Merge pull request #667 from laujan/add-new-directories

add all v4 and v2.1 to new directories; remove ignite 2023 flags

Author: JamesJBarnett

articles/ai-services/.openpublishing.redirection.ai-services.json

@@ -479,6 +479,87 @@
       "source_path_from_root": "/articles/ai-services/language-service/summarization/custom/quickstart.md",
       "redirect_url": "/azure/ai-services//language-service/summarization/overview",
       "redirect_document_id": false
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/document-intelligence/concept-accuracy-confidence.md",
+      "redirect_url": "/azure/ai-services/document-intelligence/v4-concept/accuracy-confidence",
+      "redirect_document_id": true
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/document-intelligence/concept-analyze-document-response.md",
+      "redirect_url": "/azure/ai-services/document-intelligence/v4-concept/analyze-document-response",
+      "redirect_document_id": true
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/document-intelligence/label-tool.md",
+      "redirect_url": "/azure/ai-services/document-intelligence/v2-1/label-tool",
+      "redirect_document_id": true
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/document-intelligence/sdk-overview-v2-1.md",
+      "redirect_url": "/azure/ai-services/document-intelligence/v2-1/sdk-overview",
+      "redirect_document_id": true
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/document-intelligence/quickstarts/try-sample-label-tool.md",
+      "redirect_url": "/azure/ai-services/document-intelligence/v2-1/try-sample-label-tool",
+      "redirect_document_id": true
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/document-intelligence/deploy-label-tool.md",
+      "redirect_url": "/azure/ai-services/document-intelligence/v2-1/deploy-label-tool",
+      "redirect_document_id": true
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/document-intelligence/supervised-table-tags.md",
+      "redirect_url": "/azure/ai-services/document-intelligence/v2-1/supervised-table-tags",
+      "redirect_document_id": true
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/document-intelligence/concept-bank-statement.md",
+      "redirect_url": "/azure/ai-services/document-intelligence/v4-model-feature/bank-statement",
+      "redirect_document_id": true
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/document-intelligence/concept-bank-check.md",
+      "redirect_url": "/azure/ai-services/document-intelligence/v4-model-feature/bank-check",
+      "redirect_document_id": true
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/document-intelligence/concept-credit-card.md",
+      "redirect_url": "/azure/ai-services/document-intelligence/v4-model-feature/credit-card",
+      "redirect_document_id": true
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/document-intelligence/concept-marriage-certificate.md",
+      "redirect_url": "/azure/ai-services/document-intelligence/v4-model-feature/marriage-certificate",
+      "redirect_document_id": true
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/document-intelligence/concept-mortgage-documents.md",
+      "redirect_url": "/azure/ai-services/document-intelligence/v4-model-feature/mortgage-documents",
+      "redirect_document_id": true
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/document-intelligence/concept-pay-stub.md",
+      "redirect_url": "/azure/ai-services/document-intelligence/v4-model-feature/pay-stub",
+      "redirect_document_id": true
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/document-intelligence/concept-batch-analysis.md",
+      "redirect_url": "/azure/ai-services/document-intelligence/v4-model-feature/batch-analysis",
+      "redirect_document_id": true
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/document-intelligence/concept-custom-generative.md",
+      "redirect_url": "/azure/ai-services/document-intelligence/v4-model-feature/custom-generative-extraction",
+      "redirect_document_id": true
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/document-intelligence/concept-incremental-classifier.md",
+      "redirect_url": "/azure/ai-services/document-intelligence/v4-concept/incremental-classifier",
+      "redirect_document_id": true
     }
+    
   ]
 }

articles/ai-services/document-intelligence/changelog-release-history.md

@@ -5,8 +5,6 @@ description: A version-based description of Document Intelligence feature and ca
 author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
-ms.custom:
-  - ignite-2023
 ms.topic: reference
 ms.date: 09/09/2024
 ms.author: lajanuar

articles/ai-services/document-intelligence/concept-add-on-capabilities.md

@@ -5,8 +5,6 @@ description: How to increase service limit capacity with add-on capabilities.
 author: jaep3347
 manager: nitinme
 ms.service: azure-ai-document-intelligence
-ms.custom:
-  - ignite-2023
 ms.topic: conceptual
 ms.date: 05/23/2024
 ms.author: lajanuar
@@ -48,7 +46,7 @@ Document Intelligence supports more sophisticated and modular analysis capabilit
 
