ai-g3sb-backman2.0/backend/utils/fewshot_selector.py

"""
Few-shot示例选择器 - 基于经验数据集动态选择相关示例

与 Schema 向量检索一致，使用 utils.embedding.get_embedder()（本地 Qwen3 或远程 OpenAI 兼容 API，
由 USE_LOCAL_EMBEDDING 及 OPENAI_* / MODELSCOPE_* / DASHSCOPE_* 等环境变量决定）。

用法：
    from utils.fewshot_selector import FewShotSelector

    selector = FewShotSelector("data/experiences/all_samples.jsonl")
    examples = selector.select(question="查询2024年1月的销售额", top_k=3)

    # 在Prompt中使用
    prompt = f"{examples}\n当前问题：{question}\nSchema：{schema}"
"""

import json
import os
from pathlib import Path
from typing import List, Dict, Optional
from dataclasses import dataclass
import numpy as np
import logging

logger = logging.getLogger(__name__)

_DEFAULT_LOCAL_EMBED_PATH = "./data/models/Qwen3-Embedding-0.6B"


@dataclass
class ExperienceSample:
    """经验数据样本"""
    qid: str
    question_zh: str
    question_en: Optional[str]
    sql: str
    explanation: str
    rating: Optional[int]
    tags: List[str]
    difficulty: str

    @classmethod
    def from_dict(cls, data: dict) -> "ExperienceSample":
        return cls(
            qid=data.get("qid", ""),
            question_zh=data.get("question_zh", ""),
            question_en=data.get("question_en"),
            sql=data.get("sql", ""),
            explanation=data.get("explanation", ""),
            rating=data.get("rating"),
            tags=data.get("tags", []),
            difficulty=data.get("difficulty", "medium")
        )

    def to_fewshot_format(self, include_explanation: bool = True) -> str:
        """转换为few-shot格式"""
        result = f"问题：{self.question_zh}\nSQL：\n{self.sql}"
        if include_explanation and self.explanation:
            result += f"\n说明：{self.explanation[:200]}"
        return result

    def to_dict(self) -> dict:
        return {
            "qid": self.qid,
            "question": self.question_zh,
            "sql": self.sql,
            "rating": self.rating,
            "tags": self.tags,
            "difficulty": self.difficulty
        }


class FewShotSelector:
    """
    Few-shot示例选择器

    根据用户问题，从经验数据集中检索最相似的示例，
    用于增强Prompt，提升LLM生成质量。
    """

    def __init__(
        self,
        samples_path: str,
        embedding_model_path: Optional[str] = None,
        use_cache: bool = True,
    ):
        """
        Args:
            samples_path: 样本JSONL文件路径
            embedding_model_path: 本地 Embedding 模型目录；None 时用环境变量
                EMBEDDING_MODEL_PATH（仅 USE_LOCAL_EMBEDDING=true 时有效）
            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）
        """
        self.samples_path = Path(samples_path)
        self.samples: List[ExperienceSample] = []
        self._embedder = None
        self.embeddings: Optional[np.ndarray] = None
        self.use_cache = use_cache
        self.cache_path: Optional[Path] = None
        self._embedding_model_path = (
            embedding_model_path
            if embedding_model_path is not None
            else os.getenv("EMBEDDING_MODEL_PATH", _DEFAULT_LOCAL_EMBED_PATH).strip()
        )

        self._load_samples()
        self._build_index()

    def _load_samples(self):
        """加载样本数据"""
        if not self.samples_path.exists():
            raise FileNotFoundError(f"样本文件不存在: {self.samples_path}")

        logger.info(f"加载样本: {self.samples_path}")
        with self.samples_path.open('r', encoding='utf-8') as f:
            for line in f:
                data = json.loads(line.strip())
                self.samples.append(ExperienceSample.from_dict(data))

        logger.info(f"[OK] 加载 {len(self.samples)} 个样本")

    def _build_index(self):
        """用项目统一 Embedder 构建语义索引"""
        from utils.embedding import get_embedder

