Implement real-time profile retrieval and saving in simulation API

- Added a new endpoint to retrieve real-time agent profiles during simulation, allowing users to monitor progress without going through the SimulationManager.
- Enhanced the profile generation process to support real-time saving of generated profiles to specified file formats (JSON for Reddit, CSV for Twitter).
- Updated the simulation configuration generator to assign appropriate agents to initial posts based on their types, improving the relevance of generated content.
- Improved error handling and logging for better traceability during profile generation and retrieval processes.

backend/app/services/oasis_profile_generator.py

@@ -853,7 +853,9 @@ class OasisProfileGenerator:
         use_llm: bool = True,
         progress_callback: Optional[callable] = None,
         graph_id: Optional[str] = None,
-        parallel_count: int = 5
+        parallel_count: int = 5,
+        realtime_output_path: Optional[str] = None,
+        output_platform: str = "reddit"
     ) -> List[OasisAgentProfile]:
         """
         批量从实体生成Agent Profile（支持并行生成）
@@ -864,6 +866,8 @@ class OasisProfileGenerator:
             progress_callback: 进度回调函数 (current, total, message)
             graph_id: 图谱ID，用于Zep检索获取更丰富上下文
             parallel_count: 并行生成数量，默认5
+            realtime_output_path: 实时写入的文件路径（如果提供，每生成一个就写入一次）
+            output_platform: 输出平台格式 ("reddit" 或 "twitter")
             
         Returns:
             Agent Profile列表
@@ -880,6 +884,37 @@ class OasisProfileGenerator:
         completed_count = [0]  # 使用列表以便在闭包中修改
         lock = Lock()
         
+        # 实时写入文件的辅助函数
+        def save_profiles_realtime():
+            """实时保存已生成的 profiles 到文件"""
+            if not realtime_output_path:
+                return
+            
+            with lock:
+                # 过滤出已生成的 profiles
+                existing_profiles = [p for p in profiles if p is not None]
+                if not existing_profiles:
+                    return
+                
+                try:
+                    if output_platform == "reddit":
+                        # Reddit JSON 格式
+                        profiles_data = [p.to_reddit_format() for p in existing_profiles]
+                        with open(realtime_output_path, 'w', encoding='utf-8') as f:
+                            json.dump(profiles_data, f, ensure_ascii=False, indent=2)
+                    else:
+                        # Twitter CSV 格式
+                        import csv
+                        profiles_data = [p.to_twitter_format() for p in existing_profiles]
+                        if profiles_data:
+                            fieldnames = list(profiles_data[0].keys())
+                            with open(realtime_output_path, 'w', encoding='utf-8', newline='') as f:
+                                writer = csv.DictWriter(f, fieldnames=fieldnames)
+                                writer.writeheader()
+                                writer.writerows(profiles_data)
+                except Exception as e:
+                    logger.warning(f"实时保存 profiles 失败: {e}")
+        
         def generate_single_profile(idx: int, entity: EntityNode) -> tuple:
             """生成单个profile的工作函数"""
             entity_type = entity.get_entity_type() or "Entity"
@@ -936,6 +971,9 @@ class OasisProfileGenerator:
                         completed_count[0] += 1
                         current = completed_count[0]
                     
+                    # 实时写入文件
+                    save_profiles_realtime()
+                    
                     if progress_callback:
                         progress_callback(
                             current, 
@@ -961,6 +999,8 @@ class OasisProfileGenerator:
                         source_entity_uuid=entity.uuid,
                         source_entity_type=entity_type,
                     )
+                    # 实时写入文件（即使是备用人设）
+                    save_profiles_realtime()
         
         print(f"\n{'='*60}")
         print(f"人设生成完成！共生成 {len([p for p in profiles if p])} 个Agent")

backend/app/services/simulation_config_generator.py

@@ -292,7 +292,7 @@ class SimulationConfigGenerator:
         
         # ========== 步骤2: 生成事件配置 ==========
         report_progress(2, "生成事件配置和热点话题...")
-        event_config_result = self._generate_event_config(context, simulation_requirement)
+        event_config_result = self._generate_event_config(context, simulation_requirement, entities)
         event_config = self._parse_event_config(event_config_result)
         reasoning_parts.append(f"事件配置: {event_config_result.get('reasoning', '成功')}")
         
