byteit 0.1.1

AI-powered document intelligence platform - Turn your data into structured data with a single line of code.

ByteIT API Library

Turn your data into AI - Transform documents into structured data with a single line of code.

ByteIT is an AI-powered document intelligence platform that extracts clean, structured data from PDFs, Word, Excel, and many other file formats. This Python SDK provides a simple, developer-first interface to ByteIT's advanced document processing capabilities.

---

Why ByteIT?

• Lightning Fast - Process documents in under 2 seconds
• AI-Powered - Advanced ML models trained on millions of documents
• Simple API - Parse documents in one line: client.parse("document.pdf")
• Developer First - Clean code, full type hints, comprehensive SDKs
• Enterprise Security - End-to-end encryption and GDPR compliance
• Smart Extraction - Extract text, tables, forms, and structured data with AI precision

---

Quick Start

Installation

pip install byteit

Basic Usage

from byteit import ByteITClient

# Initialize client
client = ByteITClient(api_key="your_api_key")

# Parse a document
result = client.parse("invoice.pdf")
print(result.decode())

That's it. Your document is now structured text.

---

Features

Parse Any Document

# Local files
result = client.parse("contract.pdf")

# Different formats
txt_result = client.parse("doc.pdf", output_format="txt")
json_result = client.parse("doc.pdf", output_format="json")
md_result = client.parse("doc.pdf", output_format="md")
html_result = client.parse("doc.pdf", output_format="html")

# Save to file
client.parse("doc.pdf", output="result.txt")

S3 Integration

Process files directly from S3 without downloading - perfect for high-volume workflows:

from byteit.connectors import S3InputConnector

# Parse from S3
result = client.parse(
    S3InputConnector(
        source_bucket="my-documents",
        source_path_inside_bucket="invoices/jan-2024.pdf"
    )
)

Job Management

Track and retrieve processing jobs:

# List all jobs
jobs = client.get_all_jobs()
for job in jobs:
    print(f"{job.id}: {job.processing_status}")

# Get specific job
job = client.get_job_by_id("job_123")

# Download result later
if job.is_completed:
    result = client.get_result(job.id)

Context Manager

Automatic resource cleanup:

with ByteITClient(api_key="your_key") as client:
    result = client.parse("document.pdf")
    # Session automatically closed

---

API Reference

ByteITClient

ByteITClient(api_key: str)

Initialize the ByteIT client.

Parameters:

• api_key (str): Your ByteIT API key

Methods:

parse(input, output_format="txt", output=None)

Parse a document and return the result.

Parameters:

• input (str | Path | InputConnector): File to parse
  • str or Path: Local file path
  • S3InputConnector: For S3 files
• output_format (str): Output format - "txt", "json", "md", or "html" (default: "txt")
• output (str | Path | None): Optional file path to save result

Returns: bytes - Parsed content

Example:

result = client.parse("doc.pdf", output_format="json")

get_all_jobs()

Get all jobs for your account.

Returns: List[Job] - List of Job objects

get_job_by_id(job_id: str)

Get a specific job by ID.

Parameters:

• job_id (str): The job ID

Returns: Job - Job object

get_result(job_id: str)

Download result for a completed job.

Parameters:

• job_id (str): The job ID

Returns: bytes - Result content

---

Connectors

LocalFileInputConnector

Read files from local filesystem.

from byteit.connectors import LocalFileInputConnector

connector = LocalFileInputConnector("path/to/file.pdf")
result = client.parse(connector)

S3InputConnector

Read files from Amazon S3 using IAM role authentication - files never pass through your machine.

Prerequisites:

1. Contact ByteIT support to set up AWS connection
2. Provide IAM role ARN for ByteIT to assume
3. Grant role read access to your bucket

from byteit.connectors import S3InputConnector

connector = S3InputConnector(
    source_bucket="my-bucket",
    source_path_inside_bucket="documents/file.pdf"
)
result = client.parse(connector)

---

Error Handling

ByteIT SDK provides specific exceptions for different error scenarios:

from byteit.exceptions import (
    APIKeyError,           # Invalid API key
    AuthenticationError,   # Authentication failed
    ValidationError,       # Invalid parameters
    ResourceNotFoundError, # Job/resource not found
    RateLimitError,        # Rate limit exceeded
    ServerError,           # Server-side error (5xx)
    JobProcessingError,    # Job processing failed
)

try:
    result = client.parse("document.pdf")
except ValidationError as e:
    print(f"Invalid input: {e.message}")
except RateLimitError:
    print("Rate limit exceeded - please wait")
except JobProcessingError as e:
    print(f"Processing failed: {e.message}")

All exceptions inherit from ByteITError:

from byteit.exceptions import ByteITError

try:
    result = client.parse("document.pdf")
except ByteITError as e:
    print(f"ByteIT error: {e.message}")
    print(f"Status code: {e.status_code}")
    print(f"Response: {e.response}")

---

Advanced Usage

Batch Processing

Process multiple files efficiently:

files = ["doc1.pdf", "doc2.pdf", "doc3.pdf"]
results = []

for file in files:
    result = client.parse(file, output_format="json")
    results.append(result)

Custom Output Paths

Organize results systematically:

from pathlib import Path

input_dir = Path("inputs")
output_dir = Path("outputs")
output_dir.mkdir(exist_ok=True)

for pdf_file in input_dir.glob("*.pdf"):
    output_file = output_dir / f"{pdf_file.stem}.txt"
    client.parse(pdf_file, output=output_file)

S3 Workflow

High-volume cloud processing:

from byteit.connectors import S3InputConnector

# Process multiple S3 files
s3_files = [
    "invoices/2024-01.pdf",
    "invoices/2024-02.pdf",
    "invoices/2024-03.pdf",
]

for s3_path in s3_files:
    connector = S3InputConnector(
        source_bucket="my-documents",
        source_path_inside_bucket=s3_path
    )
    result = client.parse(connector, output_format="json")
    # Process result...

---

Configuration

Environment Variables

Set your API key via environment variable:

export BYTEIT_API_KEY="your_api_key_here"

import os
from byteit import ByteITClient

client = ByteITClient(api_key=os.getenv("BYTEIT_API_KEY"))

Custom Base URL

For testing or custom deployments:

from byteit import ByteITClient

# Set custom URL (for development/testing)
ByteITClient.BASE_URL = "http://localhost:8000"
client = ByteITClient(api_key="test_key")

---

Testing

The SDK includes comprehensive unit and integration tests.

Run Unit Tests

pytest

Run Integration Tests

Integration tests require a running ByteIT API and valid API key:

export BYTEIT_API_KEY="your_api_key"
pytest -m integration

Run All Tests

pytest -m ""

---

Requirements

• Python 3.8+
• requests library

---

About ByteIT

ByteIT transforms unstructured documents into clean, structured data with AI-powered precision. Built for scale, designed for developers.

Get started today: Start Processing Free - 1,000 free pages/month

---

Support & Resources

• Website: https://byteit.ai
• Pricing: https://byteit.ai/pricing
• Support: https://byteit.ai/support
• Contact: https://byteit.ai/contact
• LinkedIn: ByteIT on LinkedIn

---

Legal

© 2026 ByteIT GmbH. All rights reserved.

• Privacy Policy: https://byteit.ai/privacy-policy
• Terms of Service: https://byteit.ai/terms
• Impressum: https://byteit.ai/impressum

This project is licensed under the terms specified in the LICENSE file.