33 GenAI in Banking & Finance :Exploring RAGAS: A Framework for evaluating the RAG application quantitatively

Exploring RAGAS: A Framework for evaluating the RAG application quantitatively

The RAGAS framework is a cutting-edge approach that combines the strengths of retrieval-based and generation-based methods to produce high-quality outputs. In this blog post, we'll explore the RAGAS framework, its components, and its applications.

What is RAGAS?

RAGAS (Retrieval-Augmented Generation and Synthesis) is a framework that integrates retrieval and generation capabilities to produce high-quality outputs. It consists of two main components:

1. Retriever: This component is responsible for retrieving relevant information from a knowledge base or database.

2. Generator: This component takes the retrieved information and generates a response or output.

How Does RAGAS Work?

The RAGAS framework works as follows:

·         Query: The user provides a query or input to the system.

·         Retrieval: The retriever component searches the knowledge base or database to retrieve relevant information related to the query.

·         Generation: The generator component takes the retrieved information and generates a response or output.

·         Synthesis: The generated output is refined and synthesized to produce a final response.

Evaluating RAGAS with Metrics

To evaluate the performance, various metrics can be used, such as:

•          Precision: Measures the accuracy of the retrieved information.
•          Recall: Measures the completeness of the retrieved information.
•          F1-score: Measures the balance between precision and recall.
•          BLEU score: Measures the quality of the generated output.
•          ROUGE score: Measures the overlap between the generated output and the reference output.

 

Understanding with an example

Let's consider an example where we're using RAGAS to generate answers to user queries. We can evaluate the performance of RAGAS using the following metrics:

Metric	Description	Example Value
Precision	Accuray of retrieved information	0.8
Recall	Completeness of retrieved information	0.7
F1-Score	Balance between precision and recall	0.75
BLEU score	Quality of Generated Output	0.6
ROUGE score	Overlap between the generated Output and Reference Output	0.55

 

Let’s now interpret the results

•          The precision of 0.8 indicates that 80% of the retrieved information is accurate.
•          The recall of 0.7 indicates that 70% of the relevant information is retrieved.
•          The F1-score of 0.75 indicates a good balance between precision and recall.
•          The BLEU score of 0.6 indicates that the generated output is of moderate quality.
•          The ROUGE score of 0.65 indicates a moderate overlap between the generated output and the reference output.

Steps for implementing RAGAS for a questions and answers application

Step 1: Prepare your evaluation dataset

• Gather or create test cases: Collect a set of questions relevant to your RAG system's purpose.
• Obtain ground truth: For each question, write down the correct answer. This is the ground truth you will compare against.
• Run your RAG system: For each question in your dataset, run your RAG system to get the generated answer and the context it used.
• Structure the data: Organize the information into a dataset format that Ragas expects. This typically requires a list of dictionaries or a dataset object, where each entry contains the question, contexts, answer, and ground_truth. 

Step 2: Set up the evaluation environment

• Install Ragas and dependencies: Install the ragas library and any other required packages, like the specific LLM provider you are using (e.g., OpenAI).
• Configure models and APIs: Set up any necessary API keys or model providers, as Ragas will use these to run its evaluation metrics.
• Select metrics: Choose which evaluation metrics you want to use. Common metrics include faithfulness, answer_relevancy, context_precision, and context_recall. 

Step 3: Run the evaluation

• Load your dataset: Load your prepared dataset into a format that can be used by Ragas.
• Execute the evaluation: Use the ragas.evaluate() function, passing your dataset and the list of chosen metrics.
• Analyze the results: Ragas will return a set of scores for each question and an overall summary. You can convert these results to a pandas DataFrame for easier analysis and visualization.
• Iterate and improve: Use the insights from the evaluation to identify weaknesses in your RAG pipeline and make improvements, then re-evaluate to see if performance has improved. 

Advantages of RAGAS

The RAGAS framework offers several benefits, including:

·         Improved Accuracy: RAGAS can improve the accuracy of models by retrieving relevant information and generating high-quality outputs.

·         Increased Efficiency: RAGAS can increase the efficiency of models by reducing the amount of computation required to generate responses.

·         Flexibility: RAGAS can be used in a wide range of applications, from question answering to content generation.

Applications of RAGAS

The RAGAS framework has a wide range of applications, including:

·         Question Answering: RAGAS can be used to build question answering systems that retrieve relevant information and generate accurate answers.

·         Content Generation: RAGAS can be used to generate high-quality content, such as articles, blog posts, and social media posts.

·         Conversational AI: RAGAS can be used to build conversational AI models that retrieve information and generate human-like responses.

Challenges and Future Directions

While the RAGAS framework offers several benefits, there are also some challenges and future directions to consider:

·         Knowledge Base Construction: Building a high-quality knowledge base is a significant challenge in RAGAS.

·         Retrieval Model: Developing effective retrieval models that can retrieve relevant information is crucial in RAGAS.

·         Generator Model: Developing generator models that can generate high-quality outputs is also crucial in RAGAS.

     Anshul Kala is results-driven AI and Data Solutions leader with extensive expertise in leveraging data analytics and artificial intelligence to drive business growth and innovation. She delivered high-impact projects and led cross-functional teams, and unlocked data-driven insights and solutions that transform businesses. She is passionate about harnessing the power of data and AI to enable informed strategic decisions and drive organizational success through data-driven innovation.