Is it possible to filter search results by metadata?

Yes, AgentDB supports hybrid search, allowing you to combine vector similarity matching with complex metadata filters like categories or date ranges.

Can I use this with different embedding models like OpenAI or Sentence-Transformers?

Absolutely. You can configure custom dimensions (e.g., 1536 for OpenAI ada-002, 768 for sentence-transformers, or 384 for MiniLM) during initialization.

How do I integrate this with Claude Code?

You can add the AgentDB MCP server to Claude Code using the 'claude mcp add' command, which exposes tools for querying, storing, and analyzing vector data directly.

Does this skill support memory optimization for large datasets?

Yes, it supports Binary, Scalar, and Product quantization strategies, which can reduce memory usage by up to 32x while maintaining high accuracy.

How much faster is AgentDB compared to traditional vector databases?

AgentDB offers operations 150x to 12,500x faster than traditional solutions, with search times typically under 100 microseconds.

AgentDB Vector Search

Name: AgentDB Vector Search
Author: ricable

byricable

•

データサイエンスとML

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

AgentDB Vector Search enables Claude to integrate advanced semantic search capabilities into any application with operations up to 12,500x faster than traditional solutions. By leveraging HNSW indexing, multi-level quantization, and sub-millisecond retrieval, it provides a robust foundation for building Retrieval-Augmented Generation (RAG) systems, semantic search engines, and context-aware knowledge bases. This skill offers seamless integration via CLI, TypeScript API, and a dedicated MCP server for Claude Code, allowing developers to manage high-dimensional embeddings and perform similarity matching with minimal latency and maximum memory efficiency.

主な機能

016 GitHub stars

02Sub-millisecond semantic search (<100µs) powered by HNSW indexing.

03Native MCP server integration for direct tool use within the Claude Code CLI.

04Multi-level quantization (Binary, Scalar, Product) for up to 32x memory reduction.

05Hybrid search capabilities combining vector similarity with metadata filtering.

06Maximal Marginal Relevance (MMR) support for diverse and relevant search results.

ユースケース

01Developing context-aware intelligent agents that require fast similarity matching across millions of vectors.

02Building high-performance Retrieval-Augmented Generation (RAG) pipelines for AI agents.

03Implementing semantic search engines for large-scale documentation or knowledge bases.

主な機能

016 GitHub stars

02Sub-millisecond semantic search (<100µs) powered by HNSW indexing.

03Native MCP server integration for direct tool use within the Claude Code CLI.

04Multi-level quantization (Binary, Scalar, Product) for up to 32x memory reduction.

05Hybrid search capabilities combining vector similarity with metadata filtering.

06Maximal Marginal Relevance (MMR) support for diverse and relevant search results.

ユースケース

01Developing context-aware intelligent agents that require fast similarity matching across millions of vectors.

02Building high-performance Retrieval-Augmented Generation (RAG) pipelines for AI agents.

03Implementing semantic search engines for large-scale documentation or knowledge bases.