From 532cda7bdb206cb6db7c44f5e7a1652b5418dd61 Mon Sep 17 00:00:00 2001 From: shaikhq Date: Mon, 21 Apr 2025 19:33:53 -0700 Subject: [PATCH 1/5] Added shoe vector search demo Signed-off-by: shaikhq --- ai-vectors/product-recommendation/.env-sample | 8 + ai-vectors/product-recommendation/.gitignore | 1 + ai-vectors/product-recommendation/README.md | 97 + .../__pycache__/utils.cpython-312.pyc | Bin 0 -> 8872 bytes ai-vectors/product-recommendation/db2.ipynb | 3542 +++++++++++++++++ .../product-recommendation/db2connect.pickle | Bin 0 -> 215 bytes .../product-recommendation/db2creds.pickle | Bin 0 -> 245 bytes .../images/FOO-7361.jpeg | Bin 0 -> 35914 bytes .../images/LOO-1396.jpeg | Bin 0 -> 56456 bytes .../images/RUN-5480.jpeg | Bin 0 -> 136948 bytes .../images/RUN-9444.jpeg | Bin 0 -> 52331 bytes .../images/STR-1144.jpeg | Bin 0 -> 43245 bytes .../images/STR-2196.jpeg | Bin 0 -> 161563 bytes .../images/STR-2507.jpeg | Bin 0 -> 53812 bytes .../images/STR-7223.jpeg | Bin 0 -> 37982 bytes .../images/ZEN-2061.jpeg | Bin 0 -> 41432 bytes .../product-recommendation/requirements.txt | 20 + .../shoes-data-generation.ipynb | 764 ++++ .../shoes-data-partitioning.ipynb | 466 +++ .../product-recommendation/shoes-search.ipynb | 1566 ++++++++ .../product-recommendation/shoes-vectors.csv | 501 +++ ai-vectors/product-recommendation/shoes.csv | 501 +++ ai-vectors/product-recommendation/utils.py | 253 ++ 23 files changed, 7719 insertions(+) create mode 100644 ai-vectors/product-recommendation/.env-sample create mode 100644 ai-vectors/product-recommendation/.gitignore create mode 100644 ai-vectors/product-recommendation/README.md create mode 100644 ai-vectors/product-recommendation/__pycache__/utils.cpython-312.pyc create mode 100644 ai-vectors/product-recommendation/db2.ipynb create mode 100644 ai-vectors/product-recommendation/db2connect.pickle create mode 100644 ai-vectors/product-recommendation/db2creds.pickle create mode 100644 ai-vectors/product-recommendation/images/FOO-7361.jpeg create mode 100644 ai-vectors/product-recommendation/images/LOO-1396.jpeg create mode 100644 ai-vectors/product-recommendation/images/RUN-5480.jpeg create mode 100644 ai-vectors/product-recommendation/images/RUN-9444.jpeg create mode 100644 ai-vectors/product-recommendation/images/STR-1144.jpeg create mode 100644 ai-vectors/product-recommendation/images/STR-2196.jpeg create mode 100644 ai-vectors/product-recommendation/images/STR-2507.jpeg create mode 100644 ai-vectors/product-recommendation/images/STR-7223.jpeg create mode 100644 ai-vectors/product-recommendation/images/ZEN-2061.jpeg create mode 100644 ai-vectors/product-recommendation/requirements.txt create mode 100644 ai-vectors/product-recommendation/shoes-data-generation.ipynb create mode 100644 ai-vectors/product-recommendation/shoes-data-partitioning.ipynb create mode 100644 ai-vectors/product-recommendation/shoes-search.ipynb create mode 100644 ai-vectors/product-recommendation/shoes-vectors.csv create mode 100644 ai-vectors/product-recommendation/shoes.csv create mode 100644 ai-vectors/product-recommendation/utils.py diff --git a/ai-vectors/product-recommendation/.env-sample b/ai-vectors/product-recommendation/.env-sample new file mode 100644 index 0000000..c88cad3 --- /dev/null +++ b/ai-vectors/product-recommendation/.env-sample @@ -0,0 +1,8 @@ +WATSONX_PROJECT=4468f0f6-ce79-46aa-9897-d363b9c610ff +WATSONX_APIKEY=kSHPOyVzLPJq3qeodlh_EtUKdGSN0Bwfj7xj5e-QfjGr +database=tpcds +hostname=localhost +port=50000 +protocol=tcpip +uid=shaikhq +pwd=7?EBx|BcxV7.jgG diff --git a/ai-vectors/product-recommendation/.gitignore b/ai-vectors/product-recommendation/.gitignore new file mode 100644 index 0000000..4c49bd7 --- /dev/null +++ b/ai-vectors/product-recommendation/.gitignore @@ -0,0 +1 @@ +.env diff --git a/ai-vectors/product-recommendation/README.md b/ai-vectors/product-recommendation/README.md new file mode 100644 index 0000000..5fc8cb4 --- /dev/null +++ b/ai-vectors/product-recommendation/README.md @@ -0,0 +1,97 @@ +1. **Install Python 3.12** + + RHEL 9.4 includes Python 3.12 in its package repositories. To install it, run: + + ```bash + sudo dnf install python3.12 + ``` + + Verify the installation: + + ```bash + python3.12 --version + ``` + +2. **Install `pip` for Python 3.12** + + To manage Python packages, install `pip` for Python 3.12: + + ```bash + sudo dnf install python3.12-pip + ``` + +3. **Install `uv` for managing python dependencies +```shell +python3.12 -m pip install uv +``` + +4. **Add `uv` to the your shell profile +Add this line to your ~/.kshrc, ~/.profile, or ~/.bashrc (depending on your shell setup). In my case, it is ~/.profile +```shell +vi ~/.profile +``` +First, find the installation path of `uv`: +```shell +python3.12 -m site --user-base +``` + +This gave me: +``` +/home/shaikhq/.local +``` + +Add the following line at the end of the file: +``` +export PATH="$HOME/.local/bin:$PATH" +``` + +Saved changes and exited vi editor. + +Apply the changes: +``` +. ~/.profile +``` + +Verify that `uv` is now on the path: +```shell +uv --version +``` + +uv --version +uv 0.6.15 + +5. **Create a python virtual environment +```shell +uv venv --python=python3.12 +``` + +6. **Activate the python virtual env: +```shell +source .venv/bin/activate +``` + +7. **Install packages from `requirements.txt` +uv pip install -r requirements.txt + +8. **Configure VS Code to Use the Virtual Environment** + + - Open the Command Palette (`Ctrl+Shift+P` or `Cmd+Shift+P` on macOS). + - Type `Python: Select Interpreter` and select the interpreter located in your `.venv` directory. + +9. **Set the Notebook's Python Interpreter in VS Code** + + - Open your Jupyter notebook in VS Code. + - Click on the kernel name at the top right corner. + - Select the interpreter from your `.venv`. + +10. Rename `.env-sample` to `.env` and populate the required API keys and metadata from your target AI platforms. Fill in the necessary values as shown below: + +```env +WATSONX_PROJECT= +WATSONX_APIKEY= +database= +hostname= +port= +protocol= +uid= +pwd= \ No newline at end of file diff --git a/ai-vectors/product-recommendation/__pycache__/utils.cpython-312.pyc b/ai-vectors/product-recommendation/__pycache__/utils.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..954cfc1711b4c7ff42287c9ae40f8686ba513a60 GIT binary patch literal 8872 zcmeG>TTC2RmR0?t=x!d}fC+wt!Vlt<|kb+n7CHcZ@m6c*$4^DVed-B6v%}nRJc00KS~J-l50byzLG(R>s>QFXtVQ zSMVi}SMm(x9=;TEFYgqpc$ZK$VdLF*Y-81Y8T8i7(Ef^DByQMGtF}mZI2hAu#a;&f zR`|b|wIJOJjh&dX)ct%tYn_EQPtU^cp$>3!HU;O9G-bw?@5@?w%d8!sZCW{DS-0xg zXi6!`gPGRorGEB^qOKus`aYnOw6r+!4)~Su49~2W-Uk_B+PB70tq|;X$XYduwH&PfU?jQQnHv%;fi^(eL`!zwV7>doyrLD zCVv4%DjG{CWY#C96rgi>Iynxs6y{~1C8cj^@Vj^nckz^cO{*mnVq6wyg;;>rBt1n%)KHQ^s}I zwdh*vTj^WvS!?*}qsL71b645j^NZ*2b}e?ToPO*&_0(H?uX(w7W%iNx^g_>5cg5Yo z#la={ixI32EDkKiR=dA-H$8_||6>1Ac;&=a{h5s`4}4GDA3q2D%p$W?^G_9fwN#Do zKP zwZQJAw=nZVrh4Ln5(Pbpf(8q6RKAgrTA|+ue;JM1Jhf-ByBRX$Z?cQ)0Xt=8m~p`n z&Ka%!EHt|FyfoSxQ;lYUyGB=@%|`nmej9`P8Enc=nnKKovBCK{Vei!lKYpTAiX7h=I=U?}b%?HL;A4F-md zb^UB0%a> zDoI90LcPHd+kLgLMV(%ty@!C9)wHunO*JJg($pH%R9Q@l2~IL;>lske4zso%(Y4ih z_rWM0Y;w5q(FAW?@s{6N!<663xpX@n5|n8vCBwlRVsA_f(hRFiatbR6iX;j* z!FZBW!j5Z}oS5cYqB6;9w=WBKtHPyXf=qTO2?@A-ZwjoEE@JpAO+FmIC_D`EMmT^* zUrfc(jN)z1wa7w-Lrca3b!jZ{9RM~_A`}k{v#|s>Eei#!tm&3H*X;N4rlFCMmiEqf z&ozND4@W{R=Q}z&n~oF0xqs35k=Jh0i%9-{jDI}G7^Ll+_4|oze!zJnX9}KIPBi?Q zp2>JZSj`R`!_%efFFYmTh~=N4#3pnCWC;H9G04Csc&jTtdp^p0bjiI(S&!NG{Izao zkE*r*P)qSAUB!ArU?YQUB+xw+EEv4PEsJ;Or+9S-LcVwW&4wzPZHNKT(P@Gp=EN9! z*EFJCSUT3XO(SS>j49_$1G@1s2KV7ZYj2F0@qOPQ8DQ;aOz6$F$$&U(0w6bU-_S@P zVrBun9oV8y&3qO2hQWjdqXWHc_rOO3!zLW$y4N{j-mqbhSj_HZ6=F#c4%7X z{|S-Cfwwi$rx?7V6|iv~aaw9Ge!>2dXAdTkQs9sG*Z zo=^uop{rJn$5X0aJ}+JfghN zWqw4>(ODWFQo~&LtAy}_UJgI*r*`4u&C?ksAA9g5_8FHYd7B$8O?C&%m5A#Au@bZH z&@NcbW?M9gSV3ws`T^Oz$*wj%h(%>4N*=7z`vCE8@Ru7P!>_lp3eVo*yrrhWQXeUI zG4nlpZ&L1x`9bZwF7I8037R8-+_{6`NxKLBOrEZT?kTe(5I~AjjClunOz-Ds00zqi zm{IVNP8t5lJR&F%imc-*^yxL-`=(=@vnYnYl(+IW-oEb8y`6K`taYkFpIIog7Gt(8 zi>sC3YFYX^eCd*PD+{oCjc0IV&JJExJE=Kd?+fKi*G*nU6YN3WP#gHisb5-nW`gV+ z{K{I}rL}qYdYQf_8t9!u0*aE(7w|Oyd*0JrGRFY5WN&k&Sw`FCoD*v0WCt#&RgjuH z>xA9IeJjOGlpf$+1#c|jL$*{a;}+;~Ar9yvW!%D7@*dv1URChE7SAI7cIzxicdo2> zzG_NXoi&ZP9nF?{&h$ALHN5R%9#J>kDgt@kj6=7yRT0$y?`Qo_9RUH}G}5 zb^Uk&iwVPVosvSypU`OnFLYt8lLZL9#-BV8vQZx|l&^q9?@v*R8?lpptE20%D7^x* zxd;rJy8h02vYsjQIu)g<_M#4G>T^Knfd_Nmtal2(O^TA4S6|fZlg-&8P-ya}vNVaZ zVzsvF(J z$xJ#0j&ATaOI(UiC!^qA05`T1oax~Ay$zSXY6Zh8;cUM}LLcrf-mMb8swKn(F7E3y zc>tSE+E0t2ErvL6a-dsQ0CdaNrVA z;UoLj-o+PH^KwLUqjK%iD?=yDmdpE3NSur#j>e%1Lzq zann$`WO!@e6lD>b311x;vvB9@8Hl7PDlLMjF;qgD65chcJ0FaQrnsb_GP-vGqAVgGy{#29 zs*CIkqeq~eE|hyz6TE8S07%;z-gn@CDo&d3q8eGKy9V(kgmg>4f+>H3zx)e09`n?* zmcWCzaxKH#Ese$LuuF5|@1jy_RMpKq3?GD1>{vt+upXEx9wYR`=VokUg+GZJiOArT()4} zDfcX$TAW(2?$jP$>B`lfUU2VJ9a@@Q{$PQ5<|7pChgXh1s%c!SS-bJ|8yjzLdj9avmhZxY8xJq<9BJJ6$^TmE(hupqdaA19Wg{{U zndM%&v|O=zbdCF}exv6>-_8+s?GjLKKl7475Qy_EjNTbR)&iRsq&vfZyIShLTf12M zdEI>Xj;HEg-E!Twry=KQ_||g*n9P)IGj%zpZspVyhTXGL-jkXT2R<9vsvLee_b|Ox z{`1GSC@k~+fD?IX!;K0tMU4N>+DQa;7q1QfA=LxN5w$oWjzg4Ai%DuFxjocM(>Or@ zcMIZ?FnliI>5%>bGSv>T&diK7j`d3UdP3l&RBMt;i5MIvcNB^Ckr\n", + "%run db2.ipynb\n", + "\n", + "\n", + "### Install Db2 Extensions\n", + "\n", + "To load the Db2 magic commands into your notebook, run the following command in your Jupyter notebook:\n", + "```\n", + "!wget https://raw.githubusercontent.com/IBM/db2-jupyter/master/db2.ipynb -O db2.ipynb\n", + "```\n", + "\n", + "### Install Db2 Python Driver\n", + "If the ibm_db driver is not installed on your system, the subsequent Db2 commands will fail. In order to install the Db2 driver, issue the following command from a Jupyter notebook cell:\n", + "```\n", + "!pip install ibm_db --user\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Db2 Extensions Loaded.\n" + ] + } + ], + "source": [ + "#\n", + "# Set up Jupyter MAGIC commands \"sql\". \n", + "# %sql will return results from a Db2 select statement or execute a DB2 command\n", + "#\n", + "# IBM 2024: George Baklarz\n", + "# Version 2024-09-16\n", + "#\n", + "\n", + "from __future__ import print_function\n", + "import multiprocessing\n", + "from IPython.display import HTML as pHTML, Image as pImage, display as pdisplay, Javascript as Javascript\n", + "from IPython.core.magic import (Magics, magics_class, line_magic,\n", + " cell_magic, line_cell_magic, needs_local_scope)\n", + "import ibm_db\n", + "import pandas\n", + "import ibm_db_dbi\n", + "import json\n", + "import getpass\n", + "import pickle\n", + "import time\n", + "import re\n", + "import warnings\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import sys\n", + "import copy\n", + "\n", + "_db2magic_version = \"2024-09-16\"\n", + "\n", + "#\n", + "# Empty settings creates a structure that we will use for saving control information\n", + "# on the database. Needs to be at the front of the code to be found in a Python \n", + "# cell.\n", + "#\n", + "\n", + "def emptySettings():\n", + "\n", + " settings = {\n", + " \"maxrows\" : 10,\n", + " \"paging\" : \"ON\",\n", + " \"display\" : \"PANDAS\",\n", + " \"threads\" : 0,\n", + " \"database\" : \"\",\n", + " \"hostname\" : \"localhost\",\n", + " \"port\" : \"50000\",\n", + " \"protocol\" : \"TCPIP\", \n", + " \"uid\" : \"\",\n", + " \"pwd\" : \"\",\n", + " \"ssl\" : \"\",\n", + " \"dsn\" : \"\",\n", + " \"sslfile\" : \"\",\n", + " \"apikey\" : \"\",\n", + " \"accesstoken\" : \"\",\n", + " }\n", + "\n", + " return settings\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "_settings = copy.deepcopy(emptySettings())\n", + "\n", + "_environment = {\n", + " \"jupyter\" : True,\n", + " \"grid\" : True,\n", + " \"gridinit\" : False\n", + "}\n", + "\n", + "# Db2 and Pandas data types\n", + "\n", + "\n", + "_db2types = [\"unknown\",\n", + " \"string\",\n", + " \"smallint\",\n", + " \"int\",\n", + " \"bigint\", \n", + " \"real\",\n", + " \"float\",\n", + " \"decfloat16\",\n", + " \"decfloat34\",\n", + " \"decimal\",\n", + " \"boolean\",\n", + " \"clob\",\n", + " \"blob\",\n", + " \"xml\",\n", + " \"date\",\n", + " \"time\",\n", + " \"timestamp\"]\n", + "\n", + "_pdtypes = [\"object\",\n", + " \"string\",\n", + " \"Int16\",\n", + " \"Int32\",\n", + " \"Int64\",\n", + " \"float32\",\n", + " \"float64\",\n", + " \"float64\",\n", + " \"float64\",\n", + " \"float64\",\n", + " \"boolean\", \n", + " \"string\", \n", + " \"object\",\n", + " \"string\",\n", + " \"string\",\n", + " \"string\",\n", + " \"datetime64[ns]\"] \n", + "\n", + "# Connection settings for statements \n", + "\n", + "_connected = False\n", + "_hdbc = None\n", + "_hdbi = None\n", + "_stmt = []\n", + "_stmtID = []\n", + "_stmtSQL = []\n", + "_vars = {}\n", + "_macros = {}\n", + "_flags = []\n", + "_debug = False\n", + "\n", + "# Db2 Error Messages and Codes\n", + "sqlcode = 0\n", + "sqlstate = \"0\"\n", + "sqlerror = \"\"\n", + "sqlelapsed = 0\n", + "\n", + "# Check to see if QGrid is installed\n", + "\n", + "try:\n", + " from itables import show, init_notebook_mode\n", + "except:\n", + " _environment['grid'] = False\n", + "\n", + "if (_environment['grid'] == False):\n", + " print(\"Warning: Grid display is unavailable for displaying results in scrollable windows.\")\n", + " print(\" Install itables if you want to enable scrolling of result sets.\")\n", + "\n", + "# Check if we are running in iPython or Jupyter\n", + "\n", + "_environment['jupyter'] = True\n", + "\n", + "try:\n", + " if (get_ipython().config == {}): \n", + " _environment['jupyter'] = False\n", + " _environment['grid'] = False\n", + " else:\n", + " _environment['jupyter'] = True\n", + "except:\n", + " _environment['jupyter'] = False\n", + " _environment['grid'] = False\n", + " \n", + "# Check if pandas supports data types in the data frame - Introduced in 1.3 of pandas\n", + "\n", + "_pandas_dtype = False\n", + "try:\n", + " _vrm = pandas.__version__.split(\".\")\n", + " _version = 0\n", + " _release = 0\n", + " _modlevel = 0\n", + " if (len(_vrm) >= 1):\n", + " _version = int(_vrm[0])\n", + " if (len(_vrm) >= 2):\n", + " _release = int(_vrm[1])\n", + " if (len(_vrm) >= 3):\n", + " _modlevel = int(_vrm[2])\n", + " if ((_version >= 1 and _release >= 3) or _version > 1):\n", + " _pandas_dtype = True\n", + " else:\n", + " _pandas_dtype = False\n", + "except:\n", + " _pandas_dtype = False\n", + "\n", + "if (_pandas_dtype == False):\n", + " print(\"Warning: PANDAS level does not support Db2 typing which will can increase memory usage.\")\n", + " print(\" Install PANDAS version 1.3+ for more efficient dataframe creation.\")\n", + " \n", + "# Check if we have parallism available\n", + "\n", + "if (sys.platform == \"darwin\"):\t\t\t\t\t# For the Mac we need multiprocess\n", + " try:\n", + " import multiprocess as mp\t\t\t\t# Check to see if it exists\n", + " from multiprocess.sharedctypes import Value, Array\n", + " _parallel = True\n", + " except:\n", + " print(\"Warning: On OSX you need to pip install the multiprocess package if you want\")\n", + " print(\" parallelism to work.\")\n", + " _parallel = False\n", + "else:\n", + " try:\n", + " import multiprocessing as mp\n", + " from multiprocessing.sharedctypes import Value, Array\n", + " _parallel = True\n", + " except:\n", + " _parallel = False\n", + " \n", + "if (_parallel == False):\n", + " print(\"Warning: Parallelism is unavailable and THREADS option will be ignored.\")\n", + " print(\" Install MULTIPROCESSING/MULTIPROCESS(OSX) if you want allow\")\n", + " print(\" multiple SQL threads to run in parallel.\") \n", + " _settings[\"threads\"] = 0\n", + "\n", + "#\n", + "# Set Options for the Db2 Magic Commands\n", + "#\n", + "\n", + "def setOptions(inSQL):\n", + "\n", + " global _settings\n", + "\n", + " cParms = inSQL.split()\n", + " cnt = 0\n", + " \n", + " if (len(cParms) == 1):\n", + " print(\"(MAXROWS) Maximum number of rows displayed: \" + str(_settings.get(\"maxrows\",10)))\n", + " print(\"(DISPLAY) Use PANDAS or GRID display format for output: \" + _settings.get(\"display\",\"PANDAS\")) \n", + " print(\"(PAGING) Use paging when displaying a grid: \" + _settings.get(\"paging\",\"ON\")) \n", + " print(\"(THREADS) Maximum number of threads to use when running SQL: \" + str(_settings.get(\"threads\",0))) \n", + " return\n", + "\n", + " while cnt < len(cParms):\n", + " \n", + " if cParms[cnt][0] == \"?\":\n", + " print(\"%sql OPTION MAXROWS n PAGING n DISPLAY n THREADS n\")\n", + " print(\"LIST - List the current option settings\")\n", + " print(\"MAXROWS n - The maximum number of rows displayed when returning results\")\n", + " print(\"THREADS n - Maximum number of parallel threads to use when running SQL\")\n", + " print(\"PAGING ON|OFF - Use paging when displaying a grid\")\n", + " return\n", + " \n", + " if cParms[cnt].upper() == 'MAXROWS':\n", + " \n", + " if cnt+1 < len(cParms):\n", + " try:\n", + " _settings[\"maxrows\"] = int(cParms[cnt+1])\n", + " if (_settings[\"maxrows\"] > 100 or _settings[\"maxrows\"] <= 0):\n", + " _settings[\"maxrows\"] = 100\n", + " except Exception as err:\n", + " errormsg(\"Invalid MAXROWS value provided.\")\n", + " pass\n", + " cnt = cnt + 1\n", + " else:\n", + " errormsg(\"No maximum rows specified for the MAXROWS option.\")\n", + " return\n", + " \n", + " elif cParms[cnt].upper() == 'PAGING':\n", + " \n", + " if cnt+1 < len(cParms):\n", + " paging = cParms[cnt+1]\n", + " paging = paging.upper().strip()\n", + " if (paging in [\"OFF\",\"FALSE\"]): # Minimum window size is 100px\n", + " paging = \"OFF\"\n", + " elif (paging in [\"ON\",\"TRUE\"]):\n", + " paging = \"ON\"\n", + " else: \n", + " errormsg(\"Invalid paging [ON|OFF] option provided with the PAGING keyword.\")\n", + " return\n", + " _settings[\"paging\"] = paging\n", + " cnt = cnt + 1\n", + " else:\n", + " errormsg(\"No paging [ON|OFF] option provided with the PAGING keyword.\")\n", + " return \n", + " \n", + " elif cParms[cnt].upper() == 'DISPLAY':\n", + " if cnt+1 < len(cParms):\n", + " if (cParms[cnt+1].upper() == 'GRID'):\n", + " _settings[\"display\"] = 'GRID'\n", + " elif (cParms[cnt+1].upper() == 'PANDAS'):\n", + " _settings[\"display\"] = 'PANDAS'\n", + " else:\n", + " errormsg(\"Invalid DISPLAY value provided.\")\n", + " cnt = cnt + 1\n", + " else:\n", + " errormsg(\"No value provided for the DISPLAY option.\")\n", + " return \n", + " \n", + " elif cParms[cnt].upper() == 'THREADS':\n", + " if cnt+1 < len(cParms):\n", + " try:\n", + " threads = int(cParms[cnt+1])\n", + " if (threads < 0):\n", + " threads = 0\n", + " elif (threads > 12):\n", + " threads = 12\n", + " else:\n", + " pass\n", + " _settings[\"threads\"] = threads\n", + " except Exception as err:\n", + " errormsg(\"Invalid THREADS value provided.\")\n", + " pass\n", + " cnt = cnt + 1\n", + " else:\n", + " errormsg(\"No thread count specified for the THREADS option.\")\n", + " return\n", + " \n", + " elif (cParms[cnt].upper() == 'LIST'):\n", + " print(\"(MAXROWS) Maximum number of rows displayed: \" + str(_settings.get(\"maxrows\",10)))\n", + " print(\"(PAGING) Use paging when displaying a grid: \" + _settings.get(\"paging\",\"ON\"))\n", + " print(\"(DISPLAY) Use PANDAS or GRID display format for output: \" + _settings.get(\"display\",\"PANDAS\"))\n", + " print(\"(THREADS) Maximum number of threads to use when running SQL: \" + str(_settings.get(\"threads\",0)))\n", + " return\n", + " else:\n", + " cnt = cnt + 1\n", + " \n", + " save_settings(_settings)\n", + "\n", + " print(\"(MAXROWS) Maximum number of rows displayed: \" + str(_settings.get(\"maxrows\",10)))\n", + " print(\"(PAGING) Use paging when displaying a grid: \" + _settings.get(\"paging\",\"ON\"))\n", + " print(\"(DISPLAY) Use PANDAS or GRID display format for output: \" + _settings.get(\"display\",\"PANDAS\"))\n", + " print(\"(THREADS) Maximum number of threads to use when running SQL: \" + str(_settings.get(\"threads\",0)))\n", + " \n", + " return\t\n", + "\n", + "#\n", + "# Display help (link to documentation)\n", + "#\n", + "\n", + "def sqlhelp():\n", + " \n", + " print(\"Db2 Magic Documentation: https://ibm.github.io/db2-jupyter/\")\n", + " return\n", + " \n", + "# Split port and IP addresses\n", + "\n", + "def split_string(in_port,splitter=\":\"):\n", + " \n", + " # Split input into an IP address and Port number\n", + "\n", + " checkports = in_port.split(splitter)\n", + " ip = checkports[0]\n", + " if (len(checkports) > 1):\n", + " port = checkports[1]\n", + " else:\n", + " port = None\n", + "\n", + " return ip, port\n", + "\n", + "# Parse the CONNECT statement and execute if possible \n", + "\n", + "def parseConnect(inSQL,local_ns):\n", + "\n", + " import shlex\n", + " \n", + " global _settings, _connected\n", + "\n", + " _connected = False\n", + " \n", + " cParms = shlex.split(inSQL) # inSQL.split()\n", + " cnt = 0\n", + "\n", + " connection = copy.deepcopy(emptySettings())\n", + "\n", + " # CONNECT {no arguments}\n", + "\n", + " if (len(cParms) == 1):\n", + " connection = copy.deepcopy(_settings)\n", + " _ = db2_doConnect(connection)\n", + " return\n", + " \n", + " # Check CONNECT arguments\n", + " \n", + " while cnt < len(cParms):\n", + "\n", + " if cParms[cnt].upper() == 'TO':\n", + " if cnt+1 < len(cParms):\n", + " connection[\"database\"] = cParms[cnt+1].upper()\n", + " cnt = cnt + 1\n", + " else:\n", + " errormsg(\"No database specified in the CONNECT statement\")\n", + " return\n", + " \n", + " elif cParms[cnt].upper() == \"SSL\":\n", + " connection[\"ssl\"] = \"SSL\"\n", + "\n", + " elif cParms[cnt].upper() == \"SSLFILE\":\n", + " if cnt+1 < len(cParms):\n", + " cert = cParms[cnt+1]\n", + " connection[\"sslfile\"] = cert\n", + " connection[\"ssl\"] = \"SSL\"\n", + " cnt = cnt + 1\n", + " else:\n", + " errormsg(\"No filename provided for the SSLFILE option\")\n", + " return\n", + " \n", + " elif cParms[cnt].upper() == 'CREDENTIALS':\n", + " if cnt+1 < len(cParms):\n", + " credentials = cParms[cnt+1]\n", + " if (credentials in local_ns):\n", + " tempid = eval(credentials,local_ns)\n", + " if (isinstance(tempid,dict) == False):\n", + " errormsg(\"The CREDENTIALS variable (\" + credentials + \") does not contain a valid Python dictionary (JSON object)\")\n", + " return\n", + " else:\n", + " tempid = None\n", + " \n", + " if (tempid == None):\n", + " fname = credentials + \".pickle\"\n", + " try:\n", + " with open(fname,'rb') as f:\n", + " _id = pickle.load(f)\n", + " except:\n", + " errormsg(\"Unable to find credential variable or file.\")\n", + " return\n", + " else:\n", + " _id = tempid\n", + " \n", + " try:\n", + " connection[\"database\"] = _id.get(\"database\",\"\")\n", + " connection[\"hostname\"] = _id.get(\"hostname\",\"\")\n", + " connection[\"port\"] = _id.get(\"port\",\"50000\")\n", + " connection[\"uid\"] = _id.get(\"uid\",\"\")\n", + " connection[\"pwd\"] = _id.get(\"pwd\",\"\")\n", + " connection[\"ssl\"] = _id.get(\"ssl\",\"\")\n", + " connection[\"sslfile\"] = _id.get(\"sslfile\",\"\")\n", + " connection[\"apikey\"] = _id.get(\"apikey\",\"\")\n", + " connection[\"accesstoken\"] = _id.get(\"accesstoken\",\"\")\n", + " connection[\"dsn\"] = _id.get(\"dsn\",\"\")\n", + " connection[\"protocol\"] = _id.get(\"protocol\",\"TCPIP\")\n", + " connection[\"paging\"] = _id.get(\"paging\",\"ON\")\n", + " connection[\"display\"] = _id.get(\"display\",\"PANDAS\")\n", + " connection[\"threads\"] = _id.get(\"threads\",0)\n", + " connection[\"maxrows\"] = _id.get(\"maxrows\",250)\n", + " \n", + " try:\n", + " fname = credentials + \".pickle\"\n", + " with open(fname,'wb') as f:\n", + " pickle.dump(connection,f)\n", + " \n", + " except:\n", + " errormsg(\"Failed trying to write Db2 Credentials.\")\n", + " return\n", + " except:\n", + " errormsg(\"Credentials file is missing information.\")\n", + " return\n", + " \n", + " else:\n", + " errormsg(\"No Credentials name supplied\")\n", + " return\n", + " \n", + " cnt = cnt + 1\n", + " \n", + " elif cParms[cnt].upper() == 'USER':\n", + " if cnt+1 < len(cParms):\n", + " connection[\"uid\"] = cParms[cnt+1]\n", + " cnt = cnt + 1\n", + " else:\n", + " errormsg(\"No userid specified in the CONNECT statement\")\n", + " return\n", + " \n", + " elif cParms[cnt].upper() == 'USING':\n", + " if cnt+1 < len(cParms):\n", + " connection[\"pwd\"] = cParms[cnt+1] \n", + " if (connection.get(\"pwd\",\"?\") == '?'):\n", + " connection[\"pwd\"] = getpass.getpass(\"Password [password]: \") or \"password\"\n", + " cnt = cnt + 1\n", + " else:\n", + " errormsg(\"No password specified in the CONNECT statement\")\n", + " return\n", + " \n", + " elif cParms[cnt].upper() == 'HOST':\n", + " if cnt+1 < len(cParms):\n", + " hostport = cParms[cnt+1]\n", + " ip, port = split_string(hostport)\n", + " if (port == None): connection[\"port\"] = \"50000\"\n", + " connection[\"hostname\"] = ip\n", + " cnt = cnt + 1\n", + " else:\n", + " errormsg(\"No hostname specified in the CONNECT statement\")\n", + " return\n", + " \n", + " elif cParms[cnt].upper() == 'PORT': \n", + " if cnt+1 < len(cParms):\n", + " connection[\"port\"] = cParms[cnt+1]\n", + " cnt = cnt + 1\n", + " else:\n", + " errormsg(\"No port specified in the CONNECT statement\")\n", + " return\n", + " \n", + " elif cParms[cnt].upper() == 'DSN': \n", + " if cnt+1 < len(cParms):\n", + " connection[\"dsn\"] = cParms[cnt+1]\n", + " cnt = cnt + 1\n", + " else:\n", + " errormsg(\"No DSN string provided in the CONNECT statement\")\n", + " return\t\n", + " \n", + " elif cParms[cnt].upper() == 'APIKEY': \n", + " if cnt+1 < len(cParms):\n", + " connection[\"apikey\"] = cParms[cnt+1]\n", + " cnt = cnt + 1\n", + " else:\n", + " errormsg(\"No APIKEY string provided in the CONNECT statement\")\n", + " return\t \n", + " \n", + " elif cParms[cnt].upper() == 'ACCESSTOKEN': \n", + " if cnt+1 < len(cParms):\n", + " connection[\"accesstoken\"] = cParms[cnt+1]\n", + " cnt = cnt + 1\n", + " else:\n", + " errormsg(\"No ACCESSTOKEN provided in the CONNECT statement\")\n", + " return\n", + " \n", + " elif cParms[cnt].upper() in ('CLOSE','RESET') :\n", + " try:\n", + " result = ibm_db.close(_hdbc)\n", + " _hdbi.close()\n", + " except:\n", + " pass\n", + " success(\"Connection closed.\") \n", + " if cParms[cnt].upper() == 'RESET': \n", + " _settings = copy.deepcopy(emptySettings())\n", + " save_settings(_settings)\n", + " return\n", + " else:\n", + " pass\n", + " \n", + " cnt = cnt + 1\n", + " \n", + " _ = db2_doConnect(connection)\n", + "\n", + "# Build the DSN string based on the connection information\n", + "\n", + "def buildDSN(connection,reconnect=False):\n", + " \n", + " database = connection.get(\"database\",None)\n", + " hostname = connection.get(\"hostname\",None)\n", + " port = connection.get(\"port\",\"50000\")\n", + " uid = connection.get(\"uid\",None)\n", + " pwd = connection.get(\"pwd\",None)\n", + " ssl = connection.get(\"ssl\",None)\n", + " sslfile = connection.get(\"sslfile\",None)\n", + " apikey = connection.get(\"apikey\",None)\n", + " accesstoken = connection.get(\"accesstoken\",None)\n", + " user_dsn = connection.get(\"dsn\",None)\n", + "\n", + " dsn = None\n", + "\n", + " while True:\n", + "\n", + " # CONNECT TO HOST PORT APIKEY \n", + "\n", + " if (apikey not in [\"\",None]): # APIKEY does not require a userid/password \n", + "\n", + " if (hostname in [None,\"\"] or port in [None,\"\"] or database in [None,\"\"]):\n", + " if (reconnect == False):\n", + " errormsg(\"Connect using an APIKEY requires HOST, PORT, and DATABASE name\")\n", + " return None \n", + "\n", + " if (uid not in [\"\",None]):\n", + " \n", + " dsn = (\"DRIVER={IBM DB2 ODBC DRIVER};\"\n", + " f\"DATABASE={database};\"\n", + " f\"HOSTNAME={hostname};\"\n", + " f\"PORT={port};\"\n", + " f\"PROTOCOL=TCPIP;ConnectTimeout=15;loginTimeout=15;LONGDATACOMPAT=1;DeferredPrepare=1;\"\n", + " f\"UID={uid};\"\n", + " f\"AUTHENTICATION=GSSplugin;SECURITY=SSL;\"\n", + " f\"APIKEY={apikey};\")\n", + " \n", + " else:\n", + "\n", + " dsn = (\"DRIVER={IBM DB2 ODBC DRIVER};\"\n", + " f\"DATABASE={database};\"\n", + " f\"HOSTNAME={hostname};\"\n", + " f\"PORT={port};\"\n", + " f\"PROTOCOL=TCPIP;ConnectTimeout=15;loginTimeout=15;LONGDATACOMPAT=1;DeferredPrepare=1;\"\n", + " f\"AUTHENTICATION=GSSplugin;SECURITY=SSL;\"\n", + " f\"APIKEY={apikey};\")\n", + "\n", + " break\n", + "\n", + " # CONNECT TO HOST PORT ACCESSTOKEN \n", + "\n", + " if (accesstoken not in [\"\",None]): # ACCESSTOKEN does not require a userid/password\n", + "\n", + " if (hostname in [None,\"\"] or port in [None,\"\"] or database in [None,\"\"]):\n", + " if (reconnect == False):\n", + " errormsg(\"Connect using an ACCESSTOKEN requires HOST, PORT, and DATABASE name\")\n", + " return None \n", + "\n", + " dsn = (\"DRIVER={IBM DB2 ODBC DRIVER};\"\n", + " f\"DATABASE={database};\"\n", + " f\"HOSTNAME={hostname};\"\n", + " f\"PORT={port};\"\n", + " f\"PROTOCOL=TCPIP;ConnectTimeout=15;loginTimeout=15;LONGDATACOMPAT=1;DeferredPrepare=1;\"\n", + " f\"AUTHENTICATION=GSSplugin;SECURITY=SSL;\"\n", + " f\"ACCESSTOKEN={accesstoken};\")\n", + "\n", + " break\n", + "\n", + " # CONNECT DSN \n", + "\n", + " if (user_dsn not in [\"\",None]): # Absolutely no checking is done on your DSN Setting\n", + "\n", + " dsn = user_dsn\n", + " break\n", + "\n", + " # CONNECT TO HOST PORT SSLFILE \n", + "\n", + " if (hostname in [None,\"\"] or port in [None,\"\"] or database in [None,\"\"]):\n", + " if (reconnect == False):\n", + " errormsg(\"Connect requires a HOST, PORT, and DATABASE name\")\n", + " return None \n", + " \n", + " if (uid in [\"\",None] or pwd in [\"\",None]): \n", + " if (reconnect == False):\n", + " errormsg(\"Connect requires a userid and password\")\n", + " return None\n", + "\n", + " if (ssl not in [\"\",None] or sslfile not in [\"\",None]): # See if you have specified SSL connection\n", + "\n", + " if (sslfile not in [\"\",None]):\n", + "\n", + " dsn = (\"DRIVER={IBM DB2 ODBC DRIVER};\"\n", + " f\"DATABASE={database};\"\n", + " f\"HOSTNAME={hostname};\"\n", + " f\"PORT={port};\"\n", + " f\"UID={uid};\"\n", + " f\"PWD={pwd};\"\n", + " f\"PROTOCOL=TCPIP;ConnectTimeout=15;loginTimeout=15;LONGDATACOMPAT=1;DeferredPrepare=1;\"\n", + " f\"SECURITY=SSL;SSLServerCertificate={sslfile};\") \n", + "\n", + " else:\n", + " \n", + " dsn = (\"DRIVER={IBM DB2 ODBC DRIVER};\"\n", + " f\"DATABASE={database};\"\n", + " f\"HOSTNAME={hostname};\"\n", + " f\"PORT={port};\"\n", + " f\"UID={uid};\"\n", + " f\"PWD={pwd};\"\n", + " f\"PROTOCOL=TCPIP;ConnectTimeout=15;loginTimeout=15;LONGDATACOMPAT=1;DeferredPrepare=1;\"\n", + " f\"SECURITY=SSL;\") \n", + "\n", + " else:\n", + " dsn = (\"DRIVER={IBM DB2 ODBC DRIVER};\"\n", + " f\"DATABASE={database};\"\n", + " f\"HOSTNAME={hostname};\"\n", + " f\"PORT={port};\"\n", + " f\"UID={uid};\"\n", + " f\"PWD={pwd};\"\n", + " f\"PROTOCOL=TCPIP;ConnectTimeout=15;loginTimeout=15;LONGDATACOMPAT=1;DeferredPrepare=1;\")\n", + "\n", + " break\n", + " \n", + " return dsn\n", + "\n", + "def db2_doConnect(connection):\n", + " \n", + " global _hdbc, _hdbi, _connected\n", + " global _settings \n", + "\n", + " reconnect = False\n", + "\n", + " if (connection == None):\n", + " reconnect = True\n", + " connection = copy.deepcopy(_settings)\n", + "\n", + " database = connection.get(\"database\",None)\n", + " hostname = connection.get(\"hostname\",None) \n", + "\n", + " dsn = buildDSN(connection,reconnect=reconnect)\n", + "\n", + " if flag([\"-e\",\"-echo\"]) and dsn not in [None, \"\"]:\n", + " print(dsn)\n", + " return False\n", + "\n", + " if (dsn in [None,\"\"]):\n", + " if (reconnect == False):\n", + " errormsg(\"Empty connection string\")\n", + " return False\n", + " \n", + " # Get a database handle (hdbc) and a statement handle (hstmt) for subsequent access to DB2\n", + "\n", + " try:\n", + " _hdbc = ibm_db.connect(dsn, \"\", \"\")\n", + " except Exception as err:\n", + " print(repr(err))\n", + " # db2_error(False,True) # errormsg(str(err))\n", + " _connected = False\n", + " _settings[\"database\"] = ''\n", + " return False\n", + " \n", + " try:\n", + " _hdbi = ibm_db_dbi.Connection(_hdbc)\n", + " except Exception as err:\n", + " print(repr(err))\n", + " # db2_error(False,True) # errormsg(str(err))\n", + " _connected = False\n", + " _settings[\"database\"] = ''\n", + " return False \n", + " \n", + " _connected = True\n", + " \n", + " # Save the values for future use\n", + " \n", + " save_settings(connection)\n", + " \n", + " success(f\"Connection successful. {database} @ {hostname} \")\n", + " return True\n", + " \n", + "\n", + "def load_settings():\n", + "\n", + " # This routine will load the settings from the previous session if they exist\n", + " \n", + " global _settings\n", + " \n", + " fname = \"db2connect.pickle\"\n", + "\n", + " try:\n", + " with open(fname,'rb') as f: \n", + " _settings = pickle.load(f) \n", + " \n", + " except: \n", + " pass\n", + " \n", + " return\n", + "\n", + "def save_settings(connection):\n", + "\n", + " # This routine will save the current settings if they exist\n", + " \n", + " global _settings\n", + " \n", + " fname = \"db2connect.pickle\"\n", + " \n", + " try:\n", + " with open(fname,'wb') as f:\n", + " pickle.dump(connection,f)\n", + " _settings = copy.deepcopy(connection)\n", + " \n", + " except:\n", + " errormsg(\"Failed trying to write Db2 Configuration Information.\")\n", + " \n", + " return \n", + "\n", + "def db2_error(quiet,connect=False):\n", + " \n", + " global sqlerror, sqlcode, sqlstate, _environment\n", + " \n", + " try:\n", + " if (connect == False):\n", + " errmsg = ibm_db.stmt_errormsg().replace('\\r',' ')\n", + " errmsg = errmsg[errmsg.rfind(\"]\")+1:].strip()\n", + " else:\n", + " errmsg = ibm_db.conn_errormsg().replace('\\r',' ')\n", + " errmsg = errmsg[errmsg.rfind(\"]\")+1:].strip()\n", + " \n", + " sqlerror = errmsg\n", + " \n", + " msg_start = errmsg.find(\"SQLSTATE=\")\n", + " if (msg_start != -1):\n", + " msg_end = errmsg.find(\" \",msg_start)\n", + " if (msg_end == -1):\n", + " msg_end = len(errmsg)\n", + " sqlstate = errmsg[msg_start+9:msg_end]\n", + " else:\n", + " sqlstate = \"0\"\n", + " \n", + " msg_start = errmsg.find(\"SQLCODE=\")\n", + " if (msg_start != -1):\n", + " msg_end = errmsg.find(\" \",msg_start)\n", + " if (msg_end == -1):\n", + " msg_end = len(errmsg)\n", + " sqlcode = errmsg[msg_start+8:msg_end]\n", + " try:\n", + " sqlcode = int(sqlcode)\n", + " except:\n", + " pass\n", + " else: \n", + " sqlcode = 0\n", + " \n", + " except:\n", + " errmsg = \"Unknown error.\"\n", + " sqlcode = -99999\n", + " sqlstate = \"-99999\"\n", + " sqlerror = errmsg\n", + " return\n", + " \n", + " \n", + " msg_start = errmsg.find(\"SQLSTATE=\")\n", + " if (msg_start != -1):\n", + " msg_end = errmsg.find(\" \",msg_start)\n", + " if (msg_end == -1):\n", + " msg_end = len(errmsg)\n", + " sqlstate = errmsg[msg_start+9:msg_end]\n", + " else:\n", + " sqlstate = \"0\"\n", + " \n", + " \n", + " msg_start = errmsg.find(\"SQLCODE=\")\n", + " if (msg_start != -1):\n", + " msg_end = errmsg.find(\" \",msg_start)\n", + " if (msg_end == -1):\n", + " msg_end = len(errmsg)\n", + " sqlcode = errmsg[msg_start+8:msg_end]\n", + " try:\n", + " sqlcode = int(sqlcode)\n", + " except:\n", + " pass\n", + " else:\n", + " sqlcode = 0\n", + " \n", + " if quiet == True: return\n", + " \n", + " if (errmsg == \"\"): return\n", + "\n", + " html = '

