chore: perform widespread code cleanup and formatting
Perform a comprehensive code cleanup across multiple modules to improve code quality, consistency, and maintainability. Key changes include: - Removing numerous unused imports. - Standardizing import order. - Eliminating trailing whitespace and inconsistent newlines. - Updating legacy type hints to modern syntax (e.g., `List` -> `list`). - Making minor improvements for code robustness and style.
This commit is contained in:
minecraft1024a
@@ -1,6 +1,6 @@
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import asyncio
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import time
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from typing import Any, TYPE_CHECKING
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from typing import TYPE_CHECKING, Any
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from src.chat.planner_actions.action_manager import ChatterActionManager
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from src.common.logger import get_logger
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@@ -6,7 +6,7 @@
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import asyncio
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import time
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from typing import Any, TYPE_CHECKING
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from typing import TYPE_CHECKING, Any
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from src.chat.energy_system import energy_manager
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from src.common.data_models.database_data_model import DatabaseMessages
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@@ -5,7 +5,7 @@
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import asyncio
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import time
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from typing import Any, TYPE_CHECKING
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from typing import TYPE_CHECKING, Any
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from src.chat.chatter_manager import ChatterManager
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from src.chat.energy_system import energy_manager
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@@ -115,12 +115,12 @@ class StreamLoopManager:
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if not context:
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logger.warning(f"无法获取流上下文: {stream_id}")
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return False
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# 快速路径：如果流已存在且不是强制启动，无需处理
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if not force and context.stream_loop_task and not context.stream_loop_task.done():
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logger.debug(f"🔄 [流循环] stream={stream_id[:8]}, 循环已在运行，跳过启动")
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return True
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# 获取或创建该流的启动锁
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if stream_id not in self._stream_start_locks:
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self._stream_start_locks[stream_id] = asyncio.Lock()
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@@ -12,7 +12,6 @@ from src.common.data_models.database_data_model import DatabaseMessages
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from src.common.database.core import get_db_session
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from src.common.database.core.models import Images, Messages
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from src.common.logger import get_logger
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from src.config.config import global_config
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from .chat_stream import ChatStream
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from .message import MessageSending
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@@ -242,9 +242,9 @@ class ChatterActionManager:
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}
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else:
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# 检查目标消息是否为表情包消息以及配置是否允许回复表情包
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if target_message and getattr(target_message, 'is_emoji', False):
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if target_message and getattr(target_message, "is_emoji", False):
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# 如果是表情包消息且配置不允许回复表情包，则跳过回复
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if not getattr(global_config.chat, 'allow_reply_to_emoji', True):
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if not getattr(global_config.chat, "allow_reply_to_emoji", True):
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logger.info(f"{log_prefix} 目标消息为表情包且配置不允许回复表情包，跳过回复")
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return {"action_type": action_name, "success": True, "reply_text": "", "skip_reason": "emoji_not_allowed"}
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@@ -376,7 +376,7 @@ class DefaultReplyer:
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if not prompt:
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logger.warning("构建prompt失败，跳过回复生成")
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return False, None, None
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from src.plugin_system.core.event_manager import event_manager
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# 触发 POST_LLM 事件（请求 LLM 之前）
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if not from_plugin:
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@@ -1878,8 +1878,8 @@ class DefaultReplyer:
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async def build_relation_info(self, sender: str, target: str):
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# 获取用户ID
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if sender == f"{global_config.bot.nickname}(你)":
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return f"你将要回复的是你自己发送的消息。"
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return "你将要回复的是你自己发送的消息。"
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person_info_manager = get_person_info_manager()
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person_id = await person_info_manager.get_person_id_by_person_name(sender)
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@@ -47,10 +47,10 @@ class BlockShuffler:
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# 复制上下文以避免修改原始字典
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shuffled_context = context_data.copy()
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# 示例：假设模板中的占位符格式为 {block_name}
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# 我们需要解析模板，找到可重排的组，并重新构建模板字符串。
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# 注意：这是一个复杂的逻辑，通常需要一个简单的模板引擎或正则表达式来完成。
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# 为保持此函数职责单一，这里仅演示核心的重排逻辑，
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# 完整的模板重建逻辑应在调用此函数的地方处理。
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@@ -58,14 +58,14 @@ class BlockShuffler:
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for group in BlockShuffler.SWAPPABLE_BLOCK_GROUPS:
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# 过滤出在当前上下文中实际存在的、非空的block
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existing_blocks = [
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block for block in group if block in context_data and context_data[block]
