RAGxplorer: A New Tool for Visualizing and Building RAG Applications

RAGxplorer: A New Tool for Visualizing and Building RAG Applications

Retrieval-Augmented Generation (RAG) is a technique that combines large language models with information retrieval systems to generate natural language responses based on external knowledge sources. RAG applications can be used for various tasks, such as question answering, content creation, and data analysis. However, developing and evaluating RAG applications can be challenging, as it requires understanding how the language model retrieves and uses relevant information from a large corpus of documents.

To address this challenge, researchers  have developed RAGxplorer, an interactive tool that supports the building of RAG applications by visualizing document chunks and the queries in the embedding space. RAGxplorer takes a document, breaks it into smaller, overlapping chunks, and converts each chunk into a numerical vector that captures its meaning and context.

RAGxplorer then displays these vectors in a 2D or 3D space, creating an interactive map of the document’s semantic landscape. Users can see how different chunks relate to each other and to specific queries, represented as dots in the embedding space. This visualization allows users to quickly assess how well the RAG model understands the document, and how it selects the most relevant chunks for generating a response.

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RAGxplorer also provides several features that make it easy and flexible to use. Users can upload PDF documents for analysis and configure the chunk size and overlap, providing adaptability to different types of content. The tool also allows users to build a vector database for efficient retrieval and visualization, enhancing the overall user experience. Users can experiment with different query expansion techniques and observe how the retrieval of relevant chunks is affected.

RAGxplorer is a novel and useful tool for researchers and developers who want to explore and build RAG applications. It enables them to gain insights into the inner workings of the RAG model and the document corpus, and to improve the quality and performance of the generated responses.

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