Docling integrations - Docs by LangChain

Docling parses PDF, DOCX, PPTX, HTML, and other formats into a rich unified representation including document layout, tables etc., making them ready for generative AI workflows like RAG. This integration provides Docling’s capabilities via the DoclingLoader document loader.

Installation and setup

Simply install langchain-docling from your package manager, e.g. pip:

pip install langchain-docling

uv add langchain-docling

Document loader

The DoclingLoader class in langchain-docling seamlessly integrates Docling into LangChain, enabling you to:

use various document types in your LLM applications with ease and speed, and
leverage Docling’s rich representation for advanced, document-native grounding.

Basic usage looks as follows:

from langchain_docling import DoclingLoader

FILE_PATH = ["https://arxiv.org/pdf/2408.09869"]  # Docling Technical Report

loader = DoclingLoader(file_path=FILE_PATH)

docs = loader.load()

For end-to-end usage check out this example.

Additional resources

Connect these docs to Claude, VSCode, and more via MCP for real-time answers.

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​Installation and setup

​Document loader

​Additional resources

Installation and setup

Document loader

Additional resources