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block for block in group if context_data.get(block)
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]
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if len(existing_blocks) > 1:
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# 随机打乱顺序
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random.shuffle(existing_blocks)
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logger.debug(f"重排block组: {group} -> {existing_blocks}")
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# 这里的实现需要调用者根据 `existing_blocks` 的新顺序
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# 去动态地重新组织 `prompt_template` 字符串。
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# 例如，找到模板中与 `group` 相关的占位符部分，然后按新顺序替换它们。
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@@ -2,7 +2,6 @@ import asyncio
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import copy
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import re
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from collections.abc import Awaitable, Callable
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from typing import List
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from src.chat.utils.prompt_params import PromptParameters
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from src.common.logger import get_logger
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@@ -119,7 +118,7 @@ class PromptComponentManager:
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async def add_injection_rule(
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self,
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prompt_name: str,
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rules: List[InjectionRule],
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rules: list[InjectionRule],
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content_provider: Callable[..., Awaitable[str]],
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source: str = "runtime",
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) -> bool:
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@@ -521,7 +520,7 @@ class PromptComponentManager:
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else:
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for name, (rule, _, _) in rules_for_target.items():
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target_copy[name] = rule
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if target_copy:
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rules_copy[target] = target_copy
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@@ -63,7 +63,7 @@ class PromptParameters:
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action_descriptions: str = ""
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notice_block: str = ""
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group_chat_reminder_block: str = ""
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# 可用动作信息
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available_actions: dict[str, Any] | None = None
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@@ -228,9 +228,9 @@ class HTMLReportGenerator:
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# 渲染模板
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# 读取CSS和JS文件内容
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async with aiofiles.open(os.path.join(self.jinja_env.loader.searchpath[0], "report.css"), "r", encoding="utf-8") as f:
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async with aiofiles.open(os.path.join(self.jinja_env.loader.searchpath[0], "report.css"), encoding="utf-8") as f:
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report_css = await f.read()
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async with aiofiles.open(os.path.join(self.jinja_env.loader.searchpath[0], "report.js"), "r", encoding="utf-8") as f:
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async with aiofiles.open(os.path.join(self.jinja_env.loader.searchpath[0], "report.js"), encoding="utf-8") as f:
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report_js = await f.read()
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# 渲染模板
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template = self.jinja_env.get_template("report.html")
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@@ -3,8 +3,6 @@ from collections import defaultdict
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from datetime import datetime, timedelta
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from typing import Any
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import aiofiles
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from src.common.database.compatibility import db_get, db_query
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from src.common.database.core.models import LLMUsage, Messages, OnlineTime
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from src.common.logger import get_logger
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@@ -16,7 +14,7 @@ logger = get_logger("maibot_statistic")
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# 彻底异步化：删除原同步包装器 _sync_db_get，所有数据库访问统一使用 await db_get。
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from .report_generator import HTMLReportGenerator, format_online_time
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from .report_generator import HTMLReportGenerator
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from .statistic_keys import *
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@@ -1,4 +1,3 @@
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# -*- coding: utf-8 -*-
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"""
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该模块用于存放统计数据相关的常量键名。
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"""
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@@ -61,4 +60,4 @@ STD_TIME_COST_BY_PROVIDER = "std_time_costs_by_provider"
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PIE_CHART_COST_BY_PROVIDER = "pie_chart_cost_by_provider"
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PIE_CHART_REQ_BY_PROVIDER = "pie_chart_req_by_provider"
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BAR_CHART_COST_BY_MODEL = "bar_chart_cost_by_model"
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BAR_CHART_REQ_BY_MODEL = "bar_chart_req_by_model"
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BAR_CHART_REQ_BY_MODEL = "bar_chart_req_by_model"
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@@ -537,7 +537,7 @@ class _PromptProcessor:
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else:
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is_truncated = True
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return content, reasoning, is_truncated
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@staticmethod
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async def _extract_reasoning(content: str) -> tuple[str, str]:
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"""
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@@ -1,4 +1,5 @@
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# 再用这个就写一行注释来混提交的我直接全部🌿飞😡
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# 🌿🌿need
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import asyncio
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import signal
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import sys
