### Install ABBYY Document AI Python SDK

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/quickstart.md

Command to install the official ABBYY Document AI Python SDK using pip, making it available for use in your project.

```bash
pip install abbyy-document-ai
```

--------------------------------

### Set Up Python Virtual Environment and Project File

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/quickstart.md

Commands to create and activate a Python virtual environment, and then create an empty Python file for the project. This ensures dependencies are isolated.

```bash
# Create a virtual environment
python3 -m venv venv
# On macOS or Linux:
source venv/bin/activate
# On Windows:
vvenv\Scripts\activate
# Create a file
touch docai.py
```

--------------------------------

### Install ABBYY Document AI SDK with pip

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/python.md

Installs the ABBYY Document AI Python SDK using the pip package manager, which is the default installer for Python packages from PyPI.

```Shell
pip install abbyy-document-ai
```

--------------------------------

### Install ABBYY Document AI SDK with Poetry

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/python.md

Adds the ABBYY Document AI Python SDK as a dependency using Poetry, a modern tool for dependency management and package publishing, utilizing a pyproject.toml file.

```Shell
poetry add abbyy-document-ai
```

--------------------------------

### Begin Basic Contract Field Extraction

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/api documentation.md

This example illustrates how to initiate the field extraction process for a Basic Contract using the ABBYY Document AI SDK. It sends a request with an input source URL pointing to the document and asserts that the extraction process has started successfully.

```python
from abbyy_document_ai import DocumentAi
import os

with DocumentAi(
    api_key_auth=os.getenv("DOCUMENTAI_API_KEY_AUTH", ""),
) as document_ai:

    res = document_ai.models.basic_contract.begin_field_extraction(request={
        "input_source": {
            "url": "https://example.com/documents/basic_contract.png",
        },
    })

    assert res.documents is not None

    # Handle response
    print(res.documents)
```

--------------------------------

### Begin Brokerage Statement Field Extraction

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/api documentation.md

This code example demonstrates how to start the field extraction process for a Brokerage Statement using the ABBYY Document AI SDK. It sends the document's URL as an input source to the service and verifies that the extraction request was successfully initiated.

```python
from abbyy_document_ai import DocumentAi
import os

with DocumentAi(
    api_key_auth=os.getenv("DOCUMENTAI_API_KEY_AUTH", ""),
) as document_ai:

    res = document_ai.models.brokerage_statement.begin_field_extraction(request={
        "input_source": {
            "url": "https://example.com/documents/brokerage_statement.png",
        },
    })

    assert res.documents is not None

    # Handle response
    print(res.documents)
```

--------------------------------

### Extract Invoice Data using ABBYY Document AI Python SDK

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/quickstart.md

Python code to initialize the Document AI SDK with an API key and send an invoice file for processing. It demonstrates how to use the 'documents/invoice' endpoint and capture the document ID from the initial response.

```python
from document_ai_sdk import DocumentAi

API_KEY="your-api-key-here"
with DocumentAi(api_key_auth=API_KEY) as document_ai:
    res = document_ai.invoice.extract_invoice_data(input_source={
        "url": "https://raw.githubusercontent.com/dotNetkow/test-cods/6c25fa2e54fb96ecf534903aef0d3cbecd71e987/Invoice%20CA_1.pdf",
    })
    
    docId = res.documents[0].id
    print(res)
```

