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Python: updated doc generation setup and some slight api enhancements (#267)
* updated doc generation setup and some slight api enhancements * small fix in index
This commit is contained in:
Eduard van Valkenburg
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139f033b05
commit
42c7a59640
@@ -4,6 +4,7 @@ import importlib.metadata
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from ._chat_client import AzureChatClient
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from ._entra_id_authentication import get_entra_auth_token
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from ._shared import AzureOpenAISettings
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try:
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__version__ = importlib.metadata.version(__name__)
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@@ -12,6 +13,7 @@ except importlib.metadata.PackageNotFoundError:
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__all__ = [
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"AzureChatClient",
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"AzureOpenAISettings",
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"__version__",
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"get_entra_auth_token",
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]
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@@ -16,8 +16,8 @@ from agent_framework import (
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TextContent,
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)
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from agent_framework.exceptions import ServiceInitializationError
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from agent_framework.openai import OpenAIModelTypes
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from agent_framework.openai._chat_client import OpenAIChatClientBase
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from agent_framework.openai._shared import OpenAIModelTypes
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from openai.lib.azure import AsyncAzureADTokenProvider, AsyncAzureOpenAI
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from openai.types.chat.chat_completion import ChatCompletion, Choice
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from openai.types.chat.chat_completion_chunk import ChatCompletionChunk
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@@ -59,25 +59,25 @@ class AzureChatClient(AzureOpenAIConfigBase, OpenAIChatClientBase):
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"""Initialize an AzureChatCompletion service.
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Args:
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api_key (str | None): The optional api key. If provided, will override the value in the
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api_key: The optional api key. If provided, will override the value in the
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env vars or .env file.
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deployment_name (str | None): The optional deployment. If provided, will override the value
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deployment_name: The optional deployment. If provided, will override the value
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(chat_deployment_name) in the env vars or .env file.
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endpoint (str | None): The optional deployment endpoint. If provided will override the value
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endpoint: The optional deployment endpoint. If provided will override the value
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in the env vars or .env file.
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base_url (str | None): The optional deployment base_url. If provided will override the value
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base_url: The optional deployment base_url. If provided will override the value
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in the env vars or .env file.
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api_version (str | None): The optional deployment api version. If provided will override the value
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api_version: The optional deployment api version. If provided will override the value
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in the env vars or .env file.
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ad_token (str | None): The Azure Active Directory token. (Optional)
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ad_token_provider (AsyncAzureADTokenProvider): The Azure Active Directory token provider. (Optional)
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token_endpoint (str | None): The token endpoint to request an Azure token. (Optional)
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default_headers (Mapping[str, str]): The default headers mapping of string keys to
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ad_token: The Azure Active Directory token. (Optional)
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ad_token_provider: The Azure Active Directory token provider. (Optional)
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token_endpoint: The token endpoint to request an Azure token. (Optional)
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default_headers: The default headers mapping of string keys to
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string values for HTTP requests. (Optional)
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async_client (AsyncAzureOpenAI | None): An existing client to use. (Optional)
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env_file_path (str | None): Use the environment settings file as a fallback to using env vars.
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env_file_encoding (str | None): The encoding of the environment settings file, defaults to 'utf-8'.
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instruction_role (str | None): The role to use for 'instruction' messages, for example, summarization
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async_client: An existing client to use. (Optional)
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env_file_path: Use the environment settings file as a fallback to using env vars.
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env_file_encoding: The encoding of the environment settings file, defaults to 'utf-8'.
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instruction_role: The role to use for 'instruction' messages, for example, summarization
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prompts could use `developer` or `system`. (Optional)
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"""
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try:
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@@ -120,7 +120,7 @@ class AzureChatClient(AzureOpenAIConfigBase, OpenAIChatClientBase):
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Args:
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settings: A dictionary of settings for the service.
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should contain keys: service_id, and optionally:
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ad_auth, ad_token_provider, default_headers
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ad_auth, ad_token_provider, default_headers
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"""
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return AzureChatClient(
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api_key=settings.get("api_key"),
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@@ -193,7 +193,7 @@ class AzureChatClient(AzureOpenAIConfigBase, OpenAIChatClientBase):
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return None # pragma: no cover
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@staticmethod
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def split_message(message: "ChatResponse") -> ChatResponse:
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def _split_message(message: "ChatResponse") -> ChatResponse:
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"""Split an Azure On Your Data response into separate ChatMessages within the ChatResponse.
