An Interactive Framework for Querying Data from Large Pdf Files

With the evolution of huge language models, it is now possible to converse with robots on any topic. However, unstructured data, such as PDF files, must still be converted into a format that these models can easily analyze. Through this study, we present a framework for creating conversational PDF applications that are powered by language models, specifically OpenAI's GPT-3 architecture. Our framework, enables users to connect their language model APIs to other data sources, such as PDF files, and allows the language model to interact with the data source. We show our framework's usefulness by breaking a big PDF document into smaller chunks, transforming each chunk into embeddings, and constructing a knowledge base that users may query. The results obtained suggest that employing embeddings rather than comparing text directly can greatly reduce data size while improving search result accuracy.