LangSmith evaluators in LangChain

Introduction

LangSmith evaluators help you check how well your language models or chains are working. They give you clear feedback so you can improve your AI tools.

When you want to see if your AI answers are correct or useful.

When you need to compare different AI models to pick the best one.

When you want to track how your AI improves over time.

When you want to automatically grade AI responses in a project.

When you want to get detailed reports about AI performance.

Syntax

LangChain

from langchain.evaluation import load_evaluator

evaluator = load_evaluator("exact_match")
result = evaluator.evaluate_strings(predictions=[prediction], references=[reference])[0]

You use load_evaluator('exact_match') to create an evaluator object and then call its evaluate_strings method with lists containing the AI's output and the correct answer.

Evaluators can be customized to check different things like accuracy, relevance, or style.

Examples

This example checks if the predicted text exactly matches the reference text.

LangChain

from langchain.evaluation import load_evaluator

evaluator = load_evaluator("exact_match")
prediction = "Hello, world!"
reference = "Hello, world!"
result = evaluator.evaluate_strings(predictions=[prediction], references=[reference])[0]
print(result)

This example evaluates yes/no answers, useful for boolean-like outputs.

LangChain

from langchain.evaluation import load_evaluator

evaluator = load_evaluator("exact_match")
prediction = "Yes"
reference = "Yes"
result = evaluator.evaluate_strings(predictions=[prediction], references=[reference])[0]
print(result)

Sample Program

This program uses the exact_match evaluator to check if the AI's answer matches the correct answer exactly. It prints the evaluation score.

LangChain

from langchain.evaluation import load_evaluator

# Create an evaluator for string comparison
evaluator = load_evaluator("exact_match")

# AI's answer
prediction = "The capital of France is Paris."

# Correct answer
reference = "The capital of France is Paris."

# Evaluate the prediction
result = evaluator.evaluate_strings(predictions=[prediction], references=[reference])[0]

print(f"Evaluation result: {result['score']}")

Important Notes

Evaluators help you measure AI quality without guessing.

Choose the right evaluator type for your task to get useful feedback.

Evaluation results can be used to improve your AI models step by step.

Summary

LangSmith evaluators check how good AI outputs are.

Use them to compare, grade, and improve AI answers.

They are easy to use by calling evaluate_strings with prediction and reference lists.

Practice

1. What is the main purpose of LangSmith evaluators in LangChain?

easy

A. To check how good AI outputs are by comparing predictions to references

B. To train new AI models from scratch

C. To store large datasets for AI training

D. To create user interfaces for AI applications

Solution

1. Step 1: Understand the role of evaluators

   LangSmith evaluators are designed to assess AI outputs by comparing them with expected answers.

2. Step 2: Identify the correct purpose

   They do not train models, store data, or build interfaces but focus on evaluation.

3. Final Answer:

   To check how good AI outputs are by comparing predictions to references -> Option A

4. Quick Check:

   Evaluator purpose = Checking AI output quality [OK]

Hint: Evaluators compare AI answers to references to check quality [OK]

Common Mistakes:

• Confusing evaluators with training tools
• Thinking evaluators store data
• Assuming evaluators build UI

2. Which of the following is the correct way to call an evaluator's evaluate method in LangSmith?

easy

A. evaluate(evaluator, prediction, reference)

B. evaluator.evaluate(prediction, reference)

C. evaluator.run(reference, prediction)

D. evaluate(prediction, reference, evaluator)

Solution

1. Step 1: Recall method usage

   The evaluate method is called on the evaluator object with prediction and reference as arguments.

2. Step 2: Match correct syntax

   evaluator.evaluate(prediction, reference) matches this pattern exactly: evaluator.evaluate(prediction, reference).

3. Final Answer:

   evaluator.evaluate(prediction, reference) -> Option B

4. Quick Check:

   Method call = evaluator.evaluate(prediction, reference) [OK]

Hint: Call evaluate on evaluator with prediction and reference [OK]

Common Mistakes:

• Swapping argument order
• Calling evaluate as a standalone function
• Using wrong method name like run

3. Given the code snippet:

evaluator = SomeEvaluator()
prediction = "The sky is blue."
reference = "The sky is clear and blue."
result = evaluator.evaluate(prediction, reference)
print(result)

What is the expected behavior of print(result)?

medium

A. It prints the reference string unchanged

B. It prints the prediction string unchanged

C. It prints a score or feedback comparing prediction to reference

D. It raises a syntax error because evaluate needs more arguments

Solution

1. Step 1: Understand evaluate output

   The evaluate method returns a score or feedback about how close the prediction matches the reference.

2. Step 2: Analyze print statement

   Printing result shows this evaluation output, not the original strings or errors.

3. Final Answer:

   It prints a score or feedback comparing prediction to reference -> Option C

4. Quick Check:

   Evaluate returns score/feedback [OK]

Hint: Evaluate returns comparison result, not original text [OK]

Common Mistakes:

• Expecting evaluate to return input strings
• Thinking evaluate raises error without extra args
• Confusing prediction and reference outputs

4. What is the error in this code snippet?

evaluator = SomeEvaluator()
result = evaluator.evaluate(reference, prediction)
print(result)

Assuming evaluate expects (prediction, reference) order.

medium

A. Arguments are reversed; prediction should come before reference

B. Missing import statement for SomeEvaluator

C. evaluate method does not exist on evaluator

D. print statement syntax is incorrect

Solution

1. Step 1: Check argument order

   The evaluate method expects prediction first, then reference, but code reverses them.

2. Step 2: Confirm other parts are correct

   Assuming SomeEvaluator is imported and evaluate exists, the main issue is argument order.

3. Final Answer:

   Arguments are reversed; prediction should come before reference -> Option A

4. Quick Check:

   Correct argument order = prediction, reference [OK]

Hint: Remember evaluate(prediction, reference) argument order [OK]

Common Mistakes:

• Swapping prediction and reference arguments
• Assuming missing imports cause this error
• Thinking print syntax is wrong

5. You want to compare multiple AI model outputs to a single reference answer using LangSmith evaluators. Which approach correctly applies evaluators to get scores for each prediction?

hard

A. Combine all predictions into one string and evaluate against reference once

B. Call evaluator.evaluate once with a list of predictions and one reference

C. Use evaluator.evaluate(reference, prediction) inside a loop over references

D. Loop over predictions, call evaluator.evaluate(prediction, reference) for each, collect results

Solution

1. Step 1: Understand evaluator usage for multiple inputs

   Evaluators typically compare one prediction to one reference at a time.

2. Step 2: Apply evaluator in a loop

   Looping over each prediction and calling evaluate separately gives individual scores.

3. Step 3: Eliminate incorrect options

   Passing lists or combining strings is not standard; argument order matters.

4. Final Answer:

   Loop over predictions, call evaluator.evaluate(prediction, reference) for each, collect results -> Option D

5. Quick Check:

   Evaluate each prediction separately in a loop [OK]

Hint: Evaluate predictions one by one in a loop against reference [OK]

Common Mistakes:

• Passing lists instead of single strings
• Mixing argument order
• Combining predictions into one string