vector-database-engineer

Optimize vector databases for RAG by selecting appropriate embedding models, configuring HNSW or IVF indexes, and implementing document chunking. This ensures fast semantic search retrieval and scalable architecture across platforms like Pinecone, Weaviate, and Qdrant.

Implement hybrid search by combining vector similarity with keyword filtering. You achieve this by applying metadata pre-filtering or post-filtering techniques alongside vector embeddings to refine search accuracy and restrict results within your vector database.

HNSW index configuration impacts semantic search latency and accuracy by structuring graph layers for rapid vector traversal. Tuning HNSW parameters alongside embedding strategies ensures efficient retrieval and scalable performance as your document volume grows.

Yes, pgvector supports large-scale RAG applications when combined with proper embedding optimization and index configuration. You scale semantic search by implementing robust monitoring, performance tuning, and structured reindexing plans to maintain retrieval speed.

Use pre-filtering to restrict the search space before vector retrieval, improving speed with high-cardinality metadata. Use post-filtering to apply strict metadata constraints after retrieval, ensuring precise semantic search matches without excluding relevant vectors from the index.