> ## Documentation Index > Fetch the complete documentation index at: https://docs.landing.ai/llms.txt > Use this file to discover all available pages before exploring further. # Parse Large Files (Parse Jobs) export const adePythonLibrary = 'ade-python'; export const dpt2 = 'DPT-2'; export const dpt1 = 'DPT-1'; export const dpt = 'Document Pre-Trained Transformer'; export const companyName = 'LandingAI'; export const extract = 'ADE Extract'; export const parse = 'ADE Parse'; export const ade = 'Agentic Document Extraction'; The [ADE Parse Jobs](https://docs.landing.ai/api-reference/tools/ade-parse-jobs) API enables you to parse large documents that exceed the size limits of the standard ADE Parse API. ## Monitor Parse Jobs You can monitor parse jobs with these APIs: * [ADE Get Parse Jobs](https://docs.landing.ai/api-reference/tools/ade-get-parse-jobs): Get the status for a specific parse job. * [ADE List Parse Jobs](https://docs.landing.ai/api-reference/tools/ade-list-parse-jobs): List all parse jobs associated with your API key. ## Rate Limits for ADE Parse Jobs The ADE Parse Jobs API allows you to parse large documents. The following table shows the limits for the ADE Parse Jobs API. | Maximum File Size | Maximum Pages | | ----------------- | ------------- | | 1 GB | 6,000 pages | To see the rate limits for all APIs, go to [Rate Limits](./ade-rate-limits). ## API Reference To learn more, go to the reference pages for the Parse Jobs APIs: * [ADE Parse Jobs](https://docs.landing.ai/api-reference/tools/ade-parse-jobs) * [ADE Get Parse Jobs](https://docs.landing.ai/api-reference/tools/ade-get-parse-jobs) * [ADE List Parse Jobs](https://docs.landing.ai/api-reference/tools/ade-list-parse-jobs) ## Save Parsed Output to a URL When calling the [ADE Parse Jobs](https://docs.landing.ai/api-reference/tools/ade-parse-jobs) endpoint, you can use the `output_save_url` parameter to save the parsed Markdown to a specified URL instead of returning it in the API response. This is useful for managing large documents, integrating with your existing storage workflow, or complying with data retention policies. ### When Parsed Output Is Saved to a URL The parsed Markdown is saved to a URL in these scenarios: * **You specify the `output_save_url` parameter**: The Markdown is saved to your specified URL. * **The parsed Markdown exceeds 1 MB**: The Markdown is automatically saved to a presigned S3 URL generated by {ade}. The URL expires after 1 hour, but time you call the [ADE Get Parse Jobs](https://docs.landing.ai/api-reference/tools/ade-get-parse-jobs) API, a new presigned URL is generated. Behavior when the output is saved to a URL: * The `output_url` field in the API response contains the URL where the Markdown is stored. * The `data` field in the API response is `None`. ### URL Requirements If you specify the `output_save_url` parameter, your URL must meet these requirements: * The URL must be a public or presigned URL that explicitly allows PUT or CREATE operations (depending on the provider). * Tested storage providers: Amazon S3, Azure Blob Storage, and Google Cloud Storage. Other storage providers may also work. * The API cannot access private URLs, such as folders in Google Drive. ### Example: Use Amazon S3 Presigned URLs If you use Amazon S3, you can generate a presigned URL and provide it as the `output_save_url` value. Presigned URLs grant temporary access to your S3 bucket without requiring authentication in the API request. For more information about presigned URLs with Amazon S3, go to the [Amazon documentation](https://docs.aws.amazon.com/AmazonS3/latest/userguide/PresignedUrlUploadObject.html). Here is a sample script that creates a presigned URL and uses it for a parsing job: ```python theme={null} import requests import json headers = { 'Authorization': 'Bearer YOUR_API_KEY' } url = 'https://api.va.landing.ai/v1/ade/parse/jobs' # Prepare the request payload output_save_url = generate_presigned_url(...) files = {'document_url': 'https://...', 'output_save_url': output_save_url} response = requests.post(url, files=files, headers=headers) print(response.json()) ``` ## ZDR Requirements When [zero data retention](./zdr) (ZDR) is enabled, you must configure the following parameters to ensure that {ade} does not store the document content: * **Pass your document in the `document_url` parameter**. You cannot use the `document` parameter with ZDR enabled. * **Include the `output_save_url` parameter**. This ensures that the parsed content is saved to your specified URL instead of being returned in the API response. To learn how to configure this parameter, go to [Save Parsed Output to a URL](#save-parsed-output-to-a-url). ## Workflow Overview 1. Parse a document with the [ADE Parse Jobs](https://docs.landing.ai/api-reference/tools/ade-parse-jobs) API. 2. Copy the `job_id` in the API response. 3. To get results from the parsing job, call the [ADE Get Parse Jobs](https://docs.landing.ai/api-reference/tools/ade-get-parse-jobs) API with the `job_id`. 4. The parsed content is returned as Markdown in `data.markdown`, or as a URL in `output_url` (in which case `data` is `None`). For more information, go to [Save Parsed Output to a URL](#save-parsed-output-to-a-url). 5. If you need to extract fields: 1. Create an extraction schema. 