feat(backend): Use deterministic content in connector ingestion

surfsense_backend/app/connectors/google_drive/content_extractor.py

@@ -141,7 +141,6 @@ async def download_and_process_file(
     task_logger: TaskLoggingService,
     log_entry: Log,
     connector_id: int | None = None,
-    enable_summary: bool = True,
 ) -> tuple[Any, str | None, dict[str, Any] | None]:
     """
     Download Google Drive file and process using Surfsense file processors.
@@ -215,8 +214,6 @@ async def download_and_process_file(
                 "source_connector": "google_drive",
             },
         }
-        # Include connector_id for de-indexing support
-        connector_info["enable_summary"] = enable_summary
         if connector_id is not None:
             connector_info["connector_id"] = connector_id
 

surfsense_backend/app/services/confluence/kb_sync_service.py

@@ -9,7 +9,6 @@ from app.utils.document_converters import (
     create_document_chunks,
     embed_text,
     generate_content_hash,
-    generate_document_summary,
     generate_unique_identifier_hash,
 )
 
@@ -65,29 +64,11 @@ class ConfluenceKBSyncService:
             if dup:
                 content_hash = unique_hash
 
-            from app.services.llm_service import get_user_long_context_llm
 
-            user_llm = await get_user_long_context_llm(
-                self.db_session,
-                user_id,
-                search_space_id,
-                disable_streaming=True,
-            )
 
-            doc_metadata_for_summary = {
-                "page_title": page_title,
-                "space_id": space_id,
-                "document_type": "Confluence Page",
-                "connector_type": "Confluence",
-            }
 
-            if user_llm:
+            summary_content = f"Confluence Page: {page_title}\n\n{page_content}"
-                summary_content, summary_embedding = await generate_document_summary(
+            summary_embedding = embed_text(summary_content)
-                    page_content, user_llm, doc_metadata_for_summary
-                )
-            else:
-                summary_content = f"Confluence Page: {page_title}\n\n{page_content}"
-                summary_embedding = embed_text(summary_content)
 
             chunks = await create_document_chunks(page_content)
             now_str = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
@@ -185,25 +166,10 @@ class ConfluenceKBSyncService:
 
             space_id = (document.document_metadata or {}).get("space_id", "")
 
-            from app.services.llm_service import get_user_long_context_llm
 
-            user_llm = await get_user_long_context_llm(
-                self.db_session, user_id, search_space_id, disable_streaming=True
-            )
 
-            if user_llm:
+            summary_content = f"Confluence Page: {page_title}\n\n{page_content}"
-                doc_meta = {
+            summary_embedding = embed_text(summary_content)
-                    "page_title": page_title,
-                    "space_id": space_id,
-                    "document_type": "Confluence Page",
-                    "connector_type": "Confluence",
-                }
-                summary_content, summary_embedding = await generate_document_summary(
-                    page_content, user_llm, doc_meta
-                )
-            else:
-                summary_content = f"Confluence Page: {page_title}\n\n{page_content}"
-                summary_embedding = embed_text(summary_content)
 
             chunks = await create_document_chunks(page_content)
 

surfsense_backend/app/services/dropbox/kb_sync_service.py

@@ -9,7 +9,6 @@ from app.utils.document_converters import (
     create_document_chunks,
     embed_text,
     generate_content_hash,
-    generate_document_summary,
 )
 
 logger = logging.getLogger(__name__)
@@ -72,29 +71,11 @@ class DropboxKBSyncService:
                 )
                 content_hash = unique_hash
 
-            from app.services.llm_service import get_user_long_context_llm
 
-            user_llm = await get_user_long_context_llm(
-                self.db_session,
-                user_id,
-                search_space_id,
-                disable_streaming=True,
-            )
 
-            doc_metadata_for_summary = {
-                "file_name": file_name,
-                "document_type": "Dropbox File",
-                "connector_type": "Dropbox",
-            }
 
-            if user_llm:
+            summary_content = f"Dropbox File: {file_name}\n\n{indexable_content}"
-                summary_content, summary_embedding = await generate_document_summary(
+            summary_embedding = embed_text(summary_content)
-                    indexable_content, user_llm, doc_metadata_for_summary
-                )
-            else:
-                logger.warning("No LLM configured — using fallback summary")
-                summary_content = f"Dropbox File: {file_name}\n\n{indexable_content}"
-                summary_embedding = embed_text(summary_content)
 
             chunks = await create_document_chunks(indexable_content)
             now_str = datetime.now().strftime("%Y-%m-%d %H:%M:%S")

surfsense_backend/app/services/gmail/kb_sync_service.py

@@ -9,7 +9,6 @@ from app.utils.document_converters import (
     create_document_chunks,
     embed_text,
     generate_content_hash,
-    generate_document_summary,
     generate_unique_identifier_hash,
 )
 
@@ -78,30 +77,11 @@ class GmailKBSyncService:
                 )
                 content_hash = unique_hash
 
-            from app.services.llm_service import get_user_long_context_llm
 
-            user_llm = await get_user_long_context_llm(
-                self.db_session,
-                user_id,
-                search_space_id,
-                disable_streaming=True,
-            )
 
-            doc_metadata_for_summary = {
-                "subject": subject,
-                "sender": sender,
-                "document_type": "Gmail Message",
-                "connector_type": "Gmail",
-            }
 
-            if user_llm:
+            summary_content = f"Gmail Message: {subject}\n\n{indexable_content}"
-                summary_content, summary_embedding = await generate_document_summary(
+            summary_embedding = await asyncio.to_thread(embed_text, summary_content)
-                    indexable_content, user_llm, doc_metadata_for_summary
-                )
-            else:
-                logger.warning("No LLM configured -- using fallback summary")
-                summary_content = f"Gmail Message: {subject}\n\n{indexable_content}"
-                summary_embedding = await asyncio.to_thread(embed_text, summary_content)
 
             chunks = await create_document_chunks(indexable_content)
             now_str = datetime.now().strftime("%Y-%m-%d %H:%M:%S")

surfsense_backend/app/services/google_calendar/kb_sync_service.py

@@ -19,7 +19,6 @@ from app.utils.document_converters import (
     create_document_chunks,
     embed_text,
     generate_content_hash,
-    generate_document_summary,
     generate_unique_identifier_hash,
 )
 
@@ -90,33 +89,13 @@ class GoogleCalendarKBSyncService:
                 )
                 content_hash = unique_hash
 
-            from app.services.llm_service import get_user_long_context_llm
 
-            user_llm = await get_user_long_context_llm(
+
-                self.db_session,
+
-                user_id,
+            summary_content = (
-                search_space_id,
+                f"Google Calendar Event: {event_summary}\n\n{indexable_content}"
-                disable_streaming=True,
             )
-
+            summary_embedding = await asyncio.to_thread(embed_text, summary_content)
-            doc_metadata_for_summary = {
-                "event_summary": event_summary,
-                "start_time": start_time,
-                "end_time": end_time,
-                "document_type": "Google Calendar Event",
-                "connector_type": "Google Calendar",
-            }
-
-            if user_llm:
-                summary_content, summary_embedding = await generate_document_summary(
-                    indexable_content, user_llm, doc_metadata_for_summary
-                )
-            else:
-                logger.warning("No LLM configured -- using fallback summary")
-                summary_content = (
-                    f"Google Calendar Event: {event_summary}\n\n{indexable_content}"
-                )
-                summary_embedding = await asyncio.to_thread(embed_text, summary_content)
 
             chunks = await create_document_chunks(indexable_content)
             now_str = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
@@ -273,29 +252,13 @@ class GoogleCalendarKBSyncService:
             if not indexable_content:
                 return {"status": "error", "message": "Event produced empty content"}
 
