98a6cba33e
重构了动作执行流程，以支持更丰富的多动作组合，例如在发送文本回复的同时发送一个表情。 主要变更： - **执行流程**: 在 `CycleProcessor` 中，将动作分为“回复”和“其他”两类。系统会先串行执行回复动作，再并行执行所有其他动作，确保了核心回复的优先性。 - **规划逻辑**: 在 `Planner` 中优化了提示词，并增加后处理步骤，以鼓励并确保在回复时触发补充性动作（如100%概率的emoji）。 - **emoji动作**: 重构了表情选择逻辑，现在会评估所有可用的表情，而不仅仅是随机抽样，提高了选择的准确性。 - **修复**: 修复了 `ActionModifier` 中随机激活概率为100%的动作可能不触发的bug。
474 lines
19 KiB
Python
474 lines
19 KiB
Python
import random
|
||
import asyncio
|
||
import hashlib
|
||
import time
|
||
from typing import List, Any, Dict, TYPE_CHECKING, Tuple
|
||
|
||
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.chat.message_receive.chat_stream import get_chat_manager, ChatMessageContext
|
||
from src.chat.planner_actions.action_manager import ActionManager
|
||
from src.chat.utils.chat_message_builder import get_raw_msg_before_timestamp_with_chat, build_readable_messages
|
||
from src.plugin_system.base.component_types import ActionInfo, ActionActivationType
|
||
from src.plugin_system.core.global_announcement_manager import global_announcement_manager
|
||
|
||
if TYPE_CHECKING:
|
||
from src.chat.message_receive.chat_stream import ChatStream
|
||
|
||
logger = get_logger("action_manager")
|
||
|
||
|
||
class ActionModifier:
|
||
"""动作处理器
|
||
|
||
用于处理Observation对象和根据激活类型处理actions。
|
||
集成了原有的modify_actions功能和新的激活类型处理功能。
|
||
支持并行判定和智能缓存优化。
|
||
"""
|
||
|
||
def __init__(self, action_manager: ActionManager, chat_id: str):
|
||
"""初始化动作处理器"""
|
||
self.chat_id = chat_id
|
||
self.chat_stream: ChatStream = get_chat_manager().get_stream(self.chat_id) # type: ignore
|
||
self.log_prefix = f"[{get_chat_manager().get_stream_name(self.chat_id) or self.chat_id}]"
|
||
|
||
self.action_manager = action_manager
|
||
|
||
# 用于LLM判定的小模型
|
||
self.llm_judge = LLMRequest(model_set=model_config.model_task_config.utils_small, request_type="action.judge")
|
||
|
||
# 缓存相关属性
|
||
self._llm_judge_cache = {} # 缓存LLM判定结果
|
||
self._cache_expiry_time = 30 # 缓存过期时间（秒）
|
||
self._last_context_hash = None # 上次上下文的哈希值
|
||
|
||
async def modify_actions(
|
||
self,
|
||
message_content: str = "",
|
||
): # sourcery skip: use-named-expression
|
||
"""
|
||
动作修改流程，整合传统观察处理和新的激活类型判定
|
||
|
||
这个方法处理完整的动作管理流程：
|
||
1. 基于观察的传统动作修改（循环历史分析、类型匹配等）
|
||
2. 基于激活类型的智能动作判定，最终确定可用动作集
|
||
|
||
处理后，ActionManager 将包含最终的可用动作集，供规划器直接使用
|
||
"""
|
||
logger.debug(f"{self.log_prefix}开始完整动作修改流程")
|
||
|
||
removals_s1: List[Tuple[str, str]] = []
|
||
removals_s2: List[Tuple[str, str]] = []
|
||
removals_s3: List[Tuple[str, str]] = []
|
||
|
||
self.action_manager.restore_actions()
|
||
all_actions = self.action_manager.get_using_actions()
|
||
|
||
# === 第0阶段：根据聊天类型过滤动作 ===
|
||
from src.plugin_system.base.component_types import ChatType
|
||
from src.plugin_system.core.component_registry import component_registry
|
||
from src.plugin_system.base.component_types import ComponentType
|
||
from src.chat.utils.utils import get_chat_type_and_target_info
|
||
|
||
# 获取聊天类型
|
||
is_group_chat, _ = get_chat_type_and_target_info(self.chat_id)
|
||
all_registered_actions = component_registry.get_components_by_type(ComponentType.ACTION)
|
||
|
||
chat_type_removals = []
|
||
for action_name in list(all_actions.keys()):
