📘 概述

ParaSeqTaskGenerator 是一个任务生成算子，它基于输入的原子任务，利用大语言模型（LLM）服务生成三种相关的任务类型：并行任务、后继任务以及将这两者结合的组合任务。

__init__函数

def __init__(self, llm_serving: LLMServingABC)

init参数说明

参数名	类型	默认值	说明
llm_serving	LLMServingABC	必需	大语言模型服务实例，用于执行推理与生成。

Prompt模板说明

Prompt 模板名称	主要用途	适用场景	特点说明

run函数

def run(self, storage: DataFlowStorage, input_task_key: str, output_parallel_task_key: str = "parallel_task",  output_subsequent_task_key: str = "subsequent_task", output_composition_task_key: str = "composition_task")

参数

名称	类型	默认值	说明
storage	DataFlowStorage	必需	数据流存储实例，负责读取与写入数据。
input_task_key	str	必需	输入列名，对应原子任务字段。
output_parallel_task_key	str	"parallel_task"	输出列名，对应生成的并行任务字段。
output_subsequent_task_key	str	"subsequent_task"	输出列名，对应生成的后继任务字段。
output_composition_task_key	str	"composition_task"	输出列名，对应生成的组合任务字段。

🧠 示例用法

from dataflow.operators.conversations import ParaSeqTaskGenerator
from dataflow.utils.storage import FileStorage
from dataflow.serving import APILLMServing_request
from dataflow.core import LLMServingABC

class ParaSeqTaskGeneratorTest:
    def __init__(self, llm_serving: LLMServingABC = None):
        self.storage = FileStorage(
            first_entry_file_name="input.jsonl",
            cache_path="./cache_local",
            file_name_prefix="dataflow_cache_step",
            cache_type="jsonl",
        )

        self.llm_serving = APILLMServing_request(
            api_url="",
            model_name="gpt-4o",
            max_workers=30
        )

        self.generator = ParaSeqTaskGenerator(
            llm_serving=self.llm_serving
        )

    def forward(self):
        self.generator.run(
            storage=self.storage.step(),
            input_task_key="atom_task",
            output_parallel_task_key="parallel_task",
            output_subsequent_task_key="subsequent_task",
            output_composition_task_key="composition_task"
        )

if __name__ == "__main__":
    pl = ParaSeqTaskGeneratorTest()
    pl.forward()

🧾 默认输出格式（Output Format）

字段	类型	说明
	str	输入的原子任务文本。
parallel_task	str	模型生成的并行任务。
subsequent_task	str	模型生成的后继任务。
composition_task	str	模型生成的组合任务。

示例输入：

{
  "atom_task": "Search for a round-trip flight from New York to London, departing on November 15th and returning on November 22nd, with a budget of $500 or less."
}

示例输出：

{
  "atom_task": "Search for a round-trip flight from New York to London, departing on November 15th and returning on November 22nd, with a budget of $500 or less.",
  "parallel_task": "Find accommodation options in London from November 15th to November 22nd within a budget of $100 per night.",
  "subsequent_task": "Calculate the total cost of flights and accommodation for the trip and check if it is within the overall trip budget.",
  "composition_task": "Determine if you can travel from New York to London and back with the flight and accommodation options while staying within the total budget for the trip."
}