Azure OpenAI Deployment Mock

This project emulates or mocks Azure OpenAI API deployments for testing and development purposes.

Features

• Mock both streaming and non-streaming responses.
• Simulate token-based streaming for testing real-time API behaviors.

Requirements

• Docker
• Python 3.12 (for local development)

Setup and Usage

Running with Docker

To run this mock API using Docker, you can either pull the pre-built image directly from Docker Hub or build it locally:

Option 1: Run with Pre-Built Image

Pull and run the Docker container from Docker Hub:

docker run -p 8000:8000 jkfran/azure-openai-mock-api

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

Option 2: Build and Run Locally

1. Clone the repository:

   git clone https://github.com/jkfran/azure-openai-deployment-mock.git
   cd azure-openai-deployment-mock

2. Build the Docker image:

   docker build -t azure-openai-mock-api .

3. Run the Docker container:

   docker run -p 8000:8000 azure-openai-mock-api

Access the API

The API will be available at http://localhost:8000. You can interact with it using tools like Postman or curl.

API Endpoints

POST /openai/deployments/{model_name}/chat/completions

This endpoint simulates an OpenAI completion request.

Request Headers:

• api-key: A required header to authenticate requests. You can use MOCK-AZURE-OPENAI-API-KEY-1234567890 as the valid key.

Request Body:

• stream: Set to true to enable streaming responses. Default is false.

Example Request:

curl -X POST "http://localhost:8000/openai/deployments/mock-deployment/chat/completions" \
     -H "api-key: MOCK-AZURE-OPENAI-API-KEY-1234567890" \
     -d '{ "stream": true }'

Local Development

To run the API locally without Docker, install the required Python dependencies:

pip install -r requirements.txt

Run the FastAPI server:

uvicorn mock_api:app --reload --host 0.0.0.0 --port 8000

Contributing

Contributions are welcome! If you'd like to improve the mock API or add more features, feel free to fork the repository, make your changes, and open a pull request.

1. Fork the project.
2. Create a feature branch.
3. Submit a pull request.

License

This project is open source and available under the MIT License.