microsoft · elbruno · Jun 1, 2026
diff --git a/03-AIPatternsAndApplications/01-embeddings-semantic-search.md b/03-AIPatternsAndApplications/01-embeddings-semantic-search.md
@@ -15,13 +15,14 @@ _Click the image to watch the video_
 Traditional search works by matching keywords. If you search for "running shoes," it finds documents containing those exact words.
 
 But what if someone searches for:
+
 - "sneakers for jogging"
 - "athletic footwear"
 - "shoes for my morning run"
 
 These all mean the same thing, but keyword search would miss them.
 
-**Semantic search** solves this by understanding *meaning*, not just words.
+**Semantic search** solves this by understanding _meaning_, not just words.
 
 ---
 
@@ -33,7 +34,7 @@ An **embedding** is a way to represent text (or images, or other data) as a list
 "I love pizza" → [0.12, -0.45, 0.89, 0.23, -0.67, ...]
 ```
 
-These numbers capture the *meaning* of the text. Similar meanings produce similar numbers.
+These numbers capture the _meaning_ of the text. Similar meanings produce similar numbers.
 
 > **Learn more:** [Understand embeddings in .NET](https://learn.microsoft.com/dotnet/ai/conceptual/embeddings) explains how embedding models work and when to use them.
 
@@ -129,6 +130,7 @@ static float CosineSimilarity(ReadOnlyMemory<float> a, ReadOnlyMemory<float> b)
 ```
 
 Cosine similarity returns a value between -1 and 1:
+
 - **1.0** = Identical meaning
 - **0.0** = Unrelated
 - **-1.0** = Opposite meaning
@@ -185,10 +187,12 @@ Notice: "sneakers for jogging" ranks high even though it shares no words with th
 ## Part 4: Vector Stores
 
 For real applications, you don't want to:
+
 1. Generate embeddings for your entire dataset on every search
 2. Loop through all embeddings to find matches
 
 **Vector stores** (or vector databases) solve this by:
+
 - Storing embeddings persistently
 - Using optimized algorithms for fast similarity search
 - Scaling to millions of documents
@@ -218,6 +222,7 @@ public class Product
 ```
 
 Key attributes:
+
 - `[VectorStoreKey]` - Unique identifier
 - `[VectorStoreData]` - Searchable/filterable data
 - `[VectorStoreVector(dimensions, distanceFunction)]` - The embedding vector
@@ -337,7 +342,7 @@ The code stays the same - just swap the vector store implementation!
 
 | Sample | Description |
 |--------|-------------|
-| [RAGSimple-02MEAIVectorsMemory](../samples/CoreSamples/RAGSimple-02MEAIVectorsMemory/) | In-memory vector store with Azure OpenAI |
+| [RAGSimple-02MEAIVectorsMemory](../samples/CoreSamples/RAGSimple-02MEAIVectorsMemory/) | Local sqlite-vec vector store with Azure OpenAI |
 | [RAGSimple-03MEAIVectorsAISearch](../samples/CoreSamples/RAGSimple-03MEAIVectorsAISearch/) | Azure AI Search vector store |
 | [RAGSimple-04MEAIVectorsQdrant](../samples/CoreSamples/RAGSimple-04MEAIVectorsQdrant/) | Qdrant vector store |
 

diff --git a/03-AIPatternsAndApplications/02-retrieval-augmented-generation.md b/03-AIPatternsAndApplications/02-retrieval-augmented-generation.md
@@ -28,9 +28,11 @@ Language models are trained on data from a specific point in time. They don't kn
 RAG has two phases:
 
 ### Phase 1: Retrieval
+
 Find relevant documents from your knowledge base using semantic search.
 
 ### Phase 2: Generation
+
 Pass those documents to the AI along with the user's question.
