666ghj / BettaFish

2024-07-01 · Source: Github Trending: All languages · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Intermediate, extended

Summary

BettaFish, also known as "Weiyu," is an innovative multi-agent public opinion analysis system designed to break through information cocoons, restore public opinion, predict trends, and assist decision-making. It features an AI-driven omnichannel monitoring cluster that operates 24/7, covering over 30 mainstream social media platforms and millions of public comments, including deep analysis of short video content. The system employs a composite analysis engine integrating five specialized agents, fine-tuned models, and statistical models, alongside a unique "Agent Forum" collaboration mechanism for collective intelligence. BettaFish also supports seamless integration of public and private domain data and offers a lightweight, highly extensible Python-based framework for one-click deployment. It is now integrated with MiroFish, a collective intelligence engine for panoramic prediction, completing a data-to-decision closed loop.

Key takeaway

For AI Engineers and Data Analysts building intelligence systems, BettaFish offers a robust, extensible framework for public opinion analysis and trend prediction. You should consider its multi-agent architecture and omnichannel data collection capabilities to enhance the depth and breadth of your analytical tools. Its integration with MiroFish for panoramic prediction provides a complete solution from raw data to intelligent decision-making, potentially streamlining your development efforts for similar applications.

Key insights

BettaFish is a multi-agent system for comprehensive public opinion analysis and trend prediction.

Principles

• Multi-agent collaboration enhances analysis depth.
• Omnichannel monitoring captures diverse public sentiment.
• Integrate public and private data for holistic insights.

Method

BettaFish uses a multi-agent "forum" mechanism for collaborative analysis, combining AI crawlers, multimodal content processing, private database mining, and report generation, all orchestrated by a Flask application.

In practice

• Deploy with Docker for quick setup.
• Customize LLM APIs using OpenAI-compatible formats.
• Integrate custom business databases for tailored analysis.

Topics

• Multi-Agent Systems
• Public Opinion Analysis
• Multimodal AI
• Social Media Monitoring
• Large Language Models

Code references

• 666ghj/BettaFish
• 666ghj/MiroFish
• 666ghj/DeepSearchAgent-Demo

Best for: AI Engineer, Machine Learning Engineer, Software Engineer

Related on AIssential

• On GATE, Text and Social Media Analysis, and Detecting Misinformation Online — A multi-layer analytical pipeline effectively extracts and categorizes digital identity aspects of agrifood products from online discourse.
• ArogyaSutra: A Multi-Agent Framework for Multimodal Medical Reasoning in Indic Languages — Multimodal medical reasoning in Indic languages is enhanced by an actor-critic framework with tool grounding and dual-memory.
• BigBodyCobain / Shadowbroker — ShadowBroker unifies diverse public intelligence feeds into a real-time, interactive geospatial platform with AI agent integration.
• From Argument Components to Graphs: A Multi-Agent Debate with Confidence Gating for Argument Relations — Multi-agent debate with confidence gating improves training-free argument relation identification and offers interpretability.
• Improve Your Agentic AI Trading With a Great Data Pipeline — High-quality, diverse data pipelines are crucial for successful agentic AI trading, outweighing model sophistication.
• WholeSum tops up Pre-Seed with $335K to fix AI’s trust problem in text analytics — WholeSum's hybrid AI and statistical inference platform provides auditable, reproducible insights from unstructured text data for high-trust sectors.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Github Trending: All languages.