Custom evaluation metrics in LangChain - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define a custom evaluation metric function that returns accuracy.

LangChain

def custom_metric(predictions, references):
    correct = sum(p == r for p, r in zip(predictions, references))
    total = len(predictions)
    return correct [1] total

Drag options to blanks, or click blank then click option'

A-

B*

C/

D+

Attempts:

3 left

💡 Hint

Common Mistakes

Using multiplication instead of division.

Subtracting instead of dividing.

Adding counts instead of dividing.

The custom metric calculates accuracy by dividing the number of correct predictions by the total predictions.

2fill in blank

medium

Complete the code to register the custom metric in LangChain's evaluation framework.

LangChain

from langchain.evaluation import Evaluation

eval = Evaluation()
eval.register_metric('accuracy', [1])

Drag options to blanks, or click blank then click option'

Acustom_metric()

Bcustom_metric()()

C'custom_metric'

Dcustom_metric

Attempts:

3 left

💡 Hint

Common Mistakes

Calling the function instead of passing it.

Passing the function name as a string.

Adding extra parentheses.

You register the function itself, not the result of calling it, so pass the function name without parentheses.

3fill in blank

hard

Fix the error in the custom metric function to handle empty prediction lists safely.

LangChain

def custom_metric(predictions, references):
    if len(predictions) == 0:
        return 0
    correct = sum(p == r for p, r in zip(predictions, references))
    return correct [1] len(predictions)

Drag options to blanks, or click blank then click option'

A/

B-

C+

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Using multiplication instead of division.

Not handling empty lists causing errors.

Returning incorrect calculations.

Division is needed to calculate accuracy; returning 0 for empty predictions avoids division by zero.

4fill in blank

hard

Fill both blanks to create a custom metric that calculates F1 score using precision and recall.

LangChain

def f1_score(precision, recall):
    return 2 * (precision [1] recall) [2] (precision + recall)

Drag options to blanks, or click blank then click option'

A*

B+

C/

D-

Attempts:

3 left

💡 Hint

Common Mistakes

Using addition instead of multiplication.

Dividing by difference instead of sum.

Incorrect order of operations.

The F1 score formula is 2 * (precision * recall) / (precision + recall).

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each label to its F1 score if recall is above 0.5.

LangChain

f1_scores = {label: f1_score(precision[label], recall[label]) for label in labels if recall[label] [1] [2]
filtered_scores = {k: v for k, v in f1_scores.items() if v [3] 0.7}

Drag options to blanks, or click blank then click option'

A>

B0.5

C>=

D0.7

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong comparison operators.

Mixing up threshold values.

Incorrect dictionary comprehension syntax.

The comprehension filters labels with recall > 0.5 and then filters scores >= 0.7.

Practice

1. What is the main purpose of creating a custom evaluation metric in Langchain?

easy

A. To speed up the AI model training process

B. To measure AI results in a way that fits your specific needs

C. To automatically fix errors in AI outputs

D. To replace the AI model with a simpler one

Solution

1. Step 1: Understand the role of evaluation metrics

   Evaluation metrics measure how well an AI model performs its task.

2. Step 2: Identify why custom metrics are used

   Custom metrics let you measure results in ways that standard metrics might not cover, fitting your unique needs.

3. Final Answer:

   To measure AI results in a way that fits your specific needs -> Option B

4. Quick Check:

   Custom metrics = tailored measurement [OK]

Hint: Custom metrics tailor scoring to your AI task [OK]

Common Mistakes:

• Thinking custom metrics speed training
• Believing they fix AI errors automatically
• Confusing metrics with model replacement

2. Which of the following is the correct way to start defining a custom evaluation metric class in Langchain?

easy

A. class MyMetric(Evaluation):

B. def MyMetric():

C. class MyMetric():

D. function MyMetric extends Evaluation {}

Solution

1. Step 1: Recall Langchain class inheritance syntax

   Custom metrics inherit from the Evaluation base class using Python class syntax.

2. Step 2: Identify correct class definition

   class MyMetric(Evaluation): correctly defines a class inheriting from Evaluation, matching Langchain patterns.

3. Final Answer:

   class MyMetric(Evaluation): -> Option A

4. Quick Check:

   Class inherits Evaluation = correct syntax [OK]

Hint: Use class inheritance with Evaluation base [OK]

Common Mistakes:

• Defining a function instead of a class
• Missing inheritance from Evaluation
• Using JavaScript syntax in Python

3. Given this custom metric class, what will metric.evaluate(['hello'], ['hello']) return?

class ExactMatch(Evaluation):
    def evaluate(self, predictions, references):
        return sum(p == r for p, r in zip(predictions, references)) / len(references)

medium

A. 1.0

B. 0.0

C. Error due to missing method

D. None

Solution

1. Step 1: Understand the evaluate method logic

   It compares each prediction to the reference and counts matches, then divides by total references.

2. Step 2: Apply inputs to the method

   With predictions=['hello'] and references=['hello'], the single pair matches, so sum is 1 and length is 1, result is 1/1 = 1.0.

3. Final Answer:

   1.0 -> Option A

4. Quick Check:

   Exact match count / total = 1.0 [OK]

Hint: Check if predictions equal references, then divide [OK]

Common Mistakes:

• Forgetting to divide by length
• Confusing sum with boolean values
• Expecting method to return a list

4. What is wrong with this custom metric class that causes an error?

class LengthDiff(Evaluation):
    def evaluate(self, predictions, references):
        return abs(len(predictions) - len(references)) / len(references)

medium

A. It returns a number instead of a score between 0 and 1

B. It does not implement the evaluate method

C. It uses abs() incorrectly causing a syntax error

D. It does not handle empty lists causing runtime error

Solution

1. Step 1: Analyze the evaluate method with empty references

   If references=[], len(references)=0 causes ZeroDivisionError in the division.

2. Step 2: Identify the runtime error cause

   The code divides by len(references) without checking if references is empty, causing runtime error.

3. Final Answer:

   It does not handle empty lists causing runtime error -> Option D

4. Quick Check:

   len(references)==0 -> ZeroDivisionError [OK]

Hint: Check how method handles empty input lists [OK]

Common Mistakes:

• Assuming abs() causes syntax error
• Thinking evaluate method is missing
• Ignoring empty list edge cases

5. You want to create a custom metric that scores AI answers higher if they contain more keywords from a reference list. Which approach fits best?

hard

A. Calculate the difference in length between prediction and reference

B. Check if prediction exactly matches the reference string

C. Count how many keywords appear in the prediction, divide by total keywords

D. Return a fixed score regardless of prediction content

Solution

1. Step 1: Understand the goal of keyword-based scoring

   The metric should reward predictions containing more keywords from the reference list.

2. Step 2: Identify the approach that measures keyword presence proportionally

   Counting keywords in prediction and dividing by total keywords gives a score reflecting keyword coverage.

3. Final Answer:

   Count how many keywords appear in the prediction, divide by total keywords -> Option C

4. Quick Check:

   Keyword coverage scoring = Count how many keywords appear in the prediction, divide by total keywords [OK]

Hint: Score by keyword matches divided by total keywords [OK]

Common Mistakes:

• Using exact match instead of keyword count
• Measuring length difference unrelated to keywords
• Returning fixed scores ignoring content