 
 ![How RAG Works](./images/how-rag-works.png)
@@ -366,7 +368,8 @@ IEmbeddingGenerator<string, Embedding<float>> embeddingGenerator =
 
 | Sample | Description |
 |--------|-------------|
-| [RAGSimple-02MEAIVectorsMemory](../samples/CoreSamples/RAGSimple-02MEAIVectorsMemory/) | RAG with in-memory vectors |
+| [RAGSimple-02MEAIVectorsMemory](../samples/CoreSamples/RAGSimple-02MEAIVectorsMemory/) | RAG with a local sqlite-vec store |
+| [DataIngestion-01-Simple](../samples/CoreSamples/DataIngestion-01-Simple/) | Document ingestion pipeline (read → chunk → embed → store) with sqlite-vec |
 | [RAGSimple-03MEAIVectorsAISearch](../samples/CoreSamples/RAGSimple-03MEAIVectorsAISearch/) | RAG with Azure AI Search |
 | [RAGSimple-04MEAIVectorsQdrant](../samples/CoreSamples/RAGSimple-04MEAIVectorsQdrant/) | RAG with Qdrant |
 | [RAGSimple-15Ollama-DeepSeekR1](../samples/CoreSamples/RAGSimple-15Ollama-DeepSeekR1/) | RAG with DeepSeek reasoning |

diff --git a/03-AIPatternsAndApplications/readme.md b/03-AIPatternsAndApplications/readme.md
@@ -25,18 +25,23 @@ _Click the image to watch the video_
 This lesson is divided into five parts:
 
 ### [Part 1: Embeddings and Semantic Search](./01-embeddings-semantic-search.md)
+
 Understand how AI represents meaning as vectors and build search that finds by intent.
 
 ### [Part 2: Retrieval-Augmented Generation (RAG)](./02-retrieval-augmented-generation.md)
+
 Ground AI responses in your own documents and data.
 
 ### [Part 3: Vision and Document Understanding](./03-vision-document-understanding.md)
+
 Process images, PDFs, and visual content with multimodal AI.
 
 ### [Part 4: Combining Patterns](./04-combining-patterns.md)
+
 Build sophisticated applications that combine multiple patterns.
 
 ### [Part 5: Local Model Runners](./05-LocalModelRunners.md)
+
 Run AI models locally using AI Toolkit, Docker Model Runner, and Foundry Local.
 
 ---
@@ -65,7 +70,7 @@ All code samples for this lesson are located in the **[`samples/CoreSamples/`](.
 
 | Category | Samples | Description |
 |----------|---------|-------------|
-| **Embeddings & RAG** | [RAGSimple-02MEAIVectorsMemory](../samples/CoreSamples/RAGSimple-02MEAIVectorsMemory/) | In-memory vector store |
+| **Embeddings & RAG** | [RAGSimple-02MEAIVectorsMemory](../samples/CoreSamples/RAGSimple-02MEAIVectorsMemory/) | Local sqlite-vec vector store |
 | | [RAGSimple-03MEAIVectorsAISearch](../samples/CoreSamples/RAGSimple-03MEAIVectorsAISearch/) | Azure AI Search |
 | | [RAGSimple-04MEAIVectorsQdrant](../samples/CoreSamples/RAGSimple-04MEAIVectorsQdrant/) | Qdrant vector store |
 | **Vision** | [Vision-01MEAI-AzureOpenAI](../samples/CoreSamples/Vision-01MEAI-AzureOpenAI/) | Vision with Azure OpenAI |

diff --git a/04-AgentsWithMAF/02-agents-with-tools.md b/04-AgentsWithMAF/02-agents-with-tools.md
@@ -374,6 +374,7 @@ static string GetOrderStatus(string orderId) { ... }
 |--------|-------------|
 | [MAF-BackgroundResponses-02-Tools](../samples/MAF/MAF-BackgroundResponses-02-Tools/) | Agent with function tools |
 | [MAF-BackgroundResponses-03-Complex](../samples/MAF/MAF-BackgroundResponses-03-Complex/) | Complex tool scenarios |
+| [MAF-ImageGen-03-Foundry](../samples/MAF/MAF-ImageGen-03-Foundry/) | Agent with an image-generation tool (GPT-Image-2 on Microsoft Foundry) |
 
 ---
 

diff --git a/04-AgentsWithMAF/03-multi-agent-workflows.md b/04-AgentsWithMAF/03-multi-agent-workflows.md
@@ -379,6 +379,7 @@ catch (AgentException ex)
 | [MAF02](../samples/MAF/MAF02/) | Simple sequential workflow |
 | [MAF-MultiAgents](../samples/MAF/MAF-MultiAgents/) | Multi-model orchestration |
 | [MAF-MultiAgents-Factory-01](../samples/MAF/MAF-MultiAgents-Factory-01/) | Advanced workflow patterns |
+| [A2A-01](../samples/MAF/A2A-01/) | Agent-to-Agent (A2A) protocol: host a MAF agent and call it as a remote agent |
 
 ---
 

diff --git a/04-AgentsWithMAF/04-model-context-protocol.md b/04-AgentsWithMAF/04-model-context-protocol.md
@@ -414,6 +414,8 @@ Need to integrate with external service?
