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fix: auto-fix code issues (cron)

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- 修复重复导入/字段
- 修复异常处理
- 修复PEP8格式问题
- 修复语法错误（运算符空格问题）
- 修复类型注解格式
This commit is contained in:
AutoFix Bot
2026-03-02 06:09:49 +08:00
parent b83265e5fd
commit e23f1fec08
84 changed files with 9492 additions and 9491 deletions
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172
backend/multimodal_entity_linker.py
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@@ -9,13 +9,13 @@ from dataclasses import dataclass
from difflib import SequenceMatcher
# Constants
UUID_LENGTH = 8 # UUID 截断长度
UUID_LENGTH = 8 # UUID 截断长度
# 尝试导入embedding库
try:
NUMPY_AVAILABLE = True
NUMPY_AVAILABLE = True
except ImportError:
NUMPY_AVAILABLE = False
NUMPY_AVAILABLE = False
@dataclass
@@ -30,11 +30,11 @@ class MultimodalEntity:
source_id: str
mention_context: str
confidence: float
modality_features: dict = None # 模态特定特征
modality_features: dict = None # 模态特定特征
def __post_init__(self):
def __post_init__(self) -> None:
if self.modality_features is None:
self.modality_features = {}
self.modality_features = {}
@dataclass
@@ -78,7 +78,7 @@ class MultimodalEntityLinker:
"""多模态实体关联器 - 跨模态实体对齐和知识融合"""
# 关联类型
LINK_TYPES = {
LINK_TYPES = {
"same_as": "同一实体",
"related_to": "相关实体",
"part_of": "组成部分",
@@ -86,16 +86,16 @@ class MultimodalEntityLinker:
}
# 模态类型
MODALITIES = ["audio", "video", "image", "document"]
MODALITIES = ["audio", "video", "image", "document"]
def __init__(self, similarity_threshold: float = 0.85) -> None:
def __init__(self, similarity_threshold: float = 0.85) -> None:
"""
初始化多模态实体关联器
Args:
similarity_threshold: 相似度阈值
"""
self.similarity_threshold = similarity_threshold
self.similarity_threshold = similarity_threshold
def calculate_string_similarity(self, s1: str, s2: str) -> float:
"""
@@ -111,7 +111,7 @@ class MultimodalEntityLinker:
if not s1 or not s2:
return 0.0
s1, s2 = s1.lower().strip(), s2.lower().strip()
s1, s2 = s1.lower().strip(), s2.lower().strip()
# 完全匹配
if s1 == s2:
@@ -136,7 +136,7 @@ class MultimodalEntityLinker:
(相似度, 匹配类型)
"""
# 名称相似度
name_sim = self.calculate_string_similarity(
name_sim = self.calculate_string_similarity(
entity1.get("name", ""), entity2.get("name", "")
)
@@ -145,8 +145,8 @@ class MultimodalEntityLinker:
return 1.0, "exact"
# 检查别名
aliases1 = set(a.lower() for a in entity1.get("aliases", []))
aliases2 = set(a.lower() for a in entity2.get("aliases", []))
aliases1 = set(a.lower() for a in entity1.get("aliases", []))
aliases2 = set(a.lower() for a in entity2.get("aliases", []))
if aliases1 & aliases2: # 有共同别名
return 0.95, "alias_match"
@@ -157,12 +157,12 @@ class MultimodalEntityLinker:
return 0.95, "alias_match"
# 定义相似度
def_sim = self.calculate_string_similarity(
def_sim = self.calculate_string_similarity(
entity1.get("definition", ""), entity2.get("definition", "")
)
# 综合相似度
combined_sim = name_sim * 0.7 + def_sim * 0.3
combined_sim = name_sim * 0.7 + def_sim * 0.3
if combined_sim >= self.similarity_threshold:
return combined_sim, "fuzzy"
@@ -170,7 +170,7 @@ class MultimodalEntityLinker:
return combined_sim, "none"
def find_matching_entity(
self, query_entity: dict, candidate_entities: list[dict], exclude_ids: set[str] = None
self, query_entity: dict, candidate_entities: list[dict], exclude_ids: set[str] = None
) -> AlignmentResult | None:
"""
在候选实体中查找匹配的实体
@@ -183,28 +183,28 @@ class MultimodalEntityLinker:
Returns:
对齐结果
"""
exclude_ids = exclude_ids or set()
best_match = None
best_similarity = 0.0
exclude_ids = exclude_ids or set()
best_match = None
best_similarity = 0.0
for candidate in candidate_entities:
if candidate.get("id") in exclude_ids:
continue
similarity, match_type = self.calculate_entity_similarity(query_entity, candidate)
similarity, match_type = self.calculate_entity_similarity(query_entity, candidate)
if similarity > best_similarity and similarity >= self.similarity_threshold:
best_similarity = similarity
best_match = candidate
