From 3c7e868d6dd658958c6ce7f1e3774af2851cad90 Mon Sep 17 00:00:00 2001 From: SengokuCola <1026294844@qq.com> Date: Sun, 14 Sep 2025 17:25:40 +0800 Subject: [PATCH] fix ruff --- scripts/test_interest_embedding.py | 613 ------------------ .../frequency_control/frequency_control.py | 1 - .../talk_frequency_control.py | 272 -------- src/chat/heart_flow/heartFC_chat.py | 5 +- .../heart_flow/heartflow_message_processor.py | 1 - src/chat/replyer/default_generator.py | 2 +- src/chat/replyer/lpmm_prompt.py | 31 +- src/chat/replyer/replyer_prompt.py | 31 +- src/chat/replyer/rewrite_prompt.py | 31 +- src/plugins/built_in/memory/plugin.py | 2 +- template/bot_config_template.toml | 2 +- 11 files changed, 8 insertions(+), 983 deletions(-) delete mode 100644 scripts/test_interest_embedding.py delete mode 100644 src/chat/frequency_control/talk_frequency_control.py diff --git a/scripts/test_interest_embedding.py b/scripts/test_interest_embedding.py deleted file mode 100644 index f5fcd195..00000000 --- a/scripts/test_interest_embedding.py +++ /dev/null @@ -1,613 +0,0 @@ -#!/usr/bin/env python3 -""" -基于Embedding的兴趣度计算测试脚本 -使用MaiBot-Core的EmbeddingStore计算兴趣描述与目标文本的关联度 -""" - -import sys -import os -sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) - -from typing import List, Dict, Tuple, Optional -import time -import json -import asyncio -from src.chat.knowledge.embedding_store import EmbeddingStore, cosine_similarity -from src.chat.knowledge.embedding_store import EMBEDDING_DATA_DIR_STR -from src.llm_models.utils_model import LLMRequest -from src.config.config import model_config - - -class InterestScorer: - """基于Embedding的兴趣度计算器""" - - def __init__(self, namespace: str = "interest_test"): - """初始化兴趣度计算器""" - self.embedding_store = EmbeddingStore(namespace, EMBEDDING_DATA_DIR_STR) - - async def get_embedding(self, text: str) -> Tuple[Optional[List[float]], float]: - """获取文本的嵌入向量""" - start_time = time.time() - try: - # 直接使用异步方式获取嵌入 - from src.llm_models.utils_model import LLMRequest - from src.config.config import model_config - - llm = LLMRequest(model_set=model_config.model_task_config.embedding, request_type="embedding") - embedding, _ = await llm.get_embedding(text) - - end_time = time.time() - elapsed = end_time - start_time - - if embedding and len(embedding) > 0: - return embedding, elapsed - return None, elapsed - except Exception as e: - print(f"获取嵌入向量失败: {e}") - return None, 0.0 - - async def calculate_similarity(self, text1: str, text2: str) -> Tuple[float, float, float]: - """计算两段文本的余弦相似度，返回(相似度, 文本1耗时, 文本2耗时)""" - emb1, time1 = await self.get_embedding(text1) - emb2, time2 = await self.get_embedding(text2) - - if emb1 is None or emb2 is None: - return 0.0, time1, time2 - - return cosine_similarity(emb1, emb2), time1, time2 - - async def calculate_interest_score(self, interest_text: str, target_text: str) -> Dict: - """ - 计算兴趣度分数 - - Args: - interest_text: 兴趣描述文本 - target_text: 目标文本 - - Returns: - 包含各种分数的字典 - """ - # 只计算语义相似度（嵌入分数） - semantic_score, interest_time, target_time = await self.calculate_similarity(interest_text, target_text) - - # 直接使用语义相似度作为最终分数 - final_score = semantic_score - - return { - "final_score": final_score, - "semantic_score": semantic_score, - "timing": { - "interest_embedding_time": interest_time, - "target_embedding_time": target_time, - "total_time": interest_time + target_time - } - } - - async def batch_calculate(self, interest_text: str, target_texts: List[str]) -> List[Dict]: - """批量计算兴趣度""" - results = [] - total_start_time = time.time() - - print(f"开始批量计算兴趣度...") - print(f"兴趣文本: {interest_text}") - print(f"目标文本数量: {len(target_texts)}") - - # 获取兴趣文本的嵌入向量（只需要一次） - interest_embedding, interest_time = await self.get_embedding(interest_text) - if interest_embedding is None: - print("无法获取兴趣文本的嵌入向量") - return [] - - print(f"兴趣文本嵌入计算耗时: {interest_time:.3f}秒") - - total_target_time = 0.0 - - for i, target_text in enumerate(target_texts): - print(f"处理第 {i+1}/{len(target_texts)} 个文本...") - - # 获取目标文本的嵌入向量 - target_embedding, target_time = await self.get_embedding(target_text) - total_target_time += target_time - - if target_embedding is None: - semantic_score = 0.0 - else: - semantic_score = cosine_similarity(interest_embedding, target_embedding) - - # 