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Antigravity Notebook - Storage Service
Handles saving and loading latent tensors to/from filesystem.
"""
import torch
import os
from pathlib import Path
from typing import List, Dict, Optional
from uuid import UUID
from sqlalchemy.orm import Session
from backend.config import settings
from backend.database import LatentTensor, Source
class StorageService:
"""Service for managing latent tensor storage"""
def __init__(self, base_dir: str = None):
self.base_dir = Path(base_dir or settings.LATENT_TENSOR_DIR)
self.base_dir.mkdir(parents=True, exist_ok=True)
def get_tensor_path(
self,
notebook_id: UUID,
source_id: UUID,
segment_index: int
) -> Path:
"""
Generate file path for a latent tensor.
Structure: data/latent_tensors/{notebook_id}/{source_id}/segment_{idx}.pt
Args:
notebook_id: Notebook UUID
source_id: Source UUID
segment_index: Segment index
Returns:
Path to tensor file
"""
path = self.base_dir / str(notebook_id) / str(source_id)
path.mkdir(parents=True, exist_ok=True)
return path / f"segment_{segment_index}.pt"
def save_tensor(
self,
tensor: torch.Tensor,
notebook_id: UUID,
source_id: UUID,
segment_index: int
) -> str:
"""
Save a latent tensor to disk.
Args:
tensor: PyTorch tensor to save
notebook_id: Notebook UUID
source_id: Source UUID
segment_index: Segment index
Returns:
Relative path to saved tensor
"""
path = self.get_tensor_path(notebook_id, source_id, segment_index)
# Save with compression
torch.save(
tensor,
path,
_use_new_zipfile_serialization=True
)
# Return relative path for database storage
relative_path = str(path.relative_to(self.base_dir))
return relative_path
def load_tensor(self, relative_path: str) -> torch.Tensor:
"""
Load a latent tensor from disk.
Args:
relative_path: Relative path from base_dir
Returns:
Loaded tensor
"""
full_path = self.base_dir / relative_path
if not full_path.exists():
raise FileNotFoundError(f"Tensor file not found: {full_path}")
tensor = torch.load(full_path, map_location="cpu")
return tensor
def get_notebook_sources(
self,
db: Session,
notebook_id: UUID
) -> List[Dict]:
"""
Get all sources for a notebook with their metadata.
Args:
db: Database session
notebook_id: Notebook UUID
Returns:
List of source dictionaries with metadata
"""
sources = db.query(Source).filter(
Source.notebook_id == notebook_id
).all()
source_data = []
for source in sources:
source_data.append({
"id": source.id,
"filename": source.filename,
"source_type": source.source_type,
"url": source.url,
"metadata": source.metadata
})
return source_data
def get_source_tensors(
self,
db: Session,
source_id: UUID
) -> List[LatentTensor]:
"""
Get all latent tensors for a source.
Args:
db: Database session
source_id: Source UUID
Returns:
List of LatentTensor objects
"""
tensors = db.query(LatentTensor).filter(
LatentTensor.source_id == source_id
).order_by(LatentTensor.segment_index).all()
return tensors
def get_notebook_tensors(
self,
db: Session,
notebook_id: UUID
) -> List[Dict]:
"""
Get ALL latent tensors for a notebook across all sources.
This is used by ContextManager to prepare the full notebook context.
Args:
db: Database session
notebook_id: Notebook UUID
Returns:
List of dicts with tensor metadata and loaded tensors
"""
# Get all sources for notebook
sources = db.query(Source).filter(
Source.notebook_id == notebook_id
).all()
all_tensors = []
for source in sources:
# Get all tensors for this source
tensors = db.query(LatentTensor).filter(
LatentTensor.source_id == source.id
).order_by(LatentTensor.segment_index).all()
for tensor_record in tensors:
# Load the actual tensor
tensor = self.load_tensor(tensor_record.tensor_path)
all_tensors.append({
"tensor": tensor,
"source_id": source.id,
"source_filename": source.filename,
"source_type": source.source_type,
"segment_index": tensor_record.segment_index,
"token_count": tensor_record.token_count,
"metadata": tensor_record.metadata
})
return all_tensors
def delete_source_tensors(
self,
db: Session,
source_id: UUID,
notebook_id: UUID
) -> int:
"""
Delete all tensors associated with a source.
Args:
db: Database session
source_id: Source UUID
notebook_id: Notebook UUID
Returns:
Number of tensors deleted
"""
# Get all tensors for source
tensors = self.get_source_tensors(db, source_id)
deleted_count = 0
for tensor in tensors:
# Delete file
full_path = self.base_dir / tensor.tensor_path
if full_path.exists():
full_path.unlink()
deleted_count += 1
# Clean up empty directories
source_dir = self.base_dir / str(notebook_id) / str(source_id)
if source_dir.exists() and not any(source_dir.iterdir()):
source_dir.rmdir()
notebook_dir = self.base_dir / str(notebook_id)
if notebook_dir.exists() and not any(notebook_dir.iterdir()):
notebook_dir.rmdir()
return deleted_count
def get_storage_stats(self) -> Dict:
"""
Get storage statistics.
Returns:
Dictionary with storage stats
"""
total_files = 0
total_size = 0
for root, dirs, files in os.walk(self.base_dir):
for file in files:
if file.endswith('.pt'):
file_path = Path(root) / file
total_files += 1
total_size += file_path.stat().st_size
return {
"total_tensors": total_files,
"total_size_bytes": total_size,
"total_size_mb": round(total_size / (1024 * 1024), 2),
"base_directory": str(self.base_dir)
}
# Global storage service instance
_storage_instance: Optional[StorageService] = None
def get_storage_service() -> StorageService:
"""Get or create the global storage service instance"""
global _storage_instance
if _storage_instance is None:
_storage_instance = StorageService()
return _storage_instance
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