1. 统一为使用基于pydantic的.env环境变量管理配置

2. 全项目基于loguru进行日志管理

MediaEngine/agent.py

@@ -8,7 +8,7 @@ import os
 import re
 from datetime import datetime
 from typing import Optional, Dict, Any, List
-
+from loguru import logger
 from .llms import LLMClient
 from .nodes import (
     ReportStructureNode,
@@ -20,29 +20,26 @@ from .nodes import (
 )
 from .state import State
 from .tools import BochaMultimodalSearch, BochaResponse
-from .utils import Config, load_config, format_search_results_for_prompt
+from .utils import settings, Settings, format_search_results_for_prompt
 
 
 class DeepSearchAgent:
     """Deep Search Agent主类"""
     
-    def __init__(self, config: Optional[Config] = None):
+    def __init__(self, config: Optional[Settings] = None):
         """
         初始化Deep Search Agent
         
         Args:
             config: 配置对象，如果不提供则自动加载
         """
-        # 加载配置
-        self.config = config or load_config()
-        os.environ["BOCHA_API_KEY"] = self.config.bocha_api_key or ""
-        os.environ["BOCHA_WEB_SEARCH_API_KEY"] = self.config.bocha_api_key or ""
+        self.config = config or settings
         
         # 初始化LLM客户端
         self.llm_client = self._initialize_llm()
         
         # 初始化搜索工具集
-        self.search_agency = BochaMultimodalSearch(api_key=self.config.bocha_api_key)
+        self.search_agency = BochaMultimodalSearch(api_key=(self.config.BOCHA_API_KEY or self.config.BOCHA_WEB_SEARCH_API_KEY))
         
         # 初始化节点
         self._initialize_nodes()
@@ -51,18 +48,18 @@ class DeepSearchAgent:
         self.state = State()
         
         # 确保输出目录存在
-        os.makedirs(self.config.output_dir, exist_ok=True)
+        os.makedirs(self.config.OUTPUT_DIR, exist_ok=True)
         
-        print(f"Meida Agent已初始化")
-        print(f"使用LLM: {self.llm_client.get_model_info()}")
-        print(f"搜索工具集: BochaMultimodalSearch (支持5种多模态搜索工具)")
+        logger.info(f"Meida Agent已初始化")
+        logger.info(f"使用LLM: {self.llm_client.get_model_info()}")
+        logger.info(f"搜索工具集: BochaMultimodalSearch (支持5种多模态搜索工具)")
     
     def _initialize_llm(self) -> LLMClient:
         """初始化LLM客户端"""
         return LLMClient(
-            api_key=self.config.llm_api_key,
-            model_name=self.config.llm_model_name,
-            base_url=self.config.llm_base_url,
+            api_key=(self.config.MEDIA_ENGINE_API_KEY or self.config.MINDSPIDER_API_KEY),
+            model_name=(self.config.MEDIA_ENGINE_MODEL_NAME or self.config.MINDSPIDER_MODEL_NAME),
+            base_url=(self.config.MEDIA_ENGINE_BASE_URL or self.config.MINDSPIDER_BASE_URL),
         )
     
     def _initialize_nodes(self):
@@ -115,7 +112,7 @@ class DeepSearchAgent:
         Returns:
             BochaResponse对象
         """
-        print(f"  → 执行搜索工具: {tool_name}")
+        logger.info(f"  → 执行搜索工具: {tool_name}")
         
         if tool_name == "comprehensive_search":
             max_results = kwargs.get("max_results", 10)
@@ -130,7 +127,7 @@ class DeepSearchAgent:
         elif tool_name == "search_last_week":
             return self.search_agency.search_last_week(query)
         else:
-            print(f"  ⚠️  未知的搜索工具: {tool_name}，使用默认综合搜索")
+            logger.info(f"  ⚠️  未知的搜索工具: {tool_name}，使用默认综合搜索")
             return self.search_agency.comprehensive_search(query)
     
     def research(self, query: str, save_report: bool = True) -> str:
@@ -144,9 +141,9 @@ class DeepSearchAgent:
         Returns:
             最终报告内容
         """
-        print(f"\n{'='*60}")
-        print(f"开始深度研究: {query}")
-        print(f"{'='*60}")
+        logger.info(f"\n{'='*60}")
+        logger.info(f"开始深度研究: {query}")
+        logger.info(f"{'='*60}")
         
         try:
             # Step 1: 生成报告结构
@@ -162,19 +159,21 @@ class DeepSearchAgent:
             if save_report:
                 self._save_report(final_report)
             
-            print(f"\n{'='*60}")
-            print("深度研究完成！")
-            print(f"{'='*60}")
+            logger.info(f"\n{'='*60}")
+            logger.info("深度研究完成！")
+            logger.info(f"{'='*60}")
             
             return final_report
             
         except Exception as e:
-            print(f"研究过程中发生错误: {str(e)}")
+            import traceback
+            error_traceback = traceback.format_exc()
+            logger.error(f"研究过程中发生错误: {str(e)} \n错误堆栈: {error_traceback}")
             raise e
     
     def _generate_report_structure(self, query: str):
         """生成报告结构"""
-        print(f"\n[步骤 1] 生成报告结构...")
+        logger.info(f"\n[步骤 1] 生成报告结构...")
         
