diff --git a/src/va_evaluator/search_metrics_evaluator.py b/src/va_evaluator/search_metrics_evaluator.py
index 060486c..adf0a05 100644
--- a/src/va_evaluator/search_metrics_evaluator.py
+++ b/src/va_evaluator/search_metrics_evaluator.py
@@ -3,7 +3,7 @@ import sqlite3
 
 import pandas as pd
 import typer
-import vertexai
+from google import genai
 from google.cloud import bigquery
 from google.cloud.aiplatform.matching_engine import MatchingEngineIndexEndpoint
 from ranx import Qrels, Run
@@ -11,7 +11,6 @@ from ranx import evaluate as ranx_evaluate
 from rich.console import Console
 from rich.progress import track
 from rich.table import Table
-from vertexai.language_models import TextEmbeddingModel
 
 from va_evaluator.config import Settings
 
@@ -111,8 +110,11 @@ def run_evaluation(
     )
     console.print(f"Index Name: [bold cyan]{settings.index.name}[/bold cyan]")
 
-    vertexai.init(project=settings.project_id, location=settings.location)
-    embedding_model = TextEmbeddingModel.from_pretrained(settings.agent.embedding_model)
+    client = genai.Client(
+        vertexai=True,
+        project=settings.project_id,
+        location=settings.location,
+    )
     index_endpoint = MatchingEngineIndexEndpoint(settings.index.require_endpoint)
 
     # Prepare qrels
@@ -126,8 +128,11 @@ def run_evaluation(
     run_data = {}
     detailed_results_list = []
     for _, row in track(df.iterrows(), total=len(df), description="Preparing run..."):
-        embeddings = embedding_model.get_embeddings([row["question"]])
-        question_embedding = embeddings[0].values
+        result = client.models.embed_content(
+            model=settings.agent.embedding_model,
+            contents=row["question"],
+        )
+        question_embedding = result.embeddings[0].values
 
         results = index_endpoint.find_neighbors(
             deployed_index_id=settings.index.require_deployment,