Does AgentDB support different embedding models?

Yes, it is model-agnostic. You can configure custom dimensions to match any model, such as OpenAI ada-002 (1536), sentence-transformers (768), or all-MiniLM-L6-v2 (384).

Can I use AgentDB directly within Claude Code?

Yes, it includes an MCP server integration. By running a simple command, you can add AgentDB tools like agentdb_query and agentdb_store directly to your Claude Code environment.

How much faster is AgentDB compared to standard solutions?

AgentDB offers search performance that is 150x to 12,500x faster than traditional vector solutions, achieving sub-millisecond retrieval times (<100µs) even with large datasets.

How does AgentDB optimize memory usage?

It provides multiple quantization strategies including Binary (32x reduction), Scalar (4x reduction), and Product quantization (8-16x reduction) to handle large vector sets on modest hardware.

What distance metrics are supported for similarity matching?

AgentDB supports Cosine Similarity (default), Euclidean Distance (L2 norm), and Dot Product, allowing you to choose the best metric for your specific embedding model.

AgentDB Vector Search

Name: AgentDB Vector Search
Author: Activer007

byActiver007

0•

データサイエンスとML

Implements high-performance semantic vector search and HNSW indexing for intelligent document retrieval and RAG systems.

AgentDB Vector Search is a high-performance database skill designed for Claude Code that enables sub-millisecond semantic retrieval and similarity matching. By leveraging advanced HNSW indexing and quantization techniques, it provides a 150x to 12,500x speed improvement over traditional solutions, making it an ideal choice for building Retrieval-Augmented Generation (RAG) pipelines, semantic search engines, and intelligent knowledge bases. It supports various distance metrics like cosine similarity and Euclidean distance, and integrates seamlessly as an MCP server to provide Claude with direct access to local or remote vector storage.

主な機能

01Built-in MCP server for seamless Claude Code tool integration (query, store, stats)

020 GitHub stars

03Sub-millisecond retrieval (<100µs) using advanced HNSW indexing

04Hybrid search capabilities combining vector similarity with metadata filtering

05Quantization options (Binary, Scalar, Product) for up to 32x memory reduction

06Maximal Marginal Relevance (MMR) for diverse and non-redundant search results

ユースケース

01Building local RAG systems for intelligent documentation retrieval

02Managing large-scale vector datasets for AI-driven pattern matching and context-aware querying

03Creating high-performance semantic search engines with low latency requirements

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add activer007/ordinary-claude-skills agentdb-vector-search

For use in Claude.ai and ChatGPT

主な機能

01Built-in MCP server for seamless Claude Code tool integration (query, store, stats)

020 GitHub stars

03Sub-millisecond retrieval (<100µs) using advanced HNSW indexing

04Hybrid search capabilities combining vector similarity with metadata filtering

05Quantization options (Binary, Scalar, Product) for up to 32x memory reduction

06Maximal Marginal Relevance (MMR) for diverse and non-redundant search results

ユースケース

01Building local RAG systems for intelligent documentation retrieval

02Managing large-scale vector datasets for AI-driven pattern matching and context-aware querying

03Creating high-performance semantic search engines with low latency requirements