Merge pull request #224069 from HeidiSteen/heidist-gh

[azure search] GH issue, confusing statement in semantic answers doc

Author: prmerger-automator[bot]

articles/search/search-get-started-python.md

@@ -8,7 +8,7 @@ ms.author: heidist
 ms.service: cognitive-search
 ms.devlang: python
 ms.topic: quickstart
-ms.date: 08/31/2022
+ms.date: 01/17/2023
 ms.custom: devx-track-python, mode-api
 ---
 
@@ -275,7 +275,7 @@ This step shows you how to query an index using the **search** method of the [se
 1. The following step executes an empty search (`search=*`), returning an unranked list (search score = 1.0) of arbitrary documents. Because there are no criteria, all documents are included in results. This query prints just two of the fields in each document. It also adds `include_total_count=True` to get a count of all documents (4) in the results.
 
     ```python
-    results =  search_client.search(search_text="*", include_total_count=True)
+    results = search_client.search(search_text="*", include_total_count=True)
 
     print ('Total Documents Matching Query:', results.get_count())
     for result in results:
@@ -285,7 +285,7 @@ This step shows you how to query an index using the **search** method of the [se
 1. The next query adds whole terms to the search expression ("wifi"). This query specifies that results contain only those fields in the `select` statement. Limiting the fields that come back minimizes the amount of data sent back over the wire and reduces search latency.
 
     ```python
-    results =  search_client.search(search_text="wifi", include_total_count=True, select='HotelId,HotelName,Tags')
+    results = search_client.search(search_text="wifi", include_total_count=True, select='HotelId,HotelName,Tags')
 
     print ('Total Documents Matching Query:', results.get_count())
     for result in results:
@@ -295,16 +295,16 @@ This step shows you how to query an index using the **search** method of the [se
 1. Next, apply a filter expression, returning only those hotels with a rating greater than 4, sorted in descending order.
 
     ```python
-    results =  search_client.search(search_text="hotels", select='HotelId,HotelName,Rating', filter='Rating gt 4', order_by='Rating desc')
+    results = search_client.search(search_text="hotels", select='HotelId,HotelName,Rating', filter='Rating gt 4', order_by='Rating desc')
 
     for result in results:
         print("{}: {} - {} rating".format(result["HotelId"], result["HotelName"], result["Rating"]))
     ```
 
-1. Add `search_fields` to scope query matching to a single field.
+1. Add `search_fields` (an array) to scope query matching to a single field.
 
     ```python
-    results =  search_client.search(search_text="sublime", search_fields='HotelName', select='HotelId,HotelName')
+    results = search_client.search(search_text="sublime", search_fields=['HotelName'], select='HotelId,HotelName')
 
     for result in results:
         print("{}: {}".format(result["HotelId"], result["HotelName"]))
@@ -313,7 +313,7 @@ This step shows you how to query an index using the **search** method of the [se
 1. Facets are labels that can be used to compose facet navigation structure. This query returns facets and counts for Category.
 
     ```python
-    results =  search_client.search(search_text="*", facets=["Category"])
+    results = search_client.search(search_text="*", facets=["Category"])
 
     facets = results.get_facets()
 

articles/search/semantic-answers.md

@@ -8,7 +8,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: cognitive-search
 ms.topic: conceptual
-ms.date: 11/02/2022
+ms.date: 01/16/2023
 ---
 
 # Return a semantic answer in Azure Cognitive Search
@@ -166,7 +166,7 @@ For best results, return semantic answers on a document corpus having the follow
 
 + The "semanticConfiguration" must include fields that offer sufficient text in which an answer is likely to be found. Fields more likely to contain answers should be listed first in "prioritizedContentFields". Only verbatim text from a document can appear as an answer.
 
-+ Query strings must not be null (search=`*`) and the string should have the characteristics of a question, as opposed to a keyword search (a sequential list of arbitrary terms or phrases). If the query string doesn't appear to be answer, answer processing is skipped, even if the request specifies "answers" as a query parameter.
++ Query strings must not be null (search=`*`) and the string should have the characteristics of a question, such as "what is" or "how to", as opposed to a keyword search consisting of terms or phrases in arbitrary order. If the query string doesn't appear to be a question, answer processing is skipped, even if the request specifies "answers" as a query parameter.
 
 + Semantic extraction and summarization have limits over how many tokens per document can be analyzed in a timely fashion. In practical terms, if you have large documents that run into hundreds of pages, try to break up the content into smaller documents first.