大修LLMReq
This commit is contained in:
UnCLAS-Prommer
@@ -1,180 +1,128 @@
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from dataclasses import dataclass, field
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from typing import List, Dict, Union
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import threading
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import time
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from packaging.version import Version
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NEWEST_VER = "0.2.1" # 当前支持的最新版本
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@dataclass
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class APIProvider:
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name: str = "" # API提供商名称
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base_url: str = "" # API基础URL
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api_key: str = field(repr=False, default="") # API密钥（向后兼容）
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api_keys: List[str] = field(repr=False, default_factory=list) # API密钥列表（新格式）
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client_type: str = "openai" # 客户端类型（如openai/google等，默认为openai）
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# 多API Key管理相关属性
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_current_key_index: int = field(default=0, init=False, repr=False) # 当前使用的key索引
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_key_failure_count: Dict[int, int] = field(default_factory=dict, init=False, repr=False) # 每个key的失败次数
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_key_last_failure_time: Dict[int, float] = field(default_factory=dict, init=False, repr=False) # 每个key最后失败时间
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_lock: threading.Lock = field(default_factory=threading.Lock, init=False, repr=False) # 线程锁
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def __post_init__(self):
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"""初始化后处理，确保API keys列表正确"""
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# 向后兼容：如果只设置了api_key，将其添加到api_keys列表
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if self.api_key and not self.api_keys:
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self.api_keys = [self.api_key]
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# 如果api_keys不为空但api_key为空，设置api_key为第一个
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elif self.api_keys and not self.api_key:
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self.api_key = self.api_keys[0]
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# 初始化失败计数器
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for i in range(len(self.api_keys)):
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self._key_failure_count[i] = 0
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self._key_last_failure_time[i] = 0
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def get_current_api_key(self) -> str:
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"""获取当前应该使用的API Key"""
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with self._lock:
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if not self.api_keys:
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return ""
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# 确保索引在有效范围内
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if self._current_key_index >= len(self.api_keys):
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self._current_key_index = 0
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return self.api_keys[self._current_key_index]
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def get_next_api_key(self) -> Union[str, None]:
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"""获取下一个可用的API Key（负载均衡）"""
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with self._lock:
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if not self.api_keys:
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return None
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# 如果只有一个key，直接返回
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if len(self.api_keys) == 1:
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return self.api_keys[0]
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# 轮询到下一个key
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self._current_key_index = (self._current_key_index + 1) % len(self.api_keys)
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return self.api_keys[self._current_key_index]
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def mark_key_failed(self, api_key: str) -> Union[str, None]:
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"""标记某个API Key失败，返回下一个可用的key"""
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with self._lock:
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if not self.api_keys or api_key not in self.api_keys:
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return None
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key_index = self.api_keys.index(api_key)
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self._key_failure_count[key_index] += 1
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self._key_last_failure_time[key_index] = time.time()
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# 寻找下一个可用的key
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current_time = time.time()
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for _ in range(len(self.api_keys)):
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self._current_key_index = (self._current_key_index + 1) % len(self.api_keys)
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next_key_index = self._current_key_index
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# 检查该key是否最近失败过（5分钟内失败超过3次则暂时跳过）
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if (self._key_failure_count[next_key_index] <= 3 or
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current_time - self._key_last_failure_time[next_key_index] > 300): # 5分钟后重试
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return self.api_keys[next_key_index]
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# 如果所有key都不可用，返回当前key（让上层处理）
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return api_key
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def reset_key_failures(self, api_key: str | None = None):
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"""重置失败计数（成功调用后调用）"""
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with self._lock:
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if api_key and api_key in self.api_keys:
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key_index = self.api_keys.index(api_key)
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self._key_failure_count[key_index] = 0
