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Using Chat API
This page explains how to use the Chat API to query models
h1 paragraph
Using Chat API
This page explains how to use the Chat API to query models
generative-apis ai-data chat-api
validation posted
2024-09-03
2024-09-03

Scaleway Generative APIs are designed as a drop-in replacement for the OpenAI APIs. If you have an LLM-driven application that uses one of OpenAI's client libraries, you can easily configure it to point to Scaleway Chat API, and get your existing applications running using open-weight instruct models hosted at Scaleway.

Create chat completion

Creates a model response for the given chat conversation.

Request sample:

curl --request POST \
     --url https://api.scaleway.ai/v1/chat/completions \
     --header 'Authorization: Bearer ${SCW_SECRET_KEY}' \
     --header 'Content-Type: application/json' \
     --data '{
     "model": "llama-3.1-8b-instruct",
     "messages": [
      {
        "role": "system",
        "content": "<string>"
      },
      {
        "role": "user",
        "content": "<string>"
      }
     ],
     "max_tokens": integer,
     "temperature": float,
     "top_p": float,
     "presence_penalty": float,
     "stop": "<string>",
     "stream": boolean,
     }'

Headers

Find required headers in this page.

Body

Required parameters

Param Type Description
messages array of objects A list of messages comprising the conversation so far.
model string The name of the model to query.

Our chat API is OpenAI compatible. Use OpenAI’s API reference for more detailed information on the usage.

Supported parameters

Unsupported parameters

  • frequency_penalty
  • n
  • top_logprobs
  • logit_bias
  • user

If you have a use case requiring one of these unsupported parameters, please contact us via Slack on #ai channel.

Going further

  1. Python code examples to query text models using Scaleway's Chat API
  2. How to use structured outputs with the response_format parameter
  3. How to use function calling with tools and tool_choice