---
title: "How to use Llama Parse to convert PDF to text and extract complex table data. For Annual Reports, 10Ks, Research Reports"
slug: llama-parse-pdf-analyze-with-chatgpt-rag
date_published: 2024-03-20T13:04:38.760Z
original_url: https://www.tigzig.com/post/llama-parse-pdf-analyze-with-chatgpt-rag
source: migrated
processed_at: 2025-12-02T10:00:00.000Z
---

# How to use Llama Parse to convert PDF to text and extract complex table data. For Annual Reports, 10Ks, Research Reports

**NEW:** [**TIGZIG: Co-Analyst**](http://app.tigzig.com/)

[app.tigzig.com](http://app.tigzig.com/) - my open-source platform with 25+ micro-apps and tooling's for AI driven analytics and data science.

Including a Llama Parse PDF to Markdown converter

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Extracting data, especially table data, from complex PDFs with tables used to be a challenge. But with the launch of LlamaParse by LlamaIndex, that period is now over.

Originally published on LinkedIn. Embedded post below.

**Note for developers doing the conversion themselves with Python/JS scripts:**

* The API call works faster than the Python package.
* Chunking the file before parsing improves speeds.
* Currently, around 50 pages seems to be the optimal chunk size.
* Parsing is faster when done in 50-page chunks versus the full file at once, even for say a 100-page report.
* Tested chunk sizes between 25 to 100 pages, with less than 50 or more than 50 pages increasing the conversion time.
* However, all this can change rapidly as LlamaParse is evolving quickly. For example, just a few days back they increased file size limit from 200 to 700 pages.