Amazon Bedrock

Introduction

The Amazon Bedrock connector enables you to enrich your Mendix app with generative AI capabilities by connecting it to Amazon Bedrock.

Typical Use Cases

Amazon Bedrock is a fully managed service that makes foundation models (FMs) from Amazon and leading AI startups available through an API, so you can choose from various FMs to find the model that is best suited for your use case. With the Amazon Bedrock serverless experience, you can quickly get started, easily experiment with FMs, and seamlessly integrate and deploy them into your applications using AWS tools and capabilities. Typical use cases include the following:

• Chatting with an AI assistant, including a chat history.
• Building an AI agent to answer questions about proprietary data.
• Generating images based on text prompts and displaying them in the Mendix app.
• Generating embedding vectors for text inputs.

Prerequisites

The Amazon Bedrock connector requires Mendix Studio Pro version 9.24.2 or above.

To authenticate with Amazon Web Service (AWS), you must also install and configure the AWS Authentication connector version 3.0.0 or higher. It is crucial for the Amazon Bedrock connector to function correctly. For more information about installing and configuring the AWS Authentication connector, see AWS Authentication.

You must also install the GenAI Commons version 1.2.0 or higher. To make integration of generative AI capabilities as easy as possible, the Amazon Bedrock connector depends on the generic domain model and operations provided by the GenAICommons module.

Licensing and Cost

This connector is available as a free download from the Mendix Marketplace, but the AWS service to which is connects may incur a usage cost. For more information, refer to AWS documentation.

The pricing of the Amazon Bedrock Connectors is dependent on the Foundational Model that you use. You can find information about the pricing in the Foundational Model overview in the AWS console.

Depending on your use case, your deployment environment, and the type of app that you want to build, you may also need a license for your Mendix app. For more information, refer to Licensing Apps.

Installation

Follow the instructions in How to Use Marketplace Content to import the Amazon Bedrock connector into your app.

Configuration

After you install the connector, you can find it in the App Explorer, in the AmazonBedrockConnector section. The connector provides a domain model and several activities that you can use to connect your app to Amazon Bedrock. Each activity can be implemented by using it in a microflow. To ensure that your app can connect to the AWS service, you must also configure AWS authentication for the connector.

Using Amazon Bedrock Models

To use Amazon Bedrock models, keep in mind some specific requirements, as listed below.

Model Lifecycle

Amazon Bedrock models have a lifecycle that consists of the Active, Legacy, and EOL stages. For more information, see Model lifecycle. Models are no longer available for use after they reach the EOL state. To ensure that your application functions as intended, make sure that you regularly monitor the state of the model that you are using. For example, you may want to use an API call to retrieve the status of the model and alert you once it reaches the Legacy state. To programmatically get information about available models and their lifecycle status, you can use the ListFoundationModels operation.

Configuring AWS Authentication

In order to use the Amazon Bedrock service, you must authenticate with AWS. To do so, you must set up a configuration profile in your Mendix app. After you set up the configuration profile, the connector module handles the authentication internally.

As of version 3.0.0 of the AWS Authentication Connector, all the resources and logic required to set up authentication are centralized inside the AWS Authentication Connector module.

The AWS Authentication Connector supports both static credentials and temporary credentials. For more information and detailed instructions please refer to the AWS Authentication Connector documentation page.

Configuring a Microflow for an AWS Service

After you configure the authentication profile for Amazon Bedrock, you can implement the functions of the connector by using the provided activities in microflows. The USE_ME folder contains several subfolders containing operations that depend on the GenAICommons module. The following example microflows have been created for each of these inside the ExampleImplementations folder:

• EXAMPLE_ChatCompletions_FunctionCalling
• EXAMPLE_ChatCompletions_Vision
• EXAMPLE_ChatCompletions_withHistory
• EXAMPLE_ChatCompletions_withoutHistory
• EXAMPLE_Embeddings_ChunkCollection
• EXAMPLE_Embeddings_SingleString
• EXAMPLE_Retrieve
• EXAMPLE_RetrieveAndGenerate
• EXAMPLE_ImageGeneration_MultipleImages

You can also take a look at the GenAI Showcase Application to get some inspiration on what you can use these operations for.

For operations that do not depend on the GenAICommons, you can take a different approach. For example, to list all foundational models, implement the List Foundation Models activity by doing the following steps:

