Source code for

"""Azure OpenAI chat wrapper."""
from __future__ import annotations

import logging
import os
from typing import Any, Callable, Dict, List, Optional, Union

import openai
from langchain_core.language_models.chat_models import LangSmithParams
from langchain_core.outputs import ChatResult
from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env

from langchain_openai.chat_models.base import BaseChatOpenAI

logger = logging.getLogger(__name__)

[docs]class AzureChatOpenAI(BaseChatOpenAI): """`Azure OpenAI` Chat Completion API. To use this class you must have a deployed model on Azure OpenAI. Use `deployment_name` in the constructor to refer to the "Model deployment name" in the Azure portal. In addition, you should have the following environment variables set or passed in constructor in lower case: - ``AZURE_OPENAI_API_KEY`` - ``AZURE_OPENAI_ENDPOINT`` - ``AZURE_OPENAI_AD_TOKEN`` - ``OPENAI_API_VERSION`` - ``OPENAI_PROXY`` For example, if you have `gpt-3.5-turbo` deployed, with the deployment name `35-turbo-dev`, the constructor should look like: .. code-block:: python from langchain_openai import AzureChatOpenAI AzureChatOpenAI( azure_deployment="35-turbo-dev", openai_api_version="2023-05-15", ) Be aware the API version may change. You can also specify the version of the model using ``model_version`` constructor parameter, as Azure OpenAI doesn't return model version with the response. Default is empty. When you specify the version, it will be appended to the model name in the response. Setting correct version will help you to calculate the cost properly. Model version is not validated, so make sure you set it correctly to get the correct cost. Any parameters that are valid to be passed to the openai.create call can be passed in, even if not explicitly saved on this class. """ azure_endpoint: Union[str, None] = None """Your Azure endpoint, including the resource. Automatically inferred from env var `AZURE_OPENAI_ENDPOINT` if not provided. Example: `` """ deployment_name: Union[str, None] = Field(default=None, alias="azure_deployment") """A model deployment. If given sets the base client URL to include `/deployments/{azure_deployment}`. Note: this means you won't be able to use non-deployment endpoints. """ openai_api_version: str = Field(default="", alias="api_version") """Automatically inferred from env var `OPENAI_API_VERSION` if not provided.""" openai_api_key: Optional[SecretStr] = Field(default=None, alias="api_key") """Automatically inferred from env var `AZURE_OPENAI_API_KEY` if not provided.""" azure_ad_token: Optional[SecretStr] = None """Your Azure Active Directory token. Automatically inferred from env var `AZURE_OPENAI_AD_TOKEN` if not provided. For more: """ azure_ad_token_provider: Union[Callable[[], str], None] = None """A function that returns an Azure Active Directory token. Will be invoked on every request. """ model_version: str = "" """Legacy, for openai<1.0.0 support.""" openai_api_type: str = "" """Legacy, for openai<1.0.0 support.""" validate_base_url: bool = True """For backwards compatibility. If legacy val openai_api_base is passed in, try to infer if it is a base_url or azure_endpoint and update accordingly. """
[docs] @classmethod def get_lc_namespace(cls) -> List[str]: """Get the namespace of the langchain object.""" return ["langchain", "chat_models", "azure_openai"]
@property def lc_secrets(self) -> Dict[str, str]: return { "openai_api_key": "AZURE_OPENAI_API_KEY", "azure_ad_token": "AZURE_OPENAI_AD_TOKEN", }
[docs] @classmethod def is_lc_serializable(cls) -> bool: return True
@root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" if values["n"] < 1: raise ValueError("n must be at least 1.") if values["n"] > 1 and values["streaming"]: raise ValueError("n must be 1 when streaming.") # Check OPENAI_KEY for backwards compatibility. # TODO: Remove OPENAI_API_KEY support to avoid possible conflict when using # other forms of azure credentials. openai_api_key = ( values["openai_api_key"] or os.getenv("AZURE_OPENAI_API_KEY") or os.getenv("OPENAI_API_KEY") ) values["openai_api_key"] = ( convert_to_secret_str(openai_api_key) if openai_api_key else None ) values["openai_api_base"] = values["openai_api_base"] or os.getenv( "OPENAI_API_BASE" ) values["openai_api_version"] = values["openai_api_version"] or os.getenv( "OPENAI_API_VERSION" ) # Check