fixing labeling

Author: aahill

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

@@ -9,7 +9,7 @@ ms.subservice: openai
 ms.topic: quickstart
 author: aahill
 ms.author: aahi
-ms.date: 08/25/2023
+ms.date: 09/05/2023
 recommendations: false
 ---
 
@@ -206,7 +206,7 @@ This option encourages the model to respond using your data only, and is selecte
 
 Azure OpenAI on your data provides several search options you can use when you add your data source, leveraging the following types of search.
 
-* [Simple search](/azure/search/search-lucene-query-architecture)
+* [Keyword search](/azure/search/search-lucene-query-architecture)
 
 * [Semantic search](/azure/search/semantic-search-overview)
 * [Vector search](/azure/search/vector-search-overview) using Ada [embedding](./understand-embeddings.md) models, available in [select regions](models.md#embeddings-models-1). 
@@ -219,11 +219,11 @@ Azure OpenAI on your data provides several search options you can use when you a
 
 | Search option       | Retrieval type | Additional pricing? |Benefits|
 |---------------------|------------------------|---------------------| -------- |
-| *simple*            | Simple 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.|
+| *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.|
 | *semantic*          |  Semantic search  |  Additional pricing for [semantic search](/azure/search/semantic-search-overview#availability-and-pricing) usage.                  |Improves the precision and relevance of search results by using a reranker (with AI models) to understand the semantic meaning of query terms and documents returned by the initial search ranker|
 | *vector*            | Vector search       | [Additional pricing](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) on your Azure OpenAI account from calling the embedding model.                    |Enables you to find documents that are similar to a given query input based on the vector embeddings of the content. |
-| *vector + simple*   | A hybrid of vector search and simple 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.|
-| *vector + semantic* + simple| A hybrid of vector search, semantic and simple search for retrieval.     | [Additional pricing](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) on your Azure OpenAI account from calling the embedding model, and additional pricing for [semantic search](/azure/search/semantic-search-overview#availability-and-pricing) usage.                    |Leverages vector embeddings, language understanding and flexible query parsing to create rich search experiences and generative AI apps that can handle complex and diverse information retrieval scenarios. |
+| *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.|
+| *hybrid (vector + keyword) + semantic* | A hybrid of vector search, semantic and keyword search for retrieval.     | [Additional pricing](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) on your Azure OpenAI account from calling the embedding model, and additional pricing for [semantic search](/azure/search/semantic-search-overview#availability-and-pricing) usage.                    |Leverages vector embeddings, language understanding and flexible query parsing to create rich search experiences and generative AI apps that can handle complex and diverse information retrieval scenarios. |
 
 The optimal search option can vary depending on your dataset and use-case. You may need to experiment with multiple options to determine which works best for your use-case.