feat：改为单planner，并解析多个动作
This commit is contained in:
SengokuCola
613
scripts/test_interest_embedding.py
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613
scripts/test_interest_embedding.py
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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"]:
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if result["target_text"] == target_text:
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scores_for_target.append(result["final_score"])
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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),
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"min": min(scores_for_target),
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"std": (sum((x - sum(scores_for_target) / len(scores_for_target)) ** 2 for x in scores_for_target) / len(scores_for_target)) ** 0.5
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}
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summary["target_stats"] = target_stats
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# 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({
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"target_key": target_key,
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"target_text": stats["target_text"],
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"average_score": stats["average"],
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"max_score": stats["max"],
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"min_score": stats["min"],
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"std_score": stats["std"]
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})
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target_rankings.sort(key=lambda x: x["average_score"], reverse=True)
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summary["target_rankings"] = target_rankings
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# 计算目标文本的整体统计
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if target_rankings:
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all_target_averages = [t["average_score"] for t in target_rankings]
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all_target_scores = []
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for stats in target_stats.values():
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all_target_scores.extend(stats["scores"])
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summary["target_overall"] = {
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"avg_of_averages": sum(all_target_averages) / len(all_target_averages),
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"overall_max": max(all_target_scores),
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"overall_min": min(all_target_scores),
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"best_target": target_rankings[0]["target_text"],
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"worst_target": target_rankings[-1]["target_text"]
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}
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return summary
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async def run_single_test():
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"""运行单个测试"""
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print("单个兴趣度测试")
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print("=" * 40)
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# 输入兴趣文本
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# interest_text = input("请输入兴趣描述文本: ").strip()
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# if not interest_text:
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# print("兴趣描述不能为空")
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# return
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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"
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# 输入目标文本
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print("请输入目标文本 (输入空行结束):")
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import random
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target_texts = [
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"AveMujica非常好看，你看了吗",
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"明日方舟这个游戏挺好玩的",
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"你能不能说点正经的",
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"明日方舟挺好玩的",
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"你的名字非常好看，你看了吗",
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"《你的名字》非常好看，你看了吗",
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"我们来聊聊苏联政治吧",
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"轻音少女非常好看，你看了吗",
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"我还挺喜欢打游戏的",
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"我嘞个原神玩家啊",
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"我心买了PlayStation5",
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"直接Steam",
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"有没有R"
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]
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random.shuffle(target_texts)
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# while True:
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# line = input().strip()
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# if not line:
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# break
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# target_texts.append(line)
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# if not target_texts:
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# print("目标文本不能为空")
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# return
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# 计算兴趣度
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scorer = InterestScorer()
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results = await scorer.batch_calculate(interest_text, target_texts)
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# 显示结果
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print(f"\n兴趣度排序结果:")
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print("-" * 80)
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print(f"{'排名':<4} {'最终分数':<10} {'语义分数':<10} {'耗时(秒)':<10} {'目标文本'}")
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print("-" * 80)
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for j, result in enumerate(results):
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target_text = result['target_text']
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if len(target_text) > 40:
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target_text = target_text[:37] + "..."
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timing = result.get('timing', {})
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item_time = timing.get('item_total_time', 0.0)
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print(f"{j+1:<4} {result['final_score']:<10.3f} {result['semantic_score']:<10.3f} "
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f"{item_time:<10.3f} {target_text}")
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async def run_paraphrase_test():
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"""运行近义句子测试"""
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print("近义句子兴趣度对比测试")
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print("=" * 40)
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# 输入兴趣文本
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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"
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# 输入目标文本
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print("请输入目标文本 (输入空行结束):")
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# target_texts = []
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# while True:
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# line = input().strip()
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# if not line:
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# break
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# target_texts.append(line)
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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()
|
||||
Reference in New Issue
Block a user