-            from app.services.llm_service import get_user_long_context_llm
 
-            user_llm = await get_user_long_context_llm(
+
-                self.db_session, user_id, search_space_id, disable_streaming=True
+
+            summary_content = (
+                f"Google Calendar Event: {event_summary}\n\n{indexable_content}"
             )
-
+            summary_embedding = await asyncio.to_thread(embed_text, summary_content)
-            doc_metadata_for_summary = {
-                "event_summary": event_summary,
-                "start_time": start_time,
-                "end_time": end_time,
-                "document_type": "Google Calendar Event",
-                "connector_type": "Google Calendar",
-            }
-
-            if user_llm:
-                summary_content, summary_embedding = await generate_document_summary(
-                    indexable_content, user_llm, doc_metadata_for_summary
-                )
-            else:
-                summary_content = (
-                    f"Google Calendar Event: {event_summary}\n\n{indexable_content}"
-                )
-                summary_embedding = await asyncio.to_thread(embed_text, summary_content)
 
             chunks = await create_document_chunks(indexable_content)
             now_str = datetime.now().strftime("%Y-%m-%d %H:%M:%S")

surfsense_backend/app/services/google_drive/kb_sync_service.py

@@ -8,7 +8,6 @@ from app.utils.document_converters import (
     create_document_chunks,
     embed_text,
     generate_content_hash,
-    generate_document_summary,
     generate_unique_identifier_hash,
 )
 
@@ -74,32 +73,13 @@ class GoogleDriveKBSyncService:
                 )
                 content_hash = unique_hash
 
-            from app.services.llm_service import get_user_long_context_llm
 
-            user_llm = await get_user_long_context_llm(
+
-                self.db_session,
+
-                user_id,
+            summary_content = (
-                search_space_id,
+                f"Google Drive File: {file_name}\n\n{indexable_content}"
-                disable_streaming=True,
             )
-
+            summary_embedding = embed_text(summary_content)
-            doc_metadata_for_summary = {
-                "file_name": file_name,
-                "mime_type": mime_type,
-                "document_type": "Google Drive File",
-                "connector_type": "Google Drive",
-            }
-
-            if user_llm:
-                summary_content, summary_embedding = await generate_document_summary(
-                    indexable_content, user_llm, doc_metadata_for_summary
-                )
-            else:
-                logger.warning("No LLM configured — using fallback summary")
-                summary_content = (
-                    f"Google Drive File: {file_name}\n\n{indexable_content}"
-                )
-                summary_embedding = embed_text(summary_content)
 
             chunks = await create_document_chunks(indexable_content)
             now_str = datetime.now().strftime("%Y-%m-%d %H:%M:%S")

surfsense_backend/app/services/linear/kb_sync_service.py

@@ -9,7 +9,6 @@ from app.utils.document_converters import (
     create_document_chunks,
     embed_text,
     generate_content_hash,
-    generate_document_summary,
     generate_unique_identifier_hash,
 )
 
@@ -84,32 +83,13 @@ class LinearKBSyncService:
                 )
                 content_hash = unique_hash
 
-            from app.services.llm_service import get_user_long_context_llm
 
-            user_llm = await get_user_long_context_llm(
+
-                self.db_session,
+
-                user_id,
+            summary_content = (
-                search_space_id,
+                f"Linear Issue {issue_identifier}: {issue_title}\n\n{issue_content}"
-                disable_streaming=True,
             )
-
+            summary_embedding = embed_text(summary_content)
-            doc_metadata_for_summary = {
-                "issue_id": issue_identifier,
-                "issue_title": issue_title,
-                "document_type": "Linear Issue",
-                "connector_type": "Linear",
-            }
-
-            if user_llm:
-                summary_content, summary_embedding = await generate_document_summary(
-                    issue_content, user_llm, doc_metadata_for_summary
-                )
-            else:
-                logger.warning("No LLM configured — using fallback summary")
-                summary_content = (
-                    f"Linear Issue {issue_identifier}: {issue_title}\n\n{issue_content}"
-                )
-                summary_embedding = embed_text(summary_content)
 
             chunks = await create_document_chunks(issue_content)
             now_str = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
@@ -227,30 +207,12 @@ class LinearKBSyncService:
             comment_count = len(formatted_issue.get("comments", []))
             formatted_issue.get("description", "")
 
-            from app.services.llm_service import get_user_long_context_llm
 
-            user_llm = await get_user_long_context_llm(
+
-                self.db_session, user_id, search_space_id, disable_streaming=True
+            summary_content = (
+                f"Linear Issue {issue_identifier}: {issue_title}\n\n{issue_content}"
             )
-
+            summary_embedding = embed_text(summary_content)
-            if user_llm:
-                document_metadata_for_summary = {
-                    "issue_id": issue_identifier,
-                    "issue_title": issue_title,
-                    "state": state,
-                    "priority": priority,
-                    "comment_count": comment_count,
-                    "document_type": "Linear Issue",
-                    "connector_type": "Linear",
-                }
-                summary_content, summary_embedding = await generate_document_summary(
-                    issue_content, user_llm, document_metadata_for_summary
-                )
-            else:
-                summary_content = (
-                    f"Linear Issue {issue_identifier}: {issue_title}\n\n{issue_content}"
-                )
-                summary_embedding = embed_text(summary_content)
 
             chunks = await create_document_chunks(issue_content)
 

surfsense_backend/app/services/notion/kb_sync_service.py

@@ -8,7 +8,6 @@ from app.utils.document_converters import (
     create_document_chunks,
     embed_text,
     generate_content_hash,
-    generate_document_summary,
     generate_unique_identifier_hash,
 )
 
@@ -73,30 +72,11 @@ class NotionKBSyncService:
                 )
                 content_hash = unique_hash
 
-            from app.services.llm_service import get_user_long_context_llm
 
-            user_llm = await get_user_long_context_llm(
-                self.db_session,
-                user_id,
-                search_space_id,
-                disable_streaming=True,
-            )
 
-            doc_metadata_for_summary = {
-                "page_title": page_title,
-                "page_id": page_id,
-                "document_type": "Notion Page",
-                "connector_type": "Notion",
-            }
 
-            if user_llm:
+            summary_content = f"Notion Page: {page_title}\n\n{markdown_content}"
-                summary_content, summary_embedding = await generate_document_summary(
+            summary_embedding = embed_text(summary_content)
-                    markdown_content, user_llm, doc_metadata_for_summary
-                )
-            else:
-                logger.warning("No LLM configured — using fallback summary")
-                summary_content = f"Notion Page: {page_title}\n\n{markdown_content}"
-                summary_embedding = embed_text(summary_content)
 
             chunks = await create_document_chunks(markdown_content)
             now_str = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
@@ -245,31 +225,11 @@ class NotionKBSyncService:
                 f"Final content length: {len(full_content)} chars, verified={content_verified}"
             )
 
-            from app.services.llm_service import get_user_long_context_llm
 
             logger.debug("Generating summary and embeddings")
-            user_llm = await get_user_long_context_llm(
-                self.db_session,
-                user_id,
-                search_space_id,
-                disable_streaming=True,  # disable streaming to avoid leaking into the chat
-            )
 
