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Legal Simplifier

Legal Simplifier is an application that allows you to simplify legal documents - from terms and conditions of an insrance policy or a business contract. This example also shows how we can summarize contents of a large documents in chunks.

Based on the document you upload, the size can vary and hence mgight find the need to split larger documents into chunks of 10,000 tokens to fit within GTP-4's context window.

Design

The script consists of three tools: a top-level tool that orchestrates everything, a summarizer that will summarize one chunk of text at a time, and a Python script that ingests the PDF and splits it into chunks and provides a specific chunk based on an index.

The summarizer tool looks at the entire summary up to the current chunk and then summarizes the current chunk and adds it onto the end. In the case of models with very small context windows, or extremely large documents, this approach may still exceed the context window, in which case another tool could be added to only give the summarizer the previous few chunk summaries instead of all of them.

Installation

Prerequisites

  • Python 3.8 or later
  • Flask
  • Other Python dependencies listed in requirements.txt.

Steps

  1. Clone the repository:

    git clone https://github.com/gptscript-ai/gptscript.git

  2. Navigate to the examples/legalsimplifier directory and install the dependencies:

    Python:

    pip install -r requirements.txt

    Node:

    npm install

  3. Setup OPENAI_API_KEY (Eg: export OPENAI_API_KEY="yourapikey123456"). You can get your API key here.

  4. Run the Flask application using flask run or python app.py

Usage

  1. Open your web browser and navigate to http://127.0.0.1:5000/.
  2. Use the web interface to upload an a legal document in .pdf format.
  3. The application will analyze the document and show a summary.

Under the hood

Below are the processes that take place when you execute the application:

  • The Python app places the uploaded image as legal.pdf in the current working directory.
  • It then executes legalsimplifier.gpt which internally calls main.py to split the large document in chunks so that they fit within the token limit of GPT-4's context.
  • The analysis will be stored in a summary.md document.
  • The app will then read this summary file and show the summary on the UI.