Source code for langchain_openai.llms.azure

from __future__ import annotations

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

import openai
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.llms.base import BaseOpenAI

logger = logging.getLogger(__name__)


[docs]class AzureOpenAI(BaseOpenAI): """Azure-specific OpenAI large language models. To use, you should have the ``openai`` python package installed, and the environment variable ``OPENAI_API_KEY`` set with your API key. 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. Example: .. code-block:: python from langchain_openai import AzureOpenAI openai = AzureOpenAI(model_name="gpt-3.5-turbo-instruct") """ azure_endpoint: Union[str, None] = None """Your Azure endpoint, including the resource. Automatically inferred from env var `AZURE_OPENAI_ENDPOINT` if not provided. Example: `https://example-resource.azure.openai.com/` """ 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: https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id. """ azure_ad_token_provider: Union[Callable[[], str], None] = None """A function that returns an Azure Active Directory token. Will be invoked on every request. """ 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", "llms", "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 whether this model can be serialized by Langchain.""" 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["streaming"] and values["n"] > 1: raise ValueError("Cannot stream results when n > 1.") if values["streaming"] and values["best_of"] > 1: raise ValueError("Cannot stream results when best_of > 1.") # 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["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_base"] = values["openai_api_base"] or os.getenv( "OPENAI_API_BASE" ) values["openai_proxy"] = get_from_dict_or_env( values, "openai_proxy", "OPENAI_PROXY", default="", ) values["openai_organization"] = ( values["openai_organization"] or os.getenv("OPENAI_ORG_ID") or os.getenv("OPENAI_ORGANIZATION") ) values["openai_api_version"] = values["openai_api_version"] or os.getenv( "OPENAI_API_VERSION" ) values["openai_api_type"] = get_from_dict_or_env( values, "openai_api_type", "OPENAI_API_TYPE", default="azure" ) # 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" ) 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 `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`." ) 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"].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 ).completions if not values.get("async_client"): async_specific = {"http_client": values["http_async_client"]} values["async_client"] = openai.AsyncAzureOpenAI( **client_params, **async_specific ).completions return values @property def _identifying_params(self) -> Mapping[str, Any]: return { **{"deployment_name": self.deployment_name}, **super()._identifying_params, } @property def _invocation_params(self) -> Dict[str, Any]: openai_params = {"model": self.deployment_name} return {**openai_params, **super()._invocation_params} @property def _llm_type(self) -> str: """Return type of llm.""" return "azure" @property def lc_attributes(self) -> Dict[str, Any]: return { "openai_api_type": self.openai_api_type, "openai_api_version": self.openai_api_version, }