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Implement Report Agent for automated report generation and interaction

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- Introduced the Report Agent module to facilitate the automatic generation of simulation analysis reports using LangChain and Zep, following the ReACT model.
- Added functionality for report outline planning, segmented content generation, and user interaction through a dialogue interface.
- Implemented new API endpoints for report generation, status checking, and retrieval, enhancing the overall reporting capabilities.
- Updated README.md to include detailed instructions on the new report generation features and API usage.
- Enhanced the project structure to accommodate the new report management functionalities, including report storage and retrieval mechanisms.
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666ghj 2025-12-09 15:10:55 +08:00
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backend/README.md
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@ -28,6 +28,8 @@
4. **模拟配置智能生成**: 使用 LLM 根据需求自动生成模拟参数(时间、活跃度、事件等)
5. **双平台模拟**: 支持 Twitter 和 Reddit 双平台并行舆论模拟(基于 OASIS 框架)
6. **图谱记忆动态更新**: 可选功能,将模拟中Agent的活动实时更新到Zep图谱,让图谱"记住"模拟过程
7. **智能报告生成**: 使用 LangChain + Zep 实现 ReACT 模式的模拟分析报告自动生成
8. **Report Agent对话**: 报告生成后可与Report Agent对话,自主调用检索工具回答问题
---
@ -42,10 +44,11 @@
│ │ API层 │ │ 服务层 │ │ 模型层 │ │
│ │ - graph.py │→ │ - 本体生成 │→ │ - Project │ │
│ │ - simulation │ │ - 图谱构建 │ │ - Task │ │
│ └────────────────┘ │ - 实体读取 │ └─────────────────┘ │
│ │ - 人设生成 │ │
│ │ - report.py │ │ - 实体读取 │ │ - Report │ │
│ └────────────────┘ │ - 人设生成 │ └─────────────────┘ │
│ │ - 配置生成 │ │
│ │ - 模拟运行 │ │
│ │ - 报告生成 │ │
│ └──────────────┘ │
├─────────────────────────────────────────────────────────────┤
│ 外部服务集成 │
@ -78,6 +81,16 @@
模拟完成 → 环境进入等待模式 → 发送Interview命令 → Agent回答 → 获取结果 → (可选)关闭环境
```
5. **报告生成流程**:
```
模拟完成 → 调用Report API → ReACT规划大纲 → 逐章节生成(多次工具调用) → 生成Markdown报告 → 解锁Interview功能
```
6. **Report Agent对话流程**:
```
用户提问 → Agent分析 → 调用Zep检索工具 → 整合信息 → 返回回答
```
---
## 技术栈
@ -89,6 +102,7 @@
### AI & 知识图谱
- **Zep Cloud SDK 2.0+**: 知识图谱构建与管理
- **OpenAI SDK 1.0+**: LLM 调用(支持 OpenAI 兼容接口)
- **LangChain 0.2+**: Report Agent框架(ReACT模式)
- **OASIS-AI**: 社交媒体模拟框架
- **CAMEL-AI**: Agent 行为模拟
@ -117,6 +131,10 @@ backend/
│ │ ├── project.json # 项目元数据
│ │ ├── files/ # 上传的文件
│ │ └── extracted_text.txt # 提取的文本
│ ├── reports/ # 报告数据
│ │ └── report_xxx/
│ │ ├── report_xxx.json # 报告元数据
│ │ └── report_xxx.md # Markdown报告
│ └── simulations/ # 模拟数据
│ └── sim_xxx/
│ ├── state.json # 模拟状态
@ -142,7 +160,8 @@ backend/
├── api/ # API路由
│ ├── __init__.py
│ ├── graph.py # 图谱相关接口
│ └── simulation.py # 模拟相关接口
│ ├── simulation.py # 模拟相关接口
│ └── report.py # 报告相关接口
├── models/ # 数据模型
│ ├── __init__.py
│ ├── project.py # 项目模型
@ -153,12 +172,14 @@ backend/
│ ├── graph_builder.py # 图谱构建
│ ├── text_processor.py # 文本处理
│ ├── zep_entity_reader.py # 实体读取
│ ├── zep_tools.py # Zep检索工具服务
│ ├── oasis_profile_generator.py # 人设生成
│ ├── simulation_config_generator.py # 配置生成
│ ├── simulation_manager.py # 模拟管理
│ ├── simulation_runner.py # 模拟运行
│ ├── simulation_ipc.py # 模拟IPC通信（Interview功能）
│ └── zep_graph_memory_updater.py # 图谱记忆动态更新
│ ├── zep_graph_memory_updater.py # 图谱记忆动态更新
│ └── report_agent.py # 报告生成Agent（ReACT模式）
└── utils/ # 工具类
├── __init__.py
├── file_parser.py # 文件解析
@ -240,6 +261,55 @@ backend/
- `SimulationIPCClient`: IPC客户端（Flask端使用）
- `SimulationIPCServer`: IPC服务器（模拟脚本端使用）
### 5. Report Agent模块（报告生成）
**功能**: 模拟完成后自动生成分析报告，支持与用户对话
**特点**:
- **ReACT模式**: Reasoning + Acting，多轮思考与工具调用
- **大纲规划**: LLM分析模拟需求，自动规划报告目录结构
- **分段生成**: 逐章节生成，每章节可多次调用Zep检索工具
- **Markdown输出**: 生成专业的Markdown格式报告
- **对话功能**: 报告完成后可与Report Agent对话，自主调用工具回答问题
**工具（MCP封装）**:
- `search_graph`: 图谱语义搜索
- `get_graph_statistics`: 获取图谱统计信息
- `get_entity_summary`: 获取实体关系摘要
- `get_simulation_context`: 获取模拟上下文
- `get_entities_by_type`: 按类型获取实体
**核心服务**:
- `ZepToolsService`: Zep检索工具封装
- `ReportAgent`: 报告生成Agent（ReACT模式）
- `ReportManager`: 报告持久化管理
**工作原理**:
```
┌─────────────────────────────────────────────────────────────┐
│ Report Agent (ReACT) │
├─────────────────────────────────────────────────────────────┤
│ │
│ 1. 规划阶段 │
│ ┌─────────────────────────────────────────────────┐ │
│ │ LLM分析模拟需求 → 获取图谱上下文 → 生成报告大纲 │ │
│ └─────────────────────────────────────────────────┘ │
│ │
│ 2. 生成阶段 (每章节) │
│ ┌─────────────────────────────────────────────────┐ │
│ │ Thought → Action → Observation → ... → Final │ │
│ │ ↓ ↓ ↓ │ │
│ │ 分析需求 调用工具 分析结果 生成内容 │ │
│ └─────────────────────────────────────────────────┘ │
│ │
│ 3. 输出阶段 │
│ ┌─────────────────────────────────────────────────┐ │
│ │ 组装章节 → 生成Markdown → 保存JSON/MD文件 │ │
│ └─────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────┘
```
**工作原理**:
```
Flask后端 模拟脚本
@ -964,6 +1034,245 @@ Flask后端 模拟脚本
- `/stop`: 强制终止模拟进程
- `/close-env`: 优雅地关闭环境，让模拟进程正常退出
---
### Report 报告接口
> **说明**: 报告生成完成后才能解锁Interview功能。Report Agent使用ReACT模式，可以在对话中自主调用Zep检索工具。
#### 1. 生成报告
**接口**: `POST /api/report/generate`
**请求参数**:
```json