1. In the App Explorer, right-click on the name of your module, and then click Add microflow.
2. Enter a name for your microflow, for example, ACT_ListFoundationModels, and then click OK.
3. From the Toolbox, drag a Create Object activity to your microflow and create an object of type ListFoundationModelsRequest.
4. In the Toolbox, in the in the Amazon Bedrock (other) section, find the ListFoundationModels activity.
5. Drag the ListFoundationModels activity onto the work area of your microflow.
6. Double-click the ListFoundationModels activity to configure the required parameters.
7. For the ENUM_Region parameter, provide a value by using a variable or an expression. This must be of the type ENUM_Region of the AWS Authentication connector.
8. For the Credentials parameter, provide a Credentials object from the AWS Authentication connector:
   1. In the App Explorer, in the AWSAuthentication > Operations section, find the GetStaticCredentials or GetTemporaryCredentials action.
   2. Drag the one you would like to use to the beginning of your microflow.
   3. Double-click the microflow action to configure the required parameters and provide a value for the AWS Region. For the ListFoundationModels parameter, provide the ListFoundationModelsRequest created in step 3.
9. The ListFoundationModelsResponse object is returned by the ListFoundationModels activity.
10. From the Toolbox, drag a Retrieve activity to your microflow and place it after the ListFoundationModels activity.
11. Double-click the Retrieve activity and make sure By Association is selected.
12. Select the FoundationModelSummary_ListFoundationModelsResponse association, which will return a list of the type FoundationModelSummary.
13. To further use the response information, you can create an implementation module with copies of the ListFoundationModelsResponse and ModelSummary Entities. This way, you can use your custom user roles and access rules for those entities and keep them when updating the connector.

You can follow a similar approach to implement any of the other operations in Amazon Bedrock (other).

Chatting with Large Language Models using the ChatCompletions Operation

A common use case of the Amazon Bedrock Connector is the development of chatbots and chat solutions. The ChatCompletions (without history / with history) operations offer an easy way to connect to most of the text-generation models available on Amazon Bedrock. The ChatCompletions operations are built on top of Bedrock’s Converse API, allowing you to talk to different models without the need of a model-specific implementation.

For an overview of supported models and model-specific capabilities and limitations, see Amazon Bedrock Converse API in the AWS documentation.

To build a simple microflow that uses the ChatCompletions operation to send a single message to the Anthropic Claude 3.5 Sonnet model and show the response on a page, perform the following steps:

1. Create a new microflow and name it, for example, AmazonBedrockChatCompletions.
2. In the Toolbox, search for the Chat Completions (without history) activity in the Amazon Bedrock (Operations) and drag it onto your microflow.
3. Double click on the activity to see its parameters.
   1. The Request and FileCollection parameters are not needed for this example, so you can set them to empty.
   2. For the UserPrompt parameter, enter a string of your choice, for example Hi, Claude!.
   3. CLick OK. The input for the Connection parameter will be created in the next step.
4. In the Toolbox, search for the Create Amazon Bedrock Connection operation and drag it to the beginning of your microflow.
5. Double-click it to configure its parameters.
   1. For the ENUM_Region parameter, enter a value of the AWSAuthentication.ENUM_Region enumeration. Choose the region where you have access to Amazon Bedrock. For example, AWSAuthentication.ENUM_Region.us_east_1.
   2. For the ModelId parameter, enter the model id of the LLM you want to send a message to. The model id of Claude 3.5 Sonnet is anthropic.claude-3-5-sonnet-20240620-v1:0.
   3. For the UseStaticCredentials parameter, enter true if you have configured static AWS Credentials, and false if you have configured temporary AWS Credentials.
   4. Click OK.
6. Double-click the ChatCompletion operation and, for the Connection parameter, pass the newly created AmazonBedrockConnection object.
7. In the Toolbox, search for the Get Model Response Text operation from the GenAI Commons (Text & Files - Response) category, and drag it to the end of your microflow.
8. Double-click on it and pass the Response from the ChatCompletions operation as parameter. The Get Model Response Text will return the response from Claude as a string.
9. Add a Show Message activity to the end of the microflow and configure it to show the returned response string.
10. Add a button that calls this microflow, run your project, and verify the results.

You can find several implementation examples for the ChatCompletions operations inside of the GenAI showcase application.

Invoking Specific Models by Using the InvokeModel Operation

Depending on your needs, you can reuse the operations inside the AmazonBedrockConnector (GenAICommons) section. You can also find guidance on how to implement the required structures in the GenAICommons documentation. Most text models can be used with the ChatCompletions operation. For an overview of the supported models and capabilities, see Supported models and model features in the AWS Bedrock documentation.

To invoke a specific model that is not covered by the ChatCompletions operation, you can make use of the Invoke Model operation by performing the following steps:

1. Choose the model with which you want to interact by using the Invoke Model operation.

2. In the Model Parameters section of the Amazon Bedrock user guide, find the request and response JSON structures of the specific model that you want to invoke.

3. Create your domain model inspired by the JSON structures that you found. You can use a tool to visualize Json structures if needed, such as JSON Crack.

4. In Mendix Studio Pro, create a JSON structure by doing the following steps:

   1. Right-click on the target folder.
   2. Click Add other > JSON structure.
   3. Create a structure for the request JSON.
   4. Repeat the previous steps to create a structure for the response JSON.
   5. Open the created JSON structure.
   6. Paste the request or response JSON into the created structure.
   7. Click OK.

