Update README.md

README.md

@@ -1,24 +1,70 @@
 # Medical Chatbot API
 
-This is a FastAPI-based medical chatbot API that provides medical information and answers questions.
+A FastAPI-based medical chatbot API powered by LangChain and custom models.
 
-## API Endpoints
+## Configuration
 
-- `GET /health`: Health check endpoint
-- `POST /chat`: Chat endpoint for medical queries
+### Environment Variables
+
+Create a `.env` file with the following variables:
+
+```env
+QDRANT_URL=your_qdrant_url
+QDRANT_API_KEY=your_qdrant_api_key
+HF_TOKEN=your_huggingface_token
+```
+
+### Model Configuration
+
+The application expects the following model structure:
+```
+models/
+    ├── embeddings/
+    └── llm/
+```
+
+### Dependencies
 
-## Environment Variables
+Key dependencies include:
+- LangChain ecosystem
+- Qdrant for vector storage
+- Unsloth for optimized model loading
+- FastAPI for the web framework
 
-The following environment variables need to be set:
-- `QDRANT_URL`: URL for the Qdrant vector database
-- `QDRANT_API_KEY`: API key for Qdrant authentication
+## Development Setup
 
-## Development
+1. Install dependencies:
+```bash
+pip install -r requirements.txt
+```
+
+2. Run the development server:
+```bash
+uvicorn app.main:app --reload --host 0.0.0.0 --port 7860
+```
 
-To run locally:
+## Docker Deployment
 
+Build and run with Docker:
 ```bash
-docker compose up --build
+docker build -t medical-chatbot .
+docker run -p 7860:7860 --env-file .env medical-chatbot
 ```
 
-The API will be available at `http://localhost:7860`
+## API Endpoints
+
+- `GET /health`: Health check endpoint
+- `POST /chat`: Chat endpoint
+  ```json
+  {
+    "question": "What are the symptoms of diabetes?",
+    "context": "Optional medical context"
+  }
+  ```
+
+## Production Deployment
+
+For Hugging Face Spaces:
+1. Set repository secrets in Space settings
+2. Deploy using the provided Dockerfile
+3. Ensure model weights are properly configured