knowledge-agent/gemini.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
简单的 Gemini 处理器
用于满足导入需求，实际功能可以根据需要扩展
"""

import os
import json
from typing import Any, Dict, Optional
from dotenv import load_dotenv
import google.generativeai as genai


class GeminiProcessor:
    """Gemini API 处理器"""
    
    def __init__(self):
        # 加载环境变量
        load_dotenv()
        
        # 获取API密钥，支持多密钥配置
        self.api_key = os.getenv('GEMINI_API_KEY')
        if not self.api_key:
            # 如果没有基础密钥，尝试使用第一个多密钥
            self.api_key = os.getenv('GEMINI_API_KEY_1')
            if not self.api_key:
                raise ValueError("未找到GEMINI_API_KEY或GEMINI_API_KEY_1环境变量")
        
        # 延迟配置Gemini，在真正使用时再设置
        self._configured = False

    def _ensure_configured(self):
        """确保Gemini已配置"""
        if not self._configured:
            genai.configure(api_key=self.api_key)
            self._configured = True
    
    def process(self, content: Any, system_prompt: str) -> Dict[str, Any]:

        try:
            # 确保Gemini已配置
            self._ensure_configured()
            
            # 处理输入内容格式
            if isinstance(content, dict):
                # 将字典转换为JSON字符串
                formatted_content = json.dumps(content, ensure_ascii=False)
            else:
                formatted_content = content
                
            # 创建带有 system_instruction 的模型实例
            model_with_system = genai.GenerativeModel(
                'gemini-2.5-flash',
                system_instruction=system_prompt
            )
            
            # 调用 Gemini API
            response = model_with_system.generate_content(
                contents=formatted_content
            )
            
            # 尝试解析 JSON 响应
            return response.text
                
        except Exception as e:
            print(f"Gemini API 调用失败: {e}")
            return {"error": str(e), "content": content} 
            
    def batch_process(self, contents: list, system_prompt: str) -> list:
        results = []
        for content in contents:
            result = self.process(content, system_prompt)
            results.append(result)
        return results