| # 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 <access_token> 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 🚀 |