LangSmith Client SDK高级技巧：自定义评估器与自动化测试完整指南

LangSmith Client SDK高级技巧自定义评估器与自动化测试完整指南【免费下载链接】langsmith-sdkLangSmith Client SDK Implementations项目地址: https://gitcode.com/gh_mirrors/la/langsmith-sdkLangSmith Client SDK是构建和评估AI应用的核心工具它提供了强大的自定义评估器和自动化测试功能帮助开发者快速评估和优化语言模型性能。无论你是AI应用开发的新手还是经验丰富的工程师掌握这些高级技巧都能显著提升你的开发效率和应用质量。为什么需要自定义评估器和自动化测试在AI应用开发中评估模型输出的质量至关重要。LangSmith SDK的自定义评估器让你能够根据具体业务需求设计评估标准而自动化测试则确保你的应用在不同场景下都能稳定运行。通过结合这两大功能你可以构建一个完整的评估和监控体系。自定义评估器打造专属评估标准LangSmith SDK提供了灵活的评估器接口让你可以轻松创建符合业务需求的评估逻辑。让我们从基础开始逐步构建高级评估器。1. 基础字符串评估器最简单的评估器是字符串评估器它比较模型输出与期望结果from langsmith.evaluation import StringEvaluator def jaccard_similarity(output: str, answer: str) - float: 计算两个字符串的Jaccard相似度 prediction_chars set(output.strip().lower()) answer_chars set(answer.strip().lower()) intersection prediction_chars.intersection(answer_chars) union prediction_chars.union(answer_chars) return len(intersection) / len(union) if union else 0.0 def grader(run_input: str, run_output: str, answer: str) - dict: 评估函数计算分数和标签 score jaccard_similarity(run_output, answer) value CORRECT if score 0.9 else INCORRECT return {score: score, value: value} # 创建评估器实例 evaluator StringEvaluator( evaluation_nameJaccardSimilarity, grading_functiongrader )2. 使用装饰器创建高级评估器LangSmith提供了run_evaluator装饰器让你可以更灵活地创建评估器from langsmith.evaluation import run_evaluator, EvaluationResult from langsmith.schemas import Run, Example run_evaluator def custom_evaluator(run: Run, example: Example) - EvaluationResult: 自定义评估器示例 # 提取输入输出 user_input run.inputs.get(question, ) model_output run.outputs.get(answer, ) expected_answer example.outputs.get(expected, ) # 自定义评估逻辑 if 错误 in model_output: return EvaluationResult( keyErrorCheck, score0.0, valueFAIL, comment输出包含错误信息 ) # 计算相关性分数 relevance_score calculate_relevance(model_output, expected_answer) return EvaluationResult( keyRelevanceScore, scorerelevance_score, valuePASS if relevance_score 0.8 else FAIL, metadata{ input_length: len(user_input), output_length: len(model_output) } )3. 异步评估器支持对于需要调用外部API或进行复杂计算的评估器LangSmith支持异步实现import asyncio from langsmith.evaluation import run_evaluator, EvaluationResult run_evaluator async def async_evaluator(run: Run, example: Example) - EvaluationResult: 异步评估器示例 # 异步调用外部API进行评估 model_output run.outputs.get(answer, ) # 调用OpenAI进行质量评估 from openai import AsyncOpenAI client AsyncOpenAI() response await client.chat.completions.create( modelgpt-4, messages[ {role: system, content: 你是一个质量评估专家}, {role: user, content: f请评估以下回答的质量{model_output}} ] ) assessment response.choices[0].message.content # 解析评估结果 if 优秀 in assessment: score 1.0 elif 良好 in assessment: score 0.7 else: score 0.3 return EvaluationResult( keyLLM_Assessment, scorescore, valueassessment[:50], # 截取前50个字符 comment基于GPT-4的评估结果 )自动化测试确保应用稳定性LangSmith与pytest深度集成提供了强大的自动化测试功能。让我们看看如何利用这个功能构建可靠的测试套件。1. 