click.option( - "--username", - required=False, - help=( - "Username for the user. If not provided, the promptui will not have " - "authentication." - ), -) -@click.option( - "--password", - required=False, - help="Password for the user. If not provided, will be prompted.", -) -@click.option( - "--appname", - required=False, - help="The share app subdomain. Requires --share and --username", -) -@click.option( - "--port", - required=False, - help="Port to run the app. If not provided, will $GRADIO_SERVER_PORT (7860)", -) -def run(run_path, share, username, password, appname, port): - """Run the UI from a config file - - Examples: - - \b - # Run with default config file - $ kh promptui run - - \b - # Run with username and password supplied - $ kh promptui run --username admin --password password - - \b - # Run with username and prompted password - $ kh promptui run --username admin - - # Run and share to promptui - # kh promptui run --username admin --password password --share --appname hey \ - --port 7861 - """ - import sys - - from kotaemon.contribs.promptui.ui import build_from_dict - - sys.path.append(os.getcwd()) - - check_config_format(run_path) - demo = build_from_dict(run_path) - - params: dict = {} - if username is not None: - if password is not None: - auth = (username, password) - else: - auth = (username, click.prompt("Password", hide_input=True)) - params["auth"] = auth - - port = int(port) if port else int(os.getenv("GRADIO_SERVER_PORT", "7860")) - params["server_port"] = port - - if share: - if username is None: - raise ValueError( - "Username must be provided to enable authentication for sharing" - ) - if appname: - from kotaemon.contribs.promptui.tunnel import Tunnel - - tunnel = Tunnel( - appname=str(appname), username=str(username), local_port=port - ) - url = tunnel.run() - print(f"App is shared at {url}") - else: - params["share"] = True - print("App is shared at Gradio") - - demo.launch(**params) - - -@main.command() -@click.argument("module", required=True) -@click.option( - "--output", default="docs.md", required=False, help="The output markdown file" -) -@click.option( - "--separation-level", required=False, default=1, help="Organize markdown layout" -) -def makedoc(module, output, separation_level): - """Make documentation for module `module` - - Example: - - \b - # Make component documentation for kotaemon library - $ kh makedoc kotaemon - """ - from kotaemon.contribs.docs import make_doc - - make_doc(module, output, separation_level) - print(f"Documentation exported to {output}") - - -@main.command() -@click.option( - "--template", - default="project-default", - required=False, - help="Template name", - show_default=True, -) -def start_project(template): - """Start a project from a template. - - Important: the value for --template corresponds to the name of the template folder, - which is located at https://github.com/Cinnamon/kotaemon/tree/main/templates - The default value is "project-default", which should work when you are starting a - client project. - """ - - print("Retrieving template...") - os.system( - "cookiecutter git@github.com:Cinnamon/kotaemon.git " - f"--directory='templates/{template}'" - ) - - -if __name__ == "__main__": - main() diff --git a/libs/kotaemon/kotaemon/contribs/docs.py b/libs/kotaemon/kotaemon/contribs/docs.py deleted file mode 100644 index 0b3e275fd..000000000 --- a/libs/kotaemon/kotaemon/contribs/docs.py +++ /dev/null @@ -1,66 +0,0 @@ -import inspect -from collections import defaultdict - -from theflow.utils.documentation import get_function_documentation_from_module - - -def from_definition_to_markdown(definition: dict) -> str: - """From definition to markdown""" - - # Handle params - params = " N/A\n" - if definition["params"]: - params = "\n| Name | Description | Type | Default |\n" - params += "| --- | --- | --- | --- |\n" - for name, p in definition["params"].items(): - type_ = p["type"].__name__ if inspect.isclass(p["type"]) else p["type"] - params += f"| {name} | {p['desc']} | {type_} | {p['default']} |\n" - - # Handle nodes - nodes = " N/A\n" - if definition["nodes"]: - nodes = "\n| Name | Description | Type | Input | Output |\n" - nodes += "| --- | --- | --- | --- | --- |\n" - for name, n in definition["nodes"].items(): - type_ = n["type"].__name__ if inspect.isclass(n["type"]) else str(n["type"]) - input_ = ( - n["input"].__name__ if inspect.isclass(n["input"]) else str(n["input"]) - ) - output_ = ( - n["output"].__name__ - if inspect.isclass(n["output"]) - else str(n["output"]) - ) - nodes += f"|{name}|{n['desc']}|{type_}|{input_}|{output_}|\n" - - description = inspect.cleandoc(definition["desc"]) - return f"{description}\n\n_**Params:**_{params}\n_**Nodes:**_{nodes}" - - -def make_doc(module: str, output: str, separation_level: int): - """Run exporting components to markdown - - Args: - module (str): module name - output_path (str): output path to save - separation_level (int): level of separation - """ - documentation = sorted( - get_function_documentation_from_module(module).items(), key=lambda x: x[0] - ) - - entries = defaultdict(list) - - for name, definition in documentation: - section = name.split(".")[separation_level].capitalize() - cls_name = name.split(".")[-1] - - markdown = from_definition_to_markdown(definition) - entries[section].append(f"### {cls_name}\n{markdown}") - - final = "\n".join( - [f"## {section}\n" + "\n".join(entries[section]) for section in entries] - ) - - with open(output, "w") as f: - f.write(final) diff --git a/libs/kotaemon/kotaemon/contribs/promptui/.gitignore b/libs/kotaemon/kotaemon/contribs/promptui/.gitignore deleted file mode 100644 index 632ca7499..000000000 --- a/libs/kotaemon/kotaemon/contribs/promptui/.gitignore +++ /dev/null @@ -1 +0,0 @@ -/frpc_* diff --git a/libs/kotaemon/kotaemon/contribs/promptui/__init__.py b/libs/kotaemon/kotaemon/contribs/promptui/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/libs/kotaemon/kotaemon/contribs/promptui/base.py b/libs/kotaemon/kotaemon/contribs/promptui/base.py deleted file mode 100644 index 8d3eeb284..000000000 --- a/libs/kotaemon/kotaemon/contribs/promptui/base.py +++ /dev/null @@ -1,43 +0,0 @@ -import gradio as gr - -COMPONENTS_CLASS = { - "text": gr.components.Textbox, - "checkbox": gr.components.CheckboxGroup, - "dropdown": gr.components.Dropdown, - "file": gr.components.File, - "image": gr.components.Image, - "number": gr.components.Number, - "radio": gr.components.Radio, - "slider": gr.components.Slider, -} -SUPPORTED_COMPONENTS = set(COMPONENTS_CLASS.keys()) -DEFAULT_COMPONENT_BY_TYPES = { - "str": "text", - "bool": "checkbox", - "int": "number", - "float": "number", - "list": "dropdown", -} - - -def get_component(component_def: dict) -> gr.components.Component: - """Get the component based on component definition""" - component_cls = None - - if "component" in component_def: - component = component_def["component"] - if component not in SUPPORTED_COMPONENTS: - raise ValueError( - f"Unsupported UI component: {component}. " - f"Must be one of {SUPPORTED_COMPONENTS}" - ) - - component_cls = COMPONENTS_CLASS[component] - else: - raise ValueError( - f"Cannot decide the component from {component_def}. " - "Please specify `component` with 1 of the following " - f"values: {SUPPORTED_COMPONENTS}" - ) - - return component_cls(**component_def.get("params", {})) diff --git a/libs/kotaemon/kotaemon/contribs/promptui/cli.py b/libs/kotaemon/kotaemon/contribs/promptui/cli.py deleted file mode 100644 index 0f3131825..000000000 --- a/libs/kotaemon/kotaemon/contribs/promptui/cli.py +++ /dev/null @@ -1 +0,0 @@ -"""CLI commands that can be imported by the kotaemon.cli module""" diff --git a/libs/kotaemon/kotaemon/contribs/promptui/config.py b/libs/kotaemon/kotaemon/contribs/promptui/config.py deleted file mode 100644 index e9098b1ff..000000000 --- a/libs/kotaemon/kotaemon/contribs/promptui/config.py +++ /dev/null @@ -1,183 +0,0 @@ -"""Get config from Pipeline""" -import inspect -from pathlib import Path -from typing import Any, Dict, Optional, Type, Union - -import yaml - -from kotaemon.base import BaseComponent -from kotaemon.chatbot import BaseChatBot - -from .base import DEFAULT_COMPONENT_BY_TYPES - - -def config_from_value(value: Any) -> dict: - """Get the config from default value - - Args: - value (Any): default value - - Returns: - dict: config - """ - component = DEFAULT_COMPONENT_BY_TYPES.get(type(value).__name__, "text") - return { - "component": component, - "params": { - "value": value, - }, - } - - -def handle_param(param: dict) -> dict: - """Convert param definition into promptui-compliant config - - Supported gradio's UI components are (https://www.gradio.app/docs/components) - - CheckBoxGroup: list (multi select) - - DropDown: list (single select) - - File - - Image - - Number: int / float - - Radio: list (single select) - - Slider: int / float - - TextBox: str - """ - params = {} - default = param.get("default", None) - if isinstance(default, str) and default.startswith("{{") and default.endswith("}}"): - default = None - if default is not None: - params["value"] = default - - ui_component = param.get("component_ui", "") - if not ui_component: - type_: str = type(default).__name__ if default is not None else "" - ui_component = DEFAULT_COMPONENT_BY_TYPES.get(type_, "text") - - return { - "component": ui_component, - "params": params, - } - - -def handle_node(node: dict) -> dict: - """Convert node definition into promptui-compliant config""" - config = {} - for name, param_def in node.get("params", {}).items(): - if isinstance(param_def["auto_callback"], str): - continue - if param_def.get("ignore_ui", False): - continue - config[name] = handle_param(param_def) - for name, node_def in node.get("nodes", {}).items(): - if isinstance(node_def["auto_callback"], str): - continue - if node_def.get("ignore_ui", False): - continue - for key, value in handle_node(node_def["default"]).items(): - config[f"{name}.{key}"] = value - for key, value in node_def.get("default_kwargs", {}).items(): - config[f"{name}.{key}"] = config_from_value(value) - - return config - - -def handle_input(pipeline: Union[BaseComponent, Type[BaseComponent]]) -> dict: - """Get the input from the pipeline""" - signature = inspect.signature(pipeline.run) - inputs: Dict[str, Dict] = {} - for name, param in signature.parameters.items(): - if name in ["self", "args", "kwargs"]: - continue - input_def: Dict[str, Optional[Any]] = {"component": "text"} - default = param.default - if default is param.empty: - inputs[name] = input_def - continue - - params = {} - params["value"] = default - type_ = type(default).__name__ if default is not None else None - ui_component = None - if type_ is not None: - ui_component = "text" - - input_def["component"] = ui_component - input_def["params"] = params - - inputs[name] = input_def - - return inputs - - -def export_pipeline_to_config( - pipeline: Union[BaseComponent, Type[BaseComponent]], - path: Optional[str] = None, -) -> dict: - """Export a pipeline to a promptui-compliant config dict""" - if inspect.isclass(pipeline): - pipeline = pipeline() - - pipeline_def = pipeline.describe() - ui_type = "chat" if isinstance(pipeline, BaseChatBot) else "simple" - if ui_type == "chat": - params = {f".bot.{k}": v for k, v in handle_node(pipeline_def).items()} - params["system_message"] = {"component": "text", "params": {"value": ""}} - outputs = [] - if hasattr(pipeline, "_promptui_outputs"): - outputs = pipeline._promptui_outputs - config_obj: dict = { - "ui-type": ui_type, - "params": params, - "inputs": {}, - "outputs": outputs, - "logs": { - "full_pipeline": { - "input": { - "step": ".", - "getter": "_get_input", - }, - "output": { - "step": ".", - "getter": "_get_output", - }, - "preference": { - "step": "preference", - }, - } - }, - } - else: - outputs = [{"step": ".", "getter": "_get_output", "component": "text"}] - if hasattr(pipeline, "_promptui_outputs"): - outputs = pipeline._promptui_outputs - config_obj = { - "ui-type": ui_type, - "params": handle_node(pipeline_def), - "inputs": handle_input(pipeline), - "outputs": outputs, - "logs": { - "full_pipeline": { - "input": { - "step": ".", - "getter": "_get_input", - }, - "output": { - "step": ".", - "getter": "_get_output", - }, - }, - }, - } - - config = {f"{pipeline.__module__}.{pipeline.__class__.__name__}": config_obj} - if path is not None: - old_config = config - if Path(path).is_file(): - with open(path) as f: - old_config = yaml.safe_load(f) - old_config.update(config) - with open(path, "w") as f: - yaml.safe_dump(old_config, f, sort_keys=False) - - return config diff --git a/libs/kotaemon/kotaemon/contribs/promptui/export.py b/libs/kotaemon/kotaemon/contribs/promptui/export.py deleted file mode 100644 index 8b174ea8b..000000000 --- a/libs/kotaemon/kotaemon/contribs/promptui/export.py +++ /dev/null @@ -1,141 +0,0 @@ -"""Export logs into Excel file""" -import os -import pickle -from pathlib import Path -from typing import Any, Dict, List, Type, Union - -import pandas as pd -import yaml -from theflow.storage import storage -from theflow.utils.modules import import_dotted_string - -from kotaemon.base import BaseComponent - -from .logs import ResultLog - - -def from_log_to_dict(pipeline_cls: Type[BaseComponent], log_config: dict) -> dict: - """Export the log to panda dataframes - - Args: - pipeline_cls (Type[BaseComponent]): Pipeline class - log_config (dict): Log config - - Returns: - dataframe - """ - # get the directory - pipeline_log_path = storage.url(pipeline_cls().config.store_result) - dirs = list(sorted([f.path for f in os.scandir(pipeline_log_path) if f.is_dir()])) - - # get resultlog callback - resultlog = getattr(pipeline_cls, "_promptui_resultlog", ResultLog) - allowed_resultlog_callbacks = {i for i in dir(resultlog) if not i.startswith("__")} - - ids = [] - params: Dict[str, List[Any]] = {} - logged_infos: Dict[str, List[Any]] = {} - - for idx, each_dir in enumerate(dirs): - ids.append(str(Path(each_dir).name)) - - # get the params - params_file = os.path.join(each_dir, "params.pkl") - if os.path.exists(params_file): - with open(params_file, "rb") as f: - each_params = pickle.load(f) - for key, value in each_params.items(): - if key not in params: - params[key] = [None] * len(dirs) - params[key][idx] = value - - # get the progress - progress_file = os.path.join(each_dir, "progress.pkl") - if os.path.exists(progress_file): - with open(progress_file, "rb") as f: - progress = pickle.load(f) - - for name, col_info in log_config.items(): - step = col_info["step"] - getter = col_info.get("getter", None) - if name not in logged_infos: - logged_infos[name] = [None] * len(dirs) - - if step not in progress: - continue - - info = progress[step] - if getter: - if getter in allowed_resultlog_callbacks: - info = getattr(resultlog, getter)(info) - else: - implicit_name = f"get_{name}" - if implicit_name in allowed_resultlog_callbacks: - info = getattr(resultlog, implicit_name)(info) - logged_infos[name][idx] = info - - return {"ids": ids, **params, **logged_infos} - - -def export(config: dict, pipeline_def, output_path): - """Export from config to Excel file""" - - pipeline_name = f"{pipeline_def.__module__}.{pipeline_def.__name__}" - - # export to Excel - if not config.get("logs", {}): - raise ValueError(f"Pipeline {pipeline_name} has no logs to export") - - pds: Dict[str, pd.DataFrame] = {} - for log_name, log_def in config["logs"].items(): - pds[log_name] = pd.DataFrame(from_log_to_dict(pipeline_def, log_def)) - - # from the list of pds, export to Excel to output_path - with pd.ExcelWriter(output_path, engine="openpyxl") as writer: # type: ignore - for log_name, df in pds.items(): - df.to_excel(writer, sheet_name=log_name) - - -def export_from_dict( - config: Union[str, dict], - pipeline: Union[str, Type[BaseComponent]], - output_path: str, -): - """CLI to export the logs of a pipeline into Excel file - - Args: - config_path (str): Path to the config file - pipeline_name (str): Name of the pipeline - output_path (str): Path to the output Excel file - """ - # get the pipeline class and the relevant config dict - config_dict: dict - if isinstance(config, str): - with open(config) as f: - config_dict = yaml.safe_load(f) - elif isinstance(config, dict): - config_dict = config - else: - raise TypeError(f"`config` must be str or dict, not {type(config)}") - - pipeline_name: str - pipeline_cls: Type[BaseComponent] - pipeline_config: dict - if isinstance(pipeline, str): - if pipeline not in config_dict: - raise ValueError(f"Pipeline {pipeline} not found in config file") - pipeline_name = pipeline - pipeline_cls = import_dotted_string(pipeline, safe=False) - pipeline_config = config_dict[pipeline] - elif isinstance(pipeline, type) and issubclass(pipeline, BaseComponent): - pipeline_name = f"{pipeline.__module__}.{pipeline.__name__}" - if pipeline_name not in config_dict: - raise ValueError(f"Pipeline {pipeline_name} not found in config file") - pipeline_cls = pipeline - pipeline_config = config_dict[pipeline_name] - else: - raise TypeError( - f"`pipeline` must be str or subclass of BaseComponent, not {type(pipeline)}" - ) - - export(pipeline_config, pipeline_cls, output_path) diff --git a/libs/kotaemon/kotaemon/contribs/promptui/logs.py b/libs/kotaemon/kotaemon/contribs/promptui/logs.py deleted file mode 100644 index f9b24d2aa..000000000 --- a/libs/kotaemon/kotaemon/contribs/promptui/logs.py +++ /dev/null @@ -1,16 +0,0 @@ -class ResultLog: - """Callback getter to get the desired log result - - The callback resolution will be as follow: - 1. Explicit string name - 2. Implicitly by: `get_` - 3. Pass through - """ - - @staticmethod - def _get_input(obj): - return obj["input"] - - @staticmethod - def _get_output(obj): - return obj["output"] diff --git a/libs/kotaemon/kotaemon/contribs/promptui/themes.py b/libs/kotaemon/kotaemon/contribs/promptui/themes.py deleted file mode 100644 index d76d9f9a9..000000000 --- a/libs/kotaemon/kotaemon/contribs/promptui/themes.py +++ /dev/null @@ -1,95 +0,0 @@ -from __future__ import annotations - -from typing import Iterable - -from gradio.themes.base import Base -from gradio.themes.utils import colors, fonts, sizes - - -class John(Base): - def __init__( - self, - *, - primary_hue: colors.Color | str = colors.neutral, - secondary_hue: colors.Color | str = colors.neutral, - neutral_hue: colors.Color | str = colors.neutral, - spacing_size: sizes.Size | str = sizes.spacing_sm, - radius_size: sizes.Size | str = sizes.radius_none, - text_size: sizes.Size | str = sizes.text_sm, - font: fonts.Font - | str - | Iterable[fonts.Font | str] = ( - fonts.GoogleFont("Quicksand"), - "ui-sans-serif", - "system-ui", - "sans-serif", - ), - font_mono: fonts.Font - | str - | Iterable[fonts.Font | str] = ( - fonts.GoogleFont("IBM Plex Mono"), - "ui-monospace", - "Consolas", - "monospace", - ), - ): - super().__init__( - primary_hue=primary_hue, - secondary_hue=secondary_hue, - neutral_hue=neutral_hue, - spacing_size=spacing_size, - radius_size=radius_size, - text_size=text_size, - font=font, - font_mono=font_mono, - ) - self.name = "monochrome" - super().set( - # Colors - slider_color="*neutral_900", - slider_color_dark="*neutral_500", - body_text_color="*neutral_900", - block_label_text_color="*body_text_color", - block_title_text_color="*body_text_color", - body_text_color_subdued="*neutral_700", - background_fill_primary_dark="*neutral_900", - background_fill_secondary_dark="*neutral_800", - block_background_fill_dark="*neutral_800", - input_background_fill_dark="*neutral_700", - # Button Colors - button_primary_background_fill="*neutral_900", - button_primary_background_fill_hover="*neutral_700", - button_primary_text_color="white", - button_primary_background_fill_dark="*neutral_600", - button_primary_background_fill_hover_dark="*neutral_600", - button_primary_text_color_dark="white", - button_secondary_background_fill=( - "linear-gradient(to bottom right, *neutral_100, *neutral_200)" - ), - button_secondary_background_fill_hover=( - "linear-gradient(to bottom right, *neutral_100, *neutral_100)" - ), - button_secondary_background_fill_dark=( - "linear-gradient(to bottom right, *neutral_600, *neutral_700)" - ), - button_secondary_background_fill_hover_dark=( - "linear-gradient(to bottom right, *neutral_600, *neutral_600)" - ), - button_cancel_background_fill="*button_primary_background_fill", - button_cancel_background_fill_hover="*button_primary_background_fill_hover", - button_cancel_text_color="*button_primary_text_color", - # Padding - checkbox_label_padding="*spacing_sm", - button_large_padding="*spacing_sm", - button_small_padding="*spacing_sm", - # Borders - block_border_width="0px", - block_border_width_dark="1px", - shadow_drop_lg="0 1px 4px 0 rgb(0 0 0 / 0.1)", - block_shadow="*shadow_drop_lg", - block_shadow_dark="none", - # Block Labels - block_title_text_weight="600", - block_label_text_weight="600", - block_label_text_size="*text_sm", - ) diff --git a/libs/kotaemon/kotaemon/contribs/promptui/tunnel.py b/libs/kotaemon/kotaemon/contribs/promptui/tunnel.py deleted file mode 100644 index 711585da0..000000000 --- a/libs/kotaemon/kotaemon/contribs/promptui/tunnel.py +++ /dev/null @@ -1,106 +0,0 @@ -import atexit -import logging -import os -import platform -import stat -import subprocess -from pathlib import Path - -import requests - -VERSION = "1.0" - -machine = platform.machine() -if machine == "x86_64": - machine = "amd64" - -BINARY_REMOTE_NAME = f"frpc_{platform.system().lower()}_{machine.lower()}" -EXTENSION = ".exe" if os.name == "nt" else "" -BINARY_URL = ( - "some-endpoint.com" f"/kotaemon/tunneling/{VERSION}/{BINARY_REMOTE_NAME}{EXTENSION}" -) - -BINARY_FILENAME = f"{BINARY_REMOTE_NAME}_v{VERSION}" -BINARY_FOLDER = Path(__file__).parent -BINARY_PATH = f"{BINARY_FOLDER / BINARY_FILENAME}" - - -logger = logging.getLogger(__name__) - - -class Tunnel: - def __init__(self, appname, username, local_port): - self.proc = None - self.url = None - self.appname = appname - self.username = username - self.local_port = local_port - - @staticmethod - def download_binary(): - if not Path(BINARY_PATH).exists(): - print("First time setting tunneling...") - resp = requests.get(BINARY_URL) - - if resp.status_code == 404: - raise OSError( - f"Cannot set up a share link as this platform is incompatible. " - "Please create a GitHub issue with information about your " - f"platform: {platform.uname()}" - ) - - if resp.status_code == 403: - raise OSError( - "You do not have permission to setup the tunneling. Please " - "make sure that you are within Cinnamon VPN or within other " - "approved IPs. If this is new server, please contact @channel " - "at #llm-productization to add your IP address" - ) - - resp.raise_for_status() - - # Save file data to local copy - with open(BINARY_PATH, "wb") as file: - file.write(resp.content) - st = os.stat(BINARY_PATH) - os.chmod(BINARY_PATH, st.st_mode | stat.S_IEXEC) - - def run(self) -> str: - """Setting up tunneling""" - if platform.system().lower() == "windows": - logger.warning("Tunneling is not fully supported on Windows.") - - self.download_binary() - self.url = self._start_tunnel(BINARY_PATH) - return self.url - - def kill(self): - if self.proc is not None: - print(f"Killing tunnel 127.0.0.1:{self.local_port} <> {self.url}") - self.proc.terminate() - self.proc = None - - def _start_tunnel(self, binary: str) -> str: - command = [ - binary, - "http", - "-l", - str(self.local_port), - "-i", - "127.0.0.1", - "--uc", - "--sd", - str(self.appname), - "-n", - str(self.appname + self.username), - "--server_addr", - "44.229.38.9:7000", - "--token", - "Wz807/DyC;#t;#/", - "--disable_log_color", - ] - self.proc = subprocess.Popen( - command, stdout=subprocess.PIPE, stderr=subprocess.PIPE - ) - atexit.register(self.kill) - return f"https://{self.appname}.promptui.dm.cinnamon.is" diff --git a/libs/kotaemon/kotaemon/contribs/promptui/ui/__init__.py b/libs/kotaemon/kotaemon/contribs/promptui/ui/__init__.py deleted file mode 100644 index b74022fe5..000000000 --- a/libs/kotaemon/kotaemon/contribs/promptui/ui/__init__.py +++ /dev/null @@ -1,45 +0,0 @@ -from typing import Union - -import gradio as gr -import yaml -from theflow.utils.modules import import_dotted_string - -from ..themes import John -from .chat import build_chat_ui -from .pipeline import build_pipeline_ui - - -def build_from_dict(config: Union[str, dict]): - """Build a full UI from YAML config file""" - - if isinstance(config, str): - with open(config) as f: - config_dict: dict = yaml.safe_load(f) - elif isinstance(config, dict): - config_dict = config - else: - raise ValueError( - f"config must be either a yaml path or a dict, got {type(config)}" - ) - - demos = [] - for key, value in config_dict.items(): - pipeline_def = import_dotted_string(key, safe=False) - if value["ui-type"] == "chat": - demos.append(build_chat_ui(value, pipeline_def).queue()) - else: - demos.append(build_pipeline_ui(value, pipeline_def).queue()) - if len(demos) == 1: - demo = demos[0] - else: - demo = gr.TabbedInterface( - demos, - tab_names=list(config_dict.keys()), - title="PromptUI from kotaemon", - analytics_enabled=False, - theme=John(), - ) - - demo.queue() - - return demo diff --git a/libs/kotaemon/kotaemon/contribs/promptui/ui/blocks.py b/libs/kotaemon/kotaemon/contribs/promptui/ui/blocks.py deleted file mode 100644 index b2ee05d78..000000000 --- a/libs/kotaemon/kotaemon/contribs/promptui/ui/blocks.py +++ /dev/null @@ -1,181 +0,0 @@ -from __future__ import annotations - -from typing import Any, AsyncGenerator - -import anyio -from gradio import ChatInterface -from gradio.components import Component, get_component_instance -from gradio.events import on -from gradio.helpers import special_args -from gradio.routes import Request - - -class ChatBlock(ChatInterface): - """The ChatBlock subclasses ChatInterface to provide extra functionalities: - - - Show additional outputs to the chat interface - - Disallow blank user message - """ - - def __init__( - self, - *args, - additional_outputs: str | Component | list[str | Component] | None = None, - **kwargs, - ): - if additional_outputs: - if not isinstance(additional_outputs, list): - additional_outputs = [additional_outputs] - self.additional_outputs = [ - get_component_instance(i) for i in additional_outputs # type: ignore - ] - else: - self.additional_outputs = [] - - super().__init__(*args, **kwargs) - - async def _submit_fn( - self, - message: str, - history_with_input: list[list[str | None]], - request: Request, - *args, - ) -> tuple[Any, ...]: - input_args = args[: -len(self.additional_outputs)] - output_args = args[-len(self.additional_outputs) :] - if not message: - return history_with_input, history_with_input, *output_args - - history = history_with_input[:-1] - inputs, _, _ = special_args( - self.fn, inputs=[message, history, *input_args], request=request - ) - - if self.is_async: - response = await self.fn(*inputs) - else: - response = await anyio.to_thread.run_sync( - self.fn, *inputs, limiter=self.limiter - ) - - output = [] - if self.additional_outputs: - text = response[0] - output = response[1:] - else: - text = response - - history.append([message, text]) - return history, history, *output - - async def _stream_fn( - self, - message: str, - history_with_input: list[list[str | None]], - *args, - ) -> AsyncGenerator: - raise NotImplementedError("Stream function not implemented for ChatBlock") - - def _display_input( - self, message: str, history: list[list[str | None]] - ) -> tuple[list[list[str | None]], list[list[str | None]]]: - """Stop displaying the input message if the message is a blank string""" - if not message: - return history, history - return super()._display_input(message, history) - - def _setup_events(self) -> None: - """Include additional outputs in the submit event""" - submit_fn = self._stream_fn if self.is_generator else self._submit_fn - submit_triggers = ( - [self.textbox.submit, self.submit_btn.click] - if self.submit_btn - else [self.textbox.submit] - ) - submit_event = ( - on( - submit_triggers, - self._clear_and_save_textbox, - [self.textbox], - [self.textbox, self.saved_input], - api_name=False, - queue=False, - ) - .then( - self._display_input, - [self.saved_input, self.chatbot_state], - [self.chatbot, self.chatbot_state], - api_name=False, - queue=False, - ) - .then( - submit_fn, - [self.saved_input, self.chatbot_state] - + self.additional_inputs - + self.additional_outputs, - [self.chatbot, self.chatbot_state] + self.additional_outputs, - api_name=False, - ) - ) - self._setup_stop_events(submit_triggers, submit_event) - - if self.retry_btn: - retry_event = ( - self.retry_btn.click( - self._delete_prev_fn, - [self.chatbot_state], - [self.chatbot, self.saved_input, self.chatbot_state], - api_name=False, - queue=False, - ) - .then( - self._display_input, - [self.saved_input, self.chatbot_state], - [self.chatbot, self.chatbot_state], - api_name=False, - queue=False, - ) - .then( - submit_fn, - [self.saved_input, self.chatbot_state] - + self.additional_inputs - + self.additional_outputs, - [self.chatbot, self.chatbot_state] + self.additional_outputs, - api_name=False, - ) - ) - self._setup_stop_events([self.retry_btn.click], retry_event) - - if self.undo_btn: - self.undo_btn.click( - self._delete_prev_fn, - [self.chatbot_state], - [self.chatbot, self.saved_input, self.chatbot_state], - api_name=False, - queue=False, - ).then( - lambda x: x, - [self.saved_input], - [self.textbox], - api_name=False, - queue=False, - ) - - if self.clear_btn: - self.clear_btn.click( - lambda: ([], [], None), - None, - [self.chatbot, self.chatbot_state, self.saved_input], - queue=False, - api_name=False, - ) - - def _setup_api(self) -> None: - api_fn = self._api_stream_fn if self.is_generator else self._api_submit_fn - - self.fake_api_btn.click( - api_fn, - [self.textbox, self.chatbot_state] + self.additional_inputs, - [self.textbox, self.chatbot_state] + self.additional_outputs, - api_name="chat", - ) diff --git a/libs/kotaemon/kotaemon/contribs/promptui/ui/chat.py b/libs/kotaemon/kotaemon/contribs/promptui/ui/chat.py deleted file mode 100644 index b3ef35612..000000000 --- a/libs/kotaemon/kotaemon/contribs/promptui/ui/chat.py +++ /dev/null @@ -1,307 +0,0 @@ -import pickle -from datetime import datetime -from pathlib import Path - -import gradio as gr -from theflow.storage import storage - -from kotaemon.chatbot import ChatConversation -from kotaemon.contribs.promptui.base import get_component -from kotaemon.contribs.promptui.export import export -from kotaemon.contribs.promptui.ui.blocks import ChatBlock - -from ..logs import ResultLog - -USAGE_INSTRUCTION = """## How to use: - -1. Set the desired parameters. -2. Click "New chat" to start a chat session with the supplied parameters. This - set of parameters will persist until the end of the chat session. During an - ongoing chat session, changing the parameters will not take any effect. -3. Chat and interact with the chat bot on the right panel. You can add any - additional input (if any), and they will be supplied to the chatbot. -4. During chat, the log of the chat will show up in the "Output" tabs. This is - empty by default, so if you want to show the log here, tell the AI developers - to configure the UI settings. -5. When finishing chat, select your preference in the radio box. Click "End chat". - This will save the chat log and the preference to disk. -6. To compare the result of different run, click "Export" to get an Excel - spreadsheet summary of different run. - -## Support: - -In case of errors, you can: - -- PromptUI instruction: - https://github.com/Cinnamon/kotaemon/wiki/Utilities#prompt-engineering-ui -- Create bug fix and make PR at: https://github.com/Cinnamon/kotaemon -- Ping any of @john @tadashi @ian @jacky in Slack channel #llm-productization - -## Contribute: - -- Follow installation at: https://github.com/Cinnamon/kotaemon/ -""" - - -def construct_chat_ui( - config, func_new_chat, func_chat, func_end_chat, func_export_to_excel -) -> gr.Blocks: - """Construct the prompt engineering UI for chat - - Args: - config: the UI config - func_new_chat: the function for starting a new chat session - func_chat: the function for chatting interaction - func_end_chat: the function for ending and saving the chat - func_export_to_excel: the function to export the logs to excel - - Returns: - the UI object - """ - inputs, outputs, params = [], [], [] - for name, component_def in config.get("inputs", {}).items(): - if "params" not in component_def: - component_def["params"] = {} - component_def["params"]["interactive"] = True - component = get_component(component_def) - if hasattr(component, "label") and not component.label: # type: ignore - component.label = name # type: ignore - - inputs.append(component) - - for name, component_def in config.get("params", {}).items(): - if "params" not in component_def: - component_def["params"] = {} - component_def["params"]["interactive"] = True - component = get_component(component_def) - if hasattr(component, "label") and not component.label: # type: ignore - component.label = name # type: ignore - - params.append(component) - - for idx, component_def in enumerate(config.get("outputs", [])): - if "params" not in component_def: - component_def["params"] = {} - component_def["params"]["interactive"] = False - component = get_component(component_def) - if hasattr(component, "label") and not component.label: # type: ignore - component.label = f"Output {idx}" # type: ignore - - outputs.append(component) - - sess = gr.State(value=None) - chatbot = gr.Chatbot(label="Chatbot", show_copy_button=True) - chat = ChatBlock( - func_chat, chatbot=chatbot, additional_inputs=[sess], additional_outputs=outputs - ) - param_state = gr.Textbox(interactive=False) - - with gr.Blocks(analytics_enabled=False, title="Welcome to PromptUI") as demo: - sess.render() - with gr.Accordion(label="HOW TO", open=False): - gr.Markdown(USAGE_INSTRUCTION) - with gr.Row(): - run_btn = gr.Button("New chat") - run_btn.click( - func_new_chat, - inputs=params, - outputs=[ - chat.chatbot, - chat.chatbot_state, - chat.saved_input, - param_state, - sess, - *outputs, - ], - ) - with gr.Accordion(label="End chat", open=False): - likes = gr.Radio(["like", "dislike", "neutral"], value="neutral") - save_log = gr.Checkbox( - value=True, - label="Save log", - info="If saved, log can be exported later", - show_label=True, - ) - end_btn = gr.Button("End chat") - end_btn.click( - func_end_chat, - inputs=[likes, save_log, sess], - outputs=[param_state, sess], - ) - with gr.Accordion(label="Export", open=False): - exported_file = gr.File( - label="Output file", show_label=True, height=100 - ) - export_btn = gr.Button("Export") - export_btn.click(func_export_to_excel, inputs=[], outputs=exported_file) - - with gr.Row(): - with gr.Column(): - with gr.Tab("Params"): - for component in params: - component.render() - with gr.Accordion(label="Session state", open=False): - param_state.render() - - with gr.Tab("Outputs"): - for component in outputs: - component.render() - with gr.Column(): - chat.render() - - return demo.queue() - - -def build_chat_ui(config, pipeline_def): - """Build the chat UI - - Args: - config: the UI config - pipeline_def: the pipeline definition - - Returns: - the UI object - """ - output_dir: Path = Path(storage.url(pipeline_def().config.store_result)) - exported_dir = output_dir.parent / "exported" - exported_dir.mkdir(parents=True, exist_ok=True) - - resultlog = getattr(pipeline_def, "_promptui_resultlog", ResultLog) - allowed_resultlog_callbacks = {i for i in dir(resultlog) if not i.startswith("__")} - - def new_chat(*args): - """Start a new chat function - - Args: - *args: the pipeline init params - - Returns: - new empty states - """ - gr.Info("Starting new session...") - param_dicts = { - name: value for name, value in zip(config["params"].keys(), args) - } - for key in param_dicts.keys(): - if config["params"][key].get("component").lower() == "file": - param_dicts[key] = param_dicts[key].name - - # TODO: currently hard-code as ChatConversation - pipeline = pipeline_def() - session = ChatConversation(bot=pipeline) - session.set(param_dicts) - session.start_session() - - param_state_str = "\n".join( - f"- {name}: {value}" for name, value in param_dicts.items() - ) - - gr.Info("New chat session started.") - return ( - [], - [], - None, - param_state_str, - session, - *[None] * len(config.get("outputs", [])), - ) - - def chat(message, history, session, *args): - """The chat interface - - # TODO: wrap the input and output of this chat function so that it - work with more types of chat conversation than simple text - - Args: - message: the message from the user - history: the gradio history of the chat - session: the chat object session - *args: the additional inputs - - Returns: - the response from the chatbot - """ - if session is None: - raise gr.Error( - "No active chat session. Please set the params and click New chat" - ) - - pred = session(message) - text_response = pred.content - - additional_outputs = [] - for output_def in config.get("outputs", []): - value = session.last_run.logs(output_def["step"]) - getter = output_def.get("getter", None) - if getter and getter in allowed_resultlog_callbacks: - value = getattr(resultlog, getter)(value) - additional_outputs.append(value) - - return text_response, *additional_outputs - - def end_chat(preference: str, save_log: bool, session): - """End the chat session - - Args: - preference: the preference of the user - save_log: whether to save the result - session: the chat object session - - Returns: - the new empty state - """ - gr.Info("Ending session...") - session.end_session() - output_dir: Path = ( - Path(storage.url(session.config.store_result)) / session.last_run.id() - ) - - if not save_log: - if output_dir.exists(): - import shutil - - shutil.rmtree(output_dir) - - session = None - param_state = "" - gr.Info("End session without saving log.") - return param_state, session - - # add preference result to progress - with (output_dir / "progress.pkl").open("rb") as fi: - progress = pickle.load(fi) - progress["preference"] = preference - with (output_dir / "progress.pkl").open("wb") as fo: - pickle.dump(progress, fo) - - # get the original params - param_dicts = {name: session.getx(name) for name in config["params"].keys()} - with (output_dir / "params.pkl").open("wb") as fo: - pickle.dump(param_dicts, fo) - - session = None - param_state = "" - gr.Info("End session and save log.") - return param_state, session - - def export_func(): - name = ( - f"{pipeline_def.__module__}.{pipeline_def.__name__}_{datetime.now()}.xlsx" - ) - path = str(exported_dir / name) - gr.Info(f"Begin exporting {name}...") - try: - export(config=config, pipeline_def=pipeline_def, output_path=path) - except Exception as e: - raise gr.Error(f"Failed to export. Please contact project's AIR: {e}") - gr.Info(f"Exported {name}. Please go to the `Exported file` tab to download") - return path - - demo = construct_chat_ui( - config=config, - func_new_chat=new_chat, - func_chat=chat, - func_end_chat=end_chat, - func_export_to_excel=export_func, - ) - return demo diff --git a/libs/kotaemon/kotaemon/contribs/promptui/ui/pipeline.py b/libs/kotaemon/kotaemon/contribs/promptui/ui/pipeline.py deleted file mode 100644 index 978a751b0..000000000 --- a/libs/kotaemon/kotaemon/contribs/promptui/ui/pipeline.py +++ /dev/null @@ -1,246 +0,0 @@ -import pickle -import time -from datetime import datetime -from pathlib import Path -from typing import Any, Dict - -import gradio as gr -import pandas as pd -from theflow.storage import storage - -from kotaemon.contribs.promptui.base import get_component -from kotaemon.contribs.promptui.export import export - -from ..logs import ResultLog - -USAGE_INSTRUCTION = """## How to use: - -1. Set the desired parameters. -2. Set the desired inputs. -3. Click "Run" to execute the pipeline with the supplied parameters and inputs -4. The pipeline output will show up in the output panel. -5. Repeat from step 1. -6. To compare the result of different run, click "Export" to get an Excel - spreadsheet summary of different run. - -## Support: - -In case of errors, you can: - -- PromptUI instruction: - https://github.com/Cinnamon/kotaemon/wiki/Utilities#prompt-engineering-ui -- Create bug fix and make PR at: https://github.com/Cinnamon/kotaemon -- Ping any of @john @tadashi @ian @jacky in Slack channel #llm-productization - -## Contribute: - -- Follow installation at: https://github.com/Cinnamon/kotaemon/ -""" - - -def construct_pipeline_ui( - config, func_run, func_save, func_load_params, func_activate_params, func_export -) -> gr.Blocks: - """Create UI from config file. Execute the UI from config file - - - Can do now: Log from stdout to UI - - In the future, we can provide some hooks and callbacks to let developers better - fine-tune the UI behavior. - """ - inputs, outputs, params = [], [], [] - for name, component_def in config.get("inputs", {}).items(): - if "params" not in component_def: - component_def["params"] = {} - component_def["params"]["interactive"] = True - component = get_component(component_def) - if hasattr(component, "label") and not component.label: # type: ignore - component.label = name # type: ignore - - inputs.append(component) - - for name, component_def in config.get("params", {}).items(): - if "params" not in component_def: - component_def["params"] = {} - component_def["params"]["interactive"] = True - component = get_component(component_def) - if hasattr(component, "label") and not component.label: # type: ignore - component.label = name # type: ignore - - params.append(component) - - for idx, component_def in enumerate(config.get("outputs", [])): - if "params" not in component_def: - component_def["params"] = {} - component_def["params"]["interactive"] = False - component = get_component(component_def) - if hasattr(component, "label") and not component.label: # type: ignore - component.label = f"Output {idx}" # type: ignore - - outputs.append(component) - - exported_file = gr.File(label="Output file", show_label=True) - history_dataframe = gr.DataFrame(wrap=True) - - temp = gr.Tab - with gr.Blocks(analytics_enabled=False, title="Welcome to PromptUI") as demo: - with gr.Accordion(label="HOW TO", open=False): - gr.Markdown(USAGE_INSTRUCTION) - with gr.Accordion(label="Params History", open=False): - with gr.Row(): - save_btn = gr.Button("Save params") - save_btn.click(func_save, inputs=params, outputs=history_dataframe) - load_params_btn = gr.Button("Reload params") - load_params_btn.click( - func_load_params, inputs=[], outputs=history_dataframe - ) - history_dataframe.render() - history_dataframe.select( - func_activate_params, inputs=params, outputs=params - ) - with gr.Row(): - run_btn = gr.Button("Run") - run_btn.click(func_run, inputs=inputs + params, outputs=outputs) - export_btn = gr.Button( - "Export (Result will be in Exported file next to Output)" - ) - export_btn.click(func_export, inputs=[], outputs=exported_file) - with gr.Row(): - with gr.Column(): - if params: - with temp("Params"): - for component in params: - component.render() - if inputs: - with temp("Inputs"): - for component in inputs: - component.render() - if not params and not inputs: - gr.Text("No params or inputs") - with gr.Column(): - with temp("Outputs"): - for component in outputs: - component.render() - with temp("Exported file"): - exported_file.render() - - return demo - - -def load_saved_params(path: str) -> Dict: - """Load the saved params from path to a dataframe""" - # get all pickle files - files = list(sorted(Path(path).glob("*.pkl"))) - data: Dict[str, Any] = {"_id": [None] * len(files)} - for idx, each_file in enumerate(files): - with open(each_file, "rb") as f: - each_data = pickle.load(f) - data["_id"][idx] = Path(each_file).stem - for key, value in each_data.items(): - if key not in data: - data[key] = [None] * len(files) - data[key][idx] = value - - return data - - -def build_pipeline_ui(config: dict, pipeline_def): - """Build a tab from config file""" - inputs_name = list(config.get("inputs", {}).keys()) - params_name = list(config.get("params", {}).keys()) - outputs_def = config.get("outputs", []) - - output_dir: Path = Path(storage.url(pipeline_def().config.store_result)) - exported_dir = output_dir.parent / "exported" - exported_dir.mkdir(parents=True, exist_ok=True) - - save_dir = ( - output_dir.parent - / "saved" - / f"{pipeline_def.__module__}.{pipeline_def.__name__}" - ) - save_dir.mkdir(parents=True, exist_ok=True) - - resultlog = getattr(pipeline_def, "_promptui_resultlog", ResultLog) - allowed_resultlog_callbacks = {i for i in dir(resultlog) if not i.startswith("__")} - - def run_func(*args): - inputs = { - name: value for name, value in zip(inputs_name, args[: len(inputs_name)]) - } - params = { - name: value for name, value in zip(params_name, args[len(inputs_name) :]) - } - pipeline = pipeline_def() - pipeline.set(params) - pipeline(**inputs) - with storage.open( - storage.url( - pipeline.config.store_result, pipeline.last_run.id(), "params.pkl" - ), - "wb", - ) as f: - pickle.dump(params, f) - if outputs_def: - outputs = [] - for output_def in outputs_def: - output = pipeline.last_run.logs(output_def["step"]) - getter = output_def.get("getter", None) - if getter and getter in allowed_resultlog_callbacks: - output = getattr(resultlog, getter)(output) - outputs.append(output) - if len(outputs_def) == 1: - return outputs[0] - return outputs - - def save_func(*args): - params = {name: value for name, value in zip(params_name, args)} - filename = save_dir / f"{int(time.time())}.pkl" - with open(filename, "wb") as f: - pickle.dump(params, f) - gr.Info("Params saved") - - data = load_saved_params(str(save_dir)) - return pd.DataFrame(data) - - def load_params_func(): - data = load_saved_params(str(save_dir)) - return pd.DataFrame(data) - - def activate_params_func(ev: gr.SelectData, *args): - data = load_saved_params(str(save_dir)) - output_args = [each for each in args] - if ev.value is None: - gr.Info(f'Blank value: "{ev.value}". Skip') - return output_args - - column = list(data.keys())[ev.index[1]] - - if column not in params_name: - gr.Info(f'Column "{column}" not in params. Skip') - return output_args - - value = data[column][ev.index[0]] - if value is None: - gr.Info(f'Blank value: "{ev.value}". Skip') - return output_args - - output_args[params_name.index(column)] = value - - return output_args - - def export_func(): - name = ( - f"{pipeline_def.__module__}.{pipeline_def.__name__}_{datetime.now()}.xlsx" - ) - path = str(exported_dir / name) - gr.Info(f"Begin exporting {name}...") - try: - export(config=config, pipeline_def=pipeline_def, output_path=path) - except Exception as e: - raise gr.Error(f"Failed to export. Please contact project's AIR: {e}") - gr.Info(f"Exported {name}. Please go to the `Exported file` tab to download") - return path - - return construct_pipeline_ui( - config, run_func, save_func, load_params_func, activate_params_func, export_func - ) diff --git a/libs/kotaemon/kotaemon/embeddings/__init__.py b/libs/kotaemon/kotaemon/embeddings/__init__.py index 7ba87c9ae..c4eb97764 100644 --- a/libs/kotaemon/kotaemon/embeddings/__init__.py +++ b/libs/kotaemon/kotaemon/embeddings/__init__.py @@ -2,8 +2,10 @@ from .endpoint_based import EndpointEmbeddings from .fastembed import FastEmbedEmbeddings from .langchain_based import ( + BaseLCEmbeddings, LCAzureOpenAIEmbeddings, LCCohereEmbeddings, + LCEmbeddingMixin, LCGoogleEmbeddings, LCHuggingFaceEmbeddings, LCMistralEmbeddings, @@ -17,6 +19,8 @@ "BaseEmbeddings", "EndpointEmbeddings", "TeiEndpointEmbeddings", + "BaseLCEmbeddings", + "LCEmbeddingMixin", "LCOpenAIEmbeddings", "LCAzureOpenAIEmbeddings", "LCCohereEmbeddings", diff --git a/libs/kotaemon/kotaemon/embeddings/base.py b/libs/kotaemon/kotaemon/embeddings/base.py index 3955c79ca..db1136b74 100644 --- a/libs/kotaemon/kotaemon/embeddings/base.py +++ b/libs/kotaemon/kotaemon/embeddings/base.py @@ -1,10 +1,18 @@ from __future__ import annotations -from kotaemon.base import BaseComponent, Document, DocumentWithEmbedding +from dataclasses import dataclass +from kotaemon.base import Document, DocumentWithEmbedding +from kotaemon.base.describe import DataclassDescribe, describe_dataclass -class BaseEmbeddings(BaseComponent): - def run( + +@dataclass(kw_only=True) +class BaseEmbeddings: + @classmethod + def describe(cls) -> DataclassDescribe: + return describe_dataclass(cls) + + def __call__( self, text: str | list[str] | Document | list[Document], *args, **kwargs ) -> list[DocumentWithEmbedding]: return self.invoke(text, *args, **kwargs) diff --git a/libs/kotaemon/kotaemon/embeddings/endpoint_based.py b/libs/kotaemon/kotaemon/embeddings/endpoint_based.py index fa9a99b78..16703faa5 100644 --- a/libs/kotaemon/kotaemon/embeddings/endpoint_based.py +++ b/libs/kotaemon/kotaemon/embeddings/endpoint_based.py @@ -1,3 +1,5 @@ +from dataclasses import dataclass, field + import requests from kotaemon.base import Document, DocumentWithEmbedding @@ -5,31 +7,20 @@ from .base import BaseEmbeddings +@dataclass(kw_only=True) class EndpointEmbeddings(BaseEmbeddings): - """ - An Embeddings component that uses an OpenAI API compatible endpoint. - - Attributes: - endpoint_url (str): The url of an OpenAI API compatible endpoint. - """ + """Embeddings via an OpenAI API compatible HTTP endpoint.""" - endpoint_url: str + endpoint_url: str = field( + metadata={"description": "OpenAI-compatible embeddings endpoint URL"}, + ) - def run( - self, text: str | list[str] | Document | list[Document] + def invoke( + self, text: str | list[str] | Document | list[Document], *args, **kwargs ) -> list[DocumentWithEmbedding]: - """ - Generate embeddings from text Args: - text (str | list[str] | Document | list[Document]): text to generate - embeddings from - Returns: - list[DocumentWithEmbedding]: embeddings - """ if not isinstance(text, list): text = [text] - - outputs = [] - + outputs: list[DocumentWithEmbedding] = [] for item in text: response = requests.post( self.endpoint_url, json={"input": str(item)} @@ -42,5 +33,9 @@ def run( prompt_tokens=response["usage"]["prompt_tokens"], ) ) - return outputs + + def run( + self, text: str | list[str] | Document | list[Document] + ) -> list[DocumentWithEmbedding]: + return self.invoke(text) diff --git a/libs/kotaemon/kotaemon/embeddings/factory.py b/libs/kotaemon/kotaemon/embeddings/factory.py new file mode 100644 index 000000000..01be7891d --- /dev/null +++ b/libs/kotaemon/kotaemon/embeddings/factory.py @@ -0,0 +1,63 @@ +from __future__ import annotations + +from enum import Enum + +from .base import BaseEmbeddings +from .endpoint_based import EndpointEmbeddings +from .fastembed import FastEmbedEmbeddings +from .langchain_based import ( + LCAzureOpenAIEmbeddings, + LCCohereEmbeddings, + LCGoogleEmbeddings, + LCHuggingFaceEmbeddings, + LCMistralEmbeddings, + LCOpenAIEmbeddings, +) +from .openai import AzureOpenAIEmbeddings, OpenAIEmbeddings +from .tei_endpoint_embed import TeiEndpointEmbeddings +from .voyageai import VoyageAIEmbeddings + + +class EmbeddingVendor(str, Enum): + AZURE_OPENAI = "AzureOpenAIEmbeddings" + OPENAI = "OpenAIEmbeddings" + VOYAGE_AI = "VoyageAIEmbeddings" + FAST_EMBED = "FastEmbedEmbeddings" + ENDPOINT = "EndpointEmbeddings" + TEI_ENDPOINT = "TeiEndpointEmbeddings" + LC_OPENAI = "LCOpenAIEmbeddings" + LC_AZURE_OPENAI = "LCAzureOpenAIEmbeddings" + LC_COHERE = "LCCohereEmbeddings" + LC_HUGGING_FACE = "LCHuggingFaceEmbeddings" + LC_GOOGLE = "LCGoogleEmbeddings" + LC_MISTRAL = "LCMistralEmbeddings" + + +MP_VENDOR_CLS: dict[EmbeddingVendor, type[BaseEmbeddings]] = { + EmbeddingVendor.AZURE_OPENAI: AzureOpenAIEmbeddings, + EmbeddingVendor.OPENAI: OpenAIEmbeddings, + EmbeddingVendor.VOYAGE_AI: VoyageAIEmbeddings, + EmbeddingVendor.FAST_EMBED: FastEmbedEmbeddings, + EmbeddingVendor.ENDPOINT: EndpointEmbeddings, + EmbeddingVendor.TEI_ENDPOINT: TeiEndpointEmbeddings, + EmbeddingVendor.LC_OPENAI: LCOpenAIEmbeddings, + EmbeddingVendor.LC_AZURE_OPENAI: LCAzureOpenAIEmbeddings, + EmbeddingVendor.LC_COHERE: LCCohereEmbeddings, + EmbeddingVendor.LC_HUGGING_FACE: LCHuggingFaceEmbeddings, + EmbeddingVendor.LC_GOOGLE: LCGoogleEmbeddings, + EmbeddingVendor.LC_MISTRAL: LCMistralEmbeddings, +} + + +class EmbeddingFactory: + @staticmethod + def get_cls(vendor: EmbeddingVendor | str) -> type[BaseEmbeddings]: + """Return the class for *vendor*, coercing bare strings.""" + key = EmbeddingVendor(vendor) + if key not in MP_VENDOR_CLS: + raise ValueError(f"Invalid embedding vendor: {vendor!r}") + return MP_VENDOR_CLS[key] + + @staticmethod + def supported_vendors() -> list[EmbeddingVendor]: + return list(MP_VENDOR_CLS.keys()) diff --git a/libs/kotaemon/kotaemon/embeddings/fastembed.py b/libs/kotaemon/kotaemon/embeddings/fastembed.py index ecaec677c..35b5caaa0 100644 --- a/libs/kotaemon/kotaemon/embeddings/fastembed.py +++ b/libs/kotaemon/kotaemon/embeddings/fastembed.py @@ -1,6 +1,8 @@ +from dataclasses import dataclass, field +from functools import cached_property from typing import TYPE_CHECKING, Optional -from kotaemon.base import Document, DocumentWithEmbedding, Param +from kotaemon.base import Document, DocumentWithEmbedding from .base import BaseEmbeddings @@ -8,44 +10,29 @@ from fastembed import TextEmbedding +@dataclass(kw_only=True) class FastEmbedEmbeddings(BaseEmbeddings): - """Utilize fastembed library for embeddings locally without GPU. + """Utilize fastembed library for embeddings locally without GPU.""" - Supported model: https://qdrant.github.io/fastembed/examples/Supported_Models/ - Code: https://github.com/qdrant/fastembed - """ - - model_name: str = Param( - "BAAI/bge-small-en-v1.5", - help=( - "Model name for fastembed. Please refer " - "[here](https://qdrant.github.io/fastembed/examples/Supported_Models/) " - "for the list of supported models." - ), - required=True, + model_name: str = field( + default="BAAI/bge-small-en-v1.5", + metadata={"description": "fastembed model name"}, ) - batch_size: int = Param( - 256, - help="Batch size for embeddings. Higher values use more memory, but are faster", + batch_size: int = field( + default=256, + metadata={"description": "Batch size for embeddings"}, ) - parallel: Optional[int] = Param( - None, - help=( - "Number of threads to use for embeddings. " - "If > 1, data-parallel encoding will be used. " - "If 0, use all available CPUs. " - "If None, use default onnxruntime threading. " - "Defaults to None." - ), + parallel: Optional[int] = field( + default=None, + metadata={"description": "Thread count for encoding"}, ) - @Param.auto() + @cached_property def client_(self) -> "TextEmbedding": try: from fastembed import TextEmbedding except ImportError: raise ImportError("Please install FastEmbed: `pip install fastembed`") - return TextEmbedding(model_name=self.model_name) def invoke( @@ -58,15 +45,11 @@ def invoke( parallel=self.parallel, ) return [ - DocumentWithEmbedding( - content=doc, - embedding=list(embedding), - ) + DocumentWithEmbedding(content=doc, embedding=list(embedding)) for doc, embedding in zip(input_, embeddings) ] async def ainvoke( self, text: str | list[str] | Document | list[Document], *args, **kwargs ) -> list[DocumentWithEmbedding]: - """Fastembed does not support async API.""" return self.invoke(text, *args, **kwargs) diff --git a/libs/kotaemon/kotaemon/embeddings/langchain_based.py b/libs/kotaemon/kotaemon/embeddings/langchain_based.py index 2a77b2841..82f052007 100644 --- a/libs/kotaemon/kotaemon/embeddings/langchain_based.py +++ b/libs/kotaemon/kotaemon/embeddings/langchain_based.py @@ -1,115 +1,80 @@ -from typing import Optional +from __future__ import annotations -from kotaemon.base import DocumentWithEmbedding, Param +from dataclasses import dataclass, field +from functools import cached_property +from typing import Any + +from kotaemon.base import Document, DocumentWithEmbedding from .base import BaseEmbeddings -class LCEmbeddingMixin: - def _get_lc_class(self): +@dataclass(kw_only=True) +class BaseLCEmbeddings(BaseEmbeddings): + """Base wrapper for LangChain embedding models.""" + + def _get_lc_class(self) -> type: raise NotImplementedError( - "Please return the relevant Langchain class in in _get_lc_class" + "Subclasses must implement _get_lc_class with the LangChain class." ) - def __init__(self, **params): - self._lc_class = self._get_lc_class() - self._obj = self._lc_class(**params) - self._kwargs: dict = params + def _lc_kwargs(self) -> dict[str, Any]: + raise NotImplementedError( + "Subclasses must implement _lc_kwargs mapping fields to LangChain." + ) - super().__init__() + @cached_property + def _lc_obj(self) -> Any: + return self._get_lc_class()(**self._lc_kwargs()) - def run(self, text): + def invoke( + self, + text: str | list[str] | Document | list[Document], + *args, + **kwargs, + ) -> list[DocumentWithEmbedding]: input_docs = self.prepare_input(text) input_ = [doc.text for doc in input_docs] - - embeddings = self._obj.embed_documents(input_) - + embeddings = self._lc_obj.embed_documents(input_) return [ DocumentWithEmbedding(content=doc, embedding=each_embedding) for doc, each_embedding in zip(input_docs, embeddings) ] - def __repr__(self): - kwargs = [] - for key, value_obj in self._kwargs.items(): - value = repr(value_obj) - kwargs.append(f"{key}={value}") - kwargs_repr = ", ".join(kwargs) - return f"{self.__class__.__name__}({kwargs_repr})" - - def __str__(self): - kwargs = [] - for key, value_obj in self._kwargs.items(): - value = str(value_obj) - if len(value) > 20: - value = f"{value[:15]}..." - kwargs.append(f"{key}={value}") - kwargs_repr = ", ".join(kwargs) - return f"{self.__class__.__name__}({kwargs_repr})" - - def __setattr__(self, name, value): - if name == "_lc_class": - return super().__setattr__(name, value) - - if name in self._lc_class.__fields__: - self._kwargs[name] = value - self._obj = self._lc_class(**self._kwargs) - else: - super().__setattr__(name, value) - - def __getattr__(self, name): - if name in self._kwargs: - return self._kwargs[name] - return getattr(self._obj, name) - - def dump(self, *args, **kwargs): - from theflow.utils.modules import serialize - - params = {key: serialize(value) for key, value in self._kwargs.items()} - return { - "__type__": f"{self.__module__}.{self.__class__.__qualname__}", - **params, - } - - def specs(self, path: str): - path = path.strip(".") - if "." in path: - raise ValueError("path should not contain '.'") - - if path in self._lc_class.__fields__: - return { - "__type__": "theflow.base.ParamAttr", - "refresh_on_set": True, - "strict_type": True, - } - - raise ValueError(f"Invalid param {path}") + def run( + self, + text: str | list[str] | Document | list[Document], + *args, + **kwargs, + ) -> list[DocumentWithEmbedding]: + return self.invoke(text, *args, **kwargs) -class LCOpenAIEmbeddings(LCEmbeddingMixin, BaseEmbeddings): - """Wrapper around Langchain's OpenAI embedding, focusing on key parameters""" +@dataclass(kw_only=True) +class LCOpenAIEmbeddings(BaseLCEmbeddings): + """Wrapper around LangChain's OpenAI embedding model.""" - def __init__( - self, - model: str = "text-embedding-ada-002", - openai_api_version: Optional[str] = None, - openai_api_base: Optional[str] = None, - openai_api_type: Optional[str] = None, - openai_api_key: Optional[str] = None, - request_timeout: Optional[float] = None, - **params, - ): - super().__init__( - model=model, - openai_api_version=openai_api_version, - openai_api_base=openai_api_base, - openai_api_type=openai_api_type, - openai_api_key=openai_api_key, - request_timeout=request_timeout, - **params, - ) + model: str = field( + default="text-embedding-ada-002", + metadata={"description": "Model name."}, + ) + openai_api_version: str | None = field( + default=None, metadata={"description": "OpenAI API version."} + ) + openai_api_base: str | None = field( + default=None, metadata={"description": "OpenAI API base URL."} + ) + openai_api_type: str | None = field( + default=None, metadata={"description": "OpenAI API type."} + ) + openai_api_key: str | None = field( + default=None, metadata={"description": "OpenAI API key."} + ) + request_timeout: float | None = field( + default=None, metadata={"description": "Request timeout in seconds."} + ) - def _get_lc_class(self): + def _get_lc_class(self) -> type: try: from langchain_openai import OpenAIEmbeddings except ImportError: @@ -117,29 +82,38 @@ def _get_lc_class(self): return OpenAIEmbeddings + def _lc_kwargs(self) -> dict[str, Any]: + return { + "model": self.model, + "openai_api_version": self.openai_api_version, + "openai_api_base": self.openai_api_base, + "openai_api_type": self.openai_api_type, + "openai_api_key": self.openai_api_key, + "request_timeout": self.request_timeout, + } -class LCAzureOpenAIEmbeddings(LCEmbeddingMixin, BaseEmbeddings): - """Wrapper around Langchain's AzureOpenAI embedding, focusing on key parameters""" - def __init__( - self, - azure_endpoint: Optional[str] = None, - deployment: Optional[str] = None, - openai_api_key: Optional[str] = None, - api_version: Optional[str] = None, - request_timeout: Optional[float] = None, - **params, - ): - super().__init__( - azure_endpoint=azure_endpoint, - deployment=deployment, - api_version=api_version, - openai_api_key=openai_api_key, - request_timeout=request_timeout, - **params, - ) +@dataclass(kw_only=True) +class LCAzureOpenAIEmbeddings(BaseLCEmbeddings): + """Wrapper around LangChain's Azure OpenAI embedding model.""" - def _get_lc_class(self): + azure_endpoint: str | None = field( + default=None, metadata={"description": "Azure OpenAI endpoint URL."} + ) + deployment: str | None = field( + default=None, metadata={"description": "Azure deployment name."} + ) + openai_api_key: str | None = field( + default=None, metadata={"description": "Azure OpenAI API key."} + ) + api_version: str | None = field( + default=None, metadata={"description": "Azure API version."} + ) + request_timeout: float | None = field( + default=None, metadata={"description": "Request timeout in seconds."} + ) + + def _get_lc_class(self) -> type: try: from langchain_openai import AzureOpenAIEmbeddings except ImportError: @@ -147,41 +121,38 @@ def _get_lc_class(self): return AzureOpenAIEmbeddings + def _lc_kwargs(self) -> dict[str, Any]: + return { + "azure_endpoint": self.azure_endpoint, + "deployment": self.deployment, + "api_version": self.api_version, + "openai_api_key": self.openai_api_key, + "request_timeout": self.request_timeout, + } + -class LCCohereEmbeddings(LCEmbeddingMixin, BaseEmbeddings): - """Wrapper around Langchain's Cohere embedding, focusing on key parameters""" +@dataclass(kw_only=True) +class LCCohereEmbeddings(BaseLCEmbeddings): + """Wrapper around LangChain's Cohere embedding model.""" - cohere_api_key: str = Param( - help="API key (https://dashboard.cohere.com/api-keys)", - default=None, - required=True, + model: str = field( + default="embed-english-v2.0", + metadata={"description": "Model name."}, ) - model: str = Param( - help="Model name to use (https://docs.cohere.com/docs/models)", - default=None, - required=True, + cohere_api_key: str | None = field( + default=None, metadata={"description": "Cohere API key."} ) - user_agent: str = Param( - help="User agent (leave default)", default="default", required=True + truncate: str | None = field( + default=None, metadata={"description": "Truncation mode."} + ) + request_timeout: float | None = field( + default=None, metadata={"description": "Request timeout in seconds."} + ) + user_agent: str | None = field( + default=None, metadata={"description": "HTTP user agent string."} ) - def __init__( - self, - model: str = "embed-english-v2.0", - cohere_api_key: Optional[str] = None, - truncate: Optional[str] = None, - request_timeout: Optional[float] = None, - **params, - ): - super().__init__( - model=model, - cohere_api_key=cohere_api_key, - truncate=truncate, - request_timeout=request_timeout, - **params, - ) - - def _get_lc_class(self): + def _get_lc_class(self) -> type: try: from langchain_cohere import CohereEmbeddings except ImportError: @@ -189,30 +160,26 @@ def _get_lc_class(self): return CohereEmbeddings + def _lc_kwargs(self) -> dict[str, Any]: + return { + "model": self.model, + "cohere_api_key": self.cohere_api_key, + "truncate": self.truncate, + "request_timeout": self.request_timeout, + "user_agent": self.user_agent, + } -class LCHuggingFaceEmbeddings(LCEmbeddingMixin, BaseEmbeddings): - """Wrapper around Langchain's HuggingFace embedding, focusing on key parameters""" - model_name: str = Param( - help=( - "Model name to use (https://huggingface.co/models?" - "pipeline_tag=sentence-similarity&sort=trending)" - ), - default=None, - required=True, - ) +@dataclass(kw_only=True) +class LCHuggingFaceEmbeddings(BaseLCEmbeddings): + """Wrapper around LangChain's HuggingFace BGE embedding model.""" - def __init__( - self, - model_name: str = "sentence-transformers/all-mpnet-base-v2", - **params, - ): - super().__init__( - model_name=model_name, - **params, - ) + model_name: str = field( + default="sentence-transformers/all-mpnet-base-v2", + metadata={"description": "HuggingFace model name."}, + ) - def _get_lc_class(self): + def _get_lc_class(self) -> type: try: from langchain_community.embeddings import HuggingFaceBgeEmbeddings except ImportError: @@ -220,74 +187,66 @@ def _get_lc_class(self): return HuggingFaceBgeEmbeddings + def _lc_kwargs(self) -> dict[str, Any]: + return {"model_name": self.model_name} -class LCGoogleEmbeddings(LCEmbeddingMixin, BaseEmbeddings): - """Wrapper around Langchain's Google GenAI embedding, focusing on key parameters""" - google_api_key: str = Param( - help="API key (https://aistudio.google.com/app/apikey)", - default=None, - required=True, - ) - model: str = Param( - help="Model name to use (https://ai.google.dev/gemini-api/docs/models/gemini#text-embedding-and-embedding)", # noqa +@dataclass(kw_only=True) +class LCGoogleEmbeddings(BaseLCEmbeddings): + """Wrapper around LangChain's Google GenAI embedding model.""" + + model: str = field( default="models/text-embedding-004", - required=True, + metadata={"description": "Model name."}, + ) + google_api_key: str | None = field( + default=None, metadata={"description": "Google API key."} ) - def __init__( - self, - model: str = "models/text-embedding-004", - google_api_key: Optional[str] = None, - **params, - ): - super().__init__( - model=model, - google_api_key=google_api_key, - **params, - ) - - def _get_lc_class(self): + def _get_lc_class(self) -> type: try: from langchain_google_genai import GoogleGenerativeAIEmbeddings - except ImportError: - raise ImportError("Please install langchain-google-genai") + except ImportError as e: + raise ImportError("Please install langchain-google-genai") from e return GoogleGenerativeAIEmbeddings + def _lc_kwargs(self) -> dict[str, Any]: + return { + "model": self.model, + "google_api_key": self.google_api_key, + } -class LCMistralEmbeddings(LCEmbeddingMixin, BaseEmbeddings): - """Wrapper around LangChain's MistralAI embedding, focusing on key parameters""" - api_key: str = Param( - help="API key (https://console.mistral.ai/api-keys)", - default=None, - required=True, - ) - model: str = Param( - help="Model name to use ('mistral-embed')", +@dataclass(kw_only=True) +class LCMistralEmbeddings(BaseLCEmbeddings): + """Wrapper around LangChain's Mistral AI embedding model.""" + + model: str = field( default="mistral-embed", - required=True, + metadata={"description": "Model name."}, + ) + api_key: str | None = field( + default=None, metadata={"description": "Mistral API key."} ) - def __init__( - self, - model: str = "mistral-embed", - api_key: Optional[str] = None, - **params, - ): - super().__init__( - model=model, - api_key=api_key, - **params, - ) - - def _get_lc_class(self): + def _get_lc_class(self) -> type: try: from langchain_mistralai import MistralAIEmbeddings - except ImportError: + except ImportError as e: raise ImportError( "Please install langchain_mistralai: " "`pip install -U langchain_mistralai`" - ) + ) from e + return MistralAIEmbeddings + + def _lc_kwargs(self) -> dict[str, Any]: + return { + "model": self.model, + "api_key": self.api_key, + } + + +# Backward-compatible alias for code that imported the old mixin name. +LCEmbeddingMixin = BaseLCEmbeddings diff --git a/libs/kotaemon/kotaemon/embeddings/openai.py b/libs/kotaemon/kotaemon/embeddings/openai.py index 604540dac..4dcb7aeb7 100644 --- a/libs/kotaemon/kotaemon/embeddings/openai.py +++ b/libs/kotaemon/kotaemon/embeddings/openai.py @@ -1,3 +1,4 @@ +from dataclasses import dataclass, field from itertools import islice from typing import Optional @@ -10,9 +11,6 @@ stop_after_attempt, wait_random_exponential, ) -from theflow.utils.modules import import_dotted_string - -from kotaemon.base import Param from .base import BaseEmbeddings, Document, DocumentWithEmbedding @@ -35,6 +33,7 @@ def split_text_by_chunk_size(text: str, chunk_size: int) -> list[list[int]]: return result +@dataclass(kw_only=True) class BaseOpenAIEmbeddings(BaseEmbeddings): """Base interface for OpenAI embedding model, using the openai library. @@ -47,25 +46,27 @@ class BaseOpenAIEmbeddings(BaseEmbeddings): _dependencies = ["openai"] - api_key: str = Param(None, help="API key", required=True) - timeout: Optional[float] = Param(None, help="Timeout for the API request.") - max_retries: Optional[int] = Param( - None, help="Maximum number of retries for the API request." + api_key: str = field(metadata={"description": "API key"}) + timeout: Optional[float] = field( + default=None, metadata={"description": "Timeout for the API request."} ) - - dimensions: Optional[int] = Param( - None, - help=( - "The number of dimensions the resulting output embeddings should have. " - "Only supported in `text-embedding-3` and later models." - ), + max_retries: Optional[int] = field( + default=None, + metadata={"description": "Maximum number of retries for the API request."}, ) - context_length: Optional[int] = Param( - None, help="The maximum context length of the embedding model" + dimensions: Optional[int] = field( + default=None, + metadata={ + "description": ("Output embedding dimensions (text-embedding-3 and later).") + }, + ) + context_length: Optional[int] = field( + default=None, + metadata={"description": "Maximum context length of the embedding model"}, ) - @Param.auto(depends_on=["max_retries"]) - def max_retries_(self): + @property + def max_retries_(self) -> int: if self.max_retries is None: from openai._constants import DEFAULT_MAX_RETRIES @@ -139,18 +140,20 @@ async def ainvoke( ] +@dataclass(kw_only=True) class OpenAIEmbeddings(BaseOpenAIEmbeddings): """OpenAI chat model""" - base_url: Optional[str] = Param(None, help="OpenAI base URL") - organization: Optional[str] = Param(None, help="OpenAI organization") - model: str = Param( - None, - help=( - "ID of the model to use. You can go to [Model overview](https://platform." - "openai.com/docs/models/overview) to see the available models." - ), - required=True, + model: str = field( + metadata={ + "description": ("Model ID (see platform.openai.com/docs/models/overview).") + } + ) + base_url: Optional[str] = field( + default=None, metadata={"description": "OpenAI base URL"} + ) + organization: Optional[str] = field( + default=None, metadata={"description": "OpenAI organization"} ) def prepare_client(self, async_version: bool = False): @@ -194,25 +197,16 @@ def openai_response(self, client, **kwargs): return client.embeddings.create(**params) +@dataclass(kw_only=True) class AzureOpenAIEmbeddings(BaseOpenAIEmbeddings): - azure_endpoint: str = Param( - None, - help=( - "HTTPS endpoint for the Azure OpenAI model. The azure_endpoint, " - "azure_deployment, and api_version parameters are used to construct " - "the full URL for the Azure OpenAI model." - ), - required=True, + azure_endpoint: str = field( + metadata={"description": ("HTTPS endpoint for the Azure OpenAI model.")} + ) + azure_deployment: str = field(metadata={"description": "Azure deployment name"}) + api_version: str = field(metadata={"description": "Azure model version"}) + azure_ad_token: Optional[str] = field( + default=None, metadata={"description": "Azure AD token"} ) - azure_deployment: str = Param(None, help="Azure deployment name", required=True) - api_version: str = Param(None, help="Azure model version", required=True) - azure_ad_token: Optional[str] = Param(None, help="Azure AD token") - azure_ad_token_provider: Optional[str] = Param(None, help="Azure AD token provider") - - @Param.auto(depends_on=["azure_ad_token_provider"]) - def azure_ad_token_provider_(self): - if isinstance(self.azure_ad_token_provider, str): - return import_dotted_string(self.azure_ad_token_provider, safe=False) def prepare_client(self, async_version: bool = False): """Get the OpenAI client @@ -225,7 +219,6 @@ def prepare_client(self, async_version: bool = False): "api_version": self.api_version, "api_key": self.api_key, "azure_ad_token": self.azure_ad_token, - "azure_ad_token_provider": self.azure_ad_token_provider_, "timeout": self.timeout, "max_retries": self.max_retries_, } diff --git a/libs/kotaemon/kotaemon/embeddings/tei_endpoint_embed.py b/libs/kotaemon/kotaemon/embeddings/tei_endpoint_embed.py index 6a436e8df..3170d44a5 100644 --- a/libs/kotaemon/kotaemon/embeddings/tei_endpoint_embed.py +++ b/libs/kotaemon/kotaemon/embeddings/tei_endpoint_embed.py @@ -1,40 +1,37 @@ +from __future__ import annotations + +from dataclasses import dataclass, field + import aiohttp import requests -from kotaemon.base import Document, DocumentWithEmbedding, Param +from kotaemon.base import Document, DocumentWithEmbedding from .base import BaseEmbeddings session = requests.session() +@dataclass(kw_only=True) class TeiEndpointEmbeddings(BaseEmbeddings): - """An Embeddings component that uses an - TEI (Text-Embedding-Inference) API compatible endpoint. - - Ref: https://github.com/huggingface/text-embeddings-inference - - Attributes: - endpoint_url (str): The url of an TEI - (Text-Embedding-Inference) API compatible endpoint. - normalize (bool): Whether to normalize embeddings to unit length. - truncate (bool): Whether to truncate embeddings - to a fixed/default length. - """ + """Embeddings via a TEI API compatible endpoint.""" - endpoint_url: str = Param(None, help="TEI embedding service api base URL") - normalize: bool = Param( - True, - help="Normalize embeddings to unit length", + endpoint_url: str = field( + default="", + metadata={"description": "TEI embedding service api base URL"}, ) - truncate: bool = Param( - True, - help="Truncate embeddings to a fixed/default length", + normalize: bool = field( + default=True, + metadata={"description": "Normalize embeddings to unit length"}, + ) + truncate: bool = field( + default=True, + metadata={"description": "Truncate embeddings to a fixed length"}, ) async def client_(self, inputs: list[str]): - async with aiohttp.ClientSession() as session: - async with session.post( + async with aiohttp.ClientSession() as http_session: + async with http_session.post( url=self.endpoint_url, json={ "inputs": inputs, @@ -42,24 +39,23 @@ async def client_(self, inputs: list[str]): "truncate": self.truncate, }, ) as resp: - embeddings = await resp.json() - return embeddings + return await resp.json() async def ainvoke( self, text: str | list[str] | Document | list[Document], *args, **kwargs ) -> list[DocumentWithEmbedding]: if not isinstance(text, list): text = [text] - text = self.prepare_input(text) + docs: list[Document] = self.prepare_input(text) - outputs = [] + outputs: list[DocumentWithEmbedding] = [] batch_size = 6 - num_batch = max(len(text) // batch_size, 1) + num_batch = max(len(docs) // batch_size, 1) for i in range(num_batch): if i == num_batch - 1: - mini_batch = text[batch_size * i :] + mini_batch = docs[batch_size * i :] else: - mini_batch = text[batch_size * i : batch_size * (i + 1)] + mini_batch = docs[batch_size * i : batch_size * (i + 1)] mini_batch = [x.content for x in mini_batch] embeddings = await self.client_(mini_batch) # type: ignore outputs.extend( @@ -68,7 +64,6 @@ async def ainvoke( for doc, embedding in zip(mini_batch, embeddings) ] ) - return outputs def invoke( @@ -76,17 +71,16 @@ def invoke( ) -> list[DocumentWithEmbedding]: if not isinstance(text, list): text = [text] + docs: list[Document] = self.prepare_input(text) - text = self.prepare_input(text) - - outputs = [] + outputs: list[DocumentWithEmbedding] = [] batch_size = 6 - num_batch = max(len(text) // batch_size, 1) + num_batch = max(len(docs) // batch_size, 1) for i in range(num_batch): if i == num_batch - 1: - mini_batch = text[batch_size * i :] + mini_batch = docs[batch_size * i :] else: - mini_batch = text[batch_size * i : batch_size * (i + 1)] + mini_batch = docs[batch_size * i : batch_size * (i + 1)] mini_batch = [x.content for x in mini_batch] embeddings = session.post( url=self.endpoint_url, diff --git a/libs/kotaemon/kotaemon/embeddings/voyageai.py b/libs/kotaemon/kotaemon/embeddings/voyageai.py index 06d37c5b3..6d5e4998a 100644 --- a/libs/kotaemon/kotaemon/embeddings/voyageai.py +++ b/libs/kotaemon/kotaemon/embeddings/voyageai.py @@ -1,9 +1,11 @@ -"""Implements embeddings from [Voyage AI](https://voyageai.com). -""" +"""Implements embeddings from [Voyage AI](https://voyageai.com).""" + +from __future__ import annotations import importlib +from dataclasses import dataclass, field -from kotaemon.base import Document, DocumentWithEmbedding, Param +from kotaemon.base import Document, DocumentWithEmbedding from .base import BaseEmbeddings @@ -18,36 +20,27 @@ def _import_voyageai(): def _format_output(texts: list[str], embeddings: list[list]): - """Formats the output of all `.embed` calls. - Args: - texts: List of original documents - embeddings: Embeddings corresponding to each document - """ return [ DocumentWithEmbedding(content=text, embedding=embedding) for text, embedding in zip(texts, embeddings) ] +@dataclass(kw_only=True) class VoyageAIEmbeddings(BaseEmbeddings): - """Voyage AI provides best-in-class embedding models and rerankers.""" + """Voyage AI embedding models.""" - api_key: str = Param(None, help="Voyage API key", required=False) - model: str = Param( - "voyage-3", - help=( - "Model name to use. The Voyage " - "[documentation](https://docs.voyageai.com/docs/embeddings) " - "provides a list of all available embedding models." - ), - required=True, + api_key: str | None = field( + default=None, metadata={"description": "Voyage API key"} + ) + model: str = field( + default="voyage-3", + metadata={"description": "Embedding model name"}, ) - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) + def __post_init__(self) -> None: if not self.api_key: raise ValueError("API key must be provided for VoyageAIEmbeddings.") - self._client = _import_voyageai().Client(api_key=self.api_key) self._aclient = _import_voyageai().AsyncClient(api_key=self.api_key) diff --git a/libs/kotaemon/kotaemon/indices/__init__.py b/libs/kotaemon/kotaemon/indices/__init__.py index 64da5f36e..1dac3c4ef 100644 --- a/libs/kotaemon/kotaemon/indices/__init__.py +++ b/libs/kotaemon/kotaemon/indices/__init__.py @@ -1,3 +1,36 @@ +from .indexing import BaseIndexing +from .retriever import BaseRetriever +from .stores import ( + ChunkRelationStore, + FileLevelChunkRelationStore, + FileLevelSourceStore, + FileSourceStore, + RelationType, + make_file_level_stores, +) from .vectorindex import VectorIndexing, VectorRetrieval +from .websearch import ( + BaseWebSearch, + JinaWebSearch, + TavilyWebSearch, + WebSearchFactory, + WebSearchVendor, +) -__all__ = ["VectorIndexing", "VectorRetrieval"] +__all__ = [ + "BaseIndexing", + "BaseRetriever", + "BaseWebSearch", + "JinaWebSearch", + "TavilyWebSearch", + "WebSearchFactory", + "WebSearchVendor", + "ChunkRelationStore", + "FileLevelChunkRelationStore", + "FileLevelSourceStore", + "FileSourceStore", + "RelationType", + "VectorIndexing", + "VectorRetrieval", + "make_file_level_stores", +] diff --git a/libs/kotaemon/kotaemon/indices/base.py b/libs/kotaemon/kotaemon/indices/base.py index a20ca6b2d..44f97c087 100644 --- a/libs/kotaemon/kotaemon/indices/base.py +++ b/libs/kotaemon/kotaemon/indices/base.py @@ -1,46 +1,23 @@ from __future__ import annotations -from abc import abstractmethod +from abc import ABC, abstractmethod from typing import Any, Type from llama_index.core.node_parser.interface import NodeParser -from kotaemon.base import BaseComponent, Document, RetrievedDocument +from kotaemon.base import Document, RetrievedDocument -class DocTransformer(BaseComponent): - """This is a base class for document transformers - - A document transformer transforms a list of documents into another list - of documents. Transforming can mean splitting a document into multiple documents, - reducing a large list of documents into a smaller list of documents, or adding - metadata to each document in a list of documents, etc. - """ - +class DocTransformer(ABC): @abstractmethod - def run( - self, - documents: list[Document], - **kwargs, - ) -> list[Document]: + def run(self, documents: list[Document], **kwargs) -> list[Document]: ... + def __call__(self, documents: list[Document], **kwargs) -> list[Document]: + return self.run(documents, **kwargs) -class LlamaIndexDocTransformerMixin: - """Allow automatically wrapping a Llama-index component into kotaemon component - - Example: - class TokenSplitter(LlamaIndexMixin, BaseSplitter): - def _get_li_class(self): - from llama_index.core.text_splitter import TokenTextSplitter - return TokenTextSplitter - - To use this mixin, please: - 1. Use this class as the 1st parent class, so that Python will prefer to use - the attributes and methods of this class whenever possible. - 2. Overwrite `_get_li_class` to return the relevant LlamaIndex component. - """ +class LlamaIndexDocTransformerMixin: def _get_li_class(self) -> Type[NodeParser]: raise NotImplementedError( "Please return the relevant LlamaIndex class in _get_li_class" @@ -50,30 +27,23 @@ def __init__(self, **params): self._li_cls = self._get_li_class() self._obj = self._li_cls(**params) self._kwargs = params - super().