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remove：移除utils_small模型，统一使用tool_use模型，移除弃用的LLM_judge类型

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This commit is contained in:
SengokuCola
2025-12-24 19:28:44 +08:00
parent a3c3fcf518
commit 7cbc2f1462
17 changed files with 309 additions and 255 deletions
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2
src/bw_learner/expression_learner.py
View File

@@ -89,7 +89,7 @@ class ExpressionLearner:
model_set=model_config.model_task_config.utils, request_type="expression.learner"
)
self.summary_model: LLMRequest = LLMRequest(
model_set=model_config.model_task_config.utils_small, request_type="expression.summary"
model_set=model_config.model_task_config.utils, request_type="expression.summary"
)
self.chat_id = chat_id
self.chat_stream = get_chat_manager().get_stream(chat_id)

2
src/bw_learner/expression_selector.py
View File

@@ -45,7 +45,7 @@ def init_prompt():
class ExpressionSelector:
def __init__(self):
self.llm_model = LLMRequest(
model_set=model_config.model_task_config.utils_small, request_type="expression.selector"
model_set=model_config.model_task_config.tool_use, request_type="expression.selector"
)
def can_use_expression_for_chat(self, chat_id: str) -> bool:

2
src/chat/brain_chat/brain_planner.py
View File

@@ -444,7 +444,7 @@ class BrainPlanner:
if action_info.activation_type == ActionActivationType.NEVER:
logger.debug(f"{self.log_prefix}动作 {action_name} 设置为 NEVER 激活类型，跳过")
continue
elif action_info.activation_type in [ActionActivationType.LLM_JUDGE, ActionActivationType.ALWAYS]:
elif action_info.activation_type == ActionActivationType.ALWAYS:
filtered_actions[action_name] = action_info
elif action_info.activation_type == ActionActivationType.RANDOM:
if random.random() < action_info.random_activation_probability:

205
src/chat/planner_actions/action_modifier.py
View File

@@ -1,12 +1,9 @@
import random
import asyncio
import hashlib
import time
from typing import List, 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.config.config import global_config
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
@@ -35,14 +32,6 @@ class ActionModifier:
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 = "",
@@ -159,9 +148,6 @@ class ActionModifier:
"""
deactivated_actions = []
# 分类处理不同激活类型的actions
llm_judge_actions: Dict[str, ActionInfo] = {}
actions_to_check = list(actions_with_info.items())
random.shuffle(actions_to_check)
@@ -185,9 +171,6 @@ class ActionModifier:
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))
@@ -196,194 +179,8 @@ class ActionModifier:
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, ActionInfo],
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: Dict[str, ActionInfo] = {}
# 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,

2
src/chat/planner_actions/planner.py
View File

@@ -591,7 +591,7 @@ class ActionPlanner:
if action_info.activation_type == ActionActivationType.NEVER:
logger.debug(f"{self.log_prefix}动作 {action_name} 设置为 NEVER 激活类型，跳过")
continue
elif action_info.activation_type in [ActionActivationType.LLM_JUDGE, ActionActivationType.ALWAYS]:
elif action_info.activation_type == ActionActivationType.ALWAYS:
filtered_actions[action_name] = action_info
elif action_info.activation_type == ActionActivationType.RANDOM:
if random.random() < action_info.random_activation_probability:

2
src/chat/replyer/private_generator.py
View File

@@ -774,7 +774,7 @@ class PrivateReplyer:
expression_habits_block, selected_expressions = results_dict["expression_habits"]
expression_habits_block: str
selected_expressions: List[int]
relation_info: str = results_dict["relation_info"]
relation_info: str = results_dict.get("relation_info") or ""
tool_info: str = results_dict["tool_info"]
prompt_info: str = results_dict["prompt_info"] # 直接使用格式化后的结果
actions_info: str = results_dict["actions_info"]

3
src/config/api_ada_configs.py
View File

@@ -105,9 +105,6 @@ class ModelTaskConfig(ConfigBase):
utils: TaskConfig
"""组件模型配置"""
utils_small: TaskConfig
"""组件小模型配置"""
replyer: TaskConfig
"""normal_chat首要回复模型模型配置"""

