review references to alphanumeric

Author: HeidiSteen

articles/search/cognitive-search-create-custom-skill-example.md

@@ -32,7 +32,7 @@ Although this example uses an Azure Function to host a web API, it isn't require
 
 1. In Visual Studio, select **New** > **Project** from the File menu.
 
-1. Choose **Azure Functions** as the template and select **Next**. Type a name for your project, and select **Create**. The function app name must be valid as a C# namespace, so don't use underscores, hyphens, or any other non-alphanumeric characters.
+1. Choose **Azure Functions** as the template and select **Next**. Type a name for your project, and select **Create**. The function app name must be valid as a C# namespace, so don't use underscores, hyphens, or any other special characters.
 
 1. Select a framework that has long term support.
 

articles/search/index-add-scoring-profiles.md

@@ -99,7 +99,7 @@ Scoring profiles supplement the default scoring algorithm by boosting the scores
 
 For standalone text queries, scoring profiles identify the maximum 1,000 matches in a [BM25-ranked search](index-similarity-and-scoring.md), and the top 50 are returned in results.
 
-For pure vectors, the query is vector-only, but if the [*k*-matching documents](vector-search-ranking.md) include alphanumeric fields that a scoring profile can process, a scoring profile is applied. The scoring profile revises the result set by boosting documents that match criteria in the profile.
+For pure vectors, the query is vector-only, but if the [*k*-matching documents](vector-search-ranking.md) include nonvector alphanumeric fields, a scoring profile is applied. The scoring profile revises the result set by boosting documents that match criteria in the profile.
 
 For text queries in a hybrid query, scoring profiles identify the maximum 1,000 matches in a BM25-ranked search. However, once those 1,000 results are identified, they're restored to their original BM25 order so that they can be rescored alongside vectors results in the final [Reciprocal Ranking Function (RRF)](hybrid-search-ranking.md) ordering, where the scoring profile (identified as "final document boosting adjustment" in the illustration) is applied to the merged results, along with [vector weighting](vector-search-how-to-query.md#vector-weighting), and [semantic ranking](semantic-search-overview.md) as the last step.
 

articles/search/search-filters.md

@@ -46,7 +46,7 @@ Filtering occurs in tandem with search, qualifying which documents to include in
 
 ## How filters are defined
 
-Filters apply to alphanumeric content on fields that are attributed as `filterable`.
+Filters apply to text and numeric (nonvector) content on fields that are attributed as `filterable`.
 
 Filters are OData expressions, articulated in the [filter syntax](search-query-odata-filter.md) supported by Azure AI Search.
 

articles/search/search-how-to-create-indexers.md

@@ -11,7 +11,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 10/24/2024
+ms.date: 12/17/2024
 ---
 
 # Create an indexer in Azure AI Search
@@ -22,9 +22,9 @@ You can use an indexer to automate data import and indexing in Azure AI Search.
 
 Indexers support two workflows:
 
-+ **Text-based indexing**: Extract strings and metadata from textual content for full text search scenarios.
++ **Raw content indexing (plain text or vectors)**: Extract strings and metadata from textual content for full text search scenarios. Extracts raw vector content for vector search (for example, vectors in an Azure SQL database or Azure Cosmos DB collection). In this workflow, indexing occurs only over existing content that you provide.
 
-+ **Skills-based indexing**: Use built-in or custom skills that add integrated machine learning for analysis over images and large undifferentiated content, extracting or inferring text and structure. Skills-based indexing enables search over content that isn't otherwise easily full text searchable. To learn more, see [AI enrichment in Azure AI Search](cognitive-search-concept-intro.md).
++ **Skills-based indexing**: Extends indexing through built-in or custom skills that create or generate new searchable content. For example, you can add integrated machine learning for analysis over images and unstructured text, extracting or inferring text and structure. Or, use skills to chunk and vectorize content from text and images. Skills-based indexing creates or generates new content that doesn't exist in your external data source. New content becomes part of your index when you add fields to the index schema that accepts the incoming data. To learn more, see [AI enrichment in Azure AI Search](cognitive-search-concept-intro.md).
 
 ## Prerequisites
 
@@ -38,11 +38,11 @@ Indexers support two workflows:
 
 ## Indexer patterns
 
-When you create an indexer, the definition is one of two patterns: *text-based indexing* or *skills-based indexing*. The patterns are the same, except that skills-based indexing has more definitions.
+When you create an indexer, the definition is one of two patterns: *content-based indexing* or *skills-based indexing*. The patterns are the same, except that skills-based indexing has more definitions.
 
