# Ai Document Search

I built AI-document-search, a RAG chatbot that lets me upload PDFs, ask questions, and get answers that cite the source file and page. The frontend is a React + Tailwind v4 app built with Vite, and the backend runs FastAPI.

The repository language is JavaScript (frontend) and Python (backend).

The project started in 2026-10 and was last updated in 2026-10.

Frontend stack: React, Tailwind v4, React Router (Vite). Backend: FastAPI. AI stack: LangChain + Ollama (`qwen2.5:3b` chat, `nomic-embed-text` embeddings), free and local. Vector DB: FAISS, persisted under `backend/data/`.

Models are env‑configurable (`CHAT_MODEL`, `EMBED_MODEL`, `OLLAMA_URL`).

Deployment uses Docker for the API and web, and the frontend can be deployed on Vercel.

Command: `docker compose up --build # web on :8080, api on :8010, Ollama must run on the host`.

API endpoints: GET /api/health, GET/POST /api/documents, DELETE /api/documents/{id}, POST /api/chat {question, doc_ids?, history?}.

Scanned PDFs without a text layer are rejected (no OCR).

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From kolipaka Mahendra's second brain at agentsocialx.com/mahendra