mirror of
https://github.com/bytedance/deer-flow.git
synced 2026-05-23 16:35:59 +00:00
feat: flush memory before summarization (#2176)
* feat: flush memory before summarization * fix: keep agent-scoped memory on summarization flush * fix: harden summarization hook plumbing * fix: address summarization review feedback * style: format memory middleware
This commit is contained in:
DanielWalnut
@@ -1,14 +1,16 @@
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import logging
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from langchain.agents import create_agent
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from langchain.agents.middleware import AgentMiddleware, SummarizationMiddleware
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from langchain.agents.middleware import AgentMiddleware
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from langchain_core.runnables import RunnableConfig
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from deerflow.agents.lead_agent.prompt import apply_prompt_template
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from deerflow.agents.memory.summarization_hook import memory_flush_hook
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from deerflow.agents.middlewares.clarification_middleware import ClarificationMiddleware
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from deerflow.agents.middlewares.loop_detection_middleware import LoopDetectionMiddleware
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from deerflow.agents.middlewares.memory_middleware import MemoryMiddleware
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from deerflow.agents.middlewares.subagent_limit_middleware import SubagentLimitMiddleware
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from deerflow.agents.middlewares.summarization_middleware import BeforeSummarizationHook, DeerFlowSummarizationMiddleware
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from deerflow.agents.middlewares.title_middleware import TitleMiddleware
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from deerflow.agents.middlewares.todo_middleware import TodoMiddleware
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from deerflow.agents.middlewares.token_usage_middleware import TokenUsageMiddleware
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@@ -17,6 +19,7 @@ from deerflow.agents.middlewares.view_image_middleware import ViewImageMiddlewar
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from deerflow.agents.thread_state import ThreadState
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from deerflow.config.agents_config import load_agent_config
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from deerflow.config.app_config import get_app_config
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from deerflow.config.memory_config import get_memory_config
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from deerflow.config.summarization_config import get_summarization_config
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from deerflow.models import create_chat_model
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@@ -38,7 +41,7 @@ def _resolve_model_name(requested_model_name: str | None = None) -> str:
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return default_model_name
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def _create_summarization_middleware() -> SummarizationMiddleware | None:
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def _create_summarization_middleware() -> DeerFlowSummarizationMiddleware | None:
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"""Create and configure the summarization middleware from config."""
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config = get_summarization_config()
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@@ -77,7 +80,11 @@ def _create_summarization_middleware() -> SummarizationMiddleware | None:
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if config.summary_prompt is not None:
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kwargs["summary_prompt"] = config.summary_prompt
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return SummarizationMiddleware(**kwargs)
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hooks: list[BeforeSummarizationHook] = []
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if get_memory_config().enabled:
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hooks.append(memory_flush_hook)
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return DeerFlowSummarizationMiddleware(**kwargs, before_summarization=hooks)
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def _create_todo_list_middleware(is_plan_mode: bool) -> TodoMiddleware | None:
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@@ -0,0 +1,109 @@
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"""Shared helpers for turning conversations into memory update inputs."""
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from __future__ import annotations
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import re
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from copy import copy
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from typing import Any
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_UPLOAD_BLOCK_RE = re.compile(r"<uploaded_files>[\s\S]*?</uploaded_files>\n*", re.IGNORECASE)
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_CORRECTION_PATTERNS = (
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re.compile(r"\bthat(?:'s| is) (?:wrong|incorrect)\b", re.IGNORECASE),
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re.compile(r"\byou misunderstood\b", re.IGNORECASE),
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re.compile(r"\btry again\b", re.IGNORECASE),
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re.compile(r"\bredo\b", re.IGNORECASE),
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re.compile(r"不对"),
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re.compile(r"你理解错了"),
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re.compile(r"你理解有误"),
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re.compile(r"重试"),
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re.compile(r"重新来"),
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re.compile(r"换一种"),
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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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def extract_message_text(message: Any) -> str:
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"""Extract plain text from message content for filtering and signal detection."""