'\n", + " \n", + " if (_environment[\"jupyter\"] == True):\n", + " pdisplay(pHTML(html+errmsg+\"

\"))\n", + " else:\n", + " print(errmsg)\n", + " \n", + "# Print out an error message\n", + "\n", + "def errormsg(message):\n", + " \n", + " global _environment\n", + " \n", + " if (message != \"\"):\n", + " html = '

'\n", + " if (_environment[\"jupyter\"] == True):\n", + " pdisplay(pHTML(html + message + \"

\")) \n", + " else:\n", + " print(message)\n", + " \n", + "def success(message):\n", + " \n", + " if (message not in (None,\"\")):\n", + " print(message)\n", + " return \n", + "\n", + "def debug(message,error=False):\n", + " \n", + " global _environment\n", + " \n", + " if (message in (None,\"\")):\n", + " return\n", + " \n", + " if (_environment[\"jupyter\"] == True):\n", + " spacer = \"
\" + \" \"\n", + " else:\n", + " spacer = \"\\n \"\n", + " \n", + " lines = message.split('\\n')\n", + " msg = \"\"\n", + " indent = 0\n", + " for line in lines:\n", + " delta = line.count(\"(\") - line.count(\")\")\n", + " if (msg == \"\"):\n", + " msg = line\n", + " indent = indent + delta\n", + " else:\n", + " if (delta < 0): indent = indent + delta\n", + " msg = msg + spacer * (indent*2) + line\n", + " if (delta > 0): indent = indent + delta \n", + "\n", + " if (indent < 0): indent = 0\n", + " if (error == True): \n", + " html = '

