Prepare backend for HF Spaces deployment

Dockerfile

@@ -6,8 +6,7 @@ ENV PYTHONDONTWRITEBYTECODE=1 \
     PYTHONUNBUFFERED=1
 
 # Set the working directory in the container
-
-WORKDIR /rag_agent_api
+WORKDIR /app
 
 # Copy the requirements file into the container
 COPY requirements.txt .
@@ -18,8 +17,8 @@ RUN pip install --no-cache-dir --upgrade -r requirements.txt
 # Copy the rest of the application code
 COPY . .
 
-# Expose the port the app runs on
-EXPOSE 8000
+# Expose the port the app runs on for Hugging Face Spaces
+EXPOSE 7860
 
-# Run the FastAPI application with uvicorn
-CMD ["uvicorn", "rag_agent_api.main:app", "--host", "0.0.0.0", "--port", "8000"]
+# Run the FastAPI application with uvicorn for Hugging Face Spaces
+CMD ["uvicorn", "rag_agent_api.main:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -1 +1,32 @@
----\ntitle: Backend Deploy\nemoji: 🚀\ncolorFrom: blue\ncolorTo: purple\nsdk: docker\npinned: false\n---\n\n# Backend FastAPI\n\nFastAPI backend deployed on Hugging Face Spaces using Docker.
+---
+title: Backend Deploy
+emoji: 🚀
+colorFrom: blue
+colorTo: purple
+sdk: docker
+pinned: false
+---
+
+# RAG Agent and API Layer
+
+This is a FastAPI application that provides a question-answering API using Gemini agents and Qdrant retrieval for RAG (Retrieval Augmented Generation) functionality.
+
+## API Endpoints
+
+- `GET /` - Root endpoint with API information
+- `POST /ask` - Main question-answering endpoint
+- `GET /health` - Health check endpoint
+- `GET /ready` - Readiness check endpoint
+- `/docs` - API documentation (Swagger UI)
+- `/redoc` - API documentation (Redoc)
+
+## Configuration
+
+The application requires the following environment variables:
+- `GEMINI_API_KEY` - API key for Google Gemini
+- `QDRANT_URL` - URL for Qdrant vector database
+- `QDRANT_API_KEY` - API key for Qdrant database
+
+## Deployment
+
+This application is configured for deployment on Hugging Face Spaces using Docker.