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document-qa

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⚡ Production-ready .NET Standard 2.0/2.1 RAG library with 🤖 multi-AI provider support, 🏢 enterprise vector storage, and 📄 intelligent document processing. 🌍 Cross-platform compatible.

  • Updated Aug 28, 2025
  • C#

AI assistant backend for document-based question answering using RAG (LangChain, OpenAI, FastAPI, ChromaDB). Features modular architecture, multi-tool agents, conversational memory, semantic search, PDF/Docx/Markdown processing, and production-ready deployment with Docker.

  • Updated Aug 27, 2025
  • Python

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.

  • Updated Aug 11, 2025
  • Python

AI-powered commission plan assistant featuring advanced RAG pipeline, Model Context Protocol (MCP) PostgreSQL server integration, multi-format document processing, and secure SELECT-only database operations. Guided 3-phase plan creation with conversational interface.

  • Updated Aug 24, 2025
  • Python

🔍 Agentic AI system that allows users to upload documents (PDFs, DOCX, etc.) and natural language questions. It uses LLM-based RAG to extract relevant information. The architecture includes multi-agent components such as document retrievers, summarizers, web searchers, and tool routers — enabling dynamic reasoning and accurate responses.

  • Updated Aug 10, 2025
  • Jupyter Notebook

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