AgentDB Vector Search

About

AgentDB Vector Search enables ultra-fast semantic document retrieval and similarity matching, boasting speeds up to 12,500x faster than traditional vector databases. It leverages advanced HNSW indexing and multiple quantization strategies to minimize memory usage while maintaining sub-millisecond search performance. This skill is ideal for developers building sophisticated RAG (Retrieval-Augmented Generation) pipelines, semantic search engines, or intelligent knowledge bases where speed and scalability are critical.

Key Features

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• Hybrid search capabilities combining vector similarity with metadata filtering
• Flexible quantization options (Binary, Scalar, Product) for up to 32x memory reduction
• Maximal Marginal Relevance (MMR) to ensure diverse and non-redundant search results
• High-performance HNSW indexing for sub-millisecond semantic search latency
• Integrated MCP server providing semantic search and storage tools directly to Claude Code

Use Cases

• Implementing semantic search engines for large-scale document repositories
• Building Retrieval-Augmented Generation (RAG) pipelines for AI agents
• Creating intelligent knowledge bases with context-aware querying