AI21 Labs Tutorial: Build a Contextual Answers App with AI21 Studio

What is AI21 Studio?

AI21 Studio is an innovative platform tailored for developers who are eager to leverage advanced language models, particularly Jurassic-2, to create a diverse array of AI-driven applications. The platform features an intuitive API and targeted endpoints that empower developers to efficiently perform various natural language processing (NLP) tasks, including text generation, summarization, paraphrasing, and more. Furthermore, AI21 Studio enables users to customize solutions, allowing them to fine-tune models in accordance with their specific needs.

Getting Started with AI21 Studio

To embark on your journey with AI21 Studio, follow these steps:

1. Create an account and register for a free trial.
2. Familiarize yourself with the platform, especially if you're new to it. Access the well-structured AI documentation and API Reference to gain an understanding of the models, tools, and APIs available.
3. After exploring, obtain your API key which will be essential for accessing the API in your applications.

Contextual API

The Contextual Answers API is a robust system engineered to deliver precise and trustworthy answers to questions based on specific document contexts. It guarantees that responses are extracted exclusively from the given context, eliminating potential factual inaccuracies common in traditional language models. If a question's answer isn't found in the document, the model explicitly states this absence rather than providing a possibly incorrect answer.

Key Features of the Contextual Answers API

• Task-specific optimization for effective and efficient performance in existing systems.
• Elimination of the need for prompt engineering, making integration straightforward.
• User-friendly interface, enhancing the user experience for accurate, context-driven answers.

Using the API

To make a successful request to the Contextual Answers API, it requires two primary parameters:

• context: A string containing the document context relevant to the question.
• question: A string posing the question based on the provided context.

The API's response includes:

• answer: A string with the answer derived from the context or null if absent.
• id: A unique identifier for tracking and logging the specific request.

Setting Up Your Project

Begin by installing Flask, a lightweight backend framework, using Python's PIP:

pip install Flask

You may also want to install Postman, an application that facilitates API development, testing, and management.

Creating the Basic Structure

Set up a basic Flask application:

touch app.py

Fill it with a minimal boilerplate code to get you started.

Implementing the AI21 API

Having set up your project, modify it to integrate the AI21 API.

You will need to adjust your get_answer() method to accommodate the API requirements:

• Store your API key in a separate file (e.g., .env).
• Establish context and question as variables based on user input.
• Set up the URL, payload, and headers for your request.
• Process and return the data from the API response.

Testing With Postman

Run your Flask app and test your API using Postman:

python app.py

Input your context and question parameters to see how the API responds.

Conclusion

This tutorial demonstrated how to create a foundational Flask web API for fetching contextual answers via the AI21 Contextual Answers API. You can further enhance this project by developing a frontend interface. If you are ready to challenge yourself and hone your skills with AI21 Studio, consider joining our upcoming AI Hackathon organized by lablab.ai, where you can test your abilities and create fantastic tools during this immersive 7-day event.

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