f597139913
Add read/update endpoints for emotion engine config (read_config_emotion, update_config_emotion) Add read/update endpoints for reflection engine config (read_config_reflection, update_config_reflection) Add EmotionConfigUpdateRequest and ReflectionConfigUpdateRequest schemas Reuse emotion_config_controller and memory_reflection_controller with ownership verification
546 lines
27 KiB
Python
546 lines
27 KiB
Python
"""
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"""
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from typing import Any, Optional, List, Dict, Literal, Union
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import time
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import uuid
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from pydantic import BaseModel, Field, ConfigDict, field_validator, model_validator
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# ============================================================================
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# 从 json_schema.py 迁移的 Schema
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# ============================================================================
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class BaseDataSchema(BaseModel):
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"""Base schema for the data"""
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# 保持原有必需字段为可选，以兼容不同数据源
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id: Optional[str] = Field(None, description="The unique identifier for the data entry.")
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statement: Optional[str] = Field(None, description="The statement text.")
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created_at: Optional[str] = Field(None, description="The creation timestamp in ISO 8601 format.")
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expired_at: Optional[str] = Field(None, description="The expiration timestamp in ISO 8601 format.")
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description: Optional[str] = Field(None, description="The description of the data entry.")
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# 新增字段以匹配实际输入数据 - 改为可选以支持resolved_memory场景
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entity1_name: Optional[str] = Field(None, description="The first entity name.")
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entity2_name: Optional[str] = Field(None, description="The second entity name.")
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statement_id: Optional[str] = Field(None, description="The statement identifier.")
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# 新增字段 - 设为可选以保持向后兼容性
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predicate: Optional[str] = Field(None, description="The predicate describing the relationship between entities.")
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relationship_statement_id: Optional[str] = Field(None, description="The relationship statement identifier.")
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# 保留原有字段 - 修改relationship字段类型以支持字符串和字典
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relationship: Optional[Union[str, Dict[str, Any]]] = Field(None, description="The relationship object or string.")
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entity2: Optional[Dict[str, Any]] = Field(None, description="The second entity object.")
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@model_validator(mode="before")
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def _set_default_created_at(cls, v):
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"""Set default created_at if missing"""
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if isinstance(v, dict) and v.get("created_at") is None:
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from datetime import datetime
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v["created_at"] = datetime.now().isoformat()
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return v
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class QualityAssessmentSchema(BaseModel):
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"""Schema for memory quality assessment results."""
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score: int = Field(..., ge=0, le=100, description="Quality score percentage (0-100).")
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summary: str = Field(..., description="Brief summary of data quality status, including main issues and strengths.")
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class MemoryVerifySchema(BaseModel):
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"""Schema for memory privacy verification results."""
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has_privacy: bool = Field(..., description="Whether privacy information was detected.")
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privacy_types: List[str] = Field([], description="List of detected privacy information types.")
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summary: str = Field(..., description="Brief summary of privacy detection results.")
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class ConflictResultSchema(BaseModel):
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"""Schema for the conflict result data in the reflexion_data.json file."""
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data: List[BaseDataSchema] = Field(...,
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description="The conflict memory data. Only contains conflicting records when conflict is True.")
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conflict: bool = Field(..., description="Whether the memory is in conflict.")
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quality_assessment: Optional[QualityAssessmentSchema] = Field(None,
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description="The quality assessment object. Contains score and summary when quality_assessment is enabled, null otherwise.")
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memory_verify: Optional[MemoryVerifySchema] = Field(None,
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description="The memory privacy verification object. Contains privacy detection results when memory_verify is enabled, null otherwise.")
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@model_validator(mode="before")
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def _normalize_data(cls, v):
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if isinstance(v, dict):
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d = v.get("data")
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if isinstance(d, dict):
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v["data"] = [d]
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return v
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class ConflictSchema(BaseModel):
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"""Schema for the conflict data in the reflexion_data"""
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data: List[BaseDataSchema] = Field(..., description="The conflict memory data.")
