diff --git a/content/en/docs/genai/concepts/_index.md b/content/en/docs/genai/concepts/_index.md index 7ded5eabdb6..d921b6834c9 100644 --- a/content/en/docs/genai/concepts/_index.md +++ b/content/en/docs/genai/concepts/_index.md @@ -133,7 +133,7 @@ An example of how this can be done with OpenAI is described in [RAG Example Impl Another way to provide the LLM with additional information and capabilities is to use function calling, also known as tool use. With function calling you can make specific microflows available to the LLM. While evaluating the prompt, the LLM will, optionally, ask to execute a particular microflow. The Mendix application will execute this microflow and return additional information for the LLM to add to the prompt being processed. -This microflow runs in the context of the user, allowing you to make sure that it only shows data that is relevant for the current user. You can also use it to execute actions on behalf of the user, or interact with page that the user is looking at. +This microflow runs in the context of the user, allowing you to make sure that it only shows data that is relevant for the current user. You can also use it to execute actions on behalf of the user, or interact with the page that the user is looking at. See [Function Calling](/agents/function-calling/) for more information on ReAct. You can see ReAct implemented in the [GenAI Showcase App](https://marketplace.mendix.com/link/component/220475) where the `GetInformationForTicketID` microflow allows an LLM to answer a question like "What is the status of ticket 42?". diff --git a/content/en/docs/genai/concepts/model-context-protocol.md b/content/en/docs/genai/concepts/model-context-protocol.md index 682877e5693..36dc2ebd455 100644 --- a/content/en/docs/genai/concepts/model-context-protocol.md +++ b/content/en/docs/genai/concepts/model-context-protocol.md @@ -28,7 +28,7 @@ The MCP host is typically the application that facilitates interaction with LLMs ### MCP Client -The MCP client usually runs inside of the MCP Host and is responsible of connecting to one MCP server outside of the MCP host application. The client follows the MCP principles and should technically be able to connect to any MCP server. A host can maintain multiple clients at the same time and thus connecting to multiple MCP servers or maintaining a client-server connection for each end-user. +The MCP client usually runs inside of the MCP Host and is responsible for connecting to one MCP server outside of the MCP host application. The client follows the MCP principles and should technically be able to connect to any MCP server. A host can maintain multiple clients at the same time and thus connecting to multiple MCP servers or maintaining a client-server connection for each end-user. ### MCP Server @@ -38,7 +38,7 @@ Servers can expose the following three main types of resources: #### Tools -The most common type of resource is [tool](https://modelcontextprotocol.io/specification/2025-06-18/server/tools), which exposes executable functionality such as microflows to clients. Those can not only contain simple retrieve logic to get information from a database, but also perform actions on behalf of the user, for example, submitting a report or changing values. Typically, a model in the MCP host chooses when to call a tool based on the context of the chat. +The most common type of resource is a [tool](https://modelcontextprotocol.io/specification/2025-06-18/server/tools), which exposes executable functionality such as microflows to clients. Those can not only contain simple retrieve logic to get information from a database, but also perform actions on behalf of the user, for example, submitting a report or changing values. Typically, a model in the MCP host chooses when to call a tool based on the context of the chat. #### Prompts @@ -52,7 +52,7 @@ In general, generic [resources](https://modelcontextprotocol.io/specification/20 An example for setting up an MCP server is available in the [GenAI Showcase App](https://marketplace.mendix.com/link/component/220475). The app guides you through available tools and prompts that will be exposed when the server is started. Additionally, it shows an example for setting up authentication using username and password. You can use a Mendix application or third-party tools such as Claude Desktop as an MCP host to connect to the server. -Furthermore, an MCP Client example teaches you how to establish a connection to a server, discover tools and prompts, and finally uses them in a chat interface. You can use this example to inspect MCP servers, similar as the [MCP Inspector](https://github.com/modelcontextprotocol/inspector) can do, by testing prompts and tools without any LLM integration. +Furthermore, an MCP Client example teaches you how to establish a connection to a server, discover tools and prompts, and finally use them in a chat interface. You can use this example to inspect MCP servers, similar as the [MCP Inspector](https://github.com/modelcontextprotocol/inspector) can do, by testing prompts and tools without any LLM integration. ## Read More diff --git a/content/en/docs/genai/concepts/prompt-engineering.md b/content/en/docs/genai/concepts/prompt-engineering.md index 31d9a99d898..5d575c15670 100644 --- a/content/en/docs/genai/concepts/prompt-engineering.md +++ b/content/en/docs/genai/concepts/prompt-engineering.md @@ -90,7 +90,7 @@ Different prompt techniques can be used to guide AI models in performing their s | Prompt technique | Description | Use Case and Prompt Example | |-------------------|---------------------------------------------------|----------------------------------| | Interview Pattern Approach| Using an interview-style approach allows the model to ask follow-up questions to the end-user to provide a better-fitting response. | Use case: Movie Recommendation Engine.
Prompt instruction: *You will act as a movie recommender expert. You will ask the user a series of detailed questions, one at a time, to understand their preferences in movies*. | -| Instruction Prompt | The instruction gives the AI model directions on how to perform a task. It can be a guidance on the type of output, such as summarization or translation, style, format, and more. | Use case: Mendix ML Kit Python Script Generator.
Prompt instruction: *You will act as an expert Python developer specializing in the Mendix ML Kit. The output/response should be given in a python script with annotations for the Mendix ML Kit*. | +| Instruction Prompt | The instruction gives the AI model directions on how to perform a task. It can be guidance on the type of output, such as summarization or translation, style, format, and more. | Use case: Mendix ML Kit Python Script Generator.
Prompt instruction: *You will act as an expert Python developer specializing in the Mendix ML Kit. The output/response should be given in a python script with annotations for the Mendix ML Kit*. | | Few-shot Prompt | It helps the model to learn a task or pattern dynamically by providing examples. It can also be part of the system prompt. | Use case: English-Spanish Translator.
Prompt instruction: *You are a kind assistant who helps translate English texts to Spanish. For example, “Good Evening” to Spanish: “Buenas Noches*” | | Chain-of-Thought | It simplifies complex tasks by turning them into discrete steps that happen in a certain order. | Use case: Medical Diagnosis for interns.
Prompt instruction: *You are a diagnosis assistant designed to help trainee doctors ask a series of questions for patients’ initial evaluation. Your goal is to identify the patient’s symptoms, health history, and other relevant variables to reach an accurate evaluation that, depending on the result, will be forwarded to nurses or doctors. Start by asking the patient about their primary symptoms and the reason for their visit. Then, ...* | | Tree-of-Thought | Similar to a decision tree, it includes several lines of thought to allow the model to evaluate and find its path to the correct outcome. | Use case: Support Assistant Bot.
Prompt instruction: *You are a helpful assistant supporting the IT department with employees’ requests, such as support tickets, licenses, or hardware inquiries. Follow the instructions below according to the type of request. If the user asks about ... If the request is vague or incomplete, ... If the request is about licenses or hardware, first ... then ... If the user wants to know about their support tickets, ...* | diff --git a/content/en/docs/genai/concepts/rag-example-implementation.md b/content/en/docs/genai/concepts/rag-example-implementation.md index 3d04fb1a78e..1e8890d130e 100644 --- a/content/en/docs/genai/concepts/rag-example-implementation.md +++ b/content/en/docs/genai/concepts/rag-example-implementation.md @@ -10,7 +10,7 @@ aliases: ## Introduction {#introduction} -Retrieval augmented generation (RAG) is a framework for an AI-based search using a private or external knowledge base that combines embeddings-based knowledge retrieval with a text generation model. The starting point is a collection of data to be considered as the private knowledge base. The final goal is that an end-user of the app can ask questions about the data and the assistant's responses are only be based on this knowledge base. +Retrieval augmented generation (RAG) is a framework for an AI-based search using a private or external knowledge base that combines embeddings-based knowledge retrieval with a text generation model. The starting point is a collection of data to be considered as the private knowledge base. The final goal is that an end-user of the app can ask questions about the data and the assistant's responses are only based on this knowledge base. {{% alert color="info" %}}This document describes how to set up RAG with PgVector. If you want to use the Bedrock Retrieval Augmented Generation capabilities, see [Bedrock Retrieval Augmented Generation](/agents/get-started/#rag).{{% /alert %}} @@ -126,4 +126,4 @@ If you want to build your own RAG setup, feel free to learn from the GenAI Showc ## Read More {#read-more} -* [Embeddings-based Search – Open AI Cookbook](https://cookbook.openai.com/examples/question_answering_using_embeddings) +* [Embeddings-based Search – OpenAI Cookbook](https://cookbook.openai.com/examples/question_answering_using_embeddings) diff --git a/content/en/docs/genai/mendix-cloud-genai/navigate-mxgenai.md b/content/en/docs/genai/mendix-cloud-genai/navigate-mxgenai.md index c625663adf8..0c3bf702abc 100644 --- a/content/en/docs/genai/mendix-cloud-genai/navigate-mxgenai.md +++ b/content/en/docs/genai/mendix-cloud-genai/navigate-mxgenai.md @@ -22,7 +22,7 @@ After clicking a specific resource, you land on its details page, which provides {{% alert color="info" %}} The **Consumption** tab is available for Model resources only.{{% /alert %}} -{{% alert color="warning" %}}A resource reaches its monthly GenAI Unit allocation is blocked. A blocked resource stays blocked until a company admin increases its GenAI Unit capacity or the current bundle month ends. For details, see [Consumption Limit Notifications](#consumption-limit-notifications).{{% /alert %}} +{{% alert color="warning" %}}A resource that reaches its monthly GenAI Unit allocation is blocked. A blocked resource stays blocked until a company admin increases its GenAI Unit capacity or the current bundle month ends. For details, see [Consumption Limit Notifications](#consumption-limit-notifications).{{% /alert %}} The **Consumption** tab provides an overview of GenAI Unit and Large Language Model (LLM) token consumption for the resource. Use this overview to see current usage, view daily consumption insights, and compare the current period with previous periods. @@ -32,7 +32,7 @@ Note that periods represent bundle months. A bundle month is the period during w **Current Consumption** shows the total GenAI Units consumed against your monthly allocation, displayed as a percentage and a number (for example, 1,521 / 1.6k). **Plan** shows the resource pack model type (for example, Anthropic Claude Opus) and the total GenAI Unit allocation for the bundle month. The bundle refill date is shown at the top of the page. -Below the summary, the page shows **Consumption Over Selected Time Range**. You can switch between weekly (**W**), bundle month (**BM**), month to date (**MTD**), and six months (**6M**) views. Use the **Model** dropdown to filter the charts by a specific model, and the **Key** dropdown to filter them by a specific resource key. The charts show the following metrics: +Below the summary, the page shows **Consumption Over Selected Time Range**. You can switch between weekly (**W**), bundle month (**BM**), month to date (**MTD**), and six months (**6M**) views. Use the **Model** drop-down list to filter the charts by a specific model, and the **Key** dropdown to filter them by a specific resource key. The charts show the following metrics: * **Monthly Bundle GenAI Units Consumption** – GenAI Units consumed per day, with a dashed line indicating the monthly allocation limit. * **Monthly Bundle Input Consumption** – Raw LLM input tokens consumed per day. @@ -48,7 +48,7 @@ Mendix notifies you when a resource approaches or reaches its monthly GenAI Unit A blocked resource stays blocked until one of the following happens: -* A company admin increases the GenAI Unit capacity of the resource so that it covers to a value above the current consumption. For more information, see [Adjusting the GenAI Unit Capacity of a Resource](#adjusting-the-genai-unit-capacity-of-a-resource). +* A company admin increases the GenAI Unit capacity of the resource so that it covers a value above the current consumption. For more information, see [Adjusting the GenAI Unit Capacity of a Resource](#adjusting-the-genai-unit-capacity-of-a-resource). * The current bundle month ends and the allocation resets. To avoid blocking, increase the allocation as soon as you receive the 80% or 100% notification. diff --git a/content/en/docs/genai/v1/how-to/byo_connector.md b/content/en/docs/genai/v1/how-to/byo_connector.md index 7c56c177a99..adbd1e3eb5f 100644 --- a/content/en/docs/genai/v1/how-to/byo_connector.md +++ b/content/en/docs/genai/v1/how-to/byo_connector.md @@ -59,7 +59,7 @@ If your provider's API differs significantly from OpenAI's, it is best to start Additionally, refer to the [GenAI Commons](/agents/agents-kit-1/reference-guide/commons/) to explore available out-of-the-box components that can help accelerate development. Pay close attention to: * The domain model (data structure) to see how existing entities can be reused. -* The **Connector Building** folders, contain useful microflows and helper activities for working with the provided entities. +* The **Connector Building** folders contain useful microflows and helper activities for working with the provided entities. If you want to explore the [GenAICommons](https://marketplace.mendix.com/link/component/227933) module, check out the [public repository](https://github.com/mendix/genai-showcase-app). @@ -155,4 +155,4 @@ To set this up: 1. Find **Echo Configurations** in the **Management** section of the home page. This will lead you to the page where the configuration can be set up for the Echo Connector. 2. Click **New**, fill in the required fields, and click **Save**. For this example, the input can be left empty as no real credentials are needed. When you click **Save**, two `EchoDeployedModel` objects are created for the new configuration. Since the Echo Connector simply returns the request content as the response, these serve as test models for the Chat UI examples. In a custom connector, this step could involve importing available models based on the configuration or allowing the admin to create models manually. 