-### Indexer example for text-based indexing
+### Indexer example for content-based indexing
 
-Text-based indexing for full text search is the primary use case for indexers. For this workflow, an indexer looks like this example.
+Content-based indexing for full text or vector search is the primary use case for indexers. For this workflow, an indexer looks like this example.
 
 ```json
 {
@@ -114,16 +114,16 @@ Indexers work with data sets. When you run an indexer, it connects to your data
 
 | Source data | Tasks |
 |-------------|-------|
-| JSON documents | Make sure the structure or shape of incoming data corresponds to the schema of your search index. Most search indexes are fairly flat, where the fields collection consists of fields at the same level. However, hierarchical or nested structures are possible through [complex fields and collections](search-howto-complex-data-types.md). |
+| JSON documents | JSON documents can contain text, numbers, and vectors. Make sure the structure or shape of incoming data corresponds to the schema of your search index. Most search indexes are fairly flat, where the fields collection consists of fields at the same level. However, hierarchical or nested structures are possible through [complex fields and collections](search-howto-complex-data-types.md). |
 | Relational | Provide data as a flattened row set, where each row becomes a full or partial search document in the index. <br><br> To flatten relational data into a row set, you should create a SQL view, or build a query that returns parent and child records in the same row. For example, the built-in hotels sample dataset is an SQL database that has 50 records (one for each hotel), linked to room records in a related table. The query that flattens the collective data into a row set embeds all of the room information in JSON documents in each hotel record. The embedded room information is a generated by a query that uses a **FOR JSON AUTO** clause. <br><br> You can learn more about this technique in [define a query that returns embedded JSON](index-sql-relational-data.md#define-a-query-that-returns-embedded-json). This is just one example; you can find other approaches that produce the same result. |
 | Files | An indexer generally creates one search document for each file, where the search document consists of fields for content and metadata. Depending on the file type, the indexer can sometimes [parse one file into multiple search documents](search-howto-index-one-to-many-blobs.md). For example, in a CSV file, each row can become a standalone search document. |
 
 Remember that you only need to pull in searchable and filterable data:
 
-+ Searchable data is text
-+ Filterable data is alphanumeric
++ Searchable data is text or vectors
++ Filterable data is text and numbers (non-vector fields)
 
-Azure AI Search can't search over binary data in any format, although it can extract and infer text descriptions of image files (see [AI enrichment](cognitive-search-concept-intro.md)) to create searchable content. Likewise, large text can be broken down and analyzed by natural language models to find structure or relevant information, generating new content that you can add to a search document.
+Azure AI Search can't do a full-text search over binary data in any format, although it can extract and infer text descriptions of image files (see [AI enrichment](cognitive-search-concept-intro.md)) to create searchable content. Likewise, large text can be broken down and analyzed by natural language models to find structure or relevant information, generating new content that you can add to a search document. It can also do vector search over embeddings, including quantized embeddings in a binary format.
 
 Given that indexers don't fix data problems, other forms of data cleansing or manipulation might be needed. For more information, you should refer to the product documentation of your [Azure database product](/azure/?product=databases).
 

articles/search/search-howto-incremental-index.md

@@ -134,7 +134,7 @@ PUT https://[YOUR-SEARCH-SERVICE].search.windows.net/indexers/[YOUR-INDEXER-NAME
     }
 ```
 
-If you now issue another GET request on the indexer, the response from the service includes an `ID` property in the cache object. The alphanumeric string is appended to the name of the container containing all the cached results and intermediate state of each document processed by this indexer. The ID is used to uniquely name the cache in Blob storage.
+If you now issue another GET request on the indexer, the response from the service includes an `ID` property in the cache object. The string is appended to the name of the container containing all the cached results and intermediate state of each document processed by this indexer. The ID is used to uniquely name the cache in Blob storage.
 
 ```http
     "cache": {

articles/search/search-import-data-portal.md

@@ -17,7 +17,7 @@ customer intent: As a developer, I want to use wizards for index creation so tha
 
 Azure AI Search has two import wizards that automate indexing and object creation so that you can begin querying immediately. If you're new to Azure AI Search, these wizards are one of the most powerful features at your disposal. With minimal effort, you can create an indexing or enrichment pipeline that exercises most of the functionality of Azure AI Search.
 
-+ **Import data wizard** supports nonvector workflows. You can extract alphanumeric text from raw documents. You can also configure applied AI and built-in skills that infer structure and generate text searchable content from image files and unstructured data.
++ **Import data wizard** supports nonvector workflows. You can extract text and numbers from raw documents. You can also configure applied AI and built-in skills that infer structure and generate text searchable content from image files and unstructured data.
 
 + **Import and vectorize data wizard** adds chunking and vectorization. You must specify an existing deployment of an embedding model, but the wizard makes the connection, formulates the request, and handles the response. It generates vector content from text or image content.