-            if user_llm:
+            summary_content = f"Notion Page: {document.document_metadata.get('page_title')}\n\n{full_content}"
-                document_metadata_for_summary = {
+            summary_embedding = embed_text(summary_content)
-                    "page_title": document.document_metadata.get("page_title"),
-                    "page_id": document.document_metadata.get("page_id"),
-                    "document_type": "Notion Page",
-                    "connector_type": "Notion",
-                }
-                summary_content, summary_embedding = await generate_document_summary(
-                    full_content, user_llm, document_metadata_for_summary
-                )
-                logger.debug(f"Generated summary length: {len(summary_content)} chars")
-            else:
-                logger.warning("No LLM configured - using fallback summary")
-                summary_content = f"Notion Page: {document.document_metadata.get('page_title')}\n\n{full_content}"
-                summary_embedding = embed_text(summary_content)
 
             logger.debug("Creating new chunks")
             chunks = await create_document_chunks(full_content)

surfsense_backend/app/services/onedrive/kb_sync_service.py

@@ -10,7 +10,6 @@ from app.utils.document_converters import (
     create_document_chunks,
     embed_text,
     generate_content_hash,
-    generate_document_summary,
 )
 
 logger = logging.getLogger(__name__)
@@ -73,30 +72,11 @@ class OneDriveKBSyncService:
                 )
                 content_hash = unique_hash
 
-            from app.services.llm_service import get_user_long_context_llm
 
-            user_llm = await get_user_long_context_llm(
-                self.db_session,
-                user_id,
-                search_space_id,
-                disable_streaming=True,
-            )
 
-            doc_metadata_for_summary = {
-                "file_name": file_name,
-                "mime_type": mime_type,
-                "document_type": "OneDrive File",
-                "connector_type": "OneDrive",
-            }
 
-            if user_llm:
+            summary_content = f"OneDrive File: {file_name}\n\n{indexable_content}"
-                summary_content, summary_embedding = await generate_document_summary(
+            summary_embedding = await asyncio.to_thread(embed_text, summary_content)
-                    indexable_content, user_llm, doc_metadata_for_summary
-                )
-            else:
-                logger.warning("No LLM configured — using fallback summary")
-                summary_content = f"OneDrive File: {file_name}\n\n{indexable_content}"
-                summary_embedding = await asyncio.to_thread(embed_text, summary_content)
 
             chunks = await create_document_chunks(indexable_content)
             now_str = datetime.now().strftime("%Y-%m-%d %H:%M:%S")

surfsense_backend/app/tasks/connector_indexers/airtable_indexer.py

@@ -14,13 +14,11 @@ from sqlalchemy.ext.asyncio import AsyncSession
 
 from app.connectors.airtable_history import AirtableHistoryConnector
 from app.db import Document, DocumentStatus, DocumentType, SearchSourceConnectorType
-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,
 )
 
@@ -394,29 +392,10 @@ async def index_airtable_records(
                     document.status = DocumentStatus.processing()
                     await session.commit()
 
-                    # Heavy processing (LLM, embeddings, chunks)
+                    # Heavy processing (embeddings, chunks)
-                    user_llm = await get_user_long_context_llm(
-                        session, user_id, search_space_id
-                    )
 
-                    if user_llm and connector.enable_summary:
+                    summary_content = f"Airtable Record: {item['record_id']}\n\n{item['markdown_content']}"
-                        document_metadata_for_summary = {
+                    summary_embedding = embed_text(summary_content)
-                            "record_id": item["record_id"],
-                            "created_time": item["record"].get("CREATED_TIME()", ""),
-                            "document_type": "Airtable Record",
-                            "connector_type": "Airtable",
-                        }
-                        (
-                            summary_content,
-                            summary_embedding,
-                        ) = await generate_document_summary(
-                            item["markdown_content"],
-                            user_llm,
-                            document_metadata_for_summary,
-                        )
-                    else:
-                        summary_content = f"Airtable Record: {item['record_id']}\n\n{item['markdown_content']}"
-                        summary_embedding = embed_text(summary_content)
 
                     chunks = await create_document_chunks(item["markdown_content"])
 

surfsense_backend/app/tasks/connector_indexers/bookstack_indexer.py

@@ -15,13 +15,11 @@ from sqlalchemy.ext.asyncio import AsyncSession
 
 from app.connectors.bookstack_connector import BookStackConnector
 from app.db import Document, DocumentStatus, DocumentType, SearchSourceConnectorType
-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,
 )
 
@@ -384,10 +382,7 @@ async def index_bookstack_pages(
                 document.status = DocumentStatus.processing()
                 await session.commit()
 
-                # Heavy processing (LLM, embeddings, chunks)
+                # Heavy processing (embeddings, chunks)
-                user_llm = await get_user_long_context_llm(
-                    session, user_id, search_space_id
-                )
 
                 # Build document metadata
                 doc_metadata = {
@@ -403,23 +398,8 @@ async def index_bookstack_pages(
                     "connector_id": connector_id,
                 }
 
-                if user_llm and connector.enable_summary:
+                summary_content = f"BookStack Page: {item['page_name']}\n\nBook ID: {item['book_id']}\n\n{item['full_content']}"
-                    summary_metadata = {
+                summary_embedding = embed_text(summary_content)
-                        "page_name": item["page_name"],
-                        "page_id": item["page_id"],
-                        "book_id": item["book_id"],
-                        "document_type": "BookStack Page",
-                        "connector_type": "BookStack",
-                    }
-                    (
-                        summary_content,
-                        summary_embedding,
-                    ) = await generate_document_summary(
-                        item["full_content"], user_llm, summary_metadata
-                    )
-                else:
-                    summary_content = f"BookStack Page: {item['page_name']}\n\nBook ID: {item['book_id']}\n\n{item['full_content']}"
-                    summary_embedding = embed_text(summary_content)
 
                 # Process chunks - using the full page content
                 chunks = await create_document_chunks(item["full_content"])

surfsense_backend/app/tasks/connector_indexers/clickup_indexer.py

@@ -16,13 +16,11 @@ from sqlalchemy.ext.asyncio import AsyncSession
 
 from app.connectors.clickup_history import ClickUpHistoryConnector
 from app.db import Document, DocumentStatus, DocumentType, SearchSourceConnectorType
-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,
 )
 
@@ -393,32 +391,10 @@ async def index_clickup_tasks(
                 document.status = DocumentStatus.processing()
                 await session.commit()
 
-                # Heavy processing (LLM, embeddings, chunks)
+                # Heavy processing (embeddings, chunks)
-                user_llm = await get_user_long_context_llm(
-                    session, user_id, search_space_id
-                )
 
-                if user_llm and connector.enable_summary:
+                summary_content = item["task_content"]
-                    document_metadata_for_summary = {
+                summary_embedding = embed_text(item["task_content"])
-                        "task_id": item["task_id"],
-                        "task_name": item["task_name"],
-                        "task_status": item["task_status"],
-                        "task_priority": item["task_priority"],
-                        "task_list": item["task_list_name"],
-                        "task_space": item["task_space_name"],
-                        "assignees": len(item["task_assignees"]),
-                        "document_type": "ClickUp Task",
-                        "connector_type": "ClickUp",
-                    }
-                    (
-                        summary_content,
-                        summary_embedding,
-                    ) = await generate_document_summary(
-                        item["task_content"], user_llm, document_metadata_for_summary
-                    )
-                else:
-                    summary_content = item["task_content"]
-                    summary_embedding = embed_text(item["task_content"])
 
                 chunks = await create_document_chunks(item["task_content"])
 

surfsense_backend/app/tasks/connector_indexers/confluence_indexer.py

@@ -14,7 +14,6 @@ from app.indexing_pipeline.indexing_pipeline_service import (
     IndexingPipelineService,
     PlaceholderInfo,
 )
-from app.services.llm_service import get_user_long_context_llm
 from app.services.task_logging_service import TaskLoggingService
 
 from .base import (
@@ -36,7 +35,6 @@ def _build_connector_doc(
     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", "")
@@ -54,10 +52,6 @@ def _build_connector_doc(
         "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,
@@ -66,8 +60,6 @@ def _build_connector_doc(
         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,
     )
 
