What makes AgentDB Vector Search faster than other databases?

AgentDB uses optimized HNSW indexing and specialized retrieval patterns to achieve search times under 100µs, outperforming traditional solutions by 150x to 12,500x depending on the scale.

Can I use this with different embedding models?

Yes, the skill supports customizable dimensions, allowing seamless integration with OpenAI (1536), sentence-transformers (768), and all-MiniLM (384) models.

Does it support filtering along with vector search?

Yes, it supports hybrid search, allowing you to combine semantic vector similarity with standard metadata filters like categories, dates, or custom tags.

How does it handle large datasets in memory?

It provides several quantization options—Binary, Scalar, and Product—that can reduce memory footprints by 4x to 32x, enabling the handling of millions of vectors on standard hardware.

AgentDB Vector Search

Name: AgentDB Vector Search
Author: Microck

byMicrock

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データサイエンスとML

Implement high-performance semantic search and vector storage for intelligent document retrieval and RAG systems.

概要

This skill integrates AgentDB into Claude Code, providing a high-performance vector database optimized for extreme speed and memory efficiency. It enables developers to build sophisticated Retrieval-Augmented Generation (RAG) pipelines, semantic search engines, and intelligent knowledge bases with sub-millisecond query times. By utilizing advanced features like HNSW indexing and multiple quantization levels, it offers significantly faster operations than traditional vector databases while maintaining a small footprint, making it ideal for agentic workflows and local AI development.

主な機能

Maximal Marginal Relevance (MMR) support for diverse and non-redundant search results.
81 GitHub stars
Native MCP server integration for direct tool-calling within Claude Code environments.
Sub-millisecond semantic search using HNSW indexing with O(log n) complexity.
Advanced quantization (Binary, Scalar, Product) for up to 32x memory reduction.
Hybrid search capabilities combining vector similarity with complex metadata filtering.

ユースケース

Implementing semantic search engines for large-scale enterprise knowledge bases.
Creating similarity-based recommendation systems using metadata-filtered vector results.
Building efficient RAG pipelines for context-aware AI document querying.

概要

主な機能

Maximal Marginal Relevance (MMR) support for diverse and non-redundant search results.
81 GitHub stars
Native MCP server integration for direct tool-calling within Claude Code environments.
Sub-millisecond semantic search using HNSW indexing with O(log n) complexity.
Advanced quantization (Binary, Scalar, Product) for up to 32x memory reduction.
Hybrid search capabilities combining vector similarity with complex metadata filtering.

ユースケース

Implementing semantic search engines for large-scale enterprise knowledge bases.
Creating similarity-based recommendation systems using metadata-filtered vector results.
Building efficient RAG pipelines for context-aware AI document querying.