Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
Docling is a powerful Python library developed by IBM Research that simplifies document processing for generative AI applications. With 48,400+ GitHub stars and 159 contributors, Docling excels at parsing diverse document formats—including advanced PDF understanding with layout analysis—and provides seamless integrations with AI frameworks like LangChain, LlamaIndex, and Model Context Protocol (MCP) servers.
from docling.document_converter import DocumentConverter
# Initialize converter
converter = DocumentConverter()
# Convert document
result = converter.convert("document.pdf")
# Export to Markdown
markdown_content = result.document.export_to_markdown()
print(markdown_content)
# Export to HTML
html_content = result.document.export_to_html()
# Export to JSON (lossless)
json_data = result.document.export_to_dict()
2. Advanced PDF Processing
from docling.document_converter import DocumentConverter
from docling.datamodel.pipeline_options import (
PdfPipelineOptions,
TableFormerMode
)
# Configure PDF processing
pipeline_options = PdfPipelineOptions()
pipeline_options.do_table_structure = True
pipeline_options.table_structure_options.mode = TableFormerMode.ACCURATE
pipeline_options.do_ocr = True# Initialize converter with options
converter = DocumentConverter(
pipeline_options=pipeline_options
)
# Convert with advanced features
result = converter.convert("complex_report.pdf")
# Access structured contentfor table in result.document.tables:
print(f"Table: {table.to_markdown()}")
for figure in result.document.pictures:
print(f"Figure caption: {figure.caption}")
3. Batch Processing with Resource Limits
from docling.document_converter import DocumentConverter
# Configure resource constraints
converter = DocumentConverter(
max_file_size=50_000_000, # 50 MB limit
max_num_pages=100# First 100 pages only
)
# Process multiple documents
documents = ["doc1.pdf", "doc2.docx", "doc3.xlsx"]
for doc_path in documents:
try:
result = converter.convert(doc_path)
output_path = doc_path.replace(".pdf", ".md")
withopen(output_path, "w") as f:
f.write(result.document.export_to_markdown())
print(f"✅ Converted: {doc_path}")
except Exception as e:
print(f"❌ Failed: {doc_path} - {e}")
4. LangChain Integration (RAG Pipeline)
from langchain_docling import DoclingLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
# Load documents with Docling
loader = DoclingLoader(
file_path="technical_manual.pdf",
export_type="markdown"# or "json" for lossless
)
documents = loader.load()
# Split documents into chunks
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200
)
splits = text_splitter.split_documents(documents)
# Create vector store
embeddings = OpenAIEmbeddings()
vectorstore = FAISS.from_documents(splits, embeddings)
# Query the documents
retriever = vectorstore.as_retriever()
relevant_docs = retriever.get_relevant_documents("What is the installation process?")
for doc in relevant_docs:
print(doc.page_content)
5. LlamaIndex Integration
from llama_index.readers.docling import DoclingReader
from llama_index.node_parser.docling import DoclingNodeParser
from llama_index.core import VectorStoreIndex
# Load documents with Docling Reader
reader = DoclingReader(export_type="json") # Lossless serialization
documents = reader.load_data(file_path="research_paper.pdf")
# Parse into nodes
node_parser = DoclingNodeParser()
nodes = node_parser.get_nodes_from_documents(documents)
# Build index
index = VectorStoreIndex(nodes)
# Query
query_engine = index.as_query_engine()
response = query_engine.query("Summarize the methodology section")
print(response)
6. Custom Pipeline Configuration
from docling.document_converter import DocumentConverter
from docling.datamodel.pipeline_options import PdfPipelineOptions
from docling.backend.pdf_backend import PyPdfiumBackend
import os
# Configure threading (for performance)
os.environ["OMP_NUM_THREADS"] = "8"# Use 8 CPU threads# Custom pipeline options
pipeline_options = PdfPipelineOptions()
pipeline_options.do_cell_matching = True# Enable table cell matching
pipeline_options.generate_page_images = True# Extract page images
pipeline_options.generate_picture_images = True# Extract figures# Use specific backend
converter = DocumentConverter(
allowed_formats=["pdf"],
format_options={"pdf": PyPdfiumBackend}
)
# Convert with custom settings
result = converter.convert(
"scientific_paper.pdf",
pipeline_options=pipeline_options
)
# Save extracted imagesfor i, image inenumerate(result.document.pictures):
image.save(f"figure_{i}.png")
7. Binary Stream Processing
from docling.document_converter import DocumentConverter, DocumentStream
