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Python: Add Durabletask samples and minor fixes (#3157)
* Add samples and minor fixes * Add redis sample and wait-for-completion * Add wait-for-completion support * ADd missing docs
This commit is contained in:
Laveesh Rohra
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3df916064c
@@ -1,6 +1,6 @@
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# Single Agent Sample
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# Single Agent
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This sample demonstrates how to use the durable agents extension to create a worker-client setup that hosts a single AI agent and provides interactive conversation via the Durable Task Scheduler.
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This sample demonstrates how to create a worker-client setup that hosts a single AI agent and provides interactive conversation via the Durable Task Scheduler.
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## Key Concepts Demonstrated
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@@ -15,18 +15,24 @@ See the [README.md](../README.md) file in the parent directory for more informat
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## Running the Sample
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With the environment setup, you can run the sample using separate worker and client processes:
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With the environment setup, you can run the sample using the combined approach or separate worker and client processes:
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**Start the worker:**
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**Option 1: Combined (Recommended for Testing)**
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```bash
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cd samples/getting_started/durabletask/01_single_agent
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python sample.py
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```
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**Option 2: Separate Processes**
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Start the worker in one terminal:
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```bash
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python worker.py
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```
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The worker will register the Joker agent and listen for requests.
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**In a new terminal, run the client:**
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In a new terminal, run the client:
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```bash
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python client.py
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@@ -58,9 +64,10 @@ Because light attracts bugs!
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You can view the state of the agent in the Durable Task Scheduler dashboard:
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1. Open your browser and navigate to `http://localhost:8082`
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2. In the dashboard, you can view the state of the Joker agent, including its conversation history and current state
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The agent maintains conversation state across multiple interactions, and you can inspect this state in the dashboard to understand how the durable agents extension manages conversation context.
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2. In the dashboard, you can view:
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- The state of the Joker agent entity (dafx-Joker)
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- Conversation history and current state
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- How the durable agents extension manages conversation context
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@@ -23,69 +23,96 @@ logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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async def main() -> None:
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"""Main entry point for the client application."""
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logger.info("Starting Durable Task Agent Client...")
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def get_client(
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taskhub: str | None = None,
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endpoint: str | None = None,
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log_handler: logging.Handler | None = None
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) -> DurableAIAgentClient:
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"""Create a configured DurableAIAgentClient.
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# Get environment variables for taskhub and endpoint with defaults
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taskhub_name = os.getenv("TASKHUB", "default")
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endpoint = os.getenv("ENDPOINT", "http://localhost:8080")
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logger.info(f"Using taskhub: {taskhub_name}")
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logger.info(f"Using endpoint: {endpoint}")
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logger.info("")
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# Set credential to None for emulator, or DefaultAzureCredential for Azure
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credential = None if endpoint == "http://localhost:8080" else DefaultAzureCredential()
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Args:
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taskhub: Task hub name (defaults to TASKHUB env var or "default")
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endpoint: Scheduler endpoint (defaults to ENDPOINT env var or "http://localhost:8080")
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log_handler: Optional logging handler for client logging
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Returns:
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Configured DurableAIAgentClient instance
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"""
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taskhub_name = taskhub or os.getenv("TASKHUB", "default")
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endpoint_url = endpoint or os.getenv("ENDPOINT", "http://localhost:8080")
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# Create a client using Azure Managed Durable Task
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client = DurableTaskSchedulerClient(
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host_address=endpoint,
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secure_channel=endpoint != "http://localhost:8080",
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logger.debug(f"Using taskhub: {taskhub_name}")
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logger.debug(f"Using endpoint: {endpoint_url}")
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credential = None if endpoint_url == "http://localhost:8080" else DefaultAzureCredential()
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dts_client = DurableTaskSchedulerClient(
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host_address=endpoint_url,
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secure_channel=endpoint_url != "http://localhost:8080",
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taskhub=taskhub_name,
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token_credential=credential
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token_credential=credential,
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log_handler=log_handler
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)
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# Wrap it with the agent client
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agent_client = DurableAIAgentClient(client)
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return DurableAIAgentClient(dts_client)
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def run_client(agent_client: DurableAIAgentClient) -> None:
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"""Run client interactions with the Joker agent.
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Args:
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agent_client: The DurableAIAgentClient instance
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"""
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# Get a reference to the Joker agent
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logger.info("Getting reference to Joker agent...")
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logger.debug("Getting reference to Joker agent...")
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joker = agent_client.get_agent("Joker")
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# Create a new thread for the conversation
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thread = joker.get_new_thread()
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logger.debug(f"Thread ID: {thread.session_id}")
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logger.info("Start chatting with the Joker agent! (Type 'exit' to quit)")
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logger.info(f"Created conversation thread: {thread.session_id}")
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logger.info("")
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# Interactive conversation loop
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while True:
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# Get user input
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try:
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user_message = input("You: ").strip()
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except (EOFError, KeyboardInterrupt):
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logger.info("\nExiting...")
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break
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# Check for exit command
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if user_message.lower() == "exit":
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logger.info("Goodbye!")
