diff --git a/notebooks/real-time-recommendation-engine/meta.toml b/notebooks/real-time-recommendation-engine/meta.toml
new file mode 100644
index 0000000..4690a2c
--- /dev/null
+++ b/notebooks/real-time-recommendation-engine/meta.toml
@@ -0,0 +1,8 @@
+[meta]
+title="Real Time Recommendation Engine"
+description="""\
+We demonstrate how to build and host a real-time recommendation engine for free with SingleStore. The notebook also leverages our new SingleStore Job Service to ensure that the latest data is ingested and used in providing recommendations.\
+    """
+icon="crystal-ball"
+tags=["openai", "vercel", "realtime", "vectordb"]
+destinations=["spaces"]
diff --git a/notebooks/real-time-recommendation-engine/notebook.ipynb b/notebooks/real-time-recommendation-engine/notebook.ipynb
new file mode 100644
index 0000000..fa378d4
--- /dev/null
+++ b/notebooks/real-time-recommendation-engine/notebook.ipynb
@@ -0,0 +1,945 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "6c991811-dee6-4315-b831-320573e8e06f",
+      "metadata": {},
+      "source": [
+        "<div id=\"singlestore-header\" style=\"display: flex; background-color: rgba(255, 167, 103, 0.25); padding: 5px;\">\n",
+        "    <div id=\"icon-image\" style=\"width: 90px; height: 90px;\">\n",
+        "        <img width=\"100%\" height=\"100%\" src=\"https://raw.githubusercontent.com/singlestore-labs/spaces-notebooks/master/common/images/header-icons/crystal-ball.png\" />\n",
+        "    </div>\n",
+        "    <div id=\"text\" style=\"padding: 5px; margin-left: 10px;\">\n",
+        "        <div id=\"badge\" style=\"display: inline-block; background-color: rgba(0, 0, 0, 0.15); border-radius: 4px; padding: 4px 8px; align-items: center; margin-top: 6px; margin-bottom: -2px; font-size: 80%\">SingleStore Notebooks</div>\n",
+        "        <h1 style=\"font-weight: 500; margin: 8px 0 0 4px;\">Real Time Recommendation Engine</h1>\n",
+        "    </div>\n",
+        "</div>"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# How to build a real-time recommendation engine with SingleStore & Vercel"
+      ]
+    },
+    {
+      "attachments": {
+        "c7f1d715-a955-408e-87f4-fdc1e1b3dc05.png": {
+          "image/png": "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"
+        }
+      },
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We will demonstrate how to build a modern real-time AI application for free using a Shared Tier Database, SingleStore Notebooks, and Job Service.\n",
+        "\n",
+        "A Free SingleStore Starter Workspace enables you to execute hybrid search, real-time analytics, and point read/writes/updates in a single database. With SingleStore Notebooks and our Job Service, you easily bring in data from various sources (APIs, MySQL / Mongo endpoints) in real-time. You can also execute Python-based transforms, such as adding embeddings, ensuring that real-time data is readily available for your downstream LLMs and applications.\n",
+        "\n",
+        "We will showcase the seamless transition from a prototype to an end-application using SingleStore. The final application will be hosted on Vercel. You can see the App we've built following this notebook [here](https://llm-recommender.vercel.app/)\n",
+        "### Architecture:\n",
+        "\n",
+        "![Screenshot 2024-01-12 at 2.13.37 PM.png](attachment:c7f1d715-a955-408e-87f4-fdc1e1b3dc05.png)"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Scenario:\n",
+        "Building a recommendation engine on what LLM you should be using for your use-case. Bringing together semantic search + real-time analytics on the performance of the LLM to make the recommendations.\n",
+        "\n",
+        "Here are the requirements we've set out for this recommendation engine:\n",
+        "1. Pull data from [Hugging Face Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)  on various Open source LLM models and their scores. Pull updated scores on these models every hour.\n",
+        "2. For each of these models, pull data from Twitter and Github on what developers are saying about these models, and how they are being used in active projects. Pull this data every hour.\n",
+        "3. Provide an easy 'search' interface to users where they can describe their use-case. When users provide describe their use-case, perform a hybrid search (vector + full-text search) across the descriptions of these models, what users are saying about it on Twitter, and which github repos are using these LLMs.\n",
+        "4. Combine the results of the semantic search with analytics on the public benchmarks, # likes, # downloads of these models.\n",
+        "6. Power the app entirely on a single SingleStore Free Shared Tier Workspace.\n",
+        "7. Ensure that all of the latest posts / scores are reflected in the App. Power this entirely with SingleStore Notebook and Job Service"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Contents\n",
+        "- Step 1: Creating a Starter Workspace\n",
+        "- Step 2: Installing & Importing required libraries\n",
+        "- Step 3: Setting Key Variables\n",
