# document-processing

> Browse 7 items tagged with document-processing.

## Items

- [PyPDF2](https://vectordb.ever.works/ar/items/pypdf2) — A pure Python PDF library for extracting text, metadata, and other content from PDF documents, commonly used in data preprocessing pipelines for vector database applications involving research papers and technical documentation.
- [Sycamore](https://vectordb.ever.works/ar/items/sycamore) — 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.
- [Unstructured](https://vectordb.ever.works/ar/items/document-parsing-unstructured) — 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.
- [LlamaParse](https://vectordb.ever.works/ar/items/llama-parse-document) — 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.
- [Document Parsing for RAG](https://vectordb.ever.works/ar/items/document-parsing-for-rag) — 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.
- [Document Loaders](https://vectordb.ever.works/ar/items/document-loaders) — 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.
- [Chunking Strategies for RAG](https://vectordb.ever.works/ar/items/chunking-strategies-for-rag) — Methods for splitting documents into optimal pieces for vector embedding and retrieval. Includes fixed-size, recursive, semantic, and agentic chunking approaches.

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