Use RAG to Chat with PDFs: Build Your Own PDF Chabot with LLMs

Retrieval-Augmented Generation (RAG) is a technique that integrates large language models (LLMs) with a retrieval system to provide accurate, contextually grounded answers by using external knowledge from documents like PDFs. The process involves indexing documents by splitting them into text chunks, converting these chunks into vector embeddings stored in a vector database, and retrieving relevant chunks when a query is made. This approach improves the accuracy of chatbot responses by grounding them in current and specific document data, rather than relying solely on potentially outdated or erroneous model training data. RAG-based chat systems are particularly useful for businesses in various sectors, such as internal knowledge base access, regulatory compliance, legal research, customer support, and research assistance, by creating a conversational interface that leverages specific document content. Eden AI offers a platform that simplifies the implementation of RAG by providing a unified API for managing LLMs, embeddings, and vector databases, allowing developers to build customized chatbots without dealing with complex infrastructure.

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