5. Generate export and import mappings for the request and response JSON structures. The export mapping creates a JSON from the request-related objects (specific to the model that you want to invoke). The JSON must be added as the request body of the InvokeModelRequest object provided as input parameter to the Invoke Model operation. The import mapping maps the response returned by the Invoke Model operation to your model-specific response objects. To create import or export mappings, perform the following steps:

   1. Right-click the target folder.
   2. Click Add other > Import/Export mapping.
   3. In the dialogue window, select the Schema source.
   4. Click JSON structure and select the appropriate request/response JSON structure.
   5. Select the relevant schema elements.
   6. Click OK.
   7. Map the relevant elements to the correct attributes by double-clicking the shown entities and choosing the correct entity attributes for the correct elements.

6. Create a microflow that invokes a specific model using the Invoke Model operation, such as in the following figure (for Claude v. 2.1):

   

Invoking an Agent with the InvokeAgent Operation

Agents in Amazon Bedrock can perform certain automated tasks such as API calls and data source interactions. For more information, see Agents in Bedrock.

To invoke a Bedrock agent for your Mendix app, do the following steps:

1. Create the agent, as described in Create an agent in the Amazon Bedrock documentation.

2. Deploy the agent to create an alias, as described in Deploy your agent in the Amazon Bedrock documentation.

3. In your Mendix app, create a new microflow and add the InvokeAgent operation as a microflow step.

4. Either pass an InvokeAgentRequest object as a parameter to the flow, or create one within the microflow. Ensure that the following attributes are populated for the request:

   • Agent ID
   • Agent Alias ID
   • Input text (the prompt to send to the agent).
   • The session id (by reusing this value in a subsequent request, it is possible to continue a conversation).
   • Make a choice on ‘EnableTrace’ to enable or disable the tracking of the reasoning process.
   • Set ‘EndSession’ to specify whether or not you want to have the option of continuing the conversation in another request.

5. Enter the desired region as a value of the AWSAuthentication.ENUM_Region type.

6. Select a Credentials object. You can put it in scope with the AWSAuthentication.GetStaticCredentials or the AWSAuthentication.GetTemporaryCredentials microflow.

7. Select a microflow that takes an AmazonBedrockConnector.InvokeAgentResponse object as an input and handles that response. This is necessary because InvokeAgent is an asynchronous operation which means that it will not necessarily finish when the process that it was invoked from finishes. By giving the operation a handler microflow, the response can be handled as soon as it arrives. For an example handler microflow, see AmazonBedrockConnector.InvokeAgentResponse_Handle in the connector module. This microflow logs the response, so you can also use it just to investigate the response.

Technical Reference

The module includes technical reference documentation for the available entities, enumerations, activities, and other items that you can use in your application. You can view the information about each object in context by using the Documentation pane in Studio Pro.

The Documentation pane displays the documentation for the currently selected element. To view it, perform the following steps:

1. In the View menu of Studio Pro, select Documentation.

2. Click on the element for which you want to view the documentation.

   

For additional information about available operations, refer to the sections below.

GenAICommons-Based Operations

ChatCompletions (Without History)

The ChatCompletions (without history) activity can be used for any conversations with a variety of supported LLMs. There is no option to keep the conversation history in mind. This operation corresponds to the ChatCompletions_WithoutHistory_AmazonBedrock microflow.

This operation leverages the Amazon Bedrock Converse API. For a full overview of supported models and model capabilities, please refer to the AWS Documentation.

The input and output for this service are shown in the table below:

Input	Output
Userprompt (string), AmazonBedrockConnection, GenAICommons.Request, FileCollection	GenAICommons.Response

GenAICommons.Request and FileCollection can be empty, in which case they are not sent to the Bedrock API.

ChatCompletions (With History)

The ChatCompletions (with history) activity can be used for any conversations with a variety of supported LLMs. It is possible for it to keep the conversation history in mind. This operation corresponds to the ChatCompletions_WithHistory_AmazonBedrock microflow.

This operation leverages the Amazon Bedrock Converse API. For a full overview of supported models and model capabilities, please refer to the AWS Documentation.

The input and output for this service are shown in the table below:

Input	Output
GenAICommons.Request, AmazonBedrockConnection	GenAICommons.Response

In order to pass a conversation history to the flow, the list of previous messages must be associated to the input request. This operation can easily be replaced or combined with the ChatCompletions (with history) operation inside of the OpenAI connector.

Some capabilities of the chat completions operations are currently only available for specific models:

• Function Calling - You can use function calling in all chat completions operations using a supported model by adding a ToolCollection with a Tool via the Tools: Add Function to Request operation. For more information about function calling, see the Function Calling Documentation.

Function calling microflows: A microflow used as a tool for function calling must satisfy the following conditions:

1. One input parameter of type String or no input parameter.
2. Return value of type String.

• Vision - This operation supports the vision capability for supported models. With vision, you can send image prompts, in addition to the traditional text prompts. You can use vision by adding a FileCollection with a File to the Message using the Files: Initialize Collection with File or the Files: Add to Collection operation. Make sure to set the FileType attribute to image.

