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Profa
dc10361133
Performance fixes: - HNSW Array.splice → BinaryHeap for O(log n) insertion (25x speedup) - vectorSearch N+1 → batch SQL query with IN clause - loadPatterns N+1 → batched concurrent Promise.all (50/batch) - Math.min(...spread) → iterative loop to prevent stack overflow at >10K vertices - Move @faker-js/faker to devDependencies (-5.2MB production) Security fixes: - SQL table name allowlist for defense-in-depth (SQLite can't parameterize identifiers) - Object.assign() → safeAssignPooled() to prevent prototype pollution (3 locations) - OAuth timingSafeCompare pads strings instead of early-return on length mismatch - OAuth no longer stores raw tokens (hash-only storage) - OAuth revokedTokens Set→Map with timestamps for 24hr expiry cleanup - Remove hardcoded GCP credentials from sync/interfaces.ts defaults - Add .env.example GCP section with placeholder comments Testing: - Add 167 tests for 5 MCP security boundary validators (command, path-traversal, input-sanitizer, crypto, regex-safety) - Include QE swarm analysis reports for v3.6.3 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
179 lines
4.4 KiB
Bash
179 lines
4.4 KiB
Bash
# Agentic QE Fleet Environment Configuration
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# Copy this file to .env and fill in your values
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# ============================================
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# LLM PROVIDER CONFIGURATION (Phase 2)
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# ============================================
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# Primary LLM Provider API Keys
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ANTHROPIC_API_KEY=sk-ant-api03-your-key-here
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OPENROUTER_API_KEY=sk-or-v1-your-key-here
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# Optional: Additional LLM Providers
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# GROQ_API_KEY=gsk_your-key-here
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# OPENAI_API_KEY=sk-proj-your-key-here
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# GOOGLE_API_KEY=AIzaSy-your-key-here
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# LLM Provider Selection
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# LLM_PROVIDER: auto, anthropic, openrouter, groq, ruvllm
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# LLM_MODE: hybrid (uses HybridRouter), cloud-only, local-only
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LLM_PROVIDER=auto
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LLM_MODE=hybrid
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# ============================================
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# RUVECTOR SELF-LEARNING
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# Enables intelligent caching and pattern learning
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# ============================================
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# Enable RuVector integration (true/false)
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AQE_RUVECTOR_ENABLED=true
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# RuVector service URL (for HTTP API)
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AQE_RUVECTOR_URL=http://localhost:8080
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# RuVector PostgreSQL connection (for vector storage)
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RUVECTOR_HOST=localhost
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RUVECTOR_PORT=5432
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RUVECTOR_DATABASE=ruvector_db
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RUVECTOR_USER=ruvector
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RUVECTOR_PASSWORD=ruvector
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# ============================================
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# PATTERN STORE (Self-Learning Patterns)
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# Stores and retrieves QE patterns for learning
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# ============================================
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AQE_PATTERN_STORE_ENABLED=true
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AQE_PATTERN_DUAL_WRITE=true
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AQE_PATTERN_STORE_PATH=./data/qe-patterns.ruvector
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AQE_PATTERN_AUTO_SYNC=true
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# ============================================
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# CODE INTELLIGENCE
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# Knowledge graph and semantic code search
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# ============================================
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# Ollama for local embeddings
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OLLAMA_URL=http://localhost:11434
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# PostgreSQL for knowledge graph (can share with RuVector)
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PGHOST=localhost
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PGPORT=5432
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PGDATABASE=ruvector_db
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PGUSER=ruvector
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PGPASSWORD=ruvector
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# ============================================
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# N8N WORKFLOW INTEGRATION
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# For n8n workflow testing agents
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# ============================================
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# N8N_API_KEY=your-n8n-api-key
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# N8N_BASE_URL=https://your-instance.app.n8n.cloud/
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# ============================================
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# AQE FLEET CONFIGURATION
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# ============================================
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# Fleet identification
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FLEET_ID=agentic-qe-fleet
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FLEET_NAME="Autonomous Quality Engineering Fleet"
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# Agent limits
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AQE_MAX_AGENTS=10
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MAX_AGENTS=20
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# Provider preference for agents
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AQE_DEFAULT_PROVIDER=claude
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# Timing configuration
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HEARTBEAT_INTERVAL=30000
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TASK_TIMEOUT=600000
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# Agent pool sizing
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TEST_EXECUTOR_COUNT=3
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QUALITY_ANALYZER_COUNT=2
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PERFORMANCE_TESTER_COUNT=1
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# ============================================
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# STORAGE CONFIGURATION
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# ============================================
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# AQE data paths
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AQE_MEMORY_PATH=.aqe/memory
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AQE_LOG_PATH=.aqe/logs
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# Fleet database (SQLite for simple setup)
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DB_TYPE=sqlite
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DB_FILENAME=./data/fleet.db
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# For PostgreSQL (production recommended):
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# DB_TYPE=postgres
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# DB_HOST=localhost
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# DB_PORT=5432
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# DB_NAME=agentic_qe
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# DB_USER=username
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# DB_PASSWORD=password
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# ============================================
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# LOGGING CONFIGURATION
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# ============================================
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# Log level: error, warn, info, debug
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LOG_LEVEL=info
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AQE_LOG_LEVEL=info
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# Log format: json, text
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LOG_FORMAT=json
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# Log outputs: console, file (comma-separated)
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LOG_OUTPUTS=console,file
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# ============================================
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# API SERVER CONFIGURATION
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# ============================================
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API_PORT=3000
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API_HOST=0.0.0.0
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API_CORS=true
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# Rate limiting
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RATE_LIMIT_WINDOW=900000
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RATE_LIMIT_MAX=100
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# ============================================
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# SECURITY CONFIGURATION
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# ============================================
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# Generate secure values for production!
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# API_KEY=your-secure-api-key
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# JWT_SECRET=your-secure-jwt-secret
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ENCRYPTION_ALGORITHM=aes-256-gcm
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ENCRYPTION_KEY_LENGTH=32
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# ============================================
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# GCP CLOUD SYNC CONFIGURATION
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# Required for v3 cloud sync features
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# ============================================
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# GCP_PROJECT=your-gcp-project-id
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# GCP_ZONE=us-central1-a
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# GCP_INSTANCE=your-postgres-instance
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# GCP_DATABASE=aqe_learning
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# GCP_USER=your-db-user
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# GCP_TUNNEL_PORT=15432
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# ============================================
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# DEVELOPMENT & DEBUGGING
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# ============================================
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# Set to development for verbose output
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NODE_ENV=development
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# Enable debug mode (true/false)
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# DEBUG=true
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# Configuration file override (optional)
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# CONFIG_FILE=./config/fleet.yaml
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