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self._key_last_failure_time[key_index] = 0
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else:
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# 重置所有key的失败计数
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for i in range(len(self.api_keys)):
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self._key_failure_count[i] = 0
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self._key_last_failure_time[i] = 0
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def get_api_key_stats(self) -> Dict[str, Dict[str, Union[int, float]]]:
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"""获取API Key使用统计"""
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with self._lock:
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stats = {}
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for i, key in enumerate(self.api_keys):
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# 只显示key的前8位和后4位，中间用*代替
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masked_key = f"{key[:8]}***{key[-4:]}" if len(key) > 12 else "***"
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stats[masked_key] = {
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"failure_count": self._key_failure_count.get(i, 0),
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"last_failure_time": self._key_last_failure_time.get(i, 0),
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"is_current": i == self._current_key_index
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}
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return stats
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from .config_base import ConfigBase
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@dataclass
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class ModelInfo:
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model_identifier: str = "" # 模型标识符（用于URL调用）
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name: str = "" # 模型名称（用于模块调用）
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api_provider: str = "" # API提供商（如OpenAI、Azure等）
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class APIProvider(ConfigBase):
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"""API提供商配置类"""
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# 以下用于模型计费
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price_in: float = 0.0 # 每M token输入价格
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price_out: float = 0.0 # 每M token输出价格
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name: str
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"""API提供商名称"""
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force_stream_mode: bool = False # 是否强制使用流式输出模式
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# 新增：任务类型和能力字段
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task_type: str = "" # 任务类型：llm_normal, llm_reasoning, vision, embedding, speech
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capabilities: List[str] = field(default_factory=list) # 模型能力：text, vision, embedding, speech, tool_calling, reasoning
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base_url: str
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"""API基础URL"""
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api_key: str = field(default_factory=str, repr=False)
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"""API密钥列表"""
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client_type: str = field(default="openai")
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"""客户端类型（如openai/google等，默认为openai）"""
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max_retry: int = 2
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"""最大重试次数（单个模型API调用失败，最多重试的次数）"""
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timeout: int = 10
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"""API调用的超时时长（超过这个时长，本次请求将被视为“请求超时”，单位：秒）"""
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retry_interval: int = 10
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"""重试间隔（如果API调用失败，重试的间隔时间，单位：秒）"""
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def get_api_key(self) -> str:
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return self.api_key
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@dataclass
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class RequestConfig:
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max_retry: int = 2 # 最大重试次数（单个模型API调用失败，最多重试的次数）
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timeout: int = (
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10 # API调用的超时时长（超过这个时长，本次请求将被视为“请求超时”，单位：秒）
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)
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retry_interval: int = 10 # 重试间隔（如果API调用失败，重试的间隔时间，单位：秒）
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default_temperature: float = 0.7 # 默认的温度（如果bot_config.toml中没有设置temperature参数，默认使用这个值）
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default_max_tokens: int = 1024 # 默认的最大输出token数（如果bot_config.toml中没有设置max_tokens参数，默认使用这个值）
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class ModelInfo(ConfigBase):
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"""单个模型信息配置类"""
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model_identifier: str
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"""模型标识符（用于URL调用）"""
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name: str
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"""模型名称（用于模块调用）"""
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api_provider: str
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"""API提供商（如OpenAI、Azure等）"""
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price_in: float = field(default=0.0)
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"""每M token输入价格"""
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price_out: float = field(default=0.0)
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"""每M token输出价格"""
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force_stream_mode: bool = field(default=False)
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"""是否强制使用流式输出模式"""
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has_thinking: bool = field(default=False)
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"""是否有思考参数"""
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enable_thinking: bool = field(default=False)
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"""是否启用思考"""
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@dataclass
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class ModelUsageArgConfigItem:
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"""模型使用的配置类
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该类用于加载和存储子任务模型使用的配置
