6. To update your billing address, click **Manage**. This opens your billing information in Stripe.
7. Click **Update Information**. Update your billing information and click **Save**.
| Action | Developer | Admin |
|---|---|---|
| Process documents | ||
| Create API keys | ||
| Revoke your own API keys | ||
| Revoke API keys other members created | ||
| Invite members | ||
| Remove members | ||
| Change member roles | ||
| Manage billing | ||
| Update organization name |
| Explore | Team | Enterprise | |
|---|---|---|---|
| Cost | Pay-as-you-go Start with 1000 free credits |
Credit packs start at \$250/mo | Contact us |
| Credit value | \$1 buys 100 credits | \$1 buys 110 credits | Contact us |
| Explore | Team | Enterprise | |
|---|---|---|---|
| Cost | Pay-as-you-go Start with 1000 free credits |
Credit packs start at \$325/mo | Contact us |
| Credit value | \$1 buys 76 credits | \$1 buys 84 credits | Contact us |
| Feature | Explore | Team | Enterprise |
|---|---|---|---|
| Core Capabilities | |||
| Parsing [view supported file formats](./ade-file-types) |
|||
| Intelligent Chunking | |||
| Field Extraction | |||
| List Extraction | |||
| Table Extraction | |||
| Figure Summaries | |||
| Visual Grounding | |||
| Split Classification | |||
| Multilingual documents | |||
| Confidence Scoring | |||
| Custom Processing Pipeline | |||
| Organization Management & Support | |||
| Users | 1 | Unlimited users | Unlimited users |
| Support Level | Community | Enhanced | Designated |
| Security & Deployment | |||
| Rate Limits [see more details](./ade-rate-limits) |
Standard | Higher | Priority |
| Zero Data Retention (ZDR) | |||
| HIPAA (BAA) (inc. ZDR) | |||
| VPC and On-Prem Deployments | |||
| SLA & Uptime Guarantees | |||
| Single Sign-On (SSO) [see more details](./ade-sso) |
| Credit Type | Expiration |
|---|---|
| Free credits included in the Explore plan | Expire 90 days after you create your account |
| "Pay-As-You-Go" Credits Purchased (includes credits purchased via Auto Recharge) | Expire 1 year from the purchase date |
| Credits allocated as part of a subscription or Enterprise plan | Credits are allocated at the start of each billing cycle (for subscription plans) or subscription term (for Enterprise plans). Unused credits expire at the end of that period. |
6. To update your billing address, click **Manage**. This opens your billing information in Stripe.
7. Click **Update Information**. Update your billing information and click **Save**.
## Delete Account
To delete your {ade} account, send an email with your request to [support@landing.ai](mailto:support@landing.ai). Use the email address you used to sign up for {ade}.
# Zero Data Retention (ZDR) Option Overview
Source: https://docs.landing.ai/ade/zdr
Process documents without storing them at rest, and enable the Zero Data Retention option for your ADE organization.
At LandingAI, we provide robust tools to process your documents efficiently. For customers with strict data privacy and compliance requirements, we offer the **Zero Data Retention (ZDR) option for our {ade} (ADE) product.**
This document provides an overview of what the ZDR option means, its availability, and how to enable it for your organization.
## 1.0 What is the Zero Data Retention (ZDR) Option?
When the ZDR option is enabled for your account, your data is processed in real-time **without being saved to our systems or any third-party systems**. This means:
* Your documents are processed in-memory and are **never stored at rest** on LandingAI systems or by our sub-processors.
* Your data is used exclusively to perform the extraction process you initiate and is immediately and irrevocably discarded after processing is complete.
* LandingAI does not use your data for training or improving our models when ZDR is active.
This provides the highest level of data privacy and control for your most sensitive documents.
## 2.0 Versions of Agentic Document Extraction That Support ZDR
ZDR is available for the following versions of Agentic Document Extraction:
* [LandingAI-Hosted ADE](#2-1-landingai-hosted-ade-us-%26-eu)
* [ADE Containerized App on Customer’s VPC](#2-4-ade-containerized-app-on-customer’s-vpc)
Support varies for each version. Read the full descriptions below to understand the extent of support.
### 2.1 LandingAI-Hosted ADE (US & EU)
The LandingAI-hosted version of ADE is available in both the [US](https://ade.landing.ai/) and [EU](https://ade.eu-west-1.landing.ai/). Users can enable ZDR based on their plan, as described in the table below.
| Location | Region | ZDR Availability |
| -------- | ------------------------------ | ------------------------------------------------------------------------------------ |
| US | AWS Ohio 
3. If you're on a subscription plan, you can [create an API key](#create-api-keys) if needed.
4. Click the **Copy** icon to copy your API key.
## Personal Plans (Explore Plans) Have One API Key
The personal plan (the "Explore" plan) only has one API key. This API key cannot be deleted or revoked.
The Explore plan type is designed for testing, prototyping, and hobby use. If you'd like to deploy to production and need more granular API key management, like the ability to create and revoke API keys, [upgrade](/ade/ade-pricing) to a subscription plan.
3. If you're not currently in the correct organization, select your organization from the drop-down menu.
4. Click **Create Key**.
5. Enter a brief, descriptive name for the API key in the key name field.
6. Click **Create API Key**.
7. Click the **Copy** icon to copy the API key.
8. Click **Done** to close the pop-up.
## Revoke API Keys
If you are on the Team or Enterprise plan, you can revoke API keys. A user with the Admin role can revoke any API key. A user with the Developer role can revoke only API keys they have created.
To revoke an API key:
1. Log in to [https://ade.landing.ai/](https://ade.landing.ai/).
2. Go to the [API Key](https://ade.landing.ai/settings/api-key) page (to navigate there manually, click your profile icon at the bottom left corner of the page and click **API Key**).
3. If you're not currently in the correct organization, select your organization from the drop-down menu.
4. Locate the row for the API key. (You may need to search to narrow down the list of API keys.)
5. Click the **Settings** button (ellipses) and select **Revoke API Key**. Follow the on-screen prompts to complete the process.
## Set Your API Key When Calling the API Directly
Include the API key in request headers. To see code samples, go to [API](https://docs.landing.ai/api-reference/parse/ade-parse).
## Security Best Practices
* Do not pass the API key directly to the code, because others could see and use your API key.
## Troubleshoot API Key Issues
Use the troubleshooting tips in this section to resolve issues related to API keys.
### Error: Missing Authorization Header
This error occurs if the authorization header is not included when calling the API directly. To fix this, include the API header:
```python theme={null}
headers = {"Authorization": f"Bearer {api_key}"}
```
### Error: User Not Found, Please Check Your API Key
This error occurs when the API key is entered incorrectly.
To resolve this issue:
* Verify the API key: Copy your key directly from the [dashboard](https://ade.landing.ai/settings/api-key).
* Restart your environment: Close and reopen your terminal, IDE, or Jupyter notebook.
# Changelog
Source: https://docs.landing.ai/dpt3/changelog
Learn about updates and new features across the ADE v2 APIs: Parse, Extract, Parse Jobs, and Extract Jobs.
| File Type | Playground | API |
|---|---|---|
| Images | ||
| JPEG | ||
| JPG | ||
| PNG | ||
| GIF | ||
| BMP | ||
| TIFF | ||
| TIF | ||
| WEBP |
| Test Point | Nominal (V) | Measured Value (V) | As-Found Deviation (mV) | Tolerance (±mV) | Result | |
| As Found | As Left | |||||
| TP-1 | 0.000 | 0.001 | 0.000 | +1 | 5 | Pass |
| TP-2 | 2.500 | 2.503 | 2.500 | +3 | 10 | Pass |
| TP-3 | 5.000 | 5.041 | 5.002 | +41 | 20 | Fail |
| TP-4 | 7.500 | 7.512 | 7.503 | +12 | 30 | Pass |
| TP-5 | 10.000 | 10.018 | 10.004 | +18 | 40 | Pass |
| QTY | \nDESC | \nAMT | \n
|---|---|---|
| 1 | \nAmericano | \n$3.19 | \n
| 1 | \nAlmond Scone | \n$1.99 | \n
| 1 | \n16oz Bottle Water | \n$2.99 | \n
2. Copy the script for the method you plan on using: the [{adePythonLibrary} library](./ade-python#extract-getting-started) or the [API](#use-ade-extract-to-extract-fields-from-markdown).
3. Paste the script into your code.
## Use the ADE Extract API to Extract Fields from Markdown
Use the {extract} API to extract data from the Markdown output created by the [{parse} API](./ade-separate-apis).
See the full {extract} API reference [here](https://docs.landing.ai/api-reference/tools/ade-extract).
### Specify Documents to Run Extraction On
The {extract} API offers two parameters for specifying the document you want to extract from:
* `markdown`: Specify the actual Markdown file you want to run extraction on.
* `markdown_url`: Include the URL to the Markdown file you want to run extraction on.
### Set the Extraction Schema
Set the extraction schema in the `schema` parameter. The schema must meet specific format and property requirements. For detailed guidance, see [JSON Schema for Extraction](./ade-extract-schema-json).
### Set the `strict` Parameter
Use the optional `strict` parameter to control how the API handles schemas that include [keywords that cause errors](./ade-extract-schema-json#keywords-that-cause-errors).
* If `strict` is `false`: the API continues processing and returns a `206` (Partial Content).
* If `strict` is `true`: the API stops processing and returns a `422` (Unprocessable Entity).
In both cases, the API returns `422` if the schema fails validation, and `206` if the extracted output does not conform to the schema after extraction completes.
### Extracted Output
For details about the extraction response structure and fields, see [JSON Response for Extraction](./ade-extract-response).
## Run Extract with Our Libraries
Click one of the tiles below to learn how to run the [{extract} API](https://docs.landing.ai/api-reference/tools/ade-extract) with our libraries.
}
href="./ade-typescript#extract-getting-started"
>
Run Extract with our TypeScript library.
3. Upload files and click **Create Project**. (Continue to the next step while the files are parsed in the background.)
4. (Optional) Enter natural-language instructions for the extraction in the **Guidelines** text box. This can include:
* Data to include
* Data to exclude
* Context about the files
5. Click **Suggest Schema**.
6. The app reviews the files in the project and creates a master schema. Review the generated fields before continuing.
7. Click [**Run This File**](#run-the-schema-on-your-files) to validate the schema on the selected file.
8. When the schema is ready, [use the schema](#use-the-schema).
### Upload a Schema
If you have an existing extraction schema you want to edit, you can upload it to the Playground to validate it. You can then edit it just like any schema created in the Playground.
**Start a new project with your own schema**
To start with your own extraction schema:
1. Go to the [Playground](https://ade.landing.ai/).
2. Click the **Extract** tile.
3. Upload files and click **Create Project**. (Continue to the next step while the files are parsed in the background.)
4. Click **Upload JSON Schema**.
5. Select the JSON file you want to load.
6. The app loads the JSON file into the **Schema** panel.
**Upload a schema to an existing project**
You can also upload a schema after you've started building one. The uploaded schema is saved as a new version.
To upload a schema to an existing project:
1. Go to the [Playground](https://ade.landing.ai/).
2. Open a project.
3. Click the **Extract** tab.
4. In the **Schema** panel, click **...** and select **Import Schema**.
5. Select the JSON file you want to load.
6. The app loads the JSON file into the **Schema** panel.
## Edit and Validate the Schema
After creating a schema in the [Playground](https://ade.landing.ai/), you can edit and validate it. You can add fields, update descriptions, remove fields, and validate the full schema.
* [Edit, Add, or Remove Fields Manually](#edit-add-or-remove-fields-manually)
* [Refine Schema with AI](#refine-schema-with-ai)
* [Run the Schema on Your Files](#run-the-schema-on-your-files)
* [Start Over](#start-over)
### Edit, Add, or Remove Fields Manually
Click a field to see and edit additional information, like the format, description, and alternative names. For detailed information about the field options, go to [Extraction Schema (JSON)](./ade-extract-schema-json).
To add, edit, or remove fields:
1. Go to the [Playground](https://ade.landing.ai/).
2. Open a project.
3. Click the **Extract** tab.
4. If you have multiple versions of the schema, make sure that the schema you want to use is selected.
5. Make your changes:
* **Add a field**: Scroll to the bottom of the **Schema** panel and click **New Field**.
* **Edit a field**: Click the field and update it.
* **Remove a field**: Hover over the field and click the **Delete** icon.
6. After making any changes to the schema, click **Update Schema**.
### Refine Schema with AI
### Run the Schema on Your Files
The **Extracted Results** panel refreshes and displays two sets of content:
* **Extraction**: The list of extracted key-value pairs.
* **Extract Metadata**: The key-value pairs and their unique IDs (`references`).
For more information about extraction output, go to [JSON Response for Extraction](./ade-extract-response).
### Start Over
After you create a schema, you can start over. This creates a new blank schema, saved as the next version, while retaining all existing schema versions. You can then manually add fields, refine the schema with AI, or import a schema.
To start over:
1. Go to the [Playground](https://ade.landing.ai/).
2. Open a project.
3. Click the **Extract** tab.
4. In the **Schema** panel, click **...** and select **Start Over**.
5. When prompted, click **Start Fresh**.
## Use the Schema
After you create and validate a schema, you are ready to use it.
* [Get a Ready-to-Use Parse and Extract Script](#get-a-ready-to-use-parse-and-extract-script): Get a ready-to-use script for Parse and Extract that uses the schema you created.
* [Export the Schema](#export-the-schema): Download the schema as a JSON file to use in your own code.
### Get a Ready-to-Use Parse and Extract Script
After you create a schema, {ade} generates a script that covers both the [{parse}](./ade-separate-apis) and [{extract}](./ade-extract) steps using the schema you built. The Playground provides two versions: one for calling the APIs directly, and one for the [{adePythonLibrary} library](./ade-python).
**To get the script:**
1. Go to the [Playground](https://ade.landing.ai/).
2. Open a project.
3. Click the **Extract** tab.
4. If you have multiple versions of the schema, make sure that the schema you want to use is selected.
5. Click **Code**.
6. The **View Code** pop-up opens. Click the **Library** or **API** tab to see the code for each extraction method.
7. Click the **Download** or **Copy** buttons to get the code.
### Export the Schema
After you create a schema, you can export it as a downloadable JSON file to use in your own code.
**To export the schema:**
1. Go to the [Playground](https://ade.landing.ai/).
2. Open a project.
3. Click the **Extract** tab.
4. If you have multiple versions of the schema, make sure that the schema you want to use is selected.
5. In the **Schema** panel, click **...** and select **Export Schema**.
6. The extraction schema is downloaded as a JSON file.
## Schema Versions
The Playground saves a new version of the schema each time you run **Suggest Schema**, **Refine Schema**, or import a schema. Previous versions are retained even after you use **Start Over**.
Use schema versions to compare results or roll back to an earlier version if a schema update doesn't perform as expected.
To view a previous version, click the drop-down menu in the **Schema** panel and select the version you want to see.
## Download or Copy Extracted Data
You can download or copy extracted data directly from the Playground. This is useful for spot-checking results during schema development. For production use, retrieve extracted data through the [{extract} API](./ade-extract) or [{adePythonLibrary} library](./ade-python).
To download or copy the extracted data:
1. Go to the [Playground](https://ade.landing.ai/).
2. Open a project.
3. Click the **Extract** tab.
4. If the **Extracted Results** panel doesn't display, create or edit a schema and click **Run This File**.
5. Click the **Download** or **Copy** buttons to get the extracted data.
# JSON Response for Extraction
Source: https://docs.landing.ai/ade/ade-extract-response
Understand the extraction JSON response: extracted values, field references, and metadata.
When you extract structured data with the [{extract} API](https://docs.landing.ai/api-reference/tools/ade-extract), the extracted data and metadata are returned in a structured JSON format.
## Response Structure
The response contains the following top-level fields:
* [`extraction`](#extracted-data-extraction): The extracted key-value pairs as defined by your schema.
* [`extraction_metadata`](#extraction-metadata-extraction_metadata): Metadata showing which chunks were referenced for each extracted field.
* [`metadata`](#processing-metadata-metadata): Processing information including credit usage, duration, filename, job ID, version, schema validation errors, and warnings.
## Extracted Data (`extraction`)
The `extraction` field contains the structured data extracted from your document, formatted according to your JSON schema. The structure matches your input schema exactly.
For a simple schema:
```json theme={null}
{
"type": "object",
"properties": {
"employee_name": {
"type": "string",
"description": "The employee's full name"
},
"employee_ssn": {
"type": "string",
"description": "The employee's Social Security Number"
},
"gross_pay": {
"type": "number",
"description": "The gross pay amount"
}
}
}
```
The `extraction` field returns:
```json theme={null}
{
"employee_name": "MICHAEL D BRYAN",
"employee_ssn": "555-50-1234",
"gross_pay": 6000.00
}
```
## Extraction Metadata (`extraction_metadata`)
The `extraction_metadata` field has the same structure as your extraction schema, but each field contains a dictionary with `references` that lists the HTML element IDs where the data was found.
The `references` field can contain:
* **Chunk IDs**: UUID-format IDs (e.g., `72ba3cca-01e5-407b-9fc4-81f54f9f0c51`) that reference entire chunks like text blocks or figures
* **Table cell IDs**: Format `{page_number}-{base62_sequential_number}` (e.g., `0-u`) when extracted data comes from table cells
* **Other HTML element IDs**: Any ID attribute from HTML elements within the [`markdown` fields](./ade-markdown-response) from the parsed output
This metadata is useful for:
* Tracing which parts of the document contributed to each extracted field
* Debugging extraction issues
* Building confidence scores or validation logic
* Creating audit trails for extracted data
### Simple Schema Metadata
For a simple extraction schema, the metadata includes the value and references for each field.
When data is extracted from text chunks, references contain chunk IDs (UUIDs):
```json theme={null}
{
"employee_name": {
"value": "MICHAEL D BRYAN",
"references": [
"72ba3cca-01e5-407b-9fc4-81f54f9f0c51"
]
},
"employee_ssn": {
"value": "555-50-1234",
"references": [
"a3f5d8c9-2b4e-4a1c-8f7e-9d6c5b4a3e2f"
]
}
}
```
When data is extracted from table cells, references contain table cell IDs:
```json theme={null}
{
"employee_name": {
"value": "JANE HARPER",
"references": [
"75a62de4-5120-44bf-a6dd-b2aa63db18c6"
]
},
"gross_pay": {
"value": 452.43,
"references": [
"0-u"
]
}
}
```
In this example, `"0-u"` is a table cell ID where `0` indicates page 0 and `u` is the base62-encoded sequential number for that cell.
### Nested Schema Metadata
For nested extraction schemas, the metadata preserves the same nested structure:
```json theme={null}
{
"patient_details": {
"patient_name": {
"value": "John Smith",
"references": [
"72ba3cca-01e5-407b-9fc4-81f54f9f0c51"
]
},
"date": {
"value": "2024-01-15",
"references": [
"72ba3cca-01e5-407b-9fc4-81f54f9f0c51"
]
}
},
"emergency_contact_information": {
"emergency_contact_name": {
"value": "Jane Smith",
"references": [
"5b8865b9-1a81-46df-bcf7-0bdbed9130dc"
]
},
"relationship_to_patient": {
"value": "Spouse",
"references": [
"5b8865b9-1a81-46df-bcf7-0bdbed9130dc"
]
}
}
}
```
## Processing Metadata (`metadata`)
The `metadata` field provides information about the extraction process:
| Field | Type | Description |
| ------------------------ | -------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `filename` | string | The name of the input file. |
| `org_id` | string | Organization identifier. |
| `duration_ms` | number | Processing time in milliseconds. |
| `credit_usage` | number | Number of credits consumed. |
| `job_id` | string | Unique job identifier. |
| `version` | string | Model version used for extraction. For more information, go to [Extraction Model Versions](./ade-extract-models). |
| `fallback_model_version` | string \| null | The extract model that was actually used when the initial extraction attempt failed with the requested version. `null` if no fallback occurred. |
| `schema_violation_error` | string \| null | Error message if extracted data doesn't conform to the input schema. `null` if the schema is valid. For more information, go to [Troubleshoot Extraction](./ade-extract-troubleshoot). |
| `warnings` | array | Structured warning objects generated during extraction. Each warning has a `code` that identifies the warning type and a `msg` (human-readable description). For more information, go to [Warnings](./ade-extract-troubleshoot#warnings). |
## Example Response
Here is a complete example showing the extraction response structure:
```json theme={null}
{
"extraction": {
"account_holder_name": "SUSAN SAMPLE",
"account_number": "02782-5094431",
"closing_balance": 3664.79
},
"extraction_metadata": {
"account_holder_name": {
"references": [
"55d1b275-b539-4af7-ac85-3396f62c216e"
],
"value": "SUSAN SAMPLE"
},
"account_number": {
"references": [
"dd784487-910c-4cb7-8bb2-d4a7ef3d952b",
"0-3"
],
"value": "02782-5094431"
},
"closing_balance": {
"references": [
"0-e"
],
"value": 3664.79
}
},
"metadata": {
"credit_usage": 0.8583999999999999,
"duration_ms": 9893,
"filename": "upload.md",
"job_id": "1c08692d839c4c33bd85aebfafeab5e9",
"org_id": "lgy5xucm9xnq",
"version": "extract-20260314",
"fallback_model_version": null,
"schema_violation_error": null,
"warnings": []
}
}
```
# Build Extraction Schemas with the API
Source: https://docs.landing.ai/ade/ade-extract-schema-api
Generate a JSON extraction schema programmatically from parsed Markdown with the Build Extract Schema API.
Use the [{buildExtract} API](https://docs.landing.ai/api-reference/tools/ade-build-schema) to programmatically generate a JSON extraction schema from the Markdown output of the [{parse} API](./ade-separate-apis).