• Document Chat - This operation supports the ability to chat with documents for supported models. To send a document to the model add a FileCollection with a System.FileDocument to the Message using the Files: Initialize Collection with File or the Files: Add to Collection operation. For Document Chat, it is not supported to create a FileContent from an URL using the above mentioned operations; Please use the System.FileDocument option. Make sure to set the FileType attribute to document.

RetrieveAndGenerate

The Retrieve and Generate activity can be used for conversations leveraging Retrieval Augmented Generation through a knowledge base. This operation corresponds to the RetrieveAndGenerate microflow. The input and output for this service are shown in the table below:

Input	Output
GenAICommons.Request, AmazonBedrockConnection	GenAICommons.Response

The request object passed to this operation must include a KnowledgeBaseTool object, which can be added to the request using the Request: Add Knowledge Base Tool to Collection operation.

Chatting with History

The RetrieveAndGenerate operation only allows a single user message to be part of the request. Unlike the ChatCompletions operation, it is not supported to send a history of messages to the model.

The history can be enabled using the SessionId parameter on the RetrieveAndGenerateRequest_Extension entity. By reusing the same SessionId value, the model will run in the context of the session.

Image Generation

The Image Generation operation can be used to generate one or more images. This operation corresponds to the ImageGeneration_AmazonBedrock microflow. Currently Amazon Titan Image Generator G1 is the only supported model for image generation of the Amazon Bedrock Connector.

The input and output for this service are shown in the table below:

Input	Output
UserPrompt (String), AmazonBedrockConnection (object), GenAICommons.ImageOptions (object)	GenAICommons.Response (object)

GenAICommons.ImageOptions can be an empty object. If provided, it allows you to set additional options for Image Generation.

GenAICommons.ImageOptions can be created by using the Image: Create Options operation of GenAI Commons.

To retrieve actual image objects from the response, the Image: Get Generated Image (Single) or Image: Get Generated Images (List) helper operations from GenAICommons can be used.

For Titan Image models, the Image Generation: Add Titan Image Extension operation can be used to configure Titan image-specific values (currently only NegativeText).

Embeddings (single string)

The Embeddings (single string) activity can be used to generate an embedding vector for a given input string with one of the Cohere Embed models or Titan Embeddings v2. This operation corresponds to the Embeddings_SingleString_AmazonBedrock microflow.

The input and output for this service are shown in the table below:

Input	Output
InputText, AmazonBedrockConnection, GenAICommons.EmbeddingsOptions (optional)	GenAICommons.EmbeddingsResponse

For Cohere Embed and Titan Embeddings, the request can be associated to their respective EmbeddingsOptions extension object which can be created with the Embeddings Options: Add Cohere Embed Extension or Embeddings Options: Add Titan Embeddings Extension operation. Through this extension, it is possible to tailor the operation to more specific needs. This operation can easily be replaced or combined with the Embeddings (single string) operation inside of the OpenAI connector.

Currently, embeddings are available for the Cohere Embed family and or Titan Embeddings v2.

Embeddings (chunk collection)

The Embeddings (chunk collection) activity can be used to generate a collection of embedding vectors for a given collection of text chunks with one of the Cohere Embed models or Titan Embeddings v2. This operation corresponds to the Embeddings_ChunkCollection_AmazonBedrock microflow.

The input and output for this service are shown in the table below:

Input	Output
GenAICommons.ChunkCollection, AmazonBedrockConnection, GenAICommons.EmbeddingsOptions (optional)	GenAICommons.EmbeddingsResponse

For each model family, the request can be associated to an extension of the EmbeddingsOptions object which can be created with either the Embeddings Options: Add Cohere Embed Extension or the Embeddings Options: Add Titan Embeddings Extension operation. Through this extension, it is possible to tailor the operation to more specific needs. This operation can easily be replaced or combined with the Embeddings (chunk collection) operation inside of the OpenAI connector.

Currently, embeddings are available for the Cohere Embed family and Titan Embeddings v2.

Retrieve

The Retrieve activity allows you to query a knowledge base and retrieve information from it. This operation corresponds to the Retrieve microflow.

The input and output for this service are shown in the table below:

Input	Output
GenAICommons.Request, AmazonBedrockConnection	GenAICommons.Response

GenAI Commons Helper Operations

Create Amazon Bedrock Connection

Use this microflow to create a new Amazon Bedrock Connection object.

This operation corresponds to the AmazonBedrockConnection_Create microflow.

| ENUM_Region (enumeration), UseStaticCredentials (Boolean), ModelId (string) | AmazonBedrockConnection (object)|

Request: Add Knowledge Base Tool to Collection

Use this microflow to add a new KnowledgeBaseTool object to your request. This is useful for adding additional parameters when using the Retrieve And Generate operation.

This operation corresponds to the RetrieveAndGenerateRequest_Extension_Create microflow.

Input	Output
GenAICommons.Request (object), KnowledgeBaseId (string)	none

Request: Add Retrieve And Generate Request Extension

Use this microflow to add a new RetrieveAndGenerateRequest_Extension object to your request. This is required in order to use the Retrieve And Generate operation successfully.

