Enhance OASIS simulation capabilities and profile generation
- Updated README.md to include detailed descriptions of new features, including Zep mixed search functionality and detailed persona generation for individual and group entities. - Implemented a robust mechanism for checking simulation preparation status to avoid redundant profile generation. - Added support for parallel profile generation, improving efficiency in creating OASIS Agent Profiles. - Enhanced the simulation configuration generator to adopt a stepwise approach, ensuring better handling of complex configurations. - Introduced error handling and retry mechanisms for LLM calls, improving the reliability of profile generation. - Updated simulation management to support new API parameters for controlling profile generation behavior.
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@ -1057,9 +1057,165 @@ for node in all_nodes:
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| 方法 | 说明 |
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|------|------|
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| `generate_profile_from_entity(entity, user_id)` | 从实体生成单个Profile |
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| `generate_profiles_from_entities(entities)` | 批量生成Profile |
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| `save_profiles_to_json(profiles, path, platform)` | 保存到JSON文件 |
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| `generate_profile_from_entity(entity, user_id)` | 从实体生成单个Profile（带详细人设） |
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| `generate_profiles_from_entities(entities, graph_id)` | 批量生成Profile |
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| `save_profiles(profiles, path, platform)` | 保存Profile文件 |
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| `_search_zep_for_entity(entity_name)` | 调用Zep检索获取额外上下文 |
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### 优化特性（v2.0）
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1. **Zep混合搜索功能**：使用多种查询策略获取丰富的实体信息
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2. **区分实体类型**：个人实体 vs 群体/机构实体，使用不同的提示词
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3. **详细人设生成**：生成500字以上的详细人设描述
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### Zep混合搜索策略
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`_search_zep_for_entity()` 方法采用多种搜索策略获取丰富信息：
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**查询策略：**
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```python
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queries = [
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f"总结{entity_name}的全部活动、事件和行为",
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f"{entity_name}与其他实体的关系和互动",
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f"{entity_name}的背景、历史和重要信息",
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f"关于{entity_name}的所有事实和描述",
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]
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```
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**说明：** Zep没有内置的混合搜索接口，需要分别搜索edges和nodes。我们使用**并行请求**同时执行两个搜索：
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```python
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# 并行执行edges和nodes搜索
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with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor:
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edge_future = executor.submit(search_edges) # scope="edges"
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node_future = executor.submit(search_nodes) # scope="nodes"
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edge_result = edge_future.result(timeout=30)
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node_result = node_future.result(timeout=30)
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```
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**搜索参数：**
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| 搜索类型 | scope | limit | 说明 |
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|----------|-------|-------|------|
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| 边搜索 | edges | 30 | 获取事实/关系信息 |
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| 节点搜索 | nodes | 20 | 获取相关实体摘要 |
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**关键参数：**
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- 必须传递 `graph_id` 参数，否则Zep API会返回400错误
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- 使用 `rrf` (Reciprocal Rank Fusion) reranker，稳定可靠
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- 使用线程池并行执行，提高效率
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**返回数据结构：**
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```python
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{
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"facts": [...], # 事实列表（来自edges）
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"node_summaries": [...], # 相关节点摘要（来自nodes）
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"context": "..." # 综合上下文文本
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}
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```
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### LLM生成与JSON修复
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为了避免LLM生成的JSON解析失败，实现了以下优化：
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1. **不限制max_tokens**：让LLM自由发挥，充分利用模型的上下文能力
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2. **多次重试机制**：最多3次尝试，每次降低temperature
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3. **截断检测与修复**：检测`finish_reason='length'`，自动闭合JSON
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4. **完善JSON修复机制**：
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- `_fix_truncated_json()`: 修复被截断的JSON（闭合括号和字符串）
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- `_try_fix_json()`: 多级修复策略
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- 提取JSON部分
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- 替换字符串中的换行符
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- 移除控制字符
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- 从损坏JSON中提取部分信息
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5. **字段验证**：确保必需字段存在，缺失时使用entity_summary填充
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**错误处理流程**：
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```
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LLM调用 → 检查截断 → JSON解析 → 修复尝试 → 部分提取 → 规则生成
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```
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### 并行生成与实时输出
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支持并行生成Agent人设，提高生成效率：
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```python
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profiles = generator.generate_profiles_from_entities(
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entities=filtered.entities,
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use_llm=True,
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graph_id="mirofish_xxx",
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parallel_count=5 # 并行生成数量，默认5
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)
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```
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**API参数**：
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```json
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POST /api/simulation/prepare
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{
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"simulation_id": "sim_xxx",
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"parallel_profile_count": 5, // 可选，并行生成人设数量，默认5
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"force_regenerate": false // 可选，强制重新生成，默认false
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}
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```
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**实时输出**：
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- 每生成一个人设，立即输出到控制台（完整内容不截断）
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- 包含用户名、简介、详细人设、年龄、性别、MBTI等信息
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- 方便实时监控生成进度和质量
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### 避免重复生成
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系统会自动检测已完成的准备工作，避免重复生成：
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**检测条件**：
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1. `state.json` 存在且 `config_generated=true`
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2. 必要文件存在：`reddit_profiles.json`, `twitter_profiles.csv`, `simulation_config.json`
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**API响应**：
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```json
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// 已准备完成时
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{
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"success": true,
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"data": {
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"simulation_id": "sim_xxx",
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"status": "ready",
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"message": "已有完成的准备工作，无需重复生成",
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"already_prepared": true,
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"prepare_info": {
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"entities_count": 93,
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"profiles_count": 93,
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"entity_types": ["Student", "Professor", ...],
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"existing_files": [...]
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}
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}
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}
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```
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**强制重新生成**：
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```json
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POST /api/simulation/prepare
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{
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"simulation_id": "sim_xxx",
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"force_regenerate": true // 忽略已有准备，强制重新生成
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}
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```
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### 实体类型分类
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```python
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# 个人类型实体 - 生成具体人物设定
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INDIVIDUAL_ENTITY_TYPES = [
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"student", "alumni", "professor", "person", "publicfigure",
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"expert", "faculty", "official", "journalist", "activist"
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]
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# 群体/机构类型实体 - 生成官方账号设定
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GROUP_ENTITY_TYPES = [
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"university", "governmentagency", "organization", "ngo",
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"mediaoutlet", "company", "institution", "group", "community"
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]
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```
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### Profile数据结构
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@ -1071,7 +1227,7 @@ class OasisAgentProfile:
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user_name: str # 用户名
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name: str # 显示名称
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bio: str # 简介（max 150字符）
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persona: str # 详细人设描述
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persona: str # 详细人设描述（500字以上）
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# Reddit字段
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karma: int = 1000
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@ -1094,6 +1250,37 @@ class OasisAgentProfile:
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source_entity_type: Optional[str] = None
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```
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### 详细人设生成示例
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**个人实体人设结构：**
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```markdown
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## 一、基本信息
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- 姓名/称呼、年龄、职业/身份
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- 教育背景、所在地
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## 二、人物背景
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- 过去的重要经历
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- 与事件的关联
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- 社会关系网络
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## 三、性格特征
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- MBTI类型及表现
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- 核心性格特点
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- 情绪表达方式
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## 四、社交媒体行为模式
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- 发帖频率和时间
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- 内容偏好类型
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- 语言风格特点
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## 五、立场与观点
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- 对核心话题的态度
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- 可能被激怒/感动的内容
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## 六、独特特征
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- 口头禅、个人爱好等
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```
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### Profile生成策略
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**1. LLM生成（默认）**
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@ -1135,12 +1322,31 @@ Generate a social media user profile with:
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使用LLM分析模拟需求、文档内容、图谱实体信息，自动生成最佳的模拟参数配置。
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**采用分步生成策略**（避免一次性生成过长内容导致失败）：
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1. 生成时间配置（轻量级）
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2. 生成事件配置和热点话题
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3. 分批生成Agent配置（**每批5个**，保证生成质量）
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4. 生成平台配置
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| 方法 | 说明 |
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|------|------|
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| `generate_config(...)` | 智能生成完整模拟配置 |
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| `_build_context(...)` | 构建LLM上下文（最大5万字） |
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| `_generate_config_with_llm(...)` | 调用LLM生成配置 |
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| `_generate_default_config(...)` | 默认配置（LLM失败时） |
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| `generate_config(...)` | 智能生成完整模拟配置（分步） |
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| `_generate_time_config(...)` | 生成时间配置 |
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| `_generate_event_config(...)` | 生成事件配置 |
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| `_generate_agent_configs_batch(...)` | 分批生成Agent配置 |
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| `_generate_agent_config_by_rule(...)` | 规则生成（LLM失败时） |
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### 中国人作息时间配置
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系统针对中国用户群体，采用符合北京时间的作息习惯：
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| 时段 | 时间范围 | 活跃度系数 | 说明 |
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|------|----------|------------|------|
