-Once the application is registered with Azure AD B2C, create the following application secrets in the Azure Static Web App's [application settings](../static-web-apps/application-settings.md). You can configure application settings via the Azure portal or with the Azure CLI. For more information, check out the [Configure application settings for Azure Static Web Apps](../static-web-apps/application-settings.md#configure-application-settings) article.

-| [Phone sign-up and sign-in](phone-authentication-user-flows.md) | GA | GA | |

-| [Conditional Access and Identity Protection](conditional-access-user-flow.md) | GA | GA | Not available for SAML applications |

-| [Multi-language support](localization.md)| GA | GA | |

-| [Custom domains](custom-domain.md)| GA | GA | |

-| [Page layout version](page-layout.md) | GA | GA | |

-| [JavaScript](javascript-and-page-layout.md) | GA | GA | |

-| [Password complexity](password-complexity.md) | GA | GA | |

-|[Apple](identity-provider-apple-id.md) | GA | GA | |

-|[WeChat](identity-provider-wechat.md) | Preview | GA | |

-| [Default SSO session provider](custom-policy-reference-sso.md#defaultssosessionprovider) | GA | |

-| [External login session provider](custom-policy-reference-sso.md#externalloginssosessionprovider) | GA | |

-| [SAML SSO session provider](custom-policy-reference-sso.md#samlssosessionprovider) | GA | |

-| [OAuth SSO Session Provider](custom-policy-reference-sso.md#oauthssosessionprovider) | GA| |

-| [Phone factor authentication](phone-factor-technical-profile.md) | GA | |

-# Export cost savings

-> Recommendations show savings individually, and may overlap with the savings shown in other recommendations, for example ΓÇô you can only benefit from savings plans for compute or reservations for virtual machines, but not from both.

-|Subscription Owner<br>Subscription Contributor|View reviews for a workload, triage recommendations linked to those reviews, manage review recommendation lifecycle.|

-|Advisor Recommendations Contributor (Assessments and Reviews)|View review recommendations, accept review recommendations, manage review recommendations' lifecycle.|

-In the Azure portal, find your Language Understanding (LUIS) authoring resource. It has the type `LUIS.Authoring`. In the resource's **Access Control (IAM)** page, add the role of **contributor** for the user that you want to contribute. For detailed steps, see [Assign Azure roles using the Azure portal](../../role-based-access-control/role-assignments-portal.md).

-Within a few minutes, the target will be assigned the selected role at the selected scope. For help with these steps, see [Assign Azure roles using the Azure portal](../../role-based-access-control/role-assignments-portal.md).

-| F0 | 10 |

-| S0 | 10 |

-In the simple case without the _reasoning_ feature, the Groundedness detection API classifies the ungroundedness of the submitted content as `true` or `false` and provides a confidence score.

-> [!TIP]

-> To test a summarization task instead of a question answering (QnA) task, use the following sample JSON body:

->

-> ```json

-> {

-> "Domain": "Medical",

-> "Task": "Summarization",

-> "Text": "Ms Johnson has been in the hospital after experiencing a stroke.",

-> "GroundingSources": ["Our patient, Ms. Johnson, presented with persistent fatigue, unexplained weight loss, and frequent night sweats. After a series of tests, she was diagnosed with HodgkinΓÇÖs lymphoma, a type of cancer that affects the lymphatic system. The diagnosis was confirmed through a lymph node biopsy revealing the presence of Reed-Sternberg cells, a characteristic of this disease. She was further staged using PET-CT scans. Her treatment plan includes chemotherapy and possibly radiation therapy, depending on her response to treatment. The medical team remains optimistic about her prognosis given the high cure rate of HodgkinΓÇÖs lymphoma."],

-> "Reasoning": false

-> }

-> ```

-| **reasoning** | (Optional) Specifies whether to use the reasoning feature. The default value is `false`. If `true`, you need to bring your own Azure OpenAI resources to provide an explanation. Be careful: using reasoning increases the processing time and incurs extra fees.| Boolean |

-| **confidenceScore** | The confidence value of the _ungrounded_ designation. The score ranges from 0 to 1. | Float |

-| -**`Text`** | The specific text that is ungrounded. | String |

-In order to use your Azure OpenAI resource to enable the reasoning feature, use Managed Identity to allow your Content Safety resource to access the Azure OpenAI resource:

-In your request to the Groundedness detection API, set the `"Reasoning"` body parameter to `true`, and provide the other needed parameters:

-| **llmResource** | (Optional) If you want to use your own Azure OpenAI resources instead of our default GPT resources, add this field and include the subfields for the resources used. If you don't want to use your own resources, remove this field from the input. | String |

-| - `resourceType `| Specifies the type of resource being used. Currently it only allows `AzureOpenAI`. | Enum|

-| **confidenceScore** | The confidence value of the _ungrounded_ designation. The score ranges from 0 to 1. | Float |

-| -**`Text`** | The specific text that is ungrounded. | String |

-| -**`Reason`** | Offers explanations for detected ungroundedness. | String |

-Within a few minutes, the target will be assigned the selected role at the selected scope. For help with these steps, see [Assign Azure roles using the Azure portal](../../role-based-access-control/role-assignments-portal.md).

-For help with role assignments, see [Assign Azure roles using the Azure portal](../../role-based-access-control/role-assignments-portal.md).

-1. **Document type confidence score**: The document type confidence is an indicator of closely the analyzed document resembleds documents in the training dataset. When the document type confidence is low, this is indicative of template or structural variations in the analyzed document. To improve the document type confidence, label a document with that specific variation and add it to your training dataset. Once the model is re-trained, it should be better equipped to handl that class of variations.

-2. **Field level confidence**: Each labled field extracted has an associated confidence score. This score reflects the model's confidence on the position of the value extracted. While evaluating the confidence you should also look at the underlying extraction confidence to generate a comprehensive confidence for the extracted result. Evaluate the OCR results for text extraction or selection marks depending on the field type to generate a composite confidence score for the field.

-3. **Word confidence score** Each word extracted within the document has an associated confidence score. The score represents the confidence of the transcription. The pages array contains an array of words, each word has an associated span and confidence. Spans from the custom field extracted values will match the spans of the extracted words.

-4. **Selection mark confidence score**: The pages array also contains an array of selection marks, each selection mark has a confidence score representing the confidence of the seletion mark and selection state detection. When a labeled field is a selection mark, the custom field selection confidence combined with the selection mark confidence is an accurate representation of the overall confidence that the field was extracted correctly.

-With the addition of table, row and cell confidence with the ```2024-02-29-preview``` API, here are some common questions that should help with interpreting the table, row and cell scores:

-|:--|:-|:-|::|

-| CustomerName | String | Invoiced customer| |

-| CustomerId | String | Customer reference ID | |

-| PurchaseOrder | String | Purchase order reference number | |

-| InvoiceId | String | ID for this specific invoice (often "Invoice Number") | |

-| InvoiceDate | Date | Date the invoice was issued | yyyy-mm-dd|

-| DueDate | Date | Date payment for this invoice is due | yyyy-mm-dd|

-| VendorName | String | Vendor name | |

-| VendorTaxId | String | The taxpayer number associated with the vendor | |

-| VendorAddress | String | Vendor mailing address| |

-| VendorAddressRecipient | String | Name associated with the VendorAddress | |

-| CustomerAddress | String | Mailing address for the Customer | |

-| CustomerTaxId | String | The taxpayer number associated with the customer | |

-| CustomerAddressRecipient | String | Name associated with the CustomerAddress | |

-| BillingAddress | String | Explicit billing address for the customer | |

-| BillingAddressRecipient | String | Name associated with the BillingAddress | |

-| ShippingAddress | String | Explicit shipping address for the customer | |

-| ShippingAddressRecipient | String | Name associated with the ShippingAddress | |

-| PaymentTerm | String | The terms of payment for the invoice | |

- |Sub&#8203;Total| Number | Subtotal field identified on this invoice | Integer |

-| TotalTax | Number | Total tax field identified on this invoice | Integer |

-| InvoiceTotal | Number (USD) | Total new charges associated with this invoice | Integer |

-| AmountDue | Number (USD) | Total Amount Due to the vendor | Integer |

-| ServiceAddress | String | Explicit service address or property address for the customer | |

-| ServiceAddressRecipient | String | Name associated with the ServiceAddress | |

-| RemittanceAddress | String | Explicit remittance or payment address for the customer | |

-| RemittanceAddressRecipient | String | Name associated with the RemittanceAddress | |

-| ServiceStartDate | Date | First date for the service period (for example, a utility bill service period) | yyyy-mm-dd |

-| ServiceEndDate | Date | End date for the service period (for example, a utility bill service period) | yyyy-mm-dd|

-| PreviousUnpaidBalance | Number | Explicit previously unpaid balance | Integer |

-| CurrencyCode | String | The currency code associated with the extracted amount | |

-| KVKNumber(NL-only) | String | A unique identifier for businesses registered in the Netherlands|12345678|

-| PaymentDetails | Array | An array that holds Payment Option details such as `IBAN`,`SWIFT`, `BPay(AU)` | |

-| TotalDiscount | Number | The total discount applied to an invoice | Integer |

-| TaxItems | Array | AN array that holds added tax information such as `CGST`, `IGST`, and `SGST`. This line item is currently only available for the Germany (`de`), Spain (`es`), Portugal (`pt`), and English Canada (`en-CA`) locales| |

-### Line items

-|Name| Type | Description | Text (line item #1) | Value (standardized output) |

-|:--|:-|:-|:-| :-|

-| Items | String | Full string text line of the line item | 3/4/2021 A123 Consulting Services 2 hours $30.00 10% $60.00 | |

-| Amount | Number | The amount of the line item | $60.00 | 100 |

-| Description | String | The text description for the invoice line item | Consulting service | Consulting service |

-| Quantity | Number | The quantity for this invoice line item | 2 | 2 |

-| UnitPrice | Number | The net or gross price (depending on the gross invoice setting of the invoice) of one unit of this item | $30.00 | 30 |

-| ProductCode | String| Product code, product number, or SKU associated with the specific line item | A123 | |

-| Unit | String| The unit of the line item, e.g, kg, lb etc. | Hours | |

-| Date | Date| Date corresponding to each line item. Often it's a date the line item was shipped | 3/4/2021| 2021-03-04 |

-| Tax | Number | Tax associated with each line item. Possible values include tax amount and tax Y/N | 10.00 | |

-| TaxRate | Number | Tax Rate associated with each line item. | 10% | |

- > * [Assign an Azure role for access to blob data](../../role-based-access-control/role-assignments-portal.md?tabs=current) to assign a role that allows for read, write, and delete permissions for your Azure storage container. *See* [Storage Blob Data Contributor](../../role-based-access-control/built-in-roles.md#storage-blob-data-contributor).

-POST https://<your-resource-name>/documentintelligence/documentModels/{modelId}:copyTo?api-version=2024-02-29-preview

-POST https://<your-resource-name>/documentintelligence/documentModels/{modelId}:copyTo?api-version=2024-02-29-preview

-Operation-Location: https://<your-resource-name>.cognitiveservices.azure.com/documentintelligence/operations/{operation-id}?api-version=2024-02-29-preview

-GET https://<your-resource-name>.cognitiveservices.azure.com/documentintelligence/operations/{<operation-id>}?api-version=2024-02-29-preview

-GET https://<your-resource-name>/documentintelligence/documentModels/{modelId}?api-version=2024-02-29-preview" -H "Ocp-Apim-Subscription-Key: <your-key>

-curl -i -X POST "<your-resource-name>/documentintelligence/documentModels:authorizeCopy?api-version=2024-02-29-preview"

-curl -i -X POST "<your-resource-name>/documentintelligence/documentModels/{modelId}:copyTo?api-version=2024-02-29-preview"

-Operation-Location: https://<your-resource-name>.cognitiveservices.azure.com/documentintelligence/operations/{operation-id}?api-version=2024-02-29-preview

-* A brief understanding of [**Azure role-based access control (Azure RBAC)**](../../role-based-access-control/role-assignments-portal.md) using the Azure portal.

-Within a few minutes, the target will be assigned the selected role at the selected scope. For help with these steps, see [Assign Azure roles using the Azure portal](../../../role-based-access-control/role-assignments-portal.md).

-* [Language detection overview](overview.md)

-You can use NER resolutions to implement actions or retrieve further information. For example, your service can extract datetime entities to extract dates and times that will be provided to a meeting scheduling system.

-Use this article to get an overview of the new API changes starting from `2023-04-15-preview` version. This API change mainly introduces two new concepts (`entity types` and `entity tags`) replacing the `category` and `subcategory` fields in the current Generally Available API.

-The changes introduce better flexibility for named entity recognition, including:

-* More granular entity recognition through introducing the tags list where an entity could be tagged by more than one entity tag.

-| Temperature | Numeric, Dimension, Temperature |

-| Speed | Numeric, Dimension, Speed |

-| Weight | Numeric, Dimension, Weight |

-| Height | Numeric, Dimension, Height |

-| Length | Numeric, Dimension, Length |

-| Volume | Numeric, Dimension, Volume |

-| Area | Numeric, Dimension, Area |

-| Information | Numeric, Dimension, Information |

-| ComputingProduct | Product, ComputingProduct |

-Named Entity Recognition (NER) is one of the features offered by [Azure AI Language](../overview.md), a collection of machine learning and AI algorithms in the cloud for developing intelligent applications that involve written language. The NER feature can identify and categorize entities in unstructured text. For example: people, places, organizations, and quantities.

-* An Azure Language Service resource and custom question qnswering project. If you donΓÇÖt have one already, then [create one](../quickstart/sdk.md).

-* [Using Azure OpenAI on your data](../../../openai/concepts/use-your-data.md)

-Assistants use the [same API for file upload as fine-tuning](/rest/api/azureopenai/files/upload?view=rest-azureopenai-2024-02-15-preview&tabs=HTTP). When uploading a file you have to specify an appropriate value for the [purpose parameter](/rest/api/azureopenai/files/upload?view=rest-azureopenai-2024-02-15-preview&tabs=HTTP#purpose).

-> Version `0613` of `gpt-35-turbo` and `gpt-35-turbo-16k` will be retired no earlier than June 13, 2024. Version `0301` of `gpt-35-turbo` will be retired no earlier than July 5, 2024. See [model updates](../how-to/working-with-models.md#model-updates) for model upgrade behavior.

-`gpt-35-turbo-0613` - fine-tuning of this model is limited to a subset of regions, and is not available in every region the base model is available.

-| `babbage-002` | North Central US <br> Sweden Central | 16,384 | Sep 2021 |

-| `davinci-002` | North Central US <br> Sweden Central | 16,384 | Sep 2021 |

-| `gpt-35-turbo` (0613) | East US2 <br> North Central US <br> Sweden Central | 4,096 | Sep 2021 |

-| `gpt-35-turbo` (1106) | East US2 <br> North Central US <br> Sweden Central | Input: 16,385<br> Output: 4,096 | Sep 2021|

-| `gpt-35-turbo` (0125) | East US2 <br> North Central US <br> Sweden Central | 16,385 | Sep 2021 |

-## To Avoid Fabrication or Ungrounded Content

-You need to connect to a data source to upload your data. When you want to use your data to chat with an Azure OpenAI model, your data is chunked in a search index so that relevant data can be found based on user queries. For some data sources such as uploading files from your local machine (preview) or data contained in a blob storage account (preview), Azure AI Search is used.

-When you choose the following data sources, your data is ingested into an Azure AI Search index.

-# [Azure Cosmos DB for MongoDB vCore](#tab/mongo-db)

-* [Azure Cosmos DB for MongoDB vCore](/azure/cosmos-db/mongodb/vcore/introduction) account

-* Only Azure Cosmos DB for MongoDB vCore is supported.

-* The search type is limited to [Azure Cosmos DB for MongoDB vCore vector search](/azure/cosmos-db/mongodb/vcore/vector-search) with an Azure OpenAI embedding model.

-Use the script provided on [GitHub](https://github.com/microsoft/sample-app-aoai-chatGPT/blob/feature/2023-9/scripts/cosmos_mongo_vcore_data_preparation.py) to prepare your data.

-To add Azure Cosmos DB for MongoDB vCore as a data source, you will need an existing Azure Cosmos DB for MongoDB vCore index containing your data, and a deployed Azure OpenAI Ada embeddings model that will be used for vector search.

-1. In the [Azure OpenAI portal](https://oai.azure.com/portal) chat playground, select **Add your data**. In the panel that appears, select **Azure Cosmos DB for MongoDB vCore** as the data source.

-When you add your Azure Cosmos DB for MongoDB vCore data source, you can specify data fields to properly map your data for retrieval.

-### Failed ingestion jobs

-To troubleshoot a failed job, always look out for errors or warnings specified either in the API response or Azure OpenAI studio. Here are some of the common errors and warnings:

-description: Learn how to create helpful AI Assistants with tools like Code Interpreter

-Azure OpenAI Assistants (Preview) allows you to create AI assistants tailored to your needs through custom instructions and augmented by advanced tools like code interpreter, and custom functions. In this article we'll provide an in-depth walkthrough of getting started with the Assistants API.

-Now let's create a thread

-A thread is essentially the record of the conversation session between the assistant and the user. It's similar to the messages array/list in a typical chat completions API call. One of the key differences, is unlike a chat completions messages array, you don't need to track tokens with each call to make sure that you're remaining below the context length of the model. Threads abstract away this management detail and will compress the thread history as needed in order to allow the conversation to continue. The ability for threads to accomplish this with larger conversations is enhanced when using the latest models, which have larger context lengths as well as support for the latest features.

-Next create the first user question to add to the thread

-description: Learn about the options for how to use the GPT-35-Turbo and GPT-4 models

-zone_pivot_groups: openai-chat

-# Learn how to work with the GPT-35-Turbo and GPT-4 models

-The GPT-35-Turbo and GPT-4 models are language models that are optimized for conversational interfaces. The models behave differently than the older GPT-3 models. Previous models were text-in and text-out, meaning they accepted a prompt string and returned a completion to append to the prompt. However, the GPT-35-Turbo and GPT-4 models are conversation-in and message-out. The models expect input formatted in a specific chat-like transcript format, and return a completion that represents a model-written message in the chat. While this format was designed specifically for multi-turn conversations, you'll find it can also work well for non-chat scenarios too.

-In Azure OpenAI there are two different options for interacting with these type of models:

-The Chat Completion API is a new dedicated API for interacting with the GPT-35-Turbo and GPT-4 models. This API is the preferred method for accessing these models. **It is also the only way to access the new GPT-4 models**.

-ChatML uses the same [completion API](../reference.md#completions) that you use for other models like text-davinci-002, it requires a unique token based prompt format known as Chat Markup Language (ChatML). This provides lower level access than the dedicated Chat Completion API, but also requires additional input validation, only supports gpt-35-turbo models, and **the underlying format is more likely to change over time**.

-This article walks you through getting started with the GPT-35-Turbo and GPT-4 models. It's important to use the techniques described here to get the best results. If you try to interact with the models the same way you did with the older model series, the models will often be verbose and provide less useful responses.

-Assistants use the [same API for file upload as fine-tuning](/rest/api/azureopenai/files/upload?view=rest-azureopenai-2024-02-15-preview&tabs=HTTP). When uploading a file you have to specify an appropriate value for the [purpose parameter](/rest/api/azureopenai/files/upload?view=rest-azureopenai-2024-02-15-preview&tabs=HTTP#purpose).

-* [File Upload API reference](/rest/api/azureopenai/files/upload?view=rest-azureopenai-2024-02-15-preview&tabs=HTTP)

-> All customers have the ability to modify the content filters to be stricter (for example, to filter content at lower severity levels than the default). Approval is required for turning the content filters partially or fully off. Managed customers only may apply for full content filtering control via this form: [Azure OpenAI Limited Access Review: Modified Content Filters](https://ncv.microsoft.com/uEfCgnITdR).

-* **Content Filtering** improves safety, but it also impacts latency. Evaluate if any of your workloads would benefit from [modified content filtering policies](./content-filters.md).

-| `Generated Completion Tokens` | Usage | Sum | Number of generated tokens (output) from an OpenAI model. Applies to PayGo, PTU, and PTU-manged SKUs | `ApiName`, `ModelDeploymentName`,`ModelName`, `Region`|

-| `Processed FineTuned Training Hours` | Usage |Sum| Number of Training Hours Processed on an OpenAI FineTuned Model | `ApiName`, `ModelDeploymentName`,`ModelName`, `Region`|

-| `Processed Inference Tokens` | Usage | Sum| Number of inference tokens processed by an OpenAI model. Calculated as prompt tokens (input) + generated tokens. Applies to PayGo, PTU, and PTU-manged SKUs.|`ApiName`, `ModelDeploymentName`,`ModelName`, `Region`|

-| `Processed Prompt Tokens` | Usage | Sum | Total number of prompt tokens (input) processed on an OpenAI model. Applies to PayGo, PTU, and PTU-managed SKUs.|`ApiName`, `ModelDeploymentName`,`ModelName`, `Region`|

-| `Provision-managed Utilization V2` | Usage | Average | Provision-managed utilization is the utilization percentage for a given provisioned-managed deployment. Calculated as (PTUs consumed/PTUs deployed)*100. When utilization is at or above 100%, calls are throttled and return a 429 error code. | `ModelDeploymentName`,`ModelName`,`ModelVersion`, `Region`, `StreamType`|

-description: Learn how to generate reproducible output (preview) with Azure OpenAI Service

-By default if you ask an Azure OpenAI Chat Completion model the same question multiple times you are likely to get a different response. The responses are therefore considered to be non-deterministic. Reproducible output is a new preview feature that allows you to selectively change the default behavior towards producing more deterministic outputs.

- api_version="2023-12-01-preview"

- model="gpt-4-1106-preview", # Model = should match the deployment name you chose for your 1106-preview model deployment

- max_tokens =200,

- api_version = '2023-12-01-preview' # may change in the future

- max_tokens = 200

-In the beginning, there was nothingness, a vast expanse of empty space, a blank canvas waiting to be painted with the wonders of existence. Then, approximately 13.8 billion years ago, something extraordinary happened, an event that would mark the birth of the universe ΓÇô the Big Bang.

-The Big Bang was not an explosion in the conventional sense but rather an expansion, an incredibly rapid stretching of space that took place everywhere in the universe at once. In just a fraction of a second, the universe grew from smaller than a single atom to an incomprehensibly large expanse.

-In these first moments, the universe was unimaginably hot and dense, filled with a seething soup of subatomic particles and radiant energy. As the universe expanded, it began to cool, allowing the first particles to form. Protons and neutrons came together to create the first simple atomic nuclei in a process known as nucleosynthesis.

-For hundreds of thousands of years, the universe continued to cool and expand

-Once upon a time, in the vast expanse of nothingness, there was a moment that would come to define everything. This moment, a tiny fraction of a second that would be forever known as the Big Bang, marked the birth of the universe as we know it.

-Before this moment, there was no space, no time, just an infinitesimally small point of pure energy, a singularity where all the laws of physics as we understand them did not apply. Then, suddenly, this singular point began to expand at an incredible rate. In a cosmic symphony of creation, matter, energy, space, and time all burst forth into existence.

-The universe was a hot, dense soup of particles, a place of unimaginable heat and pressure. It was in this crucible of creation that the simplest elements were formed. Hydrogen and helium, the building blocks of the cosmos, came into being.

-As the universe continued to expand and cool, these primordial elements began to co

-Once upon a time, in the vast expanse of nothingness, there was a singularity, an infinitely small and infinitely dense point where all the mass and energy of what would become the universe were concentrated. This singularity was like a tightly wound cosmic spring holding within it the potential of everything that would ever exist.

-Then, approximately 13.8 billion years ago, something extraordinary happened. This singularity began to expand in an event we now call the Big Bang. In just a fraction of a second, the universe grew exponentially during a period known as cosmic inflation. It was like a symphony's first resounding chord, setting the stage for a cosmic performance that would unfold over billions of years.

-As the universe expanded and cooled, the fundamental forces of nature that we know today ΓÇô gravity, electromagnetism, and the strong and weak nuclear forces ΓÇô began to take shape. Particles of matter were created and began to clump together under the force of gravity, forming the first atoms

- api_version="2023-12-01-preview"

- model="gpt-4-1106-preview", # Model = should match the deployment name you chose for your 1106-preview model deployment

- max_tokens =200,

- api_version = '2023-12-01-preview' # may change in the future

- max_tokens = 200

-In the beginning, there was nothing but a vast emptiness, a void without form or substance. Then, from this nothingness, a singular event occurred that would change the course of existence foreverΓÇöThe Big Bang.

-Around 13.8 billion years ago, an infinitely hot and dense point, no larger than a single atom, began to expand at an inconceivable speed. This was the birth of our universe, a moment where time and space came into being. As this primordial fireball grew, it cooled, and the fundamental forces that govern the cosmosΓÇögravity, electromagnetism, and the strong and weak nuclear forcesΓÇöbegan to take shape.

-Matter coalesced into the simplest elements, hydrogen and helium, which later formed vast clouds in the expanding universe. These clouds, driven by the force of gravity, began to collapse in on themselves, creating the first stars. The stars were crucibles of nuclear fusion, forging heavier elements like carbon, nitrogen, and oxygen

-In the beginning, there was nothing but a vast emptiness, a void without form or substance. Then, from this nothingness, a singular event occurred that would change the course of existence foreverΓÇöThe Big Bang.

-Around 13.8 billion years ago, an infinitely hot and dense point, no larger than a single atom, began to expand at an inconceivable speed. This was the birth of our universe, a moment where time and space came into being. As this primordial fireball grew, it cooled, and the fundamental forces that govern the cosmosΓÇögravity, electromagnetism, and the strong and weak nuclear forcesΓÇöbegan to take shape.

-Matter coalesced into the simplest elements, hydrogen and helium, which later formed vast clouds in the expanding universe. These clouds, driven by the force of gravity, began to collapse in on themselves, creating the first stars. The stars were crucibles of nuclear fusion, forging heavier elements like carbon, nitrogen, and oxygen

-In the beginning, there was nothing but a vast emptiness, a void without form or substance. Then, from this nothingness, a singular event occurred that would change the course of existence foreverΓÇöThe Big Bang.

-Around 13.8 billion years ago, an infinitely hot and dense point, no larger than a single atom, began to expand at an inconceivable speed. This was the birth of our universe, a moment where time and space came into being. As this primordial fireball grew, it cooled, and the fundamental forces that govern the cosmosΓÇögravity, electromagnetism, and the strong and weak nuclear forcesΓÇöbegan to take shape.

-Matter coalesced into the simplest elements, hydrogen and helium, which later formed vast clouds in the expanding universe. These clouds, driven by the force of gravity, began to collapse in on themselves, creating the first stars. The stars were crucibles of nuclear fusion, forging heavier elements like carbon, nitrogen, and oxygen

-By using the same `seed` parameter of 42 for each of our three requests we're able to produce much more consistent (in this case identical) results.

-Within a few minutes, the target will be assigned the selected role at the selected scope. For help with these steps, see [Assign Azure roles using the Azure portal](../../../role-based-access-control/role-assignments-portal.md).

-description: Learn about the changes you need to make to your code to swap back and forth between OpenAI and Azure OpenAI endpoints.

-# How to switch between OpenAI and Azure OpenAI endpoints with Python

-While OpenAI and Azure OpenAI Service rely on a [common Python client library](https://github.com/openai/openai-python), there are small changes you need to make to your code in order to swap back and forth between endpoints. This article walks you through the common changes and differences you'll experience when working across OpenAI and Azure OpenAI.

-This article only shows examples with the new OpenAI Python 1.x API library. For information on migrating from `0.28.1` to `1.x` refer to our [migration guide](./migration.md).

-## Authentication

-We recommend using environment variables. If you haven't done this before our [Python quickstarts](../quickstart.md) walk you through this configuration.

