Agentdb vector search optimization

Skill ComeOnOliver/skillshub/skills/aiskillstore/marketplace/dnyoussef/agentdb-vector-search-optimization

🧠 The right skill, one API call. AI agent skills registry with token-efficient skill resolution. 5,000+ skills from 500+ top repos.

Install

npx -y skills add ComeOnOliver/skillshub --skill agentdb-vector-search-optimization

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

What its author says it does

Copied from the file, not written here

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.

SKILL.md

3.2 KB, 562 tokens by cl100k_base, as published. Nobody here has run it

AgentDB Vector Search Optimization

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