 * [`languages`](#language-detection)
 
-Starting with `2024-07-31-preview` release, the Read model supports searchable PDF output:
+For `2024-07-31-preview` release and later, the Read model supports searchable PDF output:
 
 * [`Searchable PDF](#searchable-pdf)
 
@@ -157,7 +155,7 @@ if result.tables:
             print(f"...Cell[{cell.row_index}][{cell.column_index}] has text '{cell.content}'")
             if cell.bounding_regions:
                 for region in cell.bounding_regions:
-                    print(f"...content on page {region.page_number} is within bounding polygon '{region.polygon}'")   
+                    print(f"...content on page {region.page_number} is within bounding polygon '{region.polygon}'")
 ```
 > [!div class="nextstepaction"]
 > [View samples on GitHub.](https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/Python(v4.0)/Add-on_capabilities/sample_analyze_addon_highres.py)
@@ -372,7 +370,7 @@ poller = document_intelligence_client.begin_analyze_document(
     "prebuilt-layout",
     AnalyzeDocumentRequest(url_source=formUrl),
     features=[DocumentAnalysisFeature.FORMULAS],  # Specify which add-on capabilities to enable
-)       
+)
 result: AnalyzeResult = poller.result()
 
 # [START analyze_formulas]
@@ -382,7 +380,7 @@ for page in result.pages:
         inline_formulas = [f for f in page.formulas if f.kind == "inline"]
         display_formulas = [f for f in page.formulas if f.kind == "display"]
 
-        # To learn the detailed concept of "polygon" in the following content, visit: https://aka.ms/bounding-region 
+        # To learn the detailed concept of "polygon" in the following content, visit: https://aka.ms/bounding-region
         print(f"Detected {len(inline_formulas)} inline formulas.")
         for formula_idx, formula in enumerate(inline_formulas):
             print(f"- Inline #{formula_idx}: {formula.value}")
@@ -509,7 +507,7 @@ poller = document_intelligence_client.begin_analyze_document(
     "prebuilt-layout",
     AnalyzeDocumentRequest(url_source=formUrl),
     features=[DocumentAnalysisFeature.STYLE_FONT]    # Specify which add-on capabilities to enable.
-)       
+)
 result: AnalyzeResult = poller.result()
 
 # [START analyze_fonts]
@@ -754,7 +752,7 @@ poller = document_intelligence_client.begin_analyze_document(
     "prebuilt-read",
     AnalyzeDocumentRequest(url_source=formUrl),
     features=[DocumentAnalysisFeature.BARCODES]    # Specify which add-on capabilities to enable.
-)       
+)
 result: AnalyzeResult = poller.result()
 
 # [START analyze_barcodes]
@@ -851,7 +849,7 @@ poller = document_intelligence_client.begin_analyze_document(
     "prebuilt-layout",
     AnalyzeDocumentRequest(url_source=formUrl),
     features=[DocumentAnalysisFeature.LANGUAGES]     # Specify which add-on capabilities to enable.
-)       
+)
 result: AnalyzeResult = poller.result()
 
 # [START analyze_languages]
@@ -934,7 +932,7 @@ for lang_idx, lang in enumerate(result.languages):
 
 ## Searchable PDF
 
-The searchable PDF capability enables you to convert an analog PDF, such as scanned-image PDF files, to a PDF with embedded text. The embedded text enables deep text search within the PDF's extracted content by overlaying the detected text entities on top of the image files. 
+The searchable PDF capability enables you to convert an analog PDF, such as scanned-image PDF files, to a PDF with embedded text. The embedded text enables deep text search within the PDF's extracted content by overlaying the detected text entities on top of the image files.
 
   > [!IMPORTANT]
   >
@@ -969,10 +967,9 @@ GET /documentModels/prebuilt-read/analyzeResults/{resultId}/pdf
 Content-Type: application/pdf
 ```
 
-
 ## Key-value Pairs
 
-In earlier API versions, the prebuilt-document model extracted key-value pairs from forms and documents. With the addition of the `keyValuePairs` feature to prebuilt-layout, the layout model now produces the same results. 
+In earlier API versions, the `prebuilt-document` model extracted key-value pairs from forms and documents. With the addition of the `keyValuePairs` feature to prebuilt-layout, the layout model now produces the same results.
 
