Context Database Improvement Recommendations

Generated: December 29, 2025 Database Version: sessions.db v1.0 Current Size: 1,603 MB (after deduplication)

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Executive Summary

The context database is functional but underutilized. Key findings:

Metric	Current	Potential	Status
Active tables	30/56 (54%)	56/56 (100%)	Pending
Semantic search	Disabled	Full RAG capability	Pending
Knowledge graph	Empty	Entity-relationship extraction	Pending
Learning system	Empty	User progress tracking	Pending
Automation	Manual triggers	Scheduled + event-driven	In Progress
Deduplication	UNIQUE constraints added	Prevention at ingestion	✅ DONE
Query Views	10 views	19 views	✅ DONE
Session Insights	1,163 sessions analyzed	All sessions	✅ DONE

Priority Recommendations:

1. P0: Enable semantic embeddings for RAG search
2. P1: Implement knowledge graph extraction
3. P1: Add ingestion-time deduplication
4. P2: Activate learning system
5. P2: Schedule automated maintenance

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Workflow Steps

1. Initialize - Set up the environment
2. Configure - Apply settings
3. Execute - Run the process
4. Validate - Check results
5. Complete - Finalize workflow

1. Content Gaps (What's Missing)

1.1 Empty Tables Analysis

Table	Purpose	Status	Action
doc_embeddings	Semantic document search	Empty (0 rows)	Enable embedding generation
entries_embeddings	Semantic entry search	Empty (0 rows)	Enable embedding generation
knowledge_entities	Entity extraction	Empty (0 rows)	Implement NER pipeline
knowledge_graph	Entity relationships	Empty (0 rows)	Implement relation extraction
file_backup_entries	File version tracking	Empty (0 rows)	Enable file backup hook
learning_* (4 tables)	User learning progress	Empty (0 rows)	Activate learning system

1.2 Underutilized Tables

Table	Rows	Expected Usage	Gap
session_insights	5,753	1 per session	✅ DONE - 1,163 sessions analyzed
project_summaries	1	1 per project (~5+)	Only one project tracked
file_snapshots	1	Many per session	File tracking disabled
workspace_registry	3	All workspaces (~10+)	Manual registration only

1.3 Missing Data Types

Currently not captured:

• Git commit metadata (author, branch, diff stats)
• Tool usage patterns (which tools, success rates)
• User preferences and settings
• External API call logs
• Performance metrics (response times)

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2. Ingestion Pipeline Improvements

2.1 Current Ingestion Points

Script	Tables Updated	Trigger
unified-message-extractor.py	entries, messages	Manual (/cx)
context-db.py	doc_index, decisions, etc.	Manual (/cxq)
component-indexer.py	components, capabilities	Manual
component-frontmatter-indexer.py	component_frontmatter	Manual

2.2 Recommended New Pipelines

A. Automatic Session Capture (Hook-based)

# hooks/post-message-capture.py
# Trigger: After each Claude message
# Action: Extract and store in real-time
def on_message(message):
    hash = compute_hash(message)
    if not exists_in_db(hash):
        store_message(message)
        extract_decisions(message)
        extract_code_patterns(message)

B. Semantic Embedding Pipeline

# scripts/generate-embeddings.py
# Trigger: After doc_index updates
# Action: Generate embeddings for semantic search
def generate_embeddings():
    unembedded = get_docs_without_embeddings()
    for doc in unembedded:
        embedding = embed(doc.content)
        store_embedding(doc.id, embedding)

C. Knowledge Graph Extraction

# scripts/extract-knowledge-graph.py
# Trigger: After decisions/patterns update
# Action: Extract entities and relationships
def extract_knowledge():
    for decision in new_decisions():
        entities = extract_entities(decision.text)
        relations = extract_relations(decision.text)
        store_graph(entities, relations)

2.3 Ingestion-Time Deduplication

Problem: Current deduplication is post-hoc (after duplicates exist).

