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style(memory): Some code style optimizations

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This commit is contained in:
Eternity
2026-03-20 18:22:20 +08:00
parent e8ae46b286
commit c17a2dad2d
8 changed files with 296 additions and 292 deletions
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74
api/app/core/memory/models/graph_models.py
View File

@@ -44,21 +44,21 @@ def parse_historical_datetime(v):
"""
if v is None:
return v
# 处理 Neo4j DateTime 对象
if hasattr(v, 'to_native'):
return v.to_native()
# 处理 Python datetime 对象
if isinstance(v, datetime):
return v
if isinstance(v, str):
# 匹配 ISO 8601 格式：YYYY-MM-DD 或 YYYY-MM-DDTHH:MM:SS[.ffffff][Z|±HH:MM]
# 支持1-4位年份
pattern = r'^(\d{1,4})-(\d{2})-(\d{2})(?:T(\d{2}):(\d{2}):(\d{2})(?:\.(\d+))?(?:Z|([+-]\d{2}:\d{2}))?)?'
match = re.match(pattern, v)
if match:
try:
year = int(match.group(1))
@@ -68,31 +68,31 @@ def parse_historical_datetime(v):
minute = int(match.group(5)) if match.group(5) else 0
second = int(match.group(6)) if match.group(6) else 0
microsecond = 0
# 处理微秒
if match.group(7):
# 补齐或截断到6位
us_str = match.group(7).ljust(6, '0')[:6]
microsecond = int(us_str)
# 处理时区
tzinfo = None
if 'Z' in v or match.group(8):
tzinfo = timezone.utc
# 创建 datetime 对象
return datetime(year, month, day, hour, minute, second, microsecond, tzinfo=tzinfo)
except (ValueError, OverflowError):
# 日期值无效（如月份13、日期32等）
return None
# 如果不匹配模式，尝试使用 fromisoformat（用于标准格式）
try:
return datetime.fromisoformat(v.replace('Z', '+00:00'))
except Exception:
return None
return v
@@ -167,7 +167,7 @@ class EntityEntityEdge(Edge):
source_statement_id: str = Field(..., description="Statement where this relationship was extracted")
valid_at: Optional[datetime] = Field(None, description="Temporal validity start")
invalid_at: Optional[datetime] = Field(None, description="Temporal validity end")
@field_validator('valid_at', 'invalid_at', mode='before')
@classmethod
def validate_datetime(cls, v):
@@ -206,7 +206,8 @@ class DialogueNode(Node):
ref_id: str = Field(..., description="Reference identifier of the dialog")
content: str = Field(..., description="Dialogue content")
dialog_embedding: Optional[List[float]] = Field(None, description="Dialog embedding vector")
config_id: Optional[int | str] = Field(None, description="Configuration ID used to process this dialogue (integer or string)")
config_id: Optional[int | str] = Field(None,
description="Configuration ID used to process this dialogue (integer or string)")
class StatementNode(Node):
@@ -241,17 +242,17 @@ class StatementNode(Node):
chunk_id: str = Field(..., description="ID of the parent chunk")
stmt_type: str = Field(..., description="Type of the statement")
statement: str = Field(..., description="The statement text content")
# Speaker identification
speaker: Optional[str] = Field(
None,
description="Speaker identifier: 'user' for user messages, 'assistant' for AI responses"
)
# Emotion fields (ordered as requested, emotion_intensity first for display)
emotion_intensity: Optional[float] = Field(
None,
ge=0.0,
None,
ge=0.0,
le=1.0,
description="Emotion intensity: 0.0-1.0 (displayed on node)"
)
@@ -264,25 +265,26 @@ class StatementNode(Node):
description="Emotion subject: self/other/object"
)
emotion_type: Optional[str] = Field(
None,
None,
description="Emotion type: joy/sadness/anger/fear/surprise/neutral"
)
emotion_keywords: Optional[List[str]] = Field(
default_factory=list,
description="Emotion keywords list, max 3 items"
)
# Temporal fields
temporal_info: TemporalInfo = Field(..., description="Temporal information")
valid_at: Optional[datetime] = Field(None, description="Temporal validity start")
invalid_at: Optional[datetime] = Field(None, description="Temporal validity end")
# Embedding and other fields
statement_embedding: Optional[List[float]] = Field(None, description="Statement embedding vector")
chunk_embedding: Optional[List[float]] = Field(None, description="Chunk embedding vector")
connect_strength: str = Field(..., description="Strong VS Weak classification of this statement")
config_id: Optional[int | str] = Field(None, description="Configuration ID used to process this statement (integer or string)")
