42b59a644d
- Add force parameter to delete_config endpoint for controlled deletion of in-use configs - Implement MemoryConfigService.delete_config with protection against deleting default configs - Add validation to prevent deletion of configs with connected end-users unless force=True - Reorganize controller imports to remove duplicates and improve maintainability - Clean up unused database connection management code from memory_storage_controller - Add detailed docstring to delete_config endpoint explaining protection mechanisms - Update error handling with specific BizCode.RESOURCE_IN_USE for configs in active use - Add comprehensive logging for deletion attempts, warnings, and affected users - Refactor ConfigParamsDelete schema usage to use MemoryConfigService directly - Improve API response structure with affected_users count and force_required flag
281 lines
9.3 KiB
Python
281 lines
9.3 KiB
Python
"""Implicit Memory Schemas
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This module defines the Pydantic schemas for the implicit memory system API.
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"""
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import datetime
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from enum import Enum
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from typing import Any, Dict, List, Optional
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from pydantic import BaseModel, ConfigDict, Field, field_serializer, field_validator
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class FrequencyPattern(str, Enum):
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"""Frequency patterns for habits."""
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DAILY = "daily"
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WEEKLY = "weekly"
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MONTHLY = "monthly"
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SEASONAL = "seasonal"
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OCCASIONAL = "occasional"
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EVENT_TRIGGERED = "event_triggered"
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# Request Schemas
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class TimeRange(BaseModel):
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"""Time range for analysis."""
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start_date: datetime.datetime
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end_date: datetime.datetime
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@field_validator('end_date')
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@classmethod
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def end_date_must_be_after_start_date(cls, v, info):
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if 'start_date' in info.data and v <= info.data['start_date']:
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raise ValueError('end_date must be after start_date')
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return v
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@field_serializer("start_date", when_used="json")
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def _serialize_start_date(self, dt: datetime.datetime):
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return int(dt.timestamp() * 1000) if dt else None
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@field_serializer("end_date", when_used="json")
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def _serialize_end_date(self, dt: datetime.datetime):
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return int(dt.timestamp() * 1000) if dt else None
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class DateRange(BaseModel):
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"""Date range for filtering."""
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start_date: Optional[datetime.datetime] = None
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end_date: Optional[datetime.datetime] = None
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@field_validator('end_date')
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@classmethod
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def end_date_must_be_after_start_date(cls, v, info):
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if v and 'start_date' in info.data and info.data['start_date'] and v <= info.data['start_date']:
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raise ValueError('end_date must be after start_date')
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return v
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@field_serializer("start_date", when_used="json")
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def _serialize_start_date(self, dt: Optional[datetime.datetime]):
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return int(dt.timestamp() * 1000) if dt else None
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@field_serializer("end_date", when_used="json")
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def _serialize_end_date(self, dt: Optional[datetime.datetime]):
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return int(dt.timestamp() * 1000) if dt else None
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class AnalysisConfig(BaseModel):
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"""Configuration for analysis operations."""
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llm_model_id: Optional[str] = None
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batch_size: int = 100
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confidence_threshold: float = 0.5
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include_historical_trends: bool = False
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max_conversations: Optional[int] = None
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# Response Schemas
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class PreferenceTagResponse(BaseModel):
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"""A user preference tag with detailed context."""
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model_config = ConfigDict(from_attributes=True)
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tag_name: str
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confidence_score: float = Field(ge=0.0, le=1.0)
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supporting_evidence: List[str]
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context_details: str
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created_at: datetime.datetime
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updated_at: datetime.datetime
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conversation_references: List[str]
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category: Optional[str] = None
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@field_serializer("created_at", when_used="json")
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def _serialize_created_at(self, dt: datetime.datetime):
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return int(dt.timestamp() * 1000) if dt else None
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@field_serializer("updated_at", when_used="json")
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def _serialize_updated_at(self, dt: datetime.datetime):
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return int(dt.timestamp() * 1000) if dt else None
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class DimensionScoreResponse(BaseModel):
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"""Score for a personality dimension."""
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model_config = ConfigDict(from_attributes=True)
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dimension_name: str
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percentage: float = Field(ge=0.0, le=100.0)
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evidence: List[str]
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reasoning: str
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confidence_level: int = Field(ge=0, le=100)
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class DimensionPortraitResponse(BaseModel):
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"""Four-dimension personality portrait."""
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model_config = ConfigDict(from_attributes=True)
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creativity: DimensionScoreResponse
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aesthetic: DimensionScoreResponse
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technology: DimensionScoreResponse
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literature: DimensionScoreResponse
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analysis_timestamp: datetime.datetime
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total_summaries_analyzed: int
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historical_trends: Optional[List[Dict[str, Any]]] = None
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@field_serializer("analysis_timestamp", when_used="json")
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def _serialize_analysis_timestamp(self, dt: datetime.datetime):
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return int(dt.timestamp() * 1000) if dt else None
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class InterestCategoryResponse(BaseModel):
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"""Interest category with percentage and evidence."""
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model_config = ConfigDict(from_attributes=True)
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category_name: str
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percentage: float = Field(ge=0.0, le=100.0)
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evidence: List[str]
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trending_direction: Optional[str] = None
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class InterestAreaDistributionResponse(BaseModel):
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"""Distribution of user interests across four areas."""