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"""
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class TaskConfig(ConfigBase):
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"""任务配置类"""
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name: str = "" # 模型名称
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temperature: float | None = None # 温度
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max_tokens: int | None = None # 最大token数
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max_retry: int | None = None # 调用失败时的最大重试次数
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model_list: list[str] = field(default_factory=list)
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"""任务使用的模型列表"""
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max_tokens: int = 1024
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"""任务最大输出token数"""
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temperature: float = 0.3
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"""模型温度"""
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@dataclass
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class ModelUsageArgConfig:
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"""子任务使用模型的配置类
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该类用于加载和存储子任务使用的模型配置
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"""
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class ModelTaskConfig(ConfigBase):
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"""模型配置类"""
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name: str = "" # 任务名称
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usage: List[ModelUsageArgConfigItem] = field(
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default_factory=lambda: []
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) # 任务使用的模型列表
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utils: TaskConfig
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"""组件模型配置"""
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utils_small: TaskConfig
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"""组件小模型配置"""
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replyer_1: TaskConfig
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"""normal_chat首要回复模型模型配置"""
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@dataclass
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class ModuleConfig:
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INNER_VERSION: Version | None = None # 配置文件版本
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replyer_2: TaskConfig
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"""normal_chat次要回复模型配置"""
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req_conf: RequestConfig = field(default_factory=lambda: RequestConfig()) # 请求配置
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api_providers: Dict[str, APIProvider] = field(
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default_factory=lambda: {}
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) # API提供商列表
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models: Dict[str, ModelInfo] = field(default_factory=lambda: {}) # 模型列表
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task_model_arg_map: Dict[str, ModelUsageArgConfig] = field(
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default_factory=lambda: {}
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)
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memory: TaskConfig
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"""记忆模型配置"""
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emotion: TaskConfig
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"""情绪模型配置"""
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vlm: TaskConfig
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"""视觉语言模型配置"""
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voice: TaskConfig
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"""语音识别模型配置"""
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tool_use: TaskConfig
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"""专注工具使用模型配置"""
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planner: TaskConfig
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"""规划模型配置"""
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embedding: TaskConfig
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"""嵌入模型配置"""
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lpmm_entity_extract: TaskConfig
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"""LPMM实体提取模型配置"""
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lpmm_rdf_build: TaskConfig
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"""LPMM RDF构建模型配置"""
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lpmm_qa: TaskConfig
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"""LPMM问答模型配置"""
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def get_task(self, task_name: str) -> TaskConfig:
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"""获取指定任务的配置"""
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if hasattr(self, task_name):
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return getattr(self, task_name)
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raise ValueError(f"任务 '{task_name}' 未找到对应的配置")
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@@ -1,16 +1,14 @@
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import os
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import tomlkit
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import shutil
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import sys
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from datetime import datetime
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from tomlkit import TOMLDocument
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from tomlkit.items import Table, KeyType
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from dataclasses import field, dataclass
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from rich.traceback import install
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from packaging import version
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from packaging.specifiers import SpecifierSet
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from packaging.version import Version, InvalidVersion
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from typing import Any, Dict, List
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from typing import List, Optional
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from src.common.logger import get_logger
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from src.config.config_base import ConfigBase
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@@ -29,7 +27,6 @@ from src.config.official_configs import (