{
"simulation_id": "sim_xxxx",
"force_regenerate": false
}
```
| 参数 | 类型 | 必填 | 默认值 | 说明 |
|------|------|------|--------|------|
| simulation_id | String | 是 | - | 模拟ID |
| force_regenerate | Boolean | 否 | false | 强制重新生成 |
**返回示例**:
```json
{
"success": true,
"data": {
"simulation_id": "sim_xxxx",
"task_id": "task_xxxx",
"status": "generating",
"message": "报告生成任务已启动",
"already_generated": false
}
}
```
**如果报告已存在**:
```json
{
"success": true,
"data": {
"simulation_id": "sim_xxxx",
"report_id": "report_xxxx",
"status": "completed",
"message": "报告已存在",
"already_generated": true
}
}
```
---
#### 2. 查询生成进度
**接口**: `POST /api/report/generate/status`
**请求参数**:
```json
{
"task_id": "task_xxxx",
"simulation_id": "sim_xxxx"
}
```
**返回示例**:
```json
{
"success": true,
"data": {
"task_id": "task_xxxx",
"status": "processing",
"progress": 45,
"message": "[generating] 正在生成章节: 关键发现 (3/5)"
}
}
```
---
#### 3. 获取报告
**接口**: `GET /api/report/{report_id}`
**返回示例**:
```json
{
"success": true,
"data": {
"report_id": "report_xxxx",
"simulation_id": "sim_xxxx",
"graph_id": "mirofish_xxxx",
"simulation_requirement": "模拟武汉大学撤销处分后的舆情走向",
"status": "completed",
"outline": {
"title": "武汉大学撤销处分事件舆情分析报告",
"summary": "基于模拟结果的全面舆情分析",
"sections": [
{"title": "执行摘要", "content": "..."},
{"title": "模拟背景", "content": "..."},
{"title": "关键发现", "content": "..."},
{"title": "舆情分析", "content": "..."},
{"title": "建议与展望", "content": "..."}
]
},
"markdown_content": "# 武汉大学撤销处分事件舆情分析报告\n\n...",
"created_at": "2025-12-09T10:00:00",
"completed_at": "2025-12-09T10:05:00"
}
}
```
---
#### 4. 根据模拟ID获取报告
**接口**: `GET /api/report/by-simulation/{simulation_id}`
**返回示例**:
```json
{
"success": true,
"data": {...},
"has_report": true
}
```
---
#### 5. 下载报告
**接口**: `GET /api/report/{report_id}/download`
**返回**: Markdown文件下载
---
#### 6. 与Report Agent对话
**接口**: `POST /api/report/chat`
**请求参数**:
```json
{
"simulation_id": "sim_xxxx",
"message": "请详细解释一下舆情的主要趋势",
"chat_history": [
{"role": "user", "content": "报告提到了哪些关键人物？"},
{"role": "assistant", "content": "根据分析，关键人物包括..."}
]
}
```
| 参数 | 类型 | 必填 | 默认值 | 说明 |
|------|------|------|--------|------|
| simulation_id | String | 是 | - | 模拟ID |
| message | String | 是 | - | 用户消息 |
| chat_history | Array | 否 | [] | 对话历史（用于上下文） |
**返回示例**:
```json
{
"success": true,
"data": {
"response": "根据模拟数据分析，舆情的主要趋势表现为...\n\n1. **初期阶段**：...\n2. **发酵阶段**：...\n3. **高峰阶段**：...",
"tool_calls": [
{"name": "search_graph", "parameters": {"query": "舆情趋势"}},
{"name": "get_graph_statistics", "parameters": {}}
],
"sources": []
}
}
```
---
#### 7. 检查报告状态
**接口**: `GET /api/report/check/{simulation_id}`
**用途**: 判断是否解锁Interview功能
**返回示例**:
```json
{
"success": true,
"data": {
"simulation_id": "sim_xxxx",
"has_report": true,
"report_status": "completed",
"report_id": "report_xxxx",
"interview_unlocked": true
}
}
```
---
#### 8. 列出所有报告
**接口**: `GET /api/report/list?simulation_id=sim_xxxx&limit=50`
**返回示例**:
```json
{
"success": true,
"data": [...],
"count": 5
}
```
---
#### 9. 删除报告
**接口**: `DELETE /api/report/{report_id}`
---
#### 10. 工具调试接口
**图谱搜索**: `POST /api/report/tools/search`
```json
{
"graph_id": "mirofish_xxxx",
"query": "舆情走向",
"limit": 10
}
```
**图谱统计**: `POST /api/report/tools/statistics`
```json
{
"graph_id": "mirofish_xxxx"
}
```
---
#### 6. 获取运行状态
**接口**: `GET /api/simulation/{simulation_id}/run-status`
@ -1192,7 +1501,50 @@ created_at: str # 创建时间
---
### 6. SimulationParameters (模拟参数)
### 6. Report (报告模型)
**文件**: `app/services/report_agent.py`
**字段**:
```python
report_id: str # 报告ID (report_xxx)
simulation_id: str # 模拟ID
graph_id: str # 图谱ID
simulation_requirement: str # 模拟需求
status: ReportStatus # 状态
outline: ReportOutline # 报告大纲
markdown_content: str # Markdown内容
created_at: str # 创建时间
completed_at: str # 完成时间
error: str # 错误信息
```
**状态枚举**:
```python
PENDING = "pending" # 等待中
PLANNING = "planning" # 规划大纲中
GENERATING = "generating" # 生成内容中
COMPLETED = "completed" # 已完成
FAILED = "failed" # 失败
```
**ReportOutline字段**:
```python
title: str # 报告标题
summary: str # 报告摘要
sections: List[ReportSection] # 章节列表
```
**ReportSection字段**:
```python
title: str # 章节标题
content: str # 章节内容
subsections: List[ReportSection] # 子章节
```
---
### 7. SimulationParameters (模拟参数)
**文件**: `app/services/simulation_config_generator.py`
@ -1815,6 +2167,270 @@ result = SimulationRunner.interview_all_agents(
---
### 10. ZepToolsService (Zep检索工具服务)
**文件**: `app/services/zep_tools.py`
**功能**: 封装多种Zep图谱检索工具，供Report Agent调用
**核心方法**:
```python
def search_graph(
graph_id: str,
query: str,
limit: int = 10
) -> SearchResult:
"""
图谱语义搜索
使用混合搜索（语义+BM25）查找相关信息
返回: facts列表、edges列表、nodes列表
"""
def get_all_nodes(graph_id: str) -> List[NodeInfo]:
"""获取图谱所有节点"""
def get_all_edges(graph_id: str) -> List[EdgeInfo]:
"""获取图谱所有边"""
def get_node_detail(node_uuid: str) -> Optional[NodeInfo]:
"""获取单个节点详情"""
def get_node_edges(node_uuid: str) -> List[EdgeInfo]:
"""获取节点相关的边"""
def get_entities_by_type(
graph_id: str,
entity_type: str
) -> List[NodeInfo]:
"""按类型获取实体"""
def get_entity_summary(
graph_id: str,
