mirror of
https://github.com/bytedance/deer-flow.git
synced 2026-05-25 01:15:58 +00:00
feat(auth): authentication module with multi-tenant isolation (RFC-001)
Introduce an always-on auth layer with auto-created admin on first boot, multi-tenant isolation for threads/stores, and a full setup/login flow. Backend - JWT access tokens with `ver` field for stale-token rejection; bump on password/email change - Password hashing, HttpOnly+Secure cookies (Secure derived from request scheme at runtime) - CSRF middleware covering both REST and LangGraph routes - IP-based login rate limiting (5 attempts / 5-min lockout) with bounded dict growth and X-Forwarded-For bypass fix - Multi-worker-safe admin auto-creation (single DB write, WAL once) - needs_setup + token_version on User model; SQLite schema migration - Thread/store isolation by owner; orphan thread migration on first admin registration - thread_id validated as UUID to prevent log injection - CLI tool to reset admin password - Decorator-based authz module extracted from auth core Frontend - Login and setup pages with SSR guard for needs_setup flow - Account settings page (change password / email) - AuthProvider + route guards; skips redirect when no users registered - i18n (en-US / zh-CN) for auth surfaces - Typed auth API client; parseAuthError unwraps FastAPI detail envelope Infra & tooling - Unified `serve.sh` with gateway mode + auto dep install - Public PyPI uv.toml pin for CI compatibility - Regenerated uv.lock with public index Tests - HTTP vs HTTPS cookie security tests - Auth middleware, rate limiter, CSRF, setup flow coverage
This commit is contained in:
@@ -182,6 +182,23 @@ class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
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return None, False
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@staticmethod
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def _append_text(content: str | list | None, text: str) -> str | list:
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"""Append *text* to AIMessage content, handling str, list, and None.
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When content is a list of content blocks (e.g. Anthropic thinking mode),
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we append a new ``{"type": "text", ...}`` block instead of concatenating
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a string to a list, which would raise ``TypeError``.
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"""
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if content is None:
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return text
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if isinstance(content, list):
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return [*content, {"type": "text", "text": f"\n\n{text}"}]
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if isinstance(content, str):
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return content + f"\n\n{text}"
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# Fallback: coerce unexpected types to str to avoid TypeError
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return str(content) + f"\n\n{text}"
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def _apply(self, state: AgentState, runtime: Runtime) -> dict | None:
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warning, hard_stop = self._track_and_check(state, runtime)
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@@ -192,7 +209,7 @@ class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
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stripped_msg = last_msg.model_copy(
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update={
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"tool_calls": [],
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"content": (last_msg.content or "") + f"\n\n{_HARD_STOP_MSG}",
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"content": self._append_text(last_msg.content, _HARD_STOP_MSG),
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}
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)
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return {"messages": [stripped_msg]}
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@@ -29,6 +29,22 @@ _CORRECTION_PATTERNS = (
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re.compile(r"改用"),
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)
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_REINFORCEMENT_PATTERNS = (
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re.compile(r"\byes[,.]?\s+(?:exactly|perfect|that(?:'s| is) (?:right|correct|it))\b", re.IGNORECASE),
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re.compile(r"\bperfect(?:[.!?]|$)", re.IGNORECASE),
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re.compile(r"\bexactly\s+(?:right|correct)\b", re.IGNORECASE),
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re.compile(r"\bthat(?:'s| is)\s+(?:exactly\s+)?(?:right|correct|what i (?:wanted|needed|meant))\b", re.IGNORECASE),
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re.compile(r"\bkeep\s+(?:doing\s+)?that\b", re.IGNORECASE),
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re.compile(r"\bjust\s+(?:like\s+)?(?:that|this)\b", re.IGNORECASE),
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re.compile(r"\bthis is (?:great|helpful)\b(?:[.!?]|$)", re.IGNORECASE),
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re.compile(r"\bthis is what i wanted\b(?:[.!?]|$)", re.IGNORECASE),
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re.compile(r"对[,,]?\s*就是这样(?:[。!?!?.]|$)"),
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re.compile(r"完全正确(?:[。!?!?.]|$)"),
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re.compile(r"(?:对[,,]?\s*)?就是这个意思(?:[。!?!?.]|$)"),
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re.compile(r"正是我想要的(?:[。!?!?.]|$)"),
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re.compile(r"继续保持(?:[。!?!?.]|$)"),
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)
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class MemoryMiddlewareState(AgentState):
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"""Compatible with the `ThreadState` schema."""
