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<div align="center">
# 📊 Weibo Public Opinion Multi-Agent Analysis System
<img src="static/image/logo_compressed.png" alt="Weibo Public Opinion Analysis System Logo" width="600">
[![GitHub Stars](https://img.shields.io/github/stars/666ghj/Weibo_PublicOpinion_AnalysisSystem?style=flat-square)](https://github.com/666ghj/Weibo_PublicOpinion_AnalysisSystem/stargazers)
[![GitHub Forks](https://img.shields.io/github/forks/666ghj/Weibo_PublicOpinion_AnalysisSystem?style=flat-square)](https://github.com/666ghj/Weibo_PublicOpinion_AnalysisSystem/network)
[![GitHub Issues](https://img.shields.io/github/issues/666ghj/Weibo_PublicOpinion_AnalysisSystem?style=flat-square)](https://github.com/666ghj/Weibo_PublicOpinion_AnalysisSystem/issues)
[![GitHub License](https://img.shields.io/github/license/666ghj/Weibo_PublicOpinion_AnalysisSystem?style=flat-square)](https://github.com/666ghj/Weibo_PublicOpinion_AnalysisSystem/blob/main/LICENSE)
[English](./README-EN.md) | [中文文档](./README.md)
</div>
<div align="center">
<img src="static/image/banner_compressed.png" alt="banner" width="800">
</div>
## 📝 Project Overview
**Weibo Public Opinion Multi-Agent Analysis System** is an innovative public opinion analysis platform built from scratch, utilizing multi-agent collaborative architecture to provide accurate, real-time, and comprehensive Weibo public opinion monitoring and analysis services. The system achieves full-process automation from data collection and sentiment analysis to report generation through the collaboration of five specialized AI agents.
### 🚀 Key Features
- **Multi-Agent Collaborative Architecture**: 5 specialized agents working together to complete the full process of public opinion analysis
- **Comprehensive Data Collection**: Integrating Weibo crawlers, news search, multimedia content, and other multi-dimensional data sources
- **Deep Sentiment Analysis**: Precise multilingual sentiment recognition based on fine-tuned BERT/GPT-2/Qwen models
- **Intelligent Report Generation**: Automatically generate structured HTML analysis reports with custom template support
- **Agent Forum Communication**: ForumEngine provides information sharing and collaborative decision-making platform for agents
- **High-Performance Asynchronous Processing**: Support concurrent processing of multiple public opinion tasks with real-time status monitoring
- **Cloud Data Support**: Convenient cloud database service with 100,000+ daily real data
## 🏗️ System Architecture
### Overall Architecture Diagram
```mermaid
graph TB
subgraph "Frontend Display Layer"
UI[Web Interface<br/>Flask + Streamlit]
end
subgraph "Multi-Agent Collaboration Layer"
QE[QueryEngine<br/>News Search Agent]
ME[MediaEngine<br/>Multimedia Search Agent]
IE[InsightEngine<br/>Deep Insight Agent]
RE[ReportEngine<br/>Report Generation Agent]
Forum[ForumEngine<br/>Agent Forum Communication Center]
end
subgraph "Data Processing Layer"
MS[MindSpider<br/>Weibo Crawler System]
SA[SentimentAnalysis<br/>Sentiment Analysis Model Collection]
DB[(MySQL<br/>Database)]
end
subgraph "External Service Layer"
LLM[LLM API<br/>DeepSeek/Kimi/Gemini]
Search[Search API<br/>Tavily/Bocha]
end
UI --> QE
UI --> ME
UI --> IE
UI --> RE
QE --> Search
ME --> Search
IE --> MS
IE --> SA
QE --> LLM
ME --> LLM
IE --> LLM
RE --> LLM
MS --> DB
SA --> DB
%% Agent Forum Communication Mechanism
QE <--> Forum
ME <--> Forum
IE <--> Forum
RE <--> Forum
```
### Agent Collaboration Workflow
The system's core workflow is based on multi-agent collaboration:
1. **QueryEngine (News Query Agent)**: Uses Tavily API to search authoritative news reports, providing official information sources
2. **MediaEngine (Multimedia Search Agent)**: Conducts multimodal content search through Bocha API to gather social media perspectives
3. **InsightEngine (Deep Insight Agent)**: Queries local Weibo database, combines multiple sentiment analysis models for deep analysis
4. **ForumEngine (Forum Monitoring Agent)**: Real-time monitoring of agent log outputs, extracts key information and promotes collaboration
5. **ReportEngine (Report Generation Agent)**: Based on analysis results from all agents, uses Gemini LLM to generate comprehensive HTML reports
### Project Code Structure
```
Weibo_PublicOpinion_AnalysisSystem/
├── QueryEngine/ # News Query Engine Agent
│ ├── agent.py # Agent main logic
│ ├── llms/ # LLM interface wrapper
│ ├── nodes/ # Processing nodes
│ ├── tools/ # Search tools
│ └── utils/ # Utility functions
├── MediaEngine/ # Multimedia Search Engine Agent
│ ├── agent.py # Agent main logic
│ ├── llms/ # LLM interfaces
│ ├── tools/ # Search tools
│ └── ... # Other modules
├── InsightEngine/ # Data Insight Engine Agent
│ ├── agent.py # Agent main logic
│ ├── llms/ # LLM interface wrapper
│ │ ├── deepseek.py # DeepSeek API
│ │ ├── kimi.py # Kimi API
