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Remove backend vision autocomplete stack

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11
surfsense_backend/app/agents/autocomplete/__init__.py
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"""Agent-based vision autocomplete with scoped filesystem exploration."""
from app.agents.autocomplete.autocomplete_agent import (
create_autocomplete_agent,
stream_autocomplete_agent,
)
__all__ = [
"create_autocomplete_agent",
"stream_autocomplete_agent",
]

495
surfsense_backend/app/agents/autocomplete/autocomplete_agent.py
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"""Vision autocomplete agent with scoped filesystem exploration.
Converts the stateless single-shot vision autocomplete into an agent that
seeds a virtual filesystem from KB search results and lets the vision LLM
explore documents via ``ls``, ``read_file``, ``glob``, ``grep``, etc.
before generating the final completion.
Performance: KB search and agent graph compilation run in parallel so
the only sequential latency is KB-search (or agent compile, whichever is
slower) + the agent's LLM turns. There is no separate "query extraction"
LLM call — the window title is used directly as the KB search query.
"""
from __future__ import annotations
import asyncio
import json
import logging
import re
import uuid
from collections.abc import AsyncGenerator
from typing import Any
from deepagents.graph import BASE_AGENT_PROMPT
from deepagents.middleware.patch_tool_calls import PatchToolCallsMiddleware
from langchain.agents import create_agent
from langchain_anthropic.middleware import AnthropicPromptCachingMiddleware
from langchain_core.language_models import BaseChatModel
from langchain_core.messages import AIMessage, ToolMessage
from app.agents.new_chat.middleware.filesystem import SurfSenseFilesystemMiddleware
from app.agents.new_chat.middleware.knowledge_search import (
build_scoped_filesystem,
search_knowledge_base,
)
from app.services.new_streaming_service import VercelStreamingService
logger = logging.getLogger(__name__)
KB_TOP_K = 10
# ---------------------------------------------------------------------------
# System prompt
# ---------------------------------------------------------------------------
AUTOCOMPLETE_SYSTEM_PROMPT = """You are a smart writing assistant that analyzes the user's screen to draft or complete text.
You will receive a screenshot of the user's screen. Your PRIMARY source of truth is the screenshot itself — the visual context determines what to write.
Your job:
1. Analyze the ENTIRE screenshot to understand what the user is working on (email thread, chat conversation, document, code editor, form, etc.).
2. Identify the text area where the user will type.
3. Generate the text the user most likely wants to write based on the visual context.
You also have access to the user's knowledge base documents via filesystem tools. However:
- ONLY consult the knowledge base if the screenshot clearly involves a topic where your KB documents are DIRECTLY relevant (e.g., the user is writing about a specific project/topic that matches a document title).
- Do NOT explore documents just because they exist. Most autocomplete requests can be answered purely from the screenshot.
- If you do read a document, only incorporate information that is 100% relevant to what the user is typing RIGHT NOW. Do not add extra details, background, or tangential information from the KB.
- Keep your output SHORT — autocomplete should feel like a natural continuation, not an essay.
Key behavior:
- If the text area is EMPTY, draft a concise response or message based on what you see on screen (e.g., reply to an email, respond to a chat message, continue a document).
- If the text area already has text, continue it naturally — typically just a sentence or two.
Rules:
- Be CONCISE. Prefer a single paragraph or a few sentences. Autocomplete is a quick assist, not a full draft.
- Match the tone and formality of the surrounding context.
- If the screen shows code, write code. If it shows a casual chat, be casual. If it shows a formal email, be formal.
- Do NOT describe the screenshot or explain your reasoning.
- Do NOT cite or reference documents explicitly — just let the knowledge inform your writing naturally.
- If you cannot determine what to write, output an empty JSON array: []
## Output Format
You MUST provide exactly 3 different suggestion options. Each should be a distinct, plausible completion — vary the tone, detail level, or angle.
Return your suggestions as a JSON array of exactly 3 strings. Output ONLY the JSON array, nothing else — no markdown fences, no explanation, no commentary.