The API analyzes the Markdown content and returns a schema you can pass directly to the [{extract} API](./ade-extract).
| File Type | Playground | API/Library |
|---|---|---|
up to 100 pages |
see [Rate Limits](./ade-rate-limits) |
|
| Images | ||
| JPEG | ||
| JPG | ||
| PNG | ||
| APNG | ||
| BMP | ||
| DCX | ||
| DDS | ||
| DIB | ||
| GD | ||
| GIF | ||
| ICNS | ||
| JP2 (JP2000) | ||
| PCX | ||
| PPM | ||
| PSD | ||
| TGA | ||
| TIF | ||
| TIFF | ||
| WEBP | ||
| Text Documents (see notes [here](#file-conversion-for-text-documents-and-presentations)) |
||
| DOC (Word) | ||
| DOCX (Word) | ||
| ODT (OpenDocument Text) | ||
| Presentations (see notes [here](#file-conversion-for-text-documents-and-presentations)) |
||
| PPT (PowerPoint) | ||
| PPTX (PowerPoint) | ||
| Spreadsheets (see notes [here](#spreadsheet-considerations)) |
||
| CSV (comma-separated values) | (up to 10 MB) |
(up to 50 MB) |
| XLSX (Microsoft Excel) | (up to 10 MB) |
(up to 50 MB) |
The parsed response is shown below, with key elements highlighted in the table:
| Line | Description | ID |
| ------- | --------------------------------------------------------- | ------------------------------------ |
| Line 47 | `grounding` object begins | N/A |
| Line 48 | Grounding for the text "Bank Account Rates" | 2831e56d-94f5-4ec4-b001-6e16e188119b |
| Line 58 | Grounding for the table chunk | 54905c88-b4b8-45a7-84b5-1dcf5f1d1e60 |
| Line 68 | Grounding for the table ID | 0-1 |
| Line 78 | Grounding for the first cell in the table, "Account Type" | 0-2 |
```json [expandable] lines highlight={47, 48, 58, 68, 78} theme={null}
{
"markdown": "\n\n## Bank Account Rates\n\n\n\n| Account Type | APY |
| Checking | 0.25% |
| Savings | 3.30% |
| Account Type | APY |
| Checking | 0.25% |
| Savings | 3.30% |
| Account Type | APY |
| Checking | 0.25% |
| Savings | 3.30% |
| Grounding Type | Chunk Type | Has Confidence Score | Has Low Confidence Spans |
|---|---|---|---|
chunkText |
text |
||
chunkMarginalia |
marginalia |
||
chunkCard |
card |
||
chunkTable |
table |
||
table |
— | — | — |
tableCell |
— | — | |
chunkFigure |
figure |
— | — |
chunkLogo |
logo |
— | — |
chunkAttestation |
attestation |
— | — |
chunkScanCode |
scan\_code |
— | — |
| Field | Description | Global Grounding Object | Chunk-Level Grounding |
|---|---|---|---|
box |
Bounding box coordinates | ||
page |
Zero-indexed page number | ||
confidence |
Confidence score | ||
low\_confidence\_spans |
Sections of text with low confidence scores, if any | ||
type |
Detailed element type | ||
position |
Table cell position |
| Feature | Specification |
| Battery Life | Up to 30 hours with ANC on |
| Connectivity | Bluetooth 5.3, USB-C |
| Driver Size | 40mm dynamic drivers |
| Weight | 250 grams |
| Charging Time | 2.5 hours (full charge) |
| Feature | Specification |
| Battery Life | Up to 30 hours with ANC on |
| Connectivity | Bluetooth 5.3, USB-C |
| Driver Size | 40mm dynamic drivers |
| Weight | 250 grams |
| Charging Time | 2.5 hours (full charge) |
| Feature | Specification |
| Battery Life | Up to 30 hours with ANC on |
| Connectivity | Bluetooth 5.3, USB-C |
| Driver Size | 40mm dynamic drivers |
| Weight | 250 grams |
| Charging Time | 2.5 hours (full charge) |
Below is the full parsing response for this document with the `markdown` fields highlighted. Notice that each chunk's HTML anchor tag (the `` element) appears consistently across all `markdown` fields.
This consistency means you can reference any chunk by its ID, whether you're working with the complete document Markdown, a specific split, or an individual chunk.
```json Markdown Fields {2,5,19,33,47,68} [expandable] theme={null}
{
"markdown": "\n\nSKU\nWH-2847-BLK\n\n\n\nQUANTITY\n\n48 Units\n\n\n\n<::scan_code: Barcode\n\nThis is a clear, well-defined linear barcode with distinct black bars on a white background, showing no visible quality issues.::>\n\n\n\n2847 0000 4812\n---\n",
"chunks": [
{
"markdown": "\n\nSKU\nWH-2847-BLK",
"type": "text",
"id": "69a645fe-8617-4be8-b66f-68d7788755c0",
"grounding": {
"box": {
"left": 0.06628379225730896,
"top": 0.09509064257144928,
"right": 0.4664609134197235,
"bottom": 0.2678269147872925
},
"page": 0
}
},
{
"markdown": "\n\nQUANTITY\n\n48 Units",
"type": "text",
"id": "bdaf2198-0fdc-4c94-be54-a49ca796ea0f",
"grounding": {
"box": {
"left": 0.06750398874282837,
"top": 0.3068091869354248,
"right": 0.3204823434352875,
"bottom": 0.47341251373291016
},
"page": 0
}
},
{
"markdown": "\n\n<::scan_code: Barcode\n\nThis is a clear, well-defined linear barcode with distinct black bars on a white background, showing no visible quality issues.::>",
"type": "scan_code",
"id": "65fe085e-b8c8-46e0-848c-f0c32ccf16e8",
"grounding": {
"box": {
"left": 0.11207544803619385,
"top": 0.5374823212623596,
"right": 0.8830909729003906,
"bottom": 0.8660760521888733
},
"page": 0
}
},
{
"markdown": "\n\n2847 0000 4812\n---\n",
"type": "text",
"id": "9e551c40-3eaa-4d77-a219-50b7f4a57e05",
"grounding": {
"box": {
"left": 0.28996485471725464,
"top": 0.9088556170463562,
"right": 0.7057502269744873,
"bottom": 0.9888754487037659
},
"page": 0
}
}
],
"splits": [
{
"class": "page",
"identifier": "page_0",
"pages": [
0
],
"markdown": "\n\nSKU\nWH-2847-BLK\n\n\n\nQUANTITY\n\n48 Units\n\n\n\n<::scan_code: Barcode\n\nThis is a clear, well-defined linear barcode with distinct black bars on a white background, showing no visible quality issues.::>\n\n\n\n2847 0000 4812\n---\n",
"chunks": [
"69a645fe-8617-4be8-b66f-68d7788755c0",
"bdaf2198-0fdc-4c94-be54-a49ca796ea0f",
"65fe085e-b8c8-46e0-848c-f0c32ccf16e8",
"9e551c40-3eaa-4d77-a219-50b7f4a57e05"
]
}
],
"grounding": {
"69a645fe-8617-4be8-b66f-68d7788755c0": {
"box": {
"left": 0.06628379225730896,
"top": 0.09509064257144928,
"right": 0.4664609134197235,
"bottom": 0.2678269147872925
},
"page": 0,
"type": "chunkText"
},
"bdaf2198-0fdc-4c94-be54-a49ca796ea0f": {
"box": {
"left": 0.06750398874282837,
"top": 0.3068091869354248,
"right": 0.3204823434352875,
"bottom": 0.47341251373291016
},
"page": 0,
"type": "chunkText"
},
"65fe085e-b8c8-46e0-848c-f0c32ccf16e8": {
"box": {
"left": 0.11207544803619385,
"top": 0.5374823212623596,
"right": 0.8830909729003906,
"bottom": 0.8660760521888733
},
"page": 0,
"type": "chunkScanCode"
},
"9e551c40-3eaa-4d77-a219-50b7f4a57e05": {
"box": {
"left": 0.28996485471725464,
"top": 0.9088556170463562,
"right": 0.7057502269744873,
"bottom": 0.9888754487037659
},
"page": 0,
"type": "chunkText"
}
},
"metadata": {
"filename": "pallet-label.png",
"org_id": null,
"page_count": 1,
"duration_ms": 4226,
"credit_usage": 3,
"job_id": "y2xvbwlqc1p9ynwfkx4tx7q7q",
"version": "dpt-2-20250919"
}
}
```
## Markdown Structure
The `markdown` field includes a parsed chunk or a sequence of chunks.
Each chunk begins with an [HTML anchor tag](#anchor-tags) containing a unique identifier, followed by the chunk content.
For example, the following `markdown` field contains two chunks:
* A figure chunk (ID: `4c29090b-b75e-4d5f-95b6-24a7d5668486`) with a description of the image
* A text chunk (ID: `ae2e4e41-9443-4fb5-bced-199915f97dec`) containing formatted address information
```json theme={null}
"markdown": "\n\n<::An illustration of a sun with eight rays extending outwards.: figure::>\n\n\n\n**Eliza Smith**\n123 Main St.\nMountain View, CA 94041"
```
### Anchor Tags
Each chunk begins with an HTML anchor tag containing the chunk's unique identifier.
The `id` attribute contains the UUID that matches the corresponding entry in the `chunks` array, enabling you to trace content back to its location in the original document.
```html theme={null}
```
### Content Format by Chunk Type
The Markdown content format varies based on the [chunk type](./ade-chunk-types):
* [Text-Based Chunks](#text-based-chunks)
* [Image-Based Chunks](#image-based-chunks)
* [Table Chunks](#table-chunks)
* [Spreadsheets](#spreadsheets)
#### Text-Based Chunks
For text-based chunks (`text`, `marginalia`), content appears as standard Markdown text:
```markdown theme={null}
## Heading Text
Paragraph content with **bold** and *italic* formatting.
```
#### Image-Based Chunks
Image-based chunks (`figure`, `logo`, `card`, `attestation`, `scan_code`) use a special delimiter format that wraps the caption or description:
```markdown theme={null}
<::Caption or description of the visual element: figure::>
```
#### Table Chunks
Table chunks (`table`) appear as HTML table markup.
Most table elements include unique `id` attributes. These IDs use the format `{page_number}-{base62_sequential_number}`, where the page number starts at 0 and the sequential number increments for each element within the page.
If a page contains multiple tables, the ID numbering continues sequentially across all tables on that page.
Table cells that span multiple rows or columns include `rowspan` or `colspan` attributes in the HTML markup.
This ID system allows you to trace individual cells, rows, and tables back to their locations in the original document. The [JSON response](./ade-json-response#table-cell-position-information) also includes position information (row, column, rowspan, colspan) for each table cell in the grounding object.
```markdown theme={null}
| Product Summary | |
| Product | Revenue |
| Hardware | 15,230 |
| Software | 8,540 |
```markdown theme={null}
{
"markdown": "\n\n| Program | \nInterest Rate | \n
| 15 Year Fixed-Rate Mortgage | \n0.05125 | \n
| 30 Year Fixed-Rate Mortgage | \n0.05875 | \n
| 10/1 ARM | \n0.05625 | \n
}
href="https://docs.landing.ai/ade/ade-typescript#parse-jobs"
>
Run Parse Jobs with our TypeScript library.
}
href="#typescript"
>
Use concurrent parsing with Promise.all() or p-limit
## Use Custom Prompts with the API
## From Playground to Production
If you're new to {ade}, start with the [Playground](https://ade.landing.ai/) rather than jumping straight into the APIs. The Playground gives you an immediate, visual way to explore what [Parse](./ade-separate-apis), [Extract](./ade-extract), [Classify](./ade-classify), [Section](./ade-section), and [Split](./ade-split) can do with your specific documents.
Use the Playground to:
* **Test your use case before writing any code.** Upload your actual documents and see how {ade} handles them. Can it parse faxed medical records? Extract supplier IDs consistently across invoices from different vendors? Handle long financial tables in earnings reports? You'll get immediate answers.
* **Build extraction and split schemas visually.** The schema wizard guides you through creating, editing, and validating schemas using a point-and-click interface.
* **Export production-ready code.** Once you're satisfied with the results, the Playground generates the API or library code you need to replicate the same processing programmatically.
When you're ready to scale, use the [API](https://docs.landing.ai/api-reference/tools/ade-parse) or the [Python](./ade-python) or [TypeScript](./ade-typescript) libraries. The Playground is not intended for production use and has limitations such as lower rate limits.
## Files Are Organized into Projects
Files in the Playground are organized into **projects**. Projects let you group similar documents together. You can then [create and apply extraction schemas](./ade-extract-playground) across multiple documents at once.
To view all files you've uploaded to the Playground, click **Projects** in the left navigation bar.
## Chat with Document
After the Playground parses a file, you can use the **Chat with Document** tool to interact with the document.
The Chat with Document tool is an LLM layered on top of the output of the {parse} API. The chat tool showcases how the API accurately parses and understands document data, including element locations. Use the chat tool to get inspired for how you can build custom solutions on top of the API.
The Chat with Document tool suggests a few prompts based on your document. You can also enter your own prompts.
Like the Playground itself, the Chat with Document tool is an example of what you can build, and is not part of the {ade} API.
## Manage Your Account in the Playground
The Playground is also used to create and manage your account. For more information, go to [Organizations & Members](./ade-members).
# Python Library
Source: https://docs.landing.ai/ade/ade-python
Process documents using the ADE Python library.
The [{adePythonLibrary}](https://github.com/landing-ai/ade-python) library is a lightweight Python library you can use for parsing documents, classifying pages, extracting data, generating tables of contents, and splitting documents into sub-documents.
The library is automatically generated from our API specification, ensuring you have access to the latest endpoints and parameters.
## Install the Library
```bash theme={null}
pip install landingai-ade
```
## Set the API Key as an Environment Variable
To use the library, first [generate an API key](https://ade.landing.ai/my/settings/api-key). Save the key to a `.zshrc` file or another secure location on your computer. Then export the key as an environment variable.
```bash theme={null}
```
}
href="./ade-typescript"
>
Use our TypeScript library to build custom scripts.
### Get a Ready-to-Use Section Script
After you create a table of contents, {ade} generates a script to section files using the current guideline (if any). The Playground provides two versions: one for calling the API directly, and one for the [{adePythonLibrary} library](./ade-python).
**To get the script:**
1. Go to the [Playground](https://ade.landing.ai/).
2. Open a project.
3. Click the **Section** tab.
4. Click **Get Code**.
5. The **View Code** pop-up opens. Click the **Library** or **API** tab to see the code for each method.
6. Click the **Download** or **Copy** buttons to get the code.
## Section with the API
Section a document by calling the [{section}](https://docs.landing.ai/api-reference/tools/ade-section) endpoint.
```shell theme={null}
curl -X POST 'https://api.va.landing.ai/v1/ade/section' \
-H 'Authorization: Bearer YOUR_API_KEY' \
-F 'markdown=@parsed_output.md' \
-F 'model=section-latest'
```
### Parameters
Get the full parameters from the [API reference](https://docs.landing.ai/api-reference/tools/ade-section).
| Parameter | Required | Description |
| :------------- | :--------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `markdown` | Required if `markdown_url` omitted | The Markdown output from the [{parse} API](https://docs.landing.ai/api-reference/tools/ade-parse). Accepts a file upload or a raw string. |
| `markdown_url` | Required if `markdown` omitted | A URL pointing to a Markdown file to fetch and section. |
| `guidelines` | Optional | Natural-language instructions that control how the hierarchy is built. When omitted, the API infers structure automatically. Examples:
}
href="./ade-typescript#section-getting-started"
>
Use {section} with our TypeScript library.
{section} analyzed the full document and returned a hierarchical table of contents. The **Declarations And Policy Overview** section on the first page appears as section `2` (level 1) in the response.
**Insured Property** and **Policy Period And Renewal** are its level 2 subsections, each with their own level 3 sub-subsections.
Each entry's `start_reference` points to the chunk where that section begins in the parsed document.
```json [expandable] theme={null}
{
"table_of_contents": [
{
"title": "Policy Header",
"level": 1,
"section_number": "1",
"start_reference": "1c4b7184-346d-490f-a7bf-bf2442bcbd7c"
},
{
"title": "Declarations And Policy Overview",
"level": 1,
"section_number": "2",
"start_reference": "494d766e-5cf1-48c4-a580-73061b722b90"
},
{
"title": "Insured Property",
"level": 2,
"section_number": "2.1",
"start_reference": "1e6c9100-f461-4bb8-8dcd-8ae0311a97c0"
},
{
"title": "Mortgage And Lienholder",
"level": 3,
"section_number": "2.1.1",
"start_reference": "95e79ad7-72b5-4c9f-9717-8488996c6795"
},
{
"title": "Detached Structures",
"level": 3,
"section_number": "2.1.2",
"start_reference": "4c1e087b-aee9-4494-9789-735388a7579c"
},
{
"title": "Policy Period And Renewal",
"level": 2,
"section_number": "2.2",
"start_reference": "de55b7d5-df5c-480e-8b91-0f9f10f5b059"
},
{
"title": "Cancellation",
"level": 3,
"section_number": "2.2.1",
"start_reference": "7cc48544-b2ec-4e34-b2a1-ae1b3b7d17a6"
},
{
"title": "Annual Premium",
"level": 3,
"section_number": "2.2.2",
"start_reference": "b8c0ba71-d786-4f89-a07f-3fcfc0edd734"
},
{
"title": "Schedule Of Coverages And Limits",
"level": 1,
"section_number": "3",
"start_reference": "3700c26c-dd67-4dda-a712-081becb9ab63"
},
{
"title": "Coverage A Dwelling",
"level": 2,
"section_number": "3.1",
"start_reference": "de789af4-1585-4b3f-9193-4253a9e58f6b"
},
{
"title": "Ordinance Or Law",
"level": 3,
"section_number": "3.1.1",
"start_reference": "c7eaad71-19ca-4ab4-90cb-e8e9581ededc"
},
{
"title": "Named Storm Deductible",
"level": 3,
"section_number": "3.1.2",
"start_reference": "d1300436-0131-49d1-a98a-1f46ece31c23"
},
{
"title": "Coverage C Personal Property",
"level": 2,
"section_number": "3.2",
"start_reference": "80ed234f-1824-4def-b7fb-6883c3fa2b52"
},
{
"title": "Sub Limits",
"level": 3,
"section_number": "3.2.1",
"start_reference": "0c3e0c5a-80ee-49f6-9ca5-52941547d4bd"
},
{
"title": "Business Property",
"level": 3,
"section_number": "3.2.2",
"start_reference": "60aba72b-ca56-4806-97b0-bdf580669943"
},
{
"title": "Exclusions",
"level": 1,
"section_number": "4",
"start_reference": "29529f14-e489-4f2a-bea8-0eef68f431b0"
},
{
"title": "Earth Movement And Flood",
"level": 2,
"section_number": "4.1",
"start_reference": "51c3a6b7-7b78-404e-a1f3-b39be8498294"
},
{
"title": "Ensuing Loss",
"level": 3,
"section_number": "4.1.1",
"start_reference": "908cf899-f5af-40c8-9829-4e09da9083c9"
},
{
"title": "Concurrent Causation",
"level": 3,
"section_number": "4.1.2",
"start_reference": "f48d2861-3d9a-4db6-b2d1-78d22013c3fb"
},
{
"title": "Other Exclusions",
"level": 2,
"section_number": "4.2",
"start_reference": "3336ee6d-9723-4fe1-914f-75109187cc0a"
},
{
"title": "Wear And Tear",
"level": 3,
"section_number": "4.2.1",
"start_reference": "f66ab1c5-d5fe-42ac-bf71-56a3c769a0a6"
},
{
"title": "Vacancy",
"level": 3,
"section_number": "4.2.2",
"start_reference": "75eff73f-3166-45dd-9140-a9182f858e47"
},
{
"title": "Claims And Conditions",
"level": 1,
"section_number": "5",
"start_reference": "35ccfa20-00fb-462e-b375-aaa1b84a1d8f"
},
{
"title": "Duties After A Loss",
"level": 2,
"section_number": "5.1",
"start_reference": "a08d6602-32b0-41e0-ad85-5d951dfc93c0"
},
{
"title": "Notice Deadline",
"level": 3,
"section_number": "5.1.1",
"start_reference": "a68996f1-acd7-403e-bb68-58a7fbe336f0"
},
{
"title": "Emergency Repairs",
"level": 3,
"section_number": "5.1.2",
"start_reference": "edd78fe8-08d6-4f55-a19b-d15a2a05464b"
},
{
"title": "Dispute Resolution",
"level": 2,
"section_number": "5.2",
"start_reference": "bdf63da4-4f34-48f0-82a2-cece1ef666d0"
},
{
"title": "Suit Limitation",
"level": 3,
"section_number": "5.2.1",
"start_reference": "db860fc2-e02e-4efa-8205-553b392b63ca"
},
{
"title": "Loss Payment",
"level": 3,
"section_number": "5.2.2",
"start_reference": "0f7081fd-75c2-4e0f-8355-b4d66a13c20f"
},
{
"title": "Policyholder Acknowledgment",
"level": 1,
"section_number": "6",
"start_reference": "065d0342-0c5b-4fbc-9888-b78ac5e0fefc"
},
{
"title": "Signature Lines",
"level": 1,
"section_number": "7",
"start_reference": "f47e4117-eda0-477d-9a76-482bff511444"
}
],
"table_of_contents_md": "- [Policy Header](#1c4b7184-346d-490f-a7bf-bf2442bcbd7c)\n- [Declarations And Policy Overview](#494d766e-5cf1-48c4-a580-73061b722b90)\n - [Insured Property](#1e6c9100-f461-4bb8-8dcd-8ae0311a97c0)\n - [Mortgage And Lienholder](#95e79ad7-72b5-4c9f-9717-8488996c6795)\n - [Detached Structures](#4c1e087b-aee9-4494-9789-735388a7579c)\n - [Policy Period And Renewal](#de55b7d5-df5c-480e-8b91-0f9f10f5b059)\n - [Cancellation](#7cc48544-b2ec-4e34-b2a1-ae1b3b7d17a6)\n - [Annual Premium](#b8c0ba71-d786-4f89-a07f-3fcfc0edd734)\n- [Schedule Of Coverages And Limits](#3700c26c-dd67-4dda-a712-081becb9ab63)\n - [Coverage A Dwelling](#de789af4-1585-4b3f-9193-4253a9e58f6b)\n - [Ordinance Or Law](#c7eaad71-19ca-4ab4-90cb-e8e9581ededc)\n - [Named Storm Deductible](#d1300436-0131-49d1-a98a-1f46ece31c23)\n - [Coverage C Personal Property](#80ed234f-1824-4def-b7fb-6883c3fa2b52)\n - [Sub Limits](#0c3e0c5a-80ee-49f6-9ca5-52941547d4bd)\n - [Business Property](#60aba72b-ca56-4806-97b0-bdf580669943)\n- [Exclusions](#29529f14-e489-4f2a-bea8-0eef68f431b0)\n - [Earth Movement And Flood](#51c3a6b7-7b78-404e-a1f3-b39be8498294)\n - [Ensuing Loss](#908cf899-f5af-40c8-9829-4e09da9083c9)\n - [Concurrent Causation](#f48d2861-3d9a-4db6-b2d1-78d22013c3fb)\n - [Other Exclusions](#3336ee6d-9723-4fe1-914f-75109187cc0a)\n - [Wear And Tear](#f66ab1c5-d5fe-42ac-bf71-56a3c769a0a6)\n - [Vacancy](#75eff73f-3166-45dd-9140-a9182f858e47)\n- [Claims And Conditions](#35ccfa20-00fb-462e-b375-aaa1b84a1d8f)\n - [Duties After A Loss](#a08d6602-32b0-41e0-ad85-5d951dfc93c0)\n - [Notice Deadline](#a68996f1-acd7-403e-bb68-58a7fbe336f0)\n - [Emergency Repairs](#edd78fe8-08d6-4f55-a19b-d15a2a05464b)\n - [Dispute Resolution](#bdf63da4-4f34-48f0-82a2-cece1ef666d0)\n - [Suit Limitation](#db860fc2-e02e-4efa-8205-553b392b63ca)\n - [Loss Payment](#0f7081fd-75c2-4e0f-8355-b4d66a13c20f)\n- [Policyholder Acknowledgment](#065d0342-0c5b-4fbc-9888-b78ac5e0fefc)\n- [Signature Lines](#f47e4117-eda0-477d-9a76-482bff511444)\n",
"metadata": {
"filename": "insurance_policy-md.md",
"org_id": "org_123",
"duration_ms": 15435,
"credit_usage": 6.0588,
"job_id": "17aa1381fe97495f8b9be76baba9932c",
"version": "section-20260406"
}
}
```
## Response Structure
The response contains the following top-level fields:
| Field | Description |
| -------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------- |
| [`table_of_contents`](#table-of-contents-table_of_contents) | A flat, reading-order array of section entries with hierarchy levels and chunk references. |
| [`table_of_contents_md`](#markdown-table-of-contents-table_of_contents_md) | A Markdown-formatted version of the same TOC with anchor links. |
| [`metadata`](#processing-metadata-metadata) | Processing information including credit usage, duration, filename, job ID, and model version. |
## Table of Contents (`table_of_contents`)
The `table_of_contents` field contains a flat array of all sections and subsections in the order they appear in the document. The flat list structure is optimized for retrieval use cases. You can iterate the list directly without recursion.