|
||
if action_name in all_registered_actions:
|
||
action_info = all_registered_actions[action_name]
|
||
chat_type_allow = getattr(action_info, "chat_type_allow", ChatType.ALL)
|
||
|
||
# 检查是否符合聊天类型限制
|
||
should_keep = (
|
||
chat_type_allow == ChatType.ALL
|
||
or (chat_type_allow == ChatType.GROUP and is_group_chat)
|
||
or (chat_type_allow == ChatType.PRIVATE and not is_group_chat)
|
||
)
|
||
|
||
if not should_keep:
|
||
chat_type_removals.append((action_name, f"不支持{'群聊' if is_group_chat else '私聊'}"))
|
||
self.action_manager.remove_action_from_using(action_name)
|
||
|
||
if chat_type_removals:
|
||
logger.info(f"{self.log_prefix} 第0阶段：根据聊天类型过滤 - 移除了 {len(chat_type_removals)} 个动作")
|
||
for action_name, reason in chat_type_removals:
|
||
logger.debug(f"{self.log_prefix} - 移除 {action_name}: {reason}")
|
||
|
||
message_list_before_now_half = get_raw_msg_before_timestamp_with_chat(
|
||
chat_id=self.chat_stream.stream_id,
|
||
timestamp=time.time(),
|
||
limit=min(int(global_config.chat.max_context_size * 0.33), 10),
|
||
)
|
||
chat_content = build_readable_messages(
|
||
message_list_before_now_half,
|
||
replace_bot_name=True,
|
||
merge_messages=False,
|
||
timestamp_mode="relative",
|
||
read_mark=0.0,
|
||
show_actions=True,
|
||
)
|
||
|
||
if message_content:
|
||
chat_content = chat_content + "\n" + f"现在，最新的消息是：{message_content}"
|
||
|
||
# === 第一阶段：去除用户自行禁用的 ===
|
||
disabled_actions = global_announcement_manager.get_disabled_chat_actions(self.chat_id)
|
||
if disabled_actions:
|
||
for disabled_action_name in disabled_actions:
|
||
if disabled_action_name in all_actions:
|
||
removals_s1.append((disabled_action_name, "用户自行禁用"))
|
||
self.action_manager.remove_action_from_using(disabled_action_name)
|
||
logger.debug(f"{self.log_prefix}阶段一移除动作: {disabled_action_name}，原因: 用户自行禁用")
|
||
|
||
# === 第二阶段：检查动作的关联类型 ===
|
||
chat_context = self.chat_stream.context
|
||
type_mismatched_actions = self._check_action_associated_types(all_actions, chat_context)
|
||
|
||
if type_mismatched_actions:
|
||
removals_s2.extend(type_mismatched_actions)
|
||
|
||
# 应用第二阶段的移除
|
||
for action_name, reason in removals_s2:
|
||
self.action_manager.remove_action_from_using(action_name)
|
||
logger.debug(f"{self.log_prefix}阶段二移除动作: {action_name}，原因: {reason}")
|
||
|
||
# === 第三阶段：激活类型判定 ===
|
||
if chat_content is not None:
|
||
logger.debug(f"{self.log_prefix}开始激活类型判定阶段")
|
||
|
||
# 获取当前使用的动作集（经过第一阶段处理）
|
||
current_using_actions = self.action_manager.get_using_actions()
|
||
|
||
# 获取因激活类型判定而需要移除的动作
|
||
removals_s3 = await self._get_deactivated_actions_by_type(
|
||
current_using_actions,
|
||
chat_content,
|
||
)
|
||
|
||
# 应用第三阶段的移除
|
||
for action_name, reason in removals_s3:
|
||
self.action_manager.remove_action_from_using(action_name)
|
||
logger.debug(f"{self.log_prefix}阶段三移除动作: {action_name}，原因: {reason}")
|
||
|
||
# === 统一日志记录 ===
|
||
all_removals = chat_type_removals + removals_s1 + removals_s2 + removals_s3
|
||
removals_summary: str = ""
|
||
if all_removals:
|
||
removals_summary = " | ".join([f"{name}({reason})" for name, reason in all_removals])
|
||
|
||
available_actions = list(self.action_manager.get_using_actions().keys())