-### API key

-<table>

-<tr>

-<td> OpenAI </td> <td> Azure OpenAI </td>

-</tr>

-<tr>

-<td>

-```python

-import os

-from openai import OpenAI

-client = OpenAI(

- api_key=os.getenv("OPENAI_API_KEY")

-)

-```

-</td>

-<td>

-```python

-import os

-from openai import AzureOpenAI

-

-client = AzureOpenAI(

- api_key=os.getenv("AZURE_OPENAI_API_KEY"),

- api_version="2023-12-01-preview",

- azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT")

-)

-```

-</td>

-</tr>

-</table>

-<a name='azure-active-directory-authentication'></a>

-### Microsoft Entra ID authentication

-<table>

-<tr>

-<td> OpenAI </td> <td> Azure OpenAI </td>

-</tr>

-<tr>

-<td>

-```python

-import os

-from openai import OpenAI

-client = OpenAI(

- api_key=os.getenv("OPENAI_API_KEY")

-)

-```

-</td>

-<td>

-```python

-from azure.identity import DefaultAzureCredential, get_bearer_token_provider

-from openai import AzureOpenAI

-token_provider = get_bearer_token_provider(

- DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"

-)

-api_version = "2023-12-01-preview"

-endpoint = "https://my-resource.openai.azure.com"

-client = AzureOpenAI(

- api_version=api_version,

- azure_endpoint=endpoint,

- azure_ad_token_provider=token_provider,

-)

-```

-</td>

-</tr>

-</table>

-## Keyword argument for model

-OpenAI uses the `model` keyword argument to specify what model to use. Azure OpenAI has the concept of unique model [deployments](create-resource.md?pivots=web-portal#deploy-a-model). When using Azure OpenAI `model` should refer to the underlying deployment name you chose when you deployed the model.

-> [!IMPORTANT]

-> When you access the model via the API in Azure OpenAI you will need to refer to the deployment name rather than the underlying model name in API calls. This is one of the [key differences](../how-to/switching-endpoints.md) between OpenAI and Azure OpenAI. OpenAI only requires the model name, Azure OpenAI always requires deployment name, even when using the model parameter. In our docs we often have examples where deployment names are represented as identical to model names to help indicate which model works with a particular API endpoint. Ultimately your deployment names can follow whatever naming convention is best for your use case.

-<table>

-<tr>

-<td> OpenAI </td> <td> Azure OpenAI </td>

-</tr>

-<tr>

-<td>

-```python

-completion = client.completions.create(

- model="gpt-3.5-turbo-instruct",

- prompt="<prompt>"

-)

-chat_completion = client.chat.completions.create(

- model="gpt-4",

- messages="<messages>"

-)

-embedding = client.embeddings.create(

- model="text-embedding-ada-002",

- input="<input>"

-)

-```

-</td>

-<td>

-```python

-completion = client.completions.create(

- model="gpt-35-turbo-instruct", # This must match the custom deployment name you chose for your model.

- prompt="<prompt>"

-)

-chat_completion = client.chat.completions.create(

- model="gpt-35-turbo", # model = "deployment_name".

- messages="<messages>"

-)

-embedding = client.embeddings.create(

- model="text-embedding-ada-002", # model = "deployment_name".

- input="<input>"

-)

-```

-</td>

-</tr>

-</table>

-## Azure OpenAI embeddings multiple input support

-OpenAI and Azure OpenAI currently support input arrays up to 2048 input items for text-embedding-ada-002. Both require the max input token limit per API request to remain under 8191 for this model.

-<table>

-<tr>

-<td> OpenAI </td> <td> Azure OpenAI </td>

-</tr>

-<tr>

-<td>

-```python

-inputs = ["A", "B", "C"]

-embedding = client.embeddings.create(

- input=inputs,

- model="text-embedding-ada-002"

-)

-```

-</td>

-<td>

-```python

-inputs = ["A", "B", "C"] #max array size=2048

-embedding = client.embeddings.create(

- input=inputs,

- model="text-embedding-ada-002" # This must match the custom deployment name you chose for your model.

- # engine="text-embedding-ada-002"

-)

-```

-</td>

-</tr>

-</table>

-## Next steps

-* Learn more about how to work with GPT-35-Turbo and the GPT-4 models with [our how-to guide](../how-to/chatgpt.md).

-* For more examples, check out the [Azure OpenAI Samples GitHub repository](https://aka.ms/AOAICodeSamples)

-## Resources configuration

-> To allow access to your Azure AI Search resource from Azure OpenAI resource, you need to submit an [application form](https://aka.ms/applyacsvpnaoaioyd). The application will be reviewed in 10 business days and you will be contacted via email about the results. If you are eligible, we will provision the private endpoint in Microsoft managed virtual network, and send a private endpoint connection request to your search service, and you will need to approve the request.

-See the [ingestion API reference article](/azure/ai-services/openai/reference#start-an-ingestion-job) for details on the request and response objects used by the ingestion API.

-The Whisper models, currently in preview, can be used to transcribe and translate speech to text.

-| Max Files per resource (fine-tuning) | 30 |

- "n": 3,

-| ```file```| file | Yes | N/A | The audio file object (not file name) to transcribe, in one of these formats: `flac`, `mp3`, `mp4`, `mpeg`, `mpga`, `m4a`, `ogg`, `wav`, or `webm`.<br/><br/>The file size limit for the Azure OpenAI Whisper model is 25 MB. If you need to transcribe a file larger than 25 MB, break it into chunks. Alternatively you can use the Azure AI Speech [batch transcription](../speech-service/batch-transcription-create.md#use-a-whisper-model) API.<br/><br/>You can get sample audio files from the [Azure AI Speech SDK repository at GitHub](https://github.com/Azure-Samples/cognitive-services-speech-sdk/tree/master/sampledata/audiofiles). |

-Azure OpenAI is deployed as a part of the Azure AI services. All Azure AI services rely on the same set of management APIs for creation, update, and delete operations. The management APIs are also used for deploying models within an OpenAI resource.

-| Python | [Source code](https://github.com/openai/openai-python) | [Package (PyPi)](https://pypi.org/project/openai/) | [Python examples](./how-to/switching-endpoints.md) |

-If you want to clean up and remove an OpenAI resource, you can delete the resource. Before deleting the resource, you must first delete any deployed models.

-If you created an OpenAI resource solely for completing this tutorial and want to clean up and remove an OpenAI resource, you'll need to delete your deployed models, and then delete the resource or associated resource group if it's dedicated to your test resource. Deleting the resource group also deletes any other resources associated with it.

-* An Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services?azure-portal=true).

-pip install openai requests tiktoken

-pip install "openai==0.28.1" requests tiktoken

-setx AZURE_OPENAI_API_KEY "REPLACE_WITH_YOUR_KEY_VALUE_HERE"

-setx AZURE_OPENAI_ENDPOINT "REPLACE_WITH_YOUR_ENDPOINT_HERE"

-

- azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT"),

- api_key=os.getenv("AZURE_OPENAI_API_KEY"),

- api_version="2024-02-01" # This API version or later is required to access fine-tuning for turbo/babbage-002/davinci-002

- file=open(training_file_name, "rb"), purpose="fine-tune"

- file=open(validation_file_name, "rb"), purpose="fine-tune"

-openai.api_key = os.getenv("AZURE_OPENAI_API_KEY")

- file=open(training_file_name, "rb"), purpose="fine-tune", user_provided_filename="training_set.jsonl"

- file=open(validation_file_name, "rb"), purpose="fine-tune", user_provided_filename="validation_set.jsonl"

- training_file=training_file_id,

- validation_file=validation_file_id,

- model="gpt-35-turbo-0613", # Enter base model name. Note that in Azure OpenAI the model name contains dashes and cannot contain dot/period characters.

- training_file=training_file_id,

- validation_file=validation_file_id,

- model="gpt-35-turbo-0613",

-

-

-#Retrieve fine_tuned_model name

-#Retrieve fine_tuned_model name

-token= os.getenv("TEMP_AUTH_TOKEN")

-subscription = "<YOUR_SUBSCRIPTION_ID>"

-model_deployment_name ="YOUR_CUSTOM_MODEL_DEPLOYMENT_NAME"

-deploy_params = {'api-version': "2023-05-01"}

- "sku": {"name": "standard", "capacity": 1},

- azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT"),

- api_key=os.getenv("AZURE_OPENAI_API_KEY"),

- api_version="2024-02-01"

- model="gpt-35-turbo-ft", # model = "Custom deployment name you chose for your fine-tuning model"

- messages=[

-openai.api_base = os.getenv("AZURE_OPENAI_ENDPOINT")

- engine="gpt-35-turbo-ft", # engine = "Custom deployment name you chose for your fine-tuning model"

- messages=[

-You can delete the deployment in [Azure OpenAI Studio](https://oai.azure.com/), via [REST API](/rest/api/aiservices/accountmanagement/deployments/delete?tabs=HTTP), [Azure CLI](/cli/azure/cognitiveservices/account/deployment#az-cognitiveservices-account-deployment-delete()), or other supported deployment methods.

-

-If you want to clean up and remove an OpenAI or Azure AI Search resource, you can delete the resource or resource group. Deleting the resource group also deletes any other resources associated with it.

-### Fine-tuning is now supported in East US 2

-Fine-tuning is now available in East US 2 with support for:

-**Content filtering is temporarily off** by default. Azure content moderation works differently than OpenAI. Azure OpenAI runs content filters during the generation call to detect harmful or abusive content and filters them from the response. [Learn MoreΓÇï](./concepts/content-filter.md)

-If you want to clean up and remove an OpenAI resource, you can delete the resource. Before deleting the resource, you must first delete any deployed models.

-QnA Maker allows multiple people to collaborate on all knowledge bases in the same QnA Maker resource. This feature is provided with [Azure role-based access control (Azure RBAC)](../../../role-based-access-control/role-assignments-portal.md).

-description: Provides an overview of available Azure AI REST APIs with links to reference documentation.

-# Azure AI REST API reference

-This article provides an overview of available Azure AI REST APIs with links to service and feature level reference documentation.

-|  [Azure AI Search](../../search/index.yml) | Bring AI-powered cloud search to your mobile and web apps | [Azure AI Search API](/rest/api/searchservice) |

-| </br>&bullet; [fine-tuning](/rest/api/azureopenai/fine-tuning) |

-|  [Bot Service](/composer/) | Create bots and connect them across channels | [Bot Service API](/azure/bot-service/rest-api/bot-framework-rest-connector-api-reference?view=azure-bot-service-4.0&preserve-view=true) |

-|  [Content Safety](../../ai-services/content-safety/index.yml) | An AI service that detects unwanted contents | [Content Safety API](https://westus.dev.cognitive.microsoft.com/docs/services/content-safety-service-2023-10-15-preview/operations/TextBlocklists_AddOrUpdateBlocklistItems) |

-|  [Custom Vision](../../ai-services/custom-vision-service/index.yml) | Customize image recognition for your business applications. |**Custom Vision APIs**<br>&bullet; [prediction](https://westus2.dev.cognitive.microsoft.com/docs/services/Custom_Vision_Prediction_3.1/operations/5eb37d24548b571998fde5f3)<br>&bullet; [training](https://westus2.dev.cognitive.microsoft.com/docs/services/Custom_Vision_Training_3.3/operations/5eb0bcc6548b571998fddebd)|

-|  [Document Intelligence](../../ai-services/document-intelligence/index.yml) | Turn documents into intelligent data-driven solutions | [Document Intelligence API](/rest/api/aiservices/document-models?view=rest-aiservices-2023-07-31&preserve-view=true) |

-|  |

-|  [Language](../../ai-services/language-service/index.yml) | Build apps with industry-leading natural language understanding capabilities | [REST API](/rest/api/language/) |

-|  |

-| |

-|  [Video Indexer](/azure/azure-video-indexer) | Extract actionable insights from your videos | [Video Indexer API](/rest/api/videoindexer/accounts?view=rest-videoindexer-2024-01-01&preserve-view=true) |

-|  [Vision](../../ai-services/computer-vision/index.yml) | Analyze content in images and videos | [Vision API](https://eastus.dev.cognitive.microsoft.com/docs/services/Cognitive_Services_Unified_Vision_API_2024-02-01/operations/61d65934cd35050c20f73ab6) |

-|  [Anomaly Detector](../../ai-services/Anomaly-Detector/index.yml) <br>(deprecated 2023) | Identify potential problems early on | [Anomaly Detector API](https://westus2.dev.cognitive.microsoft.com/docs/services/AnomalyDetector-v1-1/operations/CreateMultivariateModel) |

-|  |

-|  [Language understanding (LUIS)](../../ai-services/luis/index.yml) <br>(deprecated 2023) | Understand natural language in your apps | [LUIS API](https://westus.dev.cognitive.microsoft.com/docs/services/luis-endpoint-api-v3-0/operations/5cb0a9459a1fe8fa44c28dd8) |

-|  [Metrics Advisor](../../ai-services/metrics-advisor/index.yml) <br>(deprecated 2023) | An AI service that detects unwanted contents | [Metrics Advisor API](https://westus.dev.cognitive.microsoft.com/docs/services/MetricsAdvisor/operations/createDataFeed) |

-|  [Personalizer](../../ai-services/personalizer/index.yml) <br>(deprecated 2023) | Create rich, personalized experiences for each user | [Personalizer API](https://westus2.dev.cognitive.microsoft.com/docs/services/personalizer-api/operations/Rank) |

-|  [QnA maker](../../ai-services/qnamaker/index.yml) <br>(deprecated 2022) | Distill information into easy-to-navigate questions and answers | [QnA Maker API](https://westus.dev.cognitive.microsoft.com/docs/services/5a93fcf85b4ccd136866eb37/operations/5ac266295b4ccd1554da75ff) |

-# Azure AI SDK reference

-> The maximum JSON payload size that will be accepted is 2 megabytes. Each Speech resource can have up to 300 batch synthesis jobs that are running concurrently.

-To create a transcription, use the [Transcriptions_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_Create) operation of the [Speech to text REST API](rest-speech-to-text.md#transcriptions). Construct the request body according to the following instructions:

-Make an HTTP POST request that uses the URI as shown in the following [Transcriptions_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_Create) example.

-The top-level `self` property in the response body is the transcription's URI. Use this URI to [get](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_Get) details such as the URI of the transcriptions and transcription report files. You also use this URI to [update](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_Update) or [delete](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_Delete) a transcription.

-You can query the status of your transcriptions with the [Transcriptions_Get](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_Get) operation.

-Call [Transcriptions_Delete](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_Delete)

-Here are some property options that you can use to configure a transcription when you call the [Transcriptions_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_Create) operation.

-|`diarization`|Indicates that the Speech service should attempt diarization analysis on the input, which is expected to be a mono channel that contains multiple voices. The feature isn't available with stereo recordings.<br/><br/>Diarization is the process of separating speakers in audio data. The batch pipeline can recognize and separate multiple speakers on mono channel recordings.<br/><br/>Specify the minimum and maximum number of people who might be speaking. You must also set the `diarizationEnabled` property to `true`. The [transcription file](batch-transcription-get.md#transcription-result-file) contains a `speaker` entry for each transcribed phrase.<br/><br/>You need to use this property when you expect three or more speakers. For two speakers, setting `diarizationEnabled` property to `true` is enough. For an example of the property usage, see [Transcriptions_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_Create).<br/><br/>The maximum number of speakers for diarization must be less than 36 and more or equal to the `minSpeakers` property. For an example, see [Transcriptions_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_Create).<br/><br/>When this property is selected, source audio length can't exceed 240 minutes per file.<br/><br/>**Note**: This property is only available with Speech to text REST API version 3.1 and later. If you set this property with any previous version, such as version 3.0, it's ignored and only two speakers are identified.|

-|`timeToLive`|A duration after the transcription job is created, when the transcription results will be automatically deleted. The value is an ISO 8601 encoded duration. For example, specify `PT12H` for 12 hours. As an alternative, you can call [Transcriptions_Delete](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_Delete) regularly after you retrieve the transcription results.|

-Whisper models by batch transcription are supported in the East US, Southeast Asia, and West Europe regions.

-You can make a [Models_ListBaseModels](https://westus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-2-preview2/operations/Models_ListBaseModels) request to get available base models for all locales.

-To get the status of the transcription job, call the [Transcriptions_Get](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_Get) operation of the [Speech to text REST API](rest-speech-to-text.md).

-The [Transcriptions_ListFiles](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_ListFiles) operation returns a list of result files for a transcription. A [transcription report](#transcription-report-file) file is provided for each submitted batch transcription job. In addition, one [transcription](#transcription-result-file) file (the end result) is provided for each successfully transcribed audio file.

-Batch transcription is used to transcribe a large amount of audio data in storage. Both the [Speech to text REST API](rest-speech-to-text.md#transcriptions) and [Speech CLI](spx-basics.md) support batch transcription.

-[Speech to text REST API](rest-speech-to-text.md) fully supports BYOS-enabled Speech resources. However, because the data is now stored within the BYOS-enabled Storage account, requests like [Get Transcription Files](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_ListFiles) interact with the BYOS-associated Storage account Blob storage, instead of Speech service internal resources. It allows using the same REST API based code for both "regular" and BYOS-enabled Speech resources.

-For maximum security use the `sasValidityInSeconds` parameter with the value set to `0` in the requests, that return data file URLs, like [Get Transcription Files](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_ListFiles) request. Here's an example request URL:

-> If `sasValidityInSeconds` parameter is omitted in [Get Transcription Files](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_ListFiles) request or similar ones, then a [User delegation SAS](../../storage/common/storage-sas-overview.md) with the validity of 30 days will be generated for each data file URL returned. This SAS is signed by the system assigned managed identity of your BYOS-enabled Speech resource. Because of it, the SAS allows access to the data, even if storage account key access is disabled. See details [here](../../storage/common/shared-key-authorization-prevent.md#understand-how-disallowing-shared-key-affects-sas-tokens).

-> Logging data is kept for 30 days. After this period the logs are automatically deleted. This is valid for BYOS-enabled Speech resources as well. If you want to keep the logs longer, copy the correspondent files and folders from `customspeech-audiologs` Blob container directly or use REST API.

-[Speech to text REST API](rest-speech-to-text.md) fully supports BYOS-enabled Speech resources. However, because the data is now stored within the BYOS-enabled Storage account, requests like [Get Base Model Logs](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_ListBaseModelLogs) interact with the BYOS-associated Storage account Blob storage, instead of Speech service internal resources. It allows using the same REST API based code for both "regular" and BYOS-enabled Speech resources.

-For maximum security use the `sasValidityInSeconds` parameter with the value set to `0` in the requests, that return data file URLs, like [Get Base Model Logs](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_ListBaseModelLogs) request. Here's an example request URL:

-> If `sasValidityInSeconds` parameter is omitted in [Get Base Model Logs](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_ListBaseModelLogs) request or similar ones, then a [User delegation SAS](../../storage/common/storage-sas-overview.md) with the validity of 30 days will be generated for each data file URL returned. This SAS is signed by the system assigned managed identity of your BYOS-enabled Speech resource. Because of it, the SAS allows access to the data, even if storage account key access is disabled. See details [here](../../storage/common/shared-key-authorization-prevent.md#understand-how-disallowing-shared-key-affects-sas-tokens).

-[Speech to text REST API](rest-speech-to-text.md) fully supports BYOS-enabled Speech resources. However, because the data is now stored within the BYOS-enabled Storage account, requests like [Get Dataset Files](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Datasets_ListFiles) interact with the BYOS-associated Storage account Blob storage, instead of Speech service internal resources. It allows using the same REST API based code for both "regular" and BYOS-enabled Speech resources.

-For maximum security use the `sasValidityInSeconds` parameter with the value set to `0` in the requests, that return data file URLs, like [Get Dataset Files](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Datasets_ListFiles) request. Here's an example request URL:

-> If `sasValidityInSeconds` parameter is omitted in [Get Dataset Files](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Datasets_ListFiles) request or similar ones, then a [User delegation SAS](../../storage/common/storage-sas-overview.md) with the validity of 30 days will be generated for each data file URL returned. This SAS is signed by the system assigned managed identity of your BYOS-enabled Speech resource. Because of it, the SAS allows access to the data, even if storage account key access is disabled. See details [here](../../storage/common/shared-key-authorization-prevent.md#understand-how-disallowing-shared-key-affects-sas-tokens).

-This section describes how to create a BYOS enabled Speech resource.

-> Check out the code samples in the [Speech SDK repository on GitHub](https://github.com/Azure-Samples/cognitive-services-speech-sdk/blob/master/samples/custom-voice/README.md) to see how to use personal voice in your application.

- <version>1.36.0</version>

- implementation 'com.microsoft.cognitiveservices.speech:client-sdk-embedded:1.36.0@aar'

-You can assign roles to the user or application using the [Azure portal](../../role-based-access-control/role-assignments-portal.md) or [PowerShell](../../role-based-access-control/role-assignments-powershell.md).

-To create a project, use the [Projects_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Projects_Create) operation of the [Speech to text REST API](rest-speech-to-text.md). Construct the request body according to the following instructions:

-Make an HTTP POST request using the URI as shown in the following [Projects_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Projects_Create) example. Replace `YourSubscriptionKey` with your Speech resource key, replace `YourServiceRegion` with your Speech resource region, and set the request body properties as previously described.

-The top-level `self` property in the response body is the project's URI. Use this URI to [get](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Projects_Get) details about the project's evaluations, datasets, models, endpoints, and transcriptions. You also use this URI to [update](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Projects_Update) or [delete](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Projects_Delete) a project.

-To create an endpoint and deploy a model, use the [Endpoints_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_Create) operation of the [Speech to text REST API](rest-speech-to-text.md). Construct the request body according to the following instructions:

-Make an HTTP POST request using the URI as shown in the following [Endpoints_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_Create) example. Replace `YourSubscriptionKey` with your Speech resource key, replace `YourServiceRegion` with your Speech resource region, and set the request body properties as previously described.

-The top-level `self` property in the response body is the endpoint's URI. Use this URI to [get](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_Get) details about the endpoint's project, model, and logs. You also use this URI to [update](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_Update) or [delete](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_Delete) the endpoint.

-To redeploy the custom endpoint with a new model, use the [Endpoints_Update](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_Update) operation of the [Speech to text REST API](rest-speech-to-text.md). Construct the request body according to the following instructions:

-To get logs for an endpoint, start by using the [Endpoints_Get](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_Get) operation of the [Speech to text REST API](rest-speech-to-text.md).

-To create a test, use the [Evaluations_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Evaluations_Create) operation of the [Speech to text REST API](rest-speech-to-text.md). Construct the request body according to the following instructions:

-The top-level `self` property in the response body is the evaluation's URI. Use this URI to [get](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Evaluations_Get) details about the evaluation's project and test results. You also use this URI to [update](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Evaluations_Update) or [delete](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Evaluations_Delete) the evaluation.

-To get test results, start by using the [Evaluations_Get](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Evaluations_Get) operation of the [Speech to text REST API](rest-speech-to-text.md).

-To create a test, use the [Evaluations_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Evaluations_Create) operation of the [Speech to text REST API](rest-speech-to-text.md). Construct the request body according to the following instructions:

-The top-level `self` property in the response body is the evaluation's URI. Use this URI to [get](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Evaluations_Get) details about the evaluation's project and test results. You also use this URI to [update](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Evaluations_Update) or [delete](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Evaluations_Delete) the evaluation.

-To get test results, start by using the [Evaluations_Get](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Evaluations_Get) operation of the [Speech to text REST API](rest-speech-to-text.md).

-|Batch transcription |[Batch transcription](batch-transcription.md) requests for expired models fail with a 4xx error. |In each [Transcriptions_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_Create) REST API request body, set the `model` property to a base model or custom model that isn't expired. Otherwise don't include the `model` property to always use the latest base model. |

-To get the training and transcription expiration dates for a base model, use the [Models_GetBaseModel](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_GetBaseModel) operation of the [Speech to text REST API](rest-speech-to-text.md). You can make a [Models_ListBaseModels](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_ListBaseModels) request to get available base models for all locales.

-To get the transcription expiration date for your custom model, use the [Models_GetCustomModel](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_GetCustomModel) operation of the [Speech to text REST API](rest-speech-to-text.md).

-If you train a custom model with audio data, choose a Speech resource region with dedicated hardware for training audio data. For more information, see footnotes in the [regions](regions.md#speech-service) table. In regions with dedicated hardware for custom speech training, the Speech service uses up to 20 hours of your audio training data, and can process about 10 hours of data per day. In other regions, the Speech service uses up to 8 hours of your audio data, and can process about 1 hour of data per day. After the model is trained, you can copy the model to another region as needed with the [Models_CopyTo](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_CopyTo) REST API.

-To create a model with datasets for training, use the [Models_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_Create) operation of the [Speech to text REST API](rest-speech-to-text.md). Construct the request body according to the following instructions:

-The top-level `self` property in the response body is the model's URI. Use this URI to [get](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_GetCustomModel) details about the model's project, manifest, and deprecation dates. You also use this URI to [update](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_Update) or [delete](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_Delete) the model.

-To copy a model to another Speech resource, use the [Models_CopyTo](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_CopyTo) operation of the [Speech to text REST API](rest-speech-to-text.md). Construct the request body according to the following instructions:

-To connect a new model to a project of the Speech resource where the model was copied, use the [Models_Update](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_Update) operation of the [Speech to text REST API](rest-speech-to-text.md). Construct the request body according to the following instructions:

-Make an HTTP PATCH request using the URI as shown in the following example. Use the URI of the new model. You can get the new model ID from the `self` property of the [Models_CopyTo](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_CopyTo) response body. Replace `YourSubscriptionKey` with your Speech resource key, replace `YourServiceRegion` with your Speech resource region, and set the request body properties as previously described.

-To create a dataset and connect it to an existing project, use the [Datasets_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Datasets_Create) operation of the [Speech to text REST API](rest-speech-to-text.md). Construct the request body according to the following instructions:

-The top-level `self` property in the response body is the dataset's URI. Use this URI to [get](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Datasets_Get) details about the dataset's project and files. You also use this URI to [update](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Datasets_Update) or [delete](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Datasets_Delete) the dataset.

-The required Transcription ID is the GUID value contained in the main `self` element of the Response body returned by requests, like [Transcriptions_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_Create).

-The following is and example response body of a [Transcriptions_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_Create) request. GUID value `537216f8-0620-4a10-ae2d-00bdb423b36f` found in the first `self` element is the Transcription ID.

-> Use the same technique to determine different IDs required for debugging issues related to [custom speech](custom-speech-overview.md), like uploading a dataset using [Datasets_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Datasets_Create) request.

-> You can also see all existing transcriptions and their Transcription IDs for a given Speech resource by using [Transcriptions_Get](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_Get) request.

-> We are retiring the standard voices from September 1, 2021 through August 31, 2024. If you used a standard voice with your Speech resource that was created prior to September 1, 2021 then you can continue to do so until August 31, 2024. To use neural voices, choose voice names that include 'Neural' in their name, for example: en-US-JennyMultilingualNeural. All other Speech resources can only use prebuilt neural voices. You can choose from the supported [neural voice names](language-support.md?tabs=tts). After August 31, 2024 the standard voices won't be supported with any Speech resource.

-To identify languages with [Batch transcription REST API](batch-transcription.md), use `languageIdentification` property in the body of your [Transcriptions_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_Create) request.

-The table in this section summarizes the 27 locales supported for pronunciation assessment, and each language is available on all [Speech to text regions](regions.md#speech-service). Latest update extends support from English to 26 more languages and quality enhancements to existing features, including accuracy, fluency and miscue assessment. You should specify the language that you're learning or practicing improving pronunciation. The default language is set as `en-US`. If you know your target learning language, [set the locale](how-to-pronunciation-assessment.md#get-pronunciation-assessment-results) accordingly. For example, if you're learning British English, you should specify the language as `en-GB`. If you're teaching a broader language, such as Spanish, and are uncertain about which locale to select, you can run various accent models (`es-ES`, `es-MX`) to determine the one that achieves the highest score to suit your specific scenario.

