Agent skill · data analytics · aiskillstore
agentdb-vector-search
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.
Why this skill is useful
Adds executable scripts for initializing, querying, and managing a vector database that enhance AI's capabilities in semantic search and document retrieval.
What it needs
Requires node installed locally. Requires openai account access. About 5k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
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 Query Vector Database Import/Export Vectors Quick Start with API Core Features 1. Vector Storage 2. Similarity Search 3. Hybrid Search (Vector + Metadata) Advanced Usage RAG (Retrieval Augmented Generation) Batch Operations MCP Server Integration Performance Benchmarks Quantization Options AgentDB provides multiple quantization strategies for memory efficiency: Binary Quantization (32x reduction) Scalar Quantization (4x reduction) Product Quantization (8-16x reduction) Distance Metrics Advanced Features HNSW Indexing O(log n) search complexity Sub-millisecond retrieval (<100µs) Automatic index building Caching 1000 pattern in-memory cache <1ms pattern retrieval Automatic cache invalidation MMR (Maximal Marginal Relevance) Diverse result sets Avoid redundancy Balance relevance and diversity Performance Tips 1. Enable HNSW indexing: Automatic with AgentDB, 10-100x faster 2. Use quantization: Binary (32x), Scalar (4x), Product (8-16x) memory reduction 3. Batch operations: 500x faster for bulk inserts 4. Match dimensions: 1536 (OpenAI), 768 (sentence-transformers), 384 (MiniLM) 5. Similarity threshold: Start at 0.7 for quality, adjust based on use case 6. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/agentdb-vector-search