--------------------------------

### ABBYY Document AI Invoice Processing API

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/quickstart.md

Documentation for the ABBYY Document AI API endpoints related to invoice processing, including initial submission and retrieval of extracted fields. It details the request parameters, response structures, and status handling.

```APIDOC
Endpoint: POST /documents/invoice
Description: Submits an invoice document for data extraction.
Parameters:
  input_source (object): Defines the source of the invoice document.
    url (string): URL to the invoice file.
Headers:
  Authorization: Bearer <your-api-key>

Response (Initial Submission):
  Type: JSON Array of Document Objects
  Example:
    [
      {
        "id": "r23qpmmjsc7f93o3",
        "name": "Invoice%20CA_1.pdf",
        "createdAt": "2025-04-01 20:41:26.510152",
        "status": "Pending",
        "pageCount": 1
      }
    ]
  Properties:
    id (string): Unique identifier for the submitted document.
    name (string): Original name of the document.
    createdAt (string): Timestamp of document submission.
    status (string): Current processing status (e.g., 'Pending', 'Processed').
    pageCount (integer): Number of pages in the document.

Endpoint: GET /documents/invoice/[document-id]
Description: Retrieves the extracted fields for a previously submitted invoice document.
Parameters:
  document_id (string): The ID of the document obtained from the initial submission.

Response (Processed Invoice Fields):
  Type: JSON Object
  Example:
    {
      "meta": {
        "id": "r23qpmmjsc7f93o3",
        "name": "Invoice%20CA_1.pdf",
        "createdAt": "2025-04-01 20:41:26.510152",
        "status": "Processed",
        "pageCount": 1
      },
      "fields": {
        "invoiceNumber": "9435435",
        "invoiceDate": "2021-11-11",
        "total": 4620,
        "currency": "CAD",
        "businessUnit": {
          "name": "M GROUP INC.",
          "address": "770-21ST AVENUE N\nSASKATOON SK S1K7R1\nCANADA",
          "country": "CA"
        },
        "vendor": {
          "name": "ANADAYA CANADA",
          "address": "262 Merrier Avenue, Toronto, Ontario Т1Т 3R29"
        },
        "reversedCharged": false,
        "invoiceType": {
          "invoice": true,
          "creditNote": false
        },
        "purchaseOrder": [
          {
            "orderChecked": false
          }
        ],
        "lineItems": [
          {
            "isValid": false,
            "position": 1,
            "articleNumberVendor": "864UW7",
            "description": "RECEIVER (19003)",
            "quantity": 2,
            "unitPrice": 385,
            "netPrice": 770,
            "currency": "CAD"
          },
          {
            "isValid": false,
            "position": 2,
            "articleNumberVendor": "864UW7",
            "description": "RECEIVER (85813)",
            "quantity": 2,
            "unitPrice": 385,
            "netPrice": 770,
            "currency": "CAD"
          },
          {
            "isValid": false,
            "position": 3,
            "articleNumberVendor": "384 UW5",
            "description": "RECEIVER (19053)",
            "quantity": 2,
            "unitPrice": 385,
            "netPrice": 770,
            "currency": "CAD"
          },
          {
            "isValid": false,
            "position": 4,
            "articleNumberVendor": "365UW8",
            "description": "RECEIVER (85583)",
            "quantity": 2,
            "unitPrice": 385,
            "netPrice": 770,
            "currency": "CAD"
          },
          {
            "isValid": false,
            "position": 5,
            "articleNumberVendor": "354UW7",
            "description": "RECEIVER (15003)",
            "quantity": 2,
            "unitPrice": 385,
            "netPrice": 770,
            "currency": "CAD"
          },
          {
            "isValid": false,
            "position": 6,
            "articleNumberVendor": "564UW7",
            "description": "RECEIVER (85513)",
            "quantity": 2,
            "unitPrice": 385,
            "netPrice": 770,
            "currency": "CAD"
          }
        ]
      }
    }
```

--------------------------------

### Extract Invoice Data and Poll for Completion (Python)

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/quickstart.md

This Python snippet demonstrates how to initialize the Document AI SDK, submit an invoice PDF for data extraction using a URL, and then poll the API until the document processing is complete. It showcases how to retrieve and print specific extracted fields like the invoice number.

```python
import time
from document_ai_sdk import DocumentAi

API_KEY="your-api-key-here"
with DocumentAi(api_key_auth=API_KEY) as document_ai:
    res = document_ai.invoice.extract_invoice_data(input_source={
        "url": "https://raw.githubusercontent.com/dotNetkow/test-cods/6c25fa2e54fb96ecf534903aef0d3cbecd71e987/Invoice%20CA_1.pdf",
    })
    
    docId = res.documents[0].id
    print(res)
    
    processed = False
    while not processed:
        time.sleep(3)  # Wait 3 seconds between checks
        res = document_ai.models.invoice.get_extracted_fields(
            document_id=docId
        )
        processed = res.invoice.meta.status == "Processed"
    
    print("Invoice number: ", res.invoice.fields.invoice_number)
```

--------------------------------

### Manage DocumentAi SDK Resources with Python Context Manager

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/python.md

This example demonstrates how to use the DocumentAi class as a context manager in Python for both synchronous and asynchronous operations. Utilizing 'with' statements ensures proper resource cleanup, such as closing HTTP connections and freeing memory, which is crucial for long-lived applications to prevent resource leaks.

```python
from abbyy_document_ai import DocumentAi
import os
def main():

        with DocumentAi(
                api_key_auth=os.getenv("DOCUMENTAI_API_KEY_AUTH", ""),
        ) as document_ai:
                # Rest of application here...


# Or when using async:
async def amain():

        async with DocumentAi(
                api_key_auth=os.getenv("DOCUMENTAI_API_KEY_AUTH", ""),
        ) as document_ai:
                # Rest of application here...
```

--------------------------------

### Begin Field Extraction for Purchase Orders using ABBYY Document AI SDK

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/api documentation.md

This Python example illustrates how to initiate the field extraction process for a purchase order document using the ABBYY Document AI SDK. It sets up the DocumentAi client and then calls the `begin_field_extraction` method on the purchase order model, providing a URL to the input document. The response contains information about the initiated extraction.

```python
from abbyy_document_ai import DocumentAi
import os

with DocumentAi(
    api_key_auth=os.getenv("DOCUMENTAI_API_KEY_AUTH", ""),
) as document_ai:

    res = document_ai.models.purchase_order.begin_field_extraction(request={
        "input_source": {
            "url": "https://example.com/documents/invoice.png",
        },
    })

    assert res.documents is not None

    # Handle response
    print(res.documents)
```

--------------------------------

### Start Packing List Field Extraction with ABBYY Document AI Python SDK

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/api documentation.md

This code initiates the field extraction process for a packing list document from a specified URL. It sets up the Document AI client using an API key and then invokes the `begin_field_extraction` method, providing the document's URL as input. The response, containing document processing details, is then printed.

```python
from abbyy_document_ai import DocumentAi
import os

with DocumentAi(
    api_key_auth=os.getenv("DOCUMENTAI_API_KEY_AUTH", ""),
) as document_ai:

    res = document_ai.models.packing_list.begin_field_extraction(request={
        "input_source": {
            "url": "https://example.com/documents/packing_list.png",
        },
    })

    assert res.documents is not None

    # Handle response
    print(res.documents)
```

