## β‘ Project Overview
**"BettaFish"** is an innovative multi-agent public opinion analysis system built from scratch. It helps break information cocoons, restore the original public sentiment, predict future trends, and assist decision-making. Users only need to raise analysis needs like chatting; the agents automatically analyze 30+ mainstream social platforms at home and abroad and millions of public comments.
> Betta is a small yet combative and beautiful fish, symbolizing "small but powerful, fearless of challenges".
See the system-generated research report on "Wuhan University Public Opinion": [In-depth Analysis Report on Wuhan University's Brand Reputation](./final_reports/final_report__20250827_131630.html)
See a complete system run example on "Wuhan University Public Opinion": [Video - In-depth Analysis Report on Wuhan University's Brand Reputation](https://www.bilibili.com/video/BV1TH1WBxEWN/?vd_source=da3512187e242ce17dceee4c537ec7a6#reply279744466833)
Beyond just report quality, compared to similar products, we have π six major advantages:
1. **AI-Driven Comprehensive Monitoring**: AI crawler clusters operate 24/7 non-stop, comprehensively covering 10+ key domestic and international social media platforms including Weibo, Xiaohongshu, TikTok, Kuaishou, etc. Not only capturing trending content in real-time, but also drilling down to massive user comments, letting you hear the most authentic and widespread public voice.
2. **Composite Analysis Engine Beyond LLM**: We not only rely on 5 types of professionally designed Agents, but also integrate middleware such as fine-tuned models and statistical models. Through multi-model collaborative work, we ensure the depth, accuracy, and multi-dimensional perspective of analysis results.
3. **Powerful Multimodal Capabilities**: Breaking through text and image limitations, capable of deep analysis of short video content from TikTok, Kuaishou, etc., and precisely extracting structured multimodal information cards such as weather, calendar, stocks from modern search engines, giving you comprehensive control over public opinion dynamics.
4. **Agent "Forum" Collaboration Mechanism**: Endowing different Agents with unique toolsets and thinking patterns, introducing a debate moderator model, conducting chain-of-thought collision and debate through the "forum" mechanism. This not only avoids the thinking limitations of single models and homogenization caused by communication, but also catalyzes higher-quality collective intelligence and decision support.
5. **Seamless Integration of Public and Private Domain Data**: The platform not only analyzes public opinion, but also provides high-security interfaces supporting seamless integration of your internal business databases with public opinion data. Breaking through data barriers, providing powerful analysis capabilities of "external trends + internal insights" for vertical businesses.
6. **Lightweight and Highly Extensible Framework**: Based on pure Python modular design, achieving lightweight, one-click deployment. Clear code structure allows developers to easily integrate custom models and business logic, enabling rapid platform expansion and deep customization.
**Starting with public opinion, but not limited to public opinion**. The goal of "WeiYu" is to become a simple and universal data analysis engine that drives all business scenarios.
> For example, you only need to simply modify the API parameters and prompts of the Agent toolset to transform it into a financial market analysis system.
>
> Here's a relatively active Linux.do project discussion thread: https://linux.do/t/topic/1009280
>
> Check out the comparison by a Linux.do fellow: [Open Source Project (BettaFish) vs manus|minimax|ChatGPT Comparison](https://linux.do/t/topic/1148040)
Say goodbye to traditional data dashboards. In "WeiYu", everything starts with a simple question - you just need to ask your analysis needs like a conversation
## πͺ Sponsors
LLM Model API Sponsor: Solomon Blog LionCC.ai; Programming Carpool codecodex.ai; Programming Computing Power VibeCodingAPI.ai:
1. Solomon Blog LionCC.ai has updated the "BettaFish WeiYu System - LionCC API Deployment Configuration Complete Guide" and is optimizing one-click deployment and cloud server invocation solutions.
2. VibeCodingapi.ai Lion Computing Platform has adapted to all LLM models of "BettaFish WeiYu System", including Claude Code, OpenAI Codex, and Gemini CLI programming development computing power. The quota price is 1:1 (100 yuan equals 100 USD quota).
3. Codecodex.ai Lion Programming Carpool System has achieved IP-free access to bypass Claude Code and OpenAI Codex restrictions. After following the official deployment tutorial, simply switch the BASE_URL invocation address and Token key invocation key to use the most powerful programming models.
Solomon LionCC BettaFish WeiYu Benefits: Open codecodex.ai Lion Programming Channel, scan the QR code to join the WeChat community, register for VibeCodingapi.ai Lion Computing, and receive 20 USD API quota (limited to the first 1,000 users).
Pay-as-you-go enterprise-grade AI resource platform offering a comprehensive set of AI models and APIs, plus multiple ready-to-use online AI apps:
302.AI is a pay-as-you-go enterprise AI resource hub that offers the latest and most comprehensive AI models and APIs on the market, along with a variety of ready-to-use online AI applications.
## ποΈ System Architecture
### Overall Architecture Diagram
**Insight Agent** Private Database Mining: AI agent for in-depth analysis of private public opinion databases
**Media Agent** Multimodal Content Analysis: AI agent with powerful multimodal capabilities
**Query Agent** Precise Information Search: AI agent with domestic and international web search capabilities
**Report Agent** Intelligent Report Generation: Multi-round report generation AI agent with built-in templates
```bash
# Enter crawler directory
cd MindSpider
# Project initialization
python main.py --setup
# Run topic extraction (get hot news and keywords)
python main.py --broad-topic
# 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
```
## βοΈ Advanced Configuration (Deprecated: Configuration has been unified to the `.env` file in the project root directory, and other sub-agents automatically inherit the root directory 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 any LLM provider that follows the OpenAI request format. You only need to fill in KEY, BASE_URL, and MODEL_NAME in `config.py`.
