[.Net] fix #3014 by adding local model function call in dotnet website (

#3044)

* add instruction in ollama-litellm function call example

* add tutorial

* fix tests

dotnet/sample/AutoGen.BasicSamples/Example13_OpenAIAgent_JsonMode.cs

@@ -1,68 +1,3 @@
 // Copyright (c) Microsoft Corporation. All rights reserved.
 // Example13_OpenAIAgent_JsonMode.cs
-
-using System.Text.Json;
-using System.Text.Json.Serialization;
-using AutoGen.Core;
-using AutoGen.OpenAI;
-using AutoGen.OpenAI.Extension;
-using Azure.AI.OpenAI;
-using FluentAssertions;
-
-namespace AutoGen.BasicSample;
-
-public class Example13_OpenAIAgent_JsonMode
-{
-    public static async Task RunAsync()
-    {
-        #region create_agent
-        var config = LLMConfiguration.GetAzureOpenAIGPT3_5_Turbo(deployName: "gpt-35-turbo"); // json mode only works with 0125 and later model.
-        var apiKey = config.ApiKey;
-        var endPoint = new Uri(config.Endpoint);
-
-        var openAIClient = new OpenAIClient(endPoint, new Azure.AzureKeyCredential(apiKey));
-        var openAIClientAgent = new OpenAIChatAgent(
-            openAIClient: openAIClient,
-            name: "assistant",
-            modelName: config.DeploymentName,
-            systemMessage: "You are a helpful assistant designed to output JSON.",
-            seed: 0, // explicitly set a seed to enable deterministic output
-            responseFormat: ChatCompletionsResponseFormat.JsonObject) // set response format to JSON object to enable JSON mode
-            .RegisterMessageConnector()
-            .RegisterPrintMessage();
-        #endregion create_agent
-
-        #region chat_with_agent
-        var reply = await openAIClientAgent.SendAsync("My name is John, I am 25 years old, and I live in Seattle.");
-
-        var person = JsonSerializer.Deserialize<Person>(reply.GetContent());
-        Console.WriteLine($"Name: {person.Name}");
-        Console.WriteLine($"Age: {person.Age}");
-
-        if (!string.IsNullOrEmpty(person.Address))
-        {
-            Console.WriteLine($"Address: {person.Address}");
-        }
-
-        Console.WriteLine("Done.");
-        #endregion chat_with_agent
-
-        person.Name.Should().Be("John");
-        person.Age.Should().Be(25);
-        person.Address.Should().BeNullOrEmpty();
-    }
-}
-
-#region person_class
-public class Person
-{
-    [JsonPropertyName("name")]
-    public string Name { get; set; }
-
-    [JsonPropertyName("age")]
-    public int Age { get; set; }
-
-    [JsonPropertyName("address")]
-    public string Address { get; set; }
-}
-#endregion person_class
+// this example has been moved to https://github.com/microsoft/autogen/blob/main/dotnet/sample/AutoGen.OpenAI.Sample/Use_Json_Mode.cs

dotnet/sample/AutoGen.BasicSamples/Example16_OpenAIChatAgent_ConnectToThirdPartyBackend.cs

