This full-stack application implements RAG a technique that combines semantic search with LLM-powered generation to create AI systems that are accurate, traceable, and production-ready.
-
Updated
Jul 4, 2026 - Python
This full-stack application implements RAG a technique that combines semantic search with LLM-powered generation to create AI systems that are accurate, traceable, and production-ready.
An AI-powered assistant that lets you ask natural language questions about Pakistan's National AI Policy 2025. Built with FastAPI, LangChain, FAISS vector search, and Groq's LLaMA 3.1 for fast, accurate, context-aware answers.
A web app that lets you chat with your CSV files using natural language. Upload a CSV, and the RAG pipeline — built with FastAPI, LangChain, Groq, HuggingFace embeddings, and FAISS — indexes your data and answers questions instantly. No SQL, no formulas, just ask.
🏛️ Local AI Document Manager — Convert PDF/DOCX/PPTX/URLs to Markdown, organize in folders, and chat with your documents using a privacy-first DeepAgents assistant. 100% local, zero data leaves your machine.
Local RAG chat app: FastAPI + LlamaIndex + Ollama (deepseek-r1:1.5b). JWT auth, multi-session chat with persistent memory, attachable documents (manual chunking + Chroma), three working function-calls (time/weather/currency). React + MUI frontend.
Self-contained document search & grounded AI chat engine in pure TypeScript. Lexical BM25 retrieval (no FAISS/embeddings). npm: @kordabjinan/deeppipe
AI-powered document Q&A system using RAG
AI-powered document analysis demo. Upload files and interact with their content through natural language. Built with a premium UX, security, and intelligent data extraction in mind.
A Retrieval-Augmented Generation (RAG) based chatbot that allows users to upload PDF documents and ask questions in natural language. The system retrieves relevant context from documents and uses Google Gemini AI to generate accurate, context-aware answers.
Add a description, image, and links to the chat-with-documents topic page so that developers can more easily learn about it.
To associate your repository with the chat-with-documents topic, visit your repo's landing page and select "manage topics."