DataFlow/dataflow/prompts/multihopqa.py

import textwrap
from typing import Dict, Literal

class MultiHopQAGeneratorPrompt:
    '''
    多跳问答生成器（严格JSON格式输出）
    根据语言参数提供完全独立的专业提示模板
    '''
    def __init__(self, lang: str = "en"):
        self.lang = lang
        self.system_text = self._build_system_prompt()
        
    def _build_system_prompt(self) -> str:
        """构建专业级多跳问答提示"""
        if self.lang == "en":
            return textwrap.dedent("""\
                You are a professional multi-hop QA specialist with strict protocols:

                █ Core Requirements
                1. Must identify 2-3 interrelated facts in context
                2. Design complex questions requiring cross-fact reasoning
                3. Reasoning chains must:
                   - Contain 2-3 logical steps (numbered)
                   - Show clear causal/progressive relationships
                   - Each step must reference specific facts
                4. Final answer must synthesize all reasoning conclusions

                █ Output Specifications
                1. Only pure JSON in this structure:
                {
                    "question": "Multi-fact reasoning question",
                    "reasoning_steps": [
                        {"step": "First step (must use Fact 1)"},
                        {"step": "Second step (must link Fact 2)"}
                    ],
                    "answer": "Synthesized final answer",
                    "supporting_facts": ["Verbatim Fact 1", "Verbatim Fact 2"],
                    "type": "domain_tag"
                }
                2. Supporting facts must:
                   - Be verbatim from context
                   - Directly support corresponding steps
                   - No paraphrasing allowed

                █ Demonstration
                Context:
                "Photosynthesis converts CO2 to oxygen. This process sustains plant growth. Plants form the base of food chains."

                Valid Output:
                {
                    "question": "How does photosynthesis impact ecosystems?",
                    "reasoning_steps": [
                        {"step": "Photosynthesis produces oxygen"},
                        {"step": "Plants using photosynthesis form food chain bases"}
                    ],
                    "answer": "It provides oxygen and sustains ecosystem food chains",
                    "supporting_facts": [
                        "Photosynthesis converts CO2 to oxygen",
                        "Plants form the base of food chains"
                    ],
                    "type": "biology"
                }

                █ Rejection Criteria
                Reject if:
                - Fewer than 2 reasoning steps
                - Unreferenced supporting facts exist
                - Any non-JSON content appears
                """)
        else:
            return textwrap.dedent("""\
                您是专业的多跳问答生成专家，必须严格遵循以下专业标准：

                █ 核心要求
                1. 必须识别上下文中的2-3个关联事实
                2. 设计需要跨事实推理的复杂问题
                3. 推理链必须满足：
                    - 至少包含2-3个逻辑步骤
                    - 每个步骤明确标注序号
                    - 步骤间存在因果或递进关系
                4. 最终答案必须整合所有推理结论

                █ 输出规范
                1. 仅允许输出以下结构的纯JSON：
                {
                    "question": "需要跨事实推理的问题",
                    "reasoning_steps": [
                        {"step": "第一推理步骤（必须引用事实1）"},
                        {"step": "第二推理步骤（必须关联事实2）"}
                    ],
                    "answer": "整合所有步骤的最终答案",
                    "supporting_facts": ["原文事实1", "原文事实2"],
                    "type": "领域标签"
                }
                2. 支撑事实必须：
                    - 从上下文逐字提取
                    - 与推理步骤严格对应
                    - 不得改写或概括

                █ 示例
                上下文：
                "量子纠缠现象由爱因斯坦提出质疑。后来贝尔实验证实了其真实性。该现象是量子计算的基础。"

                合格输出：
                {
                    "question": "为什么量子纠缠现象对量子计算很重要？",
                    "reasoning_steps": [
                        {"step": "贝尔实验证实了量子纠缠的真实性"},
                        {"step": "该现象是量子计算的基础"}
                    ],
                    "answer": "因为量子纠缠被证实真实且是量子计算的基础",
                    "supporting_facts": [
                        "后来贝尔实验证实了其真实性",
                        "该现象是量子计算的基础"
                    ],
                    "type": "量子物理"
                }

                █ 违规处理
                以下情况将拒绝输出：
                - 推理步骤少于2步
                - 存在未引用的支撑事实
                - JSON外出现任何附加文本
                """)

    def _multihop_qa_generator_user_prompt(self, text: str) -> str:
        """生成完全专业化的用户提示"""
        if self.lang == "en":
            user_prompt = textwrap.dedent(f"""\
            Generate professional multi-hop QA from:

            Context:
            {text}

            Strict requirements:
            1. Extract exactly 2-3 interrelated facts
            2. Question must demonstrate cross-fact reasoning
            3. Use this exact JSON structure (include all quotes/braces):
            {{
                "question": "...",
                "reasoning_steps": [
                    {{"step": "Must explicitly use Fact 1"}},
                    {{"step": "Must explicitly link Fact 2"}}
                ],
                "answer": "...",
                "supporting_facts": ["Verbatim Fact 1", "Verbatim Fact 2"],
                "type": "..."
            }}
            """)
        else:
            user_prompt = textwrap.dedent(f"""\
                请基于以下上下文生成专业级多跳问答：

                上下文：
                {text}

                严格按照以下要求执行：
                1. 必须从上述上下文中提取2-3个关联事实
                2. 问题需体现跨事实推理的复杂性
                3. 使用此精确JSON结构（包括所有引号和括号）：
                {{
                    "question": "...",
                    "reasoning_steps": [
                        {{"step": "必须明确引用事实1"}},
                        {{"step": "必须明确关联事实2"}}
                    ],
                    "answer": "...",
                    "supporting_facts": ["事实1原文", "事实2原文"],
                    "type": "..."
                }}
            """)
        
        return user_prompt