Integrate Helicone with Ragas, an open-source framework for evaluating Retrieval-Augmented Generation (RAG) systems. Monitor and analyze the performance of your RAG pipelines.
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.
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.
Install required packages
Install the necessary Python packages for the integration:
Set up the environment
Configure your environment with the Helicone API key and OpenAI API key:
Replace "your_helicone_api_key_here"
and "your_openai_api_key_here"
with your actual API keys.
Prepare your dataset
Create a dataset for evaluation using the Hugging Face datasets
library:
Evaluate with Ragas
Use Ragas to evaluate your RAG system:
View results in Helicone
The API calls made during the Ragas evaluation are automatically logged in Helicone. To view the results:
Analyze these logs to understand:
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.
If you encounter any issues with the integration, please check the following:
If you’re still experiencing problems, please contact Helicone support for assistance.
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.
Integrate Helicone with Ragas, an open-source framework for evaluating Retrieval-Augmented Generation (RAG) systems. Monitor and analyze the performance of your RAG pipelines.
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.
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.
Install required packages
Install the necessary Python packages for the integration:
Set up the environment
Configure your environment with the Helicone API key and OpenAI API key:
Replace "your_helicone_api_key_here"
and "your_openai_api_key_here"
with your actual API keys.
Prepare your dataset
Create a dataset for evaluation using the Hugging Face datasets
library:
Evaluate with Ragas
Use Ragas to evaluate your RAG system:
View results in Helicone
The API calls made during the Ragas evaluation are automatically logged in Helicone. To view the results:
Analyze these logs to understand:
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.
If you encounter any issues with the integration, please check the following:
If you’re still experiencing problems, please contact Helicone support for assistance.
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.