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Csharp conversation quickstarts
Signed-off-by: Alice Gibbons <alice@diagrid.io>
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‎conversation/csharp/http/README.md

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# Dapr Conversation API (C# HTTP)
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In this quickstart, you'll send an input to a mock Large Language Model (LLM) using Dapr's Conversation API. This API is responsible for providing one consistent API entry point to talk to underlying LLM providers.
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Visit [this](https://v1-15.docs.dapr.io/developing-applications/building-blocks/conversation/conversation-overview/) link for more information about Dapr and the Conversation API.
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> **Note:** This example leverages HTTP `requests` only. If you are looking for the example using the Dapr Client SDK (recommended) [click here](../sdk/).
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This quickstart includes one app:
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- Conversation, responsible for sending an input to the underlying LLM and retrieving an output.
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## Run the app with the template file
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This section shows how to run the application using the [multi-app run template file](https://docs.dapr.io/developing-applications/local-development/multi-app-dapr-run/multi-app-overview/) and Dapr CLI with `dapr run -f .`.
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This example uses the default LLM Component provided by Dapr which simply echoes the input provided, for testing purposes. Integrate with popular LLM models by using one of the other [supported conversation components](https://v1-15.docs.dapr.io/reference/components-reference/supported-conversation/).
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Open a new terminal window and run the multi app run template:
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<!-- STEP
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name: Run multi app run template
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expected_stdout_lines:
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- '== APP - conversation == Input sent: What is dapr?'
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- '== APP - conversation == Output response: What is dapr?'
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expected_stderr_lines:
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output_match_mode: substring
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match_order: none
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background: true
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sleep: 15
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timeout_seconds: 15
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-->
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```bash
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dapr run -f .
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```
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The terminal console output should look similar to this, where:
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- The app sends an input `What is dapr?` to the `echo` Component mock LLM.
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- The mock LLM echoes `What is dapr?`.
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```text
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== APP - conversation == Input sent: What is dapr?
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== APP - conversation == Output response: What is dapr?
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```
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<!-- END_STEP -->
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2. Stop and clean up application processes.
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<!-- STEP
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name: Stop multi-app run
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-->
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```bash
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dapr stop -f .
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```
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<!-- END_STEP -->
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## Run the app individually
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1. Open a terminal and run the `conversation` app. Build the dependencies if you haven't already.
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```bash
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cd ./conversation
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dotnet build
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```
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2. Run the Dapr process alongside the application.
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```bash
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dapr run --app-id conversation --resources-path ../../../components/ -- dotnet run
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```
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The terminal console output should look similar to this, where:
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- The app sends an input `What is dapr?` to the `echo` Component mock LLM.
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- The mock LLM echoes `What is dapr?`.
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```text
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== APP - conversation == Input sent: What is dapr?
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== APP - conversation == Output response: What is dapr?
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```

‎conversation/csharp/http/conversation/Program.cs

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/*
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Copyright 2024 The Dapr Authors
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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*/
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using System.Net.Http;
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using System.Text.Json;
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using System.Text;
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class Program
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{
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private const string ConversationComponentName = "echo";
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static async Task Main(string[] args)
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{
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var daprHost = Environment.GetEnvironmentVariable("DAPR_HOST") ?? "http://localhost";
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var daprHttpPort = Environment.GetEnvironmentVariable("DAPR_HTTP_PORT") ?? "3500";
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var client = new HttpClient
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{
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Timeout = TimeSpan.FromSeconds(15)
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};
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var inputBody = new
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{
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name = "echo",
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inputs = new[] { new { message = "What is dapr?" } },
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parameters = new { },
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metadata = new { }
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};
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var daprUrl = $"{daprHost}:{daprHttpPort}/v1.0-alpha1/conversation/{ConversationComponentName}/converse";
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try
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{
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var content = new StringContent(JsonSerializer.Serialize(inputBody), Encoding.UTF8, "application/json");
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// Send a request to the echo mock LLM component
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var response = await client.PostAsync(daprUrl, content);
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response.EnsureSuccessStatusCode();
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Console.WriteLine("Input sent: " + inputBody.inputs[0].message);
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var responseBody = await response.Content.ReadAsStringAsync();
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// Parse the response
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var data = JsonSerializer.Deserialize<Dictionary<string, List<Dictionary<string, string>>>>(responseBody);
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var result = data?["outputs"]?[0]?["result"];
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Console.WriteLine("Output response: " + result);
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}
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catch (Exception ex)
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{
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Console.WriteLine("Error: " + ex.Message);
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}
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}
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}