--------------------------------

### Begin Field Extraction for Remittance Advice using ABBYY Document AI SDK

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/api documentation.md

This Python example illustrates how to initiate the field extraction process for a remittance advice document using the ABBYY Document AI SDK. It sets up the DocumentAi client and then calls the `begin_field_extraction` method on the remittance advice model, providing a URL to the input document. The response contains information about the initiated extraction.

```python
from abbyy_document_ai import DocumentAi
import os

with DocumentAi(
    api_key_auth=os.getenv("DOCUMENTAI_API_KEY_AUTH", ""),
) as document_ai:

    res = document_ai.models.remittance_advice.begin_field_extraction(request={
        "input_source": {
            "url": "https://example.com/documents/invoice.png",
        },
    })

    assert res.documents is not None

    # Handle response
    print(res.documents)
```

--------------------------------

### Override ABBYY Document AI SDK Server URL in Python

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/python.md

This example shows how to configure the ABBYY Document AI SDK to use a custom server URL instead of the default one. It initializes the SDK with a specified `server_url` parameter, allowing for connection to different API endpoints.

```python
from abbyy_document_ai import DocumentAi
import os

with DocumentAi(
        server_url="https://api.abbyy.com/document-ai",
        api_key_auth=os.getenv("DOCUMENTAI_API_KEY_AUTH", ""),
) as document_ai:

        res = document_ai.documents.list(cursor="<optional_pagination_cursor_goes_here>")

        while res is not None:
                # Handle items

                res = res.next()
```

--------------------------------

### Wrap Custom HTTP Client for ABBYY Document AI Python SDK

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/python.md

This advanced example demonstrates how to create a custom HTTP client class that wraps an existing `AsyncHttpClient` to inject custom logic, such as modifying request headers before sending. It shows how to implement the `send` method to intercept and modify requests, providing fine-grained control over network operations.

```python
from abbyy_document_ai import DocumentAi
from abbyy_document_ai.httpclient import AsyncHttpClient
import httpx

class CustomClient(AsyncHttpClient):
        client: AsyncHttpClient

        def __init__(self, client: AsyncHttpClient):
                self.client = client

        async def send(
                self,
                request: httpx.Request,
                *,
                stream: bool = False,
                auth: Union[
                        httpx._types.AuthTypes, httpx._client.UseClientDefault, None
                ] = httpx.USE_CLIENT_DEFAULT,
                follow_redirects: Union[
                        bool, httpx._client.UseClientDefault
                ] = httpx.USE_CLIENT_DEFAULT,
        ) -> httpx.Response:
                request.headers["Client-Level-Header"] = "added by client"

                return await self.client.send(
                        request, stream=stream, auth=auth, follow_redirects=follow_redirects
                )

        def build_request(
                self,
                method: str,
                url: httpx._types.URLTypes,
                *,
                content: Optional[httpx._types.RequestContent] = None,
                data: Optional[httpx._types.RequestData] = None,
                files: Optional[httpx._types.RequestFiles] = None,
                json: Optional[Any] = None,
                params: Optional[httpx._types.QueryParamTypes] = None,
                headers: Optional[httpx._types.HeaderTypes] = None,
                cookies: Optional[httpx._types.CookieTypes] = None,
                timeout: Union[
                        httpx._types.TimeoutTypes, httpx._client.UseClientDefault
                ] = httpx.USE_CLIENT_DEFAULT,
                extensions: Optional[httpx._types.RequestExtensions] = None,
        ) -> httpx.Request:
```

--------------------------------

### Begin Field Extraction for Delivery Notes using ABBYY Document AI SDK

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/api documentation.md

This Python example illustrates how to initiate the field extraction process for a delivery note document using the ABBYY Document AI SDK. It sets up the DocumentAi client and then calls the `begin_field_extraction` method on the delivery note model, providing a URL to the input document. The response contains information about the initiated extraction.

```python
from abbyy_document_ai import DocumentAi
import os

with DocumentAi(
    api_key_auth=os.getenv("DOCUMENTAI_API_KEY_AUTH", ""),
) as document_ai:

    res = document_ai.models.delivery_note.begin_field_extraction(request={
        "input_source": {
            "url": "https://example.com/documents/delivery_note.png",
        },
    })

    assert res.documents is not None

    # Handle response
    print(res.documents)
```

--------------------------------

### Retrieve Document Conversion Status with ABBYY Document AI Python SDK

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/api documentation.md

This example demonstrates how to retrieve the status and results of a previously initiated document conversion using its document ID. It queries the `get_conversion` method to check the progress and obtain the conversion results, which can then be processed or downloaded.

```python
from abbyy_document_ai import DocumentAi
import os

with DocumentAi(
    api_key_auth=os.getenv("DOCUMENTAI_API_KEY_AUTH", ""),
) as document_ai:

    res = document_ai.models.document_conversion.get_conversion(document_id="wh23anb5xjf0ntw5taase5qz")

    assert res.conversion_results is not None

    # Handle response
    print(res.conversion_results)
```

