.NET: Add rag samples with sample TextSearchStore (#1664)

* Port store for adding text to a vector store to AF * Fix typo. * Change TextSearchStore to sample, and add sample to use it and do rag with a custom schema * Add more tests and fix broken ones * Fix merge issue * Fix sample after merge. * Convert TextSearchStore to use Dynamic mode to be AOT compatible. * Add some more clarification on when to use assistant messages in rag searches.
2026-06-16 21:04:09 +08:00 · 2025-11-04 16:24:26 +00:00
parent 64fc3f381f
commit ada5b83c80
18 changed files with 1079 additions and 42 deletions
@@ -65,9 +65,11 @@
    <PackageVersion Include="Microsoft.Extensions.Logging.Abstractions" Version="9.0.10" />
    <PackageVersion Include="Microsoft.Extensions.Logging.Console" Version="9.0.10" />
    <PackageVersion Include="Microsoft.Extensions.ServiceDiscovery" Version="$(AspireAppHostSdkVersion)" />
+    <PackageVersion Include="Microsoft.Extensions.VectorData.Abstractions" Version="9.7.0" />
    <!-- Vector Stores -->
    <PackageVersion Include="Microsoft.SemanticKernel" Version="1.66.0" />
    <PackageVersion Include="Microsoft.SemanticKernel.Connectors.InMemory" Version="1.66.0-preview" />
+    <PackageVersion Include="Microsoft.SemanticKernel.Connectors.Qdrant" Version="1.66.0-preview" />
    <PackageVersion Include="Microsoft.SemanticKernel.Agents.Core" Version="1.66.0" />
    <PackageVersion Include="Microsoft.SemanticKernel.Agents.OpenAI" Version="1.66.0-preview" />
    <PackageVersion Include="Microsoft.SemanticKernel.Agents.AzureAI" Version="1.66.0-preview" />
@@ -67,6 +67,11 @@
    <Project Path="samples/GettingStarted/AgentWithOpenAI/Agent_OpenAI_Step01_Running/Agent_OpenAI_Step01_Running.csproj" />
    <Project Path="samples/GettingStarted/AgentWithOpenAI/Agent_OpenAI_Step02_Reasoning/Agent_OpenAI_Step02_Reasoning.csproj" />
  </Folder>
+  <Folder Name="/Samples/GettingStarted/AgentWithRAG/">
+    <File Path="samples/GettingStarted/AgentWithRAG/README.md" />
+    <Project Path="samples/GettingStarted/AgentWithRAG/AgentWithRAG_Step01_BasicTextRAG/AgentWithRAG_Step01_BasicTextRAG.csproj" />
+    <Project Path="samples/GettingStarted/AgentWithRAG/AgentWithRAG_Step02_ExternalDataSourceRAG/AgentWithRAG_Step02_ExternalDataSourceRAG.csproj" />
+  </Folder>
  <Folder Name="/Samples/GettingStarted/ModelContextProtocol/">
    <File Path="samples/GettingStarted/ModelContextProtocol/README.md" />
    <Project Path="samples/GettingStarted/ModelContextProtocol/Agent_MCP_Server/Agent_MCP_Server.csproj" />
@@ -52,9 +52,9 @@ static Task<IEnumerable<TextSearchProvider.TextSearchResult>> MockSearchAsync(st
    {
        results.Add(new()
        {
-            Name = "Contoso Outdoors Return Policy",
-            Link = "https://contoso.com/policies/returns",
-            Value = "Customers may return any item within 30 days of delivery. Items should be unused and include original packaging. Refunds are issued to the original payment method within 5 business days of inspection."
+            SourceName = "Contoso Outdoors Return Policy",
+            SourceLink = "https://contoso.com/policies/returns",
+            Text = "Customers may return any item within 30 days of delivery. Items should be unused and include original packaging. Refunds are issued to the original payment method within 5 business days of inspection."
        });
    }

@@ -62,9 +62,9 @@ static Task<IEnumerable<TextSearchProvider.TextSearchResult>> MockSearchAsync(st
    {
        results.Add(new()
        {
-            Name = "Contoso Outdoors Shipping Guide",
-            Link = "https://contoso.com/help/shipping",
-            Value = "Standard shipping is free on orders over $50 and typically arrives in 3-5 business days within the continental United States. Expedited options are available at checkout."
+            SourceName = "Contoso Outdoors Shipping Guide",
+            SourceLink = "https://contoso.com/help/shipping",
+            Text = "Standard shipping is free on orders over $50 and typically arrives in 3-5 business days within the continental United States. Expedited options are available at checkout."
        });
    }

@@ -72,9 +72,9 @@ static Task<IEnumerable<TextSearchProvider.TextSearchResult>> MockSearchAsync(st
    {
        results.Add(new()
        {
-            Name = "TrailRunner Tent Care Instructions",
-            Link = "https://contoso.com/manuals/trailrunner-tent",
-            Value = "Clean the tent fabric with lukewarm water and a non-detergent soap. Allow it to air dry completely before storage and avoid prolonged UV exposure to extend the lifespan of the waterproof coating."
+            SourceName = "TrailRunner Tent Care Instructions",
+            SourceLink = "https://contoso.com/manuals/trailrunner-tent",
+            Text = "Clean the tent fabric with lukewarm water and a non-detergent soap. Allow it to air dry completely before storage and avoid prolonged UV exposure to extend the lifespan of the waterproof coating."
        });
    }

