From 8d7c9134f7b1aabfa464a77a10c8e1cceda58476 Mon Sep 17 00:00:00 2001
From: Marco Cetraro <cetrarom2@nih.gov>
Date: Fri, 18 Oct 2024 13:38:12 +0000
Subject: [PATCH 01/12] I am committing GCP_RAG_for_Structure_Data.ipynb

---
 images/gcp_rag_structure_data_01.png          | Bin 0 -> 16270 bytes
 .../GenAI/GCP_RAG_for_Structure_Data.ipynb    | 710 ++++++++++++++++++
 2 files changed, 710 insertions(+)
 create mode 100644 images/gcp_rag_structure_data_01.png
 create mode 100644 notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb

diff --git a/images/gcp_rag_structure_data_01.png b/images/gcp_rag_structure_data_01.png
new file mode 100644
index 0000000000000000000000000000000000000000..a9cedd885c6c716900c5734a397876b5f30f8ff5
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diff --git a/notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb b/notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb
new file mode 100644
index 0000000..a7d082e
--- /dev/null
+++ b/notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb
@@ -0,0 +1,710 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6e38b3f2-2b29-4788-a29a-f9e5d10f8365",
+   "metadata": {},
+   "source": [
+    "##### __Skill Level:__ <span style=\"color:blue\">Intermediate</span>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c0533e3-4fbd-47b1-8458-fd193bcb263f",
+   "metadata": {},
+   "source": [
+    "# Creating a Chatbot for Structure Data using a RAG"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76cf4b02-6422-4a48-882c-c00315e34c8a",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Overview"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b7870dd-09e0-4f6a-91a9-7c30529af720",
+   "metadata": {},
+   "source": [
+    "Generative AI (GenAI) represents a transformative technology capable of producing human-like text, images, code, and various other types of content. While much of the focus has been on unstructured data—such as PDFs, text documents, image files, and websites—many GenAI implementations rely on a parameter called \"top K.\" This algorithm retrieves only the highest-scoring pieces of content relevant to a user's query, which can be limiting. Users seeking insights from structured data formats like CSV and JSON often require access to all relevant occurrences, rather than just a subset.\n",
+    "\n",
+    "In this tutorial, we present a technique that leverages SQL databases. By formulating a query based on the user's request, the model submits this query to the database, providing comprehensive results. This approach not only ensures users receive all pertinent information but also reduces the likelihood of exceeding token limits."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d513a9fe-1ac4-4610-b53c-6696e461f828",
+   "metadata": {},
+   "source": [
+    "## Prerequisites"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbdb09aa-73d6-4cee-b3e7-5d6b243e3d58",
+   "metadata": {},
+   "source": [
+    "We assume you have access to Vertex AI and have enabled the necessary APIs.\n",
+    "\n",
+    "In this tutorial, we will be using Google Gemini Pro 1.5, which does not require deployment. However, if you prefer to use a different model, you can select one from the Model Garden via the console. This will allow you to add the model to your registry, create an endpoint (or utilize an existing one), and deploy the model—all in a single step. Here is a link for more information: [model deployment](https://cloud.google.com/vertex-ai/docs/general/deployment).\n",
+    "\n",
+    "Before we begin, you'll need to create a Vertex AI RAG Data Service Agent service account. To do this, go to the IAM section of the console. Ensure you check the box for \"Include Google-provided role grant.\" If the role is not listed, click \"Grant Access\" and add \"Vertex AI RAG Data Service Agent\" as a role."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eeb24716-0db8-462f-8faa-0b8ad9d61feb",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Learning objectives"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5216a43-31ee-4c3e-821a-4b81f4be8e54",
+   "metadata": {},
+   "source": [
+    "In this tutorial you will learn about:\n",
+    "- How to set up a BigQuery dataset and a table.\n",
+    "- How to load data to a BigQuery table.\n",
+    "- How to use the langchain ChatVertexAI agent to extract information froim the table. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "348910aa-9802-4318-8fcb-86603158923b",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Pricing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0dca31b2-880d-4cbc-bf76-ede96998e371",
+   "metadata": {},
+   "source": [
+    "<ul>\n",
+    " <li>BigQuery Standard 1TB Storage.</li>\n",
+    " <li>Model for Chat: 5 request per day. Average Input and Output characters 5,000.</li>\n",
+    " <li>Total $23.89 per month.</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7d9e3ee-1c7e-4baa-a818-c13330c665dd",
+   "metadata": {},
+   "source": [
+    "## Get Start"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "137defcd-fd2b-421d-9b2e-fe2b878fb325",
+   "metadata": {},
+   "source": [
+    "### Install Packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "55c14a38-ba51-466d-90b5-1512f5a7fae0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "!pip install google-cloud-bigquery\n",
+    "!pip install gcloud\n",
+    "!pip install langchain\n",
+    "!pip install -U langchain-community\n",
+    "!pip install -qU langchain_google_vertexai\n",
+    "!pip install pandas-gbq"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64e22773-9a03-47de-9b6d-7946abcba722",
+   "metadata": {},
+   "source": [
+    "Set your project id, location, and bucket variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7193a9ac-3169-45c1-9c5d-ba5e2f31458f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project_id='<PROJECT_ID>'\n",
+    "location='<LOCATION> (e.g.us-east4)'\n",
+    "bucket = '<BUCKET_NAME>'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df0d9eda-ee54-4c4c-86e6-9166b2b82e2d",
+   "metadata": {},
+   "source": [
+    "Create a bucket where the data used for tutorial is going to be placed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b583234-2d92-4e5b-841d-c7d79dd87d45",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# make bucket\n",
+    "!gsutil mb -l {location} gs://{bucket}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5781a07c-84ed-416c-a782-b1dc881e5c18",
+   "metadata": {},
+   "source": [
+    "Provide the names of the bucket name, source file name path, and destination blob name to upload the CSV source file to the bucket."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4829094e-61a6-4dd7-854f-6b21dc19b1b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.cloud import storage\n",
+    "\n",
+    "# The ID of your GCS bucket\n",
+    "bucket_name = bucket\n",
+    "    # The path to your file to upload\n",
+    "source_file_name = \"<SOURCE FILE NAME>\"\n",
+    "    # The ID of your GCS object\n",
+    "destination_blob_name = \"<DESTINATION BLOB NAME>\"\n",
+    "\n",
+    "storage_client = storage.Client()\n",
+    "bucket = storage_client.bucket(bucket_name)\n",
+    "blob = bucket.blob(destination_blob_name)\n",
+    "blob.upload_from_filename(source_file_name)\n",
+    "\n",
+    "print(\n",
+    "    \"File {} uploaded to {}.\".format(\n",
+    "        source_file_name, destination_blob_name\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a00dc83-8713-4ea5-9345-08a9f3d785d0",
+   "metadata": {},
+   "source": [
+    "The next step is to create a database in BigQuery. To accomplish this, we need to define a dataset_id and construct a dataset object that will be sent to the API for creation. Note that in BigQuery, a dataset is analogous to a database."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1750e13d-ec05-4335-82a2-ffe54c5b5f32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.cloud import bigquery\n",
+    "\n",
+    "client = bigquery.Client()\n",
+    "\n",
+    "# Set dataset_id to the ID of the dataset to create.\n",
+    "dataset_id = project_id.'<Name of the Dataset>'\n",
+    "\n",
+    "# Construct a full Dataset object to send to the API.\n",
+    "dataset = bigquery.Dataset(dataset_id)\n",
+    "\n",
+    "# Specify the geographic location where the dataset should reside.\n",
+    "dataset.location = location\n",
+    "\n",
+    "# Send the dataset to the API for creation, with an explicit timeout.\n",
+    "# Raises google.api_core.exceptions.Conflict if the Dataset already\n",
+    "# exists within the project.\n",
+    "dataset = client.create_dataset(dataset, timeout=30)  # Make an API request.\n",
+    "print(\"Created dataset {}.{}\".format(client.project, dataset.dataset_id))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "544c191c-2bc0-4a47-a0a7-cea81035597a",
+   "metadata": {},
+   "source": [
+    "Once the dataset is created, the next step is to create a table. First, we define a table ID, followed by outlining the table's schema. Finally, we send an API request to create the table within the dataset established in the previous step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "68206b14-1b5e-49c3-9f07-52ba6c70a93e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set table_id to the ID of the table to create.\n",
+    "\n",
+    "client = bigquery.Client()\n",
+    "\n",
+    "table_id = project_id.dataset_id.\"<Table Name>\"\n",
+    "\n",
+    "schema = [\n",
+    "    bigquery.SchemaField(\"id\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"age\", \"INTEGER\", mode=\"NULLABLE\"),    \n",
+    "    bigquery.SchemaField(\"gender\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"height\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"weight\", \"FLOAT\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"ap_hi\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"ap_lo\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"cholesterol\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"gluc\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"smoke\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"alco\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"active\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"cardio\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"ageinyr\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"bmi\", \"FLOAT\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"bmicat\", \"STRING\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"agegroup\", \"STRING\", mode=\"NULLABLE\"),\n",
+    "\n",
+    "]\n",
+    "\n",
+    "table = bigquery.Table(table_id, schema=schema)\n",
+    "table = client.create_table(table)  # Make an API request.\n",
+    "print(\n",
+    "    \"Created table {}.{}.{}\".format(table.project, table.dataset_id, table.table_id)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e97b4c6-429c-4d36-a4c1-3963b079af6a",
+   "metadata": {},
+   "source": [
+    "This step is optional. We can make an API request to verify whether the dataset has been successfully created under our project."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "72f11cfd-8b20-48bf-b157-aaf791cd3a9f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datasets = list(client.list_datasets())  # Make an API request.\n",
+    "project = project_id\n",
+    "\n",
+    "if datasets:\n",
+    "    print(\"Datasets in project {}:\".format(project))\n",
+    "    for dataset in datasets:\n",
+    "        print(\"\\t{}\".format(dataset.dataset_id))\n",
+    "else:\n",
+    "    print(\"{} project does not contain any datasets.\".format(project))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dad4f96d-ae52-43f2-8290-c04b25960ea8",
+   "metadata": {},
+   "source": [
+    "We can perform a similar check to confirm that the table we created is located within the appropriate dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "80ea826c-9d70-4268-b4db-a657d5cdcaf0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set dataset_id to the ID of the dataset to fetch.\n",
+    "\n",
+    "dataset = client.get_dataset(dataset_id)  # Make an API request.\n",
+    "\n",
+    "full_dataset_id = \"{}.{}\".format(dataset.project, dataset.dataset_id)\n",
+    "friendly_name = dataset.friendly_name\n",
+    "print(\n",
+    "    \"Got dataset '{}' with friendly_name '{}'.\".format(\n",
+    "        full_dataset_id, friendly_name\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "# View dataset properties.\n",
+    "print(\"Description: {}\".format(dataset.description))\n",
+    "print(\"Labels:\")\n",
+    "labels = dataset.labels\n",
+    "if labels:\n",
+    "    for label, value in labels.items():\n",
+    "        print(\"\\t{}: {}\".format(label, value))\n",
+    "else:\n",
+    "    print(\"\\tDataset has no labels defined.\")\n",
+    "\n",
+    "# View tables in dataset.\n",
+    "print(\"Tables:\")\n",
+    "tables = list(client.list_tables(dataset))  # Make an API request(s).\n",
+    "if tables:\n",
+    "    for table in tables:\n",
+    "        print(\"\\t{}\".format(table.table_id))\n",
+    "else:\n",
+    "    print(\"\\tThis dataset does not contain any tables.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "736080b9-0cd9-44fd-9580-dadb10a22a59",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "To upload the data from our file stored in the bucket, we first need to create a DataFrame. In this example, we will be using a file that contains health screening data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b614cc1a-b318-4364-8d47-1dea9700c792",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.cloud import storage\n",
+    "import pandas as pd\n",
+    "from io import StringIO\n",
+    "\n",
+    "# Initialize a client\n",
+    "client = storage.Client()\n",
+    "my_bucket = bucket\n",
+    "storage_client = storage.Client()\n",
+    "bucket = storage_client.get_bucket(my_bucket)\n",
+    "blob = bucket.blob(destination_blob_name)\n",
+    "path = \"<gsutil URI>\"\n",
+    "df = pd.read_csv(path)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad42d610-daa6-4d92-ac14-2d90349dafe1",
+   "metadata": {},
+   "source": [
+    "We need to use the \"to_gbq\" function from the pandas-gbq library, which allows us to write a Pandas DataFrame to a Google BigQuery table. This enables us to populate our BigQuery table with the data from the DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7cfd1d66-a0fb-403d-a9b1-27250cc81f91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_gbq(table_id, project_id=project_id)\n",
+    "\n",
+    "from google.cloud import storage\n",
+    "import pandas as pd\n",
+    "from io import StringIO\n",
+    "\n",
+    "client.load_table_from_dataframe(df, table_id).result()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22b4dd65-9bd9-48ef-9c98-85e3e9a82701",
+   "metadata": {},
+   "source": [
+    "This step is also optional. We can execute a simple query on the BigQuery table to count the number of records."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "29e91fc1-735f-4d6c-9fcf-3a2d745363c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"\"\"\n",
+    "    SELECT count(gender) as cgender\n",
+    "    FROM `project_id.dataset_id.table_id`\n",
+    "\"\"\"\n",
+    "\n",
+    "# Execute the query\n",
+    "query_job = client.query(query)\n",
+    "\n",
+    "# Fetch the results\n",
+    "results = query_job.result()\n",
+    "\n",
+    "# Iterate through the rows\n",
+    "for row in results:\n",
+    "    #print(f\"gender: {row.name}, age: {row.age}\")\n",
+    "    print(f\"cgender: {row.cgender}\")\n",
+    "\n",
+    "try:\n",
+    "    query_job = client.query(query)\n",
+    "    results = query_job.result()\n",
+    "    print(results)\n",
+    "except Exception as e:\n",
+    "    print(f\"Error executing query: {str(e)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce65ead3-694d-4d46-9376-38d35b8d0852",
+   "metadata": {},
+   "source": [
+    "cgender: 139920\n",
+    "<google.cloud.bigquery.table.RowIterator object at 0x7f226b88ab90>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6fb53b8-9f93-4f37-8730-33c5871720fc",
+   "metadata": {},
+   "source": [
+    "To interact with the BigQuery table using a Pythonic domain language, we utilize SQLAlchemy. SQLAlchemy is a Python SQL toolkit that enables developers to access and manage SQL databases, allowing users to write queries as strings or chain Python objects for similar queries. However, to do this, we need specific credentials, which can be accessed through a service account file. For more information on how to create a service account file, please visit the following link: [Service Account Creation](https://cloud.google.com/iam/docs/service-accounts-create#python)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d1d29b90-7bcd-46b8-82b9-dc5e58f9edee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.cloud import bigquery\n",
+    "from sqlalchemy import *\n",
+    "from sqlalchemy.engine import create_engine\n",
+    "from sqlalchemy.schema import *\n",
+    "import os\n",
+    "from langchain_community.agent_toolkits import create_sql_agent\n",
+    "from langchain.agents import create_sql_agent\n",
+    "from langchain.agents.agent_toolkits import SQLDatabaseToolkit\n",
+    "from langchain.sql_database import SQLDatabase\n",
+    "service_account_file = \"<Service_Account_File>\" # Change to where your service account key file is located\n",
+    "project_id = project_id\n",
+    "dataset = \"<Name of the Dataset>\"\n",
+    "table = \"<Table Name>\"\n",
+    "sqlalchemy_url = f'bigquery://{project_id}/{dataset}?credentials_path={service_account_file}'\n",
+    "print(sqlalchemy_url)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9194cc1d-7ef1-4c4e-8e65-e7d9ada5be24",
+   "metadata": {},
+   "source": [
+    "Next, we import the __ChatVertexAI__ agent from Langchain and configure it with the appropriate hyperparameters. In this instance, we are using the __gemini-1.5-pro__ LLM model, after which we create the SQL agent to enable querying the BigQuery table using a natural language string as a prompt."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee1899a2-dd79-4cf4-a02d-c7c35b99040b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_google_vertexai import VertexAI, ChatVertexAI\n",
+    "\n",
+    "llm = ChatVertexAI(\n",
+    "    model=\"gemini-1.5-pro\",\n",
+    "    temperature=0,\n",
+    "    max_tokens=8190,\n",
+    "    timeout=None,\n",
+    "    max_retries=2,\n",
+    ")\n",
+    "db = SQLDatabase.from_uri(sqlalchemy_url)\n",
+    "toolkit = SQLDatabaseToolkit(db=db, llm=llm)\n",
+    "agent_executor = create_sql_agent(\n",
+    "    llm=llm,\n",
+    "    toolkit=toolkit,\n",
+    "    verbose=True,\n",
+    "    top_k=100000\n",
+    "    )\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3551426-8644-48ba-b8c1-bf7d5eb843b3",
+   "metadata": {},
+   "source": [
+    "For our initial query, we check the number of rows to compare the result with what we obtained in a previous step using a simple SQL query against the BigQuery table. We can confirm that we received the same number of rows: 139,920."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e9753533-e82b-47af-936a-a401d512308a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent_executor.run(\"How many rows are in the table screening? \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "099154a1-34e2-4f6b-b146-cec6f5943cf7",
+   "metadata": {},
+   "source": [
+    "> Finished chain.\n",
+    "'The table screening has 139920 rows.'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65f645ed-8efa-436f-b993-72c45a7aadbe",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "We know the output of the model are correct. The image below is a reminder of what the actual results are calculated from."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8160ee6e-f95f-44c3-ab27-4415b44ba461",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "![image.png](../../images/gcp_rag_structure_data_01.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2b62012-b2d2-48c1-ae69-6f8816065551",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "Next, we pose a question that requires applying a filter, and we successfully obtain the accurate number of obese individuals in the table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5563d317-81ab-4a37-8225-e1b0dce20b36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent_executor.run(\"How many obese are in the table screen?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40e1846d-1114-4ba1-b35a-65b47a838468",
+   "metadata": {},
+   "source": [
+    "> Finished chain.\n",
+    "'There are 37204 obese people in the table.'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b96ea414-3f2d-4e66-ab0b-a034dfe507b0",
+   "metadata": {},
+   "source": [
+    "As a more complex query, we inquired about the number of female smokers, and the SQL agent accurately returned the answer of 1,626 female smokers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a456ff90-a3a5-4ee4-81da-fba4fcdf7f2d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent_executor.run(\"How many smokers are female in the table screen? the value 1 in smoke means that is a smoker and the value 2 in gender means that is a female\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ea7810f-9701-4cb0-9430-181a57929884",
+   "metadata": {},
+   "source": [
+    "> Finished chain.\n",
+    "'There are 1626 female smokers in the table.'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca0fb657-19e0-4488-b9f6-25ca3971064f",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Conclusion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f85e560-231f-4841-b1d0-334326e4b347",
+   "metadata": {},
+   "source": [
+    "You have learned how to create a dataset and a table in BigQuery, as well as how to set up an agent that enables you to query the table using natural language instead of SQL queries, allowing you to obtain the results you need."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41573388-7feb-44b6-9e8a-0511cc8ae62e",
+   "metadata": {},
+   "source": [
+    "## Clean Up"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f0edc0b-f2cc-478c-855f-9acff3792f4d",
+   "metadata": {},
+   "source": [
+    "Please remember to delete or stop your Jupyter notebook and delete your data store to prevent incurring charges. And if you have created any other services like buckets, please remember to delete them as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe73dde2-056a-46a3-bacb-0a6671f7128f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "environment": {
+   "kernel": "python3",
+   "name": "common-cpu.m125",
+   "type": "gcloud",
+   "uri": "us-docker.pkg.dev/deeplearning-platform-release/gcr.io/base-cpu:m125"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (Local)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 639e2864625b42b3106bc0ea13faf702daaff0ac Mon Sep 17 00:00:00 2001
From: cetrarom2 <cetrarom2@nih.gov>
Date: Mon, 21 Oct 2024 17:25:05 -0400
Subject: [PATCH 02/12] Add files via upload

---
 .../GenAI/GCP_RAG_for_Structure_Data.ipynb    | 160 +++---------------
 1 file changed, 24 insertions(+), 136 deletions(-)

diff --git a/notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb b/notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb
index a7d082e..e32bc5d 100644
--- a/notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb
+++ b/notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb
@@ -33,7 +33,7 @@
    "source": [
     "Generative AI (GenAI) represents a transformative technology capable of producing human-like text, images, code, and various other types of content. While much of the focus has been on unstructured data—such as PDFs, text documents, image files, and websites—many GenAI implementations rely on a parameter called \"top K.\" This algorithm retrieves only the highest-scoring pieces of content relevant to a user's query, which can be limiting. Users seeking insights from structured data formats like CSV and JSON often require access to all relevant occurrences, rather than just a subset.\n",
     "\n",
-    "In this tutorial, we present a technique that leverages SQL databases. By formulating a query based on the user's request, the model submits this query to the database, providing comprehensive results. This approach not only ensures users receive all pertinent information but also reduces the likelihood of exceeding token limits."
+    "In this tutorial, we use a RAG agent together with a VertexAI chat model gemini-1.5-pro to query the BigQuery table. A Retrieval Augmented Generation (RAG) agent is a key part of a RAG application that enhances the capabilities of the large language models (LLMs) by integrating external data retrieval. AI agents empower LLMs to interact with the world through actions and tools."
    ]
   },
   {
@@ -53,7 +53,9 @@
     "\n",
     "In this tutorial, we will be using Google Gemini Pro 1.5, which does not require deployment. However, if you prefer to use a different model, you can select one from the Model Garden via the console. This will allow you to add the model to your registry, create an endpoint (or utilize an existing one), and deploy the model—all in a single step. Here is a link for more information: [model deployment](https://cloud.google.com/vertex-ai/docs/general/deployment).\n",
     "\n",
-    "Before we begin, you'll need to create a Vertex AI RAG Data Service Agent service account. To do this, go to the IAM section of the console. Ensure you check the box for \"Include Google-provided role grant.\" If the role is not listed, click \"Grant Access\" and add \"Vertex AI RAG Data Service Agent\" as a role."
+    "Before we begin, you'll need to create a Vertex AI RAG Data Service Agent service account. To do this, go to the IAM section of the console. Ensure you check the box for \"Include Google-provided role grant.\" If the role is not listed, click \"Grant Access\" and add \"Vertex AI RAG Data Service Agent\" as a role.\n",
+    "\n",
+    "We are using a e2-standard-4 (Efficient Instance: 4 vCPUs, 16 GB RAM) for this tutorial. "
    ]
   },
   {
@@ -74,7 +76,7 @@
     "In this tutorial you will learn about:\n",
     "- How to set up a BigQuery dataset and a table.\n",
     "- How to load data to a BigQuery table.\n",
-    "- How to use the langchain ChatVertexAI agent to extract information froim the table. \n"
+    "- How to use the langchain ChatVertexAI agent to extract information from the table. \n"
    ]
   },
   {
@@ -92,11 +94,7 @@
    "id": "0dca31b2-880d-4cbc-bf76-ede96998e371",
    "metadata": {},
    "source": [
-    "<ul>\n",
-    " <li>BigQuery Standard 1TB Storage.</li>\n",
-    " <li>Model for Chat: 5 request per day. Average Input and Output characters 5,000.</li>\n",
-    " <li>Total $23.89 per month.</li>\n",
-    "</ul>"
+    "If you are following this tutorial in one sitting it will cost $23.89 per month. Completing the process in multiple sessions or using a method different from the tutorial may result in increased costs."
    ]
   },
   {
@@ -176,7 +174,7 @@
    "id": "5781a07c-84ed-416c-a782-b1dc881e5c18",
    "metadata": {},
    "source": [
-    "Provide the names of the bucket name, source file name path, and destination blob name to upload the CSV source file to the bucket."
+    "Once the bucket is created, we need to access the CSV source file. In this tutorial, I transferred the data file to our Jupyter notebook by simply dragging and dropping it from my local folder. Next, we need to specify the bucket name and the path of the data source in order to upload the CSV file to the bucket. It is important to keep in mind that the name of the bucket has to be unique."
    ]
   },
   {
@@ -186,25 +184,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from google.cloud import storage\n",
-    "\n",
-    "# The ID of your GCS bucket\n",
-    "bucket_name = bucket\n",
-    "    # The path to your file to upload\n",
-    "source_file_name = \"<SOURCE FILE NAME>\"\n",
-    "    # The ID of your GCS object\n",
-    "destination_blob_name = \"<DESTINATION BLOB NAME>\"\n",
-    "\n",
-    "storage_client = storage.Client()\n",
-    "bucket = storage_client.bucket(bucket_name)\n",
-    "blob = bucket.blob(destination_blob_name)\n",
-    "blob.upload_from_filename(source_file_name)\n",
-    "\n",
-    "print(\n",
-    "    \"File {} uploaded to {}.\".format(\n",
-    "        source_file_name, destination_blob_name\n",
-    "    )\n",
-    ")\n"
+    "!gsutil cp '<Path to your csv>' gs://{bucket}"
    ]
   },
   {
@@ -226,8 +206,9 @@
     "\n",
     "client = bigquery.Client()\n",
     "\n",
+    "dataset_name = '<Name of the Dataset>'\n",
     "# Set dataset_id to the ID of the dataset to create.\n",
-    "dataset_id = project_id.'<Name of the Dataset>'\n",
+    "dataset_id = f'{project_id}.{dataset_name}'\n",
     "\n",
     "# Construct a full Dataset object to send to the API.\n",
     "dataset = bigquery.Dataset(dataset_id)\n",
@@ -261,7 +242,8 @@
     "\n",
     "client = bigquery.Client()\n",
     "\n",
-    "table_id = project_id.dataset_id.\"<Table Name>\"\n",
+    "table_name = \"<Table Name>\"\n",
+    "table_id = f'{project_id}.{dataset_id}.{table_name}'\n",
     "\n",
     "schema = [\n",
     "    bigquery.SchemaField(\"id\", \"INTEGER\", mode=\"NULLABLE\"),\n",
@@ -319,105 +301,34 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dad4f96d-ae52-43f2-8290-c04b25960ea8",
+   "id": "ad42d610-daa6-4d92-ac14-2d90349dafe1",
    "metadata": {},
    "source": [
-    "We can perform a similar check to confirm that the table we created is located within the appropriate dataset."
+    "We need to use the \"to_gbq\" function from the pandas-gbq library, which allows us to write a Pandas DataFrame to a Google BigQuery table. This enables us to populate our BigQuery table with the data from the DataFrame."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "80ea826c-9d70-4268-b4db-a657d5cdcaf0",
+   "id": "7cfd1d66-a0fb-403d-a9b1-27250cc81f91",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Set dataset_id to the ID of the dataset to fetch.\n",
-    "\n",
-    "dataset = client.get_dataset(dataset_id)  # Make an API request.\n",
-    "\n",
-    "full_dataset_id = \"{}.{}\".format(dataset.project, dataset.dataset_id)\n",
-    "friendly_name = dataset.friendly_name\n",
-    "print(\n",
-    "    \"Got dataset '{}' with friendly_name '{}'.\".format(\n",
-    "        full_dataset_id, friendly_name\n",
-    "    )\n",
-    ")\n",
-    "\n",
-    "# View dataset properties.\n",
-    "print(\"Description: {}\".format(dataset.description))\n",
-    "print(\"Labels:\")\n",
-    "labels = dataset.labels\n",
-    "if labels:\n",
-    "    for label, value in labels.items():\n",
-    "        print(\"\\t{}: {}\".format(label, value))\n",
-    "else:\n",
-    "    print(\"\\tDataset has no labels defined.\")\n",
+    "df.to_gbq(table_id, project_id=project_id)\n",
     "\n",
-    "# View tables in dataset.\n",
-    "print(\"Tables:\")\n",
-    "tables = list(client.list_tables(dataset))  # Make an API request(s).\n",
-    "if tables:\n",
-    "    for table in tables:\n",
-    "        print(\"\\t{}\".format(table.table_id))\n",
-    "else:\n",
-    "    print(\"\\tThis dataset does not contain any tables.\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "736080b9-0cd9-44fd-9580-dadb10a22a59",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "To upload the data from our file stored in the bucket, we first need to create a DataFrame. In this example, we will be using a file that contains health screening data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b614cc1a-b318-4364-8d47-1dea9700c792",
-   "metadata": {},
-   "outputs": [],
-   "source": [
     "from google.cloud import storage\n",
     "import pandas as pd\n",
     "from io import StringIO\n",
     "\n",
-    "# Initialize a client\n",
-    "client = storage.Client()\n",
-    "my_bucket = bucket\n",
-    "storage_client = storage.Client()\n",
-    "bucket = storage_client.get_bucket(my_bucket)\n",
-    "blob = bucket.blob(destination_blob_name)\n",
-    "path = \"<gsutil URI>\"\n",
-    "df = pd.read_csv(path)\n",
-    "df.head()"
+    "client.load_table_from_dataframe(df, table_id).result()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ad42d610-daa6-4d92-ac14-2d90349dafe1",
+   "id": "4b16a292-afdc-415c-a8a7-071bc90281c3",
    "metadata": {},
    "source": [
-    "We need to use the \"to_gbq\" function from the pandas-gbq library, which allows us to write a Pandas DataFrame to a Google BigQuery table. This enables us to populate our BigQuery table with the data from the DataFrame."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7cfd1d66-a0fb-403d-a9b1-27250cc81f91",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.to_gbq(table_id, project_id=project_id)\n",
-    "\n",
-    "from google.cloud import storage\n",
-    "import pandas as pd\n",
-    "from io import StringIO\n",
-    "\n",
-    "client.load_table_from_dataframe(df, table_id).result()"
+    "![image.png](attachment:974666d1-25e5-47d9-9800-e82c434686b0.png)"
    ]
   },
   {
@@ -473,7 +384,7 @@
    "id": "b6fb53b8-9f93-4f37-8730-33c5871720fc",
    "metadata": {},
    "source": [
-    "To interact with the BigQuery table using a Pythonic domain language, we utilize SQLAlchemy. SQLAlchemy is a Python SQL toolkit that enables developers to access and manage SQL databases, allowing users to write queries as strings or chain Python objects for similar queries. However, to do this, we need specific credentials, which can be accessed through a service account file. For more information on how to create a service account file, please visit the following link: [Service Account Creation](https://cloud.google.com/iam/docs/service-accounts-create#python)"
+    "To interact with the BigQuery table using a Pythonic domain language, we utilize SQLAlchemy. SQLAlchemy is a Python SQL toolkit that enables developers to access and manage SQL databases, allowing users to write queries as strings or chain Python objects for similar queries. "
    ]
   },
   {
@@ -492,11 +403,8 @@
     "from langchain.agents import create_sql_agent\n",
     "from langchain.agents.agent_toolkits import SQLDatabaseToolkit\n",
     "from langchain.sql_database import SQLDatabase\n",
-    "service_account_file = \"<Service_Account_File>\" # Change to where your service account key file is located\n",
-    "project_id = project_id\n",
-    "dataset = \"<Name of the Dataset>\"\n",
-    "table = \"<Table Name>\"\n",
-    "sqlalchemy_url = f'bigquery://{project_id}/{dataset}?credentials_path={service_account_file}'\n",
+    "\n",
+    "sqlalchemy_url = f'bigquery://{project_id}/{dataset}'\n",
     "print(sqlalchemy_url)"
    ]
   },
@@ -505,7 +413,7 @@
    "id": "9194cc1d-7ef1-4c4e-8e65-e7d9ada5be24",
    "metadata": {},
    "source": [
-    "Next, we import the __ChatVertexAI__ agent from Langchain and configure it with the appropriate hyperparameters. In this instance, we are using the __gemini-1.5-pro__ LLM model, after which we create the SQL agent to enable querying the BigQuery table using a natural language string as a prompt."
+    "Next, we import the __ChatVertexAI__ agent from Langchain and configure it with the appropriate hyperparameters. In this instance, we are using the __gemini-1.5-pro__ LLM model, after which we create the SQL agent to enable querying the BigQuery table using a natural language string as a prompt. Temperature regulates randomness, with higher temperatures resulting in more varied and unpredictable outputs. Top-k sampling selects from the k most probable next tokens at each step, where a lower k emphasizes higher-probability tokens. The max tokens hyperparameter specifies the maximum number of tokens in the response from the large language model. Max retries indicates how many responses we will receive from the model."
    ]
   },
   {
@@ -561,26 +469,6 @@
     "'The table screening has 139920 rows.'"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "65f645ed-8efa-436f-b993-72c45a7aadbe",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "We know the output of the model are correct. The image below is a reminder of what the actual results are calculated from."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8160ee6e-f95f-44c3-ab27-4415b44ba461",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "![image.png](../../images/gcp_rag_structure_data_01.png)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "f2b62012-b2d2-48c1-ae69-6f8816065551",
@@ -668,7 +556,7 @@
    "id": "1f0edc0b-f2cc-478c-855f-9acff3792f4d",
    "metadata": {},
    "source": [
-    "Please remember to delete or stop your Jupyter notebook and delete your data store to prevent incurring charges. And if you have created any other services like buckets, please remember to delete them as well."
+    "Please remember to delete or stop your Jupyter notebook and delete your BigQuery dataset and table to prevent incurring charges. And if you have created any other services like buckets, please remember to delete them as well."
    ]
   },
   {

