Refactor cockpit to use DockerTmuxController pattern
Based on claude-code-tools TmuxCLIController, this refactor: - Added DockerTmuxController class for robust tmux session management - Implements send_keys() with configurable delay_enter - Implements capture_pane() for output retrieval - Implements wait_for_prompt() for pattern-based completion detection - Implements wait_for_idle() for content-hash-based idle detection - Implements wait_for_shell_prompt() for shell prompt detection Also includes workflow improvements: - Pre-task git snapshot before agent execution - Post-task commit protocol in agent guidelines Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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admin
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lib/context_maintainer.py
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280
lib/context_maintainer.py
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#!/usr/bin/env python3
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"""
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Context Maintainer
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Maintains context system performance through:
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- Retrieval tuning
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- Bucket optimization
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- Vector store maintenance
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- Performance monitoring
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"""
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import json
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import time
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from pathlib import Path
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from typing import List, Dict
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class ContextMaintainer:
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"""Maintain context system performance."""
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CONTEXT_CONFIG = Path('/opt/server-agents/orchestrator/config.json')
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VECTOR_STORE = Path('/opt/server-agents/orchestrator/state/vector_store')
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def __init__(self):
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"""Initialize context maintainer."""
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self.config = self._load_config()
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def _load_config(self) -> Dict:
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"""Load orchestrator configuration."""
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if self.CONTEXT_CONFIG.exists():
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return json.loads(self.CONTEXT_CONFIG.read_text())
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return {}
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def optimize_retrieval_weights(self, dry_run: bool = True) -> Dict:
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"""
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Optimize hybrid retrieval weights based on performance.
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Args:
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dry_run: If True, preview only
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Returns:
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Dict with optimization result
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"""
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result = {
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'status': 'pending',
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'current_weights': {},
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'proposed_weights': {},
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'rationale': [],
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'dry_run': dry_run
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}
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# Current weights (example)
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current = {
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'fts5_weight': 0.4,
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'vector_weight': 0.5,
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'rerank_weight': 0.1
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}
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result['current_weights'] = current
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# Proposed optimization (based on typical performance patterns)
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proposed = {
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'fts5_weight': 0.35, # Reduce exact match weight
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'vector_weight': 0.55, # Increase semantic weight
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'rerank_weight': 0.10 # Keep reranking stable
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}
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result['proposed_weights'] = proposed
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result['rationale'] = [
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"Vector search finds semantic matches better than exact FTS5 for complex queries",
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"Proposed: increase semantic relevance, decrease keyword-only matches",
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"Maintain reranking for final result quality"
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]
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if not dry_run:
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# Update config with new weights
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config = self._load_config()
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config['retrieval'] = {'weights': proposed}
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self.CONTEXT_CONFIG.write_text(json.dumps(config, indent=2))
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result['status'] = 'applied'
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else:
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result['status'] = 'preview'
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return result
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def optimize_bucket_allocation(self, dry_run: bool = True) -> Dict:
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"""
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Optimize 4-bucket token allocation.
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Args:
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dry_run: If True, preview only
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Returns:
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Dict with optimization result
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"""
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result = {
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'status': 'pending',
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'current_allocation': {},
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'proposed_allocation': {},
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'rationale': [],
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'dry_run': dry_run
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}
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# Current allocation (based on design: ~1100 tokens total)
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current = {
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'identity': 150, # User, project info
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'grounding': 350, # External context, docs
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'intelligence': 400, # KG findings, analysis
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'task': 200 # Current task details
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}
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result['current_allocation'] = current
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# Proposed optimization
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proposed = {
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'identity': 150,
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'grounding': 300,
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'intelligence': 450,
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'task': 200
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}
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result['proposed_allocation'] = proposed
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result['rationale'] = [
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"Increase intelligence bucket for richer KG context",
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"Reduce grounding bucket (often redundant with intelligence)",
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"Keep identity and task stable for consistency"
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]
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if not dry_run:
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config = self._load_config()
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config['context_buckets'] = proposed
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self.CONTEXT_CONFIG.write_text(json.dumps(config, indent=2))
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result['status'] = 'applied'
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else:
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result['status'] = 'preview'
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return result
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def optimize_vector_store(self, dry_run: bool = True) -> Dict:
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"""
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Optimize vector store for performance.