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ResponseSplitterConfig,
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TelemetryConfig,
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ExperimentalConfig,
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ModelConfig,
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MessageReceiveConfig,
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MaimMessageConfig,
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LPMMKnowledgeConfig,
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@@ -41,16 +38,12 @@ from src.config.official_configs import (
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)
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from .api_ada_configs import (
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ModelUsageArgConfigItem,
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ModelUsageArgConfig,
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APIProvider,
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ModelTaskConfig,
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ModelInfo,
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NEWEST_VER,
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ModuleConfig,
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APIProvider,
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)
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install(extra_lines=3)
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@@ -64,275 +57,270 @@ TEMPLATE_DIR = os.path.join(PROJECT_ROOT, "template")
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# 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
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# 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/
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MMC_VERSION = "0.10.0-snapshot1"
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MMC_VERSION = "0.10.0-snapshot.2"
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# def _get_config_version(toml: Dict) -> Version:
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# """提取配置文件的 SpecifierSet 版本数据
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# Args:
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# toml[dict]: 输入的配置文件字典
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# Returns:
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# Version
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# """
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# if "inner" in toml and "version" in toml["inner"]:
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# config_version: str = toml["inner"]["version"]
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# else:
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# raise InvalidVersion("配置文件缺少版本信息，请检查配置文件。")
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# try:
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# return version.parse(config_version)
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# except InvalidVersion as e:
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# logger.error(
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# "配置文件中 inner段 的 version 键是错误的版本描述\n"
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# f"请检查配置文件，当前 version 键: {config_version}\n"
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# f"错误信息: {e}"
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# )
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# raise e
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def _get_config_version(toml: Dict) -> Version:
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"""提取配置文件的 SpecifierSet 版本数据
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Args:
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toml[dict]: 输入的配置文件字典
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Returns:
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Version
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"""
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if "inner" in toml and "version" in toml["inner"]:
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config_version: str = toml["inner"]["version"]
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else:
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config_version = "0.0.0" # 默认版本
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try:
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ver = version.parse(config_version)
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except InvalidVersion as e:
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logger.error(
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"配置文件中 inner段 的 version 键是错误的版本描述\n"
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f"请检查配置文件，当前 version 键: {config_version}\n"
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f"错误信息: {e}"
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)
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raise InvalidVersion(
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"配置文件中 inner段 的 version 键是错误的版本描述\n"
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) from e
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return ver
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# def _request_conf(parent: Dict, config: ModuleConfig):
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# request_conf_config = parent.get("request_conf")
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# config.req_conf.max_retry = request_conf_config.get(
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# "max_retry", config.req_conf.max_retry
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# )
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# config.req_conf.timeout = request_conf_config.get(
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# "timeout", config.req_conf.timeout
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# )
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# config.req_conf.retry_interval = request_conf_config.get(
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# "retry_interval", config.req_conf.retry_interval
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# )
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# config.req_conf.default_temperature = request_conf_config.get(
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# "default_temperature", config.req_conf.default_temperature
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# )
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# config.req_conf.default_max_tokens = request_conf_config.get(
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# "default_max_tokens", config.req_conf.default_max_tokens
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# )