From 51dec5dff8c02cd48a69de9542c1e1ee9c42fdbd Mon Sep 17 00:00:00 2001
From: cetrarom2 <cetrarom2@nih.gov>
Date: Mon, 21 Oct 2024 17:28:22 -0400
Subject: [PATCH 03/12] Add files via upload


From 8e1a04414fd8761eacb83216ea798d6647d5b88d Mon Sep 17 00:00:00 2001
From: Marco Cetraro <cetrarom2@nih.gov>
Date: Mon, 21 Oct 2024 21:36:46 +0000
Subject: [PATCH 04/12] update changes

---
 .../gcp_rag_structure_data_01-checkpoint.png  | Bin 0 -> 16270 bytes
 ...CP_RAG_for_Structure_Data-checkpoint.ipynb | 598 +++++++++++++++
 ...AG_for_Structure_Data_old-checkpoint.ipynb | 710 ++++++++++++++++++
 .../GenAI/GCP_RAG_for_Structure_Data.ipynb    | 160 +---
 .../GCP_RAG_for_Structure_Data_old.ipynb      | 710 ++++++++++++++++++
 5 files changed, 2042 insertions(+), 136 deletions(-)
 create mode 100644 images/.ipynb_checkpoints/gcp_rag_structure_data_01-checkpoint.png
 create mode 100644 notebooks/GenAI/.ipynb_checkpoints/GCP_RAG_for_Structure_Data-checkpoint.ipynb
 create mode 100644 notebooks/GenAI/.ipynb_checkpoints/GCP_RAG_for_Structure_Data_old-checkpoint.ipynb
 create mode 100644 notebooks/GenAI/GCP_RAG_for_Structure_Data_old.ipynb