-To turn off logging for a custom endpoint, use the [Endpoints_Update](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_Update) operation of the [Speech to text REST API](rest-speech-to-text.md). Construct the request body according to the following instructions:

-Here's a sample output of [Endpoints_ListLogs](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_ListLogs). For simplicity, only one log set is shown:

-description: This document helps developers migrate code from v2 to v3 of the Speech to text REST API.speech-to-text REST API.

-# Migrate code from v2.0 to v3.0 of the REST API

-> [!IMPORTANT]

-> The Speech to text REST API v2.0 is retired as of February 29, 2024. Please migrate your applications to the Speech to text REST API v3.2. Complete the steps in this article and then see the Speech to text REST API [v3.0 to v3.1](migrate-v3-0-to-v3-1.md) and [v3.1 to v3.2](migrate-v3-1-to-v3-2.md) migration guides for additional requirements.

-## Forward compatibility

-All entities from v2.0 can also be found in the v3.0 API under the same identity. Where the schema of a result has changed (such as transcriptions), the result of a GET in the v3 version of the API uses the v3 schema. The result of a GET in the v2 version of the API uses the same v2 schema. Newly created entities on v3 aren't available in responses from v2 APIs.

-## Migration steps

-This is a summary list of items you need to be aware of when you're preparing for migration. Details are found in the individual links. Depending on your current use of the API not all steps listed here might apply. Only a few changes require nontrivial changes in the calling code. Most changes just require a change to item names.

-General changes:

-1. [Change the host name](#host-name-changes)

-1. [Rename the property ID to self in your client code](#identity-of-an-entity)

-1. [Change code to iterate over collections of entities](#working-with-collections-of-entities)

-1. [Rename the property name to displayName in your client code](#name-of-an-entity)

-1. [Adjust the retrieval of the metadata of referenced entities](#accessing-referenced-entities)

-1. If you use Batch transcription:

- * [Adjust code for creating batch transcriptions](#creating-transcriptions)

- * [Adapt code to the new transcription results schema](#format-of-v3-transcription-results)

- * [Adjust code for how results are retrieved](#getting-the-content-of-entities-and-the-results)

-1. If you use Custom model training/testing APIs:

- * [Apply modifications to custom model training](#customizing-models)

- * [Change how base and custom models are retrieved](#retrieving-base-and-custom-models)

- * [Rename the path segment accuracy tests to evaluations in your client code](#accuracy-tests)

-1. If you use endpoints APIs:

- * [Change how endpoint logs are retrieved](#retrieving-endpoint-logs)

-1. Other minor changes:

- * [Pass all custom properties as customProperties instead of properties in your POST requests](#using-custom-properties)

- * [Read the location from response header Location instead of Operation-Location](#response-headers)

-## Breaking changes

-### Host name changes

-Endpoint host names changed from `{region}.cris.ai` to `{region}.api.cognitive.microsoft.com`. Paths to the new endpoints no longer contain `api/` because it's part of the hostname. The [Speech to text REST API v3.0](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0) reference documentation lists valid regions and paths.

->[!IMPORTANT]

->Change the hostname from `{region}.cris.ai` to `{region}.api.cognitive.microsoft.com` where region is the region of your speech subscription. Also remove `api/`from any path in your client code.

-### Identity of an entity

-The property `id` is now `self`. In v2, an API user had to know how our paths on the API are being created. This was non-extensible and required unnecessary work from the user. The property `id` (uuid) is replaced by `self` (string), which is location of the entity (URL). The value is still unique between all your entities. If `id` is stored as a string in your code, a rename is enough to support the new schema. You can now use the `self` content as the URL for the `GET`, `PATCH`, and `DELETE` REST calls for your entity.

-If the entity has more functionality available through other paths, they're listed under `links`. The following example for transcription shows a separate method to `GET` the content of the transcription:

->[!IMPORTANT]

->Rename the property `id` to `self` in your client code. Change the type from `uuid` to `string` if needed.

-**v2 transcription:**

-```json

-{

- "id": "9891c965-bb32-4880-b14b-6d44efb158f3",

- "createdDateTime": "2019-01-07T11:34:12Z",

- "lastActionDateTime": "2019-01-07T11:36:07Z",

- "status": "Succeeded",

- "locale": "en-US",

- "name": "Transcription using locale en-US"

-}

-```

-**v3 transcription:**

-```json

-{

- "self": "https://eastus.api.cognitive.microsoft.com/speechtotext/v3.0/transcriptions/9891c965-bb32-4880-b14b-6d44efb158f3",

- "createdDateTime": "2019-01-07T11:34:12Z",

- "lastActionDateTime": "2019-01-07T11:36:07Z",

- "status": "Succeeded",

- "locale": "en-US",

- "displayName": "Transcription using locale en-US",

- "links": {

- "files": "https://eastus.api.cognitive.microsoft.com/speechtotext/v3.0/transcriptions/9891c965-bb32-4880-b14b-6d44efb158f3/files"

- }

-}

-```

-Depending on your code's implementation, it might not be enough to rename the property. We recommend using the returned `self` and `links` values as the target urls of your REST calls, rather than generating paths in your client. By using the returned URLs, you can be sure that future changes in paths won't break your client code.

-### Working with collections of entities

-Previously the v2 API returned all available entities in a result. To allow a more fine grained control over the expected response size in v3, all collection results are paginated. You have control over the count of returned entities and the starting offset of the page. This behavior makes it easy to predict the runtime of the response processor.

-The basic shape of the response is the same for all collections:

-```json

-{

- "values": [

- {

- }

- ],

- "@nextLink": "https://{region}.api.cognitive.microsoft.com/speechtotext/v3.0/{collection}?skip=100&top=100"

-}

-```

-The `values` property contains a subset of the available collection entities. The count and offset can be controlled using the `skip` and `top` query parameters. When `@nextLink` isn't `null`, there's more data available and the next batch of data can be retrieved by doing a GET on `$.@nextLink`.

-This change requires calling the `GET` for the collection in a loop until all elements are returned.

->[!IMPORTANT]

->When the response of a GET to `speechtotext/v3.1/{collection}` contains a value in `$.@nextLink`, continue issuing `GETs` on `$.@nextLink` until `$.@nextLink` is not set to retrieve all elements of that collection.

-### Creating transcriptions

-A detailed description on how to create batches of transcriptions can be found in [Batch transcription How-to](./batch-transcription.md).

-The v3 transcription API lets you set specific transcription options explicitly. All (optional) configuration properties can now be set in the `properties` property.

-Version v3 also supports multiple input files, so it requires a list of URLs rather than a single URL as v2 did. The v2 property name `recordingsUrl` is now `contentUrls` in v3. The functionality of analyzing sentiment in transcriptions is removed in v3. See [Text Analysis](https://azure.microsoft.com/services/cognitive-services/text-analytics/) for sentiment analysis options.

-The new property `timeToLive` under `properties` can help prune the existing completed entities. The `timeToLive` specifies a duration after which a completed entity is deleted automatically. Set it to a high value (for example `PT12H`) when the entities are continuously tracked, consumed, and deleted and therefore usually processed long before 12 hours have passed.

-**v2 transcription POST request body:**

-```json

-{

- "locale": "en-US",

- "name": "Transcription using locale en-US",

- "recordingsUrl": "https://contoso.com/mystoragelocation",

- "properties": {

- "AddDiarization": "False",

- "AddWordLevelTimestamps": "False",

- "PunctuationMode": "DictatedAndAutomatic",

- "ProfanityFilterMode": "Masked"

- }

-}

-```

-**v3 transcription POST request body:**

-```json

-{

- "locale": "en-US",

- "displayName": "Transcription using locale en-US",

- "contentUrls": [

- "https://contoso.com/mystoragelocation",

- "https://contoso.com/myotherstoragelocation"

- ],

- "properties": {

- "diarizationEnabled": false,

- "wordLevelTimestampsEnabled": false,

- "punctuationMode": "DictatedAndAutomatic",

- "profanityFilterMode": "Masked"

- }

-}

-```

->[!IMPORTANT]

->Rename the property `recordingsUrl` to `contentUrls` and pass an array of urls instead of a single url. Pass settings for `diarizationEnabled` or `wordLevelTimestampsEnabled` as `bool` instead of `string`.

-### Format of v3 transcription results

-The schema of transcription results has changed slightly to align with transcriptions created by real-time endpoints. Find an in-depth description of the new format in the [Batch transcription How-to](./batch-transcription.md). The schema of the result is published in our [GitHub sample repository](https://aka.ms/csspeech/samples) under `samples/batch/transcriptionresult_v3.schema.json`.

-Property names are now camel-cased and the values for `channel` and `speaker` now use integer types. Formats for durations now use the structure described in ISO 8601, which matches duration formatting used in other Azure APIs.

-Sample of a v3 transcription result. The differences are described in the comments.

-```json

-{

- "source": "...", // (new in v3) was AudioFileName / AudioFileUrl

- "timestamp": "2020-06-16T09:30:21Z", // (new in v3)

- "durationInTicks": 41200000, // (new in v3) was AudioLengthInSeconds

- "duration": "PT4.12S", // (new in v3)

- "combinedRecognizedPhrases": [ // (new in v3) was CombinedResults

- {

- "channel": 0, // (new in v3) was ChannelNumber

- "lexical": "hello world",

- "itn": "hello world",

- "maskedITN": "hello world",

- "display": "Hello world."

- }

- ],

- "recognizedPhrases": [ // (new in v3) was SegmentResults

- {

- "recognitionStatus": "Success", //

- "channel": 0, // (new in v3) was ChannelNumber

- "offset": "PT0.07S", // (new in v3) new format, was OffsetInSeconds

- "duration": "PT1.59S", // (new in v3) new format, was DurationInSeconds

- "offsetInTicks": 700000.0, // (new in v3) was Offset

- "durationInTicks": 15900000.0, // (new in v3) was Duration

- // possible transcriptions of the current phrase with confidences

- "nBest": [

- {

- "confidence": 0.898652852,phrase

- "speaker": 1,

- "lexical": "hello world",

- "itn": "hello world",

- "maskedITN": "hello world",

- "display": "Hello world.",

- "words": [

- {

- "word": "hello",

- "offset": "PT0.09S",

- "duration": "PT0.48S",

- "offsetInTicks": 900000.0,

- "durationInTicks": 4800000.0,

- "confidence": 0.987572

- },

- {

- "word": "world",

- "offset": "PT0.59S",

- "duration": "PT0.16S",

- "offsetInTicks": 5900000.0,

- "durationInTicks": 1600000.0,

- "confidence": 0.906032

- }

- ]

- }

- ]

- }

- ]

-}

-```

->[!IMPORTANT]

->Deserialize the transcription result into the new type as shown previously. Instead of a single file per audio channel, distinguish channels by checking the property value of `channel` for each element in `recognizedPhrases`. There is now a single result file for each input file.

-### Getting the content of entities and the results

-In v2, the links to the input or result files are inline with the rest of the entity metadata. As an improvement in v3, there's a clear separation between entity metadata (which is returned by a GET on `$.self`) and the details and credentials to access the result files. This separation helps protect customer data and allows fine control over the duration of validity of the credentials.

-In v3, `links` include a sub-property called `files` in case the entity exposes data (datasets, transcriptions, endpoints, or evaluations). A GET on `$.links.files` returns a list of files and a SAS URL

-to access the content of each file. To control the validity duration of the SAS URLs, the query parameter `sasValidityInSeconds` can be used to specify the lifetime.

-**v2 transcription:**

-```json

-{

- "id": "9891c965-bb32-4880-b14b-6d44efb158f3",

- "status": "Succeeded",

- "reportFileUrl": "https://contoso.com/report.txt?st=2018-02-09T18%3A07%3A00Z&se=2018-02-10T18%3A07%3A00Z&sp=rl&sv=2017-04-17&sr=b&sig=6c044930-3926-4be4-be76-f728327c53b5",

- "resultsUrls": {

- "channel_0": "https://contoso.com/audiofile1.wav?st=2018-02-09T18%3A07%3A00Z&se=2018-02-10T18%3A07%3A00Z&sp=rl&sv=2017-04-17&sr=b&sig=6c044930-3926-4be4-be76-f72832e6600c",

- "channel_1": "https://contoso.com/audiofile2.wav?st=2018-02-09T18%3A07%3A00Z&se=2018-02-10T18%3A07%3A00Z&sp=rl&sv=2017-04-17&sr=b&sig=3e0163f1-0029-4d4a-988d-3fba7d7c53b5"

- }

-}

-```

-**v3 transcription:**

-```json

-{

- "self": "https://eastus.api.cognitive.microsoft.com/speechtotext/v3.0/transcriptions/9891c965-bb32-4880-b14b-6d44efb158f3",

- "links": {

- "files": "https://eastus.api.cognitive.microsoft.com/speechtotext/v3.0/transcriptions/9891c965-bb32-4880-b14b-6d44efb158f3/files"

- }

-}

-```

-**A GET on `$.links.files` would result in:**

-```json

-{

- "values": [

- {

- "self": "https://eastus.api.cognitive.microsoft.com/speechtotext/v3.0/transcriptions/9891c965-bb32-4880-b14b-6d44efb158f3/files/f23e54f5-ed74-4c31-9730-2f1a3ef83ce8",

- "name": "Name",

- "kind": "Transcription",

- "properties": {

- "size": 200

- },

- "createdDateTime": "2020-01-13T08:00:00Z",

- "links": {

- "contentUrl": "https://customspeech-usw.blob.core.windows.net/artifacts/mywavefile1.wav.json?st=2018-02-09T18%3A07%3A00Z&se=2018-02-10T18%3A07%3A00Z&sp=rl&sv=2017-04-17&sr=b&sig=e05d8d56-9675-448b-820c-4318ae64c8d5"

- }

- },

- {

- "self": "https://eastus.api.cognitive.microsoft.com/speechtotext/v3.0/transcriptions/9891c965-bb32-4880-b14b-6d44efb158f3/files/28bc946b-c251-4a86-84f6-ea0f0a2373ef",

- "name": "Name",

- "kind": "TranscriptionReport",

- "properties": {

- "size": 200

- },

- "createdDateTime": "2020-01-13T08:00:00Z",

- "links": {

- "contentUrl": "https://customspeech-usw.blob.core.windows.net/artifacts/report.json?st=2018-02-09T18%3A07%3A00Z&se=2018-02-10T18%3A07%3A00Z&sp=rl&sv=2017-04-17&sr=b&sig=e05d8d56-9675-448b-820c-4318ae64c8d5"

- }

- }

- ],

- "@nextLink": "https://eastus.api.cognitive.microsoft.com/speechtotext/v3.0/transcriptions/9891c965-bb32-4880-b14b-6d44efb158f3/files?skip=2&top=2"

-}

-```

-The `kind` property indicates the format of content of the file. For transcriptions, the files of kind `TranscriptionReport` are the summary of the job and files of the kind `Transcription` are the result of the job itself.

->[!IMPORTANT]

->To get the results of operations, use a `GET` on `/speechtotext/v3.0/{collection}/{id}/files`, they are no longer contained in the responses of `GET` on `/speechtotext/v3.0/{collection}/{id}` or `/speechtotext/v3.0/{collection}`.

-### Customizing models

-Before v3, there was a distinction between an _acoustic model_ and a _language model_ when a model was being trained. This distinction resulted in the need to specify multiple models when creating endpoints or transcriptions. To simplify this process for a caller, we removed the differences and made everything depend on the content of the datasets that are being used for model training. With this change, the model creation now supports mixed datasets (language data and acoustic data). Endpoints and transcriptions now require only one model.

-With this change, the need for a `kind` in the `POST` operation is removed and the `datasets[]` array can now contain multiple datasets of the same or mixed kinds.

-To improve the results of a trained model, the acoustic data is automatically used internally during language training. In general, models created through the v3 API deliver more accurate results than models created with the v2 API.

->[!IMPORTANT]

->To customize both the acoustic and language model part, pass all of the required language and acoustic datasets in `datasets[]` of the POST to `/speechtotext/v3.0/models`. This will create a single model with both parts customized.

-### Retrieving base and custom models

-To simplify getting the available models, v3 has separated the collections of "base models" from the customer owned "customized models". The two routes are now

-`GET /speechtotext/v3.0/models/base` and `GET /speechtotext/v3.0/models/`.

-In v2, all models were returned together in a single response.

->[!IMPORTANT]

->To get a list of provided base models for customization, use `GET` on `/speechtotext/v3.0/models/base`. You can find your own customized models with a `GET` on `/speechtotext/v3.0/models`.

-### Name of an entity

-The `name` property is now `displayName`. This is consistent with other Azure APIs to not indicate identity properties. The value of this property must not be unique and can be changed after entity creation with a `PATCH` operation.

-**v2 transcription:**

-```json

-{

- "name": "Transcription using locale en-US"

-}

-```

-**v3 transcription:**

-```json

-{

- "displayName": "Transcription using locale en-US"

-}

-```

->[!IMPORTANT]

->Rename the property `name` to `displayName` in your client code.

-### Accessing referenced entities

-In v2, referenced entities were always inlined, for example the used models of an endpoint. The nesting of entities resulted in large responses and consumers rarely consumed the nested content. To shrink the response size and improve performance, the referenced entities are no longer inlined in the response. Instead, a reference to the other entity appears, and can directly be used for a subsequent `GET` (it's a URL as well), following the same pattern as the `self` link.

-**v2 transcription:**

-```json

-{

- "id": "9891c965-bb32-4880-b14b-6d44efb158f3",

- "models": [

- {

- "id": "827712a5-f942-4997-91c3-7c6cde35600b",

- "modelKind": "Language",

- "lastActionDateTime": "2019-01-07T11:36:07Z",

- "status": "Running",

- "createdDateTime": "2019-01-07T11:34:12Z",

- "locale": "en-US",

- "name": "Acoustic model",

- "description": "Example for an acoustic model",

- "datasets": [

- {

- "id": "702d913a-8ba6-4f66-ad5c-897400b081fb",

- "dataImportKind": "Language",

- "lastActionDateTime": "2019-01-07T11:36:07Z",

- "status": "Succeeded",

- "createdDateTime": "2019-01-07T11:34:12Z",

- "locale": "en-US",

- "name": "Language dataset",

- }

- ]

- },

- ]

-}

-```

-**v3 transcription:**

-```json

-{

- "self": "https://eastus.api.cognitive.microsoft.com/speechtotext/v3.0/transcriptions/9891c965-bb32-4880-b14b-6d44efb158f3",

- "model": {

- "self": "https://eastus.api.cognitive.microsoft.com/speechtotext/v3.0/models/021a72d0-54c4-43d3-8254-27336ead9037"

- }

-}

-```

-If you need to consume the details of a referenced model as shown in the above example, just issue a GET on `$.model.self`.

->[!IMPORTANT]

->To retrieve the metadata of referenced entities, issue a GET on `$.{referencedEntity}.self`, for example to retrieve the model of a transcription do a `GET` on `$.model.self`.

-### Retrieving endpoint logs

-Version v2 of the service supported logging endpoint results. To retrieve the results of an endpoint with v2, you would create a "data export", which represented a snapshot of the results defined by a time range. The process of exporting batches of data was inflexible. The v3 API gives access to each individual file and allows iteration through them.

-**A successfully running v3 endpoint:**

-```json

-{

- "self": "https://eastus.api.cognitive.microsoft.com/speechtotext/v3.0/endpoints/afa0669c-a01e-4693-ae3a-93baf40f26d6",

- "links": {

- "logs": "https://eastus.api.cognitive.microsoft.com/speechtotext/v3.0/endpoints/afa0669c-a01e-4693-ae3a-93baf40f26d6/files/logs"

- }

-}

-```

-**Response of GET `$.links.logs`:**

-```json

-{

- "values": [

- {

- "self": "https://eastus.api.cognitive.microsoft.com/speechtotext/v3.0/endpoints/6d72ad7e-f286-4a6f-b81b-a0532ca6bcaa/files/logs/2019-09-20_080000_3b5f4628-e225-439d-bd27-8804f9eed13f.wav",

- "name": "2019-09-20_080000_3b5f4628-e225-439d-bd27-8804f9eed13f.wav",

- "kind": "Audio",

- "properties": {

- "size": 12345

- },

- "createdDateTime": "2020-01-13T08:00:00Z",

- "links": {

- "contentUrl": "https://customspeech-usw.blob.core.windows.net/artifacts/2019-09-20_080000_3b5f4628-e225-439d-bd27-8804f9eed13f.wav?st=2018-02-09T18%3A07%3A00Z&se=2018-02-10T18%3A07%3A00Z&sp=rl&sv=2017-04-17&sr=b&sig=e05d8d56-9675-448b-820c-4318ae64c8d5"

- }

- }

- ],

- "@nextLink": "https://eastus.api.cognitive.microsoft.com/speechtotext/v3.0/endpoints/afa0669c-a01e-4693-ae3a-93baf40f26d6/files/logs?top=2&SkipToken=2!188!MDAwMDk1ITZhMjhiMDllLTg0MDYtNDViMi1hMGRkLWFlNzRlOGRhZWJkNi8yMDIwLTA0LTAxLzEyNDY0M182MzI5NGRkMi1mZGYzLTRhZmEtOTA0NC1mODU5ZTcxOWJiYzYud2F2ITAwMDAyOCE5OTk5LTEyLTMxVDIzOjU5OjU5Ljk5OTk5OTlaIQ--"

-}

-```

-Pagination for endpoint logs works similar to all other collections, except that no offset can be specified. Due to the large amount of available data, pagination is determined by the server.

-In v3, each endpoint log can be deleted individually by issuing a `DELETE` operation on the `self` of a file, or by using `DELETE` on `$.links.logs`. To specify an end date, the query parameter `endDate` can be added to the request.

-> [!IMPORTANT]

-> Instead of creating log exports on `/api/speechtotext/v2.0/endpoints/{id}/data` use `/v3.0/endpoints/{id}/files/logs/` to access log files individually.

-### Using custom properties

-To separate custom properties from the optional configuration properties, all explicitly named properties are now located in the `properties` property and all properties defined by the callers are now located in the `customProperties` property.

-**v2 transcription entity:**

-```json

-{

- "properties": {

- "customerDefinedKey": "value",

- "diarizationEnabled": "False",

- "wordLevelTimestampsEnabled": "False"

- }

-}

-```

-**v3 transcription entity:**

-```json

-{

- "properties": {

- "diarizationEnabled": false,

- "wordLevelTimestampsEnabled": false

- },

- "customProperties": {

- "customerDefinedKey": "value"

- }

-}

-```

-This change also lets you use correct types on all explicitly named properties under `properties` (for example boolean instead of string).

->[!IMPORTANT]

->Pass all custom properties as `customProperties` instead of `properties` in your `POST` requests.

-### Response headers

-v3 no longer returns the `Operation-Location` header in addition to the `Location` header on `POST` requests. The value of both headers in v2 was the same. Now only `Location` is returned.

-Because the new API version is now managed by Azure API management (APIM), the throttling related headers `X-RateLimit-Limit`, `X-RateLimit-Remaining`, and `X-RateLimit-Reset` aren't contained in the response headers.

->[!IMPORTANT]

->Read the location from response header `Location` instead of `Operation-Location`. In case of a 429 response code, read the `Retry-After` header value instead of `X-RateLimit-Limit`, `X-RateLimit-Remaining`, or `X-RateLimit-Reset`.

-### Accuracy tests

-Accuracy tests have been renamed to evaluations because the new name describes better what they represent. The new paths are: `https://{region}.api.cognitive.microsoft.com/speechtotext/v3.0/evaluations`.

->[!IMPORTANT]

->Rename the path segment `accuracytests` to `evaluations` in your client code.

-## Next steps

-* [Speech to text REST API](rest-speech-to-text.md)

-* [Speech to text REST API v3.0 reference](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0)

-In the [Transcriptions_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_Create) operation the following three properties are added:

-The `filter` property is added to the [Transcriptions_List](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_List), [Transcriptions_ListFiles](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_ListFiles), and [Projects_ListTranscriptions](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Projects_ListTranscriptions) operations. The `filter` expression can be used to select a subset of the available resources. You can filter by `displayName`, `description`, `createdDateTime`, `lastActionDateTime`, `status`, and `locale`. For example: `filter=createdDateTime gt 2022-02-01T11:00:00Z`

-To support model adaptation with [structured text in markdown](how-to-custom-speech-test-and-train.md#structured-text-data-for-training) data, the [Datasets_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Datasets_Create) operation now supports the **LanguageMarkdown** data kind. For more information, see [upload datasets](how-to-custom-speech-upload-data.md#upload-datasets).

-The [Models_ListBaseModels](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_ListBaseModels) and [Models_GetBaseModel](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_GetBaseModel) operations return information on the type of adaptation supported by each base model.

-The [Models_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_Create) operation has a new `customModelWeightPercent` property where you can specify the weight used when the Custom Language Model (trained from plain or structured text data) is combined with the Base Language Model. Valid values are integers between 1 and 100. The default value is currently 30.

-Added the [Models_ListFiles](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_ListFiles) operation to get the files of the model identified by the given ID.

-Added the [Models_GetFile](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_GetFile) operation to get one specific file (identified with fileId) from a model (identified with ID). This lets you retrieve a **ModelReport** file that provides information on the data processed during training.

-The name of each `operationId` in version 3.1 is prefixed with the object name. For example, the `operationId` for "Create Model" changed from [CreateModel](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/CreateModel) in version 3.0 to [Models_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_Create) in version 3.1.

