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Diffstat (limited to '.venv/lib/python3.12/site-packages/azure/ai/inference/models/_models.py')
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diff --git a/.venv/lib/python3.12/site-packages/azure/ai/inference/models/_models.py b/.venv/lib/python3.12/site-packages/azure/ai/inference/models/_models.py new file mode 100644 index 00000000..53934528 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/azure/ai/inference/models/_models.py @@ -0,0 +1,1458 @@ +# pylint: disable=too-many-lines +# coding=utf-8 +# -------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for license information. +# Code generated by Microsoft (R) Python Code Generator. +# Changes may cause incorrect behavior and will be lost if the code is regenerated. +# -------------------------------------------------------------------------- +# pylint: disable=useless-super-delegation + +import datetime +from typing import Any, Dict, List, Literal, Mapping, Optional, TYPE_CHECKING, Union, overload + +from .. import _model_base +from .._model_base import rest_discriminator, rest_field +from ._enums import ChatRole + +if TYPE_CHECKING: + from .. import models as _models + + +class ContentItem(_model_base.Model): + """An abstract representation of a structured content item within a chat message. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + ImageContentItem, AudioContentItem, TextContentItem + + :ivar type: The discriminated object type. Required. Default value is None. + :vartype type: str + """ + + __mapping__: Dict[str, _model_base.Model] = {} + type: str = rest_discriminator(name="type") + """The discriminated object type. Required. Default value is None.""" + + @overload + def __init__( + self, + *, + type: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class AudioContentItem(ContentItem, discriminator="input_audio"): + """A structured chat content item containing an audio content. + + :ivar type: The discriminated object type: always 'input_audio' for this type. Required. + Default value is "input_audio". + :vartype type: str + :ivar input_audio: The details of the input audio. Required. + :vartype input_audio: ~azure.ai.inference.models.InputAudio + """ + + type: Literal["input_audio"] = rest_discriminator(name="type") # type: ignore + """The discriminated object type: always 'input_audio' for this type. Required. Default value is + \"input_audio\".""" + input_audio: "_models.InputAudio" = rest_field() + """The details of the input audio. Required.""" + + @overload + def __init__( + self, + *, + input_audio: "_models.InputAudio", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="input_audio", **kwargs) + + +class ChatChoice(_model_base.Model): + """The representation of a single prompt completion as part of an overall chat completions + request. + Generally, ``n`` choices are generated per provided prompt with a default value of 1. + Token limits and other settings may limit the number of choices generated. + + + :ivar index: The ordered index associated with this chat completions choice. Required. + :vartype index: int + :ivar finish_reason: The reason that this chat completions choice completed its generated. + Required. Known values are: "stop", "length", "content_filter", and "tool_calls". + :vartype finish_reason: str or ~azure.ai.inference.models.CompletionsFinishReason + :ivar message: The chat message for a given chat completions prompt. Required. + :vartype message: ~azure.ai.inference.models.ChatResponseMessage + """ + + index: int = rest_field() + """The ordered index associated with this chat completions choice. Required.""" + finish_reason: Union[str, "_models.CompletionsFinishReason"] = rest_field() + """The reason that this chat completions choice completed its generated. Required. Known values + are: \"stop\", \"length\", \"content_filter\", and \"tool_calls\".""" + message: "_models.ChatResponseMessage" = rest_field() + """The chat message for a given chat completions prompt. Required.""" + + @overload + def __init__( + self, + *, + index: int, + finish_reason: Union[str, "_models.CompletionsFinishReason"], + message: "_models.ChatResponseMessage", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class ChatCompletions(_model_base.Model): + """Representation of the response data from a chat completions request. + Completions support a wide variety of tasks and generate text that continues from or + "completes" + provided prompt data. + + + :ivar id: A unique identifier associated with this chat completions response. Required. + :vartype id: str + :ivar created: The first timestamp associated with generation activity for this completions + response, + represented as seconds since the beginning of the Unix epoch of 00:00 on 1 Jan 1970. Required. + :vartype created: ~datetime.datetime + :ivar model: The model used for the chat completion. Required. + :vartype model: str + :ivar choices: The collection of completions choices associated with this completions response. + Generally, ``n`` choices are generated per provided prompt with a default value of 1. + Token limits and other settings may limit the number of choices generated. Required. + :vartype choices: list[~azure.ai.inference.models.ChatChoice] + :ivar usage: Usage information for tokens processed and generated as part of this completions + operation. Required. + :vartype usage: ~azure.ai.inference.models.CompletionsUsage + """ + + id: str = rest_field() + """A unique identifier associated with this chat completions response. Required.""" + created: datetime.datetime = rest_field(format="unix-timestamp") + """The first timestamp associated with generation activity for this completions response, + represented as seconds since the beginning of the Unix epoch of 00:00 on 1 Jan 1970. Required.""" + model: str = rest_field() + """The model used for the chat completion. Required.""" + choices: List["_models.ChatChoice"] = rest_field() + """The collection of completions choices associated with this completions response. + Generally, ``n`` choices are generated per provided prompt with a default value of 1. + Token limits and other settings may limit the number of choices generated. Required.""" + usage: "_models.CompletionsUsage" = rest_field() + """Usage information for tokens processed and generated as part of this completions operation. + Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + created: datetime.datetime, + model: str, + choices: List["_models.ChatChoice"], + usage: "_models.CompletionsUsage", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class ChatCompletionsNamedToolChoice(_model_base.Model): + """A tool selection of a specific, named function tool that will limit chat completions to using + the named function. + + :ivar type: The type of the tool. Currently, only ``function`` is supported. Required. Default + value is "function". + :vartype type: str + :ivar function: The function that should be called. Required. + :vartype function: ~azure.ai.inference.models.ChatCompletionsNamedToolChoiceFunction + """ + + type: Literal["function"] = rest_field() + """The type of the tool. Currently, only ``function`` is supported. Required. Default value is + \"function\".""" + function: "_models.ChatCompletionsNamedToolChoiceFunction" = rest_field() + """The function that should be called. Required.""" + + @overload + def __init__( + self, + *, + function: "_models.ChatCompletionsNamedToolChoiceFunction", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.type: Literal["function"] = "function" + + +class ChatCompletionsNamedToolChoiceFunction(_model_base.Model): + """A tool selection of a specific, named function tool that will limit chat completions to using + the named function. + + :ivar name: The name of the function that should be called. Required. + :vartype name: str + """ + + name: str = rest_field() + """The name of the function that should be called. Required.""" + + @overload + def __init__( + self, + *, + name: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class ChatCompletionsResponseFormat(_model_base.Model): + """Represents the format that the model must output. Use this to enable JSON mode instead of the + default text mode. + Note that to enable JSON mode, some AI models may also require you to instruct the model to + produce JSON + via a system or user message. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + ChatCompletionsResponseFormatJsonObject, ChatCompletionsResponseFormatJsonSchema, + ChatCompletionsResponseFormatText + + :ivar type: The response format type to use for chat completions. Required. Default value is + None. + :vartype type: str + """ + + __mapping__: Dict[str, _model_base.Model] = {} + type: str = rest_discriminator(name="type") + """The response format type to use for chat completions. Required. Default value is None.""" + + @overload + def __init__( + self, + *, + type: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class ChatCompletionsResponseFormatJsonObject(ChatCompletionsResponseFormat, discriminator="json_object"): + """A response format for Chat Completions that restricts responses to emitting valid JSON objects. + Note that to enable JSON mode, some AI models may also require you to instruct the model to + produce JSON + via a system or user message. + + :ivar type: Response format type: always 'json_object' for this object. Required. Default value + is "json_object". + :vartype type: str + """ + + type: Literal["json_object"] = rest_discriminator(name="type") # type: ignore + """Response format type: always 'json_object' for this object. Required. Default value is + \"json_object\".""" + + @overload + def __init__( + self, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="json_object", **kwargs) + + +class ChatCompletionsResponseFormatJsonSchema(ChatCompletionsResponseFormat, discriminator="json_schema"): + """A response format for Chat Completions that restricts responses to emitting valid JSON objects, + with a + JSON schema specified by the caller. + + :ivar type: The type of response format being defined: ``json_schema``. Required. Default value + is "json_schema". + :vartype type: str + :ivar json_schema: The definition of the required JSON schema in the response, and associated + metadata. Required. + :vartype json_schema: ~azure.ai.inference.models.JsonSchemaFormat + """ + + type: Literal["json_schema"] = rest_discriminator(name="type") # type: ignore + """The type of response format being defined: ``json_schema``. Required. Default value is + \"json_schema\".""" + json_schema: "_models.JsonSchemaFormat" = rest_field() + """The definition of the required JSON schema in the response, and associated metadata. Required.""" + + @overload + def __init__( + self, + *, + json_schema: "_models.JsonSchemaFormat", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="json_schema", **kwargs) + + +class ChatCompletionsResponseFormatText(ChatCompletionsResponseFormat, discriminator="text"): + """A response format for Chat Completions that emits text responses. This is the default response + format. + + :ivar type: Response format type: always 'text' for this object. Required. Default value is + "text". + :vartype type: str + """ + + type: Literal["text"] = rest_discriminator(name="type") # type: ignore + """Response format type: always 'text' for this object. Required. Default value is \"text\".""" + + @overload + def __init__( + self, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="text", **kwargs) + + +class ChatCompletionsToolCall(_model_base.Model): + """A function tool call requested by the AI model. + + :ivar id: The ID of the tool call. Required. + :vartype id: str + :ivar type: The type of tool call. Currently, only ``function`` is supported. Required. Default + value is "function". + :vartype type: str + :ivar function: The details of the function call requested by the AI model. Required. + :vartype function: ~azure.ai.inference.models.FunctionCall + """ + + id: str = rest_field() + """The ID of the tool call. Required.""" + type: Literal["function"] = rest_field() + """The type of tool call. Currently, only ``function`` is supported. Required. Default value is + \"function\".""" + function: "_models.FunctionCall" = rest_field() + """The details of the function call requested by the AI model. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + function: "_models.FunctionCall", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.type: Literal["function"] = "function" + + +class ChatCompletionsToolDefinition(_model_base.Model): + """The definition of a chat completions tool that can call a function. + + :ivar type: The type of the tool. Currently, only ``function`` is supported. Required. Default + value is "function". + :vartype type: str + :ivar function: The function definition details for the function tool. Required. + :vartype function: ~azure.ai.inference.models.FunctionDefinition + """ + + type: Literal["function"] = rest_field() + """The type of the tool. Currently, only ``function`` is supported. Required. Default value is + \"function\".""" + function: "_models.FunctionDefinition" = rest_field() + """The function definition details for the function tool. Required.""" + + @overload + def __init__( + self, + *, + function: "_models.FunctionDefinition", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self.type: Literal["function"] = "function" + + +class ChatRequestMessage(_model_base.Model): + """An abstract representation of a chat message as provided in a request. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + ChatRequestAssistantMessage, ChatRequestDeveloperMessage, ChatRequestSystemMessage, + ChatRequestToolMessage, ChatRequestUserMessage + + :ivar role: The chat role associated with this message. Required. Known values are: "system", + "user", "assistant", "tool", and "developer". + :vartype role: str or ~azure.ai.inference.models.ChatRole + """ + + __mapping__: Dict[str, _model_base.Model] = {} + role: str = rest_discriminator(name="role") + """The chat role associated with this message. Required. Known values are: \"system\", \"user\", + \"assistant\", \"tool\", and \"developer\".""" + + @overload + def __init__( + self, + *, + role: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class ChatRequestAssistantMessage(ChatRequestMessage, discriminator="assistant"): + """A request chat message representing response or action from the assistant. + + :ivar role: The chat role associated with this message, which is always 'assistant' for + assistant messages. Required. The role that provides responses to system-instructed, + user-prompted input. + :vartype role: str or ~azure.ai.inference.models.ASSISTANT + :ivar content: The content of the message. + :vartype content: str + :ivar tool_calls: The tool calls that must be resolved and have their outputs appended to + subsequent input messages for the chat + completions request to resolve as configured. + :vartype tool_calls: list[~azure.ai.inference.models.ChatCompletionsToolCall] + """ + + role: Literal[ChatRole.ASSISTANT] = rest_discriminator(name="role") # type: ignore + """The chat role associated with this message, which is always 'assistant' for assistant messages. + Required. The role that provides responses to system-instructed, user-prompted input.""" + content: Optional[str] = rest_field() + """The content of the message.""" + tool_calls: Optional[List["_models.ChatCompletionsToolCall"]] = rest_field() + """The tool calls that must be resolved and have their outputs appended to subsequent input + messages for the chat + completions request to resolve as configured.""" + + @overload + def __init__( + self, + *, + content: Optional[str] = None, + tool_calls: Optional[List["_models.ChatCompletionsToolCall"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, role=ChatRole.ASSISTANT, **kwargs) + + +class ChatRequestDeveloperMessage(ChatRequestMessage, discriminator="developer"): + """A request chat message containing system instructions that influence how the model will + generate a chat completions + response. Some AI models support a developer message instead of a system message. + + :ivar role: The chat role associated with this message, which