@@ -1,62 +1,3 @@
 // Copyright (c) Microsoft Corporation. All rights reserved.
 // Example16_OpenAIChatAgent_ConnectToThirdPartyBackend.cs
-#region using_statement
-using AutoGen.Core;
-using AutoGen.OpenAI;
-using AutoGen.OpenAI.Extension;
-using Azure.AI.OpenAI;
-using Azure.Core.Pipeline;
-#endregion using_statement
-
-namespace AutoGen.BasicSample;
-
-#region CustomHttpClientHandler
-public sealed class CustomHttpClientHandler : HttpClientHandler
-{
-    private string _modelServiceUrl;
-
-    public CustomHttpClientHandler(string modelServiceUrl)
-    {
-        _modelServiceUrl = modelServiceUrl;
-    }
-
-    protected override Task<HttpResponseMessage> SendAsync(HttpRequestMessage request, CancellationToken cancellationToken)
-    {
-        request.RequestUri = new Uri($"{_modelServiceUrl}{request.RequestUri.PathAndQuery}");
-
-        return base.SendAsync(request, cancellationToken);
-    }
-}
-#endregion CustomHttpClientHandler
-
-public class Example16_OpenAIChatAgent_ConnectToThirdPartyBackend
-{
-    public static async Task RunAsync()
-    {
-        #region create_agent
-        using var client = new HttpClient(new CustomHttpClientHandler("http://localhost:11434"));
-        var option = new OpenAIClientOptions(OpenAIClientOptions.ServiceVersion.V2024_04_01_Preview)
-        {
-            Transport = new HttpClientTransport(client),
-        };
-
-        // api-key is not required for local server
-        // so you can use any string here
-        var openAIClient = new OpenAIClient("api-key", option);
-        var model = "llama3";
-
-        var agent = new OpenAIChatAgent(
-            openAIClient: openAIClient,
-            name: "assistant",
-            modelName: model,
-            systemMessage: "You are a helpful assistant designed to output JSON.",
-            seed: 0)
-            .RegisterMessageConnector()
-            .RegisterPrintMessage();
-        #endregion create_agent
-
-        #region send_message
-        await agent.SendAsync("Can you write a piece of C# code to calculate 100th of fibonacci?");
-        #endregion send_message
-    }
-}
+// this example has been moved to https://github.com/microsoft/autogen/blob/main/dotnet/sample/AutoGen.OpenAI.Sample/Connect_To_Ollama.cs

dotnet/sample/AutoGen.OpenAI.Sample/Tool_Call_With_Ollama_And_LiteLLM.cs

@@ -8,6 +8,7 @@
 
 namespace AutoGen.OpenAI.Sample;
 
+#region Function
 public partial class Function
 {
     [Function]
@@ -16,25 +17,37 @@ public async Task<string> GetWeatherAsync(string city)
         return await Task.FromResult("The weather in " + city + " is 72 degrees and sunny.");
     }
 }
+#endregion Function
+
 public class Tool_Call_With_Ollama_And_LiteLLM
 {
     public static async Task RunAsync()
     {
-        #region Create_Agent
-        var liteLLMUrl = "http://localhost:4000";
-        using var httpClient = new HttpClient(new CustomHttpClientHandler(liteLLMUrl));
-        var option = new OpenAIClientOptions(OpenAIClientOptions.ServiceVersion.V2024_04_01_Preview)
-        {
-            Transport = new HttpClientTransport(httpClient),
-        };
+        // Before running this code, make sure you have
+        // - Ollama:
+        //  - Install dolphincoder:latest in Ollama
+        //  - Ollama running on http://localhost:11434
+        // - LiteLLM
+        //  - Install LiteLLM
+        //  - Start LiteLLM with the following command:
+        //    - litellm --model ollama_chat/dolphincoder --port 4000
 
+        # region Create_tools
         var functions = new Function();
         var functionMiddleware = new FunctionCallMiddleware(
             functions: [functions.GetWeatherAsyncFunctionContract],
             functionMap: new Dictionary<string, Func<string, Task<string>>>
             {
                 { functions.GetWeatherAsyncFunctionContract.Name!, functions.GetWeatherAsyncWrapper },
             });
+        #endregion Create_tools
+        #region Create_Agent
+        var liteLLMUrl = "http://localhost:4000";
+        using var httpClient = new HttpClient(new CustomHttpClientHandler(liteLLMUrl));
+        var option = new OpenAIClientOptions(OpenAIClientOptions.ServiceVersion.V2024_04_01_Preview)
+        {
+            Transport = new HttpClientTransport(httpClient),
+        };
 
         // api-key is not required for local server
         // so you can use any string here
@@ -43,7 +56,7 @@ public static async Task RunAsync()
         var agent = new OpenAIChatAgent(
             openAIClient: openAIClient,
             name: "assistant",
-            modelName: "placeholder",
+            modelName: "dolphincoder:latest",
             systemMessage: "You are a helpful AI assistant")
             .RegisterMessageConnector()
             .RegisterMiddleware(functionMiddleware)

