mirror of https://github.com/dapr/docs.git
Merge pull request #4528 from hhunter-ms/quick-convo-update
[Conversation] Add HTTP python and js tabs to the quickstart
This commit is contained in:
Hannah Hunter
GitHub
commit
021bc388e1
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@ -10,7 +10,7 @@ no_list: true
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Hit the ground running with our Dapr quickstarts, complete with code samples aimed to get you started quickly with Dapr.
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{{% alert title="Note" color="primary" %}}
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We are actively working on adding to our quickstart library. In the meantime, you can explore Dapr through our [tutorials]({{< ref "getting-started/tutorials/_index.md" >}}).
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Each release, the quickstart library has new examples added for the APIs and SDKs. You can also explore Dapr through the [tutorials]({{< ref "getting-started/tutorials/_index.md" >}}).
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{{% /alert %}}
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@ -33,4 +33,4 @@ Hit the ground running with our Dapr quickstarts, complete with code samples aim
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| [Resiliency]({{< ref resiliency >}}) | Define and apply fault-tolerance policies to your Dapr API requests. |
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| [Cryptography]({{< ref cryptography-quickstart.md >}}) | Encrypt and decrypt data using Dapr's cryptographic APIs. |
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| [Jobs]({{< ref jobs-quickstart.md >}}) | Schedule, retrieve, and delete jobs using Dapr's jobs APIs. |
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| [Conversation]({{< ref conversation-quickstart.md >}}) | Securely and reliably interact with Large Language Models (LLMs). |
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@ -17,10 +17,276 @@ You can try out this conversation quickstart by either:
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- [Running the application in this sample with the Multi-App Run template file]({{< ref "#run-the-app-with-the-template-file" >}}), or
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- [Running the application without the template]({{< ref "#run-the-app-without-the-template" >}})
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{{% alert title="Note" color="primary" %}}
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Currently, only the HTTP quickstart sample is available in Python and JavaScript.
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{{% /alert %}}
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## Run the app with the template file
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{{< tabs ".NET" Go >}}
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{{< tabs Python JavaScript ".NET" Go >}}
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<!-- Python -->
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{{% codetab %}}
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### Step 1: Pre-requisites
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For this example, you will need:
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- [Dapr CLI and initialized environment](https://docs.dapr.io/getting-started).
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- [Python 3.7+ installed](https://www.python.org/downloads/).
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<!-- IGNORE_LINKS -->
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- [Docker Desktop](https://www.docker.com/products/docker-desktop)
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<!-- END_IGNORE -->
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### Step 2: Set up the environment
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Clone the [sample provided in the Quickstarts repo](https://github.com/dapr/quickstarts/tree/master/conversation).
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```bash
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git clone https://github.com/dapr/quickstarts.git
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```
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From the root of the Quickstarts directory, navigate into the conversation directory:
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```bash
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cd conversation/python/http/conversation
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```
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Install the dependencies:
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```bash
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pip3 install -r requirements.txt
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```
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### Step 3: Launch the conversation service
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Navigate back to the `http` directory and start the conversation service with the following command:
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```bash
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dapr run -f .
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```
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**Expected output**
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```
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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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### What happened?
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When you ran `dapr init` during Dapr install, the [`dapr.yaml` Multi-App Run template file]({{< ref "#dapryaml-multi-app-run-template-file" >}}) was generated in the `.dapr/components` directory.
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Running `dapr run -f .` in this Quickstart started [conversation.go]({{< ref "#programcs-conversation-app" >}}).
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#### `dapr.yaml` Multi-App Run template file
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Running the [Multi-App Run template file]({{< ref multi-app-dapr-run >}}) with `dapr run -f .` starts all applications in your project. This Quickstart has only one application, so the `dapr.yaml` file contains the following:
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```yml
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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"]
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```
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#### Echo mock LLM component
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In [`conversation/components`](https://github.com/dapr/quickstarts/tree/master/conversation/components) directly of the quickstart, the [`conversation.yaml` file](https://github.com/dapr/quickstarts/tree/master/conversation/components/conversation.yml) configures the echo LLM component.
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```yml
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apiVersion: dapr.io/v1alpha1
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kind: Component
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metadata:
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name: echo
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spec:
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type: conversation.echo
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version: v1
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```
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To interface with a real LLM, swap out the mock component with one of [the supported conversation components]({{< ref "supported-conversation" >}}). For example, to use an OpenAI component, see the [example in the conversation how-to guide]({{< ref "howto-conversation-layer.md#use-the-openai-component" >}})
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#### `app.py` conversation app
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In the application code:
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- The app sends an input "What is dapr?" to the echo mock LLM component.
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- The mock LLM echoes "What is dapr?".
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```python
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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)
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```
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{{% /codetab %}}
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<!-- JavaScript -->
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{{% codetab %}}
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### Step 1: Pre-requisites
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For this example, you will need:
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- [Dapr CLI and initialized environment](https://docs.dapr.io/getting-started).
