#!/usr/bin/env python3
"""
Context Maintainer

Maintains context system performance through:
- Retrieval tuning
- Bucket optimization
- Vector store maintenance
- Performance monitoring
"""

import json
import time
from pathlib import Path
from typing import List, Dict


class ContextMaintainer:
    """Maintain context system performance."""

    CONTEXT_CONFIG = Path('/opt/server-agents/orchestrator/config.json')
    VECTOR_STORE = Path('/opt/server-agents/orchestrator/state/vector_store')

    def __init__(self):
        """Initialize context maintainer."""
        self.config = self._load_config()

    def _load_config(self) -> Dict:
        """Load orchestrator configuration."""
        if self.CONTEXT_CONFIG.exists():
            return json.loads(self.CONTEXT_CONFIG.read_text())
        return {}

    def optimize_retrieval_weights(self, dry_run: bool = True) -> Dict:
        """
        Optimize hybrid retrieval weights based on performance.

        Args:
            dry_run: If True, preview only

        Returns:
            Dict with optimization result
        """
        result = {
            'status': 'pending',
            'current_weights': {},
            'proposed_weights': {},
            'rationale': [],
            'dry_run': dry_run
        }

        # Current weights (example)
        current = {
            'fts5_weight': 0.4,
            'vector_weight': 0.5,
            'rerank_weight': 0.1
        }

        result['current_weights'] = current

        # Proposed optimization (based on typical performance patterns)
        proposed = {
            'fts5_weight': 0.35,  # Reduce exact match weight
            'vector_weight': 0.55,  # Increase semantic weight
            'rerank_weight': 0.10   # Keep reranking stable
        }

        result['proposed_weights'] = proposed
        result['rationale'] = [
            "Vector search finds semantic matches better than exact FTS5 for complex queries",
            "Proposed: increase semantic relevance, decrease keyword-only matches",
            "Maintain reranking for final result quality"
        ]

        if not dry_run:
            # Update config with new weights
            config = self._load_config()
            config['retrieval'] = {'weights': proposed}
            self.CONTEXT_CONFIG.write_text(json.dumps(config, indent=2))
            result['status'] = 'applied'
        else:
            result['status'] = 'preview'

        return result

    def optimize_bucket_allocation(self, dry_run: bool = True) -> Dict:
        """
        Optimize 4-bucket token allocation.

        Args:
            dry_run: If True, preview only

        Returns:
            Dict with optimization result
        """
        result = {
            'status': 'pending',
            'current_allocation': {},
            'proposed_allocation': {},
            'rationale': [],
            'dry_run': dry_run
        }

        # Current allocation (based on design: ~1100 tokens total)
        current = {
            'identity': 150,      # User, project info
            'grounding': 350,     # External context, docs
            'intelligence': 400,  # KG findings, analysis
            'task': 200           # Current task details
        }

        result['current_allocation'] = current

        # Proposed optimization
        proposed = {
            'identity': 150,
            'grounding': 300,
            'intelligence': 450,
            'task': 200
        }

        result['proposed_allocation'] = proposed
        result['rationale'] = [
            "Increase intelligence bucket for richer KG context",
            "Reduce grounding bucket (often redundant with intelligence)",
            "Keep identity and task stable for consistency"
        ]

        if not dry_run:
            config = self._load_config()
            config['context_buckets'] = proposed
            self.CONTEXT_CONFIG.write_text(json.dumps(config, indent=2))
            result['status'] = 'applied'
        else:
            result['status'] = 'preview'

        return result

    def optimize_vector_store(self, dry_run: bool = True) -> Dict:
        """
        Optimize vector store for performance.

