Automated evaluation pipelines in LangChain - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the LangChain evaluation module.

LangChain

from langchain.evaluation import [1]

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

Aload_evaluator

Bload_evaluator_chain

Cload_evaluation

Dload_chain

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'load_chain' which loads general chains, not evaluators.

Using 'load_evaluation' which is not a valid function.

The load_evaluator_chain function is used to load evaluation chains in LangChain.

2fill in blank

medium

Complete the code to create an evaluator chain with a language model.

LangChain

evaluator = load_evaluator_chain(llm=[1])

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AChatOpenAI()

BGPT4All()

COpenAI()

DTextLLM()

Attempts:

3 left

💡 Hint

Common Mistakes

Using OpenAI() which is not chat-based.

Using undefined models like TextLLM().

ChatOpenAI() is the recommended language model for evaluation chains in LangChain.

3fill in blank

hard

Fix the error in the code to run the evaluation chain on predictions and references.

LangChain

result = evaluator.evaluate(predictions=[1], references=references)

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Aoutputs

Bpreds

Cpredictions

Danswers

Attempts:

3 left

💡 Hint

Common Mistakes

Using variable names that don't match the parameter name.

Passing undefined variables.

The parameter name must be predictions to match the evaluator's API.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps inputs to their evaluation scores.

LangChain

scores = {input_text: result['[1]'] for input_text, result in [2].items()}

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Ascore

Baccuracy

Cevaluations

Dresults

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong keys like 'accuracy' which may not exist.

Using incorrect variable names for the dictionary.

The dictionary comprehension extracts the 'score' from each result in the 'results' dictionary.

5fill in blank

hard

Fill all three blanks to define a function that runs evaluation and returns the score for each input.

LangChain

def run_evaluation(data):
    evaluator = load_evaluator_chain(llm=[1])
    results = evaluator.evaluate(predictions=data['[2]'], references=data['[3]'])
    return {k: v['score'] for k, v in results.items()}

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

AChatOpenAI()

Bpredictions

Creferences

DOpenAI()

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong model instances.

Mixing up the keys for predictions and references.

The function uses ChatOpenAI() as the model, and accesses 'predictions' and 'references' keys from the data dictionary.

Practice

1. What is the main purpose of an automated evaluation pipeline in Langchain?

easy

A. To quickly test language model outputs against expected answers

B. To train new language models from scratch

C. To manually review each model output for quality

D. To deploy language models to production servers

Solution

1. Step 1: Understand the role of evaluation pipelines

   Evaluation pipelines automatically compare model outputs to expected answers to check correctness.

2. Step 2: Identify the main benefit

   This automation speeds up testing and helps catch errors early without manual review.

3. Final Answer:

   To quickly test language model outputs against expected answers -> Option A

4. Quick Check:

   Automated testing = Quick evaluation [OK]

Hint: Evaluation pipelines compare outputs to expected answers fast [OK]

Common Mistakes:

• Confusing evaluation with training
• Thinking evaluation is manual
• Assuming deployment is part of evaluation

2. Which of the following is the correct way to create an evaluation pipeline in Langchain?

easy

A. pipeline = EvaluationPipeline(inputs, model, expected_outputs)

B. pipeline = EvaluationPipeline(model, inputs, expected_outputs)

C. pipeline = EvaluationPipeline(expected_outputs, inputs, model)

D. pipeline = EvaluationPipeline(inputs, expected_outputs, model)

Solution

1. Step 1: Recall the order of parameters

   The EvaluationPipeline constructor expects inputs first, then the model, then expected outputs.

2. Step 2: Match the correct parameter order

   pipeline = EvaluationPipeline(inputs, model, expected_outputs) matches this order exactly, others mix the sequence causing errors.

3. Final Answer:

   pipeline = EvaluationPipeline(inputs, model, expected_outputs) -> Option A

4. Quick Check:

   Inputs, model, expected outputs order [OK]

Hint: Remember: inputs first, then model, then expected outputs [OK]

Common Mistakes:

• Swapping model and inputs order
• Putting expected outputs before inputs
• Using wrong parameter sequence causing errors

3. Given this code snippet, what will be the output of results?

inputs = ["Hello", "World"]
model = lambda x: x.lower()
expected = ["hello", "world"]
pipeline = EvaluationPipeline(inputs, model, expected)
results = pipeline.run()

medium

A. [True, False]

B. [False, False]

C. [True, True]

D. RuntimeError

Solution

1. Step 1: Understand the model function

   The model converts each input string to lowercase, so "Hello" -> "hello" and "World" -> "world".

2. Step 2: Compare model outputs to expected

   Both outputs match the expected list exactly, so evaluation returns True for both.

3. Final Answer:

   [True, True] -> Option C

4. Quick Check:

   Lowercase matches expected = True [OK]

Hint: Check if model output matches expected exactly [OK]

Common Mistakes:

• Assuming case does not matter
• Expecting runtime error from lambda
• Mixing up True and False results

4. You wrote this evaluation pipeline but it raises an error:

inputs = ["Test"]
model = "not a function"
expected = ["test"]
pipeline = EvaluationPipeline(inputs, model, expected)
pipeline.run()

What is the likely cause?

medium

A. Inputs list cannot have only one item

B. Model must be a callable function, not a string

C. Expected outputs must be integers

D. EvaluationPipeline requires three arguments, but only two were given

Solution

1. Step 1: Check the model parameter type

   The model should be a function that processes inputs, but here it is a string, which is not callable.

2. Step 2: Understand the error cause

   Calling pipeline.run() tries to call the model on inputs, causing a TypeError because strings can't be called like functions.

3. Final Answer:

   Model must be a callable function, not a string -> Option B

4. Quick Check:

   Model callable required, string given [OK]

Hint: Model must be a function, not a string [OK]

Common Mistakes:

• Thinking inputs size causes error
• Expecting output type to be integer
• Miscounting constructor arguments

5. You want to evaluate a language model that sometimes returns empty strings for some inputs. How should you modify your automated evaluation pipeline to handle this edge case correctly?

hard

A. Replace empty string outputs with None before evaluation

B. Treat empty string outputs as incorrect regardless of expected answer

C. Ignore inputs that produce empty strings in the evaluation

D. Filter out empty string outputs before comparing to expected answers

Solution

1. Step 1: Identify the problem with empty strings

   Empty string outputs can cause false negatives if compared directly to expected answers.

2. Step 2: Implement filtering before comparison

   Filtering out empty strings ensures only meaningful outputs are evaluated, avoiding misleading failures.

3. Step 3: Avoid ignoring inputs or forcing None

   Ignoring inputs or replacing outputs can hide real issues or cause errors in evaluation.

4. Final Answer:

   Filter out empty string outputs before comparing to expected answers -> Option D

5. Quick Check:

   Filter empty outputs to avoid false errors [OK]

Hint: Filter empty outputs before evaluation to avoid false failures [OK]

Common Mistakes:

• Ignoring inputs with empty outputs
• Replacing empty strings with None causing errors
• Counting empty strings as always wrong