config_id: Optional[int | str] = Field(None,
description="Configuration ID used to process this statement (integer or string)")
# ACT-R Memory Activation Properties
importance_score: float = Field(
default=0.5,
@@ -309,13 +311,13 @@ class StatementNode(Node):
ge=0,
description="Total number of times this node has been accessed"
)
@field_validator('valid_at', 'invalid_at', mode='before')
@classmethod
def validate_datetime(cls, v):
"""使用通用的历史日期解析函数"""
return parse_historical_datetime(v)
@field_validator('emotion_type', mode='before')
@classmethod
def validate_emotion_type(cls, v):
@@ -326,7 +328,7 @@ class StatementNode(Node):
if v not in valid_types:
raise ValueError(f"emotion_type must be one of {valid_types}, got {v}")
return v
@field_validator('emotion_subject', mode='before')
@classmethod
def validate_emotion_subject(cls, v):
@@ -337,7 +339,7 @@ class StatementNode(Node):
if v not in valid_subjects:
raise ValueError(f"emotion_subject must be one of {valid_subjects}, got {v}")
return v
@field_validator('emotion_keywords', mode='before')
@classmethod
def validate_emotion_keywords(cls, v):
@@ -405,19 +407,20 @@ class ExtractedEntityNode(Node):
entity_type: str = Field(..., description="Type of the entity")
description: str = Field(..., description="Entity description")
example: str = Field(
default="",
default="",
description="A concise example (around 20 characters) to help understand the entity"
)
aliases: List[str] = Field(
default_factory=list,
default_factory=list,
description="Entity aliases - alternative names for this entity"
)
name_embedding: Optional[List[float]] = Field(default_factory=list, description="Name embedding vector")
# TODO: fact_summary 功能暂时禁用，待后续开发完善后启用
# fact_summary: str = Field(default="", description="Summary of the fact about this entity")
connect_strength: str = Field(..., description="Strong VS Weak about this entity")
config_id: Optional[int | str] = Field(None, description="Configuration ID used to process this entity (integer or string)")
config_id: Optional[int | str] = Field(None,
description="Configuration ID used to process this entity (integer or string)")
# ACT-R Memory Activation Properties
importance_score: float = Field(
default=0.5,
@@ -444,16 +447,16 @@ class ExtractedEntityNode(Node):
ge=0,
description="Total number of times this node has been accessed"
)
# Explicit Memory Classification
is_explicit_memory: bool = Field(
default=False,
description="Whether this entity represents explicit/semantic memory (knowledge, concepts, definitions, theories, principles)"
)
@field_validator('aliases', mode='before')
@classmethod
def validate_aliases_field(cls, v): # 字段验证器 自动清理和验证 aliases 字段
def validate_aliases_field(cls, v): # 字段验证器 自动清理和验证 aliases 字段
"""Validate and clean aliases field using utility function.
This validator ensures that the aliases field is always a valid list of strings.
@@ -507,8 +510,9 @@ class MemorySummaryNode(Node):
memory_type: Optional[str] = Field(None, description="Type/category of the episodic memory")
summary_embedding: Optional[List[float]] = Field(None, description="Embedding vector for the summary")
metadata: dict = Field(default_factory=dict, description="Additional metadata for the summary")
config_id: Optional[int | str] = Field(None, description="Configuration ID used to process this summary (integer or string)")
config_id: Optional[int | str] = Field(None,
description="Configuration ID used to process this summary (integer or string)")
# ACT-R Forgetting Engine Properties
original_statement_id: Optional[str] = Field(
None,
@@ -522,7 +526,7 @@ class MemorySummaryNode(Node):
None,
description="Timestamp when the nodes were merged"
)
# ACT-R Memory Activation Properties
importance_score: float = Field(
default=0.5,

3
api/app/core/memory/storage_services/extraction_engine/knowledge_extraction/embedding_generation.py
View File

@@ -227,7 +227,8 @@ class EmbeddingGenerator:
# 打印前几个嵌入向量的维度
for i in range(min(5, len(embeddings))):
print(f"实体 '{entity_texts[i]}' 嵌入向量维度: {len(embeddings[i])}")
print(f"实体 '{entity_texts[i]}' "
f"嵌入向量维度: {len(embeddings[i])}")
# 将嵌入向量赋值给实体
for ent, emb in zip(entity_refs, embeddings):