inardini

import os
import vertexai
from vertexai import agent_engines
from typing import Optional, Dict, Any

def query_multimodal_agent(
    project_id: str,
    location: str,
    agent_engine_id: str,
    file_gcs_uri: str,

inardini

# 1. Install dependencies (run in terminal)
# pip install 'google-cloud-aiplatform>=1.84.0' 'openai' 'google-auth' 'requests'

# 2. Import libraries
import vertexai
from vertexai.preview import model_garden

# 3. Initialize Vertex AI SDK (replace placeholders)
vertexai.init(project="your-project", location="your-region")

inardini

  import pandas as pd
  from vertexai.preview.evaluation import PairwiseMetric, EvalTask, MetricPromptTemplateExamples
  from vertexai.preview.evaluation.autorater_utils import evaluate_autorater

  # Prepare the evaluation dataset with the human rating data column.
  human_rated_dataset = pd.DataFrame({
    "prompt": ["Researchers at ... ", "Introducing the 'SilentStep'..."],
    "response": ["A new solar panel ...", "The 'SilentStep' treadmill..."],
    "baseline_model_response": ["Researchers developed...", "This is the 'SilentStep'..."],
    "pairwise_fluency/human_pairwise_choice": ["CANDIDATE", "BASELINE"]

inardini

### Pseudo code of Cloud Function v2 retraining trigger

import base64
import json
import yaml
from pathlib import Path as p
from datetime import datetime
from google.cloud import aiplatform as vertex_ai

# Get config variables

inardini

# Vertex AI BQML pipeline pseudo code

@dsl.pipeline(
   name=PIPELINE_NAME,
   description="A batch pipeline to train ARIMA PLUS and generate predictions using BQML",
)
def pipeline(
):

 from google_cloud_pipeline_components.v1.bigquery import \