Feature/behavior analysis (#53)

* init behavior analysis

* init behavior analysis

* feat(implicit-memory): add implicit memory analytics system

api/app/schemas/implicit_memory_schema.py

@@ -0,0 +1,212 @@
+"""Implicit Memory Schemas
+
+This module defines the Pydantic schemas for the implicit memory system API.
+"""
+
+import datetime
+from enum import Enum
+from typing import Any, Dict, List, Optional
+
+from pydantic import BaseModel, ConfigDict, Field, field_validator
+
+
+class ConfidenceLevel(str, Enum):
+    """Confidence levels for analysis results."""
+    HIGH = "high"
+    MEDIUM = "medium"
+    LOW = "low"
+
+
+class FrequencyPattern(str, Enum):
+    """Frequency patterns for habits."""
+    DAILY = "daily"
+    WEEKLY = "weekly"
+    MONTHLY = "monthly"
+    SEASONAL = "seasonal"
+    OCCASIONAL = "occasional"
+    EVENT_TRIGGERED = "event_triggered"
+
+
+# Request Schemas
+
+class TimeRange(BaseModel):
+    """Time range for analysis."""
+    start_date: datetime.datetime
+    end_date: datetime.datetime
+
+    @field_validator('end_date')
+    @classmethod
+    def end_date_must_be_after_start_date(cls, v, info):
+        if 'start_date' in info.data and v <= info.data['start_date']:
+            raise ValueError('end_date must be after start_date')
+        return v
+
+
+class DateRange(BaseModel):
+    """Date range for filtering."""
+    start_date: Optional[datetime.datetime] = None
+    end_date: Optional[datetime.datetime] = None
+
+    @field_validator('end_date')
+    @classmethod
+    def end_date_must_be_after_start_date(cls, v, info):
+        if v and 'start_date' in info.data and info.data['start_date'] and v <= info.data['start_date']:
+            raise ValueError('end_date must be after start_date')
+        return v
+
+
+class AnalysisConfig(BaseModel):
+    """Configuration for analysis operations."""
+    llm_model_id: Optional[str] = None
+    batch_size: int = 100
+    confidence_threshold: float = 0.5
+    include_historical_trends: bool = False
+    max_conversations: Optional[int] = None
+
+
+# Response Schemas
+
+class PreferenceTagResponse(BaseModel):
+    """A user preference tag with detailed context."""
+    model_config = ConfigDict(from_attributes=True)
+    
+    tag_name: str
+    confidence_score: float = Field(ge=0.0, le=1.0)
+    supporting_evidence: List[str]
+    context_details: str
+    created_at: datetime.datetime
+    updated_at: datetime.datetime
+    conversation_references: List[str]
+    category: Optional[str] = None
+
+
+class DimensionScoreResponse(BaseModel):
+    """Score for a personality dimension."""
+    model_config = ConfigDict(from_attributes=True)
+    
+    dimension_name: str
+    percentage: float = Field(ge=0.0, le=100.0)
+    evidence: List[str]
+    reasoning: str
+    confidence_level: ConfidenceLevel
+
+
+class DimensionPortraitResponse(BaseModel):
+    """Four-dimension personality portrait."""
+    model_config = ConfigDict(from_attributes=True)
+    
+    user_id: str
+    creativity: DimensionScoreResponse
+    aesthetic: DimensionScoreResponse
+    technology: DimensionScoreResponse
+    literature: DimensionScoreResponse
+    analysis_timestamp: datetime.datetime
+    total_summaries_analyzed: int
+    historical_trends: Optional[List[Dict[str, Any]]] = None
+
+
+class InterestCategoryResponse(BaseModel):
+    """Interest category with percentage and evidence."""
+    model_config = ConfigDict(from_attributes=True)
+    
+    category_name: str
+    percentage: float = Field(ge=0.0, le=100.0)
+    evidence: List[str]
+    trending_direction: Optional[str] = None
+
+
+class InterestAreaDistributionResponse(BaseModel):
+    """Distribution of user interests across four areas."""
+    model_config = ConfigDict(from_attributes=True)
+    
+    user_id: str
+    tech: InterestCategoryResponse
+    lifestyle: InterestCategoryResponse
+    music: InterestCategoryResponse
+    art: InterestCategoryResponse
+    analysis_timestamp: datetime.datetime
+    total_summaries_analyzed: int
+    
+    @property
+    def total_percentage(self) -> float:
+        """Calculate total percentage across all interest areas."""
+        return self.tech.percentage + self.lifestyle.percentage + self.music.percentage + self.art.percentage
+
+
+class BehaviorHabitResponse(BaseModel):
+    """A behavioral habit identified from conversations."""
+    model_config = ConfigDict(from_attributes=True)
+    
+    habit_description: str
+    frequency_pattern: FrequencyPattern
+    time_context: str
+    confidence_level: ConfidenceLevel
+    supporting_summaries: List[str]
+    first_observed: datetime.datetime
+    last_observed: datetime.datetime
+    is_current: bool = True
+    specific_examples: List[str]
+
+
+class UserProfileResponse(BaseModel):
+    """Comprehensive user profile."""
+    model_config = ConfigDict(from_attributes=True)
+    
+    user_id: str
+    preference_tags: List[PreferenceTagResponse]
+    dimension_portrait: DimensionPortraitResponse
+    interest_area_distribution: InterestAreaDistributionResponse
+    behavior_habits: List[BehaviorHabitResponse]
+    profile_version: int
+    created_at: datetime.datetime
+    updated_at: datetime.datetime
+    total_summaries_analyzed: int
+    analysis_completeness_score: float = Field(ge=0.0, le=1.0)
+
+
+# Internal/Business Logic Schemas
+
+class MemorySummary(BaseModel):
+    """Memory summary from existing storage system."""
+    model_config = ConfigDict(from_attributes=True)
+    
+    summary_id: str
+    content: str
+    timestamp: datetime.datetime
+    participants: List[str]
+    summary_type: str
+
+
+class UserMemorySummary(BaseModel):
+    """Memory summary filtered for specific user content."""
+    model_config = ConfigDict(from_attributes=True)
+    
+    summary_id: str
+    user_id: str
+    user_content: str
+    timestamp: datetime.datetime
+    confidence_score: float = Field(ge=0.0, le=1.0)
+    summary_type: str
+
+
+class SummaryAnalysisResult(BaseModel):
+    """Result of analyzing memory summaries."""
+    model_config = ConfigDict(from_attributes=True)
+    
+    user_id: str
+    preferences: List[PreferenceTagResponse]
+    dimension_evidence: Dict[str, List[str]]
+    interest_evidence: Dict[str, List[str]]
+    habit_evidence: List[Dict[str, Any]]
+    analysis_timestamp: datetime.datetime
+    summaries_analyzed: List[str]
+
+
+# Aliases for backward compatibility with existing code
+PreferenceTag = PreferenceTagResponse
+DimensionScore = DimensionScoreResponse
+DimensionPortrait = DimensionPortraitResponse
+InterestCategory = InterestCategoryResponse
+InterestAreaDistribution = InterestAreaDistributionResponse
+BehaviorHabit = BehaviorHabitResponse
+UserProfile = UserProfileResponse