# Build a chat agent app with Amazon Bedrock Build a chat agent app An Amazon Bedrock in SageMaker Unified Studio chat agent app allows users to chat with an Amazon Bedrock model through a conversational interface, typically by sending text messages and receiving responses. The model analyzes the user's input, formulate an appropriate response, and carries on a dialogue with the user. You can use a chat agent apps for various purposes, such as providing customer service, answering questions, offering recommendations, or engaging in open-ended conversations on a wide range of topics. You can enhance a chat agent app app by integrating the following Amazon Bedrock features: + **[Data sources](data-sources.md)** — Enrich model responses by including context generated from an Amazon Bedrock knowledge base. + **[Guardrails](guardrails.md)** — Lets you implement safeguards for your chat agent app based on your use cases and responsible AI policies. + **[Functions](functions.md)** — Lets a model call a function to access a specific capability when handling a prompt. When you first create an chat agent app, you have a working draft of the app. Changes you make to your chat agent app apply to the working draft. You iterate on your working draft until you're satisfied with the behavior of your app. At any time you can save your chat agent app. Once you create and save chat agent app, you can do the following: + [Share the chat agent app](app-share.md) with other users. + [Export the chat agent app](app-export.md) for use outside of Amazon SageMaker Unified Studio. + Use the chat agent app as an agent node in a flow app. To use a chat agent app in a flow app you need to [deploy](app-deploy-app.md) the app. Amazon Bedrock in SageMaker Unified Studio deploys the app for you whenever you share the app with other users. In this section you learn how to create chat agent app that uses Amazon Bedrock in SageMaker Unified Studio components such as a [data source](data-sources.md) and a [guardrail](guardrails.md). You also learn how to share your app with other users. **Topics** + [ # Create a chat agent app with Amazon Bedrock ](create-chat-app-with-components.md) + [ # Deploy an Amazon Bedrock chat agent app ](app-deploy.md) + [ # Share an Amazon Bedrock chat agent app ](app-share.md) # Create a chat agent app with Amazon Bedrock In this section, you learn how create a simple Amazon Bedrock in SageMaker Unified Studio chat agent app that creates playlists for a radio station. The app can generate playlists and get the dates and locations of upcoming shows. Later, you add the following Amazon Bedrock features. + A guardrail to prevent songs with inappropriate song titles. + A knowledge base that lets the app create playlists using your unique song information. + A function that gets today's top 10 songs. **Topics** + [ ## Step 1: Create the initial chat agent app ](#chat-app-create-app) + [ ## Step 2: Add a guardrail to your chat agent app ](#chat-app-add-guardrail) + [ ## Step 3: Add a knowledge base to your chat agent app ](#chat-app-add-data-source) + [ ## Step 4: Add a function call to your chat agent app ](#chat-app-add-function-call) ## Step 1: Create the initial chat agent app In this step you create a chat agent app that generates playlists for a radio station. To create the app, you first need to create an Amazon SageMaker Unified Studio [project](projects.md). A project can contain multiple apps and is also where you can add the Amazon Bedrock components that you want your apps to use. Later you will add guardail, knowledge base, and function components to your app. You can share a project with other users and groups of users. For more information, see [Share an Amazon Bedrock chat agent app](app-share.md). To help guide users of the app, you can set user interface (UI) text, such as hint text for the beginning of a chat. In the app, you can experiment with the randomness and diversity of the response that the model returns by changing the [inference parameters](explore-prompts.md#inference-parameters). While you develop your app, you work on the current draft. You can save the current draft to the app history. Later you might want to restart work from a previous draft. For more information, see [Use app history to view and restore versions of an Amazon Bedrock app](app-history.md). **Warning** Generative AI may give inaccurate responses. Avoid sharing sensitive information. Chats may be visible to others in your organization. **To create an Amazon Bedrock chat agent app** 1. Navigate to the Amazon SageMaker Unified Studio landing page by using the URL from your administrator. 