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-sdk

LangSmith 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_name="JaccardSimilarity", grading_function=grader )

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( key="ErrorCheck", score=0.0, value="FAIL", 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( key="RelevanceScore", score=relevance_score, value="PASS" 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( model="gpt-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( key="LLM_Assessment", score=score, value=assessment[: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, data=test_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( key="Accuracy", score=score, value="CORRECT" 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": "1+1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 = { "1+1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, data=test_data, evaluators=[accuracy_evaluator], experiment_name="QA_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, data=test_data) slow_times = [r["execution_time"] for r in slow_results] # 测试快速模型 fast_results = test(fast_model, data=test_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( key="Accuracy", score=score, value="HIGH" 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( key="Relevance", score=relevance, 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 0.9

2. 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/checkout@v3 - name: Set up Python uses: actions/setup-python@v4 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-artifact@v3 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_name=project_name, start_time=start_time, end_time=end_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：

1. 构建精准的评估体系：根据业务需求设计专属的评估标准
2. 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
3. 建立监控机制：实时跟踪应用性能和质量指标
4. 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