Losita/mai-bot
0
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

ruff reformatted

Browse Source
This commit is contained in:
春河晴
2025-04-08 15:31:13 +09:00
parent 0d7068acab
commit 7840a6080d
40 changed files with 1227 additions and 1336 deletions
Download Patch File Download Diff File Expand all files Collapse all files

5
src/plugins/chat_module/reasoning_chat/reasoning_chat.py
View File

@@ -27,6 +27,7 @@ chat_config = LogConfig(
logger = get_module_logger("reasoning_chat", config=chat_config)
class ReasoningChat:
def __init__(self):
self.storage = MessageStorage()
@@ -224,13 +225,13 @@ class ReasoningChat:
do_reply = False
if random() < reply_probability:
do_reply = True
# 创建思考消息
timer1 = time.time()
thinking_id = await self._create_thinking_message(message, chat, userinfo, messageinfo)
timer2 = time.time()
timing_results["创建思考消息"] = timer2 - timer1
# 生成回复
timer1 = time.time()
response_set = await self.gpt.generate_response(message)

5
src/plugins/chat_module/reasoning_chat/reasoning_generator.py
View File

@@ -40,7 +40,7 @@ class ResponseGenerator:
async def generate_response(self, message: MessageThinking) -> Optional[Union[str, List[str]]]:
"""根据当前模型类型选择对应的生成函数"""
#从global_config中获取模型概率值并选择模型
# 从global_config中获取模型概率值并选择模型
if random.random() < global_config.MODEL_R1_PROBABILITY:
self.current_model_type = "深深地"
current_model = self.model_reasoning
@@ -51,7 +51,6 @@ class ResponseGenerator:
logger.info(
f"{self.current_model_type}思考:{message.processed_plain_text[:30] + '...' if len(message.processed_plain_text) > 30 else message.processed_plain_text}"
) # noqa: E501
model_response = await self._generate_response_with_model(message, current_model)
@@ -189,4 +188,4 @@ class ResponseGenerator:
# print(f"得到了处理后的llm返回{processed_response}")
return processed_response
return processed_response

