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https://github.com/OpenSPG/openspg.git
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Merge remote-tracking branch 'upstream/branch0101' into branch0101
This commit is contained in:
matthewhyx
commit
851a575514
@ -14,24 +14,18 @@ class IndicatorFuse(FuseOp):
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self.search_client = SearchClient("Financial.Indicator")
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def invoke(self, subject_records: List[SPGRecord]) -> List[SPGRecord]:
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print("##########IndicatorFuse###########")
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print("####################IndicatorFuse#####################")
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print("IndicatorFuse(Input): ")
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print(subject_records)
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print("----------------------")
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[print(r) for r in subject_records]
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fused_records = []
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for record in subject_records:
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query = {"match": {"name": record.get_property("name", "")}}
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recall_records = self.search_client.search(query, start=0, size=10)
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if recall_records is not None and len(recall_records) > 0:
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rerank_record = SPGRecord(
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"Financial.Indicator",
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{
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"id": recall_records[0].doc_id,
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"name": recall_records[0].properties.get("name", ""),
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},
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)
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rerank_record.update_property("name", record.get_property("name"))
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fused_records.append(rerank_record)
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continue
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fused_records.append(record)
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print("IndicatorFuse(Output): ")
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print(fused_records)
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print("##########IndicatorFuse###########")
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print("----------------------")
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[print(r) for r in fused_records]
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return fused_records
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@ -20,35 +20,18 @@ ${rel}
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"""
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def build_prompt(self, variables: Dict[str, str]):
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"""
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record: {
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"input": "济南市财政收入质量及自给能力均较好，但土地出让收入大幅下降致综合财力明显下滑。济南市财政收入质量及自给能力均较好，但土地出让收入大幅下降致综合
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财力明显下滑。2022年济南市一般公共预算收入1,000.21亿元，扣除留 抵退税因素后同比增长8%，规模在山东省下辖地市中排名第2位；其中税收收入690.31亿元，税收占比69.02%；一般公共 预算支出1,260.23亿元，财政自给率79.37%。政
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府性基金收入547.29亿元，同比大幅下降48.38%，主要系土地出让收入 同比由966.74亿元降至453.74亿元；转移性收入285.78亿元（上年同期为233.11亿元）；综合财力约1,833.28亿元（上年 同期为2,301.02亿元）。"
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"ner": "[{'财政': ['财政收入质量', '财政自给能力', '土地出让收入', '一般公共预算收入', '留抵退税', '税收收入', '税收收入/一般公共预算收入', '一般公共预算支出', '财政自给率', '政府性基金收入', '转移性收入', '综合财力']}]",
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"rel": "[{'subject': '一般公共预算收入', 'predicate': '包含', 'object': ['税收收入']}, {'subject': '税收收入', 'predicate': '包含', 'object': ['留抵退税']}, {'subject': '政府性基金收入', 'predicate': '包含', 'object': ['土地出让收入', '转移性收入']}, {'subject': '综合财力', 'predicate': '包含', 'object': ['一般公共预算收入', '政府性基金收入']}]",
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"id": "财政",
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"name": "财政",
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"hasA": "财政收入质量,财政自给能力,土地出让收入....."
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}
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"""
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return (
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template = (
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self.template.replace("${input}", variables.get("input"))
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.replace("${ner}", variables.get("ner"))
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.replace("${rel}", variables.get("rel"))
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.replace("${ner}", variables.get("IndicatorNER"))
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.replace("${rel}", variables.get("IndicatorREL"))
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)
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print("####################IndicatorLOGIC(状态逻辑抽取)#####################")
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print("LLM(Input): ")
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print("----------------------")
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print(template)
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return template
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def parse_response(self, response: str) -> List[SPGRecord]:
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"""
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response: "[{\"subject\": \"土地出让收入大幅下降\", \"predicate\": \"顺承\", \"object\": [\"综合财力明显下滑\"]}]"
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"""
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response = (
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'[{"subject": "土地出让收入大幅下降", "predicate": "顺承", "object": ["综合财力明显下滑"]}]'
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)
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print("##########IndicatorLOGIC###########")
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print("IndicatorLOGIC(Input): ")
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print(response)
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output_list = json.loads(response)
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logic_result = []
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@ -61,7 +44,4 @@ ${rel}
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elif k == "object":
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properties["causeOf"] = ",".join(v)
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logic_result.append(SPGRecord("Financial.State", properties=properties))
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print("IndicatorLOGIC(Output): ")
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print(logic_result)
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print("##########IndicatorLOGIC###########")
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return logic_result
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@ -17,22 +17,18 @@ ${input}
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"""
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def build_prompt(self, variables: Dict[str, str]):
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return self.template.replace("${input}", variables.get("input"))
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template = self.template.replace("${input}", variables.get("input"))
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print("####################IndicatorNER(指标抽取)#####################")
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print("LLM(Input): ")
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print("----------------------")
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print(template)
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return template
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def parse_response(self, response: str) -> List[SPGRecord]:
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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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print("##########IndicatorNER###########")
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print("IndicatorNER(Input): ")
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print(response)
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output_list = json.loads(response.replace("'", '"'))
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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 category, indicator_list in output.items():
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# '财政', ['财政收入....]