entity_name: str
) -> Dict[str, Any]:
"""获取实体关系摘要"""
def get_graph_statistics(graph_id: str) -> Dict[str, Any]:
"""
获取图谱统计信息
返回:
- total_nodes: 节点总数
- total_edges: 边总数
- entity_types: 实体类型分布
- relation_types: 关系类型分布
"""
def get_simulation_context(
graph_id: str,
simulation_requirement: str,
limit: int = 30
) -> Dict[str, Any]:
"""
获取模拟相关上下文
综合搜索与模拟需求相关的所有信息
"""
```
**容错机制**:
- 所有API调用带3次重试
- 指数退避策略
- 搜索失败返回空结果而非抛出异常
---
### 11. ReportAgent (报告生成Agent)
**文件**: `app/services/report_agent.py`
**功能**: 使用ReACT模式生成模拟分析报告
**核心类**:
```python
class ReportAgent:
"""
Report Agent - 模拟报告生成Agent
采用ReACT（Reasoning + Acting）模式：
1. 规划阶段：分析模拟需求，规划报告目录结构
2. 生成阶段：逐章节生成内容，每章节可多次调用工具获取信息
3. 对话阶段：支持与用户对话，自主调用检索工具
"""
# 配置
MAX_TOOL_CALLS_PER_SECTION = 5 # 每章节最大工具调用次数
MAX_REFLECTION_ROUNDS = 2 # 最大反思轮数
```
**核心方法**:
```python
def plan_outline(
progress_callback: Optional[Callable] = None
) -> ReportOutline:
"""
规划报告大纲
步骤:
1. 获取模拟上下文（图谱统计、相关事实）
2. 使用LLM分析并生成大纲结构
3. 返回包含章节列表的大纲对象
"""
def _generate_section_react(
section: ReportSection,
outline: ReportOutline,
previous_sections: List[str],
progress_callback: Optional[Callable] = None
) -> str:
"""
使用ReACT模式生成单个章节
ReACT循环:
1. Thought（思考）- 分析需要什么信息
2. Action（行动）- 调用工具获取信息
3. Observation（观察）- 分析工具返回结果
4. 重复直到信息足够或达到最大次数
5. Final Answer（最终回答）- 生成章节内容
"""
def generate_report(
progress_callback: Optional[Callable] = None
) -> Report:
"""
生成完整报告
步骤:
1. 规划大纲
2. 逐章节生成（ReACT模式）
3. 组装Markdown报告
4. 保存报告文件
"""
def chat(
message: str,
chat_history: List[Dict[str, str]] = None
) -> Dict[str, Any]:
"""
与Report Agent对话
在对话中Agent可以自主调用检索工具来回答问题
Returns:
{
"response": "Agent回复",
"tool_calls": [调用的工具列表],
"sources": [信息来源]
}
"""
```
**工具调用格式**:
Agent使用以下格式调用工具：
```
<tool_call>
{"name": "search_graph", "parameters": {"query": "舆情走向", "limit": 10}}
</tool_call>
```
或者：
```
[TOOL_CALL] search_graph(query="舆情走向", limit="10")
```
**报告大纲结构示例**:
```json
{
"title": "武汉大学撤销处分事件舆情分析报告",
"summary": "基于模拟结果的全面舆情分析",
"sections": [
{
"title": "执行摘要",
"description": "简要总结模拟结果和关键发现"
},
{
"title": "模拟背景",
"description": "描述模拟的初始条件和场景设定"
},
{
"title": "关键发现",
"description": "分析模拟中的重要发现和趋势"
},
{
"title": "舆情分析",
"description": "分析舆论走向、情绪变化、关键意见领袖"
},
{
"title": "影响评估",
"description": "评估事件的影响范围和程度"
},
{
"title": "建议与展望",
"description": "基于分析结果提出建议"
}
]
}
```
---
### 12. ReportManager (报告管理器)
**文件**: `app/services/report_agent.py`
**功能**: 报告的持久化存储和检索
**核心方法**:
```python
@classmethod
def save_report(cls, report: Report) -> None:
"""
保存报告
同时保存:
- JSON文件（报告元数据）
- Markdown文件（报告内容）
"""
@classmethod
def get_report(cls, report_id: str) -> Optional[Report]:
"""获取报告"""
@classmethod
def get_report_by_simulation(cls, simulation_id: str) -> Optional[Report]:
"""根据模拟ID获取报告"""
@classmethod
def list_reports(
cls,
simulation_id: Optional[str] = None,
limit: int = 50
) -> List[Report]:
"""列出报告"""
@classmethod
def delete_report(cls, report_id: str) -> bool:
"""删除报告"""
```
**存储结构**:
```
uploads/reports/
├── report_abc123.json # 报告元数据
└── report_abc123.md # Markdown报告
```
---
## 工具类
### 1. FileParser (文件解析器)
@ -1962,6 +2578,11 @@ ZEP_API_KEY=z_xxx
# OASIS模拟配置
OASIS_DEFAULT_MAX_ROUNDS=10
# Report Agent配置（可选）
REPORT_AGENT_MAX_TOOL_CALLS=5
REPORT_AGENT_MAX_REFLECTION_ROUNDS=2
REPORT_AGENT_TEMPERATURE=0.5
```
### 配置项说明
@ -1977,6 +2598,9 @@ OASIS_DEFAULT_MAX_ROUNDS=10
| LLM_MODEL_NAME | String | gpt-4o-mini | LLM模型名称 |
| ZEP_API_KEY | String | - | Zep API密钥(必填) |
| OASIS_DEFAULT_MAX_ROUNDS | Integer | 10 | 默认模拟轮数 |
| REPORT_AGENT_MAX_TOOL_CALLS | Integer | 5 | 每章节最大工具调用次数 |
| REPORT_AGENT_MAX_REFLECTION_ROUNDS | Integer | 2 | 最大反思轮数 |
| REPORT_AGENT_TEMPERATURE | Float | 0.5 | 报告生成温度参数 |
---
@ -2102,7 +2726,40 @@ curl -X POST http://localhost:5001/api/simulation/{sim_xxx}/interview/all \
# Step 12: 获取Interview历史
curl http://localhost:5001/api/simulation/{sim_xxx}/interview/history
# Step 13: 关闭模拟环境（优雅退出）
# Step 13: 生成模拟分析报告
curl -X POST http://localhost:5001/api/report/generate \
-H "Content-Type: application/json" \
-d '{
"simulation_id": "sim_xxx"
}'
# 返回: task_id
# Step 14: 查询报告生成进度
curl -X POST http://localhost:5001/api/report/generate/status \
-H "Content-Type: application/json" \
-d '{
"task_id": "task_xxx",
"simulation_id": "sim_xxx"
}'
# 等待status=completed
# Step 15: 获取报告
curl http://localhost:5001/api/report/by-simulation/sim_xxx
# Step 16: 与Report Agent对话
curl -X POST http://localhost:5001/api/report/chat \
-H "Content-Type: application/json" \
-d '{
"simulation_id": "sim_xxx",
"message": "请解释一下舆情的主要趋势"
}'
# Step 17: 下载Markdown报告
curl -O http://localhost:5001/api/report/{report_id}/download
# Step 18: 关闭模拟环境（优雅退出）
curl -X POST http://localhost:5001/api/simulation/close-env \
-H "Content-Type: application/json" \
-d '{
@ -2278,11 +2935,31 @@ MIT License
---
**最后更新**: 2025-12-08
**版本**: v1.2.0
**最后更新**: 2025-12-09
**版本**: v1.3.0
### 更新日志
**v1.3.0 (2025-12-09)**:
- 新增 Report Agent 模拟报告生成功能
- 使用 LangChain + Zep 实现 ReACT 模式
- 自动规划报告大纲，分段生成内容
- 每章节可多次调用Zep检索工具获取信息
- 生成专业的Markdown格式报告
- 新增 Report Agent 对话功能
- 报告完成后可与Agent对话
- Agent自主调用检索工具回答问题
- 新增 Zep 检索工具服务
- 封装图谱搜索、节点读取、边查询等工具
