ewoooc/services/elephant_alpha_orchestrator.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Elephant Alpha AI Agent Super Orchestrator

Elephant Alpha (100B, 256K context) - AI Agent 3.0  autonomous decision-making
- Deep strategic reasoning across all AI agents
- Long-term memory integration
- Autonomous workflow orchestration
- Self-learning and adaptation

Position: Super Orchestrator above Hermes/NemoTron/OpenClaw
"""

import os
import json
import asyncio
from datetime import datetime, timedelta
from typing import Dict, List, Any, Optional
from dataclasses import dataclass
from services.logger_manager import SystemLogger
from database.manager import get_session
from sqlalchemy import text
from services.ai_provider import ai_provider_service
from services.elephant_service import elephant_service

# Elephant Alpha Configuration removed - now handled by elephant_service

logger = SystemLogger("ElephantAlphaOrchestrator").get_logger()

@dataclass
class AgentCapability:
    """AI Agent capability definition"""
    name: str
    model: str
    strengths: List[str]
    limitations: List[str]
    cost_per_token: float
    max_context: int

@dataclass
class StrategicDecision:
    """High-level strategic decision from Elephant Alpha"""
    priority: str  # critical/high/medium/low
    agents_required: List[str]
    reasoning: str
    expected_outcome: str
    confidence: float
    execution_plan: List[Dict[str, Any]]
    resource_requirements: Dict[str, Any]

class ElephantAlphaOrchestrator:
    """
    Elephant Alpha Super Orchestrator
    
    Capabilities:
    - Cross-agent coordination and optimization
    - Strategic long-term planning
    - Autonomous decision making
    - Resource allocation optimization
    - Self-learning integration
    """
    
    def __init__(self):
        self.ai_provider = ai_provider_service
        self.elephant = elephant_service
        
        # Agent Registry
        self.agents = {
            "hermes": AgentCapability(
                name="Hermes Analyst",
                model="hermes3:latest",
                strengths=["price_competition_analysis", "threat_detection", "market_intelligence"],
                limitations=["context_window", "real_time_data"],
                cost_per_token=0.0,
                max_context=8000
            ),
            "nemotron": AgentCapability(
                name="NemoTron Dispatcher", 
                model="meta/llama-3.1-8b-instruct",
                strengths=["tool_calling", "action_dispatch", "decision_making"],
                limitations=["strategic_depth", "long_term_planning"],
                cost_per_token=0.0,
                max_context=128000
            ),
            "openclaw": AgentCapability(
                name="OpenClaw Strategist",
                model="gemini-2.0-flash",
                strengths=["strategic_planning", "market_analysis", "insight_generation"],
                limitations=["real_time_execution", "direct_actions"],
                cost_per_token=0.0,
                max_context=32000
            )
        }
        
        # System context for Elephant Alpha
        self.system_prompt = self._build_system_prompt()
    
    def _build_system_prompt(self) -> str:
        """Build comprehensive system prompt for Elephant Alpha"""
        return f"""You are Elephant Alpha, the Super Orchestrator for momo-pro-system e-commerce AI platform.

CURRENT ARCHITECTURE:
- You coordinate 3 specialized AI agents: Hermes (Analyst), NemoTron (Dispatcher), OpenClaw (Strategist)
- Your context window: 256,000 tokens - enables deep strategic reasoning
- Your role: Autonomous decision-making and agent orchestration

AGENT CAPABILITIES:
1. HERMES (hermes3:latest)
   - Strengths: Price competition analysis, threat detection, market intelligence
   - Limitations: Limited context window, no real-time data access
   - Best for: Analyzing large datasets, identifying patterns, threat assessment

2. NEMOTRON (meta/llama-3.1-8b-instruct)
   - Strengths: Tool calling, action dispatch, immediate decision making
   - Limitations: Limited strategic depth, short-term focus
   - Best for: Executing actions, immediate responses, tool-based operations

3. OPENCLAW (gemini-2.0-flash)
   - Strengths: Strategic planning, market analysis, insight generation
   - Limitations: No direct execution capabilities, analysis-only
   - Best for: Long-term strategy, market insights, recommendation generation

YOUR SUPERVISORY CAPABILITIES:
- Cross-agent coordination and optimization
- Strategic long-term planning (weeks/months ahead)
- Autonomous decision making with confidence scoring
- Resource allocation optimization
- Self-learning integration from past outcomes
- Conflict resolution between agent recommendations
- Priority-based task scheduling
- Risk assessment and mitigation planning

