"""Code generated by Speakeasy (https://speakeasy.com). DO NOT EDIT.""" # @generated-id: 18cb2b2415d4 from __future__ import annotations from .assistantmessage import AssistantMessage, AssistantMessageTypedDict from .codeinterpretertool import CodeInterpreterTool, CodeInterpreterToolTypedDict from .customconnector import CustomConnector, CustomConnectorTypedDict from .documentlibrarytool import DocumentLibraryTool, DocumentLibraryToolTypedDict from .guardrailconfig import GuardrailConfig, GuardrailConfigTypedDict from .imagegenerationtool import ImageGenerationTool, ImageGenerationToolTypedDict from .mistralpromptmode import MistralPromptMode from .prediction import Prediction, PredictionTypedDict from .reasoningeffort import ReasoningEffort from .responseformat import ResponseFormat, ResponseFormatTypedDict from .systemmessage import SystemMessage, SystemMessageTypedDict from .tool import Tool, ToolTypedDict from .toolchoice import ToolChoice, ToolChoiceTypedDict from .toolchoiceenum import ToolChoiceEnum from .toolmessage import ToolMessage, ToolMessageTypedDict from .usermessage import UserMessage, UserMessageTypedDict from .websearchpremiumtool import WebSearchPremiumTool, WebSearchPremiumToolTypedDict from .websearchtool import WebSearchTool, WebSearchToolTypedDict from mistralai.client.types import ( BaseModel, Nullable, OptionalNullable, UNSET, UNSET_SENTINEL, ) from mistralai.client.utils import get_discriminator from pydantic import Discriminator, Tag, model_serializer from typing import Any, Dict, List, Optional, Union from typing_extensions import Annotated, NotRequired, TypeAliasType, TypedDict ChatCompletionStreamRequestStopTypedDict = TypeAliasType( "ChatCompletionStreamRequestStopTypedDict", Union[str, List[str]] ) r"""Stop generation if this token is detected. Or if one of these tokens is detected when providing an array""" ChatCompletionStreamRequestStop = TypeAliasType( "ChatCompletionStreamRequestStop", Union[str, List[str]] ) r"""Stop generation if this token is detected. Or if one of these tokens is detected when providing an array""" ChatCompletionStreamRequestMessageTypedDict = TypeAliasType( "ChatCompletionStreamRequestMessageTypedDict", Union[ SystemMessageTypedDict, UserMessageTypedDict, AssistantMessageTypedDict, ToolMessageTypedDict, ], ) ChatCompletionStreamRequestMessage = Annotated[ Union[ Annotated[AssistantMessage, Tag("assistant")], Annotated[SystemMessage, Tag("system")], Annotated[ToolMessage, Tag("tool")], Annotated[UserMessage, Tag("user")], ], Discriminator(lambda m: get_discriminator(m, "role", "role")), ] ChatCompletionStreamRequestToolTypedDict = TypeAliasType( "ChatCompletionStreamRequestToolTypedDict", Union[ ToolTypedDict, WebSearchToolTypedDict, WebSearchPremiumToolTypedDict, CodeInterpreterToolTypedDict, ImageGenerationToolTypedDict, DocumentLibraryToolTypedDict, CustomConnectorTypedDict, ], ) ChatCompletionStreamRequestTool = Union[ Tool, WebSearchTool, WebSearchPremiumTool, CodeInterpreterTool, ImageGenerationTool, DocumentLibraryTool, CustomConnector, ] ChatCompletionStreamRequestToolChoiceTypedDict = TypeAliasType( "ChatCompletionStreamRequestToolChoiceTypedDict", Union[ToolChoiceTypedDict, ToolChoiceEnum], ) r"""Controls which (if any) tool is called by the model. `none` means the model will not call any tool and instead generates a message. `auto` means the model can pick between generating a message or calling one or more tools. `any` or `required` means the model must call one or more tools. Specifying a particular tool via `{\"type\": \"function\", \"function\": {\"name\": \"my_function\"}}` forces the model to call that tool.""" ChatCompletionStreamRequestToolChoice = TypeAliasType( "ChatCompletionStreamRequestToolChoice", Union[ToolChoice, ToolChoiceEnum] ) r"""Controls which (if any) tool is called by the model. `none` means the model will not call any tool and instead generates a message. `auto` means the model can pick between generating a message or calling one or more tools. `any` or `required` means the model must call one or more tools. Specifying a particular tool via `{\"type\": \"function\", \"function\": {\"name\": \"my_function\"}}` forces the model to call that tool.""" class ChatCompletionStreamRequestTypedDict(TypedDict): model: str r"""ID of the model to