If you build AI pipelines that read PDFs, you already know the dilemma: some parsers are fast but lose structure, while others preserve quality but increase latency.

LiteParse is an open-source parser from the LlamaIndex ecosystem designed for speed + useful structure in local workflows.

Why LiteParse matters for RAG

In RAG, extraction quality is not just about extracting text. It is about preserving signals that improve retrieval and reasoning:

• Relative position of text blocks
• Column alignment
• Table-like spacing
• Page boundaries

LiteParse preserves the original document layout by projecting text into a spatial representation (instead of trying to aggressively rewrite everything into markdown).

This matters because modern LLMs are very good at reading these layout patterns. Keeping this structure improves:

• Chunk fidelity during indexing
• Recall in retrieval
• More grounded answers (less hallucination from broken context)

Example: parsing a PDF in Python

Install the package:

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pip install liteparse

Run the code:

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from liteparse import LiteParse

parser = LiteParse()
result = parser.parse("financial-report.pdf")

# Layout-aware plain text
print(result.text)

This output preserves important spacing and alignment cues from the original PDF.

Example: online test

You can test LiteParse 100% free online with any PDF file here: https://simonw.github.io/liteparse/.

OCR and Complex Documents

LiteParse supports OCR and can be used with default OCR or external OCR servers for more complex cases.

For scanned PDFs, you can keep a single Python workflow and still get layout-preserving output.

References

• LiteParse: Local Document Parsing for AI Agents (LlamaIndex blog)
• LiteParse GitHub Repository