Using the CLI

Installation
Install the CLI via the Python package manager pip​
>> $ pip install lucidtech-las-cli
Make a prediction on a document
List models that are available for predictions
>> $ las models list
{
  "models": [
    {
      "modelId": "las:model:<hex>",
      ...
    }
  ],
  "nextToken": null
}
Upload a document
>> $ las documents create invoice.pdf
{
  "documentId": "las:document:<hex>",
  "contentType": "application/pdf"
}
Run inference on the document using a model
>> $ las predictions create las:document:<hex> las:model:<hex>
{
  "documentId": "las:document:<hex>",
  "predictions": [
    ...
  ]
}
Set ground truth of document
When uploading data that will be used for training and evaluation, we need to provide a ground truth. We can then use the optional parameters --ground-truth-path or --ground-truth-fields.
>> $ las documents create invoice.pdf --ground-truth-path ground_truth.json
{
  "documentId": "las:document:<hex>",
  "contentType": "application/pdf"
  "groundTruth": [
    ...
  ]
}
In this case the ground_truth.json should be on the following format
{
    "total_amount": "299.00",
    "due_date": "2020-03-20"
}
Update an existing document
If for instance a prediction reveals incorrect values in the ground truth of a document, we can update the existing document with new ground truth values.
>> $ las documents update las:document:<hex> --ground-truth-fields total_amount=300.00 due_date=2020-02-28
{
  "documentId": "las:document:<hex>",
  "groundTruth": [
    ...
  ]
}
Create a document with consent id
Consent ID is an identifier you can assign to documents to keep track of document ownership for your customers.
>> $ las documents create invoice.pdf --consent-id las:consent:<hex>
{
  "documentId": "las:document:<hex>",
  "contentType": "application/pdf",
  "consentId": "las:consent:<hex>"
}
Get document and download document content
>> $ las documents create invoice.pdf --consent-id las:consent:<hex>
{
  "documentId": "las:document:<hex>",
  "contentType": "application/pdf",
  "consentId": "las:consent:<hex>"
}
>> $ las documents get las:document:<hex> --download-content invoice2.pdf
{
  "documentId": "las:document:<hex>",
  "contentType": "application/pdf",
  "consentId": "las:consent:<hex>",
  "content": "XXXXXXXXX... [TRUNCATED]"
}
Revoking consent and deleting documents
Suppose we wish to delete all documents associated with a customer in our ERP database or other systems. We need to provide a consent_id to the prediction method that uniquely identifies the customer and use that consent_id to delete documents.
>> $ las consents delete las:consent:<hex>
{
  "consentId": "las:consent:<hex>",
  "documentIds": [
    ...
  ]
}
Create a batch and associate a few documents with it
Creating a batch is a way to group documents. This is useful for specifying batches of documents to use in improving the model later.
>> $ las batches create
{
  "batchId": "las:batch:<hex>",
  ...
}
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Quickstart
Python
Last updated 1 month ago