#!/usr/bin/env python3
"""
Genesis GHL Knowledge Absorber
===============================
Systematically absorbs and synthesizes GHL expertise.
Consolidates API, MCP, and MaStanley docs for all Genesis agents.
"""

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

# Add genesis-system to path
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))

from notebook_lm_skill import NotebookLMAgent
from blackboard import Blackboard, EntryType, TaskStatus

class GHLKnowledgeAbsorber:
    """
    Agent responsible for systematic GHL knowledge absorption.
    """
    
    def __init__(self):
        self.notebook = NotebookLMAgent()
        self.bb = Blackboard()
        self.kb_path = r"E:\genesis-system\knowledge-bases\ghl_mastery.json"
        os.makedirs(os.path.dirname(self.kb_path), exist_ok=True)
        
        print("[OK] GHL Knowledge Absorber Initialized.")

    def absorb_sequence(self):
        """
        Executes the systematic absorption sequence.
        1. GHL Core/API
        2. GHL MCP
        3. MaStanley MCP
        """
        print("[GHL] Beginning Systematic Absorption Sequence...")
        
        sequences = [
            {"topic": "GHL API v2", "context": "Focus on highlevel API documentation, authentication, and endpoint mapping."},
            {"topic": "GHL MCP Architecture", "description": "Analyzing Model Context Protocol integration with GoHighLevel tools."},
            {"topic": "MaStanley MCP Mastery", "description": "Deep dive into MaStanley's specialized GHL automation framework."}
        ]
        
        consolidated_intel = {}
        
        for seq in sequences:
            topic = seq.get("topic")
            print(f"[GHL] Absorbing: {topic}...")
            
            # Use NotebookLM skill for deep synthesis of each topic
            intel = self.notebook.deep_research(
                f"Consolidate all known expertise for: {topic}. {seq.get('context', '')} {seq.get('description', '')}",
                depth=1
            )
            
            consolidated_intel[topic] = intel.get("report")
            
            # Log progress to Blackboard
            self.bb.write(
                entry_type=EntryType.FINDING,
                content={"topic": topic, "status": "absorbed", "timestamp": datetime.now().isoformat()},
                author="ghl_knowledge_absorber",
                tags=["ghl_mastery", "knowledge_absorption"]
            )
            
        # Store as Unified Knowledge Base
        with open(self.kb_path, "w") as f:
            json.dump(consolidated_intel, f, indent=4)
            
        print(f"[OK] GHL Mastery KB stored at: {self.kb_path}")
        return self.kb_path

if __name__ == "__main__":
    absorber = GHLKnowledgeAbsorber()
    # absorber.absorb_sequence() # Executed via directive