DECISION FRAMEWORK:
1. Analyze the business context and objectives
2. Evaluate which agents are best suited for the task
3. Determine optimal sequencing and parallelization
4. Assess resource requirements and constraints
5. Generate confidence-scored recommendations
6. Create detailed execution plans
7. Monitor and adapt based on outcomes

BUSINESS CONTEXT:
- E-commerce platform (momo-pro-system)
- Real-time price competition monitoring
- Automated threat detection and response
- Strategic market positioning
- Revenue optimization and growth
- Customer experience enhancement

RESPONSE FORMAT:
Always respond with structured JSON:
{{
  "strategic_assessment": "Overall strategic evaluation",
  "priority": "critical|high|medium|low",
  "agents_required": ["agent1", "agent2"],
  "reasoning": "Detailed reasoning for decision",
  "expected_outcome": "Expected business impact",
  "confidence": 0.85,
  "execution_plan": [
    {{
      "step": 1,
      "agent": "hermes",
      "action": "analyze_price_competition",
      "parameters": {{}},
      "expected_duration": "2-3 minutes"
    }}
  ],
  "resource_requirements": {{
    "compute_cost": "$0.00",
    "time_estimate": "5-10 minutes",
    "human_oversight": "minimal|moderate|required"
  }},
  "risk_factors": ["potential_risk1", "potential_risk2"],
  "contingency_plans": ["backup_plan1", "backup_plan2"]
}}

AUTONOMY LEVEL:
You have autonomous decision-making authority. Make decisions confidently based on available data and strategic objectives. Request human intervention only for critical business impacts or ethical considerations.

CURRENT DATE: {datetime.now().strftime('%Y-%m-%d')}
BUSINESS OBJECTIVE: Optimize e-commerce performance through intelligent automation
"""

    async def analyze_and_coordinate(self, business_context: Dict[str, Any]) -> StrategicDecision:
        """
        Main coordination method - analyze business context and orchestrate agents
        
        Args:
            business_context: Current business situation, objectives, constraints
            
        Returns:
            StrategicDecision: Comprehensive strategic plan with agent coordination
        """
        start_time = datetime.now()
        
        try:
            # Build comprehensive prompt with business context
            prompt = self._build_coordination_prompt(business_context)
            
            # Call Elephant Alpha via unified service
            response = self.elephant.generate(
                prompt=prompt,
                system_prompt=self.system_prompt,
                json_mode=True,
                temperature=0.3,
                timeout=180
            )
            
            if not response.success:
                raise RuntimeError(response.error)

            # Parse and validate response
            decision_data = json.loads(response.content)
            decision = self._parse_strategic_decision(decision_data)
            
            # Log decision for learning
            await self._log_decision(decision, business_context, start_time)
            
            return decision
            
        except Exception as e:
            logger.error(f"[ElephantAlpha] Coordination failed: {e}")
            # Fallback to conservative decision
            return self._fallback_decision(business_context)
    
    def _build_coordination_prompt(self, context: Dict[str, Any]) -> str:
        """Build detailed coordination prompt for Elephant Alpha"""
        
        # Get recent performance data for context
        recent_performance = self._get_recent_performance_metrics()
        
        # Get current agent statuses
        agent_status = self._get_agent_status()
        
        prompt = f"""
BUSINESS CONTEXT:
{json.dumps(context, indent=2)}

RECENT PERFORMANCE METRICS:
{json.dumps(recent_performance, indent=2)}

CURRENT AGENT STATUS:
{json.dumps(agent_status, indent=2)}

CURRENT SITUATION:
- Date: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
- System Load: {self._get_system_load()}
- Pending Actions: {self._get_pending_actions_count()}

REQUIRED DECISION:
Based on the current business context and system state, determine the optimal strategy and agent coordination. Consider:
1. Immediate priorities vs long-term objectives
2. Resource constraints and optimization opportunities
3. Risk factors and mitigation strategies
4. Agent capabilities and current workload
5. Historical performance and learning outcomes

Provide your strategic decision in the specified JSON format.
"""
        return prompt
    
