Discover how to build a scalable, real-time document processing pipeline that transforms PDFs, reports, and contracts into searchable, enriched data. Learn how LlamaParse powers intelligent parsing and chunking, while MongoDB enables flexible storage, indexing, and vector search. ...

Llama Index Report Generation Agent Overview - Llama Index introduces a report generation agent capable of creating reports with interspersed text and images from complex PDFs like research papers [1] - The agent leverages Llama Parse to extract text and images, including charts, from PDFs [2][3][4][5] - The workflow involves creating chat history, retrieving document chunks, and generating reports [6] - The agent can switch between document retrieval and chunk retrieval, facilitating comparisons across multiple research papers [16][17] Technical Implementation - Llama Parse is initialized with "PAS with agent mode" to extract high-resolution OCR, full-page screenshots, and extracted charts [8][9] - A structured LLM, using a Pydantic model called "report output," defines the structure of the report, allowing for text and image blocks [11][12] - The agent uses a system prompt and a structured LLM to generate the report [12][13] - The agent utilizes chunk retriever and document retriever tools [14] Application and Use Cases - The generated reports can analyze specific topics, such as MetaGPT experimental techniques, with relevant images embedded [7][15] - The technique is applicable to research papers, presentations, quarterly reports, and other documents with charts and figures [16]