mirror of https://github.com/Mai-with-u/MaiBot.git
Merge branch 'dev' of github.com:MaiM-with-u/MaiBot into dev
UnCLAS-Prommer
commit
4f5dc810ef
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@ -1,613 +0,0 @@
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#!/usr/bin/env python3
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"""
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基于Embedding的兴趣度计算测试脚本
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使用MaiBot-Core的EmbeddingStore计算兴趣描述与目标文本的关联度
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"""
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import sys
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import os
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from typing import List, Dict, Tuple, Optional
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import time
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import json
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import asyncio
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from src.chat.knowledge.embedding_store import EmbeddingStore, cosine_similarity
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from src.chat.knowledge.embedding_store import EMBEDDING_DATA_DIR_STR
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from src.llm_models.utils_model import LLMRequest
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from src.config.config import model_config
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class InterestScorer:
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"""基于Embedding的兴趣度计算器"""
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def __init__(self, namespace: str = "interest_test"):
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"""初始化兴趣度计算器"""
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self.embedding_store = EmbeddingStore(namespace, EMBEDDING_DATA_DIR_STR)
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async def get_embedding(self, text: str) -> Tuple[Optional[List[float]], float]:
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"""获取文本的嵌入向量"""
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start_time = time.time()
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try:
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# 直接使用异步方式获取嵌入
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from src.llm_models.utils_model import LLMRequest
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from src.config.config import model_config
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llm = LLMRequest(model_set=model_config.model_task_config.embedding, request_type="embedding")
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embedding, _ = await llm.get_embedding(text)
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end_time = time.time()
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elapsed = end_time - start_time
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if embedding and len(embedding) > 0:
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return embedding, elapsed
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return None, elapsed
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except Exception as e:
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print(f"获取嵌入向量失败: {e}")
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return None, 0.0
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async def calculate_similarity(self, text1: str, text2: str) -> Tuple[float, float, float]:
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"""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)"""
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emb1, time1 = await self.get_embedding(text1)
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emb2, time2 = await self.get_embedding(text2)
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if emb1 is None or emb2 is None:
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return 0.0, time1, time2
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return cosine_similarity(emb1, emb2), time1, time2
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async def calculate_interest_score(self, interest_text: str, target_text: str) -> Dict:
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"""
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计算兴趣度分数
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Args:
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interest_text: 兴趣描述文本
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target_text: 目标文本
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Returns:
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包含各种分数的字典
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"""
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# 只计算语义相似度（嵌入分数）
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semantic_score, interest_time, target_time = await self.calculate_similarity(interest_text, target_text)
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# 直接使用语义相似度作为最终分数
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final_score = semantic_score
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return {
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"final_score": final_score,
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"semantic_score": semantic_score,
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"timing": {
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"interest_embedding_time": interest_time,
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"target_embedding_time": target_time,
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"total_time": interest_time + target_time
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}
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}
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async def batch_calculate(self, interest_text: str, target_texts: List[str]) -> List[Dict]:
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"""批量计算兴趣度"""
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results = []
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total_start_time = time.time()
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print(f"开始批量计算兴趣度...")
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print(f"兴趣文本: {interest_text}")
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print(f"目标文本数量: {len(target_texts)}")
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# 获取兴趣文本的嵌入向量（只需要一次）
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interest_embedding, interest_time = await self.get_embedding(interest_text)
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if interest_embedding is None:
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print("无法获取兴趣文本的嵌入向量")
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return []
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print(f"兴趣文本嵌入计算耗时: {interest_time:.3f}秒")
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total_target_time = 0.0
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for i, target_text in enumerate(target_texts):
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print(f"处理第 {i+1}/{len(target_texts)} 个文本...")
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# 获取目标文本的嵌入向量
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target_embedding, target_time = await self.get_embedding(target_text)
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total_target_time += target_time
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if target_embedding is None:
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semantic_score = 0.0
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else:
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semantic_score = cosine_similarity(interest_embedding, target_embedding)
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# 直接使用语义相似度作为最终分数
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final_score = semantic_score
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results.append({
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"target_text": target_text,
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"final_score": final_score,
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"semantic_score": semantic_score,
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"timing": {
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"target_embedding_time": target_time,
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"item_total_time": target_time
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}
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})
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# 按分数排序
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results.sort(key=lambda x: x["final_score"], reverse=True)
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total_time = time.time() - total_start_time
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avg_target_time = total_target_time / len(target_texts) if target_texts else 0
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print(f"\n=== 性能统计 ===")
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print(f"兴趣文本嵌入计算耗时: {interest_time:.3f}秒")
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print(f"目标文本嵌入计算总耗时: {total_target_time:.3f}秒")
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print(f"目标文本嵌入计算平均耗时: {avg_target_time:.3f}秒")
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print(f"总耗时: {total_time:.3f}秒")
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print(f"平均每个目标文本处理耗时: {total_time / len(target_texts):.3f}秒")
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return results
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async def generate_paraphrases(self, original_text: str, num_sentences: int = 5) -> List[str]:
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"""
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使用LLM生成近义句子
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Args:
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original_text: 原始文本
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num_sentences: 生成句子数量
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Returns:
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近义句子列表
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"""
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try:
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# 创建LLM请求实例
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llm_request = LLMRequest(
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model_set=model_config.model_task_config.replyer,
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request_type="paraphrase_generator"
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)
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# 构建生成近义句子的提示词
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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：
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原始兴趣描述：{original_text}
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要求：
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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
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2. 句子结构要有所变化
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3. 可以适当调整语气和重点
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4. 每个句子都要完整且自然
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5. 只返回句子，不要编号，每行一个句子
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生成的近义句子："""
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print(f"正在生成近义句子...")
