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Getting Started with the App

This document provides a step-by-step tutorial on how to get the AI Ops App configured and how to use it.

Install the App

To install the App, please follow the instructions detailed in the Installation Guide.

First Steps with the App

After installing the app, follow these steps to get started:

Step 1: Configure an LLM Model

Before you can use the AI Chat Assistant, you need to configure at least one LLM model.

  1. Navigate to AI Platform > Configuration > LLM Models in the Nautobot menu
  2. Click the + Add button to create a new model
  3. Fill in the required fields:
  4. Name: Azure deployment name (e.g., gpt-4o, gpt-4-turbo)
  5. Description: A description of the model's purpose and capabilities
  6. Model Secret Key: Name of the Nautobot Secret containing your Azure OpenAI API key
  7. Azure Endpoint: Your Azure OpenAI endpoint URL (e.g., https://your-resource.openai.azure.com/)
  8. API Version: API version (default: 2024-02-15-preview)
  9. Is Default: Check this box to make this the default model

  10. Temperature: Set the model temperature (0.0 for deterministic, higher for creative)

  11. Click Create to save the model

Tip

For your first model, mark it as the default model by checking the "Is Default" checkbox. This ensures the chat assistant knows which model to use.

Step 2: Create Secrets for API Keys (Production)

For production environments, you should store API keys securely using Nautobot Secrets:

  1. Navigate to Secrets > Secrets in Nautobot
  2. Create a new Secret with your Azure OpenAI API key
  3. Name the secret (e.g., azure_gpt4o_api_key)
  4. Configure the secret provider and value
  5. Use this secret name in your LLM Model configuration

Note

In LAB/development environments, the app can use environment variables for API keys instead of Secrets.

Step 3: Configure MCP Servers (Optional)

MCP (Model Context Protocol) servers extend the capabilities of your AI agent by providing additional tools and context:

  1. Navigate to AI Platform > Configuration > MCP Servers
  2. Click + Add to create a new server
  3. Fill in the fields:
  4. Name: Unique identifier for the server
  5. Status: Select "Healthy" to enable the server
  6. Protocol: Choose "HTTP" or "STDIO"
  7. URL: Server endpoint URL
  8. Health Check: Health check endpoint (default: /health)
  9. Description: Purpose of this MCP server

  10. MCP Type: "Internal" for internal servers, "External" for third-party

  11. Click Create to save the server

Info

MCP servers are optional. The AI Chat Assistant will work without them, but MCP servers can provide additional capabilities like code execution, file access, or integration with external systems.

Step 4: Use the AI Chat Assistant

Now you're ready to use the AI Chat Assistant:

  1. Navigate to AI Platform > Chat & Assistance > AI Chat Assistant
  2. Type your question or request in the chat input box
  3. Press Enter or click the Send button
  4. The AI agent will process your message and respond

The chat interface maintains conversation history, allowing for contextual multi-turn conversations.

Step 5: Monitor and Maintain

Check MCP Server Health

MCP server health is automatically monitored. You can view the status:

  1. Go to AI Platform > Configuration > MCP Servers
  2. Check the Status column for each server
  3. Servers with "Healthy" status are actively used by the agent
  4. Failed servers will be automatically excluded from agent operations

Schedule Checkpoint Cleanup

To prevent Redis from accumulating old conversation data:

  1. Navigate to Jobs > Jobs
  2. Find the job AI Agents > Cleanup Old Checkpoints
  3. Click Run Job Now to run it manually, or
  4. Click Schedule Job to set up automatic recurring execution

Tip

Schedule the cleanup job to run daily or weekly depending on your usage patterns and storage constraints.

What are the next steps?

After completing the initial setup, you can:

  • Explore Use Cases: Check out the Use Cases section for examples of what you can do with the AI Chat Assistant
  • Configure Multiple Models: Set up different models for different use cases (e.g., fast model for quick responses, larger model for complex analysis)
  • Integrate MCP Servers: Add MCP servers to extend agent capabilities with custom tools and integrations
  • Review API Documentation: Learn about the REST API in External Interactions
  • Customize Prompts: Advanced users can modify system prompts in the code (see Developer Guide)

Troubleshooting

Common Issues

Chat not responding?

  • Verify that at least one LLM Model exists and is marked as default
  • Check that the API key Secret is configured correctly
  • Review Nautobot logs for error messages

MCP Server showing as Failed?

  • Verify the server URL is accessible from the Nautobot instance
  • Check the health check endpoint returns a successful response
  • Review the MCP server logs for connection issues

Conversation history not persisting?

  • Ensure Redis is properly configured in nautobot_config.py
  • Verify the Redis connection using the checkpointer configuration
  • Check that LANGGRAPH_REDIS_DB environment variable is set

For more help, check the FAQ or contact your administrator.