From f1260561b0c99d68631e6f9d82ab37ef2f1628ac Mon Sep 17 00:00:00 2001 From: Elif Sema Balcioglu Date: Wed, 3 Jun 2026 13:34:49 +0000 Subject: [PATCH] Add Oracle Vector Store Integration --- docs/components/vectordbs/dbs/oracledb.mdx | 71 + docs/docs.json | 3 +- mem0/configs/vector_stores/oracledb.py | 159 + mem0/utils/factory.py | 6 +- mem0/vector_stores/configs.py | 1 + mem0/vector_stores/oracledb.py | 444 + pyproject.toml | 3 +- tests/vector_stores/test_oraclevs.py | 590 + uv.lock | 13086 +++++++++++++++++++ 9 files changed, 14360 insertions(+), 3 deletions(-) create mode 100644 docs/components/vectordbs/dbs/oracledb.mdx create mode 100644 mem0/configs/vector_stores/oracledb.py create mode 100644 mem0/vector_stores/oracledb.py create mode 100644 tests/vector_stores/test_oraclevs.py create mode 100644 uv.lock diff --git a/docs/components/vectordbs/dbs/oracledb.mdx b/docs/components/vectordbs/dbs/oracledb.mdx new file mode 100644 index 0000000000..427c37f2ff --- /dev/null +++ b/docs/components/vectordbs/dbs/oracledb.mdx @@ -0,0 +1,71 @@ +[Oracle AI Vector Search](https://www.oracle.com/database/ai-vector-search/) is designed for AI workloads and lets you query data based on semantics rather than keywords. A key benefit is the ability to combine semantic search on unstructured data with relational search on business data in a single system. + +## Overview + +The `oracledb` provider integrates mem0 with Oracle Database’s AI Vector Search feature. It stores embeddings in an Oracle table that uses the `VECTOR` data type and leverages `CREATE VECTOR INDEX` for fast similarity search. + +This backend uses the official `python-oracledb` driver. You’ll need a database user with privileges to create tables and vector indexes. + +Install the required library: +```bash +python -m pip install -U oracledb +``` + +### Usage + + +```python Python +import os +from mem0 import Memory + +os.environ["OPENAI_API_KEY"] = "sk-xx" + +config = { + "vector_store": { + "provider": "oracledb", + "config": { + "connection_params": { + "user": "", + "password": "", + "dsn": "" + }, + } + } +} + +m = Memory.from_config(config) +messages = [ + {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"}, + {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."}, + {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."}, + {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."} +] +m.add(messages, user_id="alice", metadata={"category": "movies"}) +``` + + +## Configuration + +### Config + +Here are the parameters available for configuring Oracle AI Vector Search: + +| Parameter | Description | Default Value | +| --- | --- | --- | +| `connection_params` | Oracle connection settings for `python-oracledb` (`user`, `password`, `dsn` or more). See https://python-oracledb.readthedocs.io/en/latest/user_guide/connection_handling.html | `None` | +| `use_connection_pool` | If `true`, a connection pool is created from `connection_params`; if `false`, a single connection is created | `True` | +| `client` | An existing `oracledb.Connection` or `oracledb.ConnectionPool` to use instead of building one from `connection_params` | `None` | +| `collection_name` | Name of the Oracle table used to store vectors and payloads | `mem0` | +| `embedding_model_dims` | Dimension of your embedding vectors (must be > 0) | `1536` | +| `distance_metric` | Distance function used for indexing and search. One of `EUCLIDEAN`, `EUCLIDEAN_SQUARED`, `COSINE`, `DOT`, `HAMMING`, `MANHATTAN` | `COSINE` | +| `do_create_index` | Whether to create a vector index on the collection | `True` | +| `index_type` | Vector index type. `HNSW` or `IVF` | `HNSW` | +| `index_name` | Name of the vector index. Defaults to `_VEC_IDX` | `None` | +| `index_parameters_str` | Additional parameters passed to `CREATE VECTOR INDEX` via `PARAMETERS(...)` | `None` | +| `index_accuracy` | Target index accuracy (1–100), used with `WITH TARGET ACCURACY ` | `None` | + +**Notes** +- When you provide a pre-built `client`, mem0 will use it instead of creating a new connection or pool. +- Do not include the index “type” in `index_parameters_str`; set it via `index_type`. See Oracle docs: https://docs.oracle.com/en/database/oracle/oracle-database/26/sqlrf/create-vector-index.html +- `connection_params` must follow `python-oracledb` conventions (`user`, `password`, `dsn` and more). +- The returned `score` from searches is Oracle’s `VECTOR_DISTANCE` (lower means closer/more similar). diff --git a/docs/docs.json b/docs/docs.json index 8a4c958654..9c7440e379 100644 --- a/docs/docs.json +++ b/docs/docs.json @@ -246,7 +246,8 @@ "components/vectordbs/dbs/s3_vectors", "components/vectordbs/dbs/databricks", "components/vectordbs/dbs/neptune_analytics", - "components/vectordbs/dbs/turbopuffer" + "components/vectordbs/dbs/turbopuffer", + "components/vectordbs/dbs/oracledb" ] } ] diff --git a/mem0/configs/vector_stores/oracledb.py b/mem0/configs/vector_stores/oracledb.py new file mode 100644 index 0000000000..92db7854d8 --- /dev/null +++ b/mem0/configs/vector_stores/oracledb.py @@ -0,0 +1,159 @@ +"""Pydantic configuration for the Oracle AI Vector Search integration.""" + +import re +from typing import Any, Dict, Optional + +from pydantic import BaseModel, Field, model_validator + + +def _quote_identifier(name: str) -> str: + name = name.strip() + reg = r'^(?:"[^"]+"|[^".]+)(?:\.(?:"[^"]+"|[^".]+))*$' + pattern_validate = re.compile(reg) + + if not pattern_validate.match(name): + raise ValueError(f"Identifier name {name} is not valid.") + + pattern_match = r'"([^"]+)"|([^".]