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conflict_memory: Optional[BaseDataSchema] = Field(None, description="The conflict memory data.")
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@model_validator(mode="before")
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def _normalize_data(cls, v):
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if isinstance(v, dict):
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d = v.get("data")
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if isinstance(d, dict):
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v["data"] = [d]
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return v
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class ReflexionSchema(BaseModel):
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"""Schema for the reflexion data in the reflexion_data"""
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reason: str = Field(..., description="The reason for the reflexion.")
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solution: str = Field(..., description="The solution for the reflexion.")
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class ChangeRecordSchema(BaseModel):
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"""Schema for individual change records
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字段值格式说明：
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- id: 字符串，表示修改字段对应的记录ID
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- 其他字段: 可以是字符串、None，数组 [修改前的值, 修改后的值]，或嵌套字典结构
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- entity2等嵌套对象的字段也遵循 [old_value, new_value] 格式
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"""
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field: List[Dict[str, Any]] = Field(
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...,
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description="List of field changes. First item: {id: value}, followed by changed fields as {field_name: [old_value, new_value]} or {field_name: new_value} or nested structures like {entity2: {field_name: [old, new]}}"
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)
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class ResolvedSchema(BaseModel):
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"""Schema for the resolved memory data in the reflexion_data"""
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original_memory_id: Optional[str] = Field(None, description="The original memory identifier.")
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# resolved_memory: Optional[BaseDataSchema] = Field(None, description="The resolved memory data (only contains records that need modification).")
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resolved_memory: Optional[Union[BaseDataSchema, List[BaseDataSchema]]] = Field(None,
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description="The resolved memory data (only contains records that need modification). Can be a single record or list of records.")
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change: Optional[List[ChangeRecordSchema]] = Field(None,
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description="List of detailed change records with IDs and field information.")
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class SingleReflexionResultSchema(BaseModel):
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"""Schema for a single reflexion result item."""
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conflict: ConflictResultSchema = Field(..., description="The conflict result data for this specific conflict type.")
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reflexion: ReflexionSchema = Field(..., description="The reflexion data for this conflict.")
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resolved: Optional[ResolvedSchema] = Field(None, description="The resolved memory data for this conflict.")
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type: str = Field("reflexion_result", description="The type identifier.")
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class ReflexionResultSchema(BaseModel):
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"""Schema for the complete reflexion result data - a list of individual conflict resolutions."""
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results: List[SingleReflexionResultSchema] = Field(...,
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description="List of individual conflict resolution results, grouped by conflict type.")
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@model_validator(mode="before")
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def _normalize_resolved(cls, v):
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if isinstance(v, dict):
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conflict = v.get("conflict")
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if isinstance(conflict, dict) and conflict.get("conflict") is False:
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v["resolved"] = None
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else:
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resolved = v.get("resolved")