- 支持语义搜索、统计分析、上下文获取
- 新增报告管理接口
- 报告生成、查询、下载、删除
- 报告状态检查（解锁Interview功能）
- 依赖更新
- 新增 langchain>=0.2.0
- 新增 langchain-core>=0.2.0
- 新增 langchain-openai>=0.1.0
**v1.2.0 (2025-12-08)**:
- 新增 Interview 采访功能
- 支持单个Agent采访

3
backend/app/__init__.py
View file

@ -63,9 +63,10 @@ def create_app(config_class=Config):
return response
# 注册蓝图
from .api import graph_bp, simulation_bp
from .api import graph_bp, simulation_bp, report_bp
app.register_blueprint(graph_bp, url_prefix='/api/graph')
app.register_blueprint(simulation_bp, url_prefix='/api/simulation')
app.register_blueprint(report_bp, url_prefix='/api/report')
# 健康检查
@app.route('/health')

2
backend/app/api/__init__.py
View file

@ -6,7 +6,9 @@ from flask import Blueprint
graph_bp = Blueprint('graph', __name__)
simulation_bp = Blueprint('simulation', __name__)
report_bp = Blueprint('report', __name__)
from . import graph # noqa: E402, F401
from . import simulation # noqa: E402, F401
from . import report # noqa: E402, F401

829
backend/app/api/report.py Normal file
View file

@ -0,0 +1,829 @@
"""
Report API路由
提供模拟报告生成、获取、对话等接口
"""
import os
import traceback
import threading
from flask import request, jsonify, send_file
from . import report_bp
from ..config import Config
from ..services.report_agent import ReportAgent, ReportManager, ReportStatus
from ..services.simulation_manager import SimulationManager
from ..models.project import ProjectManager
from ..models.task import TaskManager, TaskStatus
from ..utils.logger import get_logger
logger = get_logger('mirofish.api.report')
# ============== 报告生成接口 ==============
@report_bp.route('/generate', methods=['POST'])
def generate_report():
"""
生成模拟分析报告（异步任务）
这是一个耗时操作，接口会立即返回task_id，
使用 GET /api/report/generate/status 查询进度
请求（JSON）：
{
"simulation_id": "sim_xxxx", // 必填，模拟ID
"force_regenerate": false // 可选，强制重新生成
}
返回：
{
"success": true,
"data": {
"simulation_id": "sim_xxxx",
"task_id": "task_xxxx",
"status": "generating",
"message": "报告生成任务已启动"
}
}
"""
try:
data = request.get_json() or {}
simulation_id = data.get('simulation_id')
if not simulation_id:
return jsonify({
"success": False,
"error": "请提供 simulation_id"
}), 400
force_regenerate = data.get('force_regenerate', False)
# 获取模拟信息
manager = SimulationManager()
state = manager.get_simulation(simulation_id)
if not state:
return jsonify({
"success": False,
"error": f"模拟不存在: {simulation_id}"
}), 404
# 检查是否已有报告
if not force_regenerate:
existing_report = ReportManager.get_report_by_simulation(simulation_id)
if existing_report and existing_report.status == ReportStatus.COMPLETED:
return jsonify({
"success": True,
"data": {
"simulation_id": simulation_id,
"report_id": existing_report.report_id,
"status": "completed",
"message": "报告已存在",
"already_generated": True
}
})
# 获取项目信息
project = ProjectManager.get_project(state.project_id)
if not project:
return jsonify({
"success": False,
"error": f"项目不存在: {state.project_id}"
}), 404
graph_id = state.graph_id or project.graph_id
if not graph_id:
return jsonify({
"success": False,
"error": "缺少图谱ID，请确保已构建图谱"
}), 400
simulation_requirement = project.simulation_requirement
if not simulation_requirement:
return jsonify({
"success": False,
"error": "缺少模拟需求描述"
}), 400
# 创建异步任务
task_manager = TaskManager()
task_id = task_manager.create_task(
task_type="report_generate",
metadata={
"simulation_id": simulation_id,
"graph_id": graph_id
}
)
# 定义后台任务
def run_generate():
try:
task_manager.update_task(
task_id,
status=TaskStatus.PROCESSING,
progress=0,
message="初始化Report Agent..."
)
# 创建Report Agent
agent = ReportAgent(
graph_id=graph_id,
simulation_id=simulation_id,
simulation_requirement=simulation_requirement
)
# 进度回调
def progress_callback(stage, progress, message):
task_manager.update_task(
task_id,
progress=progress,
message=f"[{stage}] {message}"
)
# 生成报告
report = agent.generate_report(progress_callback=progress_callback)
# 保存报告
ReportManager.save_report(report)
if report.status == ReportStatus.COMPLETED:
task_manager.complete_task(
task_id,
result={
"report_id": report.report_id,
"simulation_id": simulation_id,
"status": "completed"
}
)
else:
task_manager.fail_task(task_id, report.error or "报告生成失败")
except Exception as e:
logger.error(f"报告生成失败: {str(e)}")
task_manager.fail_task(task_id, str(e))
# 启动后台线程
thread = threading.Thread(target=run_generate, daemon=True)
thread.start()
return jsonify({
"success": True,
"data": {
"simulation_id": simulation_id,
"task_id": task_id,
"status": "generating",
"message": "报告生成任务已启动，请通过 /api/report/generate/status 查询进度",
"already_generated": False
}
})
except Exception as e:
logger.error(f"启动报告生成任务失败: {str(e)}")
return jsonify({
"success": False,
"error": str(e),
"traceback": traceback.format_exc()
}), 500
@report_bp.route('/generate/status', methods=['POST'])
def get_generate_status():
"""
查询报告生成任务进度
请求（JSON）：
{
"task_id": "task_xxxx", // 可选，generate返回的task_id
"simulation_id": "sim_xxxx" // 可选，模拟ID
}
返回：
{
"success": true,
"data": {
"task_id": "task_xxxx",
"status": "processing|completed|failed",
"progress": 45,
"message": "..."