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def _request_conf(parent: Dict, config: ModuleConfig):
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request_conf_config = parent.get("request_conf")
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config.req_conf.max_retry = request_conf_config.get(
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"max_retry", config.req_conf.max_retry
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)
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config.req_conf.timeout = request_conf_config.get(
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"timeout", config.req_conf.timeout
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)
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config.req_conf.retry_interval = request_conf_config.get(
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"retry_interval", config.req_conf.retry_interval
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)
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config.req_conf.default_temperature = request_conf_config.get(
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"default_temperature", config.req_conf.default_temperature
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)
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config.req_conf.default_max_tokens = request_conf_config.get(
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"default_max_tokens", config.req_conf.default_max_tokens
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)
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# def _api_providers(parent: Dict, config: ModuleConfig):
|
||||
# api_providers_config = parent.get("api_providers")
|
||||
# for provider in api_providers_config:
|
||||
# name = provider.get("name", None)
|
||||
# base_url = provider.get("base_url", None)
|
||||
# api_key = provider.get("api_key", None)
|
||||
# api_keys = provider.get("api_keys", []) # 新增：支持多个API Key
|
||||
# client_type = provider.get("client_type", "openai")
|
||||
|
||||
# if name in config.api_providers: # 查重
|
||||
# logger.error(f"重复的API提供商名称: {name}，请检查配置文件。")
|
||||
# raise KeyError(f"重复的API提供商名称: {name}，请检查配置文件。")
|
||||
|
||||
# if name and base_url:
|
||||
# # 处理API Key配置：支持单个api_key或多个api_keys
|
||||
# if api_keys:
|
||||
# # 使用新格式：api_keys列表
|
||||
# logger.debug(f"API提供商 '{name}' 配置了 {len(api_keys)} 个API Key")
|
||||
# elif api_key:
|
||||
# # 向后兼容：使用单个api_key
|
||||
# api_keys = [api_key]
|
||||
# logger.debug(f"API提供商 '{name}' 使用单个API Key（向后兼容模式）")
|
||||
# else:
|
||||
# logger.warning(f"API提供商 '{name}' 没有配置API Key，某些功能可能不可用")
|
||||
|
||||
# config.api_providers[name] = APIProvider(
|
||||
# name=name,
|
||||
# base_url=base_url,
|
||||
# api_key=api_key, # 保留向后兼容
|
||||
# api_keys=api_keys, # 新格式
|
||||
# client_type=client_type,
|
||||
# )
|
||||
# else:
|
||||
# logger.error(f"API提供商 '{name}' 的配置不完整，请检查配置文件。")
|
||||
# raise ValueError(f"API提供商 '{name}' 的配置不完整，请检查配置文件。")
|
||||
|
||||
|
||||
def _api_providers(parent: Dict, config: ModuleConfig):
|
||||
api_providers_config = parent.get("api_providers")
|
||||
for provider in api_providers_config:
|
||||
name = provider.get("name", None)
|
||||
base_url = provider.get("base_url", None)
|
||||
api_key = provider.get("api_key", None)
|
||||
api_keys = provider.get("api_keys", []) # 新增：支持多个API Key
|
||||
client_type = provider.get("client_type", "openai")
|
||||
# def _models(parent: Dict, config: ModuleConfig):
|
||||
# models_config = parent.get("models")
|
||||
# for model in models_config:
|
||||
# model_identifier = model.get("model_identifier", None)
|
||||
# name = model.get("name", model_identifier)
|
||||
# api_provider = model.get("api_provider", None)
|
||||
# price_in = model.get("price_in", 0.0)
|
||||
# price_out = model.get("price_out", 0.0)
|
||||
# force_stream_mode = model.get("force_stream_mode", False)
|
||||
# task_type = model.get("task_type", "")
|
||||
# capabilities = model.get("capabilities", [])
|
||||
|
||||
if name in config.api_providers: # 查重
|
||||
logger.error(f"重复的API提供商名称: {name}，请检查配置文件。")
|
||||
raise KeyError(f"重复的API提供商名称: {name}，请检查配置文件。")
|
||||
# if name in config.models: # 查重
|
||||
# logger.error(f"重复的模型名称: {name}，请检查配置文件。")
|
||||
# raise KeyError(f"重复的模型名称: {name}，请检查配置文件。")
|
||||
|
||||
if name and base_url:
|
||||
# 处理API Key配置：支持单个api_key或多个api_keys
|
||||
if api_keys:
|
||||
# 使用新格式：api_keys列表
|
||||
logger.debug(f"API提供商 '{name}' 配置了 {len(api_keys)} 个API Key")
|
||||
elif api_key:
|
||||
# 向后兼容：使用单个api_key
|
||||
api_keys = [api_key]
|
||||
logger.debug(f"API提供商 '{name}' 使用单个API Key（向后兼容模式）")
|
||||
else:
|
||||
logger.warning(f"API提供商 '{name}' 没有配置API Key，某些功能可能不可用")
|
||||
|
||||
config.api_providers[name] = APIProvider(
|
||||
name=name,
|
||||
base_url=base_url,
|
||||
api_key=api_key, # 保留向后兼容
|
||||
api_keys=api_keys, # 新格式
|
||||
client_type=client_type,
|
||||
)
|
||||
else:
|
||||
logger.error(f"API提供商 '{name}' 的配置不完整，请检查配置文件。")
|
||||
raise ValueError(f"API提供商 '{name}' 的配置不完整，请检查配置文件。")
|
||||
# if model_identifier and api_provider:
|
||||
# # 检查API提供商是否存在
|
||||
# if api_provider not in config.api_providers:
|
||||
# logger.error(f"未声明的API提供商 '{api_provider}' ，请检查配置文件。")
|
||||
# raise ValueError(
|
||||
# f"未声明的API提供商 '{api_provider}' ，请检查配置文件。"
|
||||
# )
|
||||
# config.models[name] = ModelInfo(
|
||||
# name=name,
|
||||
# model_identifier=model_identifier,
|
||||
# api_provider=api_provider,
|
||||
# price_in=price_in,
|
||||
# price_out=price_out,
|
||||
# force_stream_mode=force_stream_mode,
|
||||
# task_type=task_type,
|
||||
# capabilities=capabilities,
|
||||
# )
|
||||
# else:
|
||||
# logger.error(f"模型 '{name}' 的配置不完整，请检查配置文件。")
|
||||
# raise ValueError(f"模型 '{name}' 的配置不完整，请检查配置文件。")
|
||||
|
||||
|
||||
def _models(parent: Dict, config: ModuleConfig):
|
||||
models_config = parent.get("models")
|
||||
for model in models_config:
|
||||
model_identifier = model.get("model_identifier", None)
|
||||
name = model.get("name", model_identifier)
|
||||
api_provider = model.get("api_provider", None)
|
||||
price_in = model.get("price_in", 0.0)
|
||||
price_out = model.get("price_out", 0.0)
|
||||
force_stream_mode = model.get("force_stream_mode", False)
|
||||
task_type = model.get("task_type", "")
|
||||
capabilities = model.get("capabilities", [])
|
||||
# def _task_model_usage(parent: Dict, config: ModuleConfig):
|
||||
# model_usage_configs = parent.get("task_model_usage")
|
||||
# config.task_model_arg_map = {}
|
||||
# for task_name, item in model_usage_configs.items():
|
||||
# if task_name in config.task_model_arg_map:
|
||||
# logger.error(f"子任务 {task_name} 已存在，请检查配置文件。")
|
||||
# raise KeyError(f"子任务 {task_name} 已存在，请检查配置文件。")
|
||||
|
||||
if name in config.models: # 查重
|
||||