@@ -0,0 +1,22 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFramework>net9.0</TargetFramework>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
+    <PackageReference Include="Microsoft.SemanticKernel.Connectors.InMemory" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,107 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to use TextSearchProvider to add retrieval augmented generation (RAG) capabilities to an AI agent.
+// The sample uses an In-Memory vector store, which can easily be replaced with any other vector store that implements the Microsoft.Extensions.VectorData abstractions.
+// The TextSearchProvider runs a search against the vector store via the TextSearchStore before each model invocation and injects the results into the model context.
+// The TextSearchStore is a sample store implementation that hardcodes a storage schema and uses the vector store to store and retrieve documents.
+
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.Data;
+using Microsoft.Agents.AI.Samples;
+using Microsoft.Extensions.AI;
+using Microsoft.Extensions.VectorData;
+using Microsoft.SemanticKernel.Connectors.InMemory;
+using OpenAI;
+
+var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var embeddingDeploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME") ?? "text-embedding-3-large";
+
+AzureOpenAIClient azureOpenAIClient = new(
+    new Uri(endpoint),
+    new AzureCliCredential());
+
+// Create an In-Memory vector store that uses the Azure OpenAI embedding model to generate embeddings.
+VectorStore vectorStore = new InMemoryVectorStore(new()
+{
+    EmbeddingGenerator = azureOpenAIClient.GetEmbeddingClient(embeddingDeploymentName).AsIEmbeddingGenerator()
+});
+
+// Create a store that defines a storage schema, and uses the vector store to store and retrieve documents.
+TextSearchStore textSearchStore = new(vectorStore, "product-and-policy-info", 3072);
+
+// Upload sample documents into the store.
+await textSearchStore.UpsertDocumentsAsync(GetSampleDocuments());
+
+// Create an adapter function that the TextSearchProvider can use to run searches against the TextSearchStore.
+Func<string, CancellationToken, Task<IEnumerable<TextSearchProvider.TextSearchResult>>> SearchAdapter = async (text, ct) =>
+{
+    // Here we are limiting the search results to the single top result to demonstrate that we are accurately matching
+    // specific search results for each question, but in a real world case, more results should be used.
+    var searchResults = await textSearchStore.SearchAsync(text, 1, ct);
+    return searchResults.Select(r => new TextSearchProvider.TextSearchResult
+    {
+        SourceName = r.SourceName,
+        SourceLink = r.SourceLink,
+        Text = r.Text ?? string.Empty,
+        RawRepresentation = r
+    });
+};
+
+// Configure the options for the TextSearchProvider.
+TextSearchProviderOptions textSearchOptions = new()
+{
+    // Run the search prior to every model invocation.
+    SearchTime = TextSearchProviderOptions.TextSearchBehavior.BeforeAIInvoke,
+};
+
+// Create the AI agent with the TextSearchProvider as the AI context provider.
+AIAgent agent = azureOpenAIClient
+    .GetChatClient(deploymentName)
+    .CreateAIAgent(new ChatClientAgentOptions
+    {
+        Instructions = "You are a helpful support specialist for Contoso Outdoors. Answer questions using the provided context and cite the source document when available.",
+        AIContextProviderFactory = ctx => ctx.SerializedState.ValueKind is not System.Text.Json.JsonValueKind.Null and not System.Text.Json.JsonValueKind.Undefined
+            ? new TextSearchProvider(SearchAdapter, ctx.SerializedState, ctx.JsonSerializerOptions, textSearchOptions)
+            : new TextSearchProvider(SearchAdapter, textSearchOptions)
+    });
+
+AgentThread thread = agent.GetNewThread();
+
+Console.WriteLine(">> Asking about returns\n");
+Console.WriteLine(await agent.RunAsync("Hi! I need help understanding the return policy.", thread));
+
+Console.WriteLine("\n>> Asking about shipping\n");
+Console.WriteLine(await agent.RunAsync("How long does standard shipping usually take?", thread));
+
+Console.WriteLine("\n>> Asking about product care\n");
+Console.WriteLine(await agent.RunAsync("What is the best way to maintain the TrailRunner tent fabric?", thread));
+
+// Produces some sample search documents.
+// Each one contains a source name and link, which the agent can use to cite sources in its responses.
+static IEnumerable<TextSearchDocument> GetSampleDocuments()
+{
+    yield return new TextSearchDocument
+    {
+        SourceId = "return-policy-001",
+        SourceName = "Contoso Outdoors Return Policy",
+        SourceLink = "https://contoso.com/policies/returns",
+        Text = "Customers may return any item within 30 days of delivery. Items should be unused and include original packaging. Refunds are issued to the original payment method within 5 business days of inspection."
+    };
+    yield return new TextSearchDocument
+    {
+        SourceId = "shipping-guide-001",
+        SourceName = "Contoso Outdoors Shipping Guide",
+        SourceLink = "https://contoso.com/help/shipping",
+        Text = "Standard shipping is free on orders over $50 and typically arrives in 3-5 business days within the continental United States. Expedited options are available at checkout."
+    };
+    yield return new TextSearchDocument
+    {
+        SourceId = "tent-care-001",
+        SourceName = "TrailRunner Tent Care Instructions",
+        SourceLink = "https://contoso.com/manuals/trailrunner-tent",
+        Text = "Clean the tent fabric with lukewarm water and a non-detergent soap. Allow it to air dry completely before storage and avoid prolonged UV exposure to extend the lifespan of the waterproof coating."
+    };
+}
@@ -0,0 +1,51 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+namespace Microsoft.Agents.AI.Samples;
+
+/// <summary>
+/// Represents a document that can be used for Retrieval Augmented Generation (RAG) that stores textual data.
+/// </summary>
+public sealed class TextSearchDocument
+{
+    /// <summary>
+    /// Gets or sets an optional list of namespaces that the document should belong to.
+    /// </summary>
+    /// <remarks>
+    /// A namespace is a logical grouping of documents, e.g. may include a group id to scope the document to a specific group of users.
+    /// </remarks>
+    public IList<string> Namespaces { get; set; } = [];
+
+    /// <summary>
+    /// Gets or sets the content as text.
+    /// </summary>
+    public string? Text { get; set; }
+
+    /// <summary>
+    /// Gets or sets an optional source ID for the document.
+    /// </summary>
+    /// <remarks>
+    /// This ID should be unique within the collection that the document is stored in, and can
+    /// be used to map back to the source artifact for this document.
+    /// If updates need to be made later or the source document was deleted and this document
+    /// also needs to be deleted, this id can be used to find the document again.
+    /// </remarks>
+    public string? SourceId { get; set; }
+
+    /// <summary>
+    /// Gets or sets an optional name for the source document.
+    /// </summary>
+    /// <remarks>
+    /// This can be used to provide display names for citation links when the document is referenced as
+    /// part of a response to a query.
+    /// </remarks>
+    public string? SourceName { get; set; }
+
+    /// <summary>
+    /// Gets or sets an optional link back to the source of the document.
+    /// </summary>
+    /// <remarks>
+    /// This can be used to provide citation links when the document is referenced as
+    /// part of a response to a query.
+    /// </remarks>
+    public string? SourceLink { get; set; }
+}
@@ -0,0 +1,392 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using System.Linq.Expressions;
+using System.Text.RegularExpressions;
+using Microsoft.Extensions.VectorData;
+
+namespace Microsoft.Agents.AI.Samples;
+
+/// <summary>
+/// A class that allows for easy storage and retrieval of documents in a Vector Store for Retrieval Augmented Generation (RAG).
+/// </summary>
+/// <remarks>
+/// <para>
+/// This class provides an opinionated schema for storing documents in a vector store. It is valuable for simple scenarios
+/// where you want to store text + embedding, or a reference to an external document + embedding without needing to customize the schema.
+/// If you want to control the schema yourself, use an implementation of <see cref="VectorStoreCollection{TKey, TRecord}"/> directly instead.
+/// </para>
+/// <para>
+/// This class and its related types are currently provided as a sample implementation, but may be promoted to a first-class supported API in future releases.
+/// </para>
+/// </remarks>
+public sealed partial class TextSearchStore : IDisposable
+{
+#if NET
+    [GeneratedRegex(@"\p{L}+", RegexOptions.IgnoreCase, "en-US")]
+    private static partial Regex AnyLanguageWordRegex();
+
+    private static readonly Func<string, ICollection<string>> s_defaultWordSegmenter = text => AnyLanguageWordRegex().Matches(text).Select(x => x.Value).ToList();
+#else
+    private static readonly Regex s_anyLanguageWordRegex = new(@"\p{L}+", RegexOptions.Compiled);
+    private static Regex AnyLanguageWordRegex() => s_anyLanguageWordRegex;
+
+    private static readonly Func<string, ICollection<string>> s_defaultWordSegmenter = text =>
+    {
+        List<string> words = new();
+        foreach (Match word in AnyLanguageWordRegex().Matches(text))
+        {
+            words.Add(word.Value);
+        }
+        return words;
+    };
+#endif
+
+    private readonly VectorStore _vectorStore;
+    private readonly TextSearchStoreOptions _options;
+    private readonly Func<string, ICollection<string>> _wordSegmenter;
+
+    private readonly VectorStoreCollection<object, Dictionary<string, object?>> _vectorStoreRecordCollection;
+    private readonly SemaphoreSlim _collectionInitializationLock = new(1, 1);
+    private bool _collectionInitialized;
+    private bool _disposedValue;
+
+    /// <summary>
+    /// Initializes a new instance of the <see cref="TextSearchStore"/> class.
+    /// </summary>
+    /// <param name="vectorStore">The vector store to store and read the memories from.</param>
+    /// <param name="collectionName">The name of the collection in the vector store to store and read the memories from.</param>
+    /// <param name="vectorDimensions">The number of dimensions to use for the memory embeddings.</param>
+    /// <param name="options">Options to configure the behavior of this class.</param>
+    /// <exception cref="NotSupportedException">Thrown if the key type provided is not supported.</exception>
+    public TextSearchStore(
+        VectorStore vectorStore,
+        string collectionName,
+        int vectorDimensions,
+        TextSearchStoreOptions? options = default)