│ │ ├── openai_llm.py # OpenAI format API
│ │ └── base.py # LLM base class
│ ├── nodes/ # Processing nodes
│ │ ├── first_search_node.py # First search node
│ │ ├── reflection_node.py # Reflection node
│ │ ├── summary_nodes.py # Summary nodes
│ │ ├── search_node.py # Search node
│ │ ├── sentiment_node.py # Sentiment analysis node
│ │ └── insight_node.py # Insight generation node
│ ├── tools/ # Database query and analysis tools
│ │ ├── media_crawler_db.py # Database query tool
│ │ └── sentiment_analyzer.py # Sentiment analysis integration tool
│ ├── state/ # State management
│ │ ├── __init__.py
│ │ └── state.py # Agent state definition
│ ├── prompts/ # Prompt templates
│ │ ├── __init__.py
│ │ └── prompts.py # Various prompts
│ └── utils/ # Utility functions
│ ├── __init__.py
│ ├── config.py # Configuration management
│ └── helpers.py # Helper functions
├── ReportEngine/ # Report Generation Engine Agent
│ ├── agent.py # Agent main logic
│ ├── llms/ # LLM interfaces
│ │ └── gemini.py # Gemini API dedicated
│ ├── nodes/ # Report generation nodes
│ │ ├── template_selection.py # Template selection node
│ │ └── html_generation.py # HTML generation node
│ ├── report_template/ # Report template library
│ │ ├── 社会公共热点事件分析.md
│ │ ├── 商业品牌舆情监测.md
│ │ └── ... # More templates
│ └── flask_interface.py # Flask API interface
├── ForumEngine/ # Forum Communication Engine Agent
│ └── monitor.py # Log monitoring and forum management
├── MindSpider/ # Weibo Crawler System
│ ├── main.py # Crawler main program
│ ├── BroadTopicExtraction/ # Topic extraction module
│ │ ├── get_today_news.py # Today's news fetching
│ │ └── topic_extractor.py # Topic extractor
│ ├── DeepSentimentCrawling/ # Deep sentiment crawling
│ │ ├── MediaCrawler/ # Media crawler core
│ │ └── platform_crawler.py # Platform crawler management
│ └── schema/ # Database schema
│ └── init_database.py # Database initialization
├── SentimentAnalysisModel/ # Sentiment Analysis Model Collection
│ ├── WeiboSentiment_Finetuned/ # Fine-tuned BERT/GPT-2 models
│ ├── WeiboMultilingualSentiment/ # Multilingual sentiment analysis
│ ├── WeiboSentiment_SmallQwen/ # Small Qwen model
│ └── WeiboSentiment_MachineLearning/ # Traditional machine learning methods
├── SingleEngineApp/ # Individual Agent Streamlit apps
│ ├── query_engine_streamlit_app.py
│ ├── media_engine_streamlit_app.py
│ └── insight_engine_streamlit_app.py
├── templates/ # Flask templates
│ └── index.html # Main interface template
├── static/ # Static resources
├── logs/ # Runtime log directory
├── app.py # Flask main application entry
├── config.py # Global configuration file
└── requirements.txt # Python dependency list
```
## 🚀 Quick Start
### System Requirements
- **Operating System**: Windows 10/11 (Linux/macOS also supported)
- **Python Version**: 3.11+
- **Conda**: Anaconda or Miniconda
- **Database**: MySQL 8.0+ (or choose our cloud database service)
- **Memory**: 8GB+ recommended
### 1. Create Conda Environment
```bash
# Create conda environment named pytorch_python11
conda create -n pytorch_python11 python=3.11
conda activate pytorch_python11
```
### 2. Install Dependencies
```bash
# Install basic dependencies
pip install -r requirements.txt
# If you need local sentiment analysis functionality, install PyTorch
# CPU version
pip install torch torchvision torchaudio
# CUDA 11.8 version (if you have GPU)
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
# Install transformers and other AI-related dependencies
pip install transformers scikit-learn xgboost
```
### 3. Install Playwright Browser Drivers
```bash
# Install browser drivers (for crawler functionality)
playwright install chromium
```
### 4. System Configuration
#### 4.1 Configure API Keys
Edit the `config.py` file and fill in your API keys:
```python
# MySQL Database Configuration
DB_HOST = "localhost"
DB_PORT = 3306
DB_USER = "your_username"
DB_PASSWORD = "your_password"
DB_NAME = "weibo_analysis"
DB_CHARSET = "utf8mb4"
# DeepSeek API (Apply at: https://www.deepseek.com/)
DEEPSEEK_API_KEY = "your_deepseek_api_key"
# Tavily Search API (Apply at: https://www.tavily.com/)
TAVILY_API_KEY = "your_tavily_api_key"
# Kimi API (Apply at: https://www.kimi.com/)
KIMI_API_KEY = "your_kimi_api_key"
# Gemini API (Apply at: https://api.chataiapi.com/)
GEMINI_API_KEY = "your_gemini_api_key"
# Bocha Search API (Apply at: https://open.bochaai.com/)
BOCHA_Web_Search_API_KEY = "your_bocha_api_key"
# Silicon Flow API (Apply at: https://siliconflow.cn/)
GUIJI_QWEN3_API_KEY = "your_guiji_api_key"
```
#### 4.2 Database Initialization
**Option 1: Use Local Database**
```bash
# Local MySQL database initialization
cd MindSpider
python schema/init_database.py
```
**Option 2: Use Cloud Database Service (Recommended)**
We provide convenient cloud database service with 100,000+ daily real Weibo data, currently **free application** during the promotion period!
