mirror of https://github.com/dapr/docs.git
update python examples for workflow; update conversation quickstart to python sdk
Signed-off-by: Hannah Hunter <hannahhunter@microsoft.com>
This commit is contained in:
Hannah Hunter
parent
3688a1d684
commit
b74c247d01
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@ -36,16 +36,31 @@ The Dapr sidecar doesn’t load any workflow definitions. Rather, the sidecar si
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<!--python-->
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Define the workflow activities you'd like your workflow to perform. Activities are a function definition and can take inputs and outputs. The following example creates a counter (activity) called `hello_act` that notifies users of the current counter value. `hello_act` is a function derived from a class called `WorkflowActivityContext`.
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Define the workflow activities you'd like your workflow to perform. Activities are a function definition and can take inputs and outputs. The following example creates task chaining activities that receive input
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```python
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def hello_act(ctx: WorkflowActivityContext, input):
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global counter
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counter += input
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print(f'New counter value is: {counter}!', flush=True)
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@wfr.activity(name='step10')
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def step1(ctx, activity_input):
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print(f'Step 1: Received input: {activity_input}.')
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# Do some work
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return activity_input + 1
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@wfr.activity
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def step2(ctx, activity_input):
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print(f'Step 2: Received input: {activity_input}.')
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# Do some work
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return activity_input * 2
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@wfr.activity
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def step3(ctx, activity_input):
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print(f'Step 3: Received input: {activity_input}.')
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# Do some work
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return activity_input ^ 2
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```
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[See the `hello_act` workflow activity in context.](https://github.com/dapr/python-sdk/blob/master/examples/demo_workflow/app.py#LL40C1-L43C59)
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[See the task chaining workflow activity in context.](https://github.com/dapr/python-sdk/blob/main/examples/workflow/task_chaining.py)
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{{% /codetab %}}
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@ -226,16 +241,19 @@ Next, register and call the activites in a workflow.
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<!--python-->
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The `hello_world_wf` function is derived from a class called `DaprWorkflowContext` with input and output parameter types. It also includes a `yield` statement that does the heavy lifting of the workflow and calls the workflow activities.
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The `random_workflow` function is a task chaining workflow pattern derived from a class called `DaprWorkflowContext` with input and output parameter types. It also includes a `yield` statement that does the heavy lifting of the workflow and calls the workflow activities.
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```python
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def hello_world_wf(ctx: DaprWorkflowContext, input):
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print(f'{input}')
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yield ctx.call_activity(hello_act, input=1)
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yield ctx.call_activity(hello_act, input=10)
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yield ctx.wait_for_external_event("event1")
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yield ctx.call_activity(hello_act, input=100)
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yield ctx.call_activity(hello_act, input=1000)
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@wfr.workflow(name='random_workflow')
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def task_chain_workflow(ctx: wf.DaprWorkflowContext, wf_input: int):
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try:
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result1 = yield ctx.call_activity(step1, input=wf_input)
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result2 = yield ctx.call_activity(step2, input=result1)
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result3 = yield ctx.call_activity(step3, input=result2)
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except Exception as e:
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yield ctx.call_activity(error_handler, input=str(e))
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raise
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return [result1, result2, result3]
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```
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[See the `hello_world_wf` workflow in context.](https://github.com/dapr/python-sdk/blob/master/examples/demo_workflow/app.py#LL32C1-L38C51)
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@ -409,82 +427,77 @@ Finally, compose the application using the workflow.
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- A Python package called `DaprClient` to receive the Python SDK capabilities.
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- A builder with extensions called:
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- `WorkflowRuntime`: Allows you to register workflows and workflow activities
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- `DaprWorkflowContext`: Allows you to [create workflows]({{< ref "#write-the-workflow" >}})
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- `WorkflowRuntime`: Allows you to register the workflow runtime.
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- `DaprWorkflowContext`: Allows you to [create workflows and workflow activities]({{< ref "#write-the-workflow" >}})
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- `WorkflowActivityContext`: Allows you to [create workflow activities]({{< ref "#write-the-workflow-activities" >}})
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- API calls. In the example below, these calls start, pause, resume, purge, and terminate the workflow.
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```python
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from dapr.ext.workflow import WorkflowRuntime, DaprWorkflowContext, WorkflowActivityContext
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from dapr.clients import DaprClient
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from durabletask import worker, task
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# ...
