b3f8de3062
* init behavior analysis * init behavior analysis * feat(implicit-memory): add implicit memory analytics system
303 lines
11 KiB
Python
303 lines
11 KiB
Python
from datetime import datetime
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from typing import Optional
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from app.core.error_codes import BizCode
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from app.core.logging_config import get_api_logger
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from app.core.response_utils import fail, success
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from app.db import get_db
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from app.dependencies import (
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cur_workspace_access_guard,
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get_current_user,
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)
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from app.models.user_model import User
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from app.schemas.response_schema import ApiResponse
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from app.services.implicit_memory_service import ImplicitMemoryService
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from fastapi import APIRouter, Depends, Query
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from sqlalchemy.orm import Session
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api_logger = get_api_logger()
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router = APIRouter(
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prefix="/memory/implicit-memory",
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tags=["Implicit Memory"],
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)
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def handle_implicit_memory_error(e: Exception, operation: str, user_id: str = None) -> dict:
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"""
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Centralized error handling for implicit memory operations.
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Args:
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e: The exception that occurred
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operation: Description of the operation that failed
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user_id: Optional user ID for logging context
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Returns:
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Standardized error response
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"""
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error_context = f"user_id={user_id}" if user_id else "unknown user"
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if isinstance(e, ValueError):
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if "user" in str(e).lower() and "not found" in str(e).lower():
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api_logger.warning(f"Invalid user ID for {operation}: {error_context}")
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return fail(BizCode.INVALID_USER_ID, "无效的用户ID", str(e))
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elif "insufficient" in str(e).lower() or "no data" in str(e).lower():
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api_logger.warning(f"Insufficient data for {operation}: {error_context}")
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return fail(BizCode.INSUFFICIENT_DATA, "数据不足，无法进行分析", str(e))
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else:
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api_logger.warning(f"Invalid parameters for {operation}: {error_context}")
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return fail(BizCode.INVALID_FILTER_PARAMS, "无效的参数", str(e))
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elif isinstance(e, KeyError):
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api_logger.warning(f"Missing required data for {operation}: {error_context}")
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return fail(BizCode.INSUFFICIENT_DATA, "缺少必要的数据", str(e))
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elif isinstance(e, (ConnectionError, TimeoutError)):
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api_logger.error(f"Service unavailable for {operation}: {error_context}")
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return fail(BizCode.SERVICE_UNAVAILABLE, "服务暂时不可用", str(e))
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elif "analysis" in str(e).lower() or "llm" in str(e).lower():
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api_logger.error(f"Analysis failed for {operation}: {error_context}", exc_info=True)
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return fail(BizCode.ANALYSIS_FAILED, "分析处理失败", str(e))
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elif "storage" in str(e).lower() or "database" in str(e).lower():
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api_logger.error(f"Storage error for {operation}: {error_context}", exc_info=True)
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return fail(BizCode.PROFILE_STORAGE_ERROR, "数据存储失败", str(e))
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else:
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api_logger.error(f"Unexpected error for {operation}: {error_context}", exc_info=True)
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return fail(BizCode.INTERNAL_ERROR, f"{operation}失败", str(e))
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def validate_user_id(user_id: str) -> None:
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"""
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Validate user ID format and constraints.
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Args:
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user_id: User ID to validate
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Raises:
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ValueError: If user ID is invalid
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"""
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if not user_id or not user_id.strip():
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raise ValueError("User ID cannot be empty")
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if len(user_id.strip()) < 1:
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raise ValueError("User ID is too short")
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def validate_date_range(start_date: Optional[datetime], end_date: Optional[datetime]) -> None:
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"""
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Validate date range parameters.
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Args:
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start_date: Start date
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end_date: End date
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Raises:
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ValueError: If date range is invalid
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"""
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if (start_date and not end_date) or (end_date and not start_date):
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raise ValueError("Both start_date and end_date must be provided together")
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if start_date and end_date and start_date >= end_date:
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raise ValueError("start_date must be before end_date")
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if start_date and start_date > datetime.now():
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raise ValueError("start_date cannot be in the future")
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def validate_confidence_threshold(threshold: float) -> None:
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"""
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Validate confidence threshold parameter.
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Args:
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threshold: Confidence threshold to validate
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Raises:
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ValueError: If threshold is invalid
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"""
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if not 0.0 <= threshold <= 1.0:
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raise ValueError("confidence_threshold must be between 0.0 and 1.0")
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@router.get("/preferences/{user_id}", response_model=ApiResponse)
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@cur_workspace_access_guard()
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async def get_preference_tags(
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user_id: str,
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confidence_threshold: float = Query(0.5, ge=0.0, le=1.0, description="Minimum confidence threshold"),
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tag_category: Optional[str] = Query(None, description="Filter by tag category"),
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start_date: Optional[datetime] = Query(None, description="Filter start date"),
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end_date: Optional[datetime] = Query(None, description="Filter end date"),
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db: Session = Depends(get_db),
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current_user: User = Depends(get_current_user)
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) -> ApiResponse:
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"""
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Get user preference tags with filtering options.
