Source code for camel.models.openai_model

# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
# Licensed under the Apache License, Version 2.0 (the “License”);
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
import os
from typing import Any, Dict, List, Optional, Union

from openai import OpenAI, Stream

from camel.configs import OPENAI_API_PARAMS
from camel.messages import OpenAIMessage
from camel.models import BaseModelBackend
from camel.types import ChatCompletion, ChatCompletionChunk, ModelType
from camel.utils import (
    BaseTokenCounter,
    OpenAITokenCounter,
    api_keys_required,
)


[docs] class OpenAIModel(BaseModelBackend): r"""OpenAI API in a unified BaseModelBackend interface.""" def __init__( self, model_type: ModelType, model_config_dict: Dict[str, Any], api_key: Optional[str] = None, url: Optional[str] = None, token_counter: Optional[BaseTokenCounter] = None, ) -> None: r"""Constructor for OpenAI backend. Args: model_type (ModelType): Model for which a backend is created, one of GPT_* series. model_config_dict (Dict[str, Any]): A dictionary that will be fed into openai.ChatCompletion.create(). api_key (Optional[str]): The API key for authenticating with the OpenAI service. (default: :obj:`None`) url (Optional[str]): The url to the OpenAI service. (default: :obj:`None`) token_counter (Optional[BaseTokenCounter]): Token counter to use for the model. If not provided, `OpenAITokenCounter` will be used. """ super().__init__( model_type, model_config_dict, api_key, url, token_counter ) self._url = url or os.environ.get("OPENAI_API_BASE_URL") self._api_key = api_key or os.environ.get("OPENAI_API_KEY") self._client = OpenAI( timeout=60, max_retries=3, base_url=self._url, api_key=self._api_key, ) @property def token_counter(self) -> BaseTokenCounter: r"""Initialize the token counter for the model backend. Returns: BaseTokenCounter: The token counter following the model's tokenization style. """ if not self._token_counter: self._token_counter = OpenAITokenCounter(self.model_type) return self._token_counter
[docs] @api_keys_required("OPENAI_API_KEY") def run( self, messages: List[OpenAIMessage], ) -> Union[ChatCompletion, Stream[ChatCompletionChunk]]: r"""Runs inference of OpenAI chat completion. Args: messages (List[OpenAIMessage]): Message list with the chat history in OpenAI API format. Returns: Union[ChatCompletion, Stream[ChatCompletionChunk]]: `ChatCompletion` in the non-stream mode, or `Stream[ChatCompletionChunk]` in the stream mode. """ # o1-preview and o1-mini have Beta limitations # reference: https://platform.openai.com/docs/guides/reasoning if self.model_type in [ModelType.O1_MINI, ModelType.O1_PREVIEW]: # Remove system message that is not supported in o1 model. messages = [msg for msg in messages if msg.get("role") != "system"] # Remove unsupported parameters and reset the fixed parameters del self.model_config_dict["stream"] del self.model_config_dict["tools"] del self.model_config_dict["tool_choice"] self.model_config_dict["temperature"] = 1.0 self.model_config_dict["top_p"] = 1.0 self.model_config_dict["n"] = 1.0 self.model_config_dict["presence_penalty"] = 0.0 self.model_config_dict["frequency_penalty"] = 0.0 response = self._client.chat.completions.create( messages=messages, model=self.model_type.value, **self.model_config_dict, ) return response
[docs] def check_model_config(self): r"""Check whether the model configuration contains any unexpected arguments to OpenAI API. Raises: ValueError: If the model configuration dictionary contains any unexpected arguments to OpenAI API. """ for param in self.model_config_dict: if param not in OPENAI_API_PARAMS: raise ValueError( f"Unexpected argument `{param}` is " "input into OpenAI model backend." )
@property def stream(self) -> bool: r"""Returns whether the model is in stream mode, which sends partial results each time. Returns: bool: Whether the model is in stream mode. """ return self.model_config_dict.get('stream', False)