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If the message does not have three contents, and those three are one each of:
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@@ -6,9 +6,9 @@ from collections.abc import Awaitable, Callable, Mapping
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from copy import copy
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from typing import Any, ClassVar, Final
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from agent_framework import AFBaseSettings, HttpsUrl
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from agent_framework._pydantic import AFBaseSettings, HttpsUrl
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from agent_framework.exceptions import ServiceInitializationError
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from agent_framework.openai import OpenAIHandler, OpenAIModelTypes
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from agent_framework.openai._shared import OpenAIHandler, OpenAIModelTypes
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from agent_framework.telemetry import USER_AGENT_KEY
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from openai.lib.azure import AsyncAzureOpenAI
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from pydantic import ConfigDict, SecretStr, validate_call
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@@ -30,75 +30,79 @@ class AzureOpenAISettings(AFBaseSettings):
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with the encoding 'utf-8'. If the settings are not found in the .env file, the settings
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are ignored; however, validation will fail alerting that the settings are missing.
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Optional settings for prefix 'AZURE_OPENAI_' are:
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- chat_deployment_name: str - The name of the Azure Chat deployment. This value
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will correspond to the custom name you chose for your deployment
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when you deployed a model. This value can be found under
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Resource Management > Deployments in the Azure portal or, alternatively,
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under Management > Deployments in Azure AI Foundry.
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(Env var AZURE_OPENAI_CHAT_DEPLOYMENT_NAME)
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- responses_deployment_name: str - The name of the Azure Responses deployment. This value
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will correspond to the custom name you chose for your deployment
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when you deployed a model. This value can be found under
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Resource Management > Deployments in the Azure portal or, alternatively,
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under Management > Deployments in Azure AI Foundry.
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(Env var AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME)
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- text_deployment_name: str - The name of the Azure Text deployment. This value
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will correspond to the custom name you chose for your deployment
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when you deployed a model. This value can be found under
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Resource Management > Deployments in the Azure portal or, alternatively,
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under Management > Deployments in Azure AI Foundry.
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(Env var AZURE_OPENAI_TEXT_DEPLOYMENT_NAME)
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- embedding_deployment_name: str - The name of the Azure Embedding deployment. This value
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will correspond to the custom name you chose for your deployment
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when you deployed a model. This value can be found under
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Resource Management > Deployments in the Azure portal or, alternatively,
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under Management > Deployments in Azure AI Foundry.
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(Env var AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME)
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- text_to_image_deployment_name: str - The name of the Azure Text to Image deployment. This
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value will correspond to the custom name you chose for your deployment
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when you deployed a model. This value can be found under
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Resource Management > Deployments in the Azure portal or, alternatively,
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under Management > Deployments in Azure AI Foundry.
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(Env var AZURE_OPENAI_TEXT_TO_IMAGE_DEPLOYMENT_NAME)
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- audio_to_text_deployment_name: str - The name of the Azure Audio to Text deployment. This
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value will correspond to the custom name you chose for your deployment
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when you deployed a model. This value can be found under
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Resource Management > Deployments in the Azure portal or, alternatively,
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under Management > Deployments in Azure AI Foundry.
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(Env var AZURE_OPENAI_AUDIO_TO_TEXT_DEPLOYMENT_NAME)
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- text_to_audio_deployment_name: str - The name of the Azure Text to Audio deployment. This
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value will correspond to the custom name you chose for your deployment
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when you deployed a model. This value can be found under
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Resource Management > Deployments in the Azure portal or, alternatively,
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under Management > Deployments in Azure AI Foundry.
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(Env var AZURE_OPENAI_TEXT_TO_AUDIO_DEPLOYMENT_NAME)
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- realtime_deployment_name: str - The name of the Azure Realtime deployment. This value
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will correspond to the custom name you chose for your deployment
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when you deployed a model. This value can be found under
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Resource Management > Deployments in the Azure portal or, alternatively,
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under Management > Deployments in Azure AI Foundry.
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(Env var AZURE_OPENAI_REALTIME_DEPLOYMENT_NAME)
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- api_key: SecretStr - The API key for the Azure deployment. This value can be
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found in the Keys & Endpoint section when examining your resource in
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the Azure portal. You can use either KEY1 or KEY2.
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(Env var AZURE_OPENAI_API_KEY)
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- base_url: HttpsUrl | None - base_url: The url of the Azure deployment. This value
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can be found in the Keys & Endpoint section when examining
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your resource from the Azure portal, the base_url consists of the endpoint,
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followed by /openai/deployments/{deployment_name}/,
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use endpoint if you only want to supply the endpoint.
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(Env var AZURE_OPENAI_BASE_URL)
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- endpoint: HttpsUrl - The endpoint of the Azure deployment. This value
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can be found in the Keys & Endpoint section when examining
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your resource from the Azure portal, the endpoint should end in openai.azure.com.
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If both base_url and endpoint are supplied, base_url will be used.
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(Env var AZURE_OPENAI_ENDPOINT)
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- api_version: str | None - The API version to use. The default value is "2024-02-01".
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(Env var AZURE_OPENAI_API_VERSION)
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- token_endpoint: str - The token endpoint to use to retrieve the authentication token.
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The default value is "https://cognitiveservices.azure.com/.default".
