Configure AI Tools in Red Hat OpenShift Dev Spaces (Bonus Lab)

Introduction

This lab explores how Red Hat Advanced Developer Suite (RHADS) integrates with other products and third-party tools to bring AI into developers' daily activities. It also shows how developer tools like Red Hat OpenShift Dev Spaces provide opportunities to adopt AI practices.

Objectives

This lab showcases how to configure an AI assistant and expand its capabilities by configuring a Tavily MCP server.

Let’s get started.

Scenario

You are part of the Python AI Development team. Your team has already built the AI agent; now it is your time to learn about this implementation.

Getting started

Access Red Hat OpenShift Dev Spaces from the component already created in Red Hat Developer Hub:
- Go back to {rhdh_url}/catalog/default/component/ai-agent[Red Hat Developer Hub UI^]
- Log in with your user credentials:
  - Username: {rhdh_user}
  - Password: {rhdh_user_password}
Next, click the ai-agent component.
From the component’s overview, click the Red Hat OpenShift Dev Spaces link.

rhdh devspaces click

Configure the AI Assistant in Red Hat OpenShift Dev Spaces

We want to use an AI assistant within OpenShift Dev Spaces. For this lab, we’ll use Continue. Organizations might use others, such as Claude or Microsoft Copilot. In many organizations, developers use AI Assistants to increase productivity. Learning about these tools is key to understanding how AI can be leveraged within Red Hat products.

In Red Hat OpenShift Dev Spaces, click Extensions.

devspaces extension

Search for the continue extension and select Install Release version.

devspaces continue install release

Click the Trust the Publisher and Install button.
After a successful installation, you will see a screen similar to this:

devspaces continue page

Configure the LLM in the AI Assistant

Next, click the continue icon.

Click the Add Chat model option, then click config file.

devspaces continue config assistant

A new file named config.yaml will be created.

devspaces continue config yaml

Copy the following content and paste it, replacing the whole current content:
- Copy the provided content by clicking the icon:

name: Local Assistant
version: 1.0.0
schema: v1
models:
  - name: llama-3-2-3b
    provider: openai
    model: llama-3-2-3b
    apiBase:
    apiKey: ""
    roles:
      - chat
      - edit
      - apply
tabAutocompleteModel:
  title: RamaLama (AutoComplete)
  provider: custom
  model: default
allowAnonymousTelemetry: false
context:
  - provider: code
  - provider: docs
  - provider: diff
  - provider: terminal
  - provider: problems
  - provider: folder
  - provider: codebase

If needed, verify the solution: config.yaml AI assistant config file
- Paste it into the config.yaml file created by continue.

Next, you will add the Model’s apiBase and apiKey from the Internal MaaS Demo from the AI BU.

Get your API key for the self-hosted Model

Configuring an AI assistant requires an LLM. Organizations might use their own self-hosted models or remote ones. For this lab, we will use the Model as a Service portal to access an LLM.

Access the URL and sign in with your Red Hat account: Maas website

Note: The Maas website is not an official Red Hat service. It is for Red Hat associates' internal demo purposes only, provided 'as-is' without support or SLA. The intended purpose is to test the connectivity of Red Hat products to models that customers may use.

Click Sign in

maas sign in

Click Authenticate with RH SSO

maas authrh

Click Google to sign in to RHOAI, where the model is deployed with your account.

maas rhoai

Click applications
Click the llama-3-2-3b model

maas model select

Copy the API Key and paste it into the config.yaml file’s apiKey:" ".

maas key

Copy the Endpoint URL and paste it into the config.yaml file’s apiBase: and add /v1 to the end of the URL.
- Save the file and close the Config update message

devspaces config updated

Test the AI Assistant

Click the recently added model llama-3-2-3b, to add it to the Continue chat:

continue llama select

Ask the AI assistant a question, such as:

continue chat1

Learn about the search_agent.py file

Learn about the implementation. Let’s start with the search_agent.py file.

@search_agent.py  tell me about this file

Note: ensure you add @search_agent.py at the beginning of the prompt

continue chat2

Learn about the requirements.txt file

@requirements.txt  tell me about this file

Note: ensure you add @requirements.txt *at the beginning of the prompt

continue chat3

Congratulations, you have successfully configured an AI Assistant in Red Hat OpenShift Dev Spaces using a self-hosted LLM and learned about the AI Agent implementation.

Configure an MCP server in the AI Assistant

The AI assistant will use Tavily for tools, such as Web Search. We want to include the Tavily MCP server in our Development environment to learn about the AI agent development.

Click the Continue chat and click the tools icon:

mcp configure tools

In the tools window, click the plus icon to configure the MCP server.

mcp configure new

A new file named new-mcp-server.yaml will be created.
Copy the following content and paste it, replacing the whole current content:
- Copy the provided content by clicking on the icon:

name: Tavily MCP
version: 0.0.1
schema: v1
mcpServers:
  - name: Tavily MCP server
    command: npx
    args:
      - -y
      - mcp-remote
      - https://mcp.tavily.com/mcp/?tavilyApiKey=
    env: {}

We have provided a configuration to set up the Tavily MCP server:
- If needed, verify the solution: tavily-mcp-server config file

Note: Other AI assistants might use JSON files instead of YAML. If you configure a different AI assistant, the MCP server web page will have configuration examples. Explore more at Tavily MCP

Copy the file content and paste it into the new-mcp-server.yaml file.
- You are replacing the MCP SERVER NAME CONFIG, MCP SERVER NAME, ARGS, as shown here:

mcp server config new

Access your Tavily account

Access https://app.tavily.com
Click Login and choose your previously selected sign-up method (Google, GitHub, Email address).

tavily web

Click continue, and once logged in, you will see a screen like this one:

tavily main

Click API KEY and copy the API KEY VALUE:

tavily apikey view

NOTE: KEEP THE API KEY SECRET. Keep it safe. DO NOT PUBLISH IT, SHARE IT, OR COMMIT IT TO ANY PUBLIC GIT REPOSITORY. THIS IS YOUR PERSONAL API KEY.

Paste your key after ?tavilyApiKey=

mcp server config apikey

Save the changes.
Tavily MCP server will now appear in the MCP server configuration.

If the server is not ready, click the refresh icon: mcp server refresh

Use the MCP server in the AI Assistant to learn about Tavily

Tavily provides tools including Tavily Search and Tavily Extract.

Click the back icon to return to the Continue chat.

mcp server back

Ensure agent is selected in the Continue terminal. This agent interacts with the MCP server tools..

mcp server select agent

An agent to interact with the mcp server tools.

Ask the AI Agent questions about Tavily Search:

tell me about tavily search

mcp server chat

Ask the AI Agent questions about Tavily Extract:

tell me about tavily extract

mcp server chat extract

Congratulations, you have successfully configured an MCP Server in Red Hat OpenShift Dev Spaces as part of the AI assistant and learned about the AI Agent’s tools.