Azure Document Intelligence - Mismatch Between Trained API Version and Response API Version

Question

Hello,

I have trained a Custom Neural Model under Document Intelligence API Version 2023-07-31.
When performing inference via the API, the "apiVersion" field is not consistent with the trained API version. Inference returns a response with API version 2024-02-29-preview. The problem is consistent across custom neural models and prebuilt-layout. The issue persists when specifying a model version directly in the HTTP request.

Can anyone recreate this behavior, and could MS please advise on or fix this issue?

Test Case 1: Custom Neural Model

Model:

Code:

import os
import yaml
from azure.core.credentials import AzureKeyCredential
from azure.ai.documentintelligence import DocumentIntelligenceClient

def load_azure_config(directory="."):
    filename = "azure_config.yaml"
    filepath = os.path.join(directory, filename)
    with open(filepath, "r") as stream:
        try:
            azure_config = yaml.safe_load(stream)
        except yaml.YAMLError as exc:   
            print(exc)
    return azure_config

def get_doc_client(AzureKeys):
    key = AzureKeys["DocumentIntelligence"]["KEY_1"]
    endpoint = AzureKeys["DocumentIntelligence"]["AZURE_ENDPOINT"]
    return DocumentIntelligenceClient(endpoint, AzureKeyCredential(key))

def analyze_document(filepath, doc_client, model_id):
    with open(filepath, "rb") as f:
        poller = doc_client.begin_analyze_document(
            model_id=model_id, 
            analyze_request=f, 
            content_type="application/octet-stream"
        )
    return poller.result()

# Main execution
if __name__ == "__main__":
    # Load Azure configuration
    AzureKeys = load_azure_config()['AzureKeys']

    # Set up the Document Intelligence client
    doc_client = get_doc_client(AzureKeys)

    # Define the input file and model ID
    input_file = r"C:\Users\xxxxxx\Downloads\AIAI2021 (1).pdf"
    model_id = "test-response"

    # Analyze the document
    result = analyze_document(input_file, doc_client, model_id)

    # Print the result (you can modify this to process the result as needed)
    print(result.as_dict())

Response:

{'apiVersion': '2024-02-29-preview', 'modelId': 'test-response', 'stringIndexType': 'textElements', 'content': 'Goldsmiths Research Online\nCitation\nGoldsmiths Research Online (GRO) is the institutional research repository for Goldsmiths, University of London\nOlaniyan, Rapheal; Stamate, Daniel and Pu, Ida. 2021. \'A Two-Step Optimised BERT-Based NLP Algorithm for Extracting Sentiment from Financial News\' ... }

Test Case 2: Prebuilt Layout Model

Model:

prebuilt-layout version 2024-07-31

Code:

import os
import yaml
import requests
import time

def load_azure_config(directory="."):
    """
    Loads Azure configuration from a YAML file.
    """
    filename = "azure_config.yaml"
    filepath = os.path.join(directory, filename)
    with open(filepath, "r") as stream:
        try:
            azure_config = yaml.safe_load(stream)
        except yaml.YAMLError as exc:
            print(exc)
    return azure_config


def analyze_document_rest(filepath, azure_keys, model_id, api_version="2023-07-31"):
    """
    Submits a document for analysis using the Azure Document Intelligence REST API and polls for results.

    Args:
        filepath (str): Path to the document file to analyze.
        azure_keys (dict): Azure keys and endpoint information.
        model_id (str): Document model ID (e.g., 'prebuilt-layout').
        api_version (str): API version to use.

    Returns:
        dict: Analysis results as a dictionary, or None if an error occurred.
    """
    endpoint = azure_keys["DocumentIntelligence"]["AZURE_ENDPOINT"]
    key = azure_keys["DocumentIntelligence"]["KEY_1"]

    # Correct the REST API URL
    url = f"{endpoint}/formrecognizer/documentModels/{model_id}:analyze?api-version={api_version}"

    headers = {
        "Ocp-Apim-Subscription-Key": key,
        "Content-Type": "application/octet-stream",
    }

    try:
        # Read the file and send the request
        with open(filepath, "rb") as file_data:
            response = requests.post(url, headers=headers, data=file_data)

        # Handle initial response
        if response.status_code == 404:
            print(
                "Error 404: Endpoint or resource not found. Check your endpoint, model ID, or API version.")
            return None
        elif response.status_code != 202:
            print(f"Error: {response.status_code}")
            try:
                print(response.json())  # Print error details if available
            except Exception:
                print(response.text)  # Fallback if JSON decoding fails
            return None

        # Poll for the result using the operation-location header
        operation_location = response.headers.get("Operation-Location")
        if not operation_location:
            print("Error: Operation-Location header not found.")
            return None

        print("Document analysis submitted. Polling for results...")

        # Polling for result
        while True:
            poll_response = requests.get(operation_location, headers={
                                         "Ocp-Apim-Subscription-Key": key})
            if poll_response.status_code != 200:
                print(f"Polling Error: {poll_response.status_code}")
                print(poll_response.text)
                return None

            poll_data = poll_response.json()
            status = poll_data.get("status")
            if status == "succeeded":
                print("Analysis succeeded.")
                return poll_data
            elif status == "failed":
                print("Analysis failed.")
                print(poll_data)
                return None

            # Wait and retry polling
            print("Waiting for 5 seconds before retrying...")
            time.sleep(5)

    except Exception as e:
        print(f"An error occurred: {e}")
        return None


# Load Azure configuration
azure_keys = load_azure_config()["AzureKeys"]

# Define the input file and model ID
input_file = r"C:\Users\xxxxxxx\Downloads\AIAI2021 (1).pdf"
model_id = "prebuilt-layout"

# Specify the API version
api_version = "2023-07-31"

# Analyze the document using REST API
result = analyze_document_rest(input_file, azure_keys, model_id, api_version)

Response:

{'apiVersion': '2024-02-29-preview',
 'modelId': 'prebuilt-layout',
 'stringIndexType': 'textElements',
 'content': 'Goldsmiths Research Online\nGoldsmiths '...}