RAG based Document Assistant for Search
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Updated
Feb 19, 2026 - Python
RAG based Document Assistant for Search
🤖 Agentic RAG - An intelligent document assistant with semantic chunking, hybrid search (vector + BM25), multi-format support (PDF, Word, CSV, Excel), and multi-channel bots (Telegram, WhatsApp). Built with FastAPI, React, PostgreSQL/pgvector, and LangChain.
RAG-based AI document assistant using Spring Boot, Spring AI, Ollama, and React for intelligent document Q&A.
🧠 Secure AI Assistant to chat with your documents. Isolated vector data storage with full citation tracing built using FastAPI and Flutter.
This repository contains materials for studying and implementing Agentic AI from ReadyTensor.
Local-first private document assistant for macOS
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NovaDocs is a multi-workspace document assistant. Upload text, Markdown, or PDF files into isolated workspaces, then ask questions grounded in your own content. Answers include citations, honest refusals when the corpus does not support a claim, and optional tools to save tasks or post Slack summaries.
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A lightweight Document Q&A chatbot using RAG. Built from scratch with FastAPI, pgvector, and local (Ollama) or remote (OpenAI) LLMs.
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Full-stack AI document Q&A app — upload PDFs (+ scanned OCR), chat with documents using local LLM (RAG pipeline). Built with Next.js, FastAPI, MongoDB Atlas & Ollama.
Small AI document assistant prototype using Python, Streamlit, prompt design, and basic document retrieval.
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