diff --git a/images/.ipynb_checkpoints/gcp_rag_structure_data_01-checkpoint.png b/images/.ipynb_checkpoints/gcp_rag_structure_data_01-checkpoint.png
new file mode 100644
index 0000000000000000000000000000000000000000..a9cedd885c6c716900c5734a397876b5f30f8ff5
GIT binary patch
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diff --git a/notebooks/GenAI/.ipynb_checkpoints/GCP_RAG_for_Structure_Data-checkpoint.ipynb b/notebooks/GenAI/.ipynb_checkpoints/GCP_RAG_for_Structure_Data-checkpoint.ipynb
new file mode 100644
index 0000000..54d4614
--- /dev/null
+++ b/notebooks/GenAI/.ipynb_checkpoints/GCP_RAG_for_Structure_Data-checkpoint.ipynb
@@ -0,0 +1,598 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6e38b3f2-2b29-4788-a29a-f9e5d10f8365",
+   "metadata": {},
+   "source": [
+    "##### __Skill Level:__ <span style=\"color:blue\">Intermediate</span>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c0533e3-4fbd-47b1-8458-fd193bcb263f",
+   "metadata": {},
+   "source": [
+    "# Creating a Chatbot for Structure Data using a RAG"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76cf4b02-6422-4a48-882c-c00315e34c8a",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Overview"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b7870dd-09e0-4f6a-91a9-7c30529af720",
+   "metadata": {},
+   "source": [
+    "Generative AI (GenAI) represents a transformative technology capable of producing human-like text, images, code, and various other types of content. While much of the focus has been on unstructured data—such as PDFs, text documents, image files, and websites—many GenAI implementations rely on a parameter called \"top K.\" This algorithm retrieves only the highest-scoring pieces of content relevant to a user's query, which can be limiting. Users seeking insights from structured data formats like CSV and JSON often require access to all relevant occurrences, rather than just a subset.\n",
+    "\n",
+    "In this tutorial, we use a RAG agent together with a VertexAI chat model gemini-1.5-pro to query the BigQuery table. A Retrieval Augmented Generation (RAG) agent is a key part of a RAG application that enhances the capabilities of the large language models (LLMs) by integrating external data retrieval. AI agents empower LLMs to interact with the world through actions and tools."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d513a9fe-1ac4-4610-b53c-6696e461f828",
+   "metadata": {},
+   "source": [
+    "## Prerequisites"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbdb09aa-73d6-4cee-b3e7-5d6b243e3d58",
+   "metadata": {},
+   "source": [
+    "We assume you have access to Vertex AI and have enabled the necessary APIs.\n",
+    "\n",
+    "In this tutorial, we will be using Google Gemini Pro 1.5, which does not require deployment. However, if you prefer to use a different model, you can select one from the Model Garden via the console. This will allow you to add the model to your registry, create an endpoint (or utilize an existing one), and deploy the model—all in a single step. Here is a link for more information: [model deployment](https://cloud.google.com/vertex-ai/docs/general/deployment).\n",
+    "\n",
+    "Before we begin, you'll need to create a Vertex AI RAG Data Service Agent service account. To do this, go to the IAM section of the console. Ensure you check the box for \"Include Google-provided role grant.\" If the role is not listed, click \"Grant Access\" and add \"Vertex AI RAG Data Service Agent\" as a role.\n",
+    "\n",
+    "We are using a e2-standard-4 (Efficient Instance: 4 vCPUs, 16 GB RAM) for this tutorial. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eeb24716-0db8-462f-8faa-0b8ad9d61feb",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Learning objectives"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5216a43-31ee-4c3e-821a-4b81f4be8e54",
+   "metadata": {},
+   "source": [
+    "In this tutorial you will learn about:\n",
+    "- How to set up a BigQuery dataset and a table.\n",
+    "- How to load data to a BigQuery table.\n",
+    "- How to use the langchain ChatVertexAI agent to extract information from the table. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "348910aa-9802-4318-8fcb-86603158923b",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Pricing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0dca31b2-880d-4cbc-bf76-ede96998e371",
+   "metadata": {},
+   "source": [
+    "If you are following this tutorial in one sitting it will cost $23.89 per month. Completing the process in multiple sessions or using a method different from the tutorial may result in increased costs."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7d9e3ee-1c7e-4baa-a818-c13330c665dd",
+   "metadata": {},
+   "source": [
+    "## Get Start"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "137defcd-fd2b-421d-9b2e-fe2b878fb325",
+   "metadata": {},
+   "source": [
+    "### Install Packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "55c14a38-ba51-466d-90b5-1512f5a7fae0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "!pip install google-cloud-bigquery\n",
+    "!pip install gcloud\n",
+    "!pip install langchain\n",
+    "!pip install -U langchain-community\n",
+    "!pip install -qU langchain_google_vertexai\n",
+    "!pip install pandas-gbq"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64e22773-9a03-47de-9b6d-7946abcba722",
+   "metadata": {},
+   "source": [
+    "Set your project id, location, and bucket variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7193a9ac-3169-45c1-9c5d-ba5e2f31458f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project_id='<PROJECT_ID>'\n",
+    "location='<LOCATION> (e.g.us-east4)'\n",
+    "bucket = '<BUCKET_NAME>'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df0d9eda-ee54-4c4c-86e6-9166b2b82e2d",
+   "metadata": {},
+   "source": [
+    "Create a bucket where the data used for tutorial is going to be placed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b583234-2d92-4e5b-841d-c7d79dd87d45",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# make bucket\n",
+    "!gsutil mb -l {location} gs://{bucket}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5781a07c-84ed-416c-a782-b1dc881e5c18",
+   "metadata": {},
+   "source": [
+    "Once the bucket is created, we need to access the CSV source file. In this tutorial, I transferred the data file to our Jupyter notebook by simply dragging and dropping it from my local folder. Next, we need to specify the bucket name and the path of the data source in order to upload the CSV file to the bucket. It is important to keep in mind that the name of the bucket has to be unique."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4829094e-61a6-4dd7-854f-6b21dc19b1b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!gsutil cp '<Path to your csv>' gs://{bucket}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a00dc83-8713-4ea5-9345-08a9f3d785d0",
+   "metadata": {},
+   "source": [
+    "The next step is to create a database in BigQuery. To accomplish this, we need to define a dataset_id and construct a dataset object that will be sent to the API for creation. Note that in BigQuery, a dataset is analogous to a database."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1750e13d-ec05-4335-82a2-ffe54c5b5f32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.cloud import bigquery\n",
+    "\n",
+    "client = bigquery.Client()\n",
+    "\n",
+    "dataset_name = '<Name of the Dataset>'\n",
+    "# Set dataset_id to the ID of the dataset to create.\n",
+    "dataset_id = f'{project_id}.{dataset_name}'\n",
+    "\n",
+    "# Construct a full Dataset object to send to the API.\n",
+    "dataset = bigquery.Dataset(dataset_id)\n",
+    "\n",
+    "# Specify the geographic location where the dataset should reside.\n",
+    "dataset.location = location\n",
+    "\n",
+    "# Send the dataset to the API for creation, with an explicit timeout.\n",
+    "# Raises google.api_core.exceptions.Conflict if the Dataset already\n",
+    "# exists within the project.\n",
+    "dataset = client.create_dataset(dataset, timeout=30)  # Make an API request.\n",
+    "print(\"Created dataset {}.{}\".format(client.project, dataset.dataset_id))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "544c191c-2bc0-4a47-a0a7-cea81035597a",
+   "metadata": {},
+   "source": [
+    "Once the dataset is created, the next step is to create a table. First, we define a table ID, followed by outlining the table's schema. Finally, we send an API request to create the table within the dataset established in the previous step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "68206b14-1b5e-49c3-9f07-52ba6c70a93e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set table_id to the ID of the table to create.\n",
+    "\n",
+    "client = bigquery.Client()\n",
+    "\n",
+    "table_name = \"<Table Name>\"\n",
+    "table_id = f'{project_id}.{dataset_id}.{table_name}'\n",
+    "\n",
+    "schema = [\n",
+    "    bigquery.SchemaField(\"id\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"age\", \"INTEGER\", mode=\"NULLABLE\"),    \n",
+    "    bigquery.SchemaField(\"gender\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"height\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"weight\", \"FLOAT\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"ap_hi\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"ap_lo\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"cholesterol\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"gluc\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"smoke\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"alco\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"active\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"cardio\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"ageinyr\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"bmi\", \"FLOAT\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"bmicat\", \"STRING\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"agegroup\", \"STRING\", mode=\"NULLABLE\"),\n",
+    "\n",
+    "]\n",
+    "\n",
+    "table = bigquery.Table(table_id, schema=schema)\n",
+    "table = client.create_table(table)  # Make an API request.\n",
+    "print(\n",
+    "    \"Created table {}.{}.{}\".format(table.project, table.dataset_id, table.table_id)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e97b4c6-429c-4d36-a4c1-3963b079af6a",
+   "metadata": {},
+   "source": [
+    "This step is optional. We can make an API request to verify whether the dataset has been successfully created under our project."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "72f11cfd-8b20-48bf-b157-aaf791cd3a9f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datasets = list(client.list_datasets())  # Make an API request.\n",
+    "project = project_id\n",
+    "\n",
+    "if datasets:\n",
+    "    print(\"Datasets in project {}:\".format(project))\n",
+    "    for dataset in datasets:\n",
+    "        print(\"\\t{}\".format(dataset.dataset_id))\n",
+    "else:\n",
+    "    print(\"{} project does not contain any datasets.\".format(project))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad42d610-daa6-4d92-ac14-2d90349dafe1",
+   "metadata": {},
+   "source": [
+    "We need to use the \"to_gbq\" function from the pandas-gbq library, which allows us to write a Pandas DataFrame to a Google BigQuery table. This enables us to populate our BigQuery table with the data from the DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7cfd1d66-a0fb-403d-a9b1-27250cc81f91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_gbq(table_id, project_id=project_id)\n",
+    "\n",
+    "from google.cloud import storage\n",
+    "import pandas as pd\n",
+    "from io import StringIO\n",
+    "\n",
+    "client.load_table_from_dataframe(df, table_id).result()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b16a292-afdc-415c-a8a7-071bc90281c3",
+   "metadata": {},
+   "source": [
+    "![image.png](../../NIHCloudLabGCP/images/gcp_rag_structure_data_01.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22b4dd65-9bd9-48ef-9c98-85e3e9a82701",
+   "metadata": {},
+   "source": [
+    "This step is also optional. We can execute a simple query on the BigQuery table to count the number of records."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "29e91fc1-735f-4d6c-9fcf-3a2d745363c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"\"\"\n",
+    "    SELECT count(gender) as cgender\n",
+    "    FROM `project_id.dataset_id.table_id`\n",
+    "\"\"\"\n",
+    "\n",
+    "# Execute the query\n",
+    "query_job = client.query(query)\n",
+    "\n",
+    "# Fetch the results\n",
+    "results = query_job.result()\n",
+    "\n",
+    "# Iterate through the rows\n",
+    "for row in results:\n",
+    "    #print(f\"gender: {row.name}, age: {row.age}\")\n",
+    "    print(f\"cgender: {row.cgender}\")\n",
+    "\n",
+    "try:\n",
+    "    query_job = client.query(query)\n",
+    "    results = query_job.result()\n",
+    "    print(results)\n",
+    "except Exception as e:\n",
+    "    print(f\"Error executing query: {str(e)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce65ead3-694d-4d46-9376-38d35b8d0852",
+   "metadata": {},
+   "source": [
+    "cgender: 139920\n",
+    "<google.cloud.bigquery.table.RowIterator object at 0x7f226b88ab90>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6fb53b8-9f93-4f37-8730-33c5871720fc",
+   "metadata": {},
+   "source": [
+    "To interact with the BigQuery table using a Pythonic domain language, we utilize SQLAlchemy. SQLAlchemy is a Python SQL toolkit that enables developers to access and manage SQL databases, allowing users to write queries as strings or chain Python objects for similar queries. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d1d29b90-7bcd-46b8-82b9-dc5e58f9edee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.cloud import bigquery\n",
+    "from sqlalchemy import *\n",
+    "from sqlalchemy.engine import create_engine\n",
+    "from sqlalchemy.schema import *\n",
+    "import os\n",
+    "from langchain_community.agent_toolkits import create_sql_agent\n",
+    "from langchain.agents import create_sql_agent\n",
+    "from langchain.agents.agent_toolkits import SQLDatabaseToolkit\n",
+    "from langchain.sql_database import SQLDatabase\n",
+    "\n",
+    "sqlalchemy_url = f'bigquery://{project_id}/{dataset}'\n",
+    "print(sqlalchemy_url)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9194cc1d-7ef1-4c4e-8e65-e7d9ada5be24",
+   "metadata": {},
+   "source": [
+    "Next, we import the __ChatVertexAI__ agent from Langchain and configure it with the appropriate hyperparameters. In this instance, we are using the __gemini-1.5-pro__ LLM model, after which we create the SQL agent to enable querying the BigQuery table using a natural language string as a prompt. Temperature regulates randomness, with higher temperatures resulting in more varied and unpredictable outputs. Top-k sampling selects from the k most probable next tokens at each step, where a lower k emphasizes higher-probability tokens. The max tokens hyperparameter specifies the maximum number of tokens in the response from the large language model. Max retries indicates how many responses we will receive from the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee1899a2-dd79-4cf4-a02d-c7c35b99040b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_google_vertexai import VertexAI, ChatVertexAI\n",
+    "\n",
+    "llm = ChatVertexAI(\n",
+    "    model=\"gemini-1.5-pro\",\n",
+    "    temperature=0,\n",
+    "    max_tokens=8190,\n",
+    "    timeout=None,\n",
+    "    max_retries=2,\n",
+    ")\n",
+    "db = SQLDatabase.from_uri(sqlalchemy_url)\n",
+    "toolkit = SQLDatabaseToolkit(db=db, llm=llm)\n",
+    "agent_executor = create_sql_agent(\n",
+    "    llm=llm,\n",
+    "    toolkit=toolkit,\n",
+    "    verbose=True,\n",
+    "    top_k=100000\n",
+    "    )\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3551426-8644-48ba-b8c1-bf7d5eb843b3",
+   "metadata": {},
+   "source": [
+    "For our initial query, we check the number of rows to compare the result with what we obtained in a previous step using a simple SQL query against the BigQuery table. We can confirm that we received the same number of rows: 139,920."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e9753533-e82b-47af-936a-a401d512308a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent_executor.run(\"How many rows are in the table screening? \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "099154a1-34e2-4f6b-b146-cec6f5943cf7",
+   "metadata": {},
+   "source": [
+    "> Finished chain.\n",
+    "'The table screening has 139920 rows.'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2b62012-b2d2-48c1-ae69-6f8816065551",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "Next, we pose a question that requires applying a filter, and we successfully obtain the accurate number of obese individuals in the table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5563d317-81ab-4a37-8225-e1b0dce20b36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent_executor.run(\"How many obese are in the table screen?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40e1846d-1114-4ba1-b35a-65b47a838468",
+   "metadata": {},
+   "source": [
+    "> Finished chain.\n",
+    "'There are 37204 obese people in the table.'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b96ea414-3f2d-4e66-ab0b-a034dfe507b0",
+   "metadata": {},
+   "source": [
+    "As a more complex query, we inquired about the number of female smokers, and the SQL agent accurately returned the answer of 1,626 female smokers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a456ff90-a3a5-4ee4-81da-fba4fcdf7f2d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent_executor.run(\"How many smokers are female in the table screen? the value 1 in smoke means that is a smoker and the value 2 in gender means that is a female\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ea7810f-9701-4cb0-9430-181a57929884",
+   "metadata": {},
+   "source": [
+    "> Finished chain.\n",
+    "'There are 1626 female smokers in the table.'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca0fb657-19e0-4488-b9f6-25ca3971064f",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Conclusion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f85e560-231f-4841-b1d0-334326e4b347",
+   "metadata": {},
+   "source": [
+    "You have learned how to create a dataset and a table in BigQuery, as well as how to set up an agent that enables you to query the table using natural language instead of SQL queries, allowing you to obtain the results you need."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41573388-7feb-44b6-9e8a-0511cc8ae62e",
+   "metadata": {},
+   "source": [
+    "## Clean Up"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f0edc0b-f2cc-478c-855f-9acff3792f4d",
+   "metadata": {},
+   "source": [
+    "Please remember to delete or stop your Jupyter notebook and delete your BigQuery dataset and table to prevent incurring charges. And if you have created any other services like buckets, please remember to delete them as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe73dde2-056a-46a3-bacb-0a6671f7128f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "environment": {
+   "kernel": "python3",
+   "name": "common-cpu.m125",
+   "type": "gcloud",
+   "uri": "us-docker.pkg.dev/deeplearning-platform-release/gcr.io/base-cpu:m125"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (Local)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/GenAI/.ipynb_checkpoints/GCP_RAG_for_Structure_Data_old-checkpoint.ipynb b/notebooks/GenAI/.ipynb_checkpoints/GCP_RAG_for_Structure_Data_old-checkpoint.ipynb
new file mode 100644
index 0000000..a7d082e
--- /dev/null
+++ b/notebooks/GenAI/.ipynb_checkpoints/GCP_RAG_for_Structure_Data_old-checkpoint.ipynb
@@ -0,0 +1,710 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6e38b3f2-2b29-4788-a29a-f9e5d10f8365",
+   "metadata": {},
+   "source": [
+    "##### __Skill Level:__ <span style=\"color:blue\">Intermediate</span>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c0533e3-4fbd-47b1-8458-fd193bcb263f",
+   "metadata": {},
+   "source": [
+    "# Creating a Chatbot for Structure Data using a RAG"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76cf4b02-6422-4a48-882c-c00315e34c8a",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Overview"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b7870dd-09e0-4f6a-91a9-7c30529af720",
+   "metadata": {},
+   "source": [
+    "Generative AI (GenAI) represents a transformative technology capable of producing human-like text, images, code, and various other types of content. While much of the focus has been on unstructured data—such as PDFs, text documents, image files, and websites—many GenAI implementations rely on a parameter called \"top K.\" This algorithm retrieves only the highest-scoring pieces of content relevant to a user's query, which can be limiting. Users seeking insights from structured data formats like CSV and JSON often require access to all relevant occurrences, rather than just a subset.\n",
+    "\n",
+    "In this tutorial, we present a technique that leverages SQL databases. By formulating a query based on the user's request, the model submits this query to the database, providing comprehensive results. This approach not only ensures users receive all pertinent information but also reduces the likelihood of exceeding token limits."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d513a9fe-1ac4-4610-b53c-6696e461f828",
+   "metadata": {},
+   "source": [
+    "## Prerequisites"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbdb09aa-73d6-4cee-b3e7-5d6b243e3d58",
+   "metadata": {},
+   "source": [
+    "We assume you have access to Vertex AI and have enabled the necessary APIs.\n",
+    "\n",
+    "In this tutorial, we will be using Google Gemini Pro 1.5, which does not require deployment. However, if you prefer to use a different model, you can select one from the Model Garden via the console. This will allow you to add the model to your registry, create an endpoint (or utilize an existing one), and deploy the model—all in a single step. Here is a link for more information: [model deployment](https://cloud.google.com/vertex-ai/docs/general/deployment).\n",
+    "\n",
+    "Before we begin, you'll need to create a Vertex AI RAG Data Service Agent service account. To do this, go to the IAM section of the console. Ensure you check the box for \"Include Google-provided role grant.\" If the role is not listed, click \"Grant Access\" and add \"Vertex AI RAG Data Service Agent\" as a role."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eeb24716-0db8-462f-8faa-0b8ad9d61feb",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Learning objectives"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5216a43-31ee-4c3e-821a-4b81f4be8e54",
+   "metadata": {},
+   "source": [
+    "In this tutorial you will learn about:\n",
+    "- How to set up a BigQuery dataset and a table.\n",
+    "- How to load data to a BigQuery table.\n",
+    "- How to use the langchain ChatVertexAI agent to extract information froim the table. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "348910aa-9802-4318-8fcb-86603158923b",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Pricing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0dca31b2-880d-4cbc-bf76-ede96998e371",
+   "metadata": {},
+   "source": [
+    "<ul>\n",
+    " <li>BigQuery Standard 1TB Storage.</li>\n",
+    " <li>Model for Chat: 5 request per day. Average Input and Output characters 5,000.</li>\n",
+    " <li>Total $23.89 per month.</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7d9e3ee-1c7e-4baa-a818-c13330c665dd",
+   "metadata": {},
+   "source": [
+    "## Get Start"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "137defcd-fd2b-421d-9b2e-fe2b878fb325",
+   "metadata": {},
+   "source": [
+    "### Install Packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "55c14a38-ba51-466d-90b5-1512f5a7fae0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "!pip install google-cloud-bigquery\n",
+    "!pip install gcloud\n",
+    "!pip install langchain\n",
+    "!pip install -U langchain-community\n",
+    "!pip install -qU langchain_google_vertexai\n",
+    "!pip install pandas-gbq"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64e22773-9a03-47de-9b6d-7946abcba722",
+   "metadata": {},
+   "source": [
+    "Set your project id, location, and bucket variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7193a9ac-3169-45c1-9c5d-ba5e2f31458f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project_id='<PROJECT_ID>'\n",
+    "location='<LOCATION> (e.g.us-east4)'\n",
+    "bucket = '<BUCKET_NAME>'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df0d9eda-ee54-4c4c-86e6-9166b2b82e2d",
+   "metadata": {},
+   "source": [
+    "Create a bucket where the data used for tutorial is going to be placed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b583234-2d92-4e5b-841d-c7d79dd87d45",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# make bucket\n",
+    "!gsutil mb -l {location} gs://{bucket}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5781a07c-84ed-416c-a782-b1dc881e5c18",
+   "metadata": {},
+   "source": [
+    "Provide the names of the bucket name, source file name path, and destination blob name to upload the CSV source file to the bucket."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4829094e-61a6-4dd7-854f-6b21dc19b1b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.cloud import storage\n",
+    "\n",
+    "# The ID of your GCS bucket\n",
+    "bucket_name = bucket\n",
+    "    # The path to your file to upload\n",
+    "source_file_name = \"<SOURCE FILE NAME>\"\n",
+    "    # The ID of your GCS object\n",
+    "destination_blob_name = \"<DESTINATION BLOB NAME>\"\n",
+    "\n",
+    "storage_client = storage.Client()\n",
+    "bucket = storage_client.bucket(bucket_name)\n",
+    "blob = bucket.blob(destination_blob_name)\n",
+    "blob.upload_from_filename(source_file_name)\n",
+    "\n",
+    "print(\n",
+    "    \"File {} uploaded to {}.\".format(\n",
+    "        source_file_name, destination_blob_name\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a00dc83-8713-4ea5-9345-08a9f3d785d0",
+   "metadata": {},
+   "source": [
+    "The next step is to create a database in BigQuery. To accomplish this, we need to define a dataset_id and construct a dataset object that will be sent to the API for creation. Note that in BigQuery, a dataset is analogous to a database."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1750e13d-ec05-4335-82a2-ffe54c5b5f32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.cloud import bigquery\n",
+    "\n",
+    "client = bigquery.Client()\n",
+    "\n",
+    "# Set dataset_id to the ID of the dataset to create.\n",
+    "dataset_id = project_id.'<Name of the Dataset>'\n",
+    "\n",
+    "# Construct a full Dataset object to send to the API.\n",
+    "dataset = bigquery.Dataset(dataset_id)\n",
+    "\n",
+    "# Specify the geographic location where the dataset should reside.\n",
+    "dataset.location = location\n",
+    "\n",
+    "# Send the dataset to the API for creation, with an explicit timeout.\n",
+    "# Raises google.api_core.exceptions.Conflict if the Dataset already\n",
+    "# exists within the project.\n",
+    "dataset = client.create_dataset(dataset, timeout=30)  # Make an API request.\n",
+    "print(\"Created dataset {}.{}\".format(client.project, dataset.dataset_id))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "544c191c-2bc0-4a47-a0a7-cea81035597a",
+   "metadata": {},
+   "source": [
+    "Once the dataset is created, the next step is to create a table. First, we define a table ID, followed by outlining the table's schema. Finally, we send an API request to create the table within the dataset established in the previous step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "68206b14-1b5e-49c3-9f07-52ba6c70a93e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set table_id to the ID of the table to create.\n",
+    "\n",
+    "client = bigquery.Client()\n",
+    "\n",
+    "table_id = project_id.dataset_id.\"<Table Name>\"\n",
+    "\n",
+    "schema = [\n",
+    "    bigquery.SchemaField(\"id\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"age\", \"INTEGER\", mode=\"NULLABLE\"),    \n",
+    "    bigquery.SchemaField(\"gender\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"height\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"weight\", \"FLOAT\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"ap_hi\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"ap_lo\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"cholesterol\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"gluc\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"smoke\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"alco\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"active\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"cardio\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"ageinyr\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"bmi\", \"FLOAT\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"bmicat\", \"STRING\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"agegroup\", \"STRING\", mode=\"NULLABLE\"),\n",
+    "\n",
+    "]\n",
+    "\n",
+    "table = bigquery.Table(table_id, schema=schema)\n",
+    "table = client.create_table(table)  # Make an API request.\n",
+    "print(\n",
+    "    \"Created table {}.{}.{}\".format(table.project, table.dataset_id, table.table_id)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e97b4c6-429c-4d36-a4c1-3963b079af6a",
+   "metadata": {},
+   "source": [
+    "This step is optional. We can make an API request to verify whether the dataset has been successfully created under our project."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "72f11cfd-8b20-48bf-b157-aaf791cd3a9f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datasets = list(client.list_datasets())  # Make an API request.\n",
+    "project = project_id\n",
+    "\n",
+    "if datasets:\n",
+    "    print(\"Datasets in project {}:\".format(project))\n",
+    "    for dataset in datasets:\n",
+    "        print(\"\\t{}\".format(dataset.dataset_id))\n",
+    "else:\n",
+    "    print(\"{} project does not contain any datasets.\".format(project))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dad4f96d-ae52-43f2-8290-c04b25960ea8",
+   "metadata": {},
+   "source": [
+    "We can perform a similar check to confirm that the table we created is located within the appropriate dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "80ea826c-9d70-4268-b4db-a657d5cdcaf0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set dataset_id to the ID of the dataset to fetch.\n",
+    "\n",
+    "dataset = client.get_dataset(dataset_id)  # Make an API request.\n",
+    "\n",
+    "full_dataset_id = \"{}.{}\".format(dataset.project, dataset.dataset_id)\n",
+    "friendly_name = dataset.friendly_name\n",
+    "print(\n",
+    "    \"Got dataset '{}' with friendly_name '{}'.\".format(\n",
+    "        full_dataset_id, friendly_name\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "# View dataset properties.\n",
+    "print(\"Description: {}\".format(dataset.description))\n",
+    "print(\"Labels:\")\n",
+    "labels = dataset.labels\n",
+    "if labels:\n",
+    "    for label, value in labels.items():\n",
+    "        print(\"\\t{}: {}\".format(label, value))\n",
+    "else:\n",
+    "    print(\"\\tDataset has no labels defined.\")\n",
+    "\n",
+    "# View tables in dataset.\n",
+    "print(\"Tables:\")\n",
+    "tables = list(client.list_tables(dataset))  # Make an API request(s).\n",
+    "if tables:\n",
+    "    for table in tables:\n",
+    "        print(\"\\t{}\".format(table.table_id))\n",
+    "else:\n",
+    "    print(\"\\tThis dataset does not contain any tables.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "736080b9-0cd9-44fd-9580-dadb10a22a59",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "To upload the data from our file stored in the bucket, we first need to create a DataFrame. In this example, we will be using a file that contains health screening data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b614cc1a-b318-4364-8d47-1dea9700c792",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.cloud import storage\n",
+    "import pandas as pd\n",
+    "from io import StringIO\n",
+    "\n",
+    "# Initialize a client\n",
+    "client = storage.Client()\n",
+    "my_bucket = bucket\n",
+    "storage_client = storage.Client()\n",
+    "bucket = storage_client.get_bucket(my_bucket)\n",
+    "blob = bucket.blob(destination_blob_name)\n",
+    "path = \"<gsutil URI>\"\n",
+    "df = pd.read_csv(path)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad42d610-daa6-4d92-ac14-2d90349dafe1",
+   "metadata": {},
+   "source": [
+    "We need to use the \"to_gbq\" function from the pandas-gbq library, which allows us to write a Pandas DataFrame to a Google BigQuery table. This enables us to populate our BigQuery table with the data from the DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7cfd1d66-a0fb-403d-a9b1-27250cc81f91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_gbq(table_id, project_id=project_id)\n",
+    "\n",
+    "from google.cloud import storage\n",
+    "import pandas as pd\n",
+    "from io import StringIO\n",
+    "\n",
+    "client.load_table_from_dataframe(df, table_id).result()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22b4dd65-9bd9-48ef-9c98-85e3e9a82701",
+   "metadata": {},
+   "source": [
+    "This step is also optional. We can execute a simple query on the BigQuery table to count the number of records."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "29e91fc1-735f-4d6c-9fcf-3a2d745363c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"\"\"\n",
+    "    SELECT count(gender) as cgender\n",
+    "    FROM `project_id.dataset_id.table_id`\n",
+    "\"\"\"\n",
+    "\n",
+    "# Execute the query\n",
+    "query_job = client.query(query)\n",
+    "\n",
+    "# Fetch the results\n",
+    "results = query_job.result()\n",
+    "\n",
+    "# Iterate through the rows\n",
+    "for row in results:\n",
+    "    #print(f\"gender: {row.name}, age: {row.age}\")\n",
+    "    print(f\"cgender: {row.cgender}\")\n",
+    "\n",
+    "try:\n",
+    "    query_job = client.query(query)\n",
+    "    results = query_job.result()\n",
+    "    print(results)\n",
+    "except Exception as e:\n",
+    "    print(f\"Error executing query: {str(e)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce65ead3-694d-4d46-9376-38d35b8d0852",
+   "metadata": {},
+   "source": [
+    "cgender: 139920\n",
+    "<google.cloud.bigquery.table.RowIterator object at 0x7f226b88ab90>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6fb53b8-9f93-4f37-8730-33c5871720fc",
+   "metadata": {},
+   "source": [
+    "To interact with the BigQuery table using a Pythonic domain language, we utilize SQLAlchemy. SQLAlchemy is a Python SQL toolkit that enables developers to access and manage SQL databases, allowing users to write queries as strings or chain Python objects for similar queries. However, to do this, we need specific credentials, which can be accessed through a service account file. For more information on how to create a service account file, please visit the following link: [Service Account Creation](https://cloud.google.com/iam/docs/service-accounts-create#python)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d1d29b90-7bcd-46b8-82b9-dc5e58f9edee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.cloud import bigquery\n",
+    "from sqlalchemy import *\n",
+    "from sqlalchemy.engine import create_engine\n",
+    "from sqlalchemy.schema import *\n",
+    "import os\n",
+    "from langchain_community.agent_toolkits import create_sql_agent\n",
+    "from langchain.agents import create_sql_agent\n",
+    "from langchain.agents.agent_toolkits import SQLDatabaseToolkit\n",
+    "from langchain.sql_database import SQLDatabase\n",
+    "service_account_file = \"<Service_Account_File>\" # Change to where your service account key file is located\n",
+    "project_id = project_id\n",
+    "dataset = \"<Name of the Dataset>\"\n",
+    "table = \"<Table Name>\"\n",
+    "sqlalchemy_url = f'bigquery://{project_id}/{dataset}?credentials_path={service_account_file}'\n",
+    "print(sqlalchemy_url)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9194cc1d-7ef1-4c4e-8e65-e7d9ada5be24",
+   "metadata": {},
+   "source": [
+    "Next, we import the __ChatVertexAI__ agent from Langchain and configure it with the appropriate hyperparameters. In this instance, we are using the __gemini-1.5-pro__ LLM model, after which we create the SQL agent to enable querying the BigQuery table using a natural language string as a prompt."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee1899a2-dd79-4cf4-a02d-c7c35b99040b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_google_vertexai import VertexAI, ChatVertexAI\n",
+    "\n",
+    "llm = ChatVertexAI(\n",
+    "    model=\"gemini-1.5-pro\",\n",
+    "    temperature=0,\n",
+    "    max_tokens=8190,\n",
+    "    timeout=None,\n",
+    "    max_retries=2,\n",
+    ")\n",
+    "db = SQLDatabase.from_uri(sqlalchemy_url)\n",
+    "toolkit = SQLDatabaseToolkit(db=db, llm=llm)\n",
+    "agent_executor = create_sql_agent(\n",
+    "    llm=llm,\n",
+    "    toolkit=toolkit,\n",
+    "    verbose=True,\n",
+    "    top_k=100000\n",
+    "    )\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3551426-8644-48ba-b8c1-bf7d5eb843b3",
+   "metadata": {},
+   "source": [
+    "For our initial query, we check the number of rows to compare the result with what we obtained in a previous step using a simple SQL query against the BigQuery table. We can confirm that we received the same number of rows: 139,920."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e9753533-e82b-47af-936a-a401d512308a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent_executor.run(\"How many rows are in the table screening? \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "099154a1-34e2-4f6b-b146-cec6f5943cf7",
+   "metadata": {},
+   "source": [
+    "> Finished chain.\n",
+    "'The table screening has 139920 rows.'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65f645ed-8efa-436f-b993-72c45a7aadbe",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "We know the output of the model are correct. The image below is a reminder of what the actual results are calculated from."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8160ee6e-f95f-44c3-ab27-4415b44ba461",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "![image.png](../../images/gcp_rag_structure_data_01.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2b62012-b2d2-48c1-ae69-6f8816065551",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "Next, we pose a question that requires applying a filter, and we successfully obtain the accurate number of obese individuals in the table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5563d317-81ab-4a37-8225-e1b0dce20b36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent_executor.run(\"How many obese are in the table screen?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40e1846d-1114-4ba1-b35a-65b47a838468",
+   "metadata": {},
+   "source": [
+    "> Finished chain.\n",
+    "'There are 37204 obese people in the table.'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b96ea414-3f2d-4e66-ab0b-a034dfe507b0",
+   "metadata": {},
+   "source": [
+    "As a more complex query, we inquired about the number of female smokers, and the SQL agent accurately returned the answer of 1,626 female smokers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a456ff90-a3a5-4ee4-81da-fba4fcdf7f2d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent_executor.run(\"How many smokers are female in the table screen? the value 1 in smoke means that is a smoker and the value 2 in gender means that is a female\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ea7810f-9701-4cb0-9430-181a57929884",
+   "metadata": {},
+   "source": [
+    "> Finished chain.\n",
+    "'There are 1626 female smokers in the table.'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca0fb657-19e0-4488-b9f6-25ca3971064f",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Conclusion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f85e560-231f-4841-b1d0-334326e4b347",
+   "metadata": {},
+   "source": [
+    "You have learned how to create a dataset and a table in BigQuery, as well as how to set up an agent that enables you to query the table using natural language instead of SQL queries, allowing you to obtain the results you need."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41573388-7feb-44b6-9e8a-0511cc8ae62e",
+   "metadata": {},
+   "source": [
+    "## Clean Up"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f0edc0b-f2cc-478c-855f-9acff3792f4d",
+   "metadata": {},
+   "source": [
+    "Please remember to delete or stop your Jupyter notebook and delete your data store to prevent incurring charges. And if you have created any other services like buckets, please remember to delete them as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe73dde2-056a-46a3-bacb-0a6671f7128f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "environment": {
+   "kernel": "python3",
+   "name": "common-cpu.m125",
+   "type": "gcloud",
+   "uri": "us-docker.pkg.dev/deeplearning-platform-release/gcr.io/base-cpu:m125"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (Local)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb b/notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb
index a7d082e..54d4614 100644
--- a/notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb
+++ b/notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb
@@ -33,7 +33,7 @@
    "source": [
     "Generative AI (GenAI) represents a transformative technology capable of producing human-like text, images, code, and various other types of content. While much of the focus has been on unstructured data—such as PDFs, text documents, image files, and websites—many GenAI implementations rely on a parameter called \"top K.\" This algorithm retrieves only the highest-scoring pieces of content relevant to a user's query, which can be limiting. Users seeking insights from structured data formats like CSV and JSON often require access to all relevant occurrences, rather than just a subset.\n",
     "\n",
-    "In this tutorial, we present a technique that leverages SQL databases. By formulating a query based on the user's request, the model submits this query to the database, providing comprehensive results. This approach not only ensures users receive all pertinent information but also reduces the likelihood of exceeding token limits."
+    "In this tutorial, we use a RAG agent together with a VertexAI chat model gemini-1.5-pro to query the BigQuery table. A Retrieval Augmented Generation (RAG) agent is a key part of a RAG application that enhances the capabilities of the large language models (LLMs) by integrating external data retrieval. AI agents empower LLMs to interact with the world through actions and tools."
    ]
   },
   {
@@ -53,7 +53,9 @@
     "\n",
     "In this tutorial, we will be using Google Gemini Pro 1.5, which does not require deployment. However, if you prefer to use a different model, you can select one from the Model Garden via the console. This will allow you to add the model to your registry, create an endpoint (or utilize an existing one), and deploy the model—all in a single step. Here is a link for more information: [model deployment](https://cloud.google.com/vertex-ai/docs/general/deployment).\n",
     "\n",
-    "Before we begin, you'll need to create a Vertex AI RAG Data Service Agent service account. To do this, go to the IAM section of the console. Ensure you check the box for \"Include Google-provided role grant.\" If the role is not listed, click \"Grant Access\" and add \"Vertex AI RAG Data Service Agent\" as a role."
+    "Before we begin, you'll need to create a Vertex AI RAG Data Service Agent service account. To do this, go to the IAM section of the console. Ensure you check the box for \"Include Google-provided role grant.\" If the role is not listed, click \"Grant Access\" and add \"Vertex AI RAG Data Service Agent\" as a role.\n",
+    "\n",
+    "We are using a e2-standard-4 (Efficient Instance: 4 vCPUs, 16 GB RAM) for this tutorial. "
    ]
   },
   {
@@ -74,7 +76,7 @@
     "In this tutorial you will learn about:\n",
     "- How to set up a BigQuery dataset and a table.\n",
     "- How to load data to a BigQuery table.\n",
-    "- How to use the langchain ChatVertexAI agent to extract information froim the table. \n"
+    "- How to use the langchain ChatVertexAI agent to extract information from the table. \n"
    ]
   },
   {
@@ -92,11 +94,7 @@
    "id": "0dca31b2-880d-4cbc-bf76-ede96998e371",
    "metadata": {},
    "source": [
-    "<ul>\n",
-    " <li>BigQuery Standard 1TB Storage.</li>\n",
-    " <li>Model for Chat: 5 request per day. Average Input and Output characters 5,000.</li>\n",
-    " <li>Total $23.89 per month.</li>\n",
-    "</ul>"
+    "If you are following this tutorial in one sitting it will cost $23.89 per month. Completing the process in multiple sessions or using a method different from the tutorial may result in increased costs."
    ]
   },
   {
@@ -176,7 +174,7 @@
    "id": "5781a07c-84ed-416c-a782-b1dc881e5c18",
    "metadata": {},
    "source": [
-    "Provide the names of the bucket name, source file name path, and destination blob name to upload the CSV source file to the bucket."
+    "Once the bucket is created, we need to access the CSV source file. In this tutorial, I transferred the data file to our Jupyter notebook by simply dragging and dropping it from my local folder. Next, we need to specify the bucket name and the path of the data source in order to upload the CSV file to the bucket. It is important to keep in mind that the name of the bucket has to be unique."
    ]
   },
   {
@@ -186,25 +184,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from google.cloud import storage\n",
-    "\n",
-    "# The ID of your GCS bucket\n",
-    "bucket_name = bucket\n",
-    "    # The path to your file to upload\n",
-    "source_file_name = \"<SOURCE FILE NAME>\"\n",
-    "    # The ID of your GCS object\n",
-    "destination_blob_name = \"<DESTINATION BLOB NAME>\"\n",
-    "\n",
-    "storage_client = storage.Client()\n",
-    "bucket = storage_client.bucket(bucket_name)\n",
-    "blob = bucket.blob(destination_blob_name)\n",
-    "blob.upload_from_filename(source_file_name)\n",
-    "\n",
-    "print(\n",
-    "    \"File {} uploaded to {}.\".format(\n",
-    "        source_file_name, destination_blob_name\n",
-    "    )\n",
-    ")\n"
+    "!gsutil cp '<Path to your csv>' gs://{bucket}"
    ]
   },
   {
@@ -226,8 +206,9 @@
     "\n",
     "client = bigquery.Client()\n",
     "\n",
+    "dataset_name = '<Name of the Dataset>'\n",
     "# Set dataset_id to the ID of the dataset to create.\n",
-    "dataset_id = project_id.'<Name of the Dataset>'\n",
+    "dataset_id = f'{project_id}.{dataset_name}'\n",
     "\n",
     "# Construct a full Dataset object to send to the API.\n",
     "dataset = bigquery.Dataset(dataset_id)\n",
@@ -261,7 +242,8 @@
     "\n",
     "client = bigquery.Client()\n",
     "\n",
-    "table_id = project_id.dataset_id.\"<Table Name>\"\n",
+    "table_name = \"<Table Name>\"\n",
+    "table_id = f'{project_id}.{dataset_id}.{table_name}'\n",
     "\n",
     "schema = [\n",
     "    bigquery.SchemaField(\"id\", \"INTEGER\", mode=\"NULLABLE\"),\n",
@@ -319,105 +301,34 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dad4f96d-ae52-43f2-8290-c04b25960ea8",
+   "id": "ad42d610-daa6-4d92-ac14-2d90349dafe1",
    "metadata": {},
    "source": [
-    "We can perform a similar check to confirm that the table we created is located within the appropriate dataset."
+    "We need to use the \"to_gbq\" function from the pandas-gbq library, which allows us to write a Pandas DataFrame to a Google BigQuery table. This enables us to populate our BigQuery table with the data from the DataFrame."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "80ea826c-9d70-4268-b4db-a657d5cdcaf0",
+   "id": "7cfd1d66-a0fb-403d-a9b1-27250cc81f91",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Set dataset_id to the ID of the dataset to fetch.\n",
-    "\n",
-    "dataset = client.get_dataset(dataset_id)  # Make an API request.\n",
-    "\n",
-    "full_dataset_id = \"{}.{}\".format(dataset.project, dataset.dataset_id)\n",
-    "friendly_name = dataset.friendly_name\n",
-    "print(\n",
-    "    \"Got dataset '{}' with friendly_name '{}'.\".format(\n",
-    "        full_dataset_id, friendly_name\n",
-    "    )\n",
-    ")\n",
-    "\n",
-    "# View dataset properties.\n",
-    "print(\"Description: {}\".format(dataset.description))\n",
-    "print(\"Labels:\")\n",
-    "labels = dataset.labels\n",
-    "if labels:\n",
-    "    for label, value in labels.items():\n",
-    "        print(\"\\t{}: {}\".format(label, value))\n",
-    "else:\n",
-    "    print(\"\\tDataset has no labels defined.\")\n",
+    "df.to_gbq(table_id, project_id=project_id)\n",
     "\n",
-    "# View tables in dataset.\n",
-    "print(\"Tables:\")\n",
-    "tables = list(client.list_tables(dataset))  # Make an API request(s).\n",
-    "if tables:\n",
-    "    for table in tables:\n",
-    "        print(\"\\t{}\".format(table.table_id))\n",
-    "else:\n",
-    "    print(\"\\tThis dataset does not contain any tables.\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "736080b9-0cd9-44fd-9580-dadb10a22a59",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "To upload the data from our file stored in the bucket, we first need to create a DataFrame. In this example, we will be using a file that contains health screening data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b614cc1a-b318-4364-8d47-1dea9700c792",
-   "metadata": {},
-   "outputs": [],
-   "source": [
     "from google.cloud import storage\n",
     "import pandas as pd\n",
     "from io import StringIO\n",
     "\n",
-    "# Initialize a client\n",
-    "client = storage.Client()\n",
-    "my_bucket = bucket\n",
-    "storage_client = storage.Client()\n",
-    "bucket = storage_client.get_bucket(my_bucket)\n",
-    "blob = bucket.blob(destination_blob_name)\n",
-    "path = \"<gsutil URI>\"\n",
-    "df = pd.read_csv(path)\n",
-    "df.head()"
+    "client.load_table_from_dataframe(df, table_id).result()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ad42d610-daa6-4d92-ac14-2d90349dafe1",
+   "id": "4b16a292-afdc-415c-a8a7-071bc90281c3",
    "metadata": {},
    "source": [
-    "We need to use the \"to_gbq\" function from the pandas-gbq library, which allows us to write a Pandas DataFrame to a Google BigQuery table. This enables us to populate our BigQuery table with the data from the DataFrame."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7cfd1d66-a0fb-403d-a9b1-27250cc81f91",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.to_gbq(table_id, project_id=project_id)\n",
-    "\n",
-    "from google.cloud import storage\n",
-    "import pandas as pd\n",
-    "from io import StringIO\n",
-    "\n",
-    "client.load_table_from_dataframe(df, table_id).result()"
+    "![image.png](../../NIHCloudLabGCP/images/gcp_rag_structure_data_01.png)"
    ]
   },
   {
@@ -473,7 +384,7 @@
    "id": "b6fb53b8-9f93-4f37-8730-33c5871720fc",
    "metadata": {},
    "source": [
-    "To interact with the BigQuery table using a Pythonic domain language, we utilize SQLAlchemy. SQLAlchemy is a Python SQL toolkit that enables developers to access and manage SQL databases, allowing users to write queries as strings or chain Python objects for similar queries. However, to do this, we need specific credentials, which can be accessed through a service account file. For more information on how to create a service account file, please visit the following link: [Service Account Creation](https://cloud.google.com/iam/docs/service-accounts-create#python)"
+    "To interact with the BigQuery table using a Pythonic domain language, we utilize SQLAlchemy. SQLAlchemy is a Python SQL toolkit that enables developers to access and manage SQL databases, allowing users to write queries as strings or chain Python objects for similar queries. "
    ]
   },
   {
@@ -492,11 +403,8 @@
     "from langchain.agents import create_sql_agent\n",
     "from langchain.agents.agent_toolkits import SQLDatabaseToolkit\n",
     "from langchain.sql_database import SQLDatabase\n",
-    "service_account_file = \"<Service_Account_File>\" # Change to where your service account key file is located\n",
-    "project_id = project_id\n",
-    "dataset = \"<Name of the Dataset>\"\n",
-    "table = \"<Table Name>\"\n",
-    "sqlalchemy_url = f'bigquery://{project_id}/{dataset}?credentials_path={service_account_file}'\n",
+    "\n",
+    "sqlalchemy_url = f'bigquery://{project_id}/{dataset}'\n",
     "print(sqlalchemy_url)"
    ]
   },
@@ -505,7 +413,7 @@
    "id": "9194cc1d-7ef1-4c4e-8e65-e7d9ada5be24",
    "metadata": {},
    "source": [
-    "Next, we import the __ChatVertexAI__ agent from Langchain and configure it with the appropriate hyperparameters. In this instance, we are using the __gemini-1.5-pro__ LLM model, after which we create the SQL agent to enable querying the BigQuery table using a natural language string as a prompt."
+    "Next, we import the __ChatVertexAI__ agent from Langchain and configure it with the appropriate hyperparameters. In this instance, we are using the __gemini-1.5-pro__ LLM model, after which we create the SQL agent to enable querying the BigQuery table using a natural language string as a prompt. Temperature regulates randomness, with higher temperatures resulting in more varied and unpredictable outputs. Top-k sampling selects from the k most probable next tokens at each step, where a lower k emphasizes higher-probability tokens. The max tokens hyperparameter specifies the maximum number of tokens in the response from the large language model. Max retries indicates how many responses we will receive from the model."
    ]
   },
   {
@@ -561,26 +469,6 @@
     "'The table screening has 139920 rows.'"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "65f645ed-8efa-436f-b993-72c45a7aadbe",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "We know the output of the model are correct. The image below is a reminder of what the actual results are calculated from."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8160ee6e-f95f-44c3-ab27-4415b44ba461",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "![image.png](../../images/gcp_rag_structure_data_01.png)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "f2b62012-b2d2-48c1-ae69-6f8816065551",
@@ -668,7 +556,7 @@
    "id": "1f0edc0b-f2cc-478c-855f-9acff3792f4d",
    "metadata": {},
    "source": [
-    "Please remember to delete or stop your Jupyter notebook and delete your data store to prevent incurring charges. And if you have created any other services like buckets, please remember to delete them as well."
+    "Please remember to delete or stop your Jupyter notebook and delete your BigQuery dataset and table to prevent incurring charges. And if you have created any other services like buckets, please remember to delete them as well."
    ]
   },
   {
diff --git a/notebooks/GenAI/GCP_RAG_for_Structure_Data_old.ipynb b/notebooks/GenAI/GCP_RAG_for_Structure_Data_old.ipynb
new file mode 100644
index 0000000..a7d082e
--- /dev/null
+++ b/notebooks/GenAI/GCP_RAG_for_Structure_Data_old.ipynb
@@ -0,0 +1,710 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6e38b3f2-2b29-4788-a29a-f9e5d10f8365",
+   "metadata": {},
+   "source": [
+    "##### __Skill Level:__ <span style=\"color:blue\">Intermediate</span>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c0533e3-4fbd-47b1-8458-fd193bcb263f",
+   "metadata": {},
+   "source": [
+    "# Creating a Chatbot for Structure Data using a RAG"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76cf4b02-6422-4a48-882c-c00315e34c8a",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Overview"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b7870dd-09e0-4f6a-91a9-7c30529af720",
+   "metadata": {},
+   "source": [
+    "Generative AI (GenAI) represents a transformative technology capable of producing human-like text, images, code, and various other types of content. While much of the focus has been on unstructured data—such as PDFs, text documents, image files, and websites—many GenAI implementations rely on a parameter called \"top K.\" This algorithm retrieves only the highest-scoring pieces of content relevant to a user's query, which can be limiting. Users seeking insights from structured data formats like CSV and JSON often require access to all relevant occurrences, rather than just a subset.\n",
+    "\n",
+    "In this tutorial, we present a technique that leverages SQL databases. By formulating a query based on the user's request, the model submits this query to the database, providing comprehensive results. This approach not only ensures users receive all pertinent information but also reduces the likelihood of exceeding token limits."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d513a9fe-1ac4-4610-b53c-6696e461f828",
+   "metadata": {},
+   "source": [
+    "## Prerequisites"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbdb09aa-73d6-4cee-b3e7-5d6b243e3d58",
+   "metadata": {},
+   "source": [
+    "We assume you have access to Vertex AI and have enabled the necessary APIs.\n",
+    "\n",
+    "In this tutorial, we will be using Google Gemini Pro 1.5, which does not require deployment. However, if you prefer to use a different model, you can select one from the Model Garden via the console. This will allow you to add the model to your registry, create an endpoint (or utilize an existing one), and deploy the model—all in a single step. Here is a link for more information: [model deployment](https://cloud.google.com/vertex-ai/docs/general/deployment).\n",
+    "\n",
+    "Before we begin, you'll need to create a Vertex AI RAG Data Service Agent service account. To do this, go to the IAM section of the console. Ensure you check the box for \"Include Google-provided role grant.\" If the role is not listed, click \"Grant Access\" and add \"Vertex AI RAG Data Service Agent\" as a role."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eeb24716-0db8-462f-8faa-0b8ad9d61feb",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Learning objectives"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5216a43-31ee-4c3e-821a-4b81f4be8e54",
+   "metadata": {},
+   "source": [
+    "In this tutorial you will learn about:\n",
+    "- How to set up a BigQuery dataset and a table.\n",
+    "- How to load data to a BigQuery table.\n",
+    "- How to use the langchain ChatVertexAI agent to extract information froim the table. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "348910aa-9802-4318-8fcb-86603158923b",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Pricing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0dca31b2-880d-4cbc-bf76-ede96998e371",
+   "metadata": {},
+   "source": [
+    "<ul>\n",
+    " <li>BigQuery Standard 1TB Storage.</li>\n",
+    " <li>Model for Chat: 5 request per day. Average Input and Output characters 5,000.</li>\n",
+    " <li>Total $23.89 per month.</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7d9e3ee-1c7e-4baa-a818-c13330c665dd",
+   "metadata": {},
+   "source": [
+    "## Get Start"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "137defcd-fd2b-421d-9b2e-fe2b878fb325",
+   "metadata": {},
+   "source": [
+    "### Install Packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "55c14a38-ba51-466d-90b5-1512f5a7fae0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "!pip install google-cloud-bigquery\n",
+    "!pip install gcloud\n",
+    "!pip install langchain\n",
+    "!pip install -U langchain-community\n",
+    "!pip install -qU langchain_google_vertexai\n",
+    "!pip install pandas-gbq"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64e22773-9a03-47de-9b6d-7946abcba722",
+   "metadata": {},
+   "source": [
+    "Set your project id, location, and bucket variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7193a9ac-3169-45c1-9c5d-ba5e2f31458f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project_id='<PROJECT_ID>'\n",
+    "location='<LOCATION> (e.g.us-east4)'\n",
+    "bucket = '<BUCKET_NAME>'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df0d9eda-ee54-4c4c-86e6-9166b2b82e2d",
+   "metadata": {},
+   "source": [
+    "Create a bucket where the data used for tutorial is going to be placed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b583234-2d92-4e5b-841d-c7d79dd87d45",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# make bucket\n",
+    "!gsutil mb -l {location} gs://{bucket}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5781a07c-84ed-416c-a782-b1dc881e5c18",
+   "metadata": {},
+   "source": [
+    "Provide the names of the bucket name, source file name path, and destination blob name to upload the CSV source file to the bucket."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4829094e-61a6-4dd7-854f-6b21dc19b1b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.cloud import storage\n",
+    "\n",
+    "# The ID of your GCS bucket\n",
+    "bucket_name = bucket\n",
+    "    # The path to your file to upload\n",
+    "source_file_name = \"<SOURCE FILE NAME>\"\n",
+    "    # The ID of your GCS object\n",
+    "destination_blob_name = \"<DESTINATION BLOB NAME>\"\n",
+    "\n",
+    "storage_client = storage.Client()\n",
+    "bucket = storage_client.bucket(bucket_name)\n",
+    "blob = bucket.blob(destination_blob_name)\n",
+    "blob.upload_from_filename(source_file_name)\n",
+    "\n",
+    "print(\n",
+    "    \"File {} uploaded to {}.\".format(\n",
+    "        source_file_name, destination_blob_name\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a00dc83-8713-4ea5-9345-08a9f3d785d0",
+   "metadata": {},
+   "source": [
+    "The next step is to create a database in BigQuery. To accomplish this, we need to define a dataset_id and construct a dataset object that will be sent to the API for creation. Note that in BigQuery, a dataset is analogous to a database."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1750e13d-ec05-4335-82a2-ffe54c5b5f32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.cloud import bigquery\n",
+    "\n",
+    "client = bigquery.Client()\n",
+    "\n",
+    "# Set dataset_id to the ID of the dataset to create.\n",
+    "dataset_id = project_id.'<Name of the Dataset>'\n",
+    "\n",
+    "# Construct a full Dataset object to send to the API.\n",
+    "dataset = bigquery.Dataset(dataset_id)\n",
+    "\n",
+    "# Specify the geographic location where the dataset should reside.\n",
+    "dataset.location = location\n",
+    "\n",
+    "# Send the dataset to the API for creation, with an explicit timeout.\n",
+    "# Raises google.api_core.exceptions.Conflict if the Dataset already\n",
+    "# exists within the project.\n",
+    "dataset = client.create_dataset(dataset, timeout=30)  # Make an API request.\n",
+    "print(\"Created dataset {}.{}\".format(client.project, dataset.dataset_id))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "544c191c-2bc0-4a47-a0a7-cea81035597a",
+   "metadata": {},
+   "source": [
+    "Once the dataset is created, the next step is to create a table. First, we define a table ID, followed by outlining the table's schema. Finally, we send an API request to create the table within the dataset established in the previous step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "68206b14-1b5e-49c3-9f07-52ba6c70a93e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set table_id to the ID of the table to create.\n",
+    "\n",
+    "client = bigquery.Client()\n",
+    "\n",
+    "table_id = project_id.dataset_id.\"<Table Name>\"\n",
+    "\n",
+    "schema = [\n",
+    "    bigquery.SchemaField(\"id\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"age\", \"INTEGER\", mode=\"NULLABLE\"),    \n",
+    "    bigquery.SchemaField(\"gender\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"height\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"weight\", \"FLOAT\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"ap_hi\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"ap_lo\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"cholesterol\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"gluc\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"smoke\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"alco\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"active\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"cardio\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"ageinyr\", \"INTEGER\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"bmi\", \"FLOAT\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"bmicat\", \"STRING\", mode=\"NULLABLE\"),\n",
+    "    bigquery.SchemaField(\"agegroup\", \"STRING\", mode=\"NULLABLE\"),\n",
+    "\n",
+    "]\n",
+    "\n",
+    "table = bigquery.Table(table_id, schema=schema)\n",
+    "table = client.create_table(table)  # Make an API request.\n",
+    "print(\n",
+    "    \"Created table {}.{}.{}\".format(table.project, table.dataset_id, table.table_id)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e97b4c6-429c-4d36-a4c1-3963b079af6a",
+   "metadata": {},
+   "source": [
+    "This step is optional. We can make an API request to verify whether the dataset has been successfully created under our project."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "72f11cfd-8b20-48bf-b157-aaf791cd3a9f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datasets = list(client.list_datasets())  # Make an API request.\n",
+    "project = project_id\n",
+    "\n",
+    "if datasets:\n",
+    "    print(\"Datasets in project {}:\".format(project))\n",
+    "    for dataset in datasets:\n",
+    "        print(\"\\t{}\".format(dataset.dataset_id))\n",
+    "else:\n",
+    "    print(\"{} project does not contain any datasets.\".format(project))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dad4f96d-ae52-43f2-8290-c04b25960ea8",
+   "metadata": {},
+   "source": [
+    "We can perform a similar check to confirm that the table we created is located within the appropriate dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "80ea826c-9d70-4268-b4db-a657d5cdcaf0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set dataset_id to the ID of the dataset to fetch.\n",
+    "\n",
+    "dataset = client.get_dataset(dataset_id)  # Make an API request.\n",
+    "\n",
+    "full_dataset_id = \"{}.{}\".format(dataset.project, dataset.dataset_id)\n",
+    "friendly_name = dataset.friendly_name\n",
+    "print(\n",
+    "    \"Got dataset '{}' with friendly_name '{}'.\".format(\n",
+    "        full_dataset_id, friendly_name\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "# View dataset properties.\n",
+    "print(\"Description: {}\".format(dataset.description))\n",
+    "print(\"Labels:\")\n",
+    "labels = dataset.labels\n",
+    "if labels:\n",
+    "    for label, value in labels.items():\n",
+    "        print(\"\\t{}: {}\".format(label, value))\n",
+    "else:\n",
+    "    print(\"\\tDataset has no labels defined.\")\n",
+    "\n",
+    "# View tables in dataset.\n",
+    "print(\"Tables:\")\n",
+    "tables = list(client.list_tables(dataset))  # Make an API request(s).\n",
+    "if tables:\n",
+    "    for table in tables:\n",
+    "        print(\"\\t{}\".format(table.table_id))\n",
+    "else:\n",
+    "    print(\"\\tThis dataset does not contain any tables.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "736080b9-0cd9-44fd-9580-dadb10a22a59",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "To upload the data from our file stored in the bucket, we first need to create a DataFrame. In this example, we will be using a file that contains health screening data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b614cc1a-b318-4364-8d47-1dea9700c792",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.cloud import storage\n",
+    "import pandas as pd\n",
+    "from io import StringIO\n",
+    "\n",
+    "# Initialize a client\n",
+    "client = storage.Client()\n",
+    "my_bucket = bucket\n",
+    "storage_client = storage.Client()\n",
+    "bucket = storage_client.get_bucket(my_bucket)\n",
+    "blob = bucket.blob(destination_blob_name)\n",
+    "path = \"<gsutil URI>\"\n",
+    "df = pd.read_csv(path)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad42d610-daa6-4d92-ac14-2d90349dafe1",
+   "metadata": {},
+   "source": [
+    "We need to use the \"to_gbq\" function from the pandas-gbq library, which allows us to write a Pandas DataFrame to a Google BigQuery table. This enables us to populate our BigQuery table with the data from the DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7cfd1d66-a0fb-403d-a9b1-27250cc81f91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_gbq(table_id, project_id=project_id)\n",
+    "\n",
+    "from google.cloud import storage\n",
+    "import pandas as pd\n",
+    "from io import StringIO\n",
+    "\n",
+    "client.load_table_from_dataframe(df, table_id).result()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22b4dd65-9bd9-48ef-9c98-85e3e9a82701",
+   "metadata": {},
+   "source": [
+    "This step is also optional. We can execute a simple query on the BigQuery table to count the number of records."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "29e91fc1-735f-4d6c-9fcf-3a2d745363c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"\"\"\n",
+    "    SELECT count(gender) as cgender\n",
+    "    FROM `project_id.dataset_id.table_id`\n",
+    "\"\"\"\n",
+    "\n",
+    "# Execute the query\n",
+    "query_job = client.query(query)\n",
+    "\n",
+    "# Fetch the results\n",
+    "results = query_job.result()\n",
+    "\n",
+    "# Iterate through the rows\n",
+    "for row in results:\n",
+    "    #print(f\"gender: {row.name}, age: {row.age}\")\n",
+    "    print(f\"cgender: {row.cgender}\")\n",
+    "\n",
+    "try:\n",
+    "    query_job = client.query(query)\n",
+    "    results = query_job.result()\n",
+    "    print(results)\n",
+    "except Exception as e:\n",
+    "    print(f\"Error executing query: {str(e)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce65ead3-694d-4d46-9376-38d35b8d0852",
+   "metadata": {},
+   "source": [
+    "cgender: 139920\n",
+    "<google.cloud.bigquery.table.RowIterator object at 0x7f226b88ab90>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6fb53b8-9f93-4f37-8730-33c5871720fc",
+   "metadata": {},
+   "source": [
+    "To interact with the BigQuery table using a Pythonic domain language, we utilize SQLAlchemy. SQLAlchemy is a Python SQL toolkit that enables developers to access and manage SQL databases, allowing users to write queries as strings or chain Python objects for similar queries. However, to do this, we need specific credentials, which can be accessed through a service account file. For more information on how to create a service account file, please visit the following link: [Service Account Creation](https://cloud.google.com/iam/docs/service-accounts-create#python)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d1d29b90-7bcd-46b8-82b9-dc5e58f9edee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.cloud import bigquery\n",
+    "from sqlalchemy import *\n",
+    "from sqlalchemy.engine import create_engine\n",
+    "from sqlalchemy.schema import *\n",
+    "import os\n",
+    "from langchain_community.agent_toolkits import create_sql_agent\n",
+    "from langchain.agents import create_sql_agent\n",
+    "from langchain.agents.agent_toolkits import SQLDatabaseToolkit\n",
+    "from langchain.sql_database import SQLDatabase\n",
+    "service_account_file = \"<Service_Account_File>\" # Change to where your service account key file is located\n",
+    "project_id = project_id\n",
+    "dataset = \"<Name of the Dataset>\"\n",
+    "table = \"<Table Name>\"\n",
+    "sqlalchemy_url = f'bigquery://{project_id}/{dataset}?credentials_path={service_account_file}'\n",
+    "print(sqlalchemy_url)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9194cc1d-7ef1-4c4e-8e65-e7d9ada5be24",
+   "metadata": {},
+   "source": [
+    "Next, we import the __ChatVertexAI__ agent from Langchain and configure it with the appropriate hyperparameters. In this instance, we are using the __gemini-1.5-pro__ LLM model, after which we create the SQL agent to enable querying the BigQuery table using a natural language string as a prompt."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee1899a2-dd79-4cf4-a02d-c7c35b99040b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_google_vertexai import VertexAI, ChatVertexAI\n",
+    "\n",
+    "llm = ChatVertexAI(\n",
+    "    model=\"gemini-1.5-pro\",\n",
+    "    temperature=0,\n",
+    "    max_tokens=8190,\n",
+    "    timeout=None,\n",
+    "    max_retries=2,\n",
+    ")\n",
+    "db = SQLDatabase.from_uri(sqlalchemy_url)\n",
+    "toolkit = SQLDatabaseToolkit(db=db, llm=llm)\n",
+    "agent_executor = create_sql_agent(\n",
+    "    llm=llm,\n",
+    "    toolkit=toolkit,\n",
+    "    verbose=True,\n",
+    "    top_k=100000\n",
+    "    )\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3551426-8644-48ba-b8c1-bf7d5eb843b3",
+   "metadata": {},
+   "source": [
+    "For our initial query, we check the number of rows to compare the result with what we obtained in a previous step using a simple SQL query against the BigQuery table. We can confirm that we received the same number of rows: 139,920."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e9753533-e82b-47af-936a-a401d512308a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent_executor.run(\"How many rows are in the table screening? \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "099154a1-34e2-4f6b-b146-cec6f5943cf7",
+   "metadata": {},
+   "source": [
+    "> Finished chain.\n",
+    "'The table screening has 139920 rows.'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65f645ed-8efa-436f-b993-72c45a7aadbe",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "We know the output of the model are correct. The image below is a reminder of what the actual results are calculated from."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8160ee6e-f95f-44c3-ab27-4415b44ba461",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "![image.png](../../images/gcp_rag_structure_data_01.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2b62012-b2d2-48c1-ae69-6f8816065551",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "Next, we pose a question that requires applying a filter, and we successfully obtain the accurate number of obese individuals in the table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5563d317-81ab-4a37-8225-e1b0dce20b36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent_executor.run(\"How many obese are in the table screen?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40e1846d-1114-4ba1-b35a-65b47a838468",
+   "metadata": {},
+   "source": [
+    "> Finished chain.\n",
+    "'There are 37204 obese people in the table.'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b96ea414-3f2d-4e66-ab0b-a034dfe507b0",
+   "metadata": {},
+   "source": [
+    "As a more complex query, we inquired about the number of female smokers, and the SQL agent accurately returned the answer of 1,626 female smokers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a456ff90-a3a5-4ee4-81da-fba4fcdf7f2d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent_executor.run(\"How many smokers are female in the table screen? the value 1 in smoke means that is a smoker and the value 2 in gender means that is a female\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ea7810f-9701-4cb0-9430-181a57929884",
+   "metadata": {},
+   "source": [
+    "> Finished chain.\n",
+    "'There are 1626 female smokers in the table.'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca0fb657-19e0-4488-b9f6-25ca3971064f",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Conclusion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f85e560-231f-4841-b1d0-334326e4b347",
+   "metadata": {},
+   "source": [
+    "You have learned how to create a dataset and a table in BigQuery, as well as how to set up an agent that enables you to query the table using natural language instead of SQL queries, allowing you to obtain the results you need."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41573388-7feb-44b6-9e8a-0511cc8ae62e",
+   "metadata": {},
+   "source": [
+    "## Clean Up"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f0edc0b-f2cc-478c-855f-9acff3792f4d",
+   "metadata": {},
+   "source": [
+    "Please remember to delete or stop your Jupyter notebook and delete your data store to prevent incurring charges. And if you have created any other services like buckets, please remember to delete them as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe73dde2-056a-46a3-bacb-0a6671f7128f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "environment": {
+   "kernel": "python3",
+   "name": "common-cpu.m125",
+   "type": "gcloud",
+   "uri": "us-docker.pkg.dev/deeplearning-platform-release/gcr.io/base-cpu:m125"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (Local)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From e34c82510dff6b782488a0077d2d31d2196a3ec3 Mon Sep 17 00:00:00 2001
From: Marco Cetraro <cetrarom2@nih.gov>
Date: Wed, 23 Oct 2024 20:41:52 +0000
Subject: [PATCH 05/12] update changes 10/23/2024 b

