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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数据科学与机器学习

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

关于

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.

主要功能

Advanced quantization (Binary, Scalar, Product) for up to 32x memory reduction
2 GitHub stars
Maximal Marginal Relevance (MMR) for diverse and non-redundant search results
Ultra-fast HNSW indexing for sub-millisecond (<100µs) vector retrieval
Seamless MCP server integration for direct database access via Claude Code
Hybrid search capabilities combining vector similarity with metadata filtering

使用场景

Implementing semantic document search in large-scale technical knowledge bases
Building enterprise-scale Retrieval-Augmented Generation (RAG) systems
Developing intelligent agents requiring fast context retrieval from long-term memory

关于

主要功能

Advanced quantization (Binary, Scalar, Product) for up to 32x memory reduction
2 GitHub stars
Maximal Marginal Relevance (MMR) for diverse and non-redundant search results
Ultra-fast HNSW indexing for sub-millisecond (<100µs) vector retrieval
Seamless MCP server integration for direct database access via Claude Code
Hybrid search capabilities combining vector similarity with metadata filtering

使用场景

Implementing semantic document search in large-scale technical knowledge bases
Building enterprise-scale Retrieval-Augmented Generation (RAG) systems
Developing intelligent agents requiring fast context retrieval from long-term memory