Create an AI agent to fill forms from your private documents
Build an AI agent capable of automatically filling forms by extracting data from your private documents. Learn how to integrate document processing and AI-driven automation.
At a Glance
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.
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:
- Automates form filling: It streamlines the process of completing forms by automatically inserting relevant information into the appropriate fields.
- 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.
- Integrates AI models: It uses IBM watsonx-hosted LLMs to interpret form requirements and generate accurate, contextually relevant responses for each field.
- 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.
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