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from kag.interface import LLMClient | ||
from kag.solver.logic.solver_pipeline import SolverPipeline | ||
from kag.solver.implementation.table.table_reasoner import TableReasoner | ||
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llm_config = { | ||
"type": "ant_deepseek", | ||
"model": "deepseek-chat", | ||
"key": "gs540iivzezmidi3", | ||
"url": "https://zdfmng.alipay.com/commonQuery/queryData", | ||
"visitDomain": "BU_altas", | ||
"visitBiz": "BU_altas_tianchang", | ||
"visitBizLine": "BU_altas_tianchang_line", | ||
} | ||
class FinStateSolver(SolverPipeline): | ||
""" | ||
solver | ||
""" | ||
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def __init__( | ||
self, max_run=3, reflector=None, reasoner=None, generator=None, **kwargs | ||
): | ||
super().__init__(max_run, reflector, reasoner, generator, **kwargs) | ||
llm: LLMClient = LLMClient.from_config(llm_config) | ||
self.table_reasoner = TableReasoner(llm_module = llm) | ||
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def run(self, question, **kwargs): | ||
""" | ||
Executes the core logic of the problem-solving system. | ||
Parameters: | ||
- question (str): The question to be answered. | ||
Returns: | ||
- tuple: answer, trace log | ||
""" | ||
return self.table_reasoner.reason(question, llm_module=None) | ||
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if __name__ == "__main__": | ||
solver = FinStateSolver(KAG_PROJECT_ID=1) | ||
#question = "阿里巴巴最新的营业收入是多少,哪个部分收入占比最高,占了百分之多少?" | ||
#question = "阿里国际数字商业集团24年截至9月30日止六个月的收入是多少?它的经营利润率是多少?" | ||
question1 = "阿里巴巴财报中,2024年-截至9月30日止六个月的收入是多少?收入中每个部分分别占比多少?" | ||
#question = "可持续发展委员会有哪些成员组成" | ||
#question = "公允价值计量表中,24年9月30日,第二级资产各项目哪个占比最高,占了百分之多少?" | ||
# question = "231243423乘以13334233等于多少?" | ||
#question = "李妈妈有12个糖果,她给李明了3个,李红4个,那么李妈妈还剩下多少个糖果?" | ||
#response = solver.run(question) | ||
response = solver.run(question1) | ||
print("*" * 80) | ||
print(question1) | ||
print("*" * 40) | ||
print(response) | ||
print("*" * 80) |