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‎.github/env/global.env

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DAPR_CLI_VERSION: 1.15.0-rc.2
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DAPR_RUNTIME_VERSION: 1.15.0-rc.5
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DAPR_CLI_VERSION: 1.15.0-rc.5
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DAPR_RUNTIME_VERSION: 1.15.0-rc.10
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DAPR_INSTALL_URL: https://raw.githubusercontent.com/dapr/cli/v${DAPR_CLI_VERSION}/install/
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DAPR_DEFAULT_IMAGE_REGISTRY: ghcr
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‎bindings/python/sdk/batch/requirements.txt

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dapr>=1.14.0a,<1.15.0
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dapr>=1.15.0rc2
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Flask
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typing-extensions
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werkzeug>=3.0.3 # not directly required, pinned by Snyk to avoid a vulnerability

‎configuration/python/sdk/order-processor/requirements.txt

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dapr>=1.14.0a,<1.15.0
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dapr>=1.15.0rc2
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typing-extensions

‎conversation/javascript/http/README.md

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# Dapr Conversation API (JS 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.
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This quickstart includes one app:
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- `index.js`, 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 files](https://docs.dapr.io/developing-applications/local-development/multi-app-dapr-run/multi-app-overview/) 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. Here are other [supported Conversation components](https://v1-15.docs.dapr.io/reference/components-reference/supported-conversation/).
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1. Install dependencies:
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<!-- STEP
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name: Install Node dependencies for conversation
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-->
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```bash
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cd ./conversation
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npm install
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```
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<!-- END_STEP -->
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2. 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: 30
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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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3. Stop and clean up application processes.
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<!-- STEP
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name: Stop multi-app run
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sleep: 5
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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 navigate to the `conversation` app. Install the dependencies if you haven't already.
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```bash
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cd ./conversation
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npm install
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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/ -- npm run start
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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/javascript/http/conversation/index.js

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const conversationComponentName = "echo";
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async function main() {
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const daprHost = process.env.DAPR_HOST || "http://localhost";
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const daprHttpPort = process.env.DAPR_HTTP_PORT || "3500";
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const inputBody = {
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name: "echo",
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inputs: [{ message: "What is dapr?" }],
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parameters: {},
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metadata: {},
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};
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const reqURL = `${daprHost}:${daprHttpPort}/v1.0-alpha1/conversation/${conversationComponentName}/converse`;
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try {
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const response = await fetch(reqURL, {
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method: "POST",
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headers: {
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"Content-Type": "application/json",
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},
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body: JSON.stringify(inputBody),
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});
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console.log("Input sent: What is dapr?");
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const data = await response.json();
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const result = data.outputs[0].result;
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console.log("Output response:", result);
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} catch (error) {
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console.error("Error:", error.message);
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process.exit(1);
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}
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}
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main().catch((error) => {
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console.error("Unhandled error:", error);
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process.exit(1);
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});

‎conversation/javascript/http/conversation/package-lock.json

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‎conversation/javascript/http/conversation/package.json

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{
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"name": "conversation",
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"version": "1.0.0",
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"description": "",
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"main": "index.js",
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"type": "module",
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"scripts": {
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"start": "node index.js",
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"start:dapr": "dapr run --app-id conversation --resources-path ../../../components/ -- npm start"
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},
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"keywords": [],
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"author": "",
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"license": "ISC"
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}

‎conversation/javascript/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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- appID: conversation
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appDirPath: ./conversation/
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daprHTTPPort: 3502
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command: ["npm", "run", "start"]

‎conversation/javascript/http/makefile

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

‎conversation/python/http/README.md

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# Dapr Conversation API (Python 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.
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This quickstart includes one app:
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- `app.py`, 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 files](https://docs.dapr.io/developing-applications/local-development/multi-app-dapr-run/multi-app-overview/) 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. Here are other [supported Conversation components](https://v1-15.docs.dapr.io/reference/components-reference/supported-conversation/).
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1. Install dependencies:
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<!-- STEP
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name: Install Python dependencies
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-->
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```bash
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cd ./conversation
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pip3 install -r requirements.txt
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cd ..
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```
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2. 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 == INFO:root:Input sent: What is dapr?'
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- '== APP == INFO:root: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: 30
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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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sleep: 5
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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 with the Dapr CLI
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1. Install dependencies:
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Open a terminal and run:
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```bash
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cd ./conversation
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pip3 install -r requirements.txt
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```
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2. Run the application:
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```bash
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dapr run --app-id conversation --resources-path ../../../components -- python3 app.py
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```
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You should see the output:
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```bash
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== APP == INFO:root:Input sent: What is dapr?
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== APP == INFO:root:Output response: What is dapr?
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```

‎conversation/python/http/conversation/app.py

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import logging
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import requests
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import os
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logging.basicConfig(level=logging.INFO)
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base_url = os.getenv('BASE_URL', 'http://localhost') + ':' + os.getenv(
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'DAPR_HTTP_PORT', '3500')
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CONVERSATION_COMPONENT_NAME = 'echo'
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input = {
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'name': 'echo',
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'inputs': [{'message':'What is dapr?'}],
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'parameters': {},
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'metadata': {}
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}
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# Send input to conversation endpoint
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result = requests.post(
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url='%s/v1.0-alpha1/conversation/%s/converse' % (base_url, CONVERSATION_COMPONENT_NAME),
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json=input
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)
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logging.info('Input sent: What is dapr?')
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# Parse conversation output
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data = result.json()
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output = data["outputs"][0]["result"]
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logging.info('Output response: ' + output)

‎conversation/python/http/conversation/requirements.txt

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requests

‎conversation/python/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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- appID: conversation
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appDirPath: ./conversation/
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command: ["python3", "app.py"]

‎conversation/python/http/makefile

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

‎cryptography/python/sdk/README.md

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- '== APP == Encrypted the message, got 856 bytes'
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- '== APP == Decrypted the message, got 24 bytes'
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- '== APP == The secret is "passw0rd"'
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- '== APP == Wrote decrypted data to encrypted.out'
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- '== APP == Wrote encrypted data to encrypted.out'
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- '== APP == Wrote decrypted data to decrypted.out.jpg'
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- "Exited App successfully"
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expected_stderr_lines:

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