Custom evaluation metrics in LangChain - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a custom evaluation metric in Langchain?

A custom evaluation metric is a user-defined way to measure how well a Langchain model or chain performs, tailored to specific needs beyond built-in metrics.

beginner

Why would you create a custom evaluation metric instead of using built-in ones?

Because built-in metrics might not capture the specific goals or nuances of your task, custom metrics let you measure exactly what matters for your use case.

intermediate

Which method do you typically override or implement to create a custom evaluation metric in Langchain?

You implement the `evaluate` method that takes predictions and references, then returns a score or result based on your custom logic.

intermediate

How can you use a custom evaluation metric in Langchain's evaluation framework?

You register your custom metric class and pass it to the evaluation runner, which will call your metric to score model outputs during evaluation.

beginner

Give an example of a simple custom evaluation metric you might create.

For example, a metric that counts how many predicted answers exactly match the correct answers, returning the accuracy as a percentage.

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

A To generate new data automatically

B To replace the Langchain core library

C To speed up model training

D To measure model performance tailored to specific needs

Custom evaluation metrics let you measure model performance in ways that fit your unique task or goals.

Which method do you implement to define a custom evaluation metric in Langchain?

A train()

B evaluate()

C predict()

D fit()

The evaluate() method is where you write your logic to score predictions against references.

Can custom evaluation metrics use multiple inputs like predictions and references?

A No, they use neither

B No, they only use predictions

C Yes, they compare predictions to references

D No, they only use references

Custom metrics usually compare model predictions to the correct answers (references) to score performance.

What is a common output of a custom evaluation metric?

A A score or number representing performance

B A new model

C A dataset

D A training log

Evaluation metrics return scores like accuracy, F1, or custom numbers to show how well the model did.

How do you integrate a custom evaluation metric into Langchain's evaluation process?

A By registering it and passing it to the evaluation runner

B By rewriting the Langchain source code

C By training a new model

D By exporting data to CSV

You register your custom metric class and provide it to the evaluation runner to use during evaluation.

Explain how to create and use a custom evaluation metric in Langchain.

Think about how you measure if the model did well or not.

Why might built-in evaluation metrics not be enough for your Langchain project?

Consider how different tasks need different ways to measure success.

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