--------------------------------

### Begin Field Extraction for Hotel Invoices using ABBYY Document AI SDK

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/api documentation.md

This Python example illustrates how to initiate the field extraction process for a hotel invoice document using the ABBYY Document AI SDK. It sets up the DocumentAi client and then calls the `begin_field_extraction` method on the hotel invoice model, providing a URL to the input document. The response contains information about the initiated extraction.

```python
from abbyy_document_ai import DocumentAi
import os

with DocumentAi(
    api_key_auth=os.getenv("DOCUMENTAI_API_KEY_AUTH", ""),
) as document_ai:

    res = document_ai.models.hotel_invoice.begin_field_extraction(request={
        "input_source": {
            "url": "https://example.com/documents/hotel_invoice.png",
        },
    })

    assert res.documents is not None

    # Handle response
    print(res.documents)
```

--------------------------------

### Start Sea Waybill Field Extraction with ABBYY Document AI Python SDK

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/api documentation.md

This code initiates the field extraction process for a sea waybill document from a specified URL. It sets up the Document AI client using an API key and then invokes the `begin_field_extraction` method, providing the document's URL as input. The response, containing document processing details, is then printed.

```python
from abbyy_document_ai import DocumentAi
import os

with DocumentAi(
    api_key_auth=os.getenv("DOCUMENTAI_API_KEY_AUTH", ""),
) as document_ai:

    res = document_ai.models.sea_waybill.begin_field_extraction(request={
        "input_source": {
            "url": "https://example.com/documents/sea_waybill.png",
        },
    })

    assert res.documents is not None

    # Handle response
    print(res.documents)
```

--------------------------------

### Extract Personal Earnings Statement Fields with Python SDK

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/model/Know Your Customer/Personal Earning Statement.md

This Python example demonstrates how to use the ABBYY Document AI SDK to initiate field extraction from a document URL and then poll for the processing status until the fields are successfully extracted. It requires an API key for authentication and handles the asynchronous nature of document processing.

```python
from abbyy_document_ai import DocumentAi
import time

with DocumentAi(api_key_auth=YOUR_API_KEY) as document_ai:
    res = document_ai.models.personal_earnings_statement.begin_field_extraction(input_source={
        "url": "https://example.com/document.pdf",
    })

    assert res.documents is not None
    doc_id = res.documents[0].id

    processed = False
    while not processed:
        time.sleep(3)  # Wait 3 seconds between checks
        res = document_ai.models.personal_earnings_statement.get_extracted_fields(
            document_id=doc_id
        )
        processed = res.personal_earnings_statement.meta.status == "Processed"

    print(res.personal_earnings_statement.fields)
```

--------------------------------

### Get Arrival Notice Extracted Fields using ABBYY Document AI Python SDK

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/api documentation.md

This Python snippet retrieves the extracted fields for a specific arrival notice document using the ABBYY Document AI SDK. It initializes the client with an API key and calls the `get_extracted_fields` method on the `arrival_notice` model, asserting the response and printing the extracted data.

```python
from abbyy_document_ai import DocumentAi
import os

with DocumentAi(
    api_key_auth=os.getenv("DOCUMENTAI_API_KEY_AUTH", ""),
) as document_ai:

    res = document_ai.models.arrival_notice.get_extracted_fields(document_id="wh23anb5xjf0ntw5taase5qz")

    assert res.arrival_notice is not None

    # Handle response
    print(res.arrival_notice)
```

--------------------------------

### Begin Field Extraction for Taxi Receipts using ABBYY Document AI SDK

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/api documentation.md

This Python example illustrates how to initiate the field extraction process for a taxi receipt document using the ABBYY Document AI SDK. It sets up the DocumentAi client and then calls the `begin_field_extraction` method on the taxi receipt model, providing a URL to the input document. The response contains information about the initiated extraction.

```python
from abbyy_document_ai import DocumentAi
import os

with DocumentAi(
    api_key_auth=os.getenv("DOCUMENTAI_API_KEY_AUTH", ""),
) as document_ai:

    res = document_ai.models.taxi_receipt.begin_field_extraction(request={
        "input_source": {
            "url": "https://example.com/documents/taxi_receipt.png",
        },
    })

    assert res.documents is not None

    # Handle response
    print(res.documents)
```

--------------------------------

### Start Customs Declaration Field Extraction with ABBYY Document AI Python SDK

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/api documentation.md

This code initiates the field extraction process for a customs declaration document from a specified URL. It sets up the Document AI client using an API key and then invokes the `begin_field_extraction` method, providing the document's URL as input. The response, containing document processing details, is then printed.

```python
from abbyy_document_ai import DocumentAi
import os

with DocumentAi(
    api_key_auth=os.getenv("DOCUMENTAI_API_KEY_AUTH", ""),
) as document_ai:

    res = document_ai.models.customs_declaration.begin_field_extraction(request={
        "input_source": {
            "url": "https://example.com/documents/customs_declaration.png",
        },
    })

    assert res.documents is not None

    # Handle response
    print(res.documents)
```

--------------------------------

### Extract Form 1040 Fields with Python SDK

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/model/Know Your Customer/Form 1040, U.S. Individual Income Tax Return, 2022.md

This Python example demonstrates how to use the ABBYY Document AI SDK to initiate field extraction for a Form 1040 document from a specified URL. It includes polling logic to wait for the document processing to complete and then prints the extracted fields. An API key is required for authentication.

```python
from abbyy_document_ai import DocumentAi

with DocumentAi(api_key_auth=YOUR_API_KEY) as document_ai:
    res = document_ai.models.us_form1040.begin_field_extraction(input_source={
        "url": "https://example.com/document.pdf",
    })

    assert res.documents is not None
    doc_id = res.documents[0].id

    processed = False
    while not processed:
        time.sleep(3)  # Wait 3 seconds between checks
        res = document_ai.models.us_form1040.get_extracted_fields(
            document_id=doc_id
        )
        processed = res.us_form1040.meta.status == "Processed"

    print(res.us_form1040.fields)
```

--------------------------------

### Extract W-2 Form Data using Python SDK

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/model/Know Your Customer/W-2 Form.md

This Python code demonstrates how to use the ABBYY Document AI SDK to initiate W-2 form data extraction from a specified URL. It includes a polling mechanism to wait for the document processing to complete and then prints the extracted fields. This example highlights the asynchronous nature of document processing.

```python
from abbyy_document_ai import DocumentAi
import time

with DocumentAi(api_key_auth=YOUR_API_KEY) as document_ai:
    res = document_ai.models.us_form_w2.begin_field_extraction(input_source={
        "url": "https://example.com/document.pdf",
    })

    assert res.documents is not None
    doc_id = res.documents[0].id

    processed = False
    while not processed:
        time.sleep(3)  # Wait 3 seconds between checks
        res = document_ai.models.us_form_w2.get_extracted_fields(
            document_id=doc_id
        )
        processed = res.us_form_w2.meta.status == "Processed"

    print(res.us_form_w2.fields)
```

--------------------------------

### Start International Consignment Note Field Extraction with ABBYY Document AI Python SDK

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/api documentation.md

This code initiates the field extraction process for an international consignment note document from a specified URL. It sets up the Document AI client using an API key and then invokes the `begin_field_extraction` method, providing the document's URL as input. The response, containing document processing details, is then printed.

```python
from abbyy_document_ai import DocumentAi
import os

with DocumentAi(
    api_key_auth=os.getenv("DOCUMENTAI_API_KEY_AUTH", ""),
) as document_ai:

    res = document_ai.models.international_consignment_note.begin_field_extraction(request={
        "input_source": {
            "url": "https://example.com/documents/international_consignment_note.png",
        },
    })

    assert res.documents is not None

    # Handle response
    print(res.documents)
```

--------------------------------

### Extract Fields from Packing List using Python

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/model/Customs Clearance/Custom Clearance.md

This Python example illustrates how to leverage the ABBYY Document AI SDK to extract structured data from a Packing List. It sets up the DocumentAi client, begins the field extraction process for a document from a URL, and continuously checks the processing status until completion, then outputs the extracted fields.

```python
from abbyy_document_ai import DocumentAi

with DocumentAi(api_key_auth=YOUR_API_KEY) as document_ai:
    res = document_ai.models.packing_list.begin_field_extraction(input_source={
        "url": "https://example.com/document.pdf",
    })

    assert res.documents is not None
    doc_id = res.documents[0].id

    processed = False
    while not processed:
            time.sleep(3)  # Wait 3 seconds between checks
            res = document_ai.models.packing_list.get_extracted_fields(
                    document_id=doc_id
            )
            processed = res.packing_list.meta.status == "Processed"

    print(res.packing_list.fields)
```

--------------------------------

### Get Extracted Fields for Remittance Advice using ABBYY Document AI SDK

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/api documentation.md

This Python snippet demonstrates how to retrieve previously extracted fields for a specific remittance advice document using the ABBYY Document AI SDK. It initializes the DocumentAi client with an API key and then calls the `get_extracted_fields` method on the remittance advice model, asserting that the remittance advice data is not None.

```python
from abbyy_document_ai import DocumentAi
import os

with DocumentAi(
    api_key_auth=os.getenv("DOCUMENTAI_API_KEY_AUTH", ""),
) as document_ai:

    res = document_ai.models.remittance_advice.get_extracted_fields(document_id="wh23anb5xjf0ntw5taase5qz")

    assert res.remittance_advice is not None

    # Handle response
    print(res.remittance_advice)
```

--------------------------------

### Start Dangerous Goods Declaration Field Extraction with ABBYY Document AI Python SDK

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/api documentation.md

This code initiates the field extraction process for a dangerous goods declaration document from a specified URL. It sets up the Document AI client using an API key and then invokes the `begin_field_extraction` method, providing the document's URL as input. The response, containing document processing details, is then printed.

```python
from abbyy_document_ai import DocumentAi
import os

with DocumentAi(
    api_key_auth=os.getenv("DOCUMENTAI_API_KEY_AUTH", ""),
) as document_ai:

    res = document_ai.models.dangerous_goods_declaration.begin_field_extraction(request={
        "input_source": {
            "url": "https://example.com/documents/dangerous_goods_declaration.png",
        },
    })

    assert res.documents is not None

    # Handle response
    print(res.documents)
```