+)' + groups = re.findall(pattern_match, name) + groups = [m[0] or m[1] for m in groups] + groups = [f'"{g}"' for g in groups] + + return ".".join(groups) + + +ALLOWED_DISTANCE_METRICS = {"EUCLIDEAN", "EUCLIDEAN_SQUARED", "COSINE", "DOT", "HAMMING", "MANHATTAN"} +ALLOWED_INDEX_TYPES = {"HNSW", "IVF"} +ALLOWED_HNSW_INDEX_PARAMETERS = {"neighbors", "efconstruction"} +ALLOWED_IVF_INDEX_PARAMETERS = {"neighbor_partitions", "samples_per_partition", "min_vectors_per_partition"} + + +def _validate_int_parameter(parameters: dict, key: str, min_value: int, max_value: Optional[int] = None) -> None: + if key not in parameters: + return + + value = parameters[key] + if isinstance(value, bool) or not isinstance(value, int): + raise ValueError(f"`index_parameters.{key}` must be an integer") + if value < min_value: + raise ValueError(f"`index_parameters.{key}` must be at least {min_value}") + if max_value is not None and value > max_value: + raise ValueError(f"`index_parameters.{key}` must be at most {max_value}") + parameters[key] = int(value) + + +def _validate_index_parameters(index_type: str, index_parameters: Optional[dict]) -> Optional[dict]: + if index_parameters is None: + return None + if not isinstance(index_parameters, dict): + raise ValueError("`index_parameters` must be a dictionary") + + parameters = dict(index_parameters) + invalid_keys = [key for key in parameters if not isinstance(key, str)] + if invalid_keys: + raise ValueError( + "`index_parameters` keys must be strings: {}".format( + ", ".join(sorted(repr(key) for key in invalid_keys)) + ) + ) + + allowed_parameters = ALLOWED_HNSW_INDEX_PARAMETERS if index_type == "HNSW" else ALLOWED_IVF_INDEX_PARAMETERS + extra_parameters = set(parameters) - allowed_parameters + if extra_parameters: + raise ValueError( + "`index_parameters` contains unsupported keys for {}: {}".format( + index_type, ", ".join(sorted(extra_parameters)) + ) + ) + + if index_type == "HNSW": + _validate_int_parameter(parameters, "neighbors", 2, 2048) + _validate_int_parameter(parameters, "efconstruction", 1, 65535) + else: + _validate_int_parameter(parameters, "neighbor_partitions", 1, 10000000) + _validate_int_parameter(parameters, "samples_per_partition", 1) + _validate_int_parameter(parameters, "min_vectors_per_partition", 0) + + return parameters + + +class OracleAIVectorSearchConfig(BaseModel): + """Configuration required to connect to an Oracle database with vector search enabled.""" + + connection_params: Optional[dict] = Field(None, description="Database connection parameters, including auth.") + use_connection_pool: Optional[bool] = Field( + True, description="Oracle Connection or ConnectionPool (overrides connection string and individual parameters)" + ) + + client: Optional[Any] = Field( + None, description="Oracle Connection or ConnectionPool (overrides connection string and individual parameters)" + ) + + collection_name: Optional[str] = Field("mem0", description="Default name for the collection") + embedding_model_dims: int = Field(1536, description="Dimension of the embedding vectors") + distance_metric: Optional[str] = Field( + "COSINE", + description="Similarity metric: EUCLIDEAN, EUCLIDEAN_SQUARED, COSINE, DOT, HAMMING or MANHATTAN. Defaults to COSINE", + ) + + do_create_index: Optional[bool] = Field(True, description="Optional whether to create index") + index_type: Optional[str] = Field("HNSW", description="Optional index type, HNSW or IVF") + index_name: Optional[str] = Field(None, description="Optional custom name for the vector index") + index_parameters: Optional[dict] = Field( + None, + description="Optional structured CREATE VECTOR INDEX parameters", + ) + index_accuracy: Optional[int] = Field(None, description="Optional index accuracy") + + @model_validator(mode="after") + def _validate_model(self): + """Normalise attributes and validate identifiers/metrics.""" + + if not self.connection_params and not self.client: + raise ValueError("Must provide at least one of `connection_params` and `client`") + + if self.distance_metric is None: + raise ValueError("`distance_metric` must not be None") + distance_metric = self.distance_metric.upper() + if distance_metric not in ALLOWED_DISTANCE_METRICS: + raise ValueError(f"`distance_metric` must be one of: {ALLOWED_DISTANCE_METRICS}") + self.distance_metric = distance_metric + + if self.index_type is None: + raise ValueError("`index_type` must not be None") + index_type = self.index_type.upper() + if index_type not in ALLOWED_INDEX_TYPES: + raise ValueError(f"`index_type` must be one of: {ALLOWED_INDEX_TYPES}") + self.index_type = index_type + + if self.index_name is None: + self.index_name = f"{self.collection_name}_VEC_IDX" + + self.index_name = _quote_identifier(self.index_name) + self.collection_name = _quote_identifier(self.collection_name) + + self.index_parameters = _validate_index_parameters(self.index_type, self.index_parameters) + + if self.index_accuracy and not (0 < self.index_accuracy <= 100): + raise ValueError("`index_accuracy` must be between 1 and 100") + + if not (0 < self.embedding_model_dims): + raise ValueError("`embedding_model_dims` must be bigger than 0") + + return self + + def canonical_index_parameters(self) -> Optional[dict]: + return _validate_index_parameters(self.index_type, self.index_parameters) + + @model_validator(mode="before") + @classmethod + def validate_extra_fields(cls, values: Dict[str, Any]) -> Dict[str, Any]: + allowed_fields = set(cls.model_fields.keys()) + extra_fields = set(values.keys()) - allowed_fields + if extra_fields: + raise ValueError( + "Extra fields not allowed: {}. Please input only the following fields: {}".format( + ", ".join(sorted(extra_fields)), ", ".join(sorted(allowed_fields)) + ) + ) + return values diff --git a/mem0/utils/factory.py b/mem0/utils/factory.py index 754ea0988b..6160f24e2e 100644 --- a/mem0/utils/factory.py +++ b/mem0/utils/factory.py @@ -190,6 +190,7 @@ class VectorStoreFactory: "cassandra": "mem0.vector_stores.cassandra.CassandraDB", "neptune": "mem0.vector_stores.neptune_analytics.NeptuneAnalyticsVector", "turbopuffer": "mem0.vector_stores.turbopuffer.TurbopufferDB", + "oracledb": "mem0.vector_stores.oracledb.OracleAIVectorSearch", } @classmethod @@ -219,7 +220,10 @@ class RerankerFactory: # Provider mappings with their config classes provider_to_class = { "cohere": ("mem0.reranker.cohere_reranker.CohereReranker", CohereRerankerConfig), - "sentence_transformer": ("mem0.reranker.sentence_transformer_reranker.SentenceTransformerReranker", SentenceTransformerRerankerConfig), + "sentence_transformer": ( + "mem0.reranker.sentence_transformer_reranker.SentenceTransformerReranker", + SentenceTransformerRerankerConfig, + ), "zero_entropy": ("mem0.reranker.zero_entropy_reranker.ZeroEntropyReranker", ZeroEntropyRerankerConfig), "llm_reranker": ("mem0.reranker.llm_reranker.LLMReranker", LLMRerankerConfig), "huggingface": ("mem0.reranker.huggingface_reranker.HuggingFaceReranker", HuggingFaceRerankerConfig), diff --git a/mem0/vector_stores/configs.py b/mem0/vector_stores/configs.py index 32459dddc3..990086c7d6 100644 --- a/mem0/vector_stores/configs.py +++ b/mem0/vector_stores/configs.py @@ -35,6 +35,7 @@ class VectorStoreConfig(BaseModel): "langchain": "LangchainConfig", "s3_vectors": "S3VectorsConfig", "turbopuffer": "TurbopufferConfig", + "oracledb": "OracleAIVectorSearchConfig", } @model_validator(mode="after") diff --git a/mem0/vector_stores/oracledb.py b/mem0/vector_stores/oracledb.py new file mode 100644 index 0000000000..53bcd6cc05 --- /dev/null +++ b/mem0/vector_stores/oracledb.py @@ -0,0 +1,444 @@ +"""Oracle AI Vector Search vector store integration for mem0.""" + +import json +import logging +from contextlib import contextmanager +from typing import Any, Dict, List, Optional +import re +import array +import uuid + +try: + import oracledb +except ImportError as exc: # pragma: no cover - dependency guard + raise ImportError("Oracle AI Vector Search requires the 'oracledb' package.") from exc + +from pydantic import BaseModel + +from mem0.configs.vector_stores.oracledb import OracleAIVectorSearchConfig +from mem0.vector_stores.base import VectorStoreBase + +logger = logging.getLogger(__name__) + + +class OutputData(BaseModel): + """Standard output structure returned from vector operations.""" + + id: Optional[str] + score: Optional[float] + payload: Optional[Dict[str, Any]] + + +# Allow letters, digits, underscore, dot, brackets, comma, *, space (for 'to') +METADATA_PATTERN = re.compile(r"[a-zA-Z0-9_\.\[\],\s\*]*") + + +def _validate_metadata_key(metadata_key: str) -> None: + if not METADATA_PATTERN.fullmatch(metadata_key): + raise ValueError( + f"Invalid metadata key '{metadata_key}'. " + "Only letters, numbers, underscores, nesting via '.', " + "and array wildcards '[*]' are allowed." + ) + + +class OracleAIVectorSearch(VectorStoreBase): + """Oracle AI Vector Search backend for mem0.""" + + def __init__(self, **kwargs: Any) -> None: + self.config = OracleAIVectorSearchConfig(**kwargs) + self.collection_name = self.config.collection_name + + if self.config.client: + logger.debug("Using Oracle connection pool: %s", self.config.client) + self.client = self.config.client + self._owns_client = False + elif self.config.use_connection_pool: + pool_kwargs = { + "min": 1, + "max": 4, + } + pool_kwargs.update(self.config.connection_params) + + logger.debug("Creating Oracle connection pool") + self.client = oracledb.create_pool(**pool_kwargs) + self._owns_client = True + else: + logger.debug("Creating Oracle connection") + self.client = oracledb.connect(**self.config.connection_params) + self._owns_client = True + + if not (hasattr(self.client, "thin") and self.client.thin): + if oracledb.clientversion()[:2] < (23, 4): + raise Exception( + f"Oracle DB client driver version {oracledb.clientversion()} not \ + supported, must be >=23.4 for vector support" + ) + + if isinstance(self.client, oracledb.Connection): + db_version = tuple([int(v) for v in self.client.version.split(".")]) + else: + with self.client.acquire() as conn: + db_version = tuple([int(v) for v in conn.version.split(".")]) + + if db_version < (23, 4): + raise Exception( + f"Oracle DB version {oracledb.__version__} not supported, \ + must be >=23.4 for vector support" + ) + + collections = self.list_cols() + if self._catalog_name(self.collection_name) not in {self._catalog_name(name) for name in collections}: + self.create_col() + + @contextmanager + def _get_cursor(self, commit: bool = False): + if isinstance(self.client, oracledb.ConnectionPool): + with self.client.acquire() as connection: + with connection.cursor() as cursor: + try: + yield cursor + if commit: + connection.commit() + except Exception: + connection.rollback() + raise + else: + with self.client.cursor() as cursor: + try: + yield cursor + if commit: + self.client.commit() + except Exception: + self.client.rollback() + raise + + # Utility helpers -------------------------------------------------- + @staticmethod + def _load_payload(value: Any) -> Dict[str, Any]: + if