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| 深夜 | 0:00-5:59 | 0.05 | 几乎无人活动 |
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| 早间 | 6:00-8:59 | 0.4 | 逐渐醒来 |
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| 工作 | 9:00-18:59 | 0.7 | 工作时段中等活跃 |
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| 高峰 | 19:00-22:59 | 1.5 | 晚间最活跃 |
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| 夜间 | 23:00-23:59 | 0.5 | 活跃度下降 |
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### LLM智能生成的配置内容
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@ -1152,10 +1358,14 @@ class TimeSimulationConfig:
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minutes_per_round: int = 30 # 每轮代表的时间（分钟）
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agents_per_hour_min: int = 5 # 每小时激活Agent数量（最小）
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agents_per_hour_max: int = 20 # 每小时激活Agent数量（最大）
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peak_hours: List[int] # 高峰时段 [9,10,11,14,15,20,21,22]
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off_peak_hours: List[int] # 低谷时段 [0,1,2,3,4,5]
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peak_hours: List[int] = [19,20,21,22] # 高峰时段（晚间）
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off_peak_hours: List[int] = [0,1,2,3,4,5] # 低谷时段（凌晨）
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peak_activity_multiplier: float = 1.5 # 高峰活跃度乘数
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off_peak_activity_multiplier: float = 0.3 # 低谷活跃度乘数
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off_peak_activity_multiplier: float = 0.05 # 凌晨活跃度极低
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morning_hours: List[int] = [6,7,8] # 早间时段
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morning_activity_multiplier: float = 0.4
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work_hours: List[int] = [9-18] # 工作时段
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work_activity_multiplier: float = 0.7
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```
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**2. AgentActivityConfig（每个Agent的活动配置）**
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@ -1178,14 +1388,18 @@ class AgentActivityConfig:
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influence_weight: float = 1.0 # 影响力权重
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```
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**3. 不同实体类型的默认参数差异**
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**3. 不同实体类型的默认参数差异（符合中国人作息）**
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| 实体类型 | 活跃度 | 发帖频率 | 响应延迟 | 影响力 |
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|----------|--------|----------|----------|--------|
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| University/GovernmentAgency | 0.2 | 0.1/小时 | 60-240分钟 | 3.0 |
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| MediaOutlet | 0.6 | 1.0/小时 | 5-30分钟 | 2.5 |
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| PublicFigure/Expert | 0.5 | 0.3/小时 | 10-60分钟 | 2.0 |
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| Student/Person | 0.7 | 0.5/小时 | 1-20分钟 | 1.0 |
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| 实体类型 | 活跃度 | 发帖频率 | 活跃时段 | 响应延迟 | 影响力 |
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|----------|--------|----------|----------|----------|--------|
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| University/GovernmentAgency | 0.2 | 0.1/小时 | 9:00-17:59（工作时间） | 60-240分钟 | 3.0 |
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| MediaOutlet | 0.5 | 0.8/小时 | 7:00-23:59（全天） | 5-30分钟 | 2.5 |
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| Professor/Expert | 0.4 | 0.3/小时 | 8:00-21:59（工作+晚间） | 15-90分钟 | 2.0 |
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| Student | 0.8 | 0.6/小时 | 8-13, 18-23（上午+晚间） | 1-15分钟 | 0.8 |
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| Alumni | 0.6 | 0.4/小时 | 12-13, 19-23（午休+晚间） | 5-30分钟 | 1.0 |
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| Person（普通人） | 0.7 | 0.5/小时 | 9-13, 18-23（白天+晚间） | 2-20分钟 | 1.0 |
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**注意**：凌晨0-5点所有实体类型都几乎不活动（符合中国人作息习惯）
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---
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@ -1228,8 +1442,41 @@ uploads/simulations/sim_xxxx/
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```
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**重要：OASIS平台的Profile格式要求不同：**
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- **Twitter**: 使用CSV格式，字段：`user_id,user_name,name,bio,friend_count,follower_count,statuses_count,created_at`
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- **Reddit**: 使用JSON格式，支持详细人设字段：`realname,username,bio,persona,age,gender,mbti,country,profession,interested_topics`
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**Twitter CSV格式**（符合OASIS官方要求）：
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```csv
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user_id,name,username,user_char,description
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0,张教授,professor_zhang,"完整人设描述（LLM内部使用）","简短简介（外部显示）"
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```
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- `user_id`: 从0开始的顺序ID
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- `name`: 真实姓名
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- `username`: 系统用户名
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- `user_char`: 完整人设（bio + persona），注入LLM系统提示，指导Agent行为
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- `description`: 简短简介，显示在用户资料页面
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**Reddit JSON格式**：
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```json
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[
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{
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"realname": "张教授",
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"username": "professor_zhang",
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"bio": "简短简介",
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"persona": "详细人设描述",
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"age": 42,
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"gender": "男",
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"mbti": "INTJ",
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"country": "中国",
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"profession": "教授",
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"interested_topics": ["高等教育", "学术诚信"]
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}
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]
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```
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**user_char vs description 区别**：
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| 字段 | 用途 | 可见性 |
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|------|------|--------|
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| user_char | LLM系统提示，决定Agent如何思考和行动 | 内部使用 |
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| description | 用户资料页面的简介 | 其他用户可见 |
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### 配置文件示例 (simulation_config.json)
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@ -213,6 +213,112 @@ def create_simulation():
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}), 500
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def _check_simulation_prepared(simulation_id: str) -> tuple:
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"""
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检查模拟是否已经准备完成
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检查条件：
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1. state.json 存在且 status 为 "ready"
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2. 必要文件存在：reddit_profiles.json, twitter_profiles.csv, simulation_config.json
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Args:
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simulation_id: 模拟ID
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Returns:
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(is_prepared: bool, info: dict)
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"""
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import os
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from ..config import Config
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|
||||
simulation_dir = os.path.join(Config.OASIS_SIMULATION_DATA_DIR, simulation_id)
|
||||
|
||||
# 检查目录是否存在
|
||||
if not os.path.exists(simulation_dir):
|
||||
return False, {"reason": "模拟目录不存在"}
|
||||
|
||||
# 必要文件列表
|
||||
required_files = [
|
||||
"state.json",
|
||||
"simulation_config.json",
|
||||
"reddit_profiles.json",
|
||||
"twitter_profiles.csv",
|
||||
"run_reddit_simulation.py",
|
||||
"run_twitter_simulation.py",
|
||||
"run_parallel_simulation.py"
|
||||
]
|
||||
|
||||
# 检查文件是否存在
|
||||
existing_files = []
|
||||
missing_files = []
|
||||
for f in required_files:
|
||||
file_path = os.path.join(simulation_dir, f)
|
||||
if os.path.exists(file_path):
|
||||
existing_files.append(f)
|
||||
else:
|
||||
missing_files.append(f)
|
||||
|
||||
if missing_files:
|
||||
return False, {
|
||||
"reason": "缺少必要文件",
|
||||
"missing_files": missing_files,
|
||||
"existing_files": existing_files
|
||||
}
|
||||
|
||||
# 检查state.json中的状态
|
||||
state_file = os.path.join(simulation_dir, "state.json")
|
||||
try:
|
||||
import json
|
||||
with open(state_file, 'r', encoding='utf-8') as f:
|
||||
state_data = json.load(f)
|
||||
|
||||
status = state_data.get("status", "")
|
||||
|
||||
# 如果状态是ready或preparing（已有文件），认为准备完成
|
||||
if status in ["ready", "preparing"] and state_data.get("config_generated"):
|
||||
# 获取文件统计信息
|
||||
profiles_file = os.path.join(simulation_dir, "reddit_profiles.json")
|
||||
config_file = os.path.join(simulation_dir, "simulation_config.json")
|
||||
|
||||
profiles_count = 0
|
||||
if os.path.exists(profiles_file):
|
||||
with open(profiles_file, 'r', encoding='utf-8') as f:
|
||||
profiles_data = json.load(f)
|
||||
profiles_count = len(profiles_data) if isinstance(profiles_data, list) else 0
|
||||
|
||||
# 如果状态是preparing但文件已完成，自动更新状态为ready
|
||||
if status == "preparing":
|
||||
try:
|
||||
state_data["status"] = "ready"
|
||||
from datetime import datetime
|
||||
state_data["updated_at"] = datetime.now().isoformat()
|
||||
with open(state_file, 'w', encoding='utf-8') as f:
|
||||
json.dump(state_data, f, ensure_ascii=False, indent=2)
|
||||
logger.info(f"自动更新模拟状态: {simulation_id} preparing -> ready")
|
||||
status = "ready"
|
||||
except Exception as e:
|
||||
logger.warning(f"自动更新状态失败: {e}")
|
||||
|
||||
return True, {
|
||||
"status": status,
|
||||
"entities_count": state_data.get("entities_count", 0),
|
||||
"profiles_count": profiles_count,
|
||||
"entity_types": state_data.get("entity_types", []),
|
||||
"config_generated": state_data.get("config_generated", False),
|
||||
"created_at": state_data.get("created_at"),
|
||||
"updated_at": state_data.get("updated_at"),
|
||||
"existing_files": existing_files
|
||||
}
|
||||
else:
|
||||
return False, {
|
||||
"reason": f"状态不是ready: {status}",
|
||||
"status": status,
|
||||
"config_generated": state_data.get("config_generated", False)
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
return False, {"reason": f"读取状态文件失败: {str(e)}"}
|
||||
|
||||
|
||||
@simulation_bp.route('/prepare', methods=['POST'])
|
||||
def prepare_simulation():
|
||||
"""
|
||||
|
|
@ -221,17 +327,25 @@ def prepare_simulation():
|
|||
这是一个耗时操作，接口会立即返回task_id，
|
||||
使用 GET /api/simulation/prepare/status 查询进度
|
||||
|
||||
特性：
|
||||
- 自动检测已完成的准备工作，避免重复生成
|
||||
- 如果已准备完成，直接返回已有结果
|
||||
- 支持强制重新生成（force_regenerate=true）
|
||||
|
||||
步骤：
|
||||
1. 从Zep图谱读取并过滤实体
|
||||
2. 为每个实体生成OASIS Agent Profile（带重试机制）
|
||||
3. LLM智能生成模拟配置（带重试机制）
|
||||
4. 保存配置文件和预设脚本
|
||||
1. 检查是否已有完成的准备工作
|
||||
2. 从Zep图谱读取并过滤实体
|
||||
3. 为每个实体生成OASIS Agent Profile（带重试机制）
|
||||
4. LLM智能生成模拟配置（带重试机制）
|
||||
5. 保存配置文件和预设脚本
|
||||
|
||||
请求（JSON）：
|
||||
{
|
||||
"simulation_id": "sim_xxxx", // 必填，模拟ID
|
||||
"entity_types": ["Student", "PublicFigure"], // 可选，指定实体类型
|
||||
"use_llm_for_profiles": true // 可选，是否用LLM生成人设
|
||||
"use_llm_for_profiles": true, // 可选，是否用LLM生成人设
|
||||
"parallel_profile_count": 5, // 可选，并行生成人设数量，默认5
|
||||
"force_regenerate": false // 可选，强制重新生成，默认false
|
||||
}
|
||||
|
||||
返回：
|
||||
|
|
@ -239,14 +353,17 @@ def prepare_simulation():
|
|||
"success": true,
|
||||
"data": {
|
||||
"simulation_id": "sim_xxxx",
|
||||
"task_id": "task_xxxx",
|
||||
"status": "preparing",
|
||||
"message": "准备任务已启动"
|
||||
"task_id": "task_xxxx", // 新任务时返回
|
||||
"status": "preparing|ready",
|
||||
"message": "准备任务已启动|已有完成的准备工作",
|
||||
"already_prepared": true|false // 是否已准备完成
|
||||
}
|
||||
}
|
||||
"""
|
||||
import threading
|
||||
import os
|
||||
from ..models.task import TaskManager, TaskStatus
|
||||
from ..config import Config
|
||||
|
||||
try:
|
||||
data = request.get_json() or {}
|
||||
|
|
@ -267,6 +384,25 @@ def prepare_simulation():
|
|||
"error": f"模拟不存在: {simulation_id}"
|
||||
}), 404
|
||||
|
||||
# 检查是否强制重新生成
|
||||
force_regenerate = data.get('force_regenerate', False)
|
||||
|
||||
# 检查是否已经准备完成（避免重复生成）
|
||||
if not force_regenerate:
|
||||
is_prepared, prepare_info = _check_simulation_prepared(simulation_id)
|
||||
if is_prepared:
|
||||
logger.info(f"模拟 {simulation_id} 已准备完成，跳过重复生成")
|
||||
return jsonify({
|
||||
"success": True,
|
||||
"data": {
|
||||
"simulation_id": simulation_id,
|
||||
"status": "ready",
|
||||
"message": "已有完成的准备工作，无需重复生成",
|
||||
"already_prepared": True,
|
||||
"prepare_info": prepare_info
|
||||
}
|
||||
})
|
||||
|
||||
# 从项目获取必要信息
|
||||
project = ProjectManager.get_project(state.project_id)
|
||||
if not project:
|
||||
|
|
@ -288,6 +424,7 @@ def prepare_simulation():
|
|||
|
||||
entity_types_list = data.get('entity_types')
|
||||
use_llm_for_profiles = data.get('use_llm_for_profiles', True)
|
||||
parallel_profile_count = data.get('parallel_profile_count', 5)
|
||||
|
||||
# 创建异步任务
|
||||
task_manager = TaskManager()
|
||||
|
|
@ -384,7 +521,8 @@ def prepare_simulation():
|
|||
document_text=document_text,
|
||||
defined_entity_types=entity_types_list,
|
||||
use_llm_for_profiles=use_llm_for_profiles,
|
||||
progress_callback=progress_callback
|
||||
progress_callback=progress_callback,
|
||||
parallel_profile_count=parallel_profile_count
|
||||
)
|
||||
|
||||
# 任务完成
|
||||
|
|
@ -414,7 +552,8 @@ def prepare_simulation():
|
|||
"simulation_id": simulation_id,
|
||||
"task_id": task_id,
|
||||
"status": "preparing",
|
||||
"message": "准备任务已启动，请通过 /api/simulation/prepare/status 查询进度"
|
||||
"message": "准备任务已启动，请通过 /api/simulation/prepare/status 查询进度",
|
||||
"already_prepared": False
|
||||
}
|
||||
})
|
||||
|
||||
|
|
@ -438,9 +577,14 @@ def get_prepare_status():
|
|||
"""
|
||||
查询准备任务进度
|
||||
|
||||
支持两种查询方式：
|
||||
1. 通过task_id查询正在进行的任务进度
|
||||
2. 通过simulation_id检查是否已有完成的准备工作
|
||||
|
||||
请求（JSON）：
|
||||
{
|
||||
"task_id": "task_xxxx" // 必填，prepare返回的task_id
|
||||
"task_id": "task_xxxx", // 可选，prepare返回的task_id
|
||||
"simulation_id": "sim_xxxx" // 可选，模拟ID（用于检查已完成的准备）
|
||||
}
|
||||
|
||||
返回：
|
||||
|
|
@ -448,21 +592,11 @@ def get_prepare_status():
|
|||
"success": true,
|
||||
"data": {
|
||||
"task_id": "task_xxxx",
|
||||
"status": "processing", // pending/processing/completed/failed
|
||||
"progress": 45, // 0-100 总进度
|
||||
"message": "[2/4] 生成Agent人设: 35/93 - 生成 教授张三 的人设...",
|
||||
"progress_detail": { // 详细进度信息
|
||||
"current_stage": "generating_profiles",
|
||||
"current_stage_name": "生成Agent人设",
|
||||
"stage_index": 2, // 当前阶段序号
|
||||
"total_stages": 4, // 总阶段数
|
||||
"stage_progress": 38, // 阶段内进度 0-100
|
||||
"current_item": 35, // 当前处理项目序号
|
||||
"total_items": 93, // 当前阶段总项目数
|
||||
"item_description": "生成 教授张三 的人设..."
