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‎.gitignore‎

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.openpublishing.buildcore.ps1
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.vscode/
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*sec.endpointdlp
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# CoPilot instructions and prompts

‎.openpublishing.publish.config.json‎

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"branch": "main",
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"branch_mapping": {}
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},
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{
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"path_to_root": "azure-search-javascript-samples",
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"url": "https://github.com/Azure-Samples/azure-search-javascript-samples",
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"branch": "main",
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"branch_mapping": {}
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},
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{
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"path_to_root": "azureai-model-inference-bicep",
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"url": "https://github.com/Azure-Samples/azureai-model-inference-bicep",

‎.vscode/settings.json‎

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‎articles/ai-foundry/concepts/architecture.md‎

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# Azure AI Foundry architecture
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> [!NOTE]
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> The architecture discussed in this article is specific to a **[!INCLUDE [hub](../includes/hub-project-name.md)]**. For more information, see [Types of projects](../what-is-azure-ai-foundry.md#project-types).
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[!INCLUDE [hub-only-alt](../includes/uses-hub-only-alt.md)]
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Azure AI Foundry provides a unified experience for AI developers and data scientists to build, evaluate, and deploy AI models through a web portal, SDK, or CLI. Azure AI Foundry is built on capabilities and services provided by other Azure services.
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‎articles/ai-foundry/concepts/concept-playgrounds.md‎

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‎articles/ai-foundry/concepts/encryption-keys-portal.md‎

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Customer-managed key encryption is configured via Azure portal in a similar way for each Azure resource:
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> [!IMPORTANT]
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> The Azure Key Vault used for encryption **must be in the same resource group** as the AI Foundry project. Key Vaults in other resource groups are not currently supported by the deployment wizards or project configuration workflows.
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1. Create a new Azure AI Foundry resource in the [Azure portal](https://portal.azure.com/).
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1. Under the **Encryption** tab, select **Customer-managed key**, **Select vault and key**, and then select the key vault and key to use.
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‎articles/ai-foundry/concepts/evaluation-evaluators/agent-evaluators.md‎

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### Intent resolution output
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The numerical score on a likert scale (integer 1 to 5) and a higher score is better. Given a numerical threshold (default to 3), we also output "pass" if the score <= threshold, or "fail" otherwise. Using the reason and additional fields can help you understand why the score is high or low.
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The numerical score on a likert scale (integer 1 to 5) and a higher score is better. Given a numerical threshold (default to 3), we also output "pass" if the score >= threshold, or "fail" otherwise. Using the reason and additional fields can help you understand why the score is high or low.
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```python
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{
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### Tool call accuracy output
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The numerical score (passing rate of correct tool calls) is 0-1 and a higher score is better. Given a numerical threshold (default to 3), we also output "pass" if the score <= threshold, or "fail" otherwise. Using the reason and tool call detail fields can help you understand why the score is high or low.
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The numerical score (passing rate of correct tool calls) is 0-1 and a higher score is better. Given a numerical threshold (default to 3), we also output "pass" if the score >= threshold, or "fail" otherwise. Using the reason and tool call detail fields can help you understand why the score is high or low.
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```python
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### Task adherence output
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The numerical score on a likert scale (integer 1 to 5) and a higher score is better. Given a numerical threshold (default to 3), we also output "pass" if the score <= threshold, or "fail" otherwise. Using the reason field can help you understand why the score is high or low.
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The numerical score on a likert scale (integer 1 to 5) and a higher score is better. Given a numerical threshold (default to 3), we also output "pass" if the score >= threshold, or "fail" otherwise. Using the reason field can help you understand why the score is high or low.
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```python
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{

‎articles/ai-foundry/concepts/evaluation-evaluators/general-purpose-evaluators.md‎

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### Coherence output
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The numerical score on a likert scale (integer 1 to 5) and a higher score is better. Given a numerical threshold (default to 3), we also output "pass" if the score <= threshold, or "fail" otherwise. Using the reason field can help you understand why the score is high or low.
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The numerical score on a likert scale (integer 1 to 5) and a higher score is better. Given a numerical threshold (default to 3), we also output "pass" if the score >= threshold, or "fail" otherwise. Using the reason field can help you understand why the score is high or low.
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```python
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### Fluency output
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The numerical score on a likert scale (integer 1 to 5) and a higher score is better. Given a numerical threshold (default to 3), we also output "pass" if the score <= threshold, or "fail" otherwise. Using the reason field can help you understand why the score is high or low.
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The numerical score on a likert scale (integer 1 to 5) and a higher score is better. Given a numerical threshold (default to 3), we also output "pass" if the score >= threshold, or "fail" otherwise. Using the reason field can help you understand why the score is high or low.
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```python
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### QA output
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While F1 score outputs a numerical score on 0-1 float scale, the other evaluators output numerical scores on a likert scale (integer 1 to 5) and a higher score is better. Given a numerical threshold (default to 3), we also output "pass" if the score <= threshold, or "fail" otherwise. Using the reason field can help you understand why the score is high or low.
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While F1 score outputs a numerical score on 0-1 float scale, the other evaluators output numerical scores on a likert scale (integer 1 to 5) and a higher score is better. Given a numerical threshold (default to 3), we also output "pass" if the score >= threshold, or "fail" otherwise. Using the reason field can help you understand why the score is high or low.
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```python
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{