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content = getattr(message, "content", "")
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if isinstance(content, list):
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text_parts: list[str] = []
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for part in content:
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if isinstance(part, str):
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text_parts.append(part)
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elif isinstance(part, dict):
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text_val = part.get("text")
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if isinstance(text_val, str):
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text_parts.append(text_val)
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return " ".join(text_parts)
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return str(content)
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def filter_messages_for_memory(messages: list[Any]) -> list[Any]:
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"""Keep only user inputs and final assistant responses for memory updates."""
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filtered = []
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skip_next_ai = False
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for msg in messages:
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msg_type = getattr(msg, "type", None)
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if msg_type == "human":
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content_str = extract_message_text(msg)
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if "<uploaded_files>" in content_str:
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stripped = _UPLOAD_BLOCK_RE.sub("", content_str).strip()
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if not stripped:
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skip_next_ai = True
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continue
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clean_msg = copy(msg)
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clean_msg.content = stripped
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filtered.append(clean_msg)
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skip_next_ai = False
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else:
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filtered.append(msg)
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skip_next_ai = False
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elif msg_type == "ai":
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tool_calls = getattr(msg, "tool_calls", None)
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if not tool_calls:
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if skip_next_ai:
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skip_next_ai = False
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continue
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filtered.append(msg)
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return filtered
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def detect_correction(messages: list[Any]) -> bool:
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"""Detect explicit user corrections in recent conversation turns."""
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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 content and any(pattern.search(content) for pattern in _CORRECTION_PATTERNS):
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return True
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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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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 content and 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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@@ -61,48 +61,88 @@ class MemoryUpdateQueue:
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return
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with self._lock:
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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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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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correction_detected=merged_correction_detected,
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reinforcement_detected=merged_reinforcement_detected,
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correction_detected=correction_detected,
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reinforcement_detected=reinforcement_detected,
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)
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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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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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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 = get_memory_config()
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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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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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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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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 = get_memory_config()
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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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# Start new timer
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self._timer = threading.Timer(
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config.debounce_seconds,
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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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logger.debug("Memory update timer set for %ss", config.debounce_seconds)
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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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@@ -110,8 +150,8 @@ class MemoryUpdateQueue:
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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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# 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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@@ -164,6 +204,13 @@ class MemoryUpdateQueue:
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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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@@ -0,0 +1,31 @@
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"""Hooks fired before summarization removes messages from state."""
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from __future__ import annotations
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from deerflow.agents.memory.message_processing import detect_correction, detect_reinforcement, filter_messages_for_memory
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from deerflow.agents.memory.queue import get_memory_queue
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from deerflow.agents.middlewares.summarization_middleware import SummarizationEvent
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from deerflow.config.memory_config import get_memory_config
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def memory_flush_hook(event: SummarizationEvent) -> None:
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"""Flush messages about to be summarized into the memory queue."""
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if not get_memory_config().enabled or not event.thread_id:
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return
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filtered_messages = filter_messages_for_memory(list(event.messages_to_summarize))
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user_messages = [message for message in filtered_messages if getattr(message, "type", None) == "human"]
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assistant_messages = [message for message in filtered_messages if getattr(message, "type", None) == "ai"]
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if not user_messages or not assistant_messages:
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return
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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_nowait(
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thread_id=event.thread_id,
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messages=filtered_messages,
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agent_name=event.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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@@ -1,50 +1,19 @@
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"""Middleware for memory mechanism."""