@@ -268,8 +260,7 @@ async def index_confluence_pages(
                     connector_id=connector_id,
                     search_space_id=search_space_id,
                     user_id=user_id,
-                    enable_summary=connector.enable_summary,
+                    )
-                )
 
                 with session.no_autoflush:
                     duplicate_by_content = await check_duplicate_document_by_hash(
@@ -297,12 +288,8 @@ async def index_confluence_pages(
 
         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,

surfsense_backend/app/tasks/connector_indexers/dropbox_indexer.py

@@ -27,7 +27,6 @@ from app.db import Document, DocumentStatus, DocumentType, SearchSourceConnector
 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
-from app.services.llm_service import get_user_long_context_llm
 from app.services.page_limit_service import PageLimitService
 from app.services.task_logging_service import TaskLoggingService
 from app.tasks.connector_indexers.base import (
@@ -126,7 +125,6 @@ def _build_connector_doc(
     connector_id: int,
     search_space_id: int,
     user_id: str,
-    enable_summary: bool,
 ) -> ConnectorDocument:
     file_id = file.get("id", "")
     file_name = file.get("name", "Unknown")
@@ -138,8 +136,6 @@ def _build_connector_doc(
         "connector_type": "Dropbox",
     }
 
-    fallback_summary = f"File: {file_name}\n\n{markdown[:4000]}"
-
     return ConnectorDocument(
         title=file_name,
         source_markdown=markdown,
@@ -148,8 +144,6 @@ def _build_connector_doc(
         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,
     )
 
@@ -161,7 +155,6 @@ async def _download_files_parallel(
     connector_id: int,
     search_space_id: int,
     user_id: str,
-    enable_summary: bool,
     max_concurrency: int = 3,
     on_heartbeat: HeartbeatCallbackType | None = None,
     vision_llm=None,
@@ -191,7 +184,6 @@ async def _download_files_parallel(
                 connector_id=connector_id,
                 search_space_id=search_space_id,
                 user_id=user_id,
-                enable_summary=enable_summary,
             )
             async with hb_lock:
                 completed_count += 1
@@ -223,7 +215,6 @@ async def _download_and_index(
     connector_id: int,
     search_space_id: int,
     user_id: str,
-    enable_summary: bool,
     on_heartbeat: HeartbeatCallbackType | None = None,
     vision_llm=None,
 ) -> tuple[int, int]:
@@ -234,7 +225,6 @@ async def _download_and_index(
         connector_id=connector_id,
         search_space_id=search_space_id,
         user_id=user_id,
-        enable_summary=enable_summary,
         on_heartbeat=on_heartbeat,
         vision_llm=vision_llm,
     )
@@ -243,13 +233,8 @@ async def _download_and_index(
     batch_failed = 0
     if connector_docs:
         pipeline = IndexingPipelineService(session)
-
-        async def _get_llm(s):
-            return await get_user_long_context_llm(s, user_id, search_space_id)
-
         _, batch_indexed, batch_failed = await pipeline.index_batch_parallel(
             connector_docs,
-            _get_llm,
             max_concurrency=3,
             on_heartbeat=on_heartbeat,
         )
@@ -289,7 +274,6 @@ async def _index_with_delta_sync(
     log_entry: object,
     max_files: int,
     on_heartbeat_callback: HeartbeatCallbackType | None = None,
-    enable_summary: bool = True,
     vision_llm=None,
 ) -> tuple[int, int, int, str]:
     """Delta sync using Dropbox cursor-based change tracking.
@@ -361,7 +345,6 @@ async def _index_with_delta_sync(
         connector_id=connector_id,
         search_space_id=search_space_id,
         user_id=user_id,
-        enable_summary=enable_summary,
         on_heartbeat=on_heartbeat_callback,
         vision_llm=vision_llm,
     )
@@ -388,7 +371,6 @@ async def _index_full_scan(
     include_subfolders: bool = True,
     incremental_sync: bool = True,
     on_heartbeat_callback: HeartbeatCallbackType | None = None,
-    enable_summary: bool = True,
     vision_llm=None,
 ) -> tuple[int, int, int]:
     """Full scan indexing of a folder.
@@ -473,7 +455,6 @@ async def _index_full_scan(
         connector_id=connector_id,
         search_space_id=search_space_id,
         user_id=user_id,
-        enable_summary=enable_summary,
         on_heartbeat=on_heartbeat_callback,
         vision_llm=vision_llm,
     )
@@ -502,7 +483,6 @@ async def _index_selected_files(
     connector_id: int,
     search_space_id: int,
     user_id: str,
-    enable_summary: bool,
     incremental_sync: bool = True,
     on_heartbeat: HeartbeatCallbackType | None = None,
     vision_llm=None,
@@ -563,7 +543,6 @@ async def _index_selected_files(
         connector_id=connector_id,
         search_space_id=search_space_id,
         user_id=user_id,
-        enable_summary=enable_summary,
         on_heartbeat=on_heartbeat,
         vision_llm=vision_llm,
     )
@@ -629,7 +608,6 @@ async def index_dropbox_files(
             )
             return 0, 0, error_msg, 0
 
-        connector_enable_summary = getattr(connector, "enable_summary", True)
         connector_enable_vision_llm = getattr(connector, "enable_vision_llm", False)
         vision_llm = None
         if connector_enable_vision_llm:
@@ -664,7 +642,6 @@ async def index_dropbox_files(
                 connector_id=connector_id,
                 search_space_id=search_space_id,
                 user_id=user_id,
-                enable_summary=connector_enable_summary,
                 incremental_sync=incremental_sync,
                 vision_llm=vision_llm,
             )
@@ -700,7 +677,6 @@ async def index_dropbox_files(
                     task_logger,
                     log_entry,
                     max_files,
-                    enable_summary=connector_enable_summary,
                     vision_llm=vision_llm,
                 )
                 folder_cursors[folder_path] = new_cursor
@@ -720,7 +696,6 @@ async def index_dropbox_files(
                     max_files,
                     include_subfolders,
                     incremental_sync=incremental_sync,
-                    enable_summary=connector_enable_summary,
                     vision_llm=vision_llm,
                 )
                 total_unsupported += unsup

surfsense_backend/app/tasks/connector_indexers/github_indexer.py

@@ -18,13 +18,11 @@ from sqlalchemy.ext.asyncio import AsyncSession
 
 from app.connectors.github_connector import GitHubConnector
 from app.db import Document, DocumentStatus, DocumentType, SearchSourceConnectorType
-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,
 )
 
@@ -351,42 +349,14 @@ async def index_github_repos(
                 document.status = DocumentStatus.processing()
                 await session.commit()
 