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break
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# Skip empty messages
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if not user_message:
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continue
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# Send message to agent and get response
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try:
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response = joker.run(user_message, thread=thread)
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logger.info(f"Joker: {response.text} \n")
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except Exception as e:
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logger.error(f"Error getting response: {e}")
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logger.info("Conversation completed.")
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async def main() -> None:
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"""Main entry point for the client application."""
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logger.debug("Starting Durable Task Agent Client...")
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# Create client using helper function
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agent_client = get_client()
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try:
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# First message
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message1 = "Tell me a short joke about cloud computing."
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logger.info(f"User: {message1}")
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logger.info("")
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# Run the agent - this blocks until the response is ready
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response1 = joker.run(message1, thread=thread)
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logger.info(f"Agent: {response1.text}")
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logger.info("")
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# Second message - continuing the conversation
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message2 = "Now tell me one about Python programming."
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logger.info(f"User: {message2}")
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logger.info("")
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response2 = joker.run(message2, thread=thread)
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logger.info(f"Agent: {response2.text}")
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logger.info("")
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logger.info(f"Conversation completed successfully!")
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logger.info(f"Thread ID: {thread.session_id}")
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run_client(agent_client)
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except Exception as e:
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logger.exception(f"Error during agent interaction: {e}")
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finally:
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logger.info("Client shutting down")
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logger.debug("Client shutting down")
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if __name__ == "__main__":
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@@ -14,122 +14,42 @@ To run this sample:
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"""
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import logging
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import os
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from agent_framework.azure import AzureOpenAIChatClient
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from agent_framework_durabletask import DurableAIAgentClient, DurableAIAgentWorker
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from azure.identity import AzureCliCredential, DefaultAzureCredential
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from dotenv import load_dotenv
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from durabletask.azuremanaged.client import DurableTaskSchedulerClient
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from durabletask.azuremanaged.worker import DurableTaskSchedulerWorker
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# Configure logging
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logging.basicConfig(level=logging.INFO)
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# Import helper functions from worker and client modules
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from client import get_client, run_client
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from worker import get_worker, setup_worker
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# Configure logging (must be after imports to override their basicConfig)
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logging.basicConfig(level=logging.INFO, force=True)
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logger = logging.getLogger(__name__)
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def create_joker_agent():
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"""Create the Joker agent using Azure OpenAI.
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Returns:
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AgentProtocol: The configured Joker agent
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"""
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return AzureOpenAIChatClient(credential=AzureCliCredential()).create_agent(
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name="Joker",
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instructions="You are good at telling jokes.",
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)
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def main():
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"""Main entry point - runs both worker and client in single process."""
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logger.info("Starting Durable Task Agent Sample (Combined Worker + Client)...")
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# Get environment variables for taskhub and endpoint with defaults
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taskhub_name = os.getenv("TASKHUB", "default")
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endpoint = os.getenv("ENDPOINT", "http://localhost:8080")
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logger.debug("Starting Durable Task Agent Sample (Combined Worker + Client)...")
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logger.info(f"Using taskhub: {taskhub_name}")
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logger.info(f"Using endpoint: {endpoint}")
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logger.info("")
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# Set credential to None for emulator, or DefaultAzureCredential for Azure
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credential = None if endpoint == "http://localhost:8080" else DefaultAzureCredential()
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secure_channel = endpoint != "http://localhost:8080"
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silent_handler = logging.NullHandler()
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# Create and start the worker using a context manager
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with DurableTaskSchedulerWorker(
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host_address=endpoint,
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secure_channel=secure_channel,
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taskhub=taskhub_name,
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token_credential=credential
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) as worker:
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# Wrap with the agent worker
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agent_worker = DurableAIAgentWorker(worker)
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# Create and register the Joker agent
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logger.info("Creating and registering Joker agent...")
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joker_agent = create_joker_agent()
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agent_worker.add_agent(joker_agent)
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logger.info(f"✓ Registered agent: {joker_agent.name}")
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logger.info(f" Entity name: dafx-{joker_agent.name}")
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logger.info("")
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# Create and start the worker using helper function and context manager
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with get_worker(log_handler=silent_handler) as dts_worker:
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# Register agents using helper function
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setup_worker(dts_worker)
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# Start the worker
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worker.start()
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logger.info("Worker started and listening for requests...")
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logger.info("")
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dts_worker.start()
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logger.debug("Worker started and listening for requests...")
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# Create the client
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client = DurableTaskSchedulerClient(
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host_address=endpoint,
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secure_channel=secure_channel,
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taskhub=taskhub_name,
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token_credential=credential
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)
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# Wrap it with the agent client
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agent_client = DurableAIAgentClient(client)
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# Get a reference to the Joker agent
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logger.info("Getting reference to Joker agent...")
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joker = agent_client.get_agent("Joker")
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# Create a new thread for the conversation
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thread = joker.get_new_thread()
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logger.info(f"Created conversation thread: {thread.session_id}")
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logger.info("")
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# Create the client using helper function
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agent_client = get_client(log_handler=silent_handler)
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try:
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# First message
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message1 = "Tell me a short joke about cloud computing."