+    {
+        // Verify
+        if (vectorStore is null)
+        {
+            throw new ArgumentNullException(nameof(vectorStore));
+        }
+
+        if (string.IsNullOrWhiteSpace(collectionName))
+        {
+            throw new ArgumentException("Collection name cannot be null or whitespace.", nameof(collectionName));
+        }
+
+        if (vectorDimensions < 1)
+        {
+            throw new ArgumentOutOfRangeException(nameof(vectorDimensions), "Vector dimensions must be greater than zero.");
+        }
+
+        if (options?.KeyType is not null && options.KeyType != typeof(string) && options.KeyType != typeof(Guid))
+        {
+            throw new NotSupportedException($"Unsupported key of type '{options.KeyType.Name}'");
+        }
+
+        if (options?.KeyType is not null && options.KeyType != typeof(string) && options?.UseSourceIdAsPrimaryKey is true)
+        {
+            throw new NotSupportedException($"The {nameof(TextSearchStoreOptions.UseSourceIdAsPrimaryKey)} option can only be used when the key type is 'string'.");
+        }
+
+        // Assign
+        this._vectorStore = vectorStore;
+        this._options = options ?? new TextSearchStoreOptions();
+        this._wordSegmenter = this._options.WordSegmenter ?? s_defaultWordSegmenter;
+
+        // Create a definition so that we can use the dimensions provided at runtime.
+        VectorStoreCollectionDefinition ragDocumentDefinition = new()
+        {
+            Properties = new List<VectorStoreProperty>()
+            {
+                new VectorStoreKeyProperty("Key", this._options.KeyType ?? typeof(string)),
+                new VectorStoreDataProperty("Namespaces", typeof(List<string>)) { IsIndexed = true },
+                new VectorStoreDataProperty("SourceId", typeof(string)) { IsIndexed = true },
+                new VectorStoreDataProperty("Text", typeof(string)) { IsFullTextIndexed = true },
+                new VectorStoreDataProperty("SourceName", typeof(string)),
+                new VectorStoreDataProperty("SourceLink", typeof(string)),
+                new VectorStoreVectorProperty("TextEmbedding", typeof(string), vectorDimensions),
+            }
+        };
+
+        this._vectorStoreRecordCollection = this._vectorStore.GetDynamicCollection(collectionName, ragDocumentDefinition);
+    }
+
+    /// <summary>
+    /// Upserts a batch of text chunks into the vector store.
+    /// </summary>
+    /// <param name="textChunks">The text chunks to upload.</param>
+    /// <param name="cancellationToken">The <see cref="CancellationToken"/> to monitor for cancellation requests. The default is <see cref="CancellationToken.None"/>.</param>
+    /// <returns>A task that completes when the documents have been upserted.</returns>
+    public async Task UpsertTextAsync(IEnumerable<string> textChunks, CancellationToken cancellationToken = default)
+    {
+        if (textChunks == null)
+        {
+            throw new ArgumentNullException(nameof(textChunks));
+        }
+
+        var vectorStoreRecordCollection = await this.EnsureCollectionExistsAsync(cancellationToken).ConfigureAwait(false);
+
+        var storageDocuments = textChunks.Select(textChunk =>
+        {
+            // Without text we cannot generate a vector.
+            if (string.IsNullOrWhiteSpace(textChunk))
+            {
+                throw new ArgumentException("One of the provided text chunks is null.", nameof(textChunks));
+            }
+
+            return new Dictionary<string, object?>
+            {
+                { "Key", this.GenerateUniqueKey(null) },
+                { "Namespaces", new List<string>() },
+                { "Text", textChunk },
+                { "TextEmbedding", textChunk },
+            };
+        });
+
+        await vectorStoreRecordCollection.UpsertAsync(storageDocuments, cancellationToken).ConfigureAwait(false);
+    }
+
+    /// <summary>
+    /// Upserts a batch of documents into the vector store.
+    /// </summary>
+    /// <param name="documents">The documents to upload.</param>
+    /// <param name="options">Optional options to control the upsert behavior.</param>
+    /// <param name="cancellationToken">The <see cref="CancellationToken"/> to monitor for cancellation requests. The default is <see cref="CancellationToken.None"/>.</param>
+    /// <returns>A task that completes when the documents have been upserted.</returns>
+    public async Task UpsertDocumentsAsync(IEnumerable<TextSearchDocument> documents, TextSearchStoreUpsertOptions? options = null, CancellationToken cancellationToken = default)
+    {
+        if (documents is null)
+        {
+            throw new ArgumentNullException(nameof(documents));
+        }
+
+        var vectorStoreRecordCollection = await this.EnsureCollectionExistsAsync(cancellationToken).ConfigureAwait(false);
+
+        var storageDocuments = documents.Select(document =>
+        {
+            if (document is null)
+            {
+                throw new ArgumentNullException(nameof(documents), "One of the provided documents is null.");
+            }
+
+            // Without text we cannot generate a vector.
+            if (string.IsNullOrWhiteSpace(document.Text))
+            {
+                throw new ArgumentException($"The {nameof(TextSearchDocument.Text)} property must be set.", nameof(document));
+            }
+
+            // If we aren't persisting the text, we need a source id or link to refer back to the original document.
+            if (options?.DoNotPersistSourceText is true && string.IsNullOrWhiteSpace(document.SourceId) && string.IsNullOrWhiteSpace(document.SourceLink))
+            {
+                throw new ArgumentException($"Either the {nameof(TextSearchDocument.SourceId)} or {nameof(TextSearchDocument.SourceLink)} properties must be set when the {nameof(TextSearchStoreUpsertOptions.DoNotPersistSourceText)} setting is true.", nameof(document));
+            }
+
+            var key = this.GenerateUniqueKey(this._options.UseSourceIdAsPrimaryKey ?? false ? document.SourceId : null);
+
+            return new Dictionary<string, object?>()
+            {
+                { "Key", key },
+                { "Namespaces", document.Namespaces.ToList() },
+                { "SourceId", document.SourceId },
+                { "Text", options?.DoNotPersistSourceText is true ? null : document.Text },
+                { "SourceName", document.SourceName },
+                { "SourceLink", document.SourceLink },
+                { "TextEmbedding", document.Text },
+            };
+        });
+
+        await vectorStoreRecordCollection.UpsertAsync(storageDocuments, cancellationToken).ConfigureAwait(false);
+    }
+
+    /// <summary>
+    /// Search the database for documents similar to the provided query.
+    /// </summary>
+    /// <param name="query">The text query to find similar documents to.</param>
+    /// <param name="top">The maximum number of results to return.</param>
+    /// <param name="cancellationToken">The <see cref="CancellationToken"/> to monitor for cancellation requests. The default is <see cref="CancellationToken.None"/>.</param>
+    /// <returns>The search results.</returns>
+    public async Task<IEnumerable<TextSearchDocument>> SearchAsync(string query, int top, CancellationToken cancellationToken = default)
+    {
+        var searchResult = await this.SearchCoreAsync(query, top, cancellationToken).ConfigureAwait(false);
+
+        return searchResult.Select(x => new TextSearchDocument()
+        {
+            Namespaces = (List<string>)x["Namespaces"]!,
+            Text = (string?)x["Text"],
+            SourceId = (string?)x["SourceId"],
+            SourceName = (string?)x["SourceName"],
+            SourceLink = (string?)x["SourceLink"],
+        });
+    }
+
+    /// <summary>
+    /// Internal search implementation with hydration of id / link only storage.
+    /// </summary>
+    /// <param name="query">The text query to find similar documents to.</param>
+    /// <param name="top">The maximum number of results to return.</param>
+    /// <param name="cancellationToken">The <see cref="CancellationToken"/> to monitor for cancellation requests. The default is <see cref="CancellationToken.None"/>.</param>
+    /// <returns>The search results.</returns>
+    private async Task<IEnumerable<Dictionary<string, object?>>> SearchCoreAsync(string query, int top, CancellationToken cancellationToken = default)
+    {
+        // Short circuit if the query is empty.
+        if (string.IsNullOrWhiteSpace(query))
+        {
+            return [];
+        }
+
+        var vectorStoreRecordCollection = await this.EnsureCollectionExistsAsync(cancellationToken).ConfigureAwait(false);
+
+        // If the user has not opted out of hybrid search, check if the vector store supports it.
+        var hybridSearchCollection = this._options.UseHybridSearch ?? true ?
+            vectorStoreRecordCollection.GetService(typeof(IKeywordHybridSearchable<Dictionary<string, object?>>)) as IKeywordHybridSearchable<Dictionary<string, object?>> :
+            null;
+
+        // Optional filter to limit the search to a specific namespace.
+        Expression<Func<Dictionary<string, object?>, bool>>? filter = string.IsNullOrWhiteSpace(this._options.SearchNamespace) ? null : x => ((List<string>)x["Namespaces"]!).Contains(this._options.SearchNamespace);
+
+        // Execute a hybrid search if possible, otherwise perform a regular vector search.
+        var searchResult = hybridSearchCollection is null
+            ? vectorStoreRecordCollection.SearchAsync(
+                query,
+                top,
+                options: new()
+                {
+                    Filter = filter,
+                },
+                cancellationToken: cancellationToken)
+            : hybridSearchCollection.HybridSearchAsync(
+                query,
+                this._wordSegmenter(query),
+                top,
+                options: new()
+                {
+                    Filter = filter,
+                },
+                cancellationToken: cancellationToken);
+
+        // Retrieve the documents from the search results.
+        List<Dictionary<string, object?>> searchResponseDocs = new();
+        await foreach (var searchResponseDoc in searchResult.WithCancellation(cancellationToken).ConfigureAwait(false))
+        {
+            searchResponseDocs.Add(searchResponseDoc.Record);
+        }
+
+        // Find any source ids and links for which the text needs to be retrieved.
+        var sourceIdsToRetrieve = searchResponseDocs
+            .Where(x => string.IsNullOrWhiteSpace((string?)x["Text"]))
+            .Select(x => new TextSearchStoreOptions.SourceRetrievalRequest((string?)x["SourceId"], (string?)x["SourceLink"]))
+            .ToList();
+
+        // If we have none, we can return early.
+        if (sourceIdsToRetrieve.Count == 0)
+        {
