AgentDB Vector Search

Speed up semantic vector search using HNSW indexing, which delivers sub-millisecond query latency for large-scale document retrieval. This approach reduces search times significantly compared to traditional vector databases by optimizing the indexing layer for high throughput.

Reduce memory usage for millions of embedding vectors by applying binary, scalar, or product quantization. These quantization options compress vector storage by 4 to 32 times, making large-scale knowledge bases cost-effective to maintain.

HNSW indexing works for RAG system development by creating a navigable small-world graph for your embeddings, enabling fast similarity matching. It allows retrieval-augmented generation chatbots to fetch relevant documents in real time with low latency.

Filter search results by metadata and ensure diversity using hybrid metadata filtering and MMR diversity. These features restrict results to specific categories while removing redundant matches, returning relevant and non-redundant documents from your knowledge base.

Vector quantization optimizes memory usage without fundamentally breaking semantic search accuracy. By using binary, scalar, or product quantization, you balance memory efficiency with retrieval precision, keeping large-scale querying cost-effective.