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45 lines
1.1 KiB
Python
45 lines
1.1 KiB
Python
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from knext.component.builder import CSVReader, KGWriter, LLMBasedExtractor, SubGraphMapping, SPGTypeMapping
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from nn4k.invoker import LLMInvoker
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from knext.api.operator import SPOPrompt
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"""
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1. 定义输入源，CSV文件
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"""
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source = CSVReader(
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local_path="job/data/Disease.csv",
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columns=["id", "input"],
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start_row=2,
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)
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"""
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2. 定义大模型抽取组件，从长文本中抽取Medical.Disease类型实体
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"""
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extract = LLMBasedExtractor(llm=LLMInvoker.from_config("openai_infer.json"),
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prompt_ops=[SPOPrompt("Medical1.Disease", ["commonSymptom", "applicableDrug"])])
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mapping = SubGraphMapping(spg_type_name="Medical1.Disease")\
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.add_mapping_field("id", "id")\
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.add_mapping_field("name", "name")\
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.add_mapping_field("commonSymptom", "commonSymptom")\
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.add_mapping_field("applicableDrug", "applicableDrug")
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"""
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4. 定义输出到图谱
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"""
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sink = KGWriter()
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"""
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5. 定义builder_chain
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"""
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builder_chain = source >> extract >> mapping >> sink
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"""
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5. 执行builder_chain，或发布成平台任务
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"""
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builder_chain.invoke()
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# builder_chain.submit()
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