__init__() def __repr__(self): - kwargs = [] - for key, value_obj in self._kwargs.items(): - value = repr(value_obj) - kwargs.append(f"{key}={value}") - kwargs_repr = ", ".join(kwargs) + kwargs_repr = ", ".join(f"{k}={v!r}" for k, v in self._kwargs.items()) return f"{self.__class__.__name__}({kwargs_repr})" def __str__(self): - kwargs = [] + parts = [] for key, value_obj in self._kwargs.items(): value = str(value_obj) if len(value) > 20: value = f"{value[:15]}..." - kwargs.append(f"{key}={value}") - kwargs_repr = ", ".join(kwargs) - return f"{self.__class__.__name__}({kwargs_repr})" + parts.append(f"{key}={value}") + return f"{self.__class__.__name__}({', '.join(parts)})" def __setattr__(self, name: str, value: Any) -> None: if name.startswith("_") or name in self._protected_keywords(): return super().__setattr__(name, value) - self._kwargs[name] = value return setattr(self._obj, name, value) @@ -82,42 +52,15 @@ def __getattr__(self, name: str) -> Any: return self._kwargs[name] return getattr(self._obj, name) - def dump(self, *args, **kwargs): - from theflow.utils.modules import serialize - - params = {key: serialize(value) for key, value in self._kwargs.items()} - return { - "__type__": f"{self.__module__}.{self.__class__.__qualname__}", - **params, - } - - def run( - self, - documents: list[Document], - **kwargs, - ) -> list[Document]: - """Run Llama-index node parser and convert the output to Document from - kotaemon - """ + def run(self, documents: list[Document], **kwargs) -> list[Document]: docs = self._obj(documents, **kwargs) # type: ignore return [Document.from_dict(doc.to_dict()) for doc in docs] -class BaseIndexing(BaseComponent): - """Define the base interface for indexing pipeline""" - - def to_retrieval_pipeline(self, **kwargs): - """Convert the indexing pipeline to a retrieval pipeline""" - raise NotImplementedError - - def to_qa_pipeline(self, **kwargs): - """Convert the indexing pipeline to a QA pipeline""" - raise NotImplementedError - - -class BaseRetrieval(BaseComponent): - """Define the base interface for retrieval pipeline""" - +class BaseRetrieval(ABC): @abstractmethod def run(self, *args, **kwargs) -> list[RetrievedDocument]: ... + + def __call__(self, *args, **kwargs) -> list[RetrievedDocument]: + return self.run(*args, **kwargs) diff --git a/libs/kotaemon/kotaemon/indices/indexing/__init__.py b/libs/kotaemon/kotaemon/indices/indexing/__init__.py new file mode 100644 index 000000000..1c989ff51 --- /dev/null +++ b/libs/kotaemon/kotaemon/indices/indexing/__init__.py @@ -0,0 +1,4 @@ +from .base import BaseIndexing +from .impl.rag import IndexDocumentPipeline, IndexPipeline + +__all__ = ["BaseIndexing", "IndexDocumentPipeline", "IndexPipeline"] diff --git a/libs/kotaemon/kotaemon/indices/indexing/base.py b/libs/kotaemon/kotaemon/indices/indexing/base.py new file mode 100644 index 000000000..cb50fb95b --- /dev/null +++ b/libs/kotaemon/kotaemon/indices/indexing/base.py @@ -0,0 +1,105 @@ +import abc +from dataclasses import dataclass +from pathlib import Path +from typing import Generator + +from kotaemon.base import Document +from kotaemon.indices.stores import ChunkRelationStore, FileSourceStore +from kotaemon.storages import BaseDocumentStore, BaseVectorStore + + +@dataclass(kw_only=True) +class BaseIndexing: + """Base interface for file index ingestion pipelines. + + Subclasses must implement: + - run(self, file_paths): run the indexing pipeline + - get_pipeline(cls, user_settings, index_settings): return a + fully-initialized pipeline, ready to be used by ktem + + Resources injected at runtime: + - file_source: indexed file metadata store + - chunk_relations: source-to-chunk relation store + - VS: VectorStore + - DS: DocStore + - FSPath: file storage path + - user_id: the current user id + """ + + file_source: FileSourceStore + chunk_relations: ChunkRelationStore + VS: BaseVectorStore + DS: BaseDocumentStore + FSPath: Path + user_id: int + private: bool + chunk_size: int + chunk_overlap: int + + @abc.abstractmethod + def run( + self, + file_paths: str | Path | list[str | Path], + *args, + **kwargs, + ) -> tuple[list[str | None], list[str | None]]: + """Run the indexing pipeline. + + Args: + file_paths: the file paths to index + + Returns: + - indexed file ids (one per input path, or None on failure) + - error messages (one per input path, or None on success) + """ + ... + + def stream( + self, + file_paths: str | Path | list[str | Path], + *args, + **kwargs, + ) -> Generator[ + Document, None, tuple[list[str | None], list[str | None], list[Document]] + ]: + """Stream the indexing pipeline. + + Args: + file_paths: the file paths to index + + Yields: + Document: output message to the UI + (must have channel == index or debug) + + Returns: + - indexed file ids (one per input path, or None on failure) + - error messages (one per input path, or None on success) + - indexed documents as list[Document] + """ + raise NotImplementedError + + @classmethod + def get_user_settings(cls) -> dict: + """Get user-facing settings for this indexer. + + Returns: + dict: settings in ``ktem.settings.SettingItem`` format + """ + return {} + + @classmethod + @abc.abstractmethod + def get_pipeline(cls, user_settings: dict, index_settings: dict) -> "BaseIndexing": + ... + + def warning(self, msg: str) -> None: + """Log a warning message. + + Args: + msg: the message to log + """ + print(msg) + + def rebuild_index(self) -> None: + """Rebuild the index.""" + raise NotImplementedError diff --git a/libs/kotaemon/kotaemon/indices/indexing/impl/__init__.py b/libs/kotaemon/kotaemon/indices/indexing/impl/__init__.py new file mode 100644 index 000000000..881ca3f68 --- /dev/null +++ b/libs/kotaemon/kotaemon/indices/indexing/impl/__init__.py @@ -0,0 +1,3 @@ +from .rag import IndexDocumentPipeline, IndexPipeline + +__all__ = ["IndexDocumentPipeline", "IndexPipeline"] diff --git a/libs/kotaemon/kotaemon/indices/indexing/impl/rag.py b/libs/kotaemon/kotaemon/indices/indexing/impl/rag.py new file mode 100644 index 000000000..b0ad22558 --- /dev/null +++ b/libs/kotaemon/kotaemon/indices/indexing/impl/rag.py @@ -0,0 +1,426 @@ +from __future__ import annotations + +import logging +import shutil +import threading +import time +from copy import deepcopy +from dataclasses import dataclass +from functools import cached_property +from hashlib import sha256 +from pathlib import Path +from typing import Generator, Optional + +import tiktoken +from llama_index.core.readers.base import BaseReader +from llama_index.core.readers.file.base import default_file_metadata_func + +from kotaemon.base import Document +from kotaemon.embeddings import BaseEmbeddings +from kotaemon.indices.indexing.base import BaseIndexing +from kotaemon.indices.ingests.files import ( + KH_DEFAULT_FILE_EXTRACTORS, + adobe_reader, + azure_reader, + docling_reader, + paddle_struct_reader, + paddle_vl_reader, + unstructured, + web_reader, +) +from kotaemon.indices.splitters import BaseSplitter, TokenSplitter +from kotaemon.indices.stores import ChunkRelationStore, FileSourceStore +from kotaemon.indices.vectorindex import VectorIndexing +from kotaemon.storages import BaseDocumentStore, BaseVectorStore + +logger = logging.getLogger(__name__) + + +def configure_readers( + vlm_endpoint: str = "", + markdown_output_dir: Path | None = None, +) -> None: + """Wire app-level config into the shared reader singletons. + + Call this once at application startup after the settings object is + available. Idempotent — safe to call multiple times. + + Args: + vlm_endpoint: URL of the Vision-Language Model endpoint. + markdown_output_dir: Directory used by document loaders + to cache intermediate markdown output. + """ + adobe_reader.vlm_endpoint = vlm_endpoint + azure_reader.vlm_endpoint = vlm_endpoint + docling_reader.vlm_endpoint = vlm_endpoint + azure_reader.cache_dir = markdown_output_dir + _mhtml_reader = KH_DEFAULT_FILE_EXTRACTORS.get(".mhtml") + if _mhtml_reader is not None and hasattr(_mhtml_reader, "cache_dir"): + _mhtml_reader.cache_dir = markdown_output_dir + + +_default_token_func = tiktoken.encoding_for_model("gpt-3.5-turbo").encode + + +@dataclass(kw_only=True) +class IndexPipeline: + """Index a single file into the vector and document stores.""" + + loader: BaseReader + splitter: BaseSplitter | None + chunk_batch_size: int = 200 + + file_source: FileSourceStore + chunk_relations: ChunkRelationStore + VS: BaseVectorStore + DS: BaseDocumentStore + FSPath: Path + user_id: int + collection_name: str = "default" + private: bool = False + run_embedding_in_thread: bool = False + embedding: BaseEmbeddings + chunks_output_dir: Path | None = None + + @cached_property + def vector_indexing(self) -> VectorIndexing: + return VectorIndexing( + vector_store=self.VS, + doc_store=self.DS, + embedding=self.embedding, + cache_dir=self.chunks_output_dir, + ) + + def handle_docs(self, docs, file_id, file_name) -> Generator[Document, None, int]: + s_time = time.time() + text_docs, non_text_docs, thumbnail_docs = [], [], [] + + for doc in docs: + doc_type = doc.metadata.get("type", "text") + if doc_type == "text": + text_docs.append(doc) + elif doc_type == "thumbnail": + thumbnail_docs.append(doc) + else: + non_text_docs.append(doc) + + print(f"Got {len(thumbnail_docs)} page thumbnails") + page_label_to_thumbnail = { + doc.metadata["page_label"]: doc.doc_id for doc in thumbnail_docs + } + + all_chunks = self.splitter(text_docs) if self.splitter else text_docs + + for chunk in all_chunks: + page_label = chunk.metadata.get("page_label") + if page_label and page_label in page_label_to_thumbnail: + chunk.metadata["thumbnail_doc_id"] = page_label_to_thumbnail[page_label] + + to_index_chunks = all_chunks + non_text_docs + thumbnail_docs + + n_chunks = 0 + batch = self.chunk_batch_size * 4 + for start in range(0, len(to_index_chunks), batch): + chunk_slice = to_index_chunks[start : start + batch] + self.handle_chunks_docstore(chunk_slice, file_id) + n_chunks += len(chunk_slice) + yield Document( + f" => [{file_name}] Processed {n_chunks} chunks", + channel="debug", + ) + + def insert_chunks_to_vectorstore(): + n = 0 + for start in range(0, len(to_index_chunks), self.chunk_batch_size): + sl = to_index_chunks[start : start + self.chunk_batch_size] + self.handle_chunks_vectorstore(sl, file_id) + n += len(sl) + if self.VS: + yield Document( + f" => [{file_name}] Created embedding for {n} chunks", + channel="debug", + ) + + if self.run_embedding_in_thread: + print("Running embedding in thread") + threading.Thread( + target=lambda: list(insert_chunks_to_vectorstore()) + ).start() + else: + yield from insert_chunks_to_vectorstore() + + print("indexing step took", time.time() - s_time) + return n_chunks + + def handle_chunks_docstore(self, chunks, file_id): + """Persist chunks to doc store and record in index table.""" + self.vector_indexing.add_to_docstore(chunks) + self.chunk_relations.add_relations( + file_id, + [chunk.doc_id for chunk in chunks], + "document", + ) + + def handle_chunks_vectorstore(self, chunks, file_id): + """Embed chunks and record vector ids in index table.""" + self.vector_indexing.add_to_vectorstore(chunks) + self.vector_indexing.write_chunk_to_file(chunks) + if self.VS: + self.chunk_relations.add_relations( + file_id, + [chunk.doc_id for chunk in chunks], + "vector", + ) + + def get_id_if_exists(self, file_path: str | Path) -> Optional[str]: + """Return the existing file id if this file is already indexed.""" + file_name = file_path.name if isinstance(file_path, Path) else file_path + return self.file_source.find_id_by_name(file_name, user_id=self.user_id) + + def store_url(self, url: str) -> str: + """Persist a URL record and return the generated file id.""" + file_hash = sha256(url.encode()).hexdigest() + return self.file_source.create_from_url(url, file_hash, user_id=self.user_id) + + def store_file(self, file_path: Path) -> str: + """Copy the file to storage, persist a record, return file id.""" + with file_path.open("rb") as fi: + file_hash = sha256(fi.read()).hexdigest() + shutil.copy(file_path, self.FSPath / file_hash) + return self.file_source.create_from_file( + file_path.name, + file_hash, + file_path.stat().st_size, + user_id=self.user_id, + ) + + def finish(self, file_id: str, file_path: str | Path) -> str: + """Populate token count and loader metadata after indexing.""" + doc_ids = self.chunk_relations.list_target_ids(file_id, "document") + token_count: int | None = None + token_func = self.get_token_func() + if doc_ids and token_func: + docs = self.DS.get(doc_ids) + token_count = sum(len(token_func(doc.text)) for doc in docs) + self.file_source.update_note( + file_id, + tokens=token_count, + loader=self.loader.__class__.__name__, + ) + return file_id + + def get_token_func(self): + """Return the tokenizer used to count tokens.""" + return _default_token_func + + def delete_file(self, file_id: str) -> None: + """Remove a file and all its indexed chunks.""" + vs_ids, ds_ids = self.chunk_relations.delete_by_source(file_id) + self.file_source.delete_source(file_id) + if vs_ids and self.VS: + self.VS.delete(vs_ids) + if ds_ids: + self.DS.delete(ds_ids) + + def run( + self, file_path: str | Path, reindex: bool, **kwargs + ) -> tuple[str, list[Document]]: + raise NotImplementedError + + def stream( + self, file_path: str | Path, reindex: bool, **kwargs + ) -> Generator[Document, None, tuple[str, list[Document]]]: + if isinstance(file_path, Path): + file_path = file_path.resolve() + + file_id = self.get_id_if_exists(file_path) + + if isinstance(file_path, Path): + if file_id is not None: + if not reindex: + raise ValueError( + f"File {file_path.name} already indexed. Rerun with " + "reindex=True to force reindexing." + ) + yield Document(f" => Removing old {file_path.name}", channel="debug") + self.delete_file(file_id) + file_id = self.store_file(file_path) + else: + file_id = self.store_file(file_path) + else: + if file_id is not None: + raise ValueError(f"URL {file_path} already indexed.") + file_id = self.store_url(file_path) + + if isinstance(file_path, Path): + extra_info = default_file_metadata_func(str(file_path)) + file_name = file_path.name + else: + extra_info = {"file_name": file_path} + file_name = file_path + + extra_info["file_id"] = file_id + extra_info["collection_name"] = self.collection_name + + yield Document(f" => Converting {file_name} to text", channel="debug") + docs = self.loader.load_data(file_path, extra_info=extra_info) + yield Document(f" => Converted {file_name} to text", channel="debug") + yield from self.handle_docs(docs, file_id, file_name) + + self.finish(file_id, file_path) + yield Document(f" => Finished indexing {file_name}", channel="debug") + return file_id, docs + + +@dataclass(kw_only=True) +class IndexDocumentPipeline(BaseIndexing): + """Route each file to the appropriate IndexPipeline and run it. + + This class is a factory + orchestrator: it decides which reader to + use per file type, constructs the right ``IndexPipeline``, and + delegates to it. + """ + + reader_mode: str + embedding: BaseEmbeddings + run_embedding_in_thread: bool = False + vlm_endpoint: str = "" + markdown_output_dir: Path | None = None + chunks_output_dir: Path | None = None + + @cached_property + def readers(self): + configure_readers(self.vlm_endpoint, self.markdown_output_dir) + readers = deepcopy(KH_DEFAULT_FILE_EXTRACTORS) + print("reader_mode", self.reader_mode) + if self.reader_mode == "adobe": + readers[".pdf"] = adobe_reader + elif self.reader_mode == "azure-di": + readers[".pdf"] = azure_reader + elif self.reader_mode == "docling": + readers[".pdf"] = docling_reader + elif self.reader_mode == "paddle-struct": + readers.update( + { + ext: paddle_struct_reader + for ext in (".pdf", ".png", ".jpeg", ".jpg", ".tiff", ".tif") + } + ) + elif self.reader_mode == "paddle-vl": + readers.update( + { + ext: paddle_vl_reader + for ext in (".pdf", ".png", ".jpeg", ".jpg", ".tiff", ".tif") + } + ) + + # dev_readers, _, _ = dev_settings() + # readers.update(dev_readers) + return readers + + def is_url(self, file_path: str | Path) -> bool: + return isinstance(file_path, str) and ( + file_path.startswith("http://") or file_path.startswith("https://") + ) + + def route(self, file_path: str | Path) -> IndexPipeline: + """Select the right IndexPipeline for this file.""" + chunk_size = self.chunk_size + chunk_overlap = self.chunk_overlap + + if self.is_url(file_path): + reader = web_reader + else: + assert isinstance(file_path, Path) + ext = file_path.suffix.lower() + reader = self.readers.get(ext, unstructured) + if reader is None: + raise NotImplementedError( + f"No supported pipeline to index {file_path.name}. " + "Please specify a pipeline for this file type in settings." + ) + + print(f"Chunk size: {chunk_size}, chunk overlap: {chunk_overlap}") + print("Using reader", reader) + return IndexPipeline( + loader=reader, + splitter=TokenSplitter( + chunk_size=chunk_size or 1024, + chunk_overlap=chunk_overlap or 256, + separator="\n\n", + backup_separators=["\n", ".", "\u200B"], + ), + run_embedding_in_thread=self.run_embedding_in_thread, + file_source=self.file_source, + chunk_relations=self.chunk_relations, + VS=self.VS, + DS=self.DS, + FSPath=self.FSPath, + user_id=self.user_id, + private=self.private, + embedding=self.embedding, + chunks_output_dir=self.chunks_output_dir, + ) + + def run( + self, file_paths: str | Path | list[str | Path], *args, **kwargs + ) -> tuple[list[str | None], list[str | None]]: + raise NotImplementedError + + def stream( + self, + file_paths: str | Path | list[str | Path], + reindex: bool = False, + **kwargs, + ) -> Generator[ + Document, None, tuple[list[str | None], list[str | None], list[Document]] + ]: + """Yield progress messages, return (file_ids, errors, docs).""" + if not isinstance(file_paths, list): + file_paths = [file_paths] + + file_ids: list[str | None] = [] + errors: list[str | None] = [] + all_docs: list[Document] = [] + + n_files = len(file_paths) + for idx, file_path in enumerate(file_paths): + file_name = file_path if self.is_url(file_path) else Path(file_path).name + if not self.is_url(file_path): + file_path = Path(file_path) + + yield Document( + content=f"Indexing [{idx + 1}/{n_files}]: {file_name}", + channel="debug", + ) + try: + pipeline = self.route(file_path) + file_id, docs = yield from pipeline.stream( + file_path, reindex=reindex, **kwargs + ) + all_docs.extend(docs) + file_ids.append(file_id) + errors.append(None) + yield Document( + content={ + "file_path": file_path, + "file_name": file_name, + "status": "success", + }, + channel="index", + ) + except Exception as e: + logger.exception(e) + file_ids.append(None) + errors.append(str(e)) + yield Document( + content={ + "file_path": file_path, + "file_name": file_name, + "status": "failed", + "message": str(e), + }, + channel="index", + ) + + return file_ids, errors, all_docs diff --git a/libs/kotaemon/kotaemon/indices/ingests/files.py b/libs/kotaemon/kotaemon/indices/ingests/files.py index b40dfbba6..25752b37b 100644 --- a/libs/kotaemon/kotaemon/indices/ingests/files.py +++ b/libs/kotaemon/kotaemon/indices/ingests/files.py @@ -1,12 +1,12 @@ +from dataclasses import dataclass, field from pathlib import Path from typing import Type from decouple import config from llama_index.core.readers.base import BaseReader from llama_index.readers.file import PDFReader -from theflow.settings import settings as flowsettings -from kotaemon.base import BaseComponent, Document, Param +from kotaemon.base import Document from kotaemon.indices.extractors import BaseDocParser from kotaemon.indices.splitters import BaseSplitter, TokenSplitter from kotaemon.loaders import ( @@ -33,18 +33,12 @@ azure_reader = AzureAIDocumentIntelligenceLoader( endpoint=str(config("AZURE_DI_ENDPOINT", default="")), credential=str(config("AZURE_DI_CREDENTIAL", default="")), - cache_dir=getattr(flowsettings, "KH_MARKDOWN_OUTPUT_DIR", None), ) docling_reader = DoclingReader() -adobe_reader.vlm_endpoint = ( - azure_reader.vlm_endpoint -) = docling_reader.vlm_endpoint = getattr(flowsettings, "KH_VLM_ENDPOINT", "") - paddle_device = str(config("PADDLE_DEVICE", default="gpu")) paddle_struct_reader = PPStructureV3Reader(device=paddle_device) paddle_vl_reader = PaddleOCRVLReader(device=paddle_device) - KH_DEFAULT_FILE_EXTRACTORS: dict[str, BaseReader] = { ".xlsx": PandasExcelReader(), ".docx": unstructured, @@ -64,38 +58,24 @@ } -class DocumentIngestor(BaseComponent): - """Ingest common office document types into Document for indexing - - Document types: - - pdf - - xlsx, xls - - docx, doc - - Args: - pdf_mode: mode for pdf extraction, one of "normal", "mathpix", "ocr" - - normal: parse pdf text - - mathpix: parse pdf text using mathpix - - ocr: parse pdf image using flax - doc_parsers: list of document parsers to parse the document - text_splitter: splitter to split the document into text nodes - override_file_extractors: override file extractors for specific file extensions - The default file extractors are stored in `KH_DEFAULT_FILE_EXTRACTORS` - """ - - pdf_mode: str = "normal" # "normal", "mathpix", "ocr", "multimodal" - doc_parsers: list[BaseDocParser] = Param(default_callback=lambda _: []) - text_splitter: BaseSplitter = TokenSplitter.withx( +def _default_text_splitter() -> TokenSplitter: + return TokenSplitter( chunk_size=1024, chunk_overlap=256, separator="\n\n", backup_separators=["\n", ".", " ", "\u200B"], ) - override_file_extractors: dict[str, Type[BaseReader]] = {} + + +@dataclass(kw_only=True) +class DocumentIngestor: + pdf_mode: str = "normal" + doc_parsers: list[BaseDocParser] = field(default_factory=list) + text_splitter: BaseSplitter = field(default_factory=_default_text_splitter) + override_file_extractors: dict[str, Type[BaseReader]] = field(default_factory=dict) def _get_reader(self, input_files: list[str | Path]): - """Get appropriate readers for the input files based on file extension""" - file_extractors: dict[str, BaseReader] = { + file_extractors = { ext: reader for ext, reader in KH_DEFAULT_FILE_EXTRACTORS.items() } for ext, cls in self.override_file_extractors.items(): @@ -110,34 +90,14 @@ def _get_reader(self, input_files: list[str | Path]): else: file_extractors[".pdf"] = MathpixPDFReader() - main_reader = DirectoryReader( - input_files=input_files, - file_extractor=file_extractors, - ) - - return main_reader + return DirectoryReader(input_files=input_files, file_extractor=file_extractors) def run(self, file_paths: list[str | Path] | str | Path) -> list[Document]: - """Ingest the file paths into Document - - Args: - file_paths: list of file paths or a single file path - - Returns: - list of parsed Documents - """ if not isinstance(file_paths, list): file_paths = [file_paths] - documents = self._get_reader(input_files=file_paths)() - print(f"Read {len(file_paths)} files into {len(documents)} documents.") nodes = self.text_splitter(documents) - print(f"Transform {len(documents)} documents into {len(nodes)} nodes.") - self.log_progress(".num_docs", num_docs=len(nodes)) - - # document parsers call if self.doc_parsers: for parser in self.doc_parsers: nodes = parser(nodes) - return nodes diff --git a/libs/kotaemon/kotaemon/indices/qa/citation.py b/libs/kotaemon/kotaemon/indices/qa/citation.py index 635876064..11cf82065 100644 --- a/libs/kotaemon/kotaemon/indices/qa/citation.py +++ b/libs/kotaemon/kotaemon/indices/qa/citation.py @@ -1,8 +1,8 @@ +from dataclasses import dataclass from typing import List from pydantic import BaseModel, Field -from kotaemon.base import BaseComponent from kotaemon.base.schema import HumanMessage, SystemMessage from kotaemon.llms import BaseLLM @@ -19,12 +19,16 @@ class CiteEvidence(BaseModel): ) -class CitationPipeline(BaseComponent): +@dataclass(kw_only=True) +class CitationPipeline: """Citation pipeline to extract cited evidences from source (based on input question)""" llm: BaseLLM + def __call__(self, context: str, question: str): + return self.run(context, question) + def run(self, context: str, question: str): return self.invoke(context, question) @@ -68,7 +72,7 @@ def invoke(self, context: str, question: str): messages, llm_kwargs = self.prepare_llm(context, question) try: print("CitationPipeline: invoking LLM") - llm_output = self.get_from_path("llm").invoke(messages, **llm_kwargs) + llm_output = self.llm.invoke(messages, **llm_kwargs) print("CitationPipeline: finish invoking LLM") if not llm_output.additional_kwargs.get("tool_calls"): return None diff --git a/libs/kotaemon/kotaemon/indices/qa/citation_qa.py b/libs/kotaemon/kotaemon/indices/qa/citation_qa.py index b56563285..2caed0bb8 100644 --- a/libs/kotaemon/kotaemon/indices/qa/citation_qa.py +++ b/libs/kotaemon/kotaemon/indices/qa/citation_qa.py @@ -1,19 +1,12 @@ import threading from collections import defaultdict -from typing import Generator +from dataclasses import dataclass, field +from typing import Any, Generator import numpy as np from decouple import config -from theflow.settings import settings as flowsettings - -from kotaemon.base import ( - AIMessage, - BaseComponent, - Document, - HumanMessage, - Node, - SystemMessage, -) + +from kotaemon.base import AIMessage, Document, HumanMessage, SystemMessage from kotaemon.llms import ChatLLM, PromptTemplate from .citation import CitationPipeline @@ -24,13 +17,6 @@ ) from .utils import find_text -try: - from ktem.llms.manager import llms - from ktem.reasoning.prompt_optimization.mindmap import CreateMindmapPipeline - from ktem.utils.render import Render -except ImportError: - raise ImportError("Please install `ktem` to use this component") - MAX_IMAGES = 10 CITATION_TIMEOUT = 5.0 CONTEXT_RELEVANT_WARNING_SCORE = config( @@ -80,7 +66,8 @@ ) # noqa -class AnswerWithContextPipeline(BaseComponent): +@dataclass(kw_only=True) +class AnswerWithContextPipeline: """Answer the question based on the evidence Args: @@ -95,28 +82,24 @@ class AnswerWithContextPipeline(BaseComponent): lang: the language of the answer. Currently support English and Japanese """ - llm: ChatLLM = Node(default_callback=lambda _: llms.get_default()) - vlm_endpoint: str = getattr(flowsettings, "KH_VLM_ENDPOINT", "") - use_multimodal: bool = getattr(flowsettings, "KH_REASONINGS_USE_MULTIMODAL", True) - citation_pipeline: CitationPipeline = Node( - default_callback=lambda _: CitationPipeline(llm=llms.get_default()) - ) - create_mindmap_pipeline: CreateMindmapPipeline = Node( - default_callback=lambda _: CreateMindmapPipeline(llm=llms.get_default()) - ) - - qa_template: str = DEFAULT_QA_TEXT_PROMPT - qa_table_template: str = DEFAULT_QA_TABLE_PROMPT - qa_chatbot_template: str = DEFAULT_QA_CHATBOT_PROMPT - qa_figure_template: str = DEFAULT_QA_FIGURE_PROMPT - - enable_citation: bool = False - enable_mindmap: bool = False - enable_citation_viz: bool = False - - system_prompt: str = "" - lang: str = "English" # support English and Japanese - n_last_interactions: int = 5 + llm: ChatLLM + citation_pipeline: CitationPipeline + vlm_endpoint: str = field(default="") + use_multimodal: bool = field(default=True) + create_mindmap_pipeline: Any | None = field(default=None) + + qa_template: str = field(default=DEFAULT_QA_TEXT_PROMPT) + qa_table_template: str = field(default=DEFAULT_QA_TABLE_PROMPT) + qa_chatbot_template: str = field(default=DEFAULT_QA_CHATBOT_PROMPT) + qa_figure_template: str = field(default=DEFAULT_QA_FIGURE_PROMPT) + + enable_citation: bool = field(default=False) + enable_mindmap: bool = field(default=False) + enable_citation_viz: bool = field(default=False) + + system_prompt: str = field(default="") + lang: str = field(default="English") # support English and Japanese + n_last_interactions: int = field(default=5) def get_prompt(self, question, evidence, evidence_mode: int): """Prepare the prompt and other information for LLM""" @@ -213,7 +196,11 @@ def citation_call(): def mindmap_call(): nonlocal mindmap - mindmap = self.create_mindmap_pipeline(context=evidence, question=question) + + if self.create_mindmap_pipeline is not None: + mindmap = self.create_mindmap_pipeline( + context=evidence, question=question + ) citation_thread = None mindmap_thread = None @@ -224,7 +211,7 @@ def mindmap_call(): citation_thread = threading.Thread(target=citation_call) citation_thread.start() - if self.enable_mindmap: + if self.enable_mindmap and self.create_mindmap_pipeline is not None: mindmap_thread = threading.Thread(target=mindmap_call) mindmap_thread.start() @@ -318,86 +305,3 @@ def match_evidence_with_context(self, answer, docs) -> dict[str, list[dict]]: # print("Matched citation:", quote, matched_excerpts), return spans - - def prepare_citations(self, answer, docs) -> tuple[list[Document], list[Document]]: - """Prepare the citations to show on the UI""" - with_citation, without_citation = [], [] - has_llm_score = any("llm_trulens_score" in doc.metadata for doc in docs) - - spans = self.match_evidence_with_context(answer, docs) - id2docs = {doc.doc_id: doc for doc in docs} - not_detected = set(id2docs.keys()) - set(spans.keys()) - - # render highlight spans - for _id, ss in spans.items(): - if not ss: - not_detected.add(_id) - continue - cur_doc = id2docs[_id] - highlight_text = "" - - ss = sorted(ss, key=lambda x: x["start"]) - last_end = 0 - text = cur_doc.text[: ss[0]["start"]] - - for idx, span in enumerate(ss): - # prevent overlapping between span - span_start = max(last_end, span["start"]) - span_end = max(last_end, span["end"]) - - to_highlight = cur_doc.text[span_start:span_end] - last_end = span_end - - # append to highlight on PDF viewer - highlight_text += (" " if highlight_text else "") + to_highlight - - span_idx = span.get("idx", None) - if span_idx is not None: - to_highlight = f"【{span_idx}】" + to_highlight - - text += Render.highlight( - to_highlight, - elem_id=str(span_idx) if span_idx is not None else None, - ) - if idx < len(ss) - 1: - text += cur_doc.text[span["end"] : ss[idx + 1]["start"]] - - text += cur_doc.text[ss[-1]["end"] :] - # add to display list - with_citation.append( - Document( - channel="info", - content=Render.collapsible_with_header_score( - cur_doc, - override_text=text, - highlight_text=highlight_text, - open_collapsible=True, - ), - ) - ) - - print("Got {} cited docs".format(len(with_citation))) - - sorted_not_detected_items_with_scores = [ - (id_, id2docs[id_].metadata.get("llm_trulens_score", 0.0)) - for id_ in not_detected - ] - sorted_not_detected_items_with_scores.sort(key=lambda x: x[1], reverse=True) - - for id_, _ in sorted_not_detected_items_with_scores: - doc = id2docs[id_] - doc_score = doc.metadata.get("llm_trulens_score", 0.0) - is_open = not has_llm_score or ( - doc_score - > CONTEXT_RELEVANT_WARNING_SCORE - # and len(with_citation) == 0 - ) - without_citation.append( - Document( - channel="info", - content=Render.collapsible_with_header_score( - doc, open_collapsible=is_open - ), - ) - ) - return with_citation, without_citation diff --git a/libs/kotaemon/kotaemon/indices/qa/citation_qa_inline.py b/libs/kotaemon/kotaemon/indices/qa/citation_qa_inline.py index aa4706529..8df910366 100644 --- a/libs/kotaemon/kotaemon/indices/qa/citation_qa_inline.py +++ b/libs/kotaemon/kotaemon/indices/qa/citation_qa_inline.py @@ -1,7 +1,7 @@ import re import threading from collections import defaultdict -from dataclasses import dataclass +from dataclasses import dataclass, field from typing import Generator import numpy as np @@ -83,10 +83,11 @@ class InlineEvidence: idx: int | None = None +@dataclass(kw_only=True) class AnswerWithInlineCitation(AnswerWithContextPipeline): """Answer the question based on the evidence with inline citation""" - qa_citation_template: str = DEFAULT_QA_CITATION_PROMPT + qa_citation_template: str = field(default=DEFAULT_QA_CITATION_PROMPT) def get_prompt(self, question, evidence, evidence_mode: int): """Prepare the prompt and other information for LLM""" @@ -214,13 +215,16 @@ def stream( # type: ignore def mindmap_call(): nonlocal mindmap - mindmap = self.create_mindmap_pipeline(context=evidence, question=question) + if self.create_mindmap_pipeline is not None: + mindmap = self.create_mindmap_pipeline( + context=evidence, question=question + ) mindmap_thread = None # execute function call in thread if evidence: - if self.enable_mindmap: + if self.enable_mindmap and self.create_mindmap_pipeline is not None: mindmap_thread = threading.Thread(target=mindmap_call) mindmap_thread.start() diff --git a/libs/kotaemon/kotaemon/indices/qa/format_context.py b/libs/kotaemon/kotaemon/indices/qa/format_context.py index 4727b1cf8..0a4a7fa32 100644 --- a/libs/kotaemon/kotaemon/indices/qa/format_context.py +++ b/libs/kotaemon/kotaemon/indices/qa/format_context.py @@ -1,9 +1,10 @@ import html +from dataclasses import dataclass from functools import partial import tiktoken -from kotaemon.base import BaseComponent, Document, RetrievedDocument +from kotaemon.base import Document, RetrievedDocument from kotaemon.indices.splitters import TokenSplitter EVIDENCE_MODE_TEXT = 0 @@ -12,7 +13,8 @@ EVIDENCE_MODE_FIGURE = 3 -class PrepareEvidencePipeline(BaseComponent): +@dataclass(kw_only=True) +class PrepareEvidencePipeline: """Prepare the evidence text from the list of retrieved documents This step usually happens after `DocumentRetrievalPipeline`. @@ -25,6 +27,9 @@ class PrepareEvidencePipeline(BaseComponent): max_context_length: int = 32000 trim_func: TokenSplitter | None = None + def __call__(self, docs: list[RetrievedDocument]) -> Document: + return self.run(docs) + def run(self, docs: list[RetrievedDocument]) -> Document: evidence = "" images = [] diff --git a/libs/kotaemon/kotaemon/indices/rankings/base.py b/libs/kotaemon/kotaemon/indices/rankings/base.py index c9c0b9b84..62e1448fa 100644 --- a/libs/kotaemon/kotaemon/indices/rankings/base.py +++ b/libs/kotaemon/kotaemon/indices/rankings/base.py @@ -1,13 +1,3 @@ -from __future__ import annotations +from kotaemon.rerankings.base import BaseReranking -from abc import abstractmethod - -from kotaemon.base import BaseComponent, Document - - -class BaseReranking(BaseComponent): - @abstractmethod - def run(self, documents: list[Document], query: str) -> list[Document]: - """Main method to transform list of documents - (re-ranking, filtering, etc)""" - ... +__all__ = ["BaseReranking"] diff --git a/libs/kotaemon/kotaemon/indices/rankings/cohere.py b/libs/kotaemon/kotaemon/indices/rankings/cohere.py index 039a00dbe..1cd369e4e 100644 --- a/libs/kotaemon/kotaemon/indices/rankings/cohere.py +++ b/libs/kotaemon/kotaemon/indices/rankings/cohere.py @@ -10,7 +10,6 @@ class CohereReranking(BaseReranking): model_name: str = "rerank-v4.0-fast" cohere_api_key: str = config("COHERE_API_KEY", "") - use_key_from_ktem: bool = False def run(self, documents: list[Document], query: str) -> list[Document]: """Use Cohere Reranker model to re-order documents @@ -19,25 +18,9 @@ def run(self, documents: list[Document], query: str) -> list[Document]: import cohere except ImportError: raise ImportError( - "Please install Cohere `pip install cohere` to use Cohere Reranking" + "Please install Cohere " "`pip install cohere` to use Cohere Reranking" ) - # try to get COHERE_API_KEY from embeddings - if not self.cohere_api_key and self.use_key_from_ktem: - try: - from ktem.embeddings.manager import ( - embedding_models_manager as embeddings, - ) - - cohere_model = embeddings.get("cohere") - ktem_cohere_api_key = cohere_model._kwargs.get( # type: ignore - "cohere_api_key" - ) - if ktem_cohere_api_key != "your-key": - self.cohere_api_key = ktem_cohere_api_key - except Exception as e: - print("Cannot get Cohere API key from `ktem`", e) - if not self.cohere_api_key: print("Cohere API key not found. Skipping rerankings.") return documents diff --git a/libs/kotaemon/kotaemon/indices/rankings/llm_trulens.py b/libs/kotaemon/kotaemon/indices/rankings/llm_trulens.py index 046998f76..05f23ef38 100644 --- a/libs/kotaemon/kotaemon/indices/rankings/llm_trulens.py +++ b/libs/kotaemon/kotaemon/indices/rankings/llm_trulens.py @@ -49,6 +49,19 @@ MAX_CONTEXT_LEN = 7500 +def _default_trim_func() -> TokenSplitter: + return TokenSplitter( + chunk_size=MAX_CONTEXT_LEN, + chunk_overlap=0, + separator=" ", + tokenizer=partial( + tiktoken.encoding_for_model("gpt-3.5-turbo").encode, + allowed_special=set(), + disallowed_special="all", + ), + ) + + def validate_rating(rating) -> int: """Validate a rating is between 0 and 10.""" @@ -99,16 +112,7 @@ class LLMTrulensScoring(LLMReranking): user_prompt_template: PromptTemplate = USER_PROMPT_TEMPLATE concurrent: bool = True normalize: float = 10 - trim_func: TokenSplitter = TokenSplitter.withx( - chunk_size=MAX_CONTEXT_LEN, - chunk_overlap=0, - separator=" ", - tokenizer=partial( - tiktoken.encoding_for_model("gpt-3.5-turbo").encode, - allowed_special=set(), - disallowed_special="all", - ), - ) + trim_func: TokenSplitter = _default_trim_func() def run( self, diff --git a/libs/kotaemon/kotaemon/indices/retriever/__init__.py b/libs/kotaemon/kotaemon/indices/retriever/__init__.py new file mode 100644 index 000000000..7f3bc2085 --- /dev/null +++ b/libs/kotaemon/kotaemon/indices/retriever/__init__.py @@ -0,0 +1,4 @@ +from .base import BaseRetriever +from .impl.rag import DocumentRetrievalPipeline + +__all__ = ["BaseRetriever", "DocumentRetrievalPipeline"] diff --git a/libs/kotaemon/kotaemon/indices/retriever/base.py b/libs/kotaemon/kotaemon/indices/retriever/base.py new file mode 100644 index 000000000..07978c1b5 --- /dev/null +++ b/libs/kotaemon/kotaemon/indices/retriever/base.py @@ -0,0 +1,50 @@ +import abc +from dataclasses import dataclass +from pathlib import Path +from typing import Optional + +from kotaemon.indices.stores import ChunkRelationStore +from kotaemon.storages import BaseDocumentStore, BaseVectorStore + + +@dataclass(kw_only=True) +class BaseRetriever: + """Base interface for file index retrieval pipelines. + + Subclasses must implement: + - run(self, *args, **kwargs): execute the retrieval + - get_pipeline(cls, user_settings, index_settings, selected): + return a fully-initialized pipeline instance + + Resources injected at runtime: + - chunk_relations: source-to-chunk relation store + - VS: VectorStore + - DS: DocStore + - FSPath: file storage path + - user_id: the current user id + """ + + chunk_relations: ChunkRelationStore + VS: BaseVectorStore + DS: BaseDocumentStore + FSPath: Path + user_id: int + + @classmethod + def get_user_settings(cls) -> dict: + """Get user-facing settings for this retriever. + + Returns: + dict: settings in ``ktem.settings.SettingItem`` format + """ + return {} + + @classmethod + @abc.abstractmethod + def get_pipeline( + cls, + user_settings: dict, + index_settings: dict, + selected: Optional[list] = None, + ) -> "BaseRetriever": + ... diff --git a/libs/kotaemon/kotaemon/indices/retriever/impl/__init__.py b/libs/kotaemon/kotaemon/indices/retriever/impl/__init__.py new file mode 100644 index 000000000..541d13156 --- /dev/null +++ b/libs/kotaemon/kotaemon/indices/retriever/impl/__init__.py @@ -0,0 +1,3 @@ +from .rag import DocumentRetrievalPipeline + +__all__ = ["DocumentRetrievalPipeline"] diff --git a/libs/kotaemon/kotaemon/indices/retriever/impl/rag.py b/libs/kotaemon/kotaemon/indices/retriever/impl/rag.py new file mode 100644 index 000000000..19b50cebc --- /dev/null +++ b/libs/kotaemon/kotaemon/indices/retriever/impl/rag.py @@ -0,0 +1,167 @@ +from __future__ import annotations + +import json +import logging +import time +import warnings +from collections import defaultdict +from dataclasses import dataclass +from functools import cached_property +from typing import Optional, Sequence + +from llama_index.core.vector_stores import ( + FilterCondition, + FilterOperator, + MetadataFilter, + MetadataFilters, +) +from llama_index.core.vector_stores.types import VectorStoreQueryMode + +from kotaemon.base import RetrievedDocument +from kotaemon.embeddings import BaseEmbeddings +from kotaemon.indices.rankings import BaseReranking, LLMReranking +from kotaemon.indices.retriever.base import BaseRetriever +from kotaemon.indices.vectorindex import VectorRetrieval + +logger = logging.getLogger(__name__) + + +@dataclass(kw_only=True) +class DocumentRetrievalPipeline(BaseRetriever): + """Retrieve relevant document excerpts from a vector/doc store. + + Args: + embedding: embedding model used for vector search + rerankers: optional reranking pipelines applied after retrieval + llm_scorer: optional LLM-based relevance scorer + get_extra_table: if True, also retrieve surrounding table nodes + top_k: number of documents to return + mmr: whether to apply MMR diversification + retrieval_mode: one of ``vector``, ``text``, or ``hybrid`` + """ + + embedding: BaseEmbeddings + rerankers: Sequence[BaseReranking] = () + llm_scorer: LLMReranking | None + get_extra_table: bool = False + mmr: bool = False + top_k: int = 5 + retrieval_mode: str = "hybrid" + doc_ids: Optional[list[str]] = None + + @cached_property + def vector_retrieval(self) -> VectorRetrieval: + return VectorRetrieval( + embedding=self.embedding, + vector_store=self.VS, + doc_store=self.DS, + retrieval_mode=self.retrieval_mode, # type: ignore + rerankers=self.rerankers, + ) + + def __call__( + self, text: str, doc_ids: Optional[list[str]] = None, *args, **kwargs + ) -> list[RetrievedDocument]: + return self.run(text, doc_ids, *args, **kwargs) + + def run( + self, + text: str, + doc_ids: Optional[list[str]] = None, + *args, + **kwargs, + ) -> list[RetrievedDocument]: + """Retrieve document excerpts similar to *text*. + + Args: + text: the query text + doc_ids: restrict retrieval to these source document ids + """ + doc_ids = doc_ids or self.doc_ids + + if doc_ids: + flatten_doc_ids: list[str] = [] + for doc_id in doc_ids: + if doc_id is None: + raise ValueError("No document is selected") + if doc_id.startswith("["): + flatten_doc_ids.extend(json.loads(doc_id)) + else: + flatten_doc_ids.append(doc_id) + doc_ids = flatten_doc_ids + + print("searching in doc_ids", doc_ids) + if not doc_ids: + logger.info(f"Skip retrieval because of no selected files: {self}") + return [] + + retrieval_kwargs: dict = {} + chunk_ids = self.chunk_relations.list_target_ids_for_sources( + doc_ids, "document" + ) + + retrieval_kwargs["do_extend"] = True + retrieval_kwargs["scope"] = chunk_ids + retrieval_kwargs["filters"] = MetadataFilters( + filters=[ + MetadataFilter( + key="file_id", + value=doc_ids, + operator=FilterOperator.IN, + ) + ], + condition=FilterCondition.OR, + ) + + if self.mmr: + retrieval_kwargs["mode"] = VectorStoreQueryMode.MMR + retrieval_kwargs["mmr_threshold"] = 0.5 + + s_time = time.time() + print(f"retrieval_kwargs: {retrieval_kwargs.keys()}") + docs = self.vector_retrieval(text=text, top_k=self.top_k, **retrieval_kwargs) + print("retrieval step took", time.time() - s_time) + + if not self.get_extra_table: + return docs + + table_pages: dict = defaultdict(list) + retrieved_id = {doc.doc_id for doc in docs} + for doc in docs: + if "page_label" not in doc.metadata: + continue + if "file_name" not in doc.metadata: + warnings.warn( + "file_name not in metadata while page_label is in metadata:" + f" {doc.metadata}" + ) + table_pages[doc.metadata["file_name"]].append(doc.metadata["page_label"]) + + queries: list[dict] = [ + {"$and": [{"file_name": {"$eq": fn}}, {"page_label": {"$in": pls}}]} + for fn, pls in table_pages.items() + ] + if queries: + try: + extra_docs = self.vector_retrieval( + text="", + top_k=50, + where=queries[0] if len(queries) == 1 else {"$or": queries}, + ) + for doc in extra_docs: + if doc.doc_id not in retrieved_id: + docs.append(doc) + except Exception: + print("Error retrieving additional tables") + + return docs + + def generate_relevant_scores( + self, query: str, documents: list[RetrievedDocument] + ) -> list[RetrievedDocument]: + """Re-score documents using the LLM scorer if configured.""" + return ( + documents + if not self.llm_scorer + else self.llm_scorer(documents=documents, query=query) + ) diff --git a/libs/kotaemon/kotaemon/indices/retrievers/__init__.py b/libs/kotaemon/kotaemon/indices/retrievers/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/libs/kotaemon/kotaemon/indices/retrievers/tavily_web_search.py b/libs/kotaemon/kotaemon/indices/retrievers/tavily_web_search.py deleted file mode 100644 index f6087d55d..000000000 --- a/libs/kotaemon/kotaemon/indices/retrievers/tavily_web_search.py +++ /dev/null @@ -1,57 +0,0 @@ -from decouple import config - -from kotaemon.base import BaseComponent, RetrievedDocument - -TAVILY_API_KEY = config("TAVILY_API_KEY", default="") - - -class WebSearch(BaseComponent): - """WebSearch component for fetching data from the web - using Jina API - """ - - def run( - self, - text: str, - *args, - **kwargs, - ) -> list[RetrievedDocument]: - if TAVILY_API_KEY == "": - raise ValueError( - "This feature requires TAVILY_API_KEY " - "(get free one from https://app.tavily.com/)" - ) - - try: - from tavily import TavilyClient - except ImportError: - raise ImportError( - "Please install `pip install tavily-python` to use this feature" - ) - - tavily_client = TavilyClient(api_key=TAVILY_API_KEY) - results = tavily_client.search( - query=text, - search_depth="advanced", - )["results"] - context = "\n\n".join( - "###URL: [{url}]({url})\n\n{content}".format( - url=result["url"], - content=result["content"], - ) - for result in results - ) - - return [ - RetrievedDocument( - text=context, - metadata={ - "file_name": "Web search", - "type": "table", - "llm_trulens_score": 1.0, - }, - ) - ] - - def generate_relevant_scores(self, text, documents: list[RetrievedDocument]): - return documents diff --git a/libs/kotaemon/kotaemon/indices/stores/__init__.py b/libs/kotaemon/kotaemon/indices/stores/__init__.py new file mode 100644 index 000000000..0c0082298 --- /dev/null +++ b/libs/kotaemon/kotaemon/indices/stores/__init__.py @@ -0,0 +1,19 @@ +from .base import ChunkRelationStore, FileSourceStore, RelationType +from .file import ( + ChunkRelationRecord, + FileLevelChunkRelationStore, + FileLevelSourceStore, + FileSourceRecord, + make_file_level_stores, +) + +__all__ = [ + "ChunkRelationRecord", + "ChunkRelationStore", + "FileLevelChunkRelationStore", + "FileLevelSourceStore", + "FileSourceRecord", + "FileSourceStore", + "RelationType", + "make_file_level_stores", +] diff --git a/libs/kotaemon/kotaemon/indices/stores/base.py b/libs/kotaemon/kotaemon/indices/stores/base.py new file mode 100644 index 000000000..490793e9e --- /dev/null +++ b/libs/kotaemon/kotaemon/indices/stores/base.py @@ -0,0 +1,53 @@ +from __future__ import annotations + +from typing import Literal, Protocol + +RelationType = Literal["document", "vector"] + + +class FileSourceStore(Protocol): + """Persistence for indexed source files (paths, URLs, metadata).""" + + def find_id_by_name(self, name: str, *, user_id: int) -> str | None: + """Return an existing source id for *name*, or None.""" + + def create_from_url(self, url: str, path_hash: str, *, user_id: int) -> str: + """Persist a URL source and return its id.""" + + def create_from_file( + self, name: str, path_hash: str, size: int, *, user_id: int + ) -> str: + """Persist a file source and return its id.""" + + def update_note(self, file_id: str, *, tokens: int | None, loader: str) -> None: + """Merge token count and loader name into the source note.""" + + def delete_source(self, file_id: str) -> None: + """Remove the source row for *file_id*.""" + + +class ChunkRelationStore(Protocol): + """Persistence for source-file to chunk / vector id relations.""" + + def add_relations( + self, + source_id: str, + target_ids: list[str], + relation_type: RelationType, + ) -> None: + """Record relations between a source file and chunk targets.""" + + def list_target_ids(self, source_id: str, relation_type: RelationType) -> list[str]: + """Return target ids linked to *source_id* with *relation_type*.""" + + def list_target_ids_for_sources( + self, source_ids: list[str], relation_type: RelationType + ) -> list[str]: + """Return target ids for many sources with *relation_type*.""" + + def delete_by_source(self, source_id: str) -> tuple[list[str], list[str]]: + """Delete all relations for *source_id*. + + Returns: + ``(vector_target_ids, document_target_ids)`` before deletion. + """ diff --git a/libs/kotaemon/kotaemon/indices/stores/file.py b/libs/kotaemon/kotaemon/indices/stores/file.py new file mode 100644 index 000000000..a893ca67a --- /dev/null +++ b/libs/kotaemon/kotaemon/indices/stores/file.py @@ -0,0 +1,220 @@ +from __future__ import annotations + +import json +import uuid +from dataclasses import asdict, dataclass, field +from pathlib import Path + +from .base import ChunkRelationStore, FileSourceStore, RelationType + + +@dataclass +class FileSourceRecord: + """Serialized source file metadata.""" + + id: str + name: str + path: str + size: int + user: str + note: dict = field(default_factory=dict) + + +@dataclass +class ChunkRelationRecord: + """Serialized source-to-chunk link.""" + + source_id: str + target_id: str + relation_type: str + + +def _read_json_list(path: Path) -> list[dict]: + if not path.exists(): + return [] + with path.open(encoding="utf-8") as f: + data = json.load(f) + if not isinstance(data, list): + raise ValueError(f"Expected a JSON list in {path}") + return data + + +def _write_json_list(path: Path, rows: list[dict]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + tmp = path.with_suffix(path.suffix + ".tmp") + with tmp.open("w", encoding="utf-8") as f: + json.dump(rows, f, indent=2) + tmp.replace(path) + + +@dataclass(kw_only=True) +class FileLevelSourceStore(FileSourceStore): + """JSON file-backed :class:`FileSourceStore` for standalone kotaemon use. + + Persists rows to ``{storage_dir}/sources.json``. Suitable for + single-process indexing without SQLAlchemy. + """ + + storage_dir: Path + private: bool = False + + @property + def _sources_path(self) -> Path: + return self.storage_dir / "sources.json" + + def _load(self) -> list[FileSourceRecord]: + return [FileSourceRecord(**row) for row in _read_json_list(self._sources_path)] + + def _save(self, rows: list[FileSourceRecord]) -> None: + _write_json_list(self._sources_path, [asdict(r) for r in rows]) + + def find_id_by_name(self, name: str, *, user_id: int) -> str | None: + user = str(user_id) + for row in self._load(): + if row.name != name: + continue + if self.private and row.user != user: + continue + return row.id + return None + + def create_from_url(self, url: str, path_hash: str, *, user_id: int) -> str: + rows = self._load() + record = FileSourceRecord( + id=str(uuid.uuid4()), + name=url, + path=path_hash, + size=0, + user=str(user_id), + ) + rows.append(record) + self._save(rows) + return record.id + + def create_from_file( + self, name: str, path_hash: str, size: int, *, user_id: int + ) -> str: + rows = self._load() + record = FileSourceRecord( + id=str(uuid.uuid4()), + name=name, + path=path_hash, + size=size, + user=str(user_id), + ) + rows.append(record) + self._save(rows) + return record.id + + def update_note(self, file_id: str, *, tokens: int | None, loader: str) -> None: + rows = self._load() + for row in rows: + if row.id != file_id: + continue + if tokens is not None: + row.note["tokens"] = tokens + row.note["loader"] = loader + self._save(rows) + return + + def delete_source(self, file_id: str) -> None: + rows = [r for r in self._load() if r.id != file_id] + self._save(rows) + + +@dataclass(kw_only=True) +class FileLevelChunkRelationStore(ChunkRelationStore): + """JSON file-backed :class:`ChunkRelationStore` for standalone kotaemon use. + + Persists rows to ``{storage_dir}/relations.json``. + """ + + storage_dir: Path + + @property + def _relations_path(self) -> Path: + return self.storage_dir / "relations.json" + + def _load(self) -> list[ChunkRelationRecord]: + return [ + ChunkRelationRecord(**row) for row in _read_json_list(self._relations_path) + ] + + def _save(self, rows: list[ChunkRelationRecord]) -> None: + _write_json_list(self._relations_path, [asdict(r) for r in rows]) + + def add_relations( + self, + source_id: str, + target_ids: list[str], + relation_type: RelationType, + ) -> None: + if not target_ids: + return + rows = self._load() + rows.extend( + ChunkRelationRecord( + source_id=source_id, + target_id=target_id, + relation_type=relation_type, + ) + for target_id in target_ids + ) + self._save(rows) + + def list_target_ids(self, source_id: str, relation_type: RelationType) -> list[str]: + return [ + row.target_id + for row in self._load() + if row.source_id == source_id and row.relation_type == relation_type + ] + + def list_target_ids_for_sources( + self, source_ids: list[str], relation_type: RelationType + ) -> list[str]: + if not source_ids: + return [] + allowed = set(source_ids) + return [ + row.target_id + for row in self._load() + if row.source_id in allowed and row.relation_type == relation_type + ] + + def delete_by_source(self, source_id: str) -> tuple[list[str], list[str]]: + vs_ids: list[str] = [] + ds_ids: list[str] = [] + kept: list[ChunkRelationRecord] = [] + for row in self._load(): + if row.source_id != source_id: + kept.append(row) + continue + if row.relation_type == "vector": + vs_ids.append(row.target_id) + elif row.relation_type == "document": + ds_ids.append(row.target_id) + self._save(kept) + return vs_ids, ds_ids + + +def make_file_level_stores( + storage_dir: str | Path, + *, + private: bool = False, +) -> tuple[FileLevelSourceStore, FileLevelChunkRelationStore]: + """Build a matched pair of file-level stores under *storage_dir*. + + Example (standalone kotaemon indexing):: + + from pathlib import Path + from kotaemon.indices.stores import make_file_level_stores + + file_source, chunk_relations = make_file_level_stores( + Path("./index_data/default") + ) + """ + root = Path(storage_dir) + return ( + FileLevelSourceStore(storage_dir=root, private=private), + FileLevelChunkRelationStore(storage_dir=root), + ) diff --git a/libs/kotaemon/kotaemon/indices/vectorindex.py b/libs/kotaemon/kotaemon/indices/vectorindex.py index d882d6bfb..677399437 100644 --- a/libs/kotaemon/kotaemon/indices/vectorindex.py +++ b/libs/kotaemon/kotaemon/indices/vectorindex.py @@ -2,39 +2,30 @@ import threading import uuid +from dataclasses import dataclass, field from pathlib import Path from typing import Optional, Sequence, cast -from theflow.settings import settings as flowsettings - -from kotaemon.base import BaseComponent, Document, RetrievedDocument +from kotaemon.base import Document, RetrievedDocument, Runnable from kotaemon.embeddings import BaseEmbeddings from kotaemon.storages import BaseDocumentStore, BaseVectorStore -from .base import BaseIndexing, BaseRetrieval +from .base import BaseRetrieval from .rankings import BaseReranking, LLMReranking VECTOR_STORE_FNAME = "vectorstore" DOC_STORE_FNAME = "docstore" -class VectorIndexing(BaseIndexing): - """Ingest the document, run through the embedding, and store the embedding in a - vector store. - - This pipeline supports the following set of inputs: - - List of documents - - List of texts - """ - - cache_dir: Optional[str] = getattr(flowsettings, "KH_CHUNKS_OUTPUT_DIR", None) +@dataclass(kw_only=True) +class VectorIndexing: vector_store: BaseVectorStore - doc_store: Optional[BaseDocumentStore] = None embedding: BaseEmbeddings + cache_dir: Optional[str] = None + doc_store: Optional[BaseDocumentStore] = None count_: int = 0 def to_retrieval_pipeline(self, *args, **kwargs): - """Convert the indexing pipeline to a retrieval pipeline""" return VectorRetrieval( vector_store=self.vector_store, doc_store=self.doc_store, @@ -43,50 +34,34 @@ def to_retrieval_pipeline(self, *args, **kwargs): ) def write_chunk_to_file(self, docs: list[Document]): - # save the chunks content into markdown format - if self.cache_dir: - file_name = docs[0].metadata.get("file_name") - if not file_name: - return - - file_name = Path(file_name) - for i in range(len(docs)): - markdown_content = "" - if "page_label" in docs[i].metadata: - page_label = str(docs[i].metadata["page_label"]) - markdown_content += f"Page label: {page_label}" - if "file_name" in docs[i].metadata: - filename = docs[i].metadata["file_name"] - markdown_content += f"\nFile name: {filename}" - if "section" in docs[i].metadata: - section = docs[i].metadata["section"] - markdown_content += f"\nSection: {section}" - if "type" in docs[i].metadata: - if docs[i].metadata["type"] == "image": - image_origin = docs[i].metadata["image_origin"] - image_origin = f'

' - markdown_content += f"\nImage origin: {image_origin}" - if docs[i].text: - markdown_content += f"\ntext:\n{docs[i].text}" - - with open( - Path(self.cache_dir) / f"{file_name.stem}_{self.count_+i}.md", - "w", - encoding="utf-8", - ) as f: - f.write(markdown_content) + if not self.cache_dir: + return + file_name = docs[0].metadata.get("file_name") + if not file_name: + return + file_name = Path(file_name) + for i, doc in enumerate(docs): + parts = [] + if "page_label" in doc.metadata: + parts.append(f"Page label: {doc.metadata['page_label']}") + if "file_name" in doc.metadata: + parts.append(f"File name: {doc.metadata['file_name']}") + if doc.text: + parts.append(f"text:\n{doc.text}") + with open( + Path(self.cache_dir) / f"{file_name.stem}_{self.count_ + i}.md", + "w", + encoding="utf-8", + ) as f: + f.write("\n".join(parts)) def add_to_docstore(self, docs: list[Document]): if self.doc_store: - print("Adding documents to doc store") self.doc_store.add(docs) def add_to_vectorstore(self, docs: list[Document]): - # in case we want to skip embedding if self.vector_store: - print(f"Getting embeddings for {len(docs)} nodes") embeddings = self.embedding(docs) - print("Adding embeddings to vector store") self.vector_store.add( embeddings=embeddings, ids=[t.doc_id for t in docs], @@ -96,7 +71,6 @@ def run(self, text: str | list[str] | Document | list[Document]): input_: list[Document] = [] if not isinstance(text, list): text = [text] - for item in cast(list, text): if isinstance(item, str): input_.append(Document(text=item, id_=str(uuid.uuid4()))) @@ -106,64 +80,40 @@ def run(self, text: str | list[str] | Document | list[Document]): raise ValueError( f"Invalid input type {type(item)}, should be str or Document" ) - self.add_to_vectorstore(input_) self.add_to_docstore(input_) self.write_chunk_to_file(input_) self.count_ += len(input_) +@dataclass(kw_only=True) class VectorRetrieval(BaseRetrieval): - """Retrieve list of documents from vector store""" - vector_store: BaseVectorStore - doc_store: Optional[BaseDocumentStore] = None embedding: BaseEmbeddings - rerankers: Sequence[BaseReranking] = [] + doc_store: Optional[BaseDocumentStore] = None + rerankers: Sequence[BaseReranking] = field(default_factory=list) top_k: int = 5 first_round_top_k_mult: int = 10 - retrieval_mode: str = "hybrid" # vector, text, hybrid + retrieval_mode: str = "hybrid" def _filter_docs( self, documents: list[RetrievedDocument], top_k: int | None = None ): - if top_k: - documents = documents[:top_k] - return documents + return documents[:top_k] if top_k else documents def run( self, text: str | Document, top_k: Optional[int] = None, **kwargs ) -> list[RetrievedDocument]: - """Retrieve a list of documents from vector store - - Args: - text: the text to retrieve similar documents - top_k: number of top similar documents to return - - Returns: - list[RetrievedDocument]: list of retrieved documents - """ if top_k is None: top_k = self.top_k - do_extend = kwargs.pop("do_extend", False) - thumbnail_count = kwargs.pop("thumbnail_count", 3) - - if do_extend: - top_k_first_round = top_k * self.first_round_top_k_mult - else: - top_k_first_round = top_k - + kwargs.pop("thumbnail_count", 3) + top_k_first_round = top_k * self.first_round_top_k_mult if do_extend else top_k if self.doc_store is None: - raise ValueError( - "doc_store is not provided. Please provide a doc_store to " - "retrieve the documents" - ) + raise ValueError("doc_store is not provided.") result: list[RetrievedDocument] = [] - # TODO: should declare scope directly in the run params scope = kwargs.pop("scope", None) - emb: list[float] if self.retrieval_mode == "vector": emb = self.embedding(text)[0].embedding @@ -177,24 +127,18 @@ def run( ] elif self.retrieval_mode == "text": query = text.text if isinstance(text, Document) else text - docs = [] - if scope: - docs = self.doc_store.query( - query, top_k=top_k_first_round, doc_ids=scope - ) + docs = ( + self.doc_store.query(query, top_k=top_k_first_round, doc_ids=scope) + if scope + else [] + ) result = [RetrievedDocument(**doc.to_dict(), score=-1.0) for doc in docs] elif self.retrieval_mode == "hybrid": - # similarity search section emb = self.embedding(text)[0].embedding - vs_docs: list[RetrievedDocument] = [] - vs_ids: list[str] = [] - vs_scores: list[float] = [] + vs_docs, vs_ids, vs_scores = [], [], [] def query_vectorstore(): - nonlocal vs_docs - nonlocal vs_scores - nonlocal vs_ids - + nonlocal vs_docs, vs_scores, vs_ids assert self.doc_store is not None _, vs_scores, vs_ids = self.vector_store.query( embedding=emb, top_k=top_k_first_round, doc_ids=scope, **kwargs @@ -202,12 +146,10 @@ def query_vectorstore(): if vs_ids: vs_docs = self.doc_store.get(vs_ids) - # full-text search section ds_docs: list[RetrievedDocument] = [] def query_docstore(): nonlocal ds_docs - assert self.doc_store is not None query = text.text if isinstance(text, Document) else text if scope: @@ -215,15 +157,12 @@ def query_docstore(): query, top_k=top_k_first_round, doc_ids=scope ) - vs_query_thread = threading.Thread(target=query_vectorstore) - ds_query_thread = threading.Thread(target=query_docstore) - - vs_query_thread.start() - ds_query_thread.start() - - vs_query_thread.join() - ds_query_thread.join() - + t1 = threading.Thread(target=query_vectorstore) + t2 = threading.Thread(target=query_docstore) + t1.start() + t2.start() + t1.join() + t2.join() result = [ RetrievedDocument(**doc.to_dict(), score=-1.0) for doc in ds_docs @@ -233,79 +172,21 @@ def query_docstore(): RetrievedDocument(**doc.to_dict(), score=score) for doc, score in zip(vs_docs, vs_scores) ] - print(f"Got {len(vs_docs)} from vectorstore") - print(f"Got {len(ds_docs)} from docstore") - # use additional reranker to re-order the document list if self.rerankers and text: for reranker in self.rerankers: - # if reranker is LLMReranking, limit the document with top_k items only if isinstance(reranker, LLMReranking): result = self._filter_docs(result, top_k=top_k) result = reranker.run(documents=result, query=text) result = self._filter_docs(result, top_k=top_k) - print(f"Got raw {len(result)} retrieved documents") - - # add page thumbnails to the result if exists - thumbnail_doc_ids: set[str] = set() - # we should copy the text from retrieved text chunk - # to the thumbnail to get relevant LLM score correctly - text_thumbnail_docs: dict[str, RetrievedDocument] = {} - - non_thumbnail_docs = [] - raw_thumbnail_docs = [] - for doc in result: - if doc.metadata.get("type") == "thumbnail": - # change type to image to display on UI - doc.metadata["type"] = "image" - raw_thumbnail_docs.append(doc) - continue - if ( - "thumbnail_doc_id" in doc.metadata - and len(thumbnail_doc_ids) < thumbnail_count - ): - thumbnail_id = doc.metadata["thumbnail_doc_id"] - thumbnail_doc_ids.add(thumbnail_id) - text_thumbnail_docs[thumbnail_id] = doc - else: - non_thumbnail_docs.append(doc) - - linked_thumbnail_docs = self.doc_store.get(list(thumbnail_doc_ids)) - print( - "thumbnail docs", - len(linked_thumbnail_docs), - "non-thumbnail docs", - len(non_thumbnail_docs), - "raw-thumbnail docs", - len(raw_thumbnail_docs), - ) - additional_docs = [] - - for thumbnail_doc in linked_thumbnail_docs: - text_doc = text_thumbnail_docs[thumbnail_doc.doc_id] - doc_dict = thumbnail_doc.to_dict() - doc_dict["_id"] = text_doc.doc_id - doc_dict["content"] = text_doc.content - doc_dict["metadata"]["type"] = "image" - for key in text_doc.metadata: - if key not in doc_dict["metadata"]: - doc_dict["metadata"][key] = text_doc.metadata[key] - - additional_docs.append(RetrievedDocument(**doc_dict, score=text_doc.score)) - - result = additional_docs + non_thumbnail_docs - - if not result: - # return output from raw retrieved thumbnails - result = self._filter_docs(raw_thumbnail_docs, top_k=thumbnail_count) - return result -class TextVectorQA(BaseComponent): +@dataclass(kw_only=True) +class TextVectorQA: retrieving_pipeline: BaseRetrieval - qa_pipeline: BaseComponent + qa_pipeline: Runnable def run(self, question, **kwargs): retrieved_documents = self.retrieving_pipeline(question, **kwargs) diff --git a/libs/kotaemon/kotaemon/indices/websearch/__init__.py b/libs/kotaemon/kotaemon/indices/websearch/__init__.py new file mode 100644 index 000000000..21b2b27c5 --- /dev/null +++ b/libs/kotaemon/kotaemon/indices/websearch/__init__.py @@ -0,0 +1,13 @@ +from .base import BaseWebSearch +from .factory import MP_VENDOR_CLS, WebSearchFactory, WebSearchVendor +from .jina import JinaWebSearch +from .tavily import TavilyWebSearch + +__all__ = [ + "BaseWebSearch", + "JinaWebSearch", + "MP_VENDOR_CLS", + "TavilyWebSearch", + "WebSearchFactory", + "WebSearchVendor", +] diff --git a/libs/kotaemon/kotaemon/indices/websearch/base.py b/libs/kotaemon/kotaemon/indices/websearch/base.py new file mode 100644 index 000000000..b517cfd43 --- /dev/null +++ b/libs/kotaemon/kotaemon/indices/websearch/base.py @@ -0,0 +1,20 @@ +from __future__ import annotations + +from dataclasses import dataclass + +from kotaemon.base import RetrievedDocument + + +@dataclass(kw_only=True) +class BaseWebSearch: + """Base class for internet search backends used as a chat retriever.""" + + def run(self, text: str, *args, **kwargs) -> list[RetrievedDocument]: + raise NotImplementedError + + def generate_relevant_scores( + self, + text: str, + documents: list[RetrievedDocument], + ) -> list[RetrievedDocument]: + return documents diff --git a/libs/kotaemon/kotaemon/indices/websearch/factory.py b/libs/kotaemon/kotaemon/indices/websearch/factory.py new file mode 100644 index 000000000..bfdbba8f8 --- /dev/null +++ b/libs/kotaemon/kotaemon/indices/websearch/factory.py @@ -0,0 +1,32 @@ +from __future__ import annotations + +from enum import Enum + +from .base import BaseWebSearch +from .jina import JinaWebSearch +from .tavily import TavilyWebSearch + + +class WebSearchVendor(str, Enum): + TAVILY = "TavilyWebSearch" + JINA = "JinaWebSearch" + + +MP_VENDOR_CLS: dict[WebSearchVendor, type[BaseWebSearch]] = { + WebSearchVendor.TAVILY: TavilyWebSearch, + WebSearchVendor.JINA: JinaWebSearch, +} + + +class WebSearchFactory: + @staticmethod + def get_cls(vendor: WebSearchVendor | str) -> type[BaseWebSearch]: + """Return the class for *vendor*, coercing bare strings.""" + key = WebSearchVendor(vendor) + if key not in MP_VENDOR_CLS: + raise ValueError(f"Invalid web search vendor: {vendor!r}") + return MP_VENDOR_CLS[key] + + @staticmethod + def supported_vendors() -> list[WebSearchVendor]: + return list(MP_VENDOR_CLS.keys()) diff --git a/libs/kotaemon/kotaemon/indices/retrievers/jina_web_search.py b/libs/kotaemon/kotaemon/indices/websearch/jina.py similarity index 54% rename from libs/kotaemon/kotaemon/indices/retrievers/jina_web_search.py rename to libs/kotaemon/kotaemon/indices/websearch/jina.py index 48fa60f5b..34b1e8645 100644 --- a/libs/kotaemon/kotaemon/indices/retrievers/jina_web_search.py +++ b/libs/kotaemon/kotaemon/indices/websearch/jina.py @@ -1,34 +1,33 @@ +from dataclasses import dataclass, field + import requests from decouple import config -from kotaemon.base import BaseComponent, RetrievedDocument +from kotaemon.base import RetrievedDocument + +from .base import BaseWebSearch -JINA_API_KEY = config("JINA_API_KEY", default="") -JINA_URL = config("JINA_URL", default="https://r.jina.ai/") +@dataclass(kw_only=True) +class JinaWebSearch(BaseWebSearch): + """Web search via the Jina Search API.""" -class WebSearch(BaseComponent): - """WebSearch component for fetching data from the web - using Jina API - """ + api_key: str = field( + default_factory=lambda: config("JINA_API_KEY", default=""), + metadata={"description": "Jina API key (https://jina.ai/reader)."}, + ) - def run( - self, - text: str, - *args, - **kwargs, - ) -> list[RetrievedDocument]: - if JINA_API_KEY == "": + def run(self, text: str, *args, **kwargs) -> list[RetrievedDocument]: + if not self.api_key: raise ValueError( "This feature requires JINA_API_KEY " - "(get free one from https://jina.ai/reader)" + "(get a free key from https://jina.ai/reader)" ) - # setup the request api_url = f"https://s.jina.ai/{text}" headers = {"X-With-Generated-Alt": "true", "Accept": "application/json"} - if JINA_API_KEY: - headers["Authorization"] = f"Bearer {JINA_API_KEY}" + if self.api_key: + headers["Authorization"] = f"Bearer {self.api_key}" response = requests.get(api_url, headers=headers) response.raise_for_status() @@ -38,9 +37,7 @@ def run( RetrievedDocument( text=( "###URL: [{url}]({url})\n\n" - "####{title}\n\n" - "{description}\n" - "{content}" + "####{title}\n\n{description}\n{content}" ).format( url=item["url"], title=item["title"], @@ -55,6 +52,3 @@ def run( ) for item in response_dict["data"] ] - - def generate_relevant_scores(self, text, documents: list[RetrievedDocument]): - return documents diff --git a/libs/kotaemon/kotaemon/indices/websearch/tavily.py b/libs/kotaemon/kotaemon/indices/websearch/tavily.py new file mode 100644 index 000000000..9b1dd5cd0 --- /dev/null +++ b/libs/kotaemon/kotaemon/indices/websearch/tavily.py @@ -0,0 +1,49 @@ +from dataclasses import dataclass, field + +from decouple import config + +from kotaemon.base import RetrievedDocument + +from .base import BaseWebSearch + + +@dataclass(kw_only=True) +class TavilyWebSearch(BaseWebSearch): + """Web search via the Tavily API.""" + + api_key: str = field( + default_factory=lambda: config("TAVILY_API_KEY", default=""), + metadata={"description": "Tavily API key (https://app.tavily.com/)."}, + ) + + def run(self, text: str, *args, **kwargs) -> list[RetrievedDocument]: + if not self.api_key: + raise ValueError( + "This feature requires TAVILY_API_KEY " + "(get a free key from https://app.tavily.com/)" + ) + try: + from tavily import TavilyClient + except ImportError as e: + raise ImportError( + "Please install tavily-python: pip install tavily-python" + ) from e + + client = TavilyClient(api_key=self.api_key) + results = client.search(query=text, search_depth="advanced")["results"] + context = "\n\n".join( + "###URL: [{url}]({url})\n\n{content}".format( + url=result["url"], content=result["content"] + ) + for result in results + ) + return [ + RetrievedDocument( + text=context, + metadata={ + "file_name": "Web search", + "type": "table", + "llm_trulens_score": 1.0, + }, + ) + ] diff --git a/libs/kotaemon/kotaemon/llms/base.py b/libs/kotaemon/kotaemon/llms/base.py index 374d13947..4ac352df7 100644 --- a/libs/kotaemon/kotaemon/llms/base.py +++ b/libs/kotaemon/kotaemon/llms/base.py @@ -2,10 +2,10 @@ from langchain_core.language_models.base import BaseLanguageModel -from kotaemon.base import BaseComponent, LLMInterface +from kotaemon.base import LLMInterface -class BaseLLM(BaseComponent): +class BaseLLM: def to_langchain_format(self) -> BaseLanguageModel: raise NotImplementedError @@ -21,5 +21,5 @@ def stream(self, *args, **kwargs) -> Iterator[LLMInterface]: def astream(self, *args, **kwargs) -> AsyncGenerator[LLMInterface, None]: raise NotImplementedError - def run(self, *args, **kwargs): + def __call__(self, *args, **kwargs) -> LLMInterface: return self.invoke(*args, **kwargs) diff --git a/libs/kotaemon/kotaemon/llms/branching.py b/libs/kotaemon/kotaemon/llms/branching.py index ee49dc547..551bc5d5b 100644 --- a/libs/kotaemon/kotaemon/llms/branching.py +++ b/libs/kotaemon/kotaemon/llms/branching.py @@ -1,187 +1,25 @@ from typing import List, Optional -from kotaemon.base import BaseComponent, Document, Param +from kotaemon.base import Document, Runnable -from .linear import GatedLinearPipeline +class SimpleBranchingPipeline: + def __init__(self, branches: List[Runnable] | None = None) -> None: + self.branches: List[Runnable] = branches or [] -class SimpleBranchingPipeline(BaseComponent): - """ - A simple branching pipeline for executing multiple branches. - - Attributes: - branches (List[BaseComponent]): The list of branches to be executed. - - Example: - ```python - from kotaemon.llms import ( - LCAzureChatOpenAI, - BasePromptComponent, - GatedLinearPipeline, - ) - from kotaemon.parsers import RegexExtractor - - def identity(x): - return x - - pipeline = SimpleBranchingPipeline() - llm = LCAzureChatOpenAI( - openai_api_base="your openai api base", - openai_api_key="your openai api key", - openai_api_version="your openai api version", - deployment_name="dummy-q2-gpt35", - temperature=0, - request_timeout=600, - ) - - for i in range(3): - pipeline.add_branch( - GatedLinearPipeline( - prompt=BasePromptComponent(template=f"what is {i} in Japanese ?"), - condition=RegexExtractor(pattern=f"{i}"), - llm=llm, - post_processor=identity, - ) - ) - print(pipeline(condition_text="1")) - print(pipeline(condition_text="2")) - print(pipeline(condition_text="12")) - ``` - """ - - branches: List[BaseComponent] = Param(default_callback=lambda *_: []) - - def add_branch(self, component: BaseComponent): - """ - Add a new branch to the pipeline. - - Args: - component (BaseComponent): The branch component to be added. - """ + def add_branch(self, component: Runnable): self.branches.append(component) def run(self, **prompt_kwargs): - """ - Execute the pipeline by running each branch and return the outputs as a list. - - Args: - **prompt_kwargs: Keyword arguments for the branches. - - Returns: - List: The outputs of each branch as a list. - """ - output = [] - for i, branch in enumerate(self.branches): - self._prepare_child(branch, name=f"branch-{i}") - output.append(branch(**prompt_kwargs)) - - return output + return [branch(**prompt_kwargs) for branch in self.branches] class GatedBranchingPipeline(SimpleBranchingPipeline): - """ - A simple gated branching pipeline for executing multiple branches based on a - condition. - - This class extends the SimpleBranchingPipeline class and adds the ability to execute - the branches until a branch returns a non-empty output based on a condition. - - Attributes: - branches (List[BaseComponent]): The list of branches to be executed. - - Example: - ```python - from kotaemon.llms import ( - LCAzureChatOpenAI, - BasePromptComponent, - GatedLinearPipeline, - ) - from kotaemon.parsers import RegexExtractor - - def identity(x): - return x - - pipeline = GatedBranchingPipeline() - llm = LCAzureChatOpenAI( - openai_api_base="your openai api base", - openai_api_key="your openai api key", - openai_api_version="your openai api version", - deployment_name="dummy-q2-gpt35", - temperature=0, - request_timeout=600, - ) - - for i in range(3): - pipeline.add_branch( - GatedLinearPipeline( - prompt=BasePromptComponent(template=f"what is {i} in Japanese ?"), - condition=RegexExtractor(pattern=f"{i}"), - llm=llm, - post_processor=identity, - ) - ) - print(pipeline(condition_text="1")) - print(pipeline(condition_text="2")) - ``` - """ - def run(self, *, condition_text: Optional[str] = None, **prompt_kwargs): - """ - Execute the pipeline by running each branch and return the output of the first - branch that returns a non-empty output based on the provided condition. - - Args: - condition_text (str): The condition text to evaluate for each branch. - Default to None. - **prompt_kwargs: Keyword arguments for the branches. - - Returns: - Union[OutputType, None]: The output of the first branch that satisfies the - condition, or None if no branch satisfies the condition. - - Raises: - ValueError: If condition_text is None - """ if condition_text is None: raise ValueError("`condition_text` must be provided.") - - for i, branch in enumerate(self.branches): - self._prepare_child(branch, name=f"branch-{i}") + for branch in self.branches: output = branch(condition_text=condition_text, **prompt_kwargs) if output: return output - return Document(None) - - -if __name__ == "__main__": - import dotenv - - from kotaemon.llms import BasePromptComponent, LCAzureChatOpenAI - from kotaemon.parsers import RegexExtractor - - def identity(x): - return x - - secrets = dotenv.dotenv_values(".env") - - pipeline = GatedBranchingPipeline() - llm = LCAzureChatOpenAI( - openai_api_base=secrets.get("OPENAI_API_BASE", ""), - openai_api_key=secrets.get("OPENAI_API_KEY", ""), - openai_api_version=secrets.get("OPENAI_API_VERSION", ""), - deployment_name="dummy-q2-gpt35", - temperature=0, - request_timeout=600, - ) - - for i in range(3): - pipeline.add_branch( - GatedLinearPipeline( - prompt=BasePromptComponent(template=f"what is {i} in Japanese ?"), - condition=RegexExtractor(pattern=f"{i}"), - llm=llm, - post_processor=identity, - ) - ) - pipeline(condition_text="1") diff --git a/libs/kotaemon/kotaemon/llms/chats/__init__.py b/libs/kotaemon/kotaemon/llms/chats/__init__.py index f3f827746..d963e019b 100644 --- a/libs/kotaemon/kotaemon/llms/chats/__init__.py +++ b/libs/kotaemon/kotaemon/llms/chats/__init__.py @@ -1,6 +1,7 @@ from .base import ChatLLM from .endpoint_based import EndpointChatLLM from .langchain_based import ( + BaseLCChat, LCAnthropicChat, LCAzureChatOpenAI, LCChatMixin, @@ -25,6 +26,7 @@ "LCOllamaChat", "LCChatOpenAI", "LCAzureChatOpenAI", + "BaseLCChat", "LCChatMixin", "LlamaCppChat", ] diff --git a/libs/kotaemon/kotaemon/llms/chats/base.py b/libs/kotaemon/kotaemon/llms/chats/base.py index 09042bc6c..3d390e0c9 100644 --- a/libs/kotaemon/kotaemon/llms/chats/base.py +++ b/libs/kotaemon/kotaemon/llms/chats/base.py @@ -1,22 +1,16 @@ from __future__ import annotations import logging +from dataclasses import dataclass -from kotaemon.base import BaseComponent +from kotaemon.base.describe import DataclassDescribe, describe_dataclass from kotaemon.llms.base import BaseLLM logger = logging.getLogger(__name__) +@dataclass(kw_only=True) class ChatLLM(BaseLLM): - def flow(self): - if self.inflow is None: - raise ValueError("No inflow provided.") - - if not isinstance(self.inflow, BaseComponent): - raise ValueError( - f"inflow must be a BaseComponent, found {type(self.inflow)}" - ) - - text = self.inflow.flow().text - return self.