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feat：拆分重命名模型配置，修复动作恢复问题

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This commit is contained in:
SengokuCola
2025-05-27 14:28:41 +08:00
parent 5b8e4c0690
commit 0391111c82
19 changed files with 119 additions and 118 deletions
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6
src/chat/focus_chat/expressors/default_expressor.py
View File

@@ -78,10 +78,10 @@ class DefaultExpressor:
self.log_prefix = "expressor"
# TODO: API-Adapter修改标记
self.express_model = LLMRequest(
model=global_config.model.normal,
temperature=global_config.model.normal["temp"],
model=global_config.model.focus_expressor,
temperature=global_config.model.focus_expressor["temp"],
max_tokens=256,
request_type="response_heartflow",
request_type="focus_expressor",
)
self.heart_fc_sender = HeartFCSender()

3
src/chat/focus_chat/info_processors/action_processor.py
View File

@@ -27,9 +27,6 @@ class ActionProcessor(BaseProcessor):
"""初始化观察处理器"""
super().__init__()
# TODO: API-Adapter修改标记
self.model_summary = LLMRequest(
model=global_config.model.observation, temperature=0.7, max_tokens=300, request_type="chat_observation"
)
async def process_info(
self,

6
src/chat/focus_chat/info_processors/mind_processor.py
View File

@@ -71,10 +71,10 @@ class MindProcessor(BaseProcessor):
self.subheartflow_id = subheartflow_id
self.llm_model = LLMRequest(
model=global_config.model.sub_heartflow,
temperature=global_config.model.sub_heartflow["temp"],
model=global_config.model.focus_chat_mind,
temperature=global_config.model.focus_chat_mind["temp"],
max_tokens=800,
request_type="sub_heart_flow",
request_type="focus_chat_mind",
)
self.current_mind = ""

6
src/chat/focus_chat/info_processors/self_processor.py
View File

@@ -54,10 +54,10 @@ class SelfProcessor(BaseProcessor):
self.subheartflow_id = subheartflow_id
self.llm_model = LLMRequest(
model=global_config.model.sub_heartflow,
temperature=global_config.model.sub_heartflow["temp"],
model=global_config.model.focus_self_recognize,
temperature=global_config.model.focus_self_recognize["temp"],
max_tokens=800,
request_type="self_identify",
request_type="focus_self_identify",
)
name = chat_manager.get_stream_name(self.subheartflow_id)

4
src/chat/focus_chat/info_processors/tool_processor.py
View File

@@ -49,9 +49,9 @@ class ToolProcessor(BaseProcessor):
self.subheartflow_id = subheartflow_id
self.log_prefix = f"[{subheartflow_id}:ToolExecutor] "
self.llm_model = LLMRequest(
model=global_config.model.tool_use,
model=global_config.model.focus_tool_use,
max_tokens=500,
request_type="tool_execution",
request_type="focus_tool",
)
self.structured_info = []

6
src/chat/focus_chat/info_processors/working_memory_processor.py
View File

@@ -61,10 +61,10 @@ class WorkingMemoryProcessor(BaseProcessor):
self.subheartflow_id = subheartflow_id
self.llm_model = LLMRequest(
model=global_config.model.sub_heartflow,
temperature=global_config.model.sub_heartflow["temp"],
model=global_config.model.focus_chat_mind,
temperature=global_config.model.focus_chat_mind["temp"],
max_tokens=800,
request_type="working_memory",
request_type="focus_working_memory",
)
name = chat_manager.get_stream_name(self.subheartflow_id)

2
src/chat/focus_chat/memory_activator.py
View File

@@ -36,7 +36,7 @@ class MemoryActivator:
def __init__(self):
# TODO: API-Adapter修改标记
self.summary_model = LLMRequest(
model=global_config.model.summary, temperature=0.7, max_tokens=50, request_type="chat_observation"
model=global_config.model.memory_summary, temperature=0.7, max_tokens=50, request_type="chat_observation"
)
self.running_memory = []