-                # Heavy processing (LLM, embeddings, chunks)
+                # Heavy processing (embeddings, chunks)
-                user_llm = await get_user_long_context_llm(
+
-                    session, user_id, search_space_id
+                summary_text = (
+                    f"# GitHub Repository: {repo_full_name}\n\n"
+                    f"## Summary\n{digest.summary}\n\n"
+                    f"## File Structure\n{digest.tree}"
                 )
-
+                summary_embedding = embed_text(summary_text)
-                document_metadata_for_summary = {
-                    "repository": repo_full_name,
-                    "document_type": "GitHub Repository",
-                    "connector_type": "GitHub",
-                    "ingestion_method": "gitingest",
-                    "file_tree": digest.tree[:2000]
-                    if len(digest.tree) > 2000
-                    else digest.tree,
-                    "estimated_tokens": digest.estimated_tokens,
-                }
-
-                if user_llm and connector.enable_summary:
-                    # Prepare content for summarization
-                    summary_content = digest.full_digest
-                    if len(summary_content) > MAX_DIGEST_CHARS:
-                        summary_content = (
-                            f"# Repository: {repo_full_name}\n\n"
-                            f"## File Structure\n\n{digest.tree}\n\n"
-                            f"## File Contents (truncated)\n\n{digest.content[: MAX_DIGEST_CHARS - len(digest.tree) - 200]}..."
-                        )
-
-                    summary_text, summary_embedding = await generate_document_summary(
-                        summary_content, user_llm, document_metadata_for_summary
-                    )
-                else:
-                    summary_text = (
-                        f"# GitHub Repository: {repo_full_name}\n\n"
-                        f"## Summary\n{digest.summary}\n\n"
-                        f"## File Structure\n{digest.tree}"
-                    )
-                    summary_embedding = embed_text(summary_text)
 
                 # Chunk the full digest content for granular search
                 try:

surfsense_backend/app/tasks/connector_indexers/google_calendar_indexer.py

@@ -21,7 +21,6 @@ from app.indexing_pipeline.indexing_pipeline_service import (
     PlaceholderInfo,
 )
 from app.services.composio_service import ComposioService
-from app.services.llm_service import get_user_long_context_llm
 from app.services.task_logging_service import TaskLoggingService
 from app.utils.google_credentials import COMPOSIO_GOOGLE_CONNECTOR_TYPES
 
@@ -53,7 +52,6 @@ def _build_connector_doc(
     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", "")
@@ -78,8 +76,6 @@ def _build_connector_doc(
         "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,
@@ -88,8 +84,6 @@ def _build_connector_doc(
         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,
     )
 
@@ -420,8 +414,7 @@ async def index_google_calendar_events(
                     connector_id=connector_id,
                     search_space_id=search_space_id,
                     user_id=user_id,
-                    enable_summary=connector.enable_summary,
+                    )
-                )
 
                 with session.no_autoflush:
                     duplicate = await check_duplicate_document_by_hash(
@@ -448,13 +441,8 @@ async def index_google_calendar_events(
 
         # ── Pipeline: migrate legacy docs + parallel index ─────────────
         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,

surfsense_backend/app/tasks/connector_indexers/google_drive_indexer.py

@@ -40,7 +40,6 @@ from app.indexing_pipeline.indexing_pipeline_service import (
     PlaceholderInfo,
 )
 from app.services.composio_service import ComposioService
-from app.services.llm_service import get_user_long_context_llm
 from app.services.page_limit_service import PageLimitService
 from app.services.task_logging_service import TaskLoggingService
 from app.tasks.connector_indexers.base import (
@@ -381,7 +380,6 @@ def _build_connector_doc(
     connector_id: int,
     search_space_id: int,
     user_id: str,
-    enable_summary: bool,
 ) -> ConnectorDocument:
     """Build a ConnectorDocument from Drive file metadata + extracted markdown."""
     file_id = file.get("id", "")
@@ -394,8 +392,6 @@ def _build_connector_doc(
         "connector_type": "Google Drive",
     }
 
-    fallback_summary = f"File: {file_name}\n\n{markdown[:4000]}"
-
     return ConnectorDocument(
         title=file_name,
         source_markdown=markdown,
@@ -404,8 +400,6 @@ def _build_connector_doc(
         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,
     )
 
@@ -461,7 +455,6 @@ async def _download_files_parallel(
     connector_id: int,
     search_space_id: int,
     user_id: str,
-    enable_summary: bool,
     max_concurrency: int = 3,
     on_heartbeat: HeartbeatCallbackType | None = None,
     vision_llm=None,
@@ -494,7 +487,6 @@ async def _download_files_parallel(
                 connector_id=connector_id,
                 search_space_id=search_space_id,
                 user_id=user_id,
-                enable_summary=enable_summary,
             )
             async with hb_lock:
                 completed_count += 1
@@ -525,7 +517,6 @@ async def _process_single_file(
     connector_id: int,
     search_space_id: int,
     user_id: str,
-    enable_summary: bool = True,
     vision_llm=None,
 ) -> tuple[int, int, int]:
     """Download, extract, and index a single Drive file via the pipeline.
@@ -561,8 +552,7 @@ async def _process_single_file(
             connector_id=connector_id,
             search_space_id=search_space_id,
             user_id=user_id,
-            enable_summary=enable_summary,
+            )
-        )
 
         pipeline = IndexingPipelineService(session)
         documents = await pipeline.prepare_for_indexing([doc])
@@ -578,10 +568,7 @@ async def _process_single_file(
             connector_doc = doc_map.get(document.unique_identifier_hash)
             if not connector_doc:
                 continue
-            user_llm = await get_user_long_context_llm(
+            await pipeline.index(document, connector_doc)
-                session, user_id, search_space_id
-            )
-            await pipeline.index(document, connector_doc, user_llm)
 
         await page_limit_service.update_page_usage(
             user_id, estimated_pages, allow_exceed=True
@@ -636,7 +623,6 @@ async def _download_and_index(
     connector_id: int,
     search_space_id: int,
     user_id: str,
-    enable_summary: bool,
     on_heartbeat: HeartbeatCallbackType | None = None,
     vision_llm=None,
 ) -> tuple[int, int]:
@@ -650,7 +636,6 @@ async def _download_and_index(
         connector_id=connector_id,
         search_space_id=search_space_id,
         user_id=user_id,
-        enable_summary=enable_summary,
         on_heartbeat=on_heartbeat,
         vision_llm=vision_llm,
     )
@@ -659,13 +644,8 @@ async def _download_and_index(
     batch_failed = 0
     if connector_docs:
         pipeline = IndexingPipelineService(session)
-
-        async def _get_llm(s):
-            return await get_user_long_context_llm(s, user_id, search_space_id)
-
         _, batch_indexed, batch_failed = await pipeline.index_batch_parallel(
             connector_docs,
-            _get_llm,
             max_concurrency=3,
             on_heartbeat=on_heartbeat,
         )
@@ -681,7 +661,6 @@ async def _index_selected_files(
     connector_id: int,
     search_space_id: int,
     user_id: str,
-    enable_summary: bool,
     on_heartbeat: HeartbeatCallbackType | None = None,
     vision_llm=None,
 ) -> tuple[int, int, int, list[str]]:
@@ -746,7 +725,6 @@ async def _index_selected_files(
         connector_id=connector_id,
         search_space_id=search_space_id,
         user_id=user_id,
-        enable_summary=enable_summary,
         on_heartbeat=on_heartbeat,
         vision_llm=vision_llm,
     )
@@ -781,7 +759,6 @@ async def _index_full_scan(
     max_files: int,
     include_subfolders: bool = False,
     on_heartbeat_callback: HeartbeatCallbackType | None = None,
-    enable_summary: bool = True,
     vision_llm=None,
 ) -> tuple[int, int, int]:
     """Full scan indexing of a folder.
@@ -911,7 +888,6 @@ async def _index_full_scan(
         connector_id=connector_id,
         search_space_id=search_space_id,
         user_id=user_id,
-        enable_summary=enable_summary,
         on_heartbeat=on_heartbeat_callback,
         vision_llm=vision_llm,
     )
@@ -946,7 +922,6 @@ async def _index_with_delta_sync(
     max_files: int,
     include_subfolders: bool = False,
     on_heartbeat_callback: HeartbeatCallbackType | None = None,
-    enable_summary: bool = True,
     vision_llm=None,
 ) -> tuple[int, int, int]:
     """Delta sync using change tracking.
@@ -1054,7 +1029,6 @@ async def _index_with_delta_sync(
         connector_id=connector_id,
         search_space_id=search_space_id,
         user_id=user_id,
-        enable_summary=enable_summary,
         on_heartbeat=on_heartbeat_callback,
         vision_llm=vision_llm,
     )
@@ -1142,7 +1116,6 @@ async def index_google_drive_files(
             )
             return 0, 0, client_error, 0
 