__call__(text) + @classmethod + def describe(cls) -> DataclassDescribe: + return describe_dataclass(cls) diff --git a/libs/kotaemon/kotaemon/llms/chats/endpoint_based.py b/libs/kotaemon/kotaemon/llms/chats/endpoint_based.py index 5ab183578..8f477840b 100644 --- a/libs/kotaemon/kotaemon/llms/chats/endpoint_based.py +++ b/libs/kotaemon/kotaemon/llms/chats/endpoint_based.py @@ -1,3 +1,5 @@ +from dataclasses import dataclass, field + import requests from kotaemon.base import ( @@ -5,38 +7,23 @@ BaseMessage, HumanMessage, LLMInterface, - Param, SystemMessage, ) from .base import ChatLLM +@dataclass(kw_only=True) class EndpointChatLLM(ChatLLM): - """ - A ChatLLM that uses an endpoint to generate responses. This expects an OpenAI API - compatible endpoint. - - Attributes: - endpoint_url (str): The url of a OpenAI API compatible endpoint. - """ + """ChatLLM that uses an OpenAI API compatible HTTP endpoint.""" - endpoint_url: str = Param( - help="URL of the OpenAI API compatible endpoint", required=True + endpoint_url: str = field( + metadata={"description": "OpenAI API compatible endpoint URL"}, ) def run( self, messages: str | BaseMessage | list[BaseMessage], **kwargs ) -> LLMInterface: - """ - Generate response from messages - Args: - messages (str | BaseMessage | list[BaseMessage]): history of messages to - generate response from - **kwargs: additional arguments to pass to the OpenAI API - Returns: - LLMInterface: generated response - """ if isinstance(messages, str): input_ = [HumanMessage(content=messages)] elif isinstance(messages, BaseMessage): @@ -47,10 +34,9 @@ def run( def decide_role(message: BaseMessage): if isinstance(message, SystemMessage): return "system" - elif isinstance(message, AIMessage): + if isinstance(message, AIMessage): return "assistant" - else: - return "user" + return "user" request_json = { "messages": [{"content": m.text, "role": decide_role(m)} for m in input_] @@ -79,7 +65,6 @@ def decide_role(message: BaseMessage): def invoke( self, messages: str | BaseMessage | list[BaseMessage], **kwargs ) -> LLMInterface: - """Same as run""" return self.run(messages, **kwargs) async def ainvoke( diff --git a/libs/kotaemon/kotaemon/llms/chats/factory.py b/libs/kotaemon/kotaemon/llms/chats/factory.py new file mode 100644 index 000000000..45aca8317 --- /dev/null +++ b/libs/kotaemon/kotaemon/llms/chats/factory.py @@ -0,0 +1,59 @@ +from __future__ import annotations + +from enum import Enum + +from .base import ChatLLM as BaseChatLLM +from .endpoint_based import EndpointChatLLM +from .langchain_based import ( + LCAnthropicChat, + LCAzureChatOpenAI, + LCChatOpenAI, + LCCohereChat, + LCGeminiChat, + LCOllamaChat, +) +from .llamacpp import LlamaCppChat +from .openai import AzureChatOpenAI, ChatOpenAI, StructuredOutputChatOpenAI + + +class LLMVendor(str, Enum): + AZURE_CHAT_OPENAI = "AzureChatOpenAI" + CHAT_OPENAI = "ChatOpenAI" + STRUCTURED_OUTPUT_CHAT_OPENAI = "StructuredOutputChatOpenAI" + ENDPOINT = "EndpointChatLLM" + LC_CHAT_OPENAI = "LCChatOpenAI" + LC_AZURE_CHAT_OPENAI = "LCAzureChatOpenAI" + LC_ANTHROPIC = "LCAnthropicChat" + LC_GEMINI = "LCGeminiChat" + LC_COHERE = "LCCohereChat" + LC_OLLAMA = "LCOllamaChat" + LLAMA_CPP = "LlamaCppChat" + + +MP_VENDOR_CLS: dict[LLMVendor, type[BaseChatLLM]] = { + LLMVendor.AZURE_CHAT_OPENAI: AzureChatOpenAI, + LLMVendor.CHAT_OPENAI: ChatOpenAI, + LLMVendor.STRUCTURED_OUTPUT_CHAT_OPENAI: StructuredOutputChatOpenAI, + LLMVendor.ENDPOINT: EndpointChatLLM, + LLMVendor.LC_CHAT_OPENAI: LCChatOpenAI, + LLMVendor.LC_AZURE_CHAT_OPENAI: LCAzureChatOpenAI, + LLMVendor.LC_ANTHROPIC: LCAnthropicChat, + LLMVendor.LC_GEMINI: LCGeminiChat, + LLMVendor.LC_COHERE: LCCohereChat, + LLMVendor.LC_OLLAMA: LCOllamaChat, + LLMVendor.LLAMA_CPP: LlamaCppChat, +} + + +class LLMFactory: + @staticmethod + def get_cls(vendor: LLMVendor | str) -> type[BaseChatLLM]: + """Return the class for *vendor*, coercing bare strings.""" + key = LLMVendor(vendor) + if key not in MP_VENDOR_CLS: + raise ValueError(f"Invalid LLM vendor: {vendor!r}") + return MP_VENDOR_CLS[key] + + @staticmethod + def supported_vendors() -> list[LLMVendor]: + return list(MP_VENDOR_CLS.keys()) diff --git a/libs/kotaemon/kotaemon/llms/chats/langchain_based.py b/libs/kotaemon/kotaemon/llms/chats/langchain_based.py index 43daa765b..c9e1fb753 100644 --- a/libs/kotaemon/kotaemon/llms/chats/langchain_based.py +++ b/libs/kotaemon/kotaemon/llms/chats/langchain_based.py @@ -1,95 +1,95 @@ from __future__ import annotations import logging -from typing import AsyncGenerator, Iterator +from dataclasses import dataclass, field +from functools import cached_property +from typing import Any, AsyncGenerator, Iterator -from kotaemon.base import BaseMessage, HumanMessage, LLMInterface, Param +from kotaemon.base import BaseMessage, HumanMessage, LLMInterface from .base import ChatLLM logger = logging.getLogger(__name__) -class LCChatMixin: - """Mixin for langchain based chat models""" +def lc_prepare_message( + messages: str | BaseMessage | list[BaseMessage], +) -> list[BaseMessage]: + if isinstance(messages, str): + return [HumanMessage(content=messages)] + if isinstance(messages, BaseMessage): + return [messages] + return messages + + +def lc_prepare_response(pred: Any) -> LLMInterface: + all_text = [each.text for each in pred.generations[0]] + all_messages = [each.message for each in pred.generations[0]] + + completion_tokens, total_tokens, prompt_tokens = 0, 0, 0 + try: + if pred.llm_output is not None: + completion_tokens = pred.llm_output["token_usage"]["completion_tokens"] + total_tokens = pred.llm_output["token_usage"]["total_tokens"] + prompt_tokens = pred.llm_output["token_usage"]["prompt_tokens"] + except Exception: + pass + + return LLMInterface( + text=all_text[0] if len(all_text) > 0 else "", + candidates=all_text, + completion_tokens=completion_tokens, + total_tokens=total_tokens, + prompt_tokens=prompt_tokens, + messages=all_messages, + logits=[], + ) + + +@dataclass(kw_only=True) +class BaseLCChat(ChatLLM): + """Base wrapper for LangChain chat models.""" + + streaming: bool = field( + default=False, + metadata={"description": "Return a streaming iterator from run()."}, + ) - def _get_lc_class(self): + def _get_lc_class(self) -> type: raise NotImplementedError( - "Please return the relevant Langchain class in in _get_lc_class" + "Subclasses must implement _get_lc_class with the LangChain class." ) - def _get_tool_call_kwargs(self): + def _lc_kwargs(self) -> dict[str, Any]: + raise NotImplementedError( + "Subclasses must implement _lc_kwargs mapping fields to LangChain." + ) + + def _get_tool_call_kwargs(self) -> dict[str, Any]: return {} - def __init__(self, stream: bool = False, **params): - self._lc_class = self._get_lc_class() - self._obj = self._lc_class(**params) - self._kwargs: dict = params - self._stream = stream + @cached_property + def _lc_obj(self) -> Any: + return self._get_lc_class()(**self._lc_kwargs()) - super().__init__() + def to_langchain_format(self) -> Any: + return self._lc_obj def run( self, messages: str | BaseMessage | list[BaseMessage], **kwargs ) -> LLMInterface: - if self._stream: - return self.stream(messages, **kwargs) # type: ignore + if self.streaming: + return self.stream(messages, **kwargs) # type: ignore[return-value] return self.invoke(messages, **kwargs) - def prepare_message(self, messages: str | BaseMessage | list[BaseMessage]): - input_: list[BaseMessage] = [] - - if isinstance(messages, str): - input_ = [HumanMessage(content=messages)] - elif isinstance(messages, BaseMessage): - input_ = [messages] - else: - input_ = messages - - return input_ - - def prepare_response(self, pred): - all_text = [each.text for each in pred.generations[0]] - all_messages = [each.message for each in pred.generations[0]] - - completion_tokens, total_tokens, prompt_tokens = 0, 0, 0 - try: - if pred.llm_output is not None: - completion_tokens = pred.llm_output["token_usage"]["completion_tokens"] - total_tokens = pred.llm_output["token_usage"]["total_tokens"] - prompt_tokens = pred.llm_output["token_usage"]["prompt_tokens"] - except Exception: - pass - - return LLMInterface( - text=all_text[0] if len(all_text) > 0 else "", - candidates=all_text, - completion_tokens=completion_tokens, - total_tokens=total_tokens, - prompt_tokens=prompt_tokens, - messages=all_messages, - logits=[], - ) - def invoke( self, messages: str | BaseMessage | list[BaseMessage], **kwargs ) -> LLMInterface: - """Generate response from messages - - Args: - messages: history of messages to generate response from - **kwargs: additional arguments to pass to the langchain chat model - - Returns: - LLMInterface: generated response - """ - input_ = self.prepare_message(messages) + input_ = lc_prepare_message(messages) if "tools_pydantic" in kwargs: - tools = kwargs.pop( - "tools_pydantic", - ) - lc_tool_call = self._obj.bind_tools(tools) + tools = kwargs.pop("tools_pydantic") + lc_tool_call = self._lc_obj.bind_tools(tools) pred = lc_tool_call.invoke( input_, **self._get_tool_call_kwargs(), @@ -99,115 +99,54 @@ def invoke( else: tool_calls = pred.additional_kwargs.get("tool_calls", []) - output = LLMInterface( + return LLMInterface( content="", additional_kwargs={"tool_calls": tool_calls}, ) - else: - pred = self._obj.generate(messages=[input_], **kwargs) - output = self.prepare_response(pred) - return output + pred = self._lc_obj.generate(messages=[input_], **kwargs) + return lc_prepare_response(pred) async def ainvoke( self, messages: str | BaseMessage | list[BaseMessage], **kwargs ) -> LLMInterface: - input_ = self.prepare_message(messages) - pred = await self._obj.agenerate(messages=[input_], **kwargs) - return self.prepare_response(pred) + input_ = lc_prepare_message(messages) + pred = await self._lc_obj.agenerate(messages=[input_], **kwargs) + return lc_prepare_response(pred) def stream( self, messages: str | BaseMessage | list[BaseMessage], **kwargs ) -> Iterator[LLMInterface]: - for response in self._obj.stream(input=messages, **kwargs): + for response in self._lc_obj.stream(input=messages, **kwargs): yield LLMInterface(content=response.content) async def astream( self, messages: str | BaseMessage | list[BaseMessage], **kwargs ) -> AsyncGenerator[LLMInterface, None]: - async for response in self._obj.astream(input=messages, **kwargs): + async for response in self._lc_obj.astream(input=messages, **kwargs): yield LLMInterface(content=response.content) - def to_langchain_format(self): - return self._obj - - def __repr__(self): - kwargs = [] - for key, value_obj in self._kwargs.items(): - value = repr(value_obj) - kwargs.append(f"{key}={value}") - kwargs_repr = ", ".join(kwargs) - return f"{self.__class__.__name__}({kwargs_repr})" - - def __str__(self): - kwargs = [] - for key, value_obj in self._kwargs.items(): - value = str(value_obj) - if len(value) > 20: - value = f"{value[:15]}..." - kwargs.append(f"{key}={value}") - kwargs_repr = ", ".join(kwargs) - return f"{self.__class__.__name__}({kwargs_repr})" - - def __setattr__(self, name, value): - if name == "_lc_class": - return super().__setattr__(name, value) - - if name in self._lc_class.__fields__: - self._kwargs[name] = value - self._obj = self._lc_class(**self._kwargs) - else: - super().__setattr__(name, value) - - def __getattr__(self, name): - if name in self._kwargs: - return self._kwargs[name] - return getattr(self._obj, name) - - def dump(self, *args, **kwargs): - from theflow.utils.modules import serialize - - params = {key: serialize(value) for key, value in self._kwargs.items()} - return { - "__type__": f"{self.__module__}.{self.__class__.__qualname__}", - **params, - } - - def specs(self, path: str): - path = path.strip(".") - if "." in path: - raise ValueError("path should not contain '.'") - if path in self._lc_class.__fields__: - return { - "__type__": "theflow.base.ParamAttr", - "refresh_on_set": True, - "strict_type": True, - } +@dataclass(kw_only=True) +class LCChatOpenAI(BaseLCChat): + """Wrapper around LangChain's OpenAI chat model.""" - raise ValueError(f"Invalid param {path}") - - -class LCChatOpenAI(LCChatMixin, ChatLLM): # type: ignore - def __init__( - self, - openai_api_base: str | None = None, - openai_api_key: str | None = None, - model: str | None = None, - temperature: float = 0.7, - request_timeout: float | None = None, - **params, - ): - super().__init__( - openai_api_base=openai_api_base, - openai_api_key=openai_api_key, - model=model, - temperature=temperature, - request_timeout=request_timeout, - **params, - ) + openai_api_base: str | None = field( + default=None, metadata={"description": "OpenAI API base URL."} + ) + openai_api_key: str | None = field( + default=None, metadata={"description": "OpenAI API key."} + ) + model: str | None = field(default=None, metadata={"description": "Model name."}) + temperature: float = field( + default=0.7, + metadata={"description": "Sampling temperature."}, + ) + request_timeout: float | None = field( + default=None, metadata={"description": "Request timeout in seconds."} + ) - def _get_lc_class(self): + def _get_lc_class(self) -> type: try: from langchain_openai import ChatOpenAI except ImportError: @@ -215,29 +154,42 @@ def _get_lc_class(self): return ChatOpenAI + def _lc_kwargs(self) -> dict[str, Any]: + return { + "openai_api_base": self.openai_api_base, + "openai_api_key": self.openai_api_key, + "model": self.model, + "temperature": self.temperature, + "request_timeout": self.request_timeout, + } + -class LCAzureChatOpenAI(LCChatMixin, ChatLLM): # type: ignore - def __init__( - self, - azure_endpoint: str | None = None, - openai_api_key: str | None = None, - openai_api_version: str = "", - deployment_name: str | None = None, - temperature: float = 0.7, - request_timeout: float | None = None, - **params, - ): - super().__init__( - azure_endpoint=azure_endpoint, - openai_api_key=openai_api_key, - openai_api_version=openai_api_version, - deployment_name=deployment_name, - temperature=temperature, - request_timeout=request_timeout, - **params, - ) +@dataclass(kw_only=True) +class LCAzureChatOpenAI(BaseLCChat): + """Wrapper around LangChain's Azure OpenAI chat model.""" + + azure_endpoint: str | None = field( + default=None, metadata={"description": "Azure OpenAI endpoint URL."} + ) + openai_api_key: str | None = field( + default=None, metadata={"description": "Azure OpenAI API key."} + ) + openai_api_version: str = field( + default="", + metadata={"description": "Azure OpenAI API version."}, + ) + deployment_name: str | None = field( + default=None, metadata={"description": "Azure deployment name."} + ) + temperature: float = field( + default=0.7, + metadata={"description": "Sampling temperature."}, + ) + request_timeout: float | None = field( + default=None, metadata={"description": "Request timeout in seconds."} + ) - def _get_lc_class(self): + def _get_lc_class(self) -> type: try: from langchain_openai import AzureChatOpenAI except ImportError: @@ -245,58 +197,67 @@ def _get_lc_class(self): return AzureChatOpenAI + def _lc_kwargs(self) -> dict[str, Any]: + return { + "azure_endpoint": self.azure_endpoint, + "openai_api_key": self.openai_api_key, + "openai_api_version": self.openai_api_version, + "deployment_name": self.deployment_name, + "temperature": self.temperature, + "request_timeout": self.request_timeout, + } + + +@dataclass(kw_only=True) +class LCAnthropicChat(BaseLCChat): + """Wrapper around LangChain's Anthropic chat model.""" -class LCAnthropicChat(LCChatMixin, ChatLLM): # type: ignore - api_key: str = Param( - help="API key (https://console.anthropic.com/settings/keys)", required=True + api_key: str | None = field( + default=None, metadata={"description": "Anthropic API key."} ) - model_name: str = Param( - help=( - "Model name to use " - "(https://docs.anthropic.com/en/docs/about-claude/models)" - ), - required=True, + model_name: str | None = field( + default=None, metadata={"description": "Model name."} + ) + temperature: float = field( + default=0.7, + metadata={"description": "Sampling temperature."}, ) - def _get_tool_call_kwargs(self): + def _get_tool_call_kwargs(self) -> dict[str, Any]: return {"tool_choice": {"type": "any"}} - def __init__( - self, - api_key: str | None = None, - model_name: str | None = None, - temperature: float = 0.7, - **params, - ): - super().__init__( - api_key=api_key, - model_name=model_name, - temperature=temperature, - **params, - ) - - def _get_lc_class(self): + def _get_lc_class(self) -> type: try: from langchain_anthropic import ChatAnthropic - except ImportError: - raise ImportError("Please install langchain-anthropic") + except ImportError as e: + raise ImportError("Please install langchain-anthropic") from e return ChatAnthropic + def _lc_kwargs(self) -> dict[str, Any]: + return { + "api_key": self.api_key, + "model_name": self.model_name, + "temperature": self.temperature, + } + + +@dataclass(kw_only=True) +class LCGeminiChat(BaseLCChat): + """Wrapper around LangChain's Google Generative AI chat model.""" -class LCGeminiChat(LCChatMixin, ChatLLM): # type: ignore - api_key: str = Param( - help="API key (https://aistudio.google.com/app/apikey)", required=True + api_key: str | None = field( + default=None, metadata={"description": "Google API key."} ) - model_name: str = Param( - help=( - "Model name to use (https://cloud.google" - ".com/vertex-ai/generative-ai/docs/learn/models)" - ), - required=True, + model_name: str | None = field( + default=None, metadata={"description": "Model name."} + ) + temperature: float = field( + default=0.7, + metadata={"description": "Sampling temperature."}, ) - def _get_tool_call_kwargs(self): + def _get_tool_call_kwargs(self) -> dict[str, Any]: return { "tool_config": { "function_calling_config": { @@ -305,93 +266,82 @@ def _get_tool_call_kwargs(self): } } - def __init__( - self, - api_key: str | None = None, - model_name: str | None = None, - temperature: float = 0.7, - **params, - ): - super().__init__( - google_api_key=api_key, - model=model_name, - temperature=temperature, - **params, - ) - - def _get_lc_class(self): + def _get_lc_class(self) -> type: try: from langchain_google_genai import ChatGoogleGenerativeAI - except ImportError: - raise ImportError("Please install langchain-google-genai") + except ImportError as e: + raise ImportError("Please install langchain-google-genai") from e return ChatGoogleGenerativeAI + def _lc_kwargs(self) -> dict[str, Any]: + return { + "google_api_key": self.api_key, + "model": self.model_name, + "temperature": self.temperature, + } -class LCCohereChat(LCChatMixin, ChatLLM): # type: ignore - api_key: str = Param( - help="API key (https://dashboard.cohere.com/api-keys)", required=True + +@dataclass(kw_only=True) +class LCCohereChat(BaseLCChat): + """Wrapper around LangChain's Cohere chat model.""" + + api_key: str | None = field( + default=None, metadata={"description": "Cohere API key."} ) - model_name: str = Param( - help=("Model name to use (https://dashboard.cohere.com/playground/chat)"), - required=True, + model_name: str | None = field( + default=None, metadata={"description": "Model name."} + ) + temperature: float = field( + default=0.7, + metadata={"description": "Sampling temperature."}, ) - def __init__( - self, - api_key: str | None = None, - model_name: str | None = None, - temperature: float = 0.7, - **params, - ): - super().__init__( - cohere_api_key=api_key, - model_name=model_name, - temperature=temperature, - **params, - ) - - def _get_lc_class(self): + def _get_lc_class(self) -> type: try: from langchain_cohere import ChatCohere - except ImportError: - raise ImportError("Please install langchain-cohere") + except ImportError as e: + raise ImportError("Please install langchain-cohere") from e return ChatCohere + def _lc_kwargs(self) -> dict[str, Any]: + return { + "cohere_api_key": self.api_key, + "model_name": self.model_name, + "temperature": self.temperature, + } + -class LCOllamaChat(LCChatMixin, ChatLLM): # type: ignore - base_url: str = Param( - help="Base Ollama URL. (default: http://localhost:11434/api/)", # noqa - required=True, +@dataclass(kw_only=True) +class LCOllamaChat(BaseLCChat): + """Wrapper around LangChain's Ollama chat model.""" + + model: str | None = field( + default=None, metadata={"description": "Ollama model name."} ) - model: str = Param( - help="Model name to use (https://ollama.com/library)", - required=True, + base_url: str | None = field( + default=None, metadata={"description": "Ollama server base URL."} ) - num_ctx: int = Param( - help="The size of the context window (default: 8192)", - required=True, + num_ctx: int | None = field( + default=None, metadata={"description": "Context window size."} ) - def __init__( - self, - model: str | None = None, - base_url: str | None = None, - num_ctx: int | None = None, - **params, - ): - super().__init__( - base_url=base_url, - model=model, - num_ctx=num_ctx, - **params, - ) - - def _get_lc_class(self): + def _get_lc_class(self) -> type: try: from langchain_ollama import ChatOllama - except ImportError: - raise ImportError("Please install langchain-ollama") + except ImportError as e: + raise ImportError("Please install langchain-ollama") from e return ChatOllama + + def _lc_kwargs(self) -> dict[str, Any]: + return { + "base_url": self.base_url, + "model": self.model, + "num_ctx": self.num_ctx, + } + + +# Backward-compatible alias for code that imported the old mixin name. +LCChatMixin = BaseLCChat diff --git a/libs/kotaemon/kotaemon/llms/chats/llamacpp.py b/libs/kotaemon/kotaemon/llms/chats/llamacpp.py index 11f9abb02..931cac111 100644 --- a/libs/kotaemon/kotaemon/llms/chats/llamacpp.py +++ b/libs/kotaemon/kotaemon/llms/chats/llamacpp.py @@ -1,6 +1,8 @@ +from dataclasses import dataclass, field +from functools import cached_property from typing import TYPE_CHECKING, Iterator, Optional, cast -from kotaemon.base import BaseMessage, HumanMessage, LLMInterface, Param +from kotaemon.base import BaseMessage, HumanMessage, LLMInterface from .base import ChatLLM @@ -9,50 +11,47 @@ from llama_cpp import Llama +@dataclass(kw_only=True) class LlamaCppChat(ChatLLM): - """Wrapper around the llama-cpp-python's Llama model""" + """Wrapper around the llama-cpp-python Llama model.""" - model_path: Optional[str] = Param( - help="Path to the model file. This is required to load the model.", + model_path: Optional[str] = field( + default=None, metadata={"description": "Path to the model file"} ) - repo_id: Optional[str] = Param( - help="Id of a repo on the HuggingFace Hub in the form of `user_name/repo_name`." + repo_id: Optional[str] = field( + default=None, metadata={"description": "HuggingFace repo id"} ) - filename: Optional[str] = Param( - help="A filename or glob pattern to match the model file in the repo." + filename: Optional[str] = field( + default=None, metadata={"description": "Model filename in repo"} ) - chat_format: str = Param( - help=( - "Chat format to use. Please refer to llama_cpp.llama_chat_format for a " - "list of supported formats. If blank, the chat format will be auto-" - "inferred." - ), - required=True, + chat_format: str = field( + metadata={"description": "Chat format (llama_cpp.llama_chat_format)"} ) - lora_base: Optional[str] = Param(None, help="Path to the base Lora model") - n_ctx: Optional[int] = Param(512, help="Text context, 0 = from model") - n_gpu_layers: Optional[int] = Param( - 0, - help="Number of layers to offload to GPU. If -1, all layers are offloaded", + lora_base: Optional[str] = field( + default=None, metadata={"description": "Path to Lora model"} ) - use_mmap: Optional[bool] = Param( - True, - help=(), + n_ctx: Optional[int] = field( + default=512, metadata={"description": "Text context size"} ) - vocab_only: Optional[bool] = Param( - False, - help="If True, only the vocabulary is loaded. This is useful for debugging.", + n_gpu_layers: Optional[int] = field( + default=0, metadata={"description": "GPU layers (-1 = all)"} + ) + use_mmap: Optional[bool] = field(default=True, metadata={"description": ""}) + vocab_only: Optional[bool] = field( + default=False, metadata={"description": "Vocabulary only (debug)"} ) - _role_mapper: dict[str, str] = { - "human": "user", - "system": "system", - "ai": "assistant", - } + _role_mapper: dict[str, str] = field( + default_factory=lambda: { + "human": "user", + "system": "system", + "ai": "assistant", + }, + repr=False, + ) - @Param.auto() + @cached_property def client_object(self) -> "Llama": - """Get the llama-cpp-python client object""" try: from llama_cpp import Llama except ImportError: @@ -63,17 +62,9 @@ def client_object(self) -> "Llama": errors = [] if not self.model_path and (not self.repo_id or not self.filename): - errors.append( - "- `model_path` or `repo_id` and `filename` are required to load the" - " model" - ) - + errors.append("- `model_path` or `repo_id` and `filename` are required") if not self.chat_format: - errors.append( - "- `chat_format` is required to know how to format the chat messages. " - "Please refer to llama_cpp.llama_chat_format for a list of supported " - "formats." - ) + errors.append("- `chat_format` is required") if errors: raise ValueError("\n".join(errors)) @@ -87,23 +78,20 @@ def client_object(self) -> "Llama": use_mmap=self.use_mmap, vocab_only=self.vocab_only, ) - else: - return Llama.from_pretrained( - repo_id=self.repo_id, - filename=self.filename, - chat_format=self.chat_format, - lora_base=self.lora_base, - n_ctx=self.n_ctx, - n_gpu_layers=self.n_gpu_layers, - use_mmap=self.use_mmap, - vocab_only=self.vocab_only, - ) + return Llama.from_pretrained( + repo_id=self.repo_id, + filename=self.filename, + chat_format=self.chat_format, + lora_base=self.lora_base, + n_ctx=self.n_ctx, + n_gpu_layers=self.n_gpu_layers, + use_mmap=self.use_mmap, + vocab_only=self.vocab_only, + ) def prepare_message( self, messages: str | BaseMessage | list[BaseMessage] ) -> list[dict]: - input_: list[BaseMessage] = [] - if isinstance(messages, str): input_ = [HumanMessage(content=messages)] elif isinstance(messages, BaseMessage): @@ -111,22 +99,18 @@ def prepare_message( else: input_ = messages - output_ = [ + return [ {"role": self._role_mapper[each.type], "content": each.content} for each in input_ ] - return output_ - def invoke( self, messages: str | BaseMessage | list[BaseMessage], **kwargs ) -> LLMInterface: - pred: "CCCR" = self.client_object.create_chat_completion( messages=self.prepare_message(messages), stream=False, ) - return LLMInterface( content=pred["choices"][0]["message"]["content"] if pred["choices"] else "", candidates=[ @@ -149,8 +133,6 @@ def stream( for chunk in pred: if not chunk["choices"]: continue - if "content" not in chunk["choices"][0]["delta"]: continue - yield LLMInterface(content=chunk["choices"][0]["delta"]["content"]) diff --git a/libs/kotaemon/kotaemon/llms/chats/openai.py b/libs/kotaemon/kotaemon/llms/chats/openai.py index a1f885435..3343c4455 100644 --- a/libs/kotaemon/kotaemon/llms/chats/openai.py +++ b/libs/kotaemon/kotaemon/llms/chats/openai.py @@ -1,14 +1,13 @@ +from dataclasses import dataclass, field from typing import TYPE_CHECKING, AsyncGenerator, Iterator, Optional, Type from pydantic import BaseModel -from theflow.utils.modules import import_dotted_string from kotaemon.base import ( AIMessage, BaseMessage, HumanMessage, LLMInterface, - Param, StructuredOutputLLMInterface, ) @@ -20,6 +19,7 @@ ) +@dataclass(kw_only=True) class BaseChatOpenAI(ChatLLM): """Base interface for OpenAI chat model, using the openai library @@ -33,104 +33,61 @@ class BaseChatOpenAI(ChatLLM): _dependencies = ["openai"] _capabilities = ["chat", "text"] # consider as mixin - api_key: str = Param(help="API key", required=True) - timeout: Optional[float] = Param(None, help="Timeout for the API request") - max_retries: Optional[int] = Param( - None, help="Maximum number of retries for the API request" + api_key: str = field(metadata={"description": "API key"}) + timeout: Optional[float] = field( + default=None, metadata={"description": "Timeout for the API request"} ) - - temperature: Optional[float] = Param( - None, - help=( - "Number between 0 and 2 that controls the randomness of the generated " - "tokens. Lower values make the model more deterministic, while higher " - "values make the model more random." - ), + max_retries: Optional[int] = field( + default=None, + metadata={"description": "Maximum number of retries for the API request"}, + ) + temperature: Optional[float] = field( + default=None, + metadata={ + "description": ( + "Number between 0 and 2 that controls the randomness of the " + "generated tokens." + ) + }, ) - max_tokens: Optional[int] = Param( - None, - help=( - "Maximum number of tokens to generate. The total length of input tokens " - "and generated tokens is limited by the model's context length." - ), + max_tokens: Optional[int] = field( + default=None, + metadata={"description": "Maximum number of tokens to generate."}, ) - n: int = Param( - 1, - help=( - "Number of completions to generate. The API will generate n completion " - "for each prompt." - ), + n: int = field( + default=1, + metadata={"description": "Number of completions to generate."}, ) - stop: Optional[str | list[str]] = Param( - None, - help=( - "Stop sequence. If a stop sequence is detected, generation will stop " - "at that point. If not specified, generation will continue until the " - "maximum token length is reached." - ), + stop: Optional[str | list[str]] = field( + default=None, metadata={"description": "Stop sequence."} ) - frequency_penalty: Optional[float] = Param( - None, - help=( - "Number between -2.0 and 2.0. Positive values penalize new tokens " - "based on their existing frequency in the text so far, decrearsing the " - "model's likelihood of repeating the same text." - ), + frequency_penalty: Optional[float] = field( + default=None, metadata={"description": "Frequency penalty."} ) - presence_penalty: Optional[float] = Param( - None, - help=( - "Number between -2.0 and 2.0. Positive values penalize new tokens " - "based on their existing presence in the text so far, decrearsing the " - "model's likelihood of repeating the same text." - ), + presence_penalty: Optional[float] = field( + default=None, metadata={"description": "Presence penalty."} ) - tool_choice: Optional[str] = Param( - None, - help=( - "Choice of tool to use for the completion. Available choices are: " - "auto, default." - ), + tool_choice: Optional[str] = field( + default=None, metadata={"description": "Tool choice for the completion."} ) - tools: Optional[list[str]] = Param( - None, - help="List of tools to use for the completion.", + tools: Optional[list[str]] = field( + default=None, metadata={"description": "Tools for the completion."} ) - logprobs: Optional[bool] = Param( - None, - help=( - "Include log probabilities on the logprobs most likely tokens, " - "as well as the chosen token." - ), + logprobs: Optional[bool] = field( + default=None, metadata={"description": "Include log probabilities."} ) - logit_bias: Optional[dict] = Param( - None, - help=( - "Dictionary of logit bias values to add to the logits of the tokens " - "in the vocabulary." - ), + logit_bias: Optional[dict] = field( + default=None, metadata={"description": "Logit bias dictionary."} ) - top_logprobs: Optional[int] = Param( - None, - help=( - "An integer between 0 and 5 specifying the number of most likely tokens " - "to return at each token position, each with an associated log " - "probability. `logprobs` must also be set to `true` if this parameter " - "is used." - ), + top_logprobs: Optional[int] = field( + default=None, metadata={"description": "Number of top logprobs to return."} ) - top_p: Optional[float] = Param( - None, - help=( - "An alternative to sampling with temperature, called nucleus sampling, " - "where the model considers the results of the token with top_p " - "probability mass. So 0.1 means that only the tokens comprising the " - "top 10% probability mass are considered." - ), + top_p: Optional[float] = field( + default=None, metadata={"description": "Nucleus sampling mass."} ) - @Param.auto(depends_on=["max_retries"]) - def max_retries_(self): + @property + def max_retries_(self) -> int: if self.max_retries is None: from openai._constants import DEFAULT_MAX_RETRIES @@ -275,12 +232,21 @@ async def astream( yield LLMInterface(content=chunk.choices[0].delta.content) +@dataclass(kw_only=True) class ChatOpenAI(BaseChatOpenAI): """OpenAI chat model""" - base_url: Optional[str] = Param(None, help="OpenAI base URL") - organization: Optional[str] = Param(None, help="OpenAI organization") - model: str = Param(help="OpenAI model", required=True) + model: str = field( + metadata={ + "description": "OpenAI model ID (see platform.openai.com/docs/models)." + } + ) + base_url: Optional[str] = field( + default=None, metadata={"description": "OpenAI base URL"} + ) + organization: Optional[str] = field( + default=None, metadata={"description": "OpenAI organization"} + ) def prepare_client(self, async_version: bool = False): """Get the OpenAI client @@ -338,11 +304,12 @@ async def aopenai_response(self, client, **kwargs): return await client.chat.completions.create(**params) +@dataclass(kw_only=True) class StructuredOutputChatOpenAI(ChatOpenAI): """OpenAI chat model that returns structured output""" - response_schema: Type[BaseModel] = Param( - help="class that subclasses pydantics BaseModel", required=True + response_schema: Type[BaseModel] = field( + metadata={"description": "Pydantic model class for structured output."} ) def prepare_output(self, resp: dict) -> StructuredOutputLLMInterface: @@ -420,26 +387,18 @@ async def aopenai_response(self, client, **kwargs): return await client.beta.chat.completions.parse(**params) +@dataclass(kw_only=True) class AzureChatOpenAI(BaseChatOpenAI): """OpenAI chat model provided by Microsoft Azure""" - azure_endpoint: str = Param( - help=( - "HTTPS endpoint for the Azure OpenAI model. The azure_endpoint, " - "azure_deployment, and api_version parameters are used to construct " - "the full URL for the Azure OpenAI model." - ), - required=True, + azure_endpoint: str = field( + metadata={"description": ("HTTPS endpoint for the Azure OpenAI model.")} + ) + azure_deployment: str = field(metadata={"description": "Azure deployment name"}) + api_version: str = field(metadata={"description": "Azure model version"}) + azure_ad_token: Optional[str] = field( + default=None, metadata={"description": "Azure AD token"} ) - azure_deployment: str = Param(help="Azure deployment name", required=True) - api_version: str = Param(help="Azure model version", required=True) - azure_ad_token: Optional[str] = Param(None, help="Azure AD token") - azure_ad_token_provider: Optional[str] = Param(None, help="Azure AD token provider") - - @Param.auto(depends_on=["azure_ad_token_provider"]) - def azure_ad_token_provider_(self): - if isinstance(self.azure_ad_token_provider, str): - return import_dotted_string(self.azure_ad_token_provider, safe=False) def prepare_client(self, async_version: bool = False): """Get the OpenAI client @@ -452,7 +411,6 @@ def prepare_client(self, async_version: bool = False): "api_version": self.api_version, "api_key": self.api_key, "azure_ad_token": self.azure_ad_token, - "azure_ad_token_provider": self.azure_ad_token_provider_, "timeout": self.timeout, "max_retries": self.max_retries_, } diff --git a/libs/kotaemon/kotaemon/llms/completions/__init__.py b/libs/kotaemon/kotaemon/llms/completions/__init__.py index b9c1a3711..bd88eb8a5 100644 --- a/libs/kotaemon/kotaemon/llms/completions/__init__.py +++ b/libs/kotaemon/kotaemon/llms/completions/__init__.py @@ -1,4 +1,17 @@ from .base import LLM -from .langchain_based import AzureOpenAI, LCCompletionMixin, LlamaCpp, OpenAI +from .langchain_based import ( + AzureOpenAI, + BaseLCCompletion, + LCCompletionMixin, + LlamaCpp, + OpenAI, +) -__all__ = ["LLM", "OpenAI", "AzureOpenAI", "LCCompletionMixin", "LlamaCpp"] +__all__ = [ + "LLM", + "OpenAI", + "AzureOpenAI", + "BaseLCCompletion", + "LCCompletionMixin", + "LlamaCpp", +] diff --git a/libs/kotaemon/kotaemon/llms/completions/base.py b/libs/kotaemon/kotaemon/llms/completions/base.py index 004fddb6b..62377a357 100644 --- a/libs/kotaemon/kotaemon/llms/completions/base.py +++ b/libs/kotaemon/kotaemon/llms/completions/base.py @@ -1,5 +1,13 @@ +from __future__ import annotations + +from dataclasses import dataclass + +from kotaemon.base.describe import DataclassDescribe, describe_dataclass from kotaemon.llms.base import BaseLLM +@dataclass(kw_only=True) class LLM(BaseLLM): - pass + @classmethod + def describe(cls) -> DataclassDescribe: + return describe_dataclass(cls) diff --git a/libs/kotaemon/kotaemon/llms/completions/langchain_based.py b/libs/kotaemon/kotaemon/llms/completions/langchain_based.py index d099dce13..68676a836 100644 --- a/libs/kotaemon/kotaemon/llms/completions/langchain_based.py +++ b/libs/kotaemon/kotaemon/llms/completions/langchain_based.py @@ -1,5 +1,9 @@ +from __future__ import annotations + import logging -from typing import Optional +from dataclasses import dataclass, field +from functools import cached_property +from typing import Any from kotaemon.base import LLMInterface @@ -8,220 +12,251 @@ logger = logging.getLogger(__name__) -class LCCompletionMixin: - def _get_lc_class(self): - raise NotImplementedError( - "Please return the relevant Langchain class in in _get_lc_class" +def lc_completion_response(pred: Any, *, lc_class_name: str) -> LLMInterface: + all_text = [each.text for each in pred.generations[0]] + + completion_tokens, total_tokens, prompt_tokens = 0, 0, 0 + try: + if pred.llm_output is not None: + completion_tokens = pred.llm_output["token_usage"]["completion_tokens"] + total_tokens = pred.llm_output["token_usage"]["total_tokens"] + prompt_tokens = pred.llm_output["token_usage"]["prompt_tokens"] + except Exception: + logger.warning( + "Cannot get token usage from LLM output for %s", + lc_class_name, ) - def __init__(self, **params): - self._lc_class = self._get_lc_class() - self._obj = self._lc_class(**params) - self._kwargs: dict = params + return LLMInterface( + text=all_text[0] if len(all_text) > 0 else "", + candidates=all_text, + completion_tokens=completion_tokens, + total_tokens=total_tokens, + prompt_tokens=prompt_tokens, + logits=[], + ) - super().__init__() - def run(self, text: str) -> LLMInterface: - pred = self._obj.generate([text]) - all_text = [each.text for each in pred.generations[0]] +@dataclass(kw_only=True) +class BaseLCCompletion(LLM): + """Base wrapper for LangChain completion (legacy) LLM models.""" - completion_tokens, total_tokens, prompt_tokens = 0, 0, 0 - try: - if pred.llm_output is not None: - completion_tokens = pred.llm_output["token_usage"]["completion_tokens"] - total_tokens = pred.llm_output["token_usage"]["total_tokens"] - prompt_tokens = pred.llm_output["token_usage"]["prompt_tokens"] - except Exception: - logger.warning( - f"Cannot get token usage from LLM output for {self._lc_class.__name__}" - ) - - return LLMInterface( - text=all_text[0] if len(all_text) > 0 else "", - candidates=all_text, - completion_tokens=completion_tokens, - total_tokens=total_tokens, - prompt_tokens=prompt_tokens, - logits=[], + def _get_lc_class(self) -> type: + raise NotImplementedError( + "Subclasses must implement _get_lc_class with the LangChain class." ) - def to_langchain_format(self): - return self._obj - - def __repr__(self): - kwargs = [] - for key, value_obj in self._kwargs.items(): - value = repr(value_obj) - kwargs.append(f"{key}={value}") - kwargs_repr = ", ".join(kwargs) - return f"{self.__class__.__name__}({kwargs_repr})" - - def __str__(self): - kwargs = [] - for key, value_obj in self._kwargs.items(): - value = str(value_obj) - if len(value) > 20: - value = f"{value[:15]}..." - kwargs.append(f"{key}={value}") - kwargs_repr = ", ".join(kwargs) - return f"{self.__class__.__name__}({kwargs_repr})" - - def __setattr__(self, name, value): - if name == "_lc_class": - return super().__setattr__(name, value) - - if name in self._lc_class.__fields__: - self._kwargs[name] = value - self._obj = self._lc_class(**self._kwargs) - else: - super().__setattr__(name, value) - - def __getattr__(self, name): - if name in self._kwargs: - return self._kwargs[name] - return getattr(self._obj, name) - - def dump(self, *args, **kwargs): - from theflow.utils.modules import serialize - - params = {key: serialize(value) for key, value in self._kwargs.items()} - return { - "__type__": f"{self.__module__}.{self.__class__.__qualname__}", - **params, - } - - def specs(self, path: str): - path = path.strip(".") - if "." in path: - raise ValueError("path should not contain '.'") - - if path in self._lc_class.__fields__: - return { - "__type__": "theflow.base.ParamAttr", - "refresh_on_set": True, - "strict_type": True, - } - - raise ValueError(f"Invalid param {path}") - - -class OpenAI(LCCompletionMixin, LLM): - """Wrapper around Langchain's OpenAI class, focusing on key parameters""" - - def __init__( - self, - openai_api_key: Optional[str] = None, - openai_api_base: Optional[str] = None, - model_name: str = "text-davinci-003", - temperature: float = 0.7, - max_tokens: int = 256, - top_p: float = 1, - frequency_penalty: float = 0, - n: int = 1, - best_of: int = 1, - request_timeout: Optional[float] = None, - max_retries: int = 2, - streaming: bool = False, - **params, - ): - super().__init__( - openai_api_key=openai_api_key, - openai_api_base=openai_api_base, - model_name=model_name, - temperature=temperature, - max_tokens=max_tokens, - top_p=top_p, - frequency_penalty=frequency_penalty, - n=n, - best_of=best_of, - request_timeout=request_timeout, - max_retries=max_retries, - streaming=streaming, - **params, + def _lc_kwargs(self) -> dict[str, Any]: + raise NotImplementedError( + "Subclasses must implement _lc_kwargs mapping fields to LangChain." ) - def _get_lc_class(self): + @cached_property + def _lc_obj(self) -> Any: + return self._get_lc_class()(**self._lc_kwargs()) + + def to_langchain_format(self) -> Any: + return self._lc_obj + + def invoke(self, text: str, **kwargs) -> LLMInterface: + pred = self._lc_obj.generate([text], **kwargs) + return lc_completion_response(pred, lc_class_name=self._get_lc_class().__name__) + + def run(self, text: str, **kwargs) -> LLMInterface: + return self.invoke(text, **kwargs) + + +@dataclass(kw_only=True) +class OpenAI(BaseLCCompletion): + """Wrapper around LangChain's OpenAI completion model.""" + + openai_api_key: str | None = field( + default=None, metadata={"description": "OpenAI API key."} + ) + openai_api_base: str | None = field( + default=None, metadata={"description": "OpenAI API base URL."} + ) + openai_api_version: str | None = field( + default=None, metadata={"description": "OpenAI API version."} + ) + deployment_name: str | None = field( + default=None, metadata={"description": "Deployment name."} + ) + model_name: str = field( + default="text-davinci-003", + metadata={"description": "Model name."}, + ) + temperature: float = field( + default=0.7, + metadata={"description": "Sampling temperature."}, + ) + max_tokens: int = field( + default=256, metadata={"description": "Maximum tokens to generate."} + ) + top_p: float = field( + default=1.0, metadata={"description": "Nucleus sampling mass."} + ) + frequency_penalty: float = field( + default=0.0, metadata={"description": "Frequency penalty."} + ) + n: int = field( + default=1, metadata={"description": "Number of completions to generate."} + ) + best_of: int = field( + default=1, metadata={"description": "Number of candidates per prompt."} + ) + request_timeout: float | None = field( + default=None, metadata={"description": "Request timeout in seconds."} + ) + max_retries: int = field( + default=2, metadata={"description": "Maximum API retries."} + ) + streaming: bool = field( + default=False, metadata={"description": "Enable streaming responses."} + ) + + def _get_lc_class(self) -> type: try: - from langchain_openai import OpenAI + from langchain_openai import OpenAI as LCOpenAI except ImportError: - from langchain.llms import OpenAI - - return OpenAI - - -class AzureOpenAI(LCCompletionMixin, LLM): - """Wrapper around Langchain's AzureOpenAI class, focusing on key parameters""" - - def __init__( - self, - azure_endpoint: Optional[str] = None, - deployment_name: Optional[str] = None, - openai_api_version: str = "", - openai_api_key: Optional[str] = None, - model_name: str = "text-davinci-003", - temperature: float = 0.7, - max_tokens: int = 256, - top_p: float = 1, - frequency_penalty: float = 0, - n: int = 1, - best_of: int = 1, - request_timeout: Optional[float] = None, - max_retries: int = 2, - streaming: bool = False, - **params, - ): - super().__init__( - azure_endpoint=azure_endpoint, - deployment_name=deployment_name, - openai_api_version=openai_api_version, - openai_api_key=openai_api_key, - model_name=model_name, - temperature=temperature, - max_tokens=max_tokens, - top_p=top_p, - frequency_penalty=frequency_penalty, - n=n, - best_of=best_of, - request_timeout=request_timeout, - max_retries=max_retries, - streaming=streaming, - **params, - ) + from langchain.llms import OpenAI as LCOpenAI + + return LCOpenAI + + def _lc_kwargs(self) -> dict[str, Any]: + return { + "openai_api_key": self.openai_api_key, + "openai_api_base": self.openai_api_base, + "openai_api_version": self.openai_api_version, + "deployment_name": self.deployment_name, + "model_name": self.model_name, + "temperature": self.temperature, + "max_tokens": self.max_tokens, + "top_p": self.top_p, + "frequency_penalty": self.frequency_penalty, + "n": self.n, + "best_of": self.best_of, + "request_timeout": self.request_timeout, + "max_retries": self.max_retries, + "streaming": self.streaming, + } + - def _get_lc_class(self): +@dataclass(kw_only=True) +class AzureOpenAI(BaseLCCompletion): + """Wrapper around LangChain's Azure OpenAI completion model.""" + + azure_endpoint: str | None = field( + default=None, metadata={"description": "Azure OpenAI endpoint URL."} + ) + deployment_name: str | None = field( + default=None, metadata={"description": "Azure deployment name."} + ) + openai_api_version: str = field( + default="", + metadata={"description": "Azure OpenAI API version."}, + ) + openai_api_key: str | None = field( + default=None, metadata={"description": "Azure OpenAI API key."} + ) + model_name: str = field( + default="text-davinci-003", + metadata={"description": "Model name."}, + ) + temperature: float = field( + default=0.7, + metadata={"description": "Sampling temperature."}, + ) + max_tokens: int = field( + default=256, metadata={"description": "Maximum tokens to generate."} + ) + top_p: float = field( + default=1.0, metadata={"description": "Nucleus sampling mass."} + ) + frequency_penalty: float = field( + default=0.0, metadata={"description": "Frequency penalty."} + ) + n: int = field( + default=1, metadata={"description": "Number of completions to generate."} + ) + best_of: int = field( + default=1, metadata={"description": "Number of candidates per prompt."} + ) + request_timeout: float | None = field( + default=None, metadata={"description": "Request timeout in seconds."} + ) + max_retries: int = field( + default=2, metadata={"description": "Maximum API retries."} + ) + streaming: bool = field( + default=False, metadata={"description": "Enable streaming responses."} + ) + + def _get_lc_class(self) -> type: try: - from langchain_openai import AzureOpenAI + from langchain_openai import AzureOpenAI as LCAzureOpenAI except ImportError: - from langchain.llms import AzureOpenAI - - return AzureOpenAI - - -class LlamaCpp(LCCompletionMixin, LLM): - """Wrapper around Langchain's LlamaCpp class, focusing on key parameters""" - - def __init__( - self, - model_path: str, - lora_base: Optional[str] = None, - n_ctx: int = 512, - n_gpu_layers: Optional[int] = None, - use_mmap: bool = True, - **params, - ): - super().__init__( - model_path=model_path, - lora_base=lora_base, - n_ctx=n_ctx, - n_gpu_layers=n_gpu_layers, - use_mmap=use_mmap, - **params, - ) + from langchain.llms import AzureOpenAI as LCAzureOpenAI + + return LCAzureOpenAI + + def _lc_kwargs(self) -> dict[str, Any]: + return { + "azure_endpoint": self.azure_endpoint, + "deployment_name": self.deployment_name, + "openai_api_version": self.openai_api_version, + "openai_api_key": self.openai_api_key, + "model_name": self.model_name, + "temperature": self.temperature, + "max_tokens": self.max_tokens, + "top_p": self.top_p, + "frequency_penalty": self.frequency_penalty, + "n": self.n, + "best_of": self.best_of, + "request_timeout": self.request_timeout, + "max_retries": self.max_retries, + "streaming": self.streaming, + } - def _get_lc_class(self): + +@dataclass(kw_only=True) +class LlamaCpp(BaseLCCompletion): + """Wrapper around LangChain's LlamaCpp completion model.""" + + model_path: str = field(metadata={"description": "Path to the GGUF model file."}) + lora_base: str | None = field( + default=None, metadata={"description": "Path to Lora model."} + ) + n_ctx: int = field(default=512, metadata={"description": "Text context size."}) + n_gpu_layers: int | None = field( + default=None, metadata={"description": "GPU layers (-1 = all)."} + ) + use_mmap: bool = field( + default=True, metadata={"description": "Use memory mapping for model."} + ) + vocab_only: bool = field( + default=False, + metadata={"description": "Load vocabulary only (debug)."}, + ) + + def _get_lc_class(self) -> type: try: - from langchain_community.llms import LlamaCpp + from langchain_community.llms import LlamaCpp as LCLlamaCpp except ImportError: - from langchain.llms import LlamaCpp + from langchain.llms import LlamaCpp as LCLlamaCpp + + return LCLlamaCpp + + def _lc_kwargs(self) -> dict[str, Any]: + return { + "model_path": self.model_path, + "lora_base": self.lora_base, + "n_ctx": self.n_ctx, + "n_gpu_layers": self.n_gpu_layers, + "use_mmap": self.use_mmap, + "vocab_only": self.vocab_only, + } + - return LlamaCpp +# Backward-compatible alias for code that imported the old mixin name. +LCCompletionMixin = BaseLCCompletion diff --git a/libs/kotaemon/kotaemon/llms/cot.py b/libs/kotaemon/kotaemon/llms/cot.py index a52f9bd36..1674f6a74 100644 --- a/libs/kotaemon/kotaemon/llms/cot.py +++ b/libs/kotaemon/kotaemon/llms/cot.py @@ -1,102 +1,31 @@ from copy import deepcopy +from dataclasses import dataclass, field +from functools import cached_property from typing import Callable, List -from theflow import Function, Node, Param +from kotaemon.base import Document -from kotaemon.base import BaseComponent, Document - -from .chats import LCAzureChatOpenAI from .completions import LLM from .prompts import BasePromptComponent -class Thought(BaseComponent): - """A thought in the chain of thought - - - Input: `**kwargs` pairs, where key is the placeholder in the prompt, and - value is the value. - - Output: an output dictionary - - _**Usage:**_ - - Create and run a thought: - - ```python - >> from kotaemon.pipelines.cot import Thought - >> thought = Thought( - prompt="How to {action} {object}?", - llm=LCAzureChatOpenAI(...), - post_process=lambda string: {"tutorial": string}, - ) - >> output = thought(action="install", object="python") - >> print(output) - {'tutorial': 'As an AI language model,...'} - ``` - - Basically, when a thought is run, it will: - - 1. Populate the prompt template with the input `**kwargs`. - 2. Run the LLM model with the populated prompt. - 3. Post-process the LLM output with the post-processor. - - This `Thought` allows chaining sequentially with the + operator. For example: - - ```python - >> llm = LCAzureChatOpenAI(...) - >> thought1 = Thought( - prompt="Word {word} in {language} is ", - llm=llm, - post_process=lambda string: {"translated": string}, - ) - >> thought2 = Thought( - prompt="Translate {translated} to Japanese", - llm=llm, - post_process=lambda string: {"output": string}, - ) - - >> thought = thought1 + thought2 - >> thought(word="hello", language="French") - {'word': 'hello', - 'language': 'French', - 'translated': '"Bonjour"', - 'output': 'こんにちは (Konnichiwa)'} - ``` - - Under the hood, when the `+` operator is used, a `ManualSequentialChainOfThought` - is created. - """ - - prompt: str = Param( - help=( - "The prompt template string. This prompt template has Python-like variable" - " placeholders, that then will be substituted with real values when this" - " component is executed" - ) - ) - llm: LLM = Node(LCAzureChatOpenAI, help="The LLM model to execute the input prompt") - post_process: Function = Node( - help=( - "The function post-processor that post-processes LLM output prediction ." - "It should take a string as input (this is the LLM output text) and return " - "a dictionary, where the key should" - ) - ) +@dataclass(kw_only=True) +class Thought: + prompt: str + llm: LLM + post_process: Callable[[str], dict] - @Node.auto(depends_on="prompt") - def prompt_template(self): - """Automatically wrap around param prompt. Can ignore""" + @cached_property + def prompt_template(self) -> BasePromptComponent: return BasePromptComponent(template=self.prompt) def run(self, **kwargs) -> Document: - """Run the chain of thought""" prompt = self.prompt_template(**kwargs).text - response = self.llm(prompt).text - response = self.post_process(response) - + response = self.post_process(self.llm(prompt).text) return Document(response) - def get_variables(self) -> List[str]: - return [] + def __call__(self, **kwargs) -> Document: + return self.run(**kwargs) def __add__(self, next_thought: "Thought") -> "ManualSequentialChainOfThought": return ManualSequentialChainOfThought( @@ -104,72 +33,19 @@ def __add__(self, next_thought: "Thought") -> "ManualSequentialChainOfThought": ) -class ManualSequentialChainOfThought(BaseComponent): - """Perform sequential chain-of-thought with manual pre-defined prompts - - This method supports variable number of steps. Each step corresponds to a - `kotaemon.pipelines.cot.Thought`. Please refer that section for - Thought's detail. This section is about chaining thought together. - - _**Usage:**_ - - **Create and run a chain of thought without "+" operator:** - - ```pycon - >>> from kotaemon.pipelines.cot import Thought, ManualSequentialChainOfThought - >>> llm = LCAzureChatOpenAI(...) - >>> thought1 = Thought( - >>> prompt="Word {word} in {language} is ", - >>> post_process=lambda string: {"translated": string}, - >>> ) - >>> thought2 = Thought( - >>> prompt="Translate {translated} to Japanese", - >>> post_process=lambda string: {"output": string}, - >>> ) - >>> thought = ManualSequentialChainOfThought(thoughts=[thought1, thought2], llm=llm) - >>> thought(word="hello", language="French") - {'word': 'hello', - 'language': 'French', - 'translated': '"Bonjour"', - 'output': 'こんにちは (Konnichiwa)'} - ``` - - **Create and run a chain of thought without "+" operator:** Please refer the - `kotaemon.pipelines.cot.Thought` section for examples. - - This chain-of-thought optionally takes a termination check callback function. - This function will be called after each thought is executed. It takes in a - dictionary of all thought outputs so far, and it returns True or False. If - True, the chain-of-thought will terminate. If unset, the default callback always - returns False. - """ - - thoughts: List[Thought] = Param( - default_callback=lambda *_: [], help="List of Thought" - ) - llm: LLM = Param(help="The LLM model to use (base of kotaemon.llms.BaseLLM)") - terminate: Callable = Param( - default=lambda _: False, - help="Callback on terminate condition. Default to always return False", - ) +@dataclass(kw_only=True) +class ManualSequentialChainOfThought: + thoughts: List[Thought] = field(default_factory=list) + llm: LLM + terminate: Callable[[dict], bool] = field(default=lambda _: False) def run(self, **kwargs) -> Document: - """Run the manual chain of thought""" - inputs = deepcopy(kwargs) - for idx, thought in enumerate(self.thoughts): + for thought in self.thoughts: if self.llm: thought.llm = self.llm - self._prepare_child(thought, f"thought{idx}") - output = thought(**inputs) inputs.update(output.content) if self.terminate(inputs): break - return Document(inputs) - - def __add__(self, next_thought: Thought) -> "ManualSequentialChainOfThought": - return ManualSequentialChainOfThought( - thoughts=self.thoughts + [next_thought], llm=self.llm - ) diff --git a/libs/kotaemon/kotaemon/llms/linear.py b/libs/kotaemon/kotaemon/llms/linear.py index 4c615971d..d69f2f4e8 100644 --- a/libs/kotaemon/kotaemon/llms/linear.py +++ b/libs/kotaemon/kotaemon/llms/linear.py @@ -1,155 +1,52 @@ -from typing import Any, Callable, Optional, Union +from __future__ import annotations + +from typing import Callable, Optional, Union + +from kotaemon.base import Runnable +from kotaemon.base.schema import Document, IO_Type -from ..base import BaseComponent -from ..base.schema import Document, IO_Type from .chats import ChatLLM from .completions import LLM from .prompts import BasePromptComponent -class SimpleLinearPipeline(BaseComponent): - """ - A simple pipeline for running a function with a prompt, a language model, and an - optional post-processor. - - Attributes: - prompt (BasePromptComponent): The prompt component used to generate the initial - input. - llm (Union[ChatLLM, LLM]): The language model component used to generate the - output. - post_processor (Union[BaseComponent, Callable[[IO_Type], IO_Type]]): An optional - post-processor component or function. - - Example Usage: - ```python - from kotaemon.llms import LCAzureChatOpenAI, BasePromptComponent - - def identity(x): - return x - - llm = LCAzureChatOpenAI( - openai_api_base="your openai api base", - openai_api_key="your openai api key", - openai_api_version="your openai api version", - deployment_name="dummy-q2-gpt35", - temperature=0, - request_timeout=600, - ) - - pipeline = SimpleLinearPipeline( - prompt=BasePromptComponent(template="what is {word} in Japanese ?"), - llm=llm, - post_processor=identity, - ) - print(pipeline(word="lone")) - ``` - """ - - prompt: BasePromptComponent - llm: Union[ChatLLM, LLM] - post_processor: Union[BaseComponent, Callable[[IO_Type], IO_Type]] +class SimpleLinearPipeline: + def __init__( + self, + prompt: BasePromptComponent, + llm: Union[ChatLLM, LLM], + post_processor: Union[Runnable, Callable[[IO_Type], IO_Type]] | None = None, + ) -> None: + self.prompt = prompt + self.llm = llm + self.post_processor = post_processor def run( self, *, - llm_kwargs: Optional[dict] = {}, - post_processor_kwargs: Optional[dict] = {}, + llm_kwargs: Optional[dict] = None, + post_processor_kwargs: Optional[dict] = None, **prompt_kwargs, ): - """ - Run the function with the given arguments and return the final output as a - Document object. - - Args: - llm_kwargs (dict): Keyword arguments for the llm call. - post_processor_kwargs (dict): Keyword arguments for the post_processor. - **prompt_kwargs: Keyword arguments for populating the prompt. - - Returns: - Document: The final output of the function as a Document object. - """ + llm_kwargs = llm_kwargs or {} + post_processor_kwargs = post_processor_kwargs or {} prompt = self.prompt(**prompt_kwargs) llm_output = self.llm(prompt.text, **llm_kwargs) if self.post_processor is not None: final_output = self.post_processor(llm_output, **post_processor_kwargs)[0] else: final_output = llm_output - return Document(final_output) class GatedLinearPipeline(SimpleLinearPipeline): - """ - A pipeline that extends the SimpleLinearPipeline class and adds a condition - attribute. - - Attributes: - condition (Callable[[IO_Type], Any]): A callable function that represents the - condition. - - Usage: - ```{.py3 title="Example Usage"} - from kotaemon.llms import LCAzureChatOpenAI, BasePromptComponent - from kotaemon.parsers import RegexExtractor + def __init__(self, prompt, llm, condition, post_processor=None): + super().__init__(prompt=prompt, llm=llm, post_processor=post_processor) + self.condition = condition - def identity(x): - return x - - llm = LCAzureChatOpenAI( - openai_api_base="your openai api base", - openai_api_key="your openai api key", - openai_api_version="your openai api version", - deployment_name="dummy-q2-gpt35", - temperature=0, - request_timeout=600, - ) - - pipeline = GatedLinearPipeline( - prompt=BasePromptComponent(template="what is {word} in Japanese ?"), - condition=RegexExtractor(pattern="some pattern"), - llm=llm, - post_processor=identity, - ) - print(pipeline(condition_text="some pattern", word="lone")) - print(pipeline(condition_text="other pattern", word="lone")) - ``` - """ - - condition: Callable[[IO_Type], Any] - - def run( - self, - *, - condition_text: Optional[str] = None, - llm_kwargs: Optional[dict] = {}, - post_processor_kwargs: Optional[dict] = {}, - **prompt_kwargs, - ) -> Document: - """ - Run the pipeline with the given arguments and return the final output as a - Document object. - - Args: - condition_text (str): The condition text to evaluate. Default to None. - llm_kwargs (dict): Additional keyword arguments for the language model call. - post_processor_kwargs (dict): Additional keyword arguments for the - post-processor. - **prompt_kwargs: Keyword arguments for populating the prompt. - - Returns: - Document: The final output of the pipeline as a Document object. - - Raises: - ValueError: If condition_text is None - """ + def run(self, *, condition_text: Optional[str] = None, **kwargs) -> Document: if condition_text is None: raise ValueError("`condition_text` must be provided") - if self.condition(condition_text)[0]: - return super().run( - llm_kwargs=llm_kwargs, - post_processor_kwargs=post_processor_kwargs, - **prompt_kwargs, - ) - + return super().run(**kwargs) return Document(None) diff --git a/libs/kotaemon/kotaemon/llms/prompts/base.py b/libs/kotaemon/kotaemon/llms/prompts/base.py index ca84d57b9..11f18f548 100644 --- a/libs/kotaemon/kotaemon/llms/prompts/base.py +++ b/libs/kotaemon/kotaemon/llms/prompts/base.py @@ -1,187 +1,33 @@ -from typing import Callable +from functools import cached_property -from theflow import Param - -from kotaemon.base import BaseComponent, Document +from kotaemon.base import Document from .template import PromptTemplate -class BasePromptComponent(BaseComponent): - """ - Base class for prompt components. - - Args: - template (PromptTemplate): The prompt template. - **kwargs: Any additional keyword arguments that will be used to populate the - given template. - """ - - class Config: - middleware_switches = {"theflow.middleware.CachingMiddleware": False} - allow_extra = True - - template: str | PromptTemplate - - @Param.auto(depends_on="template") - def template__(self): - return ( - self.template - if isinstance(self.template, PromptTemplate) - else PromptTemplate(self.template) - ) - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.__set(**kwargs) - - def __check_redundant_kwargs(self, **kwargs): - """ - Check for redundant keyword arguments. - - Parameters: - **kwargs (dict): A dictionary of keyword arguments. - - Raises: - ValueError: If any keys provided are not in the template. - - Returns: - None - """ - self.template__.check_redundant_kwargs(**kwargs) - - def __check_unset_placeholders(self): - """ - Check if all the placeholders in the template are set. - - This function checks if all the expected placeholders in the template are set as - attributes of the object. If any placeholders are missing, a `ValueError` - is raised with the names of the missing keys. - - Parameters: - None - - Returns: - None - """ - self.template__.check_missing_kwargs(**self.__dict__) - - def __validate_value_type(self, **kwargs): - """ - Validates the value types of the given keyword arguments. - - Parameters: - **kwargs (dict): A dictionary of keyword arguments to be validated. - - Raises: - ValueError: If any of the values in the kwargs dictionary have an - unsupported type. - - Returns: - None - """ - type_error = [] - for k, v in kwargs.items(): - if k.startswith("template"): - continue - if not isinstance(v, (str, int, Document, Callable)): # type: ignore - type_error.append((k, type(v))) - - if type_error: - raise ValueError( - "Type of values must be either int, str, Document, Callable, " - f"found unsupported type for (key, type): {type_error}" - ) - - def __set(self, **kwargs): - """ - Set the values of the attributes in the object based on the provided keyword - arguments. - - Args: - kwargs (dict): A dictionary with the attribute names as keys and the new - values as values. - - Returns: - None - """ - self.__check_redundant_kwargs(**kwargs) - self.__validate_value_type(**kwargs) - +class BasePromptComponent: + def __init__(self, template: str | PromptTemplate, **kwargs) -> None: + self.template = template self.__dict__.update(kwargs) - def __prepare_value(self): - """ - Generate a dictionary of keyword arguments based on the template's placeholders - and the current instance's attributes. - - Returns: - dict: A dictionary of keyword arguments. - """ - - def __prepare(key, value): - if isinstance(value, str): - return value - if isinstance(value, (int, Document)): - return str(value) - - raise ValueError( - f"Unsupported type {type(value)} for template value of key {key}" - ) - - kwargs = {} - for k in self.template__.placeholders: - v = getattr(self, k) - - # if get a callable, execute to get its output - if isinstance(v, Callable): # type: ignore[arg-type] - v = v() - - if isinstance(v, list): - v = str([__prepare(k, each) for each in v]) - elif isinstance(v, (str, int, Document)): - v = __prepare(k, v) - else: - raise ValueError( - f"Unsupported type {type(v)} for template value of key `{k}`" - ) - kwargs[k] = v - - return kwargs - - def set_value(self, **kwargs): - """ - Similar to `__set` but for external use. - - Set the values of the attributes in the object based on the provided keyword - arguments. - - Args: - kwargs (dict): A dictionary with the attribute names as keys and the new - values as values. - - Returns: - None - """ - self.__set(**kwargs) + @cached_property + def template__(self) -> PromptTemplate: + if isinstance(self.template, PromptTemplate): + return self.template + return PromptTemplate(self.template) def run(self, **kwargs): - """ - Run the function with the given keyword arguments. - - Args: - **kwargs: The keyword arguments to pass to the function. - - Returns: - The result of calling the `populate` method of the `template` object - with the given keyword arguments. - """ - self.__set(**kwargs) - self.__check_unset_placeholders() - prepared_kwargs = self.__prepare_value() - - text = self.template__.populate(**prepared_kwargs) - return Document(text=text, metadata={"origin": "PromptComponent"}) + self.__dict__.update(kwargs) + prepared = { + k: str( + getattr(self, k)() if callable(getattr(self, k)) else getattr(self, k) + ) + for k in self.template__.placeholders + } + return Document( + text=self.template__.populate(**prepared), + metadata={"origin": "PromptComponent"}, + ) - def flow(self): - return self.__call__() + def __call__(self, **kwargs): + return self.run(**kwargs) diff --git a/libs/kotaemon/kotaemon/loaders/azureai_document_intelligence_loader.py b/libs/kotaemon/kotaemon/loaders/azureai_document_intelligence_loader.py index c0b27d345..76bf4efbb 100644 --- a/libs/kotaemon/kotaemon/loaders/azureai_document_intelligence_loader.py +++ b/libs/kotaemon/kotaemon/loaders/azureai_document_intelligence_loader.py @@ -1,12 +1,14 @@ import base64 import os +from dataclasses import dataclass, field +from functools import cached_property from io import BytesIO from pathlib import Path from typing import Optional from PIL import Image -from kotaemon.base import Document, Param +from kotaemon.base import Document from .base import BaseReader from .utils.adobe import generate_single_figure_caption @@ -56,58 +58,40 @@ def crop_image(file_path: Path, bbox: list[float], page_number: int = 0) -> Imag ) +@dataclass(kw_only=True) class AzureAIDocumentIntelligenceLoader(BaseReader): - """Utilize Azure AI Document Intelligence to parse document - - As of April 24, the supported file formats are: pdf, jpeg/jpg, png, bmp, tiff, - heif, docx, xlsx, pptx and html. - """ + """Utilize Azure AI Document Intelligence to parse document.""" _dependencies = ["azure-ai-documentintelligence", "PyMuPDF", "Pillow"] - endpoint: str = Param( - os.environ.get("AZUREAI_DOCUMENT_INTELLIGENT_ENDPOINT", None), - help="Endpoint of Azure AI Document Intelligence", - ) - credential: str = Param( - os.environ.get("AZUREAI_DOCUMENT_INTELLIGENT_CREDENTIAL", None), - help="Credential of Azure AI Document Intelligence", - ) - model: str = Param( - "prebuilt-layout", - help=( - "Model to use for document analysis. Default is prebuilt-layout. " - "As of April 24, you can view the supported models [here]" - "(https://learn.microsoft.com/en-us/azure/ai-services/" - "document-intelligence/concept-model-overview?view=doc-intel-4.0.0" - "#model-analysis-features)" - ), - ) - output_content_format: str = Param( - "markdown", - help="Output content format. Can be 'markdown' or 'text'.Default is markdown", - ) - vlm_endpoint: str = Param( - help=( - "Default VLM endpoint for figure captioning. If not provided, will not " - "caption the figures" + endpoint: str | None = field( + default_factory=lambda: os.environ.get( + "AZUREAI_DOCUMENT_INTELLIGENT_ENDPOINT", None ) ) - figure_friendly_filetypes: list[str] = Param( - [".pdf", ".jpeg", ".jpg", ".png", ".bmp", ".tiff", ".heif", ".tif"], - help=( - "File types that we can reliably open and extract figures. " - "For files like .docx or .html, the visual layout may be different " - "when viewed from different tools, hence we cannot use Azure DI " - "location to extract figures." - ), + credential: str | None = field( + default_factory=lambda: os.environ.get( + "AZUREAI_DOCUMENT_INTELLIGENT_CREDENTIAL", None + ) ) - cache_dir: str = Param( - None, - help="Directory to cache the downloaded files. Default is None", + model: str = "prebuilt-layout" + output_content_format: str = "markdown" + vlm_endpoint: str | None = None + figure_friendly_filetypes: list[str] = field( + default_factory=lambda: [ + ".pdf", + ".jpeg", + ".jpg", + ".png", + ".bmp", + ".tiff", + ".heif", + ".tif", + ] ) + cache_dir: str | None = None - @Param.auto(depends_on=["endpoint", "credential"]) + @cached_property def client_(self): try: from azure.ai.documentintelligence import DocumentIntelligenceClient @@ -120,9 +104,9 @@ def client_(self): ) def run( - self, file_path: str | Path, extra_info: Optional[dict] = None, **kwargs + self, file: str | Path, extra_info: Optional[dict] = None, **kwargs ) -> list[Document]: - return self.load_data(Path(file_path), extra_info=extra_info, **kwargs) + return self.load_data(Path(file), extra_info=extra_info, **kwargs) def load_data( self, file_path: Path, extra_info: Optional[dict] = None, **kwargs diff --git a/libs/kotaemon/kotaemon/loaders/base.py b/libs/kotaemon/kotaemon/loaders/base.py index 7f000563b..a31efe77c 100644 --- a/libs/kotaemon/kotaemon/loaders/base.py +++ b/libs/kotaemon/kotaemon/loaders/base.py @@ -1,34 +1,28 @@ +from abc import ABC, abstractmethod from pathlib import Path from typing import TYPE_CHECKING, Any, List, Type, Union -from kotaemon.base import BaseComponent, Document +from kotaemon.base import Document if TYPE_CHECKING: from llama_index.core.readers.base import BaseReader as LIBaseReader -class BaseReader(BaseComponent): - """The base class for all readers""" +class BaseReader(ABC): + """The base class for all readers.""" - ... + @abstractmethod + def run(self, file: Union[Path, str], **kwargs: Any) -> List[Document]: + """Load documents from *file*.""" class AutoReader(BaseReader): """General auto reader for a variety of files. (based on llama-hub)""" def __init__(self, reader_type: Union[str, Type["LIBaseReader"]]) -> None: - """Init reader using string identifier or class name from llama-hub. - - When a string is given, first attempts a direct import from - ``llama_index.readers.file`` (works in uv/pip-less venvs where the - package is already installed). Falls back to the deprecated - ``download_loader`` only if the direct import fails. - """ import importlib if isinstance(reader_type, str): - # Try direct import first — avoids pip-install side-effect of - # download_loader, which fails in venvs without pip (e.g. uv). try: module = importlib.import_module("llama_index.readers.file") reader_cls = getattr(module, reader_type) @@ -39,50 +33,30 @@ def __init__(self, reader_type: Union[str, Type["LIBaseReader"]]) -> None: self._reader = download_loader(reader_type)() else: self._reader = reader_type() - super().__init__() def load_data(self, file: Union[Path, str], **kwargs: Any) -> List[Document]: documents = self._reader.load_data(file=file, **kwargs) - - # convert Document to new base class from kotaemon - converted_documents = [Document.from_dict(doc.to_dict()) for doc in documents] - return converted_documents + return [Document.from_dict(doc.to_dict()) for doc in documents] def run(self, file: Union[Path, str], **kwargs: Any) -> List[Document]: return self.load_data(file=file, **kwargs) -class LIReaderMixin(BaseComponent): - """Base wrapper around llama-index reader - - To use the LIBaseReader, you need to implement the _get_wrapped_class method to - return the relevant llama-index reader class that you want to wrap. - - Example: - - ```python - class DirectoryReader(LIBaseReader): - def _get_wrapped_class(self) -> Type["BaseReader"]: - from llama_index import SimpleDirectoryReader - - return SimpleDirectoryReader - ``` - """ +class LIReaderMixin(BaseReader): + """Base wrapper around llama-index reader.""" def _get_wrapped_class(self) -> Type["LIBaseReader"]: raise NotImplementedError( - "Please return the relevant llama-index class in in _get_wrapped_class" + "Please return the relevant llama-index class in _get_wrapped_class" ) def __init__(self, *args, **kwargs): self._reader_class = self._get_wrapped_class() self._reader = self._reader_class(*args, **kwargs) - super().__init__() def __setattr__(self, name: str, value: Any) -> None: if name.startswith("_"): return super().