RAG / 向量检索排行

Agentic AI framework built using LangGraph and Multi-Agent Control Plane (MCP) for building structured, goal-driven multi-agent systems.

Telegram bot that answers questions over your PDFs using retrieval-augmented generation (LangChain + FAISS).

Pull high-quality, efficient embeddings for PubMed, arXiv and Wikipedia from Huggingface and use for local LLM inference/Retrieval Augmented Generation (RAG)

A modular, high-performance Retrieval-Augmented Generation framework with multi-path retrieval, graph extraction, and fusion ranking

Code repository for AI Builders Bootcamp #2

👨🏻‍💻 Meet Lumina – my personal AI assistant powered by hybrid RAG with Pinecone vector search and Neo4j graph traversal, Google AI, LangChain, and an MCP server with 30+ tools. Features real-time streaming, conversation branching, and a multi-agent agentic AI pipeline for intelligent, grounded responses with inline citations.

Interpretable text embeddings by asking LLMs yes/no questions (NeurIPS 2024)

A curated list of PHP libraries and tools for AI, Machine Learning, and Natural Language Processing. Find everything you need to integrate modern AI capabilities into your PHP applications.

The whole local AI stack in one executable: it runs and manages local AI models across every GPU, and it's a search engine you can talk to, with cited answers from your files, code, and the web. MCP server for coding agents, web crawler, TUI, CLI, REST API, Python library. No Ollama or LM Studio needed, works with both.

🎯 Your free LLM evaluation toolkit helps you assess the accuracy of facts, how well it understands context, its tone, and more. This helps you see how good your LLM applications are.

An 18 notebook course that isolates and measures each component of agentic loop engineering on real, industry standard software datasets.

Layered guardrails to make agentic AI safer and more reliable.

YiShape Vector Database is a specialized vector database management system and application assistance system for new artificial intelligence applications such as large language models, natural language processing, and image retrieval.

Drop-in AI memory layer with 2x faster response and 10x lower cost. Fully compatible with Mem0 API. Migrate in 5 minutes without any code changes. Self-host for free.

'Talk to your slide deck' (Multimodal RAG) using foundation models (FMs) hosted on Amazon Bedrock and Amazon SageMaker

Improving langchain knowledge graphs using baml

学习基于langchaingo结合ollama实现的rag应用流程

Bounded, inspectable LLM inference pipelines from declared YAML — runs offline against Ollama or any OpenAI-compatible local server, emitting JSONL traces, inspect reports, and stable exit codes.

A practical guide to AI: from running your first local model to building your own agents. 52 files covering LLMs, Ollama, RAG, prompt engineering, machine learning, AI agents, and local AI tools.

Build LLM systems you actually control. A free, open engineering book + course — from tokenization to serving your own models. Mechanisms, trade-offs, and numbers, not prompt tips