3. After the configuration and the models have been created, go back to the home page and open one of the showcases in the **Conversational UI** section. -4. In the **Model** dropdown, select one of the models created by the Echo Connector and start chatting. +4. In the **Model** drop-down list, select one of the models created by the Echo Connector and start chatting. diff --git a/content/en/docs/genai/v1/how-to/creating-agents/shared-setup.md b/content/en/docs/genai/v1/how-to/creating-agents/shared-setup.md index 9084d40c7a5..3ff2cf5c042 100644 --- a/content/en/docs/genai/v1/how-to/creating-agents/shared-setup.md +++ b/content/en/docs/genai/v1/how-to/creating-agents/shared-setup.md @@ -206,7 +206,7 @@ You have now successfully created your first function microflow to link to the a #### Get Ticket by Identifier 1. Create a new microflow named `Ticket_GetTicketByID`. -2. In the new microflow, Add a *String* input parameter called `Identifier`. +2. In the new microflow, add a *String* input parameter called `Identifier`. 3. Add a `Retrieve` action to retrieve the ticket of the given identifier: diff --git a/content/en/docs/genai/v1/how-to/ground_your_llm_in_data.md b/content/en/docs/genai/v1/how-to/ground_your_llm_in_data.md index ba2c5ad9e68..2f921c92e08 100644 --- a/content/en/docs/genai/v1/how-to/ground_your_llm_in_data.md +++ b/content/en/docs/genai/v1/how-to/ground_your_llm_in_data.md @@ -45,7 +45,7 @@ Before implementing this capability into your app, make sure you meet the follow Since this document focuses on the [Mendix Cloud GenAI Resource Packs](/agents/mx-cloud-genai/resource-packs/), ensure that you have the [Mendix Cloud GenAI Connector](https://marketplace.mendix.com/link/component/239449) installed. -Follow the instructions in the [Navigate through the Mendix Cloud GenAI Portal](/agents/mx-cloud-genai/Navigate-MxGenAI/) guide to collect the resources keys and configure the connector within your application. The keys bridge the gap between your app and the resources, enabling you to access models and add to or retrieve data from a Mendix Cloud GenAI knowledge base. +Follow the instructions in the [Navigate through the Mendix Cloud GenAI Portal](/agents/mx-cloud-genai/Navigate-MxGenAI/) guide to collect the resource keys and configure the connector within your application. The keys bridge the gap between your app and the resources, enabling you to access models and add to or retrieve data from a Mendix Cloud GenAI knowledge base. While this documentation focuses on adding data to your knowledge base from a Mendix application, you can also fill the knowledge base directly within the portal, for example, by uploading files. diff --git a/content/en/docs/genai/v1/how-to/integrate_function_calling.md b/content/en/docs/genai/v1/how-to/integrate_function_calling.md index 48d54769037..a503157c086 100644 --- a/content/en/docs/genai/v1/how-to/integrate_function_calling.md +++ b/content/en/docs/genai/v1/how-to/integrate_function_calling.md @@ -26,7 +26,7 @@ Through this document, you will: Before integrating function calling into your app, make sure you meet the following requirements: -* An existing app: To simplify your first use case, start building from a preconfigured set up [Blank GenAI Starter App](https://marketplace.mendix.com/link/component/227934). For more information, see [Build a Chatbot from Scratch Using the Blank GenAI App](/agents/agents-kit-1/how-to/blank-app/). +* An existing app: To simplify your first use case, start building from the preconfigured [Blank GenAI Starter App](https://marketplace.mendix.com/link/component/227934). For more information, see [Build a Chatbot from Scratch Using the Blank GenAI App](/agents/agents-kit-1/how-to/blank-app/). * Be on Mendix Studio Pro 10.12.4 or higher. diff --git a/content/en/docs/genai/v1/how-to/prompt_engineering-runtime.md b/content/en/docs/genai/v1/how-to/prompt_engineering-runtime.md index f5a5796572f..b54515ead1e 100644 --- a/content/en/docs/genai/v1/how-to/prompt_engineering-runtime.md +++ b/content/en/docs/genai/v1/how-to/prompt_engineering-runtime.md @@ -46,7 +46,7 @@ Agent Commons enables users to create powerful agents at runtime, enriching requ If you are using a GenAI starter app such as the Blank GenAI app, you can skip ahead to [the next section](#configuration) because the following steps are already completed. Otherwise, follow these setup steps to add Agent Commons capabilities to your app and navigation: -1. Install the [Agent Commons module](/agents/agents-kit-1/reference-guide/agent-commons/) module and its dependencies from Mendix Marketplace. +1. Install the [Agent Commons module](/agents/agents-kit-1/reference-guide/agent-commons/) and its dependencies from Mendix Marketplace. 2. Open your app's [Security](/refguide/security/#user-role) settings and edit the user role that you want to be able to create agents at runtime. This is typically the Administrator role, but this may vary depending on your use case. Follow these steps: 1. For the Agent Commons module, assign the **AgentAdmin** module role. @@ -104,11 +104,11 @@ You can now create your first agent in the user interface. The final agent will To further improve your prompts and the user experience, you can add some placeholder variables. -1. Once you have saved an agent version, you cannot edit its fields anymore. So, in the **Agent version** dropdown, click **Draft** to start a new version and edit the fields again. +1. Once you have saved an agent version, you cannot edit its fields anymore. So, in the **Agent version** drop-down list, click **Draft** to start a new version and edit the fields again. 2. Change the **User Prompt** to `Generate a short product description for a {{ProductName}}. The description should not be longer than {{NumberOfWords}} words.` -3. Notice that two variables appear in the **Test Case** card on the right. You can use these variables later in your in your application to allow users to dynamically modify the user prompt without needing to understand what a prompt is, and without requiring any changes or restarts to the application. Enter the following values for the variables: +3. Notice that two variables appear in the **Test Case** card on the right. You can use these variables later in your application to allow users to dynamically modify the user prompt without needing to understand what a prompt is, and without requiring any changes or restarts to the application. Enter the following values for the variables: * `30` for **NumberOfWords** * `chair` for **ProductName** @@ -123,7 +123,7 @@ To further improve your prompts and the user experience, you can add some placeh To further refine the agent's responses, add a system prompt that defines the assistant's role and create an additional test case for comparison. -1. In the **Agent version** dropdown, click **Draft** so that you can edit the fields again. This time, add instructions in the [System Prompt](/agents/prompt-engineering/#system-prompt) field. Enter the following: `You are a sales assistant that can write engaging and inspiring product descriptions for our online marketplace. The user asks you to create a description for various products. You should always respond in {{Language}}.` Notice that the **Language** variable appears in the **Test Card**. +1. In the **Agent version** drop-down list, click **Draft** so that you can edit the fields again. This time, add instructions in the [System Prompt](/agents/prompt-engineering/#system-prompt) field. Enter the following: `You are a sales assistant that can write engaging and inspiring product descriptions for our online marketplace. The user asks you to create a description for various products. You should always respond in {{Language}}.` Notice that the **Language** variable appears in the **Test Card**. 2. Add a new test case by clicking **New Blank Test Case** ({{% icon name="add"%}}) next to the **Test variable input** dropdown. @@ -132,7 +132,7 @@ To further refine the agent's responses, add a system prompt that defines the as 3. Click **Test** to test the new input, then save the test case with the title `Chair 30 words German`. -4. Now that you have saved at least two test cases, open the dropdown next to the **Test** button, and click **Test All**. This runs both test cases, allowing you to compare the different input values. Note that the **Language** variable was not set in the first test case, as it did not exist at the time. As a result, the model's response may be in English or another random language. +4. Now that you have saved at least two test cases, open the drop-down list next to the **Test** button, and click **Test All**. This runs both test cases, allowing you to compare the different input values. Note that the **Language** variable was not set in the first test case, as it did not exist at the time. As a result, the model's response may be in English or another random language. 5. Once you are satisfied with your agent, save the version with the title `Added system prompt and language`. diff --git a/content/en/docs/genai/v1/how-to/start_from_a_starter_app.md b/content/en/docs/genai/v1/how-to/start_from_a_starter_app.md index 8367fb3e0e1..019edcf22dd 100644 --- a/content/en/docs/genai/v1/how-to/start_from_a_starter_app.md +++ b/content/en/docs/genai/v1/how-to/start_from_a_starter_app.md @@ -11,7 +11,7 @@ aliases: ## Introduction -This document guides on building a smart app using a starter template. Alternatively, you can create your smart app from scratch using a blank GenAI app template. For more details, see [Build a Smart App from a Blank GenAI App](/agents/agents-kit-1/how-to/blank-app/). +This document guides you on building a smart app using a starter template. Alternatively, you can create your smart app from scratch using a blank GenAI app template. For more details, see [Build a Smart App from a Blank GenAI App](/agents/agents-kit-1/how-to/blank-app/). ### Prerequisites @@ -45,7 +45,7 @@ To simplify your first use case, start building a chatbot using the [AI Bot Star Selecting the infrastructure for integrating GenAI into your Mendix application is the first step. Depending on your use case and preferences, you can choose from the following options: -* [Mendix Cloud GenAI Resources Packs](/agents/mx-cloud-genai/resource-packs/): The [Mendix Cloud GenAI Connector](https://marketplace.mendix.com/link/component/239449) integrates LLMs by dragging and dropping common operations from its toolbox in Studio Pro. +* [Mendix Cloud GenAI Resource Packs](/agents/mx-cloud-genai/resource-packs/): The [Mendix Cloud GenAI Connector](https://marketplace.mendix.com/link/component/239449) integrates LLMs by dragging and dropping common operations from its toolbox in Studio Pro. * [OpenAI](/agents/agents-kit-1/reference-guide/external-connectors/openai/): The [OpenAI Connector](https://marketplace.mendix.com/link/component/220472) supports OpenAI's platform and Microsoft Foundry. @@ -65,7 +65,7 @@ Next, follow the steps below based on the infrastructure you chose. #### Mendix Cloud GenAI Configuration -Follow these steps to configure the Mendix Cloud GenAI Resources Packs for your application and for more background information, look at the [Mendix Cloud GenAI Configuration](/agents/agents-kit-1/mx-cloud-genai/mxgenai-connector/#configuration) documentation: +Follow these steps to configure the Mendix Cloud GenAI Resource Packs for your application and for more background information, look at the [Mendix Cloud GenAI Configuration](/agents/agents-kit-1/mx-cloud-genai/mxgenai-connector/#configuration) documentation: 1. Run the application locally. diff --git a/content/en/docs/genai/v1/how-to/start_from_blank_app.md b/content/en/docs/genai/v1/how-to/start_from_blank_app.md index 0cfbaba2562..b5032e721de 100644 --- a/content/en/docs/genai/v1/how-to/start_from_blank_app.md +++ b/content/en/docs/genai/v1/how-to/start_from_blank_app.md @@ -49,13 +49,13 @@ The [Blank GenAI App Template](https://marketplace.mendix.com/link/component/227 * The [Conversational UI](/agents/agents-kit-1/reference-guide/conversational-ui/) module: offers UI elements for chat interfaces and usage data monitoring. -* The [Mendix Cloud GenAI Resources Packs](/agents/mx-cloud-genai/resource-packs/) connector: supports the usage of LLMs in your applications. +* The [Mendix Cloud GenAI Resource Packs](/agents/mx-cloud-genai/resource-packs/) connector: supports the usage of LLMs in your applications. ### Choosing the Infrastructure Selecting the infrastructure for integrating GenAI into your Mendix application is the first step. Depending on your use case and preferences, you can choose from the following options: -* [Mendix Cloud GenAI Resources Packs](/agents/mx-cloud-genai/resource-packs/): The [Mendix Cloud GenAI Connector](https://marketplace.mendix.com/link/component/239449) integrates LLMs by dragging and dropping common operations from its toolbox in Studio Pro. +* [Mendix Cloud GenAI Resource Packs](/agents/mx-cloud-genai/resource-packs/): The [Mendix Cloud GenAI Connector](https://marketplace.mendix.com/link/component/239449) integrates LLMs by dragging and dropping common operations from its toolbox in Studio Pro. * [OpenAI](/agents/agents-kit-1/reference-guide/external-connectors/openai/): The [OpenAI Connector](https://marketplace.mendix.com/link/component/220472) supports both OpenAI's platform and Microsoft Foundry. * [Amazon Bedrock](/agents/agents-kit-1/reference-guide/external-connectors/bedrock/): The [Amazon Bedrock Connector](https://marketplace.mendix.com/link/component/215042) allows you to leverage Amazon Bedrock’s fully managed service to integrate foundation models from Amazon and leading AI providers. @@ -106,7 +106,7 @@ You may encounter an error about allowed roles. To resolve this, go to the **Pro #### Mendix Cloud GenAI Configuration -Follow these steps to configure the Mendix Cloud GenAI Resources Packs for your application and more background information, look at the [Mendix Cloud GenAI Configuration](/agents/agents-kit-1/mx-cloud-genai/mxgenai-connector/#configuration) documentation: +Follow these steps to configure the Mendix Cloud GenAI Resource Packs for your application and more background information, look at the [Mendix Cloud GenAI Configuration](/agents/agents-kit-1/mx-cloud-genai/mxgenai-connector/#configuration) documentation: 1. Run the application locally. diff --git a/content/en/docs/genai/v1/migration-guide.md b/content/en/docs/genai/v1/migration-guide.md index f4bd32dac50..efec627c80c 100644 --- a/content/en/docs/genai/v1/migration-guide.md +++ b/content/en/docs/genai/v1/migration-guide.md @@ -35,7 +35,7 @@ Mendix recommends following the steps below per release to ensure a smooth upgra * Read the full migration guide for the specific release and ensure that you cover each module used in your app. * Perform the upgrade in a non-production environment first. -* Keep the back up of your database before starting. +* Keep a backup of your database before starting. * Upgrade all modules to the versions listed in the upgrade matrix for the release. * Update any custom application logic by referencing deprecated entities, associations, attributes, microflows, or enumerations. * Run all required migration microflows upon starting the application (for example, as part of the after-startup microflow). @@ -94,7 +94,7 @@ The following modules require an upgrade: ###### Impact -Existing custom code that use any of the renamed pages and microflows will show consistency errors in the Studio Pro. Furthermore, agent definitions containing Single MCP tools require migration to prevent failing agent calls at runtime. +Existing custom code that uses any of the renamed pages and microflows will show consistency errors in Studio Pro. Furthermore, agent definitions containing Single MCP tools require migration to prevent failing agent calls at runtime. Data migration is only required if your app uses Agent definitions containing Single MCP tools. @@ -175,7 +175,7 @@ To prevent the need to recreate existing data related to Agent definitions and k 6. If your app has the PgVector Knowledge Base module: 1. Upgrade the PgVector Knowledge Base module to V6.0.1 in your Mendix app. - 2. Include logic to run the data migration microflow upon starting your application (forexample, include it in the after-startup): **PgVectorKnowledgeBase** > **USE_ME** > **Migration** > `ConsumedKnowledgeBase_Migrate`. This microflow makes sure the new attributes on the generalization are set properly, and the `DisplayName` field is migrated. + 2. Include logic to run the data migration microflow upon starting your application (for example, include it in the after-startup): **PgVectorKnowledgeBase** > **USE_ME** > **Migration** > `ConsumedKnowledgeBase_Migrate`. This microflow makes sure the new attributes on the generalization are set properly, and the `DisplayName` field is migrated. 