__setattr__(name, value) - return setattr(self._reader, name, value) def __getattr__(self, name: str) -> Any: @@ -90,10 +64,7 @@ def __getattr__(self, name: str) -> Any: def load_data(self, *args, **kwargs: Any) -> List[Document]: documents = self._reader.load_data(*args, **kwargs) - - # convert Document to new base class from kotaemon - converted_documents = [Document.from_dict(doc.to_dict()) for doc in documents] - return converted_documents + return [Document.from_dict(doc.to_dict()) for doc in documents] def run(self, *args, **kwargs: Any) -> List[Document]: return self.load_data(*args, **kwargs) diff --git a/libs/kotaemon/kotaemon/loaders/docling_loader.py b/libs/kotaemon/kotaemon/loaders/docling_loader.py index 149834268..f2d4399cd 100644 --- a/libs/kotaemon/kotaemon/loaders/docling_loader.py +++ b/libs/kotaemon/kotaemon/loaders/docling_loader.py @@ -1,47 +1,40 @@ import base64 from collections import defaultdict +from dataclasses import dataclass, field +from functools import cached_property from io import BytesIO from pathlib import Path from typing import List, Optional -from kotaemon.base import Document, Param +from kotaemon.base import Document from .azureai_document_intelligence_loader import crop_image from .base import BaseReader from .utils.adobe import generate_single_figure_caption, make_markdown_table +@dataclass(kw_only=True) class DoclingReader(BaseReader): """Using Docling to extract document structure and content""" _dependencies = ["docling"] - vlm_endpoint: str = Param( - help=( - "Default VLM endpoint for figure captioning. " - "If not provided, will not caption the figures" - ) - ) - - max_figure_to_caption: int = Param( - 100, - help=( - "The maximum number of figures to caption. " - "The rest will be indexed without captions." - ), - ) - - figure_friendly_filetypes: list[str] = Param( - [".pdf", ".jpeg", ".jpg", ".png", ".bmp", ".tiff", ".heif", ".tif"], - help=( - "File types that we can reliably open and extract figures. " - "For files like .docx or .html, the visual layout may be different " - "when viewed from different tools, hence we cannot use Azure DI location " - "to extract figures." - ), + vlm_endpoint: str | None = None + max_figure_to_caption: int = 100 + figure_friendly_filetypes: list[str] = field( + default_factory=lambda: [ + ".pdf", + ".jpeg", + ".jpg", + ".png", + ".bmp", + ".tiff", + ".heif", + ".tif", + ] ) - @Param.auto(cache=True) + @cached_property def converter_(self): try: from docling.document_converter import DocumentConverter @@ -51,9 +44,9 @@ def converter_(self): return DocumentConverter() def run( - self, file_path: str | Path, extra_info: Optional[dict] = None, **kwargs + self, file: str | Path, extra_info: Optional[dict] = None, **kwargs ) -> List[Document]: - return self.load_data(file_path, extra_info, **kwargs) + return self.load_data(file, extra_info, **kwargs) def load_data( self, file_path: str | Path, extra_info: Optional[dict] = None, **kwargs diff --git a/libs/kotaemon/kotaemon/loaders/html_loader.py b/libs/kotaemon/kotaemon/loaders/html_loader.py index 555efe50f..d2df1975c 100644 --- a/libs/kotaemon/kotaemon/loaders/html_loader.py +++ b/libs/kotaemon/kotaemon/loaders/html_loader.py @@ -3,7 +3,6 @@ from typing import Optional from llama_index.core.readers.base import BaseReader -from theflow.settings import settings as flowsettings from kotaemon.base import Document @@ -79,9 +78,7 @@ class MhtmlReader(BaseReader): def __init__( self, - cache_dir: Optional[str] = getattr( - flowsettings, "KH_MARKDOWN_OUTPUT_DIR", None - ), + cache_dir: Optional[str] = None, open_encoding: Optional[str] = None, bs_kwargs: Optional[dict] = None, get_text_separator: str = "", diff --git a/libs/kotaemon/kotaemon/loaders/paddleocr_loader/paddleocr_vl_loader.py b/libs/kotaemon/kotaemon/loaders/paddleocr_loader/paddleocr_vl_loader.py index d7da3b7a9..ef80ddd91 100644 --- a/libs/kotaemon/kotaemon/loaders/paddleocr_loader/paddleocr_vl_loader.py +++ b/libs/kotaemon/kotaemon/loaders/paddleocr_loader/paddleocr_vl_loader.py @@ -1,11 +1,14 @@ +from dataclasses import dataclass, field +from functools import cached_property from pathlib import Path -from kotaemon.base import Document, Param +from kotaemon.base import Document from kotaemon.loaders.base import BaseReader from .adapter import PaddleOCRResult +@dataclass(kw_only=True) class PaddleOCRVLReader(BaseReader): """Multilingual document parsing via PaddleOCR-VL-1.5 (0.9B VLM). @@ -17,46 +20,51 @@ class PaddleOCRVLReader(BaseReader): _dependencies = ["paddleocr[doc-parser]"] - device: str = Param( - "gpu:0", - help="Device for inference: gpu:0, cpu, npu:0, xpu:0", + device: str = "gpu:0" + + supported_file_types: list[str] = field( + default_factory=lambda: [ + ".pdf", + ".jpg", + ".jpeg", + ".png", + ".bmp", + ".tiff", + ".tif", + ".webp", + ] ) - supported_file_types: list[str] = Param( - [".pdf", ".jpg", ".jpeg", ".png", ".bmp", ".tiff", ".tif", ".webp"], - help="Supported file extensions", - ) - - pipeline_version: str | None = Param("v1.5") - layout_detection_model_name: str | None = Param(None) - layout_detection_model_dir: str | None = Param(None) - layout_threshold: float | None = Param(None) - layout_nms: float | None = Param(None) - layout_unclip_ratio: float | None = Param(None) - layout_merge_bboxes_mode: str | None = Param(None) - vl_rec_model_name: str | None = Param(None) - vl_rec_model_dir: str | None = Param(None) - vl_rec_backend: str | None = Param(None) - vl_rec_server_url: str | None = Param(None) - vl_rec_max_concurrency: int | None = Param(None) - vl_rec_api_model_name: str | None = Param(None) - vl_rec_api_key: str | None = Param(None) - doc_orientation_classify_model_name: str | None = Param(None) - doc_orientation_classify_model_dir: str | None = Param(None) - doc_unwarping_model_name: str | None = Param(None) - doc_unwarping_model_dir: str | None = Param(None) - use_doc_orientation_classify: bool | None = Param(None) - use_doc_unwarping: bool | None = Param(None) - use_layout_detection: bool | None = Param(None) - use_chart_recognition: bool | None = Param(None) - use_seal_recognition: bool | None = Param(None) - use_ocr_for_image_block: bool | None = Param(None) - format_block_content: bool | None = Param(None) - merge_layout_blocks: bool | None = Param(None) - markdown_ignore_labels: list[str] | None = Param(None) - use_queues: bool | None = Param(None) - - @Param.auto(cache=True) + pipeline_version: str | None = "v1.5" + layout_detection_model_name: str | None = None + layout_detection_model_dir: str | None = None + layout_threshold: float | None = None + layout_nms: float | None = None + layout_unclip_ratio: float | None = None + layout_merge_bboxes_mode: str | None = None + vl_rec_model_name: str | None = None + vl_rec_model_dir: str | None = None + vl_rec_backend: str | None = None + vl_rec_server_url: str | None = None + vl_rec_max_concurrency: int | None = None + vl_rec_api_model_name: str | None = None + vl_rec_api_key: str | None = None + doc_orientation_classify_model_name: str | None = None + doc_orientation_classify_model_dir: str | None = None + doc_unwarping_model_name: str | None = None + doc_unwarping_model_dir: str | None = None + use_doc_orientation_classify: bool | None = None + use_doc_unwarping: bool | None = None + use_layout_detection: bool | None = None + use_chart_recognition: bool | None = None + use_seal_recognition: bool | None = None + use_ocr_for_image_block: bool | None = None + format_block_content: bool | None = None + merge_layout_blocks: bool | None = None + markdown_ignore_labels: list[str] | None = None + use_queues: bool | None = None + + @cached_property def pipeline_(self): """Lazy-load the PaddleOCRVL pipeline.""" try: diff --git a/libs/kotaemon/kotaemon/loaders/paddleocr_loader/ppstructure_v3_loader.py b/libs/kotaemon/kotaemon/loaders/paddleocr_loader/ppstructure_v3_loader.py index a45a941a5..7bd2a4927 100644 --- a/libs/kotaemon/kotaemon/loaders/paddleocr_loader/ppstructure_v3_loader.py +++ b/libs/kotaemon/kotaemon/loaders/paddleocr_loader/ppstructure_v3_loader.py @@ -1,11 +1,14 @@ +from dataclasses import dataclass, field +from functools import cached_property from pathlib import Path -from kotaemon.base import Document, Param +from kotaemon.base import Document from kotaemon.loaders.base import BaseReader from .adapter import PaddleOCRResult +@dataclass(kw_only=True) class PPStructureV3Reader(BaseReader): """Document structure extraction via PaddleOCR PPStructureV3. @@ -15,85 +18,81 @@ class PPStructureV3Reader(BaseReader): _dependencies = ["paddleocr[doc-parser]"] - device: str = Param( - "gpu:0", - help="Device for inference: gpu:0, cpu, npu:0, xpu:0", - ) + device: str = "gpu:0" - supported_file_types: list[str] = Param( - [".pdf", ".jpg", ".jpeg", ".png", ".tiff", ".tif"], - help="Supported file extensions", + supported_file_types: list[str] = field( + default_factory=lambda: [".pdf", ".jpg", ".jpeg", ".png", ".tiff", ".tif"] ) - layout_detection_model_name: str | None = Param(None) - layout_detection_model_dir: str | None = Param(None) - layout_threshold: float | None = Param(None) - layout_nms: float | None = Param(None) - layout_unclip_ratio: float | None = Param(None) - layout_merge_bboxes_mode: str | None = Param(None) - chart_recognition_model_name: str | None = Param(None) - chart_recognition_model_dir: str | None = Param(None) - chart_recognition_batch_size: int | None = Param(None) - region_detection_model_name: str | None = Param(None) - region_detection_model_dir: str | None = Param(None) - doc_orientation_classify_model_name: str | None = Param(None) - doc_orientation_classify_model_dir: str | None = Param(None) - doc_unwarping_model_name: str | None = Param(None) - doc_unwarping_model_dir: str | None = Param(None) - text_detection_model_name: str | None = Param(None) - text_detection_model_dir: str | None = Param(None) - text_det_limit_side_len: int | None = Param(None) - text_det_limit_type: str | None = Param(None) - text_det_thresh: float | None = Param(None) - text_det_box_thresh: float | None = Param(None) - text_det_unclip_ratio: float | None = Param(None) - textline_orientation_model_name: str | None = Param(None) - textline_orientation_model_dir: str | None = Param(None) - textline_orientation_batch_size: int | None = Param(None) - text_recognition_model_name: str | None = Param(None) - text_recognition_model_dir: str | None = Param(None) - text_recognition_batch_size: int | None = Param(None) - text_rec_score_thresh: float | None = Param(None) - table_classification_model_name: str | None = Param(None) - table_classification_model_dir: str | None = Param(None) - wired_table_structure_recognition_model_name: str | None = Param(None) - wired_table_structure_recognition_model_dir: str | None = Param(None) - wireless_table_structure_recognition_model_name: str | None = Param(None) - wireless_table_structure_recognition_model_dir: str | None = Param(None) - wired_table_cells_detection_model_name: str | None = Param(None) - wired_table_cells_detection_model_dir: str | None = Param(None) - wireless_table_cells_detection_model_name: str | None = Param(None) - wireless_table_cells_detection_model_dir: str | None = Param(None) - table_orientation_classify_model_name: str | None = Param(None) - table_orientation_classify_model_dir: str | None = Param(None) - seal_text_detection_model_name: str | None = Param(None) - seal_text_detection_model_dir: str | None = Param(None) - seal_det_limit_side_len: int | None = Param(None) - seal_det_limit_type: str | None = Param(None) - seal_det_thresh: float | None = Param(None) - seal_det_box_thresh: float | None = Param(None) - seal_det_unclip_ratio: float | None = Param(None) - seal_text_recognition_model_name: str | None = Param(None) - seal_text_recognition_model_dir: str | None = Param(None) - seal_text_recognition_batch_size: int | None = Param(None) - seal_rec_score_thresh: float | None = Param(None) - formula_recognition_model_name: str | None = Param(None) - formula_recognition_model_dir: str | None = Param(None) - formula_recognition_batch_size: int | None = Param(None) - use_doc_orientation_classify: bool | None = Param(None) - use_doc_unwarping: bool | None = Param(None) - use_textline_orientation: bool | None = Param(None) - use_seal_recognition: bool | None = Param(None) - use_table_recognition: bool | None = Param(None) - use_formula_recognition: bool | None = Param(None) - use_chart_recognition: bool | None = Param(None) - use_region_detection: bool | None = Param(None) - format_block_content: bool | None = Param(None) - markdown_ignore_labels: list[str] | None = Param(None) - lang: str | None = Param(None) - ocr_version: str | None = Param(None) - - @Param.auto(cache=True) + layout_detection_model_name: str | None = None + layout_detection_model_dir: str | None = None + layout_threshold: float | None = None + layout_nms: float | None = None + layout_unclip_ratio: float | None = None + layout_merge_bboxes_mode: str | None = None + chart_recognition_model_name: str | None = None + chart_recognition_model_dir: str | None = None + chart_recognition_batch_size: int | None = None + region_detection_model_name: str | None = None + region_detection_model_dir: str | None = None + doc_orientation_classify_model_name: str | None = None + doc_orientation_classify_model_dir: str | None = None + doc_unwarping_model_name: str | None = None + doc_unwarping_model_dir: str | None = None + text_detection_model_name: str | None = None + text_detection_model_dir: str | None = None + text_det_limit_side_len: int | None = None + text_det_limit_type: str | None = None + text_det_thresh: float | None = None + text_det_box_thresh: float | None = None + text_det_unclip_ratio: float | None = None + textline_orientation_model_name: str | None = None + textline_orientation_model_dir: str | None = None + textline_orientation_batch_size: int | None = None + text_recognition_model_name: str | None = None + text_recognition_model_dir: str | None = None + text_recognition_batch_size: int | None = None + text_rec_score_thresh: float | None = None + table_classification_model_name: str | None = None + table_classification_model_dir: str | None = None + wired_table_structure_recognition_model_name: str | None = None + wired_table_structure_recognition_model_dir: str | None = None + wireless_table_structure_recognition_model_name: str | None = None + wireless_table_structure_recognition_model_dir: str | None = None + wired_table_cells_detection_model_name: str | None = None + wired_table_cells_detection_model_dir: str | None = None + wireless_table_cells_detection_model_name: str | None = None + wireless_table_cells_detection_model_dir: str | None = None + table_orientation_classify_model_name: str | None = None + table_orientation_classify_model_dir: str | None = None + seal_text_detection_model_name: str | None = None + seal_text_detection_model_dir: str | None = None + seal_det_limit_side_len: int | None = None + seal_det_limit_type: str | None = None + seal_det_thresh: float | None = None + seal_det_box_thresh: float | None = None + seal_det_unclip_ratio: float | None = None + seal_text_recognition_model_name: str | None = None + seal_text_recognition_model_dir: str | None = None + seal_text_recognition_batch_size: int | None = None + seal_rec_score_thresh: float | None = None + formula_recognition_model_name: str | None = None + formula_recognition_model_dir: str | None = None + formula_recognition_batch_size: int | None = None + use_doc_orientation_classify: bool | None = None + use_doc_unwarping: bool | None = None + use_textline_orientation: bool | None = None + use_seal_recognition: bool | None = None + use_table_recognition: bool | None = None + use_formula_recognition: bool | None = None + use_chart_recognition: bool | None = None + use_region_detection: bool | None = None + format_block_content: bool | None = None + markdown_ignore_labels: list[str] | None = None + lang: str | None = None + ocr_version: str | None = None + + @cached_property def pipeline_(self): """Lazy-load the PPStructureV3 pipeline.""" try: diff --git a/libs/kotaemon/kotaemon/loaders/txt_loader.py b/libs/kotaemon/kotaemon/loaders/txt_loader.py index 5a0d48cf5..5387791ed 100644 --- a/libs/kotaemon/kotaemon/loaders/txt_loader.py +++ b/libs/kotaemon/kotaemon/loaders/txt_loader.py @@ -8,9 +8,9 @@ class TxtReader(BaseReader): def run( - self, file_path: str | Path, extra_info: Optional[dict] = None, **kwargs + self, file: str | Path, extra_info: Optional[dict] = None, **kwargs ) -> list[Document]: - return self.load_data(Path(file_path), extra_info=extra_info, **kwargs) + return self.load_data(Path(file), extra_info=extra_info, **kwargs) def load_data( self, file_path: Path, extra_info: Optional[dict] = None, **kwargs diff --git a/libs/kotaemon/kotaemon/loaders/web_loader.py b/libs/kotaemon/kotaemon/loaders/web_loader.py index fc73aa68e..e97f30194 100644 --- a/libs/kotaemon/kotaemon/loaders/web_loader.py +++ b/libs/kotaemon/kotaemon/loaders/web_loader.py @@ -14,9 +14,9 @@ class WebReader(BaseReader): def run( - self, file_path: str | Path, extra_info: Optional[dict] = None, **kwargs + self, file: str | Path, extra_info: Optional[dict] = None, **kwargs ) -> list[Document]: - return self.load_data(Path(file_path), extra_info=extra_info, **kwargs) + return self.load_data(Path(file), extra_info=extra_info, **kwargs) def fetch_url(self, url: str): # setup the request diff --git a/libs/kotaemon/kotaemon/parsers/regex_extractor.py b/libs/kotaemon/kotaemon/parsers/regex_extractor.py index f773fe910..81d2f58e4 100644 --- a/libs/kotaemon/kotaemon/parsers/regex_extractor.py +++ b/libs/kotaemon/kotaemon/parsers/regex_extractor.py @@ -3,85 +3,41 @@ import re from typing import Callable -from kotaemon.base import BaseComponent, Document, ExtractorOutput, Param +from kotaemon.base import Document, ExtractorOutput -class RegexExtractor(BaseComponent): - """ - Simple class for extracting text from a document using a regex pattern. - - Args: - pattern (List[str]): The regex pattern(s) to use. - output_map (dict, optional): A mapping from extracted text to the - desired output. Defaults to None. - """ - - class Config: - middleware_switches = {"theflow.middleware.CachingMiddleware": False} - +class RegexExtractor: pattern: list[str] - output_map: dict[str, str] | Callable[[str], str] = Param( - default_callback=lambda *_: {} - ) - - def __init__(self, pattern: str | list[str], **kwargs): + output_map: dict[str, str] | Callable[[str], str] + + def __init__( + self, + pattern: str | list[str], + *, + output_map: dict[str, str] | Callable[[str], str] | None = None, + ) -> None: if isinstance(pattern, str): pattern = [pattern] - super().__init__(pattern=pattern, **kwargs) + self.pattern = pattern + self.output_map = output_map if output_map is not None else {} @staticmethod def run_raw_static(pattern: str, text: str) -> list[str]: - """ - Finds all non-overlapping occurrences of a pattern in a string. - - Parameters: - pattern (str): The regular expression pattern to search for. - text (str): The input string to search in. - - Returns: - List[str]: A list of all non-overlapping occurrences of the pattern in the - string. - """ return re.findall(pattern, text) @staticmethod def map_output(text, output_map) -> str: - """ - Maps the given `text` to its corresponding value in the `output_map` dictionary. - - Parameters: - text (str): The input text to be mapped. - output_map (dict): A dictionary containing mapping of input text to output - values. - - Returns: - str: The corresponding value from the `output_map` if `text` is found in the - dictionary, otherwise returns the original `text`. - """ if not output_map: return text - if isinstance(output_map, dict): return output_map.get(text, text) - return output_map(text) def run_raw(self, text: str) -> ExtractorOutput: - """ - Matches the raw text against the pattern and rans the output mapping, returning - an instance of ExtractorOutput. - - Args: - text (str): The raw text to be processed. - - Returns: - ExtractorOutput: The processed output as a list of ExtractorOutput. - """ output: list[str] = sum( [self.run_raw_static(p, text) for p in self.pattern], [] ) - output = [self.map_output(text, self.output_map) for text in output] - + output = [self.map_output(item, self.output_map) for item in output] return ExtractorOutput( text=output[0] if output else "", matches=output, @@ -91,29 +47,9 @@ def run_raw(self, text: str) -> ExtractorOutput: def run( self, text: str | list[str] | Document | list[Document] ) -> list[ExtractorOutput]: - """Match the input against a pattern and return the output for each input - - Parameters: - text: contains the input string to be processed - - Returns: - A list contains the output ExtractorOutput for each input - - Example: - ```pycon - >>> document1 = Document(...) - >>> document2 = Document(...) - >>> document_batch = [document1, document2] - >>> batch_output = self(document_batch) - >>> print(batch_output) - [output1_document1, output1_document2] - ``` - """ - # TODO: this conversion seems common input_: list[str] = [] if not isinstance(text, list): text = [text] - for item in text: if isinstance(item, str): input_.append(item) @@ -123,28 +59,23 @@ def run( raise ValueError( f"Invalid input type {type(item)}, should be str or Document" ) + return [self.run_raw(each) for each in input_] - output = [] - for each_input in input_: - output.append(self.run_raw(each_input)) - - return output + def __call__(self, *args, **kwargs): + return self.run(*args, **kwargs) class FirstMatchRegexExtractor(RegexExtractor): - pattern: list[str] - def run_raw(self, text: str) -> ExtractorOutput: for p in self.pattern: output = self.run_raw_static(p, text) if output: - output = [self.map_output(text, self.output_map) for text in output] + output = [self.map_output(item, self.output_map) for item in output] return ExtractorOutput( text=output[0], matches=output, metadata={"origin": "FirstMatchRegexExtractor"}, ) - return ExtractorOutput( text=None, matches=[], metadata={"origin": "FirstMatchRegexExtractor"} ) diff --git a/libs/kotaemon/kotaemon/rerankings/__init__.py b/libs/kotaemon/kotaemon/rerankings/__init__.py index 23de862c4..fa0a9a090 100644 --- a/libs/kotaemon/kotaemon/rerankings/__init__.py +++ b/libs/kotaemon/kotaemon/rerankings/__init__.py @@ -1,6 +1,15 @@ from .base import BaseReranking from .cohere import CohereReranking +from .factory import MP_VENDOR_CLS, RerankingFactory, RerankingVendor from .tei_fast_rerank import TeiFastReranking from .voyageai import VoyageAIReranking -__all__ = ["BaseReranking", "TeiFastReranking", "CohereReranking", "VoyageAIReranking"] +__all__ = [ + "BaseReranking", + "TeiFastReranking", + "CohereReranking", + "VoyageAIReranking", + "RerankingVendor", + "RerankingFactory", + "MP_VENDOR_CLS", +] diff --git a/libs/kotaemon/kotaemon/rerankings/base.py b/libs/kotaemon/kotaemon/rerankings/base.py index c9c0b9b84..e03d4382a 100644 --- a/libs/kotaemon/kotaemon/rerankings/base.py +++ b/libs/kotaemon/kotaemon/rerankings/base.py @@ -1,13 +1,37 @@ from __future__ import annotations -from abc import abstractmethod +from dataclasses import dataclass -from kotaemon.base import BaseComponent, Document +from kotaemon.base import Document +from kotaemon.base.describe import DataclassDescribe, describe_dataclass -class BaseReranking(BaseComponent): - @abstractmethod - def run(self, documents: list[Document], query: str) -> list[Document]: - """Main method to transform list of documents - (re-ranking, filtering, etc)""" - ... +@dataclass(kw_only=True) +class BaseReranking: + @classmethod + def describe(cls) -> DataclassDescribe: + return describe_dataclass(cls) + + def __call__( + self, + documents: list[Document], + query: str, + *args, + **kwargs, + ) -> list[Document]: + return self.run(documents, query, *args, **kwargs) + + def prepare_input(self, documents: list[str] | list[Document]) -> list[Document]: + """Coerce a list of strings to a list of Documents.""" + return [ + d if isinstance(d, Document) else Document(content=d) for d in documents + ] + + def run( + self, + documents: list[Document], + query: str, + *args, + **kwargs, + ) -> list[Document]: + raise NotImplementedError diff --git a/libs/kotaemon/kotaemon/rerankings/cohere.py b/libs/kotaemon/kotaemon/rerankings/cohere.py index ffc3baf5c..44edc9fe7 100644 --- a/libs/kotaemon/kotaemon/rerankings/cohere.py +++ b/libs/kotaemon/kotaemon/rerankings/cohere.py @@ -1,45 +1,38 @@ from __future__ import annotations import os +from dataclasses import dataclass, field from decouple import config -from kotaemon.base import Document, Param +from kotaemon.base import Document from .base import BaseReranking +@dataclass(kw_only=True) class CohereReranking(BaseReranking): """Cohere Reranking model""" - model_name: str = Param( - "rerank-v4.0-fast", - help=( - "ID of the model to use. See [Cohere Rerank models]" - "(https://docs.cohere.com/docs/models#rerank) for supported IDs " - "(e.g. rerank-v4.0-fast, rerank-v4.0-pro, rerank-multilingual-v3.0)." - ), - required=True, + model_name: str = field( + default="rerank-v4.0-fast", + metadata={"description": "Cohere rerank model ID"}, ) - cohere_api_key: str = Param( - config("COHERE_API_KEY", ""), - help="Cohere API key", - required=True, + cohere_api_key: str = field( + default_factory=lambda: config("COHERE_API_KEY", ""), + metadata={"description": "Cohere API key"}, ) - base_url: str = Param( - None, - help="Rerank API base url. Default is https://api.cohere.com", - required=False, + base_url: str | None = field( + default=None, + metadata={"description": "Rerank API base url"}, ) def run(self, documents: list[Document], query: str) -> list[Document]: - """Use Cohere Reranker model to re-order documents - with their relevance score""" try: import cohere except ImportError: raise ImportError( - "Please install Cohere " "`pip install cohere` to use Cohere Reranking" + "Please install Cohere `pip install cohere` to use Cohere Reranking" ) if not self.cohere_api_key or "COHERE_API_KEY" in self.cohere_api_key: @@ -51,7 +44,7 @@ def run(self, documents: list[Document], query: str) -> list[Document]: ) compressed_docs: list[Document] = [] - if not documents: # to avoid empty api call + if not documents: return compressed_docs _docs = [d.content for d in documents] diff --git a/libs/kotaemon/kotaemon/rerankings/factory.py b/libs/kotaemon/kotaemon/rerankings/factory.py new file mode 100644 index 000000000..73c74618d --- /dev/null +++ b/libs/kotaemon/kotaemon/rerankings/factory.py @@ -0,0 +1,35 @@ +from __future__ import annotations + +from enum import Enum + +from .base import BaseReranking +from .cohere import CohereReranking +from .tei_fast_rerank import TeiFastReranking +from .voyageai import VoyageAIReranking + + +class RerankingVendor(str, Enum): + COHERE = "CohereReranking" + VOYAGE_AI = "VoyageAIReranking" + TEI_FAST = "TeiFastReranking" + + +MP_VENDOR_CLS: dict[RerankingVendor, type[BaseReranking]] = { + RerankingVendor.COHERE: CohereReranking, + RerankingVendor.VOYAGE_AI: VoyageAIReranking, + RerankingVendor.TEI_FAST: TeiFastReranking, +} + + +class RerankingFactory: + @staticmethod + def get_cls(vendor: RerankingVendor | str) -> type[BaseReranking]: + """Return the class for *vendor*, coercing bare strings.""" + key = RerankingVendor(vendor) + if key not in MP_VENDOR_CLS: + raise ValueError(f"Invalid reranking vendor: {vendor!r}") + return MP_VENDOR_CLS[key] + + @staticmethod + def supported_vendors() -> list[RerankingVendor]: + return list(MP_VENDOR_CLS.keys()) diff --git a/libs/kotaemon/kotaemon/rerankings/tei_fast_rerank.py b/libs/kotaemon/kotaemon/rerankings/tei_fast_rerank.py index adf1246e3..a481a0f03 100644 --- a/libs/kotaemon/kotaemon/rerankings/tei_fast_rerank.py +++ b/libs/kotaemon/kotaemon/rerankings/tei_fast_rerank.py @@ -1,67 +1,57 @@ from __future__ import annotations -from typing import Optional +from dataclasses import dataclass, field import requests -from kotaemon.base import Document, Param +from kotaemon.base import Document from .base import BaseReranking session = requests.session() +@dataclass(kw_only=True) class TeiFastReranking(BaseReranking): - """Text Embeddings Inference (TEI) Reranking model - (https://huggingface.co/docs/text-embeddings-inference/en/index) - """ + """TEI (Text Embeddings Inference) reranking model.""" - endpoint_url: str = Param( - None, help="TEI Reranking service api base URL", required=True + endpoint_url: str = field( + metadata={"description": "TEI reranking service api base URL"}, ) - model_name: Optional[str] = Param( - None, - help=( - "ID of the model to use. You can go to [Supported Models]" - "(https://github.com/huggingface" - "/text-embeddings-inference?tab=readme-ov-file" - "#supported-models) to see the supported models" - ), + model_name: str | None = field( + default=None, + metadata={"description": "Model ID (optional)"}, ) - is_truncated: Optional[bool] = Param(True, help="Whether to truncate the inputs") - max_tokens: Optional[int] = Param( - 512, - help=( - "This option is used to specify the " - "maximum number of tokens supported by the reranker model." - ), + is_truncated: bool = field( + default=True, + metadata={"description": "Whether to truncate inputs"}, + ) + max_tokens: int = field( + default=512, + metadata={"description": "Max tokens when truncating"}, ) - def client(self, query, texts): + def client(self, query: str, texts: list[str]): + truncated_texts = texts if self.is_truncated: - max_tokens = self.max_tokens # default is 512 tokens. - truncated_texts = [text[:max_tokens] for text in texts] + truncated_texts = [text[: self.max_tokens] for text in texts] - response = session.post( + return session.post( url=self.endpoint_url, json={ "query": query, "texts": truncated_texts, - "is_truncated": self.is_truncated, # default is True + "is_truncated": self.is_truncated, }, ).json() - return response def run(self, documents: list[Document], query: str) -> list[Document]: - """Use the deployed TEI rerankings service to re-order documents - with their relevance score""" if not self.endpoint_url: print("TEI API reranking URL not found. Skipping rerankings.") return documents compressed_docs: list[Document] = [] - - if not documents: # to avoid empty api call + if not documents: return compressed_docs if isinstance(documents[0], str): @@ -82,7 +72,8 @@ def run(self, documents: list[Document], query: str) -> list[Document]: doc.metadata["reranking_score"] = r["score"] compressed_docs.append(doc) - compressed_docs = sorted( - compressed_docs, key=lambda x: x.metadata["reranking_score"], reverse=True + return sorted( + compressed_docs, + key=lambda x: x.metadata["reranking_score"], + reverse=True, ) - return compressed_docs diff --git a/libs/kotaemon/kotaemon/rerankings/voyageai.py b/libs/kotaemon/kotaemon/rerankings/voyageai.py index fe97db0e9..ac4a9d3bf 100644 --- a/libs/kotaemon/kotaemon/rerankings/voyageai.py +++ b/libs/kotaemon/kotaemon/rerankings/voyageai.py @@ -1,10 +1,11 @@ from __future__ import annotations import importlib +from dataclasses import dataclass, field from decouple import config -from kotaemon.base import Document, Param +from kotaemon.base import Document from .base import BaseReranking @@ -18,37 +19,28 @@ def _import_voyageai(): return vo +@dataclass(kw_only=True) class VoyageAIReranking(BaseReranking): """VoyageAI Reranking model""" - model_name: str = Param( - "rerank-2", - help=( - "ID of the model to use. You can go to [Supported Models]" - "(https://docs.voyageai.com/docs/reranker) to see the supported models" - ), - required=True, + model_name: str = field( + default="rerank-2", + metadata={"description": "Rerank model ID"}, ) - api_key: str = Param( - config("VOYAGE_API_KEY", ""), - help="VoyageAI API key", - required=True, + api_key: str = field( + default_factory=lambda: config("VOYAGE_API_KEY", ""), + metadata={"description": "VoyageAI API key"}, ) - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) + def __post_init__(self) -> None: if not self.api_key: - raise ValueError("API key must be provided for VoyageAIEmbeddings.") - + raise ValueError("API key must be provided for VoyageAIReranking.") self._client = _import_voyageai().Client(api_key=self.api_key) self._aclient = _import_voyageai().AsyncClient(api_key=self.api_key) def run(self, documents: list[Document], query: str) -> list[Document]: - """Use VoyageAI Reranker model to re-order documents - with their relevance score""" compressed_docs: list[Document] = [] - - if not documents: # to avoid empty api call + if not documents: return compressed_docs _docs = [d.content for d in documents] diff --git a/libs/kotaemon/kotaemon/storages/__init__.py b/libs/kotaemon/kotaemon/storages/__init__.py index 5638db806..f1ace7fd7 100644 --- a/libs/kotaemon/kotaemon/storages/__init__.py +++ b/libs/kotaemon/kotaemon/storages/__init__.py @@ -1,5 +1,7 @@ from .docstores import ( BaseDocumentStore, + DocStoreFactory, + DocStoreVendor, ElasticsearchDocumentStore, InMemoryDocumentStore, LanceDBDocumentStore, @@ -13,6 +15,8 @@ MilvusVectorStore, QdrantVectorStore, SimpleFileVectorStore, + VectorStoreFactory, + VectorStoreVendor, ) __all__ = [ @@ -22,6 +26,8 @@ "ElasticsearchDocumentStore", "SimpleFileDocumentStore", "LanceDBDocumentStore", + "DocStoreVendor", + "DocStoreFactory", # Vector stores "BaseVectorStore", "ChromaVectorStore", @@ -30,4 +36,6 @@ "LanceDBVectorStore", "MilvusVectorStore", "QdrantVectorStore", + "VectorStoreVendor", + "VectorStoreFactory", ] diff --git a/libs/kotaemon/kotaemon/storages/docstores/__init__.py b/libs/kotaemon/kotaemon/storages/docstores/__init__.py index 8bd0788aa..8e0cf006f 100644 --- a/libs/kotaemon/kotaemon/storages/docstores/__init__.py +++ b/libs/kotaemon/kotaemon/storages/docstores/__init__.py @@ -1,5 +1,6 @@ from .base import BaseDocumentStore from .elasticsearch import ElasticsearchDocumentStore +from .factory import MP_VENDOR_CLS, DocStoreFactory, DocStoreVendor from .in_memory import InMemoryDocumentStore from .lancedb import LanceDBDocumentStore from .simple_file import SimpleFileDocumentStore @@ -10,4 +11,7 @@ "ElasticsearchDocumentStore", "SimpleFileDocumentStore", "LanceDBDocumentStore", + "DocStoreVendor", + "DocStoreFactory", + "MP_VENDOR_CLS", ] diff --git a/libs/kotaemon/kotaemon/storages/docstores/base.py b/libs/kotaemon/kotaemon/storages/docstores/base.py index 4b6f397cb..8dc0ddbb2 100644 --- a/libs/kotaemon/kotaemon/storages/docstores/base.py +++ b/libs/kotaemon/kotaemon/storages/docstores/base.py @@ -1,12 +1,30 @@ from abc import ABC, abstractmethod -from typing import List, Optional, Union +from typing import Any, Dict, List, Optional, Union + +from pydantic_settings import BaseSettings +from typing_extensions import Self from kotaemon.base import Document +class BaseDocumentStoreEnv(BaseSettings): + DOCSTORE_COLLECTION_NAME: str = "docstore" + + @classmethod + def override_envs(cls, overriding_envvars: Dict[str, Any] | None = None) -> Self: + settings = cls().model_dump() + settings.update(overriding_envvars or {}) + return cls.model_validate(settings) + + class BaseDocumentStore(ABC): """A document store is in charged of storing and managing documents""" + @classmethod + @abstractmethod + def from_env(cls, overriding_envvars: Dict[str, Any] | None = None) -> Self: + ... + @abstractmethod def __init__(self, *args, **kwargs): ... diff --git a/libs/kotaemon/kotaemon/storages/docstores/elasticsearch.py b/libs/kotaemon/kotaemon/storages/docstores/elasticsearch.py index 27045cf34..81c27c081 100644 --- a/libs/kotaemon/kotaemon/storages/docstores/elasticsearch.py +++ b/libs/kotaemon/kotaemon/storages/docstores/elasticsearch.py @@ -1,12 +1,20 @@ -from typing import List, Optional, Union +from typing import Any, Dict, List, Optional, Union + +from typing_extensions import Self from kotaemon.base import Document -from .base import BaseDocumentStore +from .base import BaseDocumentStore, BaseDocumentStoreEnv MAX_DOCS_TO_GET = 10**4 +class ElasticsearchEnv(BaseDocumentStoreEnv): + ELASTICSEARCH_URL: str = "http://localhost:9200" + ELASTICSEARCH_K1: float = 2.0 + ELASTICSEARCH_B: float = 0.75 + + class ElasticsearchDocumentStore(BaseDocumentStore): """Simple memory document store that store document in a dictionary""" @@ -60,6 +68,17 @@ def __init__( index=self.index_name, mappings=mappings, settings=settings ) + @classmethod + def from_env(cls, overriding_envvars: Dict[str, Any] | None = None) -> Self: + envs = ElasticsearchEnv.override_envs(overriding_envvars) + print(f"Loaded Elasticsearch with envs={envs}") + return cls( + collection_name=envs.DOCSTORE_COLLECTION_NAME, + elasticsearch_url=envs.ELASTICSEARCH_URL, + k1=envs.ELASTICSEARCH_K1, + b=envs.ELASTICSEARCH_B, + ) + def add( self, docs: Union[Document, List[Document]], diff --git a/libs/kotaemon/kotaemon/storages/docstores/factory.py b/libs/kotaemon/kotaemon/storages/docstores/factory.py new file mode 100644 index 000000000..a9bf255ce --- /dev/null +++ b/libs/kotaemon/kotaemon/storages/docstores/factory.py @@ -0,0 +1,69 @@ +"""Docstore vendor registry and factory. + +Usage:: + + from kotaemon.storages.docstores.factory import ( + DocStoreFactory, + DocStoreVendor, + ) + + cls = DocStoreFactory.get_cls(DocStoreVendor.LANCEDB) + store = cls(path="/data/docstore", collection_name="default") +""" + +from __future__ import annotations + +from enum import Enum + +from .base import BaseDocumentStore +from .elasticsearch import ElasticsearchDocumentStore +from .in_memory import InMemoryDocumentStore +from .lancedb import LanceDBDocumentStore +from .simple_file import SimpleFileDocumentStore + + +class DocStoreVendor(str, Enum): + """Supported docstore back-ends.""" + + LANCEDB = "LanceDBDocumentStore" + ELASTICSEARCH = "ElasticsearchDocumentStore" + IN_MEMORY = "InMemoryDocumentStore" + SIMPLE_FILE = "SimpleFileDocumentStore" + + +MP_VENDOR_CLS: dict[DocStoreVendor, type[BaseDocumentStore]] = { + DocStoreVendor.LANCEDB: LanceDBDocumentStore, + DocStoreVendor.ELASTICSEARCH: ElasticsearchDocumentStore, + DocStoreVendor.IN_MEMORY: InMemoryDocumentStore, + DocStoreVendor.SIMPLE_FILE: SimpleFileDocumentStore, +} + + +class DocStoreFactory: + """Resolve a :class:`DocStoreVendor` to its implementation class.""" + + @staticmethod + def get_cls( + vendor: DocStoreVendor | str, + ) -> type[BaseDocumentStore]: + """Return the class for *vendor*, coercing bare strings. + + Args: + vendor: A :class:`DocStoreVendor` value or a plain string + matching one (e.g. ``"LanceDBDocumentStore"``). + + Returns: + The concrete :class:`BaseDocumentStore` subclass. + + Raises: + ValueError: When *vendor* does not match any registered class. + """ + key = DocStoreVendor(vendor) + if key not in MP_VENDOR_CLS: + raise ValueError(f"Invalid docstore vendor: {vendor!r}") + return MP_VENDOR_CLS[key] + + @staticmethod + def supported_vendors() -> list[DocStoreVendor]: + """Return all registered docstore vendors.""" + return list(MP_VENDOR_CLS.keys()) diff --git a/libs/kotaemon/kotaemon/storages/docstores/in_memory.py b/libs/kotaemon/kotaemon/storages/docstores/in_memory.py index 0df7a3e58..750174342 100644 --- a/libs/kotaemon/kotaemon/storages/docstores/in_memory.py +++ b/libs/kotaemon/kotaemon/storages/docstores/in_memory.py @@ -1,6 +1,8 @@ import json from pathlib import Path -from typing import List, Optional, Union +from typing import Any, Dict, List, Optional, Union + +from typing_extensions import Self from kotaemon.base import Document @@ -13,6 +15,10 @@ class InMemoryDocumentStore(BaseDocumentStore): def __init__(self): self._store = {} + @classmethod + def from_env(cls, _overriding_envvars: Dict[str, Any] | None = None) -> Self: + return cls() + def add( self, docs: Union[Document, List[Document]], diff --git a/libs/kotaemon/kotaemon/storages/docstores/lancedb.py b/libs/kotaemon/kotaemon/storages/docstores/lancedb.py index 11b09a52e..2dc109451 100644 --- a/libs/kotaemon/kotaemon/storages/docstores/lancedb.py +++ b/libs/kotaemon/kotaemon/storages/docstores/lancedb.py @@ -1,13 +1,19 @@ import json -from typing import List, Optional, Union +from typing import Any, Dict, List, Optional, Union + +from typing_extensions import Self from kotaemon.base import Document -from .base import BaseDocumentStore +from .base import BaseDocumentStore, BaseDocumentStoreEnv MAX_DOCS_TO_GET = 10**4 +class LanceDBEnv(BaseDocumentStoreEnv): + LANCEDB_URI: str = "lancedb" + + class LanceDBDocumentStore(BaseDocumentStore): """LancdDB document store which support full-text search query""" @@ -23,6 +29,12 @@ def __init__(self, path: str = "lancedb", collection_name: str = "docstore"): self.collection_name = collection_name self.db_connection = lancedb.connect(self.db_uri) # type: ignore + @classmethod + def from_env(cls, overriding_envvars: Dict[str, Any] | None = None) -> Self: + envs = LanceDBEnv.override_envs(overriding_envvars) + print(f"Loaded LanceDB with envs={envs}") + return cls(path=envs.LANCEDB_URI, collection_name=envs.DOCSTORE_COLLECTION_NAME) + def add( self, docs: Union[Document, List[Document]], diff --git a/libs/kotaemon/kotaemon/storages/docstores/simple_file.py b/libs/kotaemon/kotaemon/storages/docstores/simple_file.py index e4f560242..7afcdd753 100644 --- a/libs/kotaemon/kotaemon/storages/docstores/simple_file.py +++ b/libs/kotaemon/kotaemon/storages/docstores/simple_file.py @@ -1,11 +1,18 @@ from pathlib import Path -from typing import List, Optional, Union +from typing import Any, Dict, List, Optional, Union + +from typing_extensions import Self from kotaemon.base import Document +from .base import BaseDocumentStoreEnv from .in_memory import InMemoryDocumentStore +class SimpleFileDocumentStoreEnv(BaseDocumentStoreEnv): + SIMPLE_FILE_PATH: str = "files" + + class SimpleFileDocumentStore(InMemoryDocumentStore): """Improve InMemoryDocumentStore by auto saving whenever the corpus is changed""" @@ -19,6 +26,14 @@ def __init__(self, path: str | Path, collection_name: str = "default"): if self._save_path.is_file(): self.load(self._save_path) + @classmethod + def from_env(cls, overriding_envvars: Dict[str, Any] | None = None) -> Self: + envs = SimpleFileDocumentStoreEnv.override_envs(overriding_envvars) + print(f"Loaded SimpleFile with envs={envs}") + return cls( + path=envs.SIMPLE_FILE_PATH, collection_name=envs.DOCSTORE_COLLECTION_NAME + ) + def get(self, ids: Union[List[str], str]) -> List[Document]: """Get document by id""" if not isinstance(ids, list): @@ -58,11 +73,3 @@ def drop(self): """Drop the document store""" super().drop() self._save_path.unlink(missing_ok=True) - - def __persist_flow__(self): - from theflow.utils.modules import serialize - - return { - "path": serialize(self._path), - "collection_name": self._collection_name, - } diff --git a/libs/kotaemon/kotaemon/storages/vectorstores/__init__.py b/libs/kotaemon/kotaemon/storages/vectorstores/__init__.py index 1cb85410d..dc7515778 100644 --- a/libs/kotaemon/kotaemon/storages/vectorstores/__init__.py +++ b/libs/kotaemon/kotaemon/storages/vectorstores/__init__.py @@ -1,5 +1,6 @@ from .base import BaseVectorStore from .chroma import ChromaVectorStore +from .factory import MP_VENDOR_CLS, VectorStoreFactory, VectorStoreVendor from .in_memory import InMemoryVectorStore from .lancedb import LanceDBVectorStore from .milvus import MilvusVectorStore @@ -14,4 +15,7 @@ "LanceDBVectorStore", "MilvusVectorStore", "QdrantVectorStore", + "VectorStoreVendor", + "VectorStoreFactory", + "MP_VENDOR_CLS", ] diff --git a/libs/kotaemon/kotaemon/storages/vectorstores/base.py b/libs/kotaemon/kotaemon/storages/vectorstores/base.py index 7d13c9de2..9a7a761e8 100644 --- a/libs/kotaemon/kotaemon/storages/vectorstores/base.py +++ b/libs/kotaemon/kotaemon/storages/vectorstores/base.py @@ -1,17 +1,34 @@ from __future__ import annotations from abc import ABC, abstractmethod -from typing import Any, Optional +from typing import Any, Dict, Optional from llama_index.core.schema import NodeRelationship, RelatedNodeInfo from llama_index.core.vector_stores.types import BasePydanticVectorStore from llama_index.core.vector_stores.types import VectorStore as LIVectorStore from llama_index.core.vector_stores.types import VectorStoreQuery +from pydantic_settings import BaseSettings +from typing_extensions import Self from kotaemon.base import DocumentWithEmbedding +class BaseVectorStoreEnv(BaseSettings): + VECTORSTORE_COLLECTION_NAME: str = "vectorstore" + + @classmethod + def override_envs(cls, overriding_envvars: Dict[str, Any] | None = None) -> Self: + settings = cls().model_dump() + settings.update(overriding_envvars or {}) + return cls.model_validate(settings) + + class BaseVectorStore(ABC): + @classmethod + @abstractmethod + def from_env(cls, overriding_envvars: Dict[str, Any] | None = None) -> Self: + ... + @abstractmethod def __init__(self, *args, **kwargs): ... diff --git a/libs/kotaemon/kotaemon/storages/vectorstores/chroma.py b/libs/kotaemon/kotaemon/storages/vectorstores/chroma.py index f8609d4da..8bb3e7abe 100644 --- a/libs/kotaemon/kotaemon/storages/vectorstores/chroma.py +++ b/libs/kotaemon/kotaemon/storages/vectorstores/chroma.py @@ -1,13 +1,35 @@ from typing import Any, Dict, List, Optional, Type, cast from llama_index.vector_stores.chroma import ChromaVectorStore as LIChromaVectorStore +from typing_extensions import Self -from .base import LlamaIndexVectorStore +from .base import BaseVectorStoreEnv, LlamaIndexVectorStore + + +class ChromaEnv(BaseVectorStoreEnv): + CHROMA_PATH: str = "./chroma" + CHROMA_HOST: str = "localhost" + CHROMA_PORT: str = "8000" + CHROMA_SSL: bool = False + CHROMA_HEADERS: Optional[Dict[str, str]] = None class ChromaVectorStore(LlamaIndexVectorStore): _li_class: Type[LIChromaVectorStore] = LIChromaVectorStore + @classmethod + def from_env(cls, overriding_envvars: Dict[str, Any] | None = None) -> Self: + envs = ChromaEnv.override_envs(overriding_envvars) + print(f"Loaded Chroma with envs={envs}") + return cls( + path=envs.CHROMA_PATH, + collection_name=envs.VECTORSTORE_COLLECTION_NAME, + host=envs.CHROMA_HOST, + port=envs.CHROMA_PORT, + ssl=envs.CHROMA_SSL, + headers=envs.CHROMA_HEADERS, + ) + def __init__( self, path: str = "./chroma", diff --git a/libs/kotaemon/kotaemon/storages/vectorstores/factory.py b/libs/kotaemon/kotaemon/storages/vectorstores/factory.py new file mode 100644 index 000000000..f1315cf5e --- /dev/null +++ b/libs/kotaemon/kotaemon/storages/vectorstores/factory.py @@ -0,0 +1,75 @@ +"""Vectorstore vendor registry and factory. + +Usage:: + + from kotaemon.storages.vectorstores.factory import ( + VectorStoreFactory, + VectorStoreVendor, + ) + + cls = VectorStoreFactory.get_cls(VectorStoreVendor.CHROMA) + store = cls(path="/data/vectorstore", collection_name="default") +""" + +from __future__ import annotations + +from enum import Enum + +from .base import BaseVectorStore +from .chroma import ChromaVectorStore +from .in_memory import InMemoryVectorStore +from .lancedb import LanceDBVectorStore +from .milvus import MilvusVectorStore +from .qdrant import QdrantVectorStore +from .simple_file import SimpleFileVectorStore + + +class VectorStoreVendor(str, Enum): + """Supported vectorstore back-ends.""" + + CHROMA = "ChromaVectorStore" + LANCEDB = "LanceDBVectorStore" + IN_MEMORY = "InMemoryVectorStore" + MILVUS = "MilvusVectorStore" + QDRANT = "QdrantVectorStore" + SIMPLE_FILE = "SimpleFileVectorStore" + + +MP_VENDOR_CLS: dict[VectorStoreVendor, type[BaseVectorStore]] = { + VectorStoreVendor.CHROMA: ChromaVectorStore, + VectorStoreVendor.LANCEDB: LanceDBVectorStore, + VectorStoreVendor.IN_MEMORY: InMemoryVectorStore, + VectorStoreVendor.MILVUS: MilvusVectorStore, + VectorStoreVendor.QDRANT: QdrantVectorStore, + VectorStoreVendor.SIMPLE_FILE: SimpleFileVectorStore, +} + + +class VectorStoreFactory: + """Resolve a :class:`VectorStoreVendor` to its implementation class.""" + + @staticmethod + def get_cls( + vendor: VectorStoreVendor | str, + ) -> type[BaseVectorStore]: + """Return the class for *vendor*, coercing bare strings. + + Args: + vendor: A :class:`VectorStoreVendor` value or a plain string + matching one (e.g. ``"ChromaVectorStore"``). + + Returns: + The concrete :class:`BaseVectorStore` subclass. + + Raises: + ValueError: When *vendor* does not match any registered class. + """ + key = VectorStoreVendor(vendor) + if key not in MP_VENDOR_CLS: + raise ValueError(f"Invalid vectorstore vendor: {vendor!r}") + return MP_VENDOR_CLS[key] + + @staticmethod + def supported_vendors() -> list[VectorStoreVendor]: + """Return all registered vectorstore vendors.""" + return list(MP_VENDOR_CLS.keys()) diff --git a/libs/kotaemon/kotaemon/storages/vectorstores/in_memory.py b/libs/kotaemon/kotaemon/storages/vectorstores/in_memory.py index 9e01766bf..136a4b2b5 100644 --- a/libs/kotaemon/kotaemon/storages/vectorstores/in_memory.py +++ b/libs/kotaemon/kotaemon/storages/vectorstores/in_memory.py @@ -1,9 +1,10 @@ """Simple vector store index.""" -from typing import Any, Optional, Type +from typing import Any, Dict, Optional, Type import fsspec from llama_index.core.vector_stores import SimpleVectorStore as LISimpleVectorStore from llama_index.core.vector_stores.simple import SimpleVectorStoreData +from typing_extensions import Self from .base import LlamaIndexVectorStore @@ -28,6 +29,10 @@ def __init__( **kwargs, ) + @classmethod + def from_env(cls, _overriding_envvars: Dict[str, Any] | None = None) -> Self: + return cls() + def save( self, save_path: str, diff --git a/libs/kotaemon/kotaemon/storages/vectorstores/lancedb.py b/libs/kotaemon/kotaemon/storages/vectorstores/lancedb.py index e0fa4c06d..dbd312b5e 100644 --- a/libs/kotaemon/kotaemon/storages/vectorstores/lancedb.py +++ b/libs/kotaemon/kotaemon/storages/vectorstores/lancedb.py @@ -1,10 +1,11 @@ -from typing import Any, List, Type, cast +from typing import Any, Dict, List, Type, cast from llama_index.core.vector_stores.types import MetadataFilters from llama_index.vector_stores.lancedb import LanceDBVectorStore as LILanceDBVectorStore from llama_index.vector_stores.lancedb import base as base_lancedb +from typing_extensions import Self -from .base import LlamaIndexVectorStore +from .base import BaseVectorStoreEnv, LlamaIndexVectorStore # custom monkey patch for LanceDB original_to_lance_filter = base_lancedb._to_lance_filter @@ -27,9 +28,21 @@ def custom_to_lance_filter( base_lancedb._to_lance_filter = custom_to_lance_filter +class LanceDBEnv(BaseVectorStoreEnv): + LANCEDB_URI: str = "lancedb" + + class LanceDBVectorStore(LlamaIndexVectorStore): _li_class: Type[LILanceDBVectorStore] = LILanceDBVectorStore + @classmethod + def from_env(cls, overriding_envvars: Dict[str, Any] | None = None) -> Self: + envs = LanceDBEnv.override_envs(overriding_envvars) + print(f"Loaded LanceDB with envs={envs}") + return cls( + path=envs.LANCEDB_URI, collection_name=envs.VECTORSTORE_COLLECTION_NAME + ) + def __init__( self, path: str = "./lancedb", diff --git a/libs/kotaemon/kotaemon/storages/vectorstores/milvus.py b/libs/kotaemon/kotaemon/storages/vectorstores/milvus.py index 1725743cf..39d300947 100644 --- a/libs/kotaemon/kotaemon/storages/vectorstores/milvus.py +++ b/libs/kotaemon/kotaemon/storages/vectorstores/milvus.py @@ -1,14 +1,31 @@ import os -from typing import Any, Optional, cast +from typing import Any, Dict, Optional, cast + +from typing_extensions import Self from kotaemon.base import DocumentWithEmbedding -from .base import LlamaIndexVectorStore +from .base import BaseVectorStoreEnv, LlamaIndexVectorStore + + +class MilvusEnv(BaseVectorStoreEnv): + MILVUS_URI: str = "./milvus.db" + MILVUS_TOKEN: Optional[str] = None class MilvusVectorStore(LlamaIndexVectorStore): _li_class = None + @classmethod + def from_env(cls, overriding_envvars: Dict[str, Any] | None = None) -> Self: + envs = MilvusEnv.override_envs(overriding_envvars) + print(f"Loaded Milvus with envs={envs}") + return cls( + uri=envs.MILVUS_URI, + collection_name=envs.VECTORSTORE_COLLECTION_NAME, + token=envs.MILVUS_TOKEN, + ) + def _get_li_class(self): try: from llama_index.vector_stores.milvus import ( diff --git a/libs/kotaemon/kotaemon/storages/vectorstores/qdrant.py b/libs/kotaemon/kotaemon/storages/vectorstores/qdrant.py index ea9811a5f..0baa4cc8b 100644 --- a/libs/kotaemon/kotaemon/storages/vectorstores/qdrant.py +++ b/libs/kotaemon/kotaemon/storages/vectorstores/qdrant.py @@ -1,11 +1,28 @@ -from typing import Any, List, Optional, cast +from typing import Any, Dict, List, Optional, cast -from .base import LlamaIndexVectorStore +from typing_extensions import Self + +from .base import BaseVectorStoreEnv, LlamaIndexVectorStore + + +class QdrantEnv(BaseVectorStoreEnv): + QDRANT_URL: str = "http://localhost:6333" + QDRANT_API_KEY: Optional[str] = None class QdrantVectorStore(LlamaIndexVectorStore): _li_class = None + @classmethod + def from_env(cls, overriding_envvars: Dict[str, Any] | None = None) -> Self: + envs = QdrantEnv.override_envs(overriding_envvars) + print(f"Loaded Qdrant with envs={envs}") + return cls( + collection_name=envs.VECTORSTORE_COLLECTION_NAME, + url=envs.QDRANT_URL, + api_key=envs.QDRANT_API_KEY, + ) + def _get_li_class(self): try: from llama_index.vector_stores.qdrant import ( diff --git a/libs/kotaemon/kotaemon/storages/vectorstores/simple_file.py b/libs/kotaemon/kotaemon/storages/vectorstores/simple_file.py index b96b67c0e..231f28581 100644 --- a/libs/kotaemon/kotaemon/storages/vectorstores/simple_file.py +++ b/libs/kotaemon/kotaemon/storages/vectorstores/simple_file.py @@ -1,14 +1,19 @@ """Simple file vector store index.""" from pathlib import Path -from typing import Any, Optional, Type +from typing import Any, Dict, Optional, Type import fsspec from llama_index.core.vector_stores import SimpleVectorStore as LISimpleVectorStore from llama_index.core.vector_stores.simple import SimpleVectorStoreData +from typing_extensions import Self from kotaemon.base import DocumentWithEmbedding -from .base import LlamaIndexVectorStore +from .base import BaseVectorStoreEnv, LlamaIndexVectorStore + + +class SimpleFileVectorStoreEnv(BaseVectorStoreEnv): + SIMPLE_FILE_PATH: str = "files" class SimpleFileVectorStore(LlamaIndexVectorStore): @@ -17,6 +22,14 @@ class SimpleFileVectorStore(LlamaIndexVectorStore): _li_class: Type[LISimpleVectorStore] = LISimpleVectorStore store_text: bool = False + @classmethod + def from_env(cls, overriding_envvars: Dict[str, Any] | None = None) -> Self: + envs = SimpleFileVectorStoreEnv.override_envs(overriding_envvars) + print(f"Loaded SimpleFile with envs={envs}") + return cls( + path=envs.SIMPLE_FILE_PATH, collection_name=envs.VECTORSTORE_COLLECTION_NAME + ) + def __init__( self, path: str | Path, diff --git a/libs/kotaemon/tests/simple_pipeline.py b/libs/kotaemon/tests/simple_pipeline.py deleted file mode 100644 index a133f18da..000000000 --- a/libs/kotaemon/tests/simple_pipeline.py +++ /dev/null @@ -1,33 +0,0 @@ -import tempfile -from typing import List - -from kotaemon.base import BaseComponent, LLMInterface, lazy -from kotaemon.embeddings import LCAzureOpenAIEmbeddings -from kotaemon.indices import VectorRetrieval -from kotaemon.llms import AzureOpenAI -from kotaemon.storages import ChromaVectorStore - - -class Pipeline(BaseComponent): - llm: AzureOpenAI = AzureOpenAI.withx( - azure_endpoint="https://test.openai.azure.com/", - openai_api_key="some-key", - openai_api_version="2023-03-15-preview", - deployment_name="gpt35turbo", - temperature=0, - request_timeout=60, - ) - - retrieving_pipeline: VectorRetrieval = VectorRetrieval.withx( - vector_store=lazy(ChromaVectorStore).withx(path=str(tempfile.mkdtemp())), - embedding=LCAzureOpenAIEmbeddings.withx( - model="text-embedding-ada-002", - deployment="embedding-deployment", - azure_endpoint="https://test.openai.azure.com/", - openai_api_key="some-key", - ), - ) - - def run(self, text: str) -> LLMInterface: - matched_texts: List[str] = self.retrieving_pipeline(text) - return self.llm("\n".join(matched_texts)) diff --git a/libs/kotaemon/tests/test_llms_completion_models.py b/libs/kotaemon/tests/test_llms_completion_models.py index dff206f6d..66bcbd2b5 100644 --- a/libs/kotaemon/tests/test_llms_completion_models.py +++ b/libs/kotaemon/tests/test_llms_completion_models.py @@ -88,4 +88,4 @@ def test_llamacpp_model(): # test initialization model = LlamaCpp(model_path=str(weight_path), vocab_only=True) - assert isinstance(model._obj, model._get_lc_class()) + assert isinstance(model._lc_obj, model._get_lc_class()) diff --git a/libs/kotaemon/tests/test_promptui.py b/libs/kotaemon/tests/test_promptui.py deleted file mode 100644 index 9d9b8e6ca..000000000 --- a/libs/kotaemon/tests/test_promptui.py +++ /dev/null @@ -1,66 +0,0 @@ -from kotaemon.contribs.promptui.config import export_pipeline_to_config -from kotaemon.contribs.promptui.export import export_from_dict -from kotaemon.contribs.promptui.ui import build_from_dict - -from .simple_pipeline import Pipeline - - -class TestPromptConfig: - def test_export_prompt_config(self): - """Test if the prompt config is exported correctly""" - pipeline = Pipeline() - config_dict = export_pipeline_to_config(pipeline) - config = list(config_dict.values())[0] - - assert "inputs" in config, "inputs should be in config" - assert "text" in config["inputs"], "inputs should have config" - - assert "params" in config, "params should be in config" - assert "llm.deployment_name" in config["params"] - assert "llm.azure_endpoint" in config["params"] - assert "llm.openai_api_key" in config["params"] - assert "llm.openai_api_version" in config["params"] - assert "llm.request_timeout" in config["params"] - assert "llm.temperature" in config["params"] - - -class TestPromptUI: - def test_uigeneration(self): - """Test if the gradio UI is exposed without any problem""" - pipeline = Pipeline() - config = export_pipeline_to_config(pipeline) - - build_from_dict(config) - - -class TestExport: - def test_export(self, tmp_path): - """Test if the export functionality works without error""" - from pathlib import Path - - import yaml - from theflow.storage import storage - - config_path = tmp_path / "config.yaml" - pipeline = Pipeline() - Path(storage.url(pipeline.config.store_result)).mkdir( - parents=True, exist_ok=True - ) - - config_dict = export_pipeline_to_config(pipeline) - pipeline_name = list(config_dict.keys())[0] - - config_dict[pipeline_name]["logs"] = { - "sheet1": { - "inputs": [{"name": "text", "step": ".", "variable": "text"}], - "outputs": [{"name": "answer", "step": "."}], - }, - } - with open(config_path, "w") as f: - yaml.safe_dump(config_dict, f) - - export_from_dict( - config=str(config_path), - pipeline=pipeline_name, - output_path=str(tmp_path / "exported.xlsx"), - ) diff --git a/libs/ktem/ktem/app.py b/libs/ktem/ktem/app.py index 25b293e38..69a90cbc2 100644 --- a/libs/ktem/ktem/app.py +++ b/libs/ktem/ktem/app.py @@ -1,17 +1,17 @@ import os from pathlib import Path -from typing import Optional +from typing import Any, Optional import gradio as gr import pluggy from ktem import extension_protocol from ktem.assets import PDFJS_PREBUILT_DIR, KotaemonTheme +from ktem.collections import CollectionManager from ktem.components import reasonings from ktem.exceptions import HookAlreadyDeclared, HookNotDeclared -from ktem.index import IndexManager +from ktem.reasoning.registry import get_reasoning_cls from ktem.settings import BaseSettingGroup, SettingGroup, SettingReasoningGroup -from theflow.settings import settings -from theflow.utils.modules import import_dotted_string +from ktem.settings_config import app_settings as settings BASE_PATH = os.environ.get("GR_FILE_ROOT_PATH", "") @@ -38,10 +38,10 @@ class BaseApp: public_events: list[str] = [] def __init__(self): - self.dev_mode = getattr(settings, "KH_MODE", "") == "dev" - self.app_name = getattr(settings, "KH_APP_NAME", "Kotaemon") - self.app_version = getattr(settings, "KH_APP_VERSION", "") - self.f_user_management = getattr(settings, "KH_FEATURE_USER_MANAGEMENT", False) + self.dev_mode = settings.KH_MODE == "dev" + self.app_name = settings.KH_APP_NAME + self.app_version = settings.KH_APP_VERSION or "" + self.f_user_management = settings.KH_FEATURE_USER_MANAGEMENT self._theme = KotaemonTheme() dir_assets = Path(__file__).parent / "assets" @@ -71,6 +71,10 @@ def __init__(self): self._callbacks: dict[str, list] = {} self._events: dict[str, list] = {} + # Set by App.ui() — declared here so type checkers see them on BaseApp + self.chat_page: Any = None + self.tabs: Any = None + self.register_extensions() self.register_reasonings() self.initialize_indices() @@ -83,10 +87,10 @@ def __init__(self): def initialize_indices(self): """Create the index manager, start indices, and register to app settings""" - self.index_manager = IndexManager(self) - self.index_manager.on_application_startup() + self.collection_manager = CollectionManager(self) + self.collection_manager.on_application_startup() - for index in self.index_manager.indices: + for index in self.collection_manager.collections: options = index.get_user_settings() self.default_settings.index.options[index.id] = BaseSettingGroup( settings=options @@ -94,14 +98,14 @@ def initialize_indices(self): def register_reasonings(self): """Register the reasoning components from app settings""" - if getattr(settings, "KH_REASONINGS", None) is None: + if not settings.KH_REASONINGS: return for value in settings.KH_REASONINGS: - reasoning_cls = import_dotted_string(value, safe=False) + reasoning_cls = get_reasoning_cls(value) rid = reasoning_cls.get_info()["id"] reasonings[rid] = reasoning_cls - options = reasoning_cls().get_user_settings() + options = reasoning_cls.get_user_settings() self.default_settings.reasoning.options[rid] = BaseSettingGroup( settings=options ) @@ -255,7 +259,7 @@ class BasePage: public_events: list[str] = [] - def __init__(self, app): + def __init__(self, app: BaseApp): self._app = app def on_building_ui(self): diff --git a/libs/ktem/ktem/collections/__init__.py b/libs/ktem/ktem/collections/__init__.py new file mode 100644 index 000000000..a153cd83c --- /dev/null +++ b/libs/ktem/ktem/collections/__init__.py @@ -0,0 +1,3 @@ +from .manager import CollectionManager + +__all__ = ["CollectionManager"] diff --git a/libs/ktem/ktem/index/base.py b/libs/ktem/ktem/collections/base.py similarity index 95% rename from libs/ktem/ktem/index/base.py rename to libs/ktem/ktem/collections/base.py index c3e666d7b..0ed57432b 100644 --- a/libs/ktem/ktem/index/base.py +++ b/libs/ktem/ktem/collections/base.py @@ -5,13 +5,14 @@ if TYPE_CHECKING: from ktem.app import BasePage - from kotaemon.base import BaseComponent + from kotaemon.indices.indexing.base import BaseIndexing + from kotaemon.indices.retriever.base import BaseRetriever logger = logging.getLogger(__name__) -class BaseIndex(abc.ABC): +class BaseCollection(abc.ABC): """The base class for the index The index is responsible for storing information in a searchable manner, and @@ -111,7 +112,7 @@ def get_admin_settings(cls) -> dict: @abc.abstractmethod def get_indexing_pipeline( self, settings: dict, user_id: Optional[int] - ) -> "BaseComponent": + ) -> "BaseIndexing": """Return the indexing pipeline that populates the entities into the index Args: @@ -127,6 +128,6 @@ def get_indexing_pipeline( def get_retriever_pipelines( self, settings: dict, user_id: int, selected: Any = None - ) -> list["BaseComponent"]: + ) -> list["BaseRetriever"]: """Return the retriever pipelines to retrieve the entity from the index""" return [] diff --git a/libs/ktem/ktem/index/file/__init__.py b/libs/ktem/ktem/collections/file/__init__.py similarity index 100% rename from libs/ktem/ktem/index/file/__init__.py rename to libs/ktem/ktem/collections/file/__init__.py diff --git a/libs/ktem/ktem/index/file/exceptions.py b/libs/ktem/ktem/collections/file/exceptions.py similarity index 100% rename from libs/ktem/ktem/index/file/exceptions.py rename to libs/ktem/ktem/collections/file/exceptions.py diff --git a/libs/ktem/ktem/index/file/index.py b/libs/ktem/ktem/collections/file/index.py similarity index 53% rename from libs/ktem/ktem/index/file/index.py rename to libs/ktem/ktem/collections/file/index.py index 7409f3cd1..bbdda11ef 100644 --- a/libs/ktem/ktem/index/file/index.py +++ b/libs/ktem/ktem/collections/file/index.py @@ -1,27 +1,28 @@ -import uuid -from datetime import datetime +from pathlib import Path from typing import Any, Optional, Type -from ktem.components import filestorage_path, get_docstore, get_vectorstore +from ktem.collections.base import BaseCollection +from ktem.collections.file.stores import ( + SqlAlchemyChunkRelationStore, + SqlAlchemyFileSourceStore, +) +from ktem.collections.registry import get_pipeline_cls +from ktem.components import get_docstore, get_vectorstore from ktem.db.engine import engine -from ktem.index.base import BaseIndex -from sqlalchemy import JSON, Column, DateTime, Integer, String, UniqueConstraint -from sqlalchemy.ext.declarative import declarative_base -from sqlalchemy.ext.mutable import MutableDict -from theflow.settings import settings as flowsettings -from theflow.utils.modules import import_dotted_string -from tzlocal import get_localzone - +from ktem.db.models.file_index import ( + get_file_chunk_relation_model, + get_file_group_model, + get_file_source_model, +) +from ktem.embeddings.manager import embedding_models_manager +from ktem.settings_config import app_settings as flowsettings + +from kotaemon.indices.indexing import BaseIndexing +from kotaemon.indices.retriever import BaseRetriever from kotaemon.storages import BaseDocumentStore, BaseVectorStore -from .base import BaseFileIndexIndexing, BaseFileIndexRetriever - - -def generate_uuid(): - return str(uuid.uuid4()) - -class FileIndex(BaseIndex): +class FileIndex(BaseCollection): """ File index to store and allow retrieval of files @@ -38,8 +39,8 @@ class FileIndex(BaseIndex): def __init__(self, app, id: int, name: str, config: dict): super().__init__(app, id, name, config) - self._indexing_pipeline_cls: Type[BaseFileIndexIndexing] - self._retriever_pipeline_cls: list[Type[BaseFileIndexRetriever]] + self._indexing_pipeline_cls: Type[BaseIndexing] + self._retriever_pipeline_cls: list[Type[BaseRetriever]] self._selector_ui_cls: Type self._selector_ui: Any = None self._index_ui_cls: Type @@ -57,102 +58,14 @@ def _setup_resources(self): - Document store - File storage path """ - Base = declarative_base() - - if self.config.get("private", False): - Source = type( - "Source", - (Base,), - { - "__tablename__": f"index__{self.id}__source", - "__table_args__": ( - UniqueConstraint("name", "user", name="_name_user_uc"), - ), - "id": Column( - String, - primary_key=True, - default=lambda: str(uuid.uuid4()), - unique=True, - ), - "name": Column(String), - "path": Column(String), - "size": Column(Integer, default=0), - "date_created": Column( - DateTime(timezone=True), default=datetime.now(get_localzone()) - ), - "user": Column(String, default=""), - "note": Column( - MutableDict.as_mutable(JSON), # type: ignore - default={}, - ), - }, - ) - else: - Source = type( - "Source", - (Base,), - { - "__tablename__": f"index__{self.id}__source", - "id": Column( - String, - primary_key=True, - default=lambda: str(uuid.uuid4()), - unique=True, - ), - "name": Column(String, unique=True), - "path": Column(String), - "size": Column(Integer, default=0), - "date_created": Column( - DateTime(timezone=True), default=datetime.now(get_localzone()) - ), - "user": Column(String, default=""), - "note": Column( - MutableDict.as_mutable(JSON), # type: ignore - default={}, - ), - }, - ) - Index = type( - "IndexTable", - (Base,), - { - "__tablename__": f"index__{self.id}__index", - "id": Column(Integer, primary_key=True, autoincrement=True), - "source_id": Column(String), - "target_id": Column(String), - "relation_type": Column(String), - "user": Column(String, default=""), - }, - ) - FileGroup = type( - "FileGroupTable", - (Base,), - { - "__tablename__": f"index__{self.id}__group", - "__table_args__": ( - UniqueConstraint("name", "user", name="_name_user_uc"), - ), - "id": Column( - String, - primary_key=True, - default=lambda: str(uuid.uuid4()), - unique=True, - ), - "date_created": Column( - DateTime(timezone=True), default=datetime.now(get_localzone()) - ), - "name": Column(String), - "user": Column(String, default=""), - "data": Column( - MutableDict.as_mutable(JSON), # type: ignore - default={"files": []}, - ), - }, - ) + private = self.config.get("private", False) + Source = get_file_source_model(self.id, private=private) + Index = get_file_chunk_relation_model(self.id) + FileGroup = get_file_group_model(self.id) self._vs: BaseVectorStore = get_vectorstore(f"index_{self.id}") self._docstore: BaseDocumentStore = get_docstore(f"index_{self.id}") - self._fs_path = filestorage_path / f"index_{self.id}" + self._fs_path = Path(flowsettings.KH_FILESTORAGE_PATH) / f"index_{self.id}" self._resources = { "Source": Source, "Index": Index, @@ -175,24 +88,24 @@ def _setup_indexing_cls(self): - The default .pipelines.IndexDocumentPipeline """ if "FILE_INDEX_PIPELINE" in self.config: - self._indexing_pipeline_cls = import_dotted_string( - self.config["FILE_INDEX_PIPELINE"], safe=False + self._indexing_pipeline_cls = get_pipeline_cls( + self.config["FILE_INDEX_PIPELINE"] ) return if hasattr(flowsettings, f"FILE_INDEX_{self.id}_PIPELINE"): - self._indexing_pipeline_cls = import_dotted_string( - getattr(flowsettings, f"FILE_INDEX_{self.id}_PIPELINE"), safe=False + self._indexing_pipeline_cls = get_pipeline_cls( + getattr(flowsettings, f"FILE_INDEX_{self.id}_PIPELINE") ) return if hasattr(flowsettings, "FILE_INDEX_PIPELINE"): - self._indexing_pipeline_cls = import_dotted_string( - getattr(flowsettings, "FILE_INDEX_PIPELINE"), safe=False + self._indexing_pipeline_cls = get_pipeline_cls( + flowsettings.FILE_INDEX_PIPELINE # type: ignore[attr-defined] ) return - from .pipelines import IndexDocumentPipeline + from .indexing import IndexDocumentPipeline self._indexing_pipeline_cls = IndexDocumentPipeline @@ -210,14 +123,14 @@ def _setup_retriever_cls(self): """ if "FILE_INDEX_RETRIEVER_PIPELINES" in self.config: self._retriever_pipeline_cls = [ - import_dotted_string(each, safe=False) + get_pipeline_cls(each) for each in self.config["FILE_INDEX_RETRIEVER_PIPELINES"] ] return if hasattr(flowsettings, f"FILE_INDEX_{self.id}_RETRIEVER_PIPELINES"): self._retriever_pipeline_cls = [ - import_dotted_string(each, safe=False) + get_pipeline_cls(each) for each in getattr( flowsettings, f"FILE_INDEX_{self.id}_RETRIEVER_PIPELINES" ) @@ -225,86 +138,16 @@ def _setup_retriever_cls(self): return if hasattr(flowsettings, "FILE_INDEX_RETRIEVER_PIPELINES"): + pipelines = flowsettings.FILE_INDEX_RETRIEVER_PIPELINES # type: ignore self._retriever_pipeline_cls = [ - import_dotted_string(each, safe=False) - for each in getattr(flowsettings, "FILE_INDEX_RETRIEVER_PIPELINES") + get_pipeline_cls(each) for each in pipelines ] return - from .pipelines import DocumentRetrievalPipeline + from .retriever import DocumentRetrievalPipeline self._retriever_pipeline_cls = [DocumentRetrievalPipeline] - def _setup_file_selector_ui_cls(self): - """Retrieve the file selector UI for the file index - - There can be multiple retriever classes. - - The retriever classes will is retrieved from the following order. Stop at the - first order found: - - `FILE_INDEX_SELECTOR_UI` in self.config - - `FILE_INDEX_{id}_SELECTOR_UI` in the flowsettings - - `FILE_INDEX_SELECTOR_UI` in the flowsettings - - The default .ui.FileSelector - """ - if "FILE_INDEX_SELECTOR_UI" in self.config: - self._selector_ui_cls = import_dotted_string( - self.config["FILE_INDEX_SELECTOR_UI"], safe=False - ) - return - - if hasattr(flowsettings, f"FILE_INDEX_{self.id}_SELECTOR_UI"): - self._selector_ui_cls = import_dotted_string( - getattr(flowsettings, f"FILE_INDEX_{self.id}_SELECTOR_UI"), - safe=False, - ) - return - - if hasattr(flowsettings, "FILE_INDEX_SELECTOR_UI"): - self._selector_ui_cls = import_dotted_string( - getattr(flowsettings, "FILE_INDEX_SELECTOR_UI"), safe=False - ) - return - - from .ui import FileSelector - - self._selector_ui_cls = FileSelector - - def _setup_file_index_ui_cls(self): - """Retrieve the Index UI class - - There can be multiple retriever classes. - - The retriever classes will is retrieved from the following order. Stop at the - first order found: - - `FILE_INDEX_UI` in self.config - - `FILE_INDEX_{id}_UI` in the flowsettings - - `FILE_INDEX_UI` in the flowsettings - - The default .ui.FileIndexPage - """ - if "FILE_INDEX_UI" in self.config: - self._index_ui_cls = import_dotted_string( - self.config["FILE_INDEX_UI"], safe=False - ) - return - - if hasattr(flowsettings, f"FILE_INDEX_{self.id}_UI"): - self._index_ui_cls = import_dotted_string( - getattr(flowsettings, f"FILE_INDEX_{self.id}_UI"), - safe=False, - ) - return - - if hasattr(flowsettings, "FILE_INDEX_UI"): - self._index_ui_cls = import_dotted_string( - getattr(flowsettings, "FILE_INDEX_UI"), safe=False - ) - return - - from .ui import FileIndexPage - - self._index_ui_cls = FileIndexPage - def on_create(self): """Create the index for the first time @@ -345,11 +188,13 @@ def on_delete(self): def on_start(self): """Setup the classes and hooks""" + from .ui import FileIndexPage, FileSelector + self._setup_resources() self._setup_indexing_cls() self._setup_retriever_cls() - self._setup_file_index_ui_cls() - self._setup_file_selector_ui_cls() + self._selector_ui_cls = FileSelector + self._index_ui_cls = FileIndexPage def get_selector_component_ui(self): if self._selector_ui is None: @@ -437,7 +282,7 @@ def get_admin_settings(cls): }, } - def get_indexing_pipeline(self, settings, user_id) -> BaseFileIndexIndexing: + def get_indexing_pipeline(self, settings, user_id) -> BaseIndexing: """Define the interface of the indexing pipeline""" prefix = f"index.options.{self.id}." @@ -446,22 +291,54 @@ def get_indexing_pipeline(self, settings, user_id) -> BaseFileIndexIndexing: if key.startswith(prefix): stripped_settings[key[len(prefix) :]] = value - obj = self._indexing_pipeline_cls.get_pipeline(stripped_settings, self.config) - obj.Source = self._resources["Source"] - obj.Index = self._resources["Index"] - obj.VS = self._vs - obj.DS = self._docstore - obj.FSPath = self._fs_path - obj.user_id = user_id - obj.private = self.config.get("private", False) - obj.chunk_size = self.config.get("chunk_size", 0) - obj.chunk_overlap = self.config.get("chunk_overlap", 0) + """ + embedding=embedding_models_manager[ + index_settings.get( + "embedding", + embedding_models_manager.get_default_name(), + ) + ], + run_embedding_in_thread=use_quick_index_mode, + reader_mode=user_settings.get("reader_mode", "default"), + """ + file_source = SqlAlchemyFileSourceStore( + engine=engine, + source_model=self._resources["Source"], + private=self.config.get("private", False), + ) + chunk_relations = SqlAlchemyChunkRelationStore( + engine=engine, + index_model=self._resources["Index"], + ) + params_input = { + "embedding": embedding_models_manager[ + self.config.get( + "embedding", embedding_models_manager.get_default_name() + ) + ], + "run_embedding_in_thread": self.config.get( + "run_embedding_in_thread", False + ), + "reader_mode": self.config.get("reader_mode", "default"), + "file_source": file_source, + "chunk_relations": chunk_relations, + "VS": self._vs, + "DS": self._docstore, + "FSPath": self._fs_path, + "user_id": user_id, + "private": self.config.get("private", False), + "chunk_size": self.config.get("chunk_size", 0), + "chunk_overlap": self.config.get("chunk_overlap", 0), + "vlm_endpoint": flowsettings.KH_VLM_ENDPOINT, + "markdown_output_dir": flowsettings.KH_MARKDOWN_OUTPUT_DIR, + "chunks_output_dir": flowsettings.KH_CHUNKS_OUTPUT_DIR, + } - return obj + return self._indexing_pipeline_cls(**params_input) def get_retriever_pipelines( self, settings: dict, user_id: int, selected: Any = None - ) -> list["BaseFileIndexRetriever"]: + ) -> list["BaseRetriever"]: # retrieval settings prefix = f"index.options.