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(Env var AZURE_OPENAI_TOKEN_ENDPOINT)
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Attributes:
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chat_deployment_name: The name of the Azure Chat deployment. This value
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will correspond to the custom name you chose for your deployment
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when you deployed a model. This value can be found under
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Resource Management > Deployments in the Azure portal or, alternatively,
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under Management > Deployments in Azure AI Foundry.
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(Env var AZURE_OPENAI_CHAT_DEPLOYMENT_NAME)
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responses_deployment_name: The name of the Azure Responses deployment. This value
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will correspond to the custom name you chose for your deployment
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when you deployed a model. This value can be found under
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Resource Management > Deployments in the Azure portal or, alternatively,
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under Management > Deployments in Azure AI Foundry.
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(Env var AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME)
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text_deployment_name: The name of the Azure Text deployment. This value
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will correspond to the custom name you chose for your deployment
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when you deployed a model. This value can be found under
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Resource Management > Deployments in the Azure portal or, alternatively,
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under Management > Deployments in Azure AI Foundry.
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(Env var AZURE_OPENAI_TEXT_DEPLOYMENT_NAME)
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embedding_deployment_name: The name of the Azure Embedding deployment. This value
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will correspond to the custom name you chose for your deployment
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when you deployed a model. This value can be found under
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Resource Management > Deployments in the Azure portal or, alternatively,
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under Management > Deployments in Azure AI Foundry.
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(Env var AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME)
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text_to_image_deployment_name: The name of the Azure Text to Image deployment. This
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value will correspond to the custom name you chose for your deployment
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when you deployed a model. This value can be found under
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Resource Management > Deployments in the Azure portal or, alternatively,
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under Management > Deployments in Azure AI Foundry.
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(Env var AZURE_OPENAI_TEXT_TO_IMAGE_DEPLOYMENT_NAME)
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audio_to_text_deployment_name: The name of the Azure Audio to Text deployment. This
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value will correspond to the custom name you chose for your deployment
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when you deployed a model. This value can be found under
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Resource Management > Deployments in the Azure portal or, alternatively,
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under Management > Deployments in Azure AI Foundry.
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(Env var AZURE_OPENAI_AUDIO_TO_TEXT_DEPLOYMENT_NAME)
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text_to_audio_deployment_name: The name of the Azure Text to Audio deployment. This
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value will correspond to the custom name you chose for your deployment
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when you deployed a model. This value can be found under
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Resource Management > Deployments in the Azure portal or, alternatively,
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under Management > Deployments in Azure AI Foundry.
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(Env var AZURE_OPENAI_TEXT_TO_AUDIO_DEPLOYMENT_NAME)
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realtime_deployment_name: The name of the Azure Realtime deployment. This value
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will correspond to the custom name you chose for your deployment
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when you deployed a model. This value can be found under
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Resource Management > Deployments in the Azure portal or, alternatively,
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under Management > Deployments in Azure AI Foundry.
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(Env var AZURE_OPENAI_REALTIME_DEPLOYMENT_NAME)
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api_key: The API key for the Azure deployment. This value can be
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found in the Keys & Endpoint section when examining your resource in
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the Azure portal. You can use either KEY1 or KEY2.
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(Env var AZURE_OPENAI_API_KEY)
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base_url: The url of the Azure deployment. This value
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can be found in the Keys & Endpoint section when examining
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your resource from the Azure portal, the base_url consists of the endpoint,
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followed by /openai/deployments/{deployment_name}/,
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use endpoint if you only want to supply the endpoint.
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(Env var AZURE_OPENAI_BASE_URL)
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endpoint: The endpoint of the Azure deployment. This value
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can be found in the Keys & Endpoint section when examining
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your resource from the Azure portal, the endpoint should end in openai.azure.com.
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If both base_url and endpoint are supplied, base_url will be used.
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(Env var AZURE_OPENAI_ENDPOINT)
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api_version: The API version to use. The default value is "2024-02-01".
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(Env var AZURE_OPENAI_API_VERSION)
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token_endpoint: The token endpoint to use to retrieve the authentication token.
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The default value is "https://cognitiveservices.azure.com/.default".
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(Env var AZURE_OPENAI_TOKEN_ENDPOINT)
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Parameters:
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env_file_path: The path to the .env file to load settings from.
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env_file_encoding: The encoding of the .env file, defaults to 'utf-8'.
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"""
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env_prefix: ClassVar[str] = "AZURE_OPENAI_"
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@@ -476,7 +476,7 @@ async def test_azure_on_your_data_split_messages(
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content = await azure_chat_client.get_response(
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messages=messages_in,
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)
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message = azure_chat_client.split_message(content)
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message = azure_chat_client._split_message(content)
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assert len(content.messages) == 1
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assert len(content.messages[0].contents) == 3
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assert isinstance(content.messages[0].contents[0], FunctionCallContent)
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