Merge branch 'dev' of https://github.com/MODSetter/SurfSense into dev

surfsense_backend/app/agents/new_chat/tools/google_calendar/update_event.py

@@ -14,6 +14,20 @@ from app.services.google_calendar import GoogleCalendarToolMetadataService
 logger = logging.getLogger(__name__)
 
 
+def _is_date_only(value: str) -> bool:
+    """Return True when *value* looks like a bare date (YYYY-MM-DD) with no time component."""
+    return len(value) <= 10 and "T" not in value
+
+
+def _build_time_body(value: str, context: dict[str, Any] | Any) -> dict[str, str]:
+    """Build a Google Calendar start/end body using ``date`` for all-day
+    events and ``dateTime`` for timed events."""
+    if _is_date_only(value):
+        return {"date": value}
+    tz = context.get("timezone", "UTC") if isinstance(context, dict) else "UTC"
+    return {"dateTime": value, "timeZone": tz}
+
+
 def create_update_calendar_event_tool(
     db_session: AsyncSession | None = None,
     search_space_id: int | None = None,
@@ -255,25 +269,13 @@ def create_update_calendar_event_tool(
             if final_new_summary is not None:
                 update_body["summary"] = final_new_summary
             if final_new_start_datetime is not None:
-                tz = (
-                    context.get("timezone", "UTC")
-                    if isinstance(context, dict)
-                    else "UTC"
+                update_body["start"] = _build_time_body(
+                    final_new_start_datetime, context
                 )
-                update_body["start"] = {
-                    "dateTime": final_new_start_datetime,
-                    "timeZone": tz,
-                }
             if final_new_end_datetime is not None:
-                tz = (
-                    context.get("timezone", "UTC")
-                    if isinstance(context, dict)
-                    else "UTC"
+                update_body["end"] = _build_time_body(
+                    final_new_end_datetime, context
                 )
-                update_body["end"] = {
-                    "dateTime": final_new_end_datetime,
-                    "timeZone": tz,
-                }
             if final_new_description is not None:
                 update_body["description"] = final_new_description
             if final_new_location is not None:

surfsense_backend/app/connectors/google_drive/__init__.py

@@ -2,13 +2,14 @@
 
 from .change_tracker import categorize_change, fetch_all_changes, get_start_page_token
 from .client import GoogleDriveClient
-from .content_extractor import download_and_process_file
+from .content_extractor import download_and_extract_content, download_and_process_file
 from .credentials import get_valid_credentials, validate_credentials
 from .folder_manager import get_file_by_id, get_files_in_folder, list_folder_contents
 
 __all__ = [
     "GoogleDriveClient",
     "categorize_change",
+    "download_and_extract_content",
     "download_and_process_file",
     "fetch_all_changes",
     "get_file_by_id",

surfsense_backend/app/connectors/google_drive/change_tracker.py

@@ -84,22 +84,50 @@ async def get_changes(
         return [], None, f"Error getting changes: {e!s}"
 
 
+async def _is_descendant_of(
+    client: GoogleDriveClient,
+    parent_ids: list[str],
+    target_folder_id: str,
+    max_depth: int = 20,
+) -> bool:
+    """Walk up the parent chain to check if any ancestor is *target_folder_id*."""
+    visited: set[str] = set()
+    to_check = list(parent_ids)
+
+    for _ in range(max_depth):
+        if not to_check:
+            return False
+
+        current = to_check.pop(0)
+        if current in visited:
+            continue
+        visited.add(current)
+
+        if current == target_folder_id:
+            return True
+
+        try:
+            service = await client.get_service()
+            meta = (
+                service.files()
+                .get(fileId=current, fields="parents", supportsAllDrives=True)
+                .execute()
+            )
+            grandparents = meta.get("parents", [])
+            to_check.extend(grandparents)
+        except Exception:
+            continue
+
+    return False
+
+
 async def _filter_changes_by_folder(
     client: GoogleDriveClient,
     changes: list[dict[str, Any]],
     folder_id: str,
 ) -> list[dict[str, Any]]:
-    """
-    Filter changes to only include files within the specified folder.
-
-    Args:
-        client: GoogleDriveClient instance
-        changes: List of changes from API
-        folder_id: Folder ID to filter by
-
-    Returns:
-        Filtered list of changes
-    """
+    """Filter changes to only include files within the specified folder
+    (direct children or nested descendants)."""
     filtered = []
 
     for change in changes:
@@ -108,14 +136,10 @@ async def _filter_changes_by_folder(
             filtered.append(change)
             continue
 
-        # Check if file is in the folder (or subfolder)
         parents = file.get("parents", [])
         if folder_id in parents:
             filtered.append(change)
-        else:
-            # Check if any parent is a descendant of folder_id
-            # This is a simplified check - full implementation would traverse hierarchy
-            # For now, we'll include it and let indexer validate
+        elif await _is_descendant_of(client, parents, folder_id):
             filtered.append(change)
 
     return filtered

surfsense_backend/app/connectors/google_drive/client.py

@@ -1,9 +1,15 @@
 """Google Drive API client."""
 
+import asyncio
 import io
+import logging
+import threading
+import time
 from typing import Any
 
+import httplib2
 from google.oauth2.credentials import Credentials
+from google_auth_httplib2 import AuthorizedHttp
 from googleapiclient.discovery import build
 from googleapiclient.errors import HttpError
 from googleapiclient.http import MediaIoBaseUpload
@@ -12,6 +18,14 @@ from sqlalchemy.ext.asyncio import AsyncSession
 from .credentials import get_valid_credentials
 from .file_types import GOOGLE_DOC, GOOGLE_SHEET
 
+logger = logging.getLogger(__name__)
+
+
+def _build_thread_http(credentials: Credentials) -> AuthorizedHttp:
+    """Create a per-thread HTTP transport so concurrent downloads don't share
+    the same ``httplib2.Http`` (which is not thread-safe)."""
+    return AuthorizedHttp(credentials, http=httplib2.Http())
+
 
 class GoogleDriveClient:
     """Client for Google Drive API operations."""
@@ -34,7 +48,9 @@ class GoogleDriveClient:
         self.session = session
         self.connector_id = connector_id
         self._credentials = credentials
+        self._resolved_credentials: Credentials | None = None
         self.service = None
+        self._service_lock = asyncio.Lock()
 
     async def get_service(self):
         """
@@ -49,6 +65,10 @@ class GoogleDriveClient:
         if self.service:
             return self.service
 
+        async with self._service_lock:
+            if self.service:
+                return self.service
+
             try:
                 if self._credentials:
                     credentials = self._credentials
@@ -56,6 +76,7 @@ class GoogleDriveClient:
                     credentials = await get_valid_credentials(
                         self.session, self.connector_id
                     )
+                self._resolved_credentials = credentials
                 self.service = build("drive", "v3", credentials=credentials)
                 return self.service
             except Exception as e:
@@ -134,6 +155,33 @@ class GoogleDriveClient:
         except Exception as e:
             return None, f"Error getting file metadata: {e!s}"
 
+    @staticmethod
+    def _sync_download_file(
+        service, file_id: str, credentials: Credentials,
+    ) -> tuple[bytes | None, str | None]:
+        """Blocking download — runs on a worker thread via ``to_thread``."""
+        thread = threading.current_thread().name
+        t0 = time.monotonic()
+        logger.info(f"[download] START file={file_id} thread={thread}")
+        try:
+            from googleapiclient.http import MediaIoBaseDownload
+
+            http = _build_thread_http(credentials)
+            request = service.files().get_media(fileId=file_id)
+            request.http = http
+            fh = io.BytesIO()
+            downloader = MediaIoBaseDownload(fh, request)
+            done = False
+            while not done:
+                _, done = downloader.next_chunk()
+            return fh.getvalue(), None
+        except HttpError as e:
+            return None, f"HTTP error downloading file: {e.resp.status}"
+        except Exception as e:
+            return None, f"Error downloading file: {e!s}"
+        finally:
+            logger.info(f"[download] END file={file_id} thread={thread} elapsed={time.monotonic() - t0:.2f}s")
+
     async def download_file(self, file_id: str) -> tuple[bytes | None, str | None]:
         """
         Download binary file content.
@@ -144,27 +192,76 @@ class GoogleDriveClient:
         Returns:
             Tuple of (file content bytes, error message)
         """
-        try:
         service = await self.get_service()
-            request = service.files().get_media(fileId=file_id)
+        return await asyncio.to_thread(
+            self._sync_download_file, service, file_id, self._resolved_credentials,
+        )
 
-            import io
-
-            fh = io.BytesIO()
+    @staticmethod
+    def _sync_download_file_to_disk(
+        service, file_id: str, dest_path: str, chunksize: int,
+        credentials: Credentials,
+    ) -> str | None:
+        """Blocking download-to-disk — runs on a worker thread via ``to_thread``."""
+        thread = threading.current_thread().name
+        t0 = time.monotonic()
+        logger.info(f"[download-to-disk] START file={file_id} thread={thread}")
+        try:
             from googleapiclient.http import MediaIoBaseDownload
 
-            downloader = MediaIoBaseDownload(fh, request)
-
+            http = _build_thread_http(credentials)
+            request = service.files().get_media(fileId=file_id)
+            request.http = http
+            with open(dest_path, "wb") as fh:
+                downloader = MediaIoBaseDownload(fh, request, chunksize=chunksize)
                 done = False
                 while not done:
                     _, done = downloader.next_chunk()
-
-            return fh.getvalue(), None
-
+            return None
         except HttpError as e:
-            return None, f"HTTP error downloading file: {e.resp.status}"
+            return f"HTTP error downloading file: {e.resp.status}"
         except Exception as e:
-            return None, f"Error downloading file: {e!s}"
+            return f"Error downloading file: {e!s}"
+        finally:
+            logger.info(f"[download-to-disk] END file={file_id} thread={thread} elapsed={time.monotonic() - t0:.2f}s")
+
+    async def download_file_to_disk(
+        self, file_id: str, dest_path: str, chunksize: int = 5 * 1024 * 1024,
+    ) -> str | None:
+        """Stream file directly to disk in chunks, avoiding full in-memory buffering.
+
+        Returns error message on failure, None on success.
+        """
+        service = await self.get_service()
+        return await asyncio.to_thread(
+            self._sync_download_file_to_disk,
+            service, file_id, dest_path, chunksize, self._resolved_credentials,
+        )
+
+    @staticmethod
+    def _sync_export_google_file(
+        service, file_id: str, mime_type: str, credentials: Credentials,
+    ) -> tuple[bytes | None, str | None]:
+        """Blocking export — runs on a worker thread via ``to_thread``."""
+        thread = threading.current_thread().name
+        t0 = time.monotonic()
+        logger.info(f"[export] START file={file_id} thread={thread}")
+        try:
+            http = _build_thread_http(credentials)
+            content = (
+                service.files()
+                .export(fileId=file_id, mimeType=mime_type)
+                .execute(http=http)
+            )
+            if not isinstance(content, bytes):
+                content = content.encode("utf-8")
+            return content, None
+        except HttpError as e:
+            return None, f"HTTP error exporting file: {e.resp.status}"
+        except Exception as e:
+            return None, f"Error exporting file: {e!s}"
+        finally:
+            logger.info(f"[export] END file={file_id} thread={thread} elapsed={time.monotonic() - t0:.2f}s")
 
     async def export_google_file(
         self, file_id: str, mime_type: str
@@ -179,24 +276,12 @@ class GoogleDriveClient:
         Returns:
             Tuple of (exported content as bytes, error message)
         """
-        try:
         service = await self.get_service()
-            content = (
-                service.files().export(fileId=file_id, mimeType=mime_type).execute()
+        return await asyncio.to_thread(
+            self._sync_export_google_file, service, file_id, mime_type,
+            self._resolved_credentials,
         )
 
-            # Content is already bytes from the API
-            # Keep as bytes to support both text and binary formats (like PDF)
-            if not isinstance(content, bytes):
-                content = content.encode("utf-8")
-
-            return content, None
-
-        except HttpError as e:
-            return None, f"HTTP error exporting file: {e.resp.status}"
-        except Exception as e:
-            return None, f"Error exporting file: {e!s}"
-
     async def create_file(
         self,
         name: str,

surfsense_backend/app/connectors/google_drive/content_extractor.py

@@ -1,8 +1,11 @@
 """Content extraction for Google Drive files."""
 
+import asyncio
 import logging
 import os
 import tempfile
+import threading
+import time
 from pathlib import Path
 from typing import Any
 
@@ -12,11 +15,182 @@ from app.db import Log
 from app.services.task_logging_service import TaskLoggingService
 
 from .client import GoogleDriveClient
-from .file_types import get_export_mime_type, is_google_workspace_file, should_skip_file
+from .file_types import (
+    get_export_mime_type,
+    get_extension_from_mime,
+    is_google_workspace_file,
+    should_skip_file,
+)
 
 logger = logging.getLogger(__name__)
 
 
+async def download_and_extract_content(
+    client: GoogleDriveClient,
+    file: dict[str, Any],
+) -> tuple[str | None, dict[str, Any], str | None]:
+    """Download a Google Drive file and extract its content as markdown.
+
+    ETL only -- no DB writes, no indexing, no summarization.
+
+    Returns:
+        (markdown_content, drive_metadata, error_message)
+        On success error_message is None.
+    """
+    file_id = file.get("id")
+    file_name = file.get("name", "Unknown")
+    mime_type = file.get("mimeType", "")
+
+    if should_skip_file(mime_type):
+        return None, {}, f"Skipping {mime_type}"
+
+    logger.info(f"Downloading file for content extraction: {file_name} ({mime_type})")
+
+    drive_metadata: dict[str, Any] = {
+        "google_drive_file_id": file_id,
+        "google_drive_file_name": file_name,
+        "google_drive_mime_type": mime_type,
+        "source_connector": "google_drive",
+    }
+    if "modifiedTime" in file:
+        drive_metadata["modified_time"] = file["modifiedTime"]
+    if "createdTime" in file:
+        drive_metadata["created_time"] = file["createdTime"]
+    if "size" in file:
+        drive_metadata["file_size"] = file["size"]
+    if "webViewLink" in file:
+        drive_metadata["web_view_link"] = file["webViewLink"]
+    if "md5Checksum" in file:
+        drive_metadata["md5_checksum"] = file["md5Checksum"]
+    if is_google_workspace_file(mime_type):
+        export_ext = get_extension_from_mime(get_export_mime_type(mime_type) or "")
+        drive_metadata["exported_as"] = export_ext.lstrip(".") if export_ext else "pdf"
+        drive_metadata["original_workspace_type"] = mime_type.split(".")[-1]
+
+    temp_file_path = None
+    try:
+        if is_google_workspace_file(mime_type):
+            export_mime = get_export_mime_type(mime_type)
+            if not export_mime:
+                return None, drive_metadata, f"Cannot export Google Workspace type: {mime_type}"
+            content_bytes, error = await client.export_google_file(file_id, export_mime)
+            if error:
+                return None, drive_metadata, error
+            extension = get_extension_from_mime(export_mime) or ".pdf"
+
+            with tempfile.NamedTemporaryFile(delete=False, suffix=extension) as tmp:
+                tmp.write(content_bytes)
+                temp_file_path = tmp.name
+        else:
+            extension = (
+                Path(file_name).suffix
+                or get_extension_from_mime(mime_type)
+                or ".bin"
+            )
+            with tempfile.NamedTemporaryFile(delete=False, suffix=extension) as tmp:
+                temp_file_path = tmp.name
+
+            error = await client.download_file_to_disk(file_id, temp_file_path)
+            if error:
+                return None, drive_metadata, error
+
+        markdown = await _parse_file_to_markdown(temp_file_path, file_name)
+        return markdown, drive_metadata, None
+
+    except Exception as e:
+        logger.warning(f"Failed to extract content from {file_name}: {e!s}")
+        return None, drive_metadata, str(e)
+    finally:
+        if temp_file_path and os.path.exists(temp_file_path):
+            try:
+                os.unlink(temp_file_path)
+            except Exception:
+                pass
+
+
+async def _parse_file_to_markdown(file_path: str, filename: str) -> str:
+    """Parse a local file to markdown using the configured ETL service."""
+    lower = filename.lower()
+
+    if lower.endswith((".md", ".markdown", ".txt")):
+        with open(file_path, encoding="utf-8") as f:
+            return f.read()
+
+    if lower.endswith((".mp3", ".mp4", ".mpeg", ".mpga", ".m4a", ".wav", ".webm")):
+        from app.config import config as app_config
+        from litellm import atranscription
+
+        stt_service_type = (
+            "local"
+            if app_config.STT_SERVICE and app_config.STT_SERVICE.startswith("local/")
+            else "external"
+        )
+        if stt_service_type == "local":
+            from app.services.stt_service import stt_service
+            t0 = time.monotonic()
+            logger.info(f"[local-stt] START file={filename} thread={threading.current_thread().name}")
+            result = await asyncio.to_thread(stt_service.transcribe_file, file_path)
+            logger.info(f"[local-stt] END file={filename} elapsed={time.monotonic() - t0:.2f}s")
+            text = result.get("text", "")
+        else:
+            with open(file_path, "rb") as audio_file:
+                kwargs: dict[str, Any] = {
+                    "model": app_config.STT_SERVICE,
+                    "file": audio_file,
+                    "api_key": app_config.STT_SERVICE_API_KEY,
+                }
+                if app_config.STT_SERVICE_API_BASE:
+                    kwargs["api_base"] = app_config.STT_SERVICE_API_BASE
+                resp = await atranscription(**kwargs)
+                text = resp.get("text", "")
+
+        if not text:
+            raise ValueError("Transcription returned empty text")
+        return f"# Transcription of {filename}\n\n{text}"
+
+    # Document files -- use configured ETL service
+    from app.config import config as app_config
+
+    if app_config.ETL_SERVICE == "UNSTRUCTURED":
+        from langchain_unstructured import UnstructuredLoader
+        from app.utils.document_converters import convert_document_to_markdown
+
+        loader = UnstructuredLoader(
+            file_path,
+            mode="elements",
+            post_processors=[],
+            languages=["eng"],
+            include_orig_elements=False,
+            include_metadata=False,
+            strategy="auto",
+        )
+        docs = await loader.aload()
+        return await convert_document_to_markdown(docs)
+
+    if app_config.ETL_SERVICE == "LLAMACLOUD":
+        from app.tasks.document_processors.file_processors import (
+            parse_with_llamacloud_retry,
+        )
+
+        result = await parse_with_llamacloud_retry(file_path=file_path, estimated_pages=50)
+        markdown_documents = await result.aget_markdown_documents(split_by_page=False)
+        if not markdown_documents:
+            raise RuntimeError(f"LlamaCloud returned no documents for {filename}")
+        return markdown_documents[0].text
+
+    if app_config.ETL_SERVICE == "DOCLING":
+        from docling.document_converter import DocumentConverter
+
+        converter = DocumentConverter()
+        t0 = time.monotonic()
+        logger.info(f"[docling] START file={filename} thread={threading.current_thread().name}")
+        result = await asyncio.to_thread(converter.convert, file_path)
+        logger.info(f"[docling] END file={filename} elapsed={time.monotonic() - t0:.2f}s")
+        return result.document.export_to_markdown()
+
+    raise RuntimeError(f"Unknown ETL_SERVICE: {app_config.ETL_SERVICE}")
+
+
 async def download_and_process_file(
     client: GoogleDriveClient,
     file: dict[str, Any],
@@ -68,14 +242,17 @@ async def download_and_process_file(
             if error:
                 return None, error
 
-            extension = ".pdf" if export_mime == "application/pdf" else ".txt"
+            extension = get_extension_from_mime(export_mime) or ".pdf"
         else:
             content_bytes, error = await client.download_file(file_id)
             if error:
                 return None, error
 
-            # Preserve original file extension
-            extension = Path(file_name).suffix or ".bin"
+            extension = (
+                Path(file_name).suffix
+                or get_extension_from_mime(mime_type)
+                or ".bin"
+            )
 
         with tempfile.NamedTemporaryFile(delete=False, suffix=extension) as tmp_file:
             tmp_file.write(content_bytes)
@@ -113,7 +290,12 @@ async def download_and_process_file(
             connector_info["metadata"]["md5_checksum"] = file["md5Checksum"]
 
         if is_google_workspace_file(mime_type):
-            connector_info["metadata"]["exported_as"] = "pdf"
+            export_ext = get_extension_from_mime(
+                get_export_mime_type(mime_type) or ""
+            )
+            connector_info["metadata"]["exported_as"] = (
+                export_ext.lstrip(".") if export_ext else "pdf"
+            )
             connector_info["metadata"]["original_workspace_type"] = mime_type.split(
                 "."
             )[-1]

surfsense_backend/app/connectors/google_drive/file_types.py

@@ -7,11 +7,34 @@ GOOGLE_FOLDER = "application/vnd.google-apps.folder"
 GOOGLE_SHORTCUT = "application/vnd.google-apps.shortcut"
 
 EXPORT_FORMATS = {
-    GOOGLE_DOC: "application/pdf",
-    GOOGLE_SHEET: "application/pdf",
+    GOOGLE_DOC: "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
+    GOOGLE_SHEET: "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
     GOOGLE_SLIDE: "application/pdf",
 }
 
+MIME_TO_EXTENSION: dict[str, str] = {
+    "application/pdf": ".pdf",
+    "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet": ".xlsx",
+    "application/vnd.openxmlformats-officedocument.wordprocessingml.document": ".docx",
+    "application/vnd.openxmlformats-officedocument.presentationml.presentation": ".pptx",
+    "application/vnd.ms-excel": ".xls",
+    "application/msword": ".doc",
+    "application/vnd.ms-powerpoint": ".ppt",
+    "text/plain": ".txt",
+    "text/csv": ".csv",
+    "text/html": ".html",
+    "text/markdown": ".md",
+    "application/json": ".json",
+    "application/xml": ".xml",
+    "image/png": ".png",
+    "image/jpeg": ".jpg",
+}
+
+
+def get_extension_from_mime(mime_type: str) -> str | None:
+    """Return a file extension (with leading dot) for a MIME type, or None."""
+    return MIME_TO_EXTENSION.get(mime_type)
+
 
 def is_google_workspace_file(mime_type: str) -> bool:
     """Check if file is a Google Workspace file that needs export."""

surfsense_backend/app/indexing_pipeline/document_hashing.py

@@ -3,10 +3,17 @@ import hashlib
 from app.indexing_pipeline.connector_document import ConnectorDocument
 
 
+def compute_identifier_hash(
+    document_type_value: str, unique_id: str, search_space_id: int
+) -> str:
+    """Return a stable SHA-256 hash from raw identity components."""
+    combined = f"{document_type_value}:{unique_id}:{search_space_id}"
+    return hashlib.sha256(combined.encode("utf-8")).hexdigest()
+
+
 def compute_unique_identifier_hash(doc: ConnectorDocument) -> str:
     """Return a stable SHA-256 hash identifying a document by its source identity."""
-    combined = f"{doc.document_type.value}:{doc.unique_id}:{doc.search_space_id}"
-    return hashlib.sha256(combined.encode("utf-8")).hexdigest()
+    return compute_identifier_hash(doc.document_type.value, doc.unique_id, doc.search_space_id)
 
 
 def compute_content_hash(doc: ConnectorDocument) -> str:

surfsense_backend/app/indexing_pipeline/indexing_pipeline_service.py

@@ -1,17 +1,21 @@
+import asyncio
 import contextlib
+import logging
 import time
+from collections.abc import Awaitable, Callable
 from datetime import UTC, datetime
 
 from sqlalchemy import delete, select
 from sqlalchemy.exc import IntegrityError
 from sqlalchemy.ext.asyncio import AsyncSession
 
-from app.db import Chunk, Document, DocumentStatus
+from app.db import NATIVE_TO_LEGACY_DOCTYPE, Chunk, Document, DocumentStatus
 from app.indexing_pipeline.connector_document import ConnectorDocument
 from app.indexing_pipeline.document_chunker import chunk_text
 from app.indexing_pipeline.document_embedder import embed_texts
 from app.indexing_pipeline.document_hashing import (
     compute_content_hash,
+    compute_identifier_hash,
     compute_unique_identifier_hash,
 )
 from app.indexing_pipeline.document_persistence import (
@@ -54,6 +58,62 @@ class IndexingPipelineService:
     def __init__(self, session: AsyncSession) -> None:
         self.session = session
 
+    async def migrate_legacy_docs(
+        self, connector_docs: list[ConnectorDocument]
+    ) -> None:
+        """Migrate legacy Composio documents to their native Google type.
+
+        For each ConnectorDocument whose document_type has a Composio equivalent
+        in NATIVE_TO_LEGACY_DOCTYPE, look up the old document by legacy hash and
+        update its unique_identifier_hash and document_type so that
+        prepare_for_indexing() can find it under the native hash.
+        """
+        for doc in connector_docs:
+            legacy_type = NATIVE_TO_LEGACY_DOCTYPE.get(doc.document_type.value)
+            if not legacy_type:
+                continue
+
+            legacy_hash = compute_identifier_hash(
+                legacy_type, doc.unique_id, doc.search_space_id
+            )
+            result = await self.session.execute(
+                select(Document).filter(
+                    Document.unique_identifier_hash == legacy_hash
+                )
+            )
+            existing = result.scalars().first()
+            if existing is None:
+                continue
+
+            native_hash = compute_identifier_hash(
+                doc.document_type.value, doc.unique_id, doc.search_space_id
+            )
+            existing.unique_identifier_hash = native_hash
+            existing.document_type = doc.document_type
+
+        await self.session.commit()
+
+    async def index_batch(
+        self, connector_docs: list[ConnectorDocument], llm
+    ) -> list[Document]:
+        """Convenience method: prepare_for_indexing then index each document.
+
+        Indexers that need heartbeat callbacks or custom per-document logic
+        should call prepare_for_indexing() + index() directly instead.
+        """
+        doc_map = {
+            compute_unique_identifier_hash(cd): cd for cd in connector_docs
+        }
+        documents = await self.prepare_for_indexing(connector_docs)
+        results: list[Document] = []
+        for document in documents:
+            connector_doc = doc_map.get(document.unique_identifier_hash)
+            if connector_doc is None:
+                continue
+            result = await self.index(document, connector_doc, llm)
+            results.append(result)
+        return results
+
     async def prepare_for_indexing(
         self, connector_docs: list[ConnectorDocument]
     ) -> list[Document]:
@@ -200,13 +260,14 @@ class IndexingPipelineService:
             )
 
             t_step = time.perf_counter()
-            chunk_texts = chunk_text(
+            chunk_texts = await asyncio.to_thread(
+                chunk_text,
                 connector_doc.source_markdown,
                 use_code_chunker=connector_doc.should_use_code_chunker,
             )
 
             texts_to_embed = [content, *chunk_texts]
-            embeddings = embed_texts(texts_to_embed)
+            embeddings = await asyncio.to_thread(embed_texts, texts_to_embed)
             summary_embedding, *chunk_embeddings = embeddings
 
             chunks = [
@@ -268,3 +329,126 @@ class IndexingPipelineService:
             await self.session.refresh(document)
 
         return document
+
+    async def index_batch_parallel(
+        self,
+        connector_docs: list[ConnectorDocument],
+        get_llm: Callable[[AsyncSession], Awaitable],
+        *,
+        max_concurrency: int = 4,
+        on_heartbeat: Callable[[int], Awaitable[None]] | None = None,
+        heartbeat_interval: float = 30.0,
+    ) -> tuple[list[Document], int, int]:
+        """Index documents in parallel with bounded concurrency.
+
+        Phase 1 (serial): prepare_for_indexing using self.session.
+        Phase 2 (parallel): index each document in an isolated session,
+        bounded by a semaphore to avoid overwhelming APIs/DB.
+        """
+        logger = logging.getLogger(__name__)
+        perf = get_perf_logger()
+        t_total = time.perf_counter()
+
+        doc_map = {
+            compute_unique_identifier_hash(cd): cd for cd in connector_docs
+        }
+        documents = await self.prepare_for_indexing(connector_docs)
+
+        if not documents:
+            return [], 0, 0
+
+        from app.tasks.celery_tasks import get_celery_session_maker
+
+        sem = asyncio.Semaphore(max_concurrency)
+        lock = asyncio.Lock()
+        indexed_count = 0
+        failed_count = 0
+        results: list[Document] = []
+        last_heartbeat = time.time()
+
+        async def _index_one(document: Document) -> Document | Exception:
+            nonlocal indexed_count, failed_count, last_heartbeat
+
+            connector_doc = doc_map.get(document.unique_identifier_hash)
+            if connector_doc is None:
+                logger.warning(
+                    "No matching ConnectorDocument for document %s, skipping",
+                    document.id,
+                )
+                async with lock:
+                    failed_count += 1
+                return document
+
+            async with sem:
+                session_maker = get_celery_session_maker()
+                async with session_maker() as isolated_session:
+                    try:
+                        refetched = await isolated_session.get(
+                            Document, document.id
+                        )
+                        if refetched is None:
+                            async with lock:
+                                failed_count += 1
+                            return document
+
+                        llm = await get_llm(isolated_session)
+                        iso_pipeline = IndexingPipelineService(isolated_session)
+                        result = await iso_pipeline.index(
+                            refetched, connector_doc, llm
+                        )
+
+                        async with lock:
+                            if DocumentStatus.is_state(
+                                result.status, DocumentStatus.READY
+                            ):
+                                indexed_count += 1
+                            else:
+                                failed_count += 1
+
+                            if on_heartbeat:
+                                now = time.time()
+                                if now - last_heartbeat >= heartbeat_interval:
+                                    await on_heartbeat(indexed_count)
+                                    last_heartbeat = now
+
+                        return result
+                    except Exception as exc:
+                        logger.error(
+                            "Parallel index failed for doc %s: %s",
+                            document.id,
+                            exc,
+                            exc_info=True,
+                        )
+                        async with lock:
+                            failed_count += 1
+                        return exc
+
+        tasks = [_index_one(doc) for doc in documents]
+        t_parallel = time.perf_counter()
+        outcomes = await asyncio.gather(*tasks, return_exceptions=True)
+        perf.info(
+            "[indexing] index_batch_parallel gather docs=%d concurrency=%d "
+            "indexed=%d failed=%d in %.3fs",
+            len(documents),
+            max_concurrency,
+            indexed_count,
+            failed_count,
+            time.perf_counter() - t_parallel,
+        )
+
+        for outcome in outcomes:
+            if isinstance(outcome, Document):
+                results.append(outcome)
+            elif isinstance(outcome, Exception):
+                pass
+
+        perf.info(
+            "[indexing] index_batch_parallel TOTAL input=%d prepared=%d "
+            "indexed=%d failed=%d in %.3fs",
+            len(connector_docs),
+            len(documents),
+            indexed_count,
+            failed_count,
+            time.perf_counter() - t_total,
+        )
+        return results, indexed_count, failed_count

surfsense_backend/app/routes/editor_routes.py

@@ -1,19 +1,43 @@
 """
 Editor routes for document editing with markdown (Plate.js frontend).
+Includes multi-format export (PDF, DOCX, HTML, LaTeX, EPUB, ODT, plain text).
 """
 
+import asyncio
+import io
+import logging
+import os
+import re
+import tempfile
 from datetime import UTC, datetime
 from typing import Any
 
-from fastapi import APIRouter, Depends, HTTPException
+import pypandoc
+import typst
+from fastapi import APIRouter, Depends, HTTPException, Query
+from fastapi.responses import StreamingResponse
 from sqlalchemy import select
 from sqlalchemy.ext.asyncio import AsyncSession
 from sqlalchemy.orm import selectinload
 
 from app.db import Document, DocumentType, Permission, User, get_async_session
+from app.routes.reports_routes import (
+    ExportFormat,
+    _FILE_EXTENSIONS,
+    _MEDIA_TYPES,
+    _normalize_latex_delimiters,
+    _strip_wrapping_code_fences,
+)
+from app.templates.export_helpers import (
+    get_html_css_path,
+    get_reference_docx_path,
+    get_typst_template_path,
+)
 from app.users import current_active_user
 from app.utils.rbac import check_permission
 
+logger = logging.getLogger(__name__)
+
 router = APIRouter()
 
 
@@ -212,3 +236,153 @@ async def save_document(
         "message": "Document saved and will be reindexed in the background",
         "updated_at": document.updated_at.isoformat(),
     }
+
+
+@router.get(
+    "/search-spaces/{search_space_id}/documents/{document_id}/export"
+)
+async def export_document(
+    search_space_id: int,
+    document_id: int,
+    format: ExportFormat = Query(
+        ExportFormat.PDF,
+        description="Export format: pdf, docx, html, latex, epub, odt, or plain",
+    ),
+    session: AsyncSession = Depends(get_async_session),
+    user: User = Depends(current_active_user),
+):
+    """Export a document in the requested format (reuses the report export pipeline)."""
+    await check_permission(
+        session,
+        user,
+        search_space_id,
+        Permission.DOCUMENTS_READ.value,
+        "You don't have permission to read documents in this search space",
+    )
+
+    result = await session.execute(
+        select(Document)
+        .options(selectinload(Document.chunks))
+        .filter(
+            Document.id == document_id,
+            Document.search_space_id == search_space_id,
+        )
+    )
+    document = result.scalars().first()
+    if not document:
+        raise HTTPException(status_code=404, detail="Document not found")
+
+    # Resolve markdown content (same priority as editor-content endpoint)
+    markdown_content: str | None = document.source_markdown
+    if markdown_content is None and document.blocknote_document:
+        from app.utils.blocknote_to_markdown import blocknote_to_markdown
+
+        markdown_content = blocknote_to_markdown(document.blocknote_document)
+    if markdown_content is None:
+        chunks = sorted(document.chunks, key=lambda c: c.id)
+        if chunks:
+            markdown_content = "\n\n".join(chunk.content for chunk in chunks)
+
+    if not markdown_content or not markdown_content.strip():
+        raise HTTPException(
+            status_code=400, detail="Document has no content to export"
+        )
+
+    markdown_content = _strip_wrapping_code_fences(markdown_content)
+    markdown_content = _normalize_latex_delimiters(markdown_content)
+
+    doc_title = document.title or "Document"
+    formatted_date = (
+        document.created_at.strftime("%B %d, %Y") if document.created_at else ""
+    )
+    input_fmt = "gfm+tex_math_dollars"
+    meta_args = ["-M", f"title:{doc_title}", "-M", f"date:{formatted_date}"]
+
+    def _convert_and_read() -> bytes:
+        if format == ExportFormat.PDF:
+            typst_template = str(get_typst_template_path())
+            typst_markup: str = pypandoc.convert_text(
+                markdown_content,
+                "typst",
+                format=input_fmt,
+                extra_args=[
+                    "--standalone",
+                    f"--template={typst_template}",
+                    "-V", "mainfont:Libertinus Serif",
+                    "-V", "codefont:DejaVu Sans Mono",
+                    *meta_args,
+                ],
+            )
+            return typst.compile(typst_markup.encode("utf-8"))
+
+        if format == ExportFormat.DOCX:
+            return _pandoc_to_tempfile(
+                format.value,
+                ["--standalone", f"--reference-doc={get_reference_docx_path()}", *meta_args],
+            )
+
+        if format == ExportFormat.HTML:
+            html_str: str = pypandoc.convert_text(
+                markdown_content,
+                "html5",
+                format=input_fmt,
+                extra_args=[
+                    "--standalone", "--embed-resources",
+                    f"--css={get_html_css_path()}",
+                    "--syntax-highlighting=pygments",
+                    *meta_args,
+                ],
+            )
+            return html_str.encode("utf-8")
+
+        if format == ExportFormat.EPUB:
+            return _pandoc_to_tempfile("epub3", ["--standalone", *meta_args])
+
+        if format == ExportFormat.ODT:
+            return _pandoc_to_tempfile("odt", ["--standalone", *meta_args])
+
+        if format == ExportFormat.LATEX:
+            tex_str: str = pypandoc.convert_text(
+                markdown_content, "latex", format=input_fmt,
+                extra_args=["--standalone", *meta_args],
+            )
+            return tex_str.encode("utf-8")
+
+        plain_str: str = pypandoc.convert_text(
+            markdown_content, "plain", format=input_fmt,
+            extra_args=["--wrap=auto", "--columns=80"],
+        )
+        return plain_str.encode("utf-8")
+
+    def _pandoc_to_tempfile(output_format: str, extra_args: list[str]) -> bytes:
+        fd, tmp_path = tempfile.mkstemp(suffix=f".{output_format}")
+        os.close(fd)
+        try:
+            pypandoc.convert_text(
+                markdown_content, output_format, format=input_fmt,
+                extra_args=extra_args, outputfile=tmp_path,
+            )
+            with open(tmp_path, "rb") as f:
+                return f.read()
+        finally:
+            os.unlink(tmp_path)
+
+    try:
+        loop = asyncio.get_running_loop()
+        output = await loop.run_in_executor(None, _convert_and_read)
+    except Exception as e:
+        logger.exception("Document export failed")
+        raise HTTPException(status_code=500, detail=f"Export failed: {e!s}") from e
+
+    safe_title = (
+        "".join(c if c.isalnum() or c in " -_" else "_" for c in doc_title)
+        .strip()[:80]
+        or "document"
+    )
+    ext = _FILE_EXTENSIONS[format]
+
+    return StreamingResponse(
+        io.BytesIO(output),
+        media_type=_MEDIA_TYPES[format],
+        headers={"Content-Disposition": f'attachment; filename="{safe_title}.{ext}"'},
+    )

surfsense_backend/app/routes/search_source_connectors_routes.py

@@ -2329,7 +2329,7 @@ async def run_google_drive_indexing(
     try:
         from app.tasks.connector_indexers.google_drive_indexer import (
             index_google_drive_files,
-            index_google_drive_single_file,
+            index_google_drive_selected_files,
         )
 
         # Parse the structured data
@@ -2402,25 +2402,23 @@ async def run_google_drive_indexing(
                     exc_info=True,
                 )
 
-        # Index each individual file
-        for file in items.files:
+        # Index all selected files together via the parallel pipeline
+        if items.files:
             try:
-                indexed_count, error_message = await index_google_drive_single_file(
+                file_tuples = [(f.id, f.name) for f in items.files]
+                indexed_count, _skipped, file_errors = await index_google_drive_selected_files(
                     session,
                     connector_id,
                     search_space_id,
                     user_id,
-                    file_id=file.id,
-                    file_name=file.name,
+                    files=file_tuples,
                 )
-                if error_message:
-                    errors.append(f"File '{file.name}': {error_message}")
-                else:
                 total_indexed += indexed_count
+                errors.extend(file_errors)
             except Exception as e:
-                errors.append(f"File '{file.name}': {e!s}")
+                errors.append(f"File batch indexing: {e!s}")
                 logger.error(
-                    f"Error indexing file {file.name} ({file.id}): {e}",
+                    f"Error batch indexing files: {e}",
                     exc_info=True,
                 )
 

surfsense_backend/app/services/google_calendar/kb_sync_service.py

@@ -209,8 +209,8 @@ class GoogleCalendarKBSyncService:
             )
 
             calendar_id = (document.document_metadata or {}).get(
-                "calendar_id", "primary"
-            )
+                "calendar_id"
+            ) or "primary"
             live_event = await loop.run_in_executor(
                 None,
                 lambda: (

surfsense_backend/app/tasks/connector_indexers/confluence_indexer.py

@@ -1,49 +1,74 @@
-"""
-Confluence connector indexer.
-
-Provides real-time document status updates during indexing using a two-phase approach:
-- Phase 1: Create all documents with PENDING status (visible in UI immediately)
-- Phase 2: Process each document one by one (PENDING → PROCESSING → READY/FAILED)
-"""
+"""Confluence connector indexer using the unified parallel indexing pipeline."""
 
 import contextlib
-import time
 from collections.abc import Awaitable, Callable
-from datetime import datetime
 
 from sqlalchemy.exc import SQLAlchemyError
 from sqlalchemy.ext.asyncio import AsyncSession
 
 from app.connectors.confluence_history import ConfluenceHistoryConnector
-from app.db import Document, DocumentStatus, DocumentType, SearchSourceConnectorType
+from app.db import DocumentType, SearchSourceConnectorType
+from app.indexing_pipeline.connector_document import ConnectorDocument
+from app.indexing_pipeline.document_hashing import compute_content_hash
+from app.indexing_pipeline.indexing_pipeline_service import IndexingPipelineService
 from app.services.llm_service import get_user_long_context_llm
 from app.services.task_logging_service import TaskLoggingService
-from app.utils.document_converters import (
-    create_document_chunks,
-    embed_text,
-    generate_content_hash,
-    generate_document_summary,
-    generate_unique_identifier_hash,
-)
 
 from .base import (
     calculate_date_range,
-    check_document_by_unique_identifier,
     check_duplicate_document_by_hash,
     get_connector_by_id,
-    get_current_timestamp,
     logger,
-    safe_set_chunks,
     update_connector_last_indexed,
 )
 
-# Type hint for heartbeat callback
 HeartbeatCallbackType = Callable[[int], Awaitable[None]]
-
-# Heartbeat interval in seconds
 HEARTBEAT_INTERVAL_SECONDS = 30
 
 
+def _build_connector_doc(
+    page: dict,
+    full_content: str,
+    *,
+    connector_id: int,
+    search_space_id: int,
+    user_id: str,
+    enable_summary: bool,
+) -> ConnectorDocument:
+    """Map a raw Confluence page dict to a ConnectorDocument."""
+    page_id = page.get("id", "")
+    page_title = page.get("title", "")
+    space_id = page.get("spaceId", "")
+    comment_count = len(page.get("comments", []))
+
+    metadata = {
+        "page_id": page_id,
+        "page_title": page_title,
+        "space_id": space_id,
+        "comment_count": comment_count,
+        "connector_id": connector_id,
+        "document_type": "Confluence Page",
+        "connector_type": "Confluence",
+    }
+
+    fallback_summary = (
+        f"Confluence Page: {page_title}\n\nSpace ID: {space_id}\n\n{full_content}"
+    )
+
+    return ConnectorDocument(
+        title=page_title,
+        source_markdown=full_content,
+        unique_id=page_id,
+        document_type=DocumentType.CONFLUENCE_CONNECTOR,
+        search_space_id=search_space_id,
+        connector_id=connector_id,
+        created_by_id=user_id,
+        should_summarize=enable_summary,
+        fallback_summary=fallback_summary,
+        metadata=metadata,
+    )
+
+
 async def index_confluence_pages(
     session: AsyncSession,
     connector_id: int,
@@ -53,26 +78,9 @@ async def index_confluence_pages(
     end_date: str | None = None,
     update_last_indexed: bool = True,
     on_heartbeat_callback: HeartbeatCallbackType | None = None,
-) -> tuple[int, str | None]:
-    """
-    Index Confluence pages and comments.
-
-    Args:
-        session: Database session
-        connector_id: ID of the Confluence connector
-        search_space_id: ID of the search space to store documents in
-        user_id: User ID
-        start_date: Start date for indexing (YYYY-MM-DD format)
-        end_date: End date for indexing (YYYY-MM-DD format)
-        update_last_indexed: Whether to update the last_indexed_at timestamp (default: True)
-        on_heartbeat_callback: Optional callback to update notification during long-running indexing.
-
-    Returns:
-        Tuple containing (number of documents indexed, error message or None)
-    """
+) -> tuple[int, int, str | None]:
+    """Index Confluence pages and comments."""
     task_logger = TaskLoggingService(session, search_space_id)
-
-    # Log task start
     log_entry = await task_logger.log_task_start(
         task_name="confluence_pages_indexing",
         source="connector_indexing_task",
@@ -86,7 +94,6 @@ async def index_confluence_pages(
     )
 
     try:
-        # Get the connector from the database
         connector = await get_connector_by_id(
             session, connector_id, SearchSourceConnectorType.CONFLUENCE_CONNECTOR
         )
@@ -98,9 +105,8 @@ async def index_confluence_pages(
                 "Connector not found",
                 {"error_type": "ConnectorNotFound"},
             )
-            return 0, f"Connector with ID {connector_id} not found"
+            return 0, 0, f"Connector with ID {connector_id} not found"
 