dotnet/sample/AutoGen.OpenAI.Sample/Use_Json_Mode.cs

@@ -0,0 +1,67 @@
+// Copyright (c) Microsoft Corporation. All rights reserved.
+// Example13_OpenAIAgent_JsonMode.cs
+
+using System.Text.Json;
+using System.Text.Json.Serialization;
+using AutoGen.Core;
+using AutoGen.OpenAI;
+using AutoGen.OpenAI.Extension;
+using Azure.AI.OpenAI;
+using FluentAssertions;
+
+namespace AutoGen.BasicSample;
+
+public class Use_Json_Mode
+{
+    public static async Task RunAsync()
+    {
+        #region create_agent
+        var apiKey = Environment.GetEnvironmentVariable("OPENAI_API_KEY") ?? throw new Exception("Please set OPENAI_API_KEY environment variable.");
+        var model = "gpt-3.5-turbo";
+
+        var openAIClient = new OpenAIClient(apiKey);
+        var openAIClientAgent = new OpenAIChatAgent(
+            openAIClient: openAIClient,
+            name: "assistant",
+            modelName: model,
+            systemMessage: "You are a helpful assistant designed to output JSON.",
+            seed: 0, // explicitly set a seed to enable deterministic output
+            responseFormat: ChatCompletionsResponseFormat.JsonObject) // set response format to JSON object to enable JSON mode
+            .RegisterMessageConnector()
+            .RegisterPrintMessage();
+        #endregion create_agent
+
+        #region chat_with_agent
+        var reply = await openAIClientAgent.SendAsync("My name is John, I am 25 years old, and I live in Seattle.");
+
+        var person = JsonSerializer.Deserialize<Person>(reply.GetContent());
+        Console.WriteLine($"Name: {person.Name}");
+        Console.WriteLine($"Age: {person.Age}");
+
+        if (!string.IsNullOrEmpty(person.Address))
+        {
+            Console.WriteLine($"Address: {person.Address}");
+        }
+
+        Console.WriteLine("Done.");
+        #endregion chat_with_agent
+
+        person.Name.Should().Be("John");
+        person.Age.Should().Be(25);
+        person.Address.Should().BeNullOrEmpty();
+    }
+}
+
+#region person_class
+public class Person
+{
+    [JsonPropertyName("name")]
+    public string Name { get; set; }
+
+    [JsonPropertyName("age")]
+    public int Age { get; set; }
+
+    [JsonPropertyName("address")]
+    public string Address { get; set; }
+}
+#endregion person_class

dotnet/test/AutoGen.OpenAI.Tests/AutoGen.OpenAI.Tests.csproj

@@ -8,6 +8,7 @@
   </PropertyGroup>
 
   <ItemGroup>
+    <ProjectReference Include="..\..\sample\AutoGen.OpenAI.Sample\AutoGen.OpenAI.Sample.csproj" />
     <ProjectReference Include="..\..\src\AutoGen.SourceGenerator\AutoGen.SourceGenerator.csproj" OutputItemType="Analyzer" ReferenceOutputAssembly="false" />
     <ProjectReference Include="..\..\src\AutoGen\AutoGen.csproj" />
     <ProjectReference Include="..\AutoGen.Tests\AutoGen.Tests.csproj" />

dotnet/test/AutoGen.Tests/BasicSampleTest.cs

@@ -37,11 +37,6 @@ public async Task AgentFunctionCallTestAsync()
             await Example03_Agent_FunctionCall.RunAsync();
         }
 
-        [ApiKeyFact("AZURE_OPENAI_API_KEY", "AZURE_OPENAI_ENDPOINT", "AZURE_OPENAI_DEPLOY_NAME")]
-        public async Task OpenAIAgent_JsonMode()
-        {
-            await Example13_OpenAIAgent_JsonMode.RunAsync();
-        }
 
         [ApiKeyFact("MISTRAL_API_KEY")]
         public async Task MistralClientAgent_TokenCount()

