From d9e97f0e33ca9fc0c0d391d64b494c4e744dcd55 Mon Sep 17 00:00:00 2001
From: Sonal Goyal <sonalgoyal4@gmail.com>
Date: Wed, 3 Jan 2024 16:17:09 +0530
Subject: [PATCH 01/36] docs showing up, but structure is off

---
 .../docs/_build/doctrees/environment.pickle   | Bin 0 -> 1083362 bytes
 python/docs/_build/doctrees/index.doctree     | Bin 0 -> 297408 bytes
 python/docs/_build/doctrees/zingg.doctree     | Bin 0 -> 297127 bytes
 python/docs/index.rst                         |   2 +-
 4 files changed, 1 insertion(+), 1 deletion(-)
 create mode 100644 python/docs/_build/doctrees/environment.pickle
 create mode 100644 python/docs/_build/doctrees/index.doctree
 create mode 100644 python/docs/_build/doctrees/zingg.doctree

diff --git a/python/docs/_build/doctrees/environment.pickle b/python/docs/_build/doctrees/environment.pickle
new file mode 100644
index 0000000000000000000000000000000000000000..613e146b91b442bd9b35f1890f8fc7e8cd704441
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diff --git a/python/docs/index.rst b/python/docs/index.rst
index 26777a748..f25596d00 100644
--- a/python/docs/index.rst
+++ b/python/docs/index.rst
@@ -4,7 +4,7 @@
    contain the root `toctree` directive.
 
 Welcome to the documentation of Zingg Entity Resolution With Python package!
-=================================
+============================================================================
    
 Contents:
 

From 9e67cb855290601695847b17ccadd819392f1ed8 Mon Sep 17 00:00:00 2001
From: Sonal Goyal <sonalgoyal4@gmail.com>
Date: Wed, 3 Jan 2024 21:21:39 +0530
Subject: [PATCH 02/36] docs appearing better, exanmple added

---
 .../docs/_build/doctrees/environment.pickle   | Bin 1083362 -> 0 bytes
 python/docs/_build/doctrees/index.doctree     | Bin 297408 -> 0 bytes
 python/docs/_build/doctrees/zingg.doctree     | Bin 297127 -> 0 bytes
 python/docs/conf.py                           |   5 +-
 python/docs/index.rst                         |  90 ++++++++++++------
 python/docs/zingg.rst                         |   8 +-
 python/zingg/client.py                        |   1 -
 7 files changed, 66 insertions(+), 38 deletions(-)
 delete mode 100644 python/docs/_build/doctrees/environment.pickle
 delete mode 100644 python/docs/_build/doctrees/index.doctree
 delete mode 100644 python/docs/_build/doctrees/zingg.doctree

diff --git a/python/docs/_build/doctrees/environment.pickle b/python/docs/_build/doctrees/environment.pickle
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diff --git a/python/docs/conf.py b/python/docs/conf.py
index 6be49c6eb..e491980be 100644
--- a/python/docs/conf.py
+++ b/python/docs/conf.py
@@ -18,12 +18,13 @@
 print(os.path.abspath('../zingg/'))
 # -- Project information -----------------------------------------------------
 
-project = 'Zingg'
+project = 'Zingg Entity Resolution'
 copyright = '2024, Zingg.AI'
 author = 'Zingg.AI'
 
 # The full version, including alpha/beta/rc tags
 release = '0.4.0'
+version = '0.4.0'
 
 
 # -- General configuration ---------------------------------------------------
@@ -53,7 +54,7 @@
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 #
-html_theme = 'classic'
+html_theme = 'sphinx_rtd_theme'
 
 
 # Add any paths that contain custom static files (such as style sheets) here,
diff --git a/python/docs/index.rst b/python/docs/index.rst
index f25596d00..914795720 100644
--- a/python/docs/index.rst
+++ b/python/docs/index.rst
@@ -3,50 +3,82 @@
    You can adapt this file completely to your liking, but it should at least
    contain the root `toctree` directive.
 
-Welcome to the documentation of Zingg Entity Resolution With Python package!
+Zingg Entity Resolution Python Package
 ============================================================================
    
-Contents:
+ 
+Zingg Python APIs for entity resolution, identity resolution, record linkage, data mastering and deduplication using ML
+(https://www.zingg.ai) 
+
+
+.. note::
+   Requires python 3.6+; spark 3.5.0
+   Otherwise, :py:func:`zingg.client.Zingg` cannot be executed
+
 
 .. toctree::
    :maxdepth: 3
-   
-   zingg
- 
 
-Zingg Python APIs for entity resolution, record linkage, data mastering and deduplication using ML
-(https://www.zingg.ai) 
+Example API Usage
+=================
 
-requires python 3.6+; spark 3.5.0
-Otherwise, :py:func:`zingg.client.Zingg` cannot be executed
+.. code:: python
+   :number-lines:
 
-.. toctree::
-   :maxdepth: 2
-   
+   from zingg.client import *
+   from zingg.pipes import *
 
-.. automodule:: zingg
-   :members:
-   :undoc-members:
-   :show-inheritance:
+   #build the arguments for zingg
+   args = Arguments()
+   #set field definitions
+   fname = FieldDefinition("fname", "string", MatchType.FUZZY)
+   lname = FieldDefinition("lname", "string", MatchType.FUZZY)
+   stNo = FieldDefinition("stNo", "string", MatchType.FUZZY)
+   add1 = FieldDefinition("add1","string", MatchType.FUZZY)
+   add2 = FieldDefinition("add2", "string", MatchType.FUZZY)
+   city = FieldDefinition("city", "string", MatchType.FUZZY)
+   areacode = FieldDefinition("areacode", "string", MatchType.FUZZY)
+   state = FieldDefinition("state", "string", MatchType.FUZZY)
+   dob = FieldDefinition("dob", "string", MatchType.FUZZY)
+   ssn = FieldDefinition("ssn", "string", MatchType.FUZZY)
 
-.. automodule:: zingg.client
-   :members:
-   :undoc-members:
-   :show-inheritance:
+   fieldDefs = [fname, lname, stNo, add1, add2, city, areacode, state, dob, ssn]
 
-.. automodule:: zingg.pipes
-   :members:
-   :undoc-members:
-   :show-inheritance:
+   args.setFieldDefinition(fieldDefs)
+   #set the modelid and the zingg dir
+   args.setModelId("100")
+   args.setZinggDir("models")
+   args.setNumPartitions(4)
+   args.setLabelDataSampleSize(0.5)
 
-Indices and tables
+   #reading dataset into inputPipe and settint it up in 'args'
+   #below line should not be required if you are reading from in memory dataset
+   #in that case, replace df with input df
+   schema = "id string, fname string, lname string, stNo string, add1 string, add2 string, city string, areacode string, state string, dob string, ssn  string"
+   inputPipe = CsvPipe("testFebrl", "examples/febrl/test.csv", schema)
+   args.setData(inputPipe)
+   outputPipe = CsvPipe("resultFebrl", "/tmp/febrlOutput")
+
+   args.setOutput(outputPipe)
+
+   options = ClientOptions([ClientOptions.PHASE,"match"])
+
+   #Zingg execution for the given phase
+   zingg = Zingg(args, options)
+   zingg.initAndExecute()
+
+
+API Reference
 ==================
+* :ref:`modindex`
 
+Index
+======
 * :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
 
-.. note::
 
-   This project is used by Zingg.AI
+* :ref:`search`
 
+.. meta::
+   :description: Zingg Entity Resolution With Python and AI
+   :keywords: entity resolution, identity resolution, record linkage
diff --git a/python/docs/zingg.rst b/python/docs/zingg.rst
index 2e5369c2b..bbeef788a 100644
--- a/python/docs/zingg.rst
+++ b/python/docs/zingg.rst
@@ -1,9 +1,5 @@
 Zingg Entity Resolution Package
-=============
-
-
-Contents
----------------
+================================
 
 Zingg Python APIs for entity resolution, record linkage, data mastering and deduplication using ML
 (https://www.zingg.ai) 
@@ -12,7 +8,7 @@ requires python 3.6+; spark 3.5.0
 Otherwise, :py:func:`zingg.client.Zingg` cannot be executed
 
 .. toctree::
-   :maxdepth: 2
+   :maxdepth: 3
    
 
 .. automodule:: zingg
diff --git a/python/zingg/client.py b/python/zingg/client.py
index 3c748a978..fd1aeb6c9 100644
--- a/python/zingg/client.py
+++ b/python/zingg/client.py
@@ -365,7 +365,6 @@ class ZinggWithSpark(Zingg):
     :type options: ClientOptions
 
     """
-
     def __init__(self, args, options):
         self.client = getJVM().zingg.spark.client.SparkClient(args.getArgs(), options.getClientOptions(), getSparkSession()._jsparkSession)
 

From 37d4989bf7420d75293bab231d2df559e73d92e2 Mon Sep 17 00:00:00 2001
From: Sonal Goyal <sonalgoyal4@gmail.com>
Date: Wed, 3 Jan 2024 21:30:06 +0530
Subject: [PATCH 03/36] added sphinx rtd theme requirement to py docs for
 readthedocs

---
 .readthedocs.yaml            | 6 +++---
 python/docs/requirements.txt | 1 +
 2 files changed, 4 insertions(+), 3 deletions(-)
 create mode 100644 python/docs/requirements.txt

diff --git a/.readthedocs.yaml b/.readthedocs.yaml
index 3a1089ff1..e6e682e71 100644
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -30,6 +30,6 @@ sphinx:
 # Optional but recommended, declare the Python requirements required
 # to build your documentation
 # See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html
-# python:
-#   install:
-#     - requirements: docs/requirements.txt
+python:
+   install:
+    - requirements: docs/requirements.txt
diff --git a/python/docs/requirements.txt b/python/docs/requirements.txt
new file mode 100644
index 000000000..52b04f2ec
--- /dev/null
+++ b/python/docs/requirements.txt
@@ -0,0 +1 @@
+sphinx_rtd_theme
\ No newline at end of file

From a4765bdba052d9e9f1cdf0e9d7bae9853013b43f Mon Sep 17 00:00:00 2001
From: Sonal Goyal <sonalgoyal4@gmail.com>
Date: Wed, 3 Jan 2024 21:50:14 +0530
Subject: [PATCH 04/36] updated rtd yaml

---
 .readthedocs.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.readthedocs.yaml b/.readthedocs.yaml
index e6e682e71..e5cc55118 100644
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -32,4 +32,4 @@ sphinx:
 # See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html
 python:
    install:
-    - requirements: docs/requirements.txt
+    - requirements: python/docs/requirements.txt

From 2ee148ab52391f32ca17d81e1917dca12eb5c243 Mon Sep 17 00:00:00 2001
From: Sonal Goyal <sonalgoyal4@gmail.com>
Date: Wed, 3 Jan 2024 21:58:20 +0530
Subject: [PATCH 05/36] added py docs

---
 python/docs/_build/doctrees/environment.pickle | Bin 0 -> 635961 bytes
 python/docs/_build/doctrees/index.doctree      | Bin 0 -> 10704 bytes
 python/docs/_build/doctrees/zingg.doctree      | Bin 0 -> 296430 bytes
 3 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 python/docs/_build/doctrees/environment.pickle
 create mode 100644 python/docs/_build/doctrees/index.doctree
 create mode 100644 python/docs/_build/doctrees/zingg.doctree

diff --git a/python/docs/_build/doctrees/environment.pickle b/python/docs/_build/doctrees/environment.pickle
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HcmV?d00001


From aff40cb306d9f0ce68aa25da1fb07672de9fff1b Mon Sep 17 00:00:00 2001
From: Sonal Goyal <sonalgoyal4@gmail.com>
Date: Wed, 3 Jan 2024 22:05:47 +0530
Subject: [PATCH 06/36] moved examples below the index

---
 .../docs/_build/doctrees/environment.pickle   | Bin 635961 -> 533566 bytes
 python/docs/_build/doctrees/index.doctree     | Bin 10704 -> 10371 bytes
 python/docs/index.rst                         |  17 +++++++----------
 3 files changed, 7 insertions(+), 10 deletions(-)

diff --git a/python/docs/_build/doctrees/environment.pickle b/python/docs/_build/doctrees/environment.pickle
index 8ccd5ea676ab1d8747f151c4cfb0e1618d24b661..6c0e28602b7c4ea9cbd8e0475ce9fa6c60e1b7d6 100644
GIT binary patch
literal 533566
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diff --git a/python/docs/_build/doctrees/index.doctree b/python/docs/_build/doctrees/index.doctree
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diff --git a/python/docs/index.rst b/python/docs/index.rst
index 914795720..ca6307762 100644
--- a/python/docs/index.rst
+++ b/python/docs/index.rst
@@ -19,6 +19,13 @@ Zingg Python APIs for entity resolution, identity resolution, record linkage, da
 .. toctree::
    :maxdepth: 3
 
+
+API Reference
+==================
+* :ref:`modindex`
+* :ref:`genindex`
+* :ref:`search`
+
 Example API Usage
 =================
 
@@ -68,16 +75,6 @@ Example API Usage
    zingg.initAndExecute()
 
 
-API Reference
-==================
-* :ref:`modindex`
-
-Index
-======
-* :ref:`genindex`
-
-
-* :ref:`search`
 
 .. meta::
    :description: Zingg Entity Resolution With Python and AI

From 9771389adb5f64bbe38281ab6e9f1d1c84021a72 Mon Sep 17 00:00:00 2001
From: Sonal Goyal <sonalgoyal4@gmail.com>
Date: Wed, 3 Jan 2024 22:17:28 +0530
Subject: [PATCH 07/36] added zingg to the index.rst

---
 .../docs/_build/doctrees/environment.pickle   | Bin 533566 -> 533605 bytes
 python/docs/_build/doctrees/index.doctree     | Bin 10371 -> 10386 bytes
 python/docs/index.rst                         |   1 +
 3 files changed, 1 insertion(+)

diff --git a/python/docs/_build/doctrees/environment.pickle b/python/docs/_build/doctrees/environment.pickle
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index db86f70dee06e7a375fe485019538c5ffd86d32b..aacb0eb1c34e290678cdb7273e51db35a45e424d 100644
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diff --git a/python/docs/index.rst b/python/docs/index.rst
index ca6307762..63b396d3e 100644
--- a/python/docs/index.rst
+++ b/python/docs/index.rst
@@ -19,6 +19,7 @@ Zingg Python APIs for entity resolution, identity resolution, record linkage, da
 .. toctree::
    :maxdepth: 3
 
+   zingg
 
 API Reference
 ==================

From adff68ad5c4732b27fb75f6838e498b6f9a0eac2 Mon Sep 17 00:00:00 2001
From: Sonal Goyal <sonalgoyal4@gmail.com>
Date: Wed, 3 Jan 2024 22:23:29 +0530
Subject: [PATCH 08/36] added pandas dep for rtd

---
 python/docs/requirements.txt | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/python/docs/requirements.txt b/python/docs/requirements.txt
index 52b04f2ec..de8aa1a15 100644
--- a/python/docs/requirements.txt
+++ b/python/docs/requirements.txt
@@ -1 +1,2 @@
-sphinx_rtd_theme
\ No newline at end of file
+sphinx_rtd_theme
+pandas
\ No newline at end of file

From 9b8f0b217d70111bb611e083de60541933b1c4ce Mon Sep 17 00:00:00 2001
From: Sonal Goyal <sonalgoyal4@gmail.com>
Date: Wed, 3 Jan 2024 22:28:22 +0530
Subject: [PATCH 09/36] updated requirements to refer to py requirements.txt

---
 .readthedocs.yaml            | 2 +-
 python/docs/requirements.txt | 2 --
 2 files changed, 1 insertion(+), 3 deletions(-)
 delete mode 100644 python/docs/requirements.txt

diff --git a/.readthedocs.yaml b/.readthedocs.yaml
index e5cc55118..9a1fb7cfb 100644
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -32,4 +32,4 @@ sphinx:
 # See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html
 python:
    install:
-    - requirements: python/docs/requirements.txt
+    - requirements: python/requirements.txt
diff --git a/python/docs/requirements.txt b/python/docs/requirements.txt
deleted file mode 100644
index de8aa1a15..000000000
--- a/python/docs/requirements.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-sphinx_rtd_theme
-pandas
\ No newline at end of file

From 1d46899ce44e15dcf90f16dea6b8872a2bc8fc81 Mon Sep 17 00:00:00 2001
From: Sonal Goyal <sonalgoyal4@gmail.com>
Date: Thu, 4 Jan 2024 18:49:52 +0530
Subject: [PATCH 10/36] added __main__ to pkg to make sure it doesnt run when
 loaded

---
 .../docs/_build/doctrees/environment.pickle   |  Bin 533605 -> 35314 bytes
 python/docs/_build/doctrees/index.doctree     |  Bin 10386 -> 10040 bytes
 python/docs/_build/doctrees/zingg.doctree     |  Bin 296430 -> 6968 bytes
 python/docs/_build/markdown/index.md          |  183 +++
 python/docs/_build/markdown/zingg.md          |  785 ++++++++++
 python/docs/conf.py                           |    3 +-
 python/docs/index.rst                         |    5 -
 python/zingg/client.py                        | 1370 ++++++++---------
 python/zingg/pipes.py                         |  439 +++---
 9 files changed, 1875 insertions(+), 910 deletions(-)
 create mode 100644 python/docs/_build/markdown/index.md
 create mode 100644 python/docs/_build/markdown/zingg.md

diff --git a/python/docs/_build/doctrees/environment.pickle b/python/docs/_build/doctrees/environment.pickle
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diff --git a/python/docs/_build/doctrees/index.doctree b/python/docs/_build/doctrees/index.doctree
index aacb0eb1c34e290678cdb7273e51db35a45e424d..399dbb7c48f3e5bb6443c3e03e9ed775c5602788 100644
GIT binary patch
delta 2508
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diff --git a/python/docs/_build/doctrees/zingg.doctree b/python/docs/_build/doctrees/zingg.doctree
index 4d5ef1240a23db39984ac416cde32cc560c5b3df..205abbb8fb5526e0d9010cdfe7a3ba1d47574a4b 100644
GIT binary patch
delta 434
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diff --git a/python/docs/_build/markdown/index.md b/python/docs/_build/markdown/index.md
new file mode 100644
index 000000000..d8b455d19
--- /dev/null
+++ b/python/docs/_build/markdown/index.md
@@ -0,0 +1,183 @@
+<!-- zingg documentation master file, created by
+sphinx-quickstart on Thu Jul  7 12:24:41 2022.
+You can adapt this file completely to your liking, but it should at least
+contain the root `toctree` directive. -->
+
+# Zingg Entity Resolution Python Package
+
+Zingg Python APIs for entity resolution, identity resolution, record linkage, data mastering and deduplication using ML
+([https://www.zingg.ai](https://www.zingg.ai))
+
+#### NOTE
+Requires python 3.6+; spark 3.5.0
+Otherwise, [`zingg.client.Zingg()`](zingg.md#zingg.client.Zingg) cannot be executed
+
+* [Zingg Entity Resolution Package](zingg.md)
+  * [zingg.client](zingg.md#zingg-client)
+  * [`Arguments`](zingg.md#zingg.client.Arguments)
+    * [`Arguments.copyArgs()`](zingg.md#zingg.client.Arguments.copyArgs)
+    * [`Arguments.createArgumentsFromJSON()`](zingg.md#zingg.client.Arguments.createArgumentsFromJSON)
+    * [`Arguments.createArgumentsFromJSONString()`](zingg.md#zingg.client.Arguments.createArgumentsFromJSONString)
+    * [`Arguments.getArgs()`](zingg.md#zingg.client.Arguments.getArgs)
+    * [`Arguments.getModelId()`](zingg.md#zingg.client.Arguments.getModelId)
+    * [`Arguments.getZinggBaseModelDir()`](zingg.md#zingg.client.Arguments.getZinggBaseModelDir)
+    * [`Arguments.getZinggBaseTrainingDataDir()`](zingg.md#zingg.client.Arguments.getZinggBaseTrainingDataDir)
+    * [`Arguments.getZinggModelDir()`](zingg.md#zingg.client.Arguments.getZinggModelDir)
+    * [`Arguments.getZinggTrainingDataMarkedDir()`](zingg.md#zingg.client.Arguments.getZinggTrainingDataMarkedDir)
+    * [`Arguments.getZinggTrainingDataUnmarkedDir()`](zingg.md#zingg.client.Arguments.getZinggTrainingDataUnmarkedDir)
+    * [`Arguments.setArgs()`](zingg.md#zingg.client.Arguments.setArgs)
+    * [`Arguments.setColumn()`](zingg.md#zingg.client.Arguments.setColumn)
+    * [`Arguments.setData()`](zingg.md#zingg.client.Arguments.setData)
+    * [`Arguments.setFieldDefinition()`](zingg.md#zingg.client.Arguments.setFieldDefinition)
+    * [`Arguments.setLabelDataSampleSize()`](zingg.md#zingg.client.Arguments.setLabelDataSampleSize)
+    * [`Arguments.setModelId()`](zingg.md#zingg.client.Arguments.setModelId)
+    * [`Arguments.setNumPartitions()`](zingg.md#zingg.client.Arguments.setNumPartitions)
+    * [`Arguments.setOutput()`](zingg.md#zingg.client.Arguments.setOutput)
+    * [`Arguments.setStopWordsCutoff()`](zingg.md#zingg.client.Arguments.setStopWordsCutoff)
+    * [`Arguments.setTrainingSamples()`](zingg.md#zingg.client.Arguments.setTrainingSamples)
+    * [`Arguments.setZinggDir()`](zingg.md#zingg.client.Arguments.setZinggDir)
+    * [`Arguments.writeArgumentsToJSON()`](zingg.md#zingg.client.Arguments.writeArgumentsToJSON)
+    * [`Arguments.writeArgumentsToJSONString()`](zingg.md#zingg.client.Arguments.writeArgumentsToJSONString)
+  * [`ClientOptions`](zingg.md#zingg.client.ClientOptions)
+    * [`ClientOptions.COLUMN`](zingg.md#zingg.client.ClientOptions.COLUMN)
+    * [`ClientOptions.CONF`](zingg.md#zingg.client.ClientOptions.CONF)
+    * [`ClientOptions.EMAIL`](zingg.md#zingg.client.ClientOptions.EMAIL)
+    * [`ClientOptions.LICENSE`](zingg.md#zingg.client.ClientOptions.LICENSE)
+    * [`ClientOptions.LOCATION`](zingg.md#zingg.client.ClientOptions.LOCATION)
+    * [`ClientOptions.MODEL_ID`](zingg.md#zingg.client.ClientOptions.MODEL_ID)
+    * [`ClientOptions.PHASE`](zingg.md#zingg.client.ClientOptions.PHASE)
+    * [`ClientOptions.REMOTE`](zingg.md#zingg.client.ClientOptions.REMOTE)
+    * [`ClientOptions.ZINGG_DIR`](zingg.md#zingg.client.ClientOptions.ZINGG_DIR)
+    * [`ClientOptions.getClientOptions()`](zingg.md#zingg.client.ClientOptions.getClientOptions)
+    * [`ClientOptions.getConf()`](zingg.md#zingg.client.ClientOptions.getConf)
+    * [`ClientOptions.getLocation()`](zingg.md#zingg.client.ClientOptions.getLocation)
+    * [`ClientOptions.getOptionValue()`](zingg.md#zingg.client.ClientOptions.getOptionValue)
+    * [`ClientOptions.getPhase()`](zingg.md#zingg.client.ClientOptions.getPhase)
+    * [`ClientOptions.hasLocation()`](zingg.md#zingg.client.ClientOptions.hasLocation)
+    * [`ClientOptions.setOptionValue()`](zingg.md#zingg.client.ClientOptions.setOptionValue)
+    * [`ClientOptions.setPhase()`](zingg.md#zingg.client.ClientOptions.setPhase)
+  * [`FieldDefinition`](zingg.md#zingg.client.FieldDefinition)
+    * [`FieldDefinition.getFieldDefinition()`](zingg.md#zingg.client.FieldDefinition.getFieldDefinition)
+    * [`FieldDefinition.setStopWords()`](zingg.md#zingg.client.FieldDefinition.setStopWords)
+    * [`FieldDefinition.stringify()`](zingg.md#zingg.client.FieldDefinition.stringify)
+  * [`Zingg`](zingg.md#zingg.client.Zingg)
+    * [`Zingg.execute()`](zingg.md#zingg.client.Zingg.execute)
+    * [`Zingg.executeLabel()`](zingg.md#zingg.client.Zingg.executeLabel)
+    * [`Zingg.executeLabelUpdate()`](zingg.md#zingg.client.Zingg.executeLabelUpdate)
+    * [`Zingg.getArguments()`](zingg.md#zingg.client.Zingg.getArguments)
+    * [`Zingg.getMarkedRecords()`](zingg.md#zingg.client.Zingg.getMarkedRecords)
+    * [`Zingg.getMarkedRecordsStat()`](zingg.md#zingg.client.Zingg.getMarkedRecordsStat)
+    * [`Zingg.getMatchedMarkedRecordsStat()`](zingg.md#zingg.client.Zingg.getMatchedMarkedRecordsStat)
+    * [`Zingg.getOptions()`](zingg.md#zingg.client.Zingg.getOptions)
+    * [`Zingg.getUnmarkedRecords()`](zingg.md#zingg.client.Zingg.getUnmarkedRecords)
+    * [`Zingg.getUnmatchedMarkedRecordsStat()`](zingg.md#zingg.client.Zingg.getUnmatchedMarkedRecordsStat)
+    * [`Zingg.getUnsureMarkedRecordsStat()`](zingg.md#zingg.client.Zingg.getUnsureMarkedRecordsStat)
+    * [`Zingg.init()`](zingg.md#zingg.client.Zingg.init)
+    * [`Zingg.initAndExecute()`](zingg.md#zingg.client.Zingg.initAndExecute)
+    * [`Zingg.processRecordsCli()`](zingg.md#zingg.client.Zingg.processRecordsCli)
+    * [`Zingg.processRecordsCliLabelUpdate()`](zingg.md#zingg.client.Zingg.processRecordsCliLabelUpdate)
+    * [`Zingg.setArguments()`](zingg.md#zingg.client.Zingg.setArguments)
+    * [`Zingg.setOptions()`](zingg.md#zingg.client.Zingg.setOptions)
+    * [`Zingg.writeLabelledOutput()`](zingg.md#zingg.client.Zingg.writeLabelledOutput)
+    * [`Zingg.writeLabelledOutputFromPandas()`](zingg.md#zingg.client.Zingg.writeLabelledOutputFromPandas)
+  * [`ZinggWithSpark`](zingg.md#zingg.client.ZinggWithSpark)
+  * [`getDfFromDs()`](zingg.md#zingg.client.getDfFromDs)
+  * [`getGateway()`](zingg.md#zingg.client.getGateway)
+  * [`getJVM()`](zingg.md#zingg.client.getJVM)
+  * [`getPandasDfFromDs()`](zingg.md#zingg.client.getPandasDfFromDs)
+  * [`getSparkContext()`](zingg.md#zingg.client.getSparkContext)
+  * [`getSparkSession()`](zingg.md#zingg.client.getSparkSession)
+  * [`getSqlContext()`](zingg.md#zingg.client.getSqlContext)
+  * [`initClient()`](zingg.md#zingg.client.initClient)
+  * [`initDataBricksConectClient()`](zingg.md#zingg.client.initDataBricksConectClient)
+  * [`initSparkClient()`](zingg.md#zingg.client.initSparkClient)
+  * [`parseArguments()`](zingg.md#zingg.client.parseArguments)
+  * [zingg.pipes](zingg.md#zingg-pipes)
+  * [`BigQueryPipe`](zingg.md#zingg.pipes.BigQueryPipe)
+    * [`BigQueryPipe.CREDENTIAL_FILE`](zingg.md#zingg.pipes.BigQueryPipe.CREDENTIAL_FILE)
+    * [`BigQueryPipe.TABLE`](zingg.md#zingg.pipes.BigQueryPipe.TABLE)
+    * [`BigQueryPipe.TEMP_GCS_BUCKET`](zingg.md#zingg.pipes.BigQueryPipe.TEMP_GCS_BUCKET)
+    * [`BigQueryPipe.VIEWS_ENABLED`](zingg.md#zingg.pipes.BigQueryPipe.VIEWS_ENABLED)
+    * [`BigQueryPipe.setCredentialFile()`](zingg.md#zingg.pipes.BigQueryPipe.setCredentialFile)
+    * [`BigQueryPipe.setTable()`](zingg.md#zingg.pipes.BigQueryPipe.setTable)
+    * [`BigQueryPipe.setTemporaryGcsBucket()`](zingg.md#zingg.pipes.BigQueryPipe.setTemporaryGcsBucket)
+    * [`BigQueryPipe.setViewsEnabled()`](zingg.md#zingg.pipes.BigQueryPipe.setViewsEnabled)
+  * [`CsvPipe`](zingg.md#zingg.pipes.CsvPipe)
+    * [`CsvPipe.setDelimiter()`](zingg.md#zingg.pipes.CsvPipe.setDelimiter)
+    * [`CsvPipe.setHeader()`](zingg.md#zingg.pipes.CsvPipe.setHeader)
+    * [`CsvPipe.setLocation()`](zingg.md#zingg.pipes.CsvPipe.setLocation)
+  * [`InMemoryPipe`](zingg.md#zingg.pipes.InMemoryPipe)
+    * [`InMemoryPipe.getDataset()`](zingg.md#zingg.pipes.InMemoryPipe.getDataset)
+    * [`InMemoryPipe.setDataset()`](zingg.md#zingg.pipes.InMemoryPipe.setDataset)
+  * [`Pipe`](zingg.md#zingg.pipes.Pipe)
+    * [`Pipe.addProperty()`](zingg.md#zingg.pipes.Pipe.addProperty)
+    * [`Pipe.getPipe()`](zingg.md#zingg.pipes.Pipe.getPipe)
+    * [`Pipe.setSchema()`](zingg.md#zingg.pipes.Pipe.setSchema)
+    * [`Pipe.toString()`](zingg.md#zingg.pipes.Pipe.toString)
+  * [`SnowflakePipe`](zingg.md#zingg.pipes.SnowflakePipe)
+    * [`SnowflakePipe.DATABASE`](zingg.md#zingg.pipes.SnowflakePipe.DATABASE)
+    * [`SnowflakePipe.DBTABLE`](zingg.md#zingg.pipes.SnowflakePipe.DBTABLE)
+    * [`SnowflakePipe.PASSWORD`](zingg.md#zingg.pipes.SnowflakePipe.PASSWORD)
+    * [`SnowflakePipe.SCHEMA`](zingg.md#zingg.pipes.SnowflakePipe.SCHEMA)
+    * [`SnowflakePipe.URL`](zingg.md#zingg.pipes.SnowflakePipe.URL)
+    * [`SnowflakePipe.USER`](zingg.md#zingg.pipes.SnowflakePipe.USER)
+    * [`SnowflakePipe.WAREHOUSE`](zingg.md#zingg.pipes.SnowflakePipe.WAREHOUSE)
+    * [`SnowflakePipe.setDatabase()`](zingg.md#zingg.pipes.SnowflakePipe.setDatabase)
+    * [`SnowflakePipe.setDbTable()`](zingg.md#zingg.pipes.SnowflakePipe.setDbTable)
+    * [`SnowflakePipe.setPassword()`](zingg.md#zingg.pipes.SnowflakePipe.setPassword)
+    * [`SnowflakePipe.setSFSchema()`](zingg.md#zingg.pipes.SnowflakePipe.setSFSchema)
+    * [`SnowflakePipe.setURL()`](zingg.md#zingg.pipes.SnowflakePipe.setURL)
+    * [`SnowflakePipe.setUser()`](zingg.md#zingg.pipes.SnowflakePipe.setUser)
+    * [`SnowflakePipe.setWarehouse()`](zingg.md#zingg.pipes.SnowflakePipe.setWarehouse)
+
+# API Reference
+
+* [Module Index](py-modindex.md)
+* [Index](genindex.md)
+* [Search Page](search.md)
+
+# Example API Usage
+
+```python
+from zingg.client import *
+from zingg.pipes import *
+
+#build the arguments for zingg
+args = Arguments()
+#set field definitions
+fname = FieldDefinition("fname", "string", MatchType.FUZZY)
+lname = FieldDefinition("lname", "string", MatchType.FUZZY)
+stNo = FieldDefinition("stNo", "string", MatchType.FUZZY)
+add1 = FieldDefinition("add1","string", MatchType.FUZZY)
+add2 = FieldDefinition("add2", "string", MatchType.FUZZY)
+city = FieldDefinition("city", "string", MatchType.FUZZY)
+areacode = FieldDefinition("areacode", "string", MatchType.FUZZY)
+state = FieldDefinition("state", "string", MatchType.FUZZY)
+dob = FieldDefinition("dob", "string", MatchType.FUZZY)
+ssn = FieldDefinition("ssn", "string", MatchType.FUZZY)
+
+fieldDefs = [fname, lname, stNo, add1, add2, city, areacode, state, dob, ssn]
+
+args.setFieldDefinition(fieldDefs)
+#set the modelid and the zingg dir
+args.setModelId("100")
+args.setZinggDir("models")
+args.setNumPartitions(4)
+args.setLabelDataSampleSize(0.5)
+
+#reading dataset into inputPipe and settint it up in 'args'
+#below line should not be required if you are reading from in memory dataset
+#in that case, replace df with input df
+schema = "id string, fname string, lname string, stNo string, add1 string, add2 string, city string, areacode string, state string, dob string, ssn  string"
+inputPipe = CsvPipe("testFebrl", "examples/febrl/test.csv", schema)
+args.setData(inputPipe)
+outputPipe = CsvPipe("resultFebrl", "/tmp/febrlOutput")
+
+args.setOutput(outputPipe)
+
+options = ClientOptions([ClientOptions.PHASE,"match"])
+
+#Zingg execution for the given phase
+zingg = Zingg(args, options)
+zingg.initAndExecute()
+```
diff --git a/python/docs/_build/markdown/zingg.md b/python/docs/_build/markdown/zingg.md
new file mode 100644
index 000000000..9467c6751
--- /dev/null
+++ b/python/docs/_build/markdown/zingg.md
@@ -0,0 +1,785 @@
+# Zingg Entity Resolution Package
+
+Zingg Python APIs for entity resolution, record linkage, data mastering and deduplication using ML
+([https://www.zingg.ai](https://www.zingg.ai))
+
+requires python 3.6+; spark 3.5.0
+Otherwise, [`zingg.client.Zingg()`](#zingg.client.Zingg) cannot be executed
+
+<a id="module-zingg"></a>
+
+<a id="module-zingg.client"></a>
+
+## zingg.client
+
+This module is the main entry point of the Zingg Python API
+
+### *class* zingg.client.Arguments
+
+Bases: `object`
+
+This class helps supply match arguments to Zingg. There are 3 basic steps in any match process.
+
+* **Defining:**
+  specifying information about data location, fields, and our notion of similarity.
+* **Training:**
+  making Zingg learn the matching rules
+* **Matching:**
+  Running the models on the entire dataset
+
+#### copyArgs(phase)
+
+#### *static* createArgumentsFromJSON(fileName, phase)
+
+Method to create an object of this class from the JSON file and phase parameter value.
+
+* **Parameters:**
+  * **fileName** (*String*) – The CONF parameter value of ClientOption object
+  * **phase** (*String*) – The PHASE parameter value of ClientOption object
+* **Returns:**
+  The pointer containing address of the this class object
+* **Return type:**
+  pointer([Arguments](#zingg.client.Arguments))
+
+#### *static* createArgumentsFromJSONString(jsonArgs, phase)
+
+#### getArgs()
+
+Method to get pointer address of this class
+
+* **Returns:**
+  The pointer containing the address of this class object
+* **Return type:**
+  pointer([Arguments](#zingg.client.Arguments))
+
+#### getModelId()
+
+#### getZinggBaseModelDir()
+
+#### getZinggBaseTrainingDataDir()
+
+Method to get the location of the folder where Zingg
+saves the training data found by findTrainingData
+
+#### getZinggModelDir()
+
+#### getZinggTrainingDataMarkedDir()
+
+Method to get the location of the folder where Zingg
+saves the marked training data labeled by the user
+
+#### getZinggTrainingDataUnmarkedDir()
+
+Method to get the location of the folder where Zingg
+saves the training data found by findTrainingData
+
+#### setArgs(argumentsObj)
+
+Method to set this class object
+
+* **Parameters:**
+  **argumentsObj** (*pointer**(*[*Arguments*](#zingg.client.Arguments)*)*) – Argument object to set this object
+
+#### setColumn(column)
+
+Method to set stopWordsCutoff parameter value
+By default, Zingg extracts 10% of the high frequency unique words from a dataset. If user wants different selection, they should set up StopWordsCutoff property
+
+* **Parameters:**
+  **stopWordsCutoff** (*float*) – The stop words cutoff parameter value of ClientOption object or file address of json file
+
+#### setData(\*pipes)
+
+Method to set the file path of the file to be matched.
+
+* **Parameters:**
+  **pipes** ([*Pipe*](#zingg.pipes.Pipe)*[**]*) – input data pipes separated by comma e.g. (pipe1,pipe2,..)
+
+#### setFieldDefinition(fieldDef)
+
+Method convert python objects to java FieldDefinition objects and set the field definitions associated with this client
+
+* **Parameters:**
+  **fieldDef** (*List**(*[*FieldDefinition*](#zingg.client.FieldDefinition)*)*) – python FieldDefinition object list
+
+#### setLabelDataSampleSize(labelDataSampleSize)
+
+Method to set labelDataSampleSize parameter value
+Set the fraction of data to be used from the complete data set to be used for seeding the labeled data Labelling is costly and we want a fast approximate way of looking at a small sample of the records and identifying expected matches and nonmatches
+
+* **Parameters:**
+  **labelDataSampleSize** (*float*) – value between 0.0 and 1.0 denoting portion of dataset to use in generating seed samples
+
+#### setModelId(id)
+
+Method to set the output directory where the match output will be saved
+
+* **Parameters:**
+  **id** (*String*) – model id value
+
+#### setNumPartitions(numPartitions)
+
+Method to set NumPartitions parameter value
+Sample size to use for seeding labeled data We don’t want to run over all the data, as we want a quick way to seed some labeled data that we can manually edit
+
+* **Parameters:**
+  **numPartitions** (*int*) – number of partitions for given data pipes
+
+#### setOutput(\*pipes)
+
+Method to set the output directory where the match result will be saved
+
+* **Parameters:**
+  **pipes** ([*Pipe*](#zingg.pipes.Pipe)*[**]*) – output data pipes separated by comma e.g. (pipe1,pipe2,..)
+
+#### setStopWordsCutoff(stopWordsCutoff)
+
+Method to set stopWordsCutoff parameter value
+By default, Zingg extracts 10% of the high frequency unique words from a dataset. If user wants different selection, they should set up StopWordsCutoff property
+
+* **Parameters:**
+  **stopWordsCutoff** (*float*) – The stop words cutoff parameter value of ClientOption object or file address of json file
+
+#### setTrainingSamples(\*pipes)
+
+Method to set existing training samples to be matched.
+
+* **Parameters:**
+  **pipes** ([*Pipe*](#zingg.pipes.Pipe)*[**]*) – input training data pipes separated by comma e.g. (pipe1,pipe2,..)
+
+#### setZinggDir(f)
+
+Method to set the location for Zingg to save its internal computations and models. Please set it to a place where the program has to write access.
+
+* **Parameters:**
+  **f** (*String*) – Zingg directory name of the models
+
+#### writeArgumentsToJSON(fileName)
+
+Method to write JSON file from the object of this class
+
+* **Parameters:**
+  **fileName** (*String*) – The CONF parameter value of ClientOption object or file address of json file
+
+#### writeArgumentsToJSONString()
+
+Method to create an object of this class from the JSON file and phase parameter value.
+
+* **Parameters:**
+  * **fileName** (*String*) – The CONF parameter value of ClientOption object
+  * **phase** (*String*) – The PHASE parameter value of ClientOption object
+* **Returns:**
+  The pointer containing address of the this class object
+* **Return type:**
+  pointer([Arguments](#zingg.client.Arguments))
+
+### *class* zingg.client.ClientOptions(argsSent=None)
+
+Bases: `object`
+
+Class that contains Client options for Zingg object
+:param phase: trainMatch, train, match, link, findAndLabel, findTrainingData, recommend etc
+:type phase: String
+:param args: Parse a list of Zingg command line options parameter values e.g. “–location” etc. optional argument for initializing this class.
+:type args: List(String) or None
+
+#### COLUMN *= <py4j.java_gateway.JavaPackage object>*
+
+Column whose stop words are to be recommended through Zingg
+
+* **Type:**
+  COLUMN
+
+#### CONF *= <py4j.java_gateway.JavaPackage object>*
+
+conf parameter for this class
+
+* **Type:**
+  CONF
+
+#### EMAIL *= <py4j.java_gateway.JavaPackage object>*
+
+e-mail parameter for this class
+
+* **Type:**
+  EMAIL
+
+#### LICENSE *= <py4j.java_gateway.JavaPackage object>*
+
+license parameter for this class
+
+* **Type:**
+  LICENSE
+
+#### LOCATION *= <py4j.java_gateway.JavaPackage object>*
+
+location parameter for this class
+
+* **Type:**
+  LOCATION
+
+#### MODEL_ID *= <py4j.java_gateway.JavaPackage object>*
+
+ZINGG_DIR/MODEL_ID is used to save the model
+
+* **Type:**
+  MODEL_ID
+
+#### PHASE *= <py4j.java_gateway.JavaPackage object>*
+
+phase parameter for this class
+
+* **Type:**
+  PHASE
+
+#### REMOTE *= <py4j.java_gateway.JavaPackage object>*
+
+remote option used internally for running on Databricks
+
+* **Type:**
+  REMOTE
+
+#### ZINGG_DIR *= <py4j.java_gateway.JavaPackage object>*
+
+location where Zingg saves the model, training data etc
+
+* **Type:**
+  ZINGG_DIR
+
+#### getClientOptions()
+
+Method to get pointer address of this class
+
+* **Returns:**
+  The pointer containing address of the this class object
+* **Return type:**
+  pointer([ClientOptions](#zingg.client.ClientOptions))
+
+#### getConf()
+
+Method to get CONF value
+
+* **Returns:**
+  The CONF parameter value
+* **Return type:**
+  String
+
+#### getLocation()
+
+Method to get LOCATION value
+
+* **Returns:**
+  The LOCATION parameter value
+* **Return type:**
+  String
+
+#### getOptionValue(option)
+
+Method to get value for the key option
+
+* **Parameters:**
+  **option** (*String*) – key to geting the value
+* **Returns:**
+  The value which is mapped for given key
+* **Return type:**
+  String
+
+#### getPhase()
+
+Method to get PHASE value
+
+* **Returns:**
+  The PHASE parameter value
+* **Return type:**
+  String
+
+#### hasLocation()
+
+Method to check if this class has LOCATION parameter set as None or not
+
+* **Returns:**
+  The boolean value if LOCATION parameter is present or not
+* **Return type:**
+  Bool
+
+#### setOptionValue(option, value)
+
+Method to map option key to the given value
+
+* **Parameters:**
+  * **option** (*String*) – key that is mapped with value
+  * **value** (*String*) – value to be set for given key
+
+#### setPhase(newValue)
+
+Method to set PHASE value
+
+* **Parameters:**
+  **newValue** (*String*) – name of the phase
+* **Returns:**
+  The pointer containing address of the this class object after seting phase
+* **Return type:**
+  pointer([ClientOptions](#zingg.client.ClientOptions))
+
+### *class* zingg.client.FieldDefinition(name, dataType, \*matchType)
+
+Bases: `object`
+
+This class defines each field that we use in matching We can use this to configure the properties of each field we use for matching in Zingg.
+
+* **Parameters:**
+  * **name** (*String*) – name of the field
+  * **dataType** (*String*) – type of the data e.g. string, float, etc.
+  * **matchType** (*MatchType*) – match type of this field e.g. FUSSY, EXACT, etc.
+
+#### getFieldDefinition()
+
+Method to get  pointer address of this class
+
+* **Returns:**
+  The pointer containing the address of this class object
+* **Return type:**
+  pointer([FieldDefinition](#zingg.client.FieldDefinition))
+
+#### setStopWords(stopWords)
+
+Method to add stopwords to this class object
+
+* **Parameters:**
+  **stopWords** (*String*) – The stop Words containing csv file’s location
+
+#### stringify(str)
+
+Method to stringify’ed the dataType before it is set in FieldDefinition object
+
+* **Parameters:**
+  **str** (*String*) – dataType of the FieldDefinition
+* **Returns:**
+  The stringify’ed value of the dataType
+* **Return type:**
+  String
+
+### *class* zingg.client.Zingg(args, options)
+
+Bases: `object`
+
+This class is the main point of interface with the Zingg matching product. Construct a client to Zingg using provided arguments and spark master. If running locally, set the master to local.
+
+* **Parameters:**
+  * **args** ([*Arguments*](#zingg.client.Arguments)) – arguments for training and matching
+  * **options** ([*ClientOptions*](#zingg.client.ClientOptions)) – client option for this class object
+
+#### execute()
+
+Method to execute this class object
+
+#### executeLabel()
+
+Method to run label phase
+
+#### executeLabelUpdate()
+
+Method to run label update phase
+
+#### getArguments()
+
+Method to get atguments of this class object
+
+* **Returns:**
+  The pointer containing address of the Arguments object of this class object
+* **Return type:**
+  pointer([Arguments](#zingg.client.Arguments))
+
+#### getMarkedRecords()
+
+Method to get marked record dataset from the inputpipe
+
+* **Returns:**
+  spark dataset containing marked records
+* **Return type:**
+  Dataset<Row>
+
+#### getMarkedRecordsStat(markedRecords, value)
+
+Method to get No. of records that is marked
+
+* **Parameters:**
+  * **markedRecords** (*Dataset<Row>*) – spark dataset containing marked records
+  * **value** (*long*) – flag value to check if markedRecord is initially matched or not
+* **Returns:**
+  The no. of marked records
+* **Return type:**
+  int
+
+#### getMatchedMarkedRecordsStat()
+
+Method to get No. of records that are marked and matched
+
+* **Returns:**
+  The bo. of matched marked records
+* **Return type:**
+  int
+
+#### getOptions()
+
+Method to get client options of this class object
+
+* **Returns:**
+  The pointer containing the address of the ClientOptions object of this class object
+* **Return type:**
+  pointer([ClientOptions](#zingg.client.ClientOptions))
+
+#### getUnmarkedRecords()
+
+Method to get unmarked record dataset from the inputpipe
+
+* **Returns:**
+  spark dataset containing unmarked records
+* **Return type:**
+  Dataset<Row>
+
+#### getUnmatchedMarkedRecordsStat()
+
+Method to get No. of records that are marked and unmatched
+
+* **Returns:**
+  The no. of unmatched marked records
+* **Return type:**
+  int
+
+#### getUnsureMarkedRecordsStat()
+
+Method to get No. of records that are marked and Not Sure if its matched or not
+
+* **Returns:**
+  The no. of Not Sure marked records
+* **Return type:**
+  int
+
+#### init()
+
+Method to initialize zingg client by reading internal configurations and functions
+
+#### initAndExecute()
+
+Method to run both init and execute methods consecutively
+
+#### processRecordsCli(unmarkedRecords, args)
+
+Method to get user input on unmarked records
+
+* **Returns:**
+  spark dataset containing updated records
+* **Return type:**
+  Dataset<Row>
+
+#### processRecordsCliLabelUpdate(lines, args)
+
+#### setArguments(args)
+
+Method to set Arguments
+
+* **Parameters:**
+  **args** ([*Arguments*](#zingg.client.Arguments)) – provide arguments for this class object
+
+#### setOptions(options)
+
+Method to set atguments of this class object
+
+* **Parameters:**
+  **options** ([*ClientOptions*](#zingg.client.ClientOptions)) – provide client options for this class object
+* **Returns:**
+  The pointer containing address of the ClientOptions object of this class object
+* **Return type:**
+  pointer([ClientOptions](#zingg.client.ClientOptions))
+
+#### writeLabelledOutput(updatedRecords, args)
+
+Method to write updated records after user input
+
+#### writeLabelledOutputFromPandas(candidate_pairs_pd, args)
+
+Method to write updated records (as pandas df) after user input
+
+### *class* zingg.client.ZinggWithSpark(args, options)
+
+Bases: [`Zingg`](#zingg.client.Zingg)
+
+This class is the main point of interface with the Zingg matching product. Construct a client to Zingg using provided arguments and spark master. If running locally, set the master to local.
+
+* **Parameters:**
+  * **args** ([*Arguments*](#zingg.client.Arguments)) – arguments for training and matching
+  * **options** ([*ClientOptions*](#zingg.client.ClientOptions)) – client option for this class object
+
+### zingg.client.getDfFromDs(data)
+
+Method to convert spark dataset to dataframe
+
+* **Parameters:**
+  **data** (*DataSet*) – provide spark dataset
+* **Returns:**
+  converted spark dataframe
+* **Return type:**
+  DataFrame
+
+### zingg.client.getGateway()
+
+### zingg.client.getJVM()
+
+### zingg.client.getPandasDfFromDs(data)
+
+Method to convert spark dataset to pandas dataframe
+
+* **Parameters:**
+  **data** (*DataSet*) – provide spark dataset
+* **Returns:**
+  converted pandas dataframe
+* **Return type:**
+  DataFrame
+
+### zingg.client.getSparkContext()
+
+### zingg.client.getSparkSession()
+
+### zingg.client.getSqlContext()
+
+### zingg.client.initClient()
+
+### zingg.client.initDataBricksConectClient()
+
+### zingg.client.initSparkClient()
+
+### zingg.client.parseArguments(argv)
+
+This method is used for checking mandatory arguments and creating an arguments list from Command line arguments
+
+* **Parameters:**
+  **argv** (*List*) – Values that are passed during the calling of the program along with the calling statement.
+* **Returns:**
+  a list containing necessary arguments to run any phase
+* **Return type:**
+  List
+
+<a id="module-zingg.pipes"></a>
+
+## zingg.pipes
+
+This module is submodule of zingg to work with different types of Pipes. Classes of this module inherit the Pipe class, and use that class to create many different types of pipes.
+
+### *class* zingg.pipes.BigQueryPipe(name)
+
+Bases: [`Pipe`](#zingg.pipes.Pipe)
+
+Pipe Class for working with BigQuery pipeline
+
+* **Parameters:**
+  **name** (*String*) – name of the pipe.
+
+#### CREDENTIAL_FILE *= 'credentialsFile'*
+
+#### TABLE *= 'table'*
+
+#### TEMP_GCS_BUCKET *= 'temporaryGcsBucket'*
+
+#### VIEWS_ENABLED *= 'viewsEnabled'*
+
+#### setCredentialFile(file)
+
+Method to set Credential file to the pipe
+
+* **Parameters:**
+  **file** (*String*) – credential file name
+
+#### setTable(table)
+
+Method to set Table to the pipe
+
+* **Parameters:**
+  **table** (*String*) – provide table parameter
+
+#### setTemporaryGcsBucket(bucket)
+
+Method to set TemporaryGcsBucket to the pipe
+
+* **Parameters:**
+  **bucket** (*String*) – provide bucket parameter
+
+#### setViewsEnabled(isEnabled)
+
+Method to set if viewsEnabled parameter is Enabled or not
+
+* **Parameters:**
+  **isEnabled** (*Bool*) – provide boolean parameter which defines if viewsEnabled option is enable or not
+
+### *class* zingg.pipes.CsvPipe(name, location=None, schema=None)
+
+Bases: [`Pipe`](#zingg.pipes.Pipe)
+
+Class CsvPipe: used for working with text files which uses a pipe symbol to separate units of text that belong in different columns.
+
+* **Parameters:**
+  * **name** (*String*) – name of the pipe.
+  * **location** (*String* *or* *None*) – (optional) location from where we read data
+  * **schema** (*Schema* *or* *None*) – (optional) json schema for the pipe
+
+#### setDelimiter(delimiter)
+
+This method is used to define delimiter of CsvPipe
+
+* **Parameters:**
+  **delimiter** (*String*) – a sequence of one or more characters for specifying the boundary between separate, independent regions in data streams
+
+#### setHeader(header)
+
+Method to set header property of pipe
+
+* **Parameters:**
+  **header** (*Boolean*) – true if pipe have header, false otherwise
+
+#### setLocation(location)
+
+Method to set location of pipe
+
+* **Parameters:**
+  **location** (*String*) – location from where we read data
+
+### *class* zingg.pipes.InMemoryPipe(name, df=None)
+
+Bases: [`Pipe`](#zingg.pipes.Pipe)
+
+Pipe Class for working with InMemory pipeline
+
+* **Parameters:**
+  * **name** (*String*) – name of the pipe
+  * **df** (*Dataset* *or* *None*) – provide dataset for this pipe (optional)
+
+#### getDataset()
+
+Method to get Dataset from pipe
+
+* **Returns:**
+  dataset of the pipe in the format of spark dataset
+* **Return type:**
+  Dataset<Row>
+
+#### setDataset(df)
+
+Method to set DataFrame of the pipe
+
+* **Parameters:**
+  **df** (*DataFrame*) – pandas or spark dataframe for the pipe
+
+### *class* zingg.pipes.Pipe(name, format)
+
+Bases: `object`
+
+Pipe class for working with different data-pipelines. Actual pipe def in the args. One pipe can be used at multiple places with different tables, locations, queries, etc
+
+* **Parameters:**
+  * **name** (*String*) – name of the pipe
+  * **format** (*Format*) – formate of pipe e.g. bigquery,InMemory, etc.
+
+#### addProperty(name, value)
+
+Method for adding different properties of pipe
+
+* **Parameters:**
+  * **name** (*String*) – name of the property
+  * **value** (*String*) – value you want to set for the property
+
+#### getPipe()
+
+Method to get Pipe
+
+* **Returns:**
+  pipe parameter values in the format of a list of string
+* **Return type:**
+  [Pipe](#zingg.pipes.Pipe)
+
+#### setSchema(s)
+
+Method to set pipe schema value
+
+* **Parameters:**
+  **s** (*Schema*) – json schema for the pipe
+
+#### toString()
+
+Method to get pipe parameter values
+
+* **Returns:**
+  pipe information in list format
+* **Return type:**
+  List[String]
+
+### *class* zingg.pipes.SnowflakePipe(name)
+
+Bases: [`Pipe`](#zingg.pipes.Pipe)
+
+Pipe Class for working with Snowflake pipeline
+
+* **Parameters:**
+  **name** (*String*) – name of the pipe
+
+#### DATABASE *= 'sfDatabase'*
+
+#### DBTABLE *= 'dbtable'*
+
+#### PASSWORD *= 'sfPassword'*
+
+#### SCHEMA *= 'sfSchema'*
+
+#### URL *= 'sfUrl'*
+
+#### USER *= 'sfUser'*
+
+#### WAREHOUSE *= 'sfWarehouse'*
+
+#### setDatabase(db)
+
+Method to set Database to the pipe
+
+* **Parameters:**
+  **db** (*Database*) – provide Database parameter.
+
+#### setDbTable(dbtable)
+
+description
+
+* **Parameters:**
+  **dbtable** (*String*) – provide bucket parameter.
+
+#### setPassword(passwd)
+
+Method to set Password to the pipe
+
+* **Parameters:**
+  **passwd** (*String*) – provide Password parameter.
+
+#### setSFSchema(schema)
+
+Method to set Schema to the pipe
+
+* **Parameters:**
+  **schema** (*Schema*) – provide schema parameter.
+
+#### setURL(url)
+
+Method to set url to the pipe
+
+* **Parameters:**
+  **url** (*String*) – provide url for this pipe
+
+#### setUser(user)
+
+Method to set User to the pipe
+
+* **Parameters:**
+  **user** (*String*) – provide User parameter.
+
+#### setWarehouse(warehouse)
+
+Method to set warehouse parameter to the pipe
+
+* **Parameters:**
+  **warehouse** (*String*) – provide warehouse parameter.
diff --git a/python/docs/conf.py b/python/docs/conf.py
index e491980be..df6be82de 100644
--- a/python/docs/conf.py
+++ b/python/docs/conf.py
@@ -37,7 +37,8 @@
     'sphinx.ext.autodoc',
     'sphinx.ext.napoleon',
     'sphinx.ext.viewcode',
-    'sphinx.ext.coverage'
+    'sphinx.ext.coverage',
+    'sphinx_markdown_builder'
 ]
 
