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fix: 更新LLMRequest类的任务名称确定逻辑，优先使用配置文件中的task_type和capabilities字段

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墨梓柒
2025-07-28 19:50:40 +08:00
parent 6cf2533bea
commit 3a11c69b8c
2 changed files with 97 additions and 5 deletions
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43
src/llm_models/utils_model.py
View File

@@ -216,24 +216,59 @@ class LLMRequest:
def _determine_task_name(self, model: dict) -> str:
"""
根据模型配置确定任务名称
优先使用配置文件中明确定义的任务类型，避免基于模型名称的脆弱推断
Args:
model: 模型配置字典
Returns:
任务名称
"""
# 兼容新旧格式的模型名称
model_name = model.get("model_name", model.get("name", ""))
# 方法1: 优先使用配置文件中明确定义的 task_type 字段
if "task_type" in model:
task_type = model["task_type"]
logger.debug(f"🎯 [任务确定] 使用配置中的 task_type: {task_type}")
return task_type
# 根据模型名称推断任务类型
# 方法2: 使用 capabilities 字段来推断主要任务类型
if "capabilities" in model:
capabilities = model["capabilities"]
if isinstance(capabilities, list):
# 按优先级顺序检查能力
if "vision" in capabilities:
logger.debug(f"🎯 [任务确定] 从 capabilities {capabilities} 推断为: vision")
return "vision"
elif "embedding" in capabilities:
logger.debug(f"🎯 [任务确定] 从 capabilities {capabilities} 推断为: embedding")
return "embedding"
elif "speech" in capabilities:
logger.debug(f"🎯 [任务确定] 从 capabilities {capabilities} 推断为: speech")
return "speech"
elif "text" in capabilities:
# 如果只有文本能力，则根据request_type细分
task = "llm_reasoning" if self.request_type == "reasoning" else "llm_normal"
logger.debug(f"🎯 [任务确定] 从 capabilities {capabilities} 和 request_type {self.request_type} 推断为: {task}")
return task
# 方法3: 向后兼容 - 基于模型名称的关键字推断（不推荐但保留兼容性）
model_name = model.get("model_name", model.get("name", ""))
logger.warning(f"⚠️ [任务确定] 配置中未找到 task_type 或 capabilities，回退到基于模型名称的推断: {model_name}")
logger.warning("⚠️ [建议] 请在 model_config.toml 中为模型添加明确的 task_type 或 capabilities 字段")
# 保留原有的关键字匹配逻辑作为fallback
if any(keyword in model_name.lower() for keyword in ["vlm", "vision", "gpt-4o", "claude", "vl-"]):
logger.debug(f"🎯 [任务确定] 从模型名称 {model_name} 推断为: vision")
return "vision"
elif any(keyword in model_name.lower() for keyword in ["embed", "text-embedding", "bge-"]):
logger.debug(f"🎯 [任务确定] 从模型名称 {model_name} 推断为: embedding")
return "embedding"
elif any(keyword in model_name.lower() for keyword in ["whisper", "speech", "voice"]):
logger.debug(f"🎯 [任务确定] 从模型名称 {model_name} 推断为: speech")
return "speech"
else:
# 根据request_type确定，映射到配置文件中定义的任务
return "llm_reasoning" if self.request_type == "reasoning" else "llm_normal"
task = "llm_reasoning" if self.request_type == "reasoning" else "llm_normal"
logger.debug(f"🎯 [任务确定] 从 request_type {self.request_type} 推断为: {task}")
return task
@staticmethod
def _init_database():