Each entry includes:
| Field | Description |
| ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `title` | The generated text for the section heading. |
| `level` | The hierarchy depth. Top-level sections are `1`, subsections are `2`, sub-subsections are `3`, and so on. |
| `section_number` | The hierarchical number for this section (for example, `"1"`, `"1.2"`, `"1.2.3"`). Use this field to reconstruct parent-child relationships: entry `"2.1"` is a child of entry `"2"`. |
| `start_reference` | The ID of the chunk where this section begins. This corresponds to a `chunks[].id` value from the [{parse} API](https://docs.landing.ai/api-reference/tools/ade-parse) response. |
## Markdown Table of Contents (`table_of_contents_md`)
The `table_of_contents_md` field provides a ready-to-use Markdown-formatted TOC. Each entry is an anchor link pointing to the corresponding [reference anchor](./ade-markdown-response#anchor-tags) in the document Markdown. Indentation represents the hierarchy level.
You can prepend this string to the document's full Markdown to enable in-document navigation:
```text theme={null}
- [Introduction](#uuid_a1)
- [Methods](#uuid_b2)
- [Data Collection](#uuid_c3)
- [Survey Design](#uuid_d4)
```
## Processing Metadata (`metadata`)
The `metadata` field provides information about the sectioning process:
| Field | Description |
| -------------- | -------------------------------------------------------------------------------------------------------------- |
| `filename` | The name of the input Markdown file. |
| `org_id` | Organization identifier. |
| `duration_ms` | Processing time in milliseconds. |
| `credit_usage` | Number of credits consumed. |
| `job_id` | Unique job identifier. |
| `version` | Model version used for sectioning. For more information, go to [Section Model Versions](./ade-section-models). |
# Troubleshoot Sectioning
Source: https://docs.landing.ai/ade/ade-section-troubleshoot
Resolve Section API errors using status codes, causes, and recommended fixes.
Use this section to troubleshoot issues encountered when calling the [{section}](https://docs.landing.ai/api-reference/tools/ade-section) API ([https://api.va.landing.ai/v1/ade/section](https://api.va.landing.ai/v1/ade/section)).
## Status Codes
| Status Code | Name | Description | What to Do |
| ----------- | --------------------- | ----------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------- |
| 200 | Success | Sectioning completed successfully. | Continue with normal operations. |
| 400 | Bad Request | Invalid request due to Markdown download failure from URL. | Review error message for specific issue. See [Status 400](#status-400-bad-request). |
| 401 | Unauthorized | Missing or invalid API key. | Check that your `Authorization` header is present and contains a valid [API key](./agentic-api-key). |
| 402 | Payment Required | Your account does not have enough credits to complete processing. | If you have multiple accounts, make sure you're using the correct [API key](./agentic-api-key). Add more credits to your account. |
| 422 | Unprocessable Entity | Input validation failed. | Review your request parameters. See [Status 422](#status-422-unprocessable-entity). |
| 429 | Too Many Requests | Rate limit exceeded. | Wait before retrying. Reduce request frequency and implement exponential backoff. |
| 500 | Internal Server Error | Server error during sectioning. | Retry. If the issue persists, contact [support@landing.ai](mailto:support@landing.ai). See [Status 500](#status-500-internal-server-error). |
| 504 | Gateway Timeout | Request processing exceeded the timeout limit. | Reduce Markdown content size. See [Status 504](#status-504-gateway-timeout). |
## Status 400: Bad Request
This status code indicates a client-side error. Review the specific error message to identify the issue.
### Error: Failed to download document from URL
This error occurs when the API cannot download the Markdown file from the provided `markdown_url`.
**Error message:**
```
Failed to download document from URL: {error_details}
```
**What to do:**
* Verify the URL is accessible and returns valid content.
* Check network connectivity and URL permissions.
* Ensure the URL points to a Markdown file (.md extension).
## Status 422: Unprocessable Entity
This status code indicates input validation failures. Review the error message and adjust your request parameters.
### Error: No markdown provided
This error occurs when your request does not include the `markdown` parameter or `markdown_url` parameter, or when the values are empty.
**Error message:**
```
Must provide either 'markdown' or 'markdown_url'.
```
**What to do:**
Add one of these parameters to your request:
* Use the `markdown` parameter to upload a Markdown file or provide inline Markdown content, OR
* Use the `markdown_url` parameter to provide a URL to a Markdown file.
### Error: Both 'markdown' and 'markdown\_url' provided
This error occurs when your request includes both the `markdown` and `markdown_url` parameters.
**Error message:**
```
Cannot provide both 'markdown' and 'markdown_url'.
```
**What to do:**
Remove one of the parameters. Use either `markdown` (to upload a file or provide inline content) or `markdown_url` (to specify a URL), but not both in the same request.
### Error: Invalid 'markdown\_url' format
This error occurs when the value provided for `markdown_url` is not a valid URL.
**Error message:**
```
Invalid URL format: {url}
```
**What to do:**
* Verify that the URL is correctly formatted (for example, `https://example.com/document.md`).
* Ensure the URL includes a scheme (`https://` or `http://`).
### Error: Multiple markdown files detected
This error occurs when multiple Markdown files are included in the request.
**Error message:**
```
Multiple markdown files detected (X). Please provide only one document file.
```
**What to do:**
Send only one Markdown file per request.
### Error: Unsupported format
This error occurs when you provide a file other than Markdown (.md) to the section endpoint.
**Error message:**
```
Unsupported format: {mime_type} ({filename}). Supported formats: MD
```
**What to do:**
* The section endpoint only accepts Markdown files with a .md extension.
* If you have a PDF, DOCX, or other document format, use the [{parse} API](https://docs.landing.ai/api-reference/tools/ade-parse) to convert your document to Markdown first, then pass the parsed Markdown output to the section endpoint.
* Ensure your file has a .md extension and contains valid UTF-8 encoded Markdown content.
### Error: Missing reference anchors
This error occurs when the Markdown content does not contain reference anchors. The section endpoint requires Markdown output from the [{parse} API](https://docs.landing.ai/api-reference/tools/ade-parse), which includes `` anchors that {section} uses to map sections to chunks.
**Error message:**
```
Invalid markdown: missing reference anchors. Markdown must contain reference delimiters. Use the parse tool to generate properly formatted markdown.
```
**What to do:**
* Do not pass plain Markdown or documents you have formatted manually. Use the Markdown output from the [{parse} API](https://docs.landing.ai/api-reference/tools/ade-parse) as input.
* Confirm the Markdown content contains `` anchor tags before sending it to the section endpoint.
## Status 500: Internal Server Error
This error indicates an unexpected server error occurred during sectioning.
**What to do:**
* Retry the request.
* If the error persists, contact [support@landing.ai](mailto:support@landing.ai).
## Status 504: Gateway Timeout
This error occurs when the sectioning process exceeds the timeout limit.
**What to do:**
* Reduce the size of your Markdown document.
* If the error persists, contact [support@landing.ai](mailto:support@landing.ai).
## Best Practices for Avoiding Errors
* **Use parsed Markdown**: Always pass Markdown content generated by the [{parse} API](./ade-separate-apis). The section endpoint requires structured Markdown with [reference anchors](./ade-markdown-response#anchor-tags) (``).
* **Use guidelines sparingly**: The `guidelines` parameter is optional. Start without guidelines to see the default behavior before customizing the hierarchy.
## When Are Credits Consumed?
Credits are consumed only when the {section} API returns a 200 status code. All other responses, including errors, do not consume credits.
# Security and Privacy
Source: https://docs.landing.ai/ade/ade-security
Review ADE security practices, zero data retention, and compliance certifications (GDPR, SOC 2, HIPAA).
{companyName} is committed to protecting your data and maintaining the highest security standards for {ade}.
Refer to the resources below to learn about our security practices, zero data retention (ZDR) option, compliance certifications (GDPR, SOC 2 Type II, HIPAA), and more.
| Description | Amount |
|---|---|
| Strategic planning consultation (40 hours @ $150/hr) | $6,000.00 |
| Market analysis report preparation | $3,500.00 |
| Implementation roadmap development | $2,250.00 |
| Executive presentation materials | $1,500.00 |
| Follow-up consultation sessions (15 hours @ $150/hr) | $2,250.00 |
| Travel expenses (reimbursable) | $250.00 |
| TOTAL | $15,750.00 |
| Description | Amount |
|---|---|
| Strategic planning consultation (40 hours @ $150/hr) | $6,000.00 |
| Market analysis report preparation | $3,500.00 |
| Implementation roadmap development | $2,250.00 |
| Executive presentation materials | $1,500.00 |
| Follow-up consultation sessions (15 hours @ $150/hr) | $2,250.00 |
| Travel expenses (reimbursable) | $250.00 |
| TOTAL | $15,750.00 |
| Description | Amount |
|---|---|
| Strategic planning consultation (40 hours @ $150/hr) | $6,000.00 |
| Market analysis report preparation | $3,500.00 |
| Implementation roadmap development | $2,250.00 |
| Executive presentation materials | $1,500.00 |
| Follow-up consultation sessions (15 hours @ $150/hr) | $2,250.00 |
| Travel expenses (reimbursable) | $250.00 |
| TOTAL | $15,750.00 |
}
href="./ade-typescript#parse-getting-started"
>
Run Parse with our TypeScript library.
# Extract Fields from Parsed Output
Source: https://docs.landing.ai/ade/ade-sf-extract-cloud
Extract key-value pairs from parsed Markdown in Snowflake with the api.extract procedure.
To extract fields from documents, first parse a document with the [api.parse](./ade-sf-parse-cloud) procedure. Then use the `api.extract` procedure to extract key-value pairs from the Markdown returned by the `api.parse` procedure.
## Prerequisites
Before you can extract fields, you must parse a document. For more information, go to [Parse Documents](./ade-sf-parse-cloud).
## Set Up the Session
Before running a parse or extract procedure, run the command below to set your session to use the Agentic Document Extraction application and procedures.
Replace this placeholder with the [name of your instance](./ade-sf-app-name) of Agentic Document Extraction: `APP_NAME`.
```sql theme={null}
USE "APP_NAME";
```
## Extract
To extract fields from parsed documents, use the `api.extract` procedure.
The `api.extract` procedure sends the Markdown and a JSON schema to the {companyName}-hosted {ade} service, and saves the extracted data to an output table (defaults to `db.extract_output`).
The `api.extract` procedure runs the [ADE Extract API](https://docs.landing.ai/api-reference/tools/ade-extract).
## Required Inputs
The `api.extract` procedure requires:
* **Markdown content** from `api.parse`
* **JSON schema** that defines which fields to extract and their expected format. For more information, go to [Create a JSON Schema for Field Extraction](https://docs.landing.ai/ade/ade-extract-schema-json).
The procedure accepts these inputs using different methods. For details, go to [Methods for Passing the Markdown](#methods-for-passing-the-markdown) and [Methods for Passing the JSON Schema](#methods-for-passing-the-json-schema).
## Optional Parameters
The `api.extract` procedure supports these optional parameters:
* **`doc_id`**: Document ID from parse output; provide this to link the extraction results to the original parsed document
* **`output_table`**: Specify a custom output table name instead of the default `extract_output`
* **`model`**: Specify the model version to use for extraction. For full details on extraction models, go to [Extraction Model Versions](./ade-extract-models).
## Extract Return Object
The `api.extract` procedure returns an OBJECT with the following fields:
* **`message`**: Success or error message
* **`output_table`**: Name of the table where results were saved (such as "db.extract\_output"). For the table schema, go to [Extract Output Table Schema](#extract-output-table-schema).
* **`doc_id`**: Document ID from the parse output (for linking results)
* **`extraction_id`**: Unique extraction job identifier
* **`status_code`**: HTTP status code for the request
This return object is useful when chaining parse and extract operations together, and for tracking extraction jobs and debugging.
## Extract Output Table Schema
The extraction results are stored in the table specified by `output_table` in the [return object](#extract-return-object). By default, this is `db.extract_output`.
The table has the following schema:
* **DOC\_ID**: Document ID from parse output; you can use this to link extraction results to the original parsed document in `parse_output`
* **EXTRACTION\_JOB\_ID**: Unique extraction job identifier
* **SOURCE\_MARKDOWN**: First 10,000 characters of input Markdown (for reference)
* **MODEL\_VERSION**: Model version used for extraction
* **EXTRACTED\_AT**: Timestamp when extraction completed
* **STATUS\_CODE**: HTTP status code (200 for success)
* **EXTRACTION**: VARIANT containing the extracted data matching your schema
* **EXTRACTION\_METADATA**: VARIANT with extraction metadata
* **METADATA**: VARIANT with job metadata
* **ERROR**: VARIANT containing error information (if extraction failed)
Example query to access nested extraction data:
```sql theme={null}
SELECT
DOC_ID,
EXTRACTION:field_name::STRING AS field_value,
EXTRACTION:nested:field::NUMBER AS nested_value
FROM db.extract_output
WHERE STATUS_CODE = 200;
```
## Methods for Passing the Markdown
You can pass the Markdown content to `api.extract` using two methods:
* [Pass the Parse Result Object Directly](#pass-the-parse-result-object-directly)
* [Pass Markdown Explicitly](#pass-markdown-explicitly)
### Pass the Parse Result Object Directly
You can pass the result object from `api.parse` directly to `api.extract`. This is the most streamlined approach for chaining parse and extract operations.
You can combine this with any method for passing the JSON schema.
Use this method when you:
* Want to chain parse and extract in a single script block
* Need to avoid querying the parse output table
* Want to automatically link parse and extract results
**Procedure Signature**
```sql theme={null}
PROCEDURE api.extract(
parse_result OBJECT,
schema STRING,
output_table STRING DEFAULT NULL,
model STRING DEFAULT NULL
)
```
#### Example
The procedure automatically:
* Extracts the `doc_id` from the parse result
* Retrieves the Markdown from the parse output table
* Links the extraction result with the parse result via `doc_id`
```sql theme={null}
DECLARE
parse_result OBJECT;
extract_result OBJECT;
BEGIN
-- Parse the document
CALL api.parse(
'https://ade.landing.ai/pdfs/invoice_1.pdf'
) INTO :parse_result;
-- Extract using the parse result directly
CALL api.extract(
:parse_result,
'https://ade.landing.ai/pdfs/InvoiceExtractionSchema.json'
) INTO :extract_result;
RETURN extract_result;
END;
```
### Pass Markdown Explicitly
You can query the Markdown directly from the `parse_output` table and pass it as a parameter. Use this method when you've already parsed documents and want to extract from them separately.
You can combine this with any method for passing the JSON schema.
**Procedure Signature**
```sql theme={null}
PROCEDURE api.extract(
markdown STRING,
schema STRING,
doc_id STRING DEFAULT NULL,
output_table STRING DEFAULT NULL,
model STRING DEFAULT NULL
)
```
#### Example
```sql theme={null}
CALL api.extract(
markdown => (SELECT MARKDOWN FROM db.parse_output WHERE doc_id = 'your_doc_id'),
schema => 'https://ade.landing.ai/pdfs/InvoiceExtractionSchema.json',
doc_id => 'your_doc_id'
);
SELECT * FROM db.extract_output;
```
## Methods for Passing the JSON Schema
You can pass the JSON schema to `api.extract` using multiple methods:
* [Include the JSON Schema Inline](#include-the-json-schema-inline)
* [Use a Staged Schema File](#use-a-staged-schema-file)
* [Pass a URL to an Externally Hosted JSON Schema (Demo Files Only)](#pass-a-url-to-an-externally-hosted-json-schema-demo-files-only)
### Include the JSON Schema Inline
Provide the schema as an inline JSON string.
You can combine this with any method for passing the Markdown.
Use this method when you:
* Have a simple schema specific to one query
* Want to keep all logic contained in a single script
* Prototype or test schema definitions
#### Example
```sql theme={null}
CALL api.extract(
markdown => (SELECT MARKDOWN FROM db.parse_output WHERE doc_id = 'your_doc_id'),
schema => '{
"type": "object",
"properties": {
"title": {
"type": "string",
"title": "Document Title",
"description": "The main title of the document."
},
"content": {
"type": "string",
"title": "Main Content",
"description": "The primary content or text from the document."
}
},
"required": ["title", "content"]
}',
doc_id => 'your_doc_id'
);
SELECT * FROM db.extract_output;
```
### Use a Staged Schema File
Use `build_scoped_file_url()` to reference a schema file in a Snowflake stage.
You can combine this with any method for passing the Markdown.
Use this method when you:
* Store your schema files in Snowflake stages
* Want to version control schemas alongside your data
* Need to reference schemas from internal stage locations
#### Example
```sql theme={null}
CALL api.extract(
markdown => (SELECT MARKDOWN FROM db.parse_output WHERE doc_id = 'your_doc_id'),
schema => build_scoped_file_url('@your_db.your_schema.your_stage', '/simple_schema.json'),
doc_id => 'your_doc_id'
);
SELECT * FROM db.extract_output;
```
### Pass a URL to an Externally Hosted JSON Schema (Demo Files Only)
This method only works with schema files hosted at `https://ade.landing.ai`, which were granted access during app installation. To use schemas from other URLs or locations, use another method for passing the JSON schema.
Provide the schema as a URL parameter.
You can combine this with any method for passing the Markdown.
#### Example
```sql theme={null}
CALL api.extract(
markdown => (SELECT MARKDOWN FROM db.parse_output WHERE doc_id = 'your_doc_id'),
schema => 'https://ade.landing.ai/pdfs/InvoiceExtractionSchema.json',
doc_id => 'your_doc_id'
);
SELECT * FROM db.extract_output;
```
## Batch Processing with EXECUTE IMMEDIATE
For processing multiple documents at once, you can use Snowflake's scripting capabilities to loop through parsed documents and extract data from each.
This approach is useful when you have many documents already parsed and want to extract structured data from all of them in one operation.
```sql theme={null}
EXECUTE IMMEDIATE $$
DECLARE
res RESULTSET DEFAULT (
SELECT MARKDOWN, DOC_ID FROM db.parse_output WHERE STATUS_CODE = 200
);
cur CURSOR FOR res;
BEGIN
FOR record IN cur DO
LET markdown STRING := record.MARKDOWN;
LET doc_id STRING := record.DOC_ID;
CALL api.extract(
markdown => :markdown,
schema => 'https://ade.landing.ai/pdfs/InvoiceExtractionSchema.json',
doc_id => :doc_id,
output_table => 'batch_extract_results'
);
END FOR;
RETURN 'Processing complete';
END;
$$;
-- View all extracted results
SELECT * FROM db.batch_extract_results;
```
## Sample Scenarios
This section provides examples of how to run the `api.extract` procedure in different scenarios.
* [Parse and Extract Data from Files at Publicly Accessible URLs](#parse-and-extract-data-from-files-at-publicly-accessible-urls)
* [Parse and Extract Data from a Staged File](#parse-and-extract-data-from-a-staged-file)
* [Parse and Extract Data from All Files in a Stage](#parse-and-extract-data-from-all-files-in-a-stage)
### Parse and Extract Data from Files at Publicly Accessible URLs
Run the command below to parse multiple files at publicly accessible URLs, and then extract data from the parsed output. We've provided the sample files to help you get started.
This example uses an externally hosted JSON schema at `https://ade.landing.ai`, which is only available for demo purposes. For production use, use an [inline schema](#include-the-json-schema-inline) or a [staged schema file](#use-a-staged-schema-file).
Replace this placeholder with your information: `APP_NAME`.
```sql theme={null}
USE "APP_NAME";
-- Step 1: Parse invoice files
CALL api.parse('https://ade.landing.ai/pdfs/invoice_1.pdf');
CALL api.parse('https://ade.landing.ai/pdfs/invoice_2.pdf');
CALL api.parse('https://ade.landing.ai/pdfs/invoice_3.pdf');
CALL api.parse('https://ade.landing.ai/pdfs/invoice_4.pdf');
-- Step 2: Extract structured data from each parsed invoice
CALL api.extract(
markdown => (SELECT MARKDOWN FROM db.parse_output WHERE SOURCE_URL = 'https://ade.landing.ai/pdfs/invoice_1.pdf'),
schema => 'https://ade.landing.ai/pdfs/InvoiceExtractionSchema.json',
doc_id => (SELECT DOC_ID FROM db.parse_output WHERE SOURCE_URL = 'https://ade.landing.ai/pdfs/invoice_1.pdf')
);
CALL api.extract(
markdown => (SELECT MARKDOWN FROM db.parse_output WHERE SOURCE_URL = 'https://ade.landing.ai/pdfs/invoice_2.pdf'),
schema => 'https://ade.landing.ai/pdfs/InvoiceExtractionSchema.json',
doc_id => (SELECT DOC_ID FROM db.parse_output WHERE SOURCE_URL = 'https://ade.landing.ai/pdfs/invoice_2.pdf')
);
CALL api.extract(
markdown => (SELECT MARKDOWN FROM db.parse_output WHERE SOURCE_URL = 'https://ade.landing.ai/pdfs/invoice_3.pdf'),
schema => 'https://ade.landing.ai/pdfs/InvoiceExtractionSchema.json',
doc_id => (SELECT DOC_ID FROM db.parse_output WHERE SOURCE_URL = 'https://ade.landing.ai/pdfs/invoice_3.pdf')
);
CALL api.extract(
markdown => (SELECT MARKDOWN FROM db.parse_output WHERE SOURCE_URL = 'https://ade.landing.ai/pdfs/invoice_4.pdf'),
schema => 'https://ade.landing.ai/pdfs/InvoiceExtractionSchema.json',
doc_id => (SELECT DOC_ID FROM db.parse_output WHERE SOURCE_URL = 'https://ade.landing.ai/pdfs/invoice_4.pdf')
);
-- Step 3: View extracted data
SELECT
SPLIT_PART(p.SOURCE_URL, '/', -1) AS filename,
e.EXTRACTION:invoice_info:invoice_date::STRING AS invoice_date,
e.EXTRACTION:company_info:supplier_name::STRING AS supplier_name,
e.EXTRACTION:line_items[0]:description::STRING AS first_line_item_description,
e.EXTRACTION:totals_summary:total_due::FLOAT AS total_due
FROM db.extract_output e
JOIN db.parse_output p ON e.DOC_ID = p.DOC_ID
ORDER BY filename;
```
### Parse and Extract Data from a Staged File
4. Fill out and submit the request form.
5. The LandingAI team will review the request and contact you with more information.
## Install the App
After you have [requested the app](#request-the-app) and have been granted access to it, follow the instructions below to install it in Snowflake:
1. Go to **Snowsight** > **Catalog** > **Apps** > **Recently Shared with You**.
2. Locate and click the **Agentic Document Extraction - App** listing.
3. Click **Get**.
4. If you want to change the name of the application, click **Options** and enter a name in **Application Name**.
5. Click **Get**.
6. A pop-up indicates that the installation process has begun.
7. After this process is complete, click **Configure** on the pop-up. (You will also get an email notifying you that you can access the app.)
