vector-database-engineer

Configure vector search by selecting an embedding model, choosing an index type like HNSW or IVF, and designing document chunking and metadata schemas. Running an initial performance and recall evaluation completes the end-to-end vector retrieval pipeline setup.

Implement hybrid search by combining vector similarity retrieval with keyword-based filtering. This approach optimizes RAG systems by ensuring fast, accurate semantic matching over large document collections while preserving exact term search capabilities.

Metadata filtering applies structured attribute constraints to vector embeddings using pre-filtering or post-filtering techniques. This narrows the search space before or after similarity computation, enabling precise document retrieval within specific data categories.

Use HNSW indexes for high recall and low latency queries, while IVF indexes suit large-scale datasets needing memory efficiency through partitioning. PQ can further compress embeddings to optimize storage and scaling trade-offs.

Document chunking strategies optimize search by splitting text into semantically coherent segments before embedding. Proper chunk sizing balances context preservation against retrieval precision, directly impacting vector database recall and performance.

Yes, you can build recommendation engines using vector similarity search across text, images, and audio. By indexing multimedia embeddings, the engine retrieves relevant items based on semantic likeness rather than exact keyword matches.