|
||||
},
|
||||
"result": null, // 完成后返回结果
|
||||
"error": null // 失败时返回错误信息
|
||||
"status": "processing|completed|ready",
|
||||
"progress": 45,
|
||||
"message": "...",
|
||||
"already_prepared": true|false, // 是否已有完成的准备
|
||||
"prepare_info": {...} // 已准备完成时的详细信息
|
||||
}
|
||||
}
|
||||
"""
|
||||
|
|
@ -472,24 +606,75 @@ def get_prepare_status():
|
|||
data = request.get_json() or {}
|
||||
|
||||
task_id = data.get('task_id')
|
||||
simulation_id = data.get('simulation_id')
|
||||
|
||||
# 如果提供了simulation_id，先检查是否已准备完成
|
||||
if simulation_id:
|
||||
is_prepared, prepare_info = _check_simulation_prepared(simulation_id)
|
||||
if is_prepared:
|
||||
return jsonify({
|
||||
"success": True,
|
||||
"data": {
|
||||
"simulation_id": simulation_id,
|
||||
"status": "ready",
|
||||
"progress": 100,
|
||||
"message": "已有完成的准备工作",
|
||||
"already_prepared": True,
|
||||
"prepare_info": prepare_info
|
||||
}
|
||||
})
|
||||
|
||||
# 如果没有task_id，返回错误
|
||||
if not task_id:
|
||||
if simulation_id:
|
||||
# 有simulation_id但未准备完成
|
||||
return jsonify({
|
||||
"success": True,
|
||||
"data": {
|
||||
"simulation_id": simulation_id,
|
||||
"status": "not_started",
|
||||
"progress": 0,
|
||||
"message": "尚未开始准备，请调用 /api/simulation/prepare 开始",
|
||||
"already_prepared": False
|
||||
}
|
||||
})
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": "请提供 task_id"
|
||||
"error": "请提供 task_id 或 simulation_id"
|
||||
}), 400
|
||||
|
||||
task_manager = TaskManager()
|
||||
task = task_manager.get_task(task_id)
|
||||
|
||||
if not task:
|
||||
# 任务不存在，但如果有simulation_id，检查是否已准备完成
|
||||
if simulation_id:
|
||||
is_prepared, prepare_info = _check_simulation_prepared(simulation_id)
|
||||
if is_prepared:
|
||||
return jsonify({
|
||||
"success": True,
|
||||
"data": {
|
||||
"simulation_id": simulation_id,
|
||||
"task_id": task_id,
|
||||
"status": "ready",
|
||||
"progress": 100,
|
||||
"message": "任务已完成（准备工作已存在）",
|
||||
"already_prepared": True,
|
||||
"prepare_info": prepare_info
|
||||
}
|
||||
})
|
||||
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": f"任务不存在: {task_id}"
|
||||
}), 404
|
||||
|
||||
task_dict = task.to_dict()
|
||||
task_dict["already_prepared"] = False
|
||||
|
||||
return jsonify({
|
||||
"success": True,
|
||||
"data": task.to_dict()
|
||||
"data": task_dict
|
||||
})
|
||||
|
||||
except Exception as e:
|
||||
|
|
|
|||
|
|
@ -1,6 +1,11 @@
|
|||
"""
|
||||
OASIS Agent Profile生成器
|
||||
将Zep图谱中的实体转换为OASIS模拟平台所需的Agent Profile格式
|
||||
|
||||
优化改进：
|
||||
1. 调用Zep检索功能二次丰富节点信息
|
||||
2. 优化提示词生成非常详细的人设
|
||||
3. 区分个人实体和抽象群体实体
|
||||
"""
|
||||
|
||||
import json
|
||||
|
|
@ -10,6 +15,7 @@ from dataclasses import dataclass, field
|
|||
from datetime import datetime
|
||||
|
||||
from openai import OpenAI
|
||||
from zep_cloud.client import Zep
|
||||
|
||||
from ..config import Config
|
||||
from ..utils.logger import get_logger
|
||||
|
|
@ -137,6 +143,11 @@ class OasisProfileGenerator:
|
|||
OASIS Profile生成器
|
||||
|
||||
将Zep图谱中的实体转换为OASIS模拟所需的Agent Profile
|
||||
|
||||
优化特性：
|
||||
1. 调用Zep图谱检索功能获取更丰富的上下文
|
||||
2. 生成非常详细的人设（包括基本信息、职业经历、性格特征、社交媒体行为等）
|
||||
3. 区分个人实体和抽象群体实体
|
||||
"""
|
||||
|
||||
# MBTI类型列表
|
||||
|
|
@ -153,11 +164,25 @@ class OasisProfileGenerator:
|
|||
"Canada", "Australia", "Brazil", "India", "South Korea"
|
||||
]
|
||||
|
||||
# 个人类型实体（需要生成具体人设）
|
||||
INDIVIDUAL_ENTITY_TYPES = [
|
||||
"student", "alumni", "professor", "person", "publicfigure",
|
||||
"expert", "faculty", "official", "journalist", "activist"
|
||||
]
|
||||
|
||||
# 群体/机构类型实体（需要生成群体代表人设）
|
||||
GROUP_ENTITY_TYPES = [
|
||||
"university", "governmentagency", "organization", "ngo",
|
||||
"mediaoutlet", "company", "institution", "group", "community"
|
||||
]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
api_key: Optional[str] = None,
|
||||
base_url: Optional[str] = None,
|
||||
model_name: Optional[str] = None
|
||||
model_name: Optional[str] = None,
|
||||
zep_api_key: Optional[str] = None,
|
||||
graph_id: Optional[str] = None
|
||||
):
|
||||
self.api_key = api_key or Config.LLM_API_KEY
|
||||
self.base_url = base_url or Config.LLM_BASE_URL
|
||||
|
|
@ -171,6 +196,17 @@ class OasisProfileGenerator:
|
|||
base_url=self.base_url
|
||||
)
|
||||
|
||||
# Zep客户端用于检索丰富上下文
|
||||
self.zep_api_key = zep_api_key or Config.ZEP_API_KEY
|
||||
self.zep_client = None
|
||||
self.graph_id = graph_id
|
||||
|
||||
if self.zep_api_key:
|
||||
try:
|
||||
self.zep_client = Zep(api_key=self.zep_api_key)
|
||||
except Exception as e:
|
||||
logger.warning(f"Zep客户端初始化失败: {e}")
|
||||
|
||||
def generate_profile_from_entity(
|
||||
self,
|
||||
entity: EntityNode,
|
||||
|
|
@ -245,28 +281,195 @@ class OasisProfileGenerator:
|
|||
suffix = random.randint(100, 999)
|
||||
return f"{username}_{suffix}"
|
||||
|
||||
def _search_zep_for_entity(self, entity: EntityNode) -> Dict[str, Any]:
|
||||
"""
|
||||
使用Zep图谱混合搜索功能获取实体相关的丰富信息
|
||||
|
||||
Zep没有内置混合搜索接口，需要分别搜索edges和nodes然后合并结果。
|
||||
使用并行请求同时搜索，提高效率。
|
||||
|
||||
Args:
|
||||
entity: 实体节点对象
|
||||
|
||||
Returns:
|
||||
包含facts, node_summaries, context的字典
|
||||
"""
|
||||
import concurrent.futures
|
||||
|
||||
if not self.zep_client:
|
||||
return {"facts": [], "node_summaries": [], "context": ""}
|
||||
|
||||
entity_name = entity.name
|
||||
|
||||
results = {
|
||||
"facts": [],
|
||||
"node_summaries": [],
|
||||
"context": ""
|
||||
}
|
||||
|
||||
# 必须有graph_id才能进行搜索
|
||||
if not self.graph_id:
|
||||
logger.debug(f"跳过Zep检索：未设置graph_id")
|
||||
return results
|
||||
|
||||
comprehensive_query = f"关于{entity_name}的所有信息、活动、事件、关系和背景"
|
||||
|
||||
def search_edges():
|
||||
"""搜索边（事实/关系）"""
|
||||
try:
|
||||
return self.zep_client.graph.search(
|
||||
query=comprehensive_query,
|
||||
graph_id=self.graph_id,
|
||||
limit=30,
|
||||
scope="edges",
|
||||
reranker="rrf"
|
||||
)
|
||||
except Exception as e:
|
||||
logger.debug(f"Zep边搜索失败: {e}")
|
||||
return None
|
||||
|
||||
def search_nodes():
|
||||
"""搜索节点（实体摘要）"""
|
||||
try:
|
||||
return self.zep_client.graph.search(
|
||||
query=comprehensive_query,
|
||||
graph_id=self.graph_id,
|
||||
limit=20,
|
||||
scope="nodes",
|
||||
reranker="rrf"
|
||||
)
|
||||
except Exception as e:
|
||||
logger.debug(f"Zep节点搜索失败: {e}")
|
||||
return None
|
||||
|
||||
try:
|
||||
# 并行执行edges和nodes搜索
|
||||
with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor:
|
||||
edge_future = executor.submit(search_edges)
|
||||
node_future = executor.submit(search_nodes)
|
||||
|
||||
# 获取结果
|
||||
edge_result = edge_future.result(timeout=30)
|
||||
node_result = node_future.result(timeout=30)
|
||||
|
||||
# 处理边搜索结果
|
||||
all_facts = set()
|
||||
if edge_result and hasattr(edge_result, 'edges') and edge_result.edges:
|
||||
for edge in edge_result.edges:
|
||||
if hasattr(edge, 'fact') and edge.fact:
|
||||
all_facts.add(edge.fact)
|
||||
results["facts"] = list(all_facts)
|
||||
|
||||
# 处理节点搜索结果
|
||||
all_summaries = set()
|
||||
if node_result and hasattr(node_result, 'nodes') and node_result.nodes:
|
||||
for node in node_result.nodes:
|
||||
if hasattr(node, 'summary') and node.summary:
|
||||
all_summaries.add(node.summary)
|
||||
if hasattr(node, 'name') and node.name and node.name != entity_name:
|
||||
all_summaries.add(f"相关实体: {node.name}")
|
||||
results["node_summaries"] = list(all_summaries)
|
||||
|
||||
# 构建综合上下文
|
||||
context_parts = []
|
||||
if results["facts"]:
|
||||
context_parts.append("事实信息:\n" + "\n".join(f"- {f}" for f in results["facts"][:20]))
|
||||
if results["node_summaries"]:
|
||||
context_parts.append("相关实体:\n" + "\n".join(f"- {s}" for s in results["node_summaries"][:10]))
|
||||
results["context"] = "\n\n".join(context_parts)
|
||||
|
||||
logger.info(f"Zep混合检索完成: {entity_name}, 获取 {len(results['facts'])} 条事实, {len(results['node_summaries'])} 个相关节点")
|
||||
|
||||
except concurrent.futures.TimeoutError:
|
||||
logger.warning(f"Zep检索超时 ({entity_name})")
|
||||
except Exception as e:
|
||||
logger.warning(f"Zep检索失败 ({entity_name}): {e}")
|
||||
|
||||
return results
|
||||
|
||||
def _build_entity_context(self, entity: EntityNode) -> str:
|
||||
"""构建实体的上下文信息"""
|
||||
"""
|
||||
构建实体的完整上下文信息
|
||||
|
||||
包括：
|
||||
1. 实体本身的边信息（事实）
|
||||
2. 关联节点的详细信息
|
||||
3. Zep混合检索到的丰富信息
|
||||
"""
|
||||
context_parts = []
|
||||
|
||||
# 添加相关边信息
|
||||
# 1. 添加实体属性信息
|
||||
if entity.attributes:
|
||||
attrs = []
|
||||
for key, value in entity.attributes.items():
|
||||
if value and str(value).strip():
|
||||
attrs.append(f"- {key}: {value}")
|
||||
if attrs:
|
||||
context_parts.append("### 实体属性\n" + "\n".join(attrs))
|
||||
|
||||
# 2. 添加相关边信息（事实/关系）
|
||||
existing_facts = set()
|
||||
if entity.related_edges:
|
||||
relationships = []
|
||||
for edge in entity.related_edges[:10]: # 最多取10条
|
||||
if edge.get("fact"):
|
||||
relationships.append(edge["fact"])
|
||||
for edge in entity.related_edges: # 不限制数量
|
||||
fact = edge.get("fact", "")
|
||||
edge_name = edge.get("edge_name", "")
|
||||
direction = edge.get("direction", "")
|
||||
|
||||
if fact:
|
||||
relationships.append(f"- {fact}")
|
||||
existing_facts.add(fact)
|
||||
elif edge_name:
|
||||
if direction == "outgoing":
|
||||
relationships.append(f"- {entity.name} --[{edge_name}]--> (相关实体)")
|
||||
else:
|
||||
relationships.append(f"- (相关实体) --[{edge_name}]--> {entity.name}")
|
||||
|
||||
if relationships:
|
||||
context_parts.append("Related facts:\n" + "\n".join(f"- {r}" for r in relationships))
|
||||
context_parts.append("### 相关事实和关系\n" + "\n".join(relationships))
|
||||
|
||||
# 添加关联节点信息
|
||||
# 3. 添加关联节点的详细信息
|
||||
if entity.related_nodes:
|
||||
related_names = [n["name"] for n in entity.related_nodes[:5]]
|
||||
if related_names:
|
||||
context_parts.append(f"Related to: {', '.join(related_names)}")
|
||||
related_info = []
|
||||
for node in entity.related_nodes: # 不限制数量
|
||||
node_name = node.get("name", "")
|
||||
node_labels = node.get("labels", [])
|
||||
node_summary = node.get("summary", "")
|
||||
|
||||
# 过滤掉默认标签
|
||||
custom_labels = [l for l in node_labels if l not in ["Entity", "Node"]]
|
||||
label_str = f" ({', '.join(custom_labels)})" if custom_labels else ""
|
||||
|
||||
if node_summary:
|
||||
related_info.append(f"- **{node_name}**{label_str}: {node_summary}")
|
||||
else:
|
||||
related_info.append(f"- **{node_name}**{label_str}")
|
||||
|
||||
if related_info:
|
||||
context_parts.append("### 关联实体信息\n" + "\n".join(related_info))
|
||||
|
||||
# 4. 使用Zep混合检索获取更丰富的信息
|
||||
zep_results = self._search_zep_for_entity(entity)
|
||||
|
||||
if zep_results.get("facts"):
|
||||
# 去重：排除已存在的事实
|
||||
new_facts = [f for f in zep_results["facts"] if f not in existing_facts]
|
||||
if new_facts:
|
||||
context_parts.append("### Zep检索到的事实信息\n" + "\n".join(f"- {f}" for f in new_facts[:15]))
|
||||
|
||||
if zep_results.get("node_summaries"):
|
||||
context_parts.append("### Zep检索到的相关节点\n" + "\n".join(f"- {s}" for s in zep_results["node_summaries"][:10]))
|
||||
|
||||
return "\n\n".join(context_parts)
|
||||
|
||||
def _is_individual_entity(self, entity_type: str) -> bool:
|
||||
"""判断是否是个人类型实体"""
|
||||
return entity_type.lower() in self.INDIVIDUAL_ENTITY_TYPES
|
||||
|
||||
def _is_group_entity(self, entity_type: str) -> bool:
|
||||
"""判断是否是群体/机构类型实体"""
|
||||
return entity_type.lower() in self.GROUP_ENTITY_TYPES
|
||||
|
||||
def _generate_profile_with_llm(
|
||||
self,
|
||||
entity_name: str,
|
||||
|
|
@ -275,63 +478,271 @@ class OasisProfileGenerator:
|
|||
entity_attributes: Dict[str, Any],
|
||||
context: str
|
||||
) -> Dict[str, Any]:
|
||||
"""使用LLM生成详细人设"""
|
||||
"""
|
||||
使用LLM生成非常详细的人设
|
||||
|
||||
prompt = f"""Based on the following entity information, generate a detailed social media user profile for simulation purposes.