'                  \n",
+    "    else:\n",
+    "        html = '
'\n",
+    "\n",
+    "    if (_environment[\"jupyter\"] == True):\n",
+    "        pdisplay(pHTML(html + msg + \"
\"))\n",
+    "    else:\n",
+    "        print(msg)\n",
+    "        \n",
+    "    return \n",
+    "\n",
+    "def setMacro(inSQL,parms):\n",
+    "      \n",
+    "    global _macros\n",
+    "    \n",
+    "    names = parms.split()\n",
+    "    if (len(names) < 2):\n",
+    "        errormsg(\"No command name supplied.\")\n",
+    "        return None\n",
+    "    \n",
+    "    macroName = names[1].upper()\n",
+    "    _macros[macroName] = inSQL # inSQL.replace(\"\\t\",\" \")\n",
+    "\n",
+    "    return\n",
+    "\n",
+    "def checkMacro(in_sql):\n",
+    "       \n",
+    "    global _macros\n",
+    "    \n",
+    "    if (len(in_sql) == 0): return(in_sql)          # Nothing to do \n",
+    "    \n",
+    "    tokens = parseArgs(in_sql,None)                # Take the string and reduce into tokens\n",
+    "    \n",
+    "    macro_name = tokens[0].upper()                 # Uppercase the name of the token\n",
+    " \n",
+    "    if (macro_name not in _macros): \n",
+    "        return(in_sql) # No macro by this name so just return the string\n",
+    "\n",
+    "    result = runMacro(_macros[macro_name],in_sql,tokens)  # Execute the macro using the tokens we found\n",
+    "\n",
+    "    return(result)                                 # Runmacro will either return the original SQL or the new one\n",
+    "\n",
+    "def splitassign(arg):\n",
+    "    \n",
+    "    var_name = \"null\"\n",
+    "    var_value = \"null\"\n",
+    "    \n",
+    "    arg = arg.strip()\n",
+    "    eq = arg.find(\"=\")\n",
+    "    if (eq != -1):\n",
+    "        var_name = arg[:eq].strip()\n",
+    "        temp_value = arg[eq+1:].strip()\n",
+    "        if (temp_value != \"\"):\n",
+    "            ch = temp_value[0]\n",
+    "            if (ch in [\"'\",'\"']):\n",
+    "                if (temp_value[-1:] == ch):\n",
+    "                    var_value = temp_value[1:-1]\n",
+    "                else:\n",
+    "                    var_value = temp_value\n",
+    "            else:\n",
+    "                var_value = temp_value\n",
+    "    else:\n",
+    "        var_value = arg\n",
+    "\n",
+    "    return var_name, var_value\n",
+    "\n",
+    "def parseArgs(argin,_vars):\n",
+    "\n",
+    "    quoteChar = \"\"\n",
+    "    blockChar = \"\"\n",
+    "    inQuote = False\n",
+    "    inBlock = False\n",
+    "    inArg = True\n",
+    "    args = []\n",
+    "    arg = ''\n",
+    "\n",
+    "    for ch in argin.lstrip():\n",
+    "        if (inBlock == True):\n",
+    "            if (ch == \")\"):\n",
+    "                inBlock = False\n",
+    "                arg = arg + ch\n",
+    "            else:\n",
+    "                arg = arg + ch\n",
+    "        elif (inQuote == True):\n",
+    "            if (ch == quoteChar):\n",
+    "                inQuote = False   \n",
+    "                arg = arg + ch #z\n",
+    "            else:\n",
+    "                arg = arg + ch\n",
+    "        elif (ch == \"(\"): # Do we have a block\n",
+    "            arg = arg + ch\n",
+    "            inBlock = True\n",
+    "        elif (ch == \"\\\"\" or ch == \"\\'\"): # Do we have a quote\n",
+    "            quoteChar = ch\n",
+    "            arg = arg + ch #z\n",
+    "            inQuote = True\n",
+    "        elif (ch == \" \"):\n",
+    "            if (arg != \"\"):\n",
+    "                arg = subvars(arg,_vars)\n",
+    "                args.append(arg)\n",
+    "            else:\n",
+    "                args.append(\"null\")\n",
+    "            arg = \"\"\n",
+    "        else:\n",
+    "            arg = arg + ch\n",
+    "\n",
+    "    if (arg != \"\"):\n",
+    "        arg = subvars(arg,_vars)\n",
+    "        args.append(arg)   \n",
+    "\n",
+    "    return(args)\n",
+    "\n",
+    "def runMacro(script,in_sql,tokens):\n",
+    "\n",
+    "    result = \"\"\n",
+    "    runIT = True \n",
+    "    code = script.split(\"\\n\")\n",
+    "    level = 0\n",
+    "    runlevel = [True,False,False,False,False,False,False,False,False,False]\n",
+    "    ifcount = 0\n",
+    "    flags = \"\"\n",
+    "    _vars = {}\n",
+    "\n",
+    "    for i in range(0,len(tokens)):\n",
+    "        vstr = str(i)\n",
+    "        _vars[vstr] = tokens[i]\n",
+    "\n",
+    "    if (len(tokens) == 0):\n",
+    "        _vars[\"argc\"] = \"0\"\n",
+    "    else:\n",
+    "        _vars[\"argc\"] = str(len(tokens)-1)\n",
+    "\n",
+    "    for line in code:\n",
+    "        line = line.strip()\n",
+    "        if (line == \"\" or line == \"\\n\"): continue\n",
+    "        if (line[0] == \"#\"): continue    # A comment line starts with a # in the first position of the line\n",
+    "        args = parseArgs(line,_vars)     # Get all of the arguments\n",
+    "        if (args[0] == \"if\"):\n",
+    "            ifcount = ifcount + 1\n",
+    "            if (runlevel[level] == False): # You can't execute this statement\n",
+    "                continue\n",
+    "            level = level + 1    \n",
+    "            if (len(args) < 4):\n",
+    "                print(\"Macro: Incorrect number of arguments for the if clause.\")\n",
+    "                return in_sql\n",
+    "            arg1 = args[1]\n",
+    "            arg2 = args[3]\n",
+    "            if (len(arg2) > 2):\n",
+    "                ch1 = arg2[0]\n",
+    "                ch2 = arg2[-1:]\n",
+    "                if (ch1 in ['\"',\"'\"] and ch1 == ch2):\n",
+    "                    arg2 = arg2[1:-1].strip()\n",
+    "\n",
+    "            op   = args[2]\n",
+    "            if (op in [\"=\",\"==\"]):\n",
+    "                if (arg1 == arg2):\n",
+    "                    runlevel[level] = True\n",
+    "                else:\n",
+    "                    runlevel[level] = False                \n",
+    "            elif (op in [\"<=\",\"=<\"]):\n",
+    "                if (arg1 <= arg2):\n",
+    "                    runlevel[level] = True\n",
+    "                else:\n",
+    "                    runlevel[level] = False                \n",
+    "            elif (op in [\">=\",\"=>\"]):                    \n",
+    "                if (arg1 >= arg2):\n",
+    "                    runlevel[level] = True\n",
+    "                else:\n",
+    "                    runlevel[level] = False                                       \n",
+    "            elif (op in [\"<>\",\"!=\"]):                    \n",
+    "                if (arg1 != arg2):\n",
+    "                    runlevel[level] = True\n",
+    "                else:\n",
+    "                    runlevel[level] = False  \n",
+    "            elif (op in [\"<\"]):\n",
+    "                if (arg1 < arg2):\n",
+    "                    runlevel[level] = True\n",
+    "                else:\n",
+    "                    runlevel[level] = False                \n",
+    "            elif (op in [\">\"]):\n",
+    "                if (arg1 > arg2):\n",
+    "                    runlevel[level] = True\n",
+    "                else:\n",
+    "                    runlevel[level] = False                \n",
+    "            else:\n",
+    "                print(\"Macro: Unknown comparison operator in the if statement:\" + op)\n",
+    "\n",
+    "                continue\n",
+    "\n",
+    "        elif (args[0] in [\"exit\",\"echo\"] and runlevel[level] == True):\n",
+    "            msg = \"\"\n",
+    "            for msgline in args[1:]:\n",
+    "                if (msg == \"\"):\n",
+    "                    msg = subvars(msgline,_vars)\n",
+    "                else:\n",
+    "                    msg = msg + \" \" + subvars(msgline,_vars)\n",
+    "            if (msg != \"\"): \n",
+    "                if (args[0] == \"echo\"):\n",
+    "                    debug(msg,error=False)\n",
+    "                else:\n",
+    "                    debug(msg,error=True)\n",
+    "            if (args[0] == \"exit\"): return ''\n",
+    "\n",
+    "        elif (args[0] == \"pass\" and runlevel[level] == True):\n",
+    "            pass\n",
+    "\n",
+    "        elif (args[0] == \"flags\" and runlevel[level] == True):\n",
+    "            if (len(args) > 1):\n",
+    "                for i in range(1,len(args)):\n",
+    "                    flags = flags + \" \" + args[i]\n",
+    "                flags = flags.strip()\n",
+    "\n",
+    "        elif (args[0] == \"var\" and runlevel[level] == True):\n",
+    "            value = \"\"\n",
+    "            for val in args[2:]:\n",
+    "                if (value == \"\"):\n",
+    "                    value = subvars(val,_vars)\n",
+    "                else:\n",
+    "                    value = value + \" \" + subvars(val,_vars)\n",
+    "            value.strip()\n",
+    "            _vars[args[1]] = value \n",
+    "\n",
+    "        elif (args[0] == 'else'):\n",
+    "\n",
+    "            if (ifcount == level):\n",
+    "                runlevel[level] = not runlevel[level]\n",
+    "\n",
+    "        elif (args[0] == 'return' and runlevel[level] == True):\n",
+    "            return(f\"{flags} {result}\")\n",
+    "\n",
+    "        elif (args[0] == \"endif\"):\n",
+    "            ifcount = ifcount - 1\n",
+    "            if (ifcount < level):\n",
+    "                level = level - 1\n",
+    "                if (level < 0):\n",
+    "                    print(\"Macro: Unmatched if/endif pairs.\")\n",
+    "                    return ''\n",
+    "\n",
+    "        else:\n",
+    "            if (runlevel[level] == True):\n",
+    "                if (result == \"\"):\n",
+    "                    result = subvars(line,_vars)\n",
+    "                else:\n",
+    "                    result = result + \"\\n\" + subvars(line,_vars)\n",
+    "\n",
+    "    return(f\"{flags} {result}\")      \n",
+    "\n",
+    "def subvars(script,_vars):\n",
+    "    \n",
+    "    if (_vars == None): return script\n",
+    "    \n",
+    "    remainder = script\n",
+    "    result = \"\"\n",
+    "    done = False\n",
+    "    \n",
+    "    while done == False:\n",
+    "        bv = remainder.find(\"{\")\n",
+    "        if (bv == -1):\n",
+    "            done = True\n",
+    "            continue\n",
+    "        ev = remainder.find(\"}\")\n",
+    "        if (ev == -1):\n",
+    "            done = True\n",
+    "            continue\n",
+    "        result = result + remainder[:bv]\n",
+    "        vvar = remainder[bv+1:ev].strip()\n",
+    "        remainder = remainder[ev+1:]\n",
+    "        \n",
+    "        modifier = \"\"\n",
+    "        \n",
+    "        if (len(vvar) == 0):\n",
+    "            errormsg(f\"No variable name supplied in the braces {{}}.\")\n",
+    "            return script\n",
+    "        \n",
+    "        upper = False\n",
+    "        allvars = False\n",
+    "        concat = \" \"\n",
+    "        \n",
+    "        if (len(vvar) > 1):\n",
+    "            modifier = vvar[0]\n",
+    "            if (modifier == \"^\"):\n",
+    "                upper = True\n",
+    "                vvar = vvar[1:]\n",
+    "            elif (modifier == \"*\"):\n",
+    "                vvar = vvar[1:]\n",
+    "                allvars = True\n",
+    "                concat = \" \"\n",
+    "            elif (vvar[0] == \",\"):\n",
+    "                vvar = vvar[1:]\n",
+    "                allvars = True\n",
+    "                concat = \",\"\n",
+    "            else:\n",
+    "                pass\n",
+    "        \n",
+    "        if (vvar in _vars):\n",
+    "            if (upper == True):\n",
+    "                items = _vars[vvar].upper()\n",
+    "            elif (allvars == True):\n",
+    "                try:\n",
+    "                    iVar = int(vvar)\n",
+    "                except:\n",
+    "                    return(script)\n",
+    "                items = \"\"\n",
+    "                sVar = str(iVar)\n",
+    "                while sVar in _vars:\n",
+    "                    if (items == \"\"):\n",
+    "                        items = _vars[sVar]\n",
+    "                    else:\n",
+    "                        items = items + concat + _vars[sVar]\n",
+    "                    iVar = iVar + 1\n",
+    "                    sVar = str(iVar)\n",
+    "            else:\n",
+    "                items = _vars[vvar]\n",
+    "        else:\n",
+    "            if (allvars == True):\n",
+    "                items = \"\"\n",
+    "            else:\n",
+    "                items = \"null\"                \n",
+    "                 \n",
+    "        result = result + items\n",
+    "                \n",
+    "    if (remainder != \"\"):\n",
+    "        result = result + remainder\n",
+    "        \n",
+    "    return(result)\n",
+    "\n",
+    "def splitargs(arguments):\n",
+    "    \n",
+    "    import types\n",
+    "    \n",
+    "    # String the string and remove the ( and ) characters if they at the beginning and end of the string\n",
+    "    \n",
+    "    results = []\n",
+    "    \n",
+    "    step1 = arguments.strip()\n",
+    "    if (len(step1) == 0): return(results)       # Not much to do here - no args found\n",
+    "    \n",
+    "    if (step1[0] == '('):\n",
+    "        if (step1[-1:] == ')'):\n",
+    "            step2 = step1[1:-1]\n",
+    "            step2 = step2.strip()\n",
+    "        else:\n",
+    "            step2 = step1\n",
+    "    else:\n",
+    "        step2 = step1\n",
+    "            \n",
+    "    # Now we have a string without brackets. Start scanning for commas\n",
+    "            \n",
+    "    quoteCH = \"\"\n",
+    "    pos = 0\n",
+    "    arg = \"\"\n",
+    "    args = []\n",
+    "            \n",
+    "    while pos < len(step2):\n",
+    "        ch = step2[pos]\n",
+    "        if (quoteCH == \"\"):                     # Are we in a quote?\n",
+    "            if (ch in ('\"',\"'\")):               # Check to see if we are starting a quote\n",
+    "                quoteCH = ch\n",
+    "                arg = arg + ch\n",
+    "                pos += 1\n",
+    "            elif (ch == \",\"):                   # Are we at the end of a parameter?\n",
+    "                arg = arg.strip()\n",
+    "                args.append(arg)\n",
+    "                arg = \"\"\n",
+    "                inarg = False \n",
+    "                pos += 1\n",
+    "            else:                               # Continue collecting the string\n",
+    "                arg = arg + ch\n",
+    "                pos += 1\n",
+    "        else:\n",
+    "            if (ch == quoteCH):                 # Are we at the end of a quote?\n",
+    "                arg = arg + ch                  # Add the quote to the string\n",
+    "                pos += 1                        # Increment past the quote\n",
+    "                quoteCH = \"\"                    # Stop quote checking (maybe!)\n",
+    "            else:\n",
+    "                pos += 1\n",
+    "                arg = arg + ch\n",
+    "\n",
+    "    if (quoteCH != \"\"):                         # So we didn't end our string\n",
+    "        arg = arg.strip()\n",
+    "        args.append(arg)\n",
+    "    elif (arg != \"\"):                           # Something left over as an argument\n",
+    "        arg = arg.strip()\n",
+    "        args.append(arg)\n",
+    "    else:\n",
+    "        pass\n",
+    "    \n",
+    "    results = []\n",
+    "    \n",
+    "    for arg in args:\n",
+    "        result = []\n",
+    "        if (len(arg) > 0):\n",
+    "            if (arg[0] in ('\"',\"'\")):\n",
+    "                value = arg[1:-1]\n",
+    "                isString = True\n",
+    "                isNumber = False\n",
+    "            else:\n",
+    "                isString = False \n",
+    "                isNumber = False \n",
+    "                try:\n",
+    "                    value = eval(arg)\n",
+    "                    if (type(value) == int):\n",
+    "                        isNumber = True\n",
+    "                    elif (isinstance(value,float) == True):\n",
+    "                        isNumber = True\n",
+    "                    else:\n",
+    "                        value = arg\n",
+    "                except:\n",
+    "                    value = arg\n",
+    "\n",
+    "        else:\n",
+    "            value = \"\"\n",
+    "            isString = False\n",
+    "            isNumber = False\n",
+    "            \n",
+    "        result = [value,isString,isNumber]\n",
+    "        results.append(result)\n",
+    "        \n",
+    "    return results\n",
+    "\n",
+    "def createDF(hdbc,hdbi,sqlin,local_ns):\n",
+    "    \n",
+    "    import datetime\n",
+    "    import ibm_db    \n",
+    "    import shlex\n",
+    "    \n",
+    "    global sqlcode, _settings, _parallel\n",
+    "    \n",
+    "    NoDF  = False\n",
+    "    YesDF = True\n",
+    "    \n",
+    "    if (hdbc == None or hdbi == None):\n",
+    "        errormsg(\"You need to connect to a database before issuing this command.\")\n",
+    "        return NoDF, None\n",
+    "    \n",
+    "    # Strip apart the command into tokens based on spaces\n",
+    "\n",
+    "    tokens = shlex.split(sqlin)\n",
+    "\n",
+    "    token_count = len(tokens)\n",
+    "    \n",
+    "    if (token_count < 5): # Not enough parameters\n",
+    "        errormsg(\"Insufficient arguments for USING command.\")\n",
+    "        return NoDF, None\n",
+    "        \n",
+    "    keyword_command = tokens[0].upper()\n",
+    "    dfName          = tokens[1]\n",
+    "    keyword_create  = tokens[2].upper()\n",
+    "    keyword_table   = tokens[3].upper()\n",
+    "    table           = tokens[4]           \n",
+    "    \n",
+    "    if (dfName not in local_ns):\n",
+    "        errormsg(f'The variable \"{dfName}\" does not exist in the local variable list.')\n",
+    "        return NoDF, None    \n",
+    "\n",
+    "    try:\n",
+    "        dfValue = eval(dfName,None,local_ns) # globals()[varName] # eval(varName)\n",
+    "    except:\n",
+    "        errormsg(f'The variable \"{dfName}\" does not contain a value.')\n",
+    "        return NoDF, None     \n",
+    "   \n",
+    "    if (keyword_create in (\"SELECT\",\"WITH\")):\n",
+    "        \n",
+    "        if (_parallel == False):\n",
+    "            errormsg(\"Parallelism is not availble on this system.\")\n",
+    "            return NoDF, None\n",
+    " \n",
+    "        thread_count = _settings.get(\"threads\",0)\n",
+    "        if (thread_count in (0,1)):\n",
+    "            errormsg(\"The THREADS option is currently set to 0 or 1 which disables parallelism.\")\n",
+    "            return NoDF, None      \n",
+    "        \n",
+    "        ok, df = dfSQL(hdbc,hdbi,sqlin,dfName,dfValue,thread_count)\n",
+    "        \n",
+    "        if (ok == False):\n",
+    "            return NoDF, None\n",
+    "        else:\n",
+    "            return YesDF, df\n",
+    "                \n",
+    "    if (isinstance(dfValue,pandas.DataFrame) == False): # Not a Pandas dataframe\n",
+    "        errormsg(f'The variable \"{dfName}\" is not a Pandas dataframe.')\n",
+    "        return NoDF, None\n",
+    "    \n",
+    "    if (keyword_create not in (\"CREATE\",\"REPLACE\",\"APPEND\",\"DECLARE\") or keyword_table != \"TABLE\"):\n",
+    "        errormsg(\"Incorrect syntax: %sql using  [create | replace | append | declare] table  [options]\")\n",
+    "        return NoDF, None\n",
+    "        \n",
+    "    flag_withdata = False\n",
+    "    flag_asis     = False\n",
+    "    flag_export   = False\n",
+    "    flag_ddl      = False\n",
+    "    clob_type     = \"CLOB\"\n",
+    "    blob_type     = \"BLOB\"\n",
+    "    char_type     = \"VARCHAR\"\n",
+    "    limit         = -1\n",
+    "    column_list   = []\n",
+    "    column_index  = []\n",
+    "    padding       = 1.0\n",
+    "        \n",
+    "    token_idx = 5\n",
+    "    while (token_idx < token_count):\n",
+    "        option_key = tokens[token_idx].upper()\n",
+    "        if (token_idx + 1 >= token_count):\n",
+    "            errormsg(f\"Insufficient arguments for the {option_key} option.\")\n",
+    "            return NoDF, None\n",
+    "        option_val = tokens[token_idx+1].upper()\n",
+    "        if (option_key == \"WITH\" and option_val == \"DATA\"):\n",
+    "            flag_withdata = True\n",
+    "            token_idx += 2\n",
+    "        elif (option_key == \"NAMES\" and option_val == \"ASIS\"):\n",
+    "            flag_asis = True\n",
+    "            token_idx += 2\n",
+    "        elif (option_key == \"DDL\"):\n",
+    "            if (option_val == \"EXPORT\"):\n",
+    "                flag_export = True\n",
+    "            elif (option_val == \"ONLY\"):\n",
+    "                flag_ddl = True\n",
+    "            else:\n",
+    "                errormsg(\"DDL option requires EXPORT or ONLY keyword\")\n",
+    "                return NoDF, None\n",
+    "            token_idx += 2  \n",
+    "        elif (option_key in [\"NCHAR\",\"PADDING\"]):\n",
+    "            try:\n",
+    "                padding = float(option_val)\n",
+    "            except:\n",
+    "                errormsg(\"The NCHAR/PADDING value must be a valid decimal value greater than 1.0\")\n",
+    "                return NoDF, None\n",
+    "            if (padding <= 1.0 or padding > 5.0):\n",
+    "                padding = 1.0 \n",
+    "            token_idx += 2\n",
+    "        elif (option_key == \"LIMIT\"):\n",
+    "            if (option_val.isnumeric() == False):\n",
+    "                errormsg(\"The LIMIT must be a valid number from -1 (unlimited) to the maximun number of rows to insert\")\n",
+    "                return NoDF, None\n",
+    "            limit = int(option_val)\n",
+    "            token_idx += 2\n",
+    "        elif (option_key == \"COLUMNS\"):\n",
+    "            column_list == []\n",
+    "            for i in range (token_idx + 1, token_count):\n",
+    "                column_list.append(tokens[i].upper())\n",
+    "            token_idx = token_count\n",
+    "        else:\n",
+    "            errormsg(\"Invalid option. [WITH DATA] [DDL (ONLY | EXPORT)] [NAMES ASIS] [PADDING x] [LIMIT count] [COLUMNS list]\")\n",
+    "            return NoDF, None   \n",
+    "\n",
+    "    if (keyword_create == \"REPLACE\" and (flag_ddl == False and flag_export == False)):\n",
+    "        sql = f\"DROP TABLE {table}\"\n",
+    "        ok = execSQL(hdbc,sql,quiet=True)   \n",
+    "\n",
+    "    sql = [] \n",
+    "    columns = dict(dfValue.dtypes)\n",
+    "    if (keyword_create == \"DECLARE\"):\n",
+    "        sql.append(f'CREATE GLOBAL TEMPORARY TABLE {table} (')\n",
+    "    else:\n",
+    "        sql.append(f'CREATE TABLE {table} (')\n",
+    "    datatypes = []\n",
+    "    sql_types = []\n",
+    "    column_names = []\n",
+    "    comma = \"\"\n",
+    "\n",
+    "    idx = -1\n",
+    "\n",
+    "    temp_column_list = []\n",
+    "    for column in columns:\n",
+    "        temp_column_list.append(column.upper())\n",
+    "\n",
+    "    for column_requested in column_list:\n",
+    "        if (column_requested not in temp_column_list):\n",
+    "            errormsg(f'Column \"{column_requested}\" was not found in the dataframe.')\n",
+    "            return NoDF, None             \n",
+    "\n",
+    "    for column in columns:\n",
+    "\n",
+    "        if (len(column_list) != 0):\n",
+    "            if (column.upper() not in column_list):\n",
+    "                idx += 1\n",
+    "                continue\n",
+    "            column_found = True\n",
+    "\n",
+    "        idx += 1\n",
+    "        datatype = str(columns[column])\n",
+    "        datatype = datatype.upper()\n",
+    "        column_index.append(idx)\n",
+    " \n",
+    "        if (datatype == \"OBJECT\"):\n",
+    "            maxlength = dfValue[column].apply(str).apply(lambda x: len(x.encode('utf-8'))).max() \n",
+    "            if (maxlength <= 0): maxlength = 1\n",
+    "            adjusted = int(float(maxlength) * padding) \n",
+    "            if (adjusted < 32000):\n",
+    "                type = f\"{char_type}({adjusted})\" #f\"VARCHAR({adjusted})\"\n",
+    "                sql_type = ibm_db.SQL_VARCHAR\n",
+    "            else:\n",
+    "                type = f\"{clob_type}({adjusted})\" # \"CLOB({adjusted})\"\n",
+    "                if (\"DBCLOB\" in clob_type):\n",
+    "                    sql_type = ibm_db.SQL_DBCLOB    \n",
+    "                else:            \n",
+    "                    sql_type = ibm_db.SQL_CLOB # lob_sqltype\n",
+    "        elif (datatype in [\"INT64\",\"INT32\",\"INT16\",\"INT8\"]):\n",
+    "            count = dfValue[column].count()\n",
+    "            if (count == 0):\n",
+    "                maxlength = dfValue[column].apply(str).apply(lambda x: len(x.encode('utf-8'))).max() \n",
+    "                if (maxlength <= 0): maxlength = 1\n",
+    "                adjusted = int(float(maxlength) * padding) \n",
+    "                type = f\"{char_type}({adjusted})\" \n",
+    "                sql_type = ibm_db.SQL_VARCHAR\n",
+    "            else:\n",
+    "                if (datatype == \"INT64\"):\n",
+    "                    sql_type = ibm_db.SQL_BIGINT\n",
+    "                    type = \"BIGINT\"\n",
+    "                elif (datatype == \"INT32\"):\n",
+    "                    sql_type = ibm_db.SQL_INTEGER\n",
+    "                    type = \"INT\"\n",
+    "                elif (datatype == \"INT16\"):\n",
+    "                    sql_type = ibm_db.SQL_SMALLINT\n",
+    "                    type = \"SMALLINT\"                     \n",
+    "                else:\n",
+    "                    sql_type = ibm_db.SQL_SMALLINT\n",
+    "                    type = \"SMALLINT\"\n",
+    "        elif (datatype in [\"FLOAT64\",\"FLOAT32\",\"FLOAT16\"]):\n",
+    "            count = dfValue[column].count()\n",
+    "            if (count == 0):\n",
+    "                maxlength = dfValue[column].apply(str).apply(lambda x: len(x.encode('utf-8'))).max() \n",
+    "                if (maxlength <= 0): maxlength = 1\n",
+    "                adjusted = int(float(maxlength) * padding) \n",
+    "                type = f\"{char_type}({adjusted})\" \n",
+    "                sql_type = ibm_db.SQL_VARCHAR\n",
+    "            else:\n",
+    "                if (datatype == \"FLOAT64\"):\n",
+    "                    sql_type = ibm_db.SQL_FLOAT\n",
+    "                    type = \"FLOAT\"\n",
+    "                elif (datatype == \"FLOAT32\"):\n",
+    "                    sql_type = ibm_db.SQL_REAL\n",
+    "                    type = \"REAL\"\n",
+    "                else:\n",
+    "                    sql_type = ibm_db.SQL_REAL\n",
+    "                    type = \"REAL\"\n",
+    "        elif (\"DATETIME64\" in datatype):\n",
+    "            type = \"TIMESTAMP\"\n",
+    "            sql_type = ibm_db.SQL_VARCHAR\n",
+    "        elif (datatype == \"BOOL\"):\n",
+    "            type = \"BINARY\"\n",
+    "            sql_type = ibm_db.SQL_BINARY\n",
+    "            maxlength = dfValue[column].apply(str).apply(len).max() \n",
+    "            if (maxlength <= 0): maxlength = 1\n",
+    "            adjusted = int(float(maxlength) * padding) \n",
+    "            if (adjusted < 32000):\n",
+    "                type = f\"{char_type}({adjusted})\" # f\"VARCHAR({adjusted})\"\n",
+    "                sql_type = ibm_db.SQL_VARCHAR\n",
+    "            else:\n",
+    "                type = f\"{blob_type}({adjusted})\" # \"CLOB({adjusted})\"\n",
+    "                sql_type = ibm_db.SQL_BLOB # lob_sqltype\n",
+    "        else:\n",
+    "            maxlength = dfValue[column].apply(str).apply(lambda x: len(x.encode('utf-8'))).max() \n",
+    "            if (maxlength <= 0): maxlength = 1\n",
+    "            adjusted = int(float(maxlength) * padding) \n",
+    "            if (adjusted < 32000):\n",
+    "                type = f\"{char_type}({adjusted})\" # \"VARCHAR({adjusted})\"\n",
+    "                sql_type = ibm_db.SQL_VARCHAR\n",
+    "            else:\n",
+    "                type = f\"{clob_type}({adjusted})\" # \"CLOB({adjusted})\"\n",
+    "                sql_type = ibm_db.SQL_CLOB # lob_sqltype\n",
+    "\n",
+    "        datatypes.append(type)   \n",
+    "        sql_types.append(sql_type) \n",
+    "\n",
+    "        if (flag_asis == False):\n",
+    "            if (isinstance(column,str) == False):\n",
+    "                column = str(column)\n",
+    "            identifier = \"ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789_\"\n",
+    "            column_name = column.strip().upper()\n",
+    "            new_name = \"\"\n",
+    "            for ch in column_name:\n",
+    "                if (ch not in identifier):\n",
+    "                    new_name = new_name + \"_\"\n",
+    "                else:\n",
+    "                    new_name = new_name + ch\n",
+    "                    \n",
+    "            new_name = new_name.lstrip('_').rstrip('_')\n",
+    "            \n",
+    "            if (new_name == \"\" or new_name[0] not in \"ABCDEFGHIJKLMNOPQRSTUVWXYZ\"):\n",
+    "                new_name = f'\"{column}\"'\n",
+    "        else:\n",
+    "            new_name = f'\"{column}\"'\n",
+    "\n",
+    "        column_names.append(new_name)\n",
+    "        \n",
+    "        sql.append(f\"    {new_name} {type}\")\n",
+    "    sql.append(\")\")\n",
+    "\n",
+    "    sqlcmd = \"\"\n",
+    "    for i in range(0,len(sql)):\n",
+    "        if (i > 0 and i < len(sql)-2):\n",
+    "            comma = \",\"\n",
+    "        else:\n",
+    "            comma = \"\"\n",
+    "        if (i == 0):\n",
+    "            sqlcmd = \"{}{}\".format(sql[i],comma)\n",
+    "        else:\n",
+    "            sqlcmd = \"{}\\n{}{}\".format(sqlcmd,sql[i],comma)\n",
+    "\n",
+    "    if (keyword_create == \"DECLARE\"):\n",
+    "        sqlcmd = \"{}\\n{}\".format(sqlcmd,\"ON COMMIT PRESERVE ROWS NOT LOGGED\")\n",
+    "\n",
+    "    if (flag_export == True or flag_ddl == True):\n",
+    "        if (flag_ddl == True):\n",
+    "            print(sqlcmd)\n",
+    "            return NoDF, None\n",
+    "        else:\n",
+    "            return NoDF, sqlcmd\n",
+    "        \n",
+    "    if (keyword_create != \"APPEND\"):\n",
+    "        print(sqlcmd)\n",
+    "        ok = execSQL(hdbc,sqlcmd,quiet=False)\n",
+    "        if (ok == False):\n",
+    "            return NoDF, None\n",
+    "\n",
+    "    if (flag_withdata == True or keyword_create == \"APPEND\"):\n",
+    "        \n",
+    "        autocommit = ibm_db.autocommit(hdbc)\n",
+    "        ibm_db.autocommit(hdbc,False)\n",
+    "\n",
+    "        row_count = 0\n",
+    "        insert_sql = \"\"\n",
+    "        rows, cols = dfValue.shape\n",
+    "\n",
+    "        markers = \"\"\n",
+    "        column_limit = len(sql_types)\n",
+    "\n",
+    "        # column_list = \"\"\n",
+    "        for i in range(0, column_limit):\n",
+    "            if (markers == \"\"):\n",
+    "                markers = \"?\"\n",
+    "            else:\n",
+    "                markers = f\"{markers},?\"\n",
+    "\n",
+    "        insert_sql = f\"INSERT INTO {table} VALUES ({markers})\"\n",
+    "\n",
+    "        try:\n",
+    "            stmt = ibm_db.prepare(hdbc,insert_sql) # Check error code here\n",
+    "            if (stmt in [False,None]): \n",
+    "                db2_error(False)\n",
+    "                return NoDF, None\n",
+    "        except:\n",
+    "            db2_error(False)\n",
+    "            return NoDF, None\n",
+    "\n",
+    "        row_count = 0\n",
+    "\n",
+    "        parms = [None] * column_limit\n",
+    "\n",
+    "        convert_table = []\n",
+    "\n",
+    "        for col in range(0, column_limit):\n",
+    "\n",
+    "            coltype = sql_types[col]\n",
+    "            \n",
+    "            if (coltype in [ibm_db.SQL_BIGINT, ibm_db.SQL_SMALLINT, ibm_db.SQL_INTEGER]):\t\t\t# Big Integer -> Python int\n",
+    "                conversion = \"int\"\n",
+    "            elif (coltype in [ibm_db.SQL_DECFLOAT,ibm_db.SQL_FLOAT,ibm_db.SQL_REAL,ibm_db.SQL_DOUBLE]):\t\t# Decfloat -> convert to character\n",
+    "                conversion = \"float\"\n",
+    "            elif (coltype == ibm_db.SQL_VARCHAR):\t\t# Varchar -> convert to character\n",
+    "                conversion = \"str\"\n",
+    "            elif (coltype == ibm_db.SQL_CLOB):\t\t\t# CLOB -> convert to character\n",
+    "                conversion = \"str\"\n",
+    "            elif (coltype == ibm_db.SQL_BINARY):\t\t# Binary -> Integer\n",
+    "                conversion = \"int\"\n",
+    "            else:\n",
+    "                conversion = \"str\"\n",
+    "            convert_table.append(conversion)\n",
+    "\n",
+    "            try:\n",
+    "                result = ibm_db.bind_param(stmt, col+1, parms[col], ibm_db.SQL_PARAM_INPUT, sql_types[col]) \n",
+    "            except:\n",
+    "                db2_error(False)\n",
+    "                return NoDF, None\n",
+    "        \n",
+    "            if (result in [False,None]):\n",
+    "                errormsg(\"SQL Prepare failed.\")      \n",
+    "                return NoDF, None\n",
+    "\n",
+    "        for row in range(0,rows):\n",
+    "            \n",
+    "            for col in range(0, column_limit):\n",
+    "\n",
+    "                colno = column_index[col]\n",
+    "                checkValue = dfValue.iat[row,colno]\n",
+    "\n",
+    "                conversion = convert_table[col]\n",
+    "                try:\n",
+    "                    if (conversion == None):\t\t\t# Nothing to do\n",
+    "                        converted = checkValue\n",
+    "                    elif (conversion == \"int\"):\t\t\t# Set it to integer\n",
+    "                        try:\n",
+    "                            converted: int = int(checkValue) \n",
+    "                        except ValueError:\n",
+    "                            converted: int = None\n",
+    "                    elif (conversion == \"float\"):\t\t# Set to float\n",
+    "                        try:\n",
+    "                            converted: float = float(checkValue)\n",
+    "                        except ValueError:\n",
+    "                            converted: float = None\n",
+    "                    elif (conversion == \"str\"):\t\t\t# Set to string\n",
+    "                        try:\n",
+    "                            converted: str = str(checkValue)\n",
+    "                        except ValueError:\n",
+    "                            converted: str = None\n",
+    "                    else:\n",
+    "                        converted = checkValue\t\t\t# Hope for the best\n",
+    "                except:\n",
+    "                    converted = checkValue\n",
+    "\n",
+    "                parms[col] = converted\n",
+    "\n",
+    "            try:\n",
+    "                result = ibm_db.execute(stmt,(*parms,)) \n",
+    "            except Exception as e:\n",
+    "                print(\"SQL Execution error due to possible datatype mismatch or column too small\")\n",
+    "                print(repr(e))\n",
+    "                db2_error(True)\n",
+    "                ibm_db.free_result(stmt)\n",
+    "                return NoDF, None\n",
+    "                \n",
+    "            if (result == False): \n",
+    "                errormsg(\"SQL Execute failed.\")      \n",
+    "                return NoDF, None\n",
+    "\n",
+    "            row_count += 1\n",
+    "            if (row_count % 1000 == 0 or row_count == limit):\n",
+    "                ibm_db.commit(hdbc)\n",
+    "                print(f\"\\r{row_count} of {rows} rows inserted.\",end=\"\")\n",
+    "                \n",
+    "            if (row_count == limit):\n",
+    "                break\n",
+    "\n",
+    "        print(f\"\\r{row_count} of {rows} rows inserted.\",end=\"\")\n",
+    "                    \n",
+    "        ibm_db.commit(hdbc)\n",
+    "        ibm_db.autocommit(hdbc,autocommit)\n",
+    "\n",
+    "        print(\"\\nInsert completed.\")\n",
+    "                      \n",
+    "    return NoDF, None\n",
+    "\n",
+    "\n",
+    "def sqlParser(sqlin,local_ns):\n",
+    "       \n",
+    "    sql_cmd = \"\"\n",
+    "    encoded_sql = sqlin\n",
+    "    \n",
+    "    firstCommand = \"(?:^\\s*)([a-zA-Z]+)(?:\\s+.*|$)\"\n",
+    "    \n",
+    "    findFirst = re.match(firstCommand,sqlin)\n",
+    "    \n",
+    "    if (findFirst == None): # We did not find a match so we just return the empty string\n",
+    "        return sql_cmd, encoded_sql\n",
+    "    \n",
+    "    cmd = findFirst.group(1)\n",
+    "    sql_cmd = cmd.upper()\n",
+    "\n",
+    "    #\n",
+    "    # Scan the input string looking for variables in the format :var. If no : is found just return.\n",
+    "    # Var must be alpha+number+_ to be valid\n",
+    "    #\n",
+    "    \n",
+    "    if (':' not in sqlin): # A quick check to see if parameters are in here, but not fool-proof!         \n",
+    "        return sql_cmd, encoded_sql    \n",
+    "    \n",
+    "    inVar = False \n",
+    "    inQuote = \"\" \n",
+    "    varName = \"\"\n",
+    "    encoded_sql = \"\"\n",
+    "    \n",
+    "    STRING = 0\n",
+    "    NUMBER = 1\n",
+    "    LIST = 2\n",
+    "    RAW = 3\n",
+    "    PANDAS = 5\n",
+    "    \n",
+    "    for ch in sqlin:\n",
+    "        if (inVar == True): # We are collecting the name of a variable\n",
+    "            if (ch.upper() in \"@_ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789[]\"):\n",
+    "                varName = varName + ch\n",
+    "                continue\n",
+    "            else:\n",
+    "                if (varName == \"\"):\n",
+    "                    encoded_sql = encoded_sql + \":\"\n",
+    "                elif (varName[0] in ('[',']')):\n",
+    "                    encoded_sql = encoded_sql + \":\" + varName\n",
+    "                else:\n",
+    "                    if (ch == '.'): # If the variable name is stopped by a period, assume no quotes are used\n",
+    "                        flag_quotes = False\n",
+    "                    else:\n",
+    "                        flag_quotes = True\n",
+    "                    varValue, varType = getContents(varName,flag_quotes,local_ns)\n",
+    "                    if (varType != PANDAS and varValue == None):                 \n",
+    "                        encoded_sql = encoded_sql + \":\" + varName\n",
+    "                    else:\n",
+    "                        if (varType == STRING):\n",
+    "                            encoded_sql = encoded_sql + varValue\n",
+    "                        elif (varType == NUMBER):\n",
+    "                            encoded_sql = encoded_sql + str(varValue)\n",
+    "                        elif (varType == RAW):\n",
+    "                            encoded_sql = encoded_sql + varValue\n",
+    "                        elif (varType == PANDAS):\n",
+    "                            insertsql = \"\"\n",
+    "                            coltypes = varValue.dtypes\n",
+    "                            rows, cols = varValue.shape\n",
+    "                            for row in range(0,rows):\n",
+    "                                insertrow = \"\"\n",
+    "                                for col in range(0, cols):\n",
+    "                                    value = varValue.iloc[row][col]\n",
+    "                                    if (coltypes[col] == \"object\"):\n",
+    "                                        value = str(value)\n",
+    "                                        value = addquotes(value,True)\n",
+    "                                    else:\n",
+    "                                        strvalue = str(value)\n",
+    "                                        if (\"NAN\" in strvalue.upper()):\n",
+    "                                            value = \"NULL\"    \n",
+    "                                    if (insertrow == \"\"):\n",
+    "                                        insertrow = f\"{value}\"\n",
+    "                                    else:\n",
+    "                                        insertrow = f\"{insertrow},{value}\"\n",
+    "                                if (insertsql == \"\"):\n",
+    "                                    insertsql = f\"({insertrow})\"\n",
+    "                                else:\n",
+    "                                    insertsql = f\"{insertsql},({insertrow})\"  \n",
+    "                            encoded_sql = encoded_sql + insertsql\n",
+    "                        elif (varType == LIST):\n",
+    "                            start = True\n",
+    "                            for v in varValue:\n",
+    "                                if (start == False):\n",
+    "                                    encoded_sql = encoded_sql + \",\"\n",
+    "                                if (isinstance(v,int) == True):         # Integer value \n",
+    "                                    encoded_sql = encoded_sql + str(v)\n",
+    "                                elif (isinstance(v,float) == True):\n",
+    "                                    encoded_sql = encoded_sql + str(v)\n",
+    "                                else:\n",
+    "                                    flag_quotes = True\n",
+    "                                    try:\n",
+    "                                        if (v.find('0x') == 0):               # Just guessing this is a hex value at beginning\n",
+    "                                            encoded_sql = encoded_sql + v\n",
+    "                                        else:\n",
+    "                                            encoded_sql = encoded_sql + addquotes(v,flag_quotes)      # String\n",
+    "                                    except:\n",
+    "                                        encoded_sql = encoded_sql + addquotes(str(v),flag_quotes)                                   \n",
+    "                                start = False\n",
+    "\n",
+    "                encoded_sql = encoded_sql + ch\n",
+    "                varName = \"\"\n",
+    "                inVar = False  \n",
+    "        elif (inQuote != \"\"):\n",
+    "            encoded_sql = encoded_sql + ch\n",
+    "            if (ch == inQuote): inQuote = \"\"\n",
+    "        elif (ch in (\"'\",'\"')):\n",
+    "            encoded_sql = encoded_sql + ch\n",
+    "            inQuote = ch\n",
+    "        elif (ch == \":\"): # This might be a variable\n",
+    "            varName = \"\"\n",
+    "            inVar = True\n",
+    "        else:\n",
+    "            encoded_sql = encoded_sql + ch\n",
+    "    \n",
+    "    if (inVar == True):\n",
+    "        varValue, varType = getContents(varName,True,local_ns) # We assume the end of a line is quoted\n",
+    "        if (varType != PANDAS and varValue == None):                 \n",
+    "            encoded_sql = encoded_sql + \":\" + varName  \n",
+    "        else:\n",
+    "            if (varType == STRING):\n",
+    "                encoded_sql = encoded_sql + varValue\n",
+    "            elif (varType == RAW):\n",
+    "                encoded_sql = encoded_sql + varValue                \n",
+    "            elif (varType == NUMBER):\n",
+    "                encoded_sql = encoded_sql + str(varValue)\n",
+    "            elif (varType == PANDAS):\n",
+    "                insertsql = \"\"\n",
+    "                coltypes = varValue.dtypes\n",
+    "                rows, cols = varValue.shape\n",
+    "                for row in range(0,rows):\n",
+    "                    insertrow = \"\"\n",
+    "                    for col in range(0, cols):\n",
+    "                        value = varValue.iloc[row][col]\n",
+    "                        if (coltypes[col] == \"object\"):\n",
+    "                            value = str(value)\n",
+    "                            value = addquotes(value,True)\n",
+    "                        else:\n",
+    "                            strvalue = str(value)\n",
+    "                            if (\"NAN\" in strvalue.upper()):\n",
+    "                                value = \"NULL\"    \n",
+    "                        if (insertrow == \"\"):\n",
+    "                            insertrow = f\"{value}\"\n",
+    "                        else:\n",
+    "                            insertrow = f\"{insertrow},{value}\"\n",
+    "                    if (insertsql == \"\"):\n",
+    "                        insertsql = f\"({insertrow})\"\n",
+    "                    else:\n",
+    "                        insertsql = f\"{insertsql},({insertrow})\"  \n",
+    "                encoded_sql = encoded_sql + insertsql                \n",
+    "            elif (varType == LIST):\n",
+    "                flag_quotes = True\n",
+    "                start = True\n",
+    "                for v in varValue:\n",
+    "                    if (start == False):\n",
+    "                        encoded_sql = encoded_sql + \",\"\n",
+    "                    if (isinstance(v,int) == True):         # Integer value \n",
+    "                        encoded_sql = encoded_sql + str(v)\n",
+    "                    elif (isinstance(v,float) == True):\n",
+    "                        encoded_sql = encoded_sql + str(v)\n",
+    "                    else:\n",
+    "                        try:\n",
+    "                            if (v.find('0x') == 0):               # Just guessing this is a hex value\n",
+    "                                encoded_sql = encoded_sql + v\n",
+    "                            else:\n",
+    "                                encoded_sql = encoded_sql + addquotes(v,flag_quotes)              # String\n",
+    "                        except:\n",
+    "                            encoded_sql = encoded_sql + addquotes(str(v),flag_quotes)                                 \n",
+    "                    start = False\n",
+    "\n",
+    "    return sql_cmd, encoded_sql\n",
+    "\n",
+    "def plotData(hdbi, sql):\n",
+    "    \n",
+    "    try:\n",
+    "        df = pandas.read_sql(sql,hdbi)\n",
+    "          \n",
+    "    except Exception as err:\n",
+    "        db2_error(False)\n",
+    "        return\n",
+    "                \n",
+    "        \n",
+    "    if df.empty:\n",
+    "        errormsg(\"No results returned\")\n",
+    "        return\n",
+    "    \n",
+    "    col_count = len(df.columns)\n",