-        # Initialize Confluence OAuth client
         await task_logger.log_task_progress(
             log_entry,
             f"Initializing Confluence OAuth client for connector {connector_id}",
@@ -114,7 +120,6 @@ async def index_confluence_pages(
             )
         )
 
-        # Calculate date range
         start_date_str, end_date_str = calculate_date_range(
             connector, start_date, end_date, default_days_back=365
         )
@@ -129,19 +134,14 @@ async def index_confluence_pages(
             },
         )
 
-        # Get pages within date range
         try:
             pages, error = await confluence_client.get_pages_by_date_range(
                 start_date=start_date_str, end_date=end_date_str, include_comments=True
             )
 
             if error:
-                # Don't treat "No pages found" as an error that should stop indexing
                 if "No pages found" in error:
                     logger.info(f"No Confluence pages found: {error}")
-                    logger.info(
-                        "No pages found is not a critical error, continuing with update"
-                    )
                     if update_last_indexed:
                         await update_connector_last_indexed(
                             session, connector, update_last_indexed
@@ -156,11 +156,10 @@ async def index_confluence_pages(
                         f"No Confluence pages found in date range {start_date_str} to {end_date_str}",
                         {"pages_found": 0},
                     )
-                    # Close client before returning
                     if confluence_client:
                         with contextlib.suppress(Exception):
                             await confluence_client.close()
-                    return 0, None
+                    return 0, 0, None
                 else:
                     logger.error(f"Failed to get Confluence pages: {error}")
                     await task_logger.log_task_failure(
@@ -169,36 +168,35 @@ async def index_confluence_pages(
                         "API Error",
                         {"error_type": "APIError"},
                     )
-                    # Close client on error
                     if confluence_client:
                         with contextlib.suppress(Exception):
                             await confluence_client.close()
-                    return 0, f"Failed to get Confluence pages: {error}"
+                    return 0, 0, f"Failed to get Confluence pages: {error}"
 
             logger.info(f"Retrieved {len(pages)} pages from Confluence API")
 
         except Exception as e:
             logger.error(f"Error fetching Confluence pages: {e!s}", exc_info=True)
-            # Close client on error
             if confluence_client:
                 with contextlib.suppress(Exception):
                     await confluence_client.close()
-            return 0, f"Error fetching Confluence pages: {e!s}"
+            return 0, 0, f"Error fetching Confluence pages: {e!s}"
+
+        if not pages:
+            logger.info("No Confluence pages found for the specified date range")
+            if update_last_indexed:
+                await update_connector_last_indexed(
+                    session, connector, update_last_indexed
+                )
+                await session.commit()
+            if confluence_client:
+                with contextlib.suppress(Exception):
+                    await confluence_client.close()
+            return 0, 0, None
 
-        # =======================================================================
-        # PHASE 1: Analyze all pages, create pending documents
-        # This makes ALL documents visible in the UI immediately with pending status
-        # =======================================================================
-        documents_indexed = 0
         documents_skipped = 0
-        documents_failed = 0
         duplicate_content_count = 0
-
-        # Heartbeat tracking - update notification periodically to prevent appearing stuck
-        last_heartbeat_time = time.time()
-
-        pages_to_process = []  # List of dicts with document and page data
-        new_documents_created = False
+        connector_docs: list[ConnectorDocument] = []
 
         for page in pages:
             try:
@@ -213,12 +211,10 @@ async def index_confluence_pages(
                     documents_skipped += 1
                     continue
 
-                # Extract page content
                 page_content = ""
                 if page.get("body") and page["body"].get("storage"):
                     page_content = page["body"]["storage"].get("value", "")
 
-                # Add comments to content
                 comments = page.get("comments", [])
                 comments_content = ""
                 if comments:
@@ -235,61 +231,25 @@ async def index_confluence_pages(
 
                         comments_content += f"**Comment by {comment_author}** ({comment_date}):\n{comment_body}\n\n"
 
-                # Combine page content with comments
                 full_content = f"# {page_title}\n\n{page_content}{comments_content}"
 
-                if not full_content.strip():
+                if not page_content.strip() and not comments:
                     logger.warning(f"Skipping page with no content: {page_title}")
                     documents_skipped += 1
                     continue
 
-                # Generate unique identifier hash for this Confluence page
-                unique_identifier_hash = generate_unique_identifier_hash(
-                    DocumentType.CONFLUENCE_CONNECTOR, page_id, search_space_id
+                doc = _build_connector_doc(
+                    page,
+                    full_content,
+                    connector_id=connector_id,
+                    search_space_id=search_space_id,
+                    user_id=user_id,
+                    enable_summary=connector.enable_summary,
                 )
 
-                # Generate content hash
-                content_hash = generate_content_hash(full_content, search_space_id)
-
-                # Check if document with this unique identifier already exists
-                existing_document = await check_document_by_unique_identifier(
-                    session, unique_identifier_hash
-                )
-
-                comment_count = len(comments)
-
-                if existing_document:
-                    # Document exists - check if content has changed
-                    if existing_document.content_hash == content_hash:
-                        # Ensure status is ready (might have been stuck in processing/pending)
-                        if not DocumentStatus.is_state(
-                            existing_document.status, DocumentStatus.READY
-                        ):
-                            existing_document.status = DocumentStatus.ready()
-                        documents_skipped += 1
-                        continue
-
-                    # Queue existing document for update (will be set to processing in Phase 2)
-                    pages_to_process.append(
-                        {
-                            "document": existing_document,
-                            "is_new": False,
-                            "full_content": full_content,
-                            "page_content": page_content,
-                            "content_hash": content_hash,
-                            "page_id": page_id,
-                            "page_title": page_title,
-                            "space_id": space_id,
-                            "comment_count": comment_count,
-                        }
-                    )
-                    continue
-
-                # Document doesn't exist by unique_identifier_hash
-                # Check if a document with the same content_hash exists (from another connector)
                 with session.no_autoflush:
                     duplicate_by_content = await check_duplicate_document_by_hash(
-                        session, content_hash
+                        session, compute_content_hash(doc)
                     )
 
                 if duplicate_by_content:
@@ -302,151 +262,29 @@ async def index_confluence_pages(
                     documents_skipped += 1
                     continue
 
-                # Create new document with PENDING status (visible in UI immediately)
-                document = Document(
-                    search_space_id=search_space_id,
-                    title=page_title,
-                    document_type=DocumentType.CONFLUENCE_CONNECTOR,
-                    document_metadata={
-                        "page_id": page_id,
-                        "page_title": page_title,
-                        "space_id": space_id,
-                        "comment_count": comment_count,
-                        "connector_id": connector_id,
-                    },
-                    content="Pending...",  # Placeholder until processed
-                    content_hash=unique_identifier_hash,  # Temporary unique value - updated when ready
-                    unique_identifier_hash=unique_identifier_hash,
-                    embedding=None,
-                    chunks=[],  # Empty at creation - safe for async
-                    status=DocumentStatus.pending(),  # Pending until processing starts
-                    updated_at=get_current_timestamp(),
-                    created_by_id=user_id,
-                    connector_id=connector_id,
-                )
-                session.add(document)
-                new_documents_created = True
-
-                pages_to_process.append(
-                    {
-                        "document": document,
-                        "is_new": True,
-                        "full_content": full_content,
-                        "page_content": page_content,
-                        "content_hash": content_hash,
-                        "page_id": page_id,
-                        "page_title": page_title,
-                        "space_id": space_id,
-                        "comment_count": comment_count,
-                    }
-                )
+                connector_docs.append(doc)
 
             except Exception as e:
-                logger.error(f"Error in Phase 1 for page: {e!s}", exc_info=True)
-                documents_failed += 1
+                logger.error(f"Error building ConnectorDocument for page: {e!s}", exc_info=True)
+                documents_skipped += 1
                 continue
 
-        # Commit all pending documents - they all appear in UI now
-        if new_documents_created:
-            logger.info(
-                f"Phase 1: Committing {len([p for p in pages_to_process if p['is_new']])} pending documents"
-            )
-            await session.commit()
+        pipeline = IndexingPipelineService(session)
+        await pipeline.migrate_legacy_docs(connector_docs)
 
-        # =======================================================================
-        # PHASE 2: Process each document one by one
-        # Each document transitions: pending → processing → ready/failed
-        # =======================================================================
-        logger.info(f"Phase 2: Processing {len(pages_to_process)} documents")
+        async def _get_llm(s: AsyncSession):
+            return await get_user_long_context_llm(s, user_id, search_space_id)
 
-        for item in pages_to_process:
-            # Send heartbeat periodically
-            if on_heartbeat_callback:
-                current_time = time.time()
-                if current_time - last_heartbeat_time >= HEARTBEAT_INTERVAL_SECONDS:
-                    await on_heartbeat_callback(documents_indexed)
-                    last_heartbeat_time = current_time
-
-            document = item["document"]
-            try:
-                # Set to PROCESSING and commit - shows "processing" in UI for THIS document only
-                document.status = DocumentStatus.processing()
-                await session.commit()
-
-                # Heavy processing (LLM, embeddings, chunks)
-                user_llm = await get_user_long_context_llm(
-                    session, user_id, search_space_id
+        _, documents_indexed, documents_failed = await pipeline.index_batch_parallel(
+            connector_docs,
+            _get_llm,
+            max_concurrency=3,
+            on_heartbeat=on_heartbeat_callback,
+            heartbeat_interval=HEARTBEAT_INTERVAL_SECONDS,
         )
 
-                if user_llm and connector.enable_summary:
-                    document_metadata = {
-                        "page_title": item["page_title"],
-                        "page_id": item["page_id"],
-                        "space_id": item["space_id"],
-                        "comment_count": item["comment_count"],
-                        "document_type": "Confluence Page",
-                        "connector_type": "Confluence",
-                    }
-                    (
-                        summary_content,
-                        summary_embedding,
-                    ) = await generate_document_summary(
-                        item["full_content"], user_llm, document_metadata
-                    )
-                else:
-                    summary_content = f"Confluence Page: {item['page_title']}\n\nSpace ID: {item['space_id']}\n\n{item['full_content']}"
-                    summary_embedding = embed_text(summary_content)
-
-                # Process chunks - using the full page content with comments
-                chunks = await create_document_chunks(item["full_content"])
-
-                # Update document to READY with actual content
-                document.title = item["page_title"]
-                document.content = summary_content
-                document.content_hash = item["content_hash"]
-                document.embedding = summary_embedding
-                document.document_metadata = {
-                    "page_id": item["page_id"],
-                    "page_title": item["page_title"],
-                    "space_id": item["space_id"],
-                    "comment_count": item["comment_count"],
-                    "indexed_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
-                    "connector_id": connector_id,
-                }
-                await safe_set_chunks(session, document, chunks)
-                document.updated_at = get_current_timestamp()
-                document.status = DocumentStatus.ready()
-
-                documents_indexed += 1
-
-                # Batch commit every 10 documents (for ready status updates)
-                if documents_indexed % 10 == 0:
-                    logger.info(
-                        f"Committing batch: {documents_indexed} Confluence pages processed so far"
-                    )
-                    await session.commit()
-
-            except Exception as e:
-                logger.error(
-                    f"Error processing page {item.get('page_title', 'Unknown')}: {e!s}",
-                    exc_info=True,
-                )
-                # Mark document as failed with reason (visible in UI)
-                try:
-                    document.status = DocumentStatus.failed(str(e))
-                    document.updated_at = get_current_timestamp()
-                except Exception as status_error:
-                    logger.error(
-                        f"Failed to update document status to failed: {status_error}"
-                    )
-                documents_failed += 1
-                continue  # Skip this page and continue with others
-
-        # CRITICAL: Always update timestamp (even if 0 documents indexed) so Zero syncs
-        # This ensures the UI shows "Last indexed" instead of "Never indexed"
         await update_connector_last_indexed(session, connector, update_last_indexed)
 
-        # Final commit to ensure all documents are persisted (safety net)
         logger.info(
             f"Final commit: Total {documents_indexed} Confluence pages processed"
         )
@@ -456,7 +294,6 @@ async def index_confluence_pages(
                 "Successfully committed all Confluence document changes to database"
             )
         except Exception as e:
-            # Handle any remaining integrity errors gracefully (race conditions, etc.)
             if (
                 "duplicate key value violates unique constraint" in str(e).lower()
                 or "uniqueviolationerror" in str(e).lower()
@@ -467,11 +304,9 @@ async def index_confluence_pages(
                     f"Rolling back and continuing. Error: {e!s}"
                 )
                 await session.rollback()
-                # Don't fail the entire task - some documents may have been successfully indexed
             else:
                 raise
 
-        # Build warning message if there were issues
         warning_parts = []
         if duplicate_content_count > 0:
             warning_parts.append(f"{duplicate_content_count} duplicate")
@@ -479,7 +314,6 @@ async def index_confluence_pages(
             warning_parts.append(f"{documents_failed} failed")
         warning_message = ", ".join(warning_parts) if warning_parts else None
 
-        # Log success
         await task_logger.log_task_success(
             log_entry,
             f"Successfully completed Confluence indexing for connector {connector_id}",
@@ -490,22 +324,19 @@ async def index_confluence_pages(
                 "duplicate_content_count": duplicate_content_count,
             },
         )
-
         logger.info(
             f"Confluence indexing completed: {documents_indexed} ready, "
             f"{documents_skipped} skipped, {documents_failed} failed "
             f"({duplicate_content_count} duplicate content)"
         )
 
-        # Close the client connection
         if confluence_client:
             await confluence_client.close()
 
-        return documents_indexed, warning_message
+        return documents_indexed, documents_skipped, warning_message
 
     except SQLAlchemyError as db_error:
         await session.rollback()
-        # Close client if it exists
         if confluence_client:
             with contextlib.suppress(Exception):
                 await confluence_client.close()
@@ -516,10 +347,9 @@ async def index_confluence_pages(
             {"error_type": "SQLAlchemyError"},
         )
         logger.error(f"Database error: {db_error!s}", exc_info=True)
-        return 0, f"Database error: {db_error!s}"
+        return 0, 0, f"Database error: {db_error!s}"
     except Exception as e:
         await session.rollback()
-        # Close client if it exists
         if confluence_client:
             with contextlib.suppress(Exception):
                 await confluence_client.close()
@@ -530,4 +360,4 @@ async def index_confluence_pages(
             {"error_type": type(e).__name__},
         )
         logger.error(f"Failed to index Confluence pages: {e!s}", exc_info=True)
-        return 0, f"Failed to index Confluence pages: {e!s}"
+        return 0, 0, f"Failed to index Confluence pages: {e!s}"

surfsense_backend/app/tasks/connector_indexers/google_calendar_indexer.py

@@ -1,12 +1,10 @@
 """
 Google Calendar connector indexer.
 
-Implements 2-phase document status updates for real-time UI feedback:
-- Phase 1: Create all documents with 'pending' status (visible in UI immediately)
-- Phase 2: Process each document: pending → processing → ready/failed
+Uses the shared IndexingPipelineService for document deduplication,
+summarization, chunking, and embedding.
 """
 
-import time
 from collections.abc import Awaitable, Callable
 from datetime import datetime, timedelta
 
@@ -15,29 +13,22 @@ from sqlalchemy.exc import SQLAlchemyError
 from sqlalchemy.ext.asyncio import AsyncSession
 
 from app.connectors.google_calendar_connector import GoogleCalendarConnector
-from app.db import Document, DocumentStatus, DocumentType, SearchSourceConnectorType
+from app.db import DocumentType, SearchSourceConnectorType
+from app.indexing_pipeline.connector_document import ConnectorDocument
+from app.indexing_pipeline.document_hashing import compute_content_hash
+from app.indexing_pipeline.indexing_pipeline_service import IndexingPipelineService
 from app.services.llm_service import get_user_long_context_llm
 from app.services.task_logging_service import TaskLoggingService
-from app.utils.document_converters import (
-    create_document_chunks,
-    embed_text,
-    generate_content_hash,
-    generate_document_summary,
-    generate_unique_identifier_hash,
-)
 from app.utils.google_credentials import (
     COMPOSIO_GOOGLE_CONNECTOR_TYPES,
     build_composio_credentials,
 )
 
 from .base import (
-    check_document_by_unique_identifier,
     check_duplicate_document_by_hash,
     get_connector_by_id,
-    get_current_timestamp,
     logger,
     parse_date_flexible,
-    safe_set_chunks,
     update_connector_last_indexed,
 )
 
@@ -46,13 +37,60 @@ ACCEPTED_CALENDAR_CONNECTOR_TYPES = {
     SearchSourceConnectorType.COMPOSIO_GOOGLE_CALENDAR_CONNECTOR,
 }
 
-# Type hint for heartbeat callback
 HeartbeatCallbackType = Callable[[int], Awaitable[None]]
-
-# Heartbeat interval in seconds
 HEARTBEAT_INTERVAL_SECONDS = 30
 
 
+def _build_connector_doc(
+    event: dict,
+    event_markdown: str,
+    *,
+    connector_id: int,
+    search_space_id: int,
+    user_id: str,
+    enable_summary: bool,
+) -> ConnectorDocument:
+    """Map a raw Google Calendar API event dict to a ConnectorDocument."""
+    event_id = event.get("id", "")
+    event_summary = event.get("summary", "No Title")
+    calendar_id = event.get("calendarId", "")
+
+    start = event.get("start", {})
+    end = event.get("end", {})
+    start_time = start.get("dateTime") or start.get("date", "")
+    end_time = end.get("dateTime") or end.get("date", "")
+    location = event.get("location", "")
+
+    metadata = {
+        "event_id": event_id,
+        "event_summary": event_summary,
+        "calendar_id": calendar_id,
+        "start_time": start_time,
+        "end_time": end_time,
+        "location": location,
+        "connector_id": connector_id,
+        "document_type": "Google Calendar Event",
+        "connector_type": "Google Calendar",
+    }
+
+    fallback_summary = (
+        f"Google Calendar Event: {event_summary}\n\n{event_markdown}"
+    )
+
+    return ConnectorDocument(
+        title=event_summary,
+        source_markdown=event_markdown,
+        unique_id=event_id,
+        document_type=DocumentType.GOOGLE_CALENDAR_CONNECTOR,
+        search_space_id=search_space_id,
+        connector_id=connector_id,
+        created_by_id=user_id,
+        should_summarize=enable_summary,
+        fallback_summary=fallback_summary,
+        metadata=metadata,
+    )
+
+
 async def index_google_calendar_events(
     session: AsyncSession,
     connector_id: int,
@@ -82,7 +120,6 @@ async def index_google_calendar_events(
     """
     task_logger = TaskLoggingService(session, search_space_id)
 
-    # Log task start
     log_entry = await task_logger.log_task_start(
         task_name="google_calendar_events_indexing",
         source="connector_indexing_task",
@@ -96,7 +133,7 @@ async def index_google_calendar_events(
     )
 
     try:
-        # Accept both native and Composio Calendar connectors
+        # ── Connector lookup ──────────────────────────────────────────
         connector = None
         for ct in ACCEPTED_CALENDAR_CONNECTOR_TYPES:
             connector = await get_connector_by_id(session, connector_id, ct)
@@ -112,7 +149,7 @@ async def index_google_calendar_events(
             )
             return 0, 0, f"Connector with ID {connector_id} not found"
 
-        # Build credentials based on connector type
+        # ── Credential building ───────────────────────────────────────
         if connector.connector_type in COMPOSIO_GOOGLE_CONNECTOR_TYPES:
             connected_account_id = connector.config.get("composio_connected_account_id")
             if not connected_account_id:
@@ -184,6 +221,7 @@ async def index_google_calendar_events(
                 )
                 return 0, 0, "Google Calendar credentials not found in connector config"
 
+        # ── Calendar client init ──────────────────────────────────────
         await task_logger.log_task_progress(
             log_entry,
             f"Initializing Google Calendar client for connector {connector_id}",
@@ -203,36 +241,26 @@ async def index_google_calendar_events(
         if end_date == "undefined" or end_date == "":
             end_date = None
 
-        # Calculate date range
-        # For calendar connectors, allow future dates to index upcoming events
+        # ── Date range calculation ────────────────────────────────────
         if start_date is None or end_date is None:
-            # Fall back to calculating dates based on last_indexed_at
-            # Default to today (users can manually select future dates if needed)
             calculated_end_date = datetime.now()
 
-            # Use last_indexed_at as start date if available, otherwise use 30 days ago
             if connector.last_indexed_at:
-                # Convert dates to be comparable (both timezone-naive)
                 last_indexed_naive = (
                     connector.last_indexed_at.replace(tzinfo=None)
                     if connector.last_indexed_at.tzinfo
                     else connector.last_indexed_at
                 )
-
-                # Allow future dates - use last_indexed_at as start date
                 calculated_start_date = last_indexed_naive
                 logger.info(
                     f"Using last_indexed_at ({calculated_start_date.strftime('%Y-%m-%d')}) as start date"
                 )
             else:
-                calculated_start_date = datetime.now() - timedelta(
-                    days=365
-                )  # Use 365 days as default for calendar events (matches frontend)
+                calculated_start_date = datetime.now() - timedelta(days=365)
                 logger.info(
                     f"No last_indexed_at found, using {calculated_start_date.strftime('%Y-%m-%d')} (365 days ago) as start date"
                 )
 
-            # Use calculated dates if not provided
             start_date_str = (
                 start_date if start_date else calculated_start_date.strftime("%Y-%m-%d")
             )
@@ -240,19 +268,14 @@ async def index_google_calendar_events(
                 end_date if end_date else calculated_end_date.strftime("%Y-%m-%d")
             )
         else:
-            # Use provided dates (including future dates)
             start_date_str = start_date
             end_date_str = end_date
 
-        # FIX: Ensure end_date is at least 1 day after start_date to avoid
-        # "start_date must be strictly before end_date" errors when dates are the same
-        # (e.g., when last_indexed_at is today)
         if start_date_str == end_date_str:
             logger.info(
                 f"Start date ({start_date_str}) equals end date ({end_date_str}), "
                 "adjusting end date to next day to ensure valid date range"
             )
-            # Parse end_date and add 1 day
             try:
                 end_dt = parse_date_flexible(end_date_str)
             except ValueError:
@@ -264,6 +287,7 @@ async def index_google_calendar_events(
             end_date_str = end_dt.strftime("%Y-%m-%d")
             logger.info(f"Adjusted end date to {end_date_str}")
 
+        # ── Fetch events ──────────────────────────────────────────────
         await task_logger.log_task_progress(
             log_entry,
             f"Fetching Google Calendar events from {start_date_str} to {end_date_str}",
@@ -274,27 +298,19 @@ async def index_google_calendar_events(
             },
         )
 
-        # Get events within date range from primary calendar
         try:
             events, error = await calendar_client.get_all_primary_calendar_events(
                 start_date=start_date_str, end_date=end_date_str
             )
 
             if error:
-                # Don't treat "No events found" as an error that should stop indexing
                 if "No events found" in error:
                     logger.info(f"No Google Calendar events found: {error}")
-                    logger.info(
-                        "No events found is not a critical error, continuing with update"
-                    )
                     if update_last_indexed:
                         await update_connector_last_indexed(
                             session, connector, update_last_indexed
                         )
                         await session.commit()
-                        logger.info(
-                            f"Updated last_indexed_at to {connector.last_indexed_at} despite no events found"
-                        )
 
                     await task_logger.log_task_success(
                         log_entry,
@@ -304,7 +320,6 @@ async def index_google_calendar_events(
                     return 0, 0, None
                 else:
                     logger.error(f"Failed to get Google Calendar events: {error}")
-                    # Check if this is an authentication error that requires re-authentication
                     error_message = error
                     error_type = "APIError"
                     if (
@@ -329,28 +344,15 @@ async def index_google_calendar_events(
             logger.error(f"Error fetching Google Calendar events: {e!s}", exc_info=True)
             return 0, 0, f"Error fetching Google Calendar events: {e!s}"
 
-        documents_indexed = 0
+        # ── Build ConnectorDocuments ──────────────────────────────────
+        connector_docs: list[ConnectorDocument] = []
         documents_skipped = 0
-        documents_failed = 0  # Track events that failed processing
-        duplicate_content_count = (
-            0  # Track events skipped due to duplicate content_hash
-        )
-
-        # Heartbeat tracking - update notification periodically to prevent appearing stuck
-        last_heartbeat_time = time.time()
-
-        # =======================================================================
-        # PHASE 1: Analyze all events, create pending documents
-        # This makes ALL documents visible in the UI immediately with pending status
-        # =======================================================================
-        events_to_process = []  # List of dicts with document and event data
-        new_documents_created = False
+        duplicate_content_count = 0
 
         for event in events:
             try:
                 event_id = event.get("id")
                 event_summary = event.get("summary", "No Title")
-                calendar_id = event.get("calendarId", "")
 
                 if not event_id:
                     logger.warning(f"Skipping event with missing ID: {event_summary}")
@@ -363,246 +365,55 @@ async def index_google_calendar_events(
                     documents_skipped += 1
                     continue
 
-                start = event.get("start", {})
-                end = event.get("end", {})
-                start_time = start.get("dateTime") or start.get("date", "")
-                end_time = end.get("dateTime") or end.get("date", "")
-                location = event.get("location", "")
-                description = event.get("description", "")
-
-                # Generate unique identifier hash for this Google Calendar event
-                unique_identifier_hash = generate_unique_identifier_hash(
-                    DocumentType.GOOGLE_CALENDAR_CONNECTOR, event_id, search_space_id
+                doc = _build_connector_doc(
+                    event,
+                    event_markdown,
+                    connector_id=connector_id,
+                    search_space_id=search_space_id,
+                    user_id=user_id,
+                    enable_summary=connector.enable_summary,
                 )
 
-                # Generate content hash
-                content_hash = generate_content_hash(event_markdown, search_space_id)
-
-                # Check if document with this unique identifier already exists
-                existing_document = await check_document_by_unique_identifier(
-                    session, unique_identifier_hash
-                )
-
-                # Fallback: legacy Composio hash
-                if not existing_document:
-                    legacy_hash = generate_unique_identifier_hash(
-                        DocumentType.COMPOSIO_GOOGLE_CALENDAR_CONNECTOR,
-                        event_id,
-                        search_space_id,
-                    )
-                    existing_document = await check_document_by_unique_identifier(
-                        session, legacy_hash
-                    )
-                    if existing_document:
-                        existing_document.unique_identifier_hash = (
-                            unique_identifier_hash
-                        )
-                        if (
-                            existing_document.document_type
-                            == DocumentType.COMPOSIO_GOOGLE_CALENDAR_CONNECTOR
-                        ):
-                            existing_document.document_type = (
-                                DocumentType.GOOGLE_CALENDAR_CONNECTOR
-                            )
-                        logger.info(
-                            f"Migrated legacy Composio Calendar document: {event_id}"
-                        )
-
-                if existing_document:
-                    # Document exists - check if content has changed
-                    if existing_document.content_hash == content_hash:
-                        # Ensure status is ready (might have been stuck in processing/pending)
-                        if not DocumentStatus.is_state(
-                            existing_document.status, DocumentStatus.READY
-                        ):
-                            existing_document.status = DocumentStatus.ready()
-                        documents_skipped += 1
-                        continue
-
-                    # Queue existing document for update (will be set to processing in Phase 2)
-                    events_to_process.append(
-                        {
-                            "document": existing_document,
-                            "is_new": False,
-                            "event_markdown": event_markdown,
-                            "content_hash": content_hash,
-                            "event_id": event_id,
-                            "event_summary": event_summary,
-                            "calendar_id": calendar_id,
-                            "start_time": start_time,
-                            "end_time": end_time,
-                            "location": location,
-                            "description": description,
-                        }
-                    )
-                    continue
-
-                # Document doesn't exist by unique_identifier_hash
-                # Check if a document with the same content_hash exists (from another connector)
                 with session.no_autoflush:
-                    duplicate_by_content = await check_duplicate_document_by_hash(
-                        session, content_hash
+                    duplicate = await check_duplicate_document_by_hash(
+                        session, compute_content_hash(doc)
                     )
-
-                if duplicate_by_content:
-                    # A document with the same content already exists (likely from Composio connector)
+                if duplicate:
                     logger.info(
-                        f"Event {event_summary} already indexed by another connector "
-                        f"(existing document ID: {duplicate_by_content.id}, "
-                        f"type: {duplicate_by_content.document_type}). Skipping to avoid duplicate content."
+                        f"Event {doc.title} already indexed by another connector "
+                        f"(existing document ID: {duplicate.id}, "
+                        f"type: {duplicate.document_type}). Skipping."
                     )
                     duplicate_content_count += 1
                     documents_skipped += 1
                     continue
 
-                # Create new document with PENDING status (visible in UI immediately)
-                document = Document(
-                    search_space_id=search_space_id,
-                    title=event_summary,
-                    document_type=DocumentType.GOOGLE_CALENDAR_CONNECTOR,
-                    document_metadata={
-                        "event_id": event_id,
-                        "event_summary": event_summary,
-                        "calendar_id": calendar_id,
-                        "start_time": start_time,
-                        "end_time": end_time,
-                        "location": location,
-                        "connector_id": connector_id,
-                    },
-                    content="Pending...",  # Placeholder until processed
-                    content_hash=unique_identifier_hash,  # Temporary unique value - updated when ready
-                    unique_identifier_hash=unique_identifier_hash,
-                    embedding=None,
-                    chunks=[],  # Empty at creation - safe for async
-                    status=DocumentStatus.pending(),  # Pending until processing starts
-                    updated_at=get_current_timestamp(),
-                    created_by_id=user_id,
-                    connector_id=connector_id,
-                )
-                session.add(document)
-                new_documents_created = True
-
-                events_to_process.append(
-                    {
-                        "document": document,
-                        "is_new": True,
-                        "event_markdown": event_markdown,
-                        "content_hash": content_hash,
-                        "event_id": event_id,
-                        "event_summary": event_summary,
-                        "calendar_id": calendar_id,
-                        "start_time": start_time,
-                        "end_time": end_time,
-                        "location": location,
-                        "description": description,
-                    }
-                )
+                connector_docs.append(doc)
 
             except Exception as e:
-                logger.error(f"Error in Phase 1 for event: {e!s}", exc_info=True)
-                documents_failed += 1
+                logger.error(f"Error building ConnectorDocument for event: {e!s}", exc_info=True)
+                documents_skipped += 1
                 continue
 
-        # Commit all pending documents - they all appear in UI now
-        if new_documents_created:
-            logger.info(
-                f"Phase 1: Committing {len([e for e in events_to_process if e['is_new']])} pending documents"
-            )
-            await session.commit()
+        # ── Pipeline: migrate legacy docs + parallel index ─────────────
+        pipeline = IndexingPipelineService(session)
 
-        # =======================================================================
-        # PHASE 2: Process each document one by one
-        # Each document transitions: pending → processing → ready/failed
-        # =======================================================================
-        logger.info(f"Phase 2: Processing {len(events_to_process)} documents")
+        await pipeline.migrate_legacy_docs(connector_docs)
 
-        for item in events_to_process:
-            # Send heartbeat periodically
-            if on_heartbeat_callback:
-                current_time = time.time()
-                if current_time - last_heartbeat_time >= HEARTBEAT_INTERVAL_SECONDS:
-                    await on_heartbeat_callback(documents_indexed)
-                    last_heartbeat_time = current_time
+        async def _get_llm(s):
+            return await get_user_long_context_llm(s, user_id, search_space_id)
 
-            document = item["document"]
-            try:
-                # Set to PROCESSING and commit - shows "processing" in UI for THIS document only
-                document.status = DocumentStatus.processing()
-                await session.commit()
-
-                # Heavy processing (LLM, embeddings, chunks)
-                user_llm = await get_user_long_context_llm(
-                    session, user_id, search_space_id
+        _, documents_indexed, documents_failed = await pipeline.index_batch_parallel(
+            connector_docs,
+            _get_llm,
+            max_concurrency=3,
+            on_heartbeat=on_heartbeat_callback,
+            heartbeat_interval=HEARTBEAT_INTERVAL_SECONDS,
         )
 
-                if user_llm and connector.enable_summary:
-                    document_metadata_for_summary = {
-                        "event_id": item["event_id"],
-                        "event_summary": item["event_summary"],
-                        "calendar_id": item["calendar_id"],
-                        "start_time": item["start_time"],
-                        "end_time": item["end_time"],
-                        "location": item["location"] or "No location",
-                        "document_type": "Google Calendar Event",
-                        "connector_type": "Google Calendar",
-                    }
-                    (
-                        summary_content,
-                        summary_embedding,
-                    ) = await generate_document_summary(
-                        item["event_markdown"], user_llm, document_metadata_for_summary
-                    )
-                else:
-                    summary_content = f"Google Calendar Event: {item['event_summary']}\n\n{item['event_markdown']}"
-                    summary_embedding = embed_text(summary_content)
-
-                chunks = await create_document_chunks(item["event_markdown"])
-
-                # Update document to READY with actual content
-                document.title = item["event_summary"]
-                document.content = summary_content
-                document.content_hash = item["content_hash"]
-                document.embedding = summary_embedding
-                document.document_metadata = {
-                    "event_id": item["event_id"],
-                    "event_summary": item["event_summary"],
-                    "calendar_id": item["calendar_id"],
-                    "start_time": item["start_time"],
-                    "end_time": item["end_time"],
-                    "location": item["location"],
-                    "indexed_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
-                    "connector_id": connector_id,
-                }
-                await safe_set_chunks(session, document, chunks)
-                document.updated_at = get_current_timestamp()
-                document.status = DocumentStatus.ready()
-
-                documents_indexed += 1
-
-                # Batch commit every 10 documents (for ready status updates)
-                if documents_indexed % 10 == 0:
-                    logger.info(
-                        f"Committing batch: {documents_indexed} Google Calendar events processed so far"
-                    )
-                    await session.commit()
-
-            except Exception as e:
-                logger.error(f"Error processing Calendar event: {e!s}", exc_info=True)
-                # Mark document as failed with reason (visible in UI)
-                try:
-                    document.status = DocumentStatus.failed(str(e))
-                    document.updated_at = get_current_timestamp()
-                except Exception as status_error:
-                    logger.error(
-                        f"Failed to update document status to failed: {status_error}"
-                    )
-                documents_failed += 1
-                continue
-
-        # CRITICAL: Always update timestamp (even if 0 documents indexed) so Zero syncs
+        # ── Finalize ──────────────────────────────────────────────────
         await update_connector_last_indexed(session, connector, update_last_indexed)
 
-        # Final commit for any remaining documents not yet committed in batches
         logger.info(
             f"Final commit: Total {documents_indexed} Google Calendar events processed"
         )
@@ -612,22 +423,18 @@ async def index_google_calendar_events(
                 "Successfully committed all Google Calendar document changes to database"
             )
         except Exception as e:
-            # Handle any remaining integrity errors gracefully (race conditions, etc.)
             if (
                 "duplicate key value violates unique constraint" in str(e).lower()
                 or "uniqueviolationerror" in str(e).lower()
             ):
                 logger.warning(
                     f"Duplicate content_hash detected during final commit. "
-                    f"This may occur if the same event was indexed by multiple connectors. "
                     f"Rolling back and continuing. Error: {e!s}"
                 )
                 await session.rollback()
-                # Don't fail the entire task - some documents may have been successfully indexed
             else:
                 raise
 
-        # Build warning message if there were issues
         warning_parts = []
         if duplicate_content_count > 0:
             warning_parts.append(f"{duplicate_content_count} duplicate")

surfsense_backend/app/tasks/connector_indexers/google_gmail_indexer.py

@@ -1,12 +1,10 @@
 """
 Google Gmail connector indexer.
 
-Implements 2-phase document status updates for real-time UI feedback:
-- Phase 1: Create all documents with 'pending' status (visible in UI immediately)
-- Phase 2: Process each document: pending → processing → ready/failed
+Uses the shared IndexingPipelineService for document deduplication,
+summarization, chunking, and embedding.
 """
 
-import time
 from collections.abc import Awaitable, Callable
 from datetime import datetime
 
@@ -15,21 +13,12 @@ from sqlalchemy.exc import SQLAlchemyError
 from sqlalchemy.ext.asyncio import AsyncSession
 
 from app.connectors.google_gmail_connector import GoogleGmailConnector
-from app.db import (
-    Document,
-    DocumentStatus,
-    DocumentType,
-    SearchSourceConnectorType,
-)
+from app.db import DocumentType, SearchSourceConnectorType
+from app.indexing_pipeline.connector_document import ConnectorDocument
+from app.indexing_pipeline.document_hashing import compute_content_hash
+from app.indexing_pipeline.indexing_pipeline_service import IndexingPipelineService
 from app.services.llm_service import get_user_long_context_llm
 from app.services.task_logging_service import TaskLoggingService
-from app.utils.document_converters import (
-    create_document_chunks,
-    embed_text,
-    generate_content_hash,
-    generate_document_summary,
-    generate_unique_identifier_hash,
-)
 from app.utils.google_credentials import (
     COMPOSIO_GOOGLE_CONNECTOR_TYPES,
     build_composio_credentials,
@@ -37,12 +26,9 @@ from app.utils.google_credentials import (
 
 from .base import (
     calculate_date_range,
-    check_document_by_unique_identifier,
     check_duplicate_document_by_hash,
     get_connector_by_id,
-    get_current_timestamp,
     logger,
-    safe_set_chunks,
     update_connector_last_indexed,
 )
 
@@ -51,13 +37,70 @@ ACCEPTED_GMAIL_CONNECTOR_TYPES = {
     SearchSourceConnectorType.COMPOSIO_GMAIL_CONNECTOR,
 }
 
-# Type hint for heartbeat callback
 HeartbeatCallbackType = Callable[[int], Awaitable[None]]
-
-# Heartbeat interval in seconds
 HEARTBEAT_INTERVAL_SECONDS = 30
 
 
+def _build_connector_doc(
+    message: dict,
+    markdown_content: str,
+    *,
+    connector_id: int,
+    search_space_id: int,
+    user_id: str,
+    enable_summary: bool,
+) -> ConnectorDocument:
+    """Map a raw Gmail API message dict to a ConnectorDocument."""
+    message_id = message.get("id", "")
+    thread_id = message.get("threadId", "")
+    payload = message.get("payload", {})
+    headers = payload.get("headers", [])
+
+    subject = "No Subject"
+    sender = "Unknown Sender"
+    date_str = "Unknown Date"
+
+    for header in headers:
+        name = header.get("name", "").lower()
+        value = header.get("value", "")
+        if name == "subject":
+            subject = value
+        elif name == "from":
+            sender = value
+        elif name == "date":
+            date_str = value
+
+    metadata = {
+        "message_id": message_id,
+        "thread_id": thread_id,
+        "subject": subject,
+        "sender": sender,
+        "date": date_str,
+        "connector_id": connector_id,
+        "document_type": "Gmail Message",
+        "connector_type": "Google Gmail",
+    }
+
+    fallback_summary = (
+        f"Google Gmail Message: {subject}\n\n"
+        f"From: {sender}\nDate: {date_str}\n\n"
+        f"{markdown_content}"
+    )
+
+    return ConnectorDocument(
+        title=subject,
+        source_markdown=markdown_content,
+        unique_id=message_id,
+        document_type=DocumentType.GOOGLE_GMAIL_CONNECTOR,
+        search_space_id=search_space_id,
+        connector_id=connector_id,
+        created_by_id=user_id,
+        should_summarize=enable_summary,
+        fallback_summary=fallback_summary,
+        metadata=metadata,
+    )
+
+
 async def index_google_gmail_messages(
     session: AsyncSession,
     connector_id: int,
@@ -80,7 +123,7 @@ async def index_google_gmail_messages(
         start_date: Start date for filtering messages (YYYY-MM-DD format)
         end_date: End date for filtering messages (YYYY-MM-DD format)
         update_last_indexed: Whether to update the last_indexed_at timestamp (default: True)
-        max_messages: Maximum number of messages to fetch (default: 100)
+        max_messages: Maximum number of messages to fetch (default: 1000)
         on_heartbeat_callback: Optional callback to update notification during long-running indexing.
 