is always 'developer' for + developer messages. Required. The role that instructs or sets the behavior of the assistant. + Some AI models support this role instead of the 'system' role. + :vartype role: str or ~azure.ai.inference.models.DEVELOPER + :ivar content: The contents of the developer message. Required. + :vartype content: str + """ + + role: Literal[ChatRole.DEVELOPER] = rest_discriminator(name="role") # type: ignore + """The chat role associated with this message, which is always 'developer' for developer messages. + Required. The role that instructs or sets the behavior of the assistant. Some AI models support + this role instead of the 'system' role.""" + content: str = rest_field() + """The contents of the developer message. Required.""" + + @overload + def __init__( + self, + *, + content: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, role=ChatRole.DEVELOPER, **kwargs) + + +class ChatRequestSystemMessage(ChatRequestMessage, discriminator="system"): + """A request chat message containing system instructions that influence how the model will + generate a chat completions + response. + + :ivar role: The chat role associated with this message, which is always 'system' for system + messages. Required. The role that instructs or sets the behavior of the assistant. + :vartype role: str or ~azure.ai.inference.models.SYSTEM + :ivar content: The contents of the system message. Required. + :vartype content: str + """ + + role: Literal[ChatRole.SYSTEM] = rest_discriminator(name="role") # type: ignore + """The chat role associated with this message, which is always 'system' for system messages. + Required. The role that instructs or sets the behavior of the assistant.""" + content: str = rest_field() + """The contents of the system message. Required.""" + + @overload + def __init__( + self, + *, + content: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, role=ChatRole.SYSTEM, **kwargs) + + +class ChatRequestToolMessage(ChatRequestMessage, discriminator="tool"): + """A request chat message representing requested output from a configured tool. + + :ivar role: The chat role associated with this message, which is always 'tool' for tool + messages. Required. The role that represents extension tool activity within a chat completions + operation. + :vartype role: str or ~azure.ai.inference.models.TOOL + :ivar content: The content of the message. + :vartype content: str + :ivar tool_call_id: The ID of the tool call resolved by the provided content. Required. + :vartype tool_call_id: str + """ + + role: Literal[ChatRole.TOOL] = rest_discriminator(name="role") # type: ignore + """The chat role associated with this message, which is always 'tool' for tool messages. Required. + The role that represents extension tool activity within a chat completions operation.""" + content: Optional[str] = rest_field() + """The content of the message.""" + tool_call_id: str = rest_field() + """The ID of the tool call resolved by the provided content. Required.""" + + @overload + def __init__( + self, + *, + tool_call_id: str, + content: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, role=ChatRole.TOOL, **kwargs) + + +class ChatRequestUserMessage(ChatRequestMessage, discriminator="user"): + """A request chat message representing user input to the assistant. + + :ivar role: The chat role associated with this message, which is always 'user' for user + messages. Required. The role that provides input for chat completions. + :vartype role: str or ~azure.ai.inference.models.USER + :ivar content: The contents of the user message, with available input types varying by selected + model. Required. Is either a str type or a [ContentItem] type. + :vartype content: str or list[~azure.ai.inference.models.ContentItem] + """ + + role: Literal[ChatRole.USER] = rest_discriminator(name="role") # type: ignore + """The chat role associated with this message, which is always 'user' for user messages. Required. + The role that provides input for chat completions.""" + content: Union["str", List["_models.ContentItem"]] = rest_field() + """The contents of the user message, with available input types varying by selected model. + Required. Is either a str type or a [ContentItem] type.""" + + @overload + def __init__( + self, + *, + content: Union[str, List["_models.ContentItem"]], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, role=ChatRole.USER, **kwargs) + + +class ChatResponseMessage(_model_base.Model): + """A representation of a chat message as received in a response. + + + :ivar role: The chat role associated with the message. Required. Known values are: "system", + "user", "assistant", "tool", and "developer". + :vartype role: str or ~azure.ai.inference.models.ChatRole + :ivar content: The content of the message. Required. + :vartype content: str + :ivar tool_calls: The tool calls that must be resolved and have their outputs appended to + subsequent input messages for the chat + completions request to resolve as configured. + :vartype tool_calls: list[~azure.ai.inference.models.ChatCompletionsToolCall] + """ + + role: Union[str, "_models.ChatRole"] = rest_field() + """The chat role associated with the message. Required. Known values are: \"system\", \"user\", + \"assistant\", \"tool\", and \"developer\".""" + content: str = rest_field() + """The content of the message. Required.""" + tool_calls: Optional[List["_models.ChatCompletionsToolCall"]] = rest_field() + """The tool calls that must be resolved and have their outputs appended to subsequent input + messages for the chat + completions request to resolve as configured.""" + + @overload + def __init__( + self, + *, + role: Union[str, "_models.ChatRole"], + content: str, + tool_calls: Optional[List["_models.ChatCompletionsToolCall"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class CompletionsUsage(_model_base.Model): + """Representation of the token counts processed for a completions request. + Counts consider all tokens across prompts, choices, choice alternates, best_of generations, and + other consumers. + + + :ivar completion_tokens: The number of tokens generated across all completions emissions. + Required. + :vartype completion_tokens: int + :ivar prompt_tokens: The number of tokens in the provided prompts for the completions request. + Required. + :vartype prompt_tokens: int + :ivar total_tokens: The total number of tokens processed for the completions request and + response. Required. + :vartype total_tokens: int + """ + + completion_tokens: int = rest_field() + """The number of tokens generated across all completions emissions. Required.""" + prompt_tokens: int = rest_field() + """The number of tokens in the provided prompts for the completions request. Required.""" + total_tokens: int = rest_field() + """The total number of tokens processed for the completions request and response. Required.""" + + @overload + def __init__( + self, + *, + completion_tokens: int, + prompt_tokens: int, + total_tokens: int, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class EmbeddingItem(_model_base.Model): + """Representation of a single embeddings relatedness comparison. + + + :ivar embedding: List of embedding values for the input prompt. These represent a measurement + of the + vector-based relatedness of the provided input. Or a base64 encoded string of the embedding + vector. Required. Is either a str type or a [float] type. + :vartype embedding: str or list[float] + :ivar index: Index of the prompt to which the EmbeddingItem corresponds. Required. + :vartype index: int + """ + + embedding: Union["str", List[float]] = rest_field() + """List of embedding values for the input prompt. These represent a measurement of the + vector-based relatedness of the provided input. Or a base64 encoded string of the embedding + vector. Required. Is either a str type or a [float] type.""" + index: int = rest_field() + """Index of the prompt to which the EmbeddingItem corresponds. Required.""" + + @overload + def __init__( + self, + *, + embedding: Union[str, List[float]], + index: int, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class EmbeddingsResult(_model_base.Model): + """Representation of the response data from an embeddings request. + Embeddings measure the relatedness of text strings and are commonly used for search, + clustering, + recommendations, and other similar scenarios. + + + :ivar id: Unique identifier for the embeddings result. Required. + :vartype id: str + :ivar data: Embedding values for the prompts submitted in the request. Required. + :vartype data: list[~azure.ai.inference.models.EmbeddingItem] + :ivar usage: Usage counts for tokens input using the embeddings API. Required. + :vartype usage: ~azure.ai.inference.models.EmbeddingsUsage + :ivar model: The model ID used to generate this result. Required. + :vartype model: str + """ + + id: str = rest_field() + """Unique identifier for the embeddings result. Required.""" + data: List["_models.EmbeddingItem"] = rest_field() + """Embedding values for the prompts submitted in the request. Required.""" + usage: "_models.EmbeddingsUsage" = rest_field() + """Usage counts for tokens input using the embeddings API. Required.""" + model: str = rest_field() + """The model ID used to generate this result. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + data: List["_models.EmbeddingItem"], + usage: "_models.EmbeddingsUsage", + model: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class EmbeddingsUsage(_model_base.Model): + """Measurement of the amount of tokens used in this request and response. + + + :ivar prompt_tokens: Number of tokens in the request. Required. + :vartype prompt_tokens: int + :ivar total_tokens: Total number of tokens transacted in this request/response. Should equal + the + number of tokens in the request. Required. + :vartype total_tokens: int + """ + + prompt_tokens: int = rest_field() + """Number of tokens in the request. Required.""" + total_tokens: int = rest_field() + """Total number of tokens transacted in this request/response. Should equal the + number of tokens in the request. Required.""" + + @overload + def __init__( + self, + *, + prompt_tokens: int, + total_tokens: int, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class FunctionCall(_model_base.Model): + """The name and arguments of a function that should be called, as generated by the model. + + + :ivar name: The name of the function to call. Required. + :vartype name: str + :ivar arguments: The arguments to call the function with, as generated by the model in JSON + format. + Note that the model does not always generate valid JSON, and may hallucinate parameters + not defined by your function schema. Validate the arguments in your code before calling + your function. Required. + :vartype arguments: str + """ + + name: str = rest_field() + """The name of the function to call. Required.""" + arguments: str = rest_field() + """The arguments to call the function with, as generated by the model in JSON format. + Note that the model does not always generate valid JSON, and may hallucinate parameters + not defined by your function schema. Validate the arguments in your code before calling + your function. Required.""" + + @overload + def __init__( + self, + *, + name: str, + arguments: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class FunctionDefinition(_model_base.Model): + """The definition of a caller-specified function that chat completions may invoke in response to + matching user input. + + :ivar name: The name of the function to be called. Required. + :vartype name: str + :ivar description: A description of what the function does. The model will use this description + when selecting the function and + interpreting its parameters. + :vartype description: str + :ivar parameters: The parameters the function accepts, described as a JSON Schema object. + :vartype parameters: any + """ + + name: str = rest_field() + """The name of the function to be called. Required.""" + description: Optional[str] = rest_field() + """A description of what the function does. The model will use this description when selecting the + function and + interpreting its parameters.""" + parameters: Optional[Any] = rest_field() + """The parameters the function accepts, described as a JSON Schema object.""" + + @overload + def __init__( + self, + *, + name: str, + description: Optional[str] = None, + parameters: Optional[Any] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class ImageContentItem(ContentItem, discriminator="image_url"): + """A structured chat content item containing an image reference. + + :ivar type: The discriminated object type: always 'image_url' for this type. Required. Default + value is "image_url". + :vartype type: str + :ivar image_url: An internet location, which must be accessible to the model,from which the + image may be retrieved. Required. + :vartype image_url: ~azure.ai.inference.models.ImageUrl + """ + + type: Literal["image_url"] = rest_discriminator(name="type") # type: ignore + """The discriminated object type: always 'image_url' for this type. Required. Default value is + \"image_url\".""" + image_url: "_models.ImageUrl" = rest_field() + """An internet location, which must be accessible to the model,from which the image may be + retrieved. Required.""" + + @overload + def __init__( + self, + *, + image_url: "_models.ImageUrl", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="image_url", **kwargs) + + +class ImageEmbeddingInput(_model_base.Model): + """Represents an image with optional text. + + :ivar image: The input image encoded in base64 string as a data URL. Example: + ``data:image/{format};base64,{data}``. Required. + :vartype image: str + :ivar text: Optional. The text input to feed into the model (like DINO, CLIP). + Returns a 422 error if the model doesn't support the value or parameter. + :vartype text: str + """ + + image: str = rest_field() + """The input image encoded in base64 string as a data URL. Example: + ``data:image/{format};base64,{data}``. Required.""" + text: Optional[str] = rest_field() + """Optional. The text input to feed into the model (like DINO, CLIP). + Returns a 422 error if the model doesn't support the value or parameter.""" + + @overload + def __init__( + self, + *, + image: str, + text: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class ImageUrl(_model_base.Model): + """An internet location from which the model may retrieve an image. + + :ivar url: The URL of the image. Required. + :vartype url: str + :ivar detail: The evaluation quality setting to use, which controls relative prioritization of + speed, token consumption, and + accuracy. Known values are: "auto", "low", and "high". + :vartype detail: str or ~azure.ai.inference.models.ImageDetailLevel + """ + + url: str = rest_field() + """The URL of the image. Required.""" + detail: Optional[Union[str, "_models.ImageDetailLevel"]] = rest_field() + """The evaluation quality setting to use, which controls relative prioritization of speed, token + consumption, and + accuracy. Known values are: \"auto\", \"low\", and \"high\".""" + + @overload + def __init__( + self, + *, + url: str, + detail: Optional[Union[str, "_models.ImageDetailLevel"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class InputAudio(_model_base.Model): + """The details of an audio chat message content part. + + :ivar data: Base64 encoded audio data. Required. + :vartype data: str + :ivar format: The audio format of the audio content. Required. Known values are: "wav" and + "mp3". + :vartype format: str or ~azure.ai.inference.models.AudioContentFormat + """ + + data: str = rest_field() + """Base64 encoded audio data. Required.""" + format: Union[str, "_models.AudioContentFormat"] = rest_field() + """The audio format of the audio content. Required. Known values are: \"wav\" and \"mp3\".""" + + @overload + def __init__( + self, + *, + data: str, + format: Union[str, "_models.AudioContentFormat"], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class JsonSchemaFormat(_model_base.Model): + """Defines the response format for chat completions as JSON with a given schema. + The AI model will need to adhere to this schema when generating