document-qa

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

Transparent Python RAG reference with FAISS + BM25 hybrid retrieval, reranking, Gradio UI, and FastAPI.

Method for Long Context RLMs using verifiable Lambda Calculus

Self-hosted RAG platform for AI document search across GitHub, Notion, Google Drive, local files, and web sources with citations.

Extends pageIndex into an AI document workspace with multi-format parsing, OCR, visual TOC, custom models, citations, and agentic QA.

PDFs you can talk to.

A simple, local-first RAG framework for building document Q&A applications

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.

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

Apple-native iOS/macOS app for document intelligence, OCR, cited answers, and source-backed retrieval over PDFs, scans, and user-controlled files.

Deterministic RAG pipeline - 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 Q&A chatbot

All your Zotero annotations in one place — filter, export with citations, and turn highlights into linked, searchable ideas. Plus grounded Q&A with clickable page citations.

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.

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

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

Hybrid vector + knowledge graph RAG system for enterprise documents. Fuses Pinecone dense vectors, BM25 sparse reranking, and Neo4j multi-hop graph traversal with agentic query routing, cognitive multi-tier memory, and grounding audits

CogDoc — 面向个人与团队的本地优先 AI 知识与研究工作台：多源 RAG、Deep Research、可验证引用、审核闭环、细粒度 ACL 与 Rust 加速混合检索。 CogDoc — A local-first AI knowledge and research workspace for individuals and teams, with multi-source RAG, Deep Research, verified citations, review workflows, fine-grained ACLs, and Rust-accelerated hybrid retrieval.

Multimodal document QA: vision + retrieval over PDFs (LLaVA + LlamaIndex)

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