🦾 CLEUR DevNet-3707 2024

🎞️ Slides and Recording 🎥 - here 🚀

This demo is built to showcase how you AI might assist you in troubleshooting network issues.

The components used by this demo are:

• Virtual IOS-XE devices running ISIS.
  • The CML Devnet sandbox was used to build the lab.
• ncpeek. A python netconf client used for telegraf.
• TIG stack with docker 20.10+ 🐳
  • Telegraf grabs telmetry data from network devices.
  • Grafana kicks a webhook when an alarm is detected. 🚨
• FastAPI.
  • Host the LLM.
  • Interacts with network devices & frontend.
• PyATS. Provides a framework to interact with network devices. 🛠️
• Webex_bot use to interact with the LLM. 🤖
• OpenAI LLM. 🧠
  • gpt-4-turbo-preview was used. 🚀

🎬 Demo

For this demo one alarm was created.

When the average number of ISIS neighbors in a lapse of 30 second is less than the average number of ISIS neighbors in a lapse of 30 minutes, the alarm will trigger a webhook for the LLM.

This signal that a stable ISIS neighbor that was working on the last 30 minutes was lost, and allows to work with N number of ISIS neighbors.

🛠️ Prepare Demo

🔑 Environment variables

📌 Mandatory variables

For the demo to work, you must set the next environment variables. You can either export the environment variables or create a .env file with them. See .env.local for an example.

OPENAI_API_KEY=<YOUR_OPENAI_API_KEY>
WEBEX_TEAMS_ACCESS_TOKEN=<YOUR_TEAM_ACCESS_TOKEN>
WEBEX_APPROVED_USERS_MAIL=<MAILS_OF_USERS_APPROVED_SEPARATED_BY_COMMAS>
WEBEX_USERNAME=<YOUR_WEBEX_USERNAME>
WEBEX_ROOM_ID=<THE_WEBEX_ROOM_ID>

NOTE: The webex variables are only needed if you interact with the LLM using webex.

If you prefer to use another client, you need to:

• Modify the notify function to accomodate your client.
• Remove/comment the start of the webex bot
• Communicate with the LLM using REST API. See send_message_to_chat_api for an example. 📡

📝 Webex considerations

To get your webex token go to https://developer.webex.com/docs/bots and create a bot.

To get the WEBEX_ROOM_ID the easiest way is to open a room with your bot in the webex app. Once you have your room, you can get the WEBEX_ROOM_ID by using API list room, use your token created before.

📌 Optional Variables

For testing, you can use the GRAFANA_WEB_HOOK env var to send webhooks to other site, such as https://webhook.site/

If you have access to smith.langchain.com (recommended for view LLM operations) add your project ID and API key.

GRAFANA_WEB_HOOK=<WEB_HOOK_URL>
LANGCHAIN_PROJECT=<YOUR_LANGCHAIN_PROJECT_ID>
LANGCHAIN_API_KEY=<YOUR_LANGCHAIN_API_KEY>
LANGCHAIN_TRACING_V2=true
LANGCHAIN_ENDPOINT=https://api.smith.langchain.com

.env.local file

The .env.local file is used to define all variables used by the containers.

In a production environment, this file should be kept out of version control using the .gitignore file.

🚀 Start the topology

This demo uses a CML instance from the Cisco DevNet sandbox. You can also use a dedicated CML instance or a NSO sandbox. 🏖️

After acquiring your sandbox, stop the default topology and wipe it out. 🧹

Then, import the topology file used for this demo and start the lab.

📦 TIG Stack

The TIG stack requires Docker and IP reachability to the CML instance. For this demo, I used my laptop.

To start the TIG stack do.

./build_run_telegraf.sh
./build_run_influxdb.sh
./build_run_grafana.sh

🚦 Verifying Telemetry on Telegraf, Influxdb, Grafana

• telegraf
  • Run docker exec -it telegraf bash and then tail -F /tmp/telegraf-grpc.log to see Telegraf logs.
• Influxdb
  • Access http://localhost:8086 with the credentials admin/admin123
• Grafana
  • Access http://localhost:3000/dashboards with the credentials admin/admin
  • Navigrate to General > Network Telemetry to see the grafana dashboard.

🏁 Starting the LLM

The llm_agent directory provides all the code used to run the LLM.

In this demo, the LLM is run using a Python virtual environment. Ensure that you install the requirementes listed.

The entry point for the application is the app file

NOTE: In the upcoming weeks, a container will be added for the LLM

🎮 Running the Demo

The demo involves shutting down one interface, causing an ISIS failure, and allowing the LLM to diagnose the issue and implement a fix.

In the images below, GigabitEthernet5 was shutting down on cat8000-v0 resulting in losing its ISIS adjacency with cat8000-v2

On Grafana, you can observe the ISIS count decreasing and triggering an alarm.

Next, you will receive a webex notification from grafana and the LLM will receive the webhook. The webhook triggers the LLM to start looking at what the issue is and how to resolve it.

📝 Notes

• You can easily run out of OpenAI tokens in your replies from netconf, so is important to filter data to what AI could need.
• Repeated alarms are suppresed by Grafana, this is controlled by the grafana policy file,
  • If you are testing continously, run ./build_run_grafana.sh to destroy and create the container.
  • This isn't an ideal scenario, but a proper solution wasn't found within the given time.
• From time to time, the answers from the LLM are lost and not sent to webex. You can find them on the terminal output.
• This is the second iteration of this exercise. The first one was presented at Cisco Impact 2023

📚 Troubleshooting

If the CML lab is not reachable from your laptop, it's usually due to a connectivity issue between the devices and the CML bridge. Here are some steps to resolve this:

• Try flapping the management interface (G1) of the devices several times.
• Ping from the devices to their Gateway (10.10.20.255).
• Go to the DevBox 10.10.20.50 (credentials: developer/C1sco12345) and ping the management interface of the devices.

Connectivity usually starts to work after about 5 minutes.

In more drastic cases, restart the cat8kv from the CML GUI.