RAG / 向量检索排行

Experimental RAG playground for exploring retrieval quality, corpus construction, and filter-chain design. Features configurable ranking and filtering pipelines, visual document grounding, chat interfaces, web search, Open WebUI integration, and rich debugging insights. Ollama and vLLM, running in Dev Containers or natively on Linux and Windows.

A semantic code search tool for intelligent, cross-repo context retrieval.

High-performance infrastructure libraries built in Rust, with seamless bindings for Python, Node.js, and WebAssembly.

A structured learning repo for retrieval-augmented generation, from foundations to production patterns.

It shows how to use mutimodal and RAG based on multi-region LLM.

a simple lightweight large language model pipeline framework.

Turn documents into high-quality instruction datasets with grounding, quality filtering, deduplication, and provenance.

A complete, hands-on roadmap to becoming an AI Engineer, from Python basics to production RAG, Agents, and Fine-Tuning. 10 modules, 20+ notebooks, 15 real projects. Free, open source, built for the community.

Advanced Coding AI Assistant that uses a Gradio interface to stream coding related responses. ChatRAG supports local and API inference and pulls context from GitHub Repos and local files.

SQL Server as a vector database, SQL Server Extenstion for RAG

AI assistant for Dota 2 with real-time game analysis via GSI and advice from LLM

Local-first vector similarity search for Jazz Tools

🧠 Stop building AI that forgets. Master MCP (Model Context Protocol) with production-ready semantic memory, hybrid RAG, and the WARNERCO Schematica teaching app. FastMCP + LangGraph + Vector/Graph stores. Your AI assistant's long-term memory starts here.

A RAG system designed to process documents with multimodal content. It can generate factual, context-aware answers to user queries, based on the documents texts, tables, figures, ...

Local NotebookLM for Claude Code via Google Antigravity (agy / Gemini 3.x): /agy:notebook turns a folder of documents into per-doc summaries + a relevance index + a cited synthesis + Q&A. Plus audio/video transcription, deep web research with citations & branded HTML reports. 13 commands, no Node runtime.

This project is the backend engine for a fully autonomous AI-powered call center. It integrates a large language model (LLM), speech recognition, and text-to-speech to manage real-time phone conversations via Asterisk.

Fine-tuning black-box OpenAI embedding models

META LLAMA3 GENAI Real World UseCases End To End Implementation Guide

Production-ready multi-agent platform delivering orchestration, session governance and audit capabilities. Stack: Go/Python/Rust microservices, multi-type data storage, Next.js admin UI, automated CI/CD pipelines, AI code review triggered via PR comment mentions.

Search knowledge by what documents mean and how they look — not one or the other.