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import logging
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import re
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from typing import Any, override
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from typing import override
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from langchain.agents import AgentState
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from langchain.agents.middleware import AgentMiddleware
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from langgraph.config import get_config
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from langgraph.runtime import Runtime
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from deerflow.agents.memory.message_processing import detect_correction, detect_reinforcement, filter_messages_for_memory
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from deerflow.agents.memory.queue import get_memory_queue
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from deerflow.config.memory_config import get_memory_config
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logger = logging.getLogger(__name__)
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_UPLOAD_BLOCK_RE = re.compile(r"<uploaded_files>[\s\S]*?</uploaded_files>\n*", re.IGNORECASE)
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_CORRECTION_PATTERNS = (
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re.compile(r"\bthat(?:'s| is) (?:wrong|incorrect)\b", re.IGNORECASE),
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re.compile(r"\byou misunderstood\b", re.IGNORECASE),
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re.compile(r"\btry again\b", re.IGNORECASE),
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re.compile(r"\bredo\b", re.IGNORECASE),
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re.compile(r"不对"),
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re.compile(r"你理解错了"),
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re.compile(r"你理解有误"),
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re.compile(r"重试"),
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re.compile(r"重新来"),
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re.compile(r"换一种"),
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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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@@ -52,125 +21,6 @@ class MemoryMiddlewareState(AgentState):
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pass
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def _extract_message_text(message: Any) -> str:
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"""Extract plain text from message content for filtering and signal detection."""
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content = getattr(message, "content", "")
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if isinstance(content, list):
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text_parts: list[str] = []
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for part in content:
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if isinstance(part, str):
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text_parts.append(part)
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elif isinstance(part, dict):
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text_val = part.get("text")
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if isinstance(text_val, str):
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text_parts.append(text_val)
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return " ".join(text_parts)
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return str(content)
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def _filter_messages_for_memory(messages: list[Any]) -> list[Any]:
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"""Filter messages to keep only user inputs and final assistant responses.
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This filters out:
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- Tool messages (intermediate tool call results)
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- AI messages with tool_calls (intermediate steps, not final responses)
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- The <uploaded_files> block injected by UploadsMiddleware into human messages
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(file paths are session-scoped and must not persist in long-term memory).
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The user's actual question is preserved; only turns whose content is entirely
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the upload block (nothing remains after stripping) are dropped along with
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their paired assistant response.
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Only keeps:
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- Human messages (with the ephemeral upload block removed)
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- AI messages without tool_calls (final assistant responses), unless the
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paired human turn was upload-only and had no real user text.
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Args:
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messages: List of all conversation messages.
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Returns:
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Filtered list containing only user inputs and final assistant responses.
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"""
|
||||
filtered = []
|
||||
skip_next_ai = False
|
||||
for msg in messages:
|
||||
msg_type = getattr(msg, "type", None)
|
||||
|
||||
if msg_type == "human":
|
||||
content_str = _extract_message_text(msg)
|
||||
if "<uploaded_files>" in content_str:
|
||||
# Strip the ephemeral upload block; keep the user's real question.
|
||||
stripped = _UPLOAD_BLOCK_RE.sub("", content_str).strip()
|
||||
if not stripped:
|
||||
# Nothing left — the entire turn was upload bookkeeping;
|
||||
# skip it and the paired assistant response.
|
||||
skip_next_ai = True
|
||||
continue
|
||||
# Rebuild the message with cleaned content so the user's question
|
||||
# is still available for memory summarisation.
|
||||
from copy import copy
|
||||
|
||||
clean_msg = copy(msg)
|
||||
clean_msg.content = stripped
|
||||
filtered.append(clean_msg)
|
||||
skip_next_ai = False
|
||||
else:
|
||||
filtered.append(msg)
|
||||
skip_next_ai = False
|
||||
elif msg_type == "ai":
|
||||
tool_calls = getattr(msg, "tool_calls", None)
|
||||
if not tool_calls:
|
||||
if skip_next_ai:
|
||||
skip_next_ai = False
|
||||
continue
|
||||
filtered.append(msg)
|
||||
# Skip tool messages and AI messages with tool_calls
|
||||
|
||||
return filtered
|
||||
|
||||
|
||||
def detect_correction(messages: list[Any]) -> bool:
|
||||
"""Detect explicit user corrections in recent conversation turns.
|
||||
|
||||
The queue keeps only one pending context per thread, so callers pass the
|
||||
latest filtered message list. Checking only recent user turns keeps signal
|
||||
detection conservative while avoiding stale corrections from long histories.