- Real Weibo data, updated in real-time
- Pre-processed sentiment annotation data
- Multi-dimensional tag classification
- High-availability cloud service
- Professional technical support
**Contact us to apply for free cloud database access: 📧 670939375@qq.com**
### 5. Launch System
#### 5.1 Complete System Launch (Recommended)
```bash
# In project root directory, activate conda environment
conda activate pytorch_python11
# Start main application (automatically starts all agents)
python app.py
```
Visit http://localhost:5000 to use the complete system
#### 5.2 Launch Individual Agents
```bash
# Start QueryEngine
streamlit run SingleEngineApp/query_engine_streamlit_app.py --server.port 8503
# Start MediaEngine
streamlit run SingleEngineApp/media_engine_streamlit_app.py --server.port 8502
# Start InsightEngine
streamlit run SingleEngineApp/insight_engine_streamlit_app.py --server.port 8501
```
#### 5.3 Standalone Crawler System
```bash
# Enter crawler directory
cd MindSpider
# Project initialization
python main.py --setup
# Run complete crawler workflow
python main.py --complete --date 2024-01-20
# Run topic extraction only
python main.py --broad-topic --date 2024-01-20
# Run deep crawling only
python main.py --deep-sentiment --platforms xhs dy wb
```
## 💾 Database Configuration
### Local Database Configuration
1. **Install MySQL 8.0+**
2. **Create Database**:
```sql
CREATE DATABASE weibo_analysis CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci;
```
3. **Run Initialization Script**:
```bash
cd MindSpider
python schema/init_database.py
```
### Auto-Crawling Configuration
Configure automatic crawling tasks for continuous data updates:
```python
# Configure crawler parameters in MindSpider/config.py
CRAWLER_CONFIG = {
'max_pages': 200, # Maximum pages to crawl
'delay': 1, # Request delay (seconds)
'timeout': 30, # Timeout (seconds)
'platforms': ['xhs', 'dy', 'wb', 'bili'], # Crawling platforms
'daily_keywords': 100, # Daily keywords count
'max_notes_per_keyword': 50, # Max content per keyword
'use_proxy': False, # Whether to use proxy
}
```
### Cloud Database Service (Recommended)
**Why Choose Our Cloud Database Service?**
- **Rich Data Sources**: 100,000+ daily real Weibo data covering hot topics across all industries
- **High-Quality Annotations**: Professional team manually annotated sentiment data with 95%+ accuracy
- **Multi-Dimensional Analysis**: Including topic classification, sentiment tendency, influence scoring and other multi-dimensional tags
- **Real-Time Updates**: 24/7 continuous data collection ensuring timeliness
- **Technical Support**: Professional team providing technical support and customization services
**Application Method**:
📧 Email Contact: 670939375@qq.com
📝 Email Subject: Apply for Weibo Public Opinion Cloud Database Access
📝 Email Content: Please describe your use case and expected data volume requirements
**Promotion Period Benefits**:
- Free basic cloud database access
- Free technical support and deployment guidance
- Priority access to new features
## ⚙️ Advanced Configuration
### Modify Key Parameters
#### Agent Configuration Parameters
Each agent has dedicated configuration files that can be adjusted according to needs:
```python
# QueryEngine/utils/config.py
class Config:
max_reflections = 2 # Reflection rounds
max_search_results = 15 # Maximum search results
max_content_length = 8000 # Maximum content length
# MediaEngine/utils/config.py
class Config:
comprehensive_search_limit = 10 # Comprehensive search limit
web_search_limit = 15 # Web search limit
# InsightEngine/utils/config.py
class Config:
default_search_topic_globally_limit = 200 # Global search limit
default_get_comments_limit = 500 # Comment retrieval limit
max_search_results_for_llm = 50 # Max results for LLM
```
#### Sentiment Analysis Model Configuration
```python
# InsightEngine/tools/sentiment_analyzer.py
SENTIMENT_CONFIG = {
'model_type': 'multilingual', # Options: 'bert', 'multilingual', 'qwen'
'confidence_threshold': 0.8, # Confidence threshold
'batch_size': 32, # Batch size
'max_sequence_length': 512, # Max sequence length
}
```
### Integrate Different LLM Models
The system supports multiple LLM providers, switchable in each agent's configuration:
```python
# Configure in each Engine's utils/config.py
class Config:
default_llm_provider = "deepseek" # Options: "deepseek", "openai", "kimi", "gemini"
# DeepSeek configuration
deepseek_api_key = "your_api_key"
deepseek_model = "deepseek-chat"
# OpenAI compatible configuration
openai_api_key = "your_api_key"
openai_model = "gpt-3.5-turbo"
openai_base_url = "https://api.openai.com/v1"
# Kimi configuration
kimi_api_key = "your_api_key"
kimi_model = "moonshot-v1-8k"
# Gemini configuration
gemini_api_key = "your_api_key"
gemini_model = "gemini-pro"
```
### Change Sentiment Analysis Models
The system integrates multiple sentiment analysis methods, selectable based on needs:
#### 1. BERT-based Fine-tuned Model (Highest Accuracy)
```bash
# Use BERT Chinese model
cd SentimentAnalysisModel/WeiboSentiment_Finetuned/BertChinese-Lora
python predict.py --text "This product is really great"
```
#### 2. GPT-2 LoRA Fine-tuned Model (Faster Speed)
```bash
cd SentimentAnalysisModel/WeiboSentiment_Finetuned/GPT2-Lora
python predict.py --text "I'm not feeling great today"
```
#### 3. Small Qwen Model (Balanced)
```bash
cd SentimentAnalysisModel/WeiboSentiment_SmallQwen
python predict_universal.py --text "This event was very successful"
```
#### 4. Traditional Machine Learning Methods (Lightweight)
```bash
cd SentimentAnalysisModel/WeiboSentiment_MachineLearning
python predict.py --model_type "svm" --text "Service attitude needs improvement"
```
#### 5. Multilingual Sentiment Analysis (Supports 22 Languages)
```bash
cd SentimentAnalysisModel/WeiboMultilingualSentiment
python predict.py --text "This product is amazing!" --lang "en"
```
### Integrate Custom Business Database
#### 1. Modify Database Connection Configuration
```python
# Add your business database configuration in config.py
BUSINESS_DB_HOST = "your_business_db_host"
BUSINESS_DB_PORT = 3306
BUSINESS_DB_USER = "your_business_user"
BUSINESS_DB_PASSWORD = "your_business_password"
BUSINESS_DB_NAME = "your_business_database"
```
#### 2. Create Custom Data Access Tools
```python
# InsightEngine/tools/custom_db_tool.py
class CustomBusinessDBTool:
"""Custom business database query tool"""
def __init__(self):
self.connection_config = {
'host': config.BUSINESS_DB_HOST,
'port': config.BUSINESS_DB_PORT,
'user': config.BUSINESS_DB_USER,
'password': config.BUSINESS_DB_PASSWORD,
'database': config.BUSINESS_DB_NAME,
}
def search_business_data(self, query: str, table: str):
"""Query business data"""
# Implement your business logic
pass
def get_customer_feedback(self, product_id: str):
"""Get customer feedback data"""
# Implement customer feedback query logic
pass
```
#### 3. Integrate into InsightEngine
```python
# Integrate custom tools in InsightEngine/agent.py
from .tools.custom_db_tool import CustomBusinessDBTool
class DeepSearchAgent:
def __init__(self, config=None):
# ... other initialization code
self.custom_db_tool = CustomBusinessDBTool()
def execute_custom_search(self, query: str):
"""Execute custom business data search"""
return self.custom_db_tool.search_business_data(query, "your_table")
```
### Custom Report Templates
#### 1. Create Template Files
Create new Markdown templates in the `ReportEngine/report_template/` directory:
```markdown
<!-- Enterprise Brand Monitoring Report.md -->
# Enterprise Brand Public Opinion Monitoring Report
## 📊 Executive Summary
{executive_summary}
## 🔍 Brand Mention Analysis
### Mention Volume Trends
{mention_trend}
### Sentiment Distribution
{sentiment_distribution}
## 📈 Competitor Analysis
{competitor_analysis}
## 🎯 Key Insights Summary
{key_insights}
## ⚠️ Risk Alerts
{risk_alerts}
## 📋 Improvement Recommendations
{recommendations}
---
*Report Type: Enterprise Brand Public Opinion Monitoring*
*Generation Time: {generation_time}*
*Data Sources: {data_sources}*
```
#### 2. Use in Web Interface
The system supports uploading custom template files (.md or .txt format), selectable when generating reports.