+    "\n",
+    "    if flag([\"-pb\",\"-bar\"]):                                    # Plot 1 = bar chart\n",
+    "    \n",
+    "        if (col_count in (1,2,3)):\n",
+    "            \n",
+    "            if (col_count == 1):\n",
+    " \n",
+    "                df.index = df.index + 1\n",
+    "                _ = df.plot(kind='bar');\n",
+    "                _ = plt.plot();\n",
+    "                \n",
+    "            elif (col_count == 2):\n",
+    " \n",
+    "                xlabel = df.columns.values[0]\n",
+    "                ylabel = df.columns.values[1]\n",
+    "                df.plot(kind='bar',x=xlabel,y=ylabel);\n",
+    "                _ = plt.plot();\n",
+    "                \n",
+    "            else:\n",
+    " \n",
+    "                values = df.columns.values[2]\n",
+    "                columns = df.columns.values[0]\n",
+    "                index = df.columns.values[1]\n",
+    "                pivoted = pandas.pivot_table(df, values=values, columns=columns, index=index) \n",
+    "                _ = pivoted.plot.bar(); \n",
+    "            \n",
+    "        else:\n",
+    "            errormsg(\"Can't determine what columns to plot\")\n",
+    "            return\n",
+    "                    \n",
+    "    elif flag([\"-pp\",\"-pie\"]):                                  # Plot 2 = pie chart\n",
+    "        \n",
+    "        if (col_count in (1,2)):  \n",
+    "                \n",
+    "            if (col_count == 1):\n",
+    "                df.index = df.index + 1\n",
+    "                yname = df.columns.values[0]\n",
+    "                _ = df.plot(kind='pie',y=yname);                \n",
+    "            else:          \n",
+    "                xlabel = df.columns.values[0]\n",
+    "                xname = df[xlabel].tolist()\n",
+    "                yname = df.columns.values[1]\n",
+    "                _ = df.plot(kind='pie',y=yname,labels=xname);\n",
+    "                \n",
+    "            plt.show();\n",
+    "            \n",
+    "        else:\n",
+    "            errormsg(\"Can't determine what columns to plot\")\n",
+    "            return\n",
+    "                    \n",
+    "    elif flag([\"-pl\",\"-line\"]):                                  # Plot 3 = line chart\n",
+    "            \n",
+    "        if (col_count in (1,2,3)): \n",
+    "            \n",
+    "            if (col_count == 1):\n",
+    "                df.index = df.index + 1  \n",
+    "                _ = df.plot(kind='line');          \n",
+    "            elif (col_count == 2):            \n",
+    "                xlabel = df.columns.values[0]\n",
+    "                ylabel = df.columns.values[1]\n",
+    "                _ = df.plot(kind='line',x=xlabel,y=ylabel) ; \n",
+    "            else:         \n",
+    "                values = df.columns.values[2]\n",
+    "                columns = df.columns.values[0]\n",
+    "                index = df.columns.values[1]\n",
+    "                pivoted = pandas.pivot_table(df, values=values, columns=columns, index=index)\n",
+    "                _ = pivoted.plot();\n",
+    "                \n",
+    "            plt.show();\n",
+    "                \n",
+    "        else:\n",
+    "            errormsg(\"Can't determine what columns to plot\")\n",
+    "            return\n",
+    "    else:\n",
+    "        return\n",
+    "\n",
+    "def getContents(varName,flag_quotes,local_ns):\n",
+    "    \n",
+    "    #\n",
+    "    # Get the contents of the variable name that is passed to the routine. Only simple\n",
+    "    # variables are checked, i.e. arrays and lists are not parsed\n",
+    "    #\n",
+    "    \n",
+    "    STRING = 0\n",
+    "    NUMBER = 1\n",
+    "    LIST = 2\n",
+    "    RAW = 3\n",
+    "    DICT = 4\n",
+    "    PANDAS = 5\n",
+    "    \n",
+    "    try:\n",
+    "        value = eval(varName,None,local_ns) # globals()[varName] # eval(varName)\n",
+    "    except:\n",
+    "        return(None,STRING)\n",
+    "    \n",
+    "    if (isinstance(value,dict) == True):          # Check to see if this is JSON dictionary\n",
+    "        return(addquotes(value,flag_quotes),STRING)\n",
+    "\n",
+    "    elif(isinstance(value,list) == True or isinstance(value,tuple) == True):         # List - tricky \n",
+    "        return(value,LIST)\n",
+    "    \n",
+    "    elif (isinstance(value,pandas.DataFrame) == True): # Pandas dataframe\n",
+    "        return(value,PANDAS)\n",
+    "\n",
+    "    elif (isinstance(value,int) == True):         # Integer value \n",
+    "        return(value,NUMBER)\n",
+    "\n",
+    "    elif (isinstance(value,float) == True):       # Float value\n",
+    "        return(value,NUMBER)\n",
+    "\n",
+    "    else:\n",
+    "        try:\n",
+    "            # The pattern needs to be in the first position (0 in Python terms)\n",
+    "            if (value.find('0x') == 0):               # Just guessing this is a hex value\n",
+    "                return(value,RAW)\n",
+    "            else:\n",
+    "                return(addquotes(value,flag_quotes),STRING)                     # String\n",
+    "        except:\n",
+    "            return(addquotes(str(value),flag_quotes),RAW)\n",
+    "\n",
+    "def addquotes(inString,flag_quotes):\n",
+    "    \n",
+    "    if (isinstance(inString,dict) == True):          # Check to see if this is JSON dictionary\n",
+    "        serialized = json.dumps(inString) \n",
+    "    else:\n",
+    "        serialized = inString\n",
+    "\n",
+    "    # Replace single quotes with '' (two quotes) and wrap everything in single quotes\n",
+    "    if (flag_quotes == False):\n",
+    "        return(serialized)\n",
+    "    else:\n",
+    "        return(\"'\"+serialized.replace(\"'\",\"''\")+\"'\")    # Convert single quotes to two single quotes\n",
+    "    \n",
+    "def checkOption(args_in, option, vFalse=False, vTrue=True):\n",
+    "    \n",
+    "    args_out = args_in.strip()\n",
+    "    found = vFalse\n",
+    "    \n",
+    "    if (args_out != \"\"):\n",
+    "        if (args_out.find(option) >= 0):\n",
+    "            args_out = args_out.replace(option,\" \")\n",
+    "            args_out = args_out.strip()\n",
+    "            found = vTrue\n",
+    "\n",
+    "    return args_out, found\n",
+    "\n",
+    "def findProc(procname):\n",
+    "    \n",
+    "    global _hdbc, _hdbi, _connected\n",
+    "    \n",
+    "    # Split the procedure name into schema.procname if appropriate\n",
+    "    upper_procname = procname.upper()\n",
+    "    schema, proc = split_string(upper_procname,\".\") # Expect schema.procname\n",
+    "    if (proc == None):\n",
+    "        proc = schema\n",
+    "\n",
+    "    # Call ibm_db.procedures to see if the procedure does exist\n",
+    "    schema = \"%\"\n",
+    "\n",
+    "    try:\n",
+    "        stmt = ibm_db.procedures(_hdbc, None, schema, proc) \n",
+    "        if (stmt == False):                         # Error executing the code\n",
+    "            errormsg(\"Procedure \" + procname + \" not found in the system catalog.\")\n",
+    "            return None\n",
+    "\n",
+    "        result = ibm_db.fetch_tuple(stmt)\n",
+    "        resultsets = result[5]\n",
+    "        if (resultsets >= 1): resultsets = 1\n",
+    "        return resultsets\n",
+    "            \n",
+    "    except Exception as err:\n",
+    "        errormsg(\"Procedure \" + procname + \" not found in the system catalog.\")\n",
+    "        return None\n",
+    "\n",
+    "def parseCallArgs(macro):\n",
+    "    \n",
+    "    quoteChar = \"\"\n",
+    "    inQuote = False\n",
+    "    inParm = False\n",
+    "    ignore = False\n",
+    "    name = \"\"\n",
+    "    parms = []\n",
+    "    parm = ''\n",
+    "    \n",
+    "    sqlin = macro.replace(\"\\n\",\"\")\n",
+    "    sqlin.lstrip()\n",
+    "    \n",
+    "    for ch in sqlin:\n",
+    "        if (inParm == False):\n",
+    "            # We hit a blank in the name, so ignore everything after the procedure name until a ( is found\n",
+    "            if (ch == \" \"): \n",
+    "                ignore == True\n",
+    "            elif (ch ==  \"(\"): # Now we have parameters to send to the stored procedure\n",
+    "                inParm = True\n",
+    "            else:\n",
+    "                if (ignore == False): name = name + ch # The name of the procedure (and no blanks)\n",
+    "        else:\n",
+    "            if (inQuote == True):\n",
+    "                if (ch == quoteChar):\n",
+    "                    inQuote = False  \n",
+    "                else:\n",
+    "                    parm = parm + ch\n",
+    "            elif (ch in (\"\\\"\",\"\\'\",\"[\")): # Do we have a quote\n",
+    "                if (ch == \"[\"):\n",
+    "                    quoteChar = \"]\"\n",
+    "                else:\n",
+    "                    quoteChar = ch\n",
+    "                inQuote = True\n",
+    "            elif (ch == \")\"):\n",
+    "                if (parm != \"\"):\n",
+    "                    parms.append(parm)\n",
+    "                parm = \"\"\n",
+    "                break\n",
+    "            elif (ch == \",\"):\n",
+    "                if (parm != \"\"):\n",
+    "                    parms.append(parm)                  \n",
+    "                else:\n",
+    "                    parms.append(\"null\")\n",
+    "                    \n",
+    "                parm = \"\"\n",
+    "\n",
+    "            else:\n",
+    "                parm = parm + ch\n",
+    "                \n",
+    "    if (inParm == True):\n",
+    "        if (parm != \"\"):\n",
+    "            parms.append(parm)    \n",
+    "                       \n",
+    "    return(name,parms)\n",
+    "\n",
+    "def getColumns(stmt):\n",
+    "       \n",
+    "    columns = []\n",
+    "    types = []\n",
+    "    colcount = 0\n",
+    "    try:\n",
+    "        colname = ibm_db.field_name(stmt,colcount)\n",
+    "        coltype = ibm_db.field_type(stmt,colcount)\n",
+    "        precision = ibm_db.field_precision(stmt,colcount)\n",
+    "        while (colname != False):\n",
+    "            if (coltype == \"real\"):\n",
+    "                if (precision == 7):\n",
+    "                    coltype = \"real\"\n",
+    "                elif (precision == 15):\n",
+    "                    coltype = \"float\"\n",
+    "                elif (precision == 16):\n",
+    "                    coltype = \"decfloat16\"\n",
+    "                elif (precision == 34):\n",
+    "                    coltype = \"decfloat34\"\n",
+    "                else:\n",
+    "                    coltype = \"real\"\n",
+    "            elif (coltype == \"int\"):\n",
+    "                if (precision == 1):\n",
+    "                    coltype = \"boolean\"\n",
+    "                elif (precision == 5):\n",
+    "                    coltype = \"smallint\"\n",
+    "                elif (precision == 10):\n",
+    "                    coltype = \"int\"\n",
+    "                else:\n",
+    "                    coltype = \"int\"\n",
+    "            columns.append(colname)\n",
+    "            types.append(coltype)\n",
+    "            colcount += 1\n",
+    "            colname = ibm_db.field_name(stmt,colcount)\n",
+    "            coltype = ibm_db.field_type(stmt,colcount)  \n",
+    "            precision = ibm_db.field_precision(stmt,colcount)     \n",
+    "            \n",
+    "        return columns,types   \n",
+    "                \n",
+    "    except Exception as err:\n",
+    "        db2_error(False)\n",
+    "        return None\n",
+    "\n",
+    "def parseCall(hdbc, inSQL, local_ns):\n",
+    "    \n",
+    "    global _hdbc, _hdbi, _connected, _environment\n",
+    "    \n",
+    "    # Check to see if we are connected first\n",
+    "    if (_connected == False):                                      # Check if you are connected \n",
+    "        db2_doConnect(None)\n",
+    "        if _connected == False: return None\n",
+    "     \n",
+    "    remainder = inSQL.strip()\n",
+    "    procName, procArgs = parseCallArgs(remainder[5:]) # Assume that CALL ... is the format\n",
+    "    \n",
+    "    resultsets = findProc(procName)\n",
+    "    if (resultsets == None): return None\n",
+    "    \n",
+    "    argvalues = []\n",
+    " \n",
+    "    if (len(procArgs) > 0): # We have arguments to consider\n",
+    "        for arg in procArgs:\n",
+    "            varname = arg\n",
+    "            if (len(varname) > 0):\n",
+    "                if (varname[0] == \":\"):\n",
+    "                    checkvar = varname[1:]\n",
+    "                    varvalue = getContents(checkvar,True,local_ns)\n",
+    "                    if (varvalue == None):\n",
+    "                        errormsg(\"Variable \" + checkvar + \" is not defined.\")\n",
+    "                        return None\n",
+    "                    argvalues.append(varvalue)\n",
+    "                else:\n",
+    "                    if (varname.upper() == \"NULL\"):\n",
+    "                        argvalues.append(None)\n",
+    "                    else:\n",
+    "                        argvalues.append(varname)\n",
+    "            else:\n",
+    "                argvalues.append(None)\n",
+    "\n",
+    "    \n",
+    "    try:\n",
+    "\n",
+    "        if (len(procArgs) > 0):\n",
+    "            argtuple = tuple(argvalues)\n",
+    "            result = ibm_db.callproc(_hdbc,procName,argtuple)\n",
+    "            stmt = result[0]\n",
+    "        else:\n",
+    "            result = ibm_db.callproc(_hdbc,procName)\n",
+    "            stmt = result\n",
+    "        \n",
+    "        if (resultsets != 0 and stmt != None): \n",
+    "\n",
+    "            columns, types = getColumns(stmt)\n",
+    "            if (columns == None): return None\n",
+    "            \n",
+    "            rows = []\n",
+    "            rowlist = ibm_db.fetch_tuple(stmt)\n",
+    "            while ( rowlist ) :\n",
+    "                row = []\n",
+    "                colcount = 0\n",
+    "                for col in rowlist:\n",
+    "                    try:\n",
+    "                        if (types[colcount] in [\"int\",\"bigint\"]):\n",
+    "                            row.append(int(col))\n",
+    "                        elif (types[colcount] in [\"decimal\",\"real\"]):\n",
+    "                            row.append(float(col))\n",
+    "                        elif (types[colcount] in [\"date\",\"time\",\"timestamp\"]):\n",
+    "                            row.append(str(col))\n",
+    "                        else:\n",
+    "                            row.append(col)\n",
+    "                    except:\n",
+    "                        row.append(col)\n",
+    "                    colcount += 1\n",
+    "                rows.append(row)\n",
+    "                rowlist = ibm_db.fetch_tuple(stmt)\n",
+    "            \n",
+    "            if flag([\"-r\",\"-array\"]):\n",
+    "                rows.insert(0,columns)\n",
+    "                if len(procArgs) > 0:\n",
+    "                    allresults = []\n",
+    "                    allresults.append(rows)\n",
+    "                    for x in result[1:]:\n",
+    "                        allresults.append(x)\n",
+    "                    return allresults # rows,returned_results\n",
+    "                else:\n",
+    "                    return rows\n",
+    "            else:\n",
+    "                df = pandas.DataFrame.from_records(rows,columns=columns)\n",
+    "                if flag(\"-grid\") or _settings.get('display',\"PANDAS\") == 'GRID':\n",
+    "                    if (_environment['grid'] == False):\n",
+    "                        with pandas.option_context('display.max_rows', None, 'display.max_columns', None):  \n",
+    "                            pdisplay(df)\n",
+    "                    else:\n",
+    "                        try:\n",
+    "                            setDisplay(True)\n",
+    "                            if (_settings[\"paging\"] == \"OFF\"):\n",
+    "                                show(df, scrollY=\"300px\", scrollCollapse=True, paging=False)\n",
+    "                            else:\n",
+    "                                show(df)\n",
+    "                            setDisplay(False)\n",
+    "                        except Exception as err:\n",
+    "                            print(repr(err))\n",
+    "                            errormsg(\"Grid control failed to diplay data. Use option -a or %sql OPTION DISPLAY PANDAS instead.\")\n",
+    "                            \n",
+    "                    return                             \n",
+    "                else:\n",
+    "                    if flag([\"-a\",\"-all\"]) or _settings.get(\"maxrows\",10) == -1 : # All of the rows\n",
+    "                        with pandas.option_context('display.max_rows', 100, 'display.max_columns', None): \n",
+    "                            pdisplay(df)\n",
+    "                    else:\n",
+    "                        return df\n",
+    "            \n",
+    "        else:\n",
+    "            if len(procArgs) > 0:\n",
+    "                allresults = []\n",
+    "                for x in result[1:]:\n",
+    "                    allresults.append(x)\n",
+    "                return allresults # rows,returned_results\n",
+    "            else:\n",
+    "                return None\n",
+    "            \n",
+    "    except Exception as err:\n",
+    "        db2_error(False)\n",
+    "        return None\n",
+    "\n",
+    "def parsePExec(hdbc, inSQL):\n",
+    "     \n",
+    "    import ibm_db    \n",
+    "    global _stmt, _stmtID, _stmtSQL, sqlcode\n",
+    "    \n",
+    "    cParms = inSQL.split()\n",
+    "    parmCount = len(cParms)\n",
+    "    if (parmCount == 0): return(None)                          # Nothing to do but this shouldn't happen\n",
+    "    \n",
+    "    keyword = cParms[0].upper()                                  # Upper case the keyword\n",
+    "    \n",
+    "    if (keyword == \"PREPARE\"):                                   # Prepare the following SQL\n",
+    "        uSQL = inSQL.upper()\n",
+    "        found = uSQL.find(\"PREPARE\")\n",
+    "        sql = inSQL[found+7:].strip()\n",
+    "\n",
+    "        try:\n",
+    "            pattern = \"\\?\\*[0-9]+\"\n",
+    "            findparm = re.search(pattern,sql)\n",
+    "            while findparm != None:\n",
+    "                found = findparm.group(0)\n",
+    "                count = int(found[2:])\n",
+    "                markers = ('?,' * count)[:-1]\n",
+    "                sql = sql.replace(found,markers)\n",
+    "                findparm = re.search(pattern,sql)\n",
+    "            \n",
+    "            stmt = ibm_db.prepare(hdbc,sql) # Check error code here\n",
+    "            if (stmt == False): \n",
+    "                db2_error(False)\n",
+    "                return(False)\n",
+    "            \n",
+    "            stmttext = str(stmt).strip()\n",
+    "            stmtID = stmttext[33:48].strip()\n",
+    "            \n",
+    "            if (stmtID in _stmtID) == False:\n",
+    "                _stmt.append(stmt)              # Prepare and return STMT to caller\n",
+    "                _stmtID.append(stmtID)\n",
+    "            else:\n",
+    "                stmtIX = _stmtID.index(stmtID)\n",
+    "                _stmt[stmtIX] = stmt\n",
+    "                 \n",
+    "            return(stmtID)\n",
+    "        \n",
+    "        except Exception as err:\n",
+    "            print(err)\n",
+    "            db2_error(False)\n",
+    "            return(False)\n",
+    "\n",
+    "    if (keyword == \"EXECUTE\"):                                  # Execute the prepare statement\n",
+    "        if (parmCount < 2): return(False)                    # No stmtID available\n",
+    "        \n",
+    "        stmtID = cParms[1].strip()\n",
+    "        if (stmtID in _stmtID) == False:\n",
+    "            errormsg(\"Prepared statement not found or invalid.\")\n",
+    "            return(False)\n",
+    "\n",
+    "        stmtIX = _stmtID.index(stmtID)\n",
+    "        stmt = _stmt[stmtIX]\n",
+    "\n",
+    "        try:        \n",
+    "\n",
+    "            if (parmCount == 2):                           # Only the statement handle available\n",
+    "                result = ibm_db.execute(stmt)               # Run it\n",
+    "            elif (parmCount == 3):                          # Not quite enough arguments\n",
+    "                errormsg(\"Missing or invalid USING clause on EXECUTE statement.\")\n",
+    "                sqlcode = -99999\n",
+    "                return(False)\n",
+    "            else:\n",
+    "                using = cParms[2].upper()\n",
+    "                if (using != \"USING\"):                     # Bad syntax again\n",
+    "                    errormsg(\"Missing USING clause on EXECUTE statement.\")\n",
+    "                    sqlcode = -99999\n",
+    "                    return(False)\n",
+    "                \n",
+    "                uSQL = inSQL.upper()\n",
+    "                found = uSQL.find(\"USING\")\n",
+    "                parmString = inSQL[found+5:].strip()\n",
+    "                parmset = splitargs(parmString)\n",
+    " \n",
+    "                if (len(parmset) == 0):\n",
+    "                    errormsg(\"Missing parameters after the USING clause.\")\n",
+    "                    sqlcode = -99999\n",
+    "                    return(False)\n",
+    "                    \n",
+    "                parm_count = 0\n",
+    "                parms = []\n",
+    "                parms.append(None)\n",
+    "                \n",
+    "                CONSTANT = 0\n",
+    "                VARIABLE = 1\n",
+    "                const = [0]\n",
+    "                const_cnt = 0\n",
+    "                \n",
+    "                for v in parmset:\n",
+    "                    \n",
+    "                    parm_count = parm_count + 1\n",
+    "                    parms.append(None)\n",
+    "                    \n",
+    "                    if (v[1] == True or v[2] == True): # v[1] true if string, v[2] true if num\n",
+    "                        \n",
+    "                        parm_type = CONSTANT                        \n",
+    "                        const_cnt = const_cnt + 1\n",
+    "                        if (v[2] == True):\n",
+    "                            if (isinstance(v[0],int) == True):         # Integer value \n",
+    "                                sql_type = ibm_db.SQL_INTEGER\n",
+    "                            elif (isinstance(v[0],float) == True):       # Float value\n",
+    "                                sql_type = ibm_db.SQL_DOUBLE\n",
+    "                            else:\n",
+    "                                sql_type = ibm_db.SQL_INTEGER\n",
+    "                        else:\n",
+    "                            sql_type = ibm_db.SQL_CHAR\n",
+    "                        \n",
+    "                        const.append(v[0])\n",
+    "\n",
+    "                        \n",
+    "                    else:\n",
+    "                    \n",
+    "                        parm_type = VARIABLE\n",
+    "                    \n",
+    "                        # See if the variable has a type associated with it varname@type\n",
+    "                    \n",
+    "                        varset = v[0].split(\"@\")\n",
+    "                        parm_name = varset[0]\n",
+    "                        \n",
+    "                        parm_datatype = \"char\"\n",
+    "\n",
+    "                        if (parm_name[0] == \":\"):\n",
+    "                            parm_name = parm_name[1:]\n",
+    "\n",
+    "                        # Does the variable exist?\n",
+    "                        if (parm_name not in globals()):\n",
+    "                            errormsg(\"SQL Execute parameter \" + parm_name + \" not found\")\n",
+    "                            sqlcode = -99999\n",
+    "                            return(False)       \n",
+    "                        \n",
+    "                        parms[parm_count] = globals()[parm_name]\n",
+    "        \n",
+    "                        if (len(varset) > 1):                # Type provided\n",
+    "                            parm_datatype = varset[1]\n",
+    "\n",
+    "                        if (parm_datatype == \"dec\" or parm_datatype == \"decimal\"):\n",
+    "                            sql_type = ibm_db.SQL_DOUBLE\n",
+    "                        elif (parm_datatype == \"bin\" or parm_datatype == \"binary\"):\n",
+    "                            sql_type = ibm_db.SQL_BINARY\n",
+    "                        elif (parm_datatype == \"int\" or parm_datatype == \"integer\"):\n",
+    "                            sql_type = ibm_db.SQL_INTEGER\n",
+    "                        elif (parm_datatype == \"file\"):\n",
+    "                            sql_type = ibm_db.SQL_CHAR\n",
+    "                        else:\n",
+    "                            sql_type = ibm_db.SQL_CHAR\n",
+    "                            parms[parm_count] = addquotes(parms[parm_count],False)\n",
+    "                    \n",
+    "                    try:\n",
+    "                        if (parm_type == VARIABLE):\n",
+    "                            if (parm_datatype == \"file\"):\n",
+    "                                result = ibm_db.bind_param(stmt, parm_count, parms[parm_count], ibm_db.PARAM_FILE,ibm_db.SQL_CLOB) \t\t\t\t\t\t\t\t\n",
+    "                            else:\n",
+    "                                result = ibm_db.bind_param(stmt, parm_count, parms[parm_count], ibm_db.SQL_PARAM_INPUT, sql_type)                           \n",
+    "                            # result = ibm_db.bind_param(stmt, parm_count, globals()[parm_name], ibm_db.SQL_PARAM_INPUT, sql_type)\n",
+    "                        else:\n",
+    "                            result = ibm_db.bind_param(stmt, parm_count, const[const_cnt], ibm_db.SQL_PARAM_INPUT, sql_type)\n",
+    "                            \n",
+    "                    except Exception as e:\n",
+    "                        print(repr(e))\n",
+    "                        result = False\n",
+    "                        \n",
+    "                    if (result == False):\n",
+    "                        errormsg(\"SQL Bind on variable \" + parm_name + \" failed.\")\n",
+    "                        sqlcode = -99999\n",
+    "                        return(False) \n",
+    "                    \n",
+    "                result = ibm_db.execute(stmt) # ,tuple(parms))\n",
+    "                \n",
+    "            if (result == False): \n",
+    "                errormsg(\"SQL Execute failed.\")      \n",
+    "                return(False)\n",
+    "            \n",
+    "            if (ibm_db.num_fields(stmt) == 0): return(True) # Command successfully completed\n",
+    "                          \n",
+    "            return(fetchResults(stmt))\n",
+    "                        \n",
+    "        except Exception as err:\n",
+    "            db2_error(False)\n",
+    "            return(False)\n",
+    "        \n",
+    "        return(False)\n",
+    "  \n",
+    "    return(False)     \n",
+    "\n",
+    "def fetchResults(stmt):\n",
+    "     \n",
+    "    global sqlcode\n",
+    "    \n",
+    "    rows = []\n",
+    "    columns, types = getColumns(stmt)\n",
+    "    \n",
+    "    # By default we assume that the data will be an array\n",
+    "    is_array = True\n",
+    "    \n",
+    "    # Check what type of data we want returned - array or json\n",
+    "    if (flag([\"-r\",\"-array\"]) == False):\n",
+    "        # See if we want it in JSON format, if not it remains as an array\n",
+    "        if (flag(\"-json\") == True):\n",
+    "            is_array = False\n",
+    "    \n",
+    "    # Set column names to lowercase for JSON records\n",
+    "    if (is_array == False):\n",
+    "        columns = [col.lower() for col in columns] # Convert to lowercase for each of access\n",
+    "    \n",
+    "    # First row of an array has the column names in it\n",
+    "    if (is_array == True):\n",
+    "        rows.append(columns)\n",
+    "        \n",
+    "    result = ibm_db.fetch_tuple(stmt)\n",
+    "    rowcount = 0\n",
+    "    while (result):\n",
+    "        \n",
+    "        rowcount += 1\n",
+    "        \n",
+    "        if (is_array == True):\n",
+    "            row = []\n",
+    "        else:\n",
+    "            row = {}\n",
+    "            \n",
+    "        colcount = 0\n",
+    "        for col in result:\n",
+    "            try:\n",
+    "                if (types[colcount] in [\"int\",\"bigint\"]):\n",
+    "                    if (is_array == True):\n",
+    "                        row.append(int(col))\n",
+    "                    else:\n",
+    "                        row[columns[colcount]] = int(col)\n",
+    "                elif (types[colcount] in [\"decimal\",\"real\"]):\n",
+    "                    if (is_array == True):\n",
+    "                        row.append(float(col))\n",
+    "                    else:\n",
+    "                        row[columns[colcount]] = float(col)\n",
+    "                elif (types[colcount] in [\"date\",\"time\",\"timestamp\"]):\n",
+    "                    if (is_array == True):\n",
+    "                        row.append(str(col))\n",
+    "                    else:\n",
+    "                        row[columns[colcount]] = str(col)\n",
+    "                else:\n",
+    "                    if (is_array == True):\n",
+    "                        row.append(col)\n",
+    "                    else:\n",
+    "                        row[columns[colcount]] = col\n",
+    "                        \n",
+    "            except:\n",
+    "                if (is_array == True):\n",
+    "                    row.append(col)\n",
+    "                else:\n",
+    "                    row[columns[colcount]] = col\n",
+    "                    \n",
+    "            colcount += 1\n",
+    "        \n",
+    "        rows.append(row)\n",
+    "        result = ibm_db.fetch_tuple(stmt)\n",
+    "        \n",
+    "    if (rowcount == 0): \n",
+    "        sqlcode = 100        \n",
+    "    else:\n",
+    "        sqlcode = 0\n",
+    "        \n",
+    "    return rows\n",
+    "            \n",
+    "\n",
+    "def parseCommit(sql):\n",
+    "    \n",
+    "    global _hdbc, _hdbi, _connected, _stmt, _stmtID, _stmtSQL\n",
+    "\n",
+    "    if (_connected == False): return                        # Nothing to do if we are not connected\n",
+    "    \n",
+    "    cParms = sql.split()\n",
+    "    if (len(cParms) == 0): return                           # Nothing to do but this shouldn't happen\n",
+    "    \n",
+    "    keyword = cParms[0].upper()                             # Upper case the keyword\n",
+    "    \n",
+    "    if (keyword == \"COMMIT\"):                               # Commit the work that was done\n",
+    "        try:\n",
+    "            result = ibm_db.commit (_hdbc)                  # Commit the connection\n",
+    "            if (len(cParms) > 1):\n",
+    "                keyword = cParms[1].upper()\n",
+    "                if (keyword == \"HOLD\"):\n",
+    "                    return\n",
+    "            \n",
+    "            del _stmt[:]\n",
+    "            del _stmtID[:]\n",
+    "\n",
+    "        except Exception as err:\n",
+    "            db2_error(False)\n",
+    "        \n",
+    "        return\n",
+    "        \n",
+    "    if (keyword == \"ROLLBACK\"):                             # Rollback the work that was done\n",
+    "        try:\n",
+    "            result = ibm_db.rollback(_hdbc)                  # Rollback the connection\n",
+    "            del _stmt[:]\n",
+    "            del _stmtID[:]            \n",
+    "\n",
+    "        except Exception as err:\n",
+    "            db2_error(False)\n",
+    "        \n",
+    "        return\n",
+    "    \n",
+    "    if (keyword == \"AUTOCOMMIT\"):                           # Is autocommit on or off\n",
+    "        if (len(cParms) > 1): \n",
+    "            op = cParms[1].upper()                          # Need ON or OFF value\n",
+    "        else:\n",
+    "            return\n",
+    "        \n",
+    "        try:\n",
+    "            if (op == \"OFF\"):\n",
+    "                ibm_db.autocommit(_hdbc, False)\n",
+    "            elif (op == \"ON\"):\n",
+    "                ibm_db.autocommit (_hdbc, True)\n",
+    "            return    \n",
+    "        \n",
+    "        except Exception as err:\n",
+    "            db2_error(False)\n",
+    "            return \n",
+    "        \n",
+    "    return\n",
+    "\n",
+    "def setFlags(inSQL,reset=False):\n",
+    "\n",
+    "    global _flags\n",
+    "\n",
+    "    if (reset == True):\n",
+    "        _flags = [] # Delete all of the current flag settings\n",
+    "\n",
+    "    pos = 0\n",
+    "    end = len(inSQL)-1\n",
+    "    inFlag = False\n",
+    "    ignore = False\n",
+    "    outSQL = \"\"\n",
+    "    flag = \"\"\n",
+    "\n",
+    "    while (pos <= end):\n",
+    "        ch = inSQL[pos]\n",
+    "        if (ignore == True):   \n",
+    "            outSQL = outSQL + ch\n",
+    "        else:\n",
+    "            if (inFlag == True):\n",
+    "                if (ch != \" \"):\n",
+    "                    flag = flag + ch\n",
+    "                else:\n",
+    "                    _flags.append(flag)\n",
+    "                    inFlag = False\n",
+    "            else:\n",
+    "                if (ch == \"-\"):\n",
+    "                    flag = \"-\"\n",
+    "                    inFlag = True\n",
+    "                elif (ch == ' '):\n",
+    "                    outSQL = outSQL + ch\n",
+    "                else:\n",
+    "                    outSQL = outSQL + ch\n",
+    "                    ignore = True\n",
+    "        pos += 1\n",
+    "\n",
+    "    if (inFlag == True):\n",
+    "        _flags.append(flag)\n",
+    "\n",
+    "    return outSQL\n",
+    "\n",
+    "def flag(inflag):\n",
+    "    \n",
+    "    global _flags\n",
+    "\n",
+    "    if isinstance(inflag,list):\n",
+    "        for x in inflag:\n",
+    "            if (x in _flags):\n",
+    "                return True\n",
+    "        return False\n",
+    "    else:\n",
+    "        if (inflag in _flags):\n",
+    "            return True\n",
+    "        else:\n",
+    "            return False\n",
+    "\n",
+    "def execSQL(hdbc,sql,quiet=True):\n",
+    "\n",
+    "    success = True\n",
+    "    try:                                                  # See if we have an answer set\n",
+    "        stmt = ibm_db.prepare(hdbc,sql)\n",
+    "        result = ibm_db.execute(stmt)                 # Run it                            \n",
+    "        if (result == False):                         # Error executing the code\n",
+    "            db2_error(quiet)\n",
+    "            success = False\n",
+    "    except:\n",
+    "        db2_error(quiet) \n",
+    "        success = False\n",
+    "\n",
+    "    return success\t            \n",
+    "\n",
+    "def splitSQL(inputString, delimiter):\n",
+    "     \n",
+    "    pos = 0\n",
+    "    arg = \"\"\n",
+    "    results = []\n",
+    "    quoteCH = \"\"\n",
+    "    \n",
+    "    inSQL = inputString.strip()\n",
+    "    if (len(inSQL) == 0): return(results)       # Not much to do here - no args found\n",
+    "            \n",
+    "    while pos < len(inSQL):\n",
+    "        ch = inSQL[pos]\n",
+    "        pos += 1\n",
+    "        if (ch in ('\"',\"'\")):                   # Is this a quote characters?\n",
+    "            arg = arg + ch                      # Keep appending the characters to the current arg\n",
+    "            if (ch == quoteCH):                 # Is this quote character we are in\n",
+    "                quoteCH = \"\"\n",
+    "            elif (quoteCH == \"\"):               # Create the quote\n",
+    "                quoteCH = ch\n",
+    "            else:\n",
+    "                None\n",
+    "        elif (quoteCH != \"\"):                   # Still in a quote\n",
+    "            arg = arg + ch\n",
+    "        elif (ch == delimiter):                 # Is there a delimiter?\n",
+    "            results.append(arg)\n",
+    "            arg = \"\"\n",
+    "        else:\n",
+    "            arg = arg + ch\n",
+    "            \n",
+    "    if (arg != \"\"):\n",
+    "        results.append(arg)\n",
+    "        \n",
+    "    return(results)\n",
+    "\n",
+    "def process_slice(connection, dfName, dfValue, pd_dtypes, sql, q, s):\n",
+    "    \n",
+    "    import numpy as np    \n",
+    "    import pandas as pd\n",
+    "\n",
+    "    if (q.empty() == False): return None\n",
+    "\n",
+    "    if (isinstance(dfValue,list) == True or isinstance(dfValue,tuple) == True):\n",
+    "        encoded_sql = \"\"\n",
+    "        start = True\n",
+    "        for v in dfValue:\n",
+    "            if (start == False):\n",
+    "                encoded_sql = encoded_sql + \",\"\n",
+    "            if (isinstance(v,str) == True):\n",
+    "                encoded_sql = encoded_sql + addquotes(v,True)\n",
+    "            else:\n",
+    "                encoded_sql = encoded_sql + str(v)\n",
+    "            start = False\n",
+    "\n",
+    "        dfValue = encoded_sql\n",
+    "    elif (isinstance(dfValue,str) == True):\n",
+    "        dfValue = addquotes(dfValue,True)\n",
+    "    else:\n",
+    "        dfValue = str(dfValue)\n",
+    "\n",
+    "    if (q.empty() == False): return None\n",
+    "\n",
+    "    dsn = buildDSN(connection)\n",
+    "\n",
+    "    if (dsn in [None,\"\"]):\n",
+    "        return None\n",
+    "   \n",
+    "    # Get a database handle (hdbc) and a statement handle (hstmt) for subsequent access to Db2\n",
+    "\n",
+    "    try:\n",
+    "        hdbc  = ibm_db.connect(dsn, \"\", \"\")\n",
+    "    except Exception as err:\n",
+    "        try:\n",
+    "            errmsg = ibm_db.conn_errormsg().replace('\\r',' ')\n",
+    "            errmsg = errmsg[errmsg.rfind(\"]\")+1:].strip()\n",
+    "        except:\n",
+    "            errmsg = \"Error attempting to retrieve error message\"\n",
+    "        q.put(errmsg)  \n",
+    "        return None\n",
+    "    \n",
+    "    try:\n",
+    "        hdbi = ibm_db_dbi.Connection(hdbc)\n",
+    "    except Exception as err:\n",
+    "        errmsg = \"Connection error when connecting through DBI adapter.\"\n",
+    "        q.put(errmsg)\n",
+    "        return None\n",
+    "\n",
+    "    if (q.empty() == False): return None\n",
+    "    \n",
+    "    # if (isinstance(dfValue,str) == True):\n",
+    "    # \tdfValue = addquotes(dfValue,True)\n",
+    "    # else:\n",
+    "    # \tdfValue = str(dfValue)\n",
+    "        \n",
+    "    protoSQL = sql.replace(f\":{dfName}\",dfValue)\n",
+    "\n",
+    "    s.put(protoSQL)\n",
+    "\n",
+    "    if (q.empty() == False): return None\n",
+    "\n",
+    "    try:\n",
+    "        if (pd_dtypes != None):\n",
+    "            df = pd.read_sql_query(protoSQL,hdbi,dtype=pd_dtypes)  \n",
+    "        else:\n",
+    "            df = pd.read_sql_query(protoSQL,hdbi)    \n",
+    "    except:\n",
+    "        try:\n",
+    "            errmsg = ibm_db.stmt_errormsg().replace('\\r',' ')\n",
+    "            errmsg = errmsg[errmsg.rfind(\"]\")+1:].strip()\t\t\n",
+    "            ibm_db.close(hdbc)\n",
+    "        except:\n",
+    "            errmsg = \"Error attempting to retrieve statement error message.\"\n",
+    "        q.put(errmsg)\n",
+    "        return None\n",
+    "\n",
+    "    if (q.empty() == False): return None\n",
+    "\n",
+    "    try:\n",
+    "        ibm_db.close(hdbc)\n",
+    "    except:\n",
+    "        pass\n",
+    "              \n",
+    "    return df\n",
+    "\n",
+    "def dfSQL(hdbc,hdbi,sqlin,dfName,dfValue,thread_count):\n",
+    "    \n",
+    "    import shlex\n",
+    "\n",
+    "    NoDF  = False\n",
+    "    YesDF = True\n",
+    "\n",
+    "    sqlin = \" \".join(shlex.split(sqlin)) \n",
+    "\n",
+    "    if (hdbc == None or hdbi == None or sqlin in (None, \"\")):\n",
+    "        return NoDF,None\n",
+    "    \n",
+    "    uSQLin = sqlin.upper()\n",
+    "      \n",
+    "    select_location = uSQLin.find(\"SELECT\")\n",
+    "    with_location   = uSQLin.find(\"WITH\")\n",
+    "\n",
+    "    if (select_location == -1):\n",
+    "        errormsg(\"SQL statement does not contain a SELECT statement.\")\n",
+    "        return NoDF, None          \n",
+    "      \n",
+    "    if (with_location != -1 and (with_location < select_location)):\n",
+    "        keyword_location = with_location\n",
+    "    else:\n",
+    "        keyword_location = select_location\n",
+    "        \n",
+    "    sql = sqlin[keyword_location:]\n",
+    "\n",
+    "    keyword_location = sql.find(f\":{dfName}\")\n",
+    "       \n",
+    "    if (keyword_location == -1):\n",
+    "        errormsg(f'The parallelism value \"{dfName}\" was not found in the SQL statement.')\n",
+    "        return NoDF, None\n",
+    "    \n",
+    "    if (isinstance(dfValue,list) == False):\n",
+    "        errormsg(f'The variable \"{dfName}\" is not an array or a list of values.')\n",
+    "        return NoDF, None\n",
+    "\n",
+    "    #\tCreate a prototype statement to make sure the SQL will run\n",
+    "    \n",
+    "    protoValue = dfValue[0]\n",
+    "\n",
+    "    if (isinstance(protoValue,list) == True or isinstance(protoValue,tuple) == True):\n",
+    "        if (len(protoValue) == 0):\n",
+    "            errormsg(f'The variable \"{dfName}\" contains array values that are empty.')\n",
+    "            return NoDF, None\n",
+    "        protoValue = protoValue[0]\n",
+    "\n",
+    "    if (isinstance(protoValue,str) == True):\n",
+    "        protoValue = addquotes(protoValue,True)\n",
+    "    else:\n",
+    "        protoValue = str(protoValue)\n",
+    "           \n",
+    "    protoSQL = sql.replace(f\":{dfName}\",protoValue)\n",
+    "    \n",
+    "    try:\n",
+    "        stmt = ibm_db.prepare(hdbc,protoSQL)\n",
+    "\n",
+    "        if (ibm_db.num_fields(stmt) == 0):                \n",
+    "            errormsg(\"The SQL statement does not return an answer set.\")\n",
+    "            return NoDF, None\n",
+    "  \n",
+    "    except Exception as err:\n",
+    "        db2_error(False)\n",
+    "        return NoDF, None\n",
+    "\n",
+    "    #\tDetermine the datatypes for a Pandas dataframe if it is supported\n",
+    "\n",
+    "    pd_dtypes = None\n",
+    "\n",
+    "    if (_pandas_dtype == True):\n",
+    "        pd_dtypes = None\n",
+    "        columns, types = getColumns(stmt)\n",
+    "        pd_dtypes={}\n",
+    "        for idx, col in enumerate(columns):\n",
+    "            try:\n",
+    "                _dindex = _db2types.index(types[idx])\n",
+    "            except:\n",
+    "                _dindex = 0\n",
+    "\n",
+    "            pd_dtypes[col] = _pdtypes[_dindex]\n",
+    "\n",
+    "        if len(pd_dtypes.keys()) == 0:\n",
+    "            pd_dtypes = None\n",
+    "    \n",
+    "    pool \t = mp.Pool(processes=thread_count)\n",
+    "    m \t\t = mp.Manager()\t\t\t# multiprocessing.Manager()\n",
+    "    q\t\t = m.Queue()\n",
+    "    tracesql = m.Queue()\n",
+    "    \n",
+    "    try:\n",
+    "        results = [pool.apply_async(process_slice, args=(_settings,dfName,x,pd_dtypes,sql,q,tracesql,)) for x in dfValue]\n",
+    "    except Exception as err:\n",
+    "        print(repr(err))\n",
+    "        return NoDF, None        \n",
+    "\n",
+    "    output=[]\n",
+    "\n",
+    "    badresults = False\n",
+    "    \n",
+    "    for p in results:\n",
+    "        try:\n",
+    "            df = p.get()\n",
+    "            if (isinstance(df,pandas.DataFrame) == True):\n",
+    "                output.append(df)\n",
+    "            else:\n",
+    "                badresults = True\n",
+    "        except Exception as err:\n",
+    "            print(repr(err))\n",
+    "            badresults = True \n",
+    "\n",
+    "    if flag([\"-e\",\"-echo\"]): \n",
+    "        while (tracesql.empty() == False):\n",
+    "            debug(tracesql.get(),False)\n",
+    "\n",
+    "    if (badresults == True):\n",
+    "        if (q.empty() == False):\n",
+    "            errormsg(q.get())\n",
+    "        return NoDF, None      \n",
+    "\n",
+    "    finaldf = pandas.concat(output)\n",
+    "    finaldf.reset_index(drop=True, inplace=True)\n",
+    "    \n",
+    "    if (len(finaldf) == 0):\n",
+    "        sqlcode = 100\n",
+    "        errormsg(\"No rows found\")\n",
+    "        return NoDF, None    \n",
+    "\n",
+    "    return YesDF, finaldf\n",
+    "\n",
+    "def stripComments(sql):\n",
+    "\n",
+    "    import re\n",
+    "\n",
+    "    if (sql in [\"\",None]):\n",
+    "        return(sql)\n",
+    "\n",
+    "    sql = re.sub(r\"(.*)--.*\\n\",r\"\\1 \",sql)\n",
+    "    sql = sql.strip()\n",
+    "    return(sql)    \n",
+    "\n",
+    "def setDisplay(onoff):\n",
+    "    global _environment, _settings\n",
+    "    # Set the display type\n",
+    "    if (_environment['grid'] == False):                       # Nothing to do since no grid control\n",
+    "        return\n",
+    "    \n",
+    "    if (onoff == True):                                   # Turn on grid \n",
+    "        if flag('-grid') or _settings.get('display',\"PANDAS\") == 'GRID':\n",
+    "            if (_environment[\"gridinit\"] == False):\n",
+    "                init_notebook_mode(all_interactive=True)\n",
+    "                _environment[\"gridinit\"] = True\n",
+    "        else:\n",
+    "            if (_environment[\"gridinit\"] == True):\n",
+    "                init_notebook_mode(all_interactive=False)\n",
+    "                _environment[\"gridinit\"] = False\n",
+    "    else:\n",
+    "        if (_environment[\"gridinit\"] == True):\n",
+    "            init_notebook_mode(all_interactive=False)\n",
+    "            _environment[\"gridinit\"] = False\n",
+    "\n",
+    "@magics_class\n",
+    "class DB2(Magics):\n",
+    "   \n",
+    "    @needs_local_scope    \n",
+    "    @line_cell_magic\n",
+    "    def sql(self, line, cell=None, local_ns=None):\n",
+    "            \n",
+    "        # Before we event get started, check to see if you have connected yet. Without a connection we \n",
+    "        # can't do anything. You may have a connection request in the code, so if that is true, we run those,\n",
