reformatted bulleted section

Author: HeidiSteen

articles/search/vector-search-how-to-create-index.md

@@ -82,7 +82,7 @@ The schema must include fields for the document key, vector fields, and any othe
 
 1. Add other fields that define the substance and structure of the textual content you're indexing. At a minimum, you need a document key. 
 
-   You should also add fields that are useful in the query or in its response. The example below shows vector fields for title and content ("titleVector", "contentVector") that are equivalent to vectosr. It also provides fields for equivalent textual content ("title", "content") useful for sorting, filtering, and reading in a search result.
+   You should also add fields that are useful in the query or in its response. The example below shows vector fields for title and content ("titleVector", "contentVector") that are equivalent to vectors. It also provides fields for equivalent textual content ("title", "content") useful for sorting, filtering, and reading in a search result.
 
    An index definition with the described elements looks like this:
 

articles/search/vector-search-how-to-query.md

@@ -88,20 +88,16 @@ Vectors aren't designed for readability, so avoid returning them in the response
 Size of the results is determined by the query parameters "k" and "top". Maximum results in a response are either:
 
 + `"k": n` results for vector-only queries
-
 + `"top": n` results for hybrid queries
 
-Ranking of results is either:
-
-+ Cosine similarity if the query is over a single vector field, assuming `cosine` is what you specified in the index `vectorConfiguration`.
-
-  Azure OpenAI embedding models use cosine similarity, so if you're using Azure OpenAI embedding models, `cosine` is the recommended metric. Other supported ranking metrics include `euclidean` and `dotProduct`.
+Ranking of results is computed by either:
 
++ The similarity metric specified in the index `vectorConfiguration` for a vector-only query.
 + Reciprocal Rank Fusion (RRF) if there are multiple sets of search results.
 
-  Multiple sets are created if the query targets multiple vector fields, or if the query is a hybrid of vector and full text search, with or without the optional semantic reranking capabilities of [semantic search](semantic-search-overview.md).
+Azure OpenAI embedding models use cosine similarity, so if you're using Azure OpenAI embedding models, `cosine` is the recommended metric. Other supported ranking metrics include `euclidean` and `dotProduct`.
 
-  Within vector search, a vector query can only target one internal vector index. So for [multiple vector fields](#query-syntax-for-vector-query-over-multiple-fields) and [multiple vector queries](#query-syntax-for-multiple-vector-queries), the search engine generates parallel queries that target the respective vector indexes of each field. Output is a set of ranked results for each query, which are fused using RRF. For more information, see [Vector query execution and scoring](vector-search-ranking.md).
+Multiple sets are created if the query targets multiple vector fields, or if the query is a hybrid of vector and full text search, with or without the optional semantic reranking capabilities of [semantic search](semantic-search-overview.md). Within vector search, a vector query can only target one internal vector index. So for [multiple vector fields](#query-syntax-for-vector-query-over-multiple-fields) and [multiple vector queries](#query-syntax-for-multiple-vector-queries), the search engine generates multiple queries that target the respective vector indexes of each field. Output is a set of ranked results for each query, which are fused using RRF. For more information, see [Vector query execution and scoring](vector-search-ranking.md).
 
 ## Query syntax for vector search