}
}
"""
try:
data = request.get_json() or {}
task_id = data.get('task_id')
simulation_id = data.get('simulation_id')
# 如果提供了simulation_id，先检查是否已有完成的报告
if simulation_id:
existing_report = ReportManager.get_report_by_simulation(simulation_id)
if existing_report and existing_report.status == ReportStatus.COMPLETED:
return jsonify({
"success": True,
"data": {
"simulation_id": simulation_id,
"report_id": existing_report.report_id,
"status": "completed",
"progress": 100,
"message": "报告已生成",
"already_completed": True
}
})
if not task_id:
return jsonify({
"success": False,
"error": "请提供 task_id 或 simulation_id"
}), 400
task_manager = TaskManager()
task = task_manager.get_task(task_id)
if not task:
return jsonify({
"success": False,
"error": f"任务不存在: {task_id}"
}), 404
return jsonify({
"success": True,
"data": task.to_dict()
})
except Exception as e:
logger.error(f"查询任务状态失败: {str(e)}")
return jsonify({
"success": False,
"error": str(e)
}), 500
# ============== 报告获取接口 ==============
@report_bp.route('/<report_id>', methods=['GET'])
def get_report(report_id: str):
"""
获取报告详情
返回：
{
"success": true,
"data": {
"report_id": "report_xxxx",
"simulation_id": "sim_xxxx",
"status": "completed",
"outline": {...},
"markdown_content": "...",
"created_at": "...",
"completed_at": "..."
}
}
"""
try:
report = ReportManager.get_report(report_id)
if not report:
return jsonify({
"success": False,
"error": f"报告不存在: {report_id}"
}), 404
return jsonify({
"success": True,
"data": report.to_dict()
})
except Exception as e:
logger.error(f"获取报告失败: {str(e)}")
return jsonify({
"success": False,
"error": str(e),
"traceback": traceback.format_exc()
}), 500
@report_bp.route('/by-simulation/<simulation_id>', methods=['GET'])
def get_report_by_simulation(simulation_id: str):
"""
根据模拟ID获取报告
返回：
{
"success": true,
"data": {
"report_id": "report_xxxx",
...
}
}
"""
try:
report = ReportManager.get_report_by_simulation(simulation_id)
if not report:
return jsonify({
"success": False,
"error": f"该模拟暂无报告: {simulation_id}",
"has_report": False
}), 404
return jsonify({
"success": True,
"data": report.to_dict(),
"has_report": True
})
except Exception as e:
logger.error(f"获取报告失败: {str(e)}")
return jsonify({
"success": False,
"error": str(e),
"traceback": traceback.format_exc()
}), 500
@report_bp.route('/list', methods=['GET'])
def list_reports():
"""
列出所有报告
Query参数：
simulation_id: 按模拟ID过滤（可选）
limit: 返回数量限制（默认50）
返回：
{
"success": true,
"data": [...],
"count": 10
}
"""
try:
simulation_id = request.args.get('simulation_id')
limit = request.args.get('limit', 50, type=int)
reports = ReportManager.list_reports(
simulation_id=simulation_id,
limit=limit
)
return jsonify({
"success": True,
"data": [r.to_dict() for r in reports],
"count": len(reports)
})
except Exception as e:
logger.error(f"列出报告失败: {str(e)}")
return jsonify({
"success": False,
"error": str(e),
"traceback": traceback.format_exc()
}), 500
@report_bp.route('/<report_id>/download', methods=['GET'])
def download_report(report_id: str):
"""
下载报告（Markdown格式）
返回Markdown文件
"""
try:
report = ReportManager.get_report(report_id)
if not report:
return jsonify({
"success": False,
"error": f"报告不存在: {report_id}"
}), 404
md_path = ReportManager._get_report_markdown_path(report_id)
if not os.path.exists(md_path):
# 如果MD文件不存在，生成一个临时文件
import tempfile
with tempfile.NamedTemporaryFile(mode='w', suffix='.md', delete=False) as f:
f.write(report.markdown_content)
temp_path = f.name
return send_file(
temp_path,
as_attachment=True,
download_name=f"{report_id}.md"
)
return send_file(
md_path,
as_attachment=True,
download_name=f"{report_id}.md"
)
except Exception as e:
logger.error(f"下载报告失败: {str(e)}")
return jsonify({
"success": False,
"error": str(e),
"traceback": traceback.format_exc()
}), 500
@report_bp.route('/<report_id>', methods=['DELETE'])
def delete_report(report_id: str):
"""删除报告"""
try:
success = ReportManager.delete_report(report_id)
if not success:
return jsonify({
"success": False,
"error": f"报告不存在: {report_id}"
}), 404
return jsonify({
"success": True,
"message": f"报告已删除: {report_id}"
})
except Exception as e:
logger.error(f"删除报告失败: {str(e)}")
return jsonify({
"success": False,
"error": str(e),
"traceback": traceback.format_exc()
}), 500
# ============== Report Agent对话接口 ==============
@report_bp.route('/chat', methods=['POST'])
def chat_with_report_agent():
"""
与Report Agent对话
Report Agent可以在对话中自主调用检索工具来回答问题
请求（JSON）：
{
"simulation_id": "sim_xxxx", // 必填，模拟ID
"message": "请解释一下舆情走向", // 必填，用户消息
"chat_history": [ // 可选，对话历史
{"role": "user", "content": "..."},
{"role": "assistant", "content": "..."}
]
}
返回：
{
"success": true,
"data": {
"response": "Agent回复...",
"tool_calls": [调用的工具列表],
"sources": [信息来源]
}
}
"""
try:
data = request.get_json() or {}
simulation_id = data.get('simulation_id')
message = data.get('message')
chat_history = data.get('chat_history', [])
if not simulation_id:
return jsonify({
"success": False,
"error": "请提供 simulation_id"
}), 400
if not message:
return jsonify({
"success": False,
"error": "请提供 message"
}), 400
# 获取模拟和项目信息
manager = SimulationManager()
state = manager.get_simulation(simulation_id)
if not state:
return jsonify({
"success": False,
"error": f"模拟不存在: {simulation_id}"
}), 404
project = ProjectManager.get_project(state.project_id)
if not project:
return jsonify({
"success": False,
"error": f"项目不存在: {state.project_id}"
}), 404
graph_id = state.graph_id or project.graph_id
if not graph_id:
return jsonify({
"success": False,
"error": "缺少图谱ID"
}), 400
simulation_requirement = project.simulation_requirement or ""
# 创建Agent并进行对话
agent = ReportAgent(
graph_id=graph_id,
simulation_id=simulation_id,
simulation_requirement=simulation_requirement
)
result = agent.chat(message=message, chat_history=chat_history)
return jsonify({
"success": True,
"data": result
})
except Exception as e:
logger.error(f"对话失败: {str(e)}")
return jsonify({
"success": False,
"error": str(e),
"traceback": traceback.format_exc()
}), 500
# ============== 报告进度与分章节接口 ==============
@report_bp.route('/<report_id>/progress', methods=['GET'])
def get_report_progress(report_id: str):
"""
获取报告生成进度（实时）
返回：
{
"success": true,
"data": {
"status": "generating",
"progress": 45,
"message": "正在生成章节: 关键发现",
"current_section": "关键发现",
"completed_sections": ["执行摘要", "模拟背景"],
"updated_at": "2025-12-09T..."
}
}
"""
try:
progress = ReportManager.get_progress(report_id)
if not progress:
return jsonify({
"success": False,
"error": f"报告不存在或进度信息不可用: {report_id}"
}), 404
return jsonify({
"success": True,
"data": progress
})
except Exception as e:
logger.error(f"获取报告进度失败: {str(e)}")
return jsonify({
"success": False,
"error": str(e),
"traceback": traceback.format_exc()
}), 500
@report_bp.route('/<report_id>/sections', methods=['GET'])
def get_report_sections(report_id: str):
"""
获取已生成的章节列表（分章节输出）
前端可以轮询此接口获取已生成的章节内容，无需等待整个报告完成
返回：
{
"success": true,
"data": {
"report_id": "report_xxxx",
"sections": [
{
"filename": "section_01.md",
"section_index": 1,
"content": "## 执行摘要\\n\\n..."