+        "- Step 4: Designing your table scheama on SingleStore\n",
+        "- Step 5: Creating Helper Functions to load data into SingleStore\n",
+        "- Step 6: Loading data with embeddings into SingleStore\n",
+        "- Step 7: Building the Recommendation Engine Algorithm on Vercel"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Step 1. Create a Starter Workspace\n",
+        "\n",
+        "Create a new Workpsace Group and select a Starter Workspace. If you do not have this enabled email pm@singlestore.com"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Step 2. Install and import required libraries"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%pip install singlestoredb openai tiktoken beautifulsoup4 pandas python-dotenv Markdown praw tweepy --quiet\n",
+        "\n",
+        "import re\n",
+        "import json\n",
+        "import openai\n",
+        "import tiktoken\n",
+        "import json\n",
+        "import requests\n",
+        "import getpass\n",
+        "import pandas as pd\n",
+        "import singlestoredb as s2\n",
+        "import tweepy\n",
+        "import praw\n",
+        "from bs4 import BeautifulSoup\n",
+        "from markdown import markdown\n",
+        "from datetime import datetime\n",
+        "from time import time, sleep"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Step 3. Seting Environment variables\n",
+        "\n",
+        "### 3.1. Set the app common variables. Do not change these"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "MODELS_LIMIT = 100\n",
+        "MODELS_TABLE_NAME = 'models'\n",
+        "MODEL_READMES_TABLE_NAME = 'model_readmes'\n",
+        "MODEL_TWITTER_POSTS_TABLE_NAME = 'model_twitter_posts'\n",
+        "MODEL_REDDIT_POSTS_TABLE_NAME = 'model_reddit_posts'\n",
+        "MODEL_GITHUB_REPOS_TABLE_NAME = 'model_github_repos'\n",
+        "LEADERBOARD_DATASET_URL = 'https://llm-recommender.vercel.app/datasets/leaderboard.json'\n",
+        "TOKENS_LIMIT = 2047\n",
+        "TOKENS_TRASHHOLD_LIMIT = TOKENS_LIMIT - 128"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### 3.2. Set the OpenAI variables\n",
+        "\n",
+        "We will be using OpenAI's embedding models to create vectors representing our data. The vectors will be stored in the SingleStore Starter Workspace as a column in the relevant tables.\n",
+        "\n",
+        "Using OpenAI's LLMs we will also generate output text after we complete the Retrieval Augmentation Generation Steps.\n",
+        "1. [Open the OpenAI API keys page](https://platform.openai.com/api-keys)\n",
+        "2. Create a new key\n",
+        "3. Copy the key and paste it into the `OPENAI_API_KEY` variable"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "OPENAI_API_KEY = getpass.getpass(\"enter openAI apikey here\")"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### 3.3. Set the HuggingFace variables\n",
+        "\n",
+        "We will be pulling data from HugginFace about the different models, the usage of these models, and how they score in several evaluation metrics.\n",
+        "1. [Open the HuggingFace Access Tokens page](https://huggingface.co/settings/tokens)\n",
+        "2. Create a new token\n",
+        "3. Copy the key and paste it into the `HF_TOKEN` variable"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "HF_TOKEN = getpass.getpass(\"enter HuggingFace apikey here\")"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### 3.4. Set the Twitter variables\n",
+        "We will be pulling data from Twitter about what users might be saying about these models. Since teh quality of these models may change over time, we want to caputre the sentiment of what people are talking about and using on twitter.\n",
+        "1. [Open the Twitter Developer Projects & Apps page](https://developer.twitter.com/en/portal/projects-and-apps)\n",
+        "2. Add a new app\n",
+        "3. Fill the form\n",
+        "4. Generate a Bearer Token and paste it into the `TWITTER_BEARER_TOKEN` variable"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "TWITTER_BEARER_TOKEN = getpass.getpass(\"enter Twitter Bearer Token here\")"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### 3.5 Set the GitHub variables\n",
+        "We will also be pulling data from various Github repos on which models are being referenced and used for which scenarios.\n",
+        "1. [Open the Register new GitHub App page](https://github.com/settings/apps/new)\n",
+        "2. Fill the form\n",
+        "3. Get an access token and paste it into the `GITHUB_ACCESS_TOKEN` variable"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "GITHUB_ACCESS_TOKEN = getpass.getpass(\"enter Github Access Token here\")"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Step 4. Designing and creating your table schemas in SingleStore\n",
+        "\n",
+        "We will be storing all of this data in a single Free Shared Tier Database. Through this database, you can write hybrid search queries, run analytics on the model's performance, and get real-time reads/updates.\n",
+        "\n",
+        "- `connection` - database connection to execute queries\n",