+            return searchResponseDocs;
+        }
+
+        if (this._options.SourceRetrievalCallback is null)
+        {
+            throw new InvalidOperationException($"The {nameof(TextSearchStoreOptions.SourceRetrievalCallback)} option must be set if retrieving documents without stored text.");
+        }
+
+        // Retrieve the source text for the documents that need it.
+        var retrievalResponses = await this._options.SourceRetrievalCallback(sourceIdsToRetrieve).ConfigureAwait(false);
+
+        if (retrievalResponses is null)
+        {
+            throw new InvalidOperationException($"The {nameof(TextSearchStoreOptions.SourceRetrievalCallback)} must return a non-null value.");
+        }
+
+        // Update the retrieved documents with the retrieved text.
+        return searchResponseDocs.GroupJoin(
+            retrievalResponses,
+            searchResponseDoc => (searchResponseDoc["SourceId"], searchResponseDoc["SourceLink"]),
+            retrievalResponse => (retrievalResponse.SourceId, retrievalResponse.SourceLink),
+            (searchResponseDoc, textRetrievalResponse) => (searchResponseDoc, textRetrievalResponse))
+            .SelectMany(
+                joinedSet => joinedSet.textRetrievalResponse.DefaultIfEmpty(),
+                (combined, textRetrievalResponse) =>
+                {
+                    combined.searchResponseDoc["Text"] = textRetrievalResponse?.Text ?? combined.searchResponseDoc["Text"];
+                    return combined.searchResponseDoc;
+                });
+    }
+
+    /// <summary>
+    /// Thread safe method to get the collection and ensure that it is created at least once.
+    /// </summary>
+    /// <param name="cancellationToken">The <see cref="CancellationToken"/> to monitor for cancellation requests. The default is <see cref="CancellationToken.None"/>.</param>
+    /// <returns>The created collection.</returns>
+    private async Task<VectorStoreCollection<object, Dictionary<string, object?>>> EnsureCollectionExistsAsync(CancellationToken cancellationToken)
+    {
+        // Return immediately if the collection is already created, no need to do any locking in this case.
+        if (this._collectionInitialized)
+        {
+            return this._vectorStoreRecordCollection;
+        }
+
+        // Wait on a lock to ensure that only one thread can create the collection.
+        await this._collectionInitializationLock.WaitAsync(cancellationToken).ConfigureAwait(false);
+
+        // If multiple threads waited on the lock, and the first already created the collection,
+        // we can return immediately without doing any work in subsequent threads.
+        if (this._collectionInitialized)
+        {
+            this._collectionInitializationLock.Release();
+            return this._vectorStoreRecordCollection;
+        }
+
+        // Only the winning thread should reach this point and create the collection.
+        try
+        {
+            await this._vectorStoreRecordCollection.EnsureCollectionExistsAsync(cancellationToken).ConfigureAwait(false);
+            this._collectionInitialized = true;
+        }
+        finally
+        {
+            this._collectionInitializationLock.Release();
+        }
+
+        return this._vectorStoreRecordCollection;
+    }
+
+    /// <summary>
+    /// Generates a unique key for the RAG document.
+    /// </summary>
+    /// <param name="sourceId">Source id of the source document for this RAG document.</param>
+    /// <returns>A new unique key.</returns>
+    /// <exception cref="NotSupportedException">Thrown if the requested key type is not supported.</exception>
+    private object GenerateUniqueKey(string? sourceId)
+        => this._options.KeyType switch
+        {
+            _ when (this._options.KeyType == null || this._options.KeyType == typeof(string)) && !string.IsNullOrWhiteSpace(sourceId) => sourceId!,
+            _ when this._options.KeyType == null || this._options.KeyType == typeof(string) => Guid.NewGuid().ToString(),
+            _ when this._options.KeyType == typeof(Guid) => Guid.NewGuid(),
+
+            _ => throw new NotSupportedException($"Unsupported key of type '{this._options.KeyType.Name}'")
+        };
+
+    /// <inheritdoc/>
+    private void Dispose(bool disposing)
+    {
+        if (!this._disposedValue)
+        {
+            if (disposing)
+            {
+                this._vectorStoreRecordCollection.Dispose();
+                this._collectionInitializationLock.Dispose();
+            }
+
+            this._disposedValue = true;
+        }
+    }
+
+    /// <inheritdoc/>
+    public void Dispose()
+    {
+        // Do not change this code. Put cleanup code in 'Dispose(bool disposing)' method
+        this.Dispose(disposing: true);
+        GC.SuppressFinalize(this);
+    }
+}
@@ -0,0 +1,140 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+namespace Microsoft.Agents.AI.Samples;
+
+/// <summary>
+/// Contains options for the <see cref="TextSearchStore"/>.
+/// </summary>
+public sealed class TextSearchStoreOptions
+{
+    /// <summary>
+    /// Gets or sets an optional namespace to pre-filter the possible
+    /// records with when doing a vector search.
+    /// </summary>
+    public string? SearchNamespace { get; init; }
+
+    /// <summary>
+    /// Gets or sets a value indicating whether to use the source ID as the primary key for records.
+    /// </summary>
+    /// <remarks>
+    /// <para>
+    /// Using the source ID as the primary key allows for easy updates from the source for any changed
+    /// records, since those records can just be upserted again, and will overwrite the previous version
+    /// of the same record.
+    /// </para>
+    /// <para>
+    /// This setting can only be used when the chosen key type is a string.
+    /// </para>
+    /// </remarks>
+    /// <value>
+    /// Defaults to <c>false</c> if not set.
+    /// </value>
+    public bool? UseSourceIdAsPrimaryKey { get; init; }
+
+    /// <summary>
+    /// Gets or sets a value indicating whether to use hybrid search if it is available for the provided vector store.
+    /// </summary>
+    /// <value>
+    /// Defaults to <c>true</c> if not set.
+    /// </value>
+    public bool? UseHybridSearch { get; init; }
+
+    /// <summary>
+    /// Gets or sets a word segmenter function to split search text into separate words for the purposes of hybrid search.
+    /// This will not be used if <see cref="UseHybridSearch"/> is set to <c>false</c>.
+    /// </summary>
+    /// <remarks>
+    /// Defaults to a simple text-character-based segmenter that splits the text by any character that is not a text character.
+    /// </remarks>
+    public Func<string, ICollection<string>>? WordSegmenter { get; init; }
+
+    /// <summary>
+    /// Gets or sets the type of key to use for records in the text search store.
+    /// </summary>
+    /// <remarks>
+    /// Make sure to pick a key type that is supported by the underlying vector store.
+    /// Note that you have to choose <see cref="string"/> when using <see cref="UseSourceIdAsPrimaryKey"/>.
+    /// </remarks>
+    /// <value>Defaults to <see cref="string"/> if not set. Only <see cref="string"/> and <see cref="Guid"/> is currently supported.</value>
+    public Type? KeyType { get; init; }
+
+    /// <summary>
+    /// Gets or sets an optional callback to load the source text using the source id or source link
+    /// if the source text is not persisted in the database.
+    /// </summary>
+    /// <remarks>
+    /// The response should include the source id or source link, as provided in the request,
+    /// plus the source text loaded from the source.
+    /// </remarks>
+    public Func<List<SourceRetrievalRequest>, Task<IEnumerable<SourceRetrievalResponse>>>? SourceRetrievalCallback { get; init; }
+
+    /// <summary>
+    /// Represents a request to the <see cref="SourceRetrievalCallback"/>.
+    /// </summary>
+    public sealed class SourceRetrievalRequest
+    {
+        /// <summary>
+        /// Initializes a new instance of the <see cref="SourceRetrievalRequest"/> class.
+        /// </summary>
+        /// <param name="sourceId">The source ID of the document to retrieve.</param>
+        /// <param name="sourceLink">The source link of the document to retrieve.</param>
+        public SourceRetrievalRequest(string? sourceId, string? sourceLink)
+        {
+            this.SourceId = sourceId;
+            this.SourceLink = sourceLink;
+        }
+
+        /// <summary>
+        /// Gets or sets the source ID of the document to retrieve.
+        /// </summary>
+        public string? SourceId { get; set; }
+
+        /// <summary>
+        /// Gets or sets the source link of the document to retrieve.
+        /// </summary>
+        public string? SourceLink { get; set; }
+    }
+
+    /// <summary>
+    /// Represents a response from the <see cref="SourceRetrievalCallback"/>.
+    /// </summary>
+    public sealed class SourceRetrievalResponse
+    {
+        /// <summary>
+        /// Initializes a new instance of the <see cref="SourceRetrievalResponse"/> class.
+        /// </summary>
+        /// <param name="request">The request matching this response.</param>
+        /// <param name="text">The source text that was retrieved.</param>
+        public SourceRetrievalResponse(SourceRetrievalRequest request, string text)
+        {
+            if (request == null)
+            {
+                throw new ArgumentNullException(nameof(request));
+            }
+
+            if (text == null)
+            {
+                throw new ArgumentNullException(nameof(text));
+            }
+
+            this.SourceId = request.SourceId;
+            this.SourceLink = request.SourceLink;
+            this.Text = text;
+        }
+
+        /// <summary>
+        /// Gets or sets the source ID of the document that was retrieved.
+        /// </summary>
+        public string? SourceId { get; set; }
+
+        /// <summary>
+        /// Gets or sets the source link of the document that was retrieved.
+        /// </summary>
+        public string? SourceLink { get; set; }
+
+        /// <summary>
+        /// Gets or sets the source text of the document that was retrieved.
+        /// </summary>
+        public string Text { get; set; }
+    }
+}
@@ -0,0 +1,17 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+namespace Microsoft.Agents.AI.Samples;
+
+/// <summary>
+/// Contains options for <see cref="TextSearchStore.UpsertDocumentsAsync(IEnumerable{TextSearchDocument}, TextSearchStoreUpsertOptions?, CancellationToken)"/>.
+/// </summary>
+public sealed class TextSearchStoreUpsertOptions
+{
+    /// <summary>
+    /// Gets or sets a value indicating whether the source text should be persisted in the database.
+    /// </summary>
+    /// <value>
+    /// Defaults to <see langword="false"/> if not set.
+    /// </value>
