From 2fcbabd3aed2193c8248921e252fa5aab9429128 Mon Sep 17 00:00:00 2001
From: juhel-phanju-intugle <juhel@intugle.ai>
Date: Thu, 28 Aug 2025 21:41:41 +0530
Subject: [PATCH 01/25] feat: Duckdb compatible

- Added duckdb adapter
- Changes made in sql generator for query compatiblity
- Added details in source
---
 notebooks/quickstart_duckdb.ipynb             | 1616 +++++
 quickstart.ipynb                              |    6 +-
 src/intugle/adapters/adapter.py               |   10 +-
 src/intugle/adapters/models.py                |    7 +
 src/intugle/adapters/types/duckdb/duckdb.py   |   72 +-
 src/intugle/adapters/types/pandas/pandas.py   |    8 +-
 src/intugle/analysis/models.py                |   37 +-
 src/intugle/libs/query_generator/operators.py |    4 +-
 .../libs/query_generator/query_generator.py   |   16 +-
 src/intugle/libs/query_generator/utils.py     |    6 +-
 .../smart_query_generator/utils/filter.py     |    4 +-
 src/intugle/link_predictor/predictor.py       |    6 +-
 src/intugle/mcp/adapter/service.py            |   35 +-
 src/intugle/mcp/semantic_layer/router.py      |    8 +-
 src/intugle/mcp/semantic_layer/service.py     |   10 +-
 src/intugle/models/resources/source.py        |    2 +-
 src/intugle/sql_generator.py                  |   36 +-
 tests/analysis/test_profile.py                |   34 +-
 tests/mcp/test_semantic_layer.py              |    4 +-
 uv.lock                                       | 5908 ++++++++---------
 20 files changed, 4775 insertions(+), 3054 deletions(-)
 create mode 100644 notebooks/quickstart_duckdb.ipynb

diff --git a/notebooks/quickstart_duckdb.ipynb b/notebooks/quickstart_duckdb.ipynb
new file mode 100644
index 0000000..9b04115
--- /dev/null
+++ b/notebooks/quickstart_duckdb.ipynb
@@ -0,0 +1,1616 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "757096f5",
+   "metadata": {},
+   "source": [
+    "# Quickstart: Building a Semantic Layer with Intugle\n",
+    "\n",
+    "This notebook provides a quick introduction to this project. You'll learn how to use its key features to automatically build a semantic layer over your data.\n",
+    "\n",
+    "**What is a Semantic Layer?**\n",
+    "\n",
+    "A semantic layer is a business-friendly representation of your data. It hides the complexity of the underlying data sources and provides a unified view of your data using familiar business terms. This makes it easier for both business users and data teams to understand and query the data, accelerating data-driven insights.\n",
+    "\n",
+    "**Who is this for?**\n",
+    "\n",
+    "This tool is designed for both **data teams** and **business teams**. \n",
+    "\n",
+    "* **Data teams** can use it to automate data profiling, schema discovery, and documentation, significantly accelerating their workflow.\n",
+    "* **Business teams** can use it to gain a better understanding of their data and to perform self-service analytics without needing to write complex SQL queries.\n",
+    "\n",
+    "**In this notebook, you will learn how to:**\n",
+    "\n",
+    "1. **Profile your data:** Analyze your data sources to understand their structure, data types, and other characteristics.\n",
+    "2. **Business Glossary Generation:** Generate a business glossary for each column, with support for industry-specific domains.\n",
+    "2. **Automatically predict links:** Use a Large Language Model (LLM) to automatically discover relationships (foreign keys) between tables.\n",
+    "3. **Generate a semantic layer:** Create YAML files file that defines your semantic layer.\n",
+    "4. **Generate SQL queries:** Use the semantic layer to generate SQL queries and retrieve data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2a1b244",
+   "metadata": {},
+   "source": [
+    "## 1. LLM Configuration\n",
+    "\n",
+    "Before running the project, you need to configure a Large Language Model (LLM). This is used for tasks like generating business glossaries and predicting links between tables.\n",
+    "\n",
+    "You can configure the LLM by setting the following environment variables:\n",
+    "\n",
+    "* `LLM_PROVIDER`: The LLM provider and model to use (e.g., `openai:gpt-3.5-turbo`). The format follows langchain's format for initializing chat models. Checkout how to specify your model [here](https://python.langchain.com/docs/integrations/chat/) \n",
+    "* `OPENAI_API_KEY`: Your API key for the LLM provider.\n",
+    "\n",
+    "Here's an example of how to set these variables in your environment:\n",
+    "\n",
+    "```bash\n",
+    "export LLM_PROVIDER=\"openai:gpt-3.5-turbo\"\n",
+    "export OPENAI_API_KEY=\"your-openai-api-key\"\n",
+    "```\n",
+    "\n",
+    "Alternatively, you can set them in the notebook like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "36454d44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"LLM_PROVIDER\"] = \"openai:gpt-3.5-turbo\"\n",
+    "os.environ[\"OPENAI_API_KEY\"] = \"your-openai-api-key\"  # Replace with your actual key\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa9fda69",
+   "metadata": {},
+   "source": [
+    "> Currently the langchain packages for OpenAI, Anthropic and Gemini is installed by default. For additional models, make sure you have the integration packages installed. E.g. you should have langchain-deepseek installed to use a DeepSeek model. You can get these packages here: [LangChain Chat Models](https://python.langchain.com/docs/integrations/chat/)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3dad9d8",
+   "metadata": {},
+   "source": [
+    "## 2. Data Profiling and Glossary Generation\n",
+    "\n",
+    "The first step in building a semantic layer is to profile your data. This involves analyzing your data sources to understand their structure, data types, and other characteristics. This tool provides a pipeline for this purpose. It can also generate business glossary for your data.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7f0e28d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from intugle import DataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "8d3e8299",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "allergies loaded\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/.venv/lib/python3.12/site-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
+      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
+      "/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/.venv/lib/python3.12/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "[!] Empty column encountered for allergies ==> stop ...\n",
+      "100%|██████████| 15/15 [00:08<00:00,  1.73it/s]\n",
+      "100%|██████████| 15/15 [00:24<00:00,  1.65s/it]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>column_name</th>\n",
+       "      <th>business_name</th>\n",
+       "      <th>table_name</th>\n",
+       "      <th>null_count</th>\n",
+       "      <th>count</th>\n",
+       "      <th>distinct_count</th>\n",
+       "      <th>uniqueness</th>\n",
+       "      <th>completeness</th>\n",
+       "      <th>sample_data</th>\n",
+       "      <th>dtype_sample</th>\n",
+       "      <th>ts</th>\n",
+       "      <th>datatype_l1</th>\n",
+       "      <th>datatype_l2</th>\n",
+       "      <th>business_glossary</th>\n",
+       "      <th>business_tags</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>start</td>\n",
+       "      <td>start</td>\n",
+       "      <td>allergies</td>\n",
+       "      <td>0</td>\n",
+       "      <td>794</td>\n",
+       "      <td>179</td>\n",
+       "      <td>0.225441</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[2006-08-07, 2001-04-09, 2011-02-04, 1958-10-2...</td>\n",
+       "      <td>[2009-02-19, 1991-11-18, 1976-04-09, 2013-07-2...</td>\n",
+       "      <td>0.036076</td>\n",
+       "      <td>date &amp; time</td>\n",
+       "      <td>L2OutputTypes.dimension</td>\n",
+       "      <td>Indicates the date and time when an allergy re...</td>\n",
+       "      <td>[Allergy Onset Date, Patient Medical History, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>stop</td>\n",
+       "      <td>stop</td>\n",
+       "      <td>allergies</td>\n",
+       "      <td>794</td>\n",
+       "      <td>794</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>0.006042</td>\n",
+       "      <td>others</td>\n",
+       "      <td>L2OutputTypes.unknown</td>\n",
+       "      <td>Indicates the conclusion or cessation of a spe...</td>\n",
+       "      <td>[AllergyEndDate, PatientCareTimeline, Clinical...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>patient</td>\n",
+       "      <td>patient</td>\n",
+       "      <td>allergies</td>\n",
+       "      <td>0</td>\n",
+       "      <td>794</td>\n",
+       "      <td>179</td>\n",
+       "      <td>0.225441</td>\n",
+       "      <td>1.0</td>\n",
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+       "      <td>[9ef4db86-c427-ddfe-a607-737f08ffb0c1, 4ba1518...</td>\n",
+       "      <td>0.006320</td>\n",
+       "      <td>alphanumeric</td>\n",
+       "      <td>L2OutputTypes.dimension</td>\n",
+       "      <td>Identifies individuals associated with recorde...</td>\n",
+       "      <td>[Patient Identifier, Healthcare Entity, Allerg...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>encounter</td>\n",
+       "      <td>encounter</td>\n",
+       "      <td>allergies</td>\n",
+       "      <td>0</td>\n",
+       "      <td>794</td>\n",
+       "      <td>179</td>\n",
+       "      <td>0.225441</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[a7d784e9-8503-e26f-b98f-e34ca8a4bad0, 704f899...</td>\n",
+       "      <td>[04effe72-a3c5-e479-b6d3-5f6fb7ac8601, be166b8...</td>\n",
+       "      <td>0.004151</td>\n",
+       "      <td>alphanumeric</td>\n",
+       "      <td>L2OutputTypes.dimension</td>\n",
+       "      <td>A unique identifier associated with a specific...</td>\n",
+       "      <td>[Patient Encounter, Clinical Visit Identifier,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>code</td>\n",
+       "      <td>code</td>\n",
+       "      <td>allergies</td>\n",
+       "      <td>0</td>\n",
+       "      <td>794</td>\n",
+       "      <td>21</td>\n",
+       "      <td>0.026448</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[264287008, 25037, 412071004, 288328004, 76295...</td>\n",
+       "      <td>[3718001, 256355007, 10831, 111088007, 1191, 4...</td>\n",
+       "      <td>0.004685</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>L2OutputTypes.dimension</td>\n",
+       "      <td>Identifies specific allergens or allergy-relat...</td>\n",
+       "      <td>[Allergy Classification, Clinical Code, Health...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "<intugle.analysis.models.DataSet at 0x71e240ae3aa0>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "allergies_data = {\n",
+    "    \"path\": \"https://raw.githubusercontent.com/Intugle/data-tools/refs/heads/main/sample_data/healthcare/allergies.csv\",\n",
+    "    \"type\": \"csv\"\n",
+    "}\n",
+    "# Create a DataSet object and run the profiling pipeline\n",
+    "dataset_allergies = DataSet(allergies_data, \"allergies\")\n",
+    "dataset_allergies.run(domain=\"Healthcare\")\n",
+    "\n",
+    "# Or you can run each step manually:\n",
+    "# dataset_allergies.profile().identify_datatypes().identify_keys().generate_glossary(domain=\"Healthcare\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f4ea58f",
+   "metadata": {},
+   "source": [
+    "The `run()` method performs a series of analysis steps, including:\n",
+    "\n",
+    "* **Profiling:** Calculates statistics for each column, such as distinct count, uniqueness, and completeness.\n",
+    "* **Datatype Identification:** Identifies the data type of each column (e.g., integer, string, datetime).\n",
+    "* **Key Identification:** Identifies potential primary keys.\n",
+    "* **Glossary Generation:** Generates a business glossary for each column using an LLM.\n",
+    "\n",
+    "> The `domain` parameter helps the LLM generate a more contextual business glossary. It specifies the industry domain that the dataset belongs to (e.g., \"Healthcare\", \"Finance\", \"E-commerce\").\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "ac8a78fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>business_glossary</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Indicates the date and time when an allergy re...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Indicates the conclusion or cessation of a spe...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Identifies individuals associated with recorde...</td>\n",
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+       "      <th>3</th>\n",
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+       "    </tr>\n",
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+       "      <th>4</th>\n",
+       "      <td>Identifies specific allergens or allergy-relat...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Identifies the source or classification framew...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Provides the name or type of allergen associat...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Categorizes the nature of a condition related ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Classifies the type of allergen relevant to an...</td>\n",
+       "    </tr>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Indicates the intensity level of an allergic r...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Indicates the specific type or category of adv...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Captures textual descriptions of observed symp...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Indicates the intensity level of an allergic r...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                    business_glossary\n",
+       "0   Indicates the date and time when an allergy re...\n",
+       "1   Indicates the conclusion or cessation of a spe...\n",
+       "2   Identifies individuals associated with recorde...\n",
+       "3   A unique identifier associated with a specific...\n",
+       "4   Identifies specific allergens or allergy-relat...\n",
+       "5   Identifies the source or classification framew...\n",
+       "6   Provides the name or type of allergen associat...\n",
+       "7   Categorizes the nature of a condition related ...\n",
+       "8   Classifies the type of allergen relevant to an...\n",
+       "9   Describes the specific physical or physiologic...\n",
+       "10  Captures the specific symptoms or manifestatio...\n",
+       "11  Indicates the intensity level of an allergic r...\n",
+       "12  Indicates the specific type or category of adv...\n",
+       "13  Captures textual descriptions of observed symp...\n",
+       "14  Indicates the intensity level of an allergic r..."
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset_allergies.result_to_pandas()[[\"business_glossary\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "86d162c1",
+   "metadata": {},
+   "outputs": [
+    {
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+       "      <th>type</th>\n",
+       "      <th>category</th>\n",
+       "      <th>reaction1</th>\n",
+       "      <th>description1</th>\n",
+       "      <th>severity1</th>\n",
+       "      <th>reaction2</th>\n",
+       "      <th>description2</th>\n",
+       "      <th>severity2</th>\n",
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+       "  <tbody>\n",
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+       "      <th>0</th>\n",
+       "      <td>2020-02-17</td>\n",
+       "      <td>None</td>\n",
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+       "      <td>111088007</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>Latex (substance)</td>\n",
+       "      <td>allergy</td>\n",
+       "      <td>environment</td>\n",
+       "      <td>247472004</td>\n",
+       "      <td>Wheal (finding)</td>\n",
+       "      <td>MILD</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
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+       "      <td>None</td>\n",
+       "      <td>b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85</td>\n",
+       "      <td>01efcc52-15d6-51e9-faa2-bee069fcbe44</td>\n",
+       "      <td>84489001</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>Mold (organism)</td>\n",
+       "      <td>allergy</td>\n",
+       "      <td>environment</td>\n",
+       "      <td>76067001</td>\n",
+       "      <td>Sneezing</td>\n",
+       "      <td>MILD</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2020-02-17</td>\n",
+       "      <td>None</td>\n",
+       "      <td>b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85</td>\n",
+       "      <td>01efcc52-15d6-51e9-faa2-bee069fcbe44</td>\n",
+       "      <td>260147004</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>House dust mite (organism)</td>\n",
+       "      <td>allergy</td>\n",
+       "      <td>environment</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2020-02-17</td>\n",
+       "      <td>None</td>\n",
+       "      <td>b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85</td>\n",
+       "      <td>01efcc52-15d6-51e9-faa2-bee069fcbe44</td>\n",
+       "      <td>264287008</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>Animal dander (substance)</td>\n",
+       "      <td>allergy</td>\n",
+       "      <td>environment</td>\n",
+       "      <td>878820003</td>\n",
+       "      <td>Rhinoconjunctivitis (disorder)</td>\n",
+       "      <td>MODERATE</td>\n",
+       "      <td>271807003</td>\n",
+       "      <td>Eruption of skin (disorder)</td>\n",
+       "      <td>MILD</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2020-02-17</td>\n",
+       "      <td>None</td>\n",
+       "      <td>b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85</td>\n",
+       "      <td>01efcc52-15d6-51e9-faa2-bee069fcbe44</td>\n",
+       "      <td>256277009</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>Grass pollen (substance)</td>\n",
+       "      <td>allergy</td>\n",
+       "      <td>environment</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>789</th>\n",
+       "      <td>1987-05-20</td>\n",
+       "      <td>None</td>\n",
+       "      <td>27cb1c2f-4ec0-6e24-d080-e46178f712d5</td>\n",
+       "      <td>04d64fc5-6137-3d7a-06b6-dd6dc2d6a7c1</td>\n",
+       "      <td>442571000124108</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>Tree nut (substance)</td>\n",
+       "      <td>allergy</td>\n",
+       "      <td>food</td>\n",
+       "      <td>39579001</td>\n",
+       "      <td>Anaphylaxis (disorder)</td>\n",
+       "      <td>SEVERE</td>\n",
+       "      <td>49727002</td>\n",
+       "      <td>Cough (finding)</td>\n",
+       "      <td>MODERATE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>790</th>\n",
+       "      <td>2001-04-09</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0cf08145-a634-329f-54d3-9cbd04ba18cc</td>\n",
+       "      <td>ef2f78b9-750e-607b-f3f4-3de39ee2eeec</td>\n",
+       "      <td>1191</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>Aspirin</td>\n",
+       "      <td>allergy</td>\n",
+       "      <td>medication</td>\n",
+       "      <td>247472004</td>\n",
+       "      <td>Wheal (finding)</td>\n",
+       "      <td>MODERATE</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>791</th>\n",
+       "      <td>2001-04-09</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0cf08145-a634-329f-54d3-9cbd04ba18cc</td>\n",
+       "      <td>ef2f78b9-750e-607b-f3f4-3de39ee2eeec</td>\n",
+       "      <td>102263004</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>Eggs (edible) (substance)</td>\n",
+       "      <td>allergy</td>\n",
+       "      <td>food</td>\n",
+       "      <td>247472004</td>\n",
+       "      <td>Wheal (finding)</td>\n",
+       "      <td>MODERATE</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>792</th>\n",
+       "      <td>1980-08-15</td>\n",
+       "      <td>None</td>\n",
+       "      <td>9c532b25-cd38-f13a-f2de-2f5538326ed6</td>\n",
+       "      <td>b3afbc77-ec8c-1cf8-05fe-740c91fa1d0f</td>\n",
+       "      <td>1191</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>Aspirin</td>\n",
+       "      <td>allergy</td>\n",
+       "      <td>medication</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>793</th>\n",
+       "      <td>1980-08-15</td>\n",
+       "      <td>None</td>\n",
+       "      <td>9c532b25-cd38-f13a-f2de-2f5538326ed6</td>\n",
+       "      <td>b3afbc77-ec8c-1cf8-05fe-740c91fa1d0f</td>\n",
+       "      <td>735029006</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>Shellfish (substance)</td>\n",
+       "      <td>allergy</td>\n",
+       "      <td>food</td>\n",
+       "      <td>402387002</td>\n",
+       "      <td>Allergic angioedema (disorder)</td>\n",
+       "      <td>MODERATE</td>\n",
+       "      <td>422587007</td>\n",
+       "      <td>Nausea (finding)</td>\n",
+       "      <td>MODERATE</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>794 rows × 15 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         start  stop                               patient  \\\n",
+       "0   2020-02-17  None  b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85   \n",
+       "1   2020-02-17  None  b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85   \n",
+       "2   2020-02-17  None  b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85   \n",
+       "3   2020-02-17  None  b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85   \n",
+       "4   2020-02-17  None  b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85   \n",
+       "..         ...   ...                                   ...   \n",
+       "789 1987-05-20  None  27cb1c2f-4ec0-6e24-d080-e46178f712d5   \n",
+       "790 2001-04-09  None  0cf08145-a634-329f-54d3-9cbd04ba18cc   \n",
+       "791 2001-04-09  None  0cf08145-a634-329f-54d3-9cbd04ba18cc   \n",
+       "792 1980-08-15  None  9c532b25-cd38-f13a-f2de-2f5538326ed6   \n",
+       "793 1980-08-15  None  9c532b25-cd38-f13a-f2de-2f5538326ed6   \n",
+       "\n",
+       "                                encounter             code   system  \\\n",
+       "0    01efcc52-15d6-51e9-faa2-bee069fcbe44        111088007  Unknown   \n",
+       "1    01efcc52-15d6-51e9-faa2-bee069fcbe44         84489001  Unknown   \n",
+       "2    01efcc52-15d6-51e9-faa2-bee069fcbe44        260147004  Unknown   \n",
+       "3    01efcc52-15d6-51e9-faa2-bee069fcbe44        264287008  Unknown   \n",
+       "4    01efcc52-15d6-51e9-faa2-bee069fcbe44        256277009  Unknown   \n",
+       "..                                    ...              ...      ...   \n",
+       "789  04d64fc5-6137-3d7a-06b6-dd6dc2d6a7c1  442571000124108  Unknown   \n",
+       "790  ef2f78b9-750e-607b-f3f4-3de39ee2eeec             1191  Unknown   \n",
+       "791  ef2f78b9-750e-607b-f3f4-3de39ee2eeec        102263004  Unknown   \n",
+       "792  b3afbc77-ec8c-1cf8-05fe-740c91fa1d0f             1191  Unknown   \n",
+       "793  b3afbc77-ec8c-1cf8-05fe-740c91fa1d0f        735029006  Unknown   \n",
+       "\n",
+       "                    description     type     category  reaction1  \\\n",
+       "0             Latex (substance)  allergy  environment  247472004   \n",
+       "1               Mold (organism)  allergy  environment   76067001   \n",
+       "2    House dust mite (organism)  allergy  environment       <NA>   \n",
+       "3     Animal dander (substance)  allergy  environment  878820003   \n",
+       "4      Grass pollen (substance)  allergy  environment       <NA>   \n",
+       "..                          ...      ...          ...        ...   \n",
+       "789        Tree nut (substance)  allergy         food   39579001   \n",
+       "790                     Aspirin  allergy   medication  247472004   \n",
+       "791   Eggs (edible) (substance)  allergy         food  247472004   \n",
+       "792                     Aspirin  allergy   medication       <NA>   \n",
+       "793       Shellfish (substance)  allergy         food  402387002   \n",
+       "\n",
+       "                       description1 severity1  reaction2  \\\n",
+       "0                   Wheal (finding)      MILD       <NA>   \n",
+       "1                          Sneezing      MILD       <NA>   \n",
+       "2                              None      None       <NA>   \n",
+       "3    Rhinoconjunctivitis (disorder)  MODERATE  271807003   \n",
+       "4                              None      None       <NA>   \n",
+       "..                              ...       ...        ...   \n",
+       "789          Anaphylaxis (disorder)    SEVERE   49727002   \n",
+       "790                 Wheal (finding)  MODERATE       <NA>   \n",
+       "791                 Wheal (finding)  MODERATE       <NA>   \n",
+       "792                            None      None       <NA>   \n",
+       "793  Allergic angioedema (disorder)  MODERATE  422587007   \n",
+       "\n",
+       "                    description2 severity2  \n",
+       "0                           None      None  \n",
+       "1                           None      None  \n",
+       "2                           None      None  \n",
+       "3    Eruption of skin (disorder)      MILD  \n",
+       "4                           None      None  \n",
+       "..                           ...       ...  \n",
+       "789              Cough (finding)  MODERATE  \n",
+       "790                         None      None  \n",
+       "791                         None      None  \n",
+       "792                         None      None  \n",
+       "793             Nausea (finding)  MODERATE  \n",
+       "\n",
+       "[794 rows x 15 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset_allergies.to_df()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "83a72c04",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/notebooks/models'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from intugle.core.settings import settings\n",
+    "\n",
+    "settings.PROJECT_BASE"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb562acd",
+   "metadata": {},
+   "source": [
+    "## 3. Automated Link Prediction\n",
+    "\n",
+    "Now that we've profiled our data, let's discover the relationships between tables. This tool uses an LLM to predict links (foreign keys) between tables.\n",
+    "\n",
+    "First, we'll load a few more tables from the sample dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0aa894eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/.venv/lib/python3.12/site-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
+      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
+      "/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/.venv/lib/python3.12/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      " 44%|████▍     | 11/25 [00:05<00:06,  2.06it/s][!] Error while llm response parsing: Got invalid JSON object. Error: Expecting ',' delimiter: line 2 column 27 (char 28)\n",
+      "For troubleshooting, visit: https://python.langchain.com/docs/troubleshooting/errors/OUTPUT_PARSING_FAILURE \n",
+      "100%|██████████| 25/25 [00:13<00:00,  1.91it/s]\n",
+      "[*] patients:   0%|          | 0/1 [00:00<?, ?it/s]/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/src/intugle/core/pipeline/key_identification/ki.py:87: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  temp.rename(columns={\"datatype_l1\": \"datatype\"}, inplace=True)\n",
+      "[*] patients: 100%|██████████| 1/1 [00:00<00:00,  1.44it/s]\n",
+      "100%|██████████| 25/25 [00:38<00:00,  1.52s/it]\n",
+      "/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/.venv/lib/python3.12/site-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
+      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
+      "/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/.venv/lib/python3.12/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "[!] Empty column encountered for claims ==> referringproviderid ...\n",
+      "100%|██████████| 31/31 [00:17<00:00,  1.78it/s]\n",
+      "[*] claims:   0%|          | 0/1 [00:00<?, ?it/s]/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/src/intugle/core/pipeline/key_identification/ki.py:87: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  temp.rename(columns={\"datatype_l1\": \"datatype\"}, inplace=True)\n",
+      "[*] claims: 100%|██████████| 1/1 [00:00<00:00,  2.29it/s]\n",
+      "100%|██████████| 31/31 [00:47<00:00,  1.53s/it]\n",
+      "/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/.venv/lib/python3.12/site-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
+      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
+      "/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/.venv/lib/python3.12/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      " 44%|████▍     | 4/9 [00:02<00:02,  1.77it/s][!] Error while llm response parsing: Got invalid JSON object. Error: Expecting ',' delimiter: line 2 column 27 (char 28)\n",
+      "For troubleshooting, visit: https://python.langchain.com/docs/troubleshooting/errors/OUTPUT_PARSING_FAILURE \n",
+      " 78%|███████▊  | 7/9 [00:03<00:00,  2.05it/s][!] Error while llm response parsing: Got invalid JSON object. Error: Expecting ',' delimiter: line 2 column 27 (char 28)\n",
+      "For troubleshooting, visit: https://python.langchain.com/docs/troubleshooting/errors/OUTPUT_PARSING_FAILURE \n",
+      "100%|██████████| 9/9 [00:04<00:00,  1.86it/s]\n",
+      "[*] careplans:   0%|          | 0/1 [00:00<?, ?it/s]/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/src/intugle/core/pipeline/key_identification/ki.py:87: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  temp.rename(columns={\"datatype_l1\": \"datatype\"}, inplace=True)\n",
+      "[*] careplans: 100%|██████████| 1/1 [00:00<00:00,  1.58it/s]\n",
+      "100%|██████████| 9/9 [00:19<00:00,  2.20s/it]\n",
+      "/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/.venv/lib/python3.12/site-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
+      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
+      "/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/.venv/lib/python3.12/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "[!] Empty column encountered for claims_transactions ==> modifier1 ...\n",
+      "[!] Empty column encountered for claims_transactions ==> modifier2 ...\n",
+      "[!] Empty column encountered for claims_transactions ==> adjustments ...\n",
+      "[!] Empty column encountered for claims_transactions ==> linenote ...\n",
+      " 30%|███       | 10/33 [00:03<00:08,  2.66it/s][!] Error while llm response parsing: Got invalid JSON object. Error: Expecting ',' delimiter: line 2 column 27 (char 28)\n",
+      "For troubleshooting, visit: https://python.langchain.com/docs/troubleshooting/errors/OUTPUT_PARSING_FAILURE \n",
+      " 39%|███▉      | 13/33 [00:05<00:08,  2.27it/s][!] Error while llm response parsing: Got invalid JSON object. Error: Expecting ',' delimiter: line 2 column 25 (char 26)\n",
+      "For troubleshooting, visit: https://python.langchain.com/docs/troubleshooting/errors/OUTPUT_PARSING_FAILURE \n",
+      "100%|██████████| 33/33 [00:18<00:00,  1.77it/s]\n",
+      "[*] claims_transactions:   0%|          | 0/1 [00:00<?, ?it/s]/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/src/intugle/core/pipeline/key_identification/ki.py:87: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  temp.rename(columns={\"datatype_l1\": \"datatype\"}, inplace=True)\n",
+      "[*] claims_transactions: 100%|██████████| 1/1 [00:00<00:00,  2.09it/s]\n",
+      "100%|██████████| 33/33 [00:49<00:00,  1.51s/it]\n",
+      "/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/.venv/lib/python3.12/site-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
+      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
+      "/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/.venv/lib/python3.12/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "100%|██████████| 13/13 [00:08<00:00,  1.56it/s]\n",
+      "100%|██████████| 13/13 [00:26<00:00,  2.00s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "table_names = [\"patients\", \"claims\", \"careplans\", \"claims_transactions\", \"medications\"]\n",
+    "datasets = [dataset_allergies]\n",
+    "\n",
+    "\n",
+    "def generate_config(table_name: str) -> str:\n",
+    "    \"\"\"Append the base URL to the table name.\"\"\"\n",
+    "    return {\n",
+    "        \"path\": f\"https://raw.githubusercontent.com/Intugle/data-tools/refs/heads/main/sample_data/healthcare/{table_name}.csv\",\n",
+    "        \"type\": \"csv\"\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "for table_name in table_names:\n",
+    "    config = generate_config(table_name)\n",
+    "    dataset = DataSet({**config}, table_name)\n",
+    "    dataset.profile_table()\n",
+    "    dataset.run(domain=\"Healthcare\")\n",
+    "    datasets.append(dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e083a8d8",
+   "metadata": {},
+   "source": [
+    "Now, let's run the link prediction pipeline."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "fe4cd56d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset 'allergies' is missing key identification. Running prerequisite analysis...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/.venv/lib/python3.12/site-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
+      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
+      "/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/.venv/lib/python3.12/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "[!] Empty column encountered for allergies ==> stop ...\n",
+      "  0%|          | 0/15 [00:00<?, ?it/s][!] Error while llm response parsing: Got invalid JSON object. Error: Expecting ',' delimiter: line 2 column 27 (char 28)\n",
+      "For troubleshooting, visit: https://python.langchain.com/docs/troubleshooting/errors/OUTPUT_PARSING_FAILURE \n",
+      "100%|██████████| 15/15 [00:11<00:00,  1.26it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset 'patients' already processed. Skipping analysis.\n",
+      "Dataset 'claims' already processed. Skipping analysis.\n",
+      "Dataset 'careplans' already processed. Skipping analysis.\n",
+      "Dataset 'claims_transactions' already processed. Skipping analysis.\n",
+      "Dataset 'medications' is missing key identification. Running prerequisite analysis...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/.venv/lib/python3.12/site-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
+      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
+      "/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/.venv/lib/python3.12/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "100%|██████████| 13/13 [00:05<00:00,  2.36it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LinkPredictor initialized with datasets: ['allergies', 'patients', 'claims', 'careplans', 'claims_transactions', 'medications']\n",
+      "\n",
+      "--- Comparing 'allergies' <=> 'patients' ---\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] allergies <==> patients Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.14s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 1 potential link(s).\n",
+      "\n",
+      "--- Comparing 'allergies' <=> 'claims' ---\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] allergies <==> claims Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.71s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No links found for this pair.\n",
+      "\n",
+      "--- Comparing 'allergies' <=> 'careplans' ---\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] allergies <==> careplans Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.27s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No links found for this pair.\n",
+      "\n",
+      "--- Comparing 'allergies' <=> 'claims_transactions' ---\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] allergies <==> claims_transactions Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.58s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No links found for this pair.\n",
+      "\n",
+      "--- Comparing 'allergies' <=> 'medications' ---\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] allergies <==> medications Runs:1: 100%|██████████| 1/1 [00:04<00:00,  4.18s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No links found for this pair.\n",
+      "\n",
+      "--- Comparing 'patients' <=> 'claims' ---\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] patients <==> claims Runs:1: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 1 potential link(s).\n",
+      "\n",
+      "--- Comparing 'patients' <=> 'careplans' ---\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] patients <==> careplans Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.05s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 1 potential link(s).\n",
+      "\n",
+      "--- Comparing 'patients' <=> 'claims_transactions' ---\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] patients <==> claims_transactions Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.32s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 1 potential link(s).\n",
+      "\n",
+      "--- Comparing 'patients' <=> 'medications' ---\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] patients <==> medications Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.18s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 1 potential link(s).\n",
+      "\n",
+      "--- Comparing 'claims' <=> 'careplans' ---\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] claims <==> careplans Runs:1: 100%|██████████| 1/1 [00:18<00:00, 18.78s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No links found for this pair.\n",
+      "\n",
+      "--- Comparing 'claims' <=> 'claims_transactions' ---\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] claims <==> claims_transactions Runs:1: 100%|██████████| 1/1 [00:16<00:00, 16.40s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 1 potential link(s).\n",
+      "\n",
+      "--- Comparing 'claims' <=> 'medications' ---\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] claims <==> medications Runs:1: 100%|██████████| 1/1 [00:07<00:00,  7.06s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No links found for this pair.\n",
+      "\n",
+      "--- Comparing 'careplans' <=> 'claims_transactions' ---\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] careplans <==> claims_transactions Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.68s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No links found for this pair.\n",
+      "\n",
+      "--- Comparing 'careplans' <=> 'medications' ---\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] careplans <==> medications Runs:1: 100%|██████████| 1/1 [00:03<00:00,  3.37s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 1 potential link(s).\n",
+      "\n",
+      "--- Comparing 'claims_transactions' <=> 'medications' ---\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] claims_transactions <==> medications Runs:1: 100%|██████████| 1/1 [00:05<00:00,  5.75s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No links found for this pair.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[PredictedLink(from_dataset='allergies', from_column='patient', to_dataset='patients', to_column='id'),\n",
+       " PredictedLink(from_dataset='patients', from_column='id', to_dataset='claims', to_column='patientid'),\n",
+       " PredictedLink(from_dataset='patients', from_column='id', to_dataset='careplans', to_column='patient'),\n",
+       " PredictedLink(from_dataset='patients', from_column='id', to_dataset='claims_transactions', to_column='patientid'),\n",
+       " PredictedLink(from_dataset='patients', from_column='id', to_dataset='medications', to_column='patient'),\n",
+       " PredictedLink(from_dataset='claims', from_column='id', to_dataset='claims_transactions', to_column='claimid'),\n",
+       " PredictedLink(from_dataset='careplans', from_column='encounter', to_dataset='medications', to_column='encounter')]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from intugle import LinkPredictor\n",
+    "\n",
+    "# Initialize the predictor\n",
+    "predictor = LinkPredictor(datasets)\n",
+    "\n",
+    "# Run the prediction\n",
+    "results = predictor.predict(save=True)\n",
+    "results.links"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d18dc421",
+   "metadata": {},
+   "source": [
+    "The `results` object contains the predicted links between the tables. You can also visualize the relationships as a graph.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "a59704ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x2000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "results.show_graph()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32fd4917",
+   "metadata": {},
+   "source": [
+    "## 4. The Semantic Layer\n",
+    "\n",
+    "The profiling and link prediction results are used to generate YAML files which are saved automatically. These files defines the semantic layer, including the models (tables) and their relationships. \n",
+    "\n",
+    "By default, these files are saved in the current working directory. You can configure this path by setting the `PROJECT_BASE` environment variable. They can also be saved manually as shown below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a0daf854",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for ds in datasets:\n",
+    "    ds.save_yaml()\n",
+    "\n",
+    "results.save_yaml(\"relationships.yml\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cf08923",
+   "metadata": {},
+   "source": [
+    "Now, we can load the YAML files and create a manifest."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d086f45",
+   "metadata": {},
+   "source": [
+    "## 5. SQL Generation\n",
+    "\n",
+    "Once you have a semantic layer, you can use the `SqlGenerator` to generate SQL queries. This allows you to query the data using business-friendly terms, without having to write complex SQL.\n",
+    "\n",
+    "Let's create an ETL model to define the query we want to generate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2570af46",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "etl = {\n",
+    "    \"name\": \"patient_names\",\n",
+    "    \"fields\": [\n",
+    "        {\"id\": \"patients.first\", \"name\": \"first_name\"},\n",
+    "        {\"id\": \"patients.last\", \"name\": \"last_name\"},\n",
+    "        {\"id\": \"allergies.start\", \"name\": \"start_date\"},\n",
+    "    ],\n",
+    "    \"filter\": {\n",
+    "        \"selections\": [{\"id\": \"claims.departmentid\", \"values\": [\"3\", \"20\"]}],\n",
+    "    },\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e4ff6be",
+   "metadata": {},
+   "source": [
+    "Now, let's use the `SqlGenerator` to generate the SQL query."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e0f9a090",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n",
+      "/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/.venv/lib/python3.12/site-packages/pydantic/_internal/_fields.py:198: UserWarning: Field name \"schema\" in \"Source\" shadows an attribute in parent \"BaseResource\"\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "from intugle.sql_generator import SqlGenerator\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f0432e77",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "careplans loaded\n",
+      "claims loaded\n",
+      "claims_transactions loaded\n",
+      "patients loaded\n",
+      "medications loaded\n",
+      "allergies loaded\n",
+      "patient_names\n",
+      "patient_names loaded\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'path': \"SELECT patients.first as first_name, patients.last as last_name, allergies.start as start_date FROM claims LEFT JOIN patients ON patients.id = claims.patientid LEFT JOIN allergies ON allergies.patient = patients.id WHERE (claims.departmentid IN ('3', '20'))\",\n",
+       " 'type': 'query'}"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Create a SqlGenerator\n",
+    "sql_generator = SqlGenerator()\n",
+    "\n",
+    "# Generate the query\n",
+    "test_etl = sql_generator.generate_product(etl)\n",
+    "\n",
+    "# Print the query\n",
+    "test_etl.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "95d2e49d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>first_name</th>\n",
+       "      <th>last_name</th>\n",
+       "      <th>start_date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Miki234</td>\n",
+       "      <td>Kozey370</td>\n",
+       "      <td>1981-05-17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Maddie576</td>\n",
+       "      <td>Gutmann970</td>\n",
+       "      <td>1960-01-05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Esther279</td>\n",
+       "      <td>Runolfsson901</td>\n",
+       "      <td>1960-05-21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Bonita405</td>\n",
+       "      <td>Bernier607</td>\n",
+       "      <td>2011-03-26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Magdalene960</td>\n",
+       "      <td>Orn563</td>\n",
+       "      <td>1995-07-11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1543</th>\n",
+       "      <td>Zelda766</td>\n",
+       "      <td>Ernser583</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1544</th>\n",
+       "      <td>Zelda766</td>\n",
+       "      <td>Ernser583</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1545</th>\n",
+       "      <td>Zelda766</td>\n",
+       "      <td>Ernser583</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1546</th>\n",
+       "      <td>Zelda766</td>\n",
+       "      <td>Ernser583</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1547</th>\n",
+       "      <td>Zelda766</td>\n",
+       "      <td>Ernser583</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1548 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        first_name      last_name start_date\n",
+       "0          Miki234       Kozey370 1981-05-17\n",
+       "1        Maddie576     Gutmann970 1960-01-05\n",
+       "2        Esther279  Runolfsson901 1960-05-21\n",
+       "3        Bonita405     Bernier607 2011-03-26\n",
+       "4     Magdalene960         Orn563 1995-07-11\n",
+       "...            ...            ...        ...\n",
+       "1543      Zelda766      Ernser583        NaT\n",
+       "1544      Zelda766      Ernser583        NaT\n",
+       "1545      Zelda766      Ernser583        NaT\n",
+       "1546      Zelda766      Ernser583        NaT\n",
+       "1547      Zelda766      Ernser583        NaT\n",
+       "\n",
+       "[1548 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_etl.to_df()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "fa02b661",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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owIABGjBgwB3Vg3vj5OSkBx54QF27dtXSpUv1ySefKCEhQR06dLB3afXi5+enRx55RMeOHVNKSoq1jf3YsWNpYw8AAAAAANBEEcgDAAC0UqmpqZo6daoqKipuO/bq1au1nq/PveMl6cr5s/++xmzWgfTltx5/4Wyt5x2dXRQyIPym8wUXz9c4bh/W56YxQd1733Egf+VCzTb3+ecya+z0P3DD+KysLJnNZjk6Our555/X559/rqKiIp07d04vvviidZy/v79iY2P1i1/8Qv3797+jmnDv+vbtq+DgYCUmJuqzzz7TpEmTNGzYsGbTAr5nz57q1q2bNm7cqA0bNljb2Pft27fZPAcAAAAAAIDWwsHeBQAAAMA+nn/++RphfNeuXTV58mTNnDlTY8fW3J1e3bL9RvXcIH/HKspKaz3v5R9Yr8DRXqGkYRgqLb1We1hYmHbv3q05c+aoT58+cnR0tI7Ly8vTF198oYiICJ05c8YutbZ2vr6++v73v6+hQ4cqJSVF33zzjfW9aw6cnJw0btw4zZ492/rhgnnz5ik3N9fepQEAAAAAAOA67JAHAABohfLz83X06FHr8eTJk7V06VJrkP3Pf/5T69atu+08DvXMvX2COynn1DFJkrObh36TcVTOrnd+726TqfbPk/q071jjODfz25vGXDxx5I7Xu3He8KmPKOHVd63H3+3prQ6eznVeHxYWpnffvTa+srJSp06dUnp6uubMmSOz2awrV67oyy+/1C9+8Ys654DtODk5KTY2Vl26dNGSJUv08ccfKyEhQZ06dbJ3afXm6+urhx9+WN9++61SUlL00Ucfafjw4Ro3bpxcXV3tXR4AAAAAAECrxw55AACAVqiysrLGsYeHhzWMz8vL0x/+8Id6zVPfXyZ7jp7w77XLSpT01iuqqqzZKr+8uEh7Uhbq6189V89Z/61tYHsFdulhPT61a7OOb11rPd6+aN4dt6uXpDYBQerQ+9/3ed+bukjHt/77gwqO/3rNjh8/rv/+7//WRx99ZH1s4cKFWrZsmcrLyyVJzs7O6tmzpx599FG5u7tbx50+ffqO60LD6t27t374wx+qTZs2mjt3rjZt2lRnV4imqkePHnruuec0btw4bd++Xe+//77279/f7J4HAAAAAABAS8MOeQAAgFYoKChInTt3trZL/+abb3Ts2DEFBQVp69atKigoqNc8jvXcIj902uPa+MXHunz+2npbvvmrDqQtU/ue/eTk7KKCi1nKOXlM5qpK+QSH3NVzGvP4c1r02n9Yj+fOeVid+4erqqpSWQd339WckjTxuV/o7z99XIZhqKq8TJ89N1Ptw/rKt0OIUpyqdPLbYzp79to971955RXrdatXr9b7778vT09P9evXT0FBQaqqqtKOHTtUVFRkHRcWFnbXtaHh+Pj46Hvf+55Wr16tVatWKTMzU9OmTZOHh4e9S6s3JycnjR07VgMHDtTKlSu1cOFC7dq1S3FxcWrXrp29ywMAAAAAAGiV2CEPAADQSt24C37Pnj1KTU1VSUmJfv3rX9drDg8nU73a1ru4e+h7734lv05drOeK8nN1fMsaHVm/UheOHZS56tqufYfr7rV+J4ZOf1z9o6dYjy1VVcrcs1XnDuySb8dQhY2ItD7m6FR3m/kb9Y6YpCn/9Yca12QfP6TD61K1dnW6NYyXVOM+8dWKi4u1bds2LVu2TMnJyTXu8d2rVy89/fTT9a4FtuXo6KiJEyfqkUce0blz5/Txxx9bP7TSnPj4+Oihhx7S448/rsLCQn300UdKTU21dmsAAAAAAABA4yGQBwAAaKUeeeQRLV68WMOGDZOrq6u8vb0VExOj9evXa/z48fWaw8FkUju3+gXo7br11PNfr9GUn/+vug0ZI08ffzk4OcnZzUP+IV3Vf8JkTXvp/zT776l39XxMJpMe+f2niv+P3ymwS5gcnV3UNrC9hs/6vmb/PVVV5WXWsV4Bd7ZbeORDP9Dz36zTqEeeVvuwvnL19JKDo6N8fX0VHh6uH/3oR0pOTtZLL71kvWb27Nn6n//5H8XGxqp79+5q27atHP91zfDhw/Xaa69p27Ztatu27V09X9hOz5499eyzz8rHx0eff/651q9f3yxbv3fv3l3PPvusxo8fr507d+rPf/6z9u3b1yyfCwAAAAAAQHNlMvjXGAAAANyDlWeLtOdSmSz2LkTS5fNn5dvh5pb3J3ds1GfPzZTFbJYk3Tf5IT30u/fveh0HSYMD3DQpxOuu50DTZ7FYlJGRoQ0bNqh79+6aPn26PD097V3WXSkoKNDKlSt16NAhde7cWfHx8QoKCrJ3WQAAAAAAAC0egTwAAADuyd68MiWfKbr9wEbw5oyRkqSQAeFqG9Be5qpK5Zw6pmObVsuwXPvIgKOzi+Z8tVpB3Xrd01rxnb000N/tnmtG03fixAktXLhQDg4Omjlzprp06WLvku7ayZMnlZycrLy8PA0dOlRRUVFyc+P7GAAAAAAAwFYI5AEAAHBPLpZUae7RK/YuQ9K1QD4383idj7t6tdFD//2B+o6Lvee1ftDbR+3cne55HjQPhYWFWrhwoU6fPq1x48YpIiJCDg7N8w5gZrNZW7Zs0dq1a+Xi4qLo6GgNGjRIJpPJ3qUBAAAAAAC0OATyAAAAuCdmw9Cbe/NkbgK/Ve5OStThtSk6f3S/ivJzVVlWKjevtgrsEqaw4ZEaNuM7ahvY/p7XcTRJLw7ylwMBZqtisVi0bt06rV27Vl27dtWMGTPk5dV8b1tw9epVrVq1SgcOHFBISIji4+PVvv29/3wAAAAAAADg3wjkAQAAcM+Wny7UwfxytYZfLB0k9fNz1QOhbexdCuzk1KlTWrhwoQzD0IwZM9StWzd7l3RPMjMzlZSUpEuXLmnIkCGKioqSu7u7vcsCAAAAAABoEQjkAQAAcM8uFFfqb8cK7F1Go3mil7eCPZztXQbsqKioSIsWLdLJkycVERGhyMjIZtvCXrrWxn7btm1as2aNnJycFB0drcGDB9PGHgAAAAAA4B4RyAMAAKBB/PXIZeWWmlv0LnmTpHbujvp+b197l4ImwDAMrV+/XmvWrFHnzp01Y8YMtW3b1t5l3ZPCwkKlpaVp37596tixo+Lj49WhQwd7lwUAAAAAANBsEcgDAACgQezLK1PSmSJ7l2FzD3T20gB/N3uXgSbk9OnTWrBggcxms6ZPn66wsDB7l3TPTp8+raSkJOXk5Cg8PFwTJkygjT0AAAAAAMBdIJAHAABAg6i0GHpvf74qLC3310tXB5N+PMBPzg608UZNJSUlWrRokY4fP67Ro0crKipKjo6O9i7rnlgsFm3fvl0ZGRlycHDQhAkTdP/999PGHgAAAAAA4A4QyAMAAKDBrDtfrM0XS1ts2/pRQe4a28HT3mWgiTIMQ5s2bVJ6ero6deqkmTNnytvb295l3bOioiKlpaVp79696tChg+Lj49WxY0d7lwUAAAAAANAsEMgDAACgwVRaDH12+LIKKiwtK5Q3LGrjZNIP+wfIid3xuI2zZ89qwYIFKi8v17Rp09SrVy97l9Qgzpw5o+TkZGVnZ+v+++/XhAkT5OHhYe+yAAAAAAAAmjQCeQAAADSorOJKzTtWYO8yGpZhqHLDQo3qF6Zx48bJycnJ3hWhiSstLdWSJUt09OhRjRgxQtHR0c2+hb10rY39jh07lJGRIUnWNvYODg52rgwAAAAAAKBpIpAHAABAg1udVaztOS2ndf3QQFc5n9ytdevWycfHR1OnTlXnzp3tXRaaOMMwtHXrVq1atUrBwcGaOXOmfH197V1WgyguLlZaWpr27Nmj4OBgxcfHq1OnTvYuCwAAAAAAoMkhkAcAAECDaymt602SfFwd9GRvXzk5mJSbm6ulS5fq3LlzGjp0qKKjo+Xi4mLvMtHEZWVlKTExUaWlpXrwwQfVp08fe5fUYM6dO6ekpCRduHBBgwcPVnR0tDw9Pe1dFgAAAAAAQJNBIA8AAACbyCqu1D+OFTT7QP7xnt7q6OlsPWexWLRt2zatXr1aHh4emjJlirp3726/ItEslJWVaenSpTp8+LCGDh2qSZMmtZhbH1gsFu3atUvp6emSpKioKA0ZMoQ29gAAAAAAACKQBwAAgA0duVyuxZmF9i7jrk3r2ka9fVxrfezy5ctatmyZTp06pcGDB2vSpElyd3dv5ArRnBiGoe3bt2vlypVq166dEhIS5OfnZ++yGkxJSYnS09O1a9cutW/fXnFxcdzaAQAAAAAAtHoE8gAAALCpvXllSj5TZO8y7lhcZy8N8ne75RjDMLR7926tXLlSTk5Oio+PV9++fRupQjRXFy5cUGJiooqKijRlyhT179/f3iU1qKysLCUlJen8+fMaNGiQoqOj5eXlZe+yAAAAAAAA7IJAHgAAADbX3EL5+oTx1yssLNSKFSt09OhR9enTR/Hx8QSQuKXy8nItX75cBw4cUHh4uGJiYuTs7Hz7C5sJwzCsbewtFosiIyM1bNgw2tgDAAAAAIBWh0AeAAAAjeLI5XIt+Vf7+qb4C6jpX/998BZt6m/FMAwdOnRISUlJslgsiomJ0aBBg2QymW5/MVql6tA6JSVF/v7+SkhIUEBAgL3LalAlJSVavXq1du7cqXbt2ik+Pl6hoaH2LgsAAAAAAKDREMgDAACg0WQVV2pZZqEKKixNLpT3cXHQlC5t1NHz3nYpl5SUKDU1Vfv27VP37t01efJk+fj4NEyRaJEuXryo+fPn6+rVq5o8ebIGDhxo75Ia3Pnz55WUlKSsrCwNGDBAEydOVJs2bexdFgAAAAAAgM0RyAMAAKBRVVoMrb9Qom05pTLJvrvlq9cf3s5dY4I95OzQcLvZv/32Wy1fvlylpaWKjo7W0KFD2S2POlVUVGjFihXat2+fBg8erPj4+BbVwl661hFgz549SktLU1VVlbWNvaOjo71LAwAAAAAAsBkCeQAAANhFU9gt31C74utSXl6utLQ07dixQyEhIZo6dWqLa0mOhrVnzx6tWLFCvr6+SkhIULt27exdUoMrLS1VRkaGduzYoYCAAMXHx6tLly72LgsAAAAAAMAmCOQBAABgN5UWQ5uzS7Qzt0zlFsPmO+ar53d1MCk80E0j2zfsrvi6ZGZmatmyZSooKFBkZKRGjhzJrmDUKTc3V/Pnz9fly5cVHx+vwYMHt8juCtnZ2UpKStLZs2fVv39/TZw4UW3btrV3WQAAAAAAAA2KQB4AAAB2V2kxdPhyuXbmlupiqbnBg3kHSRZJQe6OGhLort6+ro0SxF+vsrJSa9as0ebNmxUUFKSpU6cqODi4UWtA81FZWank5GTt3r1bAwcO1AMPPCAXFxd7l9XgDMPQ3r17lZaWpsrKSo0dO1YjRozgAysAAAAAAKDFIJAHAABAk3KhuFK7LpXp0OVymf/1m2p1oF5f1493NEl9fV11f6Cbgj3sf0/u8+fPa8mSJcrNzdXo0aM1btw4OTk52bssNFH79u3T8uXL1bZtW82aNUtBQUH2LskmysrKlJGRoe3bt8vf319xcXHq1q2bvcsCAAAAAAC4ZwTyAAAAaJIshqFLZWZll1Qpu6RK54srlVtmtob0tXE0SYFujurg6az2Hk5q7+GkADdHOTSxdt9ms1kbNmzQunXr5Ofnp6lTpyokJMTeZaGJunTpkhITE3Xp0iXFxsYqPDy8Rbawl6SLFy8qKSlJZ86cUd++fTVp0iR5e3vbuywAAAAAAIC7RiAPAACAZsNiGLpSblGlxZDZMFRlSE4mydFkkrODST6uDk0ufL+VnJwcLV26VFlZWRo2bJgmTJjQItuS495VVlYqNTVVO3fuVL9+/TRlyhS5urrauyybMAxD+/fv16pVq1ReXq6xY8dq5MiRtLEHAAAAAADNEoE8AAAAYEcWi0Vbt27V6tWr5enpqSlTpqh79+72LgtN1MGDB7V06VJ5eXkpISFBwcHB9i7JZsrLy7VmzRpt3bpVfn5+iouL42cDAAAAAAA0OwTyAAAAQBNw+fJlLVu2TKdOndLgwYM1adIkubu727ssNEH5+flKTExUTk6OJk2apKFDh7bYFvbStU4SSUlJOn36tPr06aOYmBja2AMAAAAAgGaDQB4AAABoIgzD0O7du7Vy5Uo5OzsrPj5effr0sXdZaIKqqqq0atUqbdu2TX369NHUqVPl5uZm77JsxjAMHTx4UCtXrlRpaakiIiI0atQoOTk52bs0AAAAAACAWyKQBwAAAJqYq1evKikpSUePHlXfvn0VFxcnLy8ve5eFJujw4cNasmSJ3N3dlZCQoI4dO9q7JJsqLy/X2rVrtXXrVvn4+Cg2NlY9evSwd1kAAAAAAAB1IpAHAAAAmqDqHcHJycmyWCyKjY3VwIEDW3Rrctydy5cva8GCBbpw4YKio6M1YsSIFv99kpubq+TkZJ06dUq9evVSbGysfHx87F0WAAAAAADATQjkAQAAgCaspKREKSkp2r9/v8LCwjR58mTun42bmM1mpaena/PmzerZs6emTZsmd3d3e5dlU4Zh6NChQ0pNTVVpaanGjBmj0aNH08YeAAAAAAA0KQTyAAAAQDNw7NgxrVixQmVlZZowYYKGDh3a4ndB484dPXpUS5YskbOzsxISEhQSEmLvkmyuoqJC69at0+bNm+Xt7a3Y2Fj17NnT3mUBAAAAAABIIpAHAAAAmo3y8nKtWrVKO3fuVOfOnTVlyhQFBATYuyw0MQUFBVqwYIHOnTunCRMmaNSoUa3iwxuXLl1SSkqKTpw4oZ49eyo2Nla+vr72LgsAAAAAALRyBPIAAABAM5OZmamlS5fq6tWrioyM1KhRo+Tg4GDvstCEmM1mZWRkaOPGjQoLC9O0adPk6elp77JszjAMHTlyRCkpKSouLtbo0aM1ZswYOTs727s0AAAAAADQShHIAwAAAM1QZWWlMjIytGXLFrVv315Tp05V+/bt7V0Wmpjjx49r0aJFcnR01MyZMxUaGmrvkhpFRUWFNmzYoE2bNqlNmzaKiYlRr169WkWnAAAAAAAA0LQQyAMAAADNWFZWlpYuXapLly5p9OjRGjt2rJycnOxdFpqQq1evauHChTpz5owiIyM1ZsyYVtNRIS8vTykpKTp+/LjCwsIUGxsrf39/e5cFAAAAAABaEQJ5AAAAoJkzm83asGGD1q1bJz8/P02dOlUhISH2LgtNiMVi0dq1a7Xu/7P331Fx5omZ932Rs0gSAuWcA5KQUAKJTBFKAWTPTE/P2OtZx12f3bW9+4x37Hf8Oj0eP7v77vGz9ni83h13T+i1UCpCkQVCKKGAEsoJJZCIEhmq7vePmcatng4KwA3F93NOn+MCBF9Rwsfmqvt3HzumBQsWaM+ePQoMDDQ7a0wYhqEbN26opKREL1++1JYtWxQXFydvb2+z0wAAAAAAwCTAIA8AAAC4iGfPnslms+nx48fatGmTkpKSGB3xirt37+rgwYNyc3PT3r17NX/+fLOTxszg4KCOHz+u2tpaBQYGKi0tTcuWLeMYewAAAAAAMKoY5AEAAAAX4nQ6dfr0aVVWViowMFBZWVlauHCh2VkYR7q6unTw4EHdu3dP8fHx2rFjx6Q5wl6S2traVFJSops3b2rhwoVKT0/X1KlTzc4CAAAAAAAuikEeAAAAcEFtbW3Kz8/X/fv3FR0drdTUVPn5+ZmdhXHC6XSqpqZG1dXVmjt3rvbu3augoCCzs8bUzZs3VVxcrM7OTm3ZskXx8fGcKAEAAAAAAEYcgzwAAADgogzD0Pnz51VWViYvLy9lZGRo+fLlZmdhHLl//74OHjwoh8OhPXv2aNGiRWYnjamhoSHV1tbq+PHj8vf3V2pqqlasWMEx9gAAAAAAYMQwyAMAAAAu7sWLFyosLNTNmze1YsUKWSwWBQYGmp2FcaK7u1uHDh3SnTt3tH37diUkJEyqI+wlqb29XSUlJbpx44bmz58vi8WiadOmmZ0FAAAAAABcAIM8AAAAMAkYhqGrV6/KbrfLMAylpaVpzZo1XAkMST/791FbW6vKykrNmjVLOTk5Cg4ONjtrzN26dUvFxcXq6OhQbGysduzYIR8fH7OzAAAAAADABMYgDwAAAEwiPT09Ki4u1uXLl7Vo0SJlZWVNyuEVn62xsVEHDhzQ4OCgdu/erSVLlpidNOaGhoZ04sQJ1dTUyM/PTykpKVq1ahUvXgEAAAAAAG+FQR4AAACYhG7evKmCggL19/crOTlZMTExDI6Q9LMXbRw5ckQ3b97Uli1blJSUJA8PD7OzxlxHR4dKS0t17do1zZs3TxaLRREREWZnAQAAAACACYZBHgAAAJik+vr6VF5ernPnzmnOnDmyWq0KDw83OwvjgGEYOnXqlMrLyxUVFaXc3FyFhISYnWWKO3fuyG63q62tTbGxsdq5cyfH2AMAAAAAgNfGIA8AAABMcvfu3VN+fr5evnypnTt3asuWLXJ3dzc7C+PAo0ePlJeXp/7+fu3atUvLli0zO8kUQ0NDOnXqlI4dOyYfHx+lpKRo9erVnCoBAAAAAAC+FIM8AAAAAA0ODuro0aM6deqUIiMjZbVaFRkZaXYWxoHe3l7ZbDZdv35dsbGxSk5Olqenp9lZpujs7FRpaakaGho0Z84cZWRkaPr06WZnAQAAAACAcYxBHgAAAMCwx48fy2azqaWlRdu2bVN8fPykHV/xLwzD0JkzZ1RWVqaIiAjl5uYqLCzM7CzT3L17V3a7Xa2trdq4caMSEhLk6+trdhYAAAAAABiHGOQBAAAAvMLhcKimpkY1NTUKCwuT1WrV7Nmzzc7COPDkyRPl5eWpp6dH2dnZWrlypdlJpnE4HDp16pSqq6vl7e2t5ORkrV27lmPsAQAAAADAKxjkAQAAAHymZ8+eyWaz6fHjx4qNjVViYqK8vb3NzoLJ+vr6VFBQoKtXryomJkZpaWmT+hSFFy9eqKysTFeuXNHs2bOVkZHB7R4AAAAAAMAwBnkAAAAAn8vpdOrUqVM6evSoAgMDlZ2drQULFpidBZMZhqFz586puLhYU6dO1b59+xQeHm52lqnu3bsnu92ulpYWxcTEKCEhQX5+fmZnAQAAAAAAkzHIAwAAAPhSbW1tys/P1/379xUdHa20tDTumQ01NTUpLy9PL1++VFZWllavXm12kqkcDofOnDmjqqoqeXp6Kjk5WdHR0RxjDwAAAADAJMYgDwAAAOC1GIah8+fPq6ysTF5eXsrMzNSyZcvMzoLJ+vv7VVhYqMuXL2vdunWyWCzy8vIyO8tUL1++VFlZmS5fvqxZs2bJYrFoxowZZmcBAAAAAAATMMgDAAAAeCMvXrxQYWGhbt68qZUrVyo9PV2BgYFmZ8FEhmHowoULstvtCgsLU25urqZNm2Z2lukePHigoqIiPXv2TBs2bFBSUhLH2AMAAAAAMMkwyAMAAAB4Y4Zh6MqVKyouLpZhGEpPT9fq1as5mnuSe/bsmfbv36/Ozk5lZGQoOjra7CTTOZ3O4WPs3d3dlZSUpPXr1/OzAgAAAADAJMEgDwAAAOCtdXd3q7i4WFeuXNGiRYuUlZWl4OBgs7NgooGBAdntdtXX12vt2rXKyMiQt7e32Vmm6+rqUnl5uS5evKgZM2YoIyNDM2fONDsLAAAAAACMMgZ5AAAAAO/sxo0bKiwsVH9/v5KTkxUTE8MVwJPcxYsXVVhYqODgYOXm5mr69OlmJ40LjY2NKioqUnNzs9avX6+kpCT5+/ubnQUAAAAAAEYJgzwAAACAEdHX16eysjKdP39ec+fOVXZ2tsLDw83OgolaWlq0f/9+tbW1yWKxaN26dbxQQz87xv7s2bOqrKyUu7u7EhMTtX79erm7u5udBgAAAAAARhiDPAAAAIARde/ePeXn5+vly5fauXOntmzZwtA4iQ0ODqq4uFjnz5/X6tWrlZmZKR8fH7OzxoXu7m6Vl5ervr5eUVFRysjI0KxZs8zOAgAAAAAAI4hBHgAAAMCIGxwcVGVlpU6fPq3IyEjt2rWLI8snuStXrig/P1+BgYHat2+fIiMjzU4aNx49eqSioiI9ffpU0dHRSk5OVkBAgNlZAAAAAABgBDDIAwAAABg1jx49ks1mU2trq7Zv3664uDh5enqanQWTtLa2Ki8vT8+fP1daWppiYmI4wv7nnE6nzp07p8rKSklSQkKCYmJiOF0CAAAAAIAJjkEeAAAAwKhyOByqqalRTU2NwsPDZbVaOZZ7EhsaGlJpaanq6uq0YsUKZWdny9fX1+yscaOnp0cVFRU6f/68IiMjlZGRodmzZ5udBQAAAAAA3hKDPAAAAIAx0dzcLJvNpidPnig2NlaJiYny9vY2OwsmaWhokM1mk7+/v3JzczVjxgyzk8aVx48fq6ioSE+ePNHatWuVnJyswMBAs7MAAAAAAMAbYpAHAAAAMGacTqdOnTqlo0ePKjAwUNnZ2VqwYIHZWTBJe3u78vLy1NTUpJSUFMXGxnKE/Sc4nU5duHBBFRUVcjqdSkhI0MaNGznGHgAAAACACYRBHgAAAMCYa2trk81m04MHD7Ru3TqlpqZybPkk5XA4VFZWptOnT2vZsmWyWq3y8/MzO2tc6enpUWVlpc6dO6fp06fLYrFo7ty5ZmcBAAAAAIDXwCAPAAAAwBSGYejcuXMqKyuTt7e3MjMztWzZMrOzYJLr16/ryJEj8vHxUW5urmbNmmV20rjz5MkTFRUV6fHjx1qzZo2Sk5MVFBRkdhYAAAAAAPgCDPIAAAAATPXixQsVFBTo1q1bWrlypSwWiwICAszOggk6Ojp04MABPXnyRElJSdqyZQtH2H+KYRiqr69XeXm5hoaGtHPnTm3atEkeHh5mpwEAAAAAgM/AIA8AAADAdIZh6MqVK7Lb7ZKk9PR0rV69mjF2EnI4HKqsrNSJEye0ePFi7d69W/7+/mZnjTu9vb06evSozp49q6lTpyojI0Pz5s0zOwsAAAAAAHwKgzwAAACAcaO7u1vFxcW6cuWKFi9erMzMTAUHB5udBRPcunVLhw4dkpeXl3JycjRnzhyzk8alp0+fym636+HDh1q1apVSUlI0ZcoUs7MAAAAAAMDPMcgDAAAAGHdu3LihwsJC9ff3KyUlRRs2bOBq+UnoxYsXOnDggB4+fKiEhARt376dfwefwTAMXbx4UeXl5RocHNSOHTsUGxvLMfYAAAAAAIwDDPIAAAAAxqW+vj6VlZXp/Pnzmjt3rrKzsxUeHm52FsaY0+lUVVWVampqtHDhQu3Zs0cBAQFmZ41LfX19Onr0qOrq6hQeHi6LxaIFCxaYnQUAAAAAwKTGIA8AAABgXLt3757y8/P18uVLJSQkaPPmzXJ3dzc7C2Pszp07OnjwoNzd3ZWTk8P90r9Ac3OzioqK1NjYqBUrVigtLY1j7AEAAAAAMAmDPAAAAIBxb2BgQEePHtXp06cVFRUlq9Wq6dOnm52FMfby5UsdPHhQDx48UHx8vOLj43lxxucwDEOXL19WWVmZ+vv7FR8fry1btnCMPQAAAAAAY4xBHgAAAMCE8ejRI9lsNrW2tmr79u2Ki4uTp6en2VkYQ06nU8eOHVN1dbXmz5+vvXv3KjAw0Oyscau/v19VVVU6ffq0wsLCZLFYtHDhQrOzAAAAAACYNBjkAQAAAEwoQ0NDqqmp0fHjxxUeHi6r1apZs2aZnYUxdu/ePR08eFCGYWjv3r3cK/1LPHv2TEVFRXrw4IGWL1+utLQ0BQcHm50FAAAAAIDLY5AHAAAAMCE1NzfLZrPpyZMn2rx5sxISEuTt7W12FsZQV1eXDh06pLt37youLk47d+7kCPsvYBiGrly5otLSUvX19SkuLk5bt27llAkAAAAAAEYRgzwAAACACcvpdOrUqVM6evSogoKClJ2drfnz55udhTFkGIaOHz+uo0ePavbs2crJydGUKVPMzhrX+vv7VV1drdOnTyskJETp6elavHix2VkAAAAAALgkBnkAAAAAE15ra6vy8/P14MEDrVu3TqmpqfL19TU7C2PowYMHOnDggBwOh3bv3s3A/BqeP38uu92ue/fuaenSpUpPT1dISIjZWQAAAAAAuBQGeQAAAAAuwTAMnTt3TmVlZfLx8VFmZqaWLl1qdhbGUE9Pjw4dOqTbt29r69atSkxMlIeHh9lZ45phGGpoaFBJSYl6e3u1fft2bdu2jWPsAQAAAAAYIQzyAAAAAFxKZ2enCgsLdevWLa1atUrp6ekKCAgwOwtjxDAMnThxQpWVlZoxY4Zyc3MVHBxsdta4NzAwoGPHjunkyZMKDg5Wenq6lixZYnYWAAAAAAATHoM8AAAAAJdjGIYuX76s4uJiSZLFYtGqVavk5uZmchnGysOHD3XgwAH19/dr9+7dnJbwmlpaWmS323X37l0tWbJE6enpCg0NNTsLAAAAAIAJi0EeAAAAgMvq7u6W3W7X1atXtXjxYmVlZWnKlClmZ2GM9Pb26siRI7px44Y2b96s5ORkjrB/DYZh6Nq1ayopKVF3d7e2bdum7du3y8vLy+w0AAAAAAAmHAZ5AAAAAC7v+vXrKiws1MDAgFJSUrRhwwaulp8kDMPQ6dOnVVZWpsjISOXm5nLF92saGBhQTU2NTp48qaCgIKWlpWnp0qX87AAAAAAA8AYY5AEAAABMCn19fSotLdWFCxc0b948ZWdnKywszOwsjJHHjx8rLy9Pvb292rVrl5YvX2520oTR2tqq4uJi3b59W4sWLZLFYuFnBwAAAACA18QgDwAAAGBSuXv3rvLz89XV1aWEhARt3rxZ7u7uZmdhDPT19clms+natWvauHGjUlNT5enpaXbWhGAYhm7cuKHi4mJ1dXVp69atiouL4xh7AAAAAAC+BIM8AAAAgElnYGBAlZWVOn36tGbMmCGr1arp06ebnYUxYBiGzp49q5KSEk2bNk379u3jau83MDg4qOPHj6u2tlaBgYFKS0vTsmXLOMYeAAAAAIDPwSAPAAAAYNJ69OiRbDabWltbFRcXp7i4OHl4eJidhTHw9OlT5eXlqaurS9nZ2Vq1apXZSRNKW1ubiouLdevWLS1cuFAWi0Xh4eFmZwEAAAAAMO4wyAMAAACY1IaGhlRTU6Pjx48rPDxcu3bt0syZM83Owhjo7+9XQUGBrly5ovXr1ys9PZ0j2N/Qx8fYv3jxQlu2bFF8fLy8vb3NzgIAAAAAYNxgkAcAAAAASc3NzTpy5IiampoUGxurxMRExtlJwDAMnT9/XsXFxQoLC9O+ffs0depUs7MmlMHBQdXW1qq2tlb+/v5KTU3VihUrOMYeAAAAAAAxyAMAAADAMKfTqZMnT6qqqkpBQUHKzs7W/Pnzzc7CGGhublZeXp46OzuVmZmptWvXmp004bS3t6ukpEQ3btzQ/PnzZbFYNG3aNLOzAAAAAAAwFYM8AAAAAHxKa2ur8vPz9eDBA61fv14pKSny9fU1OwujbGBgQEVFRbp48aKio6NlsVg4fv0t3Lp1S3a7XZ2dndq8ebPi4+Pl4+NjdhYAAAAAAKZgkAcAAACAz2AYhs6ePavy8nL5+PgoMzNTS5cuNTsLY6C+vl5FRUUKCQlRbm6uIiIizE6acIaGhnTixAnV1NTIz89PqampWrlyJcfYAwAAAAAmHQZ5AAAAAPgCnZ2dKigo0O3bt7Vq1Sqlp6crICDA7CyMsufPn2v//v1qb29XRkaGoqOjGZPfQkdHh0pLS3Xt2jXNmzdPFouFFzgAAAAAACYVBnkAAAAA+BKGYejy5csqLi6Wm5ub0tPTtWrVKgZaFzc4OCi73a4LFy5ozZo1yszM5Aj7t3T79m3Z7Xa1t7crNjZWO3fu5Bh7AAAAAMCkwCAPAAAAAK+pu7tbdrtdV69e1ZIlS5SZmakpU6aYnYVRdunSJRUUFGjKlCnKzc1VZGSk2UkT0tDQkE6dOqVjx47Jx8dHKSkpWr16NS9sAQAAAAC4NAZ5AAAAAHhD169fV2FhoQYHB5WSkqL169czKrq41tZW7d+/Xy0tLUpPT9eGDRt4zt9SZ2enSktL1dDQoLlz58pisWj69OlmZwEAAAAAMCoY5AEAAADgLfT29qqsrEwXLlzQvHnzlJ2drbCwMLOzMIqGhoZUXFysc+fOaeXKlcrOzubY9Xdw9+5d2e12tba2atOmTdq5c6d8fX3NzgIAAAAAYEQxyAMAAADAO7h7967y8/PV1dWlxMRExcbGyt3d3ewsjKKrV6/KZrMpICBA+/btU1RUlNlJE5bD4dCpU6dUXV0tb29vJScna+3atZw+AAAAAABwGQzyAAAAAPCOBgYGVFlZqdOnT2vmzJmyWq2KiIgwOwujqK2tTXl5eXr27JlSU1O1ceNGRuR38OLFC5WVlenKlSuaPXu2MjIyFBkZaXYWAAAAAADvjEEeAAAAAEbIw4cPZbPZ1NbWpri4OMXFxcnDw8PsLIySoaEhlZWV6cyZM1q+fLmsVitHrr+je/fuyW63q6WlRTExMUpMTOR7CgAAAACY0BjkAQAAAGAEDQ0N6dixY6qtrdXUqVNltVo1c+ZMs7Mwiq5duyabzSZfX1/l5ubyfL8jh8OhM2fOqKqqSp6enkpOTlZ0dDQnEAAAAAAAJiQGeQAAAAAYBU1NTbLZbGpqatLmzZuVkJAgLy8vs7MwSjo6OpSXl6enT58qOTlZmzdvZkB+Ry9fvlRZWZkuX76sWbNmKSMjQ1FRUWZnAQAAAADwRhjkAQAAAGCUOJ1OnTx5UlVVVQoKCpLVatW8efPMzsIocTgcqqio0MmTJ7VkyRLt3r1bfn5+ZmdNeA8ePFBRUZGePXumDRs2KCkpie8rAAAAAGDCYJAHAAAAgFHW2toqm82mxsZGrV+/XikpKdwX24XdvHlThw8flpeXl3JzczV79myzkyY8p9M5fIy9u7u7kpKStH79ek4hAAAAAACMewzyAAAAADAGDMPQ2bNnVV5eLh8fH2VlZWnJkiVmZ2GUdHZ26sCBA3r06JESExO1bds2xuMR0NXVpfLycl28eFEzZsxQRkaGZs6caXYWAAAAAACfi0EeAAAAAMZQZ2enCgoKdPv2ba1evVppaWkKCAgwOwujwOFwqKqqSsePH9eiRYu0e/dunusR0tjYqKKiIjU3N2v9+vVKSkqSv7+/2VkAAAAAAPwCBnkAAAAAGGOGYejSpUsqKSmRm5ubLBaLVq5cyRXULur27ds6dOiQPDw8lJOTo7lz55qd5BKcTqfOnj2ryspKubu7KzExUevXr5e7u7vZaQAAAAAADGOQBwAAAACTdHV1yW63q6GhQUuWLFFmZqamTJlidhZGwcuXL3XgwAE1NjZq586d2r59O8PxCOnu7lZ5ebnq6+sVFRWljIwMzZo1y+wsAAAAAAAkMcgDAAAAgOmuXbumoqIiDQ4OKiUlRevXr+dqeRfkdDpVXV2tY8eOaf78+dq7d68CAwPNznIZDx8+VFFRkZqamhQdHa3k5GRuEQAAAAAAMB2DPAAAAACMA729vSotLVV9fb3mzZun7OxshYWFmZ2FUXD37l0dPHhQkrR3714tWLDA5CLX4XQ6de7cOVVWVkqSEhISFBMTw2kEAAAAAADTMMgDAAAAwDhy584dFRQUqKurS4mJiYqNjWVMdEFdXV06ePCg7t27p/j4eO3YsYPneQT19PSovLxcFy5cUGRkpDIyMjR79myzswAAAAAAkxCDPAAAAACMMwMDA6qoqNCZM2c0c+ZMWa1WRUREmJ2FEeZ0OnX8+HFVVVVpzpw5ysnJUVBQkNlZLuXx48cqKirSkydPtHbtWiUnJ3ObAAAAAADAmGKQBwAAAIBx6uHDh7LZbGpra1N8fLy2b98uDw8Ps7Mwwu7fv6+DBw/K4XBoz549WrRokdlJLsXpdOrChQuqqKiQ0+lUQkKCNm7cyIkEAAAAAIAxwSAPAAAAAOPY0NCQjh07ptraWk2dOlVWq1UzZ840OwsjrLu7W4cPH9bt27e1bds2JSYmMhiPsJ6eHlVWVurcuXOaPn26LBaL5s6da3YWAAAAAMDFMcgDAAAAwATQ1NQkm82mpqYmbd68WQkJCfLy8jI7CyPIMAzV1taqsrJSs2bNUk5OjoKDg83OcjlPnjxRUVGRHj9+rDVr1ig5OZlbBQAAAAAARg2DPAAAAABMEE6nUydOnFBVVZWCg4OVnZ2tefPmmZ2FEdbY2KgDBw5ocHBQu3fv1pIlS8xOcjmGYQwfYz80NKSdO3dq06ZN3BICAAAAADDiGOQBAAAAYIJpaWlRfn6+GhsbtWHDBqWkpMjHx8fsLIygnp4eHTlyRDdv3tSWLVuUlJTEWDwKent7h4+xnzp1qjIyMniRCwAAAABgRDHIAwAAAMAEZBiGzp49q/Lycvn6+iozM5MrqV2MYRg6deqUysvLFRUVpdzcXIWEhJid5ZKePn2qoqIiPXr0SKtWrVJqairH2AMAAAAARgSDPAAAAABMYB0dHSooKNCdO3e0evVqpaeny9/f3+wsjKBHjx4pLy9P/f392rVrl5YtW2Z2kksyDEMXL15UWVmZhoaGtGPHDsXGxnIyAQAAAADgnTDIAwAAAMAEZxiGLl26pOLiYrm7u8tisWjlypVyc3MzOw0jpLe3VzabTdevX9emTZuUkpIiT09Ps7NcUl9fn44ePaq6ujqFh4crIyND8+fPNzsLAAAAADBBMcgDAAAAgIvo6uqS3W5XQ0ODli5dqszMTI7ddiGGYaiurk6lpaWKiIhQbm6uwsLCzM5yWU1NTbLb7WpsbNTKlSuVmpqqKVOmmJ0FAAAAAJhgGOQBAAAAwMVcu3ZNRUVFGhwcVGpqqtatW8fV8i7kyZMnysvLU09Pj7Kzs7Vy5Uqzk1zWx6dPlJWVaWBgQPHx8dqyZQvH2AMAAAAAXhuDPAAAAAC4oN7eXpWWlqq+vl7z589Xdna2QkNDzc7CCOnr61NBQYGuXr2qmJgYpaWlcYT9KOrr61NVVZXOnDmjsLAwWSwWLVy40OwsAAAAAMAEwCAPAAAAAC7szp07ys/PV3d3txITExUbGyt3d3ezszACDMPQuXPnVFxcrKlTp2rfvn0KDw83O8ulPXv2TEVFRXrw4IGWL1+utLQ0BQcHm50FAAAAABjHGOQBAAAAwMUNDAyooqJCZ86c0cyZM2W1WhUREWF2FkZIU1OT8vLy9PLlS2VlZWn16tVmJ7k0wzB05coVlZaWqq+vb/gYe04oAAAAAAB8FgZ5AAAAAJgkGhsbZbPZ1N7ervj4eG3fvp17YbuI/v5+FRYW6vLly1q3bp0sFou8vLzMznJp/f39qq6u1unTpxUSEiKLxaJFixaZnQUAAAAAGGcY5AEAAABgEhkaGlJ1dbVqa2s1bdo07dq1SzNmzDA7CyPAMAzV19erqKhIYWFhys3N1bRp08zOcnnPnz9XUVGR7t+/r6VLlyo9PV0hISFmZwEAAAAAxgkGeQAAAACYhJqamnTkyBE1Nzdry5Yt2rlzJ1dUu4hnz54pLy9PHR0dysjIUHR0tNlJLs8wDDU0NKikpES9vb3avn27tm3bxjH2AAAAAAAGeQAAAACYrBwOh06ePKmqqioFBwcrOztb8+bNMzsLI2BgYEB2u1319fVau3atMjIy5O3tbXaWyxsYGNCxY8d08uRJBQcHKz09XUuWLDE7CwAAAABgIgZ5AAAAAJjkWlpaZLPZ9PDhQ23YsEEpKSny8fExOwsj4OLFiyosLFRwcLByc3M1ffp0s5MmhZaWFtntdt29e1dLlixRenq6QkNDzc4CAAAAAJiAQR4AAAAAIMMwVFdXp/Lycvn5+SkrK0uLFy82OwsjoKWlRfv371dbW5vS09O1fv16ubm5mZ3l8gzD0LVr11RSUqLu7m5t27ZN27dv59YQAAAAADDJMMgDAAAAAIZ1dHSooKBAd+7c0Zo1a5SWliZ/f3+zs/COBgcHVVxcrPPnz2vVqlXKysriFIQxMjAwoJqaGp08eVJBQUFKS0vT0qVLeVEEAAAAAEwSDPIAAAAAgFcYhqGLFy+qpKRE7u7uysjI0IoVKxgQXcCVK1eUn5+vwMBA5ebmKioqyuykSaO1tVXFxcW6ffu2Fi1aJIvForCwMLOzAAAAAACjjEEeAAAAAPCZurq6VFRUpGvXrmnp0qXKzMxUUFCQ2Vl4R62trcrLy9Pz58+VlpammJgYXmwxRgzD0I0bN1RcXKyuri5t3bpVcXFxHGMPAAAAAC6MQR4AAAAA8IUaGhpUVFSkoaEhpaamat26dQy4E9zQ0JBKS0tVV1enFStWKDs7W76+vmZnTRqDg4M6fvy4amtrFRgYqLS0NC1btoyfKwAAAABwQQzyAAAAAIAv1dvbq9LSUtXX12v+/PnKzs5WaGio2Vl4Rw0NDbLZbPLz81Nubq5mzpxpdtKk0tbWpuLiYt26dUsLFy6UxWJReHi42VkAAAAAgBHEIA8AAAAAeG23b99WQUGBenp6lJiYqE2bNsnd3d3sLLyD9vZ25eXlqampSSkpKYqNjeVK7TH28TH2L1680JYtWxQfHy9vb2+zswAAAAAAI4BBHgAAAADwRvr7+1VRUaG6ujrNmjVLVqtV06ZNMzsL78DhcKisrEynT5/W0qVLtWvXLvn5+ZmdNakMDg6qtrZWtbW18vf3V2pqqlasWMGLIwAAAABggmOQBwAAAAC8lcbGRtlsNnV0dCguLk7bt2+Xh4eH2Vl4Bzdu3NDhw4fl4+Oj3NxczZo1y+ykSae9vV0lJSW6ceOG5s+fL4vFwgteAAAAAGACY5AHAAAAALy1oaEhVVdXq7a2VhEREbJarZoxY4bZWXgHHR0dOnDggJ48eaLExERt3bqVq7RNcOvWLdntdnV2dmrz5s2Kj4+Xj4+P2VkAAAAAgDfEIA8AAAAAeGdPnz6VzWZTc3OztmzZop07d8rLy8vsLLwlh8OhyspKnThxQosXL9bu3bvl7+9vdtakMzQ0pBMnTqimpkZ+fn5KTU3VypUreYEEAAAAAEwgDPIAAAAAgBHhcDh04sQJVVdXKzg4WFarVXPnzjU7C+/g1q1bOnTokDw9PZWTk8PzaZKOjg6VlJTo+vXrmjdvniwWiyIiIszOAgAAAAC8BgZ5AAAAAMCIamlpkc1m08OHDxUTE6Pk5GSO2p7AXrx4oQMHDujhw4dKSEjQ9u3buULbJLdv35bdbldHR4c2bdqknTt38rMFAAAAAOMcgzwAAAAAYMQZhqG6ujqVl5fLz89PWVlZWrx4sdlZeEtOp1NVVVWqqanRwoULtWfPHgUEBJidNSkNDQ3p5MmTqqmpkY+Pj1JSUrR69WpeJAEAAAAA4xSDPAAAAABg1HR0dCg/P193797VmjVrlJaWxr3IJ7A7d+7o0KFDcnNz0969ezV//nyzkyatzs5OlZaWqqGhQXPnzpXFYtH06dPNzgIAAAAAfAqDPAAAAABgVBmGoYsXL6qkpETu7u7KyMjQihUruKJ3gnr58qUOHjyoBw8eKD4+XvHx8XJ3dzc7a9K6c+eO7Ha72traho+x9/X1NTsLAAAAAPBzDPIAAAAAgDHR1dWloqIiXbt2TcuWLVNGRoaCgoLMzsJbcDqdOnbsmKqrqzV//nzt2bOH59JEDodDp06dUnV1tby9vZWSkqI1a9bwohcAAAAAGAcY5AEAAAAAY6qhoUFFRUUaGhpSWlqaoqOjGQ4nqHv37ungwYMyDEN79uzRwoULzU6a1F68eKHS0lJdvXpVs2fPVkZGhiIjI83OAgAAAIBJjUEeAAAAADDment7VVJSoosXL2rBggXKyspSaGio2Vl4C93d3Tp06JDu3LmjuLg47dy5kyPsTXbv3j3Z7Xa1tLQoJiZGiYmJHGMPAAAAACZhkAcAAAAAmOb27dsqKChQT0+PEhMTtWnTJsbcCcgwDB0/flxHjx7V7NmzlZOToylTppidNak5HA6dPn1a1dXV8vT0VHJyMqdRAAAAAIAJGOQBAAAAAKbq7+9XRUWF6urqNGvWLFmtVk2bNs3sLLyFxsZG5eXlaWhoSHv27NHixYvNTpr0Xr58qbKyMl2+fFmzZs1SRkaGoqKizM4CAAAAgEmDQR4AAAAAMC40NjbKZrOpo6ND8fHx2rZtmzw8PMzOwhvq6enR4cOHdevWLW3dulWJiYk8j+PA/fv3Zbfb9ezZs+Fj7P38/MzOAgAAAACXxyAPAAAAABg3hoaGVFVVpRMnTigiIkJWq1UzZswwOwtvyDAMnTx5UhUVFZoxY4Zyc3MVHBxsdtak53A4VFdXp6qqKrm7uyspKUnr16/nGHsAAAAAGEUM8gAAAACAcefp06ey2Wxqbm7W1q1btWPHDnl5eZmdhTf06NEj5eXlqb+/X7t379bSpUvNToKkrq4ulZeX6+LFi5oxY4YyMjI0c+ZMs7MAAAAAwCUxyAMAAAAAxiWHw6ETJ06ourpaISEhys7O1ty5c83Owhvq7e3VkSNHdOPGDcXGxiolJYUj7MeJxsZGFRUVqbm5WevXr1dSUpL8/f3NzgIAAAAAl8IgDwAAAAAY11paWmSz2fTw4UPFxMQoOTlZPj4+ZmfhDRiGodOnT6usrEyRkZHKzc1VaGio2VmQ5HQ6dfbsWVVWVsrd3V2JiYlav3693N3dzU4DAAAAAJfAIA8AAAAAGPecTqfq6upUUVEhPz8/ZWdna9GiRWZn4Q09fvxYeXl56u3tldVq1YoVK8xOws91d3ervLxc9fX1ioqKUkZGhmbNmmV2FgAAAABMeAzyAAAAAIAJo6OjQ/n5+bp7967Wrl2r1NRUjtieYPr6+pSfn6+GhgZt3LhRqamp8vT0NDsLP/fw4UMVFRWpqalJ0dHRSk5OVkBAgNlZAAAAADBhMcgDAAAAACYUwzB08eJFlZSUyMPDQxkZGVq+fLnc3NzMTsNrMgxDZ8+eVUlJiaZNm6bc3FyFh4ebnYWfczqdOnfunCorKyVJCQkJiomJ4Rh7AAAAAHgLDPIAAAAAgAnp5cuXKioq0vXr17Vs2TJlZGQoKCjI7Cy8gaamJu3fv19dXV3Kzs7WqlWrzE7CJ3R3d6uiokIXLlxQZGSkMjIyNHv2bLOzAAAAAGBCYZAHAAAAAExYhmHo2rVrKioqksPhUGpqqqKjo7lafgLp7+9XQUGBrly5ovXr1ys9PV1eXl5mZ+ETHj9+rKKiIj158kRr165VcnKyAgMDzc4CAAAAgAmBQR4AAAAAMOH19PSotLRUFy9e1IIFC5Sdna2QkBCzs/CaDMPQhQsXZLfbFRYWpn379mnq1KlmZ+ETnE6nLly4oIqKCjmdTiUkJGjjxo0cYw8AAAAAX4JBHgAAAADgMm7fvq2CggL19PQoKSmJwXCCaW5uVl5enjo7O5WZmam1a9eanYRP6enpUWVlpc6dO6fp06fLYrFo7ty5ZmcBAAAAwLjFIA8AAAAAcCn9/f2qqKhQXV2dZs+erezsbE2bNs3sLLymgYEBFRUV6eLFi4qOjpbFYpG3t7fZWfiUJ0+eqKioSI8fP9aaNWuUnJysoKAgs7MAAAAAYNxhkAcAAAAAuKQHDx7IZrOps7NT8fHx2rZtmzw8PMzOwmuqr69XUVGRQkJClJubq4iICLOT8Ckf32qgoqJCQ0ND2rlzpzZt2sTPGQAAAAB8AoM8AAAAAMBlDQ4Oqrq6WidOnND06dNltVoVFRVldhZe0/Pnz7V//361t7crIyND0dHRcnNzMzsLn9Lb2zt8jP20adNksVg0b948s7MAAAAAYFxgkAcAAAAAuLwnT57IZrPp2bNn2rp1q3bs2CEvLy+zs/AaBgcHZbfbdeHCBa1evVqZmZny8fExOwuf4enTpyoqKtKjR4+0atUqpaamcow9AAAAgEmPQR4AAAAAMCk4HA7V1tbq2LFjCgkJkdVq1Zw5c8zOwmu6fPmyCgoKFBQUpNzcXEVGRpqdhM9gGIYuXryosrIyDQ0NaceOHYqNjeUYewAAAACTFoM8AAAAAGBSef78uWw2mx49eqSNGzcqKSmJK64niNbWVu3fv18tLS1KT0/Xhg0bOMJ+nOrr69PRo0dVV1en8PBwZWRkaP78+WZnAQAAAMCYY5AHAAAAAEw6TqdTdXV1qqiokL+/v7KysrRo0SKzs/AahoaGVFJSorNnz2rlypXKzs7mBRXjWFNTk+x2uxobG7Vy5UqlpqZqypQpZmcBAAAAwJhhkAcAAAAATFrt7e0qKCjQ3bt3tXbtWqWlpcnPz8/sLLyGq1evKj8/X/7+/srNzdWMGTPMTsLnMAxDly5dUllZmQYGBhQfH68tW7ZwjD0AAACASYFBHgAAAAAwqRmGofr6epWUlMjT01MZGRlasWKF2Vl4DW1tbcrLy9OzZ8+UkpKiTZs2cYT9ONbX16eqqiqdOXNGYWFhslgsWrhwodlZAAAAADCqGOQBAAAAAJD08uVLFRUV6fr161q+fLksFouCgoLMzsKXGBoaUllZmc6cOaNly5bJarVyysE419zcLLvdrgcPHmj58uVKS0tTcHCw2VkAAAAAMCoY5AEAAAAA+DnDMNTQ0CC73S6Hw6G0tDStXbuWq64ngOvXr+vIkSPy9fVVTk6OZs2aZXYSvoBhGLpy5YpKS0vV19c3fIy9p6en2WkAAAAAMKIY5AEAAAAA+JSenh6VlJTo0qVLWrBggbKzsxUSEmJ2Fr5ER0eH8vLy9PTpUyUnJ2vz5s28mGKc6+/vV3V1tU6fPq2QkBBZLBYtWrTI7CwAAAAAGDEM8gAAAAAAfI5bt26poKBAvb29SkpK4h7lE4DD4VBFRYVOnjypJUuWaNeuXfL39zc7C1/i+fPnKioq0v3797Vs2TKlpaXxIhgAAAAALoFBHgAAAACAL9Df36/y8nKdPXtWs2fPltVq1dSpU83Owpe4efOmDh8+LC8vL+Xk5GjOnDlmJ+FLGIahq1evqrS0VL29vdq+fbu2bdvGMfYAAAAAJjQGeQAAAAAAXsODBw9ks9nU2dmpHTt2aOvWrfLw8DA7C1+gs7NTBw4c0KNHj5SYmKht27ZxwsEEMDAwoOrqap06dUrBwcFKT0/XkiVLzM4CAAAAgLfCIA8AAAAAwGsaHBxUVVWVTp48qenTp8tqtSoqKsrsLHwBp9Opo0eP6vjx41q0aJF2796tgIAAs7PwGlpaWmS323X37l0tWbJE6enpCg0NNTsLAAAAAN4IgzwAAAAAAG/oyZMnstlsevbsmbZt26YdO3ZwrPY4d/v2bR06dEgeHh7au3ev5s2bZ3YSXoNhGLp27ZpKSkrU3d09fIy9l5eX2WkAAAAA8FoY5AEAAAAAeAsOh0O1tbU6duyYQkJCZLVauU/5OPfy5UsdPHhQDx480I4dOxQXFyd3d3ezs/AaBgYGVFNToxMnTmjKlCnDx9hzCwIAAAAA4x2DPAAAAAAA7+D58+ey2Wx69OiRNm7cqKSkJPn4+Jidhc/hdDpVXV2tY8eOaf78+dq7d68CAwPNzsJram1tld1u1507d7Ro0SJZLBaFhYWZnQUAAAAAn4tBHgAAAACAd+R0OnXmzBlVVlbK399fWVlZWrRokdlZ+AJ3797VwYMHJUl79+7VggULTC7C6zIMQ9evX1dJSYm6urq0detWxcXFcYw9AAAAgHGJQR4AAAAAgBHS3t6u/Px83bt3T2vXrlVaWpr8/PzMzsLn6Orq0qFDh3T37l3Fx8drx44dHGE/gQwODg4fYx8YGKi0tDQtW7aMY+wBAAAAjCsM8gAAAAAAjCDDMFRfX6+SkhJ5enoqIyNDK1asMDsLn8PpdOr48eOqqqrSnDlzlJOTo6CgILOz8Aba2tpUXFysW7duaeHChbJYLAoPDzc7CwAAAAAkMcgDAAAAADAqXr58qcLCQt24cUPLly9XRkYG9yofx+7fv6+DBw/K4XBoz5493HJggjEMQzdv3lRxcbFevHihLVu2KD4+Xt7e3manAQAAAJjkGOQBAAAAABglhmGooaFBRUVFcjqdSktL09q1azlSe5zq7u7W4cOHdfv2bW3btk2JiYkcYT/BDA4Oqra2VrW1tfL391dqaqpWrFjBzxwAAAAA0zDIAwAAAAAwynp6elRSUqJLly5p4cKFysrKUkhIiNlZ+AyGYai2tlaVlZWaNWuWcnJyFBwcbHYW3lB7e7uKi4t18+ZNLViwQOnp6Zo2bZrZWQAAAAAmIQZ5AAAAAADGyK1bt1RQUKDe3l4lJydr48aNXLk7Tj18+FB5eXkaHBzU7t27tWTJErOT8BY+Psa+s7NTmzdvVnx8vHx8fMzOAgAAADCJMMgDAAAAADCG+vv7VV5errNnz2r27NmyWq2aOnWq2Vn4DD09PTpy5Ihu3rypzZs3Kzk5WR4eHmZn4Q0NDQ3pxIkTqqmpkZ+fn1JTU7Vy5UpeDAMAAABgTDDIAwAAAABggvv37ys/P1+dnZ3asWOHtm7dytg7DhmGoVOnTqm8vFxRUVHKzc3ldgMTVEdHh0pKSnT9+nXNmzdPFotFERERZmcBAAAAcHEM8gAAAAAAmGRwcFBVVVU6efKkpk+fLqvVqqioKLOz8BkeP36svLw89fX1yWq1avny5WYn4S3dvn1bdrtdHR0d2rRpk3bu3Mkx9gAAAABGDYM8AAAAAAAme/LkiY4cOaLnz59r27Zt2rFjhzw9Pc3Owqf09fXJZrPp2rVr2rRpk1JSUnieJqihoSGdPHlSNTU18vHxUUpKilavXs0x9gAAAABGHIM8AAAAAADjgMPh0PHjx3Xs2DGFhobKarVqzpw5ZmfhUwzDUF1dnUpLSxUREaHc3FyFhYWZnYW31NnZqdLSUjU0NGju3LmyWCyaPn262VkAAAAAXAiDPAAAAAAA48izZ89ks9n0+PFjbdq0SUlJSfL29jY7C5/y9OlT7d+/X93d3bJarVq5cqXZSXgHd+7ckd1uV1tb2/Ax9r6+vmZnAQAAAHABDPIAAAAAAIwzTqdTZ86cUWVlpfz9/ZWdna2FCxeanYVP6e/vV35+vq5evaoNGzYoPT2dI+wnMIfDoVOnTqm6ulre3t5KSUnRmjVrOMYeAAAAwDthkAcAAAAAYJxqb29Xfn6+7t27p+joaKWmpsrPz8/sLHyCYRg6f/687Ha7pk6dqn379ik8PNzsLLyDFy9eqLS0VFevXtXs2bOVkZGhyMhIs7MAAAAATFAM8gAAAAAAjGOGYejChQsqLS2Vl5eXMjIytHz5crOz8CnNzc3av3+/Xrx4oaysLK1Zs8bsJLyje/fuqaioSK2trYqJiVFiYiLH2AMAAAB4YwzyAAAAAABMAC9evFBRUZFu3Lih5cuXKyMjQ4GBgWZn4RMGBgZUWFioS5cuad26dbJYLPLy8jI7C+/A4XDo9OnTqq6ulqenp5KTkxUdHc0x9gAAAABeG4M8AAAAAAAThGEYunr1qux2u5xOp9LT07nH9ThjGIbq6+tVVFSk0NBQ7du3T9OmTTM7C+/o5cuXKisr0+XLlzVr1ixlZGQoKirK7CwAAAAAEwCDPAAAAAAAE0xPT4+Ki4t1+fJlLVy4UFlZWQoJCTE7C5/w7Nkz5eXlqaOjQxkZGYqOjjY7CSPg/v37stvtevbs2fAx9n5+fmZnAQAAABjHGOQBAAAAAJigbt68qcLCQvX19SkpKUkbN27kavlxZGBgQHa7XfX19Vq7dq0yMjLk7e1tdhbekcPhUF1dnaqqquTu7q7k5GStW7eOnz0AAAAAn4lBHgAAAACACay/v19lZWU6d+6c5syZo+zsbE2dOtXsLHzCxYsXVVhYqODgYOXm5mr69OlmJ2EEdHV1qaysTJcuXdLMmTOVkZGhGTNmmJ0FAAAAYJxhkAcAAAAAwAXcv39f+fn56uzs1M6dO7V161a5u7ubnYWfa2lp0f79+9XW1qb09HStX7+eK6pdRGNjo4qKitTc3Kz169crKSlJ/v7+ZmcBAAAAGCcY5AEAAAAAcBGDg4M6evSoTp06pcjISFmtVkVGRpqdhZ8bHBxUSUmJzp07p1WrVikrK0s+Pj5mZ2EEOJ1O1dXV6ejRo3J3d1diYqLWr1/Pi2IAAAAAMMgDAAAAAOBqHj9+LJvNppaWFm3btk3x8fHy9PQ0Ows/d+XKFeXn5yswMFC5ubmKiooyOwkjpKurSxUVFaqvr1dUVJQyMjI0a9Yss7MAAAAAmIhBHgAAAAAAF+RwOHT8+HEdO3ZMYWFhslqtmj17ttlZ+Lm2tjbt379fz58/V1pammJiYjjC3oU8fPhQRUVFampq0rp165SUlKSAgACzswAAAACYgEEeAAAAAAAX9uzZM9lsNj1+/FibNm1SUlKSvL29zc6CpKGhIZWWlqqurk4rVqxQdna2fH19zc7CCHE6nTp37pwqKyslSYmJidqwYQPH2AMAAACTDIM8AAAAAAAuzul06vTp06qsrFRgYKCysrK0cOFCs7Pwcw0NDbLZbPLz81Nubq5mzpxpdhJGUHd3tyoqKnThwgVFRkYqIyOD0yoAAACASYRBHgAAAACASaKtrU35+fm6f/++oqOjlZqaKj8/P7OzIKm9vV15eXlqampSSkqKYmNjOcLexTx69EhFRUV6+vSpoqOjlZyczDH2AAAAwCTAIA8AAAAAwCRiGIYuXLig0tJSeXl5KSMjQ8uXLzc7C5IcDofKy8t16tQpLV26VLt27eIFEy7G6XTq/PnzqqyslNPpVEJCgjZu3Mgx9gAAAIALY5AHAAAAAGASevHihQoLC3Xz5k2tWLFCFotFgYGBZmdB0o0bN3T48GH5+PgoJyeH481dUE9PjyoqKnT+/HlNnz5dFotFc+fONTsLAAAAwChgkAcAAAAAYJIyDENXr16V3W6X0+lUenq61qxZw1Hp40BnZ6fy8vL05MkTJSYmauvWrTwvLujJkycqKirS48ePtWbNGqWkpPDCGAAAAMDFMMgDAAAAADDJ9fT0qLi4WJcvX9aiRYuUlZWl4OBgs7MmPYfDocrKSp04cUKLFy/W7t275e/vb3YWRtjHt5EoLy+X0+nUzp07tXHjRnl4eJidBgAAAGAEMMgDAAAAAABJ0s2bN1VQUKD+/n4lJycrJiaGq7LHgVu3bunw4cPy8PBQTk4OR5u7qN7eXlVWVurs2bOKiIiQxWLRvHnzzM4CAAAA8I4Y5AEAAAAAwLC+vj6Vl5fr3LlzmjNnjqxWq8LDw83OmvRevHihAwcO6OHDh0pISND27dt5sYSLevr0qYqKivTo0SOtWrVKqampCgoKMjsLAAAAwFtikAcAAAAAAL/g3r17ys/P14sXL7Rz505t3bpV7u7uZmdNak6nU1VVVaqpqdGCBQu0Z88e7jfuogzDUH19vcrLyzU0NKQdO3YoNjaWY+wBAACACYhBHgAAAAAAfKbBwUEdPXpUp06dUmRkpKxWqyIjI83OmvTu3LmjQ4cOyc3NTXv37tX8+fPNTsIo6evr09GjR1VXV6fw8HBlZGTwfAMAAAATDIM8AAAAAAD4Qo8fP5bNZlNLS4u2bdum+Ph4eXp6mp01qb18+VKHDh3S/fv3FR8fr/j4eE4wcGFNTU2y2+1qbGzUypUrlZqaqilTppidBQAAAOA1MMgDAAAAAIAv5XA4VFNTo5qaGoWFhclqtWr27NlmZ01qTqdTNTU1qq6u1ty5c7V3717uNe7CDMPQpUuXVFZWpoGBAcXHx2vLli0cYw8AAACMcwzyAAAAAADgtT179kw2m02PHz9WbGysEhMT5e3tbXbWpHb//n0dOHBATqdTe/fu1cKFC81Owijq6+tTVVWVzpw5o7CwMFksFp5zAAAAYBxjkAcAAAAAAG/E6XTq9OnTqqysVGBgoLKzs7VgwQKzsya17u5uHTp0SHfu3NH27duVkJDAEfYurrm5WXa7XQ8ePNDy5cuVlpam4OBgs7MAAAAAfAqDPAAAAAAAeCttbW3Kz8/X/fv3FR0drbS0NPn6+pqdNWkZhqHjx4/r6NGjmj17tnJycrjPuIszDENXrlxRaWmp+vv7FRcXpy1btsjT09PsNAAAAAA/xyAPAAAAAADemmEYOn/+vMrKyuTl5aXMzEwtW7bM7KxJrbGxUXl5eRoaGtKePXu0ePFis5Mwyvr7+1VdXa3Tp08rJCREFotFixYtMjsLAAAAgBjkAQAAAADACHjx4oUKCwt18+ZNrVixQhaLRYGBgWZnTVo9PT06fPiwbt26pa1btyoxMVEeHh5mZ2GUPX/+XEVFRbp//76WLVumtLQ0hYSEmJ0FAAAATGoM8gAAAAAAYER8fHx2cXGxDMNQenq6Vq9eLTc3N7PTJiXDMHTy5ElVVFRoxowZysnJYZydBAzD0NWrV1VaWqre3l5t375d27Ztm7DH2DsMQ539Tg06DQ0ZhhyG5OEmebq5ycvdTcE+7vLgf8cAAABgHGOQBwAAAAAAI6q7u1vFxcW6cuWKFi1apKysLAUHB5udNWk9evRIeXl56u/v165du7ilwCQxMDCg6upqnTp1SsHBwUpPT9eSJUvMzvpCDsNQS69DTb1Dau4Z0pPuQT3vc8jxBb+99HCTpvl6aEaAl6b7eyrSz1NT/TwY6QEAADBuMMgDAAAAAIBRcePGDRUWFqq/v1/JycmKiYnhanmT9Pb2ymaz6fr164qNjVVKSgpH2E8SLS0tstvtunv3rpYsWaL09HSFhoaanfWKp92DOtfSp2vt/cPju7sk5xt8jk9+vIebtDzURxum+SrK32tkYwEAAIA3xCAPAAAAAABGTV9fn8rKynT+/HnNnTtX2dnZCg8PNztrUjIMQ2fOnFFpaakiIyOVm5s77oZZjA7DMHTt2jWVlJSou7t7+Bh7Ly/zxupBp6Fr7f06+7xXz3odcpM0kr+k/PjzTffz0IZpfloe6iMvd14QBAAAgLHHIA8AAAAAAEbdvXv3lJ+fr5cvX2rnzp3asmWL3N3dzc6alJ48eaK8vDz19PTIarVqxYoVZidhjAwMDKimpkYnTpzQlClTho+xH8uTKwadhk429ejs8z4NOI0RH+I/7ePP7+3upphpvtoS6c8wDwAAgDHFIA8AAAAAAMbE4OCgKisrdfr0aUVGRspqtSoyMtLsrEmpr69P+fn5amhoUExMjNLS0uTp6Wl2FsZIa2ur7Ha77ty5o8WLFys9PV1hYWGj/nUfdw8q//5LdQ44R3WE/zxukoK93ZU9L0gzAzjKHgAAAGODQR4AAAAAAIypR48eyWazqbW1Vdu2bVN8fDxjsAkMw9DZs2dVUlKiadOmKTc3l9sJTCKGYej69esqKSlRV1eXtm7dqri4uFE5xn7QaajmaY/OPOsd9Sviv8zHX39ThJ/iorhaHgAAAKOPQR4AAAAAAIw5h8Ohmpoa1dTUKDw8XFarVbNmzTI7a1JqamrS/v371dXVpaysLK1evdrsJIyhwcHB4WPsAwMDlZaWpmXLlo3YMfZmXxX/RUK4Wh4AAABjgEEeAAAAAACYprm5WTabTU+ePFFsbKwSExPl7e1tdtak09/fr8LCQl2+fFnr169Xenr6qFwpjfGrra1NxcXFunXrlhYuXCiLxfLOJyZcb+/XkfsvJZl7Vfzn+fglB7vmBWlZqI+pLQAAAHBdDPIAAAAAAMBUTqdTp06d0tGjRxUYGKjs7GwtWLDA7KxJxzAMXbhwQXa7XWFhYdq3b5+mTp1qdhbGkGEYunnzpoqLi/XixYvhY+zf5kUyF1v7ZG/sGoXK0WGZE6i14b5mZwAAAMAFMcgDAAAAAIBxoa2tTTabTQ8ePNC6deuUmpoqX18GsrHW3NysvLw8dXZ2KjMzU2vXrjU7CWNscHBQtbW1On78uAICApSWlqbly5e/9jH2E22M/xijPAAAAEYDgzwAAAAAABg3DMPQ+fPnVVpaKm9vb2VmZmrZsmVmZ006AwMDKioq0sWLFxUdHS2LxcKtBCah9vZ2FRcX6+bNm1qwYIEsFsuXnppwvb1fh39+TP1EtJvj6wEAADDCGOQBAAAAAMC48+LFCxUUFOjWrVtauXKlLBaLAgICzM6adOrr61VUVKSQkBDl5uYqIiLC7CSY4ONj7Ds7O7V582bt2LHjM1+g8bh7UD+62Tku7xf/utwkfX1JsGYGeJmdAgAAABfBIA8AAAAAAMYlwzB05coV2e12SVJ6erpWr1792sdmY2Q8f/5ceXl5amtrk8Vi0bp163gOJqGhoaHhY+z9/PyUmpqqlStXDv9bGHQa+sdr7eoccE74QT7Y212/tjxUXu78OwcAAMC7Y5AHAAAAAADjWnd3t4qLi3XlyhUtXrxYmZmZCg4ONjtrUhkcHJTdbteFCxe0evVqZWZmyseHY70no46ODpWUlOj69euaN2+eLBaLIiIiVPm4W3XPeif0GP9JsRF+SpjJqRwAAAB4dwzyAAAAAABgQrhx44YKCwvV39+vlJQUbdiwgSu1x9jly5dVUFCgoKAg5ebmKjIy0uwkmOT27duy2+3q6OjQym2Juhm61OykEfc+R9cDAABgBDDIAwAAAACACaOvr09lZWU6f/685s6dq+zsbIWHh5udNam0trZq//79amlpUXp6Oi+MmMSGhoZ0/OQpnXKfITf/KS5zdbzE0fUAAAAYOQzyAAAAAABgwrl3757y8/P18uVL7dy5U1u2bJG7u7vZWZPG0NCQSkpKdPbsWa1cuVJZWVny9fU1OwsmOPakWyebXeeo+k/bOt1P8TM4uh4AAABvj0EeAAAAAABMSAMDAzp69KhOnz6tqKgoWa1WTZ8+3eysSeXq1avKz8+Xv7+/cnNzNWPGDLOTMIYGnYb+5nKbBpyu++tFH3c3/ZvVYVwlDwAAgLfGIA8AAAAAACa0R48eyWazqbW1Vdu3b1dcXJw8PT3Nzpo02tralJeXp2fPniklJUWbNm3iCPtJ4lJrn4oau8zOGHWZcwK1OpwTIAAAAPB2GOQBAAAAAMCENzQ0pJqaGh0/flzh4eGyWq2aNWuW2VmTxtDQkMrLy3X69GktW7ZMVqtVfn5+ZmdhlP2v6+163utw2ePqpZ/dSz7Cz0O/uizU7BQAAABMUAzyAAAAAADAZTQ3N8tms+nJkyfavHmzEhIS5O3tbXbWpHH9+nUdOXJEPj4+ys3N5UURLuxp96D+6Wan2Rlj5ptLghUV4GV2BgAAACYgBnkAAAAAAOBSnE6nTp06paNHjyooKEjZ2dmaP3++2VmTRkdHh/Ly8vT06VMlJSVpy5YtHGHvggoevNTVtn6Xvjr+Y+6SVob5KHNukNkpAAAAmIAY5AEAAAAAgEtqa2uTzWbTgwcPtG7dOqWmpsrXl/tAjwWHw6GKigqdPHlSS5Ys0a5du+Tv7292FkaIwzD0Xy+2yvHz3yqWf/97qvjBXw+//z8WnFPojDmSpHO2nyrvu787/L5//YPDWhCzbUx738RfZa5Xx9OHkqT5G7bq1//hiCTJw036vbXhcufFJQAAAHhD7mYHAAAAAAAAjIawsDB985vfVGZmpq5evaq//du/1Y0bN8zOmhQ8PDyUmpqqr371q3r48KH+/u//Xo2NjWZnYYS09DqGx/iJovz739O3108b/q/9yZv9e3QYUkufY5TqAAAA4Mo8zQ4AAAAAAAAYLW5uboqJidHixYtVWFiojz76SCtXrpTFYlFAQIDZeS5vyZIl+s3f/E3l5eXphz/8oRITE7Vt2zaOsJ/gmnqHXvtjQ2fM0aqkrOHHAaHho5E0YpZuT1Z323NJUsSCZa+8r6lnSBF+/DoVAAAAb4b/CxIAAAAAALi84OBgffWrX9Xly5dVXFys//E//ocsFotWrVrFODzKpkyZol/5lV/R0aNHVVFRofv372vPnj28IGICa+4Zkrsk52t87IKYbeP6iPpP2/3t733m2931s0F+zfh+PQEAAADGIY6sBwAAAAAAk4Kbm5vWrFmj3/md39HChQt18OBB/fSnP1VnZ6fZaS7P3d1dSUlJeu+99/T06VN9//vf1/37983OGteqqqrk5uY2/N8Pf/hDVVRUaMeOHQoMDFRERIS+9a1vqbW1VZL04MEDff3rX9e0adPk7++vLVu2qLS09DM/d11dnX7lV35FCxculL+/vwICArRq1Sp9+9vf1vPnzz+36R//8R+1bt06ZS2epj9NXqF//uPfUWfzky/8e5yz/fSVo+Lvnq39hY952fpMZX/3V/of30jTn+xYpO9smqG/SF2pH3zLqmP/9P++8rGXy47owP/33+n/fS9Zf5m+Rn+0Zbb+aPMs/d8Z0frw976pa9Uln/n1P3mPe0n6XtaG4aa/ylw//Pa/ylw//PYf/Otdw293SnrSPSin06mf/vSnysjIUGRkpLy9vRUSEqJNmzbpT//0T9XW1vYLf78f/vCHrzyXVVVVOn78uDIyMhQSEiJ/f39t3br1c5+v/fv3KyMjQ1FRUfL29lZgYKDmz5+vlJQU/dEf/ZGuXr36hc8BAAAAzOVmGMYEu+MTAAAAAADAu7tx44YKCgo0MDCglJQUbdiwgavlx8DLly918OBBPXjwQDt27FBcXJzc3blm5NOqqqqUkJAw/Nhisai4uFif/lXeqlWr9JOf/ESJiYlqaWl55X0eHh4qKyt75fP88R//sf7sz/7sFz7Px6ZPn67CwkJt2LDhlbf/u3/37/Tf//t//4WPDwidqoWb4nSp5NDw2/5jwTmFzpgj6WeDeN53f3f4ff/6B4dfuWL+Rm25/s9//i31vuj4zB7fwCn6/xy7M/z4+/8qUw/qz3zmx35s61d/Xdl/8Oef+fU/S0jUbP2nwvOSfjbIdzx9KEmav2Grfv0fjgx/3GBPl6r++NdUWVn5uZ8rKipK+fn5r3z/fvjDH+pXf/VXhx//0i/9kvbv3/8Lz8FnPV9/8id/ou9+97tf2P+nf/qn+s53vvOFHwMAAADzcGQ9AAAAAACYlJYuXaq5c+eqtLRUhYWFunLliqxWq8LCwsxOc2lBQUF6//33VV1draqqKj148EB79+5VYGCg2Wnjmt1uV1BQkGJjY3X58mU1NzdLkq5cuaLY2Fj19vYqNjZWfX19unjxoiTJ4XDoT/7kT4YH3n/4h3/Qn/7pnw5/ztDQUG3atEl9fX2qra3V0NCQmpublZ2drYaGBoWEhAx/7U+P8TNXRMvLx1cPL597ZYx/E023r+nHf/CvNNjXO/w2/5AwRS1eKTcPDz1uqJfh/MWD8b18/TVt3iL5B4fK289fPZ3tenL9kgZ6eyRJJ376A61N26M5a2KG72H/7O5NPbt3c/hzLNmWJG9fP0lSQNi01+r95+/+rq58YowPDQ1VTEyM7t27p9u3b0uSnj59qqysLF27dm34+/cLn+ef/1kBAQHatGmT7t27N3xaxKefr4GBAX3ve/9yhH5QUJA2b94sT09PPXz4UHfu3FFvb+9nfQkAAACMIwzyAAAAAABg0vL19ZXVatWqVauUn5+vv/u7v1NCQoI2b97MVdujyN3dXQkJCZo3b54OHjyo73//+9q7d68WLFhgdtq4NWXKFJ09e1aLFy/W7du3tXjx4uH39fb26q//+q/1+7//+3I6ndq6datOnz4tSTpx4oQGBgbk4eGhP/qjPxr+Mxs3blRlZeXwCyHOnDmjzZs3yzAMPX36VH/7t3+rP/zDP5Qk/bf/9t9eadn17e9p876fXfF992yt/udv7PncK+6/SOU//D+vjPHRllzt+c7/I2+/AEnS0OCALpceeeXP7PnOf1X47Pny9PJ+5e1d7S366+wYDfR0S5KuVBRozpqY4XvYl3//e68cW7/7298bvor/dTy9eVVXyvOHHy9dulTHjh1TRESEnE6nfu3Xfk0//OEPJUlNTU2vfP8+berUqaqtrdWSJUuGX0hx+fJlSf/yfHl7e6u9vV09PT3Df664uFhbt24dftzX1/fKcwgAAIDxif/PEgAAAAAATHoLFizQb/3Wb2nDhg0qKyvTP/7jPw5fgYzRM3/+fP3Gb/yGpk+frg8//FBHjx6V8zOuiIb0la98ZXiEX7RokUJDQ4ffN2XKFP3bf/tvJf3sxQ7bt28fft/g4KBaW1t17ty5V/5N9/X16Vd+5VeUm5ur3Nxcfe9735O397+M3Ha7XZI0NDSkmpqa4bfPmDVbm3K+Ofx4Qcw2LYrd8cZ/H6fDoRu1FcOP/YJDtfs///XwGC9Jnl7eWpe575U/Fxo1Syc/+p/6wbes+vPkFfrOphn69vpp+vOk5cNjvCS1NN7RSLp5ouKVx7/3e7+niIgIST/7nv/FX/zFK+8vLi7+3M/1m7/5m1qyZIkkyc/PTzt37hx+38fPlyRFREQoKCho+H1/9md/pg8++ECnT59WZ2enfH19lZGRofj4+Hf6uwEAAGB0cYU8AAAAAACAJG9vb6Wnp2vVqlWy2Wz6wQ9+oO3btysuLk6envwKZbQEBgbqvffe0/Hjx185wn7KlClmp40rK1aseOVxYGCg2tvbJf3sBSU+Pj6vvO+T+vv7h49F/9jly5eHr8r+LA8ePJAktbS0qK+vb/jtC5cs/YXTIyIWLtOtU1Wv/XeRpJ7OtlcG9KjFK+Tj/8VXevd3d+nvfjVDzbevfenn7+9++UY9X6b96aNXHq9cufKVx1FRUQoNDR1+Tj7+/n2WdevWvfI4ODj4lcf9/f2SJDc3N/3n//yf9X/9X/+XpJ+9SOLjF0pI0vLly/XVr35V/+E//AcFBAQIAAAA4xNXyAMAAAAAAHzCrFmz9Ou//uvavn27jh8/rh/84Ad69OjRl/9BvDV3d3fFx8frm9/8ptra2vT3f//3w/fkxs98erT95Cj+6feNhE8elf7K13VzG/Gv9bpO/vM/vjLGe/v5a+GmeK1KytKqpCx5+foPv+9tjtAfK2FhYa889vDw+NyP/U//6T/p0KFD2rVrl6ZPn/7K+65du6Y//uM/1je+8Y1R6QQAAMDIYJAHAAAAAAD4FE9PTyUkJOjXf/3X5enpqf/1v/6XSkpKNDg4aHaaS5s7d65+4zd+QzNmzNCPf/xjlZeXy+FwmJ3lEubOnfvK4z/5kz+RYRif+19LS4ukn93v3NfXd/jP3b554xfG7uf3br5xj39wmLz9/+Wq7qe3GtTf0/WFf6bxYt3w/+zp7aP/cOiUvvX9A3rvr/+3vvp//0994WsF3vGFBCGRM1953NDQ8Mrjp0+fDl8dL0lz5rz+/em/zO7du3X48GE1NTWpo6NDJ0+eVG5u7vD7Dx48qKamphH7egAAABhZDPIAAAAAAACfY/r06frWt76lpKQknT17Vn/3d3+ne/fumZ3l0gICAvS1r31NycnJOnHihP7pn/5JnZ2dZmdNeBs2bNC0adOGH//N3/yNLl68+Asfd+nSJf3BH/yBDh8+LOlnL0755D3pHz9s1JkDHww/vnf+5BsfVy9J7h4eWrI1cfhxb2e7Dv/5H2ig91+OsXcMDanefuATj//lBTFu7u7y9P7ZMf2GYajiB3+tgd7Pvqpfkrx8fF95/OL5mw3YS7clvfL4v/yX/zL8ogWn06nvfOc7r7w/PT39jT7/5/nLv/zLV24tEBwcrM2bN//C5/+iI/IBAABgLm6ABgAAAAAA8AXc3d21bds2LVu2TPn5+frggw+0fv16paSkvHLlMEaOm5ubtm3bpjlz5igvL09///d/r127dmnp0qVmp01Ynp6e+u53v6vf+Z3fkfSze8OvW7dOGzZs0IwZM/Ty5Us1NDSoublZ0qv3SP/3//7fq7y8fPjx4b/4fZ098mN5+fiq8fI5GU7nWzUl/evf1/WaMg31/+we9fX2PN08WamoJavk7uGhJ9cuyTE0qGhLjiRp1sp1unmiUpI02Ner/1/uds1cEa22R/f0/P5tubm5fe5R9eGz57/y+Ee//yuavWqDPDw9tSwuVRusX/3C1qglq7QqMVNXKgslSdevX9fSpUsVExOje/fu6datW8MfGxERod/6rd96q+/Jp/35n/+5/vAP/1AzZ87U0qVLNWXKFLW0tOjUqVPDH+Pu7q758+d/wWcBAACAmbhCHgAAAAAA4DWEh4frm9/8pjIzM3XlyhX97d/+rW7cuGF2lkubPXu2fvM3f1Nz5szRRx99pJKSEo6wfwe//du/rW9/+9ty+/nx7YZh6OzZs7LZbDp69OjwGC+9el/zjIyM4SH/Y4+uXtC98yfl6eOj5fFpb9UTuXiFvvZX/yjfwCnDb+vpaNOdM8d06+RRdXe0vvLxW77yLU2JiBp+3NX2XDeOl+n5/dvauOfrCo6c9blfa8nWBAWG/8sJAV2tz3WtulhXKgr05MaV1+r97b/6H9qxY8fw47a2NpWWlr4yxk+fPl35+fm/cJ/4d/X48WNVVlbq8OHDOn78uIaGhobf9x//439URETEiH49AAAAjBwGeQAAAAAAgNfk5uammJgY/fZv/7amT5+ujz76SAcOHFB3d/eX/2G8FT8/P/3yL/+yUlNTdebMGf3v//2/X7lXN97MX/zFX+jMmTP6tV/7NS1dulQBAQHy9PTU1KlTtWXLFv3e7/2eampq9P7777/y5/7mb/5GP/jBD7RmzRp5+fgoICRca1J369/8qFwzlq15657l8an69wdqlfCt/6CZK6LlGzhF7p6eCgyfpnnrNmvnv/p3wx8bGDpVv/VDu9am75VfcKg8fXw1feEyZf3+n2nPd/7rF34db78A/drfHdDy+DT5TQkZflHC63KXtHB6uCoqKvThhx8qPT1dERER8vT0VFBQkGJiYvTd735XV69e1aZNm97iO/HZPvzwQ/3u7/6uNm3apJkzZ8rHx0fe3t6aNWuWrFarDh06pL/8y78csa8HAACAkedmfN45TgAAAAAAAPhchmHo8uXLKi4uliRZLBatWrXqjYc+vL7Hjx8rLy9Pvb292rVrl5YvX2520qR0sbVP9sYuszPGXMacQK0J5zYVAAAAeDMM8gAAAAAAAO+gu7tbdrtdV69e1ZIlS5SZmakpU6Z8+R/EW+nr65PNZtO1a9e0ceNGpaamytPT0+ysSaW5Z0j/+0aH2Rlj7l8tC1GEH//WAAAA8GYY5AEAAAAAAEbA9evXVVhYqMHBQaWkpGj9+vVcLT9KDMNQXV2dSktLFRERodzc3BG/Zzc+n8Mw9F8vtsoxiX6r6OEm/d7acLnzMw0AAIA3xCAPAAAAAAAwQvr6+lRaWqoLFy5o3rx5ys7OZigeRU+fPtX+/fvV3d0tq9WqlStXmp00aRQ8eKmrbf2aDL9YdJe0MsxHmXODzE4BAADABMQgDwAAAAAAMMLu3r2r/Px8dXV1KSEhQZs3b5a7u7vZWS6pv79f+fn5unr1qjZs2KC0tDR5eXmZneXynnYP6p9udpqdMWa+uTRYUf78uwIAAMCbY5AHAAAAAAAYBQMDA6qsrNTp06c1Y8YM7dq1SxEREWZnuSTDMHT+/HnZ7XZNnTpVubm5mjp1qtlZLu9/XW/X816HS18l7yYpws9Dv7os1OwUAAAATFAM8gAAAAAAAKPo4cOHstlsamtrU1xcnOLi4uTh4WF2lktqbm7W/v379eLFC2VlZWnNmjVmJ7m0S619KmrsMjtj1GXOCdTqcF+zMwAAADBBMcgDAAAAAACMsqGhIR07dky1tbWaOnWqrFarZs6caXaWSxoYGFBhYaEuXbqk6OhoZWRkcIT9KBl0Gvqby20acLrurxd93N30b1aHycvdzewUAAAATFAM8gAAAAAAAGOkqalJNptNTU1N2rx5sxISEhiLR4FhGKqvr1dRUZFCQ0O1b98+TZs2zewsl3TsSbdONve67LH1W6f7KX5GgNkZAAAAmMAY5AEAAAAAAMaQ0+nUyZMnVVVVpaCgIFmtVs2bN8/sLJf07Nkz5eXlqb29XRkZGYqOjpabG1c6j6RBp6F/vNauzgGnS43ybpJCfNz1a8tC5cnV8QAAAHgHDPIAAAAAAAAmaG1tlc1mU2Njo9avX6+UlBT5+nKf6pE2ODiooqIi1dfXa82aNcrMzJS3t7fZWS7lcfegPrzZaXbGiHt/SbBmBnCCBQAAAN4NgzwAAAAAAIBJDMPQ2bNnVV5eLh8fH2VlZWnJkiVmZ7mkS5cuqaCgQMHBwcrNzdX06dPNTnIplY+7VffMdY6uj43wU8JMjqoHAADAu2OQBwAAAAAAMFlnZ6cKCgp0+/ZtrVq1Sunp6QoIYAwcaS0tLcrLy1Nra6vS09O1fv16jrAfIa5ydD1H1QMAAGCkMcgDAAAAAACMA4Zh6NKlSyopKZGbm5ssFotWrlzJYDzCBgcHVVJSonPnzmnVqlXKysqSj4+P2Vku4XH3oH50s3PCD/Jf56h6AAAAjCAGeQAAAAAAgHGkq6tLxcXFunr1qpYsWaLMzExNmTLF7CyXc+XKFeXn5yswMFC5ubmKiooyO8klXG/v1+H7L83OeHM//xWpdW6AVoT7mxwDAAAAV8IgDwAAAAAAMA5dv35dhYWFGhwcVEpKCserj4K2tjbl5eXp2bNnSk1N1caNG/kej4CLrX2yN3aZnfHGnJeqNNu9V7/8y78sb29vs3MAAADgIhjkAQAAAAAAxqne3l6Vlpaqvr5e8+bNU3Z2tsLCwszOcilDQ0MqKyvTmTNntHz5clmtVvn6+pqdNeFNtFHeMidQwS+b9NOf/lTTp0/X1772Nf4dAAAAYEQwyAMAAAAAAIxzd+/eVX5+vrq6upSYmKjY2Fi5u7ubneVSrl27piNHjsjPz0+5ubmaOXOm2UkT3vX2fh35+fH14/EXkB+fhbBrfpCWhfhIkh49eqQf//jHCg0N1XvvvaeAgADzAgEAAOASGOQBAAAAAAAmgIGBAVVWVur06dOaOXOmrFarIiIizM5yKe3t7crLy1NTU5NSUlIUGxvLEfbv6HH3oPLvv1TngHPcjfIh3u7KnhekmQFer7y9ublZH374ofz8/PSNb3xDQUFBJhUCAADAFTDIAwAAAAAATCAPHz6UzWZTW1ub4uLiFBcXJw8PD7OzXIbD4VB5eblOnTqlpUuXateuXfLz8zM7a0IbdBqqedqjM8965SZzr5b/+OvHRvhpe5S/vNw/+wUXra2t+uCDD+Th4aH3339foaGhY9oJAAAA18EgDwAAAAAAMMEMDQ3p2LFjqq2t1dSpU2W1WjlifYTduHFDhw8flo+Pj3JycjR79myzkya88XC1/OddFf9ZOjo69OGHH2pwcFDf+MY3NHXq1DEoBAAAgKthkAcAAAAAAJigmpqaZLPZ1NTUpM2bNyshIUFeXl8+NOL1dHZ26sCBA3r06JGSkpK0detWjrB/R4NOQyebenTueZ/6ncaoXzH/8ef3cXfThmm+2hL5+VfFf5auri59+OGH6urq0te//nVFRUWNWisAAABcE4M8AAAAAADABOZ0OnXixAlVVVUpODhY2dnZmjdvntlZLsPhcOjo0aOqra3VokWLtHv3bgUEBJidNeENOg1da+/Xuee9au51jPgw7y7JKWm6n4dipvlpWajPGw3xn9Tb26sf/ehHam1t1XvvvcdpCQAAAHgjDPIAAAAAAAAuoKWlRfn5+WpsbNSGDRuUnJwsX19fs7Ncxu3bt3Xo0CF5eHgoJydHc+fONTvJZTztHtT5lj41tPfL8fPfVH48qL+uT368h5u0ItRH66f5Ksp/ZE6M6O/v109/+lM9efJEX/nKV7RgwYIR+bwAAABwfQzyAAAAAAAALsIwDJ09e1bl5eXy8fFRVlaWlixZYnaWy3jx4oUOHjyoxsZG7dy5U3FxcRxhP4KchqGWPoeaeobU1DOkJ92Det7nGB7pP4uHmzTN10MzArwU6e+pSH9PTfX1kPsoPC+Dg4P653/+Z927d0/79u3T0qVLR/xrAAAAwPUwyAMAAAAAALiYjo4OFRYW6vbt21q9erXS09Pl7+9vdpZLcDqdqqqqUk1NjRYsWKA9e/YoMDDQ7CyX5TQMdfQ7Neg05DAMDRmSp5vk4eYmL3c3hfi4j8r4/nkcDocOHDig69eva8+ePVq9evWYfW0AAABMTAzyAAAAAAAALsgwDF26dEnFxcVyd3eXxWL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+      "text/plain": [
+       "<Figure size 2000x2000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sql_generator.plot_sources_graph()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bfde8c96",
+   "metadata": {},
+   "source": [
+    "## 6. MCP Server: Interacting with Your Semantic Layer\n",
+    "\n",
+    "Now that you have a semantic layer, you can serve it as a MCP server to interact with it using natural language. The MCP server exposes your semantic layer as a set of tools that can be used by any MCP client.\n",
+    "\n",
+    "### Starting the MCP Server\n",
+    "\n",
+    "To start the MCP server, run the following command in your terminal:\n",
+    "\n",
+    "```bash\n",
+    "intugle-mcp\n",
+    "```\n",
+    "\n",
+    "This will start a server on `localhost:8000`.\n",
+    "\n",
+    "### Connecting to the MCP Server\n",
+    "\n",
+    "Once the server is running, you can connect to it from any MCP client. The endpoint for the MCP server is:\n",
+    "\n",
+    "`http://localhost:8000/semantic_layer/mcp`\n",
+    "\n",
+    "You can use a variety of MCP clients to connect to the server, such as Claude Desktop, Gemini CLI etc.\n",
+    "\n",
+    "### Use Cases\n",
+    "\n",
+    "Once connected, you can interact with your semantic layer using natural language. Here are some exciting applications:\n",
+    "\n",
+    "*   **Generate SQL Queries:** Ask questions in natural language and have the MCP server generate the corresponding SQL query.\n",
+    "*   **Data Discovery:** Ask questions about the tables and columns in your semantic layer to better understand your data.\n",
+    "\n",
+    "> To execute the generated SQL queries, you can also connect your database instance as a MCP tool to your client."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c29d33de",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "You've learned how to:\n",
+    "\n",
+    "* Configure your LLM provider.\n",
+    "* Profile your data to understand its characteristics.\n",
+    "* Use an LLM to automatically predict links between tables.\n",
+    "* Generate a semantic layer.\n",
+    "* Use the semantic layer to generate SQL queries.\n",
+    "* Interact with your semantic layer using the MCP server.\n",
+    "\n",
+    "This is just a starting point. This project has many other features to explore. We encourage you to try it with your own data and see how it can help you build a powerful semantic layer.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "data-tools",
+   "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": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/quickstart.ipynb b/quickstart.ipynb
index 669d62f..a381ff0 100644
--- a/quickstart.ipynb
+++ b/quickstart.ipynb
@@ -120,7 +120,7 @@
     "dataset_allergies.run(domain=\"Healthcare\")\n",
     "\n",
     "# Or you can run each step manually:\n",
-    "#dataset_allergies.profile().identify_datatypes().identify_keys().generate_glossary(domain=\"Healthcare\")"
+    "# dataset_allergies.profile().identify_datatypes().identify_keys().generate_glossary(domain=\"Healthcare\")"
    ]
   },
   {
@@ -169,7 +169,7 @@
     "for table_name in table_names:\n",
     "    df = pd.read_csv(generate_table_url(table_name))\n",
     "    dataset = DataSet(df, table_name)\n",
-    "    dataset.run(domain=\"Healthcare\")\n",
+    "    dataset.run(domain=\"Healthcare\", save=True)\n",
     "    datasets.append(dataset)"
    ]
   },
@@ -194,7 +194,7 @@
     "predictor = LinkPredictor(datasets)\n",
     "\n",
     "# Run the prediction\n",
-    "results = predictor.predict()\n",
+    "results = predictor.predict(save=True)\n",
     "results.links"
    ]
   },
diff --git a/src/intugle/adapters/adapter.py b/src/intugle/adapters/adapter.py
index 786e0c8..864b555 100644
--- a/src/intugle/adapters/adapter.py
+++ b/src/intugle/adapters/adapter.py
@@ -3,13 +3,14 @@
 
