About

Imported from aiskillstore/marketplace (skills/dnyoussef/agentdb-vector-search-optimization/SKILL.md). Install upstream with npx skills add aiskillstore/marketplace --skill agentdb-vector-search-optimization. Copyright stays with the author.

AgentDB Vector Search Optimization

Name: agentdb-vector-search-optimization
Author: aiskillstore

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

Memory reduction: 4-32x
Search speedup: 150x
Accuracy maintained: > 95%
Cache hit rate: > 70%

Additional Resources

Full docs: SKILL.md
AgentDB Optimization: https://agentdb.dev/docs/optimization

Use it

Copy one of these into your project. Installing also returns the manifest and these snippets.

yaml

targets:
  - https://api.opensmartroute.ai/api/v1/registry/aiskillstore-marketplace-agentdb-vector-search-optimization/manifest   # or paste the manifest below

Manifest

An Open Capability Manifest: the router reads it to know what this does, what it costs and when to pick it.

aiskillstore-marketplace-agentdb-vector-search-optimization.ocm.jsonjson

{
  "ocm": "1",
  "id": "aiskillstore-marketplace-agentdb-vector-search-optimization",
  "kind": "skill",
  "name": "agentdb-vector-search-optimization",
  "description": "Optimize AgentDB vector search performance using quantization for 4-32x memory reduction, HNSW indexing for 150x faster search, caching, and batch operations for scaling to millions of vectors.",
  "publisher": "aiskillstore",
  "version": "1.0.0",
  "capabilities": {
    "domains": [
      "math"
    ],
    "tags": [
      "skill-md",
      "agentdb",
      "optimization",
      "quantization",
      "hnsw-indexing",
      "performance",
      "skills-sh"
    ],
    "languages": [
      "en"
    ]
  },
  "quality_prior": 0.6,
  "examples": [
    "Optimize AgentDB vector search performance using quantization for 4-32x memory reduction, HNSW indexing for 150x faster search, caching, and batch operations for scaling to millions of vectors."
  ],
  "primary": false,
  "metadata": {
    "source": {
      "provider": "skills.sh",
      "repository": "https://github.com/aiskillstore/marketplace",
      "path": "skills/dnyoussef/agentdb-vector-search-optimization/SKILL.md",
      "ref": "HEAD",
      "url": "https://github.com/aiskillstore/marketplace/blob/HEAD/skills/dnyoussef/agentdb-vector-search-optimization/SKILL.md",
      "key": "aiskillstore/marketplace/skills/dnyoussef/agentdb-vector-search-optimization/SKILL.md"
    }
  },
  "instructions": "# AgentDB Vector Search Optimization\n\n## Overview\n\nOptimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations for scaling to millions of vectors.\n\n## SOP Framework: 5-Phase Optimization\n\n### Phase 1: Baseline Performance (1 hour)\n- Measure current metrics (latency, throughput, memory)\n- Identify bottlenecks\n- Set optimization targets\n\n### Phase 2: Apply Quantization (1-2 hours)\n- Configure product quantization\n- Train codebooks\n- Apply compression\n- Validate accuracy\n\n### Phase 3: Implement HNSW Indexing (1-2 hours",
  "cost": {
    "context_tokens": 530
  }
}

Fetch it by URL: GET /api/v1/registry/aiskillstore-marketplace-agentdb-vector-search-optimization/manifest?version=1.0.0

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