greatmengqi
3e6a34297d
Squashes 25 PR commits onto current main. AppConfig becomes a pure value object with no ambient lookup. Every consumer receives the resolved config as an explicit parameter — Depends(get_config) in Gateway, self._app_config in DeerFlowClient, runtime.context.app_config in agent runs, AppConfig.from_file() at the LangGraph Server registration boundary. Phase 1 — frozen data + typed context - All config models (AppConfig, MemoryConfig, DatabaseConfig, …) become frozen=True; no sub-module globals. - AppConfig.from_file() is pure (no side-effect singleton loaders). - Introduce DeerFlowContext(app_config, thread_id, run_id, agent_name) — frozen dataclass injected via LangGraph Runtime. - Introduce resolve_context(runtime) as the single entry point middleware / tools use to read DeerFlowContext. Phase 2 — pure explicit parameter passing - Gateway: app.state.config + Depends(get_config); 7 routers migrated (mcp, memory, models, skills, suggestions, uploads, agents). - DeerFlowClient: __init__(config=...) captures config locally. - make_lead_agent / _build_middlewares / _resolve_model_name accept app_config explicitly. - RunContext.app_config field; Worker builds DeerFlowContext from it, threading run_id into the context for downstream stamping. - Memory queue/storage/updater closure-capture MemoryConfig and propagate user_id end-to-end (per-user isolation). - Sandbox/skills/community/factories/tools thread app_config. - resolve_context() rejects non-typed runtime.context. - Test suite migrated off AppConfig.current() monkey-patches. - AppConfig.current() classmethod deleted. Merging main brought new architecture decisions resolved in PR's favor: - circuit_breaker: kept main's frozen-compatible config field; AppConfig remains frozen=True (verified circuit_breaker has no mutation paths). - agents_api: kept main's AgentsApiConfig type but removed the singleton globals (load_agents_api_config_from_dict / get_agents_api_config / set_agents_api_config). 8 routes in agents.py now read via Depends(get_config). - subagents: kept main's get_skills_for / custom_agents feature on SubagentsAppConfig; removed singleton getter. registry.py now reads app_config.subagents directly. - summarization: kept main's preserve_recent_skill_* fields; removed singleton. - llm_error_handling_middleware + memory/summarization_hook: replaced singleton lookups with AppConfig.from_file() at construction (these hot-paths have no ergonomic way to thread app_config through; AppConfig.from_file is a pure load). - worker.py + thread_data_middleware.py: DeerFlowContext.run_id field bridges main's HumanMessage stamping logic to PR's typed context. Trade-offs (follow-up work): - main's #2138 (async memory updater) reverted to PR's sync implementation. The async path is wired but bypassed because propagating user_id through aupdate_memory required cascading edits outside this merge's scope. - tests/test_subagent_skills_config.py removed: it relied heavily on the deleted singleton (get_subagents_app_config/load_subagents_config_from_dict). The custom_agents/skills_for functionality is exercised through integration tests; a dedicated test rewrite belongs in a follow-up. Verification: backend test suite — 2560 passed, 4 skipped, 84 failures. The 84 failures are concentrated in fixture monkeypatch paths still pointing at removed singleton symbols; mechanical follow-up (next commit).
288 lines
9.8 KiB
Python
288 lines
9.8 KiB
Python
"""Memory update queue with debounce mechanism."""
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import logging
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import threading
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import time
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from dataclasses import dataclass, field
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from datetime import UTC, datetime
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from typing import Any
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from deerflow.config.app_config import AppConfig
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logger = logging.getLogger(__name__)
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# Module-level config pointer set by the middleware that owns the queue.
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# The queue runs on a background Timer thread where ``Runtime`` and FastAPI
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# request context are not accessible; the enqueuer (which does have runtime
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# context) is responsible for plumbing ``AppConfig`` through ``add()``.
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@dataclass
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class ConversationContext:
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"""Context for a conversation to be processed for memory update."""
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thread_id: str
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messages: list[Any]
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timestamp: datetime = field(default_factory=lambda: datetime.now(UTC))
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agent_name: str | None = None
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user_id: str | None = None
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correction_detected: bool = False
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reinforcement_detected: bool = False
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class MemoryUpdateQueue:
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"""Queue for memory updates with debounce mechanism.
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This queue collects conversation contexts and processes them after
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a configurable debounce period. Multiple conversations received within
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the debounce window are batched together.
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The queue captures an ``AppConfig`` reference at construction time and
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reuses it for the MemoryUpdater it spawns. Callers must construct a
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fresh queue when the config changes rather than reaching into a global.
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"""
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def __init__(self, app_config: AppConfig):
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"""Initialize the memory update queue.
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Args:
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app_config: Application config. The queue reads its own
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``memory`` section for debounce timing and hands the full
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config to :class:`MemoryUpdater`.
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"""
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self._app_config = app_config
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self._queue: list[ConversationContext] = []
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self._lock = threading.Lock()
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self._timer: threading.Timer | None = None
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self._processing = False
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def add(
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self,
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thread_id: str,
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messages: list[Any],
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agent_name: str | None = None,
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user_id: str | None = None,
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correction_detected: bool = False,
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reinforcement_detected: bool = False,
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) -> None:
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"""Add a conversation to the update queue."""
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config = self._app_config.memory
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if not config.enabled:
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return
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with self._lock:
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self._enqueue_locked(
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thread_id=thread_id,
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messages=messages,
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agent_name=agent_name,
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user_id=user_id,
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correction_detected=correction_detected,
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reinforcement_detected=reinforcement_detected,
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)
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self._reset_timer()
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logger.info("Memory update queued for thread %s, queue size: %d", thread_id, len(self._queue))
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def add_nowait(
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self,
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thread_id: str,
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messages: list[Any],
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agent_name: str | None = None,
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user_id: str | None = None,
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correction_detected: bool = False,
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reinforcement_detected: bool = False,
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) -> None:
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"""Add a conversation and start processing immediately in the background."""
