📄 🤖 AI for medical and scientific papers
Building blocks for rapid development of GenAI applications
小遥搜索，听懂你的话、看懂你的图，用AI找到本地任何文件。让搜索像聊天一样简单。XiaoyaoSearch: Understands your words, reads your images, finds any local file with AI. Making search as easy as chatting.
🥥 Coco AI App - Search, Connect, Collaborate, Personal AI Search and Assistant, all in one space.
VectorRAG.Net is a .NET-native high-performance vector database library for semantic search and RAG (Retrieval-Augmented Generation). Core search is based on Random Hyperplane LSH candidate generation with exact rerank by dot/cosine.
Anything — 파일을 찾지 말고, 내용을 찾으세요. HWPX · PDF · Office 수천 건의 본문을 1초 만에. 100% 로컬 · 완전 오프라인 · AI Q&A 옵션 (Tauri · React · Rust) | Local content search for Korean PCs — HWP-first, fully offline
A lightweight, production-ready RAG (Retrieval Augmented Generation) library in Go.
Smart local file search app that understands your files
A JavaScript library that brings vector search and RAG to your browser!
Vector search demo with the arXiv paper dataset, RedisVL, HuggingFace, OpenAI, Cohere, FastAPI, React, and Redis.
🥥 Coco AI Server - Search, Connect, Collaborate, AI-powered Enterprise Search, all in one space.
This open source chatbot project lets you create a chatbot that uses your own data to answer questions, thanks to the power of the OpenAI GPT-3.5 model.
BM25 search implemented in PL/pgSQL
Self-hosted RAG search engine — 34 formats, BM25+hybrid search, multi-LLM (Gemini/OpenAI/Claude/Ollama), FastAPI + Docker, production-ready in 3 min
[knowledge-rag] - Drop docs, search instantly from Claude Code — 12 MCP tools, 20 format parsers, hybrid search + reranking. Zero servers, zero API keys, 100% local.
Local AI-powered document search and editing with first-in-class hybrid retrieval, LLM answers, WebUI, REST API and MCP support for AI clients.
An in-memory NoSQL database implemented in Python.
Search through all your personal data efficiently like web search.
Self-hosted RAG platform for AI document search across GitHub, Notion, Google Drive, local files, and web sources with citations.
Finding all pairs of similar documents time- and memory-efficiently
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