vector-database-engineer

Optimize vector databases for semantic search by selecting appropriate architectures, embedding strategies, chunking methods, and index configurations like HNSW or IVF. You must specify evaluation criteria and performance targets to ensure reliable recall and low latency in production RAG deployments.

Configure hybrid search in pgvector by combining vector similarity scores with keyword matching and applying structured metadata filters. Designing a precise metadata schema alongside vector indexes ensures accurate filtered retrieval and improves overall search relevance.

Choose between HNSW, IVF, and PQ index types by evaluating your specific latency and recall requirements. HNSW offers fast queries with higher memory usage, while IVF and PQ provide scalable alternatives, requiring configuration tuning to hit production performance targets.

Yes, vector database selection applies across Pinecone, Weaviate, Qdrant, Milvus, and pgvector. The process involves evaluating each platform's architecture and hybrid search capabilities to design a scalable semantic search solution tailored to your specific retrieval needs.

Low recall in semantic search usually stems from poorly optimized embedding strategies, inadequate chunking, or mismatched index configurations. Validating performance targets, adjusting index parameters, and implementing robust reindexing strategies are required to resolve retrieval bottlenecks.

Implement reindexing strategies for vector embeddings when modifying chunking strategies, updating embedding models, or experiencing latency drift in production. Regular performance monitoring identifies when index degradation occurs, ensuring continuous reliable retrieval for RAG applications.