--------------------------------

### Run Python shell with ABBYY Document AI SDK using uvx

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/python.md

Launches a Python shell with the ABBYY Document AI SDK available, utilizing the uvx command for quick execution without needing to set up a full project environment.

```Shell
uvx --from abbyy-document-ai python
```

--------------------------------

### Perform Asynchronous Document Listing with ABBYY Document AI SDK

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/python.md

Demonstrates how to use the `DocumentAi` SDK asynchronously to list documents, including handling pagination. It initializes the client with an API key from an environment variable and iterates through results using `list_async`.

```python
from abbyy_document_ai import DocumentAi
import asyncio
import os

async def main():

        async with DocumentAi(
                api_key_auth=os.getenv("DOCUMENTAI_API_KEY_AUTH", ""),
        ) as document_ai:

                res = await document_ai.documents.list_async(cursor="<optional_pagination_cursor_goes_here>")

                while res is not None:
                        # Handle items

                        res = res.next()

asyncio.run(main())
```

--------------------------------

### Get Brokerage Statement Fields

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/api documentation.md

This snippet illustrates how to retrieve the extracted fields for a Brokerage Statement after its processing has been completed. It uses the document ID to query the ABBYY Document AI service and then prints the extracted data for the brokerage statement.

```python
from abbyy_document_ai import DocumentAi
import os

with DocumentAi(
    api_key_auth=os.getenv("DOCUMENTAI_API_KEY_AUTH", ""),
) as document_ai:

    res = document_ai.models.brokerage_statement.get_extracted_fields(document_id="wh23anb5xjf0ntw5taase5qz")

    assert res.brokerage_statement is not None

    # Handle response
    print(res.brokerage_statement)
```

--------------------------------

### Synchronous usage of ABBYY Document AI SDK to list documents

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/python.md

Illustrates how to initialize the ABBYY Document AI SDK client with an API key and perform a synchronous API call to list documents, including handling pagination through the `res.next()` method.

```Python
from abbyy_document_ai import DocumentAi
import os

with DocumentAi(
        api_key_auth=os.getenv("DOCUMENTAI_API_KEY_AUTH", ""),
) as document_ai:

        res = document_ai.documents.list(cursor="<optional_pagination_cursor_goes_here>")

        while res is not None:
                # Handle items

                res = res.next()
```

--------------------------------

### List Documents with ABBYY Document AI Python SDK

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/api documentation.md

This snippet demonstrates how to list documents using the ABBYY Document AI Python SDK. It initializes the client with an API key and then iteratively fetches documents, handling pagination with the `cursor` and `next()` method to retrieve all available items.

```python
from abbyy_document_ai import DocumentAi
import os

with DocumentAi(
    api_key_auth=os.getenv("DOCUMENTAI_API_KEY_AUTH", ""),
) as document_ai:

    res = document_ai.documents.list(cursor="xyz")

    while res is not None:
        # Handle items
        res = res.next()
```

--------------------------------

### Retrieve Extracted Text with ABBYY Document AI Python SDK

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/api documentation.md

This example demonstrates how to retrieve the text extracted from an image after an image-to-text extraction process has completed. It uses the document ID to fetch the results, providing access to the recognized text content.

```python
from abbyy_document_ai import DocumentAi
import os

with DocumentAi(
    api_key_auth=os.getenv("DOCUMENTAI_API_KEY_AUTH", ""),
) as document_ai:

    res = document_ai.models.image_to_text.get_extracted_text(document_id="wh23anb5xjf0ntw5taase5qz")

    assert res.extracted_text is not None

    # Handle response
    print(res.extracted_text)
```

--------------------------------

### Create and run standalone Python script with ABBYY Document AI SDK

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/python.md

Demonstrates how to write a self-contained Python script that uses the ABBYY Document AI SDK, including dependency declaration within the script for execution with `uv run`.

```Python
#!/usr/bin/env -S uv run --script
# /// script
# requires-python = ">=3.9"
# dependencies = [
#     "abbyy-document-ai",
# ]
# ///

from abbyy_document_ai import DocumentAi

sdk = DocumentAi(
    # SDK arguments
)

# Rest of script here...
```

--------------------------------

### Get Basic Contract Fields

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/api documentation.md

This Python snippet shows how to retrieve the results of a previously initiated field extraction for a Basic Contract. It uses the document ID to fetch the processed data from the ABBYY Document AI service and prints the extracted contract information.

```python
from abbyy_document_ai import DocumentAi
import os

with DocumentAi(
    api_key_auth=os.getenv("DOCUMENTAI_API_KEY_AUTH", ""),
) as document_ai:

    res = document_ai.models.basic_contract.get_extracted_fields(document_id="wh23anb5xjf0ntw5taase5qz")

    assert res.basic_contract is not None

    # Handle response
    print(res.basic_contract)
```

