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Create an AI agent to fill forms from your private documents

IntermediateGuided Project

Use retrieval-augmented generation (RAG) and large language models (LLM) to process your private documents and automate the completion of forms. This project extracts required information with prompt engineering and then completes HTML forms. Using LLAMA2 hosted by IBM watsonx.ai for text analysis and Flask as a back-end web app, this system significantly improves efficiency in handling form fields -- reducing the need for manual input and expediting the entire form-filling process.

4.5 (97 Reviews)

Language

  • English

Topic

  • Artificial Intelligence

Industries

  • Information Technology, Government

Enrollment Count

  • 863

Skills You Will Learn

  • Python, Artificial Intelligence

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 90 min

Platform

  • SkillsNetwork

Last Update

  • December 26, 2024
About this Guided Project
No one likes completing forms. But, they're everywhere and in every part of life — applying for a loan, a job, a visa, or funding. So much time is wasted when completing forms, and there should be a quicker, easier way to complete them. Our time is wasted just reading information and putting it into a form. What if you could implement an AI agent that could read all of the required information and automatically complete the forms and their fields, instantly and accurately? This project does just that.

In this project, you use a simple tax form to showcase an AI process that completes the forms for you. The project provides a PDF file with information about an imaginary person. The project reads the form fields, and the AI agent completes the fields, accordingly. Overview of the AI automated form filler

A look at the project ahead

The following image shows how the AI auto-form filler app works. The app:
  1. Automates form filling: It streamlines the process of completing forms by automatically inserting relevant information into the appropriate fields.
  2. Processes and understands documents: It efficiently processes and analyzes a collection of documents, extracting and understanding the content to find information that is relevant to the forms being completed.
  3. Integrates AI models: It uses IBM watsonx-hosted LLMs to interpret form requirements and generate accurate, contextually relevant responses for each field.
  4. Creates an accessible interface: It uses Flask to provide a practical and accessible way for you to interact with the form-filling service, leveraging its capabilities to handle web requests, integrate with other Python tools, and offer a scalable and deployable solution.

What you'll need to know

To get the full scope of this project, you need an understanding of Python basics as well as the foundations of AI.

Instructors

Sina Nazeri

Data Scientist at IBM

I am grateful to have had the opportunity to work as a Research Associate, Ph.D., and IBM Data Scientist. Through my work, I have gained experience in unraveling complex data structures to extract insights and provide valuable guidance.

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Contributors

Vicky Kuo

Data Scientist

I believe that success isn't just about individual milestones, but also about uplifting and encouraging others to reach their potential. This is why I'm passionate about combining my technical background with my eagerness to help people overcome technological hurdles and accelerate growth. When I’m not on the job, I love hiking with my two dogs or relaxing in a coffee shop. There's nothing better than having an insightful conversation over coffee, or even better, some volunteer work! Please feel free to reach out to me on LinkedIn.

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Kang Wang

Data Scientist

I am a Data Scientist in the IBM. I am also a PhD Candidate in the University of Waterloo.

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