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Fim

(fim)

Overview

Fill-in-the-middle API.

Available Operations

complete

FIM completion.

Example Usage

from mistralai import Mistral
import os

with Mistral(
    api_key=os.getenv("MISTRAL_API_KEY", ""),
) as s:
    res = s.fim.complete(model="codestral-2405", prompt="def", suffix="return a+b")

    if res is not None:
        # handle response
        pass

Parameters

Parameter Type Required Description Example
model Nullable[str] ✔️ ID of the model to use. Only compatible for now with:
- codestral-2405
- codestral-latest		 codestral-2405
prompt str ✔️ The text/code to complete. def
temperature OptionalNullable[float] ➖ What sampling temperature to use, we recommend between 0.0 and 0.7. Higher values like 0.7 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. The default value varies depending on the model you are targeting. Call the /models endpoint to retrieve the appropriate value.
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.
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.FIMCompletionRequestStop] ➖ 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.
suffix OptionalNullable[str] ➖ Optional text/code that adds more context for the model. When given a prompt and a suffix the model will fill what is between them. When suffix is not provided, the model will simply execute completion starting with prompt. return a+b
min_tokens OptionalNullable[int] ➖ The minimum number of tokens to generate in the completion.
retries Optional[utils.RetryConfig] ➖ Configuration to override the default retry behavior of the client.

Response

models.FIMCompletionResponse

Errors

Error Type Status Code Content Type
models.HTTPValidationError 422 application/json
models.SDKError 4XX, 5XX */*

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 import Mistral
import os

with Mistral(
    api_key=os.getenv("MISTRAL_API_KEY", ""),
) as s:
    res = s.fim.stream(model="codestral-2405", prompt="def", suffix="return a+b")

    if res is not None:
        with res as event_stream:
            for event in event_stream:
                # handle event
                print(event, flush=True)

Parameters

Parameter Type Required Description Example
model Nullable[str] ✔️ ID of the model to use. Only compatible for now with:
- codestral-2405
- codestral-latest		 codestral-2405
prompt str ✔️ The text/code to complete. def
temperature OptionalNullable[float] ➖ What sampling temperature to use, we recommend between 0.0 and 0.7. Higher values like 0.7 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. The default value varies depending on the model you are targeting. Call the /models endpoint to retrieve the appropriate value.
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.
stream Optional[bool] ➖ N/A
stop Optional[models.FIMCompletionStreamRequestStop] ➖ 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.
suffix OptionalNullable[str] ➖ Optional text/code that adds more context for the model. When given a prompt and a suffix the model will fill what is between them. When suffix is not provided, the model will simply execute completion starting with prompt. return a+b
min_tokens OptionalNullable[int] ➖ The minimum number of tokens to generate in the completion.
retries Optional[utils.RetryConfig] ➖ Configuration to override the default retry behavior of the client.

Response

Union[eventstreaming.EventStream[models.CompletionEvent], eventstreaming.EventStreamAsync[models.CompletionEvent]]

Errors

Error Type Status Code Content Type
models.HTTPValidationError 422 application/json
models.SDKError 4XX, 5XX */*