How much faster is AgentDB compared to traditional vector stores?

AgentDB offers search operations that are 150x to 12,500x faster than traditional solutions, with sub-millisecond retrieval times (<100µs).

Is there an MCP server integration?

Yes, it includes a built-in MCP server that allows Claude Code to directly perform semantic queries, storage, and database management tasks.

What is the benefit of using quantization in AgentDB?

Quantization reduces memory usage by 4x to 32x, allowing you to store large-scale vector datasets efficiently on limited hardware.

Can I use this for hybrid search?

Yes, AgentDB supports hybrid search, allowing you to combine semantic vector similarity with traditional metadata filtering.

Does this skill support different embedding models?

Yes, it supports customizable dimensions for models like OpenAI ada-002 (1536), sentence-transformers (768), and all-MiniLM-L6-v2 (384).

AgentDB Vector Search

Name: AgentDB Vector Search
Author: Activer007

byActiver007

Ciencia de Datos y ML

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

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AgentDB Vector Search provides a high-performance vector database solution specifically optimized for Claude Code, offering search speeds up to 12,500x faster than traditional methods. It features advanced HNSW indexing, multiple quantization strategies (Binary, Scalar, Product) for up to 32x memory reduction, and seamless integration with RAG pipelines. This skill enables developers to build context-aware knowledge bases and semantic search engines with sub-millisecond retrieval times and efficient hybrid search capabilities, making it ideal for managing large-scale AI memory and document contexts.

Características Principales

0 GitHub stars
Maximal Marginal Relevance (MMR) for diverse and non-redundant search results
High-performance HNSW indexing for O(log n) search complexity
Full CLI and MCP server integration for Claude Code workflows
Hybrid search combining vector similarity with metadata filtering
Advanced quantization (Binary, Scalar, Product) for up to 32x memory efficiency

Casos de Uso

Implementing intelligent knowledge bases with memory-efficient vector storage
Building ultra-fast Retrieval Augmented Generation (RAG) systems for AI agents
Creating semantic search engines with sub-millisecond document retrieval

Acerca de

Características Principales

0 GitHub stars
Maximal Marginal Relevance (MMR) for diverse and non-redundant search results
High-performance HNSW indexing for O(log n) search complexity
Full CLI and MCP server integration for Claude Code workflows
Hybrid search combining vector similarity with metadata filtering
Advanced quantization (Binary, Scalar, Product) for up to 32x memory efficiency

Casos de Uso

Implementing intelligent knowledge bases with memory-efficient vector storage
Building ultra-fast Retrieval Augmented Generation (RAG) systems for AI agents
Creating semantic search engines with sub-millisecond document retrieval