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黑话提取新模式

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This commit is contained in:
SengokuCola
2025-12-18 21:01:32 +08:00
parent f5b5c009bf
commit bc2431ce98
8 changed files with 138 additions and 41 deletions
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15
src/chat/brain_chat/brain_chat.py
View File

@@ -588,12 +588,27 @@ class BrainChatting:
elif action_planner_info.action_type == "reply":
try:
# 从 Planner 的 action_data 中提取未知词语列表（仅在 reply 时使用）
unknown_words = None
if isinstance(action_planner_info.action_data, dict):
uw = action_planner_info.action_data.get("unknown_words")
if isinstance(uw, list):
cleaned_uw: List[str] = []
for item in uw:
if isinstance(item, str):
s = item.strip()
if s:
cleaned_uw.append(s)
if cleaned_uw:
unknown_words = cleaned_uw
success, llm_response = await generator_api.generate_reply(
chat_stream=self.chat_stream,
reply_message=action_planner_info.action_message,
available_actions=available_actions,
chosen_actions=chosen_action_plan_infos,
reply_reason=action_planner_info.reasoning or "",
unknown_words=unknown_words,
enable_tool=global_config.tool.enable_tool,
request_type="replyer",
from_plugin=False,

15
src/chat/heart_flow/heartFC_chat.py
View File

@@ -638,12 +638,27 @@ class HeartFChatting:
action_reasoning=reason,
)
# 从 Planner 的 action_data 中提取未知词语列表（仅在 reply 时使用）
unknown_words = None
if isinstance(action_planner_info.action_data, dict):
uw = action_planner_info.action_data.get("unknown_words")
if isinstance(uw, list):
cleaned_uw: List[str] = []
for item in uw:
if isinstance(item, str):
s = item.strip()
if s:
cleaned_uw.append(s)
if cleaned_uw:
unknown_words = cleaned_uw
success, llm_response = await generator_api.generate_reply(
chat_stream=self.chat_stream,
reply_message=action_planner_info.action_message,
available_actions=available_actions,
chosen_actions=chosen_action_plan_infos,
reply_reason=planner_reasoning,
unknown_words=unknown_words,
enable_tool=global_config.tool.enable_tool,
request_type="replyer",
from_plugin=False,

15
src/chat/planner_actions/planner.py
View File

@@ -51,6 +51,8 @@ reply
3.最好一次对一个话题进行回复，免得啰嗦或者回复内容太乱。
4.不要选择回复你自己发送的消息
5.不要单独对表情包进行回复
6.将上下文中所有含义不明的，疑似黑话的，缩写词均写入unknown_words中
7.用一句简单的话来描述当前回复场景，不超过10个字
{reply_action_example}
no_reply
@@ -478,10 +480,19 @@ class ActionPlanner:
name_block = f"你的名字是{bot_name}{bot_nickname}，请注意哪些是你自己的发言。"
# 根据 think_mode 配置决定 reply action 的示例 JSON
# 在 JSON 中直接作为 action 参数携带 unknown_words
if global_config.chat.think_mode == "classic":
reply_action_example = '{{"action":"reply", "target_messamge_id":"消息id(m+数字)"}}'
reply_action_example = (
'{{"action":"reply", "target_messamge_id":"消息id(m+数字)", '
'"unknown_words":["词语1","词语2"]}}'
)
else:
reply_action_example = '5.think_level表示思考深度，0表示该回复不需要思考和回忆，1表示该回复需要进行回忆和思考\n{{"action":"reply", "think_level":数值等级(0或1), "target_messamge_id":"消息id(m+数字)"}}'
reply_action_example = (
"5.think_level表示思考深度，0表示该回复不需要思考和回忆，1表示该回复需要进行回忆和思考\n"
+ '{{"action":"reply", "think_level":数值等级(0或1), '
'"target_messamge_id":"消息id(m+数字)", '
'"unknown_words":["词语1","词语2"]}}'
)
planner_prompt_template = await global_prompt_manager.get_prompt_async("planner_prompt")
prompt = planner_prompt_template.format(

