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A modular FastAPI application to interact with xAI Grok API for image generation, image understanding, and chat functionalities.

• Image Generation: Generate images using xAI Grok models based on text prompts
• Image Vision/Understanding: Analyze and understand image content
• Chat Completions: Generate text responses using xAI Grok chat models
• Streaming Support: Stream chat responses in real-time as they are generated
• Docker Ready: Production-ready Docker setup with Nginx reverse proxy
• Security Hardened: SSL/TLS, security headers, and non-root user setup
• Modular architecture for easy extension
• Support for multiple models
• Rate limiting and request logging
• API key authentication

This API is fully compatible with the OpenAI SDK! You can use the same SDK you might already be using for OpenAI, just pointing to our xAI API instead.

importosfromopenaiimportOpenAI# Set up clientclient=OpenAI(api_key=os.environ.get("XAI_API_KEY"),base_url="https://yourdomain.com/api/v1"# Point to your API server)# Chat completionchat_response=client.chat.completions.create(model="grok-3-mini-beta",messages=[        {"role":"user","content":"What is the capital of France?"}    ],temperature=0.7)print(chat_response.choices[0].message.content)# Streaming chat completionstream=client.chat.completions.create(model="grok-3-mini-beta",messages=[        {"role":"user","content":"Write a short story about space travel"}    ],stream=True,temperature=0.7)forchunkinstream:ifchunk.choices[0].delta.contentisnotNone:print(chunk.choices[0].delta.content,end="",flush=True)# Vision analysisvision_response=client.chat.completions.create(model="grok-2-vision-latest",messages=[        {"role":"user","content": [                {"type":"image_url","image_url": {"url":"https://example.com/image.jpg","detail":"high",                    },                },                {"type":"text","text":"What's in this image?",                },            ],        }    ],temperature=0.01)print(vision_response.choices[0].message.content)# Image generationimage_response=client.images.generate(model="grok-2-image",prompt="A beautiful sunset over mountains",n=1)print(f"Image URL:{image_response.data[0].url}")

• Python 3.8+
• pip
• Docker and Docker Compose (for containerized deployment)

1. Clone the repository:

   git clone https://github.com/bigsk1/xai-apicd xai-api

2. Create a virtual environment:

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

3. Install dependencies:

   pip install -r requirements.txt

4. Create a.env file in the root directory with your API keys:

   XAI_API_KEY=your_xai_api_keyXAI_API_BASE=https://api.x.ai/v1# Optional configurationDEFAULT_CHAT_MODEL=grok-3-mini-betaDEFAULT_IMAGE_GEN_MODEL=grok-2-imageDEFAULT_VISION_MODEL=grok-2-vision-latest # Optional: Rate limitingAPI_RATE_LIMIT=100API_RATE_LIMIT_PERIOD=3600

uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload

The API will be available athttp://localhost:8000.

This project includes a production-ready Docker setup with Nginx for secure deployment:

1. Generate SSL certificates (for development):

   cd docker/nginx/ssl./generate-self-signed-cert.sh

2. Start the containers:

   docker-compose -f docker/docker-compose.yaml up -d

3. The API will be available at:

   • HTTPS:https://localhost/api/v1 (secured with SSL/TLS)
   • HTTP:http://localhost/api/v1 (redirects to HTTPS)

4. Run the test script to verify all endpoints:

   ./docker/test_api.sh

For more details on the Docker setup, seeDocker Documentation andTesting Documentation.

Once the server is running, you can access the auto-generated API documentation:

• Swagger UI:http://localhost:8000/docs
• ReDoc:http://localhost:8000/redoc

For detailed examples of how to use the API, check the documentation in thedocs folder:

• API Usage Examples - Examples of using each endpoint with curl
• Streaming API - Guide to using streaming responses for real-time output
• Ready-to-Use Curl Commands - Copy-pastable curl commands for testing
• API Integration Guide - Code examples for integrating with different programming languages

• POST /api/v1/images/generate: Generate images based on text prompts

• POST /api/v1/vision/analyze: Analyze image content using vision models

• POST /api/v1/chat/completions: Generate chat completions

• GET /health: Check the API health status

The application is configurable through environment variables or a.env file:

• XAI_API_KEY: Your xAI API key (required)
• XAI_API_BASE: Base URL for xAI API (default: "https://api.x.ai/v1")
• DEFAULT_CHAT_MODEL: Default model for chat completions (default: "grok-3-mini-beta")
• DEFAULT_IMAGE_GEN_MODEL: Default model for image generation (default: "grok-2-image")
• DEFAULT_VISION_MODEL: Default model for image vision (default: "grok-2-vision-latest")
• API_RATE_LIMIT: Maximum number of requests per time window (default: 100)
• API_RATE_LIMIT_PERIOD: Time window in seconds for rate limiting (default: 3600)

• API key authentication
• Rate limiting
• CORS configuration
• Request logging
• When using Docker:
  • SSL/TLS encryption with modern cipher configuration
  • Security headers (HSTS, CSP, X-Content-Type-Options, etc.)
  • Non-root container user
  • Read-only filesystem where possible
  • Nginx reverse proxy with request buffering
  • No privilege escalation
  • Temporary filesystem for volatile data

This API currently serves as a secure proxy to xAI's Grok API services. However, there are many opportunities to enhance its capabilities:

• Specialized Endpoints: Create purpose-built endpoints for summarization, data extraction, and research
• External Integrations: Connect to additional data sources, vector databases, and APIs
• Custom Logic: Add pre-processing and post-processing capabilities to improve responses
• Enhanced Features: Combine image generation with text generation, validate code outputs, etc.

For a detailed roadmap and technical implementation ideas, see theFuture API Enhancements documentation.

MIT