@@ -318,6 +318,12 @@ class SimulationConfigGenerator:
         
         reasoning_parts.append(f"Agent配置: 成功生成 {len(all_agent_configs)} 个")
         
+        # ========== 为初始帖子分配发布者 Agent ==========
+        logger.info("为初始帖子分配合适的发布者 Agent...")
+        event_config = self._assign_initial_post_agents(event_config, all_agent_configs)
+        assigned_count = len([p for p in event_config.initial_posts if p.get("poster_agent_id") is not None])
+        reasoning_parts.append(f"初始帖子分配: {assigned_count} 个帖子已分配发布者")
+        
         # ========== 最后一步: 生成平台配置 ==========
         report_progress(total_steps, "生成平台配置...")
         twitter_config = None
@@ -583,32 +589,63 @@ class SimulationConfigGenerator:
             peak_activity_multiplier=1.5
         )
     
-    def _generate_event_config(self, context: str, simulation_requirement: str) -> Dict[str, Any]:
+    def _generate_event_config(
+        self, 
+        context: str, 
+        simulation_requirement: str,
+        entities: List[EntityNode]
+    ) -> Dict[str, Any]:
         """生成事件配置"""
+        
+        # 获取可用的实体类型列表，供 LLM 参考
+        entity_types_available = list(set(
+            e.get_entity_type() or "Unknown" for e in entities
+        ))
+        
+        # 为每种类型列出代表性实体名称
+        type_examples = {}
+        for e in entities:
+            etype = e.get_entity_type() or "Unknown"
+            if etype not in type_examples:
+                type_examples[etype] = []
+            if len(type_examples[etype]) < 3:
+                type_examples[etype].append(e.name)
+        
+        type_info = "\n".join([
+            f"- {t}: {', '.join(examples)}" 
+            for t, examples in type_examples.items()
+        ])
+        
         prompt = f"""基于以下模拟需求，生成事件配置。
 
 模拟需求: {simulation_requirement}
 
 {context[:3000]}
 
+## 可用实体类型及示例
+{type_info}
+
 ## 任务
 请生成事件配置JSON：
 - 提取热点话题关键词
 - 描述舆论发展方向
-- 设计初始帖子内容
+- 设计初始帖子内容，**每个帖子必须指定 poster_type（发布者类型）**
+
+**重要**: poster_type 必须从上面的"可用实体类型"中选择，这样初始帖子才能分配给合适的 Agent 发布。
+例如：官方声明应由 Official/University 类型发布，新闻由 MediaOutlet 发布，学生观点由 Student 发布。
 
 返回JSON格式（不要markdown）：
 {{
     "hot_topics": ["关键词1", "关键词2", ...],
     "narrative_direction": "<舆论发展方向描述>",
     "initial_posts": [
-        {{"content": "帖子内容", "poster_type": "MediaOutlet"}},
+        {{"content": "帖子内容", "poster_type": "实体类型（必须从可用类型中选择）"}},
         ...
     ],
     "reasoning": "<简要说明>"
 }}"""
 
-        system_prompt = "你是舆论分析专家。返回纯JSON格式。"
+        system_prompt = "你是舆论分析专家。返回纯JSON格式。注意 poster_type 必须精确匹配可用实体类型。"
         
         try:
             return self._call_llm_with_retry(prompt, system_prompt)
@@ -630,6 +667,91 @@ class SimulationConfigGenerator:
             narrative_direction=result.get("narrative_direction", "")
         )
     
