RAG / 向量检索排行

Open-source, end-to-end platform for evaluating, observing, and improving LLM and AI agent applications. Tracing · Evals · Simulations · Datasets · Gateway · Guardrails. Self-hostable. Apache 2.0.

Collecting awesome papers of RAG for AIGC. We propose a taxonomy of RAG foundations, enhancements, and applications in paper "Retrieval-Augmented Generation for AI-Generated Content: A Survey".

WFGY is heading toward WFGY 5.0 Polaris Protocol, a major open-source release for AI reasoning, RAG, agents, and real-world workflows. Includes Problem Map, Global Debug Card, WFGY 4.0, and the CFV Easter Egg.

Fast and efficient unstructured data extraction. Written in Rust with bindings for many languages.

Fast BM25 search in Python, powered by Numpy and Numba

The official implementation of RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval

Agentic-RAG explores advanced Retrieval-Augmented Generation systems enhanced with AI LLM agents.

A Go framework for building production agent systems with graph workflows, tools, memory, A2A, AG-UI, MCP, evaluation, and observability.

A curated collection of AI agent research papers released in 2026, covering agent engineering, memory, evaluation, workflows, and autonomous systems.

Create characters in Unity with LLMs!

Building blocks for rapid development of GenAI applications

Trench — Open-Source Analytics Infrastructure. A single production-ready Docker image built on ClickHouse, Kafka, and Node.js for tracking events. Easily build product analytics dashboards, LLM RAGs, observability platforms, or any other analytics product.

This repository provides an advanced Retrieval-Augmented Generation (RAG) solution for complex question answering. It uses sophisticated graph based algorithm to handle the tasks.

WikiChat is an improved RAG. It stops the hallucination of large language models by retrieving data from a corpus.

A project-based course repository for developing AI agents using LangChain v1+ and LangGraph: search agents, RAG systems, reflection agents, and code interpreters.

Demystify RAG by building it from scratch. Local LLMs, no black boxes - real understanding of embeddings, vector search, retrieval, and context-augmented generation.

Hivemind turns your traces into reusable skills across agents

[EMNLP 2025 Oral] MemoryOS is designed to provide a memory operating system for personalized AI agents.

Talk to your Mac, query your docs, no cloud required. On-device voice AI + RAG

A toolkit to create optimal Production-readyRetrieval Augmented Generation(RAG) setup for your data