mirror of
https://github.com/microsoft/agent-framework.git
synced 2026-06-16 21:04:09 +08:00
Dmytro Struk
@@ -142,11 +142,11 @@ You:
|
||||
Besides the Aspire Dashboard and the Application Insights native UI, you can also use Grafana to visualize the telemetry data in Application Insights. There are two tailored dashboards for you to get started quickly:
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|
||||
### Agent Overview dashboard
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Open dashboard in Azure portal: <https://aka.ms/amg/dash/af-agent>
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Grafana Dashboard Gallery link: <https://aka.ms/amg/dash/af-agent>
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||||
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||||
### Workflow Overview dashboard
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Open dashboard in Azure portal: <https://aka.ms/amg/dash/af-workflow>
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Grafana Dashboard Gallery link: <https://aka.ms/amg/dash/af-workflow>
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## Key Features Demonstrated
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@@ -1,9 +0,0 @@
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# Agent Framework Retrieval Augmented Generation (RAG)
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||||
These samples show how to create an agent with the Agent Framework that uses Memory to remember previous conversations or facts from previous conversations.
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|Sample|Description|
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|---|---|
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||||
|[Chat History memory](./AgentWithMemory_Step01_ChatHistoryMemory/)|This sample demonstrates how to enable an agent to remember messages from previous conversations.|
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||||
|[Memory with MemoryStore](./AgentWithMemory_Step02_MemoryUsingMem0/)|This sample demonstrates how to create and run an agent that uses the Mem0 service to extract and retrieve individual memories.|
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|[Custom Memory Implementation](./AgentWithMemory_Step03_CustomMemory/)|This sample demonstrates how to create a custom memory component and attach it to an agent.|
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+1
-1
@@ -1,6 +1,6 @@
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// Copyright (c) Microsoft. All rights reserved.
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// This sample shows how to use Qdrant with a custom schema to add retrieval augmented generation (RAG) capabilities to an AI agent.
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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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@@ -5,5 +5,4 @@ These samples show how to create an agent with the Agent Framework that uses Ret
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|Sample|Description|
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|---|---|
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|[Basic Text RAG](./AgentWithRAG_Step01_BasicTextRAG/)|This sample demonstrates how to create and run a basic agent with simple text Retrieval Augmented Generation (RAG).|
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|[RAG with Vector Store and custom schema](./AgentWithRAG_Step02_CustomVectorStoreRAG/)|This sample demonstrates how to create and run an agent that uses Retrieval Augmented Generation (RAG) with a vector store. It also uses a custom schema for the documents stored in the vector store.|
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|[RAG with custom RAG data source](./AgentWithRAG_Step03_CustomRAGDataSource/)|This sample demonstrates how to create and run an agent that uses Retrieval Augmented Generation (RAG) with a custom RAG data source.|
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|[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.|
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+28
@@ -0,0 +1,28 @@
