8 Popular Tools for RAG Applications

In today’s AI landscape, the ability to integrate external knowledge into models, beyond the data they were initially trained on, has become a game-changer. This advancement is driven by Retrieval Augmented Generation, in short RAG. RAG allows AI systems to dynamically access and utilize external information. Various tools have emerged to simplify both the integration and augmentation processes to build efficient and scalable RAG applications. In this article, we will explore some of the most popular tools for RAG applications and how they are shaping the future of AI.

What is RAG and How Does it Work?

Retrieval augmented generation (RAG) is an AI approach that combines retrieval-based systems with generative models. Based on a query, the RAG model first retrieves relevant information from external knowledge sources, such as databases, documents, and content repositories. This retrieved information is used to augment the input to the generative model, which then generates a more accurate and context-aware response.

For example, imagine you are looking to buy new clothes that match your style from previous purchases.

1. Finding Your Previous Purchase: The system checks your shopping history to retrieve the details of clothes you have bought before, such as the type, brand, colour, pattern, and size.
2. Understanding Your Style: The system then analyzes your previous choices to understand your fashion preferences.
3. Generate Personalized Suggestions: It then looks through the current collection to find items that align with your taste and displays items similar to your previous purchase. This approach helps ensure the suggestions are both relevant and up-to-date.

Popular Tools for RAG Applications

Specialized tools make it easier to build RAG applications for specific use cases, ranging from document searches to searching information from videos. Some of the most popular tools for RAG applications include:

1. NotebookLM (by Google)
2. ChatPdf
3. NoteGPT.io
4. Open Notebook LM (open source)
5. AskYourPDF
6. PDF.ai
7. ChatDoc
8. Chatize

Let’s first compare these tools and examine the tasks each tool is capable of performing in the table below.

Whether you’re working with text-based RAG systems or vision-based applications, these tools offer the building blocks for creating effective, high-performance solutions in the evolving field of AI.

Now, let’s explore the 3 most popular tools used for RAG applications.

1. NotebookLM

NotebookLM is a customizable RAG tool powered by Google’s LLM, Gemini 1.5 Pro. It allows the model to generate content based on the provided information, reducing the risk of hallucinations and irrelevant responses. The input can come from various file types, including PDFs, Google Docs, and YouTube videos. The model can produce summaries, answer questions, and generate audio content, creating engaging conversations and personalized podcasts.

Let’s try out NotebookLM. In this example, I am going to try generating a summary of the novel ‘Pride and Prejudice’ and get the tool to answer a few questions about the book.

STEP 1: Sign In

To access NotebookLM, visit NotebookLM. From the middle of the screen, select Try NotebookLM. Sign in using your email address, and click Create to start a new notebook.

STEP 2: Add Sources

Add the relevant resources that you want the tool to work with. Three options are available for adding resources:

• Google Drive: Upload Google Slides and Docs.
• Link: Add website URLs or YouTube video links.
• Paste Text: Copy and paste the text directly as your resource.

Note that the model can interact with up to 50 resources within a single notebook.

For example: To get a summary of the novel Pride and Prejudice, you can either upload the PDF of the novel or paste the URL link of the e-book.

I will use the URL link of the e-book to generate the summary.

After uploading, the model will quickly generate a brief summary.

STEP 3: Ask Your Query

You can type your questions at the bottom of the screen to get answers from the provided information. You can also interact with multiple sources by simply clicking the + icon on the screen and adding more resources.

STEP 4: Generate Podcast

To create a podcast of the summary, click on Generate at the top right corner.

That’s all! Now, you can listen to this summary anytime.

You’ve seen how easy it is to extract information from any file using NotebookLM in just a few simple steps. To learn more about NotebookLM, you can check out this blog, How to Use NotebookLM.

Open NotebookLM, is another similar tool for RAG applications. It is built using open source models and it is hosted on HuggingFace. Even this tool allows you to accomplish various tasks, including generating summaries and creating podcasts.

2. ChatPDF

ChatPDF is an AI-powered tool that allows users to interact with PDF documents in a conversational format. You can upload a PDF file and ask questions to extract specific information from it without needing to read the entire document.

So, let’s see how ChatPDF works.

STEP 1: Sign Up

Go to ChatPDF and log in using your Gmail account to save your chat history.

STEP 2: Upload a PDF File

Click on Drop PDF at the center of the screen. You will have two options: either browse your computer for a file or paste a URL link. Choose one to upload the relevant document.

most popular tools for rag applications

For example, I have uploaded the ‘Attention is All You Need‘  paper. You can download this research paper and upload it using the ‘Browse My Computer’ option.

Once uploaded, you will see the document on the left side of the screen. The option to chat will appear on the right side, where you can ask your queries.

This tool is widely used by students, researchers, and professionals who need to process large volumes of information quickly and efficiently.

Some similar RAG tools are Ask Your PDF, PDF.ai, ChatDoc, and Chatize. They also work by uploading relevant PDFs or documents, and answering queries based on the provided document. This saves a lot of time for professionals and enhances their productivity.

3. NoteGPT.io

NoteGPT.io is a versatile AI-powered tool designed to enhance learning through features like summarization, note-taking, document interaction, etc.

Let’s explore how NoteGPT.io works:

STEP1: Sign Up

Head to https://notegpt.io/ and sign up using your Gmail account.

STEP 2: Upload Files

Select “Create” from the left side of the screen. You will be presented with three options:

• URL: You can paste a URL link from YouTube, Google Podcasts, webpages, articles, online PDFs, Word documents, PPTs, images, audio, or video files.
• Upload: Choose a file from your desktop to upload.
• Text: You can directly copy and paste the text.

Choose the appropriate option, then click on ‘Summarize Now’.

For example, I used a free course link to a generative AI video from Analytics Vidhya, pasted the link in the URL section, and clicked ‘Summarize Now’.

The summary of the entire video appeared on the right side of the screen under ‘AI Notes.

STEP 3: Ask Query

You can ask questions related to the file in the AI chat section.

This allows you to interact with the video content easily with the help of NoteGPTi.o.

You will find all the files that you have uploaded or linked to in the Notes section.

Exciting, right? You can easily access this vast content in a short time using these tools.

Conclusion

RAG is revolutionizing how models access and utilize external knowledge to provide contextually accurate responses. With the rise in RAG applications, a variety of tools are now available to streamline its development for different use cases. Tools like Google’s NotebookLM, ChatPDF, NoteGPT.io  allow users to access relevant information from large datasets and documents. Whether summarizing content, interacting with files, or generating podcasts, these RAforG tools simplify the process of building efficient, high-performing AI models. As the landscape of RAGs continues to evolve, more tools will emerge, facilitating more diverse and complex use cases across industries. Let’s wait and watch!

Frequently Asked Questions

Diksha Kumari

As an Instructional Designer at Analytics Vidhya, Diksha has experience creating dynamic educational content on the latest technologies and trends in data science. With a knack for crafting engaging, cutting-edge content, Diksha empowers learners to navigate and excel in the evolving tech landscape, ensuring educational excellence in this rapidly advancing field.