AgentDB Vector Search

Semantic vector search uses vector embeddings and HNSW indexing to find contextually relevant documents rapidly. You initialize an AgentDB vector store, embed your documents, and query the configurable search API to retrieve accurate matches.

Yes, hybrid search is supported. You can combine vector similarity search with metadata filtering to refine results, ensuring that contextually relevant passages are retrieved accurately from your knowledge base or RAG system.

The best way to implement RAG retrieval is using a vector store with embedding-powered indexing. AgentDB provides RAG-ready integration, using HNSW indexing and quantization to deliver fast, accurate document retrieval for AI assistants.

To scale knowledge base search without losing accuracy, use a vector store like AgentDB that supports HNSW indexing and quantization. These features maintain rapid, context-aware retrieval performance as your document collections grow.

Yes, you can use AgentDB vector search for documentation search. It applies vector embeddings to enable context-aware results, making it suitable for powering intelligent assistants and search engines that require rapid, accurate passage retrieval.

Yes, you need the AgentDB vector store. The Skill requires this specific vector store to support embeddings, HNSW indexing, and quantization, enabling fast semantic vector search and scalable document retrieval.