PyPDF2

An open-source, LLM-powered document processing engine for ETL, RAG, and analytics on unstructured data, featuring a DocSet abstraction similar to Apache Spark and delivering 6x more accurate data chunking with 2x improved recall for hybrid search.

Open-source library for preprocessing unstructured documents (PDFs, Word, HTML, images) for RAG and LLM applications. Handles extraction, chunking, and cleaning of diverse document types.

Advanced document parsing service from LlamaIndex for extracting structured data from PDFs, PowerPoints, and Word documents. Uses LLMs to understand document structure and maintain layout information.

Critical preprocessing step for RAG systems involving extraction of text, tables, and images from various document formats (PDF, DOCX, HTML) using tools like Unstructured, LlamaParse, and PyPDF.

Components in LLM frameworks that fetch and parse data from various sources (PDFs, websites, databases) into a standardized format for processing. Essential first step in RAG pipelines for converting raw data into processable documents.

Methods for splitting documents into optimal pieces for vector embedding and retrieval. Includes fixed-size, recursive, semantic, and agentic chunking approaches.