 # Add any paths that contain templates here, relative to this directory.
diff --git a/python/docs/index.rst b/python/docs/index.rst
index 63b396d3e..d04663503 100644
--- a/python/docs/index.rst
+++ b/python/docs/index.rst
@@ -75,8 +75,3 @@ Example API Usage
    zingg = Zingg(args, options)
    zingg.initAndExecute()
 
-
-
-.. meta::
-   :description: Zingg Entity Resolution With Python and AI
-   :keywords: entity resolution, identity resolution, record linkage
diff --git a/python/zingg/client.py b/python/zingg/client.py
index fd1aeb6c9..58617a296 100644
--- a/python/zingg/client.py
+++ b/python/zingg/client.py
@@ -3,759 +3,759 @@
 ------------------------
 This module is the main entry point of the Zingg Python API
 """
+if __name__ == '__main__':
+    import logging
+    import argparse
+    import pandas as pd
+    from pyspark.sql import DataFrame
+
+    from pyspark import SparkConf, SparkContext, SQLContext
+
+    from py4j.java_collections import SetConverter, MapConverter, ListConverter
+
+    from pyspark.sql import SparkSession
+    import os
+    LOG = logging.getLogger("zingg")
+
+    _spark_ctxt = None
+    _sqlContext = None
+    _spark = None
+    _zingg_jar = 'zingg-0.4.0.jar'
+
+    def initSparkClient():
+        global _spark_ctxt
+        global _sqlContext
+        global _spark    
+        _spark_ctxt = SparkContext.getOrCreate()
+        _sqlContext = SQLContext(_spark_ctxt)
+        _spark = SparkSession.builder.getOrCreate()
+        return 1
 
-import logging
-import argparse
-import pandas as pd
-from pyspark.sql import DataFrame
-
-from pyspark import SparkConf, SparkContext, SQLContext
-
-from py4j.java_collections import SetConverter, MapConverter, ListConverter
-
-from pyspark.sql import SparkSession
-import os
-LOG = logging.getLogger("zingg")
-
-_spark_ctxt = None
-_sqlContext = None
-_spark = None
-_zingg_jar = 'zingg-0.4.0.jar'
-
-def initSparkClient():
-    global _spark_ctxt
-    global _sqlContext
-    global _spark    
-    _spark_ctxt = SparkContext.getOrCreate()
-    _sqlContext = SQLContext(_spark_ctxt)
-    _spark = SparkSession.builder.getOrCreate()
-    return 1
-
-def initDataBricksConectClient():
-    global _spark_ctxt
-    global _sqlContext
-    global _spark    
-    jar_path = os.getenv('ZINGG_HOME')+'/'+_zingg_jar
-    _spark = SparkSession.builder.config('spark.jars', jar_path).getOrCreate()
-    _spark_ctxt = _spark.sparkContext
-    _sqlContext = SQLContext(_spark_ctxt)
-    return 1
-
-def initClient():
-    global _spark_ctxt
-    global _sqlContext
-    global _spark    
-    if _spark_ctxt is None:
-        DATABRICKS_CONNECT = os.getenv('DATABRICKS_CONNECT')
-        if DATABRICKS_CONNECT=='Y' or DATABRICKS_CONNECT=='y':
-            return initDataBricksConectClient()
-        else:
-            return initSparkClient()
-    else:
+    def initDataBricksConectClient():
+        global _spark_ctxt
+        global _sqlContext
+        global _spark    
+        jar_path = os.getenv('ZINGG_HOME')+'/'+_zingg_jar
+        _spark = SparkSession.builder.config('spark.jars', jar_path).getOrCreate()
+        _spark_ctxt = _spark.sparkContext
+        _sqlContext = SQLContext(_spark_ctxt)
         return 1
 
-def getSparkContext():
-    if _spark_ctxt is None:
-        initClient()
-    return _spark_ctxt
-
-def getSparkSession():
-    if _spark is None:
-        initClient()
-    return _spark
-
-def getSqlContext():
-    if _sqlContext is None:
-        initClient()
-    return _sqlContext
-
-def getJVM():
-    return getSparkContext()._jvm
-
-def getGateway():
-    return getSparkContext()._gateway
-
-ColName = getJVM().zingg.common.client.util.ColName
-MatchType = getJVM().zingg.common.client.MatchType
-ClientOptions = getJVM().zingg.common.client.ClientOptions
-ZinggOptions = getJVM().zingg.common.client.ZinggOptions
-LabelMatchType = getJVM().zingg.common.core.util.LabelMatchType
-UpdateLabelMode = 'Overwrite'
-
-def getDfFromDs(data):
-    """ Method to convert spark dataset to dataframe
-
-    :param data: provide spark dataset
-    :type data: DataSet
-    :return: converted spark dataframe
-    :rtype: DataFrame 
-    """
-    return DataFrame(data.df(), getSqlContext())
-
-def getPandasDfFromDs(data):
-    """ Method to convert spark dataset to pandas dataframe
-
-    :param data: provide spark dataset
-    :type data: DataSet
-    :return: converted pandas dataframe
-    :rtype: DataFrame 
-    """
-    df = getDfFromDs(data)
-    return pd.DataFrame(df.collect(), columns=df.columns)
-
-
-class Zingg:
-    """ This class is the main point of interface with the Zingg matching product. Construct a client to Zingg using provided arguments and spark master. If running locally, set the master to local.
-
-    :param args: arguments for training and matching
-    :type args: Arguments
-    :param options: client option for this class object
-    :type options: ClientOptions
-
-    """
-
-    def __init__(self, args, options):
-        self.inpArgs = args
-        self.inpOptions = options
-        self.client = getJVM().zingg.spark.client.SparkClient(args.getArgs(), options.getClientOptions())
-       
-    def init(self):
-        """ Method to initialize zingg client by reading internal configurations and functions """
-        self.client.init()
-
-    def execute(self):
-        """ Method to execute this class object """
-        self.client.execute()
-        
-    def initAndExecute(self):
-        """ Method to run both init and execute methods consecutively """
-        self.client.init()
-        DATABRICKS_CONNECT = os.getenv('DATABRICKS_CONNECT')
-        if DATABRICKS_CONNECT=='Y' or DATABRICKS_CONNECT=='y':
-            options = self.client.getOptions()
-            inpPhase = options.get(ClientOptions.PHASE).getValue()
-            if (inpPhase==ZinggOptions.LABEL.getValue()):
-                self.executeLabel()
-            elif (inpPhase==ZinggOptions.UPDATE_LABEL.getValue()):
-                self.executeLabelUpdate()
+    def initClient():
+        global _spark_ctxt
+        global _sqlContext
+        global _spark    
+        if _spark_ctxt is None:
+            DATABRICKS_CONNECT = os.getenv('DATABRICKS_CONNECT')
+            if DATABRICKS_CONNECT=='Y' or DATABRICKS_CONNECT=='y':
+                return initDataBricksConectClient()
             else:
-                self.client.execute()
+                return initSparkClient()
         else:
-            self.client.execute()
+            return 1
 
-    def executeLabel(self):
-        """ Method to run label phase """
-        self.client.getTrainingDataModel().setMarkedRecordsStat(self.getMarkedRecords())
-        unmarkedRecords = self.getUnmarkedRecords()
-        updatedRecords = self.processRecordsCli(unmarkedRecords,self.inpArgs)
-        self.writeLabelledOutput(updatedRecords,self.inpArgs)
+    def getSparkContext():
+        if _spark_ctxt is None:
+            initClient()
+        return _spark_ctxt
 
-    def executeLabelUpdate(self):
-        """ Method to run label update phase """
-        self.processRecordsCliLabelUpdate(self.getMarkedRecords(),self.inpArgs)
+    def getSparkSession():
+        if _spark is None:
+            initClient()
+        return _spark
 
-    def getMarkedRecords(self):
-        """ Method to get marked record dataset from the inputpipe
+    def getSqlContext():
+        if _sqlContext is None:
+            initClient()
+        return _sqlContext
 
-        :return: spark dataset containing marked records
-        :rtype: Dataset<Row> 
-        """
-        return self.client.getMarkedRecords()
+    def getJVM():
+        return getSparkContext()._jvm
 
-    def getUnmarkedRecords(self):
-        """ Method to get unmarked record dataset from the inputpipe
+    def getGateway():
+        return getSparkContext()._gateway
 
-        :return: spark dataset containing unmarked records
-        :rtype: Dataset<Row> 
-        """
-        return self.client.getUnmarkedRecords()
+    ColName = getJVM().zingg.common.client.util.ColName
+    MatchType = getJVM().zingg.common.client.MatchType
+    ClientOptions = getJVM().zingg.common.client.ClientOptions
+    ZinggOptions = getJVM().zingg.common.client.ZinggOptions
+    LabelMatchType = getJVM().zingg.common.core.util.LabelMatchType
+    UpdateLabelMode = 'Overwrite'
 
-    def processRecordsCli(self,unmarkedRecords,args):
-        """ Method to get user input on unmarked records
+    def getDfFromDs(data):
+        """ Method to convert spark dataset to dataframe
 
-        :return: spark dataset containing updated records
-        :rtype: Dataset<Row> 
+        :param data: provide spark dataset
+        :type data: DataSet
+        :return: converted spark dataframe
+        :rtype: DataFrame 
         """
-        trainingDataModel = self.client.getTrainingDataModel()
-        labelDataViewHelper = self.client.getLabelDataViewHelper()
-
-        if unmarkedRecords is not None and unmarkedRecords.count() > 0:
-            labelDataViewHelper.printMarkedRecordsStat(trainingDataModel.getPositivePairsCount(),trainingDataModel.getNegativePairsCount(),trainingDataModel.getNotSurePairsCount(),trainingDataModel.getTotalCount())
-            unmarkedRecords = unmarkedRecords.cache()
-            displayCols = labelDataViewHelper.getDisplayColumns(unmarkedRecords, args.getArgs())
-            clusterIdZFrame = labelDataViewHelper.getClusterIdsFrame(unmarkedRecords)
-            clusterIDs = labelDataViewHelper.getClusterIds(clusterIdZFrame)
-            totalPairs = clusterIDs.size()
-            updatedRecords = None
-            for index in range(totalPairs):
-                currentPair = labelDataViewHelper.getCurrentPair(unmarkedRecords, index, clusterIDs, clusterIdZFrame)
-
-                score = labelDataViewHelper.getScore(currentPair)
-                prediction = labelDataViewHelper.getPrediction(currentPair)
-
-                msg1 = labelDataViewHelper.getMsg1(index, totalPairs)
-                msg2 = labelDataViewHelper.getMsg2(prediction, score)
-                labelDataViewHelper.displayRecords(labelDataViewHelper.getDSUtil().select(currentPair, displayCols), msg1, msg2)
-                selected_option = input()
-                while int(selected_option) not in [0,1,2,9]:
-                    print('Please enter valid option')
-                    selected_option = input("Enter choice: ")
-                if int(selected_option) == 9:
-                    print("User has quit in the middle. Updating the records.")
-                    break               
-                trainingDataModel.updateLabellerStat(int(selected_option), 1)
-                labelDataViewHelper.printMarkedRecordsStat(trainingDataModel.getPositivePairsCount(),trainingDataModel.getNegativePairsCount(),trainingDataModel.getNotSurePairsCount(),trainingDataModel.getTotalCount())
-                updatedRecords = trainingDataModel.updateRecords(int(selected_option), currentPair, updatedRecords)            
-            print("Processing finished.")
-            return updatedRecords
-        else:
-            print("It seems there are no unmarked records at this moment. Please run findTrainingData job to build some pairs to be labelled and then run this labeler.")
-            return None
-    
-    def processRecordsCliLabelUpdate(self,lines,args):
-        trainingDataModel = self.client.getTrainingDataModel()
-        labelDataViewHelper = self.client.getLabelDataViewHelper()
-        if (lines is not None and lines.count() > 0):
-            trainingDataModel.setMarkedRecordsStat(lines)
-            labelDataViewHelper.printMarkedRecordsStat(trainingDataModel.getPositivePairsCount(),trainingDataModel.getNegativePairsCount(),trainingDataModel.getNotSurePairsCount(),trainingDataModel.getTotalCount())
-            displayCols = labelDataViewHelper.getDSUtil().getFieldDefColumns(lines, args.getArgs(), False, args.getArgs().getShowConcise())
-            updatedRecords = None
-            recordsToUpdate = lines
-            selectedOption = -1
-
-            while (str(selectedOption) != '9'):
-                cluster_id = input("\n\tPlease enter the cluster id (or 9 to exit): ")
-                if str(cluster_id) == '9':
-                    print("User has exit in the middle. Updating the records.")
-                    break
-                currentPair = lines.filter(lines.equalTo(ColName.CLUSTER_COLUMN, cluster_id))
-                if currentPair.isEmpty():
-                    print("\tInvalid cluster id. Enter '9' to exit")
-                    continue
-
-                matchFlag = currentPair.getAsInt(currentPair.head(),ColName.MATCH_FLAG_COL)
-                preMsg = "\n\tThe record pairs belonging to the input cluster id "+cluster_id+" are:"
-                postMsg = "\tThe above pair is labeled as "+str(matchFlag)+"\n"
-                labelDataViewHelper.displayRecords(labelDataViewHelper.getDSUtil().select(currentPair, displayCols), preMsg, postMsg)
-                selectedOption = input()
-                trainingDataModel.updateLabellerStat(int(selectedOption), 1)
-                trainingDataModel.updateLabellerStat(matchFlag, -1)
-                labelDataViewHelper.printMarkedRecordsStat(trainingDataModel.getPositivePairsCount(),trainingDataModel.getNegativePairsCount(),trainingDataModel.getNotSurePairsCount(),trainingDataModel.getTotalCount())
-
-                if (str(selectedOption) == '9'):
-                    print("User has quit in the middle. Updating the records.")
-                    break
-
-                recordsToUpdate = recordsToUpdate.filter(recordsToUpdate.notEqual(ColName.CLUSTER_COLUMN,cluster_id))
-
-                if (updatedRecords is not None):
-                    updatedRecords = updatedRecords.filter(updatedRecords.notEqual(ColName.CLUSTER_COLUMN,cluster_id))
+        return DataFrame(data.df(), getSqlContext())
 
-                updatedRecords = trainingDataModel.updateRecords(int(selectedOption), currentPair, updatedRecords)
+    def getPandasDfFromDs(data):
+        """ Method to convert spark dataset to pandas dataframe
 
-            if updatedRecords is not None:
-                updatedRecords = updatedRecords.union(recordsToUpdate)
-
-            outPipe = trainingDataModel.getOutputPipe(args.getArgs())
-            outPipe.setMode(UpdateLabelMode)
-
-            trainingDataModel.writeLabelledOutput(updatedRecords,args.getArgs(),outPipe)
-            print("Processing finished.")
-            return updatedRecords
-        else:
-            print("There is no marked record for updating. Please run findTrainingData/label jobs to generate training data.")
-            return None
-
-
-    def writeLabelledOutput(self,updatedRecords,args):
-        """ Method to write updated records after user input
+        :param data: provide spark dataset
+        :type data: DataSet
+        :return: converted pandas dataframe
+        :rtype: DataFrame 
         """
-        trainingDataModel = self.client.getTrainingDataModel()
-        if updatedRecords is not None:
-            trainingDataModel.writeLabelledOutput(updatedRecords,args.getArgs())
+        df = getDfFromDs(data)
+        return pd.DataFrame(df.collect(), columns=df.columns)
 
-    def writeLabelledOutputFromPandas(self,candidate_pairs_pd,args):
-        """ Method to write updated records (as pandas df) after user input
-        """
-        markedRecordsAsDS = (getSparkSession().createDataFrame(candidate_pairs_pd))._jdf
-        # pands df gives z_isMatch as long so needs to be cast
-        markedRecordsAsDS = markedRecordsAsDS.withColumn(ColName.MATCH_FLAG_COL,markedRecordsAsDS.col(ColName.MATCH_FLAG_COL).cast("int"))
-        updatedRecords = getJVM().zingg.spark.client.SparkFrame(markedRecordsAsDS)
-        self.writeLabelledOutput(updatedRecords,args)
 
-    def setArguments(self, args):
-        """ Method to set Arguments
+    class Zingg:
+        """ This class is the main point of interface with the Zingg matching product. Construct a client to Zingg using provided arguments and spark master. If running locally, set the master to local.
 
-        :param args: provide arguments for this class object
+        :param args: arguments for training and matching
         :type args: Arguments
-        """
-        self.client.setArguments()
-
-    def getArguments(self):
-        """ Method to get atguments of this class object
-
-        :return: The pointer containing address of the Arguments object of this class object
-        :rtype: pointer(Arguments)
-        """
-        return self.client.getArguments()
-
-    def getOptions(self):
-        """ Method to get client options of this class object
-
-        :return: The pointer containing the address of the ClientOptions object of this class object
-        :rtype: pointer(ClientOptions)
-        """
-        return self.client.getOptions()
-
-    def setOptions(self, options):
-        """ Method to set atguments of this class object
-
-        :param options: provide client options for this class object
+        :param options: client option for this class object
         :type options: ClientOptions
-        :return: The pointer containing address of the ClientOptions object of this class object
-        :rtype: pointer(ClientOptions) 
-        """
-        return self.client.setOptions(options)
-
-    def getMarkedRecordsStat(self, markedRecords, value):
-        """ Method to get No. of records that is marked
-
-        :param markedRecords: spark dataset containing marked records
-        :type markedRecords: Dataset<Row>
-        :param value: flag value to check if markedRecord is initially matched or not
-        :type value: long
-        :return: The no. of marked records
-        :rtype: int 
-        """
-        return self.client.getMarkedRecordsStat(markedRecords, value)
-
-    def getMatchedMarkedRecordsStat(self):
-        """ Method to get No. of records that are marked and matched
 
-        :return: The bo. of matched marked records
-        :rtype: int 
         """
-        return self.client.getMatchedMarkedRecordsStat(self.getMarkedRecords())
 
-    def getUnmatchedMarkedRecordsStat(self):
-        """ Method to get No. of records that are marked and unmatched
-
-        :return: The no. of unmatched marked records
-        :rtype: int 
-        """
-        return self.client.getUnmatchedMarkedRecordsStat(self.getMarkedRecords())
-
-    def getUnsureMarkedRecordsStat(self):
-        """ Method to get No. of records that are marked and Not Sure if its matched or not
-
-        :return: The no. of Not Sure marked records
-        :rtype: int 
-        """
-        return self.client.getUnsureMarkedRecordsStat(self.getMarkedRecords())
-
-    
-
-class ZinggWithSpark(Zingg):
+        def __init__(self, args, options):
+            self.inpArgs = args
+            self.inpOptions = options
+            self.client = getJVM().zingg.spark.client.SparkClient(args.getArgs(), options.getClientOptions())
+        
+        def init(self):
+            """ Method to initialize zingg client by reading internal configurations and functions """
+            self.client.init()
 
-    """ This class is the main point of interface with the Zingg matching product. Construct a client to Zingg using provided arguments and spark master. If running locally, set the master to local.
+        def execute(self):
+            """ Method to execute this class object """
+            self.client.execute()
+            
+        def initAndExecute(self):
+            """ Method to run both init and execute methods consecutively """
+            self.client.init()
+            DATABRICKS_CONNECT = os.getenv('DATABRICKS_CONNECT')
+            if DATABRICKS_CONNECT=='Y' or DATABRICKS_CONNECT=='y':
+                options = self.client.getOptions()
+                inpPhase = options.get(ClientOptions.PHASE).getValue()
+                if (inpPhase==ZinggOptions.LABEL.getValue()):
+                    self.executeLabel()
+                elif (inpPhase==ZinggOptions.UPDATE_LABEL.getValue()):
+                    self.executeLabelUpdate()
+                else:
+                    self.client.execute()
+            else:
+                self.client.execute()
 
-    :param args: arguments for training and matching
-    :type args: Arguments
-    :param options: client option for this class object
-    :type options: ClientOptions
+        def executeLabel(self):
+            """ Method to run label phase """
+            self.client.getTrainingDataModel().setMarkedRecordsStat(self.getMarkedRecords())
+            unmarkedRecords = self.getUnmarkedRecords()
+            updatedRecords = self.processRecordsCli(unmarkedRecords,self.inpArgs)
+            self.writeLabelledOutput(updatedRecords,self.inpArgs)
 
-    """
-    def __init__(self, args, options):
-        self.client = getJVM().zingg.spark.client.SparkClient(args.getArgs(), options.getClientOptions(), getSparkSession()._jsparkSession)
+        def executeLabelUpdate(self):
+            """ Method to run label update phase """
+            self.processRecordsCliLabelUpdate(self.getMarkedRecords(),self.inpArgs)
 
-    
-class Arguments:
-    """ This class helps supply match arguments to Zingg. There are 3 basic steps in any match process.
+        def getMarkedRecords(self):
+            """ Method to get marked record dataset from the inputpipe
 
-    :Defining: specifying information about data location, fields, and our notion of similarity.
-    :training: making Zingg learn the matching rules
-    :Matching: Running the models on the entire dataset
-    """
+            :return: spark dataset containing marked records
+            :rtype: Dataset<Row> 
+            """
+            return self.client.getMarkedRecords()
 
-    def __init__(self):
-        self.args = getJVM().zingg.common.client.Arguments()
+        def getUnmarkedRecords(self):
+            """ Method to get unmarked record dataset from the inputpipe
 
-    def setFieldDefinition(self, fieldDef):
-        """ Method convert python objects to java FieldDefinition objects and set the field definitions associated with this client
+            :return: spark dataset containing unmarked records
+            :rtype: Dataset<Row> 
+            """
+            return self.client.getUnmarkedRecords()
 
-        :param fieldDef: python FieldDefinition object list
-        :type fieldDef: List(FieldDefinition)
-        """
-        javaFieldDef = []
-        for f in fieldDef:
-            javaFieldDef.append(f.getFieldDefinition())
-        self.args.setFieldDefinition(javaFieldDef)
+        def processRecordsCli(self,unmarkedRecords,args):
+            """ Method to get user input on unmarked records
 
-    def getArgs(self):
-        """ Method to get pointer address of this class
+            :return: spark dataset containing updated records
+            :rtype: Dataset<Row> 
+            """
+            trainingDataModel = self.client.getTrainingDataModel()
+            labelDataViewHelper = self.client.getLabelDataViewHelper()
 
-        :return: The pointer containing the address of this class object
-        :rtype: pointer(Arguments)
+            if unmarkedRecords is not None and unmarkedRecords.count() > 0:
+                labelDataViewHelper.printMarkedRecordsStat(trainingDataModel.getPositivePairsCount(),trainingDataModel.getNegativePairsCount(),trainingDataModel.getNotSurePairsCount(),trainingDataModel.getTotalCount())
+                unmarkedRecords = unmarkedRecords.cache()
+                displayCols = labelDataViewHelper.getDisplayColumns(unmarkedRecords, args.getArgs())
+                clusterIdZFrame = labelDataViewHelper.getClusterIdsFrame(unmarkedRecords)
+                clusterIDs = labelDataViewHelper.getClusterIds(clusterIdZFrame)
+                totalPairs = clusterIDs.size()
+                updatedRecords = None
+                for index in range(totalPairs):
+                    currentPair = labelDataViewHelper.getCurrentPair(unmarkedRecords, index, clusterIDs, clusterIdZFrame)
+
+                    score = labelDataViewHelper.getScore(currentPair)
+                    prediction = labelDataViewHelper.getPrediction(currentPair)
+
+                    msg1 = labelDataViewHelper.getMsg1(index, totalPairs)
+                    msg2 = labelDataViewHelper.getMsg2(prediction, score)
+                    labelDataViewHelper.displayRecords(labelDataViewHelper.getDSUtil().select(currentPair, displayCols), msg1, msg2)
+                    selected_option = input()
+                    while int(selected_option) not in [0,1,2,9]:
+                        print('Please enter valid option')
+                        selected_option = input("Enter choice: ")
+                    if int(selected_option) == 9:
+                        print("User has quit in the middle. Updating the records.")
+                        break               
+                    trainingDataModel.updateLabellerStat(int(selected_option), 1)
+                    labelDataViewHelper.printMarkedRecordsStat(trainingDataModel.getPositivePairsCount(),trainingDataModel.getNegativePairsCount(),trainingDataModel.getNotSurePairsCount(),trainingDataModel.getTotalCount())
+                    updatedRecords = trainingDataModel.updateRecords(int(selected_option), currentPair, updatedRecords)            
+                print("Processing finished.")
+                return updatedRecords
+            else:
+                print("It seems there are no unmarked records at this moment. Please run findTrainingData job to build some pairs to be labelled and then run this labeler.")
+                return None
         
-        """
-        return self.args
-
-    def setArgs(self, argumentsObj):
-        """ Method to set this class object
-
-        :param argumentsObj: Argument object to set this object
-        :type argumentsObj: pointer(Arguments)
-        """
-        self.args = argumentsObj
-
-    def setData(self, *pipes):
-        """ Method to set the file path of the file to be matched.
+        def processRecordsCliLabelUpdate(self,lines,args):
+            trainingDataModel = self.client.getTrainingDataModel()
+            labelDataViewHelper = self.client.getLabelDataViewHelper()
+            if (lines is not None and lines.count() > 0):
+                trainingDataModel.setMarkedRecordsStat(lines)
+                labelDataViewHelper.printMarkedRecordsStat(trainingDataModel.getPositivePairsCount(),trainingDataModel.getNegativePairsCount(),trainingDataModel.getNotSurePairsCount(),trainingDataModel.getTotalCount())
+                displayCols = labelDataViewHelper.getDSUtil().getFieldDefColumns(lines, args.getArgs(), False, args.getArgs().getShowConcise())
+                updatedRecords = None
+                recordsToUpdate = lines
+                selectedOption = -1
+
+                while (str(selectedOption) != '9'):
+                    cluster_id = input("\n\tPlease enter the cluster id (or 9 to exit): ")
+                    if str(cluster_id) == '9':
+                        print("User has exit in the middle. Updating the records.")
+                        break
+                    currentPair = lines.filter(lines.equalTo(ColName.CLUSTER_COLUMN, cluster_id))
+                    if currentPair.isEmpty():
+                        print("\tInvalid cluster id. Enter '9' to exit")
+                        continue
+
+                    matchFlag = currentPair.getAsInt(currentPair.head(),ColName.MATCH_FLAG_COL)
+                    preMsg = "\n\tThe record pairs belonging to the input cluster id "+cluster_id+" are:"
+                    postMsg = "\tThe above pair is labeled as "+str(matchFlag)+"\n"
+                    labelDataViewHelper.displayRecords(labelDataViewHelper.getDSUtil().select(currentPair, displayCols), preMsg, postMsg)
+                    selectedOption = input()
+                    trainingDataModel.updateLabellerStat(int(selectedOption), 1)
+                    trainingDataModel.updateLabellerStat(matchFlag, -1)
+                    labelDataViewHelper.printMarkedRecordsStat(trainingDataModel.getPositivePairsCount(),trainingDataModel.getNegativePairsCount(),trainingDataModel.getNotSurePairsCount(),trainingDataModel.getTotalCount())
+
+                    if (str(selectedOption) == '9'):
+                        print("User has quit in the middle. Updating the records.")
+                        break
+
+                    recordsToUpdate = recordsToUpdate.filter(recordsToUpdate.notEqual(ColName.CLUSTER_COLUMN,cluster_id))
+
+                    if (updatedRecords is not None):
+                        updatedRecords = updatedRecords.filter(updatedRecords.notEqual(ColName.CLUSTER_COLUMN,cluster_id))
+
+                    updatedRecords = trainingDataModel.updateRecords(int(selectedOption), currentPair, updatedRecords)
+
+                if updatedRecords is not None:
+                    updatedRecords = updatedRecords.union(recordsToUpdate)
+
+                outPipe = trainingDataModel.getOutputPipe(args.getArgs())
+                outPipe.setMode(UpdateLabelMode)
+
+                trainingDataModel.writeLabelledOutput(updatedRecords,args.getArgs(),outPipe)
+                print("Processing finished.")
+                return updatedRecords
+            else:
+                print("There is no marked record for updating. Please run findTrainingData/label jobs to generate training data.")
+                return None
 
-        :param pipes: input data pipes separated by comma e.g. (pipe1,pipe2,..)
-        :type pipes: Pipe[]
-        """
-        dataPipe = getGateway().new_array(getJVM().zingg.common.client.pipe.Pipe, len(pipes))
-        for idx, pipe in enumerate(pipes):
-            dataPipe[idx] = pipe.getPipe()
-        self.args.setData(dataPipe)
 
-    def setOutput(self, *pipes):
-        """ Method to set the output directory where the match result will be saved
+        def writeLabelledOutput(self,updatedRecords,args):
+            """ Method to write updated records after user input
+            """
+            trainingDataModel = self.client.getTrainingDataModel()
+            if updatedRecords is not None:
+                trainingDataModel.writeLabelledOutput(updatedRecords,args.getArgs())
+
+        def writeLabelledOutputFromPandas(self,candidate_pairs_pd,args):
+            """ Method to write updated records (as pandas df) after user input
+            """
+            markedRecordsAsDS = (getSparkSession().createDataFrame(candidate_pairs_pd))._jdf
+            # pands df gives z_isMatch as long so needs to be cast
+            markedRecordsAsDS = markedRecordsAsDS.withColumn(ColName.MATCH_FLAG_COL,markedRecordsAsDS.col(ColName.MATCH_FLAG_COL).cast("int"))
+            updatedRecords = getJVM().zingg.spark.client.SparkFrame(markedRecordsAsDS)
+            self.writeLabelledOutput(updatedRecords,args)
+
+        def setArguments(self, args):
+            """ Method to set Arguments
+
+            :param args: provide arguments for this class object
+            :type args: Arguments
+            """
+            self.client.setArguments()
+
+        def getArguments(self):
+            """ Method to get atguments of this class object
+
+            :return: The pointer containing address of the Arguments object of this class object
+            :rtype: pointer(Arguments)
+            """
+            return self.client.getArguments()
+
+        def getOptions(self):
+            """ Method to get client options of this class object
+
+            :return: The pointer containing the address of the ClientOptions object of this class object
+            :rtype: pointer(ClientOptions)
+            """
+            return self.client.getOptions()
+
+        def setOptions(self, options):
+            """ Method to set atguments of this class object
+
+            :param options: provide client options for this class object
+            :type options: ClientOptions
+            :return: The pointer containing address of the ClientOptions object of this class object
+            :rtype: pointer(ClientOptions) 
+            """
+            return self.client.setOptions(options)
+
+        def getMarkedRecordsStat(self, markedRecords, value):
+            """ Method to get No. of records that is marked
+
+            :param markedRecords: spark dataset containing marked records
+            :type markedRecords: Dataset<Row>
+            :param value: flag value to check if markedRecord is initially matched or not
+            :type value: long
+            :return: The no. of marked records
+            :rtype: int 
+            """
+            return self.client.getMarkedRecordsStat(markedRecords, value)
+
+        def getMatchedMarkedRecordsStat(self):
+            """ Method to get No. of records that are marked and matched
+
+            :return: The bo. of matched marked records
+            :rtype: int 
+            """
+            return self.client.getMatchedMarkedRecordsStat(self.getMarkedRecords())
+
+        def getUnmatchedMarkedRecordsStat(self):
+            """ Method to get No. of records that are marked and unmatched
+
+            :return: The no. of unmatched marked records
+            :rtype: int 
+            """
+            return self.client.getUnmatchedMarkedRecordsStat(self.getMarkedRecords())
+
+        def getUnsureMarkedRecordsStat(self):
+            """ Method to get No. of records that are marked and Not Sure if its matched or not
+
+            :return: The no. of Not Sure marked records
+            :rtype: int 
+            """
+            return self.client.getUnsureMarkedRecordsStat(self.getMarkedRecords())
 
-        :param pipes: output data pipes separated by comma e.g. (pipe1,pipe2,..)
-        :type pipes: Pipe[]
-        """
-        outputPipe = getGateway().new_array(getJVM().zingg.common.client.pipe.Pipe, len(pipes))
-        for idx, pipe in enumerate(pipes):
-            outputPipe[idx] = pipe.getPipe()
-        self.args.setOutput(outputPipe)
-    
-    def getZinggBaseModelDir(self):
-        return self.args.getZinggBaseModelDir()
+        
 
-    def getZinggModelDir(self):
-        return self.args.getZinggModelDir()
-    
-    def getZinggBaseTrainingDataDir(self):
-        """ Method to get the location of the folder where Zingg 
-        saves the training data found by findTrainingData  
-        """
-        return self.args.getZinggBaseTrainingDataDir()
+    class ZinggWithSpark(Zingg):
 
-    def getZinggTrainingDataUnmarkedDir(self):
-        """ Method to get the location of the folder where Zingg 
-        saves the training data found by findTrainingData  
-        """
-        return self.args.getZinggTrainingDataUnmarkedDir()
-    
-    def getZinggTrainingDataMarkedDir(self):
-        """ Method to get the location of the folder where Zingg 
-        saves the marked training data labeled by the user  
-        """
-        return self.args.getZinggTrainingDataMarkedDir()
-    
-    def setTrainingSamples(self, *pipes):
-        """ Method to set existing training samples to be matched.
+        """ This class is the main point of interface with the Zingg matching product. Construct a client to Zingg using provided arguments and spark master. If running locally, set the master to local.
 
-        :param pipes: input training data pipes separated by comma e.g. (pipe1,pipe2,..)
-        :type pipes: Pipe[]
-        """
-        dataPipe = getGateway().new_array(getJVM().zingg.common.client.pipe.Pipe, len(pipes))
-        for idx, pipe in enumerate(pipes):
-            dataPipe[idx] = pipe.getPipe()
-        self.args.setTrainingSamples(dataPipe)
-
-    def setModelId(self, id):
-        """ Method to set the output directory where the match output will be saved
+        :param args: arguments for training and matching
+        :type args: Arguments
+        :param options: client option for this class object
+        :type options: ClientOptions
 
-        :param id: model id value 
-        :type id: String
         """
-        self.args.setModelId(id)
-    
-    def getModelId(self):
-        return self.args.getModelId()
+        def __init__(self, args, options):
+            self.client = getJVM().zingg.spark.client.SparkClient(args.getArgs(), options.getClientOptions(), getSparkSession()._jsparkSession)
 
+        
+    class Arguments:
+        """ This class helps supply match arguments to Zingg. There are 3 basic steps in any match process.
+
+        :Defining: specifying information about data location, fields, and our notion of similarity.
+        :training: making Zingg learn the matching rules
+        :Matching: Running the models on the entire dataset
+        """
+
+        def __init__(self):
+            self.args = getJVM().zingg.common.client.Arguments()
+
+        def setFieldDefinition(self, fieldDef):
+            """ Method convert python objects to java FieldDefinition objects and set the field definitions associated with this client
+
+            :param fieldDef: python FieldDefinition object list
+            :type fieldDef: List(FieldDefinition)
+            """
+            javaFieldDef = []
+            for f in fieldDef:
+                javaFieldDef.append(f.getFieldDefinition())
+            self.args.setFieldDefinition(javaFieldDef)
+
+        def getArgs(self):
+            """ Method to get pointer address of this class
+
+            :return: The pointer containing the address of this class object
+            :rtype: pointer(Arguments)
+            
+            """
+            return self.args
+
+        def setArgs(self, argumentsObj):
+            """ Method to set this class object
+
+            :param argumentsObj: Argument object to set this object
+            :type argumentsObj: pointer(Arguments)
+            """
+            self.args = argumentsObj
+
+        def setData(self, *pipes):
+            """ Method to set the file path of the file to be matched.
+
+            :param pipes: input data pipes separated by comma e.g. (pipe1,pipe2,..)
+            :type pipes: Pipe[]
+            """
+            dataPipe = getGateway().new_array(getJVM().zingg.common.client.pipe.Pipe, len(pipes))
+            for idx, pipe in enumerate(pipes):
+                dataPipe[idx] = pipe.getPipe()
+            self.args.setData(dataPipe)
+
+        def setOutput(self, *pipes):
+            """ Method to set the output directory where the match result will be saved
+
+            :param pipes: output data pipes separated by comma e.g. (pipe1,pipe2,..)
+            :type pipes: Pipe[]
+            """
+            outputPipe = getGateway().new_array(getJVM().zingg.common.client.pipe.Pipe, len(pipes))
+            for idx, pipe in enumerate(pipes):
+                outputPipe[idx] = pipe.getPipe()
+            self.args.setOutput(outputPipe)
+        
+        def getZinggBaseModelDir(self):
+            return self.args.getZinggBaseModelDir()
 
-    def setZinggDir(self, f):
-        """ Method to set the location for Zingg to save its internal computations and models. Please set it to a place where the program has to write access.
+        def getZinggModelDir(self):
+            return self.args.getZinggModelDir()
+        
+        def getZinggBaseTrainingDataDir(self):
+            """ Method to get the location of the folder where Zingg 
+            saves the training data found by findTrainingData  
+            """
+            return self.args.getZinggBaseTrainingDataDir()
+
+        def getZinggTrainingDataUnmarkedDir(self):
+            """ Method to get the location of the folder where Zingg 
+            saves the training data found by findTrainingData  
+            """
+            return self.args.getZinggTrainingDataUnmarkedDir()
+        
+        def getZinggTrainingDataMarkedDir(self):
+            """ Method to get the location of the folder where Zingg 
+            saves the marked training data labeled by the user  
+            """
+            return self.args.getZinggTrainingDataMarkedDir()
+        
+        def setTrainingSamples(self, *pipes):
+            """ Method to set existing training samples to be matched.
+
+            :param pipes: input training data pipes separated by comma e.g. (pipe1,pipe2,..)
+            :type pipes: Pipe[]
+            """
+            dataPipe = getGateway().new_array(getJVM().zingg.common.client.pipe.Pipe, len(pipes))
+            for idx, pipe in enumerate(pipes):
+                dataPipe[idx] = pipe.getPipe()
+            self.args.setTrainingSamples(dataPipe)
+
+        def setModelId(self, id):
+            """ Method to set the output directory where the match output will be saved
+
+            :param id: model id value 
+            :type id: String
+            """
+            self.args.setModelId(id)
+        
+        def getModelId(self):
+            return self.args.getModelId()
 
-        :param f: Zingg directory name of the models
-        :type f: String
-        """
-        self.args.setZinggDir(f)
 
-    def setNumPartitions(self, numPartitions):
-        """ Method to set NumPartitions parameter value
-        Sample size to use for seeding labeled data We don't want to run over all the data, as we want a quick way to seed some labeled data that we can manually edit
+        def setZinggDir(self, f):
+            """ Method to set the location for Zingg to save its internal computations and models. Please set it to a place where the program has to write access.
 