use. You can use the [List Available Models](/api/#tag/models/operation/list_models_v1_models_get) API to see all of your available models, or see our [Model overview](/models) for model descriptions.""" messages: List[ChatCompletionStreamRequestMessageTypedDict] r"""The prompt(s) to generate completions for, encoded as a list of dict with role and content.""" temperature: NotRequired[Nullable[float]] r"""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: NotRequired[Nullable[float]] r"""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: NotRequired[Nullable[int]] r"""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: NotRequired[bool] stop: NotRequired[Nullable[ChatCompletionStreamRequestStopTypedDict]] r"""Stop generation if this token is detected. Or if one of these tokens is detected when providing an array""" random_seed: NotRequired[Nullable[int]] r"""The seed to use for random sampling. If set, different calls will generate deterministic results.""" metadata: NotRequired[Nullable[Dict[str, Any]]] response_format: NotRequired[ResponseFormatTypedDict] r"""Specify the format that the model must output. By default it will use `{ \"type\": \"text\" }`. Setting to `{ \"type\": \"json_object\" }` enables JSON mode, which guarantees the message the model generates is in JSON. When using JSON mode you MUST also instruct the model to produce JSON yourself with a system or a user message. Setting to `{ \"type\": \"json_schema\" }` enables JSON schema mode, which guarantees the message the model generates is in JSON and follows the schema you provide.""" tools: NotRequired[Nullable[List[ChatCompletionStreamRequestToolTypedDict]]] r"""A list of tools the model may call. Use this to provide a list of functions the model may generate JSON inputs for.""" tool_choice: NotRequired[ChatCompletionStreamRequestToolChoiceTypedDict] r"""Controls which (if any) tool is called by the model. `none` means the model will not call any tool and instead generates a message. `auto` means the model can pick between generating a message or calling one or more tools. `any` or `required` means the model must call one or more tools. Specifying a particular tool via `{\"type\": \"function\", \"function\": {\"name\": \"my_function\"}}` forces the model to call that tool.""" presence_penalty: NotRequired[Nullable[float]] r"""The `presence_penalty` determines how much the model penalizes the repetition of words or phrases. A higher presence penalty encourages the model to use a wider variety of words and phrases, making the output more diverse and creative.""" frequency_penalty: NotRequired[Nullable[float]] r"""The `frequency_penalty` penalizes the repetition of words based on their frequency in the generated text. A higher frequency penalty discourages the model from repeating words that have already appeared frequently in the output, promoting diversity and reducing repetition.""" n: NotRequired[Nullable[int]] r"""Number of completions to return for each request, input tokens are only billed once.""" prediction: NotRequired[PredictionTypedDict] r"""Enable users to specify an expected completion, optimizing response times by leveraging known or predictable content.""" parallel_tool_calls: NotRequired[bool] r"""Whether to enable parallel function calling during tool use, when enabled the model can call multiple tools in parallel.""" reasoning_effort: NotRequired[Nullable[ReasoningEffort]] prompt_mode: NotRequired[Nullable[MistralPromptMode]] r"""Allows toggling between the reasoning mode and no system prompt. When set to `reasoning` the system prompt for reasoning models will be used.""" guardrails: NotRequired[Nullable[List[GuardrailConfigTypedDict]]] safe_prompt: NotRequired[bool] r"""Whether to inject a safety prompt before all conversations.""" class ChatCompletionStreamRequest(BaseModel): model: str r"""ID of the model to use. You can use the [List Available Models](/api/#tag/models/operation/list_models_v1_models_get) API to see all of your available models, or see our [Model overview](/models) for model descriptions.""" messages: List[ChatCompletionStreamRequestMessage] r"""The prompt(s) to generate completions for, encoded as a list of dict with role and content.""" temperature: OptionalNullable[float] = UNSET r"""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: OptionalNullable[float] = UNSET r"""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] = UNSET r"""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] = True stop: OptionalNullable[ChatCompletionStreamRequestStop] = UNSET r"""Stop generation if this token is detected. Or if one of these tokens is detected when providing an array""" random_seed: OptionalNullable[int] = UNSET r"""The seed to use for random sampling. If set, different calls will generate deterministic results.""" metadata: OptionalNullable[Dict[str, Any]] = UNSET response_format: Optional[ResponseFormat] = None r"""Specify the format that the model must output. By default it will use `{ \"type\": \"text\" }`. Setting to `{ \"type\": \"json_object\" }` enables JSON mode, which guarantees the message the model generates is in JSON. When using JSON mode you MUST also instruct the model to produce JSON yourself with a system or a user message. Setting to `{ \"type\": \"json_schema\" }` enables JSON schema mode, which guarantees the message the model generates is in JSON and follows the schema you provide.""" tools: OptionalNullable[List[ChatCompletionStreamRequestTool]] = UNSET r"""A list of tools the model may call. Use this to provide a list of functions the model may generate JSON inputs for.""" tool_choice: Optional[ChatCompletionStreamRequestToolChoice] = None r"""Controls which (if any) tool is called by the model. `none` means the model will not call any tool and instead generates a message. `auto` means the model can pick between generating a message or calling one or more tools. `any` or `required` means the model must call one or more tools. Specifying a particular tool via `{\"type\": \"function\", \"function\": {\"name\": \"my_function\"}}` forces the model to call that tool.""" presence_penalty: OptionalNullable[float] = UNSET r"""The `presence_penalty` determines how much the model penalizes the repetition of words or phrases. A higher presence penalty encourages the model to use a wider variety of words and phrases, making the output more diverse and creative.""" frequency_penalty: OptionalNullable[float] = UNSET r"""The `frequency_penalty` penalizes the repetition of words based on their frequency in the generated text. A higher frequency penalty discourages the model from repeating words that have already appeared frequently in the output, promoting diversity and reducing repetition.""" n: OptionalNullable[int] = UNSET r"""Number of completions to return for each request, input tokens are only billed once.""" prediction: Optional[Prediction] = None r"""Enable users to specify an expected completion, optimizing response times by leveraging known or predictable content.""" parallel_tool_calls: Optional[bool] = None r"""Whether to enable parallel function calling during tool use, when enabled the model can call multiple tools in parallel.""" reasoning_effort: OptionalNullable[ReasoningEffort] = UNSET prompt_mode: OptionalNullable[MistralPromptMode] = UNSET r"""Allows toggling between the reasoning mode and no system prompt. When set to `reasoning` the system prompt for reasoning models will be used.""" guardrails: OptionalNullable[List[GuardrailConfig]] = UNSET safe_prompt: Optional[bool] = None r"""Whether to inject a safety prompt before all conversations.""" @model_serializer(mode="wrap") def serialize_model(self, handler): optional_fields = set( [ "temperature", "top_p", "max_tokens", "stream", "stop", "random_seed", "metadata", "response_format", "tools", "tool_choice", "presence_penalty", "frequency_penalty", "n", "prediction", "parallel_tool_calls", "reasoning_effort", "prompt_mode", "guardrails", "safe_prompt", ] ) nullable_fields = set( [ "temperature", "top_p", "max_tokens", "stop", "random_seed", "metadata", "tools", "presence_penalty", "frequency_penalty", "n", "reasoning_effort", "prompt_mode", "guardrails", ] ) serialized = handler(self) m = {} for n, f in type(self).model_fields.items(): k = f.alias or n val = serialized.get(k, serialized.get(n)) is_nullable_and_explicitly_set = ( k in nullable_fields and (self.__pydantic_fields_set__.intersection({n})) # pylint: disable=no-member ) if val != UNSET_SENTINEL: if ( val is not None or k not in optional_fields or is_nullable_and_explicitly_set ): m[k] = val return m