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feat(kfc):独立私聊回复模型配置

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- 在 ModelTaskConfig 中为私聊场景添加 `replyer_private` 字段
- 更新 KFC 回复器和统一模块以使用新的私聊配置
- 配置模板版本升级至 1.4.2，并更新 DeepSeek 模型名称
- 增强 KokoroFlowChatter 的执行日志
This commit is contained in:
tt-P607
2025-12-13 19:38:06 +08:00
parent f1bfcd1cff
commit 87704702ad
5 changed files with 34 additions and 26 deletions
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3
src/config/api_ada_configs.py
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@@ -130,7 +130,8 @@ class ModelTaskConfig(ValidatedConfigBase):
# 必需配置项
utils: TaskConfig = Field(..., description="组件模型配置")
utils_small: TaskConfig = Field(..., description="组件小模型配置")
replyer: TaskConfig = Field(..., description="normal_chat首要回复模型模型配置")
replyer: TaskConfig = Field(..., description="normal_chat首要回复模型模型配置（群聊使用）")
replyer_private: TaskConfig = Field(..., description="normal_chat首要回复模型模型配置（私聊使用）")
maizone: TaskConfig = Field(..., description="maizone专用模型")
emotion: TaskConfig = Field(..., description="情绪模型配置")
vlm: TaskConfig = Field(..., description="视觉语言模型配置")

4
src/plugins/built_in/kokoro_flow_chatter/chatter.py
View File

@@ -206,7 +206,8 @@ class KokoroFlowChatter(BaseChatter):
exec_results = []
has_reply = False
for action in plan_response.actions:
for idx, action in enumerate(plan_response.actions, 1):
logger.debug(f"[KFC] 执行第 {idx}/{len(plan_response.actions)} 个动作: {action.type}")
action_data = action.params.copy()
result = await self.action_manager.execute_action(
@@ -218,6 +219,7 @@ class KokoroFlowChatter(BaseChatter):
thinking_id=None,
log_prefix="[KFC]",
)
logger.debug(f"[KFC] 动作 {action.type} 执行结果: success={result.get('success')}, reply_text={result.get('reply_text', '')[:50]}")
exec_results.append(result)
if result.get("success") and action.type in ("kfc_reply", "respond"):
has_reply = True

6
src/plugins/built_in/kokoro_flow_chatter/replyer.py
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@@ -61,12 +61,12 @@ async def generate_reply_text(
if global_config and global_config.debug.show_prompt:
logger.info(f"[KFC Replyer] 生成的回复提示词:\n{prompt}")
# 2. 获取 replyer 模型配置并调用 LLM
# 2. 获取 replyer_private 模型配置并调用 LLM（KFC私聊专用）
models = llm_api.get_available_models()
replyer_config = models.get("replyer")
replyer_config = models.get("replyer_private")
if not replyer_config:
logger.error("[KFC Replyer] 未找到 replyer 模型配置")
logger.error("[KFC Replyer] 未找到 replyer_private 模型配置")
return False, "（回复生成失败：未找到模型配置）"
success, raw_response, _reasoning, _model_name = await llm_api.generate_with_model(

8
src/plugins/built_in/kokoro_flow_chatter/unified.py
View File

@@ -389,13 +389,13 @@ async def generate_unified_response(
f"--- PROMPT END ---"
)
# 获取 replyer 模型配置并调用 LLM
# 获取 replyer_private 模型配置并调用 LLM（KFC私聊专用）
models = llm_api.get_available_models()
replyer_config = models.get("replyer")
replyer_config = models.get("replyer_private")
if not replyer_config:
logger.error("[KFC Unified] 未找到 replyer 模型配置")
return LLMResponse.create_error_response("未找到 replyer 模型配置")
logger.error("[KFC Unified] 未找到 replyer_private 模型配置")
return LLMResponse.create_error_response("未找到 replyer_private 模型配置")
# 调用 LLM（使用合并后的提示词）
success, raw_response, _reasoning, _model_name = await llm_api.generate_with_model(

