Does this skill support memory-constrained environments?

Yes, it includes multiple quantization strategies like Binary, Scalar, and Product quantization that can reduce memory footprints by 4x to 32x.

How fast is AgentDB Vector Search compared to other solutions?

AgentDB is highly optimized for performance, offering operations 150x to 12,500x faster than traditional vector solutions, with search latencies often under 100 microseconds.

Can I use AgentDB tools directly inside the Claude CLI?

Absolutely. This skill includes an MCP (Model Context Protocol) server integration, allowing Claude to perform semantic queries and store documents via natural language.

Which embedding models are compatible with this skill?

It is model-agnostic and supports any embedding model, including OpenAI ada-002 (1536 dimensions), sentence-transformers (768), and MiniLM (384).

What distance metrics are available for search?

AgentDB supports Cosine Similarity, Euclidean Distance (L2 norm), and Dot Product metrics to suit different embedding model requirements.

AgentDB Vector Search

Name: AgentDB Vector Search
Author: Activer007

byActiver007

0•

데이터 과학 및 ML

Provides ultra-fast semantic vector search and intelligent document retrieval using AgentDB's high-performance HNSW indexing.

AgentDB Vector Search is a specialized Claude Code skill designed for building high-performance RAG systems and semantic search engines. It leverages AgentDB's HNSW indexing and quantization techniques to deliver search results 150x to 12,500x faster than traditional solutions, often achieving sub-millisecond latency. Whether you are managing small document sets or millions of vectors, this skill provides the CLI tools and API patterns needed for efficient storage, hybrid search, and context-aware querying directly within your development environment.

주요 기능

01High-performance HNSW indexing for sub-millisecond vector retrieval

02Native MCP server integration for direct interaction within Claude Code

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

04Advanced quantization (Binary, Scalar, Product) for up to 32x memory reduction

05Hybrid search capabilities combining vector similarity with metadata filtering

060 GitHub stars

사용 사례

01Developing high-scale vector applications with memory-efficient storage and fast batch inserts

02Implementing semantic document search and similarity matching in knowledge bases

03Building Retrieval-Augmented Generation (RAG) pipelines for intelligent AI agents

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

Download Skill