langchain/langchain/chat_models/azure_openai.py
Davis Chase f01dfe858d
OpenAI lint (#5273)
Causing lint issues if you have openai installed, annoying for local dev
2023-05-25 16:20:06 -07:00

142 lines
4.7 KiB
Python

"""Azure OpenAI chat wrapper."""
from __future__ import annotations
import logging
from typing import Any, Dict, Mapping
from pydantic import root_validator
from langchain.chat_models.openai import ChatOpenAI
from langchain.schema import ChatResult
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
class AzureChatOpenAI(ChatOpenAI):
"""Wrapper around 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:
- ``OPENAI_API_TYPE`` (default: ``azure``)
- ``OPENAI_API_KEY``
- ``OPENAI_API_BASE``
- ``OPENAI_API_VERSION``
- ``OPENAI_PROXY``
For exmaple, if you have `gpt-35-turbo` deployed, with the deployment name
`35-turbo-dev`, the constructor should look like:
.. code-block:: python
AzureChatOpenAI(
deployment_name="35-turbo-dev",
openai_api_version="2023-03-15-preview",
)
Be aware the API version may change.
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.
"""
deployment_name: str = ""
openai_api_type: str = "azure"
openai_api_base: str = ""
openai_api_version: str = ""
openai_api_key: str = ""
openai_organization: str = ""
openai_proxy: str = ""
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
openai_api_key = get_from_dict_or_env(
values,
"openai_api_key",
"OPENAI_API_KEY",
)
openai_api_base = get_from_dict_or_env(
values,
"openai_api_base",
"OPENAI_API_BASE",
)
openai_api_version = get_from_dict_or_env(
values,
"openai_api_version",
"OPENAI_API_VERSION",
)
openai_api_type = get_from_dict_or_env(
values,
"openai_api_type",
"OPENAI_API_TYPE",
)
openai_organization = get_from_dict_or_env(
values,
"openai_organization",
"OPENAI_ORGANIZATION",
default="",
)
openai_proxy = get_from_dict_or_env(
values,
"openai_proxy",
"OPENAI_PROXY",
default="",
)
try:
import openai
openai.api_type = openai_api_type
openai.api_base = openai_api_base
openai.api_version = openai_api_version
openai.api_key = openai_api_key
if openai_organization:
openai.organization = openai_organization
if openai_proxy:
openai.proxy = {"http": openai_proxy, "https": openai_proxy} # type: ignore[assignment] # noqa: E501
except ImportError:
raise ImportError(
"Could not import openai python package. "
"Please install it with `pip install openai`."
)
try:
values["client"] = openai.ChatCompletion
except AttributeError:
raise ValueError(
"`openai` has no `ChatCompletion` attribute, this is likely "
"due to an old version of the openai package. Try upgrading it "
"with `pip install --upgrade openai`."
)
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.")
return values
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling OpenAI API."""
return {
**super()._default_params,
"engine": self.deployment_name,
}
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {**self._default_params}
@property
def _llm_type(self) -> str:
return "azure-openai-chat"
def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
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"
)
return super()._create_chat_result(response)