I’m trying to set up automated testing for my language model outputs and I’ve heard that combining Pytest with LangSmith can be really effective for this purpose. However, I’m struggling to understand the proper workflow and implementation steps.
What I want to achieve is running systematic evaluations of my LLM responses to ensure they meet quality standards. I need to test things like response accuracy, consistency, and adherence to specific prompts.
Can someone walk me through the basic setup process? I’m particularly interested in understanding how to structure the test files, configure the LangSmith connection, and define meaningful evaluation metrics. Any code examples or best practices would be incredibly helpful for getting started with this testing approach.
The Problem: You’re having trouble effectively managing and analyzing large datasets while using pytest and LangSmith for automated language model testing. The challenges include version control for test data, handling numerous prompt variations, robust error handling (especially for API rate limits), and implementing rollback mechanisms for failed test runs. You need a more scalable and robust workflow for managing test data and dealing with potential failures.
Understanding the “Why” (The Root Cause): Manually managing test data and responses becomes increasingly difficult as the number of prompts and models grows. Lack of proper version control can lead to data loss and non-reproducible results. Inadequate error handling (particularly for API rate limits) slows down testing, and without rollback mechanisms, failed tests can corrupt results or leave your system inconsistent. A well-structured, automated solution is crucial for managing this complexity and ensuring reliable, reproducible outcomes.
Step-by-Step Guide:
Implement a Version Control System for Test Datasets: Use Git to manage your test datasets (CSV, JSON, etc.). This enables tracking changes, reverting to previous versions if needed, and maintaining a clear history of your test data. Organize datasets into a well-structured directory, perhaps separating them by model, prompt type, or version.
Structure Your Test Suite for Scalability: Organize your pytest tests into logically grouped modules. This improves readability and maintainability, particularly with many tests. For example, have separate modules for accuracy tests, consistency checks, and prompt adherence tests.
Implement Robust Error Handling and Retries: Incorporate comprehensive error handling into your test scripts. Specifically, address potential API rate limits with retry logic using exponential backoff. This prevents tests from being stalled by transient network or API issues. Example (Python with requests and tenacity library):
from tenacity import retry, stop_after_attempt, wait_exponential
@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=2, max=60))
def call_llm_api(prompt, model):
try:
response = requests.post(f"{ollama_endpoint}/{model}", json={"prompt": prompt})
response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx)
return response.json()["response"]
except requests.exceptions.RequestException as e:
print(f"Error calling LLM API: {e}")
raise
Build Rollback Mechanisms for Failed Test Runs: Implement mechanisms to automatically roll back changes or restore the system to a known good state if a test run fails. This might involve restoring previous versions of your datasets or using transactional database operations to ensure data integrity.
Efficient Data Storage and Retrieval: For exceptionally large datasets, consider a database (like SQLite or PostgreSQL) for efficient storage and querying. This allows easy filtering and analysis of results based on various criteria (model, prompt type, date, etc.).
Run Incremental Tests During Development: Run a smaller subset of your tests during development to quickly identify and fix issues. This is more efficient than running the entire suite after every minor code change. Only execute the complete test suite before deploying changes to a production environment.
Common Pitfalls & What to Check Next:
Data Versioning: Ensure you’re properly versioning your test datasets and tracking changes using a version control system (Git is highly recommended).
Test Data Organization: Maintain a well-structured directory for your test data, separating it logically to avoid confusion and maintain clarity.
Error Handling Coverage: Thoroughly test your error handling to ensure it gracefully handles various scenarios, including network errors, API errors, and unexpected response formats.
API Key Management: Securely store and manage your API keys. Avoid hardcoding them directly into your scripts; use environment variables or a more secure secrets management system.
Database Performance: If using a database, ensure it’s appropriately sized and optimized to handle your dataset size.
Still running into issues? Share your (sanitized) config files, the exact command you ran, and any other relevant details. The community is here to help!
totally agree! langsmith’s docs r super helpful. make sure to use pytest fixtures for api keys, and mock those external calls - itll save u so much cash on credits! happy testing!
Been there with the evaluation headaches. Pytest and LangSmith work but get messy fast when you scale.
I switched to automating the whole pipeline instead of patching tools together. Way cleaner.
Set up workflows that trigger your LLM tests, collect responses, run quality checks, and generate reports. No more juggling pytest configs or wrestling with LangSmith auth issues.
