"""
Governed Multi-Agent RAG Chain Experiment

This experiment demonstrates how the Agent Control Plane governs a multi-agent
retrieval-augmented generation (RAG) chain, ensuring:
1. Agents cooperate within governance constraints
2. No unauthorized data access
3. Complete audit trail
4. Safe coordination patterns

The experiment involves three agents:
- Retriever: Fetches relevant documents from vector store
- Processor: Analyzes and synthesizes information
- Validator: Verifies outputs meet safety/quality standards
"""

import sys
import os
import json
import argparse
import random
from typing import List, Dict, Any
from datetime import datetime

# Add parent directory to path
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))


class RAGChainExperiment:
    """
    Governed Multi-Agent RAG Chain Experiment
    
    This is a placeholder implementation demonstrating the governance
    of a multi-agent RAG chain. A full implementation would include:
    - Actual vector store (FAISS, Pinecone, etc.)
    - Real embedding models
    - Document retrieval and processing
    """
    
    def __init__(self, config: Dict[str, Any], seed: int = 42):
        self.config = config
        self.seed = seed
        random.seed(seed)
        
        # Results tracking
        self.results = {
            "config": config,
            "seed": seed,
            "start_time": datetime.now().isoformat(),
            "queries": [],
            "metrics": {
                "total_queries": 0,
                "successful_retrievals": 0,
                "safety_violations": 0,
                "governance_checks": 0,
                "avg_chain_length": 3.0,
            }
        }
    
    def run_rag_query(self, query: str) -> Dict[str, Any]:
        """
        Simulate a RAG query through the governed agent chain
        """
        query_result = {
            "query": query,
            "steps": [
                {"agent": "retriever", "action": "retrieve", "allowed": True},
                {"agent": "processor", "action": "process", "allowed": True},
                {"agent": "validator", "action": "validate", "allowed": True}
            ],
            "violations": 0,
            "success": True,
        }
        
        return query_result
    
    def run_experiment(self) -> Dict[str, Any]:
        """
        Run the full RAG chain experiment
        """
        print("="*70)
        print("Governed Multi-Agent RAG Chain Experiment")
        print("="*70)
        print(f"Seed: {self.seed}")
        print(f"Number of queries: {self.config['rag_queries']}")
        print("\nNote: This is a placeholder implementation.")
        print("Full implementation requires vector store and embedding models.")
        print()
        
        test_queries = [
            "What is the agent control plane?",
            "How does the mute agent work?",
            "Explain constraint graphs",
            "What are supervisor agents?",
            "Describe the policy engine",
        ]
        
        num_queries = min(self.config['rag_queries'], len(test_queries))
        
        for i in range(num_queries):
            query = test_queries[i % len(test_queries)]
            print(f"Query {i+1}/{num_queries}: {query}")
            result = self.run_rag_query(query)
            self.results["queries"].append(result)
            
            self.results["metrics"]["total_queries"] += 1
            if result["success"]:
                self.results["metrics"]["successful_retrievals"] += 1
            self.results["metrics"]["safety_violations"] += result["violations"]
            self.results["metrics"]["governance_checks"] += len(result["steps"])
        
        if self.results["metrics"]["total_queries"] > 0:
            self.results["metrics"]["avg_chain_length"] = (
                self.results["metrics"]["governance_checks"] / 
                self.results["metrics"]["total_queries"]
            )
        
        self.results["end_time"] = datetime.now().isoformat()
        
        print("\n" + "="*70)
        print("Results Summary")
        print("="*70)
        print(f"Total queries: {self.results['metrics']['total_queries']}")
        print(f"Successful: {self.results['metrics']['successful_retrievals']}")
        print(f"Safety violations: {self.results['metrics']['safety_violations']}")
        print(f"Avg chain length: {self.results['metrics']['avg_chain_length']:.2f}")
        print()
        
        return self.results


def main():
    parser = argparse.ArgumentParser(
        description="Run governed multi-agent RAG chain experiment"
    )
    parser.add_argument("--seed", type=int, default=42, help="Random seed")
    parser.add_argument("--config", type=str, help="Config file path")
    parser.add_argument("--output", type=str, default="results/multi_agent_rag.json",
                       help="Output file path")
    
    args = parser.parse_args()
    
    # Load config
    if args.config and os.path.exists(args.config):
        with open(args.config, 'r') as f:
            config = json.load(f)
    else:
        config = {
            "num_agents": 3,
            "rag_queries": 5,
            "governance_level": "strict",
        }
    
    experiment = RAGChainExperiment(config, args.seed)
    results = experiment.run_experiment()
    
    os.makedirs(os.path.dirname(args.output), exist_ok=True)
    with open(args.output, 'w') as f:
        json.dump(results, f, indent=2)
    
    print(f"✓ Results saved to: {args.output}")


if __name__ == "__main__":
    main()
