claudegoodies
Skill

AgentDB Vector Search

From ruvnet

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

Facts

Repository
ruvnet/ruflo
Status
Actively maintained
Last commit
Source file
.agents/skills/agentdb-vector-search/SKILL.md

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The instructions Claude Code reads when this skill runs.

# AgentDB Vector Search

## What This Skill Does

Implements vector-based semantic search using AgentDB's high-performance vector database with **150x-12,500x faster** operations than traditional solutions. Features HNSW indexing, quantization, and sub-millisecond search (<100µs).

## Prerequisites

- Node.js 18+
- AgentDB v1.0.7+ (via agentic-flow or standalone)
- OpenAI API key (for embeddings) or custom embedding model

## Quick Start with CLI

### Initialize Vector Database

```bash
# Initialize with default dimensions (1536 for OpenAI ada-002)
npx agentdb@latest init ./vectors.db

# Custom dimensions for different embedding models
npx agentdb@latest init ./vectors.db --dimension 768  # sentence-transformers
npx agentdb@latest init ./vectors.db --dimension 384  # all-MiniLM-L6-v2

# Use preset configurations
npx agentdb@latest init ./vectors.db --preset small   # <10K vectors
npx agentdb@latest init ./vectors.db --preset medium  # 10K-100K vectors
npx agentdb@latest init ./vectors.db --preset large   # >100K vectors

# In-memory database for testing
npx agentdb@latest init ./vectors.db --in-memory
```

### Query Vector Database

```bash
# Basic similarity search
npx agentdb@latest query ./vectors.db "[0.1,0.2,0.3,...]"

# Top-k results
npx agentdb@latest query ./vectors.db "[0.1,0.2,0.3]" -k 10

# With similarity threshold (cosine similarity)
npx agentdb@latest query ./vect
View full source on GitHub →

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