RAG / 向量检索排行

Privacy-first document intelligence engine — parse PDFs, DOCX, PPTX, XLSX & CSV into AI-ready chunks for RAG pipelines. Includes HITL review, 3-layer memory chat, and a production FastAPI server.

Multi-model AI agent runtime. Define agents in YAML, route each role to a model, orchestrate with 7 patterns (ReAct, Plan & Execute, Fan-Out, Pipeline, Supervisor, Swarm, Glyph), and deploy as a REST/WebSocket API with RAG, memory, MCP tools, guardrails and OpenTelemetry observability.

Anthropic's Contextual Retrieval implementation with visual chunk comparison. Preview context enrichment before/after embedding.

Versioned, fast and scalable nearest neighbor search.

Knowledge for GPTScript

A tool for summarizing search results and website content using FAISS, LLMs, and the Retrieval-Augmented Generation (RAG) technique.

Production-ready RAG framework for Python — multi-tenant chatbots with streaming, tool calling, agent mode (LangGraph), vector search (FAISS), and persistent MongoDB memory. Built on LangChain.

Un framework in Italiano ed Inglese, che permette di chattare con i propri documenti in RAG, anche multimediali (audio, video, immagini e OCR). It is an Italian and English framework, which allows you to chat with your documents in RAG, including multimedia (audio, video, images and OCR).

A pure-Python structured memory benchmark for multi-agent LLM systems — context graph vs vector RAG vs raw history dump, five scenarios, 18 graded queries, zero API calls.

Multi-Modal RAG for .NET — query databases, documents, images and audio in natural language. Production-ready with multi-AI support, vector storage, and multi-database coordination.

Self-hosted multitenant platform for AI agents and Telegram bots with event-driven architecture

ContextAtlas — context infrastructure for AI coding agents: hybrid retrieval, project memory and retrieval observability via CLI, MCP server or embeddable library. Tree-sitter indexing, LanceDB vector search, FTS5 and token-aware context packing.

Train linear embedding adapters with triplet loss to align retrieval embeddings with your queries (RAG).

LightweightEmbeddings is a fast, free, and unlimited API service for multilingual embeddings and reranking, with support for both text and images and guaranteed uptime.

Absurdly fast embedded graph database with built-in vector search. Rust core, Node.js and Python connectors, GQL, graph algorithms, all in-process with no server.

Use OpenAI, Redis, and streamlit to recommend hotels using Large Language Models

Simple, High Quality, Open Source RAG solution for chatting with your documents

An advanced Self-Centered Intelligence (SCI) prototype that represents a new paradigm in AI-human interaction.

[CVPR 2026 Highlight] ReAG: Reasoning-Augmented Generation for Knowledge-based Visual Question Answering

Self-hosted, private family AI health steward. Multimodal AI structures health data into a person-centric profile with dashboard & AI consultation (Feishu/Lark integration). 开源可私有化部署的家庭 AI 健康管家：多模态大模型 + 健康画像看板 + AI 咨询 + 飞书渠道。