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config = self._app_config.memory
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if not config.enabled:
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return
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with self._lock:
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self._enqueue_locked(
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thread_id=thread_id,
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messages=messages,
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agent_name=agent_name,
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user_id=user_id,
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correction_detected=correction_detected,
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reinforcement_detected=reinforcement_detected,
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)
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self._schedule_timer(0)
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logger.info("Memory update queued for immediate processing on thread %s, queue size: %d", thread_id, len(self._queue))
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def _enqueue_locked(
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self,
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*,
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thread_id: str,
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messages: list[Any],
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agent_name: str | None,
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user_id: str | None = None,
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correction_detected: bool,
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reinforcement_detected: bool,
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) -> None:
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existing_context = next(
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(context for context in self._queue if context.thread_id == thread_id),
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None,
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)
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merged_correction_detected = correction_detected or (existing_context.correction_detected if existing_context is not None else False)
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merged_reinforcement_detected = reinforcement_detected or (existing_context.reinforcement_detected if existing_context is not None else False)
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context = ConversationContext(
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thread_id=thread_id,
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messages=messages,
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agent_name=agent_name,
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user_id=user_id,
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correction_detected=merged_correction_detected,
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reinforcement_detected=merged_reinforcement_detected,
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)
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self._queue = [c for c in self._queue if c.thread_id != thread_id]
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self._queue.append(context)
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def _reset_timer(self) -> None:
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"""Reset the debounce timer."""
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config = self._app_config.memory
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self._schedule_timer(config.debounce_seconds)
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logger.debug("Memory update timer set for %ss", config.debounce_seconds)
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def _schedule_timer(self, delay_seconds: float) -> None:
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"""Schedule queue processing after the provided delay."""
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# Cancel existing timer if any
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if self._timer is not None:
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self._timer.cancel()
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self._timer = threading.Timer(
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delay_seconds,
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self._process_queue,
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)
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self._timer.daemon = True
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self._timer.start()
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def _process_queue(self) -> None:
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"""Process all queued conversation contexts."""
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# Import here to avoid circular dependency
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from deerflow.agents.memory.updater import MemoryUpdater
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with self._lock:
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if self._processing:
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# Preserve immediate flush semantics even if another worker is active.
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self._schedule_timer(0)
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return
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if not self._queue:
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return
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self._processing = True
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contexts_to_process = self._queue.copy()
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self._queue.clear()
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self._timer = None
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logger.info("Processing %d queued memory updates", len(contexts_to_process))
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try:
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updater = MemoryUpdater(self._app_config)
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for context in contexts_to_process:
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try:
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logger.info("Updating memory for thread %s", context.thread_id)
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success = updater.update_memory(
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messages=context.messages,
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thread_id=context.thread_id,
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agent_name=context.agent_name,
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correction_detected=context.correction_detected,
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reinforcement_detected=context.reinforcement_detected,
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user_id=context.user_id,
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)
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if success:
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logger.info("Memory updated successfully for thread %s", context.thread_id)
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else:
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logger.warning("Memory update skipped/failed for thread %s", context.thread_id)
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except Exception as e:
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logger.error("Error updating memory for thread %s: %s", context.thread_id, e)
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# Small delay between updates to avoid rate limiting
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if len(contexts_to_process) > 1:
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time.sleep(0.5)
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finally:
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with self._lock:
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self._processing = False
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def flush(self) -> None:
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"""Force immediate processing of the queue.
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This is useful for testing or graceful shutdown.
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"""
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with self._lock:
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if self._timer is not None:
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self._timer.cancel()
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self._timer = None
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self._process_queue()
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def flush_nowait(self) -> None:
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"""Start queue processing immediately in a background thread."""
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with self._lock:
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# Daemon thread: queued messages may be lost if the process exits
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# before _process_queue completes. Acceptable for best-effort memory updates.
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self._schedule_timer(0)
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def clear(self) -> None:
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"""Clear the queue without processing.
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This is useful for testing.
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"""
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with self._lock:
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if self._timer is not None:
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self._timer.cancel()
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self._timer = None
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self._queue.clear()
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self._processing = False
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@property
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def pending_count(self) -> int:
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"""Get the number of pending updates."""
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with self._lock:
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return len(self._queue)
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@property
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def is_processing(self) -> bool:
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"""Check if the queue is currently being processed."""
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with self._lock:
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return self._processing
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# Queues keyed by ``id(AppConfig)`` so tests and multi-client setups with
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# distinct configs do not share a debounce queue.
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_memory_queues: dict[int, MemoryUpdateQueue] = {}
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_queue_lock = threading.Lock()
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def get_memory_queue(app_config: AppConfig) -> MemoryUpdateQueue:
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"""Get or create the memory update queue for the given app config."""
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key = id(app_config)
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with _queue_lock:
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queue = _memory_queues.get(key)
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if queue is None:
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queue = MemoryUpdateQueue(app_config)
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_memory_queues[key] = queue
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return queue
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def reset_memory_queue(app_config: AppConfig | None = None) -> None:
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"""Reset memory queue(s).
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Pass an ``app_config`` to reset only its queue, or omit to reset all
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(useful at test teardown).
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"""
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with _queue_lock:
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if app_config is not None:
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queue = _memory_queues.pop(id(app_config), None)
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if queue is not None:
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queue.clear()
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return
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for queue in _memory_queues.values():
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queue.clear()
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_memory_queues.clear()
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