Merge upstream/dev

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, summary_embedding = await generate_document_summary(
-                    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)
+            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:
-                doc_meta = {
-                    "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)
+            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/docling_service.py

@@ -191,149 +191,6 @@ class DoclingService:
             logger.error(f"Full traceback: {traceback.format_exc()}")
             raise RuntimeError(f"Docling processing failed: {e}") from e
 
-    async def process_large_document_summary(
-        self, content: str, llm, document_title: str = "Document"
-    ) -> str:
-        """
-        Process large documents using chunked LLM summarization.
-
-        Args:
-            content: The full document content
-            llm: The language model to use for summarization
-            document_title: Title of the document for context
-
-        Returns:
-            Final summary of the document
-        """
-        # Large document threshold (100K characters ≈ 25K tokens)
-        large_document_threshold = 100_000
-
-        if len(content) <= large_document_threshold:
-            # For smaller documents, use direct processing
-            logger.info(
-                f"📄 Document size: {len(content)} chars - using direct processing"
-            )
-            from app.prompts import SUMMARY_PROMPT_TEMPLATE
-
-            summary_chain = SUMMARY_PROMPT_TEMPLATE | llm
-            result = await summary_chain.ainvoke({"document": content})
-            return result.content
-
-        logger.info(
-            f"📚 Large document detected: {len(content)} chars - using chunked processing"
-        )
-
-        # Import chunker from config
-        # Create LLM-optimized chunks (8K tokens max for safety)
-        from chonkie import OverlapRefinery, RecursiveChunker
-        from langchain_core.prompts import PromptTemplate
-
-        llm_chunker = RecursiveChunker(
-            chunk_size=8000  # Conservative for most LLMs
-        )
-
-        # Apply overlap refinery for context preservation (10% overlap = 800 tokens)
-        overlap_refinery = OverlapRefinery(
-            context_size=0.1,  # 10% overlap for context preservation
-            method="suffix",  # Add next chunk context to current chunk
-        )
-
-        # First chunk the content, then apply overlap refinery
-        initial_chunks = llm_chunker.chunk(content)
-        chunks = overlap_refinery.refine(initial_chunks)
-        total_chunks = len(chunks)
-
-        logger.info(f"📄 Split into {total_chunks} chunks for LLM processing")
-
-        # Template for chunk processing
-        chunk_template = PromptTemplate(
-            input_variables=["chunk", "chunk_number", "total_chunks"],
-            template="""<INSTRUCTIONS>
-You are summarizing chunk {chunk_number} of {total_chunks} from a large document.
-
-Create a comprehensive summary of this document chunk. Focus on:
-- Key concepts, facts, and information
-- Important details and context
-- Main topics and themes
-
-Provide a clear, structured summary that captures the essential content.
-
-Chunk {chunk_number}/{total_chunks}:
-<document_chunk>
-{chunk}
-</document_chunk>
-</INSTRUCTIONS>""",
-        )
-
-        # Process each chunk individually
-        chunk_summaries = []
-        for i, chunk in enumerate(chunks, 1):
-            try:
-                logger.info(
-                    f"🔄 Processing chunk {i}/{total_chunks} ({len(chunk.text)} chars)"
-                )
-
-                chunk_chain = chunk_template | llm
-                chunk_result = await chunk_chain.ainvoke(
-                    {
-                        "chunk": chunk.text,
-                        "chunk_number": i,
-                        "total_chunks": total_chunks,
-                    }
-                )
-
-                chunk_summary = chunk_result.content
-                chunk_summaries.append(f"=== Section {i} ===\n{chunk_summary}")
-
-                logger.info(f"✅ Completed chunk {i}/{total_chunks}")
-
-            except Exception as e:
-                logger.error(f"❌ Failed to process chunk {i}/{total_chunks}: {e}")
-                chunk_summaries.append(f"=== Section {i} ===\n[Processing failed]")
-
-        # Combine summaries into final document summary
-        logger.info(f"🔄 Combining {len(chunk_summaries)} chunk summaries")
-
-        try:
-            combine_template = PromptTemplate(
-                input_variables=["summaries", "document_title"],
-                template="""<INSTRUCTIONS>
-You are combining multiple section summaries into a final comprehensive document summary.
-
-Create a unified, coherent summary from the following section summaries of "{document_title}".
-Ensure:
-- Logical flow and organization
-- No redundancy or repetition  
-- Comprehensive coverage of all key points
-- Professional, objective tone
-
-<section_summaries>
-{summaries}
-</section_summaries>
-</INSTRUCTIONS>""",
-            )
-
-            combined_summaries = "\n\n".join(chunk_summaries)
-            combine_chain = combine_template | llm
-
-            final_result = await combine_chain.ainvoke(
-                {"summaries": combined_summaries, "document_title": document_title}
-            )
-
-            final_summary = final_result.content
-            logger.info(
-                f"✅ Large document processing complete: {len(final_summary)} chars summary"
-            )
-
-            return final_summary
-
-        except Exception as e:
-            logger.error(f"❌ Failed to combine summaries: {e}")
-            # Fallback: return concatenated chunk summaries
-            fallback_summary = "\n\n".join(chunk_summaries)
-            logger.warning("⚠️ Using fallback combined summary")
-            return fallback_summary
-
 
