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Simplify AzureChatOpenAI implementation. (#1902)
Change AzureChatOpenAI class implementation as Azure just added support for chat completion API. See: https://learn.microsoft.com/en-us/azure/cognitive-services/openai/how-to/chatgpt?pivots=programming-language-chat-completions. This should make the code much simpler.
This commit is contained in:
Eric Zhu
GitHub
parent
f155d9d3ec
commit
273e9bf296
@ -2,62 +2,50 @@
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from __future__ import annotations
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import logging
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from typing import Any, Dict, List, Mapping, Optional, Tuple
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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 (
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ChatOpenAI,
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acompletion_with_retry,
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)
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from langchain.schema import (
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AIMessage,
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BaseMessage,
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ChatGeneration,
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ChatResult,
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)
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from langchain.utils import get_from_dict_or_env
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logger = logging.getLogger(__file__)
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def _create_chat_prompt(messages: List[BaseMessage]) -> str:
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"""Create a prompt for Azure OpenAI using ChatML."""
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prompt = "\n".join([message.format_chatml() for message in messages])
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return prompt + "\n<|im_start|>assistant\n"
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def _create_chat_result(response: Mapping[str, Any]) -> ChatResult:
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generations = []
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for res in response["choices"]:
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message = AIMessage(content=res["text"])
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gen = ChatGeneration(message=message)
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generations.append(gen)
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return ChatResult(generations=generations)
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class AzureChatOpenAI(ChatOpenAI):
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"""Wrapper around Azure OpenAI Chat large language models.
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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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To use, you should have the ``openai`` python package installed, and the
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following environment variables set:
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- ``OPENAI_API_TYPE``
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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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Example:
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.. code-block:: python
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from langchain.chat_models import AzureChatOpenAI
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openai = AzureChatOpenAI(deployment_name="<your deployment name>")
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"""
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deployment_name: str = ""
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stop: List[str] = ["<|im_end|>"]
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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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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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@ -95,10 +83,10 @@ class AzureChatOpenAI(ChatOpenAI):
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"Please it install it with `pip install openai`."
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)
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try:
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values["client"] = openai.Completion
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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 `Completion` attribute, this is likely "
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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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@ -113,66 +101,5 @@ class AzureChatOpenAI(ChatOpenAI):
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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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"stop": self.stop,
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"engine": self.deployment_name,
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}
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def _generate(
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self, messages: List[BaseMessage], stop: Optional[List[str]] = None
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) -> ChatResult:
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prompt, params = self._create_prompt(messages, stop)
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if self.streaming:
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inner_completion = ""
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params["stream"] = True
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for stream_resp in self.completion_with_retry(prompt=prompt, **params):
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token = stream_resp["choices"][0]["delta"].get("text", "")
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inner_completion += token
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self.callback_manager.on_llm_new_token(
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token,
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verbose=self.verbose,
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)
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message = AIMessage(content=inner_completion)
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return ChatResult(generations=[ChatGeneration(message=message)])
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response = self.completion_with_retry(prompt=prompt, **params)
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return _create_chat_result(response)
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def _create_prompt(
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self, messages: List[BaseMessage], stop: Optional[List[str]]
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) -> Tuple[str, Dict[str, Any]]:
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params: Dict[str, Any] = {
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**{"model": self.model_name, "engine": self.deployment_name},
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**self._default_params,
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}
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if stop is not None:
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if "stop" in params:
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raise ValueError("`stop` found in both the input and default params.")
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params["stop"] = stop
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prompt = _create_chat_prompt(messages)
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return prompt, params
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async def _agenerate(
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self, messages: List[BaseMessage], stop: Optional[List[str]] = None
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) -> ChatResult:
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prompt, params = self._create_prompt(messages, stop)
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if self.streaming:
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inner_completion = ""
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params["stream"] = True
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async for stream_resp in await acompletion_with_retry(
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self, prompt=prompt, **params
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):
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token = stream_resp["choices"][0]["delta"].get("text", "")
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inner_completion += token
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if self.callback_manager.is_async:
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await self.callback_manager.on_llm_new_token(
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token,
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verbose=self.verbose,
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)
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else:
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self.callback_manager.on_llm_new_token(
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token,
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verbose=self.verbose,
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)
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message = AIMessage(content=inner_completion)
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return ChatResult(generations=[ChatGeneration(message=message)])
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else:
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response = await acompletion_with_retry(self, prompt=prompt, **params)
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return _create_chat_result(response)
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@ -60,9 +60,6 @@ class BaseMessage(BaseModel):
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content: str
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additional_kwargs: dict = Field(default_factory=dict)
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def format_chatml(self) -> str:
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raise NotImplementedError()
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@property
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@abstractmethod
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def type(self) -> str:
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@ -72,9 +69,6 @@ class BaseMessage(BaseModel):
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class HumanMessage(BaseMessage):
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"""Type of message that is spoken by the human."""
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def format_chatml(self) -> str:
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return f"<|im_start|>user\n{self.content}\n<|im_end|>"
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@property
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def type(self) -> str:
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"""Type of the message, used for serialization."""
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@ -84,9 +78,6 @@ class HumanMessage(BaseMessage):
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class AIMessage(BaseMessage):
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"""Type of message that is spoken by the AI."""
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def format_chatml(self) -> str:
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return f"<|im_start|>assistant\n{self.content}\n<|im_end|>"
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@property
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def type(self) -> str:
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"""Type of the message, used for serialization."""
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@ -96,9 +87,6 @@ class AIMessage(BaseMessage):
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class SystemMessage(BaseMessage):
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"""Type of message that is a system message."""
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def format_chatml(self) -> str:
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return f"<|im_start|>system\n{self.content}\n<|im_end|>"
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@property
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def type(self) -> str:
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"""Type of the message, used for serialization."""
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@ -110,9 +98,6 @@ class ChatMessage(BaseMessage):
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role: str
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def format_chatml(self) -> str:
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return f"<|im_start|>{self.role}\n{self.content}\n<|im_end|>"
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@property
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def type(self) -> str:
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"""Type of the message, used for serialization."""
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