Agentic RAG Chatbot

A powerful Retrieval-Augmented Generation (RAG) chatbot service built with FastAPI and LangChain.

Features

• Document ingestion and processing
• Vector-based semantic search
• Conversational memory
• RESTful API endpoints
• Document upload support
• Health check endpoint

Prerequisites

• Python 3.12 or higher
• OpenAI API key

Setup

1. Clone the repository:

git clone <repository-url>
cd chatbot-v2

2. Create a virtual environment and activate it:

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install dependencies:

pip install -e .

4. Create a .env file in the root directory with the following content:

OPENAI_API_KEY=your_openai_api_key_here
MODEL_NAME=gpt-3.5-turbo
EMBEDDING_MODEL=text-embedding-3-small
CHROMA_PERSIST_DIRECTORY=./data/chroma

Running the Application

Start the FastAPI server:

uvicorn app.main:app --reload

The API will be available at http://localhost:8000

API Endpoints

• POST /chat: Send a message to the chatbot

  {
    "message": "Your question here"
  }

• POST /upload: Upload a document for processing

  • Use multipart/form-data with a file field named "file"

• GET /health: Check the API health status

API Documentation

Once the server is running, you can access the interactive API documentation at:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

Project Structure

chatbot-v2/
├── app/
│   ├── __init__.py
│   ├── main.py
│   ├── config.py
│   ├── rag_chain.py
│   └── vector_store.py
├── data/
│   └── chroma/
├── uploads/
├── .env
├── pyproject.toml
└── README.md

Contributing

Feel free to submit issues and enhancement requests!

Additional Changes

The following code block was added to the main.py file:

app.mount("/static", StaticFiles(directory="static"), name="static")
templates = Jinja2Templates(directory="templates")

@app.get("/", response_class=HTMLResponse)
def serve_ui(request: Request):
    return templates.TemplateResponse("index.html", {"request": request})