直接使用语义相似度作为最终分数 - final_score = semantic_score - - results.append({ - "target_text": target_text, - "final_score": final_score, - "semantic_score": semantic_score, - "timing": { - "target_embedding_time": target_time, - "item_total_time": target_time - } - }) - - # 按分数排序 - results.sort(key=lambda x: x["final_score"], reverse=True) - - total_time = time.time() - total_start_time - avg_target_time = total_target_time / len(target_texts) if target_texts else 0 - - print(f"\n=== 性能统计 ===") - print(f"兴趣文本嵌入计算耗时: {interest_time:.3f}秒") - print(f"目标文本嵌入计算总耗时: {total_target_time:.3f}秒") - print(f"目标文本嵌入计算平均耗时: {avg_target_time:.3f}秒") - print(f"总耗时: {total_time:.3f}秒") - print(f"平均每个目标文本处理耗时: {total_time / len(target_texts):.3f}秒") - - return results - - async def generate_paraphrases(self, original_text: str, num_sentences: int = 5) -> List[str]: - """ - 使用LLM生成近义句子 - - Args: - original_text: 原始文本 - num_sentences: 生成句子数量 - - Returns: - 近义句子列表 - """ - try: - # 创建LLM请求实例 - llm_request = LLMRequest( - model_set=model_config.model_task_config.replyer, - request_type="paraphrase_generator" - ) - - # 构建生成近义句子的提示词 - prompt = f"""请为以下兴趣描述生成{num_sentences}个意义相近但表达不同的句子： - -原始兴趣描述：{original_text} - -要求： -1. 保持原意不变，但尽量自由发挥，使用不同的表达方式，内容也可以有差异 -2. 句子结构要有所变化 -3. 可以适当调整语气和重点 -4. 每个句子都要完整且自然 -5. 只返回句子，不要编号，每行一个句子 - -生成的近义句子：""" - - print(f"正在生成近义句子...") - content, (reasoning, model_name, tool_calls) = await llm_request.generate_response_async(prompt) - - # 解析生成的句子 - sentences = [] - for line in content.strip().split('\n'): - line = line.strip() - if line and not line.startswith('生成') and not line.startswith('近义'): - sentences.append(line) - - # 确保返回指定数量的句子 - sentences = sentences[:num_sentences] - print(f"成功生成 {len(sentences)} 个近义句子") - print(f"使用的模型: {model_name}") - - return sentences - - except Exception as e: - print(f"生成近义句子失败: {e}") - return [] - - async def evaluate_all_paraphrases(self, original_text: str, target_texts: List[str], num_sentences: int = 5) -> Dict: - """ - 评估原始文本和所有近义句子的兴趣度 - - Args: - original_text: 原始兴趣描述文本 - target_texts: 目标文本列表 - num_sentences: 生成近义句子数量 - - Returns: - 包含所有评估结果的字典 - """ - print(f"\n=== 开始近义句子兴趣度评估 ===") - print(f"原始兴趣描述: {original_text}") - print(f"目标文本数量: {len(target_texts)}") - print(f"生成近义句子数量: {num_sentences}") - - # 生成近义句子 - paraphrases = await self.generate_paraphrases(original_text, num_sentences) - if not paraphrases: - print("生成近义句子失败，使用原始文本进行评估") - paraphrases = [] - - # 所有待评估的文本（原始文本 + 近义句子） - all_texts = [original_text] + paraphrases - - # 对每个文本进行兴趣度评估 - evaluation_results = {} - - for i, text in enumerate(all_texts): - text_type = "原始文本" if i == 0 else f"近义句子{i}" - print(f"\n--- 评估 {text_type} ---") - print(f"文本内容: {text}") - - # 计算兴趣度 - results = await self.batch_calculate(text, target_texts) - evaluation_results[text_type] = { - "text": text, - "results": results, - "top_score": results[0]["final_score"] if results else 0.0, - "average_score": sum(r["final_score"] for r in results) / len(results) if results else 0.0 - } - - return { - "original_text": original_text, - "paraphrases": paraphrases, - "evaluations": evaluation_results, - "summary": self._generate_summary(evaluation_results, target_texts) - } - - def _generate_summary(self, evaluation_results: Dict, target_texts: List[str]) -> Dict: - """生成评估摘要 - 关注目标句子的表现""" - summary = { - "best_performer": None, - "worst_performer": None, - "average_scores": {}, - "max_scores": {}, - "rankings": [], - "target_stats": {}, - "target_rankings": [] - } - - scores = [] - - for text_type, data in evaluation_results.items(): - scores.append({ - "text_type": text_type, - "text": data["text"], - "top_score": data["top_score"], - "average_score": data["average_score"] - }) - - # 按top_score排序 - scores.sort(key=lambda x: x["top_score"], reverse=True) - - summary["rankings"] = scores - summary["best_performer"] = scores[0] if scores else None - summary["worst_performer"] = scores[-1] if scores else None - - # 计算原始文本统计 - original_score = next((s for s in scores if s["text_type"] == "原始文本"), None) - if original_score: - summary["average_scores"]["original"] = original_score["average_score"] - summary["max_scores"]["original"] = original_score["top_score"] - - # 计算目标句子的统计信息 - target_stats = {} - for i, target_text in enumerate(target_texts): - target_key = f"目标{i+1}" - scores_for_target = [] - - # 收集所有兴趣描述对该目标文本的分数 - for text_type, data in evaluation_results.items(): - for result in data["results"]: - if result["target_text"] == target_text: - scores_for_target.append(result["final_score"]) - - if scores_for_target: - target_stats[target_key] = { - "target_text": target_text, - "scores": scores_for_target, - "average": sum(scores_for_target) / len(scores_for_target), - "max": max(scores_for_target), - "min": min(scores_for_target), - "std": (sum((x - sum(scores_for_target) / len(scores_for_target)) ** 2 for x in scores_for_target) / len(scores_for_target)) ** 0.5 - } - - summary["target_stats"] = target_stats - - # 按平均分对目标文本排序 - target_rankings = [] - for target_key, stats in target_stats.items(): - target_rankings.append({ - "target_key": target_key, - "target_text": stats["target_text"], - "average_score": stats["average"], - "max_score": stats["max"], - "min_score": stats["min"], - "std_score": stats["std"] - }) - - target_rankings.sort(key=lambda x: x["average_score"], reverse=True) - summary["target_rankings"] = target_rankings - - # 计算目标文本的整体统计 - if target_rankings: - all_target_averages = [t["average_score"] for t in target_rankings] - all_target_scores = [] - for stats in target_stats.values(): - all_target_scores.extend(stats["scores"]) - - summary["target_overall"] = { - "avg_of_averages": sum(all_target_averages) / len(all_target_averages), - "overall_max": max(all_target_scores), - "overall_min": min(all_target_scores), - "best_target": target_rankings[0]["target_text"], - "worst_target": target_rankings[-1]["target_text"] - } - - return summary - - -async def run_single_test(): - """运行单个测试""" - print("单个兴趣度测试") - print("=" * 40) - - # 输入兴趣文本 - # interest_text = input("请输入兴趣描述文本: ").strip() - # if not interest_text: - # print("兴趣描述不能为空") - # return - - interest_text ="对技术相关话题，游戏和动漫相关话题感兴趣，也对日常话题感兴趣，不喜欢太过沉重严肃的话题" - - # 输入目标文本 - print("请输入目标文本 (输入空行结束):") - import random - target_texts = [ - "AveMujica非常好看，你看了吗", - "明日方舟这个游戏挺好玩的", - "你能不能说点正经的", - "明日方舟挺好玩的", - "你的名字非常好看，你看了吗", - "《你的名字》非常好看，你看了吗", - "我们来聊聊苏联政治吧", - "轻音少女非常好看，你看了吗", - "我还挺喜欢打游戏的", - "我嘞个原神玩家啊", - "我心买了PlayStation5", - "直接Steam", - "有没有R" - ] - random.shuffle(target_texts) - # while True: - # line = input().strip() - # if not line: - # break - # target_texts.append(line) - - # if not target_texts: - # print("目标文本不能为空") - # return - - # 计算兴趣度 - scorer = InterestScorer() - results = await scorer.batch_calculate(interest_text, target_texts) - - # 显示结果 - print(f"\n兴趣度排序结果:") - print("-" * 80) - print(f"{'排名':<4} {'最终分数':<10} {'语义分数':<10} {'耗时(秒)':<10} {'目标文本'}") - print("-" * 80) - - for j, result in enumerate(results): - target_text = result['target_text'] - if len(target_text) > 40: - target_text = target_text[:37] + "..." - - timing = result.get('timing', {}) - item_time = timing.get('item_total_time', 0.0) - - print(f"{j+1:<4} {result['final_score']:<10.3f} {result['semantic_score']:<10.3f} " - f"{item_time:<10.3f} {target_text}") - - -async def run_paraphrase_test(): - """运行近义句子测试""" - print("近义句子兴趣度对比测试") - print("=" * 40) - - # 输入兴趣文本 - interest_text = "对技术相关话题，游戏和动漫相关话题感兴趣，比如明日方舟和原神，也对日常话题感兴趣，不喜欢太过沉重严肃的话题" - - # 输入目标文本 - print("请输入目标文本 (输入空行结束):") - # target_texts = [] - # while True: - # line = input().strip() - # if not line: - # break - # target_texts.append(line) - target_texts = [ - "AveMujica非常好看，你看了吗", - "明日方舟这个游戏挺好玩的", - "你能不能说点正经的", - "明日方舟挺好玩的", - "你的名字非常好看，你看了吗", - "《你的名字》非常好看，你看了吗", - "我们来聊聊苏联政治吧", - "轻音少女非常好看，你看了吗", - "我还挺喜欢打游戏的", - "刚加好友就视奸空间14条", - "可乐老大加我好友，我先日一遍空间", - "鸟一茬茬的", - "可乐可以是m，群友可以是s" - ] - - if not target_texts: - print("目标文本不能为空") - return - - # 创建评估器 - scorer = InterestScorer() - - # 运行评估 - result = await scorer.evaluate_all_paraphrases(interest_text, target_texts, num_sentences=5) - - # 显示结果 - display_paraphrase_results(result, target_texts) - - -def display_paraphrase_results(result: Dict, target_texts: List[str]): - """显示近义句子评估结果""" - print("\n" + "=" * 80) - print("近义句子兴趣度评估结果") - print("=" * 80) - - # 显示目标文本 - print(f"\n📋 目标文本列表:") - print("-" * 40) - for i, target in enumerate(target_texts): - print(f"{i+1}. {target}") - - # 