value is None: + return {} + if isinstance(value, dict): + return value + if hasattr(value, "read"): + value = value.read() + if isinstance(value, bytes): + value = value.decode("utf-8") + try: + return json.loads(value) + except json.JSONDecodeError: + logger.debug("Failed to decode payload JSON") + raise + + @staticmethod + def _catalog_name(name: str) -> str: + return name.replace('"', "") + + def _create_index_ddl(self) -> str: + accuracy_str = "" + if self.config.index_accuracy: + accuracy_str = f"WITH TARGET ACCURACY {self.config.index_accuracy}" + + parameters = self._index_parameters() + parameters_str = f"PARAMETERS ({parameters})" if parameters else "" + + distance_metric = self.config.distance_metric + + create_index = ( + f"CREATE VECTOR INDEX IF NOT EXISTS {self.config.index_name} ON {self.collection_name} (vector) " + f"ORGANIZATION {'INMEMORY NEIGHBOR GRAPH' if self.config.index_type == 'HNSW' else 'NEIGHBOR PARTITIONS'}" + f" DISTANCE {distance_metric} {accuracy_str} {parameters_str}" + ) + + return create_index + + def _index_parameters(self) -> str: + index_parameters = self.config.canonical_index_parameters() + if not index_parameters: + return "" + + parameters = [f"type {self.config.index_type}"] + if self.config.index_type == "HNSW": + if "neighbors" in index_parameters: + parameters.append(f"neighbors {index_parameters['neighbors']}") + if "efconstruction" in index_parameters: + parameters.append(f"efconstruction {index_parameters['efconstruction']}") + else: + if "neighbor_partitions" in index_parameters: + parameters.append(f"neighbor partitions {index_parameters['neighbor_partitions']}") + if "samples_per_partition" in index_parameters: + parameters.append(f"samples_per_partition {index_parameters['samples_per_partition']}") + if "min_vectors_per_partition" in index_parameters: + parameters.append(f"min_vectors_per_partition {index_parameters['min_vectors_per_partition']}") + + return ", ".join(parameters) + + # Vector store API ------------------------------------------------- + def create_col(self) -> None: + """ + Create a new collection (table in Oracle). + Will also initialize vector search index if specified. + """ + with self._get_cursor(commit=True) as cursor: + cursor.execute( + f""" + CREATE TABLE IF NOT EXISTS {self.collection_name} ( + id VARCHAR2(36) PRIMARY KEY, + vector VECTOR({self.config.embedding_model_dims}), + payload JSON + ) + """ + ) + + if self.config.do_create_index: + ddl = self._create_index_ddl() + cursor.execute(ddl) + + def insert( + self, + vectors: List[List[float]], + payloads: Optional[List[Dict[str, Any]]] = None, + ids: Optional[List[str]] = None, + ) -> None: + logger.info(f"Inserting {len(vectors)} vectors into collection {self.collection_name}") + + if payloads is not None and len(payloads) != len(vectors): + raise ValueError(f"Payload count must match vector count. Expected {len(vectors)} got {len(payloads)}.") + if ids is not None and len(ids) != len(vectors): + raise ValueError(f"ID count must match vector count. Expected {len(vectors)} got {len(ids)}.") + + _ids = ids + if not _ids: + _ids = [str(uuid.uuid4()) for _ in vectors] + + data = [] + for vector, payload, _id in zip(vectors, payloads or [{}] * len(vectors), _ids): + document = {"id": _id, "vector": array.array("f", vector), "payload": payload} + data.append(document) + + with self._get_cursor(commit=True) as cursor: + cursor.setinputsizes( + vector=oracledb.DB_TYPE_VECTOR, + payload=oracledb.DB_TYPE_JSON, + ) + cursor.executemany( + f"INSERT INTO {self.collection_name} (id, vector, payload) VALUES (:id, :vector, :payload)", data + ) + + def search( + self, + query: str, + vectors: List[float], + limit: int = 5, + filters: Optional[Dict[str, Any]] = None, + ) -> List[OutputData]: + """ + Search for similar vectors using the vector search index. + + Args: + query (str): Query string + vectors (List[float]): Query vector. + limit (int, optional): Number of results to return. Defaults to 5. + filters (Dict, optional): Filters to apply to the search. + + Returns: + List[OutputData]: Search results. + """ + filter_clause, params = self._build_filters(filters) + + distance_metric = self.config.distance_metric + + sql = ( + f"SELECT id, payload, VECTOR_DISTANCE(vector, :query_vec, {distance_metric}) distance " + f"FROM {self.collection_name} {filter_clause} ORDER BY VECTOR_DISTANCE(vector, :query_vec, {distance_metric}) FETCH FIRST :limit ROWS ONLY" + ) + + with self._get_cursor() as cursor: + cursor.execute(sql, query_vec=array.array("f", vectors), limit=limit, **params) + rows = cursor.fetchall() + + return [ + OutputData( + id=row[0], + payload=self._load_payload(row[1]), + score=float(row[2]), + ) + for row in rows + ] + + def _build_filters(self, filters: Optional[Dict[str, Any]]) -> tuple[str, Dict[str, Any]]: + if not filters: + return "", {} + + clauses: List[str] = [] + params: Dict[str, Any] = {} + + for idx, (key, value) in enumerate(filters.items()): + _validate_metadata_key(key) + param = f"f_{idx}" + + # Build JSON path like @."a"."b" or @."a"[*]."b" + path_parts: List[str] = [] + for part in key.split("."): + if part.endswith("[*]"): + base = part[:-3] + path_parts.append(f'."{base}"[*]') + else: + path_parts.append(f'."{part}"') + json_path = "".join(path_parts) + + # Use JSON_EXISTS with PASSING BY VALUE to preserve types + clauses.append(f"JSON_EXISTS(payload, '$?