Run LLM Evals with Pytest and LangSmith

Table of Contents

1. Why Evals Matter for LLM Applications

Constant Quality Assurance 📈

Writing tests is a common practice for software engineers to verify that their applications perform as intended. With LLM applications, the need for robust testing becomes even more critical. This integration helps maintain consistent quality as your software goes through updates.

Example: Imagine you’ve built an LLM-based marketing assistant. As you update features, running evals ensures that previous functionalities still work correctly.
Surprising Fact: Over 50% of developers identify bugs only after multiple testing cycles, reinforcing the importance of continuous testing rather than waiting for final release stages.

Practical Tip: Regularly schedule evaluation tests for your applications after every significant update. This ensures that you’re catching issues early, rather than in production.

2. LangSmith and Pytest Integration Benefits

Familiarity with Pytest Functions 🔧

This integration leverages your existing Pytest knowledge and experience. It retains all Pytest functionalities such as commands, helper functions, and command-line usage.

Example: If you are used to running tests using the command pytest tests, you can do the same after integrating LangSmith—making your transition smooth.

Enhanced Debugging Experience 🔍

Unlike traditional Pytest outputs, the integration offers detailed insights into the results. This includes logs of various metrics beyond just passing and failing.

Surprising Fact: Using enhanced debugging tools can reduce the time spent on solving issues by up to 50%.
Practical Tip: When something goes wrong with your LLM app, leverage the logged metrics to pinpoint where the problem originated, instead of relying solely on binary pass/fail outputs.

3. Metrics Beyond Pass/Fail 🎯

Comprehensive Evaluation Metrics 🚀

LLM applications require more nuanced testing metrics. The integration allows you to log numerical and categorical data.

Example: Suppose you’re evaluating responses generated by an LLM. You might want to track factors like user engagement, response length, and relevance—parameters that don’t fit neatly into pass/fail categories.

Practical Tip: Design your evaluation tests to log diverse metrics such as accuracy scores, response lengths, and engagement levels. This can guide your future development priorities.

4. Team Collaboration Through Shared Results 🤝

Sharing Results Easily 📊

This integration provides a centralized platform for logging eval results, making it easier for cross-functional teams to collaborate—whether software engineers, product managers, or subject matter experts.

Example: Imagine a product manager needing to review LLM outputs for a presentation. With the centralized logging, they can access results without technical assistance.

Practical Tip: Set up a shared dashboard using LangSmith to allow stakeholders to view test results and metrics at any time, facilitating quick iterations and improvements.

5. Quick Setup and Ease of Use ⚙️

Seamless Integration Process 🖥️

Integrating LangSmith with Pytest requires minimal setup, allowing developers to enhance their testing regime without unnecessary interruptions.

Step-by-Step Example:
Mark test cases as @lsmith_test_case.
Use testing.log_inputs() to log your input data.
Call testing.log_outputs() to record the outputs generated.

Following these simple steps turns your traditional Pytest setup into a powerful LLM testing environment.

Surprising Fact: Streamlined integrations like this can reduce onboarding time for new developers by up to 30%.

Practical Tip: Use the LangSmith documentation to find code snippets that can aid in adding logs and tracing efficiently. Here’s a helpful link: How-to-guide.

Resource Toolbox 🛠️

LangChain Documentation – Comprehensive and detailed resources for better understanding: LangChain Docs.
LangSmith How-To Guide – Offers step-by-step setups for integrating with Pytest: How-to-guide.
LangSmith Tutorial – In-depth training on testing LLM applications: Tutorial.
Pytest Framework – The base framework utilized for testing: Pytest Official Site.
OpenAI API Docs – Explore the intricacies of the API used in LLM applications: OpenAI Docs.

Wrapping Up ✨

Embracing the integration between LangSmith and Pytest bridges the gap between traditional software engineering practices and modern machine learning model evaluations. By employing this combined approach, developers can ensure their LLM applications not only function correctly but continuously improve over time.