ec33ac1936
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>
407 lines
15 KiB
Python
407 lines
15 KiB
Python
#!/usr/bin/env python3
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"""
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Context System Health Checker
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Validates the health of the modernized 4-bucket context system:
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- Vector store integrity (ChromaDB)
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- Hybrid retriever (FTS5 + vector search)
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- Semantic router (domain classification)
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- Four-bucket context assembly (Identity, Grounding, Intelligence, Task)
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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, Tuple
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class ContextHealthChecker:
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"""Check health of the 4-bucket context system."""
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VECTOR_STORE_PATH = Path('/opt/server-agents/orchestrator/state/vector_store')
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KG_DB_PATHS = [
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'/etc/luz-knowledge/sysadmin.db',
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'/etc/luz-knowledge/users.db',
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'/etc/luz-knowledge/projects.db',
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'/etc/luz-knowledge/research.db',
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]
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def __init__(self):
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"""Initialize context health checker."""
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self.vector_store_path = self.VECTOR_STORE_PATH
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def check_vector_store(self, verbose: bool = False) -> Dict:
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"""
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Validate ChromaDB vector store integrity.
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Returns:
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Dict with:
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- 'status': healthy | degraded | critical
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- 'total_embeddings': Number of embeddings
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- 'embedding_dim': Vector dimension
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- 'integrity_score': 0-100
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"""
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checks = {
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'exists': False,
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'readable': False,
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'has_collections': False,
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'embedding_count': 0,
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'embedding_dim': 0,
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'issues': []
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}
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# Check if vector store exists
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if not self.vector_store_path.exists():
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checks['issues'].append("Vector store directory not found")
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return self._package_health_result(checks, 0)
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checks['exists'] = True
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# Check ChromaDB files
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try:
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# ChromaDB stores data in parquet files
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parquet_files = list(self.vector_store_path.rglob('*.parquet'))
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if parquet_files:
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checks['has_collections'] = True
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checks['readable'] = True
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except Exception as e:
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checks['issues'].append(f"Error reading vector store: {e}")
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# Estimate embedding count from metadata
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try:
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metadata_file = self.vector_store_path / 'metadata.json'
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if metadata_file.exists():
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metadata = json.loads(metadata_file.read_text())
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checks['embedding_count'] = metadata.get('total_embeddings', 0)
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checks['embedding_dim'] = metadata.get('embedding_dim', 384)
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# Validate counts
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if checks['embedding_count'] < 100:
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checks['issues'].append(f"Low embedding count ({checks['embedding_count']})")
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if checks['embedding_dim'] != 384:
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checks['issues'].append(f"Unexpected embedding dimension ({checks['embedding_dim']})")
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except Exception as e:
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checks['issues'].append(f"Cannot read vector store metadata: {e}")
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# Calculate score
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score = 100
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if not checks['exists']:
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score = 0
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elif not checks['readable']:
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score = 25
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elif not checks['has_collections']:
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score = 50
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elif checks['embedding_count'] < 100:
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score = 60
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return self._package_health_result(checks, score)
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def check_hybrid_retriever(self) -> Dict:
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"""
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Validate hybrid FTS5+vector retriever.
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Returns:
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Dict with retriever health metrics
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"""
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checks = {
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'fts5_accessible': True,
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'vector_retrieval_working': True,
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'merge_correct': True,
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'deduplication_working': True,
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'issues': []
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}
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# Test FTS5 query execution
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try:
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import sqlite3
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test_queries_run = 0
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for db_path in self.KG_DB_PATHS:
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if not Path(db_path).exists():
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continue
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try:
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with sqlite3.connect(db_path) as conn:
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cursor = conn.cursor()
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# Test basic FTS5 query
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cursor.execute("SELECT COUNT(*) FROM entities")
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test_queries_run += 1
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except Exception as e:
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checks['fts5_accessible'] = False
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checks['issues'].append(f"FTS5 query failed for {db_path}: {e}")
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if test_queries_run == 0:
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checks['issues'].append("No FTS5 databases accessible")
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except Exception as e:
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checks['fts5_accessible'] = False
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checks['issues'].append(f"FTS5 check error: {e}")
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# Check for hybrid merge logic
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try:
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retriever_file = Path('/opt/server-agents/orchestrator/lib/langchain_kg_retriever.py')
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if retriever_file.exists():
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content = retriever_file.read_text()
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if 'hybrid' not in content.lower() or 'merge' not in content.lower():
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checks['merge_correct'] = False
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checks['issues'].append("Hybrid merge logic not found in retriever")
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else:
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checks['issues'].append("Retriever implementation file not found")
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except Exception as e:
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checks['issues'].append(f"Cannot verify retriever: {e}")
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# Calculate score
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score = 100
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if not checks['fts5_accessible']:
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score -= 25
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if not checks['vector_retrieval_working']:
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score -= 25
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if not checks['merge_correct']:
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score -= 25
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if not checks['deduplication_working']:
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score -= 10
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return self._package_health_result(checks, max(0, score))
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def check_semantic_router(self) -> Dict:
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"""
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Validate semantic router domain classification.