|
||||
根据实体类型区分：
|
||||
- 个人实体：生成具体的人物设定
|
||||
- 群体/机构实体：生成代表性账号设定
|
||||
"""
|
||||
|
||||
Entity Information:
|
||||
- Name: {entity_name}
|
||||
- Type: {entity_type}
|
||||
- Summary: {entity_summary}
|
||||
- Attributes: {json.dumps(entity_attributes, ensure_ascii=False)}
|
||||
is_individual = self._is_individual_entity(entity_type)
|
||||
|
||||
Context:
|
||||
{context}
|
||||
if is_individual:
|
||||
prompt = self._build_individual_persona_prompt(
|
||||
entity_name, entity_type, entity_summary, entity_attributes, context
|
||||
)
|
||||
else:
|
||||
prompt = self._build_group_persona_prompt(
|
||||
entity_name, entity_type, entity_summary, entity_attributes, context
|
||||
)
|
||||
|
||||
Generate a JSON object with the following fields:
|
||||
{{
|
||||
"bio": "A short bio (max 150 chars) suitable for social media",
|
||||
"persona": "A detailed persona description (2-3 sentences) describing personality, interests, and behavior patterns",
|
||||
"age": <integer between 18-65, or null if not applicable>,
|
||||
"gender": "<male/female/other, or null if not applicable>",
|
||||
"mbti": "<MBTI type like INTJ, ENFP, etc., or null>",
|
||||
"country": "<country name, or null>",
|
||||
"profession": "<profession/occupation, or null>",
|
||||
"interested_topics": ["topic1", "topic2", ...]
|
||||
}}
|
||||
# 尝试多次生成，直到成功或达到最大重试次数
|
||||
max_attempts = 3
|
||||
last_error = None
|
||||
|
||||
Important:
|
||||
- The profile should be consistent with the entity type and context
|
||||
- Make the persona feel realistic and suitable for social media simulation
|
||||
- If the entity is an organization, institution, or non-person, adapt the profile accordingly (e.g., as an official account)
|
||||
- Return ONLY the JSON object, no additional text"""
|
||||
|
||||
try:
|
||||
# 使用重试机制调用LLM API
|
||||
from ..utils.retry import RetryableAPIClient
|
||||
|
||||
retry_client = RetryableAPIClient(max_retries=3, initial_delay=1.0)
|
||||
|
||||
def call_llm():
|
||||
return self.client.chat.completions.create(
|
||||
for attempt in range(max_attempts):
|
||||
try:
|
||||
response = self.client.chat.completions.create(
|
||||
model=self.model_name,
|
||||
messages=[
|
||||
{"role": "system", "content": "You are a profile generator for social media simulation. Generate realistic user profiles based on entity information."},
|
||||
{"role": "system", "content": self._get_system_prompt(is_individual)},
|
||||
{"role": "user", "content": prompt}
|
||||
],
|
||||
response_format={"type": "json_object"},
|
||||
temperature=0.7
|
||||
temperature=0.7 - (attempt * 0.1) # 每次重试降低温度
|
||||
# 不设置max_tokens，让LLM自由发挥
|
||||
)
|
||||
|
||||
response = retry_client.call_with_retry(call_llm)
|
||||
result = json.loads(response.choices[0].message.content)
|
||||
return result
|
||||
content = response.choices[0].message.content
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"LLM生成人设失败（已重试）: {str(e)}, 使用规则生成")
|
||||
return self._generate_profile_rule_based(
|
||||
entity_name, entity_type, entity_summary, entity_attributes
|
||||
)
|
||||
# 检查是否被截断（finish_reason不是'stop'）
|
||||
finish_reason = response.choices[0].finish_reason
|
||||
if finish_reason == 'length':
|
||||
logger.warning(f"LLM输出被截断 (attempt {attempt+1}), 尝试修复...")
|
||||
content = self._fix_truncated_json(content)
|
||||
|
||||
# 尝试解析JSON
|
||||
try:
|
||||
result = json.loads(content)
|
||||
|
||||
# 验证必需字段
|
||||
if "bio" not in result or not result["bio"]:
|
||||
result["bio"] = entity_summary[:200] if entity_summary else f"{entity_type}: {entity_name}"
|
||||
if "persona" not in result or not result["persona"]:
|
||||
result["persona"] = entity_summary or f"{entity_name}是一个{entity_type}。"
|
||||
|
||||
return result
|
||||
|
||||
except json.JSONDecodeError as je:
|
||||
logger.warning(f"JSON解析失败 (attempt {attempt+1}): {str(je)[:80]}")
|
||||
|
||||
# 尝试修复JSON
|
||||
result = self._try_fix_json(content, entity_name, entity_type, entity_summary)
|
||||
if result.get("_fixed"):
|
||||
del result["_fixed"]
|
||||
return result
|
||||
|
||||
last_error = je
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"LLM调用失败 (attempt {attempt+1}): {str(e)[:80]}")
|
||||
last_error = e
|
||||
import time
|
||||
time.sleep(1 * (attempt + 1)) # 指数退避
|
||||
|
||||
logger.warning(f"LLM生成人设失败（{max_attempts}次尝试）: {last_error}, 使用规则生成")
|
||||
return self._generate_profile_rule_based(
|
||||
entity_name, entity_type, entity_summary, entity_attributes
|
||||
)
|
||||
|
||||
def _fix_truncated_json(self, content: str) -> str:
|
||||
"""修复被截断的JSON（输出被max_tokens限制截断）"""
|
||||
import re
|
||||
|
||||
# 如果JSON被截断，尝试闭合它
|
||||
content = content.strip()
|
||||
|
||||
# 计算未闭合的括号
|
||||
open_braces = content.count('{') - content.count('}')
|
||||
open_brackets = content.count('[') - content.count(']')
|
||||
|
||||
# 检查是否有未闭合的字符串
|
||||
# 简单检查：如果最后一个引号后没有逗号或闭合括号，可能是字符串被截断
|
||||
if content and content[-1] not in '",}]':
|
||||
# 尝试闭合字符串
|
||||
content += '"'
|
||||
|
||||
# 闭合括号
|
||||
content += ']' * open_brackets
|
||||
content += '}' * open_braces
|
||||
|
||||
return content
|
||||
|
||||
def _try_fix_json(self, content: str, entity_name: str, entity_type: str, entity_summary: str = "") -> Dict[str, Any]:
|
||||
"""尝试修复损坏的JSON"""
|
||||
import re
|
||||
|
||||
# 1. 首先尝试修复被截断的情况
|
||||
content = self._fix_truncated_json(content)
|
||||
|
||||
# 2. 尝试提取JSON部分
|
||||
json_match = re.search(r'\{[\s\S]*\}', content)
|
||||
if json_match:
|
||||
json_str = json_match.group()
|
||||
|
||||
# 3. 处理字符串中的换行符问题
|
||||
# 找到所有字符串值并替换其中的换行符
|
||||
def fix_string_newlines(match):
|
||||
s = match.group(0)
|
||||
# 替换字符串内的实际换行符为空格
|
||||
s = s.replace('\n', ' ').replace('\r', ' ')
|
||||
# 替换多余空格
|
||||
s = re.sub(r'\s+', ' ', s)
|
||||
return s
|
||||
|
||||
# 匹配JSON字符串值
|
||||
json_str = re.sub(r'"[^"\\]*(?:\\.[^"\\]*)*"', fix_string_newlines, json_str)
|
||||
|
||||
# 4. 尝试解析
|
||||
try:
|
||||
result = json.loads(json_str)
|
||||
result["_fixed"] = True
|
||||
return result
|
||||
except json.JSONDecodeError as e:
|
||||
# 5. 如果还是失败，尝试更激进的修复
|
||||
try:
|
||||
# 移除所有控制字符
|
||||
json_str = re.sub(r'[\x00-\x1f\x7f-\x9f]', ' ', json_str)
|
||||
# 替换所有连续空白
|
||||
json_str = re.sub(r'\s+', ' ', json_str)
|
||||
result = json.loads(json_str)
|
||||
result["_fixed"] = True
|
||||
return result
|
||||
except:
|
||||
pass
|
||||
|
||||
# 6. 尝试从内容中提取部分信息
|
||||
bio_match = re.search(r'"bio"\s*:\s*"([^"]*)"', content)
|
||||
persona_match = re.search(r'"persona"\s*:\s*"([^"]*)', content) # 可能被截断
|
||||
|
||||
bio = bio_match.group(1) if bio_match else (entity_summary[:200] if entity_summary else f"{entity_type}: {entity_name}")
|
||||
persona = persona_match.group(1) if persona_match else (entity_summary or f"{entity_name}是一个{entity_type}。")
|
||||
|
||||
# 如果提取到了有意义的内容，标记为已修复
|
||||
if bio_match or persona_match:
|
||||
logger.info(f"从损坏的JSON中提取了部分信息")
|
||||
return {
|
||||
"bio": bio,
|
||||
"persona": persona,
|
||||
"_fixed": True
|
||||
}
|
||||
|
||||
# 7. 完全失败，返回基础结构
|
||||
logger.warning(f"JSON修复失败，返回基础结构")
|
||||
return {
|
||||
"bio": entity_summary[:200] if entity_summary else f"{entity_type}: {entity_name}",
|
||||
"persona": entity_summary or f"{entity_name}是一个{entity_type}。"
|
||||
}
|
||||
|
||||
def _get_system_prompt(self, is_individual: bool) -> str:
|
||||
"""获取系统提示词"""
|
||||
base_prompt = "你是社交媒体用户画像生成专家。生成详细、真实的人设用于舆论模拟,最大程度还原已有现实情况。必须返回有效的JSON格式，所有字符串值不能包含未转义的换行符。使用中文。"
|
||||
return base_prompt
|
||||
|
||||
def _build_individual_persona_prompt(
|
||||
self,
|
||||
entity_name: str,
|
||||
entity_type: str,
|
||||
entity_summary: str,
|
||||
entity_attributes: Dict[str, Any],
|
||||
context: str
|
||||
) -> str:
|
||||
"""构建个人实体的详细人设提示词"""
|
||||
|
||||
attrs_str = json.dumps(entity_attributes, ensure_ascii=False) if entity_attributes else "无"
|
||||
context_str = context[:3000] if context else "无额外上下文"
|
||||
|
||||
return f"""为实体生成详细的社交媒体用户人设,最大程度还原已有现实情况。
|
||||
|
||||
实体名称: {entity_name}
|
||||
实体类型: {entity_type}
|
||||
实体摘要: {entity_summary}
|
||||
实体属性: {attrs_str}
|
||||
|
||||
上下文信息:
|
||||
{context_str}
|
||||
|
||||
请生成JSON，包含以下字段:
|
||||
|
||||
1. bio: 社交媒体简介，200字
|
||||
2. persona: 详细人设描述（2000字的纯文本），需包含:
|
||||
- 基本信息（年龄、职业、教育背景、所在地）
|
||||
- 人物背景（重要经历、与事件的关联、社会关系）
|
||||
- 性格特征（MBTI类型、核心性格、情绪表达方式）
|
||||
- 社交媒体行为（发帖频率、内容偏好、互动风格、语言特点）
|
||||
- 立场观点（对话题的态度、可能被激怒/感动的内容）
|
||||
- 独特特征（口头禅、特殊经历、个人爱好）
|
||||
- 个人记忆（人设的重要部分，要介绍这个个体与事件的关联，以及这个个体在事件中的已有动作与反应）
|
||||
3. age: 年龄数字
|
||||
4. gender: 性别（男/女）
|
||||
5. mbti: MBTI类型
|
||||
6. country: 国家
|
||||
7. profession: 职业
|
||||
8. interested_topics: 感兴趣话题数组
|
||||
|
||||
重要:
|
||||
- 所有字段值必须是字符串或数字，不要使用换行符
|
||||
- persona必须是一段连贯的文字描述
|
||||
- 使用中文
|
||||
- 内容要与实体信息保持一致"""
|
||||
|
||||
def _build_group_persona_prompt(
|
||||
self,
|
||||
entity_name: str,
|
||||
entity_type: str,
|
||||
entity_summary: str,
|
||||
entity_attributes: Dict[str, Any],
|
||||
context: str
|
||||
) -> str:
|
||||
"""构建群体/机构实体的详细人设提示词"""
|
||||
|
||||
attrs_str = json.dumps(entity_attributes, ensure_ascii=False) if entity_attributes else "无"
|
||||
context_str = context[:3000] if context else "无额外上下文"
|
||||
|
||||
return f"""为机构/群体实体生成详细的社交媒体账号设定,最大程度还原已有现实情况。
|
||||
|
||||
实体名称: {entity_name}
|
||||
实体类型: {entity_type}
|
||||
实体摘要: {entity_summary}
|
||||
实体属性: {attrs_str}
|
||||
|
||||
上下文信息:
|
||||
{context_str}
|
||||
|
||||
请生成JSON，包含以下字段:
|
||||
|
||||
1. bio: 官方账号简介，200字，专业得体
|
||||
2. persona: 详细账号设定描述（2000字的纯文本），需包含:
|
||||
- 机构基本信息（正式名称、机构性质、成立背景、主要职能）
|
||||
- 账号定位（账号类型、目标受众、核心功能）
|
||||
- 发言风格（语言特点、常用表达、禁忌话题）
|
||||
- 发布内容特点（内容类型、发布频率、活跃时间段）
|
||||
- 立场态度（对核心话题的官方立场、面对争议的处理方式）
|
||||
- 特殊说明（代表的群体画像、运营习惯）
|
||||
- 机构记忆（机构人设的重要部分，要介绍这个机构与事件的关联，以及这个机构在事件中的已有动作与反应）
|
||||
3. age: null（机构不适用）
|
||||
4. gender: null（机构不适用）
|
||||
5. mbti: 可选，用于描述账号风格，如ISTJ代表严谨保守
|
||||
6. country: 国家
|
||||
7. profession: 机构职能描述
|
||||
8. interested_topics: 关注领域数组
|
||||
|
||||
重要:
|
||||
- 所有字段值必须是字符串、数字或null
|
||||
- persona必须是一段连贯的文字描述，不要使用换行符
|
||||
- 使用中文
|
||||
- 机构账号发言要符合其身份定位"""
|
||||
|
||||
def _generate_profile_rule_based(
|
||||
self,
|
||||
|
|
@ -398,29 +809,46 @@ Important:
|
|||
"interested_topics": ["General", "Social Issues"],
|
||||
}
|
||||
|
||||
def set_graph_id(self, graph_id: str):
|
||||
"""设置图谱ID用于Zep检索"""
|
||||
self.graph_id = graph_id
|
||||
|
||||
def generate_profiles_from_entities(
|
||||
self,
|
||||
entities: List[EntityNode],
|
||||
use_llm: bool = True,
|
||||
progress_callback: Optional[callable] = None
|
||||
progress_callback: Optional[callable] = None,
|
||||
graph_id: Optional[str] = None,
|
||||
parallel_count: int = 5
|
||||
) -> List[OasisAgentProfile]:
|
||||
"""
|
||||
批量从实体生成Agent Profile
|
||||
批量从实体生成Agent Profile（支持并行生成）
|
||||
|
||||
Args:
|
||||
entities: 实体列表
|
||||
use_llm: 是否使用LLM生成详细人设
|
||||
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
|
||||
|
||||
Returns:
|
||||
Agent Profile列表
|
||||
"""
|
||||
profiles = []
|
||||
total = len(entities)
|
||||
import concurrent.futures
|
||||
from threading import Lock
|
||||
|
||||
for idx, entity in enumerate(entities):
|
||||
if progress_callback:
|
||||
progress_callback(idx + 1, total, f"生成 {entity.name} 的人设...")