This operation corresponds to the Request_AddKnowledgeBaseTool microflow.

Input	Output
GenAICommons.Request (object), KmsKeyARN (string), SessionId (string), Enum_RetrieveAndGenerateType (enumeration)	RetrieveAndGenerateRequest_Extension (object)

KmsKeyARN, SessionId, and Enum_RetrieveAndGenerateType can be empty, in which case they are not sent to the Bedrock API.

Image Generation: Add Titan Image Extension

Use this microflow to add a new TitanImageOptions_Extension object to your GenAICommons.ImageOptions object. This will allow you to configure the NegativeText attribute.

This operation corresponds to the TitanImageOptions_Extension_Create microflow.

Input	Output
GenAICommons.ImageOptions (object), NegativeText (string)	TitanImageOptions_Extension (object)

Image Generation: Set Image Size (Titan Image)

Use this microflow to set the Height and Width attributes of your GenAICommons.ImageOptions object to any valid image size supported by Titan Image models. The ENUM_ImageSize_TitanImage enumeration contains all valid height-width combinations to choose from.

This operation corresponds to the ImageOptions_SetImageSize_TitanImage microflow.

Input	Output
GenAICommons.ImageOptions (object), ENUM_ImageSize_TitanImage (enumeration)	none

Image Generation: Set Randomness

Use this microflow to set the Seed and CfgScale attributes of your GenAICommons.ImageOptions object. These attributes can be used to influence the randomness of the image generation.

For more information, please refer to the specific model documentation such as Titan Image Generator G1.

This operation corresponds to the ImageOptions_SetRandomness microflow.

Input	Output
GenAICommons.ImageOptions (object), Seed (integer), CfgScale (decimal)	none

Seed and GfgScale can be empty, in which case they are not sent to the Bedrock API.

Embeddings Options: Add Cohere Embed Extension

Use this microflow to add a new CohereEmbedOptions_Extension object to your EmbeddingsOptions object. You can use it to include parameters that are unique to Cohere Embed models.

This operation corresponds to the CohereEmbedOptions_Extension_Create microflow.

Input	Output
GenAICommons.EmbeddingsOptions (object), InputType (enumeration), EmbeddingTypes (enumeration, optional), Truncate (enumeration, optional)	CohereEmbedOptions_Extension (object)

Embeddings Options: Add Titan Embeddings Extension

Use this microflow to add a new TitanEmbeddingsOptions_Extension object to your EmbeddingsOptions object. You can use it to include parameters that are unique to Titan Embeddings models.

This operation corresponds to the TitanEmbeddingsOptions_Extension_Create microflow.

Input	Output
GenAICommons.EmbeddingsOptions (object), Normalize (boolean)	TitanEmbeddingsOptions_Extension (object)

Request: Add Retrieve Request Extension

Use this microflow to add a new RetrieveRequest_Extension object to your request. This is required in order to use the Retrieve activity. It requires Connection, and RetrieveRequest as input parameters.

To use this activity, you must set up a knowledge base in your Amazon Bedrock Environment. For more information, see Knowledge Base.

The input and output for this service are shown in the table below:

Input	Output
ENUM_Region (enumeration), Credentials (object), RetrieveRequest (object)	RetrieveResponse (object)

Request: Add Additional Request Parameter

Use this microflow to add an additional model-specific request parameter to your request. Please follow this link to find available additional request parameters: Inference parameters

This operation corresponds to the Request_CreateAdditionalRequestParameter microflow.

Input	Output
GenAICommons.Request (object), Key (string), StringValue (string), DecimalValue (decimal), IntegerValue (integer)	none

You need to provide a value using either the StringValue, DecimalValue or IntegerValue parameters. For example, if you providing a StringValue as value of the parameter, DecimalValue and IntegerValue should be left empty.

Request: Add Additional Response Field

Some models can return additional information that is not part of the GenAICommons.Response entity. Use this microflow to add an additional model-specific response field to your request.

You can retrieve the additional requested response fields using the Response: Get Requested Response Fields operation.

This operation corresponds to the Request_CreateResponseFieldRequest microflow.

Input	Output
GenAICommons.Request (object), FieldName (string)	none

If the used model supports that response field, it will be returned as a ChatCompletionsResponse object as part of the response.

Response: Get Requested Response Fields

Use this microflow to retrieve all requested model-specific response fields from the response.

Some models can return additional information that is not part of the GenAICommons.Response entity. You can request additional request parameters using the Request: Add Additional Response Fields operation.

This operation corresponds to the Response_GetRequestedResponseFields microflow.

Input	Output
GenAICommons.Response (object)	RequestedResponseField (list)

Response: Get NextToken

Use this microflow to retrieve the NextToken from the response after using the Retrieve operation.

This operation corresponds to the Response_GetNextToken microflow.