3. If the **Agent Commons** is part of your app and there are Agents defined using knowledge bases, include the following initially excluded sub-microflow into the project and add it as a microflow call according to the annotation in the above-mentioned microflow: **PgVectorKnowledgeBase** > **USE_ME** > **Migration** > `PgVector_KnowledgeBase_Migrate`. This microflow sets the `CollectionIdentifier` field on the `KnowledgeBase` entity and outgoing reference to the `ConsumedKnowledgeBase`. 4. Set the `DisplayName` field for each `ConsumedKnowledgeBase` object by logging into the running app and using the `DatabaseConfiguration_Overview` page. diff --git a/content/en/docs/genai/v1/reference-guide/agent-commons.md b/content/en/docs/genai/v1/reference-guide/agent-commons.md index fb07f5548b5..754ccf3cd56 100644 --- a/content/en/docs/genai/v1/reference-guide/agent-commons.md +++ b/content/en/docs/genai/v1/reference-guide/agent-commons.md @@ -171,7 +171,7 @@ After a few quick iterations, the first version of the agent is typically ready #### Creating a Version -New agents will be created in the draft status by default, meaning they are still being worked on and can be tested using the agent commons module only. Once an agent is ready to be integrated into the app logic (that is, logic triggered by end-users), it must be saved as a version. This will store a snapshot of the prompt texts and the configured microflows as tools and knowledge bases. To select the active version for the agent, use the three-dot ({{% icon name="three-dots-menu-horizontal" %}}) menu option on the agent overview and click **Select Version in use**. +New agents will be created in the draft status by default, meaning they are still being worked on and can be tested using the agent commons module only. Once an agent is ready to be integrated into the app logic (that is, logic triggered by end-users), it must be saved as a version. This will store a snapshot of the prompt texts and the configured microflows as tools and knowledge bases. To select the active version for the agent, use the three-dot ({{% icon name="three-dots-menu-horizontal" %}}) menu option on the agent overview and click **Select Version in use**. #### Calling the Agent from a Microflow {#call-agent-microflow} diff --git a/content/en/docs/genai/v1/reference-guide/agent-editor.md b/content/en/docs/genai/v1/reference-guide/agent-editor.md index 9ca0c10d295..ba11faf9c73 100644 --- a/content/en/docs/genai/v1/reference-guide/agent-editor.md +++ b/content/en/docs/genai/v1/reference-guide/agent-editor.md @@ -198,7 +198,7 @@ To link a knowledge base to an agent, use the **Knowledge bases** section in Age In the knowledge base entry: * Select the configured knowledge base document in the **Knowledge base**. -* In **Collection**, select one of the available collections from the dropdown. Alternatively, type or paste a collection name to reference a collection that does not exist yet. +* In **Collection**, select one of the available collections from the drop-down list. Alternatively, type or paste a collection name to reference a collection that does not exist yet. * Provide **Name** and **Description** so the LLM can determine when to use this knowledge base. This serves the same purpose as naming tools. * Optionally configure retrieval settings: * **Max results** controls the maximum number of chunks returned in a single retrieval. diff --git a/content/en/docs/genai/v1/reference-guide/conversational-ui.md b/content/en/docs/genai/v1/reference-guide/conversational-ui.md index 4c10a6a87f6..72e7dc7b9d4 100644 --- a/content/en/docs/genai/v1/reference-guide/conversational-ui.md +++ b/content/en/docs/genai/v1/reference-guide/conversational-ui.md @@ -344,7 +344,7 @@ To manage trace data retention, you can enable the daily scheduled event `ScE_Tr #### Traceability Page {#traceability-pages} -The ConversationalUI module includes a dedicated page in the **USE_ME > Traceability** folder for viewing trace data. the page **Trace_Overview** provides a high-level view of all traces in the system, allowing administrators to browse and search through GenAI traces. It displays key information such as trace ID, agent information (if applicable), start time, duration. You can filter for specific traces and agents' invocations. The data can be visualized over time to identify patterns or anomalies. By double-clicking, users are navigated to the details page to learn more about a particular trace, including all associated spans, tool calls, and performance metrics. +The ConversationalUI module includes a dedicated page in the **USE_ME > Traceability** folder for viewing trace data. The page **Trace_Overview** provides a high-level view of all traces in the system, allowing administrators to browse and search through GenAI traces. It displays key information such as trace ID, agent information (if applicable), start time, and duration. You can filter for specific traces and agents' invocations. The data can be visualized over time to identify patterns or anomalies. By double-clicking, users are navigated to the details page to learn more about a particular trace, including all associated spans, tool calls, and performance metrics. These pages are designed for administrators and developers who need to monitor GenAI usage and investigate specific interactions. They provide the primary interface for accessing traceability data without requiring custom development. diff --git a/content/en/docs/genai/v1/reference-guide/external-platforms/bedrock.md b/content/en/docs/genai/v1/reference-guide/external-platforms/bedrock.md index 9834c513f20..bea4ef048c3 100644 --- a/content/en/docs/genai/v1/reference-guide/external-platforms/bedrock.md +++ b/content/en/docs/genai/v1/reference-guide/external-platforms/bedrock.md @@ -96,12 +96,12 @@ The following actions only list the available resources as non-persistent object #### Adding Custom Models to your Bedrock Configuration {#adding-custom-model} -When syncing your Bedrock configuration, only publicly available foundation models can be retrieved using the `List Foundation Models` action from the Bedrock Connector. To use cross-region inference (CRI) or provisioned-throughput models that you have setup in your AWS console, you must manually add these models to your database. To add a custom model, follow the steps below: +When syncing your Bedrock configuration, only publicly available foundation models can be retrieved using the `List Foundation Models` action from the Bedrock Connector. To use cross-region inference (CRI) or provisioned-throughput models that you have set up in your AWS console, you must manually add these models to your database. To add a custom model, follow the steps below: 1. Click **Add model** above the model's table. 2. Fill out the form with the relevant information of your custom model. - The display name is shown in the model dropdowns while choosing a model to use. The model ID is required to use the model with Amazon Bedrock. The other fields must be filled out according to the functionalities the model can use. + The display name is shown in the model drop-down lists while choosing a model to use. The model ID is required to use the model with Amazon Bedrock. The other fields must be filled out according to the functionalities the model can use. 3. Save the model. @@ -139,7 +139,7 @@ For operations that do not depend on the GenAI Commons, you can take a different 1. In the **App Explorer**, right-click 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. +4. In the **Toolbox**, 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. @@ -281,7 +281,7 @@ For additional general information about function calling, see [Function Calling * **Vision** – This operation supports the *vision* capability for [supported models](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference-supported-models-features.html). 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](/agents/agents-kit-1/reference-guide/commons/#initialize-filecollection) or the [Files: Add to Collection](/agents/agents-kit-1/reference-guide/commons/#add-file-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](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference-supported-models-features.html). To send a document to the model add a `FileCollection` with a `System.FileDocument` to the `Message` using the [Files: Initialize Collection with File](/agents/agents-kit-1/reference-guide/commons/#initialize-filecollection) or the [Files: Add to Collection](/agents/agents-kit-1/reference-guide/commons/#add-file-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**. +* **Document Chat** – This operation supports the ability to chat with documents for [supported models](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference-supported-models-features.html). To send a document to the model add a `FileCollection` with a `System.FileDocument` to the `Message` using the [Files: Initialize Collection with File](/agents/agents-kit-1/reference-guide/commons/#initialize-filecollection) or the [Files: Add to Collection](/agents/agents-kit-1/reference-guide/commons/#add-file-to-collection) operation. For Document Chat, it is not supported to create a `FileContent` from a URL using the above mentioned operations; please use the `System.FileDocument` option. Make sure to set the `FileType` attribute to **document**. ##### Tool Choice @@ -574,7 +574,7 @@ The input and output for this service are shown in the table below: #### GetIngestionJob {#get-ingestion-job} -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. +The `GetIngestionJob` activity allows you to retrieve information about 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 `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](#knowledge-base). @@ -712,7 +712,7 @@ The input and output for this service are shown in the table below: 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, for example, 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. +This can be useful to, for example, associate a chatbot configuration to an available model by selecting the model via a drop-down list at 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. The syncing process works the same for all of these operations: @@ -804,7 +804,7 @@ The guardrail feature will allow you to: 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](https://aws.amazon.com/blogs/aws/guardrails-for-amazon-bedrock-helps-implement-safeguards-customized-to-your-use-cases-and-responsible-ai-policies-preview/) and in the [AWS documentation](https://aws.amazon.com/en/bedrock/guardrails/). +More information about guardrails can be found in this [AWS blog post](https://aws.amazon.com/blogs/aws/guardrails-for-amazon-bedrock-helps-implement-safeguards-customized-to-your-use-cases-and-responsible-ai-policies-preview/) and in the [AWS documentation](https://aws.amazon.com/en/bedrock/guardrails/). ### Advanced Prompts for Agents @@ -841,7 +841,7 @@ To solve this issue, follow these steps: 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. +After the status of the models changes to **Access Granted**, you can use them with the Amazon Bedrock connector. ### Error Code 403 - AccessDeniedException diff --git a/content/en/docs/genai/v1/reference-guide/external-platforms/gemini.md b/content/en/docs/genai/v1/reference-guide/external-platforms/gemini.md index 502ca7c4099..e6485ddbc00 100644 --- a/content/en/docs/genai/v1/reference-guide/external-platforms/gemini.md +++ b/content/en/docs/genai/v1/reference-guide/external-platforms/gemini.md @@ -104,7 +104,7 @@ When JSON mode is used, the model is programmatically instructed to return valid Function calling enables LLMs to connect with external tools to gather information, execute actions, convert natural language into structured data, and much more. Function calling thus enables the model to intelligently decide when to let the Mendix app call one or more predefined function microflows to gather additional information to include in the assistant's response. -Gemini does not call the function. The model returns a tool called JSON structure that is used to build the input of the function (or functions) so that they can be executed as part of the chat completions operation. Functions in Mendix are essentially microflows that can be registered within the request to the LLM​. The OpenAI connector takes care of handling the tool call response as well as executing the function microflows until the API returns the assistant's final response for Gemini. +Gemini does not call the function. The model returns a tool call JSON structure that is used to build the input of the function (or functions) so that they can be executed as part of the chat completions operation. Functions in Mendix are essentially microflows that can be registered within the request to the LLM​. The OpenAI connector takes care of handling the tool call response as well as executing the function microflows until the API returns the assistant's final response for Gemini. This is all part of the implementation that is executed by the GenAI Commons chat completions operations. As a developer, make the system aware of your functions and what is done by registering the functions with the request. This is done using the GenAI Commons operation [Tools: Add Function to Request](/agents/agents-kit-1/reference-guide/commons/#add-function-to-request) once per function before passing the request to the chat completions operation. @@ -173,7 +173,7 @@ The **Documentation** pane displays the documentation for the currently selected ### Tool Choice -Gemini supports the following [tool choice types](/agents/agents-kit-1/reference-guide/commons/#enum-toolchoice) of GenAI Commons for the [Tools: Set Tool Choice](/agents/agents-kit-1/reference-guide/commons/#set-toolchoice) action is supported. For API mapping reference, see the table below: +Gemini supports the following [tool choice types](/agents/agents-kit-1/reference-guide/commons/#enum-toolchoice) of GenAI Commons for the [Tools: Set Tool Choice](/agents/agents-kit-1/reference-guide/commons/#set-toolchoice) action. For API mapping reference, see the table below: | GenAI Commons (Mendix) | Gemini | | ----------------------- | ------- | diff --git a/content/en/docs/genai/v1/reference-guide/external-platforms/mistral.md b/content/en/docs/genai/v1/reference-guide/external-platforms/mistral.md index fa3dfdca45e..d7e776d2d53 100644 --- a/content/en/docs/genai/v1/reference-guide/external-platforms/mistral.md +++ b/content/en/docs/genai/v1/reference-guide/external-platforms/mistral.md @@ -112,7 +112,7 @@ JSON mode instructs the model to return valid JSON. To use JSON mode with the Mi Function calling enables LLMs to connect with external tools to gather information, run actions, convert natural language into structured data, and more. Function calling enables the model to intelligently decide when to let the Mendix app call one or more predefined function microflows