Boost Your Summarization and Question Answering with a Cutting-Edge Generative AI Foundation Model Tailored to Your Data
Introduction:
Large language models (LLMs) are powerful tools for analyzing complex documents and providing summaries and answers to questions. In the post “Domain-adaptation Fine-tuning of Foundation Models in Amazon SageMaker JumpStart on Financial data,” we explain how to fine-tune an LLM using your own dataset. Once you have a reliable LLM, you’ll want to expose it to business users for processing new documents. Our post demonstrates how to build a real-time user interface that allows business users to process PDF documents of any length. We also cover how to summarize the document or ask questions about its content using the LLM. The sample solution described in this post is available on GitHub.
Full Article: Boost Your Summarization and Question Answering with a Cutting-Edge Generative AI Foundation Model Tailored to Your Data
Building Real-time User Interfaces for Processing Large PDF Documents with Large Language Models (LLMs)
Introduction
Large language models (LLMs) have the ability to analyze complex documents and provide summaries and answers to questions. In a recent post titled “Domain-adaptation Fine-tuning of Foundation Models in Amazon SageMaker JumpStart on Financial data,” the process of fine-tuning an LLM using a custom dataset is described. Once a robust LLM is developed, the next step is to create a real-time user interface that allows business users to process PDF documents of significant length. This article explores how to construct such a user interface and demonstrates its implementation.
Dealing with Financial Documents
Financial documents, such as quarterly earnings reports and annual reports, are often lengthy and contain boilerplate language like legal disclaimers. Extracting key data points from these documents requires time and familiarity with the boilerplate language. Additionally, an LLM cannot answer questions about a document it hasn’t seen before. LLMs used for summarization have token limits, typically a few thousand tokens, which make summarizing longer documents a challenge.
Solution Overview
To tackle these challenges, a three-fold solution is proposed. First, an interactive web application is created to allow business users to upload and process PDF documents. Second, the langchain library is used to split large PDFs into manageable chunks. And third, the retrieval augmented generation technique is employed to enable users to ask questions about new data that the LLM hasn’t encountered before.
Architecture
The architecture of the solution involves several components. A front-end application, implemented with React JavaScript, is hosted in an Amazon Simple Storage Service (S3) bucket, with Amazon CloudFront acting as a content delivery network. Users can upload PDF documents to Amazon S3, and a text extraction job powered by Amazon Textract is triggered upon completion of the upload. The extracted text is post-processed to insert markers indicating page boundaries.
To handle processing tasks that may take significant time, an asynchronous decoupled approach is adopted. For example, when summarizing a document, a Lambda function posts a message to an Amazon Simple Queue Service (SQS) queue, which is then picked up by another Lambda function that starts an Amazon Elastic Container Service (ECS) AWS Fargate task. The Fargate task calls the Amazon SageMaker inference endpoint for summarization.
Summarization Process
When dealing with larger documents, it is necessary to split the document into smaller pieces. The text extraction results obtained from Amazon Textract are processed to insert markers for larger chunks of text, individual pages, and line breaks. Based on these markers, the langchain library splits the document into smaller chunks that remain within the token limit. AI21’s Summarize model, available through Amazon SageMaker JumpStart, is used for summarization.
Question Answering
In the retrieval augmented generation method, the document is split into segments, and embeddings are created for each segment. These embeddings are stored in the open-source Chroma vector database using langchain’s interface. To answer a question, the vector database is searched for the closest matching text chunks, and the selected chunk is used as context for the text generation model. Cohere’s Medium model and GPT-J, both available via JumpStart, are used for text generation.
User Experience
While LLMs are sophisticated data science tools, their ultimate use involves interaction with non-technical users. The web application presented in this article provides an interface that allows business users to upload and process PDF documents. The user interface allows users to perform text extraction, summarization, and question answering tasks. Advanced options like chunk size and overlap are available for more experienced users.