2. Send the Markdown to the [{extract}](https://docs.landing.ai/api-reference/tools/ade-extract) API. ## End-to-End Workflow: Parse and Extract the Output This tutorial walks you through how to parse a document with the ADE Parse Jobs API and then extract a subset of fields from it using the {extract} API. For the sake of simplicity, we use a 2-page PDF in this example, with the intention that you will use larger documents in your own use case. We provide a separate script for each endpoint, so you can choose to skip the extraction steps if you don't need them. In this tutorial, we will: * Parse this PDF: MRI Report * Extract these fields: **Exam Date** and **Procedure** ### 1. Download the Document to Process Download the MRI Report and save it to a local directory. ### 2. Create Parse Job & Get Job ID #### Create the Script Copy the script below and save it as `create-parse-job.py` in the same directory as the PDF. ```python [expandable] theme={null} import requests import json headers = { 'Authorization': 'Bearer YOUR_API_KEY' } url = 'https://api.va.landing.ai/v1/ade/parse/jobs' # Upload a document document = open('mri-report.pdf', 'rb') files = {'document': document} data = {'model': 'dpt-2-latest'} response = requests.post(url, files=files, data=data, headers=headers) print(response.json()) ``` #### Run the Script Run the script from the same directory: ```bash theme={null} python create-parse-job.py ``` This returns the `job_id`: ```json theme={null} {'job_id': 'cmfx34ewm0000hyoqkh9dzd8n'} ``` ### 3. Use job\_id to Get Parsing Results #### Create the Script Copy the script below and save it as `get-parse-results.py` in the same directory as the PDF. Replace `{jobId}` with the `job_id` from the previous step. ```python [expandable] theme={null} import requests headers = { 'Authorization': 'Bearer YOUR_API_KEY' } url = f'https://api.va.landing.ai/v1/ade/parse/jobs/{jobId}' response = requests.get(url, headers=headers) response_data = response.json() # Print the full response print(response_data) # Check if job is completed if response_data.get('status') == 'completed': # Check if markdown content is available in data if 'data' in response_data and response_data['data'].get('markdown'): markdown_content = response_data['data']['markdown'] # Save markdown content to file with open('markdown-mri-report.md', 'w', encoding='utf-8') as f: f.write(markdown_content) print("\nMarkdown content saved to a Markdown file.") # Check if output_url is available instead elif response_data.get('output_url'): print("Use the Markdown file specified in `output_url`.") else: print("No Markdown content or `output_url` found in the completed job response.") else: print(f"\nJob status: {response_data.get('status', 'unknown')}.") ``` #### Run the Script Run the script from the same directory: ```bash theme={null} python get-parse-results.py ``` When parsing is complete, the script saves the output to `markdown-mri-report.md`. You will pass this file to the Extract API in the next step. ### 4. Extract Fields from Markdown Now that we have the parsed output in a Markdown file, we're ready to extract these fields: **Exam Date** and **Procedure**. #### Create the Script Copy the script below and save it as `extract-mri-report.py` in the same directory as the Markdown file. ```python [expandable] theme={null} import requests import json headers = { 'Authorization': 'Bearer YOUR_API_KEY' } url = 'https://api.va.landing.ai/v1/ade/extract' # Define the extraction schema schema = json.dumps({ "type": "object", "properties": { "exam_date": { "description": "The date on which the medical examination or procedure was performed.", "format": "YYYY-MM-DD", "x-alternativeNames": [ "Exam Date", "Date of Exam", "Examination Date" ], "type": "string" }, "procedure": { "description": "The specific medical procedure or examination that was conducted, such as an MRI or X-ray.", "x-alternativeNames": [ "Procedure", "Medical Procedure", "Performed Procedure" ], "type": "string" } } }) # Prepare files and data files = {'markdown': open('markdown-mri-report.md', 'rb')} data = {'schema': schema, 'model': 'extract-latest'} # Run extraction response = requests.post(url, files=files, data=data, headers=headers) # Save the results to a JSON file with open('mri-report_extract_output.json', 'w') as f: json.dump(response.json(), f, indent=2) ``` #### Run the Script Run the script from the same directory: ```bash theme={null} python extract-mri-report.py ``` #### View the Output The results are saved to `mri-report_extract_output.json`. The file includes the extracted fields and metadata: ```json [expandable] theme={null} { "extraction": { "exam_date": "2010-05-20", "procedure": "MRI OF THE LUMBAR SPINE WITH AND WITHOUT CONTRAST" }, "extraction_metadata": { "exam_date": { "references": [ "b5183837-035c-4a54-b324-a4c9e8a68027", "1-3" ], "value": "2010-05-20" }, "procedure": { "references": [ "56340fbe-8a51-46a0-a309-d91abd9b8b00" ], "value": "MRI OF THE LUMBAR SPINE WITH AND WITHOUT CONTRAST" } }, "metadata": { "filename": "markdown-mri-report.md", "org_id": null, "duration_ms": 15080, "credit_usage": 1.3256, "job_id": "dfef14d3b66045b8abaf39788b9d17e8", "version": "extract-20260314", "schema_violation_error": null, "fallback_model_version": null, "warnings": [] } } ``` ## Run Parse Jobs with Our Libraries Click one of the tiles below to learn how to run Parse Jobs with our libraries. Run Parse Jobs with our Python library. TypeScript TypeScript } href="https://docs.landing.ai/ade/ade-typescript#parse-jobs" > Run Parse Jobs with our TypeScript library.