@@ -20,7 +20,7 @@ Through [AI enrichment](cognitive-search-concept-intro.md), Azure AI Search give
 + [Image Analysis](cognitive-search-skill-image-analysis.md) that describes images through visual features
 + [Custom skills](#passing-images-to-custom-skills) to invoke any external image processing that you want to provide
 
-By using OCR, you can extract text from photos or pictures containing alphanumeric text, such as the word *STOP* in a stop sign. Through image analysis, you can generate a text representation of an image, such as *dandelion* for a photo of a dandelion, or the color *yellow*. You can also extract metadata about the image, such as its size.
+By using OCR, you can extract text and from photos or pictures, such as the word *STOP* in a stop sign. Through image analysis, you can generate a text representation of an image, such as *dandelion* for a photo of a dandelion, or the color *yellow*. You can also extract metadata about the image, such as its size.
 
 This article covers the fundamentals of working with images, and also describes several common scenarios, such as working with embedded images, custom skills, and overlaying visualizations on original images.
 

@@ -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.
 

@@ -47,7 +47,7 @@ To create a custom analyzer, specify it in the `analyzers` section of an index a
 
 An analyzer definition includes a name, type, one or more character filters, a maximum of one tokenizer, and one or more token filters for post-tokenization processing. Character filters are applied before tokenization. Token filters and character filters are applied from left to right.
 
-- Names in a custom analyzer must be unique and can't be the same as any of the built-in analyzers, tokenizers, token filters, or characters filters. It must only contain letters, digits, spaces, dashes or underscores, can only start and end with alphanumeric characters, and is limited to 128 characters. 
+- Names in a custom analyzer must be unique and can't be the same as any of the built-in analyzers, tokenizers, token filters, or characters filters. Names consist of letters, digits, spaces, dashes or underscores. Names must start and end with plain text characters. Names must be under 128 characters in length.
 
 - Type must be #Microsoft.Azure.Search.CustomAnalyzer.
 

@@ -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 fields with human-readable ocntent, a scoring profile can be 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.
 

@@ -70,7 +70,7 @@ You can embed Boolean operators in a query string to improve the precision of a
 
 ## Prefix queries
 
-For "starts with" queries, add a suffix operator (`*`) as the placeholder for the remainder of a term. A prefix query must begin with at least one alphanumeric character before you can add the suffix operator.
+For "starts with" queries, add a suffix operator (`*`) as the placeholder for the remainder of a term. A prefix query must begin with at least one plain text character before you can add the suffix operator.
 
 | Character | Example | Usage |
 |----------- |--------|-------|

@@ -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.
 

@@ -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).
 

@@ -64,7 +64,7 @@ Use this checklist to assist the design decisions for your search index.
 
 1. Identify which source fields can be used as filters. Numeric content and short text fields, particularly those with repeating values, are good choices. When working with filters, remember:
 
-   + Filters can be used in vector and nonvector queries, but the filter itself is applied to alphanumeric (nonvector) fields in your index.
+   + Filters can be used in vector and nonvector queries, but the filter itself is applied to human-readable (nonvector) fields in your index.
 
    + Filterable fields can optionally be used in faceted navigation.
 

@@ -18,11 +18,11 @@ This article explains how to import documents into a predefined search index. In
 
 ## How data import works
 
-A search service accepts JSON documents that conform to the index schema. A search service imports and indexes plain text and vectors in JSON, used in full text search, vector search, hybrid search, and knowledge mining scenarios. 
+A search service accepts JSON documents that conform to the index schema. A search service can import and index plain text content and vector content in JSON documents.
 
-+ Plain text content is obtainable from alphanumeric fields in the external data source, metadata that's useful in search scenarios, or enriched content created by a [skillset](cognitive-search-working-with-skillsets.md) (skills can extract or infer textual descriptions from images and unstructured content). 
++ Plain text content is retrieved from fields in the external data source, from metadata properties, or from enriched content that's generated by a [skillset](cognitive-search-working-with-skillsets.md) (skills can extract or infer textual descriptions from images and unstructured content).
 
-+ Vector content is vectorized using an [external embedding model](vector-search-how-to-generate-embeddings.md) or [integrated vectorization](vector-search-integrated-vectorization.md) using Azure AI Search features that integrate with applied AI.
++ Vector content is retrieved from a data source that provides it, or it's created by a skillset that implements [integrated vectorization](vector-search-integrated-vectorization.md) in an Azure AI Search indexer workload.
 