1. Access Amazon SageMaker Unified Studio using your IAM or single sign-on (SSO) credentials. For more information, see [Access Amazon SageMaker Unified Studio](getting-started-access-the-portal.md). 1. On the Amazon SageMaker Unified Studio home page, in the **Amazon Bedrock in SageMaker Unified Studio** tile, choose **Build chat agent app** to create a new chat agent app. The **Select or create a new project to continue** dialog box opens. 1. In the **Select or create a new project to continue** dialog box, do one of the following: + If you want to use a new project, follow the instructions at [Create a new project](create-new-project.md). For the **Project profile** in step 1, choose **Generative AI application development**. + If you want to use an existing project, select the project that you want to use and then choose **Continue**. 1. In **Untitled App - nnnn**, enter **Radio show** as the name for your app. 1. In the **Configs** pane, do the following: 1. For **Model**, select a model that supports Guardrails, Data, and Function components. The description of the model tells you the components that a model supports. For full information about the model, choose **View full model details** in the information panel. For more information, see [Find serverless models with the Amazon Bedrock model catalog](model-catalog.md). If you don't have access to an appropriate model, contact your administrator. Different models might not support all features. 1. For **Enter a system instruction** in **Instructions for chat agent & examples**, enter **You are a chat agent app that creates 2 hour long playlists for a radio station that plays rock and pop music.**. 1. In the **UI** section, update the user interface for the app by doing the following: 1. In **Hint text for empty chat** enter **Hi\$1 I'm your radio show playlist creator.**. 1. In **Hint text for user input** enter **Enter a prompt that describes the playlist that you want.**. 1. In **Quick start prompts** choose **Edit**. 1. Choose **Reset** to clear the list of quick start prompts 1. For **Quick-start prompt 1**, enter **Create a playlist of pop music songs.**. 1. (Optional). Enter quick start prompts of your choice in the remaining quick start prompt text boxes. 1. Choose **Back to configs**. 1. Choose **Save** to save the current working draft of your app. 1. In the **Quick start prompts** section of the **Preview** pane, run the quick start prompt that you just created by choosing the prompt. The app shows the prompt and the response from the model in the **Preview** pane. 1. In the prompt text box (the text should read **Enter a prompt that describes the playlist that you want**), enter **Create a playlist of songs where each song on the list is related to the next song, by musician, bands, or other connections. Be sure to explain the connection from one song to the next. **. 1. Choose the run button (or press Enter on your keyboard) to send the prompt to the model. 1. (Optional) In the **Inference parameters** section change the inference parameters. For example, include less familiar songs in the playlist by increasing the **Temperature** inference parameter. The inference parameters you can change are *Temperature*, *Top P*, and *Top K*. Not all models support each of these inference parameters. For more information, see [Inference parameters](explore-prompts.md#inference-parameters). 1. (Optional) In **Stop sequences**, add one or more stop sequences for the model. A stop sequence ensures that the model stops generating text immediately after it generates text that matches the stop sequence. Stop sequences are useful for getting precise answers without unnecessary additional text. Not all models support stop sequences. 1. (Optional) In **Reasoning** select **Model reasoning** to enable model reasoning. Model reasoning is when a model uses chain of thought reasoning to take a large, complex task and break it down into smaller, simpler steps. You can only enable or disable model reasoning at the start of a new chat. You can specify the number of tokens to use, which includes both output and reasoning tokens. Not all models support model reasoning. For more information, see [Enhance model responses with model reasoning](https://docs.aws.amazon.com/bedrock/latest/userguide/inference-reasoning.html) in the *Amazon Bedrock user guide*. 1. (Optional) Share your app with others by following the instructions at [Share an Amazon Bedrock chat agent app](app-share.md). 1. (Optional) Deploy your app for use in a flow app by following the instructions at [Deploy an Amazon Bedrock chat agent app](app-deploy.md). 