How does quantization help with large datasets?

Quantization reduces the memory footprint of your vectors by 4x to 32x, enabling the processing of millions of vectors on standard hardware without sacrificing significant search accuracy.

What makes AgentDB faster than other vector databases?

AgentDB utilizes HNSW indexing and optimized retrieval patterns to achieve search speeds 150x to 12,500x faster than traditional solutions, with search latencies often under 100µs.

Can I use this for RAG applications in Claude Code?

Yes, this skill is specifically designed for Retrieval-Augmented Generation, allowing Claude to efficiently search and retrieve relevant context from localized vector databases.

Which embedding models are supported?

AgentDB is model-agnostic and supports any embedding model, including OpenAI's text-embedding-3-small, sentence-transformers, and all-MiniLM-L6-v2.

AgentDB Vector Search

Name: AgentDB Vector Search
Author: adebold

byadebold

Database Management

Implements high-performance semantic vector search and RAG capabilities for intelligent document retrieval.

About

AgentDB Vector Search is a specialized capability for Claude Code designed to integrate lightning-fast vector database operations into AI-driven workflows. It leverages HNSW indexing and multiple quantization strategies—including Binary, Scalar, and Product quantization—to achieve sub-millisecond search latencies and significant memory efficiency. This skill is ideal for developers building Retrieval-Augmented Generation (RAG) systems, semantic search engines, or intelligent knowledge bases that require high-speed similarity matching and hybrid search across large datasets.

Key Features

Advanced quantization options for up to 32x memory reduction
Sub-millisecond semantic search using HNSW indexing (<100µs)
0 GitHub stars
Hybrid search combining vector similarity with metadata filtering
Seamless MCP server integration for Claude Code command access
Maximal Marginal Relevance (MMR) for diverse result sets

Use Cases

Optimizing memory-constrained environments using vector quantization
Implementing semantic search engines for large documentation libraries
Building high-performance RAG pipelines for AI applications

About

Key Features

Advanced quantization options for up to 32x memory reduction
Sub-millisecond semantic search using HNSW indexing (<100µs)
0 GitHub stars
Hybrid search combining vector similarity with metadata filtering
Seamless MCP server integration for Claude Code command access
Maximal Marginal Relevance (MMR) for diverse result sets

Use Cases

Optimizing memory-constrained environments using vector quantization
Implementing semantic search engines for large documentation libraries
Building high-performance RAG pipelines for AI applications