to gather additional information for the assistant's response. -Mistral does not call the function. The model returns a tool called JSON structure that is used to build the input of the function (or functions) so they can run as part of the chat completions operation. Functions in Mendix are essentially microflows that can be registered within the request to the LLM. The OpenAI connector handles the tool call response and runs the function microflows until the API returns the assistant's final response for Mistral. +Mistral does not call the function. The model returns a tool call JSON structure that is used to build the input of the function (or functions) so they can run as part of the chat completions operation. Functions in Mendix are essentially microflows that can be registered within the request to the LLM. The OpenAI connector handles the tool call response and runs the function microflows until the API returns the assistant's final response for Mistral. The GenAI Commons chat completions operations mentioned earlier run this implementation. As a developer, you must make the system aware of your functions and their purposes by registering the functions to the request. To do so, use the GenAI Commons operation [Tools: Add Function to Request](/agents/agents-kit-1/reference-guide/commons/#add-function-to-request) once per function before passing the request to the chat completions operation. diff --git a/content/en/docs/genai/v1/reference-guide/external-platforms/mx-genai-connector.md b/content/en/docs/genai/v1/reference-guide/external-platforms/mx-genai-connector.md index 991d3ce095a..d7e5849e255 100644 --- a/content/en/docs/genai/v1/reference-guide/external-platforms/mx-genai-connector.md +++ b/content/en/docs/genai/v1/reference-guide/external-platforms/mx-genai-connector.md @@ -129,7 +129,7 @@ You can optionally control both reference creation and the output returned for t Function calling enables LLMs to connect with external tools to gather information, execute actions, convert natural language into structured data, and more. Function calling thus enables the model to intelligently decide when to let the Mendix app call one or more predefined function microflows to gather additional information to include in the assistant's response. -The model does not call the function. Instead, it returns a tool called JSON structure that builds the input of the function (or functions) so they can be executed as part of the chat completions operation. Functions in Mendix are microflows that can be registered within the request to the LLM. The connector handles the tool call response and executes the function microflows until the API returns the assistant's final response. +The model does not call the function. Instead, it returns a tool call JSON structure that builds the input of the function (or functions) so they can be executed as part of the chat completions operation. Functions in Mendix are microflows that can be registered within the request to the LLM. The connector handles the tool call response and executes the function microflows until the API returns the assistant's final response. Function microflows can have none, a single, or multiple primitive input parameters such as Boolean, Datetime, Decimal, Enumeration, Integer, or String. Additionally, they may accept the [Request](/agents/agents-kit-1/reference-guide/commons/#request) or [Tool](/agents/agents-kit-1/reference-guide/commons/#tool) objects as inputs. The function microflow must return a String value. diff --git a/content/en/docs/genai/v1/reference-guide/external-platforms/openai.md b/content/en/docs/genai/v1/reference-guide/external-platforms/openai.md index ec691ca7adb..b338c450930 100644 --- a/content/en/docs/genai/v1/reference-guide/external-platforms/openai.md +++ b/content/en/docs/genai/v1/reference-guide/external-platforms/openai.md @@ -16,7 +16,7 @@ Looking for Mendix's newest agentic and generative AI features? [This page has b ## Introduction {#introduction} -The [OpenAI connector](https://marketplace.mendix.com/link/component/220472) lets you generative AI capabilities into Mendix apps. It is compatible with [OpenAI's platform](https://platform.openai.com/) and [Microsoft Foundry](https://learn.microsoft.com/en-us/azure/ai-foundry/what-is-ai-foundry), where you can access OpenAI models. +The [OpenAI connector](https://marketplace.mendix.com/link/component/220472) lets you integrate generative AI capabilities into Mendix apps. It is compatible with [OpenAI's platform](https://platform.openai.com/) and [Microsoft Foundry](https://learn.microsoft.com/en-us/azure/ai-foundry/what-is-ai-foundry), where you can access OpenAI models. ### Features {#features} @@ -160,7 +160,7 @@ JSON mode instructs the model to return valid JSON. To use JSON mode with OpenAI Function calling enables LLMs to connect with external tools to gather information, run actions, convert natural language into structured data, and more. Function calling enables the model to intelligently decide when to let the Mendix app call one or more predefined function microflows to gather additional information for the assistant's response. -OpenAI does not call the function. The model returns a tool called JSON structure that builds the input of the function (or functions) so they can run as part of the chat completions operation. Functions in Mendix are essentially microflows that can be registered within the request to the LLM. The OpenAI connector handles the tool call response and runs the function microflows until the API returns the assistant's final response. +OpenAI does not call the function. The model returns a tool call JSON structure that builds the input of the function (or functions) so they can run as part of the chat completions operation. Functions in Mendix are essentially microflows that can be registered within the request to the LLM. The OpenAI connector handles the tool call response and runs the function microflows until the API returns the assistant's final response. The GenAI Commons chat completions operations mentioned earlier run this implementation. As a developer, you must make the system aware of your functions and their purposes by registering the functions to the request. To do so, use the GenAI Commons operation [Tools: Add Function to Request](/agents/agents-kit-1/reference-guide/commons/#add-function-to-request) once per function before passing the request to the chat completions operation. @@ -178,11 +178,11 @@ For more information, see [Function Calling](/agents/function-calling/). Adding Azure indexes to a call enables LLMs to retrieve information when related topics are mentioned. By including these indexes in the request object along with a name and description, the model can intelligently decide when to let the Mendix app call one or more predefined indexes, allowing the assistant to include additional information in its response. -OpenAI does not directly connect to the Azure AI Search resource. The model returns a tool called JSON structure that builds the input of the retrievals so they can run as part of the chat completions operation. The OpenAI connector handles the tool call response and runs the function microflows until the API returns the assistant's final response. +OpenAI does not directly connect to the Azure AI Search resource. The model returns a tool call JSON structure that builds the input of the retrievals so they can run as part of the chat completions operation. The OpenAI connector handles the tool call response and runs the function microflows until the API returns the assistant's final response. The GenAI Commons chat completions operations mentioned earlier run this functionality. As a developer, make the system aware of your indexes and their purpose by registering them with the request. Use the GenAI Commons operation [Tools: Add Knowledge Base](/agents/agents-kit-1/reference-guide/commons/#add-knowledge-base-to-request), which must be called once per index before passing the request to the chat completions operation. -Note that the retrieval process is independent of the model provider and can be used with any model that supports function calling, as it relies on the generalized `GenAICommons.ConsumedKnowledgeBase`entity. For Azure indexes specifically, as part of this module, when collection identifiers need to be passed to operations, the `Name` of the `Index` should be used. +Note that the retrieval process is independent of the model provider and can be used with any model that supports function calling, as it relies on the generalized `GenAICommons.ConsumedKnowledgeBase` entity. For Azure indexes specifically, as part of this module, when collection identifiers need to be passed to operations, the `Name` of the `Index` should be used. #### Vision {#chatcompletions-vision} diff --git a/content/en/docs/genai/v1/reference-guide/external-platforms/pg-vector-knowledge-base/vector-database-setup.md b/content/en/docs/genai/v1/reference-guide/external-platforms/pg-vector-knowledge-base/vector-database-setup.md index 9c927015b38..ef9e2e08dcf 100644 --- a/content/en/docs/genai/v1/reference-guide/external-platforms/pg-vector-knowledge-base/vector-database-setup.md +++ b/content/en/docs/genai/v1/reference-guide/external-platforms/pg-vector-knowledge-base/vector-database-setup.md @@ -118,7 +118,7 @@ The following steps use sample values suitable for experimentation: 8. [Add the pgVector extension to the allowed extensions list](https://learn.microsoft.com/en-us/azure/postgresql/flexible-server/how-to-use-pgvector): 1. Search for **Server parameters** in the search bar on the left. A list of parameters is loaded. 2. Search for **azure.extensions**. - 3. In the column *VALUE*, search in the dropdown for **VECTOR** (this is the extension name in Azure, not *pgVector*). + 3. In the column *VALUE*, search in the drop-down list for **VECTOR** (this is the extension name in Azure, not *pgVector*). 4. Save the changes. 9. Search for **Databases** in the search bar on the left. Verify that you have a PostgreSQL database with schema type "User", or create a new database by clicking **Add**. @@ -214,7 +214,7 @@ To resolve this, connect to a network that allows these connections, such as a p ## Read More {#read-more} -* [Embeddings-based Search – Open AI Cookbook](https://cookbook.openai.com/examples/question_answering_using_embeddings) +* [Embeddings-based Search – OpenAI Cookbook](https://cookbook.openai.com/examples/question_answering_using_embeddings) * [Vector Database Options on AWS](https://aws.amazon.com/blogs/database/the-role-of-vector-datastores-in-generative-ai-applications/) * [Vector Database Options – OpenAI Cookbook](https://cookbook.openai.com/examples/vector_databases/readme) * [How to: AI-powered search in AWS Relational Database Service (RDS) For PostgreSQL Using pgvector](https://aws.amazon.com/blogs/database/building-ai-powered-search-in-postgresql-using-amazon-sagemaker-and-pgvector/) diff --git a/content/en/docs/genai/v1/reference-guide/genai-commons.md b/content/en/docs/genai/v1/reference-guide/genai-commons.md index ae5b8ea953a..b8a2db6c404 100644 --- a/content/en/docs/genai/v1/reference-guide/genai-commons.md +++ b/content/en/docs/genai/v1/reference-guide/genai-commons.md @@ -458,7 +458,7 @@ It is recommended that you adapt to the same interface when developing custom ch ##### Chat Completions (With History) {#chat-completions-with-history} -The `Chat Completions (with history)` operation supports more complex use cases where a list of (historical) messages (for example, comprising the conversation or context so far) is sent as part of the request to the LLM. Note that the response might not be complete if tools with [UserAccessApproval](#enum-useraccessapproval) other than `HiddenForUser` are added or the request specifies that the tool messages should be stored ([SaveToolCallHistory](#request)). In such cases, implement the logic to call the action again, with [toolcalls](#toolcall) appended to the assistant's message as well as messages of role tool to the request. If you are using the [ConversationalUI](/agents/agents-kit-1/reference-guide/conversational-ui/#human-in-the-loop) module, this is automatically handled. +The `Chat Completions (with history)` operation supports more complex use cases where a list of (historical) messages (for example, comprising the conversation or context so far) is sent as part of the request to the LLM. Note that the response might not be complete if tools with [UserAccessApproval](#enum-useraccessapproval) other than `HiddenForUser` are added or the request specifies that the tool messages should be stored ([SaveToolCallHistory](#request)). In such cases, implement the logic to call the action again, with [tool calls](#toolcall) appended to the assistant's message as well as messages of role tool to the request. If you are using the [ConversationalUI](/agents/agents-kit-1/reference-guide/conversational-ui/#human-in-the-loop) module, this is automatically handled. ###### Input Parameters @@ -475,7 +475,7 @@ The `Chat Completions (with history)` operation supports more complex use cases ##### Chat Completions (Without History) {#chat-completions-without-history} -The `Chat Completions (without history)` operation supports scenarios where there is no need to send a list of (historic) messages comprising the conversation so far as part of the request. Note that the response might not be complete if tools with [UserAccessApproval](#enum-useraccessapproval) other than `HiddenForUser` are added or the request specifies that the tool messages should be stored ([SaveToolCallHistory](#request)). In such cases, implement a logic to call the action again, with [toolcalls](#toolcall) appended to the assistant's message as well as messages of role tool to the request. For more information, refer to [Human in the loop](/agents/agents-kit-1/reference-guide/conversational-ui/#human-in-the-loop). +The `Chat Completions (without history)` operation supports scenarios where there is no need to send a list of (historic) messages comprising the conversation so far as part of the request. Note that the response might not be complete if tools with [UserAccessApproval](#enum-useraccessapproval) other than `HiddenForUser` are added or the request specifies that the tool messages should be stored ([SaveToolCallHistory](#request)). In such cases, implement a logic to call the action again, with [tool calls](#toolcall) appended to the assistant's message as well as messages of role tool to the request. For more information, refer to [Human in the loop](/agents/agents-kit-1/reference-guide/conversational-ui/#human-in-the-loop). ###### Input Parameters @@ -508,7 +508,7 @@ The `Generate Embeddings (Chunk Collection)` operation allows the invocation of | Name | Type | Description | | --- | --- | --- | -| `EmbeddingsResponse` | [EmbeddingsResponse](#embeddingsresponse-entity) | An response object that contains the token usage statistics and the corresponding embedding vector as part of a ChunkCollection. | +| `EmbeddingsResponse` | [EmbeddingsResponse](#embeddingsresponse-entity) | A response object that contains the token usage statistics and the corresponding embedding vector as part of a ChunkCollection. | ##### Generate Embeddings (String) {#embeddings-string} @@ -671,7 +671,7 @@ This microflow does not have a return value. ##### Tools: Add Knowledge Base {#add-knowledge-base-to-request} -This tool adds a function that performs a retrieval from a knowledge base to a [ToolCollection](#toolcollection) that is part of a Request. Use this microflow when you have knowledge bases in your application that may be called to retrieve the required information as part of a GenAI interaction. If you want the model to be aware of these knowledge base, you can use this operation to add them as functions to the request. If supported by the LLM