基础测试用例在test_qa.py中创建基础测试import pytest from langsmith import test from langsmith.testing import expect pytest.mark.langsmith def test_qa_system(): 测试问答系统的准确性 # 定义测试数据集 test_cases [ { input: {question: 什么是机器学习}, expected: {answer: 机器学习是人工智能的一个分支} }, { input: {question: Python的主要特点是什么}, expected: {answer: Python的主要特点包括简洁易读} } ] # 定义预测函数 def predict(inputs: dict) - dict: # 这里调用你的AI模型 question inputs[question] # 模拟模型响应 return {answer: f这是对{question}的回答} # 运行评估 results test(predict, datatest_cases) # 断言验证 for result in results: expect(result[output][answer]).to_contain(回答)2. 集成自定义评估器将自定义评估器集成到自动化测试中import pytest from langsmith import test, evaluate from langsmith.evaluation import EvaluationResult def accuracy_evaluator(run, example): 准确率评估器 predicted run.outputs.get(answer, ) expected example.outputs.get(expected, ) # 简单字符串匹配 score 1.0 if predicted expected else 0.0 return EvaluationResult( keyAccuracy, scorescore, valueCORRECT if score 1.0 else INCORRECT ) pytest.mark.langsmith def test_with_custom_evaluators(): 使用自定义评估器进行测试 test_data [ { input: {question: 11等于几}, output: {answer: 2} }, { input: {question: 中国的首都是哪里}, output: {answer: 北京} } ] def predict(inputs): question inputs[question] # 这里应该是你的模型推理逻辑 answers { 11等于几: 2, 中国的首都是哪里: 北京 } return {answer: answers.get(question, 不知道)} # 运行测试并评估 results evaluate( predict, datatest_data, evaluators[accuracy_evaluator], experiment_nameQA_System_Test ) # 分析结果 total_score sum(r[evaluation_results][results][0].score for r in results) average_accuracy total_score / len(results) assert average_accuracy 0.9, f准确率过低: {average_accuracy}3. 性能基准测试创建性能基准测试来监控模型响应时间import time import pytest from langsmith import test, traceable pytest.mark.langsmith def test_performance_benchmark(): 性能基准测试 traceable def slow_model(inputs): 模拟慢速模型 time.sleep(0.5) # 模拟处理时间 return {answer: 这是一个回答} traceable def fast_model(inputs): 模拟快速模型 time.sleep(0.1) # 模拟处理时间 return {answer: 这是另一个回答} test_data [{input: {question: 测试问题}} for _ in range(10)] # 测试慢速模型 slow_results test(slow_model, datatest_data) slow_times [r[execution_time] for r in slow_results] # 测试快速模型 fast_results test(fast_model, datatest_data) fast_times [r[execution_time] for r in fast_results] # 性能断言 avg_slow sum(slow_times) / len(slow_times) avg_fast sum(fast_times) / len(fast_times) print(f慢速模型平均响应时间: {avg_slow:.3f}秒) print(f快速模型平均响应时间: {avg_fast:.3f}秒) # 确保快速模型确实更快 assert avg_fast avg_slow * 0.5, 性能改进不足实战技巧构建完整的评估流水线1. 多维度评估体系创建覆盖多个维度的评估体系from langsmith.evaluation import run_evaluator, EvaluationResult from typing import List class MultiDimensionEvaluator: 多维度评估器 def __init__(self): self.evaluators [ self._accuracy_evaluator, self._relevance_evaluator, self._safety_evaluator, self._fluency_evaluator ] run_evaluator def evaluate(self, run, example) - List[EvaluationResult]: 执行所有评估维度 results [] for evaluator in self.evaluators: result evaluator(run, example) results.append(result) return results def _accuracy_evaluator(self, run, example): 准确率评估 predicted run.outputs.get(answer, ) expected example.outputs.get(expected, ) score self._calculate_similarity(predicted, expected) return EvaluationResult( keyAccuracy, scorescore, valueHIGH if score 0.8 else LOW ) def _relevance_evaluator(self, run, example): 相关性评估 question run.inputs.get(question, ) answer run.outputs.get(answer, ) relevance self._check_relevance(question, answer) return EvaluationResult( keyRelevance, scorerelevance, comment回答与问题的相关性评分 ) def _calculate_similarity(self, text1, text2): 计算文本相似度 # 实现你的相似度算法 return 0.85 def _check_relevance(self, question, answer): 检查相关性 # 实现相关性检查逻辑 return 0.92. 