+    "        # otherwise we connect immediately\n",
+    "        \n",
+    "        # If your statement is not a connect, and you haven't connected, we need to do it for you\n",
+    "    \n",
+    "        global _settings, _environment\n",
+    "        global _hdbc, _hdbi, _connected, sqlstate, sqlerror, sqlcode, sqlelapsed\n",
+    "             \n",
+    "        # If you use %sql (line) we just run the SQL. If you use %%SQL the entire cell is run.\n",
+    "        \n",
+    "        flag_cell = False\n",
+    "        flag_output = False\n",
+    "        sqlstate = \"0\"\n",
+    "        sqlerror = \"\"\n",
+    "        sqlcode = 0\n",
+    "        sqlelapsed = 0\n",
+    "        start_time = time.time()       \n",
+    "        \n",
+    "        # Macros gets expanded before anything is done\n",
+    "\n",
+    "        SQL1 = stripComments(line)\n",
+    "        \n",
+    "        SQL1 = line.replace(\"\\n\",\" \").strip()\n",
+    "        SQL1 = setFlags(SQL1,reset=True)  \n",
+    "        SQL1 = checkMacro(SQL1)                                   # Update the SQL if any macros are in there\n",
+    "        SQL1 = setFlags(SQL1)\n",
+    "        SQL2 = stripComments(cell)    \n",
+    "        \n",
+    "        if SQL1 == \"?\" or flag([\"-h\",\"-help\"]):                   # Are you asking for help\n",
+    "            sqlhelp()\n",
+    "            return\n",
+    "        \n",
+    "        if len(SQL1) == 0 and SQL2 == None: return                # Nothing to do here\n",
+    "                \n",
+    "        # Check for help  \n",
+    "        \n",
+    "        sqlType,remainder = sqlParser(SQL1,local_ns)              # What type of command do you have?\n",
+    "\n",
+    "        if (sqlType == \"CONNECT\"):                                # A connect request \n",
+    "            parseConnect(SQL1,local_ns)\n",
+    "            return \n",
+    "        elif (sqlType == \"USING\"):                                # You want to use a dataframe to create a table?\n",
+    "            pdReturn, df = createDF(_hdbc,_hdbi, SQL1,local_ns)\n",
+    "            if (pdReturn == True):\n",
+    "                if flag(\"-grid\") or _settings.get('display',\"PANDAS\") == 'GRID':   # Check to see if we can display the results\n",
+    "                    if (_environment['grid'] == False):\n",
+    "                        with pandas.option_context('display.max_rows', 100, 'display.max_columns', None):  \n",
+    "                            print(df.to_string())\n",
+    "                    else:\n",
+    "                        try:\n",
+    "                            setDisplay(True)\n",
+    "                            if (_settings[\"paging\"] == \"OFF\"):\n",
+    "                                show(df, scrollY=\"300px\", scrollCollapse=True, paging=False)\n",
+    "                            else:\n",
+    "                                show(df)\n",
+    "                            setDisplay(False)\n",
+    "                        except Exception as err:\n",
+    "                            print(repr(err))\n",
+    "                            errormsg(\"Grid control failed to diplay data. Use option -a or %sql OPTION DISPLAY PANDAS instead.\")\n",
+    "                    return \n",
+    "                else:\n",
+    "                    if flag([\"-a\",\"-all\"]) or _settings.get(\"maxrows\",10) == -1 : # All of the rows\n",
+    "                        pandas.options.display.max_rows = 100\n",
+    "                        pandas.options.display.max_columns = None\n",
+    "                        return df # print(df.to_string())\n",
+    "                    else:\n",
+    "                        pandas.options.display.max_rows = _settings.get(\"maxrows\",10)\n",
+    "                        pandas.options.display.max_columns = None\n",
+    "                        return df # pdisplay(df) # print(df.to_string())\n",
+    "            else:\n",
+    "                if (df != None):\n",
+    "                    return df\n",
+    "                else:\n",
+    "                    return\n",
+    "        elif (sqlType == \"DEFINE\"):                               # Create a macro from the body\n",
+    "            result = setMacro(SQL2,remainder)\n",
+    "            return\n",
+    "        elif (sqlType in (\"OPTION\",\"OPTIONS\")):\n",
+    "            setOptions(SQL1)\n",
+    "            return \n",
+    "        elif (sqlType == 'COMMIT' or sqlType == 'ROLLBACK' or sqlType == 'AUTOCOMMIT'):\n",
+    "            parseCommit(remainder)\n",
+    "            return\n",
+    "        elif (sqlType == \"PREPARE\"):\n",
+    "            pstmt = parsePExec(_hdbc, remainder)\n",
+    "            return(pstmt)\n",
+    "        elif (sqlType == \"EXECUTE\"):\n",
+    "            result = parsePExec(_hdbc, remainder)\n",
+    "            return(result)    \n",
+    "        elif (sqlType == \"CALL\"):\n",
+    "            result = parseCall(_hdbc, remainder, local_ns)\n",
+    "            return(result)\n",
+    "        else:\n",
+    "            pass        \n",
+    " \n",
+    "        sql = SQL1\n",
+    "    \n",
+    "        if (sql == \"\"): sql = SQL2\n",
+    "        \n",
+    "        if (sql == \"\"): return                                   # Nothing to do here\n",
+    "    \n",
+    "        if (_connected == False):\n",
+    "            if (db2_doConnect(None) == False):\n",
+    "                errormsg('A CONNECT statement must be issued before running SQL statements.')\n",
+    "                return      \n",
+    "        \n",
+    "        if _settings.get(\"maxrows\",10) == -1:                                 # Set the return result size\n",
+    "            pandas.reset_option('display.max_rows')\n",
+    "        else:\n",
+    "            pandas.options.display.max_rows = _settings.get(\"maxrows\",10)\n",
+    "      \n",
+    "        runSQL = re.sub('.*?--.*$',\"\",sql,flags=re.M)\n",
+    "        remainder = runSQL.replace(\"\\n\",\" \") \n",
+    "\n",
+    "        if flag([\"-d\",\"-delim\"]):\n",
+    "            sqlLines = splitSQL(remainder,\"@\")\n",
+    "        else:\n",
+    "            sqlLines = splitSQL(remainder,\";\")\n",
+    "        flag_cell = True\n",
+    "                      \n",
+    "        # For each line figure out if you run it as a command (db2) or select (sql)\n",
+    "\n",
+    "        for sqlin in sqlLines:          # Run each command\n",
+    "            \n",
+    "            sqlin = checkMacro(sqlin)                                 # Update based on any macros\n",
+    "\n",
+    "            sqlType, sql = sqlParser(sqlin,local_ns)                           # Parse the SQL  \n",
+    "            if (sql.strip() == \"\"): continue\n",
+    "\n",
+    "            if flag([\"-e\",\"-echo\"]): \n",
+    "                debug(sql,False)\n",
+    "                \n",
+    "            if flag([\"-pb\",\"-bar\",\"-pp\",\"-pie\",\"-pl\",\"-line\"]): # We are plotting some results              \n",
+    "                plotData(_hdbi, sql)                            # Plot the data and return\n",
+    "                return                \n",
+    "\n",
+    "            try:                                                  # See if we have an answer set\n",
+    "                stmt = ibm_db.prepare(_hdbc,sql)\n",
+    "                if (ibm_db.num_fields(stmt) == 0):                # No, so we just execute the code\n",
+    "                    start_time = time.time()\n",
+    "                    result = ibm_db.execute(stmt)                 # Run it                            \n",
+    "                    sqlelapsed = time.time() - start_time\n",
+    "                    if (result == False):                         # Error executing the code\n",
+    "                        db2_error(flag([\"-q\",\"-quiet\"])) \n",
+    "                        continue\n",
+    "                        \n",
+    "                    rowcount = ibm_db.num_rows(stmt)    \n",
+    "                \n",
+    "                    if (rowcount == 0 and flag([\"-q\",\"-quiet\"]) == False):\n",
+    "                        errormsg(\"No rows found.\")     \n",
+    "                        \n",
+    "                    continue                                      # Continue running\n",
+    "                \n",
+    "                elif flag([\"-r\",\"-array\",\"-j\",\"-json\"]):                     # raw, json, format json\n",
+    "                    row_count = 0\n",
+    "                    resultSet = []\n",
+    "                    try:\n",
+    "                        start_time = time.time()                 \n",
+    "                        result = ibm_db.execute(stmt)             # Run it\n",
+    "                        sqlelapsed = time.time() - start_time                            \n",
+    "                        if (result == False):                         # Error executing the code\n",
+    "                            db2_error(flag([\"-q\",\"-quiet\"]))  \n",
+    "                            return\n",
+    "                            \n",
+    "                        if flag(\"-j\"):                          # JSON single output\n",
+    "                            row_count = 0\n",
+    "                            json_results = []\n",
+    "                            while( ibm_db.fetch_row(stmt) ):\n",
+    "                                row_count = row_count + 1\n",
+    "                                jsonVal = ibm_db.result(stmt,0)\n",
+    "                                jsonDict = json.loads(jsonVal)\n",
+    "                                json_results.append(jsonDict)\n",
+    "                                flag_output = True                                    \n",
+    "                            \n",
+    "                            if (row_count == 0): sqlcode = 100\n",
+    "                            return(json_results)\n",
+    "                        \n",
+    "                        else:\n",
+    "                            return(fetchResults(stmt))\n",
+    "                                \n",
+    "                    except Exception as err:\n",
+    "                        db2_error(flag([\"-q\",\"-quiet\"]))\n",
+    "                        return\n",
+    "                        \n",
+    "                else:\n",
+    "\n",
+    "                    # New for pandas 1.3. We can coerce the PD datatypes to mimic those of Db2\n",
+    "                    \n",
+    "                    pd_dtypes = None\n",
+    "                    \n",
+    "                    if (_pandas_dtype == True):\n",
+    "                        pd_dtypes = None\n",
+    "                        columns, types = getColumns(stmt)\n",
+    "                        pd_dtypes={}\n",
+    "                        for idx, col in enumerate(columns):\n",
+    "                            try:\n",
+    "                                _dindex = _db2types.index(types[idx])\n",
+    "                            except:\n",
+    "                                _dindex = 0\n",
+    "\n",
+    "                            pd_dtypes[col] = _pdtypes[_dindex]\n",
+    "\n",
+    "                        if len(pd_dtypes.keys()) == 0:\n",
+    "                            pd_dtypes = None\n",
+    "                    try:\n",
+    "                        \n",
+    "                        start_time = time.time()    \n",
+    "                        if (_pandas_dtype == True):\n",
+    "                            df = pandas.read_sql_query(sql,_hdbi,dtype=pd_dtypes)  \n",
+    "                        else:\n",
+    "                            df = pandas.read_sql_query(sql,_hdbi)                        \n",
+    "                        sqlelapsed = time.time() - start_time                                \n",
+    "                            \n",
+    "                    except Exception as err:\n",
+    "                        print(repr(err))\n",
+    "                        sqlelapsed = 0\n",
+    "                        db2_error(False)\n",
+    "                        return\n",
+    "                                                \n",
+    "                    if (len(df) == 0):\n",
+    "                        sqlcode = 100\n",
+    "                        if (flag([\"-q\",\"-quiet\"]) == False): \n",
+    "                            errormsg(\"No rows found\")\n",
+    "                        continue                    \n",
+    "                \n",
+    "                    flag_output = True\n",
+    "                    if flag(\"-grid\") or _settings.get('display',\"PANDAS\") == 'GRID':   # Check to see if we can display the results\n",
+    "                        if (_environment['grid'] == False):\n",
+    "                            with pandas.option_context('display.max_rows', None, 'display.max_columns', None):  \n",
+    "                                print(df.to_string())\n",
+    "                        else:\n",
+    "                            try:\n",
+    "                                setDisplay(True)\n",
+    "                                if (_settings[\"paging\"] == \"OFF\"):\n",
+    "                                    show(df, scrollY=\"300px\", scrollCollapse=True, paging=False)\n",
+    "                                else:\n",
+    "                                    show(df)\n",
+    "                                setDisplay(False)\n",
+    "                            except Exception as err:\n",
+    "                                print(repr(err))\n",
+    "                                errormsg(\"Grid control failed to diplay data. Use option -a or %sql OPTION DISPLAY PANDAS instead.\")\n",
+    "                                return \n",
+    "                    else:\n",
+    "                        if flag([\"-a\",\"-all\"]) or _settings.get(\"maxrows\",10) == -1 : # All of the rows\n",
+    "                            pandas.options.display.max_rows = 100\n",
+    "                            pandas.options.display.max_columns = None\n",
+    "                            return df # print(df.to_string())\n",
+    "                        else:\n",
+    "                            pandas.options.display.max_rows = _settings.get(\"maxrows\",10)\n",
+    "                            pandas.options.display.max_columns = None\n",
+    "                            return df # pdisplay(df) # print(df.to_string())\n",
+    "\n",
+    "            except:\n",
+    "                db2_error(flag([\"-q\",\"-quiet\"]))\n",
+    "                continue # return\n",
+    "                \n",
+    "        sqlelapsed = time.time() - start_time\n",
+    "        if (flag_output == False and flag([\"-q\",\"-quiet\"]) == False): print(\"Command completed.\")\n",
+    "            \n",
+    "# Register the Magic extension in Jupyter    \n",
+    "ip = get_ipython()          \n",
+    "ip.register_magics(DB2)\n",
+    "load_settings()\n",
+    "\n",
+    "macro_list = '''\n",
+    "#\n",
+    "# The LIST macro is used to list all of the tables in the current schema or for all schemas\n",
+    "#\n",
+    "var syntax Syntax: LIST TABLES [FOR ALL | FOR SCHEMA name]\n",
+    "# \n",
+    "# Only LIST TABLES is supported by this macro\n",
+    "#\n",
+    "flags -a\n",
+    "if {^1} <> 'TABLES'\n",
+    "    exit {syntax}\n",
+    "endif\n",
+    "\n",
+    "#\n",
+    "# This SQL is a temporary table that contains the description of the different table types\n",
+    "#\n",
+    "WITH TYPES(TYPE,DESCRIPTION) AS (\n",
+    "  VALUES\n",
+    "    ('A','Alias'),\n",
+    "    ('G','Created temporary table'),\n",
+    "    ('H','Hierarchy table'),\n",
+    "    ('L','Detached table'),\n",
+    "    ('N','Nickname'),\n",
+    "    ('S','Materialized query table'),\n",
+    "    ('T','Table'),\n",
+    "    ('U','Typed table'),\n",
+    "    ('V','View'),\n",
+    "    ('W','Typed view')\n",
+    ")\n",
+    "SELECT TABNAME, TABSCHEMA, T.DESCRIPTION FROM SYSCAT.TABLES S, TYPES T\n",
+    "       WHERE T.TYPE = S.TYPE \n",
+    "\n",
+    "#\n",
+    "# Case 1: No arguments - LIST TABLES\n",
+    "#\n",
+    "if {argc} == 1\n",
+    "   AND OWNER = CURRENT USER\n",
+    "   ORDER BY TABNAME, TABSCHEMA\n",
+    "   return\n",
+    "endif \n",
+    "\n",
+    "#\n",
+    "# Case 2: Need 3 arguments - LIST TABLES FOR ALL\n",
+    "#\n",
+    "if {argc} == 3\n",
+    "    if {^2}&{^3} == 'FOR&ALL'\n",
+    "        ORDER BY TABNAME, TABSCHEMA\n",
+    "        return\n",
+    "    endif\n",
+    "    exit {syntax}\n",
+    "endif\n",
+    "\n",
+    "#\n",
+    "# Case 3: Need FOR SCHEMA something here\n",
+    "#\n",
+    "if {argc} == 4\n",
+    "    if {^2}&{^3} == 'FOR&SCHEMA'\n",
+    "        AND TABSCHEMA = '{^4}'\n",
+    "        ORDER BY TABNAME, TABSCHEMA\n",
+    "        return\n",
+    "    else\n",
+    "        exit {syntax}\n",
+    "    endif\n",
+    "endif\n",
+    "\n",
+    "#\n",
+    "# Nothing matched - Error\n",
+    "#\n",
+    "exit {syntax}\n",
+    "'''\n",
+    "DB2.sql(None, \"define LIST\", cell=macro_list, local_ns=locals())\n",
+    "\n",
+    "macro_describe = '''\n",
+    "#\n",
+    "# The DESCRIBE command can either use the syntax DESCRIBE TABLE  or DESCRIBE TABLE SELECT ...\n",
+    "#\n",
+    "var syntax Syntax: DESCRIBE [TABLE name | SELECT statement] \n",
+    "#\n",
+    "# Check to see what count of variables is... Must be at least 2 items DESCRIBE TABLE x or SELECT x\n",
+    "#\n",
+    "flags -a\n",
+    "if {argc} < 2\n",
+    "   exit {syntax}\n",
+    "endif\n",
+    "\n",
+    "CALL ADMIN_CMD('{*0}');\n",
+    "'''\n",
+    "\n",
+    "DB2.sql(None,\"define describe\", cell=macro_describe, local_ns=locals())\n",
+    "\n",
+    "create_sample = \"\"\"\n",
+    "flags -d\n",
+    "BEGIN\n",
+    "DECLARE FOUND INTEGER;\n",
+    "SET FOUND = (SELECT COUNT(*) FROM SYSIBM.SYSTABLES WHERE NAME='DEPARTMENT' AND CREATOR=CURRENT USER);\n",
+    "IF FOUND = 0 THEN\n",
+    "    EXECUTE IMMEDIATE('CREATE TABLE DEPARTMENT(DEPTNO CHAR(3) NOT NULL, DEPTNAME VARCHAR(36) NOT NULL,\n",
+    "                        MGRNO CHAR(6),ADMRDEPT CHAR(3) NOT NULL)');\n",
+    "    EXECUTE IMMEDIATE('INSERT INTO DEPARTMENT VALUES\n",
+    "        (''A00'',''SPIFFY COMPUTER SERVICE DIV.'',''000010'',''A00''),\n",
+    "        (''B01'',''PLANNING'',''000020'',''A00''),\n",
+    "        (''C01'',''INFORMATION CENTER'',''000030'',''A00''),\n",
+    "        (''D01'',''DEVELOPMENT CENTER'',NULL,''A00''),\n",
+    "        (''D11'',''MANUFACTURING SYSTEMS'',''000060'',''D01''),\n",
+    "        (''D21'',''ADMINISTRATION SYSTEMS'',''000070'',''D01''),\n",
+    "        (''E01'',''SUPPORT SERVICES'',''000050'',''A00''),\n",
+    "        (''E11'',''OPERATIONS'',''000090'',''E01''),\n",
+    "        (''E21'',''SOFTWARE SUPPORT'',''000100'',''E01''),\n",
+    "        (''F22'',''BRANCH OFFICE F2'',NULL,''E01''),\n",
+    "        (''G22'',''BRANCH OFFICE G2'',NULL,''E01''),\n",
+    "        (''H22'',''BRANCH OFFICE H2'',NULL,''E01''),\n",
+    "        (''I22'',''BRANCH OFFICE I2'',NULL,''E01''),\n",
+    "        (''J22'',''BRANCH OFFICE J2'',NULL,''E01'')');\n",
+    "END IF;\n",
+    "\n",
+    "SET FOUND = (SELECT COUNT(*) FROM SYSIBM.SYSTABLES WHERE NAME='EMPLOYEE' AND CREATOR=CURRENT USER);\n",
+    "IF FOUND = 0 THEN\n",
+    "    EXECUTE IMMEDIATE('CREATE TABLE EMPLOYEE(\n",
+    "                        EMPNO CHAR(6) NOT NULL,\n",
+    "                        FIRSTNME VARCHAR(12) NOT NULL,\n",
+    "                        MIDINIT CHAR(1),\n",
+    "                        LASTNAME VARCHAR(15) NOT NULL,\n",
+    "                        WORKDEPT CHAR(3),\n",
+    "                        PHONENO CHAR(4),\n",
+    "                        HIREDATE DATE,\n",
+    "                        JOB CHAR(8),\n",
+    "                        EDLEVEL SMALLINT NOT NULL,\n",
+    "                        SEX CHAR(1),\n",
+    "                        BIRTHDATE DATE,\n",
+    "                        SALARY DECIMAL(9,2),\n",
+    "                        BONUS DECIMAL(9,2),\n",
+    "                        COMM DECIMAL(9,2)\n",
+    "                        )');\n",
+    "    EXECUTE IMMEDIATE('INSERT INTO EMPLOYEE VALUES\n",
+    "        (''000010'',''CHRISTINE'',''I'',''HAAS''      ,''A00'',''3978'',''1995-01-01'',''PRES    '',18,''F'',''1963-08-24'',152750.00,1000.00,4220.00),\n",
+    "        (''000020'',''MICHAEL''  ,''L'',''THOMPSON''  ,''B01'',''3476'',''2003-10-10'',''MANAGER '',18,''M'',''1978-02-02'',94250.00,800.00,3300.00),\n",
+    "        (''000030'',''SALLY''    ,''A'',''KWAN''      ,''C01'',''4738'',''2005-04-05'',''MANAGER '',20,''F'',''1971-05-11'',98250.00,800.00,3060.00),\n",
+    "        (''000050'',''JOHN''     ,''B'',''GEYER''     ,''E01'',''6789'',''1979-08-17'',''MANAGER '',16,''M'',''1955-09-15'',80175.00,800.00,3214.00),\n",
+    "        (''000060'',''IRVING''   ,''F'',''STERN''     ,''D11'',''6423'',''2003-09-14'',''MANAGER '',16,''M'',''1975-07-07'',72250.00,500.00,2580.00),\n",
+    "        (''000070'',''EVA''      ,''D'',''PULASKI''   ,''D21'',''7831'',''2005-09-30'',''MANAGER '',16,''F'',''2003-05-26'',96170.00,700.00,2893.00),\n",
+    "        (''000090'',''EILEEN''   ,''W'',''HENDERSON'' ,''E11'',''5498'',''2000-08-15'',''MANAGER '',16,''F'',''1971-05-15'',89750.00,600.00,2380.00),\n",
+    "        (''000100'',''THEODORE'' ,''Q'',''SPENSER''   ,''E21'',''0972'',''2000-06-19'',''MANAGER '',14,''M'',''1980-12-18'',86150.00,500.00,2092.00),\n",
+    "        (''000110'',''VINCENZO'' ,''G'',''LUCCHESSI'' ,''A00'',''3490'',''1988-05-16'',''SALESREP'',19,''M'',''1959-11-05'',66500.00,900.00,3720.00),\n",
+    "        (''000120'',''SEAN''     ,'' '',''O`CONNELL'' ,''A00'',''2167'',''1993-12-05'',''CLERK   '',14,''M'',''1972-10-18'',49250.00,600.00,2340.00),\n",
+    "        (''000130'',''DELORES''  ,''M'',''QUINTANA''  ,''C01'',''4578'',''2001-07-28'',''ANALYST '',16,''F'',''1955-09-15'',73800.00,500.00,1904.00),\n",
+    "        (''000140'',''HEATHER''  ,''A'',''NICHOLLS''  ,''C01'',''1793'',''2006-12-15'',''ANALYST '',18,''F'',''1976-01-19'',68420.00,600.00,2274.00),\n",
+    "        (''000150'',''BRUCE''    ,'' '',''ADAMSON''   ,''D11'',''4510'',''2002-02-12'',''DESIGNER'',16,''M'',''1977-05-17'',55280.00,500.00,2022.00),\n",
+    "        (''000160'',''ELIZABETH'',''R'',''PIANKA''    ,''D11'',''3782'',''2006-10-11'',''DESIGNER'',17,''F'',''1980-04-12'',62250.00,400.00,1780.00),\n",
+    "        (''000170'',''MASATOSHI'',''J'',''YOSHIMURA'' ,''D11'',''2890'',''1999-09-15'',''DESIGNER'',16,''M'',''1981-01-05'',44680.00,500.00,1974.00),\n",
+    "        (''000180'',''MARILYN''  ,''S'',''SCOUTTEN''  ,''D11'',''1682'',''2003-07-07'',''DESIGNER'',17,''F'',''1979-02-21'',51340.00,500.00,1707.00),\n",
+    "        (''000190'',''JAMES''    ,''H'',''WALKER''    ,''D11'',''2986'',''2004-07-26'',''DESIGNER'',16,''M'',''1982-06-25'',50450.00,400.00,1636.00),\n",
+    "        (''000200'',''DAVID''    ,'' '',''BROWN''     ,''D11'',''4501'',''2002-03-03'',''DESIGNER'',16,''M'',''1971-05-29'',57740.00,600.00,2217.00),\n",
+    "        (''000210'',''WILLIAM''  ,''T'',''JONES''     ,''D11'',''0942'',''1998-04-11'',''DESIGNER'',17,''M'',''2003-02-23'',68270.00,400.00,1462.00),\n",
+    "        (''000220'',''JENNIFER'' ,''K'',''LUTZ''      ,''D11'',''0672'',''1998-08-29'',''DESIGNER'',18,''F'',''1978-03-19'',49840.00,600.00,2387.00),\n",
+    "        (''000230'',''JAMES''    ,''J'',''JEFFERSON'' ,''D21'',''2094'',''1996-11-21'',''CLERK   '',14,''M'',''1980-05-30'',42180.00,400.00,1774.00),\n",
+    "        (''000240'',''SALVATORE'',''M'',''MARINO''    ,''D21'',''3780'',''2004-12-05'',''CLERK   '',17,''M'',''2002-03-31'',48760.00,600.00,2301.00),\n",
+    "        (''000250'',''DANIEL''   ,''S'',''SMITH''     ,''D21'',''0961'',''1999-10-30'',''CLERK   '',15,''M'',''1969-11-12'',49180.00,400.00,1534.00),\n",
+    "        (''000260'',''SYBIL''    ,''P'',''JOHNSON''   ,''D21'',''8953'',''2005-09-11'',''CLERK   '',16,''F'',''1976-10-05'',47250.00,300.00,1380.00),\n",
+    "        (''000270'',''MARIA''    ,''L'',''PEREZ''     ,''D21'',''9001'',''2006-09-30'',''CLERK   '',15,''F'',''2003-05-26'',37380.00,500.00,2190.00),\n",
+    "        (''000280'',''ETHEL''    ,''R'',''SCHNEIDER'' ,''E11'',''8997'',''1997-03-24'',''OPERATOR'',17,''F'',''1976-03-28'',36250.00,500.00,2100.00),\n",
+    "        (''000290'',''JOHN''     ,''R'',''PARKER''    ,''E11'',''4502'',''2006-05-30'',''OPERATOR'',12,''M'',''1985-07-09'',35340.00,300.00,1227.00),\n",
+    "        (''000300'',''PHILIP''   ,''X'',''SMITH''     ,''E11'',''2095'',''2002-06-19'',''OPERATOR'',14,''M'',''1976-10-27'',37750.00,400.00,1420.00),\n",
+    "        (''000310'',''MAUDE''    ,''F'',''SETRIGHT''  ,''E11'',''3332'',''1994-09-12'',''OPERATOR'',12,''F'',''1961-04-21'',35900.00,300.00,1272.00),\n",
+    "        (''000320'',''RAMLAL''   ,''V'',''MEHTA''     ,''E21'',''9990'',''1995-07-07'',''FIELDREP'',16,''M'',''1962-08-11'',39950.00,400.00,1596.00),\n",
+    "        (''000330'',''WING''     ,'' '',''LEE''       ,''E21'',''2103'',''2006-02-23'',''FIELDREP'',14,''M'',''1971-07-18'',45370.00,500.00,2030.00),\n",
+    "        (''000340'',''JASON''    ,''R'',''GOUNOT''    ,''E21'',''5698'',''1977-05-05'',''FIELDREP'',16,''M'',''1956-05-17'',43840.00,500.00,1907.00),\n",
+    "        (''200010'',''DIAN''     ,''J'',''HEMMINGER'' ,''A00'',''3978'',''1995-01-01'',''SALESREP'',18,''F'',''1973-08-14'',46500.00,1000.00,4220.00),\n",
+    "        (''200120'',''GREG''     ,'' '',''ORLANDO''   ,''A00'',''2167'',''2002-05-05'',''CLERK   '',14,''M'',''1972-10-18'',39250.00,600.00,2340.00),\n",
+    "        (''200140'',''KIM''      ,''N'',''NATZ''      ,''C01'',''1793'',''2006-12-15'',''ANALYST '',18,''F'',''1976-01-19'',68420.00,600.00,2274.00),\n",
+    "        (''200170'',''KIYOSHI''  ,'' '',''YAMAMOTO''  ,''D11'',''2890'',''2005-09-15'',''DESIGNER'',16,''M'',''1981-01-05'',64680.00,500.00,1974.00),\n",
+    "        (''200220'',''REBA''     ,''K'',''JOHN''      ,''D11'',''0672'',''2005-08-29'',''DESIGNER'',18,''F'',''1978-03-19'',69840.00,600.00,2387.00),\n",
+    "        (''200240'',''ROBERT''   ,''M'',''MONTEVERDE'',''D21'',''3780'',''2004-12-05'',''CLERK   '',17,''M'',''1984-03-31'',37760.00,600.00,2301.00),\n",
+    "        (''200280'',''EILEEN''   ,''R'',''SCHWARTZ''  ,''E11'',''8997'',''1997-03-24'',''OPERATOR'',17,''F'',''1966-03-28'',46250.00,500.00,2100.00),\n",
+    "        (''200310'',''MICHELLE'' ,''F'',''SPRINGER''  ,''E11'',''3332'',''1994-09-12'',''OPERATOR'',12,''F'',''1961-04-21'',35900.00,300.00,1272.00),\n",
+    "        (''200330'',''HELENA''   ,'' '',''WONG''      ,''E21'',''2103'',''2006-02-23'',''FIELDREP'',14,''F'',''1971-07-18'',35370.00,500.00,2030.00),\n",
+    "        (''200340'',''ROY''      ,''R'',''ALONZO''    ,''E21'',''5698'',''1997-07-05'',''FIELDREP'',16,''M'',''1956-05-17'',31840.00,500.00,1907.00)');                             \n",
+    "END IF;\n",
+    "END\"\"\"\n",
+    "\n",
+    "DB2.sql(None,\"define sampledata\", cell=create_sample, local_ns=locals())\n",
+    "\n",
+    "create_set = '''\n",
+    "#\n",
+    "# Convert a SET statement into an OPTION statement\n",
+    "#\n",
+    "\n",
+    "# Display settings\n",
+    "if {^1} == 'DISPLAY'\n",
+    "    if {^2} == \"PANDAS\" \n",
+    "        OPTION DISPLAY PANDAS\n",
+    "        return\n",
+    "    else\n",
+    "        if {^2} == \"GRID\"\n",
+    "            OPTION DISPLAY GRID\n",
+    "            return\n",
+    "        endif\n",
+    "    endif\n",
+    "endif\n",
+    "\n",
+    "# Multithreading\n",
+    "if {^1} == 'THREADS'\n",
+    "    OPTION THREADS {2}\n",
+    "    return\n",
+    "endif\n",
+    "        \n",
+    "# Maximum number of rows displayed\n",
+    "if {^1} == 'MAXROWS'\n",
+    "    OPTION MAXROWS {2}\n",
+    "    return\n",
+    "endif\n",
+    "\n",
+    "# Is paging on or off?\n",
+    "if {^1} == 'PAGING'\n",
+    "    OPTION PAGING {2}\n",
+    "    return\n",
+    "endif\n",
+    "        \n",
+    "{*0}\n",
+    "return\n",
+    "'''\n",
+    "\n",
+    "DB2.sql(None,\"define set\", cell=create_set, local_ns=locals())\n",
+    "   \n",
+    "success(f\"Db2 Extensions Loaded. Version: {_db2magic_version}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Credits: IBM 2024, George Baklarz [baklarz@ca.ibm.com]"
+   ]
+  }
+ ],
+ "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": "3.8.10"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/ai-vectors/product-recommendation/db2connect.pickle b/ai-vectors/product-recommendation/db2connect.pickle
new file mode 100644
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diff --git a/ai-vectors/product-recommendation/requirements.txt b/ai-vectors/product-recommendation/requirements.txt
new file mode 100644
index 0000000..6576e88
--- /dev/null
+++ b/ai-vectors/product-recommendation/requirements.txt
@@ -0,0 +1,20 @@
+ipykernel
+ibm-watsonx-ai
+python-dotenv
+rich
+transformers
+torch
+torchvision 
+torchaudio
+pysqlite3-binary
+langchain_community
+beautifulsoup4
+langchain_chroma
+langchain_ibm
+nbformat
+pandas
+matplotlib
+ibm_db
+itables
+scikit-learn
+faker
\ No newline at end of file
diff --git a/ai-vectors/product-recommendation/shoes-data-generation.ipynb b/ai-vectors/product-recommendation/shoes-data-generation.ipynb
new file mode 100644
index 0000000..a2118e1
--- /dev/null
+++ b/ai-vectors/product-recommendation/shoes-data-generation.ipynb
@@ -0,0 +1,764 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import random\n",
+    "from faker import Faker\n",
+    "import os\n",
+    "from dotenv import load_dotenv\n",
+    "from ibm_watsonx_ai import APIClient, Credentials\n",
+    "from ibm_watsonx_ai.foundation_models import Embeddings\n",
+    "import csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fake = Faker()\n",
+    "\n",
+    "ont_cities = [\n",
+    "    \"Toronto\", \"Ottawa\"\n",
+    "]\n",
+    "\n",
+    "# Definitions\n",
+    "brands = ['Zentrax', 'FootFlex', 'StrideOne', 'Loopic', 'RunXpress']\n",
+    "types = ['Running', 'Walking']\n",
+    "classes = ['Men', 'Women']\n",
+    "materials = ['Synthetic', 'Knit']\n",
+    "colors = ['Black', 'White']\n",
+    "arch_supports = ['High', 'Flat']\n",
+    "weather_resistances = ['Waterproof', 'Resistant']\n",
+    "sizes = [round(s, 1) for s in range(6, 13)] + [s + 0.5 for s in range(6, 13)]\n",
+    "store_ids = range(1, 21)\n",
+    "\n",
+    "# Helper: create a fake product name\n",
+    "def create_product_name(brand, shoe_type):\n",
+    "    return f\"{brand} {random.choice(['Ultra', 'Flex', 'Pro', 'X', 'Max'])} {shoe_type}\"\n",
+    "\n",
+    "# Helper: create fake keywords\n",
+    "def generate_keywords(shoe_type, material):\n",
+    "    keywords = [shoe_type.lower(), material.lower()]\n",
+    "    keywords += random.sample(['lightweight', 'durable', 'breathable', 'cushioned', 'supportive', 'flexible'], 3)\n",
+    "    return ', '.join(keywords)\n",
+    "\n",
+    "def generate_shoe_data(n=500):\n",
+    "    data = []\n",
+    "    used_skus = set()\n",
+    "\n",
+    "    for _ in range(n):\n",
+    "        brand = random.choice(brands)\n",
+    "        shoe_type = random.choice(types)\n",
+    "        shoe_class = random.choice(classes)\n",
+    "        material = random.choice(materials)\n",
+    "        size = random.choice(sizes)\n",
+    "        color = random.choice(colors)\n",
+    "        arch = random.choice(arch_supports)\n",
+    "        weather = random.choice(weather_resistances)\n",
+    "        store_id = random.choice(store_ids)\n",
+    "        city = random.choice(ont_cities)\n",
+    "                \n",
+    "        price = round(random.uniform(29.99, 149.99), 2)\n",
+    "        rating = round(random.uniform(3.0, 5.0), 1)\n",
+    "        product_name = create_product_name(brand, shoe_type)\n",
+    "\n",
+    "        # Ensure SKU uniqueness\n",
+    "        while True:\n",
+    "            sku = f\"{brand[:3].upper()}-{random.randint(1000, 9999)}\"\n",
+    "            if sku not in used_skus:\n",
+    "                used_skus.add(sku)\n",
+    "                break\n",
+    "\n",
+    "        data.append({\n",
+    "            'SKU': sku,\n",
+    "            'PRODUCT_NAME': product_name,\n",
+    "            'BRAND': brand,\n",
+    "            'CLASS': shoe_class,\n",
+    "            'TYPE': shoe_type,\n",
+    "            'MATERIAL': material,\n",
+    "            'COLOR': color,\n",
+    "            'WEATHER_RESISTANCE': weather,\n",
+    "            'ARCH_SUPPORT': arch,\n",
+    "            'SIZE': size,\n",
+    "            'PRICE': price,\n",
+    "            'RATING': rating,\n",
+    "            'STORE_ID': store_id,\n",
+    "            'CITY': city\n",
+    "        })\n",
+    "\n",
+    "    return pd.DataFrame(data)\n",
+    "\n",
+    "# Generate and save\n",
+    "df_shoes = generate_shoe_data(500)\n",
+    "# sq_shoes.to_csv(\"shoes.csv\", index=False)\n",
+    "# print(\"Dataset saved as 'shoes.csv'\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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SKUPRODUCT_NAMEBRANDCLASSTYPEMATERIALCOLORWEATHER_RESISTANCEARCH_SUPPORTSIZEPRICERATINGSTORE_IDCITY
0ZEN-5999Zentrax X WalkingZentraxWomenWalkingSyntheticWhiteResistantHigh8.0108.654.31Ottawa
1RUN-4723RunXpress Max WalkingRunXpressWomenWalkingSyntheticWhiteWaterproofHigh8.5103.223.020Ottawa
2ZEN-8968Zentrax Pro RunningZentraxMenRunningSyntheticBlackWaterproofHigh7.5100.253.713Toronto
3RUN-7569RunXpress X WalkingRunXpressMenWalkingSyntheticBlackWaterproofFlat7.583.843.24Ottawa
4RUN-1083RunXpress X WalkingRunXpressWomenWalkingSyntheticBlackResistantFlat12.5139.703.82Toronto
\n",
+       "
"
+      ],
+      "text/plain": [
+       "        SKU           PRODUCT_NAME      BRAND  CLASS     TYPE   MATERIAL  \\\n",
+       "0  ZEN-5999      Zentrax X Walking    Zentrax  Women  Walking  Synthetic   \n",
+       "1  RUN-4723  RunXpress Max Walking  RunXpress  Women  Walking  Synthetic   \n",
+       "2  ZEN-8968    Zentrax Pro Running    Zentrax    Men  Running  Synthetic   \n",
+       "3  RUN-7569    RunXpress X Walking  RunXpress    Men  Walking  Synthetic   \n",
+       "4  RUN-1083    RunXpress X Walking  RunXpress  Women  Walking  Synthetic   \n",
+       "\n",
+       "   COLOR WEATHER_RESISTANCE ARCH_SUPPORT  SIZE   PRICE  RATING  STORE_ID  \\\n",
+       "0  White          Resistant         High   8.0  108.65     4.3         1   \n",
+       "1  White         Waterproof         High   8.5  103.22     3.0        20   \n",
+       "2  Black         Waterproof         High   7.5  100.25     3.7        13   \n",
+       "3  Black         Waterproof         Flat   7.5   83.84     3.2         4   \n",
+       "4  Black          Resistant         Flat  12.5  139.70     3.8         2   \n",
+       "\n",
+       "      CITY  \n",
+       "0   Ottawa  \n",
+       "1   Ottawa  \n",
+       "2  Toronto  \n",
+       "3   Ottawa  \n",
+       "4  Toronto  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_shoes.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embedding_cols = ['TYPE', 'MATERIAL', 'COLOR', 'WEATHER_RESISTANCE', 'ARCH_SUPPORT']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "  \n",
+       "
TYPEMATERIALCOLORWEATHER_RESISTANCEARCH_SUPPORT
0WalkingSyntheticWhiteResistantHigh
1WalkingSyntheticWhiteWaterproofHigh
2RunningSyntheticBlackWaterproofHigh
3WalkingSyntheticBlackWaterproofFlat
4WalkingSyntheticBlackResistantFlat
\n",
+       "
"
+      ],
+      "text/plain": [
+       "      TYPE   MATERIAL  COLOR WEATHER_RESISTANCE ARCH_SUPPORT\n",
+       "0  Walking  Synthetic  White          Resistant         High\n",
+       "1  Walking  Synthetic  White         Waterproof         High\n",
+       "2  Running  Synthetic  Black         Waterproof         High\n",
+       "3  Walking  Synthetic  Black         Waterproof         Flat\n",
+       "4  Walking  Synthetic  Black          Resistant         Flat"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_shoes[embedding_cols].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Generating embedding vetors for the shoes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Combine all embedding columns into a single string for each row, including column names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_shoes['COMBINED'] = df_shoes.apply(\n",
+    "    lambda row: ' [SEP] '.join([f\"{col_name}: {row[col_name]}\" for col_name in embedding_cols]), \n",
+    "    axis=1\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
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+       "    \n",
+       "  \n",
+       "
TYPEMATERIALCOLORWEATHER_RESISTANCEARCH_SUPPORTCOMBINED
0WalkingSyntheticWhiteResistantHighTYPE: Walking [SEP] MATERIAL: Synthetic [SEP] ...
1WalkingSyntheticWhiteWaterproofHighTYPE: Walking [SEP] MATERIAL: Synthetic [SEP] ...
2RunningSyntheticBlackWaterproofHighTYPE: Running [SEP] MATERIAL: Synthetic [SEP] ...
3WalkingSyntheticBlackWaterproofFlatTYPE: Walking [SEP] MATERIAL: Synthetic [SEP] ...
4WalkingSyntheticBlackResistantFlatTYPE: Walking [SEP] MATERIAL: Synthetic [SEP] ...
\n",
+       "
"
+      ],
+      "text/plain": [
+       "      TYPE   MATERIAL  COLOR WEATHER_RESISTANCE ARCH_SUPPORT  \\\n",
+       "0  Walking  Synthetic  White          Resistant         High   \n",
+       "1  Walking  Synthetic  White         Waterproof         High   \n",
+       "2  Running  Synthetic  Black         Waterproof         High   \n",
+       "3  Walking  Synthetic  Black         Waterproof         Flat   \n",
+       "4  Walking  Synthetic  Black          Resistant         Flat   \n",
+       "\n",
+       "                                            COMBINED  \n",
+       "0  TYPE: Walking [SEP] MATERIAL: Synthetic [SEP] ...  \n",
+       "1  TYPE: Walking [SEP] MATERIAL: Synthetic [SEP] ...  \n",
+       "2  TYPE: Running [SEP] MATERIAL: Synthetic [SEP] ...  \n",
+       "3  TYPE: Walking [SEP] MATERIAL: Synthetic [SEP] ...  \n",
+       "4  TYPE: Walking [SEP] MATERIAL: Synthetic [SEP] ...  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cols_to_show = ['TYPE', 'MATERIAL', 'COLOR', 'WEATHER_RESISTANCE', 'ARCH_SUPPORT', 'COMBINED']\n",
+    "df_shoes[cols_to_show].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'TYPE: Walking [SEP] MATERIAL: Synthetic [SEP] COLOR: White [SEP] WEATHER_RESISTANCE: Resistant [SEP] ARCH_SUPPORT: High'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_shoes.iloc[0]['COMBINED']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Setting up `wx.ai` embedding API connection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "load_dotenv(os.getcwd()+\"/.env\", override=True)\n",
+    "credentials = Credentials(\n",
+    "                url = \"https://us-south.ml.cloud.ibm.com\",\n",
+    "                api_key = os.getenv(\"WATSONX_APIKEY\", \"\")\n",
+    "                )\n",
+    "\n",
+    "client = APIClient(credentials)\n",
+    "\n",
+    "project_id = os.getenv(\"WATSONX_PROJECT\", \"\")\n",
+    "client.set.default_project(project_id)\n",
+    "\n",
+    "embeddings = Embeddings(\n",
+    "    model_id=client.foundation_models.EmbeddingModels.MULTILINGUAL_E5_LARGE,\n",
+    "    credentials=credentials,\n",
+    "    project_id=project_id,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Showing a few sample rows with their embedding vectors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "row_combined = df_shoes['COMBINED'].tolist()\n",
+    "shoe_vectors = embeddings.embed_documents(texts=row_combined)\n",
+    "df_shoes['EMBEDDING'] = shoe_vectors\n",
+    "df_shoes['EMBEDDING'] = df_shoes['EMBEDDING'].apply(lambda x: '[' + ', '.join(map(str, x)) + ']')\n",
+    "df_shoes.drop(['COMBINED'], axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
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+       "      \n",
+       "    \n",
+       "  \n",
+       "
TYPEMATERIALCOLORWEATHER_RESISTANCEARCH_SUPPORTEMBEDDING
0WalkingSyntheticWhiteResistantHigh[0.037853196, 0.0047503645, -0.042236183, -0.0...
1WalkingSyntheticWhiteWaterproofHigh[0.035730753, 0.0032045494, -0.039743163, -0.0...
2RunningSyntheticBlackWaterproofHigh[0.023130681, 0.0013610307, -0.042692233, -0.0...
3WalkingSyntheticBlackWaterproofFlat[0.02354851, 0.004738677, -0.03867846, -0.0353...
4WalkingSyntheticBlackResistantFlat[0.02477072, 0.0077166175, -0.041874062, -0.02...
\n",
+       "
"
+      ],
+      "text/plain": [
+       "      TYPE   MATERIAL  COLOR WEATHER_RESISTANCE ARCH_SUPPORT  \\\n",
+       "0  Walking  Synthetic  White          Resistant         High   \n",
+       "1  Walking  Synthetic  White         Waterproof         High   \n",
+       "2  Running  Synthetic  Black         Waterproof         High   \n",
+       "3  Walking  Synthetic  Black         Waterproof         Flat   \n",
+       "4  Walking  Synthetic  Black          Resistant         Flat   \n",
+       "\n",
+       "                                           EMBEDDING  \n",
+       "0  [0.037853196, 0.0047503645, -0.042236183, -0.0...  \n",
+       "1  [0.035730753, 0.0032045494, -0.039743163, -0.0...  \n",
+       "2  [0.023130681, 0.0013610307, -0.042692233, -0.0...  \n",
+       "3  [0.02354851, 0.004738677, -0.03867846, -0.0353...  \n",
+       "4  [0.02477072, 0.0077166175, -0.041874062, -0.02...  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cols_to_show = ['TYPE', 'MATERIAL', 'COLOR', 'WEATHER_RESISTANCE', 'ARCH_SUPPORT', 'EMBEDDING']\n",
+    "df_shoes[cols_to_show].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# df_shoes.iloc[0]['EMBEDDING']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['SKU', 'PRODUCT_NAME', 'BRAND', 'CLASS', 'TYPE', 'MATERIAL', 'COLOR',\n",
+       "       'WEATHER_RESISTANCE', 'ARCH_SUPPORT', 'SIZE', 'PRICE', 'RATING',\n",
+       "       'STORE_ID', 'CITY', 'EMBEDDING'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_shoes.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Save the shoes dataframe into a .csv file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_shoes.to_csv(\n",
+    "    'shoes-vectors.csv',\n",
+    "    index=False,\n",
+    "    quoting=csv.QUOTE_NONNUMERIC\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/ai-vectors/product-recommendation/shoes-data-partitioning.ipynb b/ai-vectors/product-recommendation/shoes-data-partitioning.ipynb
new file mode 100644
index 0000000..0dc5078
--- /dev/null
+++ b/ai-vectors/product-recommendation/shoes-data-partitioning.ipynb
@@ -0,0 +1,466 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "dcaa3841",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import csv\n",
+    "\n",
+    "df = pd.read_csv('shoes-vectors.csv', quoting=csv.QUOTE_NONNUMERIC)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e4e8b65b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "    \n",
+       "  \n",
+       "
SKUPRODUCT_NAMEBRANDCLASSTYPEMATERIALCOLORWEATHER_RESISTANCEARCH_SUPPORTSIZEPRICERATINGSTORE_IDCITYEMBEDDING
0ZEN-5999Zentrax X WalkingZentraxWomenWalkingSyntheticWhiteResistantHigh8.0108.654.31.0Ottawa[0.037853196, 0.0047503645, -0.042236183, -0.0...
1RUN-4723RunXpress Max WalkingRunXpressWomenWalkingSyntheticWhiteWaterproofHigh8.5103.223.020.0Ottawa[0.035730753, 0.0032045494, -0.039743163, -0.0...
2ZEN-8968Zentrax Pro RunningZentraxMenRunningSyntheticBlackWaterproofHigh7.5100.253.713.0Toronto[0.023130681, 0.0013610307, -0.042692233, -0.0...
3RUN-7569RunXpress X WalkingRunXpressMenWalkingSyntheticBlackWaterproofFlat7.583.843.24.0Ottawa[0.02354851, 0.004738677, -0.03867846, -0.0353...
4RUN-1083RunXpress X WalkingRunXpressWomenWalkingSyntheticBlackResistantFlat12.5139.703.82.0Toronto[0.02477072, 0.0077166175, -0.041874062, -0.02...
\n",
+       "
"
+      ],
+      "text/plain": [
+       "        SKU           PRODUCT_NAME      BRAND  CLASS     TYPE   MATERIAL  \\\n",
+       "0  ZEN-5999      Zentrax X Walking    Zentrax  Women  Walking  Synthetic   \n",
+       "1  RUN-4723  RunXpress Max Walking  RunXpress  Women  Walking  Synthetic   \n",
+       "2  ZEN-8968    Zentrax Pro Running    Zentrax    Men  Running  Synthetic   \n",
+       "3  RUN-7569    RunXpress X Walking  RunXpress    Men  Walking  Synthetic   \n",
+       "4  RUN-1083    RunXpress X Walking  RunXpress  Women  Walking  Synthetic   \n",
+       "\n",
+       "   COLOR WEATHER_RESISTANCE ARCH_SUPPORT  SIZE   PRICE  RATING  STORE_ID  \\\n",
+       "0  White          Resistant         High   8.0  108.65     4.3       1.0   \n",
+       "1  White         Waterproof         High   8.5  103.22     3.0      20.0   \n",
+       "2  Black         Waterproof         High   7.5  100.25     3.7      13.0   \n",
+       "3  Black         Waterproof         Flat   7.5   83.84     3.2       4.0   \n",
+       "4  Black          Resistant         Flat  12.5  139.70     3.8       2.0   \n",
+       "\n",
+       "      CITY                                          EMBEDDING  \n",
+       "0   Ottawa  [0.037853196, 0.0047503645, -0.042236183, -0.0...  \n",
+       "1   Ottawa  [0.035730753, 0.0032045494, -0.039743163, -0.0...  \n",
+       "2  Toronto  [0.023130681, 0.0013610307, -0.042692233, -0.0...  \n",
+       "3   Ottawa  [0.02354851, 0.004738677, -0.03867846, -0.0353...  \n",
+       "4  Toronto  [0.02477072, 0.0077166175, -0.041874062, -0.02...  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "b30395a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def format_embedding(embedding_list):\n",
+    "    return f\"VECTOR({embedding_list}, 1024, FLOAT32)\"\n",
+    "\n",
+    "df['EMBEDDING'] = df['EMBEDDING'].apply(format_embedding)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "2f810974",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "
SKUPRODUCT_NAMEBRANDCLASSTYPEMATERIALCOLORWEATHER_RESISTANCEARCH_SUPPORTSIZEPRICERATINGSTORE_IDCITYEMBEDDING
0ZEN-5999Zentrax X WalkingZentraxWomenWalkingSyntheticWhiteResistantHigh8.0108.654.31.0OttawaVECTOR([0.037853196, 0.0047503645, -0.04223618...
1RUN-4723RunXpress Max WalkingRunXpressWomenWalkingSyntheticWhiteWaterproofHigh8.5103.223.020.0OttawaVECTOR([0.035730753, 0.0032045494, -0.03974316...
2ZEN-8968Zentrax Pro RunningZentraxMenRunningSyntheticBlackWaterproofHigh7.5100.253.713.0TorontoVECTOR([0.023130681, 0.0013610307, -0.04269223...
3RUN-7569RunXpress X WalkingRunXpressMenWalkingSyntheticBlackWaterproofFlat7.583.843.24.0OttawaVECTOR([0.02354851, 0.004738677, -0.03867846, ...
4RUN-1083RunXpress X WalkingRunXpressWomenWalkingSyntheticBlackResistantFlat12.5139.703.82.0TorontoVECTOR([0.02477072, 0.0077166175, -0.041874062...
\n",
+       "
"
+      ],
+      "text/plain": [
+       "        SKU           PRODUCT_NAME      BRAND  CLASS     TYPE   MATERIAL  \\\n",
+       "0  ZEN-5999      Zentrax X Walking    Zentrax  Women  Walking  Synthetic   \n",
+       "1  RUN-4723  RunXpress Max Walking  RunXpress  Women  Walking  Synthetic   \n",
