AgentDB Vector Search

Semantic vector search over large corpora is performed by storing embeddings and applying HNSW indexing for fast similarity retrieval. This Skill uses AgentDB to surface relevant documents based on vector proximity rather than exact keyword matches.

For RAG pipelines, the best way to retrieve relevant policy documents is using hybrid search that combines vector similarity with metadata filtering. This ensures precise context-aware retrieval from knowledge bases for downstream generation.

Yes, AgentDB supports HNSW indexing and quantization options to enable fast and accurate similarity search. It provides an API to store, query, and manage vectors efficiently for document retrieval.

Initialize the vector database using 'npx agentdb@latest init' to create the storage file. Query it by running 'npx agentdb@latest query' with your search string and the desired number of top results.

Yes, hybrid search combines vector similarity with metadata filtering to deliver precise document retrieval results. This allows you to narrow down relevant documents within large repositories based on specific contextual constraints.

Quantization options in AgentDB should be used when managing large-scale vector embeddings to optimize memory usage during storage and retrieval. This maintains fast similarity search performance across extensive document repositories.