-        connector_enable_summary = getattr(connector, "enable_summary", True)
         connector_enable_vision_llm = getattr(connector, "enable_vision_llm", False)
         vision_llm = None
         if connector_enable_vision_llm:
@@ -1189,7 +1162,6 @@ async def index_google_drive_files(
                 max_files,
                 include_subfolders,
                 on_heartbeat_callback,
-                connector_enable_summary,
                 vision_llm=vision_llm,
             )
             documents_unsupported += du
@@ -1208,7 +1180,6 @@ async def index_google_drive_files(
                 max_files,
                 include_subfolders,
                 on_heartbeat_callback,
-                connector_enable_summary,
                 vision_llm=vision_llm,
             )
             documents_indexed += ri
@@ -1234,7 +1205,6 @@ async def index_google_drive_files(
                 max_files,
                 include_subfolders,
                 on_heartbeat_callback,
-                connector_enable_summary,
                 vision_llm=vision_llm,
             )
 
@@ -1346,7 +1316,6 @@ async def index_google_drive_single_file(
             )
             return 0, client_error
 
-        connector_enable_summary = getattr(connector, "enable_summary", True)
         connector_enable_vision_llm = getattr(connector, "enable_vision_llm", False)
         vision_llm = None
         if connector_enable_vision_llm:
@@ -1370,7 +1339,6 @@ async def index_google_drive_single_file(
             connector_id,
             search_space_id,
             user_id,
-            connector_enable_summary,
             vision_llm=vision_llm,
         )
         await session.commit()
@@ -1467,7 +1435,6 @@ async def index_google_drive_selected_files(
             )
             return 0, 0, [error_msg]
 
-        connector_enable_summary = getattr(connector, "enable_summary", True)
         connector_enable_vision_llm = getattr(connector, "enable_vision_llm", False)
         vision_llm = None
         if connector_enable_vision_llm:
@@ -1481,7 +1448,6 @@ async def index_google_drive_selected_files(
             connector_id=connector_id,
             search_space_id=search_space_id,
             user_id=user_id,
-            enable_summary=connector_enable_summary,
             on_heartbeat=on_heartbeat_callback,
             vision_llm=vision_llm,
         )

surfsense_backend/app/tasks/connector_indexers/google_gmail_indexer.py

@@ -21,7 +21,6 @@ from app.indexing_pipeline.indexing_pipeline_service import (
     PlaceholderInfo,
 )
 from app.services.composio_service import ComposioService
-from app.services.llm_service import get_user_long_context_llm
 from app.services.task_logging_service import TaskLoggingService
 from app.utils.google_credentials import COMPOSIO_GOOGLE_CONNECTOR_TYPES
 
@@ -105,7 +104,6 @@ def _build_connector_doc(
     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", "")
@@ -138,12 +136,6 @@ def _build_connector_doc(
         "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,
@@ -152,8 +144,6 @@ def _build_connector_doc(
         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,
     )
 
@@ -454,8 +444,7 @@ async def index_google_gmail_messages(
                     connector_id=connector_id,
                     search_space_id=search_space_id,
                     user_id=user_id,
-                    enable_summary=connector.enable_summary,
+                    )
-                )
 
                 with session.no_autoflush:
                     duplicate = await check_duplicate_document_by_hash(
@@ -483,13 +472,8 @@ async def index_google_gmail_messages(
 
         # ── Pipeline: migrate legacy docs + parallel index ─────────────
         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,

surfsense_backend/app/tasks/connector_indexers/linear_indexer.py

@@ -18,7 +18,6 @@ from app.indexing_pipeline.indexing_pipeline_service import (
     IndexingPipelineService,
     PlaceholderInfo,
 )
-from app.services.llm_service import get_user_long_context_llm
 from app.services.task_logging_service import TaskLoggingService
 
 from .base import (
@@ -41,7 +40,6 @@ def _build_connector_doc(
     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", "")
@@ -63,11 +61,6 @@ def _build_connector_doc(
         "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,
@@ -76,8 +69,6 @@ def _build_connector_doc(
         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,
     )
 
@@ -277,8 +268,7 @@ async def index_linear_issues(
                     connector_id=connector_id,
                     search_space_id=search_space_id,
                     user_id=user_id,
-                    enable_summary=connector.enable_summary,
+                    )
-                )
 
                 with session.no_autoflush:
                     duplicate = await check_duplicate_document_by_hash(
@@ -306,13 +296,8 @@ async def index_linear_issues(
 
         # ── Pipeline: migrate legacy docs + parallel index ────────────
         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,

surfsense_backend/app/tasks/connector_indexers/local_folder_indexer.py

@@ -33,7 +33,6 @@ from app.db import (
 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
-from app.services.llm_service import get_user_long_context_llm
 from app.services.page_limit_service import PageLimitExceededError, PageLimitService
 from app.services.task_logging_service import TaskLoggingService
 from app.tasks.celery_tasks import get_celery_session_maker
@@ -478,7 +477,6 @@ def _build_connector_doc(
     *,
     search_space_id: int,
     user_id: str,
-    enable_summary: bool,
 ) -> ConnectorDocument:
     """Build a ConnectorDocument from a local file's extracted content."""
     unique_id = f"{folder_name}:{relative_path}"
@@ -488,7 +486,6 @@ def _build_connector_doc(
         "document_type": "Local Folder File",
         "connector_type": "Local Folder",
     }
-    fallback_summary = f"File: {title}\n\n{content[:4000]}"
 
     return ConnectorDocument(
         title=title,
@@ -498,8 +495,6 @@ def _build_connector_doc(
         search_space_id=search_space_id,
         connector_id=None,
         created_by_id=user_id,
-        should_summarize=enable_summary,
-        fallback_summary=fallback_summary,
         metadata=metadata,
     )
 
@@ -513,7 +508,6 @@ async def index_local_folder(
     exclude_patterns: list[str] | None = None,
     file_extensions: list[str] | None = None,
     root_folder_id: int | None = None,
-    enable_summary: bool = False,
     target_file_paths: list[str] | None = None,
     on_heartbeat_callback: HeartbeatCallbackType | None = None,
 ) -> tuple[int, int, int | None, str | None]:
@@ -574,8 +568,7 @@ async def index_local_folder(
                         folder_path=folder_path,
                         folder_name=folder_name,
                         target_file_path=target_file_paths[0],
-                        enable_summary=enable_summary,
+                                root_folder_id=root_folder_id,
-                        root_folder_id=root_folder_id,
                         task_logger=task_logger,
                         log_entry=log_entry,
                     )
@@ -587,8 +580,7 @@ async def index_local_folder(
                     folder_path=folder_path,
                     folder_name=folder_name,
                     target_file_paths=target_file_paths,
-                    enable_summary=enable_summary,
+                        root_folder_id=root_folder_id,
-                    root_folder_id=root_folder_id,
                     on_progress_callback=on_heartbeat_callback,
                 )
                 if err:
@@ -774,8 +766,7 @@ async def index_local_folder(
                     folder_name=folder_name,
                     search_space_id=search_space_id,
                     user_id=user_id,
-                    enable_summary=enable_summary,
+                    )
-                )
                 connector_docs.append(doc)
                 file_meta_map[unique_identifier] = {
                     "relative_path": relative_path,
@@ -845,15 +836,13 @@ async def index_local_folder(
             doc_map = {compute_unique_identifier_hash(cd): cd for cd in connector_docs}
             documents = await pipeline.prepare_for_indexing(connector_docs)
 
