How fast is AgentDB Vector Search compared to other solutions?

AgentDB provides 150x to 12,500x faster operations than traditional vector databases, with search latencies under 100 microseconds thanks to optimized HNSW indexing.

How do I integrate this with Claude Code?

You can use the built-in MCP server integration by running 'npx agentdb@latest mcp' and adding it to Claude Code for direct access to vector storage and retrieval tools.

Can I use this with OpenAI embeddings?

Absolutely. It supports standard 1536-dimension OpenAI embeddings, as well as 768 or 384-dimension models from sentence-transformers or other providers.

Does this skill support memory-efficient storage?

Yes, it includes multiple quantization strategies like Binary, Scalar, and Product quantization, which can reduce memory usage by up to 32x while maintaining high accuracy.

AgentDB Vector Search

Name: AgentDB Vector Search
Author: Microck

byMicrock

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데이터 과학 및 ML

Implements high-performance semantic vector search and intelligent document retrieval for Claude-powered RAG systems.

소개

AgentDB Vector Search empowers Claude with a lightning-fast vector database capable of sub-millisecond similarity matching and context-aware querying. It significantly boosts the performance of Retrieval-Augmented Generation (RAG) workflows by offering 150x to 12,500x faster operations compared to traditional solutions. With built-in HNSW indexing, memory-efficient quantization, and support for various distance metrics, this skill allows developers to seamlessly store, retrieve, and filter embeddings directly through CLI, API, or Model Context Protocol (MCP) tools.

주요 기능

Integrated MCP server for direct semantic querying within the Claude Code environment
Advanced quantization options (Binary, Scalar, Product) for up to 32x memory reduction
Support for multiple distance metrics including Cosine, Euclidean, and Dot Product
Hybrid search capabilities combining vector similarity with metadata filtering
Ultra-fast HNSW indexing for sub-millisecond (<100µs) vector retrieval
89 GitHub stars

사용 사례

Implementing intelligent document matching and similarity recommendation systems
Developing semantic search engines for large-scale enterprise knowledge bases
Building high-performance Retrieval-Augmented Generation (RAG) pipelines

소개

주요 기능

Integrated MCP server for direct semantic querying within the Claude Code environment
Advanced quantization options (Binary, Scalar, Product) for up to 32x memory reduction
Support for multiple distance metrics including Cosine, Euclidean, and Dot Product
Hybrid search capabilities combining vector similarity with metadata filtering
Ultra-fast HNSW indexing for sub-millisecond (<100µs) vector retrieval
89 GitHub stars

사용 사례

Implementing intelligent document matching and similarity recommendation systems
Developing semantic search engines for large-scale enterprise knowledge bases
Building high-performance Retrieval-Augmented Generation (RAG) pipelines