Integrating AI APIs into your Python applications can significantly enhance their capabilities. LightPDF AI API offers powerful tools for document processing, conversion, and analysis. This article provides code examples and patterns to help you implement LightPDF AI API effectively using Python.

Getting Started with LightPDF AI API

Before diving into code, ensure you have an API key from LightPDF. You can obtain one by signing up on their official website. Once you have your API key, you can start making requests to the API endpoints.

Basic Setup and Authentication

Install the required Python libraries. We will use requests for HTTP requests.

```python pip install requests ```

Set up your API key and base URL.

```python import requests API_KEY = 'your_lightpdf_api_key' BASE_URL = 'https://api.lightpdf.com/v1' headers = { 'Authorization': f'Bearer {API_KEY}', 'Content-Type': 'application/json' } ```

Example 1: Converting a Document to PDF

Use the conversion endpoint to upload and convert documents.

```python def convert_to_pdf(file_path): url = f'{BASE_URL}/convert' files = {'file': open(file_path, 'rb')} response = requests.post(url, headers={'Authorization': f'Bearer {API_KEY}'}, files=files) if response.status_code == 200: result = response.json() print('Conversion successful:', result['output_url']) else: print('Error:', response.text) convert_to_pdf('example.docx') ```

Example 2: Extracting Text from a PDF

Use the text extraction endpoint to retrieve text content.

```python def extract_text(pdf_url): url = f'{BASE_URL}/extract-text' payload = {'file_url': pdf_url} response = requests.post(url, headers=headers, json=payload) if response.status_code == 200: data = response.json() print('Extracted Text:', data['text']) else: print('Error:', response.text) extract_text('https://example.com/sample.pdf') ```

Pattern: Handling Asynchronous Tasks

Some API operations are asynchronous. Use polling to check task status.

```python import time def check_task_status(task_id): url = f'{BASE_URL}/tasks/{task_id}' while True: response = requests.get(url, headers=headers) if response.status_code == 200: status = response.json()['status'] if status == 'completed': print('Task completed.') return response.json() elif status == 'failed': print('Task failed.') return None else: print('Processing...') time.sleep(5) else: print('Error fetching task status.') break ```

Security and Best Practices

Keep your API keys secure. Do not hardcode them in publicly accessible code. Use environment variables or secure vaults.

Implement error handling for robustness. Check response status codes and handle exceptions appropriately.

Conclusion

Implementing LightPDF AI API with Python involves setting up authentication, making requests to various endpoints, and handling asynchronous operations. By following the patterns and examples provided, you can integrate powerful document processing features into your Python applications efficiently.