Refactor audio storage to MongoDB chunks and enhance cleanup settings…

backends/advanced/src/advanced_omi_backend/app_factory.py

@@ -54,12 +54,12 @@ async def lifespan(app: FastAPI):
     try:
         from beanie import init_beanie
         from advanced_omi_backend.models.conversation import Conversation
-        from advanced_omi_backend.models.audio_file import AudioFile
+        from advanced_omi_backend.models.audio_chunk import AudioChunkDocument
         from advanced_omi_backend.models.user import User
 
         await init_beanie(
             database=config.db,
-            document_models=[User, Conversation, AudioFile],
+            document_models=[User, Conversation, AudioChunkDocument],
         )
         application_logger.info("Beanie initialized for all document models")
     except Exception as e:

backends/advanced/src/advanced_omi_backend/config.py

@@ -9,7 +9,9 @@
 import logging
 import os
 import shutil
+from dataclasses import dataclass, asdict
 from pathlib import Path
+from typing import Optional
 
 logger = logging.getLogger(__name__)
 
@@ -131,6 +133,101 @@ def save_diarization_settings_to_file(settings):
         return False
 
 
+# ============================================================================
+# Cleanup Settings (JSON file-based with in-memory caching)
+# ============================================================================
+
+@dataclass
+class CleanupSettings:
+    """Cleanup configuration for soft-deleted conversations."""
+    auto_cleanup_enabled: bool = False
+    retention_days: int = 30
+
+# Global cache for cleanup settings
+_cleanup_settings: Optional[CleanupSettings] = None
+
+
+def get_cleanup_config_path() -> Path:
+    """Get path to cleanup settings JSON file."""
+    data_dir = Path(os.getenv("DATA_DIR", "/app/data"))
+    data_dir.mkdir(parents=True, exist_ok=True)
+    return data_dir / "cleanup_config.json"
+
+
+def load_cleanup_settings_from_file() -> CleanupSettings:
+    """
+    Load cleanup settings from JSON file or return defaults.
+
+    Returns cached settings if available, otherwise loads from file.
+    If file doesn't exist, returns default settings.
+    """
+    global _cleanup_settings
+
+    # Return cached settings if available
+    if _cleanup_settings is not None:
+        return _cleanup_settings
+
+    config_path = get_cleanup_config_path()
+
+    # Try to load from file
+    if config_path.exists():
+        try:
+            with open(config_path, "r") as f:
+                data = json.load(f)
+                _cleanup_settings = CleanupSettings(**data)
+                logger.info(f"✅ Loaded cleanup settings: auto_cleanup={_cleanup_settings.auto_cleanup_enabled}, retention={_cleanup_settings.retention_days}d")
+                return _cleanup_settings
+        except Exception as e:
+            logger.error(f"❌ Failed to load cleanup settings from {config_path}: {e}")
+
+    # Return defaults if file doesn't exist or failed to load
+    _cleanup_settings = CleanupSettings()
+    logger.info("Using default cleanup settings (auto_cleanup_enabled=False, retention_days=30)")
+    return _cleanup_settings
+
+
+def save_cleanup_settings_to_file(settings: CleanupSettings) -> None:
+    """
+    Save cleanup settings to JSON file and update in-memory cache.
+
+    Args:
+        settings: CleanupSettings to persist
+
+    Raises:
+        Exception: If file write fails
+    """
+    global _cleanup_settings
+
+    config_path = get_cleanup_config_path()
+
+    try:
+        # Save to JSON file
+        with open(config_path, "w") as f:
+            json.dump(asdict(settings), f, indent=2)
+
+        # Update in-memory cache
+        _cleanup_settings = settings
+
+        logger.info(f"✅ Saved cleanup settings: auto_cleanup={settings.auto_cleanup_enabled}, retention={settings.retention_days}d")
+    except Exception as e:
+        logger.error(f"❌ Failed to save cleanup settings to {config_path}: {e}")
+        raise
+
+
+def get_cleanup_settings() -> dict:
+    """
+    Get current cleanup settings as dict (for API responses).
+
+    Returns:
+        Dict with auto_cleanup_enabled and retention_days
+    """
+    settings = load_cleanup_settings_from_file()
+    return {
+        "auto_cleanup_enabled": settings.auto_cleanup_enabled,
+        "retention_days": settings.retention_days,
+    }
+
+
 def get_speech_detection_settings():
     """Get speech detection settings from environment or defaults."""
 

backends/advanced/src/advanced_omi_backend/controllers/audio_controller.py

@@ -17,8 +17,9 @@
 
 from advanced_omi_backend.utils.audio_utils import (
     AudioValidationError,
-    write_audio_file,
+    validate_and_prepare_audio,
 )
+from advanced_omi_backend.utils.audio_chunk_utils import convert_audio_to_chunks
 from advanced_omi_backend.models.job import JobPriority
 from advanced_omi_backend.models.user import User
 from advanced_omi_backend.models.conversation import create_conversation
@@ -86,33 +87,19 @@ async def upload_and_process_audio_files(
                 # Generate audio UUID and timestamp
                 if source == "gdrive":
                     audio_uuid = getattr(file, "audio_uuid", None)
-                    if not audio_uuid: 
+                    if not audio_uuid:
                         audio_logger.error(f"Missing audio_uuid for gdrive file: {file.filename}")
-                        audio_uuid = str(uuid.uuid4()) 
-                else: 
+                        audio_uuid = str(uuid.uuid4())
+                else:
                     audio_uuid = str(uuid.uuid4())
                 timestamp = int(time.time() * 1000)
 
