mirror of
https://github.com/microsoft/agent-framework.git
synced 2026-06-16 21:04:09 +08:00
Dmytro Struk
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<Project Sdk="Microsoft.NET.Sdk">
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<PropertyGroup>
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<OutputType>Exe</OutputType>
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<TargetFramework>net9.0</TargetFramework>
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<Nullable>enable</Nullable>
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<ImplicitUsings>enable</ImplicitUsings>
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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="Azure.AI.OpenAI" />
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<PackageReference Include="Azure.Identity" />
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<PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
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<PackageReference Include="Microsoft.SemanticKernel.Connectors.Qdrant" />
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</ItemGroup>
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<ItemGroup>
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<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
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</ItemGroup>
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</Project>
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// Copyright (c) Microsoft. All rights reserved.
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// This sample shows how to use Qdrant to add retrieval augmented generation (RAG) capabilities to an AI agent.
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// While the sample is using Qdrant, it can easily be replaced with any other vector store that implements the Microsoft.Extensions.VectorData abstractions.
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// The TextSearchProvider runs a search against the vector store before each model invocation and injects the results into the model context.
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using Azure.AI.OpenAI;
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using Azure.Identity;
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using Microsoft.Agents.AI;
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using Microsoft.Agents.AI.Data;
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using Microsoft.Extensions.AI;
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using Microsoft.Extensions.VectorData;
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using Microsoft.SemanticKernel.Connectors.Qdrant;
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using OpenAI;
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using Qdrant.Client;
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var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
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var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
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var embeddingDeploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME") ?? "text-embedding-3-large";
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var afOverviewUrl = "https://github.com/MicrosoftDocs/semantic-kernel-docs/blob/main/agent-framework/overview/agent-framework-overview.md";
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var afMigrationUrl = "https://raw.githubusercontent.com/MicrosoftDocs/semantic-kernel-docs/refs/heads/main/agent-framework/migration-guide/from-semantic-kernel/index.md";
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AzureOpenAIClient azureOpenAIClient = new(
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new Uri(endpoint),
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new AzureCliCredential());
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// Create a Qdrant vector store that uses the Azure OpenAI embedding model to generate embeddings.
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QdrantClient client = new("localhost");
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VectorStore vectorStore = new QdrantVectorStore(client, ownsClient: true, new()
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{
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EmbeddingGenerator = azureOpenAIClient.GetEmbeddingClient(embeddingDeploymentName).AsIEmbeddingGenerator()
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});
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// Create a collection and upsert some text into it.
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var documentationCollection = vectorStore.GetCollection<Guid, DocumentationChunk>("documentation");
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await documentationCollection.EnsureCollectionDeletedAsync(); // Clear out any data from previous runs.
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await documentationCollection.EnsureCollectionExistsAsync();
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await UploadDataFromMarkdown(afOverviewUrl, "Microsoft Agent Framework Overview", documentationCollection, 2000, 200);
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await UploadDataFromMarkdown(afMigrationUrl, "Semantic Kernel to Microsoft Agent Framework Migration Guide", documentationCollection, 2000, 200);
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// Create an adapter function that the TextSearchProvider can use to run searches against the collection.
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Func<string, CancellationToken, Task<IEnumerable<TextSearchProvider.TextSearchResult>>> SearchAdapter = async (text, ct) =>
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{
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List<TextSearchProvider.TextSearchResult> results = [];
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await foreach (var result in documentationCollection.SearchAsync(text, 5, cancellationToken: ct))
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{
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results.Add(new TextSearchProvider.TextSearchResult
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{
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SourceName = result.Record.SourceName,
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SourceLink = result.Record.SourceLink,
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Text = result.Record.Text ?? string.Empty,
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RawRepresentation = result
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});
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}
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return results;
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};
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// Configure the options for the TextSearchProvider.
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TextSearchProviderOptions textSearchOptions = new()
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{
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// Run the search prior to every model invocation.
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SearchTime = TextSearchProviderOptions.TextSearchBehavior.BeforeAIInvoke,
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// Use up to 4 recent messages when searching so that searches
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// still produce valuable results even when the user is referring
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// back to previous messages in their request.
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RecentMessageMemoryLimit = 5
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};
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// Create the AI agent with the TextSearchProvider as the AI context provider.
