Introduce the error retransmission mechanism to enhance robustness.

InsightEngine/agent.py

@@ -62,7 +62,7 @@ class DeepSearchAgent:
         # 确保输出目录存在
         os.makedirs(self.config.output_dir, exist_ok=True)
         
-        print(f"Deep Search Agent 已初始化")
+        print(f"Insight Agent已初始化")
         print(f"使用LLM: {self.llm_client.get_model_info()}")
         print(f"搜索工具集: MediaCrawlerDB (支持5种本地数据库查询工具)")
         print(f"情感分析: WeiboMultilingualSentiment (支持22种语言的情感分析)")

InsightEngine/llms/deepseek.py

@@ -7,6 +7,24 @@ import os
 from typing import Optional, Dict, Any
 from openai import OpenAI
 from .base import BaseLLM
+import sys
+
+# 添加utils目录到Python路径
+current_dir = os.path.dirname(os.path.abspath(__file__))
+root_dir = os.path.dirname(os.path.dirname(current_dir))
+utils_dir = os.path.join(root_dir, 'utils')
+if utils_dir not in sys.path:
+    sys.path.append(utils_dir)
+
+try:
+    from retry_helper import with_retry, with_graceful_retry, LLM_RETRY_CONFIG
+except ImportError:
+    # 如果无法导入重试模块，使用空装饰器
+    def with_retry(config):
+        def decorator(func):
+            return func
+        return decorator
+    LLM_RETRY_CONFIG = None
 
 
 class DeepSeekLLM(BaseLLM):
@@ -39,6 +57,7 @@ class DeepSeekLLM(BaseLLM):
         """获取默认模型名称"""
         return "deepseek-chat"
     
+    @with_retry(LLM_RETRY_CONFIG)
     def invoke(self, system_prompt: str, user_prompt: str, **kwargs) -> str:
         """
         调用DeepSeek API生成回复

InsightEngine/llms/kimi.py

@@ -4,11 +4,28 @@ Kimi LLM实现
 """
 
 import os
+import sys
 from typing import Optional, Dict, Any
 from openai import OpenAI
 # 假设 .base 模块和 BaseLLM 类已存在
 from .base import BaseLLM
 
+# 添加utils目录到Python路径并导入重试模块
+try:
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    root_dir = os.path.dirname(os.path.dirname(current_dir))
+    utils_dir = os.path.join(root_dir, 'utils')
+    if utils_dir not in sys.path:
+        sys.path.append(utils_dir)
+    from retry_helper import with_retry, with_graceful_retry, LLM_RETRY_CONFIG
+except ImportError:
+    # 如果无法导入重试模块，使用空装饰器避免报错
+    def with_retry(config):
+        def decorator(func):
+            return func
+        return decorator
+    LLM_RETRY_CONFIG = None
+
 
 class KimiLLM(BaseLLM):
     """Kimi LLM实现类"""
@@ -40,6 +57,7 @@ class KimiLLM(BaseLLM):
         """获取默认模型名称"""
         return "kimi-k2-0711-preview"
 
+    @with_retry(LLM_RETRY_CONFIG)
     def invoke(self, system_prompt: str, user_prompt: str, **kwargs) -> str:
         """
         调用Kimi API生成回复

InsightEngine/llms/openai_llm.py

@@ -4,10 +4,27 @@ OpenAI LLM实现
 """
 
 import os
+import sys
 from typing import Optional, Dict, Any
 from openai import OpenAI
 from .base import BaseLLM
 
+# 添加utils目录到Python路径并导入重试模块
+try:
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    root_dir = os.path.dirname(os.path.dirname(current_dir))
+    utils_dir = os.path.join(root_dir, 'utils')
+    if utils_dir not in sys.path:
+        sys.path.append(utils_dir)
+    from retry_helper import with_retry, with_graceful_retry, LLM_RETRY_CONFIG
+except ImportError:
+    # 如果无法导入重试模块，使用空装饰器避免报错
+    def with_retry(config):
+        def decorator(func):
+            return func
+        return decorator
+    LLM_RETRY_CONFIG = None
+
 
 class OpenAILLM(BaseLLM):
     """OpenAI LLM实现类"""
@@ -35,6 +52,7 @@ class OpenAILLM(BaseLLM):
         """获取默认模型名称"""
         return "gpt-4o-mini"
     
+    @with_retry(LLM_RETRY_CONFIG)
     def invoke(self, system_prompt: str, user_prompt: str, **kwargs) -> str:
         """
         调用OpenAI API生成回复

InsightEngine/tools/keyword_optimizer.py

@@ -14,6 +14,15 @@ from dataclasses import dataclass
 sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
 from config import GUIJI_QWEN3_API_KEY
 
+# 添加utils目录到Python路径
+current_dir = os.path.dirname(os.path.abspath(__file__))
+root_dir = os.path.dirname(os.path.dirname(current_dir))
+utils_dir = os.path.join(root_dir, 'utils')
+if utils_dir not in sys.path:
+    sys.path.append(utils_dir)
+
+from retry_helper import with_graceful_retry, SEARCH_API_RETRY_CONFIG
+
 @dataclass
 class KeywordOptimizationResponse:
     """关键词优化响应"""
@@ -164,6 +173,7 @@ class KeywordOptimizer:
         
         return prompt
     
+    @with_graceful_retry(SEARCH_API_RETRY_CONFIG, default_return={"success": False, "error": "关键词优化服务暂时不可用"})
     def _call_qwen_api(self, system_prompt: str, user_prompt: str) -> Dict[str, Any]:
         """调用Qwen API"""
         headers = {