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<Project Sdk="Microsoft.NET.Sdk">
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||||
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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.Plugins.OpenApi" />
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</ItemGroup>
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||||
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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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||||
|
||||
<ItemGroup>
|
||||
<None Update="OpenAPISpec.json">
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||||
<CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
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||||
</None>
|
||||
</ItemGroup>
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||||
|
||||
</Project>
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+354
@@ -0,0 +1,354 @@
|
||||
{
|
||||
"openapi": "3.0.1",
|
||||
"info": {
|
||||
"title": "Github Versions API",
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"servers": [
|
||||
{
|
||||
"url": "https://api.github.com"
|
||||
}
|
||||
],
|
||||
"components": {
|
||||
"schemas": {
|
||||
"basic-error": {
|
||||
"title": "Basic Error",
|
||||
"description": "Basic Error",
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"message": {
|
||||
"type": "string"
|
||||
},
|
||||
"documentation_url": {
|
||||
"type": "string"
|
||||
},
|
||||
"url": {
|
||||
"type": "string"
|
||||
},
|
||||
"status": {
|
||||
"type": "string"
|
||||
}
|
||||
}
|
||||
},
|
||||
"label": {
|
||||
"title": "Label",
|
||||
"description": "Color-coded labels help you categorize and filter your issues (just like labels in Gmail).",
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"id": {
|
||||
"description": "Unique identifier for the label.",
|
||||
"type": "integer",
|
||||
"format": "int64",
|
||||
"example": 208045946
|
||||
},
|
||||
"node_id": {
|
||||
"type": "string",
|
||||
"example": "MDU6TGFiZWwyMDgwNDU5NDY="
|
||||
},
|
||||
"url": {
|
||||
"description": "URL for the label",
|
||||
"example": "https://api.github.com/repositories/42/labels/bug",
|
||||
"type": "string",
|
||||
"format": "uri"
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||||
},
|
||||
"name": {
|
||||
"description": "The name of the label.",
|
||||
"example": "bug",
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||||
"type": "string"
|
||||
},
|
||||
"description": {
|
||||
"description": "Optional description of the label, such as its purpose.",
|
||||
"type": "string",
|
||||
"example": "Something isn't working",
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"nullable": true
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||||
},
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||||
"color": {
|
||||
"description": "6-character hex code, without the leading #, identifying the color",
|
||||
"example": "FFFFFF",
|
||||
"type": "string"
|
||||
},
|
||||
"default": {
|
||||
"description": "Whether this label comes by default in a new repository.",
|
||||
"type": "boolean",
|
||||
"example": true
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||||
}
|
||||
},
|
||||
"required": [
|
||||
"id",
|
||||
"node_id",
|
||||
"url",
|
||||
"name",
|
||||
"description",
|
||||
"color",
|
||||
"default"
|
||||
]
|
||||
},
|
||||
"tag": {
|
||||
"title": "Tag",
|
||||
"description": "Tag",
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"name": {
|
||||
"type": "string",
|
||||
"example": "v0.1"
|
||||
},
|
||||
"commit": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"sha": {
|
||||
"type": "string"
|
||||
},
|
||||
"url": {
|
||||
"type": "string",
|
||||
"format": "uri"
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"sha",
|
||||
"url"
|
||||
]
|
||||
},
|
||||
"zipball_url": {
|
||||
"type": "string",
|
||||
"format": "uri",
|
||||
"example": "https://github.com/octocat/Hello-World/zipball/v0.1"
|
||||
},
|
||||
"tarball_url": {
|
||||
"type": "string",
|
||||
"format": "uri",
|
||||
"example": "https://github.com/octocat/Hello-World/tarball/v0.1"
|