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@@ -21,7 +22,6 @@ from src.common.message import get_global_api
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# 全局背景任务集合
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_background_tasks = set()
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from src.common.remote import TelemetryHeartBeatTask
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from src.common.server import Server, get_global_server
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from src.config.config import global_config
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from src.individuality.individuality import Individuality, get_individuality
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@@ -507,7 +507,7 @@ class PersistenceManager:
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GraphStore 对象
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"""
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try:
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async with aiofiles.open(input_file, "r", encoding="utf-8") as f:
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async with aiofiles.open(input_file, encoding="utf-8") as f:
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content = await f.read()
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data = json.loads(content)
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@@ -98,7 +98,7 @@ class MemoryTools:
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graph_store=graph_store,
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embedding_generator=embedding_generator,
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)
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# 初始化路径扩展器（延迟初始化，仅在启用时创建）
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self.path_expander: PathScoreExpansion | None = None
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@@ -573,7 +573,7 @@ class MemoryTools:
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# 检查是否启用路径扩展算法
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use_path_expansion = getattr(global_config.memory, "enable_path_expansion", False) and expand_depth > 0
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expanded_memory_scores = {}
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if expand_depth > 0 and initial_memory_ids:
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# 获取查询的embedding
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query_embedding = None
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@@ -582,12 +582,12 @@ class MemoryTools:
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query_embedding = await self.builder.embedding_generator.generate(query)
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except Exception as e:
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logger.warning(f"生成查询embedding失败: {e}")
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if query_embedding is not None:
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if use_path_expansion:
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# 🆕 使用路径评分扩展算法
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logger.info(f"🔬 使用路径评分扩展算法: 初始{len(similar_nodes)}个节点, 深度={expand_depth}")
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# 延迟初始化路径扩展器
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if self.path_expander is None:
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path_config = PathExpansionConfig(
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@@ -607,7 +607,7 @@ class MemoryTools:
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vector_store=self.vector_store,
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config=path_config
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)
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try:
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# 执行路径扩展（传递偏好类型）
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path_results = await self.path_expander.expand_with_path_scoring(
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@@ -616,11 +616,11 @@ class MemoryTools:
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top_k=top_k,
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prefer_node_types=all_prefer_types # 🆕 传递偏好类型
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)
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# 路径扩展返回的是 [(Memory, final_score, paths), ...]
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# 我们需要直接返回这些记忆，跳过后续的传统评分
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logger.info(f"✅ 路径扩展返回 {len(path_results)} 条记忆")
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# 直接构建返回结果
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path_memories = []
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for memory, score, paths in path_results:
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@@ -635,25 +635,25 @@ class MemoryTools:
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"max_path_depth": max(p.depth for p in paths) if paths else 0
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}
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})
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logger.info(f"🎯 路径扩展最终返回: {len(path_memories)} 条记忆")
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return {
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"success": True,
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"results": path_memories,
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"total": len(path_memories),
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"expansion_method": "path_scoring"
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}
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except Exception as e:
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logger.error(f"路径扩展失败: {e}", exc_info=True)
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logger.info("回退到传统图扩展算法")
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# 继续执行下面的传统图扩展
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# 传统图扩展（仅在未启用路径扩展或路径扩展失败时执行）
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if not use_path_expansion or expanded_memory_scores == {}:
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logger.info(f"开始传统图扩展: 初始记忆{len(initial_memory_ids)}个, 深度={expand_depth}")
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try:
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# 使用共享的图扩展工具函数
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expanded_results = await expand_memories_with_semantic_filter(
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@@ -9,10 +9,10 @@ from src.memory_graph.utils.time_parser import TimeParser
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__all__ = [
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"EmbeddingGenerator",
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"Path",
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"PathExpansionConfig",
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"PathScoreExpansion",
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"TimeParser",
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"cosine_similarity",
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"get_embedding_generator",
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"PathScoreExpansion",
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"PathExpansionConfig",
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"Path",
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]
