Add new graph notebook (#321)

* Add graph notebook including ADB client

Signed-off-by: Rahul Tasker <rahul.tasker@oracle.com>

* add graph notebook

* Updated index and README

---------

Signed-off-by: Rahul Tasker <rahul.tasker@oracle.com>
Co-authored-by: John DeSanto <202220+jdesanto@users.noreply.github.com>

Author: rahul-tasker

notebook_examples/README.md

@@ -12,10 +12,11 @@ The ADS SDK can be downloaded from [PyPi](https://pypi.org/project/oracle-ads/),
 
 
 ## Topics
-<img src="https://img.shields.io/badge/deploy model-7-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/register model-7-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/train model-7-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/automlx-5-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/data flow-4-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/pyspark-4-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/bds-3-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/oracle open data-3-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/scikit learn-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/big data service-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/nlp-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/autonomous database-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/language services-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/string manipulation-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/regex-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/regular expression-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/natural language processing-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/NLP-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/part of speech tagging-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/named entity recognition-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/sentiment analysis-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/custom plugins-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/data catalog metastore-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/xgboost-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/text classification-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/classification-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/regression-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/intel-1-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/intel extension-1-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/scikit learn-1-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> 
+<img src="https://img.shields.io/badge/deploy model-7-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/register model-7-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/train model-7-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/automlx-5-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/data flow-4-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/pyspark-4-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/oracle open data-3-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/bds-3-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/scikit learn-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/classification-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/language services-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/string manipulation-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/regex-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/regular expression-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/natural language processing-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/NLP-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/part of speech tagging-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/named entity recognition-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/sentiment analysis-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/custom plugins-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/regression-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/text classification-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/big data service-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/nlp-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/autonomous database-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/data catalog metastore-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/xgboost-2-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/intel-1-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/intel extension-1-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> <img src="https://img.shields.io/badge/scikit learn-1-brightgreen?style=for-the-badge&logo=pypi&logoColor=white"> 
 
 ## Contents
  - [Audi Autonomous Driving Dataset Repository](#audi-autonomous_driving-oracle_open_data.ipynb)
+ - [Bank Graph Example Notebook](#graph_insight-autonomous_database.ipynb)
  - [Building a Forecaster using AutoMLx](#automlx-forecasting.ipynb)
  - [Building and Explaining a Classifier using AutoMLx](#automlx-classifier.ipynb)
  - [Building and Explaining a Regressor using AutoMLx](#automlx-regression.ipynb)
@@ -73,34 +74,34 @@ This notebook was developed on the conda pack with slug: `automlx_p38_cpu_v2`
 <sub>Universal Permissive License v 1.0</sup>
 
 ---
-### <a name="automlx-text_classification.ipynb"></a> - Building and Explaining a Text Classifier using AutoMLx
+### <a name="automlx-classifier.ipynb"></a> - Building and Explaining a Classifier using AutoMLx
 
 <sub>Updated: 05/29/2023</sub>
-#### [`automlx-text_classification.ipynb`](automlx-text_classification.ipynb)
+#### [`automlx-classifier.ipynb`](automlx-classifier.ipynb)
 
 
-build a classifier using the Oracle AutoMLx tool for the public 20newsgroup dataset
+Build a classifier using the Oracle AutoMLx tool and binary data set of Census income data.
 
 This notebook was developed on the conda pack with slug: `automlx_p38_cpu_v3`
 
 
-`automlx`  `text classification`  `text classifier`
+`automlx`  `classification`  `classifier`
 
-<sub>Universal Permissive License v 1.0.</sup>
+<sub>Universal Permissive License v 1.0</sup>
 
 ---
-### <a name="automlx-classifier.ipynb"></a> - Building and Explaining a Classifier using AutoMLx
+### <a name="automlx-fairness.ipynb"></a> - Fairness with AutoMLx
 
 <sub>Updated: 05/29/2023</sub>
-#### [`automlx-classifier.ipynb`](automlx-classifier.ipynb)
+#### [`automlx-fairness.ipynb`](automlx-fairness.ipynb)
 
 
-Build a classifier using the Oracle AutoMLx tool and binary data set of Census income data.
+Develop a model and evaluate its fairness
 