||||
},
|
||||
"node_id": {
|
||||
"type": "string"
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"name",
|
||||
"node_id",
|
||||
"commit",
|
||||
"zipball_url",
|
||||
"tarball_url"
|
||||
]
|
||||
}
|
||||
},
|
||||
"examples": {
|
||||
"label-items": {
|
||||
"value": [
|
||||
{
|
||||
"id": 208045946,
|
||||
"node_id": "MDU6TGFiZWwyMDgwNDU5NDY=",
|
||||
"url": "https://api.github.com/repos/octocat/Hello-World/labels/bug",
|
||||
"name": "bug",
|
||||
"description": "Something isn't working",
|
||||
"color": "f29513",
|
||||
"default": true
|
||||
},
|
||||
{
|
||||
"id": 208045947,
|
||||
"node_id": "MDU6TGFiZWwyMDgwNDU5NDc=",
|
||||
"url": "https://api.github.com/repos/octocat/Hello-World/labels/enhancement",
|
||||
"name": "enhancement",
|
||||
"description": "New feature or request",
|
||||
"color": "a2eeef",
|
||||
"default": false
|
||||
}
|
||||
]
|
||||
},
|
||||
"tag-items": {
|
||||
"value": [
|
||||
{
|
||||
"name": "v0.1",
|
||||
"commit": {
|
||||
"sha": "c5b97d5ae6c19d5c5df71a34c7fbeeda2479ccbc",
|
||||
"url": "https://api.github.com/repos/octocat/Hello-World/commits/c5b97d5ae6c19d5c5df71a34c7fbeeda2479ccbc"
|
||||
},
|
||||
"zipball_url": "https://github.com/octocat/Hello-World/zipball/v0.1",
|
||||
"tarball_url": "https://github.com/octocat/Hello-World/tarball/v0.1",
|
||||
"node_id": "MDQ6VXNlcjE="
|
||||
}
|
||||
]
|
||||
}
|
||||
},
|
||||
"parameters": {
|
||||
"owner": {
|
||||
"name": "owner",
|
||||
"description": "The account owner of the repository. The name is not case sensitive.",
|
||||
"in": "path",
|
||||
"required": true,
|
||||
"schema": {
|
||||
"type": "string"
|
||||
}
|
||||
},
|
||||
"repo": {
|
||||
"name": "repo",
|
||||
"description": "The name of the repository without the `.git` extension. The name is not case sensitive.",
|
||||
"in": "path",
|
||||
"required": true,
|
||||
"schema": {
|
||||
"type": "string"
|
||||
}
|
||||
},
|
||||
"per-page": {
|
||||
"name": "per_page",
|
||||
"description": "The number of results per page (max 100). For more information, see \"[Using pagination in the REST API](https://docs.github.com/rest/using-the-rest-api/using-pagination-in-the-rest-api).\"",
|
||||
"in": "query",
|
||||
"schema": {
|
||||
"type": "integer",
|
||||
"default": 30
|
||||
}
|
||||
},
|
||||
"page": {
|
||||
"name": "page",
|
||||
"description": "The page number of the results to fetch. For more information, see \"[Using pagination in the REST API](https://docs.github.com/rest/using-the-rest-api/using-pagination-in-the-rest-api).\"",
|
||||
"in": "query",
|
||||
"schema": {
|
||||
"type": "integer",
|
||||
"default": 1
|
||||
}
|
||||
}
|
||||
},
|
||||
"responses": {
|
||||
"not_found": {
|
||||
"description": "Resource not found",
|
||||
"content": {
|
||||
"application/json": {
|
||||
"schema": {
|
||||
"$ref": "#/components/schemas/basic-error"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
"headers": {
|
||||
"link": {
|
||||
"example": "<https://api.github.com/resource?page=2>; rel=\"next\", <https://api.github.com/resource?page=5>; rel=\"last\"",
|
||||
"schema": {
|
||||
"type": "string"
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
"paths": {
|
||||
"/repos/{owner}/{repo}/tags": {
|
||||
"get": {
|
||||
"summary": "List repository tags",
|
||||
"description": "",
|
||||
"tags": [
|
||||
"repos"
|
||||
],
|
||||
"operationId": "repos/list-tags",
|
||||
"externalDocs": {
|
||||
"description": "API method documentation",
|
||||
"url": "https://docs.github.com/rest/repos/repos#list-repository-tags"
|
||||
},
|
||||
"parameters": [
|
||||
{
|
||||
"$ref": "#/components/parameters/owner"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/parameters/repo"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/parameters/per-page"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/parameters/page"
|
||||
}
|
||||
],
|
||||
"responses": {
|
||||
"200": {
|
||||
"description": "Response",
|
||||
"content": {
|
||||
"application/json": {
|
||||
"schema": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"$ref": "#/components/schemas/tag"
|
||||
}
|
||||
},
|
||||
"examples": {
|
||||
"default": {
|
||||