## 🤝 Contributing Guide
We welcome all forms of contributions!
### How to Contribute
1. **Fork the project** to your GitHub account
2. **Create Feature branch**: `git checkout -b feature/AmazingFeature`
3. **Commit changes**: `git commit -m 'Add some AmazingFeature'`
4. **Push to branch**: `git push origin feature/AmazingFeature`
5. **Open Pull Request**
### Contribution Types
- 🐛 Bug fixes
- ✨ New feature development
- 📚 Documentation improvements
- 🎨 UI/UX improvements
- ⚡ Performance optimization
- 🧪 Test case additions
### Development Standards
- Code follows PEP8 standards
- Commit messages use clear Chinese/English descriptions
- New features need corresponding test cases
- Update related documentation
## 📄 License
This project is licensed under the [MIT License](LICENSE). Please see the LICENSE file for details.
## 🎉 Support & Contact
### Get Help
- **Project Homepage**: [GitHub Repository](https://github.com/666ghj/Weibo_PublicOpinion_AnalysisSystem)
- **Issue Reporting**: [Issues Page](https://github.com/666ghj/Weibo_PublicOpinion_AnalysisSystem/issues)
- **Feature Requests**: [Discussions Page](https://github.com/666ghj/Weibo_PublicOpinion_AnalysisSystem/discussions)
### Contact Information
- 📧 **Email**: 670939375@qq.com
- 💬 **QQ Group**: [Join Technical Discussion Group]
- 🐦 **WeChat**: [Scan QR Code for Technical Support]
### Business Cooperation
- 🏢 **Enterprise Custom Development**
- 📊 **Big Data Services**
- 🎓 **Academic Collaboration**
- 💼 **Technical Training**
### Cloud Service Application
**Free Cloud Database Service Application**:
📧 Send email to: 670939375@qq.com
📝 Subject: Weibo Public Opinion Cloud Database Application
📝 Description: Your use case and requirements
## 👥 Contributors
Thanks to these excellent contributors:
[![Contributors](https://contrib.rocks/image?repo=666ghj/Weibo_PublicOpinion_AnalysisSystem)](https://github.com/666ghj/Weibo_PublicOpinion_AnalysisSystem/graphs/contributors)
---
<div align="center">
**⭐ If this project helps you, please give us a star!**
Made with ❤️ by [Weibo Public Opinion Analysis Team](https://github.com/666ghj)
</div>
README.md
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<div align="center">
<!-- # 📊 Weibo Public Opinion Analysis System -->
<img src="static/image/logo_compressed.png" alt="Weibo Public Opinion Analysis System Logo" width="600">
<img src="https://github.com/666ghj/Weibo_PublicOpinion_AnalysisSystem/blob/main/static/image/logo_compressed.png" alt="Weibo Public Opinion Analysis System Logo" width="800">
# 微舆 - 致力于打造简洁通用的舆情分析平台
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[English](./README-EN.md) | [中文文档](./README.md)
</div>
<div align="center">
<img src="https://github.com/666ghj/Weibo_PublicOpinion_AnalysisSystem/blob/main/static/image/banner_compressed.png" alt="banner" width="800">
<img src="static/image/system_schematic.png" alt="banner" width="800">
</div>
## 项目概述
## 📝 项目概述
**Weibo舆情分析多智能体系统** 是一个从零构建的创新型舆情分析平台，采用多Agent协作架构，致力于提供准确、实时、全面的微博舆情监测与分析服务。系统通过多个专门化的AI Agent协同工作，实现了从数据采集、情感分析到报告生成的全流程自动化。
**微博舆情分析多智能体系统**是一个从零构建的创新型舆情分析平台，采用多Agent协作架构，致力于提供准确、实时、全面的微博舆情监测与分析服务。系统通过五个专门化的AI Agent协同工作，实现了从数据采集、情感分析到报告生成的全流程自动化。
### 核心特色
### 🚀 核心亮点
- **多智能体协作架构**：5个专门化Agent协同工作，各司其职
- **全方位数据采集**：整合微博爬虫、新闻搜索、网络信息多维度数据源
- **深度情感分析**：基于微调BERT/GPT-2/Qwen模型的精准情感识别
- **智能报告生成**：自动生成结构化HTML分析报告
- **Agent论坛交流**：Forum Engine提供Agent间信息共享和协作决策平台
- **高性能异步处理**：支持并发处理多个舆情任务
- **多智能体协作架构**：5个专门化Agent各司其职，协同工作完成舆情分析全流程
- **全方位数据采集**：整合微博爬虫、新闻搜索、多媒体内容等多维度数据源
- **深度情感分析**：基于微调BERT/GPT-2/Qwen模型的精准多语言情感识别
- **智能报告生成**：自动生成结构化HTML分析报告，支持自定义模板
- **Agent论坛交流**：ForumEngine提供Agent间信息共享和协作决策平台
- **高性能异步处理**：支持并发处理多个舆情任务，实时状态监控
- **云端数据支持**：提供便捷云数据库服务，日均10万+真实数据
## 系统架构
## 🏗️ 系统架构
### 整体架构图
@@ -49,7 +51,7 @@ graph TB