1. (Optional) Export your snapshot from Amazon SageMaker Unified Studio by following the instructions at [Use your app outside of Amazon SageMaker Unified Studio](app-export.md). 1. Next step: Add a guardrail to your app by following the instructions at [Step 2: Add a guardrail to your chat agent app](#chat-app-add-guardrail). ## Step 2: Add a guardrail to your chat agent app Add a guardrail Guardrails for Amazon Bedrock lets you implement safeguards for your Amazon Bedrock in SageMaker Unified Studio app based on your use cases and responsible AI policies. You can create multiple guardrails tailored to different use cases and apply them across multiple foundation models, providing a consistent user experience and standardizing safety controls across generative AI apps. You can configure denied topics to disallow undesirable topics and content filters to block harmful content in the prompts you send to a model and to the responses you get from a model. You can use guardrails with text-only foundation models. For more information, see [Safeguard your Amazon Bedrock app with a guardrail](guardrails.md). ### Add a guardrail This procedure shows you how to use a guardrail to safeguard the app you created in [Step 1: Create the initial chat agent app](#chat-app-create-app). The guardrail prevents inappropriate language in song titles and filters out unwanted music genres. **To add a guardrail to an Amazon Bedrock app** 1. Open the app that you created in [Step 1: Create the initial chat agent app](#chat-app-create-app). 1. In the **Configs** pane, choose **Guardrails** and then **Create new guardrail**. 1. For **Guardrail name**, enter **prevent\$1unwanted\$1songs**. 1. For **Guardrail description**, enter **Prevents inappropriate or undesirable songs.**. 1. In **Content filters** make sure **Enable content filters** is selected. For more information, see [Content filters](guardrails.md#guardrails-studio-content-filters). 1. In **Filter for prompts** make sure the filter for each category is set to **High**. 1. Make sure **Apply the same filters for responses** is selected. 1. In **Blocked messsaging** do the following. 1. For **Blocked messaging for prompts**, enter **Sorry, your prompt contained inappropriate text.**. 1. Clear **Apply the same message for blocked responses**. 1. For **Blocked messaging for responses**, enter **Sorry, but I can't respond with information that contains inappropriate text.**. 1. Choose **Create** to create the guardrail. 1. In the **Configs** pane, in the **Guardrails** section, select the guardrail that you just created (**prevent\$1unwanted\$1songs**). It might take a minute for the guardrail to appear in the list. 1. Test the guardrail by entering **Create a list of 10 songs where each song has a swear word in the title.** In the prompt edit box. 1. Choose the run button to send the prompt to the model. The model should respond with the message **Sorry, but I can't respond with information that contains inappropriate text.** 1. Use a denied topic filter to prevent requests for music from a specific music genere. For information about denied topics, see [Denied topics](guardrails.md#guardrails-topic-policies). To add the filter, do the following. 1. In the **Guardrails** section of the **Configs** pane, select the guardrail and choose **Preview**. 1. Choose **Edit** to edit the guardrail. 1. In **Denied topics**, choose **Add topic**. 1. For **Name**, enter **heavy metal**. 1. For **Definition for topic**, enter **Avoid mentioning songs that are from the heavy metal genre of music.**. 1. In **Sample phrases - optional**, enter **Create a playlist of heavy metal songs**. 1. (Optional) Choose **Add phrase** to add other phrases. 1. Choose **Save**. 1. Om the **Edit guardrail** page, choose **Update** to update the guardrail. 1. Test the guardrail by entering **Create a list of heavy metal songs.** in the prompt edit box. 1. Choose the run button to send the prompt to the model. The model should respond with the message **Sorry, your prompt contained inappropriate text**. 