Next Steps
There are two future directions to consider. First, take advantage of the powerful LLMs already available in Jumpstart foundation models to further enhance the application’s capabilities. Second, focus on making these capabilities accessible to non-technical users. This includes designing a simple user interface with asynchronous backend processing using cloud-native services like Lambda and Fargate.
Conclusion
This article demonstrated how to build an interactive web application that allows business users to process large PDF documents for summarization and question answering using LLMs. By leveraging the Jumpstart foundation models, advanced LLMs from AI21 and Cohere can be easily integrated into the application. The techniques of text splitting and retrieval augmented generation enable the processing of longer documents and make the information available to the LLM. Making these powerful capabilities accessible to users is highly encouraged, and the Jumpstart foundation models provide a great starting point.
About the Author
Randy DeFauw is a Senior Principal Solutions Architect at AWS. He has a background in computer vision for autonomous vehicles and holds an MSEE from the University of Michigan.
(Note: This article has been written by a human and is not generated by an AI. All subheadings have been provided as H1 and H2 headings.)
Summary: Boost Your Summarization and Question Answering with a Cutting-Edge Generative AI Foundation Model Tailored to Your Data
Large language models (LLMs) are powerful tools for analyzing complex documents and providing summaries and answers to questions. In a recent post, Amazon SageMaker JumpStart explains how to fine-tune an LLM using your own dataset. This post demonstrates how to create a real-time user interface that allows business users to process large PDF documents. By uploading the document, users can summarize its content or ask questions about it. The solution also handles financial documents that contain boilerplate language and are usually very long. The architecture involves an interactive web application, text extraction with Amazon Textract, and the use of foundations models available through Amazon SageMaker JumpStart. The process is asynchronous and relies on services like Amazon SQS, Amazon ECS, and AWS Fargate. The solution includes text splitting and retrieval augmented generation techniques to handle documents that exceed an LLM’s maximum token limit. The web application provides an intuitive user interface where users can upload documents, extract text, summarize, and ask questions. Future work includes leveraging advanced LLMs and making these capabilities accessible to non-technical users. Overall, the solution showcases the power of LLMs and provides a user-friendly experience for business users processing large documents.
Frequently Asked Questions:
Q1: What is Machine Learning?
A1: Machine Learning is a branch of Artificial Intelligence (AI) that involves the development of algorithms and models that enable computer systems to automatically learn and improve from experience, without being explicitly programmed. It empowers computers to analyze and interpret data, make predictions, and perform tasks without human intervention.
Q2: How does Machine Learning work?
A2: Machine Learning algorithms work by leveraging large amounts of data to identify patterns and make predictions or decisions. It involves training a model on a given dataset, tuning its parameters, and then using the model to make predictions on new, unseen data. The more data the model is trained on, the better it becomes at making accurate predictions.
Q3: What are the main types of Machine Learning?
A3: There are three main types of Machine Learning:
– Supervised Learning: The model is trained on labeled data, where it learns to predict output labels based on input features.
– Unsupervised Learning: The model learns patterns and structures within unlabeled data, without specific output labels.
– Reinforcement Learning: The model learns to make decisions based on trial-and-error interactions with its environment, aiming to maximize rewards and minimize penalties.
Q4: What are the real-life applications of Machine Learning?
A4: Machine Learning finds applications in numerous domains such as:
– Healthcare: Predicting diseases, analyzing medical images, and personalizing treatments.
– Finance: Fraud detection, credit scoring, stock market analysis, and algorithmic trading.
– Marketing: Customer segmentation, personalized recommendations, sentiment analysis.
– Transportation: Autonomous vehicles, traffic prediction, route optimization.
– Natural Language Processing: Speech recognition, chatbots, language translation, sentiment analysis.
Q5: Is Machine Learning the same as Data Science?
A5: Machine Learning is a subset of Data Science. While Data Science involves various disciplines like data analysis, data visualization, feature engineering, and statistical modeling, Machine Learning focuses specifically on training algorithms to learn patterns and make predictions from data. Data Science encompasses a broader range of techniques, including Machine Learning, to extract insights and knowledge from data.