 You can prepare these documents yourself, but if content resides in a [supported data source](search-indexer-overview.md#supported-data-sources), running an [indexer](search-indexer-overview.md) or using an Import wizard can automate document retrieval, JSON serialization, and indexing.
 

@@ -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": {

@@ -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.
 

@@ -21,7 +21,7 @@ Azure AI Search supports Microsoft and Lucene analyzers. By default, the search
 
 In Azure AI Search, the two patterns for supporting multiple languages include:
 
-+ Create language-specific indexes where all of the alphanumeric content is in the same language, and all searchable string fields are attributed to use the same [language analyzer](index-add-language-analyzers.md).
++ Create language-specific indexes where all of the human readable content is in the same language, and all searchable string fields are attributed to use the same [language analyzer](index-add-language-analyzers.md).
 
 + Create a blended index with language-specific versions of each field (for example, description_en, description_fr, description_ko), and then constrain full text search to just those fields at query time. This approach is useful for scenarios where language variants are only needed on a few fields, like a description.
 

@@ -24,9 +24,9 @@ Azure AI Search supports query constructs for a broad range of scenarios, from f
 | [full text search](search-lucene-query-architecture.md) | Inverted indexes of tokenized terms. | Full text queries iterate over inverted indexes that are structured for fast scans, where a match can be found in potentially any field, within any number of search documents. Text is analyzed and tokenized for full text search.|
 | [Vector search](vector-search-overview.md) | Vector indexes of generated embeddings. | Vector queries iterate over vector fields in a search index. |
 | [Hybrid search](hybrid-search-overview.md) | All of the above, in a single search index. | Combines text search and vector search in a single query request. Text search works on plain text content in "searchable" and "filterable" fields. Vector search works on content in vector fields. |
-| Others | Plain text and alphanumeric content.| Raw content, extracted verbatim from source documents, supporting filters and pattern matching queries like geo-spatial search, fuzzy search, and fielded search. |
+| Others | Plain text and human-readable content.| Raw content, extracted verbatim from source documents, supporting filters and pattern matching queries like geo-spatial search, fuzzy search, and fielded search. |
 
-This article brings focus to the last category: queries that work on plain text and alphanumeric content, extracted intact from original source, used for filters and other specialized query forms.
+This article brings focus to the last category: queries that work on plain text and human-readable content, extracted intact from original source, used for filters and other specialized query forms.
 
 ## Autocomplete and suggested queries
 

@@ -52,9 +52,9 @@ Make sure your documents:
 
    Vector fields contain an array generated by embedding models, one embedding per field, where the field is a top-level field (not part of a nested or complex type). For the simplest integration, we recommend the embedding models in [Azure OpenAI](https://aka.ms/oai/access), such as **text-embedding-ada-002** for text documents or the [Image Retrieval REST API](/rest/api/computervision/2023-02-01-preview/image-retrieval/vectorize-image) for images.
 
-   If you can take a dependency on indexers and skillsets, consider using [integrated vectorization](vector-search-integrated-vectorization.md) that encodes images and alphanumeric content during indexing. Your field definitions are for vector fields, but source data can be text or images, with vector arrays created during indexing.
+   If you can take a dependency on indexers and skillsets, consider using [integrated vectorization](vector-search-integrated-vectorization.md) that encodes images and textual content during indexing. Your field definitions are for vector fields, but incoming source data can be text or images, represented as vector arrays created during indexing.
 
-1. Provide other fields with human-readable alphanumeric content for the query response, and for hybrid query scenarios that include full text search or semantic ranking in the same request. 
+1. Provide other fields with human-readable content for the query response, and for hybrid query scenarios that include full text search or semantic ranking in the same request. 
 
 Your search index should include fields and content for all of the query scenarios you want to support. Suppose you want to search or filter over product names, versions, metadata, or addresses. In this case, similarity search isn't especially helpful. Keyword search, geo-search, or filters would be a better choice. A search index that includes a comprehensive field collection of vector and nonvector data provides maximum flexibility for query construction and response composition.
 
@@ -467,7 +467,7 @@ Vector fields are characterized by [their data type](/rest/api/searchservice/sup
 
 ## Load vector data for indexing
 
-Content that you provide for indexing must conform to the index schema and include a unique string value for the document key. Prevectorized data is loaded into one or more vector fields, which can coexist with other fields containing alphanumeric content.
+Content that you provide for indexing must conform to the index schema and include a unique string value for the document key. Prevectorized data is loaded into one or more vector fields, which can coexist with other fields containing nonvector content.
 
 You can use either [push or pull methodologies](search-what-is-data-import.md) for data ingestion.