--------------------------------

### Begin Document Conversion with ABBYY Document AI Python SDK

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/api documentation.md

This snippet initiates a document conversion process using the ABBYY Document AI Python SDK. It specifies an input document via a URL and defines the desired output format, such as PDF. The response contains information about the newly created converted document.

```python
import abbyy_document_ai
from abbyy_document_ai import DocumentAi
import os

with DocumentAi(
    api_key_auth=os.getenv("DOCUMENTAI_API_KEY_AUTH", ""),
) as document_ai:

    res = document_ai.models.document_conversion.begin_conversion(request={
        "input_source": {
            "url": "https://example.com/documents/invoice.png",
        },
        "options": {
            "format_": abbyy_document_ai.DocumentConversionOutputFormat.PDF,
        },
    })

    assert res.documents is not None

    # Handle response
    print(res.documents)
```

--------------------------------

### Get International Consignment Note Fields

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/api documentation.md

This Python snippet demonstrates how to retrieve the extracted fields for an International Consignment Note using the ABBYY Document AI SDK. It requires a document ID for a previously processed document and asserts that the response contains the expected data.

```python
from abbyy_document_ai import DocumentAi
import os

with DocumentAi(
    api_key_auth=os.getenv("DOCUMENTAI_API_KEY_AUTH", ""),
) as document_ai:

    res = document_ai.models.international_consignment_note.get_extracted_fields(document_id="wh23anb5xjf0ntw5taase5qz")

    assert res.international_consignment_note is not None

    # Handle response
    print(res.international_consignment_note)
```

--------------------------------

### Poll ABBYY Document AI API for Processed Invoice Results

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/quickstart.md

Python code demonstrating how to poll the Document AI API to check the processing status of an invoice. It repeatedly calls the API until the document's status changes to 'Processed', then accesses and prints an extracted field.

```python
# import time module at the top of the file for waiting between field checks
import time

processed = False
while not processed:
    time.sleep(3)  # Wait 3 seconds between checks
    res = document_ai.models.invoice.get_extracted_fields(
        document_id=docId
    )
    processed = res.invoice.meta.status == "Processed"

print("Invoice number: ", res.invoice.fields.invoice_number)
```

--------------------------------

### Authenticate and List Documents Synchronously with ABBYY Document AI SDK

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/python.md

Illustrates synchronous authentication and document listing using the `DocumentAi` SDK. The API key is loaded from an environment variable, and the `documents.list` method is called to retrieve and paginate through document results.

```python
from abbyy_document_ai import DocumentAi
import os

with DocumentAi(
        api_key_auth=os.getenv("DOCUMENTAI_API_KEY_AUTH", ""),
) as document_ai:

        res = document_ai.documents.list(cursor="<optional_pagination_cursor_goes_here>")

        while res is not None:
                # Handle items

                res = res.next()
```

--------------------------------

### Extract Receipt Fields with ABBYY Document AI (Python)

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/model/Expense Management/Receipt.md

This Python example demonstrates how to use the `abbyy_document_ai` library to initiate and poll for structured data extraction from a receipt. It shows the workflow from beginning field extraction with a URL input to retrieving and printing the processed receipt fields.

```python
from abbyy_document_ai import DocumentAi
import time

with DocumentAi(api_key_auth=YOUR_API_KEY) as document_ai:
    res = document_ai.models.receipt.begin_field_extraction(input_source={
        "url": "https://example.com/document.pdf",
    })

    assert res.documents is not None
    doc_id = res.documents[0].id

    processed = False
    while not processed:
        time.sleep(3)  # Wait 3 seconds between checks
        res = document_ai.models.receipt.get_extracted_fields(
            document_id=doc_id
        )
        processed = res.receipt.meta.status == "Processed"

    print(res.receipt.fields)
```

--------------------------------

### Begin Arrival Notice Field Extraction using ABBYY Document AI Python SDK

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/api documentation.md

This Python snippet initiates the field extraction process for an arrival notice using the ABBYY Document AI SDK. It configures the client with an API key and sends a request with the URL of the arrival notice image to the `begin_field_extraction` method, asserting the response and printing the document details.

```python
from abbyy_document_ai import DocumentAi
import os

with DocumentAi(
    api_key_auth=os.getenv("DOCUMENTAI_API_KEY_AUTH", ""),
) as document_ai:

    res = document_ai.models.arrival_notice.begin_field_extraction(request={
        "input_source": {
            "url": "https://example.com/documents/arrival_notice.png",
        },
    })

    assert res.documents is not None

    # Handle response
    print(res.documents)
```

--------------------------------

### Extract Brokerage Statement Fields with Python

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/model/Contract and Brokeage Statement/Contract and Brokerage.md

This Python snippet uses the ABBYY Document AI SDK to extract data from a brokerage statement. It starts the extraction process for a document provided via URL, then repeatedly checks the processing status until completion, and outputs the extracted fields.

```python
from abbyy_document_ai import DocumentAi

with DocumentAi(api_key_auth=YOUR_API_KEY) as document_ai:
    res = document_ai.models.brokerage_statement.begin_field_extraction(input_source={
        "url": "https://example.com/document.pdf",
    })

    assert res.documents is not None
    doc_id = res.documents[0].id

    processed = False
    while not processed:
            time.sleep(3)  # Wait 3 seconds between checks
            res = document_ai.models.brokerage_statement.get_extracted_fields(
                    document_id=doc_id
            )
            processed = res.brokerage_statement.meta.status == "Processed"

    print(res.brokerage_statement.fields)
```