 from intugle.adapters.models import (
     ColumnProfile,
+    DataSetData,
     ProfilingOutput,
 )
 
 
 class Adapter(ABC):
     @abstractmethod
-    def profile(self, data: Any) -> ProfilingOutput:
+    def profile(self, data: Any, table_name: str) -> ProfilingOutput:
         pass
 
     @abstractmethod
@@ -31,3 +32,10 @@ def load():
     @abstractmethod
     def execute():
         raise NotImplementedError()
+    
+    @abstractmethod
+    def to_df(self: DataSetData, date, table_name: str):
+        raise NotImplementedError()
+    
+    def get_details(self, _: DataSetData):
+        return None
diff --git a/src/intugle/adapters/models.py b/src/intugle/adapters/models.py
index 0592390..2de485f 100644
--- a/src/intugle/adapters/models.py
+++ b/src/intugle/adapters/models.py
@@ -1,8 +1,15 @@
 from enum import Enum
 from typing import Any, List, Optional
 
+import pandas as pd
+
 from pydantic import BaseModel, Field
 
+from intugle.adapters.types.duckdb.models import DuckdbConfig
+
+# FIXME load dynamically
+DataSetData = pd.DataFrame | DuckdbConfig
+
 
 class ProfilingOutput(BaseModel):
     count: int
diff --git a/src/intugle/adapters/types/duckdb/duckdb.py b/src/intugle/adapters/types/duckdb/duckdb.py
index 9f12f12..555087d 100644
--- a/src/intugle/adapters/types/duckdb/duckdb.py
+++ b/src/intugle/adapters/types/duckdb/duckdb.py
@@ -5,7 +5,6 @@
 import duckdb
 import numpy as np
 import pandas as pd
-import pandas.api.types as ptypes
 
 from intugle.adapters.adapter import Adapter
 from intugle.adapters.factory import AdapterFactory
@@ -20,7 +19,16 @@
 
 
 class DuckdbAdapter(Adapter):
-    def profile(self, data: DuckdbConfig) -> ProfilingOutput:
+
+    @staticmethod
+    def check_data(data: Any) -> DuckdbConfig:
+        try:
+            data = DuckdbConfig.model_validate(data)
+        except Exception:
+            raise TypeError("Input must be a duckdb config.")
+        return data
+
+    def profile(self, data: DuckdbConfig, table_name: str) -> ProfilingOutput:
         """
         Generates a profile of a file.
 
@@ -33,8 +41,7 @@ def profile(self, data: DuckdbConfig) -> ProfilingOutput:
             - "columns": List of column names.
             - "dtypes": A dictionary mapping column names to generalized data types.
         """
-        if not isinstance(data, DuckdbConfig):
-            raise TypeError("Input must be a duckdb config.")
+        data = self.check_data(data)
 
         def __format_dtype__(dtype: Any) -> str:
             """Maps dtype to a generalized type string."""
@@ -47,7 +54,6 @@ def __format_dtype__(dtype: Any) -> str:
             }
             return type_map.get(dtype, "string")
 
-        table_name = "__profile_table__"
         self.load(data, table_name)
 
         # Fetching total count of table
@@ -101,9 +107,8 @@ def column_profile(
             A dictionary containing the profile for the column, or None if the
             column does not exist.
         """
-        if not isinstance(data, DuckdbConfig):
-            raise TypeError("Input must be a duckdb config.")
-        
+        data = self.check_data(data)
+
         self.load(data, table_name)
 
         start_ts = time.time()
@@ -111,10 +116,10 @@ def column_profile(
         # --- Calculations --- #
         query = f"""
         SELECT 
-            COUNT(DISTINCT {column_name}) AS distinct_count,
-            SUM(CASE WHEN {column_name} IS NULL THEN 1 ELSE 0 END) AS null_count
+            COUNT(DISTINCT "{column_name}") AS distinct_count,
+            SUM(CASE WHEN "{column_name}" IS NULL THEN 1 ELSE 0 END) AS null_count
         FROM  
-            {table_name}
+            "{table_name}"
         """
         distinct_null_data = duckdb.execute(query).fetchall()
 
@@ -125,11 +130,11 @@ def column_profile(
         # 1. Get a sample of distinct values.
         sample_query = f"""
         SELECT 
-            DISTINCT CAST( {column_name} AS VARCHAR) AS sample_values 
+            DISTINCT CAST( "{column_name}" AS VARCHAR) AS sample_values 
         FROM 
-            {table_name} 
+            "{table_name}"
         WHERE 
-            {column_name} IS NOT NULL LIMIT {dtype_sample_limit}
+            "{column_name}" IS NOT NULL LIMIT {dtype_sample_limit}
         """
         data = duckdb.execute(sample_query).fetchall()
         distinct_values = [d[0] for d in data]
@@ -193,17 +198,50 @@ def _get_load_func(data: DuckdbConfig):
 
         return f"{ld_func}('{data.path}')"
 
-    def load(self, data: DuckdbConfig, table_name: str):
-        ld_func = self._get_load_func(data)
+    def load_view(self, data: DuckdbConfig, table_name: str):
+        query = f"""CREATE OR REPLACE VIEW {table_name} AS {data.path}"""
+        duckdb.execute(query)
 
+    def load_file(self, data: DuckdbConfig, table_name: str):
+        ld_func = self._get_load_func(data)
         query = f"""CREATE TABLE IF NOT EXISTS {table_name} AS SELECT * FROM {ld_func};"""
 
         duckdb.execute(query)
 
-    def execute(): ...
+    def load(self, data: DuckdbConfig, table_name: str):
+        data = self.check_data(data)
+        if data.type == "query":
+            self.load_view(data, table_name)
+            return
+        self.load_file(data, table_name)
+    
+    def execute_df(self, query):
+        df = duckdb.sql(query).to_df()
+        return df
+
+    def to_df(self, _: DuckdbConfig, table_name: str):
+        query = f"SELECT * from {table_name}"
+        df = self.execute_df(query)
+        return df
+
+    def execute(self, query):
+        df = self.execute_df(query)
+        data = df.to_dict(orient="records")
+        return data
+
+    async def aexecute(self, query):
+        result = duckdb.sql(query).fetchnumpy()
+        df = pd.DataFrame(result)
+        data = df.to_dict(orient="records")
+        return data
+
+    def get_details(self, data: DuckdbConfig):
+        data = self.check_data(data)
+        return data.model_dump()
 
 
 def can_handle_pandas(df: Any) -> bool:
+    df = DuckdbAdapter.check_data(df)
     return isinstance(df, DuckdbConfig)
 
 
diff --git a/src/intugle/adapters/types/pandas/pandas.py b/src/intugle/adapters/types/pandas/pandas.py
index 95933ff..f8bfb48 100644
--- a/src/intugle/adapters/types/pandas/pandas.py
+++ b/src/intugle/adapters/types/pandas/pandas.py
@@ -18,7 +18,7 @@
 
 
 class PandasAdapter(Adapter):
-    def profile(self, data: pd.DataFrame) -> ProfilingOutput:
+    def profile(self, data: pd.DataFrame, _: str) -> ProfilingOutput:
         """
         Generates a profile of a pandas DataFrame.
 
