How fast is AgentDB compared to other vector databases?

AgentDB is significantly faster, offering operations between 150x and 12,500x faster than traditional solutions, with search times under 100µs.

Which embedding models are compatible?

It is compatible with any model, including OpenAI (1536 dims), sentence-transformers (768 dims), and all-MiniLM-L6-v2 (384 dims).

What is AgentDB Vector Search used for?

It is used for semantic document retrieval, similarity matching, and building Retrieval-Augmented Generation (RAG) systems with high performance.

Does this skill support memory-efficient storage?

Yes, it provides multiple quantization strategies including Binary (32x reduction), Scalar (4x reduction), and Product quantization.

Can I use AgentDB with Claude Code via MCP?

Yes, it includes a dedicated MCP server integration allowing Claude to query, store, and manage vectors directly through the CLI.

AgentDB Vector Search

Name: AgentDB Vector Search
Author: ellisapotheosis

byellisapotheosis

データサイエンスとML

Implement ultra-fast semantic vector search and intelligent document retrieval for RAG systems using AgentDB's high-performance HNSW indexing.

概要

AgentDB Vector Search is a specialized skill that empowers Claude to handle high-performance semantic retrieval and similarity matching tasks. By leveraging HNSW indexing and advanced quantization techniques like Binary and Product quantization, it achieves search operations significantly faster than traditional solutions with sub-millisecond latency. This skill is ideal for developers building Retrieval-Augmented Generation (RAG) pipelines, intelligent knowledge bases, and context-aware AI applications that require massive scalability and extreme memory efficiency.

主な機能

0 GitHub stars
High-performance HNSW indexing with sub-100µs search latency
Advanced quantization (Binary, Scalar, Product) for up to 32x memory reduction
Support for Cosine, Euclidean, and Dot Product distance metrics
Hybrid search capabilities combining vector similarity with metadata filtering
Native MCP server integration for seamless use within Claude Code

ユースケース

Building efficient Retrieval-Augmented Generation (RAG) pipelines for AI agents
Implementing real-time similarity matching and recommendation systems
Creating semantic search engines for large-scale documentation and knowledge bases

概要

主な機能

0 GitHub stars
High-performance HNSW indexing with sub-100µs search latency
Advanced quantization (Binary, Scalar, Product) for up to 32x memory reduction
Support for Cosine, Euclidean, and Dot Product distance metrics
Hybrid search capabilities combining vector similarity with metadata filtering
Native MCP server integration for seamless use within Claude Code

ユースケース

Building efficient Retrieval-Augmented Generation (RAG) pipelines for AI agents
Implementing real-time similarity matching and recommendation systems
Creating semantic search engines for large-scale documentation and knowledge bases