mockllm 0.0.2

A mock server that mimics OpenAI and Anthropic API formats for testing

Mock LLM Server

A FastAPI-based mock LLM server that mimics OpenAI and Anthropic API formats. Instead of calling actual language models, it uses predefined responses from a YAML configuration file.

This is made for when you want a deterministic response for testing or development purposes.

Check out the CodeGate when you're done here!

Project Structure

mockllm/
├── src/
│   └── mockllm/
│       ├── __init__.py
│       ├── config.py      # Response configuration handling
│       ├── models.py      # Pydantic models for API
│       └── server.py      # FastAPI server implementation
├── tests/
│   └── test_server.py     # Test suite
├── example.responses.yml   # Example response configuration
├── LICENSE                # MIT License
├── MANIFEST.in           # Package manifest
├── README.md             # This file
├── pyproject.toml        # Project configuration
└── requirements.txt      # Dependencies

Features

• OpenAI and Anthropic compatible API endpoints
• Streaming support (character-by-character response streaming)
• Configurable responses via YAML file
• Hot-reloading of response configurations
• JSON logging
• Error handling
• Mock token counting

Installation

From PyPI

pip install mockllm

From Source

1. Clone the repository:

git clone https://github.com/lukehinds/mockllm.git
cd mockllm

2. Create a virtual environment and activate it:

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

3. Install dependencies:

pip install -e ".[dev]"  # Install with development dependencies
# or
pip install -e .         # Install without development dependencies

Usage

1. Set up the responses.yml

cp example.responses.yml responses.yml

2. Start the server:

python -m mockllm

Or using uvicorn directly:

uvicorn mockllm.server:app --reload

The server will start on http://localhost:8000

3. Send requests to the API endpoints:

OpenAI Format

Regular request:

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "mock-llm",
    "messages": [
      {"role": "user", "content": "what colour is the sky?"}
    ]
  }'

Streaming request:

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "mock-llm",
    "messages": [
      {"role": "user", "content": "what colour is the sky?"}
    ],
    "stream": true
  }'

Anthropic Format

Regular request:

curl -X POST http://localhost:8000/v1/messages \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-3-sonnet-20240229",
    "messages": [
      {"role": "user", "content": "what colour is the sky?"}
    ]
  }'

Streaming request:

curl -X POST http://localhost:8000/v1/messages \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-3-sonnet-20240229",
    "messages": [
      {"role": "user", "content": "what colour is the sky?"}
    ],
    "stream": true
  }'

Configuration

Response Configuration

Responses are configured in responses.yml. The file has two main sections:

1. responses: Maps input prompts to predefined responses
2. defaults: Contains default configurations like the unknown response message

Example responses.yml:

responses:
  "what colour is the sky?": "The sky is blue during a clear day due to a phenomenon called Rayleigh scattering."
  "what is 2+2?": "2+2 equals 9."

defaults:
  unknown_response: "I don't know the answer to that. This is a mock response."

Hot Reloading

The server automatically detects changes to responses.yml and reloads the configuration without requiring a restart.

API Format

OpenAI Format

Request Format

{
  "model": "mock-llm",
  "messages": [
    {"role": "user", "content": "what colour is the sky?"}
  ],
  "temperature": 0.7,
  "max_tokens": 150,
  "stream": false
}

Response Format

Regular response:

{
  "id": "mock-123",
  "object": "chat.completion",
  "created": 1700000000,
  "model": "mock-llm",
  "choices": [
    {
      "message": {
        "role": "assistant",
        "content": "The sky is blue during a clear day due to a phenomenon called Rayleigh scattering."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 10,
    "completion_tokens": 5,
    "total_tokens": 15
  }
}

Streaming response (Server-Sent Events format):

data: {"id":"mock-123","object":"chat.completion.chunk","created":1700000000,"model":"mock-llm","choices":[{"delta":{"role":"assistant"},"index":0}]}

data: {"id":"mock-124","object":"chat.completion.chunk","created":1700000000,"model":"mock-llm","choices":[{"delta":{"content":"T"},"index":0}]}

data: {"id":"mock-125","object":"chat.completion.chunk","created":1700000000,"model":"mock-llm","choices":[{"delta":{"content":"h"},"index":0}]}

... (character by character)

data: {"id":"mock-999","object":"chat.completion.chunk","created":1700000000,"model":"mock-llm","choices":[{"delta":{},"index":0,"finish_reason":"stop"}]}

data: [DONE]

Anthropic Format

Request Format

{
  "model": "claude-3-sonnet-20240229",
  "messages": [
    {"role": "user", "content": "what colour is the sky?"}
  ],
  "max_tokens": 1024,
  "stream": false
}

Response Format

Regular response:

{
  "id": "mock-123",
  "type": "message",
  "role": "assistant",
  "model": "claude-3-sonnet-20240229",
  "content": [
    {
      "type": "text",
      "text": "The sky is blue during a clear day due to a phenomenon called Rayleigh scattering."
    }
  ],
  "usage": {
    "input_tokens": 10,
    "output_tokens": 5,
    "total_tokens": 15
  }
}

Streaming response (Server-Sent Events format):

data: {"type":"message_delta","id":"mock-123","delta":{"type":"content_block_delta","index":0,"delta":{"text":"T"}}}

data: {"type":"message_delta","id":"mock-123","delta":{"type":"content_block_delta","index":0,"delta":{"text":"h"}}}

... (character by character)

data: [DONE]

Development

Running Tests

pip install -e ".[dev]"  # Install development dependencies
pytest tests/

Code Quality

# Format code
black .
isort .

# Type checking
mypy src/

# Linting
ruff check .

Error Handling

The server includes comprehensive error handling:

• Invalid requests return 400 status codes with descriptive messages
• Server errors return 500 status codes with error details
• All errors are logged using JSON format

Logging

The server uses JSON-formatted logging for:

• Incoming request details
• Response configuration loading
• Error messages and stack traces

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.