@Marvin Adding interactive=True to a cf.run results in the task agent repeatedly asking the same prompt over and over. The prompt is using a tool that loads a pdf document’s contents with PyPDFLoader.

It seems there aren't any specific GitHub issues or directly relevant documentation excerpts about the problem you're experiencing with

interactive=True

in a

cf.run

causing repeated prompts. However, this might be related to how the interactive mode is implemented and handled in your flow. Here are a few things you might consider checking or trying: 1. Loop Handling: Ensure that your flow doesn't inadvertently re-trigger the prompt due to a loop or repeated execution within the flow logic. The interactive mode might be waiting for a specific user input or condition to continue, which could cause it to loop if not handled correctly. 2. Exception Handling: If the flow encounters a

ValidationError

or similar exception, it might be falling back into a state that prompts again. Consider implementing a try-except block to catch and handle such exceptions gracefully. 3. State Management: Check how the state of the flow and tasks are managed. If the state isn't updated or managed correctly after user input, it might cause the flow to revert to a previous state, prompting again. 4. Debugging and Logging: Add detailed logging to your flow to trace the execution path and identify where the repeated prompting occurs. This might help in pinpointing if it's a logic issue or something else. For further assistance, you might want to explore the Prefect community forums or GitHub discussions for similar experiences or reach out with more specific details of your flow setup. If you need more specific guidance, providing a minimal reproducible example could be helpful for further investigation.

@Marvin can you give an example of how I can handle #1 after the document has successfully uploaded. Perhaps via instructions or logic ?

To handle the situation where you want to proceed with specific logic after a document has successfully uploaded, you can use conditional logic within your flow. Prefect 3.x provides tools to manage flow control, including checking task states and proceeding based on conditions. Here's a simple example using Prefect's imperative programming style:

python
from prefect import flow, task, get_run_logger
from prefect.states import Completed

@task
def upload_document():
    # Simulate document upload
    logger = get_run_logger()
    <http://logger.info|logger.info>("Uploading document...")
    # Assume the upload is successful and return a success state
    return Completed()

@task
def process_after_upload():
    logger = get_run_logger()
    <http://logger.info|logger.info>("Processing after successful upload.")

@flow
def document_flow():
    upload_state = upload_document()

    # Check if the upload was successful
    if isinstance(upload_state, Completed):
        process_after_upload()
    else:
        logger = get_run_logger()
        logger.error("Upload failed, skipping further processing.")

document_flow()

### Key Points: - Task States: Use task states to determine the outcome of tasks. In this example,

Completed()

is used to simulate a successful upload. - Conditional Logic: Use simple

if

statements to decide the flow path based on task outcomes. - Logging: Use

get_run_logger()

to log messages for better traceability. This approach ensures that your flow only proceeds with subsequent tasks if the document upload is successful. You can expand this logic to include retries or alternative paths based on different outcomes. For more detailed information, you can refer to the Prefect documentation on writing flows.

@Marvin hmm, this is controlflow.ai

In ControlFlow, handling interactivity and conditional logic after a task has successfully completed involves using specific patterns and settings. Here's a brief overview based on the ControlFlow documentation: ### Interactivity in ControlFlow - Interactive Mode: By setting

interactive=True

, your agents can engage in user interactions. This mode allows agents to send messages to users and receive inputs, which can guide decision-making processes. This is particularly useful for creating dynamic workflows that adapt to real-time user needs. - Interactive Tasks and Agents: You can mark tasks or agents as interactive. If a task is interactive, all assigned agents can interact with the user. Conversely, if an agent is interactive, it can interact with the user across all tasks. ### Conditional Logic After Task Success - Run Until Conditions: ControlFlow supports flexible termination logic using

run_until

conditions. This allows you to specify when an orchestration run should end based on task completion, failure, or custom criteria. For example, you can set a condition to continue running tasks until a specific task completes successfully or until a maximum number of calls is reached. ### Example Scenario If you want to proceed with specific logic after a document upload task completes successfully, you can use these features to manage the flow:

python
from controlflow import run, run_until, Task

# Define your task
def upload_document():
    # Logic for uploading document
    return "success"

# Define your condition
def check_success(task_result):
    return task_result == "success"

# Run your task with a condition
run(
    tasks=[upload_document],
    run_until=run_until(check_success)
)

### Key Points: - Interactive Tasks: Use

interactive=True

for tasks or agents that require user input. - Conditional Execution: Utilize

run_until

to control flow execution based on task outcomes. For more details, you can explore the ControlFlow documentation on Interactivity and Running Tasks.