ref:修改默认推荐模型为v3.2和r1

pull/1325/head
SengokuCola 2025-10-26 23:06:11 +08:00
parent c5b9bc4927
commit 7660be7076
4 changed files with 33 additions and 26 deletions

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@ -1009,6 +1009,9 @@ class DefaultReplyer:
prompt prompt
) )
# 移除 content 前后的换行符和空格
content = content.strip()
logger.info(f"使用 {model_name} 生成回复内容: {content}") logger.info(f"使用 {model_name} 生成回复内容: {content}")
return content, reasoning_content, model_name, tool_calls return content, reasoning_content, model_name, tool_calls

View File

@ -930,6 +930,8 @@ class PrivateReplyer:
content, (reasoning_content, model_name, tool_calls) = await self.express_model.generate_response_async( content, (reasoning_content, model_name, tool_calls) = await self.express_model.generate_response_async(
prompt prompt
) )
content = content.strip()
logger.info(f"使用 {model_name} 生成回复内容: {content}") logger.info(f"使用 {model_name} 生成回复内容: {content}")
return content, reasoning_content, model_name, tool_calls return content, reasoning_content, model_name, tool_calls

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@ -128,9 +128,9 @@ class GetMemoryTool(BaseTool):
content_parts = [f"问题:{question}"] content_parts = [f"问题:{question}"]
if memory_answer: if memory_answer:
content_parts.append(f"记忆仓库答案{memory_answer}") content_parts.append(f"对问题'{question}',你回忆的信息是{memory_answer}")
else: else:
content_parts.append(f"记忆仓库:对问题'{question}',没有什么印象") content_parts.append(f"对问题'{question}',没有什么印象")
if chat_answer: if chat_answer:
content_parts.append(f"对问题'{question}',基于聊天记录的回答:{chat_answer}") content_parts.append(f"对问题'{question}',基于聊天记录的回答:{chat_answer}")

