PaLM2 Tutorial: Build Your Own AI Assistant

Introduction to Google's PaLM2

In the evolving landscape of artificial intelligence, Google's PaLM2 stands out as a next-generation large language model designed to enhance performance across various reasoning tasks. Building on Google's legacy of prominent breakthroughs in machine learning and responsible AI, PaLM2 presents notable improvements over its predecessor, PaLM. It excels in tasks such as code generation, mathematical reasoning, classification, question answering, translation, and natural language generation.

Prerequisites for Using PaLM2

To harness the capabilities of Google's PaLM2 API, certain prerequisites must be met:

• Join the waitlist for the PaLM2 API access on Google's official site.
• Create an account on Streamlit, which allows developers to deploy their applications easily. It's recommended to sign up using a GitHub account for streamlined deployment on Streamlit Sharing Cloud.

Getting Started with Streamlit and PaLM2

Step 1: Set Up the Project Directory

Follow these steps to set up your project directory:

1. Create a new project directory and navigate to it via your terminal.
2. Create and activate a virtual environment to manage dependencies.
3. Install Streamlit and all necessary dependencies for Google's PaLM API.

Step 2: Build Your Streamlit App

Now, let's create our Streamlit application:

1. Create a new file called app.py.
2. Import the necessary libraries from Streamlit and Google's PaLM API.
3. Set your app's title using st.title().
4. Initialize the messages state for chat input.

Form Creation for User Input

Using st.form(), create a form that captures user input. You can layout the form using st.columns(), providing one column for user input and the other for the 'Send' button.

Step 3: Configure Google's PaLM API

To communicate with the PaLM API:

1. Configure it with your unique API key.
2. Implement functionality to get the response from the API based on user input.
3. Optionally, create a function to clear the chat history for a better user experience.

Step 4: Run the App

Execute the app using a command like streamlit run app.py. You should see a user-friendly interface resembling a chat application where input and output can be easily communicated.

Step 5: Deploy Your App

Once your app is ready to be shared, follow the detailed tutorial on deploying your app on Streamlit Sharing Cloud.

Conclusion

In this tutorial, we successfully built an AI-powered Virtual Assistant utilizing Google’s PaLM2 model integrated with Streamlit. This powerful combination allows developers to create applications swiftly and efficiently using Python.

To explore the full implementation, visit the link here. Thank you for following along. For any queries or discussions, feel free to connect with me on LinkedIn or Twitter. I would love to hear from you!