init datamate

runtime/ops/llms/qa_condition_evaluator/process.py

@@ -0,0 +1,98 @@
+# -- encoding: utf-8 --
+
+"""
+Description: 基于LLM通过用户设置维度和相应描述进行QA对评估
+Create: 2023/11/7 9:26
+"""
+import json
+import re
+import time
+from pathlib import Path
+from typing import List, Dict, Any
+
+from loguru import logger
+
+
+from datamate.core.base_op import LLM
+
+
+class QAConditionEvaluator(LLM):
+    def __init__(self, *args, **kwargs):
+        super(QAConditionEvaluator, self).__init__(*args, **kwargs)
+        self.pattern = r'结果[:：] ?[YN]'
+        self.template_path = Path(__file__).parent / "resources/template.txt"
+        self.examples_path = Path(__file__).parent / "resources/examples.json"
+        self.task_id = kwargs.get("taskId", "default_id")
+        self.dimensions = kwargs.get("dimension", [
+                    {
+                        "dimension": "回答是否有针对性",
+                        "description": "回答应对问题中的所有疑问点提供正面、直接的回答，"
+                                       "不应引起疑惑。同时，答案不应有任何内容的遗漏，需构成一个完整的陈述。"
+                    },
+                    {
+                        "dimension": "问题是否独立",
+                        "description": "仅分析问题，问题的主体和客体都比较明确，即使有省略，也符合语言习惯。"
+                                       "在不需要补充其他信息的情况下不会引起疑惑。"
+                    },
+                    {
+                        "dimension": "语法是否错误",
+                        "description": "问题为疑问句，答案为陈述句; 不存在词语搭配不当的情况;连接词和标点符号不存在错用情况；"
+                                       "逻辑混乱的情况不存在；语法结构都正确且完整;"
+                    }
+                ])
+
+        self.llm = self.get_llm(*args, **kwargs)
+        self.prompts = self.build_llm_prompt(*args, **kwargs)
+
+    @staticmethod
+    def _process_examples(dimension_example: List) -> str:
+        if not dimension_example:
+            return "\n"
+        res = "\n以下是一些案例供你参考："
+        for single_example in dimension_example:
+            res += (f"\n问题：{single_example['question']}"
+                    f"\n回答：{single_example['answer']}"
+                    f"\n分析思路：{single_example['evaluate']}"
+                    f"\n结果：{single_example['result']}\n")
+        return res
+
+    def execute(self, sample: Dict[str, Any]) -> Dict[str, Any]:
+        start = time.time()
+        qas = json.loads(sample[self.text_key])
+        single_content_res = []
+        for qa in qas:
+            single_qa_res = []
+            for dimension, prompt in self.prompts.items():
+                local_result = self._llm_call_parse(qa, prompt, retry=2)
+                single_qa_res.append({"dimension": dimension, "result": local_result})
+            qa_response = {"qaId": qa["qaId"], "result": single_qa_res}
+            single_content_res.append(qa_response)
+
+        sample[self.text_key] = "Sucess"
+        self.save_sample(single_content_res, sample)
+        cost_time = time.time() - start
+        logger.info(f"task id: {self.task_id}, method: QAConditionEvaluator costs {cost_time:.6f} s")
+        return sample
+
+    def build_llm_prompt(self, *args, **kwargs) -> Dict:
+        templates = self.template_path.read_text(encoding="utf-8")
+        examples_dict = json.loads(self.examples_path.read_text(encoding="utf-8"))
+        prompts_dict = {}
+        for dimension in self.dimensions:
+            name, des = dimension["dimension"], dimension["description"]
+            dimension_example = self._process_examples(examples_dict.get(name))
+            dimension_prompt = templates.format(criterion=des, examples=dimension_example, question="{question}",
+                                                answer="{answer}")
+            prompts_dict[name] = dimension_prompt
+        return prompts_dict
+
+    def _llm_call_parse(self, data: Dict, prompt: str, retry: int = 2):
+        try:
+            for _ in range(retry):
+                response = self.llm(prompt.format(question=data["question"], answer=data["answer"]))
+                result = re.findall(self.pattern, response)
+                if result:
+                    return "Y" in result[0]
+        except RuntimeError as e:
+            logger.error(f"method: QAConditionEvaluator execution error, cause by {e}")
+        return False