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Chapter 2. Playground prerequisites

Before you can configure and use the gen AI playground feature, you must meet prerequisites at both the cluster and user levels.

2.1. Cluster administrator prerequisites
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Before a user can configure a playground instance, a cluster administrator must complete the following setup tasks:

  • Ensure that OpenShift AI is installed on an OpenShift cluster running version 4.19 or later.
  • Set the value of the spec.dashboardConfig.genAiStudio dashboard configuration option to true. For more information, see Dashboard configuration options.
  • If using OpenShift AI groups, add users to the rhods-users and rhods-admins OpenShift group.
  • Ensure that the Llama Stack Operator is enabled on the OpenShift cluster by setting its managementState field to Managed in the DataScienceCluster custom resource (CR) of the OpenShift AI Operator. For more information, see Activating the Llama Stack Operator.
  • Configure the Model Context Protocol (MCP) servers to test models with external tools. For more information, see Configuring model context protocol servers.

2.2. User prerequisites
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After the cluster administrator completes the setup, you must complete the following tasks before you can configure your playground instance:

  • You are logged in to OpenShift AI.
  • If you are using OpenShift AI groups, you are a member of the appropriate user or admin group.
  • Create a project. The playground instance is tied to a project context. For more information, see Creating a project.
  • Add a connection to your project. For more information about creating connections, see Adding a connection to your project.
  • Deploy a model in your project and make it available as an AI asset endpoint. For more information, see Deploying models on the model serving platform.

After you complete these tasks, the project is ready for you to configure your playground instance.

A cluster administrator must configure and enable the Model Context Protocol (MCP) servers at the platform level before users can interact with external tools in the Generative AI Playground. This configuration is done by creating a ConfigMap in the redhat-ods-applications namespace, which holds the necessary information for each MCP server.

Prerequisites

  • You have cluster admin privileges for your OpenShift cluster.

  • You have installed the OpenShift CLI (oc) as described in the appropriate documentation for your cluster:

Procedure

  1. Create a file named gen-ai-aa-mcp-servers.yaml with the following YAML content. You can add multiple server entries under the data: field. 

    kind: ConfigMap
apiVersion: v1
metadata:
  name: gen-ai-aa-mcp-servers
  namespace: redhat-ods-applications
data:
  GitHub-MCP-Server: |
    {
      "url": "https://api.githubcopilot.com/mcp/x/repos/readonly",
      "description": "The GitHub MCP server enables exploration and interaction with repositories, code, and developer resources on GitHub. It provides programmatic access to repositories, issues, pull requests, and related project data, allowing automation and integration within development workflows. With this service, developers can query repositories, discover project metadata, and streamline code-related tasks through MCP-compatible tools."
    }
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    Important

    The ConfigMap key (GitHub-MCP-Server) is case-sensitive and must be unique. The content provided under this key must be valid JSON format.

  2. Apply the ConfigMap to the cluster by running the following command: 

    oc apply -f gen-ai-aa-mcp-servers.yaml
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Verification

  • Confirm that the ConfigMap was successfully applied by running the following command: 

    oc get configmap gen-ai-aa-mcp-servers -n redhat-ods-applications -o yaml | grep GitHub-MCP-Server
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  • The output should contain the key name, confirming its successful creation: 

    GitHub-MCP-Server: |
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To successfully use the retrieval augmented generation (RAG) and Model Context Protocol (MCP) features in the playground, the model you deploy must meet specific requirements. Not all models offer the same capabilities.

2.4.1. Key model selection factors
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Tool calling capabilities
The model must support tool calling to interact with the playground’s RAG and MCP features. You must check the model card (for example, on Hugging Face) to verify this capability. For more information, see Tool calling in the vLLM documentation.
Context length
Models with larger context windows are recommended for RAG applications. A larger context window allows the model to process more retrieved documents and maintain longer conversation histories.
vLLM version and configuration

Tool calling functionality depends heavily on the version of vLLM used in your model serving runtime.

  • Version: Use the latest vLLM version included in Red Hat OpenShift AI for optimal compatibility.

  • Runtime arguments: You must configure specific runtime arguments in the model serving runtime to enable tool calling. Common arguments include (not exhaustive):

    • --enable-auto-tool-choice
    • --tool-call-parser
    • --chat-template=/opt/app-root/template/<template_file>.jinja
Important
  • If these requirements are not met, the model might fail to search documents or execute tools without returning a clear error message.
  • Tool calling functionality varies by model family, such as Llama, Mistral, Qwen and so on. For a complete list of supported models, compatible parsers, and template filenames, see Tool calling in the vLLM documentation.
  • When you specify a chat template, use the absolute path /opt/app-root/template/ to locate the standard Jinja template files provided in the Red Hat OpenShift AI image. For example, /opt/app-root/template/tool_chat_template_llama3.1_json.jinja. Do not use relative paths, such as examples/. Relative paths cause model deployment to fail.

2.4.2. Example model configuration
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The following table describes an example configuration for the Qwen/Qwen3-14B-AWQ model for use in the playground. You can use this as a reference when configuring your own model runtime arguments.

Expand
Table 2.1. Example configuration for Qwen/Qwen3-14B-AWQ
FieldConfiguration Details

Model

Qwen/Qwen3-14B-AWQ

vLLM Runtime

vLLM NVIDIA GPU ServingRuntime for KServe

Hardware Profile

NVIDIA A10G (24GB VRAM)

Custom Runtime Arguments

--dtype=auto
--max-model-len=32768
--enable-auto-tool-choice
--tool-call-parser=hermes
--reasoning-parser=qwen3
--gpu-memory-utilization=0.90

2.5. About the AI assets endpoints page
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Important

This feature is currently available in Red Hat OpenShift AI 3.2 as a Technology Preview feature. Technology Preview features are not supported with Red Hat production service level agreements (SLAs) and might not be functionally complete. Red Hat does not recommend using them in production. These features provide early access to upcoming product features, enabling customers to test functionality and provide feedback during the development process.

For more information about the support scope of Red Hat Technology Preview features, see Technology Preview Features Support Scope.

The AI asset endpoints page is a central dashboard for managing the generative AI assets available for you to use within your project.

The page organizes assets into two categories:

  • Models: Lists all generative AI models deployed in your project that have been designated as available assets. For a model to be available, you must select the Add as AI asset endpoint check box when deploying it. For more information, see Deploying models on the model serving platform.
  • Model Context Protocol (MCP) Server: Lists all available MCP servers configured in the cluster in a config map. For more information, see Configuring Model Context Protocol servers.

The primary purpose of this page is to provide a starting point for using these assets. From here, you can perform actions such as adding a model to a playground instance for testing.

Important

The assets listed on the AI assets endpoints page are scoped to your currently selected project. You only see models and servers that are deployed and available within that specific project.

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