+       "2  ZEN-8968    Zentrax Pro Running    Zentrax    Men  Running  Synthetic   \n",
+       "3  RUN-7569    RunXpress X Walking  RunXpress    Men  Walking  Synthetic   \n",
+       "4  RUN-1083    RunXpress X Walking  RunXpress  Women  Walking  Synthetic   \n",
+       "\n",
+       "   COLOR WEATHER_RESISTANCE ARCH_SUPPORT  SIZE   PRICE  RATING  STORE_ID  \\\n",
+       "0  White          Resistant         High   8.0  108.65     4.3       1.0   \n",
+       "1  White         Waterproof         High   8.5  103.22     3.0      20.0   \n",
+       "2  Black         Waterproof         High   7.5  100.25     3.7      13.0   \n",
+       "3  Black         Waterproof         Flat   7.5   83.84     3.2       4.0   \n",
+       "4  Black          Resistant         Flat  12.5  139.70     3.8       2.0   \n",
+       "\n",
+       "      CITY                                          EMBEDDING  \n",
+       "0   Ottawa  VECTOR([0.037853196, 0.0047503645, -0.04223618...  \n",
+       "1   Ottawa  VECTOR([0.035730753, 0.0032045494, -0.03974316...  \n",
+       "2  Toronto  VECTOR([0.023130681, 0.0013610307, -0.04269223...  \n",
+       "3   Ottawa  VECTOR([0.02354851, 0.004738677, -0.03867846, ...  \n",
+       "4  Toronto  VECTOR([0.02477072, 0.0077166175, -0.041874062...  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7c138072",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(500, 15)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0ee0dc8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['SKU', 'PRODUCT_NAME', 'BRAND', 'CLASS', 'TYPE', 'MATERIAL', 'COLOR',\n",
+       "       'WEATHER_RESISTANCE', 'ARCH_SUPPORT', 'SIZE', 'PRICE', 'RATING',\n",
+       "       'STORE_ID', 'CITY', 'EMBEDDING'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "cd514ccd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import csv\n",
+    "\n",
+    "# Assuming df_shoes is your original DataFrame\n",
+    "cols_data = [\n",
+    "    'SKU', 'PRODUCT_NAME', 'BRAND', 'CLASS', 'TYPE', 'MATERIAL', 'COLOR',\n",
+    "    'WEATHER_RESISTANCE', 'ARCH_SUPPORT', 'SIZE', 'PRICE', 'RATING',\n",
+    "    'STORE_ID', 'CITY'\n",
+    "]\n",
+    "\n",
+    "cols_vectors = ['SKU', 'EMBEDDING']\n",
+    "\n",
+    "# Split into two DataFrames\n",
+    "df_data = df[cols_data]\n",
+    "df_vectors = df[cols_vectors]\n",
+    "\n",
+    "# Save to CSVs\n",
+    "df_data.to_csv('shoes.csv', index=False, quoting=csv.QUOTE_NONNUMERIC)\n",
+    "df_vectors.to_csv('vectors.csv', index=False, quoting=csv.QUOTE_NONNUMERIC)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/ai-vectors/product-recommendation/shoes-search.ipynb b/ai-vectors/product-recommendation/shoes-search.ipynb
new file mode 100644
index 0000000..f903e26
--- /dev/null
+++ b/ai-vectors/product-recommendation/shoes-search.ipynb
@@ -0,0 +1,1566 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Finding Similar Shoes Using Vector Search in Db2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Db2 Extensions Loaded. Version: 2024-09-16\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<>:1708: SyntaxWarning: invalid escape sequence '\\s'\n",
+      "<>:2305: SyntaxWarning: invalid escape sequence '\\?'\n",
+      "/tmp/ipykernel_32259/2299624180.py:1708: SyntaxWarning: invalid escape sequence '\\s'\n",
+      "  firstCommand = \"(?:^\\s*)([a-zA-Z]+)(?:\\s+.*|$)\"\n",
+      "/tmp/ipykernel_32259/2299624180.py:2305: SyntaxWarning: invalid escape sequence '\\?'\n",
+      "  pattern = \"\\?\\*[0-9]+\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import random\n",
+    "from faker import Faker\n",
+    "import os\n",
+    "from dotenv import dotenv_values, load_dotenv\n",
+    "from ibm_watsonx_ai import APIClient, Credentials\n",
+    "from ibm_watsonx_ai.foundation_models import Embeddings\n",
+    "import csv\n",
+    "from IPython.display import Image, display\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.manifold import TSNE\n",
+    "import numpy as np\n",
+    "import ast\n",
+    "import matplotlib.image as mpimg\n",
+    "from utils import *\n",
+    "pd.set_option('display.max_colwidth', None)  # Show full column content\n",
+    "\n",
+    "# Enable Db2 Magic Commands Extensions for Jupyter Notebook\n",
+    "if not os.path.isfile('db2.ipynb'):\n",
+    "    os.system('wget https://raw.githubusercontent.com/IBM/db2-jupyter/master/db2.ipynb')\n",
+    "\n",
+    "%run db2.ipynb"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setting up wx.ai API for embedding generation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "load_dotenv(os.getcwd()+\"/.env\", override=True)\n",
+    "\n",
+    "credentials = Credentials(\n",
+    "                url = \"https://us-south.ml.cloud.ibm.com\",\n",
+    "                api_key = os.getenv(\"WATSONX_APIKEY\", \"\")\n",
+    "                )\n",
+    "\n",
+    "client = APIClient(credentials)\n",
+    "\n",
+    "project_id = os.getenv(\"WATSONX_PROJECT\", \"\")\n",
+    "client.set.default_project(project_id)\n",
+    "\n",
+    "embeddings = Embeddings(\n",
+    "    model_id=client.foundation_models.EmbeddingModels.MULTILINGUAL_E5_LARGE,\n",
+    "    credentials=credentials,\n",
+    "    project_id=project_id,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setting up Db2 Connection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Connection successful. tpcds @ localhost \n"
+     ]
+    }
+   ],
+   "source": [
+    "db2creds = dotenv_values('.env')\n",
+    "%sql CONNECT CREDENTIALS db2creds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Setting up a Shoes Table at Db2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Command completed.\n",
+      "Command completed.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%sql DROP TABLE SQ_SHOES\n",
+    "\n",
+    "# Create the table\n",
+    "sql = get_create_sq_shoes_table_sql()\n",
+    "%sql {sql}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture output\n",
+    "sql = f'''\"IMPORT FROM 'shoes-vectors.csv' OF DEL skipcount 1 INSERT INTO SQ_SHOES\"'''\n",
+    "_ = ! db2 \"connect to TPCDS\"\n",
+    "\n",
+    "output = %system db2 {sql}\n",
+    "print(output)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Searching for a Running Shoe of Size `12`, for `Men`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
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+       "\n",
+       "\n",
+       "  \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "  \n",
+       "  \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "  \n",
+       "
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+       "
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+      ],
+      "text/plain": [
+       "     1\n",
+       "0  500"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%sql SELECT count(*) FROM SQ_SHOES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
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+     "metadata": {},
+     "output_type": "display_data"
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+       "
SKUPRODUCT_NAMEBRANDTYPEMATERIALCOLORWEATHER_RESISTANCEARCH_SUPPORTPRICERATINGCITY
0STR-1144StrideOne Max RunningStrideOneRunningKnitWhiteWaterproofHigh98.133.4Ottawa
1RUN-5480RunXpress Pro WalkingRunXpressWalkingKnitWhiteWaterproofHigh97.244.7Toronto
2LOO-1396Loopic Pro RunningLoopicRunningKnitBlackResistantFlat43.984.6Toronto
3ZEN-2061Zentrax Flex RunningZentraxRunningKnitBlackWaterproofFlat134.654.9Ottawa
4RUN-9444RunXpress X RunningRunXpressRunningSyntheticBlackWaterproofFlat106.103.6Ottawa
5FOO-7361FootFlex Pro RunningFootFlexRunningSyntheticBlackWaterproofHigh83.214.8Ottawa
\n",
+       "
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+      ],
+      "text/plain": [
+       "        SKU           PRODUCT_NAME      BRAND     TYPE   MATERIAL  COLOR  \\\n",
+       "0  STR-1144  StrideOne Max Running  StrideOne  Running       Knit  White   \n",
+       "1  RUN-5480  RunXpress Pro Walking  RunXpress  Walking       Knit  White   \n",
+       "2  LOO-1396     Loopic Pro Running     Loopic  Running       Knit  Black   \n",
+       "3  ZEN-2061   Zentrax Flex Running    Zentrax  Running       Knit  Black   \n",
+       "4  RUN-9444    RunXpress X Running  RunXpress  Running  Synthetic  Black   \n",
+       "5  FOO-7361   FootFlex Pro Running   FootFlex  Running  Synthetic  Black   \n",
+       "\n",
+       "  WEATHER_RESISTANCE ARCH_SUPPORT   PRICE  RATING     CITY  \n",
+       "0         Waterproof         High   98.13     3.4   Ottawa  \n",
+       "1         Waterproof         High   97.24     4.7  Toronto  \n",
+       "2          Resistant         Flat   43.98     4.6  Toronto  \n",
+       "3         Waterproof         Flat  134.65     4.9   Ottawa  \n",
+       "4         Waterproof         Flat  106.10     3.6   Ottawa  \n",
+       "5         Waterproof         High   83.21     4.8   Ottawa  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sql = get_men_shoes_sql()\n",
+    "df_shoe_search = %sql {sql}\n",
+    "\n",
+    "sku_list = df_shoe_search['SKU'].tolist()\n",
+    "display_sku_images(sku_list)\n",
+    "\n",
+    "df_shoe_search.head(6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### My chosen shoe is available at the Ottawa location"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_choice_sku = 'ZEN-2061'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Searching for a similar shoes at the Toronto Location"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+       "
SKUPRODUCT_NAMEBRANDTYPEMATERIALCOLORWEATHER_RESISTANCEARCH_SUPPORTPRICERATINGDISTANCE
0LOO-1396Loopic Pro RunningLoopicRunningKnitBlackResistantFlat43.984.60.135494
1STR-2196StrideOne Ultra RunningStrideOneRunningKnitWhiteResistantHigh145.573.70.276512
2RUN-5480RunXpress Pro WalkingRunXpressWalkingKnitWhiteWaterproofHigh97.244.70.295729
\n",
+       "
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+      ],
+      "text/plain": [
+       "        SKU             PRODUCT_NAME      BRAND     TYPE MATERIAL  COLOR  \\\n",
+       "0  LOO-1396       Loopic Pro Running     Loopic  Running     Knit  Black   \n",
+       "1  STR-2196  StrideOne Ultra Running  StrideOne  Running     Knit  White   \n",
+       "2  RUN-5480    RunXpress Pro Walking  RunXpress  Walking     Knit  White   \n",
+       "\n",
+       "  WEATHER_RESISTANCE ARCH_SUPPORT   PRICE  RATING  DISTANCE  \n",
+       "0          Resistant         Flat   43.98     4.6  0.135494  \n",
+       "1          Resistant         High  145.57     3.7  0.276512  \n",
+       "2         Waterproof         High   97.24     4.7  0.295729  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sql = get_similar_shoes_sql()\n",
+    "df_shoes_results = %sql {sql}\n",
+    "sku_list = df_shoes_results['SKU'].tolist()\n",
+    "\n",
+    "display_sku_images(sku_list)\n",
+    "df_shoes_results.head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Comparing the Search Results with my Preferred Shoe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
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+       "      \n",
+       "    \n",
+       "  \n",
+       "
012
SKUZEN-2061LOO-1396RUN-5480
TYPERunningRunningWalking
MATERIALKnitKnitKnit
COLORBlackBlackWhite
WEATHER_RESISTANCEWaterproofResistantWaterproof
ARCH_SUPPORTFlatFlatHigh
\n",
+       "
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+      ],
+      "text/plain": [
+       "                             0          1           2\n",
+       "SKU                   ZEN-2061   LOO-1396    RUN-5480\n",
+       "TYPE                   Running    Running     Walking\n",
+       "MATERIAL                  Knit       Knit        Knit\n",
+       "COLOR                    Black      Black       White\n",
+       "WEATHER_RESISTANCE  Waterproof  Resistant  Waterproof\n",
+       "ARCH_SUPPORT              Flat       Flat        High"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sku_list = df_shoes_results.iloc[[0, 2]]['SKU'].tolist()\n",
+    "sku_list.append(my_choice_sku)\n",
+    "\n",
+    "sku_sql_in = ', '.join(f\"'{sku}'\" for sku in sku_list)\n",
+    "\n",
+    "sql = get_distance_for_sku_list_sql(sku_sql_in)\n",
+    "\n",
+    "df_result = %sql {sql}\n",
+    "\n",
+    "cols_show = ['SKU', 'TYPE', 'MATERIAL', 'COLOR', 'WEATHER_RESISTANCE', 'ARCH_SUPPORT']\n",
+    "df_result[cols_show].transpose()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Looking under the hood"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. original table without a `VECTOR` column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Command completed.\n",
+      "Command completed.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
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+       "
SKUPRODUCT_NAMEBRANDCLASSTYPEMATERIALCOLORWEATHER_RESISTANCEARCH_SUPPORTSIZEPRICERATINGSTORE_IDCITY
0ZEN-5999Zentrax X WalkingZentraxWomenWalkingSyntheticWhiteResistantHigh8.0108.654.31Ottawa
1RUN-4723RunXpress Max WalkingRunXpressWomenWalkingSyntheticWhiteWaterproofHigh8.5103.223.020Ottawa
2ZEN-8968Zentrax Pro RunningZentraxMenRunningSyntheticBlackWaterproofHigh7.5100.253.713Toronto
\n",
+       "
"
+      ],
+      "text/plain": [
+       "        SKU           PRODUCT_NAME      BRAND  CLASS     TYPE   MATERIAL  \\\n",
+       "0  ZEN-5999      Zentrax X Walking    Zentrax  Women  Walking  Synthetic   \n",
+       "1  RUN-4723  RunXpress Max Walking  RunXpress  Women  Walking  Synthetic   \n",
+       "2  ZEN-8968    Zentrax Pro Running    Zentrax    Men  Running  Synthetic   \n",
+       "\n",
+       "   COLOR WEATHER_RESISTANCE ARCH_SUPPORT  SIZE   PRICE  RATING  STORE_ID  \\\n",
+       "0  White          Resistant         High   8.0  108.65     4.3         1   \n",
+       "1  White         Waterproof         High   8.5  103.22     3.0        20   \n",
+       "2  Black         Waterproof         High   7.5  100.25     3.7        13   \n",
+       "\n",
+       "      CITY  \n",
+       "0   Ottawa  \n",
+       "1   Ottawa  \n",
+       "2  Toronto  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%sql DROP TABLE SQ_SHOES\n",
+    "\n",
+    "# Create the table\n",
+    "sql = get_create_sq_shoes_table_data_only()\n",
+    "%sql {sql}\n",
+    "\n",
+    "sql = f'''\"IMPORT FROM 'shoes.csv' OF DEL skipcount 1 INSERT INTO SQ_SHOES\"'''\n",
+    "_ = ! db2 \"connect to TPCDS\"\n",
+    "\n",
+    "output = %system db2 {sql}\n",
+    "\n",
+    "%sql SELECT * FROM SQ_SHOES FETCH FIRST 3 ROWS ONLY"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Adding a `VECTOR` column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Command completed.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%sql\n",
+    "ALTER TABLE SQ_SHOES\n",
+    "ADD COLUMN EMBEDDING VECTOR(1024, FLOAT32);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['',\n",
+       " '                                Data type                     Column',\n",
+       " 'Column name                     schema    Data type name      Length     Scale Nulls',\n",
+       " '------------------------------- --------- ------------------- ---------- ----- ------',\n",
+       " 'SKU                             SYSIBM    VARCHAR                      8     0 Yes   ',\n",
+       " 'PRODUCT_NAME                    SYSIBM    VARCHAR                     23     0 Yes   ',\n",
+       " 'BRAND                           SYSIBM    VARCHAR                      9     0 Yes   ',\n",
+       " 'CLASS                           SYSIBM    VARCHAR                      5     0 Yes   ',\n",
+       " 'TYPE                            SYSIBM    VARCHAR                      7     0 Yes   ',\n",
+       " 'MATERIAL                        SYSIBM    VARCHAR                      9     0 Yes   ',\n",
+       " 'COLOR                           SYSIBM    VARCHAR                      5     0 Yes   ',\n",
+       " 'WEATHER_RESISTANCE              SYSIBM    VARCHAR                     10     0 Yes   ',\n",
+       " 'ARCH_SUPPORT                    SYSIBM    VARCHAR                      4     0 Yes   ',\n",
+       " 'SIZE                            SYSIBM    DOUBLE                       8     0 Yes   ',\n",
+       " 'PRICE                           SYSIBM    DOUBLE                       8     0 Yes   ',\n",
+       " 'RATING                          SYSIBM    DOUBLE                       8     0 Yes   ',\n",
+       " 'STORE_ID                        SYSIBM    BIGINT                       8     0 Yes   ',\n",
+       " 'CITY                            SYSIBM    VARCHAR                      7     0 Yes   ',\n",
+       " 'EMBEDDING                       SYSIBM    VECTOR(FLOAT32)           1024     0 Yes   ',\n",
+       " '',\n",
+       " '  15 record(s) selected.',\n",
+       " '']"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sql = f'''\"DESCRIBE TABLE SQ_SHOES\"'''\n",
+    "_ = ! db2 \"connect to TPCDS\"\n",
+    "\n",
+    "%system db2 {sql}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
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+       "
SKUPRODUCT_NAMEBRANDCLASSTYPEMATERIALCOLORWEATHER_RESISTANCEARCH_SUPPORTSIZEPRICERATINGSTORE_IDCITYEMBEDDING
0ZEN-5999Zentrax X WalkingZentraxWomenWalkingSyntheticWhiteResistantHigh8.0108.654.31Ottawa<NA>
1RUN-4723RunXpress Max WalkingRunXpressWomenWalkingSyntheticWhiteWaterproofHigh8.5103.223.020Ottawa<NA>
2ZEN-8968Zentrax Pro RunningZentraxMenRunningSyntheticBlackWaterproofHigh7.5100.253.713Toronto<NA>
\n",
+       "
"
+      ],
+      "text/plain": [
+       "        SKU           PRODUCT_NAME      BRAND  CLASS     TYPE   MATERIAL  \\\n",
+       "0  ZEN-5999      Zentrax X Walking    Zentrax  Women  Walking  Synthetic   \n",
+       "1  RUN-4723  RunXpress Max Walking  RunXpress  Women  Walking  Synthetic   \n",
+       "2  ZEN-8968    Zentrax Pro Running    Zentrax    Men  Running  Synthetic   \n",
+       "\n",
+       "   COLOR WEATHER_RESISTANCE ARCH_SUPPORT  SIZE   PRICE  RATING  STORE_ID  \\\n",
+       "0  White          Resistant         High   8.0  108.65     4.3         1   \n",
+       "1  White         Waterproof         High   8.5  103.22     3.0        20   \n",
+       "2  Black         Waterproof         High   7.5  100.25     3.7        13   \n",
+       "\n",
+       "      CITY EMBEDDING  \n",
+       "0   Ottawa        \n",
+       "1   Ottawa        \n",
+       "2  Toronto        "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%sql SELECT * FROM SQ_SHOES FETCH FIRST 3 ROWS ONLY"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. selected shoe features for generating shoe vectors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
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+       "\n",
+       "\n",
+       "  \n",
+       "    \n",
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+       "    \n",
+       "  \n",
+       "
TYPEMATERIALCOLORWEATHER_RESISTANCEARCH_SUPPORT
0RunningKnitBlackWaterproofFlat
\n",
+       "
"
+      ],
+      "text/plain": [
+       "      TYPE MATERIAL  COLOR WEATHER_RESISTANCE ARCH_SUPPORT\n",
+       "0  Running     Knit  Black         Waterproof         Flat"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "embedding_cols = ['TYPE', 'MATERIAL', 'COLOR', 'WEATHER_RESISTANCE', 'ARCH_SUPPORT']\n",
+    "shoe_sample = %sql SELECT * FROM SQ_SHOES WHERE SKU = :my_choice_sku\n",
+    "shoe_sample[embedding_cols]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. combined the text features of each shoe as follows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'TYPE: Running [SEP] MATERIAL: Knit [SEP] COLOR: Black [SEP] WEATHER_RESISTANCE: Waterproof [SEP] ARCH_SUPPORT: Flat'"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Combine all columns into a single string for each row, including column names\n",
+    "shoe_sample['combined'] = shoe_sample.apply(\n",
+    "    lambda row: ' [SEP] '.join([f\"{col_name}: {row[col_name]}\" for col_name in embedding_cols]), \n",
+    "    axis=1\n",
+    ")\n",
+    "\n",
+    "shoe_sample.iloc[0]['combined']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. generated embedding vectors for the shoe text obtained in the previous step. For generating embedding vectors, used `watsonx.ai`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "input text:  TYPE: Running [SEP] MATERIAL: Knit [SEP] COLOR: Black [SEP] WEATHER_RESISTANCE: Waterproof [SEP] ARCH_SUPPORT: Flat\n",
+      "generated embedding vector:  [0.018356532, 0.016739532, -0.043080304, -0.024207275, 0.024635633, -0.04653779, -0.039383117, 0.067312725, 0.04778032, -0.008429668, 0.04462195, 0.02995529, -0.025633719, 0.011672391, -0.030393964, -0.0029873757, -0.03703966, 0.036439303, 0.044415813, -0.029581932, 0.042920265, -0.011383696, -0.04923202, -0.033802535, -0.018248053, -0.010518033, -0.036249846, -0.03555203, -0.006186654, -0.031357873, -0.0007059911, 0.0062761037, -0.024753371, -0.028718058, -0.058127377, 0.0048069307, 0.035525076, 0.045073558, -0.040165797, 0.027472366, -0.037680116, 0.07650787, 0.002180276, -0.013313014, 0.0066697826, 0.025946569, 0.04761589, -0.017814677, -0.013444244, 0.021717995, 0.045873262, 0.015344814, -0.03655906, -0.03055815, -0.030357162, 0.033440053, -0.011115993, 0.0013662616, -0.025815329, 0.007793427, -0.023909122, 0.0062568327, 0.03182204, -0.016662259, -0.037518673, 0.016322153, 0.010001751, 0.0037034906, -0.06874117, 0.016399004, -0.031211587, 0.0342059, -0.0052034673, -0.012294922, -0.014884096, -0.045349687, 0.013356172, -0.0040074214, 0.0070084375, -0.001885637, 0.058992002, -0.014754689, 0.020468213, 5.323983e-05, 0.013309533, 0.0033008622, 0.043872662, 0.03631291, 0.02827073, 0.017439328, 0.006253376, 0.06080418, 0.017733183, -0.044588655, -0.037274633, 0.024579398, 0.041496474, 0.017027322, 0.03658258, 0.018404478, -0.028737783, -0.029258737, 0.018402796, -0.026102504, -0.029240927, 0.0034474938, 0.02394656, 0.030381713, 0.023185588, -0.0068694307, 0.03299803, 0.04879574, 0.0089901155, -0.024252197, -0.020116242, 0.0025532418, 0.00013939654, -0.01611388, -0.020574015, 0.03047761, 0.013228186, 0.035088364, 0.0038374912, -0.033108894, 0.04399222, -0.011376842, -0.00092374237, 0.045334984, 0.012177831, 0.06287884, 0.012377942, 0.017395081, -0.024599226, -0.015391439, -0.027699836, -0.037346333, -0.011008362, -0.0052378788, -0.023574373, 0.028913282, -0.005251813, 0.03635035, 0.038289856, -0.03336983, -0.01471766, -0.03146721, -0.060331333, -0.010738448, -0.024146128, -0.011852695, 0.008307694, -0.008944146, -0.008999175, 0.030492557, 0.013358244, -0.011641766, 0.017579468, 0.06484657, 0.014106955, -0.0078127915, -0.06054368, -0.043107063, -0.0040495214, -0.012238848, 0.0060263244, -0.0384844, 0.044552702, 0.031245, 0.014581195, -0.029247792, -0.028483499, -0.0491414, 0.05058327, -0.05128686, 0.022084018, 0.02737291, 0.035537176, -0.0009304694, 0.057989825, -0.05785187, -0.0011272512, 0.02973273, 0.049165662, -0.027106907, -0.039722953, 0.025630474, 0.058284074, 0.028953206, 0.03577578, 0.035351504, 0.014288987, -0.0008035807, -0.018250583, 0.0002783224, -0.019769343, 0.009995123, 0.023639124, -0.009662511, -0.04887629, -0.030108023, 0.0012597011, 0.010098899, -0.017649245, 0.022021933, 0.008383836, -0.008405848, -0.059839875, -0.020180907, 0.00928244, -0.06735911, -0.091197945, -0.018413484, 0.041689977, 0.04866101, -0.026367405, -0.04970615, -0.041674487, -0.026323617, 0.03742511, 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+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "row_combined = shoe_sample['combined'].tolist()\n",
+    "print('input text: ', row_combined[0])\n",
+    "\n",
+    "shoe_vectors = embeddings.embed_documents(texts=row_combined)\n",
+    "print('generated embedding vector: ', shoe_vectors[0])\n",
+    "\n",
+    "shoe_sample['embedding'] = shoe_vectors\n",
+    "shoe_sample['embedding'] = shoe_sample['embedding'].apply(lambda x: '[' + ', '.join(map(str, x)) + ']')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4. stored vectors in Db2 in a Vector column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
\n",
+       "\n",
+       "\n",
+       "  \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "  \n",
+       "  \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "  \n",
+       "
EMBEDDING
0<NA>
\n",
+       "
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+      ],
+      "text/plain": [
+       "  EMBEDDING\n",
+       "0      "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%sql SELECT EMBEDDING FROM SQ_SHOES WHERE SKU = :my_choice_sku"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Command completed.\n"
+     ]
+    }
+   ],
+   "source": [
+    "update_sql = f\"\"\" \n",
+    "UPDATE SQ_SHOES SET EMBEDDING = VECTOR('{shoe_vectors[0]}', 1024, FLOAT32) WHERE SKU = :my_choice_sku\n",
+    "\"\"\"\n",
+    "\n",
+    "%sql {update_sql}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
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+       "\n",
+       "\n",
+       "  \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "  \n",
+       "  \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "  \n",
+       "
EMBEDDING
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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      EMBEDDING\n",
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+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%sql SELECT EMBEDDING FROM SQ_SHOES WHERE SKU = :my_choice_sku"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5. Loading the vectors of all the shoes - to save demo time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Command completed.\n",
+      "Command completed.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%sql DROP TABLE SQ_SHOES\n",
+    "\n",
+    "# Create the table\n",
+    "sql = get_create_sq_shoes_table_sql()\n",
+    "%sql {sql}\n",
+    "\n",
+    "sql = f'''\"IMPORT FROM 'shoes-vectors.csv' OF DEL skipcount 1 INSERT INTO SQ_SHOES\"'''\n",
+    "_ = ! db2 \"connect to TPCDS\"\n",
+    "\n",
+    "output = %system db2 {sql}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6. Finding matching shoes using the `VECTOR_DISTANCE` function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "      \n",
+       "      \n",
+       "    \n",
+       "  \n",
+       "  \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "  \n",
+       "
SKUPRODUCT_NAMEBRANDTYPEMATERIALCOLORWEATHER_RESISTANCEARCH_SUPPORTPRICERATINGDISTANCE
0LOO-1396Loopic Pro RunningLoopicRunningKnitBlackResistantFlat43.984.60.135494
1STR-2196StrideOne Ultra RunningStrideOneRunningKnitWhiteResistantHigh145.573.70.276512
2RUN-5480RunXpress Pro WalkingRunXpressWalkingKnitWhiteWaterproofHigh97.244.70.295729
3STR-2507StrideOne Max RunningStrideOneRunningSyntheticWhiteWaterproofHigh123.494.50.306206
4STR-7223StrideOne Max WalkingStrideOneWalkingSyntheticWhiteResistantHigh130.033.90.372026
\n",
+       "
"
+      ],
+      "text/plain": [
+       "        SKU             PRODUCT_NAME      BRAND     TYPE   MATERIAL  COLOR  \\\n",
+       "0  LOO-1396       Loopic Pro Running     Loopic  Running       Knit  Black   \n",
+       "1  STR-2196  StrideOne Ultra Running  StrideOne  Running       Knit  White   \n",
+       "2  RUN-5480    RunXpress Pro Walking  RunXpress  Walking       Knit  White   \n",
+       "3  STR-2507    StrideOne Max Running  StrideOne  Running  Synthetic  White   \n",
+       "4  STR-7223    StrideOne Max Walking  StrideOne  Walking  Synthetic  White   \n",
+       "\n",
+       "  WEATHER_RESISTANCE ARCH_SUPPORT   PRICE  RATING  DISTANCE  \n",
+       "0          Resistant         Flat   43.98     4.6  0.135494  \n",
+       "1          Resistant         High  145.57     3.7  0.276512  \n",
+       "2         Waterproof         High   97.24     4.7  0.295729  \n",
+       "3         Waterproof         High  123.49     4.5  0.306206  \n",
+       "4          Resistant         High  130.03     3.9  0.372026  "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sql = f\"\"\"\n",
+    "SELECT \n",
+    "    SKU, \n",
+    "    PRODUCT_NAME, \n",
+    "    BRAND, \n",
+    "    TYPE, \n",
+    "    MATERIAL, \n",
+    "    COLOR, \n",
+    "    WEATHER_RESISTANCE, \n",
+    "    ARCH_SUPPORT, \n",
+    "    PRICE, \n",
+    "    RATING,\n",
+    "    VECTOR_DISTANCE(\n",
+    "        (SELECT EMBEDDING FROM SQ_SHOES WHERE SKU = :my_choice_sku), \n",
+    "        EMBEDDING, \n",
+    "        EUCLIDEAN\n",
+    "    ) AS DISTANCE\n",
+    "FROM \n",
+    "    SQ_SHOES\n",
+    "WHERE \n",
+    "    SKU <> :my_choice_sku\n",
+    "    AND CITY = 'Toronto'\n",
+    "    AND SIZE = 12\n",
+    "    AND CLASS = 'Men'\n",
+    "ORDER BY \n",
+    "    DISTANCE ASC\n",
+    "FETCH FIRST 5 ROWS ONLY\n",
+    "\"\"\"\n",
+    "\n",
+    "top_shoes = %sql {sql}\n",
+    "top_shoes.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 7. Visualizing vector search"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "sku_list = top_shoes['SKU'].tolist()\n",
+    "sku_list.append(my_choice_sku)\n",
+    "sku_sql_in = ', '.join(f\"'{sku}'\" for sku in sku_list)\n",
+    "sql = get_similarity_query(sku_sql_in)\n",
+    "\n",
+    "top_matching_vectors = %sql {sql}\n",
+    "\n",
+    "plot_similarity_tsne(top_matching_vectors, my_choice_sku)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Command completed.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%sql DROP TABLE SQ_SHOES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Connection closed.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%sql CONNECT RESET"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/ai-vectors/product-recommendation/shoes-vectors.csv b/ai-vectors/product-recommendation/shoes-vectors.csv
new file mode 100644
index 0000000..a29c353
--- /dev/null
+++ b/ai-vectors/product-recommendation/shoes-vectors.csv
@@ -0,0 +1,501 @@
+"SKU","PRODUCT_NAME","BRAND","CLASS","TYPE","MATERIAL","COLOR","WEATHER_RESISTANCE","ARCH_SUPPORT","SIZE","PRICE","RATING","STORE_ID","CITY","EMBEDDING"
+"ZEN-5999","Zentrax X Walking","Zentrax","Women","Walking","Synthetic","White","Resistant","High",8.0,108.65,4.3,1,"Ottawa","[0.037853196, 0.0047503645, -0.042236183, -0.023286333, 0.025220515, -0.059019625, -0.037894115, 0.091906, 0.041301135, -0.0059495494, 0.058937304, 0.055801105, -0.023764633, 0.0014579415, -0.027988022, -0.0015831171, -0.04727508, 0.017276177, 0.049292743, -0.03363558, 0.02288617, -0.017494783, -0.047991946, -0.039499708, -0.0033078673, -0.014013269, -0.027293429, -0.035684317, 0.0043387455, -0.02781687, -0.0021022134, 0.0058820313, -0.023342246, -0.028928408, -0.0347362, -0.0009870783, 0.035189915, 0.027085623, -0.044766575, 0.021104794, -0.036405396, 0.04171475, -0.0075976173, 0.0014318504, -0.015827492, 0.017645454, 0.027092984, -0.015472854, -0.0015612696, 0.016907789, 0.042744685, -0.004297024, -0.041870743, -0.034852132, -0.042689048, 0.034820072, -0.028583705, 0.009490473, -0.020777518, 0.008220448, -0.02307375, 0.016156167, 0.045190006, -0.023913974, 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0.019641194, 0.01735744, 0.04955331, -0.015278533, -0.06417095, 0.0048628673, -0.010464309, 0.015367795, 0.035641983, -0.016642757, 0.021023192, -0.010934618, -0.034909178, -0.03383901, 0.013905422, -0.0065203216, 0.040356614, -0.016635625, 0.01754132, 0.0052183405, 0.029602623, -0.0132019445, 0.0042904033, 0.014189753, -0.018611005, -0.030726163, -0.02602095, 0.03556757, 0.05551324, 0.03527323, 0.036614053, -0.000457852, -0.0030278517, 0.03647431, 0.029965695, -0.02670364, -0.03010368, -0.02970417, -0.022100944, -0.046983063, -0.025455097, -0.033814043, -0.012099763, -0.04315842, 0.016263824, -0.0017008653, -0.044376966, 0.0120320525, 0.021173403, 0.0014537976, -0.04444309, -0.034066245, -0.007779884, 0.04666089, 0.015489517, -0.02211805, -0.027515022, 0.015780194, 0.03308855, 0.013539091, 0.024814125, -0.006245241, -0.0135074295, -0.04633733, 0.037686232, -0.053801924, 0.03626295, 0.038545117, -0.018688267, -0.019289166, -0.006999873, -0.053072467, -0.014576887, -0.031682726, 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0.004395546, 0.0036149984, -0.0062337155, 0.07489935, 0.0021195642, 0.03533891, 0.008940203, 0.03437911, -0.003559498, -0.009041754, 0.034696788, 0.03246317, 0.07949995, 0.029227376, 0.024927733, -0.041332457, 0.020068645, -0.022524618, 0.0027720055, -0.049873415, 0.011852741, 0.036012318, -0.03456813, 0.008503248, 0.007149607, -0.017921906, 0.0010975271, 0.035348825, -0.012081595, 0.028761445, 0.0026910685, -0.0069993255, -0.0268019, -0.020776581, -0.0005201282, -0.019771313, 0.02501875, 0.030961769, 0.0016758109, 0.02262523, 0.032564245, 0.028742397, -0.010020618, 0.032523774, -0.008571346, 3.4808952e-05, 0.029291723, 0.034944557, 0.050267544, -0.02146174, -0.011174748, -0.016465636, 0.011475959, -0.015925525, 0.019786347, -0.03599372, -0.016918005, 0.0016325398, 0.0398531, 0.010125999, 0.0040133907, -0.030545494, -0.014505829, 0.023839805, 0.000582805, 0.013744977, -0.0034717373, 0.001199983, -0.037860174, -0.043605812, 0.009794843]"
diff --git a/ai-vectors/product-recommendation/shoes.csv b/ai-vectors/product-recommendation/shoes.csv
new file mode 100644
index 0000000..ef83831
--- /dev/null
+++ b/ai-vectors/product-recommendation/shoes.csv
@@ -0,0 +1,501 @@
+"SKU","PRODUCT_NAME","BRAND","CLASS","TYPE","MATERIAL","COLOR","WEATHER_RESISTANCE","ARCH_SUPPORT","SIZE","PRICE","RATING","STORE_ID","CITY"
+"ZEN-5999","Zentrax X Walking","Zentrax","Women","Walking","Synthetic","White","Resistant","High",8.0,108.65,4.3,1.0,"Ottawa"
+"RUN-4723","RunXpress Max Walking","RunXpress","Women","Walking","Synthetic","White","Waterproof","High",8.5,103.22,3.0,20.0,"Ottawa"
+"ZEN-8968","Zentrax Pro Running","Zentrax","Men","Running","Synthetic","Black","Waterproof","High",7.5,100.25,3.7,13.0,"Toronto"
+"RUN-7569","RunXpress X Walking","RunXpress","Men","Walking","Synthetic","Black","Waterproof","Flat",7.5,83.84,3.2,4.0,"Ottawa"
+"RUN-1083","RunXpress X Walking","RunXpress","Women","Walking","Synthetic","Black","Resistant","Flat",12.5,139.7,3.8,2.0,"Toronto"
+"LOO-1505","Loopic X Walking","Loopic","Men","Walking","Synthetic","White","Resistant","High",11.5,115.43,3.7,5.0,"Toronto"
+"LOO-7876","Loopic Flex Walking","Loopic","Men","Walking","Knit","White","Resistant","Flat",11.0,45.42,4.6,6.0,"Toronto"
+"FOO-3873","FootFlex Max Walking","FootFlex","Men","Walking","Knit","White","Resistant","High",7.5,114.41,3.7,15.0,"Ottawa"
+"STR-3321","StrideOne Pro Running","StrideOne","Men","Running","Knit","Black","Waterproof","High",8.0,126.96,4.1,6.0,"Ottawa"
+"RUN-9657","RunXpress Ultra Running","RunXpress","Women","Running","Synthetic","White","Resistant","High",10.0,117.52,4.2,3.0,"Toronto"
+"ZEN-2275","Zentrax Flex Running","Zentrax","Women","Running","Knit","White","Resistant","High",11.5,136.3,3.7,10.0,"Toronto"
+"LOO-2201","Loopic X Running","Loopic","Men","Running","Synthetic","White","Waterproof","High",9.5,71.18,4.3,10.0,"Ottawa"
+"STR-1144","StrideOne Max Running","StrideOne","Men","Running","Knit","White","Waterproof","High",12.0,98.13,3.4,13.0,"Ottawa"
+"FOO-5535","FootFlex Flex Walking","FootFlex","Men","Walking","Synthetic","Black","Waterproof","High",9.0,70.42,3.3,17.0,"Toronto"
+"LOO-5310","Loopic Max Walking","Loopic","Women","Walking","Synthetic","Black","Resistant","High",7.0,104.33,3.5,20.0,"Ottawa"
+"FOO-3385","FootFlex Max Walking","FootFlex","Women","Walking","Synthetic","White","Waterproof","Flat",10.5,100.82,4.1,5.0,"Toronto"
+"ZEN-5121","Zentrax Flex Walking","Zentrax","Men","Walking","Knit","Black","Waterproof","Flat",6.0,114.34,3.0,18.0,"Toronto"
+"LOO-2183","Loopic Flex Walking","Loopic","Women","Walking","Knit","Black","Resistant","High",6.0,39.52,3.1,17.0,"Ottawa"
+"ZEN-5696","Zentrax Max Running","Zentrax","Men","Running","Synthetic","Black","Waterproof","High",9.0,127.76,3.6,6.0,"Toronto"
+"STR-7245","StrideOne Pro Walking","StrideOne","Women","Walking","Knit","White","Resistant","High",6.0,128.85,4.5,3.0,"Ottawa"
+"LOO-8554","Loopic Flex Running","Loopic","Women","Running","Synthetic","Black","Resistant","High",6.5,131.62,4.9,11.0,"Toronto"
+"RUN-5216","RunXpress Ultra Walking","RunXpress","Women","Walking","Synthetic","Black","Waterproof","Flat",11.5,61.52,3.4,11.0,"Toronto"
+"ZEN-5125","Zentrax Pro Walking","Zentrax","Women","Walking","Knit","Black","Resistant","High",11.0,88.31,4.6,17.0,"Ottawa"
+"ZEN-7425","Zentrax Flex Walking","Zentrax","Women","Walking","Knit","White","Waterproof","High",10.0,70.42,4.6,6.0,"Toronto"
+"RUN-6498","RunXpress Max Walking","RunXpress","Men","Walking","Synthetic","White","Waterproof","Flat",8.5,71.75,4.5,18.0,"Toronto"
+"FOO-8432","FootFlex Flex Walking","FootFlex","Women","Walking","Synthetic","Black","Resistant","Flat",12.0,137.67,4.7,18.0,"Ottawa"
+"LOO-7300","Loopic Ultra Running","Loopic","Men","Running","Knit","White","Resistant","High",7.0,79.29,4.3,11.0,"Ottawa"
+"RUN-1492","RunXpress X Walking","RunXpress","Women","Walking","Synthetic","Black","Resistant","High",9.0,122.26,4.3,12.0,"Ottawa"
+"RUN-5161","RunXpress Pro Walking","RunXpress","Women","Walking","Knit","Black","Waterproof","Flat",11.5,124.14,3.2,14.0,"Ottawa"
+"LOO-8764","Loopic Max Walking","Loopic","Women","Walking","Synthetic","Black","Resistant","Flat",10.0,125.54,4.6,5.0,"Toronto"
+"STR-4150","StrideOne Ultra Running","StrideOne","Men","Running","Knit","Black","Waterproof","High",8.0,42.79,4.2,20.0,"Ottawa"
+"RUN-5480","RunXpress Pro Walking","RunXpress","Men","Walking","Knit","White","Waterproof","High",12.0,97.24,4.7,12.0,"Toronto"
+"FOO-3128","FootFlex Pro Walking","FootFlex","Men","Walking","Synthetic","Black","Resistant","Flat",6.5,147.7,3.4,16.0,"Toronto"
+"STR-9557","StrideOne Ultra Walking","StrideOne","Women","Walking","Synthetic","Black","Waterproof","High",10.0,57.09,4.6,3.0,"Ottawa"
+"ZEN-8148","Zentrax Pro Walking","Zentrax","Women","Walking","Knit","Black","Waterproof","High",7.0,69.16,3.9,15.0,"Ottawa"
+"LOO-9552","Loopic X Running","Loopic","Men","Running","Knit","White","Waterproof","High",6.5,88.64,3.0,20.0,"Ottawa"
+"ZEN-6334","Zentrax Max Running","Zentrax","Men","Running","Synthetic","White","Waterproof","Flat",6.5,116.2,4.4,10.0,"Ottawa"
+"FOO-1619","FootFlex Max Walking","FootFlex","Men","Walking","Knit","White","Waterproof","High",7.5,134.91,3.1,10.0,"Toronto"
+"RUN-6902","RunXpress Flex Walking","RunXpress","Women","Walking","Synthetic","Black","Waterproof","Flat",11.0,95.21,4.7,19.0,"Toronto"
+"FOO-9245","FootFlex Flex Walking","FootFlex","Women","Walking","Synthetic","White","Waterproof","High",7.0,39.93,3.8,3.0,"Ottawa"
+"STR-4210","StrideOne Flex Running","StrideOne","Men","Running","Knit","Black","Waterproof","Flat",11.0,119.36,4.5,3.0,"Toronto"
+"RUN-3891","RunXpress Max Walking","RunXpress","Women","Walking","Knit","White","Waterproof","Flat",6.5,149.7,3.9,18.0,"Toronto"
+"ZEN-7615","Zentrax Max Walking","Zentrax","Women","Walking","Knit","White","Waterproof","High",10.0,60.12,3.8,2.0,"Toronto"
+"RUN-3427","RunXpress X Walking","RunXpress","Women","Walking","Knit","Black","Resistant","High",6.0,138.12,4.3,17.0,"Ottawa"
+"LOO-1396","Loopic Pro Running","Loopic","Men","Running","Knit","Black","Resistant","Flat",12.0,43.98,4.6,4.0,"Toronto"
+"STR-2330","StrideOne X Running","StrideOne","Men","Running","Synthetic","Black","Waterproof","High",6.0,97.79,3.7,2.0,"Toronto"
+"STR-1819","StrideOne Flex Running","StrideOne","Women","Running","Knit","White","Resistant","Flat",10.0,38.54,3.3,7.0,"Toronto"
+"ZEN-2334","Zentrax Ultra Walking","Zentrax","Men","Walking","Synthetic","Black","Waterproof","Flat",8.5,59.39,3.4,20.0,"Toronto"
+"FOO-6944","FootFlex Ultra Walking","FootFlex","Men","Walking","Knit","White","Resistant","Flat",6.5,69.0,3.8,8.0,"Ottawa"
+"STR-7181","StrideOne X Running","StrideOne","Women","Running","Synthetic","White","Waterproof","Flat",10.0,122.42,3.7,12.0,"Toronto"
+"LOO-4267","Loopic Pro Running","Loopic","Men","Running","Synthetic","Black","Resistant","Flat",9.5,118.25,4.3,3.0,"Toronto"
+"RUN-1157","RunXpress Max Walking","RunXpress","Women","Walking","Knit","Black","Resistant","Flat",11.5,106.6,4.1,5.0,"Ottawa"
+"LOO-5640","Loopic Flex Running","Loopic","Women","Running","Synthetic","Black","Waterproof","Flat",7.0,127.1,4.0,8.0,"Ottawa"
+"FOO-8401","FootFlex Max Walking","FootFlex","Men","Walking","Synthetic","White","Waterproof","Flat",11.0,133.32,3.0,6.0,"Toronto"
+"RUN-3632","RunXpress Ultra Running","RunXpress","Men","Running","Synthetic","White","Resistant","High",12.5,90.99,3.3,14.0,"Ottawa"
+"STR-1253","StrideOne Ultra Walking","StrideOne","Women","Walking","Knit","White","Resistant","High",12.0,149.74,4.5,10.0,"Ottawa"
+"LOO-3364","Loopic Pro Running","Loopic","Men","Running","Knit","White","Waterproof","High",10.0,45.62,3.7,8.0,"Ottawa"
+"ZEN-2061","Zentrax Flex Running","Zentrax","Men","Running","Knit","Black","Waterproof","Flat",12.0,134.65,4.9,11.0,"Ottawa"
+"RUN-2499","RunXpress Flex Running","RunXpress","Women","Running","Knit","White","Resistant","High",10.0,106.05,3.7,3.0,"Ottawa"
+"STR-2124","StrideOne X Running","StrideOne","Men","Running","Knit","White","Waterproof","High",8.0,87.28,4.2,16.0,"Toronto"
+"ZEN-5366","Zentrax Ultra Running","Zentrax","Women","Running","Knit","Black","Waterproof","High",6.0,142.46,4.1,9.0,"Toronto"
+"FOO-9325","FootFlex Pro Walking","FootFlex","Women","Walking","Synthetic","Black","Resistant","High",10.5,123.09,4.9,2.0,"Toronto"
+"RUN-9444","RunXpress X Running","RunXpress","Men","Running","Synthetic","Black","Waterproof","Flat",12.0,106.1,3.6,10.0,"Ottawa"
+"LOO-3084","Loopic Max Walking","Loopic","Men","Walking","Synthetic","White","Waterproof","Flat",7.5,33.8,4.3,5.0,"Ottawa"
+"STR-5227","StrideOne Max Walking","StrideOne","Women","Walking","Synthetic","White","Resistant","High",10.0,75.14,4.9,4.0,"Ottawa"
+"ZEN-6851","Zentrax Flex Walking","Zentrax","Men","Walking","Knit","Black","Waterproof","High",7.0,81.78,4.7,4.0,"Toronto"
+"ZEN-1991","Zentrax Pro Walking","Zentrax","Women","Walking","Knit","Black","Waterproof","High",6.5,119.67,4.4,3.0,"Ottawa"
+"FOO-2335","FootFlex Flex Running","FootFlex","Women","Running","Synthetic","White","Resistant","High",12.5,115.57,4.2,16.0,"Toronto"
+"STR-6954","StrideOne Flex Walking","StrideOne","Men","Walking","Synthetic","White","Waterproof","Flat",8.5,66.18,3.8,19.0,"Ottawa"
+"STR-1510","StrideOne Ultra Running","StrideOne","Men","Running","Synthetic","Black","Resistant","Flat",11.5,46.54,4.3,6.0,"Toronto"
+"ZEN-7687","Zentrax Max Running","Zentrax","Women","Running","Knit","Black","Waterproof","Flat",11.5,142.2,4.5,4.0,"Toronto"
+"STR-4486","StrideOne Ultra Walking","StrideOne","Women","Walking","Knit","White","Resistant","High",11.0,130.85,4.7,4.0,"Toronto"
+"FOO-9748","FootFlex Flex Running","FootFlex","Men","Running","Synthetic","Black","Resistant","High",11.0,93.67,4.2,15.0,"Toronto"
+"RUN-3556","RunXpress Flex Running","RunXpress","Men","Running","Knit","Black","Waterproof","Flat",9.0,38.35,4.0,19.0,"Toronto"
+"RUN-1890","RunXpress Pro Running","RunXpress","Women","Running","Synthetic","White","Waterproof","Flat",12.5,84.7,3.8,3.0,"Toronto"
+"RUN-7463","RunXpress Flex Walking","RunXpress","Men","Walking","Synthetic","White","Waterproof","Flat",12.5,37.26,4.7,16.0,"Ottawa"
+"LOO-7936","Loopic Ultra Walking","Loopic","Men","Walking","Knit","White","Waterproof","Flat",9.5,32.8,4.3,5.0,"Toronto"
+"RUN-5486","RunXpress X Walking","RunXpress","Women","Walking","Synthetic","White","Resistant","Flat",7.5,102.99,4.4,4.0,"Ottawa"
+"FOO-8974","FootFlex Flex Walking","FootFlex","Women","Walking","Synthetic","White","Waterproof","Flat",8.5,58.62,4.8,6.0,"Ottawa"
+"RUN-1859","RunXpress X Walking","RunXpress","Men","Walking","Synthetic","Black","Waterproof","Flat",11.0,89.12,3.1,17.0,"Ottawa"
+"FOO-9370","FootFlex Ultra Walking","FootFlex","Men","Walking","Knit","Black","Resistant","Flat",6.0,138.22,3.0,16.0,"Toronto"
+"STR-5974","StrideOne X Running","StrideOne","Women","Running","Knit","Black","Resistant","High",9.0,57.82,4.5,10.0,"Ottawa"
+"ZEN-2333","Zentrax Flex Running","Zentrax","Women","Running","Knit","White","Waterproof","Flat",10.5,138.11,4.5,18.0,"Toronto"
+"LOO-1413","Loopic Flex Running","Loopic","Women","Running","Knit","White","Resistant","Flat",10.0,128.16,4.1,9.0,"Toronto"
+"STR-7643","StrideOne Flex Running","StrideOne","Women","Running","Synthetic","White","Resistant","High",10.0,133.95,3.7,12.0,"Ottawa"
+"LOO-1675","Loopic Flex Running","Loopic","Men","Running","Synthetic","White","Resistant","Flat",10.5,117.61,4.7,15.0,"Ottawa"
+"STR-7840","StrideOne Pro Walking","StrideOne","Women","Walking","Knit","White","Resistant","High",12.5,99.76,4.4,18.0,"Ottawa"
+"ZEN-9237","Zentrax X Walking","Zentrax","Men","Walking","Synthetic","White","Waterproof","High",9.5,44.38,5.0,9.0,"Toronto"
+"RUN-3364","RunXpress Flex Walking","RunXpress","Women","Walking","Knit","Black","Waterproof","Flat",7.5,111.51,4.0,20.0,"Ottawa"
+"STR-5275","StrideOne Flex Walking","StrideOne","Men","Walking","Synthetic","Black","Resistant","High",9.0,92.29,3.1,11.0,"Toronto"
+"FOO-2470","FootFlex Pro Running","FootFlex","Women","Running","Synthetic","White","Waterproof","Flat",10.0,119.31,4.0,1.0,"Toronto"
+"FOO-2211","FootFlex Pro Running","FootFlex","Men","Running","Knit","Black","Resistant","Flat",6.0,106.69,3.1,3.0,"Toronto"
+"FOO-4876","FootFlex Flex Walking","FootFlex","Men","Walking","Knit","White","Resistant","Flat",8.5,98.73,3.5,3.0,"Toronto"
+"FOO-7166","FootFlex X Running","FootFlex","Men","Running","Synthetic","White","Waterproof","Flat",11.5,50.58,3.6,14.0,"Ottawa"
+"FOO-5229","FootFlex Max Walking","FootFlex","Women","Walking","Synthetic","Black","Waterproof","High",10.0,121.8,4.8,3.0,"Ottawa"
+"RUN-6591","RunXpress Ultra Running","RunXpress","Men","Running","Knit","White","Resistant","High",8.0,117.29,3.6,2.0,"Ottawa"
+"ZEN-6109","Zentrax X Walking","Zentrax","Men","Walking","Synthetic","White","Waterproof","Flat",7.0,134.5,4.3,3.0,"Ottawa"
+"RUN-8824","RunXpress Pro Walking","RunXpress","Men","Walking","Knit","Black","Waterproof","Flat",11.5,30.35,4.0,13.0,"Ottawa"
+"ZEN-3095","Zentrax X Running","Zentrax","Women","Running","Synthetic","Black","Waterproof","Flat",7.0,39.8,3.2,6.0,"Ottawa"
+"RUN-1481","RunXpress Pro Running","RunXpress","Women","Running","Synthetic","Black","Resistant","Flat",11.0,38.61,3.4,2.0,"Toronto"
+"ZEN-3968","Zentrax Pro Running","Zentrax","Women","Running","Knit","White","Resistant","Flat",8.0,135.15,4.2,9.0,"Ottawa"
+"FOO-7945","FootFlex Max Walking","FootFlex","Women","Walking","Knit","White","Waterproof","Flat",6.5,83.48,3.5,8.0,"Toronto"
+"STR-1964","StrideOne Pro Walking","StrideOne","Men","Walking","Synthetic","White","Resistant","High",7.0,34.36,4.2,7.0,"Toronto"
+"RUN-2617","RunXpress Ultra Walking","RunXpress","Men","Walking","Knit","Black","Waterproof","Flat",6.0,90.6,4.8,18.0,"Ottawa"
+"ZEN-8522","Zentrax Pro Running","Zentrax","Women","Running","Synthetic","White","Waterproof","Flat",10.5,59.78,3.4,17.0,"Toronto"
+"ZEN-7777","Zentrax Pro Running","Zentrax","Men","Running","Knit","Black","Waterproof","Flat",9.0,101.62,4.3,15.0,"Ottawa"
