**London**: Supply Chain Data Scientist Samir Saci shares his journey transitioning from traditional coding with LangChain to the user-friendly n8n platform. He highlights how low-code solutions enhance analytics and streamline processes, making advanced technology accessible to consultants with limited programming skills.
A Supply Chain Data Scientist has recently highlighted the advancements made in creating AI agents for supply chain analytics using low-code platforms, particularly n8n. In an article published on Towards Data Science, Samir Saci discusses his experiences and findings while shifting from a more traditional coding approach with LangChain to the more user-friendly environment of n8n.
In Saci’s initial exploration, detailed in an article from late 2023, he outlined his endeavours with LangChain to establish a control tower for supply chain management. At that time, he constructed an agent capable of processing user requests in plain English, generating SQL queries, querying databases, and delivering clear responses, all through a series of iterations that refined the chain structure and prompts until achieving desirable outcomes.
However, a year later, Saci discovered n8n, an open-source workflow automation tool that simplifies the process of building such solutions. “To offer this as a service, I needed tools that would make the solution easier to deploy, maintain, and improve — even with limited Python knowledge,” Saci noted, recognising the necessity of accessible technology for broader use within his team of Supply Chain consultants, who possess limited programming skills.
Saci’s workflow using n8n integrates various applications, allowing automated processing with minimal coding. The tool connects pre-built nodes to facilitate interactions amongst a range of platforms, including email services, CRMs, and API frameworks. One of the example workflows described involves an AI-powered email parser that processes emails while sending relevant data to a Google Sheet, showcasing the simplicity and efficiency achievable with n8n.
The n8n platform allows for the creation of more complex sub-workflows that incorporate features such as chat interfaces and AI agents. These elements work together to generate and execute SQL queries seamlessly, streamlining processes that were previously cumbersome in Python. “The results are comparable to those of the Python version,” Saci confirmed, indicating that the functionality and performance remain robust despite the shift to low-code solutions.
Saci further elaborated on the connectivity of n8n, expressing that additional functionalities—like integrating communication interfaces or logging interactions—can be added to existing workflows with ease. While he acknowledged the limitations that come with low-code platforms, he maintains that these tools provide valuable support rather than a complete replacement for traditional coding methods. His developing service offerings aim to enhance the analytics process, enabling teams to adapt and maintain workflows with straightforward training.
Beyond the technical specifics, Saci extends an invitation for collaboration and consultation in analytics and supply chain transformation through Logigreen Consulting, advocating for an exchange of ideas and innovation within this evolving field. His insights reflect a practical perspective on leveraging both advanced frameworks and accessible tools to improve supply chain logistics and efficiency.
Source: Noah Wire Services
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