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better：优化分割，优化表达使用，优化Planner选择和联动，优化记忆总结，优化回复Log

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This commit is contained in:
SengokuCola
2025-12-18 10:52:58 +08:00
parent 3ea775af92
commit 1e159213cf
9 changed files with 252 additions and 58 deletions
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54
src/chat/replyer/group_generator.py
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@@ -98,8 +98,10 @@ class DefaultReplyer:
available_actions = {}
try:
# 3. 构建 Prompt
timing_logs = []
almost_zero_str = ""
with Timer("构建Prompt", {}): # 内部计时器，可选保留
prompt, selected_expressions = await self.build_prompt_reply_context(
prompt, selected_expressions, timing_logs, almost_zero_str = await self.build_prompt_reply_context(
extra_info=extra_info,
available_actions=available_actions,
chosen_actions=chosen_actions,
@@ -136,9 +138,22 @@ class DefaultReplyer:
content, reasoning_content, model_name, tool_call = await self.llm_generate_content(prompt)
# logger.debug(f"replyer生成内容: {content}")
logger.info(f"模型: [{model_name}][思考等级:{think_level}]生成内容: {content}")
if global_config.debug.show_replyer_reasoning and reasoning_content:
logger.info(f"模型: [{model_name}][思考等级:{think_level}]生成推理:\n{reasoning_content}")
# 统一输出所有日志信息，使用try-except确保即使某个步骤出错也能输出
try:
# 1. 输出回复准备日志
timing_log_str = f"回复准备: {'; '.join(timing_logs)}; {almost_zero_str} <0.1s" if timing_logs or almost_zero_str else "回复准备: 无计时信息"
logger.info(timing_log_str)
# 2. 输出Prompt日志
if global_config.debug.show_replyer_prompt:
logger.info(f"\n{prompt}\n")
else:
logger.debug(f"\nreplyer_Prompt:{prompt}\n")
# 3. 输出模型生成内容和推理日志
logger.info(f"模型: [{model_name}][思考等级:{think_level}]生成内容: {content}")
if global_config.debug.show_replyer_reasoning and reasoning_content:
logger.info(f"模型: [{model_name}][思考等级:{think_level}]生成推理:\n{reasoning_content}")
except Exception as e:
logger.warning(f"输出日志时出错: {e}")
llm_response.content = content
llm_response.reasoning = reasoning_content
@@ -162,6 +177,21 @@ class DefaultReplyer:
except Exception as llm_e:
# 精简报错信息
logger.error(f"LLM 生成失败: {llm_e}")
# 即使LLM生成失败，也尝试输出已收集的日志信息
try:
# 1. 输出回复准备日志
timing_log_str = f"回复准备: {'; '.join(timing_logs)}; {almost_zero_str} <0.1s" if timing_logs or almost_zero_str else "回复准备: 无计时信息"
logger.info(timing_log_str)
# 2. 输出Prompt日志
if global_config.debug.show_replyer_prompt:
logger.info(f"\n{prompt}\n")
else:
logger.debug(f"\nreplyer_Prompt:{prompt}\n")
# 3. 输出模型生成失败信息
logger.info("模型生成失败，无法输出生成内容和推理")
except Exception as log_e:
logger.warning(f"输出日志时出错: {log_e}")
return False, llm_response # LLM 调用失败则无法生成回复
return True, llm_response
@@ -705,7 +735,7 @@ class DefaultReplyer:
enable_tool: bool = True,
reply_time_point: Optional[float] = time.time(),
think_level: int = 1,
) -> Tuple[str, List[int]]:
) -> Tuple[str, List[int], List[str], str]:
"""
构建回复器上下文
@@ -838,7 +868,8 @@ class DefaultReplyer:
continue
timing_logs.append(f"{chinese_name}: {duration:.1f}s")
logger.info(f"回复准备: {'; '.join(timing_logs)}; {almost_zero_str} <0.1s")
# 不再在这里输出日志，而是返回给调用者统一输出
# logger.info(f"回复准备: {'; '.join(timing_logs)}; {almost_zero_str} <0.1s")
expression_habits_block, selected_expressions = results_dict["expression_habits"]
expression_habits_block: str
@@ -915,7 +946,7 @@ class DefaultReplyer:
memory_retrieval=memory_retrieval,
chat_prompt=chat_prompt_block,
planner_reasoning=planner_reasoning,
), selected_expressions
), selected_expressions, timing_logs, almost_zero_str
async def build_prompt_rewrite_context(
self,
@@ -1046,10 +1077,11 @@ class DefaultReplyer:
# 直接使用已初始化的模型实例
# logger.info(f"\n{prompt}\n")
if global_config.debug.show_replyer_prompt:
logger.info(f"\n{prompt}\n")
else:
logger.debug(f"\nreplyer_Prompt:{prompt}\n")
# 不再在这里输出日志，而是返回给调用者统一输出
# if global_config.debug.show_replyer_prompt:
# logger.info(f"\n{prompt}\n")
# else:
# logger.debug(f"\nreplyer_Prompt:{prompt}\n")
content, (reasoning_content, model_name, tool_calls) = await self.express_model.generate_response_async(
prompt