 |--------|-------------|
 | [MCP-01-HuggingFace](../samples/CoreSamples/MCP-01-HuggingFace/) | MCP with Azure OpenAI |
 | [MCP-02-HuggingFace-Ollama](../samples/CoreSamples/MCP-02-HuggingFace-Ollama/) | MCP with local Ollama models |
+| [MCP-03-MicrosoftLearn](../samples/CoreSamples/MCP-03-MicrosoftLearn/) | Before/after demo grounding answers with the Microsoft Learn MCP server |
+| [MAF-MCP-01](../samples/MAF/MAF-MCP-01/) | A MAF agent that owns Microsoft Learn MCP tools |
 | Aspire MCP Sample | See [04-PracticalSamples](../04-PracticalSamples/) for Aspire + MCP integration |
 
 ---

diff --git a/samples/CoreSamples/BasicChat-05AIFoundryModels/README.md b/samples/CoreSamples/BasicChat-05AIFoundryModels/README.md
@@ -0,0 +1,66 @@
+# BasicChat-05AIFoundryModels — Foundry model swap + Integrated Security
+
+A Foundations / MEAI sample. One `IChatClient`, one
+**Microsoft Foundry** endpoint, many models — swap the model by changing a single string,
+and authenticate the way you should in Foundry: **Integrated Security (Microsoft Entra ID)**.
+
+## What it shows
+
+1. **Chat with a deployment name + endpoint + apikey** — the familiar key-based path.
+2. **Foundry model swap** — change only `AzureOpenAI:Deployment` to switch
+   **`gpt-5.5` → `grok-4`** (same code, same endpoint, same `IChatClient`).
+3. **Integrated Security (recommended)** — drop the key entirely and use
+   `AzureCliCredential` / Microsoft Entra ID. This is the way to work in Foundry.
+4. **Simple one-shot prompt** — the sample asks a hardcoded question so the demo
+   stays focused on `IChatClient`, model swapping, and auth.
+
+> Then the demo switches to **`BasicChat-03Ollama`** to run the same `IChatClient`
+> against a local model — zero changes to app logic.
+
+## Config (User Secrets)
+
+```bash
+cd samples/CoreSamples/BasicChat-05AIFoundryModels
+
+# Foundry endpoint (one endpoint hosts many models)
+dotnet user-secrets set "AzureOpenAI:Endpoint" "https://<your-foundry-resource>.openai.azure.com/"
+
+# Model = deployment name. Swap this to change models (gpt-5.5 -> grok-4 -> ...)