-        :param numPartitions: number of partitions for given data pipes
-        :type numPartitions: int
-        """
-        self.args.setNumPartitions(numPartitions)
+            :param f: Zingg directory name of the models
+            :type f: String
+            """
+            self.args.setZinggDir(f)
 
-    def setLabelDataSampleSize(self, labelDataSampleSize):
-        """ Method to set labelDataSampleSize parameter value
-        Set the fraction of data to be used from the complete data set to be used for seeding the labeled data Labelling is costly and we want a fast approximate way of looking at a small sample of the records and identifying expected matches and nonmatches
+        def setNumPartitions(self, numPartitions):
+            """ Method to set NumPartitions parameter value
+            Sample size to use for seeding labeled data We don't want to run over all the data, as we want a quick way to seed some labeled data that we can manually edit
 
-        :param labelDataSampleSize: value between 0.0 and 1.0 denoting portion of dataset to use in generating seed samples
-        :type labelDataSampleSize: float
-        """
-        self.args.setLabelDataSampleSize(labelDataSampleSize)
+            :param numPartitions: number of partitions for given data pipes
+            :type numPartitions: int
+            """
+            self.args.setNumPartitions(numPartitions)
 
-    def writeArgumentsToJSON(self, fileName):
-        """ Method to write JSON file from the object of this class 
+        def setLabelDataSampleSize(self, labelDataSampleSize):
+            """ Method to set labelDataSampleSize parameter value
+            Set the fraction of data to be used from the complete data set to be used for seeding the labeled data Labelling is costly and we want a fast approximate way of looking at a small sample of the records and identifying expected matches and nonmatches
 
-        :param fileName: The CONF parameter value of ClientOption object or file address of json file
-        :type fileName: String
-        """
-        getJVM().zingg.common.client.ArgumentsUtil().writeArgumentsToJSON(fileName, self.args)
+            :param labelDataSampleSize: value between 0.0 and 1.0 denoting portion of dataset to use in generating seed samples
+            :type labelDataSampleSize: float
+            """
+            self.args.setLabelDataSampleSize(labelDataSampleSize)
 
-    def setStopWordsCutoff(self, stopWordsCutoff):
-        """ Method to set stopWordsCutoff parameter value
-        By default, Zingg extracts 10% of the high frequency unique words from a dataset. If user wants different selection, they should set up StopWordsCutoff property
+        def writeArgumentsToJSON(self, fileName):
+            """ Method to write JSON file from the object of this class 
 
-        :param stopWordsCutoff: The stop words cutoff parameter value of ClientOption object or file address of json file
-        :type stopWordsCutoff: float
-        """
-        self.args.setStopWordsCutoff(stopWordsCutoff)
-    
-    def setColumn(self, column):
-        """ Method to set stopWordsCutoff parameter value
-        By default, Zingg extracts 10% of the high frequency unique words from a dataset. If user wants different selection, they should set up StopWordsCutoff property
+            :param fileName: The CONF parameter value of ClientOption object or file address of json file
+            :type fileName: String
+            """
+            getJVM().zingg.common.client.ArgumentsUtil().writeArgumentsToJSON(fileName, self.args)
 
-        :param stopWordsCutoff: The stop words cutoff parameter value of ClientOption object or file address of json file
-        :type stopWordsCutoff: float
-        """
-        self.args.setColumn(column)
+        def setStopWordsCutoff(self, stopWordsCutoff):
+            """ Method to set stopWordsCutoff parameter value
+            By default, Zingg extracts 10% of the high frequency unique words from a dataset. If user wants different selection, they should set up StopWordsCutoff property
 
-    @staticmethod
-    def createArgumentsFromJSON(fileName, phase):
-        """ Method to create an object of this class from the JSON file and phase parameter value.
+            :param stopWordsCutoff: The stop words cutoff parameter value of ClientOption object or file address of json file
+            :type stopWordsCutoff: float
+            """
+            self.args.setStopWordsCutoff(stopWordsCutoff)
         
-        :param fileName: The CONF parameter value of ClientOption object
-        :type fileName: String
-        :param phase: The PHASE parameter value of ClientOption object
-        :type phase: String
-        :return: The pointer containing address of the this class object
-        :rtype: pointer(Arguments)
-        """
-        obj = Arguments()
-        obj.args = getJVM().zingg.common.client.ArgumentsUtil().createArgumentsFromJSON(fileName, phase)
-        return obj
-    
-    
-    def writeArgumentsToJSONString(self):
-        """ Method to create an object of this class from the JSON file and phase parameter value.
+        def setColumn(self, column):
+            """ Method to set stopWordsCutoff parameter value
+            By default, Zingg extracts 10% of the high frequency unique words from a dataset. If user wants different selection, they should set up StopWordsCutoff property
+
+            :param stopWordsCutoff: The stop words cutoff parameter value of ClientOption object or file address of json file
+            :type stopWordsCutoff: float
+            """
+            self.args.setColumn(column)
+
+        @staticmethod
+        def createArgumentsFromJSON(fileName, phase):
+            """ Method to create an object of this class from the JSON file and phase parameter value.
+            
+            :param fileName: The CONF parameter value of ClientOption object
+            :type fileName: String
+            :param phase: The PHASE parameter value of ClientOption object
+            :type phase: String
+            :return: The pointer containing address of the this class object
+            :rtype: pointer(Arguments)
+            """
+            obj = Arguments()
+            obj.args = getJVM().zingg.common.client.ArgumentsUtil().createArgumentsFromJSON(fileName, phase)
+            return obj
         
-        :param fileName: The CONF parameter value of ClientOption object
-        :type fileName: String
-        :param phase: The PHASE parameter value of ClientOption object
-        :type phase: String
-        :return: The pointer containing address of the this class object
-        :rtype: pointer(Arguments)
-        """
-        return getJVM().zingg.common.client.ArgumentsUtil().writeArgumentstoJSONString(self.args)
-    
-    @staticmethod
-    def createArgumentsFromJSONString(jsonArgs, phase):
-        obj = Arguments()
-        obj.args = getJVM().zingg.common.client.ArgumentsUtil().createArgumentsFromJSONString(jsonArgs, phase)
-        return obj
-    
-    
-    def copyArgs(self, phase):
-        argsString = self.writeArgumentsToJSONString()
-        return self.createArgumentsFromJSONString(argsString, phase)
-
-   
-    
-
+        
+        def writeArgumentsToJSONString(self):
+            """ Method to create an object of this class from the JSON file and phase parameter value.
+            
+            :param fileName: The CONF parameter value of ClientOption object
+            :type fileName: String
+            :param phase: The PHASE parameter value of ClientOption object
+            :type phase: String
+            :return: The pointer containing address of the this class object
+            :rtype: pointer(Arguments)
+            """
+            return getJVM().zingg.common.client.ArgumentsUtil().writeArgumentstoJSONString(self.args)
+        
+        @staticmethod
+        def createArgumentsFromJSONString(jsonArgs, phase):
+            obj = Arguments()
+            obj.args = getJVM().zingg.common.client.ArgumentsUtil().createArgumentsFromJSONString(jsonArgs, phase)
+            return obj
+        
+        
+        def copyArgs(self, phase):
+            argsString = self.writeArgumentsToJSONString()
+            return self.createArgumentsFromJSONString(argsString, phase)
 
-class ClientOptions:
-    """ Class that contains Client options for Zingg object
-    :param phase: trainMatch, train, match, link, findAndLabel, findTrainingData, recommend etc
-    :type phase: String
-    :param args: Parse a list of Zingg command line options parameter values e.g. "--location" etc. optional argument for initializing this class.
-    :type args: List(String) or None
-    """
-    PHASE = getJVM().zingg.common.client.ClientOptions.PHASE 
-    """:PHASE: phase parameter for this class"""
-    CONF = getJVM().zingg.common.client.ClientOptions.CONF
-    """:CONF: conf parameter for this class"""
-    LICENSE = getJVM().zingg.common.client.ClientOptions.LICENSE
-    """:LICENSE: license parameter for this class"""
-    EMAIL = getJVM().zingg.common.client.ClientOptions.EMAIL
-    """:EMAIL: e-mail parameter for this class"""
-    LOCATION = getJVM().zingg.common.client.ClientOptions.LOCATION
-    """:LOCATION: location parameter for this class"""
-    REMOTE = getJVM().zingg.common.client.ClientOptions.REMOTE
-    """:REMOTE: remote option used internally for running on Databricks"""
-    ZINGG_DIR = getJVM().zingg.common.client.ClientOptions.ZINGG_DIR
-    """:ZINGG_DIR: location where Zingg saves the model, training data etc"""
-    MODEL_ID = getJVM().zingg.common.client.ClientOptions.MODEL_ID
-    """:MODEL_ID: ZINGG_DIR/MODEL_ID is used to save the model"""
-    COLUMN = getJVM().zingg.common.client.ClientOptions.COLUMN
-    """:COLUMN: Column whose stop words are to be recommended through Zingg"""
-
-    def __init__(self, argsSent=None):
-        print(argsSent)
-        if(argsSent == None):
-            args = []
-        else:
-            args = argsSent.copy()
-        if (not (self.PHASE in args)):
-            args.append(self.PHASE)
-            args.append("peekModel")
-        if (not (self.LICENSE in args)):
-            args.append(self.LICENSE)
-            args.append("zinggLic.txt")
-        if (not (self.EMAIL in args)):
-            args.append(self.EMAIL)
-            args.append("zingg@zingg.ai")
-        if (not (self.CONF in args)):
-            args.append(self.CONF)
-            args.append("dummyConf.json")
-        print("arguments for client options are ", args) 
-        self.co = getJVM().zingg.common.client.ClientOptions(args)
-    
     
-    def getClientOptions(self):
-        """ Method to get pointer address of this class
-
-        :return: The pointer containing address of the this class object
-        :rtype: pointer(ClientOptions)
-        """
-        return self.co
-
-    def getOptionValue(self, option):
-        """ Method to get value for the key option
-
-        :param option: key to geting the value
-        :type option: String
-        :return: The value which is mapped for given key 
-        :rtype: String 
-        """
-        return self.co.getOptionValue(option)
-
-    def setOptionValue(self, option, value):
-        """ Method to map option key to the given value
-
-        :param option: key that is mapped with value
-        :type option: String
-        :param value: value to be set for given key
-        :type value: String
-        """
-        self.co.get(option).setValue(value)
-
-    def getPhase(self):
-        """ Method to get PHASE value
-
-        :return: The PHASE parameter value
-        :rtype: String 
-        """
-        return self.co.get(ClientOptions.PHASE).getValue()
-
-    def setPhase(self, newValue):
-        """ Method to set PHASE value
-
-        :param newValue: name of the phase
-        :type newValue: String
-        :return: The pointer containing address of the this class object after seting phase
-        :rtype: pointer(ClientOptions) 
-        """
-        self.co.get(ClientOptions.PHASE).setValue(newValue)
-
-    def getConf(self):
-        """ Method to get CONF value
-
-        :return: The CONF parameter value
-        :rtype: String 
-        """
-        return self.co.get(ClientOptions.CONF).getValue()
+        
 
-    def hasLocation(self):
-        """ Method to check if this class has LOCATION parameter set as None or not
 
-        :return: The boolean value if LOCATION parameter is present or not
-        :rtype: Bool 
-        """
-        if(self.co.get(ClientOptions.LOCATION)==None):
-            return False
-        else:
-            return True
+    class ClientOptions:
+        """ Class that contains Client options for Zingg object
+        :param phase: trainMatch, train, match, link, findAndLabel, findTrainingData, recommend etc
+        :type phase: String
+        :param args: Parse a list of Zingg command line options parameter values e.g. "--location" etc. optional argument for initializing this class.
+        :type args: List(String) or None
+        """
+        PHASE = getJVM().zingg.common.client.ClientOptions.PHASE 
+        """:PHASE: phase parameter for this class"""
+        CONF = getJVM().zingg.common.client.ClientOptions.CONF
+        """:CONF: conf parameter for this class"""
+        LICENSE = getJVM().zingg.common.client.ClientOptions.LICENSE
+        """:LICENSE: license parameter for this class"""
+        EMAIL = getJVM().zingg.common.client.ClientOptions.EMAIL
+        """:EMAIL: e-mail parameter for this class"""
+        LOCATION = getJVM().zingg.common.client.ClientOptions.LOCATION
+        """:LOCATION: location parameter for this class"""
+        REMOTE = getJVM().zingg.common.client.ClientOptions.REMOTE
+        """:REMOTE: remote option used internally for running on Databricks"""
+        ZINGG_DIR = getJVM().zingg.common.client.ClientOptions.ZINGG_DIR
+        """:ZINGG_DIR: location where Zingg saves the model, training data etc"""
+        MODEL_ID = getJVM().zingg.common.client.ClientOptions.MODEL_ID
+        """:MODEL_ID: ZINGG_DIR/MODEL_ID is used to save the model"""
+        COLUMN = getJVM().zingg.common.client.ClientOptions.COLUMN
+        """:COLUMN: Column whose stop words are to be recommended through Zingg"""
+
+        def __init__(self, argsSent=None):
+            print(argsSent)
+            if(argsSent == None):
+                args = []
+            else:
+                args = argsSent.copy()
+            if (not (self.PHASE in args)):
+                args.append(self.PHASE)
+                args.append("peekModel")
+            if (not (self.LICENSE in args)):
+                args.append(self.LICENSE)
+                args.append("zinggLic.txt")
+            if (not (self.EMAIL in args)):
+                args.append(self.EMAIL)
+                args.append("zingg@zingg.ai")
+            if (not (self.CONF in args)):
+                args.append(self.CONF)
+                args.append("dummyConf.json")
+            print("arguments for client options are ", args) 
+            self.co = getJVM().zingg.common.client.ClientOptions(args)
+        
+        
+        def getClientOptions(self):
+            """ Method to get pointer address of this class
+
+            :return: The pointer containing address of the this class object
+            :rtype: pointer(ClientOptions)
+            """
+            return self.co
+
+        def getOptionValue(self, option):
+            """ Method to get value for the key option
+
+            :param option: key to geting the value
+            :type option: String
+            :return: The value which is mapped for given key 
+            :rtype: String 
+            """
+            return self.co.getOptionValue(option)
+
+        def setOptionValue(self, option, value):
+            """ Method to map option key to the given value
+
+            :param option: key that is mapped with value
+            :type option: String
+            :param value: value to be set for given key
+            :type value: String
+            """
+            self.co.get(option).setValue(value)
+
+        def getPhase(self):
+            """ Method to get PHASE value
+
+            :return: The PHASE parameter value
+            :rtype: String 
+            """
+            return self.co.get(ClientOptions.PHASE).getValue()
+
+        def setPhase(self, newValue):
+            """ Method to set PHASE value
+
+            :param newValue: name of the phase
+            :type newValue: String
+            :return: The pointer containing address of the this class object after seting phase
+            :rtype: pointer(ClientOptions) 
+            """
+            self.co.get(ClientOptions.PHASE).setValue(newValue)
+
+        def getConf(self):
+            """ Method to get CONF value
+
+            :return: The CONF parameter value
+            :rtype: String 
+            """
+            return self.co.get(ClientOptions.CONF).getValue()
+
+        def hasLocation(self):
+            """ Method to check if this class has LOCATION parameter set as None or not
+
+            :return: The boolean value if LOCATION parameter is present or not
+            :rtype: Bool 
+            """
+            if(self.co.get(ClientOptions.LOCATION)==None):
+                return False
+            else:
+                return True
 
-    def getLocation(self):
-        """ Method to get LOCATION value
+        def getLocation(self):
+            """ Method to get LOCATION value
 
-        :return: The LOCATION parameter value
-        :rtype: String 
-        """
-        return self.co.get(ClientOptions.LOCATION).getValue()
-
-
-class FieldDefinition:
-    """ This class defines each field that we use in matching We can use this to configure the properties of each field we use for matching in Zingg.
-
-    :param name: name of the field
-    :type name: String
-    :param dataType: type of the data e.g. string, float, etc.
-    :type dataType: String
-    :param matchType: match type of this field e.g. FUSSY, EXACT, etc.
-    :type matchType: MatchType
-    """
-
-    def __init__(self, name, dataType, *matchType):
-        self.fd = getJVM().zingg.common.client.FieldDefinition()
-        self.fd.setFieldName(name)
-        self.fd.setDataType(self.stringify(dataType))
-        self.fd.setMatchType(matchType)
-        self.fd.setFields(name)
-    
-    def setStopWords(self, stopWords):
-        """ Method to add stopwords to this class object
+            :return: The LOCATION parameter value
+            :rtype: String 
+            """
+            return self.co.get(ClientOptions.LOCATION).getValue()
 
-        :param stopWords: The stop Words containing csv file's location
-        :type stopWords: String
-        """
-        self.fd.setStopWords(stopWords)
 
-    def getFieldDefinition(self):
-        """ Method to get  pointer address of this class
+    class FieldDefinition:
+        """ This class defines each field that we use in matching We can use this to configure the properties of each field we use for matching in Zingg.
 
-        :return: The pointer containing the address of this class object
-        :rtype: pointer(FieldDefinition)
+        :param name: name of the field
+        :type name: String
+        :param dataType: type of the data e.g. string, float, etc.
+        :type dataType: String
+        :param matchType: match type of this field e.g. FUSSY, EXACT, etc.
+        :type matchType: MatchType
         """
-        return self.fd
 
-    #  should be stringify'ed before it is set in fd object
-    def stringify(self, str):
-        """ Method to stringify'ed the dataType before it is set in FieldDefinition object
+        def __init__(self, name, dataType, *matchType):
+            self.fd = getJVM().zingg.common.client.FieldDefinition()
+            self.fd.setFieldName(name)
+            self.fd.setDataType(self.stringify(dataType))
+            self.fd.setMatchType(matchType)
+            self.fd.setFields(name)
         
-        :param str: dataType of the FieldDefinition
-        :type str: String
-        :return: The stringify'ed value of the dataType
-        :rtype: String
-        """
-        
-        return str
-        
-
-def parseArguments(argv):
-    """ This method is used for checking mandatory arguments and creating an arguments list from Command line arguments
-
-    :param argv: Values that are passed during the calling of the program along with the calling statement.
-    :type argv: List
-    :return: a list containing necessary arguments to run any phase
-    :rtype: List
-    """
-    parser = argparse.ArgumentParser(description='Zingg\'s python APIs')
-    mandatoryOptions = parser.add_argument_group('mandatory arguments')
-    mandatoryOptions.add_argument('--phase', required=True,
-                        help='python phase e.g. assessModel')
-    mandatoryOptions.add_argument('--conf', required=True,
-                        help='JSON configuration with data input output locations and field definitions')
-
-    args, remaining_args = parser.parse_known_args(argv)
-    LOG.debug("args: ", args)
-    return args
+        def setStopWords(self, stopWords):
+            """ Method to add stopwords to this class object
+
+            :param stopWords: The stop Words containing csv file's location
+            :type stopWords: String
+            """
+            self.fd.setStopWords(stopWords)
+
+        def getFieldDefinition(self):
+            """ Method to get  pointer address of this class
+
+            :return: The pointer containing the address of this class object
+            :rtype: pointer(FieldDefinition)
+            """
+            return self.fd
+
+        #  should be stringify'ed before it is set in fd object
+        def stringify(self, str):
+            """ Method to stringify'ed the dataType before it is set in FieldDefinition object
+            
+            :param str: dataType of the FieldDefinition
+            :type str: String
+            :return: The stringify'ed value of the dataType
+            :rtype: String
+            """
+            
+            return str
+            
+
+    def parseArguments(argv):
+        """ This method is used for checking mandatory arguments and creating an arguments list from Command line arguments
+
+        :param argv: Values that are passed during the calling of the program along with the calling statement.
+        :type argv: List
+        :return: a list containing necessary arguments to run any phase
+        :rtype: List
+        """
+        parser = argparse.ArgumentParser(description='Zingg\'s python APIs')
+        mandatoryOptions = parser.add_argument_group('mandatory arguments')
+        mandatoryOptions.add_argument('--phase', required=True,
+                            help='python phase e.g. assessModel')
+        mandatoryOptions.add_argument('--conf', required=True,
+                            help='JSON configuration with data input output locations and field definitions')
+
+        args, remaining_args = parser.parse_known_args(argv)
+        LOG.debug("args: ", args)
+        return args
diff --git a/python/zingg/pipes.py b/python/zingg/pipes.py
index 94db83699..bb505d2ec 100644
--- a/python/zingg/pipes.py
+++ b/python/zingg/pipes.py
@@ -4,270 +4,271 @@
 This module is submodule of zingg to work with different types of Pipes. Classes of this module inherit the Pipe class, and use that class to create many different types of pipes.
 """
 
+if __name__ == '__main__':
 
-import logging
-from zingg.client import *
+    import logging
+    from zingg.client import *
 
-LOG = logging.getLogger("zingg.pipes")
+    LOG = logging.getLogger("zingg.pipes")
 
-JPipe = getJVM().zingg.spark.client.pipe.SparkPipe
-FilePipe = getJVM().zingg.common.client.pipe.FilePipe
-JStructType = getJVM().org.apache.spark.sql.types.StructType
+    JPipe = getJVM().zingg.spark.client.pipe.SparkPipe
+    FilePipe = getJVM().zingg.common.client.pipe.FilePipe
+    JStructType = getJVM().org.apache.spark.sql.types.StructType
 
-class Pipe:
-    """ Pipe class for working with different data-pipelines. Actual pipe def in the args. One pipe can be used at multiple places with different tables, locations, queries, etc
+    class Pipe:
+        """ Pipe class for working with different data-pipelines. Actual pipe def in the args. One pipe can be used at multiple places with different tables, locations, queries, etc
 
-    :param name: name of the pipe
-    :type name: String
-    :param format: formate of pipe e.g. bigquery,InMemory, etc.
-    :type format: Format
-    """
+        :param name: name of the pipe
+        :type name: String
+        :param format: formate of pipe e.g. bigquery,InMemory, etc.
+        :type format: Format
+        """
 
-    def __init__(self, name, format):
-        self.pipe = getJVM().zingg.spark.client.pipe.SparkPipe()
-        self.pipe.setName(name)
-        self.pipe.setFormat(format)
+        def __init__(self, name, format):
+            self.pipe = getJVM().zingg.spark.client.pipe.SparkPipe()
+            self.pipe.setName(name)
+            self.pipe.setFormat(format)
 
-    def getPipe(self):
-        """ Method to get Pipe 
+        def getPipe(self):
+            """ Method to get Pipe 
 
-        :return: pipe parameter values in the format of a list of string 
-        :rtype: Pipe
-        """
-        return self.pipe
+            :return: pipe parameter values in the format of a list of string 
+            :rtype: Pipe
+            """
+            return self.pipe
+
+        def addProperty(self, name, value):
+            """ Method for adding different properties of pipe
+
+            :param name: name of the property
+            :type name: String
+            :param value: value you want to set for the property
+            :type value: String
+            """
+            self.pipe.setProp(name, value)
+        
+        def setSchema(self, s):
+            """ Method to set pipe schema value
+
+            :param s: json schema for the pipe
+            :type s: Schema
+            """
+            self.pipe.setSchema(s)
 
-    def addProperty(self, name, value):
-        """ Method for adding different properties of pipe
+        def toString(self):
+            """ Method to get pipe parameter values
 
-        :param name: name of the property
+            :return: pipe information in list format
+            :rtype: List[String]
+            """
+            return self.pipe.toString()
+
+
+    class CsvPipe(Pipe):
+        """ Class CsvPipe: used for working with text files which uses a pipe symbol to separate units of text that belong in different columns.
+
+        :param name: name of the pipe.
         :type name: String
-        :param value: value you want to set for the property
-        :type value: String
+        :param location: (optional) location from where we read data
+        :type location: String or None
+        :param schema: (optional) json schema for the pipe
+        :type schema: Schema or None
         """
-        self.pipe.setProp(name, value)
-    
-    def setSchema(self, s):
-        """ Method to set pipe schema value
+        def __init__(self, name, location = None, schema = None):
+            Pipe.__init__(self, name, JPipe.FORMAT_CSV)
+            if(location != None):
+                Pipe.addProperty(self, FilePipe.LOCATION, location)
+                if(schema != None):
+                    #df = spark.read.format(JPipe.FORMAT_CSV).schema(schema).load(location)
+                    #s = JStructType.fromDDL(schema)
+                    Pipe.setSchema(self, schema)
+                    print("set schema ")
+        
+        def setDelimiter(self, delimiter):
+            """ This method is used to define delimiter of CsvPipe
 
-        :param s: json schema for the pipe
-        :type s: Schema
-        """
-        self.pipe.setSchema(s)
+            :param delimiter: a sequence of one or more characters for specifying the boundary between separate, independent regions in data streams
+            :type delimiter: String
+            """
+            Pipe.addProperty(self, "delimiter", delimiter)
 
-    def toString(self):
-        """ Method to get pipe parameter values
+        def setLocation(self, location):
+            """ Method to set location of pipe
 
-        :return: pipe information in list format
-        :rtype: List[String]
-        """
-        return self.pipe.toString()
-
-
-class CsvPipe(Pipe):
-    """ Class CsvPipe: used for working with text files which uses a pipe symbol to separate units of text that belong in different columns.
-
-    :param name: name of the pipe.
-    :type name: String
-    :param location: (optional) location from where we read data
-    :type location: String or None
-    :param schema: (optional) json schema for the pipe
-    :type schema: Schema or None
-    """
-    def __init__(self, name, location = None, schema = None):
-        Pipe.__init__(self, name, JPipe.FORMAT_CSV)
-        if(location != None):
+            :param location: location from where we read data
+            :type location: String
+            """
             Pipe.addProperty(self, FilePipe.LOCATION, location)
-            if(schema != None):
-                #df = spark.read.format(JPipe.FORMAT_CSV).schema(schema).load(location)
-                #s = JStructType.fromDDL(schema)
-                Pipe.setSchema(self, schema)
-                print("set schema ")
-    
-    def setDelimiter(self, delimiter):
-        """ This method is used to define delimiter of CsvPipe
-
-        :param delimiter: a sequence of one or more characters for specifying the boundary between separate, independent regions in data streams
-        :type delimiter: String
-        """
-        Pipe.addProperty(self, "delimiter", delimiter)
 
-    def setLocation(self, location):
-        """ Method to set location of pipe
+        def setHeader(self, header):
+            """ Method to set header property of pipe
 
-        :param location: location from where we read data
-        :type location: String
-        """
-        Pipe.addProperty(self, FilePipe.LOCATION, location)
+            :param header: true if pipe have header, false otherwise
+            :type header: Boolean
+            """
+            Pipe.addProperty(self, FilePipe.HEADER, header)
 
-    def setHeader(self, header):
-        """ Method to set header property of pipe
+    class BigQueryPipe(Pipe):
+        """ Pipe Class for working with BigQuery pipeline
 
-        :param header: true if pipe have header, false otherwise
-        :type header: Boolean
+        :param name: name of the pipe.
+        :type name: String
         """
-        Pipe.addProperty(self, FilePipe.HEADER, header)
 
-class BigQueryPipe(Pipe):
-    """ Pipe Class for working with BigQuery pipeline
+        VIEWS_ENABLED = "viewsEnabled"
+        CREDENTIAL_FILE = "credentialsFile"
+        TABLE = "table"
+        TEMP_GCS_BUCKET="temporaryGcsBucket"
 
-    :param name: name of the pipe.
-    :type name: String
-    """
+        def __init__(self,name):
+            Pipe.__init__(self, name, JPipe.FORMAT_BIGQUERY)
 
-    VIEWS_ENABLED = "viewsEnabled"
-    CREDENTIAL_FILE = "credentialsFile"
-    TABLE = "table"
-    TEMP_GCS_BUCKET="temporaryGcsBucket"
+        def setCredentialFile(self, file):
+            """ Method to set Credential file to the pipe
 
-    def __init__(self,name):
-        Pipe.__init__(self, name, JPipe.FORMAT_BIGQUERY)
+            :param file: credential file name
+            :type file: String
+            """
+            Pipe.addProperty(self, "credentialsFile", file)
 
-    def setCredentialFile(self, file):
-        """ Method to set Credential file to the pipe
+        def setTable(self, table):
+            """ Method to set Table to the pipe
 
-        :param file: credential file name
-        :type file: String
-        """
-        Pipe.addProperty(self, "credentialsFile", file)
+            :param table: provide table parameter
+            :type table: String
+            """
+            Pipe.addProperty(self, "table", table)
 
-    def setTable(self, table):
-        """ Method to set Table to the pipe
+        def setTemporaryGcsBucket(self, bucket):
+            """ Method to set TemporaryGcsBucket to the pipe
 
-        :param table: provide table parameter
-        :type table: String
-        """
-        Pipe.addProperty(self, "table", table)
+            :param bucket: provide bucket parameter
+            :type bucket: String
+            """
+            Pipe.addProperty(self, "temporaryGcsBucket", bucket)
 
-    def setTemporaryGcsBucket(self, bucket):
-        """ Method to set TemporaryGcsBucket to the pipe
+        def setViewsEnabled(self, isEnabled):
+            """ Method to set if viewsEnabled parameter is Enabled or not
+
+            :param isEnabled: provide boolean parameter which defines if viewsEnabled option is enable or not
+            :type isEnabled: Bool
+            """
+            Pipe.addProperty(self, "viewsEnabled", isEnabled)
 
-        :param bucket: provide bucket parameter
-        :type bucket: String
-        """
-        Pipe.addProperty(self, "temporaryGcsBucket", bucket)
 
-    def setViewsEnabled(self, isEnabled):
-        """ Method to set if viewsEnabled parameter is Enabled or not
+    class SnowflakePipe(Pipe):
+        """ Pipe Class for working with Snowflake pipeline
 
-        :param isEnabled: provide boolean parameter which defines if viewsEnabled option is enable or not
-        :type isEnabled: Bool
+        :param name: name of the pipe
+        :type name: String
         """
-        Pipe.addProperty(self, "viewsEnabled", isEnabled)
-
-
-class SnowflakePipe(Pipe):
-    """ Pipe Class for working with Snowflake pipeline
-
-    :param name: name of the pipe
-    :type name: String
-    """
-    URL = "sfUrl"
-    USER = "sfUser"
-    PASSWORD = "sfPassword"
-    DATABASE ="sfDatabase"
-    SCHEMA = "sfSchema"
-    WAREHOUSE = "sfWarehouse"
-    DBTABLE = "dbtable"
-
-    def __init__(self,name):
-        Pipe.__init__(self, name, JPipe.FORMAT_SNOWFLAKE)
-        Pipe.addProperty(self, "application", "zinggai_zingg")
-        
+        URL = "sfUrl"
+        USER = "sfUser"
+        PASSWORD = "sfPassword"
+        DATABASE ="sfDatabase"
+        SCHEMA = "sfSchema"
+        WAREHOUSE = "sfWarehouse"
+        DBTABLE = "dbtable"
+
+        def __init__(self,name):
+            Pipe.__init__(self, name, JPipe.FORMAT_SNOWFLAKE)
+            Pipe.addProperty(self, "application", "zinggai_zingg")
+            
 
-    def setURL(self, url):
-        """ Method to set url to the pipe
+        def setURL(self, url):
+            """ Method to set url to the pipe
 
-        :param url: provide url for this pipe
-        :type url: String
-        """
-        Pipe.addProperty(self, "sfUrl", url)
+            :param url: provide url for this pipe
+            :type url: String
+            """
+            Pipe.addProperty(self, "sfUrl", url)
 
-    def setUser(self, user):
-        """ Method to set User to the pipe
-        
-        :param user: provide User parameter.
-        :type user: String
-        """
-        Pipe.addProperty(self, "sfUser", user)
+        def setUser(self, user):
+            """ Method to set User to the pipe
+            
+            :param user: provide User parameter.
+            :type user: String
+            """
+            Pipe.addProperty(self, "sfUser", user)
 
-    def setPassword(self, passwd):
-        """ Method to set Password to the pipe
-        
-        :param passwd: provide Password parameter.
-        :type passwd: String
-        """
-        Pipe.addProperty(self, "sfPassword", passwd)
+        def setPassword(self, passwd):
+            """ Method to set Password to the pipe
+            
+            :param passwd: provide Password parameter.
+            :type passwd: String
+            """
+            Pipe.addProperty(self, "sfPassword", passwd)
 
-    def setDatabase(self, db):
-        """ Method to set Database to the pipe
-        
-        :param db: provide Database parameter.
-        :type db: Database
-        """
-        Pipe.addProperty(self, "sfDatabase", db)
+        def setDatabase(self, db):
+            """ Method to set Database to the pipe
+            
+            :param db: provide Database parameter.
+            :type db: Database
+            """
+            Pipe.addProperty(self, "sfDatabase", db)
 
-    def setSFSchema(self, schema):
-        """ Method to set Schema to the pipe
-        
-        :param schema: provide schema parameter.
-        :type schema: Schema
-        """
-        Pipe.addProperty(self, "sfSchema", schema)
+        def setSFSchema(self, schema):
+            """ Method to set Schema to the pipe
+            
+            :param schema: provide schema parameter.
+            :type schema: Schema
+            """
+            Pipe.addProperty(self, "sfSchema", schema)
 
-    def setWarehouse(self, warehouse):
-        """ Method to set warehouse parameter to the pipe
-        
-        :param warehouse: provide warehouse parameter.
-        :type warehouse: String
-        """
-        Pipe.addProperty(self, "sfWarehouse", warehouse)
+        def setWarehouse(self, warehouse):
+            """ Method to set warehouse parameter to the pipe
+            
+            :param warehouse: provide warehouse parameter.
+            :type warehouse: String
+            """
+            Pipe.addProperty(self, "sfWarehouse", warehouse)
 
-    def setDbTable(self, dbtable):
-        """ description
-        
-        :param dbtable: provide bucket parameter.
-        :type dbtable: String
-        """
-        Pipe.addProperty(self, "dbtable", dbtable)     
-        
+        def setDbTable(self, dbtable):
+            """ description
+            
+            :param dbtable: provide bucket parameter.
+            :type dbtable: String
+            """
+            Pipe.addProperty(self, "dbtable", dbtable)     
+            
 
-class InMemoryPipe(Pipe):
-    """ Pipe Class for working with InMemory pipeline
+    class InMemoryPipe(Pipe):
+        """ Pipe Class for working with InMemory pipeline
 
-    :param name: name of the pipe
-    :type name: String
-    :param df: provide dataset for this pipe (optional)
-    :type df: Dataset or None
-    """    
+        :param name: name of the pipe
+        :type name: String
+        :param df: provide dataset for this pipe (optional)
+        :type df: Dataset or None
+        """    
 
-    def __init__(self, name, df = None):
-        Pipe.__init__(self, name, JPipe.FORMAT_INMEMORY)
-        if (df is not None):
-            self.setDataset(df)
+        def __init__(self, name, df = None):
+            Pipe.__init__(self, name, JPipe.FORMAT_INMEMORY)
+            if (df is not None):
+                self.setDataset(df)
 
-    def setDataset(self, df):
-        """ Method to set DataFrame of the pipe
-        
-        :param df: pandas or spark dataframe for the pipe
-        :type df: DataFrame
-        """
-        if (isinstance(df, pd.DataFrame)):
-            print('schema of pandas df is ' , Pipe.getPipe(self).getSchema())
-            if (Pipe.getPipe(self).getSchema() is not None):
-                ds = getSparkSession().createDataFrame(df, schema=Pipe.getPipe(self).getSchema())
+        def setDataset(self, df):
+            """ Method to set DataFrame of the pipe
+            
+            :param df: pandas or spark dataframe for the pipe
+            :type df: DataFrame
+            """
+            if (isinstance(df, pd.DataFrame)):
+                print('schema of pandas df is ' , Pipe.getPipe(self).getSchema())
+                if (Pipe.getPipe(self).getSchema() is not None):
+                    ds = getSparkSession().createDataFrame(df, schema=Pipe.getPipe(self).getSchema())
+                else:
+                    ds = getSparkSession().createDataFrame(df)
+                
+                Pipe.getPipe(self).setDataset(ds._jdf)
+            elif (isinstance(df, DataFrame)):
+                Pipe.getPipe(self).setDataset(df._jdf)
             else:
-                ds = getSparkSession().createDataFrame(df)
+                LOG.error(" setDataset(): NUll or Unsupported type: %s", type(df))
+
+        def getDataset(self):
+            """ Method to get Dataset from pipe
             
-            Pipe.getPipe(self).setDataset(ds._jdf)
-        elif (isinstance(df, DataFrame)):
-            Pipe.getPipe(self).setDataset(df._jdf)
-        else:
-            LOG.error(" setDataset(): NUll or Unsupported type: %s", type(df))
-
-    def getDataset(self):
-        """ Method to get Dataset from pipe
-        
-        :return: dataset of the pipe in the format of spark dataset 
-        :rtype: Dataset<Row>
-        """
-        return Pipe.getPipe(self).getDataset().df()
+            :return: dataset of the pipe in the format of spark dataset 
+            :rtype: Dataset<Row>
+            """
+            return Pipe.getPipe(self).getDataset().df()

From 248f971c570b6878122dd5e6fd3ae12457a4d397 Mon Sep 17 00:00:00 2001
From: Sonal Goyal <sonalgoyal4@gmail.com>
Date: Thu, 4 Jan 2024 20:25:14 +0530
Subject: [PATCH 11/36] Revert "added __main__ to pkg to make sure it doesnt
 run when loaded"

This reverts commit 1d46899ce44e15dcf90f16dea6b8872a2bc8fc81.
---
 .../docs/_build/doctrees/environment.pickle   |  Bin 35314 -> 533605 bytes
 python/docs/_build/doctrees/index.doctree     |  Bin 10040 -> 10386 bytes
 python/docs/_build/doctrees/zingg.doctree     |  Bin 6968 -> 296430 bytes
 python/docs/_build/markdown/index.md          |  183 ---
 python/docs/_build/markdown/zingg.md          |  785 ----------
 python/docs/conf.py                           |    3 +-
 python/docs/index.rst                         |    5 +
 python/zingg/client.py                        | 1370 ++++++++---------
 python/zingg/pipes.py                         |  439 +++---
 9 files changed, 910 insertions(+), 1875 deletions(-)
 delete mode 100644 python/docs/_build/markdown/index.md
 delete mode 100644 python/docs/_build/markdown/zingg.md

diff --git a/python/docs/_build/doctrees/environment.pickle b/python/docs/_build/doctrees/environment.pickle
index e7641c78c77993d02a78bcbc34987c18902a7986..d8e6b105f093f0aa3a3bf2506bdccbef9e112a9d 100644
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diff --git a/python/docs/_build/doctrees/index.doctree b/python/docs/_build/doctrees/index.doctree
index 399dbb7c48f3e5bb6443c3e03e9ed775c5602788..aacb0eb1c34e290678cdb7273e51db35a45e424d 100644
GIT binary patch
delta 2853
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diff --git a/python/docs/_build/doctrees/zingg.doctree b/python/docs/_build/doctrees/zingg.doctree
index 205abbb8fb5526e0d9010cdfe7a3ba1d47574a4b..4d5ef1240a23db39984ac416cde32cc560c5b3df 100644
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diff --git a/python/docs/_build/markdown/index.md b/python/docs/_build/markdown/index.md
deleted file mode 100644
index d8b455d19..000000000
--- a/python/docs/_build/markdown/index.md
+++ /dev/null
@@ -1,183 +0,0 @@
-<!-- zingg documentation master file, created by
-sphinx-quickstart on Thu Jul  7 12:24:41 2022.
-You can adapt this file completely to your liking, but it should at least
-contain the root `toctree` directive. -->
-
-# Zingg Entity Resolution Python Package
-
-Zingg Python APIs for entity resolution, identity resolution, record linkage, data mastering and deduplication using ML
-([https://www.zingg.ai](https://www.zingg.ai))
-
-#### NOTE
-Requires python 3.6+; spark 3.5.0
-Otherwise, [`zingg.client.Zingg()`](zingg.md#zingg.client.Zingg) cannot be executed
-
-* [Zingg Entity Resolution Package](zingg.md)
-  * [zingg.client](zingg.md#zingg-client)
-  * [`Arguments`](zingg.md#zingg.client.Arguments)
-    * [`Arguments.copyArgs()`](zingg.md#zingg.client.Arguments.copyArgs)
-    * [`Arguments.createArgumentsFromJSON()`](zingg.md#zingg.client.Arguments.createArgumentsFromJSON)
-    * [`Arguments.createArgumentsFromJSONString()`](zingg.md#zingg.client.Arguments.createArgumentsFromJSONString)
-    * [`Arguments.getArgs()`](zingg.md#zingg.client.Arguments.getArgs)
-    * [`Arguments.getModelId()`](zingg.md#zingg.client.Arguments.getModelId)
-    * [`Arguments.getZinggBaseModelDir()`](zingg.md#zingg.client.Arguments.getZinggBaseModelDir)
-    * [`Arguments.getZinggBaseTrainingDataDir()`](zingg.md#zingg.client.Arguments.getZinggBaseTrainingDataDir)
-    * [`Arguments.getZinggModelDir()`](zingg.md#zingg.client.Arguments.getZinggModelDir)
-    * [`Arguments.getZinggTrainingDataMarkedDir()`](zingg.md#zingg.client.Arguments.getZinggTrainingDataMarkedDir)
-    * [`Arguments.getZinggTrainingDataUnmarkedDir()`](zingg.md#zingg.client.Arguments.getZinggTrainingDataUnmarkedDir)
-    * [`Arguments.setArgs()`](zingg.md#zingg.client.Arguments.setArgs)
-    * [`Arguments.setColumn()`](zingg.md#zingg.client.Arguments.setColumn)
-    * [`Arguments.setData()`](zingg.md#zingg.client.Arguments.setData)
-    * [`Arguments.setFieldDefinition()`](zingg.md#zingg.client.Arguments.setFieldDefinition)
-    * [`Arguments.setLabelDataSampleSize()`](zingg.md#zingg.client.Arguments.setLabelDataSampleSize)
-    * [`Arguments.setModelId()`](zingg.md#zingg.client.Arguments.setModelId)
-    * [`Arguments.setNumPartitions()`](zingg.md#zingg.client.Arguments.setNumPartitions)
-    * [`Arguments.setOutput()`](zingg.md#zingg.client.Arguments.setOutput)
-    * [`Arguments.setStopWordsCutoff()`](zingg.md#zingg.client.Arguments.setStopWordsCutoff)
-    * [`Arguments.setTrainingSamples()`](zingg.md#zingg.client.Arguments.setTrainingSamples)
-    * [`Arguments.setZinggDir()`](zingg.md#zingg.client.Arguments.setZinggDir)
-    * [`Arguments.writeArgumentsToJSON()`](zingg.md#zingg.client.Arguments.writeArgumentsToJSON)
-    * [`Arguments.writeArgumentsToJSONString()`](zingg.md#zingg.client.Arguments.writeArgumentsToJSONString)
-  * [`ClientOptions`](zingg.md#zingg.client.ClientOptions)
-    * [`ClientOptions.COLUMN`](zingg.md#zingg.client.ClientOptions.COLUMN)
-    * [`ClientOptions.CONF`](zingg.md#zingg.client.ClientOptions.CONF)
-    * [`ClientOptions.EMAIL`](zingg.md#zingg.client.ClientOptions.EMAIL)
-    * [`ClientOptions.LICENSE`](zingg.md#zingg.client.ClientOptions.LICENSE)
-    * [`ClientOptions.LOCATION`](zingg.md#zingg.client.ClientOptions.LOCATION)
-    * [`ClientOptions.MODEL_ID`](zingg.md#zingg.client.ClientOptions.MODEL_ID)
-    * [`ClientOptions.PHASE`](zingg.md#zingg.client.ClientOptions.PHASE)
-    * [`ClientOptions.REMOTE`](zingg.md#zingg.client.ClientOptions.REMOTE)
-    * [`ClientOptions.ZINGG_DIR`](zingg.md#zingg.client.ClientOptions.ZINGG_DIR)
-    * [`ClientOptions.getClientOptions()`](zingg.md#zingg.client.ClientOptions.getClientOptions)
-    * [`ClientOptions.getConf()`](zingg.md#zingg.client.ClientOptions.getConf)
-    * [`ClientOptions.getLocation()`](zingg.md#zingg.client.ClientOptions.getLocation)
-    * [`ClientOptions.getOptionValue()`](zingg.md#zingg.client.ClientOptions.getOptionValue)
-    * [`ClientOptions.getPhase()`](zingg.md#zingg.client.ClientOptions.getPhase)
-    * [`ClientOptions.hasLocation()`](zingg.md#zingg.client.ClientOptions.hasLocation)
-    * [`ClientOptions.setOptionValue()`](zingg.md#zingg.client.ClientOptions.setOptionValue)
-    * [`ClientOptions.setPhase()`](zingg.md#zingg.client.ClientOptions.setPhase)
-  * [`FieldDefinition`](zingg.md#zingg.client.FieldDefinition)
-    * [`FieldDefinition.getFieldDefinition()`](zingg.md#zingg.client.FieldDefinition.getFieldDefinition)
-    * [`FieldDefinition.setStopWords()`](zingg.md#zingg.client.FieldDefinition.setStopWords)
-    * [`FieldDefinition.stringify()`](zingg.md#zingg.client.FieldDefinition.stringify)
-  * [`Zingg`](zingg.md#zingg.client.Zingg)
-    * [`Zingg.execute()`](zingg.md#zingg.client.Zingg.execute)
-    * [`Zingg.executeLabel()`](zingg.md#zingg.client.Zingg.executeLabel)
-    * [`Zingg.executeLabelUpdate()`](zingg.md#zingg.client.Zingg.executeLabelUpdate)
-    * [`Zingg.getArguments()`](zingg.md#zingg.client.Zingg.getArguments)
-    * [`Zingg.getMarkedRecords()`](zingg.md#zingg.client.Zingg.getMarkedRecords)
-    * [`Zingg.getMarkedRecordsStat()`](zingg.md#zingg.client.Zingg.getMarkedRecordsStat)
-    * [`Zingg.getMatchedMarkedRecordsStat()`](zingg.md#zingg.client.Zingg.getMatchedMarkedRecordsStat)
-    * [`Zingg.getOptions()`](zingg.md#zingg.client.Zingg.getOptions)
-    * [`Zingg.getUnmarkedRecords()`](zingg.md#zingg.client.Zingg.getUnmarkedRecords)
-    * [`Zingg.getUnmatchedMarkedRecordsStat()`](zingg.md#zingg.client.Zingg.getUnmatchedMarkedRecordsStat)
-    * [`Zingg.getUnsureMarkedRecordsStat()`](zingg.md#zingg.client.Zingg.getUnsureMarkedRecordsStat)
-    * [`Zingg.init()`](zingg.md#zingg.client.Zingg.init)
-    * [`Zingg.initAndExecute()`](zingg.md#zingg.client.Zingg.initAndExecute)
-    * [`Zingg.processRecordsCli()`](zingg.md#zingg.client.Zingg.processRecordsCli)
-    * [`Zingg.processRecordsCliLabelUpdate()`](zingg.md#zingg.client.Zingg.processRecordsCliLabelUpdate)
-    * [`Zingg.setArguments()`](zingg.md#zingg.client.Zingg.setArguments)
-    * [`Zingg.setOptions()`](zingg.md#zingg.client.Zingg.setOptions)
-    * [`Zingg.writeLabelledOutput()`](zingg.md#zingg.client.Zingg.writeLabelledOutput)
-    * [`Zingg.writeLabelledOutputFromPandas()`](zingg.md#zingg.client.Zingg.writeLabelledOutputFromPandas)
-  * [`ZinggWithSpark`](zingg.md#zingg.client.ZinggWithSpark)
-  * [`getDfFromDs()`](zingg.md#zingg.client.getDfFromDs)
-  * [`getGateway()`](zingg.md#zingg.client.getGateway)
-  * [`getJVM()`](zingg.md#zingg.client.getJVM)
-  * [`getPandasDfFromDs()`](zingg.md#zingg.client.getPandasDfFromDs)
-  * [`getSparkContext()`](zingg.md#zingg.client.getSparkContext)
-  * [`getSparkSession()`](zingg.md#zingg.client.getSparkSession)
-  * [`getSqlContext()`](zingg.md#zingg.client.getSqlContext)
-  * [`initClient()`](zingg.md#zingg.client.initClient)
-  * [`initDataBricksConectClient()`](zingg.md#zingg.client.initDataBricksConectClient)
-  * [`initSparkClient()`](zingg.md#zingg.client.initSparkClient)
-  * [`parseArguments()`](zingg.md#zingg.client.parseArguments)
-  * [zingg.pipes](zingg.md#zingg-pipes)
-  * [`BigQueryPipe`](zingg.md#zingg.pipes.BigQueryPipe)
-    * [`BigQueryPipe.CREDENTIAL_FILE`](zingg.md#zingg.pipes.BigQueryPipe.CREDENTIAL_FILE)
-    * [`BigQueryPipe.TABLE`](zingg.md#zingg.pipes.BigQueryPipe.TABLE)
-    * [`BigQueryPipe.TEMP_GCS_BUCKET`](zingg.md#zingg.pipes.BigQueryPipe.TEMP_GCS_BUCKET)
-    * [`BigQueryPipe.VIEWS_ENABLED`](zingg.md#zingg.pipes.BigQueryPipe.VIEWS_ENABLED)
-    * [`BigQueryPipe.setCredentialFile()`](zingg.md#zingg.pipes.BigQueryPipe.setCredentialFile)
-    * [`BigQueryPipe.setTable()`](zingg.md#zingg.pipes.BigQueryPipe.setTable)
-    * [`BigQueryPipe.setTemporaryGcsBucket()`](zingg.md#zingg.pipes.BigQueryPipe.setTemporaryGcsBucket)
-    * [`BigQueryPipe.setViewsEnabled()`](zingg.md#zingg.pipes.BigQueryPipe.setViewsEnabled)
-  * [`CsvPipe`](zingg.md#zingg.pipes.CsvPipe)
-    * [`CsvPipe.setDelimiter()`](zingg.md#zingg.pipes.CsvPipe.setDelimiter)
-    * [`CsvPipe.setHeader()`](zingg.md#zingg.pipes.CsvPipe.setHeader)
-    * [`CsvPipe.setLocation()`](zingg.md#zingg.pipes.CsvPipe.setLocation)
-  * [`InMemoryPipe`](zingg.md#zingg.pipes.InMemoryPipe)
-    * [`InMemoryPipe.getDataset()`](zingg.md#zingg.pipes.InMemoryPipe.getDataset)
-    * [`InMemoryPipe.setDataset()`](zingg.md#zingg.pipes.InMemoryPipe.setDataset)
-  * [`Pipe`](zingg.md#zingg.pipes.Pipe)
-    * [`Pipe.addProperty()`](zingg.md#zingg.pipes.Pipe.addProperty)
-    * [`Pipe.getPipe()`](zingg.md#zingg.pipes.Pipe.getPipe)
-    * [`Pipe.setSchema()`](zingg.md#zingg.pipes.Pipe.setSchema)
-    * [`Pipe.toString()`](zingg.md#zingg.pipes.Pipe.toString)
-  * [`SnowflakePipe`](zingg.md#zingg.pipes.SnowflakePipe)
-    * [`SnowflakePipe.DATABASE`](zingg.md#zingg.pipes.SnowflakePipe.DATABASE)
-    * [`SnowflakePipe.DBTABLE`](zingg.md#zingg.pipes.SnowflakePipe.DBTABLE)
-    * [`SnowflakePipe.PASSWORD`](zingg.md#zingg.pipes.SnowflakePipe.PASSWORD)
-    * [`SnowflakePipe.SCHEMA`](zingg.md#zingg.pipes.SnowflakePipe.SCHEMA)
-    * [`SnowflakePipe.URL`](zingg.md#zingg.pipes.SnowflakePipe.URL)
-    * [`SnowflakePipe.USER`](zingg.md#zingg.pipes.SnowflakePipe.USER)
-    * [`SnowflakePipe.WAREHOUSE`](zingg.md#zingg.pipes.SnowflakePipe.WAREHOUSE)
-    * [`SnowflakePipe.setDatabase()`](zingg.md#zingg.pipes.SnowflakePipe.setDatabase)
-    * [`SnowflakePipe.setDbTable()`](zingg.md#zingg.pipes.SnowflakePipe.setDbTable)
-    * [`SnowflakePipe.setPassword()`](zingg.md#zingg.pipes.SnowflakePipe.setPassword)
-    * [`SnowflakePipe.setSFSchema()`](zingg.md#zingg.pipes.SnowflakePipe.setSFSchema)
-    * [`SnowflakePipe.setURL()`](zingg.md#zingg.pipes.SnowflakePipe.setURL)
-    * [`SnowflakePipe.setUser()`](zingg.md#zingg.pipes.SnowflakePipe.setUser)
-    * [`SnowflakePipe.setWarehouse()`](zingg.md#zingg.pipes.SnowflakePipe.setWarehouse)
-
-# API Reference
-
-* [Module Index](py-modindex.md)
-* [Index](genindex.md)
-* [Search Page](search.md)
-
-# Example API Usage
-
-```python
-from zingg.client import *
-from zingg.pipes import *
-
-#build the arguments for zingg
-args = Arguments()
-#set field definitions
-fname = FieldDefinition("fname", "string", MatchType.FUZZY)
-lname = FieldDefinition("lname", "string", MatchType.FUZZY)
-stNo = FieldDefinition("stNo", "string", MatchType.FUZZY)
-add1 = FieldDefinition("add1","string", MatchType.FUZZY)
-add2 = FieldDefinition("add2", "string", MatchType.FUZZY)
-city = FieldDefinition("city", "string", MatchType.FUZZY)
-areacode = FieldDefinition("areacode", "string", MatchType.FUZZY)
-state = FieldDefinition("state", "string", MatchType.FUZZY)
-dob = FieldDefinition("dob", "string", MatchType.FUZZY)
-ssn = FieldDefinition("ssn", "string", MatchType.FUZZY)
-
-fieldDefs = [fname, lname, stNo, add1, add2, city, areacode, state, dob, ssn]
-
-args.setFieldDefinition(fieldDefs)
-#set the modelid and the zingg dir
-args.setModelId("100")
-args.setZinggDir("models")
-args.setNumPartitions(4)
-args.setLabelDataSampleSize(0.5)
-
-#reading dataset into inputPipe and settint it up in 'args'
-#below line should not be required if you are reading from in memory dataset
-#in that case, replace df with input df
-schema = "id string, fname string, lname string, stNo string, add1 string, add2 string, city string, areacode string, state string, dob string, ssn  string"
-inputPipe = CsvPipe("testFebrl", "examples/febrl/test.csv", schema)
-args.setData(inputPipe)
-outputPipe = CsvPipe("resultFebrl", "/tmp/febrlOutput")
-
-args.setOutput(outputPipe)
-
-options = ClientOptions([ClientOptions.PHASE,"match"])
-
-#Zingg execution for the given phase
-zingg = Zingg(args, options)
-zingg.initAndExecute()
-```
diff --git a/python/docs/_build/markdown/zingg.md b/python/docs/_build/markdown/zingg.md
deleted file mode 100644
index 9467c6751..000000000
--- a/python/docs/_build/markdown/zingg.md
+++ /dev/null
@@ -1,785 +0,0 @@
-# Zingg Entity Resolution Package
-
-Zingg Python APIs for entity resolution, record linkage, data mastering and deduplication using ML
-([https://www.zingg.ai](https://www.zingg.ai))
-
-requires python 3.6+; spark 3.5.0
-Otherwise, [`zingg.client.Zingg()`](#zingg.client.Zingg) cannot be executed
-
-<a id="module-zingg"></a>
-
-<a id="module-zingg.client"></a>
-
-## zingg.client
-
-This module is the main entry point of the Zingg Python API
-
-### *class* zingg.client.Arguments
-
-Bases: `object`
-
-This class helps supply match arguments to Zingg. There are 3 basic steps in any match process.
-
-* **Defining:**
-  specifying information about data location, fields, and our notion of similarity.
-* **Training:**
-  making Zingg learn the matching rules
-* **Matching:**
-  Running the models on the entire dataset
-
-#### copyArgs(phase)
-
-#### *static* createArgumentsFromJSON(fileName, phase)
-
-Method to create an object of this class from the JSON file and phase parameter value.
-
-* **Parameters:**
-  * **fileName** (*String*) – The CONF parameter value of ClientOption object
-  * **phase** (*String*) – The PHASE parameter value of ClientOption object
-* **Returns:**
-  The pointer containing address of the this class object
-* **Return type:**
-  pointer([Arguments](#zingg.client.Arguments))
-
-#### *static* createArgumentsFromJSONString(jsonArgs, phase)
-
-#### getArgs()
-
-Method to get pointer address of this class
-
-* **Returns:**
-  The pointer containing the address of this class object
-* **Return type:**
-  pointer([Arguments](#zingg.client.Arguments))
-
-#### getModelId()
-
-#### getZinggBaseModelDir()
-
-#### getZinggBaseTrainingDataDir()
-
-Method to get the location of the folder where Zingg
-saves the training data found by findTrainingData
-
-#### getZinggModelDir()
-
-#### getZinggTrainingDataMarkedDir()
-
-Method to get the location of the folder where Zingg
-saves the marked training data labeled by the user
-
-#### getZinggTrainingDataUnmarkedDir()
-
-Method to get the location of the folder where Zingg
-saves the training data found by findTrainingData
-
-#### setArgs(argumentsObj)
-
-Method to set this class object
-
-* **Parameters:**
-  **argumentsObj** (*pointer**(*[*Arguments*](#zingg.client.Arguments)*)*) – Argument object to set this object
-
-#### setColumn(column)
-
-Method to set stopWordsCutoff parameter value
-By default, Zingg extracts 10% of the high frequency unique words from a dataset. If user wants different selection, they should set up StopWordsCutoff property
-
-* **Parameters:**
-  **stopWordsCutoff** (*float*) – The stop words cutoff parameter value of ClientOption object or file address of json file
-
-#### setData(\*pipes)
-
-Method to set the file path of the file to be matched.
-
-* **Parameters:**
-  **pipes** ([*Pipe*](#zingg.pipes.Pipe)*[**]*) – input data pipes separated by comma e.g. (pipe1,pipe2,..)
-
-#### setFieldDefinition(fieldDef)
-
-Method convert python objects to java FieldDefinition objects and set the field definitions associated with this client
-
-* **Parameters:**
-  **fieldDef** (*List**(*[*FieldDefinition*](#zingg.client.FieldDefinition)*)*) – python FieldDefinition object list
-
-#### setLabelDataSampleSize(labelDataSampleSize)
-
-Method to set labelDataSampleSize parameter value
-Set the fraction of data to be used from the complete data set to be used for seeding the labeled data Labelling is costly and we want a fast approximate way of looking at a small sample of the records and identifying expected matches and nonmatches
-
-* **Parameters:**
-  **labelDataSampleSize** (*float*) – value between 0.0 and 1.0 denoting portion of dataset to use in generating seed samples
-
-#### setModelId(id)
-
-Method to set the output directory where the match output will be saved
-
-* **Parameters:**
-  **id** (*String*) – model id value
-
-#### setNumPartitions(numPartitions)
-
-Method to set NumPartitions parameter value
-Sample size to use for seeding labeled data We don’t want to run over all the data, as we want a quick way to seed some labeled data that we can manually edit
-
-* **Parameters:**
-  **numPartitions** (*int*) – number of partitions for given data pipes
-
-#### setOutput(\*pipes)
-
-Method to set the output directory where the match result will be saved
-
-* **Parameters:**
-  **pipes** ([*Pipe*](#zingg.pipes.Pipe)*[**]*) – output data pipes separated by comma e.g. (pipe1,pipe2,..)
-
-#### setStopWordsCutoff(stopWordsCutoff)
-
-Method to set stopWordsCutoff parameter value
-By default, Zingg extracts 10% of the high frequency unique words from a dataset. If user wants different selection, they should set up StopWordsCutoff property
-
-* **Parameters:**
-  **stopWordsCutoff** (*float*) – The stop words cutoff parameter value of ClientOption object or file address of json file
-
-#### setTrainingSamples(\*pipes)
-
-Method to set existing training samples to be matched.
-
-* **Parameters:**
-  **pipes** ([*Pipe*](#zingg.pipes.Pipe)*[**]*) – input training data pipes separated by comma e.g. (pipe1,pipe2,..)
-
-#### setZinggDir(f)
-
-Method to set the location for Zingg to save its internal computations and models. Please set it to a place where the program has to write access.
-
-* **Parameters:**
-  **f** (*String*) – Zingg directory name of the models
-
-#### writeArgumentsToJSON(fileName)
-
-Method to write JSON file from the object of this class
-
-* **Parameters:**
-  **fileName** (*String*) – The CONF parameter value of ClientOption object or file address of json file
-
-#### writeArgumentsToJSONString()
-
-Method to create an object of this class from the JSON file and phase parameter value.
-
-* **Parameters:**
-  * **fileName** (*String*) – The CONF parameter value of ClientOption object
-  * **phase** (*String*) – The PHASE parameter value of ClientOption object
-* **Returns:**
-  The pointer containing address of the this class object
-* **Return type:**
-  pointer([Arguments](#zingg.client.Arguments))
-
-### *class* zingg.client.ClientOptions(argsSent=None)
-
-Bases: `object`
-
-Class that contains Client options for Zingg object
-:param phase: trainMatch, train, match, link, findAndLabel, findTrainingData, recommend etc
-:type phase: String
-:param args: Parse a list of Zingg command line options parameter values e.g. “–location” etc. optional argument for initializing this class.
-:type args: List(String) or None
-
-#### COLUMN *= <py4j.java_gateway.JavaPackage object>*
-
-Column whose stop words are to be recommended through Zingg
-
-* **Type:**
-  COLUMN
-
-#### CONF *= <py4j.java_gateway.JavaPackage object>*
-
-conf parameter for this class
-
-* **Type:**
-  CONF
-
-#### EMAIL *= <py4j.java_gateway.JavaPackage object>*
-
-e-mail parameter for this class
-
-* **Type:**
-  EMAIL
-
-#### LICENSE *= <py4j.java_gateway.JavaPackage object>*
-
-license parameter for this class
-
-* **Type:**
-  LICENSE
-
-#### LOCATION *= <py4j.java_gateway.JavaPackage object>*
-
-location parameter for this class
-
-* **Type:**
-  LOCATION
-
-#### MODEL_ID *= <py4j.java_gateway.JavaPackage object>*
-
-ZINGG_DIR/MODEL_ID is used to save the model
-
-* **Type:**
-  MODEL_ID
-
-#### PHASE *= <py4j.java_gateway.JavaPackage object>*
-
-phase parameter for this class
-
-* **Type:**
-  PHASE
-
-#### REMOTE *= <py4j.java_gateway.JavaPackage object>*
-
-remote option used internally for running on Databricks
-
-* **Type:**
-  REMOTE
-
-#### ZINGG_DIR *= <py4j.java_gateway.JavaPackage object>*
-
-location where Zingg saves the model, training data etc
-
-* **Type:**
-  ZINGG_DIR
-
-#### getClientOptions()
-
-Method to get pointer address of this class
-
-* **Returns:**
-  The pointer containing address of the this class object
-* **Return type:**
-  pointer([ClientOptions](#zingg.client.ClientOptions))
-
-#### getConf()
-
-Method to get CONF value
-
-* **Returns:**
-  The CONF parameter value
-* **Return type:**
-  String
-
-#### getLocation()
-
-Method to get LOCATION value
-
-* **Returns:**
-  The LOCATION parameter value
-* **Return type:**
-  String
-
-#### getOptionValue(option)
-
-Method to get value for the key option
-
-* **Parameters:**
-  **option** (*String*) – key to geting the value
-* **Returns:**
-  The value which is mapped for given key
-* **Return type:**
-  String
-
-#### getPhase()
-
-Method to get PHASE value
-
-* **Returns:**
-  The PHASE parameter value
-* **Return type:**
-  String
-
-#### hasLocation()
-
-Method to check if this class has LOCATION parameter set as None or not
-
-* **Returns:**
-  The boolean value if LOCATION parameter is present or not
-* **Return type:**
-  Bool
-
-#### setOptionValue(option, value)
-
-Method to map option key to the given value
-
-* **Parameters:**
-  * **option** (*String*) – key that is mapped with value
-  * **value** (*String*) – value to be set for given key
-
-#### setPhase(newValue)
-
-Method to set PHASE value
-
-* **Parameters:**
-  **newValue** (*String*) – name of the phase
-* **Returns:**
-  The pointer containing address of the this class object after seting phase
-* **Return type:**
-  pointer([ClientOptions](#zingg.client.ClientOptions))
-
-### *class* zingg.client.FieldDefinition(name, dataType, \*matchType)
-
-Bases: `object`
-
-This class defines each field that we use in matching We can use this to configure the properties of each field we use for matching in Zingg.
-
-* **Parameters:**
-  * **name** (*String*) – name of the field
-  * **dataType** (*String*) – type of the data e.g. string, float, etc.
-  * **matchType** (*MatchType*) – match type of this field e.g. FUSSY, EXACT, etc.
-
-#### getFieldDefinition()
-
-Method to get  pointer address of this class
-
-* **Returns:**
-  The pointer containing the address of this class object
-* **Return type:**
-  pointer([FieldDefinition](#zingg.client.FieldDefinition))
-
-#### setStopWords(stopWords)
-
-Method to add stopwords to this class object
-
-* **Parameters:**
-  **stopWords** (*String*) – The stop Words containing csv file’s location
-
-#### stringify(str)
-
-Method to stringify’ed the dataType before it is set in FieldDefinition object
-
-* **Parameters:**
-  **str** (*String*) – dataType of the FieldDefinition
-* **Returns:**
-  The stringify’ed value of the dataType
-* **Return type:**
-  String
-
-### *class* zingg.client.Zingg(args, options)
-
-Bases: `object`
-
-This class is the main point of interface with the Zingg matching product. Construct a client to Zingg using provided arguments and spark master. If running locally, set the master to local.
-
-* **Parameters:**
-  * **args** ([*Arguments*](#zingg.client.Arguments)) – arguments for training and matching
-  * **options** ([*ClientOptions*](#zingg.client.ClientOptions)) – client option for this class object
-
-#### execute()
-
-Method to execute this class object
-
-#### executeLabel()
-
-Method to run label phase
-
-#### executeLabelUpdate()
-
-Method to run label update phase
-
-#### getArguments()
-
-Method to get atguments of this class object
-
-* **Returns:**
-  The pointer containing address of the Arguments object of this class object
-* **Return type:**
-  pointer([Arguments](#zingg.client.Arguments))
-
-#### getMarkedRecords()
-
-Method to get marked record dataset from the inputpipe
-
-* **Returns:**
-  spark dataset containing marked records
-* **Return type:**
-  Dataset<Row>
-
-#### getMarkedRecordsStat(markedRecords, value)
-
-Method to get No. of records that is marked
-
-* **Parameters:**
-  * **markedRecords** (*Dataset<Row>*) – spark dataset containing marked records
-  * **value** (*long*) – flag value to check if markedRecord is initially matched or not
-* **Returns:**
-  The no. of marked records
-* **Return type:**
-  int
-
-#### getMatchedMarkedRecordsStat()
-
-Method to get No. of records that are marked and matched
-
-* **Returns:**
-  The bo. of matched marked records
-* **Return type:**
-  int
-
-#### getOptions()
-
-Method to get client options of this class object
-
-* **Returns:**
-  The pointer containing the address of the ClientOptions object of this class object
-* **Return type:**
-  pointer([ClientOptions](#zingg.client.ClientOptions))
-
-#### getUnmarkedRecords()
-
-Method to get unmarked record dataset from the inputpipe
-
-* **Returns:**
-  spark dataset containing unmarked records
-* **Return type:**
-  Dataset<Row>
-
-#### getUnmatchedMarkedRecordsStat()
-
-Method to get No. of records that are marked and unmatched
-
-* **Returns:**
-  The no. of unmatched marked records
-* **Return type:**
-  int
-
-#### getUnsureMarkedRecordsStat()
-
-Method to get No. of records that are marked and Not Sure if its matched or not
-
-* **Returns:**
-  The no. of Not Sure marked records
-* **Return type:**
-  int
-
-#### init()
-
-Method to initialize zingg client by reading internal configurations and functions
-
-#### initAndExecute()
-
-Method to run both init and execute methods consecutively
-
-#### processRecordsCli(unmarkedRecords, args)
-
-Method to get user input on unmarked records
-
-* **Returns:**
-  spark dataset containing updated records
-* **Return type:**
-  Dataset<Row>
-
-#### processRecordsCliLabelUpdate(lines, args)
-
-#### setArguments(args)
-
-Method to set Arguments
-
-* **Parameters:**
-  **args** ([*Arguments*](#zingg.client.Arguments)) – provide arguments for this class object
-
-#### setOptions(options)
-
-Method to set atguments of this class object
-
-* **Parameters:**
-  **options** ([*ClientOptions*](#zingg.client.ClientOptions)) – provide client options for this class object
-* **Returns:**
-  The pointer containing address of the ClientOptions object of this class object
-* **Return type:**
-  pointer([ClientOptions](#zingg.client.ClientOptions))
-
-#### writeLabelledOutput(updatedRecords, args)
-
-Method to write updated records after user input
-
-#### writeLabelledOutputFromPandas(candidate_pairs_pd, args)
-
-Method to write updated records (as pandas df) after user input
-
-### *class* zingg.client.ZinggWithSpark(args, options)
-
-Bases: [`Zingg`](#zingg.client.Zingg)
-
-This class is the main point of interface with the Zingg matching product. Construct a client to Zingg using provided arguments and spark master. If running locally, set the master to local.
-
-* **Parameters:**
-  * **args** ([*Arguments*](#zingg.client.Arguments)) – arguments for training and matching
-  * **options** ([*ClientOptions*](#zingg.client.ClientOptions)) – client option for this class object
-
-### zingg.client.getDfFromDs(data)
-
-Method to convert spark dataset to dataframe
-
-* **Parameters:**
-  **data** (*DataSet*) – provide spark dataset
-* **Returns:**
-  converted spark dataframe
-* **Return type:**
-  DataFrame
-
-### zingg.client.getGateway()
-
-### zingg.client.getJVM()
-
-### zingg.client.getPandasDfFromDs(data)
-
-Method to convert spark dataset to pandas dataframe
-
-* **Parameters:**
-  **data** (*DataSet*) – provide spark dataset
-* **Returns:**
-  converted pandas dataframe
-* **Return type:**
-  DataFrame
-
-### zingg.client.getSparkContext()
-
-### zingg.client.getSparkSession()
-
-### zingg.client.getSqlContext()
-
-### zingg.client.initClient()
-
-### zingg.client.initDataBricksConectClient()
-
-### zingg.client.initSparkClient()
-
-### zingg.client.parseArguments(argv)
-
-This method is used for checking mandatory arguments and creating an arguments list from Command line arguments
-
-* **Parameters:**
-  **argv** (*List*) – Values that are passed during the calling of the program along with the calling statement.
-* **Returns:**
-  a list containing necessary arguments to run any phase
-* **Return type:**
-  List
-
-<a id="module-zingg.pipes"></a>
-
-## zingg.pipes
-
-This module is submodule of zingg to work with different types of Pipes. Classes of this module inherit the Pipe class, and use that class to create many different types of pipes.
-
-### *class* zingg.pipes.BigQueryPipe(name)
-
-Bases: [`Pipe`](#zingg.pipes.Pipe)
-
-Pipe Class for working with BigQuery pipeline
-
-* **Parameters:**
-  **name** (*String*) – name of the pipe.
-
-#### CREDENTIAL_FILE *= 'credentialsFile'*
-
-#### TABLE *= 'table'*
-
-#### TEMP_GCS_BUCKET *= 'temporaryGcsBucket'*
-
-#### VIEWS_ENABLED *= 'viewsEnabled'*
-
-#### setCredentialFile(file)
-
-Method to set Credential file to the pipe
-
-* **Parameters:**
-  **file** (*String*) – credential file name
-
-#### setTable(table)
-
-Method to set Table to the pipe
-
-* **Parameters:**
-  **table** (*String*) – provide table parameter
-
-#### setTemporaryGcsBucket(bucket)
-
-Method to set TemporaryGcsBucket to the pipe
-
-* **Parameters:**
-  **bucket** (*String*) – provide bucket parameter
-
-#### setViewsEnabled(isEnabled)
-
-Method to set if viewsEnabled parameter is Enabled or not
-
-* **Parameters:**
-  **isEnabled** (*Bool*) – provide boolean parameter which defines if viewsEnabled option is enable or not
-
-### *class* zingg.pipes.CsvPipe(name, location=None, schema=None)
-
-Bases: [`Pipe`](#zingg.pipes.Pipe)
-
-Class CsvPipe: used for working with text files which uses a pipe symbol to separate units of text that belong in different columns.
-
-* **Parameters:**
-  * **name** (*String*) – name of the pipe.
-  * **location** (*String* *or* *None*) – (optional) location from where we read data
-  * **schema** (*Schema* *or* *None*) – (optional) json schema for the pipe
-
-#### setDelimiter(delimiter)
-
-This method is used to define delimiter of CsvPipe
-
-* **Parameters:**
-  **delimiter** (*String*) – a sequence of one or more characters for specifying the boundary between separate, independent regions in data streams
-
-#### setHeader(header)
-
-Method to set header property of pipe
-
-* **Parameters:**
-  **header** (*Boolean*) – true if pipe have header, false otherwise
-
-#### setLocation(location)
-
-Method to set location of pipe
-
-* **Parameters:**
-  **location** (*String*) – location from where we read data
-
-### *class* zingg.pipes.InMemoryPipe(name, df=None)
-
-Bases: [`Pipe`](#zingg.pipes.Pipe)
-
-Pipe Class for working with InMemory pipeline
-
-* **Parameters:**
-  * **name** (*String*) – name of the pipe
-  * **df** (*Dataset* *or* *None*) – provide dataset for this pipe (optional)
-
-#### getDataset()
-
-Method to get Dataset from pipe
-
-* **Returns:**
-  dataset of the pipe in the format of spark dataset
-* **Return type:**
-  Dataset<Row>
-
-#### setDataset(df)
-
-Method to set DataFrame of the pipe
-
-* **Parameters:**
-  **df** (*DataFrame*) – pandas or spark dataframe for the pipe
-
-### *class* zingg.pipes.Pipe(name, format)
-
-Bases: `object`
-
-Pipe class for working with different data-pipelines. Actual pipe def in the args. One pipe can be used at multiple places with different tables, locations, queries, etc
-
-* **Parameters:**
-  * **name** (*String*) – name of the pipe
-  * **format** (*Format*) – formate of pipe e.g. bigquery,InMemory, etc.
-
-#### addProperty(name, value)
-
-Method for adding different properties of pipe
-
-* **Parameters:**
-  * **name** (*String*) – name of the property
-  * **value** (*String*) – value you want to set for the property
-
-#### getPipe()
-
-Method to get Pipe
-
-* **Returns:**
-  pipe parameter values in the format of a list of string
-* **Return type:**
-  [Pipe](#zingg.pipes.Pipe)
-
-#### setSchema(s)
-
-Method to set pipe schema value
-
-* **Parameters:**
-  **s** (*Schema*) – json schema for the pipe
-
-#### toString()
-
-Method to get pipe parameter values
-
-* **Returns:**
-  pipe information in list format
-* **Return type:**
-  List[String]
-
-### *class* zingg.pipes.SnowflakePipe(name)
-
-Bases: [`Pipe`](#zingg.pipes.Pipe)
-
-Pipe Class for working with Snowflake pipeline
-
-* **Parameters:**
-  **name** (*String*) – name of the pipe
-
-#### DATABASE *= 'sfDatabase'*
-
-#### DBTABLE *= 'dbtable'*
-
-#### PASSWORD *= 'sfPassword'*
-
-#### SCHEMA *= 'sfSchema'*
-
-#### URL *= 'sfUrl'*
-
-#### USER *= 'sfUser'*
-
-#### WAREHOUSE *= 'sfWarehouse'*
-
-#### setDatabase(db)
-
-Method to set Database to the pipe
-
-* **Parameters:**
-  **db** (*Database*) – provide Database parameter.
-
-#### setDbTable(dbtable)
-
-description
-
-* **Parameters:**
-  **dbtable** (*String*) – provide bucket parameter.
-
-#### setPassword(passwd)
-
-Method to set Password to the pipe
-
-* **Parameters:**
-  **passwd** (*String*) – provide Password parameter.
-
-#### setSFSchema(schema)
-
-Method to set Schema to the pipe
-
-* **Parameters:**
-  **schema** (*Schema*) – provide schema parameter.
-
-#### setURL(url)
-
-Method to set url to the pipe
-
-* **Parameters:**
-  **url** (*String*) – provide url for this pipe
-
-#### setUser(user)
-
-Method to set User to the pipe
-
-* **Parameters:**
-  **user** (*String*) – provide User parameter.
-
-#### setWarehouse(warehouse)
-
-Method to set warehouse parameter to the pipe
-
-* **Parameters:**
-  **warehouse** (*String*) – provide warehouse parameter.
diff --git a/python/docs/conf.py b/python/docs/conf.py
index df6be82de..e491980be 100644
--- a/python/docs/conf.py
+++ b/python/docs/conf.py
@@ -37,8 +37,7 @@
     'sphinx.ext.autodoc',
     'sphinx.ext.napoleon',
     'sphinx.ext.viewcode',
-    'sphinx.ext.coverage',
-    'sphinx_markdown_builder'
+    'sphinx.ext.coverage'
 ]
 
 # Add any paths that contain templates here, relative to this directory.
diff --git a/python/docs/index.rst b/python/docs/index.rst
index d04663503..63b396d3e 100644
--- a/python/docs/index.rst
+++ b/python/docs/index.rst
@@ -75,3 +75,8 @@ Example API Usage
    zingg = Zingg(args, options)
    zingg.initAndExecute()
 
+
+
+.. meta::
+   :description: Zingg Entity Resolution With Python and AI
+   :keywords: entity resolution, identity resolution, record linkage
diff --git a/python/zingg/client.py b/python/zingg/client.py
index 58617a296..fd1aeb6c9 100644
--- a/python/zingg/client.py
+++ b/python/zingg/client.py
@@ -3,759 +3,759 @@
 ------------------------
 This module is the main entry point of the Zingg Python API
 """
-if __name__ == '__main__':
-    import logging
-    import argparse
-    import pandas as pd
-    from pyspark.sql import DataFrame
-
-    from pyspark import SparkConf, SparkContext, SQLContext
-
-    from py4j.java_collections import SetConverter, MapConverter, ListConverter
-
-    from pyspark.sql import SparkSession
-    import os
-    LOG = logging.getLogger("zingg")
-
-    _spark_ctxt = None
-    _sqlContext = None
-    _spark = None
-    _zingg_jar = 'zingg-0.4.0.jar'
-
-    def initSparkClient():
-        global _spark_ctxt
-        global _sqlContext
-        global _spark    
-        _spark_ctxt = SparkContext.getOrCreate()
-        _sqlContext = SQLContext(_spark_ctxt)
-        _spark = SparkSession.builder.getOrCreate()
-        return 1
 
-    def initDataBricksConectClient():
-        global _spark_ctxt
-        global _sqlContext
-        global _spark    
-        jar_path = os.getenv('ZINGG_HOME')+'/'+_zingg_jar
-        _spark = SparkSession.builder.config('spark.jars', jar_path).getOrCreate()
-        _spark_ctxt = _spark.sparkContext
-        _sqlContext = SQLContext(_spark_ctxt)
+import logging
+import argparse
+import pandas as pd
+from pyspark.sql import DataFrame
+
+from pyspark import SparkConf, SparkContext, SQLContext
+
+from py4j.java_collections import SetConverter, MapConverter, ListConverter
+
+from pyspark.sql import SparkSession
+import os
+LOG = logging.getLogger("zingg")
+
+_spark_ctxt = None
+_sqlContext = None
+_spark = None
+_zingg_jar = 'zingg-0.4.0.jar'
+
+def initSparkClient():
+    global _spark_ctxt
+    global _sqlContext
+    global _spark    
+    _spark_ctxt = SparkContext.getOrCreate()
+    _sqlContext = SQLContext(_spark_ctxt)
+    _spark = SparkSession.builder.getOrCreate()
+    return 1
+
+def initDataBricksConectClient():
+    global _spark_ctxt
+    global _sqlContext
+    global _spark    
+    jar_path = os.getenv('ZINGG_HOME')+'/'+_zingg_jar
+    _spark = SparkSession.builder.config('spark.jars', jar_path).getOrCreate()
+    _spark_ctxt = _spark.sparkContext
+    _sqlContext = SQLContext(_spark_ctxt)
+    return 1
+
+def initClient():
+    global _spark_ctxt
+    global _sqlContext
+    global _spark    
+    if _spark_ctxt is None:
+        DATABRICKS_CONNECT = os.getenv('DATABRICKS_CONNECT')
+        if DATABRICKS_CONNECT=='Y' or DATABRICKS_CONNECT=='y':
+            return initDataBricksConectClient()
+        else:
+            return initSparkClient()
+    else:
         return 1
 
-    def initClient():
-        global _spark_ctxt
-        global _sqlContext
-        global _spark    
-        if _spark_ctxt is None:
-            DATABRICKS_CONNECT = os.getenv('DATABRICKS_CONNECT')
-            if DATABRICKS_CONNECT=='Y' or DATABRICKS_CONNECT=='y':
-                return initDataBricksConectClient()
+def getSparkContext():
+    if _spark_ctxt is None:
+        initClient()
+    return _spark_ctxt
+
+def getSparkSession():
+    if _spark is None:
+        initClient()
+    return _spark
+
+def getSqlContext():
+    if _sqlContext is None:
+        initClient()
+    return _sqlContext
+
+def getJVM():
+    return getSparkContext()._jvm
+
+def getGateway():
+    return getSparkContext()._gateway
+
+ColName = getJVM().zingg.common.client.util.ColName
+MatchType = getJVM().zingg.common.client.MatchType
+ClientOptions = getJVM().zingg.common.client.ClientOptions
+ZinggOptions = getJVM().zingg.common.client.ZinggOptions
+LabelMatchType = getJVM().zingg.common.core.util.LabelMatchType
+UpdateLabelMode = 'Overwrite'
+
+def getDfFromDs(data):
+    """ Method to convert spark dataset to dataframe
+
+    :param data: provide spark dataset
+    :type data: DataSet
+    :return: converted spark dataframe
+    :rtype: DataFrame 
+    """
+    return DataFrame(data.df(), getSqlContext())
+
+def getPandasDfFromDs(data):
+    """ Method to convert spark dataset to pandas dataframe
+
+    :param data: provide spark dataset
+    :type data: DataSet
+    :return: converted pandas dataframe
+    :rtype: DataFrame 
+    """
+    df = getDfFromDs(data)
+    return pd.DataFrame(df.collect(), columns=df.columns)
+
+
+class Zingg:
+    """ This class is the main point of interface with the Zingg matching product. Construct a client to Zingg using provided arguments and spark master. If running locally, set the master to local.
+
+    :param args: arguments for training and matching
+    :type args: Arguments
+    :param options: client option for this class object
+    :type options: ClientOptions
+
+    """
+
+    def __init__(self, args, options):
+        self.inpArgs = args
+        self.inpOptions = options
+        self.client = getJVM().zingg.spark.client.SparkClient(args.getArgs(), options.getClientOptions())
+       
+    def init(self):
+        """ Method to initialize zingg client by reading internal configurations and functions """
+        self.client.init()
+
+    def execute(self):
+        """ Method to execute this class object """
+        self.client.execute()
+        
+    def initAndExecute(self):
+        """ Method to run both init and execute methods consecutively """
+        self.client.init()
+        DATABRICKS_CONNECT = os.getenv('DATABRICKS_CONNECT')
+        if DATABRICKS_CONNECT=='Y' or DATABRICKS_CONNECT=='y':
+            options = self.client.getOptions()
+            inpPhase = options.get(ClientOptions.PHASE).getValue()
+            if (inpPhase==ZinggOptions.LABEL.getValue()):
+                self.executeLabel()
+            elif (inpPhase==ZinggOptions.UPDATE_LABEL.getValue()):
+                self.executeLabelUpdate()
             else:
-                return initSparkClient()
+                self.client.execute()
         else:
-            return 1
+            self.client.execute()
 
-    def getSparkContext():
-        if _spark_ctxt is None:
-            initClient()
-        return _spark_ctxt
+    def executeLabel(self):
+        """ Method to run label phase """
+        self.client.getTrainingDataModel().setMarkedRecordsStat(self.getMarkedRecords())
+        unmarkedRecords = self.getUnmarkedRecords()
+        updatedRecords = self.processRecordsCli(unmarkedRecords,self.inpArgs)
+        self.writeLabelledOutput(updatedRecords,self.inpArgs)
 
-    def getSparkSession():
-        if _spark is None:
-            initClient()
-        return _spark
+    def executeLabelUpdate(self):
+        """ Method to run label update phase """
+        self.processRecordsCliLabelUpdate(self.getMarkedRecords(),self.inpArgs)
 
-    def getSqlContext():
-        if _sqlContext is None:
-            initClient()
-        return _sqlContext
+    def getMarkedRecords(self):
+        """ Method to get marked record dataset from the inputpipe
 
-    def getJVM():
-        return getSparkContext()._jvm
+        :return: spark dataset containing marked records
+        :rtype: Dataset<Row> 
+        """
+        return self.client.getMarkedRecords()
 
-    def getGateway():
-        return getSparkContext()._gateway
+    def getUnmarkedRecords(self):
+        """ Method to get unmarked record dataset from the inputpipe
 
-    ColName = getJVM().zingg.common.client.util.ColName
-    MatchType = getJVM().zingg.common.client.MatchType
-    ClientOptions = getJVM().zingg.common.client.ClientOptions
-    ZinggOptions = getJVM().zingg.common.client.ZinggOptions
-    LabelMatchType = getJVM().zingg.common.core.util.LabelMatchType
-    UpdateLabelMode = 'Overwrite'
+        :return: spark dataset containing unmarked records
+        :rtype: Dataset<Row> 
+        """
+        return self.client.getUnmarkedRecords()
 
-    def getDfFromDs(data):
-        """ Method to convert spark dataset to dataframe
+    def processRecordsCli(self,unmarkedRecords,args):
+        """ Method to get user input on unmarked records
 
-        :param data: provide spark dataset
-        :type data: DataSet
-        :return: converted spark dataframe
-        :rtype: DataFrame 
+        :return: spark dataset containing updated records
+        :rtype: Dataset<Row> 
         """
-        return DataFrame(data.df(), getSqlContext())
+        trainingDataModel = self.client.getTrainingDataModel()
+        labelDataViewHelper = self.client.getLabelDataViewHelper()
+
+        if unmarkedRecords is not None and unmarkedRecords.count() > 0:
+            labelDataViewHelper.printMarkedRecordsStat(trainingDataModel.getPositivePairsCount(),trainingDataModel.getNegativePairsCount(),trainingDataModel.getNotSurePairsCount(),trainingDataModel.getTotalCount())
+            unmarkedRecords = unmarkedRecords.cache()
+            displayCols = labelDataViewHelper.getDisplayColumns(unmarkedRecords, args.getArgs())
+            clusterIdZFrame = labelDataViewHelper.getClusterIdsFrame(unmarkedRecords)
+            clusterIDs = labelDataViewHelper.getClusterIds(clusterIdZFrame)
+            totalPairs = clusterIDs.size()
+            updatedRecords = None
+            for index in range(totalPairs):
+                currentPair = labelDataViewHelper.getCurrentPair(unmarkedRecords, index, clusterIDs, clusterIdZFrame)
+
+                score = labelDataViewHelper.getScore(currentPair)
+                prediction = labelDataViewHelper.getPrediction(currentPair)
+
+                msg1 = labelDataViewHelper.getMsg1(index, totalPairs)
+                msg2 = labelDataViewHelper.getMsg2(prediction, score)
+                labelDataViewHelper.displayRecords(labelDataViewHelper.getDSUtil().select(currentPair, displayCols), msg1, msg2)
+                selected_option = input()
+                while int(selected_option) not in [0,1,2,9]:
+                    print('Please enter valid option')
+                    selected_option = input("Enter choice: ")
+                if int(selected_option) == 9:
+                    print("User has quit in the middle. Updating the records.")
+                    break               
+                trainingDataModel.updateLabellerStat(int(selected_option), 1)
+                labelDataViewHelper.printMarkedRecordsStat(trainingDataModel.getPositivePairsCount(),trainingDataModel.getNegativePairsCount(),trainingDataModel.getNotSurePairsCount(),trainingDataModel.getTotalCount())
+                updatedRecords = trainingDataModel.updateRecords(int(selected_option), currentPair, updatedRecords)            
+            print("Processing finished.")
+            return updatedRecords
+        else:
+            print("It seems there are no unmarked records at this moment. Please run findTrainingData job to build some pairs to be labelled and then run this labeler.")
+            return None
+    
+    def processRecordsCliLabelUpdate(self,lines,args):
+        trainingDataModel = self.client.getTrainingDataModel()
+        labelDataViewHelper = self.client.getLabelDataViewHelper()
+        if (lines is not None and lines.count() > 0):
+            trainingDataModel.setMarkedRecordsStat(lines)
+            labelDataViewHelper.printMarkedRecordsStat(trainingDataModel.getPositivePairsCount(),trainingDataModel.getNegativePairsCount(),trainingDataModel.getNotSurePairsCount(),trainingDataModel.getTotalCount())
+            displayCols = labelDataViewHelper.getDSUtil().getFieldDefColumns(lines, args.getArgs(), False, args.getArgs().getShowConcise())
+            updatedRecords = None
+            recordsToUpdate = lines
+            selectedOption = -1
+
+            while (str(selectedOption) != '9'):
+                cluster_id = input("\n\tPlease enter the cluster id (or 9 to exit): ")
+                if str(cluster_id) == '9':
+                    print("User has exit in the middle. Updating the records.")
+                    break
+                currentPair = lines.filter(lines.equalTo(ColName.CLUSTER_COLUMN, cluster_id))
+                if currentPair.isEmpty():
+                    print("\tInvalid cluster id. Enter '9' to exit")
+                    continue
+
+                matchFlag = currentPair.getAsInt(currentPair.head(),ColName.MATCH_FLAG_COL)
+                preMsg = "\n\tThe record pairs belonging to the input cluster id "+cluster_id+" are:"
+                postMsg = "\tThe above pair is labeled as "+str(matchFlag)+"\n"
+                labelDataViewHelper.displayRecords(labelDataViewHelper.getDSUtil().select(currentPair, displayCols), preMsg, postMsg)
+                selectedOption = input()
+                trainingDataModel.updateLabellerStat(int(selectedOption), 1)
+                trainingDataModel.updateLabellerStat(matchFlag, -1)
+                labelDataViewHelper.printMarkedRecordsStat(trainingDataModel.getPositivePairsCount(),trainingDataModel.getNegativePairsCount(),trainingDataModel.getNotSurePairsCount(),trainingDataModel.getTotalCount())
+
+                if (str(selectedOption) == '9'):
+                    print("User has quit in the middle. Updating the records.")
+                    break
+
+                recordsToUpdate = recordsToUpdate.filter(recordsToUpdate.notEqual(ColName.CLUSTER_COLUMN,cluster_id))
+
+                if (updatedRecords is not None):
+                    updatedRecords = updatedRecords.filter(updatedRecords.notEqual(ColName.CLUSTER_COLUMN,cluster_id))
 