76
src/chat/replyer/group_generator.py
View File

@@ -35,7 +35,7 @@ from src.chat.replyer.prompt.lpmm_prompt import init_lpmm_prompt
from src.chat.replyer.prompt.replyer_prompt import init_replyer_prompt
from src.chat.replyer.prompt.rewrite_prompt import init_rewrite_prompt
from src.memory_system.memory_retrieval import init_memory_retrieval_prompt, build_memory_retrieval_prompt
from src.bw_learner.jargon_explainer import explain_jargon_in_context
from src.bw_learner.jargon_explainer import explain_jargon_in_context, retrieve_concepts_with_jargon
init_lpmm_prompt()
init_replyer_prompt()
@@ -73,6 +73,7 @@ class DefaultReplyer:
reply_message: Optional[DatabaseMessages] = None,
reply_time_point: Optional[float] = time.time(),
think_level: int = 1,
unknown_words: Optional[List[str]] = None,
) -> Tuple[bool, LLMGenerationDataModel]:
# sourcery skip: merge-nested-ifs
"""
@@ -110,6 +111,7 @@ class DefaultReplyer:
reply_reason=reply_reason,
reply_time_point=reply_time_point,
think_level=think_level,
unknown_words=unknown_words,
)
llm_response.prompt = prompt
llm_response.selected_expressions = selected_expressions
@@ -492,6 +494,53 @@ class DefaultReplyer:
"""当关闭黑话解释时使用的占位协程，避免额外的LLM调用"""
return ""
async def _build_unknown_words_jargon(self, unknown_words: Optional[List[str]], chat_id: str) -> str:
"""针对 Planner 提供的未知词语列表执行黑话检索"""
if not unknown_words:
return ""
# 清洗未知词语列表，只保留非空字符串
concepts: List[str] = []
for item in unknown_words:
if isinstance(item, str):
s = item.strip()
if s:
concepts.append(s)
if not concepts:
return ""
try:
return await retrieve_concepts_with_jargon(concepts, chat_id)
except Exception as e:
logger.error(f"未知词语黑话检索失败: {e}")
return ""
async def _build_jargon_explanation(
self,
chat_id: str,
messages_short: List[DatabaseMessages],
chat_talking_prompt_short: str,
unknown_words: Optional[List[str]],
) -> str:
"""
统一的黑话解释构建函数：
- 根据 enable_jargon_explanation / jargon_mode 决定具体策略
"""
enable_jargon_explanation = getattr(global_config.expression, "enable_jargon_explanation", True)
if not enable_jargon_explanation:
return ""
jargon_mode = getattr(global_config.expression, "jargon_mode", "context")
# planner 模式：仅使用 Planner 的 unknown_words
if jargon_mode == "planner":
return await self._build_unknown_words_jargon(unknown_words, chat_id)
# 默认 / context 模式：使用上下文自动匹配黑话
try:
return await explain_jargon_in_context(chat_id, messages_short, chat_talking_prompt_short)
except Exception as e:
logger.error(f"上下文黑话解释失败: {e}")
return ""
def build_chat_history_prompts(
self, message_list_before_now: List[DatabaseMessages], target_user_id: str, sender: str
) -> Tuple[str, str]:
@@ -676,16 +725,10 @@ class DefaultReplyer:
# 判断是否为群聊
is_group = stream_type == "group"
# 使用与 ChatStream.get_stream_id 相同的逻辑生成 chat_id
import hashlib
if is_group:
components = [platform, str(id_str)]
else:
components = [platform, str(id_str), "private"]
key = "_".join(components)
chat_id = hashlib.md5(key.encode()).hexdigest()
# 使用 ChatManager 提供的接口生成 chat_id，避免在此重复实现逻辑
from src.chat.message_receive.chat_stream import get_chat_manager
chat_id = get_chat_manager().get_stream_id(platform, str(id_str), is_group=is_group)
return chat_id, prompt_content
except (ValueError, IndexError):
@@ -739,6 +782,7 @@ class DefaultReplyer:
enable_tool: bool = True,
reply_time_point: Optional[float] = time.time(),
think_level: int = 1,
unknown_words: Optional[List[str]] = None,
) -> Tuple[str, List[int], List[str], str]:
"""
构建回复器上下文
@@ -823,14 +867,12 @@ class DefaultReplyer:
show_actions=True,
)
# 根据配置决定是否启用黑话解释
enable_jargon_explanation = getattr(global_config.expression, "enable_jargon_explanation", True)
if enable_jargon_explanation:
jargon_coroutine = explain_jargon_in_context(chat_id, message_list_before_short, chat_talking_prompt_short)
else:
jargon_coroutine = self._build_disabled_jargon_explanation()
# 统一黑话解释构建：根据配置选择上下文或 Planner 模式
jargon_coroutine = self._build_jargon_explanation(
chat_id, message_list_before_short, chat_talking_prompt_short, unknown_words
)
# 并行执行八个构建任务（包括黑话解释，可配置关闭）
# 并行执行构建任务（包括黑话解释，可配置关闭）
task_results = await asyncio.gather(
self._time_and_run_task(
self.build_expression_habits(chat_talking_prompt_short, target, reply_reason, think_level=think_level),