agent-rag

title: Agent Retrieval Augmented Generation (RAG)
description: Learn how to use Retrieval Augmented Generation (RAG) with Agent Framework
zone_pivot_groups: programming-languages
author: westey-m
ms.topic: reference
ms.author: westey
ms.date: 11/11/2025
ms.service: agent-framework

Agent Retrieval Augmented Generation (RAG)

Microsoft Agent Framework supports adding Retrieval Augmented Generation (RAG) capabilities to agents easily by adding AI Context Providers to the agent.

::: zone pivot="programming-language-csharp"

Using TextSearchProvider

The `TextSearchProvider` class is an out-of-the-box implementation of a RAG context provider.

It can easily be attached to a `ChatClientAgent` using the `AIContextProviderFactory` option to provide RAG capabilities to the agent.

The factory is an async function that receives a context object and a cancellation token.

// Create the AI agent with the TextSearchProvider as the AI context provider.
AIAgent agent = azureOpenAIClient
    .GetChatClient(deploymentName)
    .AsAIAgent(new ChatClientAgentOptions
    {
        ChatOptions = new() { 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, ct) => new ValueTask<AIContextProvider>(
            new TextSearchProvider(SearchAdapter, ctx.SerializedState, ctx.JsonSerializerOptions, textSearchOptions))
    });

The `TextSearchProvider` requires a function that provides the search results given a query. This can be implemented using any search technology, e.g. Azure AI Search, or a web search engine.

Here is an example of a mock search function that returns pre-defined results based on the query. `SourceName` and `SourceLink` are optional, but if provided will be used by the agent to cite the source of the information when answering the user's question.

static Task<IEnumerable<TextSearchProvider.TextSearchResult>> SearchAdapter(string query, CancellationToken cancellationToken)
{
    // The mock search inspects the user's question and returns pre-defined snippets
    // that resemble documents stored in an external knowledge source.
    List<TextSearchProvider.TextSearchResult> results = new();

    if (query.Contains("return", StringComparison.OrdinalIgnoreCase) || query.Contains("refund", StringComparison.OrdinalIgnoreCase))
    {
        results.Add(new()
        {
            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."
        });
    }

    return Task.FromResult<IEnumerable<TextSearchProvider.TextSearchResult>>(results);
}

TextSearchProvider Options

The `TextSearchProvider` can be customized via the `TextSearchProviderOptions` class. Here is an example of creating options to run the search prior to every model invocation and keep a short rolling window of conversation context.

TextSearchProviderOptions textSearchOptions = new()
{
    // Run the search prior to every model invocation and keep a short rolling window of conversation context.
    SearchTime = TextSearchProviderOptions.TextSearchBehavior.BeforeAIInvoke,
    RecentMessageMemoryLimit = 6,
};

The `TextSearchProvider` class supports the following options via the `TextSearchProviderOptions` class.

| Option | Type | Description | Default | |--------|------|-------------|---------| | SearchTime | `TextSearchProviderOptions.TextSearchBehavior` | Indicates when the search should be executed. There are two options, each time the agent is invoked, or on-demand via function calling. | `TextSearchProviderOptions.TextSearchBehavior.BeforeAIInvoke` | | FunctionToolName | `string` | The name of the exposed search tool when operating in on-demand mode. | "Search" | | FunctionToolDescription | `string` | The description of the exposed search tool when operating in on-demand mode. | "Allows searching for additional information to help answer the user question." | | ContextPrompt | `string` | The context prompt prefixed to results when operating in `BeforeAIInvoke` mode. | "## Additional Context\nConsider the following information from source documents when responding to the user:" | | CitationsPrompt | `string` | The instruction appended after results to request citations when operating in `BeforeAIInvoke` mode. | "Include citations to the source document with document name and link if document name and link is available." | | ContextFormatter | `Func<IList<TextSearchProvider.TextSearchResult>, string>` | Optional delegate to fully customize formatting of the result list when operating in `BeforeAIInvoke` mode. If provided, `ContextPrompt` and `CitationsPrompt` are ignored. | `null` | | RecentMessageMemoryLimit | `int` | The number of recent conversation messages (both user and assistant) to keep in memory and include when constructing the search input for `BeforeAIInvoke` searches. | `0` (disabled) | | RecentMessageRolesIncluded | `List<ChatRole>` | The list of `ChatRole` types to filter recent messages to when deciding which recent messages to include when constructing the search input. | `ChatRole.User` |

::: zone-end ::: zone pivot="programming-language-python"

Using Semantic Kernel VectorStore with Agent Framework

Agent Framework supports using Semantic Kernel's VectorStore collections to provide RAG capabilities to agents. This is achieved through the bridge functionality that converts Semantic Kernel search functions into Agent Framework tools.

> [!IMPORTANT] > This feature requires `semantic-kernel` version 1.38 or higher.

Creating a Search Tool from VectorStore

The `create_search_function` method from a Semantic Kernel Vector