Learn How to Build a RAG Application using GPU Droplets

Introduction to Retrieval Augmented Generation (RAG) for Language Models

In this article, you will learn how to create a Retrieval-Augmented Generation (RAG) application that can work with your PDFs or other data sources. This type of application is useful for handling large amounts of text data, such as books or lecture notes, to help create a chatbot that can answer any query based on the provided data. The best part is that we will be using an open-source model, so there is no need to pay for API access.

RAG has gained immense popularity and is one of the most used AI frameworks for creating customized chatbots. It’s also a powerful tool for building knowledge-driven AI applications.

RAG can be thought of as an AI assistant that is well-versed in user data and human language. When asked a question, it utilizes a library of information to provide a detailed and accurate answer. It is a powerful combination of an information retrieval system and a robust LLM.

Retrieval-Augmented Generation (RAG) significantly improves accuracy by retrieving relevant information from various external knowledge external sources, enhancing the context and precision of generated responses. By using factual data during the retrieval process, RAG can also reduce hallucinations, a common issue in large language models. Additionally, RAG enhances in-context learning by retrieving specific, up-to-date information, making it ideal for use cases like Q&A, document summarization, and interactive workflows.

For example, we used the research paper of YOLO v9 as the data source, and the LLM was Llama-2-7b-chat. We then asked a few questions about the research paper, and here are relevant results from the replies.

response=query_engine.query("What is YOLOv9")

YOLOv9 is a new object detection algorithm that improves upon existing methods in several ways. First, it uses a new architecture that reduces the number of parameters and computations while maintaining the same level of accuracy. Second, it uses a new technique called PGI to accurately retain and extract the information needed to map the data to the target. Finally, it is shown to have significantly improved performance compared to existing methods in all aspects.

response_2=query_engine.query("what is GELAN architecture?")

GELAN architecture is a deep neural network designed for object detection tasks. It consists of multiple prediction branches, each of which is connected to a deep supervision branch. The deep supervision branch is responsible for providing auxiliary information to the main branch, which is used to improve the accuracy of object detection. The GELAN architecture also includes an integration network that combines returned gradients from different prediction heads, allowing the main branch to retain complete information about all target objects. This allows the method to alleviate the broken information problem in deep supervision and improve the overall performance of object detection tasks.

response_3=query_engine.query("What is new with YOLO v9?")

YOLO v9 introduces several new features, including:

1. Conventional convolution: YOLO v9 uses conventional convolution instead of depth-wise convolution, which leads to better parameter utilization. 
2. PGI: YOLO v9 uses a new technique called PGI (Progressive Gating and Integration) to accurately retain and extract information needed to map the data to the target. 
3. Large models: YOLO v9 shows huge advantages in using large models, requiring only 66% of the parameters while maintaining accuracy as RT DETR-X. 
4. Comparison with state-of-the-arts: YOLO v9 compares with other train-from-scratch real-time object detectors, showing significant improvement in all aspects.

Please let me know if you have any further questions or if there's anything else I can help you with.

We even tried the application with some personal data, and here is the result.

response=query_engine.query("Who is Shaoni")

Shaoni Mukherjee is a seasoned Technical Writer and AI Specialist with a deep passion for Generative AI and its transformative potential. With over four years of experience in data science and a strong foundation in AI/ML technologies, she specializes in creating in-depth, technical content that simplifies complex concepts. Currently contributing to DigitalOcean, Shaoni focuses on topics like GPU acceleration, deep learning, and large language models (LLMs), ensuring that developers and businesses alike can harness cutting-edge technology. Her expertise lies in breaking down technical innovations into digestible, actionable insights, making her a trusted voice in the world of AI.