+    public bool DoNotPersistSourceText { get; init; }
+}
@@ -0,0 +1,22 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFramework>net9.0</TargetFramework>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
+    <PackageReference Include="Microsoft.SemanticKernel.Connectors.Qdrant" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,134 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to use Qdrant to add retrieval augmented generation (RAG) capabilities to an AI agent.
+// While the sample is using Qdrant, it can easily be replaced with any other vector store that implements the Microsoft.Extensions.VectorData abstractions.
+// The TextSearchProvider runs a search against the vector store before each model invocation and injects the results into the model context.
+
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.Data;
+using Microsoft.Extensions.AI;
+using Microsoft.Extensions.VectorData;
+using Microsoft.SemanticKernel.Connectors.Qdrant;
+using OpenAI;
+using Qdrant.Client;
+
+var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var embeddingDeploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME") ?? "text-embedding-3-large";
+var afOverviewUrl = "https://github.com/MicrosoftDocs/semantic-kernel-docs/blob/main/agent-framework/overview/agent-framework-overview.md";
+var afMigrationUrl = "https://raw.githubusercontent.com/MicrosoftDocs/semantic-kernel-docs/refs/heads/main/agent-framework/migration-guide/from-semantic-kernel/index.md";
+
+AzureOpenAIClient azureOpenAIClient = new(
+    new Uri(endpoint),
+    new AzureCliCredential());
+
+// Create a Qdrant vector store that uses the Azure OpenAI embedding model to generate embeddings.
+QdrantClient client = new("localhost");
+VectorStore vectorStore = new QdrantVectorStore(client, ownsClient: true, new()
+{
+    EmbeddingGenerator = azureOpenAIClient.GetEmbeddingClient(embeddingDeploymentName).AsIEmbeddingGenerator()
+});
+
+// Create a collection and upsert some text into it.
+var documentationCollection = vectorStore.GetCollection<Guid, DocumentationChunk>("documentation");
+await documentationCollection.EnsureCollectionDeletedAsync(); // Clear out any data from previous runs.
+await documentationCollection.EnsureCollectionExistsAsync();
+await UploadDataFromMarkdown(afOverviewUrl, "Microsoft Agent Framework Overview", documentationCollection, 2000, 200);
+await UploadDataFromMarkdown(afMigrationUrl, "Semantic Kernel to Microsoft Agent Framework Migration Guide", documentationCollection, 2000, 200);
+
+// Create an adapter function that the TextSearchProvider can use to run searches against the collection.
+Func<string, CancellationToken, Task<IEnumerable<TextSearchProvider.TextSearchResult>>> SearchAdapter = async (text, ct) =>
+{
+    List<TextSearchProvider.TextSearchResult> results = [];
+    await foreach (var result in documentationCollection.SearchAsync(text, 5, cancellationToken: ct))
+    {
+        results.Add(new TextSearchProvider.TextSearchResult
+        {
+            SourceName = result.Record.SourceName,
+            SourceLink = result.Record.SourceLink,
+            Text = result.Record.Text ?? string.Empty,
+            RawRepresentation = result
+        });
+    }
+    return results;
+};
+
+// Configure the options for the TextSearchProvider.
+TextSearchProviderOptions textSearchOptions = new()
+{
+    // Run the search prior to every model invocation.
+    SearchTime = TextSearchProviderOptions.TextSearchBehavior.BeforeAIInvoke,
+    // Use up to 4 recent messages when searching so that searches
+    // still produce valuable results even when the user is referring
+    // back to previous messages in their request.
+    RecentMessageMemoryLimit = 5
+};
+
+// Create the AI agent with the TextSearchProvider as the AI context provider.
+AIAgent agent = azureOpenAIClient
+    .GetChatClient(deploymentName)
+    .CreateAIAgent(new ChatClientAgentOptions
+    {
+        Instructions = "You are a helpful support specialist for the Microsoft Agent Framework. Answer questions using the provided context and cite the source document when available. Keep responses brief.",
+        AIContextProviderFactory = ctx => ctx.SerializedState.ValueKind is not System.Text.Json.JsonValueKind.Null and not System.Text.Json.JsonValueKind.Undefined
+            ? new TextSearchProvider(SearchAdapter, ctx.SerializedState, ctx.JsonSerializerOptions, textSearchOptions)
+            : new TextSearchProvider(SearchAdapter, textSearchOptions)
+    });
+
+AgentThread thread = agent.GetNewThread();
+
+Console.WriteLine(">> Asking about SK threads\n");
+Console.WriteLine(await agent.RunAsync("Hi! How do I create a thread in Semantic Kernel?", thread));
+
+// Here we are asking a very vague question when taken out of context,
+// but since we are including previous messages in our search using RecentMessageMemoryLimit
+// the RAG search should still produce useful results.
+Console.WriteLine("\n>> Asking about AF threads\n");
+Console.WriteLine(await agent.RunAsync("and in Agent Framework?", thread));
+
+Console.WriteLine("\n>> Contrasting Approaches\n");
+Console.WriteLine(await agent.RunAsync("Please contrast the two approaches", thread));
+
+Console.WriteLine("\n>> Asking about ancestry\n");
+Console.WriteLine(await agent.RunAsync("What are the predecessors to the Agent Framework?", thread));
+
+static async Task UploadDataFromMarkdown(string markdownUrl, string sourceName, VectorStoreCollection<Guid, DocumentationChunk> vectorStoreCollection, int chunkSize, int overlap)
+{
+    // Download the markdown from the given url.
+    using HttpClient client = new();
+    var markdown = await client.GetStringAsync(new Uri(markdownUrl));
+
+    // Chunk it into separate parts with some overlap between chunks
+    var chunks = new List<DocumentationChunk>();
+    for (int i = 0; i < markdown.Length; i += chunkSize)
+    {
+        var chunk = new DocumentationChunk
+        {
+            Key = Guid.NewGuid(),
+            SourceLink = markdownUrl,
+            SourceName = sourceName,
+            Text = markdown.Substring(i, Math.Min(chunkSize + overlap, markdown.Length - i))
+        };
+        chunks.Add(chunk);
+    }
+
+    // Upsert each chunk into the provided vector store.
+    await vectorStoreCollection.UpsertAsync(chunks);
+}
+
+// Data model that defines the database schema we want to use.
+internal sealed class DocumentationChunk
+{
+    [VectorStoreKey]
+    public Guid Key { get; set; }
+    [VectorStoreData]
+    public string SourceLink { get; set; } = string.Empty;
+    [VectorStoreData]
+    public string SourceName { get; set; } = string.Empty;
+    [VectorStoreData]
+    public string Text { get; set; } = string.Empty;
+    [VectorStoreVector(Dimensions: 3072)]
+    public string Embedding => this.Text;
+}
@@ -0,0 +1,60 @@
+# Agent Framework Retrieval Augmented Generation (RAG) with an external Vector Store with a custom schema
+
+This sample demonstrates how to create and run an agent that uses Retrieval Augmented Generation (RAG) with an external vector store.
+It also uses a custom schema for the documents stored in the vector store.
+This sample uses Qdrant for the vector store, but this can easily be swapped out for any vector store that has a Microsoft.Extensions.VectorStore implementation.
+
+## Prerequisites
+
+- .NET 8.0 SDK or later
+- Azure OpenAI service endpoint
+- Both a chat completion and embedding deployment configured in the Azure OpenAI resource
+- Azure CLI installed and authenticated (for Azure credential authentication)
+- User has the `Cognitive Services OpenAI Contributor` role for the Azure OpenAI resource.
+- An existing Qdrant instance. You can use a managed service or run a local instance using Docker, but the sample assumes the instance is running locally.
+
+**Note**: These samples use Azure OpenAI models. For more information, see [how to deploy Azure OpenAI models with Azure AI Foundry](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/deploy-models-openai).
+
+**Note**: These samples use Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure OpenAI resource and have the `Cognitive Services OpenAI Contributor` role. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+
+## Running the sample from the console
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/" # Replace with your Azure OpenAI resource endpoint
+$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini
+$env:AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME="text-embedding-3-large"  # Optional, defaults to text-embedding-3-large
+```
+
+If the variables are not set, you will be prompted for the values when running the samples.
+
+To use Qdrant in docker locally, start your Qdrant instance using the default port mappings.
+
+```powershell
+docker run -d --name qdrant -p 6333:6333 -p 6334:6334 qdrant/qdrant:latest
+```
+
+Execute the following command to build the sample:
+
+```powershell
+dotnet build
+```
+
+Execute the following command to run the sample:
+
+```powershell
+dotnet run --no-build
+```
+
+Or just build and run in one step:
+
+```powershell
+dotnet run
+```
+
+## Running the sample from Visual Studio
+
+Open the solution in Visual Studio and set the sample project as the startup project. Then, run the project using the built-in debugger or by pressing `F5`.
+
+You will be prompted for any required environment variables if they are not already set.
@@ -0,0 +1,8 @@
+# Agent Framework Retrieval Augmented Generation (RAG)
+
+These samples show how to create an agent with the Agent Framework that uses Retrieval Augmented Generation (RAG) to enhance its responses with information from a knowledge base.
+
+|Sample|Description|
+|---|---|
+|[Basic Text RAG](./AgentWithRAG_Step01_BasicTextRAG/)|This sample demonstrates how to create and run a basic agent with simple text Retrieval Augmented Generation (RAG).|
+|[RAG with external Vector Store and custom schema](./AgentWithRAG_Step02_ExternalDataSourceRAG/)|This sample demonstrates how to create and run an agent that uses Retrieval Augmented Generation (RAG) with an external vector store. It also uses a custom schema for the documents stored in the vector store.|
@@ -28,7 +28,9 @@ AIAgent agent = new AzureOpenAIClient(
    .CreateAIAgent(new ChatClientAgentOptions
    {
        Instructions = "You are a helpful support specialist for Contoso Outdoors. Answer questions using the provided context and cite the source document when available.",
-        AIContextProviderFactory = _ => new TextSearchProvider(MockSearchAsync, textSearchOptions)
+        AIContextProviderFactory = ctx => ctx.SerializedState.ValueKind is not System.Text.Json.JsonValueKind.Null and not System.Text.Json.JsonValueKind.Undefined
+            ? new TextSearchProvider(MockSearchAsync, ctx.SerializedState, ctx.JsonSerializerOptions, textSearchOptions)
+            : new TextSearchProvider(MockSearchAsync, textSearchOptions)
    });

