179b048f07
- 引入了一个新的指标计算模块，用于检索和答案评估。 -通过整合基准生成和评估指标来简化评估服务。 -为新指标和基准生成功能添加了单元测试。 - 更新了前端组件，以利用新的图标，并改进了评估基准的样式。 -将与评估相关的逻辑整合为专门的“知识/评估”结构，以更好地组织。
153 lines
5.3 KiB
Python
153 lines
5.3 KiB
Python
"""
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RAG评估指标计算工具
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简化版：只保留Recall/F1（检索）和 LLM Judge（答案准确性）
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"""
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import json
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import textwrap
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from typing import Any
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from yuxi.utils import logger
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class RetrievalMetrics:
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"""检索评估指标计算"""
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@staticmethod
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def precision_at_k(retrieved_ids: list[str], relevant_ids: list[str], k: int) -> float:
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"""计算Precision@K"""
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if not retrieved_ids[:k]:
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return 0.0
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retrieved_set = set(retrieved_ids[:k])
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relevant_set = set(relevant_ids)
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return len(retrieved_set & relevant_set) / k
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@staticmethod
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def recall_at_k(retrieved_ids: list[str], relevant_ids: list[str], k: int) -> float:
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"""计算Recall@K"""
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if not relevant_ids:
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return 0.0
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retrieved_set = set(retrieved_ids[:k])
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relevant_set = set(relevant_ids)
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return len(retrieved_set & relevant_set) / len(relevant_set)
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@staticmethod
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def f1_score_at_k(retrieved_ids: list[str], relevant_ids: list[str], k: int) -> float:
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"""计算F1@K"""
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precision = RetrievalMetrics.precision_at_k(retrieved_ids, relevant_ids, k)
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recall = RetrievalMetrics.recall_at_k(retrieved_ids, relevant_ids, k)
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if precision + recall == 0:
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return 0.0
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return 2 * precision * recall / (precision + recall)
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class AnswerMetrics:
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"""答案评估指标计算"""
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@staticmethod
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async def judge_correctness(query: str, generated_answer: str, gold_answer: str, judge_llm: Any) -> dict[str, Any]:
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"""
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使用LLM判断生成的答案是否正确
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"""
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if not generated_answer:
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return {"score": 0.0, "reasoning": "未生成答案"}
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if not gold_answer:
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return {"score": 0.0, "reasoning": "无参考答案"}
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prompt = textwrap.dedent(f"""你是一个公正的评判者，请评估AI生成的答案相对于标准答案的准确性。
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问题：{query}
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标准答案：
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{gold_answer}
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AI生成的答案：
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{generated_answer}
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请判断AI生成的答案是否在事实层面与标准答案一致。
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忽略措辞、标点符号或格式上的细微差异。
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只关注核心事实是否准确包含。
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请返回以下JSON格式的结果（不要包含其他文本）：
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{{
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"score": 1.0, // 如果答案正确返回 1.0， 错误返回 0.0
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"reasoning": "简要说明判定理由"
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}}
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""")
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try:
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response = await judge_llm.call(prompt, stream=False)
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content = response.content.strip()
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# 尝试清理可能的 markdown 代码块
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if content.startswith("```json"):
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content = content[7:]
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if content.endswith("```"):
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content = content[:-3]
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content = content.strip()
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result = json.loads(content)
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return {"score": float(result.get("score", 0.0)), "reasoning": result.get("reasoning", "")}
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except Exception as e:
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logger.error(f"LLM 评判失败: {e}")
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return {"score": 0.0, "reasoning": f"评判出错: {str(e)}"}
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class EvaluationMetricsCalculator:
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"""综合评估指标计算器"""
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@staticmethod
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def calculate_retrieval_metrics(
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retrieved_chunks: list[dict[str, Any]], gold_chunk_ids: list[str], k_values: list[int] = [1, 3, 5, 10]
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) -> dict[str, float]:
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"""计算检索指标 (Recall, F1)"""
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if not retrieved_chunks or not gold_chunk_ids:
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return {}
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# 提取 ID
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retrieved_ids = []
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for chunk in retrieved_chunks:
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chunk_id = chunk.get("chunk_id") or chunk.get("metadata", {}).get("chunk_id")
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retrieved_ids.append(str(chunk_id) if chunk_id else "")
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metrics = {}
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for k in k_values:
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metrics[f"recall@{k}"] = RetrievalMetrics.recall_at_k(retrieved_ids, gold_chunk_ids, k)
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metrics[f"f1@{k}"] = RetrievalMetrics.f1_score_at_k(retrieved_ids, gold_chunk_ids, k)
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return metrics
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@staticmethod
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async def calculate_answer_metrics(
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query: str, generated_answer: str, gold_answer: str, judge_llm: Any = None
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) -> dict[str, Any]:
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"""计算答案指标 (LLM Judge)"""
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if not judge_llm:
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return {}
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return await AnswerMetrics.judge_correctness(query, generated_answer, gold_answer, judge_llm)
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@staticmethod
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def calculate_overall_score(
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retrieval_metrics_list: list[dict[str, float]], answer_metrics_list: list[dict[str, Any]]
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) -> float:
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"""计算整体平均分"""
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total_score = 0.0
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count = 0
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# 简单的平均策略：将所有retrieval metric的值和answer metric的score一起平均
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# 用户可能希望分开看，但calculate_overall_score返回一个单值。
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# 计算检索平均分
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for m in retrieval_metrics_list:
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if m:
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total_score += sum(m.values()) / len(m)
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count += 1
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# 计算答案平均分
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for m in answer_metrics_list:
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if "score" in m:
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total_score += m["score"]
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count += 1
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return total_score / count if count > 0 else 0.0
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