Regression testing for chains in LangChain - Mini Project: Build & Apply

Regression Testing for Chains in Langchain

📖 Scenario: You are working on a Langchain project that uses chains to process text inputs and generate outputs. To keep your project reliable, you want to add regression tests that check if your chains produce the expected results after changes.

🎯 Goal: Build a simple regression test setup for a Langchain chain that verifies the output for a given input text.

📋 What You'll Learn

Create a Langchain chain with a simple prompt template

Set up a test input string

Run the chain with the test input and capture the output

Write an assertion to check the output matches the expected result

💡 Why This Matters

🌍 Real World

Regression testing helps keep your Langchain chains reliable when you update or refactor your code. It ensures your chains still produce the expected outputs for known inputs.

💼 Career

Many jobs in AI and software development require writing tests to maintain code quality. Knowing how to test Langchain chains is useful for roles involving AI workflows and automation.

1

Create a Langchain chain with a prompt template

Import LLMChain and PromptTemplate from langchain. Create a prompt template called prompt with the template string 'Say hello to {name}.'. Then create a chain called chain using LLMChain with the prompt you created.

LangChain

# Your code here to import and create prompt and chain

Hint

Use PromptTemplate to define how the input variable name is used in the prompt string.

2

Set up a test input string

Create a variable called test_input and set it to a dictionary with the key 'name' and the value 'Alice'.

LangChain

from langchain import LLMChain, PromptTemplate

prompt = PromptTemplate(template='Say hello to {name}.', input_variables=['name'])
chain = LLMChain(prompt=prompt)

# Create test_input dictionary with name 'Alice'
# Your code here

Hint

Use a dictionary with the key exactly 'name' and value 'Alice'.

3

Run the chain with the test input and capture the output

Call chain.run() with the argument test_input['name'] and assign the result to a variable called output.

LangChain

from langchain import LLMChain, PromptTemplate

prompt = PromptTemplate(template='Say hello to {name}.', input_variables=['name'])
chain = LLMChain(prompt=prompt)

test_input = {'name': 'Alice'}

# Run the chain with test_input['name'] and save to output
# Your code here

Hint

Use chain.run() with the string value from test_input.

4

Write an assertion to check the output matches the expected result

Write an assert statement that checks if output is equal to the string 'Say hello to Alice.'.

LangChain

from langchain import LLMChain, PromptTemplate

prompt = PromptTemplate(template='Say hello to {name}.', input_variables=['name'])
chain = LLMChain(prompt=prompt)

test_input = {'name': 'Alice'}

output = chain.run(test_input['name'])

# Assert output equals 'Say hello to Alice.'
# Your code here

Hint

Use assert to compare output with the exact expected string.

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