Cohere's Rerank Model: Build Your Streamlit App with ElevenLabs Integration

Introduction

Cohere, a leading AI company, offers a suite of powerful language models that can transform how you work with text data. This tutorial will guide you through using the Cohere Rerank (Beta) model to optimize search algorithms and build a Streamlit app with it. To learn more about Cohere LLMs, check out our How to Get Started with Cohere LLMs Tutorial.

Understanding ElevenLabs

ElevenLabs is a voice AI research and deployment company dedicated to making content accessible in any language and voice. They specialize in creating highly realistic, versatile, and contextually aware AI audio, allowing the generation of speech in numerous voices and languages. Founded in 2022 by Piotr and Mati, former engineers and strategists, ElevenLabs was inspired by the desire to eliminate linguistic barriers in content, particularly poor dubbing in Hollywood movies.

What is Streamlit?

Streamlit is a pure Python framework for building web applications. It's user-friendly and perfect for those looking to create interactive applications in Python. For a more in-depth understanding, familiarize yourself with Streamlit's documentation.

Prerequisites

• Download Visual Studio Code compatible with your operating system or use other code editors like IntelliJ IDEA or PyCharm.
• Sign up for a Cohere account to get your API key.
• Sign up for an ElevenLabs account to access their voice AI API key.
• Create an account on Streamlit for app deployment.
• Have a cup of coffee and a laptop ready for the coding session!

Learning Outcomes

• Learn how to use the Cohere Rerank (Beta) model via API.
• Build web apps using Streamlit.
• Create an app with Cohere Rerank (Beta).
• Deploy the app on the Streamlit Sharing Cloud.

Getting Started

Create a New Project

Start by creating a new folder for your project. Open Visual Studio Code and create a new folder named cohere-rerank-tutorial.

Create a Virtual Environment

Create a Python virtual environment and activate it by using the following command:

python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

Install All Dependencies

Next, install the required dependencies using:

pip install -r requirements.txt

Creating the Streamlit App

After setting up the initial project structure, create a new file named app.py in your project directory.

Setting Up the App

• Import necessary libraries at the top of the app.py file.
• Configure the logger for debugging purposes.
• Set up the Streamlit page with a title and description.
• Create a sidebar for handling API keys and uploaded files.

Handling User Input

Utilize Streamlit's st.form() to create a form for user input, and use st.columns() for organizing the layout.

Implementing Voice Generation

Implement a function to generate voiceovers for each message entered.

Displaying Responses

Get the response from the Cohere API and display it on the screen.

Clearing Chat History

Optionally, implement a function to clear the chat history.

Running the App

Run the app by executing:

streamlit run app.py

Visit http://localhost:8501 to view your Streamlit app.

Deploying the App

Push Code to GitHub

Create a new GitHub repository and push the code to it. Ensure to include the requirements.txt file for deployment.

Deploy on Streamlit Sharing Cloud

Log in to Streamlit Sharing Cloud, click on 'New app', fill out the details, and click on the Deploy! button.

Testing the App

To test the app, enter your Cohere API key, upload your desired file, and enter your query before clicking on the Send button.

Conclusion

Thank you for following along with this tutorial! With these steps, you've successfully built and deployed a Streamlit app using Cohere Rerank (Beta) and ElevenLabs voice AI.