         # 创建报告结构节点
         report_structure_node = ReportStructureNode(self.llm_client, query)
@@ -182,17 +181,18 @@ class DeepSearchAgent:
         # 生成结构并更新状态
         self.state = report_structure_node.mutate_state(state=self.state)
         
-        print(f"报告结构已生成，共 {len(self.state.paragraphs)} 个段落:")
+        _message = f"报告结构已生成，共 {len(self.state.paragraphs)} 个段落:"
         for i, paragraph in enumerate(self.state.paragraphs, 1):
-            print(f"  {i}. {paragraph.title}")
+            _message += f"\n  {i}. {paragraph.title}"
+        logger.info(_message)
     
     def _process_paragraphs(self):
         """处理所有段落"""
         total_paragraphs = len(self.state.paragraphs)
         
         for i in range(total_paragraphs):
-            print(f"\n[步骤 2.{i+1}] 处理段落: {self.state.paragraphs[i].title}")
-            print("-" * 50)
+            logger.info(f"\n[步骤 2.{i+1}] 处理段落: {self.state.paragraphs[i].title}")
+            logger.info("-" * 50)
             
             # 初始搜索和总结
             self._initial_search_and_summary(i)
@@ -204,7 +204,7 @@ class DeepSearchAgent:
             self.state.paragraphs[i].research.mark_completed()
             
             progress = (i + 1) / total_paragraphs * 100
-            print(f"段落处理完成 ({progress:.1f}%)")
+            logger.info(f"段落处理完成 ({progress:.1f}%)")
     
     def _initial_search_and_summary(self, paragraph_index: int):
         """执行初始搜索和总结"""
@@ -217,18 +217,18 @@ class DeepSearchAgent:
         }
         
         # 生成搜索查询和工具选择
-        print("  - 生成搜索查询...")
+        logger.info("  - 生成搜索查询...")
         search_output = self.first_search_node.run(search_input)
         search_query = search_output["search_query"]
         search_tool = search_output.get("search_tool", "comprehensive_search")  # 默认工具
         reasoning = search_output["reasoning"]
         
-        print(f"  - 搜索查询: {search_query}")
-        print(f"  - 选择的工具: {search_tool}")
-        print(f"  - 推理: {reasoning}")
+        logger.info(f"  - 搜索查询: {search_query}")
+        logger.info(f"  - 选择的工具: {search_tool}")
+        logger.info(f"  - 推理: {reasoning}")
         
         # 执行搜索
-        print("  - 执行网络搜索...")
+        logger.info("  - 执行网络搜索...")
         
         # 处理特殊参数（新的工具集不需要日期参数处理）
         search_kwargs = {}
@@ -254,24 +254,25 @@ class DeepSearchAgent:
                 })
         
         if search_results:
-            print(f"  - 找到 {len(search_results)} 个搜索结果")
+            _message = f"  - 找到 {len(search_results)} 个搜索结果" 
             for j, result in enumerate(search_results, 1):
                 date_info = f" (发布于: {result.get('published_date', 'N/A')})" if result.get('published_date') else ""
-                print(f"    {j}. {result['title'][:50]}...{date_info}")
+                _message += f"\n    {j}. {result['title'][:50]}...{date_info}"
+            logger.info(_message)
         else:
-            print("  - 未找到搜索结果")
+            logger.info("  - 未找到搜索结果")
         
         # 更新状态中的搜索历史
         paragraph.research.add_search_results(search_query, search_results)
         
         # 生成初始总结
-        print("  - 生成初始总结...")
+        logger.info("  - 生成初始总结...")
         summary_input = {
             "title": paragraph.title,
             "content": paragraph.content,
             "search_query": search_query,
             "search_results": format_search_results_for_prompt(
-                search_results, self.config.max_content_length
+                search_results, self.config.SEARCH_CONTENT_MAX_LENGTH
             )
         }
         
@@ -280,14 +281,14 @@ class DeepSearchAgent:
             summary_input, self.state, paragraph_index
         )
         
-        print("  - 初始总结完成")
+        logger.info("  - 初始总结完成")
     
     def _reflection_loop(self, paragraph_index: int):
         """执行反思循环"""
         paragraph = self.state.paragraphs[paragraph_index]
         
-        for reflection_i in range(self.config.max_reflections):
-            print(f"  - 反思 {reflection_i + 1}/{self.config.max_reflections}...")
+        for reflection_i in range(self.config.MAX_REFLECTIONS):
+            logger.info(f"  - 反思 {reflection_i + 1}/{self.config.MAX_REFLECTIONS}...")
             
