Using LangChain to Benchmark LLM Application Performance

Oladimeji Sowole

Evaluating the performance of applications built with large language models (LLMs) is essential to ensure they meet required accuracy and usability standards. LangChain, a powerful framework for LLM-based applications, offers tools to streamline this process, allowing developers to benchmark models, experiment with various configurations and make data-driven improvements.

This tutorial explores how to set up effective benchmarking for LLM applications using LangChain. This guide will take you through each step, from setting up evaluation metrics to comparing different model configurations and retrieval strategies.

Start Benchmarking Your LLM Apps

What you’ll need to begin:

Basic knowledge of Python programming
Familiarity with LangChain and LLMs
LangChain and OpenAI API access
Active LangChang and OpenAI installations, which you can install with:

pip install langchain openai

Step 1: Set Up Your Environment

To begin, import the necessary libraries and configure your LLM provider. For this tutorial, I’ll use OpenAI’s models.

import openai
from langchain import LLMChain, PromptTemplate
from langchain.evaluation import load_evaluator

# Set your OpenAI API key
openai.api_key = "your_openai_api_key"

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Step 2: Design a Prompt Template

Prompt templates are foundational components in LangChain’s framework. Set up a template that defines the structure of your prompts to pass to the LLM:

prompt_template = PromptTemplate(
    input_variables=["question"],
    template="You are an expert. Answer the question concisely: {question}"
)

This template takes in a question and formats it as an input prompt for the LLM. You’ll use this prompt to evaluate different models or configurations in the upcoming steps.

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Step 3: Create an LLM Chain

An LLM chain allows you to connect your prompt template to the LLM, making it easier to generate responses in a structured manner.

llm_chain = LLMChain(
    llm=openai.Completion(engine="text-davinci-003"),
    prompt_template=prompt_template
)

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I’m using OpenAI’s text-davinci-003 engine, but you can replace it with any other model available in OpenAI’s suite.

Step 4: Define the Evaluation Metrics

Evaluation metrics help quantify your LLM’s performance. Common metrics include accuracy, precision and recall. LangChain provides tools like criteria and QAEvalChain for evaluation. I’m using a criteria-based evaluator to measure performance.

# Load an evaluator
evaluator = load_evaluator("criteria", criteria="conciseness")

# Example evaluation
eval_result = evaluator.evaluate_strings(
    prediction="Your generated text here.",
    input="Your input prompt here."
)
print(eval_result)

This snippet specifies conciseness as the evaluation criterion. You can add or customize criteria based on your application needs.

Step 5: Create a Test Data Set

To evaluate your LLM effectively, prepare a data set with sample inputs and expected outputs. This data set will serve as the baseline for evaluating various configurations.

test_data = [
    {"question": "What is the capital of France?", "expected_answer": "Paris"},
    {"question": "Who wrote '1984'?", "expected_answer": "George Orwell"},
    {"question": "What is the chemical symbol for water?", "expected_answer": "H2O"},
]

Step 6: Run Evaluations

Use the QAEvalChain to evaluate the LLM on the test data set. The evaluator will compare each generated response to the expected answer and compute the accuracy.

from langchain.evaluation import QAEvalChain
from langchain.chat_models import ChatOpenAI

llm = ChatOpenAI(temperature=0.0, model="gpt-3.5-turbo")
eval_chain = QAEvalChain.from_llm(llm)

# Example evaluation with QAEvalChain
graded_outputs = eval_chain.evaluate(
    examples=test_data,
    predictions=[{"question": example["question"], "generated_answer": example["expected_answer"]} for example in test_data]
)

# Display results
for result in graded_outputs:
    print(f"Question: {result['question']}")
    print(f"Expected: {result['expected_answer']}")
    print(f"Generated: {result['generated_answer']}")
    print(f"Correct: {result['is_correct']}")
    print()

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Step 7: Experiment with Different Configurations

To enhance accuracy, you may experiment with various configurations, such as changing the LLM or adjusting the prompt style. Try modifying the model engine and evaluating the results again.

# Using a different LLM engine
llm_chain_alternative = LLMChain(
    llm=openai.Completion(engine="gpt-4"),
    prompt_template=prompt_template
)

# Re-evaluate with the new model
evaluator_alternative = QAEvalChain.from_llm(ChatOpenAI(model="gpt-4"))
results_alternative = evaluator_alternative.evaluate(test_data)

# Display alternative results
for result in results_alternative:
    print(f"Question: {result['question']}")
    print(f"Expected: {result['expected_answer']}")
    print(f"Generated: {result['generated_answer']}")
    print(f"Correct: {result['is_correct']}")
    print()

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Step 8: Use Vector Stores for Retrieval

LangChain supports vector-based retrieval, which can improve the relevance of responses in complex applications. By incorporating vector stores, you can benchmark how well retrieval-based approaches perform compared to simple prompt-response models.

from langchain.vectorstores import FAISS

# Define and initialize your vector store
vector_store = FAISS.from_texts(
    [data["expected_answer"] for data in test_data],
    embedding=openai.Embedding(model="text-embedding-ada-002")
)

# Integrate vector store with your evaluator
evaluator_with_retrieval = QAEvalChain(
    llm_chain=llm_chain,
    vector_store=vector_store
)

# Run evaluation with vector store retrieval
retrieval_results = evaluator_with_retrieval.evaluate(test_data)

# Display retrieval results
for result in retrieval_results:
    print(f"Question: {result['question']}")
    print(f"Expected: {result['expected_answer']}")
    print(f"Generated: {result['generated_answer']}")
    print(f"Retrieved: {result.get('retrieved_answer')}")
    print(f"Correct: {result['is_correct']}")
    print()

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Step 9: Analyze and Interpret Results

After completing evaluations across various configurations, analyze the results to identify the best setup. This step involves comparing metrics like accuracy and F1 scores across models, prompts and retrieval methods.

def analyze_results(results):
    correct_responses = sum([1 for r in results if r['is_correct']])
    accuracy = correct_responses / len(results)
    print(f"Accuracy: {accuracy:.2f}")

# Analyze results from each configuration
print("Original Model Evaluation:")
analyze_results(graded_outputs)

print("\nAlternative Model Evaluation:")
analyze_results(results_alternative)

print("\nRetrieval Model Evaluation:")
analyze_results(retrieval_results)

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Conclusion

Evaluating LLM applications is essential for optimizing performance, especially when working with complex tasks, dynamic requirements or multiple model configurations. Using LangChain for benchmarking provides a structured approach to testing and improving LLM applications, offering tools to measure accuracy, assess retrieval strategies and compare different model configurations.

By adopting a systematic evaluation pipeline with LangChain, you can ensure your application’s performance is both robust and adaptable, meeting real-world demands effectively.

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About The Author: Oladimeji Sowole

Oladimeji Sowole is a member of the Andela Talent Network, a private marketplace for global tech talent. A Data Scientist and Data Analyst with more than 6 years of professional experience building data visualizations with different tools and predictive models for actionable insights, he has hands-on expertise in implementing technologies such as Python, R, and SQL to develop solutions that drive client satisfaction. A collaborative team player, he has a great passion for solving problems.