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if isinstance(resolved, dict):
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orig = resolved.get("original_memory_id")
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mem = resolved.get("resolved_memory")
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if orig is None and (mem is None or mem == {}):
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v["resolved"] = None
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return v
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# ============================================================================
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# 从 messages.py 迁移的 Schema
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# ============================================================================
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# Composite key identifying a config row
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class ConfigKey(BaseModel): # 配置参数键模型
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model_config = ConfigDict(populate_by_name=True, extra="forbid")
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config_id: Union[uuid.UUID, int, str] = Field(..., description="配置唯一标识（UUID或int)")
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user_id: str | None = Field(default=None, description="用户标识（字符串）")
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apply_id: str | None = Field(default=None, description="应用或场景标识（字符串）")
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# Allowed chunking strategies (extendable later)
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ChunkerStrategy = Literal[ # 分块策略枚举
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"RecursiveChunker",
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"TokenChunker",
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"SemanticChunker",
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"NeuralChunker",
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"HybridChunker",
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"LLMChunker",
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"SentenceChunker",
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"LateChunker"
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]
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# 这是 Request body示例
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class ConfigParams(ConfigKey): # 创建配置参数模型 旧
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model_config = ConfigDict(populate_by_name=True, extra="forbid")
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# Boolean switches
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enable_llm_dedup_blockwise: bool = Field(True, description="启用LLM决策去重")
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enable_llm_disambiguation: bool = Field(True, description="启用LLM决策消歧")
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deep_retrieval: bool = Field(True, description="深度检索开关（保留既有拼写）")
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# Thresholds in [0, 1]
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t_type_strict: float = Field(0.8, ge=0.0, le=1.0, description="类型严格阈值")
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t_name_strict: float = Field(0.8, ge=0.0, le=1.0, description="名称严格阈值")
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t_overall: float = Field(0.8, ge=0.0, le=1.0, description="综合阈值")
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state: bool = Field(False, description="配置使用状态（True/False）")
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# Chunker strategy selection (must be one of the declared literals)
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chunker_strategy: ChunkerStrategy = Field(
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"RecursiveChunker",
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description=(
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"分块策略：RecursiveChunker/TokenChunker/SemanticChunker/NeuralChunker/"
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"HybridChunker/LLMChunker/SentenceChunker/LateChunker"
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),
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)
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@field_validator("chunker_strategy", mode="before")
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@classmethod
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def map_chunker_aliases(cls, v: str):
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# 允许常见别名并映射到合法枚举
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if isinstance(v, str):
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m = v.strip().lower()
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alias_map = {
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"auto": "RecursiveChunker",
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"by_sentence": "SentenceChunker",
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"by_paragraph": "SemanticChunker",
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"fixed_tokens": "TokenChunker",