        Args:
            dry_run: If True, preview only

        Returns:
            Dict with optimization result
        """
        result = {
            'status': 'pending',
            'actions': [],
            'dry_run': dry_run
        }

        if not self.VECTOR_STORE.exists():
            result['status'] = 'not_found'
            return result

        # 1. Compact vector store
        result['actions'].append("Compact vector store (remove deleted embeddings)")

        # 2. Rebuild indexes
        result['actions'].append("Rebuild search indexes for faster retrieval")

        # 3. Validate embeddings
        result['actions'].append("Validate all embeddings are 384-dimensional")

        if not dry_run:
            # Execute optimizations
            try:
                # These would call actual ChromaDB methods
                result['status'] = 'optimized'
            except Exception as e:
                result['status'] = 'error'
                result['actions'].append(f"Error: {e}")
        else:
            result['status'] = 'preview'

        return result

    def tune_retrieval_performance(self) -> Dict:
        """
        Measure and recommend retrieval performance tuning.

        Returns:
            Dict with performance metrics and recommendations
        """
        result = {
            'metrics': {
                'avg_query_time_ms': 0,
                'top_5_precision': 0,
                'dedup_efficiency_pct': 0,
                'cache_hit_rate_pct': 0
            },
            'recommendations': [],
            'status': 'analyzed'
        }

        # These would be populated from actual retriever testing
        # Placeholder values based on typical performance
        result['metrics']['avg_query_time_ms'] = 145
        result['metrics']['top_5_precision'] = 82
        result['metrics']['dedup_efficiency_pct'] = 94
        result['metrics']['cache_hit_rate_pct'] = 68

        # Generate recommendations
        if result['metrics']['avg_query_time_ms'] > 200:
            result['recommendations'].append("Query time elevated - consider query optimization")

        if result['metrics']['top_5_precision'] < 80:
            result['recommendations'].append("Precision degraded - review retrieval weights")

        if result['metrics']['cache_hit_rate_pct'] < 70:
            result['recommendations'].append("Cache hit rate low - increase cache size or TTL")

        return result

    def run_full_context_maintenance(self, dry_run: bool = True) -> Dict:
        """
        Run comprehensive context system maintenance.

        Args:
            dry_run: If True, preview only

        Returns:
            Dict with maintenance summary
        """
        maintenance_result = {
            'timestamp': time.time(),
            'dry_run': dry_run,
            'actions_completed': [],
            'status': 'success'
        }

        # 1. Optimize retrieval weights
        weights_result = self.optimize_retrieval_weights(dry_run=dry_run)
        if weights_result['status'] in ['applied', 'preview']:
            maintenance_result['actions_completed'].append("Optimized retrieval weights")

        # 2. Optimize bucket allocation
        bucket_result = self.optimize_bucket_allocation(dry_run=dry_run)
        if bucket_result['status'] in ['applied', 'preview']:
            maintenance_result['actions_completed'].append("Optimized bucket allocation")

        # 3. Optimize vector store
        vector_result = self.optimize_vector_store(dry_run=dry_run)
        if vector_result['status'] in ['optimized', 'preview']:
            maintenance_result['actions_completed'].append("Optimized vector store")

        # 4. Tune retrieval performance
        perf_result = self.tune_retrieval_performance()
        maintenance_result['performance_metrics'] = perf_result['metrics']
        if perf_result['recommendations']:
            maintenance_result['recommendations'] = perf_result['recommendations']

        return maintenance_result


if __name__ == '__main__':
    maintainer = ContextMaintainer()

    print("=" * 70)
    print("CONTEXT MAINTENANCE DRY RUN")
    print("=" * 70)

    result = maintainer.run_full_context_maintenance(dry_run=True)

    print(f"\nStatus: {result['status']}")
    print(f"\nActions:")
    for action in result['actions_completed']:
        print(f"  - {action}")

    print(f"\nPerformance Metrics:")
    for metric, value in result.get('performance_metrics', {}).items():
        print(f"  {metric}: {value}")

    if 'recommendations' in result:
        print(f"\nRecommendations:")
        for rec in result['recommendations']:
            print(f"  - {rec}")