Solution: Check before insert:

-- Pattern: INSERT OR IGNORE with UNIQUE constraint
INSERT OR IGNORE INTO decisions (decision, type, project, confidence)
VALUES (?, ?, ?, ?);

-- Pattern: UPSERT (update if exists)
INSERT INTO code_patterns (code, name, language, type, usage_count)
VALUES (?, ?, ?, ?, 1)
ON CONFLICT(code) DO UPDATE SET
    usage_count = usage_count + 1,
    last_used = datetime('now');

Required Changes:

1. Add UNIQUE constraints to all content-hash columns ✅ DONE (Dec 29, 2025)
   • idx_messages_unique_hash on messages(hash)
   • idx_entries_unique_hash on entries(hash)
   • idx_error_solutions_unique_hash on error_solutions(error_hash)
   • idx_decisions_unique on decisions(decision)
   • idx_patterns_unique_code on code_patterns(code)
   • idx_doc_unique_path on doc_index(file_path)
2. Modify ingestion scripts to use INSERT OR IGNORE
3. Add upsert logic for counters (usage_count, access_count)

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3. Indexing Improvements

3.1 Current Index Coverage

Table	Indexes	Coverage	Gap
messages	6	Good	Missing: content FTS
entries	7	Good	Missing: content FTS
decisions	6	Good	None
code_patterns	7	Good	None
doc_index	10	Excellent	None
components	5	Moderate	Missing: full-text name/description

3.2 Recommended New Indexes

A. Full-Text Search Indexes (Already exist but verify populated)

-- Verify FTS tables are populated
SELECT COUNT(*) FROM messages_fts;      -- Should match messages
SELECT COUNT(*) FROM decisions_fts;     -- Should match decisions
SELECT COUNT(*) FROM doc_search;        -- Should match doc_index
SELECT COUNT(*) FROM component_search;  -- Should match components

B. Composite Indexes for Common Queries ✅ DONE (Dec 29, 2025)

-- Frequently used query pattern: recent by role and session
CREATE INDEX IF NOT EXISTS idx_entries_session_role_time
ON entries(session_id, role, timestamp DESC);

-- Query pattern: components by type and status
CREATE INDEX IF NOT EXISTS idx_components_type_status
ON components(type, status);

-- Query pattern: docs by workspace and type
CREATE INDEX IF NOT EXISTS idx_doc_workspace_type
ON doc_index(workspace_id, doc_type);

C. Partial Indexes (for filtered queries) ✅ DONE (Dec 29, 2025)

-- Only index active components (538 of 1937)
CREATE INDEX IF NOT EXISTS idx_components_active
ON components(type, name) WHERE status = 'active';

-- High-confidence decisions (skipped - all decisions at 0.4 confidence)
-- CREATE INDEX IF NOT EXISTS idx_decisions_high_conf
-- ON decisions(type, project) WHERE confidence >= 0.8;

3.3 Semantic Search Indexes

Current: doc_embeddings and entries_embeddings are empty.

Action: Implement vector similarity search:

# Option 1: Use SQLite with manual cosine similarity (slow for large datasets)
# Option 2: Use sqlite-vec extension (requires compilation)
# Option 3: Use external vector DB (Chroma, Qdrant) with SQLite metadata

# Recommended: Option 3 - Chroma integration exists in scripts/core/chromadb_setup.py

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4. Automation Recommendations

4.1 Scheduled Tasks

Task	Frequency	Script	Priority
Backup to GCS	Daily	backup-context-db.sh	P0
Integrity check	Weekly	PRAGMA integrity_check	P0
Statistics update	Weekly	ANALYZE	P1
Orphan cleanup	Weekly	delete-orphans.py	P1
Vacuum	Monthly	VACUUM	P2

Cron configuration:

# /etc/cron.d/coditect-context-db
0 2 * * * /path/to/backup-context-db.sh       # Daily 2 AM
0 3 * * 0 sqlite3 sessions.db "PRAGMA integrity_check" # Weekly Sunday 3 AM
0 3 * * 1 sqlite3 sessions.db "ANALYZE"        # Weekly Monday 3 AM
0 4 1 * * sqlite3 sessions.db "VACUUM"         # Monthly 1st, 4 AM

