vector-databases

Build a vector database pipeline by ingesting data points as vectors with attached metadata payloads, then execute similarity queries that respect your specific payload constraints to retrieve targeted results.

Hybrid retrieval improves RAG search accuracy by combining multiple search signals, such as dense vector embeddings and sparse keyword search, through multi-stage query planning to deliver more relevant results than either method alone.

Yes, you can attach metadata payloads to vector embeddings during the ingestion process. This allows you to perform filtered similarity searches that restrict results based on specific payload fields like user permissions or document source.

The best way to combine dense and sparse vectors for RAG retrieval is using a multi-stage hybrid search approach, which integrates embedding-based similarity with keyword matching to optimize overall retrieval quality and relevance.

Maintain and optimize vector index performance by applying index maintenance operations during data changes, ensuring the database sustains efficient similarity search retrieval speeds as your vector collection grows and updates over time.

Yes, this vector search approach supports working with Weaviate and Qdrant, enabling you to design and implement ingestion pipelines, filtered similarity searches, and hybrid retrieval flows across these vector database platforms.