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58 lines
2.3 KiB
Python
58 lines
2.3 KiB
Python
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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def build_prompt(self, variables: Dict[str, str]):
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return self.template.replace("${input}", variables.get("input"))
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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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#
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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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#
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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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print("##########IndicatorNER###########")
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ner_result = [SPGRecord(spg_type_name="Financial.Indicator", properties={"id": "土地出让收入", "name": "土地出让收入"})]
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print(ner_result)
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print("##########IndicatorNER###########")
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return ner_result
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def build_next_variables(
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self, variables: 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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response = ""
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return [{"input": variables["input"], "ner": response}]
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