Example format:
["First suggestion text here.", "Second suggestion — a different take.", "Third option with another approach."]
## Filesystem Tools `ls`, `read_file`, `write_file`, `edit_file`, `glob`, `grep`
All file paths must start with a `/`.
- ls: list files and directories at a given path.
- read_file: read a file from the filesystem.
- write_file: create a temporary file in the session (not persisted).
- edit_file: edit a file in the session (not persisted for /documents/ files).
- glob: find files matching a pattern (e.g., "**/*.xml").
- grep: search for text within files.
## When to Use Filesystem Tools
BEFORE reaching for any tool, ask yourself: "Can I write a good completion purely from the screenshot?" If yes, just write it — do NOT explore the KB.
Only use tools when:
- The user is clearly writing about a specific topic that likely has detailed information in their KB.
- You need a specific fact, name, number, or reference that the screenshot doesn't provide.
When you do use tools, be surgical:
- Check the `ls` output first. If no document title looks relevant, stop — do not read files just to see what's there.
- If a title looks relevant, read only the `<chunk_index>` (first ~20 lines) and jump to matched chunks. Do not read entire documents.
- Extract only the specific information you need and move on to generating the completion.
## Reading Documents Efficiently
Documents are formatted as XML. Each document contains:
- `<document_metadata>` — title, type, URL, etc.
- `<chunk_index>` — a table of every chunk with its **line range** and a
`matched="true"` flag for chunks that matched the search query.
- `<document_content>` — the actual chunks in original document order.
**Workflow**: read the first ~20 lines to see the `<chunk_index>`, identify
chunks marked `matched="true"`, then use `read_file(path, offset=<start_line>,
limit=<lines>)` to jump directly to those sections."""
APP_CONTEXT_BLOCK = """
The user is currently working in "{app_name}" (window: "{window_title}"). Use this to understand the type of application and adapt your tone and format accordingly."""
def _build_autocomplete_system_prompt(app_name: str, window_title: str) -> str:
prompt = AUTOCOMPLETE_SYSTEM_PROMPT
if app_name:
prompt += APP_CONTEXT_BLOCK.format(app_name=app_name, window_title=window_title)
return prompt
# ---------------------------------------------------------------------------
# Pre-compute KB filesystem (runs in parallel with agent compilation)
# ---------------------------------------------------------------------------
class _KBResult:
"""Container for pre-computed KB filesystem results."""
__slots__ = ("files", "ls_ai_msg", "ls_tool_msg")
def __init__(
self,
files: dict[str, Any] | None = None,
ls_ai_msg: AIMessage | None = None,
ls_tool_msg: ToolMessage | None = None,
) -> None:
self.files = files
self.ls_ai_msg = ls_ai_msg
self.ls_tool_msg = ls_tool_msg
@property
def has_documents(self) -> bool:
return bool(self.files)
async def precompute_kb_filesystem(
search_space_id: int,
query: str,
top_k: int = KB_TOP_K,
) -> _KBResult:
"""Search the KB and build the scoped filesystem outside the agent.
This is designed to be called via ``asyncio.gather`` alongside agent
graph compilation so the two run concurrently.
"""
if not query:
return _KBResult()
try:
search_results = await search_knowledge_base(
query=query,
search_space_id=search_space_id,
top_k=top_k,
)
if not search_results:
return _KBResult()
new_files, _ = await build_scoped_filesystem(
documents=search_results,
search_space_id=search_space_id,
)
if not new_files:
return _KBResult()
doc_paths = [
p
for p, v in new_files.items()
if p.startswith("/documents/") and v is not None
]
tool_call_id = f"auto_ls_{uuid.uuid4().hex[:12]}"
ai_msg = AIMessage(
content="",
tool_calls=[
{"name": "ls", "args": {"path": "/documents"}, "id": tool_call_id}
],
)
tool_msg = ToolMessage(
content=str(doc_paths) if doc_paths else "No documents found.",
tool_call_id=tool_call_id,
)
return _KBResult(files=new_files, ls_ai_msg=ai_msg, ls_tool_msg=tool_msg)
except Exception:
logger.warning(
"KB pre-computation failed, proceeding without KB", exc_info=True
)
return _KBResult()
# ---------------------------------------------------------------------------
# Filesystem middleware — no save_document, no persistence
# ---------------------------------------------------------------------------
class AutocompleteFilesystemMiddleware(SurfSenseFilesystemMiddleware):
"""Filesystem middleware for autocomplete — read-only exploration only.
