Merge pull request #252327 from markjbrown/vector-search

rename rag data to vector search

Author: Jill Grant

articles/cosmos-db/.openpublishing.redirection.cosmos-db.json

@@ -6209,6 +6209,11 @@
       "source_path_from_root": "/articles/cosmos-db/troubleshoot-local-emulator.md",
       "redirect_url": "/azure/cosmos-db/emulator",
       "redirect_document_id": false
+    },
+    {
+      "source_path_from_root": "/articles/cosmos-db/rag-data-openai.md",
+      "redirect_url": "/azure/cosmos-db/vector-search",
+      "redirect_document_id": true
     }
   ]
 }

articles/cosmos-db/TOC.yml

@@ -13,8 +13,8 @@
       href: distributed-nosql.md
     - name: Distributed relational
       href: distributed-relational.md
-    - name: Integrate with Azure OpenAI
-      href: rag-data-openai.md
+    - name: Integrate Vector Search with Azure OpenAI
+      href: vector-search.md
 - name: NoSQL
   href: nosql/toc.yml
 - name: MongoDB

articles/cosmos-db/index.yml

@@ -30,9 +30,9 @@ highlightedContent:
     - title: Q&A
       itemType: reference
       url: /answers/tags/187/azure-cosmos-db
-    - title: Use Azure Cosmos DB with Azure OpenAI
+    - title: Vector Search with Azure Cosmos DB
       itemType: concept
-      url: rag-data-openai.md
+      url: vector-search.md
     - title: Choose an API in Azure Cosmos DB
       itemType: concept
       url: choose-api.md

articles/cosmos-db/introduction.md

@@ -20,7 +20,7 @@ Today's applications are required to be highly responsive and always online. To
 
 Azure Cosmos DB is a fully managed NoSQL and relational database for modern app development. Azure Cosmos DB offers single-digit millisecond response times, automatic and instant scalability, along with guaranteed speed at any scale. Business continuity is assured with [SLA-backed](https://azure.microsoft.com/support/legal/sla/cosmos-db) availability and enterprise-grade security.
 
-Use Retrieval Augmented Generation (RAG) to bring the most semantically relevant data to enrich your AI-powered applications built with Azure OpenAI models like GPT-3.5 and GPT-4. For more information, see [Retrieval Augmented Generation (RAG) with Azure Cosmos DB](rag-data-openai.md).
+Use Retrieval Augmented Generation (RAG) to bring the most semantically relevant data to enrich your AI-powered applications built with Azure OpenAI models like GPT-3.5 and GPT-4. For more information, see [Retrieval Augmented Generation (RAG) with Azure Cosmos DB](vector-search.md#retrieval-augmented-generation).
 
 App development is faster and more productive thanks to:
 

articles/cosmos-db/media/rag-data-openai/vector-search-infosheet.png renamed to articles/cosmos-db/media/vector-search/vector-search-infosheet.png

@@ -368,7 +368,7 @@
             - name: Integrate with Azure services
               href: integrations-overview.md              
             - name: RAG with Azure OpenAI
-              href: ../rag-data-openai.md
+              href: ../vector-search.md#retrieval-augmented-generation
             - name: Vercel
               href: ../vercel-integration.md
         - name: Migrate data to Azure Cosmos DB

articles/cosmos-db/mongodb/TOC.yml

@@ -874,7 +874,7 @@
     - name: Integrate with other Azure services
       items:
         - name: RAG with Azure OpenAI
-          href: ../rag-data-openai.md
+          href: ../vector-search.md#retrieval-augmented-generation
         - name: Change feed Ecommerce solution
           href: changefeed-ecommerce-solution.md
         - name: Azure App Service

articles/cosmos-db/nosql/TOC.yml

@@ -1,15 +1,15 @@
 ---
-title: Use data with Azure OpenAI
+title: Vector search using Azure Cosmos DB
 titleSuffix: Azure Cosmos DB
 description: Use Retrieval Augmented Generation (RAG) and vector search to ground your Azure OpenAI models with data stored in Azure Cosmos DB. 
 author: jacodel
 ms.author: sidandrews
 ms.service: cosmos-db
 ms.topic: conceptual
-ms.date: 08/16/2023
+ms.date: 09/20/2023
 ---
 
-# Use Azure Cosmos DB data with Azure OpenAI
+# Vector search with data in Azure Cosmos DB
 
 [!INCLUDE[NoSQL, MongoDB vCore, PostgreSQL](includes/appliesto-nosql-mongodbvcore-postgresql.md)]
 
@@ -29,7 +29,7 @@ To jump right into tutorials and sample code for RAG patterns with Azure Cosmos
 
 This section includes key concepts that are critical to implementing RAG with Azure Cosmos DB and Azure OpenAI.
 
-### Retrieval Augmented Generation (RAG)
+### Retrieval Augmented Generation (RAG) <a id="retrieval-augmented-generation"></a>
 
 RAG involves the process of retrieving supplementary data to provide the LLM with the ability to use this data when it generates responses. When presented with a user's question or prompt, RAG aims to select the most pertinent and current domain-specific knowledge from external sources, such as articles or documents. This retrieved information serves as a valuable reference for the model when generating its response. For example, a simple RAG pattern using Azure Cosmos DB for NoSQL could be:
 
@@ -88,7 +88,7 @@ Prior to sending a request to Azure OpenAI, the user input/query/request is also
 
 There are multiple ways to use RAG and vector search with your data stored in Azure Cosmos DB. 
 
-:::image type="content" source="media/rag-data-openai/vector-search-infosheet.png" alt-text="Infosheet that shows options for performing Vector Search with Azure Cosmos DB to bring your data to your AI applications." lightbox="media/rag-data-openai/vector-search-infosheet.png":::
+:::image type="content" source="media/vector-search/vector-search-infosheet.png" alt-text="Infosheet that shows options for performing Vector Search with Azure Cosmos DB to bring your data to your AI applications." lightbox="media/vector-search/vector-search-infosheet.png":::
 
 ## Azure Cosmos DB for NoSQL and Azure Cognitive Search