ChatGPT as an Effective PDF Summarizer: A Detailed Guide

Name

Viktor Zinchenko

Published on 8/19/2023

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Artificial Intelligence is rapidly making inroads into numerous domains, with Language Processing being a noteworthy application. An intriguing example of such application is using ChatGPT to summarize PDF files. 'PDF GPT' is a fascinating project that has capitalized on this technology. But what makes ChatGPT a potent PDF summarizer, and what features does it offer? This article elucidates the process in depth.

Unveiling the Problem Statement and Current Solutions

Handling massive volumes of textual data, especially those in PDF format, often encounters a couple of substantial roadblocks. Firstly, platforms like OpenAI possess a 4K token limit, which implies that they can't accept an entire PDF file as input. Secondly, the AI might occasionally return responses unrelated to the query due to inferior embeddings.

Current solutions to this dilemma include services like ChatPDF (opens in a new tab), BeSpacific (opens in a new tab), and FileChat (opens in a new tab). However, these services often grapple with maintaining quality content and fall prey to the 'hallucination' problem - generating content that lacks accuracy or relevance. To address these issues, it is proposed to enhance embeddings using the Universal Sentence Encoder family of algorithms.

Exploring the Solution: The Intricacies of PDF GPT

PDF GPT presents an innovative solution enabling you to interact with an uploaded PDF file using GPT's capabilities. It skillfully maneuvers around the problem of vast text and the 4K token limit by segmenting the document into smaller chunks and employing a robust Deep Averaging Network Encoder to generate embeddings.

This application first conducts a semantic search on your PDF content and then feeds the most relevant embeddings to OpenAI. It utilizes custom logic to generate precise responses. A standout feature of this tool is that it can cite the page number where the information is located, adding credibility to the responses and aiding in swiftly pinpointing essential information.

For instance, consider the question, "What's the cap on room rent?" asked from a PDF containing an insurance policy. The AI could respond: "Room rent is subject to a maximum of INR 5,000 per day as specified in the Arogya Sanjeevani Policy [Page no. 1]."

PDF GPT also extends its functionality to production, facilitated by langchain-serve, which activates APIs on production. It offers a demo (opens in a new tab) and the source code is openly available on Hugging Face (opens in a new tab).

Diving Into the Local Playground and Cloud Deployment

PDF GPT is equipped with a local playground, which can be activated using langchain-serve. This local playground can be started by running the command:

lc-serve deploy local api

In another terminal, you can run:

python app.py

This initiates a local gradio playground. You can then open http://localhost:7860 on your browser and start interacting with the application.

Cloud deployment is also facilitated by making the application production-ready and deploying it on Jina Cloud with the following command:

bash
lc-serve deploy jcloud api

Interaction via cURL is also possible by modifying the URL to your endpoint, an example for which is provided in the original GitHub README.

Leveraging Docker and Running on Localhost

The project provides Docker Compose commands to be used with Docker Compose. For instance, running the application with Docker Compose involves this command:

docker-compose -f docker-compose.yaml up

The image can be pulled by running:

docker pull registry.hf.space/bhaskartripathi-pdfchatter:latest

For local usage, the Universal Sentence Encoder needs to be downloaded to your project's root folder. It is crucial to avoid downloading the 915 MB encoder at runtime every time you run the application.

If you've downloaded it locally, replace the line in the API file:

self.use = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')

with:

self.use = hub.load('./Universal Sentence Encoder/')

To run PDF-GPT, enter the following command:

docker run -it -p 7860:7860 --platform=linux/amd64 registry.hf.space/bhaskartripathi-pdfchatter:latest python app.py

Extending Your Contribution to PDF GPT

The creator of the project invites contributors from the open-source community. There is a standing invitation to voluntarily take up backlog items and maintain the application collaboratively.

Conclusion

ChatGPT, as a PDF summarizer, particularly through the usage of PDF GPT, represents a significant leap in AI-powered document processing. By enhancing embeddings, generating concise responses, and efficiently handling large PDFs, PDF GPT is setting the stage for an intelligent and efficient future of AI in document summarization.