connector, the chat completion operation calls the appropriate knowledge base function based on the LLM response and continue the process until the assistant's final response is returned. +This tool adds a function that performs a retrieval from a knowledge base to a [ToolCollection](#toolcollection) that is part of a Request. Use this microflow when you have knowledge bases in your application that may be called to retrieve the required information as part of a GenAI interaction. If you want the model to be aware of these knowledge bases, you can use this operation to add them as functions to the request. If supported by the LLM connector, the chat completion operation calls the appropriate knowledge base function based on the LLM response and continues the process until the assistant's final response is returned. `ConsumedKnowledgeBase` objects have provider-specific specializations, for example, `MxCloudKnowledgeBaseResource` for Mendix Cloud. @@ -746,7 +746,7 @@ Use this microflow to get the list of references that may be included in the mod | Name | Type | Description | |---|---|---| -| `ReferenceList` | List of [Reference](#reference) | The references with optional citations were part of the response message. | +| `ReferenceList` | List of [Reference](#reference) | The references with optional citations that were part of the response message. | ##### Get Response Text {#chat-get-model-response-text} @@ -789,8 +789,8 @@ This microflow creates new [ImageOptions](#imageoptions-entity). | Name | Type | Notes | Description | |--- |--- |--- |--- | -| `Height` | Integer/Long | optional | To set Width. | -| `Width` | Integer/Long | optional | To set Height. | +| `Height` | Integer/Long | optional | To set Height. | +| `Width` | Integer/Long | optional | To set Width. | | `NumberOfImages` | Integer/Long | optional | To set NumberOfImages to create. | ###### Return Value @@ -998,7 +998,7 @@ This microflow creates a new [MetadataCollection](#metadatacollection-entity) an #### `ENUM_ModelModality` {#enum-modalmodality} -`ENUM_ModelModality` describes the modalities that the model supports input or output. +`ENUM_ModelModality` describes the modalities that the model supports as input or output. | Name | Caption | Description | | --- | --- | --- | diff --git a/content/en/docs/genai/v1/reference-guide/mcp-modules/mcp-client.md b/content/en/docs/genai/v1/reference-guide/mcp-modules/mcp-client.md index f76e42b92b9..9460855f53a 100644 --- a/content/en/docs/genai/v1/reference-guide/mcp-modules/mcp-client.md +++ b/content/en/docs/genai/v1/reference-guide/mcp-modules/mcp-client.md @@ -47,7 +47,7 @@ If you start from a standard Mendix blank app or have an existing project, you m The `Create MCP Client` action creates a sync client that is connected to an (externally) running MCP server and returns the `MCPClient` object. The action requires an `MCPServerConfiguration` object that contains all required attributes to facilitate the connection. -The `MCPServerConfiguration` objects can be created by users with the `MCPClient.Administrator` userrole via the `MCPServerConfiguration_Overview` page. If the MCP server expects HTTP headers (for example, for authentication), you can select a `GetCredentialsMicroflow` which should return a list of `System.HttpHeader` objects. You can use the `Config: Create Http Header and Add to List` toolbox action in this microflow. The `GetCredentialsMicroflow` cannot have any input parameters. Take a look at the `GetCredentials_EXAMPLE` in the **Example Implementations** folder for an example. +The `MCPServerConfiguration` objects can be created by users with the `MCPClient.Administrator` user role via the `MCPServerConfiguration_Overview` page. If the MCP server expects HTTP headers (for example, for authentication), you can select a `GetCredentialsMicroflow` which should return a list of `System.HttpHeader` objects. You can use the `Config: Create Http Header and Add to List` toolbox action in this microflow. The `GetCredentialsMicroflow` cannot have any input parameters. Take a look at the `GetCredentials_EXAMPLE` in the **Example Implementations** folder for an example. You can use the returned `MCPClient` object for all other actions, for example, to discover tools and prompts, to get a specific prompt, or call a tool. An MCP Client can be reused across multiple actions or throughout an entire chat conversation. It is recommended to close connections after use by calling the `Close MCP Client` action. @@ -61,7 +61,7 @@ When creating an MCP client, specify a `ProtocolVersion`. On the official MCP do The actions `List Prompts` and `List Tools` send a request to the MCP server to discover prompts and tools, respectively. Create the MCP Client beforehand and pass it as an input. Both actions create the necessary objects, such as `Prompt` and `PromptArgument` for prompts and `Tool`, `ToolArgument`, and `EnumValue` for tools. If the prompt or tool requires arguments, the objects help you understand what needs to be passed and how to format it. -In general, prompts are often exposed to end-users in a chat to start or continue a conversation, while tools are passed to an LLM. If you want users to be able to view tools and prompts, you can assign them the `User` userrole. For more information, see the [Using MCP Client Module with GenAI Commons](#use-with-genai-commons) section below. +In general, prompts are often exposed to end-users in a chat to start or continue a conversation, while tools are passed to an LLM. If you want users to be able to view tools and prompts, you can assign them the `User` user role. For more information, see the [Using MCP Client Module with GenAI Commons](#use-with-genai-commons) section below. ### Using Resources {#use-resources} @@ -71,7 +71,7 @@ For both actions, you can pass an `ArgumentCollection` if the prompt or tool req ### Using MCP Client Module with GenAI Commons and Conversational UI {#use-with-genai-commons} -To add all tools from an MCP server to a `GenAICommons.Request`, you can use the `Request: Add all tools from MCP server` toolbox action. This action will first list all tools from the provided MCP server configuration, iterate over them, and adding them one by one to the tool collection. The request can then be passed to a Chat Completions operation. +To add all tools from an MCP server to a `GenAICommons.Request`, you can use the `Request: Add all tools from MCP server` toolbox action. This action will first list all tools from the provided MCP server configuration, iterate over them, and add them one by one to the tool collection. The request can then be passed to a Chat Completions operation. You can also find an example [action microflow](/agents/agents-kit-1/reference-guide/conversational-ui/#action-microflow) `ChatCompletions_MCPClient_ActionMicroflow` in the **Example Implementations** folder of the module. This microflow demonstrates how a Conversational UI chat action including MCP tools can be facilitated. Duplicate and include this microflow into your custom module and modify it according to your requirements. diff --git a/content/en/docs/genai/v1/reference-guide/mcp-modules/mcp-server.md b/content/en/docs/genai/v1/reference-guide/mcp-modules/mcp-server.md index 66b06633f93..bab0dadcb01 100644 --- a/content/en/docs/genai/v1/reference-guide/mcp-modules/mcp-server.md +++ b/content/en/docs/genai/v1/reference-guide/mcp-modules/mcp-server.md @@ -18,7 +18,7 @@ Looking for Mendix's newest agentic and generative AI features? [This page has b The [MCP Server](https://marketplace.mendix.com/link/component/240380) module provides easy low-code capability to set up an MCP ([Model Context Protocol](/agents/mcp/)) server within a Mendix app. An MCP server can seamlessly expose resources (such as tools or prompts) to other external AI applications that support MCP. The Mendix MCP Server module builds a bridge between Mendix and MCP client applications, such as Claude Desktop, through the [MCP Java SDK](https://github.com/modelcontextprotocol/java-sdk). With the current implementation, it is possible to: * Expose reusable prompts, including the ability to use prompt parameters -* List and execute microflow implemented in the application as tools +* List and execute microflows implemented in the application as tools * The `Path` of the MCP Server cannot contain `/`, so your server cannot have a multi-level path. To use function calling within the same Mendix application and integrating to an LLM, consider [function calling](/agents/function-calling/). diff --git a/content/en/docs/genai/v2/how-to/creating-agents/shared-setup.md b/content/en/docs/genai/v2/how-to/creating-agents/shared-setup.md index 8b138eed2db..221a3f7bd7a 100644 --- a/content/en/docs/genai/v2/how-to/creating-agents/shared-setup.md +++ b/content/en/docs/genai/v2/how-to/creating-agents/shared-setup.md @@ -203,7 +203,7 @@ You have now successfully created your first function microflow to link to the a #### Get Ticket by Identifier 1. Create a new microflow named `Ticket_GetTicketByID`. -2. In the new microflow, Add a *String* input parameter called `Identifier`. +2. In the new microflow, add a *String* input parameter called `Identifier`. 3. Add a `Retrieve` action to retrieve the ticket of the given identifier: diff --git a/content/en/docs/genai/v2/how-to/ground_your_llm_in_data.md b/content/en/docs/genai/v2/how-to/ground_your_llm_in_data.md index 572a7783c7d..2c3ffaa5ca4 100644 --- a/content/en/docs/genai/v2/how-to/ground_your_llm_in_data.md +++ b/content/en/docs/genai/v2/how-to/ground_your_llm_in_data.md @@ -31,7 +31,7 @@ Before implementing this capability into your app, ensure you meet the following ### Choosing the Infrastructure -This document focuses on the [Mendix Cloud GenAI Resource Packs](/agents/mx-cloud-genai/resource-packs/). Follow the instructions in [Navigate through the Mendix Cloud GenAI Portal](/agents/mx-cloud-genai/Navigate-MxGenAI/) to collect the resources keys and configure the connector within your application. The keys bridge the gap between your app and the resources, enabling you to access models and add to or retrieve data from a Mendix Cloud GenAI knowledge base. +This document focuses on the [Mendix Cloud GenAI Resource Packs](/agents/mx-cloud-genai/resource-packs/). Follow the instructions in [Navigate through the Mendix Cloud GenAI Portal](/agents/mx-cloud-genai/Navigate-MxGenAI/) to collect the resource keys and configure the connector within your application. The keys bridge the gap between your app and the resources, enabling you to access models and add to or retrieve data from a Mendix Cloud GenAI knowledge base. While this documentation focuses on adding data to your knowledge base from a Mendix application, you can also fill the knowledge base directly within the portal, for example, by uploading files. diff --git a/content/en/docs/genai/v2/how-to/integrate_function_calling.md b/content/en/docs/genai/v2/how-to/integrate_function_calling.md index e02a361a49d..adea7337833 100644 --- a/content/en/docs/genai/v2/how-to/integrate_function_calling.md +++ b/content/en/docs/genai/v2/how-to/integrate_function_calling.md @@ -145,7 +145,7 @@ As shown in the image, two key steps must be completed to enable the execution o ### Optional: Changing the System Prompt {#edit-systemprompt} -Optionally, you can change the [system prompt](/agents/glossary/#system-prompt) to provide the model with additional instructions, such as specifying its the tone. +Optionally, you can change the [system prompt](/agents/glossary/#system-prompt) to provide the model with additional instructions, such as specifying the tone. 1. Open the copied `ACT_FullScreenChat_Open` microflow from your `MyFirstBot` module. 2. Locate the **New Chat** action. @@ -159,7 +159,7 @@ When adding tools to a request, you can optionally set a [User Access Approval]( ## Testing and Troubleshooting {#testing-troubleshooting} -Before testing, ensure that you have completed the connector's configuration as described in its documentation. For example, if you are using Mendix Cloud GenAI, follow the intructions in the [Configuration](/agents/agents-kit-2/mx-cloud-genai/mxgenai-connector/#configuration) section of *Mendix Cloud GenAI Connector*. +Before testing, ensure that you have completed the connector's configuration as described in its documentation. For example, if you are using Mendix Cloud GenAI, follow the instructions in the [Configuration](/agents/agents-kit-2/mx-cloud-genai/mxgenai-connector/#configuration) section of *Mendix Cloud GenAI Connector*. To test the chatbot, go to the **Home** icon to open the chatbot interface. Start interacting with your chatbot by typing in the chat box. For example, type `Write a message to my colleague Max asking about a meeting to discuss the content for our next GenAI how-to.` or `How many bank holidays do I have in December?`. diff --git a/content/en/docs/genai/v2/how-to/prompt_engineering-runtime.md b/content/en/docs/genai/v2/how-to/prompt_engineering-runtime.md index 4384277e6df..8ade55a69f4 100644 --- a/content/en/docs/genai/v2/how-to/prompt_engineering-runtime.md +++ b/content/en/docs/genai/v2/how-to/prompt_engineering-runtime.md @@ -44,7 +44,7 @@ Agent Commons enables users to create powerful agents at runtime, enriching requ If you are using an Agents Kit starter app, you can skip ahead to [the next section](#configuration) because the following steps are already completed. Otherwise, follow these setup steps to add Agent Commons capabilities to your app and navigation: -1. Install the [Agent Commons module](/agents/agents-kit-2/reference-guide/agent-commons/) module and its dependencies from Mendix Marketplace. +1. Install the [Agent Commons module](/agents/agents-kit-2/reference-guide/agent-commons/) and its dependencies from Mendix Marketplace. 2. Open your app's [Security](/refguide/security/#user-role) settings and edit the user role that you want to be able to create agents at runtime. This is typically the Administrator role, but this may vary depending on your use case. Follow these steps: 1. For the Agent Commons module, assign the **AgentAdmin** module role. @@ -102,11 +102,11 @@ You can now create your first agent in the user interface. The final agent will To further improve your prompts and the user experience, you can add some placeholder variables. -1. Once you have saved an agent version, you cannot edit its fields anymore. So, in the **Agent version** dropdown, click **Draft** to start a new version and edit the fields again. +1. Once you have saved an agent version, you cannot edit its fields anymore. So, in the **Agent version** drop-down list, click **Draft** to start a new version and edit the fields again. 2. Change the **User Prompt** to `Generate a short product description for a {{ProductName}}. The description should not be longer than {{NumberOfWords}} words.` -3. Notice that two variables appear in the **Test Case** card on the right. You can use these variables later in your in your application to allow users to dynamically modify the user prompt without needing to understand what a prompt is, and without requiring any changes or restarts to the application. Enter the following values for the variables: +3. Notice that two variables appear in the **Test Case** card on the right. You can use these variables later in your application to allow users to dynamically modify the user prompt without needing to understand what a prompt is, and without requiring any changes or restarts to the application. Enter the following values for the variables: * `30` for **NumberOfWords** * `chair` for **ProductName** @@ -121,7 +121,7 @@ To further improve your prompts and the user experience, you can add some placeh To further refine the agent's responses, add a [system prompt](/agents/glossary/#system-prompt) that defines the assistant's role and create an additional test case for comparison. -1. In the **Agent version** dropdown, click **Draft** so that you can edit the fields again. This time, add instructions in the [System Prompt](/agents/prompt-engineering/#system-prompt) field. Enter the following: `You are a sales assistant that can write engaging and inspiring product descriptions for our online marketplace. The user asks you to create a description for various products. You should always respond in {{Language}}.