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@@ -132,6 +148,29 @@ def detect_correction(messages: list[Any]) -> bool:
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return False
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def detect_reinforcement(messages: list[Any]) -> bool:
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"""Detect explicit positive reinforcement signals in recent conversation turns.
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Complements detect_correction() by identifying when the user confirms the
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agent's approach was correct. This allows the memory system to record what
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worked well, not just what went wrong.
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The queue keeps only one pending context per thread, so callers pass the
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latest filtered message list. Checking only recent user turns keeps signal
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detection conservative while avoiding stale signals from long histories.
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"""
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recent_user_msgs = [msg for msg in messages[-6:] if getattr(msg, "type", None) == "human"]
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for msg in recent_user_msgs:
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content = _extract_message_text(msg).strip()
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if not content:
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continue
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if any(pattern.search(content) for pattern in _REINFORCEMENT_PATTERNS):
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return True
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return False
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class MemoryMiddleware(AgentMiddleware[MemoryMiddlewareState]):
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"""Middleware that queues conversation for memory update after agent execution.
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@@ -196,12 +235,14 @@ class MemoryMiddleware(AgentMiddleware[MemoryMiddlewareState]):
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# Queue the filtered conversation for memory update
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correction_detected = detect_correction(filtered_messages)
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reinforcement_detected = not correction_detected and detect_reinforcement(filtered_messages)
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queue = get_memory_queue()
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queue.add(
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thread_id=thread_id,
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messages=filtered_messages,
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agent_name=self._agent_name,
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correction_detected=correction_detected,
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reinforcement_detected=reinforcement_detected,
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)
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return None
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@@ -101,44 +101,33 @@ class TitleMiddleware(AgentMiddleware[TitleMiddlewareState]):
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return user_msg if user_msg else "New Conversation"
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def _generate_title_result(self, state: TitleMiddlewareState) -> dict | None:
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"""Synchronously generate a title. Returns state update or None."""
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"""Generate a local fallback title without blocking on an LLM call."""
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if not self._should_generate_title(state):
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return None
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prompt, user_msg = self._build_title_prompt(state)
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config = get_title_config()
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model = create_chat_model(name=config.model_name, thinking_enabled=False)
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try:
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response = model.invoke(prompt)
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title = self._parse_title(response.content)
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if not title:
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title = self._fallback_title(user_msg)
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except Exception:
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logger.exception("Failed to generate title (sync)")
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title = self._fallback_title(user_msg)
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return {"title": title}
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_, user_msg = self._build_title_prompt(state)
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return {"title": self._fallback_title(user_msg)}
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async def _agenerate_title_result(self, state: TitleMiddlewareState) -> dict | None:
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"""Asynchronously generate a title. Returns state update or None."""
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"""Generate a title asynchronously and fall back locally on failure."""