{self.id}." stripped_settings = {} @@ -472,17 +349,24 @@ def get_retriever_pipelines( # transform selected id selected_ids: Optional[list[str]] = self._selector_ui.get_selected_ids(selected) + chunk_relations = SqlAlchemyChunkRelationStore( + engine=engine, + index_model=self._resources["Index"], + ) retrievers = [] for cls in self._retriever_pipeline_cls: - obj = cls.get_pipeline(stripped_settings, self.config, selected_ids) + obj = cls.get_pipeline( + stripped_settings, + self.config, + chunk_relations, + self._vs, + self._docstore, + self._fs_path, + user_id, + selected=selected_ids, + ) if obj is None: continue - obj.Source = self._resources["Source"] - obj.Index = self._resources["Index"] - obj.VS = self._vs - obj.DS = self._docstore - obj.FSPath = self._fs_path - obj.user_id = user_id retrievers.append(obj) return retrievers diff --git a/libs/ktem/ktem/collections/file/indexing/__init__.py b/libs/ktem/ktem/collections/file/indexing/__init__.py new file mode 100644 index 000000000..7a0fa6ca4 --- /dev/null +++ b/libs/ktem/ktem/collections/file/indexing/__init__.py @@ -0,0 +1,11 @@ +from kotaemon.indices.indexing import BaseIndexing +from kotaemon.indices.indexing.impl.rag import IndexPipeline + +from .impl.rag import IndexDocumentPipeline, IndexingUserSettings + +__all__ = [ + "BaseIndexing", + "IndexDocumentPipeline", + "IndexingUserSettings", + "IndexPipeline", +] diff --git a/libs/ktem/ktem/collections/file/indexing/impl/__init__.py b/libs/ktem/ktem/collections/file/indexing/impl/__init__.py new file mode 100644 index 000000000..bd44ad110 --- /dev/null +++ b/libs/ktem/ktem/collections/file/indexing/impl/__init__.py @@ -0,0 +1,3 @@ +from .rag import IndexDocumentPipeline + +__all__ = ["IndexDocumentPipeline"] diff --git a/libs/ktem/ktem/collections/file/indexing/impl/rag.py b/libs/ktem/ktem/collections/file/indexing/impl/rag.py new file mode 100644 index 000000000..c30615694 --- /dev/null +++ b/libs/ktem/ktem/collections/file/indexing/impl/rag.py @@ -0,0 +1,85 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import Literal, TypedDict + +from ktem.collections.file.types import IndexSettings +from ktem.embeddings.manager import embedding_models_manager +from typing_extensions import NotRequired + +from kotaemon.indices.indexing import BaseIndexing +from kotaemon.indices.indexing.impl.rag import ( + IndexDocumentPipeline as _IndexDocumentPipeline, +) + +ReaderMode = Literal[ + "default", + "adobe", + "azure-di", + "docling", + "paddle-struct", + "paddle-vl", +] + + +class IndexingUserSettings(TypedDict): + """User-facing settings consumed by + :meth:`IndexDocumentPipeline.get_pipeline`. + + All keys are optional; defaults are applied inside ``get_pipeline`` + when a key is absent. + """ + + reader_mode: NotRequired[ReaderMode] + quick_index_mode: NotRequired[bool] + + +@dataclass(kw_only=True) +class IndexDocumentPipeline(_IndexDocumentPipeline): + """RAG indexing pipeline wired to ktem managers. + + Extends the kotaemon core with ktem-specific factory and settings. + """ + + @classmethod + def get_user_settings(cls) -> dict: + return { + "reader_mode": { + "name": "File loader", + "value": "default", + "choices": [ + ("Default (open-source)", "default"), + ("Adobe API (figure+table extraction)", "adobe"), + ( + "Azure AI Document Intelligence" " (figure+table extraction)", + "azure-di", + ), + ("Docling (figure+table extraction)", "docling"), + ( + "PaddleOCR PPStructureV3 (table+figure extraction)", + "paddle-struct", + ), + ("PaddleOCR-VL (VLM document parsing)", "paddle-vl"), + ], + "component": "dropdown", + }, + } + + @classmethod + def get_pipeline( + cls, + user_settings: IndexingUserSettings, + index_settings: IndexSettings, + ) -> BaseIndexing: + use_quick_index_mode = user_settings.get("quick_index_mode", False) + print("use_quick_index_mode", use_quick_index_mode) + return cls( + embedding=embedding_models_manager[ + index_settings.get( + "embedding", + embedding_models_manager.get_default_name(), + ) + ], + run_embedding_in_thread=use_quick_index_mode, + reader_mode=user_settings.get("reader_mode", "default"), + ) diff --git a/libs/ktem/ktem/collections/file/retriever/__init__.py b/libs/ktem/ktem/collections/file/retriever/__init__.py new file mode 100644 index 000000000..31071e253 --- /dev/null +++ b/libs/ktem/ktem/collections/file/retriever/__init__.py @@ -0,0 +1,9 @@ +from kotaemon.indices.retriever import BaseRetriever + +from .impl.rag import DocumentRetrievalPipeline, RetrievalUserSettings + +__all__ = [ + "BaseRetriever", + "DocumentRetrievalPipeline", + "RetrievalUserSettings", +] diff --git a/libs/ktem/ktem/collections/file/retriever/impl/__init__.py b/libs/ktem/ktem/collections/file/retriever/impl/__init__.py new file mode 100644 index 000000000..541d13156 --- /dev/null +++ b/libs/ktem/ktem/collections/file/retriever/impl/__init__.py @@ -0,0 +1,3 @@ +from .rag import DocumentRetrievalPipeline + +__all__ = ["DocumentRetrievalPipeline"] diff --git a/libs/ktem/ktem/collections/file/retriever/impl/rag.py b/libs/ktem/ktem/collections/file/retriever/impl/rag.py new file mode 100644 index 000000000..78f3e1523 --- /dev/null +++ b/libs/ktem/ktem/collections/file/retriever/impl/rag.py @@ -0,0 +1,156 @@ +from __future__ import annotations + +import logging +from dataclasses import dataclass +from pathlib import Path +from typing import Literal, Optional, TypedDict + +from decouple import config +from ktem.collections.file.types import IndexSettings +from ktem.embeddings.manager import embedding_models_manager +from ktem.llms.manager import llms + +from kotaemon.indices.rankings import LLMReranking, LLMTrulensScoring +from kotaemon.indices.retriever.impl.rag import ( + DocumentRetrievalPipeline as _DocumentRetrievalPipeline, +) +from kotaemon.indices.stores import ChunkRelationStore +from kotaemon.storages import BaseDocumentStore, BaseVectorStore + +logger = logging.getLogger(__name__) + +RetrievalMode = Literal["vector", "text", "hybrid"] + + +class RetrievalUserSettings(TypedDict): + """User-facing settings consumed by + :meth:`DocumentRetrievalPipeline.get_pipeline`. + + All keys must be present; ``get_user_settings`` provides the + defaults that are persisted before ``get_pipeline`` is called. + """ + + reranking_llm: Optional[str] + num_retrieval: int + retrieval_mode: RetrievalMode + prioritize_table: bool + mmr: bool + use_reranking: bool + use_llm_reranking: bool + + +@dataclass(kw_only=True) +class DocumentRetrievalPipeline(_DocumentRetrievalPipeline): + """RAG retrieval pipeline wired to ktem managers. + + Extends the kotaemon core with ktem-specific factory and settings. + """ + + @classmethod + def get_user_settings(cls) -> dict: + try: + reranking_llm = llms.get_default_name() + reranking_llm_choices = list(llms.options().keys()) + except Exception as e: + logger.error(e) + reranking_llm = None + reranking_llm_choices = [] + + return { + "reranking_llm": { + "name": "LLM for relevant scoring", + "value": reranking_llm, + "component": "dropdown", + "choices": reranking_llm_choices, + "special_type": "llm", + }, + "num_retrieval": { + "name": "Number of document chunks to retrieve", + "value": 10, + "component": "number", + }, + "retrieval_mode": { + "name": "Retrieval mode", + "value": "hybrid", + "choices": ["vector", "text", "hybrid"], + "component": "dropdown", + }, + "prioritize_table": { + "name": "Prioritize table", + "value": False, + "choices": [True, False], + "component": "checkbox", + }, + "mmr": { + "name": "Use MMR", + "value": False, + "choices": [True, False], + "component": "checkbox", + }, + "use_reranking": { + "name": "Use reranking", + "value": True, + "choices": [True, False], + "component": "checkbox", + }, + "use_llm_reranking": { + "name": "Use LLM relevant scoring", + "value": not config("USE_LOW_LLM_REQUESTS", default=False, cast=bool), + "choices": [True, False], + "component": "checkbox", + }, + } + + @classmethod + def get_pipeline( + cls, + user_settings: RetrievalUserSettings, + index_settings: IndexSettings, + chunk_relations: ChunkRelationStore, + VS: BaseVectorStore, + DS: BaseDocumentStore, + FSPath: Path, + user_id: int, + selected: Optional[list] = None, + ) -> "DocumentRetrievalPipeline": + use_llm_reranking = user_settings.get("use_llm_reranking", False) + retriever = cls( + chunk_relations=chunk_relations, + VS=VS, + DS=DS, + FSPath=FSPath, + user_id=user_id, + get_extra_table=user_settings["prioritize_table"], + top_k=user_settings["num_retrieval"], + mmr=user_settings["mmr"], + embedding=embedding_models_manager[ + index_settings.get( + "embedding", embedding_models_manager.get_default_name() + ) + ], + retrieval_mode=user_settings["retrieval_mode"], + llm_scorer=(LLMTrulensScoring() if use_llm_reranking else None), + rerankers=[ + # reranking_models_manager[ + # index_settings.get( + # "reranking", + # reranking_models_manager.get_default_name(), + # ) + # ] + ], + doc_ids=selected, + ) + if not user_settings["use_reranking"]: + retriever.rerankers = [] # type: ignore + + reranking_llm_name: str = ( + user_settings.get("reranking_llm") or llms.get_default_name() + ) + for reranker in retriever.rerankers: + if isinstance(reranker, LLMReranking): + reranker.llm = llms.get(reranking_llm_name, llms.get_default()) + + if retriever.llm_scorer: + retriever.llm_scorer.llm = llms.get(reranking_llm_name, llms.get_default()) + + return retriever diff --git a/libs/ktem/ktem/collections/file/stores.py b/libs/ktem/ktem/collections/file/stores.py new file mode 100644 index 000000000..19906653a --- /dev/null +++ b/libs/ktem/ktem/collections/file/stores.py @@ -0,0 +1,89 @@ +from __future__ import annotations + +from dataclasses import dataclass + +from ktem.db.cruds.file_chunk_relation import FileChunkRelationCRUD +from ktem.db.cruds.file_source import FileSourceCRUD +from ktem.db.models.file_index import BaseFileChunkRelation, BaseFileSource +from sqlalchemy.engine import Engine + +from kotaemon.indices.stores import RelationType + + +@dataclass(kw_only=True) +class SqlAlchemyFileSourceStore: + """SQLAlchemy-backed :class:`~kotaemon.indices.stores.FileSourceStore`. + + Delegates persistence to :class:`~ktem.db.cruds.file_source.FileSourceCRUD`. + """ + + engine: Engine + source_model: type[BaseFileSource] + private: bool = False + + def find_id_by_name(self, name: str, *, user_id: int) -> str | None: + with FileSourceCRUD( + self.engine, self.source_model, private=self.private + ) as crud: + return crud.get_id_by_name(name, user_id=user_id) + + def create_from_url(self, url: str, path_hash: str, *, user_id: int) -> str: + with FileSourceCRUD( + self.engine, self.source_model, private=self.private + ) as crud: + return crud.create_url(url, path_hash, user_id=user_id) + + def create_from_file( + self, name: str, path_hash: str, size: int, *, user_id: int + ) -> str: + with FileSourceCRUD( + self.engine, self.source_model, private=self.private + ) as crud: + return crud.create_file(name, path_hash, size, user_id=user_id) + + def update_note(self, file_id: str, *, tokens: int | None, loader: str) -> None: + with FileSourceCRUD( + self.engine, self.source_model, private=self.private + ) as crud: + crud.update_note(file_id, tokens=tokens, loader=loader) + + def delete_source(self, file_id: str) -> None: + with FileSourceCRUD( + self.engine, self.source_model, private=self.private + ) as crud: + crud.delete(file_id) + + +@dataclass(kw_only=True) +class SqlAlchemyChunkRelationStore: + """SQLAlchemy-backed :class:`~kotaemon.indices.stores.ChunkRelationStore`. + + Delegates persistence to + :class:`~ktem.db.cruds.file_chunk_relation.FileChunkRelationCRUD`. + """ + + engine: Engine + index_model: type[BaseFileChunkRelation] + + def add_relations( + self, + source_id: str, + target_ids: list[str], + relation_type: RelationType, + ) -> None: + with FileChunkRelationCRUD(self.engine, self.index_model) as crud: + crud.add_relations(source_id, target_ids, relation_type) + + def list_target_ids(self, source_id: str, relation_type: RelationType) -> list[str]: + with FileChunkRelationCRUD(self.engine, self.index_model) as crud: + return crud.list_target_ids(source_id, relation_type) + + def list_target_ids_for_sources( + self, source_ids: list[str], relation_type: RelationType + ) -> list[str]: + with FileChunkRelationCRUD(self.engine, self.index_model) as crud: + return crud.list_target_ids_for_sources(source_ids, relation_type) + + def delete_by_source(self, source_id: str) -> tuple[list[str], list[str]]: + with FileChunkRelationCRUD(self.engine, self.index_model) as crud: + return crud.delete_by_source(source_id) diff --git a/libs/ktem/ktem/collections/file/types.py b/libs/ktem/ktem/collections/file/types.py new file mode 100644 index 000000000..c8c168a72 --- /dev/null +++ b/libs/ktem/ktem/collections/file/types.py @@ -0,0 +1,16 @@ +from __future__ import annotations + +from typing import TypedDict + +__all__ = ["IndexSettings"] + + +class IndexSettings(TypedDict, total=False): + """Typed settings stored per-index (managed by the index, not the user). + + Both ``embedding`` and ``reranking`` are optional; callers fall + back to the manager's default when a key is absent. + """ + + embedding: str + reranking: str diff --git a/libs/ktem/ktem/index/file/ui.py b/libs/ktem/ktem/collections/file/ui.py similarity index 99% rename from libs/ktem/ktem/index/file/ui.py rename to libs/ktem/ktem/collections/file/ui.py index 5c7d558c0..c59d54fdd 100644 --- a/libs/ktem/ktem/index/file/ui.py +++ b/libs/ktem/ktem/collections/file/ui.py @@ -14,17 +14,17 @@ from gradio.utils import NamedString from ktem.app import BasePage from ktem.db.engine import engine +from ktem.settings_config import app_settings as flowsettings from ktem.utils.render import Render from sqlalchemy import select from sqlalchemy.orm import Session -from theflow.settings import settings as flowsettings from ...utils.commands import WEB_SEARCH_COMMAND from ...utils.rate_limit import check_rate_limit from .utils import download_arxiv_pdf, is_arxiv_url -KH_DEMO_MODE = getattr(flowsettings, "KH_DEMO_MODE", False) -KH_SSO_ENABLED = getattr(flowsettings, "KH_SSO_ENABLED", False) +KH_DEMO_MODE = flowsettings.KH_DEMO_MODE +KH_SSO_ENABLED = flowsettings.KH_SSO_ENABLED DOWNLOAD_MESSAGE = "Start download" MAX_FILENAME_LENGTH = 20 MAX_FILE_COUNT = 200 @@ -1173,7 +1173,7 @@ def _on_app_created(self): outputs=[self.group_files], ) - def _may_extract_zip(self, files, zip_dir: str): + def _may_extract_zip(self, files, zip_dir: str | Path): """Handle zip files""" zip_files = [file for file in files if file.endswith(".zip")] remaining_files = [file for file in files if not file.endswith("zip")] diff --git a/libs/ktem/ktem/index/file/utils.py b/libs/ktem/ktem/collections/file/utils.py similarity index 100% rename from libs/ktem/ktem/index/file/utils.py rename to libs/ktem/ktem/collections/file/utils.py diff --git a/libs/ktem/ktem/index/manager.py b/libs/ktem/ktem/collections/manager.py similarity index 60% rename from libs/ktem/ktem/index/manager.py rename to libs/ktem/ktem/collections/manager.py index 91e4270ba..1d20136fe 100644 --- a/libs/ktem/ktem/index/manager.py +++ b/libs/ktem/ktem/collections/manager.py @@ -1,15 +1,14 @@ from typing import Optional, Type -from ktem.db.models import engine -from sqlmodel import Session, select -from theflow.settings import settings -from theflow.utils.modules import import_dotted_string +from ktem.collections.registry import get_index_cls +from ktem.db.cruds import IndexCRUD +from ktem.db.engine import engine +from ktem.settings_config import app_settings as settings -from .base import BaseIndex -from .models import Index +from .base import BaseCollection -class IndexManager: +class CollectionManager: """Manage the application indices The index manager is responsible for: @@ -22,8 +21,8 @@ class IndexManager: def __init__(self, app): self._app = app - self._indices = [] - self._index_types: dict[str, Type[BaseIndex]] = {} + self._collections = [] + self._index_types: dict[str, Type[BaseCollection]] = {} @property def index_types(self) -> dict: @@ -44,28 +43,23 @@ def build_index(self, name: str, config: dict, index_type: str): generated automatically. Defaults to None. Returns: - BaseIndex: the index object + BaseCollection: the index object """ - with Session(engine) as sess: - entry = Index(name=name, config=config, index_type=index_type) - sess.add(entry) - sess.commit() - sess.refresh(entry) + with IndexCRUD(engine) as crud: + entry = crud.create(name=name, index_type=index_type, config=config) + if entry.id is None: + raise ValueError(f'Cannot create index "{name}": missing id') + index_id = entry.id try: - # build the index - index_cls = import_dotted_string(index_type, safe=False) - index = index_cls(app=self._app, id=entry.id, name=name, config=config) + index_cls = get_index_cls(index_type) + index = index_cls(app=self._app, id=index_id, name=name, config=config) index.on_create() - - # update the entry - entry.config = index.config - sess.commit() + crud.update(index_id, config=index.config) except Exception as e: - sess.delete(entry) - sess.commit() - raise ValueError(f'Cannot create index "{name}": {e}') + crud.delete(index_id) + raise ValueError(f'Cannot create index "{name}": {e}') from e return index @@ -77,16 +71,10 @@ def update_index(self, id: int, name: str, config: dict): name: the new name of the index config: the new config of the index """ - with Session(engine) as sess: - entry = sess.get(Index, id) - if entry is None: - raise ValueError(f"Index with id {id} does not exist") - - entry.name = name - entry.config = config - sess.commit() + with IndexCRUD(engine) as crud: + crud.update(id, name=name, config=config) - for index in self._indices: + for index in self._collections: if index.id == id: index.name = name index.config = config @@ -101,17 +89,17 @@ def start_index(self, id: int, name: str, config: dict, index_type: str): config (dict): the config of the index index_type (str): the type of the index """ - index_cls = import_dotted_string(index_type, safe=False) + index_cls = get_index_cls(index_type) index = index_cls(app=self._app, id=id, name=name, config=config) index.on_start() - self._indices.append(index) + self._collections.append(index) return index def delete_index(self, id: int): """Delete the index from the database""" - index: Optional[BaseIndex] = None - for _ in self._indices: + index: Optional[BaseCollection] = None + for _ in self._collections: if _.id == id: index = _ break @@ -124,35 +112,29 @@ def delete_index(self, id: int): try: try: - # clean up index.on_delete() except Exception as e: print(f"Error while deleting index {index.name}: {e}") - # remove from database - with Session(engine) as sess: - item = sess.query(Index).filter_by(id=id).first() - sess.delete(item) - sess.commit() + with IndexCRUD(engine) as crud: + crud.delete(id) - new_indices = [_ for _ in self._indices if _.id != id] - self._indices = new_indices + new_indices = [_ for _ in self._collections if _.id != id] + self._collections = new_indices except Exception as e: - raise ValueError(f"Cannot delete index {index.name}: {e}") + raise ValueError(f"Cannot delete index {index.name}: {e}") from e def load_index_types(self): """Load the supported index types""" self._index_types = {} - # built-in index types from .file.index import FileIndex for index in [FileIndex]: self._index_types[f"{index.__module__}.{index.__qualname__}"] = index - # developer-defined custom index types for index_str in settings.KH_INDEX_TYPES: - cls: Type[BaseIndex] = import_dotted_string(index_str, safe=False) + cls = get_index_cls(index_str) self._index_types[f"{cls.__module__}.{cls.__qualname__}"] = cls def exists(self, id: Optional[int] = None, name: Optional[str] = None) -> bool: @@ -164,15 +146,11 @@ def exists(self, id: Optional[int] = None, name: Optional[str] = None) -> bool: Returns: bool: True if the index exists, False otherwise """ - if id: - with Session(engine) as sess: - index = sess.get(Index, id) - return index is not None - - if name: - with Session(engine) as sess: - index = sess.exec(select(Index).where(Index.name == name)).one_or_none() - return index is not None + with IndexCRUD(engine) as crud: + if id: + return crud.get(id) is not None + if name: + return crud.get_by_name(name) is not None return False @@ -187,14 +165,21 @@ def on_application_startup(self): if not self.exists(name=index["name"]): self.build_index(**index) - with Session(engine) as sess: - index_defs = sess.exec(select(Index)) + with IndexCRUD(engine) as crud: + index_defs = crud.list_all() for index_def in index_defs: - self.start_index(**index_def.model_dump()) + if index_def.id is None: + continue + self.start_index( + id=index_def.id, + name=index_def.name, + config=index_def.config, + index_type=index_def.index_type, + ) @property - def indices(self): - return self._indices + def collections(self): + return self._collections[:1] def info(self): - return {index.id: index for index in self._indices} + return {index.id: index for index in self._collections} diff --git a/libs/ktem/ktem/collections/registry.py b/libs/ktem/ktem/collections/registry.py new file mode 100644 index 000000000..0d886b824 --- /dev/null +++ b/libs/ktem/ktem/collections/registry.py @@ -0,0 +1,49 @@ +"""Explicit registry for collection / index types and file-index pipelines.""" + +from __future__ import annotations + +from enum import Enum + +from ktem.collections.base import BaseCollection + + +class IndexKind(str, Enum): + FILE = "FileIndex" + + +class IndexPipelineKind(str, Enum): + INDEX = "IndexDocumentPipeline" + RETRIEVER = "DocumentRetrievalPipeline" + SELECTOR_UI = "FileSelector" + INDEX_UI = "FileIndexPage" + + +def get_index_cls(value: str | IndexKind) -> type[BaseCollection]: + from ktem.collections.file.index import FileIndex + + mp: dict[IndexKind, type[BaseCollection]] = {IndexKind.FILE: FileIndex} + if isinstance(value, IndexKind): + return mp[value] + try: + return mp[IndexKind(value)] + except ValueError as exc: + raise ValueError(f"Unknown index type: {value!r}") from exc + + +def get_pipeline_cls(value: str | IndexPipelineKind) -> type: + from ktem.collections.file.indexing import IndexDocumentPipeline + from ktem.collections.file.retriever.impl.rag import DocumentRetrievalPipeline + from ktem.collections.file.ui import FileIndexPage, FileSelector + + mp = { + IndexPipelineKind.INDEX: IndexDocumentPipeline, + IndexPipelineKind.RETRIEVER: DocumentRetrievalPipeline, + IndexPipelineKind.SELECTOR_UI: FileSelector, + IndexPipelineKind.INDEX_UI: FileIndexPage, + } + if isinstance(value, IndexPipelineKind): + return mp[value] + try: + return mp[IndexPipelineKind(value)] + except ValueError as exc: + raise ValueError(f"Unknown index pipeline: {value!r}") from exc diff --git a/libs/ktem/ktem/index/ui.py b/libs/ktem/ktem/collections/ui.py similarity index 97% rename from libs/ktem/ktem/index/ui.py rename to libs/ktem/ktem/collections/ui.py index 6cf4f6907..773d9124d 100644 --- a/libs/ktem/ktem/index/ui.py +++ b/libs/ktem/ktem/collections/ui.py @@ -1,10 +1,10 @@ import gradio as gr import pandas as pd import yaml -from ktem.app import BasePage +from ktem.app import BaseApp, BasePage from ktem.utils.file import YAMLNoDateSafeLoader -from .manager import IndexManager +from .manager import CollectionManager # UGLY way to restart gradio server by updating atime @@ -33,9 +33,9 @@ def format_description(cls): class IndexManagement(BasePage): - def __init__(self, app): + def __init__(self, app: BaseApp): self._app = app - self.manager: IndexManager = app.index_manager + self.manager: CollectionManager = app.collection_manager self.spec_desc_default = ( "# Spec description\n\nSelect an index to view the spec description." ) @@ -244,7 +244,7 @@ def create_index(self, name: str, index_type: str, config: str): if not name: raise gr.Error("Name must not be empty") - existing_names = {idx.name for idx in self.manager.indices} + existing_names = {idx.name for idx in self.manager.collections} if name in existing_names: raise gr.Error(f"Index '{name}' already exists. Please use a unique name.") @@ -261,7 +261,7 @@ def create_index(self, name: str, index_type: str, config: str): def list_indices(self): """List the indices constructed by the user""" items = [] - for item in self.manager.indices: + for item in self.manager.collections: record = {} record["id"] = item.id record["name"] = item.name @@ -319,7 +319,7 @@ def update_index(self, selected_index_id: int, name: str, config: str): raise gr.Error("Name must not be empty") # Check uniqueness (excluding current index) - for idx in self.manager.indices: + for idx in self.manager.collections: if idx.name == name and idx.id != selected_index_id: raise gr.Error( f"Index '{name}' already exists. Please use a unique name." diff --git a/libs/ktem/ktem/components.py b/libs/ktem/ktem/components.py index 6674430c5..22967624e 100644 --- a/libs/ktem/ktem/components.py +++ b/libs/ktem/ktem/components.py @@ -2,38 +2,42 @@ import logging from functools import cache -from pathlib import Path -from typing import Optional +from typing import Any, Optional -from theflow.settings import settings -from theflow.utils.modules import deserialize +from ktem.settings_config import app_settings as settings -from kotaemon.base import BaseComponent from kotaemon.storages import BaseDocumentStore, BaseVectorStore +from kotaemon.storages.docstores.factory import DocStoreFactory +from kotaemon.storages.vectorstores.factory import VectorStoreFactory logger = logging.getLogger(__name__) -filestorage_path = Path(settings.KH_FILESTORAGE_PATH) -filestorage_path.mkdir(parents=True, exist_ok=True) +# filestorage_path = Path(settings.KH_FILESTORAGE_PATH) +# filestorage_path.mkdir(parents=True, exist_ok=True) @cache -def get_docstore(collection_name: str = "default") -> BaseDocumentStore: - from copy import deepcopy +def get_docstore(collection_name: str | None = None) -> BaseDocumentStore: + """Instantiate and return the configured docstore for *collection_name*.""" + overriding_envvars = {} + if collection_name: + overriding_envvars["DOCSTORE_COLLECTION_NAME"] = collection_name - ds_conf = deepcopy(settings.KH_DOCSTORE) - ds_conf["collection_name"] = collection_name - return deserialize(ds_conf, safe=False) + return DocStoreFactory.get_cls(settings.KH_DOCSTORE_VENDOR).from_env( + overriding_envvars=overriding_envvars + ) @cache -def get_vectorstore(collection_name: str = "default") -> BaseVectorStore: - from copy import deepcopy - - vs_conf = deepcopy(settings.KH_VECTORSTORE) - vs_conf["collection_name"] = collection_name - return deserialize(vs_conf, safe=False) +def get_vectorstore(collection_name: str | None = None) -> BaseVectorStore: + """Instantiate and return the configured vectorstore for *collection_name*.""" + overriding_envvars = {} + if collection_name: + overriding_envvars["VECTORSTORE_COLLECTION_NAME"] = collection_name + return VectorStoreFactory.get_cls(settings.KH_VECTORSTORE_VENDOR).from_env( + overriding_envvars=overriding_envvars + ) class ModelPool: @@ -43,13 +47,14 @@ def __init__(self, category: str, conf: dict): self._category = category self._conf = conf - self._models: dict[str, BaseComponent] = {} + self._models: dict[str, Any] = {} self._accuracy: list[str] = [] self._cost: list[str] = [] self._default: list[str] = [] for name, model in conf.items(): - self._models[name] = deserialize(model["spec"], safe=False) + # TODO: replace with vendor-factory pattern once ModelPool + # is used with non-empty conf (mirrors LLM/embedding managers). if model.get("default", False): self._default.append(name) @@ -58,11 +63,11 @@ def __init__(self, category: str, conf: dict): ) self._cost = list(sorted(conf, key=lambda x: conf[x].get("cost", float("inf")))) - def __getitem__(self, key: str) -> BaseComponent: + def __getitem__(self, key: str) -> Any: """Get model by name""" return self._models[key] - def __setitem__(self, key: str, value: BaseComponent): + def __setitem__(self, key: str, value: Any): """Set model by name""" self._models[key] = value @@ -74,9 +79,7 @@ def __contains__(self, key: str) -> bool: """Check if model exists""" return key in self._models - def get( - self, key: str, default: Optional[BaseComponent] = None - ) -> Optional[BaseComponent]: + def get(self, key: str, default: Optional[Any] = None) -> Optional[Any]: """Get model by name with default value""" return self._models.get(key, default) @@ -124,19 +127,12 @@ def get_default_name(self) -> str: return self.get_random_name() - def get_random(self) -> BaseComponent: + def get_random(self) -> Any: """Get random model""" return self._models[self.get_random_name()] - def get_default(self) -> BaseComponent: - """Get default model - - In case there is no default model, choose random model from pool. In - case there are multiple default models, choose random from them. - - Returns: - BaseComponent: model - """ + def get_default(self) -> Any: + """Get default model.""" return self._models[self.get_default_name()] def get_highest_accuracy_name(self) -> str: @@ -149,12 +145,8 @@ def get_highest_accuracy_name(self) -> str: raise ValueError("No models in pool") return self._accuracy[-1] - def get_highest_accuracy(self) -> BaseComponent: - """Get model with highest accuracy - - Returns: - BaseComponent: model - """ + def get_highest_accuracy(self) -> Any: + """Get model with highest accuracy.""" if not self._conf: raise ValueError("No models in pool") @@ -170,12 +162,8 @@ def get_lowest_cost_name(self) -> str: raise ValueError("No models in pool") return self._cost[0] - def get_lowest_cost(self) -> BaseComponent: - """Get model with lowest cost - - Returns: - BaseComponent: model - """ + def get_lowest_cost(self) -> Any: + """Get model with lowest cost.""" if not self._conf: raise ValueError("No models in pool") diff --git a/libs/ktem/ktem/db/base_models.py b/libs/ktem/ktem/db/base_models.py deleted file mode 100644 index 1cec718a3..000000000 --- a/libs/ktem/ktem/db/base_models.py +++ /dev/null @@ -1,102 +0,0 @@ -import datetime -import uuid -from typing import Optional - -from sqlalchemy import JSON, Column -from sqlmodel import Field, SQLModel -from tzlocal import get_localzone - - -class BaseConversation(SQLModel): - """Store the chat conversation between the user and the bot - - Attributes: - id: canonical id to identify the conversation - name: human-friendly name of the conversation - user: the user id - data_source: the data source of the conversation - date_created: the date the conversation was created - date_updated: the date the conversation was updated - """ - - __table_args__ = {"extend_existing": True} - - id: str = Field( - default_factory=lambda: uuid.uuid4().hex, primary_key=True, index=True - ) - name: str = Field( - default_factory=lambda: "Untitled - {}".format( - datetime.datetime.now(get_localzone()).strftime("%Y-%m-%d %H:%M:%S") - ) - ) - user: str = Field(default="") # For now we only have one user - - is_public: bool = Field(default=False) - - # contains messages + current files + chat_suggestions - data_source: dict = Field(default={}, sa_column=Column(JSON)) - - date_created: datetime.datetime = Field( - default_factory=lambda: datetime.datetime.now(get_localzone()) - ) - date_updated: datetime.datetime = Field( - default_factory=lambda: datetime.datetime.now(get_localzone()) - ) - - -class BaseUser(SQLModel): - """Store the user information - - Attributes: - id: canonical id to identify the user - username: the username of the user - password: the hashed password of the user - """ - - __table_args__ = {"extend_existing": True} - - id: str = Field( - default_factory=lambda: uuid.uuid4().hex, primary_key=True, index=True - ) - username: str = Field(unique=True) - username_lower: str = Field(unique=True) - password: str - admin: bool = Field(default=False) - - -class BaseSettings(SQLModel): - """Record of user settings - - Attributes: - id: canonical id to identify the settings - user: the user id - setting: the user settings (in dict/json format) - """ - - __table_args__ = {"extend_existing": True} - - id: str = Field( - default_factory=lambda: uuid.uuid4().hex, primary_key=True, index=True - ) - user: str = Field(default="") - setting: dict = Field(default={}, sa_column=Column(JSON)) - - -class BaseIssueReport(SQLModel): - """Store user-reported issues - - Attributes: - id: canonical id to identify the issue report - issues: the issues reported by the user, formatted as a dict - chat: the conversation id when the user reported the issue - settings: the user settings at the time of the issue report - user: the user id - """ - - __table_args__ = {"extend_existing": True} - - id: Optional[int] = Field(default=None, primary_key=True) - issues: dict = Field(default={}, sa_column=Column(JSON)) - chat: Optional[dict] = Field(default=None, sa_column=Column(JSON)) - settings: Optional[dict] = Field(default=None, sa_column=Column(JSON)) - user: Optional[str] = Field(default=None) diff --git a/libs/ktem/ktem/db/cruds/__init__.py b/libs/ktem/ktem/db/cruds/__init__.py new file mode 100644 index 000000000..f8c04d68c --- /dev/null +++ b/libs/ktem/ktem/db/cruds/__init__.py @@ -0,0 +1,27 @@ +from .base import BaseCRUD +from .conversation import ConversationCRUD +from .embedding import EmbeddingCRUD +from .file_chunk_relation import FileChunkRelationCRUD +from .file_source import FileSourceCRUD +from .index import IndexCRUD +from .issue_report import IssueReportCRUD +from .llm import LLMCRUD +from .mcp import MCPCRUD +from .reranking import RerankingCRUD +from .settings import SettingsCRUD +from .user import UserCRUD + +__all__ = [ + "BaseCRUD", + "ConversationCRUD", + "EmbeddingCRUD", + "FileChunkRelationCRUD", + "FileSourceCRUD", + "IndexCRUD", + "IssueReportCRUD", + "LLMCRUD", + "MCPCRUD", + "RerankingCRUD", + "SettingsCRUD", + "UserCRUD", +] diff --git a/libs/ktem/ktem/db/cruds/base.py b/libs/ktem/ktem/db/cruds/base.py new file mode 100644 index 000000000..4b0f699d9 --- /dev/null +++ b/libs/ktem/ktem/db/cruds/base.py @@ -0,0 +1,50 @@ +from types import TracebackType + +from sqlalchemy.engine import Engine +from sqlalchemy.orm import Session +from typing_extensions import Self + + +class BaseCRUD: + """Context manager that provides a SQLAlchemy Session. + + Usage:: + + with SomeCRUD(engine) as crud: + item = crud.get("id") + crud.delete("id") + """ + + def __init__(self, engine: Engine) -> None: + self.engine = engine + self._session: Session | None = None + + @property + def session(self) -> Session: + if self._session is None: + raise RuntimeError( + "Session not initialised. " "Use this CRUD as a context manager." + ) + return self._session + + def commit(self) -> None: + """Commit, rolling back automatically on failure.""" + try: + self.session.commit() + except Exception: + self.session.rollback() + raise + + def __enter__(self) -> Self: + self._session = Session(self.engine) + return self + + def __exit__( + self, + exc_type: type[BaseException] | None, + exc_value: BaseException | None, + traceback: TracebackType | None, + ) -> None: + if self._session: + self._session.close() + self._session = None diff --git a/libs/ktem/ktem/db/cruds/conversation.py b/libs/ktem/ktem/db/cruds/conversation.py new file mode 100644 index 000000000..6a831d956 --- /dev/null +++ b/libs/ktem/ktem/db/cruds/conversation.py @@ -0,0 +1,147 @@ +from typing import Any + +from ktem.db.models import Conversation +from sqlalchemy import or_, select + +from .base import BaseCRUD + + +class ConversationCRUD(BaseCRUD): + """CRUD operations for the Conversation table.""" + + def create(self, user: str) -> Conversation: + """Create a new conversation owned by *user*. + + Args: + user: the user id that owns the conversation. + + Returns: + The newly created Conversation row. + """ + item = Conversation(user=user) + self.session.add(item) + self.commit() + self.session.refresh(item) + return item + + def get(self, id: str) -> Conversation | None: + """Return the conversation with the given *id*, or None. + + Args: + id: primary-key id of the conversation. + """ + return self.session.get(Conversation, id) + + def list_by_user(self, user_id: str) -> list[Conversation]: + """Return all conversations owned by *user_id*, newest first. + + Args: + user_id: the user id to filter by. + """ + stmt = ( + select(Conversation) + .where(Conversation.user == user_id) + .order_by(Conversation.date_created.desc()) # type: ignore[attr-defined] + ) + return list(self.session.scalars(stmt).all()) + + def list_by_user_or_public(self, user_id: str) -> list[Conversation]: + """Return conversations owned by *user_id* plus all public ones. + + Public conversations appear first, then sorted by creation date. + + Args: + user_id: the user id to filter by. + """ + stmt = ( + select(Conversation) + .where( + or_( + Conversation.user == user_id, + Conversation.is_public, + ) + ) + .order_by( + Conversation.is_public.desc(), # type: ignore[attr-defined] + Conversation.date_created.desc(), # type: ignore[attr-defined] + ) + ) + return list(self.session.scalars(stmt).all()) + + def update_name(self, id: str, name: str) -> Conversation: + """Rename a conversation. + + Args: + id: primary-key id of the conversation. + name: the new name. + + Returns: + The updated Conversation row. + + Raises: + ValueError: if no conversation with *id* exists. + """ + item = self.session.get(Conversation, id) + if item is None: + raise ValueError(f"Conversation '{id}' not found") + item.name = name + self.commit() + self.session.refresh(item) + return item + + def update_data_source(self, id: str, data_source: dict[str, Any]) -> Conversation: + """Replace the data_source payload of a conversation. + + Args: + id: primary-key id of the conversation. + data_source: the new data_source dict. + + Returns: + The updated Conversation row. + + Raises: + ValueError: if no conversation with *id* exists. + """ + item = self.session.get(Conversation, id) + if item is None: + raise ValueError(f"Conversation '{id}' not found") + item.data_source = data_source + self.commit() + self.session.refresh(item) + return item + + def update_public(self, id: str, *, is_public: bool) -> Conversation: + """Toggle the public flag of a conversation. + + Args: + id: primary-key id of the conversation. + is_public: new value for the public flag. + + Returns: + The updated Conversation row. + + Raises: + ValueError: if no conversation with *id* exists. + """ + item = self.session.get(Conversation, id) + if item is None: + raise ValueError(f"Conversation '{id}' not found") + item.is_public = is_public + self.commit() + self.session.refresh(item) + return item + + def delete(self, id: str) -> None: + """Delete the conversation with the given *id*. + + Args: + id: primary-key id of the conversation. + + Raises: + ValueError: if no conversation with *id* exists. + """ + item = self.session.get(Conversation, id) + if item is None: + raise ValueError(f"Conversation '{id}' not found") + self.session.delete(item) + self.commit() diff --git a/libs/ktem/ktem/db/cruds/embedding.py b/libs/ktem/ktem/db/cruds/embedding.py new file mode 100644 index 000000000..b410869b4 --- /dev/null +++ b/libs/ktem/ktem/db/cruds/embedding.py @@ -0,0 +1,118 @@ +from typing import Any + +from ktem.db.models import EmbeddingTable +from sqlalchemy import select, update + +from kotaemon.embeddings.factory import EmbeddingVendor + +from .base import BaseCRUD + + +class EmbeddingCRUD(BaseCRUD): + """CRUD operations for the EmbeddingTable.""" + + def clear_defaults(self) -> None: + """Set all embedding rows to non-default within the current tx.""" + self.session.execute(update(EmbeddingTable).values(default=False)) + + def create( + self, + name: str, + vendor: EmbeddingVendor, + spec: dict[str, Any], + *, + default: bool = False, + ) -> EmbeddingTable: + """Insert a new embedding model entry. + + If *default* is True all other entries are demoted first. + + Args: + name: unique name for this embedding model. + vendor: vendor class identifier for factory lookup. + spec: constructor parameters for the vendor class. + default: make this model the pool default. + + Returns: + The newly created EmbeddingTable row. + + Raises: + ValueError: if an entry with *name* already exists. + """ + if not name: + raise ValueError("Name must not be empty") + if self.get(name) is not None: + raise ValueError(f"Embedding model '{name}' already exists") + if default: + self.clear_defaults() + item = EmbeddingTable(name=name, vendor=vendor, spec=spec, default=default) + self.session.add(item) + self.commit() + self.session.refresh(item) + return item + + def get(self, name: str) -> EmbeddingTable | None: + """Return the entry for *name*, or None. + + Args: + name: primary-key name of the embedding model. + """ + return self.session.get(EmbeddingTable, name) + + def list_all(self) -> list[EmbeddingTable]: + """Return all embedding model entries.""" + return list(self.session.scalars(select(EmbeddingTable)).all()) + + def update( + self, + name: str, + *, + vendor: EmbeddingVendor | None = None, + spec: dict[str, Any] | None = None, + default: bool | None = None, + ) -> EmbeddingTable: + """Partially update an embedding model entry. + + If *default* is True all other entries are demoted first. + + Args: + name: primary-key name of the entry to update. + vendor: new vendor class id; unchanged when None. + spec: new spec; unchanged when None. + default: new default flag; unchanged when None. + + Returns: + The updated EmbeddingTable row. + + Raises: + ValueError: if no entry with *name* exists. + """ + item = self.get(name) + if item is None: + raise ValueError(f"Embedding model '{name}' not found") + if default is True: + self.clear_defaults() + if vendor is not None: + item.vendor = vendor + if spec is not None: + item.spec = spec + if default is not None: + item.default = default + self.commit() + self.session.refresh(item) + return item + + def delete(self, name: str) -> None: + """Delete the entry for *name*. + + Args: + name: primary-key name of the embedding model. + + Raises: + ValueError: if no entry with *name* exists. + """ + item = self.get(name) + if item is None: + raise ValueError(f"Embedding model '{name}' not found") + self.session.delete(item) + self.commit() diff --git a/libs/ktem/ktem/db/cruds/file_chunk_relation.py b/libs/ktem/ktem/db/cruds/file_chunk_relation.py new file mode 100644 index 000000000..9f7bffa67 --- /dev/null +++ b/libs/ktem/ktem/db/cruds/file_chunk_relation.py @@ -0,0 +1,98 @@ +from __future__ import annotations + +from ktem.db.models.file_index import BaseFileChunkRelation +from sqlalchemy import select +from sqlalchemy.engine import Engine + +from kotaemon.indices.stores import RelationType + +from .base import BaseCRUD + + +class FileChunkRelationCRUD(BaseCRUD): + """CRUD for :class:`~ktem.db.models.file_index.BaseFileChunkRelation`.""" + + def __init__( + self, engine: Engine, index_model: type[BaseFileChunkRelation] + ) -> None: + super().__init__(engine) + self._index_model = index_model + + def add_relations( + self, + source_id: str, + target_ids: list[str], + relation_type: RelationType, + ) -> None: + """Insert source-to-chunk relations.""" + if not source_id: + raise ValueError("source_id must not be empty") + if not target_ids: + return + self.session.add_all( + [ + self._index_model( + source_id=source_id, + target_id=target_id, + relation_type=relation_type, + ) + for target_id in target_ids + ] + ) + self.commit() + + def list_target_ids( + self, + source_id: str, + relation_type: RelationType, + ) -> list[str]: + """Return target ids for one source and relation type.""" + if not source_id: + raise ValueError("source_id must not be empty") + return list( + self.session.scalars( + select(self._index_model.target_id).where( + self._index_model.source_id == source_id, + self._index_model.relation_type == relation_type, + ) + ).all() + ) + + def list_target_ids_for_sources( + self, + source_ids: list[str], + relation_type: RelationType, + ) -> list[str]: + """Return target ids for many sources and one relation type.""" + if not source_ids: + return [] + return [ + row.target_id + for row in self.session.scalars( + select(self._index_model).where( + self._index_model.relation_type == relation_type, + self._index_model.source_id.in_(source_ids), + ) + ).all() + ] + + def delete_by_source(self, source_id: str) -> tuple[list[str], list[str]]: + """Delete all relations for *source_id*. + + Returns: + ``(vector_target_ids, document_target_ids)`` collected before delete. + """ + if not source_id: + raise ValueError("source_id must not be empty") + vs_ids: list[str] = [] + ds_ids: list[str] = [] + for row in self.session.scalars( + select(self._index_model).where(self._index_model.source_id == source_id) + ).all(): + if row.relation_type == "vector": + vs_ids.append(row.target_id) + elif row.relation_type == "document": + ds_ids.append(row.target_id) + self.session.delete(row) + self.commit() + return vs_ids, ds_ids diff --git a/libs/ktem/ktem/db/cruds/file_source.py b/libs/ktem/ktem/db/cruds/file_source.py new file mode 100644 index 000000000..7e73a5b2b --- /dev/null +++ b/libs/ktem/ktem/db/cruds/file_source.py @@ -0,0 +1,103 @@ +from __future__ import annotations + +from ktem.db.models.file_index import BaseFileSource +from sqlalchemy import delete, select +from sqlalchemy.engine import Engine + +from .base import BaseCRUD + + +class FileSourceCRUD(BaseCRUD): + """CRUD for a per-collection :class:`~ktem.db.models.file_index.BaseFileSource`.""" + + def __init__( + self, + engine: Engine, + source_model: type[BaseFileSource], + *, + private: bool = False, + ) -> None: + super().__init__(engine) + self._source_model = source_model + self._private = private + + def get_id_by_name(self, name: str, *, user_id: int) -> str | None: + """Return the source id for *name*, or None if not indexed.""" + if not name: + raise ValueError("Name must not be empty") + cond = ( + (self._source_model.name == name, self._source_model.user == str(user_id)) + if self._private + else (self._source_model.name == name,) + ) + item = self.session.scalars(select(self._source_model).where(*cond)).first() + if item is None: + return None + return item.id + + def create_url(self, url: str, path_hash: str, *, user_id: int) -> str: + """Insert a URL source row and return its id.""" + if not url: + raise ValueError("URL must not be empty") + item = self._source_model( + name=url, + path=path_hash, + size=0, + user=str(user_id), + ) + self.session.add(item) + self.commit() + self.session.refresh(item) + return item.id + + def create_file( + self, + name: str, + path_hash: str, + size: int, + *, + user_id: int, + ) -> str: + """Insert a file source row and return its id.""" + if not name: + raise ValueError("Name must not be empty") + item = self._source_model( + name=name, + path=path_hash, + size=size, + user=str(user_id), + ) + self.session.add(item) + self.commit() + self.session.refresh(item) + return item.id + + def update_note( + self, + file_id: str, + *, + tokens: int | None, + loader: str, + ) -> None: + """Merge token count and loader into the source note JSON.""" + if not file_id: + raise ValueError("file_id must not be empty") + item = self.session.scalars( + select(self._source_model).where(self._source_model.id == file_id) + ).first() + if item is None: + return + if tokens is not None: + item.note["tokens"] = tokens + item.note["loader"] = loader + self.session.add(item) + self.commit() + + def delete(self, file_id: str) -> None: + """Delete the source row for *file_id*.""" + if not file_id: + raise ValueError("file_id must not be empty") + self.session.execute( + delete(self._source_model).where(self._source_model.id == file_id) + ) + self.commit() diff --git a/libs/ktem/ktem/db/cruds/index.py b/libs/ktem/ktem/db/cruds/index.py new file mode 100644 index 000000000..9eeb65f1f --- /dev/null +++ b/libs/ktem/ktem/db/cruds/index.py @@ -0,0 +1,87 @@ +from typing import Any, Optional + +from ktem.db.models import Index +from sqlalchemy import select + +from .base import BaseCRUD + + +class IndexCRUD(BaseCRUD): + """CRUD operations for the Index (collection) table.""" + + def create( + self, + name: str, + index_type: str, + config: dict[str, Any], + ) -> Index: + """Insert a new index entry. + + Args: + name: unique collection name. + index_type: registry key for the index class. + config: constructor kwargs for the index. + + Returns: + The newly created Index row. + + Raises: + ValueError: if *name* is empty or already exists. + """ + if not name: + raise ValueError("Name must not be empty") + if self.get_by_name(name) is not None: + raise ValueError(f'Index "{name}" already exists') + item = Index(name=name, index_type=index_type, config=config) + self.session.add(item) + self.commit() + self.session.refresh(item) + return item + + def get(self, id: int) -> Index | None: + """Return the index with the given *id*, or None.""" + return self.session.get(Index, id) + + def get_by_name(self, name: str) -> Index | None: + """Return the index with the given *name*, or None.""" + stmt = select(Index).where(Index.name == name) + return self.session.scalars(stmt).one_or_none() + + def list_all(self) -> list[Index]: + """Return all index entries.""" + return list(self.session.scalars(select(Index)).all()) + + def update( + self, + id: int, + *, + name: Optional[str] = None, + config: Optional[dict[str, Any]] = None, + ) -> Index: + """Partially update an index entry. + + Raises: + ValueError: if no entry with *id* exists. + """ + item = self.get(id) + if item is None: + raise ValueError(f"Index with id {id} does not exist") + if name is not None: + item.name = name + if config is not None: + item.config = config + self.commit() + self.session.refresh(item) + return item + + def delete(self, id: int) -> None: + """Delete the index with the given *id*. + + Raises: + ValueError: if no entry with *id* exists. + """ + item = self.get(id) + if item is None: + raise ValueError(f"Index with id {id} does not exist") + self.session.delete(item) + self.commit() diff --git a/libs/ktem/ktem/db/cruds/issue_report.py b/libs/ktem/ktem/db/cruds/issue_report.py new file mode 100644 index 000000000..0e2c4553a --- /dev/null +++ b/libs/ktem/ktem/db/cruds/issue_report.py @@ -0,0 +1,47 @@ +from typing import Any, Optional + +from ktem.db.models import IssueReport + +from .base import BaseCRUD + + +class IssueReportCRUD(BaseCRUD): + """CRUD operations for the IssueReport table.""" + + def create( + self, + issues: dict[str, Any], + *, + chat: Optional[dict[str, Any]] = None, + settings: Optional[dict[str, Any]] = None, + user: Optional[str] = None, + ) -> IssueReport: + """Persist a new issue report. + + Args: + issues: structured issue data (correctness, labels, etc.). + chat: snapshot of the conversation at report time. + settings: snapshot of user settings at report time. + user: id of the reporting user. + + Returns: + The newly created IssueReport row. + """ + item = IssueReport( + issues=issues, + chat=chat, + settings=settings, + user=user, + ) + self.session.add(item) + self.commit() + self.session.refresh(item) + return item + + def get(self, id: int) -> IssueReport | None: + """Return the issue report with the given *id*, or None. + + Args: + id: primary-key id of the issue report. + """ + return self.session.get(IssueReport, id) diff --git a/libs/ktem/ktem/db/cruds/llm.py b/libs/ktem/ktem/db/cruds/llm.py new file mode 100644 index 000000000..6debaf4da --- /dev/null +++ b/libs/ktem/ktem/db/cruds/llm.py @@ -0,0 +1,118 @@ +from typing import Any + +from ktem.db.models import LLMTable +from sqlalchemy import select, update + +from kotaemon.llms.chats.factory import LLMVendor + +from .base import BaseCRUD + + +class LLMCRUD(BaseCRUD): + """CRUD operations for the LLMTable.""" + + def clear_defaults(self) -> None: + """Set all LLM rows to non-default within the current tx.""" + self.session.execute(update(LLMTable).values(default=False)) + + def create( + self, + name: str, + vendor: LLMVendor, + spec: dict[str, Any], + *, + default: bool = False, + ) -> LLMTable: + """Insert a new LLM entry. + + If *default* is True all other entries are demoted first. + + Args: + name: unique name for this LLM. + vendor: vendor class identifier for factory lookup. + spec: constructor parameters for the vendor class. + default: make this model the pool default. + + Returns: + The newly created LLMTable row. + + Raises: + ValueError: if an entry with *name* already exists. + """ + if not name: + raise ValueError("Name must not be empty") + if self.get(name) is not None: + raise ValueError(f"LLM '{name}' already exists") + if default: + self.clear_defaults() + item = LLMTable(name=name, vendor=vendor, spec=spec, default=default) + self.session.add(item) + self.commit() + self.session.refresh(item) + return item + + def get(self, name: str) -> LLMTable | None: + """Return the entry for *name*, or None. + + Args: + name: primary-key name of the LLM. + """ + return self.session.get(LLMTable, name) + + def list_all(self) -> list[LLMTable]: + """Return all LLM entries.""" + return list(self.session.scalars(select(LLMTable)).all()) + + def update( + self, + name: str, + *, + vendor: LLMVendor | None = None, + spec: dict[str, Any] | None = None, + default: bool | None = None, + ) -> LLMTable: + """Partially update an LLM entry. + + If *default* is True all other entries are demoted first. + + Args: + name: primary-key name of the entry to update. + vendor: new vendor class id; unchanged when None. + spec: new spec; unchanged when None. + default: new default flag; unchanged when None. + + Returns: + The updated LLMTable row. + + Raises: + ValueError: if no entry with *name* exists. + """ + item = self.get(name) + if item is None: + raise ValueError(f"LLM '{name}' not found") + if default is True: + self.clear_defaults() + if vendor is not None: + item.vendor = vendor + if spec is not None: + item.spec = spec + if default is not None: + item.default = default + self.commit() + self.session.refresh(item) + return item + + def delete(self, name: str) -> None: + """Delete the entry for *name*. + + Args: + name: primary-key name of the LLM. + + Raises: + ValueError: if no entry with *name* exists. + """ + item = self.get(name) + if item is None: + raise ValueError(f"LLM '{name}' not found") + self.session.delete(item) + self.commit() diff --git a/libs/ktem/ktem/db/cruds/mcp.py b/libs/ktem/ktem/db/cruds/mcp.py new file mode 100644 index 000000000..8b53291ab --- /dev/null +++ b/libs/ktem/ktem/db/cruds/mcp.py @@ -0,0 +1,75 @@ +from typing import Any + +from ktem.mcp.db import MCPTable +from sqlalchemy import select + +from .base import BaseCRUD + + +class MCPCRUD(BaseCRUD): + """CRUD operations for the MCPTable.""" + + def create(self, name: str, config: dict[str, Any]) -> MCPTable: + """Insert a new MCP server configuration. + + Args: + name: unique server name. + config: constructor / connection kwargs. + + Returns: + The newly created MCPTable row. + + Raises: + ValueError: if *name* is empty or already exists. + """ + name = name.strip() + if not name: + raise ValueError("Name must not be empty") + if self.get(name) is not None: + raise ValueError(f"MCP server '{name}' already exists") + item = MCPTable(name=name, config=config) + self.session.add(item) + self.commit() + self.session.refresh(item) + return item + + def get(self, name: str) -> MCPTable | None: + """Return the entry for *name*, or None.""" + return self.session.get(MCPTable, name) + + def list_all(self) -> list[MCPTable]: + """Return all MCP server configurations.""" + return list(self.session.scalars(select(MCPTable)).all()) + + def update(self, name: str, config: dict[str, Any]) -> MCPTable: + """Replace the config for an existing MCP server. + + Args: + name: primary-key name of the server. + config: new configuration dict. + + Returns: + The updated MCPTable row. + + Raises: + ValueError: if no entry with *name* exists. + """ + item = self.get(name) + if item is None: + raise ValueError(f"MCP server '{name}' not found") + item.config = config + self.commit() + self.session.refresh(item) + return item + + def delete(self, name: str) -> None: + """Delete the MCP server with the given *name*. + + Raises: + ValueError: if no entry with *name* exists. + """ + item = self.get(name) + if item is None: + raise ValueError(f"MCP server '{name}' not found") + self.session.delete(item) + self.commit() diff --git a/libs/ktem/ktem/db/cruds/reranking.py b/libs/ktem/ktem/db/cruds/reranking.py new file mode 100644 index 000000000..65b035469 --- /dev/null +++ b/libs/ktem/ktem/db/cruds/reranking.py @@ -0,0 +1,88 @@ +from typing import Any + +from ktem.db.models import RerankingTable +from sqlalchemy import select, update + +from kotaemon.rerankings.factory import RerankingVendor + +from .base import BaseCRUD + + +class RerankingCRUD(BaseCRUD): + """CRUD operations for the reranking model table.""" + + def clear_defaults(self) -> None: + self.session.execute(update(RerankingTable).values(default=False)) + + def create( + self, + name: str, + vendor: RerankingVendor, + spec: dict[str, Any], + *, + default: bool = False, + ) -> RerankingTable: + if not name: + raise ValueError("Name must not be empty") + if self.get(name) is not None: + raise ValueError(f"Reranking model '{name}' already exists") + if default: + self.clear_defaults() + item = RerankingTable(name=name, vendor=vendor, spec=spec, default=default) + self.session.add(item) + self.commit() + self.session.refresh(item) + return item + + def get(self, name: str) -> RerankingTable | None: + return self.session.get(RerankingTable, name) + + def list_all(self) -> list[RerankingTable]: + return list(self.session.scalars(select(RerankingTable)).all()) + + def update( + self, + name: str, + *, + vendor: RerankingVendor | None = None, + spec: dict[str, Any] | None = None, + default: bool | None = None, + new_name: str | None = None, + ) -> RerankingTable: + item = self.get(name) + if item is None: + raise ValueError(f"Reranking model '{name}' not found") + if new_name and new_name != name: + if self.get(new_name) is not None: + raise ValueError(f"Reranking model '{new_name}' already exists") + if default: + self.clear_defaults() + new_item = RerankingTable( + name=new_name, + vendor=vendor if vendor is not None else item.vendor, + spec=spec if spec is not None else item.spec, + default=default if default is not None else item.default, + ) + self.session.delete(item) + self.session.add(new_item) + self.commit() + self.session.refresh(new_item) + return new_item + if default: + self.clear_defaults() + if vendor is not None: + item.vendor = vendor + if spec is not None: + item.spec = spec + if default is not None: + item.default = default + self.commit() + self.session.refresh(item) + return item + + def delete(self, name: str) -> None: + item = self.get(name) + if item is None: + raise ValueError(f"Reranking model '{name}' not found") + self.session.delete(item) + self.commit() diff --git a/libs/ktem/ktem/db/cruds/settings.py b/libs/ktem/ktem/db/cruds/settings.py new file mode 100644 index 000000000..e1e61162b --- /dev/null +++ b/libs/ktem/ktem/db/cruds/settings.py @@ -0,0 +1,50 @@ +from typing import Any + +from ktem.db.models import Settings +from sqlalchemy import select + +from .base import BaseCRUD + + +class SettingsCRUD(BaseCRUD): + """CRUD operations for the Settings table.""" + + def get_by_user(self, user_id: str) -> Settings | None: + """Return the settings record for *user_id*, or None. + + Args: + user_id: the user id to look up. + """ + stmt = select(Settings).where(Settings.user == user_id) + return self.session.scalars(stmt).first() + + def upsert(self, user_id: str, setting: dict[str, Any]) -> Settings: + """Create or replace the settings record for *user_id*. + + Args: + user_id: the user id that owns the settings. + setting: the settings payload to store. + + Returns: + The created or updated Settings row. + """ + item = self.get_by_user(user_id) + if item is None: + item = Settings(user=user_id, setting=setting) + self.session.add(item) + else: + item.setting = setting + self.commit() + self.session.refresh(item) + return item + + def delete_by_user(self, user_id: str) -> None: + """Delete the settings record for *user_id* if one exists. + + Args: + user_id: the user id whose settings should be removed. + """ + item = self.get_by_user(user_id) + if item is not None: + self.session.delete(item) + self.commit() diff --git a/libs/ktem/ktem/db/cruds/user.py b/libs/ktem/ktem/db/cruds/user.py new file mode 100644 index 000000000..b15c1a76e --- /dev/null +++ b/libs/ktem/ktem/db/cruds/user.py @@ -0,0 +1,150 @@ +import hashlib +from typing import Optional + +from ktem.db.models import User +from sqlalchemy import select + +from .base import BaseCRUD + + +def hash_password(password: str) -> str: + """Return the SHA-256 hex digest used for stored passwords.""" + return hashlib.sha256(password.encode()).hexdigest() + + +class UserCRUD(BaseCRUD): + """CRUD operations for the User table.""" + + def create( + self, + username: str, + password: str, + *, + admin: bool = False, + user_id: Optional[str] = None, + ) -> User: + """Create a new user. + + Args: + username: the display username. + password: the already-hashed password. + admin: whether the user has admin privileges. + user_id: explicit id; auto-generated when omitted. + + Returns: + The newly created User row. + + Raises: + ValueError: if the username already exists. + """ + if self.get_by_username(username) is not None: + raise ValueError(f"Username '{username}' already exists") + kwargs: dict = dict( + username=username, + username_lower=username.lower(), + password=password, + admin=admin, + ) + if user_id is not None: + kwargs["id"] = user_id + item = User(**kwargs) + self.session.add(item) + self.commit() + self.session.refresh(item) + return item + + def get(self, id: str) -> User | None: + """Return the user with the given *id*, or None. + + Args: + id: primary-key id of the user. + """ + return self.session.get(User, id) + + def get_by_username(self, username: str) -> User | None: + """Return the user whose username (case-insensitive) matches, or None. + + Args: + username: the username to look up. + """ + stmt = select(User).where(User.username_lower == username.lower()) + return self.session.scalars(stmt).first() + + def authenticate(self, username: str, password: str) -> User | None: + """Return the user when *username* and *password* match, else None.""" + stmt = select(User).where( + User.username_lower == username.lower().strip(), + User.password == hash_password(password), + ) + return self.session.scalars(stmt).first() + + def list_all(self) -> list[User]: + """Return all users.""" + return list(self.session.scalars(select(User)).all()) + + def username_taken(self, username: str, exclude_id: Optional[str] = None) -> bool: + """Check whether *username* is already in use. + + Args: + username: username to check. + exclude_id: user id to exclude from the check (used + when renaming an existing user). + + Returns: + True if the username is taken by another user. + """ + stmt = select(User).where(User.username_lower == username.lower()) + if exclude_id is not None: + stmt = stmt.where(User.id != exclude_id) + return self.session.scalars(stmt).first() is not None + + def update( + self, + id: str, + *, + username: Optional[str] = None, + password: Optional[str] = None, + admin: Optional[bool] = None, + ) -> User: + """Partially update a user record. + + Args: + id: primary-key id of the user. + username: new username; unchanged when None. + password: new hashed password; unchanged when None. + admin: new admin flag; unchanged when None. + + Returns: + The updated User row. + + Raises: + ValueError: if the user is not found. + """ + item = self.session.get(User, id) + if item is None: + raise ValueError(f"User '{id}' not found") + if username is not None: + item.username = username + item.username_lower = username.lower() + if password is not None: + item.password = password + if admin is not None: + item.admin = admin + self.commit() + self.session.refresh(item) + return item + + def delete(self, id: str) -> None: + """Delete the user with the given *id*. + + Args: + id: primary-key id of the user. + + Raises: + ValueError: if the user is not found. + """ + item = self.session.get(User, id) + if item is None: + raise ValueError(f"User '{id}' not found") + self.session.delete(item) + self.commit() diff --git a/libs/ktem/ktem/db/engine.py b/libs/ktem/ktem/db/engine.py index 05f946ec4..7cad61528 100644 --- a/libs/ktem/ktem/db/engine.py +++ b/libs/ktem/ktem/db/engine.py @@ -1,4 +1,4 @@ -from sqlmodel import create_engine -from theflow.settings import settings +from ktem.settings_config import app_settings as settings +from sqlalchemy import create_engine engine = create_engine(settings.KH_DATABASE) diff --git a/libs/ktem/ktem/db/models.py b/libs/ktem/ktem/db/models.py deleted file mode 100644 index 641ab851d..000000000 --- a/libs/ktem/ktem/db/models.py +++ /dev/null @@ -1,49 +0,0 @@ -import ktem.db.base_models as base_models -from ktem.db.engine import engine -from sqlmodel import SQLModel -from theflow.settings import settings -from theflow.utils.modules import import_dotted_string - -_base_conv = ( - import_dotted_string(settings.KH_TABLE_CONV, safe=False) - if hasattr(settings, "KH_TABLE_CONV") - else base_models.BaseConversation -) - -_base_user = ( - import_dotted_string(settings.KH_TABLE_USER, safe=False) - if hasattr(settings, "KH_TABLE_USER") - else base_models.BaseUser -) - -_base_settings = ( - import_dotted_string(settings.KH_TABLE_SETTINGS, safe=False) - if hasattr(settings, "KH_TABLE_SETTINGS") - else base_models.BaseSettings -) - -_base_issue_report = ( - import_dotted_string(settings.KH_TABLE_ISSUE_REPORT, safe=False) - if hasattr(settings, "KH_TABLE_ISSUE_REPORT") - else base_models.BaseIssueReport -) - - -class Conversation(_base_conv, table=True): # type: ignore - """Conversation record""" - - -class User(_base_user, table=True): # type: ignore - """User table""" - - -class Settings(_base_settings, table=True): # type: ignore - """Record of settings""" - - -class IssueReport(_base_issue_report, table=True): # type: ignore - """Record of issues""" - - -if not getattr(settings, "KH_ENABLE_ALEMBIC", False): - SQLModel.metadata.create_all(engine) diff --git a/libs/ktem/ktem/db/models/__init__.py b/libs/ktem/ktem/db/models/__init__.py new file mode 100644 index 000000000..ed0a1ceae --- /dev/null +++ b/libs/ktem/ktem/db/models/__init__.py @@ -0,0 +1,59 @@ +from ktem.db.engine import engine + +from .base import Base +from .conversation import BaseConversation +from .embedding import BaseEmbedding +from .file_index import ( + BaseFileChunkRelation, + BaseFileGroup, + BaseFileSource, + get_file_chunk_relation_model, + get_file_group_model, + get_file_source_model, +) +from .issue_report import BaseIssueReport +from .llm import BaseLLM +from .reranking import BaseReranking +from .settings import BaseSettings +from .tables import ( + Conversation, + EmbeddingTable, + Index, + IssueReport, + LLMTable, + RerankingTable, + Settings, + User, +) +from .user import BaseUser + +__all__ = [ + # engine — re-exported so callers can do: + # from ktem.db.models import Conversation, engine + "engine", + # declarative base + "Base", + # abstract base models + "BaseConversation", + "BaseFileChunkRelation", + "BaseFileGroup", + "BaseFileSource", + "get_file_chunk_relation_model", + "get_file_group_model", + "get_file_source_model", + "BaseEmbedding", + "BaseIssueReport", + "BaseLLM", + "BaseReranking", + "BaseSettings", + "BaseUser", + # concrete table models + "Conversation", + "EmbeddingTable", + "Index", + "IssueReport", + "LLMTable", + "RerankingTable", + "Settings", + "User", +] diff --git a/libs/ktem/ktem/db/models/base.py b/libs/ktem/ktem/db/models/base.py new file mode 100644 index 000000000..fa2b68a5d --- /dev/null +++ b/libs/ktem/ktem/db/models/base.py @@ -0,0 +1,5 @@ +from sqlalchemy.orm import DeclarativeBase + + +class Base(DeclarativeBase): + pass diff --git a/libs/ktem/ktem/db/models/conversation.py b/libs/ktem/ktem/db/models/conversation.py new file mode 100644 index 000000000..77364338f --- /dev/null +++ b/libs/ktem/ktem/db/models/conversation.py @@ -0,0 +1,49 @@ +import datetime +import uuid +from typing import Any + +from sqlalchemy import JSON, Boolean, DateTime, String +from sqlalchemy.orm import Mapped, mapped_column +from tzlocal import get_localzone + +from .base import Base + + +class BaseConversation(Base): + """Store the chat conversation between the user and the bot. + + Attributes: + id: canonical id to identify the conversation + name: human-friendly name of the conversation + user: the user id + is_public: whether the conversation is public + data_source: messages, selected files, chat_suggestions + date_created: creation timestamp + date_updated: last-updated timestamp + """ + + __abstract__ = True + + id: Mapped[str] = mapped_column( + String, + primary_key=True, + index=True, + default=lambda: uuid.uuid4().hex, + ) + name: Mapped[str] = mapped_column( + String, + default=lambda: "Untitled - {}".format( + datetime.datetime.now(get_localzone()).strftime("%Y-%m-%d %H:%M:%S") + ), + ) + user: Mapped[str] = mapped_column(String, default="") + is_public: Mapped[bool] = mapped_column(Boolean, default=False) + data_source: Mapped[dict[str, Any]] = mapped_column(JSON, default=dict) + date_created: Mapped[datetime.datetime] = mapped_column( + DateTime(timezone=True), + default=lambda: datetime.datetime.now(datetime.timezone.utc), + ) + date_updated: Mapped[datetime.datetime] = mapped_column( + DateTime(timezone=True), + default=lambda: datetime.datetime.now(datetime.timezone.utc), + ) diff --git a/libs/ktem/ktem/db/models/embedding.py b/libs/ktem/ktem/db/models/embedding.py new file mode 100644 index 000000000..ef54cc295 --- /dev/null +++ b/libs/ktem/ktem/db/models/embedding.py @@ -0,0 +1,34 @@ +from typing import Any + +from sqlalchemy import JSON, Boolean, String +from sqlalchemy.orm import Mapped, mapped_column + +from kotaemon.embeddings.factory import EmbeddingVendor + +from .base import Base + + +class BaseEmbedding(Base): + """Store embedding model pool entries. + + Attributes: + name: unique name of the embedding model + vendor: vendor class identifier used for factory lookup + spec: constructor parameters for the vendor class + default: whether this model is the default in the pool + """ + + __abstract__ = True + + name: Mapped[str] = mapped_column(String, primary_key=True) + vendor: Mapped[EmbeddingVendor] = mapped_column(String) + spec: Mapped[dict[str, Any]] = mapped_column(JSON, default=dict) + default: Mapped[bool] = mapped_column(Boolean, default=False) + + @property + def ui(self) -> dict[str, Any]: + return { + "name": self.name, + "vendor": self.vendor, + "default": self.default, + } diff --git a/libs/ktem/ktem/db/models/file_index.py b/libs/ktem/ktem/db/models/file_index.py new file mode 100644 index 000000000..ce3c5aa17 --- /dev/null +++ b/libs/ktem/ktem/db/models/file_index.py @@ -0,0 +1,127 @@ +from __future__ import annotations + +import uuid +from datetime import datetime +from typing import Any + +from sqlalchemy import JSON, DateTime, Integer, String, UniqueConstraint +from sqlalchemy.ext.mutable import MutableDict +from sqlalchemy.orm import Mapped, mapped_column +from tzlocal import get_localzone + +from .base import Base + + +def _new_uuid() -> str: + return str(uuid.uuid4()) + + +def _now_local() -> datetime: + return datetime.now(get_localzone()) + + +class BaseFileSource(Base): + """Indexed file or URL in a file collection (per-index table).""" + + __abstract__ = True + + id: Mapped[str] = mapped_column(String, primary_key=True, default=_new_uuid) + name: Mapped[str] = mapped_column(String) + path: Mapped[str] = mapped_column(String) + size: Mapped[int] = mapped_column(Integer, default=0) + date_created: Mapped[datetime] = mapped_column( + DateTime(timezone=True), default=_now_local + ) + user: Mapped[str] = mapped_column(String, default="") + note: Mapped[dict[str, Any]] = mapped_column( + MutableDict.as_mutable(JSON), # type: ignore[arg-type] + default=lambda: {}, + ) + + +class BaseFileChunkRelation(Base): + """Link from a source file to a doc-store or vector-store chunk id.""" + + __abstract__ = True + + id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True) + source_id: Mapped[str] = mapped_column(String) + target_id: Mapped[str] = mapped_column(String) + relation_type: Mapped[str] = mapped_column(String) + user: Mapped[str] = mapped_column(String, default="") + + +class BaseFileGroup(Base): + """Named group of source file ids within a file collection.""" + + __abstract__ = True + + id: Mapped[str] = mapped_column(String, primary_key=True, default=_new_uuid) + date_created: Mapped[datetime] = mapped_column( + DateTime(timezone=True), default=_now_local + ) + name: Mapped[str] = mapped_column(String) + user: Mapped[str] = mapped_column(String, default="") + data: Mapped[dict[str, Any]] = mapped_column( + MutableDict.as_mutable(JSON), # type: ignore[arg-type] + default=lambda: {"files": []}, + ) + + +_mp_file_source_models: dict[tuple[int, bool], type[BaseFileSource]] = {} +_mp_file_chunk_relation_models: dict[int, type[BaseFileChunkRelation]] = {} +_mp_file_group_models: dict[int, type[BaseFileGroup]] = {} + + +def get_file_source_model(index_id: int, *, private: bool) -> type[BaseFileSource]: + """Return the mapped Source class for a file collection.""" + key = (index_id, private) + if key in _mp_file_source_models: + return _mp_file_source_models[key] + + table_args = ( + (UniqueConstraint("name", "user", name="_name_user_uc"),) + if private + else (UniqueConstraint("name", name="_name_uc"),) + ) + model = type( + f"FileSource_{index_id}", + (BaseFileSource,), + { + "__tablename__": f"index__{index_id}__source", + "__table_args__": table_args, + }, + ) + _mp_file_source_models[key] = model + return model + + +def get_file_chunk_relation_model(index_id: int) -> type[BaseFileChunkRelation]: + """Return the mapped chunk-relation class for a file collection.""" + if index_id in _mp_file_chunk_relation_models: + return _mp_file_chunk_relation_models[index_id] + + model = type( + f"FileChunkRelation_{index_id}", + (BaseFileChunkRelation,), + {"__tablename__": f"index__{index_id}__index"}, + ) + _mp_file_chunk_relation_models[index_id] = model + return model + + +def get_file_group_model(index_id: int) -> type[BaseFileGroup]: + """Return the mapped FileGroup class for a file collection.""" + if index_id in _mp_file_group_models: + return _mp_file_group_models[index_id] + + model = type( + f"FileGroup_{index_id}", + (BaseFileGroup,), + { + "__tablename__": f"index__{index_id}__group", + "__table_args__": (UniqueConstraint("name", "user", name="_name_user_uc"),), + }, + ) + _mp_file_group_models[index_id] = model + return model diff --git a/libs/ktem/ktem/db/models/issue_report.py b/libs/ktem/ktem/db/models/issue_report.py new file mode 100644 index 000000000..b8be09e7b --- /dev/null +++ b/libs/ktem/ktem/db/models/issue_report.py @@ -0,0 +1,28 @@ +from typing import Any, Optional + +from sqlalchemy import JSON, Integer, String +from sqlalchemy.orm import Mapped, mapped_column + +from .base import Base + + +class BaseIssueReport(Base): + """Store user-reported issues. + + Attributes: + id: auto-increment integer primary key + issues: the issues reported, formatted as a dict + chat: the conversation at the time of the report + settings: the user settings at the time of the report + user: the user id + """ + + __abstract__ = True + + id: Mapped[Optional[int]] = mapped_column( + Integer, primary_key=True, autoincrement=True + ) + issues: Mapped[dict[str, Any]] = mapped_column(JSON, default=dict) + chat: Mapped[Optional[dict[str, Any]]] = mapped_column(JSON, nullable=True) + settings: Mapped[Optional[dict[str, Any]]] = mapped_column(JSON, nullable=True) + user: Mapped[Optional[str]] = mapped_column(String, nullable=True) diff --git a/libs/ktem/ktem/db/models/llm.py b/libs/ktem/ktem/db/models/llm.py new file mode 100644 index 000000000..3ba07c243 --- /dev/null +++ b/libs/ktem/ktem/db/models/llm.py @@ -0,0 +1,34 @@ +from typing import Any + +from sqlalchemy import JSON, Boolean, String +from sqlalchemy.orm import Mapped, mapped_column + +from kotaemon.llms.chats.factory import LLMVendor + +from .base import Base + + +class BaseLLM(Base): + """Store LLM model pool entries. + + Attributes: + name: unique name of the LLM + vendor: vendor class identifier used for factory lookup + spec: constructor parameters for the vendor class + default: whether this model is the default in the pool + """ + + __abstract__ = True + + name: Mapped[str] = mapped_column(String, primary_key=True) + vendor: Mapped[LLMVendor] = mapped_column(String) + spec: Mapped[dict[str, Any]] = mapped_column(JSON, default=dict) + default: Mapped[bool] = mapped_column(Boolean, default=False) + + @property + def ui(self) -> dict[str, Any]: + return { + "name": self.name, + "vendor": self.vendor, + "default": self.default, + } diff --git a/libs/ktem/ktem/db/models/reranking.py b/libs/ktem/ktem/db/models/reranking.py new file mode 100644 index 000000000..91eb9bb9a --- /dev/null +++ b/libs/ktem/ktem/db/models/reranking.py @@ -0,0 +1,27 @@ +from typing import Any + +from sqlalchemy import JSON, Boolean, String +from sqlalchemy.orm import Mapped, mapped_column + +from kotaemon.rerankings.factory import RerankingVendor + +from .base import Base + + +class BaseReranking(Base): + """Store reranking model pool entries.""" + + __abstract__ = True + + name: Mapped[str] = mapped_column(String, primary_key=True) + vendor: Mapped[RerankingVendor] = mapped_column(String) + spec: Mapped[dict[str, Any]] = mapped_column(JSON, default=dict) + default: Mapped[bool] = mapped_column(Boolean, default=False) + + @property + def ui(self) -> dict[str, Any]: + return { + "name": self.name, + "vendor": self.vendor, + "default": self.default, + } diff --git a/libs/ktem/ktem/db/models/settings.py b/libs/ktem/ktem/db/models/settings.py new file mode 100644 index 000000000..2b412e405 --- /dev/null +++ b/libs/ktem/ktem/db/models/settings.py @@ -0,0 +1,28 @@ +import uuid +from typing import Any + +from sqlalchemy import JSON, String +from sqlalchemy.orm import Mapped, mapped_column + +from .base import Base + + +class BaseSettings(Base): + """Record of user settings. + + Attributes: + id: canonical id to identify the settings record + user: the user id + setting: the user settings serialised as JSON + """ + + __abstract__ = True + + id: Mapped[str] = mapped_column( + String, + primary_key=True, + index=True, + default=lambda: uuid.uuid4().hex, + ) + user: Mapped[str] = mapped_column(String, default="") + setting: Mapped[dict[str, Any]] = mapped_column(JSON, default=dict) diff --git a/libs/ktem/ktem/db/models/tables.py b/libs/ktem/ktem/db/models/tables.py new file mode 100644 index 000000000..e4d22de2d --- /dev/null +++ b/libs/ktem/ktem/db/models/tables.py @@ -0,0 +1,58 @@ +from typing import Any, Optional + +from ktem.db.engine import engine +from ktem.settings_config import app_settings as settings +from sqlalchemy import JSON, Integer, String +from sqlalchemy.orm import Mapped, mapped_column + +from .base import Base +from .conversation import BaseConversation +from .embedding import BaseEmbedding +from .issue_report import BaseIssueReport +from .llm import BaseLLM +from .reranking import BaseReranking +from .settings import BaseSettings +from .user import BaseUser + + +class Conversation(BaseConversation): + __tablename__ = "conversation" + + +class User(BaseUser): + __tablename__ = "user" + + +class Settings(BaseSettings): + __tablename__ = "settings" + + +class IssueReport(BaseIssueReport): + __tablename__ = "issuereport" + + +class EmbeddingTable(BaseEmbedding): + __tablename__ = "embedding" + + +class LLMTable(BaseLLM): + __tablename__ = "llm_table" + + +class RerankingTable(BaseReranking): + __tablename__ = "reranking" + + +class Index(Base): + __tablename__ = "ktem__index" + + id: Mapped[Optional[int]] = mapped_column( + Integer, primary_key=True, autoincrement=True + ) + name: Mapped[str] = mapped_column(String, unique=True) + index_type: Mapped[str] = mapped_column(String) + config: Mapped[dict[str, Any]] = mapped_column(JSON, default=dict) + + +if not settings.KH_ENABLE_ALEMBIC: + Base.metadata.create_all(engine) diff --git a/libs/ktem/ktem/db/models/user.py b/libs/ktem/ktem/db/models/user.py new file mode 100644 index 000000000..bbd156fcf --- /dev/null +++ b/libs/ktem/ktem/db/models/user.py @@ -0,0 +1,32 @@ +import uuid + +from sqlalchemy import Boolean, String +from sqlalchemy.orm import Mapped, mapped_column + +from .base import Base + + +class BaseUser(Base): + """Store the user information. + + Attributes: + id: canonical id to identify the user + username: the username of the user + username_lower: lowercased username for case-insensitive + lookup + password: the hashed password of the user + admin: whether the user has admin privileges + """ + + __abstract__ = True + + id: Mapped[str] = mapped_column( + String, + primary_key=True, + index=True, + default=lambda: uuid.uuid4().hex, + ) + username: Mapped[str] = mapped_column(String, unique=True) + username_lower: Mapped[str] = mapped_column(String, unique=True) + password: Mapped[str] = mapped_column(String) + admin: Mapped[bool] = mapped_column(Boolean, default=False) diff --git a/libs/ktem/ktem/embeddings/db.py b/libs/ktem/ktem/embeddings/db.py deleted file mode 100644 index 4ddb9fb04..000000000 --- a/libs/ktem/ktem/embeddings/db.py +++ /dev/null @@ -1,36 +0,0 @@ -from typing import Type - -from ktem.db.engine import engine -from sqlalchemy import JSON, Boolean, Column, String -from sqlalchemy.orm import DeclarativeBase -from theflow.settings import settings as flowsettings -from theflow.utils.modules import import_dotted_string - - -class Base(DeclarativeBase): - pass - - -class BaseEmbeddingTable(Base): - """Base table to store language model""" - - __abstract__ = True - - name = Column(String, primary_key=True, unique=True) - spec = Column(JSON, default={}) - default = Column(Boolean, default=False) - - -_base_llm: Type[BaseEmbeddingTable] = ( - import_dotted_string(flowsettings.KH_EMBEDDING_LLM, safe=False) - if hasattr(flowsettings, "KH_EMBEDDING_LLM") - else BaseEmbeddingTable -) - - -class EmbeddingTable(_base_llm): # type: ignore - __tablename__ = "embedding" - - -if not getattr(flowsettings, "KH_ENABLE_ALEMBIC", False): - EmbeddingTable.metadata.create_all(engine) diff --git a/libs/ktem/ktem/embeddings/manager.py b/libs/ktem/ktem/embeddings/manager.py index 023dca6b1..f27f309bf 100644 --- a/libs/ktem/ktem/embeddings/manager.py +++ b/libs/ktem/ktem/embeddings/manager.py @@ -1,226 +1,174 @@ -from typing import Optional, Type +from typing import Optional -from sqlalchemy import select -from sqlalchemy.orm import Session -from theflow.settings import settings as flowsettings -from theflow.utils.modules import deserialize +from ktem.db.cruds import EmbeddingCRUD +from ktem.db.engine import engine +from ktem.db.models import EmbeddingTable from kotaemon.embeddings.base import BaseEmbeddings - -from .db import EmbeddingTable, engine +from kotaemon.embeddings.factory import MP_VENDOR_CLS, EmbeddingFactory, EmbeddingVendor class EmbeddingManager: - """Represent a pool of models""" + """Represent a pool of embedding models.""" def __init__(self): self._models: dict[str, BaseEmbeddings] = {} - self._info: dict[str, dict] = {} + self._info: dict[str, EmbeddingTable] = {} self._default: str = "" - self._vendors: list[Type] = [] - - # populate the pool if empty - if hasattr(flowsettings, "KH_EMBEDDINGS"): - with Session(engine) as sess: - count = sess.query(EmbeddingTable).count() - if not count: - for name, model in flowsettings.KH_EMBEDDINGS.items(): - self.add( - name=name, - spec=model["spec"], - default=model.get("default", False), - ) self.load() - self.load_vendors() - def load(self): - """Load the model pool from database""" + def load(self) -> None: + """Load the model pool from database.""" self._models, self._info, self._default = {}, {}, "" - with Session(engine) as sess: - stmt = select(EmbeddingTable) - items = sess.execute(stmt) - - for (item,) in items: - self._models[item.name] = deserialize(item.spec, safe=False) - self._info[item.name] = { - "name": item.name, - "spec": item.spec, - "default": item.default, - } + + with EmbeddingCRUD(engine) as crud: + for item in crud.list_all(): + self._models[item.name] = EmbeddingFactory.get_cls(item.vendor)( + **item.spec + ) + self._info[item.name] = item if item.default: self._default = item.name - self._models["default"] = self._models[item.name] - - def load_vendors(self): - from kotaemon.embeddings import ( - AzureOpenAIEmbeddings, - FastEmbedEmbeddings, - LCCohereEmbeddings, - LCGoogleEmbeddings, - LCHuggingFaceEmbeddings, - LCMistralEmbeddings, - OpenAIEmbeddings, - TeiEndpointEmbeddings, - VoyageAIEmbeddings, - ) - - self._vendors = [ - AzureOpenAIEmbeddings, - OpenAIEmbeddings, - FastEmbedEmbeddings, - LCCohereEmbeddings, - LCHuggingFaceEmbeddings, - LCGoogleEmbeddings, - LCMistralEmbeddings, - TeiEndpointEmbeddings, - VoyageAIEmbeddings, - ] def __getitem__(self, key: str) -> BaseEmbeddings: - """Get model by name""" + """Get model by name.""" + if key == "default": + key = self._default return self._models[key] def __contains__(self, key: str) -> bool: - """Check if model exists""" + """Check if model exists.""" return key in self._models def get( self, key: str, default: Optional[BaseEmbeddings] = None ) -> Optional[BaseEmbeddings]: - """Get model by name with default value""" + """Get model by name with a fallback default.""" return self._models.get(key, default) def settings(self) -> dict: - """Present model pools option for gradio""" + """Return Gradio dropdown settings for the pool.""" return { "label": "Embedding", "choices": list(self._models.keys()), "value": self.get_default_name(), } - def options(self) -> dict: - """Present a dict of models""" + def options(self) -> dict[str, BaseEmbeddings]: + """Return all models keyed by name.""" return self._models def get_random_name(self) -> str: - """Get the name of random model - - Returns: - str: random model name in the pool - """ + """Return a random model name from the pool.""" import random if not self._models: raise ValueError("No models in pool") - return random.choice(list(self._models.keys())) def get_default_name(self) -> str: - """Get the name of default model - - In case there is no default model, choose random model from pool. In - case there are multiple default models, choose random from them. - - Returns: - str: model name - """ + """Return the default model name, or a random one if unset.""" if not self._models: raise ValueError("No models in pool") - - if not self._default: - return self.get_random_name() - - return self._default + return self._default or self.get_random_name() def get_random(self) -> BaseEmbeddings: - """Get random model""" + """Return a random model instance.""" return self._models[self.get_random_name()] def get_default(self) -> BaseEmbeddings: - """Get default model - - In case there is no default model, choose random model from pool. In - case there are multiple default models, choose random from them. - - Returns: - BaseEmbeddings: model - """ + """Return the default model instance.""" return self._models[self.get_default_name()] - def info(self) -> dict: - """List all models""" + def info(self) -> dict[str, EmbeddingTable]: + """Return all model metadata keyed by name.""" return self._info - def add(self, name: str, spec: dict, default: bool): - """Add a new model to the pool""" - if not name: - raise ValueError("Name must not be empty") - + def add( + self, + name: str, + vendor: EmbeddingVendor, + spec: dict, + default: bool, + ) -> None: + """Add a new model to the pool. + + Args: + name: unique model name. + vendor: vendor class identifier for factory lookup. + spec: constructor parameters for the vendor class. + default: make this model the pool default. + + Raises: + ValueError: on validation or database errors. + """ try: - with Session(engine) as sess: - if default: - # turn all models to non-default - sess.query(EmbeddingTable).update({"default": False}) - sess.commit() - - item = EmbeddingTable(name=name, spec=spec, default=default) - sess.add(item) - sess.commit() + with EmbeddingCRUD(engine) as crud: + crud.create(name=name, vendor=vendor, spec=spec, default=default) + except ValueError: + raise except Exception as e: - raise ValueError(f"Failed to add model {name}: {e}") - + raise ValueError(f"Failed to add embedding model '{name}': {e}") from e self.load() - def delete(self, name: str): - """Delete a model from the pool""" - try: - with Session(engine) as sess: - item = sess.query(EmbeddingTable).filter_by(name=name).first() - sess.delete(item) - sess.commit() - except Exception as e: - raise ValueError(f"Failed to delete model {name}: {e}") + def delete(self, name: str) -> None: + """Remove a model from the pool. + + Args: + name: name of the model to remove. + Raises: + ValueError: if the model does not exist. + """ + with EmbeddingCRUD(engine) as crud: + crud.delete(name) self.load() - def update(self, name: str, spec: dict, default: bool, new_name: str = ""): - """Update a model in the pool, optionally renaming it.""" + def update( + self, + name: str, + vendor: EmbeddingVendor, + spec: dict, + default: bool, + new_name: str = "", + ) -> None: + """Update a model, optionally renaming it. + + Args: + name: current model name. + vendor: vendor class identifier for factory lookup. + spec: constructor parameters for the vendor class. + default: new default flag. + new_name: rename the model when non-empty. + + Raises: + ValueError: on validation or database errors. + """ if not name: raise ValueError("Name must not be empty") - # If update name if new_name and new_name != name: if new_name in self._info: raise ValueError( - f"Model '{new_name}' already exists. Use a unique name." + f"Model '{new_name}' already exists." " Use a unique name." ) self.delete(name) - self.add(new_name, spec=spec, default=default) + self.add(new_name, vendor=vendor, spec=spec, default=default) return try: - with Session(engine) as sess: - - if default: - # turn all models to non-default - sess.query(EmbeddingTable).update({"default": False}) - sess.commit() - - item = sess.query(EmbeddingTable).filter_by(name=name).first() - if not item: - raise ValueError(f"Model {name} not found") - item.spec = spec - item.default = default - sess.commit() + with EmbeddingCRUD(engine) as crud: + crud.update(name, vendor=vendor, spec=spec, default=default) + except ValueError: + raise except Exception as e: - raise ValueError(f"Failed to update model {name}: {e}") - + raise ValueError(f"Failed to update embedding model '{name}': {e}") from e self.load() - def vendors(self) -> dict: - """Return list of vendors""" - return {vendor.