Beef up MEVD docs: expanded conceptual article, new how-to guide, and working code snippets

.openpublishing.redirection.ai.json

@@ -18,6 +18,11 @@
             "redirect_url": "/dotnet/ai/evaluation/libraries",
             "redirect_document_id": true
         },
+        {
+            "source_path_from_root": "/docs/ai/conceptual/vector-databases.md",
+            "redirect_url": "/dotnet/ai/vector-stores/overview",
+            "redirect_document_id": true
+        },
         {
             "source_path_from_root": "/docs/ai/get-started/dotnet-ai-overview.md",
             "redirect_url": "/dotnet/ai/overview",
@@ -35,6 +40,11 @@
             "source_path_from_root": "/docs/ai/how-to/work-with-local-models.md",
             "redirect_url": "/dotnet/ai"
         },
+        {
+            "source_path_from_root": "/docs/ai/quickstarts/build-vector-search-app.md",
+            "redirect_url": "/dotnet/ai/vector-stores/how-to/build-vector-search-app",
+            "redirect_document_id": true
+        },
         {
             "source_path_from_root": "/docs/ai/quickstarts/evaluate-ai-response.md",
             "redirect_url": "/dotnet/ai/evaluation/evaluate-ai-response",
@@ -93,6 +103,11 @@
             "source_path_from_root": "/docs/ai/tutorials/evaluate-with-reporting.md",
             "redirect_url": "/dotnet/ai/evaluation/evaluate-with-reporting",
             "redirect_document_id": true
+        },
+        {
+            "source_path_from_root": "/docs/ai/tutorials/tutorial-ai-vector-search.md",
+            "redirect_url": "/dotnet/ai/vector-stores/tutorial-vector-search",
+            "redirect_document_id": true
         }
     ]
 }

docs/ai/conceptual/embeddings.md

@@ -49,7 +49,7 @@ You generate embeddings for your raw data by using an AI embedding model, which
 
 ### Store and process embeddings in a vector database
 
-After you generate embeddings, you'll need a way to store them so you can later retrieve them with calls to an LLM. Vector databases are designed to store and process vectors, so they're a natural home for embeddings. Different vector databases offer different processing capabilities, so you should choose one based on your raw data and your goals. For information about your options, see [Vector databases for .NET + AI](vector-databases.md).
+After you generate embeddings, you'll need a way to store them so you can later retrieve them with calls to an LLM. Vector databases are designed to store and process vectors, so they're a natural home for embeddings. Different vector databases offer different processing capabilities, so you should choose one based on your raw data and your goals. For information about your options, see [Vector databases for .NET + AI](../vector-stores/overview.md).
 
 ### Using embeddings in your LLM solution
 

docs/ai/conceptual/understanding-tokens.md

@@ -106,4 +106,4 @@ Generative AI services might also be limited regarding the maximum number of tok
 - [Use Microsoft.ML.Tokenizers for text tokenization](../how-to/use-tokenizers.md)
 - [How generative AI and LLMs work](how-genai-and-llms-work.md)
 - [Understand embeddings](embeddings.md)
-- [Work with vector databases](vector-databases.md)
+- [Work with vector databases](../vector-stores/overview.md)

docs/ai/dotnet-ai-ecosystem.md

@@ -20,7 +20,7 @@ The .NET ecosystem provides many powerful tools, libraries, and services to deve
 
 ## Other AI-related Microsoft.Extensions libraries
 
-The [📦 Microsoft.Extensions.VectorData.Abstractions package](https://www.nuget.org/packages/Microsoft.Extensions.VectorData.Abstractions/) provides a unified layer of abstractions for interacting with a variety of vector stores. It lets you store processed chunks in vector stores such as Qdrant, Azure SQL, CosmosDB, MongoDB, ElasticSearch, and many more. For more information, see [Build a .NET AI vector search app](quickstarts/build-vector-search-app.md).
+The [📦 Microsoft.Extensions.VectorData.Abstractions package](https://www.nuget.org/packages/Microsoft.Extensions.VectorData.Abstractions/) provides a unified layer of abstractions for interacting with a variety of vector stores. It lets you store processed chunks in vector stores such as Qdrant, Azure SQL, CosmosDB, MongoDB, ElasticSearch, and many more. For more information, see [Build a .NET AI vector search app](vector-stores/how-to/build-vector-search-app.md).
 