-                # Determine output directory (with optional subfolder)
-                from advanced_omi_backend.config import CHUNK_DIR
-                if folder:
-                    chunk_dir = CHUNK_DIR / folder
-                    chunk_dir.mkdir(parents=True, exist_ok=True)
-                else:
-                    chunk_dir = CHUNK_DIR
-
-                # Validate, write audio file and create AudioSession (all in one)
+                # Validate and prepare audio (read format from WAV file)
                 try:
-                    relative_audio_path, file_path, duration = await write_audio_file(
-                        raw_audio_data=content,
-                        audio_uuid=audio_uuid,
-                        source=source,
-                        client_id=client_id,
-                        user_id=user.user_id,
-                        user_email=user.email,
-                        timestamp=timestamp,
-                        chunk_dir=chunk_dir,
-                        validate=True,  # Validate WAV format, convert stereo→mono
+                    audio_data, sample_rate, sample_width, channels, duration = await validate_and_prepare_audio(
+                        audio_data=content,
+                        expected_sample_rate=16000,  # Expecting 16kHz
+                        convert_to_mono=True  # Convert stereo to mono
                     )
                 except AudioValidationError as e:
                     processed_files.append({
@@ -123,7 +110,7 @@ async def upload_and_process_audio_files(
                     continue
 
                 audio_logger.info(
-                    f"📊 {file.filename}: {duration:.1f}s → {relative_audio_path}"
+                    f"📊 {file.filename}: {duration:.1f}s ({sample_rate}Hz, {channels}ch, {sample_width} bytes/sample)"
                 )
 
                 # Create conversation immediately for uploaded files (conversation_id auto-generated)
@@ -139,20 +126,36 @@ async def upload_and_process_audio_files(
                     title=title,
                     summary="Processing uploaded audio file..."
                 )
-                # Use the relative path returned by write_audio_file (already includes folder prefix if applicable)
-                conversation.audio_path = relative_audio_path
                 await conversation.insert()
                 conversation_id = conversation.conversation_id  # Get the auto-generated ID
 
                 audio_logger.info(f"📝 Created conversation {conversation_id} for uploaded file")
 
+                # Convert audio directly to MongoDB chunks
+                try:
+                    num_chunks = await convert_audio_to_chunks(
+                        conversation_id=conversation_id,
+                        audio_data=audio_data,
+                        sample_rate=sample_rate,
+                        channels=channels,
+                        sample_width=sample_width,
+                    )
+                    audio_logger.info(
+                        f"📦 Converted uploaded file to {num_chunks} MongoDB chunks "
+                        f"(conversation {conversation_id[:12]})"
+                    )
+                except Exception as chunk_error:
+                    audio_logger.error(
+                        f"Failed to convert uploaded file to chunks: {chunk_error}",
+                        exc_info=True
+                    )
+
                 # Enqueue post-conversation processing job chain
                 from advanced_omi_backend.controllers.queue_controller import start_post_conversation_jobs
 
                 job_ids = start_post_conversation_jobs(
                     conversation_id=conversation_id,
                     audio_uuid=audio_uuid,
-                    audio_file_path=file_path,
                     user_id=user.user_id,
                     post_transcription=True,  # Run batch transcription for uploads
                     client_id=client_id  # Pass client_id for UI tracking
@@ -217,45 +220,3 @@ async def upload_and_process_audio_files(
         return JSONResponse(
             status_code=500, content={"error": f"File upload failed: {str(e)}"}
         )
-
-
-async def get_conversation_audio_path(conversation_id: str, user: User) -> Path:
-    """
-    Get the file path for a conversation's audio file.
-
-    Args:
-        conversation_id: The conversation ID
-        user: The authenticated user
-
-    Returns:
-        Path object for the audio file
-
-    Raises:
-        ValueError: If conversation not found, access denied, or audio file not available
-    """
-    # Get conversation by conversation_id (UUID field, not _id)
-    conversation = await Conversation.find_one(Conversation.conversation_id == conversation_id)
-
-    if not conversation:
-        raise ValueError("Conversation not found")
-
-    # Check ownership (admins can access all files)
-    if not user.is_superuser and conversation.user_id != str(user.user_id):
-        raise ValueError("Access denied")
-
-    # Get the audio path
-    audio_path = conversation.audio_path
-
-    if not audio_path:
-        raise ValueError(f"No audio file available for this conversation")
-
-    # Build full file path
-    from advanced_omi_backend.app_config import get_audio_chunk_dir
-    audio_dir = get_audio_chunk_dir()
-    file_path = audio_dir / audio_path
-
-    # Check if file exists
-    if not file_path.exists() or not file_path.is_file():
-        raise ValueError("Audio file not found on disk")
-
-    return file_path