Merge branch 'release-1.15' into jobs-python

.github/env/global.env

@@ -1,5 +1,5 @@
-DAPR_CLI_VERSION: 1.15.0-rc.2
-DAPR_RUNTIME_VERSION: 1.15.0-rc.5
+DAPR_CLI_VERSION: 1.15.0-rc.5
+DAPR_RUNTIME_VERSION: 1.15.0-rc.10
 DAPR_INSTALL_URL: https://raw.githubusercontent.com/dapr/cli/v${DAPR_CLI_VERSION}/install/
 DAPR_DEFAULT_IMAGE_REGISTRY: ghcr
 

bindings/python/sdk/batch/requirements.txt

@@ -1,4 +1,4 @@
-dapr>=1.14.0a,<1.15.0
+dapr>=1.15.0rc2
 Flask
 typing-extensions
 werkzeug>=3.0.3 # not directly required, pinned by Snyk to avoid a vulnerability

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

@@ -1,2 +1,2 @@
-dapr>=1.14.0a,<1.15.0
+dapr>=1.15.0rc2
 typing-extensions

conversation/javascript/http/README.md

@@ -0,0 +1,99 @@
+# Dapr Conversation API (JS 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:
+
+- `index.js`, 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 Node dependencies for conversation
+-->
+
+```bash
+cd ./conversation
+npm install
+```
+
+<!-- END_STEP -->
+
+2. Open a new terminal window and run the multi app run template:
+
+<!-- STEP
+name: Run multi app run template
+expected_stdout_lines:
+  - '== APP - conversation == Input sent: What is dapr?'
+  - '== APP - conversation == 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 -->
+
+3. Stop and clean up application processes.
+
+<!-- STEP
+name: Stop multi-app run
+sleep: 5
+-->
+
+```bash
+dapr stop -f .
+```
+
+<!-- END_STEP -->
+
+## Run the app individually
+
+1. Open a terminal and navigate to the `conversation` app. Install the dependencies if you haven't already.
+
+```bash
+cd ./conversation
+npm install
+```
+
+2. Run the Dapr process alongside the application.
+
+```bash
+dapr run --app-id conversation --resources-path ../../../components/ -- npm run start
+```
+
+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?
+```

conversation/javascript/http/conversation/index.js

@@ -0,0 +1,39 @@
+const conversationComponentName = "echo";
+
+async function main() {
+  const daprHost = process.env.DAPR_HOST || "http://localhost";
+  const daprHttpPort = process.env.DAPR_HTTP_PORT || "3500";
+
+  const inputBody = {
+    name: "echo",
+    inputs: [{ message: "What is dapr?" }],
+    parameters: {},
+    metadata: {},
+  };
+
+  const reqURL = `${daprHost}:${daprHttpPort}/v1.0-alpha1/conversation/${conversationComponentName}/converse`;
+
+  try {
+    const response = await fetch(reqURL, {
+      method: "POST",
+      headers: {
+        "Content-Type": "application/json",
+      },
+      body: JSON.stringify(inputBody),
+    });
+
+    console.log("Input sent: What is dapr?");
+
+    const data = await response.json();
+    const result = data.outputs[0].result;
+    console.log("Output response:", result);
+  } catch (error) {
+    console.error("Error:", error.message);
+    process.exit(1);
+  }
+}
+
+main().catch((error) => {
+  console.error("Unhandled error:", error);
+  process.exit(1);
+});

conversation/javascript/http/conversation/package.json

@@ -0,0 +1,14 @@
+{
+  "name": "conversation",
+  "version": "1.0.0",
+  "description": "",
+  "main": "index.js",
+  "type": "module",
+  "scripts": {
+    "start": "node index.js",
+    "start:dapr": "dapr run --app-id conversation --resources-path ../../../components/ -- npm start"
+  },
+  "keywords": [],
+  "author": "",
+  "license": "ISC"
+}

conversation/javascript/http/dapr.yaml

@@ -0,0 +1,8 @@
+version: 1
+common:
+  resourcesPath: ../../components/
+apps:
+  - appID: conversation
+    appDirPath: ./conversation/
+    daprHTTPPort: 3502
+    command: ["npm", "run", "start"]

conversation/javascript/http/makefile

@@ -0,0 +1,2 @@
+include ../../../docker.mk
+include ../../../validate.mk

conversation/python/http/README.md

@@ -0,0 +1,97 @@
+# 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

@@ -0,0 +1,31 @@
+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

@@ -0,0 +1 @@
+requests

conversation/python/http/dapr.yaml

@@ -0,0 +1,7 @@
+version: 1
+common:
+  resourcesPath: ../../components/
+apps:
+  - appID: conversation
+    appDirPath: ./conversation/
+    command: ["python3", "app.py"]

conversation/python/http/makefile

@@ -0,0 +1,2 @@
+include ../../../docker.mk
+include ../../../validate.mk

cryptography/python/sdk/README.md

@@ -60,7 +60,7 @@ expected_stdout_lines:
   - '== APP == Encrypted the message, got 856 bytes'
   - '== APP == Decrypted the message, got 24 bytes'
   - '== APP == The secret is "passw0rd"'
-  - '== APP == Wrote decrypted data to encrypted.out'
+  - '== APP == Wrote encrypted data to encrypted.out'
   - '== APP == Wrote decrypted data to decrypted.out.jpg'
   - "Exited App successfully"
 expected_stderr_lines: