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Send chat completions to deployed models using the Adaptive SDK, OpenAI Python library, or any HTTP client. If you omit model, requests route to the project’s default model, or to a model in an active A/B test. Interactions (prompt + completion pairs) are logged automatically. See Interactions for details.

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Chat completions

response = adaptive.chat.create(
    model="llama-3.1-8b-instruct",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"}
    ],
    labels={"project": "support-bot"},
)
print(response.choices[0].message.content)
ParameterTypeDescription
modelstrModel key. Omit to use the project default.
messageslistChat messages with role and content
labelsdictKey-value pairs for filtering interactions
streamboolEnable streaming (default: False)
temperaturefloatSampling temperature
max_tokensintMaximum tokens to generate
stoplistStop sequences
top_pfloatTop-p sampling threshold
session_idstr or UUIDSession ID for KV-cache reuse across turns
storeboolWhether to log the interaction (default: True)

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Streaming

stream = adaptive.chat.create(
    messages=[{"role": "user", "content": "Hello!"}],
    stream=True,
)
for chunk in stream:
    if chunk.choices:
        print(chunk.choices[0].delta.content, end="", flush=True)

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Get the completion ID

Use completion_id to log Metrics against the response:
completion_id = response.choices[0].completion_id

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Vision requests

Models with the Multimodal tag accept images alongside text. Images must be base64-encoded data URIs (JPEG, PNG, WebP, or GIF, up to 10 MB each).
import base64

with open("photo.png", "rb") as f:
    image_data = base64.b64encode(f.read()).decode()

response = adaptive.chat.create(
    model="your-vlm-key",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What's in this image?"},
                {"type": "image_url", "image_url": f"data:image/png;base64,{image_data}"},
            ],
        }
    ],
)
See SDK Reference for all chat methods.

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OpenAI compatibility

Use the OpenAI Python library with your Adaptive deployment: 
from openai import OpenAI

client = OpenAI(
    base_url=f"{ADAPTIVE_URL}/api/v1",
    api_key=ADAPTIVE_API_KEY,
)

response = client.chat.completions.create(
    model="project_key/model_key",
    messages=[{"role": "user", "content": "Hello!"}],
)
Set model to project_key/model_key. Use metadata instead of labels.
Multimodal image format differs between Adaptive and OpenAI:
# Adaptive format (flat string)
{"type": "image_url", "image_url": "data:image/png;base64,..."}

# OpenAI format (nested object)
{"type": "image_url", "image_url": {"url": "data:image/png;base64,..."}}

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HTTP requests

Use any HTTP client to call the chat completions endpoint directly. 
import requests

headers = {"Authorization": "Bearer ADAPTIVE_API_KEY"}
payload = {
    "model": "project_key/model_key",
    "messages": [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"}
    ],
    "labels": {"project": "support-bot"},
}

response = requests.post(
    url="ADAPTIVE_URL/api/v1/chat/completions",
    json=payload,
    headers=headers,
)
completion_text = response.json()["choices"][0]["message"]["content"]
See API Reference for the full endpoint specification.