+dotnet user-secrets set "AzureOpenAI:Deployment" "gpt-5.5"
+
+# Auth mode: "integrated" (recommended) or "apikey"
+dotnet user-secrets set "AzureOpenAI:AuthMode" "integrated"
+
+# Only needed when AuthMode=apikey
+dotnet user-secrets set "AzureOpenAI:ApiKey" "<your-key>"
+```
+
+## Run the demo (suggested order)
+
+```bash
+# 1. Key auth with gpt-5.5
+dotnet user-secrets set "AzureOpenAI:AuthMode" "apikey"
+dotnet user-secrets set "AzureOpenAI:Deployment" "gpt-5.5"
+dotnet run app.cs
+
+# 2. Foundry model swap: gpt-5.5 -> grok-4 (same endpoint, same code)
+dotnet user-secrets set "AzureOpenAI:Deployment" "grok-4"
+dotnet run app.cs
+
+# 3. Integrated Security (the recommended way) — no key in config
+dotnet user-secrets set "AzureOpenAI:AuthMode" "integrated"
+az login
+dotnet run app.cs
+```
+
+The console prints the active **model** and **auth** mode, runs one hardcoded question,
+and labels the answer with `AI [<deployment>]:` so the model swap is visible at runtime.
+
+## Suggested prompt
+
+Use this question to make the model identity visible after the deployment swap:
+
+`what is your model name?`
+
+> Integrated Security requires the signed-in account to have the **Azure AI Developer**
+> role on the Foundry resource.
diff --git a/samples/CoreSamples/BasicChat-05AIFoundryModels/app.cs b/samples/CoreSamples/BasicChat-05AIFoundryModels/app.cs
@@ -5,52 +5,49 @@
 #:package Microsoft.Extensions.Configuration.UserSecrets@10.0.3
 #:property UserSecretsId=genai-beginners-dotnet
 
-using Azure.AI.OpenAI;
+using Azure;
+using Azure.AI.OpenAI;
 using Azure.Identity;
 using Microsoft.Extensions.AI;
 using Microsoft.Extensions.Configuration;
-using System.Text;
 
 var config = new ConfigurationBuilder().AddUserSecrets<Program>().Build();
+
+// One Microsoft Foundry endpoint can host MANY models behind it.
 var endpoint = config["AzureOpenAI:Endpoint"]
     ?? throw new InvalidOperationException("Set AzureOpenAI:Endpoint in User Secrets. See: https://github.com/microsoft/Generative-AI-for-beginners-dotnet/blob/main/01-IntroductionToGenerativeAI/setup-azure-openai.md");
-var deploymentName = config["AzureOpenAI:Deployment"] ?? "gpt-5-mini";
 
-IChatClient client = new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential())
+// Swap the model by changing ONLY the deployment name.
+// In Microsoft Foundry you deploy several models behind the same endpoint, e.g.:
+//   gpt-5.5  ->  grok-4.3  ->  phi-4  ->  ...   (same code, same endpoint, just this string).
+//var deploymentName = config["AzureOpenAI:Deployment"] ?? "gpt-5-mini";
+var deploymentName = "Kimi-K2.6";
+
+// Auth mode. "integrated" = Microsoft Entra ID (recommended for Foundry: no keys to leak).
+// "apikey" = key from the Foundry portal (endpoint + deployment + apikey).
+var authMode = config["AzureOpenAI:AuthMode"] ?? "integrated";
+
+AzureOpenAIClient azureClient = authMode.Equals("apikey", StringComparison.OrdinalIgnoreCase)
+    // Key auth: deployment name + endpoint + apikey.
+    ? new AzureOpenAIClient(new Uri(endpoint),
+        new AzureKeyCredential(config["AzureOpenAI:ApiKey"]
+            ?? throw new InvalidOperationException("Set AzureOpenAI:ApiKey in User Secrets when AuthMode=apikey.")))
+    // Integrated Security (recommended): Microsoft Entra ID via `az login`, no secrets.
+    : new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential());
+
+IChatClient client = azureClient
     .GetChatClient(deploymentName)
-    .AsIChatClient()
-    .AsBuilder()
-    .Build();
-
-var history = new List<ChatMessage>
-{
-    new(ChatRole.Assistant, "You are a useful chatbot. If you don't know an answer, say 'I don't know!'. Always reply in a funny way. Use emojis if possible.")