best_match_type = match_type
best_similarity = similarity
best_match = candidate
best_match_type = match_type
if best_match:
return AlignmentResult(
entity_id=query_entity.get("id"),
matched_entity_id=best_match.get("id"),
similarity=best_similarity,
match_type=best_match_type,
confidence=best_similarity,
entity_id = query_entity.get("id"),
matched_entity_id = best_match.get("id"),
similarity = best_similarity,
match_type = best_match_type,
confidence = best_similarity,
)
return None
@@ -230,10 +230,10 @@ class MultimodalEntityLinker:
Returns:
实体关联列表
"""
links = []
links = []
# 合并所有实体
all_entities = {
all_entities = {
"audio": audio_entities,
"video": video_entities,
"image": image_entities,
@@ -246,24 +246,24 @@ class MultimodalEntityLinker:
if mod1 >= mod2: # 避免重复比较
continue
entities1 = all_entities.get(mod1, [])
entities2 = all_entities.get(mod2, [])
entities1 = all_entities.get(mod1, [])
entities2 = all_entities.get(mod2, [])
for ent1 in entities1:
# 在另一个模态中查找匹配
result = self.find_matching_entity(ent1, entities2)
result = self.find_matching_entity(ent1, entities2)
if result and result.matched_entity_id:
link = EntityLink(
id=str(uuid.uuid4())[:UUID_LENGTH],
project_id=project_id,
source_entity_id=ent1.get("id"),
target_entity_id=result.matched_entity_id,
link_type="same_as" if result.similarity > 0.95 else "related_to",
source_modality=mod1,
target_modality=mod2,
confidence=result.confidence,
evidence=f"Cross-modal alignment: {result.match_type}",
link = EntityLink(
id = str(uuid.uuid4())[:UUID_LENGTH],
project_id = project_id,
source_entity_id = ent1.get("id"),
target_entity_id = result.matched_entity_id,
link_type = "same_as" if result.similarity > 0.95 else "related_to",
source_modality = mod1,
target_modality = mod2,
confidence = result.confidence,
evidence = f"Cross-modal alignment: {result.match_type}",
)
links.append(link)
@@ -284,7 +284,7 @@ class MultimodalEntityLinker:
融合结果
"""
# 收集所有属性
fused_properties = {
fused_properties = {
"names": set(),
"definitions": [],
"aliases": set(),
@@ -293,7 +293,7 @@ class MultimodalEntityLinker:
"contexts": [],
}
merged_ids = []
merged_ids = []
for entity in linked_entities:
merged_ids.append(entity.get("id"))
@@ -318,21 +318,21 @@ class MultimodalEntityLinker:
fused_properties["contexts"].append(mention.get("mention_context"))
# 选择最佳定义（最长的那个）
best_definition = (
max(fused_properties["definitions"], key=len) if fused_properties["definitions"] else ""
best_definition = (
max(fused_properties["definitions"], key = len) if fused_properties["definitions"] else ""
)
# 选择最佳名称（最常见的那个）
from collections import Counter
name_counts = Counter(fused_properties["names"])
best_name = name_counts.most_common(1)[0][0] if name_counts else ""
name_counts = Counter(fused_properties["names"])
best_name = name_counts.most_common(1)[0][0] if name_counts else ""
# 构建融合结果
return FusionResult(
canonical_entity_id=entity_id,
merged_entity_ids=merged_ids,
fused_properties={
canonical_entity_id = entity_id,
merged_entity_ids = merged_ids,
fused_properties = {
"name": best_name,
"definition": best_definition,
"aliases": list(fused_properties["aliases"]),
@@ -340,8 +340,8 @@ class MultimodalEntityLinker:
"modalities": list(fused_properties["modalities"]),
"contexts": fused_properties["contexts"][:10], # 最多10个上下文
},
source_modalities=list(fused_properties["modalities"]),
confidence=min(1.0, len(linked_entities) * 0.2 + 0.5),
source_modalities = list(fused_properties["modalities"]),
confidence = min(1.0, len(linked_entities) * 0.2 + 0.5),
)
def detect_entity_conflicts(self, entities: list[dict]) -> list[dict]:
@@ -354,30 +354,30 @@ class MultimodalEntityLinker:
Returns:
冲突列表
"""
conflicts = []
conflicts = []
# 按名称分组
name_groups = {}
name_groups = {}
for entity in entities:
name = entity.get("name", "").lower()
name = entity.get("name", "").lower()
if name:
if name not in name_groups:
name_groups[name] = []
name_groups[name] = []
name_groups[name].append(entity)
# 检测同名但定义不同的实体
for name, group in name_groups.items():
