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Chat

(chat)

Overview

Chat Completion API.

Available Operations

stream

Mistral AI provides the ability to stream responses back to a client in order to allow partial results for certain requests. Tokens will be sent as data-only server-sent events as they become available, with the stream terminated by a data: [DONE] message. Otherwise, the server will hold the request open until the timeout or until completion, with the response containing the full result as JSON.

Example Usage

from mistralai_gcp import MistralGCP
import os

s = MistralGCP()


res = s.chat.stream(messages=[
    {
        "content": "Who is the best French painter? Answer in one short sentence.",
        "role": "user",
    },
], model="mistral-small-latest")

if res is not None:
    for event in res:
        # handle event
        print(event)

Parameters

Parameter Type Required Description Example
messages List[models.Messages] ✔️ The prompt(s) to generate completions for, encoded as a list of dict with role and content. {
"role": "user",
"content": "Who is the best French painter? Answer in one short sentence."
}
model OptionalNullable[str] ➖ ID of the model to use. You can use the List Available Models API to see all of your available models, or see our Model overview for model descriptions. mistral-small-latest
temperature Optional[float] ➖ What sampling temperature to use, between 0.0 and 1.0. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. We generally recommend altering this or top_p but not both.
top_p Optional[float] ➖ Nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or temperature but not both.
max_tokens OptionalNullable[int] ➖ The maximum number of tokens to generate in the completion. The token count of your prompt plus max_tokens cannot exceed the model's context length.
min_tokens OptionalNullable[int] ➖ The minimum number of tokens to generate in the completion.
stream Optional[bool] ➖ N/A
stop Optional[models.Stop] ➖ Stop generation if this token is detected. Or if one of these tokens is detected when providing an array
random_seed OptionalNullable[int] ➖ The seed to use for random sampling. If set, different calls will generate deterministic results.
response_format Optional[models.ResponseFormat] ➖ N/A
tools List[models.Tool] ➖ N/A
tool_choice Optional[models.ToolChoice] ➖ N/A
retries Optional[utils.RetryConfig] ➖ Configuration to override the default retry behavior of the client.

Response

Union[Generator[models.CompletionEvent, None, None], AsyncGenerator[models.CompletionEvent, None]]

Errors

Error Object Status Code Content Type
models.SDKError 4xx-5xx /

create

Chat Completion

Example Usage

from mistralai_gcp import MistralGCP
import os

s = MistralGCP()


res = s.chat.complete(messages=[
    {
        "content": "Who is the best French painter? Answer in one short sentence.",
        "role": "user",
    },
], model="mistral-small-latest")

if res is not None:
    # handle response
    pass

Parameters

Parameter Type Required Description Example
messages List[models.ChatCompletionRequestMessages] ✔️ The prompt(s) to generate completions for, encoded as a list of dict with role and content. {
"role": "user",
"content": "Who is the best French painter? Answer in one short sentence."
}
model OptionalNullable[str] ➖ ID of the model to use. You can use the List Available Models API to see all of your available models, or see our Model overview for model descriptions. mistral-small-latest
temperature Optional[float] ➖ What sampling temperature to use, between 0.0 and 1.0. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. We generally recommend altering this or top_p but not both.
top_p Optional[float] ➖ Nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or temperature but not both.
max_tokens OptionalNullable[int] ➖ The maximum number of tokens to generate in the completion. The token count of your prompt plus max_tokens cannot exceed the model's context length.
min_tokens OptionalNullable[int] ➖ The minimum number of tokens to generate in the completion.
stream Optional[bool] ➖ Whether to stream back partial progress. If set, tokens will be sent as data-only server-side events as they become available, with the stream terminated by a data: [DONE] message. Otherwise, the server will hold the request open until the timeout or until completion, with the response containing the full result as JSON.
stop Optional[models.ChatCompletionRequestStop] ➖ Stop generation if this token is detected. Or if one of these tokens is detected when providing an array
random_seed OptionalNullable[int] ➖ The seed to use for random sampling. If set, different calls will generate deterministic results.
response_format Optional[models.ResponseFormat] ➖ N/A
tools List[models.Tool] ➖ N/A
tool_choice Optional[models.ChatCompletionRequestToolChoice] ➖ N/A
retries Optional[utils.RetryConfig] ➖ Configuration to override the default retry behavior of the client.

Response

models.ChatCompletionResponse

Errors

Error Object Status Code Content Type
models.HTTPValidationError 422 application/json
models.SDKError 4xx-5xx /