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content, (reasoning, model_name, tool_calls) = await llm_request.generate_response_async(prompt)
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# 解析生成的句子
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sentences = []
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for line in content.strip().split('\n'):
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line = line.strip()
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if line and not line.startswith('生成') and not line.startswith('近义'):
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sentences.append(line)
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# 确保返回指定数量的句子
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sentences = sentences[:num_sentences]
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print(f"成功生成 {len(sentences)} 个近义句子")
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print(f"使用的模型: {model_name}")
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return sentences
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except Exception as e:
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print(f"生成近义句子失败: {e}")
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return []
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async def evaluate_all_paraphrases(self, original_text: str, target_texts: List[str], num_sentences: int = 5) -> Dict:
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"""
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评估原始文本和所有近义句子的兴趣度
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Args:
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original_text: 原始兴趣描述文本
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target_texts: 目标文本列表
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num_sentences: 生成近义句子数量
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Returns:
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包含所有评估结果的字典
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"""
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print(f"\n=== 开始近义句子兴趣度评估 ===")
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print(f"原始兴趣描述: {original_text}")
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print(f"目标文本数量: {len(target_texts)}")
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print(f"生成近义句子数量: {num_sentences}")
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# 生成近义句子
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paraphrases = await self.generate_paraphrases(original_text, num_sentences)
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if not paraphrases:
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print("生成近义句子失败，使用原始文本进行评估")
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paraphrases = []
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# 所有待评估的文本（原始文本 + 近义句子）
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all_texts = [original_text] + paraphrases
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# 对每个文本进行兴趣度评估
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evaluation_results = {}
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for i, text in enumerate(all_texts):
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text_type = "原始文本" if i == 0 else f"近义句子{i}"
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print(f"\n--- 评估 {text_type} ---")
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print(f"文本内容: {text}")
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# 计算兴趣度
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results = await self.batch_calculate(text, target_texts)
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evaluation_results[text_type] = {
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"text": text,
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"results": results,
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"top_score": results[0]["final_score"] if results else 0.0,
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"average_score": sum(r["final_score"] for r in results) / len(results) if results else 0.0
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}
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return {
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"original_text": original_text,
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"paraphrases": paraphrases,
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"evaluations": evaluation_results,
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"summary": self._generate_summary(evaluation_results, target_texts)
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}
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def _generate_summary(self, evaluation_results: Dict, target_texts: List[str]) -> Dict:
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"""生成评估摘要 - 关注目标句子的表现"""
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summary = {
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"best_performer": None,