 This notebook was developed on the conda pack with slug: `automlx_p38_cpu_v3`
 
 
-`automlx`  `classification`  `classifier`
+`automlx`  `fairness`
 
 <sub>Universal Permissive License v 1.0</sup>
 
@@ -121,20 +122,20 @@ This notebook was developed on the conda pack with slug: `automlx_p38_cpu_v3`
 <sub>Universal Permissive License v 1.0</sup>
 
 ---
-### <a name="automlx-fairness.ipynb"></a> - Fairness with AutoMLx
+### <a name="automlx-text_classification.ipynb"></a> - Building and Explaining a Text Classifier using AutoMLx
 
 <sub>Updated: 05/29/2023</sub>
-#### [`automlx-fairness.ipynb`](automlx-fairness.ipynb)
+#### [`automlx-text_classification.ipynb`](automlx-text_classification.ipynb)
 
 
-Develop a model and evaluate its fairness
+build a classifier using the Oracle AutoMLx tool for the public 20newsgroup dataset
 
 This notebook was developed on the conda pack with slug: `automlx_p38_cpu_v3`
 
 
-`automlx`  `fairness`
+`automlx`  `text classification`  `text classifier`
 
-<sub>Universal Permissive License v 1.0</sup>
+<sub>Universal Permissive License v 1.0.</sup>
 
 ---
 ### <a name="audi-autonomous_driving-oracle_open_data.ipynb"></a> - Audi Autonomous Driving Dataset Repository
@@ -280,6 +281,22 @@ This notebook was developed on the conda pack with slug: `generalml_p38_cpu_v1`
 
 <sub>Universal Permissive License v 1.0</sup>
 
+---
+### <a name="graph_insight-autonomous_database.ipynb"></a> - Bank Graph Example Notebook
+
+<sub>Updated: 06/02/2023</sub>
+#### [`graph_insight-autonomous_database.ipynb`](graph_insight-autonomous_database.ipynb)
+
+ 
+Access
+
+This notebook was developed on the conda pack with slug: `pypgx2310_p38_cpu_v1`
+
+ 
+`graph_insight`  `autonomous_database`
+
+<sub>Universal Permissive License v 1.0</sup>
+
 ---
 ### <a name="train-register-deploy-huggingface-pipeline.ipynb"></a> - Train, register, and deploy HuggingFace Pipeline
 
@@ -345,31 +362,31 @@ This notebook was developed on the conda pack with slug: `pyspark30_p37_cpu_v5`
 <sub>Universal Permissive License v 1.0</sup>
 
 ---
-### <a name="natural_language_processing.ipynb"></a> - Natural Language Processing
+### <a name="automlx-forecasting.ipynb"></a> - Building a Forecaster using AutoMLx
 
-<sub>Updated: 03/26/2023</sub>
-#### [`natural_language_processing.ipynb`](natural_language_processing.ipynb)
+<sub>Updated: 05/29/2023</sub>
+#### [`automlx-forecasting.ipynb`](automlx-forecasting.ipynb)
 
 
-Use the ADS SDK to process and manipulate strings. This notebook includes regular expression matching and natural language (NLP) parsing, including part-of-speech tagging, named entity recognition, and sentiment analysis. It also shows how to create and use custom plugins specific to your specific needs.
+Use Oracle AutoMLx to build a forecast model with real-world data sets.
 
-This notebook was developed on the conda pack with slug: `nlp_p37_cpu_v2`
+This notebook was developed on the conda pack with slug: `automlx_p38_cpu_v3`
 
 
 `language services`  `string manipulation`  `regex`  `regular expression`  `natural language processing`  `NLP`  `part-of-speech tagging`  `named entity recognition`  `sentiment analysis`  `custom plugins`
 
 <sub>Universal Permissive License v 1.0</sup>
 
 ---
-### <a name="automlx-forecasting.ipynb"></a> - Building a Forecaster using AutoMLx
+### <a name="natural_language_processing.ipynb"></a> - Natural Language Processing
 
-<sub>Updated: 05/29/2023</sub>
-#### [`automlx-forecasting.ipynb`](automlx-forecasting.ipynb)
+<sub>Updated: 03/26/2023</sub>
+#### [`natural_language_processing.ipynb`](natural_language_processing.ipynb)
 
 
-Use Oracle AutoMLx to build a forecast model with real-world data sets.
+Use the ADS SDK to process and manipulate strings. This notebook includes regular expression matching and natural language (NLP) parsing, including part-of-speech tagging, named entity recognition, and sentiment analysis. It also shows how to create and use custom plugins specific to your specific needs.
 
