LLM Sherpa

Health v2 · maintenance only

Adoption not part of the score

1749 stars · 164 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

LLM Sherpa is a Python client library providing APIs for layout-aware PDF and document parsing to feed LLM applications, backed by the open-source nlm-ingestor service. It extracts hierarchical structure such as sections, paragraphs, tables, and lists from PDFs, DOCX, PPTX, HTML, and other formats to enable smart chunking for RAG pipelines.

Use cases

• parse pdfs preserving layout structure for rag
• chunk pdf documents intelligently for vector indexing
• extract sections headings tables and lists from pdfs
• self-host a document ingestion server for llm apps
• convert docx pptx html files to structured text for llms
• remove repeated headers footers and watermarks from pdf text

When to choose

• you need layout-aware pdf parsing with section hierarchy for rag pipelines
• you want to self-host a document ingestion service supporting multiple file formats
• you need smart chunking that respects paragraphs tables and nested lists

When to avoid

• you only need plain text extraction without layout information
• you need OCR for scanned pdfs without a text layer
• you need a fully managed cloud parsing service

Facets

library · maturity active

parser pdf ocr rag llm-inference sdk pdf large-language-models developer-tools python self-hosted cross-platform pdf-parsing layout-aware-chunking document-ingestion layoutpdfreader smart-chunking retrieval-augmented-generation natural-language-processing docker

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