 The [📦 Microsoft.Extensions.DataIngestion package](https://www.nuget.org/packages/Microsoft.Extensions.DataIngestion) provides foundational .NET building blocks for data ingestion. It enables developers to read, process, and prepare documents for AI and machine learning workflows, especially retrieval-augmented generation (RAG) scenarios. For more information, see [Data ingestion](conceptual/data-ingestion.md).
 

docs/ai/overview.md

@@ -53,7 +53,7 @@ We recommend the following sequence of tutorials and articles for an introductio
 |-----------------------------|-------------------------------------------------------------------------|
 | Create a chat application   | [Build an Azure AI chat app with .NET](./quickstarts/build-chat-app.md) |
 | Summarize text              | [Summarize text using Azure AI chat app](./quickstarts/prompt-model.md) |
-| Chat with your data         | [Get insight about your data from a .NET Azure AI chat app](./quickstarts/build-vector-search-app.md) |
+| Chat with your data         | [Get insight about your data from a .NET Azure AI chat app](./vector-stores/how-to/build-vector-search-app.md) |
 | Call .NET functions with AI | [Extend Azure AI using tools and execute a local function with .NET](./quickstarts/use-function-calling.md) |
 | Generate images             | [Generate images from text](./quickstarts/text-to-image.md) |
 | Train your own model        | [ML.NET tutorial](https://dotnet.microsoft.com/learn/ml-dotnet/get-started-tutorial/intro) |

docs/ai/toc.yml

@@ -20,7 +20,6 @@ items:
   - name: C# SDK for MCP
     href: get-started-mcp.md
 - name: Quickstarts
-  expanded: true
   items:
   - name: Connect to and prompt an AI model
     href: quickstarts/prompt-model.md
@@ -29,7 +28,7 @@ items:
   - name: Request structured output
     href: quickstarts/structured-output.md
   - name: Build a .NET AI vector search app
-    href: quickstarts/build-vector-search-app.md
+    href: vector-stores/how-to/build-vector-search-app.md
   - name: Execute a local .NET function
     href: quickstarts/use-function-calling.md
   - name: Chat with a local AI model
@@ -48,8 +47,6 @@ items:
     href: conceptual/understanding-tokens.md
   - name: Embeddings
     href: conceptual/embeddings.md
-  - name: Vector databases
-    href: conceptual/vector-databases.md
   - name: Data ingestion
     href: conceptual/data-ingestion.md
   - name: Prompt engineering
@@ -81,7 +78,7 @@ items:
     - name: Get started with the RAG sample
       href: get-started-app-chat-template.md
     - name: Implement RAG using vector search
-      href: tutorials/tutorial-ai-vector-search.md
+      href: vector-stores/tutorial-vector-search.md
     - name: Scale Azure OpenAI with Azure Container Apps
       href: get-started-app-chat-scaling-with-azure-container-apps.md
 - name: MCP client/server
@@ -96,6 +93,31 @@ items:
   items:
     - name: Use Microsoft.ML.Tokenizers
       href: how-to/use-tokenizers.md
+- name: Vector stores
+  items:
+  - name: overview
+    href: vector-stores/overview.md
+    displayName: vector databases, vector stores
+  - name: How-to
+    items:
+    - name: Use vector stores
+      href: vector-stores/how-to/use-vector-stores.md
+    - name: Build a vector search app
+      href: vector-stores/how-to/build-vector-search-app.md
+    - name: Ingest data into a vector store
+      href: vector-stores/how-to/vector-store-data-ingestion.md
+  - name: Define your data model
+    href: vector-stores/defining-your-data-model.md
+  - name: Define schema with record definitions
+    href: vector-stores/schema-with-record-definition.md
+  - name: Dynamic data model
+    href: vector-stores/dynamic-data-model.md
+  - name: Generate embeddings
+    href: vector-stores/embedding-generation.md
+  - name: Vector search
+    href: vector-stores/vector-search.md
+  - name: Hybrid search
+    href: vector-stores/hybrid-search.md
 - name: Security and content safety
   items:
   - name: Authentication for Azure-hosted apps and services