-|Path|Method|Version 3.1 Operation ID|Version 3.0 Operation ID|

-|||||

-|`/datasets`|GET|[Datasets_List](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Datasets_List)|[GetDatasets](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetDatasets)|

-|`/datasets`|POST|[Datasets_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Datasets_Create)|[CreateDataset](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/CreateDataset)|

-|`/datasets/{id}`|DELETE|[Datasets_Delete](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Datasets_Delete)|[DeleteDataset](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/DeleteDataset)|

-|`/datasets/{id}`|GET|[Datasets_Get](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Datasets_Get)|[GetDataset](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetDataset)|

-|`/datasets/{id}`|PATCH|[Datasets_Update](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Datasets_Update)|[UpdateDataset](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/UpdateDataset)|

-|`/datasets/{id}/blocks:commit`|POST|[Datasets_CommitBlocks](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Datasets_CommitBlocks)|Not applicable|

-|`/datasets/{id}/blocks`|GET|[Datasets_GetBlocks](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Datasets_GetBlocks)|Not applicable|

-|`/datasets/{id}/blocks`|PUT|[Datasets_UploadBlock](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Datasets_UploadBlock)|Not applicable|

-|`/datasets/{id}/files`|GET|[Datasets_ListFiles](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Datasets_ListFiles)|[GetDatasetFiles](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetDatasetFiles)|

-|`/datasets/{id}/files/{fileId}`|GET|[Datasets_GetFile](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Datasets_GetFile)|[GetDatasetFile](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetDatasetFile)|

-|`/datasets/locales`|GET|[Datasets_ListSupportedLocales](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Datasets_ListSupportedLocales)|[GetSupportedLocalesForDatasets](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetSupportedLocalesForDatasets)|

-|`/datasets/upload`|POST|[Datasets_Upload](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Datasets_Upload)|[UploadDatasetFromForm](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/UploadDatasetFromForm)|

-|`/endpoints`|GET|[Endpoints_List](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_List)|[GetEndpoints](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetEndpoints)|

-|`/endpoints`|POST|[Endpoints_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_Create)|[CreateEndpoint](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/CreateEndpoint)|

-|`/endpoints/{id}`|DELETE|[Endpoints_Delete](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_Delete)|[DeleteEndpoint](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/DeleteEndpoint)|

-|`/endpoints/{id}`|GET|[Endpoints_Get](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_Get)|[GetEndpoint](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetEndpoint)|

-|`/endpoints/{id}`|PATCH|[Endpoints_Update](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_Update)|[UpdateEndpoint](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/UpdateEndpoint)|

-|`/endpoints/{id}/files/logs`|DELETE|[Endpoints_DeleteLogs](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_DeleteLogs)|[DeleteEndpointLogs](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/DeleteEndpointLogs)|

-|`/endpoints/{id}/files/logs`|GET|[Endpoints_ListLogs](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_ListLogs)|[GetEndpointLogs](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetEndpointLogs)|

-|`/endpoints/{id}/files/logs/{logId}`|DELETE|[Endpoints_DeleteLog](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_DeleteLog)|[DeleteEndpointLog](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/DeleteEndpointLog)|

-|`/endpoints/{id}/files/logs/{logId}`|GET|[Endpoints_GetLog](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_GetLog)|[GetEndpointLog](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetEndpointLog)|

-|`/endpoints/base/{locale}/files/logs`|DELETE|[Endpoints_DeleteBaseModelLogs](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_DeleteBaseModelLogs)|[DeleteBaseModelLogs](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/DeleteBaseModelLogs)|

-|`/endpoints/base/{locale}/files/logs`|GET|[Endpoints_ListBaseModelLogs](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_ListBaseModelLogs)|[GetBaseModelLogs](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetBaseModelLogs)|

-|`/endpoints/base/{locale}/files/logs/{logId}`|DELETE|[Endpoints_DeleteBaseModelLog](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_DeleteBaseModelLog)|[DeleteBaseModelLog](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/DeleteBaseModelLog)|

-|`/endpoints/base/{locale}/files/logs/{logId}`|GET|[Endpoints_GetBaseModelLog](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_GetBaseModelLog)|[GetBaseModelLog](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetBaseModelLog)|

-|`/endpoints/locales`|GET|[Endpoints_ListSupportedLocales](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_ListSupportedLocales)|[GetSupportedLocalesForEndpoints](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetSupportedLocalesForEndpoints)|

-|`/evaluations`|GET|[Evaluations_List](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Evaluations_List)|[GetEvaluations](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetEvaluations)|

-|`/evaluations`|POST|[Evaluations_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Evaluations_Create)|[CreateEvaluation](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/CreateEvaluation)|

-|`/evaluations/{id}`|DELETE|[Evaluations_Delete](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Evaluations_Delete)|[DeleteEvaluation](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/DeleteEvaluation)|

-|`/evaluations/{id}`|GET|[Evaluations_Get](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Evaluations_Get)|[GetEvaluation](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetEvaluation)|

-|`/evaluations/{id}`|PATCH|[Evaluations_Update](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Evaluations_Update)|[UpdateEvaluation](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/UpdateEvaluation)|

-|`/evaluations/{id}/files`|GET|[Evaluations_ListFiles](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Evaluations_ListFiles)|[GetEvaluationFiles](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetEvaluationFiles)|

-|`/evaluations/{id}/files/{fileId}`|GET|[Evaluations_GetFile](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Evaluations_GetFile)|[GetEvaluationFile](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetEvaluationFile)|

-|`/evaluations/locales`|GET|[Evaluations_ListSupportedLocales](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Evaluations_ListSupportedLocales)|[GetSupportedLocalesForEvaluations](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetSupportedLocalesForEvaluations)|

-|`/healthstatus`|GET|[HealthStatus_Get](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/HealthStatus_Get)|[GetHealthStatus](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetHealthStatus)|

-|`/models`|GET|[Models_ListCustomModels](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_ListCustomModels)|[GetModels](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetModels)|

-|`/models`|POST|[Models_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_Create)|[CreateModel](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/CreateModel)|

-|`/models/{id}:copyto`¹|POST|[Models_CopyTo](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_CopyTo)|[CopyModelToSubscription](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/CopyModelToSubscription)|

-|`/models/{id}`|DELETE|[Models_Delete](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_Delete)|[DeleteModel](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/DeleteModel)|

-|`/models/{id}`|GET|[Models_GetCustomModel](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_GetCustomModel)|[GetModel](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetModel)|

-|`/models/{id}`|PATCH|[Models_Update](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_Update)|[UpdateModel](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/UpdateModel)|

-|`/models/{id}/files`|GET|[Models_ListFiles](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_ListFiles)|Not applicable|

-|`/models/{id}/files/{fileId}`|GET|[Models_GetFile](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_GetFile)|Not applicable|

-|`/models/{id}/manifest`|GET|[Models_GetCustomModelManifest](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_GetCustomModelManifest)|[GetModelManifest](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetModelManifest)|

-|`/models/base`|GET|[Models_ListBaseModels](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_ListBaseModels)|[GetBaseModels](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetBaseModels)|

-|`/models/base/{id}`|GET|[Models_GetBaseModel](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_GetBaseModel)|[GetBaseModel](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetBaseModel)|

-|`/models/base/{id}/manifest`|GET|[Models_GetBaseModelManifest](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_GetBaseModelManifest)|[GetBaseModelManifest](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetBaseModelManifest)|

-|`/models/locales`|GET|[Models_ListSupportedLocales](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_ListSupportedLocales)|[GetSupportedLocalesForModels](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetSupportedLocalesForModels)|

-|`/projects`|GET|[Projects_List](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Projects_List)|[GetProjects](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetProjects)|

-|`/projects`|POST|[Projects_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Projects_Create)|[CreateProject](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/CreateProject)|

-|`/projects/{id}`|DELETE|[Projects_Delete](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Projects_Delete)|[DeleteProject](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/DeleteProject)|

-|`/projects/{id}`|GET|[Projects_Get](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Projects_Get)|[GetProject](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetProject)|

-|`/projects/{id}`|PATCH|[Projects_Update](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Projects_Update)|[UpdateProject](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/UpdateProject)|

-|`/projects/{id}/datasets`|GET|[Projects_ListDatasets](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Projects_ListDatasets)|[GetDatasetsForProject](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetDatasetsForProject)|

-|`/projects/{id}/endpoints`|GET|[Projects_ListEndpoints](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Projects_ListEndpoints)|[GetEndpointsForProject](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetEndpointsForProject)|

-|`/projects/{id}/evaluations`|GET|[Projects_ListEvaluations](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Projects_ListEvaluations)|[GetEvaluationsForProject](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetEvaluationsForProject)|

-|`/projects/{id}/models`|GET|[Projects_ListModels](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Projects_ListModels)|[GetModelsForProject](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetModelsForProject)|

-|`/projects/{id}/transcriptions`|GET|[Projects_ListTranscriptions](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Projects_ListTranscriptions)|[GetTranscriptionsForProject](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetTranscriptionsForProject)|

-|`/projects/locales`|GET|[Projects_ListSupportedLocales](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Projects_ListSupportedLocales)|[GetSupportedProjectLocales](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetSupportedProjectLocales)|

-|`/transcriptions`|GET|[Transcriptions_List](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_List)|[GetTranscriptions](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetTranscriptions)|

-|`/transcriptions`|POST|[Transcriptions_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_Create)|[CreateTranscription](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/CreateTranscription)|

-|`/transcriptions/{id}`|DELETE|[Transcriptions_Delete](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_Delete)|[DeleteTranscription](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/DeleteTranscription)|

-|`/transcriptions/{id}`|GET|[Transcriptions_Get](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_Get)|[GetTranscription](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetTranscription)|

-|`/transcriptions/{id}`|PATCH|[Transcriptions_Update](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_Update)|[UpdateTranscription](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/UpdateTranscription)|

-|`/transcriptions/{id}/files`|GET|[Transcriptions_ListFiles](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_ListFiles)|[GetTranscriptionFiles](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetTranscriptionFiles)|

-|`/transcriptions/{id}/files/{fileId}`|GET|[Transcriptions_GetFile](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_GetFile)|[GetTranscriptionFile](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetTranscriptionFile)|

-|`/transcriptions/locales`|GET|[Transcriptions_ListSupportedLocales](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_ListSupportedLocales)|[GetSupportedLocalesForTranscriptions](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetSupportedLocalesForTranscriptions)|

-|`/webhooks`|GET|[WebHooks_List](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/WebHooks_List)|[GetHooks](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetHooks)|

-|`/webhooks`|POST|[WebHooks_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/WebHooks_Create)|[CreateHook](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/CreateHook)|

-|`/webhooks/{id}:ping`²|POST|[WebHooks_Ping](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/WebHooks_Ping)|[PingHook](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/PingHook)|

-|`/webhooks/{id}:test`³|POST|[WebHooks_Test](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/WebHooks_Test)|[TestHook](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/TestHook)|

-|`/webhooks/{id}`|DELETE|[WebHooks_Delete](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/WebHooks_Delete)|[DeleteHook](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/DeleteHook)|

-|`/webhooks/{id}`|GET|[WebHooks_Get](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/WebHooks_Get)|[GetHook](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetHook)|

-|`/webhooks/{id}`|PATCH|[WebHooks_Update](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/WebHooks_Update)|[UpdateHook](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/UpdateHook)|

-¹ The `/models/{id}/copyto` operation (includes '/') in version 3.0 is replaced by the `/models/{id}:copyto` operation (includes ':') in version 3.1.

-² The `/webhooks/{id}/ping` operation (includes '/') in version 3.0 is replaced by the `/webhooks/{id}:ping` operation (includes ':') in version 3.1.

-³ The `/webhooks/{id}/test` operation (includes '/') in version 3.0 is replaced by the `/webhooks/{id}:test` operation (includes ':') in version 3.1.

-* [Speech to text REST API v3.1 reference](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1)

-* [Speech to text REST API v3.0 reference](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0)

-To support model adaptation with [custom display text formatting](how-to-custom-speech-test-and-train.md#custom-display-text-formatting-data-for-training) data, the [Datasets_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-2-preview2/operations/Datasets_Create) operation supports the **OutputFormatting** data kind. For more information, see [upload datasets](how-to-custom-speech-upload-data.md#upload-datasets).

-* [Speech to text REST API v3.2 (preview)](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-2-preview2)

-* [Speech to text REST API v3.1 reference](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1)

-* [Speech to text REST API v3.0 reference](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0)

-> We are retiring the standard voices from September 1, 2021 through August 31, 2024. If you used a standard voice with your Speech resource that was created prior to September 1, 2021 then you can continue to do so until August 31, 2024. All other Speech resources can only use prebuilt neural voices. You can choose from the supported [neural voice names](language-support.md?tabs=tts). After August 31, 2024 the standard voices won't be supported with any Speech resource.

-| **Speech output audio format** | opus, mp3, aac, flac | opus, mp3, pcm, truesilk | opus, mp3, pcm, truesilk |

-The following table outlines the Speech Synthesis Markup Language (SSML) elements supported by OpenAI text to speech voices in Azure AI speech. Only a subset of SSML tags are supported for OpenAI voices. See [SSML document structure and events](speech-synthesis-markup-structure.md) for more information.

-The demo in Speech Studio is made available to approved customers. You can apply for access [here](https://aka.ms/customneural).

-1. Select **Request demo access**.

- :::image type="content" source="./media/personal-voice/personal-voice-request-access.png" alt-text="Screenshot of the button to request access to personal voice in Speech Studio." lightbox="./media/personal-voice/personal-voice-request-access.png":::

-1. After your access is approved, you can record your own voice and try the voice output samples in different languages. The demo includes a subset of the languages supported by personal voice.

-This article describes how to use [Power Automate](/power-automate/getting-started) and the [Azure AI services for Batch Speech to text connector](/connectors/cognitiveservicesspe/) to transcribe audio files from an Azure Storage container. The connector uses the [Batch Transcription REST API](batch-transcription.md), but you don't need to write any code to use it. If the connector doesn't meet your requirements, you can still use the [REST API](rest-speech-to-text.md#transcriptions) directly.

-2. Run the [Models_CopyTo](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_CopyTo) operation to replicate the custom model to all prepared regions (Secondary).

-> [See the Speech to text REST API v3.2 (preview)](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-2-preview2)

-> [See the Speech to text REST API v3.1 reference documentation](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/)

-> [See the Speech to text REST API v3.0 reference documentation](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/)

-## Datasets

-Datasets are applicable for [custom speech](custom-speech-overview.md). You can use datasets to train and test the performance of different models. For example, you can compare the performance of a model trained with a specific dataset to the performance of a model trained with a different dataset.

-See [Upload training and testing datasets](how-to-custom-speech-upload-data.md?pivots=rest-api) for examples of how to upload datasets. This table includes all the operations that you can perform on datasets.

-|Path|Method|Version 3.1|Version 3.0|

-|||||

-|`/datasets`|GET|[Datasets_List](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Datasets_List)|[GetDatasets](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetDatasets)|

-|`/datasets`|POST|[Datasets_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Datasets_Create)|[CreateDataset](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/CreateDataset)|

-|`/datasets/{id}`|DELETE|[Datasets_Delete](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Datasets_Delete)|[DeleteDataset](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/DeleteDataset)|

-|`/datasets/{id}`|GET|[Datasets_Get](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Datasets_Get)|[GetDataset](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetDataset)|

-|`/datasets/{id}`|PATCH|[Datasets_Update](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Datasets_Update)|[UpdateDataset](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/UpdateDataset)|

-|`/datasets/{id}/blocks:commit`|POST|[Datasets_CommitBlocks](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Datasets_CommitBlocks)|Not applicable|

-|`/datasets/{id}/blocks`|GET|[Datasets_GetBlocks](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Datasets_GetBlocks)|Not applicable|

-|`/datasets/{id}/blocks`|PUT|[Datasets_UploadBlock](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Datasets_UploadBlock)|Not applicable|

-|`/datasets/{id}/files`|GET|[Datasets_ListFiles](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Datasets_ListFiles)|[GetDatasetFiles](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetDatasetFiles)|

-|`/datasets/{id}/files/{fileId}`|GET|[Datasets_GetFile](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Datasets_GetFile)|[GetDatasetFile](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetDatasetFile)|

-|`/datasets/locales`|GET|[Datasets_ListSupportedLocales](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Datasets_ListSupportedLocales)|[GetSupportedLocalesForDatasets](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetSupportedLocalesForDatasets)|

-|`/datasets/upload`|POST|[Datasets_Upload](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Datasets_Upload)|[UploadDatasetFromForm](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/UploadDatasetFromForm)|

-## Endpoints

-Endpoints are applicable for [custom speech](custom-speech-overview.md). You must deploy a custom endpoint to use a custom speech model.

-See [Deploy a model](how-to-custom-speech-deploy-model.md?pivots=rest-api) for examples of how to manage deployment endpoints. This table includes all the operations that you can perform on endpoints.

-|Path|Method|Version 3.1|Version 3.0|

-|||||

-|`/endpoints`|GET|[Endpoints_List](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_List)|[GetEndpoints](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetEndpoints)|

-|`/endpoints`|POST|[Endpoints_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_Create)|[CreateEndpoint](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/CreateEndpoint)|

-|`/endpoints/{id}`|DELETE|[Endpoints_Delete](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_Delete)|[DeleteEndpoint](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/DeleteEndpoint)|

-|`/endpoints/{id}`|GET|[Endpoints_Get](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_Get)|[GetEndpoint](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetEndpoint)|

-|`/endpoints/{id}`|PATCH|[Endpoints_Update](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_Update)|[UpdateEndpoint](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/UpdateEndpoint)|

-|`/endpoints/{id}/files/logs`|DELETE|[Endpoints_DeleteLogs](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_DeleteLogs)|[DeleteEndpointLogs](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/DeleteEndpointLogs)|

-|`/endpoints/{id}/files/logs`|GET|[Endpoints_ListLogs](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_ListLogs)|[GetEndpointLogs](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetEndpointLogs)|

-|`/endpoints/{id}/files/logs/{logId}`|DELETE|[Endpoints_DeleteLog](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_DeleteLog)|[DeleteEndpointLog](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/DeleteEndpointLog)|

-|`/endpoints/{id}/files/logs/{logId}`|GET|[Endpoints_GetLog](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_GetLog)|[GetEndpointLog](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetEndpointLog)|

-|`/endpoints/base/{locale}/files/logs`|DELETE|[Endpoints_DeleteBaseModelLogs](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_DeleteBaseModelLogs)|[DeleteBaseModelLogs](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/DeleteBaseModelLogs)|

-|`/endpoints/base/{locale}/files/logs`|GET|[Endpoints_ListBaseModelLogs](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_ListBaseModelLogs)|[GetBaseModelLogs](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetBaseModelLogs)|

-|`/endpoints/base/{locale}/files/logs/{logId}`|DELETE|[Endpoints_DeleteBaseModelLog](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_DeleteBaseModelLog)|[DeleteBaseModelLog](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/DeleteBaseModelLog)|

-|`/endpoints/base/{locale}/files/logs/{logId}`|GET|[Endpoints_GetBaseModelLog](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_GetBaseModelLog)|[GetBaseModelLog](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetBaseModelLog)|

-|`/endpoints/locales`|GET|[Endpoints_ListSupportedLocales](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Endpoints_ListSupportedLocales)|[GetSupportedLocalesForEndpoints](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetSupportedLocalesForEndpoints)|

-## Evaluations

-Evaluations are applicable for [custom speech](custom-speech-overview.md). You can use evaluations to compare the performance of different models. For example, you can compare the performance of a model trained with a specific dataset to the performance of a model trained with a different dataset.

-See [Test recognition quality](how-to-custom-speech-inspect-data.md?pivots=rest-api) and [Test accuracy](how-to-custom-speech-evaluate-data.md?pivots=rest-api) for examples of how to test and evaluate custom speech models. This table includes all the operations that you can perform on evaluations.

-|Path|Method|Version 3.1|Version 3.0|

-|||||

-|`/evaluations`|GET|[Evaluations_List](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Evaluations_List)|[GetEvaluations](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetEvaluations)|

-|`/evaluations`|POST|[Evaluations_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Evaluations_Create)|[CreateEvaluation](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/CreateEvaluation)|

-|`/evaluations/{id}`|DELETE|[Evaluations_Delete](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Evaluations_Delete)|[DeleteEvaluation](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/DeleteEvaluation)|

-|`/evaluations/{id}`|GET|[Evaluations_Get](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Evaluations_Get)|[GetEvaluation](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetEvaluation)|

-|`/evaluations/{id}`|PATCH|[Evaluations_Update](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Evaluations_Update)|[UpdateEvaluation](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/UpdateEvaluation)|

-|`/evaluations/{id}/files`|GET|[Evaluations_ListFiles](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Evaluations_ListFiles)|[GetEvaluationFiles](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetEvaluationFiles)|

-|`/evaluations/{id}/files/{fileId}`|GET|[Evaluations_GetFile](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Evaluations_GetFile)|[GetEvaluationFile](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetEvaluationFile)|

-|`/evaluations/locales`|GET|[Evaluations_ListSupportedLocales](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Evaluations_ListSupportedLocales)|[GetSupportedLocalesForEvaluations](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetSupportedLocalesForEvaluations)|

-## Health status

-Health status provides insights about the overall health of the service and subcomponents.

-|Path|Method|Version 3.1|Version 3.0|

-|||||

-|`/healthstatus`|GET|[HealthStatus_Get](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/HealthStatus_Get)|[GetHealthStatus](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetHealthStatus)|

-## Models

-Models are applicable for [custom speech](custom-speech-overview.md) and [Batch Transcription](batch-transcription.md). You can use models to transcribe audio files. For example, you can use a model trained with a specific dataset to transcribe audio files.

-See [Train a model](how-to-custom-speech-train-model.md?pivots=rest-api) and [custom speech model lifecycle](how-to-custom-speech-model-and-endpoint-lifecycle.md?pivots=rest-api) for examples of how to train and manage custom speech models. This table includes all the operations that you can perform on models.

-|Path|Method|Version 3.1|Version 3.0|

-|||||

-|`/models`|GET|[Models_ListCustomModels](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_ListCustomModels)|[GetModels](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetModels)|

-|`/models`|POST|[Models_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_Create)|[CreateModel](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/CreateModel)|

-|`/models/{id}:copyto`¹|POST|[Models_CopyTo](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_CopyTo)|[CopyModelToSubscription](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/CopyModelToSubscription)|

-|`/models/{id}`|DELETE|[Models_Delete](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_Delete)|[DeleteModel](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/DeleteModel)|

-|`/models/{id}`|GET|[Models_GetCustomModel](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_GetCustomModel)|[GetModel](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetModel)|

-|`/models/{id}`|PATCH|[Models_Update](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_Update)|[UpdateModel](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/UpdateModel)|

-|`/models/{id}/files`|GET|[Models_ListFiles](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_ListFiles)|Not applicable|

-|`/models/{id}/files/{fileId}`|GET|[Models_GetFile](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_GetFile)|Not applicable|

-|`/models/{id}/manifest`|GET|[Models_GetCustomModelManifest](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_GetCustomModelManifest)|[GetModelManifest](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetModelManifest)|

-|`/models/base`|GET|[Models_ListBaseModels](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_ListBaseModels)|[GetBaseModels](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetBaseModels)|

-|`/models/base/{id}`|GET|[Models_GetBaseModel](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_GetBaseModel)|[GetBaseModel](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetBaseModel)|

-|`/models/base/{id}/manifest`|GET|[Models_GetBaseModelManifest](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_GetBaseModelManifest)|[GetBaseModelManifest](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetBaseModelManifest)|

-|`/models/locales`|GET|[Models_ListSupportedLocales](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_ListSupportedLocales)|[GetSupportedLocalesForModels](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetSupportedLocalesForModels)|

-## Projects

-Projects are applicable for [custom speech](custom-speech-overview.md). Custom speech projects contain models, training and testing datasets, and deployment endpoints. Each project is specific to a [locale](language-support.md?tabs=stt). For example, you might create a project for English in the United States.

-See [Create a project](how-to-custom-speech-create-project.md?pivots=rest-api) for examples of how to create projects. This table includes all the operations that you can perform on projects.

-|Path|Method|Version 3.1|Version 3.0|

-|||||

-|`/projects`|GET|[Projects_List](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Projects_List)|[GetProjects](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetProjects)|

-|`/projects`|POST|[Projects_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Projects_Create)|[CreateProject](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/CreateProject)|

-|`/projects/{id}`|DELETE|[Projects_Delete](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Projects_Delete)|[DeleteProject](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/DeleteProject)|

-|`/projects/{id}`|GET|[Projects_Get](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Projects_Get)|[GetProject](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetProject)|

-|`/projects/{id}`|PATCH|[Projects_Update](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Projects_Update)|[UpdateProject](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/UpdateProject)|

-|`/projects/{id}/datasets`|GET|[Projects_ListDatasets](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Projects_ListDatasets)|[GetDatasetsForProject](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetDatasetsForProject)|

-|`/projects/{id}/endpoints`|GET|[Projects_ListEndpoints](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Projects_ListEndpoints)|[GetEndpointsForProject](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetEndpointsForProject)|

-|`/projects/{id}/evaluations`|GET|[Projects_ListEvaluations](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Projects_ListEvaluations)|[GetEvaluationsForProject](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetEvaluationsForProject)|

-|`/projects/{id}/models`|GET|[Projects_ListModels](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Projects_ListModels)|[GetModelsForProject](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetModelsForProject)|

-|`/projects/{id}/transcriptions`|GET|[Projects_ListTranscriptions](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Projects_ListTranscriptions)|[GetTranscriptionsForProject](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetTranscriptionsForProject)|

-|`/projects/locales`|GET|[Projects_ListSupportedLocales](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Projects_ListSupportedLocales)|[GetSupportedProjectLocales](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetSupportedProjectLocales)|

-## Transcriptions

-Transcriptions are applicable for [Batch Transcription](batch-transcription.md). Batch transcription is used to transcribe a large amount of audio in storage. You should send multiple files per request or point to an Azure Blob Storage container with the audio files to transcribe.

-See [Create a transcription](batch-transcription-create.md?pivots=rest-api) for examples of how to create a transcription from multiple audio files. This table includes all the operations that you can perform on transcriptions.

-|Path|Method|Version 3.1|Version 3.0|

-|||||

-|`/transcriptions`|GET|[Transcriptions_List](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_List)|[GetTranscriptions](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetTranscriptions)|

-|`/transcriptions`|POST|[Transcriptions_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_Create)|[CreateTranscription](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/CreateTranscription)|

-|`/transcriptions/{id}`|DELETE|[Transcriptions_Delete](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_Delete)|[DeleteTranscription](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/DeleteTranscription)|

-|`/transcriptions/{id}`|GET|[Transcriptions_Get](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_Get)|[GetTranscription](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetTranscription)|

-|`/transcriptions/{id}`|PATCH|[Transcriptions_Update](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_Update)|[UpdateTranscription](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/UpdateTranscription)|

-|`/transcriptions/{id}/files`|GET|[Transcriptions_ListFiles](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_ListFiles)|[GetTranscriptionFiles](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetTranscriptionFiles)|

-|`/transcriptions/{id}/files/{fileId}`|GET|[Transcriptions_GetFile](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_GetFile)|[GetTranscriptionFile](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetTranscriptionFile)|

-|`/transcriptions/locales`|GET|[Transcriptions_ListSupportedLocales](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Transcriptions_ListSupportedLocales)|[GetSupportedLocalesForTranscriptions](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetSupportedLocalesForTranscriptions)|

-## Web hooks

-Web hooks are applicable for [custom speech](custom-speech-overview.md) and [Batch Transcription](batch-transcription.md). In particular, web hooks apply to [datasets](#datasets), [endpoints](#endpoints), [evaluations](#evaluations), [models](#models), and [transcriptions](#transcriptions). Web hooks can be used to receive notifications about creation, processing, completion, and deletion events.

-This table includes all the web hook operations that are available with the Speech to text REST API.

-|Path|Method|Version 3.1|Version 3.0|

-|||||

-|`/webhooks`|GET|[WebHooks_List](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/WebHooks_List)|[GetHooks](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetHooks)|

-|`/webhooks`|POST|[WebHooks_Create](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/WebHooks_Create)|[CreateHook](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/CreateHook)|

-|`/webhooks/{id}:ping`¹|POST|[WebHooks_Ping](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/WebHooks_Ping)|[PingHook](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/PingHook)|

-|`/webhooks/{id}:test`²|POST|[WebHooks_Test](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/WebHooks_Test)|[TestHook](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/TestHook)|

-|`/webhooks/{id}`|DELETE|[WebHooks_Delete](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/WebHooks_Delete)|[DeleteHook](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/DeleteHook)|

-|`/webhooks/{id}`|GET|[WebHooks_Get](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/WebHooks_Get)|[GetHook](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/GetHook)|

-|`/webhooks/{id}`|PATCH|[WebHooks_Update](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/WebHooks_Update)|[UpdateHook](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-0/operations/UpdateHook)|

-¹ The `/webhooks/{id}/ping` operation (includes '/') in version 3.0 is replaced by the `/webhooks/{id}:ping` operation (includes ':') in version 3.1.

-² The `/webhooks/{id}/test` operation (includes '/') in version 3.0 is replaced by the `/webhooks/{id}:test` operation (includes ':') in version 3.1.

-For finer-grained resource access control, you can [add or remove roles](../../role-based-access-control/role-assignments-portal.md?tabs=current) using the Azure portal. For example, you could create a custom role with permission to upload custom speech datasets, but without permission to deploy a custom speech model to an endpoint.