     Returns:
@@ -88,7 +131,6 @@ async def index_google_gmail_messages(
     """
     task_logger = TaskLoggingService(session, search_space_id)
 
-    # Log task start
     log_entry = await task_logger.log_task_start(
         task_name="google_gmail_messages_indexing",
         source="connector_indexing_task",
@@ -103,7 +145,7 @@ async def index_google_gmail_messages(
     )
 
     try:
-        # Accept both native and Composio Gmail connectors
+        # ── Connector lookup ──────────────────────────────────────────
         connector = None
         for ct in ACCEPTED_GMAIL_CONNECTOR_TYPES:
             connector = await get_connector_by_id(session, connector_id, ct)
@@ -117,7 +159,7 @@ async def index_google_gmail_messages(
             )
             return 0, 0, error_msg
 
-        # Build credentials based on connector type
+        # ── Credential building ───────────────────────────────────────
         if connector.connector_type in COMPOSIO_GOOGLE_CONNECTOR_TYPES:
             connected_account_id = connector.config.get("composio_connected_account_id")
             if not connected_account_id:
@@ -189,6 +231,7 @@ async def index_google_gmail_messages(
                 )
                 return 0, 0, "Google gmail credentials not found in connector config"
 
+        # ── Gmail client init ─────────────────────────────────────────
         await task_logger.log_task_progress(
             log_entry,
             f"Initializing Google gmail client for connector {connector_id}",
@@ -199,14 +242,11 @@ async def index_google_gmail_messages(
             credentials, session, user_id, connector_id
         )
 
-        # Calculate date range using last_indexed_at if dates not provided
-        # This ensures Gmail uses the same date logic as other connectors
-        # (uses last_indexed_at → now, or 365 days back for first-time indexing)
         calculated_start_date, calculated_end_date = calculate_date_range(
             connector, start_date, end_date, default_days_back=365
         )
 
-        # Fetch recent Google gmail messages
+        # ── Fetch messages ────────────────────────────────────────────
         logger.info(
             f"Fetching emails for connector {connector_id} "
             f"from {calculated_start_date} to {calculated_end_date}"
@@ -218,7 +258,6 @@ async def index_google_gmail_messages(
         )
 
         if error:
-            # Check if this is an authentication error that requires re-authentication
             error_message = error
             error_type = "APIError"
             if (
@@ -243,286 +282,74 @@ async def index_google_gmail_messages(
 
         logger.info(f"Found {len(messages)} Google gmail messages to index")
 
-        documents_indexed = 0
+        # ── Build ConnectorDocuments ──────────────────────────────────
+        connector_docs: list[ConnectorDocument] = []
         documents_skipped = 0
-        documents_failed = 0  # Track messages that failed processing
-        duplicate_content_count = (
-            0  # Track messages skipped due to duplicate content_hash
-        )
-
-        # Heartbeat tracking - update notification periodically to prevent appearing stuck
-        last_heartbeat_time = time.time()
-
-        # =======================================================================
-        # PHASE 1: Analyze all messages, create pending documents
-        # This makes ALL documents visible in the UI immediately with pending status
-        # =======================================================================
-        messages_to_process = []  # List of dicts with document and message data
-        new_documents_created = False
+        duplicate_content_count = 0
 
         for message in messages:
             try:
-                # Extract message information
                 message_id = message.get("id", "")
-                thread_id = message.get("threadId", "")
-
-                # Extract headers for subject and sender
-                payload = message.get("payload", {})
-                headers = payload.get("headers", [])
-
-                subject = "No Subject"
-                sender = "Unknown Sender"
-                date_str = "Unknown Date"
-
-                for header in headers:
-                    name = header.get("name", "").lower()
-                    value = header.get("value", "")
-                    if name == "subject":
-                        subject = value
-                    elif name == "from":
-                        sender = value
-                    elif name == "date":
-                        date_str = value
-
                 if not message_id:
-                    logger.warning(f"Skipping message with missing ID: {subject}")
+                    logger.warning("Skipping message with missing ID")
                     documents_skipped += 1
                     continue
 
-                # Format message to markdown
                 markdown_content = gmail_connector.format_message_to_markdown(message)
-
                 if not markdown_content.strip():
-                    logger.warning(f"Skipping message with no content: {subject}")
+                    logger.warning(f"Skipping message with no content: {message_id}")
                     documents_skipped += 1
                     continue
 
-                # Generate unique identifier hash for this Gmail message
-                unique_identifier_hash = generate_unique_identifier_hash(
-                    DocumentType.GOOGLE_GMAIL_CONNECTOR, message_id, search_space_id
+                doc = _build_connector_doc(
+                    message,
+                    markdown_content,
+                    connector_id=connector_id,
+                    search_space_id=search_space_id,
+                    user_id=user_id,
+                    enable_summary=connector.enable_summary,
                 )
 
-                # Generate content hash
-                content_hash = generate_content_hash(markdown_content, search_space_id)
-
-                # Check if document with this unique identifier already exists
-                existing_document = await check_document_by_unique_identifier(
-                    session, unique_identifier_hash
-                )
-
-                # Fallback: legacy Composio hash
-                if not existing_document:
-                    legacy_hash = generate_unique_identifier_hash(
-                        DocumentType.COMPOSIO_GMAIL_CONNECTOR,
-                        message_id,
-                        search_space_id,
-                    )
-                    existing_document = await check_document_by_unique_identifier(
-                        session, legacy_hash
-                    )
-                    if existing_document:
-                        existing_document.unique_identifier_hash = (
-                            unique_identifier_hash
-                        )
-                        if (
-                            existing_document.document_type
-                            == DocumentType.COMPOSIO_GMAIL_CONNECTOR
-                        ):
-                            existing_document.document_type = (
-                                DocumentType.GOOGLE_GMAIL_CONNECTOR
-                            )
-                        logger.info(
-                            f"Migrated legacy Composio Gmail document: {message_id}"
-                        )
-
-                if existing_document:
-                    # Document exists - check if content has changed
-                    if existing_document.content_hash == content_hash:
-                        # Ensure status is ready (might have been stuck in processing/pending)
-                        if not DocumentStatus.is_state(
-                            existing_document.status, DocumentStatus.READY
-                        ):
-                            existing_document.status = DocumentStatus.ready()
-                        documents_skipped += 1
-                        continue
-
-                    # Queue existing document for update (will be set to processing in Phase 2)
-                    messages_to_process.append(
-                        {
-                            "document": existing_document,
-                            "is_new": False,
-                            "markdown_content": markdown_content,
-                            "content_hash": content_hash,
-                            "message_id": message_id,
-                            "thread_id": thread_id,
-                            "subject": subject,
-                            "sender": sender,
-                            "date_str": date_str,
-                        }
-                    )
-                    continue
-
-                # Document doesn't exist by unique_identifier_hash
-                # Check if a document with the same content_hash exists (from another connector)
                 with session.no_autoflush:
-                    duplicate_by_content = await check_duplicate_document_by_hash(
-                        session, content_hash
+                    duplicate = await check_duplicate_document_by_hash(
+                        session, compute_content_hash(doc)
                     )
-
-                if duplicate_by_content:
+                if duplicate:
                     logger.info(
-                        f"Gmail message {subject} already indexed by another connector "
-                        f"(existing document ID: {duplicate_by_content.id}, "
-                        f"type: {duplicate_by_content.document_type}). Skipping."
+                        f"Gmail message {doc.title} already indexed by another connector "
+                        f"(existing document ID: {duplicate.id}, "
+                        f"type: {duplicate.document_type}). Skipping."
                     )
                     duplicate_content_count += 1
                     documents_skipped += 1
                     continue
 
-                # Create new document with PENDING status (visible in UI immediately)
-                document = Document(
-                    search_space_id=search_space_id,
-                    title=subject,
-                    document_type=DocumentType.GOOGLE_GMAIL_CONNECTOR,
-                    document_metadata={
-                        "message_id": message_id,
-                        "thread_id": thread_id,
-                        "subject": subject,
-                        "sender": sender,
-                        "date": date_str,
-                        "connector_id": connector_id,
-                    },
-                    content="Pending...",  # Placeholder until processed
-                    content_hash=unique_identifier_hash,  # Temporary unique value - updated when ready
-                    unique_identifier_hash=unique_identifier_hash,
-                    embedding=None,
-                    chunks=[],  # Empty at creation - safe for async
-                    status=DocumentStatus.pending(),  # Pending until processing starts
-                    updated_at=get_current_timestamp(),
-                    created_by_id=user_id,
-                    connector_id=connector_id,
-                )
-                session.add(document)
-                new_documents_created = True
-
-                messages_to_process.append(
-                    {
-                        "document": document,
-                        "is_new": True,
-                        "markdown_content": markdown_content,
-                        "content_hash": content_hash,
-                        "message_id": message_id,
-                        "thread_id": thread_id,
-                        "subject": subject,
-                        "sender": sender,
-                        "date_str": date_str,
-                    }
-                )
+                connector_docs.append(doc)
 
             except Exception as e:
-                logger.error(f"Error in Phase 1 for message: {e!s}", exc_info=True)
-                documents_failed += 1
+                logger.error(f"Error building ConnectorDocument for message: {e!s}", exc_info=True)
+                documents_skipped += 1
                 continue
 
-        # Commit all pending documents - they all appear in UI now
-        if new_documents_created:
-            logger.info(
-                f"Phase 1: Committing {len([m for m in messages_to_process if m['is_new']])} pending documents"
-            )
-            await session.commit()
+        # ── Pipeline: migrate legacy docs + parallel index ─────────────
+        pipeline = IndexingPipelineService(session)
 
-        # =======================================================================
-        # PHASE 2: Process each document one by one
-        # Each document transitions: pending → processing → ready/failed
-        # =======================================================================
-        logger.info(f"Phase 2: Processing {len(messages_to_process)} documents")
+        await pipeline.migrate_legacy_docs(connector_docs)
 
-        for item in messages_to_process:
-            # Send heartbeat periodically
-            if on_heartbeat_callback:
-                current_time = time.time()
-                if current_time - last_heartbeat_time >= HEARTBEAT_INTERVAL_SECONDS:
-                    await on_heartbeat_callback(documents_indexed)
-                    last_heartbeat_time = current_time
+        async def _get_llm(s):
+            return await get_user_long_context_llm(s, user_id, search_space_id)
 
-            document = item["document"]
-            try:
-                # Set to PROCESSING and commit - shows "processing" in UI for THIS document only
-                document.status = DocumentStatus.processing()
-                await session.commit()
-
-                # Heavy processing (LLM, embeddings, chunks)
-                user_llm = await get_user_long_context_llm(
-                    session, user_id, search_space_id
+        _, documents_indexed, documents_failed = await pipeline.index_batch_parallel(
+            connector_docs,
+            _get_llm,
+            max_concurrency=3,
+            on_heartbeat=on_heartbeat_callback,
+            heartbeat_interval=HEARTBEAT_INTERVAL_SECONDS,
         )
 
-                if user_llm and connector.enable_summary:
-                    document_metadata_for_summary = {
-                        "message_id": item["message_id"],
-                        "thread_id": item["thread_id"],
-                        "subject": item["subject"],
-                        "sender": item["sender"],
-                        "date": item["date_str"],
-                        "document_type": "Gmail Message",
-                        "connector_type": "Google Gmail",
-                    }
-                    (
-                        summary_content,
-                        summary_embedding,
-                    ) = await generate_document_summary(
-                        item["markdown_content"],
-                        user_llm,
-                        document_metadata_for_summary,
-                    )
-                else:
-                    summary_content = f"Google Gmail Message: {item['subject']}\n\nFrom: {item['sender']}\nDate: {item['date_str']}\n\n{item['markdown_content']}"
-                    summary_embedding = embed_text(summary_content)
-
-                chunks = await create_document_chunks(item["markdown_content"])
-
-                # Update document to READY with actual content
-                document.title = item["subject"]
-                document.content = summary_content
-                document.content_hash = item["content_hash"]
-                document.embedding = summary_embedding
-                document.document_metadata = {
-                    "message_id": item["message_id"],
-                    "thread_id": item["thread_id"],
-                    "subject": item["subject"],
-                    "sender": item["sender"],
-                    "date": item["date_str"],
-                    "connector_id": connector_id,
-                }
-                await safe_set_chunks(session, document, chunks)
-                document.updated_at = get_current_timestamp()
-                document.status = DocumentStatus.ready()
-
-                documents_indexed += 1
-
-                # Batch commit every 10 documents (for ready status updates)
-                if documents_indexed % 10 == 0:
-                    logger.info(
-                        f"Committing batch: {documents_indexed} Gmail messages processed so far"
-                    )
-                    await session.commit()
-
-            except Exception as e:
-                logger.error(f"Error processing Gmail message: {e!s}", exc_info=True)
-                # Mark document as failed with reason (visible in UI)
-                try:
-                    document.status = DocumentStatus.failed(str(e))
-                    document.updated_at = get_current_timestamp()
-                except Exception as status_error:
-                    logger.error(
-                        f"Failed to update document status to failed: {status_error}"
-                    )
-                documents_failed += 1
-                continue
-
-        # CRITICAL: Always update timestamp (even if 0 documents indexed) so Zero syncs
+        # ── Finalize ──────────────────────────────────────────────────
         await update_connector_last_indexed(session, connector, update_last_indexed)
 
-        # Final commit for any remaining documents not yet committed in batches
         logger.info(f"Final commit: Total {documents_indexed} Gmail messages processed")
         try:
             await session.commit()
@@ -530,22 +357,18 @@ async def index_google_gmail_messages(
                 "Successfully committed all Google Gmail document changes to database"
             )
         except Exception as e:
-            # Handle any remaining integrity errors gracefully (race conditions, etc.)
             if (
                 "duplicate key value violates unique constraint" in str(e).lower()
                 or "uniqueviolationerror" in str(e).lower()
             ):
                 logger.warning(
                     f"Duplicate content_hash detected during final commit. "
-                    f"This may occur if the same message was indexed by multiple connectors. "
                     f"Rolling back and continuing. Error: {e!s}"
                 )
                 await session.rollback()
-                # Don't fail the entire task - some documents may have been successfully indexed
             else:
                 raise
 
-        # Build warning message if there were issues
         warning_parts = []
         if duplicate_content_count > 0:
             warning_parts.append(f"{duplicate_content_count} duplicate")
@@ -555,7 +378,6 @@ async def index_google_gmail_messages(
 
         total_processed = documents_indexed
 
-        # Log success
         await task_logger.log_task_success(
             log_entry,
             f"Successfully completed Google Gmail indexing for connector {connector_id}",

surfsense_backend/app/tasks/connector_indexers/jira_indexer.py

@@ -1,49 +1,80 @@
-"""
-Jira connector indexer.
-
-Provides real-time document status updates during indexing using a two-phase approach:
-- Phase 1: Create all documents with PENDING status (visible in UI immediately)
-- Phase 2: Process each document one by one (PENDING → PROCESSING → READY/FAILED)
-"""
+"""Jira connector indexer using the unified parallel indexing pipeline."""
 
 import contextlib
-import time
 from collections.abc import Awaitable, Callable
-from datetime import datetime
 
 from sqlalchemy.exc import SQLAlchemyError
 from sqlalchemy.ext.asyncio import AsyncSession
 
 from app.connectors.jira_history import JiraHistoryConnector
-from app.db import Document, DocumentStatus, DocumentType, SearchSourceConnectorType
+from app.db import DocumentType, SearchSourceConnectorType
+from app.indexing_pipeline.connector_document import ConnectorDocument
+from app.indexing_pipeline.document_hashing import compute_content_hash
+from app.indexing_pipeline.indexing_pipeline_service import IndexingPipelineService
 from app.services.llm_service import get_user_long_context_llm
 from app.services.task_logging_service import TaskLoggingService
-from app.utils.document_converters import (
-    create_document_chunks,
-    embed_text,
-    generate_content_hash,
-    generate_document_summary,
-    generate_unique_identifier_hash,
-)
 
 from .base import (
     calculate_date_range,
-    check_document_by_unique_identifier,
     check_duplicate_document_by_hash,
     get_connector_by_id,
-    get_current_timestamp,
     logger,
-    safe_set_chunks,
     update_connector_last_indexed,
 )
 
-# Type hint for heartbeat callback
 HeartbeatCallbackType = Callable[[int], Awaitable[None]]
-
-# Heartbeat interval in seconds - update notification every 30 seconds
 HEARTBEAT_INTERVAL_SECONDS = 30
 
 
+def _build_connector_doc(
+    issue: dict,
+    formatted_issue: dict,
+    issue_content: str,
+    *,
+    connector_id: int,
+    search_space_id: int,
+    user_id: str,
+    enable_summary: bool,
+) -> ConnectorDocument:
+    """Map a raw Jira issue dict to a ConnectorDocument."""
+    issue_id = issue.get("key", "")
+    issue_identifier = issue.get("key", "")
+    issue_title = issue.get("id", "")
+    state = formatted_issue.get("status", "Unknown")
+    priority = formatted_issue.get("priority", "Unknown")
+    comment_count = len(formatted_issue.get("comments", []))
+
+    metadata = {
+        "issue_id": issue_id,
+        "issue_identifier": issue_identifier,
+        "issue_title": issue_title,
+        "state": state,
+        "priority": priority,
+        "comment_count": comment_count,
+        "connector_id": connector_id,
+        "document_type": "Jira Issue",
+        "connector_type": "Jira",
+    }
+
+    fallback_summary = (
+        f"Jira Issue {issue_identifier}: {issue_title}\n\n"
+        f"Status: {state}\n\n{issue_content}"
+    )
+
+    return ConnectorDocument(
+        title=f"{issue_identifier}: {issue_title}",
+        source_markdown=issue_content,
+        unique_id=issue_id,
+        document_type=DocumentType.JIRA_CONNECTOR,
+        search_space_id=search_space_id,
+        connector_id=connector_id,
+        created_by_id=user_id,
+        should_summarize=enable_summary,
+        fallback_summary=fallback_summary,
+        metadata=metadata,
+    )
+
+
 async def index_jira_issues(
     session: AsyncSession,
     connector_id: int,
@@ -53,26 +84,9 @@ async def index_jira_issues(
     end_date: str | None = None,
     update_last_indexed: bool = True,
     on_heartbeat_callback: HeartbeatCallbackType | None = None,
-) -> tuple[int, str | None]:
-    """
-    Index Jira issues and comments.
-
-    Args:
-        session: Database session
-        connector_id: ID of the Jira connector
-        search_space_id: ID of the search space to store documents in
-        user_id: User ID
-        start_date: Start date for indexing (YYYY-MM-DD format)
-        end_date: End date for indexing (YYYY-MM-DD format)
-        update_last_indexed: Whether to update the last_indexed_at timestamp (default: True)
-        on_heartbeat_callback: Optional callback to update notification during long-running indexing.
-
-    Returns:
-        Tuple containing (number of documents indexed, error message or None)
-    """
+) -> tuple[int, int, str | None]:
+    """Index Jira issues and comments."""
     task_logger = TaskLoggingService(session, search_space_id)
-
-    # Log task start
     log_entry = await task_logger.log_task_start(
         task_name="jira_issues_indexing",
         source="connector_indexing_task",
@@ -86,7 +100,6 @@ async def index_jira_issues(
     )
 
     try:
-        # Get the connector from the database
         connector = await get_connector_by_id(
             session, connector_id, SearchSourceConnectorType.JIRA_CONNECTOR
         )
@@ -98,24 +111,15 @@ async def index_jira_issues(
                 "Connector not found",
                 {"error_type": "ConnectorNotFound"},
             )
-            return 0, f"Connector with ID {connector_id} not found"
+            return 0, 0, f"Connector with ID {connector_id} not found"
 
-        # Initialize Jira client with internal refresh capability
-        # Token refresh will happen automatically when needed
         await task_logger.log_task_progress(
             log_entry,
             f"Initializing Jira client for connector {connector_id}",
             {"stage": "client_initialization"},
         )
-
-        logger.info(f"Initializing Jira client for connector {connector_id}")
-
-        # Create connector with session and connector_id for internal refresh
-        # Token refresh will happen automatically when needed
         jira_client = JiraHistoryConnector(session=session, connector_id=connector_id)
 
-        # Calculate date range
-        # Handle "undefined" strings from frontend
         if start_date == "undefined" or start_date == "":
             start_date = None
         if end_date == "undefined" or end_date == "":
@@ -135,19 +139,14 @@ async def index_jira_issues(
             },
         )
 
-        # Get issues within date range
         try:
             issues, error = await jira_client.get_issues_by_date_range(
                 start_date=start_date_str, end_date=end_date_str, include_comments=True
             )
 
             if error:
-                # Don't treat "No issues found" as an error that should stop indexing
                 if "No issues found" in error:
                     logger.info(f"No Jira issues found: {error}")
-                    logger.info(
-                        "No issues found is not a critical error, continuing with update"
-                    )
                     if update_last_indexed:
                         await update_connector_last_indexed(
                             session, connector, update_last_indexed
@@ -162,7 +161,8 @@ async def index_jira_issues(
                         f"No Jira issues found in date range {start_date_str} to {end_date_str}",
                         {"issues_found": 0},
                     )
-                    return 0, None
+                    await jira_client.close()
+                    return 0, 0, None
                 else:
                     logger.error(f"Failed to get Jira issues: {error}")
                     await task_logger.log_task_failure(
@@ -171,29 +171,30 @@ async def index_jira_issues(
                         "API Error",
                         {"error_type": "APIError"},
                     )
-                    return 0, f"Failed to get Jira issues: {error}"
+                    await jira_client.close()
+                    return 0, 0, f"Failed to get Jira issues: {error}"
 
             logger.info(f"Retrieved {len(issues)} issues from Jira API")
 
         except Exception as e:
             logger.error(f"Error fetching Jira issues: {e!s}", exc_info=True)
-            return 0, f"Error fetching Jira issues: {e!s}"
+            await jira_client.close()
+            return 0, 0, f"Error fetching Jira issues: {e!s}"
 
-        # =======================================================================
-        # PHASE 1: Analyze all issues, create pending documents
-        # This makes ALL documents visible in the UI immediately with pending status
-        # =======================================================================
-        documents_indexed = 0
+        if not issues:
+            logger.info("No Jira issues found for the specified date range")
+            if update_last_indexed:
+                await update_connector_last_indexed(
+                    session, connector, update_last_indexed
+                )
+                await session.commit()
+            await jira_client.close()
+            return 0, 0, None
+
+        connector_docs: list[ConnectorDocument] = []
         documents_skipped = 0
-        documents_failed = 0
         duplicate_content_count = 0
 
-        # Heartbeat tracking - update notification periodically to prevent appearing stuck
-        last_heartbeat_time = time.time()
-
-        issues_to_process = []  # List of dicts with document and issue data
-        new_documents_created = False
-
         for issue in issues:
             try:
                 issue_id = issue.get("key")
@@ -207,10 +208,7 @@ async def index_jira_issues(
                     documents_skipped += 1
                     continue
 
-                # Format the issue for better readability
                 formatted_issue = jira_client.format_issue(issue)
-
-                # Convert to markdown
                 issue_content = jira_client.format_issue_to_markdown(formatted_issue)
 
                 if not issue_content:
@@ -220,53 +218,19 @@ async def index_jira_issues(
                     documents_skipped += 1
                     continue
 
-                # Generate unique identifier hash for this Jira issue
-                unique_identifier_hash = generate_unique_identifier_hash(
-                    DocumentType.JIRA_CONNECTOR, issue_id, search_space_id
+                doc = _build_connector_doc(
+                    issue,
+                    formatted_issue,
+                    issue_content,
+                    connector_id=connector_id,
+                    search_space_id=search_space_id,
+                    user_id=user_id,
+                    enable_summary=connector.enable_summary,
                 )
 
-                # Generate content hash
-                content_hash = generate_content_hash(issue_content, search_space_id)
-
-                # Check if document with this unique identifier already exists
-                existing_document = await check_document_by_unique_identifier(
-                    session, unique_identifier_hash
-                )
-
-                comment_count = len(formatted_issue.get("comments", []))
-
-                if existing_document:
-                    # Document exists - check if content has changed
-                    if existing_document.content_hash == content_hash:
-                        # Ensure status is ready (might have been stuck in processing/pending)
-                        if not DocumentStatus.is_state(
-                            existing_document.status, DocumentStatus.READY
-                        ):
-                            existing_document.status = DocumentStatus.ready()
-                        documents_skipped += 1
-                        continue
-
-                    # Queue existing document for update (will be set to processing in Phase 2)
-                    issues_to_process.append(
-                        {
-                            "document": existing_document,
-                            "is_new": False,
-                            "issue_content": issue_content,
-                            "content_hash": content_hash,
-                            "issue_id": issue_id,
-                            "issue_identifier": issue_identifier,
-                            "issue_title": issue_title,
-                            "formatted_issue": formatted_issue,
-                            "comment_count": comment_count,
-                        }
-                    )
-                    continue
-
-                # Document doesn't exist by unique_identifier_hash
-                # Check if a document with the same content_hash exists (from another connector)
                 with session.no_autoflush:
                     duplicate_by_content = await check_duplicate_document_by_hash(
-                        session, content_hash
+                        session, compute_content_hash(doc)
                     )
 
                 if duplicate_by_content:
@@ -279,160 +243,37 @@ async def index_jira_issues(
                     documents_skipped += 1
                     continue
 
-                # Create new document with PENDING status (visible in UI immediately)
-                document = Document(
-                    search_space_id=search_space_id,
-                    title=f"{issue_identifier}: {issue_title}",
-                    document_type=DocumentType.JIRA_CONNECTOR,
-                    document_metadata={
-                        "issue_id": issue_id,
-                        "issue_identifier": issue_identifier,
-                        "issue_title": issue_title,
-                        "state": formatted_issue.get("status", "Unknown"),
-                        "comment_count": comment_count,
-                        "connector_id": connector_id,
-                    },
-                    content="Pending...",  # Placeholder until processed
-                    content_hash=unique_identifier_hash,  # Temporary unique value - updated when ready
-                    unique_identifier_hash=unique_identifier_hash,
-                    embedding=None,
-                    chunks=[],  # Empty at creation - safe for async
-                    status=DocumentStatus.pending(),  # Pending until processing starts
-                    updated_at=get_current_timestamp(),
-                    created_by_id=user_id,
-                    connector_id=connector_id,
-                )
-                session.add(document)
-                new_documents_created = True
-
-                issues_to_process.append(
-                    {
-                        "document": document,
-                        "is_new": True,
-                        "issue_content": issue_content,
-                        "content_hash": content_hash,
-                        "issue_id": issue_id,
-                        "issue_identifier": issue_identifier,
-                        "issue_title": issue_title,
-                        "formatted_issue": formatted_issue,
-                        "comment_count": comment_count,
-                    }
-                )
-
-            except Exception as e:
-                logger.error(f"Error in Phase 1 for issue: {e!s}", exc_info=True)
-                documents_failed += 1
-                continue
-
-        # Commit all pending documents - they all appear in UI now
-        if new_documents_created:
-            logger.info(
-                f"Phase 1: Committing {len([i for i in issues_to_process if i['is_new']])} pending documents"
-            )
-            await session.commit()
-
-        # =======================================================================
-        # PHASE 2: Process each document one by one
-        # Each document transitions: pending → processing → ready/failed
-        # =======================================================================
-        logger.info(f"Phase 2: Processing {len(issues_to_process)} documents")
-
-        for item in issues_to_process:
-            # Send heartbeat periodically
-            if on_heartbeat_callback:
-                current_time = time.time()
-                if current_time - last_heartbeat_time >= HEARTBEAT_INTERVAL_SECONDS:
-                    await on_heartbeat_callback(documents_indexed)
-                    last_heartbeat_time = current_time
-
-            document = item["document"]
-            try:
-                # Set to PROCESSING and commit - shows "processing" in UI for THIS document only
-                document.status = DocumentStatus.processing()
-                await session.commit()
-
-                # Heavy processing (LLM, embeddings, chunks)
-                user_llm = await get_user_long_context_llm(
-                    session, user_id, search_space_id
-                )
-
-                if user_llm and connector.enable_summary:
-                    document_metadata = {
-                        "issue_key": item["issue_identifier"],
-                        "issue_title": item["issue_title"],
-                        "status": item["formatted_issue"].get("status", "Unknown"),
-                        "priority": item["formatted_issue"].get("priority", "Unknown"),
-                        "comment_count": item["comment_count"],
-                        "document_type": "Jira Issue",
-                        "connector_type": "Jira",
-                    }
-                    (
-                        summary_content,
-                        summary_embedding,
-                    ) = await generate_document_summary(
-                        item["issue_content"], user_llm, document_metadata
-                    )
-                else:
-                    summary_content = f"Jira Issue {item['issue_identifier']}: {item['issue_title']}\n\n{item['issue_content']}"
-                    summary_embedding = embed_text(summary_content)
-
-                # Process chunks - using the full issue content with comments
-                chunks = await create_document_chunks(item["issue_content"])
-
-                # Update document to READY with actual content
-                document.title = f"{item['issue_identifier']}: {item['issue_title']}"
-                document.content = summary_content
-                document.content_hash = item["content_hash"]
-                document.embedding = summary_embedding
-                document.document_metadata = {
-                    "issue_id": item["issue_id"],
-                    "issue_identifier": item["issue_identifier"],
-                    "issue_title": item["issue_title"],
-                    "state": item["formatted_issue"].get("status", "Unknown"),
-                    "comment_count": item["comment_count"],
-                    "indexed_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
-                    "connector_id": connector_id,
-                }
-                await safe_set_chunks(session, document, chunks)
-                document.updated_at = get_current_timestamp()
-                document.status = DocumentStatus.ready()
-
-                documents_indexed += 1
-
-                # Batch commit every 10 documents (for ready status updates)
-                if documents_indexed % 10 == 0:
-                    logger.info(
-                        f"Committing batch: {documents_indexed} Jira issues processed so far"
-                    )
-                    await session.commit()
+                connector_docs.append(doc)
 
             except Exception as e:
                 logger.error(
-                    f"Error processing issue {item.get('issue_identifier', 'Unknown')}: {e!s}",
+                    f"Error building ConnectorDocument for issue {issue_identifier}: {e!s}",
                     exc_info=True,
                 )
-                # Mark document as failed with reason (visible in UI)
-                try:
-                    document.status = DocumentStatus.failed(str(e))
-                    document.updated_at = get_current_timestamp()
-                except Exception as status_error:
-                    logger.error(
-                        f"Failed to update document status to failed: {status_error}"
-                    )
-                documents_failed += 1
-                continue  # Skip this issue and continue with others
+                documents_skipped += 1
+                continue
+
+        pipeline = IndexingPipelineService(session)
+        await pipeline.migrate_legacy_docs(connector_docs)
+
+        async def _get_llm(s: AsyncSession):
+            return await get_user_long_context_llm(s, user_id, search_space_id)
+
+        _, documents_indexed, documents_failed = await pipeline.index_batch_parallel(
+            connector_docs,
+            _get_llm,
+            max_concurrency=3,
+            on_heartbeat=on_heartbeat_callback,
+            heartbeat_interval=HEARTBEAT_INTERVAL_SECONDS,
+        )
 
-        # CRITICAL: Always update timestamp (even if 0 documents indexed) so Zero syncs
-        # This ensures the UI shows "Last indexed" instead of "Never indexed"
         await update_connector_last_indexed(session, connector, update_last_indexed)
 
-        # Final commit to ensure all documents are persisted (safety net)
         logger.info(f"Final commit: Total {documents_indexed} Jira issues processed")
         try:
             await session.commit()
             logger.info("Successfully committed all JIRA document changes to database")
         except Exception as e:
-            # Handle any remaining integrity errors gracefully (race conditions, etc.)
             if (
                 "duplicate key value violates unique constraint" in str(e).lower()
                 or "uniqueviolationerror" in str(e).lower()
@@ -447,7 +288,6 @@ async def index_jira_issues(
             else:
                 raise
 
-        # Build warning message if there were issues
         warning_parts = []
         if duplicate_content_count > 0:
             warning_parts.append(f"{duplicate_content_count} duplicate")
@@ -455,7 +295,6 @@ async def index_jira_issues(
             warning_parts.append(f"{documents_failed} failed")
         warning_message = ", ".join(warning_parts) if warning_parts else None
 
-        # Log success
         await task_logger.log_task_success(
             log_entry,
             f"Successfully completed JIRA indexing for connector {connector_id}",
@@ -466,17 +305,13 @@ async def index_jira_issues(
                 "duplicate_content_count": duplicate_content_count,
             },
         )
-
         logger.info(
             f"JIRA indexing completed: {documents_indexed} ready, "
             f"{documents_skipped} skipped, {documents_failed} failed "
             f"({duplicate_content_count} duplicate content)"
         )
-
-        # Clean up the connector
         await jira_client.close()
-
-        return documents_indexed, warning_message
+        return documents_indexed, documents_skipped, warning_message
 
     except SQLAlchemyError as db_error:
         await session.rollback()
@@ -487,11 +322,10 @@ async def index_jira_issues(
             {"error_type": "SQLAlchemyError"},
         )
         logger.error(f"Database error: {db_error!s}", exc_info=True)
-        # Clean up the connector in case of error
         if "jira_client" in locals():
             with contextlib.suppress(Exception):
                 await jira_client.close()
-        return 0, f"Database error: {db_error!s}"
+        return 0, 0, f"Database error: {db_error!s}"
     except Exception as e:
         await session.rollback()
         await task_logger.log_task_failure(
@@ -501,8 +335,7 @@ async def index_jira_issues(
             {"error_type": type(e).__name__},
         )
         logger.error(f"Failed to index JIRA issues: {e!s}", exc_info=True)
-        # Clean up the connector in case of error
         if "jira_client" in locals():
             with contextlib.suppress(Exception):
                 await jira_client.close()
-        return 0, f"Failed to index JIRA issues: {e!s}"
+        return 0, 0, f"Failed to index JIRA issues: {e!s}"

surfsense_backend/app/tasks/connector_indexers/linear_indexer.py

@@ -1,48 +1,84 @@
 """
 Linear connector indexer.
 
-Implements 2-phase document status updates for real-time UI feedback:
-- Phase 1: Create all documents with 'pending' status (visible in UI immediately)
-- Phase 2: Process each document: pending → processing → ready/failed
+Uses the shared IndexingPipelineService for document deduplication,
+summarization, chunking, and embedding with bounded parallel indexing.
 """
 
-import time
 from collections.abc import Awaitable, Callable
-from datetime import datetime
 
 from sqlalchemy.exc import SQLAlchemyError
 from sqlalchemy.ext.asyncio import AsyncSession
 
 from app.connectors.linear_connector import LinearConnector
-from app.db import Document, DocumentStatus, DocumentType, SearchSourceConnectorType
+from app.db import DocumentType, SearchSourceConnectorType
+from app.indexing_pipeline.connector_document import ConnectorDocument
+from app.indexing_pipeline.document_hashing import compute_content_hash
+from app.indexing_pipeline.indexing_pipeline_service import IndexingPipelineService
 from app.services.llm_service import get_user_long_context_llm
 from app.services.task_logging_service import TaskLoggingService
-from app.utils.document_converters import (
-    create_document_chunks,
-    embed_text,
-    generate_content_hash,
-    generate_document_summary,
-    generate_unique_identifier_hash,
-)
 
 from .base import (
     calculate_date_range,
-    check_document_by_unique_identifier,
     check_duplicate_document_by_hash,
     get_connector_by_id,
-    get_current_timestamp,
     logger,
-    safe_set_chunks,
     update_connector_last_indexed,
 )
 
-# Type hint for heartbeat callback
 HeartbeatCallbackType = Callable[[int], Awaitable[None]]
-
-# Heartbeat interval in seconds - update notification every 30 seconds
 HEARTBEAT_INTERVAL_SECONDS = 30
 
 
+def _build_connector_doc(
+    issue: dict,
+    formatted_issue: dict,
+    issue_content: str,
+    *,
+    connector_id: int,
+    search_space_id: int,
+    user_id: str,
+    enable_summary: bool,
+) -> ConnectorDocument:
+    """Map a raw Linear issue dict to a ConnectorDocument."""
+    issue_id = issue.get("id", "")
+    issue_identifier = issue.get("identifier", "")
+    issue_title = issue.get("title", "")
+    state = formatted_issue.get("state", "Unknown")
+    priority = formatted_issue.get("priority", "Unknown")
+    comment_count = len(formatted_issue.get("comments", []))
+
+    metadata = {
+        "issue_id": issue_id,
+        "issue_identifier": issue_identifier,
+        "issue_title": issue_title,
+        "state": state,
+        "priority": priority,
+        "comment_count": comment_count,
+        "connector_id": connector_id,
+        "document_type": "Linear Issue",
+        "connector_type": "Linear",
+    }
+
+    fallback_summary = (
+        f"Linear Issue {issue_identifier}: {issue_title}\n\n"
+        f"Status: {state}\n\n{issue_content}"
+    )
+
+    return ConnectorDocument(
+        title=f"{issue_identifier}: {issue_title}",
+        source_markdown=issue_content,
+        unique_id=issue_id,
+        document_type=DocumentType.LINEAR_CONNECTOR,
+        search_space_id=search_space_id,
+        connector_id=connector_id,
+        created_by_id=user_id,
+        should_summarize=enable_summary,
+        fallback_summary=fallback_summary,
+        metadata=metadata,
+    )
+
+
 async def index_linear_issues(
     session: AsyncSession,
     connector_id: int,
@@ -52,26 +88,15 @@ async def index_linear_issues(
     end_date: str | None = None,
     update_last_indexed: bool = True,
     on_heartbeat_callback: HeartbeatCallbackType | None = None,
-) -> tuple[int, str | None]:
+) -> tuple[int, int, str | None]:
     """
     Index Linear issues and comments.
 