Strips ``save_document`` (permanent KB persistence) and passes
``search_space_id=None`` so ``write_file`` / ``edit_file`` stay ephemeral.
"""
def __init__(self) -> None:
super().__init__(search_space_id=None, created_by_id=None)
self.tools = [t for t in self.tools if t.name != "save_document"]
# ---------------------------------------------------------------------------
# Agent factory
# ---------------------------------------------------------------------------
async def _compile_agent(
llm: BaseChatModel,
app_name: str,
window_title: str,
) -> Any:
"""Compile the agent graph (CPU-bound, runs in a thread)."""
system_prompt = _build_autocomplete_system_prompt(app_name, window_title)
final_system_prompt = system_prompt + "\n\n" + BASE_AGENT_PROMPT
middleware = [
AutocompleteFilesystemMiddleware(),
PatchToolCallsMiddleware(),
AnthropicPromptCachingMiddleware(unsupported_model_behavior="ignore"),
]
agent = await asyncio.to_thread(
create_agent,
llm,
system_prompt=final_system_prompt,
tools=[],
middleware=middleware,
)
return agent.with_config({"recursion_limit": 200})
async def create_autocomplete_agent(
llm: BaseChatModel,
*,
search_space_id: int,
kb_query: str,
app_name: str = "",
window_title: str = "",
) -> tuple[Any, _KBResult]:
"""Create the autocomplete agent and pre-compute KB in parallel.
Returns ``(agent, kb_result)`` so the caller can inject the pre-computed
filesystem into the agent's initial state without any middleware delay.
"""
agent, kb = await asyncio.gather(
_compile_agent(llm, app_name, window_title),
precompute_kb_filesystem(search_space_id, kb_query),
)
return agent, kb
# ---------------------------------------------------------------------------
# JSON suggestion parsing (with fallback)
# ---------------------------------------------------------------------------
def _parse_suggestions(raw: str) -> list[str]:
"""Extract a list of suggestion strings from the agent's output.
Tries, in order:
1. Direct ``json.loads``
2. Extract content between ```json ... ``` fences
3. Find the first ``[`` … ``]`` span
Falls back to wrapping the raw text as a single suggestion.
"""
text = raw.strip()
if not text:
return []
for candidate in _json_candidates(text):
try:
parsed = json.loads(candidate)
if isinstance(parsed, list) and all(isinstance(s, str) for s in parsed):
return [s for s in parsed if s.strip()]
except (json.JSONDecodeError, ValueError):
continue
return [text]
def _json_candidates(text: str) -> list[str]:
"""Yield candidate JSON strings from raw text."""
candidates = [text]
fence = re.search(r"```(?:json)?\s*\n?(.*?)```", text, re.DOTALL)
if fence:
candidates.append(fence.group(1).strip())
bracket = re.search(r"\[.*]", text, re.DOTALL)
if bracket:
candidates.append(bracket.group(0))
return candidates
# ---------------------------------------------------------------------------
# Streaming helper
# ---------------------------------------------------------------------------
async def stream_autocomplete_agent(
agent: Any,
input_data: dict[str, Any],
streaming_service: VercelStreamingService,
*,
emit_message_start: bool = True,
) -> AsyncGenerator[str, None]:
"""Stream agent events as Vercel SSE, with thinking steps for tool calls.
When ``emit_message_start`` is False the caller has already sent the
``message_start`` event (e.g. to show preparation steps before the agent
runs).