-};
-
-while (true)
-{
-    Console.Write("Q: ");
-    var userQ = Console.ReadLine();
-    if (string.IsNullOrEmpty(userQ))
-    {
-        break;
-    }
-    history.Add(new ChatMessage(ChatRole.User, userQ));
-
-    var sb = new StringBuilder();
-    var result = client.GetStreamingResponseAsync(history);
-    Console.Write("AI: ");
-    await foreach (var item in result)
-    {
-        // validate if the item is null or has no contents
-        if (item == null || item.Contents.Count == 0)
-        {
-            continue; // skip to the next item if it's null or empty
-        }
-        sb.Append(item);
-        Console.Write(item.Contents[0].ToString());
-    }
-    Console.WriteLine();
-
-    history.Add(new ChatMessage(ChatRole.Assistant, sb.ToString()));
-}
+    .AsIChatClient();
+
+Console.WriteLine($"Microsoft Foundry chat  ·  model: {deploymentName}  ·  auth: {authMode}");
+Console.WriteLine("(swap the model with AzureOpenAI:Deployment, e.g. gpt-5.5 -> grok-4)");
+Console.WriteLine("(switch auth with AzureOpenAI:AuthMode = apikey or integrated)");
+Console.WriteLine();
+
+var question = "what is your model name?";
+Console.WriteLine($"Q: {question}");
+
+var response = await client.GetResponseAsync(question);
+
+Console.WriteLine();
+Console.WriteLine($"AI [{deploymentName}]: {response.Text}");
diff --git a/samples/CoreSamples/CoreGenerativeAITechniques.sln b/samples/CoreSamples/CoreGenerativeAITechniques.sln
@@ -65,6 +65,10 @@ Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "MCP-02-HuggingFace-Ollama",
 EndProject
 Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "VideoGeneration-AzureSora2-01", "VideoGeneration-AzureSora2-01\VideoGeneration-AzureSora2-01.csproj", "{8E3D5B50-B346-43C5-B71C-43C4427B39C9}"
 EndProject
+Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "MCP-03-MicrosoftLearn", "MCP-03-MicrosoftLearn\MCP-03-MicrosoftLearn.csproj", "{06279A2D-4582-4854-ADDB-FE81DEEC5443}"
+EndProject
+Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "DataIngestion-01-Simple", "DataIngestion-01-Simple\DataIngestion-01-Simple.csproj", "{E5E21F6D-230A-4862-9B38-AAE41242B25C}"
+EndProject
 Global
 	GlobalSection(SolutionConfigurationPlatforms) = preSolution
 		Debug|Any CPU = Debug|Any CPU
@@ -327,6 +331,30 @@ Global
 		{8E3D5B50-B346-43C5-B71C-43C4427B39C9}.Release|x64.Build.0 = Release|Any CPU
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 		{8E3D5B50-B346-43C5-B71C-43C4427B39C9}.Release|x86.Build.0 = Release|Any CPU
+		{06279A2D-4582-4854-ADDB-FE81DEEC5443}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
+		{06279A2D-4582-4854-ADDB-FE81DEEC5443}.Debug|Any CPU.Build.0 = Debug|Any CPU
+		{06279A2D-4582-4854-ADDB-FE81DEEC5443}.Debug|x64.ActiveCfg = Debug|Any CPU
+		{06279A2D-4582-4854-ADDB-FE81DEEC5443}.Debug|x64.Build.0 = Debug|Any CPU
+		{06279A2D-4582-4854-ADDB-FE81DEEC5443}.Debug|x86.ActiveCfg = Debug|Any CPU
+		{06279A2D-4582-4854-ADDB-FE81DEEC5443}.Debug|x86.Build.0 = Debug|Any CPU
+		{06279A2D-4582-4854-ADDB-FE81DEEC5443}.Release|Any CPU.ActiveCfg = Release|Any CPU