},
...
],
"total_sections": 3,
"is_complete": false
}
}
"""
try:
sections = ReportManager.get_generated_sections(report_id)
# 获取报告状态
report = ReportManager.get_report(report_id)
is_complete = report is not None and report.status == ReportStatus.COMPLETED
return jsonify({
"success": True,
"data": {
"report_id": report_id,
"sections": sections,
"total_sections": len(sections),
"is_complete": is_complete
}
})
except Exception as e:
logger.error(f"获取章节列表失败: {str(e)}")
return jsonify({
"success": False,
"error": str(e),
"traceback": traceback.format_exc()
}), 500
@report_bp.route('/<report_id>/section/<int:section_index>', methods=['GET'])
def get_single_section(report_id: str, section_index: int):
"""
获取单个章节内容
返回：
{
"success": true,
"data": {
"filename": "section_01.md",
"content": "## 执行摘要\\n\\n..."
}
}
"""
try:
section_path = ReportManager._get_section_path(report_id, section_index)
if not os.path.exists(section_path):
return jsonify({
"success": False,
"error": f"章节不存在: section_{section_index:02d}.md"
}), 404
with open(section_path, 'r', encoding='utf-8') as f:
content = f.read()
return jsonify({
"success": True,
"data": {
"filename": f"section_{section_index:02d}.md",
"section_index": section_index,
"content": content
}
})
except Exception as e:
logger.error(f"获取章节内容失败: {str(e)}")
return jsonify({
"success": False,
"error": str(e),
"traceback": traceback.format_exc()
}), 500
# ============== 报告状态检查接口 ==============
@report_bp.route('/check/<simulation_id>', methods=['GET'])
def check_report_status(simulation_id: str):
"""
检查模拟是否有报告，以及报告状态
用于前端判断是否解锁Interview功能
返回：
{
"success": true,
"data": {
"simulation_id": "sim_xxxx",
"has_report": true,
"report_status": "completed",
"report_id": "report_xxxx",
"interview_unlocked": true
}
}
"""
try:
report = ReportManager.get_report_by_simulation(simulation_id)
has_report = report is not None
report_status = report.status.value if report else None
report_id = report.report_id if report else None
# 只有报告完成后才解锁interview
interview_unlocked = has_report and report.status == ReportStatus.COMPLETED
return jsonify({
"success": True,
"data": {
"simulation_id": simulation_id,
"has_report": has_report,
"report_status": report_status,
"report_id": report_id,
"interview_unlocked": interview_unlocked
}
})
except Exception as e:
logger.error(f"检查报告状态失败: {str(e)}")
return jsonify({
"success": False,
"error": str(e),
"traceback": traceback.format_exc()
}), 500
# ============== 工具调用接口（供调试使用）==============
@report_bp.route('/tools/search', methods=['POST'])
def search_graph_tool():
"""
图谱搜索工具接口（供调试使用）
请求（JSON）：
{
"graph_id": "mirofish_xxxx",
"query": "搜索查询",
"limit": 10
}
"""
try:
data = request.get_json() or {}
graph_id = data.get('graph_id')
query = data.get('query')
limit = data.get('limit', 10)
if not graph_id or not query:
return jsonify({
"success": False,
"error": "请提供 graph_id 和 query"
}), 400
from ..services.zep_tools import ZepToolsService
tools = ZepToolsService()
result = tools.search_graph(
graph_id=graph_id,
query=query,
limit=limit
)
return jsonify({
"success": True,
"data": result.to_dict()
})
except Exception as e:
logger.error(f"图谱搜索失败: {str(e)}")
return jsonify({
"success": False,
"error": str(e),
"traceback": traceback.format_exc()
}), 500
@report_bp.route('/tools/statistics', methods=['POST'])
def get_graph_statistics_tool():
"""
图谱统计工具接口（供调试使用）
请求（JSON）：
{
"graph_id": "mirofish_xxxx"
}
"""
try:
data = request.get_json() or {}
graph_id = data.get('graph_id')
if not graph_id:
return jsonify({
"success": False,
"error": "请提供 graph_id"
}), 400
from ..services.zep_tools import ZepToolsService
tools = ZepToolsService()
result = tools.get_graph_statistics(graph_id)
return jsonify({
"success": True,
"data": result
})
except Exception as e:
logger.error(f"获取图谱统计失败: {str(e)}")
return jsonify({
"success": False,
"error": str(e),
"traceback": traceback.format_exc()
}), 500

5
backend/app/config.py
View file

@ -58,6 +58,11 @@ class Config:
'TREND', 'REFRESH', 'DO_NOTHING', 'FOLLOW', 'MUTE'
]
# Report Agent配置
REPORT_AGENT_MAX_TOOL_CALLS = int(os.environ.get('REPORT_AGENT_MAX_TOOL_CALLS', '5'))
REPORT_AGENT_MAX_REFLECTION_ROUNDS = int(os.environ.get('REPORT_AGENT_MAX_REFLECTION_ROUNDS', '2'))
REPORT_AGENT_TEMPERATURE = float(os.environ.get('REPORT_AGENT_TEMPERATURE', '0.5'))
@classmethod
def validate(cls):
"""验证必要配置"""

1296
backend/app/services/report_agent.py Normal file
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621
backend/app/services/zep_tools.py Normal file
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@ -0,0 +1,621 @@
"""
Zep检索工具服务
封装图谱搜索、节点读取、边查询等工具，供Report Agent使用
"""
import time
from typing import Dict, Any, List, Optional
from dataclasses import dataclass
from zep_cloud.client import Zep
from ..config import Config
from ..utils.logger import get_logger
logger = get_logger('mirofish.zep_tools')
@dataclass
class SearchResult:
"""搜索结果"""
facts: List[str]
edges: List[Dict[str, Any]]
nodes: List[Dict[str, Any]]
query: str
total_count: int
def to_dict(self) -> Dict[str, Any]:
return {
"facts": self.facts,
"edges": self.edges,
"nodes": self.nodes,
"query": self.query,
"total_count": self.total_count
}
def to_text(self) -> str:
"""转换为文本格式，供LLM理解"""
text_parts = [f"搜索查询: {self.query}", f"找到 {self.total_count} 条相关信息"]
if self.facts:
text_parts.append("\n### 相关事实:")
for i, fact in enumerate(self.facts, 1):
text_parts.append(f"{i}. {fact}")
return "\n".join(text_parts)
@dataclass
class NodeInfo:
"""节点信息"""
uuid: str
name: str
labels: List[str]
summary: str
attributes: Dict[str, Any]
def to_dict(self) -> Dict[str, Any]:
return {
"uuid": self.uuid,
"name": self.name,
"labels": self.labels,
"summary": self.summary,
"attributes": self.attributes
}
def to_text(self) -> str:
"""转换为文本格式"""
entity_type = next((l for l in self.labels if l not in ["Entity", "Node"]), "未知类型")
return f"实体: {self.name} (类型: {entity_type})\n摘要: {self.summary}"
@dataclass
class EdgeInfo:
"""边信息"""
uuid: str
name: str
fact: str
source_node_uuid: str
target_node_uuid: str
source_node_name: Optional[str] = None
target_node_name: Optional[str] = None
def to_dict(self) -> Dict[str, Any]:
return {
"uuid": self.uuid,
"name": self.name,
"fact": self.fact,
"source_node_uuid": self.source_node_uuid,
"target_node_uuid": self.target_node_uuid,
"source_node_name": self.source_node_name,
"target_node_name": self.target_node_name
}
def to_text(self) -> str:
"""转换为文本格式"""
source = self.source_node_name or self.source_node_uuid[:8]
target = self.target_node_name or self.target_node_uuid[:8]
return f"关系: {source} --[{self.name}]--> {target}\n事实: {self.fact}"
class ZepToolsService:
"""
Zep检索工具服务
提供多种图谱检索工具，可以被Report Agent调用：
1. search_graph - 图谱语义搜索
2. get_all_nodes - 获取图谱所有节点
3. get_all_edges - 获取图谱所有边
4. get_node_detail - 获取节点详细信息
5. get_node_edges - 获取节点相关的边
6. get_entities_by_type - 按类型获取实体
7. get_entity_summary - 获取实体的关系摘要
"""
# 重试配置
MAX_RETRIES = 3
RETRY_DELAY = 2.0
def __init__(self, api_key: Optional[str] = None):
self.api_key = api_key or Config.ZEP_API_KEY
if not self.api_key:
raise ValueError("ZEP_API_KEY 未配置")
self.client = Zep(api_key=self.api_key)
logger.info("ZepToolsService 初始化完成")
def _call_with_retry(self, func, operation_name: str, max_retries: int = None):
"""带重试机制的API调用"""
max_retries = max_retries or self.MAX_RETRIES
last_exception = None
delay = self.RETRY_DELAY
for attempt in range(max_retries):
try:
return func()
except Exception as e:
last_exception = e
if attempt < max_retries - 1:
logger.warning(
f"Zep {operation_name} 第 {attempt + 1} 次尝试失败: {str(e)[:100]}, "
f"{delay:.1f}秒后重试..."
