proffesor-for-testing__agentic-qe/docs/architecture/agentdb-schema.md

AgentDB Schema Documentation

Overview

The AgentDB database (.agentic-qe/agentdb.db) serves as the consolidated learning and pattern storage system for all 19 QE agents in the Agentic QE Fleet.

Version: 1.1.0 Schema Type: SQLite with WAL mode Location: .agentic-qe/agentdb.db

Schema Architecture

Design Principles

1. Single Source of Truth: All QE learning data consolidated in one database
2. Performance Optimized: HNSW vector indexing for 150x faster similarity search
3. Framework Agnostic: Support for Jest, Mocha, Cypress, Vitest, Playwright, AVA, Jasmine
4. Cross-Project Learning: Pattern sharing across different projects and frameworks
5. Graceful Degradation: FTS5 falls back to regular indexed tables when unavailable

Database Tables

1. patterns (Base AgentDB Table)

Purpose: Core vector embeddings storage for AgentDB

CREATE TABLE patterns (
  id TEXT PRIMARY KEY,
  type TEXT NOT NULL,
  confidence REAL NOT NULL DEFAULT 0.5,
  embedding BLOB,
  metadata TEXT,
  created_at INTEGER NOT NULL DEFAULT (unixepoch())
)

Indexes:

• idx_patterns_type on type

Used By: AgentDB core, all reasoning agents

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2. test_patterns (QE Learning Core)

Purpose: Store test patterns with code signatures for deduplication

CREATE TABLE test_patterns (
  id TEXT PRIMARY KEY NOT NULL,
  pattern_type TEXT NOT NULL,  -- edge-case, integration, boundary, etc.
  framework TEXT NOT NULL,      -- jest, mocha, cypress, vitest, etc.
  language TEXT NOT NULL DEFAULT 'typescript',
  code_signature_hash TEXT NOT NULL,
  code_signature TEXT NOT NULL,
  test_template TEXT NOT NULL,
  metadata TEXT NOT NULL,
  version TEXT NOT NULL DEFAULT '1.0.0',
  created_at INTEGER NOT NULL DEFAULT (unixepoch()),
  updated_at INTEGER NOT NULL DEFAULT (unixepoch()),
  CHECK(pattern_type IN ('edge-case', 'integration', 'boundary',
                         'error-handling', 'unit', 'e2e',
                         'performance', 'security')),
  CHECK(framework IN ('jest', 'mocha', 'cypress', 'vitest',
                      'playwright', 'ava', 'jasmine'))
)

Indexes:

• idx_patterns_framework_type on (framework, pattern_type)
• idx_patterns_signature_hash on code_signature_hash
• idx_patterns_dedup UNIQUE on (code_signature_hash, framework)

Used By: TestGeneratorAgent, CoverageAnalyzerAgent, all pattern-based agents

---

3. pattern_usage (Quality Metrics)

Purpose: Track pattern usage, success rates, and quality metrics per project

CREATE TABLE pattern_usage (
  id INTEGER PRIMARY KEY AUTOINCREMENT,
  pattern_id TEXT NOT NULL,
  project_id TEXT NOT NULL,
  usage_count INTEGER NOT NULL DEFAULT 0,
  success_count INTEGER NOT NULL DEFAULT 0,
  failure_count INTEGER NOT NULL DEFAULT 0,
  avg_execution_time REAL NOT NULL DEFAULT 0.0,
  avg_coverage_gain REAL NOT NULL DEFAULT 0.0,
  flaky_count INTEGER NOT NULL DEFAULT 0,
  quality_score REAL NOT NULL DEFAULT 0.0,
  first_used INTEGER NOT NULL DEFAULT (unixepoch()),
  last_used INTEGER NOT NULL DEFAULT (unixepoch()),
  FOREIGN KEY (pattern_id) REFERENCES test_patterns(id) ON DELETE CASCADE,
  UNIQUE(pattern_id, project_id)
)

Indexes:

• idx_usage_pattern on pattern_id
• idx_usage_quality on quality_score DESC

Used By: Pattern quality scoring, improvement loop, analytics

---

4. cross_project_mappings (Framework Translation)

Purpose: Enable pattern sharing across different testing frameworks

CREATE TABLE cross_project_mappings (
  id INTEGER PRIMARY KEY AUTOINCREMENT,
  pattern_id TEXT NOT NULL,
  source_framework TEXT NOT NULL,
  target_framework TEXT NOT NULL,
  transformation_rules TEXT NOT NULL,
  compatibility_score REAL NOT NULL DEFAULT 1.0,
  project_count INTEGER NOT NULL DEFAULT 0,
  success_rate REAL NOT NULL DEFAULT 0.0,
  created_at INTEGER NOT NULL DEFAULT (unixepoch()),
  updated_at INTEGER NOT NULL DEFAULT (unixepoch()),
  FOREIGN KEY (pattern_id) REFERENCES test_patterns(id) ON DELETE CASCADE,
  UNIQUE(pattern_id, source_framework, target_framework)
)

Use Cases:

• Jest → Vitest migrations
• Cypress → Playwright conversions
• Cross-framework pattern reuse

Used By: Framework migration tools, pattern adapters

---

5. pattern_similarity_index (Fast Similarity Queries)

