> For the complete documentation index, see [llms.txt](https://docs.mindee.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.mindee.com/split-models/extraction-model-chaining.md).
# Extraction Model Chaining
Extract data from detected split ranges.
Use Split ranges to automatically extract document data, allowing for several different extractions on a multi-page file.
## Extraction Set Up
### At Model Creation
When creating your Split model, you'll be adding document classes in the creation window.
For each document class, you can set one of your [Extraction Models](/extraction-models/extraction-models-overview.md) for chaining. The Extraction Model must exist prior to the Split Model creation.
Use the search field to filter available extraction models.
### After Model Creation
This works exactly like when creating at model creation.
Simply go to your Split model's "Utility Configuration" page and adjust as needed.
You can add new classes, remove classes, and change Extraction Models.
### Selectively Extracting
If a detected class has no linked Extraction Model, no extraction runs for that split range.
This allows selectively extracting some sections of the file while ignoring others.
Let's say you receive large multi-page PDFs from your users, where each PDF is a bundle of different scanned documents: plane tickets, travel receipts, driver license, and passport.
You need only the passports.
In your Split configuration, add a `passport` class and an `other` class, and only link an extraction model to the `passport` class.
All split ranges will get classified, but only those linked to an Extraction Model will have extraction results.
{% hint style="info" icon="lightbulb" %}
Add an `other` class in addition to classes of interest to you, unless all potential document types are known in advance.
{% endhint %}
### Putting It All Together
#### Sample Use Case: KYC
As a sample use case, let's say you have a KYC workflow, and your users upload their files. Crucially, some users upload all their documents in a single multi-page PDF, while others send them in separate files.
As a result, you receive basically anything and everything mixed together in the pipeline. Some users upload single files with documents such as : driver licenses, passports, student IDs, employee badges, social security cards, etc. And on top of that, some users send multiple people's identity in single multi-page PDF files.
As a first example, assume you only process passports. In your Split configuration, add a `passport` class and an `other` class, then chain a [Passport extraction model](/use-cases/extraction-models/passport.md) on the `passport` class. Do not chain any models on the `other` class.
Another example, if you need only government IDs (so driver license and passport), you have two choices:
* Create an `official_government_id` class and chain it to an [International ID extraction model](/use-cases/extraction-models/international-id-card.md).\
This is recommended if you accept a variety of ID documents.
* Create a `driving_license` and `passport` class and chain the [Driver's License](/use-cases/extraction-models/drivers-license.md) and [Passport](/use-cases/extraction-models/passport.md) extraction models to them, respectively.\
This is recommended if you only accept specific ID documents.
As in the earlier example, do not chain any models on the `other` class.
In the above examples, the documents classified as `other` will **not** have extraction results, while those with specific classes **will** have results.
You can of course use **any** extraction model, including fully custom ones.
#### Sample Use Case: Removing Unused Pages
It's also possible to remove pages that are never used in the Extraction. For example to remove terms and conditions from invoices, set up the classes `terms_and_conditions_page` and `invoice_page` , and only link an Extraction Model to the `invoice_page` class.
This can speed up processing, as often terms and conditions pages are slower to process than regular invoice pages. You'll also save on Extraction costs, as detailed in the [token usage section](#token-usage-with-chaining).
## Token Usage With Chaining
The initial model (Split, Crop, Classification) is provided at no additional cost to you if it triggers additional processing using an Extraction model.
The possible credit consumption scenarios are as follows:
* extraction model not chained ⇒ token usage of initial model
* extraction models chained ⇒ token usage of the extraction model, including any activated [optional features](/extraction-models/optional-features.md).
Token consumption is calculated per page as is standard for our models.
For Split models specifically, there can be multiple chained extractions on the same file. However, split ranges are non-overlapping, meaning the total number of pages processed cannot be greater than the total number of pages in the source document.
Detected split ranges not chained to an extraction model consume Split credits, not extraction credits.
Some examples:
* 2-page PDF file, a single 2-page document is detected and chained ⇒ 2 pages of Extraction credit consumed.
* 75-page PDF file, no documents detected ⇒ 75 pages of Split credit consumed.
* JPEG image file, a single document is detected and chained ⇒ 1 page of Extraction credit consumed.
* 23-page PDF file, a single 5-page document is detected and chained ⇒ 5 pages of Extraction credit consumed, 18 pages of Split credit consumed.
* 10-page PDF file, four 2-page documents are detected and chained, a single 2-page document is detected but **not** chained ⇒ 8 pages of Extraction credit consumed, 2 pages of Split credit consumed.
## Access Extraction Results
When an Extraction model is linked, each Split Range with the detected class will contain an Extraction Response object, which is identical when making an Extraction request for a single document.
Check the [Split Result](/split-models/sdk-integration/split-result.md) section for more details.
---
# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.
## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.
Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:
```
GET https://docs.mindee.com/split-models/extraction-model-chaining.md?ask=&goal=
```
`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