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"worst_performer": None,
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"average_scores": {},
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"max_scores": {},
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"rankings": [],
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"target_stats": {},
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"target_rankings": []
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}
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scores = []
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for text_type, data in evaluation_results.items():
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scores.append({
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"text_type": text_type,
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"text": data["text"],
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"top_score": data["top_score"],
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"average_score": data["average_score"]
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})
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# 按top_score排序
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scores.sort(key=lambda x: x["top_score"], reverse=True)
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summary["rankings"] = scores
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summary["best_performer"] = scores[0] if scores else None
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summary["worst_performer"] = scores[-1] if scores else None
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# 计算原始文本统计
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original_score = next((s for s in scores if s["text_type"] == "原始文本"), None)
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if original_score:
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summary["average_scores"]["original"] = original_score["average_score"]
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summary["max_scores"]["original"] = original_score["top_score"]
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# 计算目标句子的统计信息
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target_stats = {}
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for i, target_text in enumerate(target_texts):
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target_key = f"目标{i+1}"
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scores_for_target = []
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# 收集所有兴趣描述对该目标文本的分数
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for text_type, data in evaluation_results.items():
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for result in data["results"]:
|
||||
if result["target_text"] == target_text:
|
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scores_for_target.append(result["final_score"])
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|
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if scores_for_target:
|
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target_stats[target_key] = {
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"target_text": target_text,
|
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"scores": scores_for_target,
|
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"average": sum(scores_for_target) / len(scores_for_target),
|
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"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
|
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target_rankings = []
|
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for target_key, stats in target_stats.items():
|
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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
|
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|
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# 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 = []
|
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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()
|
||||
|
|
@ -16,7 +16,7 @@ from src.chat.message_receive.chat_stream import get_chat_manager
|
|||
|
||||
|
||||
MAX_EXPRESSION_COUNT = 300
|
||||
DECAY_DAYS = 30 # 30天衰减到0.01
|
||||
DECAY_DAYS = 15 # 30天衰减到0.01
|
||||
DECAY_MIN = 0.01 # 最小衰减值
|
||||
|
||||
logger = get_logger("expressor")
|
||||
|
|
@ -45,10 +45,10 @@ def init_prompt() -> None:
|
|||
例如：当"AAAAA"时，可以"BBBBB", AAAAA代表某个具体的场景，不超过20个字。BBBBB代表对应的语言风格，特定句式或表达方式，不超过20个字。
|
||||
|
||||
例如：
|
||||
当"对某件事表示十分惊叹，有些意外"时，使用"我嘞个xxxx"
|
||||
当"表示讽刺的赞同，不想讲道理"时，使用"对对对"
|
||||
当"想说明某个具体的事实观点，但懒得明说，或者不便明说，或表达一种默契"，使用"懂的都懂"
|
||||
当"当涉及游戏相关时，表示意外的夸赞，略带戏谑意味"时，使用"这么强！"
|
||||
当"对某件事表示十分惊叹"时，使用"我嘞个xxxx"
|
||||
当"表示讽刺的赞同，不讲道理"时，使用"对对对"
|
||||
当"想说明某个具体的事实观点，但懒得明说，使用"懂的都懂"
|
||||
当"当涉及游戏相关时，夸赞，略带戏谑意味"时，使用"这么强！"
|
||||
|
||||
请注意：不要总结你自己（SELF）的发言，尽量保证总结内容的逻辑性
|
||||
现在请你概括
|
||||
|
|
|
|||
|
|
@ -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")
