Automated evaluation pipelines in LangChain

Introduction

Automated evaluation pipelines help check how well your language models or chains work without doing it by hand. They save time and make sure your results are reliable.

You want to test if your chatbot answers questions correctly after updates.

You need to compare different language models to pick the best one.

You want to measure how well your text summarizer performs on many documents.

You want to automatically check if your AI system meets quality standards before release.

Syntax

LangChain

from langchain.evaluation.qa import QAEvalChain

# Create an evaluation chain with a model and criteria
evaluation_chain = QAEvalChain.from_llm(
    llm=your_llm,
    question_key="question",
    answer_key="answer",
    reference_key="reference"
)

# Run evaluation on a list of examples
results = evaluation_chain.evaluate(examples)

The QAEvalChain runs your model and compares outputs to references automatically.

You provide examples with inputs, expected outputs, and the model's outputs to get scores.

Examples

This example shows how to set up an evaluation chain to check if answers match expected ones.

LangChain

from langchain.evaluation.qa import QAEvalChain

# Simple evaluation chain setup
evaluation_chain = QAEvalChain.from_llm(
    llm=my_llm,
    question_key="question",
    answer_key="answer",
    reference_key="correct_answer"
)

results = evaluation_chain.evaluate([
    {"question": "What is 2+2?", "answer": "4", "correct_answer": "4"},
    {"question": "Capital of France?", "answer": "Paris", "correct_answer": "Paris"}
])

You can customize keys to match your data structure for inputs, predictions, and references.

LangChain

from langchain.evaluation.qa import QAEvalChain

# Using a custom evaluation prompt
custom_eval = QAEvalChain.from_llm(
    llm=my_llm,
    question_key="input_text",
    answer_key="generated_text",
    reference_key="expected_text"
)

results = custom_eval.evaluate(examples)

Sample Program

This program sets up a simple evaluation pipeline that checks if the model's answers match the correct answers for a few questions.

LangChain

from langchain.llms import OpenAI
from langchain.evaluation.qa import QAEvalChain

# Initialize a language model
llm = OpenAI(model_name="gpt-4", temperature=0)

# Create evaluation chain
evaluation_chain = QAEvalChain.from_llm(
    llm=llm,
    question_key="question",
    answer_key="answer",
    reference_key="correct_answer"
)

# Define examples to evaluate
examples = [
    {"question": "What is the capital of Italy?", "answer": "Rome", "correct_answer": "Rome"},
    {"question": "What color is the sky?", "answer": "Blue", "correct_answer": "Blue"},
    {"question": "2 + 2 equals?", "answer": "4", "correct_answer": "4"}
]

# Run evaluation
results = evaluation_chain.evaluate(examples)

print(results)

Important Notes

Make sure your examples have matching keys for input, prediction, and reference.

Evaluation pipelines can be extended with custom metrics or prompts for more complex checks.

Use low temperature in your LLM during evaluation to get consistent outputs.

Summary

Automated evaluation pipelines help test your language models quickly and reliably.

You set them up by linking inputs, model outputs, and expected answers.

They save time and improve your AI system's quality by catching errors early.

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