显示生成的近义句子 - print(f"\n📝 生成的近义句子 (作为兴趣描述):") - print("-" * 40) - for i, paraphrase in enumerate(result["paraphrases"]): - print(f"{i+1}. {paraphrase}") - - # 显示摘要 - summary = result["summary"] - print(f"\n📊 评估摘要:") - print("-" * 40) - - if summary["best_performer"]: - print(f"最佳表现: {summary['best_performer']['text_type']} (最高分: {summary['best_performer']['top_score']:.3f})") - - if summary["worst_performer"]: - print(f"最差表现: {summary['worst_performer']['text_type']} (最高分: {summary['worst_performer']['top_score']:.3f})") - - print(f"原始文本平均分: {summary['average_scores'].get('original', 0):.3f}") - - # 显示目标文本的整体统计 - if "target_overall" in summary: - overall = summary["target_overall"] - print(f"\n📈 目标文本整体统计:") - print("-" * 40) - print(f"目标文本数量: {len(summary['target_rankings'])}") - print(f"平均分的平均值: {overall['avg_of_averages']:.3f}") - print(f"所有匹配中的最高分: {overall['overall_max']:.3f}") - print(f"所有匹配中的最低分: {overall['overall_min']:.3f}") - print(f"最佳匹配目标: {overall['best_target'][:50]}...") - print(f"最差匹配目标: {overall['worst_target'][:50]}...") - - # 显示目标文本排名 - if "target_rankings" in summary and summary["target_rankings"]: - print(f"\n🏆 目标文本排名 (按平均分):") - print("-" * 80) - print(f"{'排名':<4} {'平均分':<8} {'最高分':<8} {'最低分':<8} {'标准差':<8} {'目标文本'}") - print("-" * 80) - - for i, target in enumerate(summary["target_rankings"]): - target_text = target["target_text"][:40] + "..." if len(target["target_text"]) > 40 else target["target_text"] - print(f"{i+1:<4} {target['average_score']:<8.3f} {target['max_score']:<8.3f} {target['min_score']:<8.3f} {target['std_score']:<8.3f} {target_text}") - - # 显示每个目标文本的详细分数分布 - if "target_stats" in summary: - print(f"\n📊 目标文本详细分数分布:") - print("-" * 80) - - for target_key, stats in summary["target_stats"].items(): - print(f"\n{target_key}: {stats['target_text']}") - print(f" 平均分: {stats['average']:.3f}") - print(f" 最高分: {stats['max']:.3f}") - print(f" 最低分: {stats['min']:.3f}") - print(f" 标准差: {stats['std']:.3f}") - print(f" 所有分数: {[f'{s:.3f}' for s in stats['scores']]}") - - # 显示最佳和最差兴趣描述的目标表现对比 - if summary["best_performer"] and summary["worst_performer"]: - print(f"\n🔍 最佳 vs 最差兴趣描述对比:") - print("-" * 80) - - best_data = result["evaluations"][summary["best_performer"]["text_type"]] - worst_data = result["evaluations"][summary["worst_performer"]["text_type"]] - - print(f"最佳兴趣描述: {summary['best_performer']['text']}") - print(f"最差兴趣描述: {summary['worst_performer']['text']}") - print(f"") - print(f"{'目标文本':<30} {'最佳分数':<10} {'最差分数':<10} {'差值'}") - print("-" * 60) - - for best_result, worst_result in zip(best_data["results"], worst_data["results"]): - if best_result["target_text"] == worst_result["target_text"]: - diff = best_result["final_score"] - worst_result["final_score"] - target_text = best_result["target_text"][:27] + "..." if len(best_result["target_text"]) > 30 else best_result["target_text"] - print(f"{target_text:<30} {best_result['final_score']:<10.3f} {worst_result['final_score']:<10.3f} {diff:+.3f}") - - # 显示排名 - print(f"\n🏆 兴趣描述性能排名:") - print("-" * 80) - print(f"{'排名':<4} {'文本类型':<10} {'最高分':<8} {'平均分':<8} {'兴趣描述内容'}") - print("-" * 80) - - for i, item in enumerate(summary["rankings"]): - text_content = item["text"][:40] + "..." if len(item["text"]) > 40 else item["text"] - print(f"{i+1:<4} {item['text_type']:<10} {item['top_score']:<8.3f} {item['average_score']:<8.3f} {text_content}") - - # 显示每个兴趣描述的详细结果 - print(f"\n🔍 详细结果:") - print("-" * 80) - - for text_type, data in result["evaluations"].items(): - print(f"\n--- {text_type} ---") - print(f"兴趣描述: {data['text']}") - print(f"最高分: {data['top_score']:.3f}") - print(f"平均分: {data['average_score']:.3f}") - - # 显示前3个匹配结果 - top_results = data["results"][:3] - print(f"前3个匹配的目标文本:") - for j, result_item in enumerate(top_results): - print(f" {j+1}. 