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@@ -12,7 +12,7 @@ from src.common.logger import get_logger
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from src.memory_graph.utils.similarity import cosine_similarity
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if TYPE_CHECKING:
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from src.memory_graph.models import Memory
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pass
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logger = get_logger(__name__)
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@@ -41,52 +41,52 @@ async def deduplicate_memories_by_similarity(
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"""
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if len(memories) <= 1:
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return memories
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logger.info(f"开始记忆去重: {len(memories)} 条记忆 (阈值={similarity_threshold})")
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# 准备数据结构
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memory_embeddings = []
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for memory, score, extra in memories:
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# 获取记忆的向量表示
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embedding = await _get_memory_embedding(memory)
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memory_embeddings.append((memory, score, extra, embedding))
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# 构建相似度矩阵并找出重复组
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duplicate_groups = _find_duplicate_groups(memory_embeddings, similarity_threshold)
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# 合并每个重复组
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deduplicated = []
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processed_indices = set()
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for group_indices in duplicate_groups:
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if any(i in processed_indices for i in group_indices):
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continue # 已经处理过
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# 标记为已处理
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processed_indices.update(group_indices)
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# 合并组内记忆
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group_memories = [memory_embeddings[i] for i in group_indices]
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merged_memory = _merge_memory_group(group_memories)
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deduplicated.append(merged_memory)
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# 添加未被合并的记忆
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for i, (memory, score, extra, _) in enumerate(memory_embeddings):
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if i not in processed_indices:
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deduplicated.append((memory, score, extra))
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# 按分数排序
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deduplicated.sort(key=lambda x: x[1], reverse=True)
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# 限制数量
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if keep_top_n is not None:
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deduplicated = deduplicated[:keep_top_n]
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logger.info(
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f"去重完成: {len(memories)} → {len(deduplicated)} 条记忆 "
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f"(合并了 {len(memories) - len(deduplicated)} 条重复)"
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)
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return deduplicated
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@@ -104,7 +104,7 @@ async def _get_memory_embedding(memory: Any) -> list[float] | None:
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# nodes 是 MemoryNode 对象列表
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first_node = memory.nodes[0]
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node_id = getattr(first_node, "id", None)
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if node_id:
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# 直接从 embedding 属性获取（如果存在）
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if hasattr(first_node, "embedding") and first_node.embedding is not None:
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@@ -114,7 +114,7 @@ async def _get_memory_embedding(memory: Any) -> list[float] | None:
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return embedding.tolist()
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elif isinstance(embedding, list):
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return embedding
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# 无法获取 embedding
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return None
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@@ -132,13 +132,13 @@ def _find_duplicate_groups(
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"""
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n = len(memory_embeddings)
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similarity_matrix = [[0.0] * n for _ in range(n)]
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||||
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||||
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||||
# 计算相似度矩阵
|
||||
for i in range(n):
|
||||
for j in range(i + 1, n):
|
||||
embedding_i = memory_embeddings[i][3]
|
||||
embedding_j = memory_embeddings[j][3]
|
||||
|
||||
|
||||
# 跳过 None 或零向量
|
||||
if (embedding_i is None or embedding_j is None or
|
||||
all(x == 0.0 for x in embedding_i) or all(x == 0.0 for x in embedding_j)):
|
||||
@@ -146,29 +146,29 @@ def _find_duplicate_groups(
|
||||
else:
|
||||
# cosine_similarity 会自动转换为 numpy 数组
|
||||
similarity = float(cosine_similarity(embedding_i, embedding_j)) # type: ignore
|
||||
|
||||
|
||||
similarity_matrix[i][j] = similarity
|
||||
similarity_matrix[j][i] = similarity
|
||||
|
||||
|
||||
# 使用并查集找出连通分量
|
||||
parent = list(range(n))
|
||||
|
||||
|
||||
def find(x):
|
||||
if parent[x] != x:
|
||||
parent[x] = find(parent[x])
|
||||
return parent[x]
|
||||
|
||||
|
||||
def union(x, y):
|
||||
px, py = find(x), find(y)
|
||||
if px != py:
|
||||
parent[px] = py
|
||||
|
||||
|
||||
# 合并相似的记忆
|
||||
for i in range(n):
|
||||
for j in range(i + 1, n):
|
||||
if similarity_matrix[i][j] >= threshold:
|
||||
union(i, j)
|
||||
|
||||
|
||||
# 构建组
|
||||
groups_dict: dict[int, list[int]] = {}
|
||||
for i in range(n):
|
||||
@@ -176,10 +176,10 @@ def _find_duplicate_groups(
|
||||
if root not in groups_dict:
|
||||
groups_dict[root] = []
|
||||
groups_dict[root].append(i)
|
||||
|
||||
|
||||
# 只返回大小 > 1 的组（真正的重复组）
|
||||
duplicate_groups = [group for group in groups_dict.values() if len(group) > 1]
|
||||
|
||||
|
||||
return duplicate_groups
|
||||
|
||||
|
||||
@@ -196,10 +196,10 @@ def _merge_memory_group(
|
||||
"""
|
||||
# 按分数排序
|
||||
sorted_group = sorted(group, key=lambda x: x[1], reverse=True)
|
||||
|
||||
|
||||
# 保留分数最高的记忆
|
||||
best_memory, best_score, best_extra, _ = sorted_group[0]
|
||||
|
||||
|
||||
# 计算合并后的分数（加权平均，权重递减）
|
||||
total_weight = 0.0
|
||||
weighted_sum = 0.0
|
||||
@@ -207,17 +207,17 @@ def _merge_memory_group(
|
||||
weight = 1.0 / (i + 1) # 第1名权重1.0，第2名0.5，第3名0.33...