|
||||
|
|
|
|||
|
|
@ -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')}"
|
||||
}
|
||||
|
|
@ -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
|
||||
|
|
|
|||
|
|
@ -1,6 +1,5 @@
|
|||
import asyncio
|
||||
import re
|
||||
import math
|
||||
import traceback
|
||||
|
||||
from typing import Tuple, TYPE_CHECKING
|
||||
|
|
@ -72,16 +71,15 @@ class HeartFCMessageReceiver:
|
|||
chat = message.chat_stream
|
||||
|
||||
# 2. 兴趣度计算与更新
|
||||
interested_rate, keywords = await _calculate_interest(message)
|
||||
_, keywords = await _calculate_interest(message)
|
||||
|
||||
await self.storage.store_message(message, chat)
|
||||
|
||||
heartflow_chat: HeartFChatting = await heartflow.get_or_create_heartflow_chat(chat.stream_id) # type: ignore
|
||||
|
||||
# subheartflow.add_message_to_normal_chat_cache(message, interested_rate, is_mentioned)
|
||||
if global_config.mood.enable_mood:
|
||||
chat_mood = mood_manager.get_mood_by_chat_id(heartflow_chat.stream_id)
|
||||
asyncio.create_task(chat_mood.update_mood_by_message(message, interested_rate))
|
||||
asyncio.create_task(chat_mood.update_mood_by_message(message))
|
||||
|
||||
# 3. 日志记录
|
||||
mes_name = chat.group_info.group_name if chat.group_info else "私聊"
|
||||
|
|
@ -109,7 +107,7 @@ class HeartFCMessageReceiver:
|
|||
replace_bot_name=True,
|
||||
)
|
||||
|
||||
logger.info(f"[{mes_name}]{userinfo.user_nickname}:{processed_plain_text}[{interested_rate:.2f}]") # type: ignore
|
||||
logger.info(f"[{mes_name}]{userinfo.user_nickname}:{processed_plain_text}") # type: ignore
|
||||
|
||||
_ = Person.register_person(
|
||||
platform=message.message_info.platform, # type: ignore
|
||||
|
|
|
|||
|
|
@ -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,
|
||||
|
|
@ -32,108 +32,17 @@ 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
|
||||
|
||||
from src.chat.replyer.lpmm_prompt import init_lpmm_prompt
|
||||
from src.chat.replyer.replyer_prompt import init_replyer_prompt
|
||||
from src.chat.replyer.rewrite_prompt import init_rewrite_prompt
|
||||
|
||||
init_lpmm_prompt()
|
||||
init_replyer_prompt()
|
||||
init_rewrite_prompt()
|
||||
|
||||
|
||||
logger = get_logger("replyer")
|
||||
|
||||
|
||||
def init_prompt():
|
||||
Prompt("你正在qq群里聊天，下面是群里在聊的内容：", "chat_target_group1")
|
||||
Prompt("你正在和{sender_name}聊天，这是你们之前聊的内容：", "chat_target_private1")
|
||||
Prompt("在群里聊天", "chat_target_group2")
|
||||
Prompt("和{sender_name}聊天", "chat_target_private2")
|
||||
|
||||
Prompt(
|
||||
"""
|
||||
{expression_habits_block}
|
||||
{relation_info_block}
|
||||
|
||||
{chat_target}
|
||||
{time_block}
|
||||
{chat_info}
|
||||
{identity}
|
||||
|
||||
你现在的心情是：{mood_state}
|
||||
你正在{chat_target_2},{reply_target_block}
|
||||
你想要对上述的发言进行回复，回复的具体内容（原句）是：{raw_reply}
|
||||
原因是：{reason}
|
||||
现在请你将这条具体内容改写成一条适合在群聊中发送的回复消息。
|
||||
你需要使用合适的语法和句法，参考聊天内容，组织一条日常且口语化的回复。请你修改你想表达的原句，符合你的表达风格和语言习惯
|
||||
{reply_style}
|
||||
你可以完全重组回复，保留最基本的表达含义就好，但重组后保持语意通顺。
|
||||
{keywords_reaction_prompt}
|
||||
{moderation_prompt}
|
||||
不要输出多余内容(包括前后缀，冒号和引号，括号，表情包，emoji,at或 @等 )，只输出一条回复就好。
|
||||
现在，你说：
|
||||
""",
|
||||
"default_expressor_prompt",
|
||||
)
|
||||
|
||||
# s4u 风格的 prompt 模板
|
||||
Prompt(
|
||||
"""{identity}
|
||||
你正在群聊中聊天，你想要回复 {sender_name} 的发言。同时，也有其他用户会参与聊天，你可以参考他们的回复内容，但是你现在想回复{sender_name}的发言。
|
||||
|
||||
{time_block}
|
||||
{background_dialogue_prompt}
|
||||
{core_dialogue_prompt}
|
||||
|
||||
{expression_habits_block}{tool_info_block}
|
||||
{knowledge_prompt}{relation_info_block}
|
||||
{extra_info_block}
|
||||
|
||||
{reply_target_block}
|
||||
你的心情：{mood_state}
|
||||
{reply_style}
|
||||
注意不要复读你说过的话
|
||||
{keywords_reaction_prompt}
|
||||
请注意不要输出多余内容(包括前后缀，冒号和引号，at或 @等 )。只输出回复内容。
|
||||
{moderation_prompt}
|
||||
不要输出多余内容(包括前后缀，冒号和引号，括号()，表情包，emoji,at或 @等 )。只输出一条回复就好
|
||||
现在，你说：""",
|
||||
"replyer_prompt",
|
||||
)
|
||||
|
||||
Prompt(
|
||||
"""{identity}
|
||||
{time_block}
|
||||
你现在正在一个QQ群里聊天，以下是正在进行的聊天内容：
|
||||
{background_dialogue_prompt}
|
||||
|
||||
{expression_habits_block}{tool_info_block}
|
||||
{knowledge_prompt}{relation_info_block}
|
||||
{extra_info_block}
|
||||
|
||||
你现在想补充说明你刚刚自己的发言内容：{target}，原因是{reason}
|
||||
请你根据聊天内容，组织一条新回复。注意，{target} 是刚刚你自己的发言，你要在这基础上进一步发言，请按照你自己的角度来继续进行回复。
|
||||
注意保持上下文的连贯性。
|
||||
你现在的心情是：{mood_state}
|
||||
{reply_style}
|
||||
{keywords_reaction_prompt}
|
||||
请注意不要输出多余内容(包括前后缀，冒号和引号，at或 @等 )。只输出回复内容。
|
||||
{moderation_prompt}
|
||||
不要输出多余内容(包括前后缀，冒号和引号，括号()，表情包，emoji,at或 @等 )。只输出一条回复就好
|
||||
现在，你说：
|
||||
""",
|
||||
"replyer_self_prompt",
|
||||
)
|
||||
|
||||
Prompt(
|
||||
"""
|
||||
你是一个专门获取知识的助手。你的名字是{bot_name}。现在是{time_now}。
|
||||
群里正在进行的聊天内容：
|
||||
{chat_history}
|
||||
|
||||
现在，{sender}发送了内容:{target_message},你想要回复ta。
|
||||
请仔细分析聊天内容，考虑以下几点：
|
||||
1. 内容中是否包含需要查询信息的问题
|
||||
2. 是否有明确的知识获取指令
|
||||
|
||||
If you need to use the search tool, please directly call the function "lpmm_search_knowledge". If you do not need to use any tool, simply output "No tool needed".
