陈世睿
917613c5ce
- crawler/:豆果(主)/下厨房/Bing 爬虫 + dishes.jsonl(3056 菜→9167 图映射)+ verify_repair 按 URL 重下 - retrieval/:三路检索(BM25 + 本地 BGE-M3 向量 + RRF 融合),FastAPI + 前端; 写死图片目录(默认 crawler/images,可 IMAGE_DIR 覆盖)、绑 0.0.0.0 局域网访问、 启动自动建索引、服务器 serve 图片 - 图片(1.7G)与向量模型(2.3G)不进 git Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
38 lines
1.1 KiB
Python
38 lines
1.1 KiB
Python
# -*- coding: utf-8 -*-
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"""中文 BM25 检索(jieba 分词 + rank_bm25)。文档 = 菜名。
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菜名通常很短(2~6 字),除 jieba 词以外再补单字,提升短词召回。
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"""
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from __future__ import annotations
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import re
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import jieba
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import numpy as np
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from rank_bm25 import BM25Okapi
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jieba.initialize()
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def tokenize(text: str) -> list[str]:
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text = text.replace("_", " ").strip()
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words = [t for t in jieba.lcut_for_search(text) if t.strip()]
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chars = [c for c in re.sub(r"\s+", "", text) if "一" <= c <= "鿿"]
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toks = words + chars
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return toks if toks else [t for t in re.sub(r"\s+", "", text)] or ["_"]
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class BM25Index:
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def __init__(self, names: list[str]):
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self.names = list(names)
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self.tokenized = [tokenize(n) for n in self.names]
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self.bm25 = BM25Okapi(self.tokenized)
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def search(self, query: str, topk: int = 30) -> list[tuple[str, float]]:
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q = tokenize(query)
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if not q:
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return []
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scores = self.bm25.get_scores(q)
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order = np.argsort(scores)[::-1][:topk]
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return [(self.names[i], float(scores[i])) for i in order if scores[i] > 0]
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