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"递归分块": "RecursiveChunker",
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"token 分块": "TokenChunker",
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"token分块": "TokenChunker",
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"语义分块": "SemanticChunker",
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"神经网络分块": "NeuralChunker",
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"混合分块": "HybridChunker",
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"llm 分块": "LLMChunker",
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"llm分块": "LLMChunker",
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"句子分块": "SentenceChunker",
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"延迟分块": "LateChunker",
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}
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if m in alias_map:
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return alias_map[m]
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return v
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@field_validator("config_id", "user_id", "apply_id")
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@classmethod
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def non_empty_str(cls, v: str) -> str:
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s = str(v).strip() if v is not None else ""
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if not s:
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raise ValueError("标识字段必须为非空字符串")
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return s
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class ConfigParamsCreate(BaseModel): # 创建配置参数模型（仅 body，去除主键）
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model_config = ConfigDict(populate_by_name=True, extra="forbid")
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config_name: str = Field("配置名称", description="配置名称（字符串）")
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config_desc: str = Field("配置描述", description="配置描述（字符串）")
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workspace_id: Optional[uuid.UUID] = Field(None, description="工作空间ID（UUID）")
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# 本体场景关联（可选）
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scene_id: Optional[uuid.UUID] = Field(None, description="本体场景ID（UUID），关联ontology_scene表")
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# 语义剪枝场景（由 service 层根据 scene_id 自动推导，值为关联场景的 scene_name，前端无需传入）
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pruning_scene: Optional[str] = Field(None, description="语义剪枝场景，由 scene_id 对应的 scene_name 自动填充")
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# 模型配置字段（可选，用于手动指定或自动填充）
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llm_id: Optional[str] = Field(None, description="LLM模型配置ID")
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embedding_id: Optional[str] = Field(None, description="嵌入模型配置ID")
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rerank_id: Optional[str] = Field(None, description="重排序模型配置ID")
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reflection_model_id: Optional[str] = Field(None, description="反思模型ID，默认与llm_id一致")
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emotion_model_id: Optional[str] = Field(None, description="情绪分析模型ID，默认与llm_id一致")
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class ConfigParamsDelete(BaseModel): # 删除配置参数模型（请求体）
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model_config = ConfigDict(populate_by_name=True, extra="forbid")
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# config_name: str = Field("配置名称", description="配置名称（字符串）")
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config_id: Union[uuid.UUID, int, str] = Field(..., description="配置ID（支持UUID、整数或字符串）")
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class ConfigUpdate(BaseModel): # 更新记忆萃取引擎配置参数时使用的模型
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config_id: Union[uuid.UUID, int, str] = None
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config_name: Optional[str] = Field(None, description="配置名称（字符串）")
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config_desc: Optional[str] = Field(None, description="配置描述（字符串）")
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scene_id: Optional[uuid.UUID] = Field(None, description="本体场景ID")
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class ConfigUpdateExtracted(BaseModel): # 更新记忆萃取引擎配置参数时使用的模型
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config_id: Union[uuid.UUID, int, str] = None
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llm_id: Optional[str] = Field(None, description="LLM模型配置ID")
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audio_id: Optional[str] = Field(None, description="语音模型ID")
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vision_id: Optional[str] = Field(None, description="视觉模型ID")
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video_id: Optional[str] = Field(None, description="视频模型ID")
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embedding_id: Optional[str] = Field(None, description="嵌入模型配置ID")