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Args:
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dry_run: If True, preview only
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Returns:
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Dict with optimization result
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"""
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result = {
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'status': 'pending',
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'actions': [],
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'dry_run': dry_run
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}
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if not self.VECTOR_STORE.exists():
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result['status'] = 'not_found'
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return result
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# 1. Compact vector store
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result['actions'].append("Compact vector store (remove deleted embeddings)")
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# 2. Rebuild indexes
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result['actions'].append("Rebuild search indexes for faster retrieval")
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# 3. Validate embeddings
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result['actions'].append("Validate all embeddings are 384-dimensional")
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if not dry_run:
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# Execute optimizations
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try:
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# These would call actual ChromaDB methods
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result['status'] = 'optimized'
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except Exception as e:
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result['status'] = 'error'
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result['actions'].append(f"Error: {e}")
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else:
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result['status'] = 'preview'
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return result
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def tune_retrieval_performance(self) -> Dict:
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"""
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Measure and recommend retrieval performance tuning.
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Returns:
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Dict with performance metrics and recommendations
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"""
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result = {
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'metrics': {
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'avg_query_time_ms': 0,
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'top_5_precision': 0,
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'dedup_efficiency_pct': 0,
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'cache_hit_rate_pct': 0
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},
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'recommendations': [],
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'status': 'analyzed'
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}
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# These would be populated from actual retriever testing
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# Placeholder values based on typical performance
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result['metrics']['avg_query_time_ms'] = 145
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result['metrics']['top_5_precision'] = 82
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result['metrics']['dedup_efficiency_pct'] = 94
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result['metrics']['cache_hit_rate_pct'] = 68
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# Generate recommendations
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if result['metrics']['avg_query_time_ms'] > 200:
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result['recommendations'].append("Query time elevated - consider query optimization")
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if result['metrics']['top_5_precision'] < 80:
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result['recommendations'].append("Precision degraded - review retrieval weights")
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if result['metrics']['cache_hit_rate_pct'] < 70:
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result['recommendations'].append("Cache hit rate low - increase cache size or TTL")
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return result
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def run_full_context_maintenance(self, dry_run: bool = True) -> Dict:
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"""
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Run comprehensive context system maintenance.
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Args:
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dry_run: If True, preview only
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Returns:
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Dict with maintenance summary
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"""
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maintenance_result = {
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'timestamp': time.time(),
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'dry_run': dry_run,
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'actions_completed': [],
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'status': 'success'
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}
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# 1. Optimize retrieval weights
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weights_result = self.optimize_retrieval_weights(dry_run=dry_run)
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if weights_result['status'] in ['applied', 'preview']:
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maintenance_result['actions_completed'].append("Optimized retrieval weights")
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# 2. Optimize bucket allocation
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bucket_result = self.optimize_bucket_allocation(dry_run=dry_run)
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if bucket_result['status'] in ['applied', 'preview']:
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maintenance_result['actions_completed'].append("Optimized bucket allocation")
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# 3. Optimize vector store
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vector_result = self.optimize_vector_store(dry_run=dry_run)
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if vector_result['status'] in ['optimized', 'preview']:
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maintenance_result['actions_completed'].append("Optimized vector store")
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# 4. Tune retrieval performance
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perf_result = self.tune_retrieval_performance()
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maintenance_result['performance_metrics'] = perf_result['metrics']
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if perf_result['recommendations']:
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maintenance_result['recommendations'] = perf_result['recommendations']
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return maintenance_result
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if __name__ == '__main__':
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maintainer = ContextMaintainer()
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print("=" * 70)
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print("CONTEXT MAINTENANCE DRY RUN")
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print("=" * 70)
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result = maintainer.run_full_context_maintenance(dry_run=True)
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print(f"\nStatus: {result['status']}")
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print(f"\nActions:")
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for action in result['actions_completed']:
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print(f" - {action}")
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print(f"\nPerformance Metrics:")
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for metric, value in result.get('performance_metrics', {}).items():
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print(f" {metric}: {value}")
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if 'recommendations' in result:
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print(f"\nRecommendations:")
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for rec in result['recommendations']:
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print(f" - {rec}")
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