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- [Latest Node.js installed](https://nodejs.org/).
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<!-- IGNORE_LINKS -->
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- [Docker Desktop](https://www.docker.com/products/docker-desktop)
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<!-- END_IGNORE -->
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### Step 2: Set up the environment
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Clone the [sample provided in the Quickstarts repo](https://github.com/dapr/quickstarts/tree/master/conversation).
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```bash
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git clone https://github.com/dapr/quickstarts.git
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```
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From the root of the Quickstarts directory, navigate into the conversation directory:
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```bash
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cd conversation/javascript/http/conversation
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```
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Install the dependencies:
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```bash
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npm install
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```
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### Step 3: Launch the conversation service
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Navigate back to the `http` directory and start the conversation service with the following command:
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```bash
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dapr run -f .
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```
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**Expected output**
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```
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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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### What happened?
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When you ran `dapr init` during Dapr install, the [`dapr.yaml` Multi-App Run template file]({{< ref "#dapryaml-multi-app-run-template-file" >}}) was generated in the `.dapr/components` directory.
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Running `dapr run -f .` in this Quickstart started [conversation.go]({{< ref "#programcs-conversation-app" >}}).
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#### `dapr.yaml` Multi-App Run template file
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Running the [Multi-App Run template file]({{< ref multi-app-dapr-run >}}) with `dapr run -f .` starts all applications in your project. This Quickstart has only one application, so the `dapr.yaml` file contains the following:
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```yml
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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"]
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```
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#### Echo mock LLM component
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In [`conversation/components`](https://github.com/dapr/quickstarts/tree/master/conversation/components) directly of the quickstart, the [`conversation.yaml` file](https://github.com/dapr/quickstarts/tree/master/conversation/components/conversation.yml) configures the echo LLM component.
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```yml
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apiVersion: dapr.io/v1alpha1
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kind: Component
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metadata:
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name: echo
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spec:
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type: conversation.echo
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version: v1
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```
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To interface with a real LLM, swap out the mock component with one of [the supported conversation components]({{< ref "supported-conversation" >}}). For example, to use an OpenAI component, see the [example in the conversation how-to guide]({{< ref "howto-conversation-layer.md#use-the-openai-component" >}})
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#### `index.js` conversation app
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In the application code:
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- The app sends an input "What is dapr?" to the echo mock LLM component.
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- The mock LLM echoes "What is dapr?".
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```javascript
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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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});
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```
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{{% /codetab %}}
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<!-- .NET -->
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{{% codetab %}}
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@ -282,7 +548,111 @@ func main() {
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## Run the app without the template
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{{< tabs ".NET" Go >}}
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{{< tabs Python JavaScript ".NET" Go >}}
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<!-- Python -->
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{{% codetab %}}
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### Step 1: Pre-requisites
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For this example, you will need:
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- [Dapr CLI and initialized environment](https://docs.dapr.io/getting-started).
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- [Python 3.7+ installed](https://www.python.org/downloads/).
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<!-- IGNORE_LINKS -->
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- [Docker Desktop](https://www.docker.com/products/docker-desktop)
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<!-- END_IGNORE -->
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### Step 2: Set up the environment
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Clone the [sample provided in the Quickstarts repo](https://github.com/dapr/quickstarts/tree/master/conversation).
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```bash
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git clone https://github.com/dapr/quickstarts.git
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```
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From the root of the Quickstarts directory, navigate into the conversation directory:
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```bash
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cd conversation/python/http/conversation
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```
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Install the dependencies:
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```bash
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pip3 install -r requirements.txt
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```
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### Step 3: Launch the conversation service
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Navigate back to the `http` directory and start the conversation service with the following command:
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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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> **Note**: Since Python3.exe is not defined in Windows, you may need to use `python app.py` instead of `python3 app.py`.
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**Expected output**
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```
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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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{{% /codetab %}}
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<!-- JavaScript -->
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{{% codetab %}}
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### Step 1: Pre-requisites
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For this example, you will need:
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- [Dapr CLI and initialized environment](https://docs.dapr.io/getting-started).
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- [Latest Node.js installed](https://nodejs.org/).
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<!-- IGNORE_LINKS -->
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- [Docker Desktop](https://www.docker.com/products/docker-desktop)
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<!-- END_IGNORE -->
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### Step 2: Set up the environment
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Clone the [sample provided in the Quickstarts repo](https://github.com/dapr/quickstarts/tree/master/conversation).
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```bash
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git clone https://github.com/dapr/quickstarts.git
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```
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From the root of the Quickstarts directory, navigate into the conversation directory:
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```bash
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cd conversation/javascript/http/conversation
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```
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Install the dependencies:
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```bash
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npm install
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```
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### Step 3: Launch the conversation service
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Navigate back to the `http` directory and start the conversation service with the following command:
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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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**Expected output**
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```
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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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{{% /codetab %}}
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<!-- .NET -->
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{{% codetab %}}
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Loading…
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