subgraph "数据处理层"
MS[MindSpider<br/>微博爬虫系统]
SA[SentimentAnalysis<br/>情感分析模型]
SA[SentimentAnalysis<br/>情感分析模型集合]
DB[(MySQL<br/>数据库)]
end
@@ -81,129 +83,110 @@ graph TB
ME <--> Forum
IE <--> Forum
RE <--> Forum
style UI fill:#e1f5fe
style QE fill:#fff3e0
style ME fill:#fff3e0
style IE fill:#fff3e0
style RE fill:#f3e5f5
style Forum fill:#e8f5e9
style MS fill:#fce4ec
style SA fill:#fce4ec
style DB fill:#fff9c4
style LLM fill:#e3f2fd
style Search fill:#e3f2fd
```
### 数据流程图
### Agent协作流程
```mermaid
sequenceDiagram
participant User as 用户
participant UI as Web界面
participant QE as QueryEngine
participant ME as MediaEngine
participant IE as InsightEngine
participant Forum as ForumEngine
participant RE as ReportEngine
participant DB as 数据库
User->>UI: 输入查询关键词
UI->>QE: 发起搜索请求
UI->>ME: 发起搜索请求
UI->>IE: 发起搜索请求
Note over QE,IE: Agent执行前先读取论坛信息
QE->>Forum: 读取论坛交流信息
ME->>Forum: 读取论坛交流信息
IE->>Forum: 读取论坛交流信息
par 并行处理与持续思维链交流
Note over QE: 结构思考→反思搜索→持续交流
QE->>QE: 确定新闻搜索结构
QE->>Forum: 思维链交流(结构思考)
QE->>QE: 多步反思与搜索分析
QE->>Forum: 思维链交流(搜索进展)
QE->>QE: 生成汇总报告
QE->>Forum: 思维链交流(关键发现)
and
Note over ME: 结构思考→反思搜索→持续交流
ME->>ME: 确定多媒体搜索结构
ME->>Forum: 思维链交流(结构思考)
ME->>ME: 多步反思与搜索分析
ME->>Forum: 思维链交流(搜索进展)
ME->>ME: 生成汇总报告
ME->>Forum: 思维链交流(关键发现)
and
Note over IE: 结构思考→反思搜索→持续交流
IE->>IE: 确定洞察分析结构
IE->>Forum: 思维链交流(结构思考)
IE->>DB: 查询微博数据
IE->>IE: 多步反思与情感洞察
IE->>Forum: 思维链交流(洞察进展)
IE->>IE: 生成汇总报告
IE->>Forum: 思维链交流(关键发现)
end
Note over Forum: 论坛汇总Agent交流信息
Forum->>RE: 触发报告生成
RE->>Forum: 读取所有Agent的交流信息
RE->>QE: 获取QueryEngine汇总报告
RE->>ME: 获取MediaEngine汇总报告
RE->>IE: 获取InsightEngine汇总报告
Note over RE: ReportEngine智能报告生成
RE->>RE: 读取模板库与样式库并选择
RE->>RE: 分步思考生成报告各部分
RE->>RE: 整合生成最终报告
RE->>UI: 生成综合HTML报告
UI->>User: 展示分析结果
```
系统核心工作流程基于多Agent协作模式：
## 项目结构
1. **QueryEngine（新闻查询Agent）**：使用Tavily API搜索权威新闻报道，提供官方信息源
2. **MediaEngine（多媒体搜索Agent）**：通过Bocha API进行多模态内容搜索，获取社交媒体观点
3. **InsightEngine（深度洞察Agent）**：查询本地微博数据库，结合多种情感分析模型进行深度分析
4. **ForumEngine（论坛监控Agent）**：实时监控各Agent日志输出，提取关键信息并促进协作
5. **ReportEngine（报告生成Agent）**：基于所有Agent的分析结果，使用Gemini LLM生成综合HTML报告
### 项目代码结构
```
Weibo_PublicOpinion_AnalysisSystem/
├── QueryEngine/ # web查询引擎Agent
│ ├── agent.py # Agent主逻辑
│ ├── llms/ # LLM接口封装
│ ├── nodes/ # 处理节点
│ ├── tools/ # 搜索工具
│ └── utils/ # 工具函数
├── MediaEngine/ # 媒体引擎Agent
│ └── (类似结构)
├── InsightEngine/ # 数据库引擎Agent
│ └── (类似结构)
├── ReportEngine/ # 报告生成Agent
│ ├── report_template/ # 报告模板
│ └── flask_interface.py # API接口
├── ForumEgine/ # 论坛交流Agent
│ └── monitor.py # 论坛交流管理器
├── MindSpider/ # 微博爬虫系统
│ ├── BroadTopicExtraction/ # 话题提取
│ ├── DeepSentimentCrawling/ # 深度爬取
│ └── schema/ # 数据库结构
├── SentimentAnalysisModel/ # 情感分析模型
│ ├── BertTopicDetection_Finetuned/
│ ├── WeiboSentiment_Finetuned/
│ └── WeiboSentiment_MachineLearning/
├── SingleEngineApp/ # Streamlit应用
├── templates/ # Flask模板
├── static/ # 静态资源
├── logs/ # 运行日志
├── app.py # 主应用入口
├── config.py # 配置文件
└── requirements.txt # 依赖包
├── QueryEngine/ # 新闻查询引擎Agent
│ ├── agent.py # Agent主逻辑
│ ├── llms/ # LLM接口封装
│ ├── nodes/ # 处理节点
│ ├── tools/ # 搜索工具
│ └── utils/ # 工具函数
├── MediaEngine/ # 多媒体搜索引擎Agent
│ ├── agent.py # Agent主逻辑
│ ├── llms/ # LLM接口
│ ├── tools/ # 搜索工具
│ └── ... # 其他模块
├── InsightEngine/ # 数据洞察引擎Agent
│ ├── agent.py # Agent主逻辑
│ ├── llms/ # LLM接口封装
│ │ ├── deepseek.py # DeepSeek API
│ │ ├── kimi.py # Kimi API
│ │ ├── openai_llm.py # OpenAI格式API
│ │ └── base.py # LLM基类
│ ├── nodes/ # 处理节点
│ │ ├── first_search_node.py # 首次搜索节点
│ │ ├── reflection_node.py # 反思节点
│ │ ├── summary_nodes.py # 总结节点
│ │ ├── search_node.py # 搜索节点
│ │ ├── sentiment_node.py # 情感分析节点
│ │ └── insight_node.py # 洞察生成节点
│ ├── tools/ # 数据库查询和分析工具
│ │ ├── media_crawler_db.py # 数据库查询工具
│ │ └── sentiment_analyzer.py # 情感分析集成工具
│ ├── state/ # 状态管理
│ │ ├── __init__.py
│ │ └── state.py # Agent状态定义
