mirror of
https://github.com/microsoft/agent-framework.git
synced 2026-06-16 21:04:09 +08:00
.NET: Add simple rag sample for catalog (#1834)
* add simple rag sample for catalog * Update dotnet/samples/Catalog/AgentWithTextSearchRag/AgentWithTextSearchRag.csproj Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
This commit is contained in:
SergeyMenshykh
committed by
GitHub
Unverified
GitHub
parent
b25b0af49b
commit
0b843d2b3e
@@ -137,6 +137,7 @@
|
||||
<Project Path="samples/GettingStarted/Workflows/_Foundational/07_MixedWorkflowAgentsAndExecutors/07_MixedWorkflowAgentsAndExecutors.csproj" />
|
||||
</Folder>
|
||||
<Folder Name="/Samples/Catalog/">
|
||||
<Project Path="samples/Catalog/AgentWithTextSearchRag/AgentWithTextSearchRag.csproj" />
|
||||
<Project Path="samples/Catalog/AgentsInWorkflows/AgentsInWorkflows.csproj" />
|
||||
<Project Path="samples/Catalog/DeepResearchAgent/DeepResearchAgent.csproj" />
|
||||
</Folder>
|
||||
|
||||
@@ -0,0 +1,21 @@
|
||||
<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" />
|
||||
</ItemGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
|
||||
</ItemGroup>
|
||||
|
||||
</Project>
|
||||
@@ -0,0 +1,82 @@
|
||||
// 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 provider runs a search against an external knowledge base
|
||||
// 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 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";
|
||||
|
||||
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,
|
||||
};
|
||||
|
||||
AIAgent agent = new AzureOpenAIClient(
|
||||
new Uri(endpoint),
|
||||
new AzureCliCredential())
|
||||
.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 = _ => new TextSearchProvider(MockSearchAsync, 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));
|
||||
|
||||
static Task<IEnumerable<TextSearchProvider.TextSearchResult>> MockSearchAsync(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()
|
||||
{
|
||||
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."
|
||||
});
|
||||
}
|
||||
|
||||
if (query.Contains("shipping", StringComparison.OrdinalIgnoreCase))
|
||||
{
|
||||
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."
|
||||
});
|
||||
}
|
||||
|
||||
if (query.Contains("tent", StringComparison.OrdinalIgnoreCase) || query.Contains("fabric", StringComparison.OrdinalIgnoreCase))
|
||||
{
|
||||
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."
|
||||
});
|
||||
}
|
||||
|
||||
return Task.FromResult<IEnumerable<TextSearchProvider.TextSearchResult>>(results);
|
||||
}
|
||||
@@ -0,0 +1,41 @@
|
||||
# What this sample demonstrates
|
||||
|
||||
This sample demonstrates how to use TextSearchProvider to add retrieval augmented generation (RAG) capabilities to an AI agent. The provider runs a search against an external knowledge base before each model invocation and injects the results into the model context.
|
||||
|
||||
Key features:
|
||||
- Configuring TextSearchProvider with custom search behavior
|
||||
- Running searches before AI invocations to provide relevant context
|
||||
- Managing conversation memory with a rolling window approach
|
||||
- Citing source documents in AI responses
|
||||
|
||||
## Prerequisites
|
||||
|
||||
Before running this sample, ensure you have:
|
||||
|
||||
1. An Azure OpenAI endpoint configured
|
||||
2. A deployment of a chat model (e.g., gpt-4o-mini)
|
||||
3. Azure CLI installed and authenticated
|
||||
|
||||
## Environment Variables
|
||||
|
||||
Set the following environment variables:
|
||||
|
||||
```powershell
|
||||
# Replace with your Azure OpenAI endpoint
|
||||
$env:AZURE_OPENAI_ENDPOINT="https://your-openai-resource.openai.azure.com/"
|
||||
|
||||
# Optional, defaults to gpt-4o-mini
|
||||
$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"
|
||||
```
|
||||
|
||||
## How It Works
|
||||
|
||||
The sample uses a mock search function that demonstrates the RAG pattern:
|
||||
|
||||
1. When the user asks a question, the TextSearchProvider intercepts it
|
||||
2. The search function looks for relevant documents based on the query
|
||||
3. Retrieved documents are injected into the model's context
|
||||
4. The AI responds using both its training and the provided context
|
||||
5. The agent can cite specific source documents in its answers
|
||||
|
||||
The mock search function returns pre-defined snippets for demonstration purposes. In a production scenario, you would replace this with actual searches against your knowledge base (e.g., Azure AI Search, vector database, etc.).
|
||||
Reference in New Issue
Block a user