59
template/model_config_template.toml
View File

@@ -1,5 +1,5 @@
[inner]
version = "0.2.0"
version = "0.2.1"
# 配置文件版本号迭代规则同bot_config.toml
#
@@ -18,6 +18,28 @@ version = "0.2.0"
# - 429频率限制：等待后重试，如果持续失败则切换Key
# - 网络错误：短暂等待后重试，失败则切换Key
# - 其他错误：按照正常重试机制处理
#
# === 任务类型和模型能力配置 ===
# 为了提高任务分配的准确性和可维护性，现在支持明确配置模型的任务类型和能力：
#
# task_type（推荐配置）:
# - 明确指定模型主要用于什么任务
# - 可选值：llm_normal, llm_reasoning, vision, embedding, speech
# - 如果不配置，系统会根据capabilities或模型名称自动推断（不推荐）
#
# capabilities（推荐配置）:
# - 描述模型支持的所有能力
# - 可选值：text, vision, embedding, speech, tool_calling, reasoning
# - 支持多个能力的组合，如：["text", "vision"]
#
# 配置优先级：
# 1. task_type（最高优先级，直接指定任务类型）
# 2. capabilities（中等优先级，根据能力推断任务类型）
# 3. 模型名称关键字（最低优先级，不推荐依赖）
#
# 向后兼容：
# - 仍然支持 model_flags 字段，但建议迁移到 capabilities
# - 未配置新字段时会自动回退到基于模型名称的推断
[request_conf] # 请求配置（此配置项数值均为默认值，如想修改，请取消对应条目的注释）
#max_retry = 2 # 最大重试次数（单个模型API调用失败，最多重试的次数）
@@ -70,6 +92,13 @@ model_identifier = "deepseek-chat"
name = "deepseek-v3"
# API服务商名称（对应在api_providers中配置的服务商名称）
api_provider = "DeepSeek"
# 任务类型（推荐配置，明确指定模型主要用于什么任务）
# 可选值：llm_normal, llm_reasoning, vision, embedding, speech
# 如果不配置，系统会根据capabilities或模型名称自动推断
task_type = "llm_normal"
# 模型能力列表（推荐配置，描述模型支持的能力）
# 可选值：text, vision, embedding, speech, tool_calling, reasoning
capabilities = ["text", "tool_calling"]
# 输入价格（用于API调用统计，单位：元/兆token）（可选，若无该字段，默认值为0）
price_in = 2.0
# 输出价格（用于API调用统计，单位：元/兆token）（可选，若无该字段，默认值为0）
@@ -82,6 +111,10 @@ price_out = 8.0
model_identifier = "deepseek-reasoner"
name = "deepseek-r1"
api_provider = "DeepSeek"
# 推理模型的配置示例
task_type = "llm_reasoning"
capabilities = ["text", "tool_calling", "reasoning"]
# 保留向后兼容的model_flags字段（已废弃，建议使用capabilities）
model_flags = [ "text", "tool_calling", "reasoning",]
price_in = 4.0
price_out = 16.0
@@ -90,6 +123,8 @@ price_out = 16.0
model_identifier = "Pro/deepseek-ai/DeepSeek-V3"
name = "siliconflow-deepseek-v3"
api_provider = "SiliconFlow"
task_type = "llm_normal"
capabilities = ["text", "tool_calling"]
price_in = 2.0
price_out = 8.0
@@ -97,6 +132,8 @@ price_out = 8.0
model_identifier = "Pro/deepseek-ai/DeepSeek-R1"
name = "siliconflow-deepseek-r1"
api_provider = "SiliconFlow"
task_type = "llm_reasoning"
capabilities = ["text", "tool_calling", "reasoning"]
price_in = 4.0
price_out = 16.0
@@ -104,6 +141,8 @@ price_out = 16.0
model_identifier = "Pro/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"
name = "deepseek-r1-distill-qwen-32b"
api_provider = "SiliconFlow"
task_type = "llm_reasoning"
capabilities = ["text", "tool_calling", "reasoning"]
price_in = 4.0
price_out = 16.0
@@ -111,6 +150,8 @@ price_out = 16.0
model_identifier = "Qwen/Qwen3-8B"
name = "qwen3-8b"
api_provider = "SiliconFlow"
task_type = "llm_normal"
capabilities = ["text"]
price_in = 0
price_out = 0
@@ -118,6 +159,8 @@ price_out = 0
model_identifier = "Qwen/Qwen3-14B"
name = "qwen3-14b"
api_provider = "SiliconFlow"
task_type = "llm_normal"
capabilities = ["text", "tool_calling"]
price_in = 0.5
price_out = 2.0
@@ -125,6 +168,8 @@ price_out = 2.0
model_identifier = "Qwen/Qwen3-30B-A3B"
name = "qwen3-30b"
api_provider = "SiliconFlow"
task_type = "llm_normal"
capabilities = ["text", "tool_calling"]
price_in = 0.7
price_out = 2.8
@@ -132,6 +177,10 @@ price_out = 2.8
model_identifier = "Qwen/Qwen2.5-VL-72B-Instruct"
name = "qwen2.5-vl-72b"
api_provider = "SiliconFlow"
# 视觉模型的配置示例
task_type = "vision"
capabilities = ["vision", "text"]
# 保留向后兼容的model_flags字段（已废弃，建议使用capabilities）
model_flags = [ "vision", "text",]
price_in = 4.13
price_out = 4.13
@@ -140,6 +189,10 @@ price_out = 4.13
model_identifier = "FunAudioLLM/SenseVoiceSmall"
name = "sensevoice-small"
api_provider = "SiliconFlow"
# 语音模型的配置示例
task_type = "speech"
capabilities = ["speech"]
# 保留向后兼容的model_flags字段（已废弃，建议使用capabilities）
model_flags = [ "audio",]
price_in = 0
price_out = 0
@@ -148,6 +201,10 @@ price_out = 0
model_identifier = "BAAI/bge-m3"
name = "bge-m3"
api_provider = "SiliconFlow"
# 嵌入模型的配置示例
task_type = "embedding"
capabilities = ["text", "embedding"]
# 保留向后兼容的model_flags字段（已废弃，建议使用capabilities）
model_flags = [ "text", "embedding",]
price_in = 0
price_out = 0