DNYoussef /============================================================================/ /* AGENTDB VECTOR SEARCH OPTIMIZATION SKILL :: VERILINGUA x VERIX EDITION / /============================================================================*/
name: AgentDB Vector Search Optimization version: 1.0.0 description: | [assert|neutral] AgentDB Vector Search Optimization skill for agentdb workflows [ground:given] [conf:0.95] [state:confirmed] category: agentdb tags:
- general author: system cognitive_frame: primary: evidential goal_analysis: first_order: "Execute AgentDB Vector Search Optimization workflow" second_order: "Ensure quality and consistency" third_order: "Enable systematic agentdb processes"
/----------------------------------------------------------------------------/ /* S0 META-IDENTITY / /----------------------------------------------------------------------------*/
[define|neutral] SKILL := { name: "AgentDB Vector Search Optimization", category: "agentdb", version: "1.0.0", layer: L1 } [ground:given] [conf:1.0] [state:confirmed]
/----------------------------------------------------------------------------/ /* S1 COGNITIVE FRAME / /----------------------------------------------------------------------------*/
[define|neutral] COGNITIVE_FRAME := { frame: "Evidential", source: "Turkish", force: "How do you know?" } [ground:cognitive-science] [conf:0.92] [state:confirmed]
Kanitsal Cerceve (Evidential Frame Activation)
Kaynak dogrulama modu etkin.
/----------------------------------------------------------------------------/ /* S2 TRIGGER CONDITIONS / /----------------------------------------------------------------------------*/
[define|neutral] TRIGGER_POSITIVE := { keywords: ["AgentDB Vector Search Optimization", "agentdb", "workflow"], context: "user needs AgentDB Vector Search Optimization capability" } [ground:given] [conf:1.0] [state:confirmed]
/----------------------------------------------------------------------------/ /* S3 CORE CONTENT / /----------------------------------------------------------------------------*/
AgentDB Vector Search Optimization
Kanitsal Cerceve (Evidential Frame Activation)
Kaynak dogrulama modu etkin.
Overview
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations for scaling to millions of vectors.
SOP Framework: 5-Phase Optimization
Phase 1: Baseline Performance (1 hour)
- Measure current metrics (latency, throughput, memory)
- Identify bottlenecks
- Set optimization targets
Phase 2: Apply Quantization (1-2 hours)
- Configure product quantization
- Train codebooks
- Apply compression
- Validate accuracy
Phase 3: Implement HNSW Indexing (1-2 hours)
- Build HNSW index
- Tune parameters (M, efConstruction, efSearch)
- Benchmark speedup
Phase 4: Configure Caching (1 hour)
- Implement query cache
- Set TTL and eviction policies
- Monitor hit rates
Phase 5: Benchmark Results (1-2 hours)
- Run comprehensive benchmarks
- Compare before/after
- Validate improvements
Quick Start
import { AgentDB, Quantization, QueryCache } from 'agentdb-optimization';
const db = new AgentDB({ name: 'optimized-db', dimensions: 1536 });
// Quantization (4x memory reduction)
const quantizer = new Quantization({
method: 'product-quantization',
compressionRatio: 4
});
await db.applyQuantization(quantizer);
// HNSW indexing (150x speedup)
await db.createIndex({
type: 'hnsw',
params: { M: 16, efConstruction: 200 }
});
// Caching
db.setCache(new QueryCache({
maxSize: 10000,
ttl: 3600000
}));
Optimization Techniques
Quantization
- Product Quantization: 4-8x compression
- Scalar Quantization: 2-4x compression
- Binary Quantization: 32x compression
Indexing
- HNSW: 150x faster, high accuracy
- IVF: Fast, partitioned search
- LSH: Approximate search
Caching
- Query Cache: LRU eviction
- Result Cache: TTL-based
- Embedding Cache: Reuse embeddings
Success Metrics
- [assert|neutral] Memory reduction: 4-32x [ground:acceptance-criteria] [conf:0.90] [state:provisional]
- [assert|neutral] Search speedup: 150x [ground:acceptance-criteria] [conf:0.90] [state:provisional]
- [assert|neutral] Accuracy maintained: > 95% [ground:acceptance-criteria] [conf:0.90] [state:provisional]
- [assert|neutral] Cache hit rate: > 70% [ground:acceptance-criteria] [conf:0.90] [state:provisional]
MCP Requirements
This skill operates using AgentDB's npm package and API only. No additional MCP servers required.