|
||||
"""
|
||||
recent_user_msgs = [msg for msg in messages[-6:] if getattr(msg, "type", None) == "human"]
|
||||
|
||||
for msg in recent_user_msgs:
|
||||
content = _extract_message_text(msg).strip()
|
||||
if not content:
|
||||
continue
|
||||
if any(pattern.search(content) for pattern in _CORRECTION_PATTERNS):
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
|
||||
def detect_reinforcement(messages: list[Any]) -> bool:
|
||||
"""Detect explicit positive reinforcement signals in recent conversation turns.
|
||||
|
||||
Complements detect_correction() by identifying when the user confirms the
|
||||
agent's approach was correct. This allows the memory system to record what
|
||||
worked well, not just what went wrong.
|
||||
|
||||
The queue keeps only one pending context per thread, so callers pass the
|
||||
latest filtered message list. Checking only recent user turns keeps signal
|
||||
detection conservative while avoiding stale signals from long histories.
|
||||
"""
|
||||
recent_user_msgs = [msg for msg in messages[-6:] if getattr(msg, "type", None) == "human"]
|
||||
|
||||
for msg in recent_user_msgs:
|
||||
content = _extract_message_text(msg).strip()
|
||||
if not content:
|
||||
continue
|
||||
if any(pattern.search(content) for pattern in _REINFORCEMENT_PATTERNS):
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
|
||||
class MemoryMiddleware(AgentMiddleware[MemoryMiddlewareState]):
|
||||
"""Middleware that queues conversation for memory update after agent execution.
|
||||
|
||||
@@ -223,7 +73,7 @@ class MemoryMiddleware(AgentMiddleware[MemoryMiddlewareState]):
|
||||
return None
|
||||
|
||||
# Filter to only keep user inputs and final assistant responses
|
||||
filtered_messages = _filter_messages_for_memory(messages)
|
||||
filtered_messages = filter_messages_for_memory(messages)
|
||||
|
||||
# Only queue if there's meaningful conversation
|
||||
# At minimum need one user message and one assistant response
|
||||
|
||||
@@ -0,0 +1,151 @@
|
||||
"""Summarization middleware extensions for DeerFlow."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from dataclasses import dataclass
|
||||
from typing import Protocol, runtime_checkable
|
||||
|
||||
from langchain.agents import AgentState
|
||||
from langchain.agents.middleware import SummarizationMiddleware
|
||||
from langchain_core.messages import AnyMessage, RemoveMessage
|
||||
from langgraph.config import get_config
|
||||
from langgraph.graph.message import REMOVE_ALL_MESSAGES
|
||||
from langgraph.runtime import Runtime
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class SummarizationEvent:
|
||||
"""Context emitted before conversation history is summarized away."""
|
||||
|
||||
messages_to_summarize: tuple[AnyMessage, ...]
|
||||
preserved_messages: tuple[AnyMessage, ...]
|
||||
thread_id: str | None
|
||||
agent_name: str | None
|
||||
runtime: Runtime
|
||||
|
||||
|
||||
@runtime_checkable
|
||||
class BeforeSummarizationHook(Protocol):
|
||||
"""Hook invoked before summarization removes messages from state."""
|
||||
|
||||
def __call__(self, event: SummarizationEvent) -> None: ...
|
||||
|
||||
|
||||
def _resolve_thread_id(runtime: Runtime) -> str | None:
|
||||
"""Resolve the current thread ID from runtime context or LangGraph config."""
|
||||
thread_id = runtime.context.get("thread_id") if runtime.context else None
|
||||
if thread_id is None:
|
||||
try:
|
||||
config_data = get_config()
|
||||
except RuntimeError:
|
||||
return None
|
||||
thread_id = config_data.get("configurable", {}).get("thread_id")
|
||||
return thread_id
|
||||
|
||||
|
||||
def _resolve_agent_name(runtime: Runtime) -> str | None:
|
||||
"""Resolve the current agent name from runtime context or LangGraph config."""