+        "- `create_tables` - function that creates empty tables in the database\n",
+        "- `drop_table` - helper function to drop a table\n",
+        "- `get_models` - helper function to get models from the models table\n",
+        "- `db_get_last_created_at` - helper function to get last `created_at` value from a table\n",
+        "\n",
+        "The `create_tables` creates the following tables:\n",
+        "- `models_table` - table with all models data from the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)\n",
+        "- `readmes_table` - table with model readme texts from the HugginFace model pages (used in semantic search)\n",
+        "- `twitter_posts` - table with tweets related to models (used in semantic search)\n",
+        "- `github_repos` - table with GitHub readme texts related to models (used in semantic search)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "connection = s2.connect(connection_url)\n",
+        "\n",
+        "\n",
+        "def create_tables():\n",
+        "    def create_models_table():\n",
+        "        with connection.cursor() as cursor:\n",
+        "            cursor.execute(f'''\n",
+        "                CREATE TABLE IF NOT EXISTS {MODELS_TABLE_NAME} (\n",
+        "                    id INT AUTO_INCREMENT PRIMARY KEY,\n",
+        "                    name VARCHAR(512) NOT NULL,\n",
+        "                    author VARCHAR(512) NOT NULL,\n",
+        "                    repo_id VARCHAR(1024) NOT NULL,\n",
+        "                    score DECIMAL(5, 2) NOT NULL,\n",
+        "                    arc DECIMAL(5, 2) NOT NULL,\n",
+        "                    hellaswag DECIMAL(5, 2) NOT NULL,\n",
+        "                    mmlu DECIMAL(5, 2) NOT NULL,\n",
+        "                    truthfulqa DECIMAL(5, 2) NOT NULL,\n",
+        "                    winogrande DECIMAL(5, 2) NOT NULL,\n",
+        "                    gsm8k DECIMAL(5, 2) NOT NULL,\n",
+        "                    link VARCHAR(255) NOT NULL,\n",
+        "                    downloads INT,\n",
+        "                    likes INT,\n",
+        "                    still_on_hub BOOLEAN NOT NULL,\n",
+        "                    created_at TIMESTAMP,\n",
+        "                    embedding BLOB\n",
+        "                )\n",
+        "            ''')\n",
+        "\n",
+        "    def create_model_readmes_table():\n",
+        "        with connection.cursor() as cursor:\n",
+        "            cursor.execute(f'''\n",
+        "                CREATE TABLE IF NOT EXISTS {MODEL_READMES_TABLE_NAME} (\n",
+        "                    id INT AUTO_INCREMENT PRIMARY KEY,\n",
+        "                    model_repo_id VARCHAR(512),\n",
+        "                    text LONGTEXT CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci,\n",
+        "                    clean_text LONGTEXT CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci,\n",
+        "                    created_at TIMESTAMP,\n",
+        "                    embedding BLOB\n",
+        "                )\n",
+        "            ''')\n",
+        "\n",
+        "    def create_model_twitter_posts_table():\n",
+        "        with connection.cursor() as cursor:\n",
+        "            cursor.execute(f'''\n",
+        "                CREATE TABLE IF NOT EXISTS {MODEL_TWITTER_POSTS_TABLE_NAME} (\n",
+        "                    id INT AUTO_INCREMENT PRIMARY KEY,\n",
+        "                    model_repo_id VARCHAR(512),\n",
+        "                    post_id VARCHAR(256),\n",
+        "                    clean_text LONGTEXT CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci,\n",
+        "                    created_at TIMESTAMP,\n",
+        "                    embedding BLOB\n",
+        "                )\n",
+        "            ''')\n",
+        "\n",
+        "    def create_model_github_repos_table():\n",
+        "        with connection.cursor() as cursor:\n",
+        "            cursor.execute(f'''\n",
+        "                CREATE TABLE IF NOT EXISTS {MODEL_GITHUB_REPOS_TABLE_NAME} (\n",
+        "                    id INT AUTO_INCREMENT PRIMARY KEY,\n",
+        "                    model_repo_id VARCHAR(512),\n",
+        "                    repo_id INT,\n",
+        "                    name VARCHAR(512) CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci,\n",
+        "                    description TEXT CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci,\n",
+        "                    clean_text LONGTEXT CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci,\n",
+        "                    link VARCHAR(256),\n",
+        "                    created_at TIMESTAMP,\n",
+        "                    embedding BLOB\n",
+        "                )\n",
+        "            ''')\n",
+        "\n",
+        "    create_models_table()\n",
+        "    create_model_readmes_table()\n",
+        "    create_model_twitter_posts_table()\n",
+        "    create_model_github_repos_table()\n",
+        "\n",
+        "\n",
+        "def drop_table(table_name: str):\n",
+        "    with connection.cursor() as cursor:\n",
