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openai[minor]: implement langchain-openai package (#15503)
Todo - [x] copy over integration tests - [x] update docs with new instructions in #15513 - [x] add linear ticket to bump core -> community, community->langchain, and core->openai deps - [ ] (optional): add `pip install langchain-openai` command to each notebook using it - [x] Update docstrings to not need `openai` install - [x] Add serialization - [x] deprecate old models Contributor steps: - [x] Add secret names to manual integrations workflow in .github/workflows/_integration_test.yml - [x] Add secrets to release workflow (for pre-release testing) in .github/workflows/_release.yml Maintainer steps (Contributors should not do these): - [x] set up pypi and test pypi projects - [x] add credential secrets to Github Actions - [ ] add package to conda-forge Functional changes to existing classes: - now relies on openai client v1 (1.6.1) via concrete dep in langchain-openai package Codebase organization - some function calling stuff moved to `langchain_core.utils.function_calling` in order to be used in both community and langchain-openai
This commit is contained in:
Erick Friis
GitHub
218
libs/partners/openai/langchain_openai/chat_models/azure.py
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218
libs/partners/openai/langchain_openai/chat_models/azure.py
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"""Azure OpenAI chat wrapper."""
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from __future__ import annotations
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import logging
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import os
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from typing import Any, Callable, Dict, List, Union
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import openai
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from langchain_core.outputs import ChatResult
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from langchain_core.pydantic_v1 import BaseModel, Field, root_validator
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from langchain_core.utils import get_from_dict_or_env
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from langchain_openai.chat_models.base import ChatOpenAI
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logger = logging.getLogger(__name__)
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class AzureChatOpenAI(ChatOpenAI):
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"""`Azure OpenAI` Chat Completion API.
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To use this class you
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must have a deployed model on Azure OpenAI. Use `deployment_name` in the
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constructor to refer to the "Model deployment name" in the Azure portal.
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In addition, you should have the
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following environment variables set or passed in constructor in lower case:
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- ``AZURE_OPENAI_API_KEY``
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- ``AZURE_OPENAI_ENDPOINT``
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- ``AZURE_OPENAI_AD_TOKEN``
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- ``OPENAI_API_VERSION``
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- ``OPENAI_PROXY``
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For example, if you have `gpt-3.5-turbo` deployed, with the deployment name
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`35-turbo-dev`, the constructor should look like:
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.. code-block:: python
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AzureChatOpenAI(
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azure_deployment="35-turbo-dev",
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openai_api_version="2023-05-15",
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)
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Be aware the API version may change.
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You can also specify the version of the model using ``model_version`` constructor
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parameter, as Azure OpenAI doesn't return model version with the response.
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Default is empty. When you specify the version, it will be appended to the
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model name in the response. Setting correct version will help you to calculate the
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cost properly. Model version is not validated, so make sure you set it correctly
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to get the correct cost.
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Any parameters that are valid to be passed to the openai.create call can be passed
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in, even if not explicitly saved on this class.
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"""
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azure_endpoint: Union[str, None] = None
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"""Your Azure endpoint, including the resource.
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Automatically inferred from env var `AZURE_OPENAI_ENDPOINT` if not provided.
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Example: `https://example-resource.azure.openai.com/`
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"""
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deployment_name: Union[str, None] = Field(default=None, alias="azure_deployment")
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"""A model deployment.
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If given sets the base client URL to include `/deployments/{azure_deployment}`.
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Note: this means you won't be able to use non-deployment endpoints.
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"""
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openai_api_version: str = Field(default="", alias="api_version")
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"""Automatically inferred from env var `OPENAI_API_VERSION` if not provided."""
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openai_api_key: Union[str, None] = Field(default=None, alias="api_key")
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"""Automatically inferred from env var `AZURE_OPENAI_API_KEY` if not provided."""
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azure_ad_token: Union[str, None] = None
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"""Your Azure Active Directory token.
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Automatically inferred from env var `AZURE_OPENAI_AD_TOKEN` if not provided.
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For more:
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https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id.
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""" # noqa: E501
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azure_ad_token_provider: Union[Callable[[], str], None] = None
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"""A function that returns an Azure Active Directory token.
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Will be invoked on every request.
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"""
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model_version: str = ""
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"""Legacy, for openai<1.0.0 support."""
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openai_api_type: str = ""
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"""Legacy, for openai<1.0.0 support."""
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validate_base_url: bool = True
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"""For backwards compatibility. If legacy val openai_api_base is passed in, try to
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infer if it is a base_url or azure_endpoint and update accordingly.