│ ├── prompts/ # 提示词模板
│ │ ├── __init__.py
│ │ └── prompts.py # 各类提示词
│ └── utils/ # 工具函数
│ ├── __init__.py
│ ├── config.py # 配置管理
│ └── helpers.py # 辅助函数
├── ReportEngine/ # 报告生成引擎Agent
│ ├── agent.py # Agent主逻辑
│ ├── llms/ # LLM接口
│ │ └── gemini.py # Gemini API专用
│ ├── nodes/ # 报告生成节点
│ │ ├── template_selection.py # 模板选择节点
│ │ └── html_generation.py # HTML生成节点
│ ├── report_template/ # 报告模板库
│ │ ├── 社会公共热点事件分析.md
│ │ ├── 商业品牌舆情监测.md
│ │ └── ... # 更多模板
│ └── flask_interface.py # Flask API接口
├── ForumEngine/ # 论坛交流引擎Agent
│ └── monitor.py # 日志监控和论坛管理
├── MindSpider/ # 微博爬虫系统
│ ├── main.py # 爬虫主程序
│ ├── BroadTopicExtraction/ # 话题提取模块
│ │ ├── get_today_news.py # 今日新闻获取
│ │ └── topic_extractor.py # 话题提取器
│ ├── DeepSentimentCrawling/ # 深度情感爬取
│ │ ├── MediaCrawler/ # 媒体爬虫核心
│ │ └── platform_crawler.py # 平台爬虫管理
│ └── schema/ # 数据库结构
│ └── init_database.py # 数据库初始化
├── SentimentAnalysisModel/ # 情感分析模型集合
│ ├── WeiboSentiment_Finetuned/ # 微调BERT/GPT-2模型
│ ├── WeiboMultilingualSentiment/ # 多语言情感分析
│ ├── WeiboSentiment_SmallQwen/ # 小型Qwen模型
│ └── WeiboSentiment_MachineLearning/ # 传统机器学习方法
├── SingleEngineApp/ # 单独Agent的Streamlit应用
│ ├── query_engine_streamlit_app.py
│ ├── media_engine_streamlit_app.py
│ └── insight_engine_streamlit_app.py
├── templates/ # Flask模板
│ └── index.html # 主界面模板
├── static/ # 静态资源
├── logs/ # 运行日志目录
├── app.py # Flask主应用入口
├── config.py # 全局配置文件
└── requirements.txt # Python依赖包清单
```
## 快速开始
## 🚀 快速开始
### 环境要求
- **操作系统**: Windows 10/11
- **操作系统**: Windows 10/11（Linux/macOS也支持）
- **Python版本**: 3.11+
- **Conda**: Anaconda或Miniconda
- **数据库**: MySQL 8.0+
- **数据库**: MySQL 8.0+（可选择我们的云数据库服务）
- **内存**: 建议8GB以上
### 1. 创建Conda环境
@@ -220,14 +203,14 @@ conda activate pytorch_python11
# 基础依赖安装
pip install -r requirements.txt
# 如果需要情感分析功能，安装PyTorch（根据CUDA版本选择）
# 如果需要本地情感分析功能，安装PyTorch
# CPU版本
pip install torch torchvision torchaudio
# CUDA 11.8版本
# CUDA 11.8版本（如有GPU）
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
# 安装transformers（用于BERT/GPT模型）
# 安装transformers等AI相关依赖
pip install transformers scikit-learn xgboost
```
@@ -272,16 +255,30 @@ BOCHA_Web_Search_API_KEY = "your_bocha_api_key"
GUIJI_QWEN3_API_KEY = "your_guiji_api_key"
```
#### 4.2 初始化数据库
#### 4.2 数据库初始化
**选择1：使用本地数据库**
```bash
# 本地MySQL数据库初始化
cd MindSpider
python schema/init_database.py
```
**选择2：使用云数据库服务（推荐）**
我们提供便捷的云数据库服务，包含日均10万+真实微博数据，目前推广期间**免费申请**！
- 真实微博数据，实时更新
- 预处理的情感标注数据
- 多维度标签分类
- 高可用云端服务
- 专业技术支持
**联系我们申请免费云数据库访问：📧 670939375@qq.com**
### 5. 启动系统
#### 方式一：完整系统启动（推荐）
#### 5.1 完整系统启动（推荐）
```bash
# 在项目根目录下，激活conda环境
@@ -291,9 +288,9 @@ conda activate pytorch_python11
python app.py
```
访问 http://localhost:5000 即可使用系统
访问 http://localhost:5000 即可使用完整系统
#### 方式二：单独启动某个Agent
#### 5.2 单独启动某个Agent
```bash
# 启动QueryEngine
@@ -306,147 +303,353 @@ streamlit run SingleEngineApp/media_engine_streamlit_app.py --server.port 8502
streamlit run SingleEngineApp/insight_engine_streamlit_app.py --server.port 8501
```
## 使用指南
#### 5.3 爬虫系统单独使用
### 基础使用流程
```bash
# 进入爬虫目录
cd MindSpider
1. **启动系统**：运行 `python app.py`，系统会自动启动所有Agent
# 项目初始化
python main.py --setup
2. **输入查询**：在Web界面搜索框输入要分析的舆情关键词
# 运行完整爬虫流程
python main.py --complete --date 2024-01-20
3. **Agent协作**：
- QueryEngine：搜索新闻和官方报道，将关键发现发布到论坛
- MediaEngine：搜索多媒体内容，与其他Agent分享重要信息
- InsightEngine：分析微博数据和情感，在论坛中交流洞察
- ForumEngine：提供Agent间交流平台，汇总协作信息
# 仅运行话题提取
python main.py --broad-topic --date 2024-01-20
4. **查看结果**：
- Agent论坛交流：查看Agent间的实时信息交换
- 分析报告：查看基于Agent协作的综合HTML报告
# 仅运行深度爬取
python main.py --deep-sentiment --platforms xhs dy wb
```
### 高级配置
## 💾 数据库配置
#### 配置爬虫系统
### 本地数据库配置
1. **安装MySQL 8.0+**
2. **创建数据库**：
```sql
CREATE DATABASE weibo_analysis CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci;
```
3. **运行初始化脚本**：
```bash
cd MindSpider