Purpose: Pre-computed similarity scores for O(1) pattern similarity queries

CREATE TABLE pattern_similarity_index (
  pattern_a TEXT NOT NULL,
  pattern_b TEXT NOT NULL,
  similarity_score REAL NOT NULL,
  structure_similarity REAL NOT NULL,
  identifier_similarity REAL NOT NULL,
  metadata_similarity REAL NOT NULL,
  algorithm TEXT NOT NULL DEFAULT 'hybrid-tfidf',
  last_computed INTEGER NOT NULL DEFAULT (unixepoch()),
  PRIMARY KEY (pattern_a, pattern_b),
  FOREIGN KEY (pattern_a) REFERENCES test_patterns(id) ON DELETE CASCADE,
  FOREIGN KEY (pattern_b) REFERENCES test_patterns(id) ON DELETE CASCADE
)

Indexes:

• idx_similarity_score on similarity_score DESC

Used By: Pattern recommendations, duplicate detection, clustering

---

6. pattern_fts (Full-Text Search)

Purpose: Fast full-text search across pattern metadata

FTS5 Version (when available):

CREATE VIRTUAL TABLE pattern_fts USING fts5(
  pattern_id UNINDEXED,
  pattern_name,
  description,
  tags,
  framework,
  pattern_type,
  content='',
  tokenize='porter ascii'
)

Fallback Version (sql.js WASM without FTS5):

CREATE TABLE pattern_fts (
  pattern_id TEXT PRIMARY KEY,
  pattern_name TEXT,
  description TEXT,
  tags TEXT,
  framework TEXT,
  pattern_type TEXT
)

Indexes (Fallback):

• idx_fts_pattern_name on pattern_name
• idx_fts_framework on framework
• idx_fts_pattern_type on pattern_type

Used By: Pattern search CLI, pattern discovery

---

7. schema_version (Migration Tracking)

Purpose: Track applied schema versions for safe migrations

CREATE TABLE schema_version (
  version TEXT PRIMARY KEY,
  applied_at INTEGER NOT NULL DEFAULT (unixepoch()),
  description TEXT
)

Current Version: 1.1.0 - "Initial QE ReasoningBank schema"

Used By: Database migration system, version compatibility checks

---

Performance Characteristics

Operation	Performance	Technology
Vector Search	<100µs	HNSW indexing (150x faster)
Pattern Retrieval	<1ms	In-memory cache
Batch Insert	2ms/100	WAL mode + transactions
Similarity Query	O(1)	Pre-computed index
Full-Text Search	<10ms	FTS5 (or indexed fallback)

Storage Estimates

Table	Typical Size	Max Size
patterns	10-100 MB	1 GB
test_patterns	50-500 MB	5 GB
pattern_usage	1-10 MB	100 MB
cross_project_mappings	100 KB - 10 MB	100 MB
pattern_similarity_index	10-100 MB	1 GB
pattern_fts	5-50 MB	500 MB
Total	~100 MB - 1 GB	~10 GB

Initialization

Tables are automatically created during aqe init via:

File: src/core/memory/RealAgentDBAdapter.ts Method: createQELearningTables() Process:

1. Enable WAL mode for concurrent access
2. Create test_patterns with indexes
3. Create pattern_usage with foreign keys
4. Create cross_project_mappings
5. Create pattern_similarity_index
6. Create pattern_fts (with FTS5 fallback)
7. Create schema_version and insert v1.1.0

Maintenance

Vacuum and Optimization

# Vacuum database (reclaim space)
sqlite3 .agentic-qe/agentdb.db "VACUUM;"

# Analyze for query optimization
sqlite3 .agentic-qe/agentdb.db "ANALYZE;"

Backup

# Create backup
sqlite3 .agentic-qe/agentdb.db ".backup .agentic-qe/agentdb.backup.db"

# Restore from backup
cp .agentic-qe/agentdb.backup.db .agentic-qe/agentdb.db

Statistics

# Table row counts
sqlite3 .agentic-qe/agentdb.db "
  SELECT 'patterns' as table_name, COUNT(*) as rows FROM patterns
  UNION ALL
  SELECT 'test_patterns', COUNT(*) FROM test_patterns
  UNION ALL
  SELECT 'pattern_usage', COUNT(*) FROM pattern_usage
  UNION ALL
  SELECT 'cross_project_mappings', COUNT(*) FROM cross_project_mappings
  UNION ALL
  SELECT 'pattern_similarity_index', COUNT(*) FROM pattern_similarity_index
  UNION ALL
  SELECT 'pattern_fts', COUNT(*) FROM pattern_fts;
"

Migration History

Version	Date	Description
1.1.0	2025-01	Initial QE ReasoningBank schema with all 7 tables

Related Documentation

• AgentDB Integration Guide
• Learning System Architecture
• Pattern Management Guide
• Adapter Architecture

Security

• SQL Injection Protection: All queries use parameterized statements
• Data Validation: CHECK constraints on pattern types and frameworks
• Foreign Key Enforcement: CASCADE deletes maintain referential integrity
• Access Control: File-based permissions via .agentic-qe/ directory

Troubleshooting

FTS5 Not Available

Symptom: Warning "FTS5 not available, using regular table" Cause: sql.js WASM build doesn't include FTS5 Impact: Reduced full-text search performance (still functional) Solution: Use native SQLite for production, or accept indexed fallback

Database Locked

Symptom: "database is locked" errors Cause: Concurrent write access without WAL mode Solution: WAL mode is enabled automatically; check file permissions

Large Database Size

Symptom: agentdb.db exceeds 1GB Cause: Many patterns or high similarity index density Solution: Run VACUUM, or archive old patterns

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Generated: 2025-01-18 Schema Version: 1.1.0 Maintained By: Agentic QE Fleet Team