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if not self._should_generate_title(state):
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return None
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prompt, user_msg = self._build_title_prompt(state)
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config = get_title_config()
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model = create_chat_model(name=config.model_name, thinking_enabled=False)
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prompt, user_msg = self._build_title_prompt(state)
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try:
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if config.model_name:
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model = create_chat_model(name=config.model_name, thinking_enabled=False)
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else:
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model = create_chat_model(thinking_enabled=False)
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response = await model.ainvoke(prompt)
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title = self._parse_title(response.content)
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if not title:
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title = self._fallback_title(user_msg)
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if title:
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return {"title": title}
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except Exception:
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logger.exception("Failed to generate title (async)")
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title = self._fallback_title(user_msg)
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return {"title": title}
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logger.debug("Failed to generate async title; falling back to local title", exc_info=True)
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return {"title": self._fallback_title(user_msg)}
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@override
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def after_model(self, state: TitleMiddlewareState, runtime: Runtime) -> dict | None:
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+1
-1
@@ -138,6 +138,6 @@ def build_subagent_runtime_middlewares(*, lazy_init: bool = True) -> list[AgentM
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"""Middlewares shared by subagent runtime before subagent-only middlewares."""
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return _build_runtime_middlewares(
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include_uploads=False,
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include_dangling_tool_call_patch=False,
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include_dangling_tool_call_patch=True,
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lazy_init=lazy_init,
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)
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@@ -10,10 +10,52 @@ from langchain_core.messages import HumanMessage
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from langgraph.runtime import Runtime
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from deerflow.config.paths import Paths, get_paths
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from deerflow.utils.file_conversion import extract_outline
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logger = logging.getLogger(__name__)
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_OUTLINE_PREVIEW_LINES = 5
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def _extract_outline_for_file(file_path: Path) -> tuple[list[dict], list[str]]:
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"""Return the document outline and fallback preview for *file_path*.
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Looks for a sibling ``<stem>.md`` file produced by the upload conversion
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pipeline.
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Returns:
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(outline, preview) where:
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- outline: list of ``{title, line}`` dicts (plus optional sentinel).
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Empty when no headings are found or no .md exists.
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- preview: first few non-empty lines of the .md, used as a content
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anchor when outline is empty so the agent has some context.
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Empty when outline is non-empty (no fallback needed).
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"""
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md_path = file_path.with_suffix(".md")
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if not md_path.is_file():
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return [], []
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outline = extract_outline(md_path)
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if outline:
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logger.debug("Extracted %d outline entries from %s", len(outline), file_path.name)
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return outline, []
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# outline is empty — read the first few non-empty lines as a content preview
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preview: list[str] = []
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try:
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with md_path.open(encoding="utf-8") as f:
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for line in f:
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stripped = line.strip()
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if stripped:
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preview.append(stripped)
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if len(preview) >= _OUTLINE_PREVIEW_LINES:
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break
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except Exception:
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logger.debug("Failed to read preview lines from %s", md_path, exc_info=True)
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return [], preview
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class UploadsMiddlewareState(AgentState):
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"""State schema for uploads middleware."""
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@@ -39,12 +81,38 @@ class UploadsMiddleware(AgentMiddleware[UploadsMiddlewareState]):
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super().__init__()
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self._paths = Paths(base_dir) if base_dir else get_paths()
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def _format_file_entry(self, file: dict, lines: list[str]) -> None:
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"""Append a single file entry (name, size, path, optional outline) to lines."""
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size_kb = file["size"] / 1024
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size_str = f"{size_kb:.1f} KB" if size_kb < 1024 else f"{size_kb / 1024:.1f} MB"
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lines.append(f"- {file['filename']} ({size_str})")
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lines.append(f" Path: {file['path']}")
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outline = file.get("outline") or []
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if outline:
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truncated = outline[-1].get("truncated", False)
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visible = [e for e in outline if not e.get("truncated")]
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lines.append(" Document outline (use `read_file` with line ranges to read sections):")
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for entry in visible:
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lines.append(f" L{entry['line']}: {entry['title']}")
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if truncated:
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lines.append(f" ... (showing first {len(visible)} headings; use `read_file` to explore further)")
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else:
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preview = file.get("outline_preview") or []
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if preview:
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lines.append(" No structural headings detected. Document begins with:")
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for text in preview:
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lines.append(f" > {text}")
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lines.append(" Use `grep` to search for keywords (e.g. `grep(pattern='keyword', path='/mnt/user-data/uploads/')`).")