分数: {result_item['final_score']:.3f} - {result_item['target_text']}") - - # 显示对比表格 - print(f"\n📈 兴趣描述对比表格:") - print("-" * 100) - header = f"{'兴趣描述':<20}" - for i, target in enumerate(target_texts): - target_name = f"目标{i+1}" - header += f" {target_name:<12}" - print(header) - print("-" * 100) - - # 原始文本行 - original_line = f"{'原始文本':<20}" - original_data = result["evaluations"]["原始文本"]["results"] - for i in range(len(target_texts)): - if i < len(original_data): - original_line += f" {original_data[i]['final_score']:<12.3f}" - else: - original_line += f" {'-':<12}" - print(original_line) - - # 近义句子行 - for i, paraphrase in enumerate(result["paraphrases"]): - text_type = f"近义句子{i+1}" - line = f"{text_type:<20}" - paraphrase_data = result["evaluations"][text_type]["results"] - for j in range(len(target_texts)): - if j < len(paraphrase_data): - line += f" {paraphrase_data[j]['final_score']:<12.3f}" - else: - line += f" {'-':<12}" - print(line) - - -def main(): - """主函数""" - print("基于Embedding的兴趣度计算测试工具") - print("1. 单个兴趣度测试") - print("2. 近义句子兴趣度对比测试") - - choice = input("\n请选择 (1/2): ").strip() - - if choice == "1": - asyncio.run(run_single_test()) - elif choice == "2": - asyncio.run(run_paraphrase_test()) - else: - print("无效选择") - - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/src/chat/frequency_control/frequency_control.py b/src/chat/frequency_control/frequency_control.py index 323368d5..361ad128 100644 --- a/src/chat/frequency_control/frequency_control.py +++ b/src/chat/frequency_control/frequency_control.py @@ -3,7 +3,6 @@ from typing import Optional, Dict from src.plugin_system.apis import message_api from src.chat.message_receive.chat_stream import ChatStream, get_chat_manager from src.common.logger import get_logger -from src.chat.frequency_control.talk_frequency_control import get_config_base_talk_frequency from src.chat.frequency_control.focus_value_control import get_config_base_focus_value logger = get_logger("frequency_control") diff --git a/src/chat/frequency_control/talk_frequency_control.py b/src/chat/frequency_control/talk_frequency_control.py deleted file mode 100644 index b0733bb3..00000000 --- a/src/chat/frequency_control/talk_frequency_control.py +++ /dev/null @@ -1,272 +0,0 @@ -from typing import Optional -from datetime import datetime, timedelta -import statistics -from src.config.config import global_config -from src.chat.frequency_control.utils import parse_stream_config_to_chat_id -from src.common.database.database_model import Messages - - -def get_config_base_talk_frequency(chat_id: Optional[str] = None) -> float: - """ - 根据当前时间和聊天流获取对应的 talk_frequency - - Args: - chat_stream_id: 聊天流ID，格式为 "platform:chat_id:type" - - Returns: - float: 对应的频率值 - """ - if not global_config.chat.talk_frequency_adjust: - return global_config.chat.talk_frequency - - # 优先检查聊天流特定的配置 - if chat_id: - stream_frequency = get_stream_specific_frequency(chat_id) - if stream_frequency is not None: - return stream_frequency - - # 检查全局时段配置（第一个元素为空字符串的配置） - global_frequency = get_global_frequency() - return global_config.chat.talk_frequency if global_frequency is None else global_frequency - - -def get_time_based_frequency(time_freq_list: list[str]) -> Optional[float]: - """ - 根据时间配置列表获取当前时段的频率 - - Args: - time_freq_list: 时间频率配置列表，格式为 ["HH:MM,frequency", ...] - - Returns: - float: 频率值，如果没有配置则返回 None - """ - from datetime import datetime - - current_time = datetime.now().strftime("%H:%M") - current_hour, current_minute = map(int, current_time.split(":")) - current_minutes = current_hour * 60 + current_minute - - # 解析时间频率配置 - time_freq_pairs = [] - for time_freq_str in time_freq_list: - try: - time_str, freq_str = time_freq_str.split(",") - hour, minute = map(int, time_str.split(":")) - frequency = float(freq_str) - minutes = hour * 60 + minute - time_freq_pairs.append((minutes, frequency)) - except (ValueError, IndexError): - continue - - if not time_freq_pairs: - return None - - # 按时间排序 - time_freq_pairs.sort(key=lambda x: x[0]) - - # 查找当前时间对应的频率 - current_frequency = None - for minutes, frequency in time_freq_pairs: - if current_minutes >= minutes: - current_frequency = frequency - else: - break - - # 如果当前时间在所有配置时间之前，使用最后一个时间段的频率（跨天逻辑） - if current_frequency is None and time_freq_pairs: - current_frequency = time_freq_pairs[-1][1] - - return current_frequency - - -def get_stream_specific_frequency(chat_stream_id: str): - """ - 获取特定聊天流在当前时间的频率 - - Args: - chat_stream_id: 聊天流ID（哈希值） - - Returns: - float: 频率值，如果没有配置则返回 None - """ - # 查找匹配的聊天流配置 - for config_item in global_config.chat.talk_frequency_adjust: - if not config_item or len(config_item) < 2: - continue - - stream_config_str = config_item[0] # 