nanonetsclient
Python SDK for the Nanonets API
Minimum Python version required: 3.7
Nanonets is an AI-powered Intelligent Document Processing platform that helps you:
- Extract structured data from invoices, receipts, forms, and more documents
- Supports pdf, images (jpg, png, tiff), excel files, scanned documents and photos
- Automate data entry and document workflows
- Convert unstructured documents into machine-readable formats
- Integrate advanced OCR and table extraction into your apps
Keywords: OCR, document extraction, invoice processing, receipt OCR, table extraction, data capture, workflow automation, AI document processing, unstructured to structured data, Python SDK, Nanonets API
Get your API Key
Sign up and get your API key from your Nanonets dashboard.
Installation
pip install nanonetsclient
Authentication
Set your API key as an environment variable:
export NANONETS_API_KEY='your_api_key'
Or pass it directly when initializing the client:
from nanonets import NanonetsClient
client = NanonetsClient(api_key='your_api_key')
Quick Start
from nanonets import NanonetsClient
client = NanonetsClient(api_key='your_api_key')
# 1. Create a workflow
workflow = client.workflows.create(
description="SDK Example Workflow",
workflow_type="" # Instant learning
)
workflow_id = workflow.get("workflow_id") or workflow.get("id")
# 2. Configure fields and table headers
fields = [
{"name": "invoice_number"},
{"name": "total_amount"},
{"name": "invoice_date"}
]
table_headers = [
{"name": "item_description"},
{"name": "quantity"},
{"name": "unit_price"},
{"name": "total"}
]
client.workflows.set_fields(
workflow_id=workflow_id,
fields=fields,
table_headers=table_headers
)
# 3. Upload a document to process
result = client.workflows.upload_document(
workflow_id=workflow_id,
file_path="invoice.pdf",
async_mode=False,
metadata={"test": "true"}
)
print("Upload result:", result)
Features
- Workflow Management
- Fields and Tables Configuration
- Document Processing
- Document Moderation
Available Methods
Workflow Management
create_workflowget_workflowlist_workflowsset_fieldsupdate_fielddelete_fieldupdate_metadataupdate_settings
Document Processing
upload_documentget_documentlist_documentsdelete_document
Document Moderation
update_field_valueadd_field_valuedelete_field_valueadd_tabledelete_tableupdate_table_celladd_table_celldelete_table_cellverify_fieldverify_table_cellverify_tableverify_document
Complete Documentation
For full documentation and advanced usage, visit:
https://apidocs.nanonets.com/docs/sdk/python-sdk/
License
MIT
Metadata
Release files for nanonetsclient 1.0.4
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| nanonetsclient-1.0.4.tar.gz | 5.1 kB | Without provenance | Details for nanonetsclient-1.0.4.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| nanonetsclient-1.0.4-py3-none-any.whl | Python 3 | none | any | Details for nanonetsclient-1.0.4-py3-none-any.whl |
Total release size: 10.7 kB
Release files / nanonetsclient-1.0.4.tar.gz
| Download URL | nanonetsclient-1.0.4.tar.gz |
|---|---|
| Size | 5.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/4.0.2 CPython/3.7.9
|
Release files / nanonetsclient-1.0.4-py3-none-any.whl
| Download URL | nanonetsclient-1.0.4-py3-none-any.whl |
|---|---|
| Size | 5.6 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.7.9
|
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