 AgentThread thread = agent.GetNewThread();
@@ -52,9 +54,9 @@ static Task<IEnumerable<TextSearchProvider.TextSearchResult>> MockSearchAsync(st
    {
        results.Add(new()
        {
-            Name = "Contoso Outdoors Return Policy",
-            Link = "https://contoso.com/policies/returns",
-            Value = "Customers may return any item within 30 days of delivery. Items should be unused and include original packaging. Refunds are issued to the original payment method within 5 business days of inspection."
+            SourceName = "Contoso Outdoors Return Policy",
+            SourceLink = "https://contoso.com/policies/returns",
+            Text = "Customers may return any item within 30 days of delivery. Items should be unused and include original packaging. Refunds are issued to the original payment method within 5 business days of inspection."
        });
    }

@@ -62,9 +64,9 @@ static Task<IEnumerable<TextSearchProvider.TextSearchResult>> MockSearchAsync(st
    {
        results.Add(new()
        {
-            Name = "Contoso Outdoors Shipping Guide",
-            Link = "https://contoso.com/help/shipping",
-            Value = "Standard shipping is free on orders over $50 and typically arrives in 3-5 business days within the continental United States. Expedited options are available at checkout."
+            SourceName = "Contoso Outdoors Shipping Guide",
+            SourceLink = "https://contoso.com/help/shipping",
+            Text = "Standard shipping is free on orders over $50 and typically arrives in 3-5 business days within the continental United States. Expedited options are available at checkout."
        });
    }