例如 "qq:1026294844:group" - - # 解析配置字符串并生成对应的 chat_id - config_chat_id = parse_stream_config_to_chat_id(stream_config_str) - if config_chat_id is None: - continue - - # 比较生成的 chat_id - if config_chat_id != chat_stream_id: - continue - - # 使用通用的时间频率解析方法 - return get_time_based_frequency(config_item[1:]) - - return None - - -def get_global_frequency() -> Optional[float]: - """ - 获取全局默认频率配置 - - Returns: - float: 频率值，如果没有配置则返回 None - """ - for config_item in global_config.chat.talk_frequency_adjust: - if not config_item or len(config_item) < 2: - continue - - # 检查是否为全局默认配置（第一个元素为空字符串） - if config_item[0] == "": - return get_time_based_frequency(config_item[1:]) - - return None - - -def get_weekly_hourly_message_stats(chat_id: str): - """ - 计算指定聊天最近一周每个小时的消息数量和用户数量 - - Args: - chat_id: 聊天ID（对应 Messages 表的 chat_id 字段） - - Returns: - dict: 包含24个小时统计数据，格式为: - { - "0": {"message_count": [5, 8, 3, 12, 6, 9, 7], "message_std_dev": 2.1}, - "1": {"message_count": [10, 15, 8, 20, 12, 18, 14], "message_std_dev": 3.2}, - ... - } - """ - # 计算一周前的时间戳 - one_week_ago = datetime.now() - timedelta(days=7) - one_week_ago_timestamp = one_week_ago.timestamp() - - # 初始化数据结构：按小时存储每天的消息计数 - hourly_data = {} - for hour in range(24): - hourly_data[f"hour_{hour}"] = {"daily_counts": []} - - try: - # 查询指定聊天最近一周的消息 - messages = Messages.select().where( - (Messages.time >= one_week_ago_timestamp) & - (Messages.chat_id == chat_id) - ) - - # 统计每个小时的数据 - for message in messages: - # 将时间戳转换为datetime - msg_time = datetime.fromtimestamp(message.time) - hour = msg_time.hour - - # 记录每天的消息计数（按日期分组） - day_key = msg_time.strftime("%Y-%m-%d") - hour_key = f"{hour}" - - # 为该小时添加当天的消息计数 - found = False - for day_count in hourly_data[hour_key]["daily_counts"]: - if day_count["date"] == day_key: - day_count["count"] += 1 - found = True - break - - if not found: - hourly_data[hour_key]["daily_counts"].append({"date": day_key, "count": 1}) - - - except Exception as e: - # 如果查询失败，返回空的统计结果 - print(f"Error getting weekly hourly message stats for chat {chat_id}: {e}") - hourly_stats = {} - for hour in range(24): - hourly_stats[f"hour_{hour}"] = { - "message_count": [], - "message_std_dev": 0.0 - } - return hourly_stats - - # 计算每个小时的统计结果 - hourly_stats = {} - for hour in range(24): - hour_key = f"hour_{hour}" - daily_counts = [day["count"] for day in hourly_data[hour_key]["daily_counts"]] - - # 计算总消息数 - total_messages = sum(daily_counts) - - # 计算标准差 - message_std_dev = 0.0 - if len(daily_counts) > 1: - message_std_dev = statistics.stdev(daily_counts) - elif len(daily_counts) == 1: - message_std_dev = 0.0 - - # 按日期排序每日消息计数 - daily_counts_sorted = sorted(hourly_data[hour_key]["daily_counts"], key=lambda x: x["date"]) - - hourly_stats[hour_key] = { - "message_count": [day["count"] for day in daily_counts_sorted], - "message_std_dev": message_std_dev - } - - return hourly_stats - -def get_recent_15min_stats(chat_id: str): - """ - 获取最近15分钟指定聊天的消息数量和发言人数 - - Args: - chat_id: 聊天ID（对应 Messages 表的 chat_id 字段） - - Returns: - dict: 包含消息数量和发言人数，格式为: - { - "message_count": 25, - "user_count": 8, - "time_range": "2025-01-01 14:30:00 - 2025-01-01 14:45:00" - } - """ - # 计算15分钟前的时间戳 - fifteen_min_ago = datetime.now() - timedelta(minutes=15) - fifteen_min_ago_timestamp = fifteen_min_ago.timestamp() - current_time = datetime.now() - - # 初始化统计结果 - message_count = 0 - user_set = set() - - try: - # 查询最近15分钟的消息 - messages = Messages.select().where( - (Messages.time >= fifteen_min_ago_timestamp) & - (Messages.chat_id == chat_id) - ) - - # 统计消息数量和用户 - for message in messages: - message_count += 1 - if message.user_id: - user_set.add(message.user_id) - - except Exception as e: - # 如果查询失败，返回空结果 - print(f"Error getting recent 15min stats for chat {chat_id}: {e}") - return { - "message_count": 0, - "user_count": 0, - "time_range": f"{fifteen_min_ago.strftime('%Y-%m-%d %H:%M:%S')} - {current_time.strftime('%Y-%m-%d %H:%M:%S')}" - } - - return { - "message_count": message_count, - "user_count": len(user_set), - "time_range": f"{fifteen_min_ago.strftime('%Y-%m-%d %H:%M:%S')} - {current_time.strftime('%Y-%m-%d %H:%M:%S')}" - } diff --git a/src/chat/heart_flow/heartFC_chat.py b/src/chat/heart_flow/heartFC_chat.py index 54057c8a..9aab694b 