Can I use this with OpenAI embeddings?

Yes, AgentDB is model-agnostic and supports any embedding dimension, including the 1536-dim vectors used by OpenAI and 768-dim vectors from Sentence-Transformers.

How does quantization help with memory usage?

Quantization reduces the footprint of vector data. For example, binary quantization can provide a 32x reduction in memory, allowing you to store millions of vectors on standard hardware.

What makes AgentDB faster than other vector databases?

AgentDB utilizes HNSW indexing and optimized caching to achieve search speeds of less than 100µs, outperforming traditional solutions by 150x to 12,500x depending on the scale.

Is there an MCP server for Claude Code integration?

Yes, the skill includes an MCP (Model Context Protocol) server that allows Claude Code to perform vector queries, storage, and statistics tracking directly.

AgentDB Vector Search

Name: AgentDB Vector Search
Author: ricable

byricable

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데이터 과학 및 ML

Implements high-performance semantic vector search and intelligent document retrieval for RAG systems and context-aware applications.

AgentDB Vector Search is a specialized skill for Claude Code that enables ultra-fast semantic search capabilities within AI agents. By leveraging HNSW indexing and advanced quantization techniques like Binary and Product quantization, it offers retrieval speeds up to 12,500x faster than traditional vector search methods. This skill is ideal for developers building production-grade Retrieval-Augmented Generation (RAG) pipelines, intelligent knowledge bases, and hybrid search engines that require sub-millisecond response times and extreme memory efficiency.

주요 기능

01Advanced quantization (Binary, Scalar, Product) for up to 32x memory reduction

022 GitHub stars

03Maximal Marginal Relevance (MMR) for diverse and non-redundant search results

04Ultra-fast HNSW indexing for sub-millisecond (<100µs) vector retrieval

05Seamless MCP server integration for direct database access via Claude Code

06Hybrid search capabilities combining vector similarity with metadata filtering

사용 사례

01Implementing semantic document search in large-scale technical knowledge bases

02Building enterprise-scale Retrieval-Augmented Generation (RAG) systems

03Developing intelligent agents requiring fast context retrieval from long-term memory

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add ricable/ultimate-ai-agent agentdb-vector-search

For use in Claude.ai and ChatGPT

Download Skill