Autive Check the Flow of Supply Chain Analytics with AI agents using N8n
Why do you build things in a difficult way that you can design a wise way?
As a Supply Chain Teach Scientist, I tested various structures such as Langchain and Langbram to create Ai Eichent using Python.
The above parable appears in the article I wrote at the end of 2023, entitled “Leveraging LLMS with Langchain at Supply Chain Analytics –
At that time, I examined how to use Langchain to create an agent that served as a supply of supply management.
After a year, I found a platform's low n8n power platform to build the same kind of solution by just a few clicks.
In this article, we will investigate how easy Agents can exchanged the flow of Chain Chain Analytics performance using the N8n.
We will also see how to send the same Ai-Powered Control Agent Agent Agent to the last 18 months – now using only the low code.
AI AI Pickers of Providing Property Using Langchain
My first AI Automation project uses N8N was designed for a customer for the provision of the provision of the interpretation interface.
Tower to control the offer set of dashuArds and reports linked to final final programs and management systems that use data to monitor sensitive events throughout the provision.
In the earliest article referred to the data science, I tried Langchain to connect the control tower to ai agent.
The idea was to build a plan for a plan-and-killer
- Processing a user request in a clear English
- Generate the relevant SQL question
- Ask Database and save results
- Build a clear answer in a clear English
After several Iterations, I found a righteous change structure and moves to bring the right results.
The solution worked well because I had already found experience using Langchain and other structures that made up of agents Ai.
How should we maintain this complex setup?
However, to provide for this as a service, I needed tools that would enable the solution to easily, maintain and improve – even the limited information of the Python.
This is where I get the N8n.
Let's get into that in the next part.
AI AI Popular Artifications Aiarine – Designed with N8n
What is N8n?
The N8N is a compatible tool for the operating source that allows you to easily connect apps (email, CRMS, messaging systems), APIs, and the AI model of the AI such as Langchain.
Formed jobs for work by connecting previously built-up areas.
For example, the flow of work above Processing emails
- The first Node collects emails from Gmail account.
- Email and Metadata content is sent to Agent Agent Node, issuing appropriate information.
- The third Node process is considering the issuing of JavaScript.
- The storage area is loading results on Google paper.
No code was needed to create this workout – outside of the third place, using two rows of JavaScript.
As I work with a group of supply chain publishers with limited skills of Python, this was a game game – me as I viewed my service offer.
They can easily use, adapt, and maintain this function after a short training session on the N8n.
AI Supply Chain Control Tower N8n Workflow
AI Supply Chain Control Workflow is more complicated – but is much easier than its Python version.
Involves two lower flow.
The main functioning of the main function includes the interviewing interface and the AI agent.
Of Agent Node of AI, you need
- Connect the llm (chat model) using a node where you add your API proof
- Enter memory location to manage conversation
- Add SQL Query Tool Node, connected to the second sub-Workflow
AI agent produces a SQL question and sends the “Node Node” tool, issuing a question.
Low work travel including Node for the code cleaning this question (to remove additional spaces and block harmful commands such as a deletion).
The release is sent to Grequery and Dadeconducting a question and returns results.
The process is very smooth and requires limited configuration:
- System Prompt (at Ai Agent Node)
- User prompt (on AI agent agent)
This setup does not require python skills and can directly be treated with my advisors.
Results are compared to the Python version.
With instructions to set up step by step, check my YouTube Tutorial 👇
Store
This example shows how easy it is to retard AI agent with Python – using N8N and a small code.
Does that mean that Python is no longer needed in making supply analytics performing? Sure!
Like many low platforms, features are limited to what is available within the frame.
That is why I use it as products of my Analytics products.
To do that, you can use the HTTP application sign to connect your work travel to your Analytics Backlend.
What else? Simple connections for many services.
Another reason I selected N8N to enrich my Analytics products how easy it is to add more connections.
For example, if you want to add a slack interface or log conversations to Google's paper, just add a new node to your work travel.
When you start your N8n trip and you need inspiration, feel free to check my templates.
about me
Let's connect to LinkedIn and Sane; I am an engineer for purchase requirements that uses data analytics to develop logistics activities and reduce costs.
In consultation or advice on analytics and extra conversion, feel free to contact me with logean Consulting.
Samir Saci | Data Science and Production
The technical blog centered on data science, personal, automated production, practical research and stability …samis.com