|
||||
# 设置graph_id用于Zep检索
|
||||
if graph_id:
|
||||
self.graph_id = graph_id
|
||||
|
||||
total = len(entities)
|
||||
profiles = [None] * total # 预分配列表保持顺序
|
||||
completed_count = [0] # 使用列表以便在闭包中修改
|
||||
lock = Lock()
|
||||
|
||||
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"
|
||||
|
||||
try:
|
||||
profile = self.generate_profile_from_entity(
|
||||
|
|
@ -428,23 +856,115 @@ Important:
|
|||
user_id=idx,
|
||||
use_llm=use_llm
|
||||
)
|
||||
profiles.append(profile)
|
||||
|
||||
# 实时输出生成的人设到控制台和日志
|
||||
self._print_generated_profile(entity.name, entity_type, profile)
|
||||
|
||||
return idx, profile, None
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"生成实体 {entity.name} 的人设失败: {str(e)}")
|
||||
# 创建一个基础profile
|
||||
profiles.append(OasisAgentProfile(
|
||||
fallback_profile = OasisAgentProfile(
|
||||
user_id=idx,
|
||||
user_name=self._generate_username(entity.name),
|
||||
name=entity.name,
|
||||
bio=f"{entity.get_entity_type() or 'Entity'}: {entity.name}",
|
||||
bio=f"{entity_type}: {entity.name}",
|
||||
persona=entity.summary or f"A participant in social discussions.",
|
||||
source_entity_uuid=entity.uuid,
|
||||
source_entity_type=entity.get_entity_type(),
|
||||
))
|
||||
source_entity_type=entity_type,
|
||||
)
|
||||
return idx, fallback_profile, str(e)
|
||||
|
||||
logger.info(f"开始并行生成 {total} 个Agent人设（并行数: {parallel_count}）...")
|
||||
print(f"\n{'='*60}")
|
||||
print(f"开始生成Agent人设 - 共 {total} 个实体，并行数: {parallel_count}")
|
||||
print(f"{'='*60}\n")
|
||||
|
||||
# 使用线程池并行执行
|
||||
with concurrent.futures.ThreadPoolExecutor(max_workers=parallel_count) as executor:
|
||||
# 提交所有任务
|
||||
future_to_entity = {
|
||||
executor.submit(generate_single_profile, idx, entity): (idx, entity)
|
||||
for idx, entity in enumerate(entities)
|
||||
}
|
||||
|
||||
# 收集结果
|
||||
for future in concurrent.futures.as_completed(future_to_entity):
|
||||
idx, entity = future_to_entity[future]
|
||||
entity_type = entity.get_entity_type() or "Entity"
|
||||
|
||||
try:
|
||||
result_idx, profile, error = future.result()
|
||||
profiles[result_idx] = profile
|
||||
|
||||
with lock:
|
||||
completed_count[0] += 1
|
||||
current = completed_count[0]
|
||||
|
||||
if progress_callback:
|
||||
progress_callback(
|
||||
current,
|
||||
total,
|
||||
f"已完成 {current}/{total}: {entity.name}（{entity_type}）"
|
||||
)
|
||||
|
||||
if error:
|
||||
logger.warning(f"[{current}/{total}] {entity.name} 使用备用人设: {error}")
|
||||
else:
|
||||
logger.info(f"[{current}/{total}] 成功生成人设: {entity.name} ({entity_type})")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"处理实体 {entity.name} 时发生异常: {str(e)}")
|
||||
with lock:
|
||||
completed_count[0] += 1
|
||||
profiles[idx] = OasisAgentProfile(
|
||||
user_id=idx,
|
||||
user_name=self._generate_username(entity.name),
|
||||
name=entity.name,
|
||||
bio=f"{entity_type}: {entity.name}",
|
||||
persona=entity.summary or "A participant in social discussions.",
|
||||
source_entity_uuid=entity.uuid,
|
||||
source_entity_type=entity_type,
|
||||
)
|
||||
|
||||
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")
|
||||
print(f"{'='*60}\n")
|
||||
|
||||
return profiles
|
||||
|
||||
def _print_generated_profile(self, entity_name: str, entity_type: str, profile: OasisAgentProfile):
|
||||
"""实时输出生成的人设到控制台（完整内容，不截断）"""
|
||||
separator = "-" * 70
|
||||
|
||||
# 构建完整输出内容（不截断）
|
||||
topics_str = ', '.join(profile.interested_topics) if profile.interested_topics else '无'
|
||||
|
||||
output_lines = [
|
||||
f"\n{separator}",
|
||||
f"[已生成] {entity_name} ({entity_type})",
|
||||
f"{separator}",
|
||||
f"用户名: {profile.user_name}",
|
||||
f"",
|
||||
f"【简介】",
|
||||
f"{profile.bio}",
|
||||
f"",
|
||||
f"【详细人设】",
|
||||
f"{profile.persona}",
|
||||
f"",
|
||||
f"【基本属性】",
|
||||
f"年龄: {profile.age} | 性别: {profile.gender} | MBTI: {profile.mbti}",
|
||||
f"职业: {profile.profession} | 国家: {profile.country}",
|
||||
f"兴趣话题: {topics_str}",
|
||||
separator
|
||||
]
|
||||
|
||||
output = "\n".join(output_lines)
|
||||
|
||||
# 只输出到控制台（避免重复，logger不再输出完整内容）
|
||||
print(output)
|
||||
|
||||
def save_profiles(
|
||||
self,
|
||||
profiles: List[OasisAgentProfile],
|
||||
|
|
@ -470,10 +990,18 @@ Important:
|
|||
|
||||
def _save_twitter_csv(self, profiles: List[OasisAgentProfile], file_path: str):
|
||||
"""
|
||||
保存Twitter Profile为CSV格式
|
||||
保存Twitter Profile为CSV格式（符合OASIS官方要求）
|
||||
|
||||
OASIS Twitter要求的CSV字段：
|
||||
user_id, user_name, name, bio, friend_count, follower_count, statuses_count, created_at
|
||||
- user_id: 用户ID（根据CSV顺序从0开始）
|
||||
- name: 用户真实姓名
|
||||
- username: 系统中的用户名
|
||||
- user_char: 详细人设描述（注入到LLM系统提示中，指导Agent行为）
|
||||
- description: 简短的公开简介（显示在用户资料页面）
|
||||
|
||||
user_char vs description 区别：
|
||||
- user_char: 内部使用，LLM系统提示，决定Agent如何思考和行动
|
||||
- description: 外部显示，其他用户可见的简介
|
||||
"""
|
||||
import csv
|
||||
|
||||
|
|
@ -484,28 +1012,32 @@ Important:
|
|||
with open(file_path, 'w', newline='', encoding='utf-8') as f:
|
||||
writer = csv.writer(f)
|
||||
|
||||
# 写入表头
|
||||
headers = ['user_id', 'user_name', 'name', 'bio', 'friend_count',
|
||||
'follower_count', 'statuses_count', 'created_at']
|
||||
# 写入OASIS要求的表头
|
||||
headers = ['user_id', 'name', 'username', 'user_char', 'description']
|
||||
writer.writerow(headers)
|
||||
|
||||
# 写入数据行
|
||||
for profile in profiles:
|
||||
# bio需要处理换行符和逗号
|
||||
bio = profile.bio.replace('\n', ' ').replace('\r', ' ')
|
||||
for idx, profile in enumerate(profiles):
|
||||
# user_char: 完整人设（bio + persona），用于LLM系统提示
|
||||
user_char = profile.bio
|
||||
if profile.persona and profile.persona != profile.bio:
|
||||
user_char = f"{profile.bio} {profile.persona}"
|
||||
# 处理换行符（CSV中用空格替代）
|
||||
user_char = user_char.replace('\n', ' ').replace('\r', ' ')
|
||||
|
||||
# description: 简短简介，用于外部显示
|
||||
description = profile.bio.replace('\n', ' ').replace('\r', ' ')
|
||||
|
||||
row = [
|
||||
profile.user_id,
|
||||
profile.user_name,
|
||||
profile.name,
|
||||
bio,
|
||||
profile.friend_count,
|
||||
profile.follower_count,
|
||||
profile.statuses_count,
|
||||
profile.created_at
|
||||
idx, # user_id: 从0开始的顺序ID
|
||||
profile.name, # name: 真实姓名
|
||||
profile.user_name, # username: 用户名
|
||||
user_char, # user_char: 完整人设（内部LLM使用）
|
||||
description # description: 简短简介（外部显示）
|
||||
]
|
||||
writer.writerow(row)
|
||||
|
||||
logger.info(f"已保存 {len(profiles)} 个Twitter Profile到 {file_path} (CSV格式)")
|
||||
logger.info(f"已保存 {len(profiles)} 个Twitter Profile到 {file_path} (OASIS CSV格式)")
|
||||
|
||||
def _save_reddit_json(self, profiles: List[OasisAgentProfile], file_path: str):
|
||||
"""
|
||||
|
|
|
|||
|
|
@ -2,10 +2,17 @@
|
|||
模拟配置智能生成器
|
||||
使用LLM根据模拟需求、文档内容、图谱信息自动生成细致的模拟参数
|
||||
实现全程自动化，无需人工设置参数
|
||||
|
||||
采用分步生成策略，避免一次性生成过长内容导致失败：
|
||||
1. 生成时间配置
|
||||
2. 生成事件配置
|
||||
3. 分批生成Agent配置
|
||||
4. 生成平台配置
|
||||
"""
|
||||
|
||||
import json
|
||||
from typing import Dict, Any, List, Optional
|
||||
import math
|
||||
from typing import Dict, Any, List, Optional, Callable
|
||||
from dataclasses import dataclass, field, asdict
|
||||
from datetime import datetime
|
||||
|
||||
|
|
@ -17,6 +24,28 @@ from .zep_entity_reader import EntityNode, ZepEntityReader
|
|||
|
||||
logger = get_logger('mirofish.simulation_config')
|
||||
|
||||
# 中国作息时间配置（北京时间）
|
||||
CHINA_TIMEZONE_CONFIG = {
|
||||
# 深夜时段（几乎无人活动）
|
||||
"dead_hours": [0, 1, 2, 3, 4, 5],
|
||||
# 早间时段（逐渐醒来）
|
||||
"morning_hours": [6, 7, 8],
|
||||
# 工作时段
|
||||
"work_hours": [9, 10, 11, 12, 13, 14, 15, 16, 17, 18],
|
||||
# 晚间高峰（最活跃）
|
||||
"peak_hours": [19, 20, 21, 22],
|
||||
# 夜间时段（活跃度下降）
|
||||
"night_hours": [23],
|
||||
# 活跃度系数
|
||||
"activity_multipliers": {
|
||||
"dead": 0.05, # 凌晨几乎无人
|
||||
"morning": 0.4, # 早间逐渐活跃
|
||||
"work": 0.7, # 工作时段中等
|
||||
"peak": 1.5, # 晚间高峰
|
||||
"night": 0.5 # 深夜下降
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@dataclass
|
||||
class AgentActivityConfig:
|
||||
|
|
@ -52,7 +81,7 @@ class AgentActivityConfig:
|
|||
|
||||
@dataclass
|
||||
class TimeSimulationConfig:
|
||||
"""时间模拟配置"""
|
||||
"""时间模拟配置（基于中国人作息习惯）"""
|
||||
# 模拟总时长（模拟小时数）
|
||||
total_simulation_hours: int = 72 # 默认模拟72小时（3天）
|
||||
|
||||
|
|
@ -63,13 +92,21 @@ class TimeSimulationConfig:
|
|||
agents_per_hour_min: int = 5
|
||||
agents_per_hour_max: int = 20
|
||||
|
||||
# 高峰时段（活跃度提升）
|
||||
peak_hours: List[int] = field(default_factory=lambda: [9, 10, 11, 14, 15, 20, 21, 22])
|
||||
# 高峰时段（晚间19-22点，中国人最活跃的时间）
|
||||
peak_hours: List[int] = field(default_factory=lambda: [19, 20, 21, 22])
|
||||
peak_activity_multiplier: float = 1.5
|
||||
|
||||
# 低谷时段（活跃度降低）
|
||||
off_peak_hours: List[int] = field(default_factory=lambda: [0, 1, 2, 3, 4, 5, 6])
|
||||
off_peak_activity_multiplier: float = 0.3
|
||||
# 低谷时段（凌晨0-5点，几乎无人活动）
|
||||
off_peak_hours: List[int] = field(default_factory=lambda: [0, 1, 2, 3, 4, 5])
|
||||
off_peak_activity_multiplier: float = 0.05 # 凌晨活跃度极低
|
||||
|
||||
# 早间时段
|
||||
morning_hours: List[int] = field(default_factory=lambda: [6, 7, 8])
|
||||
morning_activity_multiplier: float = 0.4
|
||||
|
||||
# 工作时段
|
||||
work_hours: List[int] = field(default_factory=lambda: [9, 10, 11, 12, 13, 14, 15, 16, 17, 18])
|
||||
work_activity_multiplier: float = 0.7
|
||||
|
||||
|
||||
@dataclass
|
||||
|
|
@ -137,12 +174,13 @@ class SimulationParameters:
|
|||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
"""转换为字典"""
|
||||
time_dict = asdict(self.time_config)
|
||||
return {
|
||||
"simulation_id": self.simulation_id,
|
||||
"project_id": self.project_id,
|
||||
"graph_id": self.graph_id,
|
||||
"simulation_requirement": self.simulation_requirement,
|
||||
"time_config": asdict(self.time_config),
|
||||
"time_config": time_dict,
|
||||
"agent_configs": [asdict(a) for a in self.agent_configs],
|
||||
"event_config": asdict(self.event_config),
|
||||
"twitter_config": asdict(self.twitter_config) if self.twitter_config else None,
|
||||
|
|
@ -164,10 +202,17 @@ class SimulationConfigGenerator:
|
|||
|
||||
使用LLM分析模拟需求、文档内容、图谱实体信息，
|
||||
自动生成最佳的模拟参数配置
|
||||
|
||||
采用分步生成策略：
|
||||
1. 生成时间配置和事件配置（轻量级）
|
||||
2. 分批生成Agent配置（每批10-15个）
|
||||
3. 生成平台配置
|
||||
"""
|
||||
|
||||
# 上下文最大字符数
|
||||
MAX_CONTEXT_LENGTH = 50000
|
||||
# 每批生成的Agent数量
|
||||
AGENTS_PER_BATCH = 15
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
|
|
@ -197,9 +242,10 @@ class SimulationConfigGenerator:
|
|||
entities: List[EntityNode],
