Indexes

Indexes are tools for optimizing query performance of a collection.

Collections can be queried without an index, but that will emit a python warning and should never be done in produciton.

query does not have a covering index for cosine_similarity. See Collection.create_index

as each query vector must be checked against every record in the collection. When the number of dimensions and/or number of records becomes large, that becomes extremely slow and computationally expensive.

An index is a heuristic datastructure that pre-computes distances among key points in the vector space. It is smaller and can be traversed more quickly than the whole collection enabling much more performant seraching.

Only one index may exist per-collection. An index optimizes a collection for searching according to a selected distance measure.

Available options distance measure are:

• cosine distance
• l2 distance
• max inner product

If you aren't sure which to use, stick with the default (cosine_distance) by omitting the parameter when creating indexes and querying.