[feat] 优化分析器执行情况，异步网络请求

src/analysis/llm_analyzer.py

@@ -3,6 +3,7 @@ LLM分析器模块
 负责协调各个分析器进行话题分析、用户称号分析和金句分析
 """
 
+import asyncio
 from typing import List, Dict, Tuple
 from astrbot.api import logger
 from ..models.data_models import SummaryTopic, UserTitle, GoldenQuote, TokenUsage
@@ -95,8 +96,68 @@ class LLMAnalyzer:
             logger.error(f"金句分析失败: {e}")
             return [], TokenUsage()
     
+    async def analyze_all_concurrent(self, messages: List[Dict], user_analysis: Dict, umo: str = None) -> Tuple[List[SummaryTopic], List[UserTitle], List[GoldenQuote], TokenUsage]:
+        """
+        并发执行所有分析任务（话题、用户称号、金句）
+        
+        Args:
+            messages: 群聊消息列表
+            user_analysis: 用户分析统计
+            umo: 模型唯一标识符
+            
+        Returns:
+            (话题列表, 用户称号列表, 金句列表, 总Token使用统计)
+        """
+        try:
+            logger.info("开始并发执行所有分析任务")
+            
+            # 并发执行三个分析任务
+            results = await asyncio.gather(
+                self.topic_analyzer.analyze_topics(messages, umo),
+                self.user_title_analyzer.analyze_user_titles(messages, user_analysis, umo),
+                self.golden_quote_analyzer.analyze_golden_quotes(messages, umo),
+                return_exceptions=True
+            )
+            
+            # 处理结果
+            topics, topic_usage = [], TokenUsage()
+            user_titles, title_usage = [], TokenUsage()
+            golden_quotes, quote_usage = [], TokenUsage()
+            
+            # 话题分析结果
+            if isinstance(results[0], Exception):
+                logger.error(f"话题分析失败: {results[0]}")
+            else:
+                topics, topic_usage = results[0]
+            
+            # 用户称号分析结果
+            if isinstance(results[1], Exception):
+                logger.error(f"用户称号分析失败: {results[1]}")
+            else:
+                user_titles, title_usage = results[1]
+            
+            # 金句分析结果
+            if isinstance(results[2], Exception):
+                logger.error(f"金句分析失败: {results[2]}")
+            else:
+                golden_quotes, quote_usage = results[2]
+            
+            # 合并Token使用统计
+            total_usage = TokenUsage(
+                prompt_tokens=topic_usage.prompt_tokens + title_usage.prompt_tokens + quote_usage.prompt_tokens,
+                completion_tokens=topic_usage.completion_tokens + title_usage.completion_tokens + quote_usage.completion_tokens,
+                total_tokens=topic_usage.total_tokens + title_usage.total_tokens + quote_usage.total_tokens
+            )
+            
+            logger.info(f"并发分析完成 - 话题: {len(topics)}, 称号: {len(user_titles)}, 金句: {len(golden_quotes)}")
+            return topics, user_titles, golden_quotes, total_usage
+            
+        except Exception as e:
+            logger.error(f"并发分析失败: {e}")
+            return [], [], [], TokenUsage()
+    
     # 向后兼容的方法，保持原有调用方式
-    async def _call_provider_with_retry(self, provider, prompt: str, max_tokens: int, 
+    async def _call_provider_with_retry(self, provider, prompt: str, max_tokens: int,
                                       temperature: float, umo: str = None):
         """
         向后兼容的LLM调用方法

src/utils/helpers.py

@@ -37,31 +37,42 @@ class MessageAnalyzer:
             # 用户分析
             user_analysis = self.user_analyzer.analyze_users(messages)
 
-            # LLM分析
+            # LLM分析 - 使用并发方式
             topics = []
             user_titles = []
             golden_quotes = []
             total_token_usage = TokenUsage()
 
-            # 话题分析
-            if self.config_manager.get_topic_analysis_enabled():
-                topics, topic_tokens = await self.llm_analyzer.analyze_topics(messages, umo=unified_msg_origin)
-                total_token_usage.prompt_tokens += topic_tokens.prompt_tokens
-                total_token_usage.completion_tokens += topic_tokens.completion_tokens
-                total_token_usage.total_tokens += topic_tokens.total_tokens
+            # 检查各个分析功能是否启用
+            topic_enabled = self.config_manager.get_topic_analysis_enabled()
+            user_title_enabled = self.config_manager.get_user_title_analysis_enabled()
+            golden_quote_enabled = self.config_manager.get_golden_quote_analysis_enabled()
+            
+            # 如果三个分析都启用，使用并发执行
+            if topic_enabled and user_title_enabled and golden_quote_enabled:
+                # 并发执行所有三个分析任务
+                topics, user_titles, golden_quotes, total_token_usage = await self.llm_analyzer.analyze_all_concurrent(
+                    messages, user_analysis, umo=unified_msg_origin
+                )
+            else:
+                # 如果只启用部分分析，则按需执行
+                if topic_enabled:
+                    topics, topic_tokens = await self.llm_analyzer.analyze_topics(messages, umo=unified_msg_origin)
+                    total_token_usage.prompt_tokens += topic_tokens.prompt_tokens
+                    total_token_usage.completion_tokens += topic_tokens.completion_tokens
+                    total_token_usage.total_tokens += topic_tokens.total_tokens
 
-            # 用户称号分析
-            if self.config_manager.get_user_title_analysis_enabled():
-                user_titles, title_tokens = await self.llm_analyzer.analyze_user_titles(messages, user_analysis, umo=unified_msg_origin)
-                total_token_usage.prompt_tokens += title_tokens.prompt_tokens
-                total_token_usage.completion_tokens += title_tokens.completion_tokens
-                total_token_usage.total_tokens += title_tokens.total_tokens
+                if user_title_enabled:
+                    user_titles, title_tokens = await self.llm_analyzer.analyze_user_titles(messages, user_analysis, umo=unified_msg_origin)
+                    total_token_usage.prompt_tokens += title_tokens.prompt_tokens
+                    total_token_usage.completion_tokens += title_tokens.completion_tokens
+                    total_token_usage.total_tokens += title_tokens.total_tokens
 
-            # 金句分析
-            golden_quotes, quote_tokens = await self.llm_analyzer.analyze_golden_quotes(messages, umo=unified_msg_origin)
-            total_token_usage.prompt_tokens += quote_tokens.prompt_tokens
-            total_token_usage.completion_tokens += quote_tokens.completion_tokens
-            total_token_usage.total_tokens += quote_tokens.total_tokens
+                if golden_quote_enabled:
+                    golden_quotes, quote_tokens = await self.llm_analyzer.analyze_golden_quotes(messages, umo=unified_msg_origin)
+                    total_token_usage.prompt_tokens += quote_tokens.prompt_tokens
+                    total_token_usage.completion_tokens += quote_tokens.completion_tokens
+                    total_token_usage.total_tokens += quote_tokens.total_tokens
 
             # 更新统计数据
             statistics.golden_quotes = golden_quotes