---
 .../GCP_RAG_for_Structure_Data-checkpoint.ipynb                 | 2 +-
 notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb                | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/notebooks/GenAI/.ipynb_checkpoints/GCP_RAG_for_Structure_Data-checkpoint.ipynb b/notebooks/GenAI/.ipynb_checkpoints/GCP_RAG_for_Structure_Data-checkpoint.ipynb
index 29891e6..54ddeb6 100644
--- a/notebooks/GenAI/.ipynb_checkpoints/GCP_RAG_for_Structure_Data-checkpoint.ipynb
+++ b/notebooks/GenAI/.ipynb_checkpoints/GCP_RAG_for_Structure_Data-checkpoint.ipynb
@@ -381,7 +381,7 @@
    "id": "4b16a292-afdc-415c-a8a7-071bc90281c3",
    "metadata": {},
    "source": [
-    "![image.png](../../../NIHCloudLabGCP/images/gcp_rag_structure_data_01.png)"
+    "![image.png](../images/gcp_rag_structure_data_01.png)"
    ]
   },
   {
diff --git a/notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb b/notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb
index 29891e6..54ddeb6 100644
--- a/notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb
+++ b/notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb
@@ -381,7 +381,7 @@
    "id": "4b16a292-afdc-415c-a8a7-071bc90281c3",
    "metadata": {},
    "source": [
-    "![image.png](../../../NIHCloudLabGCP/images/gcp_rag_structure_data_01.png)"
+    "![image.png](../images/gcp_rag_structure_data_01.png)"
    ]
   },
   {

From 07aa4cf97e6588ff76c7b5f88009227f03911a5d Mon Sep 17 00:00:00 2001
From: Marco Cetraro <cetrarom2@nih.gov>
Date: Wed, 23 Oct 2024 20:52:03 +0000
Subject: [PATCH 06/12] update changes 10/23/2024 c

---
 notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb b/notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb
index 54ddeb6..0858c6c 100644
--- a/notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb
+++ b/notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb
@@ -381,7 +381,7 @@
    "id": "4b16a292-afdc-415c-a8a7-071bc90281c3",
    "metadata": {},
    "source": [
-    "![image.png](../images/gcp_rag_structure_data_01.png)"
+    "![image.png](../../../images/gcp_rag_structure_data_01.png)"
    ]
   },
   {

From d43bb43444fd64d018b60bdd6b130834032576f6 Mon Sep 17 00:00:00 2001
From: Marco Cetraro <cetrarom2@nih.gov>
Date: Fri, 25 Oct 2024 20:26:52 +0000
Subject: [PATCH 07/12] update again

---
 images/VertexAIStudioGCP_new01.png            | Bin 0 -> 115956 bytes
 images/VertexAIStudioGCP_new05.png            | Bin 0 -> 109389 bytes
 images/VertexAIStudioGCP_new06.png            | Bin 0 -> 203925 bytes
 images/VertexAIStudioGCP_new07.png            | Bin 0 -> 214724 bytes
 ...CP_RAG_for_Structure_Data-checkpoint.ipynb | 119 ++++++------
 .../VertexAIStudioGCP-checkpoint.ipynb        | 172 ++++++++++++++++++
 .../GenAI/GCP_RAG_for_Structure_Data.ipynb    | 119 ++++++------
 notebooks/GenAI/VertexAIStudioGCP.ipynb       |  26 +--
 8 files changed, 305 insertions(+), 131 deletions(-)
 create mode 100644 images/VertexAIStudioGCP_new01.png
 create mode 100644 images/VertexAIStudioGCP_new05.png
 create mode 100644 images/VertexAIStudioGCP_new06.png
 create mode 100644 images/VertexAIStudioGCP_new07.png
 create mode 100644 notebooks/GenAI/.ipynb_checkpoints/VertexAIStudioGCP-checkpoint.ipynb

diff --git a/images/VertexAIStudioGCP_new01.png b/images/VertexAIStudioGCP_new01.png
new file mode 100644
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HcmV?d00001

