A Python client library for Nutrient Document Web Services (DWS) API. This library provides a fully async, type-safe, and ergonomic interface for document processing operations including conversion, merging, compression, watermarking, OCR, and text extraction.
Note: This package is published as
nutrient-dwson PyPI. The package provides full type support and is designed for async Python environments (Python 3.10+).
- 📄 Powerful document processing: Convert, OCR, edit, compress, watermark, redact, and digitally sign documents
- 🤖 LLM friendly: Built-in support for popular Coding Agents (Claude Code, GitHub Copilot, JetBrains Junie, Cursor, Windsurf) with auto-generated rules
- 🔄 100% mapping with DWS Processor API: Complete coverage of all Nutrient DWS Processor API capabilities
- 🛠️ Convenient functions with sane defaults: Simple interfaces for common operations with smart default settings
- ⛓️ Chainable operations: Build complex document workflows with intuitive method chaining
- 🚀 Fully async: Built from the ground up with async/await support for optimal performance
- 🔐 Flexible authentication and security: Support for API keys and async token providers with secure handling
- ✅ Highly tested: Comprehensive test suite ensuring reliability and stability
- 🔒 Type-safe: Full type annotations with comprehensive type definitions
- 🐍 Pythonic: Follows Python conventions and best practices
pip install nutrient-dwsThis package has built-in support with popular coding agents like Claude Code, GitHub Copilot, Cursor, and Windsurf by exposing scripts that will inject rules instructing the coding agents on how to use the package. This ensures that the coding agent doesn't hallucinate documentation, as well as making full use of all the features offered in Nutrient DWS Python Client.
# Adding code rule to Claude Code
dws-add-claude-code-rule
# Adding code rule to GitHub Copilot
dws-add-github-copilot-rule
# Adding code rule to Junie (Jetbrains)
dws-add-junie-rule
# Adding code rule to Cursor
dws-add-cursor-rule
# Adding code rule to Windsurf
dws-add-windsurf-ruleThe documentation for Nutrient DWS Python Client is also available on Context7
from nutrient_dws import NutrientClient
client = NutrientClient(api_key='your_api_key')The client provides numerous async methods for document processing:
import asyncio
from nutrient_dws import NutrientClient
async def main():
client = NutrientClient(api_key='your_api_key')
# Convert a document
pdf_result = await client.convert('document.docx', 'pdf')
# Extract text
text_result = await client.extract_text('document.pdf')
# Add a watermark
watermarked_doc = await client.watermark_text('document.pdf', 'CONFIDENTIAL')
# Merge multiple documents
merged_pdf = await client.merge(['doc1.pdf', 'doc2.pdf', 'doc3.pdf'])
asyncio.run(main())For a complete list of available methods with examples, see the Methods Documentation.
client.parse() exposes Nutrient's Data Extraction API. It's designed for
content-extraction workflows where you need to feed document content into a
downstream pipeline rather than render or transform the document itself:
Heads up — separate API key. DWS Extract is a different product from DWS Processor and has its own API key. Pass it as
NutrientClient(api_key=..., extract_api_key=...); the Extract key is used only forparse(), while every other method continues to use the Processor key. Using the Processor key against/extraction/parsereturns403. Ifextract_api_keyis omitted,parse()falls back to the mainapi_key— that path works once your tenant moves to global DWS API keys.
- RAG (retrieval-augmented generation) pipelines — pull a clean Markdown representation of a document for chunking, embedding, and indexing in a vector store.
- Search indexing and content migration — convert documents into Markdown for full-text search or for migration into a new content management system.
- Form and invoice extraction — pull structured fields (key/value pairs, tables, semantic regions) out of business documents with bounding boxes and confidence scores attached to every element.
- Layout-aware document understanding — get a typed, page-anchored element list (paragraphs with semantic roles, tables with cell spans, formulas in LaTeX, pictures, handwriting) suitable for building document-comprehension tooling, including agentic workflows.
| Format | Best for | Shape |
|---|---|---|
markdown |
RAG, search indexing, content migration — anywhere structured text beats spatial data | One whole-document Markdown string at response['output']['markdown'] |
spatial (default) |
Form/invoice extraction, layout reconstruction, flows that need per-element confidence | Flat list of typed elements at response['output']['elements'] |
Spatial output requires an OCR-capable mode (structure, understand, or
agentic); mode='text' is markdown-only and the client rejects the
text + spatial combination before the request goes out.
import asyncio
from nutrient_dws import NutrientClient
async def main():
client = NutrientClient(
api_key='your_processor_key',
extract_api_key='your_extract_key',
)
# Spatial elements (default) — paragraphs, tables, formulas, pictures, etc.
response = await client.parse('contract.pdf', mode='understand')
for element in response['output']['elements']:
if element['type'] == 'table':
print(element['rowCount'], element['columnCount'])
# Whole-document Markdown from a born-digital PDF
response = await client.parse(
'report.pdf', mode='text', output_format='markdown',
)
print(response['output']['markdown'])
asyncio.run(main())| Mode | Credits / page | When to use |
|---|---|---|
text |
1 | Born-digital documents only. No OCR, no AI. Fastest and cheapest path to Markdown. |
structure |
1.5 | OCR-based segmentation with bounding boxes. Handles scanned documents, images, and any input requiring OCR. |
understand |
9 | Full pipeline with AI augmentation on top of OCR. Most accurate for documents with tables, multi-column layouts, formulas, and form fields. |
agentic |
18 | Builds on understand and adds a vision-language model. Best for image descriptions, complex visual layouts, and deeper semantic understanding. |
RAG ingestion — PDF → Markdown → chunks → embeddings → vector store:
response = await client.parse('whitepaper.pdf', mode='text', output_format='markdown')
markdown = response['output']['markdown']
# Then: chunk on headings, embed, push to your vector store of choice.For born-digital PDFs, mode='text' is the cheapest path (1 credit/page).
For scanned PDFs or images, switch to mode='structure' so OCR runs.
Form/invoice extraction — PDF → spatial elements → structured dict:
response = await client.parse('invoice.pdf', mode='understand')
elements = response['output']['elements']
# Pull key/value pairs from form regions
fields = {}
for element in elements:
if element['type'] == 'keyValueRegion':
for pair in element['pairs']:
fields[pair['key']['value']] = pair['value']['value']
# Walk tables — each cell carries row/col indices and span counts
for element in elements:
if element['type'] == 'table':
print(f"Table: {element['rowCount']}×{element['columnCount']}")
for cell in element['cells']:
print(f" [{cell['row']}][{cell['column']}] {cell['text']}")For complex layouts that mix dense images with text, step up to
mode='agentic' so the VLM can produce image descriptions and semantic
classifications (18 credits/page).
The Data Extraction API is billed against extraction credits, which are a
separate billing bucket from the processor API credits consumed by
/build, /sign, OCR, and the other Processor API endpoints used by this
client (convert, watermark_text, merge, etc.). The response surfaces the
extraction-credit accounting under response['usage']['data_extraction_credits']:
usage = response['usage']['data_extraction_credits']
print(f"Cost: {usage['cost']} extraction credits, "
f"remaining: {usage['remainingCredits']}")The client also provides a fluent builder pattern with staged interfaces to create document processing workflows:
from nutrient_dws.builder.constant import BuildActions
async def main():
client = NutrientClient(api_key='your_api_key')
result = await (client
.workflow()
.add_file_part('document.pdf')
.add_file_part('appendix.pdf')
.apply_action(BuildActions.watermark_text('CONFIDENTIAL', {
'opacity': 0.5,
'fontSize': 48
}))
.output_pdf({
'optimize': {
'mrcCompression': True,
'imageOptimizationQuality': 2
}
})
.execute())
asyncio.run(main())The workflow system follows a staged approach:
- Add document parts (files, HTML, pages)
- Apply actions (optional)
- Set output format
- Execute or perform a dry run
For detailed information about the workflow system, including examples and best practices, see the Workflow Documentation.
The library provides a comprehensive error hierarchy:
from nutrient_dws import (
NutrientClient,
NutrientError,
ValidationError,
APIError,
AuthenticationError,
NetworkError
)
async def main():
client = NutrientClient(api_key='your_api_key')
try:
result = await client.convert('file.docx', 'pdf')
except ValidationError as error:
# Invalid input parameters
print(f'Invalid input: {error.message} - Details: {error.details}')
except AuthenticationError as error:
# Authentication failed
print(f'Auth error: {error.message} - Status: {error.status_code}')
except APIError as error:
# API returned an error
print(f'API error: {error.message} - Status: {error.status_code} - Details: {error.details}')
except NetworkError as error:
# Network request failed
print(f'Network error: {error.message} - Details: {error.details}')
asyncio.run(main())The library includes comprehensive unit and integration tests:
# Run all tests
python -m pytest
# Run with coverage report
python -m pytest --cov=nutrient_dws --cov-report=html
# Run only unit tests
python -m pytest tests/unit/
# Run integration tests (requires API key)
NUTRIENT_API_KEY=your_key python -m pytest tests/test_integration.pyThe library maintains high test coverage across all API methods, including:
- Unit tests for all public methods
- Integration tests for real API interactions
- Type checking with mypy
For development, install the package in development mode:
# Clone the repository
git clone https://github.com/PSPDFKit/nutrient-dws-client-python.git
cd nutrient-dws-client-python
# Install in development mode
pip install -e ".[dev]"
# Run type checking
mypy src/
# Run linting
ruff check src/
# Run formatting
ruff format src/We welcome contributions to improve the library! Please follow our development standards to ensure code quality and maintainability.
Quick start for contributors:
- Clone and setup the repository
- Make changes following atomic commit practices
- Use conventional commits for clear change history
- Include appropriate tests for new features
- Ensure type checking passes with mypy
- Follow Python code style with ruff
For detailed contribution guidelines, see the Contributing Guide.
src/
├── nutrient_dws/
│ ├── builder/ # Builder classes and constants
│ ├── generated/ # Generated type definitions
│ ├── types/ # Type definitions
│ ├── client.py # Main NutrientClient class
│ ├── errors.py # Error classes
│ ├── http.py # HTTP layer
│ ├── inputs.py # Input handling
│ ├── workflow.py # Workflow factory
│ └── __init__.py # Public exports
├── nutrient_dws_scripts/ # CLI scripts for coding agents
└── tests/ # Test files
This library supports Python 3.10 and higher. The async-first design requires modern Python features for optimal performance and type safety.
This project is licensed under the MIT License - see the LICENSE file for details.
For issues and feature requests, please use the GitHub issue tracker.
For questions about the Nutrient DWS Processor API, refer to the official documentation.