358 lines
12 KiB
Python
358 lines
12 KiB
Python
"""
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Few-shot示例选择器 - 基于经验数据集动态选择相关示例
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与 Schema 向量检索一致，使用 utils.embedding.get_embedder()（本地 Qwen3 或远程 OpenAI 兼容 API，
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由 USE_LOCAL_EMBEDDING 及 OPENAI_* / MODELSCOPE_* / DASHSCOPE_* 等环境变量决定）。
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用法：
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from utils.fewshot_selector import FewShotSelector
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selector = FewShotSelector("data/experiences/all_samples.jsonl")
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examples = selector.select(question="查询2024年1月的销售额", top_k=3)
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# 在Prompt中使用
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prompt = f"{examples}\n当前问题：{question}\nSchema：{schema}"
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"""
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import json
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import os
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from pathlib import Path
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from typing import List, Dict, Optional
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from dataclasses import dataclass
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import numpy as np
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import logging
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logger = logging.getLogger(__name__)
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_DEFAULT_LOCAL_EMBED_PATH = "./data/models/Qwen3-Embedding-0.6B"
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@dataclass
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class ExperienceSample:
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"""经验数据样本"""
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qid: str
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question_zh: str
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question_en: Optional[str]
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sql: str
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explanation: str
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rating: Optional[int]
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tags: List[str]
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difficulty: str
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@classmethod
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def from_dict(cls, data: dict) -> "ExperienceSample":
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return cls(
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qid=data.get("qid", ""),
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question_zh=data.get("question_zh", ""),
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question_en=data.get("question_en"),
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sql=data.get("sql", ""),
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explanation=data.get("explanation", ""),
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rating=data.get("rating"),
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tags=data.get("tags", []),
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difficulty=data.get("difficulty", "medium")
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)
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def to_fewshot_format(self, include_explanation: bool = True) -> str:
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"""转换为few-shot格式"""
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result = f"问题：{self.question_zh}\nSQL：\n{self.sql}"
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if include_explanation and self.explanation:
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result += f"\n说明：{self.explanation[:200]}"
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return result
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def to_dict(self) -> dict:
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return {
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"qid": self.qid,
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"question": self.question_zh,
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"sql": self.sql,
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"rating": self.rating,
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"tags": self.tags,
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"difficulty": self.difficulty
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}
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class FewShotSelector:
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"""
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Few-shot示例选择器
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根据用户问题，从经验数据集中检索最相似的示例，
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用于增强Prompt，提升LLM生成质量。
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"""
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def __init__(
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self,
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samples_path: str,
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embedding_model_path: Optional[str] = None,
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use_cache: bool = True,
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):
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"""
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Args:
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samples_path: 样本JSONL文件路径
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embedding_model_path: 本地 Embedding 模型目录；None 时用环境变量
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EMBEDDING_MODEL_PATH（仅 USE_LOCAL_EMBEDDING=true 时有效）
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use_cache: 是否缓存样本向量（按向量维度分文件，换模型会自动重建）
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"""
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self.samples_path = Path(samples_path)
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self.samples: List[ExperienceSample] = []
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self._embedder = None
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self.embeddings: Optional[np.ndarray] = None
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self.use_cache = use_cache
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self.cache_path: Optional[Path] = None
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self._embedding_model_path = (
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embedding_model_path
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if embedding_model_path is not None
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else os.getenv("EMBEDDING_MODEL_PATH", _DEFAULT_LOCAL_EMBED_PATH).strip()
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)
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self._load_samples()
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self._build_index()
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def _load_samples(self):
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"""加载样本数据"""
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if not self.samples_path.exists():
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raise FileNotFoundError(f"样本文件不存在: {self.samples_path}")
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logger.info(f"加载样本: {self.samples_path}")
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with self.samples_path.open('r', encoding='utf-8') as f:
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for line in f:
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data = json.loads(line.strip())
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self.samples.append(ExperienceSample.from_dict(data))
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logger.info(f"[OK] 加载 {len(self.samples)} 个样本")
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def _build_index(self):
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"""用项目统一 Embedder 构建语义索引"""
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from utils.embedding import get_embedder
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self._embedder = get_embedder(self._embedding_model_path)
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probe = self._embedder.encode(
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[" "],
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batch_size=1,
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normalize=True,
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show_progress=False,
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)