 def create_docling_service() -> DoclingService:
     """Create a Docling service instance."""

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, 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"Dropbox File: {file_name}\n\n{indexable_content}"
-                summary_embedding = embed_text(summary_content)
+            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, 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"Gmail Message: {subject}\n\n{indexable_content}"
-                summary_embedding = await asyncio.to_thread(embed_text, summary_content)
+            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,
-                search_space_id,
-                disable_streaming=True,
+
+
+            summary_content = (
+                f"Google Calendar Event: {event_summary}\n\n{indexable_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)
+            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}"
             )
-
-            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)
+            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,
-                search_space_id,
-                disable_streaming=True,
+
+
+            summary_content = (
+                f"Google Drive File: {file_name}\n\n{indexable_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)
+            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,
-                search_space_id,
-                disable_streaming=True,
+
+
+            summary_content = (
+                f"Linear Issue {issue_identifier}: {issue_title}\n\n{issue_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)
+            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}"
             )
-
-            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)
+            summary_embedding = embed_text(summary_content)
 
             chunks = await create_document_chunks(issue_content)
 

surfsense_backend/app/services/llm_service.py

@@ -68,7 +68,6 @@ def _is_interactive_auth_provider(
 
 class LLMRole:
     AGENT = "agent"  # For agent/chat operations
-    DOCUMENT_SUMMARY = "document_summary"  # For document summarization
 
 
 def get_global_llm_config(llm_config_id: int) -> dict | None:
@@ -268,7 +267,7 @@ async def get_search_space_llm_instance(
     Args:
         session: Database session
         search_space_id: Search Space ID
-        role: LLM role ('agent' or 'document_summary')
+        role: LLM role ('agent')
 
     Returns:
         ChatLiteLLM or ChatLiteLLMRouter instance, or None if not found
@@ -285,11 +284,8 @@ async def get_search_space_llm_instance(
             return None
 
         # Get the appropriate LLM config ID based on role
-        llm_config_id = None
         if role == LLMRole.AGENT:
             llm_config_id = search_space.agent_llm_id
-        elif role == LLMRole.DOCUMENT_SUMMARY:
-            llm_config_id = search_space.document_summary_llm_id
         else:
             logger.error(f"Invalid LLM role: {role}")
             return None
@@ -476,20 +472,13 @@ async def get_search_space_llm_instance(
 
 
 async def get_agent_llm(
-    session: AsyncSession, search_space_id: int
-) -> ChatLiteLLM | ChatLiteLLMRouter | None:
-    """Get the search space's agent LLM instance for chat operations."""
-    return await get_search_space_llm_instance(session, search_space_id, LLMRole.AGENT)
-
-
-async def get_document_summary_llm(
     session: AsyncSession, search_space_id: int, disable_streaming: bool = False
 ) -> ChatLiteLLM | ChatLiteLLMRouter | None:
-    """Get the search space's document summary LLM instance."""
+    """Get the search space's agent LLM instance for chat operations."""
     return await get_search_space_llm_instance(
         session,
         search_space_id,
-        LLMRole.DOCUMENT_SUMMARY,
+        LLMRole.AGENT,
         disable_streaming=disable_streaming,
     )
 