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Args:
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user_id: Target user ID
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confidence_threshold: Minimum confidence score (0.0-1.0)
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tag_category: Optional category filter
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start_date: Optional start date filter
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end_date: Optional end date filter
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Returns:
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List of preference tags matching the filters
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"""
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api_logger.info(f"Preference tags requested for user: {user_id}")
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try:
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# Validate inputs
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validate_user_id(user_id)
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validate_confidence_threshold(confidence_threshold)
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validate_date_range(start_date, end_date)
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# Create service with user-specific config
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service = ImplicitMemoryService(db=db, end_user_id=user_id)
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# Build date range
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date_range = None
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if start_date and end_date:
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from app.schemas.implicit_memory_schema import DateRange
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date_range = DateRange(start_date=start_date, end_date=end_date)
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# Get preference tags
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tags = await service.get_preference_tags(
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user_id=user_id,
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confidence_threshold=confidence_threshold,
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tag_category=tag_category,
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date_range=date_range
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)
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api_logger.info(f"Retrieved {len(tags)} preference tags for user: {user_id}")
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return success(data=[tag.dict() for tag in tags], msg="偏好标签获取成功")
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except Exception as e:
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return handle_implicit_memory_error(e, "偏好标签获取", user_id)
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@router.get("/portrait/{user_id}", response_model=ApiResponse)
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@cur_workspace_access_guard()
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async def get_dimension_portrait(
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user_id: str,
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include_history: bool = Query(False, description="Include historical trends"),
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db: Session = Depends(get_db),
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current_user: User = Depends(get_current_user)
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) -> ApiResponse:
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"""
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Get user's four-dimension personality portrait.
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Args:
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user_id: Target user ID
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include_history: Whether to include historical trend data
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Returns:
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Four-dimension personality portrait with scores and evidence
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"""
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api_logger.info(f"Dimension portrait requested for user: {user_id}")
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try:
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# Validate inputs
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validate_user_id(user_id)
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# Create service with user-specific config
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service = ImplicitMemoryService(db=db, end_user_id=user_id)
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portrait = await service.get_dimension_portrait(
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user_id=user_id,
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include_history=include_history
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)
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api_logger.info(f"Dimension portrait retrieved for user: {user_id}")
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return success(data=portrait.dict(), msg="四维画像获取成功")
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except Exception as e:
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return handle_implicit_memory_error(e, "四维画像获取", user_id)
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@router.get("/interest-areas/{user_id}", response_model=ApiResponse)
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@cur_workspace_access_guard()
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async def get_interest_area_distribution(
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user_id: str,
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include_trends: bool = Query(False, description="Include trend analysis"),
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db: Session = Depends(get_db),
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current_user: User = Depends(get_current_user)
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) -> ApiResponse:
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"""
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Get user's interest area distribution across four areas.
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Args:
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user_id: Target user ID
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include_trends: Whether to include trend analysis data
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Returns:
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Interest area distribution with percentages and evidence
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"""
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api_logger.info(f"Interest area distribution requested for user: {user_id}")
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try:
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# Validate inputs
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validate_user_id(user_id)
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# Create service with user-specific config
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service = ImplicitMemoryService(db=db, end_user_id=user_id)
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distribution = await service.get_interest_area_distribution(
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user_id=user_id,
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include_trends=include_trends
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)
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api_logger.info(f"Interest area distribution retrieved for user: {user_id}")
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return success(data=distribution.dict(), msg="兴趣领域分布获取成功")
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except Exception as e:
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return handle_implicit_memory_error(e, "兴趣领域分布获取", user_id)
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@router.get("/habits/{user_id}", response_model=ApiResponse)
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@cur_workspace_access_guard()
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async def get_behavior_habits(
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user_id: str,
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confidence_level: Optional[str] = Query(None, regex="^(high|medium|low)$", description="Filter by confidence level"),
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frequency_pattern: Optional[str] = Query(None, regex="^(daily|weekly|monthly|seasonal|occasional|event_triggered)$", description="Filter by frequency pattern"),
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time_period: Optional[str] = Query(None, regex="^(current|past)$", description="Filter by time period"),
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db: Session = Depends(get_db),
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current_user: User = Depends(get_current_user)
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) -> ApiResponse:
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"""
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Get user's behavioral habits with filtering options.
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Args:
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user_id: Target user ID
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confidence_level: Filter by confidence level (high, medium, low)
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frequency_pattern: Filter by frequency pattern (daily, weekly, monthly, seasonal, occasional, event_triggered)
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time_period: Filter by time period (current, past)
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Returns:
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List of behavioral habits matching the filters
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"""
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api_logger.info(f"Behavior habits requested for user: {user_id}")
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try:
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# Validate inputs
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validate_user_id(user_id)
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# Create service with user-specific config
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service = ImplicitMemoryService(db=db, end_user_id=user_id)
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habits = await service.get_behavior_habits(
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user_id=user_id,
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confidence_level=confidence_level,
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frequency_pattern=frequency_pattern,
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time_period=time_period
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)
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api_logger.info(f"Retrieved {len(habits)} behavior habits for user: {user_id}")
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return success(data=[habit.dict() for habit in habits], msg="行为习惯获取成功")
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except Exception as e:
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return handle_implicit_memory_error(e, "行为习惯获取", user_id)
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