Custom evaluation metrics in LangChain - Performance & Optimization

Performance: Custom evaluation metrics

MEDIUM IMPACT

Custom evaluation metrics affect the speed and responsiveness of model output evaluation during runtime.

Evaluating model outputs with custom metrics during runtime

LangChain

def fast_metric(output, reference):
    output_set = set(output)
    reference_set = set(reference)
    score = len(output_set & reference_set) / len(reference_set)
    return score

Uses set operations which are faster and avoid nested loops, reducing evaluation time.

📈 Performance GainReduces blocking to under 10 ms, improving interaction responsiveness

Evaluating model outputs with custom metrics during runtime

LangChain

def slow_metric(output, reference):
    # Complex nested loops and heavy computations
    score = 0
    for o in output:
        for r in reference:
            if o == r:
                score += 1
    return score / len(reference)

This metric uses nested loops causing slow evaluation especially with large outputs, blocking UI updates.

📉 Performance CostBlocks rendering for 100+ ms on large inputs, causing input delay

Performance Comparison

Pattern	DOM Operations	Reflows	Paint Cost	Verdict
Nested loops metric	Minimal	0	High due to blocking JS	[X] Bad
Set operations metric	Minimal	0	Low due to fast JS	[OK] Good

Rendering Pipeline

Custom evaluation metrics run after model output generation and before UI update, affecting the interaction to next paint phase.

→JavaScript Execution

→Layout

→Paint

⚠️ BottleneckJavaScript Execution due to heavy synchronous computations

Core Web Vital Affected

INP

Custom evaluation metrics affect the speed and responsiveness of model output evaluation during runtime.

Optimization Tips

1Avoid nested loops in metric calculations to reduce blocking.

2Use efficient data structures like sets for faster evaluation.

3Run heavy computations asynchronously to keep UI responsive.

Performance Quiz - 3 Questions

What is the main performance issue with using nested loops in custom evaluation metrics?

A They increase DOM node count

B They trigger multiple reflows

C They cause blocking JavaScript execution slowing interaction

D They increase CSS selector complexity

DevTools: Performance

How to check: Record a performance profile while running the evaluation metric; look for long scripting tasks.

What to look for: Long blocking JavaScript tasks indicate slow metric evaluation affecting responsiveness.

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