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logger.info(f"User: {message1}")
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logger.info("")
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# Run the agent - this blocks until the response is ready
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response1 = joker.run(message1, thread=thread)
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logger.info(f"Agent: {response1.text}; {response1}")
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logger.info("")
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# Second message - continuing the conversation
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message2 = "Now tell me one about Python programming."
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logger.info(f"User: {message2}")
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logger.info("")
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response2 = joker.run(message2, thread=thread)
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logger.info(f"Agent: {response2.text}; {response2}")
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logger.info("")
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logger.info(f"Conversation completed successfully!")
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logger.info(f"Thread ID: {thread.session_id}")
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# Run client interactions using helper function
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run_client(agent_client)
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except Exception as e:
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logger.exception(f"Error during agent interaction: {e}")
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logger.info("")
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logger.info("Sample completed. Worker shutting down...")
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logger.debug("Sample completed. Worker shutting down...")
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if __name__ == "__main__":
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@@ -19,7 +19,7 @@ from azure.identity import AzureCliCredential, DefaultAzureCredential
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from durabletask.azuremanaged.worker import DurableTaskSchedulerWorker
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# Configure logging
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logging.basicConfig(level=logging.INFO)
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logging.basicConfig(level=logging.WARNING)
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logger = logging.getLogger(__name__)
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@@ -35,39 +35,71 @@ def create_joker_agent():
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)
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async def main():
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"""Main entry point for the worker process."""
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logger.info("Starting Durable Task Agent Worker...")
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def get_worker(
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taskhub: str | None = None,
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endpoint: str | None = None,
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log_handler: logging.Handler | None = None
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) -> DurableTaskSchedulerWorker:
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"""Create a configured DurableTaskSchedulerWorker.
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# Get environment variables for taskhub and endpoint with defaults
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taskhub_name = os.getenv("TASKHUB", "default")
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endpoint = os.getenv("ENDPOINT", "http://localhost:8080")
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logger.info(f"Using taskhub: {taskhub_name}")
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logger.info(f"Using endpoint: {endpoint}")
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# Set credential to None for emulator, or DefaultAzureCredential for Azure
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credential = None if endpoint == "http://localhost:8080" else DefaultAzureCredential()
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Args:
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taskhub: Task hub name (defaults to TASKHUB env var or "default")
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endpoint: Scheduler endpoint (defaults to ENDPOINT env var or "http://localhost:8080")
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log_handler: Optional logging handler for worker logging
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Returns:
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Configured DurableTaskSchedulerWorker instance
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"""
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taskhub_name = taskhub or os.getenv("TASKHUB", "default")
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endpoint_url = endpoint or os.getenv("ENDPOINT", "http://localhost:8080")
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# Create a worker using Azure Managed Durable Task
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worker = DurableTaskSchedulerWorker(
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host_address=endpoint,
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secure_channel=endpoint != "http://localhost:8080",
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logger.debug(f"Using taskhub: {taskhub_name}")
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logger.debug(f"Using endpoint: {endpoint_url}")
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credential = None if endpoint_url == "http://localhost:8080" else DefaultAzureCredential()
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return DurableTaskSchedulerWorker(
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host_address=endpoint_url,
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secure_channel=endpoint_url != "http://localhost:8080",
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taskhub=taskhub_name,
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token_credential=credential
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token_credential=credential,
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log_handler=log_handler
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)
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def setup_worker(worker: DurableTaskSchedulerWorker) -> DurableAIAgentWorker:
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"""Set up the worker with agents registered.
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Args:
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worker: The DurableTaskSchedulerWorker instance
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Returns:
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DurableAIAgentWorker with agents registered
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"""
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# Wrap it with the agent worker
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agent_worker = DurableAIAgentWorker(worker)
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# Create and register the Joker agent
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logger.info("Creating and registering Joker agent...")
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logger.debug("Creating and registering Joker agent...")
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joker_agent = create_joker_agent()
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agent_worker.add_agent(joker_agent)
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logger.info(f"✓ Registered agent: {joker_agent.name}")
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logger.info(f" Entity name: dafx-{joker_agent.name}")
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logger.info("")
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logger.debug(f"✓ Registered agent: {joker_agent.name}")
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logger.debug(f" Entity name: dafx-{joker_agent.name}")
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return agent_worker
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async def main():
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"""Main entry point for the worker process."""
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logger.debug("Starting Durable Task Agent Worker...")
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# Create a worker using the helper function
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worker = get_worker()
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# Setup worker with agents
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setup_worker(worker)
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logger.info("Worker is ready and listening for requests...")
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logger.info("Press Ctrl+C to stop.")
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logger.info("")
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@@ -80,9 +112,9 @@ async def main():
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while True:
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await asyncio.sleep(1)
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except KeyboardInterrupt:
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logger.info("Worker shutdown initiated")
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logger.debug("Worker shutdown initiated")
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logger.info("Worker stopped")
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logger.debug("Worker stopped")
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if __name__ == "__main__":
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