@@ -72,9 +74,9 @@ static Task<IEnumerable<TextSearchProvider.TextSearchResult>> MockSearchAsync(st
    {
        results.Add(new()
        {
-            Name = "TrailRunner Tent Care Instructions",
-            Link = "https://contoso.com/manuals/trailrunner-tent",
-            Value = "Clean the tent fabric with lukewarm water and a non-detergent soap. Allow it to air dry completely before storage and avoid prolonged UV exposure to extend the lifespan of the waterproof coating."
+            SourceName = "TrailRunner Tent Care Instructions",
+            SourceLink = "https://contoso.com/manuals/trailrunner-tent",
+            Text = "Clean the tent fabric with lukewarm water and a non-detergent soap. Allow it to air dry completely before storage and avoid prolonged UV exposure to extend the lifespan of the waterproof coating."
        });
    }

@@ -45,6 +45,7 @@ public sealed class TextSearchProvider : AIContextProvider
    private readonly AITool[] _tools;
    private readonly Queue<string> _recentMessagesText;
    private readonly TextSearchProviderOptions _options;
+    private readonly List<ChatRole> _recentMessageRolesIncluded;

    /// <summary>
    /// Initializes a new instance of the <see cref="TextSearchProvider"/> class.
@@ -60,6 +61,7 @@ public sealed class TextSearchProvider : AIContextProvider
        Throw.IfLessThan(this._options.RecentMessageMemoryLimit, 0);
        this._logger = loggerFactory?.CreateLogger<TextSearchProvider>();
        this._recentMessagesText = new();
+        this._recentMessageRolesIncluded = this._options.RecentMessageRolesIncluded ?? [ChatRole.User];

        // Create the on-demand search tool (only used if behavior is OnDemandFunctionCalling)
        this._tools =
@@ -91,6 +93,7 @@ public sealed class TextSearchProvider : AIContextProvider
        this._options = options ?? new();
        Throw.IfLessThan(this._options.RecentMessageMemoryLimit, 0);
        this._logger = loggerFactory?.CreateLogger<TextSearchProvider>();
+        this._recentMessageRolesIncluded = this._options.RecentMessageRolesIncluded ?? [ChatRole.User];

        List<string>? restoredMessages = null;

@@ -163,7 +166,7 @@ public sealed class TextSearchProvider : AIContextProvider

        return new AIContext
        {
-            Messages = [new ChatMessage(ChatRole.User, formatted)]
+            Messages = [new ChatMessage(ChatRole.User, formatted) { AdditionalProperties = new AdditionalPropertiesDictionary() { ["IsTextSearchProviderOutput"] = true } }]
        };
    }

@@ -183,7 +186,12 @@ public sealed class TextSearchProvider : AIContextProvider

        var messagesText = context.RequestMessages
            .Concat(context.ResponseMessages ?? [])
-            .Where(m => (m.Role == ChatRole.User || m.Role == ChatRole.Assistant) && !string.IsNullOrWhiteSpace(m.Text))
+            .Where(m =>
+                this._recentMessageRolesIncluded.Contains(m.Role) &&
+                !string.IsNullOrWhiteSpace(m.Text) &&
+                // Filter out any messages that were added by this class in InvokingAsync, since we don't want
+                // a feedback loop where previous search results are used to find new search results.
+                (m.AdditionalProperties == null || m.AdditionalProperties.TryGetValue("IsTextSearchProviderOutput", out bool isTextSearchProviderOutput) == false || !isTextSearchProviderOutput))
            .Select(m => m.Text)
            .ToList();
        if (messagesText.Count > limit)
@@ -262,15 +270,15 @@ public sealed class TextSearchProvider : AIContextProvider
        for (int i = 0; i < results.Count; i++)
        {
            var result = results[i];
-            if (!string.IsNullOrWhiteSpace(result.Name))
+            if (!string.IsNullOrWhiteSpace(result.SourceName))
            {
-                sb.AppendLine($"SourceDocName: {result.Name}");
+                sb.AppendLine($"SourceDocName: {result.SourceName}");
            }
-            if (!string.IsNullOrWhiteSpace(result.Link))
+            if (!string.IsNullOrWhiteSpace(result.SourceLink))
            {
-                sb.AppendLine($"SourceDocLink: {result.Link}");
+                sb.AppendLine($"SourceDocLink: {result.SourceLink}");
            }
-            sb.AppendLine($"Contents: {result.Value}");
+            sb.AppendLine($"Contents: {result.Text}");
            sb.AppendLine("----");
        }
        sb.AppendLine(this._options.CitationsPrompt ?? DefaultCitationsPrompt);
@@ -286,17 +294,17 @@ public sealed class TextSearchProvider : AIContextProvider
        /// <summary>
        /// Gets or sets the display name of the source document (optional).
        /// </summary>
-        public string? Name { get; set; }
+        public string? SourceName { get; set; }

        /// <summary>
        /// Gets or sets a link/URL to the source document (optional).
        /// </summary>
-        public string? Link { get; set; }
+        public string? SourceLink { get; set; }

        /// <summary>
        /// Gets or sets the textual content of the retrieved chunk.
        /// </summary>
-        public string Value { get; set; } = string.Empty;
+        public string Text { get; set; } = string.Empty;

        /// <summary>
        /// Gets or sets the raw representation of the search result from the data source.
@@ -59,6 +59,26 @@ public sealed class TextSearchProviderOptions
    /// </value>
    public int RecentMessageMemoryLimit { get; set; }

+    /// <summary>
+    /// Gets or sets the list of <see cref="ChatRole"/> types to filter recent messages to
+    /// when deciding which recent messages to include when constructing the search input.
+    /// </summary>
+    /// <remarks>
+    /// <para>
+    /// Depending on your scenario, you may want to use only user messages, only assistant messages,
+    /// or both. For example, if the assistant may often provide clarifying questions or if the conversation
+    /// is expected to be particularly chatty, you may want to include assistant messages in the search context as well.
+    /// </para>
+    /// <para>
+    /// Be careful when including assistant messages though, as they may skew the search results towards
+    /// information that has already been provided by the assistant, rather than focusing on the user's current needs.
+    /// </para>
+    /// </remarks>
+    /// <value>
+    /// When not specified, defaults to only <see cref="ChatRole.User"/>.
+    /// </value>
+    public List<ChatRole>? RecentMessageRolesIncluded { get; set; }
+
    /// <summary>
    /// Behavior choices for the provider.
    /// </summary>
@@ -19,6 +19,7 @@
  <ItemGroup>
    <ProjectReference Include="..\Microsoft.Agents.AI.Abstractions\Microsoft.Agents.AI.Abstractions.csproj" />
    <PackageReference Include="Microsoft.Extensions.AI" />
+    <PackageReference Include="Microsoft.Extensions.VectorData.Abstractions" />
    <PackageReference Include="Microsoft.Extensions.DependencyInjection.Abstractions" />
    <PackageReference Include="System.Diagnostics.DiagnosticSource" />
  </ItemGroup>
@@ -31,6 +32,7 @@

  <ItemGroup>
    <InternalsVisibleTo Include="Microsoft.Agents.AI.UnitTests" />
+    <InternalsVisibleTo Include="DynamicProxyGenAssembly2, PublicKey=0024000004800000940000000602000000240000525341310004000001000100c547cac37abd99c8db225ef2f6c8a3602f3b3606cc9891605d02baa56104f4cfc0734aa39b93bf7852f7d9266654753cc297e7d2edfe0bac1cdcf9f717241550e0a7b191195b7667bb4f64bcb8e2121380fd1d9d46ad2d92d2d15605093924cceaf74c4861eff62abf69b9291ed0a340e113be11e6a7d3113e92484cf7045cc7"/>
  </ItemGroup>