 Key-value pairs are specific spans within the document that identify a label or key and its associated response or value. In a structured form, these pairs could be the label and the value the user entered for that field. In an unstructured document, they could be the date a contract was executed on based on the text in a paragraph. The AI model is trained to extract identifiable keys and values based on a wide variety of document types, formats, and structures.
 
@@ -1030,7 +1027,7 @@ For query field extraction, specify the fields you want to extract and Document
 formUrl = "https://github.com/Azure-Samples/document-intelligence-code-samples/blob/main/Data/invoice/simple-invoice.png?raw=true"
 poller = document_intelligence_client.begin_analyze_document(
     "prebuilt-layout",
-    AnalyzeDocumentRequest(url_source=formUrl),   
+    AnalyzeDocumentRequest(url_source=formUrl),
     features=[DocumentAnalysisFeature.QUERY_FIELDS],    # Specify which add-on capabilities to enable.
     query_fields=["Address", "InvoiceNumber"],  # Set the features and provide a comma-separated list of field names.
 )

articles/ai-services/document-intelligence/concept-business-card.md

@@ -5,8 +5,6 @@ description: OCR and machine learning based business card scanning in Document I
 author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
-ms.custom:
-  - ignite-2023
 ms.topic: conceptual
 ms.date: 05/23/2024
 ms.author: lajanuar

articles/ai-services/document-intelligence/concept-composed-models.md

@@ -46,7 +46,7 @@ In previous versions, the `model compose` operation performed an implicit classi
 
 The new `model compose` operation requires you to train an explicit classifier and provides several benefits.
 
-* **Continual incremental improvement**. You can consistently improve the quality of the classifier by adding more samples and [incrementally improving classification]( concept-incremental-classifier.md). This fine tuning ensures your documents are always routed to the right model for extraction.
+* **Continual incremental improvement**. You can consistently improve the quality of the classifier by adding more samples and [incrementally improving classification]( v4-concept/incremental-classifier.md). This fine tuning ensures your documents are always routed to the right model for extraction.
 
 * **Complete control over routing**. By adding confidence-based routing, you provide a confidence threshold for the document type and the classification response.
 
@@ -84,7 +84,7 @@ Composed models are billed the same as individual custom models. The pricing is
 
 ::: moniker range="<=doc-intel-3.1.0"
 
-## Use model compose
+## Use the model compose operation
 
 * Start by creating a list of all the model IDs you want to compose into a single model.
 

articles/ai-services/document-intelligence/concept-contract.md

@@ -5,8 +5,6 @@ description: Automate contract data extraction with Document Intelligence's cont
 author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
-ms.custom:
-  - ignite-2023
 ms.topic: conceptual
 ms.date: 08/07/2024
 ms.author: lajanuar
@@ -106,7 +104,7 @@ The following are the fields extracted from a contract in the JSON output respon
 | ExpirationDate | Date |Date when the contract ends| One year |
 | EffectiveDate  | Date |Date when the contract is in effect| immediately |
 | RenewalDate | Date |Date when the contract needs to be renewed| `On this twenty-third day of February two thousand and twenty two` |
-| ContractDuration | String | Contract terms | 5 years |
+| ContractDuration | String | Contract terms | Five (5) years |
 | Jurisdictions | Array | List of jurisdictions| |
 
 The contract key-value pairs and line items extracted are in the `documentResults` section of the JSON output.

articles/ai-services/document-intelligence/concept-custom-classifier.md

@@ -67,7 +67,7 @@ With custom models, you need to maintain access to the training dataset to updat
 >
 > Incremental training is only supported with models trained with the same API version. If you are trying to extend a model, use the API version the original model was trained with to extend the model. Incremental training is only supported with API version **2024-07-31-preview** or later.
 
-Incremental training requires that you provide the original model ID as the `baseClassifierId`. See [incremental training](concept-incremental-classifier.md) to learn more about how to use incremental training.
+Incremental training requires that you provide the original model ID as the `baseClassifierId`. See [incremental training](v4-concept/incremental-classifier.md) to learn more about how to use incremental training.
 