|
||||
agent_name = runtime.context.get("agent_name") if runtime.context else None
|
||||
if agent_name is None:
|
||||
try:
|
||||
config_data = get_config()
|
||||
except RuntimeError:
|
||||
return None
|
||||
agent_name = config_data.get("configurable", {}).get("agent_name")
|
||||
return agent_name
|
||||
|
||||
|
||||
class DeerFlowSummarizationMiddleware(SummarizationMiddleware):
|
||||
"""Summarization middleware with pre-compression hook dispatch."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
*args,
|
||||
before_summarization: list[BeforeSummarizationHook] | None = None,
|
||||
**kwargs,
|
||||
) -> None:
|
||||
super().__init__(*args, **kwargs)
|
||||
self._before_summarization_hooks = before_summarization or []
|
||||
|
||||
def before_model(self, state: AgentState, runtime: Runtime) -> dict | None:
|
||||
return self._maybe_summarize(state, runtime)
|
||||
|
||||
async def abefore_model(self, state: AgentState, runtime: Runtime) -> dict | None:
|
||||
return await self._amaybe_summarize(state, runtime)
|
||||
|
||||
def _maybe_summarize(self, state: AgentState, runtime: Runtime) -> dict | None:
|
||||
messages = state["messages"]
|
||||
self._ensure_message_ids(messages)
|
||||
|
||||
total_tokens = self.token_counter(messages)
|
||||
if not self._should_summarize(messages, total_tokens):
|
||||
return None
|
||||
|
||||
cutoff_index = self._determine_cutoff_index(messages)
|
||||
if cutoff_index <= 0:
|
||||
return None
|
||||
|
||||
messages_to_summarize, preserved_messages = self._partition_messages(messages, cutoff_index)
|
||||
self._fire_hooks(messages_to_summarize, preserved_messages, runtime)
|
||||
summary = self._create_summary(messages_to_summarize)
|
||||
new_messages = self._build_new_messages(summary)
|
||||
|
||||
return {
|
||||
"messages": [
|
||||
RemoveMessage(id=REMOVE_ALL_MESSAGES),
|
||||
*new_messages,
|
||||
*preserved_messages,
|
||||
]
|
||||
}
|
||||
|
||||
async def _amaybe_summarize(self, state: AgentState, runtime: Runtime) -> dict | None:
|
||||
messages = state["messages"]
|
||||
self._ensure_message_ids(messages)
|
||||
|
||||
total_tokens = self.token_counter(messages)
|
||||
if not self._should_summarize(messages, total_tokens):
|
||||
return None
|
||||
|
||||
cutoff_index = self._determine_cutoff_index(messages)
|
||||
if cutoff_index <= 0:
|
||||
return None
|
||||
|
||||
messages_to_summarize, preserved_messages = self._partition_messages(messages, cutoff_index)
|
||||
self._fire_hooks(messages_to_summarize, preserved_messages, runtime)
|
||||
summary = await self._acreate_summary(messages_to_summarize)
|
||||
new_messages = self._build_new_messages(summary)
|
||||
|
||||
return {
|
||||
"messages": [
|
||||
RemoveMessage(id=REMOVE_ALL_MESSAGES),
|
||||
*new_messages,
|
||||
*preserved_messages,
|
||||
]
|
||||
}
|
||||
|
||||
def _fire_hooks(
|
||||
self,
|
||||
messages_to_summarize: list[AnyMessage],
|
||||
preserved_messages: list[AnyMessage],
|
||||
runtime: Runtime,
|
||||
) -> None:
|
||||
if not self._before_summarization_hooks:
|
||||
return
|
||||
|
||||
event = SummarizationEvent(
|
||||
messages_to_summarize=tuple(messages_to_summarize),
|
||||
preserved_messages=tuple(preserved_messages),
|
||||
thread_id=_resolve_thread_id(runtime),
|
||||
agent_name=_resolve_agent_name(runtime),
|
||||
runtime=runtime,
|
||||
)
|
||||
|
||||
for hook in self._before_summarization_hooks:
|
||||
try:
|
||||
hook(event)
|
||||
except Exception:
|
||||
hook_name = getattr(hook, "__name__", None) or type(hook).__name__
|
||||
logger.exception("before_summarization hook %s failed", hook_name)
|
||||
Reference in New Issue
Block a user