gardel/Mofox-Core
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

调整对应的调用

Browse Source
This commit is contained in:
UnCLAS-Prommer
2025-07-30 17:07:55 +08:00
parent 3c40ceda4c
commit 6c0edd0ad7
40 changed files with 580 additions and 1236 deletions
Download Patch File Download Diff File Expand all files Collapse all files

80
src/chat/replyer/default_generator.py
View File

@@ -8,7 +8,8 @@ from typing import List, Optional, Dict, Any, Tuple
from datetime import datetime
from src.mais4u.mai_think import mai_thinking_manager
from src.common.logger import get_logger
from src.config.config import global_config
from src.config.config import global_config, model_config
from src.config.api_ada_configs import TaskConfig
from src.individuality.individuality import get_individuality
from src.llm_models.utils_model import LLMRequest
from src.chat.message_receive.message import UserInfo, Seg, MessageRecv, MessageSending
@@ -106,31 +107,36 @@ class DefaultReplyer:
def __init__(
self,
chat_stream: ChatStream,
model_configs: Optional[List[Dict[str, Any]]] = None,
model_set_with_weight: Optional[List[Tuple[TaskConfig, float]]] = None,
request_type: str = "focus.replyer",
):
self.request_type = request_type
if model_configs:
self.express_model_configs = model_configs
if model_set_with_weight:
# self.express_model_configs = model_configs
self.model_set: List[Tuple[TaskConfig, float]] = model_set_with_weight
else:
# 当未提供配置时，使用默认配置并赋予默认权重
model_config_1 = global_config.model.replyer_1.copy()
model_config_2 = global_config.model.replyer_2.copy()
# model_config_1 = global_config.model.replyer_1.copy()
# model_config_2 = global_config.model.replyer_2.copy()
prob_first = global_config.chat.replyer_random_probability
model_config_1["weight"] = prob_first
model_config_2["weight"] = 1.0 - prob_first
# model_config_1["weight"] = prob_first
# model_config_2["weight"] = 1.0 - prob_first
self.express_model_configs = [model_config_1, model_config_2]
# self.express_model_configs = [model_config_1, model_config_2]
self.model_set = [
(model_config.model_task_config.replyer_1, prob_first),
(model_config.model_task_config.replyer_2, 1.0 - prob_first),
]
if not self.express_model_configs:
logger.warning("未找到有效的模型配置，回复生成可能会失败。")
# 提供一个最终的回退，以防止在空列表上调用 random.choice
fallback_config = global_config.model.replyer_1.copy()
fallback_config.setdefault("weight", 1.0)
self.express_model_configs = [fallback_config]
# if not self.express_model_configs:
# logger.warning("未找到有效的模型配置，回复生成可能会失败。")
# # 提供一个最终的回退，以防止在空列表上调用 random.choice
# fallback_config = global_config.model.replyer_1.copy()
# fallback_config.setdefault("weight", 1.0)
# self.express_model_configs = [fallback_config]
self.chat_stream = chat_stream
self.is_group_chat, self.chat_target_info = get_chat_type_and_target_info(self.chat_stream.stream_id)
@@ -139,14 +145,15 @@ class DefaultReplyer:
self.memory_activator = MemoryActivator()
self.instant_memory = InstantMemory(chat_id=self.chat_stream.stream_id)
from src.plugin_system.core.tool_use import ToolExecutor # 延迟导入ToolExecutor，不然会循环依赖
from src.plugin_system.core.tool_use import ToolExecutor # 延迟导入ToolExecutor，不然会循环依赖
self.tool_executor = ToolExecutor(chat_id=self.chat_stream.stream_id, enable_cache=True, cache_ttl=3)
def _select_weighted_model_config(self) -> Dict[str, Any]:
def _select_weighted_models_config(self) -> Tuple[TaskConfig, float]:
"""使用加权随机选择来挑选一个模型配置"""
configs = self.express_model_configs
configs = self.model_set
# 提取权重，如果模型配置中没有'weight'键，则默认为1.0
weights = [config.get("weight", 1.0) for config in configs]
weights = [weight for _, weight in configs]
return random.choices(population=configs, weights=weights, k=1)[0]
@@ -188,12 +195,11 @@ class DefaultReplyer:
# 4. 调用 LLM 生成回复
content = None
# TODO: 复活这里
# reasoning_content = None
# model_name = "unknown_model"
reasoning_content = None
model_name = "unknown_model"
try:
content = await self.llm_generate_content(prompt)
content, reasoning_content, model_name, _ = await self.llm_generate_content(prompt)
logger.debug(f"replyer生成内容: {content}")
except Exception as llm_e:
@@ -236,15 +242,14 @@ class DefaultReplyer:
)
content = None
# TODO: 复活这里
# reasoning_content = None
# model_name = "unknown_model"
reasoning_content = None
model_name = "unknown_model"
if not prompt:
logger.error("Prompt 构建失败，无法生成回复。")
return False, None, None
try:
content = await self.llm_generate_content(prompt)
content, reasoning_content, model_name, _ = await self.llm_generate_content(prompt)
logger.info(f"想要表达：{raw_reply}||理由：{reason}||生成回复: {content}\n")
except Exception as llm_e:
@@ -843,7 +848,7 @@ class DefaultReplyer:
raw_reply: str,
reason: str,
reply_to: str,
) -> str:
) -> str: # sourcery skip: remove-redundant-if
chat_stream = self.chat_stream
chat_id = chat_stream.stream_id
is_group_chat = bool(chat_stream.group_info)
@@ -977,30 +982,23 @@ class DefaultReplyer:
display_message=display_message,
)
async def llm_generate_content(self, prompt: str) -> str:
async def llm_generate_content(self, prompt: str):
with Timer("LLM生成", {}): # 内部计时器，可选保留
# 加权随机选择一个模型配置
selected_model_config = self._select_weighted_model_config()
model_display_name = selected_model_config.get('model_name') or selected_model_config.get('name', 'N/A')
logger.info(
f"使用模型生成回复: {model_display_name} (选中概率: {selected_model_config.get('weight', 1.0)})"
)
selected_model_config, weight = self._select_weighted_models_config()
logger.info(f"使用模型集生成回复: {selected_model_config} (选中概率: {weight})")
express_model = LLMRequest(
model=selected_model_config,
request_type=self.request_type,
)
express_model = LLMRequest(model_set=selected_model_config, request_type=self.request_type)
if global_config.debug.show_prompt:
logger.info(f"\n{prompt}\n")
else:
logger.debug(f"\n{prompt}\n")
# TODO: 这里的_应该做出替换
content, _ = await express_model.generate_response_async(prompt)
content, (reasoning_content, model_name, tool_calls) = await express_model.generate_response_async(prompt)
logger.debug(f"replyer生成内容: {content}")
return content
return content, reasoning_content, model_name, tool_calls
def weighted_sample_no_replacement(items, weights, k) -> list: