Documentation Index Fetch the complete documentation index at: https://mintlify.com/docling-project/docling/llms.txt
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Docling is available as an official LlamaIndex extension, providing two powerful components: the Docling Reader for document loading and the Docling Node Parser for intelligent chunking.
Overview The LlamaIndex Docling integration provides:
Docling Reader - Load documents with high-fidelity structural preservationDocling Node Parser - Parse documents into LlamaIndex nodes with structure awarenessLossless Serialization - Preserve complete document structure as JSONFlexible Export - Export to simplified formats like Markdown when needed
Installation pip install llama-index-readers-docling llama-index-node-parser-docling
Components Docling Reader The Docling Reader loads document files and populates LlamaIndex Document objects with Docling’s rich data model.
Basic Usage from llama_index.readers.docling import DoclingReader # Create reader reader = DoclingReader() # Load documents documents = reader.load_data( file_path = "document.pdf" ) # Access document content for doc in documents: print (doc.text) print (doc.metadata)
from llama_index.readers.docling import DoclingReader from docling.datamodel.base_models import FormatOptions # Export as Markdown reader = DoclingReader( export_type = "markdown" ) docs = reader.load_data( file_path = "document.pdf" ) # Export as JSON (lossless) reader = DoclingReader( export_type = "json" ) docs = reader.load_data( file_path = "document.pdf" )
Docling Node Parser The Docling Node Parser uses knowledge of Docling’s format to intelligently parse documents into LlamaIndex Node objects for downstream usage.
Basic Usage from llama_index.readers.docling import DoclingReader from llama_index.node_parser.docling import DoclingNodeParser # Load documents with Docling Reader reader = DoclingReader( export_type = "json" ) documents = reader.load_data( file_path = "document.pdf" ) # Parse into nodes parser = DoclingNodeParser() nodes = parser.get_nodes_from_documents(documents) # Nodes are ready for embedding and retrieval for node in nodes: print (node.text) print (node.metadata)
Advanced Parsing Options from llama_index.node_parser.docling import DoclingNodeParser # Configure parser parser = DoclingNodeParser( include_metadata = True , chunk_size = 1024 , chunk_overlap = 128 ) nodes = parser.get_nodes_from_documents(documents)
Complete RAG Pipeline Here’s a full example combining both components:
from llama_index.readers.docling import DoclingReader from llama_index.node_parser.docling import DoclingNodeParser from llama_index.core import VectorStoreIndex from llama_index.embeddings.openai import OpenAIEmbedding # 1. Load documents reader = DoclingReader( export_type = "json" ) documents = reader.load_data( file_path = "document.pdf" ) # 2. Parse into nodes parser = DoclingNodeParser() nodes = parser.get_nodes_from_documents(documents) # 3. Create embeddings and index embed_model = OpenAIEmbedding() index = VectorStoreIndex(nodes, embed_model = embed_model) # 4. Query the index query_engine = index.as_query_engine() response = query_engine.query( "What is the main topic of this document?" ) print (response)
Features
Structure-Aware Preserves document hierarchy and relationships
Lossless Export JSON export maintains complete document structure
Smart Chunking Node parser respects document structure when chunking
Rich Metadata Includes page numbers, headings, and structural information
Use Cases Knowledge Base RAG # Process multiple documents reader = DoclingReader( export_type = "json" ) documents = [] for file in [ "doc1.pdf" , "doc2.docx" , "doc3.pptx" ]: docs = reader.load_data( file_path = file ) documents.extend(docs) parser = DoclingNodeParser() nodes = parser.get_nodes_from_documents(documents) index = VectorStoreIndex(nodes)
Table-Aware Retrieval # Docling preserves table structure reader = DoclingReader( export_type = "markdown" , pipeline_options = { "do_table_structure" : True } ) documents = reader.load_data( file_path = "report.pdf" ) parser = DoclingNodeParser() nodes = parser.get_nodes_from_documents(documents) # Tables are preserved in node content for node in nodes: if "table" in node.metadata.get( "type" , "" ).lower(): print ( "Found table:" , node.text)
Integration Benefits
Official Components
Maintained as official LlamaIndex integrations
Two-Component System
Reader and Parser work together seamlessly
Format Flexibility
Choose between lossless JSON or simplified Markdown
Production Ready
Used in real-world RAG applications
Resources
Tutorial Step-by-step guide
Reader Docs API reference for Docling Reader
Parser Docs API reference for Node Parser
PyPI Packages Next Steps