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lines.append("")
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def _create_files_message(self, new_files: list[dict], historical_files: list[dict]) -> str:
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"""Create a formatted message listing uploaded files.
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Args:
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new_files: Files uploaded in the current message.
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historical_files: Files uploaded in previous messages.
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Each file dict may contain an optional ``outline`` key — a list of
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``{title, line}`` dicts extracted from the converted Markdown file.
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Returns:
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Formatted string inside <uploaded_files> tags.
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@@ -55,25 +123,24 @@ class UploadsMiddleware(AgentMiddleware[UploadsMiddlewareState]):
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lines.append("")
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if new_files:
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for file in new_files:
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size_kb = file["size"] / 1024
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size_str = f"{size_kb:.1f} KB" if size_kb < 1024 else f"{size_kb / 1024:.1f} MB"
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lines.append(f"- {file['filename']} ({size_str})")
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lines.append(f" Path: {file['path']}")
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lines.append("")
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self._format_file_entry(file, lines)
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else:
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lines.append("(empty)")
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lines.append("")
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if historical_files:
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lines.append("The following files were uploaded in previous messages and are still available:")
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lines.append("")
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for file in historical_files:
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size_kb = file["size"] / 1024
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size_str = f"{size_kb:.1f} KB" if size_kb < 1024 else f"{size_kb / 1024:.1f} MB"
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lines.append(f"- {file['filename']} ({size_str})")
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lines.append(f" Path: {file['path']}")
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lines.append("")
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self._format_file_entry(file, lines)
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lines.append("You can read these files using the `read_file` tool with the paths shown above.")
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lines.append("To work with these files:")
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lines.append("- Read from the file first — use the outline line numbers and `read_file` to locate relevant sections.")
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lines.append("- Use `grep` to search for keywords when you are not sure which section to look at")
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lines.append(" (e.g. `grep(pattern='revenue', path='/mnt/user-data/uploads/')`).")
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lines.append("- Use `glob` to find files by name pattern")
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lines.append(" (e.g. `glob(pattern='**/*.md', path='/mnt/user-data/uploads/')`).")
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lines.append("- Only fall back to web search if the file content is clearly insufficient to answer the question.")
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lines.append("</uploaded_files>")
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return "\n".join(lines)
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@@ -147,6 +214,13 @@ class UploadsMiddleware(AgentMiddleware[UploadsMiddlewareState]):
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# Resolve uploads directory for existence checks
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thread_id = (runtime.context or {}).get("thread_id")
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if thread_id is None:
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try:
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from langgraph.config import get_config
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thread_id = get_config().get("configurable", {}).get("thread_id")
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except RuntimeError:
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pass # get_config() raises outside a runnable context (e.g. unit tests)
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uploads_dir = self._paths.sandbox_uploads_dir(thread_id) if thread_id else None
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# Get newly uploaded files from the current message's additional_kwargs.files
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@@ -159,15 +233,26 @@ class UploadsMiddleware(AgentMiddleware[UploadsMiddlewareState]):
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for file_path in sorted(uploads_dir.iterdir()):
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if file_path.is_file() and file_path.name not in new_filenames:
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stat = file_path.stat()
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outline, preview = _extract_outline_for_file(file_path)
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historical_files.append(
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{
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"filename": file_path.name,
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"size": stat.st_size,
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"path": f"/mnt/user-data/uploads/{file_path.name}",
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"extension": file_path.suffix,
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"outline": outline,
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"outline_preview": preview,
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}
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)
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# Attach outlines to new files as well
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if uploads_dir:
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for file in new_files:
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phys_path = uploads_dir / file["filename"]
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outline, preview = _extract_outline_for_file(phys_path)
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file["outline"] = outline
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file["outline_preview"] = preview
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if not new_files and not historical_files:
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return None
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Block a user