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Restructure repository to prepare for dotnet samples
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‎README.md

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## Contents
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- [Samples](#samples)
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- [Document Classification](#document-classification)
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- [Document Redaction](#document-redaction)
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- [Document Extraction](#document-extraction)
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- [Getting Started](#getting-started)
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- [Setup on GitHub Codespaces](#setup-on-github-codespaces)
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- [Setup on Local](#setup-on-local)
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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 is calculated based on OCR analysis confidence and `logprobs` in Azure OpenAI requests.
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| Sample | Description | Example Use Cases |
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| --------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| [Data Extraction - Azure AI Document Intelligence + Azure OpenAI GPT-4o](./samples/extraction/text-based/document-intelligence-openai.ipynb) | Demonstrates how to use Azure AI Document Intelligence pre-built layout and Azure OpenAI GPT models to extract structured data from documents. | Predominantly text-based documents such as invoices, receipts, and forms. |
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| [Data Extraction - Azure AI Document Intelligence + Phi](./samples/extraction/text-based/document-intelligence-phi.ipynb) | Demonstrates how to use Azure AI Document Intelligence pre-built layout and Microsoft's Phi small language models to extract structured data from documents. | Predominantly text-based documents such as invoices, receipts, and forms. |
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| [Data Extraction - Azure OpenAI GPT-4o with Vision](./samples/extraction/vision-based/openai.ipynb) | Demonstrates how to use Azure OpenAI GPT-4o and GPT-4o-mini models to extract structured data from documents using their built-in 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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| [Data Extraction - Comprehensive Azure AI Document Intelligence + Azure OpenAI GPT-4o with Vision](./samples/extraction/vision-based/comprehensive.ipynb) | Demonstrates how to improve the accuracy and confidence in extracting structured data from documents by combining Azure AI Document Intelligence and Azure OpenAI GPT-4o models with vision capabilities. | Any structured or unstructured document type. |
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| [Classification - Azure OpenAI GPT-4o with Vision](./samples/classification/openai.ipynb) | Demonstrates how to use Azure OpenAI GPT-4o and GPT-4o-mini 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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| [Classification - Azure AI Document Intelligence + Embeddings](./samples/classification/document-intelligence-embeddings.ipynb) | Demonstrates how to use Azure AI Document Intelligence pre-built layout and embeddings models to classify documents based on their content. | Processing multiple documents types or documents with varying purposes, such as contracts, legal documents, and emails. |
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| [Redaction - Azure AI Language PII Native Document Analysis](./samples/redaction/language-pii-redaction.ipynb) | Demonstrates how to use Azure AI Language Native Document Analysis features to redact personally identifiable information (PII) from documents. | Redacting sensitive information from documents, such as names, addresses, and phone numbers. |
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| [Redaction - Azure AI Document Intelligence + Azure OpenAI GPT-4o](./samples/redaction/document-intelligence-openai-redaction.ipynb) | Demonstrates how to use Azure AI Document Intelligence pre-built layout and Azure OpenAI GPT models to redact sensitive information from documents using natural language instruction to determine redaction areas. | Need for more complex redaction rules, such as redacting based on context or relationships. Also works well for redacting PII, including names, addresses, and phone numbers. |
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### Document Classification
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| Sample | Description | Example Use Cases |
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| --------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
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| [Classification - Azure OpenAI GPT-4o with Vision](./samples/python/classification/document-classification-gpt-vision.ipynb) | Demonstrates how to use Azure OpenAI GPT-4o and GPT-4o-mini 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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| [Classification - Azure AI Document Intelligence + Embeddings](./samples/python/classification/document-classification-text-embeddings.ipynb) | Demonstrates how to use Azure AI Document Intelligence pre-built layout and embeddings models to classify documents based on their content. | Processing multiple documents types or documents with varying purposes, such as contracts, legal documents, and emails. |
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### Document Redaction
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| Sample | Description | Example Use Cases |
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| ------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| [Redaction - Azure AI Document Intelligence + Azure OpenAI GPT-4o](./samples/python/redaction/document-redaction-gpt.ipynb) | Demonstrates how to use Azure AI Document Intelligence pre-built layout and Azure OpenAI GPT models to redact sensitive information from documents using natural language instruction to determine redaction areas. | Need for more complex redaction rules, such as redacting based on context or relationships. Also works well for redacting PII, including names, addresses, and phone numbers. |
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| [Redaction - Azure AI Language PII Native Document Analysis](./samples/python/redaction/document-redaction-language-native-document.ipynb) | Demonstrates how to use Azure AI Language Native Document Analysis features to redact personally identifiable information (PII) from documents. | Redacting sensitive information from documents, such as names, addresses, and phone numbers. |
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### Document Extraction
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| Sample | Description | Example Use Cases |
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| ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
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| [Data Extraction - Azure AI Document Intelligence + Azure OpenAI GPT-4o](./samples/python/extraction/text/document-extraction-gpt.ipynb) | Demonstrates how to use Azure AI Document Intelligence pre-built layout and Azure OpenAI GPT models to extract structured data from documents. | Predominantly text-based documents such as invoices, receipts, and forms. |
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| [Data Extraction - Azure AI Document Intelligence + Phi](./samples/python/extraction/text/document-extraction-phi.ipynb) | Demonstrates how to use Azure AI Document Intelligence pre-built layout and Microsoft's Phi small language models to extract structured data from documents. | Predominantly text-based documents such as invoices, receipts, and forms. |
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| [Data Extraction - Azure OpenAI GPT-4o with Vision](./samples/python/extraction/vision/document-extraction-gpt-vision.ipynb) | Demonstrates how to use Azure OpenAI GPT-4o and GPT-4o-mini models to extract structured data from documents using their built-in 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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| [Data Extraction - Comprehensive Azure AI Document Intelligence + Azure OpenAI GPT-4o with Vision](./samples/python/extraction/multimodal/document-extraction-gpt-text-and-vision.ipynb) | Demonstrates how to improve the accuracy and confidence in extracting structured data from documents by combining Azure AI Document Intelligence and Azure OpenAI GPT-4o models with vision capabilities. | Any structured or unstructured document type. |
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## Getting Started
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‎samples/classification/README.md

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‎samples/extraction/README.md

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