(@{json_path} == ${param})' PASSING :{param} AS \"{param}\")") + params[param] = value + + return ("WHERE " + " AND ".join(clauses), params) + + def delete(self, vector_id: str) -> None: + """ + Delete a vector by ID. + + Args: + vector_id (str): ID of the vector to delete. + """ + with self._get_cursor(commit=True) as cursor: + cursor.execute(f"DELETE FROM {self.collection_name} WHERE id = :id", id=vector_id) + + def update( + self, + vector_id: str, + vector: Optional[List[float]] = None, + payload: Optional[Dict[str, Any]] = None, + ) -> None: + """ + Update a vector and its payload. + + Args: + vector_id (str): ID of the vector to update. + vector (List[float], optional): Updated vector. + payload (Dict, optional): Updated payload. + """ + if vector is None and payload is None: + return + + with self._get_cursor(commit=True) as cursor: + if vector is not None: + cursor.setinputsizes(vector=oracledb.DB_TYPE_VECTOR) + cursor.execute( + f"UPDATE {self.collection_name} SET vector = :vector WHERE id = :vector_id", + {"vector": array.array("f", vector), "vector_id": vector_id}, + ) + if payload is not None: + cursor.setinputsizes(payload=oracledb.DB_TYPE_JSON) + cursor.execute( + f"UPDATE {self.collection_name} SET payload = :payload WHERE id = :vector_id", + {"payload": payload, "vector_id": vector_id}, + ) + + def get(self, vector_id: str) -> Optional[OutputData]: + """ + Retrieve a vector by ID. + + Args: + vector_id (str): ID of the vector to retrieve. + + Returns: + OutputData: Retrieved vector. + """ + with self._get_cursor() as cursor: + cursor.execute( + f"SELECT id, payload FROM {self.collection_name} WHERE id = :vector_id", + vector_id=vector_id, + ) + row = cursor.fetchone() + if row is None: + return None + return OutputData(id=row[0], score=None, payload=self._load_payload(row[1])) + + def list_cols(self) -> List[str]: + """ + List all collections. + + Returns: + List[str]: List of collection names. + """ + with self._get_cursor() as cursor: + cursor.execute("SELECT table_name FROM user_tables") + tables = [row[0] for row in cursor.fetchall()] + return tables + + def delete_col(self) -> None: + """Delete a collection.""" + with self._get_cursor(commit=True) as cursor: + cursor.execute(f"DROP TABLE {self.collection_name} PURGE") + + def col_info(self) -> Dict[str, Any]: + """ + Get information about a collection. + + Returns: + Dict[str, Any]: Collection information. + """ + sql = f""" + SELECT + table_name, + (SELECT COUNT(*) FROM {self.collection_name}) AS row_count, + (SELECT + ROUND(SUM(bytes) / 1024 / 1024, 2) || ' MB' + FROM user_segments + WHERE segment_name = :table_name + AND segment_type = 'TABLE' + ) AS total_size + FROM user_tables + WHERE table_name = :table_name + """ + + with self._get_cursor() as cursor: + cursor.execute( + sql, + table_name=self.collection_name.replace('"', ""), + ) + result = cursor.fetchone() + + return {"name": result[0], "count": result[1], "size": result[2]} + + def list(self, filters: Optional[Dict[str, Any]] = None, limit: Optional[int] = None) -> List[OutputData]: + """ + List all vectors in a collection. + + Args: + filters (Dict, optional): Filters to apply to the list. + limit (int, optional): Number of vectors to return. Defaults to 100. + + Returns: + List[OutputData]: List of vectors. + """ + filter_clause, params = self._build_filters(filters) + + limit_clause = "" + if limit is not None: + limit_clause = " FETCH FIRST :limit ROWS ONLY" + params["limit"] = limit + + sql = f"SELECT id, payload FROM {self.collection_name} {filter_clause} {limit_clause}" + + with self._get_cursor() as cursor: + cursor.execute(sql, **params) + rows = cursor.fetchall() + + return [OutputData(id=row[0], score=None, payload=self._load_payload(row[1])) for row in rows] + + def reset(self) -> None: + """Reset the index by deleting and recreating it.""" + logger.warning("Resetting collection %s", self.collection_name) + self.delete_col() + self.create_col() + + def __del__(self) -> None: + """ + Close the database connection pool when the object is deleted. + """ + try: + if getattr(self, "_owns_client", False): + self.client.close() + except Exception: + pass diff --git a/pyproject.toml b/pyproject.toml index 13eb2fac61..1b7ee8227b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -50,7 +50,8 @@ vector_stores = [ "redisvl>=0.1.0,<1.0.0", "elasticsearch>=8.0.0,<9.0.0", "pymilvus>=2.4.0,<2.6.0", - "langchain-aws>=0.2.23,<0.3.0", + "langchain-aws>=0.2.23", + "oracledb>=2.2.0", ] llms = [ "groq>=0.3.0", diff --git a/tests/vector_stores/test_oraclevs.py b/tests/vector_stores/test_oraclevs.py new file mode 100644 index 0000000000..09d8d5b559 --- /dev/null +++ b/tests/vector_stores/test_oraclevs.py @@ -0,0 +1,590 @@ +import os +import uuid +from typing import List, Dict, Any + +import pytest +import oracledb + +from mem0.configs.vector_stores.oracledb import OracleAIVectorSearchConfig +from mem0.vector_stores.oracledb import OracleAIVectorSearch, OutputData + + +# Global connection settings (override via env to run in different envs) +ORACLE_USER = os.environ.get("ORACLE_USER") or "" +ORACLE_PASSWORD = os.environ.get("ORACLE_PASSWORD") or "" +ORACLE_DSN = os.environ.get("ORACLE_DSN") or "" + +DIM = 128 + + +def _unique_collection_name() -> str: + # Keep under Oracle's 30-char identifier limit + return f"TEST_MEM0_{uuid.uuid4().hex[:8]}" + + +def _unwrap_results(results: Any) -> List[OutputData]: + # Current implementation of list() returns [outputs] (nested); unwrap for assertions. + if isinstance(results, list) and len(results) == 1 and isinstance(results[0], list): + return results[0] + return results + + +# Representative coverage of the old