"$ref": "#/components/examples/tag-items"
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
"headers": {
|
||||
"Link": {
|
||||
"$ref": "#/components/headers/link"
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
"x-github": {
|
||||
"githubCloudOnly": false,
|
||||
"enabledForGitHubApps": true,
|
||||
"category": "repos",
|
||||
"subcategory": "repos"
|
||||
}
|
||||
}
|
||||
},
|
||||
"/repos/{owner}/{repo}/labels": {
|
||||
"get": {
|
||||
"summary": "List labels for a repository",
|
||||
"description": "Lists all labels for a repository.",
|
||||
"tags": [
|
||||
"issues"
|
||||
],
|
||||
"operationId": "issues/list-labels-for-repo",
|
||||
"externalDocs": {
|
||||
"description": "API method documentation",
|
||||
"url": "https://docs.github.com/rest/issues/labels#list-labels-for-a-repository"
|
||||
},
|
||||
"parameters": [
|
||||
{
|
||||
"$ref": "#/components/parameters/owner"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/parameters/repo"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/parameters/per-page"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/parameters/page"
|
||||
}
|
||||
],
|
||||
"responses": {
|
||||
"200": {
|
||||
"description": "Response",
|
||||
"content": {
|
||||
"application/json": {
|
||||
"schema": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"$ref": "#/components/schemas/label"
|
||||
}
|
||||
},
|
||||
"examples": {
|
||||
"default": {
|
||||
"$ref": "#/components/examples/label-items"
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
"headers": {
|
||||
"Link": {
|
||||
"$ref": "#/components/headers/link"
|
||||
}
|
||||
}
|
||||
},
|
||||
"404": {
|
||||
"$ref": "#/components/responses/not_found"
|
||||
}
|
||||
},
|
||||
"x-github": {
|
||||
"githubCloudOnly": false,
|
||||
"enabledForGitHubApps": true,
|
||||
"category": "issues",
|
||||
"subcategory": "labels"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
+33
@@ -0,0 +1,33 @@
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
// This sample demonstrates how to use a ChatClientAgent with function tools provided via an OpenAPI spec.
|
||||
// It uses functionality from Semantic Kernel to parse the OpenAPI spec and create function tools to use with the Agent Framework Agent.
|
||||
|
||||
using Azure.AI.OpenAI;
|
||||
using Azure.Identity;
|
||||
using Microsoft.Agents.AI;
|
||||
using Microsoft.Extensions.AI;
|
||||
using Microsoft.SemanticKernel;
|
||||
using Microsoft.SemanticKernel.Plugins.OpenApi;
|
||||
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";
|
||||
|
||||
// Load the OpenAPI Spec from a file.
|
||||
KernelPlugin plugin = await OpenApiKernelPluginFactory.CreateFromOpenApiAsync("github", "OpenAPISpec.json");
|
||||
|
||||
// Convert the Semantic Kernel plugin to Agent Framework function tools.
|
||||
// This requires a dummy Kernel instance, since KernelFunctions cannot execute without one.
|
||||
Kernel kernel = new();
|
||||
List<AITool> tools = plugin.Select(x => x.WithKernel(kernel)).Cast<AITool>().ToList();
|
||||
|
||||
// Create the chat client and agent, and provide the OpenAPI function tools to the agent.
|
||||
AIAgent agent = new AzureOpenAIClient(
|
||||
new Uri(endpoint),
|
||||
new AzureCliCredential())
|
||||
.GetChatClient(deploymentName)
|
||||
.CreateAIAgent(instructions: "You are a helpful assistant", tools: tools);
|
||||
|
||||
// Run the agent with the OpenAPI function tools.
|
||||
Console.WriteLine(await agent.RunAsync("Please list the names, colors and descriptions of all the labels available in the microsoft/agent-framework repository on github."));
|
||||
+3
-3
@@ -1,11 +1,11 @@
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
// This sample shows how to use TextSearchProvider to add retrieval augmented generation (RAG)
|
||||
// capabilities to an AI agent. This shows a mock implementation of a search function,
|
||||
// which can be replaced with any custom search logic to query any external knowledge base.
|
||||
// The provider invokes the custom search function
|
||||
// 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.
|
||||
|
||||
// Also see the AgentWithRAG folder for more advanced RAG scenarios.