diff --git a/notebooks/GenAI/.ipynb_checkpoints/GCP_RAG_for_Structure_Data-checkpoint.ipynb b/notebooks/GenAI/.ipynb_checkpoints/GCP_RAG_for_Structure_Data-checkpoint.ipynb
index 54ddeb6..212cfb0 100644
--- a/notebooks/GenAI/.ipynb_checkpoints/GCP_RAG_for_Structure_Data-checkpoint.ipynb
+++ b/notebooks/GenAI/.ipynb_checkpoints/GCP_RAG_for_Structure_Data-checkpoint.ipynb
@@ -127,7 +127,8 @@
     "!pip install langchain\n",
     "!pip install -U langchain-community\n",
     "!pip install -qU langchain_google_vertexai\n",
-    "!pip install pandas-gbq"
+    "!pip install pandas-gbq\n",
+    "!pip install sqlalchemy-bigquery"
    ]
   },
   {
@@ -225,12 +226,43 @@
     "print(\"Created dataset {}.{}\".format(client.project, dataset.dataset_id))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "4e18540d-defa-414b-8b78-b86bce3dd46a",
+   "metadata": {},
+   "source": [
+    "The following code will auto detect the table schema and upload the data to the table in one step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "671aa2a3-d259-42e3-8cc3-8ab6b1e4a7d1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "table_id = f'{dataset_id}.{table_name}'\n",
+    "\n",
+    "job_config = bigquery.LoadJobConfig(\n",
+    "    autodetect=True, source_format=bigquery.SourceFormat.CSV, skip_leading_rows=1,\n",
+    ")\n",
+    "uri = f\"gs://{bucket}/ds_salaries.csv\"\n",
+    "load_job = client.load_table_from_uri(\n",
+    "    uri, table_id, job_config=job_config\n",
+    ")  # Make an API request.\n",
+    "load_job.result()  # Waits for the job to complete.\n",
+    "destination_table = client.get_table(table_id)\n",
+    "print(\"Loaded {} rows.\".format(destination_table.num_rows))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "544c191c-2bc0-4a47-a0a7-cea81035597a",
    "metadata": {},
    "source": [
-    "Once the dataset is created, the next step is to create a table. First, we define a table ID, followed by outlining the table's schema. Finally, we send an API request to create the table within the dataset established in the previous step."
+    "Alternatively, if we choose to define the table schema and create the dataset table beforehand, we can use the following code. Afterward, we can upload the CSV dataset into a pandas DataFrame and then insert it into the newly created BigQuery table."
    ]
   },
   {
@@ -277,103 +309,72 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4e18540d-defa-414b-8b78-b86bce3dd46a",
-   "metadata": {},
-   "source": [
-    "As an option, the following code will auto detect and upload the data to the table in one step."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "671aa2a3-d259-42e3-8cc3-8ab6b1e4a7d1",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "table_id = f'{dataset_id}.{table_name}'\n",
-    "\n",
-    "job_config = bigquery.LoadJobConfig(\n",
-    "    autodetect=True, source_format=bigquery.SourceFormat.CSV, skip_leading_rows=1,\n",
-    ")\n",
-    "uri = f\"gs://{bucket}/ds_salaries.csv\"\n",
-    "load_job = client.load_table_from_uri(\n",
-    "    uri, table_id, job_config=job_config\n",
-    ")  # Make an API request.\n",
-    "load_job.result()  # Waits for the job to complete.\n",
-    "destination_table = client.get_table(table_id)\n",
-    "print(\"Loaded {} rows.\".format(destination_table.num_rows))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6e97b4c6-429c-4d36-a4c1-3963b079af6a",
+   "id": "0c87a0f1-de43-4cce-a5e2-8952ce2283a0",
    "metadata": {},
    "source": [
-    "This step is optional. We can make an API request to verify whether the dataset has been successfully created under our project."
+    "We load our dataset located in our bucket to a pandas dataframe."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "72f11cfd-8b20-48bf-b157-aaf791cd3a9f",
+   "id": "2486d4e3-b462-42de-85ab-3c6d5a8ba363",
    "metadata": {},
    "outputs": [],
    "source": [
-    "datasets = list(client.list_datasets())  # Make an API request.\n",
-    "project = project_id\n",
+    "from google.cloud import storage\n",
+    "import pandas as pd\n",
+    "from io import StringIO\n",
     "\n",
-    "if datasets:\n",
-    "    print(\"Datasets in project {}:\".format(project))\n",
-    "    for dataset in datasets:\n",
-    "        print(\"\\t{}\".format(dataset.dataset_id))\n",
-    "else:\n",
-    "    print(\"{} project does not contain any datasets.\".format(project))"
+    "path = \"<gsutil URI>\"\n",
+    "df = pd.read_csv(path)\n",
+    "df.head()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0c87a0f1-de43-4cce-a5e2-8952ce2283a0",
+   "id": "2dad23f6-cc3f-4bc6-b5ff-a66d17740a65",
    "metadata": {},
    "source": [
-    "We load our dataset located in our bucket to a pandas dataframe."
+    "We need to use the \"to_gbq\" function from the pandas-gbq library, which allows us to write a Pandas DataFrame to a Google BigQuery table. This enables us to populate our BigQuery table with the data from the DataFrame."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2486d4e3-b462-42de-85ab-3c6d5a8ba363",
+   "id": "7cfd1d66-a0fb-403d-a9b1-27250cc81f91",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from google.cloud import storage\n",
-    "import pandas as pd\n",
-    "from io import StringIO\n",
+    "df.to_gbq(table_id, project_id=project_id)\n",
     "\n",
-    "path = \"<gsutil URI>\"\n",
-    "df = pd.read_csv(path)\n",
-    "df.head()"
+    "client.load_table_from_dataframe(df, table_id).result()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ad42d610-daa6-4d92-ac14-2d90349dafe1",
+   "id": "6e97b4c6-429c-4d36-a4c1-3963b079af6a",
    "metadata": {},
    "source": [
-    "We need to use the \"to_gbq\" function from the pandas-gbq library, which allows us to write a Pandas DataFrame to a Google BigQuery table. This enables us to populate our BigQuery table with the data from the DataFrame."
+    "This step is optional. We can make an API request to verify whether the dataset has been successfully created under our project."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7cfd1d66-a0fb-403d-a9b1-27250cc81f91",
+   "id": "72f11cfd-8b20-48bf-b157-aaf791cd3a9f",
    "metadata": {},
    "outputs": [],
    "source": [
-    "df.to_gbq(table_id, project_id=project_id)\n",
+    "datasets = list(client.list_datasets())  # Make an API request.\n",
+    "project = project_id\n",
     "\n",
-    "client.load_table_from_dataframe(df, table_id).result()"
+    "if datasets:\n",
+    "    print(\"Datasets in project {}:\".format(project))\n",
+    "    for dataset in datasets:\n",
+    "        print(\"\\t{}\".format(dataset.dataset_id))\n",
+    "else:\n",
+    "    print(\"{} project does not contain any datasets.\".format(project))"
    ]
   },
   {
@@ -381,7 +382,7 @@
    "id": "4b16a292-afdc-415c-a8a7-071bc90281c3",
    "metadata": {},
    "source": [
-    "![image.png](../images/gcp_rag_structure_data_01.png)"
+    "![image.png](../../images/gcp_rag_structure_data_01.png)"
    ]
   },
   {
diff --git a/notebooks/GenAI/.ipynb_checkpoints/VertexAIStudioGCP-checkpoint.ipynb b/notebooks/GenAI/.ipynb_checkpoints/VertexAIStudioGCP-checkpoint.ipynb
new file mode 100644
index 0000000..2a76783
--- /dev/null
+++ b/notebooks/GenAI/.ipynb_checkpoints/VertexAIStudioGCP-checkpoint.ipynb
@@ -0,0 +1,172 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Hny4I-ODTIS6"
+   },
+   "source": [
+    "# Vertex AI Studio on GCP - Article Summary\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-nLS57E2TO5y"
+   },
+   "source": [
+    "## Overview\n",
+    "\n",
+    "In research you often need to read several papers to understand new methods which can be very time consuming. In this tutorial we will use generative AI to summarize long documents but with the goal of perserving the most important information using Vertex AI Studio's Article Summary model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "+ You need access to Vertex AI"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Learning objectives\n",
+    "+ Learn how to use LLMs for summarization\n",
+    "+ Learn how to navigate Vertex AI to deploy and use LLMs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "skXAu__iqks_"
+   },
+   "source": [
+    "### A note on costs\n",
+    "\n",
+    "This tutorial uses billable components of Google Cloud:\n",
+    "- Vertex AI Studio\n",
+    "\n",
+    "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing), [Generative AI pricing](https://cloud.google.com/vertex-ai/pricing#generative_ai_models), and use the [Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Get Started"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "x_xMwRLuyDrj"
+   },
+   "source": [
+    "Here you will use LLM via the API to summarize the extracted texts. Please note that LLMs currently have input text limit and stuffing a large input text might not be accepted. You can read more about quotas and limits [here](https://cloud.google.com/vertex-ai/docs/quotas)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Go to the Vertex AI Studio console by navigating to Vertex AI via the search bar on the console. On the left side menu scroll down to Vertex AI Studio, click **Chat**.\n",
+    "\n",
+    " <img src=\"../../images/VertexAIStudioGCP_new01.png\" width=\"500\" height=\"500\">\n",
+    "\n",
+    "\n",
+    "Click on **\"Browse prompt gallery\"** then scroll down to **Summarizing texts** and click on it. You will see the system instructions and the prompt on the chat that are populated with the summarizing example. Since we want to summarize our own article, we clear the prompt by clicking **Clear prompt** on the top right and then **Insert Media**, select **ByURL** if you want to upload a document from the web or **Upload** if you want to upload it from your local machine. Paste the url link and press **Insert**. The document will be loaded into the Prompt and you will have to enter some instruction of what to do with the uploaded document i.e. \"Please summarize the uploaded document.\" For this tutorial this article is about how gut microbiota affects Alzeheimer's disease because of the gut-brain-microbiota axis network [here](https://www.aging-us.com/article/102930/pdf).\n",
+    "\n",
+    " <img src=\"../../images/VertexAIStudioGCP_new05.png\" width=\"500\" height=\"500\">\n",
+    "\n",
+    "To the right you can control the parameters that we have been using before this is a great way to test what each parameter does and how they effect each other. Once you are done click the submit arrow, you should have a similar output as below. For explainations on the parameters **temperature, Output token limit, top p, and top k** see the following article [here](https://cloud.google.com/vertex-ai/docs/generative-ai/text/test-text-prompts#generative-ai-test-text-prompt-drest).\n",
+    "\n",
+    " <img src=\"../../images/VertexAIStudioGCP_new06.png\" width=\"600\" height=\"600\">\n",
+    " \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lets try increasing the temperature parameter and see if we recieve a different output."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"../../images/VertexAIStudioGCP_new07.png\" width=\"600\" height=\"600\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you can see our output becomes shorter and straight to the point this is because the temperature parameter controls the degree of randomness in token selection. Lower temperatures are good for prompts that require a more deterministic and less creative responses, while higher temperatures can lead to more diverse or creative results. A temperature of 0 is deterministic, meaning that the highest probability response is always selected."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "N5aVrDWkJs3Y"
+   },
+   "source": [
+    "### Troubleshooting\n",
+    "\n",
+    "If you model responds with an error its generally because the extracted text is too long for the generative model to process. In order for the model to work best try to not include any abstract or reference sections of your article, if errors still come up try limiting the article even more by removing other sections such as the intro or extracting the first 30,000 words of the article."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Vtp21WX3T7d_"
+   },
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "Although full text is too large for the model, you have managed to create a concise, paragraph of the most important information from a portion of the PDF using the model. This method is the most simiplest and is ideal for shorter documents but can still be used when you limit the character number you want the model to read. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clean Up\n",
+    "Shut down your Vertex AI instance"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "name": "summarization_large_documents.ipynb",
+   "toc_visible": true
+  },
+  "environment": {
+   "kernel": "python3",
+   "name": "common-cpu.m125",
+   "type": "gcloud",
+   "uri": "us-docker.pkg.dev/deeplearning-platform-release/gcr.io/base-cpu:m125"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (Local)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb b/notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb
index 0858c6c..212cfb0 100644
--- a/notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb
+++ b/notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb
@@ -127,7 +127,8 @@
     "!pip install langchain\n",
     "!pip install -U langchain-community\n",
     "!pip install -qU langchain_google_vertexai\n",
-    "!pip install pandas-gbq"
+    "!pip install pandas-gbq\n",
+    "!pip install sqlalchemy-bigquery"
    ]
   },
   {
@@ -225,12 +226,43 @@
     "print(\"Created dataset {}.{}\".format(client.project, dataset.dataset_id))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "4e18540d-defa-414b-8b78-b86bce3dd46a",
+   "metadata": {},
+   "source": [
+    "The following code will auto detect the table schema and upload the data to the table in one step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "671aa2a3-d259-42e3-8cc3-8ab6b1e4a7d1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "table_id = f'{dataset_id}.{table_name}'\n",
+    "\n",
+    "job_config = bigquery.LoadJobConfig(\n",
+    "    autodetect=True, source_format=bigquery.SourceFormat.CSV, skip_leading_rows=1,\n",
+    ")\n",
+    "uri = f\"gs://{bucket}/ds_salaries.csv\"\n",
+    "load_job = client.load_table_from_uri(\n",
+    "    uri, table_id, job_config=job_config\n",
+    ")  # Make an API request.\n",
+    "load_job.result()  # Waits for the job to complete.\n",
+    "destination_table = client.get_table(table_id)\n",
+    "print(\"Loaded {} rows.\".format(destination_table.num_rows))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "544c191c-2bc0-4a47-a0a7-cea81035597a",
    "metadata": {},
    "source": [
-    "Once the dataset is created, the next step is to create a table. First, we define a table ID, followed by outlining the table's schema. Finally, we send an API request to create the table within the dataset established in the previous step."
+    "Alternatively, if we choose to define the table schema and create the dataset table beforehand, we can use the following code. Afterward, we can upload the CSV dataset into a pandas DataFrame and then insert it into the newly created BigQuery table."
    ]
   },
   {
@@ -277,103 +309,72 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4e18540d-defa-414b-8b78-b86bce3dd46a",
-   "metadata": {},
-   "source": [
-    "As an option, the following code will auto detect and upload the data to the table in one step."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "671aa2a3-d259-42e3-8cc3-8ab6b1e4a7d1",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "table_id = f'{dataset_id}.{table_name}'\n",
-    "\n",
-    "job_config = bigquery.LoadJobConfig(\n",
-    "    autodetect=True, source_format=bigquery.SourceFormat.CSV, skip_leading_rows=1,\n",
-    ")\n",
-    "uri = f\"gs://{bucket}/ds_salaries.csv\"\n",
-    "load_job = client.load_table_from_uri(\n",
-    "    uri, table_id, job_config=job_config\n",
-    ")  # Make an API request.\n",
-    "load_job.result()  # Waits for the job to complete.\n",
-    "destination_table = client.get_table(table_id)\n",
-    "print(\"Loaded {} rows.\".format(destination_table.num_rows))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6e97b4c6-429c-4d36-a4c1-3963b079af6a",
+   "id": "0c87a0f1-de43-4cce-a5e2-8952ce2283a0",
    "metadata": {},
    "source": [
-    "This step is optional. We can make an API request to verify whether the dataset has been successfully created under our project."
+    "We load our dataset located in our bucket to a pandas dataframe."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "72f11cfd-8b20-48bf-b157-aaf791cd3a9f",
+   "id": "2486d4e3-b462-42de-85ab-3c6d5a8ba363",
    "metadata": {},
    "outputs": [],
    "source": [
-    "datasets = list(client.list_datasets())  # Make an API request.\n",
-    "project = project_id\n",
+    "from google.cloud import storage\n",
+    "import pandas as pd\n",
+    "from io import StringIO\n",
     "\n",
-    "if datasets:\n",
-    "    print(\"Datasets in project {}:\".format(project))\n",
-    "    for dataset in datasets:\n",
-    "        print(\"\\t{}\".format(dataset.dataset_id))\n",
-    "else:\n",
-    "    print(\"{} project does not contain any datasets.\".format(project))"
+    "path = \"<gsutil URI>\"\n",
+    "df = pd.read_csv(path)\n",
+    "df.head()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0c87a0f1-de43-4cce-a5e2-8952ce2283a0",
+   "id": "2dad23f6-cc3f-4bc6-b5ff-a66d17740a65",
    "metadata": {},
    "source": [
-    "We load our dataset located in our bucket to a pandas dataframe."
+    "We need to use the \"to_gbq\" function from the pandas-gbq library, which allows us to write a Pandas DataFrame to a Google BigQuery table. This enables us to populate our BigQuery table with the data from the DataFrame."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2486d4e3-b462-42de-85ab-3c6d5a8ba363",
+   "id": "7cfd1d66-a0fb-403d-a9b1-27250cc81f91",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from google.cloud import storage\n",
-    "import pandas as pd\n",
-    "from io import StringIO\n",
+    "df.to_gbq(table_id, project_id=project_id)\n",
     "\n",
-    "path = \"<gsutil URI>\"\n",
-    "df = pd.read_csv(path)\n",
-    "df.head()"
+    "client.load_table_from_dataframe(df, table_id).result()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ad42d610-daa6-4d92-ac14-2d90349dafe1",
+   "id": "6e97b4c6-429c-4d36-a4c1-3963b079af6a",
    "metadata": {},
    "source": [
-    "We need to use the \"to_gbq\" function from the pandas-gbq library, which allows us to write a Pandas DataFrame to a Google BigQuery table. This enables us to populate our BigQuery table with the data from the DataFrame."
+    "This step is optional. We can make an API request to verify whether the dataset has been successfully created under our project."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7cfd1d66-a0fb-403d-a9b1-27250cc81f91",
+   "id": "72f11cfd-8b20-48bf-b157-aaf791cd3a9f",
    "metadata": {},
    "outputs": [],
    "source": [
-    "df.to_gbq(table_id, project_id=project_id)\n",
+    "datasets = list(client.list_datasets())  # Make an API request.\n",
+    "project = project_id\n",
     "\n",
-    "client.load_table_from_dataframe(df, table_id).result()"
+    "if datasets:\n",
+    "    print(\"Datasets in project {}:\".format(project))\n",
+    "    for dataset in datasets:\n",
+    "        print(\"\\t{}\".format(dataset.dataset_id))\n",
+    "else:\n",
+    "    print(\"{} project does not contain any datasets.\".format(project))"
    ]
   },
   {
@@ -381,7 +382,7 @@
    "id": "4b16a292-afdc-415c-a8a7-071bc90281c3",
    "metadata": {},
    "source": [
-    "![image.png](../../../images/gcp_rag_structure_data_01.png)"
+    "![image.png](../../images/gcp_rag_structure_data_01.png)"
    ]
   },
   {
diff --git a/notebooks/GenAI/VertexAIStudioGCP.ipynb b/notebooks/GenAI/VertexAIStudioGCP.ipynb
index 8f436d6..2a76783 100644
--- a/notebooks/GenAI/VertexAIStudioGCP.ipynb
+++ b/notebooks/GenAI/VertexAIStudioGCP.ipynb
@@ -71,18 +71,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Go to the Vertex AI Studio console by navigating to Vertex AI via the search bar on the console. On the left side menu scroll down to Vertex AI Studio, click **Language**.\n",
+    "Go to the Vertex AI Studio console by navigating to Vertex AI via the search bar on the console. On the left side menu scroll down to Vertex AI Studio, click **Chat**.\n",
     "\n",
-    " <img src=\"../../images/GCPGenStudio2.png\" width=\"500\" height=\"500\">\n",
+    " <img src=\"../../images/VertexAIStudioGCP_new01.png\" width=\"500\" height=\"500\">\n",
     "\n",
     "\n",
-    "Scroll down **\"Prompt examples\"** then to **Summarization** and click **\"Open\"** on **Article Summary**. You will see a prompt session were you will need to enter in the contents of your article as the console does not allow you to upload files. For this tutorial this article is about how gut microbiota affects Alzeheimer's disease because of the gut-brain-microbiota axis network [here](https://www.aging-us.com/article/102930/pdf).\n",
+    "Click on **\"Browse prompt gallery\"** then scroll down to **Summarizing texts** and click on it. You will see the system instructions and the prompt on the chat that are populated with the summarizing example. Since we want to summarize our own article, we clear the prompt by clicking **Clear prompt** on the top right and then **Insert Media**, select **ByURL** if you want to upload a document from the web or **Upload** if you want to upload it from your local machine. Paste the url link and press **Insert**. The document will be loaded into the Prompt and you will have to enter some instruction of what to do with the uploaded document i.e. \"Please summarize the uploaded document.\" For this tutorial this article is about how gut microbiota affects Alzeheimer's disease because of the gut-brain-microbiota axis network [here](https://www.aging-us.com/article/102930/pdf).\n",
     "\n",
-    " <img src=\"../../images/GCPGenStudio2.png\" width=\"500\" height=\"500\">\n",
+    " <img src=\"../../images/VertexAIStudioGCP_new05.png\" width=\"500\" height=\"500\">\n",
     "\n",
-    "To the right you can control the parameters that we have been using before this is a great way to test what each parameter does and how they effect each other. Once you are done click **submit**, you should have a similar output as below. For explainations on the parameters **temperature, Output token limit, top p, and top k** see the following article [here](https://cloud.google.com/vertex-ai/docs/generative-ai/text/test-text-prompts#generative-ai-test-text-prompt-drest).\n",
+    "To the right you can control the parameters that we have been using before this is a great way to test what each parameter does and how they effect each other. Once you are done click the submit arrow, you should have a similar output as below. For explainations on the parameters **temperature, Output token limit, top p, and top k** see the following article [here](https://cloud.google.com/vertex-ai/docs/generative-ai/text/test-text-prompts#generative-ai-test-text-prompt-drest).\n",
     "\n",
-    " <img src=\"../../images/GCPGenStudio3.png\" width=\"600\" height=\"600\">\n",
+    " <img src=\"../../images/VertexAIStudioGCP_new06.png\" width=\"600\" height=\"600\">\n",
     " \n"
    ]
   },
@@ -97,7 +97,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../images/GCPGenStudio4.png\" width=\"600\" height=\"600\">"
+    "<img src=\"../../images/VertexAIStudioGCP_new07.png\" width=\"600\" height=\"600\">"
    ]
   },
   {
@@ -144,15 +144,15 @@
    "toc_visible": true
   },
   "environment": {
-   "kernel": "conda-root-py",
-   "name": "workbench-notebooks.m119",
+   "kernel": "python3",
+   "name": "common-cpu.m125",
    "type": "gcloud",
-   "uri": "us-docker.pkg.dev/deeplearning-platform-release/gcr.io/workbench-notebooks:m119"
+   "uri": "us-docker.pkg.dev/deeplearning-platform-release/gcr.io/base-cpu:m125"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel) (Local)",
+   "display_name": "Python 3 (Local)",
    "language": "python",
-   "name": "conda-root-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -164,7 +164,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.10.15"
   }
  },
  "nbformat": 4,

From c96377945aab07d29e555c9605f8b13671b8f971 Mon Sep 17 00:00:00 2001
From: Marco Cetraro <cetrarom2@nih.gov>
Date: Mon, 28 Oct 2024 20:22:41 +0000
Subject: [PATCH 08/12] update again2

---
 images/VertexAIIntro01.png                    | Bin 0 -> 138809 bytes
 images/VertexAIIntro02.png                    | Bin 0 -> 148994 bytes
 images/VertexAIIntro03.png                    | Bin 0 -> 162978 bytes
 images/VertexAIIntro04.png                    | Bin 0 -> 93353 bytes
 images/VertexAIIntro05.png                    | Bin 0 -> 235783 bytes
 .../GCP_Grounding-checkpoint.ipynb            | 454 ++++++++++++
 .../Gemini_Intro-checkpoint.ipynb             | 685 ++++++++++++++++++
 notebooks/GenAI/GCP_Grounding.ipynb           |  10 +-
 notebooks/GenAI/Gemini_Intro.ipynb            |  77 +-
 9 files changed, 1203 insertions(+), 23 deletions(-)
 create mode 100644 images/VertexAIIntro01.png
 create mode 100644 images/VertexAIIntro02.png
 create mode 100644 images/VertexAIIntro03.png
 create mode 100644 images/VertexAIIntro04.png
 create mode 100644 images/VertexAIIntro05.png
 create mode 100644 notebooks/GenAI/.ipynb_checkpoints/GCP_Grounding-checkpoint.ipynb
 create mode 100644 notebooks/GenAI/.ipynb_checkpoints/Gemini_Intro-checkpoint.ipynb