+		{06279A2D-4582-4854-ADDB-FE81DEEC5443}.Release|Any CPU.Build.0 = Release|Any CPU
+		{06279A2D-4582-4854-ADDB-FE81DEEC5443}.Release|x64.ActiveCfg = Release|Any CPU
+		{06279A2D-4582-4854-ADDB-FE81DEEC5443}.Release|x64.Build.0 = Release|Any CPU
+		{06279A2D-4582-4854-ADDB-FE81DEEC5443}.Release|x86.ActiveCfg = Release|Any CPU
+		{06279A2D-4582-4854-ADDB-FE81DEEC5443}.Release|x86.Build.0 = Release|Any CPU
+		{E5E21F6D-230A-4862-9B38-AAE41242B25C}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
+		{E5E21F6D-230A-4862-9B38-AAE41242B25C}.Debug|Any CPU.Build.0 = Debug|Any CPU
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+		{E5E21F6D-230A-4862-9B38-AAE41242B25C}.Debug|x64.Build.0 = Debug|Any CPU
+		{E5E21F6D-230A-4862-9B38-AAE41242B25C}.Debug|x86.ActiveCfg = Debug|Any CPU
+		{E5E21F6D-230A-4862-9B38-AAE41242B25C}.Debug|x86.Build.0 = Debug|Any CPU
+		{E5E21F6D-230A-4862-9B38-AAE41242B25C}.Release|Any CPU.ActiveCfg = Release|Any CPU
+		{E5E21F6D-230A-4862-9B38-AAE41242B25C}.Release|Any CPU.Build.0 = Release|Any CPU
+		{E5E21F6D-230A-4862-9B38-AAE41242B25C}.Release|x64.ActiveCfg = Release|Any CPU
+		{E5E21F6D-230A-4862-9B38-AAE41242B25C}.Release|x64.Build.0 = Release|Any CPU
+		{E5E21F6D-230A-4862-9B38-AAE41242B25C}.Release|x86.ActiveCfg = Release|Any CPU
+		{E5E21F6D-230A-4862-9B38-AAE41242B25C}.Release|x86.Build.0 = Release|Any CPU
 	EndGlobalSection
 	GlobalSection(SolutionProperties) = preSolution
 		HideSolutionNode = FALSE

diff --git a/samples/CoreSamples/DataIngestion-01-Simple/DataIngestion-01-Simple.csproj b/samples/CoreSamples/DataIngestion-01-Simple/DataIngestion-01-Simple.csproj
@@ -0,0 +1,30 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFramework>net10.0</TargetFramework>
+    <RootNamespace>DataIngestion_01_Simple</RootNamespace>
+    <ImplicitUsings>enable</ImplicitUsings>
+    <Nullable>enable</Nullable>
+    <UserSecretsId>genai-beginners-dotnet</UserSecretsId>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <None Include="data\**\*" CopyToOutputDirectory="PreserveNewest" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" Version="2.8.0-beta.1" />
+    <PackageReference Include="Azure.Identity" Version="1.18.0" />
+    <PackageReference Include="ElBruno.Connectors.SqliteVec" Version="0.5.2" />
+    <PackageReference Include="Microsoft.Bcl.Memory" Version="10.0.8" />
+    <PackageReference Include="Microsoft.Extensions.AI" Version="10.6.0" />
+    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" Version="10.6.0" />
+    <PackageReference Include="Microsoft.Extensions.Configuration.UserSecrets" Version="10.0.3" />
+    <PackageReference Include="Microsoft.Extensions.DataIngestion" Version="10.6.0-preview.1.26261.4" />
+    <PackageReference Include="Microsoft.Extensions.DataIngestion.Markdig" Version="10.6.0-preview.1.26261.4" />
+    <PackageReference Include="Microsoft.Extensions.Logging.Console" Version="10.0.3" />
+    <PackageReference Include="Microsoft.ML.Tokenizers.Data.O200kBase" Version="3.0.0-preview.26160.2" />
+  </ItemGroup>
+
+</Project>