@@ -148,11 +148,15 @@ def column_profile(
             ts=time.time() - start_ts,
         )
     
-    def load():
+    def load(self, data: pd.DataFrame, table_name: str):
         ...
+        # duckdb.sql(f"CREATE TABLE {table_name} AS SELECT * FROM data")
     
     def execute():
         ...
+    
+    def to_df(self, data):
+        return data
 
 
 def can_handle_pandas(data: Any) -> bool:
diff --git a/src/intugle/analysis/models.py b/src/intugle/analysis/models.py
index b7b95bb..139b8c9 100644
--- a/src/intugle/analysis/models.py
+++ b/src/intugle/analysis/models.py
@@ -12,6 +12,7 @@
     BusinessGlossaryOutput,
     ColumnGlossary,
     ColumnProfile,
+    DataSetData,
     DataTypeIdentificationL1Output,
     DataTypeIdentificationL2Input,
     DataTypeIdentificationL2Output,
@@ -27,9 +28,6 @@
 from intugle.models.resources.model import Column, ColumnProfilingMetrics
 from intugle.models.resources.source import Source, SourceTables
 
-# FIXME add type
-DataSetData = pd.DataFrame
-
 
 class DataSet:
     """
@@ -49,11 +47,21 @@ def __init__(self, data: DataSetData, name: str):
         # A dictionary to store the results of each analysis step
         self.results: Dict[str, Any] = {}
 
+        self.load()
+    
+    def load(self):
+        try:
+            self.adapter.load(self.data, self.name)
+            print(f"{self.name} loaded")
+        except Exception as e:
+            print("eee", e)
+            ...
+
     def profile_table(self) -> Self:
         """
         Profiles the table and stores the result in the 'results' dictionary.
         """
-        self.results["table_profile"] = self.adapter.profile(self.data)
+        self.results["table_profile"] = self.adapter.profile(self.data, self.name)
         return self
 
     def profile_columns(self) -> Self:
@@ -145,8 +153,6 @@ def profile(self) -> Self:
         Profiles the dataset including table and columns and stores the result in the 'results' dictionary.
         This is a convenience method to run profiling on the raw dataframe.
         """
-        if self.data.empty:
-            raise ValueError("The raw dataframe is empty. Cannot perform profiling.")
         self.profile_table().profile_columns()
         return self
 
@@ -155,8 +161,6 @@ def identify_datatypes(self) -> Self:
         Identifies the data types for the dataset and stores the result in the 'results' dictionary.
         This is a convenience method to run data type identification on the raw dataframe.
         """
-        if self.data.empty:
-            raise ValueError("The raw dataframe is empty. Cannot perform data type identification.")
         self.identify_datatypes_l1().identify_datatypes_l2()
         return self
 
@@ -194,15 +198,11 @@ def generate_glossary(self, domain: str = "") -> Self:
         self.results["table_glossary"] = glossary_output.table_glossary
         return self
 
-
     def run(self, domain: str, save: bool = True) -> Self:
-        """Run all stages """
+        """Run all stages"""
+
+        self.profile().identify_datatypes().identify_keys().generate_glossary(domain=domain)
 
-        self.profile()\
-            .identify_datatypes()\
-            .identify_keys()\
-            .generate_glossary(domain=domain)
-        
         if save:
             self.save_yaml()
 
@@ -244,7 +244,9 @@ def save_yaml(self, file_path: Optional[str] = None) -> None:
             )
             columns.append(column)
 
-        table = SourceTables(name=self.name, description=table_description, columns=columns)
+        details = self.adapter.get_details(self.data)
+
+        table = SourceTables(name=self.name, description=table_description, columns=columns, details=details)
 
         source = Source(name="healthcare", description=table_description, schema="public", database="", table=table)
 
@@ -253,6 +255,9 @@ def save_yaml(self, file_path: Optional[str] = None) -> None:
         # Save the YAML representation of the sources
         with open(file_path, "w") as file:
             yaml.dump(sources, file, sort_keys=False, default_flow_style=False)
+    
+    def to_df(self):
+        return self.adapter.to_df(self.data, self.name)
 
     def result_to_pandas(self):
         column_profiles = self.results.get("column_profiles")
diff --git a/src/intugle/libs/query_generator/operators.py b/src/intugle/libs/query_generator/operators.py
index 1a34d63..4511e70 100644
--- a/src/intugle/libs/query_generator/operators.py
+++ b/src/intugle/libs/query_generator/operators.py
@@ -5,11 +5,11 @@
 class Operators:
     @classmethod
     def _in(cls, field, value, **_):
-        return "{} IN {}".format(field, value)
+        return "{} IN {}".format(field, tuple(value))
 
     @classmethod
     def _not_in(cls, field, value, **_):
-        return "{} NOT IN {}".format(field, value)
+        return "{} NOT IN {}".format(field, tuple(value))
 
     @classmethod
     def _like(cls, field, value, **_):
diff --git a/src/intugle/libs/query_generator/query_generator.py b/src/intugle/libs/query_generator/query_generator.py
index 7e8f532..7240fbc 100644
--- a/src/intugle/libs/query_generator/query_generator.py
+++ b/src/intugle/libs/query_generator/query_generator.py
@@ -4,7 +4,7 @@
 
 from .operators import Operators
 from .transformation import Transformations
-from .utils import get_formatted_dataset_field
+from .utils import get_formatted_dataset_field, get_formatted_name
 
 
 class QueryGeneratorModel(BaseModel):
@@ -53,7 +53,7 @@ def get_select_fields(cls, selectFields):
 
                 renameCol = field.get("renameCol", None)
                 if renameCol:
-                    expr = f"{expr} as `{renameCol}`"
+                    expr = f"{expr} as {get_formatted_name(renameCol)}"
 
                 selectedFields.append(expr)
             selectedFieldsExpr = ", ".join(selectedFields)
@@ -70,7 +70,7 @@ def get_join_expr(cls, joinFields):
         for _i in range(tbl_cnt):
             i = str(_i)
             if _i == 0:
-                join_query += f"`{joinFields[i]['dataset']}`"
+                join_query += f"{get_formatted_name(joinFields[i]['dataset'])}"
             else:
                 joining_cndns = []
                 for join_col in joinFields[i]["fields"]:
@@ -92,7 +92,7 @@ def get_join_expr(cls, joinFields):
                     " "
                     + joinFields[i].get("join_type", "").upper()
                     + " JOIN "
-                    + f"`{joinFields[i]['dataset']}`"
+                    + f"{get_formatted_name(joinFields[i]['dataset'])}"
                     + " ON "
                     + str(joining_cndn)
                 )
@@ -132,7 +132,7 @@ def get_groupby_expr(cls, groupbyFields):
                 # if _dataset is not None:
                 #     _dataset_field = get_formatted_dataset_field(_dataset, _field)
                 # else:
-                #     _dataset_field = f"`{_field}`"
+                #     _dataset_field = f"{_field}"
 
                 # _func = field.get("function", None)
                 # if _func is not None:
@@ -140,7 +140,7 @@ def get_groupby_expr(cls, groupbyFields):
                 #     _dataset_field = f"{_func}({_dataset_field})"
                 renameCol = field.get("renameCol", None)
                 if renameCol is not None:
-                    _dataset_field = f"`{renameCol}`"
+                    _dataset_field = get_formatted_name(renameCol)
                 else:
                     _dataset_field = field["sql_code"]
                 groupbyCols.append(_dataset_field)
@@ -163,7 +163,7 @@ def get_sort_expr(cls, sortFields):
             for sortField in sortFields:
                 renameCol = sortField.get("renameCol", None)
                 if renameCol is not None:
-                    sortCol = f"`{renameCol}`"
+                    sortCol = get_formatted_name(renameCol)
                 else:
                     sortCol = get_formatted_dataset_field(
                         sortField["dataset"], sortField["field"]
@@ -200,7 +200,7 @@ def getQuery(cls, config: QueryGeneratorModel):
 
         # From dataset
         # if "from" in config:
-        #     sqlQuery = sqlQuery + " FROM " + f"`{config['from']}`"
+        #     sqlQuery = sqlQuery + " FROM " + f"{config['from']}"
 
         if config.join != {}:
             sqlQuery += cls.get_join_expr(config.join)
diff --git a/src/intugle/libs/query_generator/utils.py b/src/intugle/libs/query_generator/utils.py
index 3f0ae86..15853eb 100644
--- a/src/intugle/libs/query_generator/utils.py
+++ b/src/intugle/libs/query_generator/utils.py
@@ -1,2 +1,6 @@
 def get_formatted_dataset_field(dataset: str, field: str) -> str:
-    return f"`{dataset}`.`{field}`"
+    return f"{dataset}.{field}"
+
+
+def get_formatted_name(name: str) -> str:
+    return name
\ No newline at end of file
diff --git a/src/intugle/libs/smart_query_generator/utils/filter.py b/src/intugle/libs/smart_query_generator/utils/filter.py
index 614fd0d..5a2b8b3 100644
--- a/src/intugle/libs/smart_query_generator/utils/filter.py
+++ b/src/intugle/libs/smart_query_generator/utils/filter.py
@@ -62,7 +62,7 @@ def build_selection(self):
                         "sql_code": field.sql_code,
                         "operator": "not_in" if selection.exclude else "in",
                         "md5_hash": field.is_pii and not self._can_view_pii,
-                        "params": {"value": selection.values},
+                        "params": {"value": selection.values, "type": "list"},
                     }
                     if selection.dimFunc:
                         sel["isFunction"] = True
@@ -76,7 +76,7 @@ def build_selection(self):
                         "sql_code": field.sql_code,
                         "operator": "is_not_null" if selection.exclude else "is_null",
                         "md5_hash": field.is_pii and not self._can_view_pii,
-                        "params": {"value": ""},
+                        "params": {"value": "", "type": "list"},
                     }
                     if selection.dimFunc:
                         sel_null["isFunction"] = True
diff --git a/src/intugle/link_predictor/predictor.py b/src/intugle/link_predictor/predictor.py
index aa01b2d..c10410e 100644
--- a/src/intugle/link_predictor/predictor.py
+++ b/src/intugle/link_predictor/predictor.py
@@ -143,7 +143,7 @@ def _predict_for_pair(
         ]
         return pair_links
 
-    def predict(self, filename='relationships.yml') -> Self:
+    def predict(self, filename='relationships.yml', save: bool = False) -> Self:
         """
         Iterates through all unique pairs of datasets, predicts the links for
         each pair, and returns the aggregated results.
@@ -165,7 +165,9 @@ def predict(self, filename='relationships.yml') -> Self:
                 print("No links found for this pair.")
 
         self.links = all_links
-        self.save_yaml(file_path=filename)
+        if save:
+            self.save_yaml(file_path=filename)
+
         return self
     
     def get_links_df(self):
diff --git a/src/intugle/mcp/adapter/service.py b/src/intugle/mcp/adapter/service.py
index 9e37fd3..55e5646 100644
--- a/src/intugle/mcp/adapter/service.py
+++ b/src/intugle/mcp/adapter/service.py
@@ -1,7 +1,7 @@
-from intugle.core.settings import settings
-from intugle.mcp.adapter.psql import PsqlPool
-
-# from intugle.parser.manifest import ManifestLoader
+# from intugle.adapters.factory import AdapterFactory
+from intugle.adapters.types.duckdb.duckdb import DuckdbAdapter
+from intugle.analysis.models import DataSet
+from intugle.mcp.manifest import manifest_loader
 
 
 class AdapterService:
@@ -9,16 +9,19 @@ class AdapterService:
     Adapter service for executing queries.
     """
 
-    def __init__(self):
-        # self.manifest = ManifestLoader.manifest
-        self.psql = PsqlPool(
-            user=settings.POSTGRES_USER,
-            password=settings.POSTGRES_PASSWORD,
-            host=settings.POSTGRES_HOST,
-            database=settings.POSTGRES_DB,
-            port=settings.POSTGRES_PORT,
-            schema=settings.POSTGRES_SCHEMA,
-        )
+    # Not good way to do it Need to create extandable and properly couple with adapter
+    def __init__(self, adapter: str = "duckdb"):
+        self.manifest = manifest_loader.manifest
+        self.adapter = DuckdbAdapter()
+        self.load_all()
+
+    def load_all(self):
+        sources = self.manifest.sources
+        for source in sources.values():
+            table_name = source.table.name
+            details = source.table.details
+
+            DataSet(data=details, name=table_name)
 
     async def execute_query(self, sql_query: str) -> list[dict]:
         """
@@ -31,11 +34,11 @@ async def execute_query(self, sql_query: str) -> list[dict]:
             list[dict]: The result of the query execution.
         """
 
-        data = await self.psql.fetch(sql_query)
+        data = self.adapter.execute(sql_query)
 
         data = [dict(record) for record in data] if data else []
 
         return data
 
 
-adapter_service = AdapterService()
\ No newline at end of file
+adapter_service = AdapterService()
diff --git a/src/intugle/mcp/semantic_layer/router.py b/src/intugle/mcp/semantic_layer/router.py
index 25239a8..c608dcf 100644
--- a/src/intugle/mcp/semantic_layer/router.py
+++ b/src/intugle/mcp/semantic_layer/router.py
@@ -56,7 +56,7 @@ async def prompt() -> str:
     return Prompts.raw_executor_prompt(settings.SQL_DIALECT, settings.DOMAIN, settings.UNIVERSAL_INSTRUCTIONS)
 
 
-# @semantic_layer_mcp.tool(name="execute_query", description="Return the result of a query execution")
-# async def execute_query(sql_query: str) -> list[dict]: 
-#     data = await adapter_service.execute_query(sql_query)
-#     return data
\ No newline at end of file
+@semantic_layer_mcp.tool(name="execute_query", description="Return the result of a query execution")
+async def execute_query(sql_query: str) -> list[dict]: 
+    data = await adapter_service.execute_query(sql_query)
+    return data
\ No newline at end of file
diff --git a/src/intugle/mcp/semantic_layer/service.py b/src/intugle/mcp/semantic_layer/service.py
index fe29a26..1c9d28a 100644
--- a/src/intugle/mcp/semantic_layer/service.py
+++ b/src/intugle/mcp/semantic_layer/service.py
@@ -1,15 +1,15 @@
 from intugle.common.exception import errors
+from intugle.mcp.manifest import manifest_loader
 
-# from intugle.mcp.manifest import manifest_loader
-from intugle.core import settings
-from intugle.parser.manifest import ManifestLoader
+# from intugle.core import settings
+# from intugle.parser.manifest import ManifestLoader
 from intugle.parser.table_schema import TableSchema
 
 
 class SemanticLayerService:
     def __init__(self):
-        manifest_loader = ManifestLoader(settings.PROJECT_BASE)
-        manifest_loader.load()
+        # manifest_loader = ManifestLoader(settings.PROJECT_BASE)
+        # manifest_loader.load()
         self.manifest = manifest_loader.manifest
 
         self.table_schema = TableSchema(self.manifest)
diff --git a/src/intugle/models/resources/source.py b/src/intugle/models/resources/source.py
index 6cf0694..39ce193 100644
--- a/src/intugle/models/resources/source.py
+++ b/src/intugle/models/resources/source.py
@@ -11,7 +11,7 @@ class SourceTables(SchemaBase):
     name: str
     description: str
     tags: Optional[List[str]] = Field(default_factory=list)
-    # details: dict
+    details: Optional[dict]
     columns: List[Column] = Field(default_factory=list)
     profiling_metrics: Optional[ModelProfilingMetrics] = None
 
diff --git a/src/intugle/sql_generator.py b/src/intugle/sql_generator.py
index a4a801e..3ef73d8 100644
--- a/src/intugle/sql_generator.py
+++ b/src/intugle/sql_generator.py
@@ -1,5 +1,6 @@
 from typing import TYPE_CHECKING
 
+from intugle.analysis.models import DataSet
 from intugle.core import settings
 from intugle.libs.smart_query_generator import SmartQueryGenerator
 from intugle.libs.smart_query_generator.models.models import ETLModel, FieldDetailsModel, LinkModel
@@ -28,6 +29,16 @@ def __init__(self, project_base: str = settings.PROJECT_BASE):
         selected_fields = set(self.field_details.keys())
         self.join = Join(self.links, selected_fields)
 
+        self.load_all()
+
+    def load_all(self):
+        sources = self.manifest.sources
+        for source in sources.values():
+            table_name = source.table.name
+            details = source.table.details
+
+            DataSet(data=details, name=table_name)
+
     def generate_query(self, etl: ETLModel) -> str:
         """Generates a SQL query based on the ETL model.
 
@@ -37,6 +48,8 @@ def generate_query(self, etl: ETLModel) -> str:
         Returns:
             str: The generated SQL query.
         """
+        etl = ETLModel.model_validate(etl)
+
         # get the field details fetcher function (Not required now as we are getting all field details)
         # and all field details
         field_details_fetcher = self.get_field_details_fetcher()
@@ -45,8 +58,28 @@ def generate_query(self, etl: ETLModel) -> str:
         sql_builder = SmartQueryGenerator(etl, field_details_fetcher, self.links, self.field_details)
         sql_builder.build()
         sql_query = sql_builder.get_query()
-
         return sql_query
+        
+    def generate_product(self, etl: ETLModel) -> DataSet:
+        """Generates a dataset on the ETL model.
+
+        Args:
+            etl (ETLModel): The ETL model containing the configuration for the query.
+
+        Returns:
+            Dataset: The dataset.
+        """
+        etl = ETLModel.model_validate(etl)
+
+        sql_query = self.generate_query(etl)
+
+        data = {
+            "path": sql_query,
+            "type": "query"
+        }
+        print(etl.name)
+        dataset = DataSet(data, etl.name)
+        return dataset
 
     def get_all_field_details(self) -> dict[str, FieldDetailsModel]:
         """Fetches all field details from the manifest."""
@@ -149,3 +182,4 @@ def plot_sources_graph(self):
         graph = self.join.generate_graph(list(assets), only_connected=False)
 
         self.plot_graph(graph)
+    
diff --git a/tests/analysis/test_profile.py b/tests/analysis/test_profile.py
index 9d1bb16..43e8df3 100644
--- a/tests/analysis/test_profile.py
+++ b/tests/analysis/test_profile.py
@@ -1,22 +1,22 @@
-# from intugle.adapters.types.duckdb.models import DuckdbConfig
-# from intugle.analysis.models import DataSet
+from intugle.adapters.types.duckdb.models import DuckdbConfig
+from intugle.analysis.models import DataSet
 
 
-# def test_profile():
-#     """
-#     Tests the profile analysis step.
-#     """
-#     # 1. Prepare dummy data
-#     data = DuckdbConfig.model_validate({
-#         "path": "",  # noqa: E501
-#         "type": "csv"
-#     })
-#     table_name = "allergies"
+def test_profile():
+    """
+    Tests the profile analysis step.
+    """
+    # 1. Prepare dummy data
+    data = DuckdbConfig.model_validate({
+        "path": "",  # noqa: E501
+        "type": "csv"
+    })
+    table_name = "allergies"
 
-#     # 2. Initialize DataSet
-#     dataset = DataSet(data, name=table_name)
+    # 2. Initialize DataSet
+    dataset = DataSet(data, name=table_name)
 
-#     # 3. Run profiling
-#     dataset.profile_table()
-#     dataset.profile_columns()
+    # 3. Run profiling
+    dataset.profile_table()
+    dataset.profile_columns()
 
diff --git a/tests/mcp/test_semantic_layer.py b/tests/mcp/test_semantic_layer.py
index d49fe2a..2ecbff4 100644
--- a/tests/mcp/test_semantic_layer.py
+++ b/tests/mcp/test_semantic_layer.py
@@ -62,8 +62,8 @@ async def get_schema(self, session: ClientSession = None):
 
     @connection_decorator()
     async def execute_query(self, session: ClientSession = None):
-        await session.call_tool(
-            name=Tools.EXECUTE_QUERY, arguments={"sql_query": "SELECT * FROM orders LIMIT 10"}
+        data = await session.call_tool(
+            name=Tools.EXECUTE_QUERY, arguments={"sql_query": "SELECT 1"}
         )
         # assert isinstance(schemas.structuredContent, dict)
 
diff --git a/uv.lock b/uv.lock
index 5b569d1..c9efc93 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 1
+revision = 3
 requires-python = ">=3.10"
 resolution-markers = [
     "python_full_version >= '3.13'",
@@ -13,9 +13,9 @@ resolution-markers = [
 name = "aiohappyeyeballs"
 version = "2.6.1"
 source = { registry = "https://pypi.org/simple" }
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+sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760, upload-time = "2025-03-12T01:42:48.764Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265, upload-time = "2025-03-12T01:42:47.083Z" },
 ]
 
 [[package]]
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     { name = "propcache" },
     { name = "yarl" },
 ]
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From b84fcaea8ceb3c0a9aafa046ee8e409ba04aad30 Mon Sep 17 00:00:00 2001
From: juhel-phanju-intugle <juhel@intugle.ai>
Date: Tue, 2 Sep 2025 17:54:40 +0530
Subject: [PATCH 02/25] feat: updated model version compatible to
 scikit-learn==1.7.1, xgboost==3.0.4

---
 pyproject.toml                                |    8 +-
 quickstart.ipynb                              |    2 +-
 .../di_l1_classifier_encoder_13052023.pkl     |  Bin 348 -> 579 bytes
 .../di_l1_classifier_xgb_13052023.pkl         |  Bin 14848033 -> 23584758 bytes
 .../datatype_identification/l1_model.py       |    6 +-
 uv.lock                                       | 6271 +++++++++--------
 6 files changed, 3208 insertions(+), 3079 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 55c8c7e..25994e6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -29,13 +29,13 @@ dependencies = [
     "langchain-openai>=0.3.28",
     "langgraph>=0.6.4",
     "nltk>=3.9.1",
-    "numpy==1.26.2",
+    "numpy>=2.3.0",
     "asyncpg>=0.30.0",
     "fastapi[standard]>=0.116.1",
     "matplotlib>=3.10.5",
     "mcp[cli]>=1.12.4",
     "networkx>=3.4.2",
-    "pandas>=2.3.1",
+    "pandas>=2.3.0",
     "pyaml>=25.7.0",
     "pydantic>=2.11.7",
     "pydantic-settings>=2.10.1",
@@ -43,14 +43,14 @@ dependencies = [
     "python-dotenv>=1.1.1",
     "symspellpy>=6.9.0",
     "trieregex>=1.0.0",
-    "xgboost==1.7.5",
+    "xgboost<=3.0.4",
     "pyyaml>=6.0.2",
     "langchain-deepseek>=0.1.4",
     "langchain-nvidia-ai-endpoints>=0.3.16",
     "langchain-xai>=0.2.5",
     "langchain-perplexity>=0.1.2",
     "duckdb>=1.3.2",
-    "scikit-learn==1.2.2",
+    "scikit-learn<=1.7.1",
     "langchain[anthropic,google-genai,openai]>=0.3.27",
 ]
 
diff --git a/quickstart.ipynb b/quickstart.ipynb
index 669d62f..608ae53 100644
--- a/quickstart.ipynb
+++ b/quickstart.ipynb
@@ -368,7 +368,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "data-tools",
+   "display_name": "intugle",
    "language": "python",
    "name": "python3"
   },
diff --git a/src/intugle/artifacts/model_objects/datatype_l1_identification/di_l1_classifier_encoder_13052023.pkl b/src/intugle/artifacts/model_objects/datatype_l1_identification/di_l1_classifier_encoder_13052023.pkl
index 7a84d3eb4b40c91a0b736a116de4e990e0f93186..19a7d30bc94f2976dbf5932a5bc74f6dd74190dd 100644
GIT binary patch
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diff --git a/src/intugle/artifacts/model_objects/datatype_l1_identification/di_l1_classifier_xgb_13052023.pkl b/src/intugle/artifacts/model_objects/datatype_l1_identification/di_l1_classifier_xgb_13052023.pkl
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diff --git a/src/intugle/core/pipeline/datatype_identification/l1_model.py b/src/intugle/core/pipeline/datatype_identification/l1_model.py
index cc6d931..7ab22b5 100644
--- a/src/intugle/core/pipeline/datatype_identification/l1_model.py
+++ b/src/intugle/core/pipeline/datatype_identification/l1_model.py
@@ -1,9 +1,9 @@
 import logging
 import os
-import pickle
 
 from datetime import datetime
 
+import joblib
 import numpy as np
 import pandas as pd
 
@@ -84,11 +84,11 @@ def model_prediction(self, test_df: pd.DataFrame, model_version=datetime.now().s
         model_path = os.path.join(self.model_objects_directory, 'di_l1_classifier_xgb_' + model_version + '.pkl')
         encoder_path = os.path.join(self.model_objects_directory, 'di_l1_classifier_encoder_' + model_version + '.pkl')
         with open(model_path, 'rb') as f:
-            clf_model = pickle.load(f) 
+            clf_model = joblib.load(f) 
         log.info("[*] Loaded L1 classifier model")
         # Loading the encoder objects for model prediction
         with open(encoder_path, 'rb') as f:
-            encoder = pickle.load(f)
+            encoder = joblib.load(f)
         log.info("[*] Loaded L1 Encoder")
         
         # Model prediction on the test dataset and inverse transform to get the labels for the encoder
diff --git a/uv.lock b/uv.lock
index 5b569d1..94c92af 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,10 +1,9 @@
 version = 1
-revision = 1
+revision = 3
 requires-python = ">=3.10"
 resolution-markers = [
     "python_full_version >= '3.13'",
-    "python_full_version >= '3.12.4' and python_full_version < '3.13'",
-    "python_full_version >= '3.12' and python_full_version < '3.12.4'",
+    "python_full_version == '3.12.*'",
     "python_full_version == '3.11.*'",
     "python_full_version < '3.11'",
 ]
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+sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760, upload-time = "2025-03-12T01:42:48.764Z" }
 wheels = [
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From 49a06c24704bd04db71c41cb17e1bb7fbed28f9f Mon Sep 17 00:00:00 2001
From: juhel-phanju-intugle <juhel@intugle.ai>
Date: Sun, 7 Sep 2025 01:07:41 +0530
Subject: [PATCH 03/25] feat: added fallback for np.float128 as it's not
 supported by all systems

---
 pyproject.toml                                |   2 +-
 quickstart.ipynb                              | 356 +++++++++++++++++-
 .../custom_features.py                        |   7 +-
 src/intugle/core/utilities/processing.py      | 112 +++---
 uv.lock                                       | 123 +-----
 5 files changed, 433 insertions(+), 167 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 25994e6..a42db5c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -29,7 +29,7 @@ dependencies = [
     "langchain-openai>=0.3.28",
     "langgraph>=0.6.4",
     "nltk>=3.9.1",
-    "numpy>=2.3.0",
+    "numpy<=2.3.0",
     "asyncpg>=0.30.0",
     "fastapi[standard]>=0.116.1",
     "matplotlib>=3.10.5",
diff --git a/quickstart.ipynb b/quickstart.ipynb
index 608ae53..fe9581d 100644
--- a/quickstart.ipynb
+++ b/quickstart.ipynb
@@ -86,10 +86,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "7f0e28d7",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/juhel-phanju/.pyenv/ttt/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n",
+      "/home/juhel-phanju/.pyenv/ttt/lib/python3.12/site-packages/pydantic/_internal/_fields.py:198: UserWarning: Field name \"schema\" in \"Source\" shadows an attribute in parent \"BaseResource\"\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "\n",
@@ -98,10 +109,185 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "0e8971bb",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
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+       "      <td>environment</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2020-02-17</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85</td>\n",
+       "      <td>01efcc52-15d6-51e9-faa2-bee069fcbe44</td>\n",
+       "      <td>264287008</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>Animal dander (substance)</td>\n",
+       "      <td>allergy</td>\n",
+       "      <td>environment</td>\n",
+       "      <td>878820003.0</td>\n",
+       "      <td>Rhinoconjunctivitis (disorder)</td>\n",
+       "      <td>MODERATE</td>\n",
+       "      <td>271807003.0</td>\n",
+       "      <td>Eruption of skin (disorder)</td>\n",
+       "      <td>MILD</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2020-02-17</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85</td>\n",
+       "      <td>01efcc52-15d6-51e9-faa2-bee069fcbe44</td>\n",
+       "      <td>256277009</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>Grass pollen (substance)</td>\n",
+       "      <td>allergy</td>\n",
+       "      <td>environment</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        start  stop                               patient  \\\n",
+       "0  2020-02-17   NaN  b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85   \n",
+       "1  2020-02-17   NaN  b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85   \n",
+       "2  2020-02-17   NaN  b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85   \n",
+       "3  2020-02-17   NaN  b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85   \n",
+       "4  2020-02-17   NaN  b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85   \n",
+       "\n",
+       "                              encounter       code   system  \\\n",
+       "0  01efcc52-15d6-51e9-faa2-bee069fcbe44  111088007  Unknown   \n",
+       "1  01efcc52-15d6-51e9-faa2-bee069fcbe44   84489001  Unknown   \n",
+       "2  01efcc52-15d6-51e9-faa2-bee069fcbe44  260147004  Unknown   \n",
+       "3  01efcc52-15d6-51e9-faa2-bee069fcbe44  264287008  Unknown   \n",
+       "4  01efcc52-15d6-51e9-faa2-bee069fcbe44  256277009  Unknown   \n",
+       "\n",
+       "                  description     type     category    reaction1  \\\n",
+       "0           Latex (substance)  allergy  environment  247472004.0   \n",
+       "1             Mold (organism)  allergy  environment   76067001.0   \n",
+       "2  House dust mite (organism)  allergy  environment          NaN   \n",
+       "3   Animal dander (substance)  allergy  environment  878820003.0   \n",
+       "4    Grass pollen (substance)  allergy  environment          NaN   \n",
+       "\n",
+       "                     description1 severity1    reaction2  \\\n",
+       "0                 Wheal (finding)      MILD          NaN   \n",
+       "1                        Sneezing      MILD          NaN   \n",
+       "2                             NaN       NaN          NaN   \n",
+       "3  Rhinoconjunctivitis (disorder)  MODERATE  271807003.0   \n",
+       "4                             NaN       NaN          NaN   \n",
+       "\n",
+       "                  description2 severity2  \n",
+       "0                          NaN       NaN  \n",
+       "1                          NaN       NaN  \n",
+       "2                          NaN       NaN  \n",
+       "3  Eruption of skin (disorder)      MILD  \n",
+       "4                          NaN       NaN  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Load the sample data\n",
     "allergies_df = pd.read_csv('https://raw.githubusercontent.com/Intugle/data-tools/refs/heads/main/sample_data/healthcare/allergies.csv')\n",
@@ -110,10 +296,165 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "8d3e8299",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/juhel-phanju/.pyenv/ttt/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3859: RuntimeWarning: Mean of empty slice.\n",
+      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
+      "/home/juhel-phanju/.pyenv/ttt/lib/python3.12/site-packages/numpy/_core/_methods.py:144: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "[!] Empty column encountered for allergies ==> stop ...\n",
+      "100%|██████████| 15/15 [00:07<00:00,  1.93it/s]\n",
+      "100%|██████████| 15/15 [00:28<00:00,  1.90s/it]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>column_name</th>\n",
+       "      <th>business_name</th>\n",
+       "      <th>table_name</th>\n",
+       "      <th>null_count</th>\n",
+       "      <th>count</th>\n",
+       "      <th>distinct_count</th>\n",
+       "      <th>uniqueness</th>\n",
+       "      <th>completeness</th>\n",
+       "      <th>sample_data</th>\n",
+       "      <th>dtype_sample</th>\n",
+       "      <th>ts</th>\n",
+       "      <th>datatype_l1</th>\n",
+       "      <th>datatype_l2</th>\n",
+       "      <th>business_glossary</th>\n",
+       "      <th>business_tags</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>start</td>\n",
+       "      <td>start</td>\n",
+       "      <td>allergies</td>\n",
+       "      <td>0</td>\n",
+       "      <td>794</td>\n",
+       "      <td>179</td>\n",
+       "      <td>0.225441</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[1961-12-13, 1980-03-10, 2004-09-28, 1961-02-0...</td>\n",
+       "      <td>[2020-02-17, 1981-05-17, 1960-01-05, 1960-05-2...</td>\n",
+       "      <td>0.009466</td>\n",
+       "      <td>date &amp; time</td>\n",
+       "      <td>L2OutputTypes.dimension</td>\n",
+       "      <td>The date and time when an allergy was first id...</td>\n",
+       "      <td>[Allergy Onset Date, Patient Medical History, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>stop</td>\n",
+       "      <td>stop</td>\n",
+       "      <td>allergies</td>\n",
+       "      <td>794</td>\n",
+       "      <td>794</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>0.001424</td>\n",
+       "      <td>others</td>\n",
+       "      <td>L2OutputTypes.unknown</td>\n",
+       "      <td>Indicates the conclusion or termination of a s...</td>\n",
+       "      <td>[End Date, Allergy Management, Data Validity]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>patient</td>\n",
+       "      <td>patient</td>\n",
+       "      <td>allergies</td>\n",
+       "      <td>0</td>\n",
+       "      <td>794</td>\n",
+       "      <td>179</td>\n",
+       "      <td>0.225441</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[2af5741b-913e-7915-e51c-890dd5c261fa, d00d8a6...</td>\n",
+       "      <td>[b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85, 0288c42...</td>\n",
+       "      <td>0.006663</td>\n",
+       "      <td>alphanumeric</td>\n",
+       "      <td>L2OutputTypes.dimension</td>\n",
+       "      <td>Identifies individuals associated with recorde...</td>\n",
+       "      <td>[Patient Identifier, Healthcare Record Linkage...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>encounter</td>\n",
+       "      <td>encounter</td>\n",
+       "      <td>allergies</td>\n",
+       "      <td>0</td>\n",
+       "      <td>794</td>\n",
+       "      <td>179</td>\n",
+       "      <td>0.225441</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[ee31926f-864e-9a18-89ed-e2ffa1230d8d, 34fc598...</td>\n",
+       "      <td>[01efcc52-15d6-51e9-faa2-bee069fcbe44, 79de99b...</td>\n",
+       "      <td>0.004941</td>\n",
+       "      <td>alphanumeric</td>\n",
+       "      <td>L2OutputTypes.dimension</td>\n",
+       "      <td>Tracks the specific interaction or event durin...</td>\n",
+       "      <td>[Patient Encounter, Clinical Visit Tracking, H...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>code</td>\n",
+       "      <td>code</td>\n",
+       "      <td>allergies</td>\n",
+       "      <td>0</td>\n",
+       "      <td>794</td>\n",
+       "      <td>21</td>\n",
+       "      <td>0.026448</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[442571000124108, 3718001, 10831, 735971005, 1...</td>\n",
+       "      <td>[111088007, 84489001, 260147004, 264287008, 25...</td>\n",
+       "      <td>0.008465</td>\n",
+       "      <td>integer</td>\n",
+       "      <td>L2OutputTypes.dimension</td>\n",
+       "      <td>Identifies specific allergy classifications or...</td>\n",
+       "      <td>[Allergy Identifier, Clinical Code, Patient Al...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "<intugle.analysis.models.DataSet at 0x78596ad003b0>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Create a DataSet object and run the profiling pipeline\n",
     "dataset_allergies = DataSet(allergies_df, \"allergies\")\n",
@@ -170,6 +511,7 @@
     "    df = pd.read_csv(generate_table_url(table_name))\n",
     "    dataset = DataSet(df, table_name)\n",
     "    dataset.run(domain=\"Healthcare\")\n",
+    "    \n",
     "    datasets.append(dataset)"
    ]
   },
@@ -368,7 +710,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "intugle",
+   "display_name": "ttt",
    "language": "python",
    "name": "python3"
   },
diff --git a/src/intugle/core/pipeline/datatype_identification/custom_features.py b/src/intugle/core/pipeline/datatype_identification/custom_features.py
index f609986..7643830 100644
--- a/src/intugle/core/pipeline/datatype_identification/custom_features.py
+++ b/src/intugle/core/pipeline/datatype_identification/custom_features.py
@@ -17,7 +17,7 @@
 from trieregex import TrieRegEx as TRE
 
 from intugle.core.settings import settings
-from intugle.core.utilities.processing import compute_stats
+from intugle.core.utilities.processing import compute_stats, to_high_precision_array
 
 from .initialize import di_initalizer
 
@@ -613,7 +613,8 @@ def additional_features(col_values: list, date_samples: int = 1000) -> list:
         if val == 1
     ]
     # int_type = list(int_type)
-    int_type = np.array(int_type, dtype=np.float128)
+    int_type = to_high_precision_array(int_type)
+
     # Iterate through each elements and get the float type data for feature creation
     log.info("Custom feature creation checkFloat started:%s", datetime.now())
     float_data = pseq(
@@ -625,7 +626,7 @@ def additional_features(col_values: list, date_samples: int = 1000) -> list:
         if val == 1
     ]
     # float_type = list(float_type)
-    float_type = np.array(float_type, dtype=np.float128)
+    float_type = to_high_precision_array(float_type)
 
     # Iterate through each elements and get the range type data for feature creation
     log.info("Custom feature creation checkRange started:%s", datetime.now())
diff --git a/src/intugle/core/utilities/processing.py b/src/intugle/core/utilities/processing.py
index 95e38a7..1f20fc1 100644
--- a/src/intugle/core/utilities/processing.py
+++ b/src/intugle/core/utilities/processing.py
@@ -9,34 +9,33 @@
 log = logging.getLogger(__name__)
 
 
-SPECIAL_PATTERN = r'[^a-zA-Z0-9\s]'
-WHITESPACE_PATTERN = r'\s{2,}'
-ASCII_PATTERN = r'[^\x00-\x7F]'
+SPECIAL_PATTERN = r"[^a-zA-Z0-9\s]"
+WHITESPACE_PATTERN = r"\s{2,}"
+ASCII_PATTERN = r"[^\x00-\x7F]"
 
 
 def remove_ascii(strs) -> str:
-    return ''.join([char for word in str(strs) for char in word if ord(char) < 128])
+    return "".join([char for word in str(strs) for char in word if ord(char) < 128])
 
 
 def string_standardization(uncleaned_data: str):
     cleaned_data = remove_ascii(uncleaned_data)
-    cleaned_data = re.sub(SPECIAL_PATTERN, ' ', cleaned_data)
-    cleaned_data = re.sub(WHITESPACE_PATTERN, ' ', cleaned_data.strip())
+    cleaned_data = re.sub(SPECIAL_PATTERN, " ", cleaned_data)
+    cleaned_data = re.sub(WHITESPACE_PATTERN, " ", cleaned_data.strip())
     cleaned_data = cleaned_data.replace(" ", "_")
     cleaned_data = cleaned_data.strip().lower()
     return cleaned_data
 
 
 def compute_stats(values):
-    
-    # Converting the values to array format 
+    # Converting the values to array format
     values = np.array(values) if not isinstance(values, np.ndarray) else values
     # Calculate the statistical results from the values
     _min = np.min(values)
     _max = np.max(values)
     _sum = np.sum(values)
     _mean = np.mean(values)
-    
+
     x = values - _mean
     _variance = np.mean(x * x)
 
@@ -45,50 +44,44 @@ def compute_stats(values):
         _skew = 0
         _kurtosis = -3
     else:
-        _skew = np.mean(x ** 3) / _variance ** 1.5
-        _kurtosis = np.mean(x ** 4) / _variance ** 2 - 3
-    
+        _skew = np.mean(x**3) / _variance**1.5
+        _kurtosis = np.mean(x**4) / _variance**2 - 3
+
     return _mean, _variance, _skew, _kurtosis, _min, _max, _sum
 
 
 def adjust_sample(sample_data, expected_size, sample=True, distinct=False, empty_return_na: bool = True):
-    
     if not isinstance(sample_data, list):
         try:
             sample_data = eval(sample_data)
         except Exception:
             log.error("[!] Error when evaluating sample_data")
             return [np.nan] * 2
-    
+
     sample_size = len(sample_data)
-    
+
     if sample_size == 0:
         if empty_return_na:
             return [np.nan] * expected_size
         else:
             return []
-        
-    if distinct:
 
+    if distinct:
         sample_data = list(set(sample_data))
-    
+
     if not sample:
-        
         return sample_data[:expected_size]
-    
-    if sample_size / expected_size <= 0.3:
 
+    if sample_size / expected_size <= 0.3:
         sample_data = sample_data + list(np.random.choice(sample_data, expected_size - sample_size))
-    
-    else:
 
+    else:
         sample_data = sample_data[:expected_size]
 
     return sample_data
 
 
 DATE_TIME_GROUPS = {
-    
     "YYYY-MM-DD": r"\b(?:20\d{2}|19\d{2}|\d{2})[-./_](0[1-9]|1[0-2])[-./_](0[1-9]|[12]\d|3[01])\b",
     "YYYY-DD-MM": r"\b(?:20\d{2}|19\d{2}|\d{2})[-./_](0[1-9]|[12]\d|3[01])[-./_](0[1-9]|1[0-2])\b",
     "MM-DD-YYYY": r"\b(0[1-9]|1[0-2])[-./_](0[1-9]|[12]\d|3[01])[-./_](?:20\d{2}|19\d{2}|\d{2})\b",
@@ -130,7 +123,7 @@ def adjust_sample(sample_data, expected_size, sample=True, distinct=False, empty
     "YYYY-DD-MMTHH:MM:SS±HHMM": r"\b(?:20\d{2}|19\d{2}|\d{2})[-/._](0[1-9]|[12]\d|3[01])[-/._](0[1-9]|1[0-2])T([01]\d|2[0-4])[:,.](0[0-9]|[1-5]\d)[:,.](0[0-9]|[1-5]\d)[+-](0[0-9]|1[0-2])(?::|\.|,)?(0[0-9]|[1-5]\d)\b",
     "YYYY-MM-DDTHH:MM AM/PM±HHMM": r"\b(?:20\d{2}|19\d{2}|\d{2})[-/._](0[1-9]|1[0-2])[-/._](0[1-9]|[12]\d|3[01])T([01]\d|2[0-4])[:,.](0[0-9]|[1-5]\d)\s*?( ?[APap][Mm])[+-](0[0-9]|1[0-2])(?::|\.|,)?(0[0-9]|[1-5]\d)\b",
     "YYYY-DD-MMTHH:MM AM/PM±HHMM": r"\b(?:20\d{2}|19\d{2}|\d{2})[-/._](0[1-9]|[12]\d|3[01])[-/._](0[1-9]|1[0-2])T([01]\d|2[0-4])[:,.](0[0-9]|[1-5]\d)\s*?( ?[APap][Mm])[+-](0[0-9]|1[0-2])(?::|\.|,)?(0[0-9]|[1-5]\d)\b",
-    "YYYY-MM-DD HH:MM:SS": r"^\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}$"
+    "YYYY-MM-DD HH:MM:SS": r"^\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}$",
 }
 
 
@@ -153,8 +146,8 @@ def classify_datetime_format(sampled_values: list) -> list | str:
             sampled_values = eval(sampled_values)
         except Exception:
             return DATETIME_TYPE
-     
-    sampled_values = sampled_values[:settings.DATE_TIME_FORMAT_LIMIT]
+
+    sampled_values = sampled_values[: settings.DATE_TIME_FORMAT_LIMIT]
 
     format_counters = dict.fromkeys(DATE_TIME_GROUPS.keys(), 0)
 
@@ -189,7 +182,6 @@ def __fraction_calculate__(strata_counts):
         strata_counts = strata_counts[:n_strata]
         total_count = sum([row["count"] for row in strata_counts])
         if len(strata_counts) <= 1:
-            
             sizes[strata_counts[0]["length"]] = min(strata_counts[0]["count"], n_samples)
         else:
             for row in strata_counts:
@@ -200,50 +192,76 @@ def __fraction_calculate__(strata_counts):
                 sizes[length] = sample_size
 
         return sizes
-    
+
     strata_counts = df.groupby("length").agg(count=("data", "count")).reset_index().to_dict(orient="records")
     sizes = __fraction_calculate__(strata_counts=strata_counts)
     samples = []
     for length, d in df.groupby("length", group_keys=False):
         if length in sizes:
-            samples += sorted(d.data.values)[:sizes[length]]
+            samples += sorted(d.data.values)[: sizes[length]]
 
     return samples
 
 
-def preprocess_profiling_data(profiling_data: pd.DataFrame,
-                              sample_limit: int = 5,
-                              dtypes_to_filter=["dimension",],
-                              truncate_sample_data: bool = False,
-                              ) -> pd.DataFrame:
+def preprocess_profiling_data(
+    profiling_data: pd.DataFrame,
+    sample_limit: int = 5,
+    dtypes_to_filter=[
+        "dimension",
+    ],
+    truncate_sample_data: bool = False,
+) -> pd.DataFrame:
     """
-        get the required profiling data with processed sample data
+    get the required profiling data with processed sample data
     """
     if dtypes_to_filter:
-        
         profiling_data = profiling_data.loc[profiling_data.datatype_l2.isin(dtypes_to_filter)].reset_index(drop=True)
 
-    def __sample_process__(sample_data, limit=5):        
+    def __sample_process__(sample_data, limit=5):
         try:
             if isinstance(sample_data, str):
-                
                 sample_data = eval(sample_data)
 
             if truncate_sample_data:
-                    
-                sample_data = [str(sample)[:20]for sample in sample_data]
+                sample_data = [str(sample)[:20] for sample in sample_data]
 
         except Exception as ex:
-            
             log.error(f"[!] Error while sampling: {ex}")
-            
+
         if len(sample_data) != 0:
-            sample_data = character_length_based_stratified_sampling(samples=sample_data, n_strata=limit, n_samples=int(settings.LLM_SAMPLE_LIMIT))
+            sample_data = character_length_based_stratified_sampling(
+                samples=sample_data, n_strata=limit, n_samples=int(settings.LLM_SAMPLE_LIMIT)
+            )
 
         return sample_data
-    
+
     profiling_data["sample_data"] = profiling_data["sample_data"].apply(__sample_process__, limit=sample_limit)
-    
+
     profiling_data["sample_data"] = profiling_data["sample_data"].astype(str)
+
+    return profiling_data
+
+
+def to_high_precision_array(data):
+    """
+    Converts input data to a NumPy array with the highest available floating-point precision.
+
+    Priority: float128 > longdouble > float64
+
+    Parameters:
+        data: array-like
+            The data to convert.
+
+    Returns:
+        np.ndarray
+            A NumPy array with the highest available float precision.
+    """
+
+    if hasattr(np, "float128"):  # Works on most Unix-like systems
+        dtype = np.float128
+    elif hasattr(np, "longdouble"):  # Often higher precision than float64
+        dtype = np.longdouble
+    else:
+        dtype = np.float64  # Fallback
     
-    return profiling_data 
\ No newline at end of file
+    return np.array(data, dtype=dtype)
diff --git a/uv.lock b/uv.lock
index 94c92af..70c8dc5 100644
--- a/uv.lock
+++ b/uv.lock
@@ -432,7 +432,7 @@ resolution-markers = [
     "python_full_version < '3.11'",
 ]
 dependencies = [
-    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "numpy", marker = "python_full_version < '3.11'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/66/54/eb9bfc647b19f2009dd5c7f5ec51c4e6ca831725f1aea7a993034f483147/contourpy-1.3.2.tar.gz", hash = "sha256:b6945942715a034c671b7fc54f9588126b0b8bf23db2696e3ca8328f3ff0ab54", size = 13466130, upload-time = "2025-04-15T17:47:53.79Z" }
 wheels = [
@@ -504,7 +504,7 @@ resolution-markers = [
     "python_full_version == '3.11.*'",
 ]
 dependencies = [
-    { name = "numpy", version = "2.3.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "numpy", marker = "python_full_version >= '3.11'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/58/01/1253e6698a07380cd31a736d248a3f2a50a7c88779a1813da27503cadc2a/contourpy-1.3.3.tar.gz", hash = "sha256:083e12155b210502d0bca491432bb04d56dc3432f95a979b429f2848c3dbe880", size = 13466174, upload-time = "2025-07-26T12:03:12.549Z" }
 wheels = [
@@ -1441,8 +1441,7 @@ dependencies = [
     { name = "networkx", version = "3.4.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
     { name = "networkx", version = "3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "nltk" },
-    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "numpy", version = "2.3.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "numpy" },
     { name = "pandas" },
     { name = "pyaml" },
     { name = "pydantic" },
@@ -1491,18 +1490,18 @@ requires-dist = [
     { name = "mcp", extras = ["cli"], specifier = ">=1.12.4" },
     { name = "networkx", specifier = ">=3.4.2" },
     { name = "nltk", specifier = ">=3.9.1" },
-    { name = "numpy" },
-    { name = "pandas" },
+    { name = "numpy", specifier = "<=2.3.0" },
+    { name = "pandas", specifier = ">=2.3.0" },
     { name = "pyaml", specifier = ">=25.7.0" },
     { name = "pydantic", specifier = ">=2.11.7" },
     { name = "pydantic-settings", specifier = ">=2.10.1" },
     { name = "pyfunctional", specifier = ">=1.5.0" },
     { name = "python-dotenv", specifier = ">=1.1.1" },
     { name = "pyyaml", specifier = ">=6.0.2" },
-    { name = "scikit-learn" },
+    { name = "scikit-learn", specifier = "<=1.7.1" },
     { name = "symspellpy", specifier = ">=6.9.0" },
     { name = "trieregex", specifier = ">=1.0.0" },
-    { name = "xgboost" },
+    { name = "xgboost", specifier = "<=3.0.4" },
 ]
 
 [package.metadata.requires-dev]
@@ -2019,8 +2018,7 @@ dependencies = [
     { name = "langchain" },
     { name = "langchain-core" },
     { name = "langsmith" },
-    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "numpy", version = "2.3.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "numpy" },
     { name = "pydantic-settings" },
     { name = "pyyaml" },
     { name = "requests" },
@@ -2326,8 +2324,7 @@ dependencies = [
     { name = "cycler" },
     { name = "fonttools" },
     { name = "kiwisolver" },
-    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "numpy", version = "2.3.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "numpy" },
     { name = "packaging" },
     { name = "pillow" },
     { name = "pyparsing" },
@@ -2647,9 +2644,6 @@ wheels = [
 name = "numpy"
 version = "2.2.6"
 source = { registry = "https://pypi.org/simple" }
-resolution-markers = [
-    "python_full_version < '3.11'",
-]
 sdist = { url = "https://files.pythonhosted.org/packages/76/21/7d2a95e4bba9dc13d043ee156a356c0a8f0c6309dff6b21b4d71a073b8a8/numpy-2.2.6.tar.gz", hash = "sha256:e29554e2bef54a90aa5cc07da6ce955accb83f21ab5de01a62c8478897b264fd", size = 20276440, upload-time = "2025-05-17T22:38:04.611Z" }
 wheels = [
     { url = "https://files.pythonhosted.org/packages/9a/3e/ed6db5be21ce87955c0cbd3009f2803f59fa08df21b5df06862e2d8e2bdd/numpy-2.2.6-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:b412caa66f72040e6d268491a59f2c43bf03eb6c96dd8f0307829feb7fa2b6fb", size = 21165245, upload-time = "2025-05-17T21:27:58.555Z" },
@@ -2708,92 +2702,6 @@ wheels = [
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 ]
 
-[[package]]
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-version = "2.3.2"
-source = { registry = "https://pypi.org/simple" }
-resolution-markers = [
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-]
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 sdist = { url = "https://files.pythonhosted.org/packages/0f/37/6964b830433e654ec7485e45a00fc9a27cf868d622838f6b6d9c5ec0d532/scipy-1.15.3.tar.gz", hash = "sha256:eae3cf522bc7df64b42cad3925c876e1b0b6c35c1337c93e12c0f366f55b0eaf", size = 59419214, upload-time = "2025-05-08T16:13:05.955Z" }
 wheels = [
@@ -4271,7 +4177,7 @@ resolution-markers = [
     "python_full_version == '3.11.*'",
 ]
 dependencies = [
-    { name = "numpy", version = "2.3.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "numpy", marker = "python_full_version >= '3.11'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/f5/4a/b927028464795439faec8eaf0b03b011005c487bb2d07409f28bf30879c4/scipy-1.16.1.tar.gz", hash = "sha256:44c76f9e8b6e8e488a586190ab38016e4ed2f8a038af7cd3defa903c0a2238b3", size = 30580861, upload-time = "2025-07-27T16:33:30.834Z" }
 wheels = [
@@ -4945,8 +4851,7 @@ name = "xgboost"
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 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "numpy", version = "2.3.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "numpy" },
     { name = "nvidia-nccl-cu12", marker = "platform_machine != 'aarch64' and sys_platform == 'linux'" },
     { name = "scipy", version = "1.15.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
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From 75b48fdbac4b87979293fc9e9712f7ada51352ac Mon Sep 17 00:00:00 2001
From: JaskaranIntugle <jaskaran@intugle.ai>
Date: Sun, 7 Sep 2025 18:40:17 +0530
Subject: [PATCH 04/25] added Knowledge Builder Module

---
 notebooks/quickstart_cleaned.ipynb            |  836 +++
 notebooks/quickstart_content.txt              |  186 +-
 notebooks/quickstart_duckdb.ipynb             | 1221 +---
 src/intugle/__init__.py                       |    6 +-
 src/intugle/adapters/types/duckdb/duckdb.py   |    9 +-
 src/intugle/adapters/types/pandas/pandas.py   |    3 +-
 .../adapters/{types/pandas => }/utils.py      |    0
 src/intugle/knowledge_builder.py              |   53 +
 src/intugle/mcp/server.py                     |    2 +-
 src/intugle/sql_generator.py                  |    1 -
 test.py                                       |   23 +
 tests/analysis/test_profile.py                |    2 +-
 tests/query_generator/test_query_generator.py |    2 -
 uv.lock                                       | 6168 +++++++++--------
 14 files changed, 4163 insertions(+), 4349 deletions(-)
 create mode 100644 notebooks/quickstart_cleaned.ipynb
 rename src/intugle/adapters/{types/pandas => }/utils.py (100%)
 create mode 100644 src/intugle/knowledge_builder.py
 create mode 100644 test.py

diff --git a/notebooks/quickstart_cleaned.ipynb b/notebooks/quickstart_cleaned.ipynb
new file mode 100644
index 0000000..c6ace8c
--- /dev/null
+++ b/notebooks/quickstart_cleaned.ipynb
@@ -0,0 +1,836 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "757096f5",
+   "metadata": {},
+   "source": [
+    "# Quickstart: Building a Semantic Layer with Intugle\n",
+    "\n",
+    "This notebook provides a quick introduction to this project. You'll learn how to use its key features to automatically build a semantic layer over your data.\n",
+    "\n",
+    "**What is a Semantic Layer?**\n",
+    "\n",
+    "A semantic layer is a business-friendly representation of your data. It hides the complexity of the underlying data sources and provides a unified view of your data using familiar business terms. This makes it easier for both business users and data teams to understand and query the data, accelerating data-driven insights.\n",
+    "\n",
+    "**Who is this for?**\n",
+    "\n",
+    "This tool is designed for both **data teams** and **business teams**. \n",
+    "\n",
+    "* **Data teams** can use it to automate data profiling, schema discovery, and documentation, significantly accelerating their workflow.\n",
+    "* **Business teams** can use it to gain a better understanding of their data and to perform self-service analytics without needing to write complex SQL queries.\n",
+    "\n",
+    "**In this notebook, you will learn how to:**\n",
+    "\n",
+    "1. **Profile your data:** Analyze your data sources to understand their structure, data types, and other characteristics.\n",
+    "2. **Business Glossary Generation:** Generate a business glossary for each column, with support for industry-specific domains.\n",
+    "2. **Automatically predict links:** Use a Large Language Model (LLM) to automatically discover relationships (foreign keys) between tables.\n",
+    "3. **Generate a semantic layer:** Create YAML files file that defines your semantic layer.\n",
+    "4. **Generate SQL queries:** Use the semantic layer to generate SQL queries and retrieve data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2a1b244",
+   "metadata": {},
+   "source": [
+    "## 1. LLM Configuration\n",
+    "\n",
+    "Before running the project, you need to configure a Large Language Model (LLM). This is used for tasks like generating business glossaries and predicting links between tables.\n",
+    "\n",
+    "You can configure the LLM by setting the following environment variables:\n",
+    "\n",
+    "* `LLM_PROVIDER`: The LLM provider and model to use (e.g., `openai:gpt-3.5-turbo`). The format follows langchain's format for initializing chat models. Checkout how to specify your model [here](https://python.langchain.com/docs/integrations/chat/) \n",
+    "* `OPENAI_API_KEY`: Your API key for the LLM provider.\n",
+    "\n",
+    "Here's an example of how to set these variables in your environment:\n",
+    "\n",
+    "```bash\n",
+    "export LLM_PROVIDER=\"openai:gpt-3.5-turbo\"\n",
+    "export OPENAI_API_KEY=\"your-openai-api-key\"\n",
+    "```\n",
+    "\n",
+    "Alternatively, you can set them in the notebook like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "36454d44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"LLM_PROVIDER\"] = \"openai:gpt-3.5-turbo\"\n",
+    "os.environ[\"OPENAI_API_KEY\"] = \"your-openai-api-key\"  # Replace with your actual key\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa9fda69",
+   "metadata": {},
+   "source": [
+    "> Currently the langchain packages for OpenAI, Anthropic and Gemini is installed by default. For additional models, make sure you have the integration packages installed. E.g. you should have langchain-deepseek installed to use a DeepSeek model. You can get these packages here: [LangChain Chat Models](https://python.langchain.com/docs/integrations/chat/)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3dad9d8",
+   "metadata": {},
+   "source": [
+    "## 2. Data Profiling and Glossary Generation\n",
+    "\n",
+    "The first step in building a semantic layer is to profile your data. This involves analyzing your data sources to understand their structure, data types, and other characteristics. This tool provides a pipeline for this purpose. It can also generate business glossary for your data.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7f0e28d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from intugle import DataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8d3e8299",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# allergies_data = {\n",
+    "#     \"path\": \"https://raw.githubusercontent.com/Intugle/data-tools/refs/heads/main/sample_data/healthcare/allergies.csv\",\n",
+    "#     \"type\": \"csv\"\n",
+    "# }\n",
+    "# # Create a DataSet object and run the profiling pipeline\n",
+    "# dataset_allergies = DataSet(allergies_data, \"allergies\")\n",
+    "# dataset_allergies.run(domain=\"Healthcare\")\n",
+    "\n",
+    "# Or you can run each step manually:\n",
+    "# dataset_allergies.profile().identify_datatypes().identify_keys().generate_glossary(domain=\"Healthcare\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f4ea58f",
+   "metadata": {},
+   "source": [
+    "The `run()` method performs a series of analysis steps, including:\n",
+    "\n",
+    "* **Profiling:** Calculates statistics for each column, such as distinct count, uniqueness, and completeness.\n",
+    "* **Datatype Identification:** Identifies the data type of each column (e.g., integer, string, datetime).\n",
+    "* **Key Identification:** Identifies potential primary keys.\n",
+    "* **Glossary Generation:** Generates a business glossary for each column using an LLM.\n",
+    "\n",
+    "> The `domain` parameter helps the LLM generate a more contextual business glossary. It specifies the industry domain that the dataset belongs to (e.g., \"Healthcare\", \"Finance\", \"E-commerce\").\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ac8a78fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataset_allergies.result_to_pandas()[[\"business_glossary\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "86d162c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dataset_allergies.to_df()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "83a72c04",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/raphael/intugle/data-tools/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n",
+      "/home/raphael/intugle/data-tools/.venv/lib/python3.12/site-packages/pydantic/_internal/_fields.py:198: UserWarning: Field name \"schema\" in \"Source\" shadows an attribute in parent \"BaseResource\"\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'/home/raphael/intugle/data-tools/testing_base'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from intugle.core.settings import settings\n",
+    "\n",
+    "settings.PROJECT_BASE"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb562acd",
+   "metadata": {},
+   "source": [
+    "## 3. Automated Link Prediction\n",
+    "\n",
+    "Now that we've profiled our data, let's discover the relationships between tables. This tool uses an LLM to predict links (foreign keys) between tables.\n",
+    "\n",
+    "First, we'll load a few more tables from the sample dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "0aa894eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def generate_config(table_name: str) -> str:\n",
+    "    \"\"\"Append the base URL to the table name.\"\"\"\n",
+    "    return {\n",
+    "        \"path\": f\"https://raw.githubusercontent.com/Intugle/data-tools/refs/heads/main/sample_data/healthcare/{table_name}.csv\",\n",
+    "        \"type\": \"csv\"\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "# table_names = [\"allergies\", \"patients\", \"claims\", \"careplans\", \"claims_transactions\", \"medications\"]\n",
+    "table_names = [\"allergies\", \"patients\", \"claims\", \"careplans\"]\n",
+    "datasets = {table: generate_config(table) for table in table_names}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3f457b6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e083a8d8",
+   "metadata": {},
+   "source": [
+    "Now, let's run the link prediction pipeline."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "fe4cd56d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "allergies loaded\n",
+      "patients loaded\n",
+      "claims loaded\n",
+      "careplans loaded\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/raphael/intugle/data-tools/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3859: RuntimeWarning: Mean of empty slice.\n",
+      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
+      "/home/raphael/intugle/data-tools/.venv/lib/python3.12/site-packages/numpy/_core/_methods.py:144: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "[!] Empty column encountered for allergies ==> stop ...\n",
+      "100%|██████████| 15/15 [00:07<00:00,  2.02it/s]\n",
+      "100%|██████████| 15/15 [00:20<00:00,  1.39s/it]\n",
+      "/home/raphael/intugle/data-tools/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3859: RuntimeWarning: Mean of empty slice.\n",
+      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
+      "/home/raphael/intugle/data-tools/.venv/lib/python3.12/site-packages/numpy/_core/_methods.py:144: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "100%|██████████| 25/25 [00:16<00:00,  1.50it/s]\n",
+      "[*] patients:   0%|          | 0/1 [00:00<?, ?it/s]/home/raphael/intugle/data-tools/src/intugle/core/pipeline/key_identification/ki.py:87: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  temp.rename(columns={\"datatype_l1\": \"datatype\"}, inplace=True)\n",
+      "[*] patients: 100%|██████████| 1/1 [00:01<00:00,  1.82s/it]\n",
+      "100%|██████████| 25/25 [01:05<00:00,  2.64s/it]\n",
+      "/home/raphael/intugle/data-tools/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3859: RuntimeWarning: Mean of empty slice.\n",
+      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
+      "/home/raphael/intugle/data-tools/.venv/lib/python3.12/site-packages/numpy/_core/_methods.py:144: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "[!] Empty column encountered for claims ==> referringproviderid ...\n",
+      "100%|██████████| 31/31 [00:31<00:00,  1.01s/it]\n",
+      "[*] claims:   0%|          | 0/1 [00:00<?, ?it/s]/home/raphael/intugle/data-tools/src/intugle/core/pipeline/key_identification/ki.py:87: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  temp.rename(columns={\"datatype_l1\": \"datatype\"}, inplace=True)\n",
+      "[*] claims: 100%|██████████| 1/1 [00:00<00:00,  1.91it/s]\n",
+      "100%|██████████| 31/31 [01:15<00:00,  2.44s/it]\n",
+      "/home/raphael/intugle/data-tools/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3859: RuntimeWarning: Mean of empty slice.\n",
+      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
+      "/home/raphael/intugle/data-tools/.venv/lib/python3.12/site-packages/numpy/_core/_methods.py:144: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "100%|██████████| 9/9 [00:08<00:00,  1.09it/s]\n",
+      "[*] careplans:   0%|          | 0/1 [00:00<?, ?it/s]/home/raphael/intugle/data-tools/src/intugle/core/pipeline/key_identification/ki.py:87: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  temp.rename(columns={\"datatype_l1\": \"datatype\"}, inplace=True)\n",
+      "[*] careplans: 100%|██████████| 1/1 [00:00<00:00,  1.18it/s]\n",
+      "100%|██████████| 9/9 [00:24<00:00,  2.67s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset 'allergies' is missing key identification. Running prerequisite analysis...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/raphael/intugle/data-tools/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3859: RuntimeWarning: Mean of empty slice.\n",
+      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
+      "/home/raphael/intugle/data-tools/.venv/lib/python3.12/site-packages/numpy/_core/_methods.py:144: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  ret = ret.dtype.type(ret / rcount)\n",
+      "[!] Empty column encountered for allergies ==> stop ...\n",
+      "100%|██████████| 15/15 [00:13<00:00,  1.09it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset 'patients' already processed. Skipping analysis.\n",
+      "Dataset 'claims' already processed. Skipping analysis.\n",
+      "Dataset 'careplans' already processed. Skipping analysis.\n",
+      "LinkPredictor initialized with datasets: ['allergies', 'patients', 'claims', 'careplans']\n",
+      "\n",
+      "--- Comparing 'allergies' <=> 'patients' ---\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] allergies <==> patients Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.14s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 1 potential link(s).\n",
+      "\n",
+      "--- Comparing 'allergies' <=> 'claims' ---\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] allergies <==> claims Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.61s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No links found for this pair.\n",
+      "\n",
+      "--- Comparing 'allergies' <=> 'careplans' ---\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] allergies <==> careplans Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.68s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No links found for this pair.\n",
+      "\n",
+      "--- Comparing 'patients' <=> 'claims' ---\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] patients <==> claims Runs:1: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 1 potential link(s).\n",
+      "\n",
+      "--- Comparing 'patients' <=> 'careplans' ---\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] patients <==> careplans Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.03s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 1 potential link(s).\n",
+      "\n",
+      "--- Comparing 'claims' <=> 'careplans' ---\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*] claims <==> careplans Runs:1: 100%|██████████| 1/1 [00:05<00:00,  5.22s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No links found for this pair.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<intugle.knowledge_builder.KnowledgeBuilder at 0x7fe1cf0bd9d0>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from intugle import KnowledgeBuilder\n",
+    "\n",
+    "# Initialize the knowledge builder\n",
+    "kb = KnowledgeBuilder(datasets, domain=\"Healthcare\")\n",
+    "\n",
+    "# Run the prediction\n",
+    "kb.build()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d18dc421",
+   "metadata": {},
+   "source": [
+    "The `results` object contains the predicted links between the tables. You can also visualize the relationships as a graph.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a59704ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x2000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "kb.visualize()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32fd4917",
+   "metadata": {},
+   "source": [
+    "## 4. The Semantic Layer\n",
+    "\n",
+    "The profiling and link prediction results are used to generate YAML files which are saved automatically. These files defines the semantic layer, including the models (tables) and their relationships. \n",
+    "\n",
+    "By default, these files are saved in the current working directory. You can configure this path by setting the `PROJECT_BASE` environment variable. They can also be saved manually as shown below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "a0daf854",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# for ds in datasets:\n",
+    "#     ds.save_yaml()\n",
+    "\n",
+    "# results.save_yaml(\"relationships.yml\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cf08923",
+   "metadata": {},
+   "source": [
+    "Now, we can load the YAML files and create a manifest."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d086f45",
+   "metadata": {},
+   "source": [
+    "## 5. SQL Generation\n",
+    "\n",
+    "Once you have a semantic layer, you can use the `SqlGenerator` to generate SQL queries. This allows you to query the data using business-friendly terms, without having to write complex SQL.\n",
+    "\n",
+    "Let's create an ETL model to define the query we want to generate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "2570af46",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "etl = {\n",
+    "    \"name\": \"patient_names\",\n",
+    "    \"fields\": [\n",
+    "        {\"id\": \"patients.first\", \"name\": \"first_name\"},\n",
+    "        {\"id\": \"patients.last\", \"name\": \"last_name\"},\n",
+    "        {\"id\": \"allergies.start\", \"name\": \"start_date\"},\n",
+    "    ],\n",
+    "    \"filter\": {\n",
+    "        \"selections\": [{\"id\": \"claims.departmentid\", \"values\": [\"3\", \"20\"]}],\n",
+    "    },\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e4ff6be",
+   "metadata": {},
+   "source": [
+    "Now, let's use the `SqlGenerator` to generate the SQL query."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "e0f9a090",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from intugle.sql_generator import SqlGenerator\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "f0432e77",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "careplans loaded\n",
+      "patients loaded\n",
+      "claims loaded\n",
+      "test_etl loaded\n",
+      "allergies loaded\n",
+      "patient_names\n",
+      "patient_names loaded\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'path': \"SELECT patients.first as first_name, patients.last as last_name, allergies.start as start_date FROM claims LEFT JOIN patients ON patients.id = claims.patientid LEFT JOIN allergies ON allergies.patient = patients.id WHERE (claims.departmentid IN ('3', '20'))\",\n",
+       " 'type': 'query'}"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Create a SqlGenerator\n",
+    "sql_generator = SqlGenerator()\n",
+    "\n",
+    "# Generate the query\n",
+    "test_etl = sql_generator.generate_product(etl)\n",
+    "\n",
+    "# Print the query\n",
+    "test_etl.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "95d2e49d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>first_name</th>\n",
+       "      <th>last_name</th>\n",
+       "      <th>start_date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Miki234</td>\n",
+       "      <td>Kozey370</td>\n",
+       "      <td>1981-05-17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Maddie576</td>\n",
+       "      <td>Gutmann970</td>\n",
+       "      <td>1960-01-05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Esther279</td>\n",
+       "      <td>Runolfsson901</td>\n",
+       "      <td>1960-05-21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Bonita405</td>\n",
+       "      <td>Bernier607</td>\n",
+       "      <td>2011-03-26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Magdalene960</td>\n",
+       "      <td>Orn563</td>\n",
+       "      <td>1995-07-11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1543</th>\n",
+       "      <td>Zelda766</td>\n",
+       "      <td>Ernser583</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1544</th>\n",
+       "      <td>Zelda766</td>\n",
+       "      <td>Ernser583</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1545</th>\n",
+       "      <td>Zelda766</td>\n",
+       "      <td>Ernser583</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1546</th>\n",
+       "      <td>Zelda766</td>\n",
+       "      <td>Ernser583</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1547</th>\n",
+       "      <td>Zelda766</td>\n",
+       "      <td>Ernser583</td>\n",
+       "      <td>NaT</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1548 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        first_name      last_name start_date\n",
+       "0          Miki234       Kozey370 1981-05-17\n",
+       "1        Maddie576     Gutmann970 1960-01-05\n",
+       "2        Esther279  Runolfsson901 1960-05-21\n",
+       "3        Bonita405     Bernier607 2011-03-26\n",
+       "4     Magdalene960         Orn563 1995-07-11\n",
+       "...            ...            ...        ...\n",
+       "1543      Zelda766      Ernser583        NaT\n",
+       "1544      Zelda766      Ernser583        NaT\n",
+       "1545      Zelda766      Ernser583        NaT\n",
+       "1546      Zelda766      Ernser583        NaT\n",
+       "1547      Zelda766      Ernser583        NaT\n",
+       "\n",
+       "[1548 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_etl.to_df()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "fa02b661",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x2000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sql_generator.plot_sources_graph()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bfde8c96",
+   "metadata": {},
+   "source": [
+    "## 6. MCP Server: Interacting with Your Semantic Layer\n",
+    "\n",
+    "Now that you have a semantic layer, you can serve it as a MCP server to interact with it using natural language. The MCP server exposes your semantic layer as a set of tools that can be used by any MCP client.\n",
+    "\n",
+    "### Starting the MCP Server\n",
+    "\n",
+    "To start the MCP server, run the following command in your terminal:\n",
+    "\n",
+    "```bash\n",
+    "intugle-mcp\n",
+    "```\n",
+    "\n",
+    "This will start a server on `localhost:8000`.\n",
+    "\n",
+    "### Connecting to the MCP Server\n",
+    "\n",
+    "Once the server is running, you can connect to it from any MCP client. The endpoint for the MCP server is:\n",
+    "\n",
+    "`http://localhost:8000/semantic_layer/mcp`\n",
+    "\n",
+    "You can use a variety of MCP clients to connect to the server, such as Claude Desktop, Gemini CLI etc.\n",
+    "\n",
+    "### Use Cases\n",
+    "\n",
+    "Once connected, you can interact with your semantic layer using natural language. Here are some exciting applications:\n",
+    "\n",
+    "*   **Generate SQL Queries:** Ask questions in natural language and have the MCP server generate the corresponding SQL query.\n",
+    "*   **Data Discovery:** Ask questions about the tables and columns in your semantic layer to better understand your data.\n",
+    "\n",
+    "> To execute the generated SQL queries, you can also connect your database instance as a MCP tool to your client."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c29d33de",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "You've learned how to:\n",
+    "\n",
+    "* Configure your LLM provider.\n",
+    "* Profile your data to understand its characteristics.\n",
+    "* Use an LLM to automatically predict links between tables.\n",
+    "* Generate a semantic layer.\n",
+    "* Use the semantic layer to generate SQL queries.\n",
+    "* Interact with your semantic layer using the MCP server.\n",
+    "\n",
+    "This is just a starting point. This project has many other features to explore. We encourage you to try it with your own data and see how it can help you build a powerful semantic layer.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "intugle",
+   "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": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/quickstart_content.txt b/notebooks/quickstart_content.txt
index 5d50c21..c82fe3c 100644
--- a/notebooks/quickstart_content.txt
+++ b/notebooks/quickstart_content.txt
@@ -1,13 +1,13 @@
 ---
 type: markdown
 ---
-# Quickstart: Building a Semantic Layer with Data-Tools
+# Quickstart: Building a Semantic Layer with Intugle
 
-This notebook provides a quick introduction to the `intugle` project. You'll learn how to use its key features to automatically build a semantic layer over your data.
+This notebook provides a quick introduction to this project. You'll learn how to use its key features to automatically build a semantic layer over your data.
 
 **What is a Semantic Layer?**
 
-A semantic layer is a business-friendly representation of your data. It hides the complexity of the underlying data sources and provides a unified view of your data using familiar business terms. This makes it easier for business users to understand and query the data without needing to be SQL experts.
+A semantic layer is a business-friendly representation of your data. It hides the complexity of the underlying data sources and provides a unified view of your data using familiar business terms. This makes it easier for both business users and data teams to understand and query the data, accelerating data-driven insights.
 
 **Who is this for?**
 
@@ -18,32 +18,21 @@ This tool is designed for both **data teams** and **business teams**.
 
 **In this notebook, you will learn how to:**
 
-1. **Configure your LLM Provider:** Set up the Large Language Model that will power the automated features.
-2. **Profile your data:** Analyze your data sources to understand their structure, data types, and other characteristics.
-3. **Automatically predict links:** Use a Large Language Model (LLM) to automatically discover relationships (foreign keys) between tables.
-4. **Generate a semantic layer:** Create a `manifest.json` file that defines your semantic layer.
-5. **Generate SQL queries:** Use the semantic layer to generate SQL queries and retrieve data.
+1. **Profile your data:** Analyze your data sources to understand their structure, data types, and other characteristics.
+2. **Business Glossary Generation:** Generate a business glossary for each column, with support for industry-specific domains.
+2. **Automatically predict links:** Use a Large Language Model (LLM) to automatically discover relationships (foreign keys) between tables.
+3. **Generate a semantic layer:** Create YAML files file that defines your semantic layer.
+4. **Generate SQL queries:** Use the semantic layer to generate SQL queries and retrieve data.
 ---
 type: markdown
 ---
-## 1. Setup
-
-First, let's install the necessary libraries. This project uses `uv` for package management.
----
-type: code
----
-!pip install uv
-!uv pip install -r ../requirements.txt
----
-type: markdown
----
-## 2. LLM Configuration
+## 1. LLM Configuration
 
 Before running the project, you need to configure a Large Language Model (LLM). This is used for tasks like generating business glossaries and predicting links between tables.
 
 You can configure the LLM by setting the following environment variables:
 
-* `LLM_PROVIDER`: The LLM provider and model to use (e.g., `openai:gpt-3.5-turbo`).
+* `LLM_PROVIDER`: The LLM provider and model to use (e.g., `openai:gpt-3.5-turbo`). The format follows langchain's format for initializing chat models. Checkout how to specify your model [here](https://python.langchain.com/docs/integrations/chat/) 
 * `OPENAI_API_KEY`: Your API key for the LLM provider.
 