|
||||
|
||||
using Azure.AI.OpenAI;
|
||||
using Azure.Identity;
|
||||
using Microsoft.Agents.AI;
|
||||
@@ -28,8 +28,8 @@ Before you begin, ensure you have the following prerequisites:
|
||||
|---|---|
|
||||
|[Running a simple agent](./Agent_Step01_Running/)|This sample demonstrates how to create and run a basic agent with instructions|
|
||||
|[Multi-turn conversation with a simple agent](./Agent_Step02_MultiturnConversation/)|This sample demonstrates how to implement a multi-turn conversation with a simple agent|
|
||||
|[Using function tools with a simple agent](./Agent_Step03_UsingFunctionTools/)|This sample demonstrates how to use function tools with a simple agent|
|
||||
|[Using OpenAPI function tools with a simple agent](https://github.com/microsoft/semantic-kernel/tree/main/dotnet/samples/AgentFrameworkMigration/AzureOpenAI/Step04_ToolCall_WithOpenAPI)|This sample demonstrates how to create function tools from an OpenAPI spec and use them with a simple agent (note that this sample is in the Semantic Kernel repository)|
|
||||
|[Using function tools with a simple agent](./Agent_Step03.1_UsingFunctionTools/)|This sample demonstrates how to use function tools with a simple agent|
|
||||
|[Using OpenAPI function tools with a simple agent](./Agent_Step03.2_UsingFunctionTools_FromOpenAPI/)|This sample demonstrates how to create function tools from an OpenAPI spec and use them with a simple agent|
|
||||
|[Using function tools with approvals](./Agent_Step04_UsingFunctionToolsWithApprovals/)|This sample demonstrates how to use function tools where approvals require human in the loop approvals before execution|
|
||||
|[Structured output with a simple agent](./Agent_Step05_StructuredOutput/)|This sample demonstrates how to use structured output with a simple agent|
|
||||
|[Persisted conversations with a simple agent](./Agent_Step06_PersistedConversations/)|This sample demonstrates how to persist conversations and reload them later. This is useful for cases where an agent is hosted in a stateless service|
|
||||
@@ -39,11 +39,14 @@ Before you begin, ensure you have the following prerequisites:
|
||||
|[Exposing a simple agent as MCP tool](./Agent_Step10_AsMcpTool/)|This sample demonstrates how to expose an agent as an MCP tool|
|
||||
|[Using images with a simple agent](./Agent_Step11_UsingImages/)|This sample demonstrates how to use image multi-modality with an AI agent|
|
||||
|[Exposing a simple agent as a function tool](./Agent_Step12_AsFunctionTool/)|This sample demonstrates how to expose an agent as a function tool|
|
||||
|[Background responses with tools and persistence](./Agent_Step13_BackgroundResponsesWithToolsAndPersistence/)|This sample demonstrates advanced background response scenarios including function calling during background operations and state persistence|
|
||||
|[Using memory with an agent](./Agent_Step13_Memory/)|This sample demonstrates how to create a simple memory component and use it with an agent|
|
||||
|[Using middleware with an agent](./Agent_Step14_Middleware/)|This sample demonstrates how to use middleware with an agent|
|
||||
|[Using plugins with an agent](./Agent_Step15_Plugins/)|This sample demonstrates how to use plugins with an agent|
|
||||
|[Reducing chat history size](./Agent_Step16_ChatReduction/)|This sample demonstrates how to reduce the chat history to constrain its size, where chat history is maintained locally|
|
||||
|[Background responses](./Agent_Step17_BackgroundResponses/)|This sample demonstrates how to use background responses for long-running operations with polling and resumption support|
