AgentDB Vector Search

AgentDB Vector Search provides a specialized, high-throughput vector database solution tailored for AI agent architectures and Claude Code environments. Designed for developers building Retrieval-Augmented Generation (RAG) pipelines, semantic search engines, and autonomous agent knowledge bases, this skill enables rapid semantic retrieval with performance benchmarks reaching 150x to 12,500x speed improvements over traditional database approaches. By leveraging advanced HNSW indexing and multiple quantization strategies—including binary, scalar, and product quantization—it significantly reduces memory overhead while maintaining sub-millisecond retrieval speeds below 100µs.

• Implements high-performance semantic vector storage using AgentDB technology for scalable intelligent document retrieval.

• Supports flexible distance metrics including Cosine Similarity, L2 (Euclidean), and Dot Product to accommodate various embedding models.

• Provides multi-dimensional embedding support, enabling configuration for standard models like OpenAI ada-002 as well as custom locally hosted models.

• Facilitates hybrid search capabilities, allowing users to combine vector similarity matching with structured metadata filtering for high-precision results.

• Includes native MCP (Model Context Protocol) server integration, enabling seamless usage within Claude Code and other agentic environments.

• Offers advanced optimization features like Maximal Marginal Relevance (MMR) for result diversity and built-in batch processing for large-scale data ingestion.

• Prerequisites: Requires Node.js 18+ and an active API key for embedding generation (e.g., OpenAI or local alternatives).

• Quick Start: Initialize databases using the npx agentdb CLI with specific presets (small, medium, large) or in-memory modes for testing.

• Input/Output: Accepts vectorized data via JSON or CLI inputs and returns ranked results with distance scores, suitable for downstream LLM prompt synthesis.

• Performance Constraints: Memory footprint can be tuned using quantization strategies (e.g., 32x reduction with binary quantization) to optimize performance on resource-constrained infrastructure.

• Best Practices: Utilize hybrid search with metadata filters for production-grade knowledge retrieval to ensure relevance beyond purely semantic matches.