15
src/chat/focus_chat/planners/action_manager.py
View File

@@ -28,8 +28,7 @@ class ActionManager:
self._registered_actions: Dict[str, ActionInfo] = {}
# 当前正在使用的动作集合，默认加载默认动作
self._using_actions: Dict[str, ActionInfo] = {}
# 临时备份原始使用中的动作
self._original_actions_backup: Optional[Dict[str, ActionInfo]] = None
# 默认动作集，仅作为快照，用于恢复默认
self._default_actions: Dict[str, ActionInfo] = {}
@@ -278,22 +277,18 @@ class ActionManager:
return True
def temporarily_remove_actions(self, actions_to_remove: List[str]) -> None:
"""临时移除使用集中的指定动作，备份原始使用集"""
if self._original_actions_backup is None:
self._original_actions_backup = self._using_actions.copy()
"""临时移除使用集中的指定动作"""
for name in actions_to_remove:
self._using_actions.pop(name, None)
def restore_actions(self) -> None:
"""恢复之前备份的原始使用集"""
if self._original_actions_backup is not None:
self._using_actions = self._original_actions_backup.copy()
self._original_actions_backup = None
"""恢复到默认动作集"""
logger.debug(f"恢复动作集: 从 {list(self._using_actions.keys())} 恢复到默认动作集 {list(self._default_actions.keys())}")
self._using_actions = self._default_actions.copy()
def restore_default_actions(self) -> None:
"""恢复默认动作集到使用集"""
self._using_actions = self._default_actions.copy()
self._original_actions_backup = None
def get_action(self, action_name: str) -> Optional[Type[BaseAction]]:
"""

12
src/chat/focus_chat/planners/planner.py
View File

@@ -78,9 +78,9 @@ class ActionPlanner:
self.log_prefix = log_prefix
# LLM规划器配置
self.planner_llm = LLMRequest(
model=global_config.model.plan,
model=global_config.model.focus_planner,
max_tokens=1000,
request_type="action_planning", # 用于动作规划
request_type="focus_planner", # 用于动作规划
)
self.action_manager = action_manager
@@ -161,6 +161,10 @@ class ActionPlanner:
action = "no_reply"
reasoning = "没有可用的动作" if not current_available_actions else "只有no_reply动作可用，跳过规划"
logger.info(f"{self.log_prefix}{reasoning}")
self.action_manager.restore_actions()
logger.debug(
f"{self.log_prefix}恢复到默认动作集, 当前可用: {list(self.action_manager.get_using_actions().keys())}"
)
return {
"action_result": {"action_type": action, "action_data": action_data, "reasoning": reasoning},
"current_mind": current_mind,
@@ -241,10 +245,10 @@ class ActionPlanner:
f"{self.log_prefix}规划器Prompt:\n{prompt}\n\n决策动作:{action},\n动作信息: '{action_data}'\n理由: {reasoning}"
)
# 恢复原始动作集
# 恢复到默认动作集
self.action_manager.restore_actions()
logger.debug(
f"{self.log_prefix}恢复了原始动作集, 当前可用: {list(self.action_manager.get_using_actions().keys())}"
f"{self.log_prefix}恢复到默认动作集, 当前可用: {list(self.action_manager.get_using_actions().keys())}"
)
action_result = {"action_type": action, "action_data": action_data, "reasoning": reasoning}

2
src/chat/focus_chat/working_memory/memory_manager.py
View File

@@ -33,7 +33,7 @@ class MemoryManager:
self._id_map: Dict[str, MemoryItem] = {}
self.llm_summarizer = LLMRequest(
model=global_config.model.summary, temperature=0.3, max_tokens=512, request_type="memory_summarization"
model=global_config.model.focus_working_memory, temperature=0.3, max_tokens=512, request_type="memory_summarization"
)
@property

