feat: 添加Redis支持和Celery任务处理

- 新增Redis服务模块用于会话状态存储和缓存
- 集成Celery用于后台任务处理
- 更新Docker Compose配置以支持开发环境
- 优化API以支持异步调用和Redis会话存储
- 更新文档以反映新的开发环境配置和使用方法

api/agents/memory_agent.py

@@ -1,7 +1,9 @@
 """
 回忆录整理 Agent：基于传记结构，将口语改写为书面语，归类到章节
+支持异步调用
 """
 import json
+import logging
 from typing import List, Dict, Optional
 
 from services.llm_service import llm_service
@@ -14,17 +16,19 @@ from .prompts import (
     CHAPTER_ORDER
 )
 
+logger = logging.getLogger(__name__)
+
 
 class MemoryAgent:
-    """回忆录整理 Agent"""
+    """回忆录整理 Agent（支持异步）"""
     
     def __init__(self):
         # 使用 LLM 服务获取 LLM 实例
         self.llm = llm_service.get_llm()
     
-    def classify_chapter(self, segments_text: str) -> str:
+    async def classify_chapter(self, segments_text: str) -> str:
         """
-        分类章节
+        异步分类章节
         
         Args:
             segments_text: 对话段落文本
@@ -36,28 +40,34 @@ class MemoryAgent:
             # 如果没有配置 LLM，返回默认类别
             return "childhood"
         
-        prompt = get_chapter_classification_prompt(segments_text)
-        
-        response = self.llm.invoke(prompt)
-        
-        # 提取类别
-        category = response.content.strip().lower()
-        
-        # 验证类别是否有效
-        if category in CHAPTER_CATEGORIES:
-            return category
+        try:
+            prompt = get_chapter_classification_prompt(segments_text)
+            
+            # 异步调用 LLM
+            response = await self.llm.ainvoke(prompt)
+            
+            # 提取类别
+            content = response.content if hasattr(response, 'content') else str(response)
+            category = content.strip().lower()
+            
+            # 验证类别是否有效
+            if category in CHAPTER_CATEGORIES:
+                return category
+            
+        except Exception as e:
+            logger.error(f"分类章节失败: {e}")
         
         # 默认返回 childhood
         return "childhood"
     
-    def rewrite_to_literary(
+    async def rewrite_to_literary(
         self,
         segments_text: str,
         chapter_category: str,
         existing_content: Optional[str] = None
     ) -> Dict:
         """
-        将口语改写为书面语
+        异步将口语改写为书面语
         
         Args:
             segments_text: 对话段落文本
@@ -76,14 +86,16 @@ class MemoryAgent:
                 "image_suggestions": []
             }
         
-        prompt = get_text_rewrite_prompt(segments_text, chapter_category, existing_content or "")
-        
-        response = self.llm.invoke(prompt)
-        
-        # 尝试解析 JSON
         try:
-            # 提取 JSON 部分
-            content = response.content.strip()
+            prompt = get_text_rewrite_prompt(segments_text, chapter_category, existing_content or "")
+            
+            # 异步调用 LLM
+            response = await self.llm.ainvoke(prompt)
+            
+            # 尝试解析 JSON
+            content = response.content if hasattr(response, 'content') else str(response)
+            content = content.strip()
+            
             # 移除可能的 markdown 代码块标记
             if content.startswith("```json"):
                 content = content[7:]
@@ -95,22 +107,31 @@ class MemoryAgent:
             
             result = json.loads(content)
             return result
+            
         except json.JSONDecodeError:
             # 如果解析失败，返回基本结构
             return {
                 "title": CHAPTER_CATEGORIES.get(chapter_category, "章节"),
-                "content": response.content,
+                "content": response.content if hasattr(response, 'content') else str(response),
+                "summary": "",
+                "image_suggestions": []
+            }
+        except Exception as e:
+            logger.error(f"改写文本失败: {e}")
+            return {
+                "title": CHAPTER_CATEGORIES.get(chapter_category, "章节"),
+                "content": segments_text,
                 "summary": "",
                 "image_suggestions": []
             }
     
-    def process_segments(
+    async def process_segments(
         self,
         segments: List[Dict],
         existing_chapters: Optional[Dict[str, Dict]] = None
     ) -> Dict[str, Dict]:
         """
-        处理对话段落，生成或更新章节
+        异步处理对话段落，生成或更新章节
         
         Args:
             segments: 对话段落列表，每个包含 transcript_text
@@ -130,8 +151,8 @@ class MemoryAgent:
             if not text:
                 continue
             
-            # 分类
-            category = self.classify_chapter(text)
+            # 异步分类
+            category = await self.classify_chapter(text)
             
             if category not in segments_by_category:
                 segments_by_category[category] = []
@@ -145,8 +166,8 @@ class MemoryAgent:
             combined_text = "\n\n".join(texts)
             existing_content = existing_chapters.get(category, {}).get("content", "")
             
-            # 改写为书面语
-            result = self.rewrite_to_literary(combined_text, category, existing_content)
+            # 异步改写为书面语
+            result = await self.rewrite_to_literary(combined_text, category, existing_content)
             
             # 更新章节
             updated_chapters[category] = {