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nextcloud-mcp-server/nextcloud_mcp_server/vector/scanner.py
T
Chris Coutinho b8010270c1 fix: Add async/await, PDF metadata, and type safety fixes
This commit addresses multiple issues with async operations, PDF metadata
extraction, and type safety in document processing and search.

## Async/Await Fixes
- processor.py:259 - Added await for chunker.chunk_text(content)
- processor.py:270 - Added await for bm25_service.encode_batch(chunk_texts)
- tests/unit/test_document_chunker.py - Converted all 12 test methods to async

## PDF Metadata Enhancement
- pymupdf.py:143 - Added file_size metadata extraction
- pymupdf.py:145-206 - Refactored to extract text page-by-page
  - Manually loop through pages instead of using page_chunks=True
  - Generate page_boundaries metadata for precise page tracking
  - Works around pymupdf.layout.activate() breaking page_chunks=True
- processor.py:32-66 - Added assign_page_numbers() helper function
  - Assigns page numbers to chunks based on overlap with page boundaries
  - Handles chunks spanning multiple pages
- processor.py:298-300 - Call assign_page_numbers() for PDF files

## Type Safety Fixes
- bm25_hybrid.py:184 - Removed int() conversion of doc_id
- semantic.py:131 - Removed int() conversion of doc_id
- viz_routes.py:275 - Removed int() conversion of doc_id
- Added comments documenting that doc_id can be int (notes) or str (file paths)

## Testing
- All 18 tests passing (12 unit + 6 integration)
- No type errors in modified files
- Container logs show successful processing
- Vector viz searches working correctly