"""
thread_id = uuid.uuid4().hex
config = {"configurable": {"thread_id": thread_id}}
text_buffer: list[str] = []
active_tool_depth = 0
thinking_step_counter = 0
tool_step_ids: dict[str, str] = {}
step_titles: dict[str, str] = {}
completed_step_ids: set[str] = set()
last_active_step_id: str | None = None
def next_thinking_step_id() -> str:
nonlocal thinking_step_counter
thinking_step_counter += 1
return f"autocomplete-step-{thinking_step_counter}"
def complete_current_step() -> str | None:
nonlocal last_active_step_id
if last_active_step_id and last_active_step_id not in completed_step_ids:
completed_step_ids.add(last_active_step_id)
title = step_titles.get(last_active_step_id, "Done")
event = streaming_service.format_thinking_step(
step_id=last_active_step_id,
title=title,
status="complete",
)
last_active_step_id = None
return event
return None
if emit_message_start:
yield streaming_service.format_message_start()
gen_step_id = next_thinking_step_id()
last_active_step_id = gen_step_id
step_titles[gen_step_id] = "Generating suggestions"
yield streaming_service.format_thinking_step(
step_id=gen_step_id,
title="Generating suggestions",
status="in_progress",
)
try:
async for event in agent.astream_events(
input_data, config=config, version="v2"
):
event_type = event.get("event", "")
if event_type == "on_chat_model_stream":
if active_tool_depth > 0:
continue
if "surfsense:internal" in event.get("tags", []):
continue
chunk = event.get("data", {}).get("chunk")
if chunk and hasattr(chunk, "content"):
content = chunk.content
if content and isinstance(content, str):
text_buffer.append(content)
elif event_type == "on_chat_model_end":
if active_tool_depth > 0:
continue
if "surfsense:internal" in event.get("tags", []):
continue
output = event.get("data", {}).get("output")
if output and hasattr(output, "content"):
if getattr(output, "tool_calls", None):
continue
content = output.content
if content and isinstance(content, str) and not text_buffer:
text_buffer.append(content)
elif event_type == "on_tool_start":
active_tool_depth += 1
tool_name = event.get("name", "unknown_tool")
run_id = event.get("run_id", "")
tool_input = event.get("data", {}).get("input", {})
step_event = complete_current_step()
if step_event:
yield step_event
tool_step_id = next_thinking_step_id()
tool_step_ids[run_id] = tool_step_id
last_active_step_id = tool_step_id
title, items = _describe_tool_call(tool_name, tool_input)
step_titles[tool_step_id] = title
yield streaming_service.format_thinking_step(
step_id=tool_step_id,
title=title,
status="in_progress",
items=items,
)
elif event_type == "on_tool_end":
active_tool_depth = max(0, active_tool_depth - 1)
run_id = event.get("run_id", "")
step_id = tool_step_ids.pop(run_id, None)
if step_id and step_id not in completed_step_ids:
completed_step_ids.add(step_id)
title = step_titles.get(step_id, "Done")
yield streaming_service.format_thinking_step(
step_id=step_id,
title=title,
status="complete",
)
if last_active_step_id == step_id:
last_active_step_id = None
step_event = complete_current_step()
if step_event:
yield step_event
raw_text = "".join(text_buffer)
suggestions = _parse_suggestions(raw_text)
yield streaming_service.format_data("suggestions", {"options": suggestions})
yield streaming_service.format_finish()
yield streaming_service.format_done()
except Exception as e:
logger.error(f"Autocomplete agent streaming error: {e}", exc_info=True)
yield streaming_service.format_error("Autocomplete failed. Please try again.")
yield streaming_service.format_done()
def _describe_tool_call(tool_name: str, tool_input: Any) -> tuple[str, list[str]]:
"""Return a human-readable (title, items) for a tool call thinking step."""
