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--- |
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title: ContextAI |
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emoji: 🤖 |
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colorFrom: blue |
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colorTo: green |
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sdk: docker |
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sdk_version: "1.0" |
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app_file: app.main |
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pinned: false |
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--- |
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# ContextAI |
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A **FastAPI-based RAG application** that lets users upload documents (PDF/TXT) and ask questions. |
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Powered by **LangChain**, **ChromaDB**, and **LLMs** for context-aware answers. |
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## 📚 FastAPI RAG App with LangChain, ChromaDB & Authentication |
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This project is a Retrieval-Augmented Generation (RAG) web application built with FastAPI. |
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It allows users to: |
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- 🔑 Sign up / Sign in (JWT-based authentication) |
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- 📂 Upload PDF or text documents |
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- 🧠 Store document embeddings in ChromaDB (vector database) |
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- 💬 Ask questions about uploaded documents |
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- ⚡ Get context-aware answers powered by LangChain + LLMs (via OpenRouter |
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) |
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## 🚀 Features |
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- User authentication with access & refresh tokens |
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- Secure file uploads (.pdf, .txt) |
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- Automatic text chunking & embedding with HuggingFace models |
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- Persistent vector store using ChromaDB |
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- RAG pipeline with LangChain’s RetrievalQA |
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- OpenRouter integration for running LLM queries |
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- CORS configured for frontend integration |
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## 🛠️ Tech Stack |
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- FastAPI |
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- LangChain |
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- ChromaDB |
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- SQLModel |
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- for user database |
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- HuggingFace Embeddings |
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- OpenRouter |
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- (for LLM access) |
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## 📂 Project Structure |
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````` |
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app/ |
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├── main.py # FastAPI routes & entrypoint |
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├── rag.py # RAG pipeline (embeddings, vector store, QA chain) |
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├── models.py # User models & schemas |
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├── auth.py # Auth logic (hashing, tokens, verification) |
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├── database.py # SQLModel setup |
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└── config.py # Settings & constants |
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uploads/ # User uploaded files (ignored in Git) |
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chroma_db/ # Vector DB storage (ignored in Git) |
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````` |
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## ⚙️ Setup & Installation |
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1️⃣ Clone the repo |
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- git clone https://github.com/your-username/fastapi-rag-app.git |
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- cd fastapi-rag-app |
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2️⃣ Create & activate virtual environment |
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- python -m venv venv |
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- source venv/bin/activate # Linux/Mac |
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- venv\Scripts\activate # Windows |
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3️⃣ Install dependencies |
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- pip install -r requirements.txt |
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4️⃣ Configure environment variables |
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- Create a .env file in the project root (or copy from .env.example): |
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- ### OpenRouter |
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- OPENROUTER=your_openrouter_api_key_here |
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- ### JWT secret |
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- SECRET_KEY=your_super_secret_key |
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⚠️ Never commit your real .env file. |
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▶️ Run the App |
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- Start the FastAPI server: |
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- uvicorn app.main:app --reload |
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- The API will be available at: |
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- 👉 http://127.0.0.1:8000 |
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## 🔑 Authentication Flow |
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- Signup → POST /signup with username & password |
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- Signin → POST /signin to receive access_token & refresh_token |
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- Use Authorization: Bearer <access_token> for protected endpoints |
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## 📂 Document Workflow |
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- User logs in |
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- Upload document → POST /upload (PDF or TXT) |
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- Ask a question → GET /ask?q=your+question |
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- The system searches your embeddings in ChromaDB and queries the LLM with context |
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## 📝 Notes |
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- uploads/ and chroma_db/ are auto-created at runtime if they don’t exist. |
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- Both folders are ignored by Git (runtime data only). |
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- Contributions & pull requests are welcome 🚀 |