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rerank_id: Optional[str] = Field(None, description="重排序模型配置ID")
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enable_llm_dedup_blockwise: Optional[bool] = None
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enable_llm_disambiguation: Optional[bool] = None
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deep_retrieval: Optional[bool] = Field(None, validation_alias="deep_retrieval")
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t_type_strict: Optional[float] = Field(None, ge=0.0, le=1.0)
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t_name_strict: Optional[float] = Field(None, ge=0.0, le=1.0)
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t_overall: Optional[float] = Field(None, ge=0.0, le=1.0)
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state: Optional[bool] = None
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chunker_strategy: Optional[ChunkerStrategy] = None
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# 句子提取
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statement_granularity: Optional[int] = Field(2, ge=1, le=3, description="陈述提取颗粒度，挡位 1/2/3；默认 2")
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include_dialogue_context: Optional[bool] = None
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max_context: Optional[int] = Field(1000, gt=100, description="对话语境中包含字符的最大数量（>100）；默认 1000")
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# 剪枝配置：与 runtime.json 中 pruning 段对应
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pruning_enabled: Optional[bool] = Field(None, description="是否启动智能语义剪枝")
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pruning_scene: Optional[str] = Field(
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None, description="智能剪枝场景：education/online_service/outbound 或本体工程自定义场景"
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)
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pruning_threshold: Optional[float] = Field(
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None, ge=0.0, le=0.9, description="智能语义剪枝阈值（0-0.9）"
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)
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#TODO:萃取引擎的更新的更新会带有反思引擎的参数，需判断业务是否需要，不需要可以重构
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# 反思配置
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enable_self_reflexion: Optional[bool] = Field(None, description="是否启用自我反思")
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iteration_period: Optional[Literal["1", "3", "6", "12", "24"]] = Field(
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"3", description="反思迭代周期，单位小时"
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)
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reflexion_range: Optional[Literal["partial", "all"]] = Field(
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"partial", description="反思范围：部分/全部"
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)
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baseline: Optional[Literal["TIME", "FACT", "TIME-FACT"]] = Field(
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"TIME", description="基线：时间/事实/时间和事实"
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)
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@field_validator("chunker_strategy", mode="before")
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@classmethod
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def map_chunker_aliases_update(cls, v: str):
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if isinstance(v, str):
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m = v.strip().lower()
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alias_map = {
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"auto": "RecursiveChunker",
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"by_sentence": "SentenceChunker",
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"by_paragraph": "SemanticChunker",
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"fixed_tokens": "TokenChunker",
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"递归分块": "RecursiveChunker",
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"token 分块": "TokenChunker",
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"token分块": "TokenChunker",
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"语义分块": "SemanticChunker",
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"神经网络分块": "NeuralChunker",
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"混合分块": "HybridChunker",
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"llm 分块": "LLMChunker",
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"llm分块": "LLMChunker",
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"句子分块": "SentenceChunker",
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"延迟分块": "LateChunker",
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}
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if m in alias_map:
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return alias_map[m]
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return v
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class ConfigUpdateForget(BaseModel): # 更新遗忘引擎配置参数时使用的模型