-    Args:
-        session: Database session
-        connector_id: ID of the Linear connector
-        search_space_id: ID of the search space to store documents in
-        user_id: ID of the user
-        start_date: Start date for indexing (YYYY-MM-DD format)
-        end_date: End date for indexing (YYYY-MM-DD format)
-        update_last_indexed: Whether to update the last_indexed_at timestamp (default: True)
-        on_heartbeat_callback: Optional callback to update notification during long-running indexing.
-
     Returns:
-        Tuple containing (number of documents indexed, error message or None)
+        Tuple of (indexed_count, skipped_count, warning_or_error_message)
     """
     task_logger = TaskLoggingService(session, search_space_id)
 
-    # Log task start
     log_entry = await task_logger.log_task_start(
         task_name="linear_issues_indexing",
         source="connector_indexing_task",
@@ -85,7 +110,7 @@ async def index_linear_issues(
     )
 
     try:
-        # Get the connector
+        # ── Connector lookup ──────────────────────────────────────────
         await task_logger.log_task_progress(
             log_entry,
             f"Retrieving Linear connector {connector_id} from database",
@@ -104,11 +129,11 @@ async def index_linear_issues(
                 {"error_type": "ConnectorNotFound"},
             )
             return (
+                0,
                 0,
                 f"Connector with ID {connector_id} not found or is not a Linear connector",
             )
 
-        # Check if access_token exists (support both new OAuth format and old API key format)
         if not connector.config.get("access_token") and not connector.config.get(
             "LINEAR_API_KEY"
         ):
@@ -118,26 +143,22 @@ async def index_linear_issues(
                 "Missing Linear access token",
                 {"error_type": "MissingToken"},
             )
-            return 0, "Linear access token not found in connector config"
+            return 0, 0, "Linear access token not found in connector config"
 
-        # Initialize Linear client with internal refresh capability
+        # ── Client init ───────────────────────────────────────────────
         await task_logger.log_task_progress(
             log_entry,
             f"Initializing Linear client for connector {connector_id}",
             {"stage": "client_initialization"},
         )
 
-        # Create connector with session and connector_id for internal refresh
-        # Token refresh will happen automatically when needed
         linear_client = LinearConnector(session=session, connector_id=connector_id)
 
-        # Handle 'undefined' string from frontend (treat as None)
         if start_date == "undefined" or start_date == "":
             start_date = None
         if end_date == "undefined" or end_date == "":
             end_date = None
 
-        # Calculate date range
         start_date_str, end_date_str = calculate_date_range(
             connector, start_date, end_date, default_days_back=365
         )
@@ -154,37 +175,34 @@ async def index_linear_issues(
             },
         )
 
-        # Get issues within date range
+        # ── Fetch issues ──────────────────────────────────────────────
         try:
             issues, error = await linear_client.get_issues_by_date_range(
-                start_date=start_date_str, end_date=end_date_str, include_comments=True
+                start_date=start_date_str,
+                end_date=end_date_str,
+                include_comments=True,
             )
 
             if error:
-                # Don't treat "No issues found" as an error that should stop indexing
                 if "No issues found" in error:
                     logger.info(f"No Linear issues found: {error}")
-                    logger.info(
-                        "No issues found is not a critical error, continuing with update"
-                    )
                     if update_last_indexed:
                         await update_connector_last_indexed(
                             session, connector, update_last_indexed
                         )
                         await session.commit()
-                        logger.info(
-                            f"Updated last_indexed_at to {connector.last_indexed_at} despite no issues found"
-                        )
-                    return 0, None
+                    return 0, 0, None
                 else:
                     logger.error(f"Failed to get Linear issues: {error}")
-                    return 0, f"Failed to get Linear issues: {error}"
+                    return 0, 0, f"Failed to get Linear issues: {error}"
 
             logger.info(f"Retrieved {len(issues)} issues from Linear API")
 
         except Exception as e:
-            logger.error(f"Exception when calling Linear API: {e!s}", exc_info=True)
-            return 0, f"Failed to get Linear issues: {e!s}"
+            logger.error(
+                f"Exception when calling Linear API: {e!s}", exc_info=True
+            )
+            return 0, 0, f"Failed to get Linear issues: {e!s}"
 
         if not issues:
             logger.info("No Linear issues found for the specified date range")
@@ -193,19 +211,12 @@ async def index_linear_issues(
                     session, connector, update_last_indexed
                 )
                 await session.commit()
-                logger.info(
-                    f"Updated last_indexed_at to {connector.last_indexed_at} despite no issues found"
-                )
-            return 0, None  # Return None instead of error message when no issues found
+            return 0, 0, None
 
-        # Track the number of documents indexed
-        documents_indexed = 0
+        # ── Build ConnectorDocuments ──────────────────────────────────
+        connector_docs: list[ConnectorDocument] = []
         documents_skipped = 0
-        documents_failed = 0  # Track issues that failed processing
-        skipped_issues = []
-
-        # Heartbeat tracking - update notification periodically to prevent appearing stuck
-        last_heartbeat_time = time.time()
+        duplicate_content_count = 0
 
         await task_logger.log_task_progress(
             log_entry,
@@ -213,13 +224,6 @@ async def index_linear_issues(
             {"stage": "process_issues", "total_issues": len(issues)},
         )
 
-        # =======================================================================
-        # PHASE 1: Analyze all issues, create pending documents
-        # This makes ALL documents visible in the UI immediately with pending status
-        # =======================================================================
-        issues_to_process = []  # List of dicts with document and issue data
-        new_documents_created = False
-
         for issue in issues:
             try:
                 issue_id = issue.get("id", "")
@@ -230,271 +234,102 @@ async def index_linear_issues(
                     logger.warning(
                         f"Skipping issue with missing ID or title: {issue_id or 'Unknown'}"
                     )
-                    skipped_issues.append(
-                        f"{issue_identifier or 'Unknown'} (missing data)"
-                    )
                     documents_skipped += 1
                     continue
 
-                # Format the issue first to get well-structured data
                 formatted_issue = linear_client.format_issue(issue)
-
-                # Convert issue to markdown format
-                issue_content = linear_client.format_issue_to_markdown(formatted_issue)
+                issue_content = linear_client.format_issue_to_markdown(
+                    formatted_issue
+                )
 
                 if not issue_content:
                     logger.warning(
                         f"Skipping issue with no content: {issue_identifier} - {issue_title}"
                     )
-                    skipped_issues.append(f"{issue_identifier} (no content)")
                     documents_skipped += 1
                     continue
 
-                # Generate unique identifier hash for this Linear issue
-                unique_identifier_hash = generate_unique_identifier_hash(
-                    DocumentType.LINEAR_CONNECTOR, issue_id, search_space_id
-                )
-
-                # Generate content hash
-                content_hash = generate_content_hash(issue_content, search_space_id)
-
-                # Check if document with this unique identifier already exists
-                existing_document = await check_document_by_unique_identifier(
-                    session, unique_identifier_hash
-                )
-
-                state = formatted_issue.get("state", "Unknown")
-                description = formatted_issue.get("description", "")
-                comment_count = len(formatted_issue.get("comments", []))
-                priority = formatted_issue.get("priority", "Unknown")
-
-                if existing_document:
-                    # Document exists - check if content has changed
-                    if existing_document.content_hash == content_hash:
-                        # Ensure status is ready (might have been stuck in processing/pending)
-                        if not DocumentStatus.is_state(
-                            existing_document.status, DocumentStatus.READY
-                        ):
-                            existing_document.status = DocumentStatus.ready()
-                        logger.info(
-                            f"Document for Linear issue {issue_identifier} unchanged. Skipping."
-                        )
-                        documents_skipped += 1
-                        continue
-
-                    # Queue existing document for update (will be set to processing in Phase 2)
-                    issues_to_process.append(
-                        {
-                            "document": existing_document,
-                            "is_new": False,
-                            "issue_content": issue_content,
-                            "content_hash": content_hash,
-                            "issue_id": issue_id,
-                            "issue_identifier": issue_identifier,
-                            "issue_title": issue_title,
-                            "state": state,
-                            "description": description,
-                            "comment_count": comment_count,
-                            "priority": priority,
-                        }
-                    )
-                    continue
-
-                # Document doesn't exist by unique_identifier_hash
-                # Check if a document with the same content_hash exists (from another connector)
-                with session.no_autoflush:
-                    duplicate_by_content = await check_duplicate_document_by_hash(
-                        session, content_hash
-                    )
-
-                if duplicate_by_content:
-                    logger.info(
-                        f"Linear issue {issue_identifier} already indexed by another connector "
-                        f"(existing document ID: {duplicate_by_content.id}, "
-                        f"type: {duplicate_by_content.document_type}). Skipping."
-                    )
-                    documents_skipped += 1
-                    continue
-
-                # Create new document with PENDING status (visible in UI immediately)
-                document = Document(
-                    search_space_id=search_space_id,
-                    title=f"{issue_identifier}: {issue_title}",
-                    document_type=DocumentType.LINEAR_CONNECTOR,
-                    document_metadata={
-                        "issue_id": issue_id,
-                        "issue_identifier": issue_identifier,
-                        "issue_title": issue_title,
-                        "state": state,
-                        "comment_count": comment_count,
-                        "connector_id": connector_id,
-                    },
-                    content="Pending...",  # Placeholder until processed
-                    content_hash=unique_identifier_hash,  # Temporary unique value - updated when ready
-                    unique_identifier_hash=unique_identifier_hash,
-                    embedding=None,
-                    chunks=[],  # Empty at creation - safe for async
-                    status=DocumentStatus.pending(),  # Pending until processing starts
-                    updated_at=get_current_timestamp(),
-                    created_by_id=user_id,
+                doc = _build_connector_doc(
+                    issue,
+                    formatted_issue,
+                    issue_content,
                     connector_id=connector_id,
-                )
-                session.add(document)
-                new_documents_created = True
-
-                issues_to_process.append(
-                    {
-                        "document": document,
-                        "is_new": True,
-                        "issue_content": issue_content,
-                        "content_hash": content_hash,
-                        "issue_id": issue_id,
-                        "issue_identifier": issue_identifier,
-                        "issue_title": issue_title,
-                        "state": state,
-                        "description": description,
-                        "comment_count": comment_count,
-                        "priority": priority,
-                    }
+                    search_space_id=search_space_id,
+                    user_id=user_id,
+                    enable_summary=connector.enable_summary,
                 )
 
-            except Exception as e:
-                logger.error(f"Error in Phase 1 for issue: {e!s}", exc_info=True)
-                documents_failed += 1
+                with session.no_autoflush:
+                    duplicate = await check_duplicate_document_by_hash(
+                        session, compute_content_hash(doc)
+                    )
+                if duplicate:
+                    logger.info(
+                        f"Linear issue {doc.title} already indexed by another connector "
+                        f"(existing document ID: {duplicate.id}, "
+                        f"type: {duplicate.document_type}). Skipping."
+                    )
+                    duplicate_content_count += 1
+                    documents_skipped += 1
                     continue
 
-        # Commit all pending documents - they all appear in UI now
-        if new_documents_created:
-            logger.info(
-                f"Phase 1: Committing {len([i for i in issues_to_process if i['is_new']])} pending documents"
-            )
-            await session.commit()
-
-        # =======================================================================
-        # PHASE 2: Process each document one by one
-        # Each document transitions: pending → processing → ready/failed
-        # =======================================================================
-        logger.info(f"Phase 2: Processing {len(issues_to_process)} documents")
-
-        for item in issues_to_process:
-            # Send heartbeat periodically
-            if on_heartbeat_callback:
-                current_time = time.time()
-                if current_time - last_heartbeat_time >= HEARTBEAT_INTERVAL_SECONDS:
-                    await on_heartbeat_callback(documents_indexed)
-                    last_heartbeat_time = current_time
-
-            document = item["document"]
-            try:
-                # Set to PROCESSING and commit - shows "processing" in UI for THIS document only
-                document.status = DocumentStatus.processing()
-                await session.commit()
-
-                # Heavy processing (LLM, embeddings, chunks)
-                user_llm = await get_user_long_context_llm(
-                    session, user_id, search_space_id
-                )
-
-                if user_llm and connector.enable_summary:
-                    document_metadata_for_summary = {
-                        "issue_id": item["issue_identifier"],
-                        "issue_title": item["issue_title"],
-                        "state": item["state"],
-                        "priority": item["priority"],
-                        "comment_count": item["comment_count"],
-                        "document_type": "Linear Issue",
-                        "connector_type": "Linear",
-                    }
-                    (
-                        summary_content,
-                        summary_embedding,
-                    ) = await generate_document_summary(
-                        item["issue_content"], user_llm, document_metadata_for_summary
-                    )
-                else:
-                    summary_content = f"Linear Issue {item['issue_identifier']}: {item['issue_title']}\n\nStatus: {item['state']}\n\n{item['issue_content']}"
-                    summary_embedding = embed_text(summary_content)
-
-                chunks = await create_document_chunks(item["issue_content"])
-
-                # Update document to READY with actual content
-                document.title = f"{item['issue_identifier']}: {item['issue_title']}"
-                document.content = summary_content
-                document.content_hash = item["content_hash"]
-                document.embedding = summary_embedding
-                document.document_metadata = {
-                    "issue_id": item["issue_id"],
-                    "issue_identifier": item["issue_identifier"],
-                    "issue_title": item["issue_title"],
-                    "state": item["state"],
-                    "comment_count": item["comment_count"],
-                    "indexed_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
-                    "connector_id": connector_id,
-                }
-                await safe_set_chunks(session, document, chunks)
-                document.updated_at = get_current_timestamp()
-                document.status = DocumentStatus.ready()
-
-                documents_indexed += 1
-
-                # Batch commit every 10 documents (for ready status updates)
-                if documents_indexed % 10 == 0:
-                    logger.info(
-                        f"Committing batch: {documents_indexed} Linear issues processed so far"
-                    )
-                    await session.commit()
+                connector_docs.append(doc)
 
             except Exception as e:
                 logger.error(
-                    f"Error processing issue {item.get('issue_identifier', 'Unknown')}: {e!s}",
+                    f"Error building ConnectorDocument for issue: {e!s}",
                     exc_info=True,
                 )
-                # Mark document as failed with reason (visible in UI)
-                try:
-                    document.status = DocumentStatus.failed(str(e))
-                    document.updated_at = get_current_timestamp()
-                except Exception as status_error:
-                    logger.error(
-                        f"Failed to update document status to failed: {status_error}"
-                    )
-                skipped_issues.append(
-                    f"{item.get('issue_identifier', 'Unknown')} (processing error)"
-                )
-                documents_failed += 1
+                documents_skipped += 1
                 continue
 
-        # CRITICAL: Always update timestamp (even if 0 documents indexed) so Zero syncs
+        # ── Pipeline: migrate legacy docs + parallel index ────────────
+        pipeline = IndexingPipelineService(session)
+
+        await pipeline.migrate_legacy_docs(connector_docs)
+
+        async def _get_llm(s):
+            return await get_user_long_context_llm(s, user_id, search_space_id)
+
+        _, documents_indexed, documents_failed = await pipeline.index_batch_parallel(
+            connector_docs,
+            _get_llm,
+            max_concurrency=3,
+            on_heartbeat=on_heartbeat_callback,
+            heartbeat_interval=HEARTBEAT_INTERVAL_SECONDS,
+        )
+
+        # ── Finalize ──────────────────────────────────────────────────
         await update_connector_last_indexed(session, connector, update_last_indexed)
 
-        # Final commit for any remaining documents not yet committed in batches
-        logger.info(f"Final commit: Total {documents_indexed} Linear issues processed")
+        logger.info(
+            f"Final commit: Total {documents_indexed} Linear issues processed"
+        )
         try:
             await session.commit()
             logger.info(
                 "Successfully committed all Linear document changes to database"
             )
         except Exception as e:
-            # Handle any remaining integrity errors gracefully (race conditions, etc.)
             if (
                 "duplicate key value violates unique constraint" in str(e).lower()
                 or "uniqueviolationerror" in str(e).lower()
             ):
                 logger.warning(
                     f"Duplicate content_hash detected during final commit. "
-                    f"This may occur if the same issue was indexed by multiple connectors. "
                     f"Rolling back and continuing. Error: {e!s}"
                 )
                 await session.rollback()
             else:
                 raise
 
-        # Build warning message if there were issues
-        warning_parts = []
+        warning_parts: list[str] = []
+        if duplicate_content_count > 0:
+            warning_parts.append(f"{duplicate_content_count} duplicate")
         if documents_failed > 0:
             warning_parts.append(f"{documents_failed} failed")
         warning_message = ", ".join(warning_parts) if warning_parts else None
 
-        # Log success
         await task_logger.log_task_success(
             log_entry,
             f"Successfully completed Linear indexing for connector {connector_id}",
@@ -503,7 +338,7 @@ async def index_linear_issues(
                 "documents_indexed": documents_indexed,
                 "documents_skipped": documents_skipped,
                 "documents_failed": documents_failed,
-                "skipped_issues_count": len(skipped_issues),
+                "duplicate_content_count": duplicate_content_count,
             },
         )
 
@@ -511,7 +346,7 @@ async def index_linear_issues(
             f"Linear indexing completed: {documents_indexed} ready, "
             f"{documents_skipped} skipped, {documents_failed} failed"
         )
-        return documents_indexed, warning_message
+        return documents_indexed, documents_skipped, warning_message
 
     except SQLAlchemyError as db_error:
         await session.rollback()
@@ -522,7 +357,7 @@ async def index_linear_issues(
             {"error_type": "SQLAlchemyError"},
         )
         logger.error(f"Database error: {db_error!s}", exc_info=True)
-        return 0, f"Database error: {db_error!s}"
+        return 0, 0, f"Database error: {db_error!s}"
     except Exception as e:
         await session.rollback()
         await task_logger.log_task_failure(
@@ -532,4 +367,4 @@ async def index_linear_issues(
             {"error_type": type(e).__name__},
         )
         logger.error(f"Failed to index Linear issues: {e!s}", exc_info=True)
-        return 0, f"Failed to index Linear issues: {e!s}"
+        return 0, 0, f"Failed to index Linear issues: {e!s}"

surfsense_backend/app/tasks/connector_indexers/notion_indexer.py

@@ -1,12 +1,10 @@
 """
 Notion connector indexer.
 
-Implements real-time document status updates using a two-phase approach:
-- Phase 1: Create all documents with PENDING status (visible in UI immediately)
-- Phase 2: Process each document one by one (pending → processing → ready/failed)
+Uses the shared IndexingPipelineService for document deduplication,
+summarization, chunking, and embedding with bounded parallel indexing.
 """
 
-import time
 from collections.abc import Awaitable, Callable
 from datetime import datetime
 
@@ -14,42 +12,64 @@ from sqlalchemy.exc import SQLAlchemyError
 from sqlalchemy.ext.asyncio import AsyncSession
 
 from app.connectors.notion_history import NotionHistoryConnector
-from app.db import Document, DocumentStatus, DocumentType, SearchSourceConnectorType
+from app.db import DocumentType, SearchSourceConnectorType
+from app.indexing_pipeline.connector_document import ConnectorDocument
+from app.indexing_pipeline.document_hashing import compute_content_hash
+from app.indexing_pipeline.indexing_pipeline_service import IndexingPipelineService
 from app.services.llm_service import get_user_long_context_llm
 from app.services.task_logging_service import TaskLoggingService
-from app.utils.document_converters import (
-    create_document_chunks,
-    embed_text,
-    generate_content_hash,
-    generate_document_summary,
-    generate_unique_identifier_hash,
-)
 from app.utils.notion_utils import process_blocks
 
 from .base import (
-    build_document_metadata_string,
     calculate_date_range,
-    check_document_by_unique_identifier,
     check_duplicate_document_by_hash,
     get_connector_by_id,
-    get_current_timestamp,
     logger,
-    safe_set_chunks,
     update_connector_last_indexed,
 )
 
-# Type alias for retry callback
-# Signature: async callback(retry_reason, attempt, max_attempts, wait_seconds) -> None
 RetryCallbackType = Callable[[str, int, int, float], Awaitable[None]]
-
-# Type alias for heartbeat callback
-# Signature: async callback(indexed_count) -> None
 HeartbeatCallbackType = Callable[[int], Awaitable[None]]
-
-# Heartbeat interval in seconds - update notification every 30 seconds
 HEARTBEAT_INTERVAL_SECONDS = 30
 
 
+def _build_connector_doc(
+    page: dict,
+    markdown_content: str,
+    *,
+    connector_id: int,
+    search_space_id: int,
+    user_id: str,
+    enable_summary: bool,
+) -> ConnectorDocument:
+    """Map a raw Notion page dict to a ConnectorDocument."""
+    page_id = page.get("page_id", "")
+    page_title = page.get("title", f"Untitled page ({page_id})")
+
+    metadata = {
+        "page_title": page_title,
+        "page_id": page_id,
+        "connector_id": connector_id,
+        "document_type": "Notion Page",
+        "connector_type": "Notion",
+    }
+
+    fallback_summary = f"Notion Page: {page_title}\n\n{markdown_content}"
+
+    return ConnectorDocument(
+        title=page_title,
+        source_markdown=markdown_content,
+        unique_id=page_id,
+        document_type=DocumentType.NOTION_CONNECTOR,
+        search_space_id=search_space_id,
+        connector_id=connector_id,
+        created_by_id=user_id,
+        should_summarize=enable_summary,
+        fallback_summary=fallback_summary,
+        metadata=metadata,
+    )
+
+
 async def index_notion_pages(
     session: AsyncSession,
     connector_id: int,
@@ -60,30 +80,15 @@ async def index_notion_pages(
     update_last_indexed: bool = True,
     on_retry_callback: RetryCallbackType | None = None,
     on_heartbeat_callback: HeartbeatCallbackType | None = None,
-) -> tuple[int, str | None]:
+) -> tuple[int, int, str | None]:
     """
     Index Notion pages from all accessible pages.
 
-    Args:
-        session: Database session
-        connector_id: ID of the Notion connector
-        search_space_id: ID of the search space to store documents in
-        user_id: ID of the user
-        start_date: Start date for indexing (YYYY-MM-DD format)
-        end_date: End date for indexing (YYYY-MM-DD format)
-        update_last_indexed: Whether to update the last_indexed_at timestamp (default: True)
-        on_retry_callback: Optional callback for retry progress notifications.
-            Signature: async callback(retry_reason, attempt, max_attempts, wait_seconds)
-            retry_reason is one of: 'rate_limit', 'server_error', 'timeout'
-        on_heartbeat_callback: Optional callback to update notification during long-running indexing.
-            Called periodically with (indexed_count) to prevent task appearing stuck.
-
     Returns:
-        Tuple containing (number of documents indexed, error message or None)
+        Tuple of (indexed_count, skipped_count, warning_or_error_message)
     """
     task_logger = TaskLoggingService(session, search_space_id)
 
-    # Log task start
     log_entry = await task_logger.log_task_start(
         task_name="notion_pages_indexing",
         source="connector_indexing_task",
@@ -97,7 +102,7 @@ async def index_notion_pages(
     )
 
     try:
-        # Get the connector
+        # ── Connector lookup ──────────────────────────────────────────
         await task_logger.log_task_progress(
             log_entry,
             f"Retrieving Notion connector {connector_id} from database",
@@ -116,11 +121,11 @@ async def index_notion_pages(
                 {"error_type": "ConnectorNotFound"},
             )
             return (
+                0,
                 0,
                 f"Connector with ID {connector_id} not found or is not a Notion connector",
             )
 
-        # Check if access_token exists (support both new OAuth format and old integration token format)
         if not connector.config.get("access_token") and not connector.config.get(
             "NOTION_INTEGRATION_TOKEN"
         ):
@@ -130,9 +135,9 @@ async def index_notion_pages(
                 "Missing Notion access token",
                 {"error_type": "MissingToken"},
             )
-            return 0, "Notion access token not found in connector config"
+            return 0, 0, "Notion access token not found in connector config"
 
-        # Initialize Notion client with internal refresh capability
+        # ── Client init ───────────────────────────────────────────────
         await task_logger.log_task_progress(
             log_entry,
             f"Initializing Notion client for connector {connector_id}",
@@ -141,18 +146,15 @@ async def index_notion_pages(
 
         logger.info(f"Initializing Notion client for connector {connector_id}")
 
-        # Handle 'undefined' string from frontend (treat as None)
         if start_date == "undefined" or start_date == "":
             start_date = None
         if end_date == "undefined" or end_date == "":
             end_date = None
 
-        # Calculate date range using the shared utility function
         start_date_str, end_date_str = calculate_date_range(
             connector, start_date, end_date, default_days_back=365
         )
 
-        # Convert YYYY-MM-DD to ISO format for Notion API
         start_date_iso = datetime.strptime(start_date_str, "%Y-%m-%d").strftime(
             "%Y-%m-%dT%H:%M:%SZ"
         )
@@ -160,13 +162,10 @@ async def index_notion_pages(
             "%Y-%m-%dT%H:%M:%SZ"
         )
 
-        # Create connector with session and connector_id for internal refresh
-        # Token refresh will happen automatically when needed
         notion_client = NotionHistoryConnector(
             session=session, connector_id=connector_id
         )
 
-        # Set retry callback if provided (for user notifications during rate limits)
         if on_retry_callback:
             notion_client.set_retry_callback(on_retry_callback)
 
@@ -182,21 +181,19 @@ async def index_notion_pages(
             },
         )
 
-        # Get all pages
+        # ── Fetch pages ───────────────────────────────────────────────
         try:
             pages = await notion_client.get_all_pages(
                 start_date=start_date_iso, end_date=end_date_iso
             )
             logger.info(f"Found {len(pages)} Notion pages")
 
-            # Get count of pages that had unsupported content skipped
             pages_with_skipped_content = notion_client.get_skipped_content_count()
             if pages_with_skipped_content > 0:
                 logger.info(
                     f"{pages_with_skipped_content} pages had Notion AI content skipped (not available via API)"
                 )
 
-            # Check if using legacy integration token and log warning
             if notion_client.is_using_legacy_token():
                 logger.warning(
                     f"Connector {connector_id} is using legacy integration token. "
@@ -204,8 +201,6 @@ async def index_notion_pages(
                 )
         except Exception as e:
             error_str = str(e)
-            # Check if this is an unsupported block type error (transcription, ai_block, etc.)
-            # These are known Notion API limitations and should be logged as warnings, not errors
             unsupported_block_errors = [
                 "transcription is not supported",
                 "ai_block is not supported",
@@ -216,7 +211,6 @@ async def index_notion_pages(
             )
 
             if is_unsupported_block_error:
-                # Log as warning since this is a known Notion API limitation
                 logger.warning(
                     f"Notion API limitation for connector {connector_id}: {error_str}. "
                     "This is a known issue with Notion AI blocks (transcription, ai_block) "
@@ -229,7 +223,6 @@ async def index_notion_pages(
                     {"error_type": "UnsupportedBlockType", "is_known_limitation": True},
                 )
             else:
-                # Log as error for other failures
                 logger.error(
                     f"Error fetching Notion pages for connector {connector_id}: {error_str}",
                     exc_info=True,
@@ -242,7 +235,7 @@ async def index_notion_pages(
                 )
 
             await notion_client.close()
-            return 0, f"Failed to get Notion pages: {e!s}"
+            return 0, 0, f"Failed to get Notion pages: {e!s}"
 
         if not pages:
             await task_logger.log_task_success(
@@ -252,21 +245,17 @@ async def index_notion_pages(
                 {"pages_found": 0},
             )
             logger.info("No Notion pages found to index")
-            # CRITICAL: Update timestamp even when no pages found so Zero syncs
-            await update_connector_last_indexed(session, connector, update_last_indexed)
+            await update_connector_last_indexed(
+                session, connector, update_last_indexed
+            )
             await session.commit()
             await notion_client.close()
-            return 0, None  # Success with 0 pages, not an error
+            return 0, 0, None
 
-        # Track the number of documents indexed
-        documents_indexed = 0
+        # ── Build ConnectorDocuments ──────────────────────────────────
+        connector_docs: list[ConnectorDocument] = []
         documents_skipped = 0
-        documents_failed = 0
         duplicate_content_count = 0
-        skipped_pages = []
-
-        # Heartbeat tracking - update notification periodically to prevent appearing stuck
-        last_heartbeat_time = time.time()
 
         await task_logger.log_task_progress(
             log_entry,
@@ -274,13 +263,6 @@ async def index_notion_pages(
             {"stage": "process_pages", "total_pages": len(pages)},
         )
 
-        # =======================================================================
-        # PHASE 1: Analyze all pages, create pending documents
-        # This makes ALL documents visible in the UI immediately with pending status
-        # =======================================================================
-        pages_to_process = []  # List of dicts with document and page data
-        new_documents_created = False
-
         for page in pages:
             try:
                 page_id = page.get("page_id")
@@ -293,225 +275,71 @@ async def index_notion_pages(
 
                 if not page_content:
                     logger.info(f"No content found in page {page_title}. Skipping.")
-                    skipped_pages.append(f"{page_title} (no content)")
                     documents_skipped += 1
                     continue
 
-                # Convert page content to markdown format
                 markdown_content = f"# Notion Page: {page_title}\n\n"
                 markdown_content += process_blocks(page_content)
 
-                # Format document metadata
-                metadata_sections = [
-                    ("METADATA", [f"PAGE_TITLE: {page_title}", f"PAGE_ID: {page_id}"]),
-                    (
-                        "CONTENT",
-                        [
-                            "FORMAT: markdown",
-                            "TEXT_START",
-                            markdown_content,
-                            "TEXT_END",
-                        ],
-                    ),
-                ]
-
-                # Build the document string
-                combined_document_string = build_document_metadata_string(
-                    metadata_sections
+                if not markdown_content.strip():
+                    logger.warning(
+                        f"Skipping page with empty markdown: {page_title}"
                     )
-
-                # Generate unique identifier hash for this Notion page
-                unique_identifier_hash = generate_unique_identifier_hash(
-                    DocumentType.NOTION_CONNECTOR, page_id, search_space_id
-                )
-
-                # Generate content hash
-                content_hash = generate_content_hash(
-                    combined_document_string, search_space_id
-                )
-
-                # Check if document with this unique identifier already exists
-                existing_document = await check_document_by_unique_identifier(
-                    session, unique_identifier_hash
-                )
-
-                if existing_document:
-                    # Document exists - check if content has changed
-                    if existing_document.content_hash == content_hash:
-                        # Ensure status is ready (might have been stuck in processing/pending)
-                        if not DocumentStatus.is_state(
-                            existing_document.status, DocumentStatus.READY
-                        ):
-                            existing_document.status = DocumentStatus.ready()
                     documents_skipped += 1
                     continue
 
-                    # Queue existing document for update (will be set to processing in Phase 2)
-                    pages_to_process.append(
-                        {
-                            "document": existing_document,
-                            "is_new": False,
-                            "markdown_content": markdown_content,
-                            "content_hash": content_hash,
-                            "page_id": page_id,
-                            "page_title": page_title,
-                        }
+                doc = _build_connector_doc(
+                    page,
+                    markdown_content,
+                    connector_id=connector_id,
+                    search_space_id=search_space_id,
+                    user_id=user_id,
+                    enable_summary=connector.enable_summary,
                 )
-                    continue
 
-                # Document doesn't exist by unique_identifier_hash
-                # Check if a document with the same content_hash exists (from another connector)
                 with session.no_autoflush:
-                    duplicate_by_content = await check_duplicate_document_by_hash(
-                        session, content_hash
+                    duplicate = await check_duplicate_document_by_hash(
+                        session, compute_content_hash(doc)
                     )
-
-                if duplicate_by_content:
+                if duplicate:
                     logger.info(
-                        f"Notion page {page_title} already indexed by another connector "
-                        f"(existing document ID: {duplicate_by_content.id}, "
-                        f"type: {duplicate_by_content.document_type}). Skipping."
+                        f"Notion page {doc.title} already indexed by another connector "
+                        f"(existing document ID: {duplicate.id}, "
+                        f"type: {duplicate.document_type}). Skipping."
                     )
                     duplicate_content_count += 1
                     documents_skipped += 1
                     continue
 
-                # Create new document with PENDING status (visible in UI immediately)
-                document = Document(
-                    search_space_id=search_space_id,
-                    title=page_title,
-                    document_type=DocumentType.NOTION_CONNECTOR,
-                    document_metadata={
-                        "page_title": page_title,
-                        "page_id": page_id,
-                        "connector_id": connector_id,
-                    },
-                    content="Pending...",  # Placeholder until processed
-                    content_hash=unique_identifier_hash,  # Temporary unique value - updated when ready
-                    unique_identifier_hash=unique_identifier_hash,
-                    embedding=None,
-                    chunks=[],  # Empty at creation - safe for async
-                    status=DocumentStatus.pending(),  # Pending until processing starts
-                    updated_at=get_current_timestamp(),
-                    created_by_id=user_id,
-                    connector_id=connector_id,
-                )
-                session.add(document)
-                new_documents_created = True
-
-                pages_to_process.append(
-                    {
-                        "document": document,
-                        "is_new": True,
-                        "markdown_content": markdown_content,
-                        "content_hash": content_hash,
-                        "page_id": page_id,
-                        "page_title": page_title,
-                    }
-                )
+                connector_docs.append(doc)
 
             except Exception as e:
-                logger.error(f"Error in Phase 1 for page: {e!s}", exc_info=True)
-                documents_failed += 1
-                continue
-
-        # Commit all pending documents - they all appear in UI now
-        if new_documents_created:
-            logger.info(
-                f"Phase 1: Committing {len([p for p in pages_to_process if p['is_new']])} pending documents"
-            )
-            await session.commit()
-
-        # =======================================================================
-        # PHASE 2: Process each document one by one
-        # Each document transitions: pending → processing → ready/failed
-        # =======================================================================
-        logger.info(f"Phase 2: Processing {len(pages_to_process)} documents")
-
-        for item in pages_to_process:
-            # Send heartbeat periodically
-            if on_heartbeat_callback:
-                current_time = time.time()
-                if current_time - last_heartbeat_time >= HEARTBEAT_INTERVAL_SECONDS:
-                    await on_heartbeat_callback(documents_indexed)
-                    last_heartbeat_time = current_time
-
-            document = item["document"]
-            try:
-                # Set to PROCESSING and commit - shows "processing" in UI for THIS document only
-                document.status = DocumentStatus.processing()
-                await session.commit()
-
-                # Heavy processing (LLM, embeddings, chunks)
-                user_llm = await get_user_long_context_llm(
-                    session, user_id, search_space_id
-                )
-
-                if user_llm and connector.enable_summary:
-                    document_metadata_for_summary = {
-                        "page_title": item["page_title"],
-                        "page_id": item["page_id"],
-                        "document_type": "Notion Page",
-                        "connector_type": "Notion",
-                    }
-                    (
-                        summary_content,
-                        summary_embedding,
-                    ) = await generate_document_summary(
-                        item["markdown_content"],
-                        user_llm,
-                        document_metadata_for_summary,
-                    )
-                else:
-                    summary_content = f"Notion Page: {item['page_title']}\n\n{item['markdown_content']}"
-                    summary_embedding = embed_text(summary_content)
-
-                chunks = await create_document_chunks(item["markdown_content"])
-
-                # Update document to READY with actual content
-                document.title = item["page_title"]
-                document.content = summary_content
-                document.content_hash = item["content_hash"]
-                document.embedding = summary_embedding
-                document.document_metadata = {
-                    "page_title": item["page_title"],
-                    "page_id": item["page_id"],
-                    "indexed_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
-                    "connector_id": connector_id,
-                }
-                await safe_set_chunks(session, document, chunks)
-                document.updated_at = get_current_timestamp()
-                document.status = DocumentStatus.ready()
-
-                documents_indexed += 1
-
-                # Batch commit every 10 documents (for ready status updates)
-                if documents_indexed % 10 == 0:
-                    logger.info(
-                        f"Committing batch: {documents_indexed} Notion pages processed so far"
-                    )
-                    await session.commit()
-
-            except Exception as e:
-                logger.error(f"Error processing Notion page: {e!s}", exc_info=True)
-                # Mark document as failed with reason (visible in UI)
-                try:
-                    document.status = DocumentStatus.failed(str(e))
-                    document.updated_at = get_current_timestamp()
-                except Exception as status_error:
                 logger.error(
-                        f"Failed to update document status to failed: {status_error}"
+                    f"Error building ConnectorDocument for page: {e!s}",
+                    exc_info=True,
                 )
-                skipped_pages.append(f"{item['page_title']} (processing error)")
-                documents_failed += 1
+                documents_skipped += 1
                 continue
 
-        # CRITICAL: Always update timestamp (even if 0 documents indexed) so Zero syncs
+        # ── Pipeline: migrate legacy docs + parallel index ────────────
+        pipeline = IndexingPipelineService(session)
+
+        await pipeline.migrate_legacy_docs(connector_docs)
+
+        async def _get_llm(s):
+            return await get_user_long_context_llm(s, user_id, search_space_id)
+
+        _, documents_indexed, documents_failed = await pipeline.index_batch_parallel(
+            connector_docs,
+            _get_llm,
+            max_concurrency=3,
+            on_heartbeat=on_heartbeat_callback,
+            heartbeat_interval=HEARTBEAT_INTERVAL_SECONDS,
+        )
+
+        # ── Finalize ──────────────────────────────────────────────────
         await update_connector_last_indexed(session, connector, update_last_indexed)
 
-        total_processed = documents_indexed
-
-        # Final commit to ensure all documents are persisted (safety net)
         logger.info(f"Final commit: Total {documents_indexed} documents processed")
         try:
             await session.commit()
@@ -519,59 +347,53 @@ async def index_notion_pages(
                 "Successfully committed all Notion document changes to database"
             )
         except Exception as e:
-            # Handle any remaining integrity errors gracefully (race conditions, etc.)
             if (
                 "duplicate key value violates unique constraint" in str(e).lower()
                 or "uniqueviolationerror" in str(e).lower()
             ):
                 logger.warning(
                     f"Duplicate content_hash detected during final commit. "
-                    f"This may occur if the same page was indexed by multiple connectors. "
                     f"Rolling back and continuing. Error: {e!s}"
                 )
                 await session.rollback()
-                # Don't fail the entire task - some documents may have been successfully indexed
             else:
                 raise
 
-        # Get final count of pages with skipped Notion AI content
+        # ── Build warning / notification message ──────────────────────
         pages_with_skipped_ai_content = notion_client.get_skipped_content_count()
 
-        # Build warning message if there were issues
-        warning_parts = []
+        warning_parts: list[str] = []
         if duplicate_content_count > 0:
             warning_parts.append(f"{duplicate_content_count} duplicate")
         if documents_failed > 0:
             warning_parts.append(f"{documents_failed} failed")
-        warning_message = ", ".join(warning_parts) if warning_parts else None
 
-        # Prepare result message with user-friendly notification about skipped content
-        result_message = None
-        if skipped_pages:
-            result_message = f"Processed {total_processed} pages. Skipped {len(skipped_pages)} pages: {', '.join(skipped_pages)}"
-        else:
-            result_message = f"Processed {total_processed} pages."
-
-        # Add user-friendly message about skipped Notion AI content
+        notification_parts: list[str] = []
         if pages_with_skipped_ai_content > 0:
-            result_message += (
-                " Audio transcriptions and AI summaries from Notion aren't accessible "
-                "via their API - all other content was saved."
+            notification_parts.append(
+                "Some Notion AI content couldn't be synced (API limitation)"
+            )
+        if notion_client.is_using_legacy_token():
+            notification_parts.append(
+                "Using legacy token. Reconnect with OAuth for better reliability."
+            )
+        if warning_parts:
+            notification_parts.append(", ".join(warning_parts))
+
+        user_notification_message = (
+            " ".join(notification_parts) if notification_parts else None
         )
 
-        # Log success
         await task_logger.log_task_success(
             log_entry,
             f"Successfully completed Notion indexing for connector {connector_id}",
             {
-                "pages_processed": total_processed,
+                "pages_processed": documents_indexed,
                 "documents_indexed": documents_indexed,
                 "documents_skipped": documents_skipped,
                 "documents_failed": documents_failed,
                 "duplicate_content_count": duplicate_content_count,
-                "skipped_pages_count": len(skipped_pages),
                 "pages_with_skipped_ai_content": pages_with_skipped_ai_content,
-                "result_message": result_message,
             },
         )
 
@@ -581,35 +403,9 @@ async def index_notion_pages(
             f"({duplicate_content_count} duplicate content)"
         )
 
-        # Clean up the async client
         await notion_client.close()
 
-        # Build user-friendly notification messages
-        # This will be shown in the notification to inform users
-        notification_parts = []
-
-        if pages_with_skipped_ai_content > 0:
-            notification_parts.append(
-                "Some Notion AI content couldn't be synced (API limitation)"
-            )
-
-        if notion_client.is_using_legacy_token():
-            notification_parts.append(
-                "Using legacy token. Reconnect with OAuth for better reliability."
-            )
-
-        # Include warning message if there were issues
-        if warning_message:
-            notification_parts.append(warning_message)
-
-        user_notification_message = (
-            " ".join(notification_parts) if notification_parts else None
-        )
-
-        return (
-            total_processed,
-            user_notification_message,
-        )
+        return documents_indexed, documents_skipped, user_notification_message
 
     except SQLAlchemyError as db_error:
         await session.rollback()
@@ -622,10 +418,9 @@ async def index_notion_pages(
         logger.error(
             f"Database error during Notion indexing: {db_error!s}", exc_info=True
         )
-        # Clean up the async client in case of error
         if "notion_client" in locals():
             await notion_client.close()
-        return 0, f"Database error: {db_error!s}"
+        return 0, 0, f"Database error: {db_error!s}"
     except Exception as e:
         await session.rollback()
         await task_logger.log_task_failure(
@@ -635,7 +430,6 @@ async def index_notion_pages(
             {"error_type": type(e).__name__},
         )
         logger.error(f"Failed to index Notion pages: {e!s}", exc_info=True)
-        # Clean up the async client in case of error
         if "notion_client" in locals():
             await notion_client.close()
-        return 0, f"Failed to index Notion pages: {e!s}"
+        return 0, 0, f"Failed to index Notion pages: {e!s}"

surfsense_backend/app/utils/document_converters.py

@@ -1,5 +1,6 @@
 import hashlib
 import logging
+import threading
 import warnings
 
 import numpy as np
@@ -11,6 +12,12 @@ from app.prompts import SUMMARY_PROMPT_TEMPLATE
 
 logger = logging.getLogger(__name__)
 
+# HuggingFace fast tokenizers (Rust-backed) are not thread-safe — concurrent
+# access from multiple threads causes "RuntimeError: Already borrowed".
+# This reentrant lock serialises tokenizer + embedding model access so that
+# asyncio.to_thread calls from index_batch_parallel don't collide.
+_embedding_lock = threading.RLock()
+
 
 def _get_embedding_max_tokens() -> int:
     """Get the max token limit for the configured embedding model.
@@ -36,6 +43,7 @@ def truncate_for_embedding(text: str) -> str:
     if len(text) // 3 <= max_tokens:
         return text
 
+    with _embedding_lock:
         tokenizer = config.embedding_model_instance.get_tokenizer()
         tokens = tokenizer.encode(text)
         if len(tokens) <= max_tokens:
@@ -52,6 +60,7 @@ def embed_text(text: str) -> np.ndarray:
     """Truncate text to fit and embed it. Drop-in replacement for
     ``config.embedding_model_instance.embed(text)`` that never exceeds the
     model's context window."""
+    with _embedding_lock:
         return config.embedding_model_instance.embed(truncate_for_embedding(text))
 
