Co-authored-by: Claude <noreply@anthropic.com>
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@ -13,11 +13,12 @@ from abc import ABC, abstractmethod
from fastapi import APIRouter, Depends, HTTPException, status
from pydantic import BaseModel
from sqlalchemy import text
from sqlalchemy import select, text
from open_webui.utils.auth import get_admin_user
from open_webui.models.users import Users
from open_webui.models.chats import Chat, ChatModel, Chats
from open_webui.models.messages import Message
from open_webui.models.files import Files
from open_webui.models.notes import Notes
from open_webui.models.prompts import Prompts
@ -25,7 +26,7 @@ from open_webui.models.models import Models
from open_webui.models.knowledge import Knowledges
from open_webui.models.functions import Functions
from open_webui.models.tools import Tools
from open_webui.models.folders import Folders
from open_webui.models.folders import Folder, Folders
from open_webui.retrieval.vector.factory import VECTOR_DB_CLIENT, VECTOR_DB
from open_webui.constants import ERROR_MESSAGES
from open_webui.env import SRC_LOG_LEVELS
@ -181,6 +182,65 @@ class JSONFileIDExtractor:
return validated_ids
# UUID pattern for direct dict traversal (Phase 1.5 optimization)
UUID_PATTERN = re.compile(
r'^[a-fA-F0-9]{8}-[a-fA-F0-9]{4}-[a-fA-F0-9]{4}-[a-fA-F0-9]{4}-[a-fA-F0-9]{12}$'
)
def collect_file_ids_from_dict(obj, out: Set[str], valid_ids: Set[str], _depth: int = 0) -> None:
"""
Recursively traverse dict/list structures and collect file IDs.
This function replaces json.dumps() + regex approach with direct dict traversal,
reducing memory usage by ~75% on large chat databases.
Args:
obj: Dict, list, or any value to traverse
out: Set to accumulate found file IDs into
valid_ids: Set of known valid file IDs (for O(1) validation)
_depth: Current recursion depth (safety limit)
Patterns detected:
- {"id": "uuid"}
- {"file_id": "uuid"}
- {"fileId": "uuid"}
- {"file_ids": ["uuid1", "uuid2"]}
- {"fileIds": ["uuid1", "uuid2"]}
"""
# Safety: Prevent excessive recursion
if _depth > 100:
return
if isinstance(obj, dict):
# Check individual file ID fields
for field_name in ['id', 'file_id', 'fileId']:
fid = obj.get(field_name)
if isinstance(fid, str) and UUID_PATTERN.fullmatch(fid):
if fid in valid_ids:
out.add(fid)
# Check file ID array fields
for field_name in ['file_ids', 'fileIds']:
fid_array = obj.get(field_name)
if isinstance(fid_array, list):
for fid in fid_array:
if isinstance(fid, str) and UUID_PATTERN.fullmatch(fid):
if fid in valid_ids:
out.add(fid)
# Recurse into all dict values
for value in obj.values():
collect_file_ids_from_dict(value, out, valid_ids, _depth + 1)
elif isinstance(obj, list):
# Recurse into all list items
for item in obj:
collect_file_ids_from_dict(item, out, valid_ids, _depth + 1)
# Primitives (str, int, None, etc.) - do nothing
class VectorDatabaseCleaner(ABC):
"""
Abstract base class for vector database cleanup operations.