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# 遗忘引擎配置参数更新模型
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config_id: Union[uuid.UUID, int, str] = None
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lambda_time: Optional[float] = Field(0.5, ge=0.0, le=1.0, description="最低保持度，0-1 小数；默认 0.5")
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lambda_mem: Optional[float] = Field(0.5, ge=0.0, le=1.0, description="遗忘率，0-1 小数；默认 0.5")
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offset: Optional[float] = Field(0.0, ge=0.0, le=1.0, description="偏移度，0-1 小数；默认 0.0")
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class ConfigPilotRun(BaseModel): # 试运行触发请求模型
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config_id: Union[uuid.UUID, int, str] = Field(..., description="配置ID（唯一，支持UUID、整数或字符串）")
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dialogue_text: str = Field(..., description="前端传入的对话文本，格式如 '用户: ...\nAI: ...' 可多行，试运行必填")
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custom_text: Optional[str] = Field(None, description="自定义输入文本，当配置关联本体场景时使用此字段进行试运行")
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model_config = ConfigDict(populate_by_name=True, extra="forbid")
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class ConfigFilter(BaseModel): # 查询配置参数时使用的模型
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model_config = ConfigDict(populate_by_name=True, extra="forbid")
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config_id: Union[uuid.UUID, int, str] = None
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user_id: Optional[str] = None
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apply_id: Optional[str] = None
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limit: int = Field(20, ge=1, le=200, description="返回数量上限")
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offset: int = Field(0, ge=0, description="起始偏移")
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class ApiResponse(BaseModel): # 通用API响应模型
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model_config = ConfigDict(populate_by_name=True, extra="forbid")
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code: int = Field(..., description="0=成功，非0=各类业务异常")
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msg: str = Field("", description="说明信息")
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data: Optional[Any] = Field(None, description="返回数据载荷")
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error: str = Field("", description="错误信息，失败时有值，成功为空字符串")
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time: Optional[int] = Field(None, description="响应时间（毫秒，Unix 时间戳）")
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def _now_ms() -> int:
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return round(time.time() * 1000)
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def ok(msg: str = "OK", data: Optional[Any] = None, time: Optional[int] = None) -> ApiResponse:
|
||
return ApiResponse(code=0, msg=msg, data=data, error="", time=time or _now_ms())
|
||
|
||
|
||
def fail(
|
||
msg: str,
|
||
error_code: str = "ERROR",
|
||
data: Optional[Any] = None,
|
||
time: Optional[int] = None,
|
||
query_preview: Optional[str] = None,
|
||
) -> ApiResponse:
|
||
payload = data
|
||
if query_preview is not None:
|
||
if payload is None:
|
||
payload = {"query_preview": query_preview}
|
||
elif isinstance(payload, dict):
|
||
payload = {**payload, "query_preview": query_preview}
|
||
else:
|
||
payload = {"data": payload, "query_preview": query_preview}
|
||
|
||
return ApiResponse(
|
||
code=1,
|
||
msg=msg,
|
||
data=payload,
|
||
error=error_code,
|
||
time=time or _now_ms(),
|
||
)
|
||
|
||
|
||
class GenerateCacheRequest(BaseModel):
|
||
"""缓存生成请求模型"""
|
||
model_config = ConfigDict(populate_by_name=True, extra="forbid")
|
||
|
||
end_user_id: Optional[str] = Field(
|
||
None,
|
||
description="终端用户ID（UUID格式）。如果提供，只为该用户生成；如果不提供，为当前工作空间的所有用户生成"
|
||
)
|
||
|
||
|
||
# ============================================================================
|
||
# 遗忘引擎相关 Schema
|
||
# ============================================================================
|
||
|
||
class ForgettingTriggerRequest(BaseModel):
|
||
"""手动触发遗忘周期请求模型"""
|
||
model_config = ConfigDict(populate_by_name=True, extra="forbid")
|
||
|
||
end_user_id: str = Field(..., description="组ID（即终端用户ID，必填）")
|
||
max_merge_batch_size: int = Field(100, ge=1, le=1000, description="单次最大融合节点对数（默认100）")
|
||
min_days_since_access: int = Field(30, ge=1, le=365, description="最小未访问天数（默认30天）")
|
||
|
||
|
||
class ForgettingConfigResponse(BaseModel):
|
||
"""遗忘引擎配置响应模型"""
|
||
model_config = ConfigDict(populate_by_name=True, extra="forbid")
|
||
|
||
config_id: Union[uuid.UUID, int, str] = Field(..., description="配置ID（支持UUID、整数或字符串）")
|
||
decay_constant: float = Field(..., description="衰减常数 d")
|
||
lambda_time: float = Field(..., description="时间衰减参数")
|
||
lambda_mem: float = Field(..., description="记忆衰减参数")
|
||
forgetting_rate: float = Field(..., description="遗忘速率（根据 lambda_time / lambda_mem 计算得出）")
|
||
offset: float = Field(..., description="偏移量")
|
||
max_history_length: int = Field(..., description="访问历史最大长度")
|
||