__qualname__: vendor for vendor in self._vendors} + def vendors(self) -> dict[EmbeddingVendor, type[BaseEmbeddings]]: + """Return all registered vendor classes keyed by EmbeddingVendor.""" + return dict(MP_VENDOR_CLS) embedding_models_manager = EmbeddingManager() diff --git a/libs/ktem/ktem/embeddings/ui.py b/libs/ktem/ktem/embeddings/ui.py index 29a52ffb0..be4c77adf 100644 --- a/libs/ktem/ktem/embeddings/ui.py +++ b/libs/ktem/ktem/embeddings/ui.py @@ -3,9 +3,10 @@ import gradio as gr import pandas as pd import yaml -from ktem.app import BasePage +from ktem.app import BaseApp, BasePage from ktem.utils.file import YAMLNoDateSafeLoader -from theflow.utils.modules import deserialize + +from kotaemon.embeddings.factory import EmbeddingFactory, EmbeddingVendor from .manager import embedding_models_manager @@ -14,14 +15,12 @@ def format_description(cls): params = cls.describe()["params"] params_lines = ["| Name | Type | Description |", "| --- | --- | --- |"] for key, value in params.items(): - if isinstance(value["auto_callback"], str): - continue params_lines.append(f"| {key} | {value['type']} | {value['help']} |") return f"{cls.__doc__}\n\n" + "\n".join(params_lines) class EmbeddingManagement(BasePage): - def __init__(self, app): + def __init__(self, app: BaseApp): self._app = app self.spec_desc_default = ( "# Spec description\n\nSelect a model to view the spec description." @@ -134,20 +133,24 @@ def _on_app_created(self): outputs=[self.emb_list], ) self._app.app.load( - lambda: gr.update(choices=list(embedding_models_manager.vendors().keys())), + lambda: gr.update( + choices=[ + vendor.value for vendor in EmbeddingFactory.supported_vendors() + ] + ), outputs=[self.emb_choices], ) - def on_emb_vendor_change(self, vendor): - vendor = embedding_models_manager.vendors()[vendor] + def on_emb_vendor_change(self, vendor: str): + vendor_cls = EmbeddingFactory.get_cls(EmbeddingVendor(vendor)) required: dict = {} - desc = vendor.describe() + desc = vendor_cls.describe() for key, value in desc["params"].items(): if value.get("required", False): required[key] = value.get("default", None) - return yaml.dump(required), format_description(vendor) + return yaml.dump(required), format_description(vendor_cls) def on_register_events(self): self.emb_choices.select( @@ -251,13 +254,11 @@ def create_emb(self, name, choices, spec, default): try: name = name.strip() spec = yaml.load(spec, Loader=YAMLNoDateSafeLoader) - spec["__type__"] = ( - embedding_models_manager.vendors()[choices].__module__ - + "." - + embedding_models_manager.vendors()[choices].__qualname__ - ) + vendor = EmbeddingVendor(choices) - embedding_models_manager.add(name, spec=spec, default=default) + embedding_models_manager.add( + name, vendor=vendor, spec=spec, default=default + ) gr.Info(f'Embedding model "{name}" created successfully') except ValueError as e: raise gr.Error(str(e)) @@ -266,13 +267,7 @@ def create_emb(self, name, choices, spec, default): def list_embeddings(self): """List the Embedding models""" - items = [] - for item in embedding_models_manager.info().values(): - record = {} - record["name"] = item["name"] - record["vendor"] = item["spec"].get("__type__", "-").split(".")[-1] - record["default"] = item["default"] - items.append(record) + items = [item.ui for _, item in embedding_models_manager.info().items()] if items: emb_list = pd.DataFrame.from_records(items) @@ -313,14 +308,13 @@ def on_selected_emb_change(self, selected_emb_name): btn_delete_yes = gr.update(visible=False) btn_delete_no = gr.update(visible=False) - info = deepcopy(embedding_models_manager.info()[selected_emb_name]) - vendor_str = info["spec"].pop("__type__", "-").split(".")[-1] - vendor = embedding_models_manager.vendors()[vendor_str] + item = embedding_models_manager.info()[selected_emb_name] + vendor = EmbeddingFactory.get_cls(EmbeddingVendor(item.vendor)) edit_name = selected_emb_name - edit_spec = yaml.dump(info["spec"]) + edit_spec = yaml.dump(item.spec) edit_spec_desc = format_description(vendor) - edit_default = info["default"] + edit_default = item.default return ( _selected_panel, @@ -348,14 +342,10 @@ def check_connection(self, selected_emb_name, selected_spec): log_content += f"- Testing model: {selected_emb_name}
" yield log_content - # Parse content & init model - info = deepcopy(embedding_models_manager.info()[selected_emb_name]) - - # Parse content & create dummy embedding - spec = yaml.load(selected_spec, Loader=YAMLNoDateSafeLoader) - info["spec"].update(spec) - - emb = deserialize(info["spec"], safe=False) + item = embedding_models_manager.info()[selected_emb_name] + params = deepcopy(item.spec) + params.update(yaml.load(selected_spec, Loader=YAMLNoDateSafeLoader)) + emb = EmbeddingFactory.get_cls(item.vendor)(**params) if emb is None: raise Exception(f"Can not found model: {selected_emb_name}") @@ -385,11 +375,13 @@ def save_emb(self, selected_emb_name, edit_name, default, spec): try: new_name = edit_name.strip() spec = yaml.load(spec, Loader=YAMLNoDateSafeLoader) - spec["__type__"] = embedding_models_manager.info()[selected_emb_name][ - "spec" - ]["__type__"] + item = embedding_models_manager.info()[selected_emb_name] embedding_models_manager.update( - selected_emb_name, spec=spec, default=default, new_name=new_name + selected_emb_name, + vendor=item.vendor, + spec=spec, + default=default, + new_name=new_name, ) final_name = ( new_name if new_name != selected_emb_name else selected_emb_name diff --git a/libs/ktem/ktem/index/__init__.py b/libs/ktem/ktem/index/__init__.py deleted file mode 100644 index 784c520e5..000000000 --- a/libs/ktem/ktem/index/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .manager import IndexManager - -__all__ = ["IndexManager"] diff --git a/libs/ktem/ktem/index/file/base.py b/libs/ktem/ktem/index/file/base.py deleted file mode 100644 index d57943ba9..000000000 --- a/libs/ktem/ktem/index/file/base.py +++ /dev/null @@ -1,161 +0,0 @@ -from pathlib import Path -from typing import Generator, Optional - -from kotaemon.base import BaseComponent, Document, Param - - -class BaseFileIndexRetriever(BaseComponent): - - Source = Param(help="The SQLAlchemy Source table") - Index = Param(help="The SQLAlchemy Index table") - VS = Param(help="The VectorStore") - DS = Param(help="The DocStore") - FSPath = Param(help="The file storage path") - user_id = Param(help="The user id") - - @classmethod - def get_user_settings(cls) -> dict: - """Get the user settings for indexing - - Returns: - dict: user settings in the dictionary format of - `ktem.settings.SettingItem` - """ - return {} - - @classmethod - def get_pipeline( - cls, - user_settings: dict, - index_settings: dict, - selected: Optional[list] = None, - ) -> "BaseFileIndexRetriever": - raise NotImplementedError - - -class BaseFileIndexIndexing(BaseComponent): - """The pipeline to index information into the data store - - You should define the following method: - - run(self, file_paths): run the indexing given the pipeline - - get_pipeline(cls, user_settings, index_settings): return the - fully-initialized pipeline, ready to be used by ktem. - - You will have access to the following resources: - - self._Source: the source table - - self._Index: the index table - - self._VS: the vector store - - self._DS: the docstore - """ - - Source = Param(help="The SQLAlchemy Source table") - Index = Param(help="The SQLAlchemy Index table") - VS = Param(help="The VectorStore") - DS = Param(help="The DocStore") - FSPath = Param(help="The file storage path") - user_id = Param(help="The user id") - private = Param(False, help="Whether this is private index") - chunk_size = Param(help="Chunk size for this index") - chunk_overlap = Param(help="Chunk overlap for this index") - - def run( - self, file_paths: str | Path | list[str | Path], *args, **kwargs - ) -> tuple[list[str | None], list[str | None]]: - """Run the indexing pipeline - - Args: - file_paths (str | Path | list[str | Path]): the file paths to index - - Returns: - - the indexed file ids (each file id corresponds to an input file path, or - None if the indexing failed for that file path) - - the error messages (each error message corresponds to an input file path, - or None if the indexing was successful for that file path) - """ - raise NotImplementedError - - def stream( - self, file_paths: str | Path | list[str | Path], *args, **kwargs - ) -> Generator[ - Document, None, tuple[list[str | None], list[str | None], list[Document]] - ]: - """Stream the indexing pipeline - - Args: - file_paths (str | Path | list[str | Path]): the file paths to index - - Yields: - Document: the output message to the UI, must have channel == index or debug - - Returns: - - the indexed file ids (each file id corresponds to an input file path, or - None if the indexing failed for that file path) - - the error messages (each error message corresponds to an input file path, - or None if the indexing was successful for that file path) - - the indexed documents in form of list[Documents] - """ - raise NotImplementedError - - @classmethod - def get_pipeline( - cls, user_settings: dict, index_settings: dict - ) -> "BaseFileIndexIndexing": - raise NotImplementedError - - @classmethod - def get_user_settings(cls) -> dict: - """Get the user settings for indexing - - Returns: - dict: user settings in the dictionary format of - `ktem.settings.SettingItem` - """ - return {} - - def copy_to_filestorage( - self, file_paths: str | Path | list[str | Path] - ) -> list[str]: - """Copy to file storage and return the new path, relative to the file storage - - Args: - file_path: the file path to copy - - Returns: - the new file paths, relative to the file storage - """ - import shutil - from hashlib import sha256 - - if not isinstance(file_paths, list): - file_paths = [file_paths] - - paths = [] - for file_path in file_paths: - with open(file_path, "rb") as f: - paths.append(sha256(f.read()).hexdigest()) - shutil.copy(file_path, self.FSPath / paths[-1]) - - return paths - - def get_filestorage_path(self, rel_paths: str | list[str]) -> list[str]: - """Get the file storage path for the relative path - - Args: - rel_paths: the relative path to the file storage - - Returns: - the absolute file storage path to the file - """ - raise NotImplementedError - - def warning(self, msg): - """Log a warning message - - Args: - msg: the message to log - """ - print(msg) - - def rebuild_index(self): - """Rebuild the index""" - raise NotImplementedError diff --git a/libs/ktem/ktem/index/file/graph/__init__.py b/libs/ktem/ktem/index/file/graph/__init__.py deleted file mode 100644 index afe1db443..000000000 --- a/libs/ktem/ktem/index/file/graph/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from .graph_index import GraphRAGIndex -from .light_graph_index import LightRAGIndex -from .nano_graph_index import NanoGraphRAGIndex - -__all__ = ["GraphRAGIndex", "NanoGraphRAGIndex", "LightRAGIndex"] diff --git a/libs/ktem/ktem/index/file/graph/graph_index.py b/libs/ktem/ktem/index/file/graph/graph_index.py deleted file mode 100644 index cf5dee2aa..000000000 --- a/libs/ktem/ktem/index/file/graph/graph_index.py +++ /dev/null @@ -1,36 +0,0 @@ -from typing import Any - -from ktem.index.file import FileIndex - -from ..base import BaseFileIndexIndexing, BaseFileIndexRetriever -from .pipelines import GraphRAGIndexingPipeline, GraphRAGRetrieverPipeline - - -class GraphRAGIndex(FileIndex): - def _setup_indexing_cls(self): - self._indexing_pipeline_cls = GraphRAGIndexingPipeline - - def _setup_retriever_cls(self): - self._retriever_pipeline_cls = [GraphRAGRetrieverPipeline] - - def get_indexing_pipeline(self, settings, user_id) -> BaseFileIndexIndexing: - """Define the interface of the indexing pipeline""" - - obj = super().get_indexing_pipeline(settings, user_id) - # disable vectorstore for this kind of Index - obj.VS = None - - return obj - - def get_retriever_pipelines( - self, settings: dict, user_id: int, selected: Any = None - ) -> list["BaseFileIndexRetriever"]: - file_ids = self._selector_ui.get_selected_ids(selected) - retrievers = [ - GraphRAGRetrieverPipeline( - file_ids=file_ids, - Index=self._resources["Index"], - ) - ] - - return retrievers diff --git a/libs/ktem/ktem/index/file/graph/light_graph_index.py b/libs/ktem/ktem/index/file/graph/light_graph_index.py deleted file mode 100644 index c86cfcffd..000000000 --- a/libs/ktem/ktem/index/file/graph/light_graph_index.py +++ /dev/null @@ -1,77 +0,0 @@ -from typing import Any, Optional -from uuid import uuid4 - -from ktem.db.engine import engine -from sqlalchemy.orm import Session - -from ..base import BaseFileIndexIndexing, BaseFileIndexRetriever -from .graph_index import GraphRAGIndex -from .lightrag_pipelines import LightRAGIndexingPipeline, LightRAGRetrieverPipeline - - -class LightRAGIndex(GraphRAGIndex): - def __init__(self, app, id: int, name: str, config: dict): - super().__init__(app, id, name, config) - self._collection_graph_id: Optional[str] = None - - def _setup_indexing_cls(self): - self._indexing_pipeline_cls = LightRAGIndexingPipeline - - def _setup_retriever_cls(self): - self._retriever_pipeline_cls = [LightRAGRetrieverPipeline] - - def _get_or_create_collection_graph_id(self): - if self._collection_graph_id: - return self._collection_graph_id - - # Try to find existing graph ID for this collection - with Session(engine) as session: - result = ( - session.query(self._resources["Index"].target_id) # type: ignore - .filter( - self._resources["Index"].relation_type == "graph" # type: ignore - ) - .first() - ) - if result: - self._collection_graph_id = result[0] - else: - self._collection_graph_id = str(uuid4()) - return self._collection_graph_id - - def get_indexing_pipeline(self, settings, user_id) -> BaseFileIndexIndexing: - pipeline = super().get_indexing_pipeline(settings, user_id) - # indexing settings - prefix = f"index.options.{self.id}." - striped_settings = { - key[len(prefix) :]: value - for key, value in settings.items() - if key.startswith(prefix) - } - # set the prompts - pipeline.prompts = striped_settings - # set collection graph id - pipeline.collection_graph_id = self._get_or_create_collection_graph_id() - # set index batch size - pipeline.index_batch_size = striped_settings.get( - "batch_size", pipeline.index_batch_size - ) - return pipeline - - def get_retriever_pipelines( - self, settings: dict, user_id: int, selected: Any = None - ) -> list["BaseFileIndexRetriever"]: - file_ids = self._selector_ui.get_selected_ids(selected) - # retrieval settings - prefix = f"index.options.{self.id}." - search_type = settings.get(prefix + "search_type", "local") - - retrievers = [ - LightRAGRetrieverPipeline( - file_ids=file_ids, - Index=self._resources["Index"], - search_type=search_type, - ) - ] - - return retrievers diff --git a/libs/ktem/ktem/index/file/graph/lightrag_pipelines.py b/libs/ktem/ktem/index/file/graph/lightrag_pipelines.py deleted file mode 100644 index 7df97df03..000000000 --- a/libs/ktem/ktem/index/file/graph/lightrag_pipelines.py +++ /dev/null @@ -1,547 +0,0 @@ -import asyncio -import glob -import logging -import os -import re -from pathlib import Path -from typing import Generator - -import numpy as np -import pandas as pd -from ktem.db.models import engine -from ktem.embeddings.manager import embedding_models_manager as embeddings -from ktem.llms.manager import llms -from sqlalchemy.orm import Session -from tenacity import ( - retry, - retry_if_exception_type, - stop_after_attempt, - wait_exponential, -) -from theflow.settings import settings - -from kotaemon.base import Document, Param, RetrievedDocument -from kotaemon.base.schema import AIMessage, HumanMessage, SystemMessage - -from ..pipelines import BaseFileIndexRetriever -from .pipelines import GraphRAGIndexingPipeline -from .visualize import create_knowledge_graph, visualize_graph - -try: - from lightrag import LightRAG, QueryParam - - # newer versions of LightRAG needs to be initialized before using - from lightrag.kg.shared_storage import initialize_pipeline_status - from lightrag.operate import ( - _find_most_related_edges_from_entities, - _find_most_related_text_unit_from_entities, - ) - from lightrag.utils import EmbeddingFunc, compute_args_hash - -except ImportError: - print( - ( - "LightRAG dependencies not installed. " - "Try `pip install git+https://github.com/HKUDS/LightRAG.git` to install. " - "LighthRAG retriever pipeline will not work properly." - ) - ) - - -logging.getLogger("lightrag").setLevel(logging.INFO) - - -filestorage_path = Path(settings.KH_FILESTORAGE_PATH) / "lightrag" -filestorage_path.mkdir(parents=True, exist_ok=True) - -INDEX_BATCHSIZE = 4 - - -def get_llm_func(model): - @retry( - stop=stop_after_attempt(3), - wait=wait_exponential(multiplier=1, min=4, max=10), - retry=retry_if_exception_type((Exception,)), - after=lambda retry_state: logging.warning( - f"LLM API call attempt {retry_state.attempt_number} failed. Retrying..." - ), - ) - async def _call_model(model, input_messages): - return (await model.ainvoke(input_messages)).text - - async def llm_func( - prompt, system_prompt=None, history_messages=[], **kwargs - ) -> str: - input_messages = [SystemMessage(text=system_prompt)] if system_prompt else [] - - hashing_kv = kwargs.pop("hashing_kv", None) - if history_messages: - for msg in history_messages: - if msg.get("role") == "user": - input_messages.append(HumanMessage(text=msg["content"])) - else: - input_messages.append(AIMessage(text=msg["content"])) - - input_messages.append(HumanMessage(text=prompt)) - - if hashing_kv is not None: - args_hash = compute_args_hash("model", input_messages) - if_cache_return = await hashing_kv.get_by_id(args_hash) - if if_cache_return is not None: - return if_cache_return["return"] - - try: - output = await _call_model(model, input_messages) - except Exception as e: - logging.error(f"Failed to call LLM API after 3 retries: {str(e)}") - raise - - print("-" * 50) - print(output, "\n", "-" * 50) - - if hashing_kv is not None: - await hashing_kv.upsert({args_hash: {"return": output, "model": "model"}}) - - return output - - return llm_func - - -def get_embedding_func(model): - async def embedding_func(texts: list[str]) -> np.ndarray: - outputs = model(texts) - embedding_outputs = np.array([doc.embedding for doc in outputs]) - - return embedding_outputs - - return embedding_func - - -def get_default_models_wrapper(): - # setup model functions - default_embedding = embeddings.get_default() - default_embedding_dim = len(default_embedding(["Hi"])[0].embedding) - embedding_func = EmbeddingFunc( - embedding_dim=default_embedding_dim, - max_token_size=8192, - func=get_embedding_func(default_embedding), - ) - print("GraphRAG embedding dim", default_embedding_dim) - - default_llm = llms.get_default() - llm_func = get_llm_func(default_llm) - - return llm_func, embedding_func, default_llm, default_embedding - - -def prepare_graph_index_path(graph_id: str): - root_path = Path(filestorage_path) / graph_id - input_path = root_path / "input" - - return root_path, input_path - - -def list_of_list_to_df(data: list[list]) -> pd.DataFrame: - df = pd.DataFrame(data[1:], columns=data[0]) - return df - - -def clean_quote(input: str) -> str: - return re.sub(r"[\"']", "", input) - - -async def lightrag_build_local_query_context( - graph_func, - query, - query_param, -): - knowledge_graph_inst = graph_func.chunk_entity_relation_graph - entities_vdb = graph_func.entities_vdb - text_chunks_db = graph_func.text_chunks - - results = await entities_vdb.query(query, top_k=query_param.top_k) - if not len(results): - raise ValueError("No results found") - - node_datas = await asyncio.gather( - *[knowledge_graph_inst.get_node(r["entity_name"]) for r in results] - ) - node_degrees = await asyncio.gather( - *[knowledge_graph_inst.node_degree(r["entity_name"]) for r in results] - ) - node_datas = [ - {**n, "entity_name": k["entity_name"], "rank": d} - for k, n, d in zip(results, node_datas, node_degrees) - if n is not None - ] - - try: - use_text_units = await _find_most_related_text_unit_from_entities( - node_datas, query_param, text_chunks_db, knowledge_graph_inst - ) - except Exception: - use_text_units = [] - - try: - use_relations = await _find_most_related_edges_from_entities( - node_datas, query_param, knowledge_graph_inst - ) - except Exception: - use_relations = [] - - logging.info( - f"Local query uses {len(node_datas)} entities, " - f"{len(use_relations)} relations, {len(use_text_units)} text units" - ) - - entites_section_list = [["id", "entity", "type", "description", "rank"]] - for i, n in enumerate(node_datas): - entites_section_list.append( - [ - str(i), - clean_quote(n["entity_name"]), - n.get("entity_type", "UNKNOWN"), - clean_quote(n.get("description", "UNKNOWN")), - n["rank"], - ] - ) - entities_df = list_of_list_to_df(entites_section_list) - - relations_section_list = [ - ["id", "source", "target", "description", "keywords", "weight", "rank"] - ] - for i, e in enumerate(use_relations): - relations_section_list.append( - [ - str(i), - clean_quote(e["src_tgt"][0]), - clean_quote(e["src_tgt"][1]), - clean_quote(e["description"]), - e["keywords"], - e["weight"], - e["rank"], - ] - ) - relations_df = list_of_list_to_df(relations_section_list) - - text_units_section_list = [["id", "content"]] - for i, t in enumerate(use_text_units): - text_units_section_list.append([str(i), t["content"]]) - sources_df = list_of_list_to_df(text_units_section_list) - - return entities_df, relations_df, sources_df - - -def build_graphrag(working_dir, llm_func, embedding_func): - graphrag_func = LightRAG( - working_dir=working_dir, - llm_model_func=llm_func, - embedding_func=embedding_func, - ) - - # newer versions of LightRAG needs to be initialized before using - asyncio.run(graphrag_func.initialize_storages()) - asyncio.run(initialize_pipeline_status()) - - return graphrag_func - - -class LightRAGIndexingPipeline(GraphRAGIndexingPipeline): - """GraphRAG specific indexing pipeline""" - - prompts: dict[str, str] = {} - collection_graph_id: str - index_batch_size: int = INDEX_BATCHSIZE - - def store_file_id_with_graph_id(self, file_ids: list[str | None]): - if not settings.USE_GLOBAL_GRAPHRAG: - return super().store_file_id_with_graph_id(file_ids) - - # Use the collection-wide graph ID for LightRAG - graph_id = self.collection_graph_id - - # Record all files under this graph_id - with Session(engine) as session: - for file_id in file_ids: - if not file_id: - continue - # Check if mapping already exists - existing = ( - session.query(self.Index) - .filter( - self.Index.source_id == file_id, - self.Index.target_id == graph_id, - self.Index.relation_type == "graph", - ) - .first() - ) - if not existing: - node = self.Index( - source_id=file_id, - target_id=graph_id, - relation_type="graph", - ) - session.add(node) - session.commit() - - return graph_id - - @classmethod - def get_user_settings(cls) -> dict: - settings_dict = dict(GraphRAGIndexingPipeline.get_user_settings()) - try: - from lightrag.prompt import PROMPTS - - blacklist_keywords = ["default", "response", "process"] - settings_dict["batch_size"] = { - "name": ("Index batch size " "(reduce if you have rate limit issues)"), - "value": INDEX_BATCHSIZE, - "component": "number", - } - settings_dict.update( - { - prompt_name: { - "name": f"Prompt for '{prompt_name}'", - "value": content, - "component": "text", - } - for prompt_name, content in PROMPTS.items() - if all( - keyword not in prompt_name.lower() - for keyword in blacklist_keywords - ) - and isinstance(content, str) - } - ) - return settings_dict - except ImportError as e: - print(e) - return settings_dict - - def call_graphrag_index(self, graph_id: str, docs: list[Document]): - from lightrag.prompt import PROMPTS - - # modify the prompt if it is set in the settings - for prompt_name, content in self.prompts.items(): - if prompt_name in PROMPTS: - PROMPTS[prompt_name] = content - - _, input_path = prepare_graph_index_path(graph_id) - input_path.mkdir(parents=True, exist_ok=True) - - ( - llm_func, - embedding_func, - default_llm, - default_embedding, - ) = get_default_models_wrapper() - print( - f"Indexing GraphRAG with LLM {default_llm} " - f"and Embedding {default_embedding}..." - ) - - all_docs = [ - doc.text - for doc in docs - if doc.metadata.get("type", "text") == "text" and len(doc.text.strip()) > 0 - ] - - yield Document( - channel="debug", - text="[GraphRAG] Creating/Updating index... This can take a long time.", - ) - - # Check if graph already exists - graph_file = input_path / "graph_chunk_entity_relation.graphml" - is_incremental = graph_file.exists() - - # Only clear cache if it's a new graph - if not is_incremental: - json_files = glob.glob(f"{input_path}/*.json") - for json_file in json_files: - os.remove(json_file) - - # Initialize or load existing GraphRAG - graphrag_func = build_graphrag( - input_path, - llm_func=llm_func, - embedding_func=embedding_func, - ) - - total_docs = len(all_docs) - process_doc_count = 0 - yield Document( - channel="debug", - text=( - f"[GraphRAG] {'Updating' if is_incremental else 'Creating'} index: " - f"{process_doc_count} / {total_docs} documents." - ), - ) - - for doc_id in range(0, len(all_docs), self.index_batch_size): - cur_docs = all_docs[doc_id : doc_id + self.index_batch_size] - combined_doc = "\n".join(cur_docs) - - # Use insert for incremental updates - graphrag_func.insert(combined_doc) - process_doc_count += len(cur_docs) - yield Document( - channel="debug", - text=( - f"[GraphRAG] {'Updated' if is_incremental else 'Indexed'} " - f"{process_doc_count} / {total_docs} documents." - ), - ) - - yield Document( - channel="debug", - text=f"[GraphRAG] {'Update' if is_incremental else 'Indexing'} finished.", - ) - - def stream( - self, file_paths: str | Path | list[str | Path], reindex: bool = False, **kwargs - ) -> Generator[ - Document, None, tuple[list[str | None], list[str | None], list[Document]] - ]: - file_ids, errors, all_docs = yield from super().stream( - file_paths, reindex=reindex, **kwargs - ) - - return file_ids, errors, all_docs - - -class LightRAGRetrieverPipeline(BaseFileIndexRetriever): - """GraphRAG specific retriever pipeline""" - - Index = Param(help="The SQLAlchemy Index table") - file_ids: list[str] = [] - search_type: str = "local" - - @classmethod - def get_user_settings(cls) -> dict: - return { - "search_type": { - "name": "Search type", - "value": "local", - "choices": ["local", "global", "hybrid"], - "component": "dropdown", - "info": "Whether to use local or global search in the graph.", - } - } - - def _build_graph_search(self): - file_id = self.file_ids[0] - - # retrieve the graph_id from the index - with Session(engine) as session: - graph_id = ( - session.query(self.Index.target_id) - .filter(self.Index.source_id == file_id) - .filter(self.Index.relation_type == "graph") - .first() - ) - graph_id = graph_id[0] if graph_id else None - assert graph_id, f"GraphRAG index not found for file_id: {file_id}" - - _, input_path = prepare_graph_index_path(graph_id) - input_path.mkdir(parents=True, exist_ok=True) - - llm_func, embedding_func, _, _ = get_default_models_wrapper() - graphrag_func = build_graphrag( - input_path, - llm_func=llm_func, - embedding_func=embedding_func, - ) - print("search_type", self.search_type) - query_params = QueryParam(mode=self.search_type, only_need_context=True) - - return graphrag_func, query_params - - def _to_document(self, header: str, context_text: str) -> RetrievedDocument: - return RetrievedDocument( - text=context_text, - metadata={ - "file_name": header, - "type": "table", - "llm_trulens_score": 1.0, - }, - score=1.0, - ) - - def format_context_records( - self, entities, relationships, sources - ) -> list[RetrievedDocument]: - docs = [] - context: str = "" - - # entities current parsing error - header = "Entities\n" - context = entities[["entity", "description"]].to_markdown(index=False) - docs.append(self._to_document(header, context)) - - header = "\nRelationships\n" - context = relationships[["source", "target", "description"]].to_markdown( - index=False - ) - docs.append(self._to_document(header, context)) - - header = "\nSources\n" - context = "" - for _, row in sources.iterrows(): - title, content = row["id"], row["content"] - context += f"\n\n
Source #{title}
\n" - context += content - docs.append(self._to_document(header, context)) - - return docs - - def plot_graph(self, relationships): - G = create_knowledge_graph(relationships) - plot = visualize_graph(G) - return plot - - def run( - self, - text: str, - ) -> list[RetrievedDocument]: - if not self.file_ids: - return [] - - graphrag_func, query_params = self._build_graph_search() - - # only local mode support graph visualization - if query_params.mode == "local": - entities, relationships, sources = asyncio.run( - lightrag_build_local_query_context(graphrag_func, text, query_params) - ) - documents = self.format_context_records(entities, relationships, sources) - plot = self.plot_graph(relationships) - documents += [ - RetrievedDocument( - text="", - metadata={ - "file_name": "GraphRAG", - "type": "plot", - "data": plot, - }, - ), - ] - else: - context = graphrag_func.query(text, query_params) - - # account for missing ``` for closing code block - context += "\n```" - - documents = [ - RetrievedDocument( - text=context, - metadata={ - "file_name": "GraphRAG {} Search".format( - query_params.mode.capitalize() - ), - "type": "table", - }, - ) - ] - - return documents diff --git a/libs/ktem/ktem/index/file/graph/nano_graph_index.py b/libs/ktem/ktem/index/file/graph/nano_graph_index.py deleted file mode 100644 index 636fc708e..000000000 --- a/libs/ktem/ktem/index/file/graph/nano_graph_index.py +++ /dev/null @@ -1,77 +0,0 @@ -from typing import Any, Optional -from uuid import uuid4 - -from ktem.db.engine import engine -from sqlalchemy.orm import Session - -from ..base import BaseFileIndexIndexing, BaseFileIndexRetriever -from .graph_index import GraphRAGIndex -from .nano_pipelines import NanoGraphRAGIndexingPipeline, NanoGraphRAGRetrieverPipeline - - -class NanoGraphRAGIndex(GraphRAGIndex): - def __init__(self, app, id: int, name: str, config: dict): - super().__init__(app, id, name, config) - self._collection_graph_id: Optional[str] = None - - def _setup_indexing_cls(self): - self._indexing_pipeline_cls = NanoGraphRAGIndexingPipeline - - def _setup_retriever_cls(self): - self._retriever_pipeline_cls = [NanoGraphRAGRetrieverPipeline] - - def _get_or_create_collection_graph_id(self): - if self._collection_graph_id: - return self._collection_graph_id - - # Try to find existing graph ID for this collection - with Session(engine) as session: - result = ( - session.query(self._resources["Index"].target_id) # type: ignore - .filter( - self._resources["Index"].relation_type == "graph" # type: ignore - ) - .first() - ) - if result: - self._collection_graph_id = result[0] - else: - self._collection_graph_id = str(uuid4()) - return self._collection_graph_id - - def get_indexing_pipeline(self, settings, user_id) -> BaseFileIndexIndexing: - pipeline = super().get_indexing_pipeline(settings, user_id) - # indexing settings - prefix = f"index.options.{self.id}." - striped_settings = { - key[len(prefix) :]: value - for key, value in settings.items() - if key.startswith(prefix) - } - # set the prompts - pipeline.prompts = striped_settings - # set collection graph id - pipeline.collection_graph_id = self._get_or_create_collection_graph_id() - # set index batch size - pipeline.index_batch_size = striped_settings.get( - "batch_size", pipeline.index_batch_size - ) - return pipeline - - def get_retriever_pipelines( - self, settings: dict, user_id: int, selected: Any = None - ) -> list["BaseFileIndexRetriever"]: - file_ids = self._selector_ui.get_selected_ids(selected) - # retrieval settings - prefix = f"index.options.{self.id}." - search_type = settings.get(prefix + "search_type", "local") - - retrievers = [ - NanoGraphRAGRetrieverPipeline( - file_ids=file_ids, - Index=self._resources["Index"], - search_type=search_type, - ) - ] - - return retrievers diff --git a/libs/ktem/ktem/index/file/graph/nano_pipelines.py b/libs/ktem/ktem/index/file/graph/nano_pipelines.py deleted file mode 100644 index 4e5d04375..000000000 --- a/libs/ktem/ktem/index/file/graph/nano_pipelines.py +++ /dev/null @@ -1,549 +0,0 @@ -import asyncio -import glob -import logging -import os -import re -from pathlib import Path -from typing import Generator - -import numpy as np -import pandas as pd -from ktem.db.models import engine -from ktem.embeddings.manager import embedding_models_manager as embeddings -from ktem.llms.manager import llms -from sqlalchemy.orm import Session -from tenacity import ( - retry, - retry_if_exception_type, - stop_after_attempt, - wait_exponential, -) -from theflow.settings import settings - -from kotaemon.base import Document, Param, RetrievedDocument -from kotaemon.base.schema import AIMessage, HumanMessage, SystemMessage - -from ..pipelines import BaseFileIndexRetriever -from .pipelines import GraphRAGIndexingPipeline -from .visualize import create_knowledge_graph, visualize_graph - -try: - from nano_graphrag import GraphRAG, QueryParam - from nano_graphrag._op import ( - _find_most_related_community_from_entities, - _find_most_related_edges_from_entities, - _find_most_related_text_unit_from_entities, - ) - from nano_graphrag._utils import EmbeddingFunc, compute_args_hash - -except ImportError: - print( - ( - "Nano-GraphRAG dependencies not installed. " - "Try `pip install nano-graphrag` to install. " - "Nano-GraphRAG retriever pipeline will not work properly." - ) - ) - - -logging.getLogger("nano-graphrag").setLevel(logging.INFO) - - -filestorage_path = Path(settings.KH_FILESTORAGE_PATH) / "nano_graphrag" -filestorage_path.mkdir(parents=True, exist_ok=True) - -INDEX_BATCHSIZE = 4 - - -def get_llm_func(model): - @retry( - stop=stop_after_attempt(3), - wait=wait_exponential(multiplier=1, min=4, max=10), - retry=retry_if_exception_type((Exception,)), - after=lambda retry_state: logging.warning( - f"LLM API call attempt {retry_state.attempt_number} failed. Retrying..." - ), - ) - async def _call_model(model, input_messages): - return (await model.ainvoke(input_messages)).text - - async def llm_func( - prompt, system_prompt=None, history_messages=[], **kwargs - ) -> str: - input_messages = [SystemMessage(text=system_prompt)] if system_prompt else [] - - hashing_kv = kwargs.pop("hashing_kv", None) - if history_messages: - for msg in history_messages: - if msg.get("role") == "user": - input_messages.append(HumanMessage(text=msg["content"])) - else: - input_messages.append(AIMessage(text=msg["content"])) - - input_messages.append(HumanMessage(text=prompt)) - - if hashing_kv is not None: - args_hash = compute_args_hash("model", input_messages) - if_cache_return = await hashing_kv.get_by_id(args_hash) - if if_cache_return is not None: - return if_cache_return["return"] - - try: - output = await _call_model(model, input_messages) - except Exception as e: - logging.error(f"Failed to call LLM API after 3 retries: {str(e)}") - raise - - print("-" * 50) - print(output, "\n", "-" * 50) - - if hashing_kv is not None: - await hashing_kv.upsert({args_hash: {"return": output, "model": "model"}}) - - return output - - return llm_func - - -def get_embedding_func(model): - async def embedding_func(texts: list[str]) -> np.ndarray: - outputs = model(texts) - embedding_outputs = np.array([doc.embedding for doc in outputs]) - - return embedding_outputs - - return embedding_func - - -def get_default_models_wrapper(): - # setup model functions - default_embedding = embeddings.get_default() - default_embedding_dim = len(default_embedding(["Hi"])[0].embedding) - embedding_func = EmbeddingFunc( - embedding_dim=default_embedding_dim, - max_token_size=8192, - func=get_embedding_func(default_embedding), - ) - print("GraphRAG embedding dim", default_embedding_dim) - - default_llm = llms.get_default() - llm_func = get_llm_func(default_llm) - - return llm_func, embedding_func, default_llm, default_embedding - - -def prepare_graph_index_path(graph_id: str): - root_path = Path(filestorage_path) / graph_id - input_path = root_path / "input" - - return root_path, input_path - - -def list_of_list_to_df(data: list[list]) -> pd.DataFrame: - df = pd.DataFrame(data[1:], columns=data[0]) - return df - - -def clean_quote(input: str) -> str: - return re.sub(r"[\"']", "", input) - - -async def nano_graph_rag_build_local_query_context( - graph_func, - query, - query_param, -): - knowledge_graph_inst = graph_func.chunk_entity_relation_graph - entities_vdb = graph_func.entities_vdb - community_reports = graph_func.community_reports - text_chunks_db = graph_func.text_chunks - - results = await entities_vdb.query(query, top_k=query_param.top_k) - if not len(results): - raise ValueError("No results found") - - node_datas = await asyncio.gather( - *[knowledge_graph_inst.get_node(r["entity_name"]) for r in results] - ) - node_degrees = await asyncio.gather( - *[knowledge_graph_inst.node_degree(r["entity_name"]) for r in results] - ) - node_datas = [ - {**n, "entity_name": k["entity_name"], "rank": d} - for k, n, d in zip(results, node_datas, node_degrees) - if n is not None - ] - use_communities = await _find_most_related_community_from_entities( - node_datas, query_param, community_reports - ) - use_text_units = await _find_most_related_text_unit_from_entities( - node_datas, query_param, text_chunks_db, knowledge_graph_inst - ) - use_relations = await _find_most_related_edges_from_entities( - node_datas, query_param, knowledge_graph_inst - ) - entites_section_list = [["id", "entity", "type", "description", "rank"]] - for i, n in enumerate(node_datas): - entites_section_list.append( - [ - str(i), - clean_quote(n["entity_name"]), - n.get("entity_type", "UNKNOWN"), - clean_quote(n.get("description", "UNKNOWN")), - n["rank"], - ] - ) - entities_df = list_of_list_to_df(entites_section_list) - - relations_section_list = [ - ["id", "source", "target", "description", "weight", "rank"] - ] - for i, e in enumerate(use_relations): - relations_section_list.append( - [ - str(i), - clean_quote(e["src_tgt"][0]), - clean_quote(e["src_tgt"][1]), - clean_quote(e["description"]), - e["weight"], - e["rank"], - ] - ) - relations_df = list_of_list_to_df(relations_section_list) - - communities_section_list = [["id", "content"]] - for i, c in enumerate(use_communities): - communities_section_list.append([str(i), c["report_string"]]) - communities_df = list_of_list_to_df(communities_section_list) - - text_units_section_list = [["id", "content"]] - for i, t in enumerate(use_text_units): - text_units_section_list.append([str(i), t["content"]]) - sources_df = list_of_list_to_df(text_units_section_list) - - return entities_df, relations_df, communities_df, sources_df - - -def build_graphrag(working_dir, llm_func, embedding_func): - graphrag_func = GraphRAG( - working_dir=working_dir, - best_model_func=llm_func, - cheap_model_func=llm_func, - embedding_func=embedding_func, - ) - return graphrag_func - - -class NanoGraphRAGIndexingPipeline(GraphRAGIndexingPipeline): - """GraphRAG specific indexing pipeline""" - - prompts: dict[str, str] = {} - collection_graph_id: str - index_batch_size: int = INDEX_BATCHSIZE - - def store_file_id_with_graph_id(self, file_ids: list[str | None]): - if not settings.USE_GLOBAL_GRAPHRAG: - return super().store_file_id_with_graph_id(file_ids) - - # Use the collection-wide graph ID for LightRAG - graph_id = self.collection_graph_id - - # Record all files under this graph_id - with Session(engine) as session: - for file_id in file_ids: - if not file_id: - continue - # Check if mapping already exists - existing = ( - session.query(self.Index) - .filter( - self.Index.source_id == file_id, - self.Index.target_id == graph_id, - self.Index.relation_type == "graph", - ) - .first() - ) - if not existing: - node = self.Index( - source_id=file_id, - target_id=graph_id, - relation_type="graph", - ) - session.add(node) - session.commit() - - return graph_id - - @classmethod - def get_user_settings(cls) -> dict: - try: - from nano_graphrag.prompt import PROMPTS - - blacklist_keywords = ["default", "response", "process"] - settings_dict = { - "batch_size": { - "name": ( - "Index batch size " "(reduce if you have rate limit issues)" - ), - "value": INDEX_BATCHSIZE, - "component": "number", - } - } - settings_dict.update( - { - prompt_name: { - "name": f"Prompt for '{prompt_name}'", - "value": content, - "component": "text", - } - for prompt_name, content in PROMPTS.items() - if all( - keyword not in prompt_name.lower() - for keyword in blacklist_keywords - ) - and isinstance(content, str) - } - ) - return settings_dict - except ImportError as e: - print(e) - return {} - - def call_graphrag_index(self, graph_id: str, docs: list[Document]): - from nano_graphrag.prompt import PROMPTS - - # modify the prompt if it is set in the settings - for prompt_name, content in self.prompts.items(): - if prompt_name in PROMPTS: - PROMPTS[prompt_name] = content - - _, input_path = prepare_graph_index_path(graph_id) - input_path.mkdir(parents=True, exist_ok=True) - - ( - llm_func, - embedding_func, - default_llm, - default_embedding, - ) = get_default_models_wrapper() - print( - f"Indexing GraphRAG with LLM {default_llm} " - f"and Embedding {default_embedding}..." - ) - - all_docs = [ - doc.text - for doc in docs - if doc.metadata.get("type", "text") == "text" and len(doc.text.strip()) > 0 - ] - - yield Document( - channel="debug", - text="[GraphRAG] Creating/Updating index... This can take a long time.", - ) - - # Check if graph already exists - graph_file = input_path / "graph_chunk_entity_relation.graphml" - is_incremental = graph_file.exists() - - # Only clear cache if it's a new graph - if not is_incremental: - json_files = glob.glob(f"{input_path}/*.json") - for json_file in json_files: - os.remove(json_file) - - # Initialize or load existing GraphRAG - graphrag_func = build_graphrag( - input_path, - llm_func=llm_func, - embedding_func=embedding_func, - ) - - total_docs = len(all_docs) - process_doc_count = 0 - yield Document( - channel="debug", - text=( - f"[GraphRAG] {'Updating' if is_incremental else 'Creating'} index: " - f"{process_doc_count} / {total_docs} documents." - ), - ) - - for doc_id in range(0, len(all_docs), self.index_batch_size): - cur_docs = all_docs[doc_id : doc_id + self.index_batch_size] - combined_doc = "\n".join(cur_docs) - - # Use insert for incremental updates - graphrag_func.insert(combined_doc) - process_doc_count += len(cur_docs) - yield Document( - channel="debug", - text=( - f"[GraphRAG] {'Updated' if is_incremental else 'Indexed'} " - f"{process_doc_count} / {total_docs} documents." - ), - ) - - yield Document( - channel="debug", - text=f"[GraphRAG] {'Update' if is_incremental else 'Indexing'} finished.", - ) - - def stream( - self, file_paths: str | Path | list[str | Path], reindex: bool = False, **kwargs - ) -> Generator[ - Document, None, tuple[list[str | None], list[str | None], list[Document]] - ]: - file_ids, errors, all_docs = yield from super().stream( - file_paths, reindex=reindex, **kwargs - ) - - return file_ids, errors, all_docs - - -class NanoGraphRAGRetrieverPipeline(BaseFileIndexRetriever): - """GraphRAG specific retriever pipeline""" - - Index = Param(help="The SQLAlchemy Index table") - file_ids: list[str] = [] - search_type: str = "local" - - @classmethod - def get_user_settings(cls) -> dict: - return { - "search_type": { - "name": "Search type", - "value": "local", - "choices": ["local", "global"], - "component": "dropdown", - "info": "Whether to use local or global search in the graph.", - } - } - - def _build_graph_search(self): - file_id = self.file_ids[0] - - # retrieve the graph_id from the index - with Session(engine) as session: - graph_id = ( - session.query(self.Index.target_id) - .filter(self.Index.source_id == file_id) - .filter(self.Index.relation_type == "graph") - .first() - ) - graph_id = graph_id[0] if graph_id else None - assert graph_id, f"GraphRAG index not found for file_id: {file_id}" - - _, input_path = prepare_graph_index_path(graph_id) - input_path.mkdir(parents=True, exist_ok=True) - - llm_func, embedding_func, _, _ = get_default_models_wrapper() - graphrag_func = build_graphrag( - input_path, - llm_func=llm_func, - embedding_func=embedding_func, - ) - print("search_type", self.search_type) - query_params = QueryParam(mode=self.search_type, only_need_context=True) - - return graphrag_func, query_params - - def _to_document(self, header: str, context_text: str) -> RetrievedDocument: - return RetrievedDocument( - text=context_text, - metadata={ - "file_name": header, - "type": "table", - "llm_trulens_score": 1.0, - }, - score=1.0, - ) - - def format_context_records( - self, entities, relationships, reports, sources - ) -> list[RetrievedDocument]: - docs = [] - context: str = "" - - # entities current parsing error - header = "Entities\n" - context = entities[["entity", "description"]].to_markdown(index=False) - docs.append(self._to_document(header, context)) - - header = "\nRelationships\n" - context = relationships[["source", "target", "description"]].to_markdown( - index=False - ) - docs.append(self._to_document(header, context)) - - header = "\nReports\n" - context = "" - for _, row in reports.iterrows(): - title, content = row["id"], row["content"] # not contain title - context += f"\n\n
Report {title}
\n" - context += content - docs.append(self._to_document(header, context)) - - header = "\nSources\n" - context = "" - for _, row in sources.iterrows(): - title, content = row["id"], row["content"] - context += f"\n\n
Source #{title}
\n" - context += content - docs.append(self._to_document(header, context)) - - return docs - - def plot_graph(self, relationships): - G = create_knowledge_graph(relationships) - plot = visualize_graph(G) - return plot - - def run( - self, - text: str, - ) -> list[RetrievedDocument]: - if not self.file_ids: - return [] - - graphrag_func, query_params = self._build_graph_search() - - # only local mode support graph visualization - if query_params.mode == "local": - entities, relationships, reports, sources = asyncio.run( - nano_graph_rag_build_local_query_context( - graphrag_func, text, query_params - ) - ) - - documents = self.format_context_records( - entities, relationships, reports, sources - ) - plot = self.plot_graph(relationships) - - documents += [ - RetrievedDocument( - text="", - metadata={ - "file_name": "GraphRAG", - "type": "plot", - "data": plot, - }, - ), - ] - else: - context = graphrag_func.query(text, query_params) - - documents = [ - RetrievedDocument( - text=context, - metadata={ - "file_name": "GraphRAG {} Search".format( - query_params.mode.capitalize() - ), - "type": "table", - }, - ) - ] - - return documents diff --git a/libs/ktem/ktem/index/file/graph/pipelines.py b/libs/ktem/ktem/index/file/graph/pipelines.py deleted file mode 100644 index 31b491b45..000000000 --- a/libs/ktem/ktem/index/file/graph/pipelines.py +++ /dev/null @@ -1,402 +0,0 @@ -import os -import shutil -import subprocess -from pathlib import Path -from shutil import rmtree -from typing import Generator -from uuid import uuid4 - -import pandas as pd -import tiktoken -import yaml -from decouple import config -from ktem.db.models import engine -from sqlalchemy.orm import Session -from theflow.settings import settings - -from kotaemon.base import Document, Param, RetrievedDocument - -from ..pipelines import BaseFileIndexRetriever, IndexDocumentPipeline, IndexPipeline -from .visualize import create_knowledge_graph, visualize_graph - -try: - from graphrag.query.context_builder.entity_extraction import EntityVectorStoreKey - from graphrag.query.indexer_adapters import ( - read_indexer_entities, - read_indexer_relationships, - read_indexer_reports, - read_indexer_text_units, - ) - from graphrag.query.input.loaders.dfs import store_entity_semantic_embeddings - from graphrag.query.llm.oai.embedding import OpenAIEmbedding - from graphrag.query.llm.oai.typing import OpenaiApiType - from graphrag.query.structured_search.local_search.mixed_context import ( - LocalSearchMixedContext, - ) - from graphrag.vector_stores.lancedb import LanceDBVectorStore -except ImportError: - print( - ( - "GraphRAG dependencies not installed. " - "Try `pip install graphrag future` to install. " - "GraphRAG retriever pipeline will not work properly." - ) - ) - - -filestorage_path = Path(settings.KH_FILESTORAGE_PATH) / "graphrag" -filestorage_path.mkdir(parents=True, exist_ok=True) - -GRAPHRAG_KEY_MISSING_MESSAGE = ( - "GRAPHRAG_API_KEY is not set. Please set it to use the GraphRAG retriever pipeline." -) - - -def check_graphrag_api_key(): - return len(os.getenv("GRAPHRAG_API_KEY", "")) > 0 - - -def prepare_graph_index_path(graph_id: str): - root_path = Path(filestorage_path) / graph_id - input_path = root_path / "input" - - return root_path, input_path - - -class GraphRAGIndexingPipeline(IndexDocumentPipeline): - """GraphRAG specific indexing pipeline""" - - def route(self, file_path: str | Path) -> IndexPipeline: - """Simply disable the splitter (chunking) for this pipeline""" - pipeline = super().route(file_path) - pipeline.splitter = None - - return pipeline - - def store_file_id_with_graph_id(self, file_ids: list[str | None]): - # create new graph_id and assign them to doc_id in self.Index - # record in the index - graph_id = str(uuid4()) - with Session(engine) as session: - nodes = [] - for file_id in file_ids: - if not file_id: - continue - nodes.append( - self.Index( - source_id=file_id, - target_id=graph_id, - relation_type="graph", - ) - ) - - session.add_all(nodes) - session.commit() - - return graph_id - - def write_docs_to_files(self, graph_id: str, docs: list[Document]): - root_path, input_path = prepare_graph_index_path(graph_id) - input_path.mkdir(parents=True, exist_ok=True) - - for doc in docs: - if doc.metadata.get("type", "text") == "text": - with open(input_path / f"{doc.doc_id}.txt", "w") as f: - f.write(doc.text) - - return root_path - - def call_graphrag_index(self, graph_id: str, all_docs: list[Document]): - if not check_graphrag_api_key(): - raise ValueError(GRAPHRAG_KEY_MISSING_MESSAGE) - - # call GraphRAG index with docs and graph_id - input_path = self.write_docs_to_files(graph_id, all_docs) - input_path = str(input_path.absolute()) - - # Construct the command - command = [ - "python", - "-m", - "graphrag.index", - "--root", - input_path, - "--reporter", - "rich", - "--init", - ] - - # Run the command - yield Document( - channel="debug", - text="[GraphRAG] Creating index... This can take a long time.", - ) - result = subprocess.run(command, capture_output=True, text=True) - print(result.stdout) - command = command[:-1] - - # copy customized GraphRAG config file if it exists - if config("USE_CUSTOMIZED_GRAPHRAG_SETTING", default="value").lower() == "true": - setting_file_path = os.path.join(os.getcwd(), "settings.yaml.example") - destination_file_path = os.path.join(input_path, "settings.yaml") - try: - shutil.copy(setting_file_path, destination_file_path) - except shutil.Error: - # Handle the error if the file copy fails - print("failed to copy customized GraphRAG config file. ") - - # Run the command and stream stdout - with subprocess.Popen(command, stdout=subprocess.PIPE, text=True) as process: - if process.stdout: - for line in process.stdout: - yield Document(channel="debug", text=line) - - def stream( - self, file_paths: str | Path | list[str | Path], reindex: bool = False, **kwargs - ) -> Generator[ - Document, None, tuple[list[str | None], list[str | None], list[Document]] - ]: - file_ids, errors, all_docs = yield from super().stream( - file_paths, reindex=reindex, **kwargs - ) - - # assign graph_id to file_ids - graph_id = self.store_file_id_with_graph_id(file_ids) - # call GraphRAG index with docs and graph_id - yield from self.call_graphrag_index(graph_id, all_docs) - - return file_ids, errors, all_docs - - -class GraphRAGRetrieverPipeline(BaseFileIndexRetriever): - """GraphRAG specific retriever pipeline""" - - Index = Param(help="The SQLAlchemy Index table") - file_ids: list[str] = [] - - @classmethod - def get_user_settings(cls) -> dict: - return { - "search_type": { - "name": "Search type", - "value": "local", - "choices": ["local"], - "component": "dropdown", - "info": "Whether to use local or global search in the graph.", - } - } - - def _build_graph_search(self): - assert ( - len(self.file_ids) <= 1 - ), "GraphRAG retriever only supports one file_id at a time" - - file_id = self.file_ids[0] - # retrieve the graph_id from the index - with Session(engine) as session: - graph_id = ( - session.query(self.Index.target_id) - .filter(self.Index.source_id == file_id) - .filter(self.Index.relation_type == "graph") - .first() - ) - graph_id = graph_id[0] if graph_id else None - assert graph_id, f"GraphRAG index not found for file_id: {file_id}" - - root_path, _ = prepare_graph_index_path(graph_id) - output_path = root_path / "output" - - INPUT_DIR = output_path - LANCEDB_URI = str(INPUT_DIR / "lancedb") - COMMUNITY_REPORT_TABLE = "create_final_community_reports" - ENTITY_TABLE = "create_final_nodes" - ENTITY_EMBEDDING_TABLE = "create_final_entities" - RELATIONSHIP_TABLE = "create_final_relationships" - TEXT_UNIT_TABLE = "create_final_text_units" - COMMUNITY_LEVEL = 2 - - # read nodes table to get community and degree data - entity_df = pd.read_parquet(f"{INPUT_DIR}/{ENTITY_TABLE}.parquet") - entity_embedding_df = pd.read_parquet( - f"{INPUT_DIR}/{ENTITY_EMBEDDING_TABLE}.parquet" - ) - entities = read_indexer_entities( - entity_df, entity_embedding_df, COMMUNITY_LEVEL - ) - - # load description embeddings to an in-memory lancedb vectorstore - # to connect to a remote db, specify url and port values. - description_embedding_store = LanceDBVectorStore( - collection_name="entity_description_embeddings", - ) - description_embedding_store.connect(db_uri=LANCEDB_URI) - if Path(LANCEDB_URI).is_dir(): - rmtree(LANCEDB_URI) - _ = store_entity_semantic_embeddings( - entities=entities, vectorstore=description_embedding_store - ) - print(f"Entity count: {len(entity_df)}") - - # Read relationships - relationship_df = pd.read_parquet(f"{INPUT_DIR}/{RELATIONSHIP_TABLE}.parquet") - relationships = read_indexer_relationships(relationship_df) - - # Read community reports - report_df = pd.read_parquet(f"{INPUT_DIR}/{COMMUNITY_REPORT_TABLE}.parquet") - reports = read_indexer_reports(report_df, entity_df, COMMUNITY_LEVEL) - - # Read text units - text_unit_df = pd.read_parquet(f"{INPUT_DIR}/{TEXT_UNIT_TABLE}.parquet") - text_units = read_indexer_text_units(text_unit_df) - - # initialize default settings - embedding_model = os.getenv( - "GRAPHRAG_EMBEDDING_MODEL", "text-embedding-3-small" - ) - embedding_api_key = os.getenv("GRAPHRAG_API_KEY") - embedding_api_base = None - - # use customized GraphRAG settings if the flag is set - if config("USE_CUSTOMIZED_GRAPHRAG_SETTING", default="value").lower() == "true": - settings_yaml_path = Path(root_path) / "settings.yaml" - with open(settings_yaml_path, "r") as f: - settings = yaml.safe_load(f) - if settings["embeddings"]["llm"]["model"]: - embedding_model = settings["embeddings"]["llm"]["model"] - if settings["embeddings"]["llm"]["api_key"]: - embedding_api_key = settings["embeddings"]["llm"]["api_key"] - if settings["embeddings"]["llm"]["api_base"]: - embedding_api_base = settings["embeddings"]["llm"]["api_base"] - - text_embedder = OpenAIEmbedding( - api_key=embedding_api_key, - api_base=embedding_api_base, - api_type=OpenaiApiType.OpenAI, - model=embedding_model, - deployment_name=embedding_model, - max_retries=20, - ) - token_encoder = tiktoken.get_encoding("cl100k_base") - - context_builder = LocalSearchMixedContext( - community_reports=reports, - text_units=text_units, - entities=entities, - relationships=relationships, - covariates=None, - entity_text_embeddings=description_embedding_store, - embedding_vectorstore_key=EntityVectorStoreKey.ID, - # if the vectorstore uses entity title as ids, - # set this to EntityVectorStoreKey.TITLE - text_embedder=text_embedder, - token_encoder=token_encoder, - ) - return context_builder - - def _to_document(self, header: str, context_text: str) -> RetrievedDocument: - return RetrievedDocument( - text=context_text, - metadata={ - "file_name": header, - "type": "table", - "llm_trulens_score": 1.0, - }, - score=1.0, - ) - - def format_context_records(self, context_records) -> list[RetrievedDocument]: - entities = context_records.get("entities", []) - relationships = context_records.get("relationships", []) - reports = context_records.get("reports", []) - sources = context_records.get("sources", []) - - docs = [] - - context: str = "" - - header = "Entities\n" - context = entities[["entity", "description"]].to_markdown(index=False) - docs.append(self._to_document(header, context)) - - header = "\nRelationships\n" - context = relationships[["source", "target", "description"]].to_markdown( - index=False - ) - docs.append(self._to_document(header, context)) - - header = "\nReports\n" - context = "" - for idx, row in reports.iterrows(): - title, content = row["title"], row["content"] - context += f"\n\n
Report {title}
\n" - context += content - docs.append(self._to_document(header, context)) - - header = "\nSources\n" - context = "" - for idx, row in sources.iterrows(): - title, content = row["id"], row["text"] - context += f"\n\n
Source #{title}