36
src/chat/heart_flow/background_tasks.py
View File

@@ -88,34 +88,34 @@ class BackgroundTaskManager:
f"聊天状态更新任务已启动 间隔:{STATE_UPDATE_INTERVAL_SECONDS}s",
"_state_update_task",
),
(
self._run_cleanup_cycle,
"info",
f"清理任务已启动 间隔:{CLEANUP_INTERVAL_SECONDS}s",
"_cleanup_task",
),
# 新增私聊激活任务配置
(
# Use lambda to pass the interval to the runner function
lambda: self._run_private_chat_activation_cycle(PRIVATE_CHAT_ACTIVATION_CHECK_INTERVAL_SECONDS),
"debug",
f"私聊激活检查任务已启动 间隔:{PRIVATE_CHAT_ACTIVATION_CHECK_INTERVAL_SECONDS}s",
"_private_chat_activation_task",
),
]
# 根据 chat_mode 条件添加专注评估任务
# 根据 chat_mode 条件添加其他任务
if not (global_config.chat.chat_mode == "normal"):
task_configs.append(
task_configs.extend([
(
self._run_cleanup_cycle,
"info",
f"清理任务已启动 间隔:{CLEANUP_INTERVAL_SECONDS}s",
"_cleanup_task",
),
# 新增私聊激活任务配置
(
# Use lambda to pass the interval to the runner function
lambda: self._run_private_chat_activation_cycle(PRIVATE_CHAT_ACTIVATION_CHECK_INTERVAL_SECONDS),
"debug",
f"私聊激活检查任务已启动 间隔:{PRIVATE_CHAT_ACTIVATION_CHECK_INTERVAL_SECONDS}s",
"_private_chat_activation_task",
),
(
self._run_into_focus_cycle,
"debug", # 设为debug，避免过多日志
f"专注评估任务已启动 间隔:{INTEREST_EVAL_INTERVAL_SECONDS}s",
"_into_focus_task",
)
)
])
else:
logger.info("聊天模式为 normal，跳过启动专注评估任务")
logger.info("聊天模式为 normal，跳过启动清理任务、私聊激活任务和专注评估任务")
# 统一启动所有任务
for task_func, log_level, log_msg, task_attr_name in task_configs:

4
src/chat/heart_flow/observation/chatting_observation.py
View File

@@ -66,10 +66,6 @@ class ChattingObservation(Observation):
self.oldest_messages = []
self.oldest_messages_str = ""
self.compressor_prompt = ""
# TODO: API-Adapter修改标记
self.model_summary = LLMRequest(
model=global_config.model.observation, temperature=0.7, max_tokens=300, request_type="chat_observation"
)
async def initialize(self):
self.is_group_chat, self.chat_target_info = await get_chat_type_and_target_info(self.chat_id)

