Source code for langchain_community.chat_models.azure_openai

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

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

from langchain_core._api.deprecation import deprecated
from langchain_core.outputs import ChatResult
from langchain_core.pydantic_v1 import BaseModel, Field, root_validator
from langchain_core.utils import get_from_dict_or_env

from langchain_community.chat_models.openai import ChatOpenAI
from langchain_community.utils.openai import is_openai_v1

logger = logging.getLogger(__name__)

[docs]@deprecated( since="0.0.10", removal="0.3.0", alternative_import="langchain_openai.AzureChatOpenAI", ) class AzureChatOpenAI(ChatOpenAI): """`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 ``openai`` python package installed, and 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-35-turbo` deployed, with the deployment name `35-turbo-dev`, the constructor should look like: .. code-block:: python 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: Union[str, None] = Field(default=None, alias="api_key") """Automatically inferred from env var `AZURE_OPENAI_API_KEY` if not provided.""" azure_ad_token: Union[str, None] = 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"]
@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. values["openai_api_key"] = ( values["openai_api_key"] or os.getenv("AZURE_OPENAI_API_KEY") or os.getenv("OPENAI_API_KEY") ) 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" ) values["azure_ad_token"] = values["azure_ad_token"] or os.getenv( "AZURE_OPENAI_AD_TOKEN" ) 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="" ) try: import openai except ImportError: raise ImportError( "Could not import openai python package. " "Please install it with `pip install openai`." ) if is_openai_v1(): # 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: values["openai_api_base"] = ( values["openai_api_base"].rstrip("/") + "/openai" ) warnings.warn( "As of openai>=1.0.0, Azure endpoints should be specified via " f"the `azure_endpoint` param not `openai_api_base` " f"(or alias `base_url`). Updating `openai_api_base` from " f"{openai_api_base} to {values['openai_api_base']}." ) if values["deployment_name"]: warnings.warn( "As of openai>=1.0.0, if `deployment_name` (or alias " "`azure_deployment`) is specified then " "`openai_api_base` (or alias `base_url`) should not be. " "Instead use `deployment_name` (or alias `azure_deployment`) " "and `azure_endpoint`." ) if values["deployment_name"] not in values["openai_api_base"]: warnings.warn( "As of openai>=1.0.0, if `openai_api_base` " "(or alias `base_url`) is specified it is expected to be " "of the form " " " # noqa: E501 f"Updating {openai_api_base} to " f"{values['openai_api_base']}." ) values["openai_api_base"] += ( "/deployments/" + values["deployment_name"] ) values["deployment_name"] = None client_params = { "api_version": values["openai_api_version"], "azure_endpoint": values["azure_endpoint"], "azure_deployment": values["deployment_name"], "api_key": values["openai_api_key"], "azure_ad_token": values["azure_ad_token"], "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"], "http_client": values["http_client"], } values["client"] = openai.AzureOpenAI(**client_params).chat.completions values["async_client"] = openai.AsyncAzureOpenAI( **client_params ).chat.completions else: values["client"] = openai.ChatCompletion return values @property def _default_params(self) -> Dict[str, Any]: """Get the default parameters for calling OpenAI API.""" if is_openai_v1(): return super()._default_params else: return { **super()._default_params, "engine": self.deployment_name, } @property def _identifying_params(self) -> Dict[str, Any]: """Get the identifying parameters.""" return {**self._default_params} @property def _client_params(self) -> Dict[str, Any]: """Get the config params used for the openai client.""" if is_openai_v1(): return super()._client_params else: return { **super()._client_params, "api_type": self.openai_api_type, "api_version": self.openai_api_version, } @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 _create_chat_result(self, response: Union[dict, BaseModel]) -> ChatResult: if not isinstance(response, dict): response = response.dict() 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}" if chat_result.llm_output is not None and isinstance( chat_result.llm_output, dict ): chat_result.llm_output["model_name"] = model return chat_result