+        "        cursor.execute(f'DROP TABLE IF EXISTS {table_name}')\n",
+        "\n",
+        "\n",
+        "def get_models(select='*', query='', as_dict=True):\n",
+        "    with connection.cursor() as cursor:\n",
+        "        _query = f'SELECT {select} FROM {MODELS_TABLE_NAME}'\n",
+        "\n",
+        "        if query:\n",
+        "            _query += f' {query}'\n",
+        "\n",
+        "        cursor.execute(_query)\n",
+        "\n",
+        "        if as_dict:\n",
+        "            columns = [desc[0] for desc in cursor.description]\n",
+        "            return [dict(zip(columns, row)) for row in cursor.fetchall()]\n",
+        "\n",
+        "        return cursor.fetchall()\n",
+        "\n",
+        "\n",
+        "def db_get_last_created_at(table, repo_id, to_string=False):\n",
+        "    with connection.cursor() as cursor:\n",
+        "        cursor.execute(f\"\"\"\n",
+        "            SELECT UNIX_TIMESTAMP(created_at) FROM {table}\n",
+        "            WHERE model_repo_id = '{repo_id}'\n",
+        "            ORDER BY created_at DESC\n",
+        "            LIMIT 1\n",
+        "        \"\"\")\n",
+        "\n",
+        "        rows = cursor.fetchone()\n",
+        "        created_at = float(rows[0]) if rows and rows[0] else None\n",
+        "\n",
+        "        if (created_at and to_string):\n",
+        "            created_at = datetime.fromtimestamp(created_at)\n",
+        "            created_at = created_at.strftime('%Y-%m-%dT%H:%M:%SZ')\n",
+        "\n",
+        "        return created_at"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Step 5. Creating helper functions to load data into SingleStore\n",
+        "\n",
+        "### 5.1. Setting up the `openai.api_key`"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "openai.api_key = OPENAI_API_KEY"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### 5.2. Create the `create_embeddings` function\n",
+        "This function will be used to create embeddings on data based on an input to the function. We will be doing this to all data pulled from Github, HuggingFace and Twitter. The vector embeddings created will be stored in the same SingleStore table as a separate column."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def count_tokens(text: str):\n",
+        "    enc = tiktoken.get_encoding('cl100k_base')\n",
+        "    return len(enc.encode(text, disallowed_special={}))\n",
+        "\n",
+        "def create_embedding(input):\n",
+        "    try:\n",
+        "        data = openai.embeddings.create(input=input, model='text-embedding-ada-002').data\n",
+        "        return data[0].embedding\n",
+        "    except Exception as e:\n",
+        "        print(e)\n",
+        "        return [[]]"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### 5.3. Create the function/Utils to help parse the data ingested from the various sources\n",
+        "This is a set of functions that ensure the JSON is in the right format and can be stored in SingleStore as a JSON column. In your Free Shared Tier workspace you can bring data of various formats (JSON, Geospatial, Vector) and interact with this data with SQL and MongoDB API."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "class JSONEncoder(json.JSONEncoder):\n",
+        "    def default(self, obj):\n",
+        "        if isinstance(obj, datetime):\n",
+        "            return obj.strftime('%Y-%m-%d %H:%M:%S')\n",
+        "        return super().default(obj)\n",
+        "\n",
+        "def list_into_chunks(lst, chunk_size=100):\n",
+        "    return [lst[i:i + chunk_size] for i in range(0, len(lst), chunk_size)]\n",
+        "\n",
+        "def string_into_chunks(string: str, max_tokens=TOKENS_LIMIT):\n",
+        "    if count_tokens(string) <= max_tokens:\n",
+        "        return [string]\n",
+        "\n",
+        "    delimiter = ' '\n",
+        "    words = string.split(delimiter)\n",
+        "    chunks = []\n",
+        "    current_chunk = []\n",
+        "\n",
+        "    for word in words:\n",
+        "        if count_tokens(delimiter.join(current_chunk + [word])) <= max_tokens:\n",
+        "            current_chunk.append(word)\n",
+        "        else:\n",
+        "            chunks.append(delimiter.join(current_chunk))\n",
+        "            current_chunk = [word]\n",
+        "\n",
+        "    if current_chunk:\n",
+        "        chunks.append(delimiter.join(current_chunk))\n",
+        "\n",
+        "    return chunks\n",
+        "\n",
+        "def clean_string(string: str):\n",
+        "    def strip_html_elements(string: str):\n",
+        "        html = markdown(string)\n",
+        "        soup = BeautifulSoup(html, \"html.parser\")\n",
+        "        text = soup.get_text()\n",
+        "        return text.strip()\n",
+        "\n",
+        "    def remove_unicode_escapes(string: str):\n",
+        "        return re.sub(r'[^\\x00-\\x7F]+', '', string)\n",
+        "\n",
+        "    def remove_string_spaces(strgin: str):\n",
+        "        new_string = re.sub(r'\\n+', '\\n', strgin)\n",