\n" - context += content - docs.append(self._to_document(header, context)) - - return docs - - def plot_graph(self, context_records): - relationships = context_records.get("relationships", []) - G = create_knowledge_graph(relationships) - plot = visualize_graph(G) - return plot - - def generate_relevant_scores(self, text, documents: list[RetrievedDocument]): - return documents - - def run( - self, - text: str, - ) -> list[RetrievedDocument]: - if not self.file_ids: - return [] - - if not check_graphrag_api_key(): - raise ValueError(GRAPHRAG_KEY_MISSING_MESSAGE) - - context_builder = self._build_graph_search() - - local_context_params = { - "text_unit_prop": 0.5, - "community_prop": 0.1, - "conversation_history_max_turns": 5, - "conversation_history_user_turns_only": True, - "top_k_mapped_entities": 10, - "top_k_relationships": 10, - "include_entity_rank": False, - "include_relationship_weight": False, - "include_community_rank": False, - "return_candidate_context": False, - "embedding_vectorstore_key": EntityVectorStoreKey.ID, - # set this to EntityVectorStoreKey.TITLE i - # f the vectorstore uses entity title as ids - "max_tokens": 12_000, - # change this based on the token limit you have on your model - # (if you are using a model with 8k limit, a good setting could be 5000) - } - - context_text, context_records = context_builder.build_context( - query=text, - conversation_history=None, - **local_context_params, - ) - documents = self.format_context_records(context_records) - plot = self.plot_graph(context_records) - - return documents + [ - RetrievedDocument( - text="", - metadata={ - "file_name": "GraphRAG", - "type": "plot", - "data": plot, - }, - ), - ] diff --git a/libs/ktem/ktem/index/file/graph/visualize.py b/libs/ktem/ktem/index/file/graph/visualize.py deleted file mode 100644 index 722dab7ff..000000000 --- a/libs/ktem/ktem/index/file/graph/visualize.py +++ /dev/null @@ -1,102 +0,0 @@ -import networkx as nx -import plotly.graph_objects as go -from plotly.io import to_json - - -def create_knowledge_graph(df): - """ - create nx Graph from DataFrame relations data - """ - G = nx.Graph() - for _, row in df.iterrows(): - source = row["source"] - target = row["target"] - attributes = {k: v for k, v in row.items() if k not in ["source", "target"]} - G.add_edge(source, target, **attributes) - - return G - - -def visualize_graph(G): - pos = nx.spring_layout(G, dim=2) - - edge_x = [] - edge_y = [] - edge_texts = nx.get_edge_attributes(G, "description") - to_display_edge_texts = [] - for edge in G.edges(): - x0, y0 = pos[edge[0]] - x1, y1 = pos[edge[1]] - edge_x.append(x0) - edge_x.append(x1) - edge_x.append(None) - edge_y.append(y0) - edge_y.append(y1) - edge_y.append(None) - to_display_edge_texts.append(edge_texts[edge]) - - edge_trace = go.Scatter( - x=edge_x, - y=edge_y, - text=to_display_edge_texts, - line=dict(width=0.5, color="#888"), - hoverinfo="text", - mode="lines", - ) - - node_x = [] - node_y = [] - for node in G.nodes(): - x, y = pos[node] - node_x.append(x) - node_y.append(y) - - node_adjacencies = [] - node_text = [] - node_size = [] - for node_id, adjacencies in enumerate(G.adjacency()): - degree = len(adjacencies[1]) - node_adjacencies.append(degree) - node_text.append(adjacencies[0]) - node_size.append(15 if degree < 5 else (30 if degree < 10 else 60)) - - node_trace = go.Scatter( - x=node_x, - y=node_y, - textfont=dict( - family="Courier New, monospace", - size=10, # Set the font size here - ), - textposition="top center", - mode="markers+text", - hoverinfo="text", - text=node_text, - marker=dict( - showscale=True, - # colorscale options - size=node_size, - colorscale="YlGnBu", - reversescale=True, - color=node_adjacencies, - colorbar=dict( - thickness=5, - xanchor="left", - titleside="right", - ), - line_width=2, - ), - ) - - fig = go.Figure( - data=[edge_trace, node_trace], - layout=go.Layout( - showlegend=False, - hovermode="closest", - margin=dict(b=20, l=5, r=5, t=40), - xaxis=dict(showgrid=False, zeroline=False, showticklabels=False), - yaxis=dict(showgrid=False, zeroline=False, showticklabels=False), - ), - ) - fig.update_layout(autosize=True) - - return to_json(fig) diff --git a/libs/ktem/ktem/index/file/knet/__init__.py b/libs/ktem/ktem/index/file/knet/__init__.py deleted file mode 100644 index 1ca8ef4f8..000000000 --- a/libs/ktem/ktem/index/file/knet/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .knet_index import KnowledgeNetworkFileIndex - -__all__ = ["KnowledgeNetworkFileIndex"] diff --git a/libs/ktem/ktem/index/file/knet/knet_index.py b/libs/ktem/ktem/index/file/knet/knet_index.py deleted file mode 100644 index f98e7a090..000000000 --- a/libs/ktem/ktem/index/file/knet/knet_index.py +++ /dev/null @@ -1,47 +0,0 @@ -from typing import Any - -from ktem.index.file import FileIndex - -from ..base import BaseFileIndexIndexing, BaseFileIndexRetriever -from .pipelines import KnetIndexingPipeline, KnetRetrievalPipeline - - -class KnowledgeNetworkFileIndex(FileIndex): - @classmethod - def get_admin_settings(cls): - admin_settings = super().get_admin_settings() - - # remove embedding from admin settings - # as we don't need it - admin_settings.pop("embedding") - return admin_settings - - def _setup_indexing_cls(self): - self._indexing_pipeline_cls = KnetIndexingPipeline - - def _setup_retriever_cls(self): - self._retriever_pipeline_cls = [KnetRetrievalPipeline] - - def get_indexing_pipeline(self, settings, user_id) -> BaseFileIndexIndexing: - """Define the interface of the indexing pipeline""" - - obj = super().get_indexing_pipeline(settings, user_id) - # disable vectorstore for this kind of Index - # also set the collection_name for API call - obj.VS = None - obj.collection_name = f"kh_index_{self.id}" - - return obj - - def get_retriever_pipelines( - self, settings: dict, user_id: int, selected: Any = None - ) -> list["BaseFileIndexRetriever"]: - retrievers = super().get_retriever_pipelines(settings, user_id, selected) - - for obj in retrievers: - # disable vectorstore for this kind of Index - # also set the collection_name for API call - obj.VS = None - obj.collection_name = f"kh_index_{self.id}" - - return retrievers diff --git a/libs/ktem/ktem/index/file/knet/pipelines.py b/libs/ktem/ktem/index/file/knet/pipelines.py deleted file mode 100644 index f49bdb1f5..000000000 --- a/libs/ktem/ktem/index/file/knet/pipelines.py +++ /dev/null @@ -1,169 +0,0 @@ -import base64 -import json -import os -from pathlib import Path -from typing import Optional, Sequence - -import requests -import yaml - -from kotaemon.base import RetrievedDocument -from kotaemon.indices.rankings import BaseReranking, LLMReranking, LLMTrulensScoring - -from ..pipelines import BaseFileIndexRetriever, IndexDocumentPipeline, IndexPipeline - - -class KnetIndexingPipeline(IndexDocumentPipeline): - """Knowledge Network specific indexing pipeline""" - - # collection name for external indexing call - collection_name: str = "default" - - @classmethod - def get_user_settings(cls): - return { - "reader_mode": { - "name": "Index parser", - "value": "knowledge_network", - "choices": [ - ("Default (KN)", "knowledge_network"), - ], - "component": "dropdown", - }, - } - - def route(self, file_path: str | Path) -> IndexPipeline: - """Simply disable the splitter (chunking) for this pipeline""" - pipeline = super().route(file_path) - pipeline.splitter = None - # assign IndexPipeline collection name to parse to loader - pipeline.collection_name = self.collection_name - - return pipeline - - -class KnetRetrievalPipeline(BaseFileIndexRetriever): - DEFAULT_KNET_ENDPOINT: str = "http://127.0.0.1:8081/retrieve" - - collection_name: str = "default" - rerankers: Sequence[BaseReranking] = [LLMReranking.withx()] - - def encode_image_base64(self, image_path: str | Path) -> bytes | str: - """Convert image to base64""" - img_base64 = "data:image/png;base64,{}" - with open(image_path, "rb") as image_file: - return img_base64.format( - base64.b64encode(image_file.read()).decode("utf-8") - ) - - def run( - self, - text: str, - doc_ids: Optional[list[str]] = None, - *args, - **kwargs, - ) -> list[RetrievedDocument]: - """Retrieve document excerpts similar to the text - - Args: - text: the text to retrieve similar documents - doc_ids: list of document ids to constraint the retrieval - """ - print("searching in doc_ids", doc_ids) - if not doc_ids: - return [] - - docs: list[RetrievedDocument] = [] - params = { - "query": text, - "collection": self.collection_name, - "meta_filters": {"doc_name": doc_ids}, - } - params["meta_filters"] = json.dumps(params["meta_filters"]) - response = requests.get(self.DEFAULT_KNET_ENDPOINT, params=params) - metadata_translation = { - "TABLE": "table", - "FIGURE": "image", - } - - if response.status_code == 200: - # Load YAML content from the response content - chunks = yaml.safe_load(response.content) - for chunk in chunks: - metadata = chunk["node"]["metadata"] - metadata["type"] = metadata_translation.get( - metadata.pop("content_type", ""), "" - ) - metadata["file_name"] = metadata.pop("company_name", "") - - # load image from returned path - image_path = metadata.get("image_path", "") - if image_path and os.path.isfile(image_path): - base64_im = self.encode_image_base64(image_path) - # explicitly set document type - metadata["type"] = "image" - metadata["image_origin"] = base64_im - - docs.append( - RetrievedDocument(text=chunk["node"]["text"], metadata=metadata) - ) - else: - raise IOError(f"{response.status_code}: {response.text}") - - for reranker in self.rerankers: - docs = reranker(documents=docs, query=text) - - return docs - - @classmethod - def get_user_settings(cls) -> dict: - from ktem.llms.manager import llms - - try: - reranking_llm = llms.get_default_name() - reranking_llm_choices = list(llms.options().keys()) - except Exception: - reranking_llm = None - reranking_llm_choices = [] - - return { - "reranking_llm": { - "name": "LLM for scoring", - "value": reranking_llm, - "component": "dropdown", - "choices": reranking_llm_choices, - "special_type": "llm", - }, - "retrieval_mode": { - "name": "Retrieval mode", - "value": "hybrid", - "choices": ["vector", "text", "hybrid"], - "component": "dropdown", - }, - } - - @classmethod - def get_pipeline(cls, user_settings, index_settings, selected): - """Get retriever objects associated with the index - - Args: - settings: the settings of the app - kwargs: other arguments - """ - from ktem.llms.manager import llms - - retriever = cls( - rerankers=[LLMTrulensScoring()], - ) - - # hacky way to input doc_ids to retriever.run() call (through theflow) - kwargs = {".doc_ids": selected} - retriever.set_run(kwargs, temp=False) - - for reranker in retriever.rerankers: - if isinstance(reranker, LLMReranking): - reranker.llm = llms.get( - user_settings["reranking_llm"], llms.get_default() - ) - - return retriever diff --git a/libs/ktem/ktem/index/file/pipelines.py b/libs/ktem/ktem/index/file/pipelines.py deleted file mode 100644 index 7d05e4d1f..000000000 --- a/libs/ktem/ktem/index/file/pipelines.py +++ /dev/null @@ -1,866 +0,0 @@ -from __future__ import annotations - -import json -import logging -import shutil -import threading -import time -import warnings -from collections import defaultdict -from copy import deepcopy -from functools import lru_cache -from hashlib import sha256 -from pathlib import Path -from typing import Generator, Optional, Sequence - -import tiktoken -from decouple import config -from ktem.db.models import engine -from ktem.embeddings.manager import embedding_models_manager -from ktem.llms.manager import llms -from ktem.rerankings.manager import reranking_models_manager -from llama_index.core.readers.base import BaseReader -from llama_index.core.readers.file.base import default_file_metadata_func -from llama_index.core.vector_stores import ( - FilterCondition, - FilterOperator, - MetadataFilter, - MetadataFilters, -) -from llama_index.core.vector_stores.types import VectorStoreQueryMode -from sqlalchemy import delete, select -from sqlalchemy.orm import Session -from theflow.settings import settings -from theflow.utils.modules import import_dotted_string - -from kotaemon.base import BaseComponent, Document, Node, Param, RetrievedDocument -from kotaemon.embeddings import BaseEmbeddings -from kotaemon.indices import VectorIndexing, VectorRetrieval -from kotaemon.indices.ingests.files import ( - KH_DEFAULT_FILE_EXTRACTORS, - adobe_reader, - azure_reader, - docling_reader, - paddle_struct_reader, - paddle_vl_reader, - unstructured, - web_reader, -) -from kotaemon.indices.rankings import BaseReranking, LLMReranking, LLMTrulensScoring -from kotaemon.indices.splitters import BaseSplitter, TokenSplitter - -from .base import BaseFileIndexIndexing, BaseFileIndexRetriever - -logger = logging.getLogger(__name__) - - -@lru_cache -def dev_settings(): - """Retrieve the developer settings from flowsettings.py""" - file_extractors = {} - - if hasattr(settings, "FILE_INDEX_PIPELINE_FILE_EXTRACTORS"): - file_extractors = { - key: import_dotted_string(value, safe=False)() - for key, value in settings.FILE_INDEX_PIPELINE_FILE_EXTRACTORS.items() - } - - chunk_size = None - if hasattr(settings, "FILE_INDEX_PIPELINE_SPLITTER_CHUNK_SIZE"): - chunk_size = settings.FILE_INDEX_PIPELINE_SPLITTER_CHUNK_SIZE - - chunk_overlap = None - if hasattr(settings, "FILE_INDEX_PIPELINE_SPLITTER_CHUNK_OVERLAP"): - chunk_overlap = settings.FILE_INDEX_PIPELINE_SPLITTER_CHUNK_OVERLAP - - return file_extractors, chunk_size, chunk_overlap - - -_default_token_func = tiktoken.encoding_for_model("gpt-3.5-turbo").encode - - -class DocumentRetrievalPipeline(BaseFileIndexRetriever): - """Retrieve relevant document - - Args: - vector_retrieval: the retrieval pipeline that return the relevant documents - given a text query - reranker: the reranking pipeline that re-rank and filter the retrieved - documents - get_extra_table: if True, for each retrieved document, the pipeline will look - for surrounding tables (e.g. within the page) - top_k: number of documents to retrieve - mmr: whether to use mmr to re-rank the documents - """ - - embedding: BaseEmbeddings - rerankers: Sequence[BaseReranking] = [] - # use LLM to create relevant scores for displaying on UI - llm_scorer: LLMReranking | None = LLMReranking.withx() - get_extra_table: bool = False - mmr: bool = False - top_k: int = 5 - retrieval_mode: str = "hybrid" - - @Node.auto(depends_on=["embedding", "VS", "DS"]) - def vector_retrieval(self) -> VectorRetrieval: - return VectorRetrieval( - embedding=self.embedding, - vector_store=self.VS, - doc_store=self.DS, - retrieval_mode=self.retrieval_mode, # type: ignore - rerankers=self.rerankers, - ) - - def run( - self, - text: str, - doc_ids: Optional[list[str]] = None, - *args, - **kwargs, - ) -> list[RetrievedDocument]: - """Retrieve document excerpts similar to the text - - Args: - text: the text to retrieve similar documents - doc_ids: list of document ids to constraint the retrieval - """ - # flatten doc_ids in case of group of doc_ids are passed - if doc_ids: - flatten_doc_ids = [] - for doc_id in doc_ids: - if doc_id is None: - raise ValueError("No document is selected") - - if doc_id.startswith("["): - flatten_doc_ids.extend(json.loads(doc_id)) - else: - flatten_doc_ids.append(doc_id) - doc_ids = flatten_doc_ids - - print("searching in doc_ids", doc_ids) - if not doc_ids: - logger.info(f"Skip retrieval because of no selected files: {self}") - return [] - - retrieval_kwargs: dict = {} - with Session(engine) as session: - stmt = select(self.Index).where( - self.Index.relation_type == "document", - self.Index.source_id.in_(doc_ids), - ) - results = session.execute(stmt) - chunk_ids = [r[0].target_id for r in results.all()] - - # do first round top_k extension - retrieval_kwargs["do_extend"] = True - retrieval_kwargs["scope"] = chunk_ids - retrieval_kwargs["filters"] = MetadataFilters( - filters=[ - MetadataFilter( - key="file_id", - value=doc_ids, - operator=FilterOperator.IN, - ) - ], - condition=FilterCondition.OR, - ) - - if self.mmr: - # TODO: double check that llama-index MMR works correctly - retrieval_kwargs["mode"] = VectorStoreQueryMode.MMR - retrieval_kwargs["mmr_threshold"] = 0.5 - - # rerank - s_time = time.time() - print(f"retrieval_kwargs: {retrieval_kwargs.keys()}") - docs = self.vector_retrieval(text=text, top_k=self.top_k, **retrieval_kwargs) - print("retrieval step took", time.time() - s_time) - - if not self.get_extra_table: - return docs - - # retrieve extra nodes relate to table - table_pages = defaultdict(list) - retrieved_id = set([doc.doc_id for doc in docs]) - for doc in docs: - if "page_label" not in doc.metadata: - continue - if "file_name" not in doc.metadata: - warnings.warn( - "file_name not in metadata while page_label is in metadata: " - f"{doc.metadata}" - ) - table_pages[doc.metadata["file_name"]].append(doc.metadata["page_label"]) - - queries: list[dict] = [ - {"$and": [{"file_name": {"$eq": fn}}, {"page_label": {"$in": pls}}]} - for fn, pls in table_pages.items() - ] - if queries: - try: - extra_docs = self.vector_retrieval( - text="", - top_k=50, - where=queries[0] if len(queries) == 1 else {"$or": queries}, - ) - for doc in extra_docs: - if doc.doc_id not in retrieved_id: - docs.append(doc) - except Exception: - print("Error retrieving additional tables") - - return docs - - def generate_relevant_scores( - self, query: str, documents: list[RetrievedDocument] - ) -> list[RetrievedDocument]: - docs = ( - documents - if not self.llm_scorer - else self.llm_scorer(documents=documents, query=query) - ) - return docs - - @classmethod - def get_user_settings(cls) -> dict: - from ktem.llms.manager import llms - - try: - reranking_llm = llms.get_default_name() - reranking_llm_choices = list(llms.options().keys()) - except Exception as e: - logger.error(e) - reranking_llm = None - reranking_llm_choices = [] - - return { - "reranking_llm": { - "name": "LLM for relevant scoring", - "value": reranking_llm, - "component": "dropdown", - "choices": reranking_llm_choices, - "special_type": "llm", - }, - "num_retrieval": { - "name": "Number of document chunks to retrieve", - "value": 10, - "component": "number", - }, - "retrieval_mode": { - "name": "Retrieval mode", - "value": "hybrid", - "choices": ["vector", "text", "hybrid"], - "component": "dropdown", - }, - "prioritize_table": { - "name": "Prioritize table", - "value": False, - "choices": [True, False], - "component": "checkbox", - }, - "mmr": { - "name": "Use MMR", - "value": False, - "choices": [True, False], - "component": "checkbox", - }, - "use_reranking": { - "name": "Use reranking", - "value": True, - "choices": [True, False], - "component": "checkbox", - }, - "use_llm_reranking": { - "name": "Use LLM relevant scoring", - "value": not config("USE_LOW_LLM_REQUESTS", default=False, cast=bool), - "choices": [True, False], - "component": "checkbox", - }, - } - - @classmethod - def get_pipeline(cls, user_settings, index_settings, selected): - """Get retriever objects associated with the index - - Args: - settings: the settings of the app - kwargs: other arguments - """ - use_llm_reranking = user_settings.get("use_llm_reranking", False) - - retriever = cls( - get_extra_table=user_settings["prioritize_table"], - top_k=user_settings["num_retrieval"], - mmr=user_settings["mmr"], - embedding=embedding_models_manager[ - index_settings.get( - "embedding", embedding_models_manager.get_default_name() - ) - ], - retrieval_mode=user_settings["retrieval_mode"], - llm_scorer=(LLMTrulensScoring() if use_llm_reranking else None), - rerankers=[ - reranking_models_manager[ - index_settings.get( - "reranking", reranking_models_manager.get_default_name() - ) - ] - ], - ) - if not user_settings["use_reranking"]: - retriever.rerankers = [] # type: ignore - - for reranker in retriever.rerankers: - if isinstance(reranker, LLMReranking): - reranker.llm = llms.get( - user_settings["reranking_llm"], llms.get_default() - ) - - if retriever.llm_scorer: - retriever.llm_scorer.llm = llms.get( - user_settings["reranking_llm"], llms.get_default() - ) - - kwargs = {".doc_ids": selected} - retriever.set_run(kwargs, temp=False) - return retriever - - -class IndexPipeline(BaseComponent): - """Index a single file""" - - loader: BaseReader - splitter: BaseSplitter | None - chunk_batch_size: int = 200 - - Source = Param(help="The SQLAlchemy Source table") - Index = Param(help="The SQLAlchemy Index table") - VS = Param(help="The VectorStore") - DS = Param(help="The DocStore") - FSPath = Param(help="The file storage path") - user_id = Param(help="The user id") - collection_name: str = "default" - private: bool = False - run_embedding_in_thread: bool = False - embedding: BaseEmbeddings - - @Node.auto(depends_on=["Source", "Index", "embedding"]) - def vector_indexing(self) -> VectorIndexing: - return VectorIndexing( - vector_store=self.VS, doc_store=self.DS, embedding=self.embedding - ) - - def handle_docs(self, docs, file_id, file_name) -> Generator[Document, None, int]: - s_time = time.time() - text_docs = [] - non_text_docs = [] - thumbnail_docs = [] - - for doc in docs: - doc_type = doc.metadata.get("type", "text") - if doc_type == "text": - text_docs.append(doc) - elif doc_type == "thumbnail": - thumbnail_docs.append(doc) - else: - non_text_docs.append(doc) - - print(f"Got {len(thumbnail_docs)} page thumbnails") - page_label_to_thumbnail = { - doc.metadata["page_label"]: doc.doc_id for doc in thumbnail_docs - } - - if self.splitter: - all_chunks = self.splitter(text_docs) - else: - all_chunks = text_docs - - # add the thumbnails doc_id to the chunks - for chunk in all_chunks: - page_label = chunk.metadata.get("page_label", None) - if page_label and page_label in page_label_to_thumbnail: - chunk.metadata["thumbnail_doc_id"] = page_label_to_thumbnail[page_label] - - to_index_chunks = all_chunks + non_text_docs + thumbnail_docs - - # add to doc store - chunks = [] - n_chunks = 0 - chunk_size = self.chunk_batch_size * 4 - for start_idx in range(0, len(to_index_chunks), chunk_size): - chunks = to_index_chunks[start_idx : start_idx + chunk_size] - self.handle_chunks_docstore(chunks, file_id) - n_chunks += len(chunks) - yield Document( - f" => [{file_name}] Processed {n_chunks} chunks", - channel="debug", - ) - - def insert_chunks_to_vectorstore(): - chunks = [] - n_chunks = 0 - chunk_size = self.chunk_batch_size - for start_idx in range(0, len(to_index_chunks), chunk_size): - chunks = to_index_chunks[start_idx : start_idx + chunk_size] - self.handle_chunks_vectorstore(chunks, file_id) - n_chunks += len(chunks) - if self.VS: - yield Document( - f" => [{file_name}] Created embedding for {n_chunks} chunks", - channel="debug", - ) - - # run vector indexing in thread if specified - if self.run_embedding_in_thread: - print("Running embedding in thread") - threading.Thread( - target=lambda: list(insert_chunks_to_vectorstore()) - ).start() - else: - yield from insert_chunks_to_vectorstore() - - print("indexing step took", time.time() - s_time) - return n_chunks - - def handle_chunks_docstore(self, chunks, file_id): - """Run chunks""" - # run embedding, add to both vector store and doc store - self.vector_indexing.add_to_docstore(chunks) - - # record in the index - with Session(engine) as session: - nodes = [] - for chunk in chunks: - nodes.append( - self.Index( - source_id=file_id, - target_id=chunk.doc_id, - relation_type="document", - ) - ) - session.add_all(nodes) - session.commit() - - def handle_chunks_vectorstore(self, chunks, file_id): - """Run chunks""" - # run embedding, add to both vector store and doc store - self.vector_indexing.add_to_vectorstore(chunks) - self.vector_indexing.write_chunk_to_file(chunks) - - if self.VS: - # record in the index - with Session(engine) as session: - nodes = [] - for chunk in chunks: - nodes.append( - self.Index( - source_id=file_id, - target_id=chunk.doc_id, - relation_type="vector", - ) - ) - session.add_all(nodes) - session.commit() - - def get_id_if_exists(self, file_path: str | Path) -> Optional[str]: - """Check if the file is already indexed - - Args: - file_path: the path to the file - - Returns: - the file id if the file is indexed, otherwise None - """ - file_name = file_path.name if isinstance(file_path, Path) else file_path - if self.private: - cond: tuple = ( - self.Source.name == file_name, - self.Source.user == self.user_id, - ) - else: - cond = (self.Source.name == file_name,) - - with Session(engine) as session: - stmt = select(self.Source).where(*cond) - item = session.execute(stmt).first() - if item: - return item[0].id - - return None - - def store_url(self, url: str) -> str: - """Store URL into the database and storage, return the file id - - Args: - url: the URL - - Returns: - the file id - """ - file_hash = sha256(url.encode()).hexdigest() - source = self.Source( - name=url, - path=file_hash, - size=0, - user=self.user_id, # type: ignore - ) - with Session(engine) as session: - session.add(source) - session.commit() - file_id = source.id - - return file_id - - def store_file(self, file_path: Path) -> str: - """Store file into the database and storage, return the file id - - Args: - file_path: the path to the file - - Returns: - the file id - """ - with file_path.open("rb") as fi: - file_hash = sha256(fi.read()).hexdigest() - - shutil.copy(file_path, self.FSPath / file_hash) - source = self.Source( - name=file_path.name, - path=file_hash, - size=file_path.stat().st_size, - user=self.user_id, # type: ignore - ) - with Session(engine) as session: - session.add(source) - session.commit() - file_id = source.id - - return file_id - - def finish(self, file_id: str, file_path: str | Path) -> str: - """Finish the indexing""" - with Session(engine) as session: - stmt = select(self.Source).where(self.Source.id == file_id) - result = session.execute(stmt).first() - if not result: - return file_id - - item = result[0] - - # populate the number of tokens - doc_ids_stmt = select(self.Index.target_id).where( - self.Index.source_id == file_id, - self.Index.relation_type == "document", - ) - doc_ids = [_[0] for _ in session.execute(doc_ids_stmt)] - token_func = self.get_token_func() - if doc_ids and token_func: - docs = self.DS.get(doc_ids) - item.note["tokens"] = sum([len(token_func(doc.text)) for doc in docs]) - - # populate the note - item.note["loader"] = self.get_from_path("loader").__class__.__name__ - - session.add(item) - session.commit() - - return file_id - - def get_token_func(self): - """Get the token function for calculating the number of tokens""" - return _default_token_func - - def delete_file(self, file_id: str): - """Delete a file from the db, including its chunks in docstore and vectorstore - - Args: - file_id: the file id - """ - with Session(engine) as session: - session.execute(delete(self.Source).where(self.Source.id == file_id)) - vs_ids, ds_ids = [], [] - index = session.execute( - select(self.Index).where(self.Index.source_id == file_id) - ).all() - for each in index: - if each[0].relation_type == "vector": - vs_ids.append(each[0].target_id) - elif each[0].relation_type == "document": - ds_ids.append(each[0].target_id) - session.delete(each[0]) - session.commit() - - if vs_ids and self.VS: - self.VS.delete(vs_ids) - if ds_ids: - self.DS.delete(ds_ids) - - def run( - self, file_path: str | Path, reindex: bool, **kwargs - ) -> tuple[str, list[Document]]: - raise NotImplementedError - - def stream( - self, file_path: str | Path, reindex: bool, **kwargs - ) -> Generator[Document, None, tuple[str, list[Document]]]: - # check if the file is already indexed - if isinstance(file_path, Path): - file_path = file_path.resolve() - - file_id = self.get_id_if_exists(file_path) - - if isinstance(file_path, Path): - if file_id is not None: - if not reindex: - raise ValueError( - f"File {file_path.name} already indexed. Please rerun with " - "reindex=True to force reindexing." - ) - else: - # remove the existing records - yield Document( - f" => Removing old {file_path.name}", channel="debug" - ) - self.delete_file(file_id) - file_id = self.store_file(file_path) - else: - # add record to db - file_id = self.store_file(file_path) - else: - if file_id is not None: - raise ValueError(f"URL {file_path} already indexed.") - else: - # add record to db - file_id = self.store_url(file_path) - - # extract the file - if isinstance(file_path, Path): - extra_info = default_file_metadata_func(str(file_path)) - file_name = file_path.name - else: - extra_info = {"file_name": file_path} - file_name = file_path - - extra_info["file_id"] = file_id - extra_info["collection_name"] = self.collection_name - - yield Document(f" => Converting {file_name} to text", channel="debug") - docs = self.loader.load_data(file_path, extra_info=extra_info) - yield Document(f" => Converted {file_name} to text", channel="debug") - yield from self.handle_docs(docs, file_id, file_name) - - self.finish(file_id, file_path) - - yield Document(f" => Finished indexing {file_name}", channel="debug") - return file_id, docs - - -class IndexDocumentPipeline(BaseFileIndexIndexing): - """Index the file. Decide which pipeline based on the file type. - - This method is essentially a factory to decide which indexing pipeline to use. - - We can decide the pipeline programmatically, and/or automatically based on an LLM. - If we based on the LLM, essentially we will log the LLM thought process in a file, - and then during the indexing, we will read that file to decide which pipeline - to use, and then log the operation in that file. Overtime, the LLM can learn to - decide which pipeline should be used. - """ - - reader_mode: str = Param("default", help="The reader mode") - embedding: BaseEmbeddings - run_embedding_in_thread: bool = False - - @Param.auto(depends_on="reader_mode") - def readers(self): - readers = deepcopy(KH_DEFAULT_FILE_EXTRACTORS) - print("reader_mode", self.reader_mode) - if self.reader_mode == "adobe": - readers[".pdf"] = adobe_reader - elif self.reader_mode == "azure-di": - readers[".pdf"] = azure_reader - elif self.reader_mode == "docling": - readers[".pdf"] = docling_reader - elif self.reader_mode == "paddle-struct": - readers.update( - { - ".pdf": paddle_struct_reader, - ".png": paddle_struct_reader, - ".jpeg": paddle_struct_reader, - ".jpg": paddle_struct_reader, - ".tiff": paddle_struct_reader, - ".tif": paddle_struct_reader, - } - ) - elif self.reader_mode == "paddle-vl": - readers.update( - { - ".pdf": paddle_vl_reader, - ".png": paddle_vl_reader, - ".jpeg": paddle_vl_reader, - ".jpg": paddle_vl_reader, - ".tiff": paddle_vl_reader, - ".tif": paddle_vl_reader, - } - ) - - dev_readers, _, _ = dev_settings() - readers.update(dev_readers) - - return readers - - @classmethod - def get_user_settings(cls): - return { - "reader_mode": { - "name": "File loader", - "value": "default", - "choices": [ - ("Default (open-source)", "default"), - ("Adobe API (figure+table extraction)", "adobe"), - ( - "Azure AI Document Intelligence (figure+table extraction)", - "azure-di", - ), - ("Docling (figure+table extraction)", "docling"), - ( - "PaddleOCR PPStructureV3 (table+figure extraction)", - "paddle-struct", - ), - ("PaddleOCR-VL (VLM document parsing)", "paddle-vl"), - ], - "component": "dropdown", - }, - } - - @classmethod - def get_pipeline(cls, user_settings, index_settings) -> BaseFileIndexIndexing: - use_quick_index_mode = user_settings.get("quick_index_mode", False) - print("use_quick_index_mode", use_quick_index_mode) - obj = cls( - embedding=embedding_models_manager[ - index_settings.get( - "embedding", embedding_models_manager.get_default_name() - ) - ], - run_embedding_in_thread=use_quick_index_mode, - reader_mode=user_settings.get("reader_mode", "default"), - ) - return obj - - def is_url(self, file_path: str | Path) -> bool: - return isinstance(file_path, str) and ( - file_path.startswith("http://") or file_path.startswith("https://") - ) - - def route(self, file_path: str | Path) -> IndexPipeline: - """Decide the pipeline based on the file type - - Can subclass this method for a more elaborate pipeline routing strategy. - """ - - _, dev_chunk_size, dev_chunk_overlap = dev_settings() - - chunk_size = self.chunk_size or dev_chunk_size - chunk_overlap = self.chunk_overlap or dev_chunk_overlap - - # check if file_path is a URL - if self.is_url(file_path): - reader = web_reader - else: - assert isinstance(file_path, Path) - ext = file_path.suffix.lower() - reader = self.readers.get(ext, unstructured) - if reader is None: - raise NotImplementedError( - f"No supported pipeline to index {file_path.name}. Please specify " - "the suitable pipeline for this file type in the settings." - ) - - print(f"Chunk size: {chunk_size}, chunk overlap: {chunk_overlap}") - - print("Using reader", reader) - pipeline: IndexPipeline = IndexPipeline( - loader=reader, - splitter=TokenSplitter( - chunk_size=chunk_size or 1024, - chunk_overlap=chunk_overlap or 256, - separator="\n\n", - backup_separators=["\n", ".", "\u200B"], - ), - run_embedding_in_thread=self.run_embedding_in_thread, - Source=self.Source, - Index=self.Index, - VS=self.VS, - DS=self.DS, - FSPath=self.FSPath, - user_id=self.user_id, - private=self.private, - embedding=self.embedding, - ) - - return pipeline - - def run( - self, file_paths: str | Path | list[str | Path], *args, **kwargs - ) -> tuple[list[str | None], list[str | None]]: - raise NotImplementedError - - def stream( - self, file_paths: str | Path | list[str | Path], reindex: bool = False, **kwargs - ) -> Generator[ - Document, None, tuple[list[str | None], list[str | None], list[Document]] - ]: - """Return a list of indexed file ids, and a list of errors""" - if not isinstance(file_paths, list): - file_paths = [file_paths] - - file_ids: list[str | None] = [] - errors: list[str | None] = [] - all_docs = [] - - n_files = len(file_paths) - for idx, file_path in enumerate(file_paths): - if self.is_url(file_path): - file_name = file_path - else: - file_path = Path(file_path) - file_name = file_path.name - - yield Document( - content=f"Indexing [{idx + 1}/{n_files}]: {file_name}", - channel="debug", - ) - - try: - pipeline = self.route(file_path) - file_id, docs = yield from pipeline.stream( - file_path, reindex=reindex, **kwargs - ) - all_docs.extend(docs) - file_ids.append(file_id) - errors.append(None) - yield Document( - content={ - "file_path": file_path, - "file_name": file_name, - "status": "success", - }, - channel="index", - ) - except Exception as e: - logger.exception(e) - file_ids.append(None) - errors.append(str(e)) - yield Document( - content={ - "file_path": file_path, - "file_name": file_name, - "status": "failed", - "message": str(e), - }, - channel="index", - ) - - return file_ids, errors, all_docs diff --git a/libs/ktem/ktem/index/models.py b/libs/ktem/ktem/index/models.py deleted file mode 100644 index 45b74e339..000000000 --- a/libs/ktem/ktem/index/models.py +++ /dev/null @@ -1,19 +0,0 @@ -from typing import Optional - -from ktem.db.engine import engine -from sqlalchemy import JSON, Column -from sqlmodel import Field, SQLModel - - -# TODO: simplify with using SQLAlchemy directly -class Index(SQLModel, table=True): - __table_args__ = {"extend_existing": True} - __tablename__ = "ktem__index" # type: ignore - - id: Optional[int] = Field(default=None, primary_key=True) - name: str = Field(unique=True) - index_type: str = Field() - config: dict = Field(default={}, sa_column=Column(JSON)) - - -Index.metadata.create_all(engine) diff --git a/libs/ktem/ktem/llms/db.py b/libs/ktem/ktem/llms/db.py deleted file mode 100644 index 628ebb7cf..000000000 --- a/libs/ktem/ktem/llms/db.py +++ /dev/null @@ -1,36 +0,0 @@ -from typing import Type - -from ktem.db.engine import engine -from sqlalchemy import JSON, Boolean, Column, String -from sqlalchemy.orm import DeclarativeBase -from theflow.settings import settings as flowsettings -from theflow.utils.modules import import_dotted_string - - -class Base(DeclarativeBase): - pass - - -class BaseLLMTable(Base): - """Base table to store language model""" - - __abstract__ = True - - name = Column(String, primary_key=True, unique=True) - spec = Column(JSON, default={}) - default = Column(Boolean, default=False) - - -_base_llm: Type[BaseLLMTable] = ( - import_dotted_string(flowsettings.KH_TABLE_LLM, safe=False) - if hasattr(flowsettings, "KH_TABLE_LLM") - else BaseLLMTable -) - - -class LLMTable(_base_llm): # type: ignore - __tablename__ = "llm_table" - - -if not getattr(flowsettings, "KH_ENABLE_ALEMBIC", False): - LLMTable.metadata.create_all(engine) diff --git a/libs/ktem/ktem/llms/manager.py b/libs/ktem/ktem/llms/manager.py index 6c723c250..d0f4a9882 100644 --- a/libs/ktem/ktem/llms/manager.py +++ b/libs/ktem/ktem/llms/manager.py @@ -1,233 +1,159 @@ -from typing import Optional, Type, overload +from typing import Optional -from sqlalchemy import select -from sqlalchemy.orm import Session -from theflow.settings import settings as flowsettings -from theflow.utils.modules import deserialize, import_dotted_string +from ktem.db.cruds import LLMCRUD +from ktem.db.engine import engine +from ktem.db.models import LLMTable from kotaemon.llms import ChatLLM - -from .db import LLMTable, engine +from kotaemon.llms.chats.factory import LLMFactory, LLMVendor class LLMManager: - """Represent a pool of models""" + """Represent a pool of language models.""" def __init__(self): self._models: dict[str, ChatLLM] = {} - self._info: dict[str, dict] = {} + self._info: dict[str, LLMTable] = {} self._default: str = "" - self._vendors: list[Type] = [] - - if hasattr(flowsettings, "KH_LLMS"): - for name, model in flowsettings.KH_LLMS.items(): - with Session(engine) as session: - stmt = select(LLMTable).where(LLMTable.name == name) - result = session.execute(stmt) - if not result.first(): - item = LLMTable( - name=name, - spec=model["spec"], - default=model.get("default", False), - ) - session.add(item) - session.commit() self.load() - self.load_vendors() - def load(self): - """Load the model pool from database""" + def load(self) -> None: + """Load the model pool from database.""" self._models, self._info, self._default = {}, {}, "" - with Session(engine) as session: - stmt = select(LLMTable) - items = session.execute(stmt) - - for (item,) in items: - self._models[item.name] = deserialize(item.spec, safe=False) - self._info[item.name] = { - "name": item.name, - "spec": item.spec, - "default": item.default, - } + + with LLMCRUD(engine) as crud: + for item in crud.list_all(): + llm_cls = LLMFactory.get_cls(LLMVendor(item.vendor)) + self._models[item.name] = llm_cls(**item.spec) + self._info[item.name] = item if item.default: self._default = item.name - def load_vendors(self): - from kotaemon.llms import ( - AzureChatOpenAI, - ChatOpenAI, - LCAnthropicChat, - LCCohereChat, - LCGeminiChat, - LCOllamaChat, - LlamaCppChat, - ) - - self._vendors = [ - ChatOpenAI, - AzureChatOpenAI, - LCAnthropicChat, - LCGeminiChat, - LCCohereChat, - LCOllamaChat, - LlamaCppChat, - ] - - for extra_vendor in getattr(flowsettings, "KH_LLM_EXTRA_VENDORS", []): - self._vendors.append(import_dotted_string(extra_vendor, safe=False)) - def __getitem__(self, key: str) -> ChatLLM: - """Get model by name""" + """Get model by name.""" + if key == "default": + key = self._default return self._models[key] def __contains__(self, key: str) -> bool: - """Check if model exists""" + """Check if model exists.""" return key in self._models - @overload - def get(self, key: str, default: None) -> Optional[ChatLLM]: - ... - - @overload - def get(self, key: str, default: ChatLLM) -> ChatLLM: - ... - def get(self, key: str, default: Optional[ChatLLM] = None) -> Optional[ChatLLM]: - """Get model by name with default value""" + """Get model by name with a fallback default.""" return self._models.get(key, default) def settings(self) -> dict: - """Present model pools option for gradio""" + """Return Gradio dropdown settings for the pool.""" return { "label": "LLM", "choices": list(self._models.keys()), "value": self.get_default_name(), } - def options(self) -> dict: - """Present a dict of models""" + def options(self) -> dict[str, ChatLLM]: + """Return all models keyed by name.""" return self._models def get_random_name(self) -> str: - """Get the name of random model - - Returns: - str: random model name in the pool - """ + """Return a random model name from the pool.""" import random if not self._models: raise ValueError("No models in pool") - return random.choice(list(self._models.keys())) def get_default_name(self) -> str: - """Get the name of default model - - In case there is no default model, choose random model from pool. In - case there are multiple default models, choose random from them. - - Returns: - str: model name - """ + """Return the default model name, or a random one if unset.""" if not self._models: raise ValueError("No models in pool") - - if not self._default: - return self.get_random_name() - - return self._default - - def get_random(self) -> ChatLLM: - """Get random model""" - return self._models[self.get_random_name()] + return self._default or self.get_random_name() def get_default(self) -> ChatLLM: - """Get default model - - In case there is no default model, choose random model from pool. In - case there are multiple default models, choose random from them. - - Returns: - ChatLLM: model - """ + """Return the default model instance.""" return self._models[self.get_default_name()] - def info(self) -> dict: - """List all models""" + def info(self) -> dict[str, LLMTable]: + """Return all model metadata keyed by name.""" return self._info - def add(self, name: str, spec: dict, default: bool): - """Add a new model to the pool""" - if not name: - raise ValueError("Name must not be empty") - - try: - with Session(engine) as session: + def add(self, name: str, vendor: LLMVendor, spec: dict, default: bool) -> None: + """Add a new model to the pool. - if default: - # turn all models to non-default - session.query(LLMTable).update({"default": False}) - session.commit() + Args: + name: unique model name. + vendor: vendor class identifier for factory lookup. + spec: constructor parameters for the vendor class. + default: make this model the pool default. - item = LLMTable(name=name, spec=spec, default=default) - session.add(item) - session.commit() + Raises: + ValueError: on validation or database errors. + """ + try: + with LLMCRUD(engine) as crud: + crud.create( + name=name, + vendor=vendor, + spec=spec, + default=default, + ) + except ValueError: + raise except Exception as e: - raise ValueError(f"Failed to add model {name}: {e}") - + raise ValueError(f"Failed to add LLM '{name}': {e}") from e self.load() - def delete(self, name: str): - """Delete a model from the pool""" - try: - with Session(engine) as session: - item = session.query(LLMTable).filter_by(name=name).first() - session.delete(item) - session.commit() - except Exception as e: - raise ValueError(f"Failed to delete model {name}: {e}") + def delete(self, name: str) -> None: + """Remove a model from the pool. - self.load() + Args: + name: name of the model to remove. - def update(self, name: str, spec: dict, default: bool, new_name: str = ""): - """Update a model in the pool, optionally renaming it.""" - if not name: - raise ValueError("Name must not be empty") + Raises: + ValueError: if the model does not exist. + """ + with LLMCRUD(engine) as crud: + crud.delete(name) + self.load() + def update( + self, + name: str, + vendor: LLMVendor, + spec: dict, + default: bool, + new_name: str = "", + ) -> None: + """Update a model, optionally renaming it. + + Args: + name: current model name. + vendor: vendor class identifier for factory lookup. + spec: constructor parameters for the vendor class. + default: new default flag. + new_name: rename the model when non-empty. + + Raises: + ValueError: on validation or database errors. + """ if new_name and new_name != name: - # Check uniqueness before destructive delete if new_name in self._info: raise ValueError( - f"Model '{new_name}' already exists. Use a unique name." + f"Model '{new_name}' already exists." " Use a unique name." ) self.delete(name) - self.add(new_name, spec=spec, default=default) + self.add(new_name, vendor=vendor, spec=spec, default=default) return try: - with Session(engine) as session: - - if default: - # turn all models to non-default - session.query(LLMTable).update({"default": False}) - session.commit() - - item = session.query(LLMTable).filter_by(name=name).first() - if not item: - raise ValueError(f"Model {name} not found") - item.spec = spec - item.default = default - session.commit() + with LLMCRUD(engine) as crud: + crud.update(name, vendor=vendor, spec=spec, default=default) + except ValueError: + raise except Exception as e: - raise ValueError(f"Failed to update model {name}: {e}") - + raise ValueError(f"Failed to update LLM '{name}': {e}") from e self.load() - def vendors(self) -> dict: - """Return list of vendors""" - return {vendor.__qualname__: vendor for vendor in self._vendors} - llms = LLMManager() diff --git a/libs/ktem/ktem/llms/ui.py b/libs/ktem/ktem/llms/ui.py index 73e6c1915..a2dac119c 100644 --- a/libs/ktem/ktem/llms/ui.py +++ b/libs/ktem/ktem/llms/ui.py @@ -3,9 +3,10 @@ import gradio as gr import pandas as pd import yaml -from ktem.app import BasePage +from ktem.app import BaseApp, BasePage from ktem.utils.file import YAMLNoDateSafeLoader -from theflow.utils.modules import deserialize + +from kotaemon.llms.chats.factory import LLMFactory, LLMVendor from .manager import llms @@ -14,14 +15,12 @@ def format_description(cls): params = cls.describe()["params"] params_lines = ["| Name | Type | Description |", "| --- | --- | --- |"] for key, value in params.items(): - if isinstance(value["auto_callback"], str): - continue params_lines.append(f"| {key} | {value['type']} | {value['help']} |") return f"{cls.__doc__}\n\n" + "\n".join(params_lines) class LLMManagement(BasePage): - def __init__(self, app): + def __init__(self, app: BaseApp): self._app = app self.spec_desc_default = ( "# Spec description\n\nSelect an LLM to view the spec description." @@ -133,20 +132,22 @@ def _on_app_created(self): outputs=[self.llm_list], ) self._app.app.load( - lambda: gr.update(choices=list(llms.vendors().keys())), + lambda: gr.update( + choices=[vendor.value for vendor in LLMFactory.supported_vendors()] + ), outputs=[self.llm_choices], ) - def on_llm_vendor_change(self, vendor): - vendor = llms.vendors()[vendor] + def on_llm_vendor_change(self, vendor: str): + vendor_cls = LLMFactory.get_cls(LLMVendor(vendor)) required: dict = {} - desc = vendor.describe() + desc = vendor_cls.describe() for key, value in desc["params"].items(): if value.get("required", False): required[key] = None - return yaml.dump(required), format_description(vendor) + return yaml.dump(required), format_description(vendor_cls) def on_register_events(self): self.llm_choices.select( @@ -250,13 +251,8 @@ def create_llm(self, name, choices, spec, default): try: name = name.strip() spec = yaml.load(spec, Loader=YAMLNoDateSafeLoader) - spec["__type__"] = ( - llms.vendors()[choices].__module__ - + "." - + llms.vendors()[choices].__qualname__ - ) - llms.add(name, spec=spec, default=default) + llms.add(name, vendor=LLMVendor(choices), spec=spec, default=default) gr.Info(f"LLM '{name}' created successfully") except ValueError as e: raise gr.Error(str(e)) @@ -265,13 +261,7 @@ def create_llm(self, name, choices, spec, default): def list_llms(self): """List the LLMs""" - items = [] - for item in llms.info().values(): - record = {} - record["name"] = item["name"] - record["vendor"] = item["spec"].get("__type__", "-").split(".")[-1] - record["default"] = item["default"] - items.append(record) + items = [item.ui for _, item in llms.info().items()] if items: llm_list = pd.DataFrame.from_records(items) @@ -312,14 +302,13 @@ def on_selected_llm_change(self, selected_llm_name): btn_delete_yes = gr.update(visible=False) btn_delete_no = gr.update(visible=False) - info = deepcopy(llms.info()[selected_llm_name]) - vendor_str = info["spec"].pop("__type__", "-").split(".")[-1] - vendor = llms.vendors()[vendor_str] + item = llms.info()[selected_llm_name] + vendor = LLMFactory.get_cls(LLMVendor(item.vendor)) edit_name = selected_llm_name - edit_spec = yaml.dump(info["spec"]) + edit_spec = yaml.dump(item.spec) edit_spec_desc = format_description(vendor) - edit_default = info["default"] + edit_default = item.default return ( _selected_panel, @@ -348,14 +337,10 @@ def check_connection(self, selected_llm_name: str, selected_spec): log_content += f"- Testing model: {selected_llm_name}
" yield log_content - # Parse content & init model - info = deepcopy(llms.info()[selected_llm_name]) - - # Parse content & create dummy embedding - spec = yaml.load(selected_spec, Loader=YAMLNoDateSafeLoader) - info["spec"].update(spec) - - llm = deserialize(info["spec"], safe=False) + item = llms.info()[selected_llm_name] + params = deepcopy(item.spec) + params.update(yaml.load(selected_spec, Loader=YAMLNoDateSafeLoader)) + llm = LLMFactory.get_cls(item.vendor)(**params) if llm is None: raise Exception(f"Can not found model: {selected_llm_name}") @@ -384,9 +369,13 @@ def save_llm(self, selected_llm_name, edit_name, default, spec): try: new_name = edit_name.strip() spec = yaml.load(spec, Loader=YAMLNoDateSafeLoader) - spec["__type__"] = llms.info()[selected_llm_name]["spec"]["__type__"] + item = llms.info()[selected_llm_name] llms.update( - selected_llm_name, spec=spec, default=default, new_name=new_name + selected_llm_name, + vendor=LLMVendor(item.vendor), + spec=spec, + default=default, + new_name=new_name, ) final_name = ( new_name if new_name != selected_llm_name else selected_llm_name diff --git a/libs/ktem/ktem/main.py b/libs/ktem/ktem/main.py index f9ff6be73..47dcba9d4 100644 --- a/libs/ktem/ktem/main.py +++ b/libs/ktem/ktem/main.py @@ -1,27 +1,19 @@ import gradio as gr -from decouple import config from ktem.app import BaseApp from ktem.pages.chat import ChatPage from ktem.pages.help import HelpPage from ktem.pages.resources import ResourcesTab from ktem.pages.settings import SettingsPage from ktem.pages.setup import SetupPage -from theflow.settings import settings as flowsettings +from ktem.settings_config import app_settings as flowsettings -KH_DEMO_MODE = getattr(flowsettings, "KH_DEMO_MODE", False) -KH_SSO_ENABLED = getattr(flowsettings, "KH_SSO_ENABLED", False) -KH_ENABLE_FIRST_SETUP = getattr(flowsettings, "KH_ENABLE_FIRST_SETUP", False) -KH_APP_DATA_EXISTS = getattr(flowsettings, "KH_APP_DATA_EXISTS", True) - -# override first setup setting -if config("KH_FIRST_SETUP", default=False, cast=bool): - KH_APP_DATA_EXISTS = False +KH_DEMO_MODE = flowsettings.KH_DEMO_MODE +KH_SSO_ENABLED = flowsettings.KH_SSO_ENABLED +KH_ENABLE_FIRST_SETUP = flowsettings.KH_ENABLE_FIRST_SETUP def toggle_first_setup_visibility(): - global KH_APP_DATA_EXISTS - is_first_setup = not KH_DEMO_MODE and not KH_APP_DATA_EXISTS - KH_APP_DATA_EXISTS = True + is_first_setup = not KH_DEMO_MODE and not flowsettings.KH_APP_DATA_DIR.exists() return gr.update(visible=is_first_setup), gr.update(visible=not is_first_setup) @@ -60,8 +52,8 @@ def ui(self): ) as self._tabs["chat-tab"]: self.chat_page = ChatPage(self) - if len(self.index_manager.indices) == 1: - for index in self.index_manager.indices: + if len(self.collection_manager.collections) == 1: + for index in self.collection_manager.collections: with gr.Tab( f"{index.name}", elem_id="indices-tab", @@ -75,7 +67,7 @@ def ui(self): ) as self._tabs[f"{index.id}-tab"]: page = index.get_index_page_ui() setattr(self, f"_index_{index.id}", page) - elif len(self.index_manager.indices) > 1: + elif len(self.collection_manager.collections) > 1: with gr.Tab( "Files", elem_id="indices-tab", @@ -83,7 +75,7 @@ def ui(self): id="indices-tab", visible=not self.f_user_management and not KH_DEMO_MODE, ) as self._tabs["indices-tab"]: - for index in self.index_manager.indices: + for index in self.collection_manager.collections: with gr.Tab( index.name, elem_id=f"{index.id}-tab", @@ -126,9 +118,8 @@ def ui(self): def on_subscribe_public_events(self): if self.f_user_management: + from ktem.db.cruds import UserCRUD from ktem.db.engine import engine - from ktem.db.models import User - from sqlmodel import Session, select def toggle_login_visibility(user_id): if not user_id: @@ -141,8 +132,8 @@ def toggle_login_visibility(user_id): for k in self._tabs.keys() ) + [gr.update(selected="login-tab")] - with Session(engine) as session: - user = session.exec(select(User).where(User.id == user_id)).first() + with UserCRUD(engine) as crud: + user = crud.get(user_id) if user is None: return list( ( diff --git a/libs/ktem/ktem/mcp/db.py b/libs/ktem/ktem/mcp/db.py index 20578665c..dc8e3ae27 100644 --- a/libs/ktem/ktem/mcp/db.py +++ b/libs/ktem/ktem/mcp/db.py @@ -1,20 +1,19 @@ +from typing import Any + from ktem.db.engine import engine -from sqlalchemy import JSON, Column, String +from ktem.db.models import Base +from sqlalchemy import JSON, String from sqlalchemy import inspect as sa_inspect -from sqlalchemy.orm import DeclarativeBase - - -class Base(DeclarativeBase): - pass +from sqlalchemy.orm import Mapped, mapped_column class BaseMCPTable(Base): - """Base table to store MCP server configurations""" + """Base table to store MCP server configurations.""" __abstract__ = True - name = Column(String, primary_key=True, unique=True) - config = Column(JSON, default={}) # Full JSON config for the MCP server + name: Mapped[str] = mapped_column(String, primary_key=True, unique=True) + config: Mapped[dict[str, Any]] = mapped_column(JSON, default=dict) class MCPTable(BaseMCPTable): diff --git a/libs/ktem/ktem/mcp/manager.py b/libs/ktem/ktem/mcp/manager.py index 98a755c95..0805384a7 100644 --- a/libs/ktem/ktem/mcp/manager.py +++ b/libs/ktem/ktem/mcp/manager.py @@ -6,10 +6,8 @@ import logging -from sqlalchemy import select -from sqlalchemy.orm import Session - -from .db import MCPTable, engine +from ktem.db.cruds import MCPCRUD +from ktem.db.engine import engine logger = logging.getLogger(__name__) @@ -26,10 +24,8 @@ def __init__(self): def load(self): """Reload configurations from the database.""" self._info = {} - with Session(engine) as session: - stmt = select(MCPTable) - items = session.execute(stmt) - for (item,) in items: + with MCPCRUD(engine) as crud: + for item in crud.list_all(): self._info[item.name] = { "name": item.name, "config": item.config, @@ -45,39 +41,23 @@ def get(self, name: str) -> dict | None: def add(self, name: str, config: dict): """Add a new MCP server configuration.""" - name = name.strip() - if not name: - raise ValueError("Name must not be empty") - - with Session(engine) as session: - item = MCPTable(name=name, config=config) - session.add(item) - session.commit() - + with MCPCRUD(engine) as crud: + crud.create(name=name, config=config) self.load() def update(self, name: str, config: dict): """Update an existing MCP server configuration.""" - if not name: - raise ValueError("Name must not be empty") - - with Session(engine) as session: - item = session.query(MCPTable).filter_by(name=name).first() - if not item: - raise ValueError(f"MCP server '{name}' not found") - item.config = config # type: ignore[assignment] - session.commit() - + with MCPCRUD(engine) as crud: + crud.update(name, config) self.load() def delete(self, name: str): """Delete an MCP server configuration.""" - with Session(engine) as session: - item = session.query(MCPTable).filter_by(name=name).first() - if item: - session.delete(item) - session.commit() - + with MCPCRUD(engine) as crud: + try: + crud.delete(name) + except ValueError: + pass self.load() def list_registered_mcp_servers(self) -> list[str]: diff --git a/libs/ktem/ktem/pages/chat/__init__.py b/libs/ktem/ktem/pages/chat/__init__.py index 132832040..3f97b18ab 100644 --- a/libs/ktem/ktem/pages/chat/__init__.py +++ b/libs/ktem/ktem/pages/chat/__init__.py @@ -1,15 +1,14 @@ -import asyncio import json import re from copy import deepcopy -from typing import Optional import gradio as gr from decouple import config -from ktem.app import BasePage +from ktem.app import BaseApp, BasePage +from ktem.collections.file.ui import File from ktem.components import reasonings -from ktem.db.models import Conversation, engine -from ktem.index.file.ui import File +from ktem.db.cruds import ConversationCRUD +from ktem.db.engine import engine from ktem.reasoning.prompt_optimization.mindmap import MINDMAP_HTML_EXPORT_TEMPLATE from ktem.reasoning.prompt_optimization.suggest_conversation_name import ( SuggestConvNamePipeline, @@ -17,10 +16,8 @@ from ktem.reasoning.prompt_optimization.suggest_followup_chat import ( SuggestFollowupQuesPipeline, ) +from ktem.settings_config import app_settings as flowsettings from plotly.io import from_json -from sqlmodel import Session, select -from theflow.settings import settings as flowsettings -from theflow.utils.modules import import_dotted_string from kotaemon.base import Document from kotaemon.indices.ingests.files import KH_DEFAULT_FILE_EXTRACTORS @@ -44,15 +41,17 @@ from .paper_list import PaperListPage from .report import ReportIssue -KH_DEMO_MODE = getattr(flowsettings, "KH_DEMO_MODE", False) -KH_SSO_ENABLED = getattr(flowsettings, "KH_SSO_ENABLED", False) -KH_WEB_SEARCH_BACKEND = getattr(flowsettings, "KH_WEB_SEARCH_BACKEND", None) +KH_DEMO_MODE = flowsettings.KH_DEMO_MODE +KH_SSO_ENABLED = flowsettings.KH_SSO_ENABLED +KH_WEB_SEARCH_BACKEND = flowsettings.KH_WEB_SEARCH_BACKEND WebSearch = None if KH_WEB_SEARCH_BACKEND: try: - WebSearch = import_dotted_string(KH_WEB_SEARCH_BACKEND, safe=False) - except (ImportError, AttributeError) as e: - print(f"Error importing {KH_WEB_SEARCH_BACKEND}: {e}") + from kotaemon.indices.websearch import WebSearchFactory + + WebSearch = WebSearchFactory.get_cls(KH_WEB_SEARCH_BACKEND) + except ValueError: + print(f"Unknown web search backend: {KH_WEB_SEARCH_BACKEND}") REASONING_LIMITS = 2 if KH_DEMO_MODE else 10 DEFAULT_SETTING = "(default)" @@ -204,18 +203,16 @@ class ChatPage(BasePage): - def __init__(self, app): + def __init__(self, app: BaseApp): self._app = app - self._indices_input = [] + self._indices_input: list = [] self.on_building_ui() self._preview_links = gr.State(value=None) self._reasoning_type = gr.State(value=None) self._conversation_renamed = gr.State(value=False) - self._use_suggestion = gr.State( - value=getattr(flowsettings, "KH_FEATURE_CHAT_SUGGESTION", False) - ) + self._use_suggestion = gr.State(value=flowsettings.KH_FEATURE_CHAT_SUGGESTION) self._info_panel_expanded = gr.State(value=True) self._command_state = gr.State(value=None) self._user_api_key = gr.Text(value="", visible=False) @@ -231,7 +228,9 @@ def on_building_ui(self): with gr.Column(scale=1, elem_id="conv-settings-panel") as self.conv_column: self.chat_control = ConversationControl(self._app) - for index_id, index in enumerate(self._app.index_manager.indices): + for index_id, index in enumerate( + self._app.collection_manager.collections + ): index.selector = None index_ui = index.get_selector_component_ui() if not index_ui: @@ -276,7 +275,7 @@ def on_building_ui(self): self.chat_suggestion = ChatSuggestion(self._app) - if len(self._app.index_manager.indices) > 0: + if len(self._app.collection_manager.collections) > 0: quick_upload_label = ( "Quick Upload" if not KH_DEMO_MODE else "Or input new paper URL" ) @@ -966,12 +965,12 @@ def submit_msg( if not conv_id: if not KH_DEMO_MODE: id_, update = self.chat_control.new_conv(user_id) - with Session(engine) as session: - statement = select(Conversation).where(Conversation.id == id_) - name = session.exec(statement).one().name - new_conv_id = id_ - conv_update = update - new_conv_name = name + with ConversationCRUD(engine) as crud: + conv = crud.get(id_) + name = conv.name if conv is not None else "" + new_conv_id = id_ + conv_update = update + new_conv_name = name else: new_conv_id, new_conv_name, conv_update = None, None, gr.update() else: @@ -1013,19 +1012,15 @@ def on_set_public_conversation(self, is_public, convo_id): gr.Warning("No conversation selected") return - with Session(engine) as session: - statement = select(Conversation).where(Conversation.id == convo_id) + with ConversationCRUD(engine) as crud: + result = crud.get(convo_id) + if result is None: + gr.Warning("Conversation not found") + return - result = session.exec(statement).one() name = result.name - if result.is_public != is_public: - # Only trigger updating when user - # select different value from the current - result.is_public = is_public - session.add(result) - session.commit() - + crud.update_public(convo_id, is_public=is_public) gr.Info( f"Conversation: {name} is {'public' if is_public else 'private'}." ) @@ -1117,7 +1112,7 @@ def persist_data_source( state["app"]["regen"] = False selecteds_ = {} - for index in self._app.index_manager.indices: + for index in self._app.collection_manager.collections: if index.selector is None: continue if isinstance(index.selector, int): @@ -1125,25 +1120,27 @@ def persist_data_source( else: selecteds_[str(index.id)] = [selecteds[i] for i in index.selector] - with Session(engine) as session: - statement = select(Conversation).where(Conversation.id == convo_id) - result = session.exec(statement).one() + with ConversationCRUD(engine) as crud: + result = crud.get(convo_id) + if result is None: + gr.Warning("Conversation not found") + return retrival_history, plot_history data_source = result.data_source old_selecteds = data_source.get("selected", {}) is_owner = result.user == user_id - # Write down to db - result.data_source = { - "selected": selecteds_ if is_owner else old_selecteds, - "messages": messages, - "retrieval_messages": retrival_history, - "plot_history": plot_history, - "state": state, - "likes": deepcopy(data_source.get("likes", [])), - } - session.add(result) - session.commit() + crud.update_data_source( + convo_id, + { + "selected": selecteds_ if is_owner else old_selecteds, + "messages": messages, + "retrieval_messages": retrival_history, + "plot_history": plot_history, + "state": state, + "likes": deepcopy(data_source.get("likes", [])), + }, + ) return retrival_history, plot_history @@ -1154,18 +1151,16 @@ def reasoning_changed(self, reasoning_type): return reasoning_type def is_liked(self, convo_id, liked: gr.LikeData): - with Session(engine) as session: - statement = select(Conversation).where(Conversation.id == convo_id) - result = session.exec(statement).one() + with ConversationCRUD(engine) as crud: + result = crud.get(convo_id) + if result is None: + return data_source = deepcopy(result.data_source) likes = data_source.get("likes", []) likes.append([liked.index, liked.value, liked.liked]) data_source["likes"] = likes - - result.data_source = data_source - session.add(result) - session.commit() + crud.update_data_source(convo_id, data_source) def message_selected(self, retrieval_history, plot_history, msg: gr.SelectData): index = msg.index[0] @@ -1255,7 +1250,7 @@ def create_pipeline( web_search = WebSearch() retrievers.append(web_search) else: - for index in self._app.index_manager.indices: + for index in self._app.collection_manager.collections: index_selected = [] if isinstance(index.selector, int): index_selected = selecteds[index.selector] @@ -1279,7 +1274,7 @@ def create_pipeline( def _has_selected_files(self, user_id: int, *selecteds) -> bool: """Return True if any index file selector has documents selected.""" - for index in self._app.index_manager.indices: + for index in self._app.collection_manager.collections: if index.selector is None: continue index_ui = getattr(self, f"_index_{index.id}", None) @@ -1322,8 +1317,6 @@ def chat_fn( default_question=DEFAULT_QUESTION, ) - queue: asyncio.Queue[Optional[dict]] = asyncio.Queue() - # construct the pipeline pipeline, reasoning_state = self.create_pipeline( settings, @@ -1338,7 +1331,7 @@ def chat_fn( *selecteds, ) print("Reasoning state", reasoning_state) - pipeline.set_output_queue(queue) + # pipeline.set_output_queue(queue) text, refs, plot, plot_gr = "", "", None, gr.update(visible=False) msg_placeholder = getattr( diff --git a/libs/ktem/ktem/pages/chat/chat_panel.py b/libs/ktem/ktem/pages/chat/chat_panel.py index 807c48cdf..449155e21 100644 --- a/libs/ktem/ktem/pages/chat/chat_panel.py +++ b/libs/ktem/ktem/pages/chat/chat_panel.py @@ -1,14 +1,13 @@ import gradio as gr from ktem.app import BasePage -from theflow.settings import settings as flowsettings +from ktem.settings_config import app_settings as flowsettings -KH_DEMO_MODE = getattr(flowsettings, "KH_DEMO_MODE", False) +KH_DEMO_MODE = flowsettings.KH_DEMO_MODE if not KH_DEMO_MODE: PLACEHOLDER_TEXT = ( "This is the beginning of a new conversation.\n" - "Start by uploading a file or a web URL. " - "Visit Files tab for more options (e.g: GraphRAG)." + "Start by uploading a file or a web URL." ) else: PLACEHOLDER_TEXT = ( diff --git a/libs/ktem/ktem/pages/chat/chat_suggestion.py b/libs/ktem/ktem/pages/chat/chat_suggestion.py index 567655687..72d71b845 100644 --- a/libs/ktem/ktem/pages/chat/chat_suggestion.py +++ b/libs/ktem/ktem/pages/chat/chat_suggestion.py @@ -1,6 +1,6 @@ import gradio as gr from ktem.app import BasePage -from theflow.settings import settings as flowsettings +from ktem.settings_config import app_settings as flowsettings class ChatSuggestion(BasePage): @@ -22,7 +22,7 @@ def on_building_ui(self): self.chat_samples = [[each] for each in self.CHAT_SAMPLES] with gr.Accordion( label="Chat Suggestion", - visible=getattr(flowsettings, "KH_FEATURE_CHAT_SUGGESTION", False), + visible=flowsettings.KH_FEATURE_CHAT_SUGGESTION, ) as self.accordion: self.default_example = gr.State( value=self.chat_samples, diff --git a/libs/ktem/ktem/pages/chat/control.py b/libs/ktem/ktem/pages/chat/control.py index fb2b50c7c..46164e5f6 100644 --- a/libs/ktem/ktem/pages/chat/control.py +++ b/libs/ktem/ktem/pages/chat/control.py @@ -4,10 +4,9 @@ import gradio as gr from ktem.app import BasePage -from ktem.db.models import Conversation, User, engine -from sqlmodel import Session, or_, select - -import flowsettings +from ktem.db.cruds import ConversationCRUD, UserCRUD +from ktem.db.engine import engine +from ktem.settings_config import app_settings as flowsettings from ...utils.conversation import sync_retrieval_n_message from .chat_suggestion import ChatSuggestion @@ -15,8 +14,8 @@ logger = logging.getLogger(__name__) -KH_DEMO_MODE = getattr(flowsettings, "KH_DEMO_MODE", False) -KH_SSO_ENABLED = getattr(flowsettings, "KH_SSO_ENABLED", False) +KH_DEMO_MODE = flowsettings.KH_DEMO_MODE +KH_SSO_ENABLED = flowsettings.KH_SSO_ENABLED ASSETS_DIR = "assets/icons" if not os.path.isdir(ASSETS_DIR): ASSETS_DIR = "libs/ktem/ktem/assets/icons" @@ -204,10 +203,8 @@ def load_chat_history(self, user_id): # In case user are admin. They can also watch the # public conversations can_see_public: bool = False - with Session(engine) as session: - statement = select(User).where(User.id == user_id) - result = session.exec(statement).one_or_none() - + with UserCRUD(engine) as crud: + result = crud.get(user_id) if result is not None: if flowsettings.KH_USER_CAN_SEE_PUBLIC: can_see_public = ( @@ -218,35 +215,13 @@ def load_chat_history(self, user_id): print(f"User-id: {user_id}, can see public conversations: {can_see_public}") - options = [] - with Session(engine) as session: - # Define condition based on admin-role: - # - can_see: can see their conversations & public files - # - can_not_see: only see their conversations + with ConversationCRUD(engine) as crud: if can_see_public: - statement = ( - select(Conversation) - .where( - or_( - Conversation.user == user_id, - Conversation.is_public, - ) - ) - .order_by( - Conversation.is_public.desc(), Conversation.date_created.desc() - ) # type: ignore - ) + convs = crud.list_by_user_or_public(user_id) else: - statement = ( - select(Conversation) - .where(Conversation.user == user_id) - .order_by(Conversation.date_created.desc()) # type: ignore - ) - - results = session.exec(statement).all() - for result in results: - options.append((result.name, result.id)) + convs = crud.list_by_user(user_id) + options = [(conv.name, conv.id) for conv in convs] return options def reload_conv(self, user_id): @@ -261,11 +236,8 @@ def new_conv(self, user_id): if user_id is None: gr.Warning("Please sign in first (Settings → User Settings)") return None, gr.update() - with Session(engine) as session: - new_conv = Conversation(user=user_id) - session.add(new_conv) - session.commit() - + with ConversationCRUD(engine) as crud: + new_conv = crud.create(user_id) id_ = new_conv.id history = self.load_chat_history(user_id) @@ -282,12 +254,8 @@ def delete_conv(self, conversation_id, user_id): gr.Warning("Please sign in first (Settings → User Settings)") return None, gr.update() - with Session(engine) as session: - statement = select(Conversation).where(Conversation.id == conversation_id) - result = session.exec(statement).one() - - session.delete(result) - session.commit() + with ConversationCRUD(engine) as crud: + crud.delete(conversation_id) history = self.load_chat_history(user_id) if history: @@ -300,10 +268,11 @@ def select_conv(self, conversation_id, user_id): """Select the conversation""" default_chat_suggestions = [[each] for each in ChatSuggestion.CHAT_SAMPLES] - with Session(engine) as session: - statement = select(Conversation).where(Conversation.id == conversation_id) + with ConversationCRUD(engine) as crud: try: - result = session.exec(statement).one() + result = crud.get(conversation_id) + if result is None: + raise ValueError(f"Conversation '{conversation_id}' not found") id_ = result.id name = result.name is_conv_public = result.is_public @@ -325,8 +294,6 @@ def select_conv(self, conversation_id, user_id): ) plot_history: list[dict] = result.data_source.get("plot_history", []) - # On initialization - # Ensure len of retrieval and messages are equal retrieval_history = sync_retrieval_n_message(chats, retrieval_history) info_panel = ( @@ -352,7 +319,7 @@ def select_conv(self, conversation_id, user_id): is_conv_public = False indices = [] - for index in self._app.index_manager.indices: + for index in self._app.collection_manager.collections: # assume that the index has selector if index.selector is None: continue @@ -394,12 +361,8 @@ def rename_conv(self, conversation_id, new_name, is_renamed, user_id): gr.update(visible=False), ) - with Session(engine) as session: - statement = select(Conversation).where(Conversation.id == conversation_id) - result = session.exec(statement).one() - result.name = new_name - session.add(result) - session.commit() + with ConversationCRUD(engine) as crud: + crud.update_name(conversation_id, new_name) history = self.load_chat_history(user_id) gr.Info("Conversation renamed.") @@ -424,18 +387,17 @@ def persist_chat_suggestions( gr.Warning("No conversation selected.") return gr.update(), "" - with Session(engine) as session: - statement = select(Conversation).where(Conversation.id == conversation_id) - result = session.exec(statement).one() + with ConversationCRUD(engine) as crud: + result = crud.get(conversation_id) + if result is None: + gr.Warning("Conversation not found") + return gr.update(), "" data_source = deepcopy(result.data_source) data_source["chat_suggestions"] = [ [x] for x in new_suggestions.iloc[:, 0].tolist() ] - - result.data_source = data_source - session.add(result) - session.commit() + crud.update_data_source(conversation_id, data_source) gr.Info("Chat suggestions updated.") diff --git a/libs/ktem/ktem/pages/chat/report.py b/libs/ktem/ktem/pages/chat/report.py index b269f4ef1..909a090e3 100644 --- a/libs/ktem/ktem/pages/chat/report.py +++ b/libs/ktem/ktem/pages/chat/report.py @@ -2,8 +2,8 @@ import gradio as gr from ktem.app import BasePage -from ktem.db.models import IssueReport, engine -from sqlmodel import Session +from ktem.db.cruds import IssueReportCRUD +from ktem.db.engine import engine class ReportIssue(BasePage): @@ -55,7 +55,7 @@ def report( *selecteds, ): selecteds_ = {} - for index in self._app.index_manager.indices: + for index in self._app.collection_manager.collections: if index.selector is not None: if isinstance(index.selector, int): selecteds_[str(index.id)] = selecteds[index.selector] @@ -64,8 +64,8 @@ def report( else: print(f"Unknown selector type: {index.selector}") - with Session(engine) as session: - issue = IssueReport( + with IssueReportCRUD(engine) as crud: + crud.create( issues={ "correctness": correctness, "issues": issues, @@ -79,8 +79,6 @@ def report( "selecteds": selecteds_, }, settings=settings, - user=user_id, + user=str(user_id) if user_id is not None else None, ) - session.add(issue) - session.commit() gr.Info("Thank you for your feedback") diff --git a/libs/ktem/ktem/pages/help.py b/libs/ktem/ktem/pages/help.py index 2ecdf7eb7..9f97a51ba 100644 --- a/libs/ktem/ktem/pages/help.py +++ b/libs/ktem/ktem/pages/help.py @@ -4,9 +4,10 @@ import gradio as gr import requests from decouple import config -from theflow.settings import settings +from ktem.app import BaseApp +from ktem.settings_config import app_settings as settings -KH_DEMO_MODE = getattr(settings, "KH_DEMO_MODE", False) +KH_DEMO_MODE = settings.KH_DEMO_MODE HF_SPACE_URL = config("HF_SPACE_URL", default="") @@ -34,8 +35,8 @@ def download_changelogs(release_url: str) -> str: class HelpPage: def __init__( self, - app, - doc_dir: str = settings.KH_DOC_DIR, + app: BaseApp, + doc_dir: str | Path = settings.KH_DOC_DIR, remote_content_url: str = "https://raw.githubusercontent.com/Cinnamon/kotaemon", app_version: str | None = settings.KH_APP_VERSION, changelogs_cache_dir: str diff --git a/libs/ktem/ktem/pages/login.py b/libs/ktem/ktem/pages/login.py index 1455cb856..128fc5280 100644 --- a/libs/ktem/ktem/pages/login.py +++ b/libs/ktem/ktem/pages/login.py @@ -1,10 +1,8 @@ -import hashlib - import gradio as gr -from ktem.app import BasePage -from ktem.db.models import User, engine +from ktem.app import BaseApp, BasePage +from ktem.db.cruds import UserCRUD +from ktem.db.engine import engine from ktem.pages.resources.user import create_user -from sqlmodel import Session, select fetch_creds = """ function() { @@ -27,7 +25,7 @@ class LoginPage(BasePage): public_events = ["onSignIn"] - def __init__(self, app): + def __init__(self, app: BaseApp): self._app = app self.on_building_ui() @@ -96,37 +94,27 @@ def login(self, usn, pwd, request: gr.Request): if user: user_id = user["sub"] - with Session(engine) as session: - stmt = select(User).where( - User.id == user_id, - ) - result = session.exec(stmt).all() - - if result: - print("Existing user:", user) - return user_id, "", "" - else: - print("Creating new user:", user) - create_user( - usn=user["email"], - pwd="", - user_id=user_id, - is_admin=False, - ) - return user_id, "", "" - else: - if not usn or not pwd: - return None, usn, pwd - - hashed_password = hashlib.sha256(pwd.encode()).hexdigest() - with Session(engine) as session: - stmt = select(User).where( - User.username_lower == usn.lower().strip(), - User.password == hashed_password, - ) - result = session.exec(stmt).all() - if result: - return result[0].id, "", "" - - gr.Warning("Invalid username or password") - return None, usn, pwd + with UserCRUD(engine) as crud: + if crud.get(user_id) is not None: + print("Existing user:", user) + return user_id, "", "" + + print("Creating new user:", user) + create_user( + usn=user["email"], + pwd="", + user_id=user_id, + is_admin=False, + ) + return user_id, "", "" + + if not usn or not pwd: + return None, usn, pwd + + with UserCRUD(engine) as crud: + result = crud.authenticate(usn, pwd) + if result is not None: + return result.id, "", "" + + gr.Warning("Invalid username or password") + return None, usn, pwd diff --git a/libs/ktem/ktem/pages/resources/__init__.py b/libs/ktem/ktem/pages/resources/__init__.py index 1fa90d378..e232cdd3a 100644 --- a/libs/ktem/ktem/pages/resources/__init__.py +++ b/libs/ktem/ktem/pages/resources/__init__.py @@ -1,12 +1,12 @@ import gradio as gr from ktem.app import BasePage -from ktem.db.models import User, engine +from ktem.collections.ui import IndexManagement +from ktem.db.cruds import UserCRUD +from ktem.db.engine import engine from ktem.embeddings.ui import EmbeddingManagement -from ktem.index.ui import IndexManagement from ktem.llms.ui import LLMManagement from ktem.mcp.ui import MCPManagement from ktem.rerankings.ui import RerankingManagement -from sqlmodel import Session, select from .user import UserManagement @@ -60,9 +60,9 @@ def on_subscribe_public_events(self): def toggle_user_management(self, user_id): """Show/hide the user management, depending on the user's role""" - with Session(engine) as session: - user = session.exec(select(User).where(User.id == user_id)).first() + with UserCRUD(engine) as crud: + user = crud.get(user_id) if user_id else None if user and user.admin: return gr.update(visible=True) - return gr.update(visible=False) + return gr.update(visible=False) diff --git a/libs/ktem/ktem/pages/resources/user.py b/libs/ktem/ktem/pages/resources/user.py index bb5cb56a2..8dc5b9cea 100644 --- a/libs/ktem/ktem/pages/resources/user.py +++ b/libs/ktem/ktem/pages/resources/user.py @@ -1,11 +1,10 @@ -import hashlib - import gradio as gr import pandas as pd from ktem.app import BasePage -from ktem.db.models import User, engine -from sqlmodel import Session, select -from theflow.settings import settings as flowsettings +from ktem.db.cruds import UserCRUD +from ktem.db.cruds.user import hash_password +from ktem.db.engine import engine +from ktem.settings_config import app_settings as flowsettings USERNAME_RULE = """**Username rule:** @@ -95,26 +94,18 @@ def validate_password(pwd, pwd_cnf): def create_user(usn, pwd, user_id=None, is_admin=True) -> bool: - with Session(engine) as session: - statement = select(User).where(User.username_lower == usn.lower()) - result = session.exec(statement).all() - if result: - print(f'User "{usn}" already exists') - return False - - else: - hashed_password = hashlib.sha256(pwd.encode()).hexdigest() - user = User( - id=user_id, - username=usn, - username_lower=usn.lower(), - password=hashed_password, + try: + with UserCRUD(engine) as crud: + crud.create( + usn, + hash_password(pwd), admin=is_admin, + user_id=user_id, ) - session.add(user) - session.commit() - - return True + return True + except ValueError: + print(f'User "{usn}" already exists') + return False class UserManagement(BasePage): @@ -295,21 +286,14 @@ def create_user(self, usn, pwd, pwd_cnf): gr.Warning(errors) return usn, pwd, pwd_cnf - with Session(engine) as session: - statement = select(User).where(User.username_lower == usn.lower()) - result = session.exec(statement).all() - if result: - gr.Warning(f'Username "{usn}" already exists') - return - - hashed_password = hashlib.sha256(pwd.encode()).hexdigest() - user = User( - username=usn, username_lower=usn.lower(), password=hashed_password - ) - session.add(user) - session.commit() - gr.Info(f'User "{usn}" created successfully') + try: + with UserCRUD(engine) as crud: + crud.create(usn, hash_password(pwd)) + except ValueError: + gr.Warning(f'Username "{usn}" already exists') + return usn, pwd, pwd_cnf + gr.Info(f'User "{usn}" created successfully') return "", "", "" def list_users(self, user_id): @@ -318,25 +302,28 @@ def list_users(self, user_id): [{"id": "-", "username": "-", "admin": "-"}] ) - with Session(engine) as session: - statement = select(User).where(User.id == user_id) - user = session.exec(statement).one() - if not user.admin: + with UserCRUD(engine) as crud: + user = crud.get(user_id) + if user is None or not user.admin: return [], pd.DataFrame.from_records( [{"id": "-", "username": "-", "admin": "-"}] ) - statement = select(User) results = [ - {"id": user.id, "username": user.username, "admin": user.admin} - for user in session.exec(statement).all() + { + "id": row.id, + "username": row.username, + "admin": row.admin, + } + for row in crud.list_all() ] - if results: - user_list = pd.DataFrame.from_records(results) - else: - user_list = pd.DataFrame.from_records( - [{"id": "-", "username": "-", "admin": "-"}] - ) + + if results: + user_list = pd.DataFrame.from_records(results) + else: + user_list = pd.DataFrame.from_records( + [{"id": "-", "username": "-", "admin": "-"}] + ) return results, user_list @@ -368,9 +355,10 @@ def on_selected_user_change(self, selected_user_id): btn_delete_yes = gr.update(visible=False) btn_delete_no = gr.update(visible=False) - with Session(engine) as session: - statement = select(User).where(User.id == selected_user_id) - user = session.exec(statement).one() + with UserCRUD(engine) as crud: + user = crud.get(selected_user_id) + if user is None: + raise ValueError(f"User '{selected_user_id}' not found") usn_edit = gr.update(value=user.username) pwd_edit = gr.update(value="") @@ -415,29 +403,23 @@ def save_user(self, selected_user_id, usn, pwd, pwd_cnf, admin): gr.Warning(errors) return pwd, pwd_cnf - with Session(engine) as session: - # Check username uniqueness (excluding current user) - statement = select(User).where( - User.username_lower == usn.lower(), - User.id != selected_user_id, - ) - existing = session.exec(statement).first() - if existing: + with UserCRUD(engine) as crud: + if crud.username_taken(usn, exclude_id=selected_user_id): gr.Warning( f'Username "{usn}" already exists. Please use a unique name.' ) return pwd, pwd_cnf - statement = select(User).where(User.id == selected_user_id) - user = session.exec(statement).one() - user.username = usn - user.username_lower = usn.lower() - user.admin = admin + kwargs: dict = {"username": usn, "admin": admin} if pwd: - user.password = hashlib.sha256(pwd.encode()).hexdigest() - session.commit() - gr.Info(f'User "{usn}" updated successfully') + kwargs["password"] = hash_password(pwd) + try: + crud.update(selected_user_id, **kwargs) + except ValueError: + gr.Warning("User not found") + return pwd, pwd_cnf + gr.Info(f'User "{usn}" updated successfully') return "", "" def delete_user(self, current_user, selected_user_id): @@ -445,10 +427,12 @@ def delete_user(self, current_user, selected_user_id): gr.Warning("You cannot delete yourself") return selected_user_id - with Session(engine) as session: - statement = select(User).where(User.id == selected_user_id) - user = session.exec(statement).one() - session.delete(user) - session.commit() - gr.Info(f'User "{user.username}" deleted successfully') + with UserCRUD(engine) as crud: + user = crud.get(selected_user_id) + if user is None: + gr.Warning("User not found") + return selected_user_id + username = user.username + crud.delete(selected_user_id) + gr.Info(f'User "{username}" deleted successfully') return -1 diff --git a/libs/ktem/ktem/pages/settings.py b/libs/ktem/ktem/pages/settings.py index b60cec5bc..5f849074d 100644 --- a/libs/ktem/ktem/pages/settings.py +++ b/libs/ktem/ktem/pages/settings.py @@ -1,14 +1,13 @@ -import hashlib - import gradio as gr -from ktem.app import BasePage +from ktem.app import BaseApp, BasePage from ktem.components import reasonings -from ktem.db.models import Settings, User, engine +from ktem.db.cruds import SettingsCRUD, UserCRUD +from ktem.db.cruds.user import hash_password +from ktem.db.engine import engine from ktem.mcp.manager import MCP_TOOL_PREFIX, mcp_manager -from sqlmodel import Session, select -from theflow.settings import settings as flowsettings +from ktem.settings_config import app_settings as flowsettings -KH_SSO_ENABLED = getattr(flowsettings, "KH_SSO_ENABLED", False) +KH_SSO_ENABLED = flowsettings.KH_SSO_ENABLED signout_js = """ @@ -66,7 +65,7 @@ class SettingsPage(BasePage): public_events = ["onSignOut"] - def __init__(self, app): + def __init__(self, app: BaseApp): """Initiate the page and render the UI""" self._app = app @@ -76,14 +75,14 @@ def __init__(self, app): self._settings_dict = self._default_settings.flatten() self._settings_keys = list(self._settings_dict.keys()) - self._components = {} - self._reasoning_mode = {} - self._reasoning_tool_components = [] - self._reasoning_tool_base_choices = [] + self._components: dict = {} + self._reasoning_mode: dict = {} + self._reasoning_tool_components: list = [] + self._reasoning_tool_base_choices: list = [] # store llms and embeddings components - self._llms = [] - self._embeddings = [] + self._llms: list = [] + self._embeddings: list = [] # render application page if there are application settings self._render_app_tab = False @@ -170,11 +169,10 @@ def on_subscribe_public_events(self): def get_name(user_id): name = "Current user: " if user_id: - with Session(engine) as session: - statement = select(User).where(User.id == user_id) - result = session.exec(statement).all() - if result: - return name + result[0].username + with UserCRUD(engine) as crud: + user = crud.get(user_id) + if user is not None: + return name + user.username return name + "___" self._app.subscribe_event( @@ -273,18 +271,13 @@ def change_password(self, user_id, password, password_confirm): gr.Warning(errors) return password, password_confirm - with Session(engine) as session: - statement = select(User).where(User.id == user_id) - result = session.exec(statement).all() - if result: - user = result[0] - hashed_password = hashlib.sha256(password.encode()).hexdigest() - user.password = hashed_password - session.add(user) - session.commit() - gr.Info("Password changed") - else: + with UserCRUD(engine) as crud: + try: + crud.update(user_id, password=hash_password(password)) + except ValueError: gr.Warning("User not found") + else: + gr.Info("Password changed") return "", "" @@ -305,7 +298,7 @@ def index_tab(self): # obj = render_setting_item(si, si.value) # self._components[f"index.{n}"] = obj - id2name = {k: v.name for k, v in self._app.index_manager.info().items()} + id2name = {k: v.name for k, v in self._app.collection_manager.info().items()} with gr.Tab("Retrieval settings", visible=self._render_index_tab): for pn, sig in self._default_settings.index.options.items(): name = id2name.get(pn, f"") @@ -378,11 +371,10 @@ def change_reasoning_mode(self, value): def load_setting(self, user_id=None): settings = self._settings_dict - with Session(engine) as session: - statement = select(Settings).where(Settings.user == user_id) - result = session.exec(statement).all() - if result: - settings = result[0].setting + with SettingsCRUD(engine) as crud: + item = crud.get_by_user(user_id) if user_id else None + if item is not None: + settings = item.setting output = [settings] output += tuple(settings[name] for name in self.component_names()) @@ -404,7 +396,7 @@ def refresh_reasoning_tool_choices(self, *current_values): updates.append(gr.update(choices=choices, value=value)) return updates - def save_setting(self, user_id: int, *args): + def save_setting(self, user_id: str, *args): """Save the setting to disk and persist the setting to session state Args: @@ -416,16 +408,8 @@ def save_setting(self, user_id: int, *args): gr.Warning("Need to login before saving settings") return setting - with Session(engine) as session: - statement = select(Settings).where(Settings.user == user_id) - try: - user_setting = session.exec(statement).one() - except Exception: - user_setting = Settings() - user_setting.user = user_id - user_setting.setting = setting - session.add(user_setting) - session.commit() + with SettingsCRUD(engine) as crud: + crud.upsert(user_id, setting) gr.Info("Setting saved") return setting diff --git a/libs/ktem/ktem/pages/setup.py b/libs/ktem/ktem/pages/setup.py index be5c917a5..fdc0cb7c4 100644 --- a/libs/ktem/ktem/pages/setup.py +++ b/libs/ktem/ktem/pages/setup.py @@ -3,13 +3,17 @@ import gradio as gr import requests from decouple import config -from ktem.app import BasePage +from ktem.app import BaseApp, BasePage from ktem.embeddings.manager import embedding_models_manager as embeddings from ktem.llms.manager import llms from ktem.rerankings.manager import reranking_models_manager as rerankers -from theflow.settings import settings as flowsettings +from ktem.settings_config import app_settings as flowsettings -KH_OLLAMA_URL = getattr(flowsettings, "KH_OLLAMA_URL", "http://localhost:11434/v1/") +from kotaemon.embeddings.factory import EmbeddingVendor +from kotaemon.llms.chats.factory import LLMVendor +from kotaemon.rerankings.factory import RerankingVendor + +KH_OLLAMA_URL = flowsettings.KH_OLLAMA_URL DEFAULT_OLLAMA_URL = KH_OLLAMA_URL.replace("v1", "api") if DEFAULT_OLLAMA_URL.endswith("/"): DEFAULT_OLLAMA_URL = DEFAULT_OLLAMA_URL[:-1] @@ -50,7 +54,7 @@ class SetupPage(BasePage): public_events = ["onFirstSetupComplete"] - def __init__(self, app): + def __init__(self, app: BaseApp): self._app = app self.on_building_ui() @@ -205,8 +209,8 @@ def update_model( if cohere_api_key: llms.update( name="cohere", + vendor=LLMVendor("LCCohereChat"), spec={ - "__type__": "kotaemon.llms.chats.LCCohereChat", "model_name": "command-r-plus-08-2024", "api_key": cohere_api_key, }, @@ -214,8 +218,8 @@ def update_model( ) embeddings.update( name="cohere", + vendor=EmbeddingVendor("LCCohereEmbeddings"), spec={ - "__type__": "kotaemon.embeddings.LCCohereEmbeddings", "model": "embed-multilingual-v3.0", "cohere_api_key": cohere_api_key, "user_agent": "default", @@ -224,8 +228,8 @@ def update_model( ) rerankers.update( name="cohere", + vendor=RerankingVendor("CohereReranking"), spec={ - "__type__": "kotaemon.rerankings.CohereReranking", "model_name": "rerank-v4.0-fast", "cohere_api_key": cohere_api_key, }, @@ -235,8 +239,8 @@ def update_model( if openai_api_key: llms.update( name="openai", + vendor=LLMVendor("ChatOpenAI"), spec={ - "__type__": "kotaemon.llms.ChatOpenAI", "base_url": "https://api.openai.com/v1", "model": "gpt-4o", "api_key": openai_api_key, @@ -246,8 +250,8 @@ def update_model( ) embeddings.update( name="openai", + vendor=EmbeddingVendor("OpenAIEmbeddings"), spec={ - "__type__": "kotaemon.embeddings.OpenAIEmbeddings", "base_url": "https://api.openai.com/v1", "model": "text-embedding-3-large", "api_key": openai_api_key, @@ -260,8 +264,8 @@ def update_model( if google_api_key: llms.update( name="google", + vendor=LLMVendor("LCGeminiChat"), spec={ - "__type__": "kotaemon.llms.chats.LCGeminiChat", "model_name": "gemini-1.5-flash", "api_key": google_api_key, }, @@ -269,8 +273,8 @@ def update_model( ) embeddings.update( name="google", + vendor=EmbeddingVendor("LCGoogleEmbeddings"), spec={ - "__type__": "kotaemon.embeddings.LCGoogleEmbeddings", "model": "models/text-embedding-004", "google_api_key": google_api_key, }, @@ -279,8 +283,8 @@ def update_model( elif radio_model_value == "ollama": llms.update( name="ollama", + vendor=LLMVendor("ChatOpenAI"), spec={ - "__type__": "kotaemon.llms.ChatOpenAI", "base_url": KH_OLLAMA_URL, "model": ollama_model_name, "api_key": "ollama", @@ -289,8 +293,8 @@ def update_model( ) embeddings.update( name="ollama", + vendor=EmbeddingVendor("OpenAIEmbeddings"), spec={ - "__type__": "kotaemon.embeddings.OpenAIEmbeddings", "base_url": KH_OLLAMA_URL, "model": ollama_emb_model_name, "api_key": "ollama", @@ -342,6 +346,7 @@ def update_model( log_content += "- Sending a message `Hi`
