From 284c248adde91863503713594f116bf907fb06c4 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Wed, 25 Jun 2025 07:55:16 +0000 Subject: [PATCH] =?UTF-8?q?=F0=9F=A4=96=20=E8=87=AA=E5=8A=A8=E6=A0=BC?= =?UTF-8?q?=E5=BC=8F=E5=8C=96=E4=BB=A3=E7=A0=81=20[skip=20ci]?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- bot.py | 4 +- src/chat/express/expression_selector.py | 53 +++++++++---------- .../expression_selector_processor.py | 6 +-- src/chat/normal_chat/normal_prompt.py | 8 +-- src/individuality/individuality.py | 30 +++++------ 5 files changed, 50 insertions(+), 51 deletions(-) diff --git a/bot.py b/bot.py index 884f39161..16c264cbb 100644 --- a/bot.py +++ b/bot.py @@ -2,6 +2,7 @@ import asyncio import hashlib import os from dotenv import load_dotenv + if os.path.exists(".env"): load_dotenv(".env", override=True) print("成功加载环境变量配置") @@ -13,9 +14,11 @@ import platform import traceback from pathlib import Path from rich.traceback import install + # maim_message imports for console input from maim_message import Seg, UserInfo, BaseMessageInfo, MessageBase from src.chat.message_receive.bot import chat_bot + # 最早期初始化日志系统，确保所有后续模块都使用正确的日志格式 from src.common.logger import initialize_logging, get_logger, shutdown_logging from src.main import MainSystem @@ -26,7 +29,6 @@ initialize_logging() logger = get_logger("main") - install(extra_lines=3) # 设置工作目录为脚本所在目录 diff --git a/src/chat/express/expression_selector.py b/src/chat/express/expression_selector.py index 6f30698c3..58a53ac60 100644 --- a/src/chat/express/expression_selector.py +++ b/src/chat/express/expression_selector.py @@ -12,6 +12,7 @@ from src.chat.utils.prompt_builder import Prompt, global_prompt_manager logger = get_logger("expression_selector") + def init_prompt(): expression_evaluation_prompt = """ 你的名字是{bot_name} @@ -42,30 +43,32 @@ def init_prompt(): """ Prompt(expression_evaluation_prompt, "expression_evaluation_prompt") + def weighted_sample(population: List[Dict], weights: List[float], k: int) -> List[Dict]: """按权重随机抽样""" if not population or not weights or k <= 0: return [] - + if len(population) <= k: return population.copy() - + # 使用累积权重的方法进行加权抽样 selected = [] population_copy = population.copy() weights_copy = weights.copy() - + for _ in range(k): if not population_copy: break - + # 选择一个元素 chosen_idx = random.choices(range(len(population_copy)), weights=weights_copy)[0] selected.append(population_copy.pop(chosen_idx)) weights_copy.pop(chosen_idx) - + return selected + class ExpressionSelector: def __init__(self): self.expression_learner = get_expression_learner() @@ -75,7 +78,9 @@ class ExpressionSelector: request_type="expression.selector", ) - def get_random_expressions(self, chat_id: str, style_num: int, grammar_num: int, personality_num: int) -> Tuple[List[Dict[str, str]], List[Dict[str, str]]]: + def get_random_expressions( + self, chat_id: str, style_num: int, grammar_num: int, personality_num: int + ) -> Tuple[List[Dict[str, str]], List[Dict[str, str]]]: ( learnt_style_expressions, learnt_grammar_expressions, @@ -88,13 +93,13 @@ class ExpressionSelector: selected_style = weighted_sample(learnt_style_expressions, style_weights, style_num) else: selected_style = [] - + if learnt_grammar_expressions: grammar_weights = [expr.get("count", 1) for expr in learnt_grammar_expressions] selected_grammar = weighted_sample(learnt_grammar_expressions, grammar_weights, grammar_num) else: selected_grammar = [] - + if personality_expressions: personality_weights = [expr.get("count", 1) for expr in personality_expressions] selected_personality = weighted_sample(personality_expressions, personality_weights, personality_num) @@ -102,7 +107,7 @@ class ExpressionSelector: selected_personality = [] return selected_style, selected_grammar, selected_personality - + def update_expression_count(self, chat_id: str, expression: Dict[str, str], multiplier: float = 1.5): """更新表达方式的count值""" if expression.get("type") == "style_personality": @@ -117,29 +122,30 @@ class ExpressionSelector: file_path = os.path.join("data", "expression", "learnt_grammar", str(chat_id), "expressions.json") else: return - + if not os.path.exists(file_path): return - + try: with open(file_path, "r", encoding="utf-8") as f: expressions = json.load(f) - + # 找到匹配的表达方式并更新count for expr in expressions: - if (expr.get("situation") == expression.get("situation") and - expr.get("style") == expression.get("style")): + if expr.get("situation") == expression.get("situation") and expr.get("style") == expression.get( + "style" + ): expr["count"] = expr.get("count", 1) * multiplier expr["last_active_time"] = time.time() break - + # 保存更新后的文件 with open(file_path, "w", encoding="utf-8") as f: json.dump(expressions, f, ensure_ascii=False, indent=2) - + except Exception as e: logger.error(f"更新表达方式count失败: {e}") - + async def select_suitable_expressions_llm(self, chat_id: str, chat_info: str) -> List[Dict[str, str]]: """使用LLM选择适合的表达方式""" @@ -188,7 +194,7 @@ class ExpressionSelector: ) print(prompt) - + # 4. 