 Here's an example of how to set these variables in your environment:
@@ -60,31 +49,35 @@ type: code
 import os
 
 os.environ["LLM_PROVIDER"] = "openai:gpt-3.5-turbo"
-os.environ["OPENAI_API_KEY"] = "your-openai-api-key" # Replace with your actual key
+os.environ["OPENAI_API_KEY"] = "your-openai-api-key"  # Replace with your actual key
 
 ---
 type: markdown
 ---
-## 3. Data Profiling
-
-The first step in building a semantic layer is to profile your data. This involves analyzing your data sources to understand their structure, data types, and other characteristics. The `intugle` project provides a pipeline for this purpose.
+> Currently the langchain packages for OpenAI, Anthropic and Gemini is installed by default. For additional models, make sure you have the integration packages installed. E.g. you should have langchain-deepseek installed to use a DeepSeek model. You can get these packages here: [LangChain Chat Models](https://python.langchain.com/docs/integrations/chat/)
 ---
-type: code
+type: markdown
 ---
-import pandas as pd
-from intugle.analysis.models import DataSet
+## 2. Data Profiling and Glossary Generation
+
+The first step in building a semantic layer is to profile your data. This involves analyzing your data sources to understand their structure, data types, and other characteristics. This tool provides a pipeline for this purpose. It can also generate business glossary for your data.
 ---
 type: code
 ---
-# Load the sample data
-allergies_df = pd.read_csv('../sample_data/healthcare/allergies.csv')
-allergies_df.head()
+# from intugle import DataSet
 ---
 type: code
 ---
-# Create a DataSet object and run the profiling pipeline
-dataset_allergies = DataSet(allergies_df, "allergies")
-dataset_allergies.run(domain="Healthcare")
+# allergies_data = {
+#     "path": "https://raw.githubusercontent.com/Intugle/data-tools/refs/heads/main/sample_data/healthcare/allergies.csv",
+#     "type": "csv"
+# }
+# # Create a DataSet object and run the profiling pipeline
+# dataset_allergies = DataSet(allergies_data, "allergies")
+# dataset_allergies.run(domain="Healthcare")
+
+# Or you can run each step manually:
+# dataset_allergies.profile().identify_datatypes().identify_keys().generate_glossary(domain="Healthcare")
 ---
 type: markdown
 ---
@@ -95,34 +88,46 @@ The `run()` method performs a series of analysis steps, including:
 * **Key Identification:** Identifies potential primary keys.
 * **Glossary Generation:** Generates a business glossary for each column using an LLM.
 
-**The `domain` parameter**
-
-The `domain` parameter helps the LLM generate a more contextual business glossary. It specifies the industry domain that the dataset belongs to (e.g., "Healthcare", "Finance", "E-commerce").
-
-Let's take a look at the profiling results:
+> The `domain` parameter helps the LLM generate a more contextual business glossary. It specifies the industry domain that the dataset belongs to (e.g., "Healthcare", "Finance", "E-commerce").
+---
+type: code
+---
+# dataset_allergies.result_to_pandas()[["business_glossary"]]
+---
+type: code
+---
+# dataset_allergies.to_df()
 ---
 type: code
 ---
-dataset_allergies.profile_df.head()
+from intugle.core.settings import settings
+
+settings.PROJECT_BASE
 ---
 type: markdown
 ---
-## 4. Automated Link Prediction
+## 3. Automated Link Prediction
 
-Now that we've profiled our data, let's discover the relationships between tables. The `intugle` project uses a Large Language Model (LLM) to predict links (foreign keys) between tables.
+Now that we've profiled our data, let's discover the relationships between tables. This tool uses an LLM to predict links (foreign keys) between tables.
 
 First, we'll load a few more tables from the sample dataset.
 ---
 type: code
 ---
-table_names = ["patients", "claims", "careplans", "claims_transactions", "medications"]
-datasets = [dataset_allergies]
+def generate_config(table_name: str) -> str:
+    """Append the base URL to the table name."""
+    return {
+        "path": f"https://raw.githubusercontent.com/Intugle/data-tools/refs/heads/main/sample_data/healthcare/{table_name}.csv",
+        "type": "csv"
+    }
+
 
-for table_name in table_names:
-    df = pd.read_csv(f'../sample_data/healthcare/{table_name}.csv')
-    dataset = DataSet(df, table_name)
-    dataset.run(domain="Healthcare")
-    datasets.append(dataset)
+# table_names = ["allergies", "patients", "claims", "careplans", "claims_transactions", "medications"]
+table_names = ["allergies", "patients", "claims", "careplans"]
+datasets = {table: generate_config(table) for table in table_names}
+---
+type: code
+---
 ---
 type: markdown
 ---
@@ -130,14 +135,13 @@ Now, let's run the link prediction pipeline.
 ---
 type: code
 ---
-from intugle.link_predictor import LinkPredictor
+from intugle import KnowledgeBuilder
 
-# Initialize the predictor
-predictor = LinkPredictor(datasets)
+# Initialize the knowledge builder
+kb = KnowledgeBuilder(datasets, domain="Healthcare")
 
 # Run the prediction
-results = predictor.predict()
-results.links
+kb.build()
 ---
 type: markdown
 ---
@@ -145,42 +149,30 @@ The `results` object contains the predicted links between the tables. You can al
 ---
 type: code
 ---
-results.show_graph()
+kb.visualize()
 ---
 type: markdown
 ---
-## 5. The Semantic Layer (Manifest)
+## 4. The Semantic Layer
 
-The profiling and link prediction results are used to generate a `manifest.json` file. This file defines the semantic layer, including the models (tables) and their relationships.
+The profiling and link prediction results are used to generate YAML files which are saved automatically. These files defines the semantic layer, including the models (tables) and their relationships. 
 
-Let's save the datasets and the predicted links to YAML files. By default, these files are saved in a `sources` directory in the current working directory. You can configure this path by setting the `PROJECT_BASE` environment variable.
+By default, these files are saved in the current working directory. You can configure this path by setting the `PROJECT_BASE` environment variable. They can also be saved manually as shown below:
 ---
 type: code
 ---
-for ds in datasets:
-    ds.save_yaml()
+# for ds in datasets:
+#     ds.save_yaml()
 
-results.save_yaml("relationships.yml")
+# results.save_yaml("relationships.yml")
 ---
 type: markdown
 ---
 Now, we can load the YAML files and create a manifest.
 ---
-type: code
----
-from intugle.parser.manifest import ManifestLoader
-
-# Load the manifest
-manifest_loader = ManifestLoader('.')
-manifest_loader.load()
-manifest = manifest_loader.manifest
-
-# Print the manifest
-print(manifest.json(indent=4))
----
 type: markdown
 ---
-## 6. SQL Generation
+## 5. SQL Generation
 
 Once you have a semantic layer, you can use the `SqlGenerator` to generate SQL queries. This allows you to query the data using business-friendly terms, without having to write complex SQL.
 
@@ -188,16 +180,18 @@ Let's create an ETL model to define the query we want to generate.
 ---
 type: code
 ---
-from intugle.libs.smart_query_generator.models.models import ETLModel, FieldModel
 
-# Create an ETL model
-etl_model = ETLModel(
-    fields=[
-        FieldModel(id="patients.FIRST"),
-        FieldModel(id="patients.LAST"),
-        FieldModel(id="claims.TOTAL_CLAIM_COST"),
-    ]
-)
+etl = {
+    "name": "patient_names",
+    "fields": [
+        {"id": "patients.first", "name": "first_name"},
+        {"id": "patients.last", "name": "last_name"},
+        {"id": "allergies.start", "name": "start_date"},
+    ],
+    "filter": {
+        "selections": [{"id": "claims.departmentid", "values": ["3", "20"]}],
+    },
+}
 ---
 type: markdown
 ---
@@ -206,19 +200,30 @@ Now, let's use the `SqlGenerator` to generate the SQL query.
 type: code
 ---
 from intugle.sql_generator import SqlGenerator
+---
+type: code
+---
 
 # Create a SqlGenerator
-sql_generator = SqlGenerator('.')
+sql_generator = SqlGenerator()
 
 # Generate the query
-sql_query = sql_generator.generate_query(etl_model)
+test_etl = sql_generator.generate_product(etl)
 
 # Print the query
-print(sql_query)
+test_etl.data
+---
+type: code
+---
+test_etl.to_df()
+---
+type: code
+---
+sql_generator.plot_sources_graph()
 ---
 type: markdown
 ---
-## 7. MCP Server: Interacting with Your Semantic Layer
+## 6. MCP Server: Interacting with Your Semantic Layer
 
 Now that you have a semantic layer, you can serve it as a MCP server to interact with it using natural language. The MCP server exposes your semantic layer as a set of tools that can be used by any MCP client.
 
@@ -248,19 +253,18 @@ Once connected, you can interact with your semantic layer using natural language
 *   **Data Discovery:** Ask questions about the tables and columns in your semantic layer to better understand your data.
 
 > To execute the generated SQL queries, you can also connect your database instance as a MCP tool to your client.
-
 ---
 type: markdown
 ---
 ## Conclusion
 
-This notebook has provided a brief overview of the `intugle` project. You've learned how to:
+You've learned how to:
 
 * Configure your LLM provider.
 * Profile your data to understand its characteristics.
 * Use an LLM to automatically predict links between tables.
-* Generate a semantic layer (`manifest.json`).
+* Generate a semantic layer.
 * Use the semantic layer to generate SQL queries.
 * Interact with your semantic layer using the MCP server.
 
-This is just a starting point. The `intugle` project has many other features to explore. We encourage you to try it with your own data and see how it can help you build a powerful semantic layer.
\ No newline at end of file
+This is just a starting point. This project has many other features to explore. We encourage you to try it with your own data and see how it can help you build a powerful semantic layer.
diff --git a/notebooks/quickstart_duckdb.ipynb b/notebooks/quickstart_duckdb.ipynb
index 9b04115..eb4f16a 100644
--- a/notebooks/quickstart_duckdb.ipynb
+++ b/notebooks/quickstart_duckdb.ipynb
@@ -86,7 +86,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "7f0e28d7",
    "metadata": {},
    "outputs": [],
@@ -96,172 +96,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "8d3e8299",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "allergies loaded\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/.venv/lib/python3.12/site-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
-      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
-      "/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/.venv/lib/python3.12/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "[!] Empty column encountered for allergies ==> stop ...\n",
-      "100%|██████████| 15/15 [00:08<00:00,  1.73it/s]\n",
-      "100%|██████████| 15/15 [00:24<00:00,  1.65s/it]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>column_name</th>\n",
-       "      <th>business_name</th>\n",
-       "      <th>table_name</th>\n",
-       "      <th>null_count</th>\n",
-       "      <th>count</th>\n",
-       "      <th>distinct_count</th>\n",
-       "      <th>uniqueness</th>\n",
-       "      <th>completeness</th>\n",
-       "      <th>sample_data</th>\n",
-       "      <th>dtype_sample</th>\n",
-       "      <th>ts</th>\n",
-       "      <th>datatype_l1</th>\n",
-       "      <th>datatype_l2</th>\n",
-       "      <th>business_glossary</th>\n",
-       "      <th>business_tags</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>start</td>\n",
-       "      <td>start</td>\n",
-       "      <td>allergies</td>\n",
-       "      <td>0</td>\n",
-       "      <td>794</td>\n",
-       "      <td>179</td>\n",
-       "      <td>0.225441</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>[2006-08-07, 2001-04-09, 2011-02-04, 1958-10-2...</td>\n",
-       "      <td>[2009-02-19, 1991-11-18, 1976-04-09, 2013-07-2...</td>\n",
-       "      <td>0.036076</td>\n",
-       "      <td>date &amp; time</td>\n",
-       "      <td>L2OutputTypes.dimension</td>\n",
-       "      <td>Indicates the date and time when an allergy re...</td>\n",
-       "      <td>[Allergy Onset Date, Patient Medical History, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>stop</td>\n",
-       "      <td>stop</td>\n",
-       "      <td>allergies</td>\n",
-       "      <td>794</td>\n",
-       "      <td>794</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>[]</td>\n",
-       "      <td>[]</td>\n",
-       "      <td>0.006042</td>\n",
-       "      <td>others</td>\n",
-       "      <td>L2OutputTypes.unknown</td>\n",
-       "      <td>Indicates the conclusion or cessation of a spe...</td>\n",
-       "      <td>[AllergyEndDate, PatientCareTimeline, Clinical...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>patient</td>\n",
-       "      <td>patient</td>\n",
-       "      <td>allergies</td>\n",
-       "      <td>0</td>\n",
-       "      <td>794</td>\n",
-       "      <td>179</td>\n",
-       "      <td>0.225441</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>[65c925be-8fda-2bf1-6702-4fcbcb725628, 246fb36...</td>\n",
-       "      <td>[9ef4db86-c427-ddfe-a607-737f08ffb0c1, 4ba1518...</td>\n",
-       "      <td>0.006320</td>\n",
-       "      <td>alphanumeric</td>\n",
-       "      <td>L2OutputTypes.dimension</td>\n",
-       "      <td>Identifies individuals associated with recorde...</td>\n",
-       "      <td>[Patient Identifier, Healthcare Entity, Allerg...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>encounter</td>\n",
-       "      <td>encounter</td>\n",
-       "      <td>allergies</td>\n",
-       "      <td>0</td>\n",
-       "      <td>794</td>\n",
-       "      <td>179</td>\n",
-       "      <td>0.225441</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>[a7d784e9-8503-e26f-b98f-e34ca8a4bad0, 704f899...</td>\n",
-       "      <td>[04effe72-a3c5-e479-b6d3-5f6fb7ac8601, be166b8...</td>\n",
-       "      <td>0.004151</td>\n",
-       "      <td>alphanumeric</td>\n",
-       "      <td>L2OutputTypes.dimension</td>\n",
-       "      <td>A unique identifier associated with a specific...</td>\n",
-       "      <td>[Patient Encounter, Clinical Visit Identifier,...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>code</td>\n",
-       "      <td>code</td>\n",
-       "      <td>allergies</td>\n",
-       "      <td>0</td>\n",
-       "      <td>794</td>\n",
-       "      <td>21</td>\n",
-       "      <td>0.026448</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>[264287008, 25037, 412071004, 288328004, 76295...</td>\n",
-       "      <td>[3718001, 256355007, 10831, 111088007, 1191, 4...</td>\n",
-       "      <td>0.004685</td>\n",
-       "      <td>integer</td>\n",
-       "      <td>L2OutputTypes.dimension</td>\n",
-       "      <td>Identifies specific allergens or allergy-relat...</td>\n",
-       "      <td>[Allergy Classification, Clinical Code, Health...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "<intugle.analysis.models.DataSet at 0x71e240ae3aa0>"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "allergies_data = {\n",
     "    \"path\": \"https://raw.githubusercontent.com/Intugle/data-tools/refs/heads/main/sample_data/healthcare/allergies.csv\",\n",
@@ -292,470 +130,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "ac8a78fa",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>business_glossary</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Indicates the date and time when an allergy re...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Indicates the conclusion or cessation of a spe...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Identifies individuals associated with recorde...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>A unique identifier associated with a specific...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Identifies specific allergens or allergy-relat...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Identifies the source or classification framew...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Provides the name or type of allergen associat...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Categorizes the nature of a condition related ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Classifies the type of allergen relevant to an...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Describes the specific physical or physiologic...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Captures the specific symptoms or manifestatio...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>Indicates the intensity level of an allergic r...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>Indicates the specific type or category of adv...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>Captures textual descriptions of observed symp...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>Indicates the intensity level of an allergic r...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                    business_glossary\n",
-       "0   Indicates the date and time when an allergy re...\n",
-       "1   Indicates the conclusion or cessation of a spe...\n",
-       "2   Identifies individuals associated with recorde...\n",
-       "3   A unique identifier associated with a specific...\n",
-       "4   Identifies specific allergens or allergy-relat...\n",
-       "5   Identifies the source or classification framew...\n",
-       "6   Provides the name or type of allergen associat...\n",
-       "7   Categorizes the nature of a condition related ...\n",
-       "8   Classifies the type of allergen relevant to an...\n",
-       "9   Describes the specific physical or physiologic...\n",
-       "10  Captures the specific symptoms or manifestatio...\n",
-       "11  Indicates the intensity level of an allergic r...\n",
-       "12  Indicates the specific type or category of adv...\n",
-       "13  Captures textual descriptions of observed symp...\n",
-       "14  Indicates the intensity level of an allergic r..."
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "dataset_allergies.result_to_pandas()[[\"business_glossary\"]]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "86d162c1",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "      <th>start</th>\n",
-       "      <th>stop</th>\n",
-       "      <th>patient</th>\n",
-       "      <th>encounter</th>\n",
-       "      <th>code</th>\n",
-       "      <th>system</th>\n",
-       "      <th>description</th>\n",
-       "      <th>type</th>\n",
-       "      <th>category</th>\n",
-       "      <th>reaction1</th>\n",
-       "      <th>description1</th>\n",
-       "      <th>severity1</th>\n",
-       "      <th>reaction2</th>\n",
-       "      <th>description2</th>\n",
-       "      <th>severity2</th>\n",
-       "    </tr>\n",
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-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2020-02-17</td>\n",
-       "      <td>None</td>\n",
-       "      <td>b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85</td>\n",
-       "      <td>01efcc52-15d6-51e9-faa2-bee069fcbe44</td>\n",
-       "      <td>111088007</td>\n",
-       "      <td>Unknown</td>\n",
-       "      <td>Latex (substance)</td>\n",
-       "      <td>allergy</td>\n",
-       "      <td>environment</td>\n",
-       "      <td>247472004</td>\n",
-       "      <td>Wheal (finding)</td>\n",
-       "      <td>MILD</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2020-02-17</td>\n",
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-       "      <td>b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85</td>\n",
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-       "      <td>84489001</td>\n",
-       "      <td>Unknown</td>\n",
-       "      <td>Mold (organism)</td>\n",
-       "      <td>allergy</td>\n",
-       "      <td>environment</td>\n",
-       "      <td>76067001</td>\n",
-       "      <td>Sneezing</td>\n",
-       "      <td>MILD</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2020-02-17</td>\n",
-       "      <td>None</td>\n",
-       "      <td>b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85</td>\n",
-       "      <td>01efcc52-15d6-51e9-faa2-bee069fcbe44</td>\n",
-       "      <td>260147004</td>\n",
-       "      <td>Unknown</td>\n",
-       "      <td>House dust mite (organism)</td>\n",
-       "      <td>allergy</td>\n",
-       "      <td>environment</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2020-02-17</td>\n",
-       "      <td>None</td>\n",
-       "      <td>b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85</td>\n",
-       "      <td>01efcc52-15d6-51e9-faa2-bee069fcbe44</td>\n",
-       "      <td>264287008</td>\n",
-       "      <td>Unknown</td>\n",
-       "      <td>Animal dander (substance)</td>\n",
-       "      <td>allergy</td>\n",
-       "      <td>environment</td>\n",
-       "      <td>878820003</td>\n",
-       "      <td>Rhinoconjunctivitis (disorder)</td>\n",
-       "      <td>MODERATE</td>\n",
-       "      <td>271807003</td>\n",
-       "      <td>Eruption of skin (disorder)</td>\n",
-       "      <td>MILD</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2020-02-17</td>\n",
-       "      <td>None</td>\n",
-       "      <td>b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85</td>\n",
-       "      <td>01efcc52-15d6-51e9-faa2-bee069fcbe44</td>\n",
-       "      <td>256277009</td>\n",
-       "      <td>Unknown</td>\n",
-       "      <td>Grass pollen (substance)</td>\n",
-       "      <td>allergy</td>\n",
-       "      <td>environment</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>789</th>\n",
-       "      <td>1987-05-20</td>\n",
-       "      <td>None</td>\n",
-       "      <td>27cb1c2f-4ec0-6e24-d080-e46178f712d5</td>\n",
-       "      <td>04d64fc5-6137-3d7a-06b6-dd6dc2d6a7c1</td>\n",
-       "      <td>442571000124108</td>\n",
-       "      <td>Unknown</td>\n",
-       "      <td>Tree nut (substance)</td>\n",
-       "      <td>allergy</td>\n",
-       "      <td>food</td>\n",
-       "      <td>39579001</td>\n",
-       "      <td>Anaphylaxis (disorder)</td>\n",
-       "      <td>SEVERE</td>\n",
-       "      <td>49727002</td>\n",
-       "      <td>Cough (finding)</td>\n",
-       "      <td>MODERATE</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>790</th>\n",
-       "      <td>2001-04-09</td>\n",
-       "      <td>None</td>\n",
-       "      <td>0cf08145-a634-329f-54d3-9cbd04ba18cc</td>\n",
-       "      <td>ef2f78b9-750e-607b-f3f4-3de39ee2eeec</td>\n",
-       "      <td>1191</td>\n",
-       "      <td>Unknown</td>\n",
-       "      <td>Aspirin</td>\n",
-       "      <td>allergy</td>\n",
-       "      <td>medication</td>\n",
-       "      <td>247472004</td>\n",
-       "      <td>Wheal (finding)</td>\n",
-       "      <td>MODERATE</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>791</th>\n",
-       "      <td>2001-04-09</td>\n",
-       "      <td>None</td>\n",
-       "      <td>0cf08145-a634-329f-54d3-9cbd04ba18cc</td>\n",
-       "      <td>ef2f78b9-750e-607b-f3f4-3de39ee2eeec</td>\n",
-       "      <td>102263004</td>\n",
-       "      <td>Unknown</td>\n",
-       "      <td>Eggs (edible) (substance)</td>\n",
-       "      <td>allergy</td>\n",
-       "      <td>food</td>\n",
-       "      <td>247472004</td>\n",
-       "      <td>Wheal (finding)</td>\n",
-       "      <td>MODERATE</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>792</th>\n",
-       "      <td>1980-08-15</td>\n",
-       "      <td>None</td>\n",
-       "      <td>9c532b25-cd38-f13a-f2de-2f5538326ed6</td>\n",
-       "      <td>b3afbc77-ec8c-1cf8-05fe-740c91fa1d0f</td>\n",
-       "      <td>1191</td>\n",
-       "      <td>Unknown</td>\n",
-       "      <td>Aspirin</td>\n",
-       "      <td>allergy</td>\n",
-       "      <td>medication</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>793</th>\n",
-       "      <td>1980-08-15</td>\n",
-       "      <td>None</td>\n",
-       "      <td>9c532b25-cd38-f13a-f2de-2f5538326ed6</td>\n",
-       "      <td>b3afbc77-ec8c-1cf8-05fe-740c91fa1d0f</td>\n",
-       "      <td>735029006</td>\n",
-       "      <td>Unknown</td>\n",
-       "      <td>Shellfish (substance)</td>\n",
-       "      <td>allergy</td>\n",
-       "      <td>food</td>\n",
-       "      <td>402387002</td>\n",
-       "      <td>Allergic angioedema (disorder)</td>\n",
-       "      <td>MODERATE</td>\n",
-       "      <td>422587007</td>\n",
-       "      <td>Nausea (finding)</td>\n",
-       "      <td>MODERATE</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>794 rows × 15 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         start  stop                               patient  \\\n",
-       "0   2020-02-17  None  b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85   \n",
-       "1   2020-02-17  None  b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85   \n",
-       "2   2020-02-17  None  b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85   \n",
-       "3   2020-02-17  None  b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85   \n",
-       "4   2020-02-17  None  b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85   \n",
-       "..         ...   ...                                   ...   \n",
-       "789 1987-05-20  None  27cb1c2f-4ec0-6e24-d080-e46178f712d5   \n",
-       "790 2001-04-09  None  0cf08145-a634-329f-54d3-9cbd04ba18cc   \n",
-       "791 2001-04-09  None  0cf08145-a634-329f-54d3-9cbd04ba18cc   \n",
-       "792 1980-08-15  None  9c532b25-cd38-f13a-f2de-2f5538326ed6   \n",
-       "793 1980-08-15  None  9c532b25-cd38-f13a-f2de-2f5538326ed6   \n",
-       "\n",
-       "                                encounter             code   system  \\\n",
-       "0    01efcc52-15d6-51e9-faa2-bee069fcbe44        111088007  Unknown   \n",
-       "1    01efcc52-15d6-51e9-faa2-bee069fcbe44         84489001  Unknown   \n",
-       "2    01efcc52-15d6-51e9-faa2-bee069fcbe44        260147004  Unknown   \n",
-       "3    01efcc52-15d6-51e9-faa2-bee069fcbe44        264287008  Unknown   \n",
-       "4    01efcc52-15d6-51e9-faa2-bee069fcbe44        256277009  Unknown   \n",
-       "..                                    ...              ...      ...   \n",
-       "789  04d64fc5-6137-3d7a-06b6-dd6dc2d6a7c1  442571000124108  Unknown   \n",
-       "790  ef2f78b9-750e-607b-f3f4-3de39ee2eeec             1191  Unknown   \n",
-       "791  ef2f78b9-750e-607b-f3f4-3de39ee2eeec        102263004  Unknown   \n",
-       "792  b3afbc77-ec8c-1cf8-05fe-740c91fa1d0f             1191  Unknown   \n",
-       "793  b3afbc77-ec8c-1cf8-05fe-740c91fa1d0f        735029006  Unknown   \n",
-       "\n",
-       "                    description     type     category  reaction1  \\\n",
-       "0             Latex (substance)  allergy  environment  247472004   \n",
-       "1               Mold (organism)  allergy  environment   76067001   \n",
-       "2    House dust mite (organism)  allergy  environment       <NA>   \n",
-       "3     Animal dander (substance)  allergy  environment  878820003   \n",
-       "4      Grass pollen (substance)  allergy  environment       <NA>   \n",
-       "..                          ...      ...          ...        ...   \n",
-       "789        Tree nut (substance)  allergy         food   39579001   \n",
-       "790                     Aspirin  allergy   medication  247472004   \n",
-       "791   Eggs (edible) (substance)  allergy         food  247472004   \n",
-       "792                     Aspirin  allergy   medication       <NA>   \n",
-       "793       Shellfish (substance)  allergy         food  402387002   \n",
-       "\n",
-       "                       description1 severity1  reaction2  \\\n",
-       "0                   Wheal (finding)      MILD       <NA>   \n",
-       "1                          Sneezing      MILD       <NA>   \n",
-       "2                              None      None       <NA>   \n",
-       "3    Rhinoconjunctivitis (disorder)  MODERATE  271807003   \n",
-       "4                              None      None       <NA>   \n",
-       "..                              ...       ...        ...   \n",
-       "789          Anaphylaxis (disorder)    SEVERE   49727002   \n",
-       "790                 Wheal (finding)  MODERATE       <NA>   \n",
-       "791                 Wheal (finding)  MODERATE       <NA>   \n",
-       "792                            None      None       <NA>   \n",
-       "793  Allergic angioedema (disorder)  MODERATE  422587007   \n",
-       "\n",
-       "                    description2 severity2  \n",
-       "0                           None      None  \n",
-       "1                           None      None  \n",
-       "2                           None      None  \n",
-       "3    Eruption of skin (disorder)      MILD  \n",
-       "4                           None      None  \n",
-       "..                           ...       ...  \n",
-       "789              Cough (finding)  MODERATE  \n",
-       "790                         None      None  \n",
-       "791                         None      None  \n",
-       "792                         None      None  \n",
-       "793             Nausea (finding)  MODERATE  \n",
-       "\n",
-       "[794 rows x 15 columns]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "dataset_allergies.to_df()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "83a72c04",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/notebooks/models'"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from intugle.core.settings import settings\n",
     "\n",
@@ -779,83 +177,7 @@
    "execution_count": null,
    "id": "0aa894eb",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/.venv/lib/python3.12/site-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
-      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
-      "/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/.venv/lib/python3.12/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      " 44%|████▍     | 11/25 [00:05<00:06,  2.06it/s][!] Error while llm response parsing: Got invalid JSON object. Error: Expecting ',' delimiter: line 2 column 27 (char 28)\n",
-      "For troubleshooting, visit: https://python.langchain.com/docs/troubleshooting/errors/OUTPUT_PARSING_FAILURE \n",
-      "100%|██████████| 25/25 [00:13<00:00,  1.91it/s]\n",
-      "[*] patients:   0%|          | 0/1 [00:00<?, ?it/s]/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/src/intugle/core/pipeline/key_identification/ki.py:87: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  temp.rename(columns={\"datatype_l1\": \"datatype\"}, inplace=True)\n",
-      "[*] patients: 100%|██████████| 1/1 [00:00<00:00,  1.44it/s]\n",
-      "100%|██████████| 25/25 [00:38<00:00,  1.52s/it]\n",
-      "/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/.venv/lib/python3.12/site-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
-      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
-      "/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/.venv/lib/python3.12/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "[!] Empty column encountered for claims ==> referringproviderid ...\n",
-      "100%|██████████| 31/31 [00:17<00:00,  1.78it/s]\n",
-      "[*] claims:   0%|          | 0/1 [00:00<?, ?it/s]/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/src/intugle/core/pipeline/key_identification/ki.py:87: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  temp.rename(columns={\"datatype_l1\": \"datatype\"}, inplace=True)\n",
-      "[*] claims: 100%|██████████| 1/1 [00:00<00:00,  2.29it/s]\n",
-      "100%|██████████| 31/31 [00:47<00:00,  1.53s/it]\n",
-      "/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/.venv/lib/python3.12/site-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
-      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
-      "/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/.venv/lib/python3.12/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      " 44%|████▍     | 4/9 [00:02<00:02,  1.77it/s][!] Error while llm response parsing: Got invalid JSON object. Error: Expecting ',' delimiter: line 2 column 27 (char 28)\n",
-      "For troubleshooting, visit: https://python.langchain.com/docs/troubleshooting/errors/OUTPUT_PARSING_FAILURE \n",
-      " 78%|███████▊  | 7/9 [00:03<00:00,  2.05it/s][!] Error while llm response parsing: Got invalid JSON object. Error: Expecting ',' delimiter: line 2 column 27 (char 28)\n",
-      "For troubleshooting, visit: https://python.langchain.com/docs/troubleshooting/errors/OUTPUT_PARSING_FAILURE \n",
-      "100%|██████████| 9/9 [00:04<00:00,  1.86it/s]\n",
-      "[*] careplans:   0%|          | 0/1 [00:00<?, ?it/s]/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/src/intugle/core/pipeline/key_identification/ki.py:87: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  temp.rename(columns={\"datatype_l1\": \"datatype\"}, inplace=True)\n",
-      "[*] careplans: 100%|██████████| 1/1 [00:00<00:00,  1.58it/s]\n",
-      "100%|██████████| 9/9 [00:19<00:00,  2.20s/it]\n",
-      "/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/.venv/lib/python3.12/site-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
-      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
-      "/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/.venv/lib/python3.12/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "[!] Empty column encountered for claims_transactions ==> modifier1 ...\n",
-      "[!] Empty column encountered for claims_transactions ==> modifier2 ...\n",
-      "[!] Empty column encountered for claims_transactions ==> adjustments ...\n",
-      "[!] Empty column encountered for claims_transactions ==> linenote ...\n",
-      " 30%|███       | 10/33 [00:03<00:08,  2.66it/s][!] Error while llm response parsing: Got invalid JSON object. Error: Expecting ',' delimiter: line 2 column 27 (char 28)\n",
-      "For troubleshooting, visit: https://python.langchain.com/docs/troubleshooting/errors/OUTPUT_PARSING_FAILURE \n",
-      " 39%|███▉      | 13/33 [00:05<00:08,  2.27it/s][!] Error while llm response parsing: Got invalid JSON object. Error: Expecting ',' delimiter: line 2 column 25 (char 26)\n",
-      "For troubleshooting, visit: https://python.langchain.com/docs/troubleshooting/errors/OUTPUT_PARSING_FAILURE \n",
-      "100%|██████████| 33/33 [00:18<00:00,  1.77it/s]\n",
-      "[*] claims_transactions:   0%|          | 0/1 [00:00<?, ?it/s]/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/src/intugle/core/pipeline/key_identification/ki.py:87: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  temp.rename(columns={\"datatype_l1\": \"datatype\"}, inplace=True)\n",
-      "[*] claims_transactions: 100%|██████████| 1/1 [00:00<00:00,  2.09it/s]\n",
-      "100%|██████████| 33/33 [00:49<00:00,  1.51s/it]\n",
-      "/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/.venv/lib/python3.12/site-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
-      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
-      "/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/.venv/lib/python3.12/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "100%|██████████| 13/13 [00:08<00:00,  1.56it/s]\n",
-      "100%|██████████| 13/13 [00:26<00:00,  2.00s/it]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "table_names = [\"patients\", \"claims\", \"careplans\", \"claims_transactions\", \"medications\"]\n",
     "datasets = [dataset_allergies]\n",
@@ -872,8 +194,6 @@
     "for table_name in table_names:\n",
     "    config = generate_config(table_name)\n",
     "    dataset = DataSet({**config}, table_name)\n",
-    "    dataset.profile_table()\n",
-    "    dataset.run(domain=\"Healthcare\")\n",
     "    datasets.append(dataset)"
    ]
   },
@@ -887,324 +207,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "fe4cd56d",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset 'allergies' is missing key identification. Running prerequisite analysis...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/.venv/lib/python3.12/site-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
-      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
-      "/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/.venv/lib/python3.12/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "[!] Empty column encountered for allergies ==> stop ...\n",
-      "  0%|          | 0/15 [00:00<?, ?it/s][!] Error while llm response parsing: Got invalid JSON object. Error: Expecting ',' delimiter: line 2 column 27 (char 28)\n",
-      "For troubleshooting, visit: https://python.langchain.com/docs/troubleshooting/errors/OUTPUT_PARSING_FAILURE \n",
-      "100%|██████████| 15/15 [00:11<00:00,  1.26it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset 'patients' already processed. Skipping analysis.\n",
-      "Dataset 'claims' already processed. Skipping analysis.\n",
-      "Dataset 'careplans' already processed. Skipping analysis.\n",
-      "Dataset 'claims_transactions' already processed. Skipping analysis.\n",
-      "Dataset 'medications' is missing key identification. Running prerequisite analysis...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/.venv/lib/python3.12/site-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
-      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
-      "/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/.venv/lib/python3.12/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "100%|██████████| 13/13 [00:05<00:00,  2.36it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "LinkPredictor initialized with datasets: ['allergies', 'patients', 'claims', 'careplans', 'claims_transactions', 'medications']\n",
-      "\n",
-      "--- Comparing 'allergies' <=> 'patients' ---\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[*] allergies <==> patients Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.14s/it]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 1 potential link(s).\n",
-      "\n",
-      "--- Comparing 'allergies' <=> 'claims' ---\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[*] allergies <==> claims Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.71s/it]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No links found for this pair.\n",
-      "\n",
-      "--- Comparing 'allergies' <=> 'careplans' ---\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[*] allergies <==> careplans Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.27s/it]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No links found for this pair.\n",
-      "\n",
-      "--- Comparing 'allergies' <=> 'claims_transactions' ---\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[*] allergies <==> claims_transactions Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.58s/it]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No links found for this pair.\n",
-      "\n",
-      "--- Comparing 'allergies' <=> 'medications' ---\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[*] allergies <==> medications Runs:1: 100%|██████████| 1/1 [00:04<00:00,  4.18s/it]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No links found for this pair.\n",
-      "\n",
-      "--- Comparing 'patients' <=> 'claims' ---\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[*] patients <==> claims Runs:1: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 1 potential link(s).\n",
-      "\n",
-      "--- Comparing 'patients' <=> 'careplans' ---\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[*] patients <==> careplans Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.05s/it]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 1 potential link(s).\n",
-      "\n",
-      "--- Comparing 'patients' <=> 'claims_transactions' ---\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[*] patients <==> claims_transactions Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.32s/it]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 1 potential link(s).\n",
-      "\n",
-      "--- Comparing 'patients' <=> 'medications' ---\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[*] patients <==> medications Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.18s/it]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 1 potential link(s).\n",
-      "\n",
-      "--- Comparing 'claims' <=> 'careplans' ---\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[*] claims <==> careplans Runs:1: 100%|██████████| 1/1 [00:18<00:00, 18.78s/it]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No links found for this pair.\n",
-      "\n",
-      "--- Comparing 'claims' <=> 'claims_transactions' ---\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[*] claims <==> claims_transactions Runs:1: 100%|██████████| 1/1 [00:16<00:00, 16.40s/it]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 1 potential link(s).\n",
-      "\n",
-      "--- Comparing 'claims' <=> 'medications' ---\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[*] claims <==> medications Runs:1: 100%|██████████| 1/1 [00:07<00:00,  7.06s/it]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No links found for this pair.\n",
-      "\n",
-      "--- Comparing 'careplans' <=> 'claims_transactions' ---\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[*] careplans <==> claims_transactions Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.68s/it]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No links found for this pair.\n",
-      "\n",
-      "--- Comparing 'careplans' <=> 'medications' ---\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[*] careplans <==> medications Runs:1: 100%|██████████| 1/1 [00:03<00:00,  3.37s/it]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 1 potential link(s).\n",
-      "\n",
-      "--- Comparing 'claims_transactions' <=> 'medications' ---\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[*] claims_transactions <==> medications Runs:1: 100%|██████████| 1/1 [00:05<00:00,  5.75s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No links found for this pair.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[PredictedLink(from_dataset='allergies', from_column='patient', to_dataset='patients', to_column='id'),\n",
-       " PredictedLink(from_dataset='patients', from_column='id', to_dataset='claims', to_column='patientid'),\n",
-       " PredictedLink(from_dataset='patients', from_column='id', to_dataset='careplans', to_column='patient'),\n",
-       " PredictedLink(from_dataset='patients', from_column='id', to_dataset='claims_transactions', to_column='patientid'),\n",
-       " PredictedLink(from_dataset='patients', from_column='id', to_dataset='medications', to_column='patient'),\n",
-       " PredictedLink(from_dataset='claims', from_column='id', to_dataset='claims_transactions', to_column='claimid'),\n",
-       " PredictedLink(from_dataset='careplans', from_column='encounter', to_dataset='medications', to_column='encounter')]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from intugle import LinkPredictor\n",
     "\n",
@@ -1226,21 +232,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "a59704ba",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-      "text/plain": [
-       "<Figure size 2000x2000 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "results.show_graph()\n"
    ]
@@ -1259,7 +254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "a0daf854",
    "metadata": {},
    "outputs": [],
@@ -1292,7 +287,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "2570af46",
    "metadata": {},
    "outputs": [],
@@ -1321,57 +316,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "e0f9a090",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n",
-      "/home/juhel-phanju/Documents/intugle/projects/data-tools/data-tools/.venv/lib/python3.12/site-packages/pydantic/_internal/_fields.py:198: UserWarning: Field name \"schema\" in \"Source\" shadows an attribute in parent \"BaseResource\"\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from intugle.sql_generator import SqlGenerator\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "f0432e77",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "careplans loaded\n",
-      "claims loaded\n",
-      "claims_transactions loaded\n",
-      "patients loaded\n",
-      "medications loaded\n",
-      "allergies loaded\n",
-      "patient_names\n",
-      "patient_names loaded\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'path': \"SELECT patients.first as first_name, patients.last as last_name, allergies.start as start_date FROM claims LEFT JOIN patients ON patients.id = claims.patientid LEFT JOIN allergies ON allergies.patient = patients.id WHERE (claims.departmentid IN ('3', '20'))\",\n",
-       " 'type': 'query'}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "\n",
     "# Create a SqlGenerator\n",
@@ -1386,151 +344,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "95d2e49d",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>first_name</th>\n",
-       "      <th>last_name</th>\n",
-       "      <th>start_date</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Miki234</td>\n",
-       "      <td>Kozey370</td>\n",
-       "      <td>1981-05-17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Maddie576</td>\n",
-       "      <td>Gutmann970</td>\n",
-       "      <td>1960-01-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Esther279</td>\n",
-       "      <td>Runolfsson901</td>\n",
-       "      <td>1960-05-21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Bonita405</td>\n",
-       "      <td>Bernier607</td>\n",
-       "      <td>2011-03-26</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Magdalene960</td>\n",
-       "      <td>Orn563</td>\n",
-       "      <td>1995-07-11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1543</th>\n",
-       "      <td>Zelda766</td>\n",
-       "      <td>Ernser583</td>\n",
-       "      <td>NaT</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1544</th>\n",
-       "      <td>Zelda766</td>\n",
-       "      <td>Ernser583</td>\n",
-       "      <td>NaT</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1545</th>\n",
-       "      <td>Zelda766</td>\n",
-       "      <td>Ernser583</td>\n",
-       "      <td>NaT</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1546</th>\n",
-       "      <td>Zelda766</td>\n",
-       "      <td>Ernser583</td>\n",
-       "      <td>NaT</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1547</th>\n",
-       "      <td>Zelda766</td>\n",
-       "      <td>Ernser583</td>\n",
-       "      <td>NaT</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1548 rows × 3 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        first_name      last_name start_date\n",
-       "0          Miki234       Kozey370 1981-05-17\n",
-       "1        Maddie576     Gutmann970 1960-01-05\n",
-       "2        Esther279  Runolfsson901 1960-05-21\n",
-       "3        Bonita405     Bernier607 2011-03-26\n",
-       "4     Magdalene960         Orn563 1995-07-11\n",
-       "...            ...            ...        ...\n",
-       "1543      Zelda766      Ernser583        NaT\n",
-       "1544      Zelda766      Ernser583        NaT\n",
-       "1545      Zelda766      Ernser583        NaT\n",
-       "1546      Zelda766      Ernser583        NaT\n",
-       "1547      Zelda766      Ernser583        NaT\n",
-       "\n",
-       "[1548 rows x 3 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "test_etl.to_df()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "fa02b661",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x2000 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "sql_generator.plot_sources_graph()"
    ]
diff --git a/src/intugle/__init__.py b/src/intugle/__init__.py
index 76422fa..07e5244 100644
--- a/src/intugle/__init__.py
+++ b/src/intugle/__init__.py
@@ -1,3 +1,3 @@
-from intugle.analysis.models import DataSet
-from intugle.link_predictor.predictor import LinkPredictor
-from intugle.sql_generator import SqlGenerator
+from intugle.analysis.models import DataSet as DataSet
+from intugle.knowledge_builder import KnowledgeBuilder as KnowledgeBuilder
+from intugle.sql_generator import SqlGenerator as SqlGenerator
diff --git a/src/intugle/adapters/types/duckdb/duckdb.py b/src/intugle/adapters/types/duckdb/duckdb.py
index 555087d..4f84e61 100644
--- a/src/intugle/adapters/types/duckdb/duckdb.py
+++ b/src/intugle/adapters/types/duckdb/duckdb.py
@@ -13,7 +13,7 @@
     ProfilingOutput,
 )
 from intugle.adapters.types.duckdb.models import DuckdbConfig
-from intugle.adapters.types.pandas.utils import convert_to_native
+from intugle.adapters.utils import convert_to_native
 from intugle.common.exception import errors
 from intugle.core.utilities.processing import string_standardization
 
@@ -241,8 +241,11 @@ def get_details(self, data: DuckdbConfig):
 
 
 def can_handle_pandas(df: Any) -> bool:
-    df = DuckdbAdapter.check_data(df)
-    return isinstance(df, DuckdbConfig)
+    try:
+        df = DuckdbAdapter.check_data(df)
+    except Exception:
+        return False
+    return True
 
 
 def register(factory: AdapterFactory):
diff --git a/src/intugle/adapters/types/pandas/pandas.py b/src/intugle/adapters/types/pandas/pandas.py
index f8bfb48..f1a7c38 100644
--- a/src/intugle/adapters/types/pandas/pandas.py
+++ b/src/intugle/adapters/types/pandas/pandas.py
@@ -12,10 +12,9 @@
     ColumnProfile,
     ProfilingOutput,
 )
+from intugle.adapters.utils import convert_to_native
 from intugle.core.utilities.processing import string_standardization
 
-from .utils import convert_to_native
-
 
 class PandasAdapter(Adapter):
     def profile(self, data: pd.DataFrame, _: str) -> ProfilingOutput:
diff --git a/src/intugle/adapters/types/pandas/utils.py b/src/intugle/adapters/utils.py
similarity index 100%
rename from src/intugle/adapters/types/pandas/utils.py
rename to src/intugle/adapters/utils.py
diff --git a/src/intugle/knowledge_builder.py b/src/intugle/knowledge_builder.py
new file mode 100644
index 0000000..9cd44c5
--- /dev/null
+++ b/src/intugle/knowledge_builder.py
@@ -0,0 +1,53 @@
+from typing import TYPE_CHECKING, Any, Dict, List
+
+from intugle.analysis.models import DataSet
+from intugle.link_predictor.predictor import LinkPredictor
+
+if TYPE_CHECKING:
+    from intugle.link_predictor.models import PredictedLink
+
+
+class KnowledgeBuilder:
+
+    def __init__(self, data_input: Dict[str, Any] | List[DataSet], domain: str = ""):
+
+        self.datasets: Dict[str, DataSet] = {}
+        self.links: list[PredictedLink] = []
+        self.domain = domain
+
+        if isinstance(data_input, dict):
+            self._initialize_from_dict(data_input)
+        elif isinstance(data_input, list):
+            self._initialize_from_list(data_input)
+        else:
+            raise TypeError("Input must be a dictionary of named dataframes or a list of DataSet objects.")
+        
+    def _initialize_from_dict(self, data_dict: Dict[str, Any]):
+        """Creates and processes DataSet objects from a dictionary of raw dataframes."""
+        for name, df in data_dict.items():
+            dataset = DataSet(df, name=name)
+            self.datasets[name] = dataset
+
+    def _initialize_from_list(self, data_list: List[DataSet]):
+        """Processes a list of existing DataSet objects"""
+        for dataset in data_list:
+            if not dataset.name:
+                raise ValueError("DataSet objects provided in a list must have a 'name' attribute.")
+            self.datasets[dataset.name] = dataset
+
+    def build(self):
+
+        # run analysis on all datasets
+        for dataset in self.datasets.values():
+            dataset.run(domain=self.domain, save=True)
+
+        # Initialize the predictor
+        self.link_predictor = LinkPredictor(list(self.datasets.values()))
+
+        # Run the prediction
+        self.links: list[PredictedLink] = self.link_predictor.predict().links
+        return self
+    
+    def visualize(self):
+        return self.link_predictor.show_graph()
+        
diff --git a/src/intugle/mcp/server.py b/src/intugle/mcp/server.py
index f9fc431..23bca17 100644
--- a/src/intugle/mcp/server.py
+++ b/src/intugle/mcp/server.py
@@ -30,7 +30,7 @@ async def lifespan(app: Starlette):
     routes=[
         # Using settings-based configuration
         Mount("/semantic_layer", app=semantic_layer_mcp.streamable_http_app()),
-        # Mount("/adapter", app=adapter_mcp.streamable_http_app()),
+        Mount("/adapter", app=adapter_mcp.streamable_http_app()),
     ],
     lifespan=lifespan
 )
diff --git a/src/intugle/sql_generator.py b/src/intugle/sql_generator.py
index 3ef73d8..6f746e9 100644
--- a/src/intugle/sql_generator.py
+++ b/src/intugle/sql_generator.py
@@ -36,7 +36,6 @@ def load_all(self):
         for source in sources.values():
             table_name = source.table.name
             details = source.table.details
-
             DataSet(data=details, name=table_name)
 
     def generate_query(self, etl: ETLModel) -> str:
diff --git a/test.py b/test.py
new file mode 100644
index 0000000..7d166c3
--- /dev/null
+++ b/test.py
@@ -0,0 +1,23 @@
+
+etl = {
+    "name": "test_etl",
+    "fields": [
+        {"id": "patients.first", "name": "first_name"},
+        {"id": "patients.last", "name": "last_name"},
+        {"id": "allergies.start", "name": "start_date"},
+    ],
+    "filter": {
+        "selections": [{"id": "claims.departmentid", "values": ["3", "20"]}],
+    },
+}
+
+from intugle.sql_generator import SqlGenerator
+
+# Create a SqlGenerator
+sql_generator = SqlGenerator()
+
+# Generate the query
+sql_query = sql_generator.generate_query(etl)
+
+# Print the query
+print(sql_query)
\ No newline at end of file
diff --git a/tests/analysis/test_profile.py b/tests/analysis/test_profile.py
index 43e8df3..7004250 100644
--- a/tests/analysis/test_profile.py
+++ b/tests/analysis/test_profile.py
@@ -8,7 +8,7 @@ def test_profile():
     """
     # 1. Prepare dummy data
     data = DuckdbConfig.model_validate({
-        "path": "",  # noqa: E501
+        "path": "https://raw.githubusercontent.com/Intugle/data-tools/refs/heads/main/sample_data/healthcare/allergies.csv",  # noqa: E501
         "type": "csv"
     })
     table_name = "allergies"
diff --git a/tests/query_generator/test_query_generator.py b/tests/query_generator/test_query_generator.py
index a49634f..9e9d7e4 100644
--- a/tests/query_generator/test_query_generator.py
+++ b/tests/query_generator/test_query_generator.py
@@ -10,8 +10,6 @@
 )
 from intugle.sql_generator import SqlGenerator
 
-# PROJECT_BASE = "/home/juhel-phanju/Documents/backup/MIGRATION/codes/poc/dbt/ecom/ecom/models"
-
 
 def get_test_data():
     inputs = {
diff --git a/uv.lock b/uv.lock
index 70c8dc5..f1178d5 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 3
+revision = 1
 requires-python = ">=3.10"
 resolution-markers = [
     "python_full_version >= '3.13'",
@@ -12,9 +12,9 @@ resolution-markers = [
 name = "aiohappyeyeballs"
 version = "2.6.1"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760, upload-time = "2025-03-12T01:42:48.764Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265, upload-time = "2025-03-12T01:42:47.083Z" },
+    { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265 },
 ]
 
 [[package]]
@@ -31,76 +31,76 @@ dependencies = [
     { name = "propcache" },
     { name = "yarl" },
 ]
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-wheels = [
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From 1481b3943d1d13d9056e64ccf0e40991ce82670a Mon Sep 17 00:00:00 2001
From: JaskaranIntugle <jaskaran@intugle.ai>
Date: Sun, 7 Sep 2025 19:31:57 +0530
Subject: [PATCH 05/25] added DataProductBuilder, updated documentation

---
 .gitignore                                    |   1 +
 README.md                                     |  75 +-
 notebooks/quickstart_cleaned.ipynb            | 836 ------------------
 notebooks/quickstart_content.txt              | 204 +++--
 notebooks/quickstart_duckdb.ipynb             |   8 +-
 pyproject.toml                                |   2 +-
 quickstart.ipynb                              | 578 +++---------
 src/intugle/__init__.py                       |   2 +-
 src/intugle/analysis/models.py                |  11 +-
 .../{sql_generator.py => dp_builder.py}       |   6 +-
 src/intugle/knowledge_builder.py              |   3 +-
 src/intugle/mcp/semantic_layer/router.py      |   8 +-
 test.py                                       |  23 -
 tests/query_generator/test_query_generator.py |   4 +-
 uv.lock                                       |   2 +-
 15 files changed, 287 insertions(+), 1476 deletions(-)
 delete mode 100644 notebooks/quickstart_cleaned.ipynb
 rename src/intugle/{sql_generator.py => dp_builder.py} (97%)
 delete mode 100644 test.py

diff --git a/.gitignore b/.gitignore
index c02218b..a94ed77 100644
--- a/.gitignore
+++ b/.gitignore
@@ -211,3 +211,4 @@ testing_base
 models
 
 settings.json
+archived/
diff --git a/README.md b/README.md
index 21db03a..e93f56f 100644
--- a/README.md
+++ b/README.md
@@ -31,7 +31,7 @@ This tool is designed for both **data teams** and **business teams**.
 *   **LLM-Powered Link Prediction:** Use GenAI to automatically discover relationships (foreign keys) between tables.
 *   **Business Glossary Generation:** Generate a business glossary for each column, with support for industry-specific domains.
 *   **Semantic Layer Generation:** Create YAML files that defines your semantic layer, including models (tables) and their relationships.
-*   **SQL Generation:** Generate SQL queries from the semantic layer, allowing you to query your data using business-friendly terms.
+*   **Data Product Creation:** Generate data products from the semantic layer, allowing you to query your data using business-friendly terms.
 
 ## Getting Started
 
@@ -59,52 +59,51 @@ export OPENAI_API_KEY="your-openai-api-key"
 
 ## Quickstart
 
-For a detailed, hands-on introduction to the project, please see the [`quickstart.ipynb`](quickstart.ipynb) notebook. It will walk you through the entire process of profiling your data, predicting links, generating a semantic layer, and querying your data.
+For a detailed, hands-on introduction to the project, please see the [`quickstart.ipynb`](quickstart.ipynb) notebook. It will walk you through the entire process of building a semantic layer, including:
 
-## Usage
-
-The core workflow of the project involves the following steps:
+*   **Building a Knowledge Base:** Use the `KnowledgeBuilder` to automatically profile your data, generate a business glossary, and predict links between tables.
+*   **Accessing Enriched Metadata:** Learn how to access the profiling results and business glossary for each dataset.
+*   **Visualizing Relationships:** Visualize the predicted links between your tables.
+*   **Generating Data Products:** Use the semantic layer to generate data products and retrieve data.
+*   **Serving the Semantic Layer:** Learn how to start the MCP server to interact with your semantic layer using natural language.
 
-1.  **Load your data:** Load your data into a DataSet object.
-2.  **Run the analysis pipeline:** Use the `run()` method to profile your data and generate a business glossary.
-3.  **Predict links:** Use the `LinkPredictor` to discover relationships between your tables.
+## Usage
 
-    ```python
-    from intugle import LinkPredictor
+The core workflow of the project involves using the `KnowledgeBuilder` to build a semantic layer, and then using the `DataProductBuilder` to generate data products from that layer.
 
-    # Initialize the predictor
-    predictor = LinkPredictor(datasets)
+```python
+from intugle import KnowledgeBuilder, DataProductBuilder
 
-    # Run the prediction
-    results = predictor.predict()
-    results.show_graph()
-    ```
+# Define your datasets
+datasets = {
+    "allergies": {"path": "path/to/allergies.csv", "type": "csv"},
+    "patients": {"path": "path/to/patients.csv", "type": "csv"},
+    # ... add other datasets
+}
 
-5.  **Generate SQL:** Use the `SqlGenerator` to generate SQL queries from the semantic layer.
+# Build the knowledge base
+kb = KnowledgeBuilder(datasets, domain="Healthcare")
+kb.build()
 
-    ```python
-    from intugle import SqlGenerator
+# Create a DataProductBuilder
+dp_builder = DataProductBuilder()
 
-    # Create a SqlGenerator
-    sql_generator = SqlGenerator()
+# Define an ETL model
+etl = {
+    "name": "patient_allergies",
+    "fields": [
+        {"id": "patients.first", "name": "first_name"},
+        {"id": "patients.last", "name": "last_name"},
+        {"id": "allergies.description", "name": "allergy"},
+    ],
+}
 
-    # Create an ETL model
-    etl = {
-        name": "test_etl",
-        fields": [
-           {"id": "patients.first", "name": "first_name"},
-           {"id": "patients.last", "name": "last_name"},
-           {"id": "allergies.start", "name": "start_date"},
-        ,
-        filter": {
-           "selections": [{"id": "claims.departmentid", "values": ["3", "20"]}],
-        ,
-    }
+# Generate the data product
+data_product = dp_builder.build(etl)
 
-    # Generate the query
-    sql_query = sql_generator.generate_query(etl_model)
-    print(sql_query)
-    ```
+# View the data product as a DataFrame
+print(data_product.to_df())
+```
 
 For detailed code examples and a complete walkthrough, please refer to the [`quickstart.ipynb`](quickstart.ipynb) notebook.
 
@@ -126,4 +125,4 @@ Contributions are welcome! Please see the [`CONTRIBUTING.md`](CONTRIBUTING.md) f
 
 ## License
 
-This project is licensed under the Apache License, Version 2.0. See the [`LICENSE`](LICENSE) file for details.
+This project is licensed under the Apache License, Version 2.0. See the [`LICENSE`](LICENSE) file for details.
\ No newline at end of file
diff --git a/notebooks/quickstart_cleaned.ipynb b/notebooks/quickstart_cleaned.ipynb
deleted file mode 100644
index c6ace8c..0000000
--- a/notebooks/quickstart_cleaned.ipynb
+++ /dev/null
@@ -1,836 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "757096f5",
-   "metadata": {},
-   "source": [
-    "# Quickstart: Building a Semantic Layer with Intugle\n",
-    "\n",
-    "This notebook provides a quick introduction to this project. You'll learn how to use its key features to automatically build a semantic layer over your data.\n",
-    "\n",
-    "**What is a Semantic Layer?**\n",
-    "\n",
-    "A semantic layer is a business-friendly representation of your data. It hides the complexity of the underlying data sources and provides a unified view of your data using familiar business terms. This makes it easier for both business users and data teams to understand and query the data, accelerating data-driven insights.\n",
-    "\n",
-    "**Who is this for?**\n",
-    "\n",
-    "This tool is designed for both **data teams** and **business teams**. \n",
-    "\n",
-    "* **Data teams** can use it to automate data profiling, schema discovery, and documentation, significantly accelerating their workflow.\n",
-    "* **Business teams** can use it to gain a better understanding of their data and to perform self-service analytics without needing to write complex SQL queries.\n",
-    "\n",
-    "**In this notebook, you will learn how to:**\n",
-    "\n",
-    "1. **Profile your data:** Analyze your data sources to understand their structure, data types, and other characteristics.\n",
-    "2. **Business Glossary Generation:** Generate a business glossary for each column, with support for industry-specific domains.\n",
-    "2. **Automatically predict links:** Use a Large Language Model (LLM) to automatically discover relationships (foreign keys) between tables.\n",
-    "3. **Generate a semantic layer:** Create YAML files file that defines your semantic layer.\n",
-    "4. **Generate SQL queries:** Use the semantic layer to generate SQL queries and retrieve data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a2a1b244",
-   "metadata": {},
-   "source": [
-    "## 1. LLM Configuration\n",
-    "\n",
-    "Before running the project, you need to configure a Large Language Model (LLM). This is used for tasks like generating business glossaries and predicting links between tables.\n",
-    "\n",
-    "You can configure the LLM by setting the following environment variables:\n",
-    "\n",
-    "* `LLM_PROVIDER`: The LLM provider and model to use (e.g., `openai:gpt-3.5-turbo`). The format follows langchain's format for initializing chat models. Checkout how to specify your model [here](https://python.langchain.com/docs/integrations/chat/) \n",
-    "* `OPENAI_API_KEY`: Your API key for the LLM provider.\n",
-    "\n",
-    "Here's an example of how to set these variables in your environment:\n",
-    "\n",
-    "```bash\n",
-    "export LLM_PROVIDER=\"openai:gpt-3.5-turbo\"\n",
-    "export OPENAI_API_KEY=\"your-openai-api-key\"\n",
-    "```\n",
-    "\n",
-    "Alternatively, you can set them in the notebook like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "36454d44",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "os.environ[\"LLM_PROVIDER\"] = \"openai:gpt-3.5-turbo\"\n",
-    "os.environ[\"OPENAI_API_KEY\"] = \"your-openai-api-key\"  # Replace with your actual key\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fa9fda69",
-   "metadata": {},
-   "source": [
-    "> Currently the langchain packages for OpenAI, Anthropic and Gemini is installed by default. For additional models, make sure you have the integration packages installed. E.g. you should have langchain-deepseek installed to use a DeepSeek model. You can get these packages here: [LangChain Chat Models](https://python.langchain.com/docs/integrations/chat/)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a3dad9d8",
-   "metadata": {},
-   "source": [
-    "## 2. Data Profiling and Glossary Generation\n",
-    "\n",
-    "The first step in building a semantic layer is to profile your data. This involves analyzing your data sources to understand their structure, data types, and other characteristics. This tool provides a pipeline for this purpose. It can also generate business glossary for your data.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "7f0e28d7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# from intugle import DataSet"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "8d3e8299",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# allergies_data = {\n",
-    "#     \"path\": \"https://raw.githubusercontent.com/Intugle/data-tools/refs/heads/main/sample_data/healthcare/allergies.csv\",\n",
-    "#     \"type\": \"csv\"\n",
-    "# }\n",
-    "# # Create a DataSet object and run the profiling pipeline\n",
-    "# dataset_allergies = DataSet(allergies_data, \"allergies\")\n",
-    "# dataset_allergies.run(domain=\"Healthcare\")\n",
-    "\n",
-    "# Or you can run each step manually:\n",
-    "# dataset_allergies.profile().identify_datatypes().identify_keys().generate_glossary(domain=\"Healthcare\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0f4ea58f",
-   "metadata": {},
-   "source": [
-    "The `run()` method performs a series of analysis steps, including:\n",
-    "\n",
-    "* **Profiling:** Calculates statistics for each column, such as distinct count, uniqueness, and completeness.\n",
-    "* **Datatype Identification:** Identifies the data type of each column (e.g., integer, string, datetime).\n",
-    "* **Key Identification:** Identifies potential primary keys.\n",
-    "* **Glossary Generation:** Generates a business glossary for each column using an LLM.\n",
-    "\n",
-    "> The `domain` parameter helps the LLM generate a more contextual business glossary. It specifies the industry domain that the dataset belongs to (e.g., \"Healthcare\", \"Finance\", \"E-commerce\").\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "ac8a78fa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# dataset_allergies.result_to_pandas()[[\"business_glossary\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "86d162c1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# dataset_allergies.to_df()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "83a72c04",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/raphael/intugle/data-tools/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n",
-      "/home/raphael/intugle/data-tools/.venv/lib/python3.12/site-packages/pydantic/_internal/_fields.py:198: UserWarning: Field name \"schema\" in \"Source\" shadows an attribute in parent \"BaseResource\"\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'/home/raphael/intugle/data-tools/testing_base'"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from intugle.core.settings import settings\n",
-    "\n",
-    "settings.PROJECT_BASE"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eb562acd",
-   "metadata": {},
-   "source": [
-    "## 3. Automated Link Prediction\n",
-    "\n",
-    "Now that we've profiled our data, let's discover the relationships between tables. This tool uses an LLM to predict links (foreign keys) between tables.\n",
-    "\n",
-    "First, we'll load a few more tables from the sample dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "0aa894eb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def generate_config(table_name: str) -> str:\n",
-    "    \"\"\"Append the base URL to the table name.\"\"\"\n",
-    "    return {\n",
-    "        \"path\": f\"https://raw.githubusercontent.com/Intugle/data-tools/refs/heads/main/sample_data/healthcare/{table_name}.csv\",\n",
-    "        \"type\": \"csv\"\n",
-    "    }\n",
-    "\n",
-    "\n",
-    "# table_names = [\"allergies\", \"patients\", \"claims\", \"careplans\", \"claims_transactions\", \"medications\"]\n",
-    "table_names = [\"allergies\", \"patients\", \"claims\", \"careplans\"]\n",
-    "datasets = {table: generate_config(table) for table in table_names}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c3f457b6",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e083a8d8",
-   "metadata": {},
-   "source": [
-    "Now, let's run the link prediction pipeline."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "fe4cd56d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "allergies loaded\n",
-      "patients loaded\n",
-      "claims loaded\n",
-      "careplans loaded\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/raphael/intugle/data-tools/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3859: RuntimeWarning: Mean of empty slice.\n",
-      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
-      "/home/raphael/intugle/data-tools/.venv/lib/python3.12/site-packages/numpy/_core/_methods.py:144: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "[!] Empty column encountered for allergies ==> stop ...\n",
-      "100%|██████████| 15/15 [00:07<00:00,  2.02it/s]\n",
-      "100%|██████████| 15/15 [00:20<00:00,  1.39s/it]\n",
-      "/home/raphael/intugle/data-tools/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3859: RuntimeWarning: Mean of empty slice.\n",
-      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
-      "/home/raphael/intugle/data-tools/.venv/lib/python3.12/site-packages/numpy/_core/_methods.py:144: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "100%|██████████| 25/25 [00:16<00:00,  1.50it/s]\n",
-      "[*] patients:   0%|          | 0/1 [00:00<?, ?it/s]/home/raphael/intugle/data-tools/src/intugle/core/pipeline/key_identification/ki.py:87: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  temp.rename(columns={\"datatype_l1\": \"datatype\"}, inplace=True)\n",
-      "[*] patients: 100%|██████████| 1/1 [00:01<00:00,  1.82s/it]\n",
-      "100%|██████████| 25/25 [01:05<00:00,  2.64s/it]\n",
-      "/home/raphael/intugle/data-tools/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3859: RuntimeWarning: Mean of empty slice.\n",
-      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
-      "/home/raphael/intugle/data-tools/.venv/lib/python3.12/site-packages/numpy/_core/_methods.py:144: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "[!] Empty column encountered for claims ==> referringproviderid ...\n",
-      "100%|██████████| 31/31 [00:31<00:00,  1.01s/it]\n",
-      "[*] claims:   0%|          | 0/1 [00:00<?, ?it/s]/home/raphael/intugle/data-tools/src/intugle/core/pipeline/key_identification/ki.py:87: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  temp.rename(columns={\"datatype_l1\": \"datatype\"}, inplace=True)\n",
-      "[*] claims: 100%|██████████| 1/1 [00:00<00:00,  1.91it/s]\n",
-      "100%|██████████| 31/31 [01:15<00:00,  2.44s/it]\n",
-      "/home/raphael/intugle/data-tools/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3859: RuntimeWarning: Mean of empty slice.\n",
-      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
-      "/home/raphael/intugle/data-tools/.venv/lib/python3.12/site-packages/numpy/_core/_methods.py:144: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "100%|██████████| 9/9 [00:08<00:00,  1.09it/s]\n",
-      "[*] careplans:   0%|          | 0/1 [00:00<?, ?it/s]/home/raphael/intugle/data-tools/src/intugle/core/pipeline/key_identification/ki.py:87: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  temp.rename(columns={\"datatype_l1\": \"datatype\"}, inplace=True)\n",
-      "[*] careplans: 100%|██████████| 1/1 [00:00<00:00,  1.18it/s]\n",
-      "100%|██████████| 9/9 [00:24<00:00,  2.67s/it]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset 'allergies' is missing key identification. Running prerequisite analysis...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/raphael/intugle/data-tools/.venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3859: RuntimeWarning: Mean of empty slice.\n",
-      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
-      "/home/raphael/intugle/data-tools/.venv/lib/python3.12/site-packages/numpy/_core/_methods.py:144: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "[!] Empty column encountered for allergies ==> stop ...\n",
-      "100%|██████████| 15/15 [00:13<00:00,  1.09it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset 'patients' already processed. Skipping analysis.\n",
-      "Dataset 'claims' already processed. Skipping analysis.\n",
-      "Dataset 'careplans' already processed. Skipping analysis.\n",
-      "LinkPredictor initialized with datasets: ['allergies', 'patients', 'claims', 'careplans']\n",
-      "\n",
-      "--- Comparing 'allergies' <=> 'patients' ---\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[*] allergies <==> patients Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.14s/it]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 1 potential link(s).\n",
-      "\n",
-      "--- Comparing 'allergies' <=> 'claims' ---\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[*] allergies <==> claims Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.61s/it]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No links found for this pair.\n",
-      "\n",
-      "--- Comparing 'allergies' <=> 'careplans' ---\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[*] allergies <==> careplans Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.68s/it]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No links found for this pair.\n",
-      "\n",
-      "--- Comparing 'patients' <=> 'claims' ---\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[*] patients <==> claims Runs:1: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 1 potential link(s).\n",
-      "\n",
-      "--- Comparing 'patients' <=> 'careplans' ---\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[*] patients <==> careplans Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.03s/it]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 1 potential link(s).\n",
-      "\n",
-      "--- Comparing 'claims' <=> 'careplans' ---\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[*] claims <==> careplans Runs:1: 100%|██████████| 1/1 [00:05<00:00,  5.22s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No links found for this pair.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<intugle.knowledge_builder.KnowledgeBuilder at 0x7fe1cf0bd9d0>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from intugle import KnowledgeBuilder\n",
-    "\n",
-    "# Initialize the knowledge builder\n",
-    "kb = KnowledgeBuilder(datasets, domain=\"Healthcare\")\n",
-    "\n",
-    "# Run the prediction\n",
-    "kb.build()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d18dc421",
-   "metadata": {},
-   "source": [
-    "The `results` object contains the predicted links between the tables. You can also visualize the relationships as a graph.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "a59704ba",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-      "text/plain": [
-       "<Figure size 2000x2000 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "kb.visualize()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "32fd4917",
-   "metadata": {},
-   "source": [
-    "## 4. The Semantic Layer\n",
-    "\n",
-    "The profiling and link prediction results are used to generate YAML files which are saved automatically. These files defines the semantic layer, including the models (tables) and their relationships. \n",
-    "\n",
-    "By default, these files are saved in the current working directory. You can configure this path by setting the `PROJECT_BASE` environment variable. They can also be saved manually as shown below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "a0daf854",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# for ds in datasets:\n",
-    "#     ds.save_yaml()\n",
-    "\n",
-    "# results.save_yaml(\"relationships.yml\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8cf08923",
-   "metadata": {},
-   "source": [
-    "Now, we can load the YAML files and create a manifest."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3d086f45",
-   "metadata": {},
-   "source": [
-    "## 5. SQL Generation\n",
-    "\n",
-    "Once you have a semantic layer, you can use the `SqlGenerator` to generate SQL queries. This allows you to query the data using business-friendly terms, without having to write complex SQL.\n",
-    "\n",
-    "Let's create an ETL model to define the query we want to generate."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "2570af46",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "etl = {\n",
-    "    \"name\": \"patient_names\",\n",
-    "    \"fields\": [\n",
-    "        {\"id\": \"patients.first\", \"name\": \"first_name\"},\n",
-    "        {\"id\": \"patients.last\", \"name\": \"last_name\"},\n",
-    "        {\"id\": \"allergies.start\", \"name\": \"start_date\"},\n",
-    "    ],\n",
-    "    \"filter\": {\n",
-    "        \"selections\": [{\"id\": \"claims.departmentid\", \"values\": [\"3\", \"20\"]}],\n",
-    "    },\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2e4ff6be",
-   "metadata": {},
-   "source": [
-    "Now, let's use the `SqlGenerator` to generate the SQL query."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "e0f9a090",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from intugle.sql_generator import SqlGenerator\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "f0432e77",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "careplans loaded\n",
-      "patients loaded\n",
-      "claims loaded\n",
-      "test_etl loaded\n",
-      "allergies loaded\n",
-      "patient_names\n",
-      "patient_names loaded\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'path': \"SELECT patients.first as first_name, patients.last as last_name, allergies.start as start_date FROM claims LEFT JOIN patients ON patients.id = claims.patientid LEFT JOIN allergies ON allergies.patient = patients.id WHERE (claims.departmentid IN ('3', '20'))\",\n",
-       " 'type': 'query'}"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "# Create a SqlGenerator\n",
-    "sql_generator = SqlGenerator()\n",
-    "\n",
-    "# Generate the query\n",
-    "test_etl = sql_generator.generate_product(etl)\n",
-    "\n",
-    "# Print the query\n",
-    "test_etl.data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "95d2e49d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>first_name</th>\n",
-       "      <th>last_name</th>\n",
-       "      <th>start_date</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Miki234</td>\n",
-       "      <td>Kozey370</td>\n",
-       "      <td>1981-05-17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Maddie576</td>\n",
-       "      <td>Gutmann970</td>\n",
-       "      <td>1960-01-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Esther279</td>\n",
-       "      <td>Runolfsson901</td>\n",
-       "      <td>1960-05-21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Bonita405</td>\n",
-       "      <td>Bernier607</td>\n",
-       "      <td>2011-03-26</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Magdalene960</td>\n",
-       "      <td>Orn563</td>\n",
-       "      <td>1995-07-11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1543</th>\n",
-       "      <td>Zelda766</td>\n",
-       "      <td>Ernser583</td>\n",
-       "      <td>NaT</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1544</th>\n",
-       "      <td>Zelda766</td>\n",
-       "      <td>Ernser583</td>\n",
-       "      <td>NaT</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1545</th>\n",
-       "      <td>Zelda766</td>\n",
-       "      <td>Ernser583</td>\n",
-       "      <td>NaT</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1546</th>\n",
-       "      <td>Zelda766</td>\n",
-       "      <td>Ernser583</td>\n",
-       "      <td>NaT</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1547</th>\n",
-       "      <td>Zelda766</td>\n",
-       "      <td>Ernser583</td>\n",
-       "      <td>NaT</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1548 rows × 3 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        first_name      last_name start_date\n",
-       "0          Miki234       Kozey370 1981-05-17\n",
-       "1        Maddie576     Gutmann970 1960-01-05\n",
-       "2        Esther279  Runolfsson901 1960-05-21\n",
-       "3        Bonita405     Bernier607 2011-03-26\n",
-       "4     Magdalene960         Orn563 1995-07-11\n",
-       "...            ...            ...        ...\n",
-       "1543      Zelda766      Ernser583        NaT\n",
-       "1544      Zelda766      Ernser583        NaT\n",
-       "1545      Zelda766      Ernser583        NaT\n",
-       "1546      Zelda766      Ernser583        NaT\n",
-       "1547      Zelda766      Ernser583        NaT\n",
-       "\n",
-       "[1548 rows x 3 columns]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "test_etl.to_df()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "fa02b661",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x2000 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sql_generator.plot_sources_graph()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bfde8c96",
-   "metadata": {},
-   "source": [
-    "## 6. MCP Server: Interacting with Your Semantic Layer\n",
-    "\n",
-    "Now that you have a semantic layer, you can serve it as a MCP server to interact with it using natural language. The MCP server exposes your semantic layer as a set of tools that can be used by any MCP client.\n",
-    "\n",
-    "### Starting the MCP Server\n",
-    "\n",
-    "To start the MCP server, run the following command in your terminal:\n",
-    "\n",
-    "```bash\n",
-    "intugle-mcp\n",
-    "```\n",
-    "\n",
-    "This will start a server on `localhost:8000`.\n",
-    "\n",
-    "### Connecting to the MCP Server\n",
-    "\n",
-    "Once the server is running, you can connect to it from any MCP client. The endpoint for the MCP server is:\n",
-    "\n",
-    "`http://localhost:8000/semantic_layer/mcp`\n",
-    "\n",
-    "You can use a variety of MCP clients to connect to the server, such as Claude Desktop, Gemini CLI etc.\n",
-    "\n",
-    "### Use Cases\n",
-    "\n",
-    "Once connected, you can interact with your semantic layer using natural language. Here are some exciting applications:\n",
-    "\n",
-    "*   **Generate SQL Queries:** Ask questions in natural language and have the MCP server generate the corresponding SQL query.\n",
-    "*   **Data Discovery:** Ask questions about the tables and columns in your semantic layer to better understand your data.\n",
-    "\n",
-    "> To execute the generated SQL queries, you can also connect your database instance as a MCP tool to your client."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c29d33de",
-   "metadata": {},
-   "source": [
-    "## Conclusion\n",
-    "\n",
-    "You've learned how to:\n",
-    "\n",
-    "* Configure your LLM provider.\n",
-    "* Profile your data to understand its characteristics.\n",
-    "* Use an LLM to automatically predict links between tables.\n",
-    "* Generate a semantic layer.\n",
-    "* Use the semantic layer to generate SQL queries.\n",
-    "* Interact with your semantic layer using the MCP server.\n",
-    "\n",
-    "This is just a starting point. This project has many other features to explore. We encourage you to try it with your own data and see how it can help you build a powerful semantic layer.\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "intugle",
-   "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": "3.12.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/quickstart_content.txt b/notebooks/quickstart_content.txt
index c82fe3c..74601e7 100644
--- a/notebooks/quickstart_content.txt
+++ b/notebooks/quickstart_content.txt
@@ -11,29 +11,29 @@ A semantic layer is a business-friendly representation of your data. It hides th
 
 **Who is this for?**
 
-This tool is designed for both **data teams** and **business teams**. 
+This tool is designed for both **data teams** and **business teams**.
 
