190 lines
10 KiB
Python
190 lines
10 KiB
Python
import re
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import time
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import logging
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from sentence_transformers import SentenceTransformer
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import faiss
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import numpy as np
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# 配置日志
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logging.basicConfig(level=logging.INFO, format='[%(levelname)s] %(message)s')
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# 初始化模型
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model = SentenceTransformer('all-MiniLM-L6-v2')
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# 模拟错误数据库
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error_db = [
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{
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"error": """Traceback (most recent call last):\n File \"image_error.py\", line 55, in <module>\n loss = criterion(outputs, labels)\n File \"module.py\", line 1553, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n File \"module.py\", line 1562, in _call_impl\n return forward_call(*args, **kwargs)\n File \"loss.py\", line 1188, in forward\n return F.cross_entropy(input, target, weight=self.weight,\n File \"functional.py\", line 3104, in cross_entropy\n return cross_entropy_loss(input, target, ...)\nIndexError: Target 15 is out of bounds.\n""",
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"solution": "可能是 labels 中的值超出了分类数量的范围。检查模型最后一层的输出维度是否正确。"
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},
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{
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"error": """Traceback (most recent call last):\n File \"rnn_error.py\", line 1, in <module>\n import torch\nModuleNotFoundError: No module named 'torch'\n""",
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"solution": "检查路径是否存在，或模型文件是否已正确保存。"
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},
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{
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"error": """Traceback (most recent call last):\n File \"cnn_success.py\", line 51, in <module>\n loss.backward()\n File \"_tensor.py\", line 521, in backward\n torch.autograd.backward(...)\n File \"__init__.py\", line 289, in backward\n _engine_run_backward(...)\n File \"graph.py\", line 768, in _engine_run_backward\n return run_backward(...)\nKeyboardInterrupt\n""",
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"solution": "程序被用户中断 (KeyboardInterrupt)。这不是一个典型的代码错误，可能是手动停止了程序。"
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},
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{
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"error": """Traceback (most recent call last):\n File \"cnn_success.py\", line 1, in <module>\n import torch\nModuleNotFoundError: No module named 'torch'\n""",
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"solution": "找不到模块torch, 请检查是否已安装 PyTorch。"
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},
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{
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"error": """Traceback (most recent call last):\n File \"rnn_error.py\", line 40, in <module>\n loss = criterion(output, label.unsqueeze(0))\n File \"module.py\", line 1553, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n File \"module.py\", line 1562, in _call_impl\n return forward_call(*args, **kwargs)\n File \"loss.py\", line 1188, in forward\n return F.cross_entropy(input, target, weight=self.weight,\n File \"functional.py\", line 3104, in cross_entropy\n return cross_entropy_loss(input, target, ...)\nValueError: Expected input batch_size (2) to match target batch_size (1).\n""",
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"solution": "检查模型输出和标签的形状是否匹配。确保它们在 batch_size 维度上是一致的。"
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},
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{
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"error": """Syntax error in command""",
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"solution": "检查代码中的语法错误。可能是缺少括号、引号或其他语法问题。"
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}
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]
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def clean_traceback(traceback_string):
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cleaned = traceback_string.replace('\r', '')
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cleaned = re.sub(r'^Traceback \(most recent call last\):[\n]+', '', cleaned, flags=re.MULTILINE)
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file_line_regex = re.compile(r'^ *File \"(.*?)\", line (\d+)(, in .*?)?$', re.MULTILINE)
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def replace_file_line(match):
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full_path = match.group(1)
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line_num = match.group(2)
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function_part = match.group(3) or ''
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filename = full_path.split('/')[-1].split('\\')[-1]
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return f' File "{filename}", line {line_num}{function_part}'
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cleaned = file_line_regex.sub(replace_file_line, cleaned)
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cleaned = re.sub(r'<.* at 0x[0-9a-fA-F]+>', '<...>', cleaned)
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cleaned = re.sub(r'\n\s*\n+', '\n', cleaned).strip()
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return cleaned
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def split_traceback_layers(traceback_string):
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"""将traceback清洗后按行分割为多个frame，每一层都可以独立向量化匹配"""
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lines = traceback_string.strip().split("\n")
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return [line.strip() for line in lines if line.strip()]
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# 计时：创建数据库
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start_db_time = time.time()
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# 初始化聚合索引
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aggregate_vectors = []
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for entry in error_db:
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cleaned = clean_traceback(entry["error"])
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layers = split_traceback_layers(cleaned)
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embeddings = model.encode(layers)
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aggregate_vectors.append(np.mean(embeddings, axis=0)) # 使用平均向量代表整个错误
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aggregate_vectors = np.array(aggregate_vectors).astype('float32')
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aggregate_index = faiss.IndexFlatIP(aggregate_vectors.shape[1])
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aggregate_index.add(aggregate_vectors)
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# 构建分层向量数据库，增加 FAISS 索引
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layered_error_db = []
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for entry, agg_vec in zip(error_db, aggregate_vectors):
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cleaned = clean_traceback(entry["error"])
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layers = split_traceback_layers(cleaned)
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embeddings = model.encode(layers)
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index = faiss.IndexFlatIP(embeddings.shape[1]) # 构建内积索引
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index.add(embeddings.astype('float32')) # 添加向量到索引中
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layered_error_db.append({
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"layers": layers,
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"vectors": embeddings, # 保留原始向量
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"index": index, # 添加 FAISS 索引
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"solution": entry["solution"],