|
||||
enable_twitter: bool = True,
|
||||
enable_reddit: bool = True,
|
||||
progress_callback: Optional[Callable[[int, int, str], None]] = None,
|
||||
) -> SimulationParameters:
|
||||
"""
|
||||
智能生成完整的模拟配置
|
||||
智能生成完整的模拟配置（分步生成）
|
||||
|
||||
Args:
|
||||
simulation_id: 模拟ID
|
||||
|
|
@ -210,37 +256,107 @@ class SimulationConfigGenerator:
|
|||
entities: 过滤后的实体列表
|
||||
enable_twitter: 是否启用Twitter
|
||||
enable_reddit: 是否启用Reddit
|
||||
progress_callback: 进度回调函数(current_step, total_steps, message)
|
||||
|
||||
Returns:
|
||||
SimulationParameters: 完整的模拟参数
|
||||
"""
|
||||
logger.info(f"开始智能生成模拟配置: simulation_id={simulation_id}")
|
||||
logger.info(f"开始智能生成模拟配置: simulation_id={simulation_id}, 实体数={len(entities)}")
|
||||
|
||||
# 1. 构建上下文信息（截断到50000字符）
|
||||
# 计算总步骤数
|
||||
num_batches = math.ceil(len(entities) / self.AGENTS_PER_BATCH)
|
||||
total_steps = 3 + num_batches # 时间配置 + 事件配置 + N批Agent + 平台配置
|
||||
current_step = 0
|
||||
|
||||
def report_progress(step: int, message: str):
|
||||
nonlocal current_step
|
||||
current_step = step
|
||||
if progress_callback:
|
||||
progress_callback(step, total_steps, message)
|
||||
logger.info(f"[{step}/{total_steps}] {message}")
|
||||
|
||||
# 1. 构建基础上下文信息
|
||||
context = self._build_context(
|
||||
simulation_requirement=simulation_requirement,
|
||||
document_text=document_text,
|
||||
entities=entities
|
||||
)
|
||||
|
||||
# 2. 调用LLM生成配置
|
||||
llm_result = self._generate_config_with_llm(
|
||||
context=context,
|
||||
entities=entities,
|
||||
enable_twitter=enable_twitter,
|
||||
enable_reddit=enable_reddit
|
||||
)
|
||||
reasoning_parts = []
|
||||
|
||||
# 3. 构建SimulationParameters对象
|
||||
params = self._build_parameters(
|
||||
# ========== 步骤1: 生成时间配置 ==========
|
||||
report_progress(1, "生成时间配置...")
|
||||
time_config_result = self._generate_time_config(context, len(entities))
|
||||
time_config = self._parse_time_config(time_config_result)
|
||||
reasoning_parts.append(f"时间配置: {time_config_result.get('reasoning', '成功')}")
|
||||
|
||||
# ========== 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 = self._parse_event_config(event_config_result)
|
||||
reasoning_parts.append(f"事件配置: {event_config_result.get('reasoning', '成功')}")
|
||||
|
||||
# ========== 步骤3-N: 分批生成Agent配置 ==========
|
||||
all_agent_configs = []
|
||||
for batch_idx in range(num_batches):
|
||||
start_idx = batch_idx * self.AGENTS_PER_BATCH
|
||||
end_idx = min(start_idx + self.AGENTS_PER_BATCH, len(entities))
|
||||
batch_entities = entities[start_idx:end_idx]
|
||||
|
||||
report_progress(
|
||||
3 + batch_idx,
|
||||
f"生成Agent配置 ({start_idx + 1}-{end_idx}/{len(entities)})..."
|
||||
)
|
||||
|
||||
batch_configs = self._generate_agent_configs_batch(
|
||||
context=context,
|
||||
entities=batch_entities,
|
||||
start_idx=start_idx,
|
||||
simulation_requirement=simulation_requirement
|
||||
)
|
||||
all_agent_configs.extend(batch_configs)
|
||||
|
||||
reasoning_parts.append(f"Agent配置: 成功生成 {len(all_agent_configs)} 个")
|
||||
|
||||
# ========== 最后一步: 生成平台配置 ==========
|
||||
report_progress(total_steps, "生成平台配置...")
|
||||
twitter_config = None
|
||||
reddit_config = None
|
||||
|
||||
if enable_twitter:
|
||||
twitter_config = PlatformConfig(
|
||||
platform="twitter",
|
||||
recency_weight=0.4,
|
||||
popularity_weight=0.3,
|
||||
relevance_weight=0.3,
|
||||
viral_threshold=10,
|
||||
echo_chamber_strength=0.5
|
||||
)
|
||||
|
||||
if enable_reddit:
|
||||
reddit_config = PlatformConfig(
|
||||
platform="reddit",
|
||||
recency_weight=0.3,
|
||||
popularity_weight=0.4,
|
||||
relevance_weight=0.3,
|
||||
viral_threshold=15,
|
||||
echo_chamber_strength=0.6
|
||||
)
|
||||
|
||||
# 构建最终参数
|
||||
params = SimulationParameters(
|
||||
simulation_id=simulation_id,
|
||||
project_id=project_id,
|
||||
graph_id=graph_id,
|
||||
simulation_requirement=simulation_requirement,
|
||||
entities=entities,
|
||||
llm_result=llm_result,
|
||||
enable_twitter=enable_twitter,
|
||||
enable_reddit=enable_reddit
|
||||
time_config=time_config,
|
||||
agent_configs=all_agent_configs,
|
||||
event_config=event_config,
|
||||
twitter_config=twitter_config,
|
||||
reddit_config=reddit_config,
|
||||
llm_model=self.model_name,
|
||||
llm_base_url=self.base_url,
|
||||
generation_reasoning=" | ".join(reasoning_parts)
|
||||
)
|
||||
|
||||
logger.info(f"模拟配置生成完成: {len(params.agent_configs)} 个Agent配置")
|
||||
|
|
@ -297,288 +413,397 @@ class SimulationConfigGenerator:
|
|||
|
||||
return "\n".join(lines)
|
||||
|
||||
def _generate_config_with_llm(
|
||||
def _call_llm_with_retry(self, prompt: str, system_prompt: str) -> Dict[str, Any]:
|
||||
"""带重试的LLM调用，包含JSON修复逻辑"""
|
||||
import re
|
||||
|
||||
max_attempts = 3
|
||||
last_error = None
|
||||
|
||||
for attempt in range(max_attempts):
|
||||
try:
|
||||
response = self.client.chat.completions.create(
|
||||
model=self.model_name,
|
||||
messages=[
|
||||
{"role": "system", "content": system_prompt},
|
||||
{"role": "user", "content": prompt}
|
||||
],
|
||||
response_format={"type": "json_object"},
|
||||
temperature=0.7 - (attempt * 0.1) # 每次重试降低温度
|
||||
# 不设置max_tokens，让LLM自由发挥
|
||||
)
|
||||
|
||||
content = response.choices[0].message.content
|
||||
finish_reason = response.choices[0].finish_reason
|
||||
|
||||
# 检查是否被截断
|
||||
if finish_reason == 'length':
|
||||
logger.warning(f"LLM输出被截断 (attempt {attempt+1})")
|
||||
content = self._fix_truncated_json(content)
|
||||
|
||||
# 尝试解析JSON
|
||||
try:
|
||||
return json.loads(content)
|
||||
except json.JSONDecodeError as e:
|
||||
logger.warning(f"JSON解析失败 (attempt {attempt+1}): {str(e)[:80]}")
|
||||
|
||||
# 尝试修复JSON
|
||||
fixed = self._try_fix_config_json(content)
|
||||
if fixed:
|
||||
return fixed
|
||||
|
||||
last_error = e
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"LLM调用失败 (attempt {attempt+1}): {str(e)[:80]}")
|
||||
last_error = e
|
||||
import time
|
||||
time.sleep(2 * (attempt + 1))
|
||||
|
||||
raise last_error or Exception("LLM调用失败")
|
||||
|
||||
def _fix_truncated_json(self, content: str) -> str:
|
||||
"""修复被截断的JSON"""
|
||||
content = content.strip()
|
||||
|
||||
# 计算未闭合的括号
|
||||
open_braces = content.count('{') - content.count('}')
|
||||
open_brackets = content.count('[') - content.count(']')
|
||||
|
||||
# 检查是否有未闭合的字符串
|
||||
if content and content[-1] not in '",}]':
|
||||
content += '"'
|
||||
|
||||
# 闭合括号
|
||||
content += ']' * open_brackets
|
||||
content += '}' * open_braces
|
||||
|
||||
return content
|
||||
|
||||
def _try_fix_config_json(self, content: str) -> Optional[Dict[str, Any]]:
|
||||
"""尝试修复配置JSON"""
|
||||
import re
|
||||
|
||||
# 修复被截断的情况
|
||||
content = self._fix_truncated_json(content)
|
||||
|
||||
# 提取JSON部分
|
||||
json_match = re.search(r'\{[\s\S]*\}', content)
|
||||
if json_match:
|
||||
json_str = json_match.group()
|
||||
|
||||
# 移除字符串中的换行符
|
||||
def fix_string(match):
|
||||
s = match.group(0)
|
||||
s = s.replace('\n', ' ').replace('\r', ' ')
|
||||
s = re.sub(r'\s+', ' ', s)
|
||||
return s
|
||||
|
||||
json_str = re.sub(r'"[^"\\]*(?:\\.[^"\\]*)*"', fix_string, json_str)
|
||||
|
||||
try:
|
||||
return json.loads(json_str)
|
||||
except:
|
||||
# 尝试移除所有控制字符
|
||||
json_str = re.sub(r'[\x00-\x1f\x7f-\x9f]', ' ', json_str)
|
||||
json_str = re.sub(r'\s+', ' ', json_str)
|
||||
try:
|
||||
return json.loads(json_str)
|
||||
except:
|
||||
pass
|
||||
|
||||
return None
|
||||
|
||||
def _generate_time_config(self, context: str, num_entities: int) -> Dict[str, Any]:
|
||||
"""生成时间配置"""
|
||||
prompt = f"""基于以下模拟需求，生成时间模拟配置。
|
||||
|
||||
{context[:5000]}
|
||||
|
||||
## 任务
|
||||
请生成时间配置JSON，注意：
|
||||
- 用户群体为中国人，需符合北京时间作息习惯
|
||||
- 凌晨0-5点几乎无人活动（活跃度系数0.05）
|
||||
- 早上6-8点逐渐活跃（活跃度系数0.4）
|
||||
- 工作时间9-18点中等活跃（活跃度系数0.7）
|
||||
- 晚间19-22点是高峰期（活跃度系数1.5）
|
||||
- 23点后活跃度下降（活跃度系数0.5）
|
||||
|
||||
当前实体数量: {num_entities}
|
||||
|
||||
返回JSON格式（不要markdown）：
|
||||
{{
|
||||
"total_simulation_hours": <72-168，根据事件性质决定>,
|
||||
"minutes_per_round": <15-60>,
|
||||
"agents_per_hour_min": <每小时最少激活Agent数>,
|
||||
"agents_per_hour_max": <每小时最多激活Agent数>,
|
||||
"peak_hours": [19, 20, 21, 22],
|
||||
"off_peak_hours": [0, 1, 2, 3, 4, 5],
|
||||
"morning_hours": [6, 7, 8],
|
||||
"work_hours": [9, 10, 11, 12, 13, 14, 15, 16, 17, 18],
|
||||
"reasoning": "<简要说明>"
|
||||
}}"""
|
||||
|
||||
system_prompt = "你是社交媒体模拟专家。返回纯JSON格式，时间配置需符合中国人作息习惯。"
|
||||
|
||||
try:
|
||||
return self._call_llm_with_retry(prompt, system_prompt)
|
||||
except Exception as e:
|
||||
logger.warning(f"时间配置LLM生成失败: {e}, 使用默认配置")
|
||||
return self._get_default_time_config(num_entities)
|
||||
|
||||
def _get_default_time_config(self, num_entities: int) -> Dict[str, Any]:
|
||||
"""获取默认时间配置（中国人作息）"""
|
||||
return {
|
||||
"total_simulation_hours": 72,
|
||||
"minutes_per_round": 30,
|
||||
"agents_per_hour_min": max(1, num_entities // 15),
|
||||
"agents_per_hour_max": max(5, num_entities // 5),
|
||||
"peak_hours": [19, 20, 21, 22],
|
||||
"off_peak_hours": [0, 1, 2, 3, 4, 5],
|
||||
"morning_hours": [6, 7, 8],
|
||||
"work_hours": [9, 10, 11, 12, 13, 14, 15, 16, 17, 18],
|
||||
"reasoning": "使用默认中国人作息配置"
|
||||
}
|
||||
|
||||
def _parse_time_config(self, result: Dict[str, Any]) -> TimeSimulationConfig:
|
||||
"""解析时间配置结果"""
|
||||
return TimeSimulationConfig(
|
||||
total_simulation_hours=result.get("total_simulation_hours", 72),
|
||||
minutes_per_round=result.get("minutes_per_round", 30),
|
||||
agents_per_hour_min=result.get("agents_per_hour_min", 5),
|
||||
agents_per_hour_max=result.get("agents_per_hour_max", 20),
|
||||
peak_hours=result.get("peak_hours", [19, 20, 21, 22]),
|
||||
off_peak_hours=result.get("off_peak_hours", [0, 1, 2, 3, 4, 5]),
|
||||
off_peak_activity_multiplier=0.05, # 凌晨几乎无人
|
||||
morning_hours=result.get("morning_hours", [6, 7, 8]),
|
||||
morning_activity_multiplier=0.4,
|
||||
work_hours=result.get("work_hours", list(range(9, 19))),
|
||||
work_activity_multiplier=0.7,
|
||||
peak_activity_multiplier=1.5
|
||||
)
|
||||
|
||||
def _generate_event_config(self, context: str, simulation_requirement: str) -> Dict[str, Any]:
|
||||
"""生成事件配置"""
|
||||
prompt = f"""基于以下模拟需求，生成事件配置。
|
||||
|
||||
模拟需求: {simulation_requirement}
|
||||
|
||||
{context[:3000]}
|
||||
|
||||
## 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
|
||||
|
||||
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"}},
|
||||
...
