mirror of
https://github.com/Mintplex-Labs/langchain-python.git
synced 2026-08-27 19:29:56 -04:00
Paul Garner
69698be3e6
re https://github.com/hwchase17/langchain/issues/439#issuecomment-1510442791 I think it's not polite for a library to use the root logger both of these forms are also used: ``` logger = logging.getLogger(__name__) logger = logging.getLogger(__file__) ``` I am not sure if there is any reason behind one vs the other? (...I am guessing maybe just contributed by different people) it seems to me it'd be better to consistently use `logging.getLogger(__name__)` this makes it easier for consumers of the library to set up log handlers, e.g. for everything with `langchain.` prefix
113 lines
3.7 KiB
Python
113 lines
3.7 KiB
Python
"""Azure OpenAI chat wrapper."""
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from __future__ import annotations
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import logging
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from typing import Any, Dict
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from pydantic import root_validator
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from langchain.chat_models.openai import ChatOpenAI
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from langchain.utils import get_from_dict_or_env
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logger = logging.getLogger(__name__)
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class AzureChatOpenAI(ChatOpenAI):
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"""Wrapper around Azure OpenAI Chat Completion API. 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 ``openai`` python package installed, and the
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following environment variables set or passed in constructor in lower case:
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- ``OPENAI_API_TYPE`` (default: ``azure``)
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- ``OPENAI_API_KEY``
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- ``OPENAI_API_BASE``
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- ``OPENAI_API_VERSION``
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For exmaple, if you have `gpt-35-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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deployment_name="35-turbo-dev",
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openai_api_version="2023-03-15-preview",
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)
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Be aware the API version may change.
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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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deployment_name: str = ""
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openai_api_type: str = "azure"
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openai_api_base: str = ""
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openai_api_version: str = ""
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openai_api_key: str = ""
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openai_organization: str = ""
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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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openai_api_key = get_from_dict_or_env(
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values,
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"openai_api_key",
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"OPENAI_API_KEY",
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)
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openai_api_base = get_from_dict_or_env(
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values,
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"openai_api_base",
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"OPENAI_API_BASE",
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)
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openai_api_version = get_from_dict_or_env(
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values,
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"openai_api_version",
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"OPENAI_API_VERSION",
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)
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openai_api_type = get_from_dict_or_env(
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values,
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"openai_api_type",
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"OPENAI_API_TYPE",
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)
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openai_organization = get_from_dict_or_env(
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values,
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"openai_organization",
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"OPENAI_ORGANIZATION",
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default="",
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)
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try:
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import openai
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openai.api_type = openai_api_type
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openai.api_base = openai_api_base
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openai.api_version = openai_api_version
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openai.api_key = openai_api_key
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if openai_organization:
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openai.organization = openai_organization
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except ImportError:
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raise ValueError(
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"Could not import openai python package. "
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"Please install it with `pip install openai`."
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)
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try:
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values["client"] = openai.ChatCompletion
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except AttributeError:
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raise ValueError(
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"`openai` has no `ChatCompletion` attribute, this is likely "
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"due to an old version of the openai package. Try upgrading it "
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"with `pip install --upgrade openai`."
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)
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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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return values
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@property
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def _default_params(self) -> Dict[str, Any]:
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"""Get the default parameters for calling OpenAI API."""
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return {
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**super()._default_params,
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"engine": self.deployment_name,
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}
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