Does this skill work directly with Claude Code?

Yes, it includes a dedicated MCP (Model Context Protocol) server, allowing Claude Code to access tools for querying, storing, and analyzing vectors natively.

Which embedding models are supported?

It is model-agnostic and supports custom dimensions for any provider, including OpenAI (1536), sentence-transformers (768), and all-MiniLM (384).

What makes AgentDB faster than traditional vector databases?

AgentDB utilizes optimized HNSW indexing and efficient batch operations, resulting in search speeds 150x to 12,500x faster than standard implementations, with sub-millisecond latency.

How does quantization improve performance?

Quantization reduces the memory footprint of vectors (up to 32x for binary quantization), allowing larger datasets to be stored in-memory for faster access and lower hardware requirements.

Can I use this for hybrid search?

Yes, AgentDB supports combining vector similarity search with metadata filtering to create more precise and contextually relevant retrieval pipelines.

AgentDB Vector Search

Name: AgentDB Vector Search
Author: Microck

byMicrock

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데이터 과학 및 ML

Integrates high-performance semantic vector search and HNSW indexing for intelligent document retrieval and RAG systems.

소개

AgentDB Vector Search empowers Claude with lightning-fast semantic search capabilities, delivering operations up to 12,500x faster than traditional solutions. By leveraging HNSW indexing and advanced quantization strategies like Binary and Product quantization, it achieves sub-millisecond retrieval times while reducing memory footprints by up to 32x. This skill is essential for developers building Retrieval-Augmented Generation (RAG) pipelines, semantic search engines, or complex knowledge bases that require context-aware querying directly within the Claude Code environment via its integrated MCP server.

주요 기능

Seamless Claude Code integration via built-in MCP server tools
81 GitHub stars
Advanced quantization options (Binary, Scalar, Product) for massive memory efficiency
High-performance HNSW indexing for sub-millisecond (<100µs) search latency
Support for Cosine, Euclidean, and Dot Product distance metrics
Maximal Marginal Relevance (MMR) to ensure diverse and relevant search results

사용 사례

Implementing semantic similarity matching for large-scale enterprise knowledge bases
Developing intelligent agents that require efficient, persistent long-term memory
Building high-speed RAG pipelines for AI-driven technical documentation

소개

주요 기능

Seamless Claude Code integration via built-in MCP server tools
81 GitHub stars
Advanced quantization options (Binary, Scalar, Product) for massive memory efficiency
High-performance HNSW indexing for sub-millisecond (<100µs) search latency
Support for Cosine, Euclidean, and Dot Product distance metrics
Maximal Marginal Relevance (MMR) to ensure diverse and relevant search results

사용 사례

Implementing semantic similarity matching for large-scale enterprise knowledge bases
Developing intelligent agents that require efficient, persistent long-term memory
Building high-speed RAG pipelines for AI-driven technical documentation