+"STR-1689","StrideOne Max Running","StrideOne","Women","Running","Knit","White","Waterproof","Flat",11.0,91.12,4.6,10.0,"Toronto"
+"FOO-5239","FootFlex Pro Walking","FootFlex","Men","Walking","Synthetic","White","Waterproof","High",8.5,102.04,3.7,9.0,"Toronto"
+"ZEN-1876","Zentrax Max Walking","Zentrax","Women","Walking","Knit","Black","Resistant","Flat",12.5,137.5,4.9,4.0,"Toronto"
+"LOO-1824","Loopic X Running","Loopic","Women","Running","Knit","White","Resistant","Flat",7.5,30.77,4.5,20.0,"Toronto"
+"RUN-2266","RunXpress Ultra Walking","RunXpress","Men","Walking","Synthetic","White","Waterproof","High",6.5,125.6,3.5,19.0,"Toronto"
+"LOO-6977","Loopic Flex Walking","Loopic","Women","Walking","Knit","Black","Resistant","Flat",6.5,57.59,3.2,9.0,"Ottawa"
+"FOO-1827","FootFlex Max Running","FootFlex","Women","Running","Knit","White","Resistant","High",10.0,42.45,4.6,15.0,"Toronto"
+"ZEN-3262","Zentrax X Running","Zentrax","Men","Running","Knit","White","Waterproof","High",6.5,87.31,4.2,14.0,"Toronto"
+"ZEN-8623","Zentrax Max Walking","Zentrax","Women","Walking","Synthetic","Black","Waterproof","Flat",6.0,52.98,3.4,16.0,"Toronto"
+"LOO-9567","Loopic Pro Walking","Loopic","Women","Walking","Synthetic","White","Resistant","High",9.0,76.37,3.5,13.0,"Ottawa"
+"ZEN-3955","Zentrax Max Walking","Zentrax","Men","Walking","Synthetic","Black","Resistant","High",10.5,119.92,4.2,5.0,"Toronto"
+"LOO-3950","Loopic Pro Walking","Loopic","Men","Walking","Synthetic","Black","Waterproof","Flat",8.5,77.46,3.8,9.0,"Ottawa"
+"STR-8879","StrideOne X Walking","StrideOne","Men","Walking","Knit","Black","Waterproof","High",10.0,59.9,3.5,13.0,"Ottawa"
+"RUN-9157","RunXpress Pro Walking","RunXpress","Women","Walking","Synthetic","Black","Resistant","Flat",11.0,81.71,3.8,3.0,"Ottawa"
+"STR-5016","StrideOne Ultra Walking","StrideOne","Women","Walking","Synthetic","White","Waterproof","High",8.0,100.65,4.2,4.0,"Ottawa"
+"STR-5323","StrideOne X Walking","StrideOne","Men","Walking","Synthetic","Black","Resistant","High",11.0,133.89,3.9,16.0,"Toronto"
+"LOO-3339","Loopic Ultra Walking","Loopic","Men","Walking","Synthetic","Black","Waterproof","Flat",11.0,123.52,3.0,4.0,"Toronto"
+"RUN-3419","RunXpress X Walking","RunXpress","Men","Walking","Synthetic","White","Resistant","High",7.5,41.96,4.6,9.0,"Toronto"
+"LOO-5379","Loopic Max Running","Loopic","Men","Running","Knit","White","Waterproof","High",11.0,100.33,4.8,17.0,"Toronto"
+"STR-5931","StrideOne X Walking","StrideOne","Women","Walking","Knit","Black","Waterproof","Flat",12.0,137.99,4.8,6.0,"Ottawa"
+"RUN-8664","RunXpress Ultra Running","RunXpress","Women","Running","Knit","Black","Waterproof","High",12.5,37.66,4.6,3.0,"Ottawa"
+"LOO-4388","Loopic X Running","Loopic","Women","Running","Synthetic","White","Waterproof","High",7.5,71.57,3.1,13.0,"Ottawa"
+"FOO-3772","FootFlex Pro Walking","FootFlex","Women","Walking","Synthetic","White","Waterproof","Flat",10.5,129.86,3.8,7.0,"Ottawa"
+"FOO-8146","FootFlex X Walking","FootFlex","Men","Walking","Knit","Black","Resistant","High",8.5,110.54,3.9,11.0,"Ottawa"
+"STR-4268","StrideOne Flex Walking","StrideOne","Women","Walking","Synthetic","White","Resistant","Flat",10.0,84.52,4.9,17.0,"Ottawa"
+"RUN-5281","RunXpress Flex Walking","RunXpress","Women","Walking","Synthetic","Black","Resistant","Flat",12.0,60.09,4.2,6.0,"Ottawa"
+"RUN-6050","RunXpress Flex Walking","RunXpress","Women","Walking","Knit","Black","Waterproof","Flat",6.5,136.34,3.8,10.0,"Ottawa"
+"LOO-8847","Loopic Ultra Running","Loopic","Men","Running","Synthetic","Black","Resistant","Flat",10.5,120.84,4.8,5.0,"Ottawa"
+"STR-6351","StrideOne Ultra Running","StrideOne","Women","Running","Synthetic","White","Waterproof","Flat",11.0,144.69,4.6,18.0,"Ottawa"
+"ZEN-2430","Zentrax Ultra Running","Zentrax","Women","Running","Synthetic","Black","Waterproof","Flat",9.5,149.21,3.8,19.0,"Ottawa"
+"LOO-8630","Loopic X Running","Loopic","Men","Running","Synthetic","White","Waterproof","High",11.5,119.76,3.5,17.0,"Toronto"
+"LOO-7234","Loopic Flex Running","Loopic","Women","Running","Knit","Black","Resistant","High",10.5,80.51,4.1,1.0,"Ottawa"
+"STR-5688","StrideOne X Walking","StrideOne","Women","Walking","Knit","White","Resistant","High",8.5,101.74,4.2,19.0,"Toronto"
+"FOO-5114","FootFlex Max Walking","FootFlex","Women","Walking","Knit","White","Resistant","Flat",8.5,144.54,4.3,6.0,"Ottawa"
+"LOO-8837","Loopic Ultra Walking","Loopic","Men","Walking","Synthetic","Black","Resistant","High",12.5,86.34,4.2,9.0,"Ottawa"
+"FOO-3647","FootFlex X Walking","FootFlex","Women","Walking","Knit","White","Waterproof","Flat",7.5,52.44,3.9,17.0,"Toronto"
+"RUN-3109","RunXpress Pro Walking","RunXpress","Men","Walking","Synthetic","Black","Resistant","Flat",10.5,145.39,3.0,16.0,"Ottawa"
+"LOO-5449","Loopic Flex Walking","Loopic","Women","Walking","Knit","Black","Waterproof","Flat",9.5,111.47,3.2,18.0,"Ottawa"
+"LOO-8373","Loopic Flex Walking","Loopic","Women","Walking","Synthetic","Black","Waterproof","High",7.0,119.63,3.1,10.0,"Ottawa"
+"ZEN-8696","Zentrax X Walking","Zentrax","Men","Walking","Knit","Black","Waterproof","High",9.0,60.29,4.2,13.0,"Ottawa"
+"RUN-1100","RunXpress X Running","RunXpress","Men","Running","Knit","Black","Waterproof","Flat",8.0,145.29,4.5,10.0,"Ottawa"
+"STR-1620","StrideOne Pro Walking","StrideOne","Women","Walking","Synthetic","Black","Waterproof","Flat",10.5,49.12,3.0,3.0,"Toronto"
+"ZEN-8355","Zentrax X Running","Zentrax","Women","Running","Synthetic","Black","Waterproof","High",12.0,74.66,3.6,11.0,"Ottawa"
+"FOO-5121","FootFlex Pro Walking","FootFlex","Women","Walking","Knit","White","Resistant","High",8.5,143.19,3.9,11.0,"Toronto"
+"FOO-3835","FootFlex Ultra Running","FootFlex","Men","Running","Knit","Black","Waterproof","High",7.0,135.3,3.3,11.0,"Ottawa"
+"LOO-1235","Loopic X Running","Loopic","Women","Running","Synthetic","White","Resistant","Flat",9.5,34.64,4.0,11.0,"Toronto"
+"FOO-5295","FootFlex X Running","FootFlex","Men","Running","Knit","Black","Waterproof","Flat",10.0,139.64,4.9,2.0,"Toronto"
+"RUN-9701","RunXpress X Walking","RunXpress","Women","Walking","Knit","Black","Waterproof","Flat",7.0,59.44,3.7,15.0,"Ottawa"
+"RUN-6101","RunXpress Max Walking","RunXpress","Women","Walking","Knit","White","Waterproof","Flat",6.0,137.69,3.6,1.0,"Ottawa"
+"STR-4361","StrideOne Flex Walking","StrideOne","Women","Walking","Synthetic","Black","Resistant","High",11.0,38.12,4.6,9.0,"Toronto"
+"LOO-7498","Loopic Flex Walking","Loopic","Women","Walking","Knit","White","Resistant","Flat",7.5,95.56,3.5,16.0,"Ottawa"
+"FOO-6784","FootFlex Max Walking","FootFlex","Men","Walking","Knit","Black","Resistant","High",12.5,44.63,4.6,3.0,"Toronto"
+"FOO-6577","FootFlex X Walking","FootFlex","Men","Walking","Knit","Black","Resistant","High",9.0,116.24,4.9,3.0,"Toronto"
+"ZEN-7487","Zentrax Ultra Running","Zentrax","Women","Running","Synthetic","White","Waterproof","High",10.5,57.99,3.7,3.0,"Toronto"
+"ZEN-7619","Zentrax Flex Running","Zentrax","Women","Running","Knit","Black","Waterproof","Flat",11.5,34.69,4.2,16.0,"Ottawa"
+"LOO-7375","Loopic Pro Running","Loopic","Women","Running","Knit","Black","Resistant","High",8.0,88.88,4.7,1.0,"Toronto"
+"RUN-6822","RunXpress Pro Walking","RunXpress","Women","Walking","Knit","White","Waterproof","Flat",11.0,78.39,3.3,16.0,"Toronto"
+"LOO-1941","Loopic Pro Walking","Loopic","Women","Walking","Synthetic","Black","Waterproof","Flat",7.5,95.97,4.5,16.0,"Toronto"
+"LOO-9274","Loopic Ultra Running","Loopic","Women","Running","Synthetic","Black","Waterproof","Flat",11.0,65.73,4.5,19.0,"Ottawa"
+"RUN-2965","RunXpress Pro Running","RunXpress","Women","Running","Synthetic","White","Waterproof","Flat",8.0,145.28,4.3,7.0,"Ottawa"
+"STR-1271","StrideOne X Running","StrideOne","Men","Running","Knit","White","Resistant","Flat",10.5,107.78,3.6,15.0,"Ottawa"
+"FOO-1003","FootFlex Ultra Walking","FootFlex","Women","Walking","Knit","Black","Waterproof","High",12.0,136.89,3.6,17.0,"Ottawa"
+"ZEN-2914","Zentrax Ultra Walking","Zentrax","Women","Walking","Knit","White","Resistant","High",7.0,148.53,3.9,9.0,"Toronto"
+"STR-7709","StrideOne Max Walking","StrideOne","Women","Walking","Synthetic","Black","Waterproof","High",6.0,123.93,4.4,17.0,"Ottawa"
+"RUN-6886","RunXpress Pro Running","RunXpress","Women","Running","Synthetic","Black","Waterproof","High",8.5,117.71,4.6,10.0,"Toronto"
+"RUN-7176","RunXpress X Running","RunXpress","Men","Running","Synthetic","White","Waterproof","Flat",10.0,98.13,3.6,1.0,"Ottawa"
+"STR-9823","StrideOne Flex Running","StrideOne","Women","Running","Knit","White","Resistant","Flat",9.5,40.89,3.0,14.0,"Ottawa"
+"LOO-8098","Loopic Ultra Walking","Loopic","Men","Walking","Synthetic","Black","Resistant","Flat",12.5,79.22,4.3,11.0,"Toronto"
+"FOO-3043","FootFlex Max Walking","FootFlex","Men","Walking","Knit","White","Waterproof","Flat",6.5,57.73,3.2,20.0,"Toronto"
+"FOO-6083","FootFlex Flex Walking","FootFlex","Men","Walking","Synthetic","Black","Waterproof","Flat",8.0,133.92,3.6,17.0,"Ottawa"
+"LOO-2535","Loopic X Running","Loopic","Women","Running","Synthetic","Black","Waterproof","High",11.0,85.65,3.0,15.0,"Toronto"
+"FOO-6731","FootFlex Flex Walking","FootFlex","Men","Walking","Synthetic","White","Resistant","High",6.0,58.74,3.5,17.0,"Toronto"
+"ZEN-5804","Zentrax X Walking","Zentrax","Women","Walking","Synthetic","Black","Waterproof","Flat",12.0,68.48,3.1,5.0,"Toronto"
+"ZEN-2314","Zentrax Pro Running","Zentrax","Men","Running","Synthetic","Black","Waterproof","High",9.5,149.4,4.7,18.0,"Toronto"
+"LOO-2361","Loopic X Running","Loopic","Women","Running","Knit","White","Waterproof","High",6.5,71.01,4.9,18.0,"Toronto"
+"RUN-4917","RunXpress Flex Walking","RunXpress","Women","Walking","Synthetic","Black","Resistant","High",8.5,49.37,4.3,13.0,"Ottawa"
+"ZEN-7012","Zentrax Ultra Walking","Zentrax","Women","Walking","Synthetic","White","Waterproof","Flat",7.5,130.62,3.9,8.0,"Toronto"
+"LOO-6689","Loopic X Walking","Loopic","Men","Walking","Synthetic","White","Waterproof","Flat",9.5,114.15,3.9,13.0,"Toronto"
+"STR-1429","StrideOne Flex Walking","StrideOne","Men","Walking","Knit","White","Resistant","Flat",9.5,56.57,3.1,7.0,"Ottawa"
+"FOO-2643","FootFlex X Walking","FootFlex","Women","Walking","Synthetic","Black","Waterproof","High",12.5,82.67,3.5,8.0,"Toronto"
+"RUN-5994","RunXpress X Walking","RunXpress","Women","Walking","Synthetic","Black","Resistant","Flat",12.0,71.81,4.0,3.0,"Ottawa"
+"ZEN-9833","Zentrax Ultra Walking","Zentrax","Women","Walking","Knit","Black","Waterproof","Flat",11.5,37.89,4.1,12.0,"Toronto"
+"STR-8307","StrideOne Ultra Walking","StrideOne","Women","Walking","Knit","White","Resistant","Flat",12.0,90.18,3.0,6.0,"Ottawa"
+"RUN-1724","RunXpress X Running","RunXpress","Men","Running","Synthetic","Black","Waterproof","High",9.5,102.79,4.7,17.0,"Ottawa"
+"FOO-4825","FootFlex Max Running","FootFlex","Men","Running","Knit","Black","Resistant","High",8.5,109.89,3.8,19.0,"Toronto"
+"ZEN-4558","Zentrax Max Running","Zentrax","Men","Running","Knit","Black","Waterproof","High",10.5,90.54,4.0,7.0,"Toronto"
+"RUN-4467","RunXpress Ultra Walking","RunXpress","Women","Walking","Knit","White","Resistant","High",6.0,84.62,3.9,8.0,"Toronto"
+"RUN-9392","RunXpress Max Walking","RunXpress","Men","Walking","Synthetic","White","Waterproof","Flat",12.5,45.7,4.6,12.0,"Ottawa"
+"STR-5961","StrideOne X Running","StrideOne","Women","Running","Knit","Black","Waterproof","High",12.0,140.49,3.8,19.0,"Toronto"
+"STR-6301","StrideOne Pro Running","StrideOne","Men","Running","Knit","White","Resistant","Flat",9.5,36.71,3.7,1.0,"Toronto"
+"RUN-7409","RunXpress Flex Walking","RunXpress","Women","Walking","Knit","White","Resistant","Flat",8.0,83.68,3.6,12.0,"Ottawa"
+"RUN-1850","RunXpress X Walking","RunXpress","Women","Walking","Knit","White","Resistant","Flat",8.0,31.96,4.2,6.0,"Toronto"
+"LOO-4231","Loopic Flex Running","Loopic","Men","Running","Synthetic","Black","Waterproof","High",6.5,94.75,4.2,12.0,"Toronto"
+"LOO-7891","Loopic Flex Running","Loopic","Women","Running","Knit","White","Resistant","High",9.0,121.89,4.6,3.0,"Ottawa"
+"STR-6767","StrideOne Max Running","StrideOne","Women","Running","Knit","Black","Resistant","High",12.0,118.71,4.7,8.0,"Toronto"
+"LOO-7093","Loopic Flex Running","Loopic","Women","Running","Knit","Black","Waterproof","High",10.5,114.72,3.9,11.0,"Ottawa"
+"ZEN-8598","Zentrax Flex Running","Zentrax","Men","Running","Knit","White","Waterproof","High",10.5,54.4,4.7,4.0,"Ottawa"
+"STR-3872","StrideOne Flex Running","StrideOne","Women","Running","Knit","White","Waterproof","High",7.0,102.86,3.6,4.0,"Ottawa"
+"FOO-1123","FootFlex X Walking","FootFlex","Men","Walking","Synthetic","White","Waterproof","High",11.5,142.04,4.6,3.0,"Ottawa"
+"STR-6119","StrideOne Flex Walking","StrideOne","Women","Walking","Knit","Black","Waterproof","Flat",6.5,86.63,3.4,9.0,"Ottawa"
+"RUN-9624","RunXpress Flex Walking","RunXpress","Women","Walking","Synthetic","Black","Resistant","Flat",10.5,72.42,3.5,6.0,"Ottawa"
+"LOO-3511","Loopic Flex Running","Loopic","Men","Running","Synthetic","White","Waterproof","High",9.5,49.97,4.9,20.0,"Ottawa"
+"LOO-6832","Loopic Ultra Running","Loopic","Women","Running","Knit","Black","Waterproof","Flat",11.5,84.52,3.4,9.0,"Ottawa"
+"LOO-2287","Loopic Flex Running","Loopic","Men","Running","Synthetic","White","Waterproof","Flat",7.5,33.24,3.4,3.0,"Toronto"
+"STR-1924","StrideOne Flex Walking","StrideOne","Women","Walking","Knit","Black","Resistant","High",11.5,105.37,3.1,2.0,"Ottawa"
+"LOO-8574","Loopic Flex Running","Loopic","Men","Running","Synthetic","Black","Resistant","High",11.0,45.84,3.9,14.0,"Ottawa"
+"STR-4125","StrideOne Max Walking","StrideOne","Women","Walking","Synthetic","White","Waterproof","Flat",12.5,122.97,4.8,8.0,"Toronto"
+"LOO-9137","Loopic Flex Walking","Loopic","Men","Walking","Synthetic","Black","Resistant","Flat",10.0,69.7,4.6,4.0,"Toronto"
+"LOO-7493","Loopic Flex Running","Loopic","Women","Running","Knit","White","Waterproof","High",9.0,143.38,4.9,17.0,"Toronto"
+"FOO-8891","FootFlex X Walking","FootFlex","Men","Walking","Synthetic","White","Waterproof","High",12.5,54.95,3.8,5.0,"Toronto"
+"LOO-2930","Loopic Flex Running","Loopic","Men","Running","Knit","Black","Resistant","High",8.5,81.79,4.8,18.0,"Toronto"
+"FOO-7388","FootFlex Max Running","FootFlex","Men","Running","Synthetic","White","Waterproof","High",10.5,73.94,4.0,11.0,"Toronto"
+"FOO-2411","FootFlex Pro Running","FootFlex","Women","Running","Knit","Black","Resistant","Flat",8.0,129.48,3.2,12.0,"Toronto"
+"FOO-7044","FootFlex Pro Running","FootFlex","Men","Running","Knit","White","Waterproof","Flat",11.5,107.32,3.9,18.0,"Ottawa"
+"FOO-7361","FootFlex Pro Running","FootFlex","Men","Running","Synthetic","Black","Waterproof","High",12.0,83.21,4.8,10.0,"Ottawa"
+"RUN-5905","RunXpress Flex Running","RunXpress","Women","Running","Knit","Black","Waterproof","High",7.5,74.91,4.3,3.0,"Toronto"
+"FOO-2221","FootFlex Max Walking","FootFlex","Women","Walking","Knit","White","Waterproof","High",11.5,39.57,4.2,7.0,"Toronto"
+"FOO-9607","FootFlex Max Running","FootFlex","Women","Running","Synthetic","White","Waterproof","High",11.5,98.87,4.7,13.0,"Toronto"
+"STR-8769","StrideOne Pro Walking","StrideOne","Men","Walking","Synthetic","Black","Resistant","High",6.0,129.24,4.5,4.0,"Toronto"
+"FOO-5312","FootFlex X Running","FootFlex","Women","Running","Synthetic","Black","Waterproof","High",12.5,135.55,4.1,7.0,"Ottawa"
+"LOO-8973","Loopic Pro Running","Loopic","Women","Running","Knit","Black","Waterproof","Flat",6.5,59.1,3.6,16.0,"Ottawa"
+"STR-7223","StrideOne Max Walking","StrideOne","Men","Walking","Synthetic","White","Resistant","High",12.0,130.03,3.9,8.0,"Toronto"
+"FOO-1542","FootFlex Ultra Walking","FootFlex","Women","Walking","Synthetic","White","Waterproof","Flat",9.0,123.66,4.9,11.0,"Ottawa"
+"STR-9795","StrideOne Pro Running","StrideOne","Men","Running","Synthetic","Black","Waterproof","Flat",12.5,66.65,3.6,4.0,"Toronto"
+"RUN-3045","RunXpress Ultra Walking","RunXpress","Men","Walking","Synthetic","Black","Resistant","High",7.0,85.36,3.5,1.0,"Toronto"
+"LOO-3892","Loopic Ultra Walking","Loopic","Women","Walking","Knit","White","Resistant","High",11.5,108.45,3.4,17.0,"Toronto"
+"STR-2168","StrideOne Pro Running","StrideOne","Women","Running","Knit","White","Resistant","High",10.0,61.98,4.2,13.0,"Ottawa"
+"ZEN-5521","Zentrax Ultra Walking","Zentrax","Women","Walking","Knit","Black","Resistant","High",6.5,51.39,4.1,17.0,"Ottawa"
+"STR-3628","StrideOne X Running","StrideOne","Women","Running","Knit","White","Waterproof","High",12.0,91.29,3.3,20.0,"Ottawa"
+"ZEN-8920","Zentrax Max Walking","Zentrax","Women","Walking","Knit","Black","Waterproof","High",9.5,83.53,4.6,4.0,"Ottawa"
+"STR-2604","StrideOne X Running","StrideOne","Women","Running","Synthetic","Black","Waterproof","High",7.5,116.6,4.6,2.0,"Ottawa"
+"RUN-8289","RunXpress Flex Walking","RunXpress","Women","Walking","Synthetic","White","Resistant","High",9.0,143.96,3.7,2.0,"Toronto"
+"LOO-2882","Loopic X Walking","Loopic","Men","Walking","Knit","Black","Resistant","Flat",10.5,32.8,4.6,8.0,"Ottawa"
+"LOO-7052","Loopic X Running","Loopic","Women","Running","Knit","White","Resistant","High",12.5,59.07,4.1,13.0,"Toronto"
+"RUN-4134","RunXpress Max Running","RunXpress","Women","Running","Synthetic","Black","Waterproof","Flat",6.0,130.55,3.3,17.0,"Toronto"
+"STR-8719","StrideOne X Running","StrideOne","Women","Running","Synthetic","White","Waterproof","Flat",11.5,43.66,4.2,14.0,"Toronto"
+"ZEN-1896","Zentrax Flex Running","Zentrax","Men","Running","Knit","Black","Resistant","High",7.5,37.81,3.1,20.0,"Ottawa"
+"FOO-3829","FootFlex Flex Walking","FootFlex","Women","Walking","Synthetic","Black","Resistant","High",10.0,58.05,3.5,3.0,"Ottawa"
+"FOO-6682","FootFlex Pro Running","FootFlex","Men","Running","Synthetic","Black","Resistant","High",7.5,98.89,3.0,1.0,"Toronto"
+"ZEN-2223","Zentrax Ultra Running","Zentrax","Men","Running","Synthetic","Black","Resistant","High",11.5,87.92,3.0,6.0,"Ottawa"
+"LOO-9018","Loopic Flex Running","Loopic","Women","Running","Synthetic","White","Resistant","Flat",11.5,109.42,5.0,18.0,"Toronto"
+"ZEN-4987","Zentrax Max Running","Zentrax","Women","Running","Knit","Black","Waterproof","Flat",6.0,30.35,4.4,16.0,"Ottawa"
+"ZEN-3852","Zentrax Ultra Running","Zentrax","Men","Running","Knit","White","Resistant","High",9.0,75.02,3.9,10.0,"Ottawa"
+"STR-9206","StrideOne Flex Walking","StrideOne","Men","Walking","Synthetic","Black","Waterproof","Flat",9.5,87.31,4.1,17.0,"Ottawa"
+"FOO-7904","FootFlex Max Running","FootFlex","Women","Running","Knit","White","Waterproof","High",8.5,69.45,4.9,3.0,"Ottawa"
+"ZEN-8824","Zentrax Max Walking","Zentrax","Men","Walking","Knit","White","Resistant","High",11.0,57.86,3.2,5.0,"Toronto"
+"STR-4952","StrideOne Pro Running","StrideOne","Women","Running","Knit","White","Waterproof","High",11.0,34.58,3.1,7.0,"Ottawa"
+"ZEN-2661","Zentrax Max Walking","Zentrax","Women","Walking","Knit","White","Waterproof","Flat",8.5,62.32,5.0,5.0,"Toronto"
+"RUN-5952","RunXpress Ultra Walking","RunXpress","Men","Walking","Knit","Black","Resistant","Flat",7.0,131.56,3.8,7.0,"Ottawa"
+"ZEN-5757","Zentrax X Walking","Zentrax","Men","Walking","Synthetic","White","Resistant","Flat",6.0,107.2,3.8,5.0,"Toronto"
+"STR-8713","StrideOne Pro Running","StrideOne","Men","Running","Knit","Black","Resistant","High",11.5,82.93,3.3,18.0,"Ottawa"
+"RUN-1871","RunXpress Pro Walking","RunXpress","Women","Walking","Synthetic","Black","Waterproof","Flat",7.5,36.25,3.4,7.0,"Toronto"
+"FOO-5614","FootFlex Max Running","FootFlex","Men","Running","Synthetic","White","Waterproof","High",6.5,113.67,3.4,2.0,"Ottawa"
+"FOO-7333","FootFlex Ultra Walking","FootFlex","Women","Walking","Knit","Black","Waterproof","High",8.5,115.31,3.1,17.0,"Toronto"
+"ZEN-5034","Zentrax Ultra Walking","Zentrax","Men","Walking","Synthetic","White","Waterproof","High",11.5,132.04,3.1,7.0,"Ottawa"
+"LOO-6151","Loopic Max Walking","Loopic","Men","Walking","Knit","Black","Waterproof","Flat",6.0,90.2,4.8,14.0,"Toronto"
+"LOO-7442","Loopic Pro Running","Loopic","Women","Running","Knit","Black","Resistant","High",11.0,110.92,3.1,16.0,"Toronto"
+"ZEN-5591","Zentrax X Walking","Zentrax","Men","Walking","Synthetic","Black","Waterproof","High",10.0,149.95,4.0,20.0,"Toronto"
+"STR-4214","StrideOne Flex Running","StrideOne","Women","Running","Knit","White","Resistant","Flat",12.0,59.69,3.7,7.0,"Ottawa"
+"LOO-3139","Loopic Max Running","Loopic","Men","Running","Synthetic","Black","Resistant","Flat",10.0,54.58,3.3,2.0,"Ottawa"
+"ZEN-1476","Zentrax Pro Running","Zentrax","Men","Running","Synthetic","White","Waterproof","Flat",9.5,86.8,4.5,20.0,"Ottawa"
+"FOO-7997","FootFlex Pro Running","FootFlex","Women","Running","Knit","Black","Waterproof","Flat",9.5,83.86,5.0,8.0,"Ottawa"
+"ZEN-9378","Zentrax Pro Walking","Zentrax","Men","Walking","Knit","White","Waterproof","Flat",9.0,87.7,3.1,11.0,"Toronto"
+"FOO-6135","FootFlex Pro Running","FootFlex","Women","Running","Synthetic","Black","Waterproof","Flat",6.0,141.67,3.6,10.0,"Ottawa"
+"STR-4957","StrideOne X Walking","StrideOne","Women","Walking","Synthetic","Black","Waterproof","High",8.0,45.17,4.2,15.0,"Ottawa"
+"STR-8755","StrideOne Pro Walking","StrideOne","Men","Walking","Synthetic","Black","Waterproof","High",8.0,78.77,3.2,16.0,"Toronto"
+"ZEN-7108","Zentrax Ultra Running","Zentrax","Women","Running","Synthetic","White","Waterproof","Flat",10.5,63.2,4.6,3.0,"Ottawa"
+"RUN-7792","RunXpress Max Running","RunXpress","Women","Running","Knit","Black","Resistant","High",10.0,144.59,4.5,11.0,"Toronto"
+"ZEN-6602","Zentrax Flex Running","Zentrax","Women","Running","Knit","Black","Resistant","Flat",6.5,101.12,3.1,7.0,"Ottawa"
+"STR-2196","StrideOne Ultra Running","StrideOne","Men","Running","Knit","White","Resistant","High",12.0,145.57,3.7,7.0,"Toronto"
+"STR-7869","StrideOne Flex Running","StrideOne","Men","Running","Knit","White","Resistant","Flat",10.5,133.15,3.2,12.0,"Ottawa"
+"STR-8206","StrideOne Ultra Running","StrideOne","Women","Running","Synthetic","Black","Waterproof","Flat",11.5,77.59,4.5,2.0,"Ottawa"
+"RUN-6756","RunXpress Ultra Running","RunXpress","Men","Running","Knit","White","Resistant","Flat",8.5,30.88,4.5,1.0,"Ottawa"
+"LOO-8207","Loopic Pro Running","Loopic","Men","Running","Synthetic","Black","Resistant","High",7.0,43.19,3.9,16.0,"Ottawa"
+"LOO-4668","Loopic Max Walking","Loopic","Women","Walking","Knit","Black","Resistant","Flat",8.0,89.27,4.1,11.0,"Toronto"
+"FOO-3900","FootFlex Pro Walking","FootFlex","Women","Walking","Synthetic","Black","Resistant","Flat",10.5,114.77,4.8,19.0,"Ottawa"
+"RUN-6437","RunXpress Pro Walking","RunXpress","Men","Walking","Knit","White","Waterproof","Flat",9.0,146.9,4.1,2.0,"Ottawa"
+"FOO-5781","FootFlex X Running","FootFlex","Men","Running","Knit","Black","Waterproof","Flat",10.5,116.79,5.0,16.0,"Toronto"
+"STR-9524","StrideOne Flex Walking","StrideOne","Women","Walking","Knit","White","Resistant","Flat",12.0,40.03,4.1,13.0,"Toronto"
+"LOO-8429","Loopic Flex Running","Loopic","Women","Running","Synthetic","Black","Waterproof","High",8.0,116.4,4.8,13.0,"Toronto"
+"ZEN-6506","Zentrax X Walking","Zentrax","Women","Walking","Synthetic","Black","Waterproof","High",7.0,104.17,4.9,7.0,"Ottawa"
+"STR-6980","StrideOne Flex Walking","StrideOne","Women","Walking","Synthetic","Black","Waterproof","Flat",6.0,67.83,4.9,12.0,"Toronto"
+"LOO-8037","Loopic Flex Running","Loopic","Men","Running","Knit","Black","Waterproof","High",9.0,38.79,4.6,7.0,"Toronto"
+"STR-4702","StrideOne Max Running","StrideOne","Women","Running","Knit","Black","Waterproof","High",6.5,110.25,3.6,15.0,"Ottawa"
+"STR-8523","StrideOne Flex Running","StrideOne","Women","Running","Knit","Black","Waterproof","High",7.5,99.69,3.8,19.0,"Toronto"
+"STR-3167","StrideOne X Walking","StrideOne","Women","Walking","Synthetic","Black","Resistant","High",7.5,85.83,4.1,7.0,"Ottawa"
+"LOO-9572","Loopic Max Walking","Loopic","Men","Walking","Knit","White","Resistant","High",8.5,36.52,4.1,15.0,"Ottawa"
+"FOO-1749","FootFlex Max Walking","FootFlex","Women","Walking","Synthetic","Black","Waterproof","Flat",7.5,145.9,4.0,18.0,"Toronto"
+"FOO-2168","FootFlex Max Running","FootFlex","Women","Running","Synthetic","Black","Waterproof","Flat",10.0,132.09,4.8,2.0,"Ottawa"
+"ZEN-9790","Zentrax X Walking","Zentrax","Women","Walking","Synthetic","Black","Waterproof","Flat",8.0,35.14,4.4,12.0,"Ottawa"
+"LOO-3212","Loopic Pro Walking","Loopic","Women","Walking","Knit","White","Resistant","Flat",7.5,134.9,4.0,14.0,"Ottawa"
+"LOO-9495","Loopic Flex Running","Loopic","Women","Running","Knit","Black","Waterproof","Flat",11.5,94.81,3.6,6.0,"Ottawa"
+"RUN-9592","RunXpress Ultra Walking","RunXpress","Women","Walking","Knit","White","Waterproof","Flat",8.0,116.31,4.3,9.0,"Toronto"
+"LOO-3771","Loopic Max Walking","Loopic","Women","Walking","Synthetic","White","Waterproof","High",9.0,32.3,3.9,17.0,"Ottawa"
+"ZEN-3115","Zentrax Max Running","Zentrax","Women","Running","Synthetic","White","Waterproof","High",6.5,43.91,4.0,16.0,"Ottawa"
+"FOO-4856","FootFlex Max Walking","FootFlex","Women","Walking","Knit","White","Resistant","Flat",8.0,42.37,4.2,18.0,"Toronto"
+"ZEN-9430","Zentrax Flex Running","Zentrax","Women","Running","Knit","Black","Waterproof","High",8.5,74.83,3.4,10.0,"Toronto"
+"ZEN-1394","Zentrax X Walking","Zentrax","Women","Walking","Synthetic","Black","Waterproof","Flat",6.0,110.87,4.5,10.0,"Toronto"
+"FOO-8283","FootFlex Max Running","FootFlex","Men","Running","Synthetic","White","Resistant","High",12.0,115.62,4.8,13.0,"Ottawa"
+"ZEN-4671","Zentrax Max Walking","Zentrax","Women","Walking","Knit","White","Resistant","Flat",9.0,99.44,4.6,4.0,"Ottawa"
+"FOO-6108","FootFlex Max Walking","FootFlex","Women","Walking","Knit","Black","Resistant","High",6.0,88.89,3.8,4.0,"Toronto"
+"RUN-7214","RunXpress X Running","RunXpress","Men","Running","Synthetic","White","Resistant","High",7.5,134.12,4.8,17.0,"Ottawa"
+"LOO-5823","Loopic Pro Walking","Loopic","Men","Walking","Knit","Black","Resistant","Flat",6.5,131.7,4.9,3.0,"Toronto"
+"RUN-1052","RunXpress Pro Running","RunXpress","Women","Running","Synthetic","Black","Waterproof","High",7.0,33.12,3.6,1.0,"Toronto"
+"FOO-5327","FootFlex X Walking","FootFlex","Women","Walking","Knit","White","Resistant","High",11.5,91.81,4.0,9.0,"Ottawa"
+"RUN-9489","RunXpress X Running","RunXpress","Women","Running","Synthetic","Black","Waterproof","Flat",8.0,41.1,3.9,9.0,"Toronto"
+"LOO-7702","Loopic Ultra Walking","Loopic","Men","Walking","Synthetic","White","Resistant","Flat",6.5,105.37,3.1,20.0,"Ottawa"
+"LOO-8403","Loopic Flex Walking","Loopic","Men","Walking","Synthetic","White","Waterproof","High",11.5,105.86,4.0,13.0,"Toronto"
+"LOO-2887","Loopic Ultra Running","Loopic","Women","Running","Synthetic","Black","Waterproof","Flat",7.0,108.84,4.2,20.0,"Toronto"
+"STR-9714","StrideOne X Running","StrideOne","Men","Running","Synthetic","Black","Waterproof","High",8.0,70.12,3.9,1.0,"Ottawa"
+"ZEN-4672","Zentrax Pro Running","Zentrax","Men","Running","Synthetic","Black","Resistant","Flat",9.5,116.79,4.3,11.0,"Ottawa"
+"ZEN-2732","Zentrax Flex Walking","Zentrax","Women","Walking","Knit","White","Waterproof","High",8.0,73.57,4.4,7.0,"Ottawa"
+"STR-1686","StrideOne Pro Running","StrideOne","Men","Running","Knit","White","Waterproof","Flat",11.5,89.78,3.5,12.0,"Toronto"
+"RUN-9738","RunXpress Pro Running","RunXpress","Men","Running","Synthetic","White","Resistant","High",12.0,93.66,3.1,16.0,"Ottawa"
+"ZEN-3523","Zentrax Max Walking","Zentrax","Men","Walking","Synthetic","Black","Resistant","High",7.5,62.27,4.8,10.0,"Toronto"
+"ZEN-5643","Zentrax Pro Running","Zentrax","Women","Running","Knit","White","Waterproof","Flat",7.0,145.73,4.2,11.0,"Toronto"
+"FOO-5279","FootFlex Max Walking","FootFlex","Men","Walking","Knit","Black","Resistant","Flat",10.0,120.82,3.9,4.0,"Ottawa"
+"ZEN-8141","Zentrax Ultra Running","Zentrax","Men","Running","Knit","White","Resistant","High",10.5,67.79,5.0,4.0,"Ottawa"
+"FOO-1442","FootFlex X Running","FootFlex","Men","Running","Knit","Black","Waterproof","Flat",6.0,85.77,3.0,3.0,"Toronto"
+"FOO-2880","FootFlex Flex Running","FootFlex","Men","Running","Knit","White","Resistant","High",12.0,62.22,3.1,4.0,"Ottawa"
+"RUN-4254","RunXpress Pro Walking","RunXpress","Women","Walking","Synthetic","Black","Waterproof","Flat",6.0,120.26,4.7,1.0,"Toronto"
+"FOO-6221","FootFlex X Running","FootFlex","Men","Running","Synthetic","White","Waterproof","Flat",12.5,126.03,3.2,13.0,"Toronto"
+"RUN-2623","RunXpress Ultra Running","RunXpress","Men","Running","Synthetic","Black","Waterproof","Flat",10.0,95.44,3.7,20.0,"Toronto"
+"STR-1861","StrideOne Max Walking","StrideOne","Women","Walking","Knit","White","Resistant","High",11.0,52.77,3.3,18.0,"Ottawa"
+"STR-2007","StrideOne Ultra Running","StrideOne","Men","Running","Synthetic","White","Waterproof","Flat",10.0,85.8,4.2,1.0,"Ottawa"
+"LOO-4374","Loopic X Running","Loopic","Men","Running","Knit","Black","Resistant","High",6.5,43.24,4.8,20.0,"Ottawa"
+"LOO-2510","Loopic Pro Walking","Loopic","Women","Walking","Synthetic","Black","Resistant","Flat",7.5,134.24,3.1,4.0,"Ottawa"
+"ZEN-5461","Zentrax X Walking","Zentrax","Women","Walking","Knit","White","Waterproof","High",12.0,85.52,3.1,14.0,"Ottawa"
+"LOO-3060","Loopic X Walking","Loopic","Men","Walking","Synthetic","White","Waterproof","Flat",10.5,82.3,3.8,15.0,"Ottawa"
+"ZEN-5619","Zentrax Max Running","Zentrax","Women","Running","Knit","Black","Resistant","Flat",7.5,121.12,3.4,17.0,"Toronto"
+"FOO-8691","FootFlex Ultra Running","FootFlex","Men","Running","Knit","Black","Resistant","High",11.0,139.2,3.3,18.0,"Toronto"
+"FOO-1296","FootFlex Ultra Walking","FootFlex","Women","Walking","Knit","Black","Resistant","High",7.5,98.86,3.7,19.0,"Ottawa"
+"RUN-9780","RunXpress Flex Walking","RunXpress","Men","Walking","Synthetic","Black","Waterproof","High",9.0,84.59,3.8,4.0,"Toronto"
+"ZEN-1721","Zentrax Flex Walking","Zentrax","Women","Walking","Knit","White","Waterproof","Flat",10.0,49.74,3.7,7.0,"Toronto"
+"RUN-4764","RunXpress Ultra Walking","RunXpress","Women","Walking","Knit","White","Waterproof","Flat",12.0,149.37,3.6,20.0,"Ottawa"
+"STR-2222","StrideOne Flex Running","StrideOne","Women","Running","Synthetic","Black","Waterproof","Flat",11.0,109.56,4.2,15.0,"Ottawa"
+"FOO-7412","FootFlex Ultra Running","FootFlex","Men","Running","Knit","Black","Waterproof","High",10.5,66.71,3.1,4.0,"Ottawa"
+"RUN-6105","RunXpress Max Running","RunXpress","Women","Running","Knit","Black","Waterproof","Flat",6.0,86.56,3.4,15.0,"Ottawa"
+"STR-5687","StrideOne Pro Running","StrideOne","Women","Running","Synthetic","White","Resistant","Flat",8.5,128.97,4.5,8.0,"Ottawa"
+"RUN-4646","RunXpress Flex Walking","RunXpress","Men","Walking","Knit","Black","Resistant","High",7.5,72.17,3.8,11.0,"Toronto"
+"STR-1680","StrideOne Ultra Running","StrideOne","Women","Running","Knit","Black","Waterproof","Flat",11.5,78.1,4.1,10.0,"Toronto"
+"RUN-2517","RunXpress Max Running","RunXpress","Women","Running","Synthetic","Black","Resistant","High",11.5,50.74,3.7,14.0,"Toronto"
+"STR-8082","StrideOne Max Walking","StrideOne","Men","Walking","Knit","Black","Waterproof","High",8.0,98.79,3.5,7.0,"Ottawa"
+"RUN-3491","RunXpress Flex Running","RunXpress","Women","Running","Knit","Black","Waterproof","High",10.5,93.29,4.8,8.0,"Toronto"
+"RUN-5502","RunXpress X Running","RunXpress","Women","Running","Knit","White","Resistant","Flat",10.0,49.2,4.2,14.0,"Ottawa"
+"LOO-7262","Loopic Ultra Running","Loopic","Women","Running","Synthetic","White","Resistant","High",10.5,104.11,3.2,2.0,"Toronto"
+"RUN-5947","RunXpress Ultra Walking","RunXpress","Women","Walking","Knit","Black","Resistant","Flat",10.5,85.7,3.8,3.0,"Ottawa"
+"STR-6848","StrideOne X Walking","StrideOne","Men","Walking","Synthetic","White","Resistant","Flat",8.0,44.24,3.2,18.0,"Ottawa"
+"LOO-6999","Loopic X Walking","Loopic","Women","Walking","Knit","White","Waterproof","Flat",7.0,79.73,4.4,7.0,"Toronto"
+"FOO-2851","FootFlex Pro Walking","FootFlex","Men","Walking","Knit","White","Resistant","High",12.5,111.01,4.0,6.0,"Toronto"
+"FOO-3321","FootFlex Max Walking","FootFlex","Women","Walking","Synthetic","Black","Waterproof","Flat",10.5,35.04,3.8,12.0,"Ottawa"
+"FOO-2839","FootFlex Flex Running","FootFlex","Men","Running","Synthetic","Black","Resistant","High",6.5,127.31,4.1,11.0,"Toronto"
+"FOO-8562","FootFlex Max Walking","FootFlex","Women","Walking","Knit","White","Waterproof","High",11.0,40.52,4.7,17.0,"Ottawa"
+"STR-5807","StrideOne Max Walking","StrideOne","Women","Walking","Synthetic","White","Waterproof","High",11.5,42.19,3.4,10.0,"Toronto"
+"FOO-8788","FootFlex Ultra Walking","FootFlex","Men","Walking","Synthetic","White","Resistant","High",10.0,130.74,3.9,4.0,"Ottawa"
+"STR-3546","StrideOne X Running","StrideOne","Women","Running","Knit","Black","Resistant","High",7.5,37.07,3.1,2.0,"Ottawa"
+"FOO-6762","FootFlex Pro Running","FootFlex","Men","Running","Synthetic","White","Waterproof","High",6.0,125.49,4.1,12.0,"Toronto"
+"ZEN-1683","Zentrax Ultra Walking","Zentrax","Men","Walking","Synthetic","White","Resistant","Flat",9.5,144.14,3.1,6.0,"Toronto"
+"ZEN-5926","Zentrax Max Running","Zentrax","Women","Running","Synthetic","White","Waterproof","Flat",12.0,147.82,4.6,18.0,"Ottawa"
+"ZEN-4160","Zentrax Flex Walking","Zentrax","Men","Walking","Knit","Black","Waterproof","Flat",8.0,44.52,4.2,17.0,"Ottawa"
+"STR-4757","StrideOne Flex Walking","StrideOne","Men","Walking","Synthetic","Black","Waterproof","Flat",12.5,102.2,4.9,14.0,"Toronto"
+"LOO-8328","Loopic Pro Walking","Loopic","Men","Walking","Synthetic","White","Resistant","High",10.0,105.38,3.6,7.0,"Ottawa"
+"FOO-6140","FootFlex Ultra Running","FootFlex","Women","Running","Synthetic","Black","Resistant","High",8.5,75.66,3.2,15.0,"Toronto"
+"ZEN-9927","Zentrax Flex Walking","Zentrax","Women","Walking","Synthetic","Black","Waterproof","High",9.0,67.27,3.4,4.0,"Toronto"
+"RUN-9606","RunXpress Max Running","RunXpress","Men","Running","Knit","Black","Resistant","Flat",7.0,59.23,4.2,2.0,"Ottawa"
+"RUN-1606","RunXpress Ultra Running","RunXpress","Women","Running","Synthetic","White","Waterproof","High",12.5,107.3,4.6,14.0,"Ottawa"
+"STR-1063","StrideOne Pro Walking","StrideOne","Men","Walking","Synthetic","Black","Waterproof","High",6.5,89.09,3.1,19.0,"Toronto"
+"STR-2788","StrideOne Pro Walking","StrideOne","Men","Walking","Synthetic","White","Resistant","Flat",6.0,100.94,3.4,18.0,"Ottawa"
+"STR-1257","StrideOne Ultra Running","StrideOne","Men","Running","Knit","Black","Waterproof","Flat",11.0,96.55,3.4,18.0,"Ottawa"
+"RUN-6084","RunXpress Ultra Running","RunXpress","Men","Running","Synthetic","White","Resistant","High",9.5,72.43,3.6,19.0,"Toronto"
+"LOO-4473","Loopic Ultra Running","Loopic","Men","Running","Knit","White","Resistant","Flat",7.5,113.77,3.8,12.0,"Ottawa"
+"LOO-6583","Loopic Flex Walking","Loopic","Women","Walking","Synthetic","Black","Resistant","High",10.5,45.17,4.7,3.0,"Toronto"
+"STR-5704","StrideOne Max Running","StrideOne","Women","Running","Knit","Black","Resistant","Flat",7.0,95.85,4.2,5.0,"Toronto"
+"ZEN-1117","Zentrax Ultra Walking","Zentrax","Men","Walking","Knit","White","Waterproof","Flat",7.0,143.48,3.1,12.0,"Ottawa"
+"LOO-7456","Loopic Flex Running","Loopic","Women","Running","Synthetic","Black","Waterproof","Flat",11.5,76.99,4.7,11.0,"Toronto"
+"ZEN-8887","Zentrax Flex Walking","Zentrax","Women","Walking","Synthetic","Black","Waterproof","Flat",11.5,107.51,3.2,17.0,"Toronto"
+"LOO-5545","Loopic Max Running","Loopic","Women","Running","Synthetic","White","Resistant","High",9.5,135.51,4.3,6.0,"Ottawa"
+"RUN-9225","RunXpress Flex Walking","RunXpress","Men","Walking","Knit","White","Resistant","Flat",6.5,129.61,4.7,5.0,"Toronto"
+"LOO-2857","Loopic X Running","Loopic","Women","Running","Knit","Black","Resistant","Flat",11.0,134.71,4.3,2.0,"Toronto"
+"ZEN-5899","Zentrax Ultra Walking","Zentrax","Women","Walking","Knit","White","Waterproof","High",8.5,72.39,3.7,18.0,"Toronto"
+"RUN-3963","RunXpress Pro Walking","RunXpress","Women","Walking","Synthetic","White","Waterproof","Flat",10.0,92.32,5.0,12.0,"Ottawa"
+"ZEN-3257","Zentrax Ultra Walking","Zentrax","Women","Walking","Knit","White","Resistant","High",11.5,76.18,5.0,8.0,"Toronto"
+"LOO-5617","Loopic Ultra Walking","Loopic","Men","Walking","Knit","White","Resistant","Flat",7.5,145.26,4.5,15.0,"Ottawa"
+"RUN-2385","RunXpress Ultra Walking","RunXpress","Women","Walking","Knit","White","Resistant","Flat",8.5,60.82,4.1,20.0,"Toronto"
+"ZEN-2766","Zentrax X Walking","Zentrax","Men","Walking","Knit","Black","Waterproof","Flat",7.5,49.11,3.8,13.0,"Toronto"
+"FOO-8028","FootFlex Ultra Walking","FootFlex","Women","Walking","Synthetic","Black","Waterproof","Flat",9.5,38.51,4.8,18.0,"Ottawa"
+"LOO-2529","Loopic X Walking","Loopic","Men","Walking","Synthetic","Black","Resistant","Flat",12.0,115.3,4.3,18.0,"Ottawa"
+"LOO-1332","Loopic Ultra Walking","Loopic","Men","Walking","Synthetic","Black","Waterproof","High",10.0,63.04,3.1,9.0,"Toronto"
+"RUN-6269","RunXpress Max Walking","RunXpress","Women","Walking","Synthetic","Black","Resistant","High",7.0,77.84,4.8,6.0,"Toronto"
+"RUN-1111","RunXpress Ultra Running","RunXpress","Men","Running","Knit","Black","Waterproof","High",6.5,40.62,3.7,3.0,"Ottawa"
+"FOO-2225","FootFlex Flex Walking","FootFlex","Women","Walking","Knit","Black","Resistant","High",6.0,80.2,3.0,1.0,"Ottawa"
+"FOO-1395","FootFlex Pro Running","FootFlex","Women","Running","Synthetic","Black","Waterproof","Flat",12.0,43.43,4.0,8.0,"Toronto"
+"ZEN-7091","Zentrax Max Walking","Zentrax","Women","Walking","Synthetic","White","Resistant","High",9.5,95.25,3.4,16.0,"Toronto"
+"LOO-7590","Loopic X Walking","Loopic","Men","Walking","Knit","White","Resistant","High",9.5,94.08,3.9,2.0,"Ottawa"
+"FOO-7018","FootFlex Max Running","FootFlex","Men","Running","Knit","White","Resistant","High",6.0,70.77,3.9,3.0,"Toronto"
+"FOO-5338","FootFlex Max Walking","FootFlex","Men","Walking","Synthetic","Black","Waterproof","High",9.5,99.62,4.7,16.0,"Ottawa"
+"LOO-8404","Loopic Max Walking","Loopic","Men","Walking","Synthetic","White","Waterproof","High",12.5,133.73,4.4,12.0,"Toronto"
+"ZEN-7902","Zentrax Max Running","Zentrax","Men","Running","Knit","Black","Waterproof","High",7.5,49.0,3.8,17.0,"Ottawa"
+"FOO-4293","FootFlex X Running","FootFlex","Men","Running","Knit","White","Waterproof","Flat",10.0,43.32,3.9,4.0,"Toronto"
+"ZEN-9279","Zentrax X Running","Zentrax","Women","Running","Synthetic","Black","Resistant","High",11.0,77.88,4.0,17.0,"Toronto"
+"LOO-8428","Loopic Pro Running","Loopic","Women","Running","Knit","Black","Resistant","High",7.5,106.71,4.3,14.0,"Toronto"
+"FOO-1631","FootFlex Max Walking","FootFlex","Men","Walking","Synthetic","White","Resistant","High",11.5,30.11,4.8,9.0,"Toronto"
+"LOO-4817","Loopic Max Running","Loopic","Men","Running","Synthetic","Black","Resistant","High",10.0,85.84,4.2,3.0,"Toronto"
+"ZEN-7371","Zentrax Ultra Running","Zentrax","Men","Running","Knit","White","Resistant","High",7.0,86.57,4.0,7.0,"Ottawa"
+"STR-5270","StrideOne Pro Running","StrideOne","Men","Running","Knit","White","Waterproof","Flat",7.5,86.5,3.2,19.0,"Toronto"
+"RUN-7740","RunXpress X Walking","RunXpress","Women","Walking","Synthetic","Black","Resistant","Flat",9.5,104.41,3.1,7.0,"Ottawa"
+"ZEN-6322","Zentrax Pro Walking","Zentrax","Men","Walking","Knit","Black","Resistant","Flat",12.0,92.27,4.1,18.0,"Ottawa"
+"FOO-6251","FootFlex Ultra Walking","FootFlex","Women","Walking","Knit","Black","Waterproof","Flat",12.0,64.17,4.5,5.0,"Toronto"
+"ZEN-7649","Zentrax Max Running","Zentrax","Women","Running","Synthetic","Black","Waterproof","Flat",10.5,31.8,4.7,12.0,"Ottawa"
+"STR-2714","StrideOne Ultra Walking","StrideOne","Women","Walking","Knit","Black","Waterproof","Flat",8.5,130.37,4.5,11.0,"Toronto"
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diff --git a/ai-vectors/product-recommendation/utils.py b/ai-vectors/product-recommendation/utils.py
new file mode 100644
index 0000000..6406c5a
--- /dev/null
+++ b/ai-vectors/product-recommendation/utils.py
@@ -0,0 +1,253 @@
+import matplotlib.pyplot as plt
+import matplotlib.image as mpimg
+import os
+import ast
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.manifold import TSNE
+
+def display_sku_images(sku_list, image_folder='images', image_ext='.jpeg', n_cols=3):
+    """
+    Display SKU images in a grid.
+
+    Args:
+        sku_list (list of str): List of SKUs.
+        image_folder (str): Folder where image files are stored.
+        image_ext (str): File extension of images (e.g., '.jpeg').
+        n_cols (int): Number of images per row.
+    """
+    n_rows = (len(sku_list) + n_cols - 1) // n_cols  # Ceiling division
+
+    plt.figure(figsize=(n_cols * 4, n_rows * 4))
+
+    for idx, sku in enumerate(sku_list):
+        image_path = os.path.join(image_folder, f"{sku}{image_ext}")
+        plt.subplot(n_rows, n_cols, idx + 1)
+
+        if os.path.exists(image_path):
+            img = mpimg.imread(image_path)
+            plt.imshow(img)
+            plt.title(sku, fontsize=10)
+        else:
+            plt.text(0.5, 0.5, "Image not found", ha='center', va='center')
+
+        plt.axis('off')
+
+    plt.tight_layout()
+    plt.show()
+    
+    
+def get_men_shoes_sql():
+    return """ 
+    SELECT SKU, PRODUCT_NAME, BRAND, TYPE, MATERIAL, COLOR, WEATHER_RESISTANCE, ARCH_SUPPORT, PRICE, RATING, CITY
+    FROM SQ_SHOES 
+    WHERE CLASS = 'Men' AND Size = 12 
+    FETCH FIRST 6 ROWS ONLY
+    """
+    
+# shoe_queries.py
+
+def get_similar_shoes_sql():
+    return """
+    SELECT 
+        SKU, 
+        PRODUCT_NAME, 
+        BRAND, 
+        TYPE, 
+        MATERIAL, 
+        COLOR, 
+        WEATHER_RESISTANCE, 
+        ARCH_SUPPORT, 
+        PRICE, 
+        RATING,
+        VECTOR_DISTANCE(
+            (SELECT EMBEDDING FROM SQ_SHOES WHERE SKU = :my_choice_sku), 
+            EMBEDDING, 
+            EUCLIDEAN
+        ) AS DISTANCE
+    FROM 
+        SQ_SHOES
+    WHERE 
+        SKU <> :my_choice_sku
+        AND CITY = 'Toronto'
+        AND SIZE = 12
+        AND CLASS = 'Men'
+    ORDER BY 
+        DISTANCE ASC
+    FETCH FIRST 3 ROWS ONLY
+    """
+
+# shoe_queries.py
+
+def get_distance_for_sku_list_sql(sku_sql_in):
+    """
+    Returns SQL query that retrieves shoes by SKU list with vector distances
+    relative to :my_choice_sku.
+
+    Args:
+        sku_sql_in (str): A string of comma-separated quoted SKUs for SQL IN clause.
+                          e.g., "'LOO-1396', 'STR-7223'"
+    """
+    return f"""
+    SELECT 
+        SKU, 
+        PRODUCT_NAME, 
+        BRAND, 
+        TYPE, 
+        MATERIAL, 
+        COLOR, 
+        WEATHER_RESISTANCE, 
+        ARCH_SUPPORT, 
+        PRICE, 
+        RATING,
+        VECTOR_DISTANCE(
+            (SELECT EMBEDDING FROM SQ_SHOES WHERE SKU = :my_choice_sku), 
+            EMBEDDING, 
+            EUCLIDEAN
+        ) AS DISTANCE
+    FROM 
+        SQ_SHOES
+    WHERE 
+        SKU IN ({sku_sql_in})
+    ORDER BY 
+        DISTANCE ASC
+    """
+    
+# shoe_ddl.py
+
+def get_create_sq_shoes_table_sql():
+    return """
+    CREATE TABLE SQ_SHOES (
+        SKU VARCHAR(8),
+        PRODUCT_NAME VARCHAR(23),
+        BRAND VARCHAR(9),
+        CLASS VARCHAR(5),
+        TYPE VARCHAR(7),
+        MATERIAL VARCHAR(9),
+        COLOR VARCHAR(5),
+        WEATHER_RESISTANCE VARCHAR(10),
+        ARCH_SUPPORT VARCHAR(4),
+        SIZE FLOAT,
+        PRICE FLOAT,
+        RATING FLOAT,
+        STORE_ID BIGINT,
+        CITY VARCHAR(7),
+        EMBEDDING VECTOR(1024,FLOAT32)
+    );
+    """
+
+def get_create_sq_shoes_table_data_only():
+    return """
+    CREATE TABLE SQ_SHOES (
+        SKU VARCHAR(8),
+        PRODUCT_NAME VARCHAR(23),
+        BRAND VARCHAR(9),
+        CLASS VARCHAR(5),
+        TYPE VARCHAR(7),
+        MATERIAL VARCHAR(9),
+        COLOR VARCHAR(5),
+        WEATHER_RESISTANCE VARCHAR(10),
+        ARCH_SUPPORT VARCHAR(4),
+        SIZE FLOAT,
+        PRICE FLOAT,
+        RATING FLOAT,
+        STORE_ID BIGINT,
+        CITY VARCHAR(7)
+    );
+    """
+
+# shoe_queries.py
+
+def get_shoe_by_sku_sql():
+    return """
+    SELECT 
+        PRODUCT_NAME, 
+        BRAND, 
+        TYPE, 
+        MATERIAL, 
+        COLOR, 
+        WEATHER_RESISTANCE, 
+        ARCH_SUPPORT, 
+        PRICE, 
+        RATING
+    FROM 
+        SQ_SHOES 
+    WHERE 
+        SKU = :my_choice_sku
+    """
+
+# shoe_query.py
+
+def get_similarity_query(sku_sql_in: str) -> str:
+    sql = f"""
+    SELECT 
+        SKU, 
+        PRODUCT_NAME, 
+        VECTOR_DISTANCE(
+            (SELECT EMBEDDING FROM SQ_SHOES WHERE SKU = :my_choice_sku), 
+            EMBEDDING, 
+            EUCLIDEAN
+        ) AS DISTANCE,
+        VECTOR_SERIALIZE(EMBEDDING) AS EMBEDDING 
+    FROM 
+        SQ_SHOES
+    WHERE 
+        SKU IN ({sku_sql_in})
+    """
+    return sql
+
+def plot_similarity_tsne(top_matching_vectors, my_choice_sku):
+    # Parse vector strings to list of floats
+    top_matching_vectors['embedding_list'] = top_matching_vectors['EMBEDDING'].apply(ast.literal_eval)
+
+    # Convert to 2D numpy array
+    X = np.array(top_matching_vectors['embedding_list'].tolist())
+
+    # Run t-SNE
+    tsne = TSNE(n_components=2, random_state=42, perplexity=5)
+    X_embedded = tsne.fit_transform(X)
+
+    # Find index of my_choice_sku
+    target_idx = top_matching_vectors[top_matching_vectors['SKU'] == my_choice_sku].index[0]
+    target_point = X_embedded[target_idx]
+
+    # Plot setup
+    plt.figure(figsize=(10, 8))
+    plt.scatter(X_embedded[:, 0], X_embedded[:, 1], color='skyblue')
+
+    # Draw lines and annotate
+    for i, row in top_matching_vectors.iterrows():
+        point = X_embedded[i]
+        sku = row['SKU']
+        product_name = row['PRODUCT_NAME']
+        distance = row['DISTANCE']
+
+        if sku != my_choice_sku:
+            plt.plot([target_point[0], point[0]], [target_point[1], point[1]],
+                     linestyle='--', color='gray', linewidth=0.8)
+            mid_x = (target_point[0] + point[0]) / 2
+            mid_y = (target_point[1] + point[1]) / 2
+            plt.text(mid_x, mid_y, f"{distance:.2f}", fontsize=8, color='blue', ha='center')
+
+        label_color = 'red' if sku == my_choice_sku else 'black'
+        font_weight = 'bold' if sku == my_choice_sku else 'normal'
+        plt.annotate(str(product_name), (point[0], point[1]), fontsize=9, ha='right',
+                     color=label_color, fontweight=font_weight)
+
+    # Highlight selected SKU with red circle
+    plt.scatter(target_point[0], target_point[1], s=200, facecolors='none',
+                edgecolors='red', linewidths=2)
+
+    # Remove all extras for clean visual
+    plt.grid(False)
+    plt.xlabel("")
+    plt.ylabel("")
+    plt.xticks([])
+    plt.yticks([])
+    ax = plt.gca()
+    for spine in ax.spines.values():
+        spine.set_visible(False)
+
+    plt.title(f"Vector Distance Between the Recommended Shoes and Sample Shoe {my_choice_sku}")
+    plt.tight_layout()
+    plt.show()
\ No newline at end of file

