Unlock the Power of Your Data: Building AI-Powered Analytics with Google’s Gemini API

In today's data-centric business world, the key to leveraging your organization's data lies in transforming it into actionable insights. Google’s Gemini API is a powerful tool that enables developers to build AI-powered analytics. This article walks you through a step-by-step guide to building your own AI-analytics system using this robust API.

Introduction to Gemini API

Google's Gemini API provides a sophisticated platform for integrating AI into your data analysis processes. It is designed to facilitate the creation of applications capable of transforming queries into SQL commands and responding in natural language. Whether you're a novice or an experienced developer, implementing Gemini API can be a game-changer for your data analytics needs.

Steps to Build AI-Powered Analytics with Gemini API

Step 1: Project Setup

Create a Cloud Shell Instance

First, we'll set up a Cloud Shell instance for the project:

Click here to create a Cloud Shell instance for SQL Talk.
This link sets up a Google Cloud Shell Editor instance and clones the repository for SQL Talk into the instance.

Get a Cloud Project ID

To enable the project to connect to Google Cloud services:

Navigate to the Google Cloud console and log in if necessary.
Select an existing Cloud Project or create a new one.
Note the Cloud Project ID.

Configure the SQL Talk Project

Within the Cloud Shell Editor, execute the following commands to set up the project:

gcloud config set project YOUR_PROJECT_ID
cd ~/cloudshell_open/generative-ai/gemini/function-calling/sql-talk-app
bash setup.sh

Step 2: Test the Project

To ensure the application is running correctly:

In the Cloud Shell Editor window, reconnect to your Cloud Project ID if necessary:

gcloud config set project YOUR_PROJECT_ID

Navigate to the project directory and run the application:

cd ~/cloudshell_open/generative-ai/gemini/function-calling/sql-talk-app
~/miniforge/bin/streamlit run app.py --server.enableCORS=false --server.enableXsrfProtection=false --server.port 8080

Preview the application in your browser by clicking the Web Preview button and selecting Port 8080.

Step 3: Modify the Application

Adding a New Function Call Definition

Enhance the application by defining a function to list recent database queries:

Open the application code file (sql-talk-app/app.py) in the Cloud Shell Editor.
Add the new function definition:

list_jobs_func = FunctionDeclaration(
    name="list_jobs",
    description="Get a list of the 10 most recent database requests to help answer the user's question",
    parameters={
        "type": "object",
        "properties": {  },
    },
)

Incorporate the new function into the sql_query_tool object:

sql_query_tool = Tool(
    function_declarations=[
        sql_query_func,
        list_datasets_func,
        list_jobs_func,  # new function added here
        list_tables_func,
        get_table_func,
    ],
)

Map the new function to its API call within the function_calling_in_process loop:

if response.function_call.name == "list_jobs":
    api_response = client.list_jobs(max_results=10)  # API request(s)
    api_response = str([job.job_id for job in api_response])
    api_requests_and_responses.append(
        [response.function_call.name, params, api_response]
    )

Additional Resources

For further details and updates, consider visiting these resources:

Conclusion and Next Steps

By following these steps, you have set the foundation for a powerful AI-powered analytics system. Google’s Gemini API provides the tools necessary to transform your organization's data into valuable insights, enabling better decision-making. Continue to explore and innovate with the resources provided to fully harness the capabilities of AI in data analytics.

Source of this article from Google AI for Developer.

References: