Retrieval Augmented Generation Application System (RAGAS) is a cutting-edge framework designed to increase the efficiency and accuracy of language models and embedding systems.
As AI technology continues to evolve, the integration of retrieval systems with generative models has become crucial for improving the relevance and precision of generated responses in various AI applications.
Understanding the Core Functions of RAGAS
RAGAS mainly focuses on two main things. One is generation, that is when a language model generates a response. Second, retrieval, that’s when the relevant information is searched from the database and provided as context to the language model. In simple terms, the first step in rag application is to ingest the data. So, the data, for example, a PDF or a text file, is ingested. Then it’s divided into chunks.
Those chunks are converted to embeddings and stored in the database.When the user asks a question, relevant information is retrieved from the database and sent as context to the language model. Finally, the language model will generate the response.
Retrieval is more related to the embedding model which we use and generation is more related to the language model used. So, in this way, you can test your language model using RAGAS and also you can test your embeddings model or your whole retrieval system using it.
How RAGAS Works
In regards to RAGAS, there are four different things which you need to focus on. One is the question, that is, a question which the user is going to ask next. The second is the ground truth, what is the real answer. So, these questions and ground truth will be provided as the input. And the two more: answer and the context will be generated by the models. The answer will be generated by the language model, the context will be generated during the time of retrieval and also used with embeddings model.
Initial Steps to Start with RAGAS
Install RAGAS
Make imports
Provide sample data
from datasets import Dataset
data_samples = {
'question': ['When was the first super bowl?', 'Who won the most super bowls?'],
'answer': ['The first superbowl was held on January 15, 1967', 'The most super bowls have been won by The New England Patriots'],
'contexts' : [['The Super Bowl....season since 1966,','replacing the NFL...in February.'],
['The Green Bay Packers...Green Bay, Wisconsin.','The Packers compete...Football Conference']],
'ground_truth': ['The first superbowl was held on January 15, 1967', 'The New England Patriots have won the Super Bowl a record six times']
}
dataset = Dataset.from_dict(data_samples)Each data set will contain ‘question’, ‘answer’, ‘contaxts’ and ‘ground_truth’.
Evaluation Metrics: Faithfulness and Answer Correctness
To check the effectiveness of RAGAS, two key metrics are used: faithfulness and answer correctness. Faithfulness measures the factual consistency of the response with respect to the provided context, ensuring the generated content is trustworthy. Answer correctness, on the other hand, evaluates the accuracy of the response against the actual facts or ground truth.
RAGAS is designed for ease of installation and integration into existing systems, supporting extensive testing and refinement of language and embedding models. This leads to faster development cycles and more robust AI applications.
The Future of AI with Increased Accuracy and Reliability
By using RAGAS, developers and enterprises can significantly improve the performance of their AI systems. RAGAS not only boosts the accuracy of responses but also ensures they are relevant and contextually appropriate, paving the way for more intelligent and dependable AI solutions.
At Next Brain AI, we use cutting-edge AI technology to help data analysts, data engineers, and business owners easily get insights from their data. Schedule your demo today to discover how our solution can deliver strategic insights from your data.
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