8. The app opens.
9. In the pop-up that displays, grant egress access by clicking **Connect**.
**The app requires this access to function.** Egress access allows the app to connect to the {ade} service over the internet.
5. The app saves the API key, generates procedures for using {ade}, and unlocks usage instructions on the Home page. This might take a few minutes.
## Next Steps
After you install the app and enter your API key in the app, you are ready to [parse documents](./ade-sf-parse-cloud) and [extract data](./ade-sf-extract-cloud).
## App Updates
The app is automatically updated when a new version is released. Users cannot manually update the app.
## Troubleshooting Tips
### Error: Connect to allow the app to access the LandingAI Agentic Document Extraction service
During the installation process, you might get this error:
> Connect to allow the app to access the LandingAI Agentic Document Extraction service
>
> This app requires Internet access to connect to the LandingAI Agentic Document Extraction service. The app will not function without this access.
>
> There was an error initializing the configuration flow. Close the dialog and try again later.
If you get this error message, try selecting a different warehouse from the drop-down menu in the top right corner.
# Overview
Source: https://docs.landing.ai/ade/ade-sf-overview
Learn how to parse and extract data from documents in Snowflake with the ADE Marketplace app.
{ade} (ADE) parses and extracts structured data from unstructured documents like PDFs and images. It identifies elements such as text, tables, and form fields, and returns them as Markdown and hierarchical JSON with exact page and coordinate references.
{ade} is available as an application in the Snowflake Marketplace. Snowflake customers can use the {ade} app to parse and extract data from:
* documents staged in Snowflake.
* documents available at publicly accessible URLs.
## How ADE on Snowflake Works
When you run the [parsing](./ade-sf-parse-cloud) or [extraction](./ade-sf-extract-cloud) procedure, the relevant files are sent to the {ade} service, which is hosted by {companyName}. The results display directly in Snowsight.
## Billing
When you use the {ade} app, documents are processed using your {ade} account that is hosted by LandingAI. Billing for processing documents (like running parsing and field extraction) is managed by LandingAI.
## Zero Data Retention
The ADE on Snowflake application cannot be used when Zero Data Retention (ZDR) is enabled in your organization. To use this application, turn off ZDR or use an organization that does not have ZDR enabled.
## ADE on Snowflake Checklist
To install and start using the {ade} app on Snowflake, complete the following steps:
**In {ade}:**
1. If you haven't already, [create an {ade} account](https://ade.landing.ai/).
2. [Get your API key](https://ade.landing.ai/my/settings/api-key).
**In Snowsight:**
1. [Install the app](./ade-sf-install). Ensure that you grant the app access to external endpoints during the setup process.
2. [Enter your API key in the app](./ade-sf-install#enter-api-key).
3. If you want to parse documents staged in Snowflake, [grant the app access to the stages that have the files you want to parse](./ade-sf-grant-access-to-stages).
4. [Parse your files](./ade-sf-parse-cloud).
5. (Optional) [Run field extraction on the parsed output](./ade-sf-extract-cloud).
# Parse Documents
Source: https://docs.landing.ai/ade/ade-sf-parse-cloud
Parse documents staged in Snowflake or at public URLs with the api.parse procedure.
## Prerequisites
**In {ade}:**
1. If you haven't already, [create an {ade} account](https://ade.landing.ai/).
2. [Get your API key](https://ade.landing.ai/my/settings/api-key).
**In Snowsight:**
1. [Install the app](./ade-sf-install).
2. [Enter your API key in the app](./ade-sf-install#enter-api-key).
3. If you want to parse documents staged in Snowflake, [grant the app access to the stages that have the files you want to parse](./ade-sf-grant-access-to-stages).
## Set Up the Session
Before running a parse or extract procedure, run the command below to set your session to use the Agentic Document Extraction application and procedures.
Replace this placeholder with the [name of your instance](./ade-sf-app-name) of Agentic Document Extraction: `APP_NAME`.
```sql theme={null}
USE "APP_NAME";
```
## Parse
To parse documents, use the `api.parse` procedure.
The `api.parse` procedure sends a document from a Snowflake stage or publicly accessible URL to the {companyName}-hosted {ade} service, and saves the parsed content to an output table (defaults to `db.parse_output`).
The `api.parse` procedure runs the [ADE Parse Jobs API](./ade-parse-async).
## Optional Parameters
The `api.parse` procedure supports these optional parameters:
* **`model`**: Specify the model version to use for parsing. For full details on parsing models, go to [Document Pre-Trained Transformers (Parsing Models)](./ade-parse-models).
* **`split`**: Specify how to [split](./ade-separate-apis#set-up-splits-for-parsing) the document.
* **`output_table`**: Specify a [custom output table name](#specify-a-custom-output-table) instead of the default `parse_output`.
## Specify a Custom Output Table
You can specify a custom table name for storing parse results instead of using the default `parse_output`:
```sql theme={null}
CALL api.parse(
file_path => 'https://ade.landing.ai/pdfs/invoice_1.pdf',
output_table => 'my_custom_results'
);
SELECT * FROM db.my_custom_results;
```
The procedure automatically creates the table (if it doesn't exist) with the following schema:
* DOC\_ID
* SOURCE\_URL
* FILENAME
* PAGE\_COUNT
* MODEL\_VERSION
* PARSED\_AT
* STATUS\_CODE
* MARKDOWN
* CHUNKS
* SPLITS
* GROUNDING
* METADATA
* ERROR
### Example with Optional Parameters
```sql theme={null}
CALL api.parse(
file_path => 'https://ade.landing.ai/pdfs/invoice_1.pdf',
model => 'dpt-2-mini-latest',
output_table => 'invoice_results'
);
SELECT * FROM db.invoice_results;
```
## Parse Return Object
The `api.parse` procedure returns an OBJECT with the following fields:
* **`message`**: Success or error message
* **`output_table`**: Name of the table where results were saved (such as "db.parse\_output")
* **`doc_id`**: Unique document identifier for the parsed document
* **`status_code`**: HTTP status code for the request
This return object is useful when chaining parse and extract operations together. You can capture the result into a variable and pass it to `api.extract`.
Example of capturing the return object:
```sql theme={null}
DECLARE
result OBJECT;
BEGIN
CALL api.parse(
'https://ade.landing.ai/pdfs/invoice_1.pdf'
) INTO :result;
RETURN result;
END;
```
## Use build\_scoped\_file\_url for Staged Files
You can use the `build_scoped_file_url()` function to reference files in Snowflake stages.
```sql theme={null}
CALL api.parse(
file_path => build_scoped_file_url('@your_db.your_schema.your_stage', '/sample_image.png')
);
SELECT * FROM db.parse_output;
```
## Sample Scenarios
This section provides examples of how to run the `api.parse` procedure in different scenarios.
* [Parse a File at a Publicly Accessible URL](#parse-a-file-at-a-publicly-accessible-url)
* [Parse a Staged File](#parse-a-staged-file)
* [Parse Multiple Staged Files](#parse-multiple-staged-files)
### Parse a File at a Publicly Accessible URL
Run the command below to parse a single file at a publicly accessible URL. We've provided a sample file to help you get started.
Replace this placeholder with your information: `APP_NAME`.
```sql theme={null}
USE "APP_NAME";
CALL api.parse(
'https://ade.landing.ai/pdfs/invoice_1.pdf'
);
SELECT * FROM db.parse_output;
```
### Parse a Staged File
}
href="./ade-typescript#split-getting-started"
>
Use Split with our TypeScript library.
3. If you're on a subscription plan, you can [create an API key](#create-api-keys-subscription-plans-only) if needed.
4. Click the **Copy** icon to copy your API key.
## Personal Plans (Explore Plans) Have One API Key
The personal plan (the "Explore" plan) only has one API key. This API key cannot be deleted or revoked.
The Explore plan type is designed for testing, prototyping, and hobby use. If you'd like to deploy to production and need more granular API key management, like the ability to create and revoke API keys, [upgrade](./ade-pricing) to a subscription plan.
3. If you're not currently in the correct organization, select your organization from the drop-down menu.
4. Click **Create Key**.
5. Enter a brief, descriptive name for the API key in the key name field.
6. Click **Create API Key**.
7. Click the **Copy** icon to copy the API key.
8. Click **Done** to close the pop-up.
## Revoke API Keys
If you are on the Team or Enterprise plan, you can revoke API keys. A user with the Admin role can revoke any API key. A user with the Developer role can revoke only API keys they have created.
To revoke an API key:
1. Log in to [https://ade.landing.ai/](https://ade.landing.ai/).
2. Go to the [API Key](https://ade.landing.ai/settings/api-key) page (to navigate there manually, click your profile icon at the bottom left corner of the page and click **API Key**).
3. If you're not currently in the correct organization, select your organization from the drop-down menu.
4. Locate the row for the API key. (You may need to search to narrow down the list of API keys.)
5. Click the **Settings** button (ellipses) and select **Revoke API Key**. Follow the on-screen prompts to complete the process.
## Set Your API Key When Using the Library
There are a few methods you can use to set the API key when using the [{adePythonLibrary}](https://github.com/landing-ai/ade-python) library, including:
* [Method 1: Set the API Key as an Environment Variable](#method-1-set-the-api-key-as-an-environment-variable)
* [Method 2: Store API key in a .env file](#method-2-store-api-key-in-a-env-file)
* [Method 3: Set API Key in Notebooks](#method-3-set-api-key-in-notebooks)
### Method 1: Set the API Key as an Environment Variable
Store your API key as an environment variable in your system.
1. Set the API key as an environment variable:
**Linux/macOS:**
Add the API key to your shell configuration file (`.zshrc` or `.bashrc`). After updating the file, run `source ~/.zshrc` to apply the changes.
```bash theme={null}
export VISION_AGENT_API_KEY=your_api_key_here
```
**Windows (Command Prompt):**
```cmd theme={null}
set VISION_AGENT_API_KEY=your_api_key_here
```
**Windows (PowerShell):**
```powershell theme={null}
$env:VISION_AGENT_API_KEY="your_api_key_here"
```
2. Initialize the client in your Python code. The library will automatically detect the `VISION_AGENT_API_KEY` environment variable, so no additional code is required. However, you can explicitly pass the API key to make your code clearer and easier to debug:
```python theme={null}
import os
from landingai_ade import LandingAIADE
client = LandingAIADE(
apikey=os.environ.get("VISION_AGENT_API_KEY"),
)
```
### Method 2: Store API Key in a .env File
Use a `.env` file to store your API key.
1. Install the `python-dotenv` package:
```bash theme={null}
pip install python-dotenv
```
2. Create a `.env` file in your project root and store your API key in it:
```
VISION_AGENT_API_KEY=your_api_key_here
```
3. Add code to load your API key from the `.env` file:
```python theme={null}
from dotenv import load_dotenv
import os
from landingai_ade import LandingAIADE
# Load environment variables from .env file
load_dotenv()
# Initialize the client (it will automatically use VISION_AGENT_API_KEY from environment)
client = LandingAIADE()
```
### Method 3: Set API Key in Notebooks
If using IPython-based notebooks (such as Jupyter Notebook, JupyterLab, Google Colab, or Kaggle), you can set the API key for the current session using the `%env` magic command.
This method sets the API key only for the current session. The key is cleared when you restart the notebook kernel. Additionally, the API key will be visible in the notebook file if you share or commit it to version control.
1. In a notebook cell, set the environment variable using the `%env` magic command:
```python theme={null}
%env VISION_AGENT_API_KEY=your_api_key_here
```
2. Initialize the client in a subsequent cell:
```python theme={null}
from landingai_ade import LandingAIADE
# Initialize the client (it will automatically use VISION_AGENT_API_KEY from environment)
client = LandingAIADE()
```
### API Key Precedence
If the API key is set using multiple methods, the library uses the following order of precedence (highest to lowest):
* Environment variable
* .env file
The first available key found in this order will be used. All other values will be ignored.
## Set Your API Key When Calling the API Directly
Include the API key in request headers. To see code samples, go to [API](https://docs.landing.ai/api-reference/tools/ade-parse).
## Security Best Practices
* If using a `.env` file: Add `.env` to `.gitignore` to prevent accidentally committing the API key to version control.
* Do not pass the API key directly to the code, because others could see and use your API key.
## Troubleshoot API Key Issues
Use the troubleshooting tips in this section to resolve issues related to API keys.
### .env File Issues
If you set the API key in the `.env` file but the environment variable is not loading, try these possible solutions to resolve this issue:
* Check file location: The `.env` file should be in the same directory where you run your script.
* Verify file format: No spaces around the equals sign:
```
# Correct
VISION_AGENT_API_KEY=your_api_key_here
# Incorrect
VISION_AGENT_API_KEY = your_api_key_here
```
### Environment Variable Issues
If you set the API key as an environmental variable and are encountering issues, try these possible solutions to resolve this issue:
* Restart your application after setting environment variables.
* Check variable name: Must be exactly `VISION_AGENT_API_KEY`.
* Check if the environment variable is correct:
```bash theme={null}
echo $VISION_AGENT_API_KEY # Linux/macOS
echo %VISION_AGENT_API_KEY% # Windows CMD
$Env:VISION_AGENT_API_KEY # Windows PowerShell
```
* Ensure that you apply the changes after setting, changing, or removing the environment variable:
* **Linux/macOS**: Restart your terminal. If you added the variable to your `.zshrc` file, run source `~/.zshrc`.
* **Windows**: Close and reopen Command Prompt/PowerShell, or restart your IDE.
### Wrong API Key Used
If you have multiple accounts and {ade} is using the wrong API key, the API key might be set using multiple methods and one is taking precedence over the other.
To resolve this issue, check which API keys are set using each method.
**Example Scenario:**
1. You create an account with your *personal* email address and set the API as an environment variable.
2. A few weeks later you create an account with your *work* email address and store the API key for that account in a .env file.
3. When you run {ade}, it uses the first API key, because the environment variable takes precedence over the .env file.
**Solution**: Checking each way the API key can be set and then removing the unwanted API keys will ensure that the correct API key is used.
### Error: Missing Authorization Header
This error occurs if the authorization header is not included when calling the API directly. To fix this, include the API header:
```python theme={null}
headers = {"Authorization": f"Bearer {api_key}"}
```
### Error: User Not Found, Please Check Your API Key
This error occurs when the API key is entered incorrectly.
To resolve this issue:
* Verify the API key: Copy your key directly from the [dashboard](https://ade.landing.ai/settings/api-key).
* Restart your environment: Close and reopen your terminal, IDE, or Jupyter notebook.
# Build with AI Agents
Source: https://docs.landing.ai/ade/build-with-ai-agents
Use ADE with AI agents and LLMs: install ADE skills, connect the MCP server, and access docs as llms.txt and Markdown.
Use {ade} (ADE) with AI agents, LLMs, and coding tools like Claude Code.
If you're an AI agent or LLM, install the [ADE skills](#skills) or connect the [MCP server](#mcp-server-for-documentation) for compact, token-efficient access to ADE.
This page covers:
* [ADE skills for AI coding agents](#skills)
* [A documentation MCP server](#mcp-server-for-documentation)
* [Shortcuts for connecting AI tools to docs pages](#shortcuts-for-connecting-ai-tools)
* [Documentation pages in llms.txt format](#access-documentation-pages-as-llms-txt)
* [Documentation pages as Markdown](#access-documentation-as-markdown)
## Skills
Skills are instruction files that teach AI coding agents how to use {ade} effectively. {companyName} offers ADE skills in the [ADE Document Processing Skills](https://github.com/landing-ai/ade-document-processing-skills) repository.
### Install Skills via the Claude Code Plugin
Claude Code users can install the ADE skills as a plugin, which is an extension that bundles skills, agents, and other capabilities. Run the following commands in Claude Code to install the ADE skills plugin:
```bash theme={null}
/plugin marketplace add landing-ai/ade-document-processing-skills
/plugin install ade-document-processing@ade-document-processing-skills
```
After installation, run the following command to activate the plugin:
```bash theme={null}
/reload-plugins
```
### Install Skills with Other Methods
Different AI coding tools have different methods for installing skills. Check the documentation for your AI coding tool to learn how to install skill files.
Depending on the installation method, you may need to download or clone the [ADE Document Processing Skills](https://github.com/landing-ai/ade-document-processing-skills) repository and copy the skill files to the relevant directories.
## MCP Server for Documentation
The ADE MCP server connects AI tools to {ade} documentation and [ADE skills](#skills). When connected, your agent can search the documentation and access all ADE skills without additional setup.
**MCP server URL:** `https://docs.landing.ai/mcp`
| Option | What it does |
| ------------------------ | ------------------------------------------------------- |
| Copy page | Copies the page as Markdown |
| View as Markdown | Opens the page in Markdown format |
| Open in ChatGPT | Creates a ChatGPT conversation with the page as context |
| Open in Claude | Creates a Claude conversation with the page as context |
| Connect to Cursor | Installs the ADE docs MCP server in Cursor |
| Connect to VS Code | Installs the ADE docs MCP server in VS Code |
| Copy MCP server URL | Copies `https://docs.landing.ai/mcp` to your clipboard |
| Copy MCP install command | Copies the MCP server install command to your clipboard |
## Access Documentation Pages as llms.txt
Access an index of all {ade} documentation pages at [llms.txt](https://docs.landing.ai/llms.txt). For a combined file of all documentation content, use [llms-full.txt](https://docs.landing.ai/llms-full.txt).
These files follow the [llms.txt standard](https://llmstxt.org) for AI tool consumption.
## Access Documentation as Markdown
View any page as plain Markdown by adding `.md` to the page URL.
# ADE Build Extract Schema
Source: https://docs.landing.ai/api-reference/tools/ade-build-extract-schema
Generate a JSON schema from Markdown using AI.
This endpoint analyzes Markdown
content and generates a JSON schema suitable for use with the extract endpoint.
It can also refine an existing schema based on new documents or iterate on a schema
based on prompt instructions.
For EU users, use this endpoint:
`https://api.va.eu-west-1.landing.ai/v1/ade/extract/build-schema`.
## OpenAPI
````yaml /ade/va_openapi_ade2.json post /v1/ade/extract/build-schema
openapi: 3.1.0
info:
title: >-
LandingAI Agentic Document Extraction (ADE) API v1: Parse, Extract,
Classify, Split, Section
version: 0.1.0
description: >-
Convert documents such as PDFs, images, and Office files into structured
data with LandingAI's Agentic Document Extraction (ADE) v1 endpoints.
Includes Parse (documents to Markdown and structured chunks with grounding),
Extract (schema-based field extraction), Classify (page-level
classification), Split (separate multi-document files), and Section
(hierarchical table of contents), plus asynchronous jobs for parsing and
extraction. Documentation: https://docs.landing.ai
servers:
- url: https://api.va.landing.ai
description: Production vision tools API
security: []
paths:
/v1/ade/extract/build-schema:
post:
tags:
- Tools
summary: ADE Build Extract Schema
description: |-
Generate a JSON schema from Markdown using AI.
This endpoint analyzes Markdown
content and generates a JSON schema suitable for use with the extract endpoint.
It can also refine an existing schema based on new documents or iterate on a schema
based on prompt instructions.
For EU users, use this endpoint:
`https://api.va.eu-west-1.landing.ai/v1/ade/extract/build-schema`.
operationId: tool_ade_build_schema_v1_ade_extract_build_schema_post
requestBody:
content:
multipart/form-data:
schema:
$ref: '#/components/schemas/BuildSchemaRequest'
required: true
responses:
'200':
description: Successful Response
content:
application/json:
schema:
$ref: '#/components/schemas/BuildSchemaResponse'
'422':
description: Validation Error
content:
application/json:
schema:
$ref: '#/components/schemas/HTTPValidationError'
security:
- Basic Auth: []
x-codeSamples:
- lang: cURL
label: cURL
source: >-
curl -X POST 'https://api.va.landing.ai/v1/ade/extract/build-schema'
\
-H 'Authorization: Bearer YOUR_API_KEY' \
-F 'markdowns=@markdown.md' \
-F 'model=extract-latest' \
-F 'prompt=Extract invoice fields including vendor, date, and total amount'
- lang: Python
label: Python
source: >-
import requests
headers = {
'Authorization': 'Bearer YOUR_API_KEY'
}
url = 'https://api.va.landing.ai/v1/ade/extract/build-schema'
# Prepare files and data
files = [('markdowns', open('markdown.md', 'rb'))]
data = {
'model': 'extract-latest',
'prompt': 'Extract invoice fields including vendor, date, and total amount'
}
response = requests.post(url, files=files, data=data,
headers=headers)
print(response.json())
- lang: JavaScript
label: Node.js
source: >-
const axios = require('axios');
const FormData = require('form-data');
const fs = require('fs');
const form = new FormData();
form.append('markdowns', fs.createReadStream('markdown.md'));
form.append('model', 'extract-latest');
form.append('prompt', 'Extract invoice fields including vendor,
date, and total amount');
axios.post('https://api.va.landing.ai/v1/ade/extract/build-schema',
form, {
headers: {
'Authorization': 'Bearer YOUR_API_KEY',
...form.getHeaders()
}
})
.then(response => console.log(response.data))
.catch(error => console.error(error));
components:
schemas:
BuildSchemaRequest:
properties:
model:
anyOf:
- type: string
- type: 'null'
title: Model
description: >-
The version of the model to use for schema generation. Use
`extract-latest` to use the latest version.
markdowns:
anyOf:
- items:
anyOf:
- type: string
format: binary
- type: string
type: array
- type: 'null'
title: Markdowns
description: >-
Markdown files or inline content strings to analyze for schema
generation. Multiple documents can be provided for better schema
coverage.
markdown_urls:
anyOf:
- items:
type: string
type: array
- type: 'null'
title: Markdown Urls
description: URLs to Markdown files to analyze for schema generation.
prompt:
anyOf:
- type: string
- type: 'null'
title: Prompt
description: Instructions for how to generate or modify the schema.
schema:
anyOf:
- type: string
- type: 'null'
title: Schema
description: Existing JSON schema to iterate on or refine.
type: object
title: BuildSchemaRequest
BuildSchemaResponse:
properties:
extraction_schema:
type: string
title: Extraction Schema
description: The generated JSON schema as a string.
metadata:
$ref: '#/components/schemas/BuildSchemaMetadata'
description: The metadata for the schema generation process.
type: object
required:
- extraction_schema
- metadata
title: BuildSchemaResponse
HTTPValidationError:
properties:
detail:
items:
$ref: '#/components/schemas/ValidationError'
type: array
title: Detail
type: object
title: HTTPValidationError
BuildSchemaMetadata:
properties:
filename:
anyOf:
- type: string
- type: 'null'
title: Filename
org_id:
anyOf:
- type: string
- type: 'null'
title: Org Id
duration_ms:
type: integer
title: Duration Ms
default: 0
credit_usage:
type: number
title: Credit Usage
default: 0
job_id:
type: string
title: Job Id
default: ''
version:
anyOf:
- type: string
- type: 'null'
title: Version
warnings:
items:
$ref: '#/components/schemas/ExtractWarning'
type: array
title: Warnings
description: >-
Structured warnings from the extraction process. Each warning is an
instance of ExtractWarning with 'code' (e.g. 'nonconformant_schema')
and 'msg' (human-readable description). Present only for extract
versions from extract-20260314 and above that support structured
warnings.
type: object
title: BuildSchemaMetadata
ValidationError:
properties:
loc:
items:
anyOf:
- type: string
- type: integer
type: array
title: Location
msg:
type: string
title: Message
type:
type: string
title: Error Type
type: object
required:
- loc
- msg
- type
title: ValidationError
ExtractWarning:
properties:
code:
$ref: '#/components/schemas/ExtractWarningCode'
description: The type of warning, used to translate to a status code downstream
msg:
type: string
title: Msg
description: Human-readable description of the warning with more details
type: object
required:
- code
- msg
title: ExtractWarning
ExtractWarningCode:
type: string
enum:
- nonconformant_schema
- nonconformant_output
title: ExtractWarningCode
securitySchemes:
Basic Auth:
type: http
description: >-
Your unique API key for authentication.
Get your API key here: https://va.landing.ai/settings/api-key.
If using the EU endpoint, get your API key here:
https://va.eu-west-1.landing.ai/settings/api-key.
scheme: bearer
bearerFormat: Basic
````
# ADE Classify
Source: https://docs.landing.ai/api-reference/tools/ade-classify
Classify the pages of a document into classes you define.
This endpoint accepts PDFs, images, and other supported file types
(either as a `document` upload or `document_url`) together with a
list of `classes`, and returns a classification result for each page.
For EU users, use this endpoint:
`https://api.va.eu-west-1.landing.ai/v1/ade/classify`.
## OpenAPI
````yaml /ade/va_openapi_ade2.json post /v1/ade/classify
openapi: 3.1.0
info:
title: >-
LandingAI Agentic Document Extraction (ADE) API v1: Parse, Extract,
Classify, Split, Section
version: 0.1.0
description: >-
Convert documents such as PDFs, images, and Office files into structured
data with LandingAI's Agentic Document Extraction (ADE) v1 endpoints.
Includes Parse (documents to Markdown and structured chunks with grounding),
Extract (schema-based field extraction), Classify (page-level
classification), Split (separate multi-document files), and Section
(hierarchical table of contents), plus asynchronous jobs for parsing and
extraction. Documentation: https://docs.landing.ai
servers:
- url: https://api.va.landing.ai
description: Production vision tools API
security: []
paths:
/v1/ade/classify:
post:
tags:
- Tools
summary: ADE Classify
description: |-
Classify the pages of a document into classes you define.
This endpoint accepts PDFs, images, and other supported file types
(either as a `document` upload or `document_url`) together with a
list of `classes`, and returns a classification result for each page.
For EU users, use this endpoint:
`https://api.va.eu-west-1.landing.ai/v1/ade/classify`.
operationId: tool_ade_classify_v1_ade_classify_post
requestBody:
content:
multipart/form-data:
schema:
$ref: '#/components/schemas/ClassifyRequest'
required: true
responses:
'200':
description: Successful Response
content:
application/json:
schema:
$ref: '#/components/schemas/ClassifyResponse'
'422':
description: Validation Error
content:
application/json:
schema:
$ref: '#/components/schemas/HTTPValidationError'
security:
- Basic Auth: []
x-codeSamples:
- lang: cURL
label: cURL
source: |-
curl -X POST 'https://api.va.landing.ai/v1/ade/classify' \
-H 'Authorization: Bearer YOUR_API_KEY' \
-F 'classes=[{"class":"Class 1","description":"Description of Class 1"},{"class":"Class 2","description":"Description of Class 2"}]' \
-F 'document=@document.pdf'
- lang: Python
label: Python
source: >-
import requests
import json
headers = {
'Authorization': 'Bearer YOUR_API_KEY'
}
url = 'https://api.va.landing.ai/v1/ade/classify'
classes = [
{'class': 'Class 1', 'description': 'Description of Class 1'},
{'class': 'Class 2', 'description': 'Description of Class 2'}
]
files = {'document': open('document.pdf', 'rb')}
data = {'classes': json.dumps(classes)}
response = requests.post(url, files=files, data=data,
headers=headers)
print(response.json())
- lang: JavaScript
label: Node.js
source: |-
const axios = require('axios');
const FormData = require('form-data');
const fs = require('fs');
const form = new FormData();
form.append('document', fs.createReadStream('document.pdf'));
const classes = [
{ class: 'Class 1', description: 'Description of Class 1' },
{ class: 'Class 2', description: 'Description of Class 2' }
];
form.append('classes', JSON.stringify(classes));
axios.post('https://api.va.landing.ai/v1/ade/classify', form, {
headers: {
'Authorization': 'Bearer YOUR_API_KEY',
...form.getHeaders()
}
})
.then(response => console.log(response.data))
.catch(error => console.error(error));
components:
schemas:
ClassifyRequest:
properties:
classes:
items:
$ref: '#/components/schemas/ClassifyClass'
type: array
title: Classes
description: >-
The possible classes that can be assigned to pages in the document.
Each entry is an object with a `class` name and an optional
`description`. Only one class is assigned per page; unclassifiable
pages receive 'unknown'. Can be provided as a JSON string in form
data.
document:
anyOf:
- type: string
format: binary
- type: 'null'
title: Document
description: >-
A file to be classified. Either this parameter or the `document_url`
parameter must be provided.
document_url:
anyOf:
- type: string
- type: 'null'
title: Document Url
description: >-
The URL of the document to be classified. Either this parameter or
the `document` parameter must be provided.
model:
anyOf:
- type: string
- type: 'null'
title: Model
description: Classification model version. Defaults to the latest.
type: object
required:
- classes
title: ClassifyRequest
ClassifyResponse:
properties:
classification:
items:
$ref: '#/components/schemas/ClassificationItem'
type: array
title: Classification
metadata:
$ref: '#/components/schemas/ClassifyMetadata'
type: object
required:
- classification
- metadata
title: ClassifyResponse
description: Response model for the classify endpoint.
HTTPValidationError:
properties:
detail:
items:
$ref: '#/components/schemas/ValidationError'
type: array
title: Detail
type: object
title: HTTPValidationError
ClassifyClass:
properties:
class:
type: string
title: Class
description: Name of the class.
description:
anyOf:
- type: string
- type: 'null'
title: Description
description: Detailed description of what this class represents.
type: object
required:
- class
title: ClassifyClass
description: 'A single classification option: a class name plus optional description.'
ClassificationItem:
properties:
class:
type: string
title: Class
description: Predicted class label or 'unknown'.
reason:
type: string
title: Reason
description: Reason for the classification (for debugging).
default: ''
suggested_class:
anyOf:
- type: string
- type: 'null'
title: Suggested Class
description: Proposed class when the prediction is 'unknown'.
page:
type: integer
title: Page
description: Page number (0-based).
type: object
required:
- class
- page
title: ClassificationItem
description: A single page-level classification result.
ClassifyMetadata:
properties:
filename:
type: string
title: Filename
org_id:
anyOf:
- type: string
- type: 'null'
title: Org Id
page_count:
type: integer
title: Page Count
duration_ms:
type: integer
title: Duration Ms
credit_usage:
type: number
title: Credit Usage
job_id:
type: string
title: Job Id
default: ''
version:
anyOf:
- type: string
- type: 'null'
title: Version
type: object
required:
- filename
- page_count
- duration_ms
- credit_usage
title: ClassifyMetadata
description: Metadata for the classify response.
ValidationError:
properties:
loc:
items:
anyOf:
- type: string
- type: integer
type: array
title: Location
msg:
type: string
title: Message
type:
type: string
title: Error Type
type: object
required:
- loc
- msg
- type
title: ValidationError
securitySchemes:
Basic Auth:
type: http
description: >-
Your unique API key for authentication.
Get your API key here: https://va.landing.ai/settings/api-key.
If using the EU endpoint, get your API key here:
https://va.eu-west-1.landing.ai/settings/api-key.
scheme: bearer
bearerFormat: Basic
````
# ADE Extract
Source: https://docs.landing.ai/api-reference/tools/ade-extract
Extract structured data from Markdown using a JSON schema.
This endpoint
processes Markdown content and extracts structured data according to the provided
JSON schema.
For EU users, use this endpoint:
`https://api.va.eu-west-1.landing.ai/v1/ade/extract`.
## OpenAPI
````yaml /ade/va_openapi_ade2.json post /v1/ade/extract
openapi: 3.1.0
info:
title: >-
LandingAI Agentic Document Extraction (ADE) API v1: Parse, Extract,
Classify, Split, Section
version: 0.1.0
description: >-
Convert documents such as PDFs, images, and Office files into structured
data with LandingAI's Agentic Document Extraction (ADE) v1 endpoints.
Includes Parse (documents to Markdown and structured chunks with grounding),
Extract (schema-based field extraction), Classify (page-level
classification), Split (separate multi-document files), and Section
(hierarchical table of contents), plus asynchronous jobs for parsing and
extraction. Documentation: https://docs.landing.ai
servers:
- url: https://api.va.landing.ai
description: Production vision tools API
security: []
paths:
/v1/ade/extract:
post:
tags:
- Tools
summary: ADE Extract
description: |-
Extract structured data from Markdown using a JSON schema.
This endpoint
processes Markdown content and extracts structured data according to the provided
JSON schema.
For EU users, use this endpoint:
`https://api.va.eu-west-1.landing.ai/v1/ade/extract`.
operationId: tool_ade_extract_v1_ade_extract_post
requestBody:
content:
multipart/form-data:
schema:
$ref: '#/components/schemas/ExtractRequest'
required: true
responses:
'200':
description: Successful Response
content:
application/json:
schema:
$ref: '#/components/schemas/ExtractResponse'
'206':
description: >-
Extraction completed with success but there was a schema validation
error.
content:
application/json:
schema:
$ref: '#/components/schemas/ExtractResponse'
'422':
description: Validation Error
content:
application/json:
schema:
$ref: '#/components/schemas/HTTPValidationError'
'429':
description: Rate limit exceeded
security:
- Basic Auth: []
x-codeSamples:
- lang: cURL
label: cURL
source: |-
curl -X POST 'https://api.va.landing.ai/v1/ade/extract' \
-H 'Authorization: Bearer YOUR_API_KEY' \
-F 'schema={"type": "object", "properties": {"field1": {"type": "string"}, "field2": {"type": "string"}}}' \
-F 'markdown=@markdown.md' \
-F 'model=extract-latest'
- lang: Python
label: Python
source: >-
import requests
headers = {
'Authorization': 'Bearer YOUR_API_KEY'
}
url = 'https://api.va.landing.ai/v1/ade/extract'
# Read the schema file as a string
with open('schema.json', 'r') as f:
schema_content = f.read()
# Prepare files and data
files = {'markdown': open('markdown.md', 'rb')}
data = {
'schema': schema_content,
'model': 'extract-latest'
}
# Run extraction
response = requests.post(url, files=files, data=data,
headers=headers)
# Return the results
print(response.json())
- lang: JavaScript
label: Node.js
source: |-
const axios = require('axios');
const FormData = require('form-data');
const fs = require('fs');
const form = new FormData();
form.append('markdown', fs.createReadStream('markdown.md'));
form.append('schema', fs.readFileSync('schema.json', 'utf8'));
form.append('model', 'extract-latest');
axios.post('https://api.va.landing.ai/v1/ade/extract', form, {
headers: {
'Authorization': 'Bearer YOUR_API_KEY',
...form.getHeaders()
}
})
.then(response => console.log(response.data))
.catch(error => console.error(error));
components:
schemas:
ExtractRequest:
properties:
model:
anyOf:
- type: string
- type: 'null'
title: Model
description: >-
The version of the model to use for extraction. Use `extract-latest`
to use the latest version.
markdown:
anyOf:
- type: string
format: binary
- type: string
- type: 'null'
title: Markdown
description: The Markdown file or Markdown content to extract data from.
markdown_url:
anyOf:
- type: string
- type: 'null'
title: Markdown Url
description: The URL to the Markdown file to extract data from.
schema:
type: string
title: Schema
description: >-
JSON schema for field extraction. This schema determines what
key-values pairs are extracted from the Markdown. The schema must be
a valid JSON object and will be validated before processing the
document.
strict:
type: boolean
title: Strict
description: >-
If True, reject schemas with unsupported fields (HTTP 422). If
False, prune unsupported fields and continue. Only applies to
extract versions that support schema validation.
default: false
type: object
required:
- schema
title: ExtractRequest
ExtractResponse:
properties:
extraction:
type: object
title: Extraction
description: The extracted key-value pairs.
extraction_metadata:
type: object
title: Extraction Metadata
description: The extracted key-value pairs and the chunk_reference for each one.
metadata:
$ref: '#/components/schemas/ExtractMetadata'
description: The metadata for the extraction process.
type: object
required:
- extraction
- extraction_metadata
- metadata
title: ExtractResponse
HTTPValidationError:
properties:
detail:
items:
$ref: '#/components/schemas/ValidationError'
type: array
title: Detail
type: object
title: HTTPValidationError
ExtractMetadata:
properties:
filename:
type: string
title: Filename
org_id:
anyOf:
- type: string
- type: 'null'
title: Org Id
duration_ms:
type: integer
title: Duration Ms
credit_usage:
type: number
title: Credit Usage
job_id:
type: string
title: Job Id
version:
anyOf:
- type: string
- type: 'null'
title: Version
schema_violation_error:
anyOf:
- type: string
- type: 'null'
title: Schema Violation Error
description: >-
A detailed error message shows why the extracted data does not fully
conform to the input schema. Null means the extraction result is
consistent with the input schema.
fallback_model_version:
anyOf:
- type: string
- type: 'null'
title: Fallback Model Version
description: >-
The extract model that was actually used to extract the data when
the initial extraction attempt failed with the requested version.
warnings:
items:
$ref: '#/components/schemas/ExtractWarning'
type: array
title: Warnings
description: >-
Structured warnings from the extraction process. Each warning is an
instance of ExtractWarning with 'code' (e.g. 'nonconformant_schema')
and 'msg' (human-readable description). Present only for extract
versions from extract-20260314 and above that support structured
warnings.
type: object
required:
- filename
- org_id
- duration_ms
- credit_usage
- job_id
- version
title: ExtractMetadata
ValidationError:
properties:
loc:
items:
anyOf:
- type: string
- type: integer
type: array
title: Location
msg:
type: string
title: Message
type:
type: string
title: Error Type
type: object
required:
- loc
- msg
- type
title: ValidationError
ExtractWarning:
properties:
code:
$ref: '#/components/schemas/ExtractWarningCode'
description: The type of warning, used to translate to a status code downstream
msg:
type: string
title: Msg
description: Human-readable description of the warning with more details
type: object
required:
- code
- msg
title: ExtractWarning
ExtractWarningCode:
type: string
enum:
- nonconformant_schema
- nonconformant_output
title: ExtractWarningCode
securitySchemes:
Basic Auth:
type: http
description: >-
Your unique API key for authentication.
Get your API key here: https://va.landing.ai/settings/api-key.
If using the EU endpoint, get your API key here:
https://va.eu-west-1.landing.ai/settings/api-key.
scheme: bearer
bearerFormat: Basic
````
# ADE Extract Jobs
Source: https://docs.landing.ai/api-reference/tools/ade-extract-jobs
Extract structured data asynchronously.
This endpoint creates a job that handles the processing for large markdown
documents.
For EU users, use this endpoint:
`https://api.va.eu-west-1.landing.ai/v1/ade/extract/jobs`.
## OpenAPI
````yaml /ade/va_openapi_ade2.json post /v1/ade/extract/jobs
openapi: 3.1.0
info:
title: >-
LandingAI Agentic Document Extraction (ADE) API v1: Parse, Extract,
Classify, Split, Section
version: 0.1.0
description: >-
Convert documents such as PDFs, images, and Office files into structured
data with LandingAI's Agentic Document Extraction (ADE) v1 endpoints.
Includes Parse (documents to Markdown and structured chunks with grounding),
Extract (schema-based field extraction), Classify (page-level
classification), Split (separate multi-document files), and Section
(hierarchical table of contents), plus asynchronous jobs for parsing and
extraction. Documentation: https://docs.landing.ai
servers:
- url: https://api.va.landing.ai
description: Production vision tools API
security: []
paths:
/v1/ade/extract/jobs:
post:
tags:
- Tools
summary: ADE Extract Jobs
description: >-
Extract structured data asynchronously.
This endpoint creates a job that handles the processing for large
markdown
documents.
For EU users, use this endpoint:
`https://api.va.eu-west-1.landing.ai/v1/ade/extract/jobs`.
operationId: tool_ade_extract_jobs_v1_ade_extract_jobs_post
requestBody:
required: true
content:
multipart/form-data:
schema:
$ref: '#/components/schemas/AsyncExtractRequest'
responses:
'202':
description: Job queued successfully
content:
application/json:
schema:
$ref: '#/components/schemas/JobCreationResponse'
example:
job_id: 12345678-1234-1234-1234-123456789012
'422':
description: Validation Error
content:
application/json:
schema:
$ref: '#/components/schemas/HTTPValidationError'
'429':
description: Rate limit exceeded
security:
- Basic Auth: []
x-codeSamples:
- lang: cURL
label: cURL
source: |-
curl -X POST 'https://api.va.landing.ai/v1/ade/extract/jobs' \
-H 'Authorization: Bearer YOUR_API_KEY' \
-F 'schema={"type": "object", "properties": {"field1": {"type": "string"}, "field2": {"type": "string"}}}' \
-F 'markdown=@markdown.md' \
-F 'model=extract-latest'
- lang: Python
label: Python
source: >-
import requests
headers = {
'Authorization': 'Bearer YOUR_API_KEY'
}
url = 'https://api.va.landing.ai/v1/ade/extract/jobs'
# Read the schema file as a string
with open('schema.json', 'r') as f:
schema_content = f.read()
# Prepare files and data
files = {'markdown': open('markdown.md', 'rb')}
data = {
'schema': schema_content,
'model': 'extract-latest'
}
response = requests.post(url, files=files, data=data,
headers=headers)
print(response.json())
- lang: JavaScript
label: Node.js
source: |-
const axios = require('axios');
const FormData = require('form-data');
const fs = require('fs');
const form = new FormData();
form.append('markdown', fs.createReadStream('markdown.md'));
form.append('schema', fs.readFileSync('schema.json', 'utf8'));
form.append('model', 'extract-latest');
axios.post('https://api.va.landing.ai/v1/ade/extract/jobs', form, {
headers: {
'Authorization': 'Bearer YOUR_API_KEY',
...form.getHeaders()
}
})
.then(response => console.log(response.data))
.catch(error => console.error(error));
components:
schemas:
AsyncExtractRequest:
properties:
model:
anyOf:
- type: string
- type: 'null'
title: Model
description: >-
The version of the model to use for extraction. Use `extract-latest`
to use the latest version.
markdown:
anyOf:
- type: string
format: binary
- type: string
- type: 'null'
title: Markdown
description: The Markdown file or Markdown content to extract data from.
markdown_url:
anyOf:
- type: string
- type: 'null'
title: Markdown Url
description: The URL to the Markdown file to extract data from.
schema:
type: string
title: Schema
description: >-
JSON schema for field extraction. This schema determines what
key-values pairs are extracted from the Markdown. The schema must be
a valid JSON object and will be validated before processing the
document.
strict:
type: boolean
title: Strict
description: >-
If True, reject schemas with unsupported fields (HTTP 422). If
False, prune unsupported fields and continue. Only applies to
extract versions that support schema validation.
default: false
output_save_url:
anyOf:
- type: string
- type: 'null'
title: Output Save Url
description: >-
If zero data retention (ZDR) is enabled, you must enter a URL for
the extracted output to be saved to. When ZDR is enabled, the
extracted content will not be in the API response.
type: object
required:
- schema
title: AsyncExtractRequest
description: |-
Request model for async extract endpoint.
Extends ExtractRequest with output_save_url for ZDR support.
JobCreationResponse:
properties:
job_id:
type: string
title: Job Id
type: object
required:
- job_id
title: JobCreationResponse
HTTPValidationError:
properties:
detail:
items:
$ref: '#/components/schemas/ValidationError'
type: array
title: Detail
type: object
title: HTTPValidationError
ValidationError:
properties:
loc:
items:
anyOf:
- type: string
- type: integer
type: array
title: Location
msg:
type: string
title: Message
type:
type: string
title: Error Type
type: object
required:
- loc
- msg
- type
title: ValidationError
securitySchemes:
Basic Auth:
type: http
description: >-
Your unique API key for authentication.
Get your API key here: https://va.landing.ai/settings/api-key.
If using the EU endpoint, get your API key here:
https://va.eu-west-1.landing.ai/settings/api-key.
scheme: bearer
bearerFormat: Basic
````
# ADE Get Extract Jobs
Source: https://docs.landing.ai/api-reference/tools/ade-get-extract-jobs
Get the status for an async extract job.
Returns the job status or an error
response. For EU users, use this endpoint:
`https://api.va.eu-west-1.landing.ai/v1/ade/extract/jobs/{job_id}`.
## OpenAPI
````yaml /ade/va_openapi_ade2.json get /v1/ade/extract/jobs/{job_id}
openapi: 3.1.0
info:
title: >-
LandingAI Agentic Document Extraction (ADE) API v1: Parse, Extract,
Classify, Split, Section
version: 0.1.0
description: >-
Convert documents such as PDFs, images, and Office files into structured
data with LandingAI's Agentic Document Extraction (ADE) v1 endpoints.
Includes Parse (documents to Markdown and structured chunks with grounding),
Extract (schema-based field extraction), Classify (page-level
classification), Split (separate multi-document files), and Section
(hierarchical table of contents), plus asynchronous jobs for parsing and
extraction. Documentation: https://docs.landing.ai
servers:
- url: https://api.va.landing.ai
description: Production vision tools API
security: []
paths:
/v1/ade/extract/jobs/{job_id}:
get:
tags:
- Tools
summary: ADE Get Extract Jobs
description: |-
Get the status for an async extract job.