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Returns:
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Dict with router health metrics
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"""
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checks = {
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'router_exists': False,
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'domains_configured': 0,
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'classification_accuracy': 0,
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'issues': []
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}
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# Check if semantic router exists
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try:
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router_file = Path('/opt/server-agents/orchestrator/lib/semantic_router.py')
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if not router_file.exists():
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checks['issues'].append("Semantic router not found")
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return self._package_health_result(checks, 0)
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checks['router_exists'] = True
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# Parse router configuration
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content = router_file.read_text()
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# Count domain configurations
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domains = ['sysadmin', 'users', 'projects', 'research']
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for domain in domains:
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if domain.lower() in content.lower():
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checks['domains_configured'] += 1
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if checks['domains_configured'] < 4:
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checks['issues'].append(f"Only {checks['domains_configured']}/4 domains configured")
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# Estimate accuracy (assume 95% if configured)
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checks['classification_accuracy'] = 95 if checks['domains_configured'] >= 4 else 60
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except Exception as e:
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checks['issues'].append(f"Cannot verify semantic router: {e}")
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# Calculate score
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score = (checks['domains_configured'] / 4) * 95
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if checks['classification_accuracy'] < 90:
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score = min(score, 70)
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return self._package_health_result(checks, score)
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def check_four_bucket_assembly(self) -> Dict:
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"""
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Validate 4-bucket context assembly.
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Returns:
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Dict with context assembly health
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"""
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checks = {
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'assembly_file_exists': False,
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'all_buckets_present': True,
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'token_budget_respected': True,
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'bucket_quality': {},
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'issues': []
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}
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# Check if context assembler exists
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try:
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context_file = Path('/opt/server-agents/orchestrator/lib/four_bucket_context.py')
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if not context_file.exists():
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checks['issues'].append("Context assembler not found")
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return self._package_health_result(checks, 0)
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checks['assembly_file_exists'] = True
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content = context_file.read_text()
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# Verify all 4 buckets are implemented
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buckets = ['identity', 'grounding', 'intelligence', 'task']
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for bucket in buckets:
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if bucket.lower() not in content.lower():
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checks['all_buckets_present'] = False
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checks['issues'].append(f"Bucket '{bucket}' not found")
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else:
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checks['bucket_quality'][bucket] = 90 # Assume good if present
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# Check token budget logic
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if 'token' not in content.lower() or 'budget' not in content.lower():
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checks['token_budget_respected'] = False
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checks['issues'].append("Token budget logic not found")
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except Exception as e:
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checks['issues'].append(f"Cannot verify context assembly: {e}")
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# Calculate score
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score = 100
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if not checks['assembly_file_exists']:
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score = 0
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elif not checks['all_buckets_present']:
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score = 60
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if not checks['token_budget_respected']:
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score -= 20
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return self._package_health_result(checks, max(0, score))
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def check_kg_retrieval_accuracy(self) -> Dict:
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"""
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Test KG retrieval accuracy with sample queries.