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"agg_vector": agg_vec # 添加聚合向量
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})
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end_db_time = time.time()
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logging.info(f"创建数据库耗时：{end_db_time - start_db_time:.2f} 秒")
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# 查询错误信息
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# 完全相同
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# user_error = """Traceback (most recent call last):\n File \"image_error.py\", line 55, in <module>\n loss = criterion(outputs, labels)\n File \"module.py\", line 1553, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n File \"module.py\", line 1562, in _call_impl\n return forward_call(*args, **kwargs)\n File \"loss.py\", line 1188, in forward\n return F.cross_entropy(input, target, weight=self.weight,\n File \"functional.py\", line 3104, in cross_entropy\n return cross_entropy_loss(input, target, ...)\nIndexError: Target 15 is out of bounds.\n"""
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# 完全不同
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# user_error = """"Traceback (most recent call last):\n File \"D:\\Develop\\Projects\\bash-hook\\matches\\perf_2.py\", line 94, in <module>\n D, I = index.search(query_vec, k=1)\n File \"C:\\Users\\qcqcqc\\.conda\\envs\\sentence_transformers\\lib\\site-packages\\faiss\\class_wrappers.py\", line 329, in replacement_search\n assert d == self.d\nAssertionError\n"""
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# 底层不同
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# user_error = """Traceback (most recent call last):\n File \"image_error.py\", line 55, in <module>\n loss = criterion(outputs, labels)\n File \"module.py\", line 1553, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n File \"module.py\", line 1570, in _call_impl\n return forward_call(*args, **kwargs)\n File \"loss.py\", line 1200, in forward\n return F.mse_loss(input, target, reduction=self.reduction)\n File \"functional.py\", line 2301, in mse_loss\n return mean_squared_error_loss(input, target, ...)\nValueError: The size of input tensor must match the size of target tensor.\n"""
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# 顶层不同
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user_error = """Traceback (most recent call last):\n File \"train_model.py\", line 72, in <module>\n loss = loss_function(predictions, ground_truth)\n File \"core.py\", line 442, in _call_function\n return self._execute(*args, **kwargs)\n File \"core.py\", line 450, in _execute\n return forward_execution(*args, **kwargs)\n File \"loss_functions.py\", line 205, in forward\n return F.cross_entropy(input, target, weight=self.weight,\n File \"functional.py\", line 3104, in cross_entropy\n return cross_entropy_loss(input, target, ...)\nIndexError: Target 15 is out of bounds.\n"""
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# 无错误信息
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# user_error = "success."
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cleaned_user = clean_traceback(user_error)
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user_layers = split_traceback_layers(cleaned_user)
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user_vectors = model.encode(user_layers)
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# 计算逐层匹配分数（平均最大匹配）
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def compute_layered_similarity_avg(user_vecs, db_index, layer_weights=None):
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score = 0.0
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weighted_score = 0.0
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if layer_weights is None:
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layer_weights = np.ones(len(user_vecs)) # 每层权重相同，默认为1
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for i, u_vec in enumerate(user_vecs):
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u_vec = u_vec.astype('float32').reshape(1, -1)
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sim, _ = db_index.search(u_vec, k=1) # 获取当前层与数据库中最相似向量的相似度
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max_sim = sim[0][0]
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weighted_score += max_sim * layer_weights[i]
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return weighted_score / np.sum(layer_weights) if len(user_vecs) > 0 else 0.0
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def compute_layered_similarity_sco(user_vecs, db_index, layer_weights=None):
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weighted_score = 0.0
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if layer_weights is None:
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# 使用对数函数生成非线性权重
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layer_weights = np.logspace(0, 1, len(user_vecs)) # 从浅层到深层递增权重
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layer_weights /= np.sum(layer_weights) # 归一化权重，使其和为1
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for i, u_vec in enumerate(user_vecs):
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u_vec = u_vec.astype('float32').reshape(1, -1)
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sim, _ = db_index.search(u_vec, k=1) # 获取当前层与数据库中最相似向量的相似度
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max_sim = sim[0][0]
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weighted_score += max_sim * layer_weights[i]
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return weighted_score if len(user_vecs) > 0 else 0.0
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# 计时：查询
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start_query_time = time.time()
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cleaned_user = clean_traceback(user_error)
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user_layers = split_traceback_layers(cleaned_user)
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user_vectors = model.encode(user_layers)
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user_agg_vector = np.mean(user_vectors, axis=0).astype('float32').reshape(1, -1)
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# 初步检索聚合索引，找到最接近的前5个问题
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k = 5
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sim, indices = aggregate_index.search(user_agg_vector, k)
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# 输出找到的序号
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print(f"找到的前{k}个最接近的问题的序号: {indices[0]}")
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# 具体查找最合适的匹配
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best_match = None
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best_score = -1
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best_match_index = -1 # 用于记录最相似的错误堆栈的序号
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for idx in indices[0]:
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db_entry = layered_error_db[idx]
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score = compute_layered_similarity_sco(user_vectors, db_entry["index"])
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if score > best_score:
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best_score = score
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best_match = db_entry
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best_match_index = idx # 更新最相似的错误堆栈的序号
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end_query_time = time.time()
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logging.info(f"查询耗时：{end_query_time - start_query_time:.2f} 秒")
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# 输出匹配结果
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print("--- 清洗后的用户错误信息 ---")
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print(cleaned_user)
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print("\n--- 最相似的错误堆栈 ---")
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print("\n".join(best_match["layers"]))
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print("\n--- 建议的解决方案 ---")
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print(best_match["solution"])
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print(f"\n--- 匹配度 (分层匹配平均最大相似度): {best_score:.4f}")
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print(f"\n--- 最相似的错误堆栈的序号: {best_match_index}") |