 
@@ -66,6 +75,7 @@ def embed_texts(texts: list[str]) -> list[np.ndarray]:
     """
     if not texts:
         return []
+    with _embedding_lock:
         truncated = [truncate_for_embedding(t) for t in texts]
         if config.is_local_embedding_model:
             return [config.embedding_model_instance.embed(t) for t in truncated]

surfsense_backend/tests/integration/indexing_pipeline/test_calendar_pipeline.py

@@ -0,0 +1,111 @@
+"""Integration tests: Calendar indexer builds ConnectorDocuments that flow through the pipeline."""
+
+import pytest
+from sqlalchemy import select
+
+from app.config import config as app_config
+from app.db import Document, DocumentStatus, DocumentType
+from app.indexing_pipeline.connector_document import ConnectorDocument
+from app.indexing_pipeline.document_hashing import compute_identifier_hash
+from app.indexing_pipeline.indexing_pipeline_service import IndexingPipelineService
+
+_EMBEDDING_DIM = app_config.embedding_model_instance.dimension
+
+pytestmark = pytest.mark.integration
+
+
+def _cal_doc(*, unique_id: str, search_space_id: int, connector_id: int, user_id: str) -> ConnectorDocument:
+    return ConnectorDocument(
+        title=f"Event {unique_id}",
+        source_markdown=f"## Calendar Event\n\nDetails for {unique_id}",
+        unique_id=unique_id,
+        document_type=DocumentType.GOOGLE_CALENDAR_CONNECTOR,
+        search_space_id=search_space_id,
+        connector_id=connector_id,
+        created_by_id=user_id,
+        should_summarize=True,
+        fallback_summary=f"Calendar: Event {unique_id}",
+        metadata={
+            "event_id": unique_id,
+            "start_time": "2025-01-15T10:00:00",
+            "end_time": "2025-01-15T11:00:00",
+            "document_type": "Google Calendar Event",
+        },
+    )
+
+
+@pytest.mark.usefixtures("patched_summarize", "patched_embed_texts", "patched_chunk_text")
+async def test_calendar_pipeline_creates_ready_document(
+    db_session, db_search_space, db_connector, db_user, mocker
+):
+    """A Calendar ConnectorDocument flows through prepare + index to a READY document."""
+    space_id = db_search_space.id
+    doc = _cal_doc(
+        unique_id="evt-1",
+        search_space_id=space_id,
+        connector_id=db_connector.id,
+        user_id=str(db_user.id),
+    )
+
+    service = IndexingPipelineService(session=db_session)
+    prepared = await service.prepare_for_indexing([doc])
+    assert len(prepared) == 1
+
+    await service.index(prepared[0], doc, llm=mocker.Mock())
+
+    result = await db_session.execute(
+        select(Document).filter(Document.search_space_id == space_id)
+    )
+    row = result.scalars().first()
+
+    assert row is not None
+    assert row.document_type == DocumentType.GOOGLE_CALENDAR_CONNECTOR
+    assert DocumentStatus.is_state(row.status, DocumentStatus.READY)
+
+
+@pytest.mark.usefixtures("patched_summarize", "patched_embed_texts", "patched_chunk_text")
+async def test_calendar_legacy_doc_migrated(
+    db_session, db_search_space, db_connector, db_user, mocker
+):
+    """A legacy Composio Calendar doc is migrated and reused."""
+    space_id = db_search_space.id
+    user_id = str(db_user.id)
+    evt_id = "evt-legacy-cal"
+
+    legacy_hash = compute_identifier_hash(
+        DocumentType.COMPOSIO_GOOGLE_CALENDAR_CONNECTOR.value, evt_id, space_id
+    )
+    legacy_doc = Document(
+        title="Old Calendar Event",
+        document_type=DocumentType.COMPOSIO_GOOGLE_CALENDAR_CONNECTOR,
+        content="old summary",
+        content_hash=f"ch-{legacy_hash[:12]}",
+        unique_identifier_hash=legacy_hash,
+        source_markdown="## Old event",
+        search_space_id=space_id,
+        created_by_id=user_id,
+        embedding=[0.1] * _EMBEDDING_DIM,
+        status={"state": "ready"},
+    )
+    db_session.add(legacy_doc)
+    await db_session.flush()
+    original_id = legacy_doc.id
+
+    connector_doc = _cal_doc(
+        unique_id=evt_id,
+        search_space_id=space_id,
+        connector_id=db_connector.id,
+        user_id=user_id,
+    )
+
+    service = IndexingPipelineService(session=db_session)
+    await service.migrate_legacy_docs([connector_doc])
+
+    result = await db_session.execute(select(Document).filter(Document.id == original_id))
+    row = result.scalars().first()
+
+    assert row.document_type == DocumentType.GOOGLE_CALENDAR_CONNECTOR
+    native_hash = compute_identifier_hash(
+        DocumentType.GOOGLE_CALENDAR_CONNECTOR.value, evt_id, space_id
+    )
+    assert row.unique_identifier_hash == native_hash

surfsense_backend/tests/integration/indexing_pipeline/test_drive_pipeline.py

@@ -0,0 +1,170 @@
+"""Integration tests: Drive indexer builds ConnectorDocuments that flow through the pipeline."""
+
+import pytest
+from sqlalchemy import select
+
+from app.config import config as app_config
+from app.db import Document, DocumentStatus, DocumentType
+from app.indexing_pipeline.connector_document import ConnectorDocument
+from app.indexing_pipeline.document_hashing import compute_identifier_hash
+from app.indexing_pipeline.indexing_pipeline_service import IndexingPipelineService
+
+_EMBEDDING_DIM = app_config.embedding_model_instance.dimension
+
+pytestmark = pytest.mark.integration
+
+
+def _drive_doc(*, unique_id: str, search_space_id: int, connector_id: int, user_id: str) -> ConnectorDocument:
+    return ConnectorDocument(
+        title=f"File {unique_id}.pdf",
+        source_markdown=f"## Document Content\n\nText from file {unique_id}",
+        unique_id=unique_id,
+        document_type=DocumentType.GOOGLE_DRIVE_FILE,
+        search_space_id=search_space_id,
+        connector_id=connector_id,
+        created_by_id=user_id,
+        should_summarize=True,
+        fallback_summary=f"File: {unique_id}.pdf",
+        metadata={
+            "google_drive_file_id": unique_id,
+            "google_drive_file_name": f"{unique_id}.pdf",
+            "document_type": "Google Drive File",
+        },
+    )
+
+
+@pytest.mark.usefixtures("patched_summarize", "patched_embed_texts", "patched_chunk_text")
+async def test_drive_pipeline_creates_ready_document(
+    db_session, db_search_space, db_connector, db_user, mocker
+):
+    """A Drive ConnectorDocument flows through prepare + index to a READY document."""
+    space_id = db_search_space.id
+    doc = _drive_doc(
+        unique_id="file-abc",
+        search_space_id=space_id,
+        connector_id=db_connector.id,
+        user_id=str(db_user.id),
+    )
+
+    service = IndexingPipelineService(session=db_session)
+    prepared = await service.prepare_for_indexing([doc])
+    assert len(prepared) == 1
+
+    await service.index(prepared[0], doc, llm=mocker.Mock())
+
+    result = await db_session.execute(
+        select(Document).filter(Document.search_space_id == space_id)
+    )
+    row = result.scalars().first()
+
+    assert row is not None
+    assert row.document_type == DocumentType.GOOGLE_DRIVE_FILE
+    assert DocumentStatus.is_state(row.status, DocumentStatus.READY)
+
+
+@pytest.mark.usefixtures("patched_summarize", "patched_embed_texts", "patched_chunk_text")
+async def test_drive_legacy_doc_migrated(
+    db_session, db_search_space, db_connector, db_user, mocker
+):
+    """A legacy Composio Drive doc is migrated and reused."""
+    space_id = db_search_space.id
+    user_id = str(db_user.id)
+    file_id = "file-legacy-drive"
+
+    legacy_hash = compute_identifier_hash(
+        DocumentType.COMPOSIO_GOOGLE_DRIVE_CONNECTOR.value, file_id, space_id
+    )
+    legacy_doc = Document(
+        title="Old Drive File",
+        document_type=DocumentType.COMPOSIO_GOOGLE_DRIVE_CONNECTOR,
+        content="old file summary",
+        content_hash=f"ch-{legacy_hash[:12]}",
+        unique_identifier_hash=legacy_hash,
+        source_markdown="## Old file content",
+        search_space_id=space_id,
+        created_by_id=user_id,
+        embedding=[0.1] * _EMBEDDING_DIM,
+        status={"state": "ready"},
+    )
+    db_session.add(legacy_doc)
+    await db_session.flush()
+    original_id = legacy_doc.id
+
+    connector_doc = _drive_doc(
+        unique_id=file_id,
+        search_space_id=space_id,
+        connector_id=db_connector.id,
+        user_id=user_id,
+    )
+
+    service = IndexingPipelineService(session=db_session)
+    await service.migrate_legacy_docs([connector_doc])
+
+    result = await db_session.execute(select(Document).filter(Document.id == original_id))
+    row = result.scalars().first()
+
+    assert row.document_type == DocumentType.GOOGLE_DRIVE_FILE
+    native_hash = compute_identifier_hash(
+        DocumentType.GOOGLE_DRIVE_FILE.value, file_id, space_id
+    )
+    assert row.unique_identifier_hash == native_hash
+
+
+async def test_should_skip_file_skips_failed_document(
+    db_session, db_search_space, db_user,
+):
+    """A FAILED document with unchanged md5 must be skipped — user can manually retry via Quick Index."""
+    import importlib
+    import sys
+    import types
+
+    pkg = "app.tasks.connector_indexers"
+    stub = pkg not in sys.modules
+    if stub:
+        mod = types.ModuleType(pkg)
+        mod.__path__ = ["app/tasks/connector_indexers"]
+        mod.__package__ = pkg
+        sys.modules[pkg] = mod
+
+    try:
+        gdm = importlib.import_module(
+            "app.tasks.connector_indexers.google_drive_indexer"
+        )
+        _should_skip_file = gdm._should_skip_file
+    finally:
+        if stub:
+            sys.modules.pop(pkg, None)
+
+    space_id = db_search_space.id
+    file_id = "file-failed-drive"
+    md5 = "abc123deadbeef"
+
+    doc_hash = compute_identifier_hash(
+        DocumentType.GOOGLE_DRIVE_FILE.value, file_id, space_id
+    )
+    failed_doc = Document(
+        title="Failed File.pdf",
+        document_type=DocumentType.GOOGLE_DRIVE_FILE,
+        content="LLM rate limit exceeded",
+        content_hash=f"ch-{doc_hash[:12]}",
+        unique_identifier_hash=doc_hash,
+        source_markdown="## Real content",
+        search_space_id=space_id,
+        created_by_id=str(db_user.id),
+        embedding=[0.1] * _EMBEDDING_DIM,
+        status=DocumentStatus.failed("LLM rate limit exceeded"),
+        document_metadata={
+            "google_drive_file_id": file_id,
+            "google_drive_file_name": "Failed File.pdf",
+            "md5_checksum": md5,
+        },
+    )
+    db_session.add(failed_doc)
+    await db_session.flush()
+
+    incoming_file = {"id": file_id, "name": "Failed File.pdf", "mimeType": "application/pdf", "md5Checksum": md5}
+
+    should_skip, msg = await _should_skip_file(db_session, incoming_file, space_id)
+
+    assert should_skip, "FAILED documents must be skipped during automatic sync"
+    assert "failed" in msg.lower()

surfsense_backend/tests/integration/indexing_pipeline/test_gmail_pipeline.py

@@ -0,0 +1,116 @@
+"""Integration tests: Gmail indexer builds ConnectorDocuments that flow through the pipeline."""
+
+import pytest
+from sqlalchemy import select
+
+from app.config import config as app_config
+from app.db import Document, DocumentStatus, DocumentType
+from app.indexing_pipeline.connector_document import ConnectorDocument
+from app.indexing_pipeline.document_hashing import (
+    compute_identifier_hash,
+    compute_unique_identifier_hash,
+)
+from app.indexing_pipeline.indexing_pipeline_service import IndexingPipelineService
+
+_EMBEDDING_DIM = app_config.embedding_model_instance.dimension
+
+pytestmark = pytest.mark.integration
+
+
+def _gmail_doc(*, unique_id: str, search_space_id: int, connector_id: int, user_id: str) -> ConnectorDocument:
+    """Build a Gmail-style ConnectorDocument like the real indexer does."""
+    return ConnectorDocument(
+        title=f"Subject for {unique_id}",
+        source_markdown=f"## Email\n\nBody of {unique_id}",
+        unique_id=unique_id,
+        document_type=DocumentType.GOOGLE_GMAIL_CONNECTOR,
+        search_space_id=search_space_id,
+        connector_id=connector_id,
+        created_by_id=user_id,
+        should_summarize=True,
+        fallback_summary=f"Gmail: Subject for {unique_id}",
+        metadata={
+            "message_id": unique_id,
+            "from": "sender@example.com",
+            "document_type": "Gmail Message",
+        },
+    )
+
+
+@pytest.mark.usefixtures("patched_summarize", "patched_embed_texts", "patched_chunk_text")
+async def test_gmail_pipeline_creates_ready_document(
+    db_session, db_search_space, db_connector, db_user, mocker
+):
+    """A Gmail ConnectorDocument flows through prepare + index to a READY document."""
+    space_id = db_search_space.id
+    doc = _gmail_doc(
+        unique_id="msg-pipeline-1",
+        search_space_id=space_id,
+        connector_id=db_connector.id,
+        user_id=str(db_user.id),
+    )
+
+    service = IndexingPipelineService(session=db_session)
+    prepared = await service.prepare_for_indexing([doc])
+    assert len(prepared) == 1
+
+    await service.index(prepared[0], doc, llm=mocker.Mock())
+
+    result = await db_session.execute(
+        select(Document).filter(Document.search_space_id == space_id)
+    )
+    row = result.scalars().first()
+
+    assert row is not None
+    assert row.document_type == DocumentType.GOOGLE_GMAIL_CONNECTOR
+    assert DocumentStatus.is_state(row.status, DocumentStatus.READY)
+    assert row.source_markdown == doc.source_markdown
+
+
+@pytest.mark.usefixtures("patched_summarize", "patched_embed_texts", "patched_chunk_text")
+async def test_gmail_legacy_doc_migrated_then_reused(
+    db_session, db_search_space, db_connector, db_user, mocker
+):
+    """A legacy Composio Gmail doc is migrated then reused by the pipeline."""
+    space_id = db_search_space.id
+    user_id = str(db_user.id)
+    msg_id = "msg-legacy-gmail"
+
+    legacy_hash = compute_identifier_hash(
+        DocumentType.COMPOSIO_GMAIL_CONNECTOR.value, msg_id, space_id
+    )
+    legacy_doc = Document(
+        title="Old Gmail",
+        document_type=DocumentType.COMPOSIO_GMAIL_CONNECTOR,
+        content="old summary",
+        content_hash=f"ch-{legacy_hash[:12]}",
+        unique_identifier_hash=legacy_hash,
+        source_markdown="## Old content",
+        search_space_id=space_id,
+        created_by_id=user_id,
+        embedding=[0.1] * _EMBEDDING_DIM,
+        status={"state": "ready"},
+    )
+    db_session.add(legacy_doc)
+    await db_session.flush()
+    original_id = legacy_doc.id
+
+    connector_doc = _gmail_doc(
+        unique_id=msg_id,
+        search_space_id=space_id,
+        connector_id=db_connector.id,
+        user_id=user_id,
+    )
+
+    service = IndexingPipelineService(session=db_session)
+    await service.migrate_legacy_docs([connector_doc])
+
+    prepared = await service.prepare_for_indexing([connector_doc])
+    assert len(prepared) == 1
+    assert prepared[0].id == original_id
+    assert prepared[0].document_type == DocumentType.GOOGLE_GMAIL_CONNECTOR
+
+    native_hash = compute_identifier_hash(
+        DocumentType.GOOGLE_GMAIL_CONNECTOR.value, msg_id, space_id
+    )
+    assert prepared[0].unique_identifier_hash == native_hash

surfsense_backend/tests/integration/indexing_pipeline/test_index_batch.py

@@ -0,0 +1,55 @@
+"""Integration tests for IndexingPipelineService.index_batch()."""
+
+import pytest
+from sqlalchemy import select
+
+from app.db import Document, DocumentStatus, DocumentType
+from app.indexing_pipeline.indexing_pipeline_service import IndexingPipelineService
+
+pytestmark = pytest.mark.integration
+
+
+@pytest.mark.usefixtures("patched_summarize", "patched_embed_texts", "patched_chunk_text")
+async def test_index_batch_creates_ready_documents(
+    db_session, db_search_space, make_connector_document, mocker
+):
+    """index_batch prepares and indexes a batch, resulting in READY documents."""
+    space_id = db_search_space.id
+    docs = [
+        make_connector_document(
+            document_type=DocumentType.GOOGLE_GMAIL_CONNECTOR,
+            unique_id="msg-batch-1",
+            search_space_id=space_id,
+            source_markdown="## Email 1\n\nBody",
+        ),
+        make_connector_document(
+            document_type=DocumentType.GOOGLE_GMAIL_CONNECTOR,
+            unique_id="msg-batch-2",
+            search_space_id=space_id,
+            source_markdown="## Email 2\n\nDifferent body",
+        ),
+    ]
+
+    service = IndexingPipelineService(session=db_session)
+    results = await service.index_batch(docs, llm=mocker.Mock())
+
+    assert len(results) == 2
+
+    result = await db_session.execute(
+        select(Document).filter(Document.search_space_id == space_id)
+    )
+    rows = result.scalars().all()
+    assert len(rows) == 2
+
+    for row in rows:
+        assert DocumentStatus.is_state(row.status, DocumentStatus.READY)
+        assert row.content is not None
+        assert row.embedding is not None
+
+
+@pytest.mark.usefixtures("patched_summarize", "patched_embed_texts", "patched_chunk_text")
+async def test_index_batch_empty_returns_empty(db_session, mocker):
+    """index_batch with empty input returns an empty list."""
+    service = IndexingPipelineService(session=db_session)
+    results = await service.index_batch([], llm=mocker.Mock())
+    assert results == []

surfsense_backend/tests/integration/indexing_pipeline/test_migrate_legacy_docs.py

@@ -0,0 +1,92 @@
+"""Integration tests for IndexingPipelineService.migrate_legacy_docs()."""
+
+import pytest
+from sqlalchemy import select
+
+from app.config import config as app_config
+from app.db import Document, DocumentType
+from app.indexing_pipeline.document_hashing import compute_identifier_hash
+from app.indexing_pipeline.indexing_pipeline_service import IndexingPipelineService
+
+_EMBEDDING_DIM = app_config.embedding_model_instance.dimension
+
+pytestmark = pytest.mark.integration
+
+
+async def test_legacy_composio_gmail_doc_migrated_in_db(
+    db_session, db_search_space, db_user, make_connector_document
+):
+    """A Composio Gmail doc in the DB gets its hash and type updated to native."""
+    space_id = db_search_space.id
+    user_id = str(db_user.id)
+    unique_id = "msg-legacy-123"
+
+    legacy_hash = compute_identifier_hash(
+        DocumentType.COMPOSIO_GMAIL_CONNECTOR.value, unique_id, space_id
+    )
+    native_hash = compute_identifier_hash(
+        DocumentType.GOOGLE_GMAIL_CONNECTOR.value, unique_id, space_id
+    )
+
+    legacy_doc = Document(
+        title="Old Gmail",
+        document_type=DocumentType.COMPOSIO_GMAIL_CONNECTOR,
+        content="legacy content",
+        content_hash=f"ch-{legacy_hash[:12]}",
+        unique_identifier_hash=legacy_hash,
+        search_space_id=space_id,
+        created_by_id=user_id,
+        embedding=[0.1] * _EMBEDDING_DIM,
+        status={"state": "ready"},
+    )
+    db_session.add(legacy_doc)
+    await db_session.flush()
+    doc_id = legacy_doc.id
+
+    connector_doc = make_connector_document(
+        document_type=DocumentType.GOOGLE_GMAIL_CONNECTOR,
+        unique_id=unique_id,
+        search_space_id=space_id,
+    )
+
+    service = IndexingPipelineService(session=db_session)
+    await service.migrate_legacy_docs([connector_doc])
+
+    result = await db_session.execute(select(Document).filter(Document.id == doc_id))
+    reloaded = result.scalars().first()
+
+    assert reloaded.unique_identifier_hash == native_hash
+    assert reloaded.document_type == DocumentType.GOOGLE_GMAIL_CONNECTOR
+
+
+async def test_no_legacy_doc_is_noop(
+    db_session, db_search_space, make_connector_document
+):
+    """When no legacy document exists, migrate_legacy_docs does nothing."""
+    connector_doc = make_connector_document(
+        document_type=DocumentType.GOOGLE_CALENDAR_CONNECTOR,
+        unique_id="evt-no-legacy",
+        search_space_id=db_search_space.id,
+    )
+
+    service = IndexingPipelineService(session=db_session)
+    await service.migrate_legacy_docs([connector_doc])
+
+    result = await db_session.execute(
+        select(Document).filter(Document.search_space_id == db_search_space.id)
+    )
+    assert result.scalars().all() == []
+
+
+async def test_non_google_type_is_skipped(
+    db_session, db_search_space, make_connector_document
+):
+    """migrate_legacy_docs skips ConnectorDocuments that are not Google types."""
+    connector_doc = make_connector_document(
+        document_type=DocumentType.CLICKUP_CONNECTOR,
+        unique_id="task-1",
+        search_space_id=db_search_space.id,
+    )
+
+    service = IndexingPipelineService(session=db_session)
+    await service.migrate_legacy_docs([connector_doc])

surfsense_backend/tests/unit/connector_indexers/conftest.py

@@ -0,0 +1,34 @@
+"""Pre-register the connector_indexers package to bypass a circular import
+in its ``__init__.py`` (airtable_indexer -> routes -> connector_indexers).
+
+This lets tests import individual indexer modules (e.g.
+``google_drive_indexer``) without triggering the full package init.
+"""
+
+import sys
+import types
+from pathlib import Path
+
+_BACKEND = Path(__file__).resolve().parents[3]
+
+
+def _stub_package(dotted: str, fs_dir: Path) -> None:
+    if dotted not in sys.modules:
+        mod = types.ModuleType(dotted)
+        mod.__path__ = [str(fs_dir)]
+        mod.__package__ = dotted
+        sys.modules[dotted] = mod
+
+    parts = dotted.split(".")
+    if len(parts) > 1:
+        parent_dotted = ".".join(parts[:-1])
+        parent = sys.modules.get(parent_dotted)
+        if parent is not None:
+            setattr(parent, parts[-1], sys.modules[dotted])
+
+
+_stub_package("app.tasks", _BACKEND / "app" / "tasks")
+_stub_package(
+    "app.tasks.connector_indexers",
+    _BACKEND / "app" / "tasks" / "connector_indexers",
+)

surfsense_backend/tests/unit/connector_indexers/test_confluence_parallel.py

@@ -0,0 +1,373 @@
+"""Tests for Confluence indexer migrated to the unified parallel pipeline."""
+
+from unittest.mock import AsyncMock, MagicMock
+
+import pytest
+
+import app.tasks.connector_indexers.confluence_indexer as _mod
+from app.db import DocumentType
+from app.tasks.connector_indexers.confluence_indexer import (
+    _build_connector_doc,
+    index_confluence_pages,
+)
+
+pytestmark = pytest.mark.unit
+
+_USER_ID = "00000000-0000-0000-0000-000000000001"
+_CONNECTOR_ID = 42
+_SEARCH_SPACE_ID = 1
+
+
+def _make_page(
+    page_id: str = "p1",
+    title: str = "Home",
+    space_id: str = "S1",
+    body: str = "<p>Hello</p>",
+    comments=None,
+):
+    return {
+        "id": page_id,
+        "title": title,
+        "spaceId": space_id,
+        "body": {"storage": {"value": body}},
+        "comments": comments or [],
+    }
+
+
+def _to_markdown(page: dict) -> str:
+    page_title = page.get("title", "")
+    page_content = page.get("body", {}).get("storage", {}).get("value", "")
+    comments = page.get("comments", [])
+    comments_content = ""
+    if comments:
+        comments_content = "\n\n## Comments\n\n"
+        for comment in comments:
+            comment_body = (
+                comment.get("body", {}).get("storage", {}).get("value", "")
+            )
+            comment_author = comment.get("version", {}).get("authorId", "Unknown")
+            comment_date = comment.get("version", {}).get("createdAt", "")
+            comments_content += (
+                f"**Comment by {comment_author}** ({comment_date}):\n"
+                f"{comment_body}\n\n"
+            )
+    return f"# {page_title}\n\n{page_content}{comments_content}"
+
+
+# ---------------------------------------------------------------------------
+# Slice 1: _build_connector_doc tracer bullet
+# ---------------------------------------------------------------------------
+
+
+async def test_build_connector_doc_produces_correct_fields():
+    page = _make_page(
+        page_id="abc-123",
+        title="Engineering Handbook",
+        space_id="ENG",
+        comments=[{"id": "c1"}],
+    )
+    markdown = _to_markdown(page)
+
+    doc = _build_connector_doc(
+        page,
+        markdown,
+        connector_id=_CONNECTOR_ID,
+        search_space_id=_SEARCH_SPACE_ID,
+        user_id=_USER_ID,
+        enable_summary=True,
+    )
+
+    assert doc.title == "Engineering Handbook"
+    assert doc.unique_id == "abc-123"
+    assert doc.document_type == DocumentType.CONFLUENCE_CONNECTOR
+    assert doc.source_markdown == markdown
+    assert doc.search_space_id == _SEARCH_SPACE_ID
+    assert doc.connector_id == _CONNECTOR_ID
+    assert doc.created_by_id == _USER_ID
+    assert doc.should_summarize is True
+    assert doc.metadata["page_id"] == "abc-123"
+    assert doc.metadata["page_title"] == "Engineering Handbook"
+    assert doc.metadata["space_id"] == "ENG"
+    assert doc.metadata["comment_count"] == 1
+    assert doc.metadata["connector_id"] == _CONNECTOR_ID
+    assert doc.metadata["document_type"] == "Confluence Page"
+    assert doc.metadata["connector_type"] == "Confluence"
+    assert doc.fallback_summary is not None
+    assert "Engineering Handbook" in doc.fallback_summary
+    assert markdown in doc.fallback_summary
+
+
+async def test_build_connector_doc_summary_disabled():
+    doc = _build_connector_doc(
+        _make_page(),
+        _to_markdown(_make_page()),
+        connector_id=_CONNECTOR_ID,
+        search_space_id=_SEARCH_SPACE_ID,
+        user_id=_USER_ID,
+        enable_summary=False,
+    )
+    assert doc.should_summarize is False
+
+
+# ---------------------------------------------------------------------------
+# Shared fixtures for Slices 2-7
+# ---------------------------------------------------------------------------
+
+
+def _mock_connector(enable_summary: bool = True):
+    c = MagicMock()
+    c.config = {"access_token": "tok"}
+    c.enable_summary = enable_summary
+    c.last_indexed_at = None
+    return c
+
+
+def _mock_confluence_client(pages=None, error=None):
+    client = MagicMock()
+    client.get_pages_by_date_range = AsyncMock(
+        return_value=(pages if pages is not None else [], error),
+    )
+    client.close = AsyncMock()
+    return client
+
+
+@pytest.fixture
+def confluence_mocks(monkeypatch):
+    mock_session = AsyncMock()
+    mock_session.no_autoflush = MagicMock()
+
+    mock_connector = _mock_connector()
+    monkeypatch.setattr(
+        _mod, "get_connector_by_id", AsyncMock(return_value=mock_connector),
+    )
+
+    confluence_client = _mock_confluence_client(pages=[_make_page()])
+    monkeypatch.setattr(
+        _mod, "ConfluenceHistoryConnector", MagicMock(return_value=confluence_client),
+    )
+
+    monkeypatch.setattr(
+        _mod, "check_duplicate_document_by_hash", AsyncMock(return_value=None),
+    )
+    monkeypatch.setattr(
+        _mod, "update_connector_last_indexed", AsyncMock(),
+    )
+    monkeypatch.setattr(
+        _mod, "calculate_date_range", MagicMock(return_value=("2025-01-01", "2025-12-31")),
+    )
+
+    mock_task_logger = MagicMock()
+    mock_task_logger.log_task_start = AsyncMock(return_value=MagicMock())
+    mock_task_logger.log_task_progress = AsyncMock()
+    mock_task_logger.log_task_success = AsyncMock()
+    mock_task_logger.log_task_failure = AsyncMock()
+    monkeypatch.setattr(
+        _mod, "TaskLoggingService", MagicMock(return_value=mock_task_logger),
+    )
+
+    batch_mock = AsyncMock(return_value=([], 1, 0))
+    pipeline_mock = MagicMock()
+    pipeline_mock.index_batch_parallel = batch_mock
+    pipeline_mock.migrate_legacy_docs = AsyncMock()
+    monkeypatch.setattr(
+        _mod, "IndexingPipelineService", MagicMock(return_value=pipeline_mock),
+    )
+
+    return {
+        "session": mock_session,
+        "connector": mock_connector,
+        "confluence_client": confluence_client,
+        "task_logger": mock_task_logger,
+        "pipeline_mock": pipeline_mock,
+        "batch_mock": batch_mock,
+    }
+
+
+async def _run_index(mocks, **overrides):
+    return await index_confluence_pages(
+        session=mocks["session"],
+        connector_id=overrides.get("connector_id", _CONNECTOR_ID),
+        search_space_id=overrides.get("search_space_id", _SEARCH_SPACE_ID),
+        user_id=overrides.get("user_id", _USER_ID),
+        start_date=overrides.get("start_date", "2025-01-01"),
+        end_date=overrides.get("end_date", "2025-12-31"),
+        update_last_indexed=overrides.get("update_last_indexed", True),
+        on_heartbeat_callback=overrides.get("on_heartbeat_callback"),
+    )
+
+
+# ---------------------------------------------------------------------------
+# Slice 2: Full pipeline wiring
+# ---------------------------------------------------------------------------
+
+
+async def test_one_page_calls_pipeline_and_returns_indexed_count(confluence_mocks):
+    indexed, skipped, warning = await _run_index(confluence_mocks)
+    assert indexed == 1
+    assert skipped == 0
+    assert warning is None
+
+    confluence_mocks["batch_mock"].assert_called_once()
+    connector_docs = confluence_mocks["batch_mock"].call_args[0][0]
+    assert len(connector_docs) == 1
+    assert connector_docs[0].document_type == DocumentType.CONFLUENCE_CONNECTOR
+
+
+async def test_pipeline_called_with_max_concurrency_3(confluence_mocks):
+    await _run_index(confluence_mocks)
+    call_kwargs = confluence_mocks["batch_mock"].call_args[1]
+    assert call_kwargs.get("max_concurrency") == 3
+
+
+async def test_migrate_legacy_docs_called_before_indexing(confluence_mocks):
+    await _run_index(confluence_mocks)
+    confluence_mocks["pipeline_mock"].migrate_legacy_docs.assert_called_once()
+
+
+# ---------------------------------------------------------------------------
+# Slice 3: Page skipping (missing id/title/content)
+# ---------------------------------------------------------------------------
+
+
+async def test_pages_with_missing_id_are_skipped(confluence_mocks):
+    pages = [
+        _make_page(page_id="p1", title="Valid"),
+        _make_page(page_id="", title="Missing id"),
+    ]
+    confluence_mocks["confluence_client"].get_pages_by_date_range.return_value = (
+        pages,
+        None,
+    )
+    _, skipped, _ = await _run_index(confluence_mocks)
+    connector_docs = confluence_mocks["batch_mock"].call_args[0][0]
+    assert len(connector_docs) == 1
+    assert skipped == 1
+
+
+async def test_pages_with_missing_title_are_skipped(confluence_mocks):
+    pages = [
+        _make_page(page_id="p1", title="Valid"),
+        _make_page(page_id="p2", title=""),
+    ]
+    confluence_mocks["confluence_client"].get_pages_by_date_range.return_value = (
+        pages,
+        None,
+    )
+    _, skipped, _ = await _run_index(confluence_mocks)
+    connector_docs = confluence_mocks["batch_mock"].call_args[0][0]
+    assert len(connector_docs) == 1
+    assert skipped == 1
+
+
+async def test_pages_with_no_content_are_skipped(confluence_mocks):
+    pages = [
+        _make_page(page_id="p1", title="Valid", body="<p>ok</p>"),
+        _make_page(page_id="p2", title="Empty", body=""),
+    ]
+    confluence_mocks["confluence_client"].get_pages_by_date_range.return_value = (
+        pages,
+        None,
+    )
+    _, skipped, _ = await _run_index(confluence_mocks)
+    connector_docs = confluence_mocks["batch_mock"].call_args[0][0]
+    assert len(connector_docs) == 1
+    assert skipped == 1
+
+
+# ---------------------------------------------------------------------------
+# Slice 4: Duplicate content skipping
+# ---------------------------------------------------------------------------
+
+
+async def test_duplicate_content_pages_are_skipped(confluence_mocks, monkeypatch):
+    pages = [
+        _make_page(page_id="p1", title="One"),
+        _make_page(page_id="p2", title="Two"),
+    ]
+    confluence_mocks["confluence_client"].get_pages_by_date_range.return_value = (
+        pages,
+        None,
+    )
+
+    call_count = 0
+
+    async def _check_dup(session, content_hash):
+        nonlocal call_count
+        call_count += 1
+        if call_count == 2:
+            dup = MagicMock()
+            dup.id = 99
+            dup.document_type = "OTHER"
+            return dup
+        return None
+
+    monkeypatch.setattr(_mod, "check_duplicate_document_by_hash", _check_dup)
+
+    _, skipped, _ = await _run_index(confluence_mocks)
+    connector_docs = confluence_mocks["batch_mock"].call_args[0][0]
+    assert len(connector_docs) == 1
+    assert skipped == 1
+
+
+# ---------------------------------------------------------------------------
+# Slice 5: Heartbeat callback forwarding
+# ---------------------------------------------------------------------------
+
+
+async def test_heartbeat_callback_forwarded_to_pipeline(confluence_mocks):
+    heartbeat_cb = AsyncMock()
+    await _run_index(confluence_mocks, on_heartbeat_callback=heartbeat_cb)
+    call_kwargs = confluence_mocks["batch_mock"].call_args[1]
+    assert call_kwargs.get("on_heartbeat") is heartbeat_cb
+
+
+# ---------------------------------------------------------------------------
+# Slice 6: Empty pages and no-data success tuple
+# ---------------------------------------------------------------------------
+
+
+async def test_empty_pages_returns_zero_tuple(confluence_mocks):
+    confluence_mocks["confluence_client"].get_pages_by_date_range.return_value = (
+        [],
+        None,
+    )
+    indexed, skipped, warning = await _run_index(confluence_mocks)
+    assert indexed == 0
+    assert skipped == 0
+    assert warning is None
+    confluence_mocks["batch_mock"].assert_not_called()
+
+
+async def test_no_pages_error_message_returns_success_tuple(confluence_mocks):
+    confluence_mocks["confluence_client"].get_pages_by_date_range.return_value = (
+        [],
+        "No pages found in date range",
+    )
+    indexed, skipped, warning = await _run_index(confluence_mocks)
+    assert indexed == 0
+    assert skipped == 0
+    assert warning is None
+
+
+async def test_api_error_still_returns_3_tuple(confluence_mocks):
+    confluence_mocks["confluence_client"].get_pages_by_date_range.return_value = (
+        [],
+        "API exploded",
+    )
+    result = await _run_index(confluence_mocks)
+    assert len(result) == 3
+    assert result[0] == 0
+    assert result[1] == 0
+    assert "Failed to get Confluence pages" in result[2]
+
+
+# ---------------------------------------------------------------------------
+# Slice 7: Failed docs warning
+# ---------------------------------------------------------------------------
+
+
+async def test_failed_docs_warning_in_result(confluence_mocks):
+    confluence_mocks["batch_mock"].return_value = ([], 0, 2)
+    _, _, warning = await _run_index(confluence_mocks)
+    assert warning is not None
+    assert "2 failed" in warning

