Getting Started with Payments in Python/Django (Feat. I'mport) - Basic Edition
Are you having trouble integrating payments into your web service? Quickly apply payments to your service using Django and I'mport.
We are releasing a tutorial on how to create a data analysis agent chatbot in 30 minutes using Python/Django.
hello.
< Create a data analysis agent chatbot in 30 minutes with Python/Django > We share the tutorial video and documentation.
Tutorial video: https://www.youtube.com/watch?v=10Fp78n3jSw
Tutorial documentation:https://django-pyhub-ai.readthedocs.io
This article contains the process of quickly creating an LLM situational chatbot and a data analysis agent chatbot using the django-pyhub-ai library that I created. This library is based on the DRY (Don't Repeat Yourself) philosophy, one of Django's core philosophies, to eliminate repetitive and cumbersome tasks and to help easily build an efficient agent-based chat service. You can implement an agent chatbot with code close to the settings without worrying about the web front end. It is based on Django Channels and HTMX, and uses Langchain, famous for its LLM library, internally.
Chatbot operation screen
I believe that by utilizing Django's models/cache/templates/storage/API/authentication, we can create more productive and valuable AI agents. We will continue to strive to deliver various Django news in the future.
Please share this widely.
I will be your Python/Django pacemaker.
thank you
Python Love Room, Lee Jin-seok Dream