|
||||
""",
|
||||
name="lpmm_get_knowledge_prompt",
|
||||
)
|
||||
|
||||
|
||||
class DefaultReplyer:
|
||||
def __init__(
|
||||
self,
|
||||
|
|
@ -369,7 +278,7 @@ class DefaultReplyer:
|
|||
expression_habits_title = ""
|
||||
if style_habits_str.strip():
|
||||
expression_habits_title = (
|
||||
"你可以参考以下的语言习惯，当情景合适就使用，但不要生硬使用，以合理的方式结合到你的回复中："
|
||||
"在回复时,你可以参考以下的语言习惯，当情景合适就使用，但不要生硬使用，以合理的方式结合到你的回复中："
|
||||
)
|
||||
expression_habits_block += f"{style_habits_str}\n"
|
||||
|
||||
|
|
@ -557,18 +466,6 @@ class DefaultReplyer:
|
|||
except Exception as e:
|
||||
logger.error(f"处理消息记录时出错: {msg}, 错误: {e}")
|
||||
|
||||
# 构建背景对话 prompt
|
||||
all_dialogue_prompt = ""
|
||||
if message_list_before_now:
|
||||
latest_25_msgs = message_list_before_now[-int(global_config.chat.max_context_size) :]
|
||||
all_dialogue_prompt_str = build_readable_messages(
|
||||
latest_25_msgs,
|
||||
replace_bot_name=True,
|
||||
timestamp_mode="normal_no_YMD",
|
||||
truncate=True,
|
||||
)
|
||||
all_dialogue_prompt = f"所有用户的发言：\n{all_dialogue_prompt_str}"
|
||||
|
||||
# 构建核心对话 prompt
|
||||
core_dialogue_prompt = ""
|
||||
if core_dialogue_list:
|
||||
|
|
@ -601,6 +498,22 @@ class DefaultReplyer:
|
|||
--------------------------------
|
||||
"""
|
||||
|
||||
|
||||
# 构建背景对话 prompt
|
||||
all_dialogue_prompt = ""
|
||||
if message_list_before_now:
|
||||
latest_25_msgs = message_list_before_now[-int(global_config.chat.max_context_size) :]
|
||||
all_dialogue_prompt_str = build_readable_messages(
|
||||
latest_25_msgs,
|
||||
replace_bot_name=True,
|
||||
timestamp_mode="normal_no_YMD",
|
||||
truncate=True,
|
||||
)
|
||||
if core_dialogue_prompt:
|
||||
all_dialogue_prompt = f"所有用户的发言：\n{all_dialogue_prompt_str}"
|
||||
else:
|
||||
all_dialogue_prompt = f"{all_dialogue_prompt_str}"
|
||||
|
||||
return core_dialogue_prompt, all_dialogue_prompt
|
||||
|
||||
def build_mai_think_context(
|
||||
|
|
@ -852,11 +765,11 @@ class DefaultReplyer:
|
|||
if sender:
|
||||
if is_group_chat:
|
||||
reply_target_block = (
|
||||
f"现在{sender}说的:{target}。引起了你的注意，你想要在群里发言或者回复这条消息。原因是{reply_reason}"
|
||||
f"现在{sender}说的:{target}。引起了你的注意"
|
||||
)
|
||||
else: # private chat
|
||||
reply_target_block = (
|
||||
f"现在{sender}说的:{target}。引起了你的注意，针对这条消息回复。原因是{reply_reason}"
|
||||
f"现在{sender}说的:{target}。引起了你的注意"
|
||||
)
|
||||
else:
|
||||
reply_target_block = ""
|
||||
|
|
@ -1148,4 +1061,4 @@ def weighted_sample_no_replacement(items, weights, k) -> list:
|
|||
return selected
|
||||
|
||||
|
||||
init_prompt()
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,24 @@
|
|||
|
||||
from src.chat.utils.prompt_builder import Prompt
|
||||
# from src.chat.memory_system.memory_activator import MemoryActivator
|
||||
|
||||
|
||||
|
||||
def init_lpmm_prompt():
|
||||
Prompt(
|
||||
"""
|
||||
你是一个专门获取知识的助手。你的名字是{bot_name}。现在是{time_now}。
|
||||
群里正在进行的聊天内容：
|
||||
{chat_history}
|
||||
|
||||
现在，{sender}发送了内容:{target_message},你想要回复ta。
|
||||
请仔细分析聊天内容，考虑以下几点：
|
||||
1. 内容中是否包含需要查询信息的问题
|
||||
2. 是否有明确的知识获取指令
|
||||
|
||||
If you need to use the search tool, please directly call the function "lpmm_search_knowledge". If you do not need to use any tool, simply output "No tool needed".