‎articles/ai-foundry/concepts/evaluation-evaluators/rag-evaluators.md‎

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### Retrieval output
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The numerical score on a likert scale (integer 1 to 5) and a higher score is better. Given a numerical threshold (a default is set), we also output "pass" if the score <= threshold, or "fail" otherwise. Using the reason field can help you understand why the score is high or low.
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The numerical score on a likert scale (integer 1 to 5) and a higher score is better. Given a numerical threshold (a default is set), we also output "pass" if the score >= threshold, or "fail" otherwise. Using the reason field can help you understand why the score is high or low.
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### Document retrieval output
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All numerical scores have `high_is_better=True` except for `holes` and `holes_ratio` which have `high_is_better=False`. Given a numerical threshold (default to 3), we also output "pass" if the score <= threshold, or "fail" otherwise.
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All numerical scores have `high_is_better=True` except for `holes` and `holes_ratio` which have `high_is_better=False`. Given a numerical threshold (default to 3), we also output "pass" if the score >= threshold, or "fail" otherwise.
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The numerical score on a likert scale (integer 1 to 5) and a higher score is better. Given a numerical threshold (default to 3), we also output "pass" if the score <= threshold, or "fail" otherwise. Using the reason field can help you understand why the score is high or low.
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The numerical score on a likert scale (integer 1 to 5) and a higher score is better. Given a numerical threshold (default to 3), we also output "pass" if the score >= threshold, or "fail" otherwise. Using the reason field can help you understand why the score is high or low.
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The numerical score on a likert scale (integer 1 to 5) and a higher score is better. Given a numerical threshold (default to 3), we also output "pass" if the score <= threshold, or "fail" otherwise. Using the reason field can help you understand why the score is high or low.
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The numerical score on a likert scale (integer 1 to 5) and a higher score is better. Given a numerical threshold (default to 3), we also output "pass" if the score >= threshold, or "fail" otherwise. Using the reason field can help you understand why the score is high or low.
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The numerical score on a likert scale (integer 1 to 5) and a higher score is better. Given a numerical threshold (default to 3), we also output "pass" if the score <= threshold, or "fail" otherwise. Using the reason field can help you understand why the score is high or low.
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The numerical score on a likert scale (integer 1 to 5) and a higher score is better. Given a numerical threshold (default to 3), we also output "pass" if the score >= threshold, or "fail" otherwise. Using the reason field can help you understand why the score is high or low.
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‎articles/ai-foundry/concepts/evaluation-evaluators/textual-similarity-evaluators.md‎

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### Similarity output
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The numerical score on a likert scale (integer 1 to 5) and a higher score means a higher degree of similarity. Given a numerical threshold (default to 3), we also output "pass" if the score <= threshold, or "fail" otherwise. Using the reason field can help you understand why the score is high or low.
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The numerical score on a likert scale (integer 1 to 5) and a higher score means a higher degree of similarity. Given a numerical threshold (default to 3), we also output "pass" if the score >= threshold, or "fail" otherwise. Using the reason field can help you understand why the score is high or low.
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The numerical score is a 0-1 float and a higher score is better. Given a numerical threshold (default to 0.5), we also output "pass" if the score <= threshold, or "fail" otherwise.
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The numerical score is a 0-1 float and a higher score is better. Given a numerical threshold (default to 0.5), we also output "pass" if the score >= threshold, or "fail" otherwise.
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The numerical score is a 0-1 float and a higher score is better. Given a numerical threshold (default to 0.5), we also output "pass" if the score <= threshold, or "fail" otherwise.
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The numerical score is a 0-1 float and a higher score is better. Given a numerical threshold (default to 0.5), we also output "pass" if the score >= threshold, or "fail" otherwise.
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The numerical score is a 0-1 float and a higher score is better. Given a numerical threshold (default to 0.5), we also output "pass" if the score <= threshold, or "fail" otherwise.
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The numerical score is a 0-1 float and a higher score is better. Given a numerical threshold (default to 0.5), we also output "pass" if the score >= threshold, or "fail" otherwise.
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The numerical score is a 0-1 float and a higher score is better. Given a numerical threshold (default to 0.5), we also output "pass" if the score <= threshold, or "fail" otherwise.
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The numerical score is a 0-1 float and a higher score is better. Given a numerical threshold (default to 0.5), we also output "pass" if the score >= threshold, or "fail" otherwise.
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The numerical score is a 0-1 float and a higher score is better. Given a numerical threshold (default to 0.5), we also output "pass" if the score <= threshold, or "fail" otherwise.
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The numerical score is a 0-1 float and a higher score is better. Given a numerical threshold (default to 0.5), we also output "pass" if the score >= threshold, or "fail" otherwise.
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