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dim = int(probe.shape[1])
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self.cache_path = (
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self.samples_path.parent / f"{self.samples_path.stem}.fewshot_dim{dim}.npy"
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)
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if self.use_cache and self.cache_path.exists():
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try:
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self.embeddings = np.load(self.cache_path)
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if (
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self.embeddings.shape[0] == len(self.samples)
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and self.embeddings.shape[1] == dim
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):
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logger.info(f"[OK] 加载 Few-shot 向量缓存: {self.cache_path}")
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return
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except Exception as e:
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logger.warning(f"Few-shot 缓存加载失败: {e}，将重新计算")
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# 远程 API 通常不接受空字符串作 input
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questions = [
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(s.question_zh or "").strip() or " "
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for s in self.samples
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]
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if not questions:
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self.embeddings = np.empty((0, dim), dtype=np.float32)
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return
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logger.info(f"计算 {len(questions)} 个 Few-shot 样本向量...")
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self.embeddings = self._embedder.encode(
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questions,
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batch_size=min(32, len(questions)),
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normalize=True,
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show_progress=True,
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)
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if self.use_cache and self.cache_path is not None:
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np.save(self.cache_path, self.embeddings)
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logger.info(f"[OK] Few-shot 向量已缓存: {self.cache_path}")
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def select(
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self,
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question: str,
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top_k: int = 3,
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min_rating: Optional[int] = None,
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required_tags: Optional[List[str]] = None,
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max_difficulty: str = "hard",
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exclude_qids: Optional[List[str]] = None
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) -> List[ExperienceSample]:
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"""
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选择最相关的few-shot示例
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Args:
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question: 用户问题
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top_k: 返回示例数量
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min_rating: 最低评分（None表示不限制）
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required_tags: 必须包含的标签（如["aggregation", "join"]）
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max_difficulty: 最大难度（过滤更难的示例）
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exclude_qids: 排除的QID（避免与当前问题相同）
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Returns:
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排序后的示例列表（最相关优先）
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"""
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if self._embedder is None or self.embeddings is None:
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logger.error("Few-shot 索引未初始化")
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return []
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if len(self.samples) == 0:
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return []
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q_emb = self._embedder.encode(
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[question],
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batch_size=1,
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normalize=True,
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show_progress=False,
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)[0]
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scores = np.dot(self.embeddings, q_emb)
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candidates = []
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for idx, (score, sample) in enumerate(zip(scores, self.samples)):
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if exclude_qids and sample.qid in exclude_qids:
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continue
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if min_rating and sample.rating and sample.rating < min_rating:
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continue
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if max_difficulty == "easy" and sample.difficulty != "easy":
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continue
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if max_difficulty == "medium" and sample.difficulty == "hard":
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continue
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if required_tags and not all(tag in sample.tags for tag in required_tags):
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continue
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candidates.append((idx, score, sample))
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candidates.sort(key=lambda x: -x[1])
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selected = [sample for _, _, sample in candidates[:top_k]]
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logger.info(
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f"Few-shot选择: 问题='{question[:30]}...' "
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f"→ 选中{len(selected)}个示例 (top_k={top_k}, min_rating={min_rating})"
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)
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for s in selected:
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logger.debug(
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f" [{s.qid}] {s.question_zh[:50]}... (rating={s.rating}, tags={s.tags[:3]})"
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)
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return selected
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def get_examples_prompt(
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self,
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question: str,
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top_k: int = 3,
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min_rating: int = 7,
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**kwargs
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) -> str:
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"""
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生成few-shot prompt片段
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Returns:
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格式化的示例字符串，可直接插入Prompt
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"""
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examples = self.select(question, top_k=top_k, min_rating=min_rating, **kwargs)
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if not examples:
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return ""
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lines = ["以下为相似问题的参考SQL示例：\n"]
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for i, ex in enumerate(examples, 1):
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lines.append(f"示例{i}：")
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lines.append(f"问题：{ex.question_zh}")
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lines.append(f"SQL：\n{ex.sql}")
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if ex.explanation:
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lines.append(f"说明：{ex.explanation[:150]}...")
