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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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Usage

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. 
import asyncio
from moss import MossClient, QueryOptions

async def main():
    client = MossClient(MOSS_PROJECT_ID, MOSS_PROJECT_KEY)
    indexes = ["products", "reviews", "faqs"]

    # Bulk-load (best-effort; one failure doesn't roll back the others).
    result = await client.load_indexes(indexes)
    print(f"loaded={result.loaded} failed={result.failed}")

    # One query across all three; global top-K, tagged by source.
    results = await client.query_multi_index(
        indexes, "wireless headphones battery life", QueryOptions(top_k=6)
    )
    for doc in results.docs:
        print(f"[{doc.index_name}] {doc.id} score={doc.score:.3f} {doc.text[:60]}")

    await client.unload_indexes(indexes)

asyncio.run(main())

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Behavior

  • All indexes must be loaded locally (via load_index / load_indexes) and share the same embedding model.
  • top_k is global, not per-index - it caps the merged result set.
  • filter and embedding work the same as in single-index query.

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Bulk lifecycle

load_indexes(names) returns a LoadIndexesResult with loaded and failed. It’s best-effort: a typo in one name doesn’t roll back the others. unload_indexes(names) releases them and is idempotent.

Retrieval

Single-index querying, filters, and hybrid search.

Python reference

query_multi_index, load_indexes, unload_indexes.