All AgentDB optimization operations are performed through:
- npm CLI:
npx agentdb@latest - TypeScript/JavaScript API:
import { AgentDB, Quantization, QueryCache } from 'agentdb-optimization'
Additional Resources
- Full docs: SKILL.md
- AgentDB Optimization: https://agentdb.dev/docs/optimization
Core Principles
AgentDB Vector Search Optimization operates on 3 fundamental principles:
Principle 1: Quantization - Trade Negligible Accuracy for Massive Memory Reduction
Vector databases face a fundamental constraint: high-dimensional embeddings (768-1536 dimensions) consume enormous memory at scale. Quantization techniques compress vectors by 4-32x through codebook encoding, enabling systems to hold millions of vectors in memory while maintaining 95%+ accuracy.
In practice:
- Apply product quantization (4-8x compression) for production workloads requiring high accuracy
- Use scalar quantization (2-4x compression) when exact distances matter for ranking
- Deploy binary quantization (32x compression) for massive-scale approximate search where recall > precision
Principle 2: HNSW Indexing - Logarithmic Search Instead of Linear Scan
Brute-force vector search scales O(n) - doubling vectors doubles search time. HNSW (Hierarchical Navigable Small World) indexes create multi-layer graphs that enable O(log n) search, delivering 150x speedups with tunable accuracy trade-offs through the efSearch parameter.
In practice:
- Build HNSW indexes
/----------------------------------------------------------------------------/ /* S4 SUCCESS CRITERIA / /----------------------------------------------------------------------------*/
[define|neutral] SUCCESS_CRITERIA := { primary: "Skill execution completes successfully", quality: "Output meets quality thresholds", verification: "Results validated against requirements" } [ground:given] [conf:1.0] [state:confirmed]
/----------------------------------------------------------------------------/ /* S5 MCP INTEGRATION / /----------------------------------------------------------------------------*/
[define|neutral] MCP_INTEGRATION := { memory_mcp: "Store execution results and patterns", tools: ["mcp__memory-mcp__memory_store", "mcp__memory-mcp__vector_search"] } [ground:witnessed:mcp-config] [conf:0.95] [state:confirmed]
/----------------------------------------------------------------------------/ /* S6 MEMORY NAMESPACE / /----------------------------------------------------------------------------*/
[define|neutral] MEMORY_NAMESPACE := { pattern: "skills/agentdb/AgentDB Vector Search Optimization/{project}/{timestamp}", store: ["executions", "decisions", "patterns"], retrieve: ["similar_tasks", "proven_patterns"] } [ground:system-policy] [conf:1.0] [state:confirmed]
[define|neutral] MEMORY_TAGGING := { WHO: "AgentDB Vector Search Optimization-{session_id}", WHEN: "ISO8601_timestamp", PROJECT: "{project_name}", WHY: "skill-execution" } [ground:system-policy] [conf:1.0] [state:confirmed]
/----------------------------------------------------------------------------/ /* S7 SKILL COMPLETION VERIFICATION / /----------------------------------------------------------------------------*/
[direct|emphatic] COMPLETION_CHECKLIST := { agent_spawning: "Spawn agents via Task()", registry_validation: "Use registry agents only", todowrite_called: "Track progress with TodoWrite", work_delegation: "Delegate to specialized agents" } [ground:system-policy] [conf:1.0] [state:confirmed]
/----------------------------------------------------------------------------/ /* S8 ABSOLUTE RULES / /----------------------------------------------------------------------------*/
[direct|emphatic] RULE_NO_UNICODE := forall(output): NOT(unicode_outside_ascii) [ground:windows-compatibility] [conf:1.0] [state:confirmed]
[direct|emphatic] RULE_EVIDENCE := forall(claim): has(ground) AND has(confidence) [ground:verix-spec] [conf:1.0] [state:confirmed]
[direct|emphatic] RULE_REGISTRY := forall(agent): agent IN AGENT_REGISTRY [ground:system-policy] [conf:1.0] [state:confirmed]
/----------------------------------------------------------------------------/ /* PROMISE / /----------------------------------------------------------------------------*/
[commit|confident] AGENTDB VECTOR SEARCH OPTIMIZATION_VERILINGUA_VERIX_COMPLIANT [ground:self-validation] [conf:0.99] [state:confirmed]