python schema/init_database.py
```
### 自动爬取配置
配置自动爬取任务，实现数据的持续更新：
1. **配置爬虫参数**：
```python
# MindSpider/config.py
# MindSpider/config.py 中配置爬虫参数
CRAWLER_CONFIG = {
'max_pages': 100, # 最大爬取页数
'delay': 1, # 请求延迟（秒）
'timeout': 30, # 超时时间（秒）
'use_proxy': False, # 是否使用代理
'max_pages': 200, # 最大爬取页数
'delay': 1, # 请求延迟（秒）
'timeout': 30, # 超时时间（秒）
'platforms': ['xhs', 'dy', 'wb', 'bili'], # 爬取平台
'daily_keywords': 100, # 每日关键词数量
'max_notes_per_keyword': 50, # 每关键词最大内容数
'use_proxy': False, # 是否使用代理
}
```
2. **运行爬虫**：
```bash
cd MindSpider
python main.py --topic "话题关键词" --days 7
### 云数据库服务（推荐）
**为什么选择我们的云数据库服务？**
- **丰富数据源**：日均10万+真实微博数据，涵盖各行业热点话题
- **高质量标注**：专业团队人工标注的情感数据，准确率95%+
- **多维度分析**：包含话题分类、情感倾向、影响力评分等多维标签
- **实时更新**：24小时不间断数据采集，确保时效性
- **技术支持**：专业团队提供技术支持和定制化服务
**申请方式**：
📧 邮件联系：670939375@qq.com
📝 邮件标题：申请微博舆情云数据库访问
📝 邮件内容：请说明您的使用场景和预期数据量需求
**推广期福利**：
- 免费提供基础版云数据库访问
- 免费技术支持和部署指导
- 优先体验新功能特性
## ⚙️ 高级配置
### 修改关键参数
#### Agent配置参数
每个Agent都有专门的配置文件，可根据需求调整：
```python
# QueryEngine/utils/config.py
class Config:
max_reflections = 2 # 反思轮次
max_search_results = 15 # 最大搜索结果数
max_content_length = 8000 # 最大内容长度
# MediaEngine/utils/config.py
class Config:
comprehensive_search_limit = 10 # 综合搜索限制
web_search_limit = 15 # 网页搜索限制
# InsightEngine/utils/config.py
class Config:
default_search_topic_globally_limit = 200 # 全局搜索限制
default_get_comments_limit = 500 # 评论获取限制
max_search_results_for_llm = 50 # 传给LLM的最大结果数
```
#### 配置情感分析模型
#### 情感分析模型配置
1. **选择模型**：
- BERT微调模型（精度高）
- GPT-2 LoRA（速度快）
- Qwen小模型（平衡型）
- 机器学习基线（轻量级）
2. **模型切换**：
```python
# InsightEngine/tools/sentiment_analyzer.py
MODEL_TYPE = "bert" # 可选: "bert", "gpt2", "qwen", "ml"
SENTIMENT_CONFIG = {
'model_type': 'multilingual', # 可选: 'bert', 'multilingual', 'qwen'
'confidence_threshold': 0.8, # 置信度阈值
'batch_size': 32, # 批处理大小
'max_sequence_length': 512, # 最大序列长度
}
```
#### 自定义报告模板
### 接入不同的LLM模型
在 `ReportEngine/report_template/` 目录下创建新模板：
系统支持多种LLM提供商，可在各Agent的配置中切换：
```python
# 在各Engine的utils/config.py中配置
class Config:
default_llm_provider = "deepseek" # 可选: "deepseek", "openai", "kimi", "gemini"
# DeepSeek配置
deepseek_api_key = "your_api_key"
deepseek_model = "deepseek-chat"
# OpenAI兼容配置
openai_api_key = "your_api_key"
openai_model = "gpt-3.5-turbo"
openai_base_url = "https://api.openai.com/v1"
# Kimi配置
kimi_api_key = "your_api_key"
kimi_model = "moonshot-v1-8k"
# Gemini配置
gemini_api_key = "your_api_key"
gemini_model = "gemini-pro"
```
### 更改情感分析模型
系统集成了多种情感分析方法，可根据需求选择：
#### 1. 基于BERT的微调模型（精度最高）
```bash
# 使用BERT中文模型
cd SentimentAnalysisModel/WeiboSentiment_Finetuned/BertChinese-Lora
python predict.py --text "这个产品真的很不错"
```
#### 2. GPT-2 LoRA微调模型（速度较快）
```bash
cd SentimentAnalysisModel/WeiboSentiment_Finetuned/GPT2-Lora
python predict.py --text "今天心情不太好"
```
#### 3. 小型Qwen模型（平衡型）
```bash
cd SentimentAnalysisModel/WeiboSentiment_SmallQwen
python predict_universal.py --text "这次活动办得很成功"
```
#### 4. 传统机器学习方法（轻量级）
```bash
cd SentimentAnalysisModel/WeiboSentiment_MachineLearning
python predict.py --model_type "svm" --text "服务态度需要改进"
```
#### 5. 多语言情感分析（支持22种语言）
```bash
cd SentimentAnalysisModel/WeiboMultilingualSentiment
python predict.py --text "This product is amazing!" --lang "en"
```
### 接入自定义业务数据库
#### 1. 修改数据库连接配置
```python
# config.py 中添加您的业务数据库配置
BUSINESS_DB_HOST = "your_business_db_host"
BUSINESS_DB_PORT = 3306
BUSINESS_DB_USER = "your_business_user"
BUSINESS_DB_PASSWORD = "your_business_password"
BUSINESS_DB_NAME = "your_business_database"
```
#### 2. 创建自定义数据访问工具
```python
# InsightEngine/tools/custom_db_tool.py
class CustomBusinessDBTool:
"""自定义业务数据库查询工具"""
def __init__(self):
self.connection_config = {
'host': config.BUSINESS_DB_HOST,
'port': config.BUSINESS_DB_PORT,
'user': config.BUSINESS_DB_USER,
'password': config.BUSINESS_DB_PASSWORD,