surfsense_backend/tests/unit/connector_indexers/test_google_drive_parallel.py

@@ -0,0 +1,671 @@
+"""Tests for parallel download + indexing in the Google Drive indexer."""
+
+import asyncio
+import time
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+from app.tasks.connector_indexers.google_drive_indexer import (
+    _download_files_parallel,
+    _index_full_scan,
+    _index_selected_files,
+    _index_with_delta_sync,
+)
+
+pytestmark = pytest.mark.unit
+
+_USER_ID = "00000000-0000-0000-0000-000000000001"
+_CONNECTOR_ID = 42
+_SEARCH_SPACE_ID = 1
+
+
+def _make_file_dict(file_id: str, name: str, mime: str = "text/plain") -> dict:
+    return {"id": file_id, "name": name, "mimeType": mime}
+
+
+def _mock_extract_ok(file_id: str, file_name: str):
+    """Return a successful (markdown, metadata, None) tuple."""
+    return (
+        f"# Content of {file_name}",
+        {"google_drive_file_id": file_id, "google_drive_file_name": file_name},
+        None,
+    )
+
+
+@pytest.fixture
+def mock_drive_client():
+    return MagicMock()
+
+
+@pytest.fixture
+def patch_extract(monkeypatch):
+    """Provide a helper to set the download_and_extract_content mock."""
+    def _patch(side_effect=None, return_value=None):
+        mock = AsyncMock(side_effect=side_effect, return_value=return_value)
+        monkeypatch.setattr(
+            "app.tasks.connector_indexers.google_drive_indexer.download_and_extract_content",
+            mock,
+        )
+        return mock
+    return _patch
+
+
+async def test_single_file_returns_one_connector_document(
+    mock_drive_client, patch_extract,
+):
+    """Tracer bullet: downloading one file produces one ConnectorDocument."""
+    patch_extract(return_value=_mock_extract_ok("f1", "test.txt"))
+
+    docs, failed = await _download_files_parallel(
+        mock_drive_client,
+        [_make_file_dict("f1", "test.txt")],
+        connector_id=_CONNECTOR_ID,
+        search_space_id=_SEARCH_SPACE_ID,
+        user_id=_USER_ID,
+        enable_summary=True,
+    )
+
+    assert len(docs) == 1
+    assert failed == 0
+    assert docs[0].title == "test.txt"
+    assert docs[0].unique_id == "f1"
+
+
+async def test_multiple_files_all_produce_documents(
+    mock_drive_client, patch_extract,
+):
+    """All files are downloaded and converted to ConnectorDocuments."""
+    files = [_make_file_dict(f"f{i}", f"file{i}.txt") for i in range(3)]
+    patch_extract(
+        side_effect=[_mock_extract_ok(f"f{i}", f"file{i}.txt") for i in range(3)]
+    )
+
+    docs, failed = await _download_files_parallel(
+        mock_drive_client,
+        files,
+        connector_id=_CONNECTOR_ID,
+        search_space_id=_SEARCH_SPACE_ID,
+        user_id=_USER_ID,
+        enable_summary=True,
+    )
+
+    assert len(docs) == 3
+    assert failed == 0
+    assert {d.unique_id for d in docs} == {"f0", "f1", "f2"}
+
+
+async def test_one_download_exception_does_not_block_others(
+    mock_drive_client, patch_extract,
+):
+    """A RuntimeError in one download still lets the other files succeed."""
+    files = [_make_file_dict(f"f{i}", f"file{i}.txt") for i in range(3)]
+    patch_extract(
+        side_effect=[
+            _mock_extract_ok("f0", "file0.txt"),
+            RuntimeError("network timeout"),
+            _mock_extract_ok("f2", "file2.txt"),
+        ]
+    )
+
+    docs, failed = await _download_files_parallel(
+        mock_drive_client,
+        files,
+        connector_id=_CONNECTOR_ID,
+        search_space_id=_SEARCH_SPACE_ID,
+        user_id=_USER_ID,
+        enable_summary=True,
+    )
+
+    assert len(docs) == 2
+    assert failed == 1
+    assert {d.unique_id for d in docs} == {"f0", "f2"}
+
+
+async def test_etl_error_counts_as_download_failure(
+    mock_drive_client, patch_extract,
+):
+    """download_and_extract_content returning an error is counted as failed."""
+    files = [_make_file_dict("f0", "good.txt"), _make_file_dict("f1", "bad.txt")]
+    patch_extract(
+        side_effect=[
+            _mock_extract_ok("f0", "good.txt"),
+            (None, {}, "ETL failed"),
+        ]
+    )
+
+    docs, failed = await _download_files_parallel(
+        mock_drive_client,
+        files,
+        connector_id=_CONNECTOR_ID,
+        search_space_id=_SEARCH_SPACE_ID,
+        user_id=_USER_ID,
+        enable_summary=True,
+    )
+
+    assert len(docs) == 1
+    assert failed == 1
+
+
+async def test_concurrency_bounded_by_semaphore(
+    mock_drive_client, monkeypatch,
+):
+    """Peak concurrent downloads never exceeds max_concurrency."""
+    lock = asyncio.Lock()
+    active = 0
+    peak = 0
+
+    async def _slow_extract(client, file):
+        nonlocal active, peak
+        async with lock:
+            active += 1
+            peak = max(peak, active)
+        await asyncio.sleep(0.05)
+        async with lock:
+            active -= 1
+        fid = file["id"]
+        return _mock_extract_ok(fid, file["name"])
+
+    monkeypatch.setattr(
+        "app.tasks.connector_indexers.google_drive_indexer.download_and_extract_content",
+        _slow_extract,
+    )
+
+    files = [_make_file_dict(f"f{i}", f"file{i}.txt") for i in range(6)]
+
+    docs, failed = await _download_files_parallel(
+        mock_drive_client,
+        files,
+        connector_id=_CONNECTOR_ID,
+        search_space_id=_SEARCH_SPACE_ID,
+        user_id=_USER_ID,
+        enable_summary=True,
+        max_concurrency=2,
+    )
+
+    assert len(docs) == 6
+    assert failed == 0
+    assert peak <= 2, f"Peak concurrency was {peak}, expected <= 2"
+
+
+async def test_heartbeat_fires_during_parallel_downloads(
+    mock_drive_client, monkeypatch,
+):
+    """on_heartbeat is called at least once when downloads take time."""
+    import app.tasks.connector_indexers.google_drive_indexer as _mod
+
+    monkeypatch.setattr(_mod, "HEARTBEAT_INTERVAL_SECONDS", 0)
+
+    async def _slow_extract(client, file):
+        await asyncio.sleep(0.05)
+        return _mock_extract_ok(file["id"], file["name"])
+
+    monkeypatch.setattr(
+        "app.tasks.connector_indexers.google_drive_indexer.download_and_extract_content",
+        _slow_extract,
+    )
+
+    heartbeat_calls: list[int] = []
+
+    async def _on_heartbeat(count: int):
+        heartbeat_calls.append(count)
+
+    files = [_make_file_dict(f"f{i}", f"file{i}.txt") for i in range(3)]
+
+    docs, failed = await _download_files_parallel(
+        mock_drive_client,
+        files,
+        connector_id=_CONNECTOR_ID,
+        search_space_id=_SEARCH_SPACE_ID,
+        user_id=_USER_ID,
+        enable_summary=True,
+        on_heartbeat=_on_heartbeat,
+    )
+
+    assert len(docs) == 3
+    assert failed == 0
+    assert len(heartbeat_calls) >= 1, "Heartbeat should have fired at least once"
+
+
+# ---------------------------------------------------------------------------
+# Slice 6, 6b, 6c -- _index_full_scan three-phase pipeline
+# ---------------------------------------------------------------------------
+
+def _folder_dict(file_id: str, name: str) -> dict:
+    return {"id": file_id, "name": name, "mimeType": "application/vnd.google-apps.folder"}
+
+
+@pytest.fixture
+def full_scan_mocks(mock_drive_client, monkeypatch):
+    """Wire up all mocks needed to call _index_full_scan in isolation."""
+    import app.tasks.connector_indexers.google_drive_indexer as _mod
+
+    mock_session = AsyncMock()
+    mock_connector = MagicMock()
+    mock_task_logger = MagicMock()
+    mock_task_logger.log_task_progress = AsyncMock()
+    mock_log_entry = MagicMock()
+
+    skip_results: dict[str, tuple[bool, str | None]] = {}
+
+    async def _fake_skip(session, file, search_space_id):
+        return skip_results.get(file["id"], (False, None))
+
+    monkeypatch.setattr(_mod, "_should_skip_file", _fake_skip)
+
+    download_mock = AsyncMock(return_value=([], 0))
+    monkeypatch.setattr(_mod, "_download_files_parallel", download_mock)
+
+    batch_mock = AsyncMock(return_value=([], 0, 0))
+    pipeline_mock = MagicMock()
+    pipeline_mock.index_batch_parallel = batch_mock
+    monkeypatch.setattr(
+        _mod, "IndexingPipelineService", MagicMock(return_value=pipeline_mock),
+    )
+
+    monkeypatch.setattr(
+        _mod, "get_user_long_context_llm", AsyncMock(return_value=MagicMock()),
+    )
+
+    return {
+        "drive_client": mock_drive_client,
+        "session": mock_session,
+        "connector": mock_connector,
+        "task_logger": mock_task_logger,
+        "log_entry": mock_log_entry,
+        "skip_results": skip_results,
+        "download_mock": download_mock,
+        "batch_mock": batch_mock,
+        "pipeline_mock": pipeline_mock,
+    }
+
+
+async def _run_full_scan(mocks, *, max_files=500, include_subfolders=False):
+    return await _index_full_scan(
+        mocks["drive_client"],
+        mocks["session"],
+        mocks["connector"],
+        _CONNECTOR_ID,
+        _SEARCH_SPACE_ID,
+        _USER_ID,
+        "folder-root",
+        "My Folder",
+        mocks["task_logger"],
+        mocks["log_entry"],
+        max_files,
+        include_subfolders=include_subfolders,
+        enable_summary=True,
+    )
+
+
+async def test_full_scan_three_phase_counts(full_scan_mocks, monkeypatch):
+    """Full scan collects files serially, downloads and indexes in parallel,
+    and returns correct (indexed, skipped) with renames counted as indexed."""
+    import app.tasks.connector_indexers.google_drive_indexer as _mod
+
+    page_files = [
+        _folder_dict("folder1", "SubFolder"),
+        _make_file_dict("skip1", "unchanged.txt"),
+        _make_file_dict("rename1", "renamed.txt"),
+        _make_file_dict("new1", "new1.txt"),
+        _make_file_dict("new2", "new2.txt"),
+    ]
+
+    monkeypatch.setattr(
+        _mod, "get_files_in_folder",
+        AsyncMock(return_value=(page_files, None, None)),
+    )
+
+    full_scan_mocks["skip_results"]["skip1"] = (True, "unchanged")
+    full_scan_mocks["skip_results"]["rename1"] = (True, "File renamed: 'old' → 'renamed.txt'")
+
+    mock_docs = [MagicMock(), MagicMock()]
+    full_scan_mocks["download_mock"].return_value = (mock_docs, 0)
+    full_scan_mocks["batch_mock"].return_value = ([], 2, 0)
+
+    indexed, skipped = await _run_full_scan(full_scan_mocks)
+
+    assert indexed == 3  # 1 renamed + 2 from batch
+    assert skipped == 1  # 1 unchanged
+
+    full_scan_mocks["download_mock"].assert_called_once()
+    call_files = full_scan_mocks["download_mock"].call_args[0][1]
+    assert len(call_files) == 2
+    assert {f["id"] for f in call_files} == {"new1", "new2"}
+
+
+async def test_full_scan_respects_max_files(full_scan_mocks, monkeypatch):
+    """Only max_files non-folder files are processed; the rest are ignored."""
+    import app.tasks.connector_indexers.google_drive_indexer as _mod
+
+    page_files = [_make_file_dict(f"f{i}", f"file{i}.txt") for i in range(10)]
+
+    monkeypatch.setattr(
+        _mod, "get_files_in_folder",
+        AsyncMock(return_value=(page_files, None, None)),
+    )
+
+    full_scan_mocks["download_mock"].return_value = ([], 0)
+    full_scan_mocks["batch_mock"].return_value = ([], 0, 0)
+
+    await _run_full_scan(full_scan_mocks, max_files=3)
+
+    download_call_files = full_scan_mocks["download_mock"].call_args[0][1]
+    assert len(download_call_files) == 3
+
+
+async def test_full_scan_uses_max_concurrency_3_for_indexing(
+    full_scan_mocks, monkeypatch,
+):
+    """index_batch_parallel is called with max_concurrency=3."""
+    import app.tasks.connector_indexers.google_drive_indexer as _mod
+
+    page_files = [_make_file_dict("f1", "file1.txt")]
+    monkeypatch.setattr(
+        _mod, "get_files_in_folder",
+        AsyncMock(return_value=(page_files, None, None)),
+    )
+
+    mock_docs = [MagicMock()]
+    full_scan_mocks["download_mock"].return_value = (mock_docs, 0)
+    full_scan_mocks["batch_mock"].return_value = ([], 1, 0)
+
+    await _run_full_scan(full_scan_mocks)
+
+    call_kwargs = full_scan_mocks["batch_mock"].call_args
+    assert call_kwargs[1].get("max_concurrency") == 3 or (
+        len(call_kwargs[0]) > 2 and call_kwargs[0][2] == 3
+    )
+
+
+# ---------------------------------------------------------------------------
+# Slice 7 -- _index_with_delta_sync three-phase pipeline
+# ---------------------------------------------------------------------------
+
+async def test_delta_sync_removals_serial_rest_parallel(monkeypatch):
+    """Removed/trashed changes call _remove_document; the rest go through
+    _download_files_parallel and index_batch_parallel."""
+    import app.tasks.connector_indexers.google_drive_indexer as _mod
+
+    changes = [
+        {"fileId": "del1", "removed": True},
+        {"fileId": "del2", "file": {"id": "del2", "trashed": True}},
+        {"fileId": "trash1", "file": {"id": "trash1", "trashed": True}},
+        {"fileId": "mod1", "file": _make_file_dict("mod1", "modified1.txt")},
+        {"fileId": "mod2", "file": _make_file_dict("mod2", "modified2.txt")},
+    ]
+
+    monkeypatch.setattr(
+        _mod, "fetch_all_changes",
+        AsyncMock(return_value=(changes, "new-token", None)),
+    )
+
+    change_types = {
+        "del1": "removed",
+        "del2": "removed",
+        "trash1": "trashed",
+        "mod1": "modified",
+        "mod2": "modified",
+    }
+    monkeypatch.setattr(
+        _mod, "categorize_change",
+        lambda change: change_types[change["fileId"]],
+    )
+
+    remove_calls: list[str] = []
+
+    async def _fake_remove(session, file_id, search_space_id):
+        remove_calls.append(file_id)
+
+    monkeypatch.setattr(_mod, "_remove_document", _fake_remove)
+
+    monkeypatch.setattr(
+        _mod, "_should_skip_file",
+        AsyncMock(return_value=(False, None)),
+    )
+
+    mock_docs = [MagicMock(), MagicMock()]
+    download_mock = AsyncMock(return_value=(mock_docs, 0))
+    monkeypatch.setattr(_mod, "_download_files_parallel", download_mock)
+
+    batch_mock = AsyncMock(return_value=([], 2, 0))
+    pipeline_mock = MagicMock()
+    pipeline_mock.index_batch_parallel = batch_mock
+    monkeypatch.setattr(
+        _mod, "IndexingPipelineService", MagicMock(return_value=pipeline_mock),
+    )
+    monkeypatch.setattr(
+        _mod, "get_user_long_context_llm", AsyncMock(return_value=MagicMock()),
+    )
+
+    mock_session = AsyncMock()
+    mock_task_logger = MagicMock()
+    mock_task_logger.log_task_progress = AsyncMock()
+
+    indexed, skipped = await _index_with_delta_sync(
+        MagicMock(),
+        mock_session,
+        MagicMock(),
+        _CONNECTOR_ID,
+        _SEARCH_SPACE_ID,
+        _USER_ID,
+        "folder-root",
+        "start-token-abc",
+        mock_task_logger,
+        MagicMock(),
+        max_files=500,
+        enable_summary=True,
+    )
+
+    assert sorted(remove_calls) == ["del1", "del2", "trash1"]
+
+    download_mock.assert_called_once()
+    downloaded_files = download_mock.call_args[0][1]
+    assert len(downloaded_files) == 2
+    assert {f["id"] for f in downloaded_files} == {"mod1", "mod2"}
+
+    assert indexed == 2
+    assert skipped == 0
+
+
+# ---------------------------------------------------------------------------
+# _index_selected_files -- parallel indexing of user-selected files
+# ---------------------------------------------------------------------------
+
+@pytest.fixture
+def selected_files_mocks(mock_drive_client, monkeypatch):
+    """Wire up mocks for _index_selected_files tests."""
+    import app.tasks.connector_indexers.google_drive_indexer as _mod
+
+    mock_session = AsyncMock()
+
+    get_file_results: dict[str, tuple[dict | None, str | None]] = {}
+
+    async def _fake_get_file(client, file_id):
+        return get_file_results.get(file_id, (None, f"Not configured: {file_id}"))
+
+    monkeypatch.setattr(_mod, "get_file_by_id", _fake_get_file)
+
+    skip_results: dict[str, tuple[bool, str | None]] = {}
+
+    async def _fake_skip(session, file, search_space_id):
+        return skip_results.get(file["id"], (False, None))
+
+    monkeypatch.setattr(_mod, "_should_skip_file", _fake_skip)
+
+    download_and_index_mock = AsyncMock(return_value=(0, 0))
+    monkeypatch.setattr(_mod, "_download_and_index", download_and_index_mock)
+
+    return {
+        "drive_client": mock_drive_client,
+        "session": mock_session,
+        "get_file_results": get_file_results,
+        "skip_results": skip_results,
+        "download_and_index_mock": download_and_index_mock,
+    }
+
+
+async def _run_selected(mocks, file_ids):
+    return await _index_selected_files(
+        mocks["drive_client"],
+        mocks["session"],
+        file_ids,
+        connector_id=_CONNECTOR_ID,
+        search_space_id=_SEARCH_SPACE_ID,
+        user_id=_USER_ID,
+        enable_summary=True,
+    )
+
+
+async def test_selected_files_single_file_indexed(selected_files_mocks):
+    """Tracer bullet: one file fetched, not skipped, indexed via parallel pipeline."""
+    selected_files_mocks["get_file_results"]["f1"] = (
+        _make_file_dict("f1", "report.pdf"),
+        None,
+    )
+    selected_files_mocks["download_and_index_mock"].return_value = (1, 0)
+
+    indexed, skipped, errors = await _run_selected(
+        selected_files_mocks, [("f1", "report.pdf")],
+    )
+
+    assert indexed == 1
+    assert skipped == 0
+    assert errors == []
+    selected_files_mocks["download_and_index_mock"].assert_called_once()
+
+
+async def test_selected_files_fetch_failure_isolation(selected_files_mocks):
+    """get_file_by_id failing for one file collects an error; others still indexed."""
+    selected_files_mocks["get_file_results"]["f1"] = (
+        _make_file_dict("f1", "first.txt"), None,
+    )
+    selected_files_mocks["get_file_results"]["f2"] = (None, "HTTP 404")
+    selected_files_mocks["get_file_results"]["f3"] = (
+        _make_file_dict("f3", "third.txt"), None,
+    )
+    selected_files_mocks["download_and_index_mock"].return_value = (2, 0)
+
+    indexed, skipped, errors = await _run_selected(
+        selected_files_mocks,
+        [("f1", "first.txt"), ("f2", "mid.txt"), ("f3", "third.txt")],
+    )
+
+    assert indexed == 2
+    assert skipped == 0
+    assert len(errors) == 1
+    assert "mid.txt" in errors[0]
+    assert "HTTP 404" in errors[0]
+
+
+async def test_selected_files_skip_rename_counting(selected_files_mocks):
+    """Unchanged files are skipped, renames counted as indexed,
+    and only new files are sent to _download_and_index."""
+    for fid, fname in [("s1", "unchanged.txt"), ("r1", "renamed.txt"),
+                       ("n1", "new1.txt"), ("n2", "new2.txt")]:
+        selected_files_mocks["get_file_results"][fid] = (
+            _make_file_dict(fid, fname), None,
+        )
+
+    selected_files_mocks["skip_results"]["s1"] = (True, "unchanged")
+    selected_files_mocks["skip_results"]["r1"] = (True, "File renamed: 'old' \u2192 'renamed.txt'")
+
+    selected_files_mocks["download_and_index_mock"].return_value = (2, 0)
+
+    indexed, skipped, errors = await _run_selected(
+        selected_files_mocks,
+        [("s1", "unchanged.txt"), ("r1", "renamed.txt"),
+         ("n1", "new1.txt"), ("n2", "new2.txt")],
+    )
+
+    assert indexed == 3   # 1 renamed + 2 batch
+    assert skipped == 1   # 1 unchanged
+    assert errors == []
+
+    mock = selected_files_mocks["download_and_index_mock"]
+    mock.assert_called_once()
+    call_files = mock.call_args[1].get("files") if "files" in (mock.call_args[1] or {}) else mock.call_args[0][2]
+    assert len(call_files) == 2
+    assert {f["id"] for f in call_files} == {"n1", "n2"}
+
+
+# ---------------------------------------------------------------------------
+# asyncio.to_thread verification — prove blocking calls run in parallel
+# ---------------------------------------------------------------------------
+
+async def test_client_download_file_runs_in_thread_parallel():
+    """Calling download_file concurrently via asyncio.gather should overlap
+    blocking work on separate threads, proving to_thread is effective.
+
+    Strategy: patch _sync_download_file with a blocking time.sleep(0.2).
+    Launch 3 concurrent calls. Serial would take >=0.6s; parallel < 0.4s.
+    """
+    from app.connectors.google_drive.client import GoogleDriveClient
+
+    BLOCK_SECONDS = 0.2
+    NUM_CALLS = 3
+
+    def _blocking_download(service, file_id, credentials):
+        time.sleep(BLOCK_SECONDS)
+        return b"fake-content", None
+
+    client = GoogleDriveClient.__new__(GoogleDriveClient)
+    client.service = MagicMock()
+    client._resolved_credentials = MagicMock()
+    client._service_lock = asyncio.Lock()
+
+    with patch.object(
+        GoogleDriveClient, "_sync_download_file", staticmethod(_blocking_download),
+    ):
+        start = time.monotonic()
+        results = await asyncio.gather(
+            *(client.download_file(f"file-{i}") for i in range(NUM_CALLS))
+        )
+        elapsed = time.monotonic() - start
+
+    for content, error in results:
+        assert content == b"fake-content"
+        assert error is None
+
+    serial_minimum = BLOCK_SECONDS * NUM_CALLS
+    assert elapsed < serial_minimum, (
+        f"Elapsed {elapsed:.2f}s >= serial minimum {serial_minimum:.2f}s — "
+        f"downloads are not running in parallel"
+    )
+
+
+async def test_client_export_google_file_runs_in_thread_parallel():
+    """Same strategy for export_google_file — verify to_thread parallelism."""
+    from app.connectors.google_drive.client import GoogleDriveClient
+
+    BLOCK_SECONDS = 0.2
+    NUM_CALLS = 3
+
+    def _blocking_export(service, file_id, mime_type, credentials):
+        time.sleep(BLOCK_SECONDS)
+        return b"exported", None
+
+    client = GoogleDriveClient.__new__(GoogleDriveClient)
+    client.service = MagicMock()
+    client._resolved_credentials = MagicMock()
+    client._service_lock = asyncio.Lock()
+
+    with patch.object(
+        GoogleDriveClient, "_sync_export_google_file", staticmethod(_blocking_export),
+    ):
+        start = time.monotonic()
+        results = await asyncio.gather(
+            *(client.export_google_file(f"file-{i}", "application/pdf")
+              for i in range(NUM_CALLS))
+        )
+        elapsed = time.monotonic() - start
+
+    for content, error in results:
+        assert content == b"exported"
+        assert error is None
+
+    serial_minimum = BLOCK_SECONDS * NUM_CALLS
+    assert elapsed < serial_minimum, (
+        f"Elapsed {elapsed:.2f}s >= serial minimum {serial_minimum:.2f}s — "
+        f"exports are not running in parallel"
+    )

surfsense_backend/tests/unit/connector_indexers/test_jira_parallel.py

@@ -0,0 +1,372 @@
+"""Tests for Jira indexer migrated to the unified parallel pipeline."""
+
+from unittest.mock import AsyncMock, MagicMock
+
+import pytest
+
+import app.tasks.connector_indexers.jira_indexer as _mod
+from app.db import DocumentType
+from app.tasks.connector_indexers.jira_indexer import (
+    _build_connector_doc,
+    index_jira_issues,
+)
+
+pytestmark = pytest.mark.unit
+
+_USER_ID = "00000000-0000-0000-0000-000000000001"
+_CONNECTOR_ID = 42
+_SEARCH_SPACE_ID = 1
+
+
+def _make_issue(
+    issue_key: str = "ENG-1",
+    issue_id: str = "10001",
+    title: str = "Fix login",
+):
+    return {"key": issue_key, "id": issue_id, "title": title}
+
+
+def _make_formatted_issue(
+    issue_key: str = "ENG-1",
+    issue_id: str = "10001",
+    title: str = "Fix login",
+    status: str = "In Progress",
+    priority: str = "High",
+    comments=None,
+):
+    return {
+        "key": issue_key,
+        "id": issue_id,
+        "title": title,
+        "status": status,
+        "priority": priority,
+        "comments": comments or [],
+    }
+
+
+# ---------------------------------------------------------------------------
+# Slice 1: _build_connector_doc tracer bullet
+# ---------------------------------------------------------------------------
+
+
+async def test_build_connector_doc_produces_correct_fields():
+    issue = _make_issue(issue_key="ENG-42", issue_id="4242", title="Fix auth bug")
+    formatted = _make_formatted_issue(
+        issue_key="ENG-42",
+        issue_id="4242",
+        title="Fix auth bug",
+        status="Done",
+        priority="Urgent",
+        comments=[{"id": "c1"}],
+    )
+    markdown = "# ENG-42: Fix auth bug\n\nBody"
+
+    doc = _build_connector_doc(
+        issue,
+        formatted,
+        markdown,
+        connector_id=_CONNECTOR_ID,
+        search_space_id=_SEARCH_SPACE_ID,
+        user_id=_USER_ID,
+        enable_summary=True,
+    )
+
+    assert doc.title == "ENG-42: 4242"
+    assert doc.unique_id == "ENG-42"
+    assert doc.document_type == DocumentType.JIRA_CONNECTOR
+    assert doc.source_markdown == markdown
+    assert doc.search_space_id == _SEARCH_SPACE_ID
+    assert doc.connector_id == _CONNECTOR_ID
+    assert doc.created_by_id == _USER_ID
+    assert doc.should_summarize is True
+    assert doc.metadata["issue_id"] == "ENG-42"
+    assert doc.metadata["issue_identifier"] == "ENG-42"
+    assert doc.metadata["issue_title"] == "4242"
+    assert doc.metadata["state"] == "Done"
+    assert doc.metadata["priority"] == "Urgent"
+    assert doc.metadata["comment_count"] == 1
+    assert doc.metadata["connector_id"] == _CONNECTOR_ID
+    assert doc.metadata["document_type"] == "Jira Issue"
+    assert doc.metadata["connector_type"] == "Jira"
+    assert doc.fallback_summary is not None
+    assert "ENG-42" in doc.fallback_summary
+    assert markdown in doc.fallback_summary
+
+
+async def test_build_connector_doc_summary_disabled():
+    doc = _build_connector_doc(
+        _make_issue(),
+        _make_formatted_issue(),
+        "# content",
+        connector_id=_CONNECTOR_ID,
+        search_space_id=_SEARCH_SPACE_ID,
+        user_id=_USER_ID,
+        enable_summary=False,
+    )
+    assert doc.should_summarize is False
+
+
+# ---------------------------------------------------------------------------
+# Shared fixtures for Slices 2-7
+# ---------------------------------------------------------------------------
+
+
+def _mock_connector(enable_summary: bool = True):
+    c = MagicMock()
+    c.config = {"access_token": "tok"}
+    c.enable_summary = enable_summary
+    c.last_indexed_at = None
+    return c
+
+
+def _mock_jira_client(issues=None, error=None):
+    client = MagicMock()
+    client.get_issues_by_date_range = AsyncMock(
+        return_value=(issues if issues is not None else [], error),
+    )
+    client.format_issue = MagicMock(
+        side_effect=lambda i: _make_formatted_issue(
+            issue_key=i.get("key", ""),
+            issue_id=i.get("id", ""),
+            title=i.get("title", ""),
+        )
+    )
+    client.format_issue_to_markdown = MagicMock(
+        side_effect=lambda fi: f"# {fi.get('key', '')}: {fi.get('id', '')}\n\nContent"
+    )
+    client.close = AsyncMock()
+    return client
+
+
+@pytest.fixture
+def jira_mocks(monkeypatch):
+    mock_session = AsyncMock()
+    mock_session.no_autoflush = MagicMock()
+
+    mock_connector = _mock_connector()
+    monkeypatch.setattr(
+        _mod, "get_connector_by_id", AsyncMock(return_value=mock_connector),
+    )
+
+    jira_client = _mock_jira_client(issues=[_make_issue()])
+    monkeypatch.setattr(
+        _mod, "JiraHistoryConnector", MagicMock(return_value=jira_client),
+    )
+
+    monkeypatch.setattr(
+        _mod, "check_duplicate_document_by_hash", AsyncMock(return_value=None),
+    )
+    monkeypatch.setattr(
+        _mod, "update_connector_last_indexed", AsyncMock(),
+    )
+    monkeypatch.setattr(
+        _mod, "calculate_date_range", MagicMock(return_value=("2025-01-01", "2025-12-31")),
+    )
+
+    mock_task_logger = MagicMock()
+    mock_task_logger.log_task_start = AsyncMock(return_value=MagicMock())
+    mock_task_logger.log_task_progress = AsyncMock()
+    mock_task_logger.log_task_success = AsyncMock()
+    mock_task_logger.log_task_failure = AsyncMock()
+    monkeypatch.setattr(
+        _mod, "TaskLoggingService", MagicMock(return_value=mock_task_logger),
+    )
+
+    batch_mock = AsyncMock(return_value=([], 1, 0))
+    pipeline_mock = MagicMock()
+    pipeline_mock.index_batch_parallel = batch_mock
+    pipeline_mock.migrate_legacy_docs = AsyncMock()
+    monkeypatch.setattr(
+        _mod, "IndexingPipelineService", MagicMock(return_value=pipeline_mock),
+    )
+
+    return {
+        "session": mock_session,
+        "connector": mock_connector,
+        "jira_client": jira_client,
+        "task_logger": mock_task_logger,
+        "pipeline_mock": pipeline_mock,
+        "batch_mock": batch_mock,
+    }
+
+
+async def _run_index(mocks, **overrides):
+    return await index_jira_issues(
+        session=mocks["session"],
+        connector_id=overrides.get("connector_id", _CONNECTOR_ID),
+        search_space_id=overrides.get("search_space_id", _SEARCH_SPACE_ID),
+        user_id=overrides.get("user_id", _USER_ID),
+        start_date=overrides.get("start_date", "2025-01-01"),
+        end_date=overrides.get("end_date", "2025-12-31"),
+        update_last_indexed=overrides.get("update_last_indexed", True),
+        on_heartbeat_callback=overrides.get("on_heartbeat_callback"),
+    )
+
+
+# ---------------------------------------------------------------------------
+# Slice 2: Full pipeline wiring
+# ---------------------------------------------------------------------------
+
+
+async def test_one_issue_calls_pipeline_and_returns_indexed_count(jira_mocks):
+    indexed, skipped, warning = await _run_index(jira_mocks)
+    assert indexed == 1
+    assert skipped == 0
+    assert warning is None
+
+    jira_mocks["batch_mock"].assert_called_once()
+    connector_docs = jira_mocks["batch_mock"].call_args[0][0]
+    assert len(connector_docs) == 1
+    assert connector_docs[0].document_type == DocumentType.JIRA_CONNECTOR
+
+
+async def test_pipeline_called_with_max_concurrency_3(jira_mocks):
+    await _run_index(jira_mocks)
+    call_kwargs = jira_mocks["batch_mock"].call_args[1]
+    assert call_kwargs.get("max_concurrency") == 3
+
+
+async def test_migrate_legacy_docs_called_before_indexing(jira_mocks):
+    await _run_index(jira_mocks)
+    jira_mocks["pipeline_mock"].migrate_legacy_docs.assert_called_once()
+
+
+# ---------------------------------------------------------------------------
+# Slice 3: Issue skipping (missing key/title/content)
+# ---------------------------------------------------------------------------
+
+
+async def test_issues_with_missing_key_are_skipped(jira_mocks):
+    issues = [
+        _make_issue(issue_key="ENG-1", issue_id="10001"),
+        {"key": "", "id": "10002", "title": "No key"},
+    ]
+    jira_mocks["jira_client"].get_issues_by_date_range.return_value = (issues, None)
+
+    _, skipped, _ = await _run_index(jira_mocks)
+    connector_docs = jira_mocks["batch_mock"].call_args[0][0]
+    assert len(connector_docs) == 1
+    assert skipped == 1
+
+
+async def test_issues_with_missing_title_are_skipped(jira_mocks):
+    issues = [
+        _make_issue(issue_key="ENG-1", issue_id="10001"),
+        {"key": "ENG-2", "id": "", "title": "Missing id used as title"},
+    ]
+    jira_mocks["jira_client"].get_issues_by_date_range.return_value = (issues, None)
+
+    _, skipped, _ = await _run_index(jira_mocks)
+    connector_docs = jira_mocks["batch_mock"].call_args[0][0]
+    assert len(connector_docs) == 1
+    assert skipped == 1
+
+
+async def test_issues_with_no_content_are_skipped(jira_mocks):
+    issues = [
+        _make_issue(issue_key="ENG-1", issue_id="10001"),
+        _make_issue(issue_key="ENG-2", issue_id="10002"),
+    ]
+    jira_mocks["jira_client"].get_issues_by_date_range.return_value = (issues, None)
+
+    jira_mocks["jira_client"].format_issue_to_markdown.side_effect = [
+        "# ENG-1: 10001\n\nContent",
+        "",
+    ]
+    _, skipped, _ = await _run_index(jira_mocks)
+    connector_docs = jira_mocks["batch_mock"].call_args[0][0]
+    assert len(connector_docs) == 1
+    assert skipped == 1
+
+
+# ---------------------------------------------------------------------------
+# Slice 4: Duplicate content skipping
+# ---------------------------------------------------------------------------
+
+
+async def test_duplicate_content_issues_are_skipped(jira_mocks, monkeypatch):
+    issues = [
+        _make_issue(issue_key="ENG-1", issue_id="10001"),
+        _make_issue(issue_key="ENG-2", issue_id="10002"),
+    ]
+    jira_mocks["jira_client"].get_issues_by_date_range.return_value = (issues, None)
+
+    call_count = 0
+
+    async def _check_dup(session, content_hash):
+        nonlocal call_count
+        call_count += 1
+        if call_count == 2:
+            dup = MagicMock()
+            dup.id = 99
+            dup.document_type = "OTHER"
+            return dup
+        return None
+
+    monkeypatch.setattr(_mod, "check_duplicate_document_by_hash", _check_dup)
+
+    _, skipped, _ = await _run_index(jira_mocks)
+    connector_docs = jira_mocks["batch_mock"].call_args[0][0]
+    assert len(connector_docs) == 1
+    assert skipped == 1
+
+
+# ---------------------------------------------------------------------------
+# Slice 5: Heartbeat callback forwarding
+# ---------------------------------------------------------------------------
+
+
+async def test_heartbeat_callback_forwarded_to_pipeline(jira_mocks):
+    heartbeat_cb = AsyncMock()
+    await _run_index(jira_mocks, on_heartbeat_callback=heartbeat_cb)
+    call_kwargs = jira_mocks["batch_mock"].call_args[1]
+    assert call_kwargs.get("on_heartbeat") is heartbeat_cb
+
+
+# ---------------------------------------------------------------------------
+# Slice 6: Empty issues and no-data success tuple
+# ---------------------------------------------------------------------------
+
+
+async def test_empty_issues_returns_zero_tuple(jira_mocks):
+    jira_mocks["jira_client"].get_issues_by_date_range.return_value = ([], None)
+    indexed, skipped, warning = await _run_index(jira_mocks)
+    assert indexed == 0
+    assert skipped == 0
+    assert warning is None
+    jira_mocks["batch_mock"].assert_not_called()
+
+
+async def test_no_issues_error_message_returns_success_tuple(jira_mocks):
+    jira_mocks["jira_client"].get_issues_by_date_range.return_value = (
+        [],
+        "No issues found in date range",
+    )
+    indexed, skipped, warning = await _run_index(jira_mocks)
+    assert indexed == 0
+    assert skipped == 0
+    assert warning is None
+
+
+async def test_api_error_still_returns_3_tuple(jira_mocks):
+    jira_mocks["jira_client"].get_issues_by_date_range.return_value = (
+        [],
+        "API exploded",
+    )
+    result = await _run_index(jira_mocks)
+    assert len(result) == 3
+    assert result[0] == 0
+    assert result[1] == 0
+    assert "Failed to get Jira issues" in result[2]
+
+
+# ---------------------------------------------------------------------------
+# Slice 7: Failed docs warning
+# ---------------------------------------------------------------------------
+
+
+async def test_failed_docs_warning_in_result(jira_mocks):
+    jira_mocks["batch_mock"].return_value = ([], 0, 2)
+    _, _, warning = await _run_index(jira_mocks)
+    assert warning is not None
+    assert "2 failed" in warning

surfsense_backend/tests/unit/connector_indexers/test_linear_parallel.py

@@ -0,0 +1,355 @@
+"""Tests for Linear indexer migrated to the unified parallel pipeline."""
+
+from unittest.mock import AsyncMock, MagicMock
+
+import pytest
+
+import app.tasks.connector_indexers.linear_indexer as _mod
+from app.db import DocumentType
+from app.tasks.connector_indexers.linear_indexer import (
+    _build_connector_doc,
+    index_linear_issues,
+)
+
+pytestmark = pytest.mark.unit
+
+_USER_ID = "00000000-0000-0000-0000-000000000001"
+_CONNECTOR_ID = 42
+_SEARCH_SPACE_ID = 1
+
+
+def _make_issue(
+    issue_id: str = "issue-1",
+    identifier: str = "ENG-1",
+    title: str = "Fix bug",
+):
+    return {"id": issue_id, "identifier": identifier, "title": title}
+
+
+def _make_formatted_issue(
+    issue_id: str = "issue-1",
+    identifier: str = "ENG-1",
+    title: str = "Fix bug",
+    state: str = "In Progress",
+    priority: str = "High",
+    comments=None,
+):
+    return {
+        "id": issue_id,
+        "identifier": identifier,
+        "title": title,
+        "state": state,
+        "priority": priority,
+        "description": "Some description",
+        "comments": comments or [],
+    }
+
+
+# ---------------------------------------------------------------------------
+# Slice 1: _build_connector_doc tracer bullet
+# ---------------------------------------------------------------------------
+
+
+async def test_build_connector_doc_produces_correct_fields():
+    """Tracer bullet: a Linear issue produces a ConnectorDocument with correct fields."""
+    issue = _make_issue(issue_id="abc-123", identifier="ENG-42", title="Fix login bug")
+    formatted = _make_formatted_issue(
+        issue_id="abc-123",
+        identifier="ENG-42",
+        title="Fix login bug",
+        state="Done",
+        priority="Urgent",
+        comments=[{"id": "c1"}],
+    )
+    markdown = "# ENG-42: Fix login bug\n\nDescription here"
+
+    doc = _build_connector_doc(
+        issue,
+        formatted,
+        markdown,
+        connector_id=_CONNECTOR_ID,
+        search_space_id=_SEARCH_SPACE_ID,
+        user_id=_USER_ID,
+        enable_summary=True,
+    )
+
+    assert doc.title == "ENG-42: Fix login bug"
+    assert doc.unique_id == "abc-123"
+    assert doc.document_type == DocumentType.LINEAR_CONNECTOR
+    assert doc.source_markdown == markdown
+    assert doc.search_space_id == _SEARCH_SPACE_ID
+    assert doc.connector_id == _CONNECTOR_ID
+    assert doc.created_by_id == _USER_ID
+    assert doc.should_summarize is True
+    assert doc.metadata["issue_id"] == "abc-123"
+    assert doc.metadata["issue_identifier"] == "ENG-42"
+    assert doc.metadata["issue_title"] == "Fix login bug"
+    assert doc.metadata["state"] == "Done"
+    assert doc.metadata["priority"] == "Urgent"
+    assert doc.metadata["comment_count"] == 1
+    assert doc.metadata["connector_id"] == _CONNECTOR_ID
+    assert doc.metadata["document_type"] == "Linear Issue"
+    assert doc.metadata["connector_type"] == "Linear"
+    assert doc.fallback_summary is not None
+    assert "ENG-42" in doc.fallback_summary
+    assert markdown in doc.fallback_summary
+
+
+async def test_build_connector_doc_summary_disabled():
+    """When enable_summary is False, should_summarize is False."""
+    doc = _build_connector_doc(
+        _make_issue(),
+        _make_formatted_issue(),
+        "# content",
+        connector_id=_CONNECTOR_ID,
+        search_space_id=_SEARCH_SPACE_ID,
+        user_id=_USER_ID,
+        enable_summary=False,
+    )
+
+    assert doc.should_summarize is False
+
+
+# ---------------------------------------------------------------------------
+# Shared fixtures for Slices 2-6
+# ---------------------------------------------------------------------------
+
+
+def _mock_connector(enable_summary: bool = True):
+    c = MagicMock()
+    c.config = {"access_token": "tok"}
+    c.enable_summary = enable_summary
+    c.last_indexed_at = None
+    return c
+
+
+def _mock_linear_client(issues=None, error=None):
+    client = MagicMock()
+    client.get_issues_by_date_range = AsyncMock(
+        return_value=(issues if issues is not None else [], error),
+    )
+    client.format_issue = MagicMock(side_effect=lambda i: _make_formatted_issue(
+        issue_id=i.get("id", ""),
+        identifier=i.get("identifier", ""),
+        title=i.get("title", ""),
+    ))
+    client.format_issue_to_markdown = MagicMock(
+        side_effect=lambda fi: f"# {fi.get('identifier', '')}: {fi.get('title', '')}\n\nContent"
+    )
+    return client
+
+
+@pytest.fixture
+def linear_mocks(monkeypatch):
+    """Wire up all external boundary mocks for index_linear_issues."""
+    mock_session = AsyncMock()
+    mock_session.no_autoflush = MagicMock()
+
+    mock_connector = _mock_connector()
+    monkeypatch.setattr(
+        _mod, "get_connector_by_id", AsyncMock(return_value=mock_connector),
+    )
+
+    linear_client = _mock_linear_client(issues=[_make_issue()])
+    monkeypatch.setattr(
+        _mod, "LinearConnector", MagicMock(return_value=linear_client),
+    )
+
+    monkeypatch.setattr(
+        _mod, "check_duplicate_document_by_hash", AsyncMock(return_value=None),
+    )
+
+    monkeypatch.setattr(
+        _mod, "update_connector_last_indexed", AsyncMock(),
+    )
+
+    monkeypatch.setattr(
+        _mod, "calculate_date_range", MagicMock(return_value=("2025-01-01", "2025-12-31")),
+    )
+
+    mock_task_logger = MagicMock()
+    mock_task_logger.log_task_start = AsyncMock(return_value=MagicMock())
+    mock_task_logger.log_task_progress = AsyncMock()
+    mock_task_logger.log_task_success = AsyncMock()
+    mock_task_logger.log_task_failure = AsyncMock()
+    monkeypatch.setattr(
+        _mod, "TaskLoggingService", MagicMock(return_value=mock_task_logger),
+    )
+
+    batch_mock = AsyncMock(return_value=([], 1, 0))
+    pipeline_mock = MagicMock()
+    pipeline_mock.index_batch_parallel = batch_mock
+    pipeline_mock.migrate_legacy_docs = AsyncMock()
+    monkeypatch.setattr(
+        _mod, "IndexingPipelineService", MagicMock(return_value=pipeline_mock),
+    )
+
+    return {
+        "session": mock_session,
+        "connector": mock_connector,
+        "linear_client": linear_client,
+        "task_logger": mock_task_logger,
+        "pipeline_mock": pipeline_mock,
+        "batch_mock": batch_mock,
+    }
+
+
+async def _run_index(mocks, **overrides):
+    return await index_linear_issues(
+        session=mocks["session"],
+        connector_id=overrides.get("connector_id", _CONNECTOR_ID),
+        search_space_id=overrides.get("search_space_id", _SEARCH_SPACE_ID),
+        user_id=overrides.get("user_id", _USER_ID),
+        start_date=overrides.get("start_date", "2025-01-01"),
+        end_date=overrides.get("end_date", "2025-12-31"),
+        update_last_indexed=overrides.get("update_last_indexed", True),
+        on_heartbeat_callback=overrides.get("on_heartbeat_callback"),
+    )
+
+
+# ---------------------------------------------------------------------------
+# Slice 2: Full pipeline wiring
+# ---------------------------------------------------------------------------
+
+
+async def test_one_issue_calls_pipeline_and_returns_indexed_count(linear_mocks):
+    """One valid issue is passed to the pipeline and the indexed count is returned."""
+    indexed, skipped, warning = await _run_index(linear_mocks)
+
+    assert indexed == 1
+    assert skipped == 0
+    assert warning is None
+
+    linear_mocks["batch_mock"].assert_called_once()
+    call_args = linear_mocks["batch_mock"].call_args
+    connector_docs = call_args[0][0]
+    assert len(connector_docs) == 1
+    assert connector_docs[0].document_type == DocumentType.LINEAR_CONNECTOR
+
+
+async def test_pipeline_called_with_max_concurrency_3(linear_mocks):
+    """index_batch_parallel is called with max_concurrency=3."""
+    await _run_index(linear_mocks)
+
+    call_kwargs = linear_mocks["batch_mock"].call_args[1]
+    assert call_kwargs.get("max_concurrency") == 3
+
+
+async def test_migrate_legacy_docs_called_before_indexing(linear_mocks):
+    """migrate_legacy_docs is called on the pipeline before index_batch_parallel."""
+    await _run_index(linear_mocks)
+
+    linear_mocks["pipeline_mock"].migrate_legacy_docs.assert_called_once()
+
+
+# ---------------------------------------------------------------------------
+# Slice 3: Issue skipping (missing ID / title)
+# ---------------------------------------------------------------------------
+
+
+async def test_issues_with_missing_id_are_skipped(linear_mocks):
+    """Issues without id are skipped and not passed to the pipeline."""
+    issues = [
+        _make_issue(issue_id="valid-1", identifier="ENG-1", title="Valid"),
+        {"id": "", "identifier": "ENG-2", "title": "No ID"},
+    ]
+    linear_mocks["linear_client"].get_issues_by_date_range.return_value = (issues, None)
+
+    indexed, skipped, _ = await _run_index(linear_mocks)
+
+    connector_docs = linear_mocks["batch_mock"].call_args[0][0]
+    assert len(connector_docs) == 1
+    assert connector_docs[0].unique_id == "valid-1"
+    assert skipped == 1
+
+
+async def test_issues_with_missing_title_are_skipped(linear_mocks):
+    """Issues without title are skipped."""
+    issues = [
+        _make_issue(issue_id="valid-1", identifier="ENG-1", title="Valid"),
+        {"id": "id-2", "identifier": "ENG-2", "title": ""},
+    ]
+    linear_mocks["linear_client"].get_issues_by_date_range.return_value = (issues, None)
+
+    indexed, skipped, _ = await _run_index(linear_mocks)
+
+    connector_docs = linear_mocks["batch_mock"].call_args[0][0]
+    assert len(connector_docs) == 1
+    assert skipped == 1
+
+
+# ---------------------------------------------------------------------------
+# Slice 4: Duplicate content skipping
+# ---------------------------------------------------------------------------
+
+
+async def test_duplicate_content_issues_are_skipped(linear_mocks, monkeypatch):
+    """Issues whose content hash matches an existing document are skipped."""
+    issues = [
+        _make_issue(issue_id="new-1", identifier="ENG-1", title="New"),
+        _make_issue(issue_id="dup-1", identifier="ENG-2", title="Dup"),
+    ]
+    linear_mocks["linear_client"].get_issues_by_date_range.return_value = (issues, None)
+
+    call_count = 0
+
+    async def _check_dup(session, content_hash):
+        nonlocal call_count
+        call_count += 1
+        if call_count == 2:
+            dup = MagicMock()
+            dup.id = 99
+            dup.document_type = "OTHER"
+            return dup
+        return None
+
+    monkeypatch.setattr(_mod, "check_duplicate_document_by_hash", _check_dup)
+
+    indexed, skipped, _ = await _run_index(linear_mocks)
+
+    connector_docs = linear_mocks["batch_mock"].call_args[0][0]
+    assert len(connector_docs) == 1
+    assert skipped == 1
+
+
+# ---------------------------------------------------------------------------
+# Slice 5: Heartbeat callback forwarding
+# ---------------------------------------------------------------------------
+
+
+async def test_heartbeat_callback_forwarded_to_pipeline(linear_mocks):
+    """on_heartbeat_callback is passed through to index_batch_parallel."""
+    heartbeat_cb = AsyncMock()
+
+    await _run_index(linear_mocks, on_heartbeat_callback=heartbeat_cb)
+
+    call_kwargs = linear_mocks["batch_mock"].call_args[1]
+    assert call_kwargs.get("on_heartbeat") is heartbeat_cb
+
+
+# ---------------------------------------------------------------------------
+# Slice 6: Empty issues early return
+# ---------------------------------------------------------------------------
+
+
+async def test_empty_issues_returns_zero_tuple(linear_mocks):
+    """When no issues are found, returns (0, 0, None) and pipeline is not called."""
+    linear_mocks["linear_client"].get_issues_by_date_range.return_value = ([], None)
+
+    indexed, skipped, warning = await _run_index(linear_mocks)
+
+    assert indexed == 0
+    assert skipped == 0
+    assert warning is None
+
+    linear_mocks["batch_mock"].assert_not_called()
+
+
+async def test_failed_docs_warning_in_result(linear_mocks):
+    """When documents fail indexing, the warning includes the count."""
+    linear_mocks["batch_mock"].return_value = ([], 0, 2)
+
+    _, _, warning = await _run_index(linear_mocks)
+
+    assert warning is not None
+    assert "2 failed" in warning