+        "        new_string = re.sub(r'\\s+', ' ', new_string)\n",
+        "        return new_string\n",
+        "\n",
+        "    def remove_links(string: str):\n",
+        "        url_pattern = r'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\\\(\\\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+'\n",
+        "        return re.sub(url_pattern, '', string)\n",
+        "\n",
+        "    new_string = strip_html_elements(string)\n",
+        "    new_string = remove_unicode_escapes(new_string)\n",
+        "    new_string = remove_string_spaces(new_string)\n",
+        "    new_string = re.sub(r'\\*\\*+', '*', new_string)\n",
+        "    new_string = re.sub(r'--+', '-', new_string)\n",
+        "    new_string = re.sub(r'====+', '=', new_string)\n",
+        "    new_string = remove_links(new_string)\n",
+        "\n",
+        "    return new_string"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Step 6. Loading Data into SingleStore"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### 6.1. Load Data on all Open-Source LLM models from [HuggingFace Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)\n",
+        "This function loads a pre-generated Open LLM Leaderboard dataset. Based on this dataset, all model data is created and inserted into the database.\n",
+        "We will also create embeddings for all of this data pulled using the OpenAI Embedding Model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def leaderboard_get_df():\n",
+        "    response = requests.get(LEADERBOARD_DATASET_URL)\n",
+        "\n",
+        "    if response.status_code == 200:\n",
+        "        data = json.loads(response.text)\n",
+        "        df = pd.DataFrame(data).head(MODELS_LIMIT)\n",
+        "        return df\n",
+        "    else:\n",
+        "        print(\"Failed to retrieve JSON file\")\n",
+        "\n",
+        "def leaderboard_insert_model(model):\n",
+        "    try:\n",
+        "        _model = {key: value for key, value in model.items() if key != 'readme'}\n",
+        "        to_embedding = json.dumps(_model, cls=JSONEncoder)\n",
+        "        embedding = str(create_embedding(to_embedding))\n",
+        "        model_to_insert = {**_model, embedding: embedding}\n",
+        "        readmes_to_insert = []\n",
+        "\n",
+        "        if model['readme']:\n",
+        "            readme = {\n",
+        "                'model_repo_id': model['repo_id'],\n",
+        "                'text': model['readme'],\n",
+        "                'created_at': time()\n",
+        "            }\n",
+        "\n",
+        "            if count_tokens(readme['text']) <= TOKENS_TRASHHOLD_LIMIT:\n",
+        "                readme['clean_text'] = clean_string(readme['text'])\n",
+        "                to_embedding = json.dumps({\n",
+        "                    'model_repo_id': readme['model_repo_id'],\n",
+        "                    'clean_text': readme['clean_text'],\n",
+        "                })\n",
+        "                readme['embedding'] = str(create_embedding(to_embedding))\n",
+        "                readmes_to_insert.append(readme)\n",
+        "            else:\n",
+        "                for i, chunk in enumerate(string_into_chunks(readme['text'])):\n",
+        "                    _readme = {\n",
+        "                        **readme,\n",
+        "                        'text': chunk,\n",
+        "                        'created_at': time()\n",
+        "                    }\n",
+        "\n",
+        "                    _readme['clean_text'] = clean_string(chunk)\n",
+        "                    to_embedding = json.dumps({\n",
+        "                        'model_repo_id': _readme['model_repo_id'],\n",
+        "                        'clean_text': chunk,\n",
+        "                    })\n",
+        "                    _readme['embedding'] = str(create_embedding(to_embedding))\n",
+        "                    readmes_to_insert.append(_readme)\n",
+        "\n",
+        "        with connection.cursor() as cursor:\n",
+        "            cursor.execute(f'''\n",
+        "                INSERT INTO {MODELS_TABLE_NAME} (name, author, repo_id, score, link, still_on_hub, arc, hellaswag, mmlu, truthfulqa, winogrande, gsm8k, downloads, likes, created_at, embedding)\n",
+        "                VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, FROM_UNIXTIME(%s), JSON_ARRAY_PACK(%s))\n",
+        "            ''', tuple(model_to_insert.values()))\n",
+        "\n",
+        "        for chunk in list_into_chunks([tuple(readme.values()) for readme in readmes_to_insert]):\n",
+        "            with connection.cursor() as cursor:\n",
+        "                cursor.executemany(f'''\n",
+        "                    INSERT INTO {MODEL_READMES_TABLE_NAME} (model_repo_id, text, created_at, clean_text, embedding)\n",
+        "                    VALUES (%s, %s, FROM_UNIXTIME(%s), %s, JSON_ARRAY_PACK(%s))\n",
+        "                ''', chunk)\n",
+        "    except Exception as e:\n",
+        "        print('Error leaderboard_insert_model: ', e)\n",
