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

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))