持续集成流水线将LangSmith测试集成到CI/CD流水线中# .github/workflows/langsmith-tests.yml name: LangSmith Tests on: push: branches: [ main, develop ] pull_request: branches: [ main ] jobs: test: runs-on: ubuntu-latest steps: - uses: actions/checkoutv3 - name: Set up Python uses: actions/setup-pythonv4 with: python-version: 3.9 - name: Install dependencies run: | pip install -U pip pip install langsmith pytest pip install -r requirements.txt - name: Run LangSmith tests env: LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }} LANGSMITH_TRACING: true run: | pytest tests/ -v --langsmith-output - name: Upload test results uses: actions/upload-artifactv3 if: always() with: name: langsmith-test-results path: langsmith_results/3. 监控和告警创建监控和告警系统from datetime import datetime from langsmith import Client class MonitoringSystem: 监控系统 def __init__(self, client: Client): self.client client self.thresholds { accuracy: 0.85, response_time: 2.0, # 秒 error_rate: 0.05 } def check_performance(self, project_name: str, days: int 7): 检查项目性能 end_time datetime.now() start_time datetime.fromtimestamp( end_time.timestamp() - days * 24 * 3600 ) # 获取运行数据 runs self.client.list_runs( project_nameproject_name, start_timestart_time, end_timeend_time ) metrics self._calculate_metrics(runs) alerts self._check_thresholds(metrics) if alerts: self._send_alerts(alerts) return metrics def _calculate_metrics(self, runs): 计算关键指标 total_runs 0 successful_runs 0 total_response_time 0 for run in runs: total_runs 1 if run.error is None: successful_runs 1 if run.execution_time: total_response_time run.execution_time return { success_rate: successful_runs / total_runs if total_runs 0 else 0, avg_response_time: total_response_time / total_runs if total_runs 0 else 0, total_runs: total_runs } def _check_thresholds(self, metrics): 检查阈值 alerts [] if metrics[success_rate] self.thresholds[accuracy]: alerts.append(f成功率过低: {metrics[success_rate]:.2%}) if metrics[avg_response_time] self.thresholds[response_time]: alerts.append(f响应时间过长: {metrics[avg_response_time]:.2f}秒) return alerts最佳实践和常见问题1. 评估器设计最佳实践保持评估器单一职责每个评估器只负责一个评估维度提供清晰的错误处理评估器应该优雅地处理异常情况添加丰富的元数据在评估结果中包含有用的调试信息支持异步操作对于耗时的评估逻辑使用异步评估器2. 测试策略建议分层测试从单元测试到集成测试逐步扩展数据驱动测试使用多样化的测试数据集性能监控定期运行性能基准测试回归测试确保新功能不影响现有功能3. 常见问题解决问题1评估器执行速度慢使用异步评估器实现缓存机制批量处理评估请求问题2测试结果不一致确保测试数据的一致性使用固定的随机种子清理测试环境状态问题3评估指标不够全面结合多个评估维度使用LLM进行质量评估收集用户反馈作为补充总结LangSmith Client SDK的自定义评估器和自动化测试功能为AI应用开发提供了强大的工具集。通过掌握这些高级技巧你可以构建精准的评估体系根据业务需求设计专属的评估标准实现自动化测试确保应用在不同场景下的稳定性和可靠性建立监控机制实时跟踪应用性能和质量指标集成CI/CD流程实现持续集成和持续部署无论你是构建聊天机器人、内容生成系统还是智能问答应用LangSmith的这些高级功能都能帮助你提升开发效率、保证应用质量并加速产品迭代过程。开始使用这些技巧让你的AI应用开发更加专业和高效【免费下载链接】langsmith-sdkLangSmith Client SDK Implementations项目地址: https://gitcode.com/gh_mirrors/la/langsmith-sdk创作声明：本文部分内容由AI辅助生成（AIGC），仅供参考