Python Conversation http Quickstart

conversation/python/http/README.md

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+# Dapr Conversation API (Python HTTP)
+
+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.
+
+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.
+
+> **Note:** This example leverages HTTP `requests` only.
+
+This quickstart includes one app:
+
+- `app.py`, responsible for sending an input to the underlying LLM and retrieving an output.
+
+## Run the app with the template file
+
+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 .`.  
+
+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/).
+
+1. Install dependencies:
+
+<!-- STEP
+name: Install Python dependencies
+-->
+
+```bash
+cd ./conversation
+pip3 install -r requirements.txt
+cd ..
+```
+
+2. Open a new terminal window and run the multi app run template:
+
+<!-- STEP
+name: Run multi app run template
+expected_stdout_lines:
+  - '== APP == INFO:root:Input sent: What is dapr?'
+  - '== APP == INFO:root:Output response: What is dapr?'
+expected_stderr_lines:
+output_match_mode: substring
+match_order: none
+background: true
+sleep: 15
+timeout_seconds: 30
+-->
+
+```bash
+dapr run -f .
+```
+
+The terminal console output should look similar to this, where:
+
+- The app sends an input `What is dapr?` to the `echo` Component mock LLM.
+- The mock LLM echoes `What is dapr?`.
+
+```text
+== APP - conversation == Input sent: What is dapr?
+== APP - conversation == Output response: What is dapr?
+```
+
+<!-- END_STEP -->
+
+2. Stop and clean up application processes.
+
+<!-- STEP
+name: Stop multi-app run 
+sleep: 5
+-->
+
+```bash
+dapr stop -f .
+```
+
+<!-- END_STEP -->
+
+## Run the app with the Dapr CLI
+
+1. Install dependencies:
+
+Open a terminal and run:
+
+```bash
+cd ./conversation
+pip3 install -r requirements.txt
+```
+
+2. Run the application:
+
+```bash
+dapr run --app-id conversation --resources-path ../../../components -- python3 app.py
+```
+
+You should see the output:
+
+```bash
+== APP == INFO:root:Input sent: What is dapr?
+== APP == INFO:root:Output response: What is dapr?
+```

conversation/python/http/conversation/app.py

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+import logging
+import requests
+import os
+
+logging.basicConfig(level=logging.INFO)
+
+base_url = os.getenv('BASE_URL', 'http://localhost') + ':' + os.getenv(
+                    'DAPR_HTTP_PORT', '3500')
+
+CONVERSATION_COMPONENT_NAME = 'echo'
+
+input = {
+		'name': 'echo',
+		'inputs': [{'message':'What is dapr?'}],
+		'parameters': {},
+		'metadata': {}
+    }
+
+# Send input to conversation endpoint
+result = requests.post(
+	url='%s/v1.0-alpha1/conversation/%s/converse' % (base_url, CONVERSATION_COMPONENT_NAME),
+	json=input
+)
+
+logging.info('Input sent: What is dapr?')
+
+# Parse conversation output
+data = result.json()
+output = data["outputs"][0]["result"]
+
+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
+common:
+  resourcesPath: ../../components/
+apps:
+  - appID: conversation
+    appDirPath: ./conversation/
+    command: ["python3", "app.py"]

conversation/python/http/makefile

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