+        "\n",
+        "\n",
+        "def leaderboard_process_models():\n",
+        "    print('Processing models')\n",
+        "\n",
+        "    existed_model_repo_ids = [i[0] for i in get_models('repo_id', as_dict=False)]\n",
+        "    leaderboard_df = leaderboard_get_df()\n",
+        "\n",
+        "    for i, row in leaderboard_df.iterrows():\n",
+        "        if not row['repo_id'] in existed_model_repo_ids:\n",
+        "            leaderboard_insert_model(row.to_dict())"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### 6.2 Loading Data from Github about model usage\n",
+        "We will search the Github API by keyword based on the model names we have above to find their usage across repos. We will then pull data from the ReadME's of the repos that reference a particular model and create an embedding for it.\n",
+        "\n",
+        "This allows us to see in which kinds of scenarios are developers using a particular LLM and incoporate it as a part of our recommendation.\n",
+        "\n",
+        "In the first step we search for the model using the github API"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def github_search_repos(keyword: str, last_created_at):\n",
+        "    repos = []\n",
+        "    headers = {'Authorization': f'token {GITHUB_ACCESS_TOKEN}'}\n",
+        "    query = f'\"{keyword}\" in:name,description,readme'\n",
+        "\n",
+        "    if last_created_at:\n",
+        "        query += f' created:>{last_created_at}'\n",
+        "\n",
+        "    try:\n",
+        "        repos_response = requests.get(\n",
+        "            \"https://api.github.com/search/repositories\",\n",
+        "            headers=headers,\n",
+        "            params={'q': query}\n",
+        "        )\n",
+        "\n",
+        "        if repos_response.status_code == 403:\n",
+        "            # Handle rate limiting\n",
+        "            rate_limit = repos_response.headers['X-RateLimit-Reset']\n",
+        "            if not rate_limit:\n",
+        "                return repos\n",
+        "\n",
+        "            sleep_time = int(rate_limit) - int(time())\n",
+        "            if sleep_time > 0:\n",
+        "                print(f\"Rate limit exceeded. Retrying in {sleep_time} seconds.\")\n",
+        "            sleep(sleep_time)\n",
+        "            return github_search_repos(keyword, last_created_at)\n",
+        "\n",
+        "        if repos_response.status_code != 200:\n",
+        "            return repos\n",
+        "\n",
+        "        for repo in repos_response.json().get('items', []):\n",
+        "            try:\n",
+        "                readme_response = requests.get(repo['contents_url'].replace('{+path}', 'README.md'), headers=headers)\n",
+        "                if repos_response.status_code != 200:\n",
+        "                    continue\n",
+        "\n",
+        "                readme_file = readme_response.json()\n",
+        "                if readme_file['size'] > 7000:\n",
+        "                    continue\n",
+        "\n",
+        "                readme_text = requests.get(readme_file['download_url']).text\n",
+        "                if not readme_text:\n",
+        "                    continue\n",
+        "\n",
+        "                repos.append({\n",
+        "                    'repo_id': repo['id'],\n",
+        "                    'name': repo['name'],\n",
+        "                    'link': repo['html_url'],\n",
+        "                    'created_at': datetime.strptime(repo['created_at'], '%Y-%m-%dT%H:%M:%SZ').timestamp(),\n",
+        "                    'description': repo.get('description', ''),\n",
+        "                    'readme': readme_text,\n",
+        "                })\n",
+        "            except:\n",
+        "                continue\n",
+        "    except:\n",
+        "        return repos\n",
+        "\n",
+        "    return repos"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "After we conduct this serach, we will insert it into another table in the database. The data inserted will have embeddings associated with it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def github_insert_model_repos(model_repo_id, repos):\n",
+        "    for repo in repos:\n",
+        "        try:\n",
+        "            values = []\n",
+        "            value = {\n",
+        "                'model_repo_id': model_repo_id,\n",
+        "                'repo_id': repo['repo_id'],\n",
+        "                'name': repo['name'],\n",
+        "                'description': repo['description'],\n",
+        "                'clean_text': clean_string(repo['readme']),\n",
+        "                'link': repo['link'],\n",
+        "                'created_at': repo['created_at'],\n",
+        "            }\n",
+        "\n",
+        "            to_embedding = {\n",
+        "                'model_repo_id': model_repo_id,\n",
+        "                'name': value['name'],\n",
+        "                'description': value['description'],\n",
