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AI/ML API Quickstart
Introduction
OpenAI API Compatibility
Examples
Models
Parameters
API Key Managmenet


AI/ML API QUICKSTART

👋 Welcome to the AI/ML API docs! AI/ML API makes integrating state-of-the-art
AI models into your applications effortless, offering seamless compatibility
with OpenAI-like interfaces.

Features Include:

   
 * Inference: Easily evaluate models for text, images, and more using our API.
   
 * API Key Management: Securely manage your API keys for controlled access.
   
 * Broad Model Selection: Access a diverse range of models for various AI tasks.
   

Get Started with Inference

   
 * Register for an Account: Sign up to generate your API key and kickstart with
   free trial tokens.
   
 * Run Your First Model:
   

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import openai
system_content = "You are a trav
el agent. Be descriptive and helpful."
user_content = "Tell me about San Francisco"
client = openai.OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.aimlapi.com/",
)
chat_completion = client.chat.completions.create(
    model="mistralai/Mixtral-8x7B-Instruct-v0.1",
    messages=[
        {"role": "system", "content": system_content},
        {"role": "user", "content": user_content},
    ],
    temperature=0.7,
    max_tokens=1024,
)
response = chat_completion.choices[0].message.content
print("Response:\n", response)

Next Steps

   
 * Explore the AI/ML API Playground to experiment with various models.
   
 * Learn how to integrate real-time streaming responses into your applications.
   
 * Check out code examples for inspiration on leveraging our API for different
   use cases.
   
 * Discover integrations for a seamless development experience with leading AI
   frameworks.
   

Dive into the world of AI/ML API and explore the limitless possibilities of AI
applications in your projects!


OPENAI API COMPATIBILITY

AI/ML API fully supports the OpenAI API structure, ensuring easy integration
into systems already using OpenAI's standards.


PYTHON SDK

Integrate with AI/ML API by updating your API key and base_url in the OpenAI
Python SDK. Here's an example with our API's chat model endpoint:

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import openai
system_content = "You are a travel agent. Be descriptive and helpful."
user_content = "Tell me about San Francisco"
client = openai.OpenAI(
    api_key="YOUR_AI_ML_API_KEY",
    base_url="https://api.aimlapi.com",
    )
chat_completion = client.chat.completions.create(
    model="mistralai/Mixtral-8x7B-Instruct-v0.1",
    messages=[
        {"role": "system", "content": system_content},
        {"role": "user", "content": user_content},
    ],
    temperature=0.7,
    max_tokens=128,
)
response = chat_completion.choices[0].message.content
print("AI/ML API:\n", response)

STREAMING WITH PYTHON SDK

To enable streaming of responses, simply add stream=True to the chat completions
create function.

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# ... (previous code setup)
stream = client.chat.completions.create(
    model="your-model-string-here",
    messages=[
        # ... (your messages)
    ],
    stream=True,
    max_tokens=1024,
)
for chunk in stream:
    print(chunk.choices[0].message.content or "", end="", flush=True)


NODE.JS SDK

If you're using Node.js, you can similarly switch to AI/ML API by updating the
apiKey and baseURL in the OpenAI Node.js SDK.

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const { Configuration, OpenAIApi } = require("openai");
const configuration = new Configuration({
  apiKey: "YOUR_AI_ML_API_KEY",
  basePath: "https://api.aimlapi.com/service/prompt",
});
const openai = new OpenAIApi(configuration);
// ... (your async function to run the code)

STREAMING IN NODE.JS

For streaming in Node.js, set stream: true in the completions create function.

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// ... (previous code setup)
const stream = await openai.createChatCompletion({
    model: "your-model-string-here",
    messages: [
        // ... (your messages)
    ],
    max_tokens: 1024,
    stream: true,
});
stream.on('data', (data) => {
    console.log(data.choices[0].message.content || "");
});


RESPONSE STRUCTURE

The response from AI/ML API's chat completion will have a similar structure to
the OpenAI API, allowing for a seamless transition.

Plain Text


# Assuming 'response' is the JSON object received from the API:
assistant_response = response['choices'][0]['message']['content']
print(assistant_response)

This code snippet will print the content of the message where the role is
'assistant'. It navigates through the nested JSON structure to reach the desired
data.