 </Project>
@@ -41,8 +41,8 @@ public sealed class TextSearchProviderTests
        // Arrange
        List<TextSearchProvider.TextSearchResult> results =
        [
-            new() { Name = "Doc1", Link = "http://example.com/doc1", Value = "Content of Doc1" },
-            new() { Name = "Doc2", Link = "http://example.com/doc2", Value = "Content of Doc2" }
+            new() { SourceName = "Doc1", SourceLink = "http://example.com/doc1", Text = "Content of Doc1" },
+            new() { SourceName = "Doc2", SourceLink = "http://example.com/doc2", Text = "Content of Doc2" }
        ];

        string? capturedInput = null;
@@ -158,8 +158,8 @@ public sealed class TextSearchProviderTests
        // Arrange
        List<TextSearchProvider.TextSearchResult> results =
        [
-            new() { Name = "Doc1", Link = "http://example.com/doc1", Value = "Content of Doc1" },
-            new() { Name = "Doc2", Link = "http://example.com/doc2", Value = "Content of Doc2" }
+            new() { SourceName = "Doc1", SourceLink = "http://example.com/doc1", Text = "Content of Doc1" },
+            new() { SourceName = "Doc2", SourceLink = "http://example.com/doc2", Text = "Content of Doc2" }
        ];

        Task<IEnumerable<TextSearchProvider.TextSearchResult>> SearchDelegateAsync(string input, CancellationToken ct)
@@ -210,8 +210,8 @@ public sealed class TextSearchProviderTests
        // Arrange
        List<TextSearchProvider.TextSearchResult> results =
        [
-            new() { Name = "Doc1", Link = "http://example.com/doc1", Value = "Content of Doc1" },
-            new() { Name = "Doc2", Link = "http://example.com/doc2", Value = "Content of Doc2" }
+            new() { SourceName = "Doc1", SourceLink = "http://example.com/doc1", Text = "Content of Doc1" },
+            new() { SourceName = "Doc2", SourceLink = "http://example.com/doc2", Text = "Content of Doc2" }
        ];

        Task<IEnumerable<TextSearchProvider.TextSearchResult>> SearchDelegateAsync(string input, CancellationToken ct)
@@ -244,8 +244,8 @@ public sealed class TextSearchProviderTests
        var payload2 = new RawPayload { Id = "R2" };
        List<TextSearchProvider.TextSearchResult> results =
        [
-            new() { Name = "Doc1", Value = "Content 1", RawRepresentation = payload1 },
-            new() { Name = "Doc2", Value = "Content 2", RawRepresentation = payload2 }
+            new() { SourceName = "Doc1", Text = "Content 1", RawRepresentation = payload1 },
+            new() { SourceName = "Doc2", Text = "Content 2", RawRepresentation = payload2 }
        ];

        Task<IEnumerable<TextSearchProvider.TextSearchResult>> SearchDelegateAsync(string input, CancellationToken ct)
@@ -335,7 +335,8 @@ public sealed class TextSearchProviderTests
        var options = new TextSearchProviderOptions
        {
            SearchTime = TextSearchProviderOptions.TextSearchBehavior.BeforeAIInvoke,
-            RecentMessageMemoryLimit = 3
+            RecentMessageMemoryLimit = 3,
+            RecentMessageRolesIncluded = [ChatRole.User, ChatRole.Assistant]
        };
        string? capturedInput = null;
        Task<IEnumerable<TextSearchProvider.TextSearchResult>> SearchDelegateAsync(string input, CancellationToken ct)
@@ -374,7 +375,8 @@ public sealed class TextSearchProviderTests
        var options = new TextSearchProviderOptions
        {
            SearchTime = TextSearchProviderOptions.TextSearchBehavior.BeforeAIInvoke,
-            RecentMessageMemoryLimit = 5
+            RecentMessageMemoryLimit = 5,
+            RecentMessageRolesIncluded = [ChatRole.User, ChatRole.Assistant]
        };
        string? capturedInput = null;
        Task<IEnumerable<TextSearchProvider.TextSearchResult>> SearchDelegateAsync(string input, CancellationToken ct)
@@ -408,6 +410,46 @@ public sealed class TextSearchProviderTests
        Assert.Equal("A\nB\nC\nD\nE\nF", capturedInput); // All retained (limit 5) + current request message.
    }

+    [Fact]
+    public async Task InvokingAsync_WithRecentMessageRolesIncluded_ShouldFilterRolesAsync()
+    {
+        // Arrange
+        var options = new TextSearchProviderOptions
+        {
+            SearchTime = TextSearchProviderOptions.TextSearchBehavior.BeforeAIInvoke,
+            RecentMessageMemoryLimit = 4,
+            RecentMessageRolesIncluded = new List<ChatRole> { ChatRole.Assistant } // Only retain assistant messages.
+        };
+        string? capturedInput = null;
+        Task<IEnumerable<TextSearchProvider.TextSearchResult>> SearchDelegateAsync(string input, CancellationToken ct)
+        {
+            capturedInput = input;
+            return Task.FromResult<IEnumerable<TextSearchProvider.TextSearchResult>>([]); // No results needed for this test.
+        }
+        var provider = new TextSearchProvider(SearchDelegateAsync, options);
+
+        // Populate memory with mixed roles; only Assistant messages (A1,A2) should be retained.
+        var initialMessages = new[]
+        {
+            new ChatMessage(ChatRole.User, "U1"),
+            new ChatMessage(ChatRole.Assistant, "A1"),
+            new ChatMessage(ChatRole.User, "U2"),
+            new ChatMessage(ChatRole.Assistant, "A2"),
+        };
+        await provider.InvokedAsync(new(initialMessages, null));
+
+        var invokingContext = new AIContextProvider.InvokingContext(new[]
+        {
+            new ChatMessage(ChatRole.User, "Question?") // Current request message always appended.
+        });
+
+        // Act
+        await provider.InvokingAsync(invokingContext, CancellationToken.None);
+
+        // Assert
+        Assert.Equal("A1\nA2\nQuestion?", capturedInput); // Only assistant messages from memory + current request.
+    }
+
    #endregion

    #region Serialization Tests
@@ -438,7 +480,8 @@ public sealed class TextSearchProviderTests
        var options = new TextSearchProviderOptions
        {
            SearchTime = TextSearchProviderOptions.TextSearchBehavior.BeforeAIInvoke,
-            RecentMessageMemoryLimit = 3
+            RecentMessageMemoryLimit = 3,
+            RecentMessageRolesIncluded = [ChatRole.User, ChatRole.Assistant]
        };
        var provider = new TextSearchProvider(this.NoResultSearchAsync, options);
        var messages = new[]
@@ -467,7 +510,8 @@ public sealed class TextSearchProviderTests
        var options = new TextSearchProviderOptions
        {
            SearchTime = TextSearchProviderOptions.TextSearchBehavior.BeforeAIInvoke,
-            RecentMessageMemoryLimit = 4
+            RecentMessageMemoryLimit = 4,
+            RecentMessageRolesIncluded = [ChatRole.User, ChatRole.Assistant]
        };
        var provider = new TextSearchProvider(this.NoResultSearchAsync, options);
        var messages = new[]
@@ -507,7 +551,8 @@ public sealed class TextSearchProviderTests
        var initialProvider = new TextSearchProvider(this.NoResultSearchAsync, new TextSearchProviderOptions
        {
            SearchTime = TextSearchProviderOptions.TextSearchBehavior.BeforeAIInvoke,
-            RecentMessageMemoryLimit = 5
+            RecentMessageMemoryLimit = 5,
+            RecentMessageRolesIncluded = [ChatRole.User, ChatRole.Assistant]
        });
        var messages = new[]
        {