Querying with “Group By” equivalents
In relational SQL, GROUP BY aggregates rows by a key. In a vector database, a “group by” style need often means: return at most one result per group (e.g. one document per category, one item per product ID) or aggregate scores/vectors by metadata. VDBs may support this via post-query grouping, partitioning by a key so each partition is queried separately, or application-side deduplication. There is no universal standard; support and performance depend on the engine and whether grouping is done before or after ANN.
Summary
- In SQL, GROUP BY aggregates by key. In a VDB, “group by” style often means: at most one result per group (e.g. one per category, one per product ID) or aggregate scores/vectors by metadata. No universal standard; support depends on the engine.
- Implementation: post-query grouping, partitioning by key (query each partition separately), or application-side deduplication. Grouping before vs. after ANN affects performance and semantics. See sorting within groups. Pipeline: run ANN, group by metadata in app, take best per group. Practical tip: over-fetch and group in app for one-per-category.
Implementation patterns
Common needs: at most one result per group (e.g. one document per category, one item per product ID) or aggregate scores/vectors by metadata. Implementation options: (1) Post-query grouping: run vector search, then in the application take the top result per group (e.g. by category) or deduplicate by a key. (2) Partition by key: use namespaces or partitions so each partition is one group; query each partition separately and merge. (3) Engine feature: some VDBs support diversity-by-field or group-by-style options.
Pipeline: run ANN (possibly with filter), then in app group by metadata field, take best per group (e.g. max score) or merge sorted lists. Trade-off: grouping after ANN is common and simple but may require over-fetching; query-per-partition gives top-K per group but changes semantics. Practical tip: over-fetch and group in app for one-per-category; use partitions when you need true top-K per group.
Group before or after ANN
Grouping after ANN (post-query) is common: run vector search, then deduplicate or take one per group. Grouping before (e.g. query per partition) can reduce work but changes semantics. Aggregating scores or vectors per group is often done in the application after retrieval. See multi-vector search for per-document aggregation and sorting for ordering within groups.
Frequently Asked Questions
Is there GROUP BY in vector databases?
No universal standard. The need is often at most one result per group (e.g. one document per category) or aggregate by metadata. Implement via post-query grouping, partitioning by key, or application-side deduplication. Support and performance depend on the engine.
How do I get one result per category?
Options: (1) Query and group in application (take top per category from results). (2) Partition by category and query each partition separately. (3) Use a VDB feature if available (e.g. diversity by field). See sorting for ordering within groups.
Group before or after ANN?
Grouping after ANN (post-query) is common: run vector search, then deduplicate or take one per group. Grouping before (e.g. query per partition) can reduce work but changes semantics (top-K per partition vs. global top-K).
What about aggregating scores or vectors?
Some use cases need aggregate scores (e.g. sum, max) or combined vectors per group. Support varies; often done in the application after retrieval. See multi-vector search for per-document aggregation.