refactor: extract autocomplete service and fix tooltip screen-edge positioning

surfsense_backend/app/routes/autocomplete_routes.py

@@ -1,118 +1,14 @@
-import logging
-from typing import AsyncGenerator
-
 from fastapi import APIRouter, Depends, Query
 from fastapi.responses import StreamingResponse
-from langchain_core.messages import HumanMessage, SystemMessage
 from sqlalchemy.ext.asyncio import AsyncSession
 
 from app.db import User, get_async_session
-from app.retriever.chunks_hybrid_search import ChucksHybridSearchRetriever
+from app.services.autocomplete_service import stream_autocomplete
-from app.services.llm_service import get_agent_llm
 from app.services.new_streaming_service import VercelStreamingService
 from app.users import current_active_user
 
-logger = logging.getLogger(__name__)
-
 router = APIRouter(prefix="/autocomplete", tags=["autocomplete"])
 
-AUTOCOMPLETE_SYSTEM_PROMPT = """You are an inline text autocomplete engine. Your job is to complete the user's text naturally.
-
-Rules:
-- Output ONLY the continuation text. Do NOT repeat what the user already typed.
-- Keep completions concise: 1-3 sentences maximum.
-- Match the user's tone, style, and language.
-- If knowledge base context is provided, use it to make the completion factually accurate and personalized.
-- Do NOT add quotes, explanations, or meta-commentary.
-- Do NOT start with a space unless grammatically required.
-- If you cannot produce a useful completion, output nothing."""
-
-KB_CONTEXT_TEMPLATE = """
-Relevant knowledge base context (use this to personalize the completion):
----
-{kb_context}
----
-"""
-
-
-async def _stream_autocomplete(
-    text: str,
-    cursor_position: int,
-    search_space_id: int,
-    session: AsyncSession,
-) -> AsyncGenerator[str, None]:
-    """Stream an autocomplete response with KB context."""
-    streaming_service = VercelStreamingService()
-
-    try:
-        # Text before cursor is what we're completing
-        text_before_cursor = text[:cursor_position] if cursor_position >= 0 else text
-
-        if not text_before_cursor.strip():
-            yield streaming_service.format_message_start()
-            yield streaming_service.format_finish()
-            yield streaming_service.format_done()
-            return
-
-        # Fast KB lookup: vector-only search, top 3 chunks, no planner LLM
-        kb_context = ""
-        try:
-            retriever = ChucksHybridSearchRetriever(session)
-            chunks = await retriever.vector_search(
-                query_text=text_before_cursor[-200:],  # last 200 chars for relevance
-                top_k=3,
-                search_space_id=search_space_id,
-            )
-            if chunks:
-                kb_snippets = []
-                for chunk in chunks:
-                    content = getattr(chunk, "content", None) or getattr(chunk, "chunk_text", "")
-                    if content:
-                        kb_snippets.append(content[:300])
-                if kb_snippets:
-                    kb_context = KB_CONTEXT_TEMPLATE.format(
-                        kb_context="\n\n".join(kb_snippets)
-                    )
-        except Exception as e:
-            logger.warning(f"KB search failed for autocomplete, proceeding without context: {e}")
-
-        # Get the search space's configured LLM
-        llm = await get_agent_llm(session, search_space_id)
-        if not llm:
-            yield streaming_service.format_message_start()
-            error_msg = "No LLM configured for this search space"
-            yield streaming_service.format_error(error_msg)
-            yield streaming_service.format_done()
-            return
-
-        system_prompt = AUTOCOMPLETE_SYSTEM_PROMPT
-        if kb_context:
-            system_prompt += kb_context
-
-        messages = [
-            SystemMessage(content=system_prompt),
-            HumanMessage(content=f"Complete this text:\n{text_before_cursor}"),
-        ]
-
-        # Stream the response
-        yield streaming_service.format_message_start()
-        text_id = streaming_service.generate_text_id()
-        yield streaming_service.format_text_start(text_id)
-
-        async for chunk in llm.astream(messages):
-            token = chunk.content if hasattr(chunk, "content") else str(chunk)
-            if token:
-                yield streaming_service.format_text_delta(text_id, token)
-
-        yield streaming_service.format_text_end(text_id)
-        yield streaming_service.format_finish()
-        yield streaming_service.format_done()
-
-    except Exception as e:
-        logger.error(f"Autocomplete streaming error: {e}")
-        yield streaming_service.format_error(str(e))
-        yield streaming_service.format_done()
-
 
 @router.post("/stream")
 async def autocomplete_stream(
@@ -122,12 +18,11 @@ async def autocomplete_stream(
     user: User = Depends(current_active_user),
     session: AsyncSession = Depends(get_async_session),
 ):
-    """Stream an autocomplete suggestion based on the current text and KB context."""
     if cursor_position < 0:
         cursor_position = len(text)
 
     return StreamingResponse(
-        _stream_autocomplete(text, cursor_position, search_space_id, session),
+        stream_autocomplete(text, cursor_position, search_space_id, session),
         media_type="text/event-stream",
         headers={
             **VercelStreamingService.get_response_headers(),

