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Database / Weaviate Vector database Interview questions

Explain the lifecycle of a hybrid search query in Weaviate?

A hybrid search request involves running two independent searches internally and then fusing their results, all within the scope of handling that single client call.

sequenceDiagram participant Client participant Weaviate participant Vectorizer as Vectorizer Module participant VectorIdx as Vector Index (HNSW/Flat) participant Inverted as Inverted Index (BM25) Client->>Weaviate: hybrid(query, alpha, limit) Weaviate->>Vectorizer: Embed the query text Vectorizer-->>Weaviate: Query vector Weaviate->>VectorIdx: Vector similarity search VectorIdx-->>Weaviate: Ranked vector results Weaviate->>Inverted: BM25 keyword search Inverted-->>Weaviate: Ranked keyword results Weaviate->>Weaviate: Fuse both ranked lists (rankedFusion/relativeScoreFusion, weighted by alpha) Weaviate-->>Client: Final fused top-K results

When a hybrid query arrives, Weaviate first embeds the query text using the collection's configured vectorizer (unless a raw vector was supplied directly), then runs a vector similarity search against the vector index and, in parallel, a BM25 keyword search against the inverted index. Each produces its own independently ranked list of candidate objects.

Those two lists are then combined using the configured fusion algorithm (rankedFusion or relativeScoreFusion), weighted according to the alpha parameter, and the resulting single fused ranking is what's actually returned to the client as the final top-K results, meaning the client never sees the two intermediate result lists separately, only the merged outcome.

During a hybrid search, Weaviate runs:
The fusion step that combines vector and BM25 results is weighted by:

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