ForcePilot/src/models/chat_model.py

import os
from openai import OpenAI
from src.utils import logger, get_docker_safe_url

class OpenAIBase():
    def __init__(self, api_key, base_url, model_name):
        self.api_key = api_key
        self.base_url = base_url
        self.client = OpenAI(api_key=api_key, base_url=base_url)
        self.model_name = model_name
        # logger.debug(f"{self.get_models()=}")

    def predict(self, message, stream=False):
        if isinstance(message, str):
            messages=[{"role": "user", "content": message}]
        else:
            messages = message

        if stream:
            return self._stream_response(messages)
        else:
            return self._get_response(messages)

    def _stream_response(self, messages):
        response = self.client.chat.completions.create(
            model=self.model_name,
            messages=messages,
            stream=True,
        )
        for chunk in response:
            yield chunk.choices[0].delta

    def _get_response(self, messages):
        response = self.client.chat.completions.create(
            model=self.model_name,
            messages=messages,
            stream=False,
        )
        return response.choices[0].message
    
    def get_models(self):
        try:
            return self.client.models.list()
        except Exception as e:
            logger.error(f"Error getting models: {e}")
            return []


class OpenModel(OpenAIBase):
    def __init__(self, model_name=None):
        model_name = model_name or "gpt-4o-mini"
        api_key = os.getenv("OPENAI_API_KEY")
        base_url = os.getenv("OPENAI_API_BASE")
        super().__init__(api_key=api_key, base_url=base_url, model_name=model_name)



class CustomModel(OpenAIBase):
    def __init__(self, model_info):
        model_name = model_info["name"]
        api_key = model_info.get("api_key", "custom_model")
        base_url = get_docker_safe_url(model_info["api_base"])
        logger.info(f"> Custom model: {model_name}, base_url: {base_url}")

        super().__init__(api_key=api_key, base_url=base_url, model_name=model_name)


class GeneralResponse:
    def __init__(self, content):
        self.content = content
        self.is_full = False


class Qianfan(OpenAIBase):
    """弃用"""

    def __init__(self, model_name="ernie_speed") -> None:
        import qianfan
        self.model_name = model_name
        access_key = os.getenv("QIANFAN_ACCESS_KEY")
        secret_key = os.getenv("QIANFAN_SECRET_KEY")
        self.client = qianfan.ChatCompletion(ak=access_key, sk=secret_key)

    def predict(self, message, stream=False):
        if isinstance(message, str):
            messages=[{"role": "user", "content": message}]
        else:
            messages = message

        if stream:
            return self._stream_response(messages)
        else:
            return self._get_response(messages)

    def _stream_response(self, messages):
        response = self.client.do(
            model=self.model_name,
            messages=messages,
            stream=True,
        )
        for chunk in response:
            yield GeneralResponse(chunk["body"]["result"])

    def _get_response(self, messages):
        response = self.client.do(
            model=self.model_name,
            messages=messages,
            stream=False,
        )
        return GeneralResponse(response["body"]["result"])



class DashScope(OpenAIBase):

    def __init__(self, model_name="qwen-max-latest") -> None:
        self.model_name = model_name
        self.api_key= os.getenv("DASHSCOPE_API_KEY")

    def predict(self, message, stream=False):
        if isinstance(message, str):
            messages=[{"role": "user", "content": message}]
        else:
            messages = message

        if stream:
            return self._stream_response(messages)
        else:
            return self._get_response(messages)

    def _stream_response(self, messages):
        import dashscope
        response = dashscope.Generation.call(
            api_key=self.api_key,
            model=self.model_name,
            messages=messages,
            result_format='message',
            stream=True,
        )
        for chunk in response:
            message = chunk.output.choices[0].message
            message.is_full = False
            yield chunk.output.choices[0].message

    def _get_response(self, messages):
        import dashscope
        response = dashscope.Generation.call(
            api_key=self.api_key,
            model=self.model_name,
            messages=messages,
            result_format='message',
            stream=False,
        )
        return response.output.choices[0].message


if __name__ == "__main__":
    pass