From 807d89b3504aa5651fd01a718b4322b97a80a84e Mon Sep 17 00:00:00 2001 From: Abhi Dangeti Date: Mon, 5 Aug 2024 13:21:13 -0700 Subject: [PATCH] Update docs/vectors.md to reflect latest (#2061) --- docs/vectors.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/docs/vectors.md b/docs/vectors.md index 53b8054a4..6ec3e2206 100644 --- a/docs/vectors.md +++ b/docs/vectors.md @@ -19,9 +19,10 @@ * The `vector` field type is an array that is to hold float32 values only. * The `vector_base64` field type to support base64 encoded strings using little endian byte ordering (v2.4.1+) -* Currently supported similarity metrics are: [`"l2_norm"`, `"dot_product"`]. +* Supported similarity metrics are: [`"cosine"` (v2.4.3+), `"dot_product"`, `"l2_norm"`]. + * `cosine` paths will additionally normalize vectors before indexing and search. * Supported dimensionality is between 1 and 2048 (v2.4.0), and up to **4096** (v2.4.1+). -* Supported vector index optimizations: `recall`, `latency`, `memory_efficient` (v2.4.1+) +* Supported vector index optimizations: `latency`, `memory_efficient` (v2.4.1+), `recall`. * Vectors from documents that do not conform to the index mapping dimensionality are simply discarded at index time. * The dimensionality of the query vector must match the dimensionality of the indexed vectors to obtain any results. * Pure kNN searches can be performed, but the `query` attribute within the search request must be set - to `{"match_none": {}}` in this case. The `query` attribute is made optional when `knn` is available with v2.4.1+.