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from dapr.ext.workflow.workflow_context import Workflow
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from dapr.ext.workflow.dapr_workflow_context import DaprWorkflowContext
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from dapr.ext.workflow.workflow_activity_context import Activity, WorkflowActivityContext
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from dapr.ext.workflow.util import getAddress
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def main():
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with DaprClient() as d:
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host = settings.DAPR_RUNTIME_HOST
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port = settings.DAPR_GRPC_PORT
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workflowRuntime = WorkflowRuntime(host, port)
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workflowRuntime = WorkflowRuntime()
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workflowRuntime.register_workflow(hello_world_wf)
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workflowRuntime.register_activity(hello_act)
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workflowRuntime.start()
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from dapr.clients import DaprInternalError
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from dapr.clients.http.client import DAPR_API_TOKEN_HEADER
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from dapr.conf import settings
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from dapr.conf.helpers import GrpcEndpoint
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from dapr.ext.workflow.logger import LoggerOptions, Logger
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wfr = wf.WorkflowRuntime()
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@wfr.workflow(name='hello_world_wf')
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def hello_world_wf(ctx: DaprWorkflowContext, wf_input):
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# Workflow definition...
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@wfr.activity(name='hello_act')
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def hello_act(ctx: WorkflowActivityContext, wf_input):
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# Activity definition...
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# Start workflow
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print("==========Start Counter Increase as per Input:==========")
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start_resp = d.start_workflow(instance_id=instanceId, workflow_component=workflowComponent,
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workflow_name=workflowName, input=inputData, workflow_options=workflowOptions)
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print(f"start_resp {start_resp.instance_id}")
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wfr = WorkflowRuntime()
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wfr.start()
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wf_client = DaprWorkflowClient()
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# ...
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# Pause workflow
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d.pause_workflow(instance_id=instanceId, workflow_component=workflowComponent)
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getResponse = d.get_workflow(instance_id=instanceId, workflow_component=workflowComponent)
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print(f"Get response from {workflowName} after pause call: {getResponse.runtime_status}")
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# Resume workflow
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d.resume_workflow(instance_id=instanceId, workflow_component=workflowComponent)
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getResponse = d.get_workflow(instance_id=instanceId, workflow_component=workflowComponent)
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print(f"Get response from {workflowName} after resume call: {getResponse.runtime_status}")
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wf_client.pause_workflow(instance_id=instance_id)
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metadata = wf_client.get_workflow_state(instance_id=instance_id)
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# ... check status ...
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wf_client.resume_workflow(instance_id=instance_id)
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sleep(1)
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# Raise workflow
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d.raise_workflow_event(instance_id=instanceId, workflow_component=workflowComponent,
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event_name=eventName, event_data=eventData)
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sleep(5)
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# Purge workflow
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d.purge_workflow(instance_id=instanceId, workflow_component=workflowComponent)
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try:
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getResponse = d.get_workflow(instance_id=instanceId, workflow_component=workflowComponent)
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except DaprInternalError as err:
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if nonExistentIDError in err._message:
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print("Instance Successfully Purged")
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# Kick off another workflow for termination purposes
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start_resp = d.start_workflow(instance_id=instanceId, workflow_component=workflowComponent,
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workflow_name=workflowName, input=inputData, workflow_options=workflowOptions)
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print(f"start_resp {start_resp.instance_id}")
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# Terminate workflow
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d.terminate_workflow(instance_id=instanceId, workflow_component=workflowComponent)
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sleep(1)
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getResponse = d.get_workflow(instance_id=instanceId, workflow_component=workflowComponent)
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print(f"Get response from {workflowName} after terminate call: {getResponse.runtime_status}")
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wf_client.raise_workflow_event(
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instance_id=instance_id,
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event_name=event_name,
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data=event_data
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)
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# Purge workflow
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d.purge_workflow(instance_id=instanceId, workflow_component=workflowComponent)
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try:
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getResponse = d.get_workflow(instance_id=instanceId, workflow_component=workflowComponent)
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except DaprInternalError as err:
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if nonExistentIDError in err._message:
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print("Instance Successfully Purged")
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state = wf_client.wait_for_workflow_completion(
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instance_id,
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timeout_in_seconds=30
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)
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wf_client.purge_workflow(instance_id=instance_id)
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workflowRuntime.shutdown()
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if __name__ == '__main__':
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main()
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wfr.start()
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sleep(10) # wait for workflow runtime to start
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wf_client = wf.DaprWorkflowClient()
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instance_id = wf_client.schedule_new_workflow(workflow=task_chain_workflow, input=42)
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print(f'Workflow started. Instance ID: {instance_id}')
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state = wf_client.wait_for_workflow_completion(instance_id)
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print(f'Workflow completed! Status: {state.runtime_status}')
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wfr.shutdown()
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```
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@ -10,7 +10,7 @@ description: Get started with the Dapr conversation building block
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The conversation building block is currently in **alpha**.
