Скачать или смотреть Fetch Data from Several PDF Files Then Combine All and Summarize Everything Using Python

This project demonstrates how Python can automate the process of extracting, combining, and summarizing text from multiple PDF documents for faster insights. By using libraries like pypdf, the script walks through folders, reads each PDF file, and consolidates the content into a single corpus. This large text corpus is then saved to a .txt file, which can be further analyzed or summarized using language models like GPT-3.5 or GPT-4. The summarization process splits the corpus into manageable chunks, processes each using the OpenAI API, and combines the results into one cohesive summary. Users can interact with the script by typing commands or questions, triggering either a full summary or specific answers depending on the input. To improve document understanding, Sentence-BERT is also used to find the most relevant text chunks based on similarity to a user’s question. This approach allows for personalized, accurate question-answering over large amounts of data. It highlights the value of semantic search and embeddings in navigating dense or technical documents. The application is ideal for reviewing complex materials like financial reports or market analysis quickly and cost-effectively. Ultimately, this workflow streamlines research, enhances decision-making, and supports knowledge retention across domains.