Running Llama 2 and other Open-Source LLMs on CPU Inference Locally for Document Q&A

🧠 纯原生 Python 实现的 RAG 框架 | FAISS + BM25 混合检索 | 支持 Ollama / SiliconFlow | 适合新手入门学习

Method for Long Context RLMs using verifiable Lambda Calculus

Open source RAG tool for AI document search - connect GitHub, Notion, Google Drive and ask questions with cited answers. Self-hosted with Ollama/OpenAI/Claude.

PDFs you can talk to.

Document Q&A chatbot

This Repositry is an experiment with an agent that searches documents and asks questions repeatedly in response to the main question. It automatically determines the optimal answer from the current documents or recognizes when there is no answer.

Open-source RAG engine for ingesting, indexing, and querying unstructured documents

Frank Bot — RAG-powered AI assistant for any business. Built on ChromaDB + Claude. Drop in your docs, ask Frank anything.

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.

Enterprise-grade RAG and document search system for extracting reliable insights from real-world data.

AI powered troubleshooting for ground support equipment. Deterministic RAG pipeline that ingests OEM maintenance manuals, answers with cited sources, and refuses when the documentation doesn't support a claim. Runs fully on-premises, no cloud APIs

document retrieval and QA pipeline

🐋 DeepSeek-R1: Retrieval-Augmented Generation for Document Q&A 📄

An advanced, fully local, and GPU-accelerated RAG pipeline. Features a sophisticated LLM-based preprocessing engine, state-of-the-art Parent Document Retriever with RAG Fusion, and a modular, Hydra-configurable architecture. Built with LangChain, Ollama, and ChromaDB for 100% private, high-performance document Q&A.

A basic web interface for your personal Q&A bot with documents, based on KnowledgeGPT

AI-powered knowledge base chatbot with RAG. Upload docs, ask questions, get accurate answers with sources. Supports OpenAI/local LLMs.

ContextAgent is a production-ready AI assistant backend with RAG, LangChain, and FastAPI. It ingests documents, uses OpenAI embeddings, and stores vectors in ChromaDB 🐙

A full-stack RAG application that enables intelligent document Q&A. Upload PDFs, DOCX, or TXT files and ask questions powered by LangChain, ChromaDB, and Claude/GPT. Features smart chunking, semantic search, conversation memory, and source citations. Built with FastAPI & React + TypeScript.

An LLM-powered Slack bot built with Langchain.