-            llm = await get_user_long_context_llm(session, user_id, search_space_id)
-
             for document in documents:
                 connector_doc = doc_map.get(document.unique_identifier_hash)
                 if connector_doc is None:
                     failed_count += 1
                     continue
 
-                result = await pipeline.index(document, connector_doc, llm)
+                result = await pipeline.index(document, connector_doc)
 
                 if DocumentStatus.is_state(result.status, DocumentStatus.READY):
                     indexed_count += 1
@@ -960,7 +949,6 @@ async def _index_batch_files(
     folder_path: str,
     folder_name: str,
     target_file_paths: list[str],
-    enable_summary: bool,
     root_folder_id: int | None,
     on_progress_callback: HeartbeatCallbackType | None = None,
 ) -> tuple[int, int, str | None]:
@@ -995,8 +983,7 @@ async def _index_batch_files(
                         folder_path=folder_path,
                         folder_name=folder_name,
                         target_file_path=file_path,
-                        enable_summary=enable_summary,
+                                root_folder_id=root_folder_id,
-                        root_folder_id=root_folder_id,
                         task_logger=task_logger,
                         log_entry=log_entry,
                     )
@@ -1036,7 +1023,6 @@ async def _index_single_file(
     folder_path: str,
     folder_name: str,
     target_file_path: str,
-    enable_summary: bool,
     root_folder_id: int | None,
     task_logger,
     log_entry,
@@ -1125,8 +1111,7 @@ async def _index_single_file(
             folder_name=folder_name,
             search_space_id=search_space_id,
             user_id=user_id,
-            enable_summary=enable_summary,
+            )
-        )
 
         if root_folder_id:
             connector_doc.folder_id = await _resolve_folder_for_file(
@@ -1134,7 +1119,6 @@ async def _index_single_file(
             )
 
         pipeline = IndexingPipelineService(session)
-        llm = await get_user_long_context_llm(session, user_id, search_space_id)
         documents = await pipeline.prepare_for_indexing([connector_doc])
 
         if not documents:
@@ -1142,7 +1126,7 @@ async def _index_single_file(
 
         db_doc = documents[0]
 
-        await pipeline.index(db_doc, connector_doc, llm)
+        await pipeline.index(db_doc, connector_doc)
 
         await session.refresh(db_doc)
         doc_meta = dict(db_doc.document_metadata or {})
@@ -1275,7 +1259,6 @@ async def index_uploaded_files(
     user_id: str,
     folder_name: str,
     root_folder_id: int,
-    enable_summary: bool,
     file_mappings: list[dict],
     on_heartbeat_callback: HeartbeatCallbackType | None = None,
     use_vision_llm: bool = False,
@@ -1318,7 +1301,6 @@ async def index_uploaded_files(
 
         page_limit_service = PageLimitService(session)
         pipeline = IndexingPipelineService(session)
-        llm = await get_user_long_context_llm(session, user_id, search_space_id)
 
         vision_llm_instance = None
         if use_vision_llm:
@@ -1414,8 +1396,7 @@ async def index_uploaded_files(
                     folder_name=folder_name,
                     search_space_id=search_space_id,
                     user_id=user_id,
-                    enable_summary=enable_summary,
+                    )
-                )
 
                 connector_doc.folder_id = await _resolve_folder_for_file(
                     session,
@@ -1432,7 +1413,7 @@ async def index_uploaded_files(
 
                 db_doc = documents[0]
 
-                await pipeline.index(db_doc, connector_doc, llm)
+                await pipeline.index(db_doc, connector_doc)
 
                 await session.refresh(db_doc)
                 doc_meta = dict(db_doc.document_metadata or {})

surfsense_backend/app/tasks/connector_indexers/luma_indexer.py

@@ -16,13 +16,11 @@ from sqlalchemy.ext.asyncio import AsyncSession
 
 from app.connectors.luma_connector import LumaConnector
 from app.db import Document, DocumentStatus, DocumentType, SearchSourceConnectorType
-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,
 )
 
@@ -437,38 +435,14 @@ async def index_luma_events(
                 document.status = DocumentStatus.processing()
                 await session.commit()
 
-                # Heavy processing (LLM, embeddings, chunks)
+                # Heavy processing (embeddings, chunks)
-                user_llm = await get_user_long_context_llm(
-                    session, user_id, search_space_id
-                )
 
-                if user_llm and connector.enable_summary:
+                summary_content = (
-                    document_metadata_for_summary = {
+                    f"Luma Event: {item['event_name']}\n\n{item['event_markdown']}"
-                        "event_id": item["event_id"],
+                )
-                        "event_name": item["event_name"],
+                summary_embedding = await asyncio.to_thread(
-                        "event_url": item["event_url"],
+                    embed_text, summary_content
-                        "start_at": item["start_at"],
+                )
-                        "end_at": item["end_at"],
-                        "timezone": item["timezone"],
-                        "location": item["location"] or "No location",
-                        "city": item["city"],
-                        "hosts": item["host_names"],
-                        "document_type": "Luma Event",
-                        "connector_type": "Luma",
-                    }
-                    (
-                        summary_content,
-                        summary_embedding,
-                    ) = await generate_document_summary(
-                        item["event_markdown"], user_llm, document_metadata_for_summary
-                    )
-                else:
-                    summary_content = (
-                        f"Luma Event: {item['event_name']}\n\n{item['event_markdown']}"
-                    )
-                    summary_embedding = await asyncio.to_thread(
-                        embed_text, summary_content
-                    )
 
                 chunks = await create_document_chunks(item["event_markdown"])
 

surfsense_backend/app/tasks/connector_indexers/notion_indexer.py

@@ -19,7 +19,6 @@ from app.indexing_pipeline.indexing_pipeline_service import (
     IndexingPipelineService,
     PlaceholderInfo,
 )
-from app.services.llm_service import get_user_long_context_llm
 from app.services.task_logging_service import TaskLoggingService
 from app.utils.notion_utils import process_blocks
 
@@ -43,7 +42,6 @@ def _build_connector_doc(
     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", "")
@@ -57,8 +55,6 @@ def _build_connector_doc(
         "connector_type": "Notion",
     }
 
-    fallback_summary = f"Notion Page: {page_title}\n\n{markdown_content}"
-
     return ConnectorDocument(
         title=page_title,
         source_markdown=markdown_content,
@@ -67,8 +63,6 @@ def _build_connector_doc(
         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,
     )
 
@@ -314,8 +308,7 @@ async def index_notion_pages(
                     connector_id=connector_id,
                     search_space_id=search_space_id,
                     user_id=user_id,
-                    enable_summary=connector.enable_summary,
+                    )
-                )
 
                 with session.no_autoflush:
                     duplicate = await check_duplicate_document_by_hash(
@@ -343,13 +336,8 @@ async def index_notion_pages(
 