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AIAgent agent = azureOpenAIClient
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.GetChatClient(deploymentName)
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.CreateAIAgent(new ChatClientAgentOptions
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{
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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.",
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AIContextProviderFactory = ctx => new TextSearchProvider(SearchAdapter, ctx.SerializedState, ctx.JsonSerializerOptions, textSearchOptions)
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});
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AgentThread thread = agent.GetNewThread();
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Console.WriteLine(">> Asking about SK threads\n");
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Console.WriteLine(await agent.RunAsync("Hi! How do I create a thread in Semantic Kernel?", thread));
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// Here we are asking a very vague question when taken out of context,
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// but since we are including previous messages in our search using RecentMessageMemoryLimit
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// the RAG search should still produce useful results.
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Console.WriteLine("\n>> Asking about AF threads\n");
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Console.WriteLine(await agent.RunAsync("and in Agent Framework?", thread));
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Console.WriteLine("\n>> Contrasting Approaches\n");
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Console.WriteLine(await agent.RunAsync("Please contrast the two approaches", thread));
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Console.WriteLine("\n>> Asking about ancestry\n");
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Console.WriteLine(await agent.RunAsync("What are the predecessors to the Agent Framework?", thread));
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static async Task UploadDataFromMarkdown(string markdownUrl, string sourceName, VectorStoreCollection<Guid, DocumentationChunk> vectorStoreCollection, int chunkSize, int overlap)
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{
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// Download the markdown from the given url.
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using HttpClient client = new();
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var markdown = await client.GetStringAsync(new Uri(markdownUrl));
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// Chunk it into separate parts with some overlap between chunks
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var chunks = new List<DocumentationChunk>();
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for (int i = 0; i < markdown.Length; i += chunkSize)
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{
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var chunk = new DocumentationChunk
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{
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Key = Guid.NewGuid(),
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SourceLink = markdownUrl,
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SourceName = sourceName,
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Text = markdown.Substring(i, Math.Min(chunkSize + overlap, markdown.Length - i))
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};
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chunks.Add(chunk);
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}
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// Upsert each chunk into the provided vector store.
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await vectorStoreCollection.UpsertAsync(chunks);
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}
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// Data model that defines the database schema we want to use.
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internal sealed class DocumentationChunk
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{
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[VectorStoreKey]
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public Guid Key { get; set; }
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[VectorStoreData]
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public string SourceLink { get; set; } = string.Empty;
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[VectorStoreData]
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public string SourceName { get; set; } = string.Empty;
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[VectorStoreData]
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public string Text { get; set; } = string.Empty;
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[VectorStoreVector(Dimensions: 3072)]
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public string Embedding => this.Text;
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}
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+60
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# Agent Framework Retrieval Augmented Generation (RAG) with an external Vector Store with a custom schema
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This sample demonstrates how to create and run an agent that uses Retrieval Augmented Generation (RAG) with an external vector store.
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It also uses a custom schema for the documents stored in the vector store.
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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.
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## Prerequisites
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- .NET 8.0 SDK or later
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- Azure OpenAI service endpoint
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- Both a chat completion and embedding deployment configured in the Azure OpenAI resource
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- Azure CLI installed and authenticated (for Azure credential authentication)
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- User has the `Cognitive Services OpenAI Contributor` role for the Azure OpenAI resource.
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- 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.
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**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).
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**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).
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## Running the sample from the console
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Set the following environment variables:
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```powershell
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$env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/" # Replace with your Azure OpenAI resource endpoint
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$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini" # Optional, defaults to gpt-4o-mini
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$env:AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME="text-embedding-3-large" # Optional, defaults to text-embedding-3-large
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```
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If the variables are not set, you will be prompted for the values when running the samples.
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To use Qdrant in docker locally, start your Qdrant instance using the default port mappings.
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```powershell
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docker run -d --name qdrant -p 6333:6333 -p 6334:6334 qdrant/qdrant:latest
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```
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Execute the following command to build the sample:
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```powershell
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dotnet build
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```
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Execute the following command to run the sample:
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```powershell
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dotnet run --no-build
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```
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Or just build and run in one step:
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```powershell
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dotnet run
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```
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## Running the sample from Visual Studio
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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`.
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You will be prompted for any required environment variables if they are not already set.
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