AgentDB Vector Search

You can implement semantic vector search by storing document embeddings in AgentDB and utilizing HNSW indexing with quantization for high-performance similarity matching. This enables fast context retrieval for your RAG architecture.

Hybrid search combines vector similarity calculations with metadata filtering to deliver precise document retrieval results. This allows you to narrow down semantically similar documents by applying specific attribute constraints to your queries.

Yes, you need Node.js and an embedding model provider like OpenAI to generate vector representations. AgentDB serves as the storage layer, utilizing HNSW indexing and quantization to ensure high-performance operations for your knowledge base.

The best way to build a support knowledge base is to store past tickets and documentation as embeddings, then use similarity matching to find relevant records based on user queries. This provides faster and more accurate contextual assistance.

Yes, you can combine vector similarity search with metadata filtering to achieve hybrid search. This approach narrows down semantically similar documents using specific metadata constraints, yielding highly targeted document retrieval outcomes.