‎conversation/csharp/http/conversation/Program.csproj

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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>net8.0</TargetFramework>
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<ImplicitUsings>enable</ImplicitUsings>
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<Nullable>enable</Nullable>
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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="System.Text.Json" Version="9.0.1" />
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</ItemGroup>
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</Project>

‎conversation/csharp/http/dapr.yaml

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version: 1
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common:
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resourcesPath: ../../components/
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apps:
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- appDirPath: ./conversation/
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appID: conversation
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daprHTTPPort: 3500
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command: ["dotnet", "run"]

‎conversation/csharp/http/makefile

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include ../../../docker.mk
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include ../../../validate.mk

‎conversation/csharp/sdk/README.md

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# Dapr Conversation API (C# SDK)
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In this quickstart, you'll send an input to a mock Large Language Model (LLM) using Dapr's Conversation API. This API is responsible for providing one consistent API entry point to talk to underlying LLM providers.
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Visit [this](https://v1-15.docs.dapr.io/developing-applications/building-blocks/conversation/conversation-overview/) link for more information about Dapr and the Conversation API.
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> **Note:** This example leverages the Dapr SDK. If you are looking for the example using the HTTP API [click here](../http/).
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This quickstart includes one app:
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- Conversation, responsible for sending an input to the underlying LLM and retrieving an output.
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## Run the app with the template file
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This section shows how to run the application using the [multi-app run template file](https://docs.dapr.io/developing-applications/local-development/multi-app-dapr-run/multi-app-overview/) and Dapr CLI with `dapr run -f .`.
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This example uses the default LLM Component provided by Dapr which simply echoes the input provided, for testing purposes. Integrate with popular LLM models by using one of the other [supported conversation components](https://v1-15.docs.dapr.io/reference/components-reference/supported-conversation/).
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Open a new terminal window and run the multi app run template:
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<!-- STEP
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name: Run multi app run template
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expected_stdout_lines:
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- '== APP - conversation == Input sent: What is dapr?'
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- '== APP - conversation == Output response: What is dapr?'
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expected_stderr_lines:
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output_match_mode: substring
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match_order: none
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background: true
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sleep: 15
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timeout_seconds: 15
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-->
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```bash
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dapr run -f .
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```
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The terminal console output should look similar to this, where:
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- The app sends an input `What is dapr?` to the `echo` Component mock LLM.
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- The mock LLM echoes `What is dapr?`.
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```text
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== APP - conversation == Input sent: What is dapr?
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== APP - conversation == Output response: What is dapr?
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```
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<!-- END_STEP -->
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2. Stop and clean up application processes.
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<!-- STEP
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name: Stop multi-app run
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-->
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```bash
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dapr stop -f .
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```
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<!-- END_STEP -->
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## Run the app individually
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1. Open a terminal and run the `conversation` app. Build the dependencies if you haven't already.
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```bash
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cd ./conversation
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dotnet build
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```
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2. Run the Dapr process alongside the application.
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```bash
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dapr run --app-id conversation --resources-path ../../../components/ -- dotnet run
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```
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The terminal console output should look similar to this, where:
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- The app sends an input `What is dapr?` to the `echo` Component mock LLM.
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- The mock LLM echoes `What is dapr?`.
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```text
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== APP - conversation == Input sent: What is dapr?
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== APP - conversation == Output response: What is dapr?
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```