4.2 Event-Driven Automation

Event	Trigger	Action
Session end	/export or session close	Extract and index
Document save	File save in workspace	Re-index doc_index
Component change	Agent/command/skill modified	Update component_frontmatter
High-value decision	Confidence >= 0.9	Add to knowledge graph

Hook implementation:

# hooks/post-session.sh (already exists - enhance)
#!/bin/bash
# After session ends:
# 1. Run /cx to extract messages
# 2. Update doc_index
# 3. Generate embeddings
# 4. Extract knowledge graph

python3 scripts/unified-message-extractor.py
python3 scripts/context-db.py index-docs
python3 scripts/generate-embeddings.py --incremental
python3 scripts/extract-knowledge-graph.py --incremental

4.3 Maintenance Automation Script

#!/bin/bash
# scripts/db-maintenance.sh

DB_PATH="context-storage/sessions.db"

echo "=== Context Database Maintenance ==="
echo "Started: $(date)"

# 1. Integrity check
echo "Checking integrity..."
sqlite3 "$DB_PATH" "PRAGMA integrity_check" | grep -q "ok" || {
    echo "ERROR: Database integrity check failed!"
    exit 1
}

# 2. Update statistics
echo "Updating statistics..."
sqlite3 "$DB_PATH" "ANALYZE"

# 3. Clean orphans
echo "Cleaning orphaned records..."
sqlite3 "$DB_PATH" "
DELETE FROM doc_embeddings WHERE doc_id NOT IN (SELECT id FROM doc_index);
DELETE FROM entries_embeddings WHERE entry_id NOT IN (SELECT id FROM entries);
DELETE FROM summaries WHERE entry_id NOT IN (SELECT id FROM entries);
"

# 4. Report sizes
echo "Table sizes:"
sqlite3 "$DB_PATH" "
SELECT name,
       (SELECT COUNT(*) FROM pragma_table_info(name)) as columns,
       page_count * 4096 / 1024 / 1024.0 as size_mb
FROM sqlite_master
WHERE type='table'
  AND name NOT LIKE 'sqlite_%'
ORDER BY page_count DESC
LIMIT 10;
"

echo "Completed: $(date)"

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5. Query Interface Improvements

5.1 Current Query Methods

Method	Interface	Limitation
/cxq	CLI command	Limited query types
context-db.py	Python script	Requires command line
Direct SQLite	sqlite3 shell	Requires SQL knowledge

5.2 Recommended Query Enhancements

A. Natural Language Query Interface

# Current: SQL-based
/cxq --sql "SELECT * FROM decisions WHERE confidence > 0.9"

# Proposed: Natural language
/cxq "show me high-confidence decisions from last week"
/cxq "what errors have I solved before?"
/cxq "find code patterns for authentication"

B. Saved/Named Queries ✅ IMPLEMENTED (Dec 29, 2025)

8 new views created for instant queries:

View	Purpose	Query
knowledge_stats	Row counts for all knowledge tables	/cxq --knowledge-stats
today_activity	Last 24h activity	/cxq --today-activity
recent_decisions	High-confidence decisions (7 days)	/cxq --view recent_decisions
error_frequency	Most common errors	/cxq --error-frequency
patterns_by_language	Code patterns by language	/cxq --patterns-summary
session_activity	Session durations	/cxq --session-summary
component_summary	Components by type/status	/cxq --component-overview
docs_by_category	Documentation by category	/cxq --docs-summary
sessions_by_topic	Session insights by topic	SELECT * FROM sessions_by_topic WHERE topic='deployment'

-- Example: Query any view directly
sqlite3 context-storage/sessions.db "SELECT * FROM knowledge_stats"
sqlite3 context-storage/sessions.db "SELECT * FROM error_frequency LIMIT 10"

-- Query sessions by topic (new)
sqlite3 context-storage/sessions.db "SELECT * FROM sessions_by_topic WHERE topic='authentication'"
sqlite3 context-storage/sessions.db "SELECT * FROM sessions_by_topic WHERE topic='deployment' ORDER BY hours DESC"