completions. + + :ivar name: A name that labels this JSON schema. Must be a-z, A-Z, 0-9, or contain underscores + and dashes, with a maximum length of 64. Required. + :vartype name: str + :ivar schema: The definition of the JSON schema. See + https://json-schema.org/overview/what-is-jsonschema. + Note that AI models usually only support a subset of the keywords defined by JSON schema. + Consult your AI model documentation to determine what is supported. Required. + :vartype schema: dict[str, any] + :ivar description: A description of the response format, used by the AI model to determine how + to generate responses in this format. + :vartype description: str + :ivar strict: If set to true, the service will error out if the provided JSON schema contains + keywords + not supported by the AI model. An example of such keyword may be ``maxLength`` for JSON type + ``string``. + If false, and the provided JSON schema contains keywords not supported by the AI model, + the AI model will not error out. Instead it will ignore the unsupported keywords. + :vartype strict: bool + """ + + name: str = rest_field() + """A name that labels this JSON schema. Must be a-z, A-Z, 0-9, or contain underscores and dashes, + with a maximum length of 64. Required.""" + schema: Dict[str, Any] = rest_field() + """The definition of the JSON schema. See https://json-schema.org/overview/what-is-jsonschema. + Note that AI models usually only support a subset of the keywords defined by JSON schema. + Consult your AI model documentation to determine what is supported. Required.""" + description: Optional[str] = rest_field() + """A description of the response format, used by the AI model to determine how to generate + responses in this format.""" + strict: Optional[bool] = rest_field() + """If set to true, the service will error out if the provided JSON schema contains keywords + not supported by the AI model. An example of such keyword may be ``maxLength`` for JSON type + ``string``. + If false, and the provided JSON schema contains keywords not supported by the AI model, + the AI model will not error out. Instead it will ignore the unsupported keywords.""" + + @overload + def __init__( + self, + *, + name: str, + schema: Dict[str, Any], + description: Optional[str] = None, + strict: Optional[bool] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class ModelInfo(_model_base.Model): + """Represents some basic information about the AI model. + + + :ivar model_name: The name of the AI model. For example: ``Phi21``. Required. + :vartype model_name: str + :ivar model_type: The type of the AI model. A Unique identifier for the profile. Required. + Known values are: "embeddings", "image_generation", "text_generation", "image_embeddings", + "audio_generation", and "chat_completion". + :vartype model_type: str or ~azure.ai.inference.models.ModelType + :ivar model_provider_name: The model provider name. For example: ``Microsoft Research``. + Required. + :vartype model_provider_name: str + """ + + model_name: str = rest_field() + """The name of the AI model. For example: ``Phi21``. Required.""" + model_type: Union[str, "_models.ModelType"] = rest_field() + """The type of the AI model. A Unique identifier for the profile. Required. Known values are: + \"embeddings\", \"image_generation\", \"text_generation\", \"image_embeddings\", + \"audio_generation\", and \"chat_completion\".""" + model_provider_name: str = rest_field() + """The model provider name. For example: ``Microsoft Research``. Required.""" + + @overload + def __init__( + self, + *, + model_name: str, + model_type: Union[str, "_models.ModelType"], + model_provider_name: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class StreamingChatChoiceUpdate(_model_base.Model): + """Represents an update to a single prompt completion when the service is streaming updates + using Server Sent Events (SSE). + Generally, ``n`` choices are generated per provided prompt with a default value of 1. + Token limits and other settings may limit the number of choices generated. + + + :ivar index: The ordered index associated with this chat completions choice. Required. + :vartype index: int + :ivar finish_reason: The reason that this chat completions choice completed its generated. + Required. Known values are: "stop", "length", "content_filter", and "tool_calls". + :vartype finish_reason: str or ~azure.ai.inference.models.CompletionsFinishReason + :ivar delta: An update to the chat message for a given chat completions prompt. Required. + :vartype delta: ~azure.ai.inference.models.StreamingChatResponseMessageUpdate + """ + + index: int = rest_field() + """The ordered index associated with this chat completions choice. Required.""" + finish_reason: Union[str, "_models.CompletionsFinishReason"] = rest_field() + """The reason that this chat completions choice completed its generated. Required. Known values + are: \"stop\", \"length\", \"content_filter\", and \"tool_calls\".""" + delta: "_models.StreamingChatResponseMessageUpdate" = rest_field() + """An update to the chat message for a given chat completions prompt. Required.""" + + @overload + def __init__( + self, + *, + index: int, + finish_reason: Union[str, "_models.CompletionsFinishReason"], + delta: "_models.StreamingChatResponseMessageUpdate", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class StreamingChatCompletionsUpdate(_model_base.Model): + """Represents