'database': config.BUSINESS_DB_NAME,
}
def search_business_data(self, query: str, table: str):
"""查询业务数据"""
# 实现您的业务逻辑
pass
def get_customer_feedback(self, product_id: str):
"""获取客户反馈数据"""
# 实现客户反馈查询逻辑
pass
```
#### 3. 集成到InsightEngine
```python
# InsightEngine/agent.py 中集成自定义工具
from .tools.custom_db_tool import CustomBusinessDBTool
class DeepSearchAgent:
def __init__(self, config=None):
# ... 其他初始化代码
self.custom_db_tool = CustomBusinessDBTool()
def execute_custom_search(self, query: str):
"""执行自定义业务数据搜索"""
return self.custom_db_tool.search_business_data(query, "your_table")
```
### 自定义报告模板
#### 1. 创建模板文件
在 `ReportEngine/report_template/` 目录下创建新的Markdown模板：
```markdown
# 自定义报告模板
## 舆情概览
${overview}
<!-- 企业品牌监测报告.md -->
# 企业品牌舆情监测报告
## 情感分析
${sentiment_analysis}
## 📊 执行摘要
{executive_summary}
## 关键观点
${key_insights}
## 🔍 品牌提及分析
### 提及量趋势
{mention_trend}
## 趋势预测
${trend_prediction}
### 情感分布
{sentiment_distribution}
## 📈 竞品对比分析
{competitor_analysis}
## 🎯 关键观点摘要
{key_insights}
## ⚠️ 风险预警
{risk_alerts}
## 📋 改进建议
{recommendations}
---
*报告类型：企业品牌舆情监测*
*生成时间：{generation_time}*
*数据来源：{data_sources}*
```
### 监控与日志
#### 2. 在Web界面中使用
#### 查看系统日志
系统支持上传自定义模板文件（.md或.txt格式），可在生成报告时选择使用。
所有日志文件位于 `logs/` 目录：
- `query.log`: QueryEngine运行日志
- `media.log`: MediaEngine运行日志
- `insight.log`: InsightEngine运行日志
- `forum.log`: ForumEngine论坛交流日志
- `report.log`: ReportEngine生成日志
#### Agent论坛交流
ForumEngine提供多Agent协作交流功能：
1. Agent行动前读取论坛交流信息
2. Agent思考后决定是否分享关键发现
3. 汇总所有Agent的交流信息
4. 为ReportEngine提供协作数据基础
## 故障排除
### 常见问题
#### 1. 端口占用
```bash
# 查看端口占用（Windows）
netstat -ano | findstr :5000
netstat -ano | findstr :8501
# 结束占用进程
taskkill /F /PID <进程ID>
```
#### 2. 编码问题
```python
# 在代码开头添加
import sys
import os
os.environ['PYTHONIOENCODING'] = 'utf-8'
os.environ['PYTHONUTF8'] = '1'
```
#### 3. Playwright安装失败
```bash
# 手动安装
python -m playwright install chromium --with-deps
```
#### 4. MySQL连接失败
- 检查MySQL服务是否启动
- 确认用户权限配置
- 检查防火墙设置
## 贡献指南
## 🤝 贡献指南
我们欢迎所有形式的贡献！
1. Fork项目
2. 创建Feature分支 (`git checkout -b feature/AmazingFeature`)
3. 提交更改 (`git commit -m 'Add some AmazingFeature'`)
4. 推送到分支 (`git push origin feature/AmazingFeature`)
5. 开启Pull Request
### 如何贡献
## 许可证
1. **Fork项目**到您的GitHub账号
2. **创建Feature分支**：`git checkout -b feature/AmazingFeature`
3. **提交更改**：`git commit -m 'Add some AmazingFeature'`
4. **推送到分支**：`git push origin feature/AmazingFeature`
5. **开启Pull Request**
本项目采用 MIT 许可证。详见 [LICENSE](LICENSE) 文件。
### 贡献类型
## 联系我们
- 🐛 Bug修复
- ✨ 新功能开发
- 📚 文档完善
- 🎨 UI/UX改进
- ⚡ 性能优化
- 🧪 测试用例添加
- 项目地址：[https://github.com/666ghj/Weibo_PublicOpinion_AnalysisSystem](https://github.com/666ghj/Weibo_PublicOpinion_AnalysisSystem)
- 邮箱：670939375@qq.com
- Issues：[项目Issues](https://github.com/666ghj/Weibo_PublicOpinion_AnalysisSystem/issues)
### 开发规范
- 代码遵循PEP8规范
- 提交信息使用清晰的中英文描述
- 新功能需要包含相应的测试用例
- 更新相关文档
## 📄 许可证
本项目采用 [MIT许可证](LICENSE)。详细信息请参阅LICENSE文件。
## 🎉 支持与联系
### 获取帮助
- **项目主页**：[GitHub仓库](https://github.com/666ghj/Weibo_PublicOpinion_AnalysisSystem)
- **问题反馈**：[Issues页面](https://github.com/666ghj/Weibo_PublicOpinion_AnalysisSystem/issues)
- **功能建议**：[Discussions页面](https://github.com/666ghj/Weibo_PublicOpinion_AnalysisSystem/discussions)
### 联系方式
- 📧 **邮箱**：670939375@qq.com
- 💬 **QQ群**：[加入技术交流群]
- 🐦 **微信**：[扫码添加技术支持]
### 商务合作
- 🏢 **企业定制开发**
- 📊 **大数据服务**
- 🎓 **学术合作**
- 💼 **技术培训**
### 云服务申请
**免费云数据库服务申请**：
📧 发送邮件至：670939375@qq.com
📝 标题：微博舆情云数据库申请
📝 说明：您的使用场景和需求
## 👥 贡献者
感谢以下优秀的贡献者们：
[![Contributors](https://contrib.rocks/image?repo=666ghj/Weibo_PublicOpinion_AnalysisSystem)](https://github.com/666ghj/Weibo_PublicOpinion_AnalysisSystem/graphs/contributors)
---
<div align="center">
**⭐ 如果这个项目对您有帮助，请给我们一个星标！**
Made with ❤️ by [微博舆情分析团队](https://github.com/666ghj)
</div>
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