             # 准备反思输入
             reflection_input = {
@@ -302,9 +303,9 @@ class DeepSearchAgent:
             search_tool = reflection_output.get("search_tool", "comprehensive_search")  # 默认工具
             reasoning = reflection_output["reasoning"]
             
-            print(f"    反思查询: {search_query}")
-            print(f"    选择的工具: {search_tool}")
-            print(f"    反思推理: {reasoning}")
+            logger.info(f"    反思查询: {search_query}")
+            logger.info(f"    选择的工具: {search_tool}")
+            logger.info(f"    反思推理: {reasoning}")
             
             # 执行反思搜索
             # 处理特殊参数
@@ -331,12 +332,13 @@ class DeepSearchAgent:
                     })
             
             if search_results:
-                print(f"    找到 {len(search_results)} 个反思搜索结果")
+                _message = f"    找到 {len(search_results)} 个反思搜索结果"
                 for j, result in enumerate(search_results, 1):
                     date_info = f" (发布于: {result.get('published_date', 'N/A')})" if result.get('published_date') else ""
-                    print(f"      {j}. {result['title'][:50]}...{date_info}")
+                    _message += f"\n      {j}. {result['title'][:50]}...{date_info}"
+                logger.info(_message)
             else:
-                print("    未找到反思搜索结果")
+                logger.info("    未找到反思搜索结果")
             
             # 更新搜索历史
             paragraph.research.add_search_results(search_query, search_results)
@@ -347,7 +349,7 @@ class DeepSearchAgent:
                 "content": paragraph.content,
                 "search_query": search_query,
                 "search_results": format_search_results_for_prompt(
-                    search_results, self.config.max_content_length
+                    search_results, self.config.SEARCH_CONTENT_MAX_LENGTH
                 ),
                 "paragraph_latest_state": paragraph.research.latest_summary
             }
@@ -357,11 +359,11 @@ class DeepSearchAgent:
                 reflection_summary_input, self.state, paragraph_index
             )
             
-            print(f"    反思 {reflection_i + 1} 完成")
+            logger.info(f"    反思 {reflection_i + 1} 完成")
     
     def _generate_final_report(self) -> str:
         """生成最终报告"""
-        print(f"\n[步骤 3] 生成最终报告...")
+        logger.info(f"\n[步骤 3] 生成最终报告...")
         
         # 准备报告数据
         report_data = []
@@ -375,7 +377,7 @@ class DeepSearchAgent:
         try:
             final_report = self.report_formatting_node.run(report_data)
         except Exception as e:
-            print(f"LLM格式化失败，使用备用方法: {str(e)}")
+            logger.info(f"LLM格式化失败，使用备用方法: {str(e)}")
             final_report = self.report_formatting_node.format_report_manually(
                 report_data, self.state.report_title
             )
@@ -384,7 +386,7 @@ class DeepSearchAgent:
         self.state.final_report = final_report
         self.state.mark_completed()
         
-        print("最终报告生成完成")
+        logger.info("最终报告生成完成")
         return final_report
     
     def _save_report(self, report_content: str):
@@ -395,20 +397,20 @@ class DeepSearchAgent:
         query_safe = query_safe.replace(' ', '_')[:30]
         
         filename = f"deep_search_report_{query_safe}_{timestamp}.md"
-        filepath = os.path.join(self.config.output_dir, filename)
+        filepath = os.path.join(self.config.OUTPUT_DIR, filename)
         
         # 保存报告
         with open(filepath, 'w', encoding='utf-8') as f:
             f.write(report_content)
         
-        print(f"报告已保存到: {filepath}")
+        logger.info(f"报告已保存到: {filepath}")
         
         # 保存状态（如果配置允许）
-        if self.config.save_intermediate_states:
+        if self.config.SAVE_INTERMEDIATE_STATES:
             state_filename = f"state_{query_safe}_{timestamp}.json"
-            state_filepath = os.path.join(self.config.output_dir, state_filename)
+            state_filepath = os.path.join(self.config.OUTPUT_DIR, state_filename)
             self.state.save_to_file(state_filepath)
-            print(f"状态已保存到: {state_filepath}")
+            logger.info(f"状态已保存到: {state_filepath}")
     
     def get_progress_summary(self) -> Dict[str, Any]:
         """获取进度摘要"""
@@ -417,12 +419,12 @@ class DeepSearchAgent:
     def load_state(self, filepath: str):
         """从文件加载状态"""
         self.state = State.load_from_file(filepath)
-        print(f"状态已从 {filepath} 加载")
+        logger.info(f"状态已从 {filepath} 加载")
     
     def save_state(self, filepath: str):
         """保存状态到文件"""
         self.state.save_to_file(filepath)
-        print(f"状态已保存到 {filepath}")
+        logger.info(f"状态已保存到 {filepath}")
 
 
 def create_agent(config_file: Optional[str] = None) -> DeepSearchAgent:
@@ -435,5 +437,5 @@ def create_agent(config_file: Optional[str] = None) -> DeepSearchAgent:
     Returns:
         DeepSearchAgent实例
     """
-    config = load_config(config_file)
-    return DeepSearchAgent(config)
+    settings = Settings()
+    return DeepSearchAgent(settings)