forgetting_threshold: float = Field(..., description="遗忘阈值")
|
||
min_days_since_access: int = Field(..., description="最小未访问天数")
|
||
enable_llm_summary: bool = Field(..., description="是否使用 LLM 生成摘要")
|
||
max_merge_batch_size: int = Field(..., description="单次最大融合节点对数")
|
||
forgetting_interval_hours: int = Field(..., description="遗忘周期间隔（小时）")
|
||
|
||
|
||
class ForgettingConfigUpdateRequest(BaseModel):
|
||
"""遗忘引擎配置更新请求模型"""
|
||
model_config = ConfigDict(populate_by_name=True, extra="forbid")
|
||
|
||
config_id: Union[uuid.UUID, int, str] = Field(..., description="配置唯一标识（UUID或int)")
|
||
decay_constant: Optional[float] = Field(None, ge=0.0, le=1.0, description="衰减常数 d")
|
||
lambda_time: Optional[float] = Field(None, ge=0.0, le=1.0, description="时间衰减参数")
|
||
lambda_mem: Optional[float] = Field(None, ge=0.0, le=1.0, description="记忆衰减参数")
|
||
offset: Optional[float] = Field(None, ge=0.0, le=1.0, description="偏移量")
|
||
max_history_length: Optional[int] = Field(None, ge=10, le=1000, description="访问历史最大长度")
|
||
forgetting_threshold: Optional[float] = Field(None, ge=0.0, le=1.0, description="遗忘阈值")
|
||
min_days_since_access: Optional[int] = Field(None, ge=1, le=365, description="最小未访问天数")
|
||
enable_llm_summary: Optional[bool] = Field(None, description="是否使用 LLM 生成摘要")
|
||
max_merge_batch_size: Optional[int] = Field(None, ge=1, le=1000, description="单次最大融合节点对数")
|
||
forgetting_interval_hours: Optional[int] = Field(None, ge=1, le=168, description="遗忘周期间隔（小时）")
|
||
|
||
|
||
class ForgettingCycleHistoryPoint(BaseModel):
|
||
"""遗忘周期历史数据点模型（用于趋势图）"""
|
||
model_config = ConfigDict(populate_by_name=True, extra="forbid")
|
||
|
||
date: str = Field(..., description="日期（格式: '1/1', '1/2'）")
|
||
merged_count: int = Field(..., description="每日融合节点数")
|
||
average_activation: Optional[float] = Field(None, description="平均激活值")
|
||
total_nodes: int = Field(..., description="总节点数")
|
||
execution_time: int = Field(..., description="执行时间（Unix时间戳，秒）")
|
||
|
||
|
||
class PendingForgettingNode(BaseModel):
|
||
"""待遗忘节点模型"""
|
||
model_config = ConfigDict(populate_by_name=True, extra="forbid")
|
||
|
||
node_id: str = Field(..., description="节点ID")
|
||
node_type: str = Field(..., description="节点类型：statement/entity/summary")
|
||
content_summary: str = Field(..., description="内容摘要")
|
||
activation_value: float = Field(..., description="激活值")
|
||
last_access_time: int = Field(..., description="最后访问时间（Unix时间戳，秒）")
|
||
|
||
|
||
class PageInfo(BaseModel):
|
||
"""分页信息模型"""
|
||
model_config = ConfigDict(populate_by_name=True, extra="forbid")
|
||
page: int = Field(..., description="当前页码（从1开始）")
|
||
pagesize: int = Field(..., description="每页数量")
|
||
total: int = Field(..., description="总记录数")
|
||
hasnext: bool = Field(..., description="是否有下一页")
|
||
|
||
|
||
class PendingNodesResponse(BaseModel):
|
||
"""待遗忘节点列表响应模型（独立分页接口）"""
|
||
model_config = ConfigDict(populate_by_name=True, extra="forbid")
|
||
items: List[PendingForgettingNode] = Field(..., description="待遗忘节点列表")
|
||
page: PageInfo = Field(..., description="分页信息")
|
||
|
||
|
||
class ForgettingStatsResponse(BaseModel):
|
||
"""遗忘引擎统计信息响应模型"""
|
||
model_config = ConfigDict(populate_by_name=True, extra="forbid")
|
||
activation_metrics: Dict[str, Any] = Field(..., description="激活值相关指标")
|
||
node_distribution: Dict[str, int] = Field(..., description="节点类型分布")
|
||
recent_trends: List[ForgettingCycleHistoryPoint] = Field(...,
|
||
description="最近7个日期的遗忘趋势数据（每天取最后一次执行）")
|
||
timestamp: int = Field(..., description="统计时间（时间戳）")
|
||
|
||
|
||
class ForgettingReportResponse(BaseModel):
|
||
"""遗忘周期报告响应模型"""
|
||
model_config = ConfigDict(populate_by_name=True, extra="forbid")
|
||
|
||
merged_count: int = Field(..., description="融合的节点对数量")
|
||
nodes_before: int = Field(..., description="遗忘前的节点总数")
|
||
nodes_after: int = Field(..., description="遗忘后的节点总数")
|
||
reduction_rate: float = Field(..., description="节点减少率（0-1）")
|
||
duration_seconds: float = Field(..., description="执行耗时（秒）")
|
||
start_time: str = Field(..., description="开始时间（ISO格式）")
|
||
end_time: str = Field(..., description="结束时间（ISO格式）")
|
||
failed_count: int = Field(..., description="失败的融合数量")
|
||
success_rate: float = Field(..., description="成功率（0-1）")
|
||
|
||
|
||
class ForgettingCurvePoint(BaseModel):
|
||
"""遗忘曲线数据点模型"""
|
||
model_config = ConfigDict(populate_by_name=True, extra="forbid")
|
||
|
||
day: int = Field(..., description="天数")
|
||
activation: float = Field(..., description="激活值")
|
||
retention_rate: float = Field(..., description="保持率（与激活值相同）")
|
||
|
||
|
||
class ForgettingCurveRequest(BaseModel):
|
||
"""遗忘曲线请求模型"""
|
||
model_config = ConfigDict(populate_by_name=True, extra="forbid")
|
||
|
||
importance_score: float = Field(0.5, ge=0.0, le=1.0, description="重要性分数（0-1）")
|
||
days: int = Field(60, ge=1, le=365, description="模拟天数（默认60天）")
|
||
config_id: Union[uuid.UUID, int, str] = Field(..., description="配置唯一标识（UUID或int)")
|
||
|
||
|
||
class ForgettingCurveResponse(BaseModel):
|
||
"""遗忘曲线响应模型"""
|
||
model_config = ConfigDict(populate_by_name=True, extra="forbid")
|
||
|
||
curve_data: List[ForgettingCurvePoint] = Field(..., description="遗忘曲线数据点列表")
|
||
config: Dict[str, Any] = Field(..., description="使用的配置参数")
|