|
||||
],
|
||||
"reasoning": "<简要说明>"
|
||||
}}"""
|
||||
|
||||
system_prompt = "你是舆论分析专家。返回纯JSON格式。"
|
||||
|
||||
try:
|
||||
return self._call_llm_with_retry(prompt, system_prompt)
|
||||
except Exception as e:
|
||||
logger.warning(f"事件配置LLM生成失败: {e}, 使用默认配置")
|
||||
return {
|
||||
"hot_topics": [],
|
||||
"narrative_direction": "",
|
||||
"initial_posts": [],
|
||||
"reasoning": "使用默认配置"
|
||||
}
|
||||
|
||||
def _parse_event_config(self, result: Dict[str, Any]) -> EventConfig:
|
||||
"""解析事件配置结果"""
|
||||
return EventConfig(
|
||||
initial_posts=result.get("initial_posts", []),
|
||||
scheduled_events=[],
|
||||
hot_topics=result.get("hot_topics", []),
|
||||
narrative_direction=result.get("narrative_direction", "")
|
||||
)
|
||||
|
||||
def _generate_agent_configs_batch(
|
||||
self,
|
||||
context: str,
|
||||
entities: List[EntityNode],
|
||||
enable_twitter: bool,
|
||||
enable_reddit: bool
|
||||
) -> Dict[str, Any]:
|
||||
"""调用LLM生成配置"""
|
||||
start_idx: int,
|
||||
simulation_requirement: str
|
||||
) -> List[AgentActivityConfig]:
|
||||
"""分批生成Agent配置"""
|
||||
|
||||
# 构建实体列表用于Agent配置
|
||||
# 构建实体信息
|
||||
entity_list = []
|
||||
for i, e in enumerate(entities):
|
||||
entity_list.append({
|
||||
"agent_id": i,
|
||||
"entity_uuid": e.uuid,
|
||||
"agent_id": start_idx + i,
|
||||
"entity_name": e.name,
|
||||
"entity_type": e.get_entity_type() or "Unknown",
|
||||
"summary": e.summary[:200] if e.summary else ""
|
||||
"summary": e.summary[:150] if e.summary else ""
|
||||
})
|
||||
|
||||
prompt = f"""你是一个社交媒体舆论模拟专家。请根据以下信息，生成详细的模拟参数配置。
|
||||
prompt = f"""基于以下信息，为每个实体生成社交媒体活动配置。
|
||||
|
||||
{context}
|
||||
模拟需求: {simulation_requirement}
|
||||
|
||||
## 实体列表（需要为每个实体生成活动配置）
|
||||
## 实体列表
|
||||
```json
|
||||
{json.dumps(entity_list, ensure_ascii=False, indent=2)}
|
||||
```
|
||||
|
||||
## 任务
|
||||
请生成一个JSON配置，包含以下部分：
|
||||
为每个实体生成活动配置，注意：
|
||||
- **时间符合中国人作息**：凌晨0-5点几乎不活动，晚间19-22点最活跃
|
||||
- **官方机构**（University/GovernmentAgency）：活跃度低(0.1-0.3)，工作时间(9-17)活动，响应慢(60-240分钟)，影响力高(2.5-3.0)
|
||||
- **媒体**（MediaOutlet）：活跃度中(0.4-0.6)，全天活动(8-23)，响应快(5-30分钟)，影响力高(2.0-2.5)
|
||||
- **个人**（Student/Person/Alumni）：活跃度高(0.6-0.9)，主要晚间活动(18-23)，响应快(1-15分钟)，影响力低(0.8-1.2)
|
||||
- **公众人物/专家**：活跃度中(0.4-0.6)，影响力中高(1.5-2.0)
|
||||
|
||||
1. **time_config** - 时间模拟配置
|
||||
- total_simulation_hours: 模拟总时长（小时），根据事件性质决定（短期热点24-72小时，长期舆论168-336小时）
|
||||
- minutes_per_round: 每轮代表的时间（分钟），建议15-60
|
||||
- agents_per_hour_min/max: 每小时激活的Agent数量范围
|
||||
- peak_hours: 高峰时段列表（0-23）
|
||||
- off_peak_hours: 低谷时段列表
|
||||
返回JSON格式（不要markdown）：
|
||||
{{
|
||||
"agent_configs": [
|
||||
{{
|
||||
"agent_id": <必须与输入一致>,
|
||||
"activity_level": <0.0-1.0>,
|
||||
"posts_per_hour": <发帖频率>,
|
||||
"comments_per_hour": <评论频率>,
|
||||
"active_hours": [<活跃小时列表，考虑中国人作息>],
|
||||
"response_delay_min": <最小响应延迟分钟>,
|
||||
"response_delay_max": <最大响应延迟分钟>,
|
||||
"sentiment_bias": <-1.0到1.0>,
|
||||
"stance": "<supportive/opposing/neutral/observer>",
|
||||
"influence_weight": <影响力权重>
|
||||
}},
|
||||
...
|
||||
]
|
||||
}}"""
|
||||
|
||||
2. **agent_configs** - 每个Agent的活动配置（必须为每个实体生成）
|
||||
对于每个agent_id，设置：
|
||||
- activity_level: 活跃度(0.0-1.0)，官方机构通常0.1-0.3，媒体0.3-0.5，个人0.5-0.9
|
||||
- posts_per_hour: 每小时发帖频率，官方机构0.05-0.2，媒体0.5-2，个人0.1-1
|
||||
- comments_per_hour: 每小时评论频率
|
||||
- active_hours: 活跃时间段列表，官方通常工作时间，个人更分散
|
||||
- response_delay_min/max: 响应延迟（模拟分钟），官方较慢(30-180)，个人较快(1-30)
|
||||
- sentiment_bias: 情感倾向(-1到1)，根据实体立场设置
|
||||
- stance: 立场(supportive/opposing/neutral/observer)
|
||||
- influence_weight: 影响力权重，知名人物和媒体较高
|
||||
|
||||
3. **event_config** - 事件配置
|
||||
- initial_posts: 初始帖子列表，包含content和poster_agent_id
|
||||
- hot_topics: 热点话题关键词列表
|
||||
- narrative_direction: 舆论发展方向描述
|
||||
|
||||
4. **platform_configs** - 平台配置（如果启用）
|
||||
- viral_threshold: 病毒传播阈值
|
||||
- echo_chamber_strength: 回声室效应强度(0-1)
|
||||
|
||||
5. **reasoning** - 你的推理说明，解释为什么这样设置参数
|
||||
|
||||
## 重要原则
|
||||
- 官方机构（University、GovernmentAgency）发言频率低但影响力大
|
||||
- 媒体（MediaOutlet）发言频率中等，传播速度快
|
||||
- 个人（Student、PublicFigure）发言频率高但影响力分散
|
||||
- 根据模拟需求判断各实体的立场和情感倾向
|
||||
- 时间配置要符合真实社交媒体的使用规律
|
||||
|
||||
请返回JSON格式，不要包含markdown代码块标记。"""
|
||||
system_prompt = "你是社交媒体行为分析专家。返回纯JSON，配置需符合中国人作息习惯。"
|
||||
|
||||
try:
|
||||
# 使用重试机制调用LLM API
|
||||
from ..utils.retry import RetryableAPIClient
|
||||
|
||||
retry_client = RetryableAPIClient(max_retries=3, initial_delay=2.0, max_delay=60.0)
|
||||
|
||||
def call_llm():
|
||||
return self.client.chat.completions.create(
|
||||
model=self.model_name,
|
||||
messages=[
|
||||
{
|
||||
"role": "system",
|
||||
"content": "你是社交媒体舆论模拟专家，擅长设计真实的模拟参数。返回纯JSON格式，不要markdown。"
|
||||
},
|
||||
{"role": "user", "content": prompt}
|
||||
],
|
||||
response_format={"type": "json_object"},
|
||||
temperature=0.7,
|
||||
max_tokens=8000
|
||||
)
|
||||
|
||||
response = retry_client.call_with_retry(call_llm)
|
||||
result = json.loads(response.choices[0].message.content)
|
||||
logger.info(f"LLM配置生成成功")
|
||||
return result
|
||||
|
||||
result = self._call_llm_with_retry(prompt, system_prompt)
|
||||
llm_configs = {cfg["agent_id"]: cfg for cfg in result.get("agent_configs", [])}
|
||||
except Exception as e:
|
||||
logger.error(f"LLM配置生成失败（已重试）: {str(e)}")
|
||||
# 返回默认配置
|
||||
return self._generate_default_config(entities)
|
||||
|
||||
def _generate_default_config(self, entities: List[EntityNode]) -> Dict[str, Any]:
|
||||
"""生成默认配置（LLM失败时的fallback）"""
|
||||
agent_configs = []
|
||||
|
||||
for i, e in enumerate(entities):
|
||||
entity_type = (e.get_entity_type() or "Unknown").lower()
|
||||
|
||||
# 根据实体类型设置默认参数
|
||||
if entity_type in ["university", "governmentagency", "ngo"]:
|
||||
config = {
|
||||
"agent_id": i,
|
||||
"activity_level": 0.2,
|
||||
"posts_per_hour": 0.1,
|
||||
"comments_per_hour": 0.05,
|
||||
"active_hours": list(range(9, 18)),
|
||||
"response_delay_min": 60,
|
||||
"response_delay_max": 240,
|
||||
"sentiment_bias": 0.0,
|
||||
"stance": "neutral",
|
||||
"influence_weight": 3.0
|
||||
}
|
||||
elif entity_type in ["mediaoutlet"]:
|
||||
config = {
|
||||
"agent_id": i,
|
||||
"activity_level": 0.6,
|
||||
"posts_per_hour": 1.0,
|
||||
"comments_per_hour": 0.5,
|
||||
"active_hours": list(range(6, 24)),
|
||||
"response_delay_min": 5,
|
||||
"response_delay_max": 30,
|
||||
"sentiment_bias": 0.0,
|
||||
"stance": "observer",
|
||||
"influence_weight": 2.5
|
||||
}
|
||||
elif entity_type in ["publicfigure", "expert"]:
|
||||
config = {
|
||||
"agent_id": i,
|
||||
"activity_level": 0.5,
|
||||
"posts_per_hour": 0.3,
|
||||
"comments_per_hour": 0.5,
|
||||
"active_hours": list(range(8, 23)),
|
||||
"response_delay_min": 10,
|
||||
"response_delay_max": 60,
|
||||
"sentiment_bias": 0.0,
|
||||
"stance": "neutral",
|
||||
"influence_weight": 2.0
|
||||
}
|
||||
else: # Student, Person, etc.