inp = tool_input if isinstance(tool_input, dict) else {}
if tool_name == "ls":
path = inp.get("path", "/")
return "Listing files", [path]
if tool_name == "read_file":
fp = inp.get("file_path", "")
display = fp if len(fp) <= 80 else "…" + fp[-77:]
return "Reading file", [display]
if tool_name == "write_file":
fp = inp.get("file_path", "")
display = fp if len(fp) <= 80 else "…" + fp[-77:]
return "Writing file", [display]
if tool_name == "edit_file":
fp = inp.get("file_path", "")
display = fp if len(fp) <= 80 else "…" + fp[-77:]
return "Editing file", [display]
if tool_name == "glob":
pat = inp.get("pattern", "")
base = inp.get("path", "/")
return "Searching files", [f"{pat} in {base}"]
if tool_name == "grep":
pat = inp.get("pattern", "")
path = inp.get("path", "")
display_pat = pat[:60] + ("…" if len(pat) > 60 else "")
return "Searching content", [
f'"{display_pat}"' + (f" in {path}" if path else "")
]
return f"Using {tool_name}", []

2
surfsense_backend/app/routes/__init__.py
View file

@ -3,7 +3,6 @@ from fastapi import APIRouter
from .airtable_add_connector_route import ( from .airtable_add_connector_route import (
router as airtable_add_connector_router, router as airtable_add_connector_router,
) )
from .autocomplete_routes import router as autocomplete_router
from .chat_comments_routes import router as chat_comments_router from .chat_comments_routes import router as chat_comments_router
from .circleback_webhook_route import router as circleback_webhook_router from .circleback_webhook_route import router as circleback_webhook_router
from .clickup_add_connector_route import router as clickup_add_connector_router from .clickup_add_connector_route import router as clickup_add_connector_router
@ -104,4 +103,3 @@ router.include_router(stripe_router) # Stripe checkout for additional page pack
router.include_router(youtube_router) # YouTube playlist resolution router.include_router(youtube_router) # YouTube playlist resolution
router.include_router(prompts_router) router.include_router(prompts_router)
router.include_router(memory_router) # User personal memory (memory.md style) router.include_router(memory_router) # User personal memory (memory.md style)
router.include_router(autocomplete_router) # Lightweight autocomplete with KB context

45
surfsense_backend/app/routes/autocomplete_routes.py
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@ -1,45 +0,0 @@
from fastapi import APIRouter, Depends
from fastapi.responses import StreamingResponse
from pydantic import BaseModel, Field
from sqlalchemy.ext.asyncio import AsyncSession
from app.db import User, get_async_session
from app.services.new_streaming_service import VercelStreamingService
from app.services.vision_autocomplete_service import stream_vision_autocomplete
from app.users import current_active_user
from app.utils.rbac import check_search_space_access
router = APIRouter(prefix="/autocomplete", tags=["autocomplete"])
MAX_SCREENSHOT_SIZE = 20 * 1024 * 1024 # 20 MB base64 ceiling
class VisionAutocompleteRequest(BaseModel):
screenshot: str = Field(..., max_length=MAX_SCREENSHOT_SIZE)
search_space_id: int
app_name: str = ""
window_title: str = ""
@router.post("/vision/stream")
async def vision_autocomplete_stream(
body: VisionAutocompleteRequest,
user: User = Depends(current_active_user),
session: AsyncSession = Depends(get_async_session),
):
await check_search_space_access(session, user, body.search_space_id)
return StreamingResponse(
stream_vision_autocomplete(
body.screenshot,
body.search_space_id,
session,
app_name=body.app_name,
window_title=body.window_title,
),
media_type="text/event-stream",
headers={
**VercelStreamingService.get_response_headers(),
"X-Accel-Buffering": "no",
},
)

158
surfsense_backend/app/services/vision_autocomplete_service.py
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@ -1,158 +0,0 @@
"""Vision autocomplete service — agent-based with scoped filesystem.
Optimized pipeline:
