ContextAI
A FastAPI-based RAG application that lets users upload documents (PDF/TXT) and ask questions.
Powered by LangChain, ChromaDB, and LLMs for context-aware answers.
📚 FastAPI RAG App with LangChain, ChromaDB & Authentication
This project is a Retrieval-Augmented Generation (RAG) web application built with FastAPI. It allows users to:
🔑 Sign up / Sign in (JWT-based authentication)
📂 Upload PDF or text documents
🧠 Store document embeddings in ChromaDB (vector database)
💬 Ask questions about uploaded documents
⚡ Get context-aware answers powered by LangChain + LLMs (via OpenRouter )
🚀 Features
User authentication with access & refresh tokens
Secure file uploads (.pdf, .txt)
Automatic text chunking & embedding with HuggingFace models
Persistent vector store using ChromaDB
RAG pipeline with LangChain’s RetrievalQA
OpenRouter integration for running LLM queries
CORS configured for frontend integration
🛠️ Tech Stack
FastAPI
LangChain
ChromaDB
SQLModel for user database
HuggingFace Embeddings
OpenRouter (for LLM access)
📂 Project Structure app/ ├── main.py # FastAPI routes & entrypoint ├── rag.py # RAG pipeline (embeddings, vector store, QA chain) ├── models.py # User models & schemas ├── auth.py # Auth logic (hashing, tokens, verification) ├── database.py # SQLModel setup └── config.py # Settings & constants uploads/ # User uploaded files (ignored in Git) chroma_db/ # Vector DB storage (ignored in Git)
⚙️ Setup & Installation 1️⃣ Clone the repo git clone https://github.com/your-username/fastapi-rag-app.git cd fastapi-rag-app
2️⃣ Create & activate virtual environment python -m venv venv source venv/bin/activate # Linux/Mac venv\Scripts\activate # Windows
3️⃣ Install dependencies pip install -r requirements.txt
4️⃣ Configure environment variables
Create a .env file in the project root (or copy from .env.example):
OpenRouter
OPENROUTER=your_openrouter_api_key_here
JWT secret
SECRET_KEY=your_super_secret_key
⚠️ Never commit your real .env file.
▶️ Run the App
Start the FastAPI server:
uvicorn app.main:app --reload
The API will be available at: 👉 http://127.0.0.1:8000
Interactive API docs: 👉 http://127.0.0.1:8000/docs
🔑 Authentication Flow
Signup → POST /signup with username & password
Signin → POST /signin to receive access_token & refresh_token
Use Authorization: Bearer for protected endpoints
📂 Document Workflow
User logs in
Upload document → POST /upload (PDF or TXT)
Ask a question → GET /ask?q=your+question
The system searches your embeddings in ChromaDB and queries the LLM with context
📝 Notes
uploads/ and chroma_db/ are auto-created at runtime if they don’t exist.
Both folders are ignored by Git (runtime data only).
Contributions & pull requests are welcome 🚀