Which embedding models are supported?

It is model-agnostic and supports various dimensions, including OpenAI's 1536-dim (ada-002), sentence-transformers (768-dim), and MiniLM (384-dim).

How does quantization help with performance?

Quantization reduces the memory footprint of vectors (up to 32x for binary), allowing larger datasets to stay in-memory for faster retrieval and lower infrastructure costs.

Is this skill easy to integrate with Claude?

Yes, it includes a built-in MCP (Model Context Protocol) server that allows you to add AgentDB tools directly to Claude Code with a single command.

Can I filter results based on metadata?

Yes, it supports hybrid search, allowing you to combine semantic vector similarity with metadata filters like category, date ranges, or custom tags.

What makes AgentDB faster than other vector databases?

AgentDB uses optimized HNSW indexing and specialized quantization, enabling search operations as fast as 100µs and batch inserts that are up to 500x faster than standard alternatives.

AgentDB Vector Search

Name: AgentDB Vector Search
Author: whrit

bywhrit

Database Management

Implement high-performance semantic vector search and intelligent document retrieval for RAG-based AI applications.

About

AgentDB Vector Search enables Claude to interface with a hyper-optimized vector database designed specifically for agentic workflows and low-latency retrieval. It provides sub-millisecond similarity matching using HNSW indexing to support Retrieval-Augmented Generation (RAG) systems, allowing developers to manage large datasets with up to 12,500x the speed of traditional solutions. This skill is essential for building intelligent knowledge bases, semantic search engines, and context-aware agents that require memory-efficient storage through advanced quantization strategies like binary and product quantization.

Key Features

0 GitHub stars
Ultra-fast HNSW indexing for sub-millisecond (<100µs) semantic search
Hybrid search capabilities combining vector similarity with metadata filtering
Maximal Marginal Relevance (MMR) support for diverse and non-redundant results
Seamless MCP server integration for immediate use within Claude Code
Advanced quantization (Binary, Scalar, Product) reducing memory usage up to 32x

Use Cases

Developing high-speed semantic search engines for large-scale knowledge bases
Implementing intelligent long-term memory for autonomous AI agents
Building RAG pipelines for specialized document retrieval and AI context

About

Key Features

0 GitHub stars
Ultra-fast HNSW indexing for sub-millisecond (<100µs) semantic search
Hybrid search capabilities combining vector similarity with metadata filtering
Maximal Marginal Relevance (MMR) support for diverse and non-redundant results
Seamless MCP server integration for immediate use within Claude Code
Advanced quantization (Binary, Scalar, Product) reducing memory usage up to 32x

Use Cases

Developing high-speed semantic search engines for large-scale knowledge bases
Implementing intelligent long-term memory for autonomous AI agents
Building RAG pipelines for specialized document retrieval and AI context