1. Next step: Add a data source to your app by following the instructions at [Step 3: Add a knowledge base to your chat agent app](#chat-app-add-data-source). ## Step 3: Add a knowledge base to your chat agent app Add a knowledge base You can use your own data into your application by adding a knowledge base to your app. Doing this allows your app to access to information that is only available to you. When your app passes a query, Amazon Bedrock in SageMaker Unified Studio generates a response that includes the query results from the knowledge base. For more information, see [Add a Knowledge Base to your Amazon Bedrock app](data-sources.md). In this topic, you update the app you created in [Step 1: Create the initial chat agent app](#chat-app-create-app) to use a CSV file as a document (local file) data source for a knowledge base. The CSV file includes information about bands that don't have public metadata such as song length, or music genre. The user can use the app to create a playlist based on criteria such as song length or music genre. **To add your own data to an Amazon Bedrock app** 1. Create a CSV file name *songs.csv* and fill with the following ficticious CSV data. ``` song,artist,genre,length-seconds "Celestial Odyssey","Starry Renegades","Cosmic Rock",240 "Neon Rapture","Synthwave Siren","Synthwave Pop",300 "Wordsmith Warriors","Lyrical Legions","Lyrical Flow",180 "Nebula Shredders","Galactic Axemen","Cosmic Rock",270 "Electro Euphoria","Neon Nomads","Synthwave Pop",210 "Rhythm Renegades","Percussive Pioneers","Lyrical Flow",240 "Stardust Rift","Cosmic Crusaders","Cosmic Rock",180 "Synthwave Serenade","Electro Enchanters","Synthwave Pop",300 "Lyrical Legends","Rhyme Royale","Lyrical Flow",240 "Supernova Shredders","Amplified Ascension","Cosmic Rock",300 "Celestial Chords","Ethereal Echoes","Cosmic Rock",240 "Neon Nirvana","Synthwave Sirens","Synthwave Pop",270 "Verbal Virtuoso","Lyrical Maestros","Lyrical Flow",210 "Cosmic Collision","Stellar Insurgents","Cosmic Rock",180 "Pop Paradox","Melodic Mavericks","Synthwave Pop",240 "Flow Fusion","Verbal Virtuosos","Lyrical Flow",300 "Shredding Shadows","Crimson Crusaders","Cosmic Rock",270 "Synth Serenade","Electro Enchanters","Synthwave Pop",180 "Wordsmith Warlords","Lyrical Legionnaires","Lyrical Flow",240 "Sonic Supernova","Amplified Ascension","Cosmic Rock",210 "Celestial Symphony","Ethereal Ensemble","Cosmic Rock",300 "Electro Euphoria","Neon Nomads","Synthwave Pop",180 "Lyrical Legends","Rhyme Royale","Lyrical Flow",270 "Crimson Crescendo","Scarlet Serenaders","Cosmic Rock",240 "Euphoric Tides","Melodic Mystics","Synthwave Pop",210 "Rhythm Renegades","Percussive Pioneers","Lyrical Flow",180 "Cosmic Collision","Stellar Insurgents","Cosmic Rock",300 "Stardust Serenade","Celestial Crooners","Synthwave Pop",240 "Wordsmith Warriors","Lyrical Legions","Lyrical Flow",270 "Sonic Supernova III","Amplified Ascension","Cosmic Rock",180 ``` 1. Open the app that you created in [Step 1: Create the initial chat agent app](#chat-app-create-app). 1. In **Data** choose **Use Knowledge Base** and then **Create Knowledge Base**. The **Create Knowledge Base** pane is shown. If you've previously created the knowledge base, go to step *10* and select the knowledge base. 1. For **Name**, enter a name for the Knowledge Base. 1. For **Description**, enter a description for the Knowledge Base. 1. In **Add data sources**, choose **Local file**. 1. Choose **Click to upload** and upload the CSV file that you created in step 1. Alternatively, add the CSV by dragging and dropping the document from your computer. For more information, see [Use a Local file as a data source](data-source-document.md). 1. For **Embeddings model**, choose a model for converting your data into vector embeddings. 1. Choose **Create**. It might take Amazon Bedrock in SageMaker Unified Studio a few minutes to create the knowledge base. 1. For **Select Knowledge Base**, select the Knowledge Base that you just created. 1. Test the data source by entering **Create a playlist of songs in the Lyrical Flow genre** in the prompt text box. 1. Choose the run button to send the prompt to the model. The model should respond with a playlist of songs from the Lyrical Flow genre that the CSV file contains. 1. Choose **Save** to save the app. ## Step 4: Add a function call to your chat agent app Add a function call Amazon Bedrock in SageMaker Unified Studio functions let a model include information that it has no previous knowledge of in its response. For example, you can use a function to include dynamic information in a model's response such as a weather forecast, sports results, or traffic conditions. To use a function in Amazon Bedrock in SageMaker Unified Studio you add a function component to your app. For more information, see [Call functions from your Amazon Bedrock chat agent app](functions.md). In Amazon Bedrock in SageMaker Unified Studio, a function calls an API hosted outside of Amazon Bedrock in SageMaker Unified Studio. You either create the API yourself, or use an existing API. To create an API, you can use [ Amazon API