|
||
available_actions_text = "、".join(available_actions) if available_actions else "无"
|
||
|
||
logger.info(f"{self.log_prefix} 当前可用动作: {available_actions_text}||移除: {removals_summary}")
|
||
|
||
def _check_action_associated_types(self, all_actions: Dict[str, ActionInfo], chat_context: ChatMessageContext):
|
||
type_mismatched_actions: List[Tuple[str, str]] = []
|
||
for action_name, action_info in all_actions.items():
|
||
if action_info.associated_types and not chat_context.check_types(action_info.associated_types):
|
||
associated_types_str = ", ".join(action_info.associated_types)
|
||
reason = f"适配器不支持（需要: {associated_types_str}）"
|
||
type_mismatched_actions.append((action_name, reason))
|
||
logger.debug(f"{self.log_prefix}决定移除动作: {action_name}，原因: {reason}")
|
||
return type_mismatched_actions
|
||
|
||
async def _get_deactivated_actions_by_type(
|
||
self,
|
||
actions_with_info: Dict[str, ActionInfo],
|
||
chat_content: str = "",
|
||
) -> List[tuple[str, str]]:
|
||
"""
|
||
根据激活类型过滤，返回需要停用的动作列表及原因
|
||
|
||
Args:
|
||
actions_with_info: 带完整信息的动作字典
|
||
chat_content: 聊天内容
|
||
|
||
Returns:
|
||
List[Tuple[str, str]]: 需要停用的 (action_name, reason) 元组列表
|
||
"""
|
||
deactivated_actions = []
|
||
|
||
# 分类处理不同激活类型的actions
|
||
llm_judge_actions = {}
|
||
|
||
actions_to_check = list(actions_with_info.items())
|
||
random.shuffle(actions_to_check)
|
||
|
||
for action_name, action_info in actions_to_check:
|
||
activation_type = action_info.activation_type or action_info.focus_activation_type
|
||
|
||
if activation_type == ActionActivationType.ALWAYS:
|
||
continue # 总是激活，无需处理
|
||
|
||
elif activation_type == ActionActivationType.RANDOM:
|
||
probability = action_info.random_activation_probability
|
||
if probability >= 1.0:
|
||
continue # 概率为100%或更高，直接激活
|
||
if random.random() > probability:
|
||
reason = f"RANDOM类型未触发（概率{probability}）"
|
||
deactivated_actions.append((action_name, reason))
|
||
logger.debug(f"{self.log_prefix}未激活动作: {action_name}，原因: {reason}")
|
||
|
||
elif activation_type == ActionActivationType.KEYWORD:
|
||
if not self._check_keyword_activation(action_name, action_info, chat_content):
|
||
keywords = action_info.activation_keywords
|
||
reason = f"关键词未匹配（关键词: {keywords}）"
|
||
deactivated_actions.append((action_name, reason))
|
||
logger.debug(f"{self.log_prefix}未激活动作: {action_name}，原因: {reason}")
|
||
|
||
elif activation_type == ActionActivationType.LLM_JUDGE:
|
||
llm_judge_actions[action_name] = action_info
|
||
|
||
elif activation_type == ActionActivationType.NEVER:
|
||
reason = "激活类型为never"
|
||
deactivated_actions.append((action_name, reason))
|
||
logger.debug(f"{self.log_prefix}未激活动作: {action_name}，原因: 激活类型为never")
|
||
|
||
else:
|
||
logger.warning(f"{self.log_prefix}未知的激活类型: {activation_type}，跳过处理")
|
||
|
||
# 并行处理LLM_JUDGE类型
|
||
if llm_judge_actions:
|
||
llm_results = await self._process_llm_judge_actions_parallel(
|
||
llm_judge_actions,
|
||
chat_content,
|
||
)
|
||
for action_name, should_activate in llm_results.items():
|
||
if not should_activate:
|
||
reason = "LLM判定未激活"
|
||
deactivated_actions.append((action_name, reason))
|
||
