Why construct issues the arduous manner when you possibly can design them the good manner?
As a Provide Chain Knowledge Scientist, I’ve explored varied frameworks like LangChain and LangGraph to construct AI brokers utilizing Python.
The illustration above is from an article I wrote on the finish of 2023, titled “Leveraging LLMs with LangChain for Provide Chain Analytics — A Management Tower Powered by GPT.”
On the time, I used to be exploring tips on how to use LangChain to construct an agent appearing as a Provide Chain Management Tower.
A 12 months later, I found the facility of the low-code platform n8n to construct the identical sort of resolution in only a few clicks.
On this article, we’ll discover tips on how to simply construct AI brokers to automate provide chain analytics workflows utilizing n8n.
We’ll additionally see tips on how to deploy the identical AI-powered Management Tower agent I initially constructed with LangChain 18 months in the past — now utilizing solely low-code.
AI Agent for Provide Chain Management Towers utilizing LangChain
My first undertaking of AI Automation undertaking utilizing n8n was for a buyer who wished a Provide Chain Management Tower geared up with a chat interface.
A Provide Chain Management Tower is a set of dashboards and studies linked to Warehouse and Transport Administration Programs that use knowledge to observe important occasions throughout the provision chain.
In an earlier article revealed on In direction of Knowledge Science, I experimented with LangChain to attach a management tower to an AI agent.
The concept was to construct a plan-and-execute agent that might
- Course of the consumer’s request written in plain English
- Generate the suitable SQL question
- Question the database and retailer the outcomes
- Formulate a transparent response in plain English
After a number of iterations, I discovered the appropriate chain construction and prompts to ship correct outcomes.
The answer labored effectively as a result of I had already gained expertise utilizing LangChain and different frameworks to construct AI brokers.
How are we supposed to keep up this advanced setup?
Nevertheless, to supply this as a service, I wanted instruments that might make the answer simpler to deploy, keep, and enhance — even with restricted Python data.
That’s after I found n8n.
Let’s dive into that within the subsequent part.
AI Agent for Provide Chain Management Towers — Constructed with n8n
What’s n8n?
n8n is an open-source workflow automation device that permits you to simply join apps (e-mail, CRMs, messaging techniques), APIs, and AI mannequin frameworks like LangChain.
You construct workflows by connecting pre-built nodes.
As an illustration, the workflow above processes emails
- The primary node collects emails from a Gmail account.
- The e-mail content material and metadata are despatched to the AI Agent node, which extracts the related info.
- The third node processes the output utilizing JavaScript.
- The ultimate node masses the outcomes right into a Google Sheet.
No code was wanted to construct this workflow — apart from the third node, which makes use of simply two strains of JavaScript.
Since I work with a crew of Provide Chain consultants who’ve restricted Python abilities, this was a game-changer for me as I seemed to develop my service providing.
They will simply use, adapt, and keep this workflow after a brief coaching session on n8n.
AI Provide Chain Management Tower n8n workflow
The AI Provide Chain Management Tower workflow is a little more advanced — however nonetheless far easier than its Python model.
It contains two sub-workflows.
The principle sub-workflow contains each a chat interface and the AI agent.
For the AI Agent node, it is advisable
- Join an LLM (chat mannequin) utilizing a node the place you enter your API credentials
- Add a reminiscence node to handle the dialog
- Add a device node for SQL querying, linked to the second sub-workflow
The AI agent generates an SQL question and sends it to the “Name Question Software” node, which executes the question.
The sub-workflow features a code node that cleans the question (eradicating further areas and blocking dangerous instructions like DELETE).
The output is distributed to a BigQuery node, which runs the question and returns the outcomes.
The method may be very easy and requires restricted configuration:
- System Immediate (within the AI Agent node)
- Consumer Immediate (within the AI Agent Node)
This setup requires no Python abilities and might be dealt with immediately by my consultants.
The outcomes are similar to these of the Python model.
For step-by-step setup directions, take a look at my YouTube tutorial 👇
Conclusion
This instance reveals how simple it’s to duplicate an AI agent constructed with Python — utilizing n8n and minimal code.
Does that imply Python is now not wanted for Provide Chain Analytics? Undoubtedly not!
Like many low-code platforms, the options are restricted to what’s accessible inside the framework.
That’s why I exploit it as a complement to my analytics merchandise.
To try this, you need to use the HTTP Request node to attach your workflow to your analytics backend.
What else? Straightforward connectivity to many companies.
One more reason I selected n8n to counterpoint my analytics merchandise is how simple it’s so as to add extra connections.
For instance, if you wish to add a Slack interface or log conversations to a Google Sheet, simply add a brand new node to your workflow.
For those who’re beginning your n8n journey and want inspiration, be at liberty to discover my templates.
About Me
Let’s join on Linkedin and Twitter; I’m a Provide Chain Engineer utilizing knowledge analytics to enhance Logistics operations and cut back prices.
For consulting or recommendation on analytics and sustainable Provide Chain transformation, be at liberty to contact me through Logigreen Consulting.
Samir Saci | Knowledge Science & Productiveness
A technical weblog specializing in Knowledge Science, Private Productiveness, Automation, Operations Analysis and Sustainable…samirsaci.com