10
src/chat/memory_system/Hippocampus.py
View File

@@ -193,7 +193,6 @@ class MemoryGraph:
class Hippocampus:
def __init__(self):
self.memory_graph = MemoryGraph()
self.llm_topic_judge = None
self.model_summary = None
self.entorhinal_cortex = None
self.parahippocampal_gyrus = None
@@ -205,8 +204,7 @@ class Hippocampus:
# 从数据库加载记忆图
self.entorhinal_cortex.sync_memory_from_db()
# TODO: API-Adapter修改标记
self.llm_topic_judge = LLMRequest(global_config.model.topic_judge, request_type="memory")
self.model_summary = LLMRequest(global_config.model.summary, request_type="memory")
self.model_summary = LLMRequest(global_config.model.memory_summary, request_type="memory")
def get_all_node_names(self) -> list:
"""获取记忆图中所有节点的名字列表"""
@@ -344,7 +342,7 @@ class Hippocampus:
# 使用LLM提取关键词
topic_num = min(5, max(1, int(len(text) * 0.1))) # 根据文本长度动态调整关键词数量
# logger.info(f"提取关键词数量: {topic_num}")
topics_response = await self.llm_topic_judge.generate_response(self.find_topic_llm(text, topic_num))
topics_response = await self.model_summary.generate_response(self.find_topic_llm(text, topic_num))
# 提取关键词
keywords = re.findall(r"<([^>]+)>", topics_response[0])
@@ -699,7 +697,7 @@ class Hippocampus:
# 使用LLM提取关键词
topic_num = min(5, max(1, int(len(text) * 0.1))) # 根据文本长度动态调整关键词数量
# logger.info(f"提取关键词数量: {topic_num}")
topics_response = await self.llm_topic_judge.generate_response(self.find_topic_llm(text, topic_num))
topics_response = await self.model_summary.generate_response(self.find_topic_llm(text, topic_num))
# 提取关键词
keywords = re.findall(r"<([^>]+)>", topics_response[0])
@@ -1126,7 +1124,7 @@ class ParahippocampalGyrus:
# 2. 使用LLM提取关键主题
topic_num = self.hippocampus.calculate_topic_num(input_text, compress_rate)
topics_response = await self.hippocampus.llm_topic_judge.generate_response(
topics_response = await self.hippocampus.model_summary.generate_response(
self.hippocampus.find_topic_llm(input_text, topic_num)
)

4
src/chat/normal_chat/normal_chat_generator.py
View File

@@ -17,7 +17,7 @@ class NormalChatGenerator:
def __init__(self):
# TODO: API-Adapter修改标记
self.model_reasoning = LLMRequest(
model=global_config.model.reasoning,
model=global_config.model.normal_chat_1,
temperature=0.7,
max_tokens=3000,
request_type="response_reasoning",
@@ -30,7 +30,7 @@ class NormalChatGenerator:
)
self.model_sum = LLMRequest(
model=global_config.model.summary, temperature=0.7, max_tokens=3000, request_type="relation"
model=global_config.model.memory_summary, temperature=0.7, max_tokens=3000, request_type="relation"
)
self.current_model_type = "r1" # 默认使用 R1
self.current_model_name = "unknown model"

3
src/chat/utils/utils_image.py
View File

@@ -130,6 +130,7 @@ class ImageManager:
# 根据配置决定是否保存图片
if global_config.emoji.save_emoji:
# 生成文件名和路径
logger.debug(f"保存表情包: {image_hash}")
current_timestamp = time.time()
filename = f"{int(current_timestamp)}_{image_hash[:8]}.{image_format}"
emoji_dir = os.path.join(self.IMAGE_DIR, "emoji")
@@ -156,7 +157,7 @@ class ImageManager:
description=description,
timestamp=current_timestamp,
)
logger.trace(f"保存表情包元数据: {file_path}")
# logger.debug(f"保存表情包元数据: {file_path}")
except Exception as e:
logger.error(f"保存表情包文件或元数据失败: {str(e)}")

