Agent skill

when-optimizing-vector-search-use-agentdb-optimization

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Install this agent skill to your Project

npx add-skill https://github.com/DNYoussef/context-cascade/tree/main/skills/platforms/when-optimizing-vector-search-use-agentdb-optimization

SKILL.md

/============================================================================/ /* 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

typescript
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

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]

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