)
time.sleep(delay)
delay *= 2
else:
logger.error(f"Zep {operation_name} 在 {max_retries} 次尝试后仍失败: {str(e)}")
raise last_exception
def search_graph(
self,
graph_id: str,
query: str,
limit: int = 10,
scope: str = "edges"
) -> SearchResult:
"""
图谱语义搜索
使用混合搜索（语义+BM25）在图谱中搜索相关信息。
如果Zep Cloud的search API不可用，则降级为本地关键词匹配。
Args:
graph_id: 图谱ID (Standalone Graph)
query: 搜索查询
limit: 返回结果数量
scope: 搜索范围，"edges" 或 "nodes"
Returns:
SearchResult: 搜索结果
"""
logger.info(f"图谱搜索: graph_id={graph_id}, query={query[:50]}...")
# 尝试使用Zep Cloud Search API
try:
search_results = self._call_with_retry(
func=lambda: self.client.graph.search(
graph_id=graph_id,
query=query,
limit=limit,
scope=scope,
reranker="cross_encoder"
),
operation_name=f"图谱搜索(graph={graph_id})"
)
facts = []
edges = []
nodes = []
# 解析边搜索结果
if hasattr(search_results, 'edges') and search_results.edges:
for edge in search_results.edges:
if hasattr(edge, 'fact') and edge.fact:
facts.append(edge.fact)
edges.append({
"uuid": getattr(edge, 'uuid_', None) or getattr(edge, 'uuid', ''),
"name": getattr(edge, 'name', ''),
"fact": getattr(edge, 'fact', ''),
"source_node_uuid": getattr(edge, 'source_node_uuid', ''),
"target_node_uuid": getattr(edge, 'target_node_uuid', ''),
})
# 解析节点搜索结果
if hasattr(search_results, 'nodes') and search_results.nodes:
for node in search_results.nodes:
nodes.append({
"uuid": getattr(node, 'uuid_', None) or getattr(node, 'uuid', ''),
"name": getattr(node, 'name', ''),
"labels": getattr(node, 'labels', []),
"summary": getattr(node, 'summary', ''),
})
# 节点摘要也算作事实
if hasattr(node, 'summary') and node.summary:
facts.append(f"[{node.name}]: {node.summary}")
logger.info(f"搜索完成: 找到 {len(facts)} 条相关事实")
return SearchResult(
facts=facts,
edges=edges,
nodes=nodes,
query=query,
total_count=len(facts)
)
except Exception as e:
logger.warning(f"Zep Search API失败，降级为本地搜索: {str(e)}")
# 降级：使用本地关键词匹配搜索
return self._local_search(graph_id, query, limit, scope)
def _local_search(
self,
graph_id: str,
query: str,
limit: int = 10,
scope: str = "edges"
) -> SearchResult:
"""
本地关键词匹配搜索（作为Zep Search API的降级方案）
获取所有边/节点，然后在本地进行关键词匹配
Args:
graph_id: 图谱ID
query: 搜索查询
limit: 返回结果数量
scope: 搜索范围
Returns:
SearchResult: 搜索结果
"""
logger.info(f"使用本地搜索: query={query[:30]}...")
facts = []
edges_result = []
nodes_result = []
# 提取查询关键词（简单分词）
query_lower = query.lower()
keywords = [w.strip() for w in query_lower.replace(',', ' ').replace('，', ' ').split() if len(w.strip()) > 1]
def match_score(text: str) -> int:
"""计算文本与查询的匹配分数"""
if not text:
return 0
text_lower = text.lower()
# 完全匹配查询
if query_lower in text_lower:
return 100
# 关键词匹配
score = 0
for keyword in keywords:
if keyword in text_lower:
score += 10
return score
try:
if scope in ["edges", "both"]:
# 获取所有边并匹配
all_edges = self.get_all_edges(graph_id)
scored_edges = []
for edge in all_edges:
score = match_score(edge.fact) + match_score(edge.name)
if score > 0:
scored_edges.append((score, edge))
# 按分数排序
scored_edges.sort(key=lambda x: x[0], reverse=True)
for score, edge in scored_edges[:limit]:
if edge.fact:
facts.append(edge.fact)
edges_result.append({
"uuid": edge.uuid,
"name": edge.name,
"fact": edge.fact,
"source_node_uuid": edge.source_node_uuid,
"target_node_uuid": edge.target_node_uuid,
})
if scope in ["nodes", "both"]:
# 获取所有节点并匹配
all_nodes = self.get_all_nodes(graph_id)
scored_nodes = []
for node in all_nodes:
score = match_score(node.name) + match_score(node.summary)
if score > 0:
scored_nodes.append((score, node))
scored_nodes.sort(key=lambda x: x[0], reverse=True)
for score, node in scored_nodes[:limit]:
nodes_result.append({
"uuid": node.uuid,
"name": node.name,
"labels": node.labels,
"summary": node.summary,
})
if node.summary:
facts.append(f"[{node.name}]: {node.summary}")
logger.info(f"本地搜索完成: 找到 {len(facts)} 条相关事实")
except Exception as e:
logger.error(f"本地搜索失败: {str(e)}")
return SearchResult(
facts=facts,
edges=edges_result,
nodes=nodes_result,
query=query,
total_count=len(facts)
)
def get_all_nodes(self, graph_id: str) -> List[NodeInfo]:
"""
获取图谱的所有节点
Args:
graph_id: 图谱ID
Returns:
节点列表
"""
logger.info(f"获取图谱 {graph_id} 的所有节点...")