+        "                'clean_text': value['clean_text']\n",
+        "            }\n",
+        "\n",
+        "            if count_tokens(value['clean_text']) <= TOKENS_TRASHHOLD_LIMIT:\n",
+        "                embedding = str(create_embedding(json.dumps(to_embedding)))\n",
+        "                values.append({**value, 'embedding': embedding})\n",
+        "            else:\n",
+        "                for chunk in string_into_chunks(value['clean_text']):\n",
+        "                    embedding = str(create_embedding(json.dumps({\n",
+        "                        **to_embedding,\n",
+        "                        'clean_text': chunk\n",
+        "                    })))\n",
+        "                    values.append({**value, 'clean_text': chunk, 'embedding': embedding})\n",
+        "\n",
+        "            for chunk in list_into_chunks([list(value.values()) for value in values]):\n",
+        "                with connection.cursor() as cursor:\n",
+        "                    cursor.executemany(f'''\n",
+        "                        INSERT INTO {MODEL_GITHUB_REPOS_TABLE_NAME} (model_repo_id, repo_id, name, description, clean_text, link, created_at, embedding)\n",
+        "                        VALUES (%s, %s, %s, %s, %s, %s, FROM_UNIXTIME(%s), JSON_ARRAY_PACK(%s))\n",
+        "                    ''', chunk)\n",
+        "        except Exception as e:\n",
+        "            print('Error github_insert_model_repos: ', e)\n",
+        "\n",
+        "\n",
+        "def github_process_models_repos(existed_models):\n",
+        "    print('Processing GitHub posts')\n",
+        "\n",
+        "    for model in existed_models:\n",
+        "        try:\n",
+        "            repo_id = model['repo_id']\n",
+        "            last_created_at = db_get_last_created_at(MODEL_GITHUB_REPOS_TABLE_NAME, repo_id, True)\n",
+        "            keyword = model['name'] if re.search(r'\\d', model['name']) else repo_id\n",
+        "            found_repos = github_search_repos(keyword, last_created_at)\n",
+        "\n",
+        "            if len(found_repos):\n",
+        "                github_insert_model_repos(repo_id, found_repos)\n",
+        "        except Exception as e:\n",
+        "            print('Error github_process_models_repos: ', e)"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### 6.3. Load Data from Twitter about these models."
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "First, we will search Twitter based on the model names we have using the API."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "twitter = tweepy.Client(TWITTER_BEARER_TOKEN)\n",
+        "def twitter_search_posts(keyword, last_created_at):\n",
+        "    posts = []\n",
+        "\n",
+        "    try:\n",
+        "        tweets = twitter.search_recent_tweets(\n",
+        "            query=f'{keyword} -is:retweet',\n",
+        "            tweet_fields=['id', 'text', 'created_at'],\n",
+        "            start_time=last_created_at,\n",
+        "            max_results=100\n",
+        "        )\n",
+        "\n",
+        "        for tweet in tweets.data:\n",
+        "            posts.append({\n",
+        "                'post_id': tweet.id,\n",
+        "                'text': tweet.text,\n",
+        "                'created_at': tweet.created_at,\n",
+        "            })\n",
+        "    except Exception:\n",
+        "        return posts\n",
+        "\n",
+        "    return posts"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Next, we will add the text from the posts per model into another table. This table will also have embeddings associated with it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def twitter_insert_model_posts(model_repo_id, posts):\n",
+        "    for post in posts:\n",
+        "        try:\n",
+        "            values = []\n",
+        "\n",
+        "            value = {\n",
+        "                'model_repo_id': model_repo_id,\n",
+        "                'post_id': post['post_id'],\n",
+        "                'clean_text': clean_string(post['text']),\n",
+        "                'created_at': post['created_at'],\n",
+        "            }\n",
+        "\n",
+        "            to_embedding = {\n",
+        "                'model_repo_id': value['model_repo_id'],\n",
+        "                'clean_text': value['clean_text']\n",
+        "            }\n",
+        "\n",
+        "            embedding = str(create_embedding(json.dumps(to_embedding)))\n",
+        "            values.append({**value, 'embedding': embedding})\n",
+        "\n",
+        "            for chunk in list_into_chunks([list(value.values()) for value in values]):\n",
+        "                with connection.cursor() as cursor:\n",
+        "                    cursor.executemany(f'''\n",
+        "                        INSERT INTO {MODEL_TWITTER_POSTS_TABLE_NAME} (model_repo_id, post_id, clean_text, created_at, embedding)\n",
+        "                        VALUES (%s, %s, %s, %s, JSON_ARRAY_PACK(%s))\n",
+        "                    ''', chunk)\n",
