What is the advantage of using Binary Quantization?

Binary Quantization provides a 32x reduction in memory usage, allowing you to store significantly larger datasets in-memory while maintaining high retrieval performance.

What makes AgentDB faster than other vector databases?

AgentDB uses optimized HNSW indexing and specialized memory management to achieve search times under 100 microseconds, making it up to 12,500x faster than some traditional solutions for large-scale queries.

Can I use this with OpenAI embeddings?

Yes, it fully supports the 1536-dimension vectors used by OpenAI's ada-002 and text-embedding-3 models, as well as other dimensions for models like sentence-transformers.

Does it support hybrid search?

Yes, the skill allows you to combine semantic vector similarity with metadata filtering, such as dates or categories, to narrow down search results effectively.

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 using AgentDB's optimized vector engine.

소개

AgentDB Vector Search is a specialized skill for building lightning-fast Retrieval-Augmented Generation (RAG) systems and semantic search engines directly within Claude Code. It utilizes HNSW indexing and advanced quantization strategies—including Binary, Scalar, and Product quantization—to deliver sub-millisecond retrieval times and up to 32x memory reduction. Whether you are managing millions of vectors or building a local knowledge base, this skill provides the CLI, API, and MCP tools necessary to handle similarity matching, hybrid filtering, and batch operations with efficiency that significantly outpaces traditional vector databases.

주요 기능

Ultra-fast HNSW indexing for sub-100µs search performance
Hybrid search capabilities combining vector similarity with metadata filters
Seamless MCP integration for direct vector database access in Claude Code
Multiple quantization levels (Binary, Scalar, Product) for maximum memory efficiency
81 GitHub stars
Support for various embedding dimensions including OpenAI and sentence-transformers

사용 사례

Creating recommendation engines based on feature similarity and distance metrics
Implementing semantic document search across large-scale knowledge bases
Building robust Retrieval-Augmented Generation (RAG) pipelines for AI applications

소개

주요 기능

Ultra-fast HNSW indexing for sub-100µs search performance
Hybrid search capabilities combining vector similarity with metadata filters
Seamless MCP integration for direct vector database access in Claude Code
Multiple quantization levels (Binary, Scalar, Product) for maximum memory efficiency
81 GitHub stars
Support for various embedding dimensions including OpenAI and sentence-transformers

사용 사례

Creating recommendation engines based on feature similarity and distance metrics
Implementing semantic document search across large-scale knowledge bases
Building robust Retrieval-Augmented Generation (RAG) pipelines for AI applications