Author labels in shared chats: bootstrap, stream prefix, route display name

surfsense_backend/app/agents/new_chat/system_prompt.py

@@ -138,7 +138,7 @@ You have access to the following tools:
     * Prioritize showing: diagrams, charts, infographics, key illustrations, or images that help explain the content.
     * Don't show every image - just the most relevant 1-3 images that enhance understanding.
 
-7. save_memory: Save facts, preferences, or context about the user for personalized responses.
+7. save_memory: Save facts, preferences, or context for personalized responses.
   - Use this when the user explicitly or implicitly shares information worth remembering.
   - Trigger scenarios:
     * User says "remember this", "keep this in mind", "note that", or similar

surfsense_backend/app/routes/new_chat_routes.py

@@ -1052,6 +1052,7 @@ async def handle_new_chat(
                 mentioned_surfsense_doc_ids=request.mentioned_surfsense_doc_ids,
                 needs_history_bootstrap=thread.needs_history_bootstrap,
                 thread_visibility=thread.visibility,
+                current_user_display_name=user.display_name or "A team member",
             ),
             media_type="text/event-stream",
             headers={
@@ -1283,6 +1284,7 @@ async def regenerate_response(
                     checkpoint_id=target_checkpoint_id,
                     needs_history_bootstrap=thread.needs_history_bootstrap,
                     thread_visibility=thread.visibility,
+                    current_user_display_name=user.display_name or "A team member",
                 ):
                     yield chunk
                 # If we get here, streaming completed successfully

surfsense_backend/app/tasks/chat/stream_new_chat.py

@@ -209,6 +209,7 @@ async def stream_new_chat(
     checkpoint_id: str | None = None,
     needs_history_bootstrap: bool = False,
     thread_visibility: ChatVisibility | None = None,
+    current_user_display_name: str | None = None,
 ) -> AsyncGenerator[str, None]:
     """
     Stream chat responses from the new SurfSense deep agent.
@@ -315,7 +316,9 @@ async def stream_new_chat(
 
         # Bootstrap history for cloned chats (no LangGraph checkpoint exists yet)
         if needs_history_bootstrap:
-            langchain_messages = await bootstrap_history_from_db(session, chat_id)
+            langchain_messages = await bootstrap_history_from_db(
+                session, chat_id, thread_visibility=visibility
+            )
 
             # Clear the flag so we don't bootstrap again on next message
             from app.db import NewChatThread
@@ -378,6 +381,9 @@ async def stream_new_chat(
             context = "\n\n".join(context_parts)
             final_query = f"{context}\n\n<user_query>{user_query}</user_query>"
 
+        if visibility == ChatVisibility.SEARCH_SPACE and current_user_display_name:
+            final_query = f"**[{current_user_display_name}]:** {final_query}"
+
         # if messages:
         #     # Convert frontend messages to LangChain format
         #     for msg in messages:

surfsense_backend/app/utils/content_utils.py

@@ -12,6 +12,7 @@ These utilities help extract and transform content for different use cases.
 from langchain_core.messages import AIMessage, HumanMessage
 from sqlalchemy import select
 from sqlalchemy.ext.asyncio import AsyncSession
+from sqlalchemy.orm import selectinload
 
 
 def extract_text_content(content: str | dict | list) -> str:
@@ -38,6 +39,7 @@ def extract_text_content(content: str | dict | list) -> str:
 async def bootstrap_history_from_db(
     session: AsyncSession,
     thread_id: int,
+    thread_visibility: "ChatVisibility | None" = None,
 ) -> list[HumanMessage | AIMessage]:
     """
     Load message history from database and convert to LangChain format.
@@ -45,20 +47,28 @@ async def bootstrap_history_from_db(
     Used for cloned chats where the LangGraph checkpointer has no state,
     but we have messages in the database that should be used as context.
 
+    When thread_visibility is SEARCH_SPACE, user messages are prefixed with
+    the author's display name so the LLM sees who said what.
+
     Args:
         session: Database session
         thread_id: The chat thread ID
+        thread_visibility: When SEARCH_SPACE, user messages get author prefix
 
     Returns:
         List of LangChain messages (HumanMessage/AIMessage)
     """
-    from app.db import NewChatMessage
+    from app.db import ChatVisibility, NewChatMessage
 
-    result = await session.execute(
+    is_shared = thread_visibility == ChatVisibility.SEARCH_SPACE
+    stmt = (
         select(NewChatMessage)
         .filter(NewChatMessage.thread_id == thread_id)
         .order_by(NewChatMessage.created_at)
     )
+    if is_shared:
+        stmt = stmt.options(selectinload(NewChatMessage.author))
+    result = await session.execute(stmt)
     db_messages = result.scalars().all()
 
     langchain_messages: list[HumanMessage | AIMessage] = []
@@ -68,6 +78,11 @@ async def bootstrap_history_from_db(
         if not text_content:
             continue
         if msg.role == "user":
+            if is_shared:
+                author_name = (
+                    (msg.author.display_name if msg.author else None) or "A team member"
+                )
+                text_content = f"**[{author_name}]:** {text_content}"
             langchain_messages.append(HumanMessage(content=text_content))
         elif msg.role == "assistant":
             langchain_messages.append(AIMessage(content=text_content))