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Introduction

Ragas is an open-source framework for evaluating Retrieval-Augmented Generation (RAG) systems. It provides metrics to assess various aspects of RAG performance, such as faithfulness, answer relevancy, and context precision.

Integrating Helicone with Ragas allows you to monitor and analyze the performance of your RAG pipelines, providing valuable insights into their effectiveness and areas for improvement.

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Integration Steps

1

Create an account + Generate an API Key

Log into Helicone or create an account. Once you have an account, you can generate an API key.

Make sure to generate a write only API key.

2

Install required packages

Install the necessary Python packages for the integration:

pip install ragas openai
3

Set up the environment

Configure your environment with the Helicone API key and OpenAI API key:

import os

HELICONE_API_KEY = "your_helicone_api_key_here"
os.environ["OPENAI_API_BASE"] = f"https://oai.helicone.ai/{HELICONE_API_KEY}/v1"
os.environ["OPENAI_API_KEY"] = "your_openai_api_key_here"

Replace "your_helicone_api_key_here" and "your_openai_api_key_here" with your actual API keys.

4

Prepare your dataset

Create a dataset for evaluation using the Hugging Face datasets library:

from datasets import Dataset

data_samples = {
    'question': ['When was the first Super Bowl?', 'Who has won the most Super Bowls?'],
    'answer': ['The first Super Bowl was held on January 15, 1967.', 'The New England Patriots have won the most Super Bowls, with six championships.'],
    'contexts': [
        ['The First AFL–NFL World Championship Game, later known as Super Bowl I, was played on January 15, 1967, at the Los Angeles Memorial Coliseum in Los Angeles, California.'],
        ['As of 2021, the New England Patriots have won the most Super Bowls with six championships, all under the leadership of quarterback Tom Brady and head coach Bill Belichick.']
    ],
    'ground_truth': ['The first Super Bowl was held on January 15, 1967.', 'The New England Patriots have won the most Super Bowls, with six championships as of 2021.']
}

dataset = Dataset.from_dict(data_samples)
5

Evaluate with Ragas

Use Ragas to evaluate your RAG system:

from ragas import evaluate
from ragas.metrics import faithfulness, answer_relevancy, context_precision

score = evaluate(dataset, metrics=[faithfulness, answer_relevancy, context_precision])
print(score.to_pandas())
6

View results in Helicone

The API calls made during the Ragas evaluation are automatically logged in Helicone. To view the results:

  1. Go to the Helicone dashboard
  2. Navigate to the ‘Requests’ section
  3. You should see the API calls made during the Ragas evaluation

Analyze these logs to understand:

  • The number of API calls made during evaluation
  • The performance of each call (latency, tokens used, etc.)
  • Any errors or issues that occurred during the evaluation

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Advanced Usage

You can customize the Ragas evaluation by using different metrics or creating your own. Refer to the Ragas documentation for more information on available metrics and customization options.

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Troubleshooting

If you encounter any issues with the integration, please check the following:

  1. Ensure that your Helicone and OpenAI API keys are correct and have the necessary permissions.
  2. Verify that you’re using the latest versions of the Ragas and OpenAI packages.
  3. Check the Helicone dashboard for any error messages or unexpected behavior in the logged API calls.

If you’re still experiencing problems, please contact Helicone support for assistance.

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Conclusion

By integrating Helicone with Ragas, you can gain valuable insights into the performance of your RAG systems. This combination allows you to monitor and analyze your RAG pipelines effectively, helping you identify areas for improvement and optimize your system’s performance.

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