deep-searcher/deepsearcher/llm/openai_llm.py

from deepsearcher.llm.base import BaseLLM


class OpenAILLM(BaseLLM):
    """
    OpenAI language model implementation.

    This class provides an interface to interact with OpenAI's language models
    through their API.

    Attributes:
        model (str): The OpenAI model identifier to use.
        client: The OpenAI client instance.
    """

    def __init__(self, model: str = "o1-mini", **kwargs):
        """
        Initialize an OpenAI language model client.

        Args:
            model (str, optional): The model identifier to use. Defaults to "o1-mini".
            **kwargs: Additional keyword arguments to pass to the OpenAI client.
                - api_key: OpenAI API key. If not provided, uses OPENAI_API_KEY environment variable.
                - base_url: OpenAI API base URL. If not provided, uses OPENAI_BASE_URL environment variable.
        """
        from openai import OpenAI

        self.model = model
        if "api_key" in kwargs:
            api_key = kwargs.pop("api_key")
        if "base_url" in kwargs:
            base_url = kwargs.pop("base_url")
        self.client = OpenAI(api_key=api_key, base_url=base_url, **kwargs)

    def chat(self, messages: list[dict]) -> str:
        """
        Send a chat message to the OpenAI model and get a response.

        Args:
            messages (List[Dict]):
                A list of message dictionaries, typically in the format
                [{"role": "system", "content": "..."}, {"role": "user", "content": "..."}]

        Returns:
            response (str)
        """
        with self.client.chat.completions.create(
            model=self.model,
            messages=messages,
            stream=True,
            temperature=0.8,
            top_p=0.9,
            presence_penalty=1.4
        ) as stream:
            # stream到控制台测试
            content = ""
            reasoning_content = ""
            for chunk in stream:
                if not chunk.choices:
                    continue
                else:
                    delta = chunk.choices[0].delta
                if hasattr(delta, 'reasoning_content') and delta.reasoning_content is not None:
                    print(delta.reasoning_content, end='', flush=True)
                    reasoning_content += delta.reasoning_content
                if hasattr(delta, 'content') and delta.content is not None:
                    print(delta.content, end="", flush=True)
                    content += delta.content
            print("\n")
        return content