From 949f81ae3da0801fa397fb4ccbd5408cc3e86d37 Mon Sep 17 00:00:00 2001
From: shaikhq 
Date: Mon, 21 Apr 2025 19:48:07 -0700
Subject: [PATCH 2/5] Added shoe vector search demo

Signed-off-by: shaikhq 
---
 ai-vectors/product-recommendation/README.md | 149 +++++++++++++-------
 1 file changed, 99 insertions(+), 50 deletions(-)

diff --git a/ai-vectors/product-recommendation/README.md b/ai-vectors/product-recommendation/README.md
index 5fc8cb4..ed5a155 100644
--- a/ai-vectors/product-recommendation/README.md
+++ b/ai-vectors/product-recommendation/README.md
@@ -1,97 +1,146 @@
-1. **Install Python 3.12**
+### Semantic Search for Product Recommendation Using Db2 Vector AI
 
-   RHEL 9.4 includes Python 3.12 in its package repositories. To install it, run:
+This repository contains code and data for implementing a **semantic product recommendation** demo using **IBM Db2's Vector AI** features, planned for release in **Db2 Version 12 Mod Pack 2 (June 2025)**.
 
-   ```bash
-   sudo dnf install python3.12
-   ```
+---
 
-   Verify the installation:
+## 🛍️ Use Case Overview
 
-   ```bash
-   python3.12 --version
-   ```
+This demo simulates a scenario at a shoe store where a customer walks in with a reference shoe they found online and wants to find a similar shoe available in the local store. We use **semantic search** over shoe descriptions stored in Db2 to recommend similar products.
 
-2. **Install `pip` for Python 3.12**
+---
 
-   To manage Python packages, install `pip` for Python 3.12:
+## ⚙️ Workflow Summary
 
-   ```bash
-   sudo dnf install python3.12-pip
-   ```
+- A Db2 table is created to store shoe records, which include descriptive attributes (e.g., color, material, type) and metadata (e.g., SKU, store location).
+- These records are populated from a **synthetically generated dataset**. You can find the data generation steps in `generate_dataset.ipynb`.
+- The full product recommendation logic is implemented in **`similar_shoes_search.ipynb`**, which is self-contained and ready to run.
+- It uses **pre-generated vector embeddings** (provided in `shoes-vectors.csv`) to power similarity search, so you can run the demo **without needing API access to external services**.
+- If you wish to regenerate the embeddings, Watsonx.ai can be used via the Python SDK.
 
-3. **Install `uv` for managing python dependencies
-```shell
-python3.12 -m pip install uv
+---
+
+## 🐍 Environment Setup (RHEL 9.4 + Python 3.12)
+
+Follow these steps to set up your environment and run the demo:
+
+### 1. Install Python 3.12
+
+RHEL 9.4 includes Python 3.12 in its package repositories.
+
+```bash
+sudo dnf install python3.12
+python3.12 --version
 ```
 
-4. **Add `uv` to the your shell profile
-Add this line to your ~/.kshrc, ~/.profile, or ~/.bashrc (depending on your shell setup). In my case, it is ~/.profile
-```shell
-vi ~/.profile
+### 2. Install `pip` for Python 3.12
+
+```bash
+sudo dnf install python3.12-pip
 ```
-First, find the installation path of `uv`:
-```shell
-python3.12 -m site --user-base
+
+### 3. Install `uv` (a modern Python package manager)
+
+```bash
+python3.12 -m pip install uv
 ```
 
-This gave me:
+### 4. Add `uv` to Your Shell Profile
+
+Determine the user base path:
+
+```bash
+python3.12 -m site --user-base
 ```
-/home/shaikhq/.local
+
+This should return something like:
+
+```bash
+/home//.local
 ```
 
-Add the following line at the end of the file:
+Edit your shell profile:
+
+```bash
+vi ~/.profile  # or ~/.bashrc, ~/.kshrc depending on your shell
 ```
+
+Add the following line:
+
+```bash
 export PATH="$HOME/.local/bin:$PATH"
 ```
 
-Saved changes and exited vi editor. 
-
 Apply the changes:
-```
-. ~/.profile
-```
 
-Verify that `uv` is now on the path:
-```shell
+```bash
+. ~/.profile
 uv --version
+# Should output: uv 0.6.15 (or newer)
 ```
 
-uv --version
-uv 0.6.15
+### 5. Create a Python Virtual Environment
 
-5. **Create a python virtual environment
-```shell
+```bash
 uv venv --python=python3.12
 ```
 
-6. **Activate the python virtual env:
-```shell
+### 6. Activate the Virtual Environment
+
+```bash
 source .venv/bin/activate
 ```
 
-7. **Install packages from `requirements.txt`
+### 7. Install Required Packages
+
+```bash
 uv pip install -r requirements.txt
+```
+
+---
+
+## 🧠 IDE Setup (VS Code)
 
-8. **Configure VS Code to Use the Virtual Environment**
+### 8. Configure VS Code to Use the Virtual Environment
 
-   - Open the Command Palette (`Ctrl+Shift+P` or `Cmd+Shift+P` on macOS).
-   - Type `Python: Select Interpreter` and select the interpreter located in your `.venv` directory.
+- Open Command Palette: `Ctrl+Shift+P` (or `Cmd+Shift+P` on macOS)
+- Search: `Python: Select Interpreter`
+- Select: The interpreter inside `.venv`
 
-9. **Set the Notebook's Python Interpreter in VS Code**
+### 9. Set Notebook Kernel to Virtual Environment
 
-   - Open your Jupyter notebook in VS Code.
-   - Click on the kernel name at the top right corner.
-   - Select the interpreter from your `.venv`.
+- Open `similar_shoes_search.ipynb`
+- Click the kernel name (top-right)
+- Choose the `.venv` Python interpreter
 
-10. Rename `.env-sample` to `.env` and populate the required API keys and metadata from your target AI platforms. Fill in the necessary values as shown below:
+---
+
+## 🔐 API Key Configuration (Optional)
+
+If you want to regenerate the vector embeddings using Watsonx.ai:
+
+### 10. Rename `.env-sample` to `.env` and Fill in Required Fields
 
 ```env
 WATSONX_PROJECT=
 WATSONX_APIKEY=
+
 database=
 hostname=
 port=
 protocol=
 uid=
-pwd=
\ No newline at end of file
+pwd=
+```
+
+> ⚠️ This step is optional. Pre-generated embeddings are already provided in `shoes-vectors.csv`.
+
+---
+
+## ✅ Ready to Run
+
+To run the full demo:
+
+1. Activate your environment
+2. Launch the `shoes_search.ipynb` notebook
+3. Run all cells to walk through the entire workflow
\ No newline at end of file

From 9702bbdadb23ed80a92f07ebe052ede1f639227a Mon Sep 17 00:00:00 2001
From: shaikhq 
Date: Mon, 21 Apr 2025 19:54:10 -0700
Subject: [PATCH 3/5] Added shoe vector search demo

Signed-off-by: shaikhq 
---
 ai-vectors/product-recommendation/.env-sample | 16 ++++++++--------
 ai-vectors/product-recommendation/.gitignore  |  1 +
 2 files changed, 9 insertions(+), 8 deletions(-)

diff --git a/ai-vectors/product-recommendation/.env-sample b/ai-vectors/product-recommendation/.env-sample
index c88cad3..3d0a807 100644
--- a/ai-vectors/product-recommendation/.env-sample
+++ b/ai-vectors/product-recommendation/.env-sample
@@ -1,8 +1,8 @@
-WATSONX_PROJECT=4468f0f6-ce79-46aa-9897-d363b9c610ff
-WATSONX_APIKEY=kSHPOyVzLPJq3qeodlh_EtUKdGSN0Bwfj7xj5e-QfjGr
-database=tpcds
-hostname=localhost
-port=50000
-protocol=tcpip
-uid=shaikhq
-pwd=7?EBx|BcxV7.jgG
+WATSONX_PROJECT=
+WATSONX_APIKEY=
+database=
+hostname=
+port=
+protocol=
+uid=
+pwd=
diff --git a/ai-vectors/product-recommendation/.gitignore b/ai-vectors/product-recommendation/.gitignore
index 4c49bd7..0884b00 100644
--- a/ai-vectors/product-recommendation/.gitignore
+++ b/ai-vectors/product-recommendation/.gitignore
@@ -1 +1,2 @@
 .env
+*.pickle

From b30af2511dd5470cb567213634a1db1af0ebca3e Mon Sep 17 00:00:00 2001
From: shaikhq 
Date: Mon, 21 Apr 2025 19:55:26 -0700
Subject: [PATCH 4/5] Added shoe vector search demo

Signed-off-by: shaikhq 
---
 .../product-recommendation/db2connect.pickle      | Bin 215 -> 0 bytes
 ai-vectors/product-recommendation/db2creds.pickle | Bin 245 -> 0 bytes
 2 files changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 ai-vectors/product-recommendation/db2connect.pickle
 delete mode 100644 ai-vectors/product-recommendation/db2creds.pickle

diff --git a/ai-vectors/product-recommendation/db2connect.pickle b/ai-vectors/product-recommendation/db2connect.pickle
deleted file mode 100644
index 465ee75fcc45a500cfe9c642960cdf192b092c7f..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 215
zcmXAfK@Ng25Jib1pkBn4apM7qZj1?%=mkbvp|R42DM1$|9$=Q^tvr;i{>}UE=fC3j
zC;I2f3rcTr@63R_pHX6=R&C8RIuuN)P-rzgn36Itw)xRduIVHQe~u^<;NT1>nIfjF
zG3Z*jd7Elefey|TThn<=)+-<0Nw&XSBQ&9FghvlM;i(OT5l+JO<7#)edQy~q@4#xg
z;+cgEFF;OslF=ySnNO5E*eg1Z$v`Ywc#NsnGr74|X$$+5lS#1*4F(iGI|QrW$wO%U
zyJkFUo-c3j*Tcu!4A0BOTeDG4q^nAerB3{%PAI;H=!&laL+eh(UVt%
Date: Mon, 21 Apr 2025 20:00:31 -0700
Subject: [PATCH 5/5] Added shoe vector search demo

Signed-off-by: shaikhq 
---
 ai-vectors/product-recommendation/README.md | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/ai-vectors/product-recommendation/README.md b/ai-vectors/product-recommendation/README.md
index ed5a155..a081247 100644
--- a/ai-vectors/product-recommendation/README.md
+++ b/ai-vectors/product-recommendation/README.md
@@ -115,9 +115,7 @@ uv pip install -r requirements.txt
 
 ---
 
-## 🔐 API Key Configuration (Optional)
-
-If you want to regenerate the vector embeddings using Watsonx.ai:
+## 🔐 API Key Configuration
 
 ### 10. Rename `.env-sample` to `.env` and Fill in Required Fields
 
@@ -133,7 +131,7 @@ uid=
 pwd=
 ```
 
-> ⚠️ This step is optional. Pre-generated embeddings are already provided in `shoes-vectors.csv`.
+> ⚠️ Using Watsonx.ai APIs is optional. Pre-generated embeddings using watsonx.ai API are already provided in `shoes-vectors.csv`.
 
 ---