-* **Data teams** can use it to automate data profiling, schema discovery, and documentation, significantly accelerating their workflow.
-* **Business teams** can use it to gain a better understanding of their data and to perform self-service analytics without needing to write complex SQL queries.
+*   **Data teams** can use it to automate data profiling, schema discovery, and documentation, significantly accelerating their workflow.
+*   **Business teams** can use it to gain a better understanding of their data and to perform self-service analytics without needing to write complex SQL queries.
 
 **In this notebook, you will learn how to:**
 
-1. **Profile your data:** Analyze your data sources to understand their structure, data types, and other characteristics.
-2. **Business Glossary Generation:** Generate a business glossary for each column, with support for industry-specific domains.
-2. **Automatically predict links:** Use a Large Language Model (LLM) to automatically discover relationships (foreign keys) between tables.
-3. **Generate a semantic layer:** Create YAML files file that defines your semantic layer.
-4. **Generate SQL queries:** Use the semantic layer to generate SQL queries and retrieve data.
+1.  **Build a Knowledge Base:** Use the `KnowledgeBuilder` to automatically profile your data, generate a business glossary, and predict links between tables.
+2.  **Generate Data Products:** Use the semantic layer to generate data products and retrieve data.
+3.  **Serve the Semantic Layer:** Learn how to start the MCP server to interact with your semantic layer using natural language.
+
 ---
 type: markdown
 ---
+
 ## 1. LLM Configuration
 
 Before running the project, you need to configure a Large Language Model (LLM). This is used for tasks like generating business glossaries and predicting links between tables.
 
 You can configure the LLM by setting the following environment variables:
 
-* `LLM_PROVIDER`: The LLM provider and model to use (e.g., `openai:gpt-3.5-turbo`). The format follows langchain's format for initializing chat models. Checkout how to specify your model [here](https://python.langchain.com/docs/integrations/chat/) 
-* `OPENAI_API_KEY`: Your API key for the LLM provider.
+*   `LLM_PROVIDER`: The LLM provider and model to use (e.g., `openai:gpt-3.5-turbo`). The format follows langchain's format for initializing chat models. Checkout how to specify your model [here](https://python.langchain.com/docs/integrations/chat/)
+*   `OPENAI_API_KEY`: Your API key for the LLM provider.
 
 Here's an example of how to set these variables in your environment:
 
@@ -43,9 +43,11 @@ export OPENAI_API_KEY="your-openai-api-key"
 ```
 
 Alternatively, you can set them in the notebook like this:
+
 ---
 type: code
 ---
+
 import os
 
 os.environ["LLM_PROVIDER"] = "openai:gpt-3.5-turbo"
@@ -54,66 +56,29 @@ os.environ["OPENAI_API_KEY"] = "your-openai-api-key"  # Replace with your actual
 ---
 type: markdown
 ---
-> Currently the langchain packages for OpenAI, Anthropic and Gemini is installed by default. For additional models, make sure you have the integration packages installed. E.g. you should have langchain-deepseek installed to use a DeepSeek model. You can get these packages here: [LangChain Chat Models](https://python.langchain.com/docs/integrations/chat/)
----
-type: markdown
----
-## 2. Data Profiling and Glossary Generation
 
-The first step in building a semantic layer is to profile your data. This involves analyzing your data sources to understand their structure, data types, and other characteristics. This tool provides a pipeline for this purpose. It can also generate business glossary for your data.
----
-type: code
----
-# from intugle import DataSet
----
-type: code
----
-# allergies_data = {
-#     "path": "https://raw.githubusercontent.com/Intugle/data-tools/refs/heads/main/sample_data/healthcare/allergies.csv",
-#     "type": "csv"
-# }
-# # Create a DataSet object and run the profiling pipeline
-# dataset_allergies = DataSet(allergies_data, "allergies")
-# dataset_allergies.run(domain="Healthcare")
+> Currently the langchain packages for OpenAI, Anthropic and Gemini is installed by default. For additional models, make sure you have the integration packages installed. E.g. you should have langchain-deepseek installed to use a DeepSeek model. You can get these packages here: [LangChain Chat Models](https://python.langchain.com/docs/integrations/chat/)
 
-# Or you can run each step manually:
-# dataset_allergies.profile().identify_datatypes().identify_keys().generate_glossary(domain="Healthcare")
 ---
 type: markdown
 ---
-The `run()` method performs a series of analysis steps, including:
 
-* **Profiling:** Calculates statistics for each column, such as distinct count, uniqueness, and completeness.
-* **Datatype Identification:** Identifies the data type of each column (e.g., integer, string, datetime).
-* **Key Identification:** Identifies potential primary keys.
-* **Glossary Generation:** Generates a business glossary for each column using an LLM.
+## 2. Building the Knowledge Base
 
-> The `domain` parameter helps the LLM generate a more contextual business glossary. It specifies the industry domain that the dataset belongs to (e.g., "Healthcare", "Finance", "E-commerce").
----
-type: code
----
-# dataset_allergies.result_to_pandas()[["business_glossary"]]
----
-type: code
----
-# dataset_allergies.to_df()
----
-type: code
----
-from intugle.core.settings import settings
+The `KnowledgeBuilder` is the entry point for building your semantic layer. It takes a dictionary of datasets as input and performs the following steps:
 
-settings.PROJECT_BASE
----
-type: markdown
----
-## 3. Automated Link Prediction
+1.  **Data Profiling:** Calculates statistics for each column, such as distinct count, uniqueness, and completeness.
+2.  **Datatype Identification:** Identifies the data type of each column (e.g., integer, string, datetime).
+3.  **Key Identification:** Identifies potential primary keys.
+4.  **Glossary Generation:** Generates a business glossary for each column using an LLM.
+5.  **Link Prediction:** Predicts the relationships (foreign keys) between tables.
 
-Now that we've profiled our data, let's discover the relationships between tables. This tool uses an LLM to predict links (foreign keys) between tables.
+Let's start by defining the datasets we want to use:
 
-First, we'll load a few more tables from the sample dataset.
 ---
 type: code
 ---
+
 def generate_config(table_name: str) -> str:
     """Append the base URL to the table name."""
     return {
@@ -122,19 +87,19 @@ def generate_config(table_name: str) -> str:
     }
 
 
-# table_names = ["allergies", "patients", "claims", "careplans", "claims_transactions", "medications"]
 table_names = ["allergies", "patients", "claims", "careplans"]
 datasets = {table: generate_config(table) for table in table_names}
----
-type: code
----
+
 ---
 type: markdown
 ---
-Now, let's run the link prediction pipeline.
+
+Now, let's use the `KnowledgeBuilder` to build our semantic layer:
+
 ---
 type: code
 ---
+
 from intugle import KnowledgeBuilder
 
 # Initialize the knowledge builder
@@ -142,41 +107,82 @@ kb = KnowledgeBuilder(datasets, domain="Healthcare")
 
 # Run the prediction
 kb.build()
+
 ---
 type: markdown
 ---
-The `results` object contains the predicted links between the tables. You can also visualize the relationships as a graph.
+
+## 3. Accessing Enriched Metadata
+
+Now that the knowledge base is built, you can easily access the enriched metadata for each dataset.
+
+### Accessing a Dataset
+
+You can access a specific dataset by its name from the `kb.datasets` dictionary:
+
 ---
 type: code
 ---
-kb.visualize()
+
+allergies_dataset = kb.datasets['allergies']
+
 ---
 type: markdown
 ---
-## 4. The Semantic Layer
 
-The profiling and link prediction results are used to generate YAML files which are saved automatically. These files defines the semantic layer, including the models (tables) and their relationships. 
+### Viewing Profiling Results
 
-By default, these files are saved in the current working directory. You can configure this path by setting the `PROJECT_BASE` environment variable. They can also be saved manually as shown below:
+The profiling results can be accessed through the `profiling_df` property of the `DataSet` object. It's a pandas DataFrame that you can easily explore:
+> The business glossary is also available in the `profile_df`:
 ---
 type: code
 ---
-# for ds in datasets:
-#     ds.save_yaml()
 
-# results.save_yaml("relationships.yml")
+allergies_dataset.profiling_df
+
 ---
 type: markdown
 ---
-Now, we can load the YAML files and create a manifest.
+
+### Visualizing Relationships
+
+The `KnowledgeBuilder` automatically discovers the relationships between your tables. You can access the predicted links as a list of `PredictedLink` objects:
+
+---
+type: code
+---
+
+kb.links
+
 ---
 type: markdown
 ---
-## 5. SQL Generation
 
-Once you have a semantic layer, you can use the `SqlGenerator` to generate SQL queries. This allows you to query the data using business-friendly terms, without having to write complex SQL.
+You can visualize these relationships as a graph:
+---
+type: code
+---
+
+kb.visualize()
+
+---
+type: markdown
+---
+
+## 4. The Semantic Layer
+
+The KnowledgeBuilder results are used to generate YAML files which are saved automatically. These files defines the semantic layer, including the models (tables) and their relationships. 
+
+By default, these files are saved in the current working directory. You can configure this path by setting the `PROJECT_BASE` environment variable.
+
+## 5. Data Product Creation
+
+The semantic layer serves as a foundation for the DataProductBuilder, which streamlines the creation of reusable data products. This allows you
+to encapsulate business logic and create standardized, trustworthy data assets that can be easily shared and reused across different teams and 
+applications.
+
+Let's define the model for the data product we want to build:
 
-Let's create an ETL model to define the query we want to generate.
 ---
 type: code
 ---
@@ -192,37 +198,44 @@ etl = {
         "selections": [{"id": "claims.departmentid", "values": ["3", "20"]}],
     },
 }
+
 ---
 type: markdown
 ---
-Now, let's use the `SqlGenerator` to generate the SQL query.
----
-type: code
----
-from intugle.sql_generator import SqlGenerator
+
+Now, let's use the `DataProductBuilder` to generate the data product:
+
 ---
 type: code
 ---
 
-# Create a SqlGenerator
-sql_generator = SqlGenerator()
+from intugle.dp_builder import DataProductBuilder
 
-# Generate the query
-test_etl = sql_generator.generate_product(etl)
+# Create a DataProductBuilder
+dp_builder = DataProductBuilder()
+
+# Generate the data product
+data_product = dp_builder.build(etl)
+
+# Print the generated SQL query
+print(data_product.data['path'])
 
-# Print the query
-test_etl.data
 ---
-type: code
+type: markdown
 ---
-test_etl.to_df()
+
+The `generate_product` function returns a `DataSet` object. You can use the `to_df()` method to view the data product as a pandas DataFrame:
+
 ---
 type: code
 ---
-sql_generator.plot_sources_graph()
+
+data_product.to_df()
+
 ---
 type: markdown
 ---
+
 ## 6. MCP Server: Interacting with Your Semantic Layer
 
 Now that you have a semantic layer, you can serve it as a MCP server to interact with it using natural language. The MCP server exposes your semantic layer as a set of tools that can be used by any MCP client.
@@ -249,22 +262,21 @@ You can use a variety of MCP clients to connect to the server, such as Claude De
 
 Once connected, you can interact with your semantic layer using natural language. Here are some exciting applications:
 
-*   **Generate SQL Queries:** Ask questions in natural language and have the MCP server generate the corresponding SQL query.
+*   **Generate and Execute SQL Queries:** Ask questions in natural language and have the MCP server generate and execute the corresponding SQL query.
 *   **Data Discovery:** Ask questions about the tables and columns in your semantic layer to better understand your data.
 
-> To execute the generated SQL queries, you can also connect your database instance as a MCP tool to your client.
 ---
 type: markdown
 ---
+
 ## Conclusion
 
 You've learned how to:
 
-* Configure your LLM provider.
-* Profile your data to understand its characteristics.
-* Use an LLM to automatically predict links between tables.
-* Generate a semantic layer.
-* Use the semantic layer to generate SQL queries.
-* Interact with your semantic layer using the MCP server.
+*   Configure your LLM provider.
+*   Build a knowledge base using the `KnowledgeBuilder`.
+*   Access enriched metadata, business glossaries and visualize the relationships between your tables.
+*   Generate data products from the semantic layer using the `DataProductBuilder`.
+*   Interact with your semantic layer using the MCP server.
 
 This is just a starting point. This project has many other features to explore. We encourage you to try it with your own data and see how it can help you build a powerful semantic layer.
diff --git a/notebooks/quickstart_duckdb.ipynb b/notebooks/quickstart_duckdb.ipynb
index eb4f16a..0328c28 100644
--- a/notebooks/quickstart_duckdb.ipynb
+++ b/notebooks/quickstart_duckdb.ipynb
@@ -135,7 +135,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "dataset_allergies.result_to_pandas()[[\"business_glossary\"]]"
+    "dataset_allergies.profiling_df[[\"business_glossary\"]]"
    ]
   },
   {
@@ -321,7 +321,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from intugle.sql_generator import SqlGenerator\n"
+    "from intugle.dp_builder import DataProductBuilder\n"
    ]
   },
   {
@@ -333,10 +333,10 @@
    "source": [
     "\n",
     "# Create a SqlGenerator\n",
-    "sql_generator = SqlGenerator()\n",
+    "sql_generator = DataProductBuilder()\n",
     "\n",
     "# Generate the query\n",
-    "test_etl = sql_generator.generate_product(etl)\n",
+    "test_etl = sql_generator.build(etl)\n",
     "\n",
     "# Print the query\n",
     "test_etl.data"
diff --git a/pyproject.toml b/pyproject.toml
index a42db5c..7f9df3e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "intugle"
-version = "0.1.1"
+version = "0.1.2"
 authors = [
     { name="Intugle", email="hello@intugle.ai" },
 ]
diff --git a/quickstart.ipynb b/quickstart.ipynb
index aebd9fa..e93c36e 100644
--- a/quickstart.ipynb
+++ b/quickstart.ipynb
@@ -15,18 +15,18 @@
     "\n",
     "**Who is this for?**\n",
     "\n",
-    "This tool is designed for both **data teams** and **business teams**. \n",
+    "This tool is designed for both **data teams** and **business teams**.\n",
     "\n",
-    "* **Data teams** can use it to automate data profiling, schema discovery, and documentation, significantly accelerating their workflow.\n",
-    "* **Business teams** can use it to gain a better understanding of their data and to perform self-service analytics without needing to write complex SQL queries.\n",
+    "*   **Data teams** can use it to automate data profiling, schema discovery, and documentation, significantly accelerating their workflow.\n",
+    "*   **Business teams** can use it to gain a better understanding of their data and to perform self-service analytics without needing to write complex SQL queries.\n",
     "\n",
     "**In this notebook, you will learn how to:**\n",
     "\n",
-    "1. **Profile your data:** Analyze your data sources to understand their structure, data types, and other characteristics.\n",
-    "2. **Business Glossary Generation:** Generate a business glossary for each column, with support for industry-specific domains.\n",
-    "2. **Automatically predict links:** Use a Large Language Model (LLM) to automatically discover relationships (foreign keys) between tables.\n",
-    "3. **Generate a semantic layer:** Create YAML files file that defines your semantic layer.\n",
-    "4. **Generate SQL queries:** Use the semantic layer to generate SQL queries and retrieve data."
+    "1.  **Build a Knowledge Base:** Use the `KnowledgeBuilder` to automatically profile your data, generate a business glossary, and predict links between tables.\n",
+    "2.  **Access Enriched Metadata:** Learn how to access the profiling results and business glossary for each dataset.\n",
+    "3.  **Visualize Relationships:** Visualize the predicted links between your tables.\n",
+    "4.  **Generate Data Products:** Use the semantic layer to generate data products and retrieve data.\n",
+    "5.  **Serve the Semantic Layer:** Learn how to start the MCP server to interact with your semantic layer using natural language."
    ]
   },
   {
@@ -34,14 +34,15 @@
    "id": "a2a1b244",
    "metadata": {},
    "source": [
+    "\n",
     "## 1. LLM Configuration\n",
     "\n",
     "Before running the project, you need to configure a Large Language Model (LLM). This is used for tasks like generating business glossaries and predicting links between tables.\n",
     "\n",
     "You can configure the LLM by setting the following environment variables:\n",
     "\n",
-    "* `LLM_PROVIDER`: The LLM provider and model to use (e.g., `openai:gpt-3.5-turbo`). The format follows langchain's format for initializing chat models. Checkout how to specify your model [here](https://python.langchain.com/docs/integrations/chat/) \n",
-    "* `OPENAI_API_KEY`: Your API key for the LLM provider.\n",
+    "*   `LLM_PROVIDER`: The LLM provider and model to use (e.g., `openai:gpt-3.5-turbo`). The format follows langchain's format for initializing chat models. Checkout how to specify your model [here](https://python.langchain.com/docs/integrations/chat/)\n",
+    "*   `API_KEY`: Your API key for the LLM provider. The exact name of the variable may vary from provider to provider\n",
     "\n",
     "Here's an example of how to set these variables in your environment:\n",
     "\n",
@@ -50,7 +51,7 @@
     "export OPENAI_API_KEY=\"your-openai-api-key\"\n",
     "```\n",
     "\n",
-    "Alternatively, you can set them in the notebook like this:"
+    "Alternatively, you can set them in the notebook like this:\n"
    ]
   },
   {
@@ -63,7 +64,7 @@
     "import os\n",
     "\n",
     "os.environ[\"LLM_PROVIDER\"] = \"openai:gpt-3.5-turbo\"\n",
-    "os.environ[\"OPENAI_API_KEY\"] = \"your-openai-api-key\" # Replace with your actual key\n"
+    "os.environ[\"OPENAI_API_KEY\"] = \"your-openai-api-key\"  # Replace with your actual key\n"
    ]
   },
   {
@@ -79,404 +80,62 @@
    "id": "a3dad9d8",
    "metadata": {},
    "source": [
-    "## 2. Data Profiling and Glossary Generation\n",
+    "## 2. Building the Knowledge Base\n",
+    "\n",
+    "The `KnowledgeBuilder` is the entry point for building your semantic layer. It takes a dictionary of datasets as input and performs the following steps:\n",
+    "\n",
+    "1.  **Data Profiling:** Calculates statistics for each column, such as distinct count, uniqueness, and completeness.\n",
+    "2.  **Datatype Identification:** Identifies the data type of each column (e.g., integer, string, datetime).\n",
+    "3.  **Key Identification:** Identifies potential primary keys.\n",
+    "4.  **Glossary Generation:** Generates a business glossary for each column using an LLM.\n",
+    "5.  **Link Prediction:** Predicts the relationships (foreign keys) between tables.\n",
     "\n",
-    "The first step in building a semantic layer is to profile your data. This involves analyzing your data sources to understand their structure, data types, and other characteristics. This tool provides a pipeline for this purpose. It can also generate business glossary for your data.\n"
+    "Let's start by defining the datasets we want to use:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "7f0e28d7",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/juhel-phanju/.pyenv/ttt/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n",
-      "/home/juhel-phanju/.pyenv/ttt/lib/python3.12/site-packages/pydantic/_internal/_fields.py:198: UserWarning: Field name \"schema\" in \"Source\" shadows an attribute in parent \"BaseResource\"\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "import pandas as pd\n",
+    "def generate_config(table_name: str) -> str:\n",
+    "    \"\"\"Append the base URL to the table name.\"\"\"\n",
+    "    return {\n",
+    "        \"path\": f\"https://raw.githubusercontent.com/Intugle/data-tools/refs/heads/main/sample_data/healthcare/{table_name}.csv\",\n",
+    "        \"type\": \"csv\"\n",
+    "    }\n",
     "\n",
-    "from intugle import DataSet"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "0e8971bb",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>start</th>\n",
-       "      <th>stop</th>\n",
-       "      <th>patient</th>\n",
-       "      <th>encounter</th>\n",
-       "      <th>code</th>\n",
-       "      <th>system</th>\n",
-       "      <th>description</th>\n",
-       "      <th>type</th>\n",
-       "      <th>category</th>\n",
-       "      <th>reaction1</th>\n",
-       "      <th>description1</th>\n",
-       "      <th>severity1</th>\n",
-       "      <th>reaction2</th>\n",
-       "      <th>description2</th>\n",
-       "      <th>severity2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2020-02-17</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85</td>\n",
-       "      <td>01efcc52-15d6-51e9-faa2-bee069fcbe44</td>\n",
-       "      <td>111088007</td>\n",
-       "      <td>Unknown</td>\n",
-       "      <td>Latex (substance)</td>\n",
-       "      <td>allergy</td>\n",
-       "      <td>environment</td>\n",
-       "      <td>247472004.0</td>\n",
-       "      <td>Wheal (finding)</td>\n",
-       "      <td>MILD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2020-02-17</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85</td>\n",
-       "      <td>01efcc52-15d6-51e9-faa2-bee069fcbe44</td>\n",
-       "      <td>84489001</td>\n",
-       "      <td>Unknown</td>\n",
-       "      <td>Mold (organism)</td>\n",
-       "      <td>allergy</td>\n",
-       "      <td>environment</td>\n",
-       "      <td>76067001.0</td>\n",
-       "      <td>Sneezing</td>\n",
-       "      <td>MILD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2020-02-17</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85</td>\n",
-       "      <td>01efcc52-15d6-51e9-faa2-bee069fcbe44</td>\n",
-       "      <td>260147004</td>\n",
-       "      <td>Unknown</td>\n",
-       "      <td>House dust mite (organism)</td>\n",
-       "      <td>allergy</td>\n",
-       "      <td>environment</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2020-02-17</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85</td>\n",
-       "      <td>01efcc52-15d6-51e9-faa2-bee069fcbe44</td>\n",
-       "      <td>264287008</td>\n",
-       "      <td>Unknown</td>\n",
-       "      <td>Animal dander (substance)</td>\n",
-       "      <td>allergy</td>\n",
-       "      <td>environment</td>\n",
-       "      <td>878820003.0</td>\n",
-       "      <td>Rhinoconjunctivitis (disorder)</td>\n",
-       "      <td>MODERATE</td>\n",
-       "      <td>271807003.0</td>\n",
-       "      <td>Eruption of skin (disorder)</td>\n",
-       "      <td>MILD</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2020-02-17</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85</td>\n",
-       "      <td>01efcc52-15d6-51e9-faa2-bee069fcbe44</td>\n",
-       "      <td>256277009</td>\n",
-       "      <td>Unknown</td>\n",
-       "      <td>Grass pollen (substance)</td>\n",
-       "      <td>allergy</td>\n",
-       "      <td>environment</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        start  stop                               patient  \\\n",
-       "0  2020-02-17   NaN  b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85   \n",
-       "1  2020-02-17   NaN  b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85   \n",
-       "2  2020-02-17   NaN  b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85   \n",
-       "3  2020-02-17   NaN  b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85   \n",
-       "4  2020-02-17   NaN  b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85   \n",
-       "\n",
-       "                              encounter       code   system  \\\n",
-       "0  01efcc52-15d6-51e9-faa2-bee069fcbe44  111088007  Unknown   \n",
-       "1  01efcc52-15d6-51e9-faa2-bee069fcbe44   84489001  Unknown   \n",
-       "2  01efcc52-15d6-51e9-faa2-bee069fcbe44  260147004  Unknown   \n",
-       "3  01efcc52-15d6-51e9-faa2-bee069fcbe44  264287008  Unknown   \n",
-       "4  01efcc52-15d6-51e9-faa2-bee069fcbe44  256277009  Unknown   \n",
-       "\n",
-       "                  description     type     category    reaction1  \\\n",
-       "0           Latex (substance)  allergy  environment  247472004.0   \n",
-       "1             Mold (organism)  allergy  environment   76067001.0   \n",
-       "2  House dust mite (organism)  allergy  environment          NaN   \n",
-       "3   Animal dander (substance)  allergy  environment  878820003.0   \n",
-       "4    Grass pollen (substance)  allergy  environment          NaN   \n",
-       "\n",
-       "                     description1 severity1    reaction2  \\\n",
-       "0                 Wheal (finding)      MILD          NaN   \n",
-       "1                        Sneezing      MILD          NaN   \n",
-       "2                             NaN       NaN          NaN   \n",
-       "3  Rhinoconjunctivitis (disorder)  MODERATE  271807003.0   \n",
-       "4                             NaN       NaN          NaN   \n",
-       "\n",
-       "                  description2 severity2  \n",
-       "0                          NaN       NaN  \n",
-       "1                          NaN       NaN  \n",
-       "2                          NaN       NaN  \n",
-       "3  Eruption of skin (disorder)      MILD  \n",
-       "4                          NaN       NaN  "
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Load the sample data\n",
-    "allergies_df = pd.read_csv('https://raw.githubusercontent.com/Intugle/data-tools/refs/heads/main/sample_data/healthcare/allergies.csv')\n",
-    "allergies_df.head()"
+    "\n",
+    "table_names = [\"allergies\", \"patients\", \"claims\"]\n",
+    "datasets = {table: generate_config(table) for table in table_names}"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "8d3e8299",
+   "cell_type": "markdown",
+   "id": "fffe1d34",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/juhel-phanju/.pyenv/ttt/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3859: RuntimeWarning: Mean of empty slice.\n",
-      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
-      "/home/juhel-phanju/.pyenv/ttt/lib/python3.12/site-packages/numpy/_core/_methods.py:144: RuntimeWarning: invalid value encountered in scalar divide\n",
-      "  ret = ret.dtype.type(ret / rcount)\n",
-      "[!] Empty column encountered for allergies ==> stop ...\n",
-      "100%|██████████| 15/15 [00:07<00:00,  1.93it/s]\n",
-      "100%|██████████| 15/15 [00:28<00:00,  1.90s/it]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>column_name</th>\n",
-       "      <th>business_name</th>\n",
-       "      <th>table_name</th>\n",
-       "      <th>null_count</th>\n",
-       "      <th>count</th>\n",
-       "      <th>distinct_count</th>\n",
-       "      <th>uniqueness</th>\n",
-       "      <th>completeness</th>\n",
-       "      <th>sample_data</th>\n",
-       "      <th>dtype_sample</th>\n",
-       "      <th>ts</th>\n",
-       "      <th>datatype_l1</th>\n",
-       "      <th>datatype_l2</th>\n",
-       "      <th>business_glossary</th>\n",
-       "      <th>business_tags</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>start</td>\n",
-       "      <td>start</td>\n",
-       "      <td>allergies</td>\n",
-       "      <td>0</td>\n",
-       "      <td>794</td>\n",
-       "      <td>179</td>\n",
-       "      <td>0.225441</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>[1961-12-13, 1980-03-10, 2004-09-28, 1961-02-0...</td>\n",
-       "      <td>[2020-02-17, 1981-05-17, 1960-01-05, 1960-05-2...</td>\n",
-       "      <td>0.009466</td>\n",
-       "      <td>date &amp; time</td>\n",
-       "      <td>L2OutputTypes.dimension</td>\n",
-       "      <td>The date and time when an allergy was first id...</td>\n",
-       "      <td>[Allergy Onset Date, Patient Medical History, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>stop</td>\n",
-       "      <td>stop</td>\n",
-       "      <td>allergies</td>\n",
-       "      <td>794</td>\n",
-       "      <td>794</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>[]</td>\n",
-       "      <td>[]</td>\n",
-       "      <td>0.001424</td>\n",
-       "      <td>others</td>\n",
-       "      <td>L2OutputTypes.unknown</td>\n",
-       "      <td>Indicates the conclusion or termination of a s...</td>\n",
-       "      <td>[End Date, Allergy Management, Data Validity]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>patient</td>\n",
-       "      <td>patient</td>\n",
-       "      <td>allergies</td>\n",
-       "      <td>0</td>\n",
-       "      <td>794</td>\n",
-       "      <td>179</td>\n",
-       "      <td>0.225441</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>[2af5741b-913e-7915-e51c-890dd5c261fa, d00d8a6...</td>\n",
-       "      <td>[b9c610cd-28a6-4636-ccb6-c7a0d2a4cb85, 0288c42...</td>\n",
-       "      <td>0.006663</td>\n",
-       "      <td>alphanumeric</td>\n",
-       "      <td>L2OutputTypes.dimension</td>\n",
-       "      <td>Identifies individuals associated with recorde...</td>\n",
-       "      <td>[Patient Identifier, Healthcare Record Linkage...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>encounter</td>\n",
-       "      <td>encounter</td>\n",
-       "      <td>allergies</td>\n",
-       "      <td>0</td>\n",
-       "      <td>794</td>\n",
-       "      <td>179</td>\n",
-       "      <td>0.225441</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>[ee31926f-864e-9a18-89ed-e2ffa1230d8d, 34fc598...</td>\n",
-       "      <td>[01efcc52-15d6-51e9-faa2-bee069fcbe44, 79de99b...</td>\n",
-       "      <td>0.004941</td>\n",
-       "      <td>alphanumeric</td>\n",
-       "      <td>L2OutputTypes.dimension</td>\n",
-       "      <td>Tracks the specific interaction or event durin...</td>\n",
-       "      <td>[Patient Encounter, Clinical Visit Tracking, H...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>code</td>\n",
-       "      <td>code</td>\n",
-       "      <td>allergies</td>\n",
-       "      <td>0</td>\n",
-       "      <td>794</td>\n",
-       "      <td>21</td>\n",
-       "      <td>0.026448</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>[442571000124108, 3718001, 10831, 735971005, 1...</td>\n",
-       "      <td>[111088007, 84489001, 260147004, 264287008, 25...</td>\n",
-       "      <td>0.008465</td>\n",
-       "      <td>integer</td>\n",
-       "      <td>L2OutputTypes.dimension</td>\n",
-       "      <td>Identifies specific allergy classifications or...</td>\n",
-       "      <td>[Allergy Identifier, Clinical Code, Patient Al...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "<intugle.analysis.models.DataSet at 0x78596ad003b0>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "# Create a DataSet object and run the profiling pipeline\n",
-    "dataset_allergies = DataSet(allergies_df, \"allergies\")\n",
-    "dataset_allergies.run(domain=\"Healthcare\")\n",
+    "Now, let's use the `KnowledgeBuilder` to build our semantic layer:\n",
     "\n",
-    "# Or you can run each step manually:\n",
-    "# dataset_allergies.profile().identify_datatypes().identify_keys().generate_glossary(domain=\"Healthcare\")"
+    "> The `domain` parameter helps the LLM generate a more contextual business glossary. It specifies the industry domain that the dataset belongs to (e.g., \"Healthcare\", \"Finance\", \"E-commerce\")."
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "0f4ea58f",
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ac8a78fa",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "The `run()` method performs a series of analysis steps, including:\n",
+    "from intugle import KnowledgeBuilder\n",
     "\n",
-    "* **Profiling:** Calculates statistics for each column, such as distinct count, uniqueness, and completeness.\n",
-    "* **Datatype Identification:** Identifies the data type of each column (e.g., integer, string, datetime).\n",
-    "* **Key Identification:** Identifies potential primary keys.\n",
-    "* **Glossary Generation:** Generates a business glossary for each column using an LLM.\n",
+    "# Initialize the knowledge builder\n",
+    "kb = KnowledgeBuilder(datasets, domain=\"Healthcare\")\n",
     "\n",
-    "> The `domain` parameter helps the LLM generate a more contextual business glossary. It specifies the industry domain that the dataset belongs to (e.g., \"Healthcare\", \"Finance\", \"E-commerce\").\n"
+    "# Run the prediction\n",
+    "kb.build()"
    ]
   },
   {
@@ -484,11 +143,13 @@
    "id": "eb562acd",
    "metadata": {},
    "source": [
-    "## 3. Automated Link Prediction\n",
+    "## 3. Accessing Enriched Metadata\n",
     "\n",
-    "Now that we've profiled our data, let's discover the relationships between tables. This tool uses an LLM to predict links (foreign keys) between tables.\n",
+    "Now that the knowledge base is built, you can easily access the enriched metadata for each dataset.\n",
     "\n",
-    "First, we'll load a few more tables from the sample dataset."
+    "### Accessing a Dataset\n",
+    "\n",
+    "You can access a specific dataset by its name from the `kb.datasets` dictionary:"
    ]
   },
   {
@@ -498,96 +159,68 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "table_names = [\"patients\", \"claims\", \"careplans\", \"claims_transactions\", \"medications\"]\n",
-    "datasets = [dataset_allergies]\n",
-    "\n",
-    "\n",
-    "def generate_table_url(table_name: str) -> str:\n",
-    "    \"\"\"Append the base URL to the table name.\"\"\"\n",
-    "    return f\"https://raw.githubusercontent.com/Intugle/data-tools/refs/heads/main/sample_data/healthcare/{table_name}.csv\"\n",
-    "\n",
-    "\n",
-    "for table_name in table_names:\n",
-    "    df = pd.read_csv(generate_table_url(table_name))\n",
-    "    dataset = DataSet(df, table_name)\n",
-    "    dataset.run(domain=\"Healthcare\", save=True)\n",
-    "    datasets.append(dataset)"
+    "allergies_dataset = kb.datasets['allergies']\n",
+    "allergies_dataset.profiling_df"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e083a8d8",
+   "id": "6b3e8bfd",
    "metadata": {},
    "source": [
-    "Now, let's run the link prediction pipeline."
+    "The profiling results can be accessed through the `profiling_df` property of the `DataSet` object. It's a pandas DataFrame that you can easily explore. \n",
+    "> The business glossary is also available in the `profiling_df`:"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fe4cd56d",
+   "cell_type": "markdown",
+   "id": "e083a8d8",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "from intugle import LinkPredictor\n",
     "\n",
-    "# Initialize the predictor\n",
-    "predictor = LinkPredictor(datasets)\n",
+    "### Visualizing Relationships\n",
     "\n",
-    "# Run the prediction\n",
-    "results = predictor.predict(save=True)\n",
-    "results.links"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d18dc421",
-   "metadata": {},
-   "source": [
-    "The `results` object contains the predicted links between the tables. You can also visualize the relationships as a graph.\n"
+    "The `KnowledgeBuilder` automatically discovers the relationships between your tables. You can access the predicted links as a list of `PredictedLink` objects:\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a59704ba",
+   "id": "fe4cd56d",
    "metadata": {},
    "outputs": [],
    "source": [
-    "results.show_graph()\n"
+    "kb.links"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "32fd4917",
+   "id": "9c8aa361",
    "metadata": {},
    "source": [
-    "## 4. The Semantic Layer\n",
-    "\n",
-    "The profiling and link prediction results are used to generate YAML files which are saved automatically. These files defines the semantic layer, including the models (tables) and their relationships. \n",
-    "\n",
-    "By default, these files are saved in the current working directory. You can configure this path by setting the `PROJECT_BASE` environment variable. They can also be saved manually as shown below:"
+    "You can also visualize these relationships as a graph:\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a0daf854",
+   "id": "a59704ba",
    "metadata": {},
    "outputs": [],
    "source": [
-    "for ds in datasets:\n",
-    "    ds.save_yaml()\n",
-    "\n",
-    "results.save_yaml(\"relationships.yml\")"
+    "kb.visualize()\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "8cf08923",
+   "id": "32fd4917",
    "metadata": {},
    "source": [
-    "Now, we can load the YAML files and create a manifest."
+    "## 4. The Semantic Layer\n",
+    "\n",
+    "The KnowledgeBuilder results are used to generate YAML files which are saved automatically. These files defines the semantic layer, including the models (tables) and their relationships. \n",
+    "\n",
+    "By default, these files are saved in the current working directory. You can configure this path by setting the `PROJECT_BASE` environment variable.\n"
    ]
   },
   {
@@ -595,11 +228,13 @@
    "id": "3d086f45",
    "metadata": {},
    "source": [
-    "## 5. SQL Generation\n",
+    "## 5. Data Product Creation\n",
     "\n",
-    "Once you have a semantic layer, you can use the `SqlGenerator` to generate SQL queries. This allows you to query the data using business-friendly terms, without having to write complex SQL.\n",
+    "The semantic layer serves as a foundation for the DataProductBuilder, which streamlines the creation of reusable data products. This allows you\n",
+    "to encapsulate business logic and create standardized, trustworthy data assets that can be easily shared and reused across different teams and \n",
+    "applications.\n",
     "\n",
-    "Let's create an ETL model to define the query we want to generate."
+    "Let's define the model for the data product we want to build:"
    ]
   },
   {
@@ -611,7 +246,7 @@
    "source": [
     "\n",
     "etl = {\n",
-    "    \"name\": \"test_etl\",\n",
+    "    \"name\": \"patient_names\",\n",
     "    \"fields\": [\n",
     "        {\"id\": \"patients.first\", \"name\": \"first_name\"},\n",
     "        {\"id\": \"patients.last\", \"name\": \"last_name\"},\n",
@@ -628,26 +263,44 @@
    "id": "2e4ff6be",
    "metadata": {},
    "source": [
-    "Now, let's use the `SqlGenerator` to generate the SQL query."
+    "Now, let's use the `ProductComposer` to generate the data product:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f0432e77",
+   "id": "e0f9a090",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from intugle.sql_generator import SqlGenerator\n",
+    "from intugle.dp_builder import DataProductBuilder\n",
     "\n",
-    "# Create a SqlGenerator\n",
-    "sql_generator = SqlGenerator()\n",
+    "# Create a DataProductBuilder\n",
+    "dp_builder = DataProductBuilder()\n",
     "\n",
-    "# Generate the query\n",
-    "sql_query = sql_generator.generate_query(etl)\n",
+    "# Generate the data product\n",
+    "data_product = dp_builder.build(etl)\n",
     "\n",
-    "# Print the query\n",
-    "print(sql_query)"
+    "data_product.to_df()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e235b222",
+   "metadata": {},
+   "source": [
+    "The `generate_product` function returns a `DataSet` object. You can also view the generated SQL query used for creating the data product:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "31a3e32a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The generated SQL query\n",
+    "data_product.sql_query\n"
    ]
   },
   {
@@ -681,10 +334,8 @@
     "\n",
     "Once connected, you can interact with your semantic layer using natural language. Here are some exciting applications:\n",
     "\n",
-    "*   **Generate SQL Queries:** Ask questions in natural language and have the MCP server generate the corresponding SQL query.\n",
-    "*   **Data Discovery:** Ask questions about the tables and columns in your semantic layer to better understand your data.\n",
-    "\n",
-    "> To execute the generated SQL queries, you can also connect your database instance as a MCP tool to your client."
+    "*   **Generate and Execute SQL Queries:** Ask questions in natural language and have the MCP server generate and execute the corresponding SQL query.\n",
+    "*   **Data Discovery:** Ask questions about the tables and columns in your semantic layer to better understand your data.\n"
    ]
   },
   {
@@ -696,12 +347,11 @@
     "\n",
     "You've learned how to:\n",
     "\n",
-    "* Configure your LLM provider.\n",
-    "* Profile your data to understand its characteristics.\n",
-    "* Use an LLM to automatically predict links between tables.\n",
-    "* Generate a semantic layer.\n",
-    "* Use the semantic layer to generate SQL queries.\n",
-    "* Interact with your semantic layer using the MCP server.\n",
+    "*   Configure your LLM provider.\n",
+    "*   Build a knowledge base using the `KnowledgeBuilder`.\n",
+    "*   Access enriched metadata, business glossaries and visualize the relationships between your tables.\n",
+    "*   Generate data products from the semantic layer using the `ProductComposer`.\n",
+    "*   Interact with your semantic layer using the MCP server.\n",
     "\n",
     "This is just a starting point. This project has many other features to explore. We encourage you to try it with your own data and see how it can help you build a powerful semantic layer.\n"
    ]
@@ -709,7 +359,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "ttt",
+   "display_name": "intugle",
    "language": "python",
    "name": "python3"
   },
diff --git a/src/intugle/__init__.py b/src/intugle/__init__.py
index 07e5244..67af0a4 100644
--- a/src/intugle/__init__.py
+++ b/src/intugle/__init__.py
@@ -1,3 +1,3 @@
 from intugle.analysis.models import DataSet as DataSet
 from intugle.knowledge_builder import KnowledgeBuilder as KnowledgeBuilder
-from intugle.sql_generator import SqlGenerator as SqlGenerator
+from intugle.dp_builder import DataProductBuilder as DataProductBuilder
diff --git a/src/intugle/analysis/models.py b/src/intugle/analysis/models.py
index 139b8c9..27732f5 100644
--- a/src/intugle/analysis/models.py
+++ b/src/intugle/analysis/models.py
@@ -48,6 +48,12 @@ def __init__(self, data: DataSetData, name: str):
         self.results: Dict[str, Any] = {}
 
         self.load()
+
+    @property
+    def sql_query(self):
+        if 'type' in self.data and self.data['type'] == 'query':
+            return self.data['path']
+        return None
     
     def load(self):
         try:
@@ -259,7 +265,8 @@ def save_yaml(self, file_path: Optional[str] = None) -> None:
     def to_df(self):
         return self.adapter.to_df(self.data, self.name)
 
-    def result_to_pandas(self):
+    @property
+    def profiling_df(self):
         column_profiles = self.results.get("column_profiles")
         if column_profiles is None:
             return "<p>No column profiles available.</p>"
@@ -267,5 +274,5 @@ def result_to_pandas(self):
         return df
 
     def _repr_html_(self):
-        df = self.result_to_pandas().head()
+        df = self.profiling_df.head()
         return df._repr_html_()
diff --git a/src/intugle/sql_generator.py b/src/intugle/dp_builder.py
similarity index 97%
rename from src/intugle/sql_generator.py
rename to src/intugle/dp_builder.py
index 6f746e9..cd41d43 100644
--- a/src/intugle/sql_generator.py
+++ b/src/intugle/dp_builder.py
@@ -11,8 +11,8 @@
     from intugle.models.resources.model import Column
 
 
-class SqlGenerator:
-    """Generates SQL queries based on the manifest and ETL configurations."""
+class DataProductBuilder:
+    """Generates data products based on the manifest and ETL configurations."""
 
     def __init__(self, project_base: str = settings.PROJECT_BASE):
         self.manifest_loader = ManifestLoader(project_base)
@@ -59,7 +59,7 @@ def generate_query(self, etl: ETLModel) -> str:
         sql_query = sql_builder.get_query()
         return sql_query
         
-    def generate_product(self, etl: ETLModel) -> DataSet:
+    def build(self, etl: ETLModel) -> DataSet:
         """Generates a dataset on the ETL model.
 
         Args:
diff --git a/src/intugle/knowledge_builder.py b/src/intugle/knowledge_builder.py
index 9cd44c5..0588c37 100644
--- a/src/intugle/knowledge_builder.py
+++ b/src/intugle/knowledge_builder.py
@@ -45,7 +45,8 @@ def build(self):
         self.link_predictor = LinkPredictor(list(self.datasets.values()))
 
         # Run the prediction
-        self.links: list[PredictedLink] = self.link_predictor.predict().links
+        self.link_predictor.predict(save=True)
+        self.links: list[PredictedLink] = self.link_predictor.links
         return self
     
     def visualize(self):
diff --git a/src/intugle/mcp/semantic_layer/router.py b/src/intugle/mcp/semantic_layer/router.py
index c608dcf..1fb7965 100644
--- a/src/intugle/mcp/semantic_layer/router.py
+++ b/src/intugle/mcp/semantic_layer/router.py
@@ -50,10 +50,10 @@ async def get_schema(table_names: list[str]) -> dict[str, str]:
     return schemas
 
 
-@semantic_layer_mcp.prompt(name="explore_data", title="Executor Agent Prompt")
-async def prompt() -> str:
-    print("Using prompt from semantic layer")
-    return Prompts.raw_executor_prompt(settings.SQL_DIALECT, settings.DOMAIN, settings.UNIVERSAL_INSTRUCTIONS)
+# @semantic_layer_mcp.prompt(name="explore_data", title="Executor Agent Prompt")
+# async def prompt() -> str:
+#     print("Using prompt from semantic layer")
+#     return Prompts.raw_executor_prompt(settings.SQL_DIALECT, settings.DOMAIN, settings.UNIVERSAL_INSTRUCTIONS)
 
 
 @semantic_layer_mcp.tool(name="execute_query", description="Return the result of a query execution")
diff --git a/test.py b/test.py
deleted file mode 100644
index 7d166c3..0000000
--- a/test.py
+++ /dev/null
@@ -1,23 +0,0 @@
-
-etl = {
-    "name": "test_etl",
-    "fields": [
-        {"id": "patients.first", "name": "first_name"},
-        {"id": "patients.last", "name": "last_name"},
-        {"id": "allergies.start", "name": "start_date"},
-    ],
-    "filter": {
-        "selections": [{"id": "claims.departmentid", "values": ["3", "20"]}],
-    },
-}
-
-from intugle.sql_generator import SqlGenerator
-
-# Create a SqlGenerator
-sql_generator = SqlGenerator()
-
-# Generate the query
-sql_query = sql_generator.generate_query(etl)
-
-# Print the query
-print(sql_query)
\ No newline at end of file
diff --git a/tests/query_generator/test_query_generator.py b/tests/query_generator/test_query_generator.py
index 9e9d7e4..516ae66 100644
--- a/tests/query_generator/test_query_generator.py
+++ b/tests/query_generator/test_query_generator.py
@@ -8,7 +8,7 @@
     SelectionModel,
     SortByModel,
 )
-from intugle.sql_generator import SqlGenerator
+from intugle.dp_builder import DataProductBuilder
 
 
 def get_test_data():
@@ -50,7 +50,7 @@ def get_test_data():
     get_test_data(),
 )
 def test_query_generator(etl, query):
-    sql_generator = SqlGenerator(project_base=settings.PROJECT_BASE)
+    sql_generator = DataProductBuilder(project_base=settings.PROJECT_BASE)
 
     out_query = sql_generator.generate_query(etl)
     print(out_query)
diff --git a/uv.lock b/uv.lock
index f1178d5..9c619b6 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1422,7 +1422,7 @@ wheels = [
 
 [[package]]
 name = "intugle"
-version = "0.1.1"
+version = "0.1.2"
 source = { editable = "." }
 dependencies = [
     { name = "asyncpg" },

From aa85d3e633bc4025e25eb03fabff5ee67cad2f6d Mon Sep 17 00:00:00 2001
From: JaskaranIntugle <jaskaran@intugle.ai>
Date: Sun, 7 Sep 2025 20:15:03 +0530
Subject: [PATCH 06/25] DataProductBuilder typo fix

---
 quickstart.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/quickstart.ipynb b/quickstart.ipynb
index e93c36e..85b19bf 100644
--- a/quickstart.ipynb
+++ b/quickstart.ipynb
@@ -263,7 +263,7 @@
    "id": "2e4ff6be",
    "metadata": {},
    "source": [
-    "Now, let's use the `ProductComposer` to generate the data product:"
+    "Now, let's use the `DataProductBuilder` to generate the data product:"
    ]
   },
   {
@@ -350,7 +350,7 @@
     "*   Configure your LLM provider.\n",
     "*   Build a knowledge base using the `KnowledgeBuilder`.\n",
     "*   Access enriched metadata, business glossaries and visualize the relationships between your tables.\n",
-    "*   Generate data products from the semantic layer using the `ProductComposer`.\n",
+    "*   Generate data products from the semantic layer using the `DataProductBuilder`.\n",
     "*   Interact with your semantic layer using the MCP server.\n",
     "\n",
     "This is just a starting point. This project has many other features to explore. We encourage you to try it with your own data and see how it can help you build a powerful semantic layer.\n"

From 464ece79d354b534400e83ed9a8bbe62ccda059d Mon Sep 17 00:00:00 2001
From: JaskaranIntugle <jaskaran@intugle.ai>
Date: Sun, 7 Sep 2025 21:31:34 +0530
Subject: [PATCH 07/25] added macos instructions

---
 README.md | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index e93f56f..c22611b 100644
--- a/README.md
+++ b/README.md
@@ -41,6 +41,14 @@ This tool is designed for both **data teams** and **business teams**.
 pip install intugle
 ```
 
+#### macOS
+
+For macOS users, you may need to install the `libomp` library:
+
+```bash
+brew install libomp
+```
+
 ### Configuration
 
 Before running the project, you need to configure a LLM. This is used for tasks like generating business glossaries and predicting links between tables.
@@ -125,4 +133,4 @@ Contributions are welcome! Please see the [`CONTRIBUTING.md`](CONTRIBUTING.md) f
 
 ## License
 
-This project is licensed under the Apache License, Version 2.0. See the [`LICENSE`](LICENSE) file for details.
\ No newline at end of file
+This project is licensed under the Apache License, Version 2.0. See the [`LICENSE`](LICENSE) file for details.

From a0c4a990c3afa7a9bfb694a5c76f90aad3394763 Mon Sep 17 00:00:00 2001
From: JaskaranIntugle <jaskaran@intugle.ai>
Date: Sun, 7 Sep 2025 21:33:27 +0530
Subject: [PATCH 08/25] updated readme to correct API KEY

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index c22611b..391bd50 100644
--- a/README.md
+++ b/README.md
@@ -56,7 +56,7 @@ Before running the project, you need to configure a LLM. This is used for tasks
 You can configure the LLM by setting the following environment variables:
 
 *   `LLM_PROVIDER`: The LLM provider and model to use (e.g., `openai:gpt-3.5-turbo`) following LangChain's [conventions](https://python.langchain.com/docs/integrations/chat/)
-*   `OPENAI_API_KEY`: Your API key for the LLM provider.
+*   `API_KEY`: Your API key for the LLM provider. The exact name of the variable may vary from provider to provider.
 
 Here's an example of how to set these variables in your environment:
 

From d29c6cc3c80a9d39d4185a538b61f759d5ab694c Mon Sep 17 00:00:00 2001
From: JaskaranIntugle <jaskaran@intugle.ai>
Date: Sun, 7 Sep 2025 21:37:32 +0530
Subject: [PATCH 09/25] added dev1

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 7f9df3e..011aaac 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "intugle"
-version = "0.1.2"
+version = "0.1.2dev1"
 authors = [
     { name="Intugle", email="hello@intugle.ai" },
 ]

From 362e8aa951dda4759b6a47f2259ed92ba74bee64 Mon Sep 17 00:00:00 2001
From: JaskaranIntugle <jaskaran@intugle.ai>
Date: Mon, 8 Sep 2025 00:11:36 +0530
Subject: [PATCH 10/25] added httpfs manually for duckdb mac

---
 src/intugle/adapters/types/duckdb/duckdb.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/src/intugle/adapters/types/duckdb/duckdb.py b/src/intugle/adapters/types/duckdb/duckdb.py
index 4f84e61..eeea2bd 100644
--- a/src/intugle/adapters/types/duckdb/duckdb.py
+++ b/src/intugle/adapters/types/duckdb/duckdb.py
@@ -20,6 +20,10 @@
 
 class DuckdbAdapter(Adapter):
 
+    def __init__(self):
+        duckdb.install_extension('httpfs')
+        duckdb.load_extension('httpfs')        
+
     @staticmethod
     def check_data(data: Any) -> DuckdbConfig:
         try:

From f9098bdc754c12ab23ffc24250179ff30d56909b Mon Sep 17 00:00:00 2001
From: JaskaranIntugle <jaskaran@intugle.ai>
Date: Mon, 8 Sep 2025 00:12:28 +0530
Subject: [PATCH 11/25] updated version to 0.1.2dev2

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 011aaac..808ea53 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "intugle"
-version = "0.1.2dev1"
+version = "0.1.2dev2"
 authors = [
     { name="Intugle", email="hello@intugle.ai" },
 ]

From 0766c305d9d3f63238a3b474f36cc87317f95f31 Mon Sep 17 00:00:00 2001
From: JaskaranIntugle <jaskaran@intugle.ai>
Date: Mon, 8 Sep 2025 00:50:57 +0530
Subject: [PATCH 12/25] added SSL certs for nltk downloads for mac users