C. Query Result Formatting

# Current: Raw output
/cxq --decisions

# Proposed: Multiple formats
/cxq --decisions --format table    # ASCII table
/cxq --decisions --format json     # JSON output
/cxq --decisions --format markdown # Markdown table
/cxq --decisions --format csv      # CSV export

5.3 Performance Query Hints

-- Use EXPLAIN QUERY PLAN to verify index usage
EXPLAIN QUERY PLAN
SELECT * FROM entries WHERE session_id = 'abc' AND role = 'assistant';

-- Should show: USING INDEX idx_entries_session

-- Common slow query patterns to avoid:
-- AVOID: LIKE '%pattern%' (can't use index)
-- USE: Full-text search instead
SELECT * FROM decisions_fts WHERE decisions_fts MATCH 'pattern';

-- AVOID: Complex JOINs without LIMIT
-- USE: Subqueries with LIMIT first
SELECT * FROM entries e
JOIN (SELECT id FROM messages ORDER BY timestamp DESC LIMIT 100) m
ON e.id = m.id;

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6. Implementation Roadmap

Phase 1: Foundation (Week 1) - ✅ COMPLETE

• Add remaining UNIQUE constraints (Dec 29, 2025)
• Create db-maintenance.sh script (Dec 29, 2025)
• Set up daily backup cron job
• Create common query views (8 new views) (Dec 29, 2025)
• Add view query commands to /cxq (Dec 29, 2025 - in progress)

Phase 2: Semantic Search (Week 2-3)

• Populate doc_embeddings table
• Populate entries_embeddings table
• Integrate Chroma for vector search
• Add /cxq --semantic query mode

Phase 3: Knowledge Graph (Week 4)

• Implement entity extraction pipeline
• Populate knowledge_entities
• Implement relation extraction
• Populate knowledge_graph

Phase 4: Automation (Week 5)

• Add post-session hook for extraction
• Add file-save hook for doc indexing
• Implement event-driven triggers
• Add monitoring and alerting

Phase 5: Query Enhancements (Week 6)

• Natural language query parsing
• Saved query management (views created) (Dec 29, 2025)
• Multiple output formats
• Query performance dashboard

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7. Metrics to Track

Metric	Current	Target	Tracking
Database size	1,603 MB	< 2 GB	du -h sessions.db
Unique messages	92,924	Growing	SELECT COUNT(*) FROM messages
Query response time	Unknown	< 100ms	Query timing
Index hit rate	100%	> 90%	EXPLAIN QUERY PLAN verified
Backup success rate	Unknown	100%	Backup logs
Duplicate rate	0%	0%	UNIQUE constraints prevent duplicates
Query views	19	20+	SELECT COUNT(*) FROM sqlite_master WHERE type='view'
UNIQUE constraints	6	6	All major tables protected
Total indexes	164	160+	Composite + partial indexes added
Session insights	5,753	All sessions	1,163 sessions analyzed
Hours tracked	1,884	Growing	Total development hours indexed

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Related Documents

• DATABASE-SCHEMA.md - Complete schema reference
• DATABASE-SCHEMA.json - Machine-readable schema
• MEMORY-MANAGEMENT-GUIDE.md - Context preservation guide
• backup-context-db.sh - Backup script
• db-maintenance.sh - Maintenance script
• deduplicate-all-tables.py - Deduplication script
• generate-session-insights.py - Session insights generator (NEW)

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Author: CODITECT Framework Review Status: Phase 1 complete, actively implementing Last Updated: December 29, 2025 Next Review: January 2026

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Changelog

Date	Change
2025-12-29	Added sessions_by_topic view (19 views total)
2025-12-29	Generated session insights: 5,753 rows, 1,163 sessions, 1,884 hours tracked
2025-12-29	Added generate-session-insights.py script
2025-12-29	Added 4 performance indexes: 3 composite + 1 partial (164 total)
2025-12-29	Added /cxq view commands (--views, --today-activity, etc.)
2025-12-29	Phase 1 complete: UNIQUE constraints, views, maintenance script
2025-12-29	Initial document created