surfsense_backend/app/services/autocomplete_service.py

@@ -0,0 +1,110 @@
+import logging
+from typing import AsyncGenerator
+
+from langchain_core.messages import HumanMessage, SystemMessage
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.retriever.chunks_hybrid_search import ChucksHybridSearchRetriever
+from app.services.llm_service import get_agent_llm
+from app.services.new_streaming_service import VercelStreamingService
+
+logger = logging.getLogger(__name__)
+
+SYSTEM_PROMPT = """You are an inline text autocomplete engine. Your job is to complete the user's text naturally.
+
+Rules:
+- Output ONLY the continuation text. Do NOT repeat what the user already typed.
+- Keep completions concise: 1-3 sentences maximum.
+- Match the user's tone, style, and language.
+- If knowledge base context is provided, use it to make the completion factually accurate and personalized.
+- Do NOT add quotes, explanations, or meta-commentary.
+- Do NOT start with a space unless grammatically required.
+- If you cannot produce a useful completion, output nothing."""
+
+KB_CONTEXT_TEMPLATE = """
+Relevant knowledge base context (use this to personalize the completion):
+---
+{kb_context}
+---
+"""
+
+
+async def _retrieve_kb_context(
+    session: AsyncSession,
+    text: str,
+    search_space_id: int,
+) -> str:
+    try:
+        retriever = ChucksHybridSearchRetriever(session)
+        chunks = await retriever.vector_search(
+            query_text=text[-200:],
+            top_k=3,
+            search_space_id=search_space_id,
+        )
+        if not chunks:
+            return ""
+        snippets = []
+        for chunk in chunks:
+            content = getattr(chunk, "content", None) or getattr(chunk, "chunk_text", "")
+            if content:
+                snippets.append(content[:300])
+        if not snippets:
+            return ""
+        return KB_CONTEXT_TEMPLATE.format(kb_context="\n\n".join(snippets))
+    except Exception as e:
+        logger.warning(f"KB search failed for autocomplete, proceeding without context: {e}")
+        return ""
+
+
+async def stream_autocomplete(
+    text: str,
+    cursor_position: int,
+    search_space_id: int,
+    session: AsyncSession,
+) -> AsyncGenerator[str, None]:
+    """Build context, call the LLM, and yield SSE-formatted tokens."""
+    streaming = VercelStreamingService()
+    text_before_cursor = text[:cursor_position] if cursor_position >= 0 else text
+
+    if not text_before_cursor.strip():
+        yield streaming.format_message_start()
+        yield streaming.format_finish()
+        yield streaming.format_done()
+        return
+
+    kb_context = await _retrieve_kb_context(session, text_before_cursor, search_space_id)
+
+    llm = await get_agent_llm(session, search_space_id)
+    if not llm:
+        yield streaming.format_message_start()
+        yield streaming.format_error("No LLM configured for this search space")
+        yield streaming.format_done()
+        return
+
+    system_prompt = SYSTEM_PROMPT
+    if kb_context:
+        system_prompt += kb_context
+
+    messages = [
+        SystemMessage(content=system_prompt),
+        HumanMessage(content=f"Complete this text:\n{text_before_cursor}"),
+    ]
+
+    try:
+        yield streaming.format_message_start()
+        text_id = streaming.generate_text_id()
+        yield streaming.format_text_start(text_id)
+
+        async for chunk in llm.astream(messages):
+            token = chunk.content if hasattr(chunk, "content") else str(chunk)
+            if token:
+                yield streaming.format_text_delta(text_id, token)
+
+        yield streaming.format_text_end(text_id)
+        yield streaming.format_finish()
+        yield streaming.format_done()
+
+    except Exception as e:
+        logger.error(f"Autocomplete streaming error: {e}")
+        yield streaming.format_error(str(e))
+        yield streaming.format_done()

surfsense_desktop/src/modules/autocomplete/suggestion-window.ts

@@ -8,14 +8,22 @@ const MAX_HEIGHT = 400;
 
 let suggestionWindow: BrowserWindow | null = null;
 let resizeTimer: ReturnType<typeof setInterval> | null = null;
+let cursorOrigin = { x: 0, y: 0 };
 
-function clampToScreen(x: number, y: number, w: number, h: number): { x: number; y: number } {
+const CURSOR_GAP = 20;
-  const display = screen.getDisplayNearestPoint({ x, y });
+
+function positionOnScreen(cursorX: number, cursorY: number, w: number, h: number): { x: number; y: number } {
+  const display = screen.getDisplayNearestPoint({ x: cursorX, y: cursorY });
   const { x: dx, y: dy, width: dw, height: dh } = display.workArea;
-  return {
+
-    x: Math.max(dx, Math.min(x, dx + dw - w)),
+  const x = Math.max(dx, Math.min(cursorX, dx + dw - w));
-    y: Math.max(dy, Math.min(y, dy + dh - h)),
+
-  };
+  const spaceBelow = (dy + dh) - (cursorY + CURSOR_GAP);
+  const y = spaceBelow >= h
+    ? cursorY + CURSOR_GAP
+    : cursorY - h - CURSOR_GAP;
+
+  return { x, y: Math.max(dy, y) };
 }
 
 function stopResizePolling(): void {
@@ -34,8 +42,8 @@ function startResizePolling(win: BrowserWindow): void {
       if (h > 0 && h !== lastH) {
         lastH = h;
         const clamped = Math.min(h, MAX_HEIGHT);
-        const bounds = win.getBounds();
+        const pos = positionOnScreen(cursorOrigin.x, cursorOrigin.y, TOOLTIP_WIDTH, clamped);
-        win.setBounds({ x: bounds.x, y: bounds.y, width: TOOLTIP_WIDTH, height: clamped });
+        win.setBounds({ x: pos.x, y: pos.y, width: TOOLTIP_WIDTH, height: clamped });
       }
     } catch {}
   }, 150);
@@ -55,8 +63,9 @@ export function destroySuggestion(): void {
 
 export function createSuggestionWindow(x: number, y: number): BrowserWindow {
   destroySuggestion();
+  cursorOrigin = { x, y };
 
-  const pos = clampToScreen(x, y + 20, TOOLTIP_WIDTH, TOOLTIP_HEIGHT);
+  const pos = positionOnScreen(x, y, TOOLTIP_WIDTH, TOOLTIP_HEIGHT);
 
   suggestionWindow = new BrowserWindow({
     width: TOOLTIP_WIDTH,