-|Speech resource key|Full access limited only by the assigned role permissions.|

-| Max text size when you're using the `text` parameter in the [Models_Create](https://westcentralus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1/operations/Models_Create/) API request | 200 KB | 500 KB |

-|REST API limit | Not available for F0 | 50 requests per 5 seconds |

-| Max JSON payload size to create a synthesis job | N/A | 500 kilobytes |

-| Concurrent active synthesis jobs | N/A | 200 |

-| Max number of text inputs per synthesis job | N/A | 1000 |

-The [Swagger specification](https://eastus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1) has options that allow you to quickly test for various paths. However, sometimes it's desirable to generate code for all paths, creating a single library of calls that you can base future solutions on. Let's take a look at the process to generate a Python library.

-1. In a browser, go to the Swagger specification for your [region](regions.md#speech-service):

- `https://<your-region>.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-1`

-1. On that page, select **API definition**, and select **Swagger**. Copy the URL of the page that appears.

-1. In a new browser, go to [https://editor.swagger.io](https://editor.swagger.io)

-1. Select **File**, select **Import URL**, paste the URL, and select **OK**.

-| properties.talkingAvatarCharacter | The character name of the talking avatar.<br/><br/>The supported avatar characters can be found [here](avatar-gestures-with-ssml.md#supported-pre-built-avatar-characters-styles-and-gestures).<br/><br/>This property is required.|

-| properties.talkingAvatarStyle | The style name of the talking avatar.<br/><br/>The supported avatar styles can be found [here](avatar-gestures-with-ssml.md#supported-pre-built-avatar-characters-styles-and-gestures).<br/><br/>This property is required for prebuilt avatar, and optional for customized avatar.|

-| properties.customized | A bool value indicating whether the avatar to be used is customized avatar or not. True for customized avatar, and false for prebuilt avatar.<br/><br/>This property is optional, and the default value is `false`.|

-| properties.videoFormat | The format for output video file, could be mp4 or webm.<br/><br/>The `webm` format is required for transparent background.<br/><br/>This property is optional, and the default value is mp4.|

-| properties.videoCodec | The codec for output video, could be h264, hevc or vp9.<br/><br/>Vp9 is required for transparent background. The synthesis speed will be slower with vp9 codec, as vp9 encoding is slower.<br/><br/>This property is optional, and the default value is hevc.|

-| properties.kBitrate (bitrateKbps) | The bitrate for output video, which is integer value, with unit kbps.<br/><br/>This property is optional, and the default value is 2000.|

-| properties.videoCrop | This property allows you to crop the video output, which means, to output a rectangle subarea of the original video. This property has two fields, which define the top-left vertex and bottom-right vertex of the rectangle.<br/><br/>This property is optional, and the default behavior is to output the full video.|

-| properties.videoCrop.topLeft |The top-left vertex of the rectangle for video crop. This property has two fields x and y, to define the horizontal and vertical position of the vertex.<br/><br/>This property is required when properties.videoCrop is set.|

-| properties.videoCrop.bottomRight | The bottom-right vertex of the rectangle for video crop. This property has two fields x and y, to define the horizontal and vertical position of the vertex.<br/><br/>This property is required when properties.videoCrop is set.|

-| properties.subtitleType | Type of subtitle for the avatar video file could be `external_file`, `soft_embedded`, `hard_embedded`, or `none`.<br/><br/>This property is optional, and the default value is `soft_embedded`.|

-| properties.backgroundColor | Background color of the avatar video, which is a string in #RRGGBBAA format. In this string: RR, GG, BB and AA mean the red, green, blue and alpha channels, with hexadecimal value range 00~FF. Alpha channel controls the transparency, with value 00 for transparent, value FF for non-transparent, and value between 00 and FF for semi-transparent.<br/><br/>This property is optional, and the default value is #FFFFFFFF (white).|

-| outputs.result | The location of the batch synthesis result file, which is a video file containing the synthesized avatar.<br/><br/>This property is read-only.|

-| properties.duration | The video output duration. The value is an ISO 8601 encoded duration.<br/><br/>This property is read-only. |

-| properties.durationInTicks | The video output duration in ticks.<br/><br/>This property is read-only. |

-| customProperties | A custom set of optional batch synthesis configuration settings.<br/><br/>This property is stored for your convenience to associate the synthesis jobs that you created with the synthesis jobs that you get or list. This property is stored, but isn't used by the Speech service.<br/><br/>You can specify up to 10 custom properties as key and value pairs. The maximum allowed key length is 64 characters, and the maximum allowed value length is 256 characters.|

-| displayName | The name of the batch synthesis. Choose a name that you can refer to later. The display name doesn't have to be unique.<br/><br/>This property is required.|

-| properties.timeToLive |A duration after the synthesis job is created, when the synthesis results will be automatically deleted. The value is an ISO 8601 encoded duration. For example, specify PT12H for 12 hours. This optional setting is P31D (31 days) by default. The maximum time to live is 31 days. The date and time of automatic deletion, for synthesis jobs with a status of "Succeeded" or "Failed" is calculated as the sum of the lastActionDateTime and timeToLive properties.<br/><br/>Otherwise, you can call the [delete synthesis method](../batch-synthesis.md#delete-batch-synthesis) to remove the job sooner. |

-| customVoices | A custom neural voice is associated with a name and its deployment ID, like this: "customVoices": {"your-custom-voice-name": "502ac834-6537-4bc3-9fd6-140114daa66d"}<br/><br/>You can use the voice name in your `synthesisConfig.voice` when `textType` is set to "PlainText", or within SSML text of inputs when `textType` is set to "SSML".<br/><br/>This property is required to use a custom voice. If you try to use a custom voice that isn't defined here, the service returns an error.|

-| inputs | The plain text or SSML to be synthesized.<br/><br/>When the textType is set to "PlainText", provide plain text as shown here: "inputs": [{"text": "The rainbow has seven colors."}]. When the textType is set to "SSML", provide text in the Speech Synthesis Markup Language (SSML) as shown here: "inputs": [{"text": "<speak version='\'1.0'\'' xml:lang='\'en-US'\''><voice xml:lang='\'en-US'\'' xml:gender='\'Female'\'' name='\'en-US-AvaMultilingualNeural'\''>The rainbow has seven colors.</voice></speak>"}].<br/><br/>Include up to 1,000 text objects if you want multiple video output files. Here's example input text that should be synthesized to two video output files: "inputs": [{"text": "synthesize this to a file"},{"text": "synthesize this to another file"}].<br/><br/>You don't need separate text inputs for new paragraphs. Within any of the (up to 1,000) text inputs, you can specify new paragraphs using the "\r\n" (newline) string. Here's example input text with two paragraphs that should be synthesized to the same audio output file: "inputs": [{"text": "synthesize this to a file\r\nsynthesize this to another paragraph in the same file"}]<br/><br/>This property is required when you create a new batch synthesis job. This property isn't included in the response when you get the synthesis job.|

-| synthesisConfig | The configuration settings to use for batch synthesis of plain text.<br/><br/>This property is only applicable when textType is set to "PlainText".|

-| synthesisConfig.pitch | The pitch of the audio output.<br/><br/>For information about the accepted values, see the [adjust prosody](../speech-synthesis-markup-voice.md#adjust-prosody) table in the Speech Synthesis Markup Language (SSML) documentation. Invalid values are ignored.<br/><br/>This optional property is only applicable when textType is set to "PlainText".|

-| synthesisConfig.rate | The rate of the audio output.<br/><br/>For information about the accepted values, see the [adjust prosody](../speech-synthesis-markup-voice.md#adjust-prosody) table in the Speech Synthesis Markup Language (SSML) documentation. Invalid values are ignored.<br/><br/>This optional property is only applicable when textType is set to "PlainText".|

-| synthesisConfig.style | For some voices, you can adjust the speaking style to express different emotions like cheerfulness, empathy, and calm. You can optimize the voice for different scenarios like customer service, newscast, and voice assistant.<br/><br/>For information about the available styles per voice, see [voice styles and roles](../language-support.md?tabs=tts#voice-styles-and-roles).<br/><br/>This optional property is only applicable when textType is set to "PlainText".|

-| synthesisConfig.voice | The voice that speaks the audio output.<br/><br/>For information about the available prebuilt neural voices, see [language and voice support](../language-support.md?tabs=tts). To use a custom voice, you must specify a valid custom voice and deployment ID mapping in the customVoices property.<br/><br/>This property is required when textType is set to "PlainText".|

-| synthesisConfig.volume | The volume of the audio output.<br/><br/>For information about the accepted values, see the [adjust prosody](../speech-synthesis-markup-voice.md#adjust-prosody) table in the Speech Synthesis Markup Language (SSML) documentation. Invalid values are ignored.<br/><br/>This optional property is only applicable when textType is set to "PlainText".|

-| textType | Indicates whether the inputs text property should be plain text or SSML. The possible case-insensitive values are "PlainText" and "SSML". When the textType is set to "PlainText", you must also set the synthesisConfig voice property.<br/><br/>This property is required.|

-The avatar batch synthesis API currently doesn't support setting background image/video directly. However, it supports generating a video with a transparent background, and then you can put any image/video behind the avatar as the background in a video editing tool.

-| [Create batch synthesis](#create-a-batch-synthesis-request) | POST | texttospeech/3.1-preview1/batchsynthesis/talkingavatar |

-| [Get batch synthesis](#get-batch-synthesis) | GET | texttospeech/3.1-preview1/batchsynthesis/talkingavatar/{SynthesisId} |

-| [List batch synthesis](#list-batch-synthesis) | GET | texttospeech/3.1-preview1/batchsynthesis/talkingavatar |

-| [Delete batch synthesis](#delete-batch-synthesis) | DELETE | texttospeech/3.1-preview1/batchsynthesis/talkingavatar/{SynthesisId} |

-To make an HTTP POST request, use the URI format shown in the following example. Replace `YourSpeechKey` with your Speech resource key, `YourSpeechRegion` with your Speech resource region, and set the request body properties as described above.

-curl -v -X POST -H "Ocp-Apim-Subscription-Key: YourSpeechKey" -H "Content-Type: application/json" -d '{

- "displayName": "avatar batch synthesis sample",

- "textType": "SSML",

- "text": "<speak version='\''1.0'\'' xml:lang='\''en-US'\''>

- <voice name='\''en-US-AvaMultilingualNeural'\''>

- The rainbow has seven colors.

- </voice>

- </speak>"

- "properties": {

-}' "https://YourSpeechRegion.customvoice.api.speech.microsoft.com/api/texttospeech/3.1-preview1/batchsynthesis/talkingavatar"

- "textType": "SSML",

- "timeToLive": "P31D",

- "outputFormat": "riff-24khz-16bit-mono-pcm",

- "kBitrate": 2000,

- },

- "lastActionDateTime": "2023-10-19T12:23:03.348Z",

- "status": "NotStarted",

- "id": "c48b4cf5-957f-4a0f-96af-a4e3e71bd6b6",

- "createdDateTime": "2023-10-19T12:23:03.348Z",

- "displayName": "avatar batch synthesis sample"

-curl -v -X GET "https://YourSpeechRegion.customvoice.api.speech.microsoft.com/api/texttospeech/3.1-preview1/batchsynthesis/talkingavatar/YourSynthesisId" -H "Ocp-Apim-Subscription-Key: YourSpeechKey"

- "textType": "SSML",

- "audioSize": 336780,

- "durationInTicks": 25200000,

- "succeededAudioCount": 1,

- "duration": "PT2.52S",

- "customNeural": 0,

- "neural": 29

- },

- "timeToLive": "P31D",

- "outputFormat": "riff-24khz-16bit-mono-pcm",

- "kBitrate": 2000,

- "result": "https://cvoiceprodwus2.blob.core.windows.net/batch-synthesis-output/c48b4cf5-957f-4a0f-96af-a4e3e71bd6b6/0001.mp4?SAS_Token",

- "summary": "https://cvoiceprodwus2.blob.core.windows.net/batch-synthesis-output/c48b4cf5-957f-4a0f-96af-a4e3e71bd6b6/summary.json?SAS_Token"

- },

- "lastActionDateTime": "2023-10-19T12:23:06.320Z",

- "status": "Succeeded",

- "id": "c48b4cf5-957f-4a0f-96af-a4e3e71bd6b6",

- "createdDateTime": "2023-10-19T12:23:03.350Z",

- "displayName": "avatar batch synthesis sample"

-Replace `YourSpeechKey` with your Speech resource key and `YourSpeechRegion` with your Speech resource region. Optionally, you can set the `skip` and `top` (page size) query parameters in the URL. The default value for `skip` is 0, and the default value for `top` is 100.

-curl -v -X GET "https://YourSpeechRegion.customvoice.api.speech.microsoft.com/api/texttospeech/3.1-preview1/batchsynthesis/talkingavatar?skip=0&top=2" -H "Ocp-Apim-Subscription-Key: YourSpeechKey"

- "values": [

- "textType": "PlainText",

- "synthesisConfig": {

- "voice": "en-US-AvaMultilingualNeural"

- },

- "audioSize": 339371,

- "durationInTicks": 25200000,

- "succeededAudioCount": 1,

- "duration": "PT2.52S",

- "customNeural": 0,

- "neural": 29

- },

- "timeToLive": "P31D",

- "outputFormat": "riff-24khz-16bit-mono-pcm",

- "talkingAvatarStyle": "graceful-sitting",

- "kBitrate": 2000,

- "result": "https://cvoiceprodwus2.blob.core.windows.net/batch-synthesis-output/8e3fea5f-4021-4734-8c24-77d3be594633/0001.mp4?SAS_Token",

- "summary": "https://cvoiceprodwus2.blob.core.windows.net/batch-synthesis-output/8e3fea5f-4021-4734-8c24-77d3be594633/summary.json?SAS_Token"

- },

- "lastActionDateTime": "2023-10-19T12:57:45.557Z",

- "status": "Succeeded",

- "id": "8e3fea5f-4021-4734-8c24-77d3be594633",

- "createdDateTime": "2023-10-19T12:57:42.343Z",

- "displayName": "avatar batch synthesis sample"

- "textType": "SSML",

- "audioSize": 336780,

- "durationInTicks": 25200000,

- "succeededAudioCount": 1,

- "duration": "PT2.52S",

- "customNeural": 0,

- "neural": 29

- },

- "timeToLive": "P31D",

- "outputFormat": "riff-24khz-16bit-mono-pcm",

- "kBitrate": 2000,

- "result": "https://cvoiceprodwus2.blob.core.windows.net/batch-synthesis-output/c48b4cf5-957f-4a0f-96af-a4e3e71bd6b6/0001.mp4?SAS_Token",

- "summary": "https://cvoiceprodwus2.blob.core.windows.net/batch-synthesis-output/c48b4cf5-957f-4a0f-96af-a4e3e71bd6b6/summary.json?SAS_Token"

- },

- "lastActionDateTime": "2023-10-19T12:23:06.320Z",

- "status": "Succeeded",

- "id": "c48b4cf5-957f-4a0f-96af-a4e3e71bd6b6",

- "createdDateTime": "2023-10-19T12:23:03.350Z",

- "displayName": "avatar batch synthesis sample"

- "@nextLink": "https://{region}.customvoice.api.speech.microsoft.com/api/texttospeech/3.1-preview1/batchsynthesis/talkingavatar?skip=2&top=2"

-The `values` property in the JSON response lists your synthesis requests. The list is paginated, with a maximum page size of 100. The `@nextLink` property is provided as needed to get the next page of the paginated list.

- "jobID": "c48b4cf5-957f-4a0f-96af-a4e3e71bd6b6",

- "status": "Succeeded",

- "results": [

- "texts": [

- "<speak version='1.0' xml:lang='en-US'>\n\t\t\t\t<voice name='en-US-AvaMultilingualNeural'>\n\t\t\t\t\tThe rainbow has seven colors.\n\t\t\t\t</voice>\n\t\t\t</speak>"

- "status": "Succeeded",

- "billingDetails": {

- "Neural": "29",

- "TalkingAvatarDuration": "2"

- },

- "videoFileName": "c48b4cf5-957f-4a0f-96af-a4e3e71bd6b6/0001.mp4",

- "TalkingAvatarCharacter": "lisa",

- "TalkingAvatarStyle": "graceful-sitting"

-After you have retrieved the audio output results and no longer need the batch synthesis job history, you can delete it. The Speech service retains each synthesis history for up to 31 days or the duration specified by the request's `timeToLive` property, whichever comes sooner. The date and time of automatic deletion, for synthesis jobs with a status of "Succeeded" or "Failed" is calculated as the sum of the `lastActionDateTime` and `timeToLive` properties.

-curl -v -X DELETE "https://YourSpeechRegion.customvoice.api.speech.microsoft.com/api/texttospeech/3.1-preview1/batchsynthesis/talkingavatar/YourSynthesisId" -H "Ocp-Apim-Subscription-Key: YourSpeechKey"

- "MicrosoftAppId": "3be0abc2-ca07-475e-b6c3-90c4476c4370",

- "MicrosoftAppPassword": "-zRhJZ~1cnc7ZIlj4Qozs_eKN.8Cq~U38G"

-description: The Translator container runtime environment is configured using the `docker run` command arguments. There are both required and optional settings.

-#

-recommendations: false

-# Configure Translator Docker containers

-Azure AI services provide each container with a common configuration framework. You can easily configure your Translator containers to build Translator application architecture optimized for robust cloud capabilities and edge locality.

-The **Translator** container runtime environment is configured using the `docker run` command arguments. This container has both required and optional settings. The required container-specific settings are the billing settings.

-## Configuration settings

-The container has the following configuration settings:

-|Required|Setting|Purpose|

-|--|--|--|

-|Yes|[ApiKey](#apikey-configuration-setting)|Tracks billing information.|

-|No|[ApplicationInsights](#applicationinsights-setting)|Enables adding [Azure Application Insights](/azure/application-insights) telemetric support to your container.|

-|Yes|[Billing](#billing-configuration-setting)|Specifies the endpoint URI of the service resource on Azure.|

-|Yes|[EULA](#eula-setting)| Indicates that you've accepted the license for the container.|

-|No|[Fluentd](#fluentd-settings)|Writes log and, optionally, metric data to a Fluentd server.|

-|No|HTTP Proxy|Configures an HTTP proxy for making outbound requests.|

-|No|[Logging](#logging-settings)|Provides ASP.NET Core logging support for your container. |

-|Yes|[Mounts](#mount-settings)|Reads and writes data from the host computer to the container and from the container back to the host computer.|

- > [!IMPORTANT]

-> The [**ApiKey**](#apikey-configuration-setting), [**Billing**](#billing-configuration-setting), and [**EULA**](#eula-setting) settings are used together, and you must provide valid values for all three of them; otherwise your container won't start. For more information about using these configuration settings to instantiate a container.

-## ApiKey configuration setting

-The `ApiKey` setting specifies the Azure resource key used to track billing information for the container. You must specify a value for the ApiKey and the value must be a valid key for the _Translator_ resource specified for the [`Billing`](#billing-configuration-setting) configuration setting.

-This setting can be found in the following place:

-* Azure portal: **Translator** resource management, under **Keys**

-## ApplicationInsights setting

-## Billing configuration setting

-The `Billing` setting specifies the endpoint URI of the _Translator_ resource on Azure used to meter billing information for the container. You must specify a value for this configuration setting, and the value must be a valid endpoint URI for a _Translator_ resource on Azure. The container reports usage about every 10 to 15 minutes.

-This setting can be found in the following place:

-* Azure portal: **Translator** Overview page labeled `Endpoint`

-| Required | Name | Data type | Description |

-| -- | - | | -- |

-| Yes | `Billing` | String | Billing endpoint URI. For more information on obtaining the billing URI, see [gathering required parameters](translator-how-to-install-container.md#required-elements). For more information and a complete list of regional endpoints, see [Custom subdomain names for Azure AI services](../../cognitive-services-custom-subdomains.md). |

-## EULA setting

-## Fluentd settings

-## HTTP/HTTPS proxy credentials settings

-If you need to configure an HTTP proxy for making outbound requests, use these two arguments:

-| Name | Data type | Description |

-|--|--|--|

-|HTTPS_PROXY|string|The proxy to use, for example, `https://proxy:8888`<br>`<proxy-url>`|

-|HTTP_PROXY_CREDS|string|Any credentials needed to authenticate against the proxy, for example, `username:password`. This value **must be in lower-case**. |

-|`<proxy-user>`|string|The user for the proxy.|

-|`<proxy-password>`|string|The password associated with `<proxy-user>` for the proxy.|

-||||

-```bash

-docker run --rm -it -p 5000:5000 \

-<registry-location>/<image-name> \

-Eula=accept \

-Billing=<endpoint> \

-ApiKey=<api-key> \

-HTTPS_PROXY=<proxy-url> \

-HTTP_PROXY_CREDS=<proxy-user>:<proxy-password> \

-```

-## Logging settings

-Translator containers support the following logging providers:

-|Provider|Purpose|

-|--|--|

-|[Console](/aspnet/core/fundamentals/logging/#console-provider)|The ASP.NET Core `Console` logging provider. All of the ASP.NET Core configuration settings and default values for this logging provider are supported.|

-|[Debug](/aspnet/core/fundamentals/logging/#debug-provider)|The ASP.NET Core `Debug` logging provider. All of the ASP.NET Core configuration settings and default values for this logging provider are supported.|

-|[Disk](#disk-logging)|The JSON logging provider. This logging provider writes log data to the output mount.|

-* The `Logging` settings manage ASP.NET Core logging support for your container. You can use the same configuration settings and values for your container that you use for an ASP.NET Core application.

-* The `Logging.LogLevel` specifies the minimum level to log. The severity of the `LogLevel` ranges from 0 to 6. When a `LogLevel` is specified, logging is enabled for messages at the specified level and higher: Trace = 0, Debug = 1, Information = 2, Warning = 3, Error = 4, Critical = 5, None = 6.

-* Currently, Translator containers have the ability to restrict logs at the **Warning** LogLevel or higher.

-The general command syntax for logging is as follows:

-```bash

- -Logging:LogLevel:{Provider}={FilterSpecs}

-```

-The following command starts the Docker container with the `LogLevel` set to **Warning** and logging provider set to **Console**. This command prints anomalous or unexpected events during the application flow to the console:

-```bash

-docker run --rm -it -p 5000:5000

-mcr.microsoft.com/azure-cognitive-services/translator/text-translation:latest

-```

-### Disk logging

-The `Disk` logging provider supports the following configuration settings:

-| Name | Data type | Description |

-||--|-|

-| `Format` | String | The output format for log files.<br/> **Note:** This value must be set to `json` to enable the logging provider. If this value is specified without also specifying an output mount while instantiating a container, an error occurs. |

-| `MaxFileSize` | Integer | The maximum size, in megabytes (MB), of a log file. When the size of the current log file meets or exceeds this value, the logging provider starts a new log file. If -1 is specified, the size of the log file is limited only by the maximum file size, if any, for the output mount. The default value is 1. |

-#### Disk provider example

-```bash

-docker run --rm -it -p 5000:5000 \

-Eula=accept \

-Billing=<endpoint> \

-ApiKey=<api-key> \

-Logging:Disk:Format=json \

-Mounts:Output=/output

-```

-For more information about configuring ASP.NET Core logging support, see [Settings file configuration](/aspnet/core/fundamentals/logging/).

-## Mount settings

-Use bind mounts to read and write data to and from the container. You can specify an input mount or output mount by specifying the `--mount` option in the [docker run](https://docs.docker.com/engine/reference/commandline/run/) command.

-## Next steps

-> [!div class="nextstepaction"]

-> [Learn more about Azure AI containers](../../cognitive-services-container-support.md)

-description: Understand the parameters, headers, and body messages for the container Translate method of Azure AI Translator to translate text.

-#

-# Container: Translate

-Translate text.

-## Request URL

-Send a `POST` request to:

-```HTTP

-http://localhost:{port}/translate?api-version=3.0

-```

-Example: http://<span></span>localhost:5000/translate?api-version=3.0

-## Request parameters

-Request parameters passed on the query string are:

-### Required parameters

-| Query parameter | Description |

-| | |

-| api-version | _Required parameter_. <br>Version of the API requested by the client. Value must be `3.0`. |

-| from | _Required parameter_. <br>Specifies the language of the input text. Find which languages are available to translate from by looking up [supported languages](../reference/v3-0-languages.md) using the `translation` scope.|

-| to | _Required parameter_. <br>Specifies the language of the output text. The target language must be one of the [supported languages](../reference/v3-0-languages.md) included in the `translation` scope. For example, use `to=de` to translate to German. <br>It's possible to translate to multiple languages simultaneously by repeating the parameter in the query string. For example, use `to=de&to=it` to translate to German and Italian. |

-### Optional parameters

-| Query parameter | Description |

-| | |

-| textType | _Optional parameter_. <br>Defines whether the text being translated is plain text or HTML text. Any HTML needs to be a well-formed, complete element. Possible values are: `plain` (default) or `html`. |

-| includeSentenceLength | _Optional parameter_. <br>Specifies whether to include sentence boundaries for the input text and the translated text. Possible values are: `true` or `false` (default). |

-Request headers include:

-| Headers | Description |

-| | |

-| Authentication header(s) | _Required request header_. <br>See [available options for authentication](../reference/v3-0-reference.md#authentication). |

-| Content-Type | _Required request header_. <br>Specifies the content type of the payload. <br>Accepted value is `application/json; charset=UTF-8`. |

-| Content-Length | _Required request header_. <br>The length of the request body. |

-| X-ClientTraceId | _Optional_. <br>A client-generated GUID to uniquely identify the request. You can omit this header if you include the trace ID in the query string using a query parameter named `ClientTraceId`. |

-## Request body

-The body of the request is a JSON array. Each array element is a JSON object with a string property named `Text`, which represents the string to translate.

-```json

-[

- {"Text":"I would really like to drive your car around the block a few times."}

-]

-```

-The following limitations apply:

-* The array can have at most 100 elements.

-* The entire text included in the request can't exceed 10,000 characters including spaces.

-## Response body

-A successful response is a JSON array with one result for each string in the input array. A result object includes the following properties:

-* `translations`: An array of translation results. The size of the array matches the number of target languages specified through the `to` query parameter. Each element in the array includes:

-* `to`: A string representing the language code of the target language.

-* `text`: A string giving the translated text.

-* `sentLen`: An object returning sentence boundaries in the input and output texts.

-* `srcSentLen`: An integer array representing the lengths of the sentences in the input text. The length of the array is the number of sentences, and the values are the length of each sentence.

-* `transSentLen`: An integer array representing the lengths of the sentences in the translated text. The length of the array is the number of sentences, and the values are the length of each sentence.

- Sentence boundaries are only included when the request parameter `includeSentenceLength` is `true`.

- * `sourceText`: An object with a single string property named `text`, which gives the input text in the default script of the source language. `sourceText` property is present only when the input is expressed in a script that's not the usual script for the language. For example, if the input were Arabic written in Latin script, then `sourceText.text` would be the same Arabic text converted into Arab script.

-Examples of JSON responses are provided in the [examples](#examples) section.

-## Response headers

-| Headers | Description |

-| | |

-| X-RequestId | Value generated by the service to identify the request. It's used for troubleshooting purposes. |

-| X-MT-System | Specifies the system type that was used for translation for each 'to' language requested for translation. The value is a comma-separated list of strings. Each string indicates a type: </br></br>&FilledVerySmallSquare; Custom - Request includes a custom system and at least one custom system was used during translation.</br>&FilledVerySmallSquare; Team - All other requests |

-## Response status codes

-If an error occurs, the request will also return a JSON error response. The error code is a 6-digit number combining the 3-digit HTTP status code followed by a 3-digit number to further categorize the error. Common error codes can be found on the [v3 Translator reference page](../reference/v3-0-reference.md#errors).

-## Examples

-### Translate a single input

-This example shows how to translate a single sentence from English to Simplified Chinese.

