Files
MemoryBear/api/app/services/memory_perceptual_service.py

174 lines
7.7 KiB
Python

import uuid
from typing import Dict, Any, Optional
from sqlalchemy.orm import Session
from app.core.error_codes import BizCode
from app.core.exceptions import BusinessException
from app.core.logging_config import get_business_logger
from app.models.memory_perceptual_model import PerceptualType, FileStorageType
from app.repositories.memory_perceptual_repository import MemoryPerceptualRepository
from app.schemas.memory_perceptual_schema import (
PerceptualQuerySchema,
PerceptualTimelineResponse,
PerceptualMemoryItem,
AudioModal, Content, VideoModal, TextModal
)
business_logger = get_business_logger()
class MemoryPerceptualService:
def __init__(self, db: Session):
self.db = db
self.repository = MemoryPerceptualRepository(db)
def get_memory_count(self, end_user_id: uuid.UUID) -> Dict[str, Any]:
"""Retrieve perceptual memory statistics for a user."""
business_logger.info(f"Fetching perceptual memory statistics: end_user_id={end_user_id}")
try:
total_count = self.repository.get_count_by_user_id(end_user_id=end_user_id)
vision_count = self.repository.get_count_by_type(end_user_id, PerceptualType.VISION)
audio_count = self.repository.get_count_by_type(end_user_id, PerceptualType.AUDIO)
text_count = self.repository.get_count_by_type(end_user_id, PerceptualType.TEXT)
conversation_count = self.repository.get_count_by_type(end_user_id, PerceptualType.CONVERSATION)
stats = {
"total": total_count,
"by_type": {
"vision": vision_count,
"audio": audio_count,
"text": text_count,
"conversation": conversation_count
}
}
business_logger.info(f"Memory statistics fetched successfully: total={total_count}")
return stats
except Exception as e:
business_logger.error(f"Failed to fetch memory statistics: {str(e)}")
raise BusinessException(f"Failed to fetch memory statistics: {str(e)}", BizCode.DB_ERROR)
def _get_latest_memory_by_type(
self,
end_user_id: uuid.UUID,
perceptual_type: PerceptualType
) -> Optional[dict[str, Any]]:
"""Internal helper to retrieve the latest memory by type."""
business_logger.info(f"Fetching latest {perceptual_type.name.lower()} memory: end_user_id={end_user_id}")
try:
memories = self.repository.get_by_type(
end_user_id=end_user_id,
perceptual_type=perceptual_type,
limit=1,
offset=0
)
if not memories:
business_logger.info(f"No {perceptual_type.name.lower()} memory found: end_user_id={end_user_id}")
return None
memory = memories[0]
meta_data = memory.meta_data or {}
modalities = meta_data.get("modalities")
content = meta_data.get("content")
if not modalities:
raise BusinessException(f"Modalities not defined, perceptual memory_id={memory.id}", BizCode.DB_ERROR)
if not content:
raise BusinessException(f"Content not defined, perceptual memory_id={memory.id}", BizCode.DB_ERROR)
content = Content(**content)
match perceptual_type:
case PerceptualType.VISION:
modal = VideoModal(**modalities)
case PerceptualType.AUDIO:
modal = AudioModal(**modalities)
case PerceptualType.TEXT:
modal = TextModal(**modalities)
case _:
raise BusinessException("Unsupported perceptual type", BizCode.DB_ERROR)
detail = modal.model_dump()
result = {
"id": str(memory.id),
"file_name": memory.file_name,
"file_path": memory.file_path,
"storage_type": memory.storage_service,
"summary": memory.summary,
"keywords": content.keywords,
"topic": content.topic,
"domain": content.domain,
"created_time": int(memory.created_time.timestamp()*1000),
**detail
}
business_logger.info(
f"Latest {perceptual_type.name.lower()} memory retrieved successfully: file={memory.file_name}")
return result
except Exception as e:
business_logger.error(f"Failed to fetch latest {perceptual_type.name.lower()} memory: {str(e)}")
raise BusinessException(f"Failed to fetch latest {perceptual_type.name.lower()} memory: {str(e)}",
BizCode.DB_ERROR)
def get_latest_visual_memory(self, end_user_id: uuid.UUID) -> Optional[Dict[str, Any]]:
return self._get_latest_memory_by_type(end_user_id, PerceptualType.VISION)
def get_latest_audio_memory(self, end_user_id: uuid.UUID) -> Optional[Dict[str, Any]]:
return self._get_latest_memory_by_type(end_user_id, PerceptualType.AUDIO)
def get_latest_text_memory(self, end_user_id: uuid.UUID) -> Optional[Dict[str, Any]]:
return self._get_latest_memory_by_type(end_user_id, PerceptualType.TEXT)
def get_time_line(self, end_user_id: uuid.UUID, query: PerceptualQuerySchema) -> PerceptualTimelineResponse:
"""Retrieve a timeline of perceptual memories for a user."""
business_logger.info(f"Fetching perceptual memory timeline: "
f"end_user_id={end_user_id}, filter={query.filter}")
try:
if query.page < 1:
raise BusinessException("Page number must be greater than 0", BizCode.INVALID_PARAMETER)
if query.page_size < 1 or query.page_size > 100:
raise BusinessException("Page size must be between 1 and 100", BizCode.INVALID_PARAMETER)
total_count, memories = self.repository.get_timeline(end_user_id, query)
memory_items = []
for memory in memories:
meta_data = memory.meta_data or {}
content = meta_data.get("content")
content = Content(**content)
memory_item = PerceptualMemoryItem(
id=memory.id,
perceptual_type=PerceptualType(memory.perceptual_type),
file_path=memory.file_path,
file_name=memory.file_name,
file_ext=memory.file_ext,
summary=memory.summary,
topic=content.topic,
domain=content.domain,
keywords=content.keywords,
created_time=int(memory.created_time.timestamp()*1000),
storage_type=FileStorageType(memory.storage_service),
)
memory_items.append(memory_item)
timeline_response = PerceptualTimelineResponse(
total=total_count,
page=query.page,
page_size=query.page_size,
total_pages=(total_count + query.page_size - 1) // query.page_size,
memories=memory_items
)
business_logger.info(f"Perceptual memory timeline retrieved successfully: "
f"total={total_count}, returned={len(memories)}")
return timeline_response
except BusinessException:
raise
except Exception as e:
business_logger.error(f"Failed to fetch perceptual memory timeline: {str(e)}")
raise BusinessException(f"Failed to fetch perceptual memory timeline: {str(e)}", BizCode.DB_ERROR)