Batch analysis result not retrievable

[martrdev]

• Package Name: azure-ai-documentintelligence
• Package Version: 1.0.2
• Operating System: Windows 10
• Python Version: 3.13.11

Describe the bug
I tried following the sample tutorials sample_analyze_batch_documents.py and sample_analyze_batch_documents_async.py, and in both cases, the mechanism to verify whether the operation is concluded does not work.
Specifically, I always get the message "The batch analyze is still in process..." even when I know for a fact that the analysis has concluded.

I also tried implementing a solution by using the method add_done_callback but this also does not work. Unfortunately, there is no sample that illustrates the correct use of this method to monitor a batch analysis.

To Reproduce
Steps to reproduce the behavior:

1. run the samples.

Expected behavior
The prints should reflect the status of the batch analysis operation.

[github-actions]

Thanks for the feedback! We are routing this to the appropriate team for follow-up. cc Vinod Kurpad (@vkurpad) Yung-Shin Lin (@yungshinlintw).

[jennifermorgan1110-commits]

#44572

[martrdev]

For anyone that is facing my same issues, I managed to find a workaround using the POST and GET requests from Python:

# coding: utf-8
import os
import time
import requests
from dotenv import load_dotenv, find_dotenv

# Load environment variables from .env file
load_dotenv(find_dotenv())

endpoint = os.environ["DOCUMENTINTELLIGENCE_ENDPOINT"]
key = os.environ["DOCUMENTINTELLIGENCE_API_KEY"]
result_container_sas_url = os.environ["RESULT_CONTAINER_SAS_URL"]
batch_training_data_container_sas_url = os.environ["TRAINING_DATA_CONTAINER_SAS_URL"]

model_id = "prebuilt-layout"
api_version = "2024-11-30"

# Step 1: Launch batch analysis
url = f"{endpoint}/documentintelligence/documentModels/{model_id}:analyzeBatch?api-version={api_version}"

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

payload = {
    "azureBlobSource": {
        "containerUrl": batch_training_data_container_sas_url
    },
    "resultContainerUrl": result_container_sas_url,
    "resultPrefix": "batchResults/"     # <--- this is optional
}

print("Submitting batch analysis request...")
response = requests.post(url, headers=headers, json=payload)
response.raise_for_status()

operation_location = response.headers.get("operation-location")
print("Batch analysis started. Polling URL:", operation_location)

# Step 2: Poll for results
while True:
    poll_response = requests.get(operation_location, headers={"Ocp-Apim-Subscription-Key": api_key})
    poll_response.raise_for_status()
    result = poll_response.json()

    status = result.get("status")
    print("Current status:", status)

    if status in ["succeeded", "failed"]:
        break

    time.sleep(5)  # wait before polling again

# Step 3: Print final result summary
if status == "succeeded":
    print("Batch analysis succeeded.")
    print(result)
else:
    print("Batch analysis failed.")
    print(result)

and this is what it should show (in this example, we are only analysing one docx file):

[krishan-patel001]

I am also seeing a similar issue. I am setting up a batch request using the async interface like the following and saving the continuation token.

batch_request = AnalyzeBatchDocumentsRequest(
    azure_blob_source=azure_blob_source,
    result_container_url=container_url_with_sas,
    result_prefix=result_prefix,
    overwrite_existing=True,  
)

poller = await self._document_intelligence_client.begin_analyze_batch_documents("prebuilt-layout", batch_request, locale="en")

continuation_token = poller.continuation_token()

Then in a queue I poll the job using the previously saved continuation token and print out all the statuses.

poller = await self._document_intelligence_client.get_analyze_batch_result(continuation_token)
print(f"Poller done: {poller.done()}")
print(f"Poller status: {poller.status()}")

result: AnalyzeBatchResult = await poller.result()
print(f"Poller succeeded count: {result.succeeded_count}")
print(f"Poller failed count: {result.failed_count}")
print(f"Poller skipped count: {result.skipped_count}")

However even when the result is present, and therefore the analysis job is completed, the done() still returns False and status() still says InProgress.

Poller done: False
Poller status: InProgress
Poller succeeded count: 5
Poller failed count: 0
Poller skipped count: 0

[krishan-patel001]

Xiang Yan (@xiangyan99) is there any update on this issue and if it will be fixed?

[jennifermorgan1110-commits]

• Package Name: azure-ai-documentintelligence
• Package Version: 1.0.2
• Operating System: Windows 10
• Python Version: 3.13.11

Describe the bug
I tried following the sample tutorials sample_analyze_batch_documents.py and sample_analyze_batch_documents_async.py, and in both cases, the mechanism to verify whether the operation is concluded does not work.
Specifically, I always get the message "The batch analyze is still in process..." even when I know for a fact that the analysis has concluded.

I also tried implementing a solution by using the method add_done_callback but this also does not work. Unfortunately, there is no sample that illustrates the correct use of this method to monitor a batch analysis.

To Reproduce
Steps to reproduce the behavior:

1. run the samples.

Expected behavior
The prints should reflect the status of the batch analysis operation.