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lines.append("") # 空行分隔
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return "\n".join(lines)
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def get_tagged_examples(self, tags: List[str], top_k_per_tag: int = 2) -> str:
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"""获取特定标签的示例"""
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tagged_samples = []
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for sample in self.samples:
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if any(tag in sample.tags for tag in tags):
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tagged_samples.append(sample)
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tagged_samples.sort(key=lambda s: -(s.rating or 0))
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selected = tagged_samples[:top_k_per_tag * len(tags)]
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lines = [f"# {tags} 相关示例\n"]
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for ex in selected:
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lines.append(f"## {ex.qid}. {ex.question_zh[:50]}")
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lines.append(f"评分: {ex.rating}/10")
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lines.append(f"标签: {', '.join(ex.tags)}")
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lines.append(f"```sql\n{ex.sql}\n```\n")
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return "\n".join(lines)
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def get_stats(self) -> Dict:
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"""获取数据集统计"""
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stats = {
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"total": len(self.samples),
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"by_rating": {},
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"by_difficulty": {},
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"by_tag": {},
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"avg_rating": 0.0
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}
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ratings = [s.rating for s in self.samples if s.rating]
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if ratings:
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stats["avg_rating"] = sum(ratings) / len(ratings)
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for r in range(1, 11):
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stats["by_rating"][r] = sum(1 for s in self.samples if s.rating == r)
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for diff in ["easy", "medium", "hard"]:
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stats["by_difficulty"][diff] = sum(
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1 for s in self.samples if s.difficulty == diff
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)
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tag_counts = {}
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for s in self.samples:
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for tag in s.tags:
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tag_counts[tag] = tag_counts.get(tag, 0) + 1
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stats["by_tag"] = tag_counts
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return stats
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def load_fewshot_selector() -> FewShotSelector:
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"""加载默认的few-shot选择器"""
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# __file__ = backend/utils/fewshot_selector.py → 仓库根为 parents[2]
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default_path = Path(__file__).resolve().parents[2] / "data" / "experiences" / "all_samples.jsonl"
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return FewShotSelector(str(default_path))
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if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser(description="Few-shot示例选择器")
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parser.add_argument("--samples", default="data/experiences/all_samples.jsonl")
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parser.add_argument("--question", help="测试问题")
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parser.add_argument("--top-k", type=int, default=3)
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parser.add_argument("--min-rating", type=int, default=7)
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parser.add_argument("--stats", action="store_true", help="显示数据集统计")
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args = parser.parse_args()
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selector = FewShotSelector(args.samples)
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if args.stats:
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stats = selector.get_stats()
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print("📊 数据集统计:")
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print(f" 总样本: {stats['total']}")
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print(f" 平均评分: {stats['avg_rating']:.1f}")
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print(f" 难度分布: {stats['by_difficulty']}")
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print(f"\n Top 10 标签:")
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sorted_tags = sorted(stats["by_tag"].items(), key=lambda x: -x[1])[:10]
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for tag, count in sorted_tags:
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print(f" {tag}: {count}")
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elif args.question:
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examples = selector.select(args.question, top_k=args.top_k, min_rating=args.min_rating)
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print(f"\n为问题 '{args.question}' 选择的示例:\n")
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for ex in examples:
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print(f"[{ex.qid}] 评分:{ex.rating} 难度:{ex.difficulty}")
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print(f"问题: {ex.question_zh}")
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print(f"SQL:\n{ex.sql}\n")
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else:
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print("请指定 --question 或 --stats")
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