logger.debug(f"{self.log_prefix}未激活动作: {action_name}，原因: {reason}")
|
||
|
||
return deactivated_actions
|
||
|
||
def _generate_context_hash(self, chat_content: str) -> str:
|
||
"""生成上下文的哈希值用于缓存"""
|
||
context_content = f"{chat_content}"
|
||
return hashlib.md5(context_content.encode("utf-8")).hexdigest()
|
||
|
||
async def _process_llm_judge_actions_parallel(
|
||
self,
|
||
llm_judge_actions: Dict[str, Any],
|
||
chat_content: str = "",
|
||
) -> Dict[str, bool]:
|
||
"""
|
||
并行处理LLM判定actions，支持智能缓存
|
||
|
||
Args:
|
||
llm_judge_actions: 需要LLM判定的actions
|
||
chat_content: 聊天内容
|
||
|
||
Returns:
|
||
Dict[str, bool]: action名称到激活结果的映射
|
||
"""
|
||
|
||
# 生成当前上下文的哈希值
|
||
current_context_hash = self._generate_context_hash(chat_content)
|
||
current_time = time.time()
|
||
|
||
results = {}
|
||
tasks_to_run = {}
|
||
|
||
# 检查缓存
|
||
for action_name, action_info in llm_judge_actions.items():
|
||
cache_key = f"{action_name}_{current_context_hash}"
|
||
|
||
# 检查是否有有效的缓存
|
||
if (
|
||
cache_key in self._llm_judge_cache
|
||
and current_time - self._llm_judge_cache[cache_key]["timestamp"] < self._cache_expiry_time
|
||
):
|
||
results[action_name] = self._llm_judge_cache[cache_key]["result"]
|
||
logger.debug(
|
||
f"{self.log_prefix}使用缓存结果 {action_name}: {'激活' if results[action_name] else '未激活'}"
|
||
)
|
||
else:
|
||
# 需要进行LLM判定
|
||
tasks_to_run[action_name] = action_info
|
||
|
||
# 如果有需要运行的任务，并行执行
|
||
if tasks_to_run:
|
||
logger.debug(f"{self.log_prefix}并行执行LLM判定，任务数: {len(tasks_to_run)}")
|
||
|
||
# 创建并行任务
|
||
tasks = []
|
||
task_names = []
|
||
|
||
for action_name, action_info in tasks_to_run.items():
|
||
task = self._llm_judge_action(
|
||
action_name,
|
||
action_info,
|
||
chat_content,
|
||
)
|
||
tasks.append(task)
|
||
task_names.append(action_name)
|
||
|
||
# 并行执行所有任务
|
||
try:
|
||
task_results = await asyncio.gather(*tasks, return_exceptions=True)
|
||
|
||
# 处理结果并更新缓存
|
||
for action_name, result in zip(task_names, task_results, strict=False):
|
||
if isinstance(result, Exception):
|
||
logger.error(f"{self.log_prefix}LLM判定action {action_name} 时出错: {result}")
|
||
results[action_name] = False
|
||
else:
|
||
results[action_name] = result
|
||
|
||
# 更新缓存
|
||
cache_key = f"{action_name}_{current_context_hash}"
|
||
self._llm_judge_cache[cache_key] = {"result": result, "timestamp": current_time}
|
||
|
||
logger.debug(f"{self.log_prefix}并行LLM判定完成，耗时: {time.time() - current_time:.2f}s")
|
||
|
||
except Exception as e:
|
||
logger.error(f"{self.log_prefix}并行LLM判定失败: {e}")
|
||
# 如果并行执行失败，为所有任务返回False
|
||
for action_name in tasks_to_run:
|
||
results[action_name] = False
|
||
|
||
# 清理过期缓存
|
||
self._cleanup_expired_cache(current_time)
|
||
|
||
return results
|
||
|
||
def _cleanup_expired_cache(self, current_time: float):
|
||
"""清理过期的缓存条目"""
|
||
expired_keys = []
|
||
expired_keys.extend(
|
||
cache_key
|
||
for cache_key, cache_data in self._llm_judge_cache.items()
|
||
if current_time - cache_data["timestamp"] > self._cache_expiry_time
|
||
)
|
||
for key in expired_keys:
|
||
del self._llm_judge_cache[key]
|
||
|
||
if expired_keys:
|
||
logger.debug(f"{self.log_prefix}清理了 {len(expired_keys)} 个过期缓存条目")