> What is the OpenAI request format? Here's a simple example:
>```python
>from openai import OpenAI
>
>client = OpenAI(api_key="your_api_key",
> base_url="https://api.siliconflow.cn/v1")
>
>response = client.chat.completions.create(
> model="Qwen/Qwen2.5-72B-Instruct",
> messages=[
> {
> 'role': 'user',
> 'content': "What new opportunities will reasoning models bring to the market?"
> }
> ],
>)
>
>complete_response = response.choices[0].message.content
>print(complete_response)
>```
### Change Sentiment Analysis Models
The system integrates multiple sentiment analysis methods, selectable based on needs:
#### 1. Multilingual Sentiment Analysis
```bash
cd SentimentAnalysisModel/WeiboMultilingualSentiment
python predict.py --text "This product is amazing!" --lang "en"
```
#### 2. Small Parameter Qwen3 Fine-tuning
```bash
cd SentimentAnalysisModel/WeiboSentiment_SmallQwen
python predict_universal.py --text "This event was very successful"
```
#### 3. BERT-based Fine-tuned Model
```bash
# Use BERT Chinese model
cd SentimentAnalysisModel/WeiboSentiment_Finetuned/BertChinese-Lora
python predict.py --text "This product is really great"
```
#### 4. GPT-2 LoRA Fine-tuned Model
```bash
cd SentimentAnalysisModel/WeiboSentiment_Finetuned/GPT2-Lora
python predict.py --text "I'm not feeling great today"
```
#### 5. Traditional Machine Learning Methods
```bash
cd SentimentAnalysisModel/WeiboSentiment_MachineLearning
python predict.py --model_type "svm" --text "Service attitude needs improvement"
```
### 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. Upload in Web Interface
The system supports uploading custom template files (.md or .txt format), selectable when generating reports.
#### 2. Create Template Files
Create new templates in the `ReportEngine/report_template/` directory, and our Agent will automatically select the most appropriate template.
## π€ Contributing Guide
We welcome all forms of contributions!
**Please read the following contribution guidelines:**
- [CONTRIBUTING-EN.md](./CONTRIBUTING-EN.md)
## π¦ Next Development Plan
The system has currently completed only the first two steps of the "three-step approach": requirement input -> detailed analysis. The missing step is prediction, and directly handing this over to LLM lacks persuasiveness.
Currently, after a long period of crawling and collection, we have accumulated massive data on topic popularity trends over time, trending events, and other change patterns across the entire network. We now have the conditions to develop prediction models. Our team will apply our technical reserves in time series models, graph neural networks, multimodal fusion, and other prediction model technologies to achieve truly data-driven public opinion prediction functionality.
## β οΈ Disclaimer
**Important Notice: This project is for educational, academic research, and learning purposes only**
1. **Compliance Statement**:
- All code, tools, and functionalities in this project are intended solely for educational, academic research, and learning purposes
- Commercial use or profit-making activities are strictly prohibited
- Any illegal, non-compliant, or rights-infringing activities are strictly prohibited
2. **Web Scraping Disclaimer**:
- The web scraping functionality in this project is intended only for technical learning and research purposes
- Users must comply with the target websites' robots.txt protocols and terms of use
- Users must comply with relevant laws and regulations and must not engage in malicious scraping or data abuse
- Users are solely responsible for any legal consequences arising from the use of web scraping functionality
3. **Data Usage Disclaimer**:
- The data analysis functionality in this project is intended only for academic research purposes
- Using analysis results for commercial decision-making or profit-making purposes is strictly prohibited
- Users should ensure the legality and compliance of the data being analyzed
4. **Technical Disclaimer**:
- This project is provided "as is" without any express or implied warranties
- The authors are not responsible for any direct or indirect losses caused by the use of this project
- Users should evaluate the applicability and risks of this project independently
5. **Liability Limitation**:
- Users should fully understand relevant laws and regulations before using this project
- Users should ensure their usage complies with local legal and regulatory requirements
- Users are solely responsible for any consequences arising from the illegal use of this project
**Please carefully read and understand the above disclaimer before using this project. Using this project indicates that you have agreed to and accepted all the above terms.**
## π License
This project is licensed under the [GPL-2.0 License](LICENSE). Please see the LICENSE file for details.
## π Support & Contact
### Get Help
FAQ: https://github.com/666ghj/BettaFish/issues/185
- **Project Homepage**: [GitHub Repository](https://github.com/666ghj/BettaFish)
- **Issue Reporting**: [Issues Page](https://github.com/666ghj/BettaFish/issues)
- **Feature Requests**: [Discussions Page](https://github.com/666ghj/BettaFish/discussions)
### Contact Information
- π§ **Email**: hangjiang@bupt.edu.cn
### Business Cooperation
- **Enterprise Custom Development**
- **Big Data Services**
- **Academic Collaboration**
- **Technical Training**
## π₯ Contributors
Thanks to these excellent contributors:
[](https://github.com/666ghj/BettaFish/graphs/contributors)
## π Project Statistics
[](https://github.com/666ghj/BettaFish/stargazers)
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[](https://github.com/666ghj/BettaFish/issues)
[](https://github.com/666ghj/BettaFish/pulls)
[](https://github.com/666ghj/BettaFish/blob/main/LICENSE)
[](https://github.com/666ghj/BettaFish)
[](https://hub.docker.com/)
[English](./README-EN.md) | [δΈζζζ‘£](./README.md)
Say goodbye to traditional data dashboards. In "WeiYu", everything starts with a simple question - you just need to ask your analysis needs like a conversation
302.AI is a pay-as-you-go enterprise AI resource hub that offers the latest and most comprehensive AI models and APIs on the market, along with a variety of ready-to-use online AI applications.
MindSpider Running Example
