add surfsense docs context formatting in stream_new_chat

surfsense_backend/app/routes/new_chat_routes.py

@@ -706,6 +706,7 @@ async def handle_new_chat(
                 llm_config_id=llm_config_id,
                 attachments=request.attachments,
                 mentioned_document_ids=request.mentioned_document_ids,
+                mentioned_surfsense_doc_ids=request.mentioned_surfsense_doc_ids,
             ),
             media_type="text/event-stream",
             headers={

surfsense_backend/app/tasks/chat/stream_new_chat.py

@@ -25,7 +25,7 @@ from app.agents.new_chat.llm_config import (
     load_agent_config,
     load_llm_config_from_yaml,
 )
-from app.db import Document
+from app.db import Document, SurfsenseDocsDocument
 from app.schemas.new_chat import ChatAttachment
 from app.services.connector_service import ConnectorService
 from app.services.new_streaming_service import VercelStreamingService
@@ -69,6 +69,29 @@ def format_mentioned_documents_as_context(documents: list[Document]) -> str:
     return "\n".join(context_parts)
 
 
+def format_mentioned_surfsense_docs_as_context(
+    documents: list[SurfsenseDocsDocument],
+) -> str:
+    """Format mentioned SurfSense documentation as context for the agent."""
+    if not documents:
+        return ""
+
+    context_parts = ["<mentioned_surfsense_docs>"]
+    context_parts.append(
+        "The user has explicitly mentioned the following SurfSense documentation pages. "
+        "These are official documentation about how to use SurfSense and should be used to answer questions about the application."
+    )
+    for i, doc in enumerate(documents, 1):
+        context_parts.append(
+            f"<surfsense_doc index='{i}' id='doc-{doc.id}' title='{doc.title}' source='{doc.source}'>"
+        )
+        context_parts.append(f"<![CDATA[{doc.content}]]>")
+        context_parts.append("</surfsense_doc>")
+    context_parts.append("</mentioned_surfsense_docs>")
+
+    return "\n".join(context_parts)
+
+
 def extract_todos_from_deepagents(command_output) -> dict:
     """
     Extract todos from deepagents' TodoListMiddleware Command output.
@@ -101,6 +124,7 @@ async def stream_new_chat(
     llm_config_id: int = -1,
     attachments: list[ChatAttachment] | None = None,
     mentioned_document_ids: list[int] | None = None,
+    mentioned_surfsense_doc_ids: list[int] | None = None,
 ) -> AsyncGenerator[str, None]:
     """
     Stream chat responses from the new SurfSense deep agent.
@@ -118,6 +142,7 @@ async def stream_new_chat(
         messages: Optional chat history from frontend (list of ChatMessage)
         attachments: Optional attachments with extracted content
         mentioned_document_ids: Optional list of document IDs mentioned with @ in the chat
+        mentioned_surfsense_doc_ids: Optional list of SurfSense doc IDs mentioned with @ in the chat
 
     Yields:
         str: SSE formatted response strings
@@ -208,7 +233,17 @@ async def stream_new_chat(
             )
             mentioned_documents = list(result.scalars().all())
 
-        # Format the user query with context (attachments + mentioned documents)
+        # Fetch mentioned SurfSense docs if any
+        mentioned_surfsense_docs: list[SurfsenseDocsDocument] = []
+        if mentioned_surfsense_doc_ids:
+            result = await session.execute(
+                select(SurfsenseDocsDocument).filter(
+                    SurfsenseDocsDocument.id.in_(mentioned_surfsense_doc_ids),
+                )
+            )
+            mentioned_surfsense_docs = list(result.scalars().all())
+
+        # Format the user query with context (attachments + mentioned documents + surfsense docs)
         final_query = user_query
         context_parts = []
 
@@ -220,6 +255,11 @@ async def stream_new_chat(
                 format_mentioned_documents_as_context(mentioned_documents)
             )
 
+        if mentioned_surfsense_docs:
+            context_parts.append(
+                format_mentioned_surfsense_docs_as_context(mentioned_surfsense_docs)
+            )
+
         if context_parts:
             context = "\n\n".join(context_parts)
             final_query = f"{context}\n\n<user_query>{user_query}</user_query>"
@@ -296,13 +336,13 @@ async def stream_new_chat(
         last_active_step_id = analyze_step_id
 
         # Determine step title and action verb based on context
-        if attachments and mentioned_documents:
+        if attachments and (mentioned_documents or mentioned_surfsense_docs):
             last_active_step_title = "Analyzing your content"
             action_verb = "Reading"
         elif attachments:
             last_active_step_title = "Reading your content"
             action_verb = "Reading"
-        elif mentioned_documents:
+        elif mentioned_documents or mentioned_surfsense_docs:
             last_active_step_title = "Analyzing referenced content"
             action_verb = "Analyzing"
         else:
@@ -342,6 +382,19 @@ async def stream_new_chat(
             else:
                 processing_parts.append(f"[{len(doc_names)} documents]")
 
+        # Add mentioned SurfSense docs inline
+        if mentioned_surfsense_docs:
+            doc_names = []
+            for doc in mentioned_surfsense_docs:
+                title = doc.title
+                if len(title) > 30:
+                    title = title[:27] + "..."
+                doc_names.append(title)
+            if len(doc_names) == 1:
+                processing_parts.append(f"[📖 {doc_names[0]}]")
+            else:
+                processing_parts.append(f"[📖 {len(doc_names)} docs]")
+
         last_active_step_items = [f"{action_verb}: {' '.join(processing_parts)}"]
 
         yield streaming_service.format_thinking_step(