` Notice that the **Language** variable appears in the **Test Card**. +1. In the **Agent version** drop-down list, click **Draft** so that you can edit the fields again. This time, add instructions in the [System Prompt](/agents/prompt-engineering/#system-prompt) field. Enter the following: `You are a sales assistant that can write engaging and inspiring product descriptions for our online marketplace. The user asks you to create a description for various products. You should always respond in {{Language}}.` Notice that the **Language** variable appears in the **Test Card**. 2. Add a new test case by clicking **New Blank Test Case** ({{% icon name="add"%}}) next to the **Test variable input** dropdown. @@ -130,7 +130,7 @@ To further refine the agent's responses, add a [system prompt](/agents/glossary/ 3. Click **Test** to test the new input, then save the test case with the title `Chair 30 words German`. -4. Now that you have saved at least two test cases, open the dropdown next to the **Test** button, and click **Test All**. This runs both test cases, allowing you to compare the different input values. Note that the **Language** variable was not set in the first test case, as it did not exist at the time. As a result, the model's response may be in English or another random language. +4. Now that you have saved at least two test cases, open the drop-down list next to the **Test** button, and click **Test All**. This runs both test cases, allowing you to compare the different input values. Note that the **Language** variable was not set in the first test case, as it did not exist at the time. As a result, the model's response may be in English or another random language. 5. Once you are satisfied with your agent, save the version with the title `Added system prompt and language`. diff --git a/content/en/docs/genai/v2/reference-guide/agent-commons.md b/content/en/docs/genai/v2/reference-guide/agent-commons.md index 8800576b816..2db8ed00f91 100644 --- a/content/en/docs/genai/v2/reference-guide/agent-commons.md +++ b/content/en/docs/genai/v2/reference-guide/agent-commons.md @@ -174,7 +174,7 @@ After a few quick iterations, the first version of the agent is typically ready #### Creating a Version -New agents will be created in the draft status by default, meaning they are still being worked on and can be tested using the agent commons module only. Once an agent is ready to be integrated into the app logic (that is, logic triggered by end-users), it must be saved as a version. This will store a snapshot of the prompt texts and the configured microflows as tools and knowledge bases. To select the active version for the agent, use the three-dot ({{% icon name="three-dots-menu-horizontal" %}}) menu option on the agent overview and click **Select Version in use**. +New agents will be created in the draft status by default, meaning they are still being worked on and can be tested using the Agent Commons module only. Once an agent is ready to be integrated into the app logic (that is, logic triggered by end-users), it must be saved as a version. This will store a snapshot of the prompt texts and the configured microflows as tools and knowledge bases. To select the active version for the agent, use the three-dot ({{% icon name="three-dots-menu-horizontal" %}}) menu option on the agent overview and click **Select Version in use**. #### Calling the Agent from a Microflow {#call-agent-microflow} diff --git a/content/en/docs/genai/v2/reference-guide/agent-editor.md b/content/en/docs/genai/v2/reference-guide/agent-editor.md index e543bef7c37..79dd744fea4 100644 --- a/content/en/docs/genai/v2/reference-guide/agent-editor.md +++ b/content/en/docs/genai/v2/reference-guide/agent-editor.md @@ -158,7 +158,7 @@ After defining the model, define the Agent document and configure the prompts an Defining an agent is also document-based and can be configured using Agent Editor: * Add an Agent document from the **App Explorer** at the module level. Right-click the module or folder where you want to create your Agent document, then select **Add other** > **Agent**. -* Select a **Model** document for the agent to call a text generation resource. Depending on the platform chosen, an additional dropdown will appear to select a model version (Mendix Cloud GenAI) or deployment (Azure AI Foundry). Make sure the agent is set up to use a text generation model. +* Select a **Model** document for the agent to call a text generation resource. Depending on the platform chosen, an additional drop-down list will appear to select a model version (Mendix Cloud GenAI) or deployment (Azure AI Foundry). Make sure the agent is set up to use a text generation model. * Configure the **System prompt**. Additionally, define a **User prompt** for task-style execution. In both prompts, include placeholders with double braces (for example, `{{variable}}`). * When you use placeholders, select a **Context entity** to resolve values at runtime. The placeholders used within the prompts must match the attribute names of the selected entity so that attribute values can be inserted instead of the placeholders at runtime. * Optionally, adjust the **Model settings** as needed (maximum tokens, temperature, and TopP), based on the supported ranges of the model provider. @@ -221,7 +221,7 @@ To link a knowledge base to an agent, use the **Knowledge bases** section in Age In the knowledge base entry: * Select the configured knowledge base document in the **Knowledge base**. -* In **Collection**, select one of the available collections from the dropdown. Alternatively, type or paste a collection name to reference a collection that does not exist yet. +* In **Collection**, select one of the available collections from the drop-down list. Alternatively, type or paste a collection name to reference a collection that does not exist yet. * Provide **Name** and **Description** so the LLM can determine when to use this knowledge base. This serves the same purpose as naming tools. * Optionally configure retrieval settings: * **Max results** controls the maximum number of chunks returned in a single retrieval. @@ -371,6 +371,6 @@ In both cases, confirm that the Agent Editor extension is loaded and enabled und You need Agent Editor version 2.4.0 and above to allow Maia to create or edit agent editor documents. -You must also give permission for Maia to be able to access the tools provided by this extensions. You can enable this in Studio Pro **View** > **Extensions** and set the checkmark next to the `register-ai-tools` permission. +You must also give permission for Maia to be able to access the tools provided by this extension. You can enable this in Studio Pro **View** > **Extensions** and set the checkmark next to the `register-ai-tools` permission. If this permission does not show up in the list of available permissions, delete the Agent Editor add-on module and download it again from Marketplace. When prompted about the Agent Editor Commons module, choose overwriting the existing module. diff --git a/content/en/docs/genai/v2/reference-guide/conversational-ui.md b/content/en/docs/genai/v2/reference-guide/conversational-ui.md index c92bb71f5f0..c4b7152dfa1 100644 --- a/content/en/docs/genai/v2/reference-guide/conversational-ui.md +++ b/content/en/docs/genai/v2/reference-guide/conversational-ui.md @@ -350,7 +350,7 @@ To manage trace data retention, you can enable the daily scheduled event `ScE_Tr #### Traceability Page {#traceability-pages} -The ConversationalUI module includes a dedicated page in the **USE_ME > Traceability** folder for viewing trace data. the page **Trace_Overview** provides a high-level view of all traces in the system, allowing administrators to browse and search through GenAI traces. It displays key information such as trace ID, agent information (if applicable), start time, duration. You can filter for specific traces and agents' invocations. The data can be visualized over time to identify patterns or anomalies. By double-clicking, users are navigated to the details page to learn more about a particular trace, including all associated spans, tool calls, and performance metrics. +The ConversationalUI module includes a dedicated page in the **USE_ME > Traceability** folder for viewing trace data. The page **Trace_Overview** provides a high-level view of all traces in the system, allowing administrators to browse and search through GenAI traces. It displays key information such as trace ID, agent information (if applicable), start time, and duration. You can filter for specific traces and agents' invocations. The data can be visualized over time to identify patterns or anomalies. By double-clicking, users are navigated to the details page to learn more about a particular trace, including all associated spans, tool calls, and performance metrics. These pages are designed for administrators and developers who need to monitor GenAI usage and investigate specific interactions. They provide the primary interface for accessing traceability data without requiring custom development. diff --git a/content/en/docs/genai/v2/reference-guide/external-platforms/bedrock.md b/content/en/docs/genai/v2/reference-guide/external-platforms/bedrock.md index f307a1210d1..6ca8aff2bfc 100644 --- a/content/en/docs/genai/v2/reference-guide/external-platforms/bedrock.md +++ b/content/en/docs/genai/v2/reference-guide/external-platforms/bedrock.md @@ -101,12 +101,12 @@ The following actions only list the available resources as non-persistent object #### Adding Custom Models to your Bedrock Configuration {#adding-custom-model} -When syncing your Bedrock configuration, only publicly available foundation models can be retrieved using the `List Foundation Models` action from the Bedrock Connector. To use cross-region inference (CRI) or provisioned-throughput models that you have setup in your AWS console, you must manually add these models to your database. To add a custom model, follow the steps below: +When syncing your Bedrock configuration, only publicly available foundation models can be retrieved using the `List Foundation Models` action from the Bedrock Connector. To use cross-region inference (CRI) or provisioned-throughput models that you have set up in your AWS console, you must manually add these models to your database. To add a custom model, follow the steps below: 1. Click **Add model** above the model's table. 2. Fill out the form with the relevant information of your custom model. - The display name is shown in the model dropdowns while choosing a model to use. The model ID is required to use the model with Amazon Bedrock. The other fields must be filled out according to the functionalities the model can use. + The display name is shown in the model drop-down lists while choosing a model to use. The model ID is required to use the model with Amazon Bedrock. The other fields must be filled out according to the functionalities the model can use. 3. Save the model. @@ -144,7 +144,7 @@ For operations that do not depend on the GenAI Commons, you can take a different 1. In the **App Explorer**, right-click 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. +4. In the **Toolbox**, 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. @@ -195,7 +195,7 @@ To invoke a specific model that is not covered by the ChatCompletions operation, 1. Choose the model with which you want to interact by using the **Invoke Model** operation. 2. In the [Model Parameters](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters.html) 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](https://jsoncrack.com/editor). +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](https://jsoncrack.com/editor). 4. In Mendix Studio Pro, create a JSON structure by doing the following steps: 1. Right-click the target folder. 2. Click **Add other** > **JSON structure**. @@ -284,9 +284,10 @@ For additional general information about function calling, see [Function Calling 2. Return value of the type String. +* **Vision** – This operation supports the *vision* capability for [supported models](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference-supported-models-features.html). 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](/agents/agents-kit-2/reference-guide/commons/#initialize-filecollection) or the [Files: Add to Collection](/agents/agents-kit-2/reference-guide/commons/#add-file-to-collection) operation. Make sure to set the `FileType` attribute to **image**. * **Vision** – This operation supports the *vision* capability for [supported models](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference-supported-models-features.html). 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](/agents/agents-kit-2/reference-guide/commons/#initialize-filecollection) or the [Files: Add to Collection](/agents/agents-kit-2/reference-guide/commons/#add-file-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](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference-supported-models-features.html). To send a document to the model add a `FileCollection` with a `System.FileDocument` to the `Message` using the [Files: Initialize Collection with File](/agents/agents-kit-2/reference-guide/commons/#initialize-filecollection) or the [Files: Add to Collection](/agents/agents-kit-2/reference-guide/commons/#add-file-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**. +* **Document Chat** – This operation supports the ability to chat with documents for [supported models](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference-supported-models-features.html). To send a document to the model add a `FileCollection` with a `System.FileDocument` to the `Message` using the [Files: Initialize Collection with File](/agents/agents-kit-2/reference-guide/commons/#initialize-filecollection) or the [Files: Add to Collection](/agents/agents-kit-2/reference-guide/commons/#add-file-to-collection) operation. For Document Chat, it is not supported to create a `FileContent` from a URL using the above mentioned operations; Please use the `System.FileDocument` option. Make sure to set the `FileType` attribute to **document**. ##### Tool Choice @@ -563,7 +564,7 @@ The input and output for this service are shown in the table below: #### GetIngestionJob {#get-ingestion-job} -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. +The `GetIngestionJob` activity allows you to retrieve information about 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 `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](#knowledge-base). @@ -701,7 +702,7 @@ The input and output for this service are shown in the table below: 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, for example, 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. +This can be useful to, for example, associate a chatbot configuration to an available model by selecting the model via a drop-down list at 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. The syncing process works the same for all of these operations: @@ -793,7 +794,7 @@ The guardrail feature will allow you to: 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](https://aws.amazon.com/blogs/aws/guardrails-for-amazon-bedrock-helps-implement-safeguards-customized-to-your-use-cases-and-responsible-ai-policies-preview/) and in the [AWS documentation](https://aws.amazon.com/en/bedrock/guardrails/). +More information about guardrails can be found in this [AWS blog post](https://aws.amazon.com/blogs/aws/guardrails-for-amazon-bedrock-helps-implement-safeguards-customized-to-your-use-cases-and-responsible-ai-policies-preview/) and in the [AWS documentation](https://aws.amazon.com/en/bedrock/guardrails/). ### Advanced Prompts for Agents @@ -830,7 +831,7 @@ To solve this issue, follow these steps: 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. +After the status of the models changes to **Access Granted**, you can use them with the Amazon Bedrock connector. ### Error Code 403 - AccessDeniedException diff --git a/content/en/docs/genai/v2/reference-guide/external-platforms/gemini.md b/content/en/docs/genai/v2/reference-guide/external-platforms/gemini.md index 50d6f435014..db3b61383eb 100644 --- a/content/en/docs/genai/v2/reference-guide/external-platforms/gemini.md +++ b/content/en/docs/genai/v2/reference-guide/external-platforms/gemini.md @@ -71,7 +71,7 @@ The following inputs are required for the Gemini configuration: A [Deployed Model](/agents/agents-kit-2/reference-guide/commons/#deployed-model) represents a GenAI model instance that can be used by the app to generate text, embeddings, or images. For every model you want to invoke from your app, you need to create a `GeminiDeployedModel` record, a specialization of `DeployedModel` (and also a specialization of `OpenAIDeployedModel`). In addition to the model display name and a technical name or identifier, a Gemini-deployed model contains a reference to the additional connection details as configured in the previous step. Only specific models for text generation are supported by the Google Gemini connector. -1. Click the **More Options**({{% icon name="three-dots-menu-horizontal-filled" %}}) icon for a Gemini configuration and open **Manage Deployed Models**. It is possible to use a predefined generation method, where available models are created according to their capabilities. +1. Click the **More Options** ({{% icon name="three-dots-menu-horizontal-filled" %}}) icon for a Gemini configuration and open **Manage Deployed Models**. It is possible to use a predefined generation method, where available models are created according to their capabilities. 