81
src/plugins/chat_module/reasoning_chat/reasoning_prompt_builder.py
View File

@@ -24,35 +24,32 @@ class PromptBuilder:
async def _build_prompt(
self, chat_stream, message_txt: str, sender_name: str = "某人", stream_id: Optional[int] = None
) -> tuple[str, str]:
# 开始构建prompt
prompt_personality = "你"
#person
# person
individuality = Individuality.get_instance()
personality_core = individuality.personality.personality_core
prompt_personality += personality_core
personality_sides = individuality.personality.personality_sides
random.shuffle(personality_sides)
prompt_personality += f",{personality_sides[0]}"
identity_detail = individuality.identity.identity_detail
random.shuffle(identity_detail)
prompt_personality += f",{identity_detail[0]}"
# 关系
who_chat_in_group = [(chat_stream.user_info.platform,
chat_stream.user_info.user_id,
chat_stream.user_info.user_nickname)]
who_chat_in_group = [
(chat_stream.user_info.platform, chat_stream.user_info.user_id, chat_stream.user_info.user_nickname)
]
who_chat_in_group += get_recent_group_speaker(
stream_id,
(chat_stream.user_info.platform, chat_stream.user_info.user_id),
limit=global_config.MAX_CONTEXT_SIZE,
)
relation_prompt = ""
for person in who_chat_in_group:
relation_prompt += await relationship_manager.build_relationship_info(person)
@@ -67,7 +64,7 @@ class PromptBuilder:
mood_prompt = mood_manager.get_prompt()
# logger.info(f"心情prompt: {mood_prompt}")
# 调取记忆
memory_prompt = ""
related_memory = await HippocampusManager.get_instance().get_memory_from_text(
@@ -84,7 +81,7 @@ class PromptBuilder:
# print(f"相关记忆：{related_memory_info}")
# 日程构建
schedule_prompt = f'''你现在正在做的事情是：{bot_schedule.get_current_num_task(num = 1,time_info = False)}'''
schedule_prompt = f"""你现在正在做的事情是：{bot_schedule.get_current_num_task(num=1, time_info=False)}"""
# 获取聊天上下文
chat_in_group = True
@@ -143,7 +140,7 @@ class PromptBuilder:
涉及政治敏感以及违法违规的内容请规避。"""
logger.info("开始构建prompt")
prompt = f"""
{relation_prompt_all}
{memory_prompt}
@@ -165,7 +162,7 @@ class PromptBuilder:
start_time = time.time()
related_info = ""
logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}")
# 1. 先从LLM获取主题，类似于记忆系统的做法
topics = []
# try:
@@ -173,7 +170,7 @@ class PromptBuilder:
# hippocampus = HippocampusManager.get_instance()._hippocampus
# topic_num = min(5, max(1, int(len(message) * 0.1)))
# topics_response = await hippocampus.llm_topic_judge.generate_response(hippocampus.find_topic_llm(message, topic_num))
# # 提取关键词
# topics = re.findall(r"<([^>]+)>", topics_response[0])
# if not topics:
@@ -184,7 +181,7 @@ class PromptBuilder:
# for topic in ",".join(topics).replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
# if topic.strip()
# ]
# logger.info(f"从LLM提取的主题: {', '.join(topics)}")
# except Exception as e:
# logger.error(f"从LLM提取主题失败: {str(e)}")
@@ -192,7 +189,7 @@ class PromptBuilder:
# words = jieba.cut(message)
# topics = [word for word in words if len(word) > 1][:5]
# logger.info(f"使用jieba提取的主题: {', '.join(topics)}")
# 如果无法提取到主题，直接使用整个消息
if not topics:
logger.info("未能提取到任何主题，使用整个消息进行查询")
@@ -200,26 +197,26 @@ class PromptBuilder:
if not embedding:
logger.error("获取消息嵌入向量失败")
return ""
related_info = self.get_info_from_db(embedding, limit=3, threshold=threshold)
logger.info(f"知识库检索完成，总耗时: {time.time() - start_time:.3f}秒")
return related_info
# 2. 对每个主题进行知识库查询
logger.info(f"开始处理{len(topics)}个主题的知识库查询")
# 优化：批量获取嵌入向量，减少API调用
embeddings = {}
topics_batch = [topic for topic in topics if len(topic) > 0]
if message: # 确保消息非空
topics_batch.append(message)
# 批量获取嵌入向量
embed_start_time = time.time()
for text in topics_batch:
if not text or len(text.strip()) == 0:
continue
try:
embedding = await get_embedding(text, request_type="prompt_build")
if embedding:
@@ -228,17 +225,17 @@ class PromptBuilder:
logger.warning(f"获取'{text}'的嵌入向量失败")
except Exception as e:
logger.error(f"获取'{text}'的嵌入向量时发生错误: {str(e)}")
logger.info(f"批量获取嵌入向量完成，耗时: {time.time() - embed_start_time:.3f}秒")
if not embeddings:
logger.error("所有嵌入向量获取失败")
return ""
# 3. 对每个主题进行知识库查询
all_results = []
query_start_time = time.time()
# 首先添加原始消息的查询结果
if message in embeddings:
original_results = self.get_info_from_db(embeddings[message], limit=3, threshold=threshold, return_raw=True)
@@ -247,12 +244,12 @@ class PromptBuilder:
result["topic"] = "原始消息"
all_results.extend(original_results)
logger.info(f"原始消息查询到{len(original_results)}条结果")
# 然后添加每个主题的查询结果
for topic in topics:
if not topic or topic not in embeddings:
continue
try:
topic_results = self.get_info_from_db(embeddings[topic], limit=3, threshold=threshold, return_raw=True)
if topic_results:
@@ -263,9 +260,9 @@ class PromptBuilder:
logger.info(f"主题'{topic}'查询到{len(topic_results)}条结果")
except Exception as e:
logger.error(f"查询主题'{topic}'时发生错误: {str(e)}")
logger.info(f"知识库查询完成，耗时: {time.time() - query_start_time:.3f}秒，共获取{len(all_results)}条结果")
# 4. 去重和过滤
process_start_time = time.time()
unique_contents = set()
@@ -275,14 +272,16 @@ class PromptBuilder:
if content not in unique_contents:
unique_contents.add(content)
filtered_results.append(result)
# 5. 按相似度排序
filtered_results.sort(key=lambda x: x["similarity"], reverse=True)
# 6. 限制总数量（最多10条）
filtered_results = filtered_results[:10]
logger.info(f"结果处理完成，耗时: {time.time() - process_start_time:.3f}秒，过滤后剩余{len(filtered_results)}条结果")
logger.info(
f"结果处理完成，耗时: {time.time() - process_start_time:.3f}秒，过滤后剩余{len(filtered_results)}条结果"
)
# 7. 格式化输出
if filtered_results:
format_start_time = time.time()
@@ -292,7 +291,7 @@ class PromptBuilder:
if topic not in grouped_results:
grouped_results[topic] = []
grouped_results[topic].append(result)
# 按主题组织输出
for topic, results in grouped_results.items():
related_info += f"【主题: {topic}】\n"
@@ -303,13 +302,15 @@ class PromptBuilder:
# related_info += f"{i}. [{similarity:.2f}] {content}\n"
related_info += f"{content}\n"
related_info += "\n"
logger.info(f"格式化输出完成，耗时: {time.time() - format_start_time:.3f}秒")
logger.info(f"知识库检索总耗时: {time.time() - start_time:.3f}秒")
return related_info
def get_info_from_db(self, query_embedding: list, limit: int = 1, threshold: float = 0.5, return_raw: bool = False) -> Union[str, list]:
def get_info_from_db(
self, query_embedding: list, limit: int = 1, threshold: float = 0.5, return_raw: bool = False
) -> Union[str, list]:
if not query_embedding:
return "" if not return_raw else []
# 使用余弦相似度计算