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{{% /alert %}}
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Let's take a look at how the [Dapr conversation building block]({{< ref conversation-overview.md >}}) makes interacting with Large Language Models (LLMs) easier. In this quickstart, you use the echo component to communicate with the mock LLM and ask it for a poem about Dapr.
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Let's take a look at how the [Dapr conversation building block]({{< ref conversation-overview.md >}}) makes interacting with Large Language Models (LLMs) easier. In this quickstart, you use the echo component to communicate with the mock LLM and ask it to define Dapr.
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You can try out this conversation quickstart by either:
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@ -18,7 +18,7 @@ You can try out this conversation quickstart by either:
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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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Currently, you can only use JavaScript for the quickstart sample using HTTP, not the JavaScript SDK.
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{{% /alert %}}
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## Run the app with the template file
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@ -50,7 +50,7 @@ git clone https://github.com/dapr/quickstarts.git
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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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cd conversation/python/sdk/conversation
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```
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Install the dependencies:
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@ -61,7 +61,7 @@ pip3 install -r requirements.txt
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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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Navigate back to the `sdk` 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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@ -117,37 +117,28 @@ In the application code:
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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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from dapr.clients import DaprClient
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from dapr.clients.grpc._request import ConversationInput
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logging.basicConfig(level=logging.INFO)
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with DaprClient() as d:
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inputs = [
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ConversationInput(content="What is dapr?", role='user', scrub_pii=True),
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]
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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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metadata = {
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'model': 'modelname',
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'key': 'authKey',
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'cacheTTL': '10m',
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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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print('Input sent: What is dapr?')
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logging.info('Input sent: What is dapr?')
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response = d.converse_alpha1(
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name='echo', inputs=inputs, temperature=0.7, context_id='chat-123', metadata=metadata
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)
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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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for output in response.outputs:
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print(f'Output response: {output.result}')
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```
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{{% /codetab %}}
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@ -575,7 +566,7 @@ git clone https://github.com/dapr/quickstarts.git
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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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cd conversation/python/sdk/conversation
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```
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Install the dependencies:
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@ -586,7 +577,7 @@ pip3 install -r requirements.txt
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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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Navigate back to the `sdk` 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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@ -251,7 +251,6 @@ class WorkflowConsoleApp:
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if __name__ == '__main__':
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app = WorkflowConsoleApp()
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app.main()
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```
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#### `order-processor/workflow.py`
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@ -276,7 +275,6 @@ wfr = WorkflowRuntime()
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logging.basicConfig(level=logging.INFO)
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@wfr.workflow(name="order_processing_workflow")
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def order_processing_workflow(ctx: DaprWorkflowContext, order_payload_str: str):
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"""Defines the order processing workflow.
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@ -343,7 +341,6 @@ def notify_activity(ctx: WorkflowActivityContext, input: Notification):
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logger = logging.getLogger('NotifyActivity')
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logger.info(input.message)
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@wfr.activity(name="process_payment_activity")
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def process_payment_activity(ctx: WorkflowActivityContext, input: PaymentRequest):
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"""Defines Process Payment Activity.This is used by the workflow to process a payment"""
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@ -353,7 +350,6 @@ def process_payment_activity(ctx: WorkflowActivityContext, input: PaymentRequest
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+' USD')
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logger.info(f'Payment for request ID {input.request_id} processed successfully')
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@wfr.activity(name="verify_inventory_activity")
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def verify_inventory_activity(ctx: WorkflowActivityContext,
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input: InventoryRequest) -> InventoryResult:
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@ -377,8 +373,6 @@ def verify_inventory_activity(ctx: WorkflowActivityContext,
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return InventoryResult(True, inventory_item)
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return InventoryResult(False, None)
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@wfr.activity(name="update_inventory_activity")
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def update_inventory_activity(ctx: WorkflowActivityContext,
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input: PaymentRequest) -> InventoryResult:
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@ -401,8 +395,6 @@ def update_inventory_activity(ctx: WorkflowActivityContext,
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client.save_state(store_name, input.item_being_purchased, new_val)
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logger.info(f'There are now {new_quantity} {input.item_being_purchased} left in stock')
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@wfr.activity(name="request_approval_activity")
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def request_approval_activity(ctx: WorkflowActivityContext,
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input: OrderPayload):
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@ -413,7 +405,6 @@ def request_approval_activity(ctx: WorkflowActivityContext,
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logger.info('Requesting approval for payment of '+f'{input["total_cost"]}'+' USD for '
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+f'{input["quantity"]}' +' ' +f'{input["item_name"]}')
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```
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{{% /codetab %}}
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