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articles/ai-foundry/model-inference/reference/reference-model-inference-chat-completions.md

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ms.service: azure-ai-studio
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ms.topic: conceptual
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ms.date: 01/28/2025
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ms.reviewer: fasantia
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articles/ai-foundry/model-inference/reference/reference-model-inference-embeddings.md

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ms.date: 01/28/2025
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articles/ai-foundry/model-inference/reference/reference-model-inference-images-embeddings.md

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articles/ai-foundry/model-inference/reference/reference-model-inference-info.md

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

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"redirect_url": "/azure/ai-studio/concepts/content-filtering",
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{
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"source_path_from_root": "/articles/ai-studio/reference/reference-model-inference-completions.md",
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articles/ai-studio/faq.yml

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### YamlMime:FAQ
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metadata:
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title: Azure AI frequently asked questions
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title: Azure AI Foundry frequently asked questions
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titleSuffix: Azure AI Foundry
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description: Get answers to the most popular questions about Azure AI services.
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title: Azure AI frequently asked questions
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title: Azure AI Foundry frequently asked questions
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summary: |
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If you can't find answers to your questions in this document, and still need help check the [Azure AI services support options guide](../ai-services/cognitive-services-support-options.md?context=/azure/ai-services/openai/context/context). Azure OpenAI is part of Azure AI services.
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- question: |
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Will there be multiple varying model benchmarks in Azure AI Foundry portal based on individual projects and data sources?
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In the model benchmarks view, customers can view varying model benchmarks published by Azure AI.
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In the model benchmarks view, customers can view varying model benchmarks published by Azure AI Foundry.
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Is prompt flow Microsoft's equivalent to LangChain?
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articles/machine-learning/.openpublishing.redirection.machine-learning.json

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"redirect_url": "/azure/machine-learning/concept-model-lifecycle-retirement",
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{
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"source_path_from_root": "/articles/machine-learning/reference-model-inference-completions.md",
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articles/machine-learning/reference-model-inference-api.md

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* [Get info](reference-model-inference-info.md): Returns the information about the model deployed under the endpoint.
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* [Text embeddings](reference-model-inference-embeddings.md): Creates an embedding vector representing the input text.
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* [Text completions](reference-model-inference-completions.md): Creates a completion for the provided prompt and parameters.
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* [Chat completions](reference-model-inference-chat-completions.md): Creates a model response for the given chat conversation.
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* [Image embeddings](reference-model-inference-images-embeddings.md): Creates an embedding vector representing the input text and image.
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* [Get info](reference-model-inference-info.md): Returns the information about the model deployed under the endpoint.
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* [Text embeddings](reference-model-inference-embeddings.md): Creates an embedding vector representing the input text.
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* [Text completions](reference-model-inference-completions.md): Creates a completion for the provided prompt and parameters.
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* [Chat completions](reference-model-inference-chat-completions.md): Creates a model response for the given chat conversation.
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* [Image embeddings](reference-model-inference-images-embeddings.md): Creates an embedding vector representing the input text and image.
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articles/machine-learning/reference-model-inference-chat-completions.md

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