A RAG system retrieves product support documents containing specialized component identifiers such as "Part-7X-A4-ENG22". Users often query using exact part numbers. Pure vector similarity search misses these identifiers. Which Vector Search feature best addresses this retrieval challenge?

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B. Hybrid keyword-similarity search, which combines vector similarity with BM25 keyword matching.. Databricks documentation explicitly states that hybrid keyword-similarity search is particularly useful in RAG applications where source data has unique keywords such as SKUs or identifiers that are not well suited to pure similarity search. The hybrid approach combines BM25 keyword matching (for exact part number terms) with vector similarity (for semantic context), merging results using Reciprocal Rank Fusion (RRF).

A RAG system retrieves product support documents containing specialized component identifiers such as "Part-7X-A4-ENG22". Users often query using exact part numbers. Pure vector similarity search misses these identifiers. Which Vector Search feature best addresses this retrieval challenge?

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