Azure ocr #14
Azure ocr #14
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WalkthroughThe changes update the project configuration and document processing functionality to integrate Azure Document Intelligence. The environment file now includes Azure-specific variables, and the application's settings class is updated accordingly. The document processing logic in the Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant Process as process_with_textract
participant Azure as Azure Document Intelligence
User->>Process: Trigger document processing (PDF file)
Process->>Azure: Initialize client with AZURE_ENDPOINT & AZURE_AI_KEY
Process->>Azure: Submit document for OCR processing
Azure-->>Process: Return processed (searchable) PDF
Process->>Process: Save processed PDF locally
Process-->>User: Return processed document details
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Actionable comments posted: 0
🧹 Nitpick comments (5)
app/tasks/process_with_textract.py (3)
48-55: Handling of the OCR-processed PDF is well-structured.
Overwriting the existing file is convenient, though watch for conflicting processes trying to read or write the file simultaneously.
57-62: Extraction and handoff of text is straightforward.
If documents become very large, consider streaming approaches to reduce memory usage when reading content intoextracted_text.
66-66: Error logging is appropriately granular.
Catch-all exceptions can mask specific Azure exceptions. You may want to handle network errors, authentication errors, etc., separately for clarity.app/config.py (1)
27-29: New Azure settings are consistent and align with environment variables.
Consider leveraging managed secrets services rather than storing keys in environment variables when operating in production.requirements.txt (1)
15-15: Pin or verify the version for better stability.
Having a pinned version (e.g.,azure-ai-documentintelligence==x.y.z) can help avoid unexpected breaking changes.
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📒 Files selected for processing (4)
.env.demo(2 hunks)app/config.py(1 hunks)app/tasks/process_with_textract.py(2 hunks)requirements.txt(1 hunks)
✅ Files skipped from review due to trivial changes (1)
- .env.demo
🔇 Additional comments (6)
app/tasks/process_with_textract.py (6)
3-5: Imports look good and necessary for Azure integration.
These additions correctly import the Azure Document Intelligence modules needed for OCR functionality.
14-18: Client initialization is appropriate.
Using environment-based credentials for the Azure endpoint key is a best practice. Ensure the endpoint includes the correct region or domain.
23-31: Docstring updates align well with the new Azure logic.
The step-by-step explanation is helpful for maintenance and onboarding.
36-36: More explicit error handling and logging is beneficial.
Raising aFileNotFoundErroris more informative than a genericException, and the additional log statement at line 38 aids in debugging.Also applies to: 38-38
64-64: Return statement is clear and descriptive.
This return structure is helpful for debugging, as it encapsulates all relevant paths in a single dictionary.
40-47:❓ Verification inconclusive
Verify the stability of the poller’s details.
Ifpoller.details["operation_id"]becomes unavailable or changes in the future, this might break the flow. Consider adding checks or try/except around the retrieval ofoperation_id.
🏁 Script executed:
#!/bin/bash # Check for usage of operation_id and confirm "operation_id" is a valid key in poller.details. rg -A 3 'poller\.details\[\"operation_id\"\]'Length of output: 166
Action Required: Enhance Error Handling for API Response
In
app/tasks/process_with_textract.py(lines 40–47), the code directly accessesoperation_id = poller.details["operation_id"]which could fail if the API changes its response structure. Please add error handling (e.g., a try/except block or explicit key checks) to ensure that the key
"operation_id"exists before attempting to use it. Manual verification of the API response is advised to confirm that the key remains stable over time.
fixes #3
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