diff --git a/images/VertexAIIntro01.png b/images/VertexAIIntro01.png
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diff --git a/notebooks/GenAI/.ipynb_checkpoints/GCP_Grounding-checkpoint.ipynb b/notebooks/GenAI/.ipynb_checkpoints/GCP_Grounding-checkpoint.ipynb
new file mode 100644
index 0000000..1bd18bc
--- /dev/null
+++ b/notebooks/GenAI/.ipynb_checkpoints/GCP_Grounding-checkpoint.ipynb
@@ -0,0 +1,454 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9551cc2f-a5b5-4fc5-a864-2fc731f2c310",
+   "metadata": {},
+   "source": [
+    "# Google Cloud Grounding (RAG on the console)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d9b6b51-5481-436c-8d0f-541c5dc9fee3",
+   "metadata": {},
+   "source": [
+    "## Overview"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbb045c4-b33f-4499-8d3d-c8545206e3ac",
+   "metadata": {},
+   "source": [
+    "Google Cloud's [Grounding](https://cloud.google.com/vertex-ai/generative-ai/docs/grounding/overview) feature is designed to improve the accuracy and relevance of search results by understanding the context of your search query and the information presented on web pages or your own data via the console. This requires a search app and a data store which both will be created in another Google Cloud product called [Agent Builder\n",
+    "](https://cloud.google.com/products/agent-builder?hl=en)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4e863bd8-8bbe-4226-bd7d-298303efaa84",
+   "metadata": {},
+   "source": [
+    "## Learning objectives"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bee49850-f4be-4d69-84f7-1492d5e2d23d",
+   "metadata": {},
+   "source": [
+    "- Learn how to create a data source and search app in Agent Builder\n",
+    "- Learn how to implement the search app on the Vertex AI Studio playground"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "107349df-4541-4f70-b5cb-8ea50811e759",
+   "metadata": {},
+   "source": [
+    "## Prerequisites"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7456c7a-a3c8-427e-acdc-5c446d05871d",
+   "metadata": {},
+   "source": [
+    "You must have enabled the Vertex AI, Compute Engine, and Agent Builder APIs."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8048ee93-3ee5-4d83-8039-685da38558f5",
+   "metadata": {},
+   "source": [
+    "## Get started"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed6e13da-8475-4fb5-9090-e68638d761f5",
+   "metadata": {},
+   "source": [
+    "### Create a search app"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bfe28a30-ec48-4257-b342-1fc8c3954510",
+   "metadata": {},
+   "source": [
+    "In order to utilize grounding with your own data you first need to create a search app. Start by searching up **'Agent Builder'** on the console. Then once you are on the page shown below click **'CREATE APP'**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9407c375-7b45-40b6-8504-3d6eced555da",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "![grounding1](../../images/grounding_1.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6906e34-31a0-4e88-85e9-62abee5ed6cd",
+   "metadata": {},
+   "source": [
+    "Click **'SELECT'** for the app named **Search**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39beb2ac-c377-4201-9973-5fdb738159d8",
+   "metadata": {},
+   "source": [
+    "![grounding1](../../images/grounding_2.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20abe50b-ebf4-4214-a494-b199ac7e56bb",
+   "metadata": {},
+   "source": [
+    "You have the option to tailor what kind of search you would like to use. For this tutorial we will be using **'Generic'**. The other two options include searching through media files and Healthcare files in FHIR format.\n",
+    "\n",
+    "Also make sure that you have enabled the **'Enterprise edition feature'** and **'Advance LLM feature'** to allow your app to be searchable via the grounding feature and if prompted set your location to **'global'**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2d4afa0-bdf7-4afc-8b74-d3e791bbaebd",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "![grounding3](../../images/grounding_3.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b89e7d4-bbf8-4ce9-890e-4ff9f17b4610",
+   "metadata": {},
+   "source": [
+    "### Create a Data Store"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2322c0da-1554-47c9-9e27-2732beda35dd",
+   "metadata": {},
+   "source": [
+    "Next we'll create a data store that will allow us to connect a data source to our search app."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7895ddda-5775-43b6-a723-7088c9b69ecb",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "![grounding4](../../images/grounding_4.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ccd0d718-eb6f-43b3-afe1-ec56d8108ef3",
+   "metadata": {},
+   "source": [
+    "As you can see from the image below Google Cloud's Agent builder supports the following data sources but for this tutorial we will use **'Cloud Storage'** as our data source."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b39671ac-7729-4581-be26-bf9458898cef",
+   "metadata": {},
+   "source": [
+    "![grounding5](../../images/grounding_5.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1df54267-b65c-44cd-a869-9723b7302771",
+   "metadata": {},
+   "source": [
+    "For this tutorial we'll select our data as unstructured but it also supports structured data in JSONL format and CSVs. Next choose your bucket and click **'CONTINUE'**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74e88355-7146-44a5-b30d-9907676dbb02",
+   "metadata": {},
+   "source": [
+    "![grounding6](../../images/grounding_6.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "332994e1-1bb8-476e-bbb0-28a1169e86b7",
+   "metadata": {},
+   "source": [
+    "Configure your data store by giving it a name and make sure you have set your location to **global**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3cd7fc3-01d4-4fa0-bde3-801ac4357545",
+   "metadata": {},
+   "source": [
+    "![grounding7](../../images/grounding_7.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ebeba3cc-72dd-4758-8584-96214f9ade6a",
+   "metadata": {},
+   "source": [
+    "### Sync your data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51310b87-7a49-4211-bd91-fe67c45274f4",
+   "metadata": {},
+   "source": [
+    "Select your data store or multiple data stores and click create to sync them to your search app. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0f2e40e-320a-4fd9-8795-d273f00557d3",
+   "metadata": {},
+   "source": [
+    "![grounding8](../../images/grounding_8.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d4002baa-ae55-492f-b9d8-e9729a872db8",
+   "metadata": {},
+   "source": [
+    "Depending on the size of your data it can take roughly ~30min to sync the date to your app but once its ready you should see a green check mark by the date of the last document import date similar to the image below. Make sure you **copy the data store ID** so we can use it later."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef5326da-e8bb-4e7e-bd15-12dc089c680e",
+   "metadata": {},
+   "source": [
+    "![grounding10](../../images/grounding_10.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a7668425-14f8-42ce-a115-e5cba868e1c1",
+   "metadata": {},
+   "source": [
+    "### Set up grounding on Vertex AI Studio"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "369c112d-2376-43fa-9552-9498479be9fd",
+   "metadata": {},
+   "source": [
+    "On the console go to Vertex AI and head down to Vertex AI Studio where the playground is. Select the **Language** playground. Grounding works on Text Prompt and Text Chat but for this tutorial we will use **Text Chat**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c971978-4925-427c-900c-0e8a996c0c91",
+   "metadata": {},
+   "source": [
+    "![grounding11](../../images/grounding_11.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5566af32-8ae4-42e2-8a03-cb2af69a6e60",
+   "metadata": {},
+   "source": [
+    "On the right side menu select **'Enable Grounding'** then click **'CUSTOMIZE'**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6d893bd-4377-491d-ab34-760b6128a876",
+   "metadata": {},
+   "source": [
+    "![grounding11](../../images/grounding_12.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "858329e8-aa1c-4472-8683-b521ab837a82",
+   "metadata": {},
+   "source": [
+    "Enter in you project ID and data store ID below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f7107b36-061c-4073-8e47-3a399273577a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "PROJECT_ID = '<PROJECT_ID>'\n",
+    "DATA_STORE_ID = '<DATA_STORE_ID>'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cff20cf3-86b2-4fdb-9b95-c5e645126f40",
+   "metadata": {},
+   "source": [
+    "Run the print command below to copy the path to your data store and enter it as the **Vertex AI datastore path**. Make sure your **grounding source is Vertex AI search** and click **'SAVE'**.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "04dac266-90ec-49e3-aa7c-9fd63d25a89b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "print(f'projects/{PROJECT_ID}/locations/global/collections/default_collection/dataStores/{DATA_STORE_ID}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08f1ccf8-1d00-45a7-89ae-4509a31e5ad2",
+   "metadata": {},
+   "source": [
+    "![grounding11](../../images/grounding_13.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "87cdcc8a-6372-44fe-9f21-2c0e2428b045",
+   "metadata": {},
+   "source": [
+    "Now we can ask questions about our data and the model will use our data store as the only source of information!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a5bb464b-0c17-4d61-83cd-56e2732bb06c",
+   "metadata": {},
+   "source": [
+    "![grounding14](../../images/grounding_14.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f995383-6c04-4e14-b063-41c8adf8e763",
+   "metadata": {},
+   "source": [
+    "### Grounding with Google search"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c8ab374-87a3-4c08-8bb7-1e181521a048",
+   "metadata": {},
+   "source": [
+    "Grounding with Google allows you to use the Google website as your data source for your model to refer to. In order to enable it you dont need a search app you simply go back to **'CUSTOMIZE'** on the grounding option then select **Google search as the grounding source** and click **'SAVE'**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77f0da56-5ff2-4223-9ebb-15fca421461d",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "![grounding15](../../images/grounding_15.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9dc3253-26dc-4770-bc90-cb387f365dfe",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "Then ask the model a question like you did before. The image below shows the grounding response using your own data at the top and Google searches response at the bottom."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6022faad-42c0-4934-9852-7856f32ee743",
+   "metadata": {},
+   "source": [
+    "![grounding16](../../images/grounding_16.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0ed4fd4-0bab-40f0-9888-c699d368cec4",
+   "metadata": {},
+   "source": [
+    "## Conclusion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "559696dc-7c26-40b1-a70a-26f9707feb06",
+   "metadata": {},
+   "source": [
+    "You have learned how to set up RAG with your own data and Google search on the console by creating a data store and a search app and enable grounding in the Vertex AI Studio playground."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd10f300-1348-4e73-924f-ae74fff5279f",
+   "metadata": {},
+   "source": [
+    "## Clean up"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7eab87e5-6564-4040-96e7-ff858d950183",
+   "metadata": {},
+   "source": [
+    "Please remember to delete or stop your **Jupyter notebook** and delete your **data store** and **search app** on Agent Builder to prevent incurring charges. And if you have created any other services like buckets, please remember to delete them as well."
+   ]
+  }
+ ],
+ "metadata": {
+  "environment": {
+   "kernel": "python3",
+   "name": "common-cpu.m114",
+   "type": "gcloud",
+   "uri": "gcr.io/deeplearning-platform-release/base-cpu:m114"
+  },
+  "kernelspec": {
+   "display_name": "Python (Local)",
+   "language": "python",
+   "name": "base"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/GenAI/.ipynb_checkpoints/Gemini_Intro-checkpoint.ipynb b/notebooks/GenAI/.ipynb_checkpoints/Gemini_Intro-checkpoint.ipynb
new file mode 100644
index 0000000..ff38fbf
--- /dev/null
+++ b/notebooks/GenAI/.ipynb_checkpoints/Gemini_Intro-checkpoint.ipynb
@@ -0,0 +1,685 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c51ed24c-53dd-4239-8be0-0e0422596ba3",
+   "metadata": {},
+   "source": [
+    "# Intro to GCP's Gemini "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fbc1ea5",
+   "metadata": {},
+   "source": [
+    "## Overview"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22cc412f-5338-45ba-8b29-9039c19208d9",
+   "metadata": {},
+   "source": [
+    "**Gemini** is a Google multimodal model that has the capability to **summarize, chat, and generate text from images or videos**. Gemini comes in two model versions **Gemini Pro** and **Gemini Pro Vision**, for this tutorial we will be looking into utilizing both models via python packages and GCPs model playground, **Vertex AI Studio**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3eb23dec",
+   "metadata": {},
+   "source": [
+    "## Learning Objectives\n",
+    "+ Learn how to interact with Gemini as a chatbot from a Jupyter notebook"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4546c201",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "+ You need access to Vertex AI"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a4a9c5d3",
+   "metadata": {},
+   "source": [
+    "## Install Packages"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8ec6d40-b5b3-434f-adc4-2838b7f49d1d",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "Update the google-cloud-aiplatform package"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ed9781dd-9764-4e9c-88ba-fcd7bb95842a",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "! pip install --upgrade google-cloud-aiplatform  langchain langchain-community"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7bbbed7-becf-48d0-98c3-6dd9942fd377",
+   "metadata": {},
+   "source": [
+    "Next we initialize the Gemini model by setting out project id and location. We are also pulling in the packages:\n",
+    "- **GenerativeModel:** Allows us to specify and launch the Gemini model we need (e.g. Gemini Pro, Gemini Pro Vision).\n",
+    "- **ChatSession:** Set Gemini Pro in chatbot mode.\n",
+    "- **Part:** Loads in files from buckets.\n",
+    "- **Image:** Loads in image files locally.\n",
+    "- **GenerationConfig:** Allows us to configure the models temperature, top p, top k, and max tokens."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "47dc9232-383f-405b-b1a8-fab64a80492d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from google.cloud import aiplatform\n",
+    "import vertexai\n",
+    "from vertexai.generative_models import GenerativeModel, Image, GenerativeModel, ChatSession, Part, GenerationConfig\n",
+    "\n",
+    "# TODO(developer): Update and un-comment below lines\n",
+    "project_id = \"<PROJECT_ID>\"\n",
+    "location = \"<LOCATION>\" #(e.g., us-central1)\n",
+    "vertexai.init(project=project_id, location=location)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1453b716",
+   "metadata": {},
+   "source": [
+    "## Get Started"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "022f24ba-4034-4424-91d8-1229682755ab",
+   "metadata": {},
+   "source": [
+    "### Gemini as a Chatbot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7dec028-6d30-4078-ac54-62b849ae9ced",
+   "metadata": {},
+   "source": [
+    "For dealing with text, code generation, natural language tasks we can use the **gemini-pro** model and to set our model in **chatbot mode** we need to use the `start_chat()` function. You will see below we also created a function named **get_chat_response** which will send the prompt or message we have for our model using the `send_message()` function and returns only the text of the chats response."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "70bc5b25-c796-4015-82dc-6bc861bb525f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "model = GenerativeModel(\"gemini-pro\")\n",
+    "chat = model.start_chat()\n",
+    "\n",
+    "def get_chat_response(chat: ChatSession, prompt: str):\n",
+    "    response = chat.send_message(prompt)\n",
+    "    return response.text"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae40e87a-76ac-469b-a504-fb903319cbaf",
+   "metadata": {},
+   "source": [
+    "Now that we have our functions lets ask our Gemini chatbot some questions!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9d3277de-ab85-417f-b1d8-b21985a7a21b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prompt = \"Hello.\"\n",
+    "print(get_chat_response(chat, prompt))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "342a0e3d-fbcb-4562-bb5f-b439a92e80e2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "prompt = \"List gen ai use cases that are Life Science or Health Care related. \"\n",
+    "print(get_chat_response(chat, prompt))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fcfda59a-c440-4489-8f00-a4316b827292",
+   "metadata": {},
+   "source": [
+    "We can even ask it to **generate code or debug code**!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f0b917b2-22b5-4011-a9c4-d8a667cf6b1d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "prompt = \"create a python code that will replace all null values to zero within a csv file\"\n",
+    "print(get_chat_response(chat, prompt))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a9d57d2-dbe1-434a-9345-fe5ae3315a21",
+   "metadata": {},
+   "source": [
+    "### Gemini as a Summarizer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4856bd61-3e24-40ea-8da4-1a6edc5f3e1d",
+   "metadata": {},
+   "source": [
+    "We can generate text like asking Gemini Pro to summarize articles we provide locally (using langchain). As of now Gemini does not support loading in documents that are not videos and images directly. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5d7df19-a625-40dc-b4e5-faff5e7ba241",
+   "metadata": {},
+   "source": [
+    "First we will load in a file using langchains text loader. You can also use langchain to load in files from your bucket following the instructions [here](https://python.langchain.com/docs/integrations/document_loaders/google_cloud_storage_file)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee4cf184-c815-425c-9742-7625123e02bf",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#download the article\n",
+    "!wget --user-agent \"Chrome\" https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10954554/pdf/41586_2024_Article_7159.pdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3becd6c2-daf0-4287-80e5-06cf419287bd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_community.document_loaders import PyPDFLoader\n",
+    "\n",
+    "loader = PyPDFLoader(\"41586_2024_Article_7159.pdf\")\n",
+    "ex_file=loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4e3d6c19-7d68-49e1-85e9-91bcd6bd1775",
+   "metadata": {},
+   "source": [
+    "We can configure our model to give us the best optimal output by setting the parameters below:\n",
+    "- **Max_Output_Token**: Max number of words to generate.\n",
+    "- **Temperature:** Controls randomness, higher values increase diversity meaning a more unique response make the model to think harder. Must be a number from 0 to 1.\n",
+    "- **Top_p (nucleus):** The cumulative probability cutoff for token selection. Lower values mean sampling from a smaller, more top-weighted nucleus. Must be a number from 0 to 1.\n",
+    "- **Top_k:** Sample from the k most likely next tokens at each step. Lower k focuses on higher probability tokens. This means the model choses the most probable words. Lower values eliminate fewer coherent words.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c4228e44-9639-40da-8f69-343be93b65b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "generation_config = GenerationConfig(\n",
+    "    temperature=0.9,\n",
+    "    top_p=1.0,\n",
+    "    top_k=32,\n",
+    "    candidate_count=1,\n",
+    "    max_output_tokens=8192,\n",
+    ")\n",
+    "\n",
+    "def summarizer(file: str) -> str:\n",
+    "        \n",
+    "    # Query the model\n",
+    "    response = model.generate_content(\n",
+    "        [\n",
+    "            # Add an example query\n",
+    "            \"summarize this file.\",\n",
+    "            file\n",
+    "        ],\n",
+    "        generation_config=generation_config,\n",
+    "    )\n",
+    "    #print(response)\n",
+    "    return response.text"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0bb722d6-ef79-4a0b-9327-04811e7f8ffc",
+   "metadata": {},
+   "source": [
+    "Here we are inputting only the page content from our document loader."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "97e7ea82-c58e-42ee-b01e-6aa51e324b05",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(summarizer(ex_file[0].page_content))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77c10bd8-1570-4dfa-9b2a-999e3f149faf",
+   "metadata": {},
+   "source": [
+    "### Gemini as a Image to Text Generator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "476052b3-ff64-4e5a-819c-7a18daf7f413",
+   "metadata": {},
+   "source": [
+    "Gemini Pro Vision can generate text from images and videos. These text can be descriptions or questions about the image or video. You can download an image or retrieve an image from your bucket or locally."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "214ee84f-f058-411a-944d-4e149cd0e9bc",
+   "metadata": {},
+   "source": [
+    "Images can only be in the following formats: \n",
+    "- PNG - image/png\n",
+    "- JPEG - image/jpeg\n",
+    "\n",
+    "Our function below takes in a prompt and the image, we have also included a if statement to recognize if the function should use `Image` to load in a image locally or `Part` to load it from a bucket."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "501efefe-d52f-43b3-b4eb-3d3fe81f4a3e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def img2text(image_path: str, img_prompt: str) -> str:\n",
+    "    multimodal_model = GenerativeModel(\"gemini-pro-vision\")\n",
+    "    if \"gs://\" in image_path:\n",
+    "        image1=Part.from_uri(image_path, mime_type=\"image/jpeg\")\n",
+    "    else: \n",
+    "        image1=Image.load_from_file(image_path)\n",
+    "        \n",
+    "    responses = multimodal_model.generate_content(\n",
+    "        [image1, img_prompt],\n",
+    "        generation_config={\n",
+    "            \"max_output_tokens\": 2048,\n",
+    "            \"temperature\": 0.4,\n",
+    "            \"top_p\": 1,\n",
+    "            \"top_k\": 32\n",
+    "        },\n",
+    "        stream=True,\n",
+    "    )\n",
+    "    for response in responses:\n",
+    "        print(response.text, end=\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4cafaaa8-f3c7-472a-8f0b-6595d2112636",
+   "metadata": {},
+   "source": [
+    "Lets look at an image locally, by loading a image first, this a image of a Covid virus from the [CDC Public Health Image Library](https://phil.cdc.gov/details.aspx?pid=23312)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b939f105-89c2-4c38-80f8-2cddf8dcb0ca",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "! wget -O example_image_covid.jpg \"https://phil.cdc.gov//PHIL_Images/23312/23312_lores.jpg\" "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "696d9fc7-0ca9-44a7-b431-a9698b1a636c",
+   "metadata": {},
+   "source": [
+    "Now run our function!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "34e81656-4943-439d-9fbe-df439e0e30df",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "print(img2text(\"example_image_covid.jpg\", \"describe this image.\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dc152659-7bc4-46bc-9ead-4736b4ad2706",
+   "metadata": {},
+   "source": [
+    "Next we'll look at an image from a bucket."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "81197d53-dd3d-4358-9835-ef513ec11d33",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "print(img2text(\"gs://generativeai-downloads/images/scones.jpg\", \"describe this image.\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "80e3c48f-66e9-40e1-8623-04f73b672507",
+   "metadata": {},
+   "source": [
+    "We can even ask for more details related to the items in our image!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d8395784-ea68-4a95-a0bb-b3d618f68054",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "img_prompt=\"How do you make whats in this image?\"\n",
+    "image=\"gs://generativeai-downloads/images/scones.jpg\"\n",
+    "print(img2text(image, img_prompt))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "960f7ba6-8236-437a-ba99-ea1d887efd64",
+   "metadata": {},
+   "source": [
+    "### Gemini as a Video to Text Generator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbdbbd35-f36f-4467-8a1d-48ccd942d7cc",
+   "metadata": {},
+   "source": [
+    "Just like images we will be using the same model Gemini Pro Vision. We can load videos locally and from a bucket just like images. Video files can only be in the following formats:\n",
+    "- MOV - video/mov\n",
+    "- MPEG - video/mpeg\n",
+    "- MP4 - video/mp4\n",
+    "- MPG - video/mpg\n",
+    "- AVI - video/avi\n",
+    "- WMV - video/wmv\n",
+    "- MPEGPS - video/mpegps\n",
+    "- FLS - video/flv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a7f94141-b5ba-49bf-a3d7-d0b68ccdd39a",
+   "metadata": {},
+   "source": [
+    "Our function below takes a video from a public bucket and asks for a prompt and the location of the video file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c18561c6-8f8f-46e4-b3ee-0d5fc96f2d31",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def video2text(video_path: str, video_prompt: str) -> str:\n",
+    "    # Query the model\n",
+    "    multimodal_model = GenerativeModel(\"gemini-pro-vision\")\n",
+    "    response = multimodal_model.generate_content(\n",
+    "        [\n",
+    "            # Add an example image\n",
+    "            Part.from_uri(\n",
+    "                video_path, mime_type=\"video/mp4\"\n",
+    "            ),\n",
+    "            # Add an example query\n",
+    "            video_prompt,\n",
+    "        ],\n",
+    "        stream=True\n",
+    "    )\n",
+    "    for chunk in response :\n",
+    "        return print(chunk.text)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "492f9100-b5a4-446c-a03d-dd89a1b4bbde",
+   "metadata": {},
+   "source": [
+    "Run the function!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "55990074-0365-45f5-9fa6-bedbe93c9932",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "video_prompt = \"What is this video about in detail?\"\n",
+    "video = \"gs://cloud-samples-data/video/Machine Learning Solving Problems Big, Small, and Prickly.mp4\"\n",
+    "print(video2text(video, video_prompt))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c32253b4-d892-4c0c-896e-320c8df479c7",
+   "metadata": {},
+   "source": [
+    "## Gemini on Vertex AI Studio"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "614883f2-e53a-4ba5-855c-209d755a6e6f",
+   "metadata": {},
+   "source": [
+    "You can also use Gemini Pro and Pro Vision in Vertex AI's playground called **Vertex AI Studio**. To locate Vertex AI Studio search Vertex AI and on the left hand side locate Vertex AI Studio as the image below shows. To utilize Gemini Pro Vision locate and click **Overview** and then click on **Open FreeForm** option. You will have the option to use your own prompt or explore some of the other set prompts such as Extract text from images, image question answering , etc."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a634e11-3bd5-4048-b6e4-46d5aac6ce34",
+   "metadata": {},
+   "source": [
+    "![Gemini1](../../images/VertexAIIntro01.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12c359d3-9d82-441f-aff8-da1353925210",
+   "metadata": {},
+   "source": [
+    "When the free form opens, you have to select the gemini-1.0-pro-vision-001 model on the right side of the screen. We will upload the COVID image we downloaded before by clicking **Insert Media** and selecting our file. Then we will ask it a question, her we ask \"Describe treatment for the item in this image\"."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eebe2f4e-206d-41d8-9d85-25d9644b83fd",
+   "metadata": {},
+   "source": [
+    "![Gemini3](../../images/VertexAIIntro03.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a88f06b-770a-43be-b875-b4054c3ccbf3",
+   "metadata": {},
+   "source": [
+    "To utilize Gemini Pro locate and click **Chat** on the left side menu. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1952cd5c-c93d-428f-8036-ac658eebfba4",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "![Gemini4](../../images/VertexAIIntro04.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7810c0db-d145-4acd-a2cc-b6ce8231fa14",
+   "metadata": {},
+   "source": [
+    "We ask the bot to describe covid and how it works."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36b4635d-d8f3-42df-959d-dff92259813c",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "![Gemini5](../../images/VertexAIIntro05.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0c5d0ad-2235-471e-98e6-8d5f295cc724",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Conclusion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d66220a-b17e-4b9d-8f85-f2c682d7a27f",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "You’ve learned how to interact with Gemini through code and the Vertex AI Studio interface. We covered how to generate text from images and videos using the Gemini-pro-vision model in a Python script. Additionally, we explored the FreeForm and Chat options in Vertex AI Studio to extract text from images and to engage in question-and-answer interactions within the Chat prompt."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a1fedcb-75d7-411f-9475-9ecb777c9dbb",
+   "metadata": {},
+   "source": [
+    "## Clean UP"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f30d1136-f67c-4bb1-8ed4-09b88ef67a1e",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "Please remember to delete or stop your Jupyter notebook and if you have created any other services like buckets, please delete them as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ee938bb-6dbb-4e01-88ae-e8511ada3ff7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "environment": {
+   "kernel": "python3",
+   "name": "common-cpu.m125",
+   "type": "gcloud",
+   "uri": "us-docker.pkg.dev/deeplearning-platform-release/gcr.io/base-cpu:m125"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (Local)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/GenAI/GCP_Grounding.ipynb b/notebooks/GenAI/GCP_Grounding.ipynb
index 1bd18bc..73fd265 100644
--- a/notebooks/GenAI/GCP_Grounding.ipynb
+++ b/notebooks/GenAI/GCP_Grounding.ipynb
@@ -427,14 +427,14 @@
  "metadata": {
   "environment": {
    "kernel": "python3",
-   "name": "common-cpu.m114",
+   "name": "common-cpu.m125",
    "type": "gcloud",
-   "uri": "gcr.io/deeplearning-platform-release/base-cpu:m114"
+   "uri": "us-docker.pkg.dev/deeplearning-platform-release/gcr.io/base-cpu:m125"
   },
   "kernelspec": {
-   "display_name": "Python (Local)",
+   "display_name": "Python 3 (Local)",
    "language": "python",
-   "name": "base"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -446,7 +446,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.10.15"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/GenAI/Gemini_Intro.ipynb b/notebooks/GenAI/Gemini_Intro.ipynb
index 712bca3..ff38fbf 100644
--- a/notebooks/GenAI/Gemini_Intro.ipynb
+++ b/notebooks/GenAI/Gemini_Intro.ipynb
@@ -10,6 +10,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "7fbc1ea5",
    "metadata": {},
    "source": [
     "## Overview"
@@ -25,6 +26,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "3eb23dec",
    "metadata": {},
    "source": [
     "## Learning Objectives\n",
@@ -33,6 +35,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "4546c201",
    "metadata": {},
    "source": [
     "## Prerequisites\n",
@@ -41,6 +44,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "a4a9c5d3",
    "metadata": {},
    "source": [
     "## Install Packages"
@@ -103,6 +107,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "1453b716",
    "metadata": {},
    "source": [
     "## Get Started"
@@ -540,7 +545,7 @@
    "id": "614883f2-e53a-4ba5-855c-209d755a6e6f",
    "metadata": {},
    "source": [
-    "You can also use Gemini Pro and Pro Vision in Vertex AI's playground called **Vertex AI Studio**. To locate Vertex AI Studio search Vertex AI and on the left hand side locate Vertex AI Studio as the image below shows. To utilize Gemini Pro Vision locate and click **Multimodal** you will have the option to use your own prompt or explore some of the other set prompts such as Extract text from images, image question answering , etc."
+    "You can also use Gemini Pro and Pro Vision in Vertex AI's playground called **Vertex AI Studio**. To locate Vertex AI Studio search Vertex AI and on the left hand side locate Vertex AI Studio as the image below shows. To utilize Gemini Pro Vision locate and click **Overview** and then click on **Open FreeForm** option. You will have the option to use your own prompt or explore some of the other set prompts such as Extract text from images, image question answering , etc."
    ]
   },
   {
@@ -548,25 +553,23 @@
    "id": "8a634e11-3bd5-4048-b6e4-46d5aac6ce34",
    "metadata": {},
    "source": [
-    "![Gemini1](../../images/Gemini_1.png)"
+    "![Gemini1](../../images/VertexAIIntro01.png)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c3716d9d-49af-40c9-acce-757c53be9a12",
+   "id": "12c359d3-9d82-441f-aff8-da1353925210",
    "metadata": {},
    "source": [
-    "For this tutorial we will select Open on the **Prompt Design** option. We will upload the COVID image we downloaded before by clicking **INSERT MEDIA** and selecting our file. Then we will ask it a question, here we asked \"Describe treatments for the item in this image\"."
+    "When the free form opens, you have to select the gemini-1.0-pro-vision-001 model on the right side of the screen. We will upload the COVID image we downloaded before by clicking **Insert Media** and selecting our file. Then we will ask it a question, her we ask \"Describe treatment for the item in this image\"."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ec72f9cd-0d06-47e4-b67c-45039372d967",
-   "metadata": {
-    "tags": []
-   },
+   "id": "eebe2f4e-206d-41d8-9d85-25d9644b83fd",
+   "metadata": {},
    "source": [
-    "![Gemini3](../../images/Gemini_3.png)"
+    "![Gemini3](../../images/VertexAIIntro03.png)"
    ]
   },
   {
@@ -574,7 +577,7 @@
    "id": "4a88f06b-770a-43be-b875-b4054c3ccbf3",
    "metadata": {},
    "source": [
-    "To utilize Gemini Pro locate and click **Language** on the left side menu. You have the option to use a prompt or chat and if you would like to focus on text or code."
+    "To utilize Gemini Pro locate and click **Chat** on the left side menu. "
    ]
   },
   {
@@ -584,7 +587,7 @@
     "tags": []
    },
    "source": [
-    "![Gemini2](../../images/Gemini_2.png)"
+    "![Gemini4](../../images/VertexAIIntro04.png)"
    ]
   },
   {
@@ -592,7 +595,7 @@
    "id": "7810c0db-d145-4acd-a2cc-b6ce8231fa14",
    "metadata": {},
    "source": [
-    "Here we picked the **TEXT CHAT** option and asked the bot to describe covid and how it works."
+    "We ask the bot to describe covid and how it works."
    ]
   },
   {
@@ -602,13 +605,51 @@
     "tags": []
    },
    "source": [
-    "![Gemini4](../../images/Gemini_4.png)"
+    "![Gemini5](../../images/VertexAIIntro05.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0c5d0ad-2235-471e-98e6-8d5f295cc724",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Conclusion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d66220a-b17e-4b9d-8f85-f2c682d7a27f",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "You’ve learned how to interact with Gemini through code and the Vertex AI Studio interface. We covered how to generate text from images and videos using the Gemini-pro-vision model in a Python script. Additionally, we explored the FreeForm and Chat options in Vertex AI Studio to extract text from images and to engage in question-and-answer interactions within the Chat prompt."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a1fedcb-75d7-411f-9475-9ecb777c9dbb",
+   "metadata": {},
+   "source": [
+    "## Clean UP"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f30d1136-f67c-4bb1-8ed4-09b88ef67a1e",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "Please remember to delete or stop your Jupyter notebook and if you have created any other services like buckets, please delete them as well."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "acc53b99-e6ed-45f1-a0e1-9b146e18e46c",
+   "id": "9ee938bb-6dbb-4e01-88ae-e8511ada3ff7",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -617,12 +658,12 @@
  "metadata": {
   "environment": {
    "kernel": "python3",
-   "name": "common-cpu.m114",
+   "name": "common-cpu.m125",
    "type": "gcloud",
-   "uri": "gcr.io/deeplearning-platform-release/base-cpu:m114"
+   "uri": "us-docker.pkg.dev/deeplearning-platform-release/gcr.io/base-cpu:m125"
   },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (Local)",
    "language": "python",
    "name": "python3"
   },
@@ -636,7 +677,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.10.15"
   }
  },
  "nbformat": 4,

From 6019c771cddf04902b34c6a06d4b7e0fa39197dc Mon Sep 17 00:00:00 2001
From: Marco Cetraro <cetrarom2@nih.gov>
Date: Tue, 29 Oct 2024 19:03:42 +0000
Subject: [PATCH 09/12] update again3

---
 images/Grounding_new01.png                    | Bin 0 -> 91340 bytes
 images/Grounding_new02.png                    | Bin 0 -> 109396 bytes
 images/Grounding_new03.png                    | Bin 0 -> 114276 bytes
 .../GCP_Grounding-checkpoint.ipynb            |  20 +++++++++---------
 notebooks/GenAI/GCP_Grounding.ipynb           |  10 ++++-----
 5 files changed, 15 insertions(+), 15 deletions(-)
 create mode 100644 images/Grounding_new01.png
 create mode 100644 images/Grounding_new02.png
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diff --git a/images/Grounding_new01.png b/images/Grounding_new01.png
new file mode 100644
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diff --git a/notebooks/GenAI/.ipynb_checkpoints/GCP_Grounding-checkpoint.ipynb b/notebooks/GenAI/.ipynb_checkpoints/GCP_Grounding-checkpoint.ipynb
index 1bd18bc..4104ebb 100644
--- a/notebooks/GenAI/.ipynb_checkpoints/GCP_Grounding-checkpoint.ipynb
+++ b/notebooks/GenAI/.ipynb_checkpoints/GCP_Grounding-checkpoint.ipynb
@@ -125,7 +125,7 @@
     "tags": []
    },
    "source": [
-    "![grounding3](../../images/grounding_3.png)"
+    "![grounding3](../../images/Grounding_new01.png)"
    ]
   },
   {
@@ -175,7 +175,7 @@
    "id": "1df54267-b65c-44cd-a869-9723b7302771",
    "metadata": {},
    "source": [
-    "For this tutorial we'll select our data as unstructured but it also supports structured data in JSONL format and CSVs. Next choose your bucket and click **'CONTINUE'**."
+    "For this tutorial we'll select our data as unstructured but it also supports structured data in CSVs format. Next choose your bucket and click **'CONTINUE'**."
    ]
   },
   {
@@ -183,7 +183,7 @@
    "id": "74e88355-7146-44a5-b30d-9907676dbb02",
    "metadata": {},
    "source": [
-    "![grounding6](../../images/grounding_6.png)"
+    "![grounding6](../../images/Grounding_new02.png)"
    ]
   },
   {
@@ -255,7 +255,7 @@
    "id": "369c112d-2376-43fa-9552-9498479be9fd",
    "metadata": {},
    "source": [
-    "On the console go to Vertex AI and head down to Vertex AI Studio where the playground is. Select the **Language** playground. Grounding works on Text Prompt and Text Chat but for this tutorial we will use **Text Chat**."
+    "On the console go to Vertex AI and head down to Vertex AI Studio where the playground is. Select the **Chat** playground. "
    ]
   },
   {
@@ -263,7 +263,7 @@
    "id": "8c971978-4925-427c-900c-0e8a996c0c91",
    "metadata": {},
    "source": [
-    "![grounding11](../../images/grounding_11.png)"
+    "![grounding11](../../images/Grounding_new03.png)"
    ]
   },
   {
@@ -427,14 +427,14 @@
  "metadata": {
   "environment": {
    "kernel": "python3",
-   "name": "common-cpu.m114",
+   "name": "common-cpu.m125",
    "type": "gcloud",
-   "uri": "gcr.io/deeplearning-platform-release/base-cpu:m114"
+   "uri": "us-docker.pkg.dev/deeplearning-platform-release/gcr.io/base-cpu:m125"
   },
   "kernelspec": {
-   "display_name": "Python (Local)",
+   "display_name": "Python 3 (Local)",
    "language": "python",
-   "name": "base"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -446,7 +446,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.10.15"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/GenAI/GCP_Grounding.ipynb b/notebooks/GenAI/GCP_Grounding.ipynb
index 73fd265..4104ebb 100644
--- a/notebooks/GenAI/GCP_Grounding.ipynb
+++ b/notebooks/GenAI/GCP_Grounding.ipynb
@@ -125,7 +125,7 @@
     "tags": []
    },
    "source": [
-    "![grounding3](../../images/grounding_3.png)"
+    "![grounding3](../../images/Grounding_new01.png)"
    ]
   },
   {
@@ -175,7 +175,7 @@
    "id": "1df54267-b65c-44cd-a869-9723b7302771",
    "metadata": {},
    "source": [
-    "For this tutorial we'll select our data as unstructured but it also supports structured data in JSONL format and CSVs. Next choose your bucket and click **'CONTINUE'**."
+    "For this tutorial we'll select our data as unstructured but it also supports structured data in CSVs format. Next choose your bucket and click **'CONTINUE'**."
    ]
   },
   {
@@ -183,7 +183,7 @@
    "id": "74e88355-7146-44a5-b30d-9907676dbb02",
    "metadata": {},
    "source": [
-    "![grounding6](../../images/grounding_6.png)"
+    "![grounding6](../../images/Grounding_new02.png)"
    ]
   },
   {
@@ -255,7 +255,7 @@
    "id": "369c112d-2376-43fa-9552-9498479be9fd",
    "metadata": {},
    "source": [
-    "On the console go to Vertex AI and head down to Vertex AI Studio where the playground is. Select the **Language** playground. Grounding works on Text Prompt and Text Chat but for this tutorial we will use **Text Chat**."
+    "On the console go to Vertex AI and head down to Vertex AI Studio where the playground is. Select the **Chat** playground. "
    ]
   },
   {
@@ -263,7 +263,7 @@
    "id": "8c971978-4925-427c-900c-0e8a996c0c91",
    "metadata": {},
    "source": [
-    "![grounding11](../../images/grounding_11.png)"
+    "![grounding11](../../images/Grounding_new03.png)"
    ]
   },
   {

From 2b7e9119fd74cb69682cd05755b9bf549a045a06 Mon Sep 17 00:00:00 2001
From: github-action <cbiit-github-action@github.com>
Date: Fri, 10 Jan 2025 18:36:01 +0000
Subject: [PATCH 10/12] Github Action: Lint Notebooks

---
 .../gcp_rag_structure_data_01-checkpoint.png  | Bin 16270 -> 0 bytes
 .../GCP_Grounding-checkpoint.ipynb            | 454 ------------
 ...CP_RAG_for_Structure_Data-checkpoint.ipynb | 652 -----------------
 .../Gemini_Intro-checkpoint.ipynb             | 685 ------------------
 .../VertexAIStudioGCP-checkpoint.ipynb        | 172 -----
 notebooks/GenAI/GCP_Grounding.ipynb           |   1 -
 .../GenAI/GCP_RAG_for_Structure_Data.ipynb    |  54 +-
 notebooks/GenAI/Gemini_Intro.ipynb            |  12 +-
 8 files changed, 11 insertions(+), 2019 deletions(-)
 delete mode 100644 images/.ipynb_checkpoints/gcp_rag_structure_data_01-checkpoint.png
 delete mode 100644 notebooks/GenAI/.ipynb_checkpoints/GCP_Grounding-checkpoint.ipynb
 delete mode 100644 notebooks/GenAI/.ipynb_checkpoints/GCP_RAG_for_Structure_Data-checkpoint.ipynb
 delete mode 100644 notebooks/GenAI/.ipynb_checkpoints/Gemini_Intro-checkpoint.ipynb
 delete mode 100644 notebooks/GenAI/.ipynb_checkpoints/VertexAIStudioGCP-checkpoint.ipynb