39
src/config/official_configs.py
View File

@@ -178,10 +178,10 @@ class EmojiConfig(ConfigBase):
check_interval: int = 120
"""表情包检查间隔（分钟）"""
save_pic: bool = False
save_pic: bool = True
"""是否保存图片"""
save_emoji: bool = False
save_emoji: bool = True
"""是否保存表情包"""
cache_emoji: bool = True
@@ -384,26 +384,32 @@ class ModelConfig(ConfigBase):
normal: dict[str, Any] = field(default_factory=lambda: {})
"""普通模型配置"""
topic_judge: dict[str, Any] = field(default_factory=lambda: {})
"""主题判断模型配置"""
summary: dict[str, Any] = field(default_factory=lambda: {})
"""摘要模型配置"""
memory_summary: dict[str, Any] = field(default_factory=lambda: {})
"""记忆的概括模型配置"""
vlm: dict[str, Any] = field(default_factory=lambda: {})
"""视觉语言模型配置"""
heartflow: dict[str, Any] = field(default_factory=lambda: {})
"""心流模型配置"""
observation: dict[str, Any] = field(default_factory=lambda: {})
"""观察模型配置"""
sub_heartflow: dict[str, Any] = field(default_factory=lambda: {})
"""子心流模型配置"""
focus_working_memory: dict[str, Any] = field(default_factory=lambda: {})
"""专注工作记忆模型配置"""
plan: dict[str, Any] = field(default_factory=lambda: {})
"""计划模型配置"""
focus_chat_mind: dict[str, Any] = field(default_factory=lambda: {})
"""专注聊天规划模型配置"""
focus_self_recognize: dict[str, Any] = field(default_factory=lambda: {})
"""专注自我识别模型配置"""
focus_tool_use: dict[str, Any] = field(default_factory=lambda: {})
"""专注工具使用模型配置"""
focus_planner: dict[str, Any] = field(default_factory=lambda: {})
"""专注规划模型配置"""
focus_expressor: dict[str, Any] = field(default_factory=lambda: {})
"""专注表达器模型配置"""
embedding: dict[str, Any] = field(default_factory=lambda: {})
"""嵌入模型配置"""
@@ -417,5 +423,6 @@ class ModelConfig(ConfigBase):
pfc_reply_checker: dict[str, Any] = field(default_factory=lambda: {})
"""PFC回复检查模型配置"""
tool_use: dict[str, Any] = field(default_factory=lambda: {})
"""工具使用模型配置"""

5
src/llm_models/utils_model.py
View File

@@ -459,6 +459,7 @@ class LLMRequest:
logger.error(
f"模型 {self.model_name} 错误码: {response.status} - {error_code_mapping.get(response.status)}"
)
print(request_content)
print(response)
# 尝试获取并记录服务器返回的详细错误信息
try:
@@ -499,8 +500,8 @@ class LLMRequest:
if global_config.model.normal.get("name") == old_model_name:
global_config.model.normal["name"] = self.model_name
logger.warning(f"将全局配置中的 llm_normal 模型临时降级至{self.model_name}")
if global_config.model.reasoning.get("name") == old_model_name:
global_config.model.reasoning["name"] = self.model_name
if global_config.model.normal_chat_1.get("name") == old_model_name:
global_config.model.normal_chat_1["name"] = self.model_name
logger.warning(f"将全局配置中的 llm_reasoning 模型临时降级至{self.model_name}")
if payload and "model" in payload:

9
src/tools/tool_use.py
View File

@@ -1,18 +1,9 @@
from src.llm_models.utils_model import LLMRequest
from src.config.config import global_config
import json
from src.common.logger_manager import get_logger
from src.tools.tool_can_use import get_all_tool_definitions, get_tool_instance
logger = get_logger("tool_use")
class ToolUser:
def __init__(self):
self.llm_model_tool = LLMRequest(
model=global_config.model.tool_use, temperature=0.2, max_tokens=1000, request_type="tool_use"
)
@staticmethod
def _define_tools():
"""获取所有已注册工具的定义