|
||||
weighted_sum += score * weight
|
||||
total_weight += weight
|
||||
|
||||
|
||||
merged_score = weighted_sum / total_weight if total_weight > 0 else best_score
|
||||
|
||||
|
||||
# 增强 extra_data
|
||||
merged_extra = best_extra if isinstance(best_extra, dict) else {}
|
||||
merged_extra["merged_count"] = len(sorted_group)
|
||||
merged_extra["original_scores"] = [score for _, score, _, _ in sorted_group]
|
||||
|
||||
|
||||
logger.debug(
|
||||
f"合并 {len(sorted_group)} 条相似记忆: "
|
||||
f"分数 {best_score:.3f} → {merged_score:.3f}"
|
||||
)
|
||||
|
||||
|
||||
return (best_memory, merged_score, merged_extra)
|
||||
|
||||
@@ -26,7 +26,6 @@ from src.memory_graph.utils.similarity import cosine_similarity
|
||||
if TYPE_CHECKING:
|
||||
import numpy as np
|
||||
|
||||
from src.memory_graph.models import Memory
|
||||
from src.memory_graph.storage.graph_store import GraphStore
|
||||
from src.memory_graph.storage.vector_store import VectorStore
|
||||
|
||||
@@ -71,7 +70,7 @@ class PathExpansionConfig:
|
||||
medium_score_threshold: float = 0.4 # 中分路径阈值
|
||||
max_active_paths: int = 1000 # 最大活跃路径数（防止爆炸）
|
||||
top_paths_retain: int = 500 # 超限时保留的top路径数
|
||||
|
||||
|
||||
# 🚀 性能优化参数
|
||||
enable_early_stop: bool = True # 启用早停（如果路径增长很少则提前结束）
|
||||
early_stop_growth_threshold: float = 0.1 # 早停阈值（路径增长率低于10%则停止）
|
||||
@@ -121,7 +120,7 @@ class PathScoreExpansion:
|
||||
self.vector_store = vector_store
|
||||
self.config = config or PathExpansionConfig()
|
||||
self.prefer_node_types: list[str] = [] # 🆕 偏好节点类型
|
||||
|
||||
|
||||
# 🚀 性能优化：邻居边缓存
|
||||
self._neighbor_cache: dict[str, list[Any]] = {}
|
||||
self._node_score_cache: dict[str, float] = {}
|
||||
@@ -212,11 +211,11 @@ class PathScoreExpansion:
|
||||
continue
|
||||
|
||||
edge_weight = self._get_edge_weight(edge)
|
||||
|
||||
|
||||
# 记录候选
|
||||
path_candidates.append((path, edge, next_node, edge_weight))
|
||||
candidate_nodes_for_batch.add(next_node)
|
||||
|
||||
|
||||
branch_count += 1
|
||||
if branch_count >= max_branches:
|
||||
break
|
||||
@@ -281,7 +280,7 @@ class PathScoreExpansion:
|
||||
# 🚀 早停检测：如果路径增长很少，提前终止
|
||||
prev_path_count = len(active_paths)
|
||||
active_paths = next_paths
|
||||
|
||||
|
||||
if self.config.enable_early_stop and prev_path_count > 0:
|
||||
growth_rate = (len(active_paths) - prev_path_count) / prev_path_count
|
||||
if growth_rate < self.config.early_stop_growth_threshold:
|
||||
@@ -346,18 +345,18 @@ class PathScoreExpansion:
|
||||
max_path_score = max(p.score for p in paths) if paths else 0
|
||||
rough_score = len(paths) * max_path_score * memory.importance
|
||||
memory_scores_rough.append((mem_id, rough_score))
|
||||
|
||||
|
||||
# 保留top候选
|
||||
memory_scores_rough.sort(key=lambda x: x[1], reverse=True)
|
||||
retained_mem_ids = set(mem_id for mem_id, _ in memory_scores_rough[:self.config.max_candidate_memories])
|
||||
|
||||
|
||||
# 过滤
|
||||
memory_paths = {
|
||||
mem_id: (memory, paths)
|
||||
for mem_id, (memory, paths) in memory_paths.items()
|
||||
if mem_id in retained_mem_ids
|
||||
}
|
||||