View File

@ -35,9 +35,9 @@ name = "SiliconFlow"
base_url = "https://api.siliconflow.cn/v1" base_url = "https://api.siliconflow.cn/v1"
api_key = "your-siliconflow-api-key" api_key = "your-siliconflow-api-key"
client_type = "openai" client_type = "openai"
max_retry = 2 max_retry = 3
timeout = 120 timeout = 120
retry_interval = 10 retry_interval = 5
[[models]] # 模型(可以配置多个) [[models]] # 模型(可以配置多个)
@ -49,11 +49,22 @@ price_out = 8.0 # 输出价格用于API调用统计
#force_stream_mode = true # 强制流式输出模式若模型不支持非流式输出请取消该注释启用强制流式输出若无该字段默认值为false #force_stream_mode = true # 强制流式输出模式若模型不支持非流式输出请取消该注释启用强制流式输出若无该字段默认值为false
[[models]] [[models]]
model_identifier = "deepseek-ai/DeepSeek-V3" model_identifier = "deepseek-ai/DeepSeek-V3.2-Exp"
name = "siliconflow-deepseek-v3" name = "siliconflow-deepseek-v3.2"
api_provider = "SiliconFlow" api_provider = "SiliconFlow"
price_in = 2.0 price_in = 2.0
price_out = 8.0 price_out = 3.0
[models.extra_params] # 可选的额外参数配置
enable_thinking = false # 不启用思考
[[models]]
model_identifier = "deepseek-ai/DeepSeek-V3.2-Exp"
name = "siliconflow-deepseek-v3.2-think"
api_provider = "SiliconFlow"
price_in = 2.0
price_out = 3.0
[models.extra_params] # 可选的额外参数配置
enable_thinking = true # 不启用思考
[[models]] [[models]]
model_identifier = "deepseek-ai/DeepSeek-R1" model_identifier = "deepseek-ai/DeepSeek-R1"
@ -63,15 +74,6 @@ price_in = 4.0
price_out = 16.0 price_out = 16.0
[[models]]
model_identifier = "Qwen/Qwen3-8B"
name = "qwen3-8b"
api_provider = "SiliconFlow"
price_in = 0
price_out = 0
[models.extra_params] # 可选的额外参数配置
enable_thinking = false # 不启用思考
[[models]] [[models]]
model_identifier = "Qwen/Qwen3-30B-A3B-Instruct-2507" model_identifier = "Qwen/Qwen3-30B-A3B-Instruct-2507"
name = "qwen3-30b" name = "qwen3-30b"
@ -80,8 +82,8 @@ price_in = 0.7
price_out = 2.8 price_out = 2.8
[[models]] [[models]]
model_identifier = "Qwen/Qwen2.5-VL-72B-Instruct" model_identifier = "Qwen/Qwen3-VL-30B-A3B-Instruct"
name = "qwen2.5-vl-72b" name = "qwen3-vl-30"
api_provider = "SiliconFlow" api_provider = "SiliconFlow"
price_in = 4.13 price_in = 4.13
price_out = 4.13 price_out = 4.13
@ -102,12 +104,12 @@ price_out = 0
[model_task_config.utils] # 在麦麦的一些组件中使用的模型,例如表情包模块,取名模块,关系模块,麦麦的情绪变化等,是麦麦必须的模型 [model_task_config.utils] # 在麦麦的一些组件中使用的模型,例如表情包模块,取名模块,关系模块,麦麦的情绪变化等,是麦麦必须的模型
model_list = ["siliconflow-deepseek-v3"] # 使用的模型列表,每个子项对应上面的模型名称(name) model_list = ["siliconflow-deepseek-v3.2"] # 使用的模型列表,每个子项对应上面的模型名称(name)
temperature = 0.2 # 模型温度新V3建议0.1-0.3 temperature = 0.2 # 模型温度新V3建议0.1-0.3
max_tokens = 2048 # 最大输出token数 max_tokens = 2048 # 最大输出token数
[model_task_config.utils_small] # 在麦麦的一些组件中使用的小模型,消耗量较大,建议使用速度较快的小模型 [model_task_config.utils_small] # 在麦麦的一些组件中使用的小模型,消耗量较大,建议使用速度较快的小模型
model_list = ["qwen3-8b","qwen3-30b"] model_list = ["qwen3-30b"]
temperature = 0.7 temperature = 0.7
max_tokens = 2048 max_tokens = 2048
@ -117,17 +119,17 @@ temperature = 0.7
max_tokens = 800 max_tokens = 800
[model_task_config.replyer] # 首要回复模型,还用于表达器和表达方式学习 [model_task_config.replyer] # 首要回复模型,还用于表达器和表达方式学习
model_list = ["siliconflow-deepseek-v3","siliconflow-deepseek-r1"] model_list = ["siliconflow-deepseek-v3.2-think","siliconflow-deepseek-r1","siliconflow-deepseek-v3.2"]
temperature = 0.3 # 模型温度新V3建议0.1-0.3 temperature = 0.3 # 模型温度新V3建议0.1-0.3
max_tokens = 800 max_tokens = 800
[model_task_config.planner] #决策:负责决定麦麦该什么时候回复的模型 [model_task_config.planner] #决策:负责决定麦麦该什么时候回复的模型
model_list = ["siliconflow-deepseek-v3"] model_list = ["siliconflow-deepseek-v3.2"]
temperature = 0.3 temperature = 0.3
max_tokens = 800 max_tokens = 800
[model_task_config.vlm] # 图像识别模型 [model_task_config.vlm] # 图像识别模型
model_list = ["qwen2.5-vl-72b"] model_list = ["qwen3-vl-30"]
max_tokens = 256 max_tokens = 256
[model_task_config.voice] # 语音识别模型 [model_task_config.voice] # 语音识别模型
@ -140,16 +142,16 @@ model_list = ["bge-m3"]
#------------LPMM知识库模型------------ #------------LPMM知识库模型------------
[model_task_config.lpmm_entity_extract] # 实体提取模型 [model_task_config.lpmm_entity_extract] # 实体提取模型
model_list = ["siliconflow-deepseek-v3"] model_list = ["siliconflow-deepseek-v3.2"]
temperature = 0.2 temperature = 0.2
max_tokens = 800 max_tokens = 800
[model_task_config.lpmm_rdf_build] # RDF构建模型 [model_task_config.lpmm_rdf_build] # RDF构建模型
model_list = ["siliconflow-deepseek-v3"] model_list = ["siliconflow-deepseek-v3.2"]
temperature = 0.2 temperature = 0.2
max_tokens = 800 max_tokens = 800
[model_task_config.lpmm_qa] # 问答模型 [model_task_config.lpmm_qa] # 问答模型
model_list = ["qwen3-30b"] model_list = ["siliconflow-deepseek-v3.2"]
temperature = 0.7 temperature = 0.7
max_tokens = 800 max_tokens = 800