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model_config = ConfigDict(from_attributes=True)
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tech: InterestCategoryResponse
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lifestyle: InterestCategoryResponse
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music: InterestCategoryResponse
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art: InterestCategoryResponse
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analysis_timestamp: datetime.datetime
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total_summaries_analyzed: int
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@property
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def total_percentage(self) -> float:
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"""Calculate total percentage across all interest areas."""
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return self.tech.percentage + self.lifestyle.percentage + self.music.percentage + self.art.percentage
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@field_serializer("analysis_timestamp", when_used="json")
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def _serialize_analysis_timestamp(self, dt: datetime.datetime):
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return int(dt.timestamp() * 1000) if dt else None
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class BehaviorHabitResponse(BaseModel):
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"""A behavioral habit identified from conversations."""
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model_config = ConfigDict(from_attributes=True)
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habit_description: str
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frequency_pattern: FrequencyPattern
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time_context: str
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confidence_level: int = Field(ge=0, le=100)
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first_observed: datetime.datetime
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last_observed: datetime.datetime
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is_current: bool = True
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specific_examples: List[str]
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@field_serializer("first_observed", when_used="json")
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def _serialize_first_observed(self, dt: datetime.datetime):
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return int(dt.timestamp() * 1000) if dt else None
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@field_serializer("last_observed", when_used="json")
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def _serialize_last_observed(self, dt: datetime.datetime):
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return int(dt.timestamp() * 1000) if dt else None
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class UserProfileResponse(BaseModel):
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"""Comprehensive user profile."""
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model_config = ConfigDict(from_attributes=True)
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preference_tags: List[PreferenceTagResponse]
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dimension_portrait: DimensionPortraitResponse
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interest_area_distribution: InterestAreaDistributionResponse
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behavior_habits: List[BehaviorHabitResponse]
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profile_version: int
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created_at: datetime.datetime
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updated_at: datetime.datetime
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total_summaries_analyzed: int
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analysis_completeness_score: float = Field(ge=0.0, le=1.0)
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@field_serializer("created_at", when_used="json")
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def _serialize_created_at(self, dt: datetime.datetime):
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return int(dt.timestamp() * 1000) if dt else None
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@field_serializer("updated_at", when_used="json")
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def _serialize_updated_at(self, dt: datetime.datetime):
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return int(dt.timestamp() * 1000) if dt else None
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# Internal/Business Logic Schemas
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class MemorySummary(BaseModel):
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"""Memory summary from existing storage system."""
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model_config = ConfigDict(from_attributes=True)
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summary_id: str
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content: str
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timestamp: datetime.datetime
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participants: List[str]
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summary_type: str
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@field_serializer("timestamp", when_used="json")
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def _serialize_timestamp(self, dt: datetime.datetime):
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return int(dt.timestamp() * 1000) if dt else None
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class UserMemorySummary(BaseModel):
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"""Memory summary filtered for specific user content."""
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model_config = ConfigDict(from_attributes=True)
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summary_id: str
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user_content: str
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timestamp: datetime.datetime
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confidence_score: float = Field(ge=0.0, le=1.0)
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summary_type: str
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@field_serializer("timestamp", when_used="json")
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def _serialize_timestamp(self, dt: datetime.datetime):
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return int(dt.timestamp() * 1000) if dt else None
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class SummaryAnalysisResult(BaseModel):
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"""Result of analyzing memory summaries."""
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model_config = ConfigDict(from_attributes=True)
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preferences: List[PreferenceTagResponse]
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dimension_evidence: Dict[str, List[str]]
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interest_evidence: Dict[str, List[str]]
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habit_evidence: List[Dict[str, Any]]
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analysis_timestamp: datetime.datetime
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summaries_analyzed: List[str]
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@field_serializer("analysis_timestamp", when_used="json")
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def _serialize_analysis_timestamp(self, dt: datetime.datetime):
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return int(dt.timestamp() * 1000) if dt else None
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# Aliases for backward compatibility with existing code
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PreferenceTag = PreferenceTagResponse
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DimensionScore = DimensionScoreResponse
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DimensionPortrait = DimensionPortraitResponse
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InterestCategory = InterestCategoryResponse
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InterestAreaDistribution = InterestAreaDistributionResponse
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BehaviorHabit = BehaviorHabitResponse
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UserProfile = UserProfileResponse
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# Cache-related Schemas
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class GenerateProfileRequest(BaseModel):
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"""生成完整用户画像请求"""
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end_user_id: str = Field(..., description="终端用户ID")
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class CompleteProfileResponse(BaseModel):
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"""完整用户画像响应（包含所有模块）"""
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preferences: List[PreferenceTagResponse]
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portrait: DimensionPortraitResponse
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interest_areas: InterestAreaDistributionResponse
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habits: List[BehaviorHabitResponse]
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generated_at: datetime.datetime
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cached: bool = Field(False, description="是否来自缓存")
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@field_serializer("generated_at", when_used="json")
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def _serialize_generated_at(self, dt: datetime.datetime):
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return int(dt.timestamp() * 1000) if dt else None
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