nanonets-extractor 0.2.2

A Python library for extracting data from any document using AI

This Project has been moved to

https://github.com/NanoNets/docstrange

Try live demo

https://docstrange.nanonets.com

Nanonets Document Extractor

A Python library for extracting data from any document using AI.

🚀 Try it instantly! Visit extraction-api.nanonets.com to access our hosted document extractors with a user-friendly interface.

Quick Start

Installation

pip install nanonets-extractor

Basic Usage

from nanonets_extractor import DocumentExtractor

# Initialize extractor
extractor = DocumentExtractor()

# Extract data from any document
result = extractor.extract(
    file_path="invoice.pdf",
    output_type="flat-json"
)

print(result)

Initialization Parameters

DocumentExtractor()

Parameter	Type	Required	Description
api_key	str	No	API key for unlimited access (optional - uses free tier if not provided)
model	str	No	AI model: "gemini" or "openai" (optional)

Examples

# Free tier (with rate limits)
extractor = DocumentExtractor()

# Unlimited access with API key
extractor = DocumentExtractor(api_key="your_api_key")

# Specify a particular model with API key
extractor = DocumentExtractor(api_key="your_api_key", model="openai")

💡 Getting Your API Key: If you hit rate limits, get your FREE API key from https://app.nanonets.com/#/keys for unlimited access.

Extract Method

extractor.extract()

Parameter	Type	Required	Description
file_path	str	Yes	Path to your document
output_type	str	No	Output format (default: "flat-json")
specified_fields	list	No	Extract only specific fields
json_schema	dict	No	Custom JSON schema for output

Output Types

Type	Description	Parameters Required
"markdown"	Clean markdown formatting	None
"html"	Semantic HTML structure	None
"fields"	Auto-detected key-value pairs	None
"tables"	Structured table data	None
"csv"	Tabular data and CSV format	None
"flat-json"	Flat key-value JSON	None
"specified-fields"	Custom field extraction	specified_fields
"specified-json"	Custom schema extraction	json_schema

Supported Document Types

Works with any document type:

• 📄 PDFs - Invoices, contracts, reports
• 🖼️ Images - Screenshots, photos, scans
• 📊 Spreadsheets - Excel, CSV files
• 📝 Text Documents - Word docs, text files
• 🆔 ID Documents - Passports, licenses, certificates
• 🧾 Receipts - Any receipt or bill

Examples

Basic Extraction

from nanonets_extractor import DocumentExtractor

extractor = DocumentExtractor()

# Extract all data as key-value pairs
result = extractor.extract("document.pdf", output_type="fields")
print(result)

Different Output Formats

# Get clean markdown formatting
result = extractor.extract("document.pdf", output_type="markdown")
print(result)

# Get semantic HTML structure
result = extractor.extract("document.pdf", output_type="html")
print(result)

# Extract structured table data
result = extractor.extract("document.pdf", output_type="tables")
print(result)

# Get CSV format for tabular data
result = extractor.extract("document.pdf", output_type="csv")
print(result)

Extract Specific Fields

# Extract only specific fields
result = extractor.extract(
    file_path="invoice.pdf",
    output_type="specified-fields", 
    specified_fields=["invoice_number", "total", "customer_name"]
)

Custom JSON Schema

# Use custom schema
schema = {
    "invoice_number": "string",
    "line_items": [
        {
            "description": "string",
            "amount": "number"
        }
    ]
}

result = extractor.extract(
    file_path="invoice.pdf",
    output_type="specified-json",
    json_schema=schema
)

Batch Processing

# Process multiple files
files = ["doc1.pdf", "doc2.jpg", "doc3.docx"]
results = extractor.extract_batch(
    file_paths=files,
    output_type="fields"
)

for file_path, result in results.items():
    print(f"{file_path}: {result}")

Command Line Interface

# Free tier (with rate limits)
nanonets-extractor document.pdf

# With API key for unlimited access
nanonets-extractor document.pdf --api-key your_api_key

# Specify output format with API key
nanonets-extractor document.pdf --api-key your_api_key --output-type markdown

# Extract specific fields
nanonets-extractor invoice.pdf --output-type specified-fields --fields "invoice_number,total,date"

# Save to file
nanonets-extractor document.pdf --output result.json

# Process multiple files
nanonets-extractor *.pdf --output-dir results/

Error Handling

from nanonets_extractor import DocumentExtractor
from nanonets_extractor.exceptions import ExtractionError, UnsupportedFileError

extractor = DocumentExtractor()

try:
    result = extractor.extract("document.pdf")
    print(result)
except UnsupportedFileError as e:
    print(f"File type not supported: {e}")
except ExtractionError as e:
    print(f"Extraction failed: {e}")
except Exception as e:
    print(f"Unexpected error: {e}")

Supported File Formats

• PDFs: .pdf
• Images: .png, .jpg, .jpeg, .tiff, .bmp, .gif
• Documents: .docx, .doc
• Spreadsheets: .xlsx, .xls, .csv
• Text: .txt, .rtf

License

This project is licensed under the MIT License - see the LICENSE file for details.