diff --git a/data/timesketch.conf b/data/timesketch.conf
index 7a03e64093..6cca7592e1 100644
--- a/data/timesketch.conf
+++ b/data/timesketch.conf
@@ -362,7 +362,9 @@ LLM_PROVIDER_CONFIGS = {
     # To use the Vertex AI provider you need to:
     # 1. Create and export a Service Account Key from the Google Cloud Console.
     # 2. Set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the full path
-    # to your service account private key file.
+    # to your service account private key file by adding it to the docker-compose.yml
+    # under environment:
+    # GOOGLE_APPLICATION_CREDENTIALS=/usr/local/src/timesketch/<key_file>.json
     # 3. Install the python libraries: $ pip3 install google-cloud-aiplatform
     #
     # IMPORTANT: Private keys must be kept secret. If you expose your private key it is
@@ -372,7 +374,7 @@ LLM_PROVIDER_CONFIGS = {
         'project_id': '',
     },
     # To use Google's AI Studio simply obtain an API key from https://aistudio.google.com/
-    # pip install google-generativeai
+    # pip3 install google-generativeai
     'aistudio': {
         'api_key': '',
         'model': 'gemini-2.0-flash-exp',
@@ -384,4 +386,3 @@ DATA_TYPES_PATH = '/etc/timesketch/nl2q/data_types.csv'
 PROMPT_NL2Q = '/etc/timesketch/nl2q/prompt_nl2q'
 EXAMPLES_NL2Q = '/etc/timesketch/nl2q/examples_nl2q'
 LLM_PROVIDER = ''
-
diff --git a/timesketch/lib/llms/interface.py b/timesketch/lib/llms/interface.py
index 014e0887b4..a54699fac8 100644
--- a/timesketch/lib/llms/interface.py
+++ b/timesketch/lib/llms/interface.py
@@ -20,9 +20,10 @@
 
 DEFAULT_TEMPERATURE = 0.1
 DEFAULT_TOP_P = 0.1
-DEFAULT_TOP_K = 0
+DEFAULT_TOP_K = 1
 DEFAULT_MAX_OUTPUT_TOKENS = 2048
 DEFAULT_STREAM = False
+DEFAULT_LOCATION = None
 
 
 class LLMProvider:
@@ -37,6 +38,7 @@ def __init__(
         top_k: int = DEFAULT_TOP_K,
         max_output_tokens: int = DEFAULT_MAX_OUTPUT_TOKENS,
         stream: bool = DEFAULT_STREAM,
+        location: Optional[str] = DEFAULT_LOCATION,
     ):
         """Initialize the LLM provider.
 
@@ -46,6 +48,7 @@ def __init__(
             top_k: The top_k to use for the response.
             max_output_tokens: The maximum number of output tokens to generate.
             stream: Whether to stream the response.
+            location: The cloud location/region to use for the provider.
 
         Attributes:
             config: The configuration for the LLM provider.
@@ -59,6 +62,7 @@ def __init__(
         config["top_k"] = top_k
         config["max_output_tokens"] = max_output_tokens
         config["stream"] = stream
+        config["location"] = location
 
         # Load the LLM provider config from the Flask app config
         config_from_flask = current_app.config.get("LLM_PROVIDER_CONFIGS").get(
diff --git a/timesketch/lib/llms/vertexai.py b/timesketch/lib/llms/vertexai.py
index 2166e6baba..e4f25f7f7e 100644
--- a/timesketch/lib/llms/vertexai.py
+++ b/timesketch/lib/llms/vertexai.py
@@ -1,4 +1,4 @@
-# Copyright 2024 Google Inc. All rights reserved.
+# Copyright 2025 Google Inc. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,6 +13,9 @@
 # limitations under the License.
 """Vertex AI LLM provider."""
 
+import json
+from typing import Optional
+
 from timesketch.lib.llms import interface
 from timesketch.lib.llms import manager
 
@@ -21,6 +24,7 @@
 try:
     from google.cloud import aiplatform
     from vertexai.preview.generative_models import GenerativeModel
+    from vertexai.preview.generative_models import GenerationConfig
 except ImportError:
     has_required_deps = False
 
@@ -30,31 +34,54 @@ class VertexAI(interface.LLMProvider):
 
     NAME = "vertexai"
 
-    def generate(self, prompt: str) -> str:
+    def generate(self, prompt: str, response_schema: Optional[dict] = None) -> str:
         """
         Generate text using the Vertex AI service.
 
         Args:
             prompt: The prompt to use for the generation.
-            temperature: The temperature to use for the generation.
-            stream: Whether to stream the generation or not.
+            response_schema: An optional JSON schema to define the expected
+                response format.
 
         Returns:
-            The generated text as a string.
+            The generated text as a string (or parsed data if
+                response_schema is provided).
         """
         aiplatform.init(
             project=self.config.get("project_id"),
+            location=self.config.get("location"),
         )
         model = GenerativeModel(self.config.get("model"))
+
+        if response_schema:
+            generation_config = GenerationConfig(
+                temperature=self.config.get("temperature"),
+                top_k=self.config.get("top_k"),
+                top_p=self.config.get("top_p"),
+                response_mime_type="application/json",
+                response_schema=response_schema,
+            )
+        else:
+            generation_config = GenerationConfig(
+                temperature=self.config.get("temperature"),
+                top_k=self.config.get("top_k"),
+                top_p=self.config.get("top_p"),
+            )
+
         response = model.generate_content(
             prompt,
-            generation_config={
-                "max_output_tokens": self.config.get("max_output_tokens"),
-                "temperature": self.config.get("temperature"),
-            },
+            generation_config=generation_config,
             stream=self.config.get("stream"),
         )
 
+        if response_schema:
+            try:
+                return json.loads(response.text)
+            except Exception as error:
+                raise ValueError(
+                    f"Error JSON parsing text: {response.text}: {error}"
+                ) from error
+
         return response.text