-```curl

-curl -X POST "http://localhost:{port}/translate?api-version=3.0&from=en&to=zh-Hans" -H "Ocp-Apim-Subscription-Key: <client-secret>" -H "Content-Type: application/json; charset=UTF-8" -d "[{'Text':'Hello, what is your name?'}]"

-```

-The response body is:

-```

-[

- {

- "translations":[

- {"text":"你好, 你叫什么名字？","to":"zh-Hans"}

- ]

- }

-]

-```

-The `translations` array includes one element, which provides the translation of the single piece of text in the input.

-### Translate multiple pieces of text

-Translating multiple strings at once is simply a matter of specifying an array of strings in the request body.

-```curl

-curl -X POST "http://localhost:{port}/translate?api-version=3.0&from=en&to=zh-Hans" -H "Ocp-Apim-Subscription-Key: <client-secret>" -H "Content-Type: application/json; charset=UTF-8" -d "[{'Text':'Hello, what is your name?'}, {'Text':'I am fine, thank you.'}]"

-```

-The response contains the translation of all pieces of text in the exact same order as in the request.

-The response body is:

-```

-[

- {

- "translations":[

- {"text":"你好, 你叫什么名字？","to":"zh-Hans"}

- ]

- },

- {

- "translations":[

- {"text":"我很好，谢谢你。","to":"zh-Hans"}

- ]

- }

-]

-```

-### Translate to multiple languages

-This example shows how to translate the same input to several languages in one request.

-```curl

-curl -X POST "http://localhost:{port}/translate?api-version=3.0&from=en&to=zh-Hans&to=de" -H "Ocp-Apim-Subscription-Key: <client-secret>" -H "Content-Type: application/json; charset=UTF-8" -d "[{'Text':'Hello, what is your name?'}]"

-```

-The response body is:

-```

-[

- {

- "translations":[

- {"text":"你好, 你叫什么名字？","to":"zh-Hans"},

- {"text":"Hallo, was ist dein Name?","to":"de"}

- ]

- }

-]

-```

-### Translate content with markup and decide what's translated

-It's common to translate content that includes markup such as content from an HTML page or content from an XML document. Include query parameter `textType=html` when translating content with tags. In addition, it's sometimes useful to exclude specific content from translation. You can use the attribute `class=notranslate` to specify content that should remain in its original language. In the following example, the content inside the first `div` element won't be translated, while the content in the second `div` element will be translated.

-```

-<div class="notranslate">This will not be translated.</div>

-<div>This will be translated. </div>

-```

-Here's a sample request to illustrate.

-```curl

-curl -X POST "http://localhost:{port}/translate?api-version=3.0&from=en&to=zh-Hans&textType=html" -H "Ocp-Apim-Subscription-Key: <client-secret>" -H "Content-Type: application/json; charset=UTF-8" -d "[{'Text':'<div class=\"notranslate\">This will not be translated.</div><div>This will be translated.</div>'}]"

-```

-The response is:

-```

-[

- {

- "translations":[

- {"text":"<div class=\"notranslate\">This will not be translated.</div><div>这将被翻译。</div>","to":"zh-Hans"}

- ]

- }

-]

-```

-### Translate with dynamic dictionary

-If you already know the translation you want to apply to a word or a phrase, you can supply it as markup within the request. The dynamic dictionary is only safe for proper nouns such as personal names and product names.

-The markup to supply uses the following syntax.

-```

-<mstrans:dictionary translation="translation of phrase">phrase</mstrans:dictionary>

-```

-For example, consider the English sentence "The word wordomatic is a dictionary entry." To preserve the word _wordomatic_ in the translation, send the request:

-```

-curl -X POST "http://localhost:{port}/translate?api-version=3.0&from=en&to=de" -H "Ocp-Apim-Subscription-Key: <client-secret>" -H "Content-Type: application/json; charset=UTF-8" -d "[{'Text':'The word <mstrans:dictionary translation=\"wordomatic\">word or phrase</mstrans:dictionary> is a dictionary entry.'}]"

-```

-The result is:

-```

-[

- {

- "translations":[

- {"text":"Das Wort \"wordomatic\" ist ein W├╢rterbucheintrag.","to":"de"}

- ]

- }

-]

-```

-This feature works the same way with `textType=text` or with `textType=html`. The feature should be used sparingly. The appropriate and far better way of customizing translation is by using Custom Translator. Custom Translator makes full use of context and statistical probabilities. If you've created training data that shows your work or phrase in context, you'll get much better results. [Learn more about Custom Translator](../custom-translator/concepts/customization.md).

-## Request limits

-Each translate request is limited to 10,000 characters, across all the target languages you're translating to. For example, sending a translate request of 3,000 characters to translate to three different languages results in a request size of 3000x3 = 9,000 characters, which satisfy the request limit. You're charged per character, not by the number of requests. It's recommended to send shorter requests.

-The following table lists array element and character limits for the Translator **translation** operation.

-| Operation | Maximum size of array element | Maximum number of array elements | Maximum request size (characters) |

-|:-|:-|:-|:-|

-| translate | 10,000 | 100 | 10,000 |

-description: Learn how to run Azure AI Translator containers in disconnected environments.

-#

-<!-- markdownlint-disable MD036 -->

-<!-- markdownlint-disable MD001 -->

-# Use Translator containers in disconnected environments

- Azure AI Translator containers allow you to use Translator Service APIs with the benefits of containerization. Disconnected containers are offered through commitment tier pricing offered at a discounted rate compared to pay-as-you-go pricing. With commitment tier pricing, you can commit to using Translator Service features for a fixed fee, at a predictable total cost, based on the needs of your workload.

-## Get started

-Before attempting to run a Docker container in an offline environment, make sure you're familiar with the following requirements to successfully download and use the container:

-* Host computer requirements and recommendations.

-* The Docker `pull` command to download the container.

-* How to validate that a container is running.

-* How to send queries to the container's endpoint, once it's running.

-## Request access to use containers in disconnected environments

-Complete and submit the [request form](https://aka.ms/csdisconnectedcontainers) to request access to the containers disconnected from the Internet.

-Access is limited to customers that meet the following requirements:

-* Your organization should be identified as strategic customer or partner with Microsoft.

-* Disconnected containers are expected to run fully offline, hence your use cases must meet at least one of these or similar requirements:

- * Environment or device(s) with zero connectivity to internet.

- * Remote location that occasionally has internet access.

- * Organization under strict regulation of not sending any kind of data back to cloud.

-* Application completed as instructed. Make certain to pay close attention to guidance provided throughout the application to ensure you provide all the necessary information required for approval.

-## Create a new resource and purchase a commitment plan

-1. Create a [Translator resource](https://portal.azure.com/#create/Microsoft.CognitiveServicesTextTranslation) in the Azure portal.

-1. Enter the applicable information to create your resource. Be sure to select **Commitment tier disconnected containers** as your pricing tier.

- > [!NOTE]

- >

- > * You will only see the option to purchase a commitment tier if you have been approved by Microsoft.

- :::image type="content" source="../media/create-resource-offline-container.png" alt-text="A screenshot showing resource creation on the Azure portal.":::

-1. Select **Review + Create** at the bottom of the page. Review the information, and select **Create**.

-## Gather required parameters

-There are three required parameters for all Azure AI services' containers:

-* The end-user license agreement (EULA) must be present with a value of *accept*.

-* The endpoint URL for your resource from the Azure portal.

-* The API key for your resource from the Azure portal.

-Both the endpoint URL and API key are needed when you first run the container to configure it for disconnected usage. You can find the key and endpoint on the **Key and endpoint** page for your resource in the Azure portal:

- :::image type="content" source="../media/quickstarts/keys-and-endpoint-portal.png" alt-text="Screenshot of Azure portal keys and endpoint page.":::

-> [!IMPORTANT]

-> You will only use your key and endpoint to configure the container to run in a disconnected environment. After you configure the container, you won't need the key and endpoint values to send API requests. Store them securely, for example, using Azure Key Vault. Only one key is necessary for this process.

-## Download a Docker container with `docker pull`

-Download the Docker container that has been approved to run in a disconnected environment. For example:

-|Docker pull command | Value |Format|

-|-|-||

-|&bullet; **`docker pull [image]`**</br>&bullet; **`docker pull [image]:latest`**|The latest container image.|&bullet; mcr.microsoft.com/azure-cognitive-services/translator/text-translation</br> </br>&bullet; mcr.microsoft.com/azure-cognitive-services/translator/text-translation: latest |

-|||

-|&bullet; **`docker pull [image]:[version]`** | A specific container image |mcr.microsoft.com/azure-cognitive-services/translator/text-translation:1.0.019410001-amd64 |

- **Example Docker pull command**

-```docker

-docker pull mcr.microsoft.com/azure-cognitive-services/translator/text-translation:latest

-```

-## Configure the container to run in a disconnected environment

-Now that you've downloaded your container, you need to execute the `docker run` command with the following parameters:

-* **`DownloadLicense=True`**. This parameter downloads a license file that enables your Docker container to run when it isn't connected to the internet. It also contains an expiration date, after which the license file is invalid to run the container. You can only use the license file in corresponding approved container.

-* **`Languages={language list}`**. You must include this parameter to download model files for the [languages](../language-support.md) you want to translate.

-> [!IMPORTANT]

-> The `docker run` command will generate a template that you can use to run the container. The template contains parameters you'll need for the downloaded models and configuration file. Make sure you save this template.

-The following example shows the formatting for the `docker run` command with placeholder values. Replace these placeholder values with your own values.

-| Placeholder | Value | Format|

-|-|-||

-| `[image]` | The container image you want to use. | `mcr.microsoft.com/azure-cognitive-services/translator/text-translation` |

-| `{LICENSE_MOUNT}` | The path where the license is downloaded, and mounted. | `/host/license:/path/to/license/directory` |

- | `{MODEL_MOUNT_PATH}`| The path where the machine translation models are downloaded, and mounted. Your directory structure must be formatted as **/usr/local/models** | `/host/translator/models:/usr/local/models`|

-| `{ENDPOINT_URI}` | The endpoint for authenticating your service request. You can find it on your resource's **Key and endpoint** page, in the Azure portal. | `https://<your-custom-subdomain>.cognitiveservices.azure.com` |

-| `{API_KEY}` | The key for your Text Translation resource. You can find it on your resource's **Key and endpoint** page, in the Azure portal. |`{string}`|

-| `{LANGUAGES_LIST}` | List of language codes separated by commas. It's mandatory to have English (en) language as part of the list.| `en`, `fr`, `it`, `zu`, `uk` |

-| `{CONTAINER_LICENSE_DIRECTORY}` | Location of the license folder on the container's local filesystem. | `/path/to/license/directory` |

- **Example `docker run` command**

-```docker

-docker run --rm -it -p 5000:5000 \

-[image]

-```

-### Translator translation models and container configuration

-After you've [configured the container](#configure-the-container-to-run-in-a-disconnected-environment), the values for the downloaded translation models and container configuration will be generated and displayed in the container output:

-```bash

- -e MODELS= usr/local/models/model1/, usr/local/models/model2/

- -e TRANSLATORSYSTEMCONFIG=/usr/local/models/Config/5a72fa7c-394b-45db-8c06-ecdfc98c0832

-```

-## Run the container in a disconnected environment

-Once the license file has been downloaded, you can run the container in a disconnected environment with your license, appropriate memory, and suitable CPU allocations. The following example shows the formatting of the `docker run` command with placeholder values. Replace these placeholders values with your own values.

-Whenever the container is run, the license file must be mounted to the container and the location of the license folder on the container's local filesystem must be specified with `Mounts:License=`. In addition, an output mount must be specified so that billing usage records can be written.

-Placeholder | Value | Format|

-|-|-||

-| `[image]`| The container image you want to use. | `mcr.microsoft.com/azure-cognitive-services/translator/text-translation` |

- `{MEMORY_SIZE}` | The appropriate size of memory to allocate for your container. | `16g` |

-| `{NUMBER_CPUS}` | The appropriate number of CPUs to allocate for your container. | `4` |

-| `{LICENSE_MOUNT}` | The path where the license is located and mounted. | `/host/translator/license:/path/to/license/directory` |

-|`{MODEL_MOUNT_PATH}`| The path where the machine translation models are downloaded, and mounted. Your directory structure must be formatted as **/usr/local/models** | `/host/translator/models:/usr/local/models`|

-|`{MODELS_DIRECTORY_LIST}`|List of comma separated directories each having a machine translation model. | `/usr/local/models/enu_esn_generalnn_2022240501,/usr/local/models/esn_enu_generalnn_2022240501` |

-| `{OUTPUT_PATH}` | The output path for logging [usage records](#usage-records). | `/host/output:/path/to/output/directory` |

-| `{CONTAINER_LICENSE_DIRECTORY}` | Location of the license folder on the container's local filesystem. | `/path/to/license/directory` |

-| `{CONTAINER_OUTPUT_DIRECTORY}` | Location of the output folder on the container's local filesystem. | `/path/to/output/directory` |

-|`{TRANSLATOR_CONFIG_JSON}`| Translator system configuration file used by container internally.| `/usr/local/models/Config/5a72fa7c-394b-45db-8c06-ecdfc98c0832` |

- **Example `docker run` command**

-```docker

-docker run --rm -it -p 5000:5000 --memory {MEMORY_SIZE} --cpus {NUMBER_CPUS} \

-[image]

-```

-## Other parameters and commands

-Here are a few more parameters and commands you may need to run the container:

-#### Usage records

-When operating Docker containers in a disconnected environment, the container will write usage records to a volume where they're collected over time. You can also call a REST API endpoint to generate a report about service usage.

-#### Arguments for storing logs

-When run in a disconnected environment, an output mount must be available to the container to store usage logs. For example, you would include `-v /host/output:{OUTPUT_PATH}` and `Mounts:Output={OUTPUT_PATH}` in the following example, replacing `{OUTPUT_PATH}` with the path where the logs are stored:

- **Example `docker run` command**

-```docker

-docker run -v /host/output:{OUTPUT_PATH} ... <image> ... Mounts:Output={OUTPUT_PATH}

-```

-#### Environment variable names in Kubernetes deployments

-Some Azure AI Containers, for example Translator, require users to pass environmental variable names that include colons (`:`) when running the container. This will work fine when using Docker, but Kubernetes does not accept colons in environmental variable names.

-To resolve this, you can replace colons with two underscore characters (`__`) when deploying to Kubernetes. See the following example of an acceptable format for environmental variable names:

-```Kubernetes

- env:

- - name: Mounts__License

- value: "/license"

- - name: Mounts__Output

- value: "/output"

-```

-This example replaces the default format for the `Mounts:License` and `Mounts:Output` environment variable names in the docker run command.

-#### Get records using the container endpoints

-The container provides two endpoints for returning records regarding its usage.

-#### Get all records

-The following endpoint provides a report summarizing all of the usage collected in the mounted billing record directory.

-```HTTP

-https://<service>/records/usage-logs/

-```

- **Example HTTPS endpoint**

- `http://localhost:5000/records/usage-logs`

-The usage-logs endpoint returns a JSON response similar to the following example:

-```json

-{

-"apiType": "string",

-"serviceName": "string",

-"meters": [

-{

- "name": "string",

- "quantity": 256345435

- }

- ]

-}

-```

-#### Get records for a specific month

-The following endpoint provides a report summarizing usage over a specific month and year:

-```HTTP

-https://<service>/records/usage-logs/{MONTH}/{YEAR}

-```

-This usage-logs endpoint returns a JSON response similar to the following example:

-```json

-{

- "apiType": "string",

- "serviceName": "string",

- "meters": [

- {

- "name": "string",

- "quantity": 56097

- }

- ]

-}

-```

-### Purchase a different commitment plan for disconnected containers

-Commitment plans for disconnected containers have a calendar year commitment period. When you purchase a plan, you're charged the full price immediately. During the commitment period, you can't change your commitment plan, however you can purchase more unit(s) at a pro-rated price for the remaining days in the year. You have until midnight (UTC) on the last day of your commitment, to end a commitment plan.

-You can choose a different commitment plan in the **Commitment tier pricing** settings of your resource under the **Resource Management** section.

-### End a commitment plan

- If you decide that you don't want to continue purchasing a commitment plan, you can set your resource's autorenewal to **Do not auto-renew**. Your commitment plan expires on the displayed commitment end date. After this date, you won't be charged for the commitment plan. You're still able to continue using the Azure resource to make API calls, charged at pay-as-you-go pricing. You have until midnight (UTC) on the last day of the year to end a commitment plan for disconnected containers. If you do so, you avoid charges for the following year.

-## Troubleshooting

-Run the container with an output mount and logging enabled. These settings enable the container to generate log files that are helpful for troubleshooting issues that occur while starting or running the container.

-> [!TIP]

-> For more troubleshooting information and guidance, see [Disconnected containers Frequently asked questions (FAQ)](../../containers/disconnected-container-faq.yml).

-That's it! You've learned how to create and run disconnected containers for Azure AI Translator Service.

-## Next steps

-> [!div class="nextstepaction"]

-> [Request parameters for Translator text containers](translator-container-supported-parameters.md)

-description: Use the Docker container for Translator API to translate text.

-#

-recommendations: false

-keywords: on-premises, Docker, container, identify

-# Install and run Translator containers

-Containers enable you to run several features of the Translator service in your own environment. Containers are great for specific security and data governance requirements. In this article you learn how to download, install, and run a Translator container.

-Translator container enables you to build a translator application architecture that is optimized for both robust cloud capabilities and edge locality.

-See the list of [languages supported](../language-support.md) when using Translator containers.

-> [!IMPORTANT]

->

-> * To use the Translator container, you must submit an online request and have it approved. For more information, _see_ [Request approval to run container](#request-approval-to-run-container).

-> * Translator container supports limited features compared to the cloud offerings. For more information, _see_ [**Container translate methods**](translator-container-supported-parameters.md).

-<!-- markdownlint-disable MD033 -->

-## Prerequisites

-To get started, you need an active [**Azure account**](https://azure.microsoft.com/free/cognitive-services/). If you don't have one, you can [**create a free account**](https://azure.microsoft.com/free/).

-You also need:

-| Required | Purpose |

-|--|--|

-| Familiarity with Docker | <ul><li>You should have a basic understanding of Docker concepts like registries, repositories, containers, and container images, as well as knowledge of basic `docker` [terminology and commands](/dotnet/architecture/microservices/container-docker-introduction/docker-terminology).</li></ul> |

-| Docker Engine | <ul><li>You need the Docker Engine installed on a [host computer](#host-computer). Docker provides packages that configure the Docker environment on [macOS](https://docs.docker.com/docker-for-mac/), [Windows](https://docs.docker.com/docker-for-windows/), and [Linux](https://docs.docker.com/engine/installation/#supported-platforms). For a primer on Docker and container basics, see the [Docker overview](https://docs.docker.com/engine/docker-overview/).</li><li> Docker must be configured to allow the containers to connect with and send billing data to Azure. </li><li> On **Windows**, Docker must also be configured to support **Linux** containers.</li></ul> |

-| Translator resource | <ul><li>An Azure [Translator](https://portal.azure.com/#create/Microsoft.CognitiveServicesTextTranslation) regional resource (not `global`) with an associated API key and endpoint URI. Both values are required to start the container and can be found on the resource overview page.</li></ul>|

-|Optional|Purpose|

-||-|

-|Azure CLI (command-line interface) |<ul><li> The [Azure CLI](/cli/azure/install-azure-cli) enables you to use a set of online commands to create and manage Azure resources. It's available to install in Windows, macOS, and Linux environments and can be run in a Docker container and Azure Cloud Shell.</li></ul> |

-## Required elements

-All Azure AI containers require three primary elements:

-* **EULA accept setting**. An end-user license agreement (EULA) set with a value of `Eula=accept`.

-* **API key** and **Endpoint URL**. The API key is used to start the container. You can retrieve the API key and Endpoint URL values by navigating to the Translator resource **Keys and Endpoint** page and selecting the `Copy to clipboard` <span class="docon docon-edit-copy x-hidden-focus"></span> icon.

-> [!IMPORTANT]

->

-> * Keys are used to access your Azure AI resource. Do not share your keys. Store them securely, for example, using Azure Key Vault. We also recommend regenerating these keys regularly. Only one key is necessary to make an API call. When regenerating the first key, you can use the second key for continued access to the service.

-## Host computer

-## Container requirements and recommendations

-The following table describes the minimum and recommended CPU cores and memory to allocate for the Translator container.

-| Container | Minimum |Recommended | Language Pair |

-|--|||-|

-| Translator |`2` cores, `4 GB` memory |`4` cores, `8 GB` memory | 2 |

-* Each core must be at least 2.6 gigahertz (GHz) or faster.

-* The core and memory correspond to the `--cpus` and `--memory` settings, which are used as part of the `docker run` command.

-> [!NOTE]

->

-> * CPU core and memory correspond to the `--cpus` and `--memory` settings, which are used as part of the docker run command.

->

-> * The minimum and recommended specifications are based on Docker limits, not host machine resources.

-## Request approval to run container

-Complete and submit the [**Azure AI services

-Application for Gated Services**](https://aka.ms/csgate-translator) to request access to the container.

-## Translator container image

-The Translator container image can be found on the `mcr.microsoft.com` container registry syndicate. It resides within the `azure-cognitive-services/translator` repository and is named `text-translation`. The fully qualified container image name is `mcr.microsoft.com/azure-cognitive-services/translator/text-translation:latest`.

-To use the latest version of the container, you can use the `latest` tag. You can find a full list of [tags on the MCR](https://mcr.microsoft.com/product/azure-cognitive-services/translator/text-translation/tags).

-## Get container images with **docker commands**

-> [!IMPORTANT]

->

-> * The docker commands in the following sections use the back slash, `\`, as a line continuation character. Replace or remove this based on your host operating system's requirements.

-> * The `EULA`, `Billing`, and `ApiKey` options must be specified to run the container; otherwise, the container won't start.

-Use the [docker run](https://docs.docker.com/engine/reference/commandline/run/) command to download a container image from Microsoft Container registry and run it.

-```Docker

-docker run --rm -it -p 5000:5000 --memory 12g --cpus 4 \

-mcr.microsoft.com/azure-cognitive-services/translator/text-translation:latest

-```

-The above command:

-* Downloads and runs a Translator container from the container image.

-* Allocates 12 gigabytes (GB) of memory and four CPU core.

-* Exposes TCP port 5000 and allocates a pseudo-TTY for the container

-* Accepts the end-user agreement (EULA)

-* Configures billing endpoint

-* Downloads translation models for languages English, French, Spanish, Arabic, and Russian

-* Automatically removes the container after it exits. The container image is still available on the host computer.

-### Run multiple containers on the same host

-If you intend to run multiple containers with exposed ports, make sure to run each container with a different exposed port. For example, run the first container on port 5000 and the second container on port 5001.

-You can have this container and a different Azure AI container running on the HOST together. You also can have multiple containers of the same Azure AI container running.

-## Query the container's Translator endpoint

- The container provides a REST-based Translator endpoint API. Here's an example request:

-```curl

-curl -X POST "http://localhost:5000/translate?api-version=3.0&from=en&to=zh-HANS"

- -H "Content-Type: application/json" -d "[{'Text':'Hello, what is your name?'}]"

-```

-> [!NOTE]

-> If you attempt the cURL POST request before the container is ready, you'll end up getting a *Service is temporarily unavailable* response. Wait until the container is ready, then try again.

-## Stop the container

-## Troubleshoot

-### Validate that a container is running

-There are several ways to validate that the container is running:

-* The container provides a homepage at `/` as a visual validation that the container is running.

-* You can open your favorite web browser and navigate to the external IP address and exposed port of the container in question. Use the following request URLs to validate the container is running. The example request URLs listed point to `http://localhost:5000`, but your specific container may vary. Keep in mind that you're navigating to your container's **External IP address** and exposed port.

-| Request URL | Purpose |

-|--|--|

-| `http://localhost:5000/` | The container provides a home page. |

-| `http://localhost:5000/ready` | Requested with GET. Provides a verification that the container is ready to accept a query against the model. This request can be used for Kubernetes [liveness and readiness probes](https://kubernetes.io/docs/tasks/configure-pod-container/configure-liveness-readiness-probes/). |

-| `http://localhost:5000/status` | Requested with GET. Verifies if the api-key used to start the container is valid without causing an endpoint query. This request can be used for Kubernetes [liveness and readiness probes](https://kubernetes.io/docs/tasks/configure-pod-container/configure-liveness-readiness-probes/). |

-| `http://localhost:5000/swagger` | The container provides a full set of documentation for the endpoints and a **Try it out** feature. With this feature, you can enter your settings into a web-based HTML form and make the query without having to write any code. After the query returns, an example CURL command is provided to demonstrate the HTTP headers and body format that's required. |

-## Text translation code samples

-### Translate text with swagger

-#### English &leftrightarrow; German

-Navigate to the swagger page: `http://localhost:5000/swagger/https://docsupdatetracker.net/index.html`

-1. Select **POST /translate**

-1. Select **Try it out**

-1. Enter the **From** parameter as `en`

-1. Enter the **To** parameter as `de`

-1. Enter the **api-version** parameter as `3.0`

-1. Under **texts**, replace `string` with the following JSON

-```json

- [

- {

- "text": "hello, how are you"

- }

- ]

-```

-Select **Execute**, the resulting translations are output in the **Response Body**. You should expect something similar to the following response:

-```json

-"translations": [

- {

- "text": "hallo, wie geht es dir",

- "to": "de"

- }

- ]

-```

-### Translate text with Python

-```python

-import requests, json

-url = 'http://localhost:5000/translate?api-version=3.0&from=en&to=fr'

-headers = { 'Content-Type': 'application/json' }

-body = [{ 'text': 'Hello, how are you' }]

-request = requests.post(url, headers=headers, json=body)

-response = request.json()

-print(json.dumps(

- response,

- sort_keys=True,

- indent=4,

- ensure_ascii=False,

- separators=(',', ': ')))

-```

-### Translate text with C#/.NET console app

-Launch Visual Studio, and create a new console application. Edit the `*.csproj` file to add the `<LangVersion>7.1</LangVersion>` nodeΓÇöspecifies C# 7.1. Add the [Newtoonsoft.Json](https://www.nuget.org/packages/Newtonsoft.Json/) NuGet package, version 11.0.2.

-In the `Program.cs` replace all the existing code with the following script:

-```csharp

-using Newtonsoft.Json;

-using System;

-using System.Net.Http;

-using System.Text;

-using System.Threading.Tasks;

-namespace TranslateContainer

-{

- class Program

- {

- const string ApiHostEndpoint = "http://localhost:5000";

- const string TranslateApi = "/translate?api-version=3.0&from=en&to=de";

- static async Task Main(string[] args)

- {

- var textToTranslate = "Sunny day in Seattle";

- var result = await TranslateTextAsync(textToTranslate);

- Console.WriteLine(result);

- Console.ReadLine();

- }

- static async Task<string> TranslateTextAsync(string textToTranslate)

- {

- var body = new object[] { new { Text = textToTranslate } };

- var requestBody = JsonConvert.SerializeObject(body);

- var client = new HttpClient();

- using (var request =

- new HttpRequestMessage

- {

- Method = HttpMethod.Post,

- RequestUri = new Uri($"{ApiHostEndpoint}{TranslateApi}"),

- Content = new StringContent(requestBody, Encoding.UTF8, "application/json")

- })

- {

- // Send the request and await a response.

- var response = await client.SendAsync(request);

- return await response.Content.ReadAsStringAsync();

- }

- }

- }

-}

-```

-## Summary

-In this article, you learned concepts and workflows for downloading, installing, and running Translator container. Now you know:

-* Translator provides Linux containers for Docker.

-* Container images are downloaded from the container registry and run in Docker.

-* You can use the REST API to call 'translate' operation in Translator container by specifying the container's host URI.

-## Next steps

-> [!div class="nextstepaction"]

-> [Learn more about Azure AI containers](../../cognitive-services-container-support.md?context=%2fazure%2fcognitive-services%2ftranslator%2fcontext%2fcontext)

-description: Learn how to create an Azure AI Translator resource and retrieve your API key and endpoint URL in the Azure portal.