|
||||
""",
|
||||
name="lpmm_get_knowledge_prompt",
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,55 @@
|
|||
|
||||
from src.chat.utils.prompt_builder import Prompt
|
||||
# from src.chat.memory_system.memory_activator import MemoryActivator
|
||||
|
||||
|
||||
|
||||
def init_replyer_prompt():
|
||||
Prompt("你正在qq群里聊天，下面是群里正在聊的内容:", "chat_target_group1")
|
||||
Prompt("你正在和{sender_name}聊天，这是你们之前聊的内容：", "chat_target_private1")
|
||||
Prompt("正在群里聊天", "chat_target_group2")
|
||||
Prompt("和{sender_name}聊天", "chat_target_private2")
|
||||
|
||||
|
||||
Prompt(
|
||||
"""{knowledge_prompt}{relation_info_block}{tool_info_block}{extra_info_block}
|
||||
{expression_habits_block}
|
||||
|
||||
你正在qq群里聊天，下面是群里正在聊的内容:
|
||||
{time_block}
|
||||
{background_dialogue_prompt}
|
||||
{core_dialogue_prompt}
|
||||
|
||||
{reply_target_block}。
|
||||
{identity}
|
||||
你正在群里聊天,现在请你读读之前的聊天记录，然后给出日常且口语化的回复，平淡一些，
|
||||
尽量简短一些。{keywords_reaction_prompt}请注意把握聊天内容，不要回复的太有条理，可以有个性。
|
||||
{reply_style}
|
||||
请注意不要输出多余内容(包括前后缀，冒号和引号，括号，表情等)，只输出回复内容。
|
||||
{moderation_prompt}不要输出多余内容(包括前后缀，冒号和引号，括号，表情包，at或 @等 )。""",
|
||||
"replyer_prompt",
|
||||
)
|
||||
|
||||
|
||||
|
||||
Prompt(
|
||||
"""{knowledge_prompt}{relation_info_block}{tool_info_block}{extra_info_block}
|
||||
{expression_habits_block}
|
||||
|
||||
|
||||
|
||||
|
||||
你正在qq群里聊天，下面是群里正在聊的内容:
|
||||
{time_block}
|
||||
{background_dialogue_prompt}
|
||||
|
||||
你现在想补充说明你刚刚自己的发言内容：{target}，原因是{reason}
|
||||
请你根据聊天内容，组织一条新回复。注意，{target} 是刚刚你自己的发言，你要在这基础上进一步发言，请按照你自己的角度来继续进行回复。注意保持上下文的连贯性。
|
||||
{identity}
|
||||
尽量简短一些。{keywords_reaction_prompt}请注意把握聊天内容，不要回复的太有条理，可以有个性。
|
||||
{reply_style}
|
||||
请注意不要输出多余内容(包括前后缀，冒号和引号，括号，表情等)，只输出回复内容。
|
||||
{moderation_prompt}不要输出多余内容(包括前后缀，冒号和引号，括号，表情包，at或 @等 )。
|
||||
""",
|
||||
"replyer_self_prompt",
|
||||
)
|
||||
|
|
@ -0,0 +1,37 @@
|
|||
|
||||
from src.chat.utils.prompt_builder import Prompt
|
||||
# from src.chat.memory_system.memory_activator import MemoryActivator
|
||||
|
||||
|
||||
|
||||
def init_rewrite_prompt():
|
||||
Prompt("你正在qq群里聊天，下面是群里正在聊的内容:", "chat_target_group1")
|
||||
Prompt("你正在和{sender_name}聊天，这是你们之前聊的内容：", "chat_target_private1")
|
||||
Prompt("正在群里聊天", "chat_target_group2")
|
||||
Prompt("和{sender_name}聊天", "chat_target_private2")
|
||||
|
||||
Prompt(
|
||||
"""
|
||||
{expression_habits_block}
|
||||
{relation_info_block}
|
||||
|
||||
{chat_target}
|
||||
{time_block}
|
||||
{chat_info}
|
||||
{identity}
|
||||
|
||||
你现在的心情是：{mood_state}
|
||||
你正在{chat_target_2},{reply_target_block}
|
||||
你想要对上述的发言进行回复，回复的具体内容（原句）是：{raw_reply}
|
||||
原因是：{reason}
|
||||
现在请你将这条具体内容改写成一条适合在群聊中发送的回复消息。
|
||||
你需要使用合适的语法和句法，参考聊天内容，组织一条日常且口语化的回复。请你修改你想表达的原句，符合你的表达风格和语言习惯
|
||||
{reply_style}
|
||||
你可以完全重组回复，保留最基本的表达含义就好，但重组后保持语意通顺。
|
||||
{keywords_reaction_prompt}
|
||||
{moderation_prompt}
|
||||
不要输出多余内容(包括前后缀，冒号和引号，括号，表情包，emoji,at或 @等 )，只输出一条回复就好。
|
||||
现在，你说：
|
||||
""",
|
||||
"default_expressor_prompt",
|
||||
)
|
||||
|
|