 ### Office document type support
 

articles/ai-services/document-intelligence/concept-custom-lifecycle.md

@@ -5,8 +5,6 @@ description: Document Intelligence custom model lifecycle and management guide.
 author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
-ms.custom:
-  - ignite-2023
 ms.topic: conceptual
 ms.date: 02/29/2024
 ms.author: lajanuar

articles/ai-services/document-intelligence/concept-custom-neural.md

@@ -106,7 +106,7 @@ Tabular fields provide **table, row and cell confidence** starting with the ```2
   * Row confidence, a measure of recognition of an individual row.
   * Cell confidence, a measure of recognition of an individual cell.
 
-* The recommended approach is to review the accuracy in a top-down manner starting with the table first, followed by the row and then the cell. See  [confidence and accuracy scores](concept-accuracy-confidence.md) to learn more about table, row, and cell confidence.
+* The recommended approach is to review the accuracy in a top-down manner starting with the table first, followed by the row and then the cell. See  [confidence and accuracy scores](v4-concept/accuracy-confidence.md) to learn more about table, row, and cell confidence.
 
 ### Supported languages and locales
 
@@ -327,7 +327,8 @@ GET /documentModels/{myCustomModel}
 For Document Intelligence versions `v3.1 (2023-07-31) and v3.0 (2022-08-31)`, you receive a maximum 30 minutes of training duration per model, and a maximum of 20 trainings for free per month. If you would like to train more than 20 model instances, you can create an [Azure support ticket](service-limits.md#create-and-submit-support-request) to increase in the training limit. For Azure support ticket, enter in the `summary` field: `Increase Document Intelligence custom neural training (TPS) limit`. 
 
 > [!IMPORTANT]
-> * When increasing the training limit, note that 2 custom neural model training sessions will be considered as 1 training hour. For more details on the pricing for increasing the number of training sessions, refer to the [pricing page](https://azure.microsoft.com/pricing/details/ai-document-intelligence/).
+>
+> * When increasing the training limit, note that 2 custom neural model training sessions will be considered as 1 training hour. For more information on the pricing for increasing the number of training sessions, *see** the [pricing page](https://azure.microsoft.com/pricing/details/ai-document-intelligence/).
 > * Azure support ticket for training limit increase can only apply at a **resource-level**, not a subscription level. You can request a training limit increase for a single Document Intelligence resource by specifying your resource ID and region in the support ticket.
 
 If you want to train models for longer durations than 30 minutes, we support **paid training** with our newest version, `v4.0 (2024-07-31-preview)`. Using the latest version, you can train your model for a longer duration to process larger documents. For more information about paid training, *see* [Billing v4.0](service-limits.md#billing).
@@ -341,7 +342,8 @@ If you want to train models for longer durations than 30 minutes, we support **p
 For Document Intelligence versions `v3.1 (2023-07-31) and v3.0 (2022-08-31)`, you receive a maximum 30 minutes of training duration per model, and a maximum of 20 trainings for free per month. If you would like to train more than 20 model instances, you can create an [Azure support ticket](service-limits.md#create-and-submit-support-request) to increase in the training limit. For Azure support ticket, enter in the `summary` field: `Increase Document Intelligence custom neural training (TPS) limit`. 
 
 > [!IMPORTANT]
-> * When increasing the training limit, note that 2 custom neural model training sessions will be considered as 1 training hour. For more details on the pricing for increasing the number of training sessions, refer to the [pricing page](https://azure.microsoft.com/pricing/details/ai-document-intelligence/).
+>
+> * When increasing the training limit, note that 2 custom neural model training sessions will be considered as 1 training hour. For more information on the pricing for increasing the number of training sessions, *see* the [pricing page](https://azure.microsoft.com/pricing/details/ai-document-intelligence/).
 > * Azure support ticket for training limit increase can only apply at a **resource-level**, not a subscription level. You can request a training limit increase for a single Document Intelligence resource by specifying your resource ID and region in the support ticket.
 
 If you want to train models for longer durations than 30 minutes, we support **paid training** with our newest version, `v4.0 (2024-07-31)`. Using the latest version, you can train your model for a longer duration to process larger documents. For more information about paid training, *see* [Billing v4.0](service-limits.md#billing).

articles/ai-services/document-intelligence/concept-custom-template.md

@@ -5,8 +5,6 @@ description: Use the custom template document model to train a model to extract
 author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
-ms.custom:
-  - ignite-2023
 ms.topic: conceptual
 ms.date: 08/07/2024
 ms.author: lajanuar