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import os |
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import warnings |
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from typing import Any, Dict, List, Optional, Union |
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from openai import OpenAI, Stream |
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from camel.configs import OPENAI_API_PARAMS, ChatGPTConfig |
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from camel.messages import OpenAIMessage |
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from camel.models import BaseModelBackend |
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from camel.types import ( |
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ChatCompletion, |
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ChatCompletionChunk, |
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ModelType, |
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) |
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from camel.utils import ( |
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BaseTokenCounter, |
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OpenAITokenCounter, |
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api_keys_required, |
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) |
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class OpenAIModel(BaseModelBackend): |
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r"""OpenAI API in a unified BaseModelBackend interface. |
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Args: |
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model_type (Union[ModelType, str]): Model for which a backend is |
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created, one of GPT_* series. |
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model_config_dict (Optional[Dict[str, Any]], optional): A dictionary |
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that will be fed into:obj:`openai.ChatCompletion.create()`. If |
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:obj:`None`, :obj:`ChatGPTConfig().as_dict()` will be used. |
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(default: :obj:`None`) |
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api_key (Optional[str], optional): The API key for authenticating |
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with the OpenAI service. (default: :obj:`None`) |
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url (Optional[str], optional): The url to the OpenAI service. |
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(default: :obj:`None`) |
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token_counter (Optional[BaseTokenCounter], optional): Token counter to |
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use for the model. If not provided, :obj:`OpenAITokenCounter` will |
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be used. (default: :obj:`None`) |
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""" |
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@api_keys_required( |
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[ |
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("api_key", "OPENAI_API_KEY"), |
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] |
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) |
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def __init__( |
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self, |
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model_type: Union[ModelType, str], |
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model_config_dict: Optional[Dict[str, Any]] = None, |
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api_key: Optional[str] = None, |
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url: Optional[str] = None, |
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token_counter: Optional[BaseTokenCounter] = None, |
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) -> None: |
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if model_config_dict is None: |
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model_config_dict = ChatGPTConfig().as_dict() |
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api_key = api_key or os.environ.get("OPENAI_API_KEY") |
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url = url or os.environ.get("OPENAI_API_BASE_URL") |
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super().__init__( |
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model_type, model_config_dict, api_key, url, token_counter |
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) |
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self._client = OpenAI( |
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timeout=5000, |
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max_retries=3, |
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base_url=self._url, |
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api_key=self._api_key, |
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) |
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@property |
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def token_counter(self) -> BaseTokenCounter: |
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r"""Initialize the token counter for the model backend. |
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Returns: |
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BaseTokenCounter: The token counter following the model's |
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tokenization style. |
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""" |
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if not self._token_counter: |
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self._token_counter = OpenAITokenCounter(self.model_type) |
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return self._token_counter |
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def run( |
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self, |
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messages: List[OpenAIMessage], |
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) -> Union[ChatCompletion, Stream[ChatCompletionChunk]]: |
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r"""Runs inference of OpenAI chat completion. |
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Args: |
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messages (List[OpenAIMessage]): Message list with the chat history |
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in OpenAI API format. |
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Returns: |
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Union[ChatCompletion, Stream[ChatCompletionChunk]]: |
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`ChatCompletion` in the non-stream mode, or |
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`Stream[ChatCompletionChunk]` in the stream mode. |
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""" |
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if self.model_type in [ |
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ModelType.O1, |
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ModelType.O1_MINI, |
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ModelType.O1_PREVIEW, |
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ModelType.O3_MINI, |
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ModelType.O3 |
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]: |
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unsupported_keys = [ |
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"temperature", |
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"top_p", |
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"presence_penalty", |
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"frequency_penalty", |
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"logprobs", |
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"top_logprobs", |
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"logit_bias", |
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] |
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for key in unsupported_keys: |
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if key in self.model_config_dict: |
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del self.model_config_dict[key] |
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if self.model_config_dict.get("response_format"): |
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if "stream" in self.model_config_dict: |
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del self.model_config_dict["stream"] |
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return self._to_chat_completion(response) |
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response = self._client.chat.completions.create( |
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messages=messages, |
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model=self.model_type, |
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**self.model_config_dict, |
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) |
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return response |
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def check_model_config(self): |
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r"""Check whether the model configuration contains any |
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unexpected arguments to OpenAI API. |
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Raises: |
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ValueError: If the model configuration dictionary contains any |
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unexpected arguments to OpenAI API. |
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""" |
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for param in self.model_config_dict: |
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if param not in OPENAI_API_PARAMS: |
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raise ValueError( |
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f"Unexpected argument `{param}` is " |
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"input into OpenAI model backend." |
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) |
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@property |
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def stream(self) -> bool: |
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r"""Returns whether the model is in stream mode, which sends partial |
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results each time. |
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Returns: |
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bool: Whether the model is in stream mode. |
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""" |
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return self.model_config_dict.get('stream', False) |
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