" yield log_content try: + assert llm is not None llm_output = llm("Hi") except Exception as e: log_content += ( diff --git a/libs/ktem/ktem/reasoning/base.py b/libs/ktem/ktem/reasoning/base.py index 6d6e48648..75f72ff50 100644 --- a/libs/ktem/ktem/reasoning/base.py +++ b/libs/ktem/ktem/reasoning/base.py @@ -1,9 +1,9 @@ +from dataclasses import dataclass from typing import Optional -from kotaemon.base import BaseComponent - -class BaseReasoning(BaseComponent): +@dataclass(kw_only=True) +class BaseReasoning: """The reasoning pipeline that handles each of the user chat messages This reasoning pipeline has access to: @@ -37,7 +37,7 @@ def get_pipeline( cls, user_settings: dict, state: dict, - retrievers: Optional[list["BaseComponent"]] = None, + retrievers: Optional[list] = None, ) -> "BaseReasoning": """Get the reasoning pipeline for the app to execute @@ -51,3 +51,6 @@ def get_pipeline( def run(self, message: str, conv_id: str, history: list, **kwargs): # type: ignore """Execute the reasoning pipeline""" raise NotImplementedError + + def report_output(self, doc: Optional[object]) -> None: # type: ignore + """Report intermediate output from the pipeline.""" diff --git a/libs/ktem/ktem/reasoning/citation_display.py b/libs/ktem/ktem/reasoning/citation_display.py new file mode 100644 index 000000000..be8c9e374 --- /dev/null +++ b/libs/ktem/ktem/reasoning/citation_display.py @@ -0,0 +1,107 @@ +"""Citation display helpers. + +Renders citation evidence into HTML for the Gradio UI. +This logic lives in ktem (app layer) because it depends on +``ktem.utils.render.Render`` which is UI-specific and must +not be imported by the ``kotaemon`` framework layer. +""" + +from __future__ import annotations + +import logging + +from ktem.utils.render import Render + +from kotaemon.base import Document +from kotaemon.indices.qa.citation_qa import ( + CONTEXT_RELEVANT_WARNING_SCORE, + AnswerWithContextPipeline, +) + +logger = logging.getLogger(__name__) + + +def prepare_citations( + pipeline: AnswerWithContextPipeline, + answer: Document, + docs: list[Document], +) -> tuple[list[Document], list[Document]]: + """Prepare citation documents for UI display. + + Delegates evidence-matching to the framework-level + ``pipeline.match_evidence_with_context``, then + renders the results with ``Render``. + """ + with_citation: list[Document] = [] + without_citation: list[Document] = [] + has_llm_score = any("llm_trulens_score" in doc.metadata for doc in docs) + + spans = pipeline.match_evidence_with_context(answer, docs) + id2docs = {doc.doc_id: doc for doc in docs} + not_detected = set(id2docs.keys()) - set(spans.keys()) + + for _id, ss in spans.items(): + if not ss: + not_detected.add(_id) + continue + cur_doc = id2docs[_id] + highlight_text = "" + + ss = sorted(ss, key=lambda x: x["start"]) + last_end = 0 + text = cur_doc.text[: ss[0]["start"]] + + for idx, span in enumerate(ss): + span_start = max(last_end, span["start"]) + span_end = max(last_end, span["end"]) + + to_highlight = cur_doc.text[span_start:span_end] + last_end = span_end + + highlight_text += (" " if highlight_text else "") + to_highlight + + span_idx = span.get("idx", None) + if span_idx is not None: + to_highlight = f"\u3010{span_idx}\u3011" + to_highlight + + text += Render.highlight( + to_highlight, + elem_id=(str(span_idx) if span_idx is not None else None), + ) + if idx < len(ss) - 1: + text += cur_doc.text[span["end"] : ss[idx + 1]["start"]] + + text += cur_doc.text[ss[-1]["end"] :] + with_citation.append( + Document( + channel="info", + content=Render.collapsible_with_header_score( + cur_doc, + override_text=text, + highlight_text=highlight_text, + open_collapsible=True, + ), + ) + ) + + logger.info("Got %d cited docs", len(with_citation)) + + sorted_not_detected = sorted( + not_detected, + key=lambda id_: id2docs[id_].metadata.get("llm_trulens_score", 0.0), + reverse=True, + ) + + for id_ in sorted_not_detected: + doc = id2docs[id_] + doc_score = doc.metadata.get("llm_trulens_score", 0.0) + is_open = not has_llm_score or (doc_score > CONTEXT_RELEVANT_WARNING_SCORE) + without_citation.append( + Document( + channel="info", + content=Render.collapsible_with_header_score( + doc, open_collapsible=is_open + ), + ) + ) + return with_citation, without_citation diff --git a/libs/ktem/ktem/reasoning/prompt_optimization/decompose_question.py b/libs/ktem/ktem/reasoning/prompt_optimization/decompose_question.py index 04ec6a856..bf1820c44 100644 --- a/libs/ktem/ktem/reasoning/prompt_optimization/decompose_question.py +++ b/libs/ktem/ktem/reasoning/prompt_optimization/decompose_question.py @@ -1,36 +1,29 @@ import logging +from dataclasses import dataclass, field from ktem.llms.manager import llms from ktem.reasoning.prompt_optimization.rewrite_question import RewriteQuestionPipeline from pydantic import BaseModel, Field -from kotaemon.base import Document, HumanMessage, Node, SystemMessage +from kotaemon.base import Document, HumanMessage, SystemMessage from kotaemon.llms import ChatLLM logger = logging.getLogger(__name__) class SubQuery(BaseModel): - """Search over a database of insurance rulebooks or financial reports""" - sub_query: str = Field( ..., description="A very specific query against the database.", ) +@dataclass(kw_only=True) class DecomposeQuestionPipeline(RewriteQuestionPipeline): - """Decompose user complex question into multiple sub-questions - - Args: - llm: the language model to rewrite question - lang: the language of the answer. Currently support English and Japanese - """ - - llm: ChatLLM = Node( - default_callback=lambda _: llms.get("openai-gpt4-turbo", llms.get_default()) + llm: ChatLLM = field( + default_factory=lambda: llms.get("openai-gpt4-turbo", llms.get_default()) ) - DECOMPOSE_SYSTEM_PROMPT_TEMPLATE = ( + DECOMPOSE_SYSTEM_PROMPT_TEMPLATE: str = ( "You are an expert at converting user complex questions into sub questions. " "Perform query decomposition using provided function_call. " "Given a user question, break it down into the most specific sub" @@ -54,12 +47,10 @@ def create_prompt(self, question): "tool_choice": "auto", "tools_pydantic": [SubQuery], } - messages = [ SystemMessage(content=self.prompt_template), HumanMessage(content=question), ] - return messages, llm_kwargs def run(self, question: str) -> list: # type: ignore @@ -70,10 +61,8 @@ def run(self, question: str) -> list: # type: ignore if tool_calls: for tool_call in tool_calls: if "function" in tool_call: - # openai and cohere format function_output = tool_call["function"]["arguments"] else: - # anthropic format function_output = tool_call["args"] if isinstance(function_output, str): @@ -81,10 +70,5 @@ def run(self, question: str) -> list: # type: ignore else: sub_query = SubQuery.parse_obj(function_output).sub_query - sub_queries.append( - Document( - content=sub_query, - ) - ) - + sub_queries.append(Document(content=sub_query)) return sub_queries diff --git a/libs/ktem/ktem/reasoning/prompt_optimization/fewshot_rewrite_question.py b/libs/ktem/ktem/reasoning/prompt_optimization/fewshot_rewrite_question.py index 3c3b7191e..5aa16aad6 100644 --- a/libs/ktem/ktem/reasoning/prompt_optimization/fewshot_rewrite_question.py +++ b/libs/ktem/ktem/reasoning/prompt_optimization/fewshot_rewrite_question.py @@ -1,5 +1,6 @@ import json import uuid +from dataclasses import dataclass, field from pathlib import Path from ktem.components import get_docstore, get_vectorstore @@ -8,52 +9,35 @@ DEFAULT_REWRITE_PROMPT, RewriteQuestionPipeline, ) -from theflow.settings import settings as flowsettings +from ktem.settings_config import app_settings as flowsettings -from kotaemon.base import AIMessage, Document, HumanMessage, Node, SystemMessage +from kotaemon.base import AIMessage, Document, HumanMessage, SystemMessage from kotaemon.embeddings import BaseEmbeddings from kotaemon.llms import ChatLLM from kotaemon.storages import BaseDocumentStore, BaseVectorStore +@dataclass(kw_only=True) class FewshotRewriteQuestionPipeline(RewriteQuestionPipeline): - """Rewrite user question - - Args: - llm: the language model to rewrite question - rewrite_template: the prompt template for llm to paraphrase a text input - lang: the language of the answer. Currently support English and Japanese - embedding: the embedding model to encode the question - vector_store: the vector store to store the encoded question - doc_store: the document store to store the original question - k: the number of examples to retrieve for rewriting - """ - - llm: ChatLLM = Node(default_callback=lambda _: llms.get_default()) + llm: ChatLLM = field(default_factory=lambda: llms.get_default()) rewrite_template: str = DEFAULT_REWRITE_PROMPT lang: str = "English" - embedding: BaseEmbeddings - vector_store: BaseVectorStore - doc_store: BaseDocumentStore - k: int = getattr(flowsettings, "N_PROMPT_OPT_EXAMPLES", 3) + embedding: BaseEmbeddings = field(repr=False) + vector_store: BaseVectorStore = field(repr=False) + doc_store: BaseDocumentStore = field(repr=False) + k: int = field(default_factory=lambda: flowsettings.N_PROMPT_OPT_EXAMPLES) def add_documents(self, examples, batch_size: int = 50): - print("Adding fewshot examples for rewriting") - documents = [] - for example in examples: - doc = Document( - text=example["input"], id_=str(uuid.uuid4()), metadata=example - ) - documents.append(doc) - + documents = [ + Document(text=ex["input"], id_=str(uuid.uuid4()), metadata=ex) + for ex in examples + ] for i in range(0, len(documents), batch_size): - embeddings = self.embedding(documents[i : i + batch_size]) - ids = [t.doc_id for t in documents[i : i + batch_size]] - self.vector_store.add( - embeddings=embeddings, - ids=ids, - ) - self.doc_store.add(documents[i : i + batch_size]) + batch = documents[i : i + batch_size] + embeddings = self.embedding(batch) + ids = [t.doc_id for t in batch] + self.vector_store.add(embeddings=embeddings, ids=ids) + self.doc_store.add(batch) @classmethod def get_pipeline( @@ -64,16 +48,15 @@ def get_pipeline( ): vector_store = get_vectorstore(collection_name) doc_store = get_docstore(collection_name) - pipeline = cls( - embedding=embedding, vector_store=vector_store, doc_store=doc_store + embedding=embedding, + vector_store=vector_store, + doc_store=doc_store, ) if doc_store.count(): return pipeline - examples = json.load(open(example_path, "r")) pipeline.add_documents(examples) - return pipeline def run(self, question: str) -> Document: # type: ignore @@ -95,6 +78,4 @@ def run(self, question: str) -> Document: # type: ignore content=self.rewrite_template.format(question=question, lang=self.lang) ) ) - - result = self.llm(messages) - return result + return self.llm(messages) diff --git a/libs/ktem/ktem/reasoning/prompt_optimization/mindmap.py b/libs/ktem/ktem/reasoning/prompt_optimization/mindmap.py index 42049ed58..5417a57b1 100644 --- a/libs/ktem/ktem/reasoning/prompt_optimization/mindmap.py +++ b/libs/ktem/ktem/reasoning/prompt_optimization/mindmap.py @@ -1,14 +1,14 @@ import logging +from dataclasses import dataclass, field from textwrap import dedent from ktem.llms.manager import llms -from kotaemon.base import BaseComponent, Document, HumanMessage, Node, SystemMessage +from kotaemon.base import Document, HumanMessage, SystemMessage from kotaemon.llms import ChatLLM, PromptTemplate logger = logging.getLogger(__name__) - MINDMAP_HTML_EXPORT_TEMPLATE = dedent( """ @@ -19,79 +19,46 @@ Mindmap - - {markmap_div} - + {markmap_div} """ ) -class CreateMindmapPipeline(BaseComponent): - """Create a mindmap from the question and context""" - - llm: ChatLLM = Node(default_callback=lambda _: llms.get_default()) - - SYSTEM_PROMPT = """ -From now on you will behave as "MapGPT" and, for every text the user will submit, you are going to create a PlantUML mind map file for the inputted text to best describe main ideas. Format it as a code and remember that the mind map should be in the same language as the inputted context. You don't have to provide a general example for the mind map format before the user inputs the text. - """ # noqa: E501 - MINDMAP_PROMPT_TEMPLATE = """ -Question: -{question} - -Context: -{context} - -Generate a sample PlantUML mindmap for based on the provided question and context above. Only includes context relevant to the question to produce the mindmap. - -Use the template like this: - -@startmindmap -* Title -** Item A -*** Item B -**** Item C -*** Item D -@endmindmap - """ # noqa: E501 +@dataclass(kw_only=True) +class CreateMindmapPipeline: + llm: ChatLLM = field(default_factory=lambda: llms.get_default()) + SYSTEM_PROMPT: str = ( + 'From now on you will behave as "MapGPT" and create PlantUML mind maps.' + ) + MINDMAP_PROMPT_TEMPLATE: str = ( + "Question:\n{question}\n\nContext:\n{context}\n\n" + "Generate PlantUML mindmap using @startmindmap ... @endmindmap." + ) prompt_template: str = MINDMAP_PROMPT_TEMPLATE @classmethod def convert_uml_to_markdown(cls, text: str) -> str: - start_phrase = "@startmindmap" - end_phrase = "@endmindmap" - try: - text = text.split(start_phrase)[-1] - text = text.split(end_phrase)[0] - text = text.strip().replace("*", "#") + text = text.split("@startmindmap")[-1].split("@endmindmap")[0] + return text.strip().replace("*", "#") except IndexError: - text = "" - - return text + return "" def run(self, question: str, context: str) -> Document: # type: ignore - prompt_template = PromptTemplate(self.prompt_template) - prompt = prompt_template.populate( - question=question, - context=context, + prompt = PromptTemplate(self.prompt_template).populate( + question=question, context=context ) - messages = [ SystemMessage(content=self.SYSTEM_PROMPT), HumanMessage(content=prompt), ] - uml_text = self.llm(messages).text - markdown_text = self.convert_uml_to_markdown(uml_text) + return Document(text=self.convert_uml_to_markdown(uml_text)) - return Document( - text=markdown_text, - ) + def __call__(self, question: str, context: str) -> Document: + return self.run(question, context) diff --git a/libs/ktem/ktem/reasoning/prompt_optimization/rewrite_question.py b/libs/ktem/ktem/reasoning/prompt_optimization/rewrite_question.py index 2ec878878..20c455826 100644 --- a/libs/ktem/ktem/reasoning/prompt_optimization/rewrite_question.py +++ b/libs/ktem/ktem/reasoning/prompt_optimization/rewrite_question.py @@ -1,6 +1,8 @@ +from dataclasses import dataclass, field + from ktem.llms.manager import llms -from kotaemon.base import BaseComponent, Document, HumanMessage, Node, SystemMessage +from kotaemon.base import Document, HumanMessage, SystemMessage from kotaemon.llms import ChatLLM, PromptTemplate DEFAULT_REWRITE_PROMPT = ( @@ -14,18 +16,12 @@ ) -class RewriteQuestionPipeline(BaseComponent): - """Rewrite user question - - Args: - llm: the language model to rewrite question - rewrite_template: the prompt template for llm to paraphrase a text input - lang: the language of the answer. Currently support English and Japanese - """ +@dataclass(kw_only=True) +class RewriteQuestionPipeline: + """Rewrite user question.""" - llm: ChatLLM = Node(default_callback=lambda _: llms.get_default()) + llm: ChatLLM = field(default_factory=lambda: llms.get_default()) rewrite_template: str = DEFAULT_REWRITE_PROMPT - lang: str = "English" def run(self, question: str) -> Document: # type: ignore @@ -36,3 +32,6 @@ def run(self, question: str) -> Document: # type: ignore HumanMessage(content=prompt), ] return self.llm(messages) + + def __call__(self, **kwargs) -> Document: + return self.run(**kwargs) diff --git a/libs/ktem/ktem/reasoning/prompt_optimization/suggest_conversation_name.py b/libs/ktem/ktem/reasoning/prompt_optimization/suggest_conversation_name.py index c5b2b258e..22c7bf05c 100644 --- a/libs/ktem/ktem/reasoning/prompt_optimization/suggest_conversation_name.py +++ b/libs/ktem/ktem/reasoning/prompt_optimization/suggest_conversation_name.py @@ -1,36 +1,31 @@ import logging +from dataclasses import dataclass, field from ktem.llms.manager import llms -from kotaemon.base import AIMessage, BaseComponent, Document, HumanMessage, Node +from kotaemon.base import AIMessage, Document, HumanMessage from kotaemon.llms import ChatLLM, PromptTemplate logger = logging.getLogger(__name__) -class SuggestConvNamePipeline(BaseComponent): - """Suggest a good conversation name based on the chat history.""" - - llm: ChatLLM = Node(default_callback=lambda _: llms.get_default()) - SUGGEST_NAME_PROMPT_TEMPLATE = ( - "You are an expert at suggesting good and memorable conversation name. " - "Based on the chat history above, " - "suggest a good conversation name (max 10 words). " - "Give answer in {lang}. Just output the conversation " - "name without any extra." +@dataclass(kw_only=True) +class SuggestConvNamePipeline: + llm: ChatLLM = field(default_factory=lambda: llms.get_default()) + prompt_template: str = ( + "Suggest a good conversation name (max 10 words) in {lang}. " + "Output only the name." ) - prompt_template: str = SUGGEST_NAME_PROMPT_TEMPLATE lang: str = "English" def run(self, chat_history: list[tuple[str, str]]) -> Document: # type: ignore - prompt_template = PromptTemplate(self.prompt_template) - prompt = prompt_template.populate(lang=self.lang) - + prompt = PromptTemplate(self.prompt_template).populate(lang=self.lang) messages = [] for human, ai in chat_history: messages.append(HumanMessage(content=human)) messages.append(AIMessage(content=ai)) - messages.append(HumanMessage(content=prompt)) - return self.llm(messages) + + def __call__(self, chat_history: list[tuple[str, str]]) -> Document: + return self.run(chat_history) diff --git a/libs/ktem/ktem/reasoning/prompt_optimization/suggest_followup_chat.py b/libs/ktem/ktem/reasoning/prompt_optimization/suggest_followup_chat.py index 0e0705700..6bf96571a 100644 --- a/libs/ktem/ktem/reasoning/prompt_optimization/suggest_followup_chat.py +++ b/libs/ktem/ktem/reasoning/prompt_optimization/suggest_followup_chat.py @@ -1,43 +1,35 @@ import logging +from dataclasses import dataclass, field from ktem.llms.manager import llms -from kotaemon.base import AIMessage, BaseComponent, Document, HumanMessage, Node +from kotaemon.base import AIMessage, Document, HumanMessage from kotaemon.llms import ChatLLM, PromptTemplate logger = logging.getLogger(__name__) -class SuggestFollowupQuesPipeline(BaseComponent): - """Suggest a list of follow-up questions based on the chat history.""" - - llm: ChatLLM = Node(default_callback=lambda _: llms.get_default()) - SUGGEST_QUESTIONS_PROMPT_TEMPLATE = ( - "Based on the chat history above. " - "your task is to generate 3 to 5 relevant follow-up questions. " - "These questions should be simple, very concise, " - "and designed to guide the conversation further. " - "Respond in JSON format with 'questions' key. " - "Answer using the language {lang} same as the question. " +@dataclass(kw_only=True) +class SuggestFollowupQuesPipeline: + llm: ChatLLM = field(default_factory=lambda: llms.get_default()) + prompt_template: str = ( + "Based on the chat history above, generate 3 to 5 follow-up questions. " + "Respond in JSON with 'questions' key. Answer in {lang}." ) - prompt_template: str = SUGGEST_QUESTIONS_PROMPT_TEMPLATE - extra_prompt: str = """Example of valid response: -```json -{ - "questions": ["the weather is good", "what's your favorite city"] -} -```""" + extra_prompt: str = 'Example: {"questions": ["question 1", "question 2"]}' lang: str = "English" def run(self, chat_history: list[tuple[str, str]]) -> Document: - prompt_template = PromptTemplate(self.prompt_template) - prompt = prompt_template.populate(lang=self.lang) + self.extra_prompt - + prompt = ( + PromptTemplate(self.prompt_template).populate(lang=self.lang) + + self.extra_prompt + ) messages = [] for human, ai in chat_history[-3:]: messages.append(HumanMessage(content=human)) messages.append(AIMessage(content=ai)) - messages.append(HumanMessage(content=prompt)) - return self.llm(messages) + + def __call__(self, chat_history: list[tuple[str, str]]) -> Document: + return self.run(chat_history) diff --git a/libs/ktem/ktem/reasoning/react.py b/libs/ktem/ktem/reasoning/react.py index d53b43c10..1ba0656d7 100644 --- a/libs/ktem/ktem/reasoning/react.py +++ b/libs/ktem/ktem/reasoning/react.py @@ -1,5 +1,6 @@ import html import logging +from dataclasses import dataclass, field from typing import AnyStr, Optional, Type from ktem.llms.manager import llms @@ -18,7 +19,8 @@ WikipediaTool, ) from kotaemon.agents.tools.mcp import create_tools_from_config -from kotaemon.base import BaseComponent, Document, HumanMessage, Node, SystemMessage +from kotaemon.base import Document, HumanMessage, SystemMessage +from kotaemon.indices.retriever.base import BaseRetriever from kotaemon.llms import ChatLLM, PromptTemplate from ..utils import SUPPORTED_LANGUAGE_MAP @@ -31,6 +33,7 @@ class DocSearchArgs(BaseModel): query: str = Field(..., description="a search query as input to the doc search") +@dataclass(kw_only=True) class DocSearchTool(BaseTool): name: str = "docsearch" description: str = ( @@ -42,7 +45,7 @@ class DocSearchTool(BaseTool): "formulate the search query as specific as possible." ) args_schema: Optional[Type[BaseModel]] = DocSearchArgs - retrievers: list[BaseComponent] = [] + retrievers: list[BaseRetriever] = field(default_factory=list) def _run_tool(self, query: AnyStr) -> AnyStr: docs = [] @@ -154,39 +157,34 @@ def prepare_evidence(self, docs, trim_len: int = 4000): ) -class RewriteQuestionPipeline(BaseComponent): - """Rewrite user question - - Args: - llm: the language model to rewrite question - rewrite_template: the prompt template for llm to paraphrase a text input - lang: the language of the answer. Currently support English and Japanese - """ - - llm: ChatLLM = Node(default_callback=lambda _: llms.get_default()) +@dataclass(kw_only=True) +class RewriteQuestionPipeline: + llm: ChatLLM = field(default_factory=lambda: llms.get_default()) rewrite_template: str = DEFAULT_REWRITE_PROMPT - lang: str = "English" def run(self, question: str) -> Document: # type: ignore - prompt_template = PromptTemplate(self.rewrite_template) - prompt = prompt_template.populate(question=question, lang=self.lang) - messages = [ - SystemMessage(content="You are a helpful assistant"), - HumanMessage(content=prompt), - ] - return self.llm(messages) - + prompt = PromptTemplate(self.rewrite_template).populate( + question=question, lang=self.lang + ) + return self.llm( + [ + SystemMessage(content="You are a helpful assistant"), + HumanMessage(content=prompt), + ] + ) -class ReactAgentPipeline(BaseReasoning): - """Question answering pipeline using ReAct agent.""" + def __call__(self, **kwargs) -> Document: + return self.run(**kwargs) - class Config: - allow_extra = True - retrievers: list[BaseComponent] - agent: ReactAgent = ReactAgent.withx() - rewrite_pipeline: RewriteQuestionPipeline = RewriteQuestionPipeline.withx() +@dataclass(kw_only=True) +class ReactAgentPipeline(BaseReasoning): + retrievers: list[BaseRetriever] = field(default_factory=list) + agent: ReactAgent = field(default_factory=ReactAgent) + rewrite_pipeline: RewriteQuestionPipeline = field( + default_factory=RewriteQuestionPipeline + ) use_rewrite: bool = False def prepare_citation(self, step_id, step, output, status) -> Document: @@ -270,7 +268,7 @@ def get_pipeline( max_context_length_setting = settings.get("reasoning.max_context_length", None) - pipeline = ReactAgentPipeline(retrievers=retrievers) + pipeline = ReactAgentPipeline(retrievers=retrievers or []) pipeline.agent.llm = llm pipeline.agent.max_iterations = settings[f"{prefix}.max_iterations"] diff --git a/libs/ktem/ktem/reasoning/registry.py b/libs/ktem/ktem/reasoning/registry.py new file mode 100644 index 000000000..e6b3429f3 --- /dev/null +++ b/libs/ktem/ktem/reasoning/registry.py @@ -0,0 +1,48 @@ +"""Explicit registry for reasoning pipeline classes.""" + +from __future__ import annotations + +from enum import Enum + +from ktem.reasoning.base import BaseReasoning + + +class ReasoningKind(str, Enum): + FULL_QA = "FullQAPipeline" + FULL_DECOMPOSE_QA = "FullDecomposeQAPipeline" + REACT = "ReactAgentPipeline" + REWOO = "RewooAgentPipeline" + + +_LEGACY_PATH_MAP: dict[str, ReasoningKind] = { + "ktem.reasoning.simple.FullQAPipeline": ReasoningKind.FULL_QA, + "ktem.reasoning.simple.FullDecomposeQAPipeline": (ReasoningKind.FULL_DECOMPOSE_QA), + "ktem.reasoning.react.ReactAgentPipeline": ReasoningKind.REACT, + "ktem.reasoning.rewoo.RewooAgentPipeline": ReasoningKind.REWOO, +} + + +def _load_classes() -> dict[ReasoningKind, type[BaseReasoning]]: + from ktem.reasoning.react import ReactAgentPipeline + from ktem.reasoning.rewoo import RewooAgentPipeline + from ktem.reasoning.simple import FullDecomposeQAPipeline, FullQAPipeline + + return { + ReasoningKind.FULL_QA: FullQAPipeline, + ReasoningKind.FULL_DECOMPOSE_QA: FullDecomposeQAPipeline, + ReasoningKind.REACT: ReactAgentPipeline, + ReasoningKind.REWOO: RewooAgentPipeline, + } + + +def get_reasoning_cls(value: str | ReasoningKind) -> type[BaseReasoning]: + """Resolve a reasoning class from enum value or legacy dotted path.""" + mp = _load_classes() + if isinstance(value, ReasoningKind): + return mp[value] + if value in _LEGACY_PATH_MAP: + return mp[_LEGACY_PATH_MAP[value]] + try: + return mp[ReasoningKind(value)] + except ValueError as exc: + raise ValueError(f"Unknown reasoning pipeline: {value!r}") from exc diff --git a/libs/ktem/ktem/reasoning/rewoo.py b/libs/ktem/ktem/reasoning/rewoo.py index 7bd14c898..2e1648e60 100644 --- a/libs/ktem/ktem/reasoning/rewoo.py +++ b/libs/ktem/ktem/reasoning/rewoo.py @@ -1,5 +1,6 @@ import html import logging +from dataclasses import dataclass, field from difflib import SequenceMatcher from typing import AnyStr, Generator, Optional, Type @@ -19,7 +20,8 @@ WikipediaTool, ) from kotaemon.agents.tools.mcp import create_tools_from_config -from kotaemon.base import BaseComponent, Document, HumanMessage, Node, SystemMessage +from kotaemon.base import Document, HumanMessage, SystemMessage +from kotaemon.indices.retriever.base import BaseRetriever from kotaemon.llms import ChatLLM, PromptTemplate from ..utils import SUPPORTED_LANGUAGE_MAP @@ -68,6 +70,7 @@ class DocSearchArgs(BaseModel): query: str = Field(..., description="a search query as input to the doc search") +@dataclass(kw_only=True) class DocSearchTool(BaseTool): name: str = "docsearch" description: str = ( @@ -79,7 +82,7 @@ class DocSearchTool(BaseTool): "formulate the search query as specific as possible." ) args_schema: Optional[Type[BaseModel]] = DocSearchArgs - retrievers: list[BaseComponent] = [] + retrievers: list[BaseRetriever] = field(default_factory=list) def _run_tool(self, query: AnyStr) -> AnyStr: docs = [] @@ -173,28 +176,25 @@ def prepare_evidence(self, docs, trim_len: int = 3000): ) -class RewriteQuestionPipeline(BaseComponent): - """Rewrite user question - - Args: - llm: the language model to rewrite question - rewrite_template: the prompt template for llm to paraphrase a text input - lang: the language of the answer. Currently support English and Japanese - """ - - llm: ChatLLM = Node(default_callback=lambda _: llms.get_default()) +@dataclass(kw_only=True) +class RewriteQuestionPipeline: + llm: ChatLLM = field(default_factory=lambda: llms.get_default()) rewrite_template: str = DEFAULT_REWRITE_PROMPT - lang: str = "English" def run(self, question: str) -> Document: # type: ignore - prompt_template = PromptTemplate(self.rewrite_template) - prompt = prompt_template.populate(question=question, lang=self.lang) - messages = [ - SystemMessage(content="You are a helpful assistant"), - HumanMessage(content=prompt), - ] - return self.llm(messages) + prompt = PromptTemplate(self.rewrite_template).populate( + question=question, lang=self.lang + ) + return self.llm( + [ + SystemMessage(content="You are a helpful assistant"), + HumanMessage(content=prompt), + ] + ) + + def __call__(self, **kwargs) -> Document: + return self.run(**kwargs) def find_text(llm_output, context): @@ -208,15 +208,13 @@ def find_text(llm_output, context): return matches +@dataclass(kw_only=True) class RewooAgentPipeline(BaseReasoning): - """Question answering pipeline using ReWOO Agent.""" - - class Config: - allow_extra = True - - retrievers: list[BaseComponent] - agent: RewooAgent = RewooAgent.withx() - rewrite_pipeline: RewriteQuestionPipeline = RewriteQuestionPipeline.withx() + retrievers: list[BaseRetriever] = field(default_factory=list) + agent: RewooAgent = field(default_factory=RewooAgent) + rewrite_pipeline: RewriteQuestionPipeline = field( + default_factory=RewriteQuestionPipeline + ) use_rewrite: bool = False enable_citation: bool = False @@ -389,7 +387,7 @@ def get_pipeline( ) -> BaseReasoning: _id = cls.get_info()["id"] prefix = f"reasoning.options.{_id}" - pipeline = RewooAgentPipeline(retrievers=retrievers) + pipeline = RewooAgentPipeline(retrievers=retrievers or []) max_context_length_setting = settings.get("reasoning.max_context_length", None) diff --git a/libs/ktem/ktem/reasoning/simple.py b/libs/ktem/ktem/reasoning/simple.py index c40c96590..0424dffd5 100644 --- a/libs/ktem/ktem/reasoning/simple.py +++ b/libs/ktem/ktem/reasoning/simple.py @@ -1,28 +1,31 @@ import logging import threading +from dataclasses import dataclass, field from textwrap import dedent from typing import Generator from decouple import config from ktem.embeddings.manager import embedding_models_manager as embeddings from ktem.llms.manager import llms +from ktem.reasoning.citation_display import prepare_citations from ktem.reasoning.prompt_optimization import ( DecomposeQuestionPipeline, RewriteQuestionPipeline, ) +from ktem.reasoning.prompt_optimization.mindmap import CreateMindmapPipeline +from ktem.settings_config import app_settings as flowsettings from ktem.utils.render import Render from ktem.utils.visualize_cited import CreateCitationVizPipeline from plotly.io import to_json from kotaemon.base import ( AIMessage, - BaseComponent, Document, HumanMessage, - Node, RetrievedDocument, SystemMessage, ) +from kotaemon.indices.qa.citation import CitationPipeline from kotaemon.indices.qa.citation_qa import ( CONTEXT_RELEVANT_WARNING_SCORE, DEFAULT_QA_TEXT_PROMPT, @@ -31,6 +34,7 @@ from kotaemon.indices.qa.citation_qa_inline import AnswerWithInlineCitation from kotaemon.indices.qa.format_context import PrepareEvidencePipeline from kotaemon.indices.qa.utils import replace_think_tag_with_details +from kotaemon.indices.retriever.base import BaseRetriever from kotaemon.llms import ChatLLM from ..utils import SUPPORTED_LANGUAGE_MAP @@ -39,10 +43,10 @@ logger = logging.getLogger(__name__) -class AddQueryContextPipeline(BaseComponent): - +@dataclass(kw_only=True) +class AddQueryContextPipeline: + llm: ChatLLM = field(default_factory=lambda: llms.get_default()) n_last_interactions: int = 5 - llm: ChatLLM = Node(default_callback=lambda _: llms.get_default()) def run(self, question: str, history: list) -> Document: messages = [ @@ -83,6 +87,7 @@ def run(self, question: str, history: list) -> Document: return Document(content=resp) +@dataclass(kw_only=True) class FullQAPipeline(BaseReasoning): """Question answering pipeline. Handle from question to answer""" @@ -93,17 +98,21 @@ class Config: trigger_context: int = 150 use_rewrite: bool = False - retrievers: list[BaseComponent] + retrievers: list[BaseRetriever] - evidence_pipeline: PrepareEvidencePipeline = PrepareEvidencePipeline.withx() - answering_pipeline: AnswerWithContextPipeline + answering_pipeline: AnswerWithContextPipeline | None = None + evidence_pipeline: PrepareEvidencePipeline = field( + default_factory=PrepareEvidencePipeline + ) rewrite_pipeline: RewriteQuestionPipeline | None = None - create_citation_viz_pipeline: CreateCitationVizPipeline = Node( - default_callback=lambda _: CreateCitationVizPipeline( + create_citation_viz_pipeline: CreateCitationVizPipeline = field( + default_factory=lambda: CreateCitationVizPipeline( embedding=embeddings.get_default() ) ) - add_query_context: AddQueryContextPipeline = AddQueryContextPipeline.withx() + add_query_context: AddQueryContextPipeline = field( + default_factory=AddQueryContextPipeline + ) def retrieve( self, message: str, history: list @@ -128,8 +137,8 @@ def retrieve( plot_docs = [] for idx, retriever in enumerate(self.retrievers): - retriever_node = self._prepare_child(retriever, f"retriever_{idx}") - retriever_docs = retriever_node(text=query) + # retriever_node = self._prepare_child(retriever, f"retriever_{idx}") + retriever_docs = retriever(text=query) retriever_docs_text = [] retriever_docs_plot = [] @@ -222,8 +231,8 @@ def prepare_citation_viz(self, answer, question, docs) -> Document | None: def show_citations_and_addons(self, answer, docs, question): # show the evidence - with_citation, without_citation = self.answering_pipeline.prepare_citations( - answer, docs + with_citation, without_citation = prepare_citations( + self.answering_pipeline, answer, docs ) mindmap_output = self.prepare_mindmap(answer) citation_plot_output = self.prepare_citation_viz(answer, question, docs) @@ -305,6 +314,7 @@ def generate_relevant_scores(): else: scoring_thread = None + assert self.answering_pipeline is not None answer = yield from self.answering_pipeline.stream( question=message, history=history, @@ -360,25 +370,46 @@ def get_pipeline(cls, settings, states, retrievers): # answering pipeline configuration use_inline_citation = settings[f"{prefix}.highlight_citation"] == "inline" + input_params = { + "llm": llm, + "citation_pipeline": CitationPipeline(llm=llm), + "create_mindmap_pipeline": CreateMindmapPipeline(llm=llm), + "n_last_interactions": settings[f"{prefix}.n_last_interactions"], + "enable_citation": settings[f"{prefix}.highlight_citation"] != "off", + "enable_mindmap": settings[f"{prefix}.create_mindmap"], + "enable_citation_viz": settings[f"{prefix}.create_citation_viz"], + "use_multimodal": settings[f"{prefix}.use_multimodal"], + "vlm_endpoint": flowsettings.KH_VLM_ENDPOINT, + "system_prompt": settings[f"{prefix}.system_prompt"], + "qa_template": settings[f"{prefix}.qa_prompt"], + "lang": SUPPORTED_LANGUAGE_MAP.get(settings["reasoning.lang"], "English"), + } + if use_inline_citation: - answer_pipeline = pipeline.answering_pipeline = AnswerWithInlineCitation() + pipeline.answering_pipeline = AnswerWithInlineCitation(**input_params) else: - answer_pipeline = pipeline.answering_pipeline = AnswerWithContextPipeline() - - answer_pipeline.llm = llm - answer_pipeline.citation_pipeline.llm = llm - answer_pipeline.n_last_interactions = settings[f"{prefix}.n_last_interactions"] - answer_pipeline.enable_citation = ( - settings[f"{prefix}.highlight_citation"] != "off" - ) - answer_pipeline.enable_mindmap = settings[f"{prefix}.create_mindmap"] - answer_pipeline.enable_citation_viz = settings[f"{prefix}.create_citation_viz"] - answer_pipeline.use_multimodal = settings[f"{prefix}.use_multimodal"] - answer_pipeline.system_prompt = settings[f"{prefix}.system_prompt"] - answer_pipeline.qa_template = settings[f"{prefix}.qa_prompt"] - answer_pipeline.lang = SUPPORTED_LANGUAGE_MAP.get( - settings["reasoning.lang"], "English" - ) + pipeline.answering_pipeline = AnswerWithContextPipeline(**input_params) + + # answer_pipeline.llm = llm + # answer_pipeline.citation_pipeline = CitationPipeline(llm=llm) + # answer_pipeline.create_mindmap_pipeline = CreateMindmapPipeline(llm=llm) + # answer_pipeline.n_last_interactions = settings[ + # f"{prefix}.n_last_interactions" + # ] + # answer_pipeline.enable_citation = ( + # settings[f"{prefix}.highlight_citation"] != "off" + # ) + # answer_pipeline.enable_mindmap = settings[f"{prefix}.create_mindmap"] + # answer_pipeline.enable_citation_viz = settings[ + # f"{prefix}.create_citation_viz" + # ] + # answer_pipeline.use_multimodal = settings[f"{prefix}.use_multimodal"] + # answer_pipeline.vlm_endpoint = getattr(flowsettings, "KH_VLM_ENDPOINT", "") + # answer_pipeline.system_prompt = settings[f"{prefix}.system_prompt"] + # answer_pipeline.qa_template = settings[f"{prefix}.qa_prompt"] + # answer_pipeline.lang = SUPPORTED_LANGUAGE_MAP.get( + # settings["reasoning.lang"], "English" + # ) pipeline.add_query_context.llm = llm pipeline.add_query_context.n_last_interactions = settings[ @@ -502,6 +533,7 @@ def answer_sub_questions( yield from infos evidence_mode, evidence, images = self.evidence_pipeline(docs).content + assert self.answering_pipeline is not None answer = yield from self.answering_pipeline.stream( question=message, history=history, @@ -552,6 +584,7 @@ def stream( # type: ignore yield from infos evidence_mode, evidence, images = self.evidence_pipeline(docs).content + assert self.answering_pipeline is not None answer = yield from self.answering_pipeline.stream( question=message, history=history, @@ -563,8 +596,8 @@ def stream( # type: ignore ) # show the evidence - with_citation, without_citation = self.answering_pipeline.prepare_citations( - answer, docs + with_citation, without_citation = prepare_citations( + self.answering_pipeline, answer, docs ) if not with_citation and not without_citation: yield Document(channel="info", content="
No evidence found.
") diff --git a/libs/ktem/ktem/rerankings/db.py b/libs/ktem/ktem/rerankings/db.py index 85049dfef..ac5763d27 100644 --- a/libs/ktem/ktem/rerankings/db.py +++ b/libs/ktem/ktem/rerankings/db.py @@ -1,36 +1,6 @@ -from typing import Type +"""Re-export reranking table and engine for backward compatibility.""" from ktem.db.engine import engine -from sqlalchemy import JSON, Boolean, Column, String -from sqlalchemy.orm import DeclarativeBase -from theflow.settings import settings as flowsettings -from theflow.utils.modules import import_dotted_string +from ktem.db.models import RerankingTable - -class Base(DeclarativeBase): - pass - - -class BaseRerankingTable(Base): - """Base table to store rerankings model""" - - __abstract__ = True - - name = Column(String, primary_key=True, unique=True) - spec = Column(JSON, default={}) - default = Column(Boolean, default=False) - - -__base_reranking: Type[BaseRerankingTable] = ( - import_dotted_string(flowsettings.KH_TABLE_RERANKING, safe=False) - if hasattr(flowsettings, "KH_TABLE_RERANKING") - else BaseRerankingTable -) - - -class RerankingTable(__base_reranking): # type: ignore - __tablename__ = "reranking" - - -if not getattr(flowsettings, "KH_ENABLE_ALEMBIC", False): - RerankingTable.metadata.create_all(engine) +__all__ = ["RerankingTable", "engine"] diff --git a/libs/ktem/ktem/rerankings/manager.py b/libs/ktem/ktem/rerankings/manager.py index aa02dd885..53e697e3b 100644 --- a/libs/ktem/ktem/rerankings/manager.py +++ b/libs/ktem/ktem/rerankings/manager.py @@ -1,207 +1,146 @@ -from typing import Optional, Type +from typing import Optional -from sqlalchemy import select -from sqlalchemy.orm import Session -from theflow.settings import settings as flowsettings -from theflow.utils.modules import deserialize +from ktem.db.cruds import RerankingCRUD +from ktem.db.engine import engine +from ktem.db.models import RerankingTable from kotaemon.rerankings.base import BaseReranking - -from .db import RerankingTable, engine +from kotaemon.rerankings.factory import MP_VENDOR_CLS, RerankingFactory, RerankingVendor class RerankingManager: - """Represent a pool of rerankings models""" + """Represent a pool of reranking models.""" def __init__(self): self._models: dict[str, BaseReranking] = {} - self._info: dict[str, dict] = {} + self._info: dict[str, RerankingTable] = {} self._default: str = "" - self._vendors: list[Type] = [] - - # populate the pool if empty - if hasattr(flowsettings, "KH_RERANKINGS"): - with Session(engine) as sess: - count = sess.query(RerankingTable).count() - if not count: - for name, model in flowsettings.KH_RERANKINGS.items(): - self.add( - name=name, - spec=model["spec"], - default=model.get("default", False), - ) self.load() - self.load_vendors() - def load(self): - """Load the model pool from database""" + def load(self) -> None: + """Load the model pool from database.""" self._models, self._info, self._default = {}, {}, "" - with Session(engine) as sess: - stmt = select(RerankingTable) - items = sess.execute(stmt) - - for (item,) in items: - self._models[item.name] = deserialize(item.spec, safe=False) - self._info[item.name] = { - "name": item.name, - "spec": item.spec, - "default": item.default, - } + + with RerankingCRUD(engine) as crud: + for item in crud.list_all(): + self._models[item.name] = RerankingFactory.get_cls(item.vendor)( + **item.spec + ) + self._info[item.name] = item if item.default: self._default = item.name - def load_vendors(self): - from kotaemon.rerankings import ( - CohereReranking, - TeiFastReranking, - VoyageAIReranking, - ) - - self._vendors = [TeiFastReranking, CohereReranking, VoyageAIReranking] - def __getitem__(self, key: str) -> BaseReranking: - """Get model by name""" + """Get model by name.""" + if key == "default": + key = self._default return self._models[key] def __contains__(self, key: str) -> bool: - """Check if model exists""" + """Check if model exists.""" return key in self._models def get( self, key: str, default: Optional[BaseReranking] = None ) -> Optional[BaseReranking]: - """Get model by name with default value""" + """Get model by name with a fallback default.""" return self._models.get(key, default) def settings(self) -> dict: - """Present model pools option for gradio""" + """Return Gradio dropdown settings for the pool.""" return { "label": "Reranking", "choices": list(self._models.keys()), "value": self.get_default_name(), } - def options(self) -> dict: - """Present a dict of models""" + def options(self) -> dict[str, BaseReranking]: + """Return all models keyed by name.""" return self._models def get_random_name(self) -> str: - """Get the name of random model - - Returns: - str: random model name in the pool - """ + """Return a random model name from the pool.""" import random if not self._models: - raise ValueError("No models is pool") - + raise ValueError("No models in pool") return random.choice(list(self._models.keys())) def get_default_name(self) -> str: - """Get the name of default model - - In case there is no default model, choose random model from pool. In - case there are multiple default models, choose random from them. - - Returns: - str: model name - """ + """Return the default model name, or a random one if unset.""" if not self._models: raise ValueError("No models in pool") - - if not self._default: - return self.get_random_name() - - return self._default + return self._default or self.get_random_name() def get_random(self) -> BaseReranking: - """Get random model""" + """Return a random model instance.""" return self._models[self.get_random_name()] def get_default(self) -> BaseReranking: - """Get default model - - In case there is no default model, choose random model from pool. In - case there are multiple default models, choose random from them. - - Returns: - BaseReranking: model - """ + """Return the default model instance.""" return self._models[self.get_default_name()] - def info(self) -> dict: - """List all models""" + def info(self) -> dict[str, RerankingTable]: + """Return all model metadata keyed by name.""" return self._info - def add(self, name: str, spec: dict, default: bool): - if not name: - raise ValueError("Name must not be empty") - + def add( + self, + name: str, + vendor: RerankingVendor, + spec: dict, + default: bool, + ) -> None: + """Add a new model to the pool.""" try: - with Session(engine) as sess: - if default: - # turn all models to non-default - sess.query(RerankingTable).update({"default": False}) - sess.commit() - - item = RerankingTable(name=name, spec=spec, default=default) - sess.add(item) - sess.commit() + with RerankingCRUD(engine) as crud: + crud.create(name=name, vendor=vendor, spec=spec, default=default) + except ValueError: + raise except Exception as e: - raise ValueError(f"Failed to add model {name}: {e}") - + raise ValueError(f"Failed to add reranking model '{name}': {e}") from e self.load() - def delete(self, name: str): - """Delete a model from the pool""" - try: - with Session(engine) as sess: - item = sess.query(RerankingTable).filter_by(name=name).first() - sess.delete(item) - sess.commit() - except Exception as e: - raise ValueError(f"Failed to delete model {name}: {e}") - + def delete(self, name: str) -> None: + """Remove a model from the pool.""" + with RerankingCRUD(engine) as crud: + crud.delete(name) self.load() - def update(self, name: str, spec: dict, default: bool, new_name: str = ""): - """Update a model in the pool, optionally renaming it.""" + def update( + self, + name: str, + vendor: RerankingVendor, + spec: dict, + default: bool, + new_name: str = "", + ) -> None: + """Update a model, optionally renaming it.""" if not name: raise ValueError("Name must not be empty") if new_name and new_name != name: if new_name in self._info: raise ValueError( - f"Model '{new_name}' already exists. Use a unique name." + f"Model '{new_name}' already exists." " Use a unique name." ) self.delete(name) - self.add(new_name, spec=spec, default=default) + self.add(new_name, vendor=vendor, spec=spec, default=default) return try: - with Session(engine) as sess: - - if default: - # turn all models to non-default - sess.query(RerankingTable).update({"default": False}) - sess.commit() - - item = sess.query(RerankingTable).filter_by(name=name).first() - if not item: - raise ValueError(f"Model {name} not found") - item.spec = spec - item.default = default - sess.commit() + with RerankingCRUD(engine) as crud: + crud.update(name, vendor=vendor, spec=spec, default=default) + except ValueError: + raise except Exception as e: - raise ValueError(f"Failed to update model {name}: {e}") - + raise ValueError(f"Failed to update reranking model '{name}': {e}") from e self.load() - def vendors(self) -> dict: - """Return list of vendors""" - return {vendor.__qualname__: vendor for vendor in self._vendors} + def vendors(self) -> dict[RerankingVendor, type[BaseReranking]]: + """Return all registered vendor classes keyed by RerankingVendor.""" + return {v: MP_VENDOR_CLS[v] for v in RerankingFactory.supported_vendors()} reranking_models_manager = RerankingManager() diff --git a/libs/ktem/ktem/rerankings/ui.py b/libs/ktem/ktem/rerankings/ui.py index 5e2c78b6c..3386bf315 100644 --- a/libs/ktem/ktem/rerankings/ui.py +++ b/libs/ktem/ktem/rerankings/ui.py @@ -5,9 +5,9 @@ import yaml from ktem.app import BasePage from ktem.utils.file import YAMLNoDateSafeLoader -from theflow.utils.modules import deserialize from kotaemon.base import Document +from kotaemon.rerankings.factory import RerankingFactory, RerankingVendor from .manager import reranking_models_manager @@ -16,8 +16,6 @@ def format_description(cls): params = cls.describe()["params"] params_lines = ["| Name | Type | Description |", "| --- | --- | --- |"] for key, value in params.items(): - if isinstance(value["auto_callback"], str): - continue params_lines.append(f"| {key} | {value['type']} | {value['help']} |") return f"{cls.__doc__}\n\n" + "\n".join(params_lines) @@ -56,7 +54,7 @@ def on_building_ui(self): ) self.edit_spec = gr.Textbox( label="Specification", - info="Specification of the Embedding model in YAML format", + info="Specification of the Reranking model in YAML format", lines=10, ) @@ -113,7 +111,7 @@ def on_building_ui(self): ) self.spec = gr.Textbox( label="Specification", - info="Specification of the Embedding model in YAML format.", + info="Specification of the Reranking model in YAML format.", ) self.default = gr.Checkbox( label="Set default", @@ -136,20 +134,24 @@ def _on_app_created(self): outputs=[self.rerank_list], ) self._app.app.load( - lambda: gr.update(choices=list(reranking_models_manager.vendors().keys())), + lambda: gr.update( + choices=[ + vendor.value for vendor in RerankingFactory.supported_vendors() + ] + ), outputs=[self.rerank_choices], ) - def on_rerank_vendor_change(self, vendor): - vendor = reranking_models_manager.vendors()[vendor] + def on_rerank_vendor_change(self, vendor: str): + vendor_cls = RerankingFactory.get_cls(RerankingVendor(vendor)) required: dict = {} - desc = vendor.describe() + desc = vendor_cls.describe() for key, value in desc["params"].items(): if value.get("required", False): required[key] = value.get("default", None) - return yaml.dump(required), format_description(vendor) + return yaml.dump(required), format_description(vendor_cls) def on_register_events(self): self.rerank_choices.select( @@ -250,13 +252,11 @@ def create_rerank(self, name, choices, spec, default): try: name = name.strip() spec = yaml.load(spec, Loader=YAMLNoDateSafeLoader) - spec["__type__"] = ( - reranking_models_manager.vendors()[choices].__module__ - + "." - + reranking_models_manager.vendors()[choices].__qualname__ - ) + vendor = RerankingVendor(choices) - reranking_models_manager.add(name, spec=spec, default=default) + reranking_models_manager.add( + name, vendor=vendor, spec=spec, default=default + ) gr.Info(f'Reranking model "{name}" created successfully') except ValueError as e: raise gr.Error(str(e)) @@ -265,13 +265,7 @@ def create_rerank(self, name, choices, spec, default): def list_rerankings(self): """List the Reranking models""" - items = [] - for item in reranking_models_manager.info().values(): - record = {} - record["name"] = item["name"] - record["vendor"] = item["spec"].get("__type__", "-").split(".")[-1] - record["default"] = item["default"] - items.append(record) + items = [item.ui for _, item in reranking_models_manager.info().items()] if items: rerank_list = pd.DataFrame.from_records(items) @@ -312,14 +306,13 @@ def on_selected_rerank_change(self, selected_rerank_name): btn_delete_yes = gr.update(visible=False) btn_delete_no = gr.update(visible=False) - info = deepcopy(reranking_models_manager.info()[selected_rerank_name]) - vendor_str = info["spec"].pop("__type__", "-").split(".")[-1] - vendor = reranking_models_manager.vendors()[vendor_str] + item = reranking_models_manager.info()[selected_rerank_name] + vendor = RerankingFactory.get_cls(RerankingVendor(item.vendor)) edit_name = selected_rerank_name - edit_spec = yaml.dump(info["spec"]) + edit_spec = yaml.dump(item.spec) edit_spec_desc = format_description(vendor) - edit_default = info["default"] + edit_default = item.default return ( _selected_panel, @@ -347,17 +340,10 @@ def check_connection(self, selected_rerank_name, selected_spec): log_content += f"- Testing model: {selected_rerank_name}
" yield log_content - # Parse content & init model - info = deepcopy(reranking_models_manager.info()[selected_rerank_name]) - - # Parse content & create dummy response - spec = yaml.load(selected_spec, Loader=YAMLNoDateSafeLoader) - info["spec"].update(spec) - - rerank = deserialize(info["spec"], safe=False) - - if rerank is None: - raise Exception(f"Can not found model: {selected_rerank_name}") + item = reranking_models_manager.info()[selected_rerank_name] + params = deepcopy(item.spec) + params.update(yaml.load(selected_spec, Loader=YAMLNoDateSafeLoader)) + rerank = RerankingFactory.get_cls(item.vendor)(**params) log_content += "- Sending a message ([`Hello`], `Hi`)
" yield log_content @@ -369,7 +355,7 @@ def check_connection(self, selected_rerank_name, selected_spec): ) yield log_content - gr.Info(f"Embedding {selected_rerank_name} connect successfully") + gr.Info(f"Reranking {selected_rerank_name} connect successfully") except Exception as e: print(e) log_content += ( @@ -384,11 +370,13 @@ def save_rerank(self, selected_rerank_name, edit_name, default, spec): try: new_name = edit_name.strip() spec = yaml.load(spec, Loader=YAMLNoDateSafeLoader) - spec["__type__"] = reranking_models_manager.info()[selected_rerank_name][ - "spec" - ]["__type__"] + item = reranking_models_manager.info()[selected_rerank_name] reranking_models_manager.update( - selected_rerank_name, spec=spec, default=default, new_name=new_name + selected_rerank_name, + vendor=item.vendor, + spec=spec, + default=default, + new_name=new_name, ) final_name = ( new_name if new_name != selected_rerank_name else selected_rerank_name diff --git a/libs/ktem/ktem/settings_config.py b/libs/ktem/ktem/settings_config.py new file mode 100644 index 000000000..34db1b765 --- /dev/null +++ b/libs/ktem/ktem/settings_config.py @@ -0,0 +1,244 @@ +from __future__ import annotations + +import os +from importlib.metadata import version +from pathlib import Path +from typing import Any, ClassVar + +from ktem.settings import SettingItem +from ktem.utils.lang import SUPPORTED_LANGUAGE_MAP +from pydantic import Field, computed_field +from pydantic_settings import BaseSettings, SettingsConfigDict + +# from kotaemon.storages.docstores.factory import DocStoreVendor +# from kotaemon.storages.vectorstores.factory import VectorStoreVendor + +# from ktem.collections.registry import IndexKind +# from ktem.reasoning.registry import ReasoningKind + +OPENAI_DEFAULT = "" +THEFLOW_DIR = ".theflow" +SUPPORTED_FILE_TYPES = ( + ".png, .jpeg, .jpg, .tiff, .tif, .pdf, .xls, .xlsx, .doc, .docx, " + ".pptx, .csv, .html, .mhtml, .txt, .md, .zip" +) + + +def resolve_project_root() -> Path: + """Resolve Kotaemon project root directory. + + Priority: + 1. ``KH_PROJECT_ROOT`` environment variable. + 2. Nearest ancestor that has both ``pyproject.toml`` and ``app.py``. + 3. Current working directory. + """ + env_root = os.getenv("KH_PROJECT_ROOT") + if env_root: + return Path(env_root) + + cwd = Path.cwd() + for candidate in (cwd, *cwd.parents): + if (candidate / "pyproject.toml").exists() and (candidate / "app.py").exists(): + return candidate + + return cwd + + +class _AppBaseSettings(BaseSettings): + """Feature flags and top-level application knobs.""" + + KH_PACKAGE_NAME: str = "kotaemon_app" + KH_APP_VERSION: str | None = None + KH_APP_NAME: str = "Kotaemon" + KH_GRADIO_SHARE: bool = False + KH_GRADIO_DEBUG: bool = True + KH_ENABLE_FIRST_SETUP: bool = True + KH_DEMO_MODE: bool = False + KH_OLLAMA_URL: str = "http://localhost:11434/v1/" + KH_PROJECT_ROOT: Path | None = None + KH_MODE: str = "dev" + KH_SSO_ENABLED: bool = False + KH_FEATURE_CHAT_SUGGESTION: bool = False + KH_FEATURE_USER_MANAGEMENT: bool = True + KH_USER_CAN_SEE_PUBLIC: str | None = None + KH_FEATURE_USER_MANAGEMENT_ADMIN: str = "admin" + KH_FEATURE_USER_MANAGEMENT_PASSWORD: str = "admin" + KH_ENABLE_ALEMBIC: bool = False + KH_WEB_SEARCH_BACKEND: str = "TavilyWebSearch" + N_PROMPT_OPT_EXAMPLES: int = 3 + + +class AzureOpenAISettings(BaseSettings): + """Azure OpenAI provider credentials.""" + + AZURE_OPENAI_ENDPOINT: str = "" + AZURE_OPENAI_API_KEY: str = "" + AZURE_OPENAI_CHAT_DEPLOYMENT: str = "" + AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT: str = "" + OPENAI_API_VERSION: str = "" + OPENAI_VISION_DEPLOYMENT_NAME: str = "gpt-4o" + + +class OpenAISettings(BaseSettings): + """OpenAI provider credentials.""" + + OPENAI_API_KEY: str = OPENAI_DEFAULT + OPENAI_API_BASE: str = "" + OPENAI_CHAT_MODEL: str = "gpt-4o-mini" + OPENAI_EMBEDDINGS_MODEL: str = "text-embedding-3-large" + + +class ProviderAPISettings(BaseSettings): + """Third-party API keys and model name overrides.""" + + GOOGLE_API_KEY: str = "your-key" + VOYAGE_API_KEY: str = "" + VOYAGE_EMBEDDINGS_MODEL: str = "voyage-3-large" + COHERE_API_KEY: str = "your-key" + MISTRAL_API_KEY: str = "your-key" + LOCAL_MODEL: str = "" + LOCAL_MODEL_EMBEDDINGS: str = "nomic-embed-text" + USE_MULTIMODAL: bool = False + + +class AppSettings( + _AppBaseSettings, + AzureOpenAISettings, + OpenAISettings, + ProviderAPISettings, +): + __ROOT: ClassVar[Path] = Path.cwd() + + model_config = SettingsConfigDict( + case_sensitive=False, + env_file=".env", + env_file_encoding="utf-8", + extra="allow", + ) + + # Storage back-end vendors — constructor kwargs are computed below. + KH_DOCSTORE_VENDOR: str = "LanceDBDocumentStore" + KH_VECTORSTORE_VENDOR: str = "ChromaVectorStore" + + # Static reasoning / index configuration. + KH_REASONINGS: list[str] = [ + "FullQAPipeline", + ] + KH_INDEX_TYPES: list[str] = ["FileIndex"] + SETTINGS_APP: dict[str, SettingItem] = Field(default_factory=dict) + SETTINGS_REASONING: dict[str, SettingItem] = Field( + default_factory=lambda: { + "use": SettingItem( + name="Reasoning options", + value=None, + choices=[], + component="radio", + ), + "lang": SettingItem( + name="Language", + value="en", + choices=[(lang, code) for code, lang in SUPPORTED_LANGUAGE_MAP.items()], + component="dropdown", + ), + "max_context_length": SettingItem( + name="Max context length (LLM)", + value=32000, + component="number", + ), + } + ) + + KH_APP_DATA_DIR: Path = Field( + default_factory=lambda: AppSettings.__ROOT / "ktem_app_data" + ) + KH_USER_DATA_DIR: Path = Field( + default_factory=lambda: AppSettings.__ROOT / "ktem_user_data" + ) + KH_MARKDOWN_OUTPUT_DIR: Path = Field( + default_factory=lambda: AppSettings.__ROOT / "ktem_markdown_output" + ) + KH_CHUNKS_OUTPUT_DIR: Path = Field( + default_factory=lambda: AppSettings.__ROOT / "ktem_chunks_output" + ) + KH_ZIP_OUTPUT_DIR: Path = Field( + default_factory=lambda: AppSettings.__ROOT / "ktem_zip_output" + ) + KH_ZIP_INPUT_DIR: Path = Field( + default_factory=lambda: AppSettings.__ROOT / "ktem_zip_input" + ) + KH_DOC_DIR: Path = Field(default_factory=lambda: AppSettings.__ROOT / "docs") + KH_DATABASE: str = Field( + default_factory=lambda: f"sqlite:///{AppSettings.__ROOT / 'sql.db'}" + ) + KH_FILESTORAGE_PATH: str = Field( + default_factory=lambda: str(AppSettings.__ROOT / "files") + ) + KH_VLM_ENDPOINT: str = "" + + # ------------------------------------------------------------------ + # Computed model / reasoning fields + # ------------------------------------------------------------------ + + # keep + @computed_field # type: ignore[misc] + @property + def KH_INDICES(self) -> list[dict[str, Any]]: + return self._build_indices() + + # ------------------------------------------------------------------ + # Startup side-effects + # ------------------------------------------------------------------ + + def initialize(self) -> None: + """Create app directories, set HF env vars, resolve version. + + Call once at application startup:: + + app_settings.initialize() + + All derived path and storage fields are computed automatically + from :attr:`KH_PROJECT_ROOT`; this method only performs the + side-effects that cannot be expressed as field defaults. + """ + for path in ( + self.KH_APP_DATA_DIR, + self.KH_USER_DATA_DIR, + self.KH_MARKDOWN_OUTPUT_DIR, + self.KH_CHUNKS_OUTPUT_DIR, + self.KH_ZIP_OUTPUT_DIR, + self.KH_ZIP_INPUT_DIR, + ): + path.mkdir(parents=True, exist_ok=True) + + hf_home = self.KH_APP_DATA_DIR / "huggingface" + os.environ["HF_HOME"] = str(hf_home) + os.environ["HF_HUB_CACHE"] = str(hf_home) + + if self.KH_GRADIO_DEBUG: + os.environ["GRADIO_DEBUG"] = "1" + + if not self.KH_APP_VERSION: + try: + self.KH_APP_VERSION = version(self.KH_PACKAGE_NAME) + except Exception: + self.KH_APP_VERSION = "local" + + # ------------------------------------------------------------------ + # Helpers + # ------------------------------------------------------------------ + + def _build_indices(self) -> list[dict[str, Any]]: + return [ + { + "name": "File Collection", + "config": { + "supported_file_types": SUPPORTED_FILE_TYPES, + "private": True, + }, + "index_type": "FileIndex", + }, + ] + + +app_settings = AppSettings() +app_settings.initialize() diff --git a/libs/ktem/ktem/utils/render.py b/libs/ktem/ktem/utils/render.py index 3cd2d7f47..0b7a67859 100644 --- a/libs/ktem/ktem/utils/render.py +++ b/libs/ktem/ktem/utils/render.py @@ -98,7 +98,7 @@ def preview( if not highlight_text: phrase = "false" try: - lang = detect(text.replace("\n", " "))["lang"] + lang = detect(text.replace("\n", " "))[0].get("lang", "en") if lang not in ["ja", "cn"]: highlight_words = [ t[:-1] if t.endswith("-") else t for t in text.split("\n") diff --git a/libs/ktem/ktem/utils/visualize_cited.py b/libs/ktem/ktem/utils/visualize_cited.py index a9602c79f..ef931444e 100644 --- a/libs/ktem/ktem/utils/visualize_cited.py +++ b/libs/ktem/ktem/utils/visualize_cited.py @@ -6,6 +6,7 @@ 1. [RAGxplorer](https://github.com/gabrielchua/RAGxplorer) 2. [RAGVizExpander](https://github.com/KKenny0/RAGVizExpander) """ +from dataclasses import dataclass from typing import List, Tuple import numpy as np @@ -13,7 +14,6 @@ import plotly.graph_objs as go import umap -from kotaemon.base import BaseComponent from kotaemon.embeddings import BaseEmbeddings VISUALIZATION_SETTINGS = { @@ -24,7 +24,8 @@ } -class CreateCitationVizPipeline(BaseComponent): +@dataclass +class CreateCitationVizPipeline: """Creating PlotData for visualizing query results""" embedding: BaseEmbeddings @@ -140,3 +141,6 @@ def run(self, context: List[str], question: str): visualization_df = pd.concat([viz_base_df, viz_query_df], axis=0) fig = self._plot_embeddings(visualization_df) return fig + + def __call__(self, context: list, question: str): + return self.run(context, question) diff --git a/libs/ktem/migrations/env.py b/libs/ktem/migrations/env.py index 19a67ac18..e1535a8d5 100644 --- a/libs/ktem/migrations/env.py +++ b/libs/ktem/migrations/env.py @@ -1,10 +1,10 @@ from logging.config import fileConfig from alembic import context -from ktem.db.models import * # noqa +from ktem.db.models import * # noqa: F401, F403 +from ktem.db.models import Base +from ktem.settings_config import app_settings as settings from sqlalchemy import engine_from_config, pool -from sqlmodel import SQLModel -from theflow.settings import settings # this is the Alembic Config object, which provides # access to the values within the .ini file in use. @@ -17,9 +17,7 @@ # add your model's MetaData object here # for 'autogenerate' support -# from myapp import mymodel -# target_metadata = mymodel.Base.metadata -target_metadata = SQLModel.metadata +target_metadata = Base.metadata # other values from the config, defined by the needs of env.py, # can be acquired: diff --git a/libs/ktem/migrations/script.py.mako b/libs/ktem/migrations/script.py.mako index 6ce335109..fbc4b07dc 100644 --- a/libs/ktem/migrations/script.py.mako +++ b/libs/ktem/migrations/script.py.mako @@ -9,7 +9,6 @@ from typing import Sequence, Union from alembic import op import sqlalchemy as sa -import sqlmodel ${imports if imports else ""} # revision identifiers, used by Alembic. diff --git a/libs/ktem/pyproject.toml b/libs/ktem/pyproject.toml index 7e6ac2ca2..8c19689bd 100644 --- a/libs/ktem/pyproject.toml +++ b/libs/ktem/pyproject.toml @@ -19,6 +19,7 @@ dynamic = ["version"] requires-python = ">= 3.10" description = "RAG-based Question and Answering Application" dependencies = [ + "kotaemon", "click>=8.1.7,<9", "platformdirs>=4.2.1,<5", "pluggy>=1.5.0,<2", @@ -43,3 +44,6 @@ classifiers = [ "Programming Language :: Python :: 3", "Operating System :: OS Independent", ] + +[tool.uv.sources] +kotaemon = { workspace = true } diff --git a/sso_app.py b/sso_app.py index 7880137d5..e93e57f4d 100644 --- a/sso_app.py +++ b/sso_app.py @@ -4,9 +4,9 @@ from decouple import config from fastapi import FastAPI from fastapi.responses import FileResponse -from theflow.settings import settings as flowsettings +from ktem.settings_config import app_settings as flowsettings -KH_APP_DATA_DIR = getattr(flowsettings, "KH_APP_DATA_DIR", ".") +KH_APP_DATA_DIR = flowsettings.KH_APP_DATA_DIR GRADIO_TEMP_DIR = os.getenv("GRADIO_TEMP_DIR", None) AUTHENTICATION_METHOD = config("AUTHENTICATION_METHOD", "GOOGLE") diff --git a/sso_app_demo.py b/sso_app_demo.py index afe348836..d9ee8eeb0 100644 --- a/sso_app_demo.py +++ b/sso_app_demo.py @@ -5,13 +5,13 @@ from decouple import config from fastapi import FastAPI, Request from fastapi.responses import FileResponse +from ktem.settings_config import app_settings as flowsettings from starlette.config import Config from starlette.middleware.sessions import SessionMiddleware from starlette.responses import RedirectResponse -from theflow.settings import settings as flowsettings -KH_DEMO_MODE = getattr(flowsettings, "KH_DEMO_MODE", False) -KH_APP_DATA_DIR = getattr(flowsettings, "KH_APP_DATA_DIR", ".") +KH_DEMO_MODE = flowsettings.KH_DEMO_MODE +KH_APP_DATA_DIR = flowsettings.KH_APP_DATA_DIR GRADIO_TEMP_DIR = os.getenv("GRADIO_TEMP_DIR", None) # override GRADIO_TEMP_DIR if it's not set if GRADIO_TEMP_DIR is None: diff --git a/templates/component-default/README.md b/templates/component-default/README.md deleted file mode 100644 index e69de29bb..000000000 diff --git a/templates/project-default/cookiecutter.json b/templates/project-default/cookiecutter.json deleted file mode 100644 index f70990c08..000000000 --- a/templates/project-default/cookiecutter.json +++ /dev/null @@ -1,4 +0,0 @@ -{ - "project_name": "prj_kotaemon", - "ptl": "john" -} diff --git a/templates/project-default/{{cookiecutter.project_name}}/.gitattributes b/templates/project-default/{{cookiecutter.project_name}}/.gitattributes deleted file mode 100644 index 919df25b6..000000000 --- a/templates/project-default/{{cookiecutter.project_name}}/.gitattributes +++ /dev/null @@ -1,23 +0,0 @@ -.gitattributes text eol=lf -.gitignore text eol=lf -*.build text eol=lf -*.c text eol=lf -*.cmake text eol=lf -*.cpp text eol=lf -*.csv text eol=lf -*.f text eol=lf -*.f90 text eol=lf -*.for text eol=lf -*.grc text eol=lf -*.h text eol=lf -*.ipynb text eol=lf -*.m text eol=lf -*.md text eol=lf -*.pas text eol=lf -*.py text eol=lf -*.rst text eol=lf -*.sh text eol=lf -*.txt text eol=lf -*.yml text eol=lf -Makefile text eol=lf -*.html linguist-documentation diff --git a/templates/project-default/{{cookiecutter.project_name}}/.gitignore b/templates/project-default/{{cookiecutter.project_name}}/.gitignore deleted file mode 100644 index 0e802459d..000000000 --- a/templates/project-default/{{cookiecutter.project_name}}/.gitignore +++ /dev/null @@ -1,459 +0,0 @@ -# Created by https://www.toptal.com/developers/gitignore/api/python,linux,macos,windows,vim,emacs,visualstudiocode,pycharm -# Edit at https://www.toptal.com/developers/gitignore?templates=python,linux,macos,windows,vim,emacs,visualstudiocode,pycharm - 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For a more nuclear -# option (not recommended) you can uncomment the following to ignore the entire idea folder. -#.idea/ - -### Python Patch ### -# Poetry local configuration file - https://python-poetry.org/docs/configuration/#local-configuration -poetry.toml - -# ruff -.ruff_cache/ - -# LSP config files -pyrightconfig.json - -### Vim ### -# Swap -[._]*.s[a-v][a-z] -!*.svg # comment out if you don't need vector files -[._]*.sw[a-p] -[._]s[a-rt-v][a-z] -[._]ss[a-gi-z] -[._]sw[a-p] - -# Session -Session.vim -Sessionx.vim - -# Temporary -.netrwhist -# Auto-generated tag files -tags -# Persistent undo -[._]*.un~ - -### VisualStudioCode ### -.vscode/* -!.vscode/settings.json -!.vscode/tasks.json -!.vscode/launch.json -!.vscode/extensions.json -!.vscode/*.code-snippets - -# Local History for Visual Studio Code -.history/ - -# Built Visual Studio Code Extensions -*.vsix - -### VisualStudioCode Patch ### -# Ignore all local history of files -.history -.ionide - -### Windows ### -# Windows thumbnail cache files -Thumbs.db -Thumbs.db:encryptable -ehthumbs.db -ehthumbs_vista.db - -# Dump file -*.stackdump - -# Folder config file -[Dd]esktop.ini - -# Recycle Bin used on file shares -$RECYCLE.BIN/ - -# Windows Installer files -*.cab -*.msi -*.msix -*.msm -*.msp - -# Windows shortcuts -*.lnk - -.theflow/ - -# End of https://www.toptal.com/developers/gitignore/api/python,linux,macos,windows,vim,emacs,visualstudiocode,pycharm - -logs/ -.gitsecret/keys/random_seed -!*.secret -credentials.txt - -S.gpg-agent* -.vscode/settings.json diff --git a/templates/project-default/{{cookiecutter.project_name}}/.pre-commit-config.yaml b/templates/project-default/{{cookiecutter.project_name}}/.pre-commit-config.yaml deleted file mode 100644 index 90d007119..000000000 --- a/templates/project-default/{{cookiecutter.project_name}}/.pre-commit-config.yaml +++ /dev/null @@ -1,51 +0,0 @@ -repos: - - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.3.0 - hooks: - - id: check-yaml - - id: check-toml - - id: end-of-file-fixer - - id: trailing-whitespace - - id: detect-aws-credentials - args: ["--allow-missing-credentials"] - - id: detect-private-key - - id: check-added-large-files - - repo: https://github.com/ambv/black - rev: 22.3.0 - hooks: - - id: black - language_version: python3 - - repo: https://github.com/pycqa/isort - rev: 5.12.0 - hooks: - - id: isort - args: ["--profile", "black"] - language_version: python3.10 - - repo: https://github.com/pycqa/flake8 - rev: 4.0.1 - hooks: - - id: flake8 - args: ["--max-line-length", "88", "--extend-ignore", "E203"] - - repo: https://github.com/myint/autoflake - rev: v1.4 - hooks: - - id: autoflake - args: - [ - "--in-place", - "--remove-unused-variables", - "--remove-all-unused-imports", - "--ignore-init-module-imports", - "--exclude=tests/*", - ] - - repo: https://github.com/pre-commit/mirrors-prettier - rev: v2.7.1 - hooks: - - id: prettier - types_or: [markdown, yaml] - - repo: https://github.com/pre-commit/mirrors-mypy - rev: "v1.5.1" - hooks: - - id: mypy - additional_dependencies: [types-PyYAML==6.0.12.11, "types-requests"] - args: ["--check-untyped-defs", "--ignore-missing-imports"] diff --git a/templates/project-default/{{cookiecutter.project_name}}/README.md b/templates/project-default/{{cookiecutter.project_name}}/README.md deleted file mode 100644 index da7d16064..000000000 --- a/templates/project-default/{{cookiecutter.project_name}}/README.md +++ /dev/null @@ -1,37 +0,0 @@ -
- -# Project {{ cookiecutter.project_name }} - -[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://github.com/Cinnamon/kotaemon) - -
- -# Install - -```bash -# Create new conda env (optional) -conda create -n {{ cookiecutter.project_name }} python=3.10 -conda activate {{ cookiecutter.project_name }} - -# Clone and install the project -git clone "<{{ cookiecutter.project_name }}-repo>" -cd "<{{ cookiecutter.project_name }}-repo>" -pip install -e . - -# Generate the project structure -cd .. -kh start-project -``` - -# Usage - -- Build the pipeline in `pipeline.py` - -For supported utilities and tools, refer: https://github.com/Cinnamon/kotaemon/wiki/Utilities - -# Contribute - -- For project issues and errors, please report in this repo issues. -- For kotaemon issues and errors, please report or make PR fixes in https://github.com/Cinnamon/kotaemon.git -- If the template for this project has issues and errors, please report or make - PR fixes in https://github.com/Cinnamon/kotaemon/tree/main/templates/project-default diff --git a/templates/project-default/{{cookiecutter.project_name}}/setup.py b/templates/project-default/{{cookiecutter.project_name}}/setup.py deleted file mode 100644 index a032d5330..000000000 --- a/templates/project-default/{{cookiecutter.project_name}}/setup.py +++ /dev/null @@ -1,20 +0,0 @@ -import setuptools - -setuptools.setup( - name="{{ cookiecutter.project_name }}", - version="0.0.1", - author="{{ cookiecutter.ptl }}", - author_email="{{ cookiecutter.ptl }}@cinnamon.is", - description="Project {{ cookiecutter.project_name }}", - long_description="Project {{ cookiecutter.project_name }}", - url="https://github.com/Cinnamon/kotaemon", - python_requires=">=3", - classifiers=[ - "Programming Language :: Python :: 3", - "License :: OSI Approved :: MIT License", - "Operating System :: OS Independent", - ], - install_requires=[ - "kotaemon@git+ssh://git@github.com/Cinnamon/kotaemon.git", - ], -) diff --git a/templates/project-default/{{cookiecutter.project_name}}/tests/__init__.py b/templates/project-default/{{cookiecutter.project_name}}/tests/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/templates/project-default/{{cookiecutter.project_name}}/{{cookiecutter.project_name}}/__init__.py b/templates/project-default/{{cookiecutter.project_name}}/{{cookiecutter.project_name}}/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/templates/project-default/{{cookiecutter.project_name}}/{{cookiecutter.project_name}}/pipeline.py b/templates/project-default/{{cookiecutter.project_name}}/{{cookiecutter.project_name}}/pipeline.py deleted file mode 100644 index 08237e4e6..000000000 --- a/templates/project-default/{{cookiecutter.project_name}}/{{cookiecutter.project_name}}/pipeline.py +++ /dev/null @@ -1,104 +0,0 @@ -import os -from typing import List - -from kotaemon.base import BaseComponent, Document, LLMInterface, Node, Param, lazy -from kotaemon.contribs.promptui.logs import ResultLog -from kotaemon.embeddings import LCAzureOpenAIEmbeddings -from kotaemon.indices import VectorIndexing, VectorRetrieval -from kotaemon.llms import LCAzureChatOpenAI -from kotaemon.storages import ChromaVectorStore, SimpleFileDocumentStore - - -class QAResultLog(ResultLog): - @staticmethod - def _get_prompt(obj): - return obj["prompt"] - - -class QuestionAnsweringPipeline(BaseComponent): - - _promptui_resultlog = QAResultLog - _promptui_outputs: list = [ - { - "step": ".prompt", - "getter": "_get_prompt", - "component": "text", - "params": {"label": "Constructed prompt to LLM"}, - }, - { - "step": ".", - "getter": "_get_output", - "component": "text", - "params": {"label": "Answer"}, - }, - ] - - retrieval_top_k: int = 1 - llm: LCAzureChatOpenAI = LCAzureChatOpenAI.withx( - azure_endpoint="https://bleh-dummy-2.openai.azure.com/", - openai_api_key=os.environ.get("OPENAI_API_KEY", "default-key"), - openai_api_version="2023-03-15-preview", - deployment_name="dummy-q2-gpt35", - temperature=0, - request_timeout=60, - ) - - retrieving_pipeline: VectorRetrieval = Node( - VectorRetrieval.withx( - vector_store=lazy(ChromaVectorStore).withx(path="./tmp"), - doc_store=lazy(SimpleFileDocumentStore).withx(path="docstore.json"), - embedding=LCAzureOpenAIEmbeddings.withx( - model="text-embedding-ada-002", - deployment="dummy-q2-text-embedding", - azure_endpoint="https://bleh-dummy-2.openai.azure.com/", - openai_api_key=os.environ.get("OPENAI_API_KEY", "default-key"), - ), - ), - ignore_ui=True, - ) - - def run(self, text: str) -> LLMInterface: - # retrieve relevant documents as context - matched_texts: List[str] = [ - _.text - for _ in self.retrieving_pipeline(text, top_k=int(self.retrieval_top_k)) - ] - context = "\n".join(matched_texts) - - # generate the answer - prompt = f'Answer the following question: "{text}". The context is: \n{context}' - self.log_progress(".prompt", prompt=prompt) - - return self.llm(prompt).text - - -class IndexingPipeline(VectorIndexing): - - vector_store: ChromaVectorStore = Param( - lazy(ChromaVectorStore).withx(path="./tmp"), - ignore_ui=True, - ) - doc_store: SimpleFileDocumentStore = Param( - lazy(SimpleFileDocumentStore).withx(path="docstore.json"), - ignore_ui=True, - ) - embedding: LCAzureOpenAIEmbeddings = LCAzureOpenAIEmbeddings.withx( - model="text-embedding-ada-002", - deployment="dummy-q2-text-embedding", - azure_endpoint="https://bleh-dummy-2.openai.azure.com/", - openai_api_key=os.environ.get("OPENAI_API_KEY", "default-key"), - ) - - def run(self, text: str) -> Document: - """Normally, this indexing pipeline returns nothing. For demonstration, - we want it to return something, so let's return the number of documents - in the vector store - """ - super().run(text) - - if self.doc_store is not None: - # persist to local anytime an indexing is created - # this can be bypassed when we have a FileDocumentStore - self.doc_store.save("docstore.json") - - return Document(self.vector_store._collection.count()) diff --git a/uv.lock b/uv.lock index ae4fd9bc3..4d24e4c5f 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 1 +revision = 3 requires-python = ">=3.10" resolution-markers = [ "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform == 'darwin'", @@ -39,9 +39,9 @@ dependencies = [ { name = "aiobotocore", extra = ["boto3"] }, { name = "aiofiles" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/a2/01/92e9ab00f36e2899315f49eefcd5b4685fbb19016c7f19a9edf06da80bb0/aioboto3-15.5.0.tar.gz", hash = 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