@ -102,9 +102,6 @@ class ModelTaskConfig(ConfigBase):
|
|||
replyer: TaskConfig
|
||||
"""normal_chat首要回复模型模型配置"""
|
||||
|
||||
emotion: TaskConfig
|
||||
"""情绪模型配置"""
|
||||
|
||||
vlm: TaskConfig
|
||||
"""视觉语言模型配置"""
|
||||
|
||||
|
|
|
|||
|
|
@ -54,7 +54,7 @@ TEMPLATE_DIR = os.path.join(PROJECT_ROOT, "template")
|
|||
|
||||
# 考虑到，实际上配置文件中的mai_version是不会自动更新的,所以采用硬编码
|
||||
# 对该字段的更新，请严格参照语义化版本规范：https://semver.org/lang/zh-CN/
|
||||
MMC_VERSION = "0.10.3-snapshot.3"
|
||||
MMC_VERSION = "0.10.3-snapshot.4"
|
||||
|
||||
|
||||
def get_key_comment(toml_table, key):
|
||||
|
|
|
|||
|
|
@ -62,11 +62,11 @@ class ChatMood:
|
|||
|
||||
self.regression_count: int = 0
|
||||
|
||||
self.mood_model = LLMRequest(model_set=model_config.model_task_config.emotion, request_type="mood")
|
||||
self.mood_model = LLMRequest(model_set=model_config.model_task_config.utils, request_type="mood")
|
||||
|
||||
self.last_change_time: float = 0
|
||||
|
||||
async def update_mood_by_message(self, message: MessageRecv, interested_rate: float):
|
||||
async def update_mood_by_message(self, message: MessageRecv):
|
||||
self.regression_count = 0
|
||||
|
||||
during_last_time = message.message_info.time - self.last_change_time # type: ignore
|
||||
|
|
@ -74,10 +74,9 @@ class ChatMood:
|
|||
base_probability = 0.05
|
||||
time_multiplier = 4 * (1 - math.exp(-0.01 * during_last_time))
|
||||
|
||||
if interested_rate <= 0:
|
||||
interest_multiplier = 0
|
||||
else:
|
||||
interest_multiplier = 2 * math.pow(interested_rate, 0.25)
|
||||
# 基于消息长度计算基础兴趣度
|
||||
message_length = len(message.message_content.content or "")
|
||||
interest_multiplier = min(2.0, 1.0 + message_length / 100)
|
||||
|
||||
logger.debug(
|
||||
f"base_probability: {base_probability}, time_multiplier: {time_multiplier}, interest_multiplier: {interest_multiplier}"
|
||||
|
|
@ -90,7 +89,7 @@ class ChatMood:
|
|||
return
|
||||
|
||||
logger.debug(
|
||||
f"{self.log_prefix} 更新情绪状态，感兴趣度: {interested_rate:.2f}, 更新概率: {update_probability:.2f}"
|
||||
f"{self.log_prefix} 更新情绪状态，更新概率: {update_probability:.2f}"
|
||||
)
|
||||
|
||||
message_time: float = message.message_info.time # type: ignore
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
@ -19,10 +19,10 @@ alias_names = ["麦叠", "牢麦"] # 麦麦的别名
|
|||
|
||||
[personality]
|
||||
# 建议120字以内，描述人格特质 和 身份特征
|
||||
personality = "是一个女大学生，现在在读大二，会刷贴吧。有时候说话不过脑子,有时候会喜欢说一些奇怪的话。年龄为19岁,有黑色的短发。"
|
||||
personality = "是一个女大学生，现在在读大二，会刷贴吧。"
|
||||
#アイデンティティがない 生まれないらららら
|
||||
# 描述麦麦说话的表达风格，表达习惯，如要修改，可以酌情新增内容
|
||||
reply_style = "回复可以简短一些。可以参考贴吧，知乎和微博的回复风格，回复不要浮夸，不要用夸张修辞，平淡一些。不要浮夸，不要夸张修辞。"
|
||||
reply_style = "请回复的平淡一些，简短一些，说中文，不要刻意突出自身学科背景。可以参考贴吧，知乎和微博的回复风格。"
|
||||
# 情感特征，影响情绪的变化情况
|
||||
emotion_style = "情绪较为稳定，但遭遇特定事件的时候起伏较大"
|
||||
# 麦麦的兴趣，会影响麦麦对什么话题进行回复
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
[inner]
|
||||
version = "1.6.0"
|
||||
version = "1.7.0"
|
||||
|
||||
# 配置文件版本号迭代规则同bot_config.toml
|
||||
|
||||
|
|
@ -12,14 +12,14 @@ max_retry = 2 # 最大重试次数（单个模型API