matrix. Every option value from the previous +# grid appears in at least one case, without creating a 1924-test DDL-heavy suite. +REPRESENTATIVE_CASES = [ + { + "name": "params-cosine-hnsw-default-noacc-noparams", + "use_connection_pool": False, + "distance_metric": "COSINE", + "index_type": "HNSW", + "custom_index_name": False, + "index_accuracy": None, + "index_parameters": False, + }, + { + "name": "params-euclidean-ivf-custom-acc90-params", + "use_connection_pool": False, + "distance_metric": "EUCLIDEAN", + "index_type": "IVF", + "custom_index_name": True, + "index_accuracy": 90, + "index_parameters": True, + }, + { + "name": "pool-cosine-ivf-default-acc90-noparams", + "use_connection_pool": True, + "distance_metric": "COSINE", + "index_type": "IVF", + "custom_index_name": False, + "index_accuracy": 90, + "index_parameters": False, + }, + { + "name": "pool-euclidean-hnsw-custom-noacc-params", + "use_connection_pool": True, + "distance_metric": "EUCLIDEAN", + "index_type": "HNSW", + "custom_index_name": True, + "index_accuracy": None, + "index_parameters": True, + }, +] + + +def _build_oracle_db(case: Dict[str, Any], *, do_create_index: bool) -> OracleAIVectorSearch: + collection_name = _unique_collection_name() + conn_params = {"user": ORACLE_USER, "password": ORACLE_PASSWORD, "dsn": ORACLE_DSN} + config_kwargs: Dict[str, Any] = { + "collection_name": collection_name, + "embedding_model_dims": DIM, + "distance_metric": case["distance_metric"], + "index_type": case["index_type"], + "do_create_index": do_create_index, + "use_connection_pool": case["use_connection_pool"], + } + + if case.get("custom_index_name"): + config_kwargs["index_name"] = f"{collection_name}_IDX" + if case.get("index_accuracy") is not None: + config_kwargs["index_accuracy"] = case["index_accuracy"] + if case.get("index_parameters"): + config_kwargs["index_parameters"] = ( + {"neighbors": 40, "efconstruction": 64} + if case["index_type"] == "HNSW" + else {"neighbor_partitions": 10} + ) + + if case.get("use_connection_pool"): + config_kwargs["client"] = oracledb.create_pool(min=1, max=4, **conn_params) + else: + config_kwargs["connection_params"] = conn_params + + return OracleAIVectorSearch(**config_kwargs) + + +@pytest.fixture( + params=[REPRESENTATIVE_CASES[0]], + ids=lambda p: p["name"], +) +def oracle_db(request): + """ + Stable Oracle fixture for CRUD/search/list behavior. + Uses a single representative config and skips vector-index creation to avoid + repeated DDL lock contention on the shared Oracle instance. + """ + db = _build_oracle_db(request.param, do_create_index=False) + + try: + yield db + finally: + try: + db.delete_col() + except Exception: + # Ignore failures (e.g., already dropped) + pass + + +@pytest.mark.parametrize("case", REPRESENTATIVE_CASES, ids=lambda case: case["name"]) +def test_initialize_create_col(case: Dict[str, Any]): + oracle_db = _build_oracle_db(case, do_create_index=False) + + try: + # Verify config normalization and DDL generation for each representative case + collection_name = oracle_db.collection_name.strip('"') + expected_index_name = ( + f'"{collection_name}_IDX"' + if case["custom_index_name"] + else f'"{collection_name}_VEC_IDX"' + ) + assert oracle_db.config.embedding_model_dims == DIM + assert oracle_db.config.distance_metric in ("COSINE", "EUCLIDEAN") + assert oracle_db.config.index_type in ("HNSW", "IVF") + assert oracle_db.config.index_name == expected_index_name + assert oracle_db.config.index_accuracy == case["index_accuracy"] + assert bool(case["index_parameters"]) == bool(oracle_db.config.index_parameters) + ddl = oracle_db._create_index_ddl() + assert oracle_db.config.index_name in ddl + assert oracle_db.collection_name in ddl + if case["index_type"] == "HNSW": + assert "INMEMORY NEIGHBOR GRAPH" in ddl + else: + assert "NEIGHBOR PARTITIONS" in ddl + if case["index_accuracy"] is not None: + assert f"WITH TARGET ACCURACY {case['index_accuracy']}" in ddl + if case["index_parameters"]: + assert "PARAMETERS (" in ddl + assert f"type {case['index_type']}" in ddl + else: + assert "PARAMETERS (" not in ddl + + tables = oracle_db.list_cols() + target = oracle_db.collection_name.strip('"').upper() + assert target in [t.upper() for t in tables] + finally: + try: + oracle_db.delete_col() + except Exception: + pass + + +def test_create_col_with_index_smoke(): + case = REPRESENTATIVE_CASES[1] + oracle_db = _build_oracle_db(case, do_create_index=True) + + try: + tables = oracle_db.list_cols() + target = oracle_db.collection_name.strip('"').upper() + assert target in [t.upper() for t in tables] + finally: + try: + oracle_db.delete_col() + except Exception: + pass + + +def test_index_parameters_are_structured_and_allowlisted(): + conn_params = {"user": ORACLE_USER, "password": ORACLE_PASSWORD, "dsn": ORACLE_DSN} + collection_name = _unique_collection_name() + oracle_db = OracleAIVectorSearch( + collection_name=collection_name, + embedding_model_dims=DIM, + connection_params=conn_params, + do_create_index=False, + index_type="HNSW", + index_parameters={"neighbors": 40, "efconstruction": 64}, + ) + + try: + ddl = oracle_db._create_index_ddl() + assert "PARAMETERS (type HNSW, neighbors 40, efconstruction 64)" in ddl + finally: + oracle_db.delete_col() + + +def test_ivf_index_parameters_are_structured_and_allowlisted(): + conn_params = {"user": ORACLE_USER, "password": ORACLE_PASSWORD, "dsn": ORACLE_DSN} + collection_name = _unique_collection_name() + oracle_db = OracleAIVectorSearch( + collection_name=collection_name, + embedding_model_dims=DIM, + connection_params=conn_params, + do_create_index=False, + index_type="IVF", + index_parameters={ + "neighbor_partitions": 10, + "samples_per_partition": 4, + "min_vectors_per_partition": 2, + }, + ) + + try: + ddl = oracle_db._create_index_ddl() + assert ( + "PARAMETERS (type IVF, neighbor partitions 10, samples_per_partition 4, " + "min_vectors_per_partition 2)" + ) in ddl + finally: + oracle_db.delete_col() + + +def test_index_parameters_reject_unsupported_fragments(): + conn_params = {"user": ORACLE_USER, "password": ORACLE_PASSWORD, "dsn": ORACLE_DSN} + + with pytest.raises(ValueError, match="unsupported keys"): + OracleAIVectorSearch( + collection_name=_unique_collection_name(), + embedding_model_dims=DIM, + connection_params=conn_params, + do_create_index=False, + index_type="HNSW", + index_parameters={"parallel": "8 NOLOGGING"}, + ) + + with pytest.raises(ValueError, match="must be an integer"): + OracleAIVectorSearch( + collection_name=_unique_collection_name(), + embedding_model_dims=DIM, + connection_params=conn_params, + do_create_index=False, + index_type="IVF", + index_parameters={"neighbor_partitions": "10) PARALLEL 8"}, + ) + + +def test_index_parameters_reject_non_string_keys(): + with pytest.raises(ValueError, match="keys must be strings"): + OracleAIVectorSearchConfig( + collection_name=_unique_collection_name(), + embedding_model_dims=DIM, + client=object(), + index_type="HNSW", + index_parameters={1: 10}, + ) + + +def test_index_parameters_canonicalize_int_subclasses(): + class FormattedInt(int): + def __format__(self, format_spec): + return "40) PARALLEL 8" + + config = OracleAIVectorSearchConfig( + collection_name=_unique_collection_name(), + embedding_model_dims=DIM, + client=object(), + index_type="HNSW", + index_parameters={"neighbors": FormattedInt(40)}, + ) + oracle_db = object.__new__(OracleAIVectorSearch) + oracle_db.config = config + oracle_db.collection_name = config.collection_name + + ddl = oracle_db._create_index_ddl() + assert type(config.index_parameters["neighbors"]) is int + assert "PARALLEL 8" not in ddl + assert "PARAMETERS (type HNSW, neighbors 40)" in ddl + + +def test_index_parameters_reject_post_validation_mutation(): + config = OracleAIVectorSearchConfig( + collection_name=_unique_collection_name(), + embedding_model_dims=DIM, + client=object(), + index_type="HNSW", + index_parameters={"neighbors": 40}, + ) + oracle_db = object.__new__(OracleAIVectorSearch) + oracle_db.config = config + oracle_db.collection_name = config.collection_name + + config.index_parameters["neighbors"] = "40) PARALLEL 8" + + with pytest.raises(ValueError, match="must be an integer"): + oracle_db._create_index_ddl() + + +def test_insert_and_get(oracle_db: OracleAIVectorSearch): + vectors = [[0.1] * DIM, [0.2] * DIM] + payloads = [{"name": "vector1"}, {"name": "vector2"}] + + oracle_db.insert(vectors, payloads=payloads) + + listed = _unwrap_results(oracle_db.list(limit=10)) + assert len(listed) >= 2 + seen_names = {item.payload.get("name") for item in listed} + assert {"vector1", "vector2"}.issubset(seen_names) + + # Fetch one by id (Oracle RAW(16) id is generated by DB) + some_id = listed[0].id + got = oracle_db.get(vector_id=some_id) + assert got is not None + assert got.id == some_id + assert isinstance(got.payload, dict) + + +def test_search(oracle_db: OracleAIVectorSearch): + # Create predictable geometry; works for COSINE or EUCLIDEAN + pos_vec = [1.0] * DIM + neg_vec = [-1.0] * DIM + mid_vec = [1.0 if i % 2 == 0 else 0.0 for i in range(DIM)] + payloads = [ + {"name": "pos", "user_id": "u1"}, + {"name": "neg", "user_id": "u2"}, + {"name": "mid", "user_id": "u3"}, + ] + oracle_db.insert([pos_vec, neg_vec, mid_vec], payloads=payloads) + + results = oracle_db.search("unused", vectors=pos_vec, limit=3) + assert isinstance(results, list) + assert len(results) >= 1 + + names = {r.payload.get("name") for r in results} + assert "pos" in names # closest to query + + +def test_search_with_filters(oracle_db: OracleAIVectorSearch): + vec = [0.5] * DIM + payloads = [ + {"name": "a", "user_id": "alice", "agent_id": "agent1", "run_id": "run1"}, + {"name": "b", "user_id": "bob", "agent_id": "agent2", "run_id": "run2"}, + ] + oracle_db.insert([vec, vec], payloads=payloads) + + filters = {"user_id": "alice", "agent_id": "agent1", "run_id": "run1"} + results = oracle_db.search("unused", vectors=vec, limit=5, filters=filters) + + assert len(results) >= 1 + for r in results: + assert r.payload.get("user_id") == "alice" + assert r.payload.get("agent_id") == "agent1" + assert r.payload.get("run_id") == "run1" + + +def test_search_with_single_filter(oracle_db: OracleAIVectorSearch): + vec = [0.7] * DIM + payloads = [ + {"name": "x", "user_id": "alice"}, + {"name": "y", "user_id": "bob"}, + ] + oracle_db.insert([vec, vec], payloads=payloads) + + results = oracle_db.search("unused", vectors=vec, limit=5, filters={"user_id": "alice"}) + assert len(results) >= 1 + for r in results: + assert r.payload.get("user_id") == "alice" + + +def test_search_with_no_filters(oracle_db: OracleAIVectorSearch): + vec = [0.33] * DIM + oracle_db.insert([vec], payloads=[{"k": "v"}]) + + results = oracle_db.search("unused", vectors=vec, limit=1, filters=None) + assert len(results) == 1 + + +def test_delete(oracle_db: OracleAIVectorSearch): + vec = [0.9] * DIM + oracle_db.insert([vec], payloads=[{"name": "to_delete"}]) + + listed = _unwrap_results(oracle_db.list(limit=10)) + assert len(listed) >= 1 + target_id = listed[0].id + + oracle_db.delete(vector_id=target_id) + got = oracle_db.get(vector_id=target_id) + assert got is None + + +def test_update(oracle_db: OracleAIVectorSearch): + vec = [0.01] * DIM + oracle_db.insert([vec], payloads=[{"name": "old"}]) + + listed = _unwrap_results(oracle_db.list(limit=10)) + assert len(listed) >= 1 + target_id = listed[0].id + + updated_vec = [0.02] * DIM + updated_payload = {"name": "new"} + oracle_db.update(vector_id=target_id, vector=updated_vec, payload=updated_payload) + + got = oracle_db.get(vector_id=target_id) + assert got is not None + assert got.payload.get("name") == "new" + + +def test_list_cols(oracle_db: OracleAIVectorSearch): + tables = oracle_db.list_cols() + target = oracle_db.collection_name.strip('"').upper() + assert target in [t.upper() for t in tables] + + +def test_delete_col_isolated(oracle_db: OracleAIVectorSearch): + # Use a separate, isolated collection to test drop; reuse current fixture's metric/index options + collection_name = _unique_collection_name() + cfg: Dict[str, Any] = { + "collection_name": collection_name, + "embedding_model_dims": DIM, + "distance_metric": oracle_db.config.distance_metric, + "index_type": oracle_db.config.index_type, + "do_create_index": False, + "connection_params": {"user": ORACLE_USER, "password": ORACLE_PASSWORD, "dsn": ORACLE_DSN}, + } + + # If the fixture used a pool object, pass it as well. + if getattr(oracle_db.config, "client", None): + cfg["client"] = oracle_db.config.client + + tmp_db = OracleAIVectorSearch(**cfg) + + tgt = tmp_db.collection_name.strip('"').upper() + tables_before = [t.upper() for t in tmp_db.list_cols()] + assert tgt in tables_before + + tmp_db.delete_col() + + tables_after = [t.upper() for t in tmp_db.list_cols()] + assert tgt not in tables_after + + +def test_col_info(oracle_db: OracleAIVectorSearch): + info = oracle_db.col_info() + # Structure sanity checks; exact values depend on DB state + assert isinstance(info, dict) + assert "name" in info and "count" in info and "size" in info + + +def test_list(oracle_db: OracleAIVectorSearch): + v1, v2 = [0.11] * DIM, [0.22] * DIM + oracle_db.insert([v1, v2], payloads=[{"key": "value1"}, {"key": "value2"}]) + + results = _unwrap_results(oracle_db.list(limit=2)) + assert len(results) <= 2 + # Both inserted might be returned if table had no prior rows + if len(results) == 2: + payloads = [r.payload for r in results] + keys = {p.get("key") for p in payloads} + assert keys.issubset({"value1", "value2"}) + + +def test_list_with_filters(oracle_db: OracleAIVectorSearch): + v = [0.44] * DIM + oracle_db.insert( + [v, v], + payloads=[ + {"user_id": "alice", "agent_id": "agent1", "run_id": "run1"}, + {"user_id": "bob", "agent_id": "agent2", "run_id": "run2"}, + ], + ) + + filters = {"user_id": "alice", "agent_id": "agent1", "run_id": "run1"} + results = _unwrap_results(oracle_db.list(filters=filters, limit=10)) + assert len(results) >= 1 + for r in results: + assert r.payload.get("user_id") == "alice" + assert r.payload.get("agent_id") == "agent1" + assert r.payload.get("run_id") == "run1" + + +def test_list_with_single_filter(oracle_db: OracleAIVectorSearch): + v = [0.55] * DIM + oracle_db.insert( + [v, v], + payloads=[ + {"user_id": "alice"}, + {"user_id": "bob"}, + ], + ) + + results = _unwrap_results(oracle_db.list(filters={"user_id": "alice"}, limit=10)) + assert len(results) >= 1 + for r in results: + assert r.payload.get("user_id") == "alice" + + +def test_list_with_no_filters(oracle_db: OracleAIVectorSearch): + v = [0.66] * DIM + oracle_db.insert([v], payloads=[{"k": "v"}]) + + results = _unwrap_results(oracle_db.list(filters=None, limit=10)) + assert len(results) >= 1 + + +def test_list_returns_flat_output(oracle_db: OracleAIVectorSearch): + oracle_db.insert([[0.12] * DIM], payloads=[{"name": "flat"}]) + + results = oracle_db.list(limit=10) + + assert isinstance(results, list) + assert results + assert not isinstance(results[0], list) + assert results[0].payload["name"] == "flat" + + +def test_update_accepts_empty_payload(oracle_db: OracleAIVectorSearch): + oracle_db.insert([[0.21] * DIM], payloads=[{"name": "before"}], ids=["row-1"]) + + oracle_db.update("row-1", payload={}) + + result = oracle_db.get("row-1") + assert result is not None + assert result.payload == {} + + +def test_does_not_close_caller_supplied_pool(): + pool = oracledb.create_pool( + min=1, + max=2, + user=ORACLE_USER, + password=ORACLE_PASSWORD, + dsn=ORACLE_DSN, + ) + db = OracleAIVectorSearch( + collection_name=_unique_collection_name(), + embedding_model_dims=4, + do_create_index=False, + client=pool, + ) + + try: + db.__del__() + with pool.acquire() as conn: + with conn.cursor() as cursor: + cursor.execute("SELECT 1 FROM dual") + assert cursor.fetchone()[0] == 1 + finally: + try: + db.delete_col() + finally: + pool.close() + + +def test_documentation(): + from mem0 import Memory + + if not os.environ.get("OPENAI_API_KEY"): + pytest.skip("OPENAI_API_KEY is required for the end-to-end documentation test") + + config = { + "vector_store": { + "provider": "oracledb", + "config": { + "connection_params": {"user": ORACLE_USER, "password": ORACLE_PASSWORD, "dsn": ORACLE_DSN}, + "do_create_index": False, + }, + }, + } + + m = Memory.from_config(config) + messages = [ + {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"}, + {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."}, + {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."}, + { + "role": "assistant", + "content": "Got it! 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