Does it support embedding models like OpenAI or HuggingFace?

Yes, you can configure custom dimensions during initialization to support OpenAI (1536), sentence-transformers (768), or MiniLM (384) models.

What distance metrics does the skill support?

The skill supports Cosine Similarity (default), Euclidean Distance (L2 norm), and Dot Product metrics for various vector matching requirements.

What quantization options are available for memory efficiency?

AgentDB supports Binary quantization (32x reduction), Scalar quantization (4x reduction), and Product quantization (8-16x reduction) to optimize memory usage.

How fast is AgentDB Vector Search compared to other databases?

AgentDB offers sub-millisecond retrieval (under 100µs) and can perform up to 12,500x faster than traditional vector solutions for large-scale queries.

Can I use AgentDB Vector Search directly in the Claude Code CLI?

Absolutely. It features a built-in MCP (Model Context Protocol) server that allows Claude to use tools like agentdb_query and agentdb_store directly.

AgentDB Vector Search

Name: AgentDB Vector Search
Author: ricable

byricable

•

数据科学与机器学习

Implements high-performance semantic vector search and intelligent document retrieval for RAG systems using AgentDB.

关于

AgentDB Vector Search is a high-performance skill designed for Claude Code that enables sub-millisecond semantic similarity matching and intelligent knowledge retrieval. Leveraging HNSW indexing and advanced quantization strategies, it achieves search speeds up to 12,500x faster than traditional solutions. This tool is essential for developers building efficient Retrieval-Augmented Generation (RAG) pipelines, semantic search engines, and context-aware AI agents that require rapid, memory-efficient vector operations.

主要功能

Native MCP server integration for direct tool access within Claude Code.
Sub-millisecond retrieval (<100µs) using HNSW indexing and pattern caching.
Hybrid search combining semantic vector similarity with complex metadata filtering.
2 GitHub stars
Advanced quantization (Binary, Scalar, Product) for up to 32x memory reduction.
Comprehensive CLI and API support for batch operations and database management.

使用场景

Building lightning-fast RAG systems for real-time AI assistant context injection.
Developing intelligent knowledge bases that utilize Maximal Marginal Relevance (MMR) for diverse results.
Implementing semantic search across millions of documents with minimal hardware overhead.

关于

主要功能

Native MCP server integration for direct tool access within Claude Code.
Sub-millisecond retrieval (<100µs) using HNSW indexing and pattern caching.
Hybrid search combining semantic vector similarity with complex metadata filtering.
2 GitHub stars
Advanced quantization (Binary, Scalar, Product) for up to 32x memory reduction.
Comprehensive CLI and API support for batch operations and database management.

使用场景

Building lightning-fast RAG systems for real-time AI assistant context injection.
Developing intelligent knowledge bases that utilize Maximal Marginal Relevance (MMR) for diverse results.
Implementing semantic search across millions of documents with minimal hardware overhead.