Input	Output
GenAICommons.Response (object)	NextToken (string)

Response: Cast RetrieveAndGenerateResponse

Use this microflow to get the RetrieveAndGenerateResponse object from the GenAiCommons.Response that is returned by the RetrieveAndGenerate operation.

The RetrieveAndGenerateResponse object contains the SessionID of the current Session that can be used in a subsequent request to chat within the same session.

This operation corresponds to the Response_Cast_RetrieveAndGenerateResponse microflow.

Input	Output
GenAICommons.Response (object)	RetrieveAndGenerateResponse (object)

Other Operations

ListFoundationModels

The ListFoundationModels activity allows you to get all the available foundational models which Amazon Bedrock provides. It requires ENUM_Region, Credentials and ListFoundationModelsRequest as input parameters.

The input and output for this service are shown in the table below:

Input	Output
ENUM_Region (enumeration), Credentials (object), ListFoundationModelsRequest (object)	ListFoundationModelsResponse (object)

InvokeModel

The InvokeModel activity allows you to invoke a model from Amazon Bedrock. This activity provides the generic parts that are equal for the invocation of every model. It requires ENUM_Region, Credentials and InvokeModelRequest as input parameters.

The InvokeModel operation provides a versatile interface for integrating with Amazon Bedrock models. Each available model in Amazon Bedrock has its own set of model-specific parameters required to be passed into the InvokeModelRequest. The Amazon Bedrock Connector contains two example implementations to showcase how to use the InvokeModel operation to invoke specific models. The Amazon Bedrock example implementation available on the Mendix Marketplace provides a more extensive reference implementation of how to configure the model-specific parameters into the generic InvokeModel operation.

The input and output for this service are shown in the table below:

Input	Output
ENUM_Region (enumeration), Credentials (object), InvokeModelRequest (object)	InvokeModelResponse (object)

ListKnowledgeBases

The ListKnowledgeBases activity allows you to list the knowledge bases in an account and get information about each of them. It requires ENUM_Region, Credentials, and ListKnowledgeBasesRequest as input parameters.

To use this activity, you must set up a knowledge base in your Amazon Bedrock Environment. For more information, see Knowledge Base.

The input and output for this service are shown in the table below:

Input	Output
ENUM_Region (enumeration), Credentials (object), ListKnowledgeBasesRequest (object)	ListKnowledgeBasesResponse (object)

StartIngestionJob

The StartIngestionJob activity allows you to begin an ingestion job, in which the contents of the data source S3 bucket is preprocessed and synced with the vector database of the knowledge base. It requires ENUM_Region, Credentials and StartIngestionJobRequest as input parameters.

To use this activity, you must set up a knowledge base in your Amazon Bedrock Environment. For more information, see Knowledge Base.

The input and output for this service are shown in the table below:

Input	Output
ENUM_Region (enumeration), Credentials (object), StartIngestionJobRequest (object)	StartIngestionJobResponse (object)

GetIngestionJob

The GetIngestionJob activity allows you to retrieve information about a ingestion job, in which the contents of the data source S3 bucket is preprocessed and synced with the vector database of the knowledge base. It requires ENUM_Region, Credentials and GetIngestionJobRequest as input parameters.

To use this activity, you must set up a knowledge base in your Amazon Bedrock Environment. For more information, see Knowledge Base.

The input and output for this service are shown in the table below:

Input	Output
ENUM_Region (enumeration), Credentials (object), GetIngestionJobRequest (object)	GetIngestionJobResponse (object)

InvokeAgent

The InvokeAgent activity allows you to invoke an agent from Amazon Bedrock, so that you can orchestrate tasks involving foundation models and enrich the process with organizational data and user input. It requires ENUM_Region, Credentials, InvokeAgentRequest, a ResponseHandlerMicroflow and a ErrorHandlerMicroflow as input parameters. The microflow parameters are necessary since InvokeAgent is an asynchronous operation. The ResponseHandlerMicroflow is required to have exactly one input parameter of the InvokeAgentResponse entity type. It is called in a background threat once the response is available. The ErrorHandlerMicroflow is required to have exactly one input parameter of type String. It will be called when there is an error during the asynchronous process and the error type will be passed to it’s string parameter. The Amazon Bedrock Connector includes sample response handler and error handler microflows to help you set up handlers for your implementation.

For more information, see Agents for Bedrock

The input and output for this service are shown in the table below:

Input	Output
ENUM_Region (enumeration), Credentials (object), InvokeModelRequest (object), ResponseHandlerMicroflow (microflow), ErrorHandlerMicroflow (microflow)	none

Handling the Asynchronous InvokeAgentResponse

The InvokeAgentResponse object is passed as a parameter to the ResponseHandler microflow. This microflow can perform any custom logic with the InvokeAgentResponse, for example storing it in the database. The microflow is called in another background thread, so the client is not automatically notified when the response is processed. If you want to display the agent’s response to the user of your app, you can use one of the following methods:

Polling

The easiest way to make sure the client gets a response is to constantly poll for it until it is available. This can be done using the Microflow Timer Widget, which allows you to configure a microflow or nanoflow to run every X number of seconds.

