Build Your Own Judicial AI Assistant: A Complete Anthropic Guide

What is Claude?

Claude is a state-of-the-art Large Language Model (LLM) developed by Anthropic. Its versatility allows it to function as a chatbot, a summarization tool, a coding assistant, and much more. Recently, Anthropic made headlines by increasing Claude's context size to an impressive 100,000 tokens, equivalent to around 75,000 words. This substantial upgrade significantly optimizes workflows involving large documents and books. Previously, processing lengthy texts could take up to five hours, but Claude can now read, analyze, summarize texts, and respond to questions in mere minutes!

Importantly, Claude is designed with a focus on safety, enhancing user experience with a more human-like interaction. This could signal the emergence of a new leader in the AI landscape, potentially leading to widespread adoption of Anthropic Apps in the near future.

So, how do we get started with using Claude?

How to Use Claude

To leverage Claude effectively, users must apply for early access. In this tutorial, I will showcase how to use the Anthropic Python SDK, which simplifies interaction with the model. Alternatively, users can also opt for the API or the TypeScript/JavaScript SDK.

Legal Tech - Harnessing AI for Law

In the intricate field of legal affairs, the ability to accurately analyze and interpret legal documents is crucial. Legal language can be complex and lengthy, making the process laborious and time-intensive. Here, we explore how Anthropic's Claude can streamline the analysis of extensive legal texts quickly, extracting vital information and insights efficiently, and addressing aspects like sentiment, repercussions, and potential pitfalls within legal passages, such as contracts.

What sets our exploration apart is not only the capabilities we're familiar with, like summarization and predictive analysis, but also understanding Claude's foundational principles as a Constitutional AI and its handling of large, complex prompts.

What Are We Building?

Our objective is to construct a straightforward API utilizing Claude's claude-v1-100k model to extract meaningful data from large prompts. Although ideally, a more robust legal database would enhance our search capabilities, we will use local files in our working directory for brevity.

To begin, we will work with PDF files, specifically focusing on those containing between 40,000 and 80,000 tokens. This allows us to test Claude's limits, as it is equipped to handle files within this range. We will utilize the PDF Reader to handle these documents efficiently.

Dependencies

First, we need to create a new directory and set up a virtual environment. This tutorial will rely on PyPDF2 and the Anthropic SDK, and we’ll also integrate FastAPI for a streamlined server environment.

Scaffolding Our API

Now, let’s import the necessary libraries and prepare our API. If you have obtained an API Key from your early access application, ensure it is ready for use.

Usage

To begin our journey, we’ll define functions that will read PDF files and leverage Claude's capabilities to analyze these documents. We will establish an output structure to facilitate the extraction of information from Claude’s responses.

Within our API, we’ll create a function to analyze legal cases based on the content of the provided PDF files. This function retrieves the file path, reads the content, checks the text length, and sends eligible text to the API for analysis!

For structuring Claude’s prompts and responses, we’ll incorporate XML tags, allowing for customization according to our specific needs. Also, it is crucial to set the stopping token as \n\nHuman.

Extracting Information from Cases

With our defined functions, let’s now create an endpoint to invoke the analysis function on our legal cases. After initiating our server, we can navigate to localhost to test the API using Swagger UI.

Results and Future Prospects

While our exploration could conclude here, let’s expand our capabilities by adding a new endpoint to analyze research papers, summarizing key findings to offer greater insight and influence our prompts effectively.

Possible exploratory exercises before hackers' meetups include: creating a 'healthy and safe' news digest using RSS feeds from questionable media outlets, identifying loopholes in convoluted contract language, or even crafting child-friendly stories from popular music lyrics. These activities will help familiarize you with tips from Anthropic for engaging effectively with Claude!

Conclusion

As demonstrated, we can distill key information from extensive legal documents (over 100 pages!) within seconds. This highlights Claude's capacity for handling significant text volumes. Future inquiries could involve summarizing case developments in court, elucidating principal arguments, and much more!

For developers interested in building their own Anthropic applications, a unique opportunity to bypass the waitlist is on the horizon! Members of the lablab.ais community who signed up for the Anthropic Hackathon before May 23rd will receive exclusive access instructions. Stay tuned for upcoming Artificial Intelligence Hackathons as more exciting opportunities are being devised for our vibrant community!