Can I use custom embedding models with this skill?

Yes, while it supports OpenAI's 1536-dimension vectors by default, you can configure it for any dimension size to match models like sentence-transformers (768) or MiniLM (384).

How fast is AgentDB Vector Search compared to other solutions?

AgentDB is designed for extreme performance, delivering search results 150x to 12,500x faster than traditional vector databases, with many operations completing in under 100 microseconds.

Does this skill work directly with Claude Code tools?

Yes, it includes an MCP (Model Context Protocol) server integration, allowing Claude Code to use tools like agentdb_query and agentdb_store directly during your coding sessions.

How does quantization help with large datasets?

Quantization reduces the memory footprint of your vectors. For example, binary quantization can provide a 32x reduction in memory usage, allowing large datasets to fit comfortably in RAM.

AgentDB Vector Search

Name: AgentDB Vector Search
Author: Activer007

byActiver007

Data Science & ML

Implements ultra-fast semantic vector search and RAG capabilities using AgentDB's high-performance indexing and quantization.

About

AgentDB Vector Search is a high-performance toolkit designed for building intelligent retrieval systems that are significantly faster than traditional vector databases. By leveraging HNSW indexing and multiple quantization strategies, it enables sub-millisecond semantic search and memory-efficient storage for millions of vectors. This skill is ideal for developers building Retrieval Augmented Generation (RAG) pipelines, semantic search engines, and intelligent knowledge bases that require low-latency responses and hybrid search capabilities combining vector similarity with metadata filtering.

Key Features

Memory-efficient quantization (Binary, Scalar, Product) for up to 32x reduction
Maximal Marginal Relevance (MMR) for diverse and non-redundant search results
0 GitHub stars
Hybrid search functionality combining vector similarity with metadata filters
Native MCP server integration for direct tool access within Claude Code
High-speed HNSW indexing for sub-100µs search performance

Use Cases

Developing memory-efficient intelligent knowledge bases for AI agents
Creating semantic search engines for large-scale technical documentation
Building high-performance Retrieval Augmented Generation (RAG) systems

About

Key Features

Memory-efficient quantization (Binary, Scalar, Product) for up to 32x reduction
Maximal Marginal Relevance (MMR) for diverse and non-redundant search results
0 GitHub stars
Hybrid search functionality combining vector similarity with metadata filters
Native MCP server integration for direct tool access within Claude Code
High-speed HNSW indexing for sub-100µs search performance

Use Cases

Developing memory-efficient intelligent knowledge bases for AI agents
Creating semantic search engines for large-scale technical documentation
Building high-performance Retrieval Augmented Generation (RAG) systems