How does quantization improve performance?

Quantization reduces the memory footprint of your vectors by 4x to 32x. This allows you to handle much larger datasets in-memory, significantly reducing latency and hardware requirements.

Can I use this for RAG applications?

Yes, this skill is specifically designed to facilitate RAG (Retrieval-Augmented Generation) by providing tools to store documents and retrieve relevant context based on semantic similarity.

Does this work with the Claude Code CLI?

Yes, it includes a built-in MCP (Model Context Protocol) server that allows Claude Code to access vector search and storage tools directly through the CLI interface.

What makes AgentDB faster than other vector databases?

AgentDB utilizes HNSW (Hierarchical Navigable Small World) indexing and optimized memory management to deliver sub-millisecond search results, performing up to 12,500x faster than traditional solutions for large datasets.

Which embedding models are supported?

AgentDB is model-agnostic and supports any embedding model, including OpenAI's ada-002 (1536 dimensions), sentence-transformers (768 dimensions), and all-MiniLM-L6-v2 (384 dimensions).

AgentDB Vector Search

Name: AgentDB Vector Search
Author: zellycloud

byzellycloud

データサイエンスとML

Implements high-performance semantic vector search and intelligent document retrieval for RAG-based Claude Code workflows.

概要

AgentDB Vector Search provides Claude Code with a high-speed vector database capability, enabling sub-millisecond semantic search and intelligent knowledge retrieval. It features advanced optimizations like HNSW indexing and quantization (Binary, Scalar, Product) to reduce memory usage by up to 32x while maintaining performance that is significantly faster than traditional solutions. This skill is essential for developers building Retrieval-Augmented Generation (RAG) systems, semantic search engines, or context-aware applications that require efficient similarity matching across large datasets directly within their development environment.

主な機能

Advanced quantization options including Binary and Scalar for up to 32x memory reduction.
High-performance HNSW indexing for sub-millisecond (<100µs) search retrieval.
Support for multiple distance metrics including Cosine, Euclidean, and Dot Product.
Hybrid search capabilities combining vector similarity with complex metadata filtering.
0 GitHub stars
Seamless MCP server integration for direct interaction with Claude Code tools.

ユースケース

Building local Retrieval-Augmented Generation (RAG) pipelines for private documentation.
Managing large-scale vector datasets with efficient memory usage on standard hardware.
Implementing semantic search engines that find relevant code or data based on meaning.

概要

主な機能

Advanced quantization options including Binary and Scalar for up to 32x memory reduction.
High-performance HNSW indexing for sub-millisecond (<100µs) search retrieval.
Support for multiple distance metrics including Cosine, Euclidean, and Dot Product.
Hybrid search capabilities combining vector similarity with complex metadata filtering.
0 GitHub stars
Seamless MCP server integration for direct interaction with Claude Code tools.

ユースケース

Building local Retrieval-Augmented Generation (RAG) pipelines for private documentation.
Managing large-scale vector datasets with efficient memory usage on standard hardware.
Implementing semantic search engines that find relevant code or data based on meaning.