This approach is only recommended for testing and for applications that do not have a large number of concurrent users. It is not preferred for scaling.

Websockets

WebSockets is a communication protocol that provides full-duplex communication channels over a single, persistent connection. Unlike traditional HTTP connections, which are request-response based and stateless, WebSockets enable real-time, bi-directional communication between a client (such as a Web browser) and a server.

The open source EZ Websocket Module from the Mendix Marketplace provides an easy way to implement real-time server-to-client communication using WebSockets without external dependencies.

Pusher

The platform-supported Pusher Module is built around the Pusher Channels offering. This module requires a Pusher account. Pusher Channels is a paid service, but it also has a Free Sandbox Plan. This module allows you to trigger a Notify event on the server to immediately trigger an action in the client application.

Working with Action Groups and Lambda Functions

Without action groups, the agent will still access associated knowledge bases, but will not be able to perform tasks that make agents an extension of simply invoking a model. Action groups are what make agents so powerful.

For example, it might be beneficial for the agent to dynamically retrieve more information via a REST endpoint or other source, rather than storing all possible information in a knowledge base. To achieve this, a lambda function must first be specified for the REST request and then associated with the agent as part of an action group.

If you would like to add lambda functions to your agent, please refer to the AWS documentation.

ListAgents

The ListAgents activity allows you to list the agents in an account and get information about each of them. It requires ENUM_Region, Credentials, and ListAgentsRequest as input parameters.

To use this activity, you must set up an agent in your Amazon Bedrock environment.

The input and output for this service are shown in the table below:

Input	Output
ENUM_Region (enumeration), Credentials (object), ListAgentsRequest (object)	ListAgentsResponse (object)

GetAgent

The GetAgent activity allows you to retrieve information about an agent. It requires ENUM_Region, Credentials, and GetAgentRequest as input parameters.

To use this activity, you must set up an agent in your Amazon Bedrock Environment.

The input and output for this service are shown in the table below:

Input	Output
ENUM_Region (enumeration), Credentials (object), GetAgentRequest (object)	GetAgentResponse (object)

Operations to Persist Amazon Bedrock Metadata inside the Application

The Amazon Bedrock Connector offers a range of operations to retrieve and store metadata information in the Mendix app’s database.

This can be useful to e.g. associate a chatbot configuration to an available model by selecting the model via dropdown in runtime. The persistent domain model allows for simple and efficient filtering capabilities on the available metadata. Further, the SNIP_Settings_Admin_BedrockConfig Snippet can be used to manage and view the synced data from an administrator perspective.

Currently, there are operations available to sync metadata about:

• Sync Models
• Sync Knowledge Bases
• Sync Agents

The syncing process works the same for all of these operations.

1. The information about models / knowledge bases / agents is persistent in the mendix app’s database on the initial sync.
2. An association to the AmazonBedrockRegion object, that represents the AWS region used when syncing, is stored.
3. On a subsequent syncing process the available data is extended and updated. No data will be removed from the app’s database - even if it is no longer available on AWS. The reason is that existing usages of the object in the running application should not be removed.

The available operations are described in the following sections.

Sync Models

The Sync Models activity allows you to retrieve and store metadata about available models on Amazon Bedrock in your app’s database. The model information is persistent in the AmazonBedrockModel entity.

Information about the models output and input modalities are stored as associations to the ModelModality entity. The input modality describes which form of data can be sent to the model. The output modality describes which form of data the model will return.

Information about the models inference type is stored as association to the ModelInferenceType entity. The inference type describes how the model can be accessed. ON Demand models are accessible by default and charged by usage.

The input and output for this service are shown in the table below:

Input	Output
ENUM_Region (enumeration), UseStaticCredentials (boolean)	Count (integer)

The operation returns an integer that indicates how many objects were created or changed during the syncing process.

Sync Knowledge Bases

The Sync Knowledge Bases activity allows you to retrieve and store metadata about available knowledge bases on Amazon Bedrock in your app’s database. The knowledge base information is persistent in the AmazonBedrockKnowledgeBase entity.

The input and output for this service are shown in the table below:

Input	Output
ENUM_Region (enumeration), UseStaticCredentials (boolean)	Count (integer)

The operation returns an integer that indicates how many objects were created or changed during the syncing process.

Sync Agents

The Sync Agents activity allows you to retrieve and store metadata about available agents on Amazon Bedrock in your app’s database. The agent information is persistent in the AmazonBedrockAgent entity.

The input and output for this service are shown in the table below:

Input	Output
ENUM_Region (enumeration), UseStaticCredentials (boolean)	Count (integer)

The operation returns an integer that indicates how many objects were created or changed during the syncing process.

Knowledge Base

In Bedrock, a knowledge base denotes a substantial storehouse of data and information. This serves as a foundational resource, enabling the AI system to glean insights and effectively comprehend and respond to natural language queries.

For more information about knowledge bases, see Knowledge Base in the Amazon Bedrock documentation.