--------------------------------

### Delete Document with ABBYY Document AI Python SDK

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/api documentation.md

This example shows how to delete a specific document by its ID using the ABBYY Document AI Python SDK. It initializes the client with the necessary API key and then calls the `delete` method on the `documents` service, asserting a successful response upon completion.

```python
from abbyy_document_ai import DocumentAi
import os

with DocumentAi(
    api_key_auth=os.getenv("DOCUMENTAI_API_KEY_AUTH", ""),
) as document_ai:

    res = document_ai.documents.delete(document_id="wh23anb5xjf0ntw5taase5qz")

    assert res is not None

    # Handle response
    print(res)
```

--------------------------------

### List Documents with ABBYY Document AI Python SDK and Error Handling

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/python.md

This snippet demonstrates how to initialize the ABBYY Document AI SDK, list documents using pagination, and implement robust error handling for various API exceptions such as BadRequestError, UnauthorizedError, TooManyRequestsError, InternalServerError, and a generic APIError.

```python
from abbyy_document_ai import DocumentAi, models
import os

with DocumentAi(
        api_key_auth=os.getenv("DOCUMENTAI_API_KEY_AUTH", ""),
) as document_ai:
        res = None
        try:

                res = document_ai.documents.list(cursor="<optional_pagination_cursor_goes_here>")

                while res is not None:
                        # Handle items

                        res = res.next()

        except models.BadRequestError as e:
                # handle e.data: models.BadRequestErrorData
                raise(e)
        except models.UnauthorizedError as e:
                # handle e.data: models.UnauthorizedErrorData
                raise(e)
        except models.TooManyRequestsError as e:
                # handle e.data: models.TooManyRequestsErrorData
                raise(e)
        except models.InternalServerError as e:
                # handle e.data: models.InternalServerErrorData
                raise(e)
        except models.APIError as e:
                # handle exception
                raise(e)
```

--------------------------------

### Retrieve Extracted Receipt Fields with ABBYY Document AI Python SDK

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/api documentation.md

This example demonstrates how to retrieve the structured data fields extracted from a receipt after the field extraction process has completed. It uses the document ID to fetch the results, providing access to parsed information like total amount, vendor, and line items.

```python
from abbyy_document_ai import DocumentAi
import os

with DocumentAi(
    api_key_auth=os.getenv("DOCUMENTAI_API_KEY_AUTH", ""),
) as document_ai:

    res = document_ai.models.receipt.get_extracted_fields(document_id="wh23anb5xjf0ntw5taase5qz")

    assert res.receipt is not None

    # Handle response
    print(res.receipt)
```

--------------------------------

### Get Extracted Fields for Invoices using ABBYY Document AI SDK

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/api documentation.md

This Python snippet demonstrates how to retrieve previously extracted fields for a specific invoice document using the ABBYY Document AI SDK. It initializes the DocumentAi client with an API key and then calls the `get_extracted_fields` method on the invoice model, asserting that the invoice data is not None.

```python
from abbyy_document_ai import DocumentAi
import os

with DocumentAi(
    api_key_auth=os.getenv("DOCUMENTAI_API_KEY_AUTH", ""),
) as document_ai:

    res = document_ai.models.invoice.get_extracted_fields(document_id="wh23anb5xjf0ntw5taase5qz")

    assert res.invoice is not None

    # Handle response
    print(res.invoice)
```

--------------------------------

### Get Certificate Of Origin Extracted Fields using ABBYY Document AI Python SDK

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/api documentation.md

This Python snippet retrieves the extracted fields for a specific certificate of origin document using the ABBYY Document AI SDK. It initializes the client with an API key and calls the `get_extracted_fields` method on the `certificate_of_origin` model, asserting the response and printing the extracted data.

```python
from abbyy_document_ai import DocumentAi
import os

with DocumentAi(
    api_key_auth=os.getenv("DOCUMENTAI_API_KEY_AUTH", ""),
) as document_ai:

    res = document_ai.models.certificate_of_origin.get_extracted_fields(document_id="wh23anb5xjf0ntw5taase5qz")

    assert res.certificate_of_origin is not None

    # Handle response
    print(res.certificate_of_origin)
```

--------------------------------

### Get Bill Of Lading Extracted Fields using ABBYY Document AI Python SDK

Source: https://github.com/sarvottam-bhagat/abbyy-documentation/blob/main/api documentation.md

This Python snippet retrieves the extracted fields for a specific bill of lading document using the ABBYY Document AI SDK. It initializes the client with an API key and calls the `get_extracted_fields` method on the `bill_of_lading` model, asserting the response and printing the extracted data.

```python
from abbyy_document_ai import DocumentAi
import os

with DocumentAi(
    api_key_auth=os.getenv("DOCUMENTAI_API_KEY_AUTH", ""),
) as document_ai:

    res = document_ai.models.bill_of_lading.get_extracted_fields(document_id="wh23anb5xjf0ntw5taase5qz")

    assert res.bill_of_lading is not None

    # Handle response
    print(res.bill_of_lading)
```