1. Start the SSE stream immediately so the UI shows progress.
2. Derive a KB search query from window_title (no separate LLM call).
3. Run KB filesystem pre-computation and agent graph compilation in PARALLEL.
4. Inject pre-computed KB files as initial state and stream the agent.
"""
import logging
from collections.abc import AsyncGenerator
from langchain_core.messages import HumanMessage
from sqlalchemy.ext.asyncio import AsyncSession
from app.agents.autocomplete import create_autocomplete_agent, stream_autocomplete_agent
from app.services.llm_service import get_vision_llm
from app.services.new_streaming_service import VercelStreamingService
logger = logging.getLogger(__name__)
PREP_STEP_ID = "autocomplete-prep"
def _derive_kb_query(app_name: str, window_title: str) -> str:
parts = [p for p in (window_title, app_name) if p]
return " ".join(parts)
def _is_vision_unsupported_error(e: Exception) -> bool:
msg = str(e).lower()
return "content must be a string" in msg or "does not support image" in msg
# ---------------------------------------------------------------------------
# Main entry point
# ---------------------------------------------------------------------------
async def stream_vision_autocomplete(
screenshot_data_url: str,
search_space_id: int,
session: AsyncSession,
*,
app_name: str = "",
window_title: str = "",
) -> AsyncGenerator[str, None]:
"""Analyze a screenshot with a vision-LLM agent and stream a text completion."""
streaming = VercelStreamingService()
vision_error_msg = (
"The selected model does not support vision. "
"Please set a vision-capable model (e.g. GPT-4o, Gemini) in your search space settings."
)
llm = await get_vision_llm(session, search_space_id)
if not llm:
yield streaming.format_message_start()
yield streaming.format_error("No Vision LLM configured for this search space")
yield streaming.format_done()
return
# Start SSE stream immediately so the UI has something to show
yield streaming.format_message_start()
kb_query = _derive_kb_query(app_name, window_title)
# Show a preparation step while KB search + agent compile run
yield streaming.format_thinking_step(
step_id=PREP_STEP_ID,
title="Searching knowledge base",
status="in_progress",
items=[kb_query] if kb_query else [],
)
try:
agent, kb = await create_autocomplete_agent(
llm,
search_space_id=search_space_id,
kb_query=kb_query,
app_name=app_name,
window_title=window_title,
)
except Exception as e:
if _is_vision_unsupported_error(e):
logger.warning("Vision autocomplete: model does not support vision: %s", e)
yield streaming.format_error(vision_error_msg)
yield streaming.format_done()
return
logger.error("Failed to create autocomplete agent: %s", e, exc_info=True)
yield streaming.format_error("Autocomplete failed. Please try again.")
yield streaming.format_done()
return
has_kb = kb.has_documents
doc_count = len(kb.files) if has_kb else 0 # type: ignore[arg-type]
yield streaming.format_thinking_step(
step_id=PREP_STEP_ID,
title="Searching knowledge base",
status="complete",
items=[f"Found {doc_count} document{'s' if doc_count != 1 else ''}"]
if kb_query
else ["Skipped"],
)
# Build agent input with pre-computed KB as initial state
if has_kb:
instruction = (
"Analyze this screenshot, then explore the knowledge base documents "
"listed above — read the chunk index of any document whose title "
"looks relevant and check matched chunks for useful facts. "
"Finally, generate a concise autocomplete for the active text area, "
"enhanced with any relevant KB information you found."
)
else:
instruction = (
"Analyze this screenshot and generate a concise autocomplete "
"for the active text area based on what you see."
)
user_message = HumanMessage(
content=[
{"type": "text", "text": instruction},
{"type": "image_url", "image_url": {"url": screenshot_data_url}},
]
)
input_data: dict = {"messages": [user_message]}
if has_kb:
input_data["files"] = kb.files
input_data["messages"] = [kb.ls_ai_msg, kb.ls_tool_msg, user_message]
logger.info(
"Autocomplete: injected %d KB files into agent initial state", doc_count
)
else:
logger.info(
"Autocomplete: no KB documents found, proceeding with screenshot only"
)
# Stream the agent (message_start already sent above)
try:
async for sse in stream_autocomplete_agent(
agent,
input_data,
streaming,
emit_message_start=False,
):
yield sse
except Exception as e:
if _is_vision_unsupported_error(e):
logger.warning("Vision autocomplete: model does not support vision: %s", e)
yield streaming.format_error(vision_error_msg)
yield streaming.format_done()
else:
logger.error("Vision autocomplete streaming error: %s", e, exc_info=True)
yield streaming.format_error("Autocomplete failed. Please try again.")
yield streaming.format_done()