a response update to a chat completions request, when the service is streaming + updates + using Server Sent Events (SSE). + Completions support a wide variety of tasks and generate text that continues from or + "completes" + provided prompt data. + + + :ivar id: A unique identifier associated with this chat completions response. Required. + :vartype id: str + :ivar created: The first timestamp associated with generation activity for this completions + response, + represented as seconds since the beginning of the Unix epoch of 00:00 on 1 Jan 1970. Required. + :vartype created: ~datetime.datetime + :ivar model: The model used for the chat completion. Required. + :vartype model: str + :ivar choices: An update to the collection of completion choices associated with this + completions response. + Generally, ``n`` choices are generated per provided prompt with a default value of 1. + Token limits and other settings may limit the number of choices generated. Required. + :vartype choices: list[~azure.ai.inference.models.StreamingChatChoiceUpdate] + :ivar usage: Usage information for tokens processed and generated as part of this completions + operation. + :vartype usage: ~azure.ai.inference.models.CompletionsUsage + """ + + id: str = rest_field() + """A unique identifier associated with this chat completions response. Required.""" + created: datetime.datetime = rest_field(format="unix-timestamp") + """The first timestamp associated with generation activity for this completions response, + represented as seconds since the beginning of the Unix epoch of 00:00 on 1 Jan 1970. Required.""" + model: str = rest_field() + """The model used for the chat completion. Required.""" + choices: List["_models.StreamingChatChoiceUpdate"] = rest_field() + """An update to the collection of completion choices associated with this completions response. + Generally, ``n`` choices are generated per provided prompt with a default value of 1. + Token limits and other settings may limit the number of choices generated. Required.""" + usage: Optional["_models.CompletionsUsage"] = rest_field() + """Usage information for tokens processed and generated as part of this completions operation.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + created: datetime.datetime, + model: str, + choices: List["_models.StreamingChatChoiceUpdate"], + usage: Optional["_models.CompletionsUsage"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class StreamingChatResponseMessageUpdate(_model_base.Model): + """A representation of a chat message update as received in a streaming response. + + :ivar role: The chat role associated with the message. If present, should always be + 'assistant'. Known values are: "system", "user", "assistant", "tool", and "developer". + :vartype role: str or ~azure.ai.inference.models.ChatRole + :ivar content: The content of the message. + :vartype content: str + :ivar tool_calls: The tool calls that must be resolved and have their outputs appended to + subsequent input messages for the chat + completions request to resolve as configured. + :vartype tool_calls: list[~azure.ai.inference.models.StreamingChatResponseToolCallUpdate] + """ + + role: Optional[Union[str, "_models.ChatRole"]] = rest_field() + """The chat role associated with the message. If present, should always be 'assistant'. Known + values are: \"system\", \"user\", \"assistant\", \"tool\", and \"developer\".""" + content: Optional[str] = rest_field() + """The content of the message.""" + tool_calls: Optional[List["_models.StreamingChatResponseToolCallUpdate"]] = rest_field() + """The tool calls that must be resolved and have their outputs appended to subsequent input + messages for the chat + completions request to resolve as configured.""" + + @overload + def __init__( + self, + *, + role: Optional[Union[str, "_models.ChatRole"]] = None, + content: Optional[str] = None, + tool_calls: Optional[List["_models.StreamingChatResponseToolCallUpdate"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class StreamingChatResponseToolCallUpdate(_model_base.Model): + """An update to the function tool call information requested by the AI model. + + + :ivar id: The ID of the tool call. Required. + :vartype id: str + :ivar function: Updates to the function call requested by the AI model. Required. + :vartype function: ~azure.ai.inference.models.FunctionCall + """ + + id: str = rest_field() + """The ID of the tool call. Required.""" + function: "_models.FunctionCall" = rest_field() + """Updates to the function call requested by the AI model. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + function: "_models.FunctionCall", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class TextContentItem(ContentItem, discriminator="text"): + """A structured chat content item containing plain text. + + :ivar type: The discriminated object type: always 'text' for this type. Required. Default value + is "text". + :vartype type: str + :ivar text: The content of the message. Required. + :vartype text: str + """ + + type: Literal["text"] = rest_discriminator(name="type") # type: ignore + """The discriminated object type: always 'text' for this type. Required. Default value is + \"text\".""" + text: str = rest_field() + """The content of the message. Required.""" + + @overload + def __init__( + self, + *, + text: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, type="text", **kwargs) |