39
template/model_config_template.toml
View File

@@ -1,5 +1,5 @@
[inner]
version = "1.4.1"
version = "1.4.2"
# 配置文件版本号迭代规则同bot_config.toml
@@ -68,8 +68,8 @@ price_out = 8.0 # 输出价格（用于API调用统计，单
#enable_semantic_variants = false # [可选] 启用语义变体。作为一种扰动策略，生成语义上相似但表达不同的提示。默认为 false。
[[models]]
model_identifier = "deepseek-ai/DeepSeek-V3.2-Exp"
name = "siliconflow-deepseek-ai/DeepSeek-V3.2-Exp"
model_identifier = "deepseek-ai/DeepSeek-V3."
name = "siliconflow-deepseek-ai/DeepSeek-V3.2"
api_provider = "SiliconFlow"
price_in = 2.0
price_out = 8.0
@@ -170,7 +170,7 @@ thinking_budget = 256 # Gemini2.5系列旧版参数，不同模型范围
#price_out = 0.0
[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，是麦麦必须的模型
model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2-Exp"] # 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-ai/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
max_tokens = 800 # 最大输出token数
#concurrency_count = 2 # 并发请求数量，默认为1（不并发），设置为2或更高启用并发
@@ -180,29 +180,34 @@ model_list = ["qwen3-8b"]
temperature = 0.7
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_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2-Exp"]
[model_task_config.replyer] # 首要回复模型（群聊使用），还用于表达器和表达方式学习
model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2"]
temperature = 0.2 # 模型温度，新V3建议0.1-0.3
max_tokens = 800
[model_task_config.replyer_private] # 私聊回复模型（KFC私聊专用）
model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2"] # 可以配置不同的模型用于私聊
temperature = 0.2 # 模型温度，新V3建议0.1-0.3
max_tokens = 800
[model_task_config.planner] #决策：负责决定麦麦该做什么的模型
model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2-Exp"]
model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2"]
temperature = 0.3
max_tokens = 800
[model_task_config.emotion] #负责麦麦的情绪变化
model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2-Exp"]
model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2"]
temperature = 0.3
max_tokens = 800
[model_task_config.mood] #负责麦麦的心情变化
model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2-Exp"]
model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2"]
temperature = 0.3
max_tokens = 800
[model_task_config.maizone] # maizone模型
model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2-Exp"]
model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2"]
temperature = 0.7
max_tokens = 800
@@ -229,22 +234,22 @@ temperature = 0.7
max_tokens = 800
[model_task_config.schedule_generator]#日程表生成模型
model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2-Exp"]
model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2"]
temperature = 0.7
max_tokens = 1000
[model_task_config.anti_injection] # 反注入检测专用模型
model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2-Exp"] # 使用快速的小模型进行检测
model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2"] # 使用快速的小模型进行检测
temperature = 0.1 # 低温度确保检测结果稳定
max_tokens = 200 # 检测结果不需要太长的输出
[model_task_config.monthly_plan_generator] # 月层计划生成模型
model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2-Exp"]
model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2"]
temperature = 0.7
max_tokens = 1000
[model_task_config.relationship_tracker] # 用户关系追踪模型
model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2-Exp"]
model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2"]
temperature = 0.7
max_tokens = 1000
@@ -258,12 +263,12 @@ embedding_dimension = 1024
#------------LPMM知识库模型------------
[model_task_config.lpmm_entity_extract] # 实体提取模型
model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2-Exp"]
model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2"]
temperature = 0.2
max_tokens = 800
[model_task_config.lpmm_rdf_build] # RDF构建模型
model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2-Exp"]
model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2"]
temperature = 0.2
max_tokens = 800
@@ -285,7 +290,7 @@ temperature = 0.2
max_tokens = 1000
[model_task_config.memory_long_term_builder] # 长期记忆构建模型（短期→长期图结构）
model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2-Exp"]
model_list = ["siliconflow-deepseek-ai/DeepSeek-V3.2"]
temperature = 0.2
max_tokens = 1500