调用LLM try: content, (_, _) = await self.llm_model.generate_response_async(prompt=prompt) @@ -216,7 +222,7 @@ class ExpressionSelector: if isinstance(idx, int) and 1 <= idx <= len(all_expressions): expression = all_expressions[idx - 1] # 索引从1开始 valid_expressions.append(expression) - + # 对选中的表达方式count数*1.5 self.update_expression_count(chat_id, expression, 1.5) @@ -226,7 +232,7 @@ class ExpressionSelector: except Exception as e: logger.error(f"LLM处理表达方式选择时出错: {e}") return [] - + init_prompt() @@ -234,10 +240,3 @@ try: expression_selector = ExpressionSelector() except Exception as e: print(f"ExpressionSelector初始化失败: {e}") - - - - - - - diff --git a/src/chat/focus_chat/info_processors/expression_selector_processor.py b/src/chat/focus_chat/info_processors/expression_selector_processor.py index 9bce1b49b..11ed4e1d1 100644 --- a/src/chat/focus_chat/info_processors/expression_selector_processor.py +++ b/src/chat/focus_chat/info_processors/expression_selector_processor.py @@ -24,7 +24,6 @@ class ExpressionSelectorProcessor(BaseProcessor): self.selection_interval = 10 # 40秒间隔 self.cached_expressions = [] # 缓存上一次选择的表达方式 - name = get_chat_manager().get_stream_name(self.subheartflow_id) self.log_prefix = f"[{name}] 表达选择器" @@ -72,7 +71,9 @@ class ExpressionSelectorProcessor(BaseProcessor): try: # LLM模式：调用LLM选择5-10个，然后随机选5个 - selected_expressions = await expression_selector.select_suitable_expressions_llm(self.subheartflow_id, chat_info) + selected_expressions = await expression_selector.select_suitable_expressions_llm( + self.subheartflow_id, chat_info + ) cache_size = len(selected_expressions) if selected_expressions else 0 mode_desc = f"LLM模式（已缓存{cache_size}个）" @@ -93,4 +94,3 @@ class ExpressionSelectorProcessor(BaseProcessor): except Exception as e: logger.error(f"{self.log_prefix} 处理表达方式选择时出错: {e}") return [] - diff --git a/src/chat/normal_chat/normal_prompt.py b/src/chat/normal_chat/normal_prompt.py index 2cf2bcf87..f5c903107 100644 --- a/src/chat/normal_chat/normal_prompt.py +++ b/src/chat/normal_chat/normal_prompt.py @@ -148,7 +148,7 @@ class PromptBuilder: read_mark=0.0, show_actions=True, ) - + message_list_before_now_half = get_raw_msg_before_timestamp_with_chat( chat_id=chat_stream.stream_id, timestamp=time.time(), @@ -162,8 +162,10 @@ class PromptBuilder: read_mark=0.0, show_actions=True, ) - - expressions = expression_selector.select_suitable_expressions_llm(chat_stream.stream_id, chat_talking_prompt_half) + + expressions = expression_selector.select_suitable_expressions_llm( + chat_stream.stream_id, chat_talking_prompt_half + ) style_habbits = [] grammar_habbits = [] if expressions: diff --git a/src/individuality/individuality.py b/src/individuality/individuality.py index 2937703e4..181946cf4 100644 --- a/src/individuality/individuality.py +++ b/src/individuality/individuality.py @@ -31,7 +31,7 @@ class Individuality: self.name = "" self.bot_person_id = "" self.meta_info_file_path = "data/personality/meta.json" - + self.model = LLMRequest( model=global_config.model.utils, request_type="individuality.compress", @@ -99,9 +99,7 @@ class Individuality: logger.info("已将完整人设更新到bot的impression中") # 创建压缩版本的short_impression - asyncio.create_task(self._create_compressed_impression( - personality_core, personality_sides, identity_detail - )) + asyncio.create_task(self._create_compressed_impression(personality_core, personality_sides, identity_detail)) asyncio.create_task(self.express_style.extract_and_store_personality_expressions()) @@ -374,12 +372,12 @@ class Individuality: self, personality_core: str, personality_sides: list, identity_detail: list ) -> str: """使用LLM创建压缩版本的impression - + Args: personality_core: 核心人格 personality_sides: 人格侧面列表 identity_detail: 身份细节列表 - + Returns: str: 压缩后的impression文本 """ @@ -387,23 +385,23 @@ class Individuality: compressed_parts = [] if personality_core: compressed_parts.append(f"{personality_core}") - + # 准备需要压缩的内容 content_to_compress = [] if personality_sides: content_to_compress.append(f"人格特质: {'、'.join(personality_sides)}") if identity_detail: content_to_compress.append(f"身份背景: {'、'.join(identity_detail)}") - + if not content_to_compress: # 如果没有需要压缩的内容，直接返回核心人格 result = "。".join(compressed_parts) return result + "。" if result else "" - + # 使用LLM压缩其他内容 try: compress_content = "、".join(content_to_compress) - + prompt = f"""请将以下人设信息进行简洁压缩，保留主要内容，用简练的中文表达： {compress_content} @@ -413,10 +411,10 @@ class Individuality: 2. 尽量简洁，不超过30字 3. 直接输出压缩后的内容，不要解释""" - response,(_,_) = await self.model.generate_response_async( + response, (_, _) = await self.model.generate_response_async( prompt=prompt, ) - + if response.strip(): compressed_parts.append(response.strip()) logger.info(f"精简人格侧面: {response.strip()}") @@ -424,15 +422,13 @@ class Individuality: logger.error(f"使用LLM压缩人设时出错: {response}") except Exception as e: logger.error(f"使用LLM压缩人设时出错: {e}") - + result = "。".join(compressed_parts) - + # 更新short_impression字段 if result: person_info_manager = get_person_info_manager() - await person_info_manager.update_one_field( - self.bot_person_id, "short_impression", result - ) + await person_info_manager.update_one_field(self.bot_person_id, "short_impression", result) logger.info("已将压缩人设更新到bot的short_impression中")