Returns the job status or an error
response. For EU users, use this endpoint:
`https://api.va.eu-west-1.landing.ai/v1/ade/extract/jobs/{job_id}`.
operationId: tool_ade_get_extract_jobs_v1_ade_extract_jobs__job_id__get
parameters:
- name: job_id
in: path
required: true
schema:
type: string
title: Job Id
responses:
'200':
description: Successful Response
content:
application/json:
schema:
$ref: '#/components/schemas/ExtractJobStatusResponse'
'206':
description: >-
Extraction completed with success but there was a schema validation
error.
content:
application/json:
schema:
$ref: '#/components/schemas/ExtractJobStatusResponse'
'404':
description: Job ID not found
'422':
description: Validation Error
content:
application/json:
schema:
$ref: '#/components/schemas/HTTPValidationError'
security:
- Basic Auth: []
x-codeSamples:
- lang: cURL
label: cURL
source: >-
curl -X GET 'https://api.va.landing.ai/v1/ade/extract/jobs/{job_id}'
\
-H 'Authorization: Bearer YOUR_API_KEY'
- lang: Python
label: Python
source: |-
import requests
headers = {
'Authorization': 'Bearer YOUR_API_KEY'
}
url = f'https://api.va.landing.ai/v1/ade/extract/jobs/{job_id}'
response = requests.get(url, headers=headers)
print(response.json())
- lang: JavaScript
label: Node.js
source: |-
const axios = require('axios');
const url = `https://api.va.landing.ai/v1/ade/extract/jobs/{jobId}`;
axios.get(url, {
headers: {
'Authorization': 'Bearer YOUR_API_KEY'
}
})
.then(response => console.log(response.data))
.catch(error => console.error(error));
components:
schemas:
ExtractJobStatusResponse:
properties:
job_id:
type: string
title: Job Id
description: A unique identifier for this extract job.
status:
type: string
title: Status
description: >-
The current state of the job: `pending`, `processing`, `completed`,
`failed`, or `cancelled`.
received_at:
type: integer
title: Received At
description: Unix timestamp (in seconds) for when the job was received.
created_at:
type: integer
title: Created At
description: Unix timestamp (in seconds) for when the job was created.
default: 0
progress:
type: number
maximum: 1
minimum: 0
title: Progress
description: Job completion. Either 0.0 (not yet complete) or 1.0 (complete).
org_id:
anyOf:
- type: string
- type: 'null'
title: Org Id
description: Organization ID.
version:
anyOf:
- type: string
- type: 'null'
title: Version
description: The exact model snapshot used for the extraction.
data:
anyOf:
- $ref: '#/components/schemas/ExtractResponse'
- type: 'null'
description: >-
The extraction results, returned here when the job is complete and
you did not set an `output_save_url`. Large results are returned
through `output_url` instead.
output_url:
anyOf:
- type: string
- type: 'null'
title: Output Url
description: >-
A URL to download the extraction results. Provided when the job is
complete and either you set an `output_save_url` or the result is
larger than 1 MB. URLs for large results are temporary and expire
one hour after you request the job.
metadata:
anyOf:
- $ref: '#/components/schemas/ExtractMetadata'
- type: 'null'
description: >-
Information about the extraction, such as the model version,
duration, credit usage, and any schema warnings.
failure_reason:
anyOf:
- type: string
- type: 'null'
title: Failure Reason
description: If the job failed, a message describing what went wrong.
type: object
required:
- job_id
- status
- received_at
- progress
title: ExtractJobStatusResponse
description: The status of an extract job, plus the results once it completes.
HTTPValidationError:
properties:
detail:
items:
$ref: '#/components/schemas/ValidationError'
type: array
title: Detail
type: object
title: HTTPValidationError
ExtractResponse:
properties:
extraction:
type: object
title: Extraction
description: The extracted key-value pairs.
extraction_metadata:
type: object
title: Extraction Metadata
description: The extracted key-value pairs and the chunk_reference for each one.
metadata:
$ref: '#/components/schemas/ExtractMetadata'
description: The metadata for the extraction process.
type: object
required:
- extraction
- extraction_metadata
- metadata
title: ExtractResponse
ExtractMetadata:
properties:
filename:
type: string
title: Filename
org_id:
anyOf:
- type: string
- type: 'null'
title: Org Id
duration_ms:
type: integer
title: Duration Ms
credit_usage:
type: number
title: Credit Usage
job_id:
type: string
title: Job Id
version:
anyOf:
- type: string
- type: 'null'
title: Version
schema_violation_error:
anyOf:
- type: string
- type: 'null'
title: Schema Violation Error
description: >-
A detailed error message shows why the extracted data does not fully
conform to the input schema. Null means the extraction result is
consistent with the input schema.
fallback_model_version:
anyOf:
- type: string
- type: 'null'
title: Fallback Model Version
description: >-
The extract model that was actually used to extract the data when
the initial extraction attempt failed with the requested version.
warnings:
items:
$ref: '#/components/schemas/ExtractWarning'
type: array
title: Warnings
description: >-
Structured warnings from the extraction process. Each warning is an
instance of ExtractWarning with 'code' (e.g. 'nonconformant_schema')
and 'msg' (human-readable description). Present only for extract
versions from extract-20260314 and above that support structured
warnings.
type: object
required:
- filename
- org_id
- duration_ms
- credit_usage
- job_id
- version
title: ExtractMetadata
ValidationError:
properties:
loc:
items:
anyOf:
- type: string
- type: integer
type: array
title: Location
msg:
type: string
title: Message
type:
type: string
title: Error Type
type: object
required:
- loc
- msg
- type
title: ValidationError
ExtractWarning:
properties:
code:
$ref: '#/components/schemas/ExtractWarningCode'
description: The type of warning, used to translate to a status code downstream
msg:
type: string
title: Msg
description: Human-readable description of the warning with more details
type: object
required:
- code
- msg
title: ExtractWarning
ExtractWarningCode:
type: string
enum:
- nonconformant_schema
- nonconformant_output
title: ExtractWarningCode
securitySchemes:
Basic Auth:
type: http
description: >-
Your unique API key for authentication.
Get your API key here: https://va.landing.ai/settings/api-key.
If using the EU endpoint, get your API key here:
https://va.eu-west-1.landing.ai/settings/api-key.
scheme: bearer
bearerFormat: Basic
````
# ADE Get Parse Jobs
Source: https://docs.landing.ai/api-reference/tools/ade-get-parse-jobs
Get the status for an async parse job.
Returns the job status or an error
response. For EU users, use this endpoint:
`https://api.va.eu-west-1.landing.ai/v1/ade/parse/jobs/{job_id}`.
## OpenAPI
````yaml /ade/va_openapi_ade2.json get /v1/ade/parse/jobs/{job_id}
openapi: 3.1.0
info:
title: >-
LandingAI Agentic Document Extraction (ADE) API v1: Parse, Extract,
Classify, Split, Section
version: 0.1.0
description: >-
Convert documents such as PDFs, images, and Office files into structured
data with LandingAI's Agentic Document Extraction (ADE) v1 endpoints.
Includes Parse (documents to Markdown and structured chunks with grounding),
Extract (schema-based field extraction), Classify (page-level
classification), Split (separate multi-document files), and Section
(hierarchical table of contents), plus asynchronous jobs for parsing and
extraction. Documentation: https://docs.landing.ai
servers:
- url: https://api.va.landing.ai
description: Production vision tools API
security: []
paths:
/v1/ade/parse/jobs/{job_id}:
get:
tags:
- Tools
summary: ADE Get Parse Jobs
description: |-
Get the status for an async parse job.
Returns the job status or an error
response. For EU users, use this endpoint:
`https://api.va.eu-west-1.landing.ai/v1/ade/parse/jobs/{job_id}`.
operationId: tool_ade_get_parse_jobs_v1_ade_parse_jobs__job_id__get
parameters:
- name: job_id
in: path
required: true
schema:
type: string
title: Job Id
responses:
'200':
description: Successful Response
content:
application/json:
schema:
$ref: '#/components/schemas/JobStatusResponse'
'206':
description: There were some pages that failed to be parsed.
content:
application/json:
schema:
$ref: '#/components/schemas/JobStatusResponse'
'404':
description: Job ID not found
'422':
description: Validation Error
content:
application/json:
schema:
$ref: '#/components/schemas/HTTPValidationError'
security:
- Basic Auth: []
x-codeSamples:
- lang: cURL
label: cURL
source: |-
curl -X GET 'https://api.va.landing.ai/v1/ade/parse/jobs/{job_id}' \
-H 'Authorization: Bearer YOUR_API_KEY'
- lang: Python
label: Python
source: |-
import requests
headers = {
'Authorization': 'Bearer YOUR_API_KEY'
}
url = f'https://api.va.landing.ai/v1/ade/parse/jobs/{job_id}'
response = requests.get(url, headers=headers)
print(response.json())
- lang: JavaScript
label: Node.js
source: |-
const axios = require('axios');
const url = `https://api.va.landing.ai/v1/ade/parse/jobs/{jobId}`;
axios.get(url, {
headers: {
'Authorization': 'Bearer YOUR_API_KEY'
}
})
.then(response => console.log(response.data))
.catch(error => console.error(error));
components:
schemas:
JobStatusResponse:
properties:
job_id:
type: string
title: Job Id
status:
type: string
title: Status
received_at:
type: integer
title: Received At
created_at:
type: integer
title: Created At
description: >-
Unix timestamp (seconds) for when the job was created. Mirrors
received_at; exposed so clients have an explicit creation time.
default: 0
progress:
type: number
maximum: 1
minimum: 0
title: Progress
description: >-
Job completion progress as a decimal from 0 to 1, where 0 is not
started, 1 is finished, and values between 0 and 1 indicate work in
progress.
org_id:
anyOf:
- type: string
- type: 'null'
title: Org Id
version:
anyOf:
- type: string
- type: 'null'
title: Version
data:
anyOf:
- $ref: '#/components/schemas/ParseResponse'
- $ref: '#/components/schemas/SpreadsheetParseResponse'
- type: 'null'
title: Data
description: >-
The parsed output (ParseResponse for documents,
SpreadsheetParseResponse for spreadsheets), if the job is complete
and the `output_save_url` parameter was not used.
output_url:
anyOf:
- type: string
- type: 'null'
title: Output Url
description: >-
The URL to the parsed content. This field contains a URL when the
job is complete and either you specified the `output_save_url`
parameter or the result is larger than 1MB. When the result exceeds
1MB, the URL is a presigned S3 URL that expires after 1 hour. Each
time you GET the job, a new presigned URL is generated.
metadata:
anyOf:
- $ref: '#/components/schemas/ParseMetadata'
- type: 'null'
failure_reason:
anyOf:
- type: string
- type: 'null'
title: Failure Reason
type: object
required:
- job_id
- status
- received_at
- progress
title: JobStatusResponse
description: Unified response for job status endpoint.
HTTPValidationError:
properties:
detail:
items:
$ref: '#/components/schemas/ValidationError'
type: array
title: Detail
type: object
title: HTTPValidationError
ParseResponse:
properties:
markdown:
type: string
title: Markdown
chunks:
items:
$ref: '#/components/schemas/ParseChunk'
type: array
title: Chunks
splits:
items:
$ref: '#/components/schemas/ParseSplit'
type: array
title: Splits
grounding:
additionalProperties:
anyOf:
- $ref: '#/components/schemas/ParseResponseGrounding'
- $ref: '#/components/schemas/ParseResponseTableCellGrounding'
type: object
title: Grounding
metadata:
$ref: '#/components/schemas/ParseMetadata'
type: object
required:
- markdown
- chunks
- splits
- metadata
title: ParseResponse
SpreadsheetParseResponse:
properties:
markdown:
type: string
title: Markdown
description: Full document as HTML with anchor tags and tables
chunks:
items:
$ref: '#/components/schemas/SpreadsheetChunk'
type: array
title: Chunks
description: List of table chunks (HTML)
splits:
items:
$ref: '#/components/schemas/SpreadsheetSplit'
type: array
title: Splits
description: Sheet-based splits
metadata:
$ref: '#/components/schemas/SpreadsheetParseMetadata'
description: Parsing metadata
type: object
required:
- markdown
- chunks
- splits
- metadata
title: SpreadsheetParseResponse
description: |-
Response from /ade/parse-spreadsheet endpoint.
Similar structure to ParseResponse but without grounding.
ParseMetadata:
properties:
filename:
type: string
title: Filename
org_id:
anyOf:
- type: string
- type: 'null'
title: Org Id
page_count:
type: integer
title: Page Count
duration_ms:
type: integer
title: Duration Ms
credit_usage:
type: number
title: Credit Usage
job_id:
type: string
title: Job Id
version:
anyOf:
- type: string
- type: 'null'
title: Version
failed_pages:
items:
type: integer
type: array
title: Failed Pages
type: object
required:
- filename
- org_id
- page_count
- duration_ms
- credit_usage
- job_id
- version
title: ParseMetadata
ValidationError:
properties:
loc:
items:
anyOf:
- type: string
- type: integer
type: array
title: Location
msg:
type: string
title: Message
type:
type: string
title: Error Type
type: object
required:
- loc
- msg
- type
title: ValidationError
ParseChunk:
properties:
markdown:
type: string
title: Markdown
type:
type: string
title: Type
id:
type: string
title: Id
grounding:
$ref: '#/components/schemas/ParseGrounding'
type: object
required:
- markdown
- type
- id
- grounding
title: ParseChunk
ParseSplit:
properties:
class:
type: string
title: Class
identifier:
type: string
title: Identifier
pages:
items:
type: integer
type: array
title: Pages
markdown:
type: string
title: Markdown
chunks:
items:
type: string
type: array
title: Chunks
type: object
required:
- class
- identifier
- pages
- markdown
- chunks
title: ParseSplit
ParseResponseGrounding:
properties:
box:
$ref: '#/components/schemas/ParseGroundingBox'
page:
type: integer
title: Page
type:
$ref: '#/components/schemas/GroundingType'
confidence:
anyOf:
- type: number
- type: 'null'
title: Confidence
low_confidence_spans:
items:
$ref: '#/components/schemas/Patch'
type: array
title: Low Confidence Spans
type: object
required:
- box
- page
- type
title: ParseResponseGrounding
ParseResponseTableCellGrounding:
properties:
box:
$ref: '#/components/schemas/ParseGroundingBox'
page:
type: integer
title: Page
type:
$ref: '#/components/schemas/GroundingType'
confidence:
anyOf:
- type: number
- type: 'null'
title: Confidence
position:
anyOf:
- $ref: '#/components/schemas/ParseResponseTableCellGroundingPosition'
- type: 'null'
type: object
required:
- box
- page
- type
title: ParseResponseTableCellGrounding
SpreadsheetChunk:
properties:
markdown:
type: string
title: Markdown
description: >-
Chunk content as HTML table with anchor tag (for tables) or parsed
markdown content (for chunks from images)
type:
type: string
title: Type
description: >-
Chunk type: 'table' for spreadsheet tables, or types from /parse
(text, table, figure, form, etc.) for chunks derived from embedded
images
id:
type: string
title: Id
description: >-
Chunk ID - format: '{sheet_name}-{cell_range}' for tables,
'{sheet_name}-image-{index}-{anchor_cell}-chunk-{i}-{type}' for
parsed image chunks
grounding:
anyOf:
- $ref: '#/components/schemas/ParseGrounding'
- type: 'null'
description: >-
Visual grounding coordinates from /parse API (only for chunks
derived from embedded images)
type: object
required:
- markdown
- type
- id
title: SpreadsheetChunk
description: |-
Chunk from spreadsheet parsing.
Can represent:
- Table chunks from spreadsheet cells
- Parsed content chunks from embedded images (text, table, figure, etc.)
SpreadsheetSplit:
properties:
class:
type: string
title: Class
description: >-
Split class: 'page' for per-sheet splits, 'full' for single split
with all content
identifier:
type: string
title: Identifier
description: >-
Split identifier: sheet name for 'page' splits, 'full' for full
split
sheets:
items:
type: integer
type: array
title: Sheets
description: >-
Sheet indices: single element for 'page' splits, all indices for
'full' split
markdown:
type: string
title: Markdown
description: Combined markdown for this split
chunks:
items:
type: string
type: array
title: Chunks
description: Chunk IDs in this split
type: object
required:
- class
- identifier
- sheets
- markdown
- chunks
title: SpreadsheetSplit
description: |-
Sheet-based split from spreadsheet parsing.
Similar to ParseSplit but grouped by sheet instead of page.
Supports both 'page' (per-sheet) and 'full' (all sheets) split types.
SpreadsheetParseMetadata:
properties:
filename:
type: string
title: Filename
description: Original filename
org_id:
anyOf:
- type: string
- type: 'null'
title: Org Id
description: Organization ID
sheet_count:
type: integer
title: Sheet Count
description: Number of sheets processed
total_rows:
type: integer
title: Total Rows
description: Total rows across all sheets
total_cells:
type: integer
title: Total Cells
description: Total non-empty cells across all sheets
total_chunks:
type: integer
title: Total Chunks
description: Total chunks (tables + images) extracted
total_images:
type: integer
title: Total Images
description: Total images extracted
default: 0
duration_ms:
type: integer
title: Duration Ms
description: Processing duration in milliseconds
credit_usage:
type: number
title: Credit Usage
description: Credits charged
default: 0
job_id:
type: string
title: Job Id
description: Inference history job ID
default: ''
version:
anyOf:
- type: string
- type: 'null'
title: Version
description: Model version for parsing images
type: object
required:
- filename
- sheet_count
- total_rows
- total_cells
- total_chunks
- duration_ms
title: SpreadsheetParseMetadata
description: Metadata for spreadsheet parsing result.
ParseGrounding:
properties:
box:
$ref: '#/components/schemas/ParseGroundingBox'
page:
type: integer
title: Page
type: object
required:
- box
- page
title: ParseGrounding
ParseGroundingBox:
properties:
left:
type: number
title: Left
top:
type: number
title: Top
right:
type: number
title: Right
bottom:
type: number
title: Bottom
type: object
required:
- left
- top
- right
- bottom
title: ParseGroundingBox
GroundingType:
type: string
enum:
- chunkLogo
- chunkCard
- chunkAttestation
- chunkScanCode
- chunkForm
- chunkTable
- chunkFigure
- chunkText
- chunkMarginalia
- chunkTitle
- chunkPageHeader
- chunkPageFooter
- chunkPageNumber
- chunkKeyValue
- table
- tableCell
title: GroundingType
Patch:
properties:
text:
type: string
title: Text
span:
prefixItems:
- type: integer
- type: integer
type: array
maxItems: 2
minItems: 2
title: Span
confidence:
type: number
title: Confidence
type: object
required:
- text
- span
- confidence
title: Patch
ParseResponseTableCellGroundingPosition:
properties:
row:
type: integer
title: Row
col:
type: integer
title: Col
rowspan:
type: integer
title: Rowspan
colspan:
type: integer
title: Colspan
chunk_id:
type: string
title: Chunk Id
type: object
required:
- row
- col
- rowspan
- colspan
- chunk_id
title: ParseResponseTableCellGroundingPosition
securitySchemes:
Basic Auth:
type: http
description: >-
Your unique API key for authentication.
Get your API key here: https://va.landing.ai/settings/api-key.
If using the EU endpoint, get your API key here:
https://va.eu-west-1.landing.ai/settings/api-key.
scheme: bearer
bearerFormat: Basic
````
# ADE List Extract Jobs
Source: https://docs.landing.ai/api-reference/tools/ade-list-extract-jobs
List all async extract jobs associated with your API key.
Returns the list of jobs or an error response. For EU users, use this endpoint:
`https://api.va.eu-west-1.landing.ai/v1/ade/extract/jobs`.
## OpenAPI
````yaml /ade/va_openapi_ade2.json get /v1/ade/extract/jobs
openapi: 3.1.0
info:
title: >-
LandingAI Agentic Document Extraction (ADE) API v1: Parse, Extract,
Classify, Split, Section
version: 0.1.0
description: >-
Convert documents such as PDFs, images, and Office files into structured
data with LandingAI's Agentic Document Extraction (ADE) v1 endpoints.
Includes Parse (documents to Markdown and structured chunks with grounding),
Extract (schema-based field extraction), Classify (page-level
classification), Split (separate multi-document files), and Section
(hierarchical table of contents), plus asynchronous jobs for parsing and
extraction. Documentation: https://docs.landing.ai
servers:
- url: https://api.va.landing.ai
description: Production vision tools API
security: []
paths:
/v1/ade/extract/jobs:
get:
tags:
- Tools
summary: ADE List Extract Jobs
description: >-
List all async extract jobs associated with your API key.
Returns the list of jobs or an error response. For EU users, use this
endpoint:
`https://api.va.eu-west-1.landing.ai/v1/ade/extract/jobs`.
operationId: tool_ade_list_extract_jobs_v1_ade_extract_jobs_get
parameters:
- name: page
in: query
required: false
schema:
type: integer
minimum: 0
description: Page number (0-indexed)
default: 0
title: Page
description: Page number (0-indexed)
- name: pageSize
in: query
required: false
schema:
type: integer
maximum: 100
minimum: 1
description: Number of items per page
default: 10
title: Pagesize
description: Number of items per page
- name: status
in: query
required: false
schema:
anyOf:
- enum:
- cancelled
- completed
- failed
- pending
- processing
type: string
- type: 'null'
description: Filter by job status.
title: Status
description: Filter by job status.
responses:
'200':
description: Successful Response
content:
application/json:
schema:
$ref: '#/components/schemas/JobsListResponse'
'422':
description: Validation Error
content:
application/json:
schema:
$ref: '#/components/schemas/HTTPValidationError'
security:
- Basic Auth: []
x-codeSamples:
- lang: cURL
label: cURL
source: |-
curl -X GET 'https://api.va.landing.ai/v1/ade/extract/jobs' \
-H 'Authorization: Bearer YOUR_API_KEY'
- lang: Python
label: Python
source: |-
import requests
headers = {
'Authorization': 'Bearer YOUR_API_KEY'
}
url = 'https://api.va.landing.ai/v1/ade/extract/jobs'
response = requests.get(url, headers=headers)
print(response.json())
- lang: JavaScript
label: Node.js
source: |-
const axios = require('axios');
const url = 'https://api.va.landing.ai/v1/ade/extract/jobs';
axios.get(url, {
headers: {
'Authorization': 'Bearer YOUR_API_KEY'
}
})
.then(response => console.log(response.data))
.catch(error => console.error(error));
components:
schemas:
JobsListResponse:
properties:
jobs:
items:
$ref: '#/components/schemas/JobSummary'
type: array
title: Jobs
org_id:
anyOf:
- type: string
- type: 'null'
title: Org Id
has_more:
type: boolean
title: Has More
default: false
type: object
required:
- jobs
title: JobsListResponse
description: Response for listing jobs.
HTTPValidationError:
properties:
detail:
items:
$ref: '#/components/schemas/ValidationError'
type: array
title: Detail
type: object
title: HTTPValidationError
JobSummary:
properties:
job_id:
type: string
title: Job Id
status:
type: string
title: Status
received_at:
type: integer
title: Received At
created_at:
type: integer
title: Created At
description: >-
Unix timestamp (seconds) for when the job was created. Mirrors
received_at; exposed so clients have an explicit creation time.
default: 0
progress:
type: number
maximum: 1
minimum: 0
title: Progress
description: Job completion as a decimal from 0 (not started) to 1 (complete).
failure_reason:
anyOf:
- type: string
- type: 'null'
title: Failure Reason
type: object
required:
- job_id
- status
- received_at
- progress
title: JobSummary
description: Summary of a job for listing.
ValidationError:
properties:
loc:
items:
anyOf:
- type: string
- type: integer
type: array
title: Location
msg:
type: string
title: Message
type:
type: string
title: Error Type
type: object
required:
- loc
- msg
- type
title: ValidationError
securitySchemes:
Basic Auth:
type: http
description: >-
Your unique API key for authentication.
Get your API key here: https://va.landing.ai/settings/api-key.
If using the EU endpoint, get your API key here:
https://va.eu-west-1.landing.ai/settings/api-key.
scheme: bearer
bearerFormat: Basic
````
# ADE List Parse Jobs
Source: https://docs.landing.ai/api-reference/tools/ade-list-parse-jobs
List all async parse jobs associated with your API key. Returns the list of jobs or an error response. For EU users, use this endpoint:
or an error response. For EU users, use this endpoint:
`https://api.va.eu-west-1.landing.ai/v1/ade/parse/jobs`.
## OpenAPI
````yaml /ade/va_openapi_ade2.json get /v1/ade/parse/jobs
openapi: 3.1.0
info:
title: >-
LandingAI Agentic Document Extraction (ADE) API v1: Parse, Extract,
Classify, Split, Section
version: 0.1.0
description: >-
Convert documents such as PDFs, images, and Office files into structured
data with LandingAI's Agentic Document Extraction (ADE) v1 endpoints.
