Regression testing for chains in LangChain

Introduction

Regression testing helps make sure that changes in your chain do not break its expected behavior. It checks that the chain still works as before.

After updating or improving a chain to confirm it still gives correct results.

When adding new features to a chain to ensure old features still work.

Before releasing a chain to users to avoid unexpected errors.

When fixing bugs in a chain to verify the fix and no new problems appear.

Syntax

LangChain

from langchain.chains import SomeChain
from langchain.chains.regression import RegressionTest

# Create your chain
chain = SomeChain(...)

# Define test inputs and expected outputs
test_cases = [
    {"input": {...}, "expected_output": {...}},
    ...
]

# Create regression test object
regression_test = RegressionTest(chain=chain, test_cases=test_cases)

# Run regression tests
results = regression_test.run()

# Check results
print(results)

You need to provide clear input and expected output pairs for testing.

The RegressionTest class runs the chain on inputs and compares outputs automatically.

Examples

A simple test case with input text and expected chain response.

LangChain

test_cases = [
    {"input": {"text": "Hello"}, "expected_output": {"response": "Hi there!"}}
]

Run regression tests and print whether each test passed or failed.

LangChain

regression_test = RegressionTest(chain=my_chain, test_cases=test_cases)
results = regression_test.run()
print(results)

Sample Program

This program creates a simple chain that uses an LLM to say hello to a name. It then runs regression tests to check if the chain output matches expected text.

LangChain

from langchain.chains import LLMChain
from langchain.llms import OpenAI
from langchain.chains.regression import RegressionTest

# Create a simple chain that echoes input text
llm = OpenAI(temperature=0)
prompt = "Say hello to {name}."
chain = LLMChain(llm=llm, prompt=prompt)

# Define test cases
test_cases = [
    {"input": {"name": "Alice"}, "expected_output": {"text": "Say hello to Alice."}},
    {"input": {"name": "Bob"}, "expected_output": {"text": "Say hello to Bob."}}
]

# Create regression test
regression_test = RegressionTest(chain=chain, test_cases=test_cases)

# Run tests
results = regression_test.run()

# Print results
print(results)

Important Notes

Regression tests help catch bugs early by comparing current outputs to saved expected outputs.

Keep test cases small and focused for easier debugging.

Update expected outputs only when you intentionally change chain behavior.

Summary

Regression testing checks that chains keep working after changes.

It uses input-output pairs to verify chain results.

Running regression tests helps maintain chain reliability.

Practice

1.

What is the main purpose of regression testing for chains in Langchain?

easy

A. To add new features to the chain

B. To improve the speed of chain execution

C. To verify that chains still produce expected outputs after changes

D. To train the chain with new data

Solution

1. Step 1: Understand regression testing concept

   Regression testing is about checking if existing functionality still works after updates.

2. Step 2: Apply to chains context

   For chains, this means verifying outputs remain correct after code or data changes.

3. Final Answer:

   To verify that chains still produce expected outputs after changes -> Option C

4. Quick Check:

   Regression testing = verify outputs after changes [OK]

Hint: Regression testing checks output correctness after updates [OK]

Common Mistakes:

• Confusing regression testing with performance tuning
• Thinking regression testing adds new features
• Assuming regression testing trains models

2.

Which of the following is the correct way to run a regression test on a Langchain chain named my_chain with input {"text": "Hello"} and expected output {"result": "Hi"}?

easy

A. assert my_chain.invoke({"text": "Hello"}) == {"result": "Hi"}

B. my_chain.test({"text": "Hello"}, {"result": "Hi"})

C. my_chain.run({"text": "Hello"}) == {"result": "Hi"}

D. my_chain.regression_test({"text": "Hello"}, {"result": "Hi"})

Solution

1. Step 1: Identify correct method to run chain and compare output

   Langchain chains use invoke or run to get output; to test, use assert to compare.

2. Step 2: Check options for syntax correctness

   assert my_chain.invoke({"text": "Hello"}) == {"result": "Hi"} uses assert with invoke and compares to expected output correctly.

3. Final Answer:

   assert my_chain.invoke({"text": "Hello"}) == {"result": "Hi"} -> Option A

4. Quick Check:

   Use assert with invoke for regression test [OK]

Hint: Use assert with invoke to compare outputs in regression tests [OK]

Common Mistakes:

• Using non-existent methods like regression_test
• Comparing outputs without assert
• Confusing run and test methods

3.

Given the following code snippet, what will be the output of the regression test?

class EchoChain:
    def invoke(self, inputs):
        return {"echo": inputs["message"]}

my_chain = EchoChain()
input_data = {"message": "Test"}
expected_output = {"echo": "Test"}
result = my_chain.invoke(input_data) == expected_output
print(result)

medium

A. True

B. False

C. SyntaxError

D. RuntimeError

Solution

1. Step 1: Understand the EchoChain invoke method

   The method returns a dictionary with key "echo" and value from inputs["message"].

2. Step 2: Compare the returned output with expected output

   Input is {"message": "Test"}, so output is {"echo": "Test"}, which matches expected_output.

3. Final Answer:

   True -> Option A

4. Quick Check:

   Output matches expected = True [OK]

Hint: Check returned dict matches expected dict exactly [OK]

Common Mistakes:

• Assuming method returns input unchanged
• Confusing keys in output dictionary
• Expecting errors from correct code

4.

Identify the error in this regression test code snippet for a Langchain chain my_chain:

input_data = {"query": "Hello"}
expected = {"answer": "Hi"}
result = my_chain.invoke(input_data) == expected
print(result)

Assuming my_chain.invoke returns {"response": "Hi"}, what is the problem?

medium

A. The print statement syntax is wrong

B. The input_data dictionary is missing required keys

C. The invoke method is called incorrectly

D. The expected output keys do not match the actual output keys

Solution

1. Step 1: Compare expected and actual output keys

   Expected output has key "answer" but actual output has key "response".

2. Step 2: Understand impact on regression test

   Mismatch in keys causes the equality check to fail, so test result is False.

3. Final Answer:

   The expected output keys do not match the actual output keys -> Option D

4. Quick Check:

   Output keys mismatch causes test failure [OK]

Hint: Check keys in expected vs actual output carefully [OK]

Common Mistakes:

• Assuming input_data is wrong without checking
• Thinking invoke method call is incorrect
• Blaming print statement for logic errors

5.

You want to create a regression test suite for a Langchain chain that processes user questions and returns answers. Which approach best ensures your tests catch unintended changes in the chain's behavior?

hard

A. Test the chain with random inputs and manually check outputs each time

B. Store a set of input questions and their exact expected answers, then assert equality on each test run

C. Update expected answers after every chain change without verification

D. Only check that the chain runs without errors, ignoring output correctness

Solution

1. Step 1: Understand regression test goal

   Regression tests should detect if outputs change unexpectedly after updates.

2. Step 2: Evaluate options for reliability

   Store a set of input questions and their exact expected answers, then assert equality on each test run uses fixed input-output pairs and asserts equality, which reliably detects changes.

3. Final Answer:

   Store a set of input questions and their exact expected answers, then assert equality on each test run -> Option B

4. Quick Check:

   Fixed input-output pairs catch unintended changes [OK]

Hint: Use fixed input-output pairs for reliable regression tests [OK]

Common Mistakes:

• Ignoring output correctness in tests
• Blindly updating expected outputs
• Relying on manual checks only