Creating a Knowledge Base

Setting up knowledge bases is usually a one-time configuration, which can be done with the AWS Console. In order to get the best results, you should consider whether you want to use one of the chunking strategies available on AWS when creating the knowledge base, or whether you want to pre-process the data beforehand.

Chunking is the practice of breaking large chunks of data into smaller segments. Chunking the data allows the embedding algorithm to process the given data in chunks, thus increasing efficiency. Chunking can also introduce a structure that helps the model understand which data belongs to the same context. You can use the default chunking strategy, or create a custom strategy if there is a specific way in which the model data should be split.

For example, when building a chatbot that gives restaurant recommendations, you should set up the knowledge base with a list of restaurant reviews. In this case, using the default chunking into 300 tokens might result in chunks containing reviews for different restaurants, which is not optimal. You will likely have better results if each chunk corresponds to reviews for one restaurant, as with that strategy it is less likely that the model will then associate a review with the wrong restaurant. You can achieve the required results by pre-processing the data so that there is one file per restaurant, and using the No chunking option when setting up the knowledge base.

For more information about creating the knowledge base, including a list of the available chunking strategies, see Create a knowledge base.

Adding Data from Your App

After a knowledge base has been set up, information from your app can be added in a file to the relevant S3 bucket, and then used during subsequent inquiries. Which information is used and how that information is exported depends on the customer’s use case and is up to the Mendix developer to implement. For more information, see Set up your data for ingestion.

Amazon Bedrock only processes the information that existed during the last sync, so the data source must be synchronized whenever a new file is added to your S3 bucket or the existing files are changed. For more information, see Sync to ingest your data sources into the knowledge base.

The sync can be done from the information page of your knowledge base in the Amazon Bedrock Console, or by using the StartIngestionJob action in the Amazon Bedrock Connector.

Safeguards

AWS has introduced safeguards for Bedrock (currently in preview). When available, there will be two features: Guardrails and Watermark detection.

The guardrail feature will allow you to:

• Filter harmful content with configurable thresholds based on your responsible AI policies.
• Determine how to handle personally identifiable information (PII).
• Deny topics.

The watermark detection feature will make it possible to tell if an image has been created using Amazon Titan.

More information about guardrails can be found in this AWS blogpost and in the AWS documentation.

Advanced Prompts for Agents

By default, an agent is configured with the following base prompt templates, one for each step in the agent sequence:

• Pre-processing
• Orchestration
• Knowledge base response generation
• Post-processing

By customizing the prompt templates and modifying these configurations, you can fine-tune your agent’s accuracy. Additionally, you can provide custom examples for a technique known as few-shot prompting. This involves providing labeled examples for specific tasks, which further enhances the model’s performance in targeted areas. For more information about advanced prompts, see Advanced prompts in the AWS documentation.

You can also use placeholder variables in agent prompt templates. For example, in the orchestration prompt template, the $prompt_session_attributes$ placeholder variable can be used to ingest the information from the PromptSessionAttribute entity into the prompt, if it was specified as part of the InvokeAgentRequest. For more information about placeholder variables available in agent prompt templates, see Prompt placeholders in the AWS documentation.

Troubleshooting

If you encounter any issues while using the Amazon Bedrock connector, use the following troubleshooting tips to help you solve them.

Error Code 400 - Bad Request

The service returns the error code 400 - Bad Request.

Cause

Your AWS organization may not have been granted access to the model which you are trying to invoke.

Solution

To solve this issue, follow these steps:

1. In your Amazon Bedrock environment, navigate to Model Access for the region in which you would like to work.
2. If the status of a model is Available, enable access to this model for your AWS organization by doing the following steps:
   1. In the top-right corner of the overview, click on Edit.
   2. Select the check boxes by the models which you want to access with your credential set.
   3. Click Save Changes.

After the status of the models changes to Access Granted, you can use it with the Amazon Bedrock connector.

Error code 403 - AccessDeniedException

When invoking a model, the error code 403 - Access denied indicates that you do not have access to the targeted resource.

Cause

Possible root causes for this error include the following:

• You do not have access to the model in the specified AWS region.

Solution

To solve this issue, ensure that you have selected an AWS Region where you have model access. You can see an overview of the models accessible to you in the AWS Management Console, in the Model Access section of your Amazon Bedrock environment.

Error code 404 - ResourceNotFoundException

When invoking a model, the error code 404 - Resource not found indicates that the targeted resource was not found.

Cause

Possible root causes for this error include the following:

• The model which you are trying to invoke is not available in your specified AWS region.
• The model which you are trying to invoke is deprecated.

Solution

To solve this issue, verify the following:

1. Ensure that you have selected an AWS Region where the targeted model exists. You can see an overview of the models accessible to you in the AWS Management Console, on the Overview page of your Amazon Bedrock environment. Make sure the region specified in the AWS Console matches the region you have configured in Mendix.
2. Ensure that the model that you have selected is not deprecated and that the model-id is currently available in Amazon Bedrock.