Includes Parse (documents to Markdown and structured chunks with grounding),
Extract (schema-based field extraction), Classify (page-level
classification), Split (separate multi-document files), and Section
(hierarchical table of contents), plus asynchronous jobs for parsing and
extraction. Documentation: https://docs.landing.ai
servers:
- url: https://api.va.landing.ai
description: Production vision tools API
security: []
paths:
/v1/ade/parse/jobs:
get:
tags:
- Tools
summary: ADE List Parse Jobs
description: >-
List all async parse jobs associated with your API key. Returns the list
of jobs
or an error response. For EU users, use this endpoint:
`https://api.va.eu-west-1.landing.ai/v1/ade/parse/jobs`.
operationId: tool_ade_list_parse_jobs_v1_ade_parse_jobs_get
parameters:
- name: page
in: query
required: false
schema:
type: integer
minimum: 0
description: Page number (0-indexed)
default: 0
title: Page
description: Page number (0-indexed)
- name: pageSize
in: query
required: false
schema:
type: integer
maximum: 100
minimum: 1
description: Number of items per page
default: 10
title: Pagesize
description: Number of items per page
- name: status
in: query
required: false
schema:
anyOf:
- enum:
- cancelled
- completed
- failed
- pending
- processing
type: string
- type: 'null'
description: Filter by job status.
title: Status
description: Filter by job status.
responses:
'200':
description: Successful Response
content:
application/json:
schema:
$ref: '#/components/schemas/JobsListResponse'
'422':
description: Validation Error
content:
application/json:
schema:
$ref: '#/components/schemas/HTTPValidationError'
security:
- Basic Auth: []
x-codeSamples:
- lang: cURL
label: cURL
source: |-
curl -X GET 'https://api.va.landing.ai/v1/ade/parse/jobs' \
-H 'Authorization: Bearer YOUR_API_KEY'
- lang: Python
label: Python
source: |-
import requests
headers = {
'Authorization': 'Bearer YOUR_API_KEY'
}
url = 'https://api.va.landing.ai/v1/ade/parse/jobs'
response = requests.get(url, headers=headers)
print(response.json())
- lang: JavaScript
label: Node.js
source: |-
const axios = require('axios');
const url = 'https://api.va.landing.ai/v1/ade/parse/jobs';
axios.get(url, {
headers: {
'Authorization': 'Bearer YOUR_API_KEY'
}
})
.then(response => console.log(response.data))
.catch(error => console.error(error));
components:
schemas:
JobsListResponse:
properties:
jobs:
items:
$ref: '#/components/schemas/JobSummary'
type: array
title: Jobs
org_id:
anyOf:
- type: string
- type: 'null'
title: Org Id
has_more:
type: boolean
title: Has More
default: false
type: object
required:
- jobs
title: JobsListResponse
description: Response for listing jobs.
HTTPValidationError:
properties:
detail:
items:
$ref: '#/components/schemas/ValidationError'
type: array
title: Detail
type: object
title: HTTPValidationError
JobSummary:
properties:
job_id:
type: string
title: Job Id
status:
type: string
title: Status
received_at:
type: integer
title: Received At
created_at:
type: integer
title: Created At
description: >-
Unix timestamp (seconds) for when the job was created. Mirrors
received_at; exposed so clients have an explicit creation time.
default: 0
progress:
type: number
maximum: 1
minimum: 0
title: Progress
description: Job completion as a decimal from 0 (not started) to 1 (complete).
failure_reason:
anyOf:
- type: string
- type: 'null'
title: Failure Reason
type: object
required:
- job_id
- status
- received_at
- progress
title: JobSummary
description: Summary of a job for listing.
ValidationError:
properties:
loc:
items:
anyOf:
- type: string
- type: integer
type: array
title: Location
msg:
type: string
title: Message
type:
type: string
title: Error Type
type: object
required:
- loc
- msg
- type
title: ValidationError
securitySchemes:
Basic Auth:
type: http
description: >-
Your unique API key for authentication.
Get your API key here: https://va.landing.ai/settings/api-key.
If using the EU endpoint, get your API key here:
https://va.eu-west-1.landing.ai/settings/api-key.
scheme: bearer
bearerFormat: Basic
````
# ADE Parse
Source: https://docs.landing.ai/api-reference/tools/ade-parse
Parse a document or spreadsheet.
This endpoint parses documents (PDF, images)
and spreadsheets (XLSX, CSV) into structured Markdown, chunks, and metadata.
For EU users, use this endpoint:
`https://api.va.eu-west-1.landing.ai/v1/ade/parse`.
## OpenAPI
````yaml /ade/va_openapi_ade2.json post /v1/ade/parse
openapi: 3.1.0
info:
title: >-
LandingAI Agentic Document Extraction (ADE) API v1: Parse, Extract,
Classify, Split, Section
version: 0.1.0
description: >-
Convert documents such as PDFs, images, and Office files into structured
data with LandingAI's Agentic Document Extraction (ADE) v1 endpoints.
Includes Parse (documents to Markdown and structured chunks with grounding),
Extract (schema-based field extraction), Classify (page-level
classification), Split (separate multi-document files), and Section
(hierarchical table of contents), plus asynchronous jobs for parsing and
extraction. Documentation: https://docs.landing.ai
servers:
- url: https://api.va.landing.ai
description: Production vision tools API
security: []
paths:
/v1/ade/parse:
post:
tags:
- Tools
summary: ADE Parse
description: |-
Parse a document or spreadsheet.
This endpoint parses documents (PDF, images)
and spreadsheets (XLSX, CSV) into structured Markdown, chunks, and metadata.
For EU users, use this endpoint:
`https://api.va.eu-west-1.landing.ai/v1/ade/parse`.
operationId: tool_ade_parse_v1_ade_parse_post
requestBody:
content:
multipart/form-data:
schema:
$ref: '#/components/schemas/ParseRequestWithEncryptedPassword'
required: true
responses:
'200':
description: Successful Response
content:
application/json:
schema:
$ref: '#/components/schemas/ParseResponse'
'206':
description: There were some pages that failed to be parsed.
content:
application/json:
schema:
$ref: '#/components/schemas/ParseResponse'
'422':
description: Validation Error
content:
application/json:
schema:
$ref: '#/components/schemas/HTTPValidationError'
'429':
description: Rate limit exceeded
security:
- Basic Auth: []
x-codeSamples:
- lang: cURL
label: cURL
source: |-
curl -X POST 'https://api.va.landing.ai/v1/ade/parse' \
-H 'Authorization: Bearer YOUR_API_KEY' \
-F 'document=@document.pdf' \
-F 'model=dpt-2-latest' # Set the model (optional)
- lang: Python
label: Python
source: >-
import requests
import json
headers = {
'Authorization': 'Bearer YOUR_API_KEY'
}
url = 'https://api.va.landing.ai/v1/ade/parse'
# Set the model (optional)
data = {
'model': 'dpt-2-latest'
}
# Upload a document
document = open('document.pdf', 'rb')
files = {'document': document}
response = requests.post(url, files=files, data=data,
headers=headers)
print(response.json())
- lang: JavaScript
label: Node.js
source: |-
const axios = require('axios');
const FormData = require('form-data');
const fs = require('fs');
const form = new FormData();
form.append('document', fs.createReadStream('document.pdf'));
// Set the model (optional)
form.append('model', 'dpt-2-latest');
axios.post('https://api.va.landing.ai/v1/ade/parse', form, {
headers: {
'Authorization': 'Bearer YOUR_API_KEY',
...form.getHeaders()
}
})
.then(response => console.log(response.data))
.catch(error => console.error(error));
components:
schemas:
ParseRequestWithEncryptedPassword:
properties:
model:
anyOf:
- type: string
- type: 'null'
title: Model
description: The version of the model to use for parsing.
document:
anyOf:
- type: string
format: binary
- type: 'null'
title: Document
description: >-
A file to be parsed. The file can be a PDF or an image. See the list
of supported file types here:
https://docs.landing.ai/ade/ade-file-types. Either this parameter or
the `document_url` parameter must be provided.
document_url:
anyOf:
- type: string
- type: 'null'
title: Document Url
description: >-
The URL to the file to be parsed. The file can be a PDF or an image.
See the list of supported file types here:
https://docs.landing.ai/ade/ade-file-types. Either this parameter or
the `document` parameter must be provided.
split:
anyOf:
- $ref: '#/components/schemas/SplitType'
- type: 'null'
description: >-
If you want to split documents into smaller sections, include the
split parameter. Set the parameter to page to split documents at the
page level. The splits object in the API output will contain a set
of data for each page.
password:
anyOf:
- type: string
- type: 'null'
title: Password
description: >-
Password for encrypted document files. If the document is
password-protected, provide the password to decrypt and process the
document. Ignored for unencrypted documents.
custom_prompts:
anyOf:
- type: string
contentMediaType: application/json
contentSchema:
$ref: '#/components/schemas/CustomPrompts'
- type: 'null'
title: Custom Prompts
description: >-
Optional JSON string mapping chunk types to custom parsing prompts.
Only the `figure` key is supported, for example '{"figure":"Describe
axis labels in detail."}'.
type: object
title: ParseRequestWithEncryptedPassword
ParseResponse:
properties:
markdown:
type: string
title: Markdown
chunks:
items:
$ref: '#/components/schemas/ParseChunk'
type: array
title: Chunks
splits:
items:
$ref: '#/components/schemas/ParseSplit'
type: array
title: Splits
grounding:
additionalProperties:
anyOf:
- $ref: '#/components/schemas/ParseResponseGrounding'
- $ref: '#/components/schemas/ParseResponseTableCellGrounding'
type: object
title: Grounding
metadata:
$ref: '#/components/schemas/ParseMetadata'
type: object
required:
- markdown
- chunks
- splits
- metadata
title: ParseResponse
HTTPValidationError:
properties:
detail:
items:
$ref: '#/components/schemas/ValidationError'
type: array
title: Detail
type: object
title: HTTPValidationError
SplitType:
type: string
enum:
- page
const: page
title: SplitType
ParseChunk:
properties:
markdown:
type: string
title: Markdown
type:
type: string
title: Type
id:
type: string
title: Id
grounding:
$ref: '#/components/schemas/ParseGrounding'
type: object
required:
- markdown
- type
- id
- grounding
title: ParseChunk
ParseSplit:
properties:
class:
type: string
title: Class
identifier:
type: string
title: Identifier
pages:
items:
type: integer
type: array
title: Pages
markdown:
type: string
title: Markdown
chunks:
items:
type: string
type: array
title: Chunks
type: object
required:
- class
- identifier
- pages
- markdown
- chunks
title: ParseSplit
ParseResponseGrounding:
properties:
box:
$ref: '#/components/schemas/ParseGroundingBox'
page:
type: integer
title: Page
type:
$ref: '#/components/schemas/GroundingType'
confidence:
anyOf:
- type: number
- type: 'null'
title: Confidence
low_confidence_spans:
items:
$ref: '#/components/schemas/Patch'
type: array
title: Low Confidence Spans
type: object
required:
- box
- page
- type
title: ParseResponseGrounding
ParseResponseTableCellGrounding:
properties:
box:
$ref: '#/components/schemas/ParseGroundingBox'
page:
type: integer
title: Page
type:
$ref: '#/components/schemas/GroundingType'
confidence:
anyOf:
- type: number
- type: 'null'
title: Confidence
position:
anyOf:
- $ref: '#/components/schemas/ParseResponseTableCellGroundingPosition'
- type: 'null'
type: object
required:
- box
- page
- type
title: ParseResponseTableCellGrounding
ParseMetadata:
properties:
filename:
type: string
title: Filename
org_id:
anyOf:
- type: string
- type: 'null'
title: Org Id
page_count:
type: integer
title: Page Count
duration_ms:
type: integer
title: Duration Ms
credit_usage:
type: number
title: Credit Usage
job_id:
type: string
title: Job Id
version:
anyOf:
- type: string
- type: 'null'
title: Version
failed_pages:
items:
type: integer
type: array
title: Failed Pages
type: object
required:
- filename
- org_id
- page_count
- duration_ms
- credit_usage
- job_id
- version
title: ParseMetadata
ValidationError:
properties:
loc:
items:
anyOf:
- type: string
- type: integer
type: array
title: Location
msg:
type: string
title: Message
type:
type: string
title: Error Type
type: object
required:
- loc
- msg
- type
title: ValidationError
ParseGrounding:
properties:
box:
$ref: '#/components/schemas/ParseGroundingBox'
page:
type: integer
title: Page
type: object
required:
- box
- page
title: ParseGrounding
ParseGroundingBox:
properties:
left:
type: number
title: Left
top:
type: number
title: Top
right:
type: number
title: Right
bottom:
type: number
title: Bottom
type: object
required:
- left
- top
- right
- bottom
title: ParseGroundingBox
GroundingType:
type: string
enum:
- chunkLogo
- chunkCard
- chunkAttestation
- chunkScanCode
- chunkForm
- chunkTable
- chunkFigure
- chunkText
- chunkMarginalia
- chunkTitle
- chunkPageHeader
- chunkPageFooter
- chunkPageNumber
- chunkKeyValue
- table
- tableCell
title: GroundingType
Patch:
properties:
text:
type: string
title: Text
span:
prefixItems:
- type: integer
- type: integer
type: array
maxItems: 2
minItems: 2
title: Span
confidence:
type: number
title: Confidence
type: object
required:
- text
- span
- confidence
title: Patch
ParseResponseTableCellGroundingPosition:
properties:
row:
type: integer
title: Row
col:
type: integer
title: Col
rowspan:
type: integer
title: Rowspan
colspan:
type: integer
title: Colspan
chunk_id:
type: string
title: Chunk Id
type: object
required:
- row
- col
- rowspan
- colspan
- chunk_id
title: ParseResponseTableCellGroundingPosition
securitySchemes:
Basic Auth:
type: http
description: >-
Your unique API key for authentication.
Get your API key here: https://va.landing.ai/settings/api-key.
If using the EU endpoint, get your API key here:
https://va.eu-west-1.landing.ai/settings/api-key.
scheme: bearer
bearerFormat: Basic
````
# ADE Parse Jobs
Source: https://docs.landing.ai/api-reference/tools/ade-parse-jobs
Parse documents asynchronously.
This endpoint creates a job that handles the
processing for both large documents and large batches of documents.
For EU
users, use this endpoint:
`https://api.va.eu-west-1.landing.ai/v1/ade/parse/jobs`.
## OpenAPI
````yaml /ade/va_openapi_ade2.json post /v1/ade/parse/jobs
openapi: 3.1.0
info:
title: >-
LandingAI Agentic Document Extraction (ADE) API v1: Parse, Extract,
Classify, Split, Section
version: 0.1.0
description: >-
Convert documents such as PDFs, images, and Office files into structured
data with LandingAI's Agentic Document Extraction (ADE) v1 endpoints.
Includes Parse (documents to Markdown and structured chunks with grounding),
Extract (schema-based field extraction), Classify (page-level
classification), Split (separate multi-document files), and Section
(hierarchical table of contents), plus asynchronous jobs for parsing and
extraction. Documentation: https://docs.landing.ai
servers:
- url: https://api.va.landing.ai
description: Production vision tools API
security: []
paths:
/v1/ade/parse/jobs:
post:
tags:
- Tools
summary: ADE Parse Jobs
description: |-
Parse documents asynchronously.
This endpoint creates a job that handles the
processing for both large documents and large batches of documents.
For EU
users, use this endpoint:
`https://api.va.eu-west-1.landing.ai/v1/ade/parse/jobs`.
operationId: tool_ade_parse_jobs_v1_ade_parse_jobs_post
requestBody:
required: true
content:
multipart/form-data:
schema:
$ref: '#/components/schemas/AsyncParseRequestWithEncryptedPassword'
responses:
'202':
description: Job queued successfully
content:
application/json:
schema:
$ref: '#/components/schemas/JobCreationResponse'
example:
job_id: 12345678-1234-1234-1234-123456789012
'422':
description: Validation Error
content:
application/json:
schema:
$ref: '#/components/schemas/HTTPValidationError'
'429':
description: Rate limit exceeded
security:
- Basic Auth: []
x-codeSamples:
- lang: cURL
label: cURL
source: |-
curl -X POST 'https://api.va.landing.ai/v1/ade/parse/jobs' \
-H 'Authorization: Bearer YOUR_API_KEY' \
-F 'document=@document.pdf' \
-F 'model=dpt-2-latest' # Set the model (optional)
- lang: Python
label: Python
source: >-
import requests
import json
headers = {
'Authorization': 'Bearer YOUR_API_KEY'
}
url = 'https://api.va.landing.ai/v1/ade/parse/jobs'
# Set the model (optional)
data = {
'model': 'dpt-2-latest'
}
# Upload a document
document = open('document.pdf', 'rb')
files = {'document': document}
response = requests.post(url, files=files, data=data,
headers=headers)
print(response.json())
- lang: JavaScript
label: Node.js
source: |-
const axios = require('axios');
const FormData = require('form-data');
const fs = require('fs');
const form = new FormData();
form.append('document', fs.createReadStream('document.pdf'));
// Set the model (optional)
form.append('model', 'dpt-2-latest');
axios.post('https://api.va.landing.ai/v1/ade/parse/jobs', form, {
headers: {
'Authorization': 'Bearer YOUR_API_KEY',
...form.getHeaders()
}
})
.then(response => console.log(response.data))
.catch(error => console.error(error));
components:
schemas:
AsyncParseRequestWithEncryptedPassword:
properties:
model:
anyOf:
- type: string
- type: 'null'
title: Model
description: The version of the model to use for parsing.
document:
anyOf:
- type: string
format: binary
- type: 'null'
title: Document
description: >-
A file to be parsed. The file can be a PDF or an image. See the list
of supported file types here:
https://docs.landing.ai/ade/ade-file-types. Either this parameter or
the `document_url` parameter must be provided.
document_url:
anyOf:
- type: string
- type: 'null'
title: Document Url
description: >-
The URL to the file to be parsed. The file can be a PDF or an image.
See the list of supported file types here:
https://docs.landing.ai/ade/ade-file-types. Either this parameter or
the `document` parameter must be provided.
split:
anyOf:
- $ref: '#/components/schemas/SplitType'
- type: 'null'
description: >-
If you want to split documents into smaller sections, include the
split parameter. Set the parameter to page to split documents at the
page level. The splits object in the API output will contain a set
of data for each page.
password:
anyOf:
- type: string
- type: 'null'
title: Password
description: >-
Password for encrypted document files. If the document is
password-protected, provide the password to decrypt and process the
document. Ignored for unencrypted documents.
custom_prompts:
anyOf:
- type: string
contentMediaType: application/json
contentSchema:
$ref: '#/components/schemas/CustomPrompts'
- type: 'null'
title: Custom Prompts
description: >-
Optional JSON string mapping chunk types to custom parsing prompts.
Only the `figure` key is supported, for example '{"figure":"Describe
axis labels in detail."}'.
output_save_url:
anyOf:
- type: string
- type: 'null'
title: Output Save Url
description: >-
If zero data retention (ZDR) is enabled, you must enter a URL for
the parsed output to be saved to. When ZDR is enabled, the parsed
content will not be in the API response.
type: object
title: AsyncParseRequestWithEncryptedPassword
JobCreationResponse:
properties:
job_id:
type: string
title: Job Id
type: object
required:
- job_id
title: JobCreationResponse
HTTPValidationError:
properties:
detail:
items:
$ref: '#/components/schemas/ValidationError'
type: array
title: Detail
type: object
title: HTTPValidationError
SplitType:
type: string
enum:
- page
const: page
title: SplitType
ValidationError:
properties:
loc:
items:
anyOf:
- type: string
- type: integer
type: array
title: Location
msg:
type: string
title: Message
type:
type: string
title: Error Type
type: object
required:
- loc
- msg
- type
title: ValidationError
securitySchemes:
Basic Auth:
type: http
description: >-
Your unique API key for authentication.
Get your API key here: https://va.landing.ai/settings/api-key.
If using the EU endpoint, get your API key here:
https://va.eu-west-1.landing.ai/settings/api-key.
scheme: bearer
bearerFormat: Basic
````
# ADE Section
Source: https://docs.landing.ai/api-reference/tools/ade-section
Section parsed markdown into a hierarchical table of contents.
This endpoint accepts the markdown output from /ade/parse
(with reference anchors) and returns a flat, reading-order list of
sections with hierarchy levels and reference ranges.
For EU users, use this endpoint:
`https://api.va.eu-west-1.landing.ai/v1/ade/section`.
## OpenAPI
````yaml /ade/va_openapi_ade2.json post /v1/ade/section
openapi: 3.1.0
info:
title: >-
LandingAI Agentic Document Extraction (ADE) API v1: Parse, Extract,
Classify, Split, Section
version: 0.1.0
description: >-
Convert documents such as PDFs, images, and Office files into structured
data with LandingAI's Agentic Document Extraction (ADE) v1 endpoints.
Includes Parse (documents to Markdown and structured chunks with grounding),
Extract (schema-based field extraction), Classify (page-level
classification), Split (separate multi-document files), and Section
(hierarchical table of contents), plus asynchronous jobs for parsing and
extraction. Documentation: https://docs.landing.ai
servers:
- url: https://api.va.landing.ai
description: Production vision tools API
security: []
paths:
/v1/ade/section:
post:
tags:
- Tools
summary: ADE Section
description: |-
Section parsed markdown into a hierarchical table of contents.
This endpoint accepts the markdown output from /ade/parse
(with reference anchors) and returns a flat, reading-order list of
sections with hierarchy levels and reference ranges.
For EU users, use this endpoint:
`https://api.va.eu-west-1.landing.ai/v1/ade/section`.
operationId: tool_ade_section_v1_ade_section_post
requestBody:
content:
multipart/form-data:
schema:
$ref: '#/components/schemas/SectionRequest'
required: true
responses:
'200':
description: Successful Response
content:
application/json:
schema:
$ref: '#/components/schemas/SectionResponse'
'422':
description: Validation Error
content:
application/json:
schema:
$ref: '#/components/schemas/HTTPValidationError'
security:
- Basic Auth: []
x-codeSamples:
- lang: cURL
label: cURL
source: |-
curl -X POST 'https://api.va.landing.ai/v1/ade/section' \
-H 'Authorization: Bearer YOUR_API_KEY' \
-F 'markdown=@parsed_output.md' \
-F 'model=section-latest'
- lang: Python
label: Python
source: >-
import requests
headers = {
'Authorization': 'Bearer YOUR_API_KEY'
}
url = 'https://api.va.landing.ai/v1/ade/section'
# Prepare files and data
files = {'markdown': open('parsed_output.md', 'rb')}
data = {
'model': 'section-latest'
}
# Run section classification
response = requests.post(url, files=files, data=data,
headers=headers)
# Return the results
print(response.json())
- lang: JavaScript
label: Node.js
source: |-
const axios = require('axios');
const FormData = require('form-data');
const fs = require('fs');
const form = new FormData();
form.append('markdown', fs.createReadStream('parsed_output.md'));
form.append('model', 'section-latest');
axios.post('https://api.va.landing.ai/v1/ade/section', form, {
headers: {
'Authorization': 'Bearer YOUR_API_KEY',
...form.getHeaders()
}
})
.then(response => console.log(response.data))
.catch(error => console.error(error));
components:
schemas:
SectionRequest:
properties:
markdown:
anyOf:
- type: string
format: binary
- type: string
- type: 'null'
title: Markdown
description: >-
Parsed markdown with reference anchors (). This is
the markdown field from a parse response.
markdown_url:
anyOf:
- type: string
- type: 'null'
title: Markdown Url
description: URL to fetch the markdown from.
guidelines:
anyOf:
- type: string
- type: 'null'
title: Guidelines
description: >-
Natural-language instructions to control hierarchy. Examples: 'Group
by topic', 'Treat each numbered section as a top-level entry'.
model:
anyOf:
- type: string
- type: 'null'
title: Model
description: Section model version. Defaults to latest.
type: object
title: SectionRequest
description: Request model for section endpoint.