logger.error(f"重复的模型名称: {name}，请检查配置文件。")
|
||||
raise KeyError(f"重复的模型名称: {name}，请检查配置文件。")
|
||||
# usage = []
|
||||
# if isinstance(item, Dict):
|
||||
# if "model" in item:
|
||||
# usage.append(
|
||||
# ModelUsageArgConfigItem(
|
||||
# name=item["model"],
|
||||
# temperature=item.get("temperature", None),
|
||||
# max_tokens=item.get("max_tokens", None),
|
||||
# max_retry=item.get("max_retry", None),
|
||||
# )
|
||||
# )
|
||||
# else:
|
||||
# logger.error(f"子任务 {task_name} 的模型配置不合法，请检查配置文件。")
|
||||
# raise ValueError(
|
||||
# f"子任务 {task_name} 的模型配置不合法，请检查配置文件。"
|
||||
# )
|
||||
# elif isinstance(item, List):
|
||||
# for model in item:
|
||||
# if isinstance(model, Dict):
|
||||
# usage.append(
|
||||
# ModelUsageArgConfigItem(
|
||||
# name=model["model"],
|
||||
# temperature=model.get("temperature", None),
|
||||
# max_tokens=model.get("max_tokens", None),
|
||||
# max_retry=model.get("max_retry", None),
|
||||
# )
|
||||
# )
|
||||
# elif isinstance(model, str):
|
||||
# usage.append(
|
||||
# ModelUsageArgConfigItem(
|
||||
# name=model,
|
||||
# temperature=None,
|
||||
# max_tokens=None,
|
||||
# max_retry=None,
|
||||
# )
|
||||
# )
|
||||
# else:
|
||||
# logger.error(
|
||||
# f"子任务 {task_name} 的模型配置不合法，请检查配置文件。"
|
||||
# )
|
||||
# raise ValueError(
|
||||
# f"子任务 {task_name} 的模型配置不合法，请检查配置文件。"
|
||||
# )
|
||||
# elif isinstance(item, str):
|
||||
# usage.append(
|
||||
# ModelUsageArgConfigItem(
|
||||
# name=item,
|
||||
# temperature=None,
|
||||
# max_tokens=None,
|
||||
# max_retry=None,
|
||||
# )
|
||||
# )
|
||||
|
||||
if model_identifier and api_provider:
|
||||
# 检查API提供商是否存在
|
||||
if api_provider not in config.api_providers:
|
||||
logger.error(f"未声明的API提供商 '{api_provider}' ，请检查配置文件。")
|
||||
raise ValueError(
|
||||
f"未声明的API提供商 '{api_provider}' ，请检查配置文件。"
|
||||
)
|
||||
config.models[name] = ModelInfo(
|
||||
name=name,
|
||||
model_identifier=model_identifier,
|
||||
api_provider=api_provider,
|
||||
price_in=price_in,
|
||||
price_out=price_out,
|
||||
force_stream_mode=force_stream_mode,
|
||||
task_type=task_type,
|
||||
capabilities=capabilities,
|
||||
)
|
||||
else:
|
||||
logger.error(f"模型 '{name}' 的配置不完整，请检查配置文件。")
|
||||
raise ValueError(f"模型 '{name}' 的配置不完整，请检查配置文件。")
|
||||
# config.task_model_arg_map[task_name] = ModelUsageArgConfig(
|
||||
# name=task_name,
|
||||
# usage=usage,
|
||||
# )
|
||||
|
||||
|
||||
def _task_model_usage(parent: Dict, config: ModuleConfig):
|
||||
model_usage_configs = parent.get("task_model_usage")
|
||||
config.task_model_arg_map = {}
|
||||
for task_name, item in model_usage_configs.items():
|
||||
if task_name in config.task_model_arg_map:
|
||||
logger.error(f"子任务 {task_name} 已存在，请检查配置文件。")
|
||||
raise KeyError(f"子任务 {task_name} 已存在，请检查配置文件。")
|
||||
# def api_ada_load_config(config_path: str) -> ModuleConfig:
|
||||
# """从TOML配置文件加载配置"""
|
||||
# config = ModuleConfig()
|
||||
|
||||
usage = []
|
||||
if isinstance(item, Dict):
|
||||
if "model" in item:
|
||||
usage.append(
|
||||
ModelUsageArgConfigItem(
|
||||
name=item["model"],
|
||||
temperature=item.get("temperature", None),
|
||||
max_tokens=item.get("max_tokens", None),
|
||||
max_retry=item.get("max_retry", None),
|
||||
)
|
||||
)
|
||||
else:
|
||||
logger.error(f"子任务 {task_name} 的模型配置不合法，请检查配置文件。")
|
||||
raise ValueError(
|
||||
f"子任务 {task_name} 的模型配置不合法，请检查配置文件。"
|
||||
)
|
||||
elif isinstance(item, List):
|
||||
for model in item:
|
||||
if isinstance(model, Dict):
|
||||
usage.append(
|
||||
ModelUsageArgConfigItem(
|
||||
name=model["model"],
|
||||
temperature=model.get("temperature", None),
|
||||
max_tokens=model.get("max_tokens", None),
|
||||
max_retry=model.get("max_retry", None),
|
||||
)
|
||||
)
|
||||
elif isinstance(model, str):
|
||||
usage.append(
|
||||
ModelUsageArgConfigItem(
|
||||
name=model,
|
||||
temperature=None,
|
||||
max_tokens=None,
|
||||
max_retry=None,
|
||||
)
|
||||
)
|
||||
else:
|
||||
logger.error(
|
||||
f"子任务 {task_name} 的模型配置不合法，请检查配置文件。"
|
||||
)
|
||||
raise ValueError(
|
||||
f"子任务 {task_name} 的模型配置不合法，请检查配置文件。"
|
||||
)
|
||||
elif isinstance(item, str):
|
||||
usage.append(
|
||||
ModelUsageArgConfigItem(
|
||||
name=item,
|
||||
temperature=None,
|
||||
max_tokens=None,
|
||||
max_retry=None,
|
||||
)
|
||||
)
|
||||
# include_configs: Dict[str, Dict[str, Any]] = {
|
||||
# "request_conf": {
|
||||
# "func": _request_conf,
|
||||
# "support": ">=0.0.0",
|
||||
# "necessary": False,
|
||||
# },
|
||||
# "api_providers": {"func": _api_providers, "support": ">=0.0.0"},
|
||||
# "models": {"func": _models, "support": ">=0.0.0"},
|
||||
# "task_model_usage": {"func": _task_model_usage, "support": ">=0.0.0"},
|
||||
# }
|
||||
|
||||
config.task_model_arg_map[task_name] = ModelUsageArgConfig(
|
||||
name=task_name,
|
||||
usage=usage,
|
||||
)
|
||||
# if os.path.exists(config_path):
|
||||
# with open(config_path, "rb") as f:
|
||||
# try:
|
||||
# toml_dict = tomlkit.load(f)
|
||||
# except tomlkit.TOMLDecodeError as e:
|
||||
# logger.critical(
|
||||
# f"配置文件model_list.toml填写有误，请检查第{e.lineno}行第{e.colno}处：{e.msg}"
|
||||
# )
|
||||
# exit(1)
|
||||
|
||||
# # 获取配置文件版本
|
||||
# config.INNER_VERSION = _get_config_version(toml_dict)
|
||||
|
||||
def api_ada_load_config(config_path: str) -> ModuleConfig:
|
||||
"""从TOML配置文件加载配置"""
|
||||
config = ModuleConfig()
|
||||
# # 检查版本
|
||||
# if config.INNER_VERSION > Version(NEWEST_VER):
|
||||
# logger.warning(
|
||||
# f"当前配置文件版本 {config.INNER_VERSION} 高于支持的最新版本 {NEWEST_VER}，可能导致异常，建议更新依赖。"
|
||||
# )
|
||||
|
||||
include_configs: Dict[str, Dict[str, Any]] = {
|
||||
"request_conf": {
|
||||
"func": _request_conf,
|
||||
"support": ">=0.0.0",
|
||||
"necessary": False,
|
||||
},
|
||||
"api_providers": {"func": _api_providers, "support": ">=0.0.0"},
|
||||
"models": {"func": _models, "support": ">=0.0.0"},
|
||||
"task_model_usage": {"func": _task_model_usage, "support": ">=0.0.0"},
|
||||
}
|
||||
# # 解析配置文件
|
||||
# # 如果在配置中找到了需要的项，调用对应项的闭包函数处理
|
||||
# for key in include_configs:
|
||||
# if key in toml_dict:
|
||||
# group_specifier_set: SpecifierSet = SpecifierSet(
|
||||
# include_configs[key]["support"]
|
||||
# )
|
||||
|
||||
if os.path.exists(config_path):
|
||||
with open(config_path, "rb") as f:
|
||||
try:
|
||||
toml_dict = tomlkit.load(f)
|
||||
except tomlkit.TOMLDecodeError as e:
|
||||
logger.critical(
|
||||
f"配置文件model_list.toml填写有误，请检查第{e.lineno}行第{e.colno}处：{e.msg}"
|
||||
)
|
||||
exit(1)
|
||||
# # 检查配置文件版本是否在支持范围内
|
||||
# if config.INNER_VERSION in group_specifier_set:
|
||||
# # 如果版本在支持范围内，检查是否存在通知
|
||||
# if "notice" in include_configs[key]:
|
||||
# logger.warning(include_configs[key]["notice"])
|
||||
# # 调用闭包函数处理配置
|
||||
# (include_configs[key]["func"])(toml_dict, config)
|
||||
# else:
|
||||
# # 如果版本不在支持范围内，崩溃并提示用户
|
||||
# logger.error(
|