|
||||
|
||||
logger.info(
|
||||
f"⚡ 粗排过滤: {len(memory_scores_rough)} → {len(memory_paths)} 条候选记忆"
|
||||
)
|
||||
@@ -398,7 +397,7 @@ class PathScoreExpansion:
|
||||
# 🚀 缓存检查
|
||||
if node_id in self._neighbor_cache:
|
||||
return self._neighbor_cache[node_id]
|
||||
|
||||
|
||||
edges = []
|
||||
|
||||
# 从图存储中获取与该节点相关的所有边
|
||||
@@ -454,7 +453,7 @@ class PathScoreExpansion:
|
||||
"""
|
||||
# 从向量存储获取节点数据
|
||||
node_data = await self.vector_store.get_node_by_id(node_id)
|
||||
|
||||
|
||||
if query_embedding is None:
|
||||
base_score = 0.5 # 默认中等分数
|
||||
else:
|
||||
@@ -493,27 +492,27 @@ class PathScoreExpansion:
|
||||
import numpy as np
|
||||
|
||||
scores = {}
|
||||
|
||||
|
||||
if query_embedding is None:
|
||||
# 无查询向量时，返回默认分数
|
||||
return {nid: 0.5 for nid in node_ids}
|
||||
|
||||
return dict.fromkeys(node_ids, 0.5)
|
||||
|
||||
# 批量获取节点数据
|
||||
node_data_list = await asyncio.gather(
|
||||
*[self.vector_store.get_node_by_id(nid) for nid in node_ids],
|
||||
return_exceptions=True
|
||||
)
|
||||
|
||||
|
||||
# 收集有效的嵌入向量
|
||||
valid_embeddings = []
|
||||
valid_node_ids = []
|
||||
node_metadata_map = {}
|
||||
|
||||
|
||||
for nid, node_data in zip(node_ids, node_data_list):
|
||||
if isinstance(node_data, Exception):
|
||||
scores[nid] = 0.3
|
||||
continue
|
||||
|
||||
|
||||
# 类型守卫：确保 node_data 是字典
|
||||
if not node_data or not isinstance(node_data, dict) or "embedding" not in node_data:
|
||||
scores[nid] = 0.3
|
||||
@@ -521,21 +520,21 @@ class PathScoreExpansion:
|
||||
valid_embeddings.append(node_data["embedding"])
|
||||
valid_node_ids.append(nid)
|
||||
node_metadata_map[nid] = node_data.get("metadata", {})
|
||||
|
||||
|
||||
if valid_embeddings:
|
||||
# 批量计算相似度（使用矩阵运算）
|
||||
embeddings_matrix = np.array(valid_embeddings)
|
||||
query_norm = np.linalg.norm(query_embedding)
|
||||
embeddings_norms = np.linalg.norm(embeddings_matrix, axis=1)
|
||||
|
||||
|
||||
# 向量化计算余弦相似度
|
||||
similarities = np.dot(embeddings_matrix, query_embedding) / (embeddings_norms * query_norm + 1e-8)
|
||||
similarities = np.clip(similarities, 0.0, 1.0)
|
||||
|
||||
|
||||
# 应用偏好类型加成
|
||||
for nid, sim in zip(valid_node_ids, similarities):
|
||||
base_score = float(sim)
|
||||
|
||||
|
||||
# 偏好类型加成
|
||||
if self.prefer_node_types and nid in node_metadata_map:
|
||||
node_type = node_metadata_map[nid].get("node_type")
|
||||
@@ -546,7 +545,7 @@ class PathScoreExpansion:
|
||||
scores[nid] = base_score
|
||||
else:
|
||||
scores[nid] = base_score
|
||||
|
||||
|
||||
return scores
|
||||
|
||||
def _calculate_path_score(self, old_score: float, edge_weight: float, node_score: float, depth: int) -> float:
|
||||
@@ -689,19 +688,19 @@ class PathScoreExpansion:
|
||||
# 使用临时字典存储路径列表
|
||||
temp_paths: dict[str, list[Path]] = {}
|
||||
temp_memories: dict[str, Any] = {} # 存储 Memory 对象
|
||||
|
||||
|
||||
# 🚀 性能优化：收集所有需要获取的记忆ID，然后批量获取
|
||||
all_memory_ids = set()
|
||||
path_to_memory_ids: dict[int, set[str]] = {} # path对象id -> 记忆ID集合
|
||||
|
||||
for path in paths:
|
||||
memory_ids_in_path = set()
|