+    def _assign_initial_post_agents(
+        self,
+        event_config: EventConfig,
+        agent_configs: List[AgentActivityConfig]
+    ) -> EventConfig:
+        """
+        为初始帖子分配合适的发布者 Agent
+        
+        根据每个帖子的 poster_type 匹配最合适的 agent_id
+        """
+        if not event_config.initial_posts:
+            return event_config
+        
+        # 按实体类型建立 agent 索引
+        agents_by_type: Dict[str, List[AgentActivityConfig]] = {}
+        for agent in agent_configs:
+            etype = agent.entity_type.lower()
+            if etype not in agents_by_type:
+                agents_by_type[etype] = []
+            agents_by_type[etype].append(agent)
+        
+        # 类型映射表（处理 LLM 可能输出的不同格式）
+        type_aliases = {
+            "official": ["official", "university", "governmentagency", "government"],
+            "university": ["university", "official"],
+            "mediaoutlet": ["mediaoutlet", "media"],
+            "student": ["student", "person"],
+            "professor": ["professor", "expert", "teacher"],
+            "alumni": ["alumni", "person"],
+            "organization": ["organization", "ngo", "company", "group"],
+            "person": ["person", "student", "alumni"],
+        }
+        
+        # 记录每种类型已使用的 agent 索引，避免重复使用同一个 agent
+        used_indices: Dict[str, int] = {}
+        
+        updated_posts = []
+        for post in event_config.initial_posts:
+            poster_type = post.get("poster_type", "").lower()
+            content = post.get("content", "")
+            
+            # 尝试找到匹配的 agent
+            matched_agent_id = None
+            
+            # 1. 直接匹配
+            if poster_type in agents_by_type:
+                agents = agents_by_type[poster_type]
+                idx = used_indices.get(poster_type, 0) % len(agents)
+                matched_agent_id = agents[idx].agent_id
+                used_indices[poster_type] = idx + 1
+            else:
+                # 2. 使用别名匹配
+                for alias_key, aliases in type_aliases.items():
+                    if poster_type in aliases or alias_key == poster_type:
+                        for alias in aliases:
+                            if alias in agents_by_type:
+                                agents = agents_by_type[alias]
+                                idx = used_indices.get(alias, 0) % len(agents)
+                                matched_agent_id = agents[idx].agent_id
+                                used_indices[alias] = idx + 1
+                                break
+                    if matched_agent_id is not None:
+                        break
+            
+            # 3. 如果仍未找到，使用影响力最高的 agent
+            if matched_agent_id is None:
+                logger.warning(f"未找到类型 '{poster_type}' 的匹配 Agent，使用影响力最高的 Agent")
+                if agent_configs:
+                    # 按影响力排序，选择影响力最高的
+                    sorted_agents = sorted(agent_configs, key=lambda a: a.influence_weight, reverse=True)
+                    matched_agent_id = sorted_agents[0].agent_id
+                else:
+                    matched_agent_id = 0
+            
+            updated_posts.append({
+                "content": content,
+                "poster_type": post.get("poster_type", "Unknown"),
+                "poster_agent_id": matched_agent_id
+            })
+            
+            logger.info(f"初始帖子分配: poster_type='{poster_type}' -> agent_id={matched_agent_id}")
+        
+        event_config.initial_posts = updated_posts
+        return event_config
+    
     def _generate_agent_configs_batch(
         self,
         context: str,

backend/app/services/simulation_manager.py

@@ -324,17 +324,30 @@ class SimulationManager:
                         item_name=msg
                     )
             
+            # 设置实时保存的文件路径（优先使用 Reddit JSON 格式）
+            realtime_output_path = None
+            realtime_platform = "reddit"
+            if state.enable_reddit:
+                realtime_output_path = os.path.join(sim_dir, "reddit_profiles.json")
+                realtime_platform = "reddit"
+            elif state.enable_twitter:
+                realtime_output_path = os.path.join(sim_dir, "twitter_profiles.csv")
+                realtime_platform = "twitter"
+            
             profiles = generator.generate_profiles_from_entities(
                 entities=filtered.entities,
                 use_llm=use_llm_for_profiles,
                 progress_callback=profile_progress,
                 graph_id=state.graph_id,  # 传入graph_id用于Zep检索
-                parallel_count=parallel_profile_count  # 并行生成数量
+                parallel_count=parallel_profile_count,  # 并行生成数量
+                realtime_output_path=realtime_output_path,  # 实时保存路径
+                output_platform=realtime_platform  # 输出格式
             )
             
             state.profiles_count = len(profiles)
             
             # 保存Profile文件（注意：Twitter使用CSV格式，Reddit使用JSON格式）
+            # Reddit 已经在生成过程中实时保存了，这里再保存一次确保完整性
             if progress_callback:
                 progress_callback(
                     "generating_profiles", 95,