100644 --- a/src/chat/heart_flow/heartFC_chat.py +++ b/src/chat/heart_flow/heartFC_chat.py @@ -1,7 +1,6 @@ import asyncio import time import traceback -import math import random from typing import List, Optional, Dict, Any, Tuple, TYPE_CHECKING from rich.traceback import install @@ -20,7 +19,7 @@ from src.chat.heart_flow.hfc_utils import CycleDetail from src.chat.heart_flow.hfc_utils import send_typing, stop_typing from src.chat.express.expression_learner import expression_learner_manager from src.person_info.person_info import Person -from src.plugin_system.base.component_types import ChatMode, EventType, ActionInfo +from src.plugin_system.base.component_types import EventType, ActionInfo from src.plugin_system.core import events_manager from src.plugin_system.apis import generator_api, send_api, message_api, database_api from src.mais4u.mai_think import mai_thinking_manager @@ -376,7 +375,7 @@ class HeartFChatting: action_success = False action_reply_text = "" - for i, result in enumerate(results): + for result in results: if isinstance(result, BaseException): logger.error(f"{self.log_prefix} 动作执行异常: {result}") continue diff --git a/src/chat/heart_flow/heartflow_message_processor.py b/src/chat/heart_flow/heartflow_message_processor.py index 1d90e468..ce4d033f 100644 --- a/src/chat/heart_flow/heartflow_message_processor.py +++ b/src/chat/heart_flow/heartflow_message_processor.py @@ -1,6 +1,5 @@ import asyncio import re -import math import traceback from typing import Tuple, TYPE_CHECKING diff --git a/src/chat/replyer/default_generator.py b/src/chat/replyer/default_generator.py index 64383d4e..832767d6 100644 --- a/src/chat/replyer/default_generator.py +++ b/src/chat/replyer/default_generator.py @@ -18,7 +18,7 @@ from src.chat.message_receive.chat_stream import ChatStream from src.chat.message_receive.uni_message_sender import UniversalMessageSender from src.chat.utils.timer_calculator import Timer # <--- Import Timer from src.chat.utils.utils import get_chat_type_and_target_info -from src.chat.utils.prompt_builder import Prompt, global_prompt_manager +from src.chat.utils.prompt_builder import global_prompt_manager from src.chat.utils.chat_message_builder import ( build_readable_messages, get_raw_msg_before_timestamp_with_chat, diff --git a/src/chat/replyer/lpmm_prompt.py b/src/chat/replyer/lpmm_prompt.py index 67baf027..d5d02664 100644 --- a/src/chat/replyer/lpmm_prompt.py +++ b/src/chat/replyer/lpmm_prompt.py @@ -1,35 +1,6 @@ -import traceback -import time -import asyncio -import random -import re -from typing import List, Optional, Dict, Any, Tuple -from datetime import datetime -from src.mais4u.mai_think import mai_thinking_manager -from src.common.logger import get_logger -from src.common.data_models.database_data_model import DatabaseMessages -from src.common.data_models.info_data_model import ActionPlannerInfo -from src.common.data_models.llm_data_model import LLMGenerationDataModel -from src.config.config import global_config, model_config -from src.llm_models.utils_model import LLMRequest -from src.chat.message_receive.message import UserInfo, Seg, MessageRecv, MessageSending -from src.chat.message_receive.chat_stream import ChatStream -from src.chat.message_receive.uni_message_sender import UniversalMessageSender -from src.chat.utils.timer_calculator import Timer # <--- Import Timer -from src.chat.utils.utils import get_chat_type_and_target_info -from src.chat.utils.prompt_builder import Prompt, global_prompt_manager -from src.chat.utils.chat_message_builder import ( - build_readable_messages, - get_raw_msg_before_timestamp_with_chat, - replace_user_references, -) -from src.chat.express.expression_selector import expression_selector +from src.chat.utils.prompt_builder import Prompt # from src.chat.memory_system.memory_activator import MemoryActivator -from src.mood.mood_manager import mood_manager -from src.person_info.person_info import Person, is_person_known -from src.plugin_system.base.component_types import ActionInfo, EventType -from src.plugin_system.apis import llm_api diff --git a/src/chat/replyer/replyer_prompt.py b/src/chat/replyer/replyer_prompt.py index 3abfcd6a..d3de1935 100644 --- a/src/chat/replyer/replyer_prompt.py +++ b/src/chat/replyer/replyer_prompt.py @@ -1,35 +1,6 @@ -import traceback -import time -import asyncio -import random -import re -from typing import List, Optional, Dict, Any, Tuple -from datetime import datetime -from src.mais4u.mai_think import mai_thinking_manager -from src.common.logger import get_logger -from src.common.data_models.database_data_model import DatabaseMessages -from src.common.data_models.info_data_model import ActionPlannerInfo -from src.common.data_models.llm_data_model import LLMGenerationDataModel -from src.config.config import global_config, model_config -from src.llm_models.utils_model import LLMRequest -from src.chat.message_receive.message import UserInfo, Seg, MessageRecv, MessageSending -from src.chat.message_receive.chat_stream import ChatStream -from src.chat.message_receive.uni_message_sender import UniversalMessageSender -from src.chat.utils.timer_calculator import Timer # <--- Import Timer -from src.chat.utils.utils import get_chat_type_and_target_info -from src.chat.utils.prompt_builder import Prompt, global_prompt_manager -from src.chat.utils.chat_message_builder import ( - build_readable_messages, - get_raw_msg_before_timestamp_with_chat, - replace_user_references, -) -from src.chat.express.expression_selector import expression_selector +from src.chat.utils.prompt_builder import Prompt # from src.chat.memory_system.memory_activator import MemoryActivator -from src.mood.mood_manager import mood_manager -from src.person_info.person_info import Person, is_person_known -from src.plugin_system.base.component_types import ActionInfo, EventType -from src.plugin_system.apis import llm_api diff --git a/src/chat/replyer/rewrite_prompt.py b/src/chat/replyer/rewrite_prompt.py index bff94585..35ca767c 100644 --- a/src/chat/replyer/rewrite_prompt.py +++ b/src/chat/replyer/rewrite_prompt.py @@ -1,35 +1,6 @@ -import traceback -import time -import asyncio -import random -import re -from typing import List, Optional, Dict, Any, Tuple -from datetime import datetime -from src.mais4u.mai_think import mai_thinking_manager -from src.common.logger import get_logger -from src.common.data_models.database_data_model import DatabaseMessages -from src.common.data_models.info_data_model import ActionPlannerInfo -from src.common.data_models.llm_data_model import LLMGenerationDataModel -from src.config.config import global_config, model_config -from src.llm_models.utils_model import LLMRequest -from src.chat.message_receive.message import UserInfo, Seg, MessageRecv, MessageSending -from src.chat.message_receive.chat_stream import ChatStream -from src.chat.message_receive.uni_message_sender import UniversalMessageSender -from src.chat.utils.timer_calculator import Timer # <--- Import Timer -from src.chat.utils.utils import get_chat_type_and_target_info -from src.chat.utils.prompt_builder import Prompt, global_prompt_manager -from src.chat.utils.chat_message_builder import ( - build_readable_messages, - get_raw_msg_before_timestamp_with_chat, - replace_user_references, -) -from src.chat.express.expression_selector import expression_selector +from src.chat.utils.prompt_builder import Prompt # from src.chat.memory_system.memory_activator import MemoryActivator -from src.mood.mood_manager import mood_manager -from src.person_info.person_info import Person, is_person_known -from src.plugin_system.base.component_types import ActionInfo, EventType -from src.plugin_system.apis import llm_api diff --git a/src/plugins/built_in/memory/plugin.py b/src/plugins/built_in/memory/plugin.py index 2ab1ec52..25f95448 100644 --- a/src/plugins/built_in/memory/plugin.py +++ b/src/plugins/built_in/memory/plugin.py @@ -1,7 +1,7 @@ from typing import List, Tuple, Type # 导入新插件系统 -from src.plugin_system import BasePlugin, register_plugin, ComponentInfo +from src.plugin_system import BasePlugin, ComponentInfo from src.plugin_system.base.config_types import ConfigField # 导入依赖的系统组件 diff --git a/template/bot_config_template.toml b/template/bot_config_template.toml index 2b3ff61a..36471bdb 100644 --- a/template/bot_config_template.toml +++ b/template/bot_config_template.toml @@ -1,5 +1,5 @@ [inner] -version = "6.12.0" +version = "6.13.0" #----以下是给开发人员阅读的，如果你只是部署了麦麦，不需要阅读---- #如果你想要修改配置文件，请递增version的值