llamacpp_python/base/docker-compose.yml

#### 4. **Deploying in a Containerized Environment**
#If you're using Docker or Podman, ensure the containers can communicate with each other. For example:

#- **Docker Compose**:
#  Create a `docker-compose.yml` file to define both the model server and the chat application:

  #```yaml
  version: "3"
  services:
    model_server:
      image: my_model_server_image
      ports:
        - "8001:8001"
      environment:
        - PORT=8001
      networks:
        - my_network
chat_app:
      image: my_chat_app_image
      environment:
        - MODEL_ENDPOINT=http://model_server:8001
      depends_on:
        - model_server
      networks:
        - my_network

  networks:
    my_network:
  #```

  #- The `MODEL_ENDPOINT` for the chat application is set to `http://model_server:8001`, which uses Docker's internal DNS to resolve the model server's container name.

#- **Docker Networking**:
#  If you're not using Docker Compose, you can create a custom network and attach both containers to it:

#  ```bash
  # Create a custom network
 # docker network create my_network

  # Run the model server container
 # docker run -d --name model_server --network my_network -p 8001:8001 my_model_server_image
 # Run the chat application container
 # docker run -d --name chat_app --network my_network -e MODEL_ENDPOINT=http://model_server:8001 my_chat_app_image




#### 5. **Testing the Endpoint**
#To ensure the model server is working as expected, you can test the endpoint directly using `curl` or a tool like Postman:

#```bash
#curl -X POST http://localhost:8001/generate -H "Content-Type: application/json" -d '{"prompt": "Hello, model!"}'
#```

#Expected response:
#```json
#{
#  "response": "Generated response for: Hello, model!"
#}