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https://gitee.com/wanwujie/deer-flow
synced 2026-04-15 19:04:45 +08:00
feat: add global memory mechanism for personalized conversations
Implement a memory system that stores user context and conversation history in memory.json, uses LLM to summarize conversations, and injects relevant context into system prompts for personalized responses. Key components: - MemoryConfig for configuration management - MemoryUpdateQueue with debounce for batch processing - MemoryUpdater for LLM-based memory extraction - MemoryMiddleware to queue conversations after agent execution - Memory injection into lead agent system prompt Note: Add memory section to config.yaml to enable (see config.example.yaml) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
hetaoBackend
191
backend/src/agents/memory/queue.py
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191
backend/src/agents/memory/queue.py
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"""Memory update queue with debounce mechanism."""
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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 datetime
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from typing import Any
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from src.config.memory_config import get_memory_config
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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=datetime.utcnow)
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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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"""
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def __init__(self):
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"""Initialize the memory update queue."""
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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(self, thread_id: str, messages: list[Any]) -> None:
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"""Add a conversation to the update queue.
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Args:
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thread_id: The thread ID.
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messages: The conversation messages.
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"""
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config = get_memory_config()
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if not config.enabled:
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return
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context = ConversationContext(
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thread_id=thread_id,
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messages=messages,
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)
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with self._lock:
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# Check if this thread already has a pending update
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# If so, replace it with the newer one
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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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# Reset or start the debounce timer
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self._reset_timer()
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print(f"Memory update queued for thread {thread_id}, queue size: {len(self._queue)}")
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def _reset_timer(self) -> None:
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"""Reset the debounce timer."""
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config = get_memory_config()
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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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# Start new timer
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self._timer = threading.Timer(
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config.debounce_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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print(f"Memory update timer set for {config.debounce_seconds}s")
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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 src.agents.memory.updater import MemoryUpdater
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with self._lock:
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if self._processing:
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# Already processing, reschedule
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self._reset_timer()
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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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print(f"Processing {len(contexts_to_process)} queued memory updates")
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try:
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updater = MemoryUpdater()
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for context in contexts_to_process:
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try:
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print(f"Updating memory for thread {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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)
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if success:
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print(f"Memory updated successfully for thread {context.thread_id}")
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else:
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print(f"Memory update skipped/failed for thread {context.thread_id}")
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except Exception as e:
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print(f"Error updating memory for thread {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 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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# Global singleton instance
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_memory_queue: MemoryUpdateQueue | None = None
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_queue_lock = threading.Lock()
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def get_memory_queue() -> MemoryUpdateQueue:
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"""Get the global memory update queue singleton.
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Returns:
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The memory update queue instance.
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"""
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global _memory_queue
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with _queue_lock:
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if _memory_queue is None:
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_memory_queue = MemoryUpdateQueue()
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return _memory_queue
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def reset_memory_queue() -> None:
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"""Reset the global memory queue.
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This is useful for testing.
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"""
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global _memory_queue
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with _queue_lock:
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if _memory_queue is not None:
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_memory_queue.clear()
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_memory_queue = None
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