---
 README.md | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/README.md b/README.md
index 391bd50..feec3c0 100644
--- a/README.md
+++ b/README.md
@@ -49,6 +49,12 @@ For macOS users, you may need to install the `libomp` library:
 brew install libomp
 ```
 
+You may also need to install SSL certificates by running the following command in your terminal. Please replace `3.XX` with your specific Python version:
+
+```bash
+/Applications/Python\ 3.XX/Install\ Certificates.command
+```
+
 ### Configuration
 
 Before running the project, you need to configure a LLM. This is used for tasks like generating business glossaries and predicting links between tables.

From 4894a3dce8bfa1cc7da190685e15c85dc3d6e10f Mon Sep 17 00:00:00 2001
From: JaskaranIntugle <jaskaran@intugle.ai>
Date: Tue, 9 Sep 2025 14:09:28 +0530
Subject: [PATCH 13/25] added warning when entering graph recursion

---
 .../core/pipeline/link_prediction/lp.py       | 93 +++++++------------
 1 file changed, 34 insertions(+), 59 deletions(-)

diff --git a/src/intugle/core/pipeline/link_prediction/lp.py b/src/intugle/core/pipeline/link_prediction/lp.py
index 3b504e9..6b3f50b 100644
--- a/src/intugle/core/pipeline/link_prediction/lp.py
+++ b/src/intugle/core/pipeline/link_prediction/lp.py
@@ -9,6 +9,7 @@
 from langchain_core.messages import HumanMessage
 from langchain_core.output_parsers import JsonOutputParser
 from langchain_core.prompts import PromptTemplate
+from langgraph.errors import GraphRecursionError
 from tqdm import tqdm
 
 from intugle.analysis.models import DataSet
@@ -108,9 +109,7 @@ def __init__(
         self.prompt_link = PromptTemplate(
             template=self.PROMPT_TEMPLATE,
             input_variables=["current_attempt", "previous_attempts", "table_info"],
-            partial_variables={
-                "format_instructions": self.parser_linkages.get_format_instructions()
-            },
+            partial_variables={"format_instructions": self.parser_linkages.get_format_instructions()},
         )
 
         self.link_identifier_llm = self.prompt_link | self.llm | self.parser_linkages
@@ -132,9 +131,7 @@ def __init__(
                 columns_required=self.PROFILING_COLUMNS_REQUIRED,
                 mapping_dtypes_to_sql=True,
             )
-            for (table_name,), gbydata in self.profiling_data.groupby(
-                ["table_name"]
-            )
+            for (table_name,), gbydata in self.profiling_data.groupby(["table_name"])
         }
 
         self.graph = self.__workflow_builder__()
@@ -174,9 +171,7 @@ def link_identifier(self, state):
             error_msg = state["error_msg"]
 
         tab1, tab2 = input_text.split(" & ")
-        table_details = (
-            self.table_ddl_statements[tab1] + "\n\n" + self.table_ddl_statements[tab2]
-        )
+        table_details = self.table_ddl_statements[tab1] + "\n\n" + self.table_ddl_statements[tab2]
 
         if iteration == 0:
             current_attempt = 1
@@ -191,13 +186,11 @@ def link_identifier(self, state):
         log.info(log_msg)
 
         try:
-            potential_link = self.link_identifier_llm.invoke(
-                {
-                    "current_attempt": "Attempt " + str(current_attempt),
-                    "table_info": table_details,
-                    "previous_attempts": previous_attempt_details,
-                }
-            )
+            potential_link = self.link_identifier_llm.invoke({
+                "current_attempt": "Attempt " + str(current_attempt),
+                "table_info": table_details,
+                "previous_attempts": previous_attempt_details,
+            })
         except Exception:
             log_msg = "[!] Killing LLM run due to halucinations in output"
             self.logs.append(log_msg)
@@ -260,9 +253,7 @@ def single_link_table_column_name_checker(self, state):
         final_error_msg_list = state["error_msg"]
         log.info("chk1")
         # check if table names present
-        stat_foreign_key = self.profiling_data.loc[
-            self.profiling_data["table_name"] == potential_link["table1"]
-        ]
+        stat_foreign_key = self.profiling_data.loc[self.profiling_data["table_name"] == potential_link["table1"]]
 
         if stat_foreign_key.shape[0] == 0:
             log.info("chk2")
@@ -282,15 +273,15 @@ def single_link_table_column_name_checker(self, state):
 
         log.info("chk3")
 
-        stat_foreign_key = stat_foreign_key.loc[
-            stat_foreign_key["column_name"] == potential_link["column1"],
-        ]
+        stat_foreign_key = stat_foreign_key.loc[stat_foreign_key["column_name"] == potential_link["column1"],]
 
         if stat_foreign_key.shape[0] == 0:
             log.info("chk4")
             table_name = potential_link["table1"]
             missing_column = potential_link["column1"]
-            error_msg = f"{missing_column} is not present in {table_name}. Check for any spelling errors in the column name."
+            error_msg = (
+                f"{missing_column} is not present in {table_name}. Check for any spelling errors in the column name."
+            )
             try:
                 final_error_msg_list.append(error_msg)
             except Exception:
@@ -306,9 +297,7 @@ def single_link_table_column_name_checker(self, state):
         # Referrenced table #########################3
         log.info("chk5")
         # check if table names present
-        stat_referrence_key = self.profiling_data.loc[
-            self.profiling_data["table_name"] == potential_link["table2"]
-        ]
+        stat_referrence_key = self.profiling_data.loc[self.profiling_data["table_name"] == potential_link["table2"]]
 
         if stat_referrence_key.shape[0] == 0:
             log.info("chk6")
@@ -327,14 +316,14 @@ def single_link_table_column_name_checker(self, state):
             return {"error_msg": final_error_msg_list, "if_error": True}
         log.info("chk7")
         # check if column names present
-        stat_referrence_key = stat_referrence_key.loc[
-            stat_referrence_key["column_name"] == potential_link["column2"]
-        ]
+        stat_referrence_key = stat_referrence_key.loc[stat_referrence_key["column_name"] == potential_link["column2"]]
         if stat_referrence_key.shape[0] == 0:
             log.info("chk8")
             table_name = potential_link["table2"]
             missing_column = potential_link["column2"]
-            error_msg = f"{missing_column} is not present in {table_name}. Check for any spelling errors in the column name."
+            error_msg = (
+                f"{missing_column} is not present in {table_name}. Check for any spelling errors in the column name."
+            )
             try:
                 final_error_msg_list.append(error_msg)
             except Exception:
@@ -460,9 +449,7 @@ def single_link_datatype_checker(self, state):
 
         # Calculate the max uniqueness for the referenced key table
 
-        referrenced_key_datatype = stat_referrence_key["datatype_l1"].values[
-            0
-        ]
+        referrenced_key_datatype = stat_referrence_key["datatype_l1"].values[0]
 
         if dtype_check(dtype1=foreign_key_datatype, dtype2=referrenced_key_datatype):
             # log.info('All Good No error found')
@@ -560,12 +547,8 @@ def __workflow_builder__(self):
             "single_link_table_column_name_checker",
             self.single_link_table_column_name_checker,
         )  # table column name checker
-        workflow.add_node(
-            "single_link_uniqueness_checker", self.single_link_uniqueness_checker
-        )  # uniqueness
-        workflow.add_node(
-            "single_link_datatype_checker", self.single_link_datatype_checker
-        )  # datatype
+        workflow.add_node("single_link_uniqueness_checker", self.single_link_uniqueness_checker)  # uniqueness
+        workflow.add_node("single_link_datatype_checker", self.single_link_datatype_checker)  # datatype
 
         # Adding edges
         workflow.add_edge(START, "link_identifier")
@@ -643,9 +626,7 @@ def __graph_invoke__(self, table1: DataSet, table2: DataSet) -> dict:
         final_output["table2"] = table2.name
 
         start_time = time.time()
-        input_message = HumanMessage(
-            content=f"{table1.name} & {table2.name}"
-        )
+        input_message = HumanMessage(content=f"{table1.name} & {table2.name}")
         init_data = {
             "input_text": input_message.content,
             "iteration": None,
@@ -659,22 +640,21 @@ def __graph_invoke__(self, table1: DataSet, table2: DataSet) -> dict:
                 stream_mode="values",
                 config={
                     "recursion_limit": 20,
-                    "metadata": {
-                        "table_combo": tuple(
-                            sorted([table1.name, table2.name])
-                        )
-                    },
+                    "metadata": {"table_combo": tuple(sorted([table1.name, table2.name]))},
                 },
             ):
                 for key in [list(event.keys())[-1]]:
                     log.info(f"Finished running: {key}:")
 
+        except GraphRecursionError as ex:
+            log.warning(f"[!] Graph went into recursion loop when running for {table1.name} <=> {table2.name}")
+            event["status"] = Status.HALLUCINATED_RECURSION
+            event["potential_link"] = "NA"
+            event["error_msg"] = ex
+
         except Exception as ex:
             import traceback
-
-            log.error(
-                f"[!] Error while running for {table1.name} <=> {table2.name}: Reason {traceback.format_exc()}"
-            )
+            log.error(f"[!] Error while running for {table1.name} <=> {table2.name}: Reason {traceback.format_exc()}")
             event["status"] = Status.HALLUCINATED_RECURSION
             event["potential_link"] = "NA"
             event["error_msg"] = ex
@@ -701,22 +681,17 @@ def __call__(self, assets: List[Tuple[DataSet]], *args, **kwds):
         pb = tqdm(assets)
         for _, (table1, table2) in enumerate(pb):
             runs = 1
-            pb.set_description(
-                desc=f"[*] {table1.name} <==> {table2.name} Runs:{runs}"
-            )
+            pb.set_description(desc=f"[*] {table1.name} <==> {table2.name} Runs:{runs}")
 
             final_output = self.__graph_invoke__(table1, table2)
 
             # If status came out as Hallucinated retry one more time
             while (
-                final_output["status"]
-                in (Status.HALLUCINATED_INVOKE, Status.HALLUCINATED_RECURSION)
+                final_output["status"] in (Status.HALLUCINATED_INVOKE, Status.HALLUCINATED_RECURSION)
                 and runs < self.HALLUCINATIONS_MAX_RETRY
             ):
                 runs += 1
-                log.info(
-                    f"[*] Hallucinated for {table1.name} <==> {table2.name} ... Retry no {runs} "
-                )
+                log.info(f"[*] Hallucinated for {table1.name} <==> {table2.name} ... Retry no {runs} ")
                 final_output = self.__graph_invoke__(table1=table1, table2=table2)
 
             results.append(final_output)

From 09038ff360cfaad93a829caa7fddf6c05a428fca Mon Sep 17 00:00:00 2001
From: JaskaranIntugle <jaskaran@intugle.ai>
Date: Tue, 9 Sep 2025 14:19:52 +0530
Subject: [PATCH 14/25] added " delimiter in dp_builder

---
 src/intugle/dp_builder.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/intugle/dp_builder.py b/src/intugle/dp_builder.py
index cd41d43..2004cb4 100644
--- a/src/intugle/dp_builder.py
+++ b/src/intugle/dp_builder.py
@@ -96,7 +96,7 @@ def get_all_field_details(self) -> dict[str, FieldDetailsModel]:
                     name=column.name,
                     datatype_l1=column.type,
                     datatype_l2=column.category,
-                    sql_code=f"{source.table.name}.{column.name}",
+                    sql_code=f"\"{source.table.name}\".\"{column.name}\"",
                     is_pii=False,
                     asset_id=source.table.name,
                     asset_name=source.table.name,
@@ -145,7 +145,7 @@ def field_details_fetcher(ids: list[str]):
                         name=column_detail.name,
                         datatype_l1=column_detail.type,
                         datatype_l2=column_detail.category,
-                        sql_code=f"{table}.{column}",
+                        sql_code=f"\"{table}\".\"{column}\"",
                         is_pii=False,
                         asset_id=table,
                         asset_name=table,

From cb0e42d3979234f6e9c05cca7db5ef61b3f60402 Mon Sep 17 00:00:00 2001
From: juhel-phanju-intugle <juhel@intugle.ai>
Date: Tue, 9 Sep 2025 15:11:20 +0530
Subject: [PATCH 15/25] semantic search

---
 pyproject.toml                                |    1 +
 quickstart.ipynb                              |    2 +-
 semantic_search.md                            |    7 +
 src/intugle/core/llms/config.py               |    3 -
 src/intugle/core/llms/embeddings.py           |  166 +
 src/intugle/core/semantic_search/crud.py      |  212 +
 src/intugle/core/semantic_search/models.py    |   23 +
 .../core/semantic_search/semantic_search.py   |  216 +
 src/intugle/core/semantic_search/utils.py     |   39 +
 src/intugle/core/settings.py                  |    3 +
 src/intugle/core/vector_store/__init__.py     |    3 +
 src/intugle/core/vector_store/models.py       |    0
 src/intugle/core/vector_store/qdrant.py       |  306 +
 src/intugle/core/vector_store/utils.py        |  117 +
 src/intugle/semantic_search.py                |   58 +
 tests/semantic_search/test_semantic_search.py |   15 +
 uv.lock                                       | 6213 +++++++++--------
 17 files changed, 4308 insertions(+), 3076 deletions(-)
 create mode 100644 semantic_search.md
 create mode 100644 src/intugle/core/llms/embeddings.py
 create mode 100644 src/intugle/core/semantic_search/crud.py
 create mode 100644 src/intugle/core/semantic_search/models.py
 create mode 100644 src/intugle/core/semantic_search/semantic_search.py
 create mode 100644 src/intugle/core/semantic_search/utils.py
 create mode 100644 src/intugle/core/vector_store/__init__.py
 create mode 100644 src/intugle/core/vector_store/models.py
 create mode 100644 src/intugle/core/vector_store/qdrant.py
 create mode 100644 src/intugle/core/vector_store/utils.py
 create mode 100644 src/intugle/semantic_search.py
 create mode 100644 tests/semantic_search/test_semantic_search.py

diff --git a/pyproject.toml b/pyproject.toml
index 808ea53..70b1bc0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -52,6 +52,7 @@ dependencies = [
     "duckdb>=1.3.2",
     "scikit-learn<=1.7.1",
     "langchain[anthropic,google-genai,openai]>=0.3.27",
+    "qdrant-client>=1.15.1",
 ]
 
 [project.urls]
diff --git a/quickstart.ipynb b/quickstart.ipynb
index 85b19bf..0ab01bb 100644
--- a/quickstart.ipynb
+++ b/quickstart.ipynb
@@ -359,7 +359,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "intugle",
+   "display_name": "intugle-new",
    "language": "python",
    "name": "python3"
   },
diff --git a/semantic_search.md b/semantic_search.md
new file mode 100644
index 0000000..8b26b5a
--- /dev/null
+++ b/semantic_search.md
@@ -0,0 +1,7 @@
+
+
+
+1. Download qudran and use it in docker
+2. Sync
+3. Use it
+
diff --git a/src/intugle/core/llms/config.py b/src/intugle/core/llms/config.py
index d65ac6d..81e01ca 100644
--- a/src/intugle/core/llms/config.py
+++ b/src/intugle/core/llms/config.py
@@ -1,5 +1,4 @@
 def get_llm_config(config: dict, type: str = "azure"):
-
     if type.__eq__("azure"):
         deployment = {
             # "model":config["API_INFO"]["DEPLOYMENT_NAME"],
@@ -17,5 +16,3 @@ def get_llm_config(config: dict, type: str = "azure"):
     else:
         raise ValueError("[!] Invalid model type")
     return deployment
-
-        
\ No newline at end of file
diff --git a/src/intugle/core/llms/embeddings.py b/src/intugle/core/llms/embeddings.py
new file mode 100644
index 0000000..678e58c
--- /dev/null
+++ b/src/intugle/core/llms/embeddings.py
@@ -0,0 +1,166 @@
+import asyncio
+
+from concurrent.futures import ThreadPoolExecutor
+from enum import Enum
+from typing import Callable, Dict, List, Optional
+
+import tiktoken
+
+from langchain.embeddings.base import init_embeddings
+
+
+class EmbeddingsType(str, Enum):
+    DENSE = "dense"
+    SPARSE = "sparse"
+    LATE = "late"
+
+
+ListEmbeddingsType = List[EmbeddingsType]
+Documents = List[str]
+
+
+class Embeddings:
+    def __init__(
+        self,
+        model_name: str,
+        config: dict = {},
+        executor: Optional[ThreadPoolExecutor] = None,
+        max_workers: int = 30,
+        tokenizer_model: str = "cl100k_base",
+        embeddings_size: Optional[int] = None,
+    ):
+        self.model_name = model_name
+        self.model = init_embeddings(model_name, **config)
+        self._embed_func: Dict[EmbeddingsType, Callable] = {
+            EmbeddingsType.DENSE: self.dense,
+            EmbeddingsType.SPARSE: self.sparse,
+            EmbeddingsType.LATE: self.late,
+        }
+        self._aembed_func: Dict[EmbeddingsType, Callable] = {
+            EmbeddingsType.DENSE: self.adense,
+            EmbeddingsType.SPARSE: self.asparse,
+            EmbeddingsType.LATE: self.alate,
+        }
+        self.max_workers = max_workers
+        self._executor = executor
+        self.tokenizer = tiktoken.get_encoding(tokenizer_model)
+        self._embeddings_size = embeddings_size
+    
+    @property
+    def embeddings_size(self):
+        if self._embeddings_size is None:
+            self._embeddings_size = len(self.model.embed_query("test"))
+        return self._embeddings_size
+
+    @property
+    def executor(self):
+        if self._executor is None:
+            self._executor = ThreadPoolExecutor(max_workers=self.max_workers)
+        return self._executor
+
+    def dense(self, documents: Documents):
+        embeddings = self.model.embed_documents(documents)
+        return embeddings
+
+    def _tokenize_and_encode(self, query: str):
+        """
+        Convert query to tokens and embed them
+        Args:
+            query (str): String to be tokenized and embedded
+        Returns:
+            _type_: _description_
+        """
+        token_ids = self.tokenizer.encode(query)
+        tokens = [self.tokenizer.decode([token_id]) for token_id in token_ids]
+        late_embeddings = self.dense(tokens)
+        return late_embeddings
+
+    def late(self, documents: Documents):
+        documents_list = [documents] if isinstance(documents, str) else documents
+        late_embeddings = []
+        for i in documents_list:
+            embedding = self._tokenize_and_encode(i)
+            late_embeddings.append(embedding)
+        return late_embeddings[0] if isinstance(documents, str) else late_embeddings
+
+    def sparse(self, documents: Documents):
+        raise NotImplementedError("Sparse Embeddings yet to implement")
+
+    def encode(self, documents: Documents, embeddings_types: ListEmbeddingsType):
+        response = {}
+        for embeddings_type in embeddings_types:
+            embed_func = self._embed_func[embeddings_type]
+            response[embeddings_type] = embed_func(documents)
+        return response
+
+    async def adense(self, documents: Documents):
+        embeddings = await self.model.aembed_documents(documents)
+        return embeddings
+
+    async def _atokenize_and_encode(self, query: str):
+        """
+        Convert query to tokens and embed them
+        Args:
+            query (str): String to be tokenized and embedded
+        Returns:
+            _type_: _description_
+        """
+
+        # Using synchrounous code in async
+        def tokenize(query):
+            token_ids = self.tokenizer.encode(query)
+            tokens = [self.tokenizer.decode([token_id]) for token_id in token_ids]
+            return tokens
+
+        loop = asyncio.get_running_loop()
+        tokens = await loop.run_in_executor(self.executor, tokenize, query)
+
+        late_embeddings = await self.adense(tokens)
+        return late_embeddings
+
+    async def alate(self, documents: Documents):
+        documents_list = [documents] if isinstance(documents, str) else documents
+        late_embeddings = []
+        for i in documents_list:
+            embedding = await self._atokenize_and_encode(i)
+            late_embeddings.append(embedding)
+        return late_embeddings[0] if isinstance(documents, str) else late_embeddings
+
+    async def asparse(self, documents: Documents):
+        raise NotImplementedError("Sparse Embeddings yet to implement for AzureOpenAIEmbeddings")
+
+    async def aencode(self, documents: Documents, embeddings_types: ListEmbeddingsType):
+        response = {}
+        try:
+            for embeddings_type in embeddings_types:
+                aembed_func = self._aembed_func[embeddings_type]
+                response[embeddings_type] = await aembed_func(documents)
+            return response
+        except Exception as e:
+            print("Error: ", e)
+            import traceback
+            print(traceback.format_exc())
+
+    @classmethod
+    def build(
+        cls,
+        model_name: str,
+        config: dict = {},
+        *args,
+        **kwargs,
+    ):
+        """
+        Args:
+            model_name: type of model either azure, openai
+            config: configuration of embeddings
+
+        Returns:
+            ChatModelLLM (obj): instance of builded ChatModelLLM
+        """
+
+        return cls(
+            model_name=model_name,
+            config={**config},
+            *args,
+            **kwargs,
+        )
diff --git a/src/intugle/core/semantic_search/crud.py b/src/intugle/core/semantic_search/crud.py
new file mode 100644
index 0000000..6d39351
--- /dev/null
+++ b/src/intugle/core/semantic_search/crud.py
@@ -0,0 +1,212 @@
+import asyncio
+import itertools
+import logging
+
+from typing import List, Optional
+from uuid import uuid4
+
+import pandas as pd
+
+from qdrant_client import models
+
+from intugle.core.llms.embeddings import Embeddings, EmbeddingsType
+from intugle.core.semantic_search.utils import batched
+from intugle.core.utilities.processing import string_standardization
+from intugle.core.vector_store import VectorStoreService
+from intugle.core.vector_store.qdrant import QdrantVectorConfiguration
+
+log = logging.getLogger(__name__)
+
+
+class SemanticSearchCRUD:
+    def __init__(self, collection_name: str, embeddings: List[Embeddings], batch_size: int = 30):
+        self.collection_name = collection_name
+        self.embeddings = embeddings
+        self.batch_size = batch_size
+
+    @property
+    def vector_store(self):
+        return VectorStoreService(collection_name=self.collection_name, collection_configurations=self.configuration)
+
+    @property
+    def configuration(self):
+        embeddings_configurations = {}
+        for embedding in self.embeddings:
+            config = {
+                f"{embedding.model_name}-{EmbeddingsType.DENSE}": models.VectorParams(
+                    size=embedding.embeddings_size, distance=models.Distance.COSINE
+                ),
+                f"{embedding.model_name}-{EmbeddingsType.LATE}": models.VectorParams(
+                    size=embedding.embeddings_size,
+                    distance=models.Distance.COSINE,
+                    multivector_config=models.MultiVectorConfig(comparator=models.MultiVectorComparator.MAX_SIM),
+                ),
+            }
+            embeddings_configurations = {**embeddings_configurations, **config}
+
+        configuration = QdrantVectorConfiguration(vectors_config=embeddings_configurations)
+
+        return configuration
+
+    def create_content_for_vectorization(self, _: int, row: pd.Series) -> pd.DataFrame:
+        """
+        Generate Contents for Vectorization from all the column details 
+        (i.e Column Name, Business Tags, Business Glossary)
+
+        Args:
+            i (int): integer
+            row (pd.Series): a column information as pandas series level
+
+        Returns:
+            pd.DataFrame: returns pandas dataframe with contents of the columns.
+        """
+
+        tags_content = []
+        glossary_content = []
+        column_name_content = []
+
+        if len(row["column_tags"]) > 0:
+            tags_content = [
+                {
+                    "content": tag,
+                    "type": "tag",
+                }
+                for tag in row["column_tags"]
+            ]
+
+        if not pd.isna(row["column_glossary"]):
+            glossary_content = [
+                {
+                    "content": row["column_glossary"],
+                    "type": "glossary",
+                }
+            ]
+
+        if not pd.isna(row["column_name"]):
+            column_name_content = [
+                {
+                    "content": row["column_name"],
+                    "type": "column_name",
+                }
+            ]
+
+        final_consolidated_content = pd.DataFrame(tags_content + glossary_content + column_name_content)
+
+        if final_consolidated_content.shape[0] <= 0:
+            return pd.DataFrame()
+
+        final_consolidated_content["column_id"] = row["id"]
+
+        # The content needs to be cleaned using String Standardization Methods
+        final_consolidated_content["content"] = final_consolidated_content.content.apply(string_standardization)
+
+        return final_consolidated_content[final_consolidated_content.content != ""]
+
+    async def vectorize(self, content: pd.DataFrame) -> List[models.PointStruct]:
+        tags_and_columns = content.loc[content.type.isin(["tag", "column_name"])].reset_index(drop=True)
+        business_glossary = content.loc[content.type.isin(["glossary"])].reset_index(drop=True)
+
+        tags_and_columns_content = tags_and_columns["content"].tolist()
+        business_glossary_content = business_glossary["content"].tolist()
+        log.info(f"tags_column: {tags_and_columns_content}")
+        log.info(f"business glossary: {business_glossary_content}")
+
+        async def run():
+            # Run tags col and glossary concurrenty
+            try:
+                tasks = {}
+                async with asyncio.TaskGroup() as tg:
+                    for embedding in self.embeddings:
+                        task = tg.create_task(
+                            embedding.aencode(tags_and_columns_content, embeddings_types=[EmbeddingsType.DENSE])
+                        )
+                        tasks[(embedding, "tags_col")] = task
+                        task = tg.create_task(
+                            embedding.aencode(business_glossary_content, embeddings_types=[EmbeddingsType.LATE])
+                        )
+                        tasks[(embedding, "glossary")] = task
+
+                results = {"tags_col": {}, "glossary": {}}
+                for embedding_model, task in tasks.items():
+                    model, typ = embedding_model
+                    result = task.result()
+                    if typ == "tags_col":
+                        results["tags_col"][model] = result
+                    else:
+                        results["glossary"][model] = result
+
+                return results
+            except Exception as ex:
+                raise Exception(f"[!] Semantic Search: Couldnot vectorize => {ex}")
+
+        # Run all type of embeddings concurrenlty
+        results = await run()
+
+        points = []
+        point = self.convert_to_qdrant_point(tags_and_columns, results["tags_col"])
+        points.extend(point)
+        point = self.convert_to_qdrant_point(business_glossary, results["glossary"])
+        points.extend(point)
+
+        return points
+
+    @staticmethod
+    def convert_to_qdrant_point(
+        content: pd.DataFrame, embeddings: dict[Embeddings, dict], ids: Optional[list[int]] = None
+    ):
+        if ids is None:
+            ids = [str(uuid4()) for _ in content]
+        points = []
+        for idx, row in content.iterrows():
+            payload = {"column_id": row["column_id"], "type": row["type"]}
+            vectors = {}
+            for embedding_model, embedding in embeddings.items():
+                for key, embed in embedding.items():
+                    vectors[f"{embedding_model.model_name}-{key}"] = embed[idx]
+            points.append(models.PointStruct(id=str(uuid4()), vector=vectors, payload=payload))
+
+        return points
+
+    @staticmethod
+    def convert_to_qdrant_points(
+        content: pd.DataFrame,
+        embeddings_ada: List[float],
+        embeddings_bge: List[float],
+        vector_name_ada: EmbeddingsType,
+        vector_name_bge: EmbeddingsType,
+        ids: Optional[int] = None,
+    ):
+        points = []
+        if ids is None:
+            ids = [str(uuid4()) for _ in embeddings_ada]
+        for (_, row), vector_ada, vector_bge_m3, _id in zip(content.iterrows(), embeddings_ada, embeddings_bge, ids):
+            payload = {"column_id": row["column_id"], "type": row["type"]}
+            points.append(
+                models.PointStruct(
+                    id=_id,
+                    vector={
+                        vector_name_ada: vector_ada,
+                        vector_name_bge: vector_bge_m3,
+                    },
+                    payload=payload,
+                )
+            )
+        return points
+
+    async def initialize(self, column_details: list[dict]):
+        async with self.vector_store as vdb:
+            all_points = await vdb.get(includes=["metadata"])
+
+            if all_points and len(all_points) > 0:
+                column_ids = tuple(set([point.payload.get("column_id")] for point in all_points))
+
+            column_details = pd.DataFrame(column_details)
+
+            for batch in batched(column_details, self.batch_size):
+                content = list(itertools.starmap(self.create_content_for_vectorization, batch.iterrows()))
+
+                content = pd.concat(content, axis=0).reset_index(drop=True)
+
+                points = await self.vectorize(content)
+
+                await vdb.bulk_insert(points)
diff --git a/src/intugle/core/semantic_search/models.py b/src/intugle/core/semantic_search/models.py
new file mode 100644
index 0000000..3578ae7
--- /dev/null
+++ b/src/intugle/core/semantic_search/models.py
@@ -0,0 +1,23 @@
+from enum import StrEnum
+
+
+class RelevancyCategory(StrEnum):
+    MOST_RELEVANT = "most-relevant"
+    RELEVANT = "relevant"
+    LESS_RELEVANT = "less-relevant"
+    NON_RELEVANT = "non-relevant"
+
+    def __repr__(
+        self,
+    ) -> str:
+        return self.value
+
+
+class ScoreStrategy(StrEnum):
+    MAX = "maximum"
+    AVG = "weighted-avg"
+
+    def __repr__(
+        self,
+    ) -> str:
+        return self.value
\ No newline at end of file
diff --git a/src/intugle/core/semantic_search/semantic_search.py b/src/intugle/core/semantic_search/semantic_search.py
new file mode 100644
index 0000000..67e85e5
--- /dev/null
+++ b/src/intugle/core/semantic_search/semantic_search.py
@@ -0,0 +1,216 @@
+import asyncio
+import logging
+import sys
+
+from typing import Optional
+
+import numpy as np
+import pandas as pd
+
+from qdrant_client import models
+
+from intugle.core.llms.embeddings import Embeddings, EmbeddingsType
+from intugle.core.semantic_search.models import RelevancyCategory
+from intugle.core.semantic_search.utils import relevancy_adder
+from intugle.core.vector_store import VectorStoreService
+from intugle.core.vector_store.qdrant import VectorSearchKwargs
+
+log = logging.getLogger(__name__)
+
+
+# class HybridDenseLateSearch:
+#     def __init__(self):
+#         pass
+
+
+class HybridDenseLateSearch:
+    def __init__(
+        self,
+        collection_name: str,
+        embeddings: Embeddings,
+        threshold_for_less_relevant_result: float = 0.5,
+        relevancy_config: Optional[dict] = None,
+    ):
+        self.collection_name = collection_name
+        self.embeddings = embeddings
+        self.threshold_for_less_relevant_result = threshold_for_less_relevant_result
+
+        if relevancy_config is None:
+            relevancy_config = (
+                {
+                    RelevancyCategory.MOST_RELEVANT: 0.85,
+                    RelevancyCategory.RELEVANT: 0.8,
+                    RelevancyCategory.LESS_RELEVANT: 0.75,
+                }
+            )
+        self.relevancy_config = relevancy_config
+
+    @property
+    def vector_store(self):
+        return VectorStoreService(collection_name=self.collection_name, collection_configurations=None)
+
+    async def __vector_search__(
+        self,
+        query_vector: np.ndarray | list,
+        using: str,
+        score_threshold: float = None,
+        query_filter: models.Filter = None,
+        limit: int = sys.maxsize,
+    ) -> pd.DataFrame:
+        query_vector = query_vector.tolist() if isinstance(query_vector, np.ndarray) else query_vector
+
+        search_result = pd.DataFrame()
+
+        qdrant_points = await self.vector_store.search(
+            query=query_vector,
+            search_using=using,
+            includes=["metadata"],
+            search_kwargs=dict(
+                VectorSearchKwargs(
+                    search_type="similarity",
+                    top_k=limit,
+                    filter=query_filter,
+                    search_params=models.SearchParams(
+                        hnsw_ef=limit,
+                        exact=True,
+                    ),
+                    score_threshold=score_threshold,
+                ),
+            ),
+        )
+
+        search_result = pd.DataFrame(qdrant_points.model_dump()["points"])
+
+        if search_result.shape[0] > 0:
+            search_result = search_result[["id", "score", "payload"]].reset_index(drop=True)
+
+            search_result = pd.concat([search_result, pd.json_normalize(search_result.payload)], axis=1)[
+                ["score", "column_id", "type"]
+            ]
+
+        else:
+            log.warning(f"[!] No results found when searched {using}")
+
+        return search_result
+
+    async def fetch(
+        self,
+        # query_term: str,
+        dense_vector: np.ndarray,
+        late_vector: np.ndarray,
+        add_relevancy: bool = True,
+        *args,
+        **kwargs,
+    ) -> pd.DataFrame:
+        hybrid_result = pd.DataFrame()
+
+        filter_condition_column_tag = models.Filter(
+            must=[models.FieldCondition(key="type", match=models.MatchAny(any=["column_name", "tag"]))]
+        )
+
+        filter_condition_glossary = models.Filter(
+            must=[models.FieldCondition(key="type", match=models.MatchValue(value="glossary"))]
+        )
+
+        async with asyncio.TaskGroup() as tg:
+            task1 = tg.create_task(
+                self.__vector_search__(
+                    query_vector=dense_vector,
+                    using=f"{self.embeddings.model_name}-{EmbeddingsType.DENSE}",
+                    score_threshold=self.threshold_for_less_relevant_result,
+                    query_filter=filter_condition_column_tag,
+                )
+            )
+            task2 = tg.create_task(
+                self.__vector_search__(
+                    query_vector=late_vector,
+                    using=f"{self.embeddings.model_name}-{EmbeddingsType.LATE}",
+                    # score_threshold=self.threshold_for_less_relevant_result,
+                    query_filter=filter_condition_glossary,
+                )
+            )
+
+        try:
+            dense_embedding_result = task1.result()
+            dense_embedding_result = dense_embedding_result[["column_id", "score"]]
+        except Exception as ex:
+            log.error(ex)
+            log.error(f"[*] Error while performing dense search \nReason: {ex}")
+            late_embedding_result_glossary = pd.DataFrame()
+            dense_embedding_result = pd.DataFrame()
+
+        try:
+            # Do a late embedding search across glossary
+            late_embedding_result_glossary = task2.result()
+            late_embedding_result_glossary = late_embedding_result_glossary[["column_id", "score"]]
+        except Exception as ex:
+            log.error(f"[*] Error while performing late search \nReason: {ex}")
+            late_embedding_result_glossary = pd.DataFrame()
+
+        if late_embedding_result_glossary.shape[0] > 0:
+            # The final score will be total score by no of query embeddings in late vectors
+            late_embedding_result_glossary["score"] = late_embedding_result_glossary["score"] / len(late_vector)
+
+            # filter out results that are below designated threshold
+            late_embedding_result_glossary = late_embedding_result_glossary.loc[
+                late_embedding_result_glossary.score >= self.threshold_for_less_relevant_result
+            ].reset_index(drop=True)
+
+        hybrid_result = pd.DataFrame()
+
+        # check if we have results in all cases
+        late_shape, dense_shape = (
+            late_embedding_result_glossary.shape[0],
+            dense_embedding_result.shape[0],
+        )
+
+        shapes = [late_shape, dense_shape]
+
+        # Should only merge result if atleast one of the cases of result is present
+        if sum(shapes) > 0:
+            # filter out results that came out empty
+            merging_results = [
+                result
+                for shape, result in zip(
+                    shapes,
+                    [
+                        late_embedding_result_glossary,
+                        dense_embedding_result,
+                    ],
+                )
+                if shape > 0
+            ]
+
+            if len(merging_results) == 1:
+                # if only one result came then no need to merge
+
+                hybrid_result = merging_results[-1]
+
+            else:
+                # merge all non empty results in one go using redude
+                hybrid_result = pd.concat(merging_results, axis=0).reset_index(drop=True)[["column_id", "score"]]
+
+        if hybrid_result.shape[0] > 0:
+            # Apply max logic to get score
+            hybrid_result = hybrid_result.groupby("column_id").agg({"score": "max"}).reset_index()
+
+            if add_relevancy:
+                # add the relevancy
+                hybrid_result = relevancy_adder(
+                    result=hybrid_result,
+                    relevancy_scores=self.relevancy_config,
+                    i_column="score",
+                    o_column="relevancy",
+                )
+
+            # hybrid_result.rename(columns={"score": self.hybrid_model}, inplace=True)
+
+        return hybrid_result
+    
+    async def search(self, query):
+
+        vectors = await self.embeddings.aencode([query], embeddings_types=[EmbeddingsType.DENSE, EmbeddingsType.LATE])
+
+        result = await self.fetch(vectors["dense"][0], vectors["late"][0])
+
+        return result
\ No newline at end of file
diff --git a/src/intugle/core/semantic_search/utils.py b/src/intugle/core/semantic_search/utils.py
new file mode 100644
index 0000000..d45abf1
--- /dev/null
+++ b/src/intugle/core/semantic_search/utils.py
@@ -0,0 +1,39 @@
+import numpy as np
+import pandas as pd
+
+from intugle.core.semantic_search.models import RelevancyCategory
+
+
+def relevancy_adder(
+    result: pd.DataFrame,
+    relevancy_scores: dict,
+    o_column="relevancy",
+    i_column="score",
+) -> pd.DataFrame:
+
+    # Add relevancy to each terms identified
+    result[o_column] = np.where(
+        result[i_column] >= relevancy_scores[RelevancyCategory.MOST_RELEVANT],
+        RelevancyCategory.MOST_RELEVANT,
+        np.where(
+            np.logical_and(
+                result[i_column] >= relevancy_scores[RelevancyCategory.RELEVANT],
+                result[i_column] < relevancy_scores[RelevancyCategory.MOST_RELEVANT],
+            ),
+            RelevancyCategory.RELEVANT,
+            np.where(
+                np.logical_and(
+                    result[i_column] >= relevancy_scores[RelevancyCategory.LESS_RELEVANT],
+                    result[i_column] < relevancy_scores[RelevancyCategory.RELEVANT],
+                ),
+                RelevancyCategory.LESS_RELEVANT,
+                RelevancyCategory.NON_RELEVANT,
+            ),
+        ),
+    )
+    return result
+
+
+def batched(data: pd.DataFrame, n):
+    for index in range(0, data.shape[0], n):
+        yield data[index : index + n]
diff --git a/src/intugle/core/settings.py b/src/intugle/core/settings.py
index 941db41..e94861d 100644
--- a/src/intugle/core/settings.py
+++ b/src/intugle/core/settings.py
@@ -101,6 +101,9 @@ class Settings(BaseSettings):
     POSTGRES_PORT: int = 5432
     POSTGRES_SCHEMA: Optional[str] = "public"
 
+    # Vector
+    VECTOR_COLLECTION_NAME: str = os.getcwd().split('/')[-1]
+
 
 @lru_cache
 def get_settings() -> Settings:
diff --git a/src/intugle/core/vector_store/__init__.py b/src/intugle/core/vector_store/__init__.py
new file mode 100644
index 0000000..a13b90f
--- /dev/null
+++ b/src/intugle/core/vector_store/__init__.py
@@ -0,0 +1,3 @@
+from intugle.core.vector_store.qdrant import AsyncQdrantService
+
+VectorStoreService = AsyncQdrantService
\ No newline at end of file
diff --git a/src/intugle/core/vector_store/models.py b/src/intugle/core/vector_store/models.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/intugle/core/vector_store/qdrant.py b/src/intugle/core/vector_store/qdrant.py
new file mode 100644
index 0000000..c2d9096
--- /dev/null
+++ b/src/intugle/core/vector_store/qdrant.py
@@ -0,0 +1,306 @@
+import asyncio
+import logging
+import sys
+
+from typing import Any, Dict, List, Literal, Mapping, Optional, Self
+
+import numpy as np
+import qdrant_client.models as models
+
+from pydantic import BaseModel
+from qdrant_client import AsyncQdrantClient
+from qdrant_client.conversions import common_types as qdrant_types
+
+from intugle.core.vector_store.utils import maximal_marginal_relevance
+
+log = logging.getLogger(__name__)
+
+
+class QdrantVectorConfiguration(BaseModel):
+    vectors_config: Optional[qdrant_types.VectorParams | Mapping[str, qdrant_types.VectorParams]] = None
+    sparse_vectors_config: Optional[Mapping[str, qdrant_types.SparseVectorParams]] = None
+
+
+# Used for standardization
+class VDocument(BaseModel):
+    id: Optional[str | int] = None
+    page_content: Optional[str] = None
+    metadata: Optional[Dict[str, Any]] = None
+    options: Dict[str, Any] = {}  # Refere to QDocumentOptions
+    embeddings: Optional[Dict[str, List[float]]] = None
+
+
+# Used for standardization
+class VectorSearchKwargs(BaseModel):
+    search_type: Literal["similarity", "mmr", "hybrid"] = "similarity"
+    top_k: Optional[int] = None
+    score_threshold: Optional[float] = None
+    fetch_k: Optional[int] = None
+    filter: Optional[models.Filter] = None
+    prefetch_using: List[str] = None
+    search_params: Optional[models.SearchParams] = None
+    lambda_mult: Optional[float] = None
+
+    def model_post_init(self, __context__):
+        if self.search_type == "similarity":
+            if self.top_k is None and self.score_threshold is None:
+                raise ValueError("did not pass top k or threshold")
+        elif self.search_type == "mmr":
+            if (self.fetch_k is None and self.score_threshold is None) or self.top_k is None:
+                raise ValueError("did not pass fetch k or threshold or top k")
+        elif self.search_type == "hybrid":
+            if self.prefetch_using is None:
+                raise ValueError("did not pass prefetch using")
+        else:
+            raise ValueError("search type not similarity, mmr, hybrid")
+
+
+class AsyncQdrantService:
+    def __init__(
+        self, collection_name: str, collection_configurations: QdrantVectorConfiguration, client_config: dict = {}
+    ):
+        self.collection_name = collection_name
+        if isinstance(collection_configurations, Dict):
+            collection_configurations = QdrantVectorConfiguration(**collection_configurations)
+        self.collection_configurations = collection_configurations
+        self.client_config = client_config
+        self._client = None
+
+    async def __aenter__(self) -> Self:
+        await self.create_collection()
+        return self
+
+    @property
+    def client(self):
+        if self._client is None:
+            log.debug("AsyncQdrantClient: init")
+            self._client = AsyncQdrantClient(**self.client_config)
+        return self._client
+
+    async def __aexit__(self, *args, **kwargs):
+        if isinstance(self.client, AsyncQdrantClient):
+            await self.client.close()
+            log.debug("AsyncQdrantService: closed")
+        else:
+            log.warning("client is not of type AsyncQdrantClient")
+
+    async def delete_collection(self) -> bool:
+        try:
+            deleted = await self.client.delete_collection(self.collection_name)
+            if deleted:
+                log.debug(f"AsyncQdrantService: deleted collection {self.collection_name}")
+            else:
+                log.warning(f"AsyncQdrantService: couldn't delete collection {self.collection_name}")
+            return deleted
+        except Exception as e:
+            log.error(f"AsyncQdrantService: Couldn't delete collection, reason: {e}")
+            raise e
+
+    async def create_collection(
+        self,
+    ) -> bool:
+        try:
+            exists = await self.client.collection_exists(collection_name=self.collection_name)
+
+            if exists:
+                log.debug(f"AsyncQdrantService: collection {self.collection_name} already exists")
+                return True
+
+            created = await self.client.create_collection(
+                collection_name=self.collection_name, **(dict(self.collection_configurations))
+            )
+            if created:
+                log.debug(f"AsyncQdrantService: created collection {self.collection_name}")
+            else:
+                log.warning(f"AsyncQdrantService: couldn't create collection {self.collection_name}")
+            return created
+
+        except Exception as e:
+            log.error(f"AsyncQdrantService: Couldn't create collection, reason: {e}")
+            raise e
+
+    async def bulk_insert(self, points: models.PointStruct | List[models.PointStruct]):
+        try:
+            task = asyncio.create_task(self.aupload_point(points))
+            result = await task
+            log.debug(f"Upload Status: {result}")
+            return result
+        except Exception as e:
+            log.error(f"Couldn't bulk insert data: {e}")
+            raise e
+
+    async def aupload_point(self, points):
+        try:
+            self.client.upload_points(
+                collection_name=self.collection_name,
+                points=points,
+                parallel=1,  # number of vectors points to insert parallely,
+                max_retries=5,
+            )
+            log.debug(f"batch uploaded: {len(points)}")
+            return True
+        except Exception as e:
+            log.error(f"Coulnd't uploading points: {e}")
+            raise e
+
+    async def get(
+        self,
+        ids: int | List[int] | str | List[str] = None,
+        filter: models.Filter = None,
+        includes: List[Literal["metadata", "embeddings"]] = [],
+        return_document: Optional[bool] = True,
+    ):
+        try:
+            with_payload = False
+            with_vectors = False
+            for include in includes:
+                if include == "metadata":
+                    with_payload = True
+                elif include == "embeddings":
+                    with_vectors = True
+            result = []
+            if ids is not None:
+                _ids = []
+                if isinstance(ids, (int, str)):
+                    _ids.append(ids)
+                elif isinstance(ids, List):
+                    _ids.extend(ids)
+                else:
+                    raise TypeError("ids is neither int or str or list")
+                result = await self.client.retrieve(
+                    collection_name=self.collection_name,
+                    ids=_ids,
+                    with_payload=with_payload,
+                    with_vectors=with_vectors,
+                )
+            # NOTE: this might become issue later, need to research
+            elif filter is not None:
+                result, _ = await self.client.scroll(
+                    collection_name=self.collection_name,
+                    scroll_filter=filter,
+                    limit=sys.maxsize,
+                    with_payload=with_payload,
+                    with_vectors=with_vectors,
+                )
+            else:
+                result, _ = await self.client.scroll(
+                    collection_name=self.collection_name,
+                    limit=sys.maxsize,
+                    with_payload=with_payload,
+                    with_vectors=with_vectors,
+                )
+            if return_document:
+                documents: List[VDocument] = []
+                for record in result:
+                    page_content = record.payload.get("content", None) if record.payload is not None else None
+                    if page_content is not None:
+                        record.payload.pop("content")
+                    documents.append(
+                        VDocument(
+                            id=record.id,
+                            page_content=page_content,
+                            metadata=record.payload,
+                            embeddings=record.vector,
+                        )
+                    )
+                return documents
+            else:
+                return result
+        except Exception as e:
+            log.error(f"Couldn't get from qdrant: {e}")
+            raise e
+
+    # NOTE: Don't use numerical ids as it causes issue in deletion
+    async def delete(
+        self,
+        ids: int | str | List[int] | List[str] = None,
+        filter: models.Filter = None,
+    ) -> bool:
+        try:
+            if ids is not None:
+                points_selector = None
+                if isinstance(ids, (str, int)):
+                    points_selector = models.PointIdsList(points=[ids])
+                elif isinstance(ids, list):
+                    points_selector = models.PointIdsList(points=[*ids])
+                else:
+                    raise TypeError("ids is neither list or ids")
+                result = await self.client.delete(
+                    collection_name=self.collection_name,
+                    points_selector=points_selector,
+                )
+            elif filter is not None:
+                result = await self.client.delete(collection_name=self.collection_name, points_selector=filter)
+            return result.status == models.UpdateStatus.COMPLETED
+        except Exception as e:
+            log.error(f"Couldn't delete from qdrant: {e}")
+            raise e
+
+    async def search(
+        self,
+        query: str | np.ndarray,
+        search_using: str,
+        search_kwargs: VectorSearchKwargs = {},
+        includes: List[Literal["metadata", "embeddings"]] = [],
+    ) -> models.QueryResponse:
+        with_payload = False
+        with_vectors = False
+        for include in includes:
+            if include == "metadata":
+                with_payload = True
+            elif include == "embeddings":
+                with_vectors = True
+
+        if not isinstance(query, (list, np.ndarray)):
+            raise ValueError(f"[!] Async QdrantClient: Query can be only list of floating point values not {type(str)}")
+
+        search_type = search_kwargs.get("search_type", "similarity")
+        if search_type == "similarity":
+            result = await self.client.query_points(
+                collection_name=self.collection_name,
+                query=query,
+                using=search_using,
+                query_filter=search_kwargs.get("filter", None),
+                search_params=search_kwargs.get("search_params", None),
+                limit=search_kwargs.get("top_k", None),
+                with_payload=with_payload,
+                with_vectors=with_vectors,
+                score_threshold=search_kwargs.get("score_threshold", None),
+            )
+        elif search_type == "mmr":
+            log.debug(f"mmr search using {search_using}")
+            result = await self.client.query_points(
+                collection_name=self.collection_name,
+                query=query,
+                using=search_using,
+                query_filter=search_kwargs.get("filter", None),
+                search_params=search_kwargs.get("search_params", None),
+                limit=search_kwargs.get("fetch_k", None),
+                with_payload=with_payload,
+                with_vectors=True,
+                score_threshold=search_kwargs.get("score_threshold", None),
+            )
+
+            result = result.points
+            if len(result):
+                vectors = [point.vector.get(search_using) for point in result]
+                indexes = maximal_marginal_relevance(
+                    query_embedding=np.array(query),
+                    embedding_list=vectors,
+                    lambda_mult=search_kwargs.get("lambda_mult", 0.5),
+                    k=search_kwargs.get("top_k", 4),
+                )
+
+                if with_vectors:
+                    result = [result[i] for i in indexes]
+                else:
+                    result = [self._remove_vector(result[i]) for i in indexes]
+        elif search_type == "hybrid":
+            log.debug("hybrid search")
+            raise NotImplementedError("[!] Hybrid search not implemented")
+        return result
+
+    def _remove_vector(self, point: models.PointStruct) -> models.ScoredPoint:
+        _d_ = point.model_dump()
+        _d_.pop("vector")
+        return models.ScoredPoint(**_d_)
diff --git a/src/intugle/core/vector_store/utils.py b/src/intugle/core/vector_store/utils.py
new file mode 100644
index 0000000..6ac9726
--- /dev/null
+++ b/src/intugle/core/vector_store/utils.py
@@ -0,0 +1,117 @@
+"""Internal utilities for the in memory implementation of VectorStore.
+
+These are part of a private API, and users should not use them directly
+as they can change without notice.
+"""
+
+import logging
+
+import numpy as np
+
+log = logging.getLogger(__name__)
+
+
+Matrix = list[list[float]] | list[np.ndarray] | np.ndarray
+
+
+def _cosine_similarity(x: Matrix, y: Matrix) -> np.ndarray:
+    """Row-wise cosine similarity between two equal-width matrices.
+
+    Args:
+        x: A matrix of shape (n, m).
+        y: A matrix of shape (k, m).
+
+    Returns:
+        A matrix of shape (n, k) where each element (i, j) is the cosine similarity
+        between the ith row of X and the jth row of Y.
+
+    Raises:
+        ValueError: If the number of columns in X and Y are not the same.
+        ImportError: If numpy is not installed.
+    """
+    try:
+        import numpy as np
+    except ImportError as e:
+        msg = "cosine_similarity requires numpy to be installed. Please install numpy with `pip install numpy`."
+        raise ImportError(msg) from e
+
+    if len(x) == 0 or len(y) == 0:
+        return np.array([])
+
+    x = np.array(x)
+    y = np.array(y)
+    if x.shape[1] != y.shape[1]:
+        msg = f"Number of columns in X and Y must be the same. X has shape {x.shape} and Y has shape {y.shape}."
+        raise ValueError(msg)
+    try:
+        import simsimd as simd  # type: ignore
+    except ImportError:
+        log.debug(
+            "Unable to import simsimd, defaulting to NumPy implementation. If you want "
+            "to use simsimd please install with `pip install simsimd`."
+        )
+        x_norm = np.linalg.norm(x, axis=1)
+        y_norm = np.linalg.norm(y, axis=1)
+        # Ignore divide by zero errors run time warnings as those are handled below.
+        with np.errstate(divide="ignore", invalid="ignore"):
+            similarity = np.dot(x, y.T) / np.outer(x_norm, y_norm)
+        similarity[np.isnan(similarity) | np.isinf(similarity)] = 0.0
+        return similarity
+
+    x = np.array(x, dtype=np.float32)
+    y = np.array(y, dtype=np.float32)
+    return 1 - np.array(simd.cdist(x, y, metric="cosine"))
+
+
+def maximal_marginal_relevance(
+    query_embedding: np.ndarray,
+    embedding_list: list,
+    lambda_mult: float = 0.5,
+    k: int = 4,
+) -> list[int]:
+    """Calculate maximal marginal relevance.
+
+    Args:
+        query_embedding: The query embedding.
+        embedding_list: A list of embeddings.
+        lambda_mult: The lambda parameter for MMR. Default is 0.5.
+        k: The number of embeddings to return. Default is 4.
+
+    Returns:
+        A list of indices of the embeddings to return.
+
+    Raises:
+        ImportError: If numpy is not installed.
+    """
+    try:
+        import numpy as np
+    except ImportError as e:
+        msg = (
+            "maximal_marginal_relevance requires numpy to be installed. Please install numpy with `pip install numpy`."
+        )
+        raise ImportError(msg) from e
+
+    if min(k, len(embedding_list)) <= 0:
+        return []
+    if query_embedding.ndim == 1:
+        query_embedding = np.expand_dims(query_embedding, axis=0)
+    similarity_to_query = _cosine_similarity(query_embedding, embedding_list)[0]
+    most_similar = int(np.argmax(similarity_to_query))
+    idxs = [most_similar]
+    selected = np.array([embedding_list[most_similar]])
+    while len(idxs) < min(k, len(embedding_list)):
+        best_score = -np.inf
+        idx_to_add = -1
+        similarity_to_selected = _cosine_similarity(embedding_list, selected)
+        for i, query_score in enumerate(similarity_to_query):
+            if i in idxs:
+                continue
+            redundant_score = max(similarity_to_selected[i])
+            equation_score = lambda_mult * query_score - (1 - lambda_mult) * redundant_score
+            if equation_score > best_score:
+                best_score = equation_score
+                idx_to_add = i
+        idxs.append(idx_to_add)
+        selected = np.append(selected, [embedding_list[idx_to_add]], axis=0)
+    return idxs
+
diff --git a/src/intugle/semantic_search.py b/src/intugle/semantic_search.py
new file mode 100644
index 0000000..8ea2c9c
--- /dev/null
+++ b/src/intugle/semantic_search.py
@@ -0,0 +1,58 @@
+from intugle.core import settings
+from intugle.core.llms.embeddings import Embeddings
+from intugle.core.semantic_search.crud import SemanticSearchCRUD
+from intugle.core.semantic_search.semantic_search import HybridDenseLateSearch
+from intugle.parser.manifest import ManifestLoader
+
+
+class SemanticSearch:
+    def __init__(
+        self, project_base: str = settings.PROJECT_BASE, collection_name: str = settings.VECTOR_COLLECTION_NAME
+    ):
+        self.manifest_loader = ManifestLoader(project_base)
+        self.manifest_loader.load()
+        self.manifest = self.manifest_loader.manifest
+        self.collection_name = collection_name
+
+        self.project_base = project_base
+
+    def get_column_details(self):
+        sources = self.manifest.sources
+        models = self.manifest.models
+
+        column_details = []
+        for source in sources.values():
+            table = source.table
+            for column in table.columns:
+                column_detail = {
+                    "id": f"{table.name}.{column.name}",
+                    "column_name": column.name,
+                    "column_glossary": column.description,
+                    "column_tags": column.tags,
+                }
+                column_details.append(column_detail)
+
+        for model in models.values():
+            for column in model.columns:
+                column_detail = {
+                    "id": f"{table.name}.{column.name}",
+                    "column_name": column.name,
+                    "column_glossary": column.description,
+                    "column_tags": column.tags,
+                }
+                column_details.append(column_detail)
+
+        return column_details
+
+    async def initiaize(self):
+        embeddings = Embeddings("azure_openai:ada")
+        semantic_search_crud = SemanticSearchCRUD(self.collection_name, [embeddings])
+        column_details = self.get_column_details()
+        await semantic_search_crud.initialize(column_details)
+
+    async def search(self, query):
+        embeddings = Embeddings("azure_openai:ada")
+        semantic_search = HybridDenseLateSearch(self.collection_name, embeddings)
+
+        data = await semantic_search.search(query)
+        return data
diff --git a/tests/semantic_search/test_semantic_search.py b/tests/semantic_search/test_semantic_search.py
new file mode 100644
index 0000000..ce8910a
--- /dev/null
+++ b/tests/semantic_search/test_semantic_search.py
@@ -0,0 +1,15 @@
+import asyncio
+
+from intugle.semantic_search import SemanticSearch
+
+
+def test_semantic_search_initiaize():
+    asyncio.run(SemanticSearch().initiaize())
+
+
+def test_semantic_search():
+    query = "reaction"
+    data = asyncio.run(SemanticSearch().search(query))
+    data.sort_values(by="score", ascending=False, inplace=True)
+
+    breakpoint()
\ No newline at end of file
diff --git a/uv.lock b/uv.lock
index 9c619b6..92e1621 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 1
+revision = 3
 requires-python = ">=3.10"
 resolution-markers = [
     "python_full_version >= '3.13'",
@@ -12,9 +12,9 @@ resolution-markers = [
 name = "aiohappyeyeballs"
 version = "2.6.1"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760 }
+sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760, upload-time = "2025-03-12T01:42:48.764Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265 },
+    { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265, upload-time = "2025-03-12T01:42:47.083Z" },
 ]
 
 [[package]]
@@ -31,76 +31,76 @@ dependencies = [
     { name = "propcache" },
     { name = "yarl" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/9b/e7/d92a237d8802ca88483906c388f7c201bbe96cd80a165ffd0ac2f6a8d59f/aiohttp-3.12.15.tar.gz", hash = "sha256:4fc61385e9c98d72fcdf47e6dd81833f47b2f77c114c29cd64a361be57a763a2", size = 7823716 }
-wheels = [
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From 05cecd6b10c746b11b5b2e4db66912f877e38c52 Mon Sep 17 00:00:00 2001
From: JaskaranIntugle <jaskaran@intugle.ai>
Date: Tue, 9 Sep 2025 16:19:47 +0530
Subject: [PATCH 16/25] knowledge builder can be resumed if failed for dataset
 pipeline, not for link prediction

---
 .gitignore                       |  1 +
 src/intugle/analysis/models.py   | 53 ++++++++++++++++++++++++++++++++
 src/intugle/knowledge_builder.py | 14 ++++++++-
 3 files changed, 67 insertions(+), 1 deletion(-)

diff --git a/.gitignore b/.gitignore
index a94ed77..a8e0c01 100644
--- a/.gitignore
+++ b/.gitignore
@@ -209,6 +209,7 @@ notes.txt
 
 testing_base
 models
+models_bak
 
 settings.json
 archived/
diff --git a/src/intugle/analysis/models.py b/src/intugle/analysis/models.py
index cce55bb..999d6f6 100644
--- a/src/intugle/analysis/models.py
+++ b/src/intugle/analysis/models.py
@@ -1,5 +1,6 @@
 import json
 import os
+import time
 import uuid
 
 from typing import Any, Dict, Optional, Self
@@ -25,6 +26,7 @@
 from intugle.core.pipeline.datatype_identification.l2_model import L2Model
 from intugle.core.pipeline.datatype_identification.pipeline import DataTypeIdentificationPipeline
 from intugle.core.pipeline.key_identification.ki import KeyIdentificationLLM
+from intugle.core.utilities.processing import string_standardization
 from intugle.models.resources.model import Column, ColumnProfilingMetrics
 from intugle.models.resources.source import Source, SourceTables
 
@@ -263,6 +265,57 @@ def save_yaml(self, file_path: Optional[str] = None) -> None:
         # Save the YAML representation of the sources
         with open(file_path, "w") as file:
             yaml.dump(sources, file, sort_keys=False, default_flow_style=False)
+
+    def load_from_yaml(self, file_path: str) -> None:
+        with open(file_path, "r") as file:
+            data = yaml.safe_load(file)
+
+        source = data.get("sources", [])[0]
+        table = source.get("table", {})
+
+        self.results["table_glossary"] = table.get("description")
+
+        columns = table.get("columns", [])
+        column_profiles = {}
+        for col in columns:
+            profiling_metrics = col.get("profiling_metrics", {})
+            count = profiling_metrics.get("count", 0)
+            null_count = profiling_metrics.get("null_count", 0)
+            distinct_count = profiling_metrics.get("distinct_count", 0)
+
+            column_profiles[col["name"]] = ColumnProfile(
+                column_name=col["name"],
+                business_name=string_standardization(col["name"]),
+                table_name=self.name,
+                business_glossary=col.get("description"),
+                datatype_l1=col.get("type"),
+                datatype_l2=col.get("category"),
+                business_tags=col.get("tags"),
+                count=count,
+                null_count=null_count,
+                distinct_count=distinct_count,
+                uniqueness=distinct_count / count if count > 0 else 0.0,
+                completeness=(count - null_count) / count if count > 0 else 0.0,
+                sample_data=profiling_metrics.get("sample_data"),
+                ts=time.time(),
+            )
+        self.results["column_profiles"] = column_profiles
+
+        self.results["business_glossary_and_tags"] = BusinessGlossaryOutput(
+            table_name=self.name,
+            table_glossary=self.results["table_glossary"],
+            columns=[
+                ColumnGlossary(
+                    column_name=col.column_name,
+                    business_glossary=col.business_glossary,
+                    business_tags=col.business_tags,
+                )
+                for col in column_profiles.values()
+            ],
+        )
+
+        # Key identification is not stored in the YAML, so it will be None
+        self.results["key"] = None
     
     def to_df(self):
         return self.adapter.to_df(self.data, self.name)
diff --git a/src/intugle/knowledge_builder.py b/src/intugle/knowledge_builder.py
index 0588c37..3de6e04 100644
--- a/src/intugle/knowledge_builder.py
+++ b/src/intugle/knowledge_builder.py
@@ -1,3 +1,5 @@
+import logging
+
 from typing import TYPE_CHECKING, Any, Dict, List
 
 from intugle.analysis.models import DataSet
@@ -6,6 +8,8 @@
 if TYPE_CHECKING:
     from intugle.link_predictor.models import PredictedLink
 
+log = logging.getLogger(__name__)
+
 
 class KnowledgeBuilder:
 
@@ -35,10 +39,18 @@ def _initialize_from_list(self, data_list: List[DataSet]):
                 raise ValueError("DataSet objects provided in a list must have a 'name' attribute.")
             self.datasets[dataset.name] = dataset
 
-    def build(self):
+    def build(self, force_recreate: bool = False):
+        import os
+
+        from intugle.core import settings
 
         # run analysis on all datasets
         for dataset in self.datasets.values():
+            file_path = os.path.join(settings.PROJECT_BASE, f"{dataset.name}.yml")
+            if os.path.exists(file_path) and not force_recreate:
+                log.info(f"Dataset {dataset.name} already processed. Loading from file.")
+                dataset.load_from_yaml(file_path)
+                continue
             dataset.run(domain=self.domain, save=True)
 
         # Initialize the predictor

From 05b13bb6e8a9f4d3baa1b4db4d77798609a034b7 Mon Sep 17 00:00:00 2001
From: JaskaranIntugle <jaskaran@intugle.ai>
Date: Tue, 9 Sep 2025 16:45:19 +0530
Subject: [PATCH 17/25] added key to yamls

---
 src/intugle/analysis/models.py         | 6 +++---
 src/intugle/models/resources/source.py | 1 +
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/src/intugle/analysis/models.py b/src/intugle/analysis/models.py
index 999d6f6..56d053f 100644
--- a/src/intugle/analysis/models.py
+++ b/src/intugle/analysis/models.py
@@ -226,6 +226,7 @@ def save_yaml(self, file_path: Optional[str] = None) -> None:
         file_path = os.path.join(settings.PROJECT_BASE, file_path)
 
         column_profiles = self.results.get("column_profiles")
+        key = self.results.get("key")
 
         table_description = self.results.get("table_glossary")
         table_tags = self.results.get("business_glossary_and_tags")
@@ -256,7 +257,7 @@ def save_yaml(self, file_path: Optional[str] = None) -> None:
 
         details = self.adapter.get_details(self.data)
 
-        table = SourceTables(name=self.name, description=table_description, columns=columns, details=details)
+        table = SourceTables(name=self.name, description=table_description, columns=columns, details=details, key=key)
 
         source = Source(name="healthcare", description=table_description, schema="public", database="", table=table)
 
@@ -314,8 +315,7 @@ def load_from_yaml(self, file_path: str) -> None:
             ],
         )
 
-        # Key identification is not stored in the YAML, so it will be None
-        self.results["key"] = None
+        self.results["key"] = table.get('key')
     
     def to_df(self):
         return self.adapter.to_df(self.data, self.name)
diff --git a/src/intugle/models/resources/source.py b/src/intugle/models/resources/source.py
index 39ce193..2b302ad 100644
--- a/src/intugle/models/resources/source.py
+++ b/src/intugle/models/resources/source.py
@@ -14,6 +14,7 @@ class SourceTables(SchemaBase):
     details: Optional[dict]
     columns: List[Column] = Field(default_factory=list)
     profiling_metrics: Optional[ModelProfilingMetrics] = None
+    key: Optional[str] = None
 
 
 class Source(BaseResource):

From 244da4330cd4632b1f05432991ea552a13e3250e Mon Sep 17 00:00:00 2001
From: JaskaranIntugle <jaskaran@intugle.ai>
Date: Tue, 9 Sep 2025 16:46:33 +0530
Subject: [PATCH 18/25] Made LLM_PROVIDER optional to decouple from downstream

---
 src/intugle/core/settings.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/intugle/core/settings.py b/src/intugle/core/settings.py
index 941db41..d503c5e 100644
--- a/src/intugle/core/settings.py
+++ b/src/intugle/core/settings.py
@@ -57,7 +57,7 @@ class Settings(BaseSettings):
     L2_SAMPLE_LIMIT: int = 10
 
     # LLM CONFIGS
-    LLM_PROVIDER: str
+    LLM_PROVIDER: Optional[str] = None
     LLM_SAMPLE_LIMIT: int = 15
     STRATA_SAMPLE_LIMIT: int = 4
     MAX_RETRIES: int = 5

From 13f36d10217cd1bc7c2d69c5c37dd43e44364a9b Mon Sep 17 00:00:00 2001
From: JaskaranIntugle <jaskaran@intugle.ai>
Date: Tue, 9 Sep 2025 17:12:06 +0530
Subject: [PATCH 19/25] updated quickstart content

---
 notebooks/quickstart_content.txt | 184 +++++++++++++------------------
 1 file changed, 76 insertions(+), 108 deletions(-)

diff --git a/notebooks/quickstart_content.txt b/notebooks/quickstart_content.txt
index 74601e7..6cf23e7 100644
--- a/notebooks/quickstart_content.txt
+++ b/notebooks/quickstart_content.txt
@@ -1,26 +1,27 @@
 ---
 type: markdown
 ---
-# Quickstart: Building a Semantic Layer with Intugle
+# Quickstart: Building a GenAI powered Semantic Intelligence with Intugle
 
-This notebook provides a quick introduction to this project. You'll learn how to use its key features to automatically build a semantic layer over your data.
+This quickstart shows you how to use Intugle’s open-source library to transform fragmented datasets into a connected semantic layer. The semantic layer captures profiling, relationships, and business meaning of your data — making it instantly usable for exploration, search, and data product creation.
 
-**What is a Semantic Layer?**
+**What is Semantic Intelligence?**
 
-A semantic layer is a business-friendly representation of your data. It hides the complexity of the underlying data sources and provides a unified view of your data using familiar business terms. This makes it easier for both business users and data teams to understand and query the data, accelerating data-driven insights.
+A semantic layer is an intelligent knowledge graph of your data. It connects tables, discovers relationships, and enriches them with business glossaries — so both data teams and business users can query with clarity, not complexity.
 
 **Who is this for?**
 
-This tool is designed for both **data teams** and **business teams**.
-
-*   **Data teams** can use it to automate data profiling, schema discovery, and documentation, significantly accelerating their workflow.
-*   **Business teams** can use it to gain a better understanding of their data and to perform self-service analytics without needing to write complex SQL queries.
+*   **Data Engineers & Architects** often spend weeks manually profiling, classifying, and stitching together fragmented data assets. With Intugle, they can automate this process end-to-end, uncovering meaningful links and relationships to instantly generate a connected semantic layer.
+*   **Data Analysts & Scientists** spend endless hours on data readiness and preparation before they can even start the real analysis. Intugle accelerates this by providing contextual intelligence, automatically generating SQL and reusable data products enriched with relationships and business meaning.
+*   **Business Analysts & Decision Makers** are slowed down by constant dependence on technical teams for answers. Intugle removes this bottleneck by enabling natural language queries and semantic search, giving them trusted insights on demand.
 
 **In this notebook, you will learn how to:**
 
-1.  **Build a Knowledge Base:** Use the `KnowledgeBuilder` to automatically profile your data, generate a business glossary, and predict links between tables.
-2.  **Generate Data Products:** Use the semantic layer to generate data products and retrieve data.
-3.  **Serve the Semantic Layer:** Learn how to start the MCP server to interact with your semantic layer using natural language.
+- **Profile and classify data** : Analyze your data sources to understand their structure, data types, and other characteristics.
+- **Discover links & relationships among data** : Reveal meaningful connections (PK & FK) across fragmented tables.
+- **Generate a business glossary & enable semantic search** : Create business-friendly terms and use them to query data with context.
+- **Create a semantic intelligence layer** : Build a unified model (YAML) of your data and relationships.
+- **Build unified data products** : Generate reusable SQL queries powered by the semantic layer.
 
 ---
 type: markdown
@@ -33,7 +34,7 @@ Before running the project, you need to configure a Large Language Model (LLM).
 You can configure the LLM by setting the following environment variables:
 
 *   `LLM_PROVIDER`: The LLM provider and model to use (e.g., `openai:gpt-3.5-turbo`). The format follows langchain's format for initializing chat models. Checkout how to specify your model [here](https://python.langchain.com/docs/integrations/chat/)
-*   `OPENAI_API_KEY`: Your API key for the LLM provider.
+*   `API_KEY`: Your API key for the LLM provider. The exact name of the variable may vary from provider to provider
 
 Here's an example of how to set these variables in your environment:
 
@@ -47,7 +48,6 @@ Alternatively, you can set them in the notebook like this:
 ---
 type: code
 ---
-
 import os
 
 os.environ["LLM_PROVIDER"] = "openai:gpt-3.5-turbo"
@@ -56,13 +56,10 @@ os.environ["OPENAI_API_KEY"] = "your-openai-api-key"  # Replace with your actual
 ---
 type: markdown
 ---
-
 > Currently the langchain packages for OpenAI, Anthropic and Gemini is installed by default. For additional models, make sure you have the integration packages installed. E.g. you should have langchain-deepseek installed to use a DeepSeek model. You can get these packages here: [LangChain Chat Models](https://python.langchain.com/docs/integrations/chat/)
-
 ---
 type: markdown
 ---
-
 ## 2. Building the Knowledge Base
 
 The `KnowledgeBuilder` is the entry point for building your semantic layer. It takes a dictionary of datasets as input and performs the following steps:
@@ -73,33 +70,49 @@ The `KnowledgeBuilder` is the entry point for building your semantic layer. It t
 4.  **Glossary Generation:** Generates a business glossary for each column using an LLM.
 5.  **Link Prediction:** Predicts the relationships (foreign keys) between tables.
 
-Let's start by defining the datasets we want to use:
-
+Let's start by defining the datasets we want to use. The path shown below can be a local file path or a remote URL.
 ---
 type: code
 ---
-
 def generate_config(table_name: str) -> str:
     """Append the base URL to the table name."""
     return {
         "path": f"https://raw.githubusercontent.com/Intugle/data-tools/refs/heads/main/sample_data/healthcare/{table_name}.csv",
-        "type": "csv"
+        "type": "csv",
     }
 
 
-table_names = ["allergies", "patients", "claims", "careplans"]
-datasets = {table: generate_config(table) for table in table_names}
+table_names = [
+    "allergies",
+    "careplans",
+    "claims",
+    "claims_transactions",
+    "conditions",
+    "devices",
+    "encounters",
+    "imaging_studies",
+    "immunizations",
+    "medications",
+    "observations",
+    "organizations",
+    "patients",
+    "payers",
+    "payer_transitions",
+    "procedures",
+    "providers",
+    "supplies",
+]
 
+datasets = {table: generate_config(table) for table in table_names}
 ---
 type: markdown
 ---
-
 Now, let's use the `KnowledgeBuilder` to build our semantic layer:
 
+> The `domain` parameter helps the LLM generate a more contextual business glossary. It specifies the industry domain that the dataset belongs to (e.g., "Healthcare", "Finance", "E-commerce").
 ---
 type: code
 ---
-
 from intugle import KnowledgeBuilder
 
 # Initialize the knowledge builder
@@ -107,11 +120,9 @@ kb = KnowledgeBuilder(datasets, domain="Healthcare")
 
 # Run the prediction
 kb.build()
-
 ---
 type: markdown
 ---
-
 ## 3. Accessing Enriched Metadata
 
 Now that the knowledge base is built, you can easily access the enriched metadata for each dataset.
@@ -119,27 +130,16 @@ Now that the knowledge base is built, you can easily access the enriched metadat
 ### Accessing a Dataset
 
 You can access a specific dataset by its name from the `kb.datasets` dictionary:
-
 ---
 type: code
 ---
-
-allergies_dataset = kb.datasets['allergies']
-
+allergies_dataset = kb.datasets['patients']
+allergies_dataset.profiling_df
 ---
 type: markdown
 ---
-
-### Viewing Profiling Results
-
-The profiling results can be accessed through the `profiling_df` property of the `DataSet` object. It's a pandas DataFrame that you can easily explore:
-> The business glossary is also available in the `profile_df`:
----
-type: code
----
-
-allergies_dataset.profiling_df
-
+The profiling results can be accessed through the `profiling_df` property of the `DataSet` object. It's a pandas DataFrame that you can easily explore. 
+> The business glossary is also available in the `profiling_df`:
 ---
 type: markdown
 ---
@@ -147,34 +147,29 @@ type: markdown
 ### Visualizing Relationships
 
 The `KnowledgeBuilder` automatically discovers the relationships between your tables. You can access the predicted links as a list of `PredictedLink` objects:
-
 ---
 type: code
 ---
-
 kb.links
-
 ---
 type: markdown
 ---
-
-You can visualize these relationships as a graph:
+You can also visualize these relationships as a graph:
 ---
 type: code
 ---
-
 kb.visualize()
-
 ---
 type: markdown
 ---
-
 ## 4. The Semantic Layer
 
 The KnowledgeBuilder results are used to generate YAML files which are saved automatically. These files defines the semantic layer, including the models (tables) and their relationships. 
 
 By default, these files are saved in the current working directory. You can configure this path by setting the `PROJECT_BASE` environment variable.
-
+---
+type: markdown
+---
 ## 5. Data Product Creation
 
 The semantic layer serves as a foundation for the DataProductBuilder, which streamlines the creation of reusable data products. This allows you
@@ -182,33 +177,45 @@ to encapsulate business logic and create standardized, trustworthy data assets t
 applications.
 
 Let's define the model for the data product we want to build:
-
 ---
 type: code
 ---
-
 etl = {
-    "name": "patient_names",
-    "fields": [
-        {"id": "patients.first", "name": "first_name"},
-        {"id": "patients.last", "name": "last_name"},
-        {"id": "allergies.start", "name": "start_date"},
-    ],
-    "filter": {
-        "selections": [{"id": "claims.departmentid", "values": ["3", "20"]}],
+  "name": "top_patients_by_claim_count",
+  "fields": [
+    {
+      "id": "patients.first",
+      "name": "first_name",
     },
+    {
+      "id": "patients.last",
+      "name": "last_name",
+    },
+    {
+      "id": "claims.id",
+      "name": "number_of_claims",
+      "category": "measure",
+      "measure_func": "count"
+    }
+  ],
+  "filter": {
+    "sort_by": [
+      {
+        "id": "claims.id",
+        "alias": "number_of_claims",
+        "direction": "desc"
+      }
+    ],
+    "limit": 10
+  }
 }
-
 ---
 type: markdown
 ---
-
 Now, let's use the `DataProductBuilder` to generate the data product:
-
 ---
 type: code
 ---
-
 from intugle.dp_builder import DataProductBuilder
 
 # Create a DataProductBuilder
@@ -217,58 +224,20 @@ dp_builder = DataProductBuilder()
 # Generate the data product
 data_product = dp_builder.build(etl)
 
-# Print the generated SQL query
-print(data_product.data['path'])
-
+data_product.to_df()
 ---
 type: markdown
 ---
-
-The `generate_product` function returns a `DataSet` object. You can use the `to_df()` method to view the data product as a pandas DataFrame:
-
+The `generate_product` function returns a `DataSet` object. You can also view the generated SQL query used for creating the data product:
 ---
 type: code
 ---
-
-data_product.to_df()
-
----
-type: markdown
----
-
-## 6. MCP Server: Interacting with Your Semantic Layer
-
-Now that you have a semantic layer, you can serve it as a MCP server to interact with it using natural language. The MCP server exposes your semantic layer as a set of tools that can be used by any MCP client.
-
-### Starting the MCP Server
-
-To start the MCP server, run the following command in your terminal:
-
-```bash
-intugle-mcp
-```
-
-This will start a server on `localhost:8000`.
-
-### Connecting to the MCP Server
-
-Once the server is running, you can connect to it from any MCP client. The endpoint for the MCP server is:
-
-`http://localhost:8000/semantic_layer/mcp`
-
-You can use a variety of MCP clients to connect to the server, such as Claude Desktop, Gemini CLI etc.
-
-### Use Cases
-
-Once connected, you can interact with your semantic layer using natural language. Here are some exciting applications:
-
-*   **Generate and Execute SQL Queries:** Ask questions in natural language and have the MCP server generate and execute the corresponding SQL query.
-*   **Data Discovery:** Ask questions about the tables and columns in your semantic layer to better understand your data.
+# The generated SQL query
+data_product.sql_query
 
 ---
 type: markdown
 ---
-
 ## Conclusion
 
 You've learned how to:
@@ -277,6 +246,5 @@ You've learned how to:
 *   Build a knowledge base using the `KnowledgeBuilder`.
 *   Access enriched metadata, business glossaries and visualize the relationships between your tables.
 *   Generate data products from the semantic layer using the `DataProductBuilder`.
-*   Interact with your semantic layer using the MCP server.
 
-This is just a starting point. This project has many other features to explore. We encourage you to try it with your own data and see how it can help you build a powerful semantic layer.
+This is just a starting point. This project has many other features to explore. We encourage you to try it with your own data and see how it can help you build a powerful semantic layer.
\ No newline at end of file

From 889ea829454efbf7be73a95a305659b4b429eaba Mon Sep 17 00:00:00 2001
From: JaskaranIntugle <jaskaran@intugle.ai>
Date: Wed, 10 Sep 2025 00:39:14 +0530
Subject: [PATCH 20/25] added configs, added syncing

---
 src/intugle/core/llms/embeddings.py           | 26 +---------
 src/intugle/core/semantic_search/crud.py      | 19 ++++---
 .../core/semantic_search/semantic_search.py   |  8 ++-
 src/intugle/core/settings.py                  |  4 ++
 src/intugle/semantic_search.py                | 30 +++++++++--
 tests/semantic_search/test_semantic_search.py | 50 ++++++++++++++++---
 6 files changed, 94 insertions(+), 43 deletions(-)

diff --git a/src/intugle/core/llms/embeddings.py b/src/intugle/core/llms/embeddings.py
index 678e58c..2f38c49 100644
--- a/src/intugle/core/llms/embeddings.py
+++ b/src/intugle/core/llms/embeddings.py
@@ -23,10 +23,10 @@ class Embeddings:
     def __init__(
         self,
         model_name: str,
+        tokenizer_model: str,
         config: dict = {},
         executor: Optional[ThreadPoolExecutor] = None,
         max_workers: int = 30,
-        tokenizer_model: str = "cl100k_base",
         embeddings_size: Optional[int] = None,
     ):
         self.model_name = model_name
@@ -140,27 +140,3 @@ async def aencode(self, documents: Documents, embeddings_types: ListEmbeddingsTy
             print("Error: ", e)
             import traceback
             print(traceback.format_exc())
-
-    @classmethod
-    def build(
-        cls,
-        model_name: str,
-        config: dict = {},
-        *args,
-        **kwargs,
-    ):
-        """
-        Args:
-            model_name: type of model either azure, openai
-            config: configuration of embeddings
-
-        Returns:
-            ChatModelLLM (obj): instance of builded ChatModelLLM
-        """
-
-        return cls(
-            model_name=model_name,
-            config={**config},
-            *args,
-            **kwargs,
-        )
diff --git a/src/intugle/core/semantic_search/crud.py b/src/intugle/core/semantic_search/crud.py
index 6d39351..fd05536 100644
--- a/src/intugle/core/semantic_search/crud.py
+++ b/src/intugle/core/semantic_search/crud.py
@@ -11,6 +11,7 @@
 
 from intugle.core.llms.embeddings import Embeddings, EmbeddingsType
 from intugle.core.semantic_search.utils import batched
+from intugle.core.settings import settings
 from intugle.core.utilities.processing import string_standardization
 from intugle.core.vector_store import VectorStoreService
 from intugle.core.vector_store.qdrant import QdrantVectorConfiguration
@@ -26,7 +27,12 @@ def __init__(self, collection_name: str, embeddings: List[Embeddings], batch_siz
 
     @property
     def vector_store(self):
-        return VectorStoreService(collection_name=self.collection_name, collection_configurations=self.configuration)
+        client_config = {"url": settings.QDRANT_URL, "api_key": settings.QDRANT_API_KEY}
+        return VectorStoreService(
+            collection_name=self.collection_name,
+            collection_configurations=self.configuration,
+            client_config=client_config,
+        )
 
     @property
     def configuration(self):
@@ -193,13 +199,14 @@ def convert_to_qdrant_points(
             )
         return points
 
-    async def initialize(self, column_details: list[dict]):
+    async def clean_collection(self):
         async with self.vector_store as vdb:
-            all_points = await vdb.get(includes=["metadata"])
-
-            if all_points and len(all_points) > 0:
-                column_ids = tuple(set([point.payload.get("column_id")] for point in all_points))
+            await vdb.delete_collection()
+            await vdb.create_collection()
 
+    async def initialize(self, column_details: list[dict]):
+        await self.clean_collection()
+        async with self.vector_store as vdb:
             column_details = pd.DataFrame(column_details)
 
             for batch in batched(column_details, self.batch_size):
diff --git a/src/intugle/core/semantic_search/semantic_search.py b/src/intugle/core/semantic_search/semantic_search.py
index 67e85e5..012b825 100644
--- a/src/intugle/core/semantic_search/semantic_search.py
+++ b/src/intugle/core/semantic_search/semantic_search.py
@@ -12,6 +12,7 @@
 from intugle.core.llms.embeddings import Embeddings, EmbeddingsType
 from intugle.core.semantic_search.models import RelevancyCategory
 from intugle.core.semantic_search.utils import relevancy_adder
+from intugle.core.settings import settings
 from intugle.core.vector_store import VectorStoreService
 from intugle.core.vector_store.qdrant import VectorSearchKwargs
 
@@ -47,7 +48,10 @@ def __init__(
 
     @property
     def vector_store(self):
-        return VectorStoreService(collection_name=self.collection_name, collection_configurations=None)
+        client_config = {"url": settings.QDRANT_URL, "api_key": settings.QDRANT_API_KEY}
+        return VectorStoreService(
+            collection_name=self.collection_name, collection_configurations=None, client_config=client_config
+        )
 
     async def __vector_search__(
         self,
@@ -213,4 +217,4 @@ async def search(self, query):
 
         result = await self.fetch(vectors["dense"][0], vectors["late"][0])
 
-        return result
\ No newline at end of file
+        return result
diff --git a/src/intugle/core/settings.py b/src/intugle/core/settings.py
index e94861d..2f98408 100644
--- a/src/intugle/core/settings.py
+++ b/src/intugle/core/settings.py
@@ -103,6 +103,10 @@ class Settings(BaseSettings):
 
     # Vector
     VECTOR_COLLECTION_NAME: str = os.getcwd().split('/')[-1]
+    QDRANT_URL: str = "http://localhost:6333"
+    QDRANT_API_KEY: Optional[str] = None
+    EMBEDDING_MODEL_NAME: str = "openai:ada"
+    TOKENIZER_MODEL_NAME: str = "cl100k_base"
 
 
 @lru_cache
diff --git a/src/intugle/semantic_search.py b/src/intugle/semantic_search.py
index 8ea2c9c..2b391ef 100644
--- a/src/intugle/semantic_search.py
+++ b/src/intugle/semantic_search.py
@@ -1,3 +1,5 @@
+import pandas as pd
+
 from intugle.core import settings
 from intugle.core.llms.embeddings import Embeddings
 from intugle.core.semantic_search.crud import SemanticSearchCRUD
@@ -29,6 +31,10 @@ def get_column_details(self):
                     "column_name": column.name,
                     "column_glossary": column.description,
                     "column_tags": column.tags,
+                    "profiling_metrics": column.profiling_metrics.model_dump(),
+                    "category": column.category,
+                    "table_name": table.name,
+                    "table_glossary": table.description,
                 }
                 column_details.append(column_detail)
 
@@ -39,20 +45,36 @@ def get_column_details(self):
                     "column_name": column.name,
                     "column_glossary": column.description,
                     "column_tags": column.tags,
+                    "profiling_metrics": column.profiling_metrics.model_dump(),
+                    "category": column.category,
+                    "table_name": table.name,
+                    "table_glossary": table.description,
                 }
                 column_details.append(column_detail)
 
         return column_details
 
-    async def initiaize(self):
-        embeddings = Embeddings("azure_openai:ada")
+    async def initialize(self):
+        embeddings = Embeddings(settings.EMBEDDING_MODEL_NAME, settings.TOKENIZER_MODEL_NAME)
         semantic_search_crud = SemanticSearchCRUD(self.collection_name, [embeddings])
         column_details = self.get_column_details()
+        column_details = pd.DataFrame.from_records(column_details)
         await semantic_search_crud.initialize(column_details)
 
-    async def search(self, query):
-        embeddings = Embeddings("azure_openai:ada")
+    async def _search(self, query):
+        embeddings = Embeddings(settings.EMBEDDING_MODEL_NAME, settings.TOKENIZER_MODEL_NAME)
         semantic_search = HybridDenseLateSearch(self.collection_name, embeddings)
 
         data = await semantic_search.search(query)
         return data
+
+    async def search(self, query):
+        search_results = await self._search(query)
+        if search_results.shape[0] == 0:
+            return search_results
+        column_details = self.get_column_details()
+        column_details = pd.DataFrame.from_records(column_details)
+        merged_df = pd.merge(
+            search_results, column_details, left_on="column_id", right_on="id", how="left"
+        ).drop(columns=["id"])
+        return merged_df
diff --git a/tests/semantic_search/test_semantic_search.py b/tests/semantic_search/test_semantic_search.py
index ce8910a..d264873 100644
--- a/tests/semantic_search/test_semantic_search.py
+++ b/tests/semantic_search/test_semantic_search.py
@@ -1,15 +1,53 @@
 import asyncio
 
+import pandas as pd
+
 from intugle.semantic_search import SemanticSearch
 
 
-def test_semantic_search_initiaize():
-    asyncio.run(SemanticSearch().initiaize())
+def test_semantic_search_initialize():
+    """
+    Tests the initialization of the semantic search.
+    """
+    search_client = SemanticSearch()
+    asyncio.run(search_client.initialize())
+
 
+def test_semantic_search_search():
+    """
+    Tests the search functionality of the semantic search.
+    """
+    # Initialize the semantic search
+    search_client = SemanticSearch()
 
-def test_semantic_search():
+    # Perform a search
     query = "reaction"
-    data = asyncio.run(SemanticSearch().search(query))
-    data.sort_values(by="score", ascending=False, inplace=True)
+    data = asyncio.run(search_client.search(query))
+
+    # Assert that the result is a pandas DataFrame
+    assert isinstance(data, pd.DataFrame)
 
-    breakpoint()
\ No newline at end of file
+    # Assert that the DataFrame is not empty
+    assert not data.empty
+
+    # Assert that the DataFrame has the expected columns
+    expected_columns = [
+        "column_id",
+        "score",
+        "relevancy",
+        "column_name",
+        "column_glossary",
+        "column_tags",
+        "profiling_metrics",
+        "category",
+        "table_name",
+        "table_glossary",
+    ]
+    assert all(col in data.columns for col in expected_columns)
+
+    # Assert that the scores are sorted in descending order
+    data.sort_values(by="score", ascending=False, inplace=True)
+    breakpoint()
+    assert data["score"].is_monotonic_decreasing
+    # Assert that the top result is the expected one
+    assert data.iloc[0]["column_id"] == "allergies.reaction2"

From ef582042381799f663fb5a55960c43f47e23fe63 Mon Sep 17 00:00:00 2001
From: JaskaranIntugle <jaskaran@intugle.ai>
Date: Wed, 10 Sep 2025 00:58:59 +0530
Subject: [PATCH 21/25] added semantic search to knowledge_builder

---
 src/intugle/knowledge_builder.py | 45 ++++++++++++++++++++++++++++----
 1 file changed, 40 insertions(+), 5 deletions(-)

diff --git a/src/intugle/knowledge_builder.py b/src/intugle/knowledge_builder.py
index 0588c37..18ff68d 100644
--- a/src/intugle/knowledge_builder.py
+++ b/src/intugle/knowledge_builder.py
@@ -1,19 +1,24 @@
+import asyncio
+import logging
+
 from typing import TYPE_CHECKING, Any, Dict, List
 
 from intugle.analysis.models import DataSet
 from intugle.link_predictor.predictor import LinkPredictor
+from intugle.semantic_search import SemanticSearch
 
 if TYPE_CHECKING:
     from intugle.link_predictor.models import PredictedLink
 
+log = logging.getLogger(__name__)
 
-class KnowledgeBuilder:
 
+class KnowledgeBuilder:
     def __init__(self, data_input: Dict[str, Any] | List[DataSet], domain: str = ""):
-
         self.datasets: Dict[str, DataSet] = {}
         self.links: list[PredictedLink] = []
         self.domain = domain
+        self._semantic_search_initialized = False
 
         if isinstance(data_input, dict):
             self._initialize_from_dict(data_input)
@@ -21,7 +26,7 @@ def __init__(self, data_input: Dict[str, Any] | List[DataSet], domain: str = "")
             self._initialize_from_list(data_input)
         else:
             raise TypeError("Input must be a dictionary of named dataframes or a list of DataSet objects.")
-        
+
     def _initialize_from_dict(self, data_dict: Dict[str, Any]):
         """Creates and processes DataSet objects from a dictionary of raw dataframes."""
         for name, df in data_dict.items():
@@ -36,7 +41,6 @@ def _initialize_from_list(self, data_list: List[DataSet]):
             self.datasets[dataset.name] = dataset
 
     def build(self):
-
         # run analysis on all datasets
         for dataset in self.datasets.values():
             dataset.run(domain=self.domain, save=True)
@@ -47,8 +51,39 @@ def build(self):
         # Run the prediction
         self.link_predictor.predict(save=True)
         self.links: list[PredictedLink] = self.link_predictor.links
+
+        # Initialize semantic search
+        try:
+            self.initialize_semantic_search()
+        except Exception as e:
+            log.warning(f"Semantic search initialization failed during build: {e}")
+
         return self
-    
+
+    def initialize_semantic_search(self):
+        """Initialize the semantic search engine."""
+        try:
+            log.info("Initializing semantic search...")
+            search_client = SemanticSearch()
+            asyncio.run(search_client.initialize())
+            self._semantic_search_initialized = True
+            log.info("Semantic search initialized.")
+        except Exception as e:
+            log.warning(f"Could not initialize semantic search: {e}")
+            raise e
+
     def visualize(self):
         return self.link_predictor.show_graph()
+
+    def search(self, query: str):
+        """Perform a semantic search on the knowledge base."""
+        if not self._semantic_search_initialized:
+            self.initialize_semantic_search()
+
+        try:
+            search_client = SemanticSearch()
+            return asyncio.run(search_client.search(query))
+        except Exception as e:
+            log.error(f"Could not perform semantic search: {e}")
+            raise e
         

From f5cf96c545868f9df1591a47b0c0f011aa5c8589 Mon Sep 17 00:00:00 2001
From: JaskaranIntugle <jaskaran@intugle.ai>
Date: Wed, 10 Sep 2025 12:02:04 +0530
Subject: [PATCH 22/25] updated tests, asyncio loop handling, sort in search

---
 src/intugle/knowledge_builder.py              | 40 +++++++++++++++++--
 src/intugle/semantic_search.py                | 30 ++++++++++++--
 tests/semantic_search/test_semantic_search.py |  7 +++-
 3 files changed, 68 insertions(+), 9 deletions(-)

diff --git a/src/intugle/knowledge_builder.py b/src/intugle/knowledge_builder.py
index 18ff68d..c4c43d7 100644
--- a/src/intugle/knowledge_builder.py
+++ b/src/intugle/knowledge_builder.py
@@ -1,7 +1,8 @@
 import asyncio
 import logging
+import threading
 
-from typing import TYPE_CHECKING, Any, Dict, List
+from typing import TYPE_CHECKING, Any, Awaitable, Dict, List, TypeVar
 
 from intugle.analysis.models import DataSet
 from intugle.link_predictor.predictor import LinkPredictor
@@ -12,6 +13,39 @@
 
 log = logging.getLogger(__name__)
 
+T = TypeVar("T")
+
+
+def _run_async_in_sync(coro: Awaitable[T]) -> T:
+    """
+    Runs an async coroutine in a sync context, handling cases where an event loop is already running.
+    """
+    try:
+        loop = asyncio.get_running_loop()
+    except RuntimeError:
+        return asyncio.run(coro)
+
+    if loop.is_running():
+        result = None
+        exc = None
+
+        def thread_target():
+            nonlocal result, exc
+            try:
+                result = asyncio.run(coro)
+            except Exception as e:
+                exc = e
+
+        thread = threading.Thread(target=thread_target)
+        thread.start()
+        thread.join()
+
+        if exc:
+            raise exc
+        return result
+    else:
+        return loop.run_until_complete(coro)
+
 
 class KnowledgeBuilder:
     def __init__(self, data_input: Dict[str, Any] | List[DataSet], domain: str = ""):
@@ -65,7 +99,7 @@ def initialize_semantic_search(self):
         try:
             log.info("Initializing semantic search...")
             search_client = SemanticSearch()
-            asyncio.run(search_client.initialize())
+            _run_async_in_sync(search_client.initialize())
             self._semantic_search_initialized = True
             log.info("Semantic search initialized.")
         except Exception as e:
@@ -82,7 +116,7 @@ def search(self, query: str):
 
         try:
             search_client = SemanticSearch()
-            return asyncio.run(search_client.search(query))
+            return _run_async_in_sync(search_client.search(query))
         except Exception as e:
             log.error(f"Could not perform semantic search: {e}")
             raise e
diff --git a/src/intugle/semantic_search.py b/src/intugle/semantic_search.py
index 2b391ef..b41cebe 100644
--- a/src/intugle/semantic_search.py
+++ b/src/intugle/semantic_search.py
@@ -26,29 +26,49 @@ def get_column_details(self):
         for source in sources.values():
             table = source.table
             for column in table.columns:
+                metrics = column.profiling_metrics.model_dump()
+                count = metrics.get("count", 0)
+                distinct_count = metrics.get("distinct_count", 0)
+                null_count = metrics.get("null_count", 0)
+
+                uniqueness = distinct_count / count if count > 0 else 0
+                completeness = (count - null_count) / count if count > 0 else 0
+
                 column_detail = {
                     "id": f"{table.name}.{column.name}",
                     "column_name": column.name,
                     "column_glossary": column.description,
                     "column_tags": column.tags,
-                    "profiling_metrics": column.profiling_metrics.model_dump(),
                     "category": column.category,
                     "table_name": table.name,
                     "table_glossary": table.description,
+                    "uniqueness": uniqueness,
+                    "completeness": completeness,
+                    **metrics,
                 }
                 column_details.append(column_detail)
 
         for model in models.values():
             for column in model.columns:
+                metrics = column.profiling_metrics.model_dump()
+                count = metrics.get("count", 0)
+                distinct_count = metrics.get("distinct_count", 0)
+                null_count = metrics.get("null_count", 0)
+
+                uniqueness = distinct_count / count if count > 0 else 0
+                completeness = (count - null_count) / count if count > 0 else 0
+
                 column_detail = {
                     "id": f"{table.name}.{column.name}",
                     "column_name": column.name,
                     "column_glossary": column.description,
                     "column_tags": column.tags,
-                    "profiling_metrics": column.profiling_metrics.model_dump(),
                     "category": column.category,
                     "table_name": table.name,
                     "table_glossary": table.description,
+                    "uniqueness": uniqueness,
+                    "completeness": completeness,
+                    **metrics,
                 }
                 column_details.append(column_detail)
 
@@ -72,9 +92,11 @@ async def search(self, query):
         search_results = await self._search(query)
         if search_results.shape[0] == 0:
             return search_results
+        search_results.sort_values(by="score", ascending=False, inplace=True)
+
         column_details = self.get_column_details()
-        column_details = pd.DataFrame.from_records(column_details)
+        column_details_df = pd.DataFrame.from_records(column_details)
         merged_df = pd.merge(
-            search_results, column_details, left_on="column_id", right_on="id", how="left"
+            search_results, column_details_df, left_on="column_id", right_on="id", how="left"
         ).drop(columns=["id"])
         return merged_df
diff --git a/tests/semantic_search/test_semantic_search.py b/tests/semantic_search/test_semantic_search.py
index d264873..205fe73 100644
--- a/tests/semantic_search/test_semantic_search.py
+++ b/tests/semantic_search/test_semantic_search.py
@@ -38,16 +38,19 @@ def test_semantic_search_search():
         "column_name",
         "column_glossary",
         "column_tags",
-        "profiling_metrics",
         "category",
         "table_name",
         "table_glossary",
+        "uniqueness",
+        "completeness",
+        "count",
+        "null_count",
+        "distinct_count"
     ]
     assert all(col in data.columns for col in expected_columns)
 
     # Assert that the scores are sorted in descending order
     data.sort_values(by="score", ascending=False, inplace=True)
-    breakpoint()
     assert data["score"].is_monotonic_decreasing
     # Assert that the top result is the expected one
     assert data.iloc[0]["column_id"] == "allergies.reaction2"

From 22e0ff8741d9ec48a4cb21331c2f46a14674b84d Mon Sep 17 00:00:00 2001
From: JaskaranIntugle <jaskaran@intugle.ai>
Date: Wed, 10 Sep 2025 12:22:08 +0530
Subject: [PATCH 23/25] removed semantic search md

---
 semantic_search.md | 7 -------
 1 file changed, 7 deletions(-)
 delete mode 100644 semantic_search.md

diff --git a/semantic_search.md b/semantic_search.md
deleted file mode 100644
index 8b26b5a..0000000
--- a/semantic_search.md
+++ /dev/null
@@ -1,7 +0,0 @@
-
-
-
-1. Download qudran and use it in docker
-2. Sync
-3. Use it
-

From bcb6fdc4ff36ada996fbfe948ce93fd0899623f1 Mon Sep 17 00:00:00 2001
From: JaskaranIntugle <jaskaran@intugle.ai>
Date: Wed, 10 Sep 2025 15:33:35 +0530
Subject: [PATCH 24/25] updated quickstart

---
 README.md                        |   71 +-
 notebooks/quickstart_content.txt |   51 +-
 pyproject.toml                   |    3 +-
 quickstart.ipynb                 | 1235 ++++++++++++++++++++----------
 src/intugle/knowledge_builder.py |    6 +-
 uv.lock                          |   73 +-
 6 files changed, 1019 insertions(+), 420 deletions(-)

diff --git a/README.md b/README.md
index 4ee49bf..f22b551 100644
--- a/README.md
+++ b/README.md
@@ -4,7 +4,7 @@
 </p>
 
 [![Release](https://img.shields.io/github/release/Intugle/data-tools)](https://github.com/Intugle/data-tools/releases/tag/v0.1.0)     
-[![Made with Python](https://img.shields.io/badge/Made_with-Python-blue?logo=python&logoColor=white)](https://www.python.org/)
+[![Made with Python](https://img.shields.io/badge/Made_with-Python-blue?logo=python&logoColor=white)](https://www.python.org/) 
 ![contributions - welcome](https://img.shields.io/badge/contributions-welcome-blue)
 [![License: Apache 2.0](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
 [![Open Issues](https://img.shields.io/github/issues-raw/Intugle/data-tools)](https://github.com/Intugle/data-tools/issues)
@@ -85,7 +85,7 @@ For a detailed, hands-on introduction to the project, please see the [`quickstar
 *   **Accessing Enriched Metadata:** Learn how to access the profiling results and business glossary for each dataset.
 *   **Visualizing Relationships:** Visualize the predicted links between your tables.
 *   **Generating Data Products:** Use the semantic layer to generate data products and retrieve data.
-*   **Serving the Semantic Layer:** Learn how to start the MCP server to interact with your semantic layer using natural language.
+*   **Searching the Knowledge Base:** Use semantic search to find relevant columns in your datasets using natural language.
 
 ## Usage
 
@@ -147,7 +147,72 @@ data_product = dp_builder.build(etl)
 print(data_product.to_df())
 ```
 
