AI/ML API

https://aimlapi.com/

Overview

LiteLLM supports AI/ML API Image Generation calls.

API Base, Key

# env variable
os.environ['AIML_API_KEY'] = "your-api-key"
os.environ['AIML_API_BASE'] = "https://api.aimlapi.com"  # [optional] 

Getting started with the AI/ML API is simple. Follow these steps to set up your integration:

1. Get Your API Key

To begin, you need an API key. You can obtain yours here:
🔑 Get Your API Key

2. Explore Available Models

Looking for a different model? Browse the full list of supported models:
📚 Full List of Models

3. Read the Documentation

For detailed setup instructions and usage guidelines, check out the official documentation:
📖 AI/ML API Docs

4. Need Help?

If you have any questions, feel free to reach out. We’re happy to assist! 🚀 Discord

Usage

You can choose from LLama, Qwen, Flux, and 200+ other open and closed-source models on aimlapi.com/models. For example:

response = litellm.completion(
    model="aiml/meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo", # The model name must include prefix "openai" + the model name from ai/ml api
    api_key="", # your aiml api-key 
    api_base="https://api.aimlapi.com/v2",
    messages=[
        {
            "role": "user",
            "content": "Hey, how's it going?",
        }
    ],
)

Streaming

response = litellm.completion(
    model="aiml/Qwen/Qwen2-72B-Instruct",  # The model name must include prefix "openai" + the model name from ai/ml api
    api_key="",  # your aiml api-key 
    api_base="https://api.aimlapi.com/v2",
    messages=[
        {
            "role": "user",
            "content": "Hey, how's it going?",
        }
    ],
    stream=True,
)
for chunk in response:
    print(chunk)

Async Completion

async def main():
    response = await litellm.acompletion(
        model="aiml/anthropic/claude-3-5-haiku",  # The model name must include prefix "openai" + the model name from ai/ml api
        api_key="",  # your aiml api-key
        api_base="https://api.aimlapi.com/v2",
        messages=[
            {
                "role": "user",
                "content": "Hey, how's it going?",
            }
        ],
    )
    print(response)

if __name__ == "__main__":
    asyncio.run(main())

Async Streaming

async def main():
    try:
        print("test acompletion + streaming")
        response = await litellm.acompletion(
            model="aiml/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF", # The model name must include prefix "openai" + the model name from ai/ml api
            api_key="", # your aiml api-key
            api_base="https://api.aimlapi.com/v2",
            messages=[{"content": "Hey, how's it going?", "role": "user"}],
            stream=True,
        )
        print(f"response: {response}")
        async for chunk in response:
            print(chunk)
    except:
        print(f"error occurred: {traceback.format_exc()}")
        pass

if __name__ == "__main__":
    asyncio.run(main())

Async Embedding

async def main():
    response = await litellm.aembedding(
        model="aiml/text-embedding-3-small", # The model name must include prefix "openai" + the model name from ai/ml api
        api_key="",  # your aiml api-key
        api_base="https://api.aimlapi.com/v1", # 👈 the URL has changed from v2 to v1
        input="Your text string",
    )
    print(response)

if __name__ == "__main__":
    asyncio.run(main())

Async Image Generation

async def main():
    response = await litellm.aimage_generation(
        model="aiml/dall-e-3",  # The model name must include prefix "openai" + the model name from ai/ml api
        api_key="",  # your aiml api-key
        api_base="https://api.aimlapi.com/v1", # 👈 the URL has changed from v2 to v1
        prompt="A cute baby sea otter",
    )
    print(response)

if __name__ == "__main__":
    asyncio.run(main())