dotnet/website/articles/Function-call-with-ollama-and-litellm.md

@@ -0,0 +1,93 @@
+This example shows how to use function call with local LLM models where [Ollama](https://ollama.com/) as local model provider and [LiteLLM](https://docs.litellm.ai/docs/) proxy server which provides an openai-api compatible interface.
+
+[![](https://img.shields.io/badge/Open%20on%20Github-grey?logo=github)](https://github.com/microsoft/autogen/blob/main/dotnet/sample/AutoGen.OpenAI.Sample/Tool_Call_With_Ollama_And_LiteLLM.cs)
+
+To run this example, the following prerequisites are required:
+- Install [Ollama](https://ollama.com/) and [LiteLLM](https://docs.litellm.ai/docs/) on your local machine.
+- A local model that supports function call. In this example `dolphincoder:latest` is used.
+
+## Install Ollama and pull `dolphincoder:latest` model
+First, install Ollama by following the instructions on the [Ollama website](https://ollama.com/).
+
+After installing Ollama, pull the `dolphincoder:latest` model by running the following command:
+```bash
+ollama pull dolphincoder:latest
+```
+
+## Install LiteLLM and start the proxy server
+
+You can install LiteLLM by following the instructions on the [LiteLLM website](https://docs.litellm.ai/docs/).
+```bash
+pip install 'litellm[proxy]'
+```
+
+Then, start the proxy server by running the following command:
+
+```bash
+litellm --model ollama_chat/dolphincoder --port 4000
+```
+
+This will start an openai-api compatible proxy server at `http://localhost:4000`. You can verify if the server is running by observing the following output in the terminal:
+
+```bash
+#------------------------------------------------------------#
+#                                                            #
+#         'The worst thing about this product is...'          #
+#        https://github.com/BerriAI/litellm/issues/new        #
+#                                                            #
+#------------------------------------------------------------#
+
+INFO:     Application startup complete.
+INFO:     Uvicorn running on http://0.0.0.0:4000 (Press CTRL+C to quit)
+```
+
+## Install AutoGen and AutoGen.SourceGenerator
+In your project, install the AutoGen and AutoGen.SourceGenerator package using the following command:
+
+```bash
+dotnet add package AutoGen
+dotnet add package AutoGen.SourceGenerator
+```
+
+The `AutoGen.SourceGenerator` package is used to automatically generate type-safe `FunctionContract` instead of manually defining them. For more information, please check out [Create type-safe function](Create-type-safe-function-call.md).
+
+And in your project file, enable structural xml document support by setting the `GenerateDocumentationFile` property to `true`:
+
+```xml
+<PropertyGroup>
+    <!-- This enables structural xml document support -->
+    <GenerateDocumentationFile>true</GenerateDocumentationFile>
+</PropertyGroup>
+```
+
+## Define `WeatherReport` function and create @AutoGen.Core.FunctionCallMiddleware
+
+Create a `public partial` class to host the methods you want to use in AutoGen agents. The method has to be a `public` instance method and its return type must be `Task<string>`. After the methods are defined, mark them with `AutoGen.Core.FunctionAttribute` attribute.
+
+[!code-csharp[Define WeatherReport function](../../sample/AutoGen.OpenAI.Sample/Tool_Call_With_Ollama_And_LiteLLM.cs?name=Function)]
+
+Then create a @AutoGen.Core.FunctionCallMiddleware and add the `WeatherReport` function to the middleware. The middleware will pass the `FunctionContract` to the agent when generating a response, and process the tool call response when receiving a `ToolCallMessage`.
+[!code-csharp[Define WeatherReport function](../../sample/AutoGen.OpenAI.Sample/Tool_Call_With_Ollama_And_LiteLLM.cs?name=Create_tools)]
+
+## Create @AutoGen.OpenAI.OpenAIChatAgent with `GetWeatherReport` tool and chat with it
+
+Because LiteLLM proxy server is openai-api compatible, we can use @AutoGen.OpenAI.OpenAIChatAgent to connect to it as a third-party openai-api provider. The agent is also registered with a @AutoGen.Core.FunctionCallMiddleware which contains the `WeatherReport` tool. Therefore, the agent can call the `WeatherReport` tool when generating a response.
+
+[!code-csharp[Create an agent with tools](../../sample/AutoGen.OpenAI.Sample/Tool_Call_With_Ollama_And_LiteLLM.cs?name=Create_Agent)]
+
+The reply from the agent will similar to the following:
+```bash
+AggregateMessage from assistant
+--------------------
+ToolCallMessage:
+ToolCallMessage from assistant
+--------------------
+- GetWeatherAsync: {"city": "new york"}
+--------------------
+
+ToolCallResultMessage:
+ToolCallResultMessage from assistant
+--------------------
+- GetWeatherAsync: The weather in new york is 72 degrees and sunny.
+--------------------
+```