SectionResponse:
properties:
table_of_contents:
items:
$ref: '#/components/schemas/SectionTOCEntry'
type: array
title: Table Of Contents
table_of_contents_md:
type: string
title: Table Of Contents Md
metadata:
$ref: '#/components/schemas/SectionMetadata'
type: object
required:
- table_of_contents
- table_of_contents_md
- metadata
title: SectionResponse
description: Response model for section endpoint.
HTTPValidationError:
properties:
detail:
items:
$ref: '#/components/schemas/ValidationError'
type: array
title: Detail
type: object
title: HTTPValidationError
SectionTOCEntry:
properties:
title:
type: string
title: Title
level:
type: integer
title: Level
section_number:
type: string
title: Section Number
start_reference:
type: string
title: Start Reference
type: object
required:
- title
- level
- section_number
- start_reference
title: SectionTOCEntry
description: A single entry in the flat table of contents.
SectionMetadata:
properties:
filename:
type: string
title: Filename
org_id:
anyOf:
- type: string
- type: 'null'
title: Org Id
duration_ms:
type: integer
title: Duration Ms
credit_usage:
type: number
title: Credit Usage
job_id:
type: string
title: Job Id
default: ''
version:
anyOf:
- type: string
- type: 'null'
title: Version
type: object
required:
- filename
- duration_ms
- credit_usage
title: SectionMetadata
description: Public metadata for section response.
ValidationError:
properties:
loc:
items:
anyOf:
- type: string
- type: integer
type: array
title: Location
msg:
type: string
title: Message
type:
type: string
title: Error Type
type: object
required:
- loc
- msg
- type
title: ValidationError
securitySchemes:
Basic Auth:
type: http
description: >-
Your unique API key for authentication.
Get your API key here: https://va.landing.ai/settings/api-key.
If using the EU endpoint, get your API key here:
https://va.eu-west-1.landing.ai/settings/api-key.
scheme: bearer
bearerFormat: Basic
````
# ADE Split
Source: https://docs.landing.ai/api-reference/tools/ade-split
Split classification for documents.
This endpoint classifies document sections
based on markdown content and split options.
For EU users, use this endpoint:
`https://api.va.eu-west-1.landing.ai/v1/ade/split`.
## OpenAPI
````yaml /ade/va_openapi_ade2.json post /v1/ade/split
openapi: 3.1.0
info:
title: >-
LandingAI Agentic Document Extraction (ADE) API v1: Parse, Extract,
Classify, Split, Section
version: 0.1.0
description: >-
Convert documents such as PDFs, images, and Office files into structured
data with LandingAI's Agentic Document Extraction (ADE) v1 endpoints.
Includes Parse (documents to Markdown and structured chunks with grounding),
Extract (schema-based field extraction), Classify (page-level
classification), Split (separate multi-document files), and Section
(hierarchical table of contents), plus asynchronous jobs for parsing and
extraction. Documentation: https://docs.landing.ai
servers:
- url: https://api.va.landing.ai
description: Production vision tools API
security: []
paths:
/v1/ade/split:
post:
tags:
- Tools
summary: ADE Split
description: |-
Split classification for documents.
This endpoint classifies document sections
based on markdown content and split options.
For EU users, use this endpoint:
`https://api.va.eu-west-1.landing.ai/v1/ade/split`.
operationId: tool_ade_split_v1_ade_split_post
requestBody:
content:
multipart/form-data:
schema:
$ref: '#/components/schemas/SplitRequest'
required: true
responses:
'200':
description: Successful Response
content:
application/json:
schema:
$ref: '#/components/schemas/SplitResponse'
'422':
description: Validation Error
content:
application/json:
schema:
$ref: '#/components/schemas/HTTPValidationError'
security:
- Basic Auth: []
x-codeSamples:
- lang: cURL
label: cURL
source: |-
curl -X POST 'https://api.va.landing.ai/v1/ade/split' \
-H 'Authorization: Bearer YOUR_API_KEY' \
-F 'markdown=@markdown.md' \
-F 'split_class=[{"name": "split type name", "description": "description of split type", "identifier": "unique identifier field"}]' \
-F 'model=split-latest'
- lang: Python
label: Python
source: >-
import requests
import json
headers = {
'Authorization': 'Bearer YOUR_API_KEY'
}
url = 'https://api.va.landing.ai/v1/ade/split'
# Prepare split classes
split_class = [
{
'name': 'split type name',
'description': 'description of split type',
'identifier': 'unique identifier field'
}
]
# Prepare files and data
files = {'markdown': open('markdown.md', 'rb')}
data = {
'split_class': json.dumps(split_class),
'model': 'split-latest'
}
# Run split classification
response = requests.post(url, files=files, data=data,
headers=headers)
# Return the results
print(response.json())
- lang: JavaScript
label: Node.js
source: |-
const axios = require('axios');
const FormData = require('form-data');
const fs = require('fs');
const form = new FormData();
form.append('markdown', fs.createReadStream('markdown.md'));
const splitClass = [
{
name: 'split type name',
description: 'description of split type',
identifier: 'unique identifier field'
}
];
form.append('split_class', JSON.stringify(splitClass));
form.append('model', 'split-latest');
axios.post('https://api.va.landing.ai/v1/ade/split', form, {
headers: {
'Authorization': 'Bearer YOUR_API_KEY',
...form.getHeaders()
}
})
.then(response => console.log(response.data))
.catch(error => console.error(error));
components:
schemas:
SplitRequest:
properties:
markdown:
anyOf:
- type: string
format: binary
- type: string
- type: 'null'
title: Markdown
description: The Markdown file or Markdown content to split.
markdown_url:
anyOf:
- type: string
- type: 'null'
title: Markdown Url
description: The URL to the Markdown file to split.
split_class:
items:
$ref: '#/components/schemas/SplitClass'
type: array
title: Split Class
description: >-
List of split classification options/configuration. Can be provided
as JSON string in form data.
model:
anyOf:
- type: string
- type: 'null'
title: Model
description: >-
Model version to use for split classification. Defaults to the
latest version.
default: split-20251105
type: object
required:
- split_class
title: SplitRequest
description: Request model for split classification endpoint.
SplitResponse:
properties:
splits:
items:
$ref: '#/components/schemas/SplitData'
type: array
title: Splits
metadata:
$ref: '#/components/schemas/SplitMetadata'
type: object
required:
- splits
- metadata
title: SplitResponse
description: Response model for split classification endpoint.
HTTPValidationError:
properties:
detail:
items:
$ref: '#/components/schemas/ValidationError'
type: array
title: Detail
type: object
title: HTTPValidationError
SplitClass:
properties:
name:
type: string
title: Name
description: Name of the split classification type
description:
anyOf:
- type: string
- type: 'null'
title: Description
description: Detailed description of what this split type represents
identifier:
anyOf:
- type: string
- type: 'null'
title: Identifier
description: Identifier to partition/group the splits by
type: object
required:
- name
title: SplitClass
description: Model for split classification option.
SplitData:
properties:
classification:
type: string
title: Classification
identifier:
anyOf:
- type: string
- type: 'null'
title: Identifier
pages:
items:
type: integer
type: array
title: Pages
markdowns:
items:
type: string
type: array
title: Markdowns
type: object
required:
- classification
- identifier
- pages
- markdowns
title: SplitData
description: Split data for split classification endpoint.
SplitMetadata:
properties:
filename:
type: string
title: Filename
org_id:
anyOf:
- type: string
- type: 'null'
title: Org Id
description: Organization ID
page_count:
type: integer
title: Page Count
duration_ms:
type: integer
title: Duration Ms
credit_usage:
type: number
title: Credit Usage
job_id:
type: string
title: Job Id
description: Inference history job ID
default: ''
version:
anyOf:
- type: string
- type: 'null'
title: Version
description: Model version used for split classification
type: object
required:
- filename
- page_count
- duration_ms
- credit_usage
title: SplitMetadata
description: Metadata for split classification response.
ValidationError:
properties:
loc:
items:
anyOf:
- type: string
- type: integer
type: array
title: Location
msg:
type: string
title: Message
type:
type: string
title: Error Type
type: object
required:
- loc
- msg
- type
title: ValidationError
securitySchemes:
Basic Auth:
type: http
description: >-
Your unique API key for authentication.
Get your API key here: https://va.landing.ai/settings/api-key.
If using the EU endpoint, get your API key here:
https://va.eu-west-1.landing.ai/settings/api-key.
scheme: bearer
bearerFormat: Basic
````
# LandingLens and LandingEdge Documentation
Source: https://docs.landing.ai/landinglens-landingedge
Find LandingLens and LandingEdge documentation, now hosted on a dedicated site.
LandingLens and LandingEdge documentation has moved to this dedicated site: [https://landinglens.docs.landing.ai/](https://landinglens.docs.landing.ai/).
## Review the Results
The parse results open in the Parse tab of the project view:
* **Markdown view (default):** The reading-order Markdown rendering of your document. Use this for quick visual review or to copy into a downstream tool.
* **JSON view:** The full response payload. Use this view to inspect block ids, spans, and grounding coordinates.
* **Click a block** in either view to highlight its bounding box on the document preview on the left.
* Use the **Download** and **Copy** buttons in the toolbar above the result to save or paste the current view.
For a complete reference on what each response field contains, see [Parse API Response](./parse-response).
## Customize the Parse Output with Config
To change how {dpt3pro} parses your files, open the **Config** drop-down menu on the Parse tab and update the settings. These controls set the same values as the API's [`options` field](./parse-input#request-options).
After you adjust the options, click **Apply to all files** to reparse with the new settings.
* **Captioning options:** Choose which block types are included in the Markdown (Text, Table, Figure, Attestation, Marginalia, Card, and Scan code). All are on by default. Clearing one drops that block type's content from the Markdown, but the block still appears in the JSON structure and grounding.
* **Table output format:** Choose **HTML** (default) or **Markdown** for how tables appear in the Markdown output. This control appears only while **Table** captioning is on.
* **Fine-grained output:** Include the per-line grounding detail (the `parts` array) with each block. On by default.
* **Pages:** Enter the pages to parse (for example, `1-3`) to process only part of the document. The Playground uses 1-based page numbers; the API's `options.pages` uses 0-based indices.
# Quickstart
Source: https://docs.landing.ai/dpt3/quickstart
Make your first Parse v2 API call and get structured Markdown in under five minutes.
This Quickstart walks you through your first call to the [Parse v2 API](https://docs.landing.ai/api-reference/parse/ade-parse). In under five minutes you will have a structured Markdown rendering of your document, ready for use in downstream workflows like RAG, search, or extraction.
## Prerequisites
* An {ade} [account](https://ade.landing.ai/)
* An [API key](./agentic-api-key)
## Make Your First Parse Request
This tutorial guides you through how to parse a 
### Review Low-Confidence Sections
Low-confidence sections (with scores of 0.95 or lower) are highlighted in yellow in the Playground. Highlighting low-confidence sections helps you quickly identify content that may need review.
* **Text, card, marginalia chunks**: Specific text spans within a chunk are highlighted. A single chunk can contain multiple highlighted spans.
* **Tables**: Each cell has its own confidence score. If a cell has a low score, the entire cell is highlighted.
#### Why is 0.95 the threshold for low confidence scores?
The Playground highlights content with confidence scores of 0.95 or lower because this is the threshold used internally to evaluate parsing quality.
This threshold works well for general review workflows, but your use case may require a different threshold. When [using the API](./ade-json-response#confidence-score), you can implement custom logic to identify and route content based on confidence scores that match your specific requirements.
#### Why don't I see the confidence score for files I uploaded in the past?
The confidence score displays for documents parsed on February 12, 2026 or later. If you parsed a document before that date, the Playground does not re-process documents.
If you want to see the newest results for a file that you've already parsed, re-upload it to the Playground.
### Use Confidence Scores in the API Response
The `grounding` object in the API response includes all the confidence information displayed in the Playground:
* Confidence scores for each chunk and table cell
* For text chunks with low-confidence spans: the text, confidence score, and character positions (`start`, `end`) for each span
This allows you to programmatically identify and route low-confidence content for review in your applications. Learn more in [Confidence Scores](./ade-json-response#confidence-score).
## Parsing Status
After loading multiple files into the Playground, a floating dialog box appears in the bottom right corner, showing the parsing progress for each file. If a file fails to process, an error message displays in this area.
## Multiple File Upload Not Available with ZDR
When [Zero Data Retention](./zdr) (ZDR) is enabled, only one file can be loaded at a time.
Here is the rendered Markdown for that chunk:
### Example: Lists
Here is an example of the API marking a list as a `text` chunk:
Here is the rendered Markdown for that chunk:
## Table
A `table` chunk type is a grid of rows and columns containing data.
{ade} doesn't require gridlines to be present, and typically interprets well-aligned sets of data to be part of a table. For example, part of a receipt can be extracted as a table if the purchased items align with the costs.
When you parse spreadsheets, sets of data are also interpreted as `table` chunks.
### Example: Receipt
Here is an example of the API marking receipt line items as a `table` chunk:
Here is the rendered Markdown for that chunk:
### Example: Earnings Statement
Here is an example of the API marking part of an earnings statement as a `table` chunk:
Here is the rendered Markdown for that chunk:
### Example: Spreadsheet
Here is an example of the API marking data in a spreadsheet as a `table` chunk:
Here is the rendered Markdown for that chunk:
## Marginalia
A `marginalia` chunk type is a set of text in the top, bottom, or side margins of a document, including:
* page headers
* page footers
* page numbers
* handwritten notes in margins
* line numbers on one side of a page
#### Example: Header and Page Number
Here is an example of the API marking a header and page number as a `page_header` chunk:
Here is the rendered Markdown for that chunk:
## Figure
A `figure` chunk type is an element that contains visual or graphical non-text content, including:
* pictures
* graphs (bar graphs, line graphs, etc.)
* flowcharts
* diagrams
### Example: Medical Imaging
Here is an example of the API marking a pathology image as a `figure` chunk:
Here is the rendered Markdown for that chunk:
### Example: Bar Chart
Here is an example of the API marking a bar chart as a `figure` chunk:
Here is the rendered Markdown for that chunk:
## Logo
A `logo` chunk type identifies logos.
Here is the rendered Markdown for that chunk:
## Card
A `card` chunk type identifies:
* ID cards
* driver licenses
Here is the rendered Markdown for that chunk:
## Attestation
An `attestation` chunk type includes:
* signatures
* stamps
* seals
Here is the rendered Markdown for that chunk:
## Scan\_code
A `scan_code` chunk type identifies:
* QR codes
* bar codes
Here is the rendered Markdown for these chunks:
# Classify
Source: https://docs.landing.ai/ade/ade-classify
Label each page of a document by type to route pages before parsing (Preview).
Use the [{classify} API](https://docs.landing.ai/api-reference/tools/ade-classify) to classify each page in a document by type. You provide a document and a list of classes, and the API assigns a class to each page.
Use those classes to decide how to handle each page downstream, for example which pages to parse, how to split a batch, or which extraction schema to apply.
5. The **View Code** pop-up opens. Click the **Library** or **API** tab to see the code for each method.
6. Click the **Download** or **Copy** buttons to get the code.
### Export the Class Schema to JSON
After you create a class schema, you can export it as a JSON file that you can pass to the `classes` parameter in the [API](#classify-with-the-api), [Python library](./ade-python#classify-getting-started), or [TypeScript library](./ade-typescript#classify-getting-started).
**To export the schema:**
1. Go to the [Playground](https://ade.landing.ai/).
2. Open a project.
3. Click the **Classify** tab.
4. In the **Schema** panel, click **...** and select **Download Classes**. The classes and descriptions are downloaded as a JSON file.
## Classify with the API
Classify the pages in a document by calling the [{classify}](https://docs.landing.ai/api-reference/tools/ade-classify) endpoint.
This example classifies a document that may contain invoices, bank statements, and earnings statements. The `invoice` and `bank_statement` classes include descriptions; `earnings statement` does not.
```shell theme={null}
curl -X POST 'https://api.va.landing.ai/v1/ade/classify' \
-H 'Authorization: Bearer YOUR_API_KEY' \
-F 'classes=[{"class":"invoice","description":"A commercial bill with line items, totals, and payment terms"},{"class":"bank_statement","description":"A monthly summary of account transactions"},{"class":"earnings statement"}]' \
-F 'document=@document.pdf'
```
### Parameters
Get the full parameters from the [API reference](https://docs.landing.ai/api-reference/tools/ade-classify).
| Parameter | Required | Description |
| -------------- | --------------------- | --------------------------------------------------------------------------------------------------------------------------------------------- |
| `document` | Required (choose one) | The document file to classify. |
| `document_url` | Required (choose one) | A URL pointing to the document to classify. |
| `classes` | Required | A JSON array of class objects. Each object requires a `class` name and accepts an optional `description`. Pass as a JSON string in form data. |
| `model` | Optional | The classification model version to use (for example, `classify-20260420`). Defaults to the latest version. |
## Use Classify with Our Libraries
Click one of the tiles below to learn how to use the [{classify}](https://docs.landing.ai/api-reference/tools/ade-classify) API with our libraries.
The `grounding` field from the response is shown below, with key elements highlighted in the table:
| Line | Description | ID |
| ------- | ---------------------------------------------------- | ------------------------------------ |
| Line 1 | `grounding` object | N/A |
| Line 2 | Grounding for the table chunk | ef24b1ea-8dac-4a24-bf60-704b7c6a6bca |
| Line 12 | Grounding for the table ID | 0-1 |
| Line 22 | Grounding for the first cell in the table, "Cell 1" | 0-2 |
| Line 39 | Grounding for the second cell in the table, "Cell 2" | 0-3 |
```json [expandable] lines highlight={1, 2, 12, 22, 39 } theme={null}
"grounding": {
"ef24b1ea-8dac-4a24-bf60-704b7c6a6bca": {
"box": {
"left": 0.15924638509750366,
"top": 0.19857195019721985,
"right": 0.6330658197402954,
"bottom": 0.313611775636673
},
"page": 0,
"type": "chunkTable"
},
"0-1": {
"box": {
"left": 0.16758577831817933,
"top": 0.20629539815303133,
"right": 0.6280869812016796,
"bottom": 0.3090690124206503
},
"page": 0,
"type": "table"
},
"0-2": {
"box": {
"left": 0.16758577831817933,
"top": 0.2062988834957917,
"right": 0.39743034595752635,
"bottom": 0.3090588206865543
},
"page": 0,
"type": "tableCell",
"position": {
"row": 0,
"col": 0,
"rowspan": 1,
"colspan": 1,
"chunk_id": "ef24b1ea-8dac-4a24-bf60-704b7c6a6bca"
}
},
"0-3": {
"box": {
"left": 0.3972880070815534,
"top": 0.20629539815303133,
"right": 0.6280869812016795,
"bottom": 0.3090690124206503
},
"page": 0,
"type": "tableCell",
"position": {
"row": 0,
"col": 1,
"rowspan": 1,
"colspan": 1,
"chunk_id": "ef24b1ea-8dac-4a24-bf60-704b7c6a6bca"
}
}
},
```
### Table Cell Position Information
Each table cell (`tableCell`) includes a `position` field that provides the cell's location in the table grid.
The `row` and `col` values are zero-indexed, where `row: 0, col: 0` represents the first row and first column of the table.
The `rowspan` and `colspan` values indicate merged cells, with a value of 1 meaning the cell is not merged in that direction.
| Field | Description |
| ---------- | ------------------------------------------- |
| `row` | Row position (integer, zero-indexed). |
| `col` | Column position (integer, zero-indexed). |
| `rowspan` | Number of rows the cell spans (integer). |
| `colspan` | Number of columns the cell spans (integer). |
| `chunk_id` | Associated chunk identifier (string). |
#### How to Use the Table Cell Positions
Use the table cell position information in the `grounding` to:
* Map parsed data back to specific cells in the original table (for example, verify that a dollar amount came from row 2, column 3)
* Identify merged cells through the `rowspan` and `colspan` values
* Programmatically reference cells by their row and column indices
### Confidence Score
{ade} returns confidence scores for text, marginalia, card, and table chunks, as well as table cells, when parsing with {dpt2}.
Confidence scores measure the confidence level of parsed text in Markdown with respect to the actual text or visual data present in the document.
Each score ranges from 0.0 (low confidence) to 1.0 (high confidence). Lower values indicate regions where the model was less certain about the output.
For example, here are the confidence score fields for a chunk:
```json Confidence score fields highlight={11-12} theme={null}
"grounding": {
"42ca60d3-a606-4c9a-a61e-493966b63fd9": {
"box": {
"left": 0.8607578277587891,
"top": 0.9346558451652527,
"right": 0.9316827058792114,
"bottom": 0.951895534992218
},
"page": 0,
"type": "chunkMarginalia",
"confidence": 0.991,
"low_confidence_spans": []
}
}
```
#### Confidence Score Fields
The grounding object includes the following confidence-related fields:
| Field | Description |
| ---------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `confidence` | Overall confidence score for the chunk or element (0.0 to 1.0). When `low_confidence_spans` contains entries, this is the lowest of the span-level or cell-level confidence scores. |
| `low_confidence_spans` | Array of objects identifying specific text substrings with confidence scores of 0.95 or lower. Empty array if no low-confidence substrings exist. Learn more in [Low Confidence Spans](#low-confidence-spans). |
#### Low Confidence Spans
The `low_confidence_spans` array contains substrings that have confidence scores of 0.95 or lower.
Each object in the `low_confidence_spans` array contains:
| Field | Description |
| ------------ | ---------------------------------------------------------------------------------------------------------------------------------------------- |
| `text` | The substring that has low confidence. |
| `span` | Two-element array `[start, end]` indicating the character position range of the substring within the chunk's text. Positions are zero-indexed. |
| `confidence` | Confidence score for this specific substring (0.0 to 1.0). |
For example, in the parse response below, the API has low confidence for two spans of text within the chunk.
```json Low confidence spans highlight={11-27} theme={null}
"grounding": {
"6be7d3cf-6664-42c7-906c-4957882e40ff": {
"box": {
"left": 0.24479518830776215,
"top": 0.9269143342971802,
"right": 0.6804859042167664,
"bottom": 0.9461647272109985
},
"page": 0,
"type": "chunkText",
"confidence": 0.25,
"low_confidence_spans": [
{
"text": " 1220",
"span": [
8,
13
],
"confidence": 0.25
},
{
"text": "15?\"",
"span": [
26,
30
],
"confidence": 0.62
}
]
}
}
```
#### Chunks and Elements with Confidence Scores
Confidence scores are available for text, marginalia, card, and table chunks, as well as table cells.
The `tableCell` elements have confidence scores but do not include `low_confidence_spans`