2
src/config/config.py
View File

@@ -57,7 +57,7 @@ TEMPLATE_DIR = os.path.join(PROJECT_ROOT, "template")
# 考虑到，实际上配置文件中的mai_version是不会自动更新的,所以采用硬编码
# 对该字段的更新，请严格参照语义化版本规范：https://semver.org/lang/zh-CN/
MMC_VERSION = "0.12.0"
MMC_VERSION = "0.12.1"
def get_key_comment(toml_table, key):

2
src/person_info/person_info.py
View File

@@ -19,7 +19,7 @@ from src.chat.message_receive.chat_stream import get_chat_manager
logger = get_logger("person_info")
relation_selection_model = LLMRequest(
model_set=model_config.model_task_config.utils_small, request_type="relation_selection"
model_set=model_config.model_task_config.tool_use, request_type="relation_selection"
)

4
src/plugin_system/base/base_action.py
View File

@@ -28,7 +28,6 @@ class BaseAction(ABC):
- keyword_case_sensitive: 关键词是否区分大小写
- parallel_action: 是否允许并行执行
- random_activation_probability: 随机激活概率
- llm_judge_prompt: LLM判断提示词
"""
def __init__(
@@ -81,8 +80,6 @@ class BaseAction(ABC):
"""激活类型"""
self.random_activation_probability: float = getattr(self.__class__, "random_activation_probability", 0.0)
"""当激活类型为RANDOM时的概率"""
self.llm_judge_prompt: str = getattr(self.__class__, "llm_judge_prompt", "") # 已弃用
"""协助LLM进行判断的Prompt"""
self.activation_keywords: list[str] = getattr(self.__class__, "activation_keywords", []).copy()
"""激活类型为KEYWORD时的KEYWORDS列表"""
self.keyword_case_sensitive: bool = getattr(self.__class__, "keyword_case_sensitive", False)
@@ -504,7 +501,6 @@ class BaseAction(ABC):
keyword_case_sensitive=getattr(cls, "keyword_case_sensitive", False),
parallel_action=getattr(cls, "parallel_action", True),
random_activation_probability=getattr(cls, "random_activation_probability", 0.0),
llm_judge_prompt=getattr(cls, "llm_judge_prompt", ""),
# 使用正确的字段名
action_parameters=getattr(cls, "action_parameters", {}).copy(),
action_require=getattr(cls, "action_require", []).copy(),

2
src/plugin_system/base/component_types.py
View File

@@ -33,7 +33,6 @@ class ActionActivationType(Enum):
NEVER = "never" # 从不激活（默认关闭）
ALWAYS = "always" # 默认参与到planner
LLM_JUDGE = "llm_judge" # LLM判定是否启动该action到planner
RANDOM = "random" # 随机启用action到planner
KEYWORD = "keyword" # 关键词触发启用action到planner
@@ -128,7 +127,6 @@ class ActionInfo(ComponentInfo):
normal_activation_type: ActionActivationType = ActionActivationType.ALWAYS # 已弃用
activation_type: ActionActivationType = ActionActivationType.ALWAYS
random_activation_probability: float = 0.0
llm_judge_prompt: str = ""
activation_keywords: List[str] = field(default_factory=list) # 激活关键词列表
keyword_case_sensitive: bool = False
# 模式和并行设置

2
src/plugins/built_in/tts_plugin/_manifest.json
View File

@@ -28,7 +28,7 @@
"type": "action",
"name": "tts_action",
"description": "将文本转换为语音进行播放",
"activation_modes": ["llm_judge", "keyword"],
"activation_modes": ["keyword"],
"keywords": ["语音", "tts", "播报", "读出来", "语音播放", "听", "朗读"]
}
],

4
src/plugins/built_in/tts_plugin/plugin.py
View File

@@ -13,7 +13,9 @@ class TTSAction(BaseAction):
"""TTS语音转换动作处理类"""
# 激活设置
activation_type = ActionActivationType.LLM_JUDGE
activation_type = ActionActivationType.KEYWORD
activation_keywords = ["语音", "tts", "播报", "读出来", "语音播放", "听", "朗读"]
keyword_case_sensitive = False
parallel_action = False
# 动作基本信息