surfsense_backend/tests/unit/connector_indexers/test_notion_parallel.py

@@ -0,0 +1,345 @@
+"""Tests for Notion indexer migrated to the unified parallel pipeline."""
+
+from unittest.mock import AsyncMock, MagicMock
+
+import pytest
+
+import app.tasks.connector_indexers.notion_indexer as _mod
+from app.db import DocumentType
+from app.tasks.connector_indexers.notion_indexer import (
+    _build_connector_doc,
+    index_notion_pages,
+)
+
+pytestmark = pytest.mark.unit
+
+_USER_ID = "00000000-0000-0000-0000-000000000001"
+_CONNECTOR_ID = 42
+_SEARCH_SPACE_ID = 1
+
+
+def _make_page(page_id: str = "page-1", title: str = "Test Page", content=None):
+    if content is None:
+        content = [{"type": "paragraph", "content": "Hello world", "children": []}]
+    return {"page_id": page_id, "title": title, "content": content}
+
+
+# ---------------------------------------------------------------------------
+# Slice 1: _build_connector_doc tracer bullet
+# ---------------------------------------------------------------------------
+
+
+async def test_build_connector_doc_produces_correct_fields():
+    """Tracer bullet: a single Notion page produces a ConnectorDocument with correct fields."""
+
+    page = _make_page(page_id="abc-123", title="My Notion Page")
+    markdown = "# My Notion Page\n\nHello world"
+
+    doc = _build_connector_doc(
+        page,
+        markdown,
+        connector_id=_CONNECTOR_ID,
+        search_space_id=_SEARCH_SPACE_ID,
+        user_id=_USER_ID,
+        enable_summary=True,
+    )
+
+    assert doc.title == "My Notion Page"
+    assert doc.unique_id == "abc-123"
+    assert doc.document_type == DocumentType.NOTION_CONNECTOR
+    assert doc.source_markdown == markdown
+    assert doc.search_space_id == _SEARCH_SPACE_ID
+    assert doc.connector_id == _CONNECTOR_ID
+    assert doc.created_by_id == _USER_ID
+    assert doc.should_summarize is True
+    assert doc.metadata["page_title"] == "My Notion Page"
+    assert doc.metadata["page_id"] == "abc-123"
+    assert doc.metadata["connector_id"] == _CONNECTOR_ID
+    assert doc.metadata["document_type"] == "Notion Page"
+    assert doc.metadata["connector_type"] == "Notion"
+    assert doc.fallback_summary is not None
+    assert "My Notion Page" in doc.fallback_summary
+    assert markdown in doc.fallback_summary
+
+
+async def test_build_connector_doc_summary_disabled():
+    """When enable_summary is False, should_summarize is False."""
+    doc = _build_connector_doc(
+        _make_page(),
+        "# content",
+        connector_id=_CONNECTOR_ID,
+        search_space_id=_SEARCH_SPACE_ID,
+        user_id=_USER_ID,
+        enable_summary=False,
+    )
+
+    assert doc.should_summarize is False
+
+
+# ---------------------------------------------------------------------------
+# Shared fixtures for Slices 2-7 (full index_notion_pages tests)
+# ---------------------------------------------------------------------------
+
+
+def _mock_connector(enable_summary: bool = True):
+    c = MagicMock()
+    c.config = {"access_token": "tok"}
+    c.enable_summary = enable_summary
+    c.last_indexed_at = None
+    return c
+
+
+def _mock_notion_client(pages=None, skipped_count=0, legacy_token=False):
+    client = MagicMock()
+    client.get_all_pages = AsyncMock(return_value=pages if pages is not None else [])
+    client.get_skipped_content_count = MagicMock(return_value=skipped_count)
+    client.is_using_legacy_token = MagicMock(return_value=legacy_token)
+    client.close = AsyncMock()
+    client.set_retry_callback = MagicMock()
+    return client
+
+
+@pytest.fixture
+def notion_mocks(monkeypatch):
+    """Wire up all external boundary mocks for index_notion_pages."""
+    mock_session = AsyncMock()
+    mock_session.no_autoflush = MagicMock()
+
+    mock_connector = _mock_connector()
+    monkeypatch.setattr(
+        _mod, "get_connector_by_id", AsyncMock(return_value=mock_connector),
+    )
+
+    notion_client = _mock_notion_client(pages=[_make_page()])
+    monkeypatch.setattr(
+        _mod, "NotionHistoryConnector", MagicMock(return_value=notion_client),
+    )
+
+    monkeypatch.setattr(
+        _mod, "check_duplicate_document_by_hash", AsyncMock(return_value=None),
+    )
+
+    monkeypatch.setattr(
+        _mod, "update_connector_last_indexed", AsyncMock(),
+    )
+
+    monkeypatch.setattr(
+        _mod, "calculate_date_range", MagicMock(return_value=("2025-01-01", "2025-12-31")),
+    )
+
+    monkeypatch.setattr(
+        _mod, "process_blocks", MagicMock(return_value="Converted markdown content"),
+    )
+
+    mock_task_logger = MagicMock()
+    mock_task_logger.log_task_start = AsyncMock(return_value=MagicMock())
+    mock_task_logger.log_task_progress = AsyncMock()
+    mock_task_logger.log_task_success = AsyncMock()
+    mock_task_logger.log_task_failure = AsyncMock()
+    monkeypatch.setattr(
+        _mod, "TaskLoggingService", MagicMock(return_value=mock_task_logger),
+    )
+
+    batch_mock = AsyncMock(return_value=([], 1, 0))
+    pipeline_mock = MagicMock()
+    pipeline_mock.index_batch_parallel = batch_mock
+    pipeline_mock.migrate_legacy_docs = AsyncMock()
+    monkeypatch.setattr(
+        _mod, "IndexingPipelineService", MagicMock(return_value=pipeline_mock),
+    )
+
+    return {
+        "session": mock_session,
+        "connector": mock_connector,
+        "notion_client": notion_client,
+        "task_logger": mock_task_logger,
+        "pipeline_mock": pipeline_mock,
+        "batch_mock": batch_mock,
+    }
+
+
+async def _run_index(mocks, **overrides):
+    return await index_notion_pages(
+        session=mocks["session"],
+        connector_id=overrides.get("connector_id", _CONNECTOR_ID),
+        search_space_id=overrides.get("search_space_id", _SEARCH_SPACE_ID),
+        user_id=overrides.get("user_id", _USER_ID),
+        start_date=overrides.get("start_date", "2025-01-01"),
+        end_date=overrides.get("end_date", "2025-12-31"),
+        update_last_indexed=overrides.get("update_last_indexed", True),
+        on_retry_callback=overrides.get("on_retry_callback"),
+        on_heartbeat_callback=overrides.get("on_heartbeat_callback"),
+    )
+
+
+# ---------------------------------------------------------------------------
+# Slice 2: Full pipeline wiring
+# ---------------------------------------------------------------------------
+
+
+async def test_one_page_calls_pipeline_and_returns_indexed_count(notion_mocks):
+    """One valid page is passed to the pipeline and the indexed count is returned."""
+    indexed, skipped, warning = await _run_index(notion_mocks)
+
+    assert indexed == 1
+    assert skipped == 0
+    assert warning is None
+
+    notion_mocks["batch_mock"].assert_called_once()
+    call_args = notion_mocks["batch_mock"].call_args
+    connector_docs = call_args[0][0]
+    assert len(connector_docs) == 1
+    assert connector_docs[0].document_type == DocumentType.NOTION_CONNECTOR
+
+
+async def test_pipeline_called_with_max_concurrency_3(notion_mocks):
+    """index_batch_parallel is called with max_concurrency=3."""
+    await _run_index(notion_mocks)
+
+    call_kwargs = notion_mocks["batch_mock"].call_args[1]
+    assert call_kwargs.get("max_concurrency") == 3
+
+
+async def test_migrate_legacy_docs_called_before_indexing(notion_mocks):
+    """migrate_legacy_docs is called on the pipeline before index_batch_parallel."""
+    await _run_index(notion_mocks)
+
+    notion_mocks["pipeline_mock"].migrate_legacy_docs.assert_called_once()
+
+
+# ---------------------------------------------------------------------------
+# Slice 3: Page skipping (no content / missing ID)
+# ---------------------------------------------------------------------------
+
+
+async def test_pages_with_missing_id_are_skipped(notion_mocks, monkeypatch):
+    """Pages without page_id are skipped and not passed to the pipeline."""
+    pages = [
+        _make_page(page_id="valid-1"),
+        {"title": "No ID page", "content": [{"type": "paragraph", "content": "text", "children": []}]},
+    ]
+    notion_mocks["notion_client"].get_all_pages.return_value = pages
+
+    _, skipped, _ = await _run_index(notion_mocks)
+
+    connector_docs = notion_mocks["batch_mock"].call_args[0][0]
+    assert len(connector_docs) == 1
+    assert connector_docs[0].unique_id == "valid-1"
+    assert skipped == 1
+
+
+async def test_pages_with_no_content_are_skipped(notion_mocks, monkeypatch):
+    """Pages with empty content are skipped."""
+    pages = [
+        _make_page(page_id="valid-1"),
+        _make_page(page_id="empty-1", content=[]),
+    ]
+    notion_mocks["notion_client"].get_all_pages.return_value = pages
+
+    _, skipped, _ = await _run_index(notion_mocks)
+
+    connector_docs = notion_mocks["batch_mock"].call_args[0][0]
+    assert len(connector_docs) == 1
+    assert skipped == 1
+
+
+# ---------------------------------------------------------------------------
+# Slice 4: Duplicate content skipping
+# ---------------------------------------------------------------------------
+
+
+async def test_duplicate_content_pages_are_skipped(notion_mocks, monkeypatch):
+    """Pages whose content hash matches an existing document are skipped."""
+    pages = [
+        _make_page(page_id="new-1"),
+        _make_page(page_id="dup-1"),
+    ]
+    notion_mocks["notion_client"].get_all_pages.return_value = pages
+
+    call_count = 0
+
+    async def _check_dup(session, content_hash):
+        nonlocal call_count
+        call_count += 1
+        if call_count == 2:
+            dup = MagicMock()
+            dup.id = 99
+            dup.document_type = "OTHER"
+            return dup
+        return None
+
+    monkeypatch.setattr(_mod, "check_duplicate_document_by_hash", _check_dup)
+
+    _, skipped, _ = await _run_index(notion_mocks)
+
+    connector_docs = notion_mocks["batch_mock"].call_args[0][0]
+    assert len(connector_docs) == 1
+    assert skipped == 1
+
+
+# ---------------------------------------------------------------------------
+# Slice 5: Heartbeat callback forwarding
+# ---------------------------------------------------------------------------
+
+
+async def test_heartbeat_callback_forwarded_to_pipeline(notion_mocks):
+    """on_heartbeat_callback is passed through to index_batch_parallel."""
+    heartbeat_cb = AsyncMock()
+
+    await _run_index(notion_mocks, on_heartbeat_callback=heartbeat_cb)
+
+    call_kwargs = notion_mocks["batch_mock"].call_args[1]
+    assert call_kwargs.get("on_heartbeat") is heartbeat_cb
+
+
+# ---------------------------------------------------------------------------
+# Slice 6: Notion-specific warning messages
+# ---------------------------------------------------------------------------
+
+
+async def test_skipped_ai_content_warning_in_result(notion_mocks):
+    """When Notion AI content was skipped, the warning message includes it."""
+    notion_mocks["notion_client"].get_skipped_content_count.return_value = 3
+
+    _, _, warning = await _run_index(notion_mocks)
+
+    assert warning is not None
+    assert "API limitation" in warning
+
+
+async def test_legacy_token_warning_in_result(notion_mocks):
+    """When using legacy token, the warning message includes a notice."""
+    notion_mocks["notion_client"].is_using_legacy_token.return_value = True
+
+    _, _, warning = await _run_index(notion_mocks)
+
+    assert warning is not None
+    assert "legacy token" in warning.lower()
+
+
+async def test_failed_docs_warning_in_result(notion_mocks):
+    """When documents fail indexing, the warning includes the count."""
+    notion_mocks["batch_mock"].return_value = ([], 0, 2)
+
+    _, _, warning = await _run_index(notion_mocks)
+
+    assert warning is not None
+    assert "2 failed" in warning
+
+
+# ---------------------------------------------------------------------------
+# Slice 7: Empty pages early return
+# ---------------------------------------------------------------------------
+
+
+async def test_empty_pages_returns_zero_tuple(notion_mocks):
+    """When no pages are found, returns (0, 0, None) and updates last_indexed."""
+    notion_mocks["notion_client"].get_all_pages.return_value = []
+
+    indexed, skipped, warning = await _run_index(notion_mocks)
+
+    assert indexed == 0
+    assert skipped == 0
+    assert warning is None
+
+    notion_mocks["batch_mock"].assert_not_called()

surfsense_backend/tests/unit/indexing_pipeline/test_document_hashing.py

@@ -3,6 +3,7 @@ import pytest
 from app.db import DocumentType
 from app.indexing_pipeline.document_hashing import (
     compute_content_hash,
+    compute_identifier_hash,
     compute_unique_identifier_hash,
 )
 
@@ -61,3 +62,23 @@ def test_different_content_produces_different_content_hash(make_connector_docume
     doc_a = make_connector_document(source_markdown="Original content")
     doc_b = make_connector_document(source_markdown="Updated content")
     assert compute_content_hash(doc_a) != compute_content_hash(doc_b)
+
+
+def test_compute_identifier_hash_matches_connector_doc_hash(make_connector_document):
+    """Raw-args hash equals ConnectorDocument hash for equivalent inputs."""
+    doc = make_connector_document(
+        document_type=DocumentType.GOOGLE_GMAIL_CONNECTOR,
+        unique_id="msg-123",
+        search_space_id=5,
+    )
+    raw_hash = compute_identifier_hash("GOOGLE_GMAIL_CONNECTOR", "msg-123", 5)
+    assert raw_hash == compute_unique_identifier_hash(doc)
+
+
+def test_compute_identifier_hash_differs_for_different_inputs():
+    """Different arguments produce different hashes."""
+    h1 = compute_identifier_hash("GOOGLE_DRIVE_FILE", "file-1", 1)
+    h2 = compute_identifier_hash("GOOGLE_DRIVE_FILE", "file-2", 1)
+    h3 = compute_identifier_hash("GOOGLE_DRIVE_FILE", "file-1", 2)
+    h4 = compute_identifier_hash("COMPOSIO_GOOGLE_DRIVE_CONNECTOR", "file-1", 1)
+    assert len({h1, h2, h3, h4}) == 4

surfsense_backend/tests/unit/indexing_pipeline/test_index_batch.py

@@ -0,0 +1,82 @@
+from unittest.mock import AsyncMock, MagicMock
+
+import pytest
+
+from app.db import Document, DocumentType
+from app.indexing_pipeline.document_hashing import compute_unique_identifier_hash
+from app.indexing_pipeline.indexing_pipeline_service import IndexingPipelineService
+
+pytestmark = pytest.mark.unit
+
+
+@pytest.fixture
+def mock_session():
+    return AsyncMock()
+
+
+@pytest.fixture
+def pipeline(mock_session):
+    return IndexingPipelineService(mock_session)
+
+
+async def test_calls_prepare_then_index_per_document(
+    pipeline, make_connector_document
+):
+    """index_batch calls prepare_for_indexing, then index() for each returned doc."""
+    doc1 = make_connector_document(
+        document_type=DocumentType.GOOGLE_GMAIL_CONNECTOR,
+        unique_id="msg-1",
+        search_space_id=1,
+    )
+    doc2 = make_connector_document(
+        document_type=DocumentType.GOOGLE_GMAIL_CONNECTOR,
+        unique_id="msg-2",
+        search_space_id=1,
+    )
+
+    orm1 = MagicMock(spec=Document)
+    orm1.unique_identifier_hash = compute_unique_identifier_hash(doc1)
+    orm2 = MagicMock(spec=Document)
+    orm2.unique_identifier_hash = compute_unique_identifier_hash(doc2)
+
+    mock_llm = MagicMock()
+
+    pipeline.prepare_for_indexing = AsyncMock(return_value=[orm1, orm2])
+    pipeline.index = AsyncMock(side_effect=lambda doc, cdoc, llm: doc)
+
+    results = await pipeline.index_batch([doc1, doc2], mock_llm)
+
+    pipeline.prepare_for_indexing.assert_awaited_once_with([doc1, doc2])
+    assert pipeline.index.await_count == 2
+    assert results == [orm1, orm2]
+
+
+async def test_empty_input_returns_empty(pipeline):
+    """Empty connector_docs list returns empty result."""
+    pipeline.prepare_for_indexing = AsyncMock(return_value=[])
+
+    results = await pipeline.index_batch([], MagicMock())
+
+    assert results == []
+
+
+async def test_skips_document_without_matching_connector_doc(
+    pipeline, make_connector_document
+):
+    """If prepare returns a doc whose hash has no matching ConnectorDocument, it's skipped."""
+    doc1 = make_connector_document(
+        document_type=DocumentType.GOOGLE_GMAIL_CONNECTOR,
+        unique_id="msg-1",
+        search_space_id=1,
+    )
+
+    orphan_orm = MagicMock(spec=Document)
+    orphan_orm.unique_identifier_hash = "nonexistent-hash"
+
+    pipeline.prepare_for_indexing = AsyncMock(return_value=[orphan_orm])
+    pipeline.index = AsyncMock()
+
+    results = await pipeline.index_batch([doc1], MagicMock())
+
+    pipeline.index.assert_not_awaited()
+    assert results == []

surfsense_backend/tests/unit/indexing_pipeline/test_index_batch_parallel.py

@@ -0,0 +1,186 @@
+import asyncio
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+from app.config import config as app_config
+from app.db import Document, DocumentStatus, DocumentType
+from app.indexing_pipeline.document_hashing import compute_unique_identifier_hash
+from app.indexing_pipeline.indexing_pipeline_service import IndexingPipelineService
+
+_EMBEDDING_DIM = app_config.embedding_model_instance.dimension
+
+pytestmark = pytest.mark.unit
+
+
+@pytest.fixture
+def mock_session():
+    session = AsyncMock()
+    session.refresh = AsyncMock()
+    return session
+
+
+@pytest.fixture
+def pipeline(mock_session):
+    return IndexingPipelineService(mock_session)
+
+
+def _make_orm_doc(connector_doc, doc_id):
+    """Create a MagicMock Document bound to a ConnectorDocument's hash."""
+    doc = MagicMock(spec=Document)
+    doc.id = doc_id
+    doc.unique_identifier_hash = compute_unique_identifier_hash(connector_doc)
+    doc.status = DocumentStatus.pending()
+    return doc
+
+
+async def test_index_calls_embed_and_chunk_via_to_thread(
+    pipeline, make_connector_document, monkeypatch
+):
+    """index() runs embed_texts and chunk_text via asyncio.to_thread, not blocking the loop."""
+    to_thread_calls = []
+    original_to_thread = asyncio.to_thread
+
+    async def tracking_to_thread(func, *args, **kwargs):
+        to_thread_calls.append(func.__name__)
+        return await original_to_thread(func, *args, **kwargs)
+
+    monkeypatch.setattr(asyncio, "to_thread", tracking_to_thread)
+
+    monkeypatch.setattr(
+        "app.indexing_pipeline.indexing_pipeline_service.summarize_document",
+        AsyncMock(return_value="Summary."),
+    )
+    mock_chunk = MagicMock(return_value=["chunk1"])
+    mock_chunk.__name__ = "chunk_text"
+    monkeypatch.setattr(
+        "app.indexing_pipeline.indexing_pipeline_service.chunk_text",
+        mock_chunk,
+    )
+    mock_embed = MagicMock(side_effect=lambda texts: [[0.1] * _EMBEDDING_DIM for _ in texts])
+    mock_embed.__name__ = "embed_texts"
+    monkeypatch.setattr(
+        "app.indexing_pipeline.indexing_pipeline_service.embed_texts",
+        mock_embed,
+    )
+
+    connector_doc = make_connector_document(
+        document_type=DocumentType.GOOGLE_GMAIL_CONNECTOR,
+        unique_id="msg-1",
+        search_space_id=1,
+    )
+    document = MagicMock(spec=Document)
+    document.id = 1
+    document.status = DocumentStatus.pending()
+
+    await pipeline.index(document, connector_doc, llm=MagicMock())
+
+    assert "chunk_text" in to_thread_calls
+    assert "embed_texts" in to_thread_calls
+
+
+def _mock_session_factory(orm_docs_by_id):
+    """Replace get_celery_session_maker with a two-level callable.
+
+    get_celery_session_maker() -> session_maker
+    session_maker()            -> async context manager yielding a mock session
+    """
+
+    def _get_maker():
+        def _make_session():
+            session = MagicMock()
+            session.get = AsyncMock(
+                side_effect=lambda model, doc_id: orm_docs_by_id.get(doc_id)
+            )
+            ctx = MagicMock()
+            ctx.__aenter__ = AsyncMock(return_value=session)
+            ctx.__aexit__ = AsyncMock(return_value=False)
+            return ctx
+
+        return _make_session
+
+    return _get_maker
+
+
+async def test_batch_parallel_indexes_all_documents(
+    pipeline, make_connector_document, monkeypatch
+):
+    """index_batch_parallel indexes all documents and returns correct counts."""
+    docs = [
+        make_connector_document(
+            document_type=DocumentType.GOOGLE_GMAIL_CONNECTOR,
+            unique_id=f"msg-{i}",
+            search_space_id=1,
+        )
+        for i in range(3)
+    ]
+
+    orm_docs = [_make_orm_doc(cd, doc_id=i + 1) for i, cd in enumerate(docs)]
+    pipeline.prepare_for_indexing = AsyncMock(return_value=orm_docs)
+
+    orm_by_id = {d.id: d for d in orm_docs}
+    monkeypatch.setattr(
+        "app.tasks.celery_tasks.get_celery_session_maker",
+        _mock_session_factory(orm_by_id),
+    )
+
+    index_calls = []
+
+    async def fake_index(self, document, connector_doc, llm):
+        index_calls.append(document.id)
+        document.status = DocumentStatus.ready()
+        return document
+
+    monkeypatch.setattr(IndexingPipelineService, "index", fake_index)
+
+    async def mock_get_llm(session):
+        return MagicMock()
+
+    _, indexed, failed = await pipeline.index_batch_parallel(
+        docs, mock_get_llm, max_concurrency=2
+    )
+
+    assert indexed == 3
+    assert failed == 0
+    assert sorted(index_calls) == [1, 2, 3]
+
+
+async def test_batch_parallel_one_failure_does_not_affect_others(
+    pipeline, make_connector_document, monkeypatch
+):
+    """One document failure doesn't prevent other documents from being indexed."""
+    docs = [
+        make_connector_document(
+            document_type=DocumentType.GOOGLE_GMAIL_CONNECTOR,
+            unique_id=f"msg-{i}",
+            search_space_id=1,
+        )
+        for i in range(3)
+    ]
+
+    orm_docs = [_make_orm_doc(cd, doc_id=i + 1) for i, cd in enumerate(docs)]
+    pipeline.prepare_for_indexing = AsyncMock(return_value=orm_docs)
+
+    orm_by_id = {d.id: d for d in orm_docs}
+    monkeypatch.setattr(
+        "app.tasks.celery_tasks.get_celery_session_maker",
+        _mock_session_factory(orm_by_id),
+    )
+
+    async def failing_index(self, document, connector_doc, llm):
+        if document.id == 2:
+            raise RuntimeError("LLM exploded")
+        document.status = DocumentStatus.ready()
+        return document
+
+    monkeypatch.setattr(IndexingPipelineService, "index", failing_index)
+
+    async def mock_get_llm(session):
+        return MagicMock()
+
+    _, indexed, failed = await pipeline.index_batch_parallel(
+        docs, mock_get_llm, max_concurrency=4
+    )
+
+    assert indexed == 2
+    assert failed == 1

surfsense_backend/tests/unit/indexing_pipeline/test_migrate_legacy_docs.py

@@ -0,0 +1,127 @@
+from unittest.mock import AsyncMock, MagicMock
+
+import pytest
+
+from app.db import Document, DocumentType
+from app.indexing_pipeline.document_hashing import compute_identifier_hash
+from app.indexing_pipeline.indexing_pipeline_service import IndexingPipelineService
+
+pytestmark = pytest.mark.unit
+
+
+@pytest.fixture
+def mock_session():
+    session = AsyncMock()
+    return session
+
+
+@pytest.fixture
+def pipeline(mock_session):
+    return IndexingPipelineService(mock_session)
+
+
+def _make_execute_side_effect(doc_by_hash: dict):
+    """Return a side_effect for session.execute that resolves documents by hash."""
+
+    async def _side_effect(stmt):
+        result = MagicMock()
+        for h, doc in doc_by_hash.items():
+            if h in str(stmt.compile(compile_kwargs={"literal_binds": True})):
+                result.scalars.return_value.first.return_value = doc
+                return result
+        result.scalars.return_value.first.return_value = None
+        return result
+
+    return _side_effect
+
+
+async def test_updates_hash_and_type_for_legacy_document(
+    pipeline, mock_session, make_connector_document
+):
+    """Legacy Composio document gets unique_identifier_hash and document_type updated."""
+    doc = make_connector_document(
+        document_type=DocumentType.GOOGLE_GMAIL_CONNECTOR,
+        unique_id="msg-abc",
+        search_space_id=1,
+    )
+
+    legacy_hash = compute_identifier_hash("COMPOSIO_GMAIL_CONNECTOR", "msg-abc", 1)
+    native_hash = compute_identifier_hash("GOOGLE_GMAIL_CONNECTOR", "msg-abc", 1)
+
+    existing = MagicMock(spec=Document)
+    existing.unique_identifier_hash = legacy_hash
+    existing.document_type = DocumentType.COMPOSIO_GMAIL_CONNECTOR
+
+    result_mock = MagicMock()
+    result_mock.scalars.return_value.first.return_value = existing
+    mock_session.execute = AsyncMock(return_value=result_mock)
+
+    await pipeline.migrate_legacy_docs([doc])
+
+    assert existing.unique_identifier_hash == native_hash
+    assert existing.document_type == DocumentType.GOOGLE_GMAIL_CONNECTOR
+    mock_session.commit.assert_awaited_once()
+
+
+async def test_noop_when_no_legacy_document_exists(
+    pipeline, mock_session, make_connector_document
+):
+    """No updates when no legacy Composio document is found in DB."""
+    doc = make_connector_document(
+        document_type=DocumentType.GOOGLE_GMAIL_CONNECTOR,
+        unique_id="msg-xyz",
+        search_space_id=1,
+    )
+
+    result_mock = MagicMock()
+    result_mock.scalars.return_value.first.return_value = None
+    mock_session.execute = AsyncMock(return_value=result_mock)
+
+    await pipeline.migrate_legacy_docs([doc])
+
+    mock_session.commit.assert_awaited_once()
+
+
+async def test_skips_non_google_doc_types(
+    pipeline, mock_session, make_connector_document
+):
+    """Non-Google doc types have no legacy mapping and trigger no DB query."""
+    doc = make_connector_document(
+        document_type=DocumentType.SLACK_CONNECTOR,
+        unique_id="slack-123",
+        search_space_id=1,
+    )
+
+    await pipeline.migrate_legacy_docs([doc])
+
+    mock_session.execute.assert_not_awaited()
+    mock_session.commit.assert_awaited_once()
+
+
+async def test_handles_all_three_google_types(
+    pipeline, mock_session, make_connector_document
+):
+    """Each native Google type correctly maps to its Composio legacy type."""
+    mappings = [
+        (DocumentType.GOOGLE_GMAIL_CONNECTOR, "COMPOSIO_GMAIL_CONNECTOR"),
+        (DocumentType.GOOGLE_CALENDAR_CONNECTOR, "COMPOSIO_GOOGLE_CALENDAR_CONNECTOR"),
+        (DocumentType.GOOGLE_DRIVE_FILE, "COMPOSIO_GOOGLE_DRIVE_CONNECTOR"),
+    ]
+    for native_type, expected_legacy in mappings:
+        doc = make_connector_document(
+            document_type=native_type,
+            unique_id="id-1",
+            search_space_id=1,
+        )
+
+        existing = MagicMock(spec=Document)
+        existing.document_type = DocumentType(expected_legacy)
+
+        result_mock = MagicMock()
+        result_mock.scalars.return_value.first.return_value = existing
+        mock_session.execute = AsyncMock(return_value=result_mock)
+        mock_session.commit = AsyncMock()
+
+        await pipeline.migrate_legacy_docs([doc])
+
+        assert existing.document_type == native_type

surfsense_web/app/(home)/changelog/page.tsx

@@ -1,8 +1,14 @@
 import { loader } from "fumadocs-core/source";
+import type { Metadata } from "next";
 import { changelog } from "@/.source/server";
 import { formatDate } from "@/lib/utils";
 import { getMDXComponents } from "@/mdx-components";
 
+export const metadata: Metadata = {
+	title: "Changelog | SurfSense",
+	description: "See what's new in SurfSense.",
+};
+
 const source = loader({
 	baseUrl: "/changelog",
 	source: changelog.toFumadocsSource(),

surfsense_web/app/(home)/contact/page.tsx

@@ -1,6 +1,11 @@
-import React from "react";
+import type { Metadata } from "next";
 import { ContactFormGridWithDetails } from "@/components/contact/contact-form";
 
+export const metadata: Metadata = {
+	title: "Contact | SurfSense",
+	description: "Get in touch with the SurfSense team.",
+};
+
 const page = () => {
 	return (
 		<div>

surfsense_web/app/(home)/pricing/page.tsx

@@ -1,6 +1,11 @@
-import React from "react";
+import type { Metadata } from "next";
 import PricingBasic from "@/components/pricing/pricing-section";
 
+export const metadata: Metadata = {
+	title: "Pricing | SurfSense",
+	description: "Explore SurfSense plans and pricing options.",
+};
+
 const page = () => {
 	return (
 		<div>

surfsense_web/app/dashboard/[search_space_id]/documents/(manage)/components/DocumentsTableShell.tsx

@@ -473,14 +473,14 @@ export function DocumentsTableShell({
 	}, [deletableSelectedIds, bulkDeleteDocuments, deleteDocument]);
 
 	const bulkDeleteBar = hasDeletableSelection ? (
-		<div className="flex items-center justify-center py-1.5 border-b border-border/50 bg-destructive/5 shrink-0 animate-in fade-in slide-in-from-top-1 duration-150">
+		<div className="absolute inset-x-0 top-0 z-10 flex items-center justify-center py-1 pointer-events-none animate-in fade-in duration-150">
 			<button
 				type="button"
 				onClick={() => setBulkDeleteConfirmOpen(true)}
-				className="flex items-center gap-1.5 px-3 py-1 rounded-md bg-destructive text-destructive-foreground shadow-sm text-xs font-medium hover:bg-destructive/90 transition-colors"
+				className="pointer-events-auto flex items-center gap-1.5 px-3 py-1 rounded-md bg-destructive text-destructive-foreground shadow-lg text-xs font-medium hover:bg-destructive/90 transition-colors"
 			>
 				<Trash2 size={12} />
-				Delete ({deletableSelectedIds.length} selected)
+				Delete {deletableSelectedIds.length} {deletableSelectedIds.length === 1 ? "item" : "items"}
 			</button>
 		</div>
 	) : null;
@@ -526,7 +526,6 @@ export function DocumentsTableShell({
 						</TableRow>
 					</TableHeader>
 				</Table>
-				{bulkDeleteBar}
 				{loading ? (
 					<div className="flex-1 overflow-auto">
 						<Table className="table-fixed w-full">
@@ -594,7 +593,8 @@ export function DocumentsTableShell({
 						)}
 					</div>
 				) : (
-					<div ref={desktopScrollRef} className="flex-1 overflow-auto">
+					<div ref={desktopScrollRef} className="flex-1 overflow-auto relative">
+						{bulkDeleteBar}
 						<Table className="table-fixed w-full">
 							<TableBody>
 								{sorted.map((doc) => {
@@ -788,9 +788,6 @@ export function DocumentsTableShell({
 				)}
 			</div>
 
-			{/* Mobile bulk delete bar */}
-			<div className="md:hidden">{bulkDeleteBar}</div>
-
 			{/* Mobile Card View */}
 			{loading ? (
 				<div className="md:hidden divide-y divide-border/50 flex-1 overflow-auto">
@@ -846,8 +843,9 @@ export function DocumentsTableShell({
 			) : (
 				<div
 					ref={mobileScrollRef}
-					className="md:hidden divide-y divide-border/50 flex-1 overflow-auto"
+					className="md:hidden divide-y divide-border/50 flex-1 overflow-auto relative"
 				>
+					{bulkDeleteBar}
 					{sorted.map((doc) => {
 						const isMentioned = mentionedDocIds?.has(doc.id) ?? false;
 						const statusState = doc.status?.state ?? "ready";

surfsense_web/app/dashboard/[search_space_id]/team/team-content.tsx

@@ -595,6 +595,7 @@ function CreateInviteDialog({
 			});
 		} catch (error) {
 			console.error("Failed to create invite:", error);
+			toast.error("Failed to create invite. Please try again.");
 		} finally {
 			setCreating(false);
 		}

surfsense_web/atoms/documents/document-mutation.atoms.ts

@@ -29,9 +29,6 @@ export const createDocumentMutationAtom = atomWithMutation((get) => {
 			queryClient.invalidateQueries({
 				queryKey: cacheKeys.documents.globalQueryParams(documentsQueryParams),
 			});
-			queryClient.invalidateQueries({
-				queryKey: cacheKeys.documents.typeCounts(searchSpaceId ?? undefined),
-			});
 		},
 	};
 });
@@ -75,9 +72,6 @@ export const updateDocumentMutationAtom = atomWithMutation((get) => {
 			queryClient.invalidateQueries({
 				queryKey: cacheKeys.documents.document(String(request.id)),
 			});
-			queryClient.invalidateQueries({
-				queryKey: cacheKeys.documents.typeCounts(searchSpaceId ?? undefined),
-			});
 		},
 	};
 });
@@ -109,9 +103,6 @@ export const deleteDocumentMutationAtom = atomWithMutation((get) => {
 			queryClient.invalidateQueries({
 				queryKey: cacheKeys.documents.document(String(request.id)),
 			});
-			queryClient.invalidateQueries({
-				queryKey: cacheKeys.documents.typeCounts(searchSpaceId ?? undefined),
-			});
 		},
 	};
 });

surfsense_web/atoms/documents/document-query.atoms.ts

@@ -1,38 +0,0 @@
-import { atomWithQuery } from "jotai-tanstack-query";
-import { activeSearchSpaceIdAtom } from "@/atoms/search-spaces/search-space-query.atoms";
-import type { SearchDocumentsRequest } from "@/contracts/types/document.types";
-import { documentsApiService } from "@/lib/apis/documents-api.service";
-import { cacheKeys } from "@/lib/query-client/cache-keys";
-import { globalDocumentsQueryParamsAtom } from "./ui.atoms";
-
-export const documentsAtom = atomWithQuery((get) => {
-	const searchSpaceId = get(activeSearchSpaceIdAtom);
-	const queryParams = get(globalDocumentsQueryParamsAtom);
-
-	return {
-		queryKey: cacheKeys.documents.globalQueryParams(queryParams),
-		enabled: !!searchSpaceId,
-		queryFn: async () => {
-			return documentsApiService.getDocuments({
-				queryParams: queryParams,
-			});
-		},
-	};
-});
-
-export const documentTypeCountsAtom = atomWithQuery((get) => {
-	const searchSpaceId = get(activeSearchSpaceIdAtom);
-
-	return {
-		queryKey: cacheKeys.documents.typeCounts(searchSpaceId ?? undefined),
-		enabled: !!searchSpaceId,
-		staleTime: 10 * 60 * 1000, // 10 minutes
-		queryFn: async () => {
-			return documentsApiService.getDocumentTypeCounts({
-				queryParams: {
-					search_space_id: searchSpaceId ?? undefined,
-				},
-			});
-		},
-	};
-});

surfsense_web/components/assistant-ui/connector-popup.tsx

@@ -4,7 +4,7 @@ import { useAtomValue, useSetAtom } from "jotai";
 import { AlertTriangle, Cable, Settings } from "lucide-react";
 import { forwardRef, useEffect, useImperativeHandle, useMemo, useState } from "react";
 import { createPortal } from "react-dom";
-import { documentTypeCountsAtom } from "@/atoms/documents/document-query.atoms";
+import { useZeroDocumentTypeCounts } from "@/hooks/use-zero-document-type-counts";
 import { statusInboxItemsAtom } from "@/atoms/inbox/status-inbox.atom";
 import {
 	globalNewLLMConfigsAtom,
@@ -72,9 +72,9 @@ export const ConnectorIndicator = forwardRef<ConnectorIndicatorHandle, Connector
 
 		const llmConfigLoading = preferencesLoading || globalConfigsLoading;
 
-		// Fetch document type counts via the lightweight /type-counts endpoint (cached 10 min)
-		const { data: documentTypeCounts, isFetching: documentTypesLoading } =
-			useAtomValue(documentTypeCountsAtom);
+		// Real-time document type counts via Zero (updates instantly as docs are indexed)
+		const documentTypeCounts = useZeroDocumentTypeCounts(searchSpaceId);
+		const documentTypesLoading = documentTypeCounts === undefined;
 
 		// Read status inbox items from shared atom (populated by LayoutDataProvider)
 		// instead of creating a duplicate useInbox("status") hook.

surfsense_web/components/assistant-ui/connector-popup/hooks/use-connector-dialog.ts

@@ -867,6 +867,9 @@ export const useConnectorDialog = () => {
 
 				setIsOpen(false);
 				setIsFromOAuth(false);
+				setIndexingConfig(null);
+				setIndexingConnector(null);
+				setIndexingConnectorConfig(null);
 
 				refreshConnectors();
 				queryClient.invalidateQueries({
@@ -898,6 +901,9 @@ export const useConnectorDialog = () => {
 	const handleSkipIndexing = useCallback(() => {
 		setIsOpen(false);
 		setIsFromOAuth(false);
+		setIndexingConfig(null);
+		setIndexingConnector(null);
+		setIndexingConnectorConfig(null);
 	}, [setIsOpen]);
 
 	// Handle starting edit mode

surfsense_web/components/documents/CreateFolderDialog.tsx

@@ -1,6 +1,5 @@
 "use client";
 
-import { FolderPlus } from "lucide-react";
 import { useCallback, useEffect, useRef, useState } from "react";
 import { Button } from "@/components/ui/button";
 import {
@@ -52,22 +51,25 @@ export function CreateFolderDialog({
 
 	return (
 		<Dialog open={open} onOpenChange={onOpenChange}>
-			<DialogContent className="max-w-sm">
-				<DialogHeader>
-					<DialogTitle className="flex items-center gap-2">
-						<FolderPlus className="size-5 text-muted-foreground" />
+			<DialogContent className="select-none max-w-[90vw] sm:max-w-sm p-4 sm:p-5 data-[state=open]:animate-none data-[state=closed]:animate-none">
+				<DialogHeader className="space-y-2 pb-2">
+					<div className="flex items-center gap-2 sm:gap-3">
+						<div className="flex-1 min-w-0">
+							<DialogTitle className="text-base sm:text-lg">
 								{isSubfolder ? "New subfolder" : "New folder"}
 							</DialogTitle>
-					<DialogDescription>
+							<DialogDescription className="text-xs sm:text-sm mt-0.5">
 								{isSubfolder
 									? `Create a new folder inside "${parentFolderName}".`
 									: "Create a new folder at the root level."}
 							</DialogDescription>
+						</div>
+					</div>
 				</DialogHeader>
 
-				<form onSubmit={handleSubmit} className="flex flex-col gap-4">
+				<form onSubmit={handleSubmit} className="flex flex-col gap-3 sm:gap-4">
 					<div className="flex flex-col gap-2">
-						<Label htmlFor="folder-name">Folder name</Label>
+						<Label htmlFor="folder-name" className="text-sm">Folder name</Label>
 						<Input
 							ref={inputRef}
 							id="folder-name"
@@ -76,14 +78,24 @@ export function CreateFolderDialog({
 							onChange={(e) => setName(e.target.value)}
 							maxLength={255}
 							autoComplete="off"
+							className="text-sm h-9 sm:h-10"
 						/>
 					</div>
 
-					<DialogFooter>
-						<Button type="button" variant="outline" onClick={() => onOpenChange(false)}>
+					<DialogFooter className="flex-row justify-end gap-2 pt-2 sm:pt-3">
+						<Button
+							type="button"
+							variant="secondary"
+							onClick={() => onOpenChange(false)}
+							className="h-8 sm:h-9 text-xs sm:text-sm"
+						>
 							Cancel
 						</Button>
-						<Button type="submit" disabled={!name.trim()}>
+						<Button
+							type="submit"
+							disabled={!name.trim()}
+							className="h-8 sm:h-9 text-xs sm:text-sm"
+						>
 							Create
 						</Button>
 					</DialogFooter>

surfsense_web/components/documents/DocumentNode.tsx

@@ -1,25 +1,32 @@
 "use client";
 
-import { Eye, MoreHorizontal, Move, Pencil, Trash2 } from "lucide-react";
-import React, { useCallback } from "react";
+import { AlertCircle, Clock, Download, Eye, MoreHorizontal, Move, PenLine, Trash2 } from "lucide-react";
+import React, { useCallback, useRef, useState } from "react";
 import { useDrag } from "react-dnd";
 import { getDocumentTypeIcon } from "@/app/dashboard/[search_space_id]/documents/(manage)/components/DocumentTypeIcon";
+import { ExportContextItems, ExportDropdownItems } from "@/components/shared/ExportMenuItems";
 import { Button } from "@/components/ui/button";
 import { Checkbox } from "@/components/ui/checkbox";
 import {
 	ContextMenu,
 	ContextMenuContent,
 	ContextMenuItem,
-	ContextMenuSeparator,
+	ContextMenuSub,
+	ContextMenuSubContent,
+	ContextMenuSubTrigger,
 	ContextMenuTrigger,
 } from "@/components/ui/context-menu";
 import {
 	DropdownMenu,
 	DropdownMenuContent,
 	DropdownMenuItem,
-	DropdownMenuSeparator,
+	DropdownMenuSub,
+	DropdownMenuSubContent,
+	DropdownMenuSubTrigger,
 	DropdownMenuTrigger,
 } from "@/components/ui/dropdown-menu";
+import { Spinner } from "@/components/ui/spinner";
+import { Tooltip, TooltipContent, TooltipTrigger } from "@/components/ui/tooltip";
 import type { DocumentTypeEnum } from "@/contracts/types/document.types";
 import { cn } from "@/lib/utils";
 import { DND_TYPES } from "./FolderNode";
@@ -41,6 +48,9 @@ interface DocumentNodeProps {
 	onEdit: (doc: DocumentNodeDoc) => void;
 	onDelete: (doc: DocumentNodeDoc) => void;
 	onMove: (doc: DocumentNodeDoc) => void;
+	onExport?: (doc: DocumentNodeDoc, format: string) => void;
+	contextMenuOpen?: boolean;
+	onContextMenuOpenChange?: (open: boolean) => void;
 }
 
 export const DocumentNode = React.memo(function DocumentNode({
@@ -52,6 +62,9 @@ export const DocumentNode = React.memo(function DocumentNode({
 	onEdit,
 	onDelete,
 	onMove,
+	onExport,
+	contextMenuOpen,
+	onContextMenuOpenChange,
 }: DocumentNodeProps) {
 	const statusState = doc.status?.state ?? "ready";
 	const isSelectable = statusState !== "pending" && statusState !== "processing";
@@ -74,48 +87,90 @@ export const DocumentNode = React.memo(function DocumentNode({
 	);
 
 	const isProcessing = statusState === "pending" || statusState === "processing";
+	const [dropdownOpen, setDropdownOpen] = useState(false);
+	const [exporting, setExporting] = useState<string | null>(null);
+	const rowRef = useRef<HTMLButtonElement>(null);
+
+	const handleExport = useCallback(
+		(format: string) => {
+			if (!onExport) return;
+			setExporting(format);
+			onExport(doc, format);
+			setTimeout(() => setExporting(null), 2000);
+		},
+		[doc, onExport]
+	);
+
+	const attachRef = useCallback(
+		(node: HTMLButtonElement | null) => {
+			(rowRef as React.MutableRefObject<HTMLButtonElement | null>).current = node;
+			drag(node);
+		},
+		[drag]
+	);
 
 	return (
-		<ContextMenu>
+		<ContextMenu onOpenChange={onContextMenuOpenChange}>
 			<ContextMenuTrigger asChild>
-				{/* biome-ignore lint/a11y/useSemanticElements: div required for drag ref */}
-				<div
-					ref={drag}
-					role="button"
-					tabIndex={0}
+				<button
+					type="button"
+					ref={attachRef}
 					className={cn(
-						"group flex h-8 items-center gap-1.5 rounded-md px-1 text-sm hover:bg-accent/50 cursor-pointer select-none",
+					"group flex h-8 w-full items-center gap-2.5 rounded-md px-1 text-sm hover:bg-accent/50 cursor-pointer select-none text-left",
 					isMentioned && "bg-accent/30",
 					isDragging && "opacity-40"
 					)}
 					style={{ paddingLeft: `${depth * 16 + 4}px` }}
 					onClick={handleCheckChange}
-					onKeyDown={(e) => {
-						if (e.key === "Enter" || e.key === " ") {
-							e.preventDefault();
-							handleCheckChange();
-						}
-					}}
 				>
-					{isSelectable ? (
+				{(() => {
+					if (statusState === "pending") {
+						return (
+							<Tooltip>
+								<TooltipTrigger asChild>
+									<span className="flex h-3.5 w-3.5 shrink-0 items-center justify-center">
+										<Clock className="h-3.5 w-3.5 text-muted-foreground/60" />
+									</span>
+								</TooltipTrigger>
+								<TooltipContent side="top">Pending - waiting to be synced</TooltipContent>
+							</Tooltip>
+						);
+					}
+					if (statusState === "processing") {
+						return (
+							<Tooltip>
+								<TooltipTrigger asChild>
+									<span className="flex h-3.5 w-3.5 shrink-0 items-center justify-center">
+										<Spinner size="xs" className="text-primary" />
+									</span>
+								</TooltipTrigger>
+								<TooltipContent side="top">Syncing</TooltipContent>
+							</Tooltip>
+						);
+					}
+					if (statusState === "failed") {
+						return (
+							<Tooltip>
+								<TooltipTrigger asChild>
+									<span className="flex h-3.5 w-3.5 shrink-0 items-center justify-center">
+										<AlertCircle className="h-3.5 w-3.5 text-destructive" />
+									</span>
+								</TooltipTrigger>
+								<TooltipContent side="top" className="max-w-xs">
+									{doc.status?.reason || "Processing failed"}
+								</TooltipContent>
+							</Tooltip>
+						);
+					}
+					return (
 						<Checkbox
 							checked={isMentioned}
 							onCheckedChange={handleCheckChange}
 							onClick={(e) => e.stopPropagation()}
 							className="h-3.5 w-3.5 shrink-0"
 						/>
-					) : (
-						<span className="flex h-3.5 w-3.5 shrink-0 items-center justify-center">
-							<span
-								className={cn(
-									"h-2 w-2 rounded-full",
-									statusState === "processing" && "animate-pulse bg-amber-500",
-									statusState === "pending" && "bg-muted-foreground/40",
-									statusState === "failed" && "bg-destructive"
-								)}
-							/>
-						</span>
-					)}
+					);
+				})()}
 