2. Close the **Manage Deployed Models** dialog box and test the configuration with the newly created deployed models. @@ -102,7 +102,7 @@ When JSON mode is used, the model is programmatically instructed to return valid Function calling enables LLMs to connect with external tools to gather information, execute actions, convert natural language into structured data, and much more. Function calling thus enables the model to intelligently decide when to let the Mendix app call one or more predefined function microflows to gather additional information to include in the assistant's response. -Gemini does not call the function. The model returns a tool called JSON structure that is used to build the input of the function (or functions) so that they can be executed as part of the chat completions operation. Functions in Mendix are essentially microflows that can be registered within the request to the LLM​. The OpenAI connector takes care of handling the tool call response as well as executing the function microflows until the API returns the assistant's final response for Gemini. +Gemini does not call the function. The model returns a tool call JSON structure that is used to build the input of the function (or functions) so that they can be executed as part of the chat completions operation. Functions in Mendix are essentially microflows that can be registered within the request to the LLM​. The OpenAI connector takes care of handling the tool call response as well as executing the function microflows until the API returns the assistant's final response for Gemini. This is all part of the implementation that is executed by the GenAI Commons chat completions operations. As a developer, make the system aware of your functions and what is done by registering the functions with the request. This is done using the GenAI Commons operation [Tools: Add Function to Request](/agents/agents-kit-2/reference-guide/commons/#add-function-to-request) once per function before passing the request to the chat completions operation. @@ -171,7 +171,7 @@ The **Documentation** pane displays the documentation for the currently selected ### Tool Choice -Gemini supports the following [tool choice types](/agents/agents-kit-2/reference-guide/commons/#enum-toolchoice) of GenAI Commons for the [Tools: Set Tool Choice](/agents/agents-kit-2/reference-guide/commons/#set-toolchoice) action is supported. For API mapping reference, see the table below: +Gemini supports the following [tool choice types](/agents/agents-kit-2/reference-guide/commons/#enum-toolchoice) of GenAI Commons for the [Tools: Set Tool Choice](/agents/agents-kit-2/reference-guide/commons/#set-toolchoice) action. For API mapping reference, see the table below: | GenAI Commons (Mendix) | Gemini | | ----------------------- | ------- | diff --git a/content/en/docs/genai/v2/reference-guide/external-platforms/mistral.md b/content/en/docs/genai/v2/reference-guide/external-platforms/mistral.md index 5b6b9b898f4..60af8d6d75a 100644 --- a/content/en/docs/genai/v2/reference-guide/external-platforms/mistral.md +++ b/content/en/docs/genai/v2/reference-guide/external-platforms/mistral.md @@ -110,7 +110,7 @@ JSON mode instructs the model to return valid JSON. To use JSON mode with the Mi Function calling enables LLMs to connect with external tools to gather information, run actions, convert natural language into structured data, and more. Function calling enables the model to intelligently decide when to let the Mendix app call one or more predefined function microflows to gather additional information for the assistant's response. -Mistral does not call the function. The model returns a tool called JSON structure that is used to build the input of the function (or functions) so they can run as part of the chat completions operation. Functions in Mendix are essentially microflows that can be registered within the request to the LLM. The OpenAI connector handles the tool call response and runs the function microflows until the API returns the assistant's final response for Mistral. +Mistral does not call the function. The model returns a tool call JSON structure that is used to build the input of the function (or functions) so they can run as part of the chat completions operation. Functions in Mendix are essentially microflows that can be registered within the request to the LLM. The OpenAI connector handles the tool call response and runs the function microflows until the API returns the assistant's final response for Mistral. The GenAI Commons chat completions operations mentioned earlier run this implementation. As a developer, you must make the system aware of your functions and their purposes by registering the functions to the request. To do so, use the GenAI Commons operation [Tools: Add Function to Request](/agents/agents-kit-2/reference-guide/commons/#add-function-to-request) once per function before passing the request to the chat completions operation. diff --git a/content/en/docs/genai/v2/reference-guide/external-platforms/mx-genai-connector.md b/content/en/docs/genai/v2/reference-guide/external-platforms/mx-genai-connector.md index 8c67a362fb3..e00f7d5d77b 100644 --- a/content/en/docs/genai/v2/reference-guide/external-platforms/mx-genai-connector.md +++ b/content/en/docs/genai/v2/reference-guide/external-platforms/mx-genai-connector.md @@ -122,7 +122,7 @@ You can optionally control both reference creation and the output returned for t Function calling enables LLMs to connect with external tools to gather information, execute actions, convert natural language into structured data, and more. Function calling thus enables the model to intelligently decide when to let the Mendix app call one or more predefined function microflows to gather additional information to include in the assistant's response. -The model does not call the function. Instead, it returns a tool called JSON structure that builds the input of the function (or functions) so they can be executed as part of the chat completions operation. Functions in Mendix are microflows that can be registered within the request to the LLM. The connector handles the tool call response and executes the function microflows until the API returns the assistant's final response. +The model does not call the function. Instead, it returns a tool call JSON structure that builds the input of the function (or functions) so they can be executed as part of the chat completions operation. Functions in Mendix are microflows that can be registered within the request to the LLM. The connector handles the tool call response and executes the function microflows until the API returns the assistant's final response. Function microflows can have none, a single, or multiple primitive input parameters such as Boolean, Datetime, Decimal, Enumeration, Integer, or String. Additionally, they may accept the [Request](/agents/agents-kit-2/reference-guide/commons/#request) or [Tool](/agents/agents-kit-2/reference-guide/commons/#tool) objects as inputs. The function microflow must return a String value. diff --git a/content/en/docs/genai/v2/reference-guide/external-platforms/openai.md b/content/en/docs/genai/v2/reference-guide/external-platforms/openai.md index 7f460e3fc4d..117ecadfec4 100644 --- a/content/en/docs/genai/v2/reference-guide/external-platforms/openai.md +++ b/content/en/docs/genai/v2/reference-guide/external-platforms/openai.md @@ -13,7 +13,7 @@ aliases: ## Introduction {#introduction} -The [OpenAI connector](https://marketplace.mendix.com/link/component/220472) lets you generative AI capabilities into Mendix apps. It is compatible with [OpenAI's platform](https://platform.openai.com/) and [Microsoft Foundry](https://learn.microsoft.com/en-us/azure/ai-foundry/what-is-ai-foundry), where you can access OpenAI models. +The [OpenAI connector](https://marketplace.mendix.com/link/component/220472) lets you integrate generative AI capabilities into Mendix apps. It is compatible with [OpenAI's platform](https://platform.openai.com/) and [Microsoft Foundry](https://learn.microsoft.com/en-us/azure/ai-foundry/what-is-ai-foundry), where you can access OpenAI models. ### Features {#features} @@ -161,7 +161,7 @@ JSON mode instructs the model to return valid JSON. To use JSON mode with OpenAI Function calling enables LLMs to connect with external tools to gather information, run actions, convert natural language into structured data, and more. Function calling enables the model to intelligently decide when to let the Mendix app call one or more predefined function microflows to gather additional information for the assistant's response. -OpenAI does not call the function. The model returns a tool called JSON structure that builds the input of the function (or functions) so they can run as part of the chat completions operation. Functions in Mendix are essentially microflows that can be registered within the request to the LLM. The OpenAI connector handles the tool call response and runs the function microflows until the API returns the assistant's final response. +OpenAI does not call the function. The model returns a tool call JSON structure that builds the input of the function (or functions) so they can run as part of the chat completions operation. Functions in Mendix are essentially microflows that can be registered within the request to the LLM. The OpenAI connector handles the tool call response and runs the function microflows until the API returns the assistant's final response. The GenAI Commons chat completions operations mentioned earlier run this implementation. As a developer, you must make the system aware of your functions and their purposes by registering the functions to the request. To do so, use the GenAI Commons operation [Tools: Add Function to Request](/agents/agents-kit-2/reference-guide/commons/#add-function-to-request) once per function before passing the request to the chat completions operation. @@ -179,11 +179,11 @@ For more information, see [Function Calling](/agents/function-calling/). Adding Azure indexes to a call enables LLMs to retrieve information when related topics are mentioned. By including these indexes in the request object along with a name and description, the model can intelligently decide when to let the Mendix app call one or more predefined indexes, allowing the assistant to include additional information in its response. -OpenAI does not directly connect to the Azure AI Search resource. The model returns a tool called JSON structure that builds the input of the retrievals so they can run as part of the chat completions operation. The OpenAI connector handles the tool call response and runs the function microflows until the API returns the assistant's final response. +OpenAI does not directly connect to the Azure AI Search resource. The model returns a tool call JSON structure that builds the input of the retrievals so they can run as part of the chat completions operation. The OpenAI connector handles the tool call response and runs the function microflows until the API returns the assistant's final response. The GenAI Commons chat completions operations mentioned earlier run this functionality. As a developer, make the system aware of your indexes and their purpose by registering them with the request. Use the GenAI Commons operation [Tools: Add Knowledge Base](/agents/agents-kit-2/reference-guide/commons/#add-knowledge-base-to-request), which must be called once per index before passing the request to the chat completions operation. -Note that the retrieval process is independent of the model provider and can be used with any model that supports function calling, as it relies on the generalized `GenAICommons.ConsumedKnowledgeBase`entity. For Azure indexes specifically, as part of this module, when collection identifiers need to be passed to operations, the `Name` of the `Index` should be used. +Note that the retrieval process is independent of the model provider and can be used with any model that supports function calling, as it relies on the generalized `GenAICommons.ConsumedKnowledgeBase` entity. For Azure indexes specifically, as part of this module, when collection identifiers need to be passed to operations, the `Name` of the `Index` should be used. #### Vision {#chatcompletions-vision} diff --git a/content/en/docs/genai/v2/reference-guide/external-platforms/pg-vector-knowledge-base/vector-database-setup.md b/content/en/docs/genai/v2/reference-guide/external-platforms/pg-vector-knowledge-base/vector-database-setup.md index 5d65fd1a713..8e2c8442e38 100644 --- a/content/en/docs/genai/v2/reference-guide/external-platforms/pg-vector-knowledge-base/vector-database-setup.md +++ b/content/en/docs/genai/v2/reference-guide/external-platforms/pg-vector-knowledge-base/vector-database-setup.md @@ -115,7 +115,7 @@ The following steps use sample values suitable for experimentation: 8. [Add the pgVector extension to the allowed extensions list](https://learn.microsoft.com/en-us/azure/postgresql/flexible-server/how-to-use-pgvector): 1. Search for **Server parameters** in the search bar on the left. A list of parameters is loaded. 2. Search for **azure.extensions**. - 3. In the column *VALUE*, search in the dropdown for **VECTOR** (this is the extension name in Azure, not *pgVector*). + 3. In the column *VALUE*, search in the drop-down list for **VECTOR** (this is the extension name in Azure, not *pgVector*). 4. Save the changes. 