63
template/bot_config_template.toml
View File

@@ -196,7 +196,7 @@ pfc_chatting = false # 是否启用PFC聊天，该功能仅作用于私聊，与
model_max_output_length = 800 # 模型单次返回的最大token数
#这个模型必须是推理模型
[model.reasoning] # 一般聊天模式的推理回复模型
[model.normal_chat_1] # 一般聊天模式的首要回复模型，推荐使用 推理模型
name = "Pro/deepseek-ai/DeepSeek-R1"
provider = "SILICONFLOW"
pri_in = 1.0 #模型的输入价格（非必填，可以记录消耗）
@@ -210,13 +210,7 @@ pri_out = 8 #模型的输出价格（非必填，可以记录消耗）
#默认temp 0.2 如果你使用的是老V3或者其他模型，请自己修改temp参数
temp = 0.2 #模型的温度，新V3建议0.1-0.3
[model.topic_judge] #主题判断模型：建议使用qwen2.5 7b
name = "Pro/Qwen/Qwen2.5-7B-Instruct"
provider = "SILICONFLOW"
pri_in = 0.35
pri_out = 0.35
[model.summary] #概括模型，建议使用qwen2.5 32b 及以上
[model.memory_summary] # 记忆的概括模型，建议使用qwen2.5 32b 及以上
name = "Qwen/Qwen2.5-32B-Instruct"
provider = "SILICONFLOW"
pri_in = 1.26
@@ -228,12 +222,6 @@ provider = "SILICONFLOW"
pri_in = 0.35
pri_out = 0.35
[model.heartflow] # 用于控制麦麦是否参与聊天的模型
name = "Qwen/Qwen2.5-32B-Instruct"
provider = "SILICONFLOW"
pri_in = 1.26
pri_out = 1.26
[model.observation] #观察模型，压缩聊天内容，建议用免费的
# name = "Pro/Qwen/Qwen2.5-7B-Instruct"
name = "Qwen/Qwen2.5-7B-Instruct"
@@ -241,19 +229,48 @@ provider = "SILICONFLOW"
pri_in = 0
pri_out = 0
[model.sub_heartflow] #心流：认真聊天时,生成麦麦的内心想法，必须使用具有工具调用能力的模型
[model.focus_working_memory] #工作记忆模型，建议使用qwen2.5 32b
# name = "Pro/Qwen/Qwen2.5-7B-Instruct"
name = "Qwen/Qwen2.5-32B-Instruct"
provider = "SILICONFLOW"
pri_in = 1.26
pri_out = 1.26
[model.focus_chat_mind] #聊天规划：认真聊天时,生成麦麦对聊天的规划想法
name = "Pro/deepseek-ai/DeepSeek-V3"
provider = "SILICONFLOW"
pri_in = 2
pri_out = 8
temp = 0.3 #模型的温度，新V3建议0.1-0.3
[model.plan] #决策：认真聊天时,负责决定麦麦该做什么
[model.focus_tool_use] #工具调用模型，需要使用支持工具调用的模型，建议使用qwen2.5 32b
name = "Qwen/Qwen2.5-32B-Instruct"
provider = "SILICONFLOW"
pri_in = 1.26
pri_out = 1.26
[model.focus_planner] #决策：认真聊天时,负责决定麦麦该做什么
name = "Pro/deepseek-ai/DeepSeek-V3"
provider = "SILICONFLOW"
pri_in = 2
pri_out = 8
#表达器模型，用于生成表达方式
[model.focus_expressor]
name = "Pro/deepseek-ai/DeepSeek-V3"
provider = "SILICONFLOW"
pri_in = 2
pri_out = 8
temp = 0.3
#自我识别模型，用于自我认知和身份识别
[model.focus_self_recognize]
name = "Pro/deepseek-ai/DeepSeek-V3"
provider = "SILICONFLOW"
pri_in = 2
pri_out = 8
temp = 0.3
#嵌入模型
[model.embedding] #嵌入
@@ -263,6 +280,9 @@ pri_in = 0
pri_out = 0
#私聊PFC：需要开启PFC功能，默认三个模型均为硅基流动v3，如果需要支持多人同时私聊或频繁调用，建议把其中的一个或两个换成官方v3或其它模型，以免撞到429
#PFC决策模型
@@ -289,15 +309,6 @@ pri_in = 2
pri_out = 8
#以下模型暂时没有使用！！
#以下模型暂时没有使用！！
#以下模型暂时没有使用！！
#以下模型暂时没有使用！！
#以下模型暂时没有使用！！
[model.tool_use] #工具调用模型，需要使用支持工具调用的模型，建议使用qwen2.5 32b
name = "Qwen/Qwen2.5-32B-Instruct"
provider = "SILICONFLOW"
pri_in = 1.26
pri_out = 1.26