nodes = self._call_with_retry(
func=lambda: self.client.graph.node.get_by_graph_id(graph_id=graph_id),
operation_name=f"获取节点(graph={graph_id})"
)
result = []
for node in nodes:
result.append(NodeInfo(
uuid=getattr(node, 'uuid_', None) or getattr(node, 'uuid', ''),
name=node.name or "",
labels=node.labels or [],
summary=node.summary or "",
attributes=node.attributes or {}
))
logger.info(f"获取到 {len(result)} 个节点")
return result
def get_all_edges(self, graph_id: str) -> List[EdgeInfo]:
"""
获取图谱的所有边
Args:
graph_id: 图谱ID
Returns:
边列表
"""
logger.info(f"获取图谱 {graph_id} 的所有边...")
edges = self._call_with_retry(
func=lambda: self.client.graph.edge.get_by_graph_id(graph_id=graph_id),
operation_name=f"获取边(graph={graph_id})"
)
result = []
for edge in edges:
result.append(EdgeInfo(
uuid=getattr(edge, 'uuid_', None) or getattr(edge, 'uuid', ''),
name=edge.name or "",
fact=edge.fact or "",
source_node_uuid=edge.source_node_uuid or "",
target_node_uuid=edge.target_node_uuid or ""
))
logger.info(f"获取到 {len(result)} 条边")
return result
def get_node_detail(self, node_uuid: str) -> Optional[NodeInfo]:
"""
获取单个节点的详细信息
Args:
node_uuid: 节点UUID
Returns:
节点信息或None
"""
logger.info(f"获取节点详情: {node_uuid[:8]}...")
try:
node = self._call_with_retry(
func=lambda: self.client.graph.node.get(uuid_=node_uuid),
operation_name=f"获取节点详情(uuid={node_uuid[:8]}...)"
)
if not node:
return None
return NodeInfo(
uuid=getattr(node, 'uuid_', None) or getattr(node, 'uuid', ''),
name=node.name or "",
labels=node.labels or [],
summary=node.summary or "",
attributes=node.attributes or {}
)
except Exception as e:
logger.error(f"获取节点详情失败: {str(e)}")
return None
def get_node_edges(self, graph_id: str, node_uuid: str) -> List[EdgeInfo]:
"""
获取节点相关的所有边
通过获取图谱所有边，然后过滤出与指定节点相关的边
Args:
graph_id: 图谱ID
node_uuid: 节点UUID
Returns:
边列表
"""
logger.info(f"获取节点 {node_uuid[:8]}... 的相关边")
try:
# 获取图谱所有边，然后过滤
all_edges = self.get_all_edges(graph_id)
result = []
for edge in all_edges:
# 检查边是否与指定节点相关（作为源或目标）
if edge.source_node_uuid == node_uuid or edge.target_node_uuid == node_uuid:
result.append(edge)
logger.info(f"找到 {len(result)} 条与节点相关的边")
return result
except Exception as e:
logger.warning(f"获取节点边失败: {str(e)}")
return []
def get_entities_by_type(
self,
graph_id: str,
entity_type: str
) -> List[NodeInfo]:
"""
按类型获取实体
Args:
graph_id: 图谱ID
entity_type: 实体类型（如 Student, PublicFigure 等）
Returns:
符合类型的实体列表
"""
logger.info(f"获取类型为 {entity_type} 的实体...")
all_nodes = self.get_all_nodes(graph_id)
filtered = []
for node in all_nodes:
# 检查labels是否包含指定类型
if entity_type in node.labels:
filtered.append(node)
logger.info(f"找到 {len(filtered)} 个 {entity_type} 类型的实体")
return filtered
def get_entity_summary(
self,
graph_id: str,
entity_name: str
) -> Dict[str, Any]:
"""
获取指定实体的关系摘要
搜索与该实体相关的所有信息，并生成摘要
Args:
graph_id: 图谱ID
entity_name: 实体名称
Returns:
实体摘要信息
"""
logger.info(f"获取实体 {entity_name} 的关系摘要...")
# 先搜索该实体相关的信息
search_result = self.search_graph(
graph_id=graph_id,
query=entity_name,
limit=20
)
# 尝试在所有节点中找到该实体
all_nodes = self.get_all_nodes(graph_id)
entity_node = None
for node in all_nodes:
if node.name.lower() == entity_name.lower():
entity_node = node
break
related_edges = []
if entity_node:
# 传入graph_id参数
related_edges = self.get_node_edges(graph_id, entity_node.uuid)
return {
"entity_name": entity_name,
"entity_info": entity_node.to_dict() if entity_node else None,
"related_facts": search_result.facts,
"related_edges": [e.to_dict() for e in related_edges],
"total_relations": len(related_edges)
}
def get_graph_statistics(self, graph_id: str) -> Dict[str, Any]:
"""
获取图谱的统计信息
Args:
graph_id: 图谱ID
Returns:
统计信息
"""
logger.info(f"获取图谱 {graph_id} 的统计信息...")
nodes = self.get_all_nodes(graph_id)
edges = self.get_all_edges(graph_id)
# 统计实体类型分布
entity_types = {}
for node in nodes:
for label in node.labels:
if label not in ["Entity", "Node"]:
entity_types[label] = entity_types.get(label, 0) + 1
# 统计关系类型分布
relation_types = {}
for edge in edges:
relation_types[edge.name] = relation_types.get(edge.name, 0) + 1
return {
"graph_id": graph_id,
"total_nodes": len(nodes),
"total_edges": len(edges),
"entity_types": entity_types,
"relation_types": relation_types
}
def get_simulation_context(
self,
graph_id: str,
simulation_requirement: str,
limit: int = 30
) -> Dict[str, Any]:
"""
获取模拟相关的上下文信息
综合搜索与模拟需求相关的所有信息
Args:
graph_id: 图谱ID
simulation_requirement: 模拟需求描述
limit: 每类信息的数量限制
Returns:
模拟上下文信息
"""
logger.info(f"获取模拟上下文: {simulation_requirement[:50]}...")
# 搜索与模拟需求相关的信息
search_result = self.search_graph(
graph_id=graph_id,
query=simulation_requirement,
limit=limit
)
# 获取图谱统计
stats = self.get_graph_statistics(graph_id)
# 获取所有实体节点
all_nodes = self.get_all_nodes(graph_id)
# 筛选有实际类型的实体（非纯Entity节点）
entities = []
for node in all_nodes:
custom_labels = [l for l in node.labels if l not in ["Entity", "Node"]]
if custom_labels:
entities.append({
"name": node.name,
"type": custom_labels[0],
"summary": node.summary
})
return {
"simulation_requirement": simulation_requirement,
"related_facts": search_result.facts,
"graph_statistics": stats,
"entities": entities[:limit], # 限制数量
"total_entities": len(entities)
}

5
backend/requirements.txt
View file

@ -24,3 +24,8 @@ werkzeug>=3.0.0
oasis-ai>=0.1.0
camel-ai>=0.2.0
# LangChain框架（用于Report Agent）
langchain>=0.2.0
langchain-core>=0.2.0
langchain-openai>=0.1.0