fixes

Author: aahill

articles/ai-services/openai/concepts/use-your-data.md

@@ -261,17 +261,9 @@ Using the Elasticsearch data source is a preview feature which is subject to the
 
 1. (optional) use a custom field mapping.  
 
-    You can customize the field mapping when you add your data source to define the fields that will get mapped when answering questions, or use the default values.  
+    You can [customize the field mapping](#index-field-mapping) when you add your data source to define the fields that will get mapped when answering questions, or use the default values.  
 
-1. Choose the search types. Azure OpenAI On Your Data provides the following search types you can use when you add your data source.  
-
-    * [Keyword search](../../../search/search-lucene-query-architecture.md) which is available for any index.  
-    * [Vector search](../../../search/vector-search-overview.md) using a `text-embedding-ada-002` embedding model, available in [selected regions](./models.md#embeddings-models). 
-
-   To enable vector search, you need:  
-
-    1. An index that is generated with vector embeddings generated from one of the embedding models in the prerequisites. 
-    1. To have an existing embedding model deployed in your Azure OpenAI resource (`text-embedding-ada-002`) or hosted on Elasticsearch.  
+1. Choose the [search type](#search-types). Azure OpenAI On Your Data provides the following search types you can use when you add your data source.
 
 1. Continue through the screens that appear and select **Save and close**.
 
@@ -282,20 +274,17 @@ Azure OpenAI On Your Data provides the following search types you can use when y
 * [Keyword search](/azure/search/search-lucene-query-architecture)
 * [Vector search](/azure/search/vector-search-overview) using Ada [embedding](./understand-embeddings.md) models, available in [selected regions](models.md#embeddings-models) 
 
-    To enable vector search, you need an existing embedding model deployed in your Azure OpenAI resource. Select your embedding deployment when connecting your data, then select one of the vector search types under **Data management**. If you're using Azure AI Search as a data source, make sure you have a vector column in the index.
-
-If you're using your own index, you can customize the [field mapping](#index-field-mapping) when you add your data source to define the fields that will get mapped when answering questions. To customize field mapping, select **Use custom field mapping** on the **Data Source** page when adding your data source.
+To enable vector search, you need an existing embedding model deployed in your Azure OpenAI resource or hosted on Elasticsearch. Select your embedding deployment when connecting your data, then select one of the vector search types under **Data management**. If you're using Azure AI Search as a data source, make sure you have a vector column in the index.
 
 | Search option       | Retrieval type | Additional pricing? |Benefits|
 |---------------------|------------------------|---------------------| -------- |
 | *keyword*            | Keyword search                       | No additional pricing.                    |Performs fast and flexible query parsing and matching over searchable fields, using terms or phrases in any supported language, with or without operators.|
 | *vector*            | Vector search       | No additional pricing |Enables you to find documents that are similar to a given query input based on the vector embeddings of the content. |
-| *hybrid (vector + keyword)*   | A hybrid of vector search and keyword search | [Additional pricing](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) on your Azure OpenAI account from calling the embedding model.            |Performs similarity search over vector fields using vector embeddings, while also supporting flexible query parsing and full text search over alphanumeric fields using term queries.|
 
 
 ### Index field mapping 
 
-If you're using your own index, you will be prompted in the Azure OpenAI Studio to define which fields you want to map for answering questions when you add your data source. You can provide multiple fields for *Content data*, and should include all fields that have text pertaining to your use case.
+You can customize the [field mapping](#index-field-mapping) when you add your data source to define the fields that will get mapped when answering questions. To customize field mapping, select **Use custom field mapping** on the **Data Source** page when adding your data source. You can provide multiple fields for *content data*, and should include all fields that have text pertaining to your use case.
 
 Mapping these fields correctly helps ensure the model has better response and citation quality. You can additionally configure this [in the API](../references/elasticsearch.md#fields-mapping-options) using the `fields_mapping` parameter.