         # ── Pipeline: migrate legacy docs + parallel index ────────────
         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,

surfsense_backend/app/tasks/connector_indexers/onedrive_indexer.py

@@ -27,7 +27,6 @@ from app.db import Document, DocumentStatus, DocumentType, SearchSourceConnector
 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
-from app.services.llm_service import get_user_long_context_llm
 from app.services.page_limit_service import PageLimitService
 from app.services.task_logging_service import TaskLoggingService
 from app.tasks.connector_indexers.base import (
@@ -133,7 +132,6 @@ def _build_connector_doc(
     connector_id: int,
     search_space_id: int,
     user_id: str,
-    enable_summary: bool,
 ) -> ConnectorDocument:
     file_id = file.get("id", "")
     file_name = file.get("name", "Unknown")
@@ -145,8 +143,6 @@ def _build_connector_doc(
         "connector_type": "OneDrive",
     }
 
-    fallback_summary = f"File: {file_name}\n\n{markdown[:4000]}"
-
     return ConnectorDocument(
         title=file_name,
         source_markdown=markdown,
@@ -155,8 +151,6 @@ def _build_connector_doc(
         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,
     )
 
@@ -168,7 +162,6 @@ async def _download_files_parallel(
     connector_id: int,
     search_space_id: int,
     user_id: str,
-    enable_summary: bool,
     max_concurrency: int = 3,
     on_heartbeat: HeartbeatCallbackType | None = None,
     vision_llm=None,
@@ -198,7 +191,6 @@ async def _download_files_parallel(
                 connector_id=connector_id,
                 search_space_id=search_space_id,
                 user_id=user_id,
-                enable_summary=enable_summary,
             )
             async with hb_lock:
                 completed_count += 1
@@ -230,7 +222,6 @@ async def _download_and_index(
     connector_id: int,
     search_space_id: int,
     user_id: str,
-    enable_summary: bool,
     on_heartbeat: HeartbeatCallbackType | None = None,
     vision_llm=None,
 ) -> tuple[int, int]:
@@ -241,7 +232,6 @@ async def _download_and_index(
         connector_id=connector_id,
         search_space_id=search_space_id,
         user_id=user_id,
-        enable_summary=enable_summary,
         on_heartbeat=on_heartbeat,
         vision_llm=vision_llm,
     )
@@ -250,13 +240,8 @@ async def _download_and_index(
     batch_failed = 0
     if connector_docs:
         pipeline = IndexingPipelineService(session)
-
-        async def _get_llm(s):
-            return await get_user_long_context_llm(s, user_id, search_space_id)
-
         _, batch_indexed, batch_failed = await pipeline.index_batch_parallel(
             connector_docs,
-            _get_llm,
             max_concurrency=3,
             on_heartbeat=on_heartbeat,
         )
@@ -294,7 +279,6 @@ async def _index_selected_files(
     connector_id: int,
     search_space_id: int,
     user_id: str,
-    enable_summary: bool,
     on_heartbeat: HeartbeatCallbackType | None = None,
     vision_llm=None,
 ) -> tuple[int, int, int, list[str]]:
@@ -345,7 +329,6 @@ async def _index_selected_files(
         connector_id=connector_id,
         search_space_id=search_space_id,
         user_id=user_id,
-        enable_summary=enable_summary,
         on_heartbeat=on_heartbeat,
         vision_llm=vision_llm,
     )
@@ -379,7 +362,6 @@ async def _index_full_scan(
     max_files: int,
     include_subfolders: bool = True,
     on_heartbeat_callback: HeartbeatCallbackType | None = None,
-    enable_summary: bool = True,
     vision_llm=None,
 ) -> tuple[int, int, int]:
     """Full scan indexing of a folder.
@@ -454,7 +436,6 @@ async def _index_full_scan(
         connector_id=connector_id,
         search_space_id=search_space_id,
         user_id=user_id,
-        enable_summary=enable_summary,
         on_heartbeat=on_heartbeat_callback,
         vision_llm=vision_llm,
     )
@@ -487,7 +468,6 @@ async def _index_with_delta_sync(
     log_entry: object,
     max_files: int,
     on_heartbeat_callback: HeartbeatCallbackType | None = None,
-    enable_summary: bool = True,
     vision_llm=None,
 ) -> tuple[int, int, int, str | None]:
     """Delta sync using OneDrive change tracking.
@@ -579,7 +559,6 @@ async def _index_with_delta_sync(
         connector_id=connector_id,
         search_space_id=search_space_id,
         user_id=user_id,
-        enable_summary=enable_summary,
         on_heartbeat=on_heartbeat_callback,
         vision_llm=vision_llm,
     )
@@ -651,7 +630,6 @@ async def index_onedrive_files(
             )
             return 0, 0, error_msg, 0
 
-        connector_enable_summary = getattr(connector, "enable_summary", True)
         connector_enable_vision_llm = getattr(connector, "enable_vision_llm", False)
         vision_llm = None
         if connector_enable_vision_llm:
@@ -681,7 +659,6 @@ async def index_onedrive_files(
                 connector_id=connector_id,
                 search_space_id=search_space_id,
                 user_id=user_id,
-                enable_summary=connector_enable_summary,
                 vision_llm=vision_llm,
             )
             total_indexed += indexed
@@ -711,7 +688,6 @@ async def index_onedrive_files(
                     task_logger,
                     log_entry,
                     max_files,
-                    enable_summary=connector_enable_summary,
                     vision_llm=vision_llm,
                 )
                 total_indexed += indexed
@@ -738,7 +714,6 @@ async def index_onedrive_files(
                     log_entry,
                     max_files,
                     include_subfolders,
-                    enable_summary=connector_enable_summary,
                     vision_llm=vision_llm,
                 )
                 total_indexed += ri
@@ -758,7 +733,6 @@ async def index_onedrive_files(
                     log_entry,
                     max_files,
                     include_subfolders,
-                    enable_summary=connector_enable_summary,
                     vision_llm=vision_llm,
                 )
                 total_indexed += indexed

surfsense_backend/app/tasks/connector_indexers/webcrawler_indexer.py

@@ -15,13 +15,11 @@ from sqlalchemy.ext.asyncio import AsyncSession
 
 from app.connectors.webcrawler_connector import WebCrawlerConnector
 from app.db import Document, DocumentStatus, DocumentType, SearchSourceConnectorType
-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.webcrawler_utils import parse_webcrawler_urls
@@ -372,29 +370,10 @@ async def index_crawled_urls(
                         documents_skipped += 1
                         continue
 
-                # Generate summary with LLM
+                # Select deterministic document content
-                user_llm = await get_user_long_context_llm(
-                    session, user_id, search_space_id
-                )
 
-                if user_llm and connector.enable_summary:
+                summary_content = f"Crawled URL: {title}\n\nURL: {url}\n\n{content}"
-                    document_metadata_for_summary = {
+                summary_embedding = embed_text(summary_content)
-                        "url": url,
-                        "title": title,
-                        "description": description,
-                        "language": language,
-                        "document_type": "Crawled URL",
-                        "crawler_type": crawler_type,
-                    }
-                    (
-                        summary_content,
-                        summary_embedding,
-                    ) = await generate_document_summary(
-                        structured_document, user_llm, document_metadata_for_summary
-                    )
-                else:
-                    summary_content = f"Crawled URL: {title}\n\nURL: {url}\n\n{content}"
-                    summary_embedding = embed_text(summary_content)
 
                 # Process chunks
                 chunks = await create_document_chunks(content)