Documentation for Venice AI APIs

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1. Getting the List of Available Models

To get the list of available models via the Venice AI API, you can use the models.list method of the OpenAI library.

from openai import OpenAI

# Configuration of the client with the API key and the Venice base URL
client = OpenAI(
    api_key="api_key",
    base_url="https://api.venice.ai/api/v1"
)

# Get the list of available models
models = client.models.list()

# Print the names of the models
for model in models.data:
    print(model.id)

Expected output:

fluently-xl
flux-dev
flux-dev-uncensored
pony-realism
stable-diffusion-3.5
lustify-sdxl
llama-3.3-70b
llama-3.2-3b
dolphin-2.9.2-qwen2-72b
llama-3.1-405b
qwen32b
deepseek-r1-llama-70b
deepseek-r1-671b

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2. Inferring via API in Normal Mode

To perform inference in normal mode, you can use the chat.completions.create method to generate text based on a given prompt. You can also specify additional parameters such as temperature, max_tokens, etc., as well as Venice AI-specific parameters via extra_body.

from openai import OpenAI

client = OpenAI(
    api_key="api_key",
    base_url="https://api.venice.ai/api/v1"
)

response = client.chat.completions.create(
    model="default",
    messages=[
        {"role": "user", "content": "Hello, how are you?"}
    ],
    temperature=0.7,
    frequency_penalty=0.2,
    presence_penalty=0.2,
    max_tokens=50,
    extra_body={
        "venice_parameters": {
            "include_venice_system_prompt": False
        }
    }
)

print(response.choices[0].message.content)

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3. Inferring via API in Streaming Mode

To perform inference in streaming mode, you can activate the stream=True parameter. This allows you to receive responses progressively, which is useful for real-time applications.

from openai import OpenAI
client = OpenAI(
    api_key="api_key",
    base_url="https://api.venice.ai/api/v1"
)

stream = client.chat.completions.create(
    model="default",
    messages=[{"role": "user", "content": "Hello, how are you?"}],
    temperature=0.7,
    frequency_penalty=0.2,
    presence_penalty=0.2,
    max_tokens=50,
    stream=True,  # Activate streaming
    extra_body={
        "venice_parameters": {
            "include_venice_system_prompt": False
        }
    }
)

for chunk in stream:
    if chunk.choices and chunk.choices[0].delta.content:
        text = chunk.choices[0].delta.content
        print(text, end='', flush=True)

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4. Using Venice AI's Utilities and Arguments

Utilities allow you to add additional features to your inference requests, such as database queries or external script execution. You can use the chat.completions.create method to send requests containing utilities.

from openai import OpenAI

client = OpenAI(
    api_key="api_key",
    base_url="https://api.venice.ai/api/v1"
)

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_time",
            "description": "Returns the current time",
            "parameters": {
                "type": "object",  # Required schema even if empty
                "properties": {}
            }
        }
    }
]

response = client.chat.completions.create(
    model="default",
    messages=[
        {"role": "system", "content": "Your system prompt"},
        {"role": "user", "content": "What time is it now?"}
    ],
    tools=tools,
    tool_choice="auto",  # Optional but recommended for control
    extra_body={
        "venice_parameters": {
            "include_venice_system_prompt": False
        }
    }
)

# Check for function calls
if response.choices[0].message.tool_calls:
    for tool_call in response.choices[0].message.tool_calls:
        print(f"Function called: {tool_call.function.name}")
        print(f"Arguments: {tool_call.function.arguments}")
else:
    print(response.choices[0].message.content)

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5. Generating Images

To generate images via the Venice AI API, you can use the /image/generate endpoint directly. Here's an example of code to generate an image and save it locally.

from openai import OpenAI
import base64

client = OpenAI(
    api_key="api_key",
    base_url="https://api.venice.ai/api/v1"
)

# Image generation parameters
params = {
    "model": "flux-dev",
    "prompt": "An image of a cat sitting on a wall.",
    "width": 1024,
    "height": 1024,
    "steps": 6,
    "hide_watermark": True,
    "return_binary": False,  # To get the image in base64
    "seed": 123,
    "cfg_scale": 7.5,
    "style_preset": "3D Model",
    "negative_prompt": "",
    "safe_mode": False
}

# Directly call the Venice endpoint
response = client._client.post(
    "/image/generate",
    json=params,
    headers={"Authorization": f"Bearer {client.api_key}"}
)

# Decode and save the image
image_data = base64.b64decode(response.json()["images"][0])
with open("generated_image.png", "wb") as f:
    f.write(image_data)

print("Image generated successfully!")