Rick Donato

Use .filter() in Config Templates to query objects during lookups and conditional logic. This exposes the Django ORM QuerySet filter directly in your templates. Here's an example: {% for iface in device.interfaces.filter(enabled=True, name__startswith="ge-") %} interface {{ iface.name }} {% endfor %}

Typically, when working with an LLM, you send your data along with a prompt and receive a response. But this pattern often introduces issues, especially with public LLMs, due to security concerns and token bloat. There is a different pattern we can use: the Schema-Driven LLM Query Pattern. With this

OpenAI Tip: If you need structured output from the LLM, use structured responses and define the expected format with JSON Schema. Here’s an example: from openai import OpenAI import os client = OpenAI(api_key=os.getenv("OPENAI_API_KEY")) # Define the exact shape you want back. schema

Ansible and NetBox Tip: Pass Ansible group_vars and host_vars into NetBox Config Templates by sending your Ansible vars, making them available directly within your Config Templates. Here is an example: # Ansible Playbook - name: Trigger NetBox to return rendered configuration ansible.builtin.uri: url: "{{ NETBOX_ENDPOINT }}/api/

Tip! Validate your env vars from your .env files before they hit your application by using Pydantic. This also eliminates the need to call python-dotenv manually. Here's an example: # uv add pydantic pydantic-settings python-dotenv from pydantic import Field, AnyHttpUrl, ValidationError from pydantic_settings import BaseSettings, SettingsConfigDict # Define settings

One of the big announcements from AutoCon4 last week came from the team behind Netpicker and Slurpit. They have now released major upgrades, and have aligned the product catalog under the umbrella of the Flock9 platform, bringing Netpicker and Slurpit even closer together. The TL;DR * Netpicker AI Assistant Netpicker

For those still working with Excel spreadsheets, here's a quick tip ... To easily work with data from an Excel spreadsheet in Python, use the Pandas library. First, import the spreadsheet, then iterate over the data, using itertuples, like so: # Install - pip install pandas import pandas as pd

🐍 Python Tip: Improve code readability by enforcing named arguments. To enforce all arguments to be named, start your parameter list with *. Here’s an example: def add_vlan(*, vlan_id, name): print(f"VLAN ID: {vlan_id}") print(f"Name: {name}") add_vlan(vlan_id=10,

pyATS Tip! By default, pyATS runs several exec/config commands and logs to stdout when connecting. You can disable these using the parameters in the example below: device.connect( init_exec_commands=[], # Disable default exec commands init_config_commands=[], # Disable default config commands log_stdout=False # Disable logging to stdout