@@ -655,22 +644,6 @@ async def get_vision_llm(
         return None
 
 
-# Backward-compatible alias (LLM preferences are now per-search-space, not per-user)
-async def get_user_long_context_llm(
-    session: AsyncSession,
-    user_id: str,
-    search_space_id: int,
-    disable_streaming: bool = False,
-) -> ChatLiteLLM | ChatLiteLLMRouter | None:
-    """
-    Deprecated: Use get_document_summary_llm instead.
-    The user_id parameter is ignored as LLM preferences are now per-search-space.
-    """
-    return await get_document_summary_llm(
-        session, search_space_id, disable_streaming=disable_streaming
-    )
-
-
 def get_planner_llm() -> ChatLiteLLM | None:
     """Return a planner LLM instance from the first global config marked
     ``is_planner: true``, or ``None`` if no planner config is defined.

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, summary_embedding = await generate_document_summary(
-                    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)
+            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:
-                document_metadata_for_summary = {
-                    "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)
+            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/obsidian_plugin_indexer.py

@@ -233,18 +233,6 @@ async def _resolve_attachment_vision_llm(
     return await get_vision_llm(session, search_space_id)
 
 
-async def _resolve_summary_llm(
-    session: AsyncSession, *, user_id: str, search_space_id: int, should_summarize: bool
-):
-    """Fetch summary LLM only when indexing summary is enabled."""
-    if not should_summarize:
-        return None
-
-    from app.services.llm_service import get_user_long_context_llm
-
-    return await get_user_long_context_llm(session, user_id, search_space_id)
-
-
 def _require_extracted_attachment_content(
     *, content: str, etl_meta: dict[str, Any], path: str
 ) -> str:
@@ -349,13 +337,6 @@ async def upsert_note(
             path=payload.path,
         )
 
-    llm = await _resolve_summary_llm(
-        session,
-        user_id=str(user_id),
-        search_space_id=search_space_id,
-        should_summarize=connector.enable_summary,
-    )
-
     document_string = _build_document_string(
         payload, vault_name, content_override=content_for_index
     )
@@ -374,8 +355,6 @@ async def upsert_note(
         search_space_id=search_space_id,
         connector_id=connector.id,
         created_by_id=str(user_id),
-        should_summarize=connector.enable_summary,
-        fallback_summary=f"Obsidian Note: {payload.name}\n\n{content_for_index}",
         metadata=metadata,
     )
 
@@ -388,7 +367,7 @@ async def upsert_note(
 
     document = prepared[0]
 
-    return await pipeline.index(document, connector_doc, llm)
+    return await pipeline.index(document, connector_doc)
 
 
 async def rename_note(

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, 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"OneDrive File: {file_name}\n\n{indexable_content}"
-                summary_embedding = await asyncio.to_thread(embed_text, summary_content)
+            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/services/task_dispatcher.py

@@ -18,7 +18,6 @@ class TaskDispatcher(Protocol):
         filename: str,
         search_space_id: int,
         user_id: str,
-        should_summarize: bool = False,
         use_vision_llm: bool = False,
         processing_mode: str = "basic",
     ) -> None: ...
@@ -35,7 +34,6 @@ class CeleryTaskDispatcher:
         filename: str,
         search_space_id: int,
         user_id: str,
-        should_summarize: bool = False,
         use_vision_llm: bool = False,
         processing_mode: str = "basic",
     ) -> None:
@@ -49,7 +47,6 @@ class CeleryTaskDispatcher:
             filename=filename,
             search_space_id=search_space_id,
             user_id=user_id,
-            should_summarize=should_summarize,
             use_vision_llm=use_vision_llm,
             processing_mode=processing_mode,
         )