Task Group

import asyncio
import typing

import parallel
import pydantic
from parallel.types import JsonSchemaParam, TaskRun, TaskSpecParam
from parallel.types.task_run_result import OutputTaskRunJsonOutput

# Define your input and output models
class CompanyInput(pydantic.BaseModel):
    company_name: str = pydantic.Field(description="Name of the company")
    company_website: str = pydantic.Field(description="Company website URL")

class CompanyOutput(pydantic.BaseModel):
    key_insights: list[str] = pydantic.Field(description="Key business insights")
    market_position: str = pydantic.Field(description="Market positioning analysis")

# Define Group API types (these will be added to the Parallel SDK in a future release)
class TaskRunInputParam(parallel.BaseModel):
    task_spec: TaskSpecParam | None = pydantic.Field(default=None)
    input: str | dict[str, str] = pydantic.Field(description="Input to the task")
    metadata: dict[str, str] | None = pydantic.Field(default=None)
    processor: str = pydantic.Field(description="Processor to use for the task")

class TaskGroupStatus(parallel.BaseModel):
    num_task_runs: int = pydantic.Field(description="Number of task runs in the group")
    task_run_status_counts: dict[str, int] = pydantic.Field(
        description="Number of task runs with each status"
    )
    is_active: bool = pydantic.Field(
        description="True if at least one run in the group is currently active"
    )
    status_message: str | None = pydantic.Field(
        description="Human-readable status message for the group"
    )

class TaskGroupRunRequest(parallel.BaseModel):
    default_task_spec: TaskSpecParam | None = pydantic.Field(default=None)
    inputs: list[TaskRunInputParam] = pydantic.Field(description="List of task runs to execute")

class TaskGroupResponse(parallel.BaseModel):
    taskgroup_id: str = pydantic.Field(description="ID of the group")
    status: TaskGroupStatus = pydantic.Field(description="Status of the group")

class TaskGroupRunResponse(parallel.BaseModel):
    status: TaskGroupStatus = pydantic.Field(description="Status of the group")
    run_ids: list[str] = pydantic.Field(description="IDs of the newly created runs")

class TaskRunEvent(parallel.BaseModel):
    type: typing.Literal["task_run"] = pydantic.Field(default="task_run")
    event_id: str = pydantic.Field(description="Cursor to resume the event stream")
    run: TaskRun = pydantic.Field(description="Task run object")
    input: TaskRunInputParam | None = pydantic.Field(default=None)
    output: OutputTaskRunJsonOutput | None = pydantic.Field(default=None)

class Error(parallel.BaseModel):
    ref_id: str = pydantic.Field(description="Reference ID for the error")
    message: str = pydantic.Field(description="Human-readable message")
    detail: dict[str, typing.Any] | None = pydantic.Field(default=None)

class ErrorResponse(parallel.BaseModel):
    type: typing.Literal["error"] = pydantic.Field(default="error")
    error: Error = pydantic.Field(description="Error")

# Create reusable task specification
task_spec = TaskSpecParam(
    input_schema=JsonSchemaParam(json_schema=CompanyInput.model_json_schema()),
    output_schema=JsonSchemaParam(json_schema=CompanyOutput.model_json_schema()),
)


async def wait_for_completion(client: parallel.AsyncParallel, taskgroup_id: str) -> None:
    while True:
        response = await client.get(
            path=f"/v1beta/tasks/groups/{taskgroup_id}", cast_to=TaskGroupResponse
        )

        status = response.status
        print(f"Status: {status.task_run_status_counts}")

        if not status.is_active:
            print("All tasks completed!")
            break

        await asyncio.sleep(10)


async def get_all_results(client: parallel.AsyncParallel, taskgroup_id: str):
    results = []

    path = f"/v1beta/tasks/groups/{taskgroup_id}/runs"
    path += "?include_input=true&include_output=true"

    result_stream = await client.get(
        path=path,
        cast_to=TaskRunEvent | ErrorResponse | None,
        stream=True,
        stream_cls=parallel.AsyncStream[TaskRunEvent | ErrorResponse],
    )

    async for event in result_stream:
        if isinstance(event, TaskRunEvent) and event.output:
            company_input = CompanyInput.model_validate(event.input.input)
            company_output = CompanyOutput.model_validate(event.output.content)

            results.append(
                {
                    "company": company_input.company_name,
                    "insights": company_output.key_insights,
                    "market_position": company_output.market_position,
                }
            )

    return results


async def batch_company_research():
    client = parallel.AsyncParallel(
        base_url="https://beta.parallel.ai",
        api_key="your-api-key",
    )

    # Create task group
    group_response = await client.post(
        path="/v1beta/tasks/groups", cast_to=TaskGroupResponse, body={}
    )
    taskgroup_id = group_response.taskgroup_id
    print(f"Created taskgroup id {taskgroup_id}")

    # Define companies to research
    companies = [
        {"company_name": "Stripe", "company_website": "https://stripe.com"},
        {"company_name": "Shopify", "company_website": "https://shopify.com"},
        {"company_name": "Salesforce", "company_website": "https://salesforce.com"},
    ]

    # Add Tasks to group
    run_inputs = []
    for company in companies:
        input_data = CompanyInput(
            company_name=company["company_name"],
            company_website=company["company_website"],
        )
        run_inputs.append(
            TaskRunInputParam(input=input_data.model_dump(), processor="pro")
        )

    response = await client.post(
        path=f"/v1beta/tasks/groups/{taskgroup_id}/runs",
        cast_to=TaskGroupRunResponse,
        body=TaskGroupRunRequest(
            default_task_spec=task_spec, inputs=run_inputs
        ).model_dump(),
    )
    print(f"Added {len(response.run_ids)} runs to taskgroup {taskgroup_id}")

    # Wait for completion and get results
    await wait_for_completion(client, taskgroup_id)
    results = await get_all_results(client, taskgroup_id)
    print(f"Successfully processed {len(results)} companies")
    return results


# Run the batch job
results = asyncio.run(batch_company_research())