        self._embedder = get_embedder(self._embedding_model_path)
        probe = self._embedder.encode(
            [" "],
            batch_size=1,
            normalize=True,
            show_progress=False,
        )
        dim = int(probe.shape[1])
        self.cache_path = (
            self.samples_path.parent / f"{self.samples_path.stem}.fewshot_dim{dim}.npy"
        )

        if self.use_cache and self.cache_path.exists():
            try:
                self.embeddings = np.load(self.cache_path)
                if (
                    self.embeddings.shape[0] == len(self.samples)
                    and self.embeddings.shape[1] == dim
                ):
                    logger.info(f"[OK] 加载 Few-shot 向量缓存: {self.cache_path}")
                    return
            except Exception as e:
                logger.warning(f"Few-shot 缓存加载失败: {e}，将重新计算")

        # 远程 API 通常不接受空字符串作 input
        questions = [
            (s.question_zh or "").strip() or " "
            for s in self.samples
        ]
        if not questions:
            self.embeddings = np.empty((0, dim), dtype=np.float32)
            return

        logger.info(f"计算 {len(questions)} 个 Few-shot 样本向量...")
        self.embeddings = self._embedder.encode(
            questions,
            batch_size=min(32, len(questions)),
            normalize=True,
            show_progress=True,
        )

        if self.use_cache and self.cache_path is not None:
            np.save(self.cache_path, self.embeddings)
            logger.info(f"[OK] Few-shot 向量已缓存: {self.cache_path}")

    def select(
        self,
        question: str,
        top_k: int = 3,
        min_rating: Optional[int] = None,
        required_tags: Optional[List[str]] = None,
        max_difficulty: str = "hard",
        exclude_qids: Optional[List[str]] = None
    ) -> List[ExperienceSample]:
        """
        选择最相关的few-shot示例

        Args:
            question: 用户问题
            top_k: 返回示例数量
            min_rating: 最低评分（None表示不限制）
            required_tags: 必须包含的标签（如["aggregation", "join"]）
            max_difficulty: 最大难度（过滤更难的示例）
            exclude_qids: 排除的QID（避免与当前问题相同）

        Returns:
            排序后的示例列表（最相关优先）
        """
        if self._embedder is None or self.embeddings is None:
            logger.error("Few-shot 索引未初始化")
            return []

        if len(self.samples) == 0:
            return []

        q_emb = self._embedder.encode(
            [question],
            batch_size=1,
            normalize=True,
            show_progress=False,
        )[0]

        scores = np.dot(self.embeddings, q_emb)

        candidates = []
        for idx, (score, sample) in enumerate(zip(scores, self.samples)):
            if exclude_qids and sample.qid in exclude_qids:
                continue
            if min_rating and sample.rating and sample.rating < min_rating:
                continue
            if max_difficulty == "easy" and sample.difficulty != "easy":
                continue
            if max_difficulty == "medium" and sample.difficulty == "hard":
                continue
            if required_tags and not all(tag in sample.tags for tag in required_tags):
                continue

            candidates.append((idx, score, sample))

        candidates.sort(key=lambda x: -x[1])

        selected = [sample for _, _, sample in candidates[:top_k]]

        logger.info(
            f"Few-shot选择: 问题='{question[:30]}...' "
            f"→ 选中{len(selected)}个示例 (top_k={top_k}, min_rating={min_rating})"
        )
        for s in selected:
            logger.debug(
                f"  [{s.qid}] {s.question_zh[:50]}... (rating={s.rating}, tags={s.tags[:3]})"
            )

        return selected

    def get_examples_prompt(
        self,
        question: str,
        top_k: int = 3,
        min_rating: int = 7,
        **kwargs
    ) -> str:
        """
        生成few-shot prompt片段