    # _call_elephant_alpha removed - logic moved to elephant_service
    
    def _parse_strategic_decision(self, decision_data: Dict[str, Any]) -> StrategicDecision:
        """Parse Elephant Alpha response into StrategicDecision object"""
        return StrategicDecision(
            priority=decision_data.get("priority", "medium"),
            agents_required=decision_data.get("agents_required", []),
            reasoning=decision_data.get("reasoning", ""),
            expected_outcome=decision_data.get("expected_outcome", ""),
            confidence=float(decision_data.get("confidence", 0.5)),
            execution_plan=decision_data.get("execution_plan", []),
            resource_requirements=decision_data.get("resource_requirements", {})
        )
    
    def _get_recent_performance_metrics(self) -> Dict[str, Any]:
        """Get recent performance metrics for context"""
        session = get_session()
        try:
            # Get recent AI insights performance
            rows = session.execute(text("""
                SELECT 
                    insight_type,
                    COUNT(*) as count,
                    AVG(confidence) as avg_confidence,
                    MAX(created_at) as last_activity
                FROM ai_insights 
                WHERE created_at >= NOW() - INTERVAL '7 days'
                GROUP BY insight_type
            """)).fetchall()
            
            metrics = {}
            for row in rows:
                metrics[row.insight_type] = {
                    "count": row.count,
                    "avg_confidence": float(row.avg_confidence) if row.avg_confidence else 0.0,
                    "last_activity": row.last_activity.isoformat() if row.last_activity else None
                }
            
            return metrics
            
        finally:
            session.close()
    
    def _get_agent_status(self) -> Dict[str, Any]:
        """Get current status of all agents"""
        return {
            "hermes": {
                "status": "active",
                "last_analysis": self._get_last_analysis_time("hermes"),
                "queue_size": 0
            },
            "nemotron": {
                "status": "active", 
                "last_dispatch": self._get_last_analysis_time("nemotron"),
                "queue_size": 0
            },
            "openclaw": {
                "status": "active",
                "last_strategy": self._get_last_analysis_time("openclaw"),
                "queue_size": 0
            }
        }
    
    def _get_last_analysis_time(self, agent: str) -> str:
        """Get last activity time for agent"""
        session = get_session()
        try:
            row = session.execute(text("""
                SELECT MAX(created_at) as last_time 
                FROM ai_insights 
                WHERE created_by = :agent
            """), {"agent": agent}).fetchone()
            
            return row.last_time.isoformat() if row and row.last_time else None
            
        finally:
            session.close()
    
    def _get_system_load(self) -> str:
        """Get current system load"""
        return "normal"  # Placeholder - could integrate with monitoring
    
    def _get_pending_actions_count(self) -> int:
        """Get count of pending actions"""
        session = get_session()
        try:
            row = session.execute(text("""
                SELECT COUNT(*) as count 
                FROM action_plans 
                WHERE status = 'pending'
            """)).fetchone()
            
            return row.count if row else 0
            
        finally:
            session.close()
    
    async def _log_decision(self, decision: StrategicDecision, context: Dict[str, Any], start_time: datetime):
        """Log decision for learning and audit"""
        session = get_session()
        try:
            # B4b FIX: metadata → metadata_json; confidence/created_by added by migration 015
            session.execute(text("""
                INSERT INTO ai_insights
                    (insight_type, content, confidence, created_by, status, metadata_json)
                VALUES
                    (:type, :content, :confidence, :created_by, :status, :metadata)
            """), {
                "type": "elephant_alpha_decision",
                "content": json.dumps({
                    "decision": decision.__dict__,
                    "context": context,
                    "duration_seconds": (datetime.now() - start_time).total_seconds()
                }),
                "confidence": decision.confidence,
                "created_by": "elephant_alpha",
                "status": "executed",
                "metadata": json.dumps({"agents_required": decision.agents_required})
            })
            session.commit()
            
        finally:
            session.close()
    
    def _fallback_decision(self, context: Dict[str, Any]) -> StrategicDecision:
        """Fallback decision if Elephant Alpha fails"""
        return StrategicDecision(
            priority="medium",
            agents_required=["openclaw"],
            reasoning="Elephant Alpha unavailable, using conservative OpenClaw strategy",
            expected_outcome="Basic strategic analysis",
            confidence=0.6,
            execution_plan=[{
                "step": 1,
                "agent": "openclaw",
                "action": "generate_market_analysis",
                "parameters": context,
                "expected_duration": "2-3 minutes"
            }],
            resource_requirements={"compute_cost": "$0.00", "time_estimate": "5 minutes"}
        )

# Singleton instance
elephant_orchestrator = ElephantAlphaOrchestrator()