MinerU Reader

pip install llama-index-readers-mineru

This reader uses the MinerU document parsing API to extract high-quality Markdown from PDF, Doc/Docx, PPT/PPTx, images, and Excel files. It supports two parsing modes:

Note: MinerU Python SDK precision mode supports extra output formats (images/JSON/docx/html/latex), but MinerUReader in this integration currently returns only result.markdown as Document.text.

Usage

Flash Mode (default, no token needed)

from llama_index.readers.mineru import MinerUReader

reader = MinerUReader()

# Parse a single PDF from URL
documents = reader.load_data(
    "https://cdn-mineru.openxlab.org.cn/demo/example.pdf"
)
print(documents[0].text)

# Parse a local file
documents = reader.load_data("/path/to/local.pdf")

# Parse multiple files at once
documents = reader.load_data(
    [
        "https://cdn-mineru.openxlab.org.cn/demo/example.pdf",
        "/path/to/local.pdf",
    ]
)

Precision Mode (token required)

Get your free token from MinerU API Management.

from llama_index.readers.mineru import MinerUReader

reader = MinerUReader(
    mode="precision",
    token="your-api-token",  # or set MINERU_TOKEN env var
    ocr=True,
    formula=True,
    table=True,
    language="en",
    pages="1-20",
)

documents = reader.load_data("/path/to/scanned_paper.pdf")

Mixed Sources (local path + URL)

You can parse local files and remote URLs in one call:

from llama_index.readers.mineru import MinerUReader

reader = MinerUReader()
documents = reader.load_data(
    [
        "/path/to/local_a.pdf",
        "/path/to/local_b.docx",
        "https://cdn-mineru.openxlab.org.cn/demo/example.pdf",
    ]
)

for doc in documents:
    print(doc.metadata["source"], "-", doc.text[:100])

Attach custom metadata with extra_info

Use extra_info when you want to merge custom metadata fields into every returned Document.metadata (for example project/tenant/tag). It does not change parsing behavior or output format.

from llama_index.readers.mineru import MinerUReader

reader = MinerUReader()
documents = reader.load_data(
    "/path/to/paper.pdf",
    extra_info={
        "project": "paper-rag",
        "tenant": "team-a",
        "source_type": "research_pdf",
    },
)

print(documents[0].metadata["project"])      # paper-rag
print(documents[0].metadata["source_type"])  # research_pdf
print(documents[0].text[:120])               # still Markdown text

Per-Page Splitting

When split_pages=True, each PDF page becomes a separate Document — ideal for RAG pipelines that need page-level granularity.

reader = MinerUReader(split_pages=True, pages="1-5")
documents = reader.load_data("/path/to/paper.pdf")

for doc in documents:
    print(f"Page {doc.metadata['page']}: {doc.text[:100]}...")

Page Range + Split Pages (PDF only)

Use pages together with split_pages=True to parse only selected PDF pages. In this mode, each selected page becomes one Document.

from llama_index.readers.mineru import MinerUReader

reader = MinerUReader(
    mode="precision",
    token="your-api-token",  # or set MINERU_TOKEN
    pages="2-4",
    split_pages=True,
    language="en",
)

documents = reader.load_data("/path/to/paper.pdf")
for doc in documents:
    print(doc.metadata.get("page"), doc.metadata.get("source"))

Use with LlamaIndex Pipeline

from llama_index.core import VectorStoreIndex
from llama_index.readers.mineru import MinerUReader

reader = MinerUReader()
documents = reader.load_data("/path/to/paper.pdf")

index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("Summarize the key findings")
print(response)

Parameters

Reader initialization (MinerUReader(...))

load_data(...) arguments

Links

• MinerU Official Website
• MinerU API Documentation
• MinerU Token Application
• MinerU Python SDK