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"""
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@classmethod
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def get_lc_namespace(cls) -> List[str]:
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"""Get the namespace of the langchain object."""
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return ["langchain", "chat_models", "azure_openai"]
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key and python package exists in environment."""
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if values["n"] < 1:
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raise ValueError("n must be at least 1.")
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if values["n"] > 1 and values["streaming"]:
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raise ValueError("n must be 1 when streaming.")
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# Check OPENAI_KEY for backwards compatibility.
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# TODO: Remove OPENAI_API_KEY support to avoid possible conflict when using
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# other forms of azure credentials.
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values["openai_api_key"] = (
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values["openai_api_key"]
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or os.getenv("AZURE_OPENAI_API_KEY")
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or os.getenv("OPENAI_API_KEY")
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)
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values["openai_api_base"] = values["openai_api_base"] or os.getenv(
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"OPENAI_API_BASE"
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)
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values["openai_api_version"] = values["openai_api_version"] or os.getenv(
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"OPENAI_API_VERSION"
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)
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# Check OPENAI_ORGANIZATION for backwards compatibility.
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values["openai_organization"] = (
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values["openai_organization"]
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or os.getenv("OPENAI_ORG_ID")
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or os.getenv("OPENAI_ORGANIZATION")
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)
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values["azure_endpoint"] = values["azure_endpoint"] or os.getenv(
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"AZURE_OPENAI_ENDPOINT"
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)
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values["azure_ad_token"] = values["azure_ad_token"] or os.getenv(
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"AZURE_OPENAI_AD_TOKEN"
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)
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values["openai_api_type"] = get_from_dict_or_env(
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values, "openai_api_type", "OPENAI_API_TYPE", default="azure"
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)
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values["openai_proxy"] = get_from_dict_or_env(
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values, "openai_proxy", "OPENAI_PROXY", default=""
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)
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# For backwards compatibility. Before openai v1, no distinction was made
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# between azure_endpoint and base_url (openai_api_base).
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openai_api_base = values["openai_api_base"]
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if openai_api_base and values["validate_base_url"]:
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if "/openai" not in openai_api_base:
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raise ValueError(
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"As of openai>=1.0.0, Azure endpoints should be specified via "
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"the `azure_endpoint` param not `openai_api_base` "
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"(or alias `base_url`)."
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)
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if values["deployment_name"]:
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raise ValueError(
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"As of openai>=1.0.0, if `deployment_name` (or alias "
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"`azure_deployment`) is specified then "
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"`openai_api_base` (or alias `base_url`) should not be. "
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"Instead use `deployment_name` (or alias `azure_deployment`) "
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"and `azure_endpoint`."
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)
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client_params = {
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"api_version": values["openai_api_version"],
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"azure_endpoint": values["azure_endpoint"],
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"azure_deployment": values["deployment_name"],
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"api_key": values["openai_api_key"],
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"azure_ad_token": values["azure_ad_token"],
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"azure_ad_token_provider": values["azure_ad_token_provider"],
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"organization": values["openai_organization"],
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"base_url": values["openai_api_base"],
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"timeout": values["request_timeout"],
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"max_retries": values["max_retries"],
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"default_headers": values["default_headers"],
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"default_query": values["default_query"],
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"http_client": values["http_client"],
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}
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values["client"] = openai.AzureOpenAI(**client_params).chat.completions
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values["async_client"] = openai.AsyncAzureOpenAI(
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**client_params
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).chat.completions
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return values
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@property
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def _identifying_params(self) -> Dict[str, Any]:
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"""Get the identifying parameters."""
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return {**self._default_params}
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@property
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def _llm_type(self) -> str:
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return "azure-openai-chat"
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@property
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def lc_attributes(self) -> Dict[str, Any]:
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return {
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"openai_api_type": self.openai_api_type,
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"openai_api_version": self.openai_api_version,
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}
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def _create_chat_result(self, response: Union[dict, BaseModel]) -> ChatResult:
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if not isinstance(response, dict):
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response = response.dict()
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for res in response["choices"]:
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if res.get("finish_reason", None) == "content_filter":
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raise ValueError(
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"Azure has not provided the response due to a content filter "
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"being triggered"
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)
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chat_result = super()._create_chat_result(response)
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if "model" in response:
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model = response["model"]
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if self.model_version:
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model = f"{model}-{self.model_version}"
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if chat_result.llm_output is not None and isinstance(
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chat_result.llm_output, dict
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):
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chat_result.llm_output["model_name"] = model
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return chat_result
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