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-20 02:37:07 +01:00

458 lines
18 KiB
Python

"""Scanner task for vector database synchronization.
Periodically scans enabled users' content and queues changed documents for processing.
"""
import logging
import os
import time
from dataclasses import dataclass
import anyio
from anyio.abc import TaskStatus
from anyio.streams.memory import MemoryObjectSendStream
from qdrant_client.models import FieldCondition, Filter, MatchValue
from nextcloud_mcp_server.client import NextcloudClient
from nextcloud_mcp_server.config import get_settings
from nextcloud_mcp_server.observability.metrics import record_vector_sync_scan
from nextcloud_mcp_server.observability.tracing import trace_operation
from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
logger = logging.getLogger(__name__)
@dataclass
class DocumentTask:
"""Document task for processing queue."""
user_id: str
doc_id: str
doc_type: str # "note", "file", "calendar"
operation: str # "index" or "delete"
modified_at: int
# Track documents potentially deleted (grace period before actual deletion)
# Format: {(user_id, doc_id): first_missing_timestamp}
_potentially_deleted: dict[tuple[str, str], float] = {}
async def get_last_indexed_timestamp(user_id: str) -> int | None:
"""Get the most recent indexed_at timestamp for user's notes in Qdrant.
This timestamp can be used as pruneBefore parameter to optimize data transfer
when fetching notes - only notes modified after this timestamp will be sent
with full data.
Args:
user_id: User to query
Returns:
Unix timestamp of most recently indexed note, or None if no notes indexed yet
"""
try:
qdrant_client = await get_qdrant_client()
# Query for user's notes, ordered by indexed_at descending, limit 1
scroll_result = await qdrant_client.scroll(
collection_name=get_settings().get_collection_name(),
scroll_filter=Filter(
must=[
FieldCondition(key="user_id", match=MatchValue(value=user_id)),
FieldCondition(key="doc_type", match=MatchValue(value="note")),
]
),
with_payload=["indexed_at"],
with_vectors=False,
limit=10000, # Get all to find max
)
# Find max indexed_at across all results
num_points = len(scroll_result[0]) if scroll_result[0] else 0
logger.info(f"Found {num_points} indexed notes in Qdrant for user {user_id}")
if scroll_result[0]:
timestamps = [
point.payload.get("indexed_at", 0) for point in scroll_result[0]
]
max_timestamp = max(timestamps)
logger.info(
f"Max indexed_at: {max_timestamp}, timestamps sample: {timestamps[:3]}"
)
return int(max_timestamp) if max_timestamp > 0 else None
logger.info(f"No indexed notes found for user {user_id}")
return None
except Exception as e:
logger.warning(f"Failed to get last indexed timestamp: {e}", exc_info=True)
return None
async def scanner_task(
send_stream: MemoryObjectSendStream[DocumentTask],
shutdown_event: anyio.Event,
wake_event: anyio.Event,
nc_client: NextcloudClient,
user_id: str,
*,
task_status: TaskStatus = anyio.TASK_STATUS_IGNORED,
):
"""
Periodic scanner that detects changed documents for enabled user.
For BasicAuth mode, scans a single user with credentials available at runtime.
Args:
send_stream: Stream to send changed documents to processors
shutdown_event: Event signaling shutdown
wake_event: Event to trigger immediate scan
nc_client: Authenticated Nextcloud client
user_id: User to scan
task_status: Status object for signaling task readiness
"""
logger.info(f"Scanner task started for user: {user_id}")
settings = get_settings()
# Signal that the task has started and is ready
task_status.started()
async with send_stream:
while not shutdown_event.is_set():
try:
# Scan user documents
await scan_user_documents(
user_id=user_id,
send_stream=send_stream,
nc_client=nc_client,
)
except Exception as e:
logger.error(f"Scanner error: {e}", exc_info=True)
# Sleep until next interval or wake event
try:
with anyio.move_on_after(settings.vector_sync_scan_interval):
# Wait for wake event or shutdown (whichever comes first)
await wake_event.wait()
except anyio.get_cancelled_exc_class():
# Shutdown, exit loop
break
logger.info("Scanner task stopped - stream closed")
async def scan_user_documents(
user_id: str,
send_stream: MemoryObjectSendStream[DocumentTask],
nc_client: NextcloudClient,
initial_sync: bool = False,
):
"""
Scan a single user's documents and send changes to processor stream.
Args:
user_id: User to scan
send_stream: Stream to send changed documents to processors
nc_client: Authenticated Nextcloud client
initial_sync: If True, send all documents (first-time sync)
"""
import random
scan_id = random.randint(1000, 9999)
logger.info(
f"[SCAN-{scan_id}] Starting scan for user: {user_id}, initial_sync={initial_sync}"
)
with trace_operation(
"vector_sync.scan_user_documents",
attributes={
"vector_sync.operation": "scan",
"vector_sync.user_id": user_id,
"vector_sync.initial_sync": initial_sync,
"vector_sync.scan_id": scan_id,
},
):
# Calculate prune timestamp for optimized data transfer
# Only notes modified after this will be sent with full data
prune_before = (
None if initial_sync else await get_last_indexed_timestamp(user_id)
)
if prune_before:
logger.info(
f"[SCAN-{scan_id}] Using pruneBefore={prune_before} to optimize data transfer"
)
# Get indexed state from Qdrant first (for incremental sync)
indexed_docs = {}
if not initial_sync:
qdrant_client = await get_qdrant_client()
scroll_result = await qdrant_client.scroll(
collection_name=get_settings().get_collection_name(),
scroll_filter=Filter(
must=[
FieldCondition(key="user_id", match=MatchValue(value=user_id)),
FieldCondition(key="doc_type", match=MatchValue(value="note")),
]
),
with_payload=["doc_id", "indexed_at"],
with_vectors=False,
limit=10000,
)
indexed_docs = {
point.payload["doc_id"]: point.payload["indexed_at"]
for point in scroll_result[0]
}
logger.debug(f"Found {len(indexed_docs)} indexed documents in Qdrant")
# Stream notes from Nextcloud and process immediately
note_count = 0
queued = 0
nextcloud_doc_ids = set()
async for note in nc_client.notes.get_all_notes(prune_before=prune_before):
note_count += 1
doc_id = str(note["id"])
nextcloud_doc_ids.add(doc_id)
modified_at = note.get("modified", 0)