|
||||
|[Adding RAG with text search](./Agent_Step18_TextSearchRag/)|This sample demonstrates how to enrich agent responses with retrieval augmented generation using the text search provider|
|
||||
|[Using Mem0-backed memory](./Agent_Step19_Mem0Provider/)|This sample demonstrates how to use the Mem0Provider to persist and recall memories across conversations|
|
||||
|[Background responses with tools and persistence](./Agent_Step20_BackgroundResponsesWithToolsAndPersistence/)|This sample demonstrates advanced background response scenarios including function calling during background operations and state persistence|
|
||||
|
||||
## Running the samples from the console
|
||||
|
||||
|
||||
@@ -64,14 +64,13 @@ internal static class Program
|
||||
return AgentWorkflowBuilder.BuildSequential(workflowName: key, agents: agents);
|
||||
}).AddAsAIAgent();
|
||||
|
||||
builder.Services.AddOpenAIResponses();
|
||||
builder.Services.AddOpenAIConversations();
|
||||
if (builder.Environment.IsDevelopment())
|
||||
{
|
||||
builder.AddDevUI();
|
||||
}
|
||||
|
||||
var app = builder.Build();
|
||||
|
||||
app.MapOpenAIResponses();
|
||||
app.MapOpenAIConversations();
|
||||
|
||||
if (builder.Environment.IsDevelopment())
|
||||
{
|
||||
app.MapDevUI();
|
||||
|
||||
-13
@@ -1,13 +0,0 @@
|
||||
{
|
||||
"profiles": {
|
||||
"DevUI_Step01_BasicUsage": {
|
||||
"commandName": "Project",
|
||||
"launchUrl": "devui",
|
||||
"launchBrowser": true,
|
||||
"environmentVariables": {
|
||||
"ASPNETCORE_ENVIRONMENT": "Development"
|
||||
},
|
||||
"applicationUrl": "https://localhost:50516;http://localhost:50518"
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -63,23 +63,17 @@ To add DevUI to your ASP.NET Core application:
|
||||
.AddAsAIAgent();
|
||||
```
|
||||
|
||||
3. Add OpenAI services and map the endpoints for OpenAI and DevUI:
|
||||
3. Add DevUI services and map the endpoint:
|
||||
```csharp
|
||||
// Register services for OpenAI responses and conversations (also required for DevUI)
|
||||
builder.Services.AddOpenAIResponses();
|
||||
builder.Services.AddOpenAIConversations();
|
||||
|
||||
builder.AddDevUI();
|
||||
var app = builder.Build();
|
||||
|
||||
// Map endpoints for OpenAI responses and conversations (also required for DevUI)
|
||||
|
||||
app.MapDevUI();
|
||||
|
||||
// Add required endpoints
|
||||
app.MapEntities();
|
||||
app.MapOpenAIResponses();
|
||||
app.MapOpenAIConversations();
|
||||
|
||||
if (builder.Environment.IsDevelopment())
|
||||
{
|
||||
// Map DevUI endpoint to /devui
|
||||
app.MapDevUI();
|
||||
}
|
||||
|
||||
app.Run();
|
||||
```
|
||||
|
||||
@@ -38,22 +38,19 @@ builder.Services.AddChatClient(chatClient);
|
||||
// Register your agents
|
||||
builder.AddAIAgent("my-agent", "You are a helpful assistant.");
|
||||
|
||||
// Register services for OpenAI responses and conversations (also required for DevUI)
|
||||
builder.Services.AddOpenAIResponses();
|
||||
builder.Services.AddOpenAIConversations();
|
||||
// Add DevUI services
|
||||
builder.AddDevUI();
|
||||
|
||||
var app = builder.Build();
|
||||
|
||||
// Map endpoints for OpenAI responses and conversations (also required for DevUI)
|
||||
// Map the DevUI endpoint
|
||||
app.MapDevUI();
|
||||
|
||||
// Add required endpoints
|
||||
app.MapEntities();
|
||||
app.MapOpenAIResponses();
|
||||
app.MapOpenAIConversations();
|
||||
|
||||
if (builder.Environment.IsDevelopment())
|
||||
{
|
||||
// Map DevUI endpoint to /devui
|
||||
app.MapDevUI();
|
||||
}
|
||||
|
||||
app.Run();
|
||||
```
|
||||
|
||||
|
||||
@@ -9,8 +9,6 @@ of the agent framework.