EXAMPLES

The AI/ML API is a versatile tool that can be applied to various tasks such as
text generation, summarization, conversation, code generation, and image
creation. Below, you’ll find examples demonstrating how to use our API for
different purposes.


TEXT GENERATION EXAMPLE

Generate creative text based on a prompt using the AI/ML API.

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import requests
response = requests.post(
    'https://api.aimlapi.com/chat/completions',
    json={
        "model": "zero-one-ai/Yi-34B-Chat",
        "prompt": "Once upon a time in a virtual world,",
        "max_tokens": 100,
        "temperature": 0.7,
    },
    headers={
        "Authorization": "Bearer <YOUR_API_KEY>",
    }
)
print(response.json())


SUMMARIZATION EXAMPLE

Summarize a lengthy text to its essential points.

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response = requests.post(
    'https://api.aimlapi.com/chat/completions',
    json={
        "model": "zero-one-ai/Yi-34B-Chat",
        "prompt": "Summarize the following text: <long text here>",
        "max_tokens": 50,
        "stop": ["\n"],
    },
    headers={
        "Authorization": "Bearer <YOUR_API_KEY>",
    }
)
print(response.json())


CONVERSATIONAL AI EXAMPLE

Create an interactive chatbot that responds to user input.

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response = requests.post(
    'https://api.aimlapi.com/chat/completions',
    json={
        "model": "openchat/openchat-3.5-1210",
        "prompt": [
            {"role": "system", "content": "You are a witty AI."},
            {"role": "user", "content": "Tell me a joke!"},
        ],
        "max_tokens": 30,
    },
    headers={
        "Authorization": "Bearer <YOUR_API_KEY>",
    }
)
print(response.json())


CODE GENERATION EXAMPLE

Automatically generate code snippets from a description.

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response = requests.post(
    'https://api.aimlapi.com/chat/completions',
    json={
        "model": "codellama/CodeLlama-70b-Python-hf",
        "prompt": "Write a Python function to calculate the factorial of a number:",
        "max_tokens": 50,
    },
    headers={
        "Authorization": "Bearer <YOUR_API_KEY>",
    }
)
print(response.json())


IMAGE CREATION EXAMPLE

Generate an image based on a descriptive prompt.

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response = requests.post(
    'https://api.aimlapi.com/images/generations',
    json={
        "model": "stabilityai/stable-diffusion-2-1",
        "prompt": "A futuristic city skyline at sunset",
        "n": 1,
        "size": "1024x1024",
    },
    headers={
        "Authorization": "Bearer <YOUR_API_KEY>",
    }
)
print(response.json())


ADDING YOUR OWN MODEL

If there’s a specific model you’d like to see integrated into our API, join our
Discord community and suggest it there. We’re continually expanding our
offerings based on user feedback.




POSTCHAT COMPLETION


api.aimlapi.com/chat/completions
AUTHORIZATIONBearer Token
Token

<token>

HEADERS
Content-Type

application/json

Bodyraw (json)
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json


{
    "model": "Qwen/Qwen1.5-0.5B",
    "messages": [
        {
            "role": "assistant",
            "content": "<string>"
        },
        {
            "role": "assistant",
            "content": "<string>"
        }
    ],
    "max_tokens": "<number>",
    "temperature": "<number>",
    "top_p": "<number>",
    "repetition_penalty": "<number>",
    "top_k": "<number>",
    "stream": "<boolean>"
}

Example Request
Chat Completion

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curl --location 'api.aimlapi.com/chat/completions' \
--header 'Content-Type: application/json' \
--data '{
    "model": "Qwen/Qwen1.5-0.5B",
    "messages": [
        {
            "role": "assistant",
            "content": "<string>"
        },
        {
            "role": "assistant",
            "content": "<string>"
        }
    ],
    "max_tokens": "<number>",
    "temperature": "<number>",
    "top_p": "<number>",
    "repetition_penalty": "<number>",
    "top_k": "<number>",
    "stream": "<boolean>"
}'

Example Response
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