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Sometimes the answer is spread across separate corpora - a product catalog, its reviews, and an FAQ - that you keep as distinct indexes. Multi-index search queries several loaded indexes in a single call and returns the global top-K, with each result tagged by its source index.

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How it works

Load the indexes (in bulk with load_indexes), then query them together with query_multi_index. Every result document carries an index_name so you know where it came from.

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Behavior

  • All indexes must be loaded locally (via load_index / load_indexes) and share the same embedding model. Querying indexes with different models returns an error.
  • top_k is global, not per-index - it caps the merged result set. The default is 10 (a single-index query defaults to 5).
  • Hybrid via alpha: alpha works as in a single-index query (default 0.8). 1.0 is embedding-only, 0.0 is keyword-only, and values in between fuse both signals with Reciprocal Rank Fusion. Keyword scoring runs each index’s own BM25 and merges hits by score before fusion, so keyword and hybrid ordering across indexes is approximate. filter and embedding work the same as in a single-index query.
  • Bulk lifecycle: load_indexes(names) returns a LoadIndexesResult with loaded and failed (best-effort; a typo in one name doesn’t roll back the others), and unload_indexes(names) releases them and is idempotent.

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Implementation

Multi-index search is available in the Python (query_multi_index), JavaScript (queryMultiIndex), and Elixir (Moss.Client.query_multi_index/4) SDKs, each with matching bulk load_indexes / unload_indexes calls. See the Python guide for a runnable example.

Retrieval

Single-index querying, filters, and hybrid search.

Python reference

query_multi_index, load_indexes, unload_indexes.