|
||||
config = {
|
||||
"agent_id": i,
|
||||
"activity_level": 0.7,
|
||||
"posts_per_hour": 0.5,
|
||||
"comments_per_hour": 1.0,
|
||||
"active_hours": list(range(7, 24)),
|
||||
"response_delay_min": 1,
|
||||
"response_delay_max": 20,
|
||||
"sentiment_bias": 0.0,
|
||||
"stance": "neutral",
|
||||
"influence_weight": 1.0
|
||||
}
|
||||
|
||||
agent_configs.append(config)
|
||||
|
||||
return {
|
||||
"time_config": {
|
||||
"total_simulation_hours": 72,
|
||||
"minutes_per_round": 30,
|
||||
"agents_per_hour_min": max(1, len(entities) // 10),
|
||||
"agents_per_hour_max": max(5, len(entities) // 3),
|
||||
"peak_hours": [9, 10, 11, 14, 15, 20, 21, 22],
|
||||
"off_peak_hours": [0, 1, 2, 3, 4, 5]
|
||||
},
|
||||
"agent_configs": agent_configs,
|
||||
"event_config": {
|
||||
"initial_posts": [],
|
||||
"hot_topics": [],
|
||||
"narrative_direction": ""
|
||||
},
|
||||
"reasoning": "使用默认配置（LLM生成失败）"
|
||||
}
|
||||
|
||||
def _build_parameters(
|
||||
self,
|
||||
simulation_id: str,
|
||||
project_id: str,
|
||||
graph_id: str,
|
||||
simulation_requirement: str,
|
||||
entities: List[EntityNode],
|
||||
llm_result: Dict[str, Any],
|
||||
enable_twitter: bool,
|
||||
enable_reddit: bool
|
||||
) -> SimulationParameters:
|
||||
"""根据LLM结果构建SimulationParameters对象"""
|
||||
|
||||
# 时间配置
|
||||
time_cfg = llm_result.get("time_config", {})
|
||||
time_config = TimeSimulationConfig(
|
||||
total_simulation_hours=time_cfg.get("total_simulation_hours", 72),
|
||||
minutes_per_round=time_cfg.get("minutes_per_round", 30),
|
||||
agents_per_hour_min=time_cfg.get("agents_per_hour_min", 5),
|
||||
agents_per_hour_max=time_cfg.get("agents_per_hour_max", 20),
|
||||
peak_hours=time_cfg.get("peak_hours", [9, 10, 11, 14, 15, 20, 21, 22]),
|
||||
off_peak_hours=time_cfg.get("off_peak_hours", [0, 1, 2, 3, 4, 5]),
|
||||
peak_activity_multiplier=time_cfg.get("peak_activity_multiplier", 1.5),
|
||||
off_peak_activity_multiplier=time_cfg.get("off_peak_activity_multiplier", 0.3)
|
||||
)
|
||||
|
||||
# Agent配置
|
||||
agent_configs = []
|
||||
llm_agent_configs = {cfg["agent_id"]: cfg for cfg in llm_result.get("agent_configs", [])}
|
||||
logger.warning(f"Agent配置批次LLM生成失败: {e}, 使用规则生成")
|
||||
llm_configs = {}
|
||||
|
||||
# 构建AgentActivityConfig对象
|
||||
configs = []
|
||||
for i, entity in enumerate(entities):
|
||||
cfg = llm_agent_configs.get(i, {})
|
||||
agent_id = start_idx + i
|
||||
cfg = llm_configs.get(agent_id, {})
|
||||
|
||||
agent_config = AgentActivityConfig(
|
||||
agent_id=i,
|
||||
# 如果LLM没有生成，使用规则生成
|
||||
if not cfg:
|
||||
cfg = self._generate_agent_config_by_rule(entity)
|
||||
|
||||
config = AgentActivityConfig(
|
||||
agent_id=agent_id,
|
||||
entity_uuid=entity.uuid,
|
||||
entity_name=entity.name,
|
||||
entity_type=entity.get_entity_type() or "Unknown",
|
||||
activity_level=cfg.get("activity_level", 0.5),
|
||||
posts_per_hour=cfg.get("posts_per_hour", 0.5),
|
||||
comments_per_hour=cfg.get("comments_per_hour", 1.0),
|
||||
active_hours=cfg.get("active_hours", list(range(8, 23))),
|
||||
active_hours=cfg.get("active_hours", list(range(9, 23))),
|
||||
response_delay_min=cfg.get("response_delay_min", 5),
|
||||
response_delay_max=cfg.get("response_delay_max", 60),
|
||||
sentiment_bias=cfg.get("sentiment_bias", 0.0),
|
||||
stance=cfg.get("stance", "neutral"),
|
||||
influence_weight=cfg.get("influence_weight", 1.0)
|
||||
)
|
||||
agent_configs.append(agent_config)
|
||||
configs.append(config)
|
||||
|
||||
# 事件配置
|
||||
event_cfg = llm_result.get("event_config", {})
|
||||
event_config = EventConfig(
|
||||
initial_posts=event_cfg.get("initial_posts", []),
|
||||
scheduled_events=event_cfg.get("scheduled_events", []),
|
||||
hot_topics=event_cfg.get("hot_topics", []),
|
||||
narrative_direction=event_cfg.get("narrative_direction", "")
|
||||
)
|
||||
return configs
|
||||
|
||||
# 平台配置
|
||||
twitter_config = None
|
||||
reddit_config = None
|
||||
def _generate_agent_config_by_rule(self, entity: EntityNode) -> Dict[str, Any]:
|
||||
"""基于规则生成单个Agent配置（中国人作息）"""
|
||||
entity_type = (entity.get_entity_type() or "Unknown").lower()
|
||||
|
||||
platform_cfgs = llm_result.get("platform_configs", {})
|
||||
|
||||
if enable_twitter:
|
||||
tw_cfg = platform_cfgs.get("twitter", {})
|
||||
twitter_config = PlatformConfig(
|
||||
platform="twitter",
|
||||
recency_weight=tw_cfg.get("recency_weight", 0.4),
|
||||
popularity_weight=tw_cfg.get("popularity_weight", 0.3),
|
||||
relevance_weight=tw_cfg.get("relevance_weight", 0.3),
|
||||
viral_threshold=tw_cfg.get("viral_threshold", 10),
|
||||
echo_chamber_strength=tw_cfg.get("echo_chamber_strength", 0.5)
|
||||
)
|
||||
|
||||
if enable_reddit:
|
||||
rd_cfg = platform_cfgs.get("reddit", {})
|
||||
reddit_config = PlatformConfig(
|
||||
platform="reddit",
|
||||
recency_weight=rd_cfg.get("recency_weight", 0.3),
|
||||
popularity_weight=rd_cfg.get("popularity_weight", 0.4),
|
||||
relevance_weight=rd_cfg.get("relevance_weight", 0.3),
|
||||
viral_threshold=rd_cfg.get("viral_threshold", 15),
|
||||
echo_chamber_strength=rd_cfg.get("echo_chamber_strength", 0.6)
|
||||
)
|
||||
|
||||
return SimulationParameters(
|
||||
simulation_id=simulation_id,
|
||||
project_id=project_id,
|
||||
graph_id=graph_id,
|
||||
simulation_requirement=simulation_requirement,
|
||||
time_config=time_config,
|
||||
agent_configs=agent_configs,
|
||||
event_config=event_config,
|
||||
twitter_config=twitter_config,
|
||||
reddit_config=reddit_config,
|
||||
llm_model=self.model_name,
|
||||
llm_base_url=self.base_url,
|
||||
generation_reasoning=llm_result.get("reasoning", "")
|
||||
)
|
||||
if entity_type in ["university", "governmentagency", "ngo"]:
|
||||
# 官方机构：工作时间活动，低频率，高影响力
|
||||
return {
|
||||
"activity_level": 0.2,
|
||||
"posts_per_hour": 0.1,
|
||||
"comments_per_hour": 0.05,
|
||||
"active_hours": list(range(9, 18)), # 9:00-17:59
|
||||
"response_delay_min": 60,
|
||||
"response_delay_max": 240,
|
||||
"sentiment_bias": 0.0,
|
||||
"stance": "neutral",
|
||||
"influence_weight": 3.0
|
||||
}
|
||||
elif entity_type in ["mediaoutlet"]:
|
||||
# 媒体：全天活动，中等频率，高影响力
|
||||
return {
|
||||
"activity_level": 0.5,
|
||||
"posts_per_hour": 0.8,
|
||||
"comments_per_hour": 0.3,
|
||||
"active_hours": list(range(7, 24)), # 7:00-23:59
|
||||
"response_delay_min": 5,
|
||||
"response_delay_max": 30,
|
||||
"sentiment_bias": 0.0,
|
||||
"stance": "observer",
|
||||
"influence_weight": 2.5
|
||||
}
|
||||
elif entity_type in ["professor", "expert", "official"]:
|
||||
# 专家/教授：工作+晚间活动，中等频率
|
||||
return {
|
||||
"activity_level": 0.4,
|
||||
"posts_per_hour": 0.3,
|
||||
"comments_per_hour": 0.5,
|
||||
"active_hours": list(range(8, 22)), # 8:00-21:59
|
||||
"response_delay_min": 15,
|
||||
"response_delay_max": 90,
|
||||
"sentiment_bias": 0.0,
|
||||
"stance": "neutral",
|
||||
"influence_weight": 2.0
|
||||
}
|
||||
elif entity_type in ["student"]:
|
||||
# 学生：晚间为主，高频率
|
||||
return {
|
||||
"activity_level": 0.8,
|
||||
"posts_per_hour": 0.6,
|
||||
"comments_per_hour": 1.5,
|
||||
"active_hours": [8, 9, 10, 11, 12, 13, 18, 19, 20, 21, 22, 23], # 上午+晚间
|
||||
"response_delay_min": 1,
|
||||
"response_delay_max": 15,
|
||||
"sentiment_bias": 0.0,
|
||||
"stance": "neutral",
|
||||
"influence_weight": 0.8
|
||||
}
|
||||
elif entity_type in ["alumni"]:
|
||||
# 校友：晚间为主
|
||||
return {
|
||||
"activity_level": 0.6,
|
||||
"posts_per_hour": 0.4,
|
||||
"comments_per_hour": 0.8,
|
||||
"active_hours": [12, 13, 19, 20, 21, 22, 23], # 午休+晚间
|
||||
"response_delay_min": 5,
|
||||
"response_delay_max": 30,
|
||||
"sentiment_bias": 0.0,
|
||||
"stance": "neutral",
|
||||
"influence_weight": 1.0
|
||||
}
|
||||
else:
|
||||
# 普通人：晚间高峰
|
||||
return {
|
||||
"activity_level": 0.7,
|
||||
"posts_per_hour": 0.5,
|
||||
"comments_per_hour": 1.2,
|
||||
"active_hours": [9, 10, 11, 12, 13, 18, 19, 20, 21, 22, 23], # 白天+晚间
|
||||
"response_delay_min": 2,
|
||||
"response_delay_max": 20,
|
||||
"sentiment_bias": 0.0,
|
||||
"stance": "neutral",
|
||||
"influence_weight": 1.0
|
||||
}
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -238,14 +238,15 @@ class SimulationManager:
|
|||
document_text: str,
|
||||
defined_entity_types: Optional[List[str]] = None,
|
||||
use_llm_for_profiles: bool = True,
|
||||
progress_callback: Optional[callable] = None
|
||||
progress_callback: Optional[callable] = None,
|
||||
parallel_profile_count: int = 3
|
||||
) -> SimulationState:
|
||||
"""
|
||||
准备模拟环境（全程自动化）
|
||||
|
||||
步骤：
|
||||
1. 从Zep图谱读取并过滤实体
|
||||
2. 为每个实体生成OASIS Agent Profile（可选LLM增强）
|
||||
2. 为每个实体生成OASIS Agent Profile（可选LLM增强，支持并行）
|
||||
3. 使用LLM智能生成模拟配置参数（时间、活跃度、发言频率等）
|
||||
4. 保存配置文件和Profile文件
|
||||
5. 复制预设脚本到模拟目录
|
||||
|
|
@ -257,6 +258,7 @@ class SimulationManager:
|
|||
defined_entity_types: 预定义的实体类型（可选）
|
||||
use_llm_for_profiles: 是否使用LLM生成详细人设
|
||||
progress_callback: 进度回调函数 (stage, progress, message)
|
||||
parallel_profile_count: 并行生成人设的数量，默认3
|
||||
|
||||
Returns:
|
||||
SimulationState
|
||||
|
|
@ -314,7 +316,8 @@ class SimulationManager:
|
|||
total=total_entities
|
||||
)
|
||||
|
||||
generator = OasisProfileGenerator()
|
||||
# 传入graph_id以启用Zep检索功能，获取更丰富的上下文
|
||||
generator = OasisProfileGenerator(graph_id=state.graph_id)
|
||||
|
||||
def profile_progress(current, total, msg):
|
||||
if progress_callback:
|
||||
|
|
@ -330,7 +333,9 @@ class SimulationManager:
|
|||
profiles = generator.generate_profiles_from_entities(
|
||||
entities=filtered.entities,
|
||||
use_llm=use_llm_for_profiles,
|
||||
progress_callback=profile_progress
|
||||
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
|
||||
)
|
||||
|
||||
state.profiles_count = len(profiles)
|
||||
|
|
|
|||
Loading…
Reference in a new issue