diff --git a/images/.ipynb_checkpoints/gcp_rag_structure_data_01-checkpoint.png b/images/.ipynb_checkpoints/gcp_rag_structure_data_01-checkpoint.png
deleted file mode 100644
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diff --git a/notebooks/GenAI/.ipynb_checkpoints/GCP_Grounding-checkpoint.ipynb b/notebooks/GenAI/.ipynb_checkpoints/GCP_Grounding-checkpoint.ipynb
deleted file mode 100644
index 4104ebb..0000000
--- a/notebooks/GenAI/.ipynb_checkpoints/GCP_Grounding-checkpoint.ipynb
+++ /dev/null
@@ -1,454 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "9551cc2f-a5b5-4fc5-a864-2fc731f2c310",
-   "metadata": {},
-   "source": [
-    "# Google Cloud Grounding (RAG on the console)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5d9b6b51-5481-436c-8d0f-541c5dc9fee3",
-   "metadata": {},
-   "source": [
-    "## Overview"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fbb045c4-b33f-4499-8d3d-c8545206e3ac",
-   "metadata": {},
-   "source": [
-    "Google Cloud's [Grounding](https://cloud.google.com/vertex-ai/generative-ai/docs/grounding/overview) feature is designed to improve the accuracy and relevance of search results by understanding the context of your search query and the information presented on web pages or your own data via the console. This requires a search app and a data store which both will be created in another Google Cloud product called [Agent Builder\n",
-    "](https://cloud.google.com/products/agent-builder?hl=en)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4e863bd8-8bbe-4226-bd7d-298303efaa84",
-   "metadata": {},
-   "source": [
-    "## Learning objectives"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bee49850-f4be-4d69-84f7-1492d5e2d23d",
-   "metadata": {},
-   "source": [
-    "- Learn how to create a data source and search app in Agent Builder\n",
-    "- Learn how to implement the search app on the Vertex AI Studio playground"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "107349df-4541-4f70-b5cb-8ea50811e759",
-   "metadata": {},
-   "source": [
-    "## Prerequisites"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f7456c7a-a3c8-427e-acdc-5c446d05871d",
-   "metadata": {},
-   "source": [
-    "You must have enabled the Vertex AI, Compute Engine, and Agent Builder APIs."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8048ee93-3ee5-4d83-8039-685da38558f5",
-   "metadata": {},
-   "source": [
-    "## Get started"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ed6e13da-8475-4fb5-9090-e68638d761f5",
-   "metadata": {},
-   "source": [
-    "### Create a search app"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bfe28a30-ec48-4257-b342-1fc8c3954510",
-   "metadata": {},
-   "source": [
-    "In order to utilize grounding with your own data you first need to create a search app. Start by searching up **'Agent Builder'** on the console. Then once you are on the page shown below click **'CREATE APP'**."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9407c375-7b45-40b6-8504-3d6eced555da",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "![grounding1](../../images/grounding_1.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e6906e34-31a0-4e88-85e9-62abee5ed6cd",
-   "metadata": {},
-   "source": [
-    "Click **'SELECT'** for the app named **Search**."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "39beb2ac-c377-4201-9973-5fdb738159d8",
-   "metadata": {},
-   "source": [
-    "![grounding1](../../images/grounding_2.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "20abe50b-ebf4-4214-a494-b199ac7e56bb",
-   "metadata": {},
-   "source": [
-    "You have the option to tailor what kind of search you would like to use. For this tutorial we will be using **'Generic'**. The other two options include searching through media files and Healthcare files in FHIR format.\n",
-    "\n",
-    "Also make sure that you have enabled the **'Enterprise edition feature'** and **'Advance LLM feature'** to allow your app to be searchable via the grounding feature and if prompted set your location to **'global'**."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d2d4afa0-bdf7-4afc-8b74-d3e791bbaebd",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "![grounding3](../../images/Grounding_new01.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6b89e7d4-bbf8-4ce9-890e-4ff9f17b4610",
-   "metadata": {},
-   "source": [
-    "### Create a Data Store"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2322c0da-1554-47c9-9e27-2732beda35dd",
-   "metadata": {},
-   "source": [
-    "Next we'll create a data store that will allow us to connect a data source to our search app."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7895ddda-5775-43b6-a723-7088c9b69ecb",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "![grounding4](../../images/grounding_4.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ccd0d718-eb6f-43b3-afe1-ec56d8108ef3",
-   "metadata": {},
-   "source": [
-    "As you can see from the image below Google Cloud's Agent builder supports the following data sources but for this tutorial we will use **'Cloud Storage'** as our data source."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b39671ac-7729-4581-be26-bf9458898cef",
-   "metadata": {},
-   "source": [
-    "![grounding5](../../images/grounding_5.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1df54267-b65c-44cd-a869-9723b7302771",
-   "metadata": {},
-   "source": [
-    "For this tutorial we'll select our data as unstructured but it also supports structured data in CSVs format. Next choose your bucket and click **'CONTINUE'**."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "74e88355-7146-44a5-b30d-9907676dbb02",
-   "metadata": {},
-   "source": [
-    "![grounding6](../../images/Grounding_new02.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "332994e1-1bb8-476e-bbb0-28a1169e86b7",
-   "metadata": {},
-   "source": [
-    "Configure your data store by giving it a name and make sure you have set your location to **global**."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a3cd7fc3-01d4-4fa0-bde3-801ac4357545",
-   "metadata": {},
-   "source": [
-    "![grounding7](../../images/grounding_7.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ebeba3cc-72dd-4758-8584-96214f9ade6a",
-   "metadata": {},
-   "source": [
-    "### Sync your data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "51310b87-7a49-4211-bd91-fe67c45274f4",
-   "metadata": {},
-   "source": [
-    "Select your data store or multiple data stores and click create to sync them to your search app. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c0f2e40e-320a-4fd9-8795-d273f00557d3",
-   "metadata": {},
-   "source": [
-    "![grounding8](../../images/grounding_8.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d4002baa-ae55-492f-b9d8-e9729a872db8",
-   "metadata": {},
-   "source": [
-    "Depending on the size of your data it can take roughly ~30min to sync the date to your app but once its ready you should see a green check mark by the date of the last document import date similar to the image below. Make sure you **copy the data store ID** so we can use it later."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ef5326da-e8bb-4e7e-bd15-12dc089c680e",
-   "metadata": {},
-   "source": [
-    "![grounding10](../../images/grounding_10.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a7668425-14f8-42ce-a115-e5cba868e1c1",
-   "metadata": {},
-   "source": [
-    "### Set up grounding on Vertex AI Studio"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "369c112d-2376-43fa-9552-9498479be9fd",
-   "metadata": {},
-   "source": [
-    "On the console go to Vertex AI and head down to Vertex AI Studio where the playground is. Select the **Chat** playground. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8c971978-4925-427c-900c-0e8a996c0c91",
-   "metadata": {},
-   "source": [
-    "![grounding11](../../images/Grounding_new03.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5566af32-8ae4-42e2-8a03-cb2af69a6e60",
-   "metadata": {},
-   "source": [
-    "On the right side menu select **'Enable Grounding'** then click **'CUSTOMIZE'**."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a6d893bd-4377-491d-ab34-760b6128a876",
-   "metadata": {},
-   "source": [
-    "![grounding11](../../images/grounding_12.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "858329e8-aa1c-4472-8683-b521ab837a82",
-   "metadata": {},
-   "source": [
-    "Enter in you project ID and data store ID below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f7107b36-061c-4073-8e47-3a399273577a",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "PROJECT_ID = '<PROJECT_ID>'\n",
-    "DATA_STORE_ID = '<DATA_STORE_ID>'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cff20cf3-86b2-4fdb-9b95-c5e645126f40",
-   "metadata": {},
-   "source": [
-    "Run the print command below to copy the path to your data store and enter it as the **Vertex AI datastore path**. Make sure your **grounding source is Vertex AI search** and click **'SAVE'**.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "04dac266-90ec-49e3-aa7c-9fd63d25a89b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "print(f'projects/{PROJECT_ID}/locations/global/collections/default_collection/dataStores/{DATA_STORE_ID}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "08f1ccf8-1d00-45a7-89ae-4509a31e5ad2",
-   "metadata": {},
-   "source": [
-    "![grounding11](../../images/grounding_13.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "87cdcc8a-6372-44fe-9f21-2c0e2428b045",
-   "metadata": {},
-   "source": [
-    "Now we can ask questions about our data and the model will use our data store as the only source of information!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a5bb464b-0c17-4d61-83cd-56e2732bb06c",
-   "metadata": {},
-   "source": [
-    "![grounding14](../../images/grounding_14.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1f995383-6c04-4e14-b063-41c8adf8e763",
-   "metadata": {},
-   "source": [
-    "### Grounding with Google search"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0c8ab374-87a3-4c08-8bb7-1e181521a048",
-   "metadata": {},
-   "source": [
-    "Grounding with Google allows you to use the Google website as your data source for your model to refer to. In order to enable it you dont need a search app you simply go back to **'CUSTOMIZE'** on the grounding option then select **Google search as the grounding source** and click **'SAVE'**."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "77f0da56-5ff2-4223-9ebb-15fca421461d",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "![grounding15](../../images/grounding_15.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d9dc3253-26dc-4770-bc90-cb387f365dfe",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "Then ask the model a question like you did before. The image below shows the grounding response using your own data at the top and Google searches response at the bottom."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6022faad-42c0-4934-9852-7856f32ee743",
-   "metadata": {},
-   "source": [
-    "![grounding16](../../images/grounding_16.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c0ed4fd4-0bab-40f0-9888-c699d368cec4",
-   "metadata": {},
-   "source": [
-    "## Conclusion"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "559696dc-7c26-40b1-a70a-26f9707feb06",
-   "metadata": {},
-   "source": [
-    "You have learned how to set up RAG with your own data and Google search on the console by creating a data store and a search app and enable grounding in the Vertex AI Studio playground."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fd10f300-1348-4e73-924f-ae74fff5279f",
-   "metadata": {},
-   "source": [
-    "## Clean up"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7eab87e5-6564-4040-96e7-ff858d950183",
-   "metadata": {},
-   "source": [
-    "Please remember to delete or stop your **Jupyter notebook** and delete your **data store** and **search app** on Agent Builder to prevent incurring charges. And if you have created any other services like buckets, please remember to delete them as well."
-   ]
-  }
- ],
- "metadata": {
-  "environment": {
-   "kernel": "python3",
-   "name": "common-cpu.m125",
-   "type": "gcloud",
-   "uri": "us-docker.pkg.dev/deeplearning-platform-release/gcr.io/base-cpu:m125"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (Local)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.15"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/GenAI/.ipynb_checkpoints/GCP_RAG_for_Structure_Data-checkpoint.ipynb b/notebooks/GenAI/.ipynb_checkpoints/GCP_RAG_for_Structure_Data-checkpoint.ipynb
deleted file mode 100644
index 212cfb0..0000000
--- a/notebooks/GenAI/.ipynb_checkpoints/GCP_RAG_for_Structure_Data-checkpoint.ipynb
+++ /dev/null
@@ -1,652 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "6e38b3f2-2b29-4788-a29a-f9e5d10f8365",
-   "metadata": {},
-   "source": [
-    "##### __Skill Level:__ <span style=\"color:blue\">Intermediate</span>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6c0533e3-4fbd-47b1-8458-fd193bcb263f",
-   "metadata": {},
-   "source": [
-    "# Creating a Chatbot for Structure Data using a RAG"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "76cf4b02-6422-4a48-882c-c00315e34c8a",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Overview"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7b7870dd-09e0-4f6a-91a9-7c30529af720",
-   "metadata": {},
-   "source": [
-    "Generative AI (GenAI) represents a transformative technology capable of producing human-like text, images, code, and various other types of content. While much of the focus has been on unstructured data—such as PDFs, text documents, image files, and websites—many GenAI implementations rely on a parameter called \"top K.\" This algorithm retrieves only the highest-scoring pieces of content relevant to a user's query, which can be limiting. Users seeking insights from structured data formats like CSV and JSON often require access to all relevant occurrences, rather than just a subset.\n",
-    "\n",
-    "In this tutorial, we use a RAG agent together with a VertexAI chat model gemini-1.5-pro to query the BigQuery table. A Retrieval Augmented Generation (RAG) agent is a key part of a RAG application that enhances the capabilities of the large language models (LLMs) by integrating external data retrieval. AI agents empower LLMs to interact with the world through actions and tools. The agent connects to BigQuery, runs the query, and then sends the results to the model. The model processes the data and generates the final output that it is displayed for the user.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d513a9fe-1ac4-4610-b53c-6696e461f828",
-   "metadata": {},
-   "source": [
-    "## Prerequisites"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fbdb09aa-73d6-4cee-b3e7-5d6b243e3d58",
-   "metadata": {},
-   "source": [
-    "We assume you have access to Vertex AI and have enabled the necessary APIs.\n",
-    "\n",
-    "In this tutorial, we will be using Google Gemini Pro 1.5, which does not require deployment. However, if you prefer to use a different model, you can select one from the Model Garden via the console. This will allow you to add the model to your registry, create an endpoint (or utilize an existing one), and deploy the model—all in a single step. Here is a link for more information: [model deployment](https://cloud.google.com/vertex-ai/docs/general/deployment).\n",
-    "\n",
-    "Before we begin, you'll need to create a Vertex AI RAG Data Service Agent service account. To do this, go to the IAM section of the console. Ensure you check the box for \"Include Google-provided role grant.\" If the role is not listed, click \"Grant Access\" and add \"Vertex AI RAG Data Service Agent\" as a role. Additionally, to create datasets and tables in BigQuery, the user must have the __bigquery.datasets.create__ and __bigquery.tables.create__ permissions.\n",
-    "\n",
-    "We are using a e2-standard-4 (Efficient Instance: 4 vCPUs, 16 GB RAM) for this tutorial. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eeb24716-0db8-462f-8faa-0b8ad9d61feb",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Learning objectives"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f5216a43-31ee-4c3e-821a-4b81f4be8e54",
-   "metadata": {},
-   "source": [
-    "In this tutorial you will learn about:\n",
-    "- How to set up a BigQuery dataset and a table.\n",
-    "- How to load data to a BigQuery table.\n",
-    "- How to use the langchain ChatVertexAI agent to extract information from the table. \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "348910aa-9802-4318-8fcb-86603158923b",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Pricing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0dca31b2-880d-4cbc-bf76-ede96998e371",
-   "metadata": {},
-   "source": [
-    "If you are following this tutorial in one sitting it will cost $23.89 per month. Completing the process in multiple sessions or using a method different from the tutorial may result in increased costs."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c7d9e3ee-1c7e-4baa-a818-c13330c665dd",
-   "metadata": {},
-   "source": [
-    "## Get Start"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "137defcd-fd2b-421d-9b2e-fe2b878fb325",
-   "metadata": {},
-   "source": [
-    "### Install Packages"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "55c14a38-ba51-466d-90b5-1512f5a7fae0",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "!pip install google-cloud-bigquery\n",
-    "!pip install gcloud\n",
-    "!pip install langchain\n",
-    "!pip install -U langchain-community\n",
-    "!pip install -qU langchain_google_vertexai\n",
-    "!pip install pandas-gbq\n",
-    "!pip install sqlalchemy-bigquery"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "64e22773-9a03-47de-9b6d-7946abcba722",
-   "metadata": {},
-   "source": [
-    "Set your project id, location, and bucket variables."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7193a9ac-3169-45c1-9c5d-ba5e2f31458f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "project_id='<PROJECT_ID>'\n",
-    "location='<LOCATION> (e.g.us-east4)'\n",
-    "bucket = '<BUCKET_NAME>'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "df0d9eda-ee54-4c4c-86e6-9166b2b82e2d",
-   "metadata": {},
-   "source": [
-    "Create a bucket where the data used for tutorial is going to be placed. It is important to keep in mind that the name of the bucket has to be unique."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7b583234-2d92-4e5b-841d-c7d79dd87d45",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# make bucket\n",
-    "!gsutil mb -l {location} gs://{bucket}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5781a07c-84ed-416c-a782-b1dc881e5c18",
-   "metadata": {},
-   "source": [
-    "Once the bucket is created, we need to access the CSV source file. In this tutorial, I transferred the data file to our Jupyter notebook by simply dragging and dropping it from my local folder. Next, we need to specify the bucket name and the path of the data source in order to upload the CSV file to the bucket. \n",
-    "\n",
-    "We are using the [health screening](https://www.kaggle.com/datasets/drateendrajha/health-screening-data) dataset from kaggle for this tutorial."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4829094e-61a6-4dd7-854f-6b21dc19b1b7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!gsutil cp '<Path to your csv>' gs://{bucket}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7a00dc83-8713-4ea5-9345-08a9f3d785d0",
-   "metadata": {},
-   "source": [
-    "The next step is to create a database in BigQuery. To accomplish this, we need to define a dataset_id and construct a dataset object that will be sent to the API for creation. Note that in BigQuery, a dataset is analogous to a database."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1750e13d-ec05-4335-82a2-ffe54c5b5f32",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from google.cloud import bigquery\n",
-    "\n",
-    "client = bigquery.Client()\n",
-    "\n",
-    "dataset_name = '<Name of the Dataset>'\n",
-    "# Set dataset_id to the ID of the dataset to create.\n",
-    "dataset_id = f'{project_id}.{dataset_name}'\n",
-    "\n",
-    "# Construct a full Dataset object to send to the API.\n",
-    "dataset = bigquery.Dataset(dataset_id)\n",
-    "\n",
-    "# Specify the geographic location where the dataset should reside.\n",
-    "dataset.location = location\n",
-    "\n",
-    "# Send the dataset to the API for creation, with an explicit timeout.\n",
-    "# Raises google.api_core.exceptions.Conflict if the Dataset already\n",
-    "# exists within the project.\n",
-    "dataset = client.create_dataset(dataset, timeout=30)  # Make an API request.\n",
-    "print(\"Created dataset {}.{}\".format(client.project, dataset.dataset_id))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4e18540d-defa-414b-8b78-b86bce3dd46a",
-   "metadata": {},
-   "source": [
-    "The following code will auto detect the table schema and upload the data to the table in one step."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "671aa2a3-d259-42e3-8cc3-8ab6b1e4a7d1",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "table_id = f'{dataset_id}.{table_name}'\n",
-    "\n",
-    "job_config = bigquery.LoadJobConfig(\n",
-    "    autodetect=True, source_format=bigquery.SourceFormat.CSV, skip_leading_rows=1,\n",
-    ")\n",
-    "uri = f\"gs://{bucket}/ds_salaries.csv\"\n",
-    "load_job = client.load_table_from_uri(\n",
-    "    uri, table_id, job_config=job_config\n",
-    ")  # Make an API request.\n",
-    "load_job.result()  # Waits for the job to complete.\n",
-    "destination_table = client.get_table(table_id)\n",
-    "print(\"Loaded {} rows.\".format(destination_table.num_rows))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "544c191c-2bc0-4a47-a0a7-cea81035597a",
-   "metadata": {},
-   "source": [
-    "Alternatively, if we choose to define the table schema and create the dataset table beforehand, we can use the following code. Afterward, we can upload the CSV dataset into a pandas DataFrame and then insert it into the newly created BigQuery table."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "68206b14-1b5e-49c3-9f07-52ba6c70a93e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set table_id to the ID of the table to create.\n",
-    "\n",
-    "client = bigquery.Client()\n",
-    "\n",
-    "table_name = \"<Table Name>\"\n",
-    "table_id = f'{project_id}.{dataset_name}.{table_name}'\n",
-    "\n",
-    "schema = [\n",
-    "    bigquery.SchemaField(\"id\", \"INTEGER\", mode=\"NULLABLE\"),\n",
-    "    bigquery.SchemaField(\"age\", \"INTEGER\", mode=\"NULLABLE\"),    \n",
-    "    bigquery.SchemaField(\"gender\", \"INTEGER\", mode=\"NULLABLE\"),\n",
-    "    bigquery.SchemaField(\"height\", \"INTEGER\", mode=\"NULLABLE\"),\n",
-    "    bigquery.SchemaField(\"weight\", \"FLOAT\", mode=\"NULLABLE\"),\n",
-    "    bigquery.SchemaField(\"ap_hi\", \"INTEGER\", mode=\"NULLABLE\"),\n",
-    "    bigquery.SchemaField(\"ap_lo\", \"INTEGER\", mode=\"NULLABLE\"),\n",
-    "    bigquery.SchemaField(\"cholesterol\", \"INTEGER\", mode=\"NULLABLE\"),\n",
-    "    bigquery.SchemaField(\"gluc\", \"INTEGER\", mode=\"NULLABLE\"),\n",
-    "    bigquery.SchemaField(\"smoke\", \"INTEGER\", mode=\"NULLABLE\"),\n",
-    "    bigquery.SchemaField(\"alco\", \"INTEGER\", mode=\"NULLABLE\"),\n",
-    "    bigquery.SchemaField(\"active\", \"INTEGER\", mode=\"NULLABLE\"),\n",
-    "    bigquery.SchemaField(\"cardio\", \"INTEGER\", mode=\"NULLABLE\"),\n",
-    "    bigquery.SchemaField(\"ageinyr\", \"INTEGER\", mode=\"NULLABLE\"),\n",
-    "    bigquery.SchemaField(\"bmi\", \"FLOAT\", mode=\"NULLABLE\"),\n",
-    "    bigquery.SchemaField(\"bmicat\", \"STRING\", mode=\"NULLABLE\"),\n",
-    "    bigquery.SchemaField(\"agegroup\", \"STRING\", mode=\"NULLABLE\"),\n",
-    "\n",
-    "]\n",
-    "\n",
-    "table = bigquery.Table(table_id, schema=schema)\n",
-    "table = client.create_table(table)  # Make an API request.\n",
-    "print(\n",
-    "    \"Created table {}.{}.{}\".format(table.project, table.dataset_id, table.table_id)\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0c87a0f1-de43-4cce-a5e2-8952ce2283a0",
-   "metadata": {},
-   "source": [
-    "We load our dataset located in our bucket to a pandas dataframe."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2486d4e3-b462-42de-85ab-3c6d5a8ba363",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from google.cloud import storage\n",
-    "import pandas as pd\n",
-    "from io import StringIO\n",
-    "\n",
-    "path = \"<gsutil URI>\"\n",
-    "df = pd.read_csv(path)\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2dad23f6-cc3f-4bc6-b5ff-a66d17740a65",
-   "metadata": {},
-   "source": [
-    "We need to use the \"to_gbq\" function from the pandas-gbq library, which allows us to write a Pandas DataFrame to a Google BigQuery table. This enables us to populate our BigQuery table with the data from the DataFrame."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7cfd1d66-a0fb-403d-a9b1-27250cc81f91",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.to_gbq(table_id, project_id=project_id)\n",
-    "\n",
-    "client.load_table_from_dataframe(df, table_id).result()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6e97b4c6-429c-4d36-a4c1-3963b079af6a",
-   "metadata": {},
-   "source": [
-    "This step is optional. We can make an API request to verify whether the dataset has been successfully created under our project."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "72f11cfd-8b20-48bf-b157-aaf791cd3a9f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "datasets = list(client.list_datasets())  # Make an API request.\n",
-    "project = project_id\n",
-    "\n",
-    "if datasets:\n",
-    "    print(\"Datasets in project {}:\".format(project))\n",
-    "    for dataset in datasets:\n",
-    "        print(\"\\t{}\".format(dataset.dataset_id))\n",
-    "else:\n",
-    "    print(\"{} project does not contain any datasets.\".format(project))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4b16a292-afdc-415c-a8a7-071bc90281c3",
-   "metadata": {},
-   "source": [
-    "![image.png](../../images/gcp_rag_structure_data_01.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "22b4dd65-9bd9-48ef-9c98-85e3e9a82701",
-   "metadata": {},
-   "source": [
-    "This step is also optional. We can execute a simple query on the BigQuery table to count the number of records."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "29e91fc1-735f-4d6c-9fcf-3a2d745363c1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "query = \"\"\"\n",
-    "    SELECT count(gender) as cgender\n",
-    "    FROM `project_id.dataset_id.table_id`\n",
-    "\"\"\"\n",
-    "\n",
-    "# Execute the query\n",
-    "query_job = client.query(query)\n",
-    "\n",
-    "# Fetch the results\n",
-    "results = query_job.result()\n",
-    "\n",
-    "# Iterate through the rows\n",
-    "for row in results:\n",
-    "    #print(f\"gender: {row.name}, age: {row.age}\")\n",
-    "    print(f\"cgender: {row.cgender}\")\n",
-    "\n",
-    "try:\n",
-    "    query_job = client.query(query)\n",
-    "    results = query_job.result()\n",
-    "    print(results)\n",
-    "except Exception as e:\n",
-    "    print(f\"Error executing query: {str(e)}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ce65ead3-694d-4d46-9376-38d35b8d0852",
-   "metadata": {},
-   "source": [
-    "cgender: 139920\n",
-    "<google.cloud.bigquery.table.RowIterator object at 0x7f226b88ab90>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b6fb53b8-9f93-4f37-8730-33c5871720fc",
-   "metadata": {},
-   "source": [
-    "To interact with the BigQuery table using a Pythonic domain language, we utilize SQLAlchemy. SQLAlchemy is a Python SQL toolkit that enables developers to access and manage SQL databases, allowing users to write queries as strings or chain Python objects for similar queries. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d1d29b90-7bcd-46b8-82b9-dc5e58f9edee",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from google.cloud import bigquery\n",
-    "from sqlalchemy import *\n",
-    "from sqlalchemy.engine import create_engine\n",
-    "from sqlalchemy.schema import *\n",
-    "import os\n",
-    "from langchain_community.agent_toolkits import create_sql_agent\n",
-    "from langchain.agents import create_sql_agent\n",
-    "from langchain.agents.agent_toolkits import SQLDatabaseToolkit\n",
-    "from langchain.sql_database import SQLDatabase\n",
-    "\n",
-    "sqlalchemy_url = f'bigquery://{project_id}/{dataset_name}'\n",
-    "print(sqlalchemy_url)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9194cc1d-7ef1-4c4e-8e65-e7d9ada5be24",
-   "metadata": {},
-   "source": [
-    "Next, we import the __ChatVertexAI__ agent from Langchain and configure it with the appropriate hyperparameters. In this instance, we are using the __gemini-1.5-pro__ LLM model, after which we create the SQL agent to enable querying the BigQuery table using a natural language string as a prompt. Temperature regulates randomness, with higher temperatures resulting in more varied and unpredictable outputs. Top-k sampling selects from the k most probable next tokens at each step, where a lower k emphasizes higher-probability tokens. The max tokens hyperparameter specifies the maximum number of tokens in the response from the large language model. Max retries indicates how many responses we will receive from the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ee1899a2-dd79-4cf4-a02d-c7c35b99040b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_google_vertexai import VertexAI, ChatVertexAI\n",
-    "\n",
-    "llm = ChatVertexAI(\n",
-    "    model=\"gemini-1.5-pro\",\n",
-    "    temperature=0,\n",
-    "    max_tokens=8190,\n",
-    "    timeout=None,\n",
-    "    max_retries=2,\n",
-    ")\n",
-    "db = SQLDatabase.from_uri(sqlalchemy_url)\n",
-    "toolkit = SQLDatabaseToolkit(db=db, llm=llm)\n",
-    "agent_executor = create_sql_agent(\n",
-    "    llm=llm,\n",
-    "    toolkit=toolkit,\n",
-    "    verbose=True,\n",
-    "    top_k=100000\n",
-    "    )\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f3551426-8644-48ba-b8c1-bf7d5eb843b3",
-   "metadata": {},
-   "source": [
-    "For our initial query, we check the number of rows to compare the result with what we obtained in a previous step using a simple SQL query against the BigQuery table. We can confirm that we received the same number of rows: 139,920."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e9753533-e82b-47af-936a-a401d512308a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "agent_executor.run(\"How many rows are in the table screening? \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "099154a1-34e2-4f6b-b146-cec6f5943cf7",
-   "metadata": {},
-   "source": [
-    "> Finished chain.\n",
-    "'The table screening has 139920 rows.'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f2b62012-b2d2-48c1-ae69-6f8816065551",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "Next, we pose a question that requires applying a filter, and we successfully obtain the accurate number of obese individuals in the table."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5563d317-81ab-4a37-8225-e1b0dce20b36",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "agent_executor.run(\"How many obese are in the table screen?\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "40e1846d-1114-4ba1-b35a-65b47a838468",
-   "metadata": {},
-   "source": [
-    "> Finished chain.\n",
-    "'There are 37204 obese people in the table.'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b96ea414-3f2d-4e66-ab0b-a034dfe507b0",
-   "metadata": {},
-   "source": [
-    "As a more complex query, we inquired about the number of female smokers, and the SQL agent accurately returned the answer of 1,626 female smokers."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a456ff90-a3a5-4ee4-81da-fba4fcdf7f2d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "agent_executor.run(\"How many smokers are female in the table screen? the value 1 in smoke means that is a smoker and the value 2 in gender means that is a female\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7ea7810f-9701-4cb0-9430-181a57929884",
-   "metadata": {},
-   "source": [
-    "> Finished chain.\n",
-    "'There are 1626 female smokers in the table.'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ca0fb657-19e0-4488-b9f6-25ca3971064f",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Conclusion"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7f85e560-231f-4841-b1d0-334326e4b347",
-   "metadata": {},
-   "source": [
-    "You have learned how to create a dataset and a table in BigQuery, as well as how to set up an agent that enables you to query the table using natural language instead of SQL queries, allowing you to obtain the results you need."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "41573388-7feb-44b6-9e8a-0511cc8ae62e",
-   "metadata": {},
-   "source": [
-    "## Clean Up"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1f0edc0b-f2cc-478c-855f-9acff3792f4d",
-   "metadata": {},
-   "source": [
-    "Please remember to delete or stop your Jupyter notebook and delete your BigQuery dataset and table to prevent incurring charges. And if you have created any other services like buckets, please remember to delete them as well."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fe73dde2-056a-46a3-bacb-0a6671f7128f",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "environment": {
-   "kernel": "python3",
-   "name": "common-cpu.m125",
-   "type": "gcloud",
-   "uri": "us-docker.pkg.dev/deeplearning-platform-release/gcr.io/base-cpu:m125"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (Local)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.15"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/GenAI/.ipynb_checkpoints/Gemini_Intro-checkpoint.ipynb b/notebooks/GenAI/.ipynb_checkpoints/Gemini_Intro-checkpoint.ipynb
deleted file mode 100644
index ff38fbf..0000000
--- a/notebooks/GenAI/.ipynb_checkpoints/Gemini_Intro-checkpoint.ipynb
+++ /dev/null
@@ -1,685 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "c51ed24c-53dd-4239-8be0-0e0422596ba3",
-   "metadata": {},
-   "source": [
-    "# Intro to GCP's Gemini "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7fbc1ea5",
-   "metadata": {},
-   "source": [
-    "## Overview"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "22cc412f-5338-45ba-8b29-9039c19208d9",
-   "metadata": {},
-   "source": [
-    "**Gemini** is a Google multimodal model that has the capability to **summarize, chat, and generate text from images or videos**. Gemini comes in two model versions **Gemini Pro** and **Gemini Pro Vision**, for this tutorial we will be looking into utilizing both models via python packages and GCPs model playground, **Vertex AI Studio**."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3eb23dec",
-   "metadata": {},
-   "source": [
-    "## Learning Objectives\n",
-    "+ Learn how to interact with Gemini as a chatbot from a Jupyter notebook"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4546c201",
-   "metadata": {},
-   "source": [
-    "## Prerequisites\n",
-    "+ You need access to Vertex AI"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a4a9c5d3",
-   "metadata": {},
-   "source": [
-    "## Install Packages"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b8ec6d40-b5b3-434f-adc4-2838b7f49d1d",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "Update the google-cloud-aiplatform package"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ed9781dd-9764-4e9c-88ba-fcd7bb95842a",
-   "metadata": {
-    "scrolled": true,
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "! pip install --upgrade google-cloud-aiplatform  langchain langchain-community"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f7bbbed7-becf-48d0-98c3-6dd9942fd377",
-   "metadata": {},
-   "source": [
-    "Next we initialize the Gemini model by setting out project id and location. We are also pulling in the packages:\n",
-    "- **GenerativeModel:** Allows us to specify and launch the Gemini model we need (e.g. Gemini Pro, Gemini Pro Vision).\n",
-    "- **ChatSession:** Set Gemini Pro in chatbot mode.\n",
-    "- **Part:** Loads in files from buckets.\n",
-    "- **Image:** Loads in image files locally.\n",
-    "- **GenerationConfig:** Allows us to configure the models temperature, top p, top k, and max tokens."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "47dc9232-383f-405b-b1a8-fab64a80492d",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from google.cloud import aiplatform\n",
-    "import vertexai\n",
-    "from vertexai.generative_models import GenerativeModel, Image, GenerativeModel, ChatSession, Part, GenerationConfig\n",
-    "\n",
-    "# TODO(developer): Update and un-comment below lines\n",
-    "project_id = \"<PROJECT_ID>\"\n",
-    "location = \"<LOCATION>\" #(e.g., us-central1)\n",
-    "vertexai.init(project=project_id, location=location)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1453b716",
-   "metadata": {},
-   "source": [
-    "## Get Started"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "022f24ba-4034-4424-91d8-1229682755ab",
-   "metadata": {},
-   "source": [
-    "### Gemini as a Chatbot"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b7dec028-6d30-4078-ac54-62b849ae9ced",
-   "metadata": {},
-   "source": [
-    "For dealing with text, code generation, natural language tasks we can use the **gemini-pro** model and to set our model in **chatbot mode** we need to use the `start_chat()` function. You will see below we also created a function named **get_chat_response** which will send the prompt or message we have for our model using the `send_message()` function and returns only the text of the chats response."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "70bc5b25-c796-4015-82dc-6bc861bb525f",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "model = GenerativeModel(\"gemini-pro\")\n",
-    "chat = model.start_chat()\n",
-    "\n",
-    "def get_chat_response(chat: ChatSession, prompt: str):\n",
-    "    response = chat.send_message(prompt)\n",
-    "    return response.text"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ae40e87a-76ac-469b-a504-fb903319cbaf",
-   "metadata": {},
-   "source": [
-    "Now that we have our functions lets ask our Gemini chatbot some questions!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9d3277de-ab85-417f-b1d8-b21985a7a21b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "prompt = \"Hello.\"\n",
-    "print(get_chat_response(chat, prompt))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "342a0e3d-fbcb-4562-bb5f-b439a92e80e2",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "prompt = \"List gen ai use cases that are Life Science or Health Care related. \"\n",
-    "print(get_chat_response(chat, prompt))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fcfda59a-c440-4489-8f00-a4316b827292",
-   "metadata": {},
-   "source": [
-    "We can even ask it to **generate code or debug code**!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f0b917b2-22b5-4011-a9c4-d8a667cf6b1d",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "prompt = \"create a python code that will replace all null values to zero within a csv file\"\n",
-    "print(get_chat_response(chat, prompt))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3a9d57d2-dbe1-434a-9345-fe5ae3315a21",
-   "metadata": {},
-   "source": [
-    "### Gemini as a Summarizer"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4856bd61-3e24-40ea-8da4-1a6edc5f3e1d",
-   "metadata": {},
-   "source": [
-    "We can generate text like asking Gemini Pro to summarize articles we provide locally (using langchain). As of now Gemini does not support loading in documents that are not videos and images directly. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b5d7df19-a625-40dc-b4e5-faff5e7ba241",
-   "metadata": {},
-   "source": [
-    "First we will load in a file using langchains text loader. You can also use langchain to load in files from your bucket following the instructions [here](https://python.langchain.com/docs/integrations/document_loaders/google_cloud_storage_file)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ee4cf184-c815-425c-9742-7625123e02bf",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "#download the article\n",
-    "!wget --user-agent \"Chrome\" https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10954554/pdf/41586_2024_Article_7159.pdf"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3becd6c2-daf0-4287-80e5-06cf419287bd",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain_community.document_loaders import PyPDFLoader\n",
-    "\n",
-    "loader = PyPDFLoader(\"41586_2024_Article_7159.pdf\")\n",
-    "ex_file=loader.load()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4e3d6c19-7d68-49e1-85e9-91bcd6bd1775",
-   "metadata": {},
-   "source": [
-    "We can configure our model to give us the best optimal output by setting the parameters below:\n",
-    "- **Max_Output_Token**: Max number of words to generate.\n",
-    "- **Temperature:** Controls randomness, higher values increase diversity meaning a more unique response make the model to think harder. Must be a number from 0 to 1.\n",
-    "- **Top_p (nucleus):** The cumulative probability cutoff for token selection. Lower values mean sampling from a smaller, more top-weighted nucleus. Must be a number from 0 to 1.\n",
-    "- **Top_k:** Sample from the k most likely next tokens at each step. Lower k focuses on higher probability tokens. This means the model choses the most probable words. Lower values eliminate fewer coherent words.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c4228e44-9639-40da-8f69-343be93b65b0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "generation_config = GenerationConfig(\n",
-    "    temperature=0.9,\n",
-    "    top_p=1.0,\n",
-    "    top_k=32,\n",
-    "    candidate_count=1,\n",
-    "    max_output_tokens=8192,\n",
-    ")\n",
-    "\n",
-    "def summarizer(file: str) -> str:\n",
-    "        \n",
-    "    # Query the model\n",
-    "    response = model.generate_content(\n",
-    "        [\n",
-    "            # Add an example query\n",
-    "            \"summarize this file.\",\n",
-    "            file\n",
-    "        ],\n",
-    "        generation_config=generation_config,\n",
-    "    )\n",
-    "    #print(response)\n",
-    "    return response.text"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0bb722d6-ef79-4a0b-9327-04811e7f8ffc",
-   "metadata": {},
-   "source": [
-    "Here we are inputting only the page content from our document loader."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "97e7ea82-c58e-42ee-b01e-6aa51e324b05",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(summarizer(ex_file[0].page_content))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "77c10bd8-1570-4dfa-9b2a-999e3f149faf",
-   "metadata": {},
-   "source": [
-    "### Gemini as a Image to Text Generator"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "476052b3-ff64-4e5a-819c-7a18daf7f413",
-   "metadata": {},
-   "source": [
-    "Gemini Pro Vision can generate text from images and videos. These text can be descriptions or questions about the image or video. You can download an image or retrieve an image from your bucket or locally."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "214ee84f-f058-411a-944d-4e149cd0e9bc",
-   "metadata": {},
-   "source": [
-    "Images can only be in the following formats: \n",
-    "- PNG - image/png\n",
-    "- JPEG - image/jpeg\n",
-    "\n",
-    "Our function below takes in a prompt and the image, we have also included a if statement to recognize if the function should use `Image` to load in a image locally or `Part` to load it from a bucket."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "501efefe-d52f-43b3-b4eb-3d3fe81f4a3e",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "def img2text(image_path: str, img_prompt: str) -> str:\n",
-    "    multimodal_model = GenerativeModel(\"gemini-pro-vision\")\n",
-    "    if \"gs://\" in image_path:\n",
-    "        image1=Part.from_uri(image_path, mime_type=\"image/jpeg\")\n",
-    "    else: \n",
-    "        image1=Image.load_from_file(image_path)\n",
-    "        \n",
-    "    responses = multimodal_model.generate_content(\n",
-    "        [image1, img_prompt],\n",
-    "        generation_config={\n",
-    "            \"max_output_tokens\": 2048,\n",
-    "            \"temperature\": 0.4,\n",
-    "            \"top_p\": 1,\n",
-    "            \"top_k\": 32\n",
-    "        },\n",
-    "        stream=True,\n",
-    "    )\n",
-    "    for response in responses:\n",
-    "        print(response.text, end=\"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4cafaaa8-f3c7-472a-8f0b-6595d2112636",
-   "metadata": {},
-   "source": [
-    "Lets look at an image locally, by loading a image first, this a image of a Covid virus from the [CDC Public Health Image Library](https://phil.cdc.gov/details.aspx?pid=23312)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b939f105-89c2-4c38-80f8-2cddf8dcb0ca",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "! wget -O example_image_covid.jpg \"https://phil.cdc.gov//PHIL_Images/23312/23312_lores.jpg\" "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "696d9fc7-0ca9-44a7-b431-a9698b1a636c",
-   "metadata": {},
-   "source": [
-    "Now run our function!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "34e81656-4943-439d-9fbe-df439e0e30df",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "print(img2text(\"example_image_covid.jpg\", \"describe this image.\"))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dc152659-7bc4-46bc-9ead-4736b4ad2706",
-   "metadata": {},
-   "source": [
-    "Next we'll look at an image from a bucket."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "81197d53-dd3d-4358-9835-ef513ec11d33",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "print(img2text(\"gs://generativeai-downloads/images/scones.jpg\", \"describe this image.\"))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "80e3c48f-66e9-40e1-8623-04f73b672507",
-   "metadata": {},
-   "source": [
-    "We can even ask for more details related to the items in our image!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d8395784-ea68-4a95-a0bb-b3d618f68054",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "img_prompt=\"How do you make whats in this image?\"\n",
-    "image=\"gs://generativeai-downloads/images/scones.jpg\"\n",
-    "print(img2text(image, img_prompt))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "960f7ba6-8236-437a-ba99-ea1d887efd64",
-   "metadata": {},
-   "source": [
-    "### Gemini as a Video to Text Generator"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fbdbbd35-f36f-4467-8a1d-48ccd942d7cc",
-   "metadata": {},
-   "source": [
-    "Just like images we will be using the same model Gemini Pro Vision. We can load videos locally and from a bucket just like images. Video files can only be in the following formats:\n",
-    "- MOV - video/mov\n",
-    "- MPEG - video/mpeg\n",
-    "- MP4 - video/mp4\n",
-    "- MPG - video/mpg\n",
-    "- AVI - video/avi\n",
-    "- WMV - video/wmv\n",
-    "- MPEGPS - video/mpegps\n",
-    "- FLS - video/flv"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a7f94141-b5ba-49bf-a3d7-d0b68ccdd39a",
-   "metadata": {},
-   "source": [
-    "Our function below takes a video from a public bucket and asks for a prompt and the location of the video file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c18561c6-8f8f-46e4-b3ee-0d5fc96f2d31",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "def video2text(video_path: str, video_prompt: str) -> str:\n",
-    "    # Query the model\n",
-    "    multimodal_model = GenerativeModel(\"gemini-pro-vision\")\n",
-    "    response = multimodal_model.generate_content(\n",
-    "        [\n",
-    "            # Add an example image\n",
-    "            Part.from_uri(\n",
-    "                video_path, mime_type=\"video/mp4\"\n",
-    "            ),\n",
-    "            # Add an example query\n",
-    "            video_prompt,\n",
-    "        ],\n",
-    "        stream=True\n",
-    "    )\n",
-    "    for chunk in response :\n",
-    "        return print(chunk.text)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "492f9100-b5a4-446c-a03d-dd89a1b4bbde",
-   "metadata": {},
-   "source": [
-    "Run the function!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "55990074-0365-45f5-9fa6-bedbe93c9932",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "video_prompt = \"What is this video about in detail?\"\n",
-    "video = \"gs://cloud-samples-data/video/Machine Learning Solving Problems Big, Small, and Prickly.mp4\"\n",
-    "print(video2text(video, video_prompt))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c32253b4-d892-4c0c-896e-320c8df479c7",
-   "metadata": {},
-   "source": [
-    "## Gemini on Vertex AI Studio"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "614883f2-e53a-4ba5-855c-209d755a6e6f",
-   "metadata": {},
-   "source": [
-    "You can also use Gemini Pro and Pro Vision in Vertex AI's playground called **Vertex AI Studio**. To locate Vertex AI Studio search Vertex AI and on the left hand side locate Vertex AI Studio as the image below shows. To utilize Gemini Pro Vision locate and click **Overview** and then click on **Open FreeForm** option. You will have the option to use your own prompt or explore some of the other set prompts such as Extract text from images, image question answering , etc."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8a634e11-3bd5-4048-b6e4-46d5aac6ce34",
-   "metadata": {},
-   "source": [
-    "![Gemini1](../../images/VertexAIIntro01.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "12c359d3-9d82-441f-aff8-da1353925210",
-   "metadata": {},
-   "source": [
-    "When the free form opens, you have to select the gemini-1.0-pro-vision-001 model on the right side of the screen. We will upload the COVID image we downloaded before by clicking **Insert Media** and selecting our file. Then we will ask it a question, her we ask \"Describe treatment for the item in this image\"."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eebe2f4e-206d-41d8-9d85-25d9644b83fd",
-   "metadata": {},
-   "source": [
-    "![Gemini3](../../images/VertexAIIntro03.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4a88f06b-770a-43be-b875-b4054c3ccbf3",
-   "metadata": {},
-   "source": [
-    "To utilize Gemini Pro locate and click **Chat** on the left side menu. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1952cd5c-c93d-428f-8036-ac658eebfba4",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "![Gemini4](../../images/VertexAIIntro04.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7810c0db-d145-4acd-a2cc-b6ce8231fa14",
-   "metadata": {},
-   "source": [
-    "We ask the bot to describe covid and how it works."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "36b4635d-d8f3-42df-959d-dff92259813c",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "![Gemini5](../../images/VertexAIIntro05.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c0c5d0ad-2235-471e-98e6-8d5f295cc724",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Conclusion"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3d66220a-b17e-4b9d-8f85-f2c682d7a27f",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "You’ve learned how to interact with Gemini through code and the Vertex AI Studio interface. We covered how to generate text from images and videos using the Gemini-pro-vision model in a Python script. Additionally, we explored the FreeForm and Chat options in Vertex AI Studio to extract text from images and to engage in question-and-answer interactions within the Chat prompt."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0a1fedcb-75d7-411f-9475-9ecb777c9dbb",
-   "metadata": {},
-   "source": [
-    "## Clean UP"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f30d1136-f67c-4bb1-8ed4-09b88ef67a1e",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "Please remember to delete or stop your Jupyter notebook and if you have created any other services like buckets, please delete them as well."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9ee938bb-6dbb-4e01-88ae-e8511ada3ff7",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "environment": {
-   "kernel": "python3",
-   "name": "common-cpu.m125",
-   "type": "gcloud",
-   "uri": "us-docker.pkg.dev/deeplearning-platform-release/gcr.io/base-cpu:m125"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (Local)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.15"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/GenAI/.ipynb_checkpoints/VertexAIStudioGCP-checkpoint.ipynb b/notebooks/GenAI/.ipynb_checkpoints/VertexAIStudioGCP-checkpoint.ipynb
deleted file mode 100644
index 2a76783..0000000
--- a/notebooks/GenAI/.ipynb_checkpoints/VertexAIStudioGCP-checkpoint.ipynb
+++ /dev/null
@@ -1,172 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Hny4I-ODTIS6"
-   },
-   "source": [
-    "# Vertex AI Studio on GCP - Article Summary\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "-nLS57E2TO5y"
-   },
-   "source": [
-    "## Overview\n",
-    "\n",
-    "In research you often need to read several papers to understand new methods which can be very time consuming. In this tutorial we will use generative AI to summarize long documents but with the goal of perserving the most important information using Vertex AI Studio's Article Summary model."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Prerequisites\n",
-    "+ You need access to Vertex AI"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Learning objectives\n",
-    "+ Learn how to use LLMs for summarization\n",
-    "+ Learn how to navigate Vertex AI to deploy and use LLMs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "skXAu__iqks_"
-   },
-   "source": [
-    "### A note on costs\n",
-    "\n",
-    "This tutorial uses billable components of Google Cloud:\n",
-    "- Vertex AI Studio\n",
-    "\n",
-    "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing), [Generative AI pricing](https://cloud.google.com/vertex-ai/pricing#generative_ai_models), and use the [Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Get Started"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "x_xMwRLuyDrj"
-   },
-   "source": [
-    "Here you will use LLM via the API to summarize the extracted texts. Please note that LLMs currently have input text limit and stuffing a large input text might not be accepted. You can read more about quotas and limits [here](https://cloud.google.com/vertex-ai/docs/quotas)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Go to the Vertex AI Studio console by navigating to Vertex AI via the search bar on the console. On the left side menu scroll down to Vertex AI Studio, click **Chat**.\n",
-    "\n",
-    " <img src=\"../../images/VertexAIStudioGCP_new01.png\" width=\"500\" height=\"500\">\n",
-    "\n",
-    "\n",
-    "Click on **\"Browse prompt gallery\"** then scroll down to **Summarizing texts** and click on it. You will see the system instructions and the prompt on the chat that are populated with the summarizing example. Since we want to summarize our own article, we clear the prompt by clicking **Clear prompt** on the top right and then **Insert Media**, select **ByURL** if you want to upload a document from the web or **Upload** if you want to upload it from your local machine. Paste the url link and press **Insert**. The document will be loaded into the Prompt and you will have to enter some instruction of what to do with the uploaded document i.e. \"Please summarize the uploaded document.\" For this tutorial this article is about how gut microbiota affects Alzeheimer's disease because of the gut-brain-microbiota axis network [here](https://www.aging-us.com/article/102930/pdf).\n",
-    "\n",
-    " <img src=\"../../images/VertexAIStudioGCP_new05.png\" width=\"500\" height=\"500\">\n",
-    "\n",
-    "To the right you can control the parameters that we have been using before this is a great way to test what each parameter does and how they effect each other. Once you are done click the submit arrow, you should have a similar output as below. For explainations on the parameters **temperature, Output token limit, top p, and top k** see the following article [here](https://cloud.google.com/vertex-ai/docs/generative-ai/text/test-text-prompts#generative-ai-test-text-prompt-drest).\n",
-    "\n",
-    " <img src=\"../../images/VertexAIStudioGCP_new06.png\" width=\"600\" height=\"600\">\n",
-    " \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Lets try increasing the temperature parameter and see if we recieve a different output."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<img src=\"../../images/VertexAIStudioGCP_new07.png\" width=\"600\" height=\"600\">"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As you can see our output becomes shorter and straight to the point this is because the temperature parameter controls the degree of randomness in token selection. Lower temperatures are good for prompts that require a more deterministic and less creative responses, while higher temperatures can lead to more diverse or creative results. A temperature of 0 is deterministic, meaning that the highest probability response is always selected."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "N5aVrDWkJs3Y"
-   },
-   "source": [
-    "### Troubleshooting\n",
-    "\n",
-    "If you model responds with an error its generally because the extracted text is too long for the generative model to process. In order for the model to work best try to not include any abstract or reference sections of your article, if errors still come up try limiting the article even more by removing other sections such as the intro or extracting the first 30,000 words of the article."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Vtp21WX3T7d_"
-   },
-   "source": [
-    "## Conclusion\n",
-    "\n",
-    "Although full text is too large for the model, you have managed to create a concise, paragraph of the most important information from a portion of the PDF using the model. This method is the most simiplest and is ideal for shorter documents but can still be used when you limit the character number you want the model to read. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Clean Up\n",
-    "Shut down your Vertex AI instance"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "summarization_large_documents.ipynb",
-   "toc_visible": true
-  },
-  "environment": {
-   "kernel": "python3",
-   "name": "common-cpu.m125",
-   "type": "gcloud",
-   "uri": "us-docker.pkg.dev/deeplearning-platform-release/gcr.io/base-cpu:m125"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (Local)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.15"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/GenAI/GCP_Grounding.ipynb b/notebooks/GenAI/GCP_Grounding.ipynb
index 48d924a..2f8b6a5 100644
--- a/notebooks/GenAI/GCP_Grounding.ipynb
+++ b/notebooks/GenAI/GCP_Grounding.ipynb
@@ -410,7 +410,6 @@
    ]
   }
  ],
-
  "metadata": {},
  "nbformat": 4,
  "nbformat_minor": 5
diff --git a/notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb b/notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb
index 212cfb0..439208e 100644
--- a/notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb
+++ b/notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb
@@ -19,9 +19,7 @@
   {
    "cell_type": "markdown",
    "id": "76cf4b02-6422-4a48-882c-c00315e34c8a",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "source": [
     "## Overview"
    ]
@@ -61,9 +59,7 @@
   {
    "cell_type": "markdown",
    "id": "eeb24716-0db8-462f-8faa-0b8ad9d61feb",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "source": [
     "## Learning objectives"
    ]
@@ -82,9 +78,7 @@
   {
    "cell_type": "markdown",
    "id": "348910aa-9802-4318-8fcb-86603158923b",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "source": [
     "## Pricing"
    ]
@@ -117,9 +111,7 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "55c14a38-ba51-466d-90b5-1512f5a7fae0",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "!pip install google-cloud-bigquery\n",
@@ -238,9 +230,7 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "671aa2a3-d259-42e3-8cc3-8ab6b1e4a7d1",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "table_id = f'{dataset_id}.{table_name}'\n",
@@ -526,9 +516,7 @@
   {
    "cell_type": "markdown",
    "id": "f2b62012-b2d2-48c1-ae69-6f8816065551",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "source": [
     "Next, we pose a question that requires applying a filter, and we successfully obtain the accurate number of obese individuals in the table."
    ]
@@ -582,9 +570,7 @@
   {
    "cell_type": "markdown",
    "id": "ca0fb657-19e0-4488-b9f6-25ca3971064f",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "source": [
     "## Conclusion"
    ]
@@ -622,31 +608,7 @@
    "source": []
   }
  ],
- "metadata": {
-  "environment": {
-   "kernel": "python3",
-   "name": "common-cpu.m125",
-   "type": "gcloud",
-   "uri": "us-docker.pkg.dev/deeplearning-platform-release/gcr.io/base-cpu:m125"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (Local)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.15"
-  }
- },
+ "metadata": {},
  "nbformat": 4,
  "nbformat_minor": 5
 }
diff --git a/notebooks/GenAI/Gemini_Intro.ipynb b/notebooks/GenAI/Gemini_Intro.ipynb
index 0e80134..c6a24d8 100644
--- a/notebooks/GenAI/Gemini_Intro.ipynb
+++ b/notebooks/GenAI/Gemini_Intro.ipynb
@@ -576,9 +576,7 @@
   {
    "cell_type": "markdown",
    "id": "c0c5d0ad-2235-471e-98e6-8d5f295cc724",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "source": [
     "## Conclusion"
    ]
@@ -586,9 +584,7 @@
   {
    "cell_type": "markdown",
    "id": "3d66220a-b17e-4b9d-8f85-f2c682d7a27f",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "source": [
     "You’ve learned how to interact with Gemini through code and the Vertex AI Studio interface. We covered how to generate text from images and videos using the Gemini-pro-vision model in a Python script. Additionally, we explored the FreeForm and Chat options in Vertex AI Studio to extract text from images and to engage in question-and-answer interactions within the Chat prompt."
    ]
@@ -604,9 +600,7 @@
   {
    "cell_type": "markdown",
    "id": "f30d1136-f67c-4bb1-8ed4-09b88ef67a1e",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "source": [
     "Please remember to delete or stop your Jupyter notebook and if you have created any other services like buckets, please delete them as well."
    ]

