🧠 RAG Intelligence Framework

Multi-agent Retrieval-Augmented Generation system with discovery, analysis, validation, consensus building, and anti-hallucination capabilities. Powered by advanced AI orchestration.

🔍Tree Search Discovery
📊MCTS Analysis
🤝LLM Consensus
🔍

Discovery Agent

Single-Turn Tree Search

Finds and crawls data sources using single-turn tree search algorithm

Purpose: Source crawling and API endpoint discovery
📊

Market Intelligence Agent

Multi-Turn Monte Carlo Tree Search

Fuses and analyzes data using Monte Carlo Tree Search (MCTS)

Purpose: Data fusion and market analysis
⚖️

Regulatory Analysis Agent

Haizing Loop Validation

Performs compliance and regulatory checks using Haizing Loop

Purpose: Compliance checking and regulatory analysis
🤝

Consensus Moderator Agent

LLM Debate Consensus

Orchestrates LLM debate consensus between multiple models

Purpose: Multi-model debate and consensus building
🛡️

Anti-Hallucination Validator

Ground-Truth Calibration

Grounds generated content against verified data sources

Purpose: Reality checking and hallucination prevention

⚡RAG Workflow Execution

🔄Workflow Loops

Discovery Loop
Execution: Sequential
Agents: Discovery Agent
Analysis Loop
Execution: Parallel
Agents: Market Intelligence, Regulatory Analysis
Consensus Loop
Execution: Sequential
Agents: Consensus Moderator, Anti-Hallucination
💡

Use Cases

High-Precision Financial Analysis:
  • • High search depth (8-10)
  • • Conservative exploration (1.0-1.2)
  • • Strict validation threshold (0.9+)
Fast Market Scanning:
  • • Lower search depth (3-5)
  • • Higher exploration (2.0+)
  • • Fewer simulations (500-1000)

📈System Metrics

5
Active Agents
3
Workflow Loops
89%
Accuracy
Low
Hallucination Risk

🧠 RAG Intelligence Framework • Multi-agent orchestration for reliable intelligence gathering

Powered by advanced AI algorithms • Tree Search • MCTS • LLM Consensus