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‎articles/ai-services/agents/how-to/tools/file-search.md‎

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We highly recommend that you ensure all files in a vector_store are fully processed before you create a run. This ensures that all the data in your vector store is searchable. You can check for vector store readiness by using the polling helpers in the SDKs, or by manually polling the vector store object to ensure the status is completed.
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As a fallback, there's a 60-second maximum wait in the run object when the thread's vector store contains files that are still being processed. This is to ensure that any files your users upload in a thread a fully searchable before the run proceeds. This fallback wait does not apply to the agent's vector store.
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As a fallback, there's a 60-second maximum wait in the run object when the thread's vector store contains files that are still being processed. This is to ensure that any files your users upload in a thread are fully searchable before the run proceeds. This fallback wait does not apply to the agent's vector store.
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## Add file search to an agent using the Azure AI Foundry portal
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‎articles/ai-services/agents/includes/azure-search/code-examples.md‎

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```python
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# AI Search resource connection ID
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# This code prints out the connection ID of all the Azure AI Search connections in the project
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# If you have more than one AI search connection, make sure to select the correct one that contains the index you want to use.
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conn_list = project_client.connections.list()
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# This code looks for the AI Search Connection ID and saves it as variable conn_id
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# If you have more than one AI search connection, try to establish the value in your .env file.
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# Extract the connection list.
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conn_list = project_client.connections._list_connections()["value"]
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conn_id = ""
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# Search in the metadata field of each connection in the list for the azure_ai_search type and get the id value to establish the variable
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for conn in conn_list:
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if conn.connection_type == "AZURE_AI_SEARCH":
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print(f"Connection ID: {conn.id}")
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metadata = conn["properties"].get("metadata", {})
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if metadata.get("type", "").upper() == "AZURE_AI_SEARCH":
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conn_id = conn["id"]
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break
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```
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# [C#](#tab/csharp)
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```csharp
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-H "Authorization: Bearer $AZURE_AI_AGENTS_TOKEN"
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```
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‎articles/ai-services/containers/disconnected-container-faq.yml‎

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- ignite-2023
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ms.topic: faq
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ms.date: 07/26/2024
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ms.date: 02/27/2025
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ms.author: aahi
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title: FAQ for Azure AI services disconnected containers
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summary: |
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Fill out and submit the [request form](https://aka.ms/csdisconnectedcontainers) to request access to the container. Access is limited to customers that meet the following requirements:
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* Your organization must have an enterprise agreement or an equivalent agreement and should be identified as strategic customer or partner with Microsoft.
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* Valid business scenario/use case - Disconnected containers are expected to run fully offline hence, your use cases must meet one of below or similar requirements:
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* Valid business scenario/use case - Disconnected containers are expected to run fully offline. Your use cases must meet one of below or similar requirements:
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* Environment or devices with zero connectivity to internet.
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* Remote location that occasionally has internet access.
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* Organization under strict regulation of not sending any kind of data back to cloud.
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* Application completed as instructed - Please pay close attention to guidance provided throughout the application to ensure you provide all the necessary information required for approval.
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* Application completed as instructed - pay close attention to guidance provided throughout the application to ensure you provide all the necessary information required for approval.
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- question: What if my use case can't satisfy the requirements listed above?
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answer: |
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If your use case can't satisfy above requirements but you're interested in running containers on premises, you might be able to use [connected containers](../cognitive-services-container-support.md).

‎articles/ai-services/speech-service/fast-transcription-create.md‎

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ms.author: eur
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ms.service: azure-ai-speech
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ms.topic: how-to
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ms.date: 11/12/2024
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ms.date: 2/28/2025
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# Customer intent: As a user who implements audio transcription, I want create transcriptions as quickly as possible.
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---
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- An Azure AI Speech resource in one of the regions where the fast transcription API is available. The supported regions are: **Australia East**, **Brazil South**, **Central India**, **East US**, **East US 2**, **French Central**, **Japan East**, **North Central US**, **North Europe**, **South Central US**, **Southeast Asia**, **Sweden Central**, **UK South**, **West Europe**, **West US**, **West US 2**, **West US 3**. For more information about regions supported for other Speech service features, see [Speech service regions](./regions.md).
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- An audio file (less than 2 hours long and less than 200 MB in size) in one of the formats and codecs supported by the batch transcription API. For more information about supported audio formats, see [supported audio formats](./batch-transcription-audio-data.md#supported-audio-formats-and-codecs).
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- An audio file (less than 2 hours long and less than 200 MB in size) in one of the formats and codecs supported by the batch transcription API: WAV, MP3, OPUS/OGG, FLAC, WMA, AAC, ALAW in WAV container, MULAW in WAV container, AMR, WebM, M4A, and SPEEX. For more information about supported audio formats, see [supported audio formats](./batch-transcription-audio-data.md#supported-audio-formats-and-codecs).
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## Use the fast transcription API
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‎articles/ai-services/use-key-vault.md‎

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ms.service: azure-ai-services
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ms.custom: devx-track-extended-java, devx-track-js, devx-track-python
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ms.date: 8/11/2024
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ms.date: 02/27/2025
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zone_pivot_groups: programming-languages-set-twenty-eight
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|Name | Value |
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|---------|---------|
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|Upload options | Manual |
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|Upload options | Generate |
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|Name | A secret name for your key or endpoint. For example: "CognitiveServicesKey" or "CognitiveServicesEndpoint" |
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|Value | Your Azure AI services resource key or endpoint. |
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‎articles/search/cognitive-search-attach-cognitive-services.md‎

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title: Attach Azure AI services to a skillset
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titleSuffix: Azure AI Search
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description: Learn how to attach an Azure AI services resource to an AI enrichment pipeline in Azure AI Search.
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author: eric-urban
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‎articles/search/reference-stopwords.md‎

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description: Reference documentation containing the stopwords list of the Microsoft language analyzers.
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‎articles/search/resource-demo-sites.md‎

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title: Demo sites for search features
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titleSuffix: Azure AI Search
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description: This page provides links to demo sites that are built on Azure AI Search. Try a web app to see how search performs.
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‎articles/search/resource-partners-knowledge-mining.md‎

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title: Microsoft partners
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description: Learn about end-to-end solutions offered by Microsoft partners that include Azure AI Search.
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‎articles/search/resource-tools.md‎

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title: Productivity tools
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description: Use existing code samples or build your own tools for working with a search index in Azure AI Search.
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