-#

-# Create a Translator resource

-In this article, you learn how to create a Translator resource in the Azure portal. [Azure AI Translator](translator-overview.md) is a cloud-based machine translation service that is part of the [Azure AI services](../what-are-ai-services.md) family. Azure resources are instances of services that you create. All API requests to Azure AI services require an *endpoint* URL and a read-only *key* for authenticating access.

-## Prerequisites

-To get started, you need an active [**Azure account**](https://azure.microsoft.com/free/cognitive-services/). If you don't have one, you can [**create a free 12-month subscription**](https://azure.microsoft.com/free/).

-## Create your resource

-With your Azure account, you can access the Translator service through two different resource types:

-* [**Single-service**](https://portal.azure.com/#create/Microsoft.CognitiveServicesTextTranslation) resource types enable access to a single service API key and endpoint.

-* [**Multi-service**](https://portal.azure.com/#create/Microsoft.CognitiveServicesAllInOne) resource types enable access to multiple Azure AI services by using a single API key and endpoint.

-## Complete your project and instance details

-After you decide which resource type you want use to access the Translator service, you can enter the details for your project and instance.

-1. **Subscription**. Select one of your available Azure subscriptions.

-1. **Resource Group**. You can create a new resource group or add your resource to a pre-existing resource group that shares the same lifecycle, permissions, and policies.

-1. **Resource Region**. Choose **Global** unless your business or application requires a specific region. If you're planning on using the Document Translation feature with [managed identity authorization](document-translation/how-to-guides/create-use-managed-identities.md), choose a geographic region such as **East US**.

-1. **Name**. Enter a name for your resource. The name you choose must be unique within Azure.

- > [!NOTE]

- > If you're using a Translator feature that requires a custom domain endpoint, such as Document Translation, the value that you enter in the Name field will be the custom domain name parameter for the endpoint.

-1. **Pricing tier**. Select a [pricing tier](https://azure.microsoft.com/pricing/details/cognitive-services/translator) that meets your needs:

- * Each subscription has a free tier.

- * The free tier has the same features and functionality as the paid plans and doesn't expire.

- * Only one free tier resource is available per subscription.

- * Document Translation is supported in paid tiers. The Language Studio only supports the S1 or D3 instance tiers. If you just want to try Document Translation, select the Standard S1 instance tier.

-1. If you've created a multi-service resource, the links at the bottom of the **Basics** tab provides technical documentation regarding the appropriate operation of the service.

-1. Select **Review + Create**.

-1. Review the service terms, and select **Create** to deploy your resource.

-1. After your resource has successfully deployed, select **Go to resource**.

-### Authentication keys and endpoint URL

-All Azure AI services API requests require an endpoint URL and a read-only key for authentication.

-* **Authentication keys**. Your key is a unique string that is passed on every request to the Translation service. You can pass your key through a query-string parameter or by specifying it in the HTTP request header.

-* **Endpoint URL**. Use the Global endpoint in your API request unless you need a specific Azure region or custom endpoint. For more information, see [Base URLs](reference/v3-0-reference.md#base-urls). The Global endpoint URL is `api.cognitive.microsofttranslator.com`.

-## Get your authentication keys and endpoint

-To authenitcate your connection to your Translator resource, you'll need to find its keys and endpoint.

-1. After your new resource deploys, select **Go to resource** or go to your resource page.

-1. In the left navigation pane, under **Resource Management**, select **Keys and Endpoint**.

-1. Copy and paste your keys and endpoint URL in a convenient location, such as Notepad.

-## Create a Text Translation client

-Text Translation supports both [global and regional endpoints](#complete-your-project-and-instance-details). Once you have your [authentication keys](#authentication-keys-and-endpoint-url), you need to create an instance of the `TextTranslationClient`, using an `AzureKeyCredential` for authentication, to interact with the Text Translation service:

-* To create a `TextTranslationClient` using a global resource endpoint, you need your resource **API key**:

- ```bash

- AzureKeyCredential credential = new('<apiKey>');

- TextTranslationClient client = new(credential);

- ```

-* To create a `TextTranslationClient` using a regional resource endpoint, you need your resource **API key** and the name of the **region** where your resource is located:

- ```bash

- AzureKeyCredential credential = new('<apiKey>');

- TextTranslationClient client = new(credential, '<region>');

- ```

-## How to delete a resource or resource group

-> [!WARNING]

->

-> Deleting a resource group also deletes all resources contained in the group.

-To delete the resource:

-1. Search and select **Resource groups** in the Azure portal, and select your resource group.

-1. Select the resources to be deleted by selecting the adjacent check box.

-1. Select **Delete** from the top menu near the right edge.

-1. Enter *delete* in the **Delete Resources** dialog box.

-1. Select **Delete**.

-To delete the resource group:

-1. Go to your Resource Group in the Azure portal.

-1. Select **Delete resource group** from the top menu bar.

-1. Confirm the deletion request by entering the resource group name and selecting **Delete**.

-## How to get started with Azure AI Translator REST APIs

-In our quickstart, you learn how to use the Translator service with REST APIs.

-> [!div class="nextstepaction"]

-> [Get Started with Translator](quickstart-text-rest-api.md)

-## Next Steps

-* [Microsoft Translator code samples](https://github.com/MicrosoftTranslator). Multi-language Translator code samples are available on GitHub.

-* [Microsoft Translator Support Forum](https://www.aka.ms/TranslatorForum)

-* [Get Started with Azure (3-minute video)](https://azure.microsoft.com/get-started/?b=16.24)

-* [A regional Translator resource (global isn't supported)](../../create-translator-resource.md).

-For more information, *see* [how to create a Translator resource](../create-translator-resource.md).

-description: Get answers to frequently asked questions about Document Translation.

-When text is translated from the source to target language, the overall length of translated text can differ from source. The result could be reflow of text across pages. The same fonts aren't always available in both source and target language. In general, the same font style is applied in target language to retain formatting closer to source.

-No. The text in an image within a document isn't translated.

-No. The service can't translate encrypted or password-protected documents. If your scanned or text-embedded PDFs are password-locked, you must remove the lock before submission.

-No. Don't include SAS token-appended URLS. Managed identities eliminate the need for you to include shared access signature tokens (SAS) with your HTTP requests.

-* A brief understanding of [**Azure role-based access control (`Azure RBAC`)**](../../../../role-based-access-control/role-assignments-portal.md) using the Azure portal.

-* Once you have your Azure subscription, create a [Translator resource](create-translator-resource.md) in the [Azure portal](https://portal.azure.com/#create/Microsoft.CognitiveServicesTextTranslation).

-> [Create a Translator resource in the Azure portal.](../create-translator-resource.md)

-* [**How-to guides**](create-translator-resource.md). Instructions for accessing and using the service in more specific or customized ways.

-* [**Create a Translator resource**](create-translator-resource.md "Go to the Azure portal.") in the Azure portal.

-* [**Get your access keys and API endpoint**](create-translator-resource.md#authentication-keys-and-endpoint-url). An endpoint URL and read-only key are required for authentication.

-Now, you're ready to get started! [**Create a Translator service**](create-translator-resource.md "Go to the Azure portal."), [**get your access keys and API endpoint**](create-translator-resource.md#authentication-keys-and-endpoint-url "An endpoint URL and read-only key are required for authentication."), and try our [**quickstart**](quickstart-text-rest-api.md "Learn to use Translator via REST.").

-In the Azure AI Studio, there are two levels of access: the Azure AI hub resource and the Azure AI project. The resource is home to the infrastructure (including virtual network setup, customer-managed keys, managed identities, and policies) as well as where you configure your Azure AI services. Azure AI hub resource access can allow you to modify the infrastructure, create new Azure AI hub resources, and create projects. Azure AI projects are a subset of the Azure AI hub resource that act as workspaces that allow you to build and deploy AI systems. Within a project you can develop flows, deploy models, and manage project assets. Project access lets you develop AI end-to-end while taking advantage of the infrastructure setup on the Azure AI hub resource.

-description: This article provides instructions on how to install and get started with the Azure AI CLI.

- - ignite-2023

-# Get started with the Azure AI CLI

-The Azure AI command-line interface (CLI) is a cross-platform command-line tool to connect to Azure AI services and execute control-plane and data-plane operations without having to write any code. The Azure AI CLI allows the execution of commands through a terminal using interactive command-line prompts or via script.

-You can easily use the Azure AI CLI to experiment with key Azure AI features and see how they work with your use cases. Within minutes, you can set up all the required Azure resources needed, and build a customized copilot using Azure OpenAI chat completions APIs and your own data. You can try it out interactively, or script larger processes to automate your own workflows and evaluations as part of your CI/CD system.

-## Prerequisites

-To use the Azure AI CLI, you need to install the prerequisites:

- * The Azure AI SDK, following the instructions [here](./sdk-install.md)

- * The Azure CLI (not the Azure `AI` CLI), following the instructions [here](/cli/azure/install-azure-cli)

- * The .NET SDK, following the instructions [here](/dotnet/core/install/) for your operating system and distro

-> [!NOTE]

-> If you launched VS Code from the Azure AI Studio, you don't need to install the prerequisites. See options without installing later in this article.

-## Install the CLI

-The following set of commands are provided for a few popular operating systems.

-# [Windows](#tab/windows)

-To install the .NET SDK, Azure CLI, and Azure AI CLI, run the following command.

-```bash

-dotnet tool install --prerelease --global Azure.AI.CLI

-```

-To update the Azure AI CLI, run the following command:

-```bash

-dotnet tool update --prerelease --global Azure.AI.CLI

-```

-# [Linux](#tab/linux)

-To install the .NET SDK, Azure CLI, and Azure AI CLI on Debian and Ubuntu, run the following command:

-```

-curl -sL https://aka.ms/InstallAzureAICLIDeb | bash

-```

-Alternatively, you can run the following command:

-```bash

-dotnet tool install --prerelease --global Azure.AI.CLI

-```

-To update the Azure AI CLI, run the following command:

-```bash

-dotnet tool update --prerelease --global Azure.AI.CLI

-```

-# [macOS](#tab/macos)

-To install the .NET SDK, Azure CLI, and Azure AI CLI on macOS 10.14 or later, run the following command:

-```bash

-dotnet tool install --prerelease --global Azure.AI.CLI

-```

-To update the Azure AI CLI, run the following command:

-```bash

-dotnet tool update --prerelease --global Azure.AI.CLI

-```

-## Run the Azure AI CLI without installing it

-You can install the Azure AI CLI locally as described previously, or run it using a preconfigured Docker container in VS Code.

-### Option 1: Using VS Code (web) in Azure AI Studio

-VS Code (web) in Azure AI Studio creates and runs the development container on a compute instance. To get started with this approach, follow the instructions in [Work with Azure AI projects in VS Code](develop-in-vscode.md).

-Our prebuilt development environments are based on a docker container that has the Azure AI SDK generative packages, the Azure AI CLI, the Prompt flow SDK, and other tools. It's configured to run VS Code remotely inside of the container. The docker container is similar to [this Dockerfile](https://github.com/Azure/aistudio-copilot-sample/blob/main/.devcontainer/Dockerfile), and is based on [Microsoft's Python 3.10 Development Container Image](https://mcr.microsoft.com/en-us/product/devcontainers/python/about).

-### OPTION 2: Visual Studio Code Dev Container

-You can run the Azure AI CLI in a Docker container using VS Code Dev Containers:

-1. Follow the [installation instructions](https://code.visualstudio.com/docs/devcontainers/containers#_installation) for VS Code Dev Containers.

-1. Clone the [aistudio-copilot-sample](https://github.com/Azure/aistudio-copilot-sample) repository and open it with VS Code:

- ```

- git clone https://github.com/azure/aistudio-copilot-sample

- code aistudio-copilot-sample

- ```

-1. Select the **Reopen in Dev Containers** button. If it doesn't appear, open the command palette (`Ctrl+Shift+P` on Windows and Linux, `Cmd+Shift+P` on Mac) and run the `Dev Containers: Reopen in Container` command.

-## Try the Azure AI CLI

-The AI CLI offers many capabilities, including an interactive chat experience, tools to work with prompt flows and search and speech services, and tools to manage AI services.

-If you plan to use the AI CLI as part of your development, we recommend you start by running `ai init`, which guides you through setting up your Azure resources and connections in your development environment.

-Try `ai help` to learn more about these capabilities.

-### ai init

-The `ai init` command allows interactive and non-interactive selection or creation of Azure AI hub resources. When an Azure AI hub resource is selected or created, the associated resource keys and region are retrieved and automatically stored in the local AI configuration datastore.

-You can initialize the Azure AI CLI by running the following command:

-```bash

-ai init

-```

-If you run the Azure AI CLI with VS Code (Web) coming from Azure AI Studio, your development environment will already be configured. The `ai init` command takes fewer steps: you confirm the existing project and attached resources.

-If your development environment hasn't already been configured with an existing project, or you select the **Initialize something else** option, there will be a few flows you can choose when running `ai init`: **Initialize a new AI project**, **Initialize an existing AI project**, or **Initialize standalone resources**.

-The following table describes the scenarios for each flow.

-| Scenario | Description |

-| | |

-| Initialize a new AI project | Choose if you don't have an existing AI project that you have been working with in the Azure AI Studio. The `ai init` command walks you through creating or attaching resources. |

-| Initialize an existing AI project | Choose if you have an existing AI project you want to work with. The `ai init` command checks your existing linked resources, and asks you to set anything that hasn't been set before. |

-| Initialize standalone resources| Choose if you're building a simple solution connected to a single AI service, or if you want to attach more resources to your development environment |

-Working with an AI project is recommended when using the Azure AI Studio and/or connecting to multiple AI services. Projects come with An Azure AI hub resource that houses related projects and shareable resources like compute and connections to services. Projects also allow you to connect code to cloud resources (storage and model deployments), save evaluation results, and host code behind online endpoints. You're prompted to create and/or attach Azure AI Services to your project.

-Initializing standalone resources is recommended when building simple solutions connected to a single AI service. You can also choose to initialize more standalone resources after initializing a project.

-The following resources can be initialized standalone, or attached to projects:

-#### Initializing a new AI project

-1. Run `ai init` and choose **Initialize new AI project**.

-1. Select your subscription. You might be prompted to sign in through an interactive flow.

-1. Select your Azure AI hub resource, or create a new one. An Azure AI hub resource can have multiple projects that can share resources.

-1. Select the name of your new project. There are some suggested names, or you can enter a custom one. Once you submit, the project might take a minute to create.

-1. Select the resources you want to attach to the project. You can skip resource types you don't want to attach.

-1. `ai init` checks you have the connections you need for the attached resources, and your development environment is configured with your new project.

-#### Initializing an existing AI project

-1. Enter `ai init` and choose "Initialize an existing AI project".

-1. Select your subscription. You might be prompted to sign in through an interactive flow.

-1. Select the project from the list.

-1. Select the resources you want to attach to the project. There should be a default selection based on what is already attached to the project. You can choose to create new resources to attach.

-1. `ai init` checks you have the connections you need for the attached resources, and your development environment is configured with the project.

-#### Initializing standalone resources

-1. Enter `ai init` and choose "Initialize standalone resources".

-1. Select the type of resource you want to initialize.

-1. Select your subscription. You might be prompted to sign in through an interactive flow.

-1. Choose the desired resources from the list(s). You can create new resources to attach inline.

-1. `ai init` checks you have the connections you need for the attached resources, and your development environment is configured with attached resources.

-## Project connections

-When working the Azure AI CLI, you want to use your project's connections. Connections are established to attached resources and allow you to integrate services with your project. You can have project-specific connections, or connections shared at the Azure AI hub resource level. For more information, see [Azure AI hub resources](../concepts/ai-resources.md) and [connections](../concepts/connections.md).

-When you run `ai init` your project connections get set in your development environment, allowing seamless integration with AI services. You can view these connections by running `ai service connection list`, and further manage these connections with `ai service connection` subcommands.

-Any updates you make to connections in the Azure AI CLI is reflected in [Azure AI Studio](https://ai.azure.com), and vice versa.

-## ai dev

-`ai dev` helps you configure the environment variables in your development environment.

-After running `ai init`, you can run the following command to set a `.env` file populated with environment variables you can reference in your code.

-```bash

-ai dev new .env

-```

-## ai service

-`ai service` helps you manage your connections to resources and services.

-## ai flow

-`ai flow` lets you work with prompt flows in an interactive way. You can create new flows, invoke and test existing flows, serve a flow locally to test an application experience, upload a local flow to the Azure AI Studio, or deploy a flow to an endpoint.

-The following steps help you test out each capability. They assume you have run `ai init`.

-1. Run `ai flow new --name mynewflow` to create a new flow folder based on a template for a chat flow.

-1. Open the `flow.dag.yaml` file that was created in the previous step.

- 1. Update the `deployment_name` to match the chat deployment attached to your project. You can run `ai config @chat.deployment` to get the correct name.

- 1. Update the connection field to be **Default_AzureOpenAI**. You can run `ai service connection list` to verify your connection names.

-1. `ai flow invoke --name mynewflow --input question=hello` - this runs the flow with provided input and return a response.

-1. `ai flow serve --name mynewflow` - this will locally serve the application and you can test interactively in a new window.

-1. `ai flow package --name mynewflow` - this packages the flow as a Dockerfile.

-1. `ai flow upload --name mynewflow` - this uploads the flow to the AI Studio, where you can continue working on it with the prompt flow UI.

-1. You can deploy an uploaded flow to an online endpoint for inferencing via the Azure AI Studio UI, see [Deploy a flow for real-time inference](./flow-deploy.md) for more details.

-

-### Project connections with flows

-As mentioned in step 2 above, your flow.dag.yaml should reference connection and deployment names matching those attached to your project.

-If you're working in your own development environment (including Codespaces), you might need to manually update these fields so that your flow runs connected to Azure resources.

-If you launched VS Code from the AI Studio, you are in an Azure-connected custom container experience, and you can work directly with flows stored in the `shared` folder. These flow files are the same underlying files prompt flow references in the Studio, so they should already be configured with your project connections and deployments. To learn more about the folder structure in the VS Code container experience, see [Work with Azure AI projects in VS Code](develop-in-vscode.md)

-## ai chat

-Once you have initialized resources and have a deployment, you can chat interactively or non-interactively with the AI language model using the `ai chat` command. The CLI has more examples of ways to use the `ai chat` capabilities, simply enter `ai chat` to try them. Once you have tested the chat capabilities, you can add in your own data.

-# [Terminal](#tab/terminal)

-Here's an example of interactive chat:

-```bash

-ai chat --interactive --system @prompt.txt

-```

-Here's an example of non-interactive chat:

-```bash

-ai chat --system @prompt.txt --user "Tell me about Azure AI Studio"

-```

-# [PowerShell](#tab/powershell)

-Here's an example of interactive chat:

-```powershell

-ai --% chat --interactive --system @prompt.txt

-```

-Here's an example of non-interactive chat:

-```powershell

-ai --% chat --system @prompt.txt --user "Tell me about Azure AI Studio"

-```

-> [!NOTE]

-> If you're using PowerShell, use the `--%` stop-parsing token to prevent the terminal from interpreting the `@` symbol as a special character.

-#### Chat with your data

-Once you have tested the basic chat capabilities, you can add your own data using an Azure AI Search vector index.

-1. Create a search index based on your data

-1. Interactively chat with an AI system grounded in your data

-1. Clear the index to prepare for other chat explorations

-```bash

-ai search index update --name <index_name> --files "*.md"

-ai chat --index-name <index_name> --interactive

-```

-When you use `search index update` to create or update an index (the first step above), `ai config` stores that index name. Run `ai config` in the CLI to see more usage details.

-If you want to set a different existing index for subsequent chats, use:

-```bash

-ai config --set search.index.name <index_name>

-```

-If you want to clear the set index name, use

-```bash

-ai config --clear search.index.name

-```

-## ai help

-The Azure AI CLI is interactive with extensive `help` commands. You can explore capabilities not covered in this document by running:

-```bash

-ai help

-```

-## Next steps

-Not available in AI CLI, but you can use [Azure Machine Learning CLI](../../machine-learning/how-to-managed-network.md#configure-a-managed-virtual-network-to-allow-internet-outbound). Use your Azure AI hub name as workspace name in Azure Machine Learning CLI.

-Not available in AI CLI, but you can use [Azure Machine Learning CLI](../../machine-learning/how-to-managed-network.md#configure-a-managed-virtual-network-to-allow-only-approved-outbound). Use your Azure AI hub name as workspace name in Azure Machine Learning CLI.

-Not available in AI CLI, but you can use [Azure Machine Learning CLI](../../machine-learning/how-to-managed-network.md#manage-outbound-rules). Use your Azure AI hub name as workspace name in Azure Machine Learning CLI.

-description: Learn how to configure a private link for Azure AI

-# How to configure a private link for Azure AI

-We have two network isolation aspects. One is the network isolation to access an Azure AI. Another is the network isolation of computing resources in your Azure AI and Azure AI projects such as Compute Instance, Serverless and Managed Online Endpoint. This document explains the former highlighted in the diagram. You can use private link to establish the private connection to your Azure AI and its default resources. This article is for Azure AI. For information on Azure AI Services, see the [Azure AI Services documentation](/azure/ai-services/cognitive-services-virtual-networks).

-You get several Azure AI default resources in your resource group. You need to configure following network isolation configurations.

-* You must have an existing virtual network to create the private endpoint in.

-After creating the Azure AI, use the [Azure networking CLI commands](/cli/azure/network/private-endpoint#az-network-private-endpoint-create) to create a private link endpoint for the Azure AI.

-# [Azure portal](#tab/azure-portal)

-1. From the [Azure portal](https://portal.azure.com), go to Azure AI Studio and choose __+ New Azure AI__.

-1. Choose network isolation mode in __Networking__ tab.

-1. Scroll down to __Workspace Inbound access__ and choose __+ Add__.

-1. Input required fields. When selecting the __Region__, select the same region as your virtual network.

-## Add a private endpoint to an Azure AI

-Use one of the following methods to add a private endpoint to an existing Azure AI:

-Use the [Azure networking CLI commands](/cli/azure/network/private-endpoint#az-network-private-endpoint-create) to create a private link endpoint for the Azure AI.

-# [Azure portal](#tab/azure-portal)

-1. From the [Azure portal](https://portal.azure.com), select your Azure AI.

-1. From the left side of the page, select __Networking__ and then select the __Private endpoint connections__ tab.

-1. When selecting the __Region__, select the same region as your virtual network.

-1. When selecting __Resource type__, use azuremlworkspace.

-1. Set the __Resource__ to your workspace name.

-Finally, select __Create__ to create the private endpoint.

-You can remove one or all private endpoints for an Azure AI. Removing a private endpoint removes the Azure AI from the VNet that the endpoint was associated with. Removing the private endpoint might prevent the Azure AI from accessing resources in that VNet, or resources in the VNet from accessing the workspace. For example, if the VNet doesn't allow access to or from the public internet.

-> Removing the private endpoints for a workspace __doesn't make it publicly accessible__. To make the workspace publicly accessible, use the steps in the [Enable public access](#enable-public-access) section.

-# [Azure portal](#tab/azure-portal)

-1. From the [Azure portal](https://portal.azure.com), select your Azure AI.

-1. From the left side of the page, select __Networking__ and then select the __Private endpoint connections__ tab.

-1. Select the endpoint to remove and then select __Remove__.

-In some situations, you might want to allow someone to connect to your secured Azure AI over a public endpoint, instead of through the VNet. Or you might want to remove the workspace from the VNet and re-enable public access.

-> Enabling public access doesn't remove any private endpoints that exist. All communications between components behind the VNet that the private endpoint(s) connect to are still secured. It enables public access only to the Azure AI, in addition to the private access through any private endpoints.

-# [Azure CLI](#tab/cli)

-Not available in AI CLI, but you can use [Azure Machine Learning CLI](../../machine-learning/how-to-configure-private-link.md#enable-public-access). Use your Azure AI name as workspace name in Azure Machine Learning CLI.

-1. From the [Azure portal](https://portal.azure.com), select your Azure AI.

-This is required if you make your storage account private. Our services need to read/write data in your private storage account using [Allow Azure services on the trusted services list to access this storage account](../../storage/common/storage-network-security.md#grant-access-to-trusted-azure-services) with below managed identity configurations. Enable system assigned managed identity of Azure AI Service and Azure AI Search, configure role-based access control for each managed identity.

-| `Storage File Data Privileged Contributor` | Azure AI project | Storage Account | Read/Write prompt flow data. | [Prompt flow doc](../../machine-learning/prompt-flow/how-to-secure-prompt-flow.md#secure-prompt-flow-with-workspace-managed-virtual-network) |

-| `Storage Blob Data Contributor` | Azure AI Search | Storage Account | Read blob and write knowledge store | [Search doc](../../search/search-howto-managed-identities-data-sources.md)|

-See [Azure Machine Learning custom dns doc](../../machine-learning/how-to-custom-dns.md#example-custom-dns-server-hosted-in-vnet) for the DNS forwarding configurations.

-If you need to configure custom dns server without dns forwarding, the following is the required A records.

-See [this documentation](../../machine-learning/how-to-custom-dns.md#find-the-ip-addresses) to check your private IP addresses for your A records. To check AI-PROJECT-GUID, go to Azure portal > Your Azure AI Project > JSON View > workspaceId.

-* Private Azure AI services and Azure AI Search aren't supported.

-* You might encounter problems trying to access the private endpoint for your Azure AI if you're using Mozilla Firefox. This problem might be related to DNS over HTTPS in Mozilla Firefox. We recommend using Microsoft Edge or Google Chrome.

-| [ai-resource.bicep](https://github.com/Azure/azure-quickstart-templates/blob/master/quickstarts/microsoft.machinelearningservices/aistudio-basics/modules/ai-resource.bicep) | Defines the Azure AI hub resource. |

-Note that disabling SSH prevents SSH access from the public internet. When a private virtual network is used, users can still SSH from within the virtual network.

-description: Learn how to deploy Llama 2 family of large language models with Azure AI Studio.

-# How to deploy Llama 2 family of large language models with Azure AI Studio

-In this article, you learn about the Llama 2 family of large language models (LLMs). You also learn how to use Azure AI Studio to deploy models from this set either as a service with pay-as you go billing or with hosted infrastructure in real-time endpoints.

-The Llama 2 family of LLMs is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. The model family also includes fine-tuned versions optimized for dialogue use cases with reinforcement learning from human feedback (RLHF), called Llama-2-chat.

-## Deploy Llama 2 models with pay-as-you-go

-Llama 2 models deployed as a service with pay-as-you-go are offered by Meta AI through Microsoft Azure Marketplace, and they might add more terms of use and pricing.

-The following models are available in Azure Marketplace for Llama 2 when deployed as a service with pay-as-you-go:

-If you need to deploy a different model, [deploy it to real-time endpoints](#deploy-llama-2-models-to-real-time-endpoints) instead.

- > For Llama 2 family models, the pay-as-you-go model deployment offering is only available with AI hubs created in **East US 2** and **West US 3** regions.

-1. If this is your first time deploying the model in the project, you have to subscribe your project for the particular offering (for example, Llama-2-70b) from Azure Marketplace. This step requires that your account has the Azure subscription permissions and resource group permissions listed in the prerequisites. Each project has its own subscription to the particular Azure Marketplace offering, which allows you to control and monitor spending. Select **Subscribe and Deploy**.

- > Subscribing a project to a particular Azure Marketplace offering (in this case, Llama-2-70b) requires that your account has **Contributor** or **Owner** access at the subscription level where the project is created. Alternatively, your user account can be assigned a custom role that has the Azure subscription permissions and resource group permissions listed in the [prerequisites](#prerequisites).