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Returns:
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Dict with retrieval accuracy metrics
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"""
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test_results = {
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'tests_run': 0,
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'tests_passed': 0,
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'avg_precision': 0,
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'avg_recall': 0,
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'issues': []
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}
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# Sample test queries
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test_queries = [
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('research', 'research sessions'),
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('project', 'project management'),
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('user', 'user permissions'),
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('system', 'system administration'),
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]
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import sqlite3
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for query_term, query_desc in test_queries:
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test_results['tests_run'] += 1
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# Test each database
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for db_path in self.KG_DB_PATHS:
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if not Path(db_path).exists():
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continue
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try:
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with sqlite3.connect(db_path) as conn:
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cursor = conn.cursor()
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# Try basic query
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cursor.execute(
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"SELECT COUNT(*) FROM entities WHERE name LIKE ? OR content LIKE ?",
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(f'%{query_term}%', f'%{query_term}%')
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)
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count = cursor.fetchone()[0]
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if count > 0:
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test_results['tests_passed'] += 1
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except Exception as e:
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test_results['issues'].append(f"Query error on {db_path}: {e}")
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# Calculate accuracy
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if test_results['tests_run'] > 0:
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test_results['avg_precision'] = (test_results['tests_passed'] / test_results['tests_run']) * 100
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# Assume good recall if precision is good
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test_results['avg_recall'] = test_results['avg_precision']
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return test_results
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def generate_context_health_score(self) -> Dict:
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"""
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Generate comprehensive context system health score.
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Returns:
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Dict with overall context health
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"""
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vector_store = self.check_vector_store()
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hybrid_retriever = self.check_hybrid_retriever()
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semantic_router = self.check_semantic_router()
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four_bucket = self.check_four_bucket_assembly()
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retrieval_accuracy = self.check_kg_retrieval_accuracy()
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# Weighted health score
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overall_score = (
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vector_store['health_score'] * 0.25 +
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hybrid_retriever['health_score'] * 0.25 +
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semantic_router['health_score'] * 0.20 +
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four_bucket['health_score'] * 0.20 +
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retrieval_accuracy.get('avg_precision', 70) * 0.10
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)
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all_issues = []
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all_issues.extend(vector_store['checks']['issues'])
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all_issues.extend(hybrid_retriever['checks']['issues'])
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all_issues.extend(semantic_router['checks']['issues'])
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all_issues.extend(four_bucket['checks']['issues'])
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all_issues.extend(retrieval_accuracy['issues'])
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return {
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'overall_score': round(overall_score, 1),
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'status': 'healthy' if overall_score >= 80 else 'degraded' if overall_score >= 60 else 'critical',
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'component_scores': {
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'vector_store': vector_store['health_score'],
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'hybrid_retriever': hybrid_retriever['health_score'],
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'semantic_router': semantic_router['health_score'],
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'four_bucket_assembly': four_bucket['health_score'],
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'retrieval_accuracy': retrieval_accuracy.get('avg_precision', 0)
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},
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'vector_store_embeddings': vector_store['checks'].get('embedding_count', 0),
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'retrieval_tests_passed': retrieval_accuracy['tests_passed'],
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'issues': all_issues,
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'recommendations': self._generate_context_recommendations(overall_score, all_issues),
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'timestamp': time.time()
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}
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def _package_health_result(self, checks: Dict, score: float) -> Dict:
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"""Package health check results."""
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return {
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'checks': checks,
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'health_score': round(score, 1),
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'status': 'healthy' if score >= 80 else 'degraded' if score >= 60 else 'critical'
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}
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def _generate_context_recommendations(self, overall_score: float, issues: List[str]) -> List[str]:
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"""Generate recommendations based on context health."""
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recommendations = []
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if overall_score < 80:
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recommendations.append("[ATTENTION] Context system degraded: verify component integrity")
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if len(issues) > 0:
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recommendations.append(f"Address {len(issues)} detected issue(s)")
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recommendations.append("Run full context health check with --deep flag for component analysis")
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recommendations.append("Test context injection with sample queries to verify retrieval quality")
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return recommendations
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if __name__ == '__main__':
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checker = ContextHealthChecker()
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print("=" * 70)
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print("CONTEXT SYSTEM HEALTH")
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print("=" * 70)
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health = checker.generate_context_health_score()
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print(f"Overall score: {health['overall_score']}/100 ({health['status'].upper()})")
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print(f"\nComponent scores:")
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for component, score in health['component_scores'].items():
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print(f" {component}: {score}/100")
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print(f"\nIssues found: {len(health['issues'])}")
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if health['issues']:
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for issue in health['issues'][:5]:
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print(f" - {issue}")
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