The project aims to simplify and automate the process of generating job expressions for building workflows in OpenFn based on natural language inputs. Currently, writing these expressions manually is time-consuming and requires expertise. Our solution involves developing a service that leverages AI to understand English text requirements, process sample input data, and integrate adaptor specifications to automatically generate accurate job expressions. By streamlining this process, we aim to enhance productivity and reduce the manual effort needed for workflow configuration.
About Mentor
Shristy Joshi Thakur is pursuing an undergraduate degree in Information Technology. She is a Domain Expert at Code for GovTech (C4GT), mentoring projects that leverage AI for workflow automation. Her interests lie in full-stack development, product management, and AI-ML technologies. Driven by her passion for technology and innovation, she joined C4GT to contribute to impactful open-source solutions and mentor the next generation of tech leaders.
Key Impact Takeaways:
- This project automates the process of generating job expressions, significantly reducing manual effort for developers and users.
- It will streamline workflows for organizations using OpenFn across multiple sectors, including healthcare, education, and humanitarian work.
- The project leverages advanced AI models to convert text-based requirements into functional job expressions, improving efficiency.
- It will contribute to better documentation management and ease of use for non-technical stakeholders.
- The solution integrates with OpenFn’s Apollo system, enhancing its adaptability and scalability for future expansions.