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64 lines
3.0 KiB
Python
64 lines
3.0 KiB
Python
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import json
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from typing import Dict, List
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from knext.operator.op import PromptOp
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from knext.operator.spg_record import SPGRecord
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class IndicatorNER(PromptOp):
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template = """
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请从以下文本中提取所有指标并给出指标类型，以json格式输出
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#####
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输出格式:
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[{{"XXX": ["XXX", "XXX"]}}, {{"XXX": ["XXX", "XXX"]}}]
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#####
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文本:
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{input}
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"""
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"""
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record: {"input": "济南市财政收入质量及自给能力均较好，但土地出让收入大幅下降致综合财力明显下滑。济南市财政收入质量及自给能力均较好，但土地出让收入大幅下降致综合
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财力明显下滑。2022年济南市一般公共预算收入1,000.21亿元，扣除留 抵退税因素后同比增长8%，规模在山东省下辖地市中排名第2位；其中税收收入690.31亿元，税收占比69.02%；一般公共 预算支出1,260.23亿元，财政自给率79.37%。政
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府性基金收入547.29亿元，同比大幅下降48.38%，主要系土地出让收入 同比由966.74亿元降至453.74亿元；转移性收入285.78亿元（上年同期为233.11亿元）；综合财力约1,833.28亿元（上年 同期为2,301.02亿元）。"}
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"""
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def build_prompt(self, record: Dict[str, str]):
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return self.template.format(**record)
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"""
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response: "[{'财政': ['财政收入质量', '财政自给能力', '土地出让收入', '一般公共预算收入', '留抵退税', '税收收入', '税收收入/一般公共预算收入', '一般公共预算支出', '财政自给率', '政府性基金收入', '转移性收入', '综合财力']}]"
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"""
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def parse_response(
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self, response: str
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) -> List[SPGRecord]:
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output_list = json.loads(response)
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ner_result = []
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# IF hasA
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for output in output_list:
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# {'财政': ['财政收入....}
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for k, v in output.items():
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# '财政', ['财政收入....]
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ner_result.append(SPGRecord("FEL.Indicator", properties={"id": k, "name": k, "hasA": ','.join(v)}))
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# ELSE isA
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# TODO 通过属性isA支持
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for output in output_list:
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# {'财政': ['财政收入....}
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for k, v in output.items():
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# '财政', ['财政收入....]
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for _v in v:
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# '财政收入....'
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ner_result.append(SPGRecord("FEL.Indicator", properties={"id": f'{k}-{_v}', "name": _v}))
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return ner_result
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def generate_placeholder(
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self, record: Dict[str, str], response: str
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) -> List[Dict[str, str]]:
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"""
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response: "[{'subject': '一般公共预算收入', 'predicate': '包含', 'object': ['税收收入']}, {'subject': '税收收入', 'predicate': '包含', 'object': ['留抵退税']}, {'subject': '政府性基金收入', 'predicate': '包含', 'object': ['土地出让收入', '转移性收入']}, {'subject': '综合财力', 'predicate': '包含', 'object': ['一般公共预算收入', '政府性基金收入']}]"
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"""
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return [{"input": record["input"], "ner": response}]
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