 					<span className="flex-1 min-w-0 truncate">{doc.title}</span>
 
@@ -126,25 +181,28 @@ export const DocumentNode = React.memo(function DocumentNode({
 						)}
 					</span>
 
-					<DropdownMenu>
+				<DropdownMenu open={dropdownOpen} onOpenChange={setDropdownOpen}>
 					<DropdownMenuTrigger asChild>
 						<Button
 							variant="ghost"
 							size="icon"
-								className="h-6 w-6 shrink-0 opacity-0 group-hover:opacity-100 transition-opacity"
+							className={cn(
+								"hidden sm:inline-flex h-6 w-6 shrink-0 hover:bg-transparent",
+								dropdownOpen ? "opacity-100 bg-accent hover:bg-accent" : "opacity-0 group-hover:opacity-100"
+							)}
 							onClick={(e) => e.stopPropagation()}
 						>
 								<MoreHorizontal className="h-3.5 w-3.5" />
 							</Button>
 						</DropdownMenuTrigger>
-						<DropdownMenuContent align="end" className="w-44">
+						<DropdownMenuContent align="end" className="w-40">
 							<DropdownMenuItem onClick={() => onPreview(doc)}>
 								<Eye className="mr-2 h-4 w-4" />
 								Open
 							</DropdownMenuItem>
 							{isEditable && (
 								<DropdownMenuItem onClick={() => onEdit(doc)}>
-									<Pencil className="mr-2 h-4 w-4" />
+									<PenLine className="mr-2 h-4 w-4" />
 									Edit
 								</DropdownMenuItem>
 							)}
@@ -152,7 +210,17 @@ export const DocumentNode = React.memo(function DocumentNode({
 								<Move className="mr-2 h-4 w-4" />
 								Move to...
 							</DropdownMenuItem>
-							<DropdownMenuSeparator />
+							{onExport && (
+								<DropdownMenuSub>
+									<DropdownMenuSubTrigger>
+										<Download className="mr-2 h-4 w-4" />
+										Export
+									</DropdownMenuSubTrigger>
+									<DropdownMenuSubContent className="min-w-[180px]">
+										<ExportDropdownItems onExport={handleExport} exporting={exporting} />
+									</DropdownMenuSubContent>
+								</DropdownMenuSub>
+							)}
 							<DropdownMenuItem
 								className="text-destructive focus:text-destructive"
 								disabled={isProcessing}
@@ -163,17 +231,18 @@ export const DocumentNode = React.memo(function DocumentNode({
 							</DropdownMenuItem>
 						</DropdownMenuContent>
 					</DropdownMenu>
-				</div>
+				</button>
 			</ContextMenuTrigger>
 
-			<ContextMenuContent className="w-44">
+			{contextMenuOpen && (
+				<ContextMenuContent className="w-40">
 					<ContextMenuItem onClick={() => onPreview(doc)}>
 						<Eye className="mr-2 h-4 w-4" />
 						Open
 					</ContextMenuItem>
 					{isEditable && (
 						<ContextMenuItem onClick={() => onEdit(doc)}>
-						<Pencil className="mr-2 h-4 w-4" />
+							<PenLine className="mr-2 h-4 w-4" />
 							Edit
 						</ContextMenuItem>
 					)}
@@ -181,7 +250,17 @@ export const DocumentNode = React.memo(function DocumentNode({
 						<Move className="mr-2 h-4 w-4" />
 						Move to...
 					</ContextMenuItem>
-				<ContextMenuSeparator />
+					{onExport && (
+						<ContextMenuSub>
+							<ContextMenuSubTrigger>
+								<Download className="mr-2 h-4 w-4" />
+								Export
+							</ContextMenuSubTrigger>
+							<ContextMenuSubContent className="min-w-[180px]">
+								<ExportContextItems onExport={handleExport} exporting={exporting} />
+							</ContextMenuSubContent>
+						</ContextMenuSub>
+					)}
 					<ContextMenuItem
 						className="text-destructive focus:text-destructive"
 						disabled={isProcessing}
@@ -191,6 +270,7 @@ export const DocumentNode = React.memo(function DocumentNode({
 						Delete
 					</ContextMenuItem>
 				</ContextMenuContent>
+			)}
 		</ContextMenu>
 	);
 });

surfsense_web/components/documents/FolderNode.tsx

@@ -8,7 +8,7 @@ import {
 	FolderPlus,
 	MoreHorizontal,
 	Move,
-	Pencil,
+	PenLine,
 	Trash2,
 } from "lucide-react";
 import React, { useCallback, useEffect, useRef, useState } from "react";
@@ -18,14 +18,12 @@ import {
 	ContextMenu,
 	ContextMenuContent,
 	ContextMenuItem,
-	ContextMenuSeparator,
 	ContextMenuTrigger,
 } from "@/components/ui/context-menu";
 import {
 	DropdownMenu,
 	DropdownMenuContent,
 	DropdownMenuItem,
-	DropdownMenuSeparator,
 	DropdownMenuTrigger,
 } from "@/components/ui/dropdown-menu";
 import { cn } from "@/lib/utils";
@@ -66,6 +64,8 @@ interface FolderNodeProps {
 	onReorderFolder?: (folderId: number, beforePos: string | null, afterPos: string | null) => void;
 	siblingPositions?: { before: string | null; after: string | null };
 	disabledDropIds?: Set<number>;
+	contextMenuOpen?: boolean;
+	onContextMenuOpenChange?: (open: boolean) => void;
 }
 
 function getDropZone(
@@ -99,6 +99,8 @@ export const FolderNode = React.memo(function FolderNode({
 	onReorderFolder,
 	siblingPositions,
 	disabledDropIds,
+	contextMenuOpen,
+	onContextMenuOpenChange,
 }: FolderNodeProps) {
 	const [renameValue, setRenameValue] = useState(folder.name);
 	const inputRef = useRef<HTMLInputElement>(null);
@@ -213,7 +215,7 @@ export const FolderNode = React.memo(function FolderNode({
 	const FolderIcon = isExpanded ? FolderOpen : Folder;
 
 	return (
-		<ContextMenu>
+		<ContextMenu onOpenChange={onContextMenuOpenChange}>
 			<ContextMenuTrigger asChild disabled={isRenaming}>
 				{/* biome-ignore lint/a11y/useSemanticElements: div required for drag/drop refs */}
 				<div
@@ -261,7 +263,8 @@ export const FolderNode = React.memo(function FolderNode({
 							onBlur={handleRenameSubmit}
 							onKeyDown={handleRenameKeyDown}
 							onClick={(e) => e.stopPropagation()}
-							className="flex-1 min-w-0 rounded border border-primary bg-background px-1 py-0.5 text-sm outline-none"
+							placeholder="Enter folder name"
+							className="flex-1 min-w-0 bg-transparent px-1 py-0.5 text-sm outline-none caret-primary placeholder:text-muted-foreground/50"
 						/>
 					) : (
 						<span className="flex-1 min-w-0 truncate">{folder.name}</span>
@@ -279,13 +282,13 @@ export const FolderNode = React.memo(function FolderNode({
 								<Button
 									variant="ghost"
 									size="icon"
-									className="h-6 w-6 shrink-0 opacity-0 group-hover:opacity-100 transition-opacity"
+									className="hidden sm:inline-flex h-6 w-6 shrink-0 opacity-0 group-hover:opacity-100 transition-opacity"
 									onClick={(e) => e.stopPropagation()}
 								>
 									<MoreHorizontal className="h-3.5 w-3.5" />
 								</Button>
 							</DropdownMenuTrigger>
-							<DropdownMenuContent align="end" className="w-48">
+							<DropdownMenuContent align="end" className="w-40">
 								<DropdownMenuItem
 									onClick={(e) => {
 										e.stopPropagation();
@@ -301,7 +304,7 @@ export const FolderNode = React.memo(function FolderNode({
 										startRename();
 									}}
 								>
-									<Pencil className="mr-2 h-4 w-4" />
+									<PenLine className="mr-2 h-4 w-4" />
 									Rename
 								</DropdownMenuItem>
 								<DropdownMenuItem
@@ -313,7 +316,6 @@ export const FolderNode = React.memo(function FolderNode({
 									<Move className="mr-2 h-4 w-4" />
 									Move to...
 								</DropdownMenuItem>
-								<DropdownMenuSeparator />
 								<DropdownMenuItem
 									className="text-destructive focus:text-destructive"
 									onClick={(e) => {
@@ -330,21 +332,20 @@ export const FolderNode = React.memo(function FolderNode({
 				</div>
 			</ContextMenuTrigger>
 
-			{!isRenaming && (
-				<ContextMenuContent className="w-48">
+			{!isRenaming && contextMenuOpen && (
+				<ContextMenuContent className="w-40">
 					<ContextMenuItem onClick={() => onCreateSubfolder(folder.id)}>
 						<FolderPlus className="mr-2 h-4 w-4" />
 						New subfolder
 					</ContextMenuItem>
 					<ContextMenuItem onClick={() => startRename()}>
-						<Pencil className="mr-2 h-4 w-4" />
+						<PenLine className="mr-2 h-4 w-4" />
 						Rename
 					</ContextMenuItem>
 					<ContextMenuItem onClick={() => onMove(folder)}>
 						<Move className="mr-2 h-4 w-4" />
 						Move to...
 					</ContextMenuItem>
-					<ContextMenuSeparator />
 					<ContextMenuItem
 						className="text-destructive focus:text-destructive"
 						onClick={() => onDelete(folder)}

surfsense_web/components/documents/FolderPickerDialog.tsx

@@ -124,10 +124,18 @@ export function FolderPickerDialog({
 
 	return (
 		<Dialog open={open} onOpenChange={onOpenChange}>
-			<DialogContent className="max-w-sm">
-				<DialogHeader>
-					<DialogTitle>{title}</DialogTitle>
-					{description && <DialogDescription>{description}</DialogDescription>}
+			<DialogContent className="select-none max-w-[90vw] sm:max-w-sm p-4 sm:p-5 data-[state=open]:animate-none data-[state=closed]:animate-none">
+				<DialogHeader className="space-y-2 pb-2">
+					<div className="flex items-center gap-2 sm:gap-3">
+						<div className="flex-1 min-w-0">
+							<DialogTitle className="text-base sm:text-lg">{title}</DialogTitle>
+							{description && (
+								<DialogDescription className="text-xs sm:text-sm mt-0.5">
+									{description}
+								</DialogDescription>
+							)}
+						</div>
+					</div>
 				</DialogHeader>
 
 				<div className="max-h-[300px] overflow-y-auto rounded-md border p-1">
@@ -147,11 +155,17 @@ export function FolderPickerDialog({
 					{renderPickerLevel(null, 1)}
 				</div>
 
-				<DialogFooter>
-					<Button variant="outline" onClick={() => onOpenChange(false)}>
+				<DialogFooter className="flex-row justify-end gap-2 pt-2 sm:pt-3">
+					<Button
+						variant="secondary"
+						onClick={() => onOpenChange(false)}
+						className="h-8 sm:h-9 text-xs sm:text-sm"
+					>
 						Cancel
 					</Button>
-					<Button onClick={handleConfirm}>Move here</Button>
+					<Button onClick={handleConfirm} className="h-8 sm:h-9 text-xs sm:text-sm">
+						Move here
+					</Button>
 				</DialogFooter>
 			</DialogContent>
 		</Dialog>

surfsense_web/components/documents/FolderTreeView.tsx

@@ -1,8 +1,8 @@
 "use client";
 
 import { useAtom } from "jotai";
-import { TreePine } from "lucide-react";
-import { useCallback, useMemo } from "react";
+import { CirclePlus } from "lucide-react";
+import { useCallback, useMemo, useState } from "react";
 import { DndProvider } from "react-dnd";
 import { HTML5Backend } from "react-dnd-html5-backend";
 import { renamingFolderIdAtom } from "@/atoms/documents/folder.atoms";
@@ -28,6 +28,7 @@ interface FolderTreeViewProps {
 	onEditDocument: (doc: DocumentNodeDoc) => void;
 	onDeleteDocument: (doc: DocumentNodeDoc) => void;
 	onMoveDocument: (doc: DocumentNodeDoc) => void;
+	onExportDocument?: (doc: DocumentNodeDoc, format: string) => void;
 	activeTypes: DocumentTypeEnum[];
 	onDropIntoFolder?: (
 		itemType: "folder" | "document",
@@ -62,6 +63,7 @@ export function FolderTreeView({
 	onEditDocument,
 	onDeleteDocument,
 	onMoveDocument,
+	onExportDocument,
 	activeTypes,
 	onDropIntoFolder,
 	onReorderFolder,
@@ -80,6 +82,8 @@ export function FolderTreeView({
 		return counts;
 	}, [folders, foldersByParent, docsByFolder]);
 
+	const [openContextMenuId, setOpenContextMenuId] = useState<string | null>(null);
+
 	// Single subscription for rename state — derived boolean passed to each FolderNode
 	const [renamingFolderId, setRenamingFolderId] = useAtom(renamingFolderIdAtom);
 	const handleStartRename = useCallback(
@@ -157,6 +161,8 @@ export function FolderTreeView({
 					onDropIntoFolder={onDropIntoFolder}
 					onReorderFolder={onReorderFolder}
 					siblingPositions={siblingPositions}
+					contextMenuOpen={openContextMenuId === `folder-${f.id}`}
+					onContextMenuOpenChange={(open) => setOpenContextMenuId(open ? `folder-${f.id}` : null)}
 				/>
 			);
 
@@ -177,6 +183,9 @@ export function FolderTreeView({
 					onEdit={onEditDocument}
 					onDelete={onDeleteDocument}
 					onMove={onMoveDocument}
+					onExport={onExportDocument}
+					contextMenuOpen={openContextMenuId === `doc-${d.id}`}
+					onContextMenuOpenChange={(open) => setOpenContextMenuId(open ? `doc-${d.id}` : null)}
 				/>
 			);
 		}
@@ -189,7 +198,7 @@ export function FolderTreeView({
 	if (treeNodes.length === 0 && folders.length === 0 && documents.length === 0) {
 		return (
 			<div className="flex flex-1 flex-col items-center justify-center gap-3 px-4 py-12 text-muted-foreground">
-				<TreePine className="h-10 w-10" />
+				<CirclePlus className="h-10 w-10 rotate-45" />
 				<p className="text-sm">No documents yet</p>
 			</div>
 		);
@@ -198,7 +207,7 @@ export function FolderTreeView({
 	if (treeNodes.length === 0 && activeTypes.length > 0) {
 		return (
 			<div className="flex flex-1 flex-col items-center justify-center gap-3 px-4 py-12 text-muted-foreground">
-				<TreePine className="h-10 w-10" />
+				<CirclePlus className="h-10 w-10 rotate-45" />
 				<p className="text-sm">No matching documents</p>
 			</div>
 		);

surfsense_web/components/hitl-edit-panel/hitl-edit-panel.tsx

@@ -185,7 +185,7 @@ function DateTimePickerField({
 				type="time"
 				value={time}
 				onChange={handleTimeChange}
-				className="w-[120px] text-sm shrink-0 pl-1.5 [&::-webkit-calendar-picker-indicator]:order-first [&::-webkit-calendar-picker-indicator]:mr-1"
+				className="w-[120px] text-sm shrink-0 appearance-none [&::-webkit-calendar-picker-indicator]:hidden [&::-webkit-calendar-picker-indicator]:appearance-none"
 			/>
 		</div>
 	);

surfsense_web/components/layout/ui/sidebar/AllPrivateChatsSidebar.tsx

@@ -396,10 +396,13 @@ export function AllPrivateChatsSidebarContent({
 												variant="ghost"
 												size="icon"
 												className={cn(
-													"h-6 w-6 shrink-0",
+													"h-6 w-6 shrink-0 hover:bg-transparent",
 													isMobile
 														? "opacity-0 pointer-events-none absolute"
+														: openDropdownId === thread.id
+															? "opacity-100"
 															: "md:opacity-0 md:group-hover:opacity-100 md:focus:opacity-100",
+													openDropdownId === thread.id && "bg-accent hover:bg-accent",
 													"transition-opacity"
 												)}
 												disabled={isBusy}

surfsense_web/components/layout/ui/sidebar/AllSharedChatsSidebar.tsx

@@ -396,10 +396,13 @@ export function AllSharedChatsSidebarContent({
 												variant="ghost"
 												size="icon"
 												className={cn(
-													"h-6 w-6 shrink-0",
+													"h-6 w-6 shrink-0 hover:bg-transparent",
 													isMobile
 														? "opacity-0 pointer-events-none absolute"
+														: openDropdownId === thread.id
+															? "opacity-100"
 															: "md:opacity-0 md:group-hover:opacity-100 md:focus:opacity-100",
+													openDropdownId === thread.id && "bg-accent hover:bg-accent",
 													"transition-opacity"
 												)}
 												disabled={isBusy}

surfsense_web/components/layout/ui/sidebar/ChatListItem.tsx

@@ -79,14 +79,21 @@ export function ChatListItem({
 						: "bg-gradient-to-l from-sidebar from-60% to-transparent group-hover/item:from-accent",
 					isMobile
 						? "opacity-0"
-						: isActive
+						: isActive || dropdownOpen
 							? "opacity-100"
 							: "opacity-0 group-hover/item:opacity-100"
 				)}
 			>
 				<DropdownMenu open={dropdownOpen} onOpenChange={setDropdownOpen}>
 					<DropdownMenuTrigger asChild>
-						<Button variant="ghost" size="icon" className="pointer-events-auto h-6 w-6">
+						<Button
+							variant="ghost"
+							size="icon"
+							className={cn(
+								"pointer-events-auto h-6 w-6 hover:bg-transparent",
+								dropdownOpen && "bg-accent hover:bg-accent"
+							)}
+						>
 							<MoreHorizontal className="h-3.5 w-3.5 text-muted-foreground" />
 							<span className="sr-only">{t("more_options")}</span>
 						</Button>

surfsense_web/components/layout/ui/sidebar/DocumentsSidebar.tsx

@@ -7,6 +7,7 @@ import { useParams } from "next/navigation";
 import { useTranslations } from "next-intl";
 import { useCallback, useEffect, useMemo, useRef, useState } from "react";
 import { toast } from "sonner";
+import { EXPORT_FILE_EXTENSIONS } from "@/components/shared/ExportMenuItems";
 import { DocumentsFilters } from "@/app/dashboard/[search_space_id]/documents/(manage)/components/DocumentsFilters";
 import {
 	DocumentsTableShell,
@@ -33,6 +34,7 @@ import { useDocumentSearch } from "@/hooks/use-document-search";
 import { useDocuments } from "@/hooks/use-documents";
 import { useMediaQuery } from "@/hooks/use-media-query";
 import { foldersApiService } from "@/lib/apis/folders-api.service";
+import { authenticatedFetch } from "@/lib/auth-utils";
 import { queries } from "@/zero/queries/index";
 import { SidebarSlideOutPanel } from "./SidebarSlideOutPanel";
 
@@ -234,6 +236,43 @@ export function DocumentsSidebar({
 		setFolderPickerOpen(true);
 	}, []);
 
+	const handleExportDocument = useCallback(
+		async (doc: DocumentNodeDoc, format: string) => {
+			const safeTitle =
+				doc.title
+					.replace(/[^a-zA-Z0-9 _-]/g, "_")
+					.trim()
+					.slice(0, 80) || "document";
+			const ext = EXPORT_FILE_EXTENSIONS[format] ?? format;
+
+			try {
+				const response = await authenticatedFetch(
+					`${process.env.NEXT_PUBLIC_FASTAPI_BACKEND_URL}/api/v1/search-spaces/${searchSpaceId}/documents/${doc.id}/export?format=${format}`,
+					{ method: "GET" }
+				);
+
+				if (!response.ok) {
+					const errorData = await response.json().catch(() => ({ detail: "Export failed" }));
+					throw new Error(errorData.detail || "Export failed");
+				}
+
+				const blob = await response.blob();
+				const url = URL.createObjectURL(blob);
+				const a = document.createElement("a");
+				a.href = url;
+				a.download = `${safeTitle}.${ext}`;
+				document.body.appendChild(a);
+				a.click();
+				document.body.removeChild(a);
+				URL.revokeObjectURL(url);
+			} catch (err) {
+				console.error(`Export ${format} failed:`, err);
+				toast.error(err instanceof Error ? err.message : `Export failed`);
+			}
+		},
+		[searchSpaceId]
+	);
+
 	const handleFolderPickerSelect = useCallback(
 		async (targetFolderId: number | null) => {
 			if (!folderPickerTarget) return;
@@ -606,6 +645,7 @@ export function DocumentsSidebar({
 						}}
 						onDeleteDocument={(doc) => handleDeleteDocument(doc.id)}
 						onMoveDocument={handleMoveDocument}
+						onExportDocument={handleExportDocument}
 						activeTypes={activeTypes}
 						onDropIntoFolder={handleDropIntoFolder}
 						onReorderFolder={handleReorderFolder}
@@ -617,7 +657,7 @@ export function DocumentsSidebar({
 				open={folderPickerOpen}
 				onOpenChange={setFolderPickerOpen}
 				folders={treeFolders}
-				title={folderPickerTarget?.type === "folder" ? "Move folder to..." : "Move document to..."}
+				title={folderPickerTarget?.type === "folder" ? "Move folder to" : "Move document to"}
 				description="Select a destination folder, or choose Root to move to the top level."
 				disabledFolderIds={folderPickerTarget?.disabledIds}
 				onSelect={handleFolderPickerSelect}

surfsense_web/components/new-chat/chat-share-button.tsx

@@ -199,7 +199,7 @@ export function ChatShareButton({ thread, onVisibilityChange, className }: ChatS
 									className={cn(
 										"w-full flex items-center gap-2.5 px-2.5 py-2 rounded-md transition-all",
 										"hover:bg-accent/50 dark:hover:bg-white/10 cursor-pointer",
-										"focus:outline-none",
+										"focus:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2",
 										isSelected && "bg-accent/80 dark:bg-white/10"
 									)}
 								>
@@ -248,7 +248,7 @@ export function ChatShareButton({ thread, onVisibilityChange, className }: ChatS
 									className={cn(
 										"w-full flex items-center gap-2.5 px-2.5 py-2 rounded-md transition-all",
 										"hover:bg-accent/50 dark:hover:bg-white/10 cursor-pointer",
-										"focus:outline-none",
+										"focus:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2",
 										"disabled:opacity-50 disabled:cursor-not-allowed"
 									)}
 								>

surfsense_web/components/onboarding-tour.tsx

@@ -7,7 +7,7 @@ import { useTheme } from "next-themes";
 import { useCallback, useEffect, useRef, useState } from "react";
 import { createPortal } from "react-dom";
 import { connectorsAtom } from "@/atoms/connectors/connector-query.atoms";
-import { documentTypeCountsAtom } from "@/atoms/documents/document-query.atoms";
+import { useZeroDocumentTypeCounts } from "@/hooks/use-zero-document-type-counts";
 import { activeSearchSpaceIdAtom } from "@/atoms/search-spaces/search-space-query.atoms";
 import { currentUserAtom } from "@/atoms/user/user-query.atoms";
 import { useIsMobile } from "@/hooks/use-mobile";
@@ -452,8 +452,8 @@ export function OnboardingTour() {
 		enabled: !!searchSpaceId,
 	});
 
-	// Get document type counts
-	const { data: documentTypeCounts } = useAtomValue(documentTypeCountsAtom);
+	// Real-time document type counts via Zero
+	const documentTypeCounts = useZeroDocumentTypeCounts(searchSpaceId);
 
 	// Get connectors
 	const { data: connectors = [] } = useAtomValue(connectorsAtom);

surfsense_web/components/report-panel/report-panel.tsx

@@ -15,10 +15,9 @@ import {
 	DropdownMenu,
 	DropdownMenuContent,
 	DropdownMenuItem,
-	DropdownMenuLabel,
-	DropdownMenuSeparator,
 	DropdownMenuTrigger,
 } from "@/components/ui/dropdown-menu";
+import { ExportDropdownItems, EXPORT_FILE_EXTENSIONS } from "@/components/shared/ExportMenuItems";
 import { useMediaQuery } from "@/hooks/use-media-query";
 import { baseApiService } from "@/lib/apis/base-api.service";
 import { authenticatedFetch } from "@/lib/auth-utils";
@@ -198,19 +197,6 @@ export function ReportPanelContent({
 		}
 	}, [currentMarkdown]);
 
-	// Maps backend format values to download file extensions
-	const FILE_EXTENSIONS: Record<string, string> = {
-		pdf: "pdf",
-		docx: "docx",
-		html: "html",
-		latex: "tex",
-		epub: "epub",
-		odt: "odt",
-		plain: "txt",
-		md: "md",
-	};
-
-	// Export report
 	const handleExport = useCallback(
 		async (format: string) => {
 			setExporting(format);
@@ -219,7 +205,7 @@ export function ReportPanelContent({
 					.replace(/[^a-zA-Z0-9 _-]/g, "_")
 					.trim()
 					.slice(0, 80) || "report";
-			const ext = FILE_EXTENSIONS[format] ?? format;
+			const ext = EXPORT_FILE_EXTENSIONS[format] ?? format;
 			try {
 				if (format === "md") {
 					if (!currentMarkdown) return;
@@ -329,68 +315,11 @@ export function ReportPanelContent({
 							align="start"
 							className={`min-w-[200px] select-none${insideDrawer ? " z-[100]" : ""}`}
 						>
-							{!shareToken && (
-								<>
-									<DropdownMenuLabel className="text-xs text-muted-foreground">
-										Documents
-									</DropdownMenuLabel>
-									<DropdownMenuItem
-										onClick={() => handleExport("pdf")}
-										disabled={exporting !== null}
-									>
-										PDF (.pdf)
-									</DropdownMenuItem>
-									<DropdownMenuItem
-										onClick={() => handleExport("docx")}
-										disabled={exporting !== null}
-									>
-										Word (.docx)
-									</DropdownMenuItem>
-									<DropdownMenuItem
-										onClick={() => handleExport("odt")}
-										disabled={exporting !== null}
-									>
-										OpenDocument (.odt)
-									</DropdownMenuItem>
-									<DropdownMenuSeparator />
-									<DropdownMenuLabel className="text-xs text-muted-foreground">
-										Web &amp; E-Book
-									</DropdownMenuLabel>
-									<DropdownMenuItem
-										onClick={() => handleExport("html")}
-										disabled={exporting !== null}
-									>
-										HTML (.html)
-									</DropdownMenuItem>
-									<DropdownMenuItem
-										onClick={() => handleExport("epub")}
-										disabled={exporting !== null}
-									>
-										EPUB (.epub)
-									</DropdownMenuItem>
-									<DropdownMenuSeparator />
-									<DropdownMenuLabel className="text-xs text-muted-foreground">
-										Source &amp; Plain
-									</DropdownMenuLabel>
-									<DropdownMenuItem
-										onClick={() => handleExport("latex")}
-										disabled={exporting !== null}
-									>
-										LaTeX (.tex)
-									</DropdownMenuItem>
-								</>
-							)}
-							<DropdownMenuItem onClick={() => handleExport("md")} disabled={exporting !== null}>
-								Markdown (.md)
-							</DropdownMenuItem>
-							{!shareToken && (
-								<DropdownMenuItem
-									onClick={() => handleExport("plain")}
-									disabled={exporting !== null}
-								>
-									Plain Text (.txt)
-								</DropdownMenuItem>
-							)}
+							<ExportDropdownItems
+								onExport={handleExport}
+								exporting={exporting}
+								showAllFormats={!shareToken}
+							/>
 						</DropdownMenuContent>
 					</DropdownMenu>
 

surfsense_web/components/settings/general-settings-manager.tsx

@@ -27,6 +27,7 @@ export function GeneralSettingsManager({ searchSpaceId }: GeneralSettingsManager
 	const {
 		data: searchSpace,
 		isLoading: loading,
+		isError,
 		refetch: fetchSearchSpace,
 	} = useQuery({
 		queryKey: cacheKeys.searchSpaces.detail(searchSpaceId.toString()),
@@ -104,6 +105,17 @@ export function GeneralSettingsManager({ searchSpaceId }: GeneralSettingsManager
 		);
 	}
 
+	if (isError) {
+		return (
+			<div className="flex flex-col items-center justify-center gap-3 py-8 text-center">
+				<p className="text-sm text-destructive">Failed to load settings.</p>
+				<Button variant="outline" size="sm" onClick={() => fetchSearchSpace()}>
+					Retry
+				</Button>
+			</div>
+		);
+	}
+
 	return (
 		<div className="space-y-4 md:space-y-6">
 			<Alert className="bg-muted/50 py-3 md:py-4">

surfsense_web/components/shared/ExportMenuItems.tsx

@@ -0,0 +1,142 @@
+"use client";
+
+import { Loader2 } from "lucide-react";
+import { DropdownMenuItem, DropdownMenuLabel, DropdownMenuSeparator } from "@/components/ui/dropdown-menu";
+import { ContextMenuItem } from "@/components/ui/context-menu";
+
+export const EXPORT_FILE_EXTENSIONS: Record<string, string> = {
+	pdf: "pdf",
+	docx: "docx",
+	html: "html",
+	latex: "tex",
+	epub: "epub",
+	odt: "odt",
+	plain: "txt",
+	md: "md",
+};
+
+interface ExportMenuItemsProps {
+	onExport: (format: string) => void;
+	exporting: string | null;
+	/** Hide server-side formats (PDF, DOCX, etc.) — only show md */
+	showAllFormats?: boolean;
+}
+
+export function ExportDropdownItems({
+	onExport,
+	exporting,
+	showAllFormats = true,
+}: ExportMenuItemsProps) {
+	const handle = (format: string) => (e: React.MouseEvent) => {
+		e.stopPropagation();
+		onExport(format);
+	};
+
+	return (
+		<>
+			{showAllFormats && (
+				<>
+					<DropdownMenuLabel className="text-xs text-muted-foreground">
+						Documents
+					</DropdownMenuLabel>
+					<DropdownMenuItem onClick={handle("pdf")} disabled={exporting !== null}>
+						{exporting === "pdf" && <Loader2 className="mr-2 h-3.5 w-3.5 animate-spin" />}
+						PDF (.pdf)
+					</DropdownMenuItem>
+					<DropdownMenuItem onClick={handle("docx")} disabled={exporting !== null}>
+						{exporting === "docx" && <Loader2 className="mr-2 h-3.5 w-3.5 animate-spin" />}
+						Word (.docx)
+					</DropdownMenuItem>
+					<DropdownMenuItem onClick={handle("odt")} disabled={exporting !== null}>
+						{exporting === "odt" && <Loader2 className="mr-2 h-3.5 w-3.5 animate-spin" />}
+						OpenDocument (.odt)
+					</DropdownMenuItem>
+					<DropdownMenuSeparator />
+					<DropdownMenuLabel className="text-xs text-muted-foreground">
+						Web &amp; E-Book
+					</DropdownMenuLabel>
+					<DropdownMenuItem onClick={handle("html")} disabled={exporting !== null}>
+						{exporting === "html" && <Loader2 className="mr-2 h-3.5 w-3.5 animate-spin" />}
+						HTML (.html)
+					</DropdownMenuItem>
+					<DropdownMenuItem onClick={handle("epub")} disabled={exporting !== null}>
+						{exporting === "epub" && <Loader2 className="mr-2 h-3.5 w-3.5 animate-spin" />}
+						EPUB (.epub)
+					</DropdownMenuItem>
+					<DropdownMenuSeparator />
+					<DropdownMenuLabel className="text-xs text-muted-foreground">
+						Source &amp; Plain
+					</DropdownMenuLabel>
+					<DropdownMenuItem onClick={handle("latex")} disabled={exporting !== null}>
+						{exporting === "latex" && <Loader2 className="mr-2 h-3.5 w-3.5 animate-spin" />}
+						LaTeX (.tex)
+					</DropdownMenuItem>
+				</>
+			)}
+			<DropdownMenuItem onClick={handle("md")} disabled={exporting !== null}>
+				{exporting === "md" && <Loader2 className="mr-2 h-3.5 w-3.5 animate-spin" />}
+				Markdown (.md)
+			</DropdownMenuItem>
+			{showAllFormats && (
+				<DropdownMenuItem onClick={handle("plain")} disabled={exporting !== null}>
+					{exporting === "plain" && <Loader2 className="mr-2 h-3.5 w-3.5 animate-spin" />}
+					Plain Text (.txt)
+				</DropdownMenuItem>
+			)}
+		</>
+	);
+}
+
+export function ExportContextItems({
+	onExport,
+	exporting,
+	showAllFormats = true,
+}: ExportMenuItemsProps) {
+	const handle = (format: string) => (e: React.MouseEvent) => {
+		e.stopPropagation();
+		onExport(format);
+	};
+
+	return (
+		<>
+			{showAllFormats && (
+				<>
+					<ContextMenuItem onClick={handle("pdf")} disabled={exporting !== null}>
+						{exporting === "pdf" && <Loader2 className="mr-2 h-3.5 w-3.5 animate-spin" />}
+						PDF (.pdf)
+					</ContextMenuItem>
+					<ContextMenuItem onClick={handle("docx")} disabled={exporting !== null}>
+						{exporting === "docx" && <Loader2 className="mr-2 h-3.5 w-3.5 animate-spin" />}
+						Word (.docx)
+					</ContextMenuItem>
+					<ContextMenuItem onClick={handle("odt")} disabled={exporting !== null}>
+						{exporting === "odt" && <Loader2 className="mr-2 h-3.5 w-3.5 animate-spin" />}
+						OpenDocument (.odt)
+					</ContextMenuItem>
+					<ContextMenuItem onClick={handle("html")} disabled={exporting !== null}>
+						{exporting === "html" && <Loader2 className="mr-2 h-3.5 w-3.5 animate-spin" />}
+						HTML (.html)
+					</ContextMenuItem>
+					<ContextMenuItem onClick={handle("epub")} disabled={exporting !== null}>
+						{exporting === "epub" && <Loader2 className="mr-2 h-3.5 w-3.5 animate-spin" />}
+						EPUB (.epub)
+					</ContextMenuItem>
+					<ContextMenuItem onClick={handle("latex")} disabled={exporting !== null}>
+						{exporting === "latex" && <Loader2 className="mr-2 h-3.5 w-3.5 animate-spin" />}
+						LaTeX (.tex)
+					</ContextMenuItem>
+				</>
+			)}
+			<ContextMenuItem onClick={handle("md")} disabled={exporting !== null}>
+				{exporting === "md" && <Loader2 className="mr-2 h-3.5 w-3.5 animate-spin" />}
+				Markdown (.md)
+			</ContextMenuItem>
+			{showAllFormats && (
+				<ContextMenuItem onClick={handle("plain")} disabled={exporting !== null}>
+					{exporting === "plain" && <Loader2 className="mr-2 h-3.5 w-3.5 animate-spin" />}
+					Plain Text (.txt)
+				</ContextMenuItem>
+			)}
+		</>
+	);
+}

surfsense_web/components/tool-ui/google-calendar/update-event.tsx

@@ -253,6 +253,12 @@ function ApprovalCard({
 		String(effectiveNewDescription ?? "") !== (event?.description ?? "");
 
 	const buildFinalArgs = useCallback(() => {
+		const base = {
+			event_id: event?.event_id,
+			document_id: event?.document_id,
+			connector_id: account?.id,
+		};
+
 		if (pendingEdits) {
 			const attendeesArr = pendingEdits.attendees
 				? pendingEdits.attendees
@@ -260,22 +266,38 @@ function ApprovalCard({
 						.map((e) => e.trim())
 						.filter(Boolean)
 				: null;
+			const origAttendees = event?.attendees?.map((a) => a.email) ?? [];
+
 			return {
-				event_id: event?.event_id,
-				document_id: event?.document_id,
-				connector_id: account?.id,
-				new_summary: pendingEdits.summary || null,
-				new_description: pendingEdits.description || null,
-				new_start_datetime: pendingEdits.start_datetime || null,
-				new_end_datetime: pendingEdits.end_datetime || null,
-				new_location: pendingEdits.location || null,
-				new_attendees: attendeesArr,
+				...base,
+				new_summary:
+					pendingEdits.summary && pendingEdits.summary !== (event?.summary ?? "")
+						? pendingEdits.summary
+						: null,
+				new_description:
+					pendingEdits.description !== (event?.description ?? "")
+						? pendingEdits.description || null
+						: null,
+				new_start_datetime:
+					pendingEdits.start_datetime && pendingEdits.start_datetime !== (event?.start ?? "")
+						? pendingEdits.start_datetime
+						: null,
+				new_end_datetime:
+					pendingEdits.end_datetime && pendingEdits.end_datetime !== (event?.end ?? "")
+						? pendingEdits.end_datetime
+						: null,
+				new_location:
+					pendingEdits.location !== (event?.location ?? "")
+						? pendingEdits.location || null
+						: null,
+				new_attendees:
+					attendeesArr && attendeesArr.join(",") !== origAttendees.join(",")
+						? attendeesArr
+						: null,
 			};
 		}
 		return {
-			event_id: event?.event_id,
-			document_id: event?.document_id,
-			connector_id: account?.id,
+			...base,
 			new_summary: actionArgs.new_summary ?? null,
 			new_description: actionArgs.new_description ?? null,
 			new_start_datetime: actionArgs.new_start_datetime ?? null,

surfsense_web/components/ui/expanded-gif-overlay.tsx

@@ -1,7 +1,7 @@
 "use client";
 
 import { AnimatePresence, motion } from "motion/react";
-import { useCallback, useEffect, useState } from "react";
+import { useCallback, useEffect, useRef, useState } from "react";
 import { createPortal } from "react-dom";
 
 function isVideoSrc(src: string) {
@@ -17,6 +17,12 @@ function ExpandedMediaOverlay({
 	alt: string;
 	onClose: () => void;
 }) {
+	const overlayRef = useRef<HTMLDivElement>(null);
+
+	useEffect(() => {
+		overlayRef.current?.focus();
+	}, []);
+
 	useEffect(() => {
 		const handleKey = (e: KeyboardEvent) => {
 			if (e.key === "Escape") onClose();
@@ -52,12 +58,20 @@ function ExpandedMediaOverlay({
 
 	return createPortal(
 		<motion.div
+			role="dialog"
+			aria-modal="true"
+			aria-label="Expanded media view"
+			tabIndex={-1}
+			ref={overlayRef}
 			initial={{ opacity: 0 }}
 			animate={{ opacity: 1 }}
 			exit={{ opacity: 0 }}
 			transition={{ duration: 0.2 }}
 			className="fixed inset-0 z-100 flex items-center justify-center bg-black/70 p-4 backdrop-blur-sm sm:p-8"
 			onClick={onClose}
+			onKeyDown={(e) => {
+				if (e.key === "Escape") onClose();
+			}}
 		>
 			{mediaElement}
 		</motion.div>,

surfsense_web/contracts/enums/toolIcons.tsx

@@ -5,10 +5,10 @@ import {
 	FileText,
 	Film,
 	Globe,
+	ImageIcon,
 	type LucideIcon,
 	Podcast,
 	ScanLine,
-	Sparkles,
 	Wrench,
 } from "lucide-react";
 
@@ -17,7 +17,7 @@ const TOOL_ICONS: Record<string, LucideIcon> = {
 	generate_podcast: Podcast,
 	generate_video_presentation: Film,
 	generate_report: FileText,
-	generate_image: Sparkles,
+	generate_image: ImageIcon,
 	scrape_webpage: ScanLine,
 	web_search: Globe,
 	search_surfsense_docs: BookOpen,

surfsense_web/hooks/use-zero-document-type-counts.ts

@@ -0,0 +1,31 @@
+"use client";
+
+import { useQuery } from "@rocicorp/zero/react";
+import { useMemo } from "react";
+import { queries } from "@/zero/queries";
+
+/**
+ * Real-time document type counts derived from Zero's live document sync.
+ * Updates instantly as documents are created, deleted, or change type.
+ */
+export function useZeroDocumentTypeCounts(
+	searchSpaceId: number | string | null
+): Record<string, number> | undefined {
+	const numericId = searchSpaceId != null ? Number(searchSpaceId) : null;
+
+	const [zeroDocuments] = useQuery(
+		queries.documents.bySpace({ searchSpaceId: numericId ?? -1 })
+	);
+
+	return useMemo(() => {
+		if (!zeroDocuments || numericId == null) return undefined;
+
+		const counts: Record<string, number> = {};
+		for (const doc of zeroDocuments) {
+			if (doc.id != null && doc.title != null && doc.title !== "") {
+				counts[doc.documentType] = (counts[doc.documentType] || 0) + 1;
+			}
+		}
+		return counts;
+	}, [zeroDocuments, numericId]);
+}

surfsense_web/lib/query-client/cache-keys.ts

@@ -17,7 +17,6 @@ export const cacheKeys = {
 		withQueryParams: (queries: GetDocumentsRequest["queryParams"]) =>
 			["documents-with-queries", ...(queries ? Object.values(queries) : [])] as const,
 		document: (documentId: string) => ["document", documentId] as const,
-		typeCounts: (searchSpaceId?: string) => ["documents", "type-counts", searchSpaceId] as const,
 		byChunk: (chunkId: string) => ["documents", "by-chunk", chunkId] as const,
 	},
 	logs: {