        Returns:
            格式化的示例字符串，可直接插入Prompt
        """
        examples = self.select(question, top_k=top_k, min_rating=min_rating, **kwargs)

        if not examples:
            return ""

        lines = ["以下为相似问题的参考SQL示例：\n"]
        for i, ex in enumerate(examples, 1):
            lines.append(f"示例{i}：")
            lines.append(f"问题：{ex.question_zh}")
            lines.append(f"SQL：\n{ex.sql}")
            if ex.explanation:
                lines.append(f"说明：{ex.explanation[:150]}...")
            lines.append("")  # 空行分隔

        return "\n".join(lines)

    def get_tagged_examples(self, tags: List[str], top_k_per_tag: int = 2) -> str:
        """获取特定标签的示例"""
        tagged_samples = []
        for sample in self.samples:
            if any(tag in sample.tags for tag in tags):
                tagged_samples.append(sample)

        tagged_samples.sort(key=lambda s: -(s.rating or 0))
        selected = tagged_samples[:top_k_per_tag * len(tags)]

        lines = [f"# {tags} 相关示例\n"]
        for ex in selected:
            lines.append(f"## {ex.qid}. {ex.question_zh[:50]}")
            lines.append(f"评分: {ex.rating}/10")
            lines.append(f"标签: {', '.join(ex.tags)}")
            lines.append(f"```sql\n{ex.sql}\n```\n")

        return "\n".join(lines)

    def get_stats(self) -> Dict:
        """获取数据集统计"""
        stats = {
            "total": len(self.samples),
            "by_rating": {},
            "by_difficulty": {},
            "by_tag": {},
            "avg_rating": 0.0
        }

        ratings = [s.rating for s in self.samples if s.rating]
        if ratings:
            stats["avg_rating"] = sum(ratings) / len(ratings)
            for r in range(1, 11):
                stats["by_rating"][r] = sum(1 for s in self.samples if s.rating == r)

        for diff in ["easy", "medium", "hard"]:
            stats["by_difficulty"][diff] = sum(
                1 for s in self.samples if s.difficulty == diff
            )

        tag_counts = {}
        for s in self.samples:
            for tag in s.tags:
                tag_counts[tag] = tag_counts.get(tag, 0) + 1
        stats["by_tag"] = tag_counts

        return stats


def load_fewshot_selector() -> FewShotSelector:
    """加载默认的few-shot选择器"""
    # __file__ = backend/utils/fewshot_selector.py → 仓库根为 parents[2]
    default_path = Path(__file__).resolve().parents[2] / "data" / "experiences" / "all_samples.jsonl"
    return FewShotSelector(str(default_path))


if __name__ == "__main__":
    import argparse

    parser = argparse.ArgumentParser(description="Few-shot示例选择器")
    parser.add_argument("--samples", default="data/experiences/all_samples.jsonl")
    parser.add_argument("--question", help="测试问题")
    parser.add_argument("--top-k", type=int, default=3)
    parser.add_argument("--min-rating", type=int, default=7)
    parser.add_argument("--stats", action="store_true", help="显示数据集统计")

    args = parser.parse_args()

    selector = FewShotSelector(args.samples)

    if args.stats:
        stats = selector.get_stats()
        print("📊 数据集统计:")
        print(f"  总样本: {stats['total']}")
        print(f"  平均评分: {stats['avg_rating']:.1f}")
        print(f"  难度分布: {stats['by_difficulty']}")
        print(f"\n  Top 10 标签:")
        sorted_tags = sorted(stats["by_tag"].items(), key=lambda x: -x[1])[:10]
        for tag, count in sorted_tags:
            print(f"    {tag}: {count}")
    elif args.question:
        examples = selector.select(args.question, top_k=args.top_k, min_rating=args.min_rating)
        print(f"\n为问题 '{args.question}' 选择的示例:\n")
        for ex in examples:
            print(f"[{ex.qid}] 评分:{ex.rating} 难度:{ex.difficulty}")
            print(f"问题: {ex.question_zh}")
            print(f"SQL:\n{ex.sql}\n")
    else:
        print("请指定 --question 或 --stats")