|
||||
|---|---|
|
||||
|[Agents](./Agents/README.md)|Step by step instructions for getting started with agents|
|
||||
|[Agent Providers](./AgentProviders/README.md)|Getting started with creating agents using various providers|
|
||||
|[Agents With Retrieval Augmented Generation (RAG)](./AgentWithRAG/README.md)|Adding Retrieval Augmented Generation (RAG) capabilities to your agents.|
|
||||
|[Agents With Memory](./AgentWithMemory/README.md)|Adding Memory capabilities to your agents.|
|
||||
|[A2A](./A2A/README.md)|Getting started with A2A (Agent-to-Agent) specific features|
|
||||
|[Agent Open Telemetry](./AgentOpenTelemetry/README.md)|Getting started with OpenTelemetry for agents|
|
||||
|[Agent With OpenAI exchange types](./AgentWithOpenAI/README.md)|Using OpenAI exchange types with agents|
|
||||
|
||||
+25
-10
@@ -16,7 +16,7 @@ internal static class WorkflowFactory
|
||||
internal static Workflow BuildWorkflow(IChatClient chatClient)
|
||||
{
|
||||
// Create executors
|
||||
var startExecutor = new ChatForwardingExecutor("Start");
|
||||
var startExecutor = new ConcurrentStartExecutor();
|
||||
var aggregationExecutor = new ConcurrentAggregationExecutor();
|
||||
AIAgent frenchAgent = GetLanguageAgent("French", chatClient);
|
||||
AIAgent englishAgent = GetLanguageAgent("English", chatClient);
|
||||
@@ -38,11 +38,33 @@ internal static class WorkflowFactory
|
||||
private static ChatClientAgent GetLanguageAgent(string targetLanguage, IChatClient chatClient) =>
|
||||
new(chatClient, instructions: $"You're a helpful assistant who always responds in {targetLanguage}.", name: $"{targetLanguage}Agent");
|
||||
|
||||
/// <summary>
|
||||
/// Executor that starts the concurrent processing by sending messages to the agents.
|
||||
/// </summary>
|
||||
private sealed class ConcurrentStartExecutor() : Executor("ConcurrentStartExecutor")
|
||||
{
|
||||
protected override RouteBuilder ConfigureRoutes(RouteBuilder routeBuilder)
|
||||
{
|
||||
return routeBuilder
|
||||
.AddHandler<List<ChatMessage>>(this.RouteMessages)
|
||||
.AddHandler<TurnToken>(this.RouteTurnTokenAsync);
|
||||
}
|
||||
|
||||
private ValueTask RouteMessages(List<ChatMessage> messages, IWorkflowContext context, CancellationToken cancellationToken)
|
||||
{
|
||||
return context.SendMessageAsync(messages, cancellationToken: cancellationToken);
|
||||
}
|
||||
|
||||
private ValueTask RouteTurnTokenAsync(TurnToken token, IWorkflowContext context, CancellationToken cancellationToken)
|
||||
{
|
||||
return context.SendMessageAsync(token, cancellationToken: cancellationToken);
|
||||
}
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Executor that aggregates the results from the concurrent agents.
|
||||
/// </summary>
|
||||
private sealed class ConcurrentAggregationExecutor() :
|
||||
Executor<List<ChatMessage>>("ConcurrentAggregationExecutor"), IResettableExecutor
|
||||
private sealed class ConcurrentAggregationExecutor() : Executor<List<ChatMessage>>("ConcurrentAggregationExecutor")
|
||||
{
|
||||
private readonly List<ChatMessage> _messages = [];
|
||||
|
||||
@@ -63,12 +85,5 @@ internal static class WorkflowFactory
|
||||
await context.YieldOutputAsync(formattedMessages, cancellationToken);
|
||||
}
|
||||
}
|
||||
|
||||
/// <inheritdoc/>
|
||||
public ValueTask ResetAsync()
|
||||
{
|
||||
this._messages.Clear();
|
||||
return default;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -42,7 +42,7 @@ internal sealed class Program
|
||||
Console.WriteLine(code);
|
||||
}
|
||||
|
||||
private const string DefaultWorkflow = "Marketing.yaml";
|
||||
private const string DefaultWorkflow = "HelloWorld.yaml";
|
||||
|
||||
private string WorkflowFile { get; }
|
||||
|
||||
|
||||
@@ -92,11 +92,11 @@ The repository has example workflows available in the root [`/workflow-samples`]
|
||||
2. Run the demo referencing a sample workflow by name:
|
||||
|
||||
```sh
|
||||
dotnet run Marketing
|
||||
dotnet run HelloWorld
|
||||
```
|
||||
|
||||
3. Run the demo with a path to any workflow file:
|
||||
|
||||
```sh
|
||||
dotnet run c:/myworkflows/Marketing.yaml
|
||||
dotnet run c:/myworkflows/HelloWorld.yaml
|
||||
```
|
||||
|
||||
Reference in New Issue
Block a user