Gateway](https://docs.aws.amazon.com/apigateway/). In this procedure, you add a function to the app that you created in [Step 1: Create the initial chat agent app](#chat-app-create-app) so that users can get a list of the top 10 songs played on the radio station that day. **To add a function to an Amazon Bedrock app** 1. Create a HTTPS server that implements a `TopSongsToday` function. Make sure the function adheres to the following schema. ``` openapi: 3.0.0 info: title: Top Songs API description: API to retrieve the top 10 songs played today version: 1.0.0 paths: /top-songs: get: operationId: TopSongsToday summary: Get the top 10 songs played today description: > This endpoint returns an array of the top 10 songs played today, ordered by popularity. The first element in the array (index 0) represents the most popular song, and the last element (index 9) represents the 10th most popular song. responses: '200': description: Successful response content: application/json: schema: $ref: '#/components/schemas/TopSongs' components: schemas: TopSongs: type: array items: $ref: '#/components/schemas/Song' description: > An array containing the top 10 songs played today. The first element (index 0) is the most popular song, and the last element (index 9) is the 10th most popular song. example: - title: 'Song Title 1' artist: 'Artist Name 1' album: 'Album Name 1' - title: 'Song Title 2' artist: 'Artist Name 2' album: 'Album Name 2' # ... up to 10 songs Song: type: object properties: title: type: string description: The title of the song artist: type: string description: The name of the artist or band album: type: string description: The name of the album the song is from required: - title - artist - album ``` 1. Open the app that you created in [Step 1: Create the initial chat agent app](#chat-app-create-app). 1. In **Models**, choose a model that supports functions. If you don't have access to an appropriate model, contact your administrator. 1. In **Functions**, choose **Create new function**. 1. In the **Create function** pane, do the following. 1. For in **Function name**, enter **Top\$1ten\$1songs\$1today**. 1. For **Function description (optional)**, enter **Today's top 10 songs.**. 1. For **Function schema**, enter the OpenAPI schema from step one. 1. Choose **Validate schema** to validate the schema. 1. In **Authentication method** choose the authentication method for your HTTP server. For more information, see [Authentication methods](functions.md#functions-authentication). 1. In **API servers**, enter the URL for your server in **Server URL**. This value is autopopulated if the server URL is in the schema. 1. Choose **Create** to create your function. It might take a few minutes to create the function. 1. For **Enter a system instruction**, update the system instruction so that it describes the function. Use the following text: **You are an app that creates 2 hour long playlists for a radio station that plays rock and pop music. The function Top\$1ten\$1songs\$1today gets the most popular song played on the radio station.**. 1. Test the function by doing the following. 1. Enter **What are today's top 10 songs?** in the prompt edit box. 1. Choose the run button to send the prompt to the model. The model should respond with the list of today's top 10 songs. # Deploy an Amazon Bedrock chat agent app Deploy a chat agent app Deployment of a chat agent app happens for the following reasons: + You want to use a chat agent app as an [agent node](flows-use-chat-agent.md) within a flow app. Before you can do so, you must first deploy the chat agent app. + You want to [share](app-share.md) a chat agent app with other users. When you share the app, Amazon Bedrock deploys the chat agent app for you. During deployment Amazon Bedrock creates, or reuses, an *alias* for the chat agent app and creates a new *version* of the chat agent app. **Aliases and versions** An alias is associated with a specific version of a chat agent app. When you use a chat agent app as an agent node in a flow app, you configure the agent node in the flow app with the alias of the chat agent app. When you share an app, the recipient can access the version of the app that is associated with the alias. You can create up to 8 aliases for a chat agent app. A version is a snapshot of a chat agent app at the time you deploy the app. Amazon Bedrock creates a new version of your chat agent app each time you deploy the app. Amazon Bedrock creates versions in numerical order, starting from 1. With aliases, you can switch efficiently between [different versions](app-change-alias-version.md) of a chat agent app, without