-To learn about billing for Llama models deployed with pay-as-you-go, see [Cost and quota considerations for Llama 2 models deployed as a service](#cost-and-quota-considerations-for-llama-2-models-deployed-as-a-service).

-### Consume Llama 2 models as a service

- - For completions models, such as `Llama-2-7b`, use the [`/v1/completions`](#completions-api) API.

- - For chat models, such as `Llama-2-7b-chat`, use the [`/v1/chat/completions`](#chat-api) API.

- For more information on using the APIs, see the [reference](#reference-for-llama-2-models-deployed-as-a-service) section.

-### Reference for Llama 2 models deployed as a service

- "max_tokens": 512,

-## Deploy Llama 2 models to real-time endpoints

-Apart from deploying with the pay-as-you-go managed service, you can also deploy Llama 2 models to real-time endpoints in AI Studio. When deployed to real-time endpoints, you can select all the details about the infrastructure running the model, including the virtual machines to use and the number of instances to handle the load you're expecting. Models deployed to real-time endpoints consume quota from your subscription. All the models in the Llama family can be deployed to real-time endpoints.

-### Create a new deployment

-# [Studio](#tab/azure-studio)

-Follow these steps to deploy a model such as `Llama-2-7b-chat` to a real-time endpoint in [Azure AI Studio](https://ai.azure.com).

-# [Python SDK](#tab/python)

-Follow these steps to deploy an open model such as `Llama-2-7b-chat` to a real-time endpoint, using the Azure AI Generative SDK.

- deployment_name = "my-llam27bchat-deployment"

-### Consume Llama 2 models deployed to real-time endpoints

-For reference about how to invoke Llama 2 models deployed to real-time endpoints, see the model's card in the Azure AI Studio [model catalog](../how-to/model-catalog.md). Each model's card has an overview page that includes a description of the model, samples for code-based inferencing, fine-tuning, and model evaluation.

-### Cost and quota considerations for Llama 2 models deployed as a service

-### Cost and quota considerations for Llama 2 models deployed as real-time endpoints

-Azure AI Studio supports developing in VS Code - Web and Desktop. In each scenario, your VS Code instance is remotely connected to a prebuilt custom container running on a virtual machine, also known as a compute instance. To work in your local environment instead, or to learn more, follow the steps in [Install the Azure AI SDK](sdk-install.md) and [Install the Azure AI CLI](cli-install.md).

-python --version # ensure you've >=3.8

-## Create an index

-1. Choose your **Source data**. You can choose source data from a list of your recent data sources, a storage URL on the cloud or even upload files and folders from the local machine. You can also add a connection to another data source such as Azure Blob Storage.

- 1. The search type defaults to **Hybrid + Semantic**, which is a combination of keyword search, vector search and semantic search to give the best possible search results.

- 1. For the hybrid option to work, you need an embedding model. Choose the Azure OpenAI resource, which has the embedding model

-

-1. Use the prefilled name or type your own name for New Vector index name

-

- > [!NOTE]

- > If you see a **DeploymentNotFound** error, you need to assign more permissions. See [mitigate DeploymentNotFound error](#mitigate-deploymentnotfound-error) for more details.

-### Mitigate DeploymentNotFound error

-When you try to create a vector index, you might see the following error at the **Review + Finish** step:

-**Failed to create vector index. DeploymentNotFound: A valid deployment for the model=text-embedding-ada-002 was not found in the workspace connection=Default_AzureOpenAI provided.**

-This can happen if you are trying to create an index using an **Owner**, **Contributor**, or **Azure AI Developer** role at the project level. To mitigate this error, you might need to assign more permissions using either of the following methods.

-> [!NOTE]

-> You need to be assigned the **Owner** role of the resource group or higher scope (like Subscription) to perform the operation in the next steps. This is because only the Owner role can assign roles to others. See details [here](/azure/role-based-access-control/built-in-roles).

-#### Method 1: Assign more permissions to the user on the Azure AI hub resource

-If the Azure AI hub resource the project uses was created through Azure AI Studio:

-1. Sign in to [Azure AI Studio](https://aka.ms/azureaistudio) and select your project via **Build** > **Projects**.

-1. Select **AI project settings** from the collapsible left menu.

-1. From the **Resource Configuration** section, select the link for your resource group name that takes you to the Azure portal.

-1. In the Azure portal under **Overview** > **Resources** select the Azure AI service type. It's named similar to "YourAzureAIResourceName-aiservices."

- :::image type="content" source="../media/roles-access/resource-group-azure-ai-service.png" alt-text="Screenshot of Azure AI service in a resource group." lightbox="../media/roles-access/resource-group-azure-ai-service.png":::

-1. Select **Access control (IAM)** > **+ Add** to add a role assignment.

-1. Add the **Cognitive Services OpenAI User** role to the user who wants to make an index. `Cognitive Services OpenAI Contributor` and `Cognitive Services Contributor` also work, but they assign more permissions than needed for creating an index in Azure AI Studio.

-> [!NOTE]

-> You can also opt to assign more permissions [on the resource group](#method-2-assign-more-permissions-on-the-resource-group). However, that method assigns more permissions than needed to mitigate the **DeploymentNotFound** error.

-#### Method 2: Assign more permissions on the resource group

-If the Azure AI hub resource the project uses was created through Azure portal:

-1. Sign in to [Azure AI Studio](https://aka.ms/azureaistudio) and select your project via **Build** > **Projects**.

-1. Select **AI project settings** from the collapsible left menu.

-1. From the **Resource Configuration** section, select the link for your resource group name that takes you to the Azure portal.

-1. Select **Access control (IAM)** > **+ Add** to add a role assignment.

-1. Add the **Cognitive Services OpenAI User** role to the user who wants to make an index. `Cognitive Services OpenAI Contributor` and `Cognitive Services Contributor` also work, but they assign more permissions than needed for creating an index in Azure AI Studio.

- :::image type="content" source="../media/index-retrieve/configure-index-lookup-tool.png" alt-text="Screenshot of Configure Index Lookup." lightbox="../media/index-retrieve/configure-index-lookup-tool.png":::

-description: This article introduces the Azure OpenAI GPT-4 Turbo with Vision tool for flows in Azure AI Studio.

-The prompt flow *Azure OpenAI GPT-4 Turbo with Vision* tool enables you to use your Azure OpenAI GPT-4 Turbo with Vision model deployment to analyze images and provide textual responses to questions about them.

- Currently, access to this service is granted only by application. You can apply for access to Azure OpenAI by completing the form at <a href="https://aka.ms/oai/access" target="_blank">https://aka.ms/oai/access</a>. Open an issue on this repo to contact us if you have an issue.

- :::image type="content" source="../../media/prompt-flow/azure-openai-gpt-4-vision-tool.png" alt-text="Screenshot of the Azure OpenAI GPT-4 Turbo with Vision tool added to a flow in Azure AI Studio." lightbox="../../media/prompt-flow/azure-openai-gpt-4-vision-tool.png":::

-1. Enter values for the Azure OpenAI GPT-4 Turbo with Vision tool input parameters described [here](#inputs). For example, you can use this example prompt:

-1. Specify an image to analyze in the `image_input` input parameter. For example, you can upload an image or enter the URL of an image to analyze. Otherwise you can paste or drag and drop an image into the tool.

-1. Add more tools to your flow as needed, or select **Run** to run the flow.

-1. The outputs are described [here](#outputs).

-The following are available input parameters:

-| prompt | string | Text prompt that the language model uses to generate its response. The Jinja template for composing prompts in this tool follows a similar structure to the chat API in the LLM tool. To represent an image input within your prompt, you can use the syntax ``. Image input can be passed in the `user`, `system` and `assistant` messages. | Yes |

-| max\_tokens | integer | Maximum number of tokens to generate in the response. Default is 512. | No |

-| temperature | float | Randomness of the generated text. Default is 1. | No |

-| stop | list | Stopping sequence for the generated text. Default is null. | No |

-| top_p | float | Probability of using the top choice from the generated tokens. Default is 1. | No |

-| presence\_penalty | float | Value that controls the model's behavior regarding repeating phrases. Default is 0. | No |

-| frequency\_penalty | float | Value that controls the model's behavior regarding generating rare phrases. Default is 0. | No |

-The following are available output parameters:

-| Return Type | Description |

-## Next step

-description: This article introduces the Content Safety tool for flows in Azure AI Studio.

-The prompt flow *Content Safety* tool enables you to use Azure AI Content Safety in Azure AI Studio.

-Create an Azure Content Safety connection:

-1. Complete all steps in the **Create a new connection** dialog box. You can use an Azure AI hub resource or Azure AI Content Safety resource. An Azure AI hub resource that supports multiple Azure AI services is recommended.

- :::image type="content" source="../../media/prompt-flow/content-safety-tool.png" alt-text="Screenshot of the Content Safety tool added to a flow in Azure AI Studio." lightbox="../../media/prompt-flow/content-safety-tool.png":::

-1. Enter values for the Content Safety tool input parameters described [here](#inputs).

-1. Add more tools to your flow as needed, or select **Run** to run the flow.

-1. The outputs are described [here](#outputs).

-The following are available input parameters:

-| hate_category | string | The moderation sensitivity for Hate category. You can choose from four options: *disable*, *low_sensitivity*, *medium_sensitivity*, or *high_sensitivity*. The *disable* option means no moderation for hate category. The other three options mean different degrees of strictness in filtering out hate content. The default option is *medium_sensitivity*. | Yes |

-| sexual_category | string | The moderation sensitivity for Sexual category. You can choose from four options: *disable*, *low_sensitivity*, *medium_sensitivity*, or *high_sensitivity*. The *disable* option means no moderation for sexual category. The other three options mean different degrees of strictness in filtering out sexual content. The default option is *medium_sensitivity*. | Yes |

-| self_harm_category | string | The moderation sensitivity for Self-harm category. You can choose from four options: *disable*, *low_sensitivity*, *medium_sensitivity*, or *high_sensitivity*. The *disable* option means no moderation for self-harm category. The other three options mean different degrees of strictness in filtering out self_harm content. The default option is *medium_sensitivity*. | Yes |

-| violence_category | string | The moderation sensitivity for Violence category. You can choose from four options: *disable*, *low_sensitivity*, *medium_sensitivity*, or *high_sensitivity*. The *disable* option means no moderation for violence category. The other three options mean different degrees of strictness in filtering out violence content. The default option is *medium_sensitivity*. | Yes |

-You can use the following parameters as inputs for this tool:

-| action_by_category | string | A binary value for each category: *Accept* or *Reject*. This value shows if the text meets the sensitivity level that you set in the request parameters for that category. |

-| suggested_action | string | An overall recommendation based on the four categories. If any category has a *Reject* value, the `suggested_action` is *Reject* as well. |

-description: This article introduces the Embedding tool for flows in Azure AI Studio.

-The prompt flow *Embedding* tool enables you to convert text into dense vector representations for various natural language processing tasks

-> For chat and completion tools, check out the [LLM tool](llm-tool.md).

- :::image type="content" source="../../media/prompt-flow/embedding-tool.png" alt-text="Screenshot of the Embedding tool added to a flow in Azure AI Studio." lightbox="../../media/prompt-flow/embedding-tool.png":::

-1. Enter values for the Embedding tool input parameters described [here](#inputs).

-1. Add more tools to your flow as needed, or select **Run** to run the flow.

-1. The outputs are described [here](#outputs).

-The following are available input parameters:

-| input | string | the input text to embed | Yes |

-| model, deployment_name | string | instance of the text-embedding engine to use | Yes |

-> Vector, Vector DB and Faiss Index Lookup tools are deprecated and will be retired soon. [Migrated to the new Index Lookup tool (preview).](index-lookup-tool.md#how-to-migrate-from-legacy-tools-to-the-index-lookup-tool)

-description: This article introduces the Index Lookup tool for flows in Azure AI Studio.

-The prompt flow *Index Lookup* tool enables the usage of common vector indices (such as Azure AI Search, FAISS, and Pinecone) for retrieval augmented generation (RAG) in prompt flow. The tool automatically detects the indices in the workspace and allows the selection of the index to be used in the flow.

- :::image type="content" source="../../media/prompt-flow/configure-index-lookup-tool.png" alt-text="Screenshot of the Index Lookup tool added to a flow in Azure AI Studio." lightbox="../../media/prompt-flow/configure-index-lookup-tool.png":::

-1. Enter values for the Index Lookup tool [input parameters](#inputs). The [LLM tool](llm-tool.md) can generate the vector input.

-1. Add more tools to your flow as needed, or select **Run** to run the flow.

-1. To learn more about the returned output, see [outputs](#outputs).

-The following are available input parameters:

-| mlindex_content | string | Type of index to be used. Input depends on the index type. An example of an Azure AI Search index JSON can be seen below the table. | Yes |

-Here's an example of an Azure AI Search index input.

-The following JSON format response is an example returned by the tool that includes the top-k scored entities. The entity follows a generic schema of vector search result provided by the `promptflow-vectordb` SDK. For the Vector Index Search, the following fields are populated:

-| Field Name | Type | Description |

-| metadata | dict | Customized key-value pairs provided by user when creating the index |

-| page_content | string | Content of the vector chunk being used in the lookup |

-| score | float | Depends on index type defined in Vector Index. If index type is Faiss, score is L2 distance. If index type is Azure AI Search, score is cosine similarity. |

-

-## How to migrate from legacy tools to the Index Lookup tool

-The Index Lookup tool looks to replace the three deprecated legacy index tools, the [Vector Index Lookup tool](./vector-index-lookup-tool.md), the [Vector DB Lookup tool](./vector-db-lookup-tool.md) and the [Faiss Index Lookup tool](./faiss-index-lookup-tool.md).

-If you have a flow that contains one of these tools, follow the steps below to upgrade your flow.

-1. Update your runtime. In order to do this navigate to the "AI project settings tab on the left blade in AI Studio. From there you should see a list of Prompt flow runtimes. Select the name of the runtime you want to update, and click on the ΓÇ£UpdateΓÇ¥ button near the top of the panel. Wait for the runtime to update itself.

-1. Navigate to your flow. You can do this by clicking on the ΓÇ£Prompt flowΓÇ¥ tab on the left blade in AI Studio, clicking on the ΓÇ£FlowsΓÇ¥ pivot tab, and then clicking on the name of your flow.

-1. Once inside the flow, click on the ΓÇ£+ More toolsΓÇ¥ button near the top of the pane. A dropdown should open and click on ΓÇ£Index Lookup [Preview]ΓÇ¥ to add an instance of the Index Lookup tool.

- :::image type="content" source="../../media/prompt-flow/upgrade-index-tools/index-dropdown.png" alt-text="Screenshot of the More Tools dropdown in promptflow." lightbox="../../media/prompt-flow/upgrade-index-tools/index-dropdown.png":::

-1. Name the new node and click ΓÇ£AddΓÇ¥.

- :::image type="content" source="../../media/prompt-flow/upgrade-index-tools/save-node.png" alt-text="Screenshot of the index lookup node with name." lightbox="../../media/prompt-flow/upgrade-index-tools/save-node.png":::

-1. In the new node, click on the ΓÇ£mlindex_contentΓÇ¥ textbox. This should be the first textbox in the list.

- :::image type="content" source="../../media/prompt-flow/upgrade-index-tools/mlindex-box.png" alt-text="Screenshot of the expanded Index Lookup node with the mlindex_content box outlined in red." lightbox="../../media/prompt-flow/upgrade-index-tools/mlindex-box.png":::

-1. In the Generate drawer that appears, follow the instructions below to upgrade from the three legacy tools:

- - If using the legacy **Vector Index Lookup** tool, select ΓÇ£Registered Index" in the ΓÇ£index_typeΓÇ¥ dropdown. Select your vector index asset from the ΓÇ£mlindex_asset_idΓÇ¥ dropdown.

- - If using the legacy **Faiss Index Lookup** tool, select ΓÇ£FaissΓÇ¥ in the ΓÇ£index_typeΓÇ¥ dropdown and specify the same path as in the legacy tool.

- - If using the legacy **Vector DB Lookup** tool, select AI Search or Pinecone depending on the DB type in the ΓÇ£index_typeΓÇ¥ dropdown and fill in the information as necessary.

-1. After filling in the necessary information, click save.

-1. Upon returning to the node, there should be information populated in the ΓÇ£mlindex_contentΓÇ¥ textbox. Click on the ΓÇ£queriesΓÇ¥ textbox next, and select the search terms you want to query. YouΓÇÖll want to select the same value as the input to the ΓÇ£embed_the_questionΓÇ¥ node, typically either ΓÇ£\${inputs.question}ΓÇ¥ or ΓÇ£${modify_query_with_history.output}ΓÇ¥ (the former if youΓÇÖre in a standard flow and the latter if youΓÇÖre in a chat flow).

- :::image type="content" source="../../media/prompt-flow/upgrade-index-tools/mlindex-with-content.png" alt-text="Screenshot of the expanded Index Lookup node with index information in the cells." lightbox="../../media/prompt-flow/upgrade-index-tools/mlindex-with-content.png":::

-1. Select a query type by clicking on the dropdown next to ΓÇ£query_type.ΓÇ¥ ΓÇ£VectorΓÇ¥ will produce identical results as the legacy flow, but depending on your index configuration, other options including "Hybrid" and "Semantic" may be available.

- :::image type="content" source="../../media/prompt-flow/upgrade-index-tools/vector-search.png" alt-text="Screenshot of the expanded Index Lookup node with vector search outlined in red." lightbox="../../media/prompt-flow/upgrade-index-tools/vector-search.png":::

-1. Edit downstream components to consume the output of your newly added node, instead of the output of the legacy Vector Index Lookup node.

-1. Delete the Vector Index Lookup node and its parent embedding node.

-description: This article introduces the LLM tool for flows in Azure AI Studio.

-The prompt flow *LLM* tool enables you to use large language models (LLM) for natural language processing.

-Prepare a prompt as described in the [prompt tool](prompt-tool.md#prerequisites) documentation. The LLM tool and Prompt tool both support [Jinja](https://jinja.palletsprojects.com/en/3.1.x/) templates. For more information and best practices, see [prompt engineering techniques](../../../ai-services/openai/concepts/advanced-prompt-engineering.md).

- :::image type="content" source="../../media/prompt-flow/llm-tool.png" alt-text="Screenshot of the LLM tool added to a flow in Azure AI Studio." lightbox="../../media/prompt-flow/llm-tool.png":::

-1. From the **Api** drop-down list, select *chat* or *completion*.

-1. Enter values for the LLM tool input parameters described [here](#inputs). If you selected the *chat* API, see [chat inputs](#chat-inputs). If you selected the *completion* API, see [text completion inputs](#text-completion-inputs). For information about how to prepare the prompt input, see [prerequisites](#prerequisites).

-1. Add more tools to your flow as needed, or select **Run** to run the flow.

-1. The outputs are described [here](#outputs).

-The following are available input parameters:

-| prompt | string | text prompt for the language model | Yes |

-| model, deployment_name | string | the language model to use | Yes |

-| max\_tokens | integer | the maximum number of tokens to generate in the completion. Default is 16. | No |

-| temperature | float | the randomness of the generated text. Default is 1. | No |

-| stop | list | the stopping sequence for the generated text. Default is null. | No |

-| suffix | string | text appended to the end of the completion | No |

-| top_p | float | the probability of using the top choice from the generated tokens. Default is 1. | No |

-| logprobs | integer | the number of log probabilities to generate. Default is null. | No |

-| echo | boolean | value that indicates whether to echo back the prompt in the response. Default is false. | No |

-| presence\_penalty | float | value that controls the model's behavior regarding repeating phrases. Default is 0. | No |

-| frequency\_penalty | float | value that controls the model's behavior regarding generating rare phrases. Default is 0. | No |

-| best\_of | integer | the number of best completions to generate. Default is 1. | No |

-| logit\_bias | dictionary | the logit bias for the language model. Default is empty dictionary. | No |

-| prompt | string | text prompt that the language model should reply to | Yes |

-| model, deployment_name | string | the language model to use | Yes |

-| max\_tokens | integer | the maximum number of tokens to generate in the response. Default is inf. | No |

-| temperature | float | the randomness of the generated text. Default is 1. | No |

-| stop | list | the stopping sequence for the generated text. Default is null. | No |

-| top_p | float | the probability of using the top choice from the generated tokens. Default is 1. | No |

-| presence\_penalty | float | value that controls the model's behavior regarding repeating phrases. Default is 0. | No |

-| frequency\_penalty | float | value that controls the model's behavior regarding generating rare phrases. Default is 0. | No |

-| logit\_bias | dictionary | the logit bias for the language model. Default is empty dictionary. | No |

-| API | Return Type | Description |

-| Completion | string | The text of one predicted completion |

-| Chat | string | The text of one response of conversation |

-The following table provides an index of tools in prompt flow.

-| Tool (set) name | Description | Environment | Package name |

-| [LLM](./llm-tool.md) | Use Azure OpenAI large language models (LLM) for tasks such as text completion or chat. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |

-| [Prompt](./prompt-tool.md) | Craft a prompt by using Jinja as the templating language. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |

-| [Python](./python-tool.md) | Run Python code. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |

-| [Azure OpenAI GPT-4 Turbo with Vision](./azure-open-ai-gpt-4v-tool.md) | Use AzureOpenAI GPT-4 Turbo with Vision model deployment to analyze images and provide textual responses to questions about them. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |

-| [Content Safety (Text)](./content-safety-tool.md) | Use Azure AI Content Safety to detect harmful content. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |

-| [Index Lookup*](./index-lookup-tool.md) | Search an Azure Machine Learning Vector Index for relevant results using one or more text queries. | Default | [promptflow-vectordb](https://pypi.org/project/promptflow-vectordb/) |

-| [Vector Index Lookup*](./vector-index-lookup-tool.md) | Search text or a vector-based query from a vector index. | Default | [promptflow-vectordb](https://pypi.org/project/promptflow-vectordb/) |

-| [Faiss Index Lookup*](./faiss-index-lookup-tool.md) | Search a vector-based query from the Faiss index file. | Default | [promptflow-vectordb](https://pypi.org/project/promptflow-vectordb/) |

-| [Vector DB Lookup*](./vector-db-lookup-tool.md) | Search a vector-based query from an existing vector database. | Default | [promptflow-vectordb](https://pypi.org/project/promptflow-vectordb/) |

-| [Embedding](./embedding-tool.md) | Use Azure OpenAI embedding models to create an embedding vector that represents the input text. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |

-| [Serp API](./serp-api-tool.md) | Use Serp API to obtain search results from a specific search engine. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |

-| [Azure AI Language tools*](https://microsoft.github.io/promptflow/integrations/tools/azure-ai-language-tool.html) | This collection of tools is a wrapper for various Azure AI Language APIs, which can help effectively understand and analyze documents and conversations. The capabilities currently supported include: Abstractive Summarization, Extractive Summarization, Conversation Summarization, Entity Recognition, Key Phrase Extraction, Language Detection, PII Entity Recognition, Conversational PII, Sentiment Analysis, Conversational Language Understanding, Translator. You can learn how to use them by the [Sample flows](https://github.com/microsoft/promptflow/tree/e4542f6ff5d223d9800a3687a7cfd62531a9607c/examples/flows/integrations/azure-ai-language). Support contact: taincidents@microsoft.com | Custom | [promptflow-azure-ai-language](https://pypi.org/project/promptflow-azure-ai-language/) |

-_*The asterisk marks indicate custom tools, which are created by the community that extend prompt flow's capabilities for specific use cases. They aren't officially maintained or endorsed by prompt flow team. When you encounter questions or issues for these tools, please prioritize using the support contact if it is provided in the description._

-To discover more custom tools developed by the open-source community, see [More custom tools](https://microsoft.github.io/promptflow/integrations/tools/https://docsupdatetracker.net/index.html).

-## Remarks

-description: This article introduces the Prompt tool for flows in Azure AI Studio.

-The prompt flow *Prompt* tool offers a collection of textual templates that serve as a starting point for creating prompts. These templates, based on the [Jinja](https://jinja.palletsprojects.com/en/3.1.x/) template engine, facilitate the definition of prompts. The tool proves useful when prompt tuning is required prior to feeding the prompts into the large language model (LLM) in prompt flow.

-Prepare a prompt. The [LLM tool](llm-tool.md) and Prompt tool both support [Jinja](https://jinja.palletsprojects.com/en/3.1.x/) templates.

-In this example, the prompt incorporates Jinja templating syntax to dynamically generate the welcome message and personalize it based on the user's name. It also presents a menu of options for the user to choose from. Depending on whether the user_name variable is provided, it either addresses the user by name or uses a generic greeting.

-For more information and best practices, see [prompt engineering techniques](../../../ai-services/openai/concepts/advanced-prompt-engineering.md).

- :::image type="content" source="../../media/prompt-flow/prompt-tool.png" alt-text="Screenshot of the Prompt tool added to a flow in Azure AI Studio." lightbox="../../media/prompt-flow/prompt-tool.png":::

-1. Enter values for the Prompt tool input parameters described [here](#inputs). For information about how to prepare the prompt input, see [prerequisites](#prerequisites).

-1. Add more tools (such as the [LLM tool](llm-tool.md)) to your flow as needed, or select **Run** to run the flow.

-1. The outputs are described [here](#outputs).

-The following are available input parameters:

-| prompt | string | The prompt template in Jinja | Yes |

-| Inputs | - | List of variables of prompt template and its assignments | - |

-Inputs

-| Variable | Type | Sample Value |

-Outputs

-Inputs

-| Variable | Type | Sample Value |

-Outputs

-description: This article introduces the Python tool for flows in Azure AI Studio.

-The prompt flow *Python* tool offers customized code snippets as self-contained executable nodes. You can quickly create Python tools, edit code, and verify results.

- :::image type="content" source="../../media/prompt-flow/python-tool.png" alt-text="Screenshot of the Python tool added to a flow in Azure AI Studio." lightbox="../../media/prompt-flow/python-tool.png":::

-1. Enter values for the Python tool input parameters described [here](#inputs). For example, in the **Code** input text box you can enter the following Python code:

-1. Add more tools to your flow as needed, or select **Run** to run the flow.

-1. The outputs are described [here](#outputs). Given the previous example Python code input, if the input message is "world", the output is `hello world`.

-The list of inputs will change based on the arguments of the tool function, after you save the code. Adding type to arguments and return values help the tool show the types properly.

-| Code | string | Python code snippet | Yes |

-| Inputs | - | List of tool function parameters and its assignments | - |

-The output is the `return` value of the python tool function. For example, consider the following python tool function:

-If the input message is "world", the output is `hello world`.

-| Connection | param: CustomConnection | Connection type will be handled specially |

-Parameters with `Connection` type annotation will be treated as connection inputs, which means:

-> [!Note]

-> `Union[...]` type annotation is only supported for connection type, for example, `param: Union[CustomConnection, OpenAIConnection]`.

-## Consume custom connection in the Python tool

-If you're developing a python tool that requires calling external services with authentication, you can use the custom connection in prompt flow. It allows you to securely store the access key and then retrieve it in your python code.

-Create a custom connection that stores all your LLM API KEY or other required credentials.

-1. Go to **AI project settings**, then select **New Connection**.

-1. Select **Custom** service. You can define your connection name, and you can add multiple *Key-value pairs* to store your credentials and keys by selecting **Add key-value pairs**.

- > Make sure at least one key-value pair is set as secret, otherwise the connection will not be created successfully. You can set one Key-Value pair as secret by **is secret** checked, which will be encrypted and stored in your key value.

- :::image type="content" source="../../media/prompt-flow/create-connection.png" alt-text="Screenshot that shows create connection in AI Studio." lightbox = "../../media/prompt-flow/create-connection.png":::

- :::image type="content" source="../../media/prompt-flow/custom-connection-keys.png" alt-text="Screenshot that shows add extra meta to custom connection in AI Studio." lightbox = "../../media/prompt-flow/custom-connection-keys.png":::

-