‎conversation/csharp/sdk/conversation/Program.cs

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/*
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Copyright 2024 The Dapr Authors
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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*/
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using Dapr.AI.Conversation;
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using Dapr.AI.Conversation.Extensions;
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class Program
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{
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private const string ConversationComponentName = "echo";
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static async Task Main(string[] args)
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{
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const string prompt = "What is dapr?";
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var builder = WebApplication.CreateBuilder(args);
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builder.Services.AddDaprConversationClient();
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var app = builder.Build();
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//Instantiate Dapr Conversation Client
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var conversationClient = app.Services.GetRequiredService<DaprConversationClient>();
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try
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{
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// Send a request to the echo mock LLM component
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var response = await conversationClient.ConverseAsync(ConversationComponentName, [new(prompt, DaprConversationRole.Generic)]);
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Console.WriteLine("Input sent: " + prompt);
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if (response != null)
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{
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Console.Write("Output response:");
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foreach (var resp in response.Outputs)
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{
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Console.WriteLine($" {resp.Result}");
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}
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}
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}
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catch (Exception ex)
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{
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Console.WriteLine("Error: " + ex.Message);
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}
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}
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}

‎conversation/csharp/sdk/conversation/Program.csproj

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<Project Sdk="Microsoft.NET.Sdk.Web">
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<PropertyGroup>
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<OutputType>Exe</OutputType>
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<TargetFramework>net8.0</TargetFramework>
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<ImplicitUsings>enable</ImplicitUsings>
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<Nullable>enable</Nullable>
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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="Dapr.AI" Version="1.15.0-rc02" />
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</ItemGroup>
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</Project>

‎conversation/csharp/sdk/dapr.yaml

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version: 1
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common:
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resourcesPath: ../../components/
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apps:
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- appDirPath: ./conversation/
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appID: conversation
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daprHTTPPort: 3500
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command: ["dotnet", "run"]

‎conversation/csharp/sdk/makefile

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include ../../../docker.mk
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include ../../../validate.mk

‎conversation/go/http/README.md

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Visit [this](https://v1-15.docs.dapr.io/developing-applications/building-blocks/conversation/conversation-overview/) link for more information about Dapr and the Conversation API.
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> **Note:** This example leverages HTTP `requests` only. If you are looking for the example using the Dapr Client SDK (recommended) [click here](../sdk/).
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> **Note:** This example leverages HTTP `requests` only. If you are looking for the example using the Dapr Client SDK (recommended) [click here](../sdk/).
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This quickstart includes one app:
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- `conversation.go`, responsible for sending and input to the underlying LLM and retrieving an output.
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- `conversation.go`, responsible for sending an input to the underlying LLM and retrieving an output.
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## Run the app with the template file
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<!-- END_STEP -->
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2. Stop and clean up application processes
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2. Stop and clean up application processes.
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<!-- STEP
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name: Stop multi-app run

‎conversation/go/sdk/README.md

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@@ -4,11 +4,11 @@ In this quickstart, you'll send an input to a mock Large Language Model (LLM) us
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Visit [this](https://v1-15.docs.dapr.io/developing-applications/building-blocks/conversation/conversation-overview/) link for more information about Dapr and the Conversation API.
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> **Note:** This example leverages the Dapr SDK. If you are looking for the example using the HTTP API [click here](../http/).
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> **Note:** This example leverages the Dapr SDK. If you are looking for the example using the HTTP API [click here](../http/).
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This quickstart includes one app:
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- `conversation.go`, responsible for sending and input to the underlying LLM and retrieving an output.
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- `conversation.go`, responsible for sending an input to the underlying LLM and retrieving an output.
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## Run the app with the template file
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<!-- END_STEP -->
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2. Stop and clean up application processes
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2. Stop and clean up application processes.
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<!-- STEP
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name: Stop multi-app run

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