having to update the flow apps that use the chat agent app. For example, if you discover an issue with the current version of your chat agent app, you can quickly update agent nodes that use the current version to a previous version. The following procedure shows you how to deploy a chat agent app. After deploying the app, you can then use the app as a [node agent](flows-use-chat-agent.md) in a flow app. You don't need to deploy a chat agent app before sharing the app, as Amazon Bedrock deploys the app for you. For more information, see [Deploy an Amazon Bedrock chat agent app](#app-deploy). **To deploy a chat agent app** 1. Navigate to the Amazon SageMaker Unified Studio landing page by using the URL from your administrator. 1. Access Amazon SageMaker Unified Studio using your IAM or single sign-on (SSO) credentials. For more information, see [Access Amazon SageMaker Unified Studio](getting-started-access-the-portal.md). 1. If the project that you want to use isn't already open, do the following: 1. Choose the current project at the top of the page. If a project isn't already open, choose **Select a project**. 1. Select **Browse all projects**. 1. In **Projects** select the project that you want to use. 1. Choose the **Build** menu option at the top of the page. 1. In **MACHINE LEARNING & GENERATIVE AI** choose **My apps**. 1. Open the app that you want to deploy. 1. Choose **Deploy**. 1. In **App description** enter a short description for the app. Make sure that that the description lets users understand the purpose of the app. 1. Assign an alias by doing one of the following: + Choose **Create a new alias** to create a new alias. Then enter an name and optional description for the alias. + Choose **Select an existing alias** to use an existing alias. Then select the existing alias that you want to use. 1. Choose **Deploy** to deploy the app. It might take a few seconds to deploy the app. 1. To use the deployed app, select **Working draft** in the configuration pane and then select the name of the alias that you used to deploy the app. 1. Enter a prompt to try your deployed app. # Modify the version of an Amazon Bedrock chat agent app Modify the version of a chat agent app You can change the version of a chat agent app that an agent node in a flow app uses, or the version of a chat agent app that is shared with other users. To change the version, you modify the alias for the chat agent app to reference the new version. After updating the alias, you don't need to update the flow app or shares of the chat agent app for them to use the new version. The following procedure shows you how to change the version of a chat agent app that an alias for the app references. **To modify the version that an alias references** 1. Navigate to the Amazon SageMaker Unified Studio landing page by using the URL from your administrator. 1. Access Amazon SageMaker Unified Studio using your IAM or single sign-on (SSO) credentials. For more information, see [Access Amazon SageMaker Unified Studio](getting-started-access-the-portal.md). 1. If the project that you want to use isn't already open, do the following: 1. Choose the current project at the top of the page. If a project isn't already open, choose **Select a project**. 1. Select **Browse all projects**. 1. In **Projects** select the project that you want to use. 1. Choose the **Build** menu option at the top of the page. 1. In **MACHINE LEARNING & GENERATIVE AI** choose **My apps**. 1. Open the app that you want to use. 1. Choose the selector on the **Deploy** button and select **View aliases**. The **View and manage aliases** pane opens. 1. For the alias that you want modify, choose **Edit**. 1. In the **Edit alias** pane, select the version that you want the alias to use in **Select version to associate with this alias**. 1. (Optional) Update the name and description for the alias. 1. Choose **Save** to save your changes. # Share an Amazon Bedrock chat agent app Share a chat agent app A snapshot of an Amazon Bedrock in SageMaker Unified Studio chat agent app is a point-in-time capture of the app's state, including its code, configuration, and any associated data. You can share a snapshot with all members of your Amazon SageMaker Unified Studio domain, or with specific users or groups in your Amazon SageMaker Unified Studio domain. When you first share a snapshot, you get a share link to the snapshot that you can send to users. If you share the snapshot with all users, Amazon SageMaker Unified Studio grants permission to a user, when they first open the share link. Amazon SageMaker Unified Studio also adds the