if initial_sync:
# Send everything on first sync
await send_stream.send(
DocumentTask(
user_id=user_id,
doc_id=doc_id,
doc_type="note",
operation="index",
modified_at=modified_at,
)
)
queued += 1
else:
# Incremental sync: compare with indexed state
indexed_at = indexed_docs.get(doc_id)
# If document reappeared, remove from potentially_deleted
doc_key = (user_id, doc_id)
if doc_key in _potentially_deleted:
logger.debug(
f"Document {doc_id} reappeared, removing from deletion grace period"
)
del _potentially_deleted[doc_key]
# Send if never indexed or modified since last index
if indexed_at is None or modified_at > indexed_at:
await send_stream.send(
DocumentTask(
user_id=user_id,
doc_id=doc_id,
doc_type="note",
operation="index",
modified_at=modified_at,
)
)
queued += 1
# Log and record metrics after streaming
logger.info(f"[SCAN-{scan_id}] Found {note_count} notes for {user_id}")
record_vector_sync_scan(note_count)
if initial_sync:
logger.info(f"Sent {queued} documents for initial sync: {user_id}")
return
# Check for deleted documents (in Qdrant but not in Nextcloud)
# Use grace period: only delete after 2 consecutive scans confirm absence
settings = get_settings()
grace_period = (
settings.vector_sync_scan_interval * 1.5
) # Allow 1.5 scan intervals
current_time = time.time()
for doc_id in indexed_docs:
if doc_id not in nextcloud_doc_ids:
doc_key = (user_id, doc_id)
if doc_key in _potentially_deleted:
# Already marked as potentially deleted, check if grace period elapsed
first_missing_time = _potentially_deleted[doc_key]
time_missing = current_time - first_missing_time
if time_missing >= grace_period:
# Grace period elapsed, send for deletion
logger.info(
f"Document {doc_id} missing for {time_missing:.1f}s "
f"(>{grace_period:.1f}s grace period), sending deletion"
)
await send_stream.send(
DocumentTask(
user_id=user_id,
doc_id=doc_id,
doc_type="note",
operation="delete",
modified_at=0,
)
)
queued += 1
# Remove from tracking after sending deletion
del _potentially_deleted[doc_key]
else:
logger.debug(
f"Document {doc_id} still missing "
f"({time_missing:.1f}s/{grace_period:.1f}s grace period)"
)
else:
# First time missing, add to grace period tracking
logger.debug(
f"Document {doc_id} missing for first time, starting grace period"
)
_potentially_deleted[doc_key] = current_time
# Scan tagged PDF files (after notes)
# Get indexed files from Qdrant (separate query for doc_type="file")
indexed_files = {}
if not initial_sync:
file_scroll_result = await qdrant_client.scroll(
collection_name=settings.get_collection_name(),
scroll_filter=Filter(
must=[
FieldCondition(key="user_id", match=MatchValue(value=user_id)),
FieldCondition(key="doc_type", match=MatchValue(value="file")),
]
),
limit=10000, # Reasonable limit for file count
with_payload=["doc_id", "indexed_at"],
with_vectors=False,
)
indexed_files = {
point.payload["doc_id"]: point.payload["indexed_at"]
for point in file_scroll_result[0]
}
logger.debug(f"Found {len(indexed_files)} indexed files in Qdrant")
# Scan for tagged PDF files
file_count = 0
file_queued = 0
nextcloud_file_paths = set()
try:
# Find files with vector-index tag using OCS Tags API
settings = get_settings()
tag_name = os.getenv("VECTOR_SYNC_PDF_TAG", "vector-index")
# Use NextcloudClient.find_files_by_tag() which uses proper OCS API
# and filters by PDF MIME type
tagged_files = await nc_client.find_files_by_tag(
tag_name, mime_type_filter="application/pdf"
)
for file_info in tagged_files:
# Files are already filtered by MIME type in find_files_by_tag()
file_count += 1
file_path = file_info["path"]
nextcloud_file_paths.add(file_path)
# Use last_modified timestamp if available, otherwise use current time
modified_at = file_info.get("last_modified_timestamp", int(time.time()))
if isinstance(file_info.get("last_modified"), str):
# Parse RFC 2822 date format if needed
from email.utils import parsedate_to_datetime
try:
dt = parsedate_to_datetime(file_info["last_modified"])
modified_at = int(dt.timestamp())
except (ValueError, KeyError):
pass
if initial_sync:
# Send everything on first sync
await send_stream.send(
DocumentTask(
user_id=user_id,
doc_id=file_path,
doc_type="file",
operation="index",
modified_at=modified_at,
)
)
file_queued += 1
else:
# Incremental sync: compare with indexed state
indexed_at = indexed_files.get(file_path)
# If file reappeared, remove from potentially_deleted
file_key = (user_id, file_path)
if file_key in _potentially_deleted:
logger.debug(
f"File {file_path} reappeared, removing from deletion grace period"
)
del _potentially_deleted[file_key]
# Send if never indexed or modified since last index
if indexed_at is None or modified_at > indexed_at:
await send_stream.send(
DocumentTask(
user_id=user_id,
doc_id=file_path,
doc_type="file",
operation="index",
modified_at=modified_at,
)
)
file_queued += 1
logger.info(
f"[SCAN-{scan_id}] Found {file_count} tagged PDFs for {user_id}"
)
record_vector_sync_scan(file_count)
# Check for deleted files (not initial sync)
if not initial_sync:
for file_path in indexed_files:
if file_path not in nextcloud_file_paths:
file_key = (user_id, file_path)
if file_key in _potentially_deleted:
# Check if grace period elapsed
first_missing_time = _potentially_deleted[file_key]
time_missing = current_time - first_missing_time
if time_missing >= grace_period:
# Grace period elapsed, send for deletion
logger.info(
f"File {file_path} missing for {time_missing:.1f}s "
f"(>{grace_period:.1f}s grace period), sending deletion"
)
await send_stream.send(
DocumentTask(
user_id=user_id,
doc_id=file_path,
doc_type="file",
operation="delete",
modified_at=0,
)
)
file_queued += 1
del _potentially_deleted[file_key]
else:
# First time missing, add to grace period tracking
logger.debug(
f"File {file_path} missing for first time, starting grace period"
)
_potentially_deleted[file_key] = current_time
except Exception as e:
logger.warning(f"Failed to scan tagged files for {user_id}: {e}")
queued += file_queued
if queued > 0:
logger.info(
f"Sent {queued} documents ({file_queued} files) for incremental sync: {user_id}"
)
else:
logger.debug(f"No changes detected for {user_id}")