agentdb-vector-search-optimization
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.
Best use case
agentdb-vector-search-optimization is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. 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.
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.
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "agentdb-vector-search-optimization" skill to help with this workflow task. Context: 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.
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/agentdb-vector-search-optimization/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How agentdb-vector-search-optimization Compares
| Feature / Agent | agentdb-vector-search-optimization | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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
```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
- 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/optimizationRelated Skills
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