if len(group) > 1:
# 检查定义是否相似
definitions = [e.get("definition", "") for e in group if e.get("definition")]
definitions = [e.get("definition", "") for e in group if e.get("definition")]
if len(definitions) > 1:
# 计算定义之间的相似度
sim_matrix = []
sim_matrix = []
for i, d1 in enumerate(definitions):
for j, d2 in enumerate(definitions):
if i < j:
sim = self.calculate_string_similarity(d1, d2)
sim = self.calculate_string_similarity(d1, d2)
sim_matrix.append(sim)
# 如果定义相似度都很低，可能是冲突
@@ -394,7 +394,7 @@ class MultimodalEntityLinker:
return conflicts
def suggest_entity_merges(
self, entities: list[dict], existing_links: list[EntityLink] = None
self, entities: list[dict], existing_links: list[EntityLink] = None
) -> list[dict]:
"""
建议实体合并
@@ -406,13 +406,13 @@ class MultimodalEntityLinker:
Returns:
合并建议列表
"""
suggestions = []
existing_pairs = set()
suggestions = []
existing_pairs = set()
# 记录已有的关联
if existing_links:
for link in existing_links:
pair = tuple(sorted([link.source_entity_id, link.target_entity_id]))
pair = tuple(sorted([link.source_entity_id, link.target_entity_id]))
existing_pairs.add(pair)
# 检查所有实体对
@@ -422,12 +422,12 @@ class MultimodalEntityLinker:
continue
# 检查是否已有关联
pair = tuple(sorted([ent1.get("id"), ent2.get("id")]))
pair = tuple(sorted([ent1.get("id"), ent2.get("id")]))
if pair in existing_pairs:
continue
# 计算相似度
similarity, match_type = self.calculate_entity_similarity(ent1, ent2)
similarity, match_type = self.calculate_entity_similarity(ent1, ent2)
if similarity >= self.similarity_threshold:
suggestions.append(
@@ -441,7 +441,7 @@ class MultimodalEntityLinker:
)
# 按相似度排序
suggestions.sort(key=lambda x: x["similarity"], reverse=True)
suggestions.sort(key = lambda x: x["similarity"], reverse = True)
return suggestions
@@ -451,8 +451,8 @@ class MultimodalEntityLinker:
entity_id: str,
source_type: str,
source_id: str,
mention_context: str = "",
confidence: float = 1.0,
mention_context: str = "",
confidence: float = 1.0,
) -> MultimodalEntity:
"""
创建多模态实体记录
@@ -469,14 +469,14 @@ class MultimodalEntityLinker:
多模态实体记录
"""
return MultimodalEntity(
id=str(uuid.uuid4())[:UUID_LENGTH],
entity_id=entity_id,
project_id=project_id,
name="", # 将在后续填充
source_type=source_type,
source_id=source_id,
mention_context=mention_context,
confidence=confidence,
id = str(uuid.uuid4())[:UUID_LENGTH],
entity_id = entity_id,
project_id = project_id,
name = "", # 将在后续填充
source_type = source_type,
source_id = source_id,
mention_context = mention_context,
confidence = confidence,
)
def analyze_modality_distribution(self, multimodal_entities: list[MultimodalEntity]) -> dict:
@@ -489,7 +489,7 @@ class MultimodalEntityLinker:
Returns:
模态分布统计
"""
distribution = {mod: 0 for mod in self.MODALITIES}
distribution = {mod: 0 for mod in self.MODALITIES}
# 统计每个模态的实体数
for me in multimodal_entities:
@@ -497,13 +497,13 @@ class MultimodalEntityLinker:
distribution[me.source_type] += 1
# 统计跨模态实体
entity_modalities = {}
entity_modalities = {}
for me in multimodal_entities:
if me.entity_id not in entity_modalities:
entity_modalities[me.entity_id] = set()
entity_modalities[me.entity_id] = set()
entity_modalities[me.entity_id].add(me.source_type)
cross_modal_count = sum(1 for mods in entity_modalities.values() if len(mods) > 1)
cross_modal_count = sum(1 for mods in entity_modalities.values() if len(mods) > 1)
return {
"modality_distribution": distribution,
@@ -517,12 +517,12 @@ class MultimodalEntityLinker:
# Singleton instance
_multimodal_entity_linker = None
_multimodal_entity_linker = None
def get_multimodal_entity_linker(similarity_threshold: float = 0.85) -> MultimodalEntityLinker:
def get_multimodal_entity_linker(similarity_threshold: float = 0.85) -> MultimodalEntityLinker:
"""获取多模态实体关联器单例"""
global _multimodal_entity_linker
if _multimodal_entity_linker is None:
_multimodal_entity_linker = MultimodalEntityLinker(similarity_threshold)
_multimodal_entity_linker = MultimodalEntityLinker(similarity_threshold)
return _multimodal_entity_linker