Can I use this with OpenAI embeddings?

Yes, it is pre-configured to handle 1536-dimension vectors common in OpenAI models like text-embedding-3-small, while also supporting custom dimensions for other models.

How do I integrate this into Claude Code?

You can integrate it seamlessly via the provided MCP server by running the command 'claude mcp add agentdb npx agentdb@latest mcp' in your terminal.

What makes AgentDB Vector Search faster than traditional databases?

It uses optimized HNSW indexing and specialized quantization techniques to achieve search speeds up to 12,500x faster than traditional relational solutions for large-scale vector sets.

Does it support hybrid search?

Absolutely. You can combine semantic vector similarity with traditional metadata filters such as categories, dates, or custom tags to ensure highly precise retrieval results.

What is the memory benefit of quantization?

Depending on the mode selected (Binary, Scalar, or Product), you can reduce the memory footprint of your vector database by 4x to 32x with minimal impact on retrieval accuracy.

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 vector search and intelligent document retrieval using AgentDB optimized HNSW indexing and quantization.

概要

AgentDB Vector Search is a high-performance skill designed to integrate lightning-fast semantic retrieval into Claude Code workflows. It leverages HNSW indexing and multiple quantization strategies—including binary, scalar, and product quantization—to achieve sub-millisecond search times while reducing memory overhead by up to 32x. This skill is essential for developers building robust Retrieval Augmented Generation (RAG) systems, semantic search engines, or large-scale knowledge bases that require efficient similarity matching and hybrid metadata filtering at scale.

主な機能

High-speed HNSW indexing for sub-millisecond search latency
Hybrid search capabilities combining vector similarity with metadata filters
Full MCP integration for native use within the Claude Code environment
Advanced retrieval patterns including Maximal Marginal Relevance (MMR)
Multiple quantization modes for up to 32x memory reduction
81 GitHub stars

ユースケース

Building RAG pipelines to provide AI models with relevant document context
Implementing semantic search engines for large unstructured datasets
Creating intelligent knowledge bases with efficient hybrid querying

概要

主な機能

High-speed HNSW indexing for sub-millisecond search latency
Hybrid search capabilities combining vector similarity with metadata filters
Full MCP integration for native use within the Claude Code environment
Advanced retrieval patterns including Maximal Marginal Relevance (MMR)
Multiple quantization modes for up to 32x memory reduction
81 GitHub stars

ユースケース

Building RAG pipelines to provide AI models with relevant document context
Implementing semantic search engines for large unstructured datasets
Creating intelligent knowledge bases with efficient hybrid querying