Automate RAG evaluation without manual intervention. Understand RAG importance and its impact on production. Learn to generate a synthetic test set and compute RAG metrics using Ragas package. Navigate through the implementation details in the accompanying notebook. Evaluate RAG with Ragas framework using VertexAI LLMs and embeddings for comprehensive analysis and understanding.
“`html
Automate the evaluation process of your Retrieval Augment Generation apps without any manual intervention
Today’s topic is evaluating your RAG without manually labeling test data. Measuring the performance of your RAG is important for building such systems and serving them in production. Evaluating your RAG provides quantitative feedback that guides experimentations and the appropriate selection of parameters. It is also crucial for clients or stakeholders who expect performance metrics to validate your project.
Automatically generating a synthetic test set from your RAG’s data
When evaluating the performance of your RAG, you need an evaluation dataset that includes questions, ground truths, predicted answers, and relevant contexts used by the RAG. To create such a dataset, you can generate questions and answers from the RAG data and run the RAG over these questions to make predictions.
The process involves steps such as splitting the data into chunks, embedding it into a vector database, fetching similar contexts, and generating questions and answers using a prompted template.
Generate a synthetic test set
To evaluate the RAG, you can use a workflow to produce questions and answers, and then start by building a vector store that includes the data used by the RAG. After splitting the data into chunks, create an index and use a LangChain wrapper to index the splits’ embeddings. Then, generate the synthetic dataset using an LLM, document splits, an embedding model, and the name of the Pinecone index.
Popular RAG metrics
Before jumping into the code, let’s cover the four basic metrics used to evaluate the RAG: Answer Relevancy, Faithfulness, Context Precision, and Answer Correctness. Each metric examines a different facet, and it’s crucial to consider multiple metrics for a comprehensive perspective when evaluating your application.
Evaluate RAGs with RAGAS
To evaluate the RAG and compute the four metrics, you can use Ragas, a framework that helps you evaluate your Retrieval Augmented Generation (RAG) pipelines. You can configure Ragas to use VertexAI LLMs and embeddings and call the evaluate function on the synthetic dataset to specify the metrics you want to compute.
Generating a synthetic dataset to evaluate your RAG is a good start, especially when you don’t have access to labeled data. However, this solution also comes with its problems. To tackle these issues, you can adjust and tune your prompts, filter irrelevant questions, create synthetic questions on specific topics, and use Ragas for dataset generation.
“`
