In the age of AI automation, extracting structured data from documents has become a key part of many business workflows. From invoices and contracts to identity documents and research papers, organizations are relying on AI models to interpret and process information accurately. LandingAI’s ADE Python SDK – an official API client for the LandingAI ADE REST API simplifies this process by providing a robust and developer-friendly way to integrate AI document extraction and parsing directly into Python applications.
Whether you’re building a document intelligence platform integrating OCR workflows, or automating data extraction pipelines, this SDK delivers high-performance tools for document parsing, asynchronous processing and schema-based extraction using LandingAI’s latest models such as DPT-2.
What is LandingAI ADE?
LandingAI ADE (AI Document Extractor) is an advanced system designed by LandingAI, the company founded by AI pioneer Andrew Ng. The ADE platform helps developers and enterprises automatically extract structured data from unstructured documents using AI models trained on complex document layouts, tables and visual data.
The LandingAI ADE Python SDK offers a seamless interface to interact with this REST API supporting both synchronous and asynchronous operations. It’s designed to work with Python 3.8+, making it compatible with most modern data pipelines and AI systems.
Key Features of LandingAI ADE Python SDK
1. Simple Installation and Setup
You can install the SDK directly from PyPI using:
pip install landingai-ade
This lightweight installation enables you to quickly start building without additional dependencies. For added security, developers can store their API keys using environment variables or .env files via python-dotenv.
2. Streamlined Document Parsing
The parse() method is the core of the SDK. It allows developers to send local or remote documents to the ADE model for extraction:
from landingai_ade import LandingAIADE
from pathlib import Path
client = LandingAIADE(apikey="YOUR_API_KEY")
response = client.parse(document=Path("path/to/file.pdf"), model="dpt-2-latest")
print(response.chunks)
This simple interface returns clean, structured results that can be integrated into dashboards, workflows or databases.
3. Asynchronous Parse Jobs for Large Files
For large or complex documents, the SDK supports asynchronous jobs using the parse_jobs API. This feature lets you upload a file, create a job, check its status and retrieve results once processing is complete ideal for batch document automation and high-volume workloads.
job = client.parse_jobs.create(document=Path("large_file.pdf"), model="dpt-2-latest")
print(f"Job ID: {job.job_id}")
4. Custom Data Extraction with JSON Schema
The SDK integrates with Pydantic models, allowing you to define custom schemas for precise extraction. For instance:
from pydantic import BaseModel, Field
from landingai_ade.lib import pydantic_to_json_schema
class Person(BaseModel):
name: str = Field(description="Person's name")
age: int = Field(description="Person's age")
schema = pydantic_to_json_schema(Person)
With this, you can extract structured information like names, dates, or totals from unstructured Markdown or PDF files. This is perfect for business intelligence and automated reporting systems.
5. Fully Asynchronous Support
LandingAI ADE supports asynchronous programming using Python’s asyncio enabling faster concurrent API calls:
from landingai_ade import AsyncLandingAIADE
import asyncio
async def main():
client = AsyncLandingAIADE(apikey="YOUR_API_KEY")
response = await client.parse(document=Path("invoice.pdf"), model="dpt-2-latest")
print(response.chunks)
asyncio.run(main())
You can even switch to the aiohttp backend for improved performance with heavy I/O workloads.
6. Advanced Error Handling
The SDK includes detailed exception classes to handle API errors gracefully:
APIConnectionError– network or timeout issuesAPIStatusError– for 4xx and 5xx errorsRateLimitError– triggered when request limits are exceeded
This ensures resilient automation pipelines that can recover or retry automatically during temporary failures.
7. Built-In Retries and Timeout Configuration
Developers can configure retry logic and timeouts per request ensuring that your application handles temporary API slowdowns effectively:
client = LandingAIADE(max_retries=3, timeout=30.0)
By default, the SDK retries failed requests twice using exponential backoff.
8. Raw and Streaming Responses
For applications that need access to HTTP headers or partial responses, the SDK offers .with_raw_response and .with_streaming_response methods ideal for logging, analytics or streaming large JSON outputs efficiently.
9. Custom HTTP Configuration
Developers can integrate proxies, custom transports and base URLs by extending the DefaultHttpxClient. This allows greater flexibility in enterprise and cloud deployments where network configurations may vary.
Why Developers Love LandingAI ADE SDK ?
The LandingAI ADE Python SDK stands out for its type safety, rich documentation and reliability. Built with Stainless SDK Generator, it provides:
- Autocomplete for all parameters in modern IDEs like VS Code.
- Typed request and response objects for fewer runtime errors.
- Comprehensive logging via environment variables (
LANDINGAI_ADE_LOG=info). - Compatibility with Python’s async ecosystem.
This combination empowers developers to build production-grade AI automation systems faster and with fewer bugs.
Use Cases
- Enterprise Document Management – Extracting structured data from contracts, invoices, and compliance documents.
- AI Agents for Business Workflows – Feeding parsed data into AI reasoning models or CRM systems.
- Research and Data Mining – Analyzing large collections of scientific or financial documents.
- Automated Auditing – Validating document contents with Pydantic schema definitions.
Conclusion
The LandingAI ADE Python SDK is more than just an API wrapper – it’s a complete toolkit for AI-driven document understanding. With its clean syntax, asynchronous support, robust error handling and integration flexibility, it allows developers to bring AI capabilities directly into their data processing pipelines.
As organizations continue to digitize their operations, tools like LandingAI ADE are bridging the gap between unstructured information and actionable insights unlocking a new era of intelligent automation in document processing.
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