snapshot to the user's shared assets list. If you share the snapshot with specific users and groups, the snapshot is immediately available in their shared assets list. They can also use the share link to access the snapshot. By default, sharing a snapshot is restricted to only those users or groups that you select. When you share a chat agent app, Amazon SageMaker Unified Studio also publishes the chat agent app to the Amazon SageMaker AI Catalog. When sharing occurs, Amazon Bedrock [deploys](app-deploy.md) the snapshot of the chat agent app. When you first share a snapshot, Amazon Bedrock creates a new alias for the chat agent app and a new version of the chat agent app that represents the snapshot. On subsequent shares, Amazon Bedrock creates a new version of the app, and associates the alias with the new version. If necessary, You can change the version that is associated with an alias. For more information, see [Modify the version of an Amazon Bedrock chat agent app](app-change-alias-version.md). **To share a chat agent app snapshot** 1. Navigate to the Amazon SageMaker Unified Studio landing page by using the URL from your administrator. 1. Access Amazon SageMaker Unified Studio using your IAM or single sign-on (SSO) credentials. For more information, see [Access Amazon SageMaker Unified Studio](getting-started-access-the-portal.md). 1. If the project that you want to use isn't already open, do the following: 1. Choose the current project at the top of the page. If a project isn't already open, choose **Select a project**. 1. Select **Browse all projects**. 1. In **Projects** select the project that you want to use. 1. Choose the **Build** menu option at the top of the page. 1. In **MACHINE LEARNING & GENERATIVE AI** choose **My apps**. 1. Open the chat agent app that you want to share. 1. Choose **Share**. 1. In **App description** enter a short description for the chat agent app. Make sure that that the description lets users understand the purpose of the chat agent app. 1. Do one of the following: + If you want to share the chat agent app snapshot with all members of your Amazon SageMaker Unified Studio domain, select turn on **Grant access with link**. + If you want to share the chat agent app snapshot with specific Amazon SageMaker Unified Studio domain users or groups, do the following in **Share with specific users or groups**: 1. For **Member type** choose **Individual user** or **Group**, depending on the type of member that you want share the chat agent app with. 1. Search for the users or groups that you want to share the chat agent app with by entering the user name or group in the **Search by alias to invite members** text box. 1. In the drop down list, select the matching user name or group that want to share the chat agent app with. 1. Choose **Add** to add the user or group. 1. Choose **Share** to share the chat agent app. 1. When the success message appears, choose **Copy link** and send the link to the users that you are sharing the chat agent app snapshot with. If **Grant access with link** is off, the link only works for users that you have explicitly granted access to the chat agent app. As the chat agent app creator, you, and other members of the chat agent app project, can make changes to the chat agent app and share a fresh snapshot of the chat agent app. Only the latest snapshot is available to users. Users that you share a snapshot to can't make any changes to the snapshot. To change who you share the chat agent app with, open the chat agent app, choose **Share** and make your changes. Choose **Done** to complete the changes. If you are sharing the snapshot with all users, turning off **Grant access with link** restricts access to users that you have specifically share the snapshot with. You can't stop sharing a snapshot without deleting the chat agent app. If you delete the chat agent app, the snapshot is no longer shared and is removed from the Amazon SageMaker AI Catalog. If you want to deny access to everyone, without deleting the chat agent app, edit the snapshot and remove all users and groups. If you shared the snapshot with all users, turn off **Grant access with link**. Note that Amazon SageMaker Unified Studio doesn't remove the snapshot from the Amazon SageMaker AI Catalog. If you need the share link later, share the chat agent app again and copy the share link. You can also change the users that you share with the chat agent app with. To see which chat agent apps you have shared, Open the app project, choose **Asset gallery** and then **My apps**. Check the **Share status** column for the chat agent app.