-For detailed code examples and a complete walkthrough, please refer to our quickstart notebooks.
+For detailed code examples and a complete walkthrough, please see the [`quickstart.ipynb`](quickstart.ipynb) notebook.
+
+### Semantic Search
+
+The semantic search feature allows you to search for columns in your datasets using natural language. It is built on top of the [Qdrant](https://qdrant.tech/) vector database.
+
+#### Prerequisites
+
+To use the semantic search feature, you need to have a running Qdrant instance. You can start one using the following Docker command:
+
+```bash
+docker run -p 6333:6333 -p 6334:6334 \
+    -v qdrant_storage:/qdrant/storage:z \
+    --name qdrant qdrant/qdrant
+```
+
+You also need to configure the Qdrant URL and API key (if using authorization) in your environment variables:
+
+```bash
+export QDRANT_URL="http://localhost:6333"
+export QDRANT_API_KEY="your-qdrant-api-key" # if authorization is used
+```
+
+Currently, the semantic search feature only supports OpenAI embedding models. Therefore, you need to have an OpenAI API key set up in your environment. The default model is `text-embedding-ada-002`. You can change the embedding model by setting the `EMBEDDING_MODEL_NAME` environment variable.
+
+**For OpenAI models:**
+
+```bash
+export OPENAI_API_KEY="your-openai-api-key"
+export EMBEDDING_MODEL_NAME="openai:ada"
+```
+
+**For Azure OpenAI models:**
+
+```bash
+export AZURE_OPENAI_API_KEY="your-azure-openai-api-key"
+export AZURE_OPENAI_ENDPOINT="your-azure-openai-endpoint"
+export OPENAI_API_VERSION="your-openai-api-version"
+export EMBEDDING_MODEL_NAME="azure_openai:ada"
+```
+
+#### Usage
+
+Once you have built the knowledge base, you can use the `search` method to perform a semantic search. The search function returns a pandas DataFrame containing the search results, including the column\'s profiling metrics, category, table name, and table glossary.
+
+```python
+from intugle import KnowledgeBuilder
+
+# Define your datasets
+datasets = {
+    "allergies": {"path": "path/to/allergies.csv", "type": "csv"},
+    "patients": {"path": "path/to/patients.csv", "type": "csv"},
+    "claims": {"path": "path/to/claims.csv", "type": "csv"},
+    # ... add other datasets
+}
+
+# Build the knowledge base
+kb = KnowledgeBuilder(datasets, domain="Healthcare")
+kb.build()
+# Perform a semantic search
+search_results = kb.search("patient allergies")
+
+# View the search results
+print(search_results)
+```
+For detailed code examples and a complete walkthrough, please see the [`quickstart.ipynb`](quickstart.ipynb) notebook.
 
 ## Community
 
diff --git a/notebooks/quickstart_content.txt b/notebooks/quickstart_content.txt
index 21e2523..5ccd701 100644
--- a/notebooks/quickstart_content.txt
+++ b/notebooks/quickstart_content.txt
@@ -29,12 +29,17 @@ type: markdown
 
 ## 1. LLM Configuration
 
-Before running the project, you need to configure a Large Language Model (LLM). This is used for tasks like generating business glossaries and predicting links between tables.
+Before running the project, you need to configure a Large Language Model (LLM). This is used for tasks like generating business glossaries and predicting links between tables. For the semantic search feature, you will also need to set up Qdrant and provide an OpenAI API key. For detailed setup instructions, please refer to the [README.md](README.md) file.
 
-You can configure the LLM by setting the following environment variables:
+You can configure the necessary services by setting the following environment variables:
 
 *   `LLM_PROVIDER`: The LLM provider and model to use (e.g., `openai:gpt-3.5-turbo`). The format follows langchain's format for initializing chat models. Checkout how to specify your model [here](https://python.langchain.com/docs/integrations/chat/)
-*   `API_KEY`: Your API key for the LLM provider. The exact name of the variable may vary from provider to provider
+*   `API_KEY`: Your API key for the LLM provider. The exact name of the variable may vary from provider to provider (e.g., `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`).
+*   `QDRANT_URL`: The URL of your Qdrant instance (e.g., `http://localhost:6333`).
+*   `QDRANT_API_KEY`: Your API key for the Qdrant instance, if authorization is enabled.
+*   `EMBEDDING_MODEL_NAME`: The embedding model to use. The format follows LangChain's conventions for initializing embedding models (e.g., `openai:ada`, `azure_openai:ada`).
+*   `OPENAI_API_KEY`: Your OpenAI API key, required if you are using an OpenAI embedding model.
+*   `AZURE_OPENAI_API_KEY`, `AZURE_OPENAI_ENDPOINT`, `OPENAI_API_VERSION`: Your Azure OpenAI credentials, required if you are using an Azure OpenAI embedding model.
 
 Here's an example of how to set these variables in your environment:
 
