Docuglean is a unified SDK for intelligent document processing using State of the Art AI models. Docuglean provides multilingual and multimodal capabilities with plug-and-play APIs for document OCR, structured data extraction, annotation, classification, summarization, and translation. It also comes with inbuilt tools and supports different types of documents out of the box.
- 🚀 Easy to Use: Simple, intuitive API with detailed documentation. Just pass in a file and get markdown in response.
- 🔍 OCR Capabilities: Extract text from images and scanned documents
- 📊 Structured Data Extraction: Use Zod/Pydantic schemas for type-safe structured data extraction
- 📑 Document Classification: Intelligently split multi-section documents by category with automatic chunking
- 📄 Multimodal Support: Process PDFs and images with ease
- 🤖 Multiple AI Providers: Support for OpenAI, Mistral, Google Gemini, and Hugging Face
- ⚡ Batch Processing: Process multiple documents concurrently with automatic error handling
- 🔒 Type Safety: Full TypeScript/Python type hints with comprehensive validation
- 📝 Document Parsers: Built-in local parsers for DOCX, PPTX, XLSX, CSV, TSV, and PDF (no API required)
- 📦 Permissive Licensing: Uses pdftext (Apache/BSD) instead of PyMuPDF (AGPL) for commercial-friendly PDF processing
Package: docuglean-ocr
npm install docuglean-ocrRepository: node-ocr/
Quick Start:
OCR Function - Pure OCR Processing Extracts text from documents and images, returning content and metadata like bounding boxes (provider-dependent).
import { ocr, extract } from 'docuglean-ocr';
// Extract raw text from documents (supports URLs and local files)
const ocrResult = await ocr({
filePath: 'https://arxiv.org/pdf/2302.12854',
provider: 'openai',
model: 'gpt-4o-mini',
apiKey: 'your-api-key'
});Extract Function - Structured Data Extraction Extracts structured data from documents using custom schemas. Also handles summarization via custom prompts and a compact schema.
import { z } from 'zod';
// Define schema for structured extraction
const ReceiptSchema = z.object({
date: z.string(),
total: z.number(),
items: z.array(z.object({
name: z.string(),
price: z.number()
}))
});
// Extract structured data from documents
const extractResult = await extract({
filePath: './receipt.pdf',
provider: 'mistral',
model: 'mistral-small-latest',
apiKey: 'your-api-key',
responseFormat: ReceiptSchema,
prompt: 'Extract receipt details including date, total, and items'
});
// Summarization via extract
const SummarySchema = z.object({
title: z.string().optional(),
summary: z.string().min(50),
keyPoints: z.array(z.string()).min(3).max(7),
});
const summary = await extract({
filePath: './long-report.pdf',
provider: 'openai',
apiKey: 'your-api-key',
responseFormat: SummarySchema,
prompt: 'Provide a concise 3-sentence summary of this document and 3–7 key points.'
});
console.log('Summary:', summary.summary);Note: you can also use extract with a targeted "search" prompt (e.g., "Find all occurrences of X and return matching passages") to perform semantic search within a document.
Package: docuglean
pip install docugleanRepository: python-ocr/
Quick Start:
OCR Function - Pure OCR Processing Extracts text from documents and images, returning content and metadata like bounding boxes (provider-dependent).
from docuglean import ocr, extract
# Extract raw text from documents (supports URLs and local files)
ocr_result = await ocr(
file_path="./test/data/testocr.png",
provider="gemini",
model="gemini-2.5-flash",
api_key="your-api-key"
)Extract Function - Structured Data Extraction Extracts structured data from documents using custom schemas. Requires a response format schema and returns parsed data.
from pydantic import BaseModel
from typing import List
# Define schema for structured extraction
class Item(BaseModel):
name: str
price: float
class Receipt(BaseModel):
date: str
total: float
items: List[Item]
# Extract structured data from documents
extract_result = await extract(
file_path="./receipt.pdf",
provider="mistral",
model="mistral-small-latest",
api_key="your-api-key",
response_format=Receipt,
prompt="Extract receipt details including date, total, and items"
)
# Summarization via extract
class Summary(BaseModel):
title: str | None = None
summary: str
keyPoints: List[str]
summary = await extract(
file_path="./long-report.pdf",
provider="openai",
api_key="your-api-key",
response_format=Summary,
prompt="Provide a concise 3-sentence summary of this document and 3–7 key points."
)
print("Summary:", summary.summary)Classify Function - Document Classification Intelligently classify and split documents into categories based on content. Perfect for processing multi-section documents.
from docuglean import classify, CategoryDescription
# Classify a patient medical record
result = await classify(
file_path="./patient-record.pdf",
categories=[
CategoryDescription(
name="Patient Intake Forms",
description="Pages with patient registration, insurance information, and consent forms"
),
CategoryDescription(
name="Medical History",
description="Pages containing past medical history, medications, allergies, and family history"
),
CategoryDescription(
name="Lab Results",
description="Pages with laboratory test results, blood work, and diagnostic reports"
),
CategoryDescription(
name="Treatment Notes",
description="Pages with doctor's notes, treatment plans, and prescriptions"
)
],
api_key="your-api-key",
provider="mistral"
)
# Access the results
for split in result.splits:
print(f"{split.name}: Pages {split.pages} (confidence: {split.conf})")Batch Processing - Process Multiple Documents Process multiple documents concurrently with automatic error handling.
from docuglean import batch_ocr, batch_extract
from docuglean.types import OCRConfig, ExtractConfig
# Batch OCR
results = await batch_ocr([
OCRConfig(file_path="./invoice1.pdf", provider="openai", api_key="your-api-key"),
OCRConfig(file_path="./invoice2.pdf", provider="mistral", api_key="your-api-key"),
OCRConfig(file_path="./receipt.png", provider="local", api_key="not-needed")
])
# Handle results - errors don't stop processing
for i, result in enumerate(results):
if result["success"]:
print(f"File {i + 1} processed successfully")
else:
print(f"File {i + 1} failed:", result["error"])Local Document Parsers - No API Required Extract text from various document formats without any AI provider.
from docuglean import parse_docx, parse_pptx, parse_spreadsheet, parse_pdf, parse_csv
# Parse DOCX files (returns HTML, Markdown, and raw text)
result = await parse_docx("./document.docx")
print(result["markdown"])
# Parse PPTX, XLSX, PDF, CSV files
pptx_result = await parse_pptx("./presentation.pptx")
xlsx_result = await parse_spreadsheet("./data.xlsx")
pdf_result = await parse_pdf("./document.pdf")
csv_result = await parse_csv("./data.csv")- 🤖 More Models. More Providers: Integration with Meta's Llama, Together AI, OpenRouter and lots more
- 🌍 Multilingual: Support for multiple languages
Currently supported providers and models:
- OpenAI:
gpt-4o-mini,gpt-4o,gpt-4-turbo,gpt-3.5-turbo,o1-mini,o1-preview - Mistral:
mistral-ocr-latest,mistral-small-latest,ministral-8b-latest - Google Gemini:
gemini-2.5-flash,gemini-2.5-pro,gemini-1.5-flash,gemini-1.5-pro - Hugging Face:
Qwen/Qwen2.5-VL-3B-Instructand other vision-language models (Python only)
cd node-ocr
npm install
npm run build
npm testcd python-ocr
uv sync
uv run pytestWe welcome contributions! Please see our Contributing Guide for details.
Apache 2.0 - see the LICENSE file for details.
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