|
||
|
||
async def _llm_judge_action(
|
||
self,
|
||
action_name: str,
|
||
action_info: ActionInfo,
|
||
chat_content: str = "",
|
||
) -> bool: # sourcery skip: move-assign-in-block, use-named-expression
|
||
"""
|
||
使用LLM判定是否应该激活某个action
|
||
|
||
Args:
|
||
action_name: 动作名称
|
||
action_info: 动作信息
|
||
observed_messages_str: 观察到的聊天消息
|
||
chat_context: 聊天上下文
|
||
extra_context: 额外上下文
|
||
|
||
Returns:
|
||
bool: 是否应该激活此action
|
||
"""
|
||
|
||
try:
|
||
# 构建判定提示词
|
||
action_description = action_info.description
|
||
action_require = action_info.action_require
|
||
custom_prompt = action_info.llm_judge_prompt
|
||
|
||
# 构建基础判定提示词
|
||
base_prompt = f"""
|
||
你需要判断在当前聊天情况下，是否应该激活名为"{action_name}"的动作。
|
||
|
||
动作描述：{action_description}
|
||
|
||
动作使用场景：
|
||
"""
|
||
for req in action_require:
|
||
base_prompt += f"- {req}\n"
|
||
|
||
if custom_prompt:
|
||
base_prompt += f"\n额外判定条件：\n{custom_prompt}\n"
|
||
|
||
if chat_content:
|
||
base_prompt += f"\n当前聊天记录：\n{chat_content}\n"
|
||
|
||
base_prompt += """
|
||
请根据以上信息判断是否应该激活这个动作。
|
||
只需要回答"是"或"否"，不要有其他内容。
|
||
"""
|
||
|
||
# 调用LLM进行判定
|
||
response, _ = await self.llm_judge.generate_response_async(prompt=base_prompt)
|
||
|
||
# 解析响应
|
||
response = response.strip().lower()
|
||
|
||
# print(base_prompt)
|
||
# print(f"LLM判定动作 {action_name}：响应='{response}'")
|
||
|
||
should_activate = "是" in response or "yes" in response or "true" in response
|
||
|
||
logger.debug(
|
||
f"{self.log_prefix}LLM判定动作 {action_name}：响应='{response}'，结果={'激活' if should_activate else '不激活'}"
|
||
)
|
||
return should_activate
|
||
|
||
except Exception as e:
|
||
logger.error(f"{self.log_prefix}LLM判定动作 {action_name} 时出错: {e}")
|
||
# 出错时默认不激活
|
||
return False
|
||
|
||
def _check_keyword_activation(
|
||
self,
|
||
action_name: str,
|
||
action_info: ActionInfo,
|
||
chat_content: str = "",
|
||
) -> bool:
|
||
"""
|
||
检查是否匹配关键词触发条件
|
||
|
||
Args:
|
||
action_name: 动作名称
|
||
action_info: 动作信息
|
||
observed_messages_str: 观察到的聊天消息
|
||
chat_context: 聊天上下文
|
||
extra_context: 额外上下文
|
||
|
||
Returns:
|
||
bool: 是否应该激活此action
|
||
"""
|
||
|
||
activation_keywords = action_info.activation_keywords
|
||
case_sensitive = action_info.keyword_case_sensitive
|
||
|
||
if not activation_keywords:
|
||
logger.warning(f"{self.log_prefix}动作 {action_name} 设置为关键词触发但未配置关键词")
|
||
return False
|
||
|
||
# 构建检索文本
|
||
search_text = ""
|
||
if chat_content:
|
||
search_text += chat_content
|
||
# if chat_context:
|
||
# search_text += f" {chat_context}"
|
||
# if extra_context:
|
||
# search_text += f" {extra_context}"
|
||
|
||
# 如果不区分大小写，转换为小写
|
||
if not case_sensitive:
|
||
search_text = search_text.lower()
|
||
|
||
# 检查每个关键词
|
||
matched_keywords = []
|
||
for keyword in activation_keywords:
|
||
check_keyword = keyword if case_sensitive else keyword.lower()
|
||
if check_keyword in search_text:
|
||
matched_keywords.append(keyword)
|
||
|
||
if matched_keywords:
|
||
logger.debug(f"{self.log_prefix}动作 {action_name} 匹配到关键词: {matched_keywords}")
|
||
return True
|
||
else:
|
||
logger.debug(f"{self.log_prefix}动作 {action_name} 未匹配到任何关键词: {activation_keywords}")
|
||
return False
|