改进匹配算法

server/file_association.py

@@ -15,6 +15,11 @@ from datetime import datetime
 import time
 import re
 import shutil
+import requests
+import math
+
+OLLAMA_EMBED_URL = os.environ.get('OLLAMA_EMBED_URL', 'http://localhost:11434/api/embeddings')
+OLLAMA_EMBED_MODEL = os.environ.get('OLLAMA_EMBED_MODEL', 'nomic/text-embedding-3-large')
 
 
 def clear_directory(dir_path: Path) -> None:
@@ -27,6 +32,93 @@ def clear_directory(dir_path: Path) -> None:
                 shutil.rmtree(item)
 
 
+def get_embedding(text: str) -> List[float]:
+    """使用 Ollama 获取文本的 embedding"""
+    try:
+        response = requests.post(
+            OLLAMA_EMBED_URL,
+            json={
+                'model': OLLAMA_EMBED_MODEL,
+                'input': text,
+            },
+            timeout=10,
+        )
+        response.raise_for_status()
+        data = response.json()
+        if isinstance(data, dict):
+            if 'embedding' in data:
+                return data['embedding']
+            if 'data' in data and isinstance(data['data'], list) and data['data']:
+                return data['data'][0].get('embedding', []) or []
+        return []
+    except Exception as e:
+        print(f"获取 embedding 失败: {e}")
+        return []
+
+
+def cosine_similarity(vec1: List[float], vec2: List[float]) -> float:
+    """计算两个向量的余弦相似度"""
+    if not vec1 or not vec2:
+        return 0.0
+    dot = sum(a * b for a, b in zip(vec1, vec2))
+    norm1 = math.sqrt(sum(a * a for a in vec1))
+    norm2 = math.sqrt(sum(b * b for b in vec2))
+    return dot / (norm1 * norm2) if norm1 and norm2 else 0.0
+
+
+def normalize_for_match(text: str) -> str:
+    """规范化文本，去掉路径和扩展名后的匹配用字符串"""
+    text = str(text).lower()
+    text = re.sub(r'[\u0020-\u002f\u003a-\u0040\u005b-\u0060\u007b-\u007e]+', ' ', text)
+    text = re.sub(r'\s+', ' ', text).strip()
+    return text
+
+
+def token_overlap_score(a: str, b: str) -> float:
+    tokens_a = set(re.findall(r'[\u4e00-\u9fff]+|\d+|[a-z]+', a))
+    tokens_b = set(re.findall(r'[\u4e00-\u9fff]+|\d+|[a-z]+', b))
+    if not tokens_a or not tokens_b:
+        return 0.0
+    return len(tokens_a & tokens_b) / max(1, len(tokens_b))
+
+
+def find_best_match(title: str, candidates: List[Tuple[Path, datetime]]) -> Tuple[Path, datetime]:
+    """从候选文件中找到与 title 最相似的文件"""
+    if not candidates:
+        return None
+
+    title_norm = normalize_for_match(title)
+    exact_candidates = []
+    for file_path, ctime in candidates:
+        stem_norm = normalize_for_match(file_path.stem)
+        if stem_norm and (stem_norm == title_norm or stem_norm in title_norm or title_norm in stem_norm):
+            exact_candidates.append((file_path, ctime, stem_norm))
+
+    if exact_candidates:
+        # 直接返回最精确的“包含匹配”候选项
+        return max(exact_candidates, key=lambda item: len(item[2]))[:2]
+
+    title_emb = get_embedding(title)
+    best_match = None
+    best_score = -1.0
+
+    for file_path, ctime in candidates:
+        stem_norm = normalize_for_match(file_path.stem)
+        overlap = token_overlap_score(title_norm, stem_norm)
+        score = overlap * 0.3
+
+        if title_emb:
+            stem_emb = get_embedding(file_path.stem)
+            if stem_emb:
+                score += cosine_similarity(title_emb, stem_emb) * 0.7
+
+        if score > best_score:
+            best_score = score
+            best_match = (file_path, ctime)
+
+    return best_match or candidates[0]
+
+
 def extract_all_zips(
     source_dir: Union[str, Path] = "cc",
     output_dir: Union[str, Path] = "data",
@@ -365,6 +457,8 @@ def build_file_associations(
         # 提取两组数据：标题、网址、时间
         title1, url1, time1 = parts[0].strip(), parts[1].strip(), parts[2].strip()
         title2, url2, time2 = parts[3].strip(), parts[4].strip(), parts[5].strip()
+        if title1=="2.1 楞次定律 课件-2022-2023学年高一下学期物理人教版（2019）选择性必修第二册" or title2=="2.1 楞次定律 课件-2022-2023学年高一下学期物理人教版（2019）选择性必修第二册":
+            print(f"调试：标题1={title1}, 标题2={title2}, url1={url1}, url2={url2}, time1={time1}, time2={time2}")
 
         # 辅助函数：处理文件路径，如果是 zip 则递归解压
         def process_file(file_path: Path) -> List[str]:
@@ -395,7 +489,11 @@ def build_file_associations(
 
         # 第一组：根据网址匹配文件
         if 'www.21cnjy.com' in url1 and twole_files:
-            file_path, ctime = twole_files.pop(0)
+            candidates = twole_files
+            best_match = find_best_match(title1, candidates)
+            if best_match:
+                twole_files.remove(best_match)
+                file_path, ctime = best_match
                 result.append({
                     "filename": process_file(file_path),
                     "title": title1,
@@ -403,7 +501,11 @@ def build_file_associations(
                     "time": time1
                 })
         elif 'www.zxxk.com' in url1 and zxxk_files:
-            file_path, ctime = zxxk_files.pop(0)
+            candidates = zxxk_files
+            best_match = find_best_match(title1, candidates)
+            if best_match:
+                zxxk_files.remove(best_match)
+                file_path, ctime = best_match
                 result.append({
                     "filename": process_file(file_path),
                     "title": title1,
@@ -413,7 +515,11 @@ def build_file_associations(
 
         # 第二组：根据网址匹配文件
         if 'www.21cnjy.com' in url2 and twole_files:
-            file_path, ctime = twole_files.pop(0)
+            candidates = twole_files
+            best_match = find_best_match(title2, candidates)
+            if best_match:
+                twole_files.remove(best_match)
+                file_path, ctime = best_match
                 result.append({
                     "filename": process_file(file_path),
                     "title": title2,
@@ -421,7 +527,11 @@ def build_file_associations(
                     "time": time2
                 })
         elif 'www.zxxk.com' in url2 and zxxk_files:
-            file_path, ctime = zxxk_files.pop(0)
+            candidates = zxxk_files
+            best_match = find_best_match(title2, candidates)
+            if best_match:
+                zxxk_files.remove(best_match)
+                file_path, ctime = best_match
                 result.append({
                     "filename": process_file(file_path),
                     "title": title2,