Define your evaluation criteria once and let automation handle the repetitive stuff. Response accuracy, consistency checks, prompt adherence - all run automatically on schedule or when code changes.
You get proper logging and monitoring built in. When something breaks, you know exactly what and when without digging through test outputs.
I use this for all our model evaluations now. Saves tons of time vs manual pytest runs and gives better visibility into performance over time.
The Problem: You’re experiencing difficulties managing and analyzing large datasets during automated testing of your language models using pytest and LangSmith. The core issues involve data versioning, handling large prompt variations, ensuring robust error handling (including API rate limits), and establishing effective rollback mechanisms for failed test runs. You need a more robust and scalable workflow for managing your test data and handling potential failures.
Understanding the “Why” (The Root Cause): Manual management of test data and responses becomes unwieldy as the number of prompts and models increases. Lack of proper version control for your datasets can lead to data loss and irreproducible results. Insufficient error handling (specifically for API rate limiting) can significantly slow down your testing process, and the absence of rollback mechanisms means failed tests can corrupt your results or leave your system in an inconsistent state. A well-structured, automated solution is necessary to handle the complexity and ensure reliable, reproducible results.
Step-by-Step Guide:
Implement a Version Control System for Test Datasets: Use a version control system like Git to manage your test datasets (CSV, JSON, etc.). This allows you to track changes, revert to previous versions if necessary, and maintain a clear history of your test data. Organize your datasets into a well-structured directory, perhaps separating them by model, prompt type, or version.
Structure Your Test Suite for Scalability: Organize your pytest tests into logically grouped modules. This improves readability and maintainability, particularly when dealing with a large number of tests. For instance, you might have separate modules for accuracy tests, consistency checks, and prompt adherence tests.
Implement Robust Error Handling and Retries: Incorporate comprehensive error handling into your test scripts. Specifically, address potential API rate limits by including retry logic with exponential backoff. This will prevent your tests from being completely stalled by transient network or API issues. Example (Python with requests and tenacity library):
from tenacity import retry, stop_after_attempt, wait_exponential
@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=2, max=60))
def call_llm_api(prompt, model):
try:
response = requests.post(f"{ollama_endpoint}/{model}", json={"prompt": prompt})
response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx)
return response.json()["response"]
except requests.exceptions.RequestException as e:
print(f"Error calling LLM API: {e}")
raise
Build Rollback Mechanisms for Failed Test Runs: Implement mechanisms to automatically roll back changes or restore the system to a known good state if a test run fails. This might involve restoring previous versions of your datasets or using transactional database operations to ensure data integrity.
Efficient Data Storage and Retrieval: If you’re dealing with exceptionally large datasets, consider using a database (like SQLite or PostgreSQL) for efficient storage and querying. This allows you to easily filter and analyze results based on various criteria (model, prompt type, date, etc.).
Run Incremental Tests During Development: Begin by running a smaller subset of your tests during development to quickly identify and fix issues. This is much more efficient than running the entire suite after every minor code change. Only execute the complete test suite before deploying your changes to a production environment.
Common Pitfalls & What to Check Next:
Data Versioning: Ensure you’re properly versioning your test datasets and tracking changes using a version control system (Git is highly recommended).
Test Data Organization: Maintain a well-structured directory for your test data, separating it logically to avoid confusion and maintain clarity.
Error Handling Coverage: Thoroughly test your error handling to ensure it gracefully handles various scenarios, including network errors, API errors, and unexpected response formats.
API Key Management: Securely store and manage your API keys. Avoid hardcoding them directly into your scripts; use environment variables or a more secure secrets management system.
Database Performance: If using a database, ensure it’s appropriately sized and optimized to handle your dataset size.
Still running into issues? Share your (sanitized) config files, the exact command you ran, and any other relevant details. The community is here to help!
Understanding the “Why” (The Root Cause): Manually managing test data and responses becomes increasingly difficult as the number of prompts and models grows. Lack of proper version control can lead to data loss and non-reproducible results. Inadequate error handling (particularly for API rate limits) slows down testing, and without rollback mechanisms, failed tests can corrupt results or leave your system inconsistent. A well-structured, automated solution is crucial for managing this complexity and ensuring reliable, reproducible outcomes.
Step-by-Step Guide:
Common Pitfalls & What to Check Next:
Still running into issues? Share your (sanitized) config files, the exact command you ran, and any other relevant details. The community is here to help!