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for indicator in indicator_list:
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ner_result.append(
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SPGRecord(
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@ -40,17 +36,4 @@ ${input}
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properties={"id": indicator, "name": indicator},
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)
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)
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print("IndicatorNER(Output): ")
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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 = "[{'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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return [{"input": variables["input"], "ner": response}]
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@ -14,8 +14,9 @@ class IndicatorPredict(PredictOp):
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self.search_client = SearchClient("Financial.Indicator")
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def invoke(self, subject_record: SPGRecord) -> List[SPGRecord]:
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print("##########IndicatorPredict###########")
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print("####################IndicatorPredict(状态关联指标预测)#####################")
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print("IndicatorPredict(Input): ")
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print("----------------------")
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print(subject_record)
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predicted_records = []
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query = {"match": {"name": subject_record.get_property("name", "")}}
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@ -30,6 +31,6 @@ class IndicatorPredict(PredictOp):
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)
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predicted_records.append(rerank_record)
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print("IndicatorPredict(Output): ")
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print(predicted_records)
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print("##########IndicatorPredict###########")
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print("----------------------")
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[print(r) for r in predicted_records]
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return predicted_records
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@ -16,26 +16,11 @@ ${ner}
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"""
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def build_prompt(self, variables: Dict[str, str]) -> str:
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"""
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record: {
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"input": "济南市财政收入质量及自给能力均较好，但土地出让收入大幅下降致综合财力明显下滑。济南市财政收入质量及自给能力均较好，但土地出让收入大幅下降致综合
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财力明显下滑。2022年济南市一般公共预算收入1,000.21亿元，扣除留 抵退税因素后同比增长8%，规模在山东省下辖地市中排名第2位；其中税收收入690.31亿元，税收占比69.02%；一般公共 预算支出1,260.23亿元，财政自给率79.37%。政
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府性基金收入547.29亿元，同比大幅下降48.38%，主要系土地出让收入 同比由966.74亿元降至453.74亿元；转移性收入285.78亿元（上年同期为233.11亿元）；综合财力约1,833.28亿元（上年 同期为2,301.02亿元）。"
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"ner": "[{'财政': ['财政收入质量', '财政自给能力', '土地出让收入', '一般公共预算收入', '留抵退税', '税收收入', '税收收入/一般公共预算收入', '一般公共预算支出', '财政自给率', '政府性基金收入', '转移性收入', '综合财力']}]",
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"id": "财政",
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"name": "财政",
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"hasA": "财政收入质量,财政自给能力,土地出让收入....."
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}
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"""
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return self.template.replace("${input}", variables.get("input")).replace(
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"${ner}", variables.get("ner")
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template = self.template.replace("${input}", variables.get("input")).replace(
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"${ner}", variables.get("IndicatorNER")
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)
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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 = "[{'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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return [{"input": variables["input"], "ner": variables["ner"], "rel": response}]
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print("####################IndicatorREL(指标关系抽取)#####################")
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print("LLM(Input): ")
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print("----------------------")
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print(template)
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return template
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@ -14,9 +14,10 @@ class StateFuse(FuseOp):
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self.search_client = SearchClient("Financial.State")
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def invoke(self, subject_records: List[SPGRecord]) -> List[SPGRecord]:
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print("##########StateFuse###########")
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print("####################StateFuse(状态融合)#####################")
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print("StateFuse(Input): ")
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print(subject_records)
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print("----------------------")
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[print(r) for r in subject_records]
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fused_records = []
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for record in subject_records:
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query = {"match": {"name": record.get_property("name", "")}}
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@ -32,6 +33,6 @@ class StateFuse(FuseOp):
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rerank_record.update_property("name", record.get_property("name"))
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fused_records.append(rerank_record)
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print("StateFuse(Output): ")
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print(fused_records)
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print("##########StateFuse###########")
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print("----------------------")
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[print(r) for r in fused_records]
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return fused_records
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@ -99,6 +99,7 @@ input:${input}
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def _render(self, spg_type: BaseSpgType, property_names: List[str]):
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spos = []
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repeat_desc = []
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for property_name in property_names:
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if property_name in ["id", "name", "description"]:
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continue
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@ -108,11 +109,11 @@ input:${input}
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if object_type:
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object_desc = object_type.desc
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spos.append(
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f"{spg_type.name_zh}({spg_type.desc or spg_type.name_zh})"
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f"-{prop.name_zh}({prop.desc or prop.name_zh})"
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f"{spg_type.name_zh}" + (f"({spg_type.desc or spg_type.name_zh})" if spg_type.name_zh not in repeat_desc else "") +
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f"-{prop.name_zh}({prop.desc or prop.name_zh})"
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f"-{prop.object_type_name_zh}({object_desc or prop.object_type_name_zh})"
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)
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schema_text = "[" + ",".join(spos) + "]"
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schema_text = "\n[" + ",\n".join(spos) + "]\n"
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self.template = self.template.replace("${schema}", schema_text)
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@ -52,8 +52,16 @@ class _BuiltInOnlineExtractor(ExtractOp):
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while retry_times < self.max_retry_times:
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try:
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query = op.build_prompt(input_param)
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# response = self.model.remote_inference(query)
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response = "test"
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op_name = op.__class__.__name__
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if op_name == "IndicatorNER":
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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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elif op_name == "IndicatorREL":
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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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elif op_name == "IndicatorLOGIC":
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response = '[{"subject": "土地出让收入大幅下降", "predicate": "顺承", "object": ["综合财力明显下滑"]}]'
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else:
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print(query)
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response = self.model.remote_inference(query)
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collector.extend(op.parse_response(response))
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next_params.extend(
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op.build_next_variables(input_param, response)
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@ -61,6 +69,9 @@ class _BuiltInOnlineExtractor(ExtractOp):
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break
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except Exception as e:
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retry_times += 1
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print(e)
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raise e
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input_params = next_params
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print("####################抽取结果#####################")
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[print(c) for c in collector]
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return collector
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@ -127,7 +127,11 @@ class PromptOp(BaseOp, ABC):
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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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return []
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variables.update({f"{self.__class__.__name__}": response})
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print("LLM(Output): ")
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print("----------------------")
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print([variables])
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return [variables]
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def invoke(self, *args):
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pass
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