OPENAI_ORGANIZATION for backwards compatibility. values["openai_organization"] = ( values["openai_organization"] or os.getenv("OPENAI_ORG_ID") or os.getenv("OPENAI_ORGANIZATION") ) values["azure_endpoint"] = values["azure_endpoint"] or os.getenv( "AZURE_OPENAI_ENDPOINT" ) azure_ad_token = values["azure_ad_token"] or os.getenv("AZURE_OPENAI_AD_TOKEN") values["azure_ad_token"] = ( convert_to_secret_str(azure_ad_token) if azure_ad_token else None ) values["openai_api_type"] = get_from_dict_or_env( values, "openai_api_type", "OPENAI_API_TYPE", default="azure" ) values["openai_proxy"] = get_from_dict_or_env( values, "openai_proxy", "OPENAI_PROXY", default="" ) # For backwards compatibility. Before openai v1, no distinction was made # between azure_endpoint and base_url (openai_api_base). openai_api_base = values["openai_api_base"] if openai_api_base and values["validate_base_url"]: if "/openai" not in openai_api_base: raise ValueError( "As of openai>=1.0.0, Azure endpoints should be specified via " "the `azure_endpoint` param not `openai_api_base` " "(or alias `base_url`)." ) if values["deployment_name"]: raise ValueError( "As of openai>=1.0.0, if `azure_deployment` (or alias " "`deployment_name`) is specified then " "`base_url` (or alias `openai_api_base`) should not be. " "If specifying `azure_deployment`/`deployment_name` then use " "`azure_endpoint` instead of `base_url`.\n\n" "For example, you could specify:\n\n" 'azure_endpoint="", ' 'azure_deployment="my-deployment"\n\n' "Or you can equivalently specify:\n\n" 'base_url=""' ) client_params = { "api_version": values["openai_api_version"], "azure_endpoint": values["azure_endpoint"], "azure_deployment": values["deployment_name"], "api_key": values["openai_api_key"].get_secret_value() if values["openai_api_key"] else None, "azure_ad_token": values["azure_ad_token"].get_secret_value() if values["azure_ad_token"] else None, "azure_ad_token_provider": values["azure_ad_token_provider"], "organization": values["openai_organization"], "base_url": values["openai_api_base"], "timeout": values["request_timeout"], "max_retries": values["max_retries"], "default_headers": values["default_headers"], "default_query": values["default_query"], } if not values.get("client"): sync_specific = {"http_client": values["http_client"]} values["client"] = openai.AzureOpenAI( **client_params, **sync_specific ).chat.completions if not values.get("async_client"): async_specific = {"http_client": values["http_async_client"]} values["async_client"] = openai.AsyncAzureOpenAI( **client_params, **async_specific ).chat.completions return values @property def _identifying_params(self) -> Dict[str, Any]: """Get the identifying parameters.""" return { **{"azure_deployment": self.deployment_name}, **super()._identifying_params, } @property def _llm_type(self) -> str: return "azure-openai-chat" @property def lc_attributes(self) -> Dict[str, Any]: return { "openai_api_type": self.openai_api_type, "openai_api_version": self.openai_api_version, } def _get_ls_params( self, stop: Optional[List[str]] = None, **kwargs: Any ) -> LangSmithParams: """Get the parameters used to invoke the model.""" params = super()._get_ls_params(stop=stop, **kwargs) params["ls_provider"] = "azure" if self.deployment_name: params["ls_model_name"] = self.deployment_name return params def _create_chat_result( self, response: Union[dict, openai.BaseModel] ) -> ChatResult: if not isinstance(response, dict): response = response.model_dump() for res in response["choices"]: if res.get("finish_reason", None) == "content_filter": raise ValueError( "Azure has not provided the response due to a content filter " "being triggered" ) chat_result = super()._create_chat_result(response) if "model" in response: model = response["model"] if self.model_version: model = f"{model}-{self.model_version}" chat_result.llm_output = chat_result.llm_output or {} chat_result.llm_output["model_name"] = model if "prompt_filter_results" in response: chat_result.llm_output = chat_result.llm_output or {} chat_result.llm_output["prompt_filter_results"] = response[ "prompt_filter_results" ] for chat_gen, response_choice in zip( chat_result.generations, response["choices"] ): chat_gen.generation_info = chat_gen.generation_info or {} chat_gen.generation_info["content_filter_results"] = response_choice.get( "content_filter_results", {} ) return chat_result