To generate responses to your questions, the application follows these steps:
PDF Loading: The app reads and extracts the text content from multiple PDF documents.
Text Chunking: The extracted text is divided into smaller, manageable chunks for effective processing.
Language Model: The application utilizes a language model to create vector representations (embeddings) of the text chunks.
Similarity Matching: When you pose a question, the app compares it to the text chunks and identifies the most semantically similar ones.
Response Generation: The selected text chunks are then passed to the language model, which generates a response based on the relevant content found in the PDFs.
In today's rapidly expanding digital landscape, the volume of data is growing exponentially. With each passing day, organizations accumulate vast amounts of information, causing valuable insights and critical knowledge to become buried within the depths of countless documents. Consequently, we find ourselves wasting precious time navigating through an endless sea of data in search of specific information. It is evident that we require a more innovative and efficient solution to address this challenge. But what if there was a more creative and intuitive solution to this problem? Imagine being able to have a conversation with our own documents—a chat-like experience where we could ask questions and receive insightful answers. This intriguing concept is made possible by leveraging the capabilities of large language models. Large language models are advanced deep-learning models that have been extensively trained to understand and generate text in a remarkably human-like manner. By extracting the text from our documents and feeding it into these models, we can effectively create a question�answering system that transforms the way we interact with our data. With the implementation of such a system, we can extract textual data from our documents and pose questions directly to them. The large language model, with its vast knowledge and linguistic prowess, can then provide intelligent and contextually appropriate answers. This transformative approach allows us to effortlessly access relevant information and derive insights simply by engaging in conversational exchanges with our own data.