|
|||
timeout = 30 # API请求超时时间（单位：秒）
|
||||
retry_interval = 10 # 重试间隔时间（单位：秒）
|
||||
|
||||
[[api_providers]] # SiliconFlow的API服务商配置
|
||||
name = "SiliconFlow"
|
||||
base_url = "https://api.siliconflow.cn/v1"
|
||||
api_key = "your-siliconflow-api-key"
|
||||
[[api_providers]] # 阿里 百炼 API服务商配置
|
||||
name = "BaiLian"
|
||||
base_url = "https://dashscope.aliyuncs.com/compatible-mode/v1"
|
||||
api_key = "your-bailian-key"
|
||||
client_type = "openai"
|
||||
max_retry = 2
|
||||
timeout = 30
|
||||
retry_interval = 10
|
||||
timeout = 15
|
||||
retry_interval = 5
|
||||
|
||||
[[api_providers]] # 特殊：Google的Gimini使用特殊API，与OpenAI格式不兼容，需要配置client为"gemini"
|
||||
name = "Google"
|
||||
|
|
@ -30,14 +30,14 @@ max_retry = 2
|
|||
timeout = 30
|
||||
retry_interval = 10
|
||||
|
||||
[[api_providers]] # 阿里 百炼 API服务商配置
|
||||
name = "BaiLian"
|
||||
base_url = "https://dashscope.aliyuncs.com/compatible-mode/v1"
|
||||
api_key = "your-bailian-key"
|
||||
[[api_providers]] # SiliconFlow的API服务商配置
|
||||
name = "SiliconFlow"
|
||||
base_url = "https://api.siliconflow.cn/v1"
|
||||
api_key = "your-siliconflow-api-key"
|
||||
client_type = "openai"
|
||||
max_retry = 2
|
||||
timeout = 15
|
||||
retry_interval = 5
|
||||
timeout = 60
|
||||
retry_interval = 10
|
||||
|
||||
|
||||
[[models]] # 模型（可以配置多个）
|
||||
|
|
@ -93,8 +93,8 @@ price_in = 0
|
|||
price_out = 0
|
||||
|
||||
|
||||
[model_task_config.utils] # 在麦麦的一些组件中使用的模型，例如表情包模块，取名模块，关系模块，是麦麦必须的模型
|
||||
model_list = ["siliconflow-deepseek-v3"] # 使用的模型列表，每个子项对应上面的模型名称(name)
|
||||
[model_task_config.utils] # 在麦麦的一些组件中使用的模型，例如表情包模块，取名模块，关系模块，麦麦的情绪变化等，是麦麦必须的模型
|
||||
model_list = ["siliconflow-deepseek-v3","qwen3-30b"] # 使用的模型列表，每个子项对应上面的模型名称(name)
|
||||
temperature = 0.2 # 模型温度，新V3建议0.1-0.3
|
||||
max_tokens = 800 # 最大输出token数
|
||||
|
||||
|
|
@ -103,6 +103,11 @@ model_list = ["qwen3-8b","qwen3-30b"]
|
|||
temperature = 0.7
|
||||
max_tokens = 800
|
||||
|
||||
[model_task_config.tool_use] #工具调用模型，需要使用支持工具调用的模型
|
||||
model_list = ["qwen3-30b"]
|
||||
temperature = 0.7
|
||||
max_tokens = 800
|
||||
|
||||
[model_task_config.replyer] # 首要回复模型，还用于表达器和表达方式学习
|
||||
model_list = ["siliconflow-deepseek-v3"]
|
||||
temperature = 0.3 # 模型温度，新V3建议0.1-0.3
|
||||
|
|
@ -113,11 +118,6 @@ model_list = ["siliconflow-deepseek-v3"]
|
|||
temperature = 0.3
|
||||
max_tokens = 800
|
||||
|
||||
[model_task_config.emotion] #负责麦麦的情绪变化
|
||||
model_list = ["qwen3-30b"]
|
||||
temperature = 0.7
|
||||
max_tokens = 800
|
||||
|
||||
[model_task_config.vlm] # 图像识别模型
|
||||
model_list = ["qwen2.5-vl-72b"]
|
||||
max_tokens = 800
|
||||
|
|
@ -125,11 +125,6 @@ max_tokens = 800
|
|||
[model_task_config.voice] # 语音识别模型
|
||||
model_list = ["sensevoice-small"]
|
||||
|
||||
[model_task_config.tool_use] #工具调用模型，需要使用支持工具调用的模型
|
||||
model_list = ["qwen3-30b"]
|
||||
temperature = 0.7
|
||||
max_tokens = 800
|
||||
|
||||
#嵌入模型
|
||||
[model_task_config.embedding]
|
||||
model_list = ["bge-m3"]
|
||||
|
|
|
|||
Loading…
Reference in New Issue