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jamesmcroftjamesmcroft
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Update references to GPT-4o
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‎samples/dotnet/classification/README.md‎

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| Sample | Description | Example Use Cases |
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| ------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------- |
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| [Vision-based Classification with Azure OpenAI GPT-4o](./document-classification-gpt-vision.ipynb) | Use Azure OpenAI GPT-4o models to classify documents using their built-in vision capabilities. | Processing multiple documents types or documents with varying purposes, such as contracts, legal documents, and emails. |
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| [Vision-based Classification with Azure OpenAI GPT-4.1](./document-classification-gpt-vision.ipynb) | Use Azure OpenAI GPT-4.1 models to classify documents using their built-in vision capabilities. | Processing multiple documents types or documents with varying purposes, such as contracts, legal documents, and emails. |
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| [Semantic Similarity Classification with Vector Embeddings](./document-classification-text-embeddings.ipynb) | Use Azure OpenAI embedding models to convert document text and classify them based on similarity to pre-defined classification lists | Processing multiple documents types or documents with varying purposes, such as contracts, legal documents, and emails. |

‎samples/dotnet/extraction/README.md‎

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> [!NOTE]
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> All data extraction samples provide both an accuracy and confidence score for the extracted data. The accuracy score is calculated based on the similarity between the extracted data and the ground truth data. The confidence score can be calculated based on OCR analysis confidence and `logprobs` in Azure OpenAI responses.
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| Sample | Description | Example Use Cases |
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| ------------------------------------------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
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| [Text-based Extraction with Azure AI Document Intelligence and Azure OpenAI GPT-4o](./text/document-extraction-gpt.ipynb) | Use Azure AI Document Intelligence `prebuilt-layout` and Azure OpenAI GPT models to extract structured data from documents using text. | Predominantly text-based documents such as invoices, receipts, and forms. |
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| [Text-based Extraction with Azure AI Document Intelligence and Microsoft Phi](./text/document-extraction-phi.ipynb) | Use Azure AI Document Intelligence `prebuilt-layout` and Microsoft's Phi models to extract structured data from documents using text. | Predominantly text-based documents such as invoices, receipts, and forms. |
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| [Vision-based Extraction with Azure OpenAI GPT-4o](./vision/document-extraction-gpt-vision.ipynb) | Use Azure OpenAI GPT-4o models to extract structured data from documents using vision capabilities. | Complex documents with a mix of text and images, including diagrams, signatures, selection marks, etc. such as reports and contracts. |
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| [Multi-Modal (Text and Vision) Extraction with Azure AI Document Intelligence and Azure OpenAI GPT-4o](./multimodal/document-extraction-gpt-text-and-vision.ipynb) | Improve the accuracy and confidence in extracting structured data from documents by combining text and images with LLMs. | Any structured or unstructured document type. |
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| Sample | Description | Example Use Cases |
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| ------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
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| [Text-based Extraction with Azure AI Document Intelligence and Azure OpenAI GPT-4.1](./text/document-extraction-gpt.ipynb) | Use Azure AI Document Intelligence `prebuilt-layout` and Azure OpenAI GPT models to extract structured data from documents using text. | Predominantly text-based documents such as invoices, receipts, and forms. |
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| [Text-based Extraction with Azure AI Document Intelligence and Microsoft Phi](./text/document-extraction-phi.ipynb) | Use Azure AI Document Intelligence `prebuilt-layout` and Microsoft's Phi models to extract structured data from documents using text. | Predominantly text-based documents such as invoices, receipts, and forms. |
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| [Vision-based Extraction with Azure OpenAI GPT-4.1](./vision/document-extraction-gpt-vision.ipynb) | Use Azure OpenAI GPT-4.1 models to extract structured data from documents using vision capabilities. | Complex documents with a mix of text and images, including diagrams, signatures, selection marks, etc. such as reports and contracts. |
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| [Multi-Modal (Text and Vision) Extraction with Azure AI Document Intelligence and Azure OpenAI GPT-4.1](./multimodal/document-extraction-gpt-text-and-vision.ipynb) | Improve the accuracy and confidence in extracting structured data from documents by combining text and images with LLMs. | Any structured or unstructured document type. |

‎samples/python/modules/samples/app_settings.py‎

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azure_resource_group: The name of the resource group.
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azure_storage_account_name: The name of the storage account.
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azure_ai_services_endpoint: The endpoint for the AI services.
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azure_openai_endpoint: The endpoint for the OpenAI service.
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azure_openai_chat_deployment: The name of the GPT-4o model deployment.
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azure_openai_text_embedding_deployment: The name of the text embedding model deployment.
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azure_ai_phi_endpoint: The endpoint for the Phi deployment.
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azure_ai_phi_primary_key: The primary key for the Phi deployment.
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azure_openai_endpoint: The endpoint for the Azure OpenAI service.
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azure_openai_chat_deployment: The name of the Azure OpenAI chat model deployment.
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azure_openai_text_embedding_deployment: The name of the Azure OpenAI text embedding model deployment.
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azure_ai_phi_endpoint: The endpoint for the Azure AI Foundry Phi deployment.
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azure_ai_phi_primary_key: The primary key for the Azure AI Foundry Phi deployment.
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"""
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def __init__(self, config: dict):

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