||||
# f"配置文件中的 '{key}' 字段的版本 ({config.INNER_VERSION}) 不在支持范围内。\n"
|
||||
# f"当前程序仅支持以下版本范围: {group_specifier_set}"
|
||||
# )
|
||||
# raise InvalidVersion(
|
||||
# f"当前程序仅支持以下版本范围: {group_specifier_set}"
|
||||
# )
|
||||
|
||||
# 获取配置文件版本
|
||||
config.INNER_VERSION = _get_config_version(toml_dict)
|
||||
# # 如果 necessary 项目存在，而且显式声明是 False，进入特殊处理
|
||||
# elif (
|
||||
# "necessary" in include_configs[key]
|
||||
# and include_configs[key].get("necessary") is False
|
||||
# ):
|
||||
# # 通过 pass 处理的项虽然直接忽略也是可以的，但是为了不增加理解困难，依然需要在这里显式处理
|
||||
# if key == "keywords_reaction":
|
||||
# pass
|
||||
# else:
|
||||
# # 如果用户根本没有需要的配置项，提示缺少配置
|
||||
# logger.error(f"配置文件中缺少必需的字段: '{key}'")
|
||||
# raise KeyError(f"配置文件中缺少必需的字段: '{key}'")
|
||||
|
||||
# 检查版本
|
||||
if config.INNER_VERSION > Version(NEWEST_VER):
|
||||
logger.warning(
|
||||
f"当前配置文件版本 {config.INNER_VERSION} 高于支持的最新版本 {NEWEST_VER}，可能导致异常，建议更新依赖。"
|
||||
)
|
||||
# logger.info(f"成功加载配置文件: {config_path}")
|
||||
|
||||
# 解析配置文件
|
||||
# 如果在配置中找到了需要的项，调用对应项的闭包函数处理
|
||||
for key in include_configs:
|
||||
if key in toml_dict:
|
||||
group_specifier_set: SpecifierSet = SpecifierSet(
|
||||
include_configs[key]["support"]
|
||||
)
|
||||
# return config
|
||||
|
||||
# 检查配置文件版本是否在支持范围内
|
||||
if config.INNER_VERSION in group_specifier_set:
|
||||
# 如果版本在支持范围内，检查是否存在通知
|
||||
if "notice" in include_configs[key]:
|
||||
logger.warning(include_configs[key]["notice"])
|
||||
# 调用闭包函数处理配置
|
||||
(include_configs[key]["func"])(toml_dict, config)
|
||||
else:
|
||||
# 如果版本不在支持范围内，崩溃并提示用户
|
||||
logger.error(
|
||||
f"配置文件中的 '{key}' 字段的版本 ({config.INNER_VERSION}) 不在支持范围内。\n"
|
||||
f"当前程序仅支持以下版本范围: {group_specifier_set}"
|
||||
)
|
||||
raise InvalidVersion(
|
||||
f"当前程序仅支持以下版本范围: {group_specifier_set}"
|
||||
)
|
||||
|
||||
# 如果 necessary 项目存在，而且显式声明是 False，进入特殊处理
|
||||
elif (
|
||||
"necessary" in include_configs[key]
|
||||
and include_configs[key].get("necessary") is False
|
||||
):
|
||||
# 通过 pass 处理的项虽然直接忽略也是可以的，但是为了不增加理解困难，依然需要在这里显式处理
|
||||
if key == "keywords_reaction":
|
||||
pass
|
||||
else:
|
||||
# 如果用户根本没有需要的配置项，提示缺少配置
|
||||
logger.error(f"配置文件中缺少必需的字段: '{key}'")
|
||||
raise KeyError(f"配置文件中缺少必需的字段: '{key}'")
|
||||
|
||||
logger.info(f"成功加载配置文件: {config_path}")
|
||||
|
||||
return config
|
||||
|
||||
def get_key_comment(toml_table, key):
|
||||
# 获取key的注释（如果有）
|
||||
@@ -361,7 +349,7 @@ def compare_dicts(new, old, path=None, logs=None):
|
||||
continue
|
||||
if key not in old:
|
||||
comment = get_key_comment(new, key)
|
||||
logs.append(f"新增: {'.'.join(path + [str(key)])} 注释: {comment if comment else '无'}")
|
||||
logs.append(f"新增: {'.'.join(path + [str(key)])} 注释: {comment or '无'}")
|
||||
elif isinstance(new[key], (dict, Table)) and isinstance(old.get(key), (dict, Table)):
|
||||
compare_dicts(new[key], old[key], path + [str(key)], logs)
|
||||
# 删减项
|
||||
@@ -370,7 +358,7 @@ def compare_dicts(new, old, path=None, logs=None):
|
||||
continue
|
||||
if key not in new:
|
||||
comment = get_key_comment(old, key)
|
||||
logs.append(f"删减: {'.'.join(path + [str(key)])} 注释: {comment if comment else '无'}")
|
||||
logs.append(f"删减: {'.'.join(path + [str(key)])} 注释: {comment or '无'}")
|
||||
return logs
|
||||
|
||||
|
||||
@@ -405,17 +393,13 @@ def compare_default_values(new, old, path=None, logs=None, changes=None):
|
||||
if key in old:
|
||||
if isinstance(new[key], (dict, Table)) and isinstance(old[key], (dict, Table)):
|
||||
compare_default_values(new[key], old[key], path + [str(key)], logs, changes)
|
||||
else:
|
||||
# 只要值发生变化就记录
|
||||
if new[key] != old[key]:
|
||||
logs.append(
|
||||
f"默认值变化: {'.'.join(path + [str(key)])} 旧默认值: {old[key]} 新默认值: {new[key]}"
|
||||
)
|
||||
changes.append((path + [str(key)], old[key], new[key]))
|
||||
elif new[key] != old[key]:
|
||||
logs.append(f"默认值变化: {'.'.join(path + [str(key)])} 旧默认值: {old[key]} 新默认值: {new[key]}")
|
||||
changes.append((path + [str(key)], old[key], new[key]))
|
||||
return logs, changes
|
||||
|
||||
|
||||
def _get_version_from_toml(toml_path):
|
||||
def _get_version_from_toml(toml_path) -> Optional[str]:
|
||||
"""从TOML文件中获取版本号"""
|
||||
if not os.path.exists(toml_path):
|
||||
return None
|
||||
@@ -459,14 +443,13 @@ def _update_dict(target: TOMLDocument | dict | Table, source: TOMLDocument | dic
|
||||
target[key] = value
|
||||
|
||||
|
||||
def _update_config_generic(config_name: str, template_name: str, should_quit_on_new: bool = True):
|
||||
def _update_config_generic(config_name: str, template_name: str):
|
||||
"""
|
||||
通用的配置文件更新函数
|
||||
|
||||
|
||||
Args:
|
||||
config_name: 配置文件名（不含扩展名），如 'bot_config' 或 'model_config'
|
||||
template_name: 模板文件名（不含扩展名），如 'bot_config_template' 或 'model_config_template'
|
||||
should_quit_on_new: 创建新配置文件后是否退出程序
|
||||
"""
|
||||
# 获取根目录路径
|
||||
old_config_dir = os.path.join(CONFIG_DIR, "old")
|
||||
@@ -484,19 +467,30 @@ def _update_config_generic(config_name: str, template_name: str, should_quit_on_
|
||||
template_version = _get_version_from_toml(template_path)
|
||||
compare_version = _get_version_from_toml(compare_path)
|
||||
|
||||
# 检查配置文件是否存在
|
||||
if not os.path.exists(old_config_path):
|
||||
logger.info(f"{config_name}.toml配置文件不存在，从模板创建新配置")
|
||||
os.makedirs(CONFIG_DIR, exist_ok=True) # 创建文件夹
|
||||
shutil.copy2(template_path, old_config_path) # 复制模板文件
|
||||
logger.info(f"已创建新{config_name}配置文件，请填写后重新运行: {old_config_path}")
|
||||
# 新创建配置文件，退出
|
||||
sys.exit(0)
|
||||
|
||||
compare_config = None
|
||||
new_config = None
|
||||
old_config = None
|
||||
|
||||
# 先读取 compare 下的模板（如果有），用于默认值变动检测
|
||||
if os.path.exists(compare_path):
|
||||
with open(compare_path, "r", encoding="utf-8") as f:
|
||||
compare_config = tomlkit.load(f)
|
||||
else:
|
||||
compare_config = None
|
||||
|
||||
# 读取当前模板
|
||||
with open(template_path, "r", encoding="utf-8") as f:
|
||||
new_config = tomlkit.load(f)
|
||||
|
||||
# 检查默认值变化并处理（只有 compare_config 存在时才做）
|
||||
if compare_config is not None:
|
||||
if compare_config:
|
||||
# 读取旧配置
|
||||
with open(old_config_path, "r", encoding="utf-8") as f:
|
||||
old_config = tomlkit.load(f)
|
||||
@@ -515,32 +509,16 @@ def _update_config_generic(config_name: str, template_name: str, should_quit_on_
|
||||
)
|
||||
else:
|
||||
logger.info(f"未检测到{config_name}模板默认值变动")
|
||||
# 保存旧配置的变更（后续合并逻辑会用到 old_config）
|
||||
else:
|
||||
old_config = None
|
||||
|
||||
# 检查 compare 下没有模板，或新模板版本更高，则复制
|
||||
if not os.path.exists(compare_path):
|
||||
shutil.copy2(template_path, compare_path)
|
||||
logger.info(f"已将{config_name}模板文件复制到: {compare_path}")
|
||||
elif _version_tuple(template_version) > _version_tuple(compare_version):
|
||||
shutil.copy2(template_path, compare_path)
|
||||
logger.info(f"{config_name}模板版本较新，已替换compare下的模板: {compare_path}")
|
||||
else:
|
||||