@@ -42,7 +47,6 @@ Here's an example of how to set these variables in your environment:
 export LLM_PROVIDER="openai:gpt-3.5-turbo"
 export OPENAI_API_KEY="your-openai-api-key"
 ```
-
 Alternatively, you can set them in the notebook like this:
 
 ---
@@ -54,6 +58,20 @@ import os
 os.environ["LLM_PROVIDER"] = "openai:gpt-3.5-turbo"
 os.environ["OPENAI_API_KEY"] = "your-openai-api-key"  # Replace with your actual key
 
+# Semantic Search Configuration (Optional)
+os.environ["QDRANT_URL"] = "http://localhost:6333"
+os.environ["QDRANT_API_KEY"] = "" # if authorization is used
+os.environ["EMBEDDING_MODEL_NAME"] = "openai:ada"
+os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
+
+# For Azure OpenAI models
+os.environ["EMBEDDING_MODEL_NAME"] = "azure_openai:ada"
+os.environ["AZURE_OPENAI_API_KEY"] = "your-azure-openai-api-key"
+os.environ["AZURE_OPENAI_ENDPOINT"] = "your-azure-openai-endpoint"
+os.environ["OPENAI_API_VERSION"] = "your-openai-api-version"
+
+
+
 ---
 type: markdown
 ---
@@ -171,7 +189,25 @@ By default, these files are saved in the current working directory. You can conf
 ---
 type: markdown
 ---
-## 5. Data Product Creation
+## 5. Semantic Search
+
+The semantic search feature allows you to search for columns in your datasets using natural language. 
+
+> **Note:** To use this feature, you need to have a running Qdrant instance and an OpenAI API key. Please refer to the [README.md](README.md) for detailed setup instructions.
+
+Once you have built the knowledge base, you can use the `search` method to perform a semantic search. The search function returns a pandas DataFrame containing the search results, including the column's profiling metrics, category, table name, and table glossary.
+---
+type: code
+---
+# Perform a semantic search
+search_results = kb.search("patient allergies")
+
+# View the search results
+search_results
+---
+type: markdown
+---
+## 6. Data Product Creation
 
 The semantic layer serves as a foundation for the DataProductBuilder, which streamlines the creation of reusable data products. This allows you
 to encapsulate business logic and create standardized, trustworthy data assets that can be easily shared and reused across different teams and 
@@ -244,9 +280,10 @@ type: markdown
 
 You've learned how to:
 
-*   Configure your LLM provider.
+*   Configure your LLM provider and Embeddings provider.
 *   Build a knowledge base using the `KnowledgeBuilder`.
 *   Access enriched metadata, business glossaries and visualize the relationships between your tables.
+*   Perform semantic search on your knowledge base.
 *   Generate data products from the semantic layer using the `DataProductBuilder`.
 
-This is just a starting point. This project has many other features to explore. We encourage you to try it with your own data and see how it can help you build a powerful semantic layer.
+This is just a starting point. This project has many other features to explore. We encourage you to try it with your own data and see how it can help you build a powerful semantic layer.
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index eb8ccc3..c277d3b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "intugle"
-version = "0.1.3"
+version = "0.1.4-dev"
 authors = [
     { name="Intugle", email="hello@intugle.ai" },
 ]
@@ -53,6 +53,7 @@ dependencies = [
     "scikit-learn<=1.7.1",
     "langchain[anthropic,google-genai,openai]>=0.3.27",
     "qdrant-client>=1.15.1",
+    "ipykernel>=6.30.1",
 ]
 
 [project.urls]
diff --git a/quickstart.ipynb b/quickstart.ipynb
index 82b34c6..48f3656 100644
--- a/quickstart.ipynb
+++ b/quickstart.ipynb
@@ -33,15 +33,19 @@
    "id": "a2a1b244",
    "metadata": {},
    "source": [
-    "\n",
     "## 1. LLM Configuration\n",
     "\n",
-    "Before running the project, you need to configure a Large Language Model (LLM). This is used for tasks like generating business glossaries and predicting links between tables.\n",
+    "Before running the project, you need to configure a Large Language Model (LLM). This is used for tasks like generating business glossaries and predicting links between tables. For the semantic search feature, you will also need to set up Qdrant and provide an OpenAI API key. For detailed setup instructions, please refer to the [README.md](README.md) file.\n",
     "\n",
-    "You can configure the LLM by setting the following environment variables:\n",
+    "You can configure the necessary services by setting the following environment variables:\n",
     "\n",
     "*   `LLM_PROVIDER`: The LLM provider and model to use (e.g., `openai:gpt-3.5-turbo`). The format follows langchain's format for initializing chat models. Checkout how to specify your model [here](https://python.langchain.com/docs/integrations/chat/)\n",
-    "*   `API_KEY`: Your API key for the LLM provider. The exact name of the variable may vary from provider to provider\n",
+    "*   `API_KEY`: Your API key for the LLM provider. The exact name of the variable may vary from provider to provider (e.g., `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`).\n",
+    "*   `QDRANT_URL`: The URL of your Qdrant instance (e.g., `http://localhost:6333`).\n",
+    "*   `QDRANT_API_KEY`: Your API key for the Qdrant instance, if authorization is enabled.\n",
+    "*   `EMBEDDING_MODEL_NAME`: The embedding model to use. The format follows LangChain's conventions for initializing embedding models (e.g., `openai:ada`, `azure_openai:ada`).\n",
+    "*   `OPENAI_API_KEY`: Your OpenAI API key, required if you are using an OpenAI embedding model.\n",
+    "*   `AZURE_OPENAI_API_KEY`, `AZURE_OPENAI_ENDPOINT`, `OPENAI_API_VERSION`: Your Azure OpenAI credentials, required if you are using an Azure OpenAI embedding model.\n",
     "\n",
     "Here's an example of how to set these variables in your environment:\n",
     "\n",
@@ -49,8 +53,7 @@
     "export LLM_PROVIDER=\"openai:gpt-3.5-turbo\"\n",
     "export OPENAI_API_KEY=\"your-openai-api-key\"\n",
     "```\n",
-    "\n",
-    "Alternatively, you can set them in the notebook like this:\n"
+    "Alternatively, you can set them in the notebook like this:"
    ]
   },
   {
@@ -63,7 +66,19 @@
     "import os\n",
     "\n",
     "os.environ[\"LLM_PROVIDER\"] = \"openai:gpt-3.5-turbo\"\n",
-    "os.environ[\"OPENAI_API_KEY\"] = \"your-openai-api-key\"  # Replace with your actual key\n"
+    "os.environ[\"OPENAI_API_KEY\"] = \"your-openai-api-key\"  # Replace with your actual key\n",
+    "\n",
+    "# Semantic Search Configuration (Optional)\n",
+    "os.environ[\"QDRANT_URL\"] = \"http://localhost:6333\"\n",
+    "os.environ[\"QDRANT_API_KEY\"] = \"\" # if authorization is used\n",
+    "os.environ[\"EMBEDDING_MODEL_NAME\"] = \"openai:ada\"\n",
+    "os.environ[\"OPENAI_API_KEY\"] = \"your-openai-api-key\"\n",
+    "\n",
+    "# For Azure OpenAI models\n",
+    "os.environ[\"EMBEDDING_MODEL_NAME\"] = \"azure_openai:ada\"\n",
+    "os.environ[\"AZURE_OPENAI_API_KEY\"] = \"your-azure-openai-api-key\"\n",
+    "os.environ[\"AZURE_OPENAI_ENDPOINT\"] = \"your-azure-openai-endpoint\"\n",
+    "os.environ[\"OPENAI_API_VERSION\"] = \"your-openai-api-version\"\n"
    ]
   },
   {
@@ -190,33 +205,33 @@
       "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/numpy/_core/_methods.py:144: RuntimeWarning: invalid value encountered in scalar divide\n",
       "  ret = ret.dtype.type(ret / rcount)\n",
       "[!] Empty column encountered for allergies ==> stop ...\n",
-      "100%|██████████| 15/15 [00:06<00:00,  2.14it/s]\n",
-      "100%|██████████| 15/15 [00:22<00:00,  1.51s/it]\n",
+      "100%|██████████| 15/15 [00:08<00:00,  1.71it/s]\n",
+      "100%|██████████| 15/15 [00:20<00:00,  1.37s/it]\n",
       "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3859: RuntimeWarning: Mean of empty slice.\n",
       "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
       "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/numpy/_core/_methods.py:144: RuntimeWarning: invalid value encountered in scalar divide\n",
       "  ret = ret.dtype.type(ret / rcount)\n",
-      "100%|██████████| 9/9 [00:07<00:00,  1.25it/s]\n",
+      "100%|██████████| 9/9 [00:06<00:00,  1.48it/s]\n",
       "[*] careplans:   0%|          | 0/1 [00:00<?, ?it/s]/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/intugle/core/pipeline/key_identification/ki.py:87: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  temp.rename(columns={\"datatype_l1\": \"datatype\"}, inplace=True)\n",
-      "[*] careplans: 100%|██████████| 1/1 [00:00<00:00,  2.22it/s]\n",
-      "100%|██████████| 9/9 [00:10<00:00,  1.15s/it]\n",
+      "[*] careplans: 100%|██████████| 1/1 [00:00<00:00,  2.11it/s]\n",
+      "100%|██████████| 9/9 [00:13<00:00,  1.46s/it]\n",
       "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3859: RuntimeWarning: Mean of empty slice.\n",
       "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
       "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/numpy/_core/_methods.py:144: RuntimeWarning: invalid value encountered in scalar divide\n",
       "  ret = ret.dtype.type(ret / rcount)\n",
       "[!] Empty column encountered for claims ==> referringproviderid ...\n",
-      "100%|██████████| 31/31 [00:14<00:00,  2.21it/s]\n",
+      "100%|██████████| 31/31 [00:16<00:00,  1.87it/s]\n",
       "[*] claims:   0%|          | 0/1 [00:00<?, ?it/s]/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/intugle/core/pipeline/key_identification/ki.py:87: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  temp.rename(columns={\"datatype_l1\": \"datatype\"}, inplace=True)\n",
-      "[*] claims: 100%|██████████| 1/1 [00:00<00:00,  1.95it/s]\n",
-      "100%|██████████| 31/31 [00:39<00:00,  1.27s/it]\n",
+      "[*] claims: 100%|██████████| 1/1 [00:00<00:00,  2.22it/s]\n",
+      "100%|██████████| 31/31 [00:47<00:00,  1.53s/it]\n",
       "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3859: RuntimeWarning: Mean of empty slice.\n",
       "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
       "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/numpy/_core/_methods.py:144: RuntimeWarning: invalid value encountered in scalar divide\n",
@@ -225,150 +240,149 @@
       "[!] Empty column encountered for claims_transactions ==> modifier2 ...\n",
       "[!] Empty column encountered for claims_transactions ==> adjustments ...\n",
       "[!] Empty column encountered for claims_transactions ==> linenote ...\n",
-      "100%|██████████| 33/33 [00:17<00:00,  1.91it/s]\n",
+      "100%|██████████| 33/33 [00:21<00:00,  1.57it/s]\n",
       "[*] claims_transactions:   0%|          | 0/1 [00:00<?, ?it/s]/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/intugle/core/pipeline/key_identification/ki.py:87: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  temp.rename(columns={\"datatype_l1\": \"datatype\"}, inplace=True)\n",
-      "[*] claims_transactions: 100%|██████████| 1/1 [00:00<00:00,  1.45it/s]\n",
-      "100%|██████████| 33/33 [00:43<00:00,  1.33s/it]\n",
+      "[*] claims_transactions: 100%|██████████| 1/1 [00:00<00:00,  1.03it/s]\n",
+      "100%|██████████| 33/33 [00:51<00:00,  1.55s/it]\n",
       "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3859: RuntimeWarning: Mean of empty slice.\n",
       "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
       "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/numpy/_core/_methods.py:144: RuntimeWarning: invalid value encountered in scalar divide\n",
       "  ret = ret.dtype.type(ret / rcount)\n",
-      "100%|██████████| 6/6 [00:03<00:00,  1.72it/s]\n",
-      "100%|██████████| 6/6 [00:07<00:00,  1.30s/it]\n",
+      "100%|██████████| 6/6 [00:03<00:00,  1.97it/s]\n",
+      "100%|██████████| 6/6 [00:09<00:00,  1.50s/it]\n",
       "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3859: RuntimeWarning: Mean of empty slice.\n",
       "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
       "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/numpy/_core/_methods.py:144: RuntimeWarning: invalid value encountered in scalar divide\n",
       "  ret = ret.dtype.type(ret / rcount)\n",
-      "100%|██████████| 7/7 [00:03<00:00,  1.80it/s]\n",
+      "100%|██████████| 7/7 [00:03<00:00,  1.99it/s]\n",
       "[*] devices:   0%|          | 0/1 [00:00<?, ?it/s]/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/intugle/core/pipeline/key_identification/ki.py:87: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  temp.rename(columns={\"datatype_l1\": \"datatype\"}, inplace=True)\n",
-      "[*] devices: 100%|██████████| 1/1 [00:00<00:00,  1.55it/s]\n",
-      "100%|██████████| 7/7 [00:09<00:00,  1.33s/it]\n",
+      "[*] devices: 100%|██████████| 1/1 [00:00<00:00,  2.00it/s]\n",
+      "100%|██████████| 7/7 [00:12<00:00,  1.78s/it]\n",
       "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3859: RuntimeWarning: Mean of empty slice.\n",
       "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
       "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/numpy/_core/_methods.py:144: RuntimeWarning: invalid value encountered in scalar divide\n",
       "  ret = ret.dtype.type(ret / rcount)\n",
-      "100%|██████████| 15/15 [00:07<00:00,  1.95it/s]\n",
+      "100%|██████████| 15/15 [00:07<00:00,  2.04it/s]\n",
       "[*] encounters:   0%|          | 0/1 [00:00<?, ?it/s]/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/intugle/core/pipeline/key_identification/ki.py:87: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  temp.rename(columns={\"datatype_l1\": \"datatype\"}, inplace=True)\n",
-      "[*] encounters: 100%|██████████| 1/1 [00:00<00:00,  1.96it/s]\n",
-      "100%|██████████| 15/15 [00:17<00:00,  1.19s/it]\n",
+      "[*] encounters: 100%|██████████| 1/1 [00:00<00:00,  2.36it/s]\n",
+      "100%|██████████| 15/15 [00:23<00:00,  1.54s/it]\n",
       "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3859: RuntimeWarning: Mean of empty slice.\n",
       "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
       "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/numpy/_core/_methods.py:144: RuntimeWarning: invalid value encountered in scalar divide\n",
       "  ret = ret.dtype.type(ret / rcount)\n",
-      "100%|██████████| 13/13 [00:07<00:00,  1.86it/s]\n",
+      "100%|██████████| 13/13 [00:07<00:00,  1.80it/s]\n",
       "[*] imaging_studies:   0%|          | 0/1 [00:00<?, ?it/s]/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/intugle/core/pipeline/key_identification/ki.py:87: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  temp.rename(columns={\"datatype_l1\": \"datatype\"}, inplace=True)\n",
-      "[*] imaging_studies: 100%|██████████| 1/1 [00:00<00:00,  1.94it/s]\n",
-      "100%|██████████| 13/13 [00:21<00:00,  1.67s/it]\n",
+      "[*] imaging_studies: 100%|██████████| 1/1 [00:00<00:00,  2.13it/s]\n",
+      "100%|██████████| 13/13 [00:17<00:00,  1.36s/it]\n",
       "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3859: RuntimeWarning: Mean of empty slice.\n",
       "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
       "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/numpy/_core/_methods.py:144: RuntimeWarning: invalid value encountered in scalar divide\n",
       "  ret = ret.dtype.type(ret / rcount)\n",
-      "100%|██████████| 6/6 [00:03<00:00,  1.84it/s]\n",
-      "100%|██████████| 6/6 [00:08<00:00,  1.47s/it]\n",
+      "100%|██████████| 6/6 [00:03<00:00,  1.94it/s]\n",
+      "100%|██████████| 6/6 [00:08<00:00,  1.46s/it]\n",
       "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3859: RuntimeWarning: Mean of empty slice.\n",
       "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
       "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/numpy/_core/_methods.py:144: RuntimeWarning: invalid value encountered in scalar divide\n",
       "  ret = ret.dtype.type(ret / rcount)\n",
-      "100%|██████████| 13/13 [00:06<00:00,  2.08it/s]\n",
-      "100%|██████████| 13/13 [00:16<00:00,  1.29s/it]\n",
+      "100%|██████████| 13/13 [00:13<00:00,  1.03s/it]\n",
+      "100%|██████████| 13/13 [00:20<00:00,  1.55s/it]\n",
       "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3859: RuntimeWarning: Mean of empty slice.\n",
       "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
       "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/numpy/_core/_methods.py:144: RuntimeWarning: invalid value encountered in scalar divide\n",
       "  ret = ret.dtype.type(ret / rcount)\n",
-      "100%|██████████| 9/9 [00:04<00:00,  1.99it/s]\n",
-      "100%|██████████| 9/9 [00:12<00:00,  1.41s/it]\n",
+      "100%|██████████| 9/9 [00:05<00:00,  1.57it/s]\n",
+      "100%|██████████| 9/9 [00:13<00:00,  1.52s/it]\n",
       "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3859: RuntimeWarning: Mean of empty slice.\n",
       "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
       "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/numpy/_core/_methods.py:144: RuntimeWarning: invalid value encountered in scalar divide\n",
       "  ret = ret.dtype.type(ret / rcount)\n",
-      "100%|██████████| 11/11 [00:05<00:00,  2.17it/s]\n",
+      "100%|██████████| 11/11 [00:06<00:00,  1.79it/s]\n",
       "[*] organizations:   0%|          | 0/1 [00:00<?, ?it/s]/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/intugle/core/pipeline/key_identification/ki.py:87: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  temp.rename(columns={\"datatype_l1\": \"datatype\"}, inplace=True)\n",
-      "[*] organizations: 100%|██████████| 1/1 [00:00<00:00,  1.95it/s]\n",
-      "100%|██████████| 11/11 [00:13<00:00,  1.25s/it]\n",
+      "[*] organizations: 100%|██████████| 1/1 [00:00<00:00,  1.57it/s]\n",
+      "100%|██████████| 11/11 [00:14<00:00,  1.34s/it]\n",
       "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3859: RuntimeWarning: Mean of empty slice.\n",
       "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
       "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/numpy/_core/_methods.py:144: RuntimeWarning: invalid value encountered in scalar divide\n",
       "  ret = ret.dtype.type(ret / rcount)\n",
-      "100%|██████████| 25/25 [00:10<00:00,  2.33it/s]\n",
+      "100%|██████████| 25/25 [00:16<00:00,  1.50it/s]\n",
       "[*] patients:   0%|          | 0/1 [00:00<?, ?it/s]/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/intugle/core/pipeline/key_identification/ki.py:87: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  temp.rename(columns={\"datatype_l1\": \"datatype\"}, inplace=True)\n",
-      "[*] patients: 100%|██████████| 1/1 [00:00<00:00,  1.43it/s]\n",
-      "100%|██████████| 25/25 [00:31<00:00,  1.25s/it]\n",
+      "[*] patients: 100%|██████████| 1/1 [00:00<00:00,  1.09it/s]\n",
+      "100%|██████████| 25/25 [00:39<00:00,  1.58s/it]\n",
       "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3859: RuntimeWarning: Mean of empty slice.\n",
       "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
       "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/numpy/_core/_methods.py:144: RuntimeWarning: invalid value encountered in scalar divide\n",
       "  ret = ret.dtype.type(ret / rcount)\n",
-      "100%|██████████| 21/21 [00:11<00:00,  1.90it/s]\n",
+      "100%|██████████| 21/21 [00:11<00:00,  1.81it/s]\n",
       "[*] payers:   0%|          | 0/1 [00:00<?, ?it/s]/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/intugle/core/pipeline/key_identification/ki.py:87: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  temp.rename(columns={\"datatype_l1\": \"datatype\"}, inplace=True)\n",
-      "[*] payers: 100%|██████████| 1/1 [00:00<00:00,  2.30it/s]\n",
-      "100%|██████████| 21/21 [00:36<00:00,  1.75s/it]\n",
+      "[*] payers: 100%|██████████| 1/1 [00:00<00:00,  1.95it/s]\n",
+      "100%|██████████| 21/21 [00:28<00:00,  1.35s/it]\n",
       "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3859: RuntimeWarning: Mean of empty slice.\n",
       "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
       "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/numpy/_core/_methods.py:144: RuntimeWarning: invalid value encountered in scalar divide\n",
       "  ret = ret.dtype.type(ret / rcount)\n",
-      "100%|██████████| 8/8 [00:03<00:00,  2.31it/s]\n",
+      "100%|██████████| 8/8 [00:05<00:00,  1.51it/s]\n",
       "[*] payer_transitions:   0%|          | 0/1 [00:00<?, ?it/s]/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/intugle/core/pipeline/key_identification/ki.py:87: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  temp.rename(columns={\"datatype_l1\": \"datatype\"}, inplace=True)\n",
-      "[!] Error while llm response parsing: Got invalid JSON object. Error: Expecting ',' delimiter: line 2 column 23 (char 24)\n",
+      "[!] Error while llm response parsing: Got invalid JSON object. Error: Expecting ',' delimiter: line 2 column 25 (char 26)\n",
       "For troubleshooting, visit: https://python.langchain.com/docs/troubleshooting/errors/OUTPUT_PARSING_FAILURE \n",
-      "[!] Error while parsing : 'NoneType' object has no attribute 'group'\n",
-      "[!]  key identified by LLM is not a valid column of payer_transitions table\n",
-      "[*] payer_transitions: 100%|██████████| 1/1 [00:03<00:00,  3.80s/it]\n",
-      "100%|██████████| 8/8 [00:10<00:00,  1.30s/it]\n",
+      "[!] None key identified by LLM is not a valid column of payer_transitions table\n",
+      "[*] payer_transitions: 100%|██████████| 1/1 [00:03<00:00,  3.18s/it]\n",
+      "100%|██████████| 8/8 [00:12<00:00,  1.55s/it]\n",
       "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3859: RuntimeWarning: Mean of empty slice.\n",
       "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
       "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/numpy/_core/_methods.py:144: RuntimeWarning: invalid value encountered in scalar divide\n",
       "  ret = ret.dtype.type(ret / rcount)\n",
-      "100%|██████████| 9/9 [00:04<00:00,  2.06it/s]\n",
-      "100%|██████████| 9/9 [00:11<00:00,  1.29s/it]\n",
+      "100%|██████████| 9/9 [00:03<00:00,  2.28it/s]\n",
+      "100%|██████████| 9/9 [00:12<00:00,  1.38s/it]\n",
       "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3859: RuntimeWarning: Mean of empty slice.\n",
       "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
       "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/numpy/_core/_methods.py:144: RuntimeWarning: invalid value encountered in scalar divide\n",
       "  ret = ret.dtype.type(ret / rcount)\n",
-      "100%|██████████| 12/12 [00:05<00:00,  2.32it/s]\n",
+      "100%|██████████| 12/12 [00:07<00:00,  1.52it/s]\n",
       "[*] providers:   0%|          | 0/1 [00:00<?, ?it/s]/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/intugle/core/pipeline/key_identification/ki.py:87: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame\n",
       "\n",
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  temp.rename(columns={\"datatype_l1\": \"datatype\"}, inplace=True)\n",
-      "[*] providers: 100%|██████████| 1/1 [00:00<00:00,  2.11it/s]\n",
-      "100%|██████████| 12/12 [00:15<00:00,  1.28s/it]\n",
+      "[*] providers: 100%|██████████| 1/1 [00:01<00:00,  1.07s/it]\n",
+      "100%|██████████| 12/12 [00:19<00:00,  1.60s/it]\n",
       "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3859: RuntimeWarning: Mean of empty slice.\n",
       "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
       "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/numpy/_core/_methods.py:144: RuntimeWarning: invalid value encountered in scalar divide\n",
       "  ret = ret.dtype.type(ret / rcount)\n",
-      "100%|██████████| 6/6 [00:03<00:00,  1.65it/s]\n",
-      "100%|██████████| 6/6 [00:07<00:00,  1.28s/it]\n"
+      "100%|██████████| 6/6 [00:02<00:00,  2.54it/s]\n",
+      "100%|██████████| 6/6 [00:09<00:00,  1.54s/it]\n"
      ]
     },
     {
@@ -402,7 +416,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] allergies <==> careplans Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.80s/it]\n"
+      "[*] allergies <==> careplans Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.65s/it]\n"
      ]
     },
     {
@@ -418,7 +432,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] allergies <==> claims Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.22s/it]\n"
+      "[*] allergies <==> claims Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.89s/it]\n"
      ]
     },
     {
@@ -434,7 +448,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] allergies <==> claims_transactions Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.11s/it]\n"
+      "[*] allergies <==> claims_transactions Runs:1: 100%|██████████| 1/1 [00:03<00:00,  3.15s/it]\n"
      ]
     },
     {
@@ -450,7 +464,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] allergies <==> conditions Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.86s/it]\n"
+      "[*] allergies <==> conditions Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.77s/it]\n"
      ]
     },
     {
@@ -466,7 +480,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] allergies <==> devices Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.30s/it]\n"
+      "[*] allergies <==> devices Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.66s/it]\n"
      ]
     },
     {
@@ -482,7 +496,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] allergies <==> encounters Runs:1: 100%|██████████| 1/1 [00:07<00:00,  7.08s/it]\n"
+      "[*] allergies <==> encounters Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.72s/it]\n"
      ]
     },
     {
@@ -498,7 +512,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] allergies <==> imaging_studies Runs:1: 100%|██████████| 1/1 [00:06<00:00,  6.52s/it]\n"
+      "[*] allergies <==> imaging_studies Runs:1: 100%|██████████| 1/1 [00:09<00:00,  9.78s/it]\n"
      ]
     },
     {
@@ -514,7 +528,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] allergies <==> immunizations Runs:1: 100%|██████████| 1/1 [00:06<00:00,  6.11s/it]\n"
+      "[*] allergies <==> immunizations Runs:1: 100%|██████████| 1/1 [00:07<00:00,  7.33s/it]\n"
      ]
     },
     {
@@ -530,7 +544,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] allergies <==> medications Runs:1: 100%|██████████| 1/1 [00:07<00:00,  7.73s/it]\n"
+      "[*] allergies <==> medications Runs:1: 100%|██████████| 1/1 [00:10<00:00, 10.54s/it]\n"
      ]
     },
     {
@@ -546,7 +560,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] allergies <==> observations Runs:1: 100%|██████████| 1/1 [00:12<00:00, 12.90s/it]\n"
+      "[*] allergies <==> observations Runs:1: 100%|██████████| 1/1 [00:09<00:00,  9.84s/it]\n"
      ]
     },
     {
@@ -562,7 +576,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] allergies <==> organizations Runs:1: 100%|██████████| 1/1 [00:03<00:00,  3.99s/it]\n"
+      "[*] allergies <==> organizations Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.83s/it]\n"
      ]
     },
     {
@@ -578,7 +592,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] allergies <==> patients Runs:1: 100%|██████████| 1/1 [00:03<00:00,  3.22s/it]\n"
+      "[*] allergies <==> patients Runs:1: 100%|██████████| 1/1 [00:05<00:00,  5.60s/it]\n"
      ]
     },
     {
@@ -594,7 +608,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] allergies <==> payers Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.08s/it]\n"
+      "[*] allergies <==> payers Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.53s/it]\n"
      ]
     },
     {
@@ -610,7 +624,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] allergies <==> payer_transitions Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.59s/it]\n"
+      "[*] allergies <==> payer_transitions Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.77s/it]\n"
      ]
     },
     {
@@ -626,7 +640,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] allergies <==> procedures Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.89s/it]\n"
+      "[*] allergies <==> procedures Runs:1: 100%|██████████| 1/1 [00:04<00:00,  4.09s/it]\n"
      ]
     },
     {
@@ -642,7 +656,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] allergies <==> providers Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.12s/it]\n"
+      "[*] allergies <==> providers Runs:1: 100%|██████████| 1/1 [00:00<00:00,  1.43it/s]\n"
      ]
     },
     {
@@ -658,7 +672,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] allergies <==> supplies Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.87s/it]\n"
+      "[*] allergies <==> supplies Runs:1: 100%|██████████| 1/1 [00:00<00:00,  1.54it/s]\n"
      ]
     },
     {
@@ -674,7 +688,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] careplans <==> claims Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.90s/it]\n"
+      "[*] careplans <==> claims Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.32s/it]\n"
      ]
     },
     {
@@ -690,7 +704,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] careplans <==> claims_transactions Runs:1: 100%|██████████| 1/1 [00:09<00:00,  9.12s/it]\n"
+      "[*] careplans <==> claims_transactions Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.39s/it]\n"
      ]
     },
     {
@@ -706,7 +720,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] careplans <==> conditions Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.29s/it]\n"
+      "[*] careplans <==> conditions Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.09s/it]\n"
      ]
     },
     {
@@ -722,7 +736,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] careplans <==> devices Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.01s/it]\n"
+      "[*] careplans <==> devices Runs:1: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]\n"
      ]
     },
     {
@@ -738,7 +752,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] careplans <==> encounters Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.98s/it]\n"
+      "[*] careplans <==> encounters Runs:1: 100%|██████████| 1/1 [00:06<00:00,  6.45s/it]\n"
      ]
     },
     {
@@ -754,14 +768,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] careplans <==> imaging_studies Runs:1: 100%|██████████| 1/1 [00:05<00:00,  5.66s/it]\n"
+      "[*] careplans <==> imaging_studies Runs:1: 100%|██████████| 1/1 [00:09<00:00,  9.00s/it]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Found 1 potential link(s).\n",
+      "No links found for this pair.\n",
       "\n",
       "--- Comparing 'careplans' <=> 'immunizations' ---\n"
      ]
@@ -770,7 +784,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] careplans <==> immunizations Runs:1: 100%|██████████| 1/1 [00:05<00:00,  5.04s/it]\n"
+      "[*] careplans <==> immunizations Runs:1: 100%|██████████| 1/1 [00:05<00:00,  5.96s/it]\n"
      ]
     },
     {
@@ -786,7 +800,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] careplans <==> medications Runs:1: 100%|██████████| 1/1 [00:09<00:00,  9.97s/it]\n"
+      "[*] careplans <==> medications Runs:1: 100%|██████████| 1/1 [00:05<00:00,  5.85s/it]\n"
      ]
     },
     {
@@ -802,7 +816,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] careplans <==> observations Runs:1: 100%|██████████| 1/1 [00:03<00:00,  3.14s/it]\n"
+      "[*] careplans <==> observations Runs:1: 100%|██████████| 1/1 [00:03<00:00,  3.28s/it]\n"
      ]
     },
     {
@@ -818,7 +832,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] careplans <==> organizations Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.90s/it]\n"
+      "[*] careplans <==> organizations Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.77s/it]\n"
      ]
     },
     {
@@ -834,7 +848,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] careplans <==> patients Runs:1: 100%|██████████| 1/1 [00:03<00:00,  3.92s/it]\n"
+      "[*] careplans <==> patients Runs:1: 100%|██████████| 1/1 [00:05<00:00,  5.23s/it]\n"
      ]
     },
     {
@@ -850,7 +864,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] careplans <==> payers Runs:1: 100%|██████████| 1/1 [00:00<00:00,  1.28it/s]\n"
+      "[*] careplans <==> payers Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.10s/it]\n"
      ]
     },
     {
@@ -866,7 +880,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] careplans <==> payer_transitions Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.74s/it]\n"
+      "[*] careplans <==> payer_transitions Runs:1: 100%|██████████| 1/1 [00:03<00:00,  3.19s/it]\n"
      ]
     },
     {
@@ -882,7 +896,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] careplans <==> procedures Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.09s/it]\n"
+      "[*] careplans <==> procedures Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.45s/it]\n"
      ]
     },
     {
@@ -898,7 +912,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] careplans <==> providers Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.21s/it]\n"
+      "[*] careplans <==> providers Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.64s/it]\n"
      ]
     },
     {
@@ -914,7 +928,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] careplans <==> supplies Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.01s/it]\n"
+      "[*] careplans <==> supplies Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.07s/it]\n"
      ]
     },
     {
@@ -930,7 +944,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] claims <==> claims_transactions Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.36s/it]\n"
+      "[*] claims <==> claims_transactions Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.56s/it]\n"
      ]
     },
     {
@@ -946,7 +960,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] claims <==> conditions Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.31s/it]\n"
+      "[*] claims <==> conditions Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.41s/it]\n"
      ]
     },
     {
@@ -962,7 +976,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] claims <==> devices Runs:1: 100%|██████████| 1/1 [00:09<00:00,  9.07s/it]\n"
+      "[*] claims <==> devices Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.39s/it]\n"
      ]
     },
     {
@@ -978,7 +992,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] claims <==> encounters Runs:1: 100%|██████████| 1/1 [00:08<00:00,  8.75s/it]\n"
+      "[*] claims <==> encounters Runs:1: 100%|██████████| 1/1 [00:12<00:00, 12.36s/it]\n"
      ]
     },
     {
@@ -994,7 +1008,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] claims <==> imaging_studies Runs:1: 100%|██████████| 1/1 [00:31<00:00, 31.51s/it]\n"
+      "[*] claims <==> imaging_studies Runs:1: 100%|██████████| 1/1 [00:14<00:00, 14.38s/it]\n"
      ]
     },
     {
@@ -1010,7 +1024,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] claims <==> immunizations Runs:1: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]\n"
+      "[*] claims <==> immunizations Runs:1: 100%|██████████| 1/1 [00:11<00:00, 11.79s/it]\n"
      ]
     },
     {
@@ -1026,7 +1040,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] claims <==> medications Runs:1: 100%|██████████| 1/1 [00:03<00:00,  3.25s/it]\n"
+      "[*] claims <==> medications Runs:1: 100%|██████████| 1/1 [00:09<00:00,  9.01s/it]\n"
      ]
     },
     {
@@ -1042,7 +1056,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] claims <==> observations Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.48s/it]\n"
+      "[*] claims <==> observations Runs:1: 100%|██████████| 1/1 [00:05<00:00,  5.83s/it]\n"
      ]
     },
     {
@@ -1058,7 +1072,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] claims <==> organizations Runs:1: 100%|██████████| 1/1 [00:10<00:00, 10.80s/it]\n"
+      "[*] claims <==> organizations Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.32s/it]\n"
      ]
     },
     {
@@ -1074,7 +1088,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] claims <==> patients Runs:1: 100%|██████████| 1/1 [00:00<00:00,  1.13it/s]\n"
+      "[*] claims <==> patients Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.44s/it]\n"
      ]
     },
     {
@@ -1090,7 +1104,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] claims <==> payers Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.14s/it]\n"
+      "[*] claims <==> payers Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.31s/it]\n"
      ]
     },
     {
@@ -1106,7 +1120,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] claims <==> payer_transitions Runs:1: 100%|██████████| 1/1 [00:19<00:00, 19.02s/it]\n"
+      "[*] claims <==> payer_transitions Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.45s/it]\n"
      ]
     },
     {
@@ -1122,7 +1136,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] claims <==> procedures Runs:1: 100%|██████████| 1/1 [00:09<00:00,  9.93s/it]\n"
+      "[*] claims <==> procedures Runs:1: 100%|██████████| 1/1 [00:11<00:00, 11.74s/it]\n"
      ]
     },
     {
@@ -1138,7 +1152,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] claims <==> providers Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.95s/it]\n"
+      "[*] claims <==> providers Runs:1: 100%|██████████| 1/1 [00:08<00:00,  8.86s/it]\n"
      ]
     },
     {
@@ -1154,7 +1168,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] claims <==> supplies Runs:1: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]\n"
+      "[*] claims <==> supplies Runs:1: 100%|██████████| 1/1 [00:05<00:00,  5.50s/it]\n"
      ]
     },
     {
@@ -1170,7 +1184,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] claims_transactions <==> conditions Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.80s/it]\n"
+      "[*] claims_transactions <==> conditions Runs:1: 100%|██████████| 1/1 [00:12<00:00, 12.08s/it]\n"
      ]
     },
     {
@@ -1186,7 +1200,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] claims_transactions <==> devices Runs:1: 100%|██████████| 1/1 [00:03<00:00,  3.54s/it]\n"
+      "[*] claims_transactions <==> devices Runs:1: 100%|██████████| 1/1 [00:08<00:00,  8.76s/it]\n"
      ]
     },
     {
@@ -1202,7 +1216,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] claims_transactions <==> encounters Runs:1: 100%|██████████| 1/1 [00:07<00:00,  7.31s/it]\n"
+      "[*] claims_transactions <==> encounters Runs:1: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]\n"
      ]
     },
     {
@@ -1218,7 +1232,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] claims_transactions <==> imaging_studies Runs:1: 100%|██████████| 1/1 [00:08<00:00,  8.06s/it]\n"
+      "[*] claims_transactions <==> imaging_studies Runs:1: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]\n"
      ]
     },
     {
@@ -1234,7 +1248,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] claims_transactions <==> immunizations Runs:1: 100%|██████████| 1/1 [00:18<00:00, 18.60s/it]\n"
+      "[*] claims_transactions <==> immunizations Runs:1: 100%|██████████| 1/1 [00:05<00:00,  5.50s/it]\n"
      ]
     },
     {
@@ -1250,7 +1264,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] claims_transactions <==> medications Runs:1: 100%|██████████| 1/1 [00:10<00:00, 10.07s/it]\n"
+      "[*] claims_transactions <==> medications Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.58s/it]\n"
      ]
     },
     {
@@ -1266,7 +1280,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] claims_transactions <==> observations Runs:1: 100%|██████████| 1/1 [00:07<00:00,  7.64s/it]\n"
+      "[*] claims_transactions <==> observations Runs:1: 100%|██████████| 1/1 [00:17<00:00, 17.91s/it]\n"
      ]
     },
     {
@@ -1282,7 +1296,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] claims_transactions <==> organizations Runs:1: 100%|██████████| 1/1 [00:00<00:00,  1.24it/s]\n"
+      "[*] claims_transactions <==> organizations Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.82s/it]\n"
      ]
     },
     {
@@ -1298,7 +1312,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] claims_transactions <==> patients Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.10s/it]\n"
+      "[*] claims_transactions <==> patients Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.28s/it]\n"
      ]
     },
     {
@@ -1314,7 +1328,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] claims_transactions <==> payers Runs:1: 100%|██████████| 1/1 [00:04<00:00,  4.25s/it]\n"
+      "[*] claims_transactions <==> payers Runs:1: 100%|██████████| 1/1 [00:08<00:00,  8.77s/it]\n"
      ]
     },
     {
@@ -1330,7 +1344,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] claims_transactions <==> payer_transitions Runs:1: 100%|██████████| 1/1 [00:12<00:00, 12.60s/it]\n"
+      "[*] claims_transactions <==> payer_transitions Runs:1: 100%|██████████| 1/1 [00:10<00:00, 10.51s/it]\n"
      ]
     },
     {
@@ -1346,7 +1360,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] claims_transactions <==> procedures Runs:1: 100%|██████████| 1/1 [00:05<00:00,  5.05s/it]\n"
+      "[*] claims_transactions <==> procedures Runs:1: 100%|██████████| 1/1 [00:05<00:00,  5.39s/it]\n"
      ]
     },
     {
@@ -1362,7 +1376,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] claims_transactions <==> providers Runs:1: 100%|██████████| 1/1 [00:15<00:00, 15.20s/it]\n"
+      "[*] claims_transactions <==> providers Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.10s/it]\n"
      ]
     },
     {
@@ -1378,7 +1392,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] claims_transactions <==> supplies Runs:1: 100%|██████████| 1/1 [00:09<00:00,  9.56s/it]\n"
+      "[*] claims_transactions <==> supplies Runs:1: 100%|██████████| 1/1 [00:03<00:00,  3.55s/it]\n"
      ]
     },
     {
@@ -1394,7 +1408,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] conditions <==> devices Runs:1: 100%|██████████| 1/1 [00:03<00:00,  3.43s/it]\n"
+      "[*] conditions <==> devices Runs:1: 100%|██████████| 1/1 [00:06<00:00,  6.17s/it]\n"
      ]
     },
     {
@@ -1410,7 +1424,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] conditions <==> encounters Runs:1: 100%|██████████| 1/1 [00:05<00:00,  5.12s/it]\n"
+      "[*] conditions <==> encounters Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.04s/it]\n"
      ]
     },
     {
@@ -1426,7 +1440,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] conditions <==> imaging_studies Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.01s/it]\n"
+      "[*] conditions <==> imaging_studies Runs:1: 100%|██████████| 1/1 [00:08<00:00,  8.68s/it]\n"
      ]
     },
     {
@@ -1442,7 +1456,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] conditions <==> immunizations Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.69s/it]\n"
+      "[*] conditions <==> immunizations Runs:1: 100%|██████████| 1/1 [00:09<00:00,  9.99s/it]\n"
      ]
     },
     {
@@ -1458,7 +1472,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] conditions <==> medications Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.72s/it]\n"
+      "[*] conditions <==> medications Runs:1: 100%|██████████| 1/1 [00:04<00:00,  4.02s/it]\n"
      ]
     },
     {
@@ -1474,7 +1488,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] conditions <==> observations Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.48s/it]\n"
+      "[*] conditions <==> observations Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.02s/it]\n"
      ]
     },
     {
@@ -1490,7 +1504,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] conditions <==> organizations Runs:1: 100%|██████████| 1/1 [00:00<00:00,  1.30it/s]\n"
+      "[*] conditions <==> organizations Runs:1: 100%|██████████| 1/1 [00:06<00:00,  6.01s/it]\n"
      ]
     },
     {
@@ -1506,7 +1520,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] conditions <==> patients Runs:1: 100%|██████████| 1/1 [00:03<00:00,  3.83s/it]\n"
+      "[*] conditions <==> patients Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.31s/it]\n"
      ]
     },
     {
@@ -1522,7 +1536,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] conditions <==> payers Runs:1: 100%|██████████| 1/1 [00:05<00:00,  6.00s/it]\n"
+      "[*] conditions <==> payers Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.98s/it]\n"
      ]
     },
     {
@@ -1538,7 +1552,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] conditions <==> payer_transitions Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.69s/it]\n"
+      "[*] conditions <==> payer_transitions Runs:1: 100%|██████████| 1/1 [00:08<00:00,  8.32s/it]\n"
      ]
     },
     {
@@ -1554,7 +1568,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] conditions <==> procedures Runs:1: 100%|██████████| 1/1 [00:03<00:00,  3.46s/it]\n"
+      "[*] conditions <==> procedures Runs:1: 100%|██████████| 1/1 [00:04<00:00,  4.84s/it]\n"
      ]
     },
     {
@@ -1570,7 +1584,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] conditions <==> providers Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.88s/it]\n"
+      "[*] conditions <==> providers Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.13s/it]\n"
      ]
     },
     {
@@ -1586,7 +1600,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] conditions <==> supplies Runs:1: 100%|██████████| 1/1 [00:12<00:00, 12.59s/it]\n"
+      "[*] conditions <==> supplies Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.18s/it]\n"
      ]
     },
     {
@@ -1602,7 +1616,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] devices <==> encounters Runs:1: 100%|██████████| 1/1 [00:03<00:00,  3.89s/it]\n"
+      "[*] devices <==> encounters Runs:1: 100%|██████████| 1/1 [00:00<00:00,  1.37it/s]\n"
      ]
     },
     {
@@ -1618,7 +1632,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] devices <==> imaging_studies Runs:1: 100%|██████████| 1/1 [00:07<00:00,  7.92s/it]\n"
+      "[*] devices <==> imaging_studies Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.44s/it]\n"
      ]
     },
     {
@@ -1634,7 +1648,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] devices <==> immunizations Runs:1: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]\n"
+      "[*] devices <==> immunizations Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.68s/it]\n"
      ]
     },
     {
@@ -1650,7 +1664,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] devices <==> medications Runs:1: 100%|██████████| 1/1 [00:03<00:00,  3.47s/it]\n"
+      "[*] devices <==> medications Runs:1: 100%|██████████| 1/1 [00:08<00:00,  8.62s/it]\n"
      ]
     },
     {
@@ -1666,7 +1680,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] devices <==> observations Runs:1: 100%|██████████| 1/1 [00:05<00:00,  5.29s/it]\n"
+      "[*] devices <==> observations Runs:1: 100%|██████████| 1/1 [00:15<00:00, 15.99s/it]\n"
      ]
     },
     {
@@ -1682,7 +1696,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] devices <==> organizations Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.09s/it]\n"
+      "[*] devices <==> organizations Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.35s/it]\n"
      ]
     },
     {
@@ -1698,7 +1712,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] devices <==> patients Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.13s/it]\n"
+      "[*] devices <==> patients Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.02s/it]\n"
      ]
     },
     {
@@ -1714,7 +1728,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] devices <==> payers Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.07s/it]\n"
+      "[*] devices <==> payers Runs:1: 100%|██████████| 1/1 [00:06<00:00,  6.17s/it]\n"
      ]
     },
     {
@@ -1730,7 +1744,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] devices <==> payer_transitions Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.24s/it]\n"
+      "[*] devices <==> payer_transitions Runs:1: 100%|██████████| 1/1 [00:12<00:00, 12.62s/it]\n"
      ]
     },
     {
@@ -1746,7 +1760,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] devices <==> procedures Runs:1: 100%|██████████| 1/1 [00:05<00:00,  5.97s/it]\n"
+      "[*] devices <==> procedures Runs:1: 100%|██████████| 1/1 [00:06<00:00,  6.53s/it]\n"
      ]
     },
     {
@@ -1762,7 +1776,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] devices <==> providers Runs:1: 100%|██████████| 1/1 [00:00<00:00,  1.04it/s]\n"
+      "[*] devices <==> providers Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.08s/it]\n"
      ]
     },
     {
@@ -1778,7 +1792,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] devices <==> supplies Runs:1: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]\n"
+      "[*] devices <==> supplies Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.80s/it]\n"
      ]
     },
     {
@@ -1794,7 +1808,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] encounters <==> imaging_studies Runs:1: 100%|██████████| 1/1 [00:15<00:00, 15.20s/it]\n"
+      "[*] encounters <==> imaging_studies Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.62s/it]\n"
      ]
     },
     {
@@ -1810,7 +1824,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] encounters <==> immunizations Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.85s/it]\n"
+      "[*] encounters <==> immunizations Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.24s/it]\n"
      ]
     },
     {
@@ -1826,7 +1840,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] encounters <==> medications Runs:1: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]\n"
+      "[*] encounters <==> medications Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.33s/it]\n"
      ]
     },
     {
@@ -1842,7 +1856,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] encounters <==> observations Runs:1: 100%|██████████| 1/1 [00:03<00:00,  3.33s/it]\n"
+      "[*] encounters <==> observations Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.13s/it]\n"
      ]
     },
     {
@@ -1858,7 +1872,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] encounters <==> organizations Runs:1: 100%|██████████| 1/1 [00:00<00:00,  1.16it/s]\n"
+      "[*] encounters <==> organizations Runs:1: 100%|██████████| 1/1 [00:00<00:00,  1.09it/s]\n"
      ]
     },
     {
@@ -1874,7 +1888,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] encounters <==> patients Runs:1: 100%|██████████| 1/1 [00:03<00:00,  3.98s/it]\n"
+      "[*] encounters <==> patients Runs:1: 100%|██████████| 1/1 [00:04<00:00,  4.16s/it]\n"
      ]
     },
     {
@@ -1890,7 +1904,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] encounters <==> payers Runs:1: 100%|██████████| 1/1 [00:00<00:00,  1.08it/s]\n"
+      "[*] encounters <==> payers Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.77s/it]\n"
      ]
     },
     {
@@ -1906,7 +1920,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] encounters <==> payer_transitions Runs:1: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]\n"
+      "[*] encounters <==> payer_transitions Runs:1: 100%|██████████| 1/1 [00:14<00:00, 14.43s/it]\n"
      ]
     },
     {
@@ -1922,7 +1936,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] encounters <==> procedures Runs:1: 100%|██████████| 1/1 [00:03<00:00,  3.03s/it]\n"
+      "[*] encounters <==> procedures Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.16s/it]\n"
      ]
     },
     {
@@ -1938,7 +1952,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] encounters <==> providers Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.21s/it]\n"
+      "[*] encounters <==> providers Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.10s/it]\n"
      ]
     },
     {
@@ -1954,7 +1968,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] encounters <==> supplies Runs:1: 100%|██████████| 1/1 [00:00<00:00,  1.10it/s]\n"
+      "[*] encounters <==> supplies Runs:1: 100%|██████████| 1/1 [00:06<00:00,  6.19s/it]\n"
      ]
     },
     {
@@ -1970,7 +1984,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] imaging_studies <==> immunizations Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.51s/it]\n"
+      "[*] imaging_studies <==> immunizations Runs:1: 100%|██████████| 1/1 [00:03<00:00,  3.35s/it]\n"
      ]
     },
     {
@@ -1986,7 +2000,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] imaging_studies <==> medications Runs:1: 100%|██████████| 1/1 [00:06<00:00,  6.67s/it]\n"
+      "[*] imaging_studies <==> medications Runs:1: 100%|██████████| 1/1 [00:10<00:00, 10.78s/it]\n"
      ]
     },
     {
@@ -2002,7 +2016,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] imaging_studies <==> observations Runs:1: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]\n"
+      "[*] imaging_studies <==> observations Runs:1: 100%|██████████| 1/1 [00:05<00:00,  5.48s/it]\n"
      ]
     },
     {
@@ -2018,7 +2032,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] imaging_studies <==> organizations Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.13s/it]\n"
+      "[*] imaging_studies <==> organizations Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.28s/it]\n"
      ]
     },
     {
@@ -2034,7 +2048,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] imaging_studies <==> patients Runs:1: 100%|██████████| 1/1 [00:12<00:00, 12.10s/it]\n"
+      "[*] imaging_studies <==> patients Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.29s/it]\n"
      ]
     },
     {
@@ -2050,7 +2064,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] imaging_studies <==> payers Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.88s/it]\n"
+      "[*] imaging_studies <==> payers Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.12s/it]\n"
      ]
     },
     {
@@ -2066,7 +2080,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] imaging_studies <==> payer_transitions Runs:1: 100%|██████████| 1/1 [00:07<00:00,  7.99s/it]\n"
+      "[*] imaging_studies <==> payer_transitions Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.95s/it]\n"
      ]
     },
     {
@@ -2082,7 +2096,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] imaging_studies <==> procedures Runs:1: 100%|██████████| 1/1 [00:09<00:00,  9.29s/it]\n"
+      "[*] imaging_studies <==> procedures Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.38s/it]\n"
      ]
     },
     {
@@ -2098,7 +2112,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] imaging_studies <==> providers Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.01s/it]\n"
+      "[*] imaging_studies <==> providers Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.32s/it]\n"
      ]
     },
     {
@@ -2114,7 +2128,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] imaging_studies <==> supplies Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.77s/it]\n"
+      "[*] imaging_studies <==> supplies Runs:1: 100%|██████████| 1/1 [00:06<00:00,  6.86s/it]\n"
      ]
     },
     {
@@ -2130,7 +2144,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] immunizations <==> medications Runs:1: 100%|██████████| 1/1 [00:03<00:00,  3.58s/it]\n"
+      "[*] immunizations <==> medications Runs:1: 100%|██████████| 1/1 [00:14<00:00, 14.46s/it]\n"
      ]
     },
     {
@@ -2146,7 +2160,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] immunizations <==> observations Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.57s/it]\n"
+      "[*] immunizations <==> observations Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.38s/it]\n"
      ]
     },
     {
@@ -2162,14 +2176,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] immunizations <==> organizations Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.84s/it]\n"
+      "[*] immunizations <==> organizations Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.15s/it]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "No links found for this pair.\n",
+      "Found 1 potential link(s).\n",
       "\n",
       "--- Comparing 'immunizations' <=> 'patients' ---\n"
      ]
@@ -2178,7 +2192,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] immunizations <==> patients Runs:1: 100%|██████████| 1/1 [00:00<00:00,  1.11it/s]\n"
+      "[*] immunizations <==> patients Runs:1: 100%|██████████| 1/1 [00:03<00:00,  3.98s/it]\n"
      ]
     },
     {
@@ -2194,7 +2208,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] immunizations <==> payers Runs:1: 100%|██████████| 1/1 [00:00<00:00,  1.27it/s]\n"
+      "[*] immunizations <==> payers Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.35s/it]\n"
      ]
     },
     {
@@ -2210,7 +2224,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] immunizations <==> payer_transitions Runs:1: 100%|██████████| 1/1 [00:03<00:00,  3.42s/it]\n"
+      "[*] immunizations <==> payer_transitions Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.97s/it]\n"
      ]
     },
     {
@@ -2226,7 +2240,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] immunizations <==> procedures Runs:1: 100%|██████████| 1/1 [00:03<00:00,  3.16s/it]\n"
+      "[*] immunizations <==> procedures Runs:1: 100%|██████████| 1/1 [00:11<00:00, 11.15s/it]\n"
      ]
     },
     {
@@ -2242,7 +2256,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] immunizations <==> providers Runs:1: 100%|██████████| 1/1 [00:04<00:00,  4.08s/it]\n"
+      "[*] immunizations <==> providers Runs:1: 100%|██████████| 1/1 [00:00<00:00,  1.03it/s]\n"
      ]
     },
     {
@@ -2258,7 +2272,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] immunizations <==> supplies Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.33s/it]\n"
+      "[*] immunizations <==> supplies Runs:1: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]\n"
      ]
     },
     {
@@ -2274,7 +2288,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] medications <==> observations Runs:1: 100%|██████████| 1/1 [00:14<00:00, 14.80s/it]\n"
+      "[*] medications <==> observations Runs:1: 100%|██████████| 1/1 [00:05<00:00,  5.61s/it]\n"
      ]
     },
     {
@@ -2290,7 +2304,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] medications <==> organizations Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.86s/it]\n"
+      "[*] medications <==> organizations Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.07s/it]\n"
      ]
     },
     {
@@ -2306,7 +2320,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] medications <==> patients Runs:1: 100%|██████████| 1/1 [00:05<00:00,  5.09s/it]\n"
+      "[*] medications <==> patients Runs:1: 100%|██████████| 1/1 [00:00<00:00,  1.01it/s]\n"
      ]
     },
     {
@@ -2322,7 +2336,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] medications <==> payers Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.08s/it]\n"
+      "[*] medications <==> payers Runs:1: 100%|██████████| 1/1 [00:00<00:00,  1.02it/s]\n"
      ]
     },
     {
@@ -2338,7 +2352,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] medications <==> payer_transitions Runs:1: 100%|██████████| 1/1 [00:07<00:00,  7.54s/it]\n"
+      "[*] medications <==> payer_transitions Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.83s/it]\n"
      ]
     },
     {
@@ -2354,7 +2368,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] medications <==> procedures Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.05s/it]\n"
+      "[*] medications <==> procedures Runs:1: 100%|██████████| 1/1 [00:05<00:00,  5.92s/it]\n"
      ]
     },
     {
@@ -2370,14 +2384,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] medications <==> providers Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.08s/it]\n"
+      "[*] medications <==> providers Runs:1: 100%|██████████| 1/1 [00:00<00:00,  1.23it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "No links found for this pair.\n",
+      "Found 1 potential link(s).\n",
       "\n",
       "--- Comparing 'medications' <=> 'supplies' ---\n"
      ]
@@ -2386,7 +2400,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] medications <==> supplies Runs:1: 100%|██████████| 1/1 [00:03<00:00,  3.52s/it]\n"
+      "[*] medications <==> supplies Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.04s/it]\n"
      ]
     },
     {
@@ -2402,7 +2416,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] observations <==> organizations Runs:1: 100%|██████████| 1/1 [00:00<00:00,  1.21it/s]\n"
+      "[*] observations <==> organizations Runs:1: 100%|██████████| 1/1 [00:10<00:00, 10.11s/it]\n"
      ]
     },
     {
@@ -2418,7 +2432,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] observations <==> patients Runs:1: 100%|██████████| 1/1 [00:00<00:00,  1.14it/s]\n"
+      "[*] observations <==> patients Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.34s/it]\n"
      ]
     },
     {
@@ -2434,7 +2448,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] observations <==> payers Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.81s/it]\n"
+      "[*] observations <==> payers Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.13s/it]\n"
      ]
     },
     {
@@ -2450,7 +2464,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] observations <==> payer_transitions Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.45s/it]\n"
+      "[*] observations <==> payer_transitions Runs:1: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]\n"
      ]
     },
     {
@@ -2466,7 +2480,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] observations <==> procedures Runs:1: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]\n"
+      "[*] observations <==> procedures Runs:1: 100%|██████████| 1/1 [00:04<00:00,  4.17s/it]\n"
      ]
     },
     {
@@ -2482,7 +2496,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] observations <==> providers Runs:1: 100%|██████████| 1/1 [00:00<00:00,  1.19it/s]\n"
+      "[*] observations <==> providers Runs:1: 100%|██████████| 1/1 [00:06<00:00,  6.87s/it]\n"
      ]
     },
     {
@@ -2498,7 +2512,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] observations <==> supplies Runs:1: 100%|██████████| 1/1 [00:05<00:00,  5.46s/it]\n"
+      "[*] observations <==> supplies Runs:1: 100%|██████████| 1/1 [00:04<00:00,  4.12s/it]\n"
      ]
     },
     {
@@ -2514,7 +2528,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] organizations <==> patients Runs:1: 100%|██████████| 1/1 [00:00<00:00,  1.09it/s]\n"
+      "[*] organizations <==> patients Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.78s/it]\n"
      ]
     },
     {
@@ -2530,7 +2544,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] organizations <==> payers Runs:1: 100%|██████████| 1/1 [00:11<00:00, 11.49s/it]\n"
+      "[*] organizations <==> payers Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.09s/it]\n"
      ]
     },
     {
@@ -2546,14 +2560,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] organizations <==> payer_transitions Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.18s/it]\n"
+      "[*] organizations <==> payer_transitions Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.36s/it]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Found 1 potential link(s).\n",
+      "No links found for this pair.\n",
       "\n",
       "--- Comparing 'organizations' <=> 'procedures' ---\n"
      ]
@@ -2562,14 +2576,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] organizations <==> procedures Runs:1: 100%|██████████| 1/1 [00:04<00:00,  4.09s/it]\n"
+      "[*] organizations <==> procedures Runs:1: 100%|██████████| 1/1 [00:04<00:00,  4.03s/it]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Found 1 potential link(s).\n",
+      "No links found for this pair.\n",
       "\n",
       "--- Comparing 'organizations' <=> 'providers' ---\n"
      ]
@@ -2578,7 +2592,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] organizations <==> providers Runs:1: 100%|██████████| 1/1 [00:05<00:00,  5.01s/it]\n"
+      "[*] organizations <==> providers Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.04s/it]\n"
      ]
     },
     {
@@ -2594,7 +2608,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] organizations <==> supplies Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.05s/it]\n"
+      "[*] organizations <==> supplies Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.03s/it]\n"
      ]
     },
     {
@@ -2610,7 +2624,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] patients <==> payers Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.07s/it]\n"
+      "[*] patients <==> payers Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.10s/it]\n"
      ]
     },
     {
@@ -2626,7 +2640,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] patients <==> payer_transitions Runs:1: 100%|██████████| 1/1 [00:06<00:00,  6.02s/it]\n"
+      "[*] patients <==> payer_transitions Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.83s/it]\n"
      ]
     },
     {
@@ -2642,7 +2656,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] patients <==> procedures Runs:1: 100%|██████████| 1/1 [00:03<00:00,  3.02s/it]\n"
+      "[*] patients <==> procedures Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.09s/it]\n"
      ]
     },
     {
@@ -2658,7 +2672,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] patients <==> providers Runs:1: 100%|██████████| 1/1 [00:00<00:00,  1.17it/s]\n"
+      "[*] patients <==> providers Runs:1: 100%|██████████| 1/1 [00:00<00:00,  1.35it/s]\n"
      ]
     },
     {
@@ -2674,7 +2688,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] patients <==> supplies Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.01s/it]\n"
+      "[*] patients <==> supplies Runs:1: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]\n"
      ]
     },
     {
@@ -2690,7 +2704,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] payers <==> payer_transitions Runs:1: 100%|██████████| 1/1 [00:00<00:00,  1.16it/s]\n"
+      "[*] payers <==> payer_transitions Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.24s/it]\n"
      ]
     },
     {
@@ -2706,7 +2720,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] payers <==> procedures Runs:1: 100%|██████████| 1/1 [00:00<00:00,  1.14it/s]\n"
+      "[*] payers <==> procedures Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.08s/it]\n"
      ]
     },
     {
@@ -2722,7 +2736,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] payers <==> providers Runs:1: 100%|██████████| 1/1 [00:00<00:00,  1.11it/s]\n"
+      "[*] payers <==> providers Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.07s/it]\n"
      ]
     },
     {
@@ -2738,7 +2752,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] payers <==> supplies Runs:1: 100%|██████████| 1/1 [00:00<00:00,  1.13it/s]\n"
+      "[*] payers <==> supplies Runs:1: 100%|██████████| 1/1 [00:09<00:00,  9.14s/it]\n"
      ]
     },
     {
@@ -2754,7 +2768,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] payer_transitions <==> procedures Runs:1: 100%|██████████| 1/1 [00:05<00:00,  5.21s/it]\n"
+      "[*] payer_transitions <==> procedures Runs:1: 100%|██████████| 1/1 [00:03<00:00,  3.50s/it]\n"
      ]
     },
     {
@@ -2770,7 +2784,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] payer_transitions <==> providers Runs:1: 100%|██████████| 1/1 [00:00<00:00,  1.14it/s]\n"
+      "[*] payer_transitions <==> providers Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.03s/it]\n"
      ]
     },
     {
@@ -2786,7 +2800,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] payer_transitions <==> supplies Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.85s/it]\n"
+      "[*] payer_transitions <==> supplies Runs:1: 100%|██████████| 1/1 [00:05<00:00,  5.45s/it]\n"
      ]
     },
     {
@@ -2802,14 +2816,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] procedures <==> providers Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.14s/it]\n"
+      "[*] procedures <==> providers Runs:1: 100%|██████████| 1/1 [00:02<00:00,  2.06s/it]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "No links found for this pair.\n",
+      "Found 1 potential link(s).\n",
       "\n",
       "--- Comparing 'procedures' <=> 'supplies' ---\n"
      ]
@@ -2818,7 +2832,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] procedures <==> supplies Runs:1: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]\n"
+      "[*] procedures <==> supplies Runs:1: 100%|██████████| 1/1 [00:10<00:00, 10.06s/it]\n"
      ]
     },
     {
@@ -2834,7 +2848,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[*] providers <==> supplies Runs:1: 100%|██████████| 1/1 [00:01<00:00,  1.83s/it]"
+      "[*] providers <==> supplies Runs:1: 100%|██████████| 1/1 [00:00<00:00,  1.07it/s]\n"
      ]
     },
     {
@@ -2848,13 +2862,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\n"
+      "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/intugle/core/vector_store/qdrant.py:77: UserWarning: Api key is used with an insecure connection.\n",
+      "  self._client = AsyncQdrantClient(**self.client_config)\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<intugle.knowledge_builder.KnowledgeBuilder at 0x7f7326f678c0>"
+       "<intugle.knowledge_builder.KnowledgeBuilder at 0x7008d5bfe540>"
       ]
      },
      "execution_count": 3,
@@ -2888,7 +2903,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 4,
    "id": "0aa894eb",
    "metadata": {},
    "outputs": [
@@ -2941,9 +2956,9 @@
        "      <td>1163</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>[ef3624d5-0c77-e990-c856-064916d06155, 139f120...</td>\n",
-       "      <td>[8c2d5e9b-0717-9616-beb9-21296a5b547d, 6319860...</td>\n",
-       "      <td>0.012747</td>\n",
+       "      <td>[1a4f3136-4eae-dc5d-f68d-046b7a2e11ea, acbb6a4...</td>\n",
+       "      <td>[217f95a3-4e10-bd5d-fb67-0cfb5e8ba075, 23d16ee...</td>\n",
+       "      <td>0.004991</td>\n",
        "      <td>alphanumeric</td>\n",
        "      <td>L2OutputTypes.dimension</td>\n",
        "      <td>Serves as a unique identifier for individual p...</td>\n",
@@ -2959,9 +2974,9 @@
        "      <td>987</td>\n",
        "      <td>0.848667</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>[1968-01-26, 1992-02-07, 1994-02-14, 1992-02-2...</td>\n",
+       "      <td>[1928-11-13, 1966-06-22, 2004-05-16, 1923-09-0...</td>\n",
        "      <td>[1993-12-23, 2018-09-16, 1970-09-14, 1956-10-2...</td>\n",
-       "      <td>0.011031</td>\n",
+       "      <td>0.004395</td>\n",
        "      <td>date &amp; time</td>\n",
        "      <td>L2OutputTypes.dimension</td>\n",
        "      <td>Indicates the date on which an individual was ...</td>\n",
@@ -2977,13 +2992,13 @@
        "      <td>163</td>\n",
        "      <td>0.140155</td>\n",
        "      <td>0.140155</td>\n",
-       "      <td>[1987-07-03, 1942-02-17, 2016-07-21, 1978-01-0...</td>\n",
-       "      <td>[1997-02-17, 1991-01-03, 2005-03-17, 1991-06-2...</td>\n",
-       "      <td>0.009140</td>\n",
+       "      <td>[2001-09-05, 1966-05-07, 2010-01-13, 1955-11-2...</td>\n",
+       "      <td>[2009-11-13, 2011-07-10, 1991-11-18, 2010-06-0...</td>\n",
+       "      <td>0.005714</td>\n",
        "      <td>date &amp; time</td>\n",
        "      <td>L2OutputTypes.dimension</td>\n",
        "      <td>Indicates the date and time when an individual...</td>\n",
-       "      <td>[Patient Mortality Date, End Of Life Record, H...</td>\n",
+       "      <td>[Patient Mortality, End Of Life Date, Healthca...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -2995,13 +3010,13 @@
        "      <td>1163</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>[999-26-7371, 999-80-5384, 999-44-9249, 999-75...</td>\n",
+       "      <td>[999-52-4852, 999-95-8884, 999-31-6931, 999-37...</td>\n",
        "      <td>[999-59-7726, 999-16-7928, 999-47-2075, 999-31...</td>\n",
-       "      <td>0.012124</td>\n",
+       "      <td>0.005779</td>\n",
        "      <td>alphanumeric</td>\n",
        "      <td>L2OutputTypes.dimension</td>\n",
        "      <td>Serves as a unique identifier for individuals ...</td>\n",
-       "      <td>[Patient Identification, Sensitive Information...</td>\n",
+       "      <td>[Patient Identification, Social Security Numbe...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -3013,13 +3028,13 @@
        "      <td>941</td>\n",
        "      <td>0.809114</td>\n",
        "      <td>0.815133</td>\n",
-       "      <td>[S99961183, S99929350, S99983478, S99942538, S...</td>\n",
+       "      <td>[S99918599, S99986354, S99954622, S99943469, S...</td>\n",
        "      <td>[S99916617, S99985809, S99999275, S99929211, S...</td>\n",
-       "      <td>0.010610</td>\n",
+       "      <td>0.003784</td>\n",
        "      <td>alphanumeric</td>\n",
        "      <td>L2OutputTypes.dimension</td>\n",
-       "      <td>Unique identifiers used to track entities asso...</td>\n",
-       "      <td>[Driver License Identifier, Patient Identifica...</td>\n",
+       "      <td>Unique identifiers used to associate individua...</td>\n",
+       "      <td>[Driver Identification, Healthcare Provider Re...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
@@ -3031,13 +3046,13 @@
        "      <td>887</td>\n",
        "      <td>0.762683</td>\n",
        "      <td>0.762683</td>\n",
-       "      <td>[X39843761X, X52134439X, X48390913X, X66548114...</td>\n",
+       "      <td>[X15711612X, X42921765X, X84876726X, X69721357...</td>\n",
        "      <td>[X75063318X, X33683613X, X77363212X, X62900787...</td>\n",
-       "      <td>0.010984</td>\n",
+       "      <td>0.006447</td>\n",
        "      <td>alphanumeric</td>\n",
        "      <td>L2OutputTypes.dimension</td>\n",
-       "      <td>Serves as a unique identifier for individuals ...</td>\n",
-       "      <td>[Identity Verification, International Patient ...</td>\n",
+       "      <td>Unique identifier used to document and track i...</td>\n",
+       "      <td>[Patient Identification, Travel Documentation,...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
@@ -3049,12 +3064,12 @@
        "      <td>3</td>\n",
        "      <td>0.002580</td>\n",
        "      <td>0.789338</td>\n",
-       "      <td>[Mr., Ms., Mrs.]</td>\n",
-       "      <td>[Mrs., Ms., Mr., Ms., Ms., Mrs., Mr., Mrs., Ms...</td>\n",
-       "      <td>0.008432</td>\n",
+       "      <td>[Mr., Mrs., Ms.]</td>\n",
+       "      <td>[Ms., Mr., Mrs., Mrs., Mrs., Ms., Ms., Mr., Mr...</td>\n",
+       "      <td>0.003622</td>\n",
        "      <td>close_ended_text</td>\n",
        "      <td>L2OutputTypes.dimension</td>\n",
-       "      <td>Identifies the formal title used to address an...</td>\n",
+       "      <td>Indicates the title or honorific used to addre...</td>\n",
        "      <td>[Patient Salutation, Personal Title, Demograph...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -3067,13 +3082,13 @@
        "      <td>1002</td>\n",
        "      <td>0.861565</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>[Helga481, Miquel905, Kristopher775, Tamar361,...</td>\n",
-       "      <td>[Damon455, Kari181, Laurena366, Shaun461, Kevi...</td>\n",
-       "      <td>0.010835</td>\n",
+       "      <td>[Elena945, Ana972, Donetta1, Chris95, Marivel8...</td>\n",
+       "      <td>[Franklyn361, Bonita405, Jonathon205, Gerry91,...</td>\n",
+       "      <td>0.006652</td>\n",
        "      <td>alphanumeric</td>\n",
        "      <td>L2OutputTypes.dimension</td>\n",
-       "      <td>Identifies individuals with a unique alphanume...</td>\n",
-       "      <td>[Patient Identifier, Unique Record Reference, ...</td>\n",
+       "      <td>Identifies individuals using a unique alphanum...</td>\n",
+       "      <td>[Patient Identifier, Healthcare Record Trackin...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
@@ -3085,13 +3100,13 @@
        "      <td>477</td>\n",
        "      <td>0.410146</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>[Huel628, Hettinger594, Langosh790, Conn188, O...</td>\n",
-       "      <td>[Bernier607, King743, Abernathy524, Glover433,...</td>\n",
-       "      <td>0.010227</td>\n",
+       "      <td>[Greenfelder433, Gerhold939, Estévez304, Hane6...</td>\n",
+       "      <td>[Walter473, Herzog843, Wisoky380, Runolfsson90...</td>\n",
+       "      <td>0.005493</td>\n",
        "      <td>alphanumeric</td>\n",
        "      <td>L2OutputTypes.dimension</td>\n",
-       "      <td>Identifies individuals using a unique alphanum...</td>\n",
-       "      <td>[Patient Identifier, Unique Code, Healthcare R...</td>\n",
+       "      <td>Identifies individuals uniquely within the pat...</td>\n",
+       "      <td>[Patient Identifier, Healthcare Record Trackin...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
@@ -3103,12 +3118,12 @@
        "      <td>3</td>\n",
        "      <td>0.002580</td>\n",
        "      <td>0.013758</td>\n",
-       "      <td>[JD, MD, PhD]</td>\n",
-       "      <td>[MD, PhD, JD, MD, MD, PhD, JD, JD, MD, PhD, JD...</td>\n",
-       "      <td>0.008151</td>\n",
+       "      <td>[JD, PhD, MD]</td>\n",
+       "      <td>[JD, MD, PhD, PhD, MD, MD, PhD, PhD, JD, MD, P...</td>\n",
+       "      <td>0.003334</td>\n",
        "      <td>close_ended_text</td>\n",
        "      <td>L2OutputTypes.dimension</td>\n",
-       "      <td>Indicates a professional or academic designati...</td>\n",
+       "      <td>Indicates the professional or academic designa...</td>\n",
        "      <td>[Professional Credentials, Patient Identificat...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -3121,13 +3136,13 @@
        "      <td>248</td>\n",
        "      <td>0.213242</td>\n",
        "      <td>0.284609</td>\n",
-       "      <td>[Boyle917, O'Keefe54, Bermúdez789, Hettinger59...</td>\n",
-       "      <td>[Botsford977, Glover433, Rempel203, Thompson59...</td>\n",
-       "      <td>0.013831</td>\n",
+       "      <td>[Trantow673, Aufderhar910, Runte676, Kuvalis36...</td>\n",
+       "      <td>[Blanda868, Funk324, Lemke654, Gislason620, Ar...</td>\n",
+       "      <td>0.005715</td>\n",
        "      <td>alphanumeric</td>\n",
        "      <td>L2OutputTypes.dimension</td>\n",
-       "      <td>Tracks a unique identifier or code associated ...</td>\n",
-       "      <td>[Patient Identifier, Unique Code, Healthcare R...</td>\n",
+       "      <td>Tracks unique identifiers or codes associated ...</td>\n",
+       "      <td>[Patient Identifier, Unique Code, Medical Reco...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
@@ -3139,13 +3154,13 @@
        "      <td>2</td>\n",
        "      <td>0.001720</td>\n",
        "      <td>0.669819</td>\n",
-       "      <td>[M, S]</td>\n",
-       "      <td>[S, M, M, S, M, M, S, S, S, M, M, M, S, M, S, ...</td>\n",
-       "      <td>0.008464</td>\n",
+       "      <td>[S, M]</td>\n",
+       "      <td>[M, S, S, M, S, S, M, S, M, S, S, M, M, M, S, ...</td>\n",
+       "      <td>0.003135</td>\n",
        "      <td>close_ended_text</td>\n",
        "      <td>L2OutputTypes.dimension</td>\n",
        "      <td>Indicates the relationship status of an indivi...</td>\n",
-       "      <td>[Marital Status, Demographics, Patient Profile]</td>\n",
+       "      <td>[Marital Status, Demographic Information, Pati...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
@@ -3157,13 +3172,13 @@
        "      <td>6</td>\n",
        "      <td>0.005159</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>[hawaiian, other, asian, native, white, black]</td>\n",
-       "      <td>[white, native, other, asian, hawaiian, black,...</td>\n",
-       "      <td>0.013222</td>\n",
+       "      <td>[hawaiian, asian, black, white, other, native]</td>\n",
+       "      <td>[white, native, black, other, hawaiian, asian,...</td>\n",
+       "      <td>0.005455</td>\n",
        "      <td>close_ended_text</td>\n",
        "      <td>L2OutputTypes.dimension</td>\n",
-       "      <td>Categorizes individuals based on their self-id...</td>\n",
-       "      <td>[Demographic Classification, Health Equity Ana...</td>\n",
+       "      <td>Categorizes individuals based on self-identifi...</td>\n",
+       "      <td>[Demographics, Patient Diversity, Health Equit...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
@@ -3176,12 +3191,12 @@
        "      <td>0.001720</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>[hispanic, nonhispanic]</td>\n",
-       "      <td>[hispanic, nonhispanic, hispanic, hispanic, no...</td>\n",
-       "      <td>0.008102</td>\n",
+       "      <td>[nonhispanic, hispanic, hispanic, hispanic, hi...</td>\n",
+       "      <td>0.004667</td>\n",
        "      <td>close_ended_text</td>\n",
        "      <td>L2OutputTypes.dimension</td>\n",
        "      <td>Categorizes individuals based on their cultura...</td>\n",
-       "      <td>[Demographic Classification, Population Health...</td>\n",
+       "      <td>[Demographic Classification, Health Equity Ana...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
@@ -3193,12 +3208,12 @@
        "      <td>2</td>\n",
        "      <td>0.001720</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>[F, M]</td>\n",
-       "      <td>[M, F, F, M, F, M, M, M, M, F, F, M, F, F, F, ...</td>\n",
-       "      <td>0.008261</td>\n",
+       "      <td>[M, F]</td>\n",
+       "      <td>[F, M, M, M, F, F, F, F, F, F, M, F, F, M, M, ...</td>\n",
+       "      <td>0.003129</td>\n",
        "      <td>close_ended_text</td>\n",
        "      <td>L2OutputTypes.dimension</td>\n",
-       "      <td>Describes the classification of individuals ba...</td>\n",
+       "      <td>Categorizes individuals based on their self-id...</td>\n",
        "      <td>[Patient Demographics, Gender Classification, ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -3211,13 +3226,13 @@
        "      <td>317</td>\n",
        "      <td>0.272571</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>[North Scituate  Massachusetts  US, Millis-Cli...</td>\n",
-       "      <td>[Medford  Massachusetts  US, Marblehead  Massa...</td>\n",
-       "      <td>0.010961</td>\n",
+       "      <td>[Buenos Aires  Ciudad de Buenos Aires  AR, Sud...</td>\n",
+       "      <td>[Danvers  Massachusetts  US, Blackstone  Massa...</td>\n",
+       "      <td>0.003767</td>\n",
        "      <td>close_ended_text</td>\n",
        "      <td>L2OutputTypes.dimension</td>\n",
-       "      <td>Identifies the geographic location associated ...</td>\n",
-       "      <td>[Patient Demographics, Geographical Location, ...</td>\n",
+       "      <td>Identifies the geographical location where an ...</td>\n",
+       "      <td>[Patient Demographics, Geographic Origin, Heal...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
@@ -3229,13 +3244,13 @@
        "      <td>1163</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>[768 Mertz Camp, 380 Gibson Street Unit 30, 49...</td>\n",
+       "      <td>[450 Kutch Bridge Unit 35, 418 Roob Trailer, 8...</td>\n",
        "      <td>[734 Brakus Pathway Unit 28, 902 Gutkowski Hol...</td>\n",
-       "      <td>0.011425</td>\n",
+       "      <td>0.007850</td>\n",
        "      <td>alphanumeric</td>\n",
        "      <td>L2OutputTypes.dimension</td>\n",
-       "      <td>Identifies the physical location associated wi...</td>\n",
-       "      <td>[Patient Residential Address, Contact Informat...</td>\n",
+       "      <td>Indicates the physical location associated wit...</td>\n",
+       "      <td>[Patient Residential Information, Contact Loca...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
@@ -3247,13 +3262,13 @@
        "      <td>256</td>\n",
        "      <td>0.220120</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>[Charlton, Douglas, East Bridgewater, North Ea...</td>\n",
+       "      <td>[Rockport, Salem, Clinton, Rockland, Holbrook,...</td>\n",
        "      <td>[Somerville, Salem, Essex, Mashpee, Southborou...</td>\n",
-       "      <td>0.009101</td>\n",
+       "      <td>0.003501</td>\n",
        "      <td>close_ended_text</td>\n",
        "      <td>L2OutputTypes.dimension</td>\n",
-       "      <td>Location information associated with an indivi...</td>\n",
-       "      <td>[Patient Residence, Geographic Location, Demog...</td>\n",
+       "      <td>Indicates the geographical location associated...</td>\n",
+       "      <td>[Patient Residence, Geographic Location, Commu...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
@@ -3267,10 +3282,10 @@
        "      <td>1.000000</td>\n",
        "      <td>[Massachusetts]</td>\n",
        "      <td>[Massachusetts, Massachusetts, Massachusetts, ...</td>\n",
-       "      <td>0.008148</td>\n",
+       "      <td>0.003719</td>\n",
        "      <td>close_ended_text</td>\n",
        "      <td>L2OutputTypes.dimension</td>\n",
-       "      <td>Indicates the geographical region associated w...</td>\n",
+       "      <td>Indicates the administrative region associated...</td>\n",
        "      <td>[Geographic Location, Patient Residency, State...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -3283,9 +3298,9 @@
        "      <td>13</td>\n",
        "      <td>0.011178</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>[Hampshire County, Worcester County, Hampden C...</td>\n",
-       "      <td>[Plymouth County, Suffolk County, Worcester Co...</td>\n",
-       "      <td>0.013330</td>\n",
+       "      <td>[Suffolk County, Norfolk County, Bristol Count...</td>\n",
+       "      <td>[Plymouth County, Norfolk County, Worcester Co...</td>\n",
+       "      <td>0.004057</td>\n",
        "      <td>close_ended_text</td>\n",
        "      <td>L2OutputTypes.dimension</td>\n",
        "      <td>Identifies the administrative region associate...</td>\n",
@@ -3301,13 +3316,13 @@
        "      <td>223</td>\n",
        "      <td>0.191745</td>\n",
        "      <td>0.531384</td>\n",
-       "      <td>[01890, 01583, 01702, 02472, 02561, 02215, 014...</td>\n",
+       "      <td>[01201, 01001, 02451, 01938, 02420, 01566, 019...</td>\n",
        "      <td>[02131, 02043, 01844, 02155, 01902, 01852, 027...</td>\n",
-       "      <td>0.008994</td>\n",
+       "      <td>0.004337</td>\n",
        "      <td>integer</td>\n",
        "      <td>L2OutputTypes.dimension</td>\n",
        "      <td>Identifies the geographic area associated with...</td>\n",
-       "      <td>[Patient Location, Geographic Identifier, Regi...</td>\n",
+       "      <td>[Geographic Location, Patient Demographics, Re...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
@@ -3319,13 +3334,13 @@
        "      <td>1163</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>[42.523433675145355, 42.30711671465368, 42.124...</td>\n",
+       "      <td>[42.29539340255773, 42.419364702071974, 42.068...</td>\n",
        "      <td>[42.29255662362827, 42.39983230495903, 42.5061...</td>\n",
-       "      <td>0.012944</td>\n",
+       "      <td>0.008021</td>\n",
        "      <td>float</td>\n",
        "      <td>L2OutputTypes.measure</td>\n",
        "      <td>Indicates the geographical latitude coordinate...</td>\n",
-       "      <td>[Geolocation, Patient Location, Latitude Coord...</td>\n",
+       "      <td>[Patient Location, Geospatial Data, Latitude C...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
@@ -3337,13 +3352,13 @@
        "      <td>1163</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>[-71.62789132166591, -71.16712435956047, -71.1...</td>\n",
-       "      <td>[-71.1066547010483, -71.02700529599232, -71.05...</td>\n",
-       "      <td>0.012352</td>\n",
+       "      <td>[-71.14378789698912, -70.73893314975992, -71.2...</td>\n",
+       "      <td>[-71.48251904737748, -71.02232158016432, -71.0...</td>\n",
+       "      <td>0.005395</td>\n",
        "      <td>float</td>\n",
        "      <td>L2OutputTypes.dimension</td>\n",
-       "      <td>Indicates the longitudinal coordinate of a loc...</td>\n",
-       "      <td>[Geolocation, Longitude, Patient Location]</td>\n",
+       "      <td>Indicates the longitudinal geographic coordina...</td>\n",
+       "      <td>[Geographic Location, Longitude Coordinate, Pa...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
@@ -3355,9 +3370,9 @@
        "      <td>1163</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>[1419447.4625, 1153357.2850000001, 1041314.967...</td>\n",
+       "      <td>[367249.88749999995, 570693.255, 2677158.173, ...</td>\n",
        "      <td>[390675.5774999999, 797287.0900000001, 1008922...</td>\n",
-       "      <td>0.012873</td>\n",
+       "      <td>0.008081</td>\n",
        "      <td>float</td>\n",
        "      <td>L2OutputTypes.measure</td>\n",
        "      <td>Tracks the monetary costs incurred for medical...</td>\n",
@@ -3373,9 +3388,9 @@
        "      <td>1036</td>\n",
        "      <td>0.890800</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>[3298.4665, 84066.21550000002, 333.62999999999...</td>\n",
+       "      <td>[3786.1574999999993, 6659.3475, 44135.6035, 12...</td>\n",
        "      <td>[18067.094999999998, 154972.8415, 115026.04749...</td>\n",
-       "      <td>0.016592</td>\n",
+       "      <td>0.008294</td>\n",
        "      <td>float</td>\n",
        "      <td>L2OutputTypes.measure</td>\n",
        "      <td>Monetary value allocated for an individual's i...</td>\n",
@@ -3441,58 +3456,58 @@
        "24            1036    0.890800      1.000000   \n",
        "\n",
        "                                          sample_data  \\\n",
-       "0   [ef3624d5-0c77-e990-c856-064916d06155, 139f120...   \n",
-       "1   [1968-01-26, 1992-02-07, 1994-02-14, 1992-02-2...   \n",
-       "2   [1987-07-03, 1942-02-17, 2016-07-21, 1978-01-0...   \n",
-       "3   [999-26-7371, 999-80-5384, 999-44-9249, 999-75...   \n",
-       "4   [S99961183, S99929350, S99983478, S99942538, S...   \n",
-       "5   [X39843761X, X52134439X, X48390913X, X66548114...   \n",
-       "6                                    [Mr., Ms., Mrs.]   \n",
-       "7   [Helga481, Miquel905, Kristopher775, Tamar361,...   \n",
-       "8   [Huel628, Hettinger594, Langosh790, Conn188, O...   \n",
-       "9                                       [JD, MD, PhD]   \n",
-       "10  [Boyle917, O'Keefe54, Bermúdez789, Hettinger59...   \n",
-       "11                                             [M, S]   \n",
-       "12     [hawaiian, other, asian, native, white, black]   \n",
+       "0   [1a4f3136-4eae-dc5d-f68d-046b7a2e11ea, acbb6a4...   \n",
+       "1   [1928-11-13, 1966-06-22, 2004-05-16, 1923-09-0...   \n",
+       "2   [2001-09-05, 1966-05-07, 2010-01-13, 1955-11-2...   \n",
+       "3   [999-52-4852, 999-95-8884, 999-31-6931, 999-37...   \n",
+       "4   [S99918599, S99986354, S99954622, S99943469, S...   \n",
+       "5   [X15711612X, X42921765X, X84876726X, X69721357...   \n",
+       "6                                    [Mr., Mrs., Ms.]   \n",
+       "7   [Elena945, Ana972, Donetta1, Chris95, Marivel8...   \n",
+       "8   [Greenfelder433, Gerhold939, Estévez304, Hane6...   \n",
+       "9                                       [JD, PhD, MD]   \n",
+       "10  [Trantow673, Aufderhar910, Runte676, Kuvalis36...   \n",
+       "11                                             [S, M]   \n",
+       "12     [hawaiian, asian, black, white, other, native]   \n",
        "13                            [hispanic, nonhispanic]   \n",
-       "14                                             [F, M]   \n",
-       "15  [North Scituate  Massachusetts  US, Millis-Cli...   \n",
-       "16  [768 Mertz Camp, 380 Gibson Street Unit 30, 49...   \n",
-       "17  [Charlton, Douglas, East Bridgewater, North Ea...   \n",
+       "14                                             [M, F]   \n",
+       "15  [Buenos Aires  Ciudad de Buenos Aires  AR, Sud...   \n",
+       "16  [450 Kutch Bridge Unit 35, 418 Roob Trailer, 8...   \n",
+       "17  [Rockport, Salem, Clinton, Rockland, Holbrook,...   \n",
        "18                                    [Massachusetts]   \n",
-       "19  [Hampshire County, Worcester County, Hampden C...   \n",
-       "20  [01890, 01583, 01702, 02472, 02561, 02215, 014...   \n",
-       "21  [42.523433675145355, 42.30711671465368, 42.124...   \n",
-       "22  [-71.62789132166591, -71.16712435956047, -71.1...   \n",
-       "23  [1419447.4625, 1153357.2850000001, 1041314.967...   \n",
-       "24  [3298.4665, 84066.21550000002, 333.62999999999...   \n",
+       "19  [Suffolk County, Norfolk County, Bristol Count...   \n",
+       "20  [01201, 01001, 02451, 01938, 02420, 01566, 019...   \n",
+       "21  [42.29539340255773, 42.419364702071974, 42.068...   \n",
+       "22  [-71.14378789698912, -70.73893314975992, -71.2...   \n",
+       "23  [367249.88749999995, 570693.255, 2677158.173, ...   \n",
+       "24  [3786.1574999999993, 6659.3475, 44135.6035, 12...   \n",
        "\n",
        "                                         dtype_sample        ts  \\\n",
-       "0   [8c2d5e9b-0717-9616-beb9-21296a5b547d, 6319860...  0.012747   \n",
-       "1   [1993-12-23, 2018-09-16, 1970-09-14, 1956-10-2...  0.011031   \n",
-       "2   [1997-02-17, 1991-01-03, 2005-03-17, 1991-06-2...  0.009140   \n",
-       "3   [999-59-7726, 999-16-7928, 999-47-2075, 999-31...  0.012124   \n",
-       "4   [S99916617, S99985809, S99999275, S99929211, S...  0.010610   \n",
-       "5   [X75063318X, X33683613X, X77363212X, X62900787...  0.010984   \n",
-       "6   [Mrs., Ms., Mr., Ms., Ms., Mrs., Mr., Mrs., Ms...  0.008432   \n",
-       "7   [Damon455, Kari181, Laurena366, Shaun461, Kevi...  0.010835   \n",
-       "8   [Bernier607, King743, Abernathy524, Glover433,...  0.010227   \n",
-       "9   [MD, PhD, JD, MD, MD, PhD, JD, JD, MD, PhD, JD...  0.008151   \n",
-       "10  [Botsford977, Glover433, Rempel203, Thompson59...  0.013831   \n",
-       "11  [S, M, M, S, M, M, S, S, S, M, M, M, S, M, S, ...  0.008464   \n",
-       "12  [white, native, other, asian, hawaiian, black,...  0.013222   \n",
-       "13  [hispanic, nonhispanic, hispanic, hispanic, no...  0.008102   \n",
-       "14  [M, F, F, M, F, M, M, M, M, F, F, M, F, F, F, ...  0.008261   \n",
-       "15  [Medford  Massachusetts  US, Marblehead  Massa...  0.010961   \n",
-       "16  [734 Brakus Pathway Unit 28, 902 Gutkowski Hol...  0.011425   \n",
-       "17  [Somerville, Salem, Essex, Mashpee, Southborou...  0.009101   \n",
-       "18  [Massachusetts, Massachusetts, Massachusetts, ...  0.008148   \n",
-       "19  [Plymouth County, Suffolk County, Worcester Co...  0.013330   \n",
-       "20  [02131, 02043, 01844, 02155, 01902, 01852, 027...  0.008994   \n",
-       "21  [42.29255662362827, 42.39983230495903, 42.5061...  0.012944   \n",
-       "22  [-71.1066547010483, -71.02700529599232, -71.05...  0.012352   \n",
-       "23  [390675.5774999999, 797287.0900000001, 1008922...  0.012873   \n",
-       "24  [18067.094999999998, 154972.8415, 115026.04749...  0.016592   \n",
+       "0   [217f95a3-4e10-bd5d-fb67-0cfb5e8ba075, 23d16ee...  0.004991   \n",
+       "1   [1993-12-23, 2018-09-16, 1970-09-14, 1956-10-2...  0.004395   \n",
+       "2   [2009-11-13, 2011-07-10, 1991-11-18, 2010-06-0...  0.005714   \n",
+       "3   [999-59-7726, 999-16-7928, 999-47-2075, 999-31...  0.005779   \n",
+       "4   [S99916617, S99985809, S99999275, S99929211, S...  0.003784   \n",
+       "5   [X75063318X, X33683613X, X77363212X, X62900787...  0.006447   \n",
+       "6   [Ms., Mr., Mrs., Mrs., Mrs., Ms., Ms., Mr., Mr...  0.003622   \n",
+       "7   [Franklyn361, Bonita405, Jonathon205, Gerry91,...  0.006652   \n",
+       "8   [Walter473, Herzog843, Wisoky380, Runolfsson90...  0.005493   \n",
+       "9   [JD, MD, PhD, PhD, MD, MD, PhD, PhD, JD, MD, P...  0.003334   \n",
+       "10  [Blanda868, Funk324, Lemke654, Gislason620, Ar...  0.005715   \n",
+       "11  [M, S, S, M, S, S, M, S, M, S, S, M, M, M, S, ...  0.003135   \n",
+       "12  [white, native, black, other, hawaiian, asian,...  0.005455   \n",
+       "13  [nonhispanic, hispanic, hispanic, hispanic, hi...  0.004667   \n",
+       "14  [F, M, M, M, F, F, F, F, F, F, M, F, F, M, M, ...  0.003129   \n",
+       "15  [Danvers  Massachusetts  US, Blackstone  Massa...  0.003767   \n",
+       "16  [734 Brakus Pathway Unit 28, 902 Gutkowski Hol...  0.007850   \n",
+       "17  [Somerville, Salem, Essex, Mashpee, Southborou...  0.003501   \n",
+       "18  [Massachusetts, Massachusetts, Massachusetts, ...  0.003719   \n",
+       "19  [Plymouth County, Norfolk County, Worcester Co...  0.004057   \n",
+       "20  [02131, 02043, 01844, 02155, 01902, 01852, 027...  0.004337   \n",
+       "21  [42.29255662362827, 42.39983230495903, 42.5061...  0.008021   \n",
+       "22  [-71.48251904737748, -71.02232158016432, -71.0...  0.005395   \n",
+       "23  [390675.5774999999, 797287.0900000001, 1008922...  0.008081   \n",
+       "24  [18067.094999999998, 154972.8415, 115026.04749...  0.008294   \n",
        "\n",
        "         datatype_l1              datatype_l2  \\\n",
        "0       alphanumeric  L2OutputTypes.dimension   \n",
@@ -3526,57 +3541,57 @@
        "1   Indicates the date on which an individual was ...   \n",
        "2   Indicates the date and time when an individual...   \n",
        "3   Serves as a unique identifier for individuals ...   \n",
-       "4   Unique identifiers used to track entities asso...   \n",
-       "5   Serves as a unique identifier for individuals ...   \n",
-       "6   Identifies the formal title used to address an...   \n",
-       "7   Identifies individuals with a unique alphanume...   \n",
-       "8   Identifies individuals using a unique alphanum...   \n",
-       "9   Indicates a professional or academic designati...   \n",
-       "10  Tracks a unique identifier or code associated ...   \n",
+       "4   Unique identifiers used to associate individua...   \n",
+       "5   Unique identifier used to document and track i...   \n",
+       "6   Indicates the title or honorific used to addre...   \n",
+       "7   Identifies individuals using a unique alphanum...   \n",
+       "8   Identifies individuals uniquely within the pat...   \n",
+       "9   Indicates the professional or academic designa...   \n",
+       "10  Tracks unique identifiers or codes associated ...   \n",
        "11  Indicates the relationship status of an indivi...   \n",
-       "12  Categorizes individuals based on their self-id...   \n",
+       "12  Categorizes individuals based on self-identifi...   \n",
        "13  Categorizes individuals based on their cultura...   \n",
-       "14  Describes the classification of individuals ba...   \n",
-       "15  Identifies the geographic location associated ...   \n",
-       "16  Identifies the physical location associated wi...   \n",
-       "17  Location information associated with an indivi...   \n",
-       "18  Indicates the geographical region associated w...   \n",
+       "14  Categorizes individuals based on their self-id...   \n",
+       "15  Identifies the geographical location where an ...   \n",
+       "16  Indicates the physical location associated wit...   \n",
+       "17  Indicates the geographical location associated...   \n",
+       "18  Indicates the administrative region associated...   \n",
        "19  Identifies the administrative region associate...   \n",
        "20  Identifies the geographic area associated with...   \n",
        "21  Indicates the geographical latitude coordinate...   \n",
-       "22  Indicates the longitudinal coordinate of a loc...   \n",
+       "22  Indicates the longitudinal geographic coordina...   \n",
        "23  Tracks the monetary costs incurred for medical...   \n",
        "24  Monetary value allocated for an individual's i...   \n",
        "\n",
        "                                        business_tags  \n",
        "0   [Patient Identifier, Unique Record Key, Health...  \n",
        "1   [Patient Demographics, Age Calculation, Health...  \n",
-       "2   [Patient Mortality Date, End Of Life Record, H...  \n",
-       "3   [Patient Identification, Sensitive Information...  \n",
-       "4   [Driver License Identifier, Patient Identifica...  \n",
-       "5   [Identity Verification, International Patient ...  \n",
+       "2   [Patient Mortality, End Of Life Date, Healthca...  \n",
+       "3   [Patient Identification, Social Security Numbe...  \n",
+       "4   [Driver Identification, Healthcare Provider Re...  \n",
+       "5   [Patient Identification, Travel Documentation,...  \n",
        "6   [Patient Salutation, Personal Title, Demograph...  \n",
-       "7   [Patient Identifier, Unique Record Reference, ...  \n",
-       "8   [Patient Identifier, Unique Code, Healthcare R...  \n",
+       "7   [Patient Identifier, Healthcare Record Trackin...  \n",
+       "8   [Patient Identifier, Healthcare Record Trackin...  \n",
        "9   [Professional Credentials, Patient Identificat...  \n",
-       "10  [Patient Identifier, Unique Code, Healthcare R...  \n",
-       "11    [Marital Status, Demographics, Patient Profile]  \n",
-       "12  [Demographic Classification, Health Equity Ana...  \n",
-       "13  [Demographic Classification, Population Health...  \n",
+       "10  [Patient Identifier, Unique Code, Medical Reco...  \n",
+       "11  [Marital Status, Demographic Information, Pati...  \n",
+       "12  [Demographics, Patient Diversity, Health Equit...  \n",
+       "13  [Demographic Classification, Health Equity Ana...  \n",
        "14  [Patient Demographics, Gender Classification, ...  \n",
-       "15  [Patient Demographics, Geographical Location, ...  \n",
-       "16  [Patient Residential Address, Contact Informat...  \n",
-       "17  [Patient Residence, Geographic Location, Demog...  \n",
+       "15  [Patient Demographics, Geographic Origin, Heal...  \n",
+       "16  [Patient Residential Information, Contact Loca...  \n",
+       "17  [Patient Residence, Geographic Location, Commu...  \n",
        "18  [Geographic Location, Patient Residency, State...  \n",
        "19  [Geographic Location, Healthcare Service Regio...  \n",
-       "20  [Patient Location, Geographic Identifier, Regi...  \n",
-       "21  [Geolocation, Patient Location, Latitude Coord...  \n",
-       "22         [Geolocation, Longitude, Patient Location]  \n",
+       "20  [Geographic Location, Patient Demographics, Re...  \n",
+       "21  [Patient Location, Geospatial Data, Latitude C...  \n",
+       "22  [Geographic Location, Longitude Coordinate, Pa...  \n",
        "23  [Healthcare Costs, Patient Financial Data, Med...  \n",
        "24  [InsuranceCoverageAmount, PatientFinancialResp...  "
       ]
      },
-     "execution_count": 27,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3620,7 +3635,6 @@
        " PredictedLink(from_dataset='careplans', from_column='encounter', to_dataset='conditions', to_column='encounter'),\n",
        " PredictedLink(from_dataset='careplans', from_column='encounter', to_dataset='devices', to_column='encounter'),\n",
        " PredictedLink(from_dataset='careplans', from_column='encounter', to_dataset='encounters', to_column='id'),\n",
-       " PredictedLink(from_dataset='careplans', from_column='encounter', to_dataset='imaging_studies', to_column='encounter'),\n",
        " PredictedLink(from_dataset='careplans', from_column='encounter', to_dataset='medications', to_column='encounter'),\n",
        " PredictedLink(from_dataset='careplans', from_column='encounter', to_dataset='observations', to_column='encounter'),\n",
        " PredictedLink(from_dataset='careplans', from_column='patient', to_dataset='patients', to_column='id'),\n",
@@ -3649,19 +3663,20 @@
        " PredictedLink(from_dataset='encounters', from_column='provider', to_dataset='providers', to_column='id'),\n",
        " PredictedLink(from_dataset='encounters', from_column='id', to_dataset='supplies', to_column='encounter'),\n",
        " PredictedLink(from_dataset='imaging_studies', from_column='patient', to_dataset='patients', to_column='id'),\n",
+       " PredictedLink(from_dataset='immunizations', from_column='encounter', to_dataset='organizations', to_column='id'),\n",
        " PredictedLink(from_dataset='immunizations', from_column='patient', to_dataset='patients', to_column='id'),\n",
        " PredictedLink(from_dataset='medications', from_column='payer', to_dataset='organizations', to_column='id'),\n",
        " PredictedLink(from_dataset='medications', from_column='patient', to_dataset='patients', to_column='id'),\n",
        " PredictedLink(from_dataset='medications', from_column='payer', to_dataset='payers', to_column='id'),\n",
+       " PredictedLink(from_dataset='medications', from_column='payer', to_dataset='providers', to_column='id'),\n",
        " PredictedLink(from_dataset='observations', from_column='patient', to_dataset='patients', to_column='id'),\n",
-       " PredictedLink(from_dataset='organizations', from_column='id', to_dataset='payer_transitions', to_column='payer'),\n",
-       " PredictedLink(from_dataset='organizations', from_column='id', to_dataset='procedures', to_column='patient'),\n",
        " PredictedLink(from_dataset='organizations', from_column='id', to_dataset='providers', to_column='organization'),\n",
        " PredictedLink(from_dataset='patients', from_column='id', to_dataset='payer_transitions', to_column='patient'),\n",
        " PredictedLink(from_dataset='patients', from_column='id', to_dataset='procedures', to_column='patient'),\n",
        " PredictedLink(from_dataset='patients', from_column='id', to_dataset='supplies', to_column='patient'),\n",
        " PredictedLink(from_dataset='payers', from_column='id', to_dataset='payer_transitions', to_column='payer'),\n",
-       " PredictedLink(from_dataset='payers', from_column='id', to_dataset='procedures', to_column='patient')]"
+       " PredictedLink(from_dataset='payers', from_column='id', to_dataset='procedures', to_column='patient'),\n",
+       " PredictedLink(from_dataset='procedures', from_column='encounter', to_dataset='providers', to_column='id')]"
       ]
      },
      "execution_count": 5,
@@ -3683,13 +3698,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "a59704ba",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n",
+      "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/pydantic/_internal/_fields.py:198: UserWarning: Field name \"schema\" in \"Source\" shadows an attribute in parent \"BaseResource\"\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "payer_transitions loaded\n",
+      "encounters loaded\n",
+      "careplans loaded\n",
+      "patients loaded\n",
+      "claims loaded\n",
+      "organizations loaded\n",
+      "medications loaded\n",
+      "providers loaded\n",
+      "supplies loaded\n",
+      "devices loaded\n",
+      "payers loaded\n",
+      "conditions loaded\n",
+      "observations loaded\n",
+      "procedures loaded\n",
+      "claims_transactions loaded\n",
+      "imaging_studies loaded\n",
+      "immunizations loaded\n",
+      "allergies loaded\n",
+      "top_patients_by_claim_count\n",
+      "top_patients_by_claim_count loaded\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 2000x2000 with 1 Axes>"
       ]
@@ -3714,12 +3765,385 @@
     "By default, these files are saved in the current working directory. You can configure this path by setting the `PROJECT_BASE` environment variable.\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "eb79c617",
+   "metadata": {},
+   "source": [
+    "## 5. Semantic Search\n",
+    "\n",
+    "The semantic search feature allows you to search for columns in your datasets using natural language. \n",
+    "\n",
+    "> **Note:** To use this feature, you need to have a running Qdrant instance and an OpenAI API key. Please refer to the [README.md](README.md) for detailed setup instructions.\n",
+    "\n",
+    "Once you have built the knowledge base, you can use the `search` method to perform a semantic search. The search function returns a pandas DataFrame containing the search results, including the column's profiling metrics, category, table name, and table glossary.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "ddbc8fd1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/intugle/core/vector_store/qdrant.py:77: UserWarning: Api key is used with an insecure connection.\n",
+      "  self._client = AsyncQdrantClient(**self.client_config)\n",
+      "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/intugle/core/vector_store/qdrant.py:77: UserWarning: Api key is used with an insecure connection.\n",
+      "  self._client = AsyncQdrantClient(**self.client_config)\n",
+      "/home/raphael/Downloads/intugle_demo/env/lib/python3.12/site-packages/intugle/core/semantic_search/semantic_search.py:156: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  late_embedding_result_glossary[\"score\"] = late_embedding_result_glossary[\"score\"] / len(late_vector)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>column_id</th>\n",
+       "      <th>score</th>\n",
+       "      <th>relevancy</th>\n",
+       "      <th>column_name</th>\n",
+       "      <th>column_glossary</th>\n",
+       "      <th>column_tags</th>\n",
+       "      <th>category</th>\n",
+       "      <th>table_name</th>\n",
+       "      <th>table_glossary</th>\n",
+       "      <th>uniqueness</th>\n",
+       "      <th>completeness</th>\n",
+       "      <th>count</th>\n",
+       "      <th>null_count</th>\n",
+       "      <th>distinct_count</th>\n",
+       "      <th>sample_data</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>careplans.reasondescription</td>\n",
+       "      <td>0.854457</td>\n",
+       "      <td>most-relevant</td>\n",
+       "      <td>reasondescription</td>\n",
+       "      <td>Describes the underlying justification or cond...</td>\n",
+       "      <td>[Care Plan Justification, Medical Condition, C...</td>\n",
+       "      <td>dimension</td>\n",
+       "      <td>careplans</td>\n",
+       "      <td>Defines structured plans outlining care activi...</td>\n",
+       "      <td>0.031500</td>\n",
+       "      <td>0.848000</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>304</td>\n",
+       "      <td>63</td>\n",
+       "      <td>[Secondary malignant neoplasm of colon, Closed...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>careplans.reasoncode</td>\n",
+       "      <td>0.854447</td>\n",
+       "      <td>most-relevant</td>\n",
+       "      <td>reasoncode</td>\n",
+       "      <td>Indicates the underlying justification or rati...</td>\n",
+       "      <td>[Care Plan Justification, Clinical Reason Code...</td>\n",
+       "      <td>dimension</td>\n",
+       "      <td>careplans</td>\n",
+       "      <td>Defines structured plans outlining care activi...</td>\n",
+       "      <td>0.031500</td>\n",
+       "      <td>0.848000</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>304</td>\n",
+       "      <td>63</td>\n",
+       "      <td>[284551006, 95417003, 284549007, 62564004, 444...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>supplies.description</td>\n",
+       "      <td>0.853322</td>\n",
+       "      <td>most-relevant</td>\n",
+       "      <td>description</td>\n",
+       "      <td>Provides a concise identification or categoriz...</td>\n",
+       "      <td>[Medical Supplies Classification, Healthcare E...</td>\n",
+       "      <td>dimension</td>\n",
+       "      <td>supplies</td>\n",
+       "      <td>Tracks the allocation and usage of medical sup...</td>\n",
+       "      <td>0.012715</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1573</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>[Alcohol disinfectant (substance), Protective ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>claims.currentillnessdate</td>\n",
+       "      <td>0.850228</td>\n",
+       "      <td>most-relevant</td>\n",
+       "      <td>currentillnessdate</td>\n",
+       "      <td>Indicates the date associated with the onset o...</td>\n",
+       "      <td>[Illness Onset Date, Claims Processing, Health...</td>\n",
+       "      <td>dimension</td>\n",
+       "      <td>claims</td>\n",
+       "      <td>Tracks financial and administrative details re...</td>\n",
+       "      <td>0.642500</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1285</td>\n",
+       "      <td>[1989-11-30 09:52:51+05:30, 2013-03-01 13:27:0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>medications.reasoncode</td>\n",
+       "      <td>0.849430</td>\n",
+       "      <td>relevant</td>\n",
+       "      <td>reasoncode</td>\n",
+       "      <td>Indicates the underlying justification or rati...</td>\n",
+       "      <td>[Medication Reason Codes, Clinical Decision Su...</td>\n",
+       "      <td>dimension</td>\n",
+       "      <td>medications</td>\n",
+       "      <td>Tracks prescribed medications, including their...</td>\n",
+       "      <td>0.012000</td>\n",
+       "      <td>0.742500</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>515</td>\n",
+       "      <td>24</td>\n",
+       "      <td>[301011002, 91302008, 40275004, 44054006, 2398...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>244</th>\n",
+       "      <td>organizations.revenue</td>\n",
+       "      <td>0.809573</td>\n",
+       "      <td>relevant</td>\n",
+       "      <td>revenue</td>\n",
+       "      <td>Monetary income generated by an organization.</td>\n",
+       "      <td>[Financial Performance, Organizational Revenue...</td>\n",
+       "      <td>measure</td>\n",
+       "      <td>organizations</td>\n",
+       "      <td>Organizations serve as entities that provide l...</td>\n",
+       "      <td>0.000887</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1127</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>[0.0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>245</th>\n",
+       "      <td>payers.revenue</td>\n",
+       "      <td>0.808273</td>\n",
+       "      <td>relevant</td>\n",
+       "      <td>revenue</td>\n",
+       "      <td>Monetary income generated by entities responsi...</td>\n",
+       "      <td>[Payer Financial Performance, Healthcare Reven...</td>\n",
+       "      <td>measure</td>\n",
+       "      <td>payers</td>\n",
+       "      <td>Organizations or entities that provide financi...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>[151906000.0, 159838800.0, 135820800.0, 141455...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>246</th>\n",
+       "      <td>allergies.description1</td>\n",
+       "      <td>0.808106</td>\n",
+       "      <td>relevant</td>\n",
+       "      <td>description1</td>\n",
+       "      <td>Captures the specific symptoms or reactions as...</td>\n",
+       "      <td>[Allergy Symptoms, Clinical Findings, Patient ...</td>\n",
+       "      <td>dimension</td>\n",
+       "      <td>allergies</td>\n",
+       "      <td>Tracks individual sensitivities or adverse rea...</td>\n",
+       "      <td>0.022670</td>\n",
+       "      <td>0.516373</td>\n",
+       "      <td>794</td>\n",
+       "      <td>384</td>\n",
+       "      <td>18</td>\n",
+       "      <td>[Anaphylaxis (disorder), Dyspnea (finding), Fi...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>247</th>\n",
+       "      <td>procedures.description</td>\n",
+       "      <td>0.806349</td>\n",
+       "      <td>relevant</td>\n",
+       "      <td>description</td>\n",
+       "      <td>Details the type or category of medical or cli...</td>\n",
+       "      <td>[Medical Procedures, Healthcare Services, Clin...</td>\n",
+       "      <td>dimension</td>\n",
+       "      <td>procedures</td>\n",
+       "      <td>Tracks detailed records of medical or diagnost...</td>\n",
+       "      <td>0.047500</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>95</td>\n",
+       "      <td>[Sputum examination (procedure), Childbirth, S...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>248</th>\n",
+       "      <td>claims.diagnosis1</td>\n",
+       "      <td>0.805703</td>\n",
+       "      <td>relevant</td>\n",
+       "      <td>diagnosis1</td>\n",
+       "      <td>Primary medical condition or diagnosis associa...</td>\n",
+       "      <td>[Primary Diagnosis Code, Clinical Condition Cl...</td>\n",
+       "      <td>dimension</td>\n",
+       "      <td>claims</td>\n",
+       "      <td>Tracks financial and administrative details re...</td>\n",
+       "      <td>0.046000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>92</td>\n",
+       "      <td>[267036007, 307731004, 44465007, 40275004, 302...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>249 rows × 15 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       column_id     score      relevancy         column_name  \\\n",
+       "0    careplans.reasondescription  0.854457  most-relevant   reasondescription   \n",
+       "1           careplans.reasoncode  0.854447  most-relevant          reasoncode   \n",
+       "2           supplies.description  0.853322  most-relevant         description   \n",
+       "3      claims.currentillnessdate  0.850228  most-relevant  currentillnessdate   \n",
+       "4         medications.reasoncode  0.849430       relevant          reasoncode   \n",
+       "..                           ...       ...            ...                 ...   \n",
+       "244        organizations.revenue  0.809573       relevant             revenue   \n",
+       "245               payers.revenue  0.808273       relevant             revenue   \n",
+       "246       allergies.description1  0.808106       relevant        description1   \n",
+       "247       procedures.description  0.806349       relevant         description   \n",
+       "248            claims.diagnosis1  0.805703       relevant          diagnosis1   \n",
+       "\n",
+       "                                       column_glossary  \\\n",
+       "0    Describes the underlying justification or cond...   \n",
+       "1    Indicates the underlying justification or rati...   \n",
+       "2    Provides a concise identification or categoriz...   \n",
+       "3    Indicates the date associated with the onset o...   \n",
+       "4    Indicates the underlying justification or rati...   \n",
+       "..                                                 ...   \n",
+       "244      Monetary income generated by an organization.   \n",
+       "245  Monetary income generated by entities responsi...   \n",
+       "246  Captures the specific symptoms or reactions as...   \n",
+       "247  Details the type or category of medical or cli...   \n",
+       "248  Primary medical condition or diagnosis associa...   \n",
+       "\n",
+       "                                           column_tags   category  \\\n",
+       "0    [Care Plan Justification, Medical Condition, C...  dimension   \n",
+       "1    [Care Plan Justification, Clinical Reason Code...  dimension   \n",
+       "2    [Medical Supplies Classification, Healthcare E...  dimension   \n",
+       "3    [Illness Onset Date, Claims Processing, Health...  dimension   \n",
+       "4    [Medication Reason Codes, Clinical Decision Su...  dimension   \n",
+       "..                                                 ...        ...   \n",
+       "244  [Financial Performance, Organizational Revenue...    measure   \n",
+       "245  [Payer Financial Performance, Healthcare Reven...    measure   \n",
+       "246  [Allergy Symptoms, Clinical Findings, Patient ...  dimension   \n",
+       "247  [Medical Procedures, Healthcare Services, Clin...  dimension   \n",
+       "248  [Primary Diagnosis Code, Clinical Condition Cl...  dimension   \n",
+       "\n",
+       "        table_name                                     table_glossary  \\\n",
+       "0        careplans  Defines structured plans outlining care activi...   \n",
+       "1        careplans  Defines structured plans outlining care activi...   \n",
+       "2         supplies  Tracks the allocation and usage of medical sup...   \n",
+       "3           claims  Tracks financial and administrative details re...   \n",
+       "4      medications  Tracks prescribed medications, including their...   \n",
+       "..             ...                                                ...   \n",
+       "244  organizations  Organizations serve as entities that provide l...   \n",
+       "245         payers  Organizations or entities that provide financi...   \n",
+       "246      allergies  Tracks individual sensitivities or adverse rea...   \n",
+       "247     procedures  Tracks detailed records of medical or diagnost...   \n",
+       "248         claims  Tracks financial and administrative details re...   \n",
+       "\n",
+       "     uniqueness  completeness  count  null_count  distinct_count  \\\n",
+       "0      0.031500      0.848000   2000         304              63   \n",
+       "1      0.031500      0.848000   2000         304              63   \n",
+       "2      0.012715      1.000000   1573           0              20   \n",
+       "3      0.642500      1.000000   2000           0            1285   \n",
+       "4      0.012000      0.742500   2000         515              24   \n",
+       "..          ...           ...    ...         ...             ...   \n",
+       "244    0.000887      1.000000   1127           0               1   \n",
+       "245    1.000000      1.000000     10           0              10   \n",
+       "246    0.022670      0.516373    794         384              18   \n",
+       "247    0.047500      1.000000   2000           0              95   \n",
+       "248    0.046000      1.000000   2000           0              92   \n",
+       "\n",
+       "                                           sample_data  \n",
+       "0    [Secondary malignant neoplasm of colon, Closed...  \n",
+       "1    [284551006, 95417003, 284549007, 62564004, 444...  \n",
+       "2    [Alcohol disinfectant (substance), Protective ...  \n",
+       "3    [1989-11-30 09:52:51+05:30, 2013-03-01 13:27:0...  \n",
+       "4    [301011002, 91302008, 40275004, 44054006, 2398...  \n",
+       "..                                                 ...  \n",
+       "244                                              [0.0]  \n",
+       "245  [151906000.0, 159838800.0, 135820800.0, 141455...  \n",
+       "246  [Anaphylaxis (disorder), Dyspnea (finding), Fi...  \n",
+       "247  [Sputum examination (procedure), Childbirth, S...  \n",
+       "248  [267036007, 307731004, 44465007, 40275004, 302...  \n",
+       "\n",
+       "[249 rows x 15 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Perform a semantic search\n",
+    "search_results = kb.search(\"reason for hospital visit\")\n",
+    "\n",
+    "# View the search results\n",
+    "search_results"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "3d086f45",
    "metadata": {},
    "source": [
-    "## 5. Data Product Creation\n",
+    "## 6. Data Product Creation\n",
     "\n",
     "The semantic layer serves as a foundation for the DataProductBuilder, which streamlines the creation of reusable data products. This allows you\n",
     "to encapsulate business logic and create standardized, trustworthy data assets that can be easily shared and reused across different teams and \n",
@@ -3730,7 +4154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "4b3ca934",
    "metadata": {},
    "outputs": [],
@@ -3776,7 +4200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "e0f9a090",
    "metadata": {},
    "outputs": [
@@ -3911,7 +4335,7 @@
        "9     Alaine226      Willms744                64"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3938,17 +4362,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "31a3e32a",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'SELECT patients.first as first_name, patients.last as last_name, count(claims.id) as number_of_claims FROM claims LEFT JOIN patients ON claims.patientid = patients.id GROUP BY patients.first , patients.last ORDER BY number_of_claims DESC LIMIT 10'"
+       "'SELECT \"patients\".\"first\" as first_name, \"patients\".\"last\" as last_name, count(\"claims\".\"id\") as number_of_claims FROM claims LEFT JOIN patients ON \"claims\".\"patientid\" = \"patients\".\"id\" GROUP BY \"patients\".\"first\" , \"patients\".\"last\" ORDER BY number_of_claims DESC LIMIT 10'"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3968,12 +4392,13 @@
     "\n",
     "You've learned how to:\n",
     "\n",
-    "*   Configure your LLM provider.\n",
+    "*   Configure your LLM provider and Embeddings provider.\n",
     "*   Build a knowledge base using the `KnowledgeBuilder`.\n",
     "*   Access enriched metadata, business glossaries and visualize the relationships between your tables.\n",
+    "*   Perform semantic search on your knowledge base.\n",
     "*   Generate data products from the semantic layer using the `DataProductBuilder`.\n",
     "\n",
-    "This is just a starting point. This project has many other features to explore. We encourage you to try it with your own data and see how it can help you build a powerful semantic layer.\n"
+    "This is just a starting point. This project has many other features to explore. We encourage you to try it with your own data and see how it can help you build a powerful semantic layer."
    ]
   }
  ],
diff --git a/src/intugle/knowledge_builder.py b/src/intugle/knowledge_builder.py
index 5f82dd9..f5fc361 100644
--- a/src/intugle/knowledge_builder.py
+++ b/src/intugle/knowledge_builder.py
@@ -86,7 +86,7 @@ def build(self, force_recreate: bool = False):
         for dataset in self.datasets.values():
             file_path = os.path.join(settings.PROJECT_BASE, f"{dataset.name}.yml")
             if os.path.exists(file_path) and not force_recreate:
-                log.info(f"Dataset {dataset.name} already processed. Loading from file.")
+                print(f"Dataset {dataset.name} already processed. Loading from file.")
                 dataset.load_from_yaml(file_path)
                 continue
             dataset.run(domain=self.domain, save=True)
@@ -109,11 +109,11 @@ def build(self, force_recreate: bool = False):
     def initialize_semantic_search(self):
         """Initialize the semantic search engine."""
         try:
-            log.info("Initializing semantic search...")
+            print("Initializing semantic search...")
             search_client = SemanticSearch()
             _run_async_in_sync(search_client.initialize())
             self._semantic_search_initialized = True
-            log.info("Semantic search initialized.")
+            print("Semantic search initialized.")
         except Exception as e:
             log.warning(f"Could not initialize semantic search: {e}")
             raise e
diff --git a/uv.lock b/uv.lock
index dbcb2a1..0e85fc2 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1305,6 +1305,28 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/04/4b/29cac41a4d98d144bf5f6d33995617b185d14b22401f75ca86f384e87ff1/h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86", size = 37515 },
 ]
 
+[[package]]
+name = "h2"
+version = "4.3.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "hpack" },
+    { name = "hyperframe" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/1d/17/afa56379f94ad0fe8defd37d6eb3f89a25404ffc71d4d848893d270325fc/h2-4.3.0.tar.gz", hash = "sha256:6c59efe4323fa18b47a632221a1888bd7fde6249819beda254aeca909f221bf1", size = 2152026 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/69/b2/119f6e6dcbd96f9069ce9a2665e0146588dc9f88f29549711853645e736a/h2-4.3.0-py3-none-any.whl", hash = "sha256:c438f029a25f7945c69e0ccf0fb951dc3f73a5f6412981daee861431b70e2bdd", size = 61779 },
+]
+
+[[package]]
+name = "hpack"
+version = "4.1.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/2c/48/71de9ed269fdae9c8057e5a4c0aa7402e8bb16f2c6e90b3aa53327b113f8/hpack-4.1.0.tar.gz", hash = "sha256:ec5eca154f7056aa06f196a557655c5b009b382873ac8d1e66e79e87535f1dca", size = 51276 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/07/c6/80c95b1b2b94682a72cbdbfb85b81ae2daffa4291fbfa1b1464502ede10d/hpack-4.1.0-py3-none-any.whl", hash = "sha256:157ac792668d995c657d93111f46b4535ed114f0c9c8d672271bbec7eae1b496", size = 34357 },
+]
+
 [[package]]
 name = "httpcore"
 version = "1.0.9"
@@ -1369,6 +1391,11 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/2a/39/e50c7c3a983047577ee07d2a9e53faf5a69493943ec3f6a384bdc792deb2/httpx-0.28.1-py3-none-any.whl", hash = "sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad", size = 73517 },
 ]
 
+[package.optional-dependencies]
+http2 = [
+    { name = "h2" },
+]
+
 [[package]]
 name = "httpx-sse"
 version = "0.4.1"
@@ -1378,6 +1405,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/25/0a/6269e3473b09aed2dab8aa1a600c70f31f00ae1349bee30658f7e358a159/httpx_sse-0.4.1-py3-none-any.whl", hash = "sha256:cba42174344c3a5b06f255ce65b350880f962d99ead85e776f23c6618a377a37", size = 8054 },
 ]
 
+[[package]]
+name = "hyperframe"
+version = "6.1.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/02/e7/94f8232d4a74cc99514c13a9f995811485a6903d48e5d952771ef6322e30/hyperframe-6.1.0.tar.gz", hash = "sha256:f630908a00854a7adeabd6382b43923a4c4cd4b821fcb527e6ab9e15382a3b08", size = 26566 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/48/30/47d0bf6072f7252e6521f3447ccfa40b421b6824517f82854703d0f5a98b/hyperframe-6.1.0-py3-none-any.whl", hash = "sha256:b03380493a519fce58ea5af42e4a42317bf9bd425596f7a0835ffce80f1a42e5", size = 13007 },
+]
+
 [[package]]
 name = "id"
 version = "1.5.0"
@@ -1422,12 +1458,13 @@ wheels = [
 
 [[package]]
 name = "intugle"
-version = "0.1.3"
+version = "0.1.4.dev0"
 source = { editable = "." }
 dependencies = [
     { name = "asyncpg" },
     { name = "duckdb" },
     { name = "fastapi", extra = ["standard"] },
+    { name = "ipykernel" },
     { name = "langchain", extra = ["anthropic", "google-genai", "openai"] },
     { name = "langchain-community" },
     { name = "langchain-deepseek" },
@@ -1450,6 +1487,7 @@ dependencies = [
     { name = "pyfunctional" },
     { name = "python-dotenv" },
     { name = "pyyaml" },
+    { name = "qdrant-client" },
     { name = "scikit-learn" },
     { name = "symspellpy" },
     { name = "trieregex" },
@@ -1479,6 +1517,7 @@ requires-dist = [
     { name = "asyncpg", specifier = ">=0.30.0" },
     { name = "duckdb", specifier = ">=1.3.2" },
     { name = "fastapi", extras = ["standard"], specifier = ">=0.116.1" },
+    { name = "ipykernel", specifier = ">=6.30.1" },
     { name = "langchain", extras = ["anthropic", "google-genai", "openai"], specifier = ">=0.3.27" },
     { name = "langchain-community", specifier = ">=0.3.21" },
     { name = "langchain-deepseek", specifier = ">=0.1.4" },
@@ -1499,6 +1538,7 @@ requires-dist = [
     { name = "pyfunctional", specifier = ">=1.5.0" },
     { name = "python-dotenv", specifier = ">=1.1.1" },
     { name = "pyyaml", specifier = ">=6.0.2" },
+    { name = "qdrant-client", specifier = ">=1.15.1" },
     { name = "scikit-learn", specifier = "<=1.7.1" },
     { name = "symspellpy", specifier = ">=6.9.0" },
     { name = "trieregex", specifier = ">=1.0.0" },
@@ -3115,6 +3155,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/54/20/4d324d65cc6d9205fabedc306948156824eb9f0ee1633355a8f7ec5c66bf/pluggy-1.6.0-py3-none-any.whl", hash = "sha256:e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746", size = 20538 },
 ]
 
+[[package]]
+name = "portalocker"
+version = "3.2.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pywin32", marker = "sys_platform == 'win32'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/5e/77/65b857a69ed876e1951e88aaba60f5ce6120c33703f7cb61a3c894b8c1b6/portalocker-3.2.0.tar.gz", hash = "sha256:1f3002956a54a8c3730586c5c77bf18fae4149e07eaf1c29fc3faf4d5a3f89ac", size = 95644 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/4b/a6/38c8e2f318bf67d338f4d629e93b0b4b9af331f455f0390ea8ce4a099b26/portalocker-3.2.0-py3-none-any.whl", hash = "sha256:3cdc5f565312224bc570c49337bd21428bba0ef363bbcf58b9ef4a9f11779968", size = 22424 },
+]
+
 [[package]]
 name = "prompt-toolkit"
 version = "3.0.52"
@@ -3701,6 +3753,25 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/06/f6/4a50187e023b8848edd3f0a8e197b1a7fb08d261d8c60aae7cb6c3d71612/pyzmq-27.0.2-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:f0944d65ba2b872b9fcece08411d6347f15a874c775b4c3baae7f278550da0fb", size = 544639 },
 ]
 
+[[package]]
+name = "qdrant-client"
+version = "1.15.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "grpcio" },
+    { name = "httpx", extra = ["http2"] },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "numpy", version = "2.3.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "portalocker" },
+    { name = "protobuf" },
+    { name = "pydantic" },
+    { name = "urllib3" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/79/8b/76c7d325e11d97cb8eb5e261c3759e9ed6664735afbf32fdded5b580690c/qdrant_client-1.15.1.tar.gz", hash = "sha256:631f1f3caebfad0fd0c1fba98f41be81d9962b7bf3ca653bed3b727c0e0cbe0e", size = 295297 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ef/33/d8df6a2b214ffbe4138db9a1efe3248f67dc3c671f82308bea1582ecbbb7/qdrant_client-1.15.1-py3-none-any.whl", hash = "sha256:2b975099b378382f6ca1cfb43f0d59e541be6e16a5892f282a4b8de7eff5cb63", size = 337331 },
+]
+
 [[package]]
 name = "readme-renderer"
 version = "44.0"

From 23b2e59e06d58956f176d35702cba6aa58dc855a Mon Sep 17 00:00:00 2001
From: JaskaranIntugle <jaskaran@intugle.ai>
Date: Wed, 10 Sep 2025 15:38:18 +0530
Subject: [PATCH 25/25] incremented version

---
 pyproject.toml   | 2 +-
 quickstart.ipynb | 2 +-
 uv.lock          | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index c277d3b..f7ba730 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "intugle"
-version = "0.1.4-dev"
+version = "0.1.4"
 authors = [
     { name="Intugle", email="hello@intugle.ai" },
 ]
diff --git a/quickstart.ipynb b/quickstart.ipynb
index 48f3656..6fbce1e 100644
--- a/quickstart.ipynb
+++ b/quickstart.ipynb
@@ -4404,7 +4404,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "env",
+   "display_name": "intugle",
    "language": "python",
    "name": "python3"
   },
diff --git a/uv.lock b/uv.lock
index 0e85fc2..95fd95d 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1458,7 +1458,7 @@ wheels = [
 
 [[package]]
 name = "intugle"
-version = "0.1.4.dev0"
+version = "0.1.4"
 source = { editable = "." }
 dependencies = [
     { name = "asyncpg" },