||||
|
||||
|
||||
# 收集路径中所有节点涉及的记忆
|
||||
for node_id in path.nodes:
|
||||
memory_ids = self.graph_store.node_to_memories.get(node_id, [])
|
||||
memory_ids_in_path.update(memory_ids)
|
||||
|
||||
|
||||
all_memory_ids.update(memory_ids_in_path)
|
||||
path_to_memory_ids[id(path)] = memory_ids_in_path
|
||||
|
||||
@@ -712,11 +711,11 @@ class PathScoreExpansion:
|
||||
memory = self.graph_store.get_memory_by_id(mem_id)
|
||||
if memory:
|
||||
memory_cache[mem_id] = memory
|
||||
|
||||
|
||||
# 构建映射关系
|
||||
for path in paths:
|
||||
memory_ids_in_path = path_to_memory_ids[id(path)]
|
||||
|
||||
|
||||
for mem_id in memory_ids_in_path:
|
||||
if mem_id in memory_cache:
|
||||
if mem_id not in temp_paths:
|
||||
@@ -745,10 +744,10 @@ class PathScoreExpansion:
|
||||
[(Memory, final_score, paths), ...]
|
||||
"""
|
||||
scored_memories = []
|
||||
|
||||
|
||||
# 🚀 性能优化：如果需要偏好类型加成，批量预加载所有节点的类型信息
|
||||
node_type_cache: dict[str, str | None] = {}
|
||||
|
||||
|
||||
if self.prefer_node_types:
|
||||
# 收集所有需要查询的节点ID
|
||||
all_node_ids = set()
|
||||
@@ -757,7 +756,7 @@ class PathScoreExpansion:
|
||||
for node in memory_nodes:
|
||||
node_id = node.id if hasattr(node, "id") else str(node)
|
||||
all_node_ids.add(node_id)
|
||||
|
||||
|
||||
# 批量获取节点数据
|
||||
if all_node_ids:
|
||||
logger.debug(f"🔍 批量预加载 {len(all_node_ids)} 个节点的类型信息")
|
||||
@@ -765,7 +764,7 @@ class PathScoreExpansion:
|
||||
*[self.vector_store.get_node_by_id(nid) for nid in all_node_ids],
|
||||
return_exceptions=True
|
||||
)
|
||||
|
||||
|
||||
# 构建类型缓存
|
||||
for nid, node_data in zip(all_node_ids, node_data_list):
|
||||
if isinstance(node_data, Exception) or not node_data or not isinstance(node_data, dict):
|
||||
@@ -805,7 +804,7 @@ class PathScoreExpansion:
|
||||
node_type = node_type_cache.get(node_id)
|
||||
if node_type and node_type in self.prefer_node_types:
|
||||
matched_count += 1
|
||||
|
||||
|
||||
if matched_count > 0:
|
||||
match_ratio = matched_count / len(memory_nodes)
|
||||
# 根据匹配比例给予加成（最高10%）
|
||||
@@ -870,4 +869,4 @@ class PathScoreExpansion:
|
||||
return recency_score
|
||||
|
||||
|
||||
__all__ = ["PathScoreExpansion", "PathExpansionConfig", "Path"]
|
||||
__all__ = ["Path", "PathExpansionConfig", "PathScoreExpansion"]
|
||||
|
||||
@@ -269,7 +269,7 @@ class RelationshipFetcher:
|
||||
platform = "unknown"
|
||||
if existing_stream:
|
||||
# 从现有记录获取platform
|
||||
platform = getattr(existing_stream, 'platform', 'unknown') or "unknown"
|
||||
platform = getattr(existing_stream, "platform", "unknown") or "unknown"
|
||||
logger.debug(f"从现有ChatStream获取到platform: {platform}, stream_id: {stream_id}")
|
||||
else:
|
||||
logger.debug(f"未找到现有ChatStream记录，使用默认platform: unknown, stream_id: {stream_id}")
|
||||
|
||||
@@ -742,7 +742,7 @@ class BaseAction(ABC):
|
||||
if not case_sensitive:
|
||||
search_text = search_text.lower()
|
||||
|
||||
matched_keywords: ClassVar = []
|
||||
matched_keywords = []
|
||||
for keyword in keywords:
|
||||
check_keyword = keyword if case_sensitive else keyword.lower()
|
||||
if check_keyword in search_text:
|
||||
|
||||
@@ -9,6 +9,7 @@ from datetime import datetime
|
||||
from typing import Any
|
||||
|
||||
import orjson
|
||||
from json_repair import repair_json
|
||||
|
||||
from src.chat.utils.chat_message_builder import (
|
||||
build_readable_messages_with_id,
|
||||
@@ -19,7 +20,6 @@ from src.common.logger import get_logger
|
||||
from src.config.config import global_config, model_config
|
||||
from src.llm_models.utils_model import LLMRequest
|
||||
from src.mood.mood_manager import mood_manager
|
||||
from json_repair import repair_json
|
||||
from src.plugin_system.base.component_types import ActionInfo, ChatType
|
||||
from src.schedule.schedule_manager import schedule_manager
|
||||
|
||||
@@ -144,7 +144,7 @@ class ChatterPlanFilter:
|
||||
plan.decided_actions = [
|
||||
ActionPlannerInfo(action_type="no_action", reasoning=f"筛选时出错: {e}")
|
||||
]
|
||||
|
||||
|
||||
# 在返回最终计划前，打印将要执行的动作
|
||||
if plan.decided_actions:
|
||||
action_types = [action.action_type for action in plan.decided_actions]
|
||||
@@ -631,7 +631,6 @@ class ChatterPlanFilter:
|
||||
candidate_ids.add(normalized_id[1:])
|
||||
|
||||
# 处理包含在文本中的ID格式 (如 "消息m123" -> 提取 m123)
|
||||
import re
|
||||
|
||||
# 尝试提取各种格式的ID
|
||||
id_patterns = [
|
||||
|
||||
@@ -10,7 +10,6 @@ from src.common.data_models.database_data_model import DatabaseMessages
|
||||
from src.common.data_models.info_data_model import Plan, TargetPersonInfo
|
||||
from src.config.config import global_config
|
||||
from src.plugin_system.base.component_types import ActionInfo, ChatMode, ChatType
|
||||
from src.plugin_system.core.component_registry import component_registry
|
||||
|
||||
|
||||
class ChatterPlanGenerator:
|
||||
|
||||
@@ -201,7 +201,7 @@ class ChatterActionPlanner:
|
||||
available_actions = list(initial_plan.available_actions.keys())
|
||||
plan_filter = ChatterPlanFilter(self.chat_id, available_actions)
|
||||
filtered_plan = await plan_filter.filter(initial_plan)
|
||||
|
||||
|
||||
# 检查reply动作是否可用
|
||||
has_reply_action = "reply" in available_actions or "respond" in available_actions
|
||||
if filtered_plan.decided_actions and has_reply_action and reply_not_available:
|
||||
|
||||
@@ -320,7 +320,7 @@ class QZoneService:
|
||||
return
|
||||
|
||||
# 1. 将评论分为用户评论和自己的回复
|
||||
user_comments = [c for c in comments if str(c.get("qq_account")) != str(qq_account)]
|
||||
user_comments = [c for c in comments if str(c.get("qq_account")) != str(qq_account)]
|
||||
|
||||
if not user_comments:
|
||||
return
|
||||
|
||||
@@ -295,7 +295,7 @@ class SystemCommand(PlusCommand):
|
||||
if injections:
|
||||
response_parts.append(f"🎯 **{target}** (注入源):")
|
||||
for inj in injections:
|
||||
source_tag = f"({inj['source']})" if inj['source'] != 'static_default' else ''
|
||||
source_tag = f"({inj['source']})" if inj["source"] != "static_default" else ""
|
||||
response_parts.append(f" ⎿ `{inj['name']}` (优先级: {inj['priority']}) {source_tag}")
|
||||
else:
|
||||
response_parts.append(f"🎯 **{target}** (无注入)")
|
||||
|
||||
Reference in New Issue
Block a user