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91 lines
3.5 KiB
Python
91 lines
3.5 KiB
Python
import json
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from typing import Dict, List
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from knext.api.operator import ExtractOp
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from knext.operator.spg_record import SPGRecord
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from nn4k.invoker import LLMInvoker
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class _BuiltInOnlineExtractor(ExtractOp):
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def __init__(self, params: Dict[str, str] = None):
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"""
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Args:
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params: {"model_name": "openai", "token": "**"}
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"""
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super().__init__(params)
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self.model = self.load_model()
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self.prompt_ops = self.load_operator()
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def load_model(self):
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model_config = json.loads(self.params["model_config"])
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return LLMInvoker.from_config(model_config)
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def load_operator(self):
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import importlib.util
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prompt_config = json.loads(self.params["prompt_config"])
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prompt_ops = []
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for op_config in prompt_config:
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# 创建模块规范和模块对象
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spec = importlib.util.spec_from_file_location(op_config["modulePath"], op_config["filePath"])
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module = importlib.util.module_from_spec(spec)
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# 加载模块
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spec.loader.exec_module(module)
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op_clazz = getattr(module, op_config["className"])
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op_obj = op_clazz(**op_config["params"])
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prompt_ops.append(op_obj)
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return prompt_ops
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def eval(self, record: Dict[str, str]) -> List[SPGRecord]:
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# 对于单条数据【record】执行多层抽取
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# 每次抽取都需要执行op.build_prompt()->model.predict()->op.parse_response()流程
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# 且每次抽取后可能得到多条结果，下次抽取需要对多条结果分别进行抽取。
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collector = []
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input_params = [record]
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# 循环所有prompt算子，算子数量决定对单条数据执行几层抽取
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for op in self.prompt_ops:
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next_params = []
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# record_list可能有多条数据，对多条数据都要进行抽取
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for input_param in input_params:
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# 生成完整query
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query = op.build_prompt(input_param)
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# 模型预测，生成模型输出结果
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# response = self.model.remote_inference(query)
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response = "test"
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# response = '{"spo": [{"subject": "甲状腺结节", "predicate": "常见症状", "object": "头疼"}]}'
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# 模型结果的后置处理，可能会拆分成多条数据 List[dict[str, str]]
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if hasattr(op, "parse_response"):
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collector.extend(op.parse_response(response))
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if hasattr(op, "build_variables"):
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next_params.extend(op.build_variables(input_param, response))
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input_params = next_params
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print(collector)
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return collector
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if __name__ == '__main__':
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config = {
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"invoker_type": "OpenAI",
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"openai_api_key": "EMPTY",
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"openai_api_base": "http://localhost:38000/v1",
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"openai_model_name": "vicuna-7b-v1.5",
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"openai_max_tokens": 1000
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}
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model = LLMInvoker.from_config(config)
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query = """
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已知SPO关系包括:[录音室专辑(录音室专辑)-发行年份-文本]。从下列句子中提取定义的这些关系。最终抽取结果以json格式输出。
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input:《范特西》是周杰伦的第二张音乐专辑，由周杰伦担任制作人，于2001年9月14日发行，共收录《爱在西元前》《威廉古堡》《双截棍》等10首歌曲 [1]。
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输出格式为:{"spo":[{"subject":,"predicate":,"object":},]}
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"output":
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"""
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response = model.remote_inference(query)
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print(response)
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