How to implement structured data extraction from images using LlamaIndex?

All answers to this question.

Multi-modal is where the framework is really pushing boundaries right now. Using LlamaParse specifically for invoices is a great idea because it converts complex layouts into Markdown, which keeps the table structures intact for the LLM to read. The step-by-step is: Use the LlamaParse reader to ingest the image/PDF, then pass that Markdown into a VectorStoreIndex. Because the structure is preserved, you can ask questions like "What was the total VAT on the invoice from July?" and get an accurate answer. Without a specialized parser like this, traditional OCR usually garbles the columns and rows into a useless mess of text.

   Answered 2025-09-12 by Cynthia Ward

Is LlamaParse a paid service, and are there any open-source alternatives within the LlamaIndex ecosystem for someone on a budget?

   Answered 2025-09-15 by Kenneth Brooks

• Kenneth, LlamaParse does have a free tier but is a managed service. For a purely open-source alternative, you can use LlamaIndex with UnstructuredIO or LayoutParser. They aren't quite as "plug-and-play" as LlamaParse, but they work well for standard layouts. The framework is flexible enough to let you plug in any OCR tool you want. However, for "messy" documents, the extra logic in LlamaParse usually pays for itself in saved developer hours.

     Commented 2025-09-17 by Larry Dixon

The multi-modal support also extends to "Multi-Modal Indices," where you can store both the image embeddings and text embeddings in the same searchable space.

   Answered 2025-09-20 by Donna Carter

• Thanks Donna! This is great for apps where you want to find an image based on a text description and then extract data from it.

     Commented 2025-09-21 by Cynthia Ward