-This notebook was developed on the conda pack with slug: `automlx_p38_cpu_v3`
+This notebook was developed on the conda pack with slug: `nlp_p37_cpu_v2`
 
 
 `language services`  `string manipulation`  `regex`  `regular expression`  `natural language processing`  `NLP`  `part-of-speech tagging`  `named entity recognition`  `sentiment analysis`  `custom plugins`
@@ -505,47 +522,47 @@ This notebook was developed on the conda pack with slug: `pypgx2310_p38_cpu_v1`
 <sub>Universal Permissive License v 1.0</sup>
 
 ---
-### <a name="pyspark-data_flow_studio-introduction.ipynb"></a> - Introduction to the Oracle Cloud Infrastructure Data Flow Studio
+### <a name="pyspark-data_flow-application.ipynb"></a> - PySpark
 
-<sub>Updated: 03/26/2023</sub>
-#### [`pyspark-data_flow_studio-introduction.ipynb`](pyspark-data_flow_studio-introduction.ipynb)
+<sub>Updated: 03/30/2023</sub>
+#### [`pyspark-data_flow-application.ipynb`](pyspark-data_flow-application.ipynb)
 
 
-Run interactive Spark workloads on a long lasting Oracle Cloud Infrastructure Data Flow Spark cluster through Apache Livy integration. Data Flow Spark Magic is used for interactively working with remote Spark clusters through Livy, a Spark REST server, in Jupyter notebooks. It includes a set of magic commands for interactively running Spark code.
+Develop local PySpark applications and work with remote clusters using Data Flow.
 
-This notebook was developed on the conda pack with slug: `pyspark32_p38_cpu_v2`
+This notebook was developed on the conda pack with slug: `pyspark24_p37_cpu_v3`
 
 
 `pyspark`  `data flow`
 
 <sub>Universal Permissive License v 1.0</sup>
 
 ---
-### <a name="pyspark-data_flow_studio-spark_nlp.ipynb"></a> - Spark NLP within Oracle Cloud Infrastructure Data Flow Studio
+### <a name="pyspark-data_flow_studio-introduction.ipynb"></a> - Introduction to the Oracle Cloud Infrastructure Data Flow Studio
 
 <sub>Updated: 03/26/2023</sub>
-#### [`pyspark-data_flow_studio-spark_nlp.ipynb`](pyspark-data_flow_studio-spark_nlp.ipynb)
+#### [`pyspark-data_flow_studio-introduction.ipynb`](pyspark-data_flow_studio-introduction.ipynb)
 
 
-Demonstrates how to use Spark NLP within a long lasting Oracle Cloud Infrastructure Data Flow cluster.
+Run interactive Spark workloads on a long lasting Oracle Cloud Infrastructure Data Flow Spark cluster through Apache Livy integration. Data Flow Spark Magic is used for interactively working with remote Spark clusters through Livy, a Spark REST server, in Jupyter notebooks. It includes a set of magic commands for interactively running Spark code.
 
-This notebook was developed on the conda pack with slug: `pyspark32_p38_cpu_v1`
+This notebook was developed on the conda pack with slug: `pyspark32_p38_cpu_v2`
 
 
 `pyspark`  `data flow`
 
 <sub>Universal Permissive License v 1.0</sup>
 
 ---
-### <a name="pyspark-data_flow-application.ipynb"></a> - PySpark
+### <a name="pyspark-data_flow_studio-spark_nlp.ipynb"></a> - Spark NLP within Oracle Cloud Infrastructure Data Flow Studio
 
-<sub>Updated: 03/30/2023</sub>
-#### [`pyspark-data_flow-application.ipynb`](pyspark-data_flow-application.ipynb)
+<sub>Updated: 03/26/2023</sub>
+#### [`pyspark-data_flow_studio-spark_nlp.ipynb`](pyspark-data_flow_studio-spark_nlp.ipynb)
 
 
-Develop local PySpark applications and work with remote clusters using Data Flow.
+Demonstrates how to use Spark NLP within a long lasting Oracle Cloud Infrastructure Data Flow cluster.
 
-This notebook was developed on the conda pack with slug: `pyspark24_p37_cpu_v3`
+This notebook was developed on the conda pack with slug: `pyspark32_p38_cpu_v1`
 
 
 `pyspark`  `data flow`