AgentDB Vector Search

소개

AgentDB Vector Search enables specialized, high-performance semantic search capabilities with operations significantly faster than traditional solutions through HNSW indexing and sub-millisecond retrieval. It offers advanced memory efficiency using multiple quantization strategies—including binary and scalar quantization—to reduce memory footprints by up to 32x. This skill is particularly useful for developers building sophisticated Retrieval-Augmented Generation (RAG) pipelines, semantic search engines, or any application requiring fast, context-aware similarity matching and intelligent document retrieval.

주요 기능

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• Hybrid search capabilities that combine vector similarity with metadata filtering
• High-performance HNSW indexing for sub-millisecond retrieval times
• Maximal Marginal Relevance (MMR) support to provide diverse search results
• Advanced quantization techniques (Binary, Scalar, Product) for significant memory reduction
• Comprehensive CLI and API support for managing vector storage and retrieval

사용 사례

• Developing high-speed semantic search engines and knowledge bases
• Building Retrieval-Augmented Generation (RAG) systems for LLMs
• Implementing real-time similarity matching in high-scale data environments