+        "        except Exception as e:\n",
+        "            print('Error twitter_insert_model_posts: ', e)\n",
+        "\n",
+        "def twitter_process_models_posts(existed_models):\n",
+        "    print('Processing Twitter posts')\n",
+        "\n",
+        "    for model in existed_models:\n",
+        "        try:\n",
+        "            repo_id = model['repo_id']\n",
+        "            last_created_at = db_get_last_created_at(MODEL_TWITTER_POSTS_TABLE_NAME, repo_id, True)\n",
+        "            keyword = model['name'] if re.search(r'\\d', model['name']) else repo_id\n",
+        "            found_posts = twitter_search_posts(keyword, last_created_at)\n",
+        "\n",
+        "            if len(found_posts):\n",
+        "                twitter_insert_model_posts(repo_id, found_posts)\n",
+        "        except Exception as e:\n",
+        "            print('Error twitter_process_models_posts: ', e)"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### 6.4. Run the functions we've created above to load the data into SingleStore\n",
+        "First, the notebook creates tables in the database if they don't exist.\n",
+        "Next, the notebook retrieves the specified number of models from the Open LLM Leaderboard dataset, creates embeddings, and enters the data into the `models` and `model_reamdes` tables.\n",
+        "Next, it executes a query to retrieve all the models in the database. Based on these models, Twitter posts, Reddit posts, and GitHub repositories are searched, converted into embeddings and inserted into tables.\n",
+        "\n",
+        "Finally, we get a ready set of data for finding the most appropriate model for any use case using semantic search."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "create_tables()\n",
+        "leaderboard_process_models()\n",
+        "existed_models = get_models('repo_id, name', f'ORDER BY score DESC LIMIT {MODELS_LIMIT}')\n",
+        "twitter_process_models_posts(existed_models)\n",
+        "github_process_models_repos(existed_models)"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### (Optional) 6.5 Run this notebook every hour using our built-in Job Service\n",
+        "By scheduling this notebook to run every hour, the latest data from Hugging Face will be pulled on new models, their scores and their likes/downloads.\n",
+        "This will ensure that you can capture the latest sentiment and usage from Twitter / Github about developers.\n",
+        "\n",
+        "SingleStore Notebook + Job Service makes it really easy to bring real-time data to your vector-based searches and AI/ML models downstream. You can ensure that the data is in the right format and apply python based transformations like creating embeddings on the most newly ingested data. This would've previously required a combination of several serverless technologies alongside your database as we wrote about this [previously](https://www.singlestore.com/blog/a-serverless-architecture-for-creating-openai-embeddings-with-singlestoredb/)"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## (Optional) Step 7: Host the app with Vercel"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Follow our github [repo](https://github.com/singlestore-labs/llm-recommender/tree/main) where we showcase how to write the front end code of the app which does the vector similarity search to provide the results.The front end is built with our [elegance SDK](https://elegancesdk.com/) and hosted with Vercel.\n",
+        "\n",
+        "See our [guide](https://docs.singlestore.com/cloud/integrate-with-singlestoredb-cloud/connect-with-vercel/) on our vercel integration with SingleStore. We have a public version of the app running for free [here](https://llm-recommender.vercel.app/)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "996c0586-1c4b-4c1f-aa37-240d11f544eb",
+      "metadata": {},
+      "source": [
+        "<div id=\"singlestore-footer\" style=\"background-color: rgba(194, 193, 199, 0.25); height:2px; margin-bottom:10px\"></div>\n",
+        "<div><img src=\"https://raw.githubusercontent.com/singlestore-labs/spaces-notebooks/master/common/images/singlestore-logo-grey.png\" style=\"padding: 0px; margin: 0px; height: 24px\"/></div>"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "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": "3.11.6"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
+}
diff --git a/notebooks/real-time-recommendation-engine/singlestore_bundle.pem b/notebooks/real-time-recommendation-engine/singlestore_bundle.pem
new file mode 100644
index 0000000..f9b1e41
--- /dev/null
+++ b/notebooks/real-time-recommendation-engine/singlestore_bundle.pem
@@ -0,0 +1,130 @@
+-----BEGIN CERTIFICATE-----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+-----END CERTIFICATE-----
+-----BEGIN CERTIFICATE-----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+-----END CERTIFICATE-----
+-----BEGIN CERTIFICATE-----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=
+-----END CERTIFICATE-----
+-----BEGIN CERTIFICATE-----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+-----END CERTIFICATE-----