9. Search for **Databases** in the search bar on the left. Verify that you have a PostgreSQL database with schema type "User", or create a new database by clicking **Add**. @@ -211,7 +211,7 @@ To resolve this, connect to a network that allows these connections, such as a p ## Read More {#read-more} -* [Embeddings-based Search – Open AI Cookbook](https://cookbook.openai.com/examples/question_answering_using_embeddings) +* [Embeddings-based Search – OpenAI Cookbook](https://cookbook.openai.com/examples/question_answering_using_embeddings) * [Vector Database Options on AWS](https://aws.amazon.com/blogs/database/the-role-of-vector-datastores-in-generative-ai-applications/) * [Vector Database Options – OpenAI Cookbook](https://cookbook.openai.com/examples/vector_databases/readme) * [How to: AI-powered search in AWS Relational Database Service (RDS) For PostgreSQL Using pgvector](https://aws.amazon.com/blogs/database/building-ai-powered-search-in-postgresql-using-amazon-sagemaker-and-pgvector/) diff --git a/content/en/docs/genai/v2/reference-guide/genai-commons.md b/content/en/docs/genai/v2/reference-guide/genai-commons.md index b944a7f1a96..514ad951ce2 100644 --- a/content/en/docs/genai/v2/reference-guide/genai-commons.md +++ b/content/en/docs/genai/v2/reference-guide/genai-commons.md @@ -445,7 +445,7 @@ It is recommended that you adapt to the same interface when developing custom ch ##### Chat Completions (With History) {#chat-completions-with-history} -The `Chat Completions (with history)` operation supports more complex use cases where a list of (historical) messages (for example, comprising the conversation or context so far) is sent as part of the request to the LLM. Note that the response might not be complete if tools with [UserAccessApproval](#enum-useraccessapproval) other than `HiddenForUser` are added or the request specifies that the tool messages should be stored ([SaveToolCallHistory](#request)). In such cases, implement the logic to call the action again, with [toolcalls](#toolcall) appended to the assistant's message as well as messages of role tool to the request. If you are using the [ConversationalUI](/agents/agents-kit-2/reference-guide/conversational-ui/#human-in-the-loop) module, this is automatically handled. +The `Chat Completions (with history)` operation supports more complex use cases where a list of (historical) messages (for example, comprising the conversation or context so far) is sent as part of the request to the LLM. Note that the response might not be complete if tools with [UserAccessApproval](#enum-useraccessapproval) other than `HiddenForUser` are added or the request specifies that the tool messages should be stored ([SaveToolCallHistory](#request)). In such cases, implement the logic to call the action again, with [tool calls](#toolcall) appended to the assistant's message as well as messages of role tool to the request. If you are using the [ConversationalUI](/agents/agents-kit-2/reference-guide/conversational-ui/#human-in-the-loop) module, this is automatically handled. ###### Input Parameters @@ -462,7 +462,7 @@ The `Chat Completions (with history)` operation supports more complex use cases ##### Chat Completions (Without History) {#chat-completions-without-history} -The `Chat Completions (without history)` operation supports scenarios where there is no need to send a list of (historic) messages comprising the conversation so far as part of the request. Note that the response might not be complete if tools with [UserAccessApproval](#enum-useraccessapproval) other than `HiddenForUser` are added or the request specifies that the tool messages should be stored ([SaveToolCallHistory](#request)). In such cases, implement a logic to call the action again, with [toolcalls](#toolcall) appended to the assistant's message as well as messages of role tool to the request. For more information, refer to [Human in the loop](/agents/agents-kit-2/reference-guide/conversational-ui/#human-in-the-loop). +The `Chat Completions (without history)` operation supports scenarios where there is no need to send a list of (historic) messages comprising the conversation so far as part of the request. Note that the response might not be complete if tools with [UserAccessApproval](#enum-useraccessapproval) other than `HiddenForUser` are added or the request specifies that the tool messages should be stored ([SaveToolCallHistory](#request)). In such cases, implement a logic to call the action again, with [tool calls](#toolcall) appended to the assistant's message as well as messages of role tool to the request. For more information, refer to [Human in the loop](/agents/agents-kit-2/reference-guide/conversational-ui/#human-in-the-loop). ###### Input Parameters @@ -495,7 +495,7 @@ The `Generate Embeddings (Chunk Collection)` operation allows the invocation of | Name | Type | Description | | --- | --- | --- | -| `EmbeddingsResponse` | [EmbeddingsResponse](#embeddingsresponse-entity) | An response object that contains the token usage statistics and the corresponding embedding vector as part of a ChunkCollection. | +| `EmbeddingsResponse` | [EmbeddingsResponse](#embeddingsresponse-entity) | A response object that contains the token usage statistics and the corresponding embedding vector as part of a ChunkCollection. | ##### Generate Embeddings (String) {#embeddings-string} @@ -658,7 +658,7 @@ This microflow does not have a return value. ##### Tools: Add Knowledge Base {#add-knowledge-base-to-request} -This tool adds a function that performs a retrieval from a knowledge base to a [ToolCollection](#toolcollection) that is part of a Request. Use this microflow when you have knowledge bases in your application that may be called to retrieve the required information as part of a GenAI interaction. If you want the model to be aware of these knowledge base, you can use this operation to add them as functions to the request. If supported by the LLM connector, the chat completion operation calls the appropriate knowledge base function based on the LLM response and continue the process until the assistant's final response is returned. +This tool adds a function that performs a retrieval from a knowledge base to a [ToolCollection](#toolcollection) that is part of a Request. Use this microflow when you have knowledge bases in your application that may be called to retrieve the required information as part of a GenAI interaction. If you want the model to be aware of these knowledge bases, you can use this operation to add them as functions to the request. If supported by the LLM connector, the chat completion operation calls the appropriate knowledge base function based on the LLM response and continues the process until the assistant's final response is returned. `ConsumedKnowledgeBase` objects have provider-specific specializations, for example, `MxCloudKnowledgeBaseResource` for Mendix Cloud. @@ -733,7 +733,7 @@ Use this microflow to get the list of references that may be included in the mod | Name | Type | Description | |---|---|---| -| `ReferenceList` | List of [Reference](#reference) | The references with optional citations were part of the response message. | +| `ReferenceList` | List of [Reference](#reference) | The references with optional citations that were part of the response message. | ##### Get Response Text {#chat-get-model-response-text} @@ -776,8 +776,8 @@ This microflow creates new [ImageOptions](#imageoptions-entity). | Name | Type | Notes | Description | |--- |--- |--- |--- | -| `Height` | Integer/Long | optional | To set Width. | -| `Width` | Integer/Long | optional | To set Height. | +| `Height` | Integer/Long | optional | To set Height. | +| `Width` | Integer/Long | optional | To set Width. | | `NumberOfImages` | Integer/Long | optional | To set NumberOfImages to create. | ###### Return Value @@ -985,7 +985,7 @@ This microflow creates a new [MetadataCollection](#metadatacollection-entity) an #### `ENUM_ModelModality` {#enum-modalmodality} -`ENUM_ModelModality` describes the modalities that the model supports input or output. +`ENUM_ModelModality` describes the modalities that the model supports as input or output. | Name | Caption | Description | | --- | --- | --- | diff --git a/content/en/docs/genai/v2/reference-guide/mcp-modules/mcp-client.md b/content/en/docs/genai/v2/reference-guide/mcp-modules/mcp-client.md index 456aac69618..45d18fb48a4 100644 --- a/content/en/docs/genai/v2/reference-guide/mcp-modules/mcp-client.md +++ b/content/en/docs/genai/v2/reference-guide/mcp-modules/mcp-client.md @@ -45,7 +45,7 @@ This page documents MCP Client module versions [compatible with Agents Kit 2](/a The `Create MCP Client` action creates a sync client that is connected to an (externally) running MCP server and returns the `MCPClient` object. The action requires an `MCPServerConfiguration` object that contains all required attributes to facilitate the connection. -The `MCPServerConfiguration` objects can be created by users with the `MCPClient.Administrator` userrole via the `MCPServerConfiguration_Overview` page. If the MCP server expects HTTP headers (for example, for authentication), you can select a `GetCredentialsMicroflow` which should return a list of `System.HttpHeader` objects. You can use the `Config: Create Http Header and Add to List` toolbox action in this microflow. The `GetCredentialsMicroflow` cannot have any input parameters. Take a look at the `GetCredentials_EXAMPLE` in the **Example Implementations** folder for an example. +The `MCPServerConfiguration` objects can be created by users with the `MCPClient.Administrator` user role via the `MCPServerConfiguration_Overview` page. If the MCP server expects HTTP headers (for example, for authentication), you can select a `GetCredentialsMicroflow` which should return a list of `System.HttpHeader` objects. You can use the `Config: Create Http Header and Add to List` toolbox action in this microflow. The `GetCredentialsMicroflow` cannot have any input parameters. Take a look at the `GetCredentials_EXAMPLE` in the **Example Implementations** folder for an example. You can use the returned `MCPClient` object for all other actions, for example, to discover tools and prompts, to get a specific prompt, or call a tool. An MCP Client can be reused across multiple actions or throughout an entire chat conversation. It is recommended to close connections after use by calling the `Close MCP Client` action. @@ -59,7 +59,7 @@ When creating an MCP client, specify a `ProtocolVersion`. On the official MCP do The actions `List Prompts` and `List Tools` send a request to the MCP server to discover prompts and tools, respectively. Create the MCP Client beforehand and pass it as an input. Both actions create the necessary objects, such as `Prompt` and `PromptArgument` for prompts and `Tool`, `ToolArgument`, and `EnumValue` for tools. If the prompt or tool requires arguments, the objects help you understand what needs to be passed and how to format it. -In general, prompts are often exposed to end-users in a chat to start or continue a conversation, while tools are passed to an LLM. If you want users to be able to view tools and prompts, you can assign them the `User` userrole. For more information, see the [Using MCP Client Module with GenAI Commons](#use-with-genai-commons) section below. +In general, prompts are often exposed to end-users in a chat to start or continue a conversation, while tools are passed to an LLM. If you want users to be able to view tools and prompts, you can assign them the `User` user role. For more information, see the [Using MCP Client Module with GenAI Commons](#use-with-genai-commons) section below. ### Using Resources {#use-resources} @@ -69,7 +69,7 @@ For both actions, you can pass an `ArgumentCollection` if the prompt or tool req ### Using MCP Client Module with GenAI Commons and Conversational UI {#use-with-genai-commons} -To add all tools from an MCP server to a `GenAICommons.Request`, you can use the `Request: Add all tools from MCP server` toolbox action. This action will first list all tools from the provided MCP server configuration, iterate over them, and adding them one by one to the tool collection. The request can then be passed to a Chat Completions operation. +To add all tools from an MCP server to a `GenAICommons.Request`, you can use the `Request: Add all tools from MCP server` toolbox action. This action will first list all tools from the provided MCP server configuration, iterate over them, and add them one by one to the tool collection. The request can then be passed to a Chat Completions operation. You can also find an example [action microflow](/agents/agents-kit-2/reference-guide/conversational-ui/#action-microflow) `ChatCompletions_MCPClient_ActionMicroflow` in the **Example Implementations** folder of the module. This microflow demonstrates how a Conversational UI chat action including MCP tools can be facilitated. Duplicate and include this microflow into your custom module and modify it according to your requirements. diff --git a/content/en/docs/genai/v2/reference-guide/mcp-modules/mcp-server.md b/content/en/docs/genai/v2/reference-guide/mcp-modules/mcp-server.md index 02c4b4a05c2..727c2ba74cd 100644 --- a/content/en/docs/genai/v2/reference-guide/mcp-modules/mcp-server.md +++ b/content/en/docs/genai/v2/reference-guide/mcp-modules/mcp-server.md @@ -15,7 +15,7 @@ aliases: The [MCP Server](https://marketplace.mendix.com/link/component/240380) module provides easy low-code capability to set up an MCP ([Model Context Protocol](/agents/mcp/)) server within a Mendix app. An MCP server can seamlessly expose resources (such as tools or prompts) to other external AI applications that support MCP. The Mendix MCP Server module builds a bridge between Mendix and MCP client applications, such as Claude Desktop, through the [MCP Java SDK](https://github.com/modelcontextprotocol/java-sdk). With the current implementation, it is possible to: * Expose reusable prompts, including the ability to use prompt parameters -* List and execute microflow implemented in the application as tools +* List and execute microflows implemented in the application as tools To use function calling within the same Mendix application and integrating to an LLM, consider [function calling](/agents/function-calling/). @@ -99,7 +99,7 @@ The MCP Server module does not validate access tokens itself. Your authenticatio 2. Validate the token. Depending on the authorization server, you can do this in one of the following ways: * Use the [OIDC SSO](/appstore/modules/oidc/) module, which supports validating tokens against the signing keys of the authorization server. This is required for authorization servers without an introspection endpoint, such as Microsoft Entra ID. For example, use the `DecodeVerifyJWTPlainText` action to verify and get the JWT token to validate further fields, such as, scopes. Note that the `APIAuthentication` action is currently not supported when called from the MCP Server module. - * In exceptional cases where your IdP issues opaque tokens that is not being a JWT or when your IdP supports token revocation, you may want to call the token introspection endpoint [(RFC 7662)](https://datatracker.ietf.org/doc/html/rfc7662#section-2) of the authorization server, for example, with a `Call REST service` activity. In most scenarios this is not needed. Common IdP-technologies such as Entra ID, Okta, Keycloak issue access tokens having the JWT profile. + * In exceptional cases where your IdP issues opaque tokens that are not JWTs or when your IdP supports token revocation, you may want to call the token introspection endpoint [(RFC 7662)](https://datatracker.ietf.org/doc/html/rfc7662#section-2) of the authorization server, for example, with a `Call REST service` activity. In most scenarios this is not needed. Common IdP technologies such as Entra ID, Okta, Keycloak issue access tokens having the JWT profile. 3. Check that the token is active and not expired, and that the audience (`aud`) is the value that the authorization server issues for your MCP Server. Otherwise, your MCP Server accepts tokens that were issued for other applications. Based on your requirements, check other fields such as scopes or issuer.