From 902a509b17b0e53e8badcac3155eac3cfb16970c Mon Sep 17 00:00:00 2001
From: Marco Cetraro <cetrarom2@nih.gov>
Date: Fri, 10 Jan 2025 18:42:38 +0000
Subject: [PATCH 11/12] additional edits

---
 .../GenAI/GCP_RAG_for_Structure_Data.ipynb    | 105 ++++++++++--------
 1 file changed, 59 insertions(+), 46 deletions(-)

diff --git a/notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb b/notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb
index 439208e..727ea8d 100644
--- a/notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb
+++ b/notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb
@@ -47,8 +47,6 @@
    "id": "fbdb09aa-73d6-4cee-b3e7-5d6b243e3d58",
    "metadata": {},
    "source": [
-    "We assume you have access to Vertex AI and have enabled the necessary APIs.\n",
-    "\n",
     "In this tutorial, we will be using Google Gemini Pro 1.5, which does not require deployment. However, if you prefer to use a different model, you can select one from the Model Garden via the console. This will allow you to add the model to your registry, create an endpoint (or utilize an existing one), and deploy the model—all in a single step. Here is a link for more information: [model deployment](https://cloud.google.com/vertex-ai/docs/general/deployment).\n",
     "\n",
     "Before we begin, you'll need to create a Vertex AI RAG Data Service Agent service account. To do this, go to the IAM section of the console. Ensure you check the box for \"Include Google-provided role grant.\" If the role is not listed, click \"Grant Access\" and add \"Vertex AI RAG Data Service Agent\" as a role. Additionally, to create datasets and tables in BigQuery, the user must have the __bigquery.datasets.create__ and __bigquery.tables.create__ permissions.\n",
@@ -88,7 +86,7 @@
    "id": "0dca31b2-880d-4cbc-bf76-ede96998e371",
    "metadata": {},
    "source": [
-    "If you are following this tutorial in one sitting it will cost $23.89 per month. Completing the process in multiple sessions or using a method different from the tutorial may result in increased costs."
+    "If you are following this tutorial in one sitting it will cost ~$0.80 . Completing the process in multiple sessions or using a method different from the tutorial may result in increased costs."
    ]
   },
   {
@@ -96,7 +94,7 @@
    "id": "c7d9e3ee-1c7e-4baa-a818-c13330c665dd",
    "metadata": {},
    "source": [
-    "## Get Start"
+    "## Get Started"
    ]
   },
   {
@@ -140,7 +138,9 @@
    "source": [
     "project_id='<PROJECT_ID>'\n",
     "location='<LOCATION> (e.g.us-east4)'\n",
-    "bucket = '<BUCKET_NAME>'"
+    "bucket = '<BUCKET_NAME>'\n",
+    "dataset_name = '<Name of the Dataset>'\n",
+    "table_name = \"<Table Name>\""
    ]
   },
   {
@@ -167,7 +167,7 @@
    "id": "5781a07c-84ed-416c-a782-b1dc881e5c18",
    "metadata": {},
    "source": [
-    "Once the bucket is created, we need to access the CSV source file. In this tutorial, I transferred the data file to our Jupyter notebook by simply dragging and dropping it from my local folder. Next, we need to specify the bucket name and the path of the data source in order to upload the CSV file to the bucket. \n",
+    "Once the bucket is created, we need to access the CSV source file. In this tutorial, we transferred the data file to our Jupyter notebook by simply dragging and dropping it from my local folder. Next, we need to specify the bucket name and the path of the data source in order to upload the CSV file to the bucket. \n",
     "\n",
     "We are using the [health screening](https://www.kaggle.com/datasets/drateendrajha/health-screening-data) dataset from kaggle for this tutorial."
    ]
@@ -182,6 +182,14 @@
     "!gsutil cp '<Path to your csv>' gs://{bucket}"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "3cb53613-326d-4608-8c55-cf9bef115d88",
+   "metadata": {},
+   "source": [
+    "### Create a database in BigQuery"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "7a00dc83-8713-4ea5-9345-08a9f3d785d0",
@@ -201,7 +209,6 @@
     "\n",
     "client = bigquery.Client()\n",
     "\n",
-    "dataset_name = '<Name of the Dataset>'\n",
     "# Set dataset_id to the ID of the dataset to create.\n",
     "dataset_id = f'{project_id}.{dataset_name}'\n",
     "\n",
@@ -218,12 +225,30 @@
     "print(\"Created dataset {}.{}\".format(client.project, dataset.dataset_id))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "35a1499e-0439-48ab-8042-cc7b8625efd8",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "Now you have two options to create your table (please pick one):"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b421e99e-e493-42b0-9bba-5ffa9d9f1f7b",
+   "metadata": {},
+   "source": [
+    "#### Option 1: Auto detect "
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "4e18540d-defa-414b-8b78-b86bce3dd46a",
    "metadata": {},
    "source": [
-    "The following code will auto detect the table schema and upload the data to the table in one step."
+    "The following code will auto detect the table schema and upload the data to the table in one step!"
    ]
   },
   {
@@ -247,6 +272,14 @@
     "print(\"Loaded {} rows.\".format(destination_table.num_rows))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "cf413f17-c0ab-4d4a-b333-94b05d0bd376",
+   "metadata": {},
+   "source": [
+    "#### Option 2: Manually create schema and upload CSV file"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "544c191c-2bc0-4a47-a0a7-cea81035597a",
@@ -266,7 +299,6 @@
     "\n",
     "client = bigquery.Client()\n",
     "\n",
-    "table_name = \"<Table Name>\"\n",
     "table_id = f'{project_id}.{dataset_name}.{table_name}'\n",
     "\n",
     "schema = [\n",
@@ -375,6 +407,14 @@
     "![image.png](../../images/gcp_rag_structure_data_01.png)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "ca9014cb-2226-4168-b96e-d01021bee230",
+   "metadata": {},
+   "source": [
+    "### Optional: Run a query"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "22b4dd65-9bd9-48ef-9c98-85e3e9a82701",
@@ -390,9 +430,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "query = \"\"\"\n",
+    "query = f\"\"\"\n",
     "    SELECT count(gender) as cgender\n",
-    "    FROM `project_id.dataset_id.table_id`\n",
+    "    FROM `{project_id.dataset_id.table_id}`\n",
     "\"\"\"\n",
     "\n",
     "# Execute the query\n",
@@ -423,6 +463,14 @@
     "<google.cloud.bigquery.table.RowIterator object at 0x7f226b88ab90>"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "b0cd8a0d-9845-4d84-abf6-2610ed946b7e",
+   "metadata": {},
+   "source": [
+    "### Connect our model and Big Query database and run a query"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "b6fb53b8-9f93-4f37-8730-33c5871720fc",
@@ -504,15 +552,6 @@
     "agent_executor.run(\"How many rows are in the table screening? \")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "099154a1-34e2-4f6b-b146-cec6f5943cf7",
-   "metadata": {},
-   "source": [
-    "> Finished chain.\n",
-    "'The table screening has 139920 rows.'"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "f2b62012-b2d2-48c1-ae69-6f8816065551",
@@ -531,15 +570,6 @@
     "agent_executor.run(\"How many obese are in the table screen?\")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "40e1846d-1114-4ba1-b35a-65b47a838468",
-   "metadata": {},
-   "source": [
-    "> Finished chain.\n",
-    "'There are 37204 obese people in the table.'"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "b96ea414-3f2d-4e66-ab0b-a034dfe507b0",
@@ -558,15 +588,6 @@
     "agent_executor.run(\"How many smokers are female in the table screen? the value 1 in smoke means that is a smoker and the value 2 in gender means that is a female\")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "7ea7810f-9701-4cb0-9430-181a57929884",
-   "metadata": {},
-   "source": [
-    "> Finished chain.\n",
-    "'There are 1626 female smokers in the table.'"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "ca0fb657-19e0-4488-b9f6-25ca3971064f",
@@ -598,14 +619,6 @@
    "source": [
     "Please remember to delete or stop your Jupyter notebook and delete your BigQuery dataset and table to prevent incurring charges. And if you have created any other services like buckets, please remember to delete them as well."
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fe73dde2-056a-46a3-bacb-0a6671f7128f",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {},

From 75079315244168f7a9397a6fcb2010c06ca8d4e4 Mon Sep 17 00:00:00 2001
From: github-action <cbiit-github-action@github.com>
Date: Fri, 10 Jan 2025 18:55:01 +0000
Subject: [PATCH 12/12] Github Action: Lint Notebooks

---
 notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb b/notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb
index 727ea8d..a5698c7 100644
--- a/notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb
+++ b/notebooks/GenAI/GCP_RAG_for_Structure_Data.ipynb
@@ -228,9 +228,7 @@
   {
    "cell_type": "markdown",
    "id": "35a1499e-0439-48ab-8042-cc7b8625efd8",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "source": [
     "Now you have two options to create your table (please pick one):"
    ]