昨天我們有看到 ai_handler，接下來我們來看看 ai_handler 是啥。ai_handler 在 PRAgent 被 implement 的時候會 init ai_handler，partial[BaseAiHandler,] 是使用 python 的 typing.Partial ，指定 BaseAiHandler 作為部分應用，而默認還是 LiteLLMAIHandler class

class PRAgent:
    def __init__(self, ai_handler: partial[BaseAiHandler,] = LiteLLMAIHandler):
        self.ai_handler = ai_handler  # will be initialized in run_action

BaseAiHandler 繼承自 ABC，代表她是抽象 class，不能直接被 implement，而是需要被繼承。BaseAiHandler 裡面的func 都使用 @abstractmethod 裝飾器，這表示它們必須在子類中被實現。而func 包含 deployment_id 跟 chat_completion 代表子 class 一定要實現。這個 class 講白一點就是畫藍圖規定後面的人一定要有此設定

from pr_agent.algo.ai_handlers.base_ai_handler import BaseAiHandler
from abc import ABC, abstractmethod


class BaseAiHandler(ABC):
    """
    This class defines the interface for an AI handler to be used by the PR Agents.  
    """

    @abstractmethod
    def __init__(self):
        pass

    @property
    @abstractmethod
    def deployment_id(self):
        pass

    @abstractmethod
    async def chat_completion(self, model: str, system: str, user: str, temperature: float = 0.2, img_path: str = None):
        """
        This method should be implemented to return a chat completion from the AI model.
        Args:
            model (str): the name of the model to use for the chat completion
            system (str): the system message string to use for the chat completion
            user (str): the user message string to use for the chat completion
            temperature (float): the temperature to use for the chat completion    
        """
        pass

LiteLLMAIHandler 會繼承 BaseAiHandler，LiteLLMAIHandler 從 get_settings() 配置各種不同 AI 服務的設定，以及包含一個核心的func chat_completion，會發送請求到 AI 模型。在 chat_completion 內，會先將昨天提到的 system & user 轉成 msg list。

            messages = [{"role": "system", "content": system}, {"role": "user", "content": user}]

如果有圖像則進行圖像處理

            if img_path:
                try:
                    # check if the image link is alive
                    r = requests.head(img_path, allow_redirects=True)
                    if r.status_code == 404:
                        error_msg = f"The image link is not [alive](img_path).\nPlease repost the original image as a comment, and send the question again with 'quote reply' (see [instructions](https://pr-agent-docs.codium.ai/tools/ask/#ask-on-images-using-the-pr-code-as-context))."
                        get_logger().error(error_msg)
                        return f"{error_msg}", "error"
                except Exception as e:
                    get_logger().error(f"Error fetching image: {img_path}", e)
                    return f"Error fetching image: {img_path}", "error"
                messages[1]["content"] = [{"type": "text", "text": messages[1]["content"]},
                                          {"type": "image_url", "image_url": {"url": img_path}}]

使用 acompletion 發送異步請求到 AI 模型，根據 Google 調查，iteLLM 是一個開源、高效能的大型語言模型代理。它支援OpenAI、Anthropic、Cohere 等多個提供商。 特點. 為多個LLM 提供商提供統一介面。就先想像他幫忙向 LLM 詢問吧～

from litellm import acompletion

response = await acompletion(**kwargs)