llama-index-readers-pdfmuse

LlamaIndex reader for pdfmuse — a deterministic PDF/DOCX parser for RAG. Same file in → same Documents out, with exact coordinates, tables and section structure.

pip install llama-index-readers-pdfmuse

Usage

from llama_index.readers.pdfmuse import PdfmuseReader
from llama_index.core import VectorStoreIndex

# RAG-optimized: one Document per block, section-aware metadata
docs = PdfmuseReader(mode="elements").load_data("report.pdf")
index = VectorStoreIndex.from_documents(docs)

Modes

Metadata (elements mode)

Kept to simple types so it indexes cleanly:

• source, source_kind (Pdf / Docx)
• page — 0-based page index
• category — Title · NarrativeText · Table
• heading_path — section breadcrumb, e.g. "Experience > Alibaba"
• bbox — "x0,y0,x1,y1" on the page (top-left origin, points)

heading_path + bbox let a retriever return "which section, where on the page" — not just a blob of text. And because the core has no probabilistic models, your index is reproducible run-to-run.

MIT OR Apache-2.0 · part of the pdfmuse project.