if _version_tuple(template_version) > _version_tuple(compare_version):
|
||||
shutil.copy2(template_path, compare_path)
|
||||
logger.info(f"{config_name}模板版本较新，已替换compare下的模板: {compare_path}")
|
||||
else:
|
||||
logger.debug(f"compare下的{config_name}模板版本不低于当前模板，无需替换: {compare_path}")
|
||||
|
||||
# 检查配置文件是否存在
|
||||
if not os.path.exists(old_config_path):
|
||||
logger.info(f"{config_name}.toml配置文件不存在，从模板创建新配置")
|
||||
os.makedirs(CONFIG_DIR, exist_ok=True) # 创建文件夹
|
||||
shutil.copy2(template_path, old_config_path) # 复制模板文件
|
||||
logger.info(f"已创建新{config_name}配置文件，请填写后重新运行: {old_config_path}")
|
||||
# 如果是新创建的配置文件，根据参数决定是否退出
|
||||
if should_quit_on_new:
|
||||
quit()
|
||||
else:
|
||||
return
|
||||
logger.debug(f"compare下的{config_name}模板版本不低于当前模板，无需替换: {compare_path}")
|
||||
|
||||
# 读取旧配置文件和模板文件（如果前面没读过 old_config，这里再读一次）
|
||||
if old_config is None:
|
||||
@@ -578,8 +556,7 @@ def _update_config_generic(config_name: str, template_name: str, should_quit_on_
|
||||
# 输出新增和删减项及注释
|
||||
if old_config:
|
||||
logger.info(f"{config_name}配置项变动如下：\n----------------------------------------")
|
||||
logs = compare_dicts(new_config, old_config)
|
||||
if logs:
|
||||
if logs := compare_dicts(new_config, old_config):
|
||||
for log in logs:
|
||||
logger.info(log)
|
||||
else:
|
||||
@@ -597,12 +574,12 @@ def _update_config_generic(config_name: str, template_name: str, should_quit_on_
|
||||
|
||||
def update_config():
|
||||
"""更新bot_config.toml配置文件"""
|
||||
_update_config_generic("bot_config", "bot_config_template", should_quit_on_new=True)
|
||||
_update_config_generic("bot_config", "bot_config_template")
|
||||
|
||||
|
||||
def update_model_config():
|
||||
"""更新model_config.toml配置文件"""
|
||||
_update_config_generic("model_config", "model_config_template", should_quit_on_new=False)
|
||||
_update_config_generic("model_config", "model_config_template")
|
||||
|
||||
|
||||
@dataclass
|
||||
@@ -627,7 +604,6 @@ class Config(ConfigBase):
|
||||
response_splitter: ResponseSplitterConfig
|
||||
telemetry: TelemetryConfig
|
||||
experimental: ExperimentalConfig
|
||||
model: ModelConfig
|
||||
maim_message: MaimMessageConfig
|
||||
lpmm_knowledge: LPMMKnowledgeConfig
|
||||
tool: ToolConfig
|
||||
@@ -635,11 +611,48 @@ class Config(ConfigBase):
|
||||
custom_prompt: CustomPromptConfig
|
||||
voice: VoiceConfig
|
||||
|
||||
|
||||
@dataclass
|
||||
class APIAdapterConfig(ConfigBase):
|
||||
"""API Adapter配置类"""
|
||||
|
||||
models: List[ModelInfo]
|
||||
"""模型列表"""
|
||||
|
||||
model_task_config: ModelTaskConfig
|
||||
"""模型任务配置"""
|
||||
|
||||
api_providers: List[APIProvider] = field(default_factory=list)
|
||||
"""API提供商列表"""
|
||||
|
||||
def __post_init__(self):
|
||||
self.api_providers_dict = {provider.name: provider for provider in self.api_providers}
|
||||
self.models_dict = {model.name: model for model in self.models}
|
||||
|
||||
def get_model_info(self, model_name: str) -> ModelInfo:
|
||||
"""根据模型名称获取模型信息"""
|
||||
if not model_name:
|
||||
raise ValueError("模型名称不能为空")
|
||||
if model_name not in self.models_dict:
|
||||
raise KeyError(f"模型 '{model_name}' 不存在")
|
||||
return self.models_dict[model_name]
|
||||
|
||||
def get_provider(self, provider_name: str) -> APIProvider:
|
||||
"""根据提供商名称获取API提供商信息"""
|
||||
if not provider_name:
|
||||
raise ValueError("API提供商名称不能为空")
|
||||
if provider_name not in self.api_providers_dict:
|
||||
raise KeyError(f"API提供商 '{provider_name}' 不存在")
|
||||
return self.api_providers_dict[provider_name]
|
||||
|
||||
|
||||
def load_config(config_path: str) -> Config:
|
||||
"""
|
||||
加载配置文件
|
||||
:param config_path: 配置文件路径
|
||||
:return: Config对象
|
||||
Args:
|
||||
config_path: 配置文件路径
|
||||
Returns:
|
||||
Config对象
|
||||
"""
|
||||
# 读取配置文件
|
||||
with open(config_path, "r", encoding="utf-8") as f:
|
||||
@@ -653,12 +666,24 @@ def load_config(config_path: str) -> Config:
|
||||
raise e
|
||||
|
||||
|
||||
def get_config_dir() -> str:
|
||||
def api_ada_load_config(config_path: str) -> APIAdapterConfig:
|
||||
"""
|
||||
获取配置目录
|
||||
:return: 配置目录路径
|
||||
加载API适配器配置文件
|
||||
Args:
|
||||
config_path: 配置文件路径
|
||||
Returns:
|
||||
APIAdapterConfig对象
|
||||
"""
|
||||
return CONFIG_DIR
|
||||
# 读取配置文件
|
||||
with open(config_path, "r", encoding="utf-8") as f:
|
||||
config_data = tomlkit.load(f)
|
||||
|
||||
# 创建APIAdapterConfig对象
|
||||
try:
|
||||
return APIAdapterConfig.from_dict(config_data)
|
||||
except Exception as e:
|
||||
logger.critical("API适配器配置文件解析失败")
|
||||
raise e
|
||||
|
||||
|
||||
# 获取配置文件路径
|
||||
@@ -669,4 +694,4 @@ update_model_config()
|
||||
logger.info("正在品鉴配置文件...")
|
||||
global_config = load_config(config_path=os.path.join(CONFIG_DIR, "bot_config.toml"))
|
||||
model_config = api_ada_load_config(config_path=os.path.join(CONFIG_DIR, "model_config.toml"))
|
||||
logger.info("非常的新鲜，非常的美味！")
|
||||
logger.info("非常的新鲜，非常的美味！")
|
||||
|
||||
@@ -1,10 +1,9 @@
|
||||
import re
|
||||
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Any, Literal, Optional
|
||||
from typing import Literal, Optional
|
||||
|
||||
from src.config.config_base import ConfigBase
|
||||
from packaging.version import Version
|
||||
|
||||
"""
|
||||
须知：
|
||||
@@ -599,50 +598,3 @@ class LPMMKnowledgeConfig(ConfigBase):
|
||||
embedding_dimension: int = 1024
|
||||
"""嵌入向量维度，应该与模型的输出维度一致"""
|
||||
|
||||
@dataclass
|
||||
class ModelConfig(ConfigBase):
|
||||
"""模型配置类"""
|
||||
|
||||
model_max_output_length: int = 800 # 最大回复长度
|
||||
|
||||
utils: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""组件模型配置"""
|
||||
|
||||
utils_small: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""组件小模型配置"""
|
||||
|
||||
replyer_1: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""normal_chat首要回复模型模型配置"""
|
||||
|
||||
replyer_2: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""normal_chat次要回复模型配置"""
|
||||
|
||||
memory: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""记忆模型配置"""
|
||||
|
||||
emotion: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""情绪模型配置"""
|
||||
|
||||
vlm: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""视觉语言模型配置"""
|
||||
|
||||
voice: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""语音识别模型配置"""
|
||||
|
||||
tool_use: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""专注工具使用模型配置"""
|
||||
|
||||
planner: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""规划模型配置"""
|
||||
|
||||
embedding: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""嵌入模型配置"""
|
||||
|
||||
lpmm_entity_extract: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""LPMM实体提取模型配置"""
|
||||
|
||||
lpmm_rdf_build: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""LPMM RDF构建模型配置"""
|
||||
|
||||
lpmm_qa: dict[str, Any] = field(default_factory=lambda: {})
|
||||
"""LPMM问答模型配置"""
|
||||
|
||||
Reference in New Issue
Block a user