from io import BytesIO
# Load PDF as binary streamwithopen("document.pdf", "rb") as f:
pdf_bytes = BytesIO(f.read())
# Create document stream
doc_stream = DocumentStream(
name="document.pdf",
stream=pdf_bytes
)
# Convert from stream
converter = DocumentConverter()
result = converter.convert(doc_stream)
markdown = result.document.export_to_markdown()
8. Remote Services (Cloud OCR)
from docling.document_converter import DocumentConverter
from docling.datamodel.pipeline_options import PdfPipelineOptions
# IMPORTANT: Explicit opt-in required for remote services# Main purpose of Docling is local execution
pipeline_options = PdfPipelineOptions()
pipeline_options.enable_remote_services = True# Explicit consent# Configure cloud OCR (if needed)
converter = DocumentConverter(pipeline_options=pipeline_options)
# Process document (may use cloud services)
result = converter.convert("scanned_document.pdf")
Advanced Features
Table Extraction Modes
from docling.datamodel.pipeline_options import TableFormerMode
# FAST mode (faster processing)
pipeline_options.table_structure_options.mode = TableFormerMode.FAST
# ACCURATE mode (better quality)
pipeline_options.table_structure_options.mode = TableFormerMode.ACCURATE
Trade-offs:
FAST: ~2x faster, good for simple tables
ACCURATE: Higher precision, better for complex tables with merged cells
Output Format Customization
Markdown Export:
# With embedded images
markdown = result.document.export_to_markdown(image_mode="embedded")
# With image references
markdown = result.document.export_to_markdown(image_mode="referenced")
HTML Export:
# With custom CSS
html = result.document.export_to_html(
include_styles=True,
custom_css="body { font-family: Arial; }"
)
Document Parsing Tool: Convert documents to structured format for AI agents
RAG Pipeline Integration: Extract and chunk documents for retrieval
Multi-Format Support: Handle various document types in agentic workflows
Performance Optimization
CPU Thread Control
# Set number of threads (default: 4)export OMP_NUM_THREADS=8
# Or in Python
import os
os.environ["OMP_NUM_THREADS"] = "8"
Memory Management
# Process documents in batches to manage memorydefprocess_batch(file_paths, batch_size=10):
converter = DocumentConverter()
for i inrange(0, len(file_paths), batch_size):
batch = file_paths[i:i+batch_size]
for file_path in batch:
result = converter.convert(file_path)
# Process result# Clear memory between batchesimport gc
gc.collect()
Prefetching Models
# Download all models in advance
docling-tools models download
# Verify modelsls$HOME/.cache/docling/models
Supported Formats
Input Formats (12+)
Format
Extension
Notes
PDF
.pdf
Advanced layout understanding, table extraction
Microsoft Word
.docx
Office 2007+ (Open XML)
Excel
.xlsx
Spreadsheet data extraction
PowerPoint
.pptx
Slide content and structure
HTML
.html, .xhtml
Web page content
Markdown
.md
Plain text markup
AsciiDoc
.adoc, .asciidoc
Technical documentation
CSV
.csv
Tabular data
Images
.png, .jpg, .tiff, .bmp, .webp
OCR processing
USPTO XML
.xml
Patent documents
JATS XML
.xml
Journal articles
WebVTT
.vtt
Video subtitle files
Output Formats
Format
Use Case
Lossless
Markdown
Human-readable, AI-friendly
No
HTML
Web rendering
No
JSON
Complete structure preservation
Yes
Plain Text
Simple text extraction
No
Doctags
Layout-aware markup
Partial
Common Workflows
1. PDF to Markdown for RAG
from docling.document_converter import DocumentConverter
converter = DocumentConverter()
result = converter.convert("whitepaper.pdf")
# Export to Markdown
markdown = result.document.export_to_markdown()
# Save for RAG ingestionwithopen("whitepaper.md", "w") as f:
f.write(markdown)
2. Extract Tables from Excel
from docling.document_converter import DocumentConverter
converter = DocumentConverter()
result = converter.convert("financial_report.xlsx")
# Extract all tablesfor table in result.document.tables:
print(table.to_markdown())
# Or: table.to_dataframe() for pandas integration
3. Multi-Format Document Collection
from docling.document_converter import DocumentConverter
from pathlib import Path
converter = DocumentConverter()
# Process directory
input_dir = Path("documents/")
output_dir = Path("processed/")
for file_path in input_dir.glob("*"):
if file_path.suffix in [".pdf", ".docx", ".xlsx", ".pptx"]:
result = converter.convert(str(file_path))
output_file = output_dir / f"{file_path.stem}.md"withopen(output_file, "w") as f:
f.write(result.document.export_to_markdown())
Troubleshooting
Model Download Issues
# Manually download models
docling-tools models download
# Check model cachels ~/.cache/docling/models
# Set custom cache locationexport DOCLING_ARTIFACTS_PATH=/custom/path