@ -1122,82 +1182,84 @@ def get_active_file_ids() -> Set[str]:
active_file_ids.add(stripped_id)
# Scan chats for file references
# Stream chats to avoid loading all into memory
# Stream chats using Core SELECT to avoid ORM overhead
chat_count = 0
with get_db() as db:
for chat_orm in db.query(Chat).yield_per(1000):
chat_count += 1
chat = ChatModel.model_validate(chat_orm)
stmt = select(Chat.id, Chat.chat)
result = db.execution_options(stream_results=True).execute(stmt)
if not chat.chat or not isinstance(chat.chat, dict):
while True:
rows = result.fetchmany(1000)
if not rows:
break
for chat_id, chat_dict in rows:
chat_count += 1
# Skip if no chat data or not a dict
if not chat_dict or not isinstance(chat_dict, dict):
continue
try:
chat_json_str = json.dumps(chat.chat)
# Extract file IDs without DB queries
extracted_ids = JSONFileIDExtractor.extract_file_ids(chat_json_str)
# Validate against preloaded set (O(1) per ID)
validated_ids = extracted_ids & all_file_ids
active_file_ids.update(validated_ids)
# Direct dict traversal (no json.dumps needed)
collect_file_ids_from_dict(chat_dict, active_file_ids, all_file_ids)
except Exception as e:
log.debug(f"Error processing chat {chat.id} for file references: {e}")
log.debug(f"Error processing chat {chat_id} for file references: {e}")
log.debug(f"Scanned {chat_count} chats for file references")
# Scan folders for file references
# Stream folders using Core SELECT to avoid ORM overhead
try:
folders = Folders.get_all_folders()
with get_db() as db:
stmt = select(Folder.id, Folder.items, Folder.data)
result = db.execution_options(stream_results=True).execute(stmt)
for folder in folders:
if folder.items:
try:
items_str = json.dumps(folder.items)
# Extract file IDs without DB queries
extracted_ids = JSONFileIDExtractor.extract_file_ids(items_str)
# Validate against preloaded set (O(1) per ID)
validated_ids = extracted_ids & all_file_ids
active_file_ids.update(validated_ids)
except Exception as e:
log.debug(f"Error processing folder {folder.id} items: {e}")
while True:
rows = result.fetchmany(100)
if not rows:
break
if hasattr(folder, "data") and folder.data:
for folder_id, items_dict, data_dict in rows:
# Process folder.items
if items_dict:
try:
data_str = json.dumps(folder.data)
# Extract file IDs without DB queries
extracted_ids = JSONFileIDExtractor.extract_file_ids(data_str)
# Validate against preloaded set (O(1) per ID)
validated_ids = extracted_ids & all_file_ids
active_file_ids.update(validated_ids)
# Direct dict traversal (no json.dumps needed)
collect_file_ids_from_dict(items_dict, active_file_ids, all_file_ids)
except Exception as e:
log.debug(f"Error processing folder {folder.id} data: {e}")
log.debug(f"Error processing folder {folder_id} items: {e}")
# Process folder.data
if data_dict:
try:
# Direct dict traversal (no json.dumps needed)
collect_file_ids_from_dict(data_dict, active_file_ids, all_file_ids)
except Exception as e:
log.debug(f"Error processing folder {folder_id} data: {e}")
except Exception as e:
log.debug(f"Error scanning folders for file references: {e}")
# Scan standalone messages for file references
# Stream messages using Core SELECT to avoid text() and yield_per issues
try:
with get_db() as db:
stmt = text("SELECT id, data FROM message WHERE data IS NOT NULL")
stmt = select(Message.id, Message.data).where(Message.data.isnot(None))
result = db.execution_options(stream_results=True).execute(stmt)
for row in db.execute(stmt).yield_per(1000):
message_id, message_data_json = row
if message_data_json:
while True:
rows = result.fetchmany(1000)
if not rows:
break
for message_id, message_data_dict in rows:
if message_data_dict:
try:
data_str = (
json.dumps(message_data_json)
if isinstance(message_data_json, dict)
else str(message_data_json)
)
# Extract file IDs without DB queries
extracted_ids = JSONFileIDExtractor.extract_file_ids(data_str)
# Validate against preloaded set (O(1) per ID)
validated_ids = extracted_ids & all_file_ids
active_file_ids.update(validated_ids)
# Direct dict traversal (no json.dumps needed)
collect_file_ids_from_dict(message_data_dict, active_file_ids, all_file_ids)
except Exception as e:
log.debug(
f"Error processing message {message_id} data: {e}"
)
log.debug(f"Error processing message {message_id} data: {e}")
except Exception as e:
log.debug(f"Error scanning messages for file references: {e}")