Analytics Tool

Getting Started

Prerequisites

• Running GKE cluster (Standard or Autopilot))
• kubectl access to a Kubernetes GKE Standard or GKE Autopilot cluster
• Agent-sandbox installed on GKE. Here is the (Installation Guide)

Deploy analytics tools

This section describes how to build Docker image that defines analytics tool for an ADK agent, and push the Docker image to a Artifact Registry repository and deploy from it.

Run the following commands:

cd analytics-tool

Create a repository in Artifact Registry:

gcloud artifacts repositories create analytics \
    --project=${PROJECT_ID} \
    --repository-format=docker \
    --location=us \
    --description="Analytics Repo"

Create a repository in Artifact Registry.

gcloud builds submit .

After build is completed, change $PROJECT_ID in sandbox-python.yaml and apply it:

kubectl apply -f sandbox-python.yaml
kubectl apply -f analytics-svc.yaml

Deploy jupyter lab

Deploy a jupyter lab to make some data analytics:

kubectl apply -f ../jupyterlab.yaml

Once it’s running, port-forward the jupyterlab and access on http://127.0.0.1:8888 by running this command:

kubectl port-forward "pod/jupyterlab-sandbox" 8888:8888

Follow the welcome.ipynb notebook.

Analytics example

In the Download the data is described the dataset that will be used in the example. In the Data analytics is described the actual data analytics. Function analyze_movies with the tool decorator is defined in this section. In the docstring is described the instruction for the LLM how to use it.

The example query looks like this:

Load /my-data/shopping_behavior_updated.csv. This data has 'Purchase Amount (USD)' column. Create a bar chart showing a sum of 'Purchase Amount (USD)' per column 'Location'.

The agent will be able to generate code that will be executed in the agent-sandbox pod. For example, the code might look like this:

import pandas as pd
import matplotlib.pyplot as plt
import io
import base64

# Load the data
df = pd.read_csv('/my-data/shopping_behavior_updated.csv')

# Group by 'Location' and sum 'Purchase Amount (USD)'
purchase_amount_by_location = df.groupby('Location')['Purchase Amount (USD)'].sum()

# Create a bar chart
plt.figure(figsize=(10, 6))
purchase_amount_by_location.plot(kind='bar')
plt.title('Total Purchase Amount (USD) by Location')
plt.xlabel('Location')
plt.ylabel('Total Purchase Amount (USD)')
plt.xticks(rotation=45, ha='right')
plt.tight_layout()

# Save to buffer
buf = io.BytesIO()
plt.savefig(buf, format='png')
buf.seek(0)
img_str = base64.b64encode(buf.read()).decode('utf-8')
print(f"<IMG>{img_str}</IMG>")

In the end the code prints an encoded image. Inside the tool definition the regex expression is used to extract this string, decode, and plot it.

Cleanup

gcloud artifacts repositories delete analytics \
    --project=${PROJECT_ID} \
    --location=us