-    def getPandasDfFromDs(data):
-        """ Method to convert spark dataset to pandas dataframe
+                updatedRecords = trainingDataModel.updateRecords(int(selectedOption), currentPair, updatedRecords)
 
-        :param data: provide spark dataset
-        :type data: DataSet
-        :return: converted pandas dataframe
-        :rtype: DataFrame 
+            if updatedRecords is not None:
+                updatedRecords = updatedRecords.union(recordsToUpdate)
+
+            outPipe = trainingDataModel.getOutputPipe(args.getArgs())
+            outPipe.setMode(UpdateLabelMode)
+
+            trainingDataModel.writeLabelledOutput(updatedRecords,args.getArgs(),outPipe)
+            print("Processing finished.")
+            return updatedRecords
+        else:
+            print("There is no marked record for updating. Please run findTrainingData/label jobs to generate training data.")
+            return None
+
+
+    def writeLabelledOutput(self,updatedRecords,args):
+        """ Method to write updated records after user input
         """
-        df = getDfFromDs(data)
-        return pd.DataFrame(df.collect(), columns=df.columns)
+        trainingDataModel = self.client.getTrainingDataModel()
+        if updatedRecords is not None:
+            trainingDataModel.writeLabelledOutput(updatedRecords,args.getArgs())
 
+    def writeLabelledOutputFromPandas(self,candidate_pairs_pd,args):
+        """ Method to write updated records (as pandas df) after user input
+        """
+        markedRecordsAsDS = (getSparkSession().createDataFrame(candidate_pairs_pd))._jdf
+        # pands df gives z_isMatch as long so needs to be cast
+        markedRecordsAsDS = markedRecordsAsDS.withColumn(ColName.MATCH_FLAG_COL,markedRecordsAsDS.col(ColName.MATCH_FLAG_COL).cast("int"))
+        updatedRecords = getJVM().zingg.spark.client.SparkFrame(markedRecordsAsDS)
+        self.writeLabelledOutput(updatedRecords,args)
 
-    class Zingg:
-        """ This class is the main point of interface with the Zingg matching product. Construct a client to Zingg using provided arguments and spark master. If running locally, set the master to local.
+    def setArguments(self, args):
+        """ Method to set Arguments
 
-        :param args: arguments for training and matching
+        :param args: provide arguments for this class object
         :type args: Arguments
-        :param options: client option for this class object
+        """
+        self.client.setArguments()
+
+    def getArguments(self):
+        """ Method to get atguments of this class object
+
+        :return: The pointer containing address of the Arguments object of this class object
+        :rtype: pointer(Arguments)
+        """
+        return self.client.getArguments()
+
+    def getOptions(self):
+        """ Method to get client options of this class object
+
+        :return: The pointer containing the address of the ClientOptions object of this class object
+        :rtype: pointer(ClientOptions)
+        """
+        return self.client.getOptions()
+
+    def setOptions(self, options):
+        """ Method to set atguments of this class object
+
+        :param options: provide client options for this class object
         :type options: ClientOptions
+        :return: The pointer containing address of the ClientOptions object of this class object
+        :rtype: pointer(ClientOptions) 
+        """
+        return self.client.setOptions(options)
 
+    def getMarkedRecordsStat(self, markedRecords, value):
+        """ Method to get No. of records that is marked
+
+        :param markedRecords: spark dataset containing marked records
+        :type markedRecords: Dataset<Row>
+        :param value: flag value to check if markedRecord is initially matched or not
+        :type value: long
+        :return: The no. of marked records
+        :rtype: int 
         """
+        return self.client.getMarkedRecordsStat(markedRecords, value)
 
-        def __init__(self, args, options):
-            self.inpArgs = args
-            self.inpOptions = options
-            self.client = getJVM().zingg.spark.client.SparkClient(args.getArgs(), options.getClientOptions())
-        
-        def init(self):
-            """ Method to initialize zingg client by reading internal configurations and functions """
-            self.client.init()
+    def getMatchedMarkedRecordsStat(self):
+        """ Method to get No. of records that are marked and matched
 
-        def execute(self):
-            """ Method to execute this class object """
-            self.client.execute()
-            
-        def initAndExecute(self):
-            """ Method to run both init and execute methods consecutively """
-            self.client.init()
-            DATABRICKS_CONNECT = os.getenv('DATABRICKS_CONNECT')
-            if DATABRICKS_CONNECT=='Y' or DATABRICKS_CONNECT=='y':
-                options = self.client.getOptions()
-                inpPhase = options.get(ClientOptions.PHASE).getValue()
-                if (inpPhase==ZinggOptions.LABEL.getValue()):
-                    self.executeLabel()
-                elif (inpPhase==ZinggOptions.UPDATE_LABEL.getValue()):
-                    self.executeLabelUpdate()
-                else:
-                    self.client.execute()
-            else:
-                self.client.execute()
+        :return: The bo. of matched marked records
+        :rtype: int 
+        """
+        return self.client.getMatchedMarkedRecordsStat(self.getMarkedRecords())
 
-        def executeLabel(self):
-            """ Method to run label phase """
-            self.client.getTrainingDataModel().setMarkedRecordsStat(self.getMarkedRecords())
-            unmarkedRecords = self.getUnmarkedRecords()
-            updatedRecords = self.processRecordsCli(unmarkedRecords,self.inpArgs)
-            self.writeLabelledOutput(updatedRecords,self.inpArgs)
+    def getUnmatchedMarkedRecordsStat(self):
+        """ Method to get No. of records that are marked and unmatched
 
-        def executeLabelUpdate(self):
-            """ Method to run label update phase """
-            self.processRecordsCliLabelUpdate(self.getMarkedRecords(),self.inpArgs)
+        :return: The no. of unmatched marked records
+        :rtype: int 
+        """
+        return self.client.getUnmatchedMarkedRecordsStat(self.getMarkedRecords())
 
-        def getMarkedRecords(self):
-            """ Method to get marked record dataset from the inputpipe
+    def getUnsureMarkedRecordsStat(self):
+        """ Method to get No. of records that are marked and Not Sure if its matched or not
 
-            :return: spark dataset containing marked records
-            :rtype: Dataset<Row> 
-            """
-            return self.client.getMarkedRecords()
+        :return: The no. of Not Sure marked records
+        :rtype: int 
+        """
+        return self.client.getUnsureMarkedRecordsStat(self.getMarkedRecords())
 
-        def getUnmarkedRecords(self):
-            """ Method to get unmarked record dataset from the inputpipe
+    
 
-            :return: spark dataset containing unmarked records
-            :rtype: Dataset<Row> 
-            """
-            return self.client.getUnmarkedRecords()
+class ZinggWithSpark(Zingg):
 
-        def processRecordsCli(self,unmarkedRecords,args):
-            """ Method to get user input on unmarked records
+    """ This class is the main point of interface with the Zingg matching product. Construct a client to Zingg using provided arguments and spark master. If running locally, set the master to local.
 
-            :return: spark dataset containing updated records
-            :rtype: Dataset<Row> 
-            """
-            trainingDataModel = self.client.getTrainingDataModel()
-            labelDataViewHelper = self.client.getLabelDataViewHelper()
+    :param args: arguments for training and matching
+    :type args: Arguments
+    :param options: client option for this class object
+    :type options: ClientOptions
 
-            if unmarkedRecords is not None and unmarkedRecords.count() > 0:
-                labelDataViewHelper.printMarkedRecordsStat(trainingDataModel.getPositivePairsCount(),trainingDataModel.getNegativePairsCount(),trainingDataModel.getNotSurePairsCount(),trainingDataModel.getTotalCount())
-                unmarkedRecords = unmarkedRecords.cache()
-                displayCols = labelDataViewHelper.getDisplayColumns(unmarkedRecords, args.getArgs())
-                clusterIdZFrame = labelDataViewHelper.getClusterIdsFrame(unmarkedRecords)
-                clusterIDs = labelDataViewHelper.getClusterIds(clusterIdZFrame)
-                totalPairs = clusterIDs.size()
-                updatedRecords = None
-                for index in range(totalPairs):
-                    currentPair = labelDataViewHelper.getCurrentPair(unmarkedRecords, index, clusterIDs, clusterIdZFrame)
-
-                    score = labelDataViewHelper.getScore(currentPair)
-                    prediction = labelDataViewHelper.getPrediction(currentPair)
-
-                    msg1 = labelDataViewHelper.getMsg1(index, totalPairs)
-                    msg2 = labelDataViewHelper.getMsg2(prediction, score)
-                    labelDataViewHelper.displayRecords(labelDataViewHelper.getDSUtil().select(currentPair, displayCols), msg1, msg2)
-                    selected_option = input()
-                    while int(selected_option) not in [0,1,2,9]:
-                        print('Please enter valid option')
-                        selected_option = input("Enter choice: ")
-                    if int(selected_option) == 9:
-                        print("User has quit in the middle. Updating the records.")
-                        break               
-                    trainingDataModel.updateLabellerStat(int(selected_option), 1)
-                    labelDataViewHelper.printMarkedRecordsStat(trainingDataModel.getPositivePairsCount(),trainingDataModel.getNegativePairsCount(),trainingDataModel.getNotSurePairsCount(),trainingDataModel.getTotalCount())
-                    updatedRecords = trainingDataModel.updateRecords(int(selected_option), currentPair, updatedRecords)            
-                print("Processing finished.")
-                return updatedRecords
-            else:
-                print("It seems there are no unmarked records at this moment. Please run findTrainingData job to build some pairs to be labelled and then run this labeler.")
-                return None
-        
-        def processRecordsCliLabelUpdate(self,lines,args):
-            trainingDataModel = self.client.getTrainingDataModel()
-            labelDataViewHelper = self.client.getLabelDataViewHelper()
-            if (lines is not None and lines.count() > 0):
-                trainingDataModel.setMarkedRecordsStat(lines)
-                labelDataViewHelper.printMarkedRecordsStat(trainingDataModel.getPositivePairsCount(),trainingDataModel.getNegativePairsCount(),trainingDataModel.getNotSurePairsCount(),trainingDataModel.getTotalCount())
-                displayCols = labelDataViewHelper.getDSUtil().getFieldDefColumns(lines, args.getArgs(), False, args.getArgs().getShowConcise())
-                updatedRecords = None
-                recordsToUpdate = lines
-                selectedOption = -1
-
-                while (str(selectedOption) != '9'):
-                    cluster_id = input("\n\tPlease enter the cluster id (or 9 to exit): ")
-                    if str(cluster_id) == '9':
-                        print("User has exit in the middle. Updating the records.")
-                        break
-                    currentPair = lines.filter(lines.equalTo(ColName.CLUSTER_COLUMN, cluster_id))
-                    if currentPair.isEmpty():
-                        print("\tInvalid cluster id. Enter '9' to exit")
-                        continue
-
-                    matchFlag = currentPair.getAsInt(currentPair.head(),ColName.MATCH_FLAG_COL)
-                    preMsg = "\n\tThe record pairs belonging to the input cluster id "+cluster_id+" are:"
-                    postMsg = "\tThe above pair is labeled as "+str(matchFlag)+"\n"
-                    labelDataViewHelper.displayRecords(labelDataViewHelper.getDSUtil().select(currentPair, displayCols), preMsg, postMsg)
-                    selectedOption = input()
-                    trainingDataModel.updateLabellerStat(int(selectedOption), 1)
-                    trainingDataModel.updateLabellerStat(matchFlag, -1)
-                    labelDataViewHelper.printMarkedRecordsStat(trainingDataModel.getPositivePairsCount(),trainingDataModel.getNegativePairsCount(),trainingDataModel.getNotSurePairsCount(),trainingDataModel.getTotalCount())
-
-                    if (str(selectedOption) == '9'):
-                        print("User has quit in the middle. Updating the records.")
-                        break
-
-                    recordsToUpdate = recordsToUpdate.filter(recordsToUpdate.notEqual(ColName.CLUSTER_COLUMN,cluster_id))
-
-                    if (updatedRecords is not None):
-                        updatedRecords = updatedRecords.filter(updatedRecords.notEqual(ColName.CLUSTER_COLUMN,cluster_id))
-
-                    updatedRecords = trainingDataModel.updateRecords(int(selectedOption), currentPair, updatedRecords)
-
-                if updatedRecords is not None:
-                    updatedRecords = updatedRecords.union(recordsToUpdate)
-
-                outPipe = trainingDataModel.getOutputPipe(args.getArgs())
-                outPipe.setMode(UpdateLabelMode)
-
-                trainingDataModel.writeLabelledOutput(updatedRecords,args.getArgs(),outPipe)
-                print("Processing finished.")
-                return updatedRecords
-            else:
-                print("There is no marked record for updating. Please run findTrainingData/label jobs to generate training data.")
-                return None
+    """
+    def __init__(self, args, options):
+        self.client = getJVM().zingg.spark.client.SparkClient(args.getArgs(), options.getClientOptions(), getSparkSession()._jsparkSession)
+
+    
+class Arguments:
+    """ This class helps supply match arguments to Zingg. There are 3 basic steps in any match process.
 
+    :Defining: specifying information about data location, fields, and our notion of similarity.
+    :training: making Zingg learn the matching rules
+    :Matching: Running the models on the entire dataset
+    """
 
-        def writeLabelledOutput(self,updatedRecords,args):
-            """ Method to write updated records after user input
-            """
-            trainingDataModel = self.client.getTrainingDataModel()
-            if updatedRecords is not None:
-                trainingDataModel.writeLabelledOutput(updatedRecords,args.getArgs())
-
-        def writeLabelledOutputFromPandas(self,candidate_pairs_pd,args):
-            """ Method to write updated records (as pandas df) after user input
-            """
-            markedRecordsAsDS = (getSparkSession().createDataFrame(candidate_pairs_pd))._jdf
-            # pands df gives z_isMatch as long so needs to be cast
-            markedRecordsAsDS = markedRecordsAsDS.withColumn(ColName.MATCH_FLAG_COL,markedRecordsAsDS.col(ColName.MATCH_FLAG_COL).cast("int"))
-            updatedRecords = getJVM().zingg.spark.client.SparkFrame(markedRecordsAsDS)
-            self.writeLabelledOutput(updatedRecords,args)
-
-        def setArguments(self, args):
-            """ Method to set Arguments
-
-            :param args: provide arguments for this class object
-            :type args: Arguments
-            """
-            self.client.setArguments()
-
-        def getArguments(self):
-            """ Method to get atguments of this class object
-
-            :return: The pointer containing address of the Arguments object of this class object
-            :rtype: pointer(Arguments)
-            """
-            return self.client.getArguments()
-
-        def getOptions(self):
-            """ Method to get client options of this class object
-
-            :return: The pointer containing the address of the ClientOptions object of this class object
-            :rtype: pointer(ClientOptions)
-            """
-            return self.client.getOptions()
-
-        def setOptions(self, options):
-            """ Method to set atguments of this class object
-
-            :param options: provide client options for this class object
-            :type options: ClientOptions
-            :return: The pointer containing address of the ClientOptions object of this class object
-            :rtype: pointer(ClientOptions) 
-            """
-            return self.client.setOptions(options)
-
-        def getMarkedRecordsStat(self, markedRecords, value):
-            """ Method to get No. of records that is marked
-
-            :param markedRecords: spark dataset containing marked records
-            :type markedRecords: Dataset<Row>
-            :param value: flag value to check if markedRecord is initially matched or not
-            :type value: long
-            :return: The no. of marked records
-            :rtype: int 
-            """
-            return self.client.getMarkedRecordsStat(markedRecords, value)
-
-        def getMatchedMarkedRecordsStat(self):
-            """ Method to get No. of records that are marked and matched
-
-            :return: The bo. of matched marked records
-            :rtype: int 
-            """
-            return self.client.getMatchedMarkedRecordsStat(self.getMarkedRecords())
-
-        def getUnmatchedMarkedRecordsStat(self):
-            """ Method to get No. of records that are marked and unmatched
-
-            :return: The no. of unmatched marked records
-            :rtype: int 
-            """
-            return self.client.getUnmatchedMarkedRecordsStat(self.getMarkedRecords())
-
-        def getUnsureMarkedRecordsStat(self):
-            """ Method to get No. of records that are marked and Not Sure if its matched or not
-
-            :return: The no. of Not Sure marked records
-            :rtype: int 
-            """
-            return self.client.getUnsureMarkedRecordsStat(self.getMarkedRecords())
+    def __init__(self):
+        self.args = getJVM().zingg.common.client.Arguments()
+
+    def setFieldDefinition(self, fieldDef):
+        """ Method convert python objects to java FieldDefinition objects and set the field definitions associated with this client
+
+        :param fieldDef: python FieldDefinition object list
+        :type fieldDef: List(FieldDefinition)
+        """
+        javaFieldDef = []
+        for f in fieldDef:
+            javaFieldDef.append(f.getFieldDefinition())
+        self.args.setFieldDefinition(javaFieldDef)
+
+    def getArgs(self):
+        """ Method to get pointer address of this class
 
+        :return: The pointer containing the address of this class object
+        :rtype: pointer(Arguments)
         
+        """
+        return self.args
 
-    class ZinggWithSpark(Zingg):
+    def setArgs(self, argumentsObj):
+        """ Method to set this class object
 
-        """ This class is the main point of interface with the Zingg matching product. Construct a client to Zingg using provided arguments and spark master. If running locally, set the master to local.
+        :param argumentsObj: Argument object to set this object
+        :type argumentsObj: pointer(Arguments)
+        """
+        self.args = argumentsObj
 
-        :param args: arguments for training and matching
-        :type args: Arguments
-        :param options: client option for this class object
-        :type options: ClientOptions
+    def setData(self, *pipes):
+        """ Method to set the file path of the file to be matched.
 
+        :param pipes: input data pipes separated by comma e.g. (pipe1,pipe2,..)
+        :type pipes: Pipe[]
         """
-        def __init__(self, args, options):
-            self.client = getJVM().zingg.spark.client.SparkClient(args.getArgs(), options.getClientOptions(), getSparkSession()._jsparkSession)
+        dataPipe = getGateway().new_array(getJVM().zingg.common.client.pipe.Pipe, len(pipes))
+        for idx, pipe in enumerate(pipes):
+            dataPipe[idx] = pipe.getPipe()
+        self.args.setData(dataPipe)
 
-        
-    class Arguments:
-        """ This class helps supply match arguments to Zingg. There are 3 basic steps in any match process.
-
-        :Defining: specifying information about data location, fields, and our notion of similarity.
-        :training: making Zingg learn the matching rules
-        :Matching: Running the models on the entire dataset
-        """
-
-        def __init__(self):
-            self.args = getJVM().zingg.common.client.Arguments()
-
-        def setFieldDefinition(self, fieldDef):
-            """ Method convert python objects to java FieldDefinition objects and set the field definitions associated with this client
-
-            :param fieldDef: python FieldDefinition object list
-            :type fieldDef: List(FieldDefinition)
-            """
-            javaFieldDef = []
-            for f in fieldDef:
-                javaFieldDef.append(f.getFieldDefinition())
-            self.args.setFieldDefinition(javaFieldDef)
-
-        def getArgs(self):
-            """ Method to get pointer address of this class
-
-            :return: The pointer containing the address of this class object
-            :rtype: pointer(Arguments)
-            
-            """
-            return self.args
-
-        def setArgs(self, argumentsObj):
-            """ Method to set this class object
-
-            :param argumentsObj: Argument object to set this object
-            :type argumentsObj: pointer(Arguments)
-            """
-            self.args = argumentsObj
-
-        def setData(self, *pipes):
-            """ Method to set the file path of the file to be matched.
-
-            :param pipes: input data pipes separated by comma e.g. (pipe1,pipe2,..)
-            :type pipes: Pipe[]
-            """
-            dataPipe = getGateway().new_array(getJVM().zingg.common.client.pipe.Pipe, len(pipes))
-            for idx, pipe in enumerate(pipes):
-                dataPipe[idx] = pipe.getPipe()
-            self.args.setData(dataPipe)
-
-        def setOutput(self, *pipes):
-            """ Method to set the output directory where the match result will be saved
-
-            :param pipes: output data pipes separated by comma e.g. (pipe1,pipe2,..)
-            :type pipes: Pipe[]
-            """
-            outputPipe = getGateway().new_array(getJVM().zingg.common.client.pipe.Pipe, len(pipes))
-            for idx, pipe in enumerate(pipes):
-                outputPipe[idx] = pipe.getPipe()
-            self.args.setOutput(outputPipe)
-        
-        def getZinggBaseModelDir(self):
-            return self.args.getZinggBaseModelDir()
+    def setOutput(self, *pipes):
+        """ Method to set the output directory where the match result will be saved
 
-        def getZinggModelDir(self):
-            return self.args.getZinggModelDir()
-        
-        def getZinggBaseTrainingDataDir(self):
-            """ Method to get the location of the folder where Zingg 
-            saves the training data found by findTrainingData  
-            """
-            return self.args.getZinggBaseTrainingDataDir()
-
-        def getZinggTrainingDataUnmarkedDir(self):
-            """ Method to get the location of the folder where Zingg 
-            saves the training data found by findTrainingData  
-            """
-            return self.args.getZinggTrainingDataUnmarkedDir()
-        
-        def getZinggTrainingDataMarkedDir(self):
-            """ Method to get the location of the folder where Zingg 
-            saves the marked training data labeled by the user  
-            """
-            return self.args.getZinggTrainingDataMarkedDir()
-        
-        def setTrainingSamples(self, *pipes):
-            """ Method to set existing training samples to be matched.
-
-            :param pipes: input training data pipes separated by comma e.g. (pipe1,pipe2,..)
-            :type pipes: Pipe[]
-            """
-            dataPipe = getGateway().new_array(getJVM().zingg.common.client.pipe.Pipe, len(pipes))
-            for idx, pipe in enumerate(pipes):
-                dataPipe[idx] = pipe.getPipe()
-            self.args.setTrainingSamples(dataPipe)
-
-        def setModelId(self, id):
-            """ Method to set the output directory where the match output will be saved
-
-            :param id: model id value 
-            :type id: String
-            """
-            self.args.setModelId(id)
-        
-        def getModelId(self):
-            return self.args.getModelId()
+        :param pipes: output data pipes separated by comma e.g. (pipe1,pipe2,..)
+        :type pipes: Pipe[]
+        """
+        outputPipe = getGateway().new_array(getJVM().zingg.common.client.pipe.Pipe, len(pipes))
+        for idx, pipe in enumerate(pipes):
+            outputPipe[idx] = pipe.getPipe()
+        self.args.setOutput(outputPipe)
+    
+    def getZinggBaseModelDir(self):
+        return self.args.getZinggBaseModelDir()
 
+    def getZinggModelDir(self):
+        return self.args.getZinggModelDir()
+    
+    def getZinggBaseTrainingDataDir(self):
+        """ Method to get the location of the folder where Zingg 
+        saves the training data found by findTrainingData  
+        """
+        return self.args.getZinggBaseTrainingDataDir()
 
-        def setZinggDir(self, f):
-            """ Method to set the location for Zingg to save its internal computations and models. Please set it to a place where the program has to write access.
+    def getZinggTrainingDataUnmarkedDir(self):
+        """ Method to get the location of the folder where Zingg 
+        saves the training data found by findTrainingData  
+        """
+        return self.args.getZinggTrainingDataUnmarkedDir()
+    
+    def getZinggTrainingDataMarkedDir(self):
+        """ Method to get the location of the folder where Zingg 
+        saves the marked training data labeled by the user  
+        """
+        return self.args.getZinggTrainingDataMarkedDir()
+    
+    def setTrainingSamples(self, *pipes):
+        """ Method to set existing training samples to be matched.
 
-            :param f: Zingg directory name of the models
-            :type f: String
-            """
-            self.args.setZinggDir(f)
+        :param pipes: input training data pipes separated by comma e.g. (pipe1,pipe2,..)
+        :type pipes: Pipe[]
+        """
+        dataPipe = getGateway().new_array(getJVM().zingg.common.client.pipe.Pipe, len(pipes))
+        for idx, pipe in enumerate(pipes):
+            dataPipe[idx] = pipe.getPipe()
+        self.args.setTrainingSamples(dataPipe)
 
-        def setNumPartitions(self, numPartitions):
-            """ Method to set NumPartitions parameter value
-            Sample size to use for seeding labeled data We don't want to run over all the data, as we want a quick way to seed some labeled data that we can manually edit
+    def setModelId(self, id):
+        """ Method to set the output directory where the match output will be saved
 
-            :param numPartitions: number of partitions for given data pipes
-            :type numPartitions: int
-            """
-            self.args.setNumPartitions(numPartitions)
+        :param id: model id value 
+        :type id: String
+        """
+        self.args.setModelId(id)
+    
+    def getModelId(self):
+        return self.args.getModelId()
 
-        def setLabelDataSampleSize(self, labelDataSampleSize):
-            """ Method to set labelDataSampleSize parameter value
-            Set the fraction of data to be used from the complete data set to be used for seeding the labeled data Labelling is costly and we want a fast approximate way of looking at a small sample of the records and identifying expected matches and nonmatches
 
-            :param labelDataSampleSize: value between 0.0 and 1.0 denoting portion of dataset to use in generating seed samples
-            :type labelDataSampleSize: float
-            """
-            self.args.setLabelDataSampleSize(labelDataSampleSize)
+    def setZinggDir(self, f):
+        """ Method to set the location for Zingg to save its internal computations and models. Please set it to a place where the program has to write access.
 
-        def writeArgumentsToJSON(self, fileName):
-            """ Method to write JSON file from the object of this class 
+        :param f: Zingg directory name of the models
+        :type f: String
+        """
+        self.args.setZinggDir(f)
 
-            :param fileName: The CONF parameter value of ClientOption object or file address of json file
-            :type fileName: String
-            """
-            getJVM().zingg.common.client.ArgumentsUtil().writeArgumentsToJSON(fileName, self.args)
+    def setNumPartitions(self, numPartitions):
+        """ Method to set NumPartitions parameter value
+        Sample size to use for seeding labeled data We don't want to run over all the data, as we want a quick way to seed some labeled data that we can manually edit
 
-        def setStopWordsCutoff(self, stopWordsCutoff):
-            """ Method to set stopWordsCutoff parameter value
-            By default, Zingg extracts 10% of the high frequency unique words from a dataset. If user wants different selection, they should set up StopWordsCutoff property
+        :param numPartitions: number of partitions for given data pipes
+        :type numPartitions: int
+        """
+        self.args.setNumPartitions(numPartitions)
 
-            :param stopWordsCutoff: The stop words cutoff parameter value of ClientOption object or file address of json file
-            :type stopWordsCutoff: float
-            """
-            self.args.setStopWordsCutoff(stopWordsCutoff)
-        
-        def setColumn(self, column):
-            """ Method to set stopWordsCutoff parameter value
-            By default, Zingg extracts 10% of the high frequency unique words from a dataset. If user wants different selection, they should set up StopWordsCutoff property
-
-            :param stopWordsCutoff: The stop words cutoff parameter value of ClientOption object or file address of json file
-            :type stopWordsCutoff: float
-            """
-            self.args.setColumn(column)
-
-        @staticmethod
-        def createArgumentsFromJSON(fileName, phase):
-            """ Method to create an object of this class from the JSON file and phase parameter value.
-            
-            :param fileName: The CONF parameter value of ClientOption object
-            :type fileName: String
-            :param phase: The PHASE parameter value of ClientOption object
-            :type phase: String
-            :return: The pointer containing address of the this class object
-            :rtype: pointer(Arguments)
-            """
-            obj = Arguments()
-            obj.args = getJVM().zingg.common.client.ArgumentsUtil().createArgumentsFromJSON(fileName, phase)
-            return obj
-        
-        
-        def writeArgumentsToJSONString(self):
-            """ Method to create an object of this class from the JSON file and phase parameter value.
-            
-            :param fileName: The CONF parameter value of ClientOption object
-            :type fileName: String
-            :param phase: The PHASE parameter value of ClientOption object
-            :type phase: String
-            :return: The pointer containing address of the this class object
-            :rtype: pointer(Arguments)
-            """
-            return getJVM().zingg.common.client.ArgumentsUtil().writeArgumentstoJSONString(self.args)
-        
-        @staticmethod
-        def createArgumentsFromJSONString(jsonArgs, phase):
-            obj = Arguments()
-            obj.args = getJVM().zingg.common.client.ArgumentsUtil().createArgumentsFromJSONString(jsonArgs, phase)
-            return obj
-        
-        
-        def copyArgs(self, phase):
-            argsString = self.writeArgumentsToJSONString()
-            return self.createArgumentsFromJSONString(argsString, phase)
+    def setLabelDataSampleSize(self, labelDataSampleSize):
+        """ Method to set labelDataSampleSize parameter value
+        Set the fraction of data to be used from the complete data set to be used for seeding the labeled data Labelling is costly and we want a fast approximate way of looking at a small sample of the records and identifying expected matches and nonmatches
+
+        :param labelDataSampleSize: value between 0.0 and 1.0 denoting portion of dataset to use in generating seed samples
+        :type labelDataSampleSize: float
+        """
+        self.args.setLabelDataSampleSize(labelDataSampleSize)
+
+    def writeArgumentsToJSON(self, fileName):
+        """ Method to write JSON file from the object of this class 
+
+        :param fileName: The CONF parameter value of ClientOption object or file address of json file
+        :type fileName: String
+        """
+        getJVM().zingg.common.client.ArgumentsUtil().writeArgumentsToJSON(fileName, self.args)
 
+    def setStopWordsCutoff(self, stopWordsCutoff):
+        """ Method to set stopWordsCutoff parameter value
+        By default, Zingg extracts 10% of the high frequency unique words from a dataset. If user wants different selection, they should set up StopWordsCutoff property
+
+        :param stopWordsCutoff: The stop words cutoff parameter value of ClientOption object or file address of json file
+        :type stopWordsCutoff: float
+        """
+        self.args.setStopWordsCutoff(stopWordsCutoff)
     
-        
+    def setColumn(self, column):
+        """ Method to set stopWordsCutoff parameter value
+        By default, Zingg extracts 10% of the high frequency unique words from a dataset. If user wants different selection, they should set up StopWordsCutoff property
 
+        :param stopWordsCutoff: The stop words cutoff parameter value of ClientOption object or file address of json file
+        :type stopWordsCutoff: float
+        """
+        self.args.setColumn(column)
 
-    class ClientOptions:
-        """ Class that contains Client options for Zingg object
-        :param phase: trainMatch, train, match, link, findAndLabel, findTrainingData, recommend etc
-        :type phase: String
-        :param args: Parse a list of Zingg command line options parameter values e.g. "--location" etc. optional argument for initializing this class.
-        :type args: List(String) or None
-        """
-        PHASE = getJVM().zingg.common.client.ClientOptions.PHASE 
-        """:PHASE: phase parameter for this class"""
-        CONF = getJVM().zingg.common.client.ClientOptions.CONF
-        """:CONF: conf parameter for this class"""
-        LICENSE = getJVM().zingg.common.client.ClientOptions.LICENSE
-        """:LICENSE: license parameter for this class"""
-        EMAIL = getJVM().zingg.common.client.ClientOptions.EMAIL
-        """:EMAIL: e-mail parameter for this class"""
-        LOCATION = getJVM().zingg.common.client.ClientOptions.LOCATION
-        """:LOCATION: location parameter for this class"""
-        REMOTE = getJVM().zingg.common.client.ClientOptions.REMOTE
-        """:REMOTE: remote option used internally for running on Databricks"""
-        ZINGG_DIR = getJVM().zingg.common.client.ClientOptions.ZINGG_DIR
-        """:ZINGG_DIR: location where Zingg saves the model, training data etc"""
-        MODEL_ID = getJVM().zingg.common.client.ClientOptions.MODEL_ID
-        """:MODEL_ID: ZINGG_DIR/MODEL_ID is used to save the model"""
-        COLUMN = getJVM().zingg.common.client.ClientOptions.COLUMN
-        """:COLUMN: Column whose stop words are to be recommended through Zingg"""
-
-        def __init__(self, argsSent=None):
-            print(argsSent)
-            if(argsSent == None):
-                args = []
-            else:
-                args = argsSent.copy()
-            if (not (self.PHASE in args)):
-                args.append(self.PHASE)
-                args.append("peekModel")
-            if (not (self.LICENSE in args)):
-                args.append(self.LICENSE)
-                args.append("zinggLic.txt")
-            if (not (self.EMAIL in args)):
-                args.append(self.EMAIL)
-                args.append("zingg@zingg.ai")
-            if (not (self.CONF in args)):
-                args.append(self.CONF)
-                args.append("dummyConf.json")
-            print("arguments for client options are ", args) 
-            self.co = getJVM().zingg.common.client.ClientOptions(args)
+    @staticmethod
+    def createArgumentsFromJSON(fileName, phase):
+        """ Method to create an object of this class from the JSON file and phase parameter value.
         
+        :param fileName: The CONF parameter value of ClientOption object
+        :type fileName: String
+        :param phase: The PHASE parameter value of ClientOption object
+        :type phase: String
+        :return: The pointer containing address of the this class object
+        :rtype: pointer(Arguments)
+        """
+        obj = Arguments()
+        obj.args = getJVM().zingg.common.client.ArgumentsUtil().createArgumentsFromJSON(fileName, phase)
+        return obj
+    
+    
+    def writeArgumentsToJSONString(self):
+        """ Method to create an object of this class from the JSON file and phase parameter value.
         
-        def getClientOptions(self):
-            """ Method to get pointer address of this class
-
-            :return: The pointer containing address of the this class object
-            :rtype: pointer(ClientOptions)
-            """
-            return self.co
-
-        def getOptionValue(self, option):
-            """ Method to get value for the key option
-
-            :param option: key to geting the value
-            :type option: String
-            :return: The value which is mapped for given key 
-            :rtype: String 
-            """
-            return self.co.getOptionValue(option)
-
-        def setOptionValue(self, option, value):
-            """ Method to map option key to the given value
-
-            :param option: key that is mapped with value
-            :type option: String
-            :param value: value to be set for given key
-            :type value: String
-            """
-            self.co.get(option).setValue(value)
-
-        def getPhase(self):
-            """ Method to get PHASE value
-
-            :return: The PHASE parameter value
-            :rtype: String 
-            """
-            return self.co.get(ClientOptions.PHASE).getValue()
-
-        def setPhase(self, newValue):
-            """ Method to set PHASE value
-
-            :param newValue: name of the phase
-            :type newValue: String
-            :return: The pointer containing address of the this class object after seting phase
-            :rtype: pointer(ClientOptions) 
-            """
-            self.co.get(ClientOptions.PHASE).setValue(newValue)
-
-        def getConf(self):
-            """ Method to get CONF value
-
-            :return: The CONF parameter value
-            :rtype: String 
-            """
-            return self.co.get(ClientOptions.CONF).getValue()
-
-        def hasLocation(self):
-            """ Method to check if this class has LOCATION parameter set as None or not
-
-            :return: The boolean value if LOCATION parameter is present or not
-            :rtype: Bool 
-            """
-            if(self.co.get(ClientOptions.LOCATION)==None):
-                return False
-            else:
-                return True
+        :param fileName: The CONF parameter value of ClientOption object
+        :type fileName: String
+        :param phase: The PHASE parameter value of ClientOption object
+        :type phase: String
+        :return: The pointer containing address of the this class object
+        :rtype: pointer(Arguments)
+        """
+        return getJVM().zingg.common.client.ArgumentsUtil().writeArgumentstoJSONString(self.args)
+    
+    @staticmethod
+    def createArgumentsFromJSONString(jsonArgs, phase):
+        obj = Arguments()
+        obj.args = getJVM().zingg.common.client.ArgumentsUtil().createArgumentsFromJSONString(jsonArgs, phase)
+        return obj
+    
+    
+    def copyArgs(self, phase):
+        argsString = self.writeArgumentsToJSONString()
+        return self.createArgumentsFromJSONString(argsString, phase)
+
+   
+    
+
+
+class ClientOptions:
+    """ Class that contains Client options for Zingg object
+    :param phase: trainMatch, train, match, link, findAndLabel, findTrainingData, recommend etc
+    :type phase: String
+    :param args: Parse a list of Zingg command line options parameter values e.g. "--location" etc. optional argument for initializing this class.
+    :type args: List(String) or None
+    """
+    PHASE = getJVM().zingg.common.client.ClientOptions.PHASE 
+    """:PHASE: phase parameter for this class"""
+    CONF = getJVM().zingg.common.client.ClientOptions.CONF
+    """:CONF: conf parameter for this class"""
+    LICENSE = getJVM().zingg.common.client.ClientOptions.LICENSE
+    """:LICENSE: license parameter for this class"""
+    EMAIL = getJVM().zingg.common.client.ClientOptions.EMAIL
+    """:EMAIL: e-mail parameter for this class"""
+    LOCATION = getJVM().zingg.common.client.ClientOptions.LOCATION
+    """:LOCATION: location parameter for this class"""
+    REMOTE = getJVM().zingg.common.client.ClientOptions.REMOTE
+    """:REMOTE: remote option used internally for running on Databricks"""
+    ZINGG_DIR = getJVM().zingg.common.client.ClientOptions.ZINGG_DIR
+    """:ZINGG_DIR: location where Zingg saves the model, training data etc"""
+    MODEL_ID = getJVM().zingg.common.client.ClientOptions.MODEL_ID
+    """:MODEL_ID: ZINGG_DIR/MODEL_ID is used to save the model"""
+    COLUMN = getJVM().zingg.common.client.ClientOptions.COLUMN
+    """:COLUMN: Column whose stop words are to be recommended through Zingg"""
+
+    def __init__(self, argsSent=None):
+        print(argsSent)
+        if(argsSent == None):
+            args = []
+        else:
+            args = argsSent.copy()
+        if (not (self.PHASE in args)):
+            args.append(self.PHASE)
+            args.append("peekModel")
+        if (not (self.LICENSE in args)):
+            args.append(self.LICENSE)
+            args.append("zinggLic.txt")
+        if (not (self.EMAIL in args)):
+            args.append(self.EMAIL)
+            args.append("zingg@zingg.ai")
+        if (not (self.CONF in args)):
+            args.append(self.CONF)
+            args.append("dummyConf.json")
+        print("arguments for client options are ", args) 
+        self.co = getJVM().zingg.common.client.ClientOptions(args)
+    
+    
+    def getClientOptions(self):
+        """ Method to get pointer address of this class
+
+        :return: The pointer containing address of the this class object
+        :rtype: pointer(ClientOptions)
+        """
+        return self.co
+
+    def getOptionValue(self, option):
+        """ Method to get value for the key option
+
+        :param option: key to geting the value
+        :type option: String
+        :return: The value which is mapped for given key 
+        :rtype: String 
+        """
+        return self.co.getOptionValue(option)
+
+    def setOptionValue(self, option, value):
+        """ Method to map option key to the given value
+
+        :param option: key that is mapped with value
+        :type option: String
+        :param value: value to be set for given key
+        :type value: String
+        """
+        self.co.get(option).setValue(value)
+
+    def getPhase(self):
+        """ Method to get PHASE value
+
+        :return: The PHASE parameter value
+        :rtype: String 
+        """
+        return self.co.get(ClientOptions.PHASE).getValue()
 
-        def getLocation(self):
-            """ Method to get LOCATION value
+    def setPhase(self, newValue):
+        """ Method to set PHASE value
 
-            :return: The LOCATION parameter value
-            :rtype: String 
-            """
-            return self.co.get(ClientOptions.LOCATION).getValue()
+        :param newValue: name of the phase
+        :type newValue: String
+        :return: The pointer containing address of the this class object after seting phase
+        :rtype: pointer(ClientOptions) 
+        """
+        self.co.get(ClientOptions.PHASE).setValue(newValue)
+
+    def getConf(self):
+        """ Method to get CONF value
 
+        :return: The CONF parameter value
+        :rtype: String 
+        """
+        return self.co.get(ClientOptions.CONF).getValue()
 
-    class FieldDefinition:
-        """ This class defines each field that we use in matching We can use this to configure the properties of each field we use for matching in Zingg.
+    def hasLocation(self):
+        """ Method to check if this class has LOCATION parameter set as None or not
 
-        :param name: name of the field
-        :type name: String
-        :param dataType: type of the data e.g. string, float, etc.
-        :type dataType: String
-        :param matchType: match type of this field e.g. FUSSY, EXACT, etc.
-        :type matchType: MatchType
+        :return: The boolean value if LOCATION parameter is present or not
+        :rtype: Bool 
         """
+        if(self.co.get(ClientOptions.LOCATION)==None):
+            return False
+        else:
+            return True
+
+    def getLocation(self):
+        """ Method to get LOCATION value
 
-        def __init__(self, name, dataType, *matchType):
-            self.fd = getJVM().zingg.common.client.FieldDefinition()
-            self.fd.setFieldName(name)
-            self.fd.setDataType(self.stringify(dataType))
-            self.fd.setMatchType(matchType)
-            self.fd.setFields(name)
+        :return: The LOCATION parameter value
+        :rtype: String 
+        """
+        return self.co.get(ClientOptions.LOCATION).getValue()
+
+
+class FieldDefinition:
+    """ This class defines each field that we use in matching We can use this to configure the properties of each field we use for matching in Zingg.
+
+    :param name: name of the field
+    :type name: String
+    :param dataType: type of the data e.g. string, float, etc.
+    :type dataType: String
+    :param matchType: match type of this field e.g. FUSSY, EXACT, etc.
+    :type matchType: MatchType
+    """
+
+    def __init__(self, name, dataType, *matchType):
+        self.fd = getJVM().zingg.common.client.FieldDefinition()
+        self.fd.setFieldName(name)
+        self.fd.setDataType(self.stringify(dataType))
+        self.fd.setMatchType(matchType)
+        self.fd.setFields(name)
+    
+    def setStopWords(self, stopWords):
+        """ Method to add stopwords to this class object
+
+        :param stopWords: The stop Words containing csv file's location
+        :type stopWords: String
+        """
+        self.fd.setStopWords(stopWords)
+
+    def getFieldDefinition(self):
+        """ Method to get  pointer address of this class
+
+        :return: The pointer containing the address of this class object
+        :rtype: pointer(FieldDefinition)
+        """
+        return self.fd
+
+    #  should be stringify'ed before it is set in fd object
+    def stringify(self, str):
+        """ Method to stringify'ed the dataType before it is set in FieldDefinition object
         
-        def setStopWords(self, stopWords):
-            """ Method to add stopwords to this class object
-
-            :param stopWords: The stop Words containing csv file's location
-            :type stopWords: String
-            """
-            self.fd.setStopWords(stopWords)
-
-        def getFieldDefinition(self):
-            """ Method to get  pointer address of this class
-
-            :return: The pointer containing the address of this class object
-            :rtype: pointer(FieldDefinition)
-            """
-            return self.fd
-
-        #  should be stringify'ed before it is set in fd object
-        def stringify(self, str):
-            """ Method to stringify'ed the dataType before it is set in FieldDefinition object
-            
-            :param str: dataType of the FieldDefinition
-            :type str: String
-            :return: The stringify'ed value of the dataType
-            :rtype: String
-            """
-            
-            return str
-            
-
-    def parseArguments(argv):
-        """ This method is used for checking mandatory arguments and creating an arguments list from Command line arguments
-
-        :param argv: Values that are passed during the calling of the program along with the calling statement.
-        :type argv: List
-        :return: a list containing necessary arguments to run any phase
-        :rtype: List
-        """
-        parser = argparse.ArgumentParser(description='Zingg\'s python APIs')
-        mandatoryOptions = parser.add_argument_group('mandatory arguments')
-        mandatoryOptions.add_argument('--phase', required=True,
-                            help='python phase e.g. assessModel')
-        mandatoryOptions.add_argument('--conf', required=True,
-                            help='JSON configuration with data input output locations and field definitions')
-
-        args, remaining_args = parser.parse_known_args(argv)
-        LOG.debug("args: ", args)
-        return args
+        :param str: dataType of the FieldDefinition
+        :type str: String
+        :return: The stringify'ed value of the dataType
+        :rtype: String
+        """
+        
+        return str
+        
+
+def parseArguments(argv):
+    """ This method is used for checking mandatory arguments and creating an arguments list from Command line arguments
+
+    :param argv: Values that are passed during the calling of the program along with the calling statement.
+    :type argv: List
+    :return: a list containing necessary arguments to run any phase
+    :rtype: List
+    """
+    parser = argparse.ArgumentParser(description='Zingg\'s python APIs')
+    mandatoryOptions = parser.add_argument_group('mandatory arguments')
+    mandatoryOptions.add_argument('--phase', required=True,
+                        help='python phase e.g. assessModel')
+    mandatoryOptions.add_argument('--conf', required=True,
+                        help='JSON configuration with data input output locations and field definitions')
+
+    args, remaining_args = parser.parse_known_args(argv)
+    LOG.debug("args: ", args)
+    return args
diff --git a/python/zingg/pipes.py b/python/zingg/pipes.py
index bb505d2ec..94db83699 100644
--- a/python/zingg/pipes.py
+++ b/python/zingg/pipes.py
@@ -4,271 +4,270 @@
 This module is submodule of zingg to work with different types of Pipes. Classes of this module inherit the Pipe class, and use that class to create many different types of pipes.
 """
 
-if __name__ == '__main__':
 
-    import logging
-    from zingg.client import *
+import logging
+from zingg.client import *
 
-    LOG = logging.getLogger("zingg.pipes")
+LOG = logging.getLogger("zingg.pipes")
 
-    JPipe = getJVM().zingg.spark.client.pipe.SparkPipe
-    FilePipe = getJVM().zingg.common.client.pipe.FilePipe
-    JStructType = getJVM().org.apache.spark.sql.types.StructType
+JPipe = getJVM().zingg.spark.client.pipe.SparkPipe
+FilePipe = getJVM().zingg.common.client.pipe.FilePipe
+JStructType = getJVM().org.apache.spark.sql.types.StructType
 
-    class Pipe:
-        """ Pipe class for working with different data-pipelines. Actual pipe def in the args. One pipe can be used at multiple places with different tables, locations, queries, etc
+class Pipe:
+    """ Pipe class for working with different data-pipelines. Actual pipe def in the args. One pipe can be used at multiple places with different tables, locations, queries, etc
 
-        :param name: name of the pipe
-        :type name: String
-        :param format: formate of pipe e.g. bigquery,InMemory, etc.
-        :type format: Format
-        """
-
-        def __init__(self, name, format):
-            self.pipe = getJVM().zingg.spark.client.pipe.SparkPipe()
-            self.pipe.setName(name)
-            self.pipe.setFormat(format)
-
-        def getPipe(self):
-            """ Method to get Pipe 
+    :param name: name of the pipe
+    :type name: String
+    :param format: formate of pipe e.g. bigquery,InMemory, etc.
+    :type format: Format
+    """
 
-            :return: pipe parameter values in the format of a list of string 
-            :rtype: Pipe
-            """
-            return self.pipe
-
-        def addProperty(self, name, value):
-            """ Method for adding different properties of pipe
-
-            :param name: name of the property
-            :type name: String
-            :param value: value you want to set for the property
-            :type value: String
-            """
-            self.pipe.setProp(name, value)
-        
-        def setSchema(self, s):
-            """ Method to set pipe schema value
+    def __init__(self, name, format):
+        self.pipe = getJVM().zingg.spark.client.pipe.SparkPipe()
+        self.pipe.setName(name)
+        self.pipe.setFormat(format)
 
-            :param s: json schema for the pipe
-            :type s: Schema
-            """
-            self.pipe.setSchema(s)
-
-        def toString(self):
-            """ Method to get pipe parameter values
-
-            :return: pipe information in list format
-            :rtype: List[String]
-            """
-            return self.pipe.toString()
+    def getPipe(self):
+        """ Method to get Pipe 
 
+        :return: pipe parameter values in the format of a list of string 
+        :rtype: Pipe
+        """
+        return self.pipe
 
-    class CsvPipe(Pipe):
-        """ Class CsvPipe: used for working with text files which uses a pipe symbol to separate units of text that belong in different columns.
+    def addProperty(self, name, value):
+        """ Method for adding different properties of pipe
 
-        :param name: name of the pipe.
+        :param name: name of the property
         :type name: String
-        :param location: (optional) location from where we read data
-        :type location: String or None
-        :param schema: (optional) json schema for the pipe
-        :type schema: Schema or None
+        :param value: value you want to set for the property
+        :type value: String
         """
-        def __init__(self, name, location = None, schema = None):
-            Pipe.__init__(self, name, JPipe.FORMAT_CSV)
-            if(location != None):
-                Pipe.addProperty(self, FilePipe.LOCATION, location)
-                if(schema != None):
-                    #df = spark.read.format(JPipe.FORMAT_CSV).schema(schema).load(location)
-                    #s = JStructType.fromDDL(schema)
-                    Pipe.setSchema(self, schema)
-                    print("set schema ")
-        
-        def setDelimiter(self, delimiter):
-            """ This method is used to define delimiter of CsvPipe
+        self.pipe.setProp(name, value)
+    
+    def setSchema(self, s):
+        """ Method to set pipe schema value
 
-            :param delimiter: a sequence of one or more characters for specifying the boundary between separate, independent regions in data streams
-            :type delimiter: String
-            """
-            Pipe.addProperty(self, "delimiter", delimiter)
+        :param s: json schema for the pipe
+        :type s: Schema
+        """
+        self.pipe.setSchema(s)
 
-        def setLocation(self, location):
-            """ Method to set location of pipe
+    def toString(self):
+        """ Method to get pipe parameter values
 
-            :param location: location from where we read data
-            :type location: String
-            """
+        :return: pipe information in list format
+        :rtype: List[String]
+        """
+        return self.pipe.toString()
+
+
+class CsvPipe(Pipe):
+    """ Class CsvPipe: used for working with text files which uses a pipe symbol to separate units of text that belong in different columns.
+
+    :param name: name of the pipe.
+    :type name: String
+    :param location: (optional) location from where we read data
+    :type location: String or None
+    :param schema: (optional) json schema for the pipe
+    :type schema: Schema or None
+    """
+    def __init__(self, name, location = None, schema = None):
+        Pipe.__init__(self, name, JPipe.FORMAT_CSV)
+        if(location != None):
             Pipe.addProperty(self, FilePipe.LOCATION, location)
+            if(schema != None):
+                #df = spark.read.format(JPipe.FORMAT_CSV).schema(schema).load(location)
+                #s = JStructType.fromDDL(schema)
+                Pipe.setSchema(self, schema)
+                print("set schema ")
+    
+    def setDelimiter(self, delimiter):
+        """ This method is used to define delimiter of CsvPipe
+
+        :param delimiter: a sequence of one or more characters for specifying the boundary between separate, independent regions in data streams
+        :type delimiter: String
+        """
+        Pipe.addProperty(self, "delimiter", delimiter)
 
-        def setHeader(self, header):
-            """ Method to set header property of pipe
+    def setLocation(self, location):
+        """ Method to set location of pipe
 
-            :param header: true if pipe have header, false otherwise
-            :type header: Boolean
-            """
-            Pipe.addProperty(self, FilePipe.HEADER, header)
+        :param location: location from where we read data
+        :type location: String
+        """
+        Pipe.addProperty(self, FilePipe.LOCATION, location)
 
-    class BigQueryPipe(Pipe):
-        """ Pipe Class for working with BigQuery pipeline
+    def setHeader(self, header):
+        """ Method to set header property of pipe
 
-        :param name: name of the pipe.
-        :type name: String
+        :param header: true if pipe have header, false otherwise
+        :type header: Boolean
         """
+        Pipe.addProperty(self, FilePipe.HEADER, header)
 
-        VIEWS_ENABLED = "viewsEnabled"
-        CREDENTIAL_FILE = "credentialsFile"
-        TABLE = "table"
-        TEMP_GCS_BUCKET="temporaryGcsBucket"
-
-        def __init__(self,name):
-            Pipe.__init__(self, name, JPipe.FORMAT_BIGQUERY)
+class BigQueryPipe(Pipe):
+    """ Pipe Class for working with BigQuery pipeline
 
-        def setCredentialFile(self, file):
-            """ Method to set Credential file to the pipe
+    :param name: name of the pipe.
+    :type name: String
+    """
 
-            :param file: credential file name
-            :type file: String
-            """
-            Pipe.addProperty(self, "credentialsFile", file)
+    VIEWS_ENABLED = "viewsEnabled"
+    CREDENTIAL_FILE = "credentialsFile"
+    TABLE = "table"
+    TEMP_GCS_BUCKET="temporaryGcsBucket"
 
-        def setTable(self, table):
-            """ Method to set Table to the pipe
+    def __init__(self,name):
+        Pipe.__init__(self, name, JPipe.FORMAT_BIGQUERY)
 
-            :param table: provide table parameter
-            :type table: String
-            """
-            Pipe.addProperty(self, "table", table)
+    def setCredentialFile(self, file):
+        """ Method to set Credential file to the pipe
 
-        def setTemporaryGcsBucket(self, bucket):
-            """ Method to set TemporaryGcsBucket to the pipe
+        :param file: credential file name
+        :type file: String
+        """
+        Pipe.addProperty(self, "credentialsFile", file)
 
-            :param bucket: provide bucket parameter
-            :type bucket: String
-            """
-            Pipe.addProperty(self, "temporaryGcsBucket", bucket)
+    def setTable(self, table):
+        """ Method to set Table to the pipe
 
-        def setViewsEnabled(self, isEnabled):
-            """ Method to set if viewsEnabled parameter is Enabled or not
+        :param table: provide table parameter
+        :type table: String
+        """
+        Pipe.addProperty(self, "table", table)
 
-            :param isEnabled: provide boolean parameter which defines if viewsEnabled option is enable or not
-            :type isEnabled: Bool
-            """
-            Pipe.addProperty(self, "viewsEnabled", isEnabled)
+    def setTemporaryGcsBucket(self, bucket):
+        """ Method to set TemporaryGcsBucket to the pipe
 
+        :param bucket: provide bucket parameter
+        :type bucket: String
+        """
+        Pipe.addProperty(self, "temporaryGcsBucket", bucket)
 
-    class SnowflakePipe(Pipe):
-        """ Pipe Class for working with Snowflake pipeline
+    def setViewsEnabled(self, isEnabled):
+        """ Method to set if viewsEnabled parameter is Enabled or not
 
-        :param name: name of the pipe
-        :type name: String
+        :param isEnabled: provide boolean parameter which defines if viewsEnabled option is enable or not
+        :type isEnabled: Bool
         """
-        URL = "sfUrl"
-        USER = "sfUser"
-        PASSWORD = "sfPassword"
-        DATABASE ="sfDatabase"
-        SCHEMA = "sfSchema"
-        WAREHOUSE = "sfWarehouse"
-        DBTABLE = "dbtable"
-
-        def __init__(self,name):
-            Pipe.__init__(self, name, JPipe.FORMAT_SNOWFLAKE)
-            Pipe.addProperty(self, "application", "zinggai_zingg")
-            
+        Pipe.addProperty(self, "viewsEnabled", isEnabled)
+
+
+class SnowflakePipe(Pipe):
+    """ Pipe Class for working with Snowflake pipeline
+
+    :param name: name of the pipe
+    :type name: String
+    """
+    URL = "sfUrl"
+    USER = "sfUser"
+    PASSWORD = "sfPassword"
+    DATABASE ="sfDatabase"
+    SCHEMA = "sfSchema"
+    WAREHOUSE = "sfWarehouse"
+    DBTABLE = "dbtable"
+
+    def __init__(self,name):
+        Pipe.__init__(self, name, JPipe.FORMAT_SNOWFLAKE)
+        Pipe.addProperty(self, "application", "zinggai_zingg")
+        
 
-        def setURL(self, url):
-            """ Method to set url to the pipe
+    def setURL(self, url):
+        """ Method to set url to the pipe
 
-            :param url: provide url for this pipe
-            :type url: String
-            """
-            Pipe.addProperty(self, "sfUrl", url)
+        :param url: provide url for this pipe
+        :type url: String
+        """
+        Pipe.addProperty(self, "sfUrl", url)
 
-        def setUser(self, user):
-            """ Method to set User to the pipe
-            
-            :param user: provide User parameter.
-            :type user: String
-            """
-            Pipe.addProperty(self, "sfUser", user)
+    def setUser(self, user):
+        """ Method to set User to the pipe
+        
+        :param user: provide User parameter.
+        :type user: String
+        """
+        Pipe.addProperty(self, "sfUser", user)
 
-        def setPassword(self, passwd):
-            """ Method to set Password to the pipe
-            
-            :param passwd: provide Password parameter.
-            :type passwd: String
-            """
-            Pipe.addProperty(self, "sfPassword", passwd)
+    def setPassword(self, passwd):
+        """ Method to set Password to the pipe
+        
+        :param passwd: provide Password parameter.
+        :type passwd: String
+        """
+        Pipe.addProperty(self, "sfPassword", passwd)
 
-        def setDatabase(self, db):
-            """ Method to set Database to the pipe
-            
-            :param db: provide Database parameter.
-            :type db: Database
-            """
-            Pipe.addProperty(self, "sfDatabase", db)
+    def setDatabase(self, db):
+        """ Method to set Database to the pipe
+        
+        :param db: provide Database parameter.
+        :type db: Database
+        """
+        Pipe.addProperty(self, "sfDatabase", db)
 
-        def setSFSchema(self, schema):
-            """ Method to set Schema to the pipe
-            
-            :param schema: provide schema parameter.
-            :type schema: Schema
-            """
-            Pipe.addProperty(self, "sfSchema", schema)
+    def setSFSchema(self, schema):
+        """ Method to set Schema to the pipe
+        
+        :param schema: provide schema parameter.
+        :type schema: Schema
+        """
+        Pipe.addProperty(self, "sfSchema", schema)
 
-        def setWarehouse(self, warehouse):
-            """ Method to set warehouse parameter to the pipe
-            
-            :param warehouse: provide warehouse parameter.
-            :type warehouse: String
-            """
-            Pipe.addProperty(self, "sfWarehouse", warehouse)
+    def setWarehouse(self, warehouse):
+        """ Method to set warehouse parameter to the pipe
+        
+        :param warehouse: provide warehouse parameter.
+        :type warehouse: String
+        """
+        Pipe.addProperty(self, "sfWarehouse", warehouse)
 
-        def setDbTable(self, dbtable):
-            """ description
-            
-            :param dbtable: provide bucket parameter.
-            :type dbtable: String
-            """
-            Pipe.addProperty(self, "dbtable", dbtable)     
-            
+    def setDbTable(self, dbtable):
+        """ description
+        
+        :param dbtable: provide bucket parameter.
+        :type dbtable: String
+        """
+        Pipe.addProperty(self, "dbtable", dbtable)     
+        
 
-    class InMemoryPipe(Pipe):
-        """ Pipe Class for working with InMemory pipeline
+class InMemoryPipe(Pipe):
+    """ Pipe Class for working with InMemory pipeline
 
-        :param name: name of the pipe
-        :type name: String
-        :param df: provide dataset for this pipe (optional)
-        :type df: Dataset or None
-        """    
+    :param name: name of the pipe
+    :type name: String
+    :param df: provide dataset for this pipe (optional)
+    :type df: Dataset or None
+    """    
 
-        def __init__(self, name, df = None):
-            Pipe.__init__(self, name, JPipe.FORMAT_INMEMORY)
-            if (df is not None):
-                self.setDataset(df)
+    def __init__(self, name, df = None):
+        Pipe.__init__(self, name, JPipe.FORMAT_INMEMORY)
+        if (df is not None):
+            self.setDataset(df)
 
-        def setDataset(self, df):
-            """ Method to set DataFrame of the pipe
-            
-            :param df: pandas or spark dataframe for the pipe
-            :type df: DataFrame
-            """
-            if (isinstance(df, pd.DataFrame)):
-                print('schema of pandas df is ' , Pipe.getPipe(self).getSchema())
-                if (Pipe.getPipe(self).getSchema() is not None):
-                    ds = getSparkSession().createDataFrame(df, schema=Pipe.getPipe(self).getSchema())
-                else:
-                    ds = getSparkSession().createDataFrame(df)
-                
-                Pipe.getPipe(self).setDataset(ds._jdf)
-            elif (isinstance(df, DataFrame)):
-                Pipe.getPipe(self).setDataset(df._jdf)
+    def setDataset(self, df):
+        """ Method to set DataFrame of the pipe
+        
+        :param df: pandas or spark dataframe for the pipe
+        :type df: DataFrame
+        """
+        if (isinstance(df, pd.DataFrame)):
+            print('schema of pandas df is ' , Pipe.getPipe(self).getSchema())
+            if (Pipe.getPipe(self).getSchema() is not None):
+                ds = getSparkSession().createDataFrame(df, schema=Pipe.getPipe(self).getSchema())
             else:
-                LOG.error(" setDataset(): NUll or Unsupported type: %s", type(df))
-
-        def getDataset(self):
-            """ Method to get Dataset from pipe
+                ds = getSparkSession().createDataFrame(df)
             
-            :return: dataset of the pipe in the format of spark dataset 
-            :rtype: Dataset<Row>
-            """
-            return Pipe.getPipe(self).getDataset().df()
+            Pipe.getPipe(self).setDataset(ds._jdf)
+        elif (isinstance(df, DataFrame)):
+            Pipe.getPipe(self).setDataset(df._jdf)
+        else:
+            LOG.error(" setDataset(): NUll or Unsupported type: %s", type(df))
+
+    def getDataset(self):
+        """ Method to get Dataset from pipe
+        
+        :return: dataset of the pipe in the format of spark dataset 
+        :rtype: Dataset<Row>
+        """
+        return Pipe.getPipe(self).getDataset().df()

From af9bef86823b3dee19cbbc2bc1995af0be39183b Mon Sep 17 00:00:00 2001
From: Sonal Goyal <sonalgoyal4@gmail.com>
Date: Mon, 8 Jan 2024 19:19:16 +0000
Subject: [PATCH 12/36] GITBOOK-24: No subject

---
 docs/README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/README.md b/docs/README.md
index 8a849e159..f5e9c7371 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -8,8 +8,8 @@ description: Hope you find us useful :-)
 
 This is the latest documentation for Zingg. Release wise documentation can be accessed through:
 
-* [v0.4.0 ](https://docs.zingg.ai/zingg0.4.0/)
-* [v0.3.4 ](https://docs.zingg.ai/zingg0.3.4/)
+* [v0.4.0](https://app.gitbook.com/o/kn0G4kXLdlfPagjso48S/s/a7sgpR3odgfck5L8KMcN/)
+* [v0.3.4](https://docs.zingg.ai/zingg0.3.4/)
 * [v0.3.3](https://docs.zingg.ai/zingg0.3.3/)
 
 ## Why?

From dfc49d34b0c9fdd472454f2217df9a9abbd25b44 Mon Sep 17 00:00:00 2001
From: Sonal Goyal <sonalgoyal4@gmail.com>
Date: Thu, 18 Jan 2024 21:56:52 +0530
Subject: [PATCH 13/36] added py docs to docs

---
 .DS_Store                                     | Bin 6148 -> 6148 bytes
 docs/SUMMARY.md                               |   1 +
 docs/python/doctrees/environment.pickle       | Bin 0 -> 634563 bytes
 .../python}/doctrees/index.doctree            | Bin 10386 -> 10040 bytes
 .../python}/doctrees/zingg.doctree            | Bin
 docs/python/markdown/index.md                 | 183 ++++
 docs/python/markdown/zingg.md                 | 785 ++++++++++++++++++
 .../docs/_build/doctrees/environment.pickle   | Bin 533605 -> 0 bytes
 python/docs/conf.py                           |   5 +-
 python/docs/index.rst                         |   4 -
 python/docs/zingg.rst                         |   5 +-
 11 files changed, 974 insertions(+), 9 deletions(-)
 create mode 100644 docs/python/doctrees/environment.pickle
 rename {python/docs/_build => docs/python}/doctrees/index.doctree (53%)
 rename {python/docs/_build => docs/python}/doctrees/zingg.doctree (100%)
 create mode 100644 docs/python/markdown/index.md
 create mode 100644 docs/python/markdown/zingg.md
 delete mode 100644 python/docs/_build/doctrees/environment.pickle

diff --git a/.DS_Store b/.DS_Store
index 0d364ad2053256f736395394b8d051407d045e6f..0993f235461ec76760d51d6d022183640313e781 100644
GIT binary patch
delta 173
zcmZoMXfc=|#>B!ku~2NHo}wrx0|Nsi1A_nqLkUB1Qh9MfQcix-=84S9nPovzYz&DE
z#Xy*fEDaRjJ~@S{P))qL+R)rsN5P<0N1@u#(7;?r!Q9-mww9AaR9W9TC_XzUH!r_)
y@=qpt-Y#UrCMPlrZ{}y>WZulq!OsD74A8>w%#-;=965k)VFD^;*c>6Uh8X}kA1KTK

delta 83
zcmZoMXfc=|#>B)qu~2NHo}wr_0|Nsi1A_nqLq0=ZS#VKaPJUkc#=_-{jO?3#v+yu)
kY<SPKnVo~51E^)QAjfy+$^0UY96(JVT`Zd;MAk3^091ezssI20

diff --git a/docs/SUMMARY.md b/docs/SUMMARY.md
index 234cf5596..bc4b78368 100644
--- a/docs/SUMMARY.md
+++ b/docs/SUMMARY.md
@@ -51,6 +51,7 @@
   * [BigQuery](dataSourcesAndSinks/bigquery.md)
   * [Exasol](dataSourcesAndSinks/exasol.md)
 * [Working With Python](working-with-python.md)
+  * [Python API](python/markdown/index.md)
 * [Running Zingg on Cloud](running/running.md)
   * [Running on AWS](running/aws.md)
   * [Running on Azure](running/azure.md)
diff --git a/docs/python/doctrees/environment.pickle b/docs/python/doctrees/environment.pickle
new file mode 100644
index 0000000000000000000000000000000000000000..60da21b6d4a4e1fa2e43096b4d209ecf338bde01
GIT binary patch
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diff --git a/python/docs/_build/doctrees/zingg.doctree b/docs/python/doctrees/zingg.doctree
similarity index 100%
rename from python/docs/_build/doctrees/zingg.doctree
rename to docs/python/doctrees/zingg.doctree
diff --git a/docs/python/markdown/index.md b/docs/python/markdown/index.md
new file mode 100644
index 000000000..d8b455d19
--- /dev/null
+++ b/docs/python/markdown/index.md
@@ -0,0 +1,183 @@
+<!-- zingg documentation master file, created by
+sphinx-quickstart on Thu Jul  7 12:24:41 2022.
+You can adapt this file completely to your liking, but it should at least
+contain the root `toctree` directive. -->
+
+# Zingg Entity Resolution Python Package
+
+Zingg Python APIs for entity resolution, identity resolution, record linkage, data mastering and deduplication using ML
+([https://www.zingg.ai](https://www.zingg.ai))
+
+#### NOTE
+Requires python 3.6+; spark 3.5.0
+Otherwise, [`zingg.client.Zingg()`](zingg.md#zingg.client.Zingg) cannot be executed
+
+* [Zingg Entity Resolution Package](zingg.md)
+  * [zingg.client](zingg.md#zingg-client)
+  * [`Arguments`](zingg.md#zingg.client.Arguments)
+    * [`Arguments.copyArgs()`](zingg.md#zingg.client.Arguments.copyArgs)
+    * [`Arguments.createArgumentsFromJSON()`](zingg.md#zingg.client.Arguments.createArgumentsFromJSON)
+    * [`Arguments.createArgumentsFromJSONString()`](zingg.md#zingg.client.Arguments.createArgumentsFromJSONString)
+    * [`Arguments.getArgs()`](zingg.md#zingg.client.Arguments.getArgs)
+    * [`Arguments.getModelId()`](zingg.md#zingg.client.Arguments.getModelId)
+    * [`Arguments.getZinggBaseModelDir()`](zingg.md#zingg.client.Arguments.getZinggBaseModelDir)
+    * [`Arguments.getZinggBaseTrainingDataDir()`](zingg.md#zingg.client.Arguments.getZinggBaseTrainingDataDir)
+    * [`Arguments.getZinggModelDir()`](zingg.md#zingg.client.Arguments.getZinggModelDir)
+    * [`Arguments.getZinggTrainingDataMarkedDir()`](zingg.md#zingg.client.Arguments.getZinggTrainingDataMarkedDir)
+    * [`Arguments.getZinggTrainingDataUnmarkedDir()`](zingg.md#zingg.client.Arguments.getZinggTrainingDataUnmarkedDir)
+    * [`Arguments.setArgs()`](zingg.md#zingg.client.Arguments.setArgs)
+    * [`Arguments.setColumn()`](zingg.md#zingg.client.Arguments.setColumn)
+    * [`Arguments.setData()`](zingg.md#zingg.client.Arguments.setData)
+    * [`Arguments.setFieldDefinition()`](zingg.md#zingg.client.Arguments.setFieldDefinition)
+    * [`Arguments.setLabelDataSampleSize()`](zingg.md#zingg.client.Arguments.setLabelDataSampleSize)
+    * [`Arguments.setModelId()`](zingg.md#zingg.client.Arguments.setModelId)
+    * [`Arguments.setNumPartitions()`](zingg.md#zingg.client.Arguments.setNumPartitions)
+    * [`Arguments.setOutput()`](zingg.md#zingg.client.Arguments.setOutput)
+    * [`Arguments.setStopWordsCutoff()`](zingg.md#zingg.client.Arguments.setStopWordsCutoff)
+    * [`Arguments.setTrainingSamples()`](zingg.md#zingg.client.Arguments.setTrainingSamples)
+    * [`Arguments.setZinggDir()`](zingg.md#zingg.client.Arguments.setZinggDir)
+    * [`Arguments.writeArgumentsToJSON()`](zingg.md#zingg.client.Arguments.writeArgumentsToJSON)
+    * [`Arguments.writeArgumentsToJSONString()`](zingg.md#zingg.client.Arguments.writeArgumentsToJSONString)
+  * [`ClientOptions`](zingg.md#zingg.client.ClientOptions)
+    * [`ClientOptions.COLUMN`](zingg.md#zingg.client.ClientOptions.COLUMN)
+    * [`ClientOptions.CONF`](zingg.md#zingg.client.ClientOptions.CONF)
+    * [`ClientOptions.EMAIL`](zingg.md#zingg.client.ClientOptions.EMAIL)
+    * [`ClientOptions.LICENSE`](zingg.md#zingg.client.ClientOptions.LICENSE)
+    * [`ClientOptions.LOCATION`](zingg.md#zingg.client.ClientOptions.LOCATION)
+    * [`ClientOptions.MODEL_ID`](zingg.md#zingg.client.ClientOptions.MODEL_ID)
+    * [`ClientOptions.PHASE`](zingg.md#zingg.client.ClientOptions.PHASE)
+    * [`ClientOptions.REMOTE`](zingg.md#zingg.client.ClientOptions.REMOTE)
+    * [`ClientOptions.ZINGG_DIR`](zingg.md#zingg.client.ClientOptions.ZINGG_DIR)
+    * [`ClientOptions.getClientOptions()`](zingg.md#zingg.client.ClientOptions.getClientOptions)
+    * [`ClientOptions.getConf()`](zingg.md#zingg.client.ClientOptions.getConf)
+    * [`ClientOptions.getLocation()`](zingg.md#zingg.client.ClientOptions.getLocation)
+    * [`ClientOptions.getOptionValue()`](zingg.md#zingg.client.ClientOptions.getOptionValue)
+    * [`ClientOptions.getPhase()`](zingg.md#zingg.client.ClientOptions.getPhase)
+    * [`ClientOptions.hasLocation()`](zingg.md#zingg.client.ClientOptions.hasLocation)
+    * [`ClientOptions.setOptionValue()`](zingg.md#zingg.client.ClientOptions.setOptionValue)
+    * [`ClientOptions.setPhase()`](zingg.md#zingg.client.ClientOptions.setPhase)
+  * [`FieldDefinition`](zingg.md#zingg.client.FieldDefinition)
+    * [`FieldDefinition.getFieldDefinition()`](zingg.md#zingg.client.FieldDefinition.getFieldDefinition)
+    * [`FieldDefinition.setStopWords()`](zingg.md#zingg.client.FieldDefinition.setStopWords)
+    * [`FieldDefinition.stringify()`](zingg.md#zingg.client.FieldDefinition.stringify)
+  * [`Zingg`](zingg.md#zingg.client.Zingg)
+    * [`Zingg.execute()`](zingg.md#zingg.client.Zingg.execute)
+    * [`Zingg.executeLabel()`](zingg.md#zingg.client.Zingg.executeLabel)
+    * [`Zingg.executeLabelUpdate()`](zingg.md#zingg.client.Zingg.executeLabelUpdate)
+    * [`Zingg.getArguments()`](zingg.md#zingg.client.Zingg.getArguments)
+    * [`Zingg.getMarkedRecords()`](zingg.md#zingg.client.Zingg.getMarkedRecords)
+    * [`Zingg.getMarkedRecordsStat()`](zingg.md#zingg.client.Zingg.getMarkedRecordsStat)
+    * [`Zingg.getMatchedMarkedRecordsStat()`](zingg.md#zingg.client.Zingg.getMatchedMarkedRecordsStat)
+    * [`Zingg.getOptions()`](zingg.md#zingg.client.Zingg.getOptions)
+    * [`Zingg.getUnmarkedRecords()`](zingg.md#zingg.client.Zingg.getUnmarkedRecords)
+    * [`Zingg.getUnmatchedMarkedRecordsStat()`](zingg.md#zingg.client.Zingg.getUnmatchedMarkedRecordsStat)
+    * [`Zingg.getUnsureMarkedRecordsStat()`](zingg.md#zingg.client.Zingg.getUnsureMarkedRecordsStat)
+    * [`Zingg.init()`](zingg.md#zingg.client.Zingg.init)
+    * [`Zingg.initAndExecute()`](zingg.md#zingg.client.Zingg.initAndExecute)
+    * [`Zingg.processRecordsCli()`](zingg.md#zingg.client.Zingg.processRecordsCli)
+    * [`Zingg.processRecordsCliLabelUpdate()`](zingg.md#zingg.client.Zingg.processRecordsCliLabelUpdate)
+    * [`Zingg.setArguments()`](zingg.md#zingg.client.Zingg.setArguments)
+    * [`Zingg.setOptions()`](zingg.md#zingg.client.Zingg.setOptions)
+    * [`Zingg.writeLabelledOutput()`](zingg.md#zingg.client.Zingg.writeLabelledOutput)
+    * [`Zingg.writeLabelledOutputFromPandas()`](zingg.md#zingg.client.Zingg.writeLabelledOutputFromPandas)
+  * [`ZinggWithSpark`](zingg.md#zingg.client.ZinggWithSpark)
+  * [`getDfFromDs()`](zingg.md#zingg.client.getDfFromDs)
+  * [`getGateway()`](zingg.md#zingg.client.getGateway)
+  * [`getJVM()`](zingg.md#zingg.client.getJVM)
+  * [`getPandasDfFromDs()`](zingg.md#zingg.client.getPandasDfFromDs)
+  * [`getSparkContext()`](zingg.md#zingg.client.getSparkContext)
+  * [`getSparkSession()`](zingg.md#zingg.client.getSparkSession)
+  * [`getSqlContext()`](zingg.md#zingg.client.getSqlContext)
+  * [`initClient()`](zingg.md#zingg.client.initClient)
+  * [`initDataBricksConectClient()`](zingg.md#zingg.client.initDataBricksConectClient)
+  * [`initSparkClient()`](zingg.md#zingg.client.initSparkClient)
+  * [`parseArguments()`](zingg.md#zingg.client.parseArguments)
+  * [zingg.pipes](zingg.md#zingg-pipes)
+  * [`BigQueryPipe`](zingg.md#zingg.pipes.BigQueryPipe)
+    * [`BigQueryPipe.CREDENTIAL_FILE`](zingg.md#zingg.pipes.BigQueryPipe.CREDENTIAL_FILE)
+    * [`BigQueryPipe.TABLE`](zingg.md#zingg.pipes.BigQueryPipe.TABLE)
+    * [`BigQueryPipe.TEMP_GCS_BUCKET`](zingg.md#zingg.pipes.BigQueryPipe.TEMP_GCS_BUCKET)
+    * [`BigQueryPipe.VIEWS_ENABLED`](zingg.md#zingg.pipes.BigQueryPipe.VIEWS_ENABLED)
+    * [`BigQueryPipe.setCredentialFile()`](zingg.md#zingg.pipes.BigQueryPipe.setCredentialFile)
+    * [`BigQueryPipe.setTable()`](zingg.md#zingg.pipes.BigQueryPipe.setTable)
+    * [`BigQueryPipe.setTemporaryGcsBucket()`](zingg.md#zingg.pipes.BigQueryPipe.setTemporaryGcsBucket)
+    * [`BigQueryPipe.setViewsEnabled()`](zingg.md#zingg.pipes.BigQueryPipe.setViewsEnabled)
+  * [`CsvPipe`](zingg.md#zingg.pipes.CsvPipe)
+    * [`CsvPipe.setDelimiter()`](zingg.md#zingg.pipes.CsvPipe.setDelimiter)
+    * [`CsvPipe.setHeader()`](zingg.md#zingg.pipes.CsvPipe.setHeader)
+    * [`CsvPipe.setLocation()`](zingg.md#zingg.pipes.CsvPipe.setLocation)
+  * [`InMemoryPipe`](zingg.md#zingg.pipes.InMemoryPipe)
+    * [`InMemoryPipe.getDataset()`](zingg.md#zingg.pipes.InMemoryPipe.getDataset)
+    * [`InMemoryPipe.setDataset()`](zingg.md#zingg.pipes.InMemoryPipe.setDataset)
+  * [`Pipe`](zingg.md#zingg.pipes.Pipe)
+    * [`Pipe.addProperty()`](zingg.md#zingg.pipes.Pipe.addProperty)
+    * [`Pipe.getPipe()`](zingg.md#zingg.pipes.Pipe.getPipe)
+    * [`Pipe.setSchema()`](zingg.md#zingg.pipes.Pipe.setSchema)
+    * [`Pipe.toString()`](zingg.md#zingg.pipes.Pipe.toString)
+  * [`SnowflakePipe`](zingg.md#zingg.pipes.SnowflakePipe)
+    * [`SnowflakePipe.DATABASE`](zingg.md#zingg.pipes.SnowflakePipe.DATABASE)
+    * [`SnowflakePipe.DBTABLE`](zingg.md#zingg.pipes.SnowflakePipe.DBTABLE)
+    * [`SnowflakePipe.PASSWORD`](zingg.md#zingg.pipes.SnowflakePipe.PASSWORD)
+    * [`SnowflakePipe.SCHEMA`](zingg.md#zingg.pipes.SnowflakePipe.SCHEMA)
+    * [`SnowflakePipe.URL`](zingg.md#zingg.pipes.SnowflakePipe.URL)
+    * [`SnowflakePipe.USER`](zingg.md#zingg.pipes.SnowflakePipe.USER)
+    * [`SnowflakePipe.WAREHOUSE`](zingg.md#zingg.pipes.SnowflakePipe.WAREHOUSE)
+    * [`SnowflakePipe.setDatabase()`](zingg.md#zingg.pipes.SnowflakePipe.setDatabase)
+    * [`SnowflakePipe.setDbTable()`](zingg.md#zingg.pipes.SnowflakePipe.setDbTable)
+    * [`SnowflakePipe.setPassword()`](zingg.md#zingg.pipes.SnowflakePipe.setPassword)
+    * [`SnowflakePipe.setSFSchema()`](zingg.md#zingg.pipes.SnowflakePipe.setSFSchema)
+    * [`SnowflakePipe.setURL()`](zingg.md#zingg.pipes.SnowflakePipe.setURL)
+    * [`SnowflakePipe.setUser()`](zingg.md#zingg.pipes.SnowflakePipe.setUser)
+    * [`SnowflakePipe.setWarehouse()`](zingg.md#zingg.pipes.SnowflakePipe.setWarehouse)
+
+# API Reference
+
+* [Module Index](py-modindex.md)
+* [Index](genindex.md)
+* [Search Page](search.md)
+
+# Example API Usage
+
+```python
+from zingg.client import *
+from zingg.pipes import *
+
+#build the arguments for zingg
+args = Arguments()
+#set field definitions
+fname = FieldDefinition("fname", "string", MatchType.FUZZY)
+lname = FieldDefinition("lname", "string", MatchType.FUZZY)
+stNo = FieldDefinition("stNo", "string", MatchType.FUZZY)
+add1 = FieldDefinition("add1","string", MatchType.FUZZY)
+add2 = FieldDefinition("add2", "string", MatchType.FUZZY)
+city = FieldDefinition("city", "string", MatchType.FUZZY)
+areacode = FieldDefinition("areacode", "string", MatchType.FUZZY)
+state = FieldDefinition("state", "string", MatchType.FUZZY)
+dob = FieldDefinition("dob", "string", MatchType.FUZZY)
+ssn = FieldDefinition("ssn", "string", MatchType.FUZZY)
+
+fieldDefs = [fname, lname, stNo, add1, add2, city, areacode, state, dob, ssn]
+
+args.setFieldDefinition(fieldDefs)
+#set the modelid and the zingg dir
+args.setModelId("100")
+args.setZinggDir("models")
+args.setNumPartitions(4)
+args.setLabelDataSampleSize(0.5)
+
+#reading dataset into inputPipe and settint it up in 'args'
+#below line should not be required if you are reading from in memory dataset
+#in that case, replace df with input df
+schema = "id string, fname string, lname string, stNo string, add1 string, add2 string, city string, areacode string, state string, dob string, ssn  string"
+inputPipe = CsvPipe("testFebrl", "examples/febrl/test.csv", schema)
+args.setData(inputPipe)
+outputPipe = CsvPipe("resultFebrl", "/tmp/febrlOutput")
+
+args.setOutput(outputPipe)
+
+options = ClientOptions([ClientOptions.PHASE,"match"])
+
+#Zingg execution for the given phase
+zingg = Zingg(args, options)
+zingg.initAndExecute()
+```
diff --git a/docs/python/markdown/zingg.md b/docs/python/markdown/zingg.md
new file mode 100644
index 000000000..9467c6751
--- /dev/null
+++ b/docs/python/markdown/zingg.md
@@ -0,0 +1,785 @@
+# Zingg Entity Resolution Package
+
+Zingg Python APIs for entity resolution, record linkage, data mastering and deduplication using ML
+([https://www.zingg.ai](https://www.zingg.ai))
+
+requires python 3.6+; spark 3.5.0
+Otherwise, [`zingg.client.Zingg()`](#zingg.client.Zingg) cannot be executed
+
+<a id="module-zingg"></a>
+
+<a id="module-zingg.client"></a>
+
+## zingg.client
+
+This module is the main entry point of the Zingg Python API
+
+### *class* zingg.client.Arguments
+
+Bases: `object`
+
+This class helps supply match arguments to Zingg. There are 3 basic steps in any match process.
+
+* **Defining:**
+  specifying information about data location, fields, and our notion of similarity.
+* **Training:**
+  making Zingg learn the matching rules
+* **Matching:**
+  Running the models on the entire dataset
+
+#### copyArgs(phase)
+
+#### *static* createArgumentsFromJSON(fileName, phase)
+
+Method to create an object of this class from the JSON file and phase parameter value.
+
+* **Parameters:**
+  * **fileName** (*String*) – The CONF parameter value of ClientOption object
+  * **phase** (*String*) – The PHASE parameter value of ClientOption object
+* **Returns:**
+  The pointer containing address of the this class object
+* **Return type:**
+  pointer([Arguments](#zingg.client.Arguments))
+
+#### *static* createArgumentsFromJSONString(jsonArgs, phase)
+
+#### getArgs()
+
+Method to get pointer address of this class
+
+* **Returns:**
+  The pointer containing the address of this class object
+* **Return type:**
+  pointer([Arguments](#zingg.client.Arguments))
+
+#### getModelId()
+
+#### getZinggBaseModelDir()
+
+#### getZinggBaseTrainingDataDir()
+
+Method to get the location of the folder where Zingg
+saves the training data found by findTrainingData
+
+#### getZinggModelDir()
+
+#### getZinggTrainingDataMarkedDir()
+
+Method to get the location of the folder where Zingg
+saves the marked training data labeled by the user
+
+#### getZinggTrainingDataUnmarkedDir()
+
+Method to get the location of the folder where Zingg
+saves the training data found by findTrainingData
+
+#### setArgs(argumentsObj)
+
+Method to set this class object
+
+* **Parameters:**
+  **argumentsObj** (*pointer**(*[*Arguments*](#zingg.client.Arguments)*)*) – Argument object to set this object
+
+#### setColumn(column)
+
+Method to set stopWordsCutoff parameter value
+By default, Zingg extracts 10% of the high frequency unique words from a dataset. If user wants different selection, they should set up StopWordsCutoff property
+
+* **Parameters:**
+  **stopWordsCutoff** (*float*) – The stop words cutoff parameter value of ClientOption object or file address of json file
+
+#### setData(\*pipes)
+
+Method to set the file path of the file to be matched.
+
+* **Parameters:**
+  **pipes** ([*Pipe*](#zingg.pipes.Pipe)*[**]*) – input data pipes separated by comma e.g. (pipe1,pipe2,..)
+
+#### setFieldDefinition(fieldDef)
+
+Method convert python objects to java FieldDefinition objects and set the field definitions associated with this client
+
+* **Parameters:**
+  **fieldDef** (*List**(*[*FieldDefinition*](#zingg.client.FieldDefinition)*)*) – python FieldDefinition object list
+
+#### setLabelDataSampleSize(labelDataSampleSize)
+
+Method to set labelDataSampleSize parameter value
+Set the fraction of data to be used from the complete data set to be used for seeding the labeled data Labelling is costly and we want a fast approximate way of looking at a small sample of the records and identifying expected matches and nonmatches
+
+* **Parameters:**
+  **labelDataSampleSize** (*float*) – value between 0.0 and 1.0 denoting portion of dataset to use in generating seed samples
+
+#### setModelId(id)
+
+Method to set the output directory where the match output will be saved
+
+* **Parameters:**
+  **id** (*String*) – model id value
+
+#### setNumPartitions(numPartitions)
+
+Method to set NumPartitions parameter value
+Sample size to use for seeding labeled data We don’t want to run over all the data, as we want a quick way to seed some labeled data that we can manually edit
+
+* **Parameters:**
+  **numPartitions** (*int*) – number of partitions for given data pipes
+
+#### setOutput(\*pipes)
+
+Method to set the output directory where the match result will be saved
+
+* **Parameters:**
+  **pipes** ([*Pipe*](#zingg.pipes.Pipe)*[**]*) – output data pipes separated by comma e.g. (pipe1,pipe2,..)
+
+#### setStopWordsCutoff(stopWordsCutoff)
+
+Method to set stopWordsCutoff parameter value
+By default, Zingg extracts 10% of the high frequency unique words from a dataset. If user wants different selection, they should set up StopWordsCutoff property
+
+* **Parameters:**
+  **stopWordsCutoff** (*float*) – The stop words cutoff parameter value of ClientOption object or file address of json file
+
+#### setTrainingSamples(\*pipes)
+
+Method to set existing training samples to be matched.
+
+* **Parameters:**
+  **pipes** ([*Pipe*](#zingg.pipes.Pipe)*[**]*) – input training data pipes separated by comma e.g. (pipe1,pipe2,..)
+
+#### setZinggDir(f)
+
+Method to set the location for Zingg to save its internal computations and models. Please set it to a place where the program has to write access.
+
+* **Parameters:**
+  **f** (*String*) – Zingg directory name of the models
+
+#### writeArgumentsToJSON(fileName)
+
+Method to write JSON file from the object of this class
+
+* **Parameters:**
+  **fileName** (*String*) – The CONF parameter value of ClientOption object or file address of json file
+
+#### writeArgumentsToJSONString()
+
+Method to create an object of this class from the JSON file and phase parameter value.
+
+* **Parameters:**
+  * **fileName** (*String*) – The CONF parameter value of ClientOption object
+  * **phase** (*String*) – The PHASE parameter value of ClientOption object
+* **Returns:**
+  The pointer containing address of the this class object
+* **Return type:**
+  pointer([Arguments](#zingg.client.Arguments))
+
+### *class* zingg.client.ClientOptions(argsSent=None)
+
+Bases: `object`
+
+Class that contains Client options for Zingg object
+:param phase: trainMatch, train, match, link, findAndLabel, findTrainingData, recommend etc
+:type phase: String
+:param args: Parse a list of Zingg command line options parameter values e.g. “–location” etc. optional argument for initializing this class.
+:type args: List(String) or None
+
+#### COLUMN *= <py4j.java_gateway.JavaPackage object>*
+
+Column whose stop words are to be recommended through Zingg
+
+* **Type:**
+  COLUMN
+
+#### CONF *= <py4j.java_gateway.JavaPackage object>*
+
+conf parameter for this class
+
+* **Type:**
+  CONF
+
+#### EMAIL *= <py4j.java_gateway.JavaPackage object>*
+
+e-mail parameter for this class
+
+* **Type:**
+  EMAIL
+
+#### LICENSE *= <py4j.java_gateway.JavaPackage object>*
+
+license parameter for this class
+
+* **Type:**
+  LICENSE
+
+#### LOCATION *= <py4j.java_gateway.JavaPackage object>*
+
+location parameter for this class
+
+* **Type:**
+  LOCATION
+
+#### MODEL_ID *= <py4j.java_gateway.JavaPackage object>*
+
+ZINGG_DIR/MODEL_ID is used to save the model
+
+* **Type:**
+  MODEL_ID
+
+#### PHASE *= <py4j.java_gateway.JavaPackage object>*
+
+phase parameter for this class
+
+* **Type:**
+  PHASE
+
+#### REMOTE *= <py4j.java_gateway.JavaPackage object>*
+
+remote option used internally for running on Databricks
+
+* **Type:**
+  REMOTE
+
+#### ZINGG_DIR *= <py4j.java_gateway.JavaPackage object>*
+
+location where Zingg saves the model, training data etc
+
+* **Type:**
+  ZINGG_DIR
+
+#### getClientOptions()
+
+Method to get pointer address of this class
+
+* **Returns:**
+  The pointer containing address of the this class object
+* **Return type:**
+  pointer([ClientOptions](#zingg.client.ClientOptions))
+
+#### getConf()
+
+Method to get CONF value
+
+* **Returns:**
+  The CONF parameter value
+* **Return type:**
+  String
+
+#### getLocation()
+
+Method to get LOCATION value
+
+* **Returns:**
+  The LOCATION parameter value
+* **Return type:**
+  String
+
+#### getOptionValue(option)
+
+Method to get value for the key option
+
+* **Parameters:**
+  **option** (*String*) – key to geting the value
+* **Returns:**
+  The value which is mapped for given key
+* **Return type:**
+  String
+
+#### getPhase()
+
+Method to get PHASE value
+
+* **Returns:**
+  The PHASE parameter value
+* **Return type:**
+  String
+
+#### hasLocation()
+
+Method to check if this class has LOCATION parameter set as None or not
+
+* **Returns:**
+  The boolean value if LOCATION parameter is present or not
+* **Return type:**
+  Bool
+
+#### setOptionValue(option, value)
+
+Method to map option key to the given value
+
+* **Parameters:**
+  * **option** (*String*) – key that is mapped with value
+  * **value** (*String*) – value to be set for given key
+
+#### setPhase(newValue)
+
+Method to set PHASE value
+
+* **Parameters:**
+  **newValue** (*String*) – name of the phase
+* **Returns:**
+  The pointer containing address of the this class object after seting phase
+* **Return type:**
+  pointer([ClientOptions](#zingg.client.ClientOptions))
+
+### *class* zingg.client.FieldDefinition(name, dataType, \*matchType)
+
+Bases: `object`
+
+This class defines each field that we use in matching We can use this to configure the properties of each field we use for matching in Zingg.
+
+* **Parameters:**
+  * **name** (*String*) – name of the field
+  * **dataType** (*String*) – type of the data e.g. string, float, etc.
+  * **matchType** (*MatchType*) – match type of this field e.g. FUSSY, EXACT, etc.
+
+#### getFieldDefinition()
+
+Method to get  pointer address of this class
+
+* **Returns:**
+  The pointer containing the address of this class object
+* **Return type:**
+  pointer([FieldDefinition](#zingg.client.FieldDefinition))
+
+#### setStopWords(stopWords)
+
+Method to add stopwords to this class object
+
+* **Parameters:**
+  **stopWords** (*String*) – The stop Words containing csv file’s location
+
+#### stringify(str)
+
+Method to stringify’ed the dataType before it is set in FieldDefinition object
+
+* **Parameters:**
+  **str** (*String*) – dataType of the FieldDefinition
+* **Returns:**
+  The stringify’ed value of the dataType
+* **Return type:**
+  String
+
+### *class* zingg.client.Zingg(args, options)
+
+Bases: `object`
+
+This class is the main point of interface with the Zingg matching product. Construct a client to Zingg using provided arguments and spark master. If running locally, set the master to local.
+
+* **Parameters:**
+  * **args** ([*Arguments*](#zingg.client.Arguments)) – arguments for training and matching
+  * **options** ([*ClientOptions*](#zingg.client.ClientOptions)) – client option for this class object
+
+#### execute()
+
+Method to execute this class object
+
+#### executeLabel()
+
+Method to run label phase
+
+#### executeLabelUpdate()
+
+Method to run label update phase
+
+#### getArguments()
+
+Method to get atguments of this class object
+
+* **Returns:**
+  The pointer containing address of the Arguments object of this class object
+* **Return type:**
+  pointer([Arguments](#zingg.client.Arguments))
+
+#### getMarkedRecords()
+
+Method to get marked record dataset from the inputpipe
+
+* **Returns:**
+  spark dataset containing marked records
+* **Return type:**
+  Dataset<Row>
+
+#### getMarkedRecordsStat(markedRecords, value)
+
+Method to get No. of records that is marked
+
+* **Parameters:**
+  * **markedRecords** (*Dataset<Row>*) – spark dataset containing marked records
+  * **value** (*long*) – flag value to check if markedRecord is initially matched or not
+* **Returns:**
+  The no. of marked records
+* **Return type:**
+  int
+
+#### getMatchedMarkedRecordsStat()
+
+Method to get No. of records that are marked and matched
+
+* **Returns:**
+  The bo. of matched marked records
+* **Return type:**
+  int
+
+#### getOptions()
+
+Method to get client options of this class object
+
+* **Returns:**
+  The pointer containing the address of the ClientOptions object of this class object
+* **Return type:**
+  pointer([ClientOptions](#zingg.client.ClientOptions))
+
+#### getUnmarkedRecords()
+
+Method to get unmarked record dataset from the inputpipe
+
+* **Returns:**
+  spark dataset containing unmarked records
+* **Return type:**
+  Dataset<Row>
+
+#### getUnmatchedMarkedRecordsStat()
+
+Method to get No. of records that are marked and unmatched
+
+* **Returns:**
+  The no. of unmatched marked records
+* **Return type:**
+  int
+
+#### getUnsureMarkedRecordsStat()
+
+Method to get No. of records that are marked and Not Sure if its matched or not
+
+* **Returns:**
+  The no. of Not Sure marked records
+* **Return type:**
+  int
+
+#### init()
+
+Method to initialize zingg client by reading internal configurations and functions
+
+#### initAndExecute()
+
+Method to run both init and execute methods consecutively
+
+#### processRecordsCli(unmarkedRecords, args)
+
+Method to get user input on unmarked records
+
+* **Returns:**
+  spark dataset containing updated records
+* **Return type:**
+  Dataset<Row>
+
+#### processRecordsCliLabelUpdate(lines, args)
+
+#### setArguments(args)
+
+Method to set Arguments
+
+* **Parameters:**
+  **args** ([*Arguments*](#zingg.client.Arguments)) – provide arguments for this class object
+
+#### setOptions(options)
+
+Method to set atguments of this class object
+
+* **Parameters:**
+  **options** ([*ClientOptions*](#zingg.client.ClientOptions)) – provide client options for this class object
+* **Returns:**
+  The pointer containing address of the ClientOptions object of this class object
+* **Return type:**
+  pointer([ClientOptions](#zingg.client.ClientOptions))
+
+#### writeLabelledOutput(updatedRecords, args)
+
+Method to write updated records after user input
+
+#### writeLabelledOutputFromPandas(candidate_pairs_pd, args)
+
+Method to write updated records (as pandas df) after user input
+
+### *class* zingg.client.ZinggWithSpark(args, options)
+
+Bases: [`Zingg`](#zingg.client.Zingg)
+
+This class is the main point of interface with the Zingg matching product. Construct a client to Zingg using provided arguments and spark master. If running locally, set the master to local.
+
+* **Parameters:**
+  * **args** ([*Arguments*](#zingg.client.Arguments)) – arguments for training and matching
+  * **options** ([*ClientOptions*](#zingg.client.ClientOptions)) – client option for this class object
+
+### zingg.client.getDfFromDs(data)
+
+Method to convert spark dataset to dataframe
+
+* **Parameters:**
+  **data** (*DataSet*) – provide spark dataset
+* **Returns:**
+  converted spark dataframe
+* **Return type:**
+  DataFrame
+
+### zingg.client.getGateway()
+
+### zingg.client.getJVM()
+
+### zingg.client.getPandasDfFromDs(data)
+
+Method to convert spark dataset to pandas dataframe
+
+* **Parameters:**
+  **data** (*DataSet*) – provide spark dataset
+* **Returns:**
+  converted pandas dataframe
+* **Return type:**
+  DataFrame
+
+### zingg.client.getSparkContext()
+
+### zingg.client.getSparkSession()
+
+### zingg.client.getSqlContext()
+
+### zingg.client.initClient()
+
+### zingg.client.initDataBricksConectClient()
+
+### zingg.client.initSparkClient()
+
+### zingg.client.parseArguments(argv)
+
+This method is used for checking mandatory arguments and creating an arguments list from Command line arguments
+
+* **Parameters:**
+  **argv** (*List*) – Values that are passed during the calling of the program along with the calling statement.
+* **Returns:**
+  a list containing necessary arguments to run any phase
+* **Return type:**
+  List
+
+<a id="module-zingg.pipes"></a>
+
+## zingg.pipes
+
+This module is submodule of zingg to work with different types of Pipes. Classes of this module inherit the Pipe class, and use that class to create many different types of pipes.
+
+### *class* zingg.pipes.BigQueryPipe(name)
+
+Bases: [`Pipe`](#zingg.pipes.Pipe)
+
+Pipe Class for working with BigQuery pipeline
+
+* **Parameters:**
+  **name** (*String*) – name of the pipe.
+
+#### CREDENTIAL_FILE *= 'credentialsFile'*
+
+#### TABLE *= 'table'*
+
+#### TEMP_GCS_BUCKET *= 'temporaryGcsBucket'*
+
+#### VIEWS_ENABLED *= 'viewsEnabled'*
+
+#### setCredentialFile(file)
+
+Method to set Credential file to the pipe
+
+* **Parameters:**
+  **file** (*String*) – credential file name
+
+#### setTable(table)
+
+Method to set Table to the pipe
+
+* **Parameters:**
+  **table** (*String*) – provide table parameter
+
+#### setTemporaryGcsBucket(bucket)
+
+Method to set TemporaryGcsBucket to the pipe
+
+* **Parameters:**
+  **bucket** (*String*) – provide bucket parameter
+
+#### setViewsEnabled(isEnabled)
+
+Method to set if viewsEnabled parameter is Enabled or not
+
+* **Parameters:**
+  **isEnabled** (*Bool*) – provide boolean parameter which defines if viewsEnabled option is enable or not
+
+### *class* zingg.pipes.CsvPipe(name, location=None, schema=None)
+
+Bases: [`Pipe`](#zingg.pipes.Pipe)
+
+Class CsvPipe: used for working with text files which uses a pipe symbol to separate units of text that belong in different columns.
+
+* **Parameters:**
+  * **name** (*String*) – name of the pipe.
+  * **location** (*String* *or* *None*) – (optional) location from where we read data
+  * **schema** (*Schema* *or* *None*) – (optional) json schema for the pipe
+
+#### setDelimiter(delimiter)
+
+This method is used to define delimiter of CsvPipe
+
+* **Parameters:**
+  **delimiter** (*String*) – a sequence of one or more characters for specifying the boundary between separate, independent regions in data streams
+
+#### setHeader(header)
+
+Method to set header property of pipe
+
+* **Parameters:**
+  **header** (*Boolean*) – true if pipe have header, false otherwise
+
+#### setLocation(location)
+
+Method to set location of pipe
+
+* **Parameters:**
+  **location** (*String*) – location from where we read data
+
+### *class* zingg.pipes.InMemoryPipe(name, df=None)
+
+Bases: [`Pipe`](#zingg.pipes.Pipe)
+
+Pipe Class for working with InMemory pipeline
+
+* **Parameters:**
+  * **name** (*String*) – name of the pipe
+  * **df** (*Dataset* *or* *None*) – provide dataset for this pipe (optional)
+
+#### getDataset()
+
+Method to get Dataset from pipe
+
+* **Returns:**
+  dataset of the pipe in the format of spark dataset
+* **Return type:**
+  Dataset<Row>
+
+#### setDataset(df)
+
+Method to set DataFrame of the pipe
+
+* **Parameters:**
+  **df** (*DataFrame*) – pandas or spark dataframe for the pipe
+
+### *class* zingg.pipes.Pipe(name, format)
+
+Bases: `object`
+
+Pipe class for working with different data-pipelines. Actual pipe def in the args. One pipe can be used at multiple places with different tables, locations, queries, etc
+
+* **Parameters:**
+  * **name** (*String*) – name of the pipe
+  * **format** (*Format*) – formate of pipe e.g. bigquery,InMemory, etc.
+
+#### addProperty(name, value)
+
+Method for adding different properties of pipe
+
+* **Parameters:**
+  * **name** (*String*) – name of the property
+  * **value** (*String*) – value you want to set for the property
+
+#### getPipe()
+
+Method to get Pipe
+
+* **Returns:**
+  pipe parameter values in the format of a list of string
+* **Return type:**
+  [Pipe](#zingg.pipes.Pipe)
+
+#### setSchema(s)
+
+Method to set pipe schema value
+
+* **Parameters:**
+  **s** (*Schema*) – json schema for the pipe
+
+#### toString()
+
+Method to get pipe parameter values
+
+* **Returns:**
+  pipe information in list format
+* **Return type:**
+  List[String]
+
+### *class* zingg.pipes.SnowflakePipe(name)
+
+Bases: [`Pipe`](#zingg.pipes.Pipe)
+
+Pipe Class for working with Snowflake pipeline
+
+* **Parameters:**
+  **name** (*String*) – name of the pipe
+
+#### DATABASE *= 'sfDatabase'*
+
+#### DBTABLE *= 'dbtable'*
+
+#### PASSWORD *= 'sfPassword'*
+
+#### SCHEMA *= 'sfSchema'*
+
+#### URL *= 'sfUrl'*
+
+#### USER *= 'sfUser'*
+
+#### WAREHOUSE *= 'sfWarehouse'*
+
+#### setDatabase(db)
+
+Method to set Database to the pipe
+
+* **Parameters:**
+  **db** (*Database*) – provide Database parameter.
+
+#### setDbTable(dbtable)
+
+description
+
+* **Parameters:**
+  **dbtable** (*String*) – provide bucket parameter.
+
+#### setPassword(passwd)
+
+Method to set Password to the pipe
+
+* **Parameters:**
+  **passwd** (*String*) – provide Password parameter.
+
+#### setSFSchema(schema)
+
+Method to set Schema to the pipe
+
+* **Parameters:**
+  **schema** (*Schema*) – provide schema parameter.
+
+#### setURL(url)
+
+Method to set url to the pipe
+
+* **Parameters:**
+  **url** (*String*) – provide url for this pipe
+
+#### setUser(user)
+
+Method to set User to the pipe
+
+* **Parameters:**
+  **user** (*String*) – provide User parameter.
+
+#### setWarehouse(warehouse)
+
+Method to set warehouse parameter to the pipe
+
+* **Parameters:**
+  **warehouse** (*String*) – provide warehouse parameter.
diff --git a/python/docs/_build/doctrees/environment.pickle b/python/docs/_build/doctrees/environment.pickle
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diff --git a/python/docs/conf.py b/python/docs/conf.py
index e491980be..d41a0c10f 100644
--- a/python/docs/conf.py
+++ b/python/docs/conf.py
@@ -37,9 +37,12 @@
     'sphinx.ext.autodoc',
     'sphinx.ext.napoleon',
     'sphinx.ext.viewcode',
-    'sphinx.ext.coverage'
+    'sphinx.ext.coverage',
+    'sphinx_markdown_builder'
 ]
 
+
+
 # Add any paths that contain templates here, relative to this directory.
 #templates_path = ['_templates']
 
diff --git a/python/docs/index.rst b/python/docs/index.rst
index 63b396d3e..0a34058ae 100644
--- a/python/docs/index.rst
+++ b/python/docs/index.rst
@@ -76,7 +76,3 @@ Example API Usage
    zingg.initAndExecute()
 
 
-
-.. meta::
-   :description: Zingg Entity Resolution With Python and AI
-   :keywords: entity resolution, identity resolution, record linkage
diff --git a/python/docs/zingg.rst b/python/docs/zingg.rst
index bbeef788a..6955d428b 100644
--- a/python/docs/zingg.rst
+++ b/python/docs/zingg.rst
@@ -24,7 +24,4 @@ Otherwise, :py:func:`zingg.client.Zingg` cannot be executed
 .. automodule:: zingg.pipes
    :members:
    :undoc-members:
-   :show-inheritance:
-
-   
-
+   :show-inheritance:
\ No newline at end of file

From 21a0b1a475c12a0883948c906c37579ae94ae1fc Mon Sep 17 00:00:00 2001
From: Sonal Goyal <sonalgoyal4@gmail.com>
Date: Fri, 8 Mar 2024 05:32:05 +0000
Subject: [PATCH 14/36] GITBOOK-4: No subject

---
 docs/running/aws.md | 18 ++++++++++++------
 1 file changed, 12 insertions(+), 6 deletions(-)

diff --git a/docs/running/aws.md b/docs/running/aws.md
index 55801167a..4965bc3df 100644
--- a/docs/running/aws.md
+++ b/docs/running/aws.md
@@ -1,15 +1,21 @@
 ---
-layout: default
 title: Running on AWS
 parent: Running Zingg on Cloud
 nav_order: 5
 ---
-## Running on AWS Elastic Map Reduce
 
-One option is to use the spark-submit option with the Zingg config and phase. 
+# Running on AWS
+
+One option is to use the spark-submit option with the Zingg config and phase. Please note that the config.json should be available locally at the driver for Zingg to use it.&#x20;
+
+`````
+aws emr create-cluster --name "Add Spark Step Cluster" --release-label emr-6.2.0 --applications Name=Zingg \
+--ec2-attributes KeyName=myKey --instance-type <instance type> --instance-count <num instances> \
+--steps Type=Spark,Name="Zingg",ActionOnFailure=CONTINUE,Args=[--class,zingg.spark.client.SparkClient,<s3 location of zingg.jar>,--phase,<name of phase - findTrainingData,match etc>,--conf,<local location of config.json>] --use-default-roles````
+`````
+
+A step by step s provided at the following [link](https://blog.infostrux.com/identity-resolution-with-zingg-ai-snowflake-and-aws-emr-for-the-canadian-football-league-22cf0850ab53). The guide mentions training locally using Zingg docker, but the findTrainingData and label phases can be executed on EMR directly.&#x20;
+
 
-    aws emr create-cluster --name "Add Spark Step Cluster" --release-label emr-6.2.0 --applications Name=Zingg \
-    --ec2-attributes KeyName=myKey --instance-type <instance type> --instance-count <num instances> \
-    --steps Type=Spark,Name="Zingg",ActionOnFailure=CONTINUE,Args=[--class,zingg.client.Client,<s3 location of zingg.jar>,--phase,<name of phase - findTrainingData,match etc>,--conf,<s3 location of config.json>] --use-default-roles````
 
 A second option is to run Zingg Python code in [AWS EMR Notebooks](https://aws.amazon.com/emr/features/notebooks/)

From aaa89c23ea74572a3d197837597942adc3c767eb Mon Sep 17 00:00:00 2001
From: SemyonSinchenko <semyonsinchenko@yahoo.com>
Date: Sat, 9 Mar 2024 15:41:15 +0100
Subject: [PATCH 15/36] Make sphinx works

A temporary solution, based on the following:

- introducing a new env variable ZINGG_DRY_RUN
- if the variable is set:
  + mimic globally used JVM-stuff
  + otherwise do nothing

++ slightly update ignore and docs/Makefile
++ apply formatting to client.py

 On branch 762-fix_sphinx_build
 Changes to be committed:
	modified:   .gitignore
	modified:   python/docs/Makefile
	new file:   python/pyproject.toml
	modified:   python/zingg/client.py
---
 .gitignore             |   7 +
 python/docs/Makefile   |   2 +
 python/pyproject.toml  |   2 +
 python/zingg/client.py | 450 +++++++++++++++++++++++++----------------
 4 files changed, 286 insertions(+), 175 deletions(-)
 create mode 100644 python/pyproject.toml

diff --git a/.gitignore b/.gitignore
index 18b75246c..4a3c2fe76 100644
--- a/.gitignore
+++ b/.gitignore
@@ -23,3 +23,10 @@ python/docs/_build/_doctrees
 **/python/build/*
 **/assembly/.classpath
 **/.DS_Store
+
+# Python stuff
+.env
+.venv
+
+# Sphinx _build
+**/_build
diff --git a/python/docs/Makefile b/python/docs/Makefile
index d4bb2cbb9..9847005dd 100644
--- a/python/docs/Makefile
+++ b/python/docs/Makefile
@@ -17,4 +17,6 @@ help:
 # Catch-all target: route all unknown targets to Sphinx using the new
 # "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
 %: Makefile
+	export ZINGG_DRY_RUN=1
 	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+	unset ZINGG_DRY_RUN
diff --git a/python/pyproject.toml b/python/pyproject.toml
new file mode 100644
index 000000000..16637529c
--- /dev/null
+++ b/python/pyproject.toml
@@ -0,0 +1,2 @@
+[tool.ruff]
+line-length = 150
diff --git a/python/zingg/client.py b/python/zingg/client.py
index fd1aeb6c9..a1450beb5 100644
--- a/python/zingg/client.py
+++ b/python/zingg/client.py
@@ -4,108 +4,183 @@
 This module is the main entry point of the Zingg Python API
 """
 
-import logging
+from __future__ import annotations
 import argparse
-import pandas as pd
-from pyspark.sql import DataFrame
+import logging
+import os
+from typing import Any
 
-from pyspark import SparkConf, SparkContext, SQLContext
+import pandas as pd
+from pyspark import SparkContext, SQLContext
+from pyspark.sql import DataFrame, SparkSession
 
-from py4j.java_collections import SetConverter, MapConverter, ListConverter
 
-from pyspark.sql import SparkSession
-import os
 LOG = logging.getLogger("zingg")
 
 _spark_ctxt = None
 _sqlContext = None
 _spark = None
-_zingg_jar = 'zingg-0.4.0.jar'
+_zingg_jar = "zingg-0.4.0.jar"
+
 
 def initSparkClient():
     global _spark_ctxt
     global _sqlContext
-    global _spark    
+    global _spark
     _spark_ctxt = SparkContext.getOrCreate()
     _sqlContext = SQLContext(_spark_ctxt)
     _spark = SparkSession.builder.getOrCreate()
     return 1
 
+
 def initDataBricksConectClient():
     global _spark_ctxt
     global _sqlContext
-    global _spark    
-    jar_path = os.getenv('ZINGG_HOME')+'/'+_zingg_jar
-    _spark = SparkSession.builder.config('spark.jars', jar_path).getOrCreate()
+    global _spark
+    jar_path = os.getenv("ZINGG_HOME") + "/" + _zingg_jar
+    _spark = SparkSession.builder.config("spark.jars", jar_path).getOrCreate()
     _spark_ctxt = _spark.sparkContext
     _sqlContext = SQLContext(_spark_ctxt)
     return 1
 
+
 def initClient():
     global _spark_ctxt
     global _sqlContext
-    global _spark    
+    global _spark
     if _spark_ctxt is None:
-        DATABRICKS_CONNECT = os.getenv('DATABRICKS_CONNECT')
-        if DATABRICKS_CONNECT=='Y' or DATABRICKS_CONNECT=='y':
+        DATABRICKS_CONNECT = os.getenv("DATABRICKS_CONNECT")
+        if DATABRICKS_CONNECT == "Y" or DATABRICKS_CONNECT == "y":
             return initDataBricksConectClient()
         else:
             return initSparkClient()
     else:
         return 1
 
+
 def getSparkContext():
     if _spark_ctxt is None:
         initClient()
     return _spark_ctxt
 
+
 def getSparkSession():
     if _spark is None:
         initClient()
     return _spark
 
+
 def getSqlContext():
     if _sqlContext is None:
         initClient()
     return _sqlContext
 
+
 def getJVM():
+    # TODO: Document this environ variable
+    is_dry_run = os.environ.get("ZINGG_DRY_RUN", 0)
+    if is_dry_run:
+
+        class Dummy:
+            """Dummy class for handling JVM-magick without actual starting of Java"""
+
+            def __init__(self, attrs: dict[str, Any]):
+                for k, v in attrs.items():
+                    self.__setattr__(k, v)
+
+        # TODO: replace this magic by Context-like implementation
+        return Dummy(
+            {
+                "org": Dummy({"apache": Dummy({"spark": Dummy({"sql": Dummy({"types": Dummy({"StructType": None})})})})}),
+                "zingg": Dummy(
+                    {
+                        "common": Dummy(
+                            {
+                                "client": Dummy(
+                                    {
+                                        "util": Dummy({"ColName": None}),
+                                        "MatchType": None,
+                                        "ClientOptions": Dummy(
+                                            {
+                                                "PHASE": None,
+                                                "CONF": None,
+                                                "LICENSE": None,
+                                                "EMAIL": None,
+                                                "LOCATION": None,
+                                                "REMOTE": None,
+                                                "ZINGG_DIR": None,
+                                                "MODEL_ID": None,
+                                                "COLUMN": None,
+                                            }
+                                        ),
+                                        "ZinggOptions": None,
+                                        "pipe": Dummy(
+                                            {
+                                                "FilePipe": None,
+                                            }
+                                        ),
+                                    }
+                                ),
+                                "core": Dummy({"util": Dummy({"LabelMatchType": None})}),
+                            }
+                        ),
+                        "spark": Dummy(
+                            {
+                                "client": Dummy(
+                                    {
+                                        "pipe": Dummy(
+                                            {
+                                                "SparkPipe": None,
+                                            }
+                                        )
+                                    }
+                                )
+                            }
+                        ),
+                    }
+                ),
+            }
+        )
     return getSparkContext()._jvm
 
+
 def getGateway():
     return getSparkContext()._gateway
 
+
 ColName = getJVM().zingg.common.client.util.ColName
 MatchType = getJVM().zingg.common.client.MatchType
 ClientOptions = getJVM().zingg.common.client.ClientOptions
 ZinggOptions = getJVM().zingg.common.client.ZinggOptions
 LabelMatchType = getJVM().zingg.common.core.util.LabelMatchType
-UpdateLabelMode = 'Overwrite'
+UpdateLabelMode = "Overwrite"
+
 
 def getDfFromDs(data):
-    """ Method to convert spark dataset to dataframe
+    """Method to convert spark dataset to dataframe
 
     :param data: provide spark dataset
     :type data: DataSet
     :return: converted spark dataframe
-    :rtype: DataFrame 
+    :rtype: DataFrame
     """
     return DataFrame(data.df(), getSqlContext())
 
+
 def getPandasDfFromDs(data):
-    """ Method to convert spark dataset to pandas dataframe
+    """Method to convert spark dataset to pandas dataframe
 
     :param data: provide spark dataset
     :type data: DataSet
     :return: converted pandas dataframe
-    :rtype: DataFrame 
+    :rtype: DataFrame
     """
     df = getDfFromDs(data)
     return pd.DataFrame(df.collect(), columns=df.columns)
 
 
 class Zingg:
-    """ This class is the main point of interface with the Zingg matching product. Construct a client to Zingg using provided arguments and spark master. If running locally, set the master to local.
+    """This class is the main point of interface with the Zingg matching product. Construct a client to Zingg using provided arguments and spark master. If running locally, set the master to local.
 
     :param args: arguments for training and matching
     :type args: Arguments
@@ -118,25 +193,25 @@ def __init__(self, args, options):
         self.inpArgs = args
         self.inpOptions = options
         self.client = getJVM().zingg.spark.client.SparkClient(args.getArgs(), options.getClientOptions())
-       
+
     def init(self):
-        """ Method to initialize zingg client by reading internal configurations and functions """
+        """Method to initialize zingg client by reading internal configurations and functions"""
         self.client.init()
 
     def execute(self):
-        """ Method to execute this class object """
+        """Method to execute this class object"""
         self.client.execute()
-        
+
     def initAndExecute(self):
-        """ Method to run both init and execute methods consecutively """
+        """Method to run both init and execute methods consecutively"""
         self.client.init()
-        DATABRICKS_CONNECT = os.getenv('DATABRICKS_CONNECT')
-        if DATABRICKS_CONNECT=='Y' or DATABRICKS_CONNECT=='y':
+        DATABRICKS_CONNECT = os.getenv("DATABRICKS_CONNECT")
+        if DATABRICKS_CONNECT == "Y" or DATABRICKS_CONNECT == "y":
             options = self.client.getOptions()
             inpPhase = options.get(ClientOptions.PHASE).getValue()
-            if (inpPhase==ZinggOptions.LABEL.getValue()):
+            if inpPhase == ZinggOptions.LABEL.getValue():
                 self.executeLabel()
-            elif (inpPhase==ZinggOptions.UPDATE_LABEL.getValue()):
+            elif inpPhase == ZinggOptions.UPDATE_LABEL.getValue():
                 self.executeLabelUpdate()
             else:
                 self.client.execute()
@@ -144,43 +219,48 @@ def initAndExecute(self):
             self.client.execute()
 
     def executeLabel(self):
-        """ Method to run label phase """
+        """Method to run label phase"""
         self.client.getTrainingDataModel().setMarkedRecordsStat(self.getMarkedRecords())
         unmarkedRecords = self.getUnmarkedRecords()
-        updatedRecords = self.processRecordsCli(unmarkedRecords,self.inpArgs)
-        self.writeLabelledOutput(updatedRecords,self.inpArgs)
+        updatedRecords = self.processRecordsCli(unmarkedRecords, self.inpArgs)
+        self.writeLabelledOutput(updatedRecords, self.inpArgs)
 
     def executeLabelUpdate(self):
-        """ Method to run label update phase """
-        self.processRecordsCliLabelUpdate(self.getMarkedRecords(),self.inpArgs)
+        """Method to run label update phase"""
+        self.processRecordsCliLabelUpdate(self.getMarkedRecords(), self.inpArgs)
 
     def getMarkedRecords(self):
-        """ Method to get marked record dataset from the inputpipe
+        """Method to get marked record dataset from the inputpipe
 
         :return: spark dataset containing marked records
-        :rtype: Dataset<Row> 
+        :rtype: Dataset<Row>
         """
         return self.client.getMarkedRecords()
 
     def getUnmarkedRecords(self):
-        """ Method to get unmarked record dataset from the inputpipe
+        """Method to get unmarked record dataset from the inputpipe
 
         :return: spark dataset containing unmarked records
-        :rtype: Dataset<Row> 
+        :rtype: Dataset<Row>
         """
         return self.client.getUnmarkedRecords()
 
-    def processRecordsCli(self,unmarkedRecords,args):
-        """ Method to get user input on unmarked records
+    def processRecordsCli(self, unmarkedRecords, args):
+        """Method to get user input on unmarked records
 
         :return: spark dataset containing updated records
-        :rtype: Dataset<Row> 
+        :rtype: Dataset<Row>
         """
         trainingDataModel = self.client.getTrainingDataModel()
         labelDataViewHelper = self.client.getLabelDataViewHelper()
 
         if unmarkedRecords is not None and unmarkedRecords.count() > 0:
-            labelDataViewHelper.printMarkedRecordsStat(trainingDataModel.getPositivePairsCount(),trainingDataModel.getNegativePairsCount(),trainingDataModel.getNotSurePairsCount(),trainingDataModel.getTotalCount())
+            labelDataViewHelper.printMarkedRecordsStat(
+                trainingDataModel.getPositivePairsCount(),
+                trainingDataModel.getNegativePairsCount(),
+                trainingDataModel.getNotSurePairsCount(),
+                trainingDataModel.getTotalCount(),
+            )
             unmarkedRecords = unmarkedRecords.cache()
             displayCols = labelDataViewHelper.getDisplayColumns(unmarkedRecords, args.getArgs())
             clusterIdZFrame = labelDataViewHelper.getClusterIdsFrame(unmarkedRecords)
@@ -195,37 +275,53 @@ def processRecordsCli(self,unmarkedRecords,args):
 
                 msg1 = labelDataViewHelper.getMsg1(index, totalPairs)
                 msg2 = labelDataViewHelper.getMsg2(prediction, score)
-                labelDataViewHelper.displayRecords(labelDataViewHelper.getDSUtil().select(currentPair, displayCols), msg1, msg2)
+                labelDataViewHelper.displayRecords(
+                    labelDataViewHelper.getDSUtil().select(currentPair, displayCols),
+                    msg1,
+                    msg2,
+                )
                 selected_option = input()
-                while int(selected_option) not in [0,1,2,9]:
-                    print('Please enter valid option')
+                while int(selected_option) not in [0, 1, 2, 9]:
+                    print("Please enter valid option")
                     selected_option = input("Enter choice: ")
                 if int(selected_option) == 9:
                     print("User has quit in the middle. Updating the records.")
-                    break               
+                    break
                 trainingDataModel.updateLabellerStat(int(selected_option), 1)
-                labelDataViewHelper.printMarkedRecordsStat(trainingDataModel.getPositivePairsCount(),trainingDataModel.getNegativePairsCount(),trainingDataModel.getNotSurePairsCount(),trainingDataModel.getTotalCount())
-                updatedRecords = trainingDataModel.updateRecords(int(selected_option), currentPair, updatedRecords)            
+                labelDataViewHelper.printMarkedRecordsStat(
+                    trainingDataModel.getPositivePairsCount(),
+                    trainingDataModel.getNegativePairsCount(),
+                    trainingDataModel.getNotSurePairsCount(),
+                    trainingDataModel.getTotalCount(),
+                )
+                updatedRecords = trainingDataModel.updateRecords(int(selected_option), currentPair, updatedRecords)
             print("Processing finished.")
             return updatedRecords
         else:
-            print("It seems there are no unmarked records at this moment. Please run findTrainingData job to build some pairs to be labelled and then run this labeler.")
+            print(
+                "It seems there are no unmarked records at this moment. Please run findTrainingData job to build some pairs to be labelled and then run this labeler."
+            )
             return None
-    
-    def processRecordsCliLabelUpdate(self,lines,args):
+
+    def processRecordsCliLabelUpdate(self, lines, args):
         trainingDataModel = self.client.getTrainingDataModel()
         labelDataViewHelper = self.client.getLabelDataViewHelper()
-        if (lines is not None and lines.count() > 0):
+        if lines is not None and lines.count() > 0:
             trainingDataModel.setMarkedRecordsStat(lines)
-            labelDataViewHelper.printMarkedRecordsStat(trainingDataModel.getPositivePairsCount(),trainingDataModel.getNegativePairsCount(),trainingDataModel.getNotSurePairsCount(),trainingDataModel.getTotalCount())
+            labelDataViewHelper.printMarkedRecordsStat(
+                trainingDataModel.getPositivePairsCount(),
+                trainingDataModel.getNegativePairsCount(),
+                trainingDataModel.getNotSurePairsCount(),
+                trainingDataModel.getTotalCount(),
+            )
             displayCols = labelDataViewHelper.getDSUtil().getFieldDefColumns(lines, args.getArgs(), False, args.getArgs().getShowConcise())
             updatedRecords = None
             recordsToUpdate = lines
             selectedOption = -1
 
-            while (str(selectedOption) != '9'):
+            while str(selectedOption) != "9":
                 cluster_id = input("\n\tPlease enter the cluster id (or 9 to exit): ")
-                if str(cluster_id) == '9':
+                if str(cluster_id) == "9":
                     print("User has exit in the middle. Updating the records.")
                     break
                 currentPair = lines.filter(lines.equalTo(ColName.CLUSTER_COLUMN, cluster_id))
@@ -233,23 +329,32 @@ def processRecordsCliLabelUpdate(self,lines,args):
                     print("\tInvalid cluster id. Enter '9' to exit")
                     continue
 
-                matchFlag = currentPair.getAsInt(currentPair.head(),ColName.MATCH_FLAG_COL)
-                preMsg = "\n\tThe record pairs belonging to the input cluster id "+cluster_id+" are:"
-                postMsg = "\tThe above pair is labeled as "+str(matchFlag)+"\n"
-                labelDataViewHelper.displayRecords(labelDataViewHelper.getDSUtil().select(currentPair, displayCols), preMsg, postMsg)
+                matchFlag = currentPair.getAsInt(currentPair.head(), ColName.MATCH_FLAG_COL)
+                preMsg = "\n\tThe record pairs belonging to the input cluster id " + cluster_id + " are:"
+                postMsg = "\tThe above pair is labeled as " + str(matchFlag) + "\n"
+                labelDataViewHelper.displayRecords(
+                    labelDataViewHelper.getDSUtil().select(currentPair, displayCols),
+                    preMsg,
+                    postMsg,
+                )
                 selectedOption = input()
                 trainingDataModel.updateLabellerStat(int(selectedOption), 1)
                 trainingDataModel.updateLabellerStat(matchFlag, -1)
-                labelDataViewHelper.printMarkedRecordsStat(trainingDataModel.getPositivePairsCount(),trainingDataModel.getNegativePairsCount(),trainingDataModel.getNotSurePairsCount(),trainingDataModel.getTotalCount())
-
-                if (str(selectedOption) == '9'):
+                labelDataViewHelper.printMarkedRecordsStat(
+                    trainingDataModel.getPositivePairsCount(),
+                    trainingDataModel.getNegativePairsCount(),
+                    trainingDataModel.getNotSurePairsCount(),
+                    trainingDataModel.getTotalCount(),
+                )
+
+                if str(selectedOption) == "9":
                     print("User has quit in the middle. Updating the records.")
                     break
 
-                recordsToUpdate = recordsToUpdate.filter(recordsToUpdate.notEqual(ColName.CLUSTER_COLUMN,cluster_id))
+                recordsToUpdate = recordsToUpdate.filter(recordsToUpdate.notEqual(ColName.CLUSTER_COLUMN, cluster_id))
 
-                if (updatedRecords is not None):
-                    updatedRecords = updatedRecords.filter(updatedRecords.notEqual(ColName.CLUSTER_COLUMN,cluster_id))
+                if updatedRecords is not None:
+                    updatedRecords = updatedRecords.filter(updatedRecords.notEqual(ColName.CLUSTER_COLUMN, cluster_id))
 
                 updatedRecords = trainingDataModel.updateRecords(int(selectedOption), currentPair, updatedRecords)
 
@@ -259,32 +364,32 @@ def processRecordsCliLabelUpdate(self,lines,args):
             outPipe = trainingDataModel.getOutputPipe(args.getArgs())
             outPipe.setMode(UpdateLabelMode)
 
-            trainingDataModel.writeLabelledOutput(updatedRecords,args.getArgs(),outPipe)
+            trainingDataModel.writeLabelledOutput(updatedRecords, args.getArgs(), outPipe)
             print("Processing finished.")
             return updatedRecords
         else:
             print("There is no marked record for updating. Please run findTrainingData/label jobs to generate training data.")
             return None
 
-
-    def writeLabelledOutput(self,updatedRecords,args):
-        """ Method to write updated records after user input
-        """
+    def writeLabelledOutput(self, updatedRecords, args):
+        """Method to write updated records after user input"""
         trainingDataModel = self.client.getTrainingDataModel()
         if updatedRecords is not None:
-            trainingDataModel.writeLabelledOutput(updatedRecords,args.getArgs())
+            trainingDataModel.writeLabelledOutput(updatedRecords, args.getArgs())
 
-    def writeLabelledOutputFromPandas(self,candidate_pairs_pd,args):
-        """ Method to write updated records (as pandas df) after user input
-        """
+    def writeLabelledOutputFromPandas(self, candidate_pairs_pd, args):
+        """Method to write updated records (as pandas df) after user input"""
         markedRecordsAsDS = (getSparkSession().createDataFrame(candidate_pairs_pd))._jdf
         # pands df gives z_isMatch as long so needs to be cast
-        markedRecordsAsDS = markedRecordsAsDS.withColumn(ColName.MATCH_FLAG_COL,markedRecordsAsDS.col(ColName.MATCH_FLAG_COL).cast("int"))
+        markedRecordsAsDS = markedRecordsAsDS.withColumn(
+            ColName.MATCH_FLAG_COL,
+            markedRecordsAsDS.col(ColName.MATCH_FLAG_COL).cast("int"),
+        )
         updatedRecords = getJVM().zingg.spark.client.SparkFrame(markedRecordsAsDS)
-        self.writeLabelledOutput(updatedRecords,args)
+        self.writeLabelledOutput(updatedRecords, args)
 
     def setArguments(self, args):
-        """ Method to set Arguments
+        """Method to set Arguments
 
         :param args: provide arguments for this class object
         :type args: Arguments
@@ -292,7 +397,7 @@ def setArguments(self, args):
         self.client.setArguments()
 
     def getArguments(self):
-        """ Method to get atguments of this class object
+        """Method to get atguments of this class object
 
         :return: The pointer containing address of the Arguments object of this class object
         :rtype: pointer(Arguments)
@@ -300,7 +405,7 @@ def getArguments(self):
         return self.client.getArguments()
 
     def getOptions(self):
-        """ Method to get client options of this class object
+        """Method to get client options of this class object
 
         :return: The pointer containing the address of the ClientOptions object of this class object
         :rtype: pointer(ClientOptions)
@@ -308,56 +413,55 @@ def getOptions(self):
         return self.client.getOptions()
 
     def setOptions(self, options):
-        """ Method to set atguments of this class object
+        """Method to set atguments of this class object
 
         :param options: provide client options for this class object
         :type options: ClientOptions
         :return: The pointer containing address of the ClientOptions object of this class object
-        :rtype: pointer(ClientOptions) 
+        :rtype: pointer(ClientOptions)
         """
         return self.client.setOptions(options)
 
     def getMarkedRecordsStat(self, markedRecords, value):
-        """ Method to get No. of records that is marked
+        """Method to get No. of records that is marked
 
         :param markedRecords: spark dataset containing marked records
         :type markedRecords: Dataset<Row>
         :param value: flag value to check if markedRecord is initially matched or not
         :type value: long
         :return: The no. of marked records
-        :rtype: int 
+        :rtype: int
         """
         return self.client.getMarkedRecordsStat(markedRecords, value)
 
     def getMatchedMarkedRecordsStat(self):
-        """ Method to get No. of records that are marked and matched
+        """Method to get No. of records that are marked and matched
 
         :return: The bo. of matched marked records
-        :rtype: int 
+        :rtype: int
         """
         return self.client.getMatchedMarkedRecordsStat(self.getMarkedRecords())
 
     def getUnmatchedMarkedRecordsStat(self):
-        """ Method to get No. of records that are marked and unmatched
+        """Method to get No. of records that are marked and unmatched
 
         :return: The no. of unmatched marked records
-        :rtype: int 
+        :rtype: int
         """
         return self.client.getUnmatchedMarkedRecordsStat(self.getMarkedRecords())
 
     def getUnsureMarkedRecordsStat(self):
-        """ Method to get No. of records that are marked and Not Sure if its matched or not
+        """Method to get No. of records that are marked and Not Sure if its matched or not
 
         :return: The no. of Not Sure marked records
-        :rtype: int 
+        :rtype: int
         """
         return self.client.getUnsureMarkedRecordsStat(self.getMarkedRecords())
 
-    
 
 class ZinggWithSpark(Zingg):
 
-    """ This class is the main point of interface with the Zingg matching product. Construct a client to Zingg using provided arguments and spark master. If running locally, set the master to local.
+    """This class is the main point of interface with the Zingg matching product. Construct a client to Zingg using provided arguments and spark master. If running locally, set the master to local.
 
     :param args: arguments for training and matching
     :type args: Arguments
@@ -368,9 +472,9 @@ class ZinggWithSpark(Zingg):
     def __init__(self, args, options):
         self.client = getJVM().zingg.spark.client.SparkClient(args.getArgs(), options.getClientOptions(), getSparkSession()._jsparkSession)
 
-    
+
 class Arguments:
-    """ This class helps supply match arguments to Zingg. There are 3 basic steps in any match process.
+    """This class helps supply match arguments to Zingg. There are 3 basic steps in any match process.
 
     :Defining: specifying information about data location, fields, and our notion of similarity.
     :training: making Zingg learn the matching rules
@@ -381,7 +485,7 @@ def __init__(self):
         self.args = getJVM().zingg.common.client.Arguments()
 
     def setFieldDefinition(self, fieldDef):
-        """ Method convert python objects to java FieldDefinition objects and set the field definitions associated with this client
+        """Method convert python objects to java FieldDefinition objects and set the field definitions associated with this client
 
         :param fieldDef: python FieldDefinition object list
         :type fieldDef: List(FieldDefinition)
@@ -392,16 +496,16 @@ def setFieldDefinition(self, fieldDef):
         self.args.setFieldDefinition(javaFieldDef)
 
     def getArgs(self):
-        """ Method to get pointer address of this class
+        """Method to get pointer address of this class
 
         :return: The pointer containing the address of this class object
         :rtype: pointer(Arguments)
-        
+
         """
         return self.args
 
     def setArgs(self, argumentsObj):
-        """ Method to set this class object
+        """Method to set this class object
 
         :param argumentsObj: Argument object to set this object
         :type argumentsObj: pointer(Arguments)
@@ -409,7 +513,7 @@ def setArgs(self, argumentsObj):
         self.args = argumentsObj
 
     def setData(self, *pipes):
-        """ Method to set the file path of the file to be matched.
+        """Method to set the file path of the file to be matched.
 
         :param pipes: input data pipes separated by comma e.g. (pipe1,pipe2,..)
         :type pipes: Pipe[]
@@ -420,7 +524,7 @@ def setData(self, *pipes):
         self.args.setData(dataPipe)
 
     def setOutput(self, *pipes):
-        """ Method to set the output directory where the match result will be saved
+        """Method to set the output directory where the match result will be saved
 
         :param pipes: output data pipes separated by comma e.g. (pipe1,pipe2,..)
         :type pipes: Pipe[]
@@ -429,33 +533,33 @@ def setOutput(self, *pipes):
         for idx, pipe in enumerate(pipes):
             outputPipe[idx] = pipe.getPipe()
         self.args.setOutput(outputPipe)
-    
+
     def getZinggBaseModelDir(self):
         return self.args.getZinggBaseModelDir()
 
     def getZinggModelDir(self):
         return self.args.getZinggModelDir()
-    
+
     def getZinggBaseTrainingDataDir(self):
-        """ Method to get the location of the folder where Zingg 
-        saves the training data found by findTrainingData  
+        """Method to get the location of the folder where Zingg
+        saves the training data found by findTrainingData
         """
         return self.args.getZinggBaseTrainingDataDir()
 
     def getZinggTrainingDataUnmarkedDir(self):
-        """ Method to get the location of the folder where Zingg 
-        saves the training data found by findTrainingData  
+        """Method to get the location of the folder where Zingg
+        saves the training data found by findTrainingData
         """
         return self.args.getZinggTrainingDataUnmarkedDir()
-    
+
     def getZinggTrainingDataMarkedDir(self):
-        """ Method to get the location of the folder where Zingg 
-        saves the marked training data labeled by the user  
+        """Method to get the location of the folder where Zingg
+        saves the marked training data labeled by the user
         """
         return self.args.getZinggTrainingDataMarkedDir()
-    
+
     def setTrainingSamples(self, *pipes):
-        """ Method to set existing training samples to be matched.
+        """Method to set existing training samples to be matched.
 
         :param pipes: input training data pipes separated by comma e.g. (pipe1,pipe2,..)
         :type pipes: Pipe[]
@@ -466,19 +570,18 @@ def setTrainingSamples(self, *pipes):
         self.args.setTrainingSamples(dataPipe)
 
     def setModelId(self, id):
-        """ Method to set the output directory where the match output will be saved
+        """Method to set the output directory where the match output will be saved
 
-        :param id: model id value 
+        :param id: model id value
         :type id: String
         """
         self.args.setModelId(id)
-    
+
     def getModelId(self):
         return self.args.getModelId()
 
-
     def setZinggDir(self, f):
-        """ Method to set the location for Zingg to save its internal computations and models. Please set it to a place where the program has to write access.
+        """Method to set the location for Zingg to save its internal computations and models. Please set it to a place where the program has to write access.
 
         :param f: Zingg directory name of the models
         :type f: String
@@ -486,7 +589,7 @@ def setZinggDir(self, f):
         self.args.setZinggDir(f)
 
     def setNumPartitions(self, numPartitions):
-        """ Method to set NumPartitions parameter value
+        """Method to set NumPartitions parameter value
         Sample size to use for seeding labeled data We don't want to run over all the data, as we want a quick way to seed some labeled data that we can manually edit
 
         :param numPartitions: number of partitions for given data pipes
@@ -495,7 +598,7 @@ def setNumPartitions(self, numPartitions):
         self.args.setNumPartitions(numPartitions)
 
     def setLabelDataSampleSize(self, labelDataSampleSize):
-        """ Method to set labelDataSampleSize parameter value
+        """Method to set labelDataSampleSize parameter value
         Set the fraction of data to be used from the complete data set to be used for seeding the labeled data Labelling is costly and we want a fast approximate way of looking at a small sample of the records and identifying expected matches and nonmatches
 
         :param labelDataSampleSize: value between 0.0 and 1.0 denoting portion of dataset to use in generating seed samples
@@ -504,7 +607,7 @@ def setLabelDataSampleSize(self, labelDataSampleSize):
         self.args.setLabelDataSampleSize(labelDataSampleSize)
 
     def writeArgumentsToJSON(self, fileName):
-        """ Method to write JSON file from the object of this class 
+        """Method to write JSON file from the object of this class
 
         :param fileName: The CONF parameter value of ClientOption object or file address of json file
         :type fileName: String
@@ -512,16 +615,16 @@ def writeArgumentsToJSON(self, fileName):
         getJVM().zingg.common.client.ArgumentsUtil().writeArgumentsToJSON(fileName, self.args)
 
     def setStopWordsCutoff(self, stopWordsCutoff):
-        """ Method to set stopWordsCutoff parameter value
+        """Method to set stopWordsCutoff parameter value
         By default, Zingg extracts 10% of the high frequency unique words from a dataset. If user wants different selection, they should set up StopWordsCutoff property
 
         :param stopWordsCutoff: The stop words cutoff parameter value of ClientOption object or file address of json file
         :type stopWordsCutoff: float
         """
         self.args.setStopWordsCutoff(stopWordsCutoff)
-    
+
     def setColumn(self, column):
-        """ Method to set stopWordsCutoff parameter value
+        """Method to set stopWordsCutoff parameter value
         By default, Zingg extracts 10% of the high frequency unique words from a dataset. If user wants different selection, they should set up StopWordsCutoff property
 
         :param stopWordsCutoff: The stop words cutoff parameter value of ClientOption object or file address of json file
@@ -531,8 +634,8 @@ def setColumn(self, column):
 
     @staticmethod
     def createArgumentsFromJSON(fileName, phase):
-        """ Method to create an object of this class from the JSON file and phase parameter value.
-        
+        """Method to create an object of this class from the JSON file and phase parameter value.
+
         :param fileName: The CONF parameter value of ClientOption object
         :type fileName: String
         :param phase: The PHASE parameter value of ClientOption object
@@ -543,11 +646,10 @@ def createArgumentsFromJSON(fileName, phase):
         obj = Arguments()
         obj.args = getJVM().zingg.common.client.ArgumentsUtil().createArgumentsFromJSON(fileName, phase)
         return obj
-    
-    
+
     def writeArgumentsToJSONString(self):
-        """ Method to create an object of this class from the JSON file and phase parameter value.
-        
+        """Method to create an object of this class from the JSON file and phase parameter value.
+
         :param fileName: The CONF parameter value of ClientOption object
         :type fileName: String
         :param phase: The PHASE parameter value of ClientOption object
@@ -556,30 +658,27 @@ def writeArgumentsToJSONString(self):
         :rtype: pointer(Arguments)
         """
         return getJVM().zingg.common.client.ArgumentsUtil().writeArgumentstoJSONString(self.args)
-    
+
     @staticmethod
     def createArgumentsFromJSONString(jsonArgs, phase):
         obj = Arguments()
         obj.args = getJVM().zingg.common.client.ArgumentsUtil().createArgumentsFromJSONString(jsonArgs, phase)
         return obj
-    
-    
+
     def copyArgs(self, phase):
         argsString = self.writeArgumentsToJSONString()
         return self.createArgumentsFromJSONString(argsString, phase)
 
-   
-    
-
 
 class ClientOptions:
-    """ Class that contains Client options for Zingg object
+    """Class that contains Client options for Zingg object
     :param phase: trainMatch, train, match, link, findAndLabel, findTrainingData, recommend etc
     :type phase: String
     :param args: Parse a list of Zingg command line options parameter values e.g. "--location" etc. optional argument for initializing this class.
     :type args: List(String) or None
     """
-    PHASE = getJVM().zingg.common.client.ClientOptions.PHASE 
+
+    PHASE = getJVM().zingg.common.client.ClientOptions.PHASE
     """:PHASE: phase parameter for this class"""
     CONF = getJVM().zingg.common.client.ClientOptions.CONF
     """:CONF: conf parameter for this class"""
@@ -600,28 +699,27 @@ class ClientOptions:
 
     def __init__(self, argsSent=None):
         print(argsSent)
-        if(argsSent == None):
+        if argsSent == None:
             args = []
         else:
             args = argsSent.copy()
-        if (not (self.PHASE in args)):
+        if self.PHASE not in args:
             args.append(self.PHASE)
             args.append("peekModel")
-        if (not (self.LICENSE in args)):
+        if self.LICENSE not in args:
             args.append(self.LICENSE)
             args.append("zinggLic.txt")
-        if (not (self.EMAIL in args)):
+        if self.EMAIL not in args:
             args.append(self.EMAIL)
             args.append("zingg@zingg.ai")
-        if (not (self.CONF in args)):
+        if self.CONF not in args:
             args.append(self.CONF)
             args.append("dummyConf.json")
-        print("arguments for client options are ", args) 
+        print("arguments for client options are ", args)
         self.co = getJVM().zingg.common.client.ClientOptions(args)
-    
-    
+
     def getClientOptions(self):
-        """ Method to get pointer address of this class
+        """Method to get pointer address of this class
 
         :return: The pointer containing address of the this class object
         :rtype: pointer(ClientOptions)
@@ -629,17 +727,17 @@ def getClientOptions(self):
         return self.co
 
     def getOptionValue(self, option):
-        """ Method to get value for the key option
+        """Method to get value for the key option
 
         :param option: key to geting the value
         :type option: String
-        :return: The value which is mapped for given key 
-        :rtype: String 
+        :return: The value which is mapped for given key
+        :rtype: String
         """
         return self.co.getOptionValue(option)
 
     def setOptionValue(self, option, value):
-        """ Method to map option key to the given value
+        """Method to map option key to the given value
 
         :param option: key that is mapped with value
         :type option: String
@@ -649,53 +747,53 @@ def setOptionValue(self, option, value):
         self.co.get(option).setValue(value)
 
     def getPhase(self):
-        """ Method to get PHASE value
+        """Method to get PHASE value
 
         :return: The PHASE parameter value
-        :rtype: String 
+        :rtype: String
         """
         return self.co.get(ClientOptions.PHASE).getValue()
 
     def setPhase(self, newValue):
-        """ Method to set PHASE value
+        """Method to set PHASE value
 
         :param newValue: name of the phase
         :type newValue: String
         :return: The pointer containing address of the this class object after seting phase
-        :rtype: pointer(ClientOptions) 
+        :rtype: pointer(ClientOptions)
         """
         self.co.get(ClientOptions.PHASE).setValue(newValue)
 
     def getConf(self):
-        """ Method to get CONF value
+        """Method to get CONF value
 
         :return: The CONF parameter value
-        :rtype: String 
+        :rtype: String
         """
         return self.co.get(ClientOptions.CONF).getValue()
 
     def hasLocation(self):
-        """ Method to check if this class has LOCATION parameter set as None or not
+        """Method to check if this class has LOCATION parameter set as None or not
 
         :return: The boolean value if LOCATION parameter is present or not
-        :rtype: Bool 
+        :rtype: Bool
         """
-        if(self.co.get(ClientOptions.LOCATION)==None):
+        if self.co.get(ClientOptions.LOCATION) == None:
             return False
         else:
             return True
 
     def getLocation(self):
-        """ Method to get LOCATION value
+        """Method to get LOCATION value
 
         :return: The LOCATION parameter value
-        :rtype: String 
+        :rtype: String
         """
         return self.co.get(ClientOptions.LOCATION).getValue()
 
 
 class FieldDefinition:
-    """ This class defines each field that we use in matching We can use this to configure the properties of each field we use for matching in Zingg.
+    """This class defines each field that we use in matching We can use this to configure the properties of each field we use for matching in Zingg.
 
     :param name: name of the field
     :type name: String
@@ -711,9 +809,9 @@ def __init__(self, name, dataType, *matchType):
         self.fd.setDataType(self.stringify(dataType))
         self.fd.setMatchType(matchType)
         self.fd.setFields(name)
-    
+
     def setStopWords(self, stopWords):
-        """ Method to add stopwords to this class object
+        """Method to add stopwords to this class object
 
         :param stopWords: The stop Words containing csv file's location
         :type stopWords: String
@@ -721,7 +819,7 @@ def setStopWords(self, stopWords):
         self.fd.setStopWords(stopWords)
 
     def getFieldDefinition(self):
-        """ Method to get  pointer address of this class
+        """Method to get  pointer address of this class
 
         :return: The pointer containing the address of this class object
         :rtype: pointer(FieldDefinition)
@@ -730,31 +828,33 @@ def getFieldDefinition(self):
 
     #  should be stringify'ed before it is set in fd object
     def stringify(self, str):
-        """ Method to stringify'ed the dataType before it is set in FieldDefinition object
-        
+        """Method to stringify'ed the dataType before it is set in FieldDefinition object
+
         :param str: dataType of the FieldDefinition
         :type str: String
         :return: The stringify'ed value of the dataType
         :rtype: String
         """
-        
+
         return str
-        
+
 
 def parseArguments(argv):
-    """ This method is used for checking mandatory arguments and creating an arguments list from Command line arguments
+    """This method is used for checking mandatory arguments and creating an arguments list from Command line arguments
 
     :param argv: Values that are passed during the calling of the program along with the calling statement.
     :type argv: List
     :return: a list containing necessary arguments to run any phase
     :rtype: List
     """
-    parser = argparse.ArgumentParser(description='Zingg\'s python APIs')
-    mandatoryOptions = parser.add_argument_group('mandatory arguments')
-    mandatoryOptions.add_argument('--phase', required=True,
-                        help='python phase e.g. assessModel')
-    mandatoryOptions.add_argument('--conf', required=True,
-                        help='JSON configuration with data input output locations and field definitions')
+    parser = argparse.ArgumentParser(description="Zingg's python APIs")
+    mandatoryOptions = parser.add_argument_group("mandatory arguments")
+    mandatoryOptions.add_argument("--phase", required=True, help="python phase e.g. assessModel")
+    mandatoryOptions.add_argument(
+        "--conf",
+        required=True,
+        help="JSON configuration with data input output locations and field definitions",
+    )
 
     args, remaining_args = parser.parse_known_args(argv)
     LOG.debug("args: ", args)

From c832d1d8a3461ef2c71860bb197a95e7ed354f54 Mon Sep 17 00:00:00 2001
From: Sonal Goyal <sonalgoyal4@gmail.com>
Date: Mon, 11 Mar 2024 16:55:29 +0530
Subject: [PATCH 16/36] added html theme to build docs in readthedocs

---
 python/docs/conf.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/docs/conf.py b/python/docs/conf.py
index d41a0c10f..7c79b872d 100644
--- a/python/docs/conf.py
+++ b/python/docs/conf.py
@@ -38,7 +38,7 @@
     'sphinx.ext.napoleon',
     'sphinx.ext.viewcode',
     'sphinx.ext.coverage',
-    'sphinx_markdown_builder'
+    'sphinx_html_builder'
 ]
 
 

From 365bcf6a5680a8d2c719bd3add0749313cee56b8 Mon Sep 17 00:00:00 2001
From: Sonal Goyal <sonalgoyal4@gmail.com>
Date: Mon, 11 Mar 2024 16:58:24 +0530
Subject: [PATCH 17/36] removed html builder extension

---
 python/docs/conf.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/python/docs/conf.py b/python/docs/conf.py
index 7c79b872d..e1b2e255d 100644
--- a/python/docs/conf.py
+++ b/python/docs/conf.py
@@ -37,8 +37,7 @@
     'sphinx.ext.autodoc',
     'sphinx.ext.napoleon',
     'sphinx.ext.viewcode',
-    'sphinx.ext.coverage',
-    'sphinx_html_builder'
+    'sphinx.ext.coverage'
 ]
 
 

From 4d63c0b3f8fe87f4a2edb2598866a6a103b40f1a Mon Sep 17 00:00:00 2001
From: Sonal <sonalgoyal4@gmail.com>
Date: Mon, 3 Jun 2024 09:46:20 +0530
Subject: [PATCH 18/36] Update faq.md with cdp and mdm information

---
 docs/faq.md | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/docs/faq.md b/docs/faq.md
index 1663a624f..9f30aaa9e 100644
--- a/docs/faq.md
+++ b/docs/faq.md
@@ -22,11 +22,13 @@ Very much! Zingg uses Spark and ML under the hood so that you don't have to worr
 
 ## Is Zingg an MDM?
 
-No, Zingg is not an MDM. An MDM is the system of record, it has its own store where linked and mastered records are saved. Zingg enables MDM but is not a system of record. You can build an MDM in a data store of your choice using Zingg however.
+An MDM is the system of record, it has its own store where linked and mastered records are saved. Zingg Community Version is not a complete MDM but it can be sed to build an MDM. You can build an MDM in a data store of your choice using Zingg Community Version. Zingg Enterprise Version is a lakehouse/warehouse native MDM. 
 
 ## Is Zingg a CDP ?
 
-No, Zingg is not a CDP, as it does not stream events or customer data through different channels. Zingg does overlap with the CDPs identity resolution and building customer 360 views. Here is an [article](https://hightouch.com/blog/warehouse-identity-resolution/) describing how you can build your own CDP on the warehouse with Zingg.
+No, Zingg is not a CDP, as it does not stream events or customer data through different channels. However, if you want to base your customer platform off your warehouse or datalake, Zing gis a great fit. You can leverage existing ETL, observability and other tools which are part of your data stack and use Zingg for identity. 
+Zingg Comminity Version can be used to build a composable CDP by identity resolution natively on the warehouse and datalake and building customer 360 views. Zingg's identity resolution is far more powerful than what is offered by any out of the box CDP. 
+Zingg Enterprise's probabilistic and deterministic matching take this further beyond. Here is an [article](https://hightouch.com/blog/warehouse-identity-resolution/) describing how you can build your own CDP on the warehouse with Zingg.
 
 ## I can do Entity Resolution using a graph database like TigerGraph/Neo4J, why do I need Zingg ?
 

From 201d9c17bbf9f2ef13faae85069dd5236693b7df Mon Sep 17 00:00:00 2001
From: Vikas Gupta <vikasgupta78@gmail.com>
Date: Thu, 20 Jun 2024 15:35:36 +0530
Subject: [PATCH 19/36] docs for enterprise features det match and run incr

---
 docs/deterministicMatching.md | 29 +++++++++++++++++++++++++++++
 docs/runIncremental.md        | 24 ++++++++++++++++++++++++
 2 files changed, 53 insertions(+)
 create mode 100644 docs/deterministicMatching.md
 create mode 100644 docs/runIncremental.md

diff --git a/docs/deterministicMatching.md b/docs/deterministicMatching.md
new file mode 100644
index 000000000..293d4dde7
--- /dev/null
+++ b/docs/deterministicMatching.md
@@ -0,0 +1,29 @@
+# Deterministic Matching
+
+Zingg enterprise offers a new feature of deterministic matching along side already available probabilistic matching. In the entity resolution space, we refer to exact matching on attributes as deterministic matching. When used together this allows cases where user has given some sort of unique id in some rows to match accurately together due to deterministic matching and they will than match with others rows where id is missing via probabilistic matching.
+
+Example for configuring it in json:
+
+"deterministicMatching":[
+	{
+		"matchCondition":[
+			{
+				"fieldName":"fname"
+			},
+			{
+				"fieldName":"stNo"
+			},
+			{
+				"fieldName":"add1"
+			}
+		]
+	}
+],
+
+
+Python code example:
+
+dm1 = DeterministicMatching('fname','stNo','add1')
+dm2 = DeterministicMatching('fname','dob','ssn')
+dm3 = DeterministicMatching('fname','stNo','lname')
+args.setDeterministicMatchingCondition(dm1,dm2,dm3)
diff --git a/docs/runIncremental.md b/docs/runIncremental.md
new file mode 100644
index 000000000..aedaa8d41
--- /dev/null
+++ b/docs/runIncremental.md
@@ -0,0 +1,24 @@
+# Run Incremental load for newer and incremental datasets
+
+Zingg enterprise offers a new feature of running incremental load on already matched dataset. As new data arrives there is no need to run the match phase on entire data. Instead we can reuse the models created earlier on newer and incremental datasets. This will assign clusters on new data sets as well as update existing dataset and reassign clusters if needed.
+
+You can invoke the incremental phase by invoking\
+`./scripts/zingg.sh --phase runIncremental --conf <location to incrementalConf.json>`
+
+Example incrementalConf.json:
+
+{	
+	"config" : "<parent config used for match>",
+	"incrementalData": <data pipe for incremental data>
+}
+
+runIncremental can also be triggerred using python by invoking\
+`./scripts/zingg.sh --run examples/FebrlExample.py`
+
+Python code example:
+
+incrArgs = IncrementalArguments()
+incrArgs.setParentArgs(args)
+incrPipe = ECsvPipe("testFebrlIncr", "examples/febrl/test-incr.csv", schema)
+incrArgs.setIncrementalData(incrPipe)
+

From 1019a4c325e4626fed9887cc6e16377a3c2a44f4 Mon Sep 17 00:00:00 2001
From: Sonal <sonalgoyal4@gmail.com>
Date: Thu, 20 Jun 2024 15:47:53 +0530
Subject: [PATCH 20/36] Update deterministicMatching.md

---
 docs/deterministicMatching.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/deterministicMatching.md b/docs/deterministicMatching.md
index 293d4dde7..9ffb2b774 100644
--- a/docs/deterministicMatching.md
+++ b/docs/deterministicMatching.md
@@ -1,6 +1,6 @@
-# Deterministic Matching
+# Deterministic Matching - Zingg Enterprise Only Feature
 
-Zingg enterprise offers a new feature of deterministic matching along side already available probabilistic matching. In the entity resolution space, we refer to exact matching on attributes as deterministic matching. When used together this allows cases where user has given some sort of unique id in some rows to match accurately together due to deterministic matching and they will than match with others rows where id is missing via probabilistic matching.
+Zingg Enterprise allows the ability to plug rule based deterministic matching along with already Zingg AI's probabilistic matching. If the data contains sure identifiers like emails, SSNs, passport ids etc, we can use these attributes to resolve records. The deterministic matching flow is weaved into Zingg's flow to ensure that each record which has a match finds one, probabilistically, deterministcally or both. If the data has known identifiers, Zingg Enterprise's deterministic matching highly improves both matching accuracy and performance.
 
 Example for configuring it in json:
 

From 64363a4b8b5aee3fabbb26e43466b08576d168d4 Mon Sep 17 00:00:00 2001
From: Sonal <sonalgoyal4@gmail.com>
Date: Thu, 20 Jun 2024 15:56:02 +0530
Subject: [PATCH 21/36] Update runIncremental.md

---
 docs/runIncremental.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/runIncremental.md b/docs/runIncremental.md
index aedaa8d41..3892f4558 100644
--- a/docs/runIncremental.md
+++ b/docs/runIncremental.md
@@ -1,8 +1,8 @@
-# Run Incremental load for newer and incremental datasets
+# Run Incremental Loads For New and Updated Records - Zingg Enterprise Only Feature
 
-Zingg enterprise offers a new feature of running incremental load on already matched dataset. As new data arrives there is no need to run the match phase on entire data. Instead we can reuse the models created earlier on newer and incremental datasets. This will assign clusters on new data sets as well as update existing dataset and reassign clusters if needed.
+Rerunning matching on entire datasets is wasteful, and we lose the lineage of matched records against a persistent identifier. Using Zingg Enterprise, incremental loads can be run to match existing pre resolved entities. The new and updated records are matched to existing clusters, and new persistent ZINGG_IDs generated for records which do not find a match. If a record gets updated and Zingg Enterprise discovers that it is a more suitable match with another cluster, it will be reassigned. Cluster assignment, merge and unmerge happens automatically in the flow. Zingg Entperirse also takes care of human feedback on previously matched data to ensure that it doesnt override the approved records. 
 
-You can invoke the incremental phase by invoking\
+The incremental phase is run as follows\
 `./scripts/zingg.sh --phase runIncremental --conf <location to incrementalConf.json>`
 
 Example incrementalConf.json:

From 9a1a3f9f17ac38937f2474831243d0380162fbe0 Mon Sep 17 00:00:00 2001
From: Vikas Gupta <vikasgupta78@gmail.com>
Date: Thu, 20 Jun 2024 16:05:11 +0530
Subject: [PATCH 22/36] more examples

---
 docs/deterministicMatching.md | 35 ++++++++++++++++-------------------
 1 file changed, 16 insertions(+), 19 deletions(-)

diff --git a/docs/deterministicMatching.md b/docs/deterministicMatching.md
index 9ffb2b774..b301728ef 100644
--- a/docs/deterministicMatching.md
+++ b/docs/deterministicMatching.md
@@ -4,26 +4,23 @@ Zingg Enterprise allows the ability to plug rule based deterministic matching al
 
 Example for configuring it in json:
 
-"deterministicMatching":[
-	{
-		"matchCondition":[
-			{
-				"fieldName":"fname"
-			},
-			{
-				"fieldName":"stNo"
-			},
-			{
-				"fieldName":"add1"
-			}
-		]
-	}
-],
+    "deterministicMatching":[
+        {
+           "matchCondition":[{"fieldName":"fname"},{"fieldName":"stNo"},{"fieldName":"add1"}]
+        },
+        {
+           "matchCondition":[{"fieldName":"fname"},{"fieldName":"dob"},{"fieldName":"ssn"}]
+        }, 
+        {
+           "matchCondition":[{"fieldName":"fname"},{"fieldName":"email"}]
+        }
+    ]
+
 
 
 Python code example:
 
-dm1 = DeterministicMatching('fname','stNo','add1')
-dm2 = DeterministicMatching('fname','dob','ssn')
-dm3 = DeterministicMatching('fname','stNo','lname')
-args.setDeterministicMatchingCondition(dm1,dm2,dm3)
+detMatchNameAdd = DeterministicMatching('fname','stNo','add1')
+detMatchNameDobSsn = DeterministicMatching('fname','dob','ssn')
+detMatchNameEmail = DeterministicMatching('fname','email')
+args.setDeterministicMatchingCondition(detMatchNameAdd,detMatchNameDobSsn,detMatchNameEmail)

From 9d27399e62a9787821a90fa599afeec1fab2bbc6 Mon Sep 17 00:00:00 2001
From: Vikas Gupta <vikasgupta78@gmail.com>
Date: Thu, 20 Jun 2024 16:29:50 +0530
Subject: [PATCH 23/36] detailed example for incremental

---
 docs/runIncremental.md | 65 ++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 63 insertions(+), 2 deletions(-)

diff --git a/docs/runIncremental.md b/docs/runIncremental.md
index 3892f4558..95c594792 100644
--- a/docs/runIncremental.md
+++ b/docs/runIncremental.md
@@ -8,8 +8,18 @@ The incremental phase is run as follows\
 Example incrementalConf.json:
 
 {	
-	"config" : "<parent config used for match>",
-	"incrementalData": <data pipe for incremental data>
+	"config" : "config.json",
+	"incrementalData": [{
+			"name":"customers_incr", 
+			"format":"csv", 
+			"props": {
+				"location": "test-incr.csv",
+				"delimiter": ",",
+				"header":false					
+			},
+			"schema": "recId string, fname string, lname string, stNo string, add1 string, add2 string, city string, state string, areacode string, dob string, ssn  string" 
+		}
+	] 
 }
 
 runIncremental can also be triggerred using python by invoking\
@@ -17,8 +27,59 @@ runIncremental can also be triggerred using python by invoking\
 
 Python code example:
 
+from zingg.client import *
+from zingg.pipes import *
+from zinggEC.enterprise.common.ApproverArguments import *
+from zinggEC.enterprise.common.IncrementalArguments import *
+from zinggEC.enterprise.common.epipes import *
+from zinggEC.enterprise.common.EArguments import *
+from zinggEC.enterprise.common.EFieldDefinition import EFieldDefinition
+from zinggES.enterprise.spark.ESparkClient import EZingg
+import os
+
+#build the arguments for zingg
+args = EArguments()
+#set field definitions
+recId = EFieldDefinition("recId", "string", MatchType.DONT_USE)
+recId.setPrimaryKey(True)
+fname = EFieldDefinition("fname", "string", MatchType.FUZZY)
+lname = EFieldDefinition("lname", "string", MatchType.FUZZY)
+stNo = EFieldDefinition("stNo", "string", MatchType.FUZZY)
+add1 = EFieldDefinition("add1","string", MatchType.FUZZY)
+add2 = EFieldDefinition("add2", "string", MatchType.FUZZY)
+city = EFieldDefinition("city", "string", MatchType.FUZZY)
+areacode = EFieldDefinition("areacode", "string", MatchType.FUZZY)
+state = EFieldDefinition("state", "string", MatchType.FUZZY)
+dob = EFieldDefinition("dob", "string", MatchType.FUZZY)
+ssn = EFieldDefinition("ssn", "string", MatchType.FUZZY)
+
+fieldDefs = [recId, fname, lname, stNo, add1, add2, city, areacode, state, dob, ssn]
+args.setFieldDefinition(fieldDefs)
+#set the modelid and the zingg dir
+args.setModelId("100")
+args.setZinggDir("/tmp/models")
+args.setNumPartitions(4)
+args.setLabelDataSampleSize(0.5)
+
+#reading dataset into inputPipe and settint it up in 'args'
+schema = "recId string, fname string, lname string, stNo string, add1 string, add2 string, city string, areacode string, state string, dob string, ssn  string"
+inputPipe = ECsvPipe("testFebrl", "examples/febrl/test.csv", schema)
+args.setData(inputPipe)
+
+outputPipe = ECsvPipe("resultFebrl", "/tmp/febrlOutput")
+outputPipe.setHeader("true")
+
+args.setOutput(outputPipe)
+
 incrArgs = IncrementalArguments()
 incrArgs.setParentArgs(args)
 incrPipe = ECsvPipe("testFebrlIncr", "examples/febrl/test-incr.csv", schema)
 incrArgs.setIncrementalData(incrPipe)
 
+options = ClientOptions([ClientOptions.PHASE,"runIncremental",ClientOptions.LICENSE,<enterprise license file location>])
+
+#Zingg execution for the given phase
+zingg = EZingg(incrArgs, options)
+
+zingg.initAndExecute()
+

From a93bf0c5b0ca27067a0db4e1d472f4d94d6d2ae7 Mon Sep 17 00:00:00 2001
From: Vikas Gupta <vikasgupta78@gmail.com>
Date: Thu, 20 Jun 2024 17:29:23 +0530
Subject: [PATCH 24/36] more explanation with all the phases

---
 docs/runIncremental.md | 17 +++++++++++++----
 1 file changed, 13 insertions(+), 4 deletions(-)

diff --git a/docs/runIncremental.md b/docs/runIncremental.md
index 95c594792..81af4ebdd 100644
--- a/docs/runIncremental.md
+++ b/docs/runIncremental.md
@@ -27,6 +27,8 @@ runIncremental can also be triggerred using python by invoking\
 
 Python code example:
 
+#import the packages
+
 from zingg.client import *
 from zingg.pipes import *
 from zinggEC.enterprise.common.ApproverArguments import *
@@ -71,15 +73,22 @@ outputPipe.setHeader("true")
 
 args.setOutput(outputPipe)
 
+#Run findAndLabel
+options = ClientOptions([ClientOptions.PHASE,"findAndLabel",ClientOptions.LICENSE,<enterprise license file location>])
+zingg = EZingg(args, options)
+zingg.initAndExecute()
+
+#Run trainMatch after above completes
+options = ClientOptions([ClientOptions.PHASE,"trainMatch",ClientOptions.LICENSE,<enterprise license file location>])
+zingg = EZingg(args, options)
+zingg.initAndExecute()
+
+#Now run incremental on output generated above
 incrArgs = IncrementalArguments()
 incrArgs.setParentArgs(args)
 incrPipe = ECsvPipe("testFebrlIncr", "examples/febrl/test-incr.csv", schema)
 incrArgs.setIncrementalData(incrPipe)
 
 options = ClientOptions([ClientOptions.PHASE,"runIncremental",ClientOptions.LICENSE,<enterprise license file location>])
-
-#Zingg execution for the given phase
 zingg = EZingg(incrArgs, options)
-
 zingg.initAndExecute()
-

From 73e07690772d9a961fe4cf4c83ea71d404886595 Mon Sep 17 00:00:00 2001
From: Vikas Gupta <vikasgupta78@gmail.com>
Date: Thu, 27 Jun 2024 15:18:52 +0530
Subject: [PATCH 25/36] deterministicMatching and run incremental

---
 docs/SUMMARY.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/docs/SUMMARY.md b/docs/SUMMARY.md
index bc4b78368..f478af1d0 100644
--- a/docs/SUMMARY.md
+++ b/docs/SUMMARY.md
@@ -23,6 +23,7 @@
       * [Input Data](stepbystep/configuration/data-input-and-output/data.md)
       * [Output](stepbystep/configuration/data-input-and-output/output.md)
     * [Field Definitions](stepbystep/configuration/field-definitions.md)
+    * [Deterministic Matching](deterministicMatching.md)
     * [Model Location](stepbystep/configuration/model-location.md)
     * [Tuning Label, Match And Link Jobs](stepbystep/configuration/tuning-label-match-and-link-jobs.md)
     * [Telemetry](stepbystep/configuration/telemetry.md)
@@ -33,6 +34,7 @@
     * [Using pre-existing training data](setup/training/addOwnTrainingData.md)
     * [Updating Labeled Pairs](updatingLabels.md)
     * [Exporting Labeled Data](setup/training/exportLabeledData.md)
+  * [Add Incremental Data](runIncremental.md)
   * [Building and saving the model](setup/train.md)
   * [Finding the matches](setup/match.md)
   * [Linking across datasets](setup/link.md)

From 8bc2d0fc037cdae36e05d12ee7a41e728bc4b00a Mon Sep 17 00:00:00 2001
From: Vikas Gupta <vikasgupta78@gmail.com>
Date: Tue, 2 Jul 2024 11:39:57 +0530
Subject: [PATCH 26/36] formatting

---
 docs/runIncremental.md | 188 ++++++++++++++++++++---------------------
 1 file changed, 94 insertions(+), 94 deletions(-)

diff --git a/docs/runIncremental.md b/docs/runIncremental.md
index 81af4ebdd..bcb7c9682 100644
--- a/docs/runIncremental.md
+++ b/docs/runIncremental.md
@@ -1,94 +1,94 @@
-# Run Incremental Loads For New and Updated Records - Zingg Enterprise Only Feature
-
-Rerunning matching on entire datasets is wasteful, and we lose the lineage of matched records against a persistent identifier. Using Zingg Enterprise, incremental loads can be run to match existing pre resolved entities. The new and updated records are matched to existing clusters, and new persistent ZINGG_IDs generated for records which do not find a match. If a record gets updated and Zingg Enterprise discovers that it is a more suitable match with another cluster, it will be reassigned. Cluster assignment, merge and unmerge happens automatically in the flow. Zingg Entperirse also takes care of human feedback on previously matched data to ensure that it doesnt override the approved records. 
-
-The incremental phase is run as follows\
-`./scripts/zingg.sh --phase runIncremental --conf <location to incrementalConf.json>`
-
-Example incrementalConf.json:
-
-{	
-	"config" : "config.json",
-	"incrementalData": [{
-			"name":"customers_incr", 
-			"format":"csv", 
-			"props": {
-				"location": "test-incr.csv",
-				"delimiter": ",",
-				"header":false					
-			},
-			"schema": "recId string, fname string, lname string, stNo string, add1 string, add2 string, city string, state string, areacode string, dob string, ssn  string" 
-		}
-	] 
-}
-
-runIncremental can also be triggerred using python by invoking\
-`./scripts/zingg.sh --run examples/FebrlExample.py`
-
-Python code example:
-
-#import the packages
-
-from zingg.client import *
-from zingg.pipes import *
-from zinggEC.enterprise.common.ApproverArguments import *
-from zinggEC.enterprise.common.IncrementalArguments import *
-from zinggEC.enterprise.common.epipes import *
-from zinggEC.enterprise.common.EArguments import *
-from zinggEC.enterprise.common.EFieldDefinition import EFieldDefinition
-from zinggES.enterprise.spark.ESparkClient import EZingg
-import os
-
-#build the arguments for zingg
-args = EArguments()
-#set field definitions
-recId = EFieldDefinition("recId", "string", MatchType.DONT_USE)
-recId.setPrimaryKey(True)
-fname = EFieldDefinition("fname", "string", MatchType.FUZZY)
-lname = EFieldDefinition("lname", "string", MatchType.FUZZY)
-stNo = EFieldDefinition("stNo", "string", MatchType.FUZZY)
-add1 = EFieldDefinition("add1","string", MatchType.FUZZY)
-add2 = EFieldDefinition("add2", "string", MatchType.FUZZY)
-city = EFieldDefinition("city", "string", MatchType.FUZZY)
-areacode = EFieldDefinition("areacode", "string", MatchType.FUZZY)
-state = EFieldDefinition("state", "string", MatchType.FUZZY)
-dob = EFieldDefinition("dob", "string", MatchType.FUZZY)
-ssn = EFieldDefinition("ssn", "string", MatchType.FUZZY)
-
-fieldDefs = [recId, fname, lname, stNo, add1, add2, city, areacode, state, dob, ssn]
-args.setFieldDefinition(fieldDefs)
-#set the modelid and the zingg dir
-args.setModelId("100")
-args.setZinggDir("/tmp/models")
-args.setNumPartitions(4)
-args.setLabelDataSampleSize(0.5)
-
-#reading dataset into inputPipe and settint it up in 'args'
-schema = "recId string, fname string, lname string, stNo string, add1 string, add2 string, city string, areacode string, state string, dob string, ssn  string"
-inputPipe = ECsvPipe("testFebrl", "examples/febrl/test.csv", schema)
-args.setData(inputPipe)
-
-outputPipe = ECsvPipe("resultFebrl", "/tmp/febrlOutput")
-outputPipe.setHeader("true")
-
-args.setOutput(outputPipe)
-
-#Run findAndLabel
-options = ClientOptions([ClientOptions.PHASE,"findAndLabel",ClientOptions.LICENSE,<enterprise license file location>])
-zingg = EZingg(args, options)
-zingg.initAndExecute()
-
-#Run trainMatch after above completes
-options = ClientOptions([ClientOptions.PHASE,"trainMatch",ClientOptions.LICENSE,<enterprise license file location>])
-zingg = EZingg(args, options)
-zingg.initAndExecute()
-
-#Now run incremental on output generated above
-incrArgs = IncrementalArguments()
-incrArgs.setParentArgs(args)
-incrPipe = ECsvPipe("testFebrlIncr", "examples/febrl/test-incr.csv", schema)
-incrArgs.setIncrementalData(incrPipe)
-
-options = ClientOptions([ClientOptions.PHASE,"runIncremental",ClientOptions.LICENSE,<enterprise license file location>])
-zingg = EZingg(incrArgs, options)
-zingg.initAndExecute()
+# Run Incremental Loads For New and Updated Records - Zingg Enterprise Only Feature  
+  
+Rerunning matching on entire datasets is wasteful, and we lose the lineage of matched records against a persistent identifier. Using Zingg Enterprise, incremental loads can be run to match existing pre resolved entities. The new and updated records are matched to existing clusters, and new persistent ZINGG_IDs generated for records which do not find a match. If a record gets updated and Zingg Enterprise discovers that it is a more suitable match with another cluster, it will be reassigned. Cluster assignment, merge and unmerge happens automatically in the flow. Zingg Entperirse also takes care of human feedback on previously matched data to ensure that it doesnt override the approved records.   
+  
+The incremental phase is run as follows\  
+`./scripts/zingg.sh --phase runIncremental --conf <location to incrementalConf.json>`  
+  
+Example incrementalConf.json:  
+  
+{	  
+	"config" : "config.json",  
+	"incrementalData": [{  
+			"name":"customers_incr",   
+			"format":"csv",   
+			"props": {  
+				"location": "test-incr.csv",  
+				"delimiter": ",",  
+				"header":false					  
+			},  
+			"schema": "recId string, fname string, lname string, stNo string, add1 string, add2 string, city string, state string, areacode string, dob string, ssn  string"   
+		}  
+	]   
+}  
+  
+runIncremental can also be triggerred using python by invoking\  
+`./scripts/zingg.sh --run examples/FebrlExample.py`  
+  
+Python code example:  
+  
+#import the packages  
+  
+from zingg.client import *  
+from zingg.pipes import *  
+from zinggEC.enterprise.common.ApproverArguments import *  
+from zinggEC.enterprise.common.IncrementalArguments import *  
+from zinggEC.enterprise.common.epipes import *  
+from zinggEC.enterprise.common.EArguments import *  
+from zinggEC.enterprise.common.EFieldDefinition import EFieldDefinition  
+from zinggES.enterprise.spark.ESparkClient import EZingg  
+import os  
+  
+#build the arguments for zingg  
+args = EArguments()  
+#set field definitions  
+recId = EFieldDefinition("recId", "string", MatchType.DONT_USE)  
+recId.setPrimaryKey(True)  
+fname = EFieldDefinition("fname", "string", MatchType.FUZZY)  
+lname = EFieldDefinition("lname", "string", MatchType.FUZZY)  
+stNo = EFieldDefinition("stNo", "string", MatchType.FUZZY)  
+add1 = EFieldDefinition("add1","string", MatchType.FUZZY)  
+add2 = EFieldDefinition("add2", "string", MatchType.FUZZY)  
+city = EFieldDefinition("city", "string", MatchType.FUZZY)  
+areacode = EFieldDefinition("areacode", "string", MatchType.FUZZY)  
+state = EFieldDefinition("state", "string", MatchType.FUZZY)  
+dob = EFieldDefinition("dob", "string", MatchType.FUZZY)  
+ssn = EFieldDefinition("ssn", "string", MatchType.FUZZY)  
+  
+fieldDefs = [recId, fname, lname, stNo, add1, add2, city, areacode, state, dob, ssn]  
+args.setFieldDefinition(fieldDefs)  
+#set the modelid and the zingg dir  
+args.setModelId("100")  
+args.setZinggDir("/tmp/models")  
+args.setNumPartitions(4)  
+args.setLabelDataSampleSize(0.5)  
+  
+#reading dataset into inputPipe and settint it up in 'args'  
+schema = "recId string, fname string, lname string, stNo string, add1 string, add2 string, city string, areacode string, state string, dob string, ssn  string"  
+inputPipe = ECsvPipe("testFebrl", "examples/febrl/test.csv", schema)  
+args.setData(inputPipe)  
+  
+outputPipe = ECsvPipe("resultFebrl", "/tmp/febrlOutput")  
+outputPipe.setHeader("true")  
+  
+args.setOutput(outputPipe)  
+  
+#Run findAndLabel  
+options = ClientOptions([ClientOptions.PHASE,"findAndLabel",ClientOptions.LICENSE,<enterprise license file location>])  
+zingg = EZingg(args, options)  
+zingg.initAndExecute()  
+  
+#Run trainMatch after above completes  
+options = ClientOptions([ClientOptions.PHASE,"trainMatch",ClientOptions.LICENSE,<enterprise license file location>])  
+zingg = EZingg(args, options)  
+zingg.initAndExecute()  
+  
+#Now run incremental on output generated above  
+incrArgs = IncrementalArguments()  
+incrArgs.setParentArgs(args)  
+incrPipe = ECsvPipe("testFebrlIncr", "examples/febrl/test-incr.csv", schema)  
+incrArgs.setIncrementalData(incrPipe)  
+  
+options = ClientOptions([ClientOptions.PHASE,"runIncremental",ClientOptions.LICENSE,<enterprise license file location>])  
+zingg = EZingg(incrArgs, options)  
+zingg.initAndExecute()  

From 7f35f3199a851bda3bb79f641639b25f46366127 Mon Sep 17 00:00:00 2001
From: Vikas Gupta <vikasgupta78@gmail.com>
Date: Tue, 2 Jul 2024 11:45:47 +0530
Subject: [PATCH 27/36] code and header corrected

---
 docs/runIncremental.md | 38 +++++++++++++++++++-------------------
 1 file changed, 19 insertions(+), 19 deletions(-)

diff --git a/docs/runIncremental.md b/docs/runIncremental.md
index bcb7c9682..11340930b 100644
--- a/docs/runIncremental.md
+++ b/docs/runIncremental.md
@@ -1,28 +1,28 @@
-# Run Incremental Loads For New and Updated Records - Zingg Enterprise Only Feature  
+# Run Incremental Loads For New and Updated Records - Zingg Enterprise Feature  
   
 Rerunning matching on entire datasets is wasteful, and we lose the lineage of matched records against a persistent identifier. Using Zingg Enterprise, incremental loads can be run to match existing pre resolved entities. The new and updated records are matched to existing clusters, and new persistent ZINGG_IDs generated for records which do not find a match. If a record gets updated and Zingg Enterprise discovers that it is a more suitable match with another cluster, it will be reassigned. Cluster assignment, merge and unmerge happens automatically in the flow. Zingg Entperirse also takes care of human feedback on previously matched data to ensure that it doesnt override the approved records.   
   
-The incremental phase is run as follows\  
+The incremental phase is run as follows:  
 `./scripts/zingg.sh --phase runIncremental --conf <location to incrementalConf.json>`  
   
 Example incrementalConf.json:  
   
-{	  
-	"config" : "config.json",  
-	"incrementalData": [{  
-			"name":"customers_incr",   
-			"format":"csv",   
-			"props": {  
-				"location": "test-incr.csv",  
-				"delimiter": ",",  
-				"header":false					  
-			},  
-			"schema": "recId string, fname string, lname string, stNo string, add1 string, add2 string, city string, state string, areacode string, dob string, ssn  string"   
-		}  
-	]   
+{      
+    "config" : "config.json",  
+    "incrementalData": [{  
+            "name":"customers_incr",   
+            "format":"csv",   
+            "props": {  
+                "location": "test-incr.csv",  
+                "delimiter": ",",  
+                "header":false  
+            },  
+            "schema": "recId string, fname string, lname string, stNo string, add1 string, add2 string, city string, state string, areacode string, dob string, ssn  string"   
+        }  
+    ]   
 }  
   
-runIncremental can also be triggerred using python by invoking\  
+runIncremental can also be triggerred using python by invoking:  
 `./scripts/zingg.sh --run examples/FebrlExample.py`  
   
 Python code example:  
@@ -74,12 +74,12 @@ outputPipe.setHeader("true")
 args.setOutput(outputPipe)  
   
 #Run findAndLabel  
-options = ClientOptions([ClientOptions.PHASE,"findAndLabel",ClientOptions.LICENSE,<enterprise license file location>])  
+options = ClientOptions([ClientOptions.PHASE,"findAndLabel"])  
 zingg = EZingg(args, options)  
 zingg.initAndExecute()  
   
 #Run trainMatch after above completes  
-options = ClientOptions([ClientOptions.PHASE,"trainMatch",ClientOptions.LICENSE,<enterprise license file location>])  
+options = ClientOptions([ClientOptions.PHASE,"trainMatch"])  
 zingg = EZingg(args, options)  
 zingg.initAndExecute()  
   
@@ -89,6 +89,6 @@ incrArgs.setParentArgs(args)
 incrPipe = ECsvPipe("testFebrlIncr", "examples/febrl/test-incr.csv", schema)  
 incrArgs.setIncrementalData(incrPipe)  
   
-options = ClientOptions([ClientOptions.PHASE,"runIncremental",ClientOptions.LICENSE,<enterprise license file location>])  
+options = ClientOptions([ClientOptions.PHASE,"runIncremental"])  
 zingg = EZingg(incrArgs, options)  
 zingg.initAndExecute()  

From 60332e7b31b98e41d8d46dda3f4b9fac6f3d7839 Mon Sep 17 00:00:00 2001
From: Vikas Gupta <vikasgupta78@gmail.com>
Date: Tue, 2 Jul 2024 11:51:07 +0530
Subject: [PATCH 28/36] added headers

---
 docs/runIncremental.md | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/docs/runIncremental.md b/docs/runIncremental.md
index 11340930b..05fce4946 100644
--- a/docs/runIncremental.md
+++ b/docs/runIncremental.md
@@ -2,11 +2,12 @@
   
 Rerunning matching on entire datasets is wasteful, and we lose the lineage of matched records against a persistent identifier. Using Zingg Enterprise, incremental loads can be run to match existing pre resolved entities. The new and updated records are matched to existing clusters, and new persistent ZINGG_IDs generated for records which do not find a match. If a record gets updated and Zingg Enterprise discovers that it is a more suitable match with another cluster, it will be reassigned. Cluster assignment, merge and unmerge happens automatically in the flow. Zingg Entperirse also takes care of human feedback on previously matched data to ensure that it doesnt override the approved records.   
   
-The incremental phase is run as follows:  
+## The incremental phase is run as follows:  
 `./scripts/zingg.sh --phase runIncremental --conf <location to incrementalConf.json>`  
   
-Example incrementalConf.json:  
-  
+## Example incrementalConf.json:  
+
+```json  
 {      
     "config" : "config.json",  
     "incrementalData": [{  
@@ -21,11 +22,12 @@ Example incrementalConf.json:
         }  
     ]   
 }  
+```
   
-runIncremental can also be triggerred using python by invoking:  
+## runIncremental can also be triggerred using python by invoking:  
 `./scripts/zingg.sh --run examples/FebrlExample.py`  
   
-Python code example:  
+### Python code example:  
   
 #import the packages  
   

From 44d559a340b4fe4b124cd283507aaf017bc22810 Mon Sep 17 00:00:00 2001
From: Vikas Gupta <vikasgupta78@gmail.com>
Date: Tue, 2 Jul 2024 12:01:44 +0530
Subject: [PATCH 29/36] formatting and more explaination

---
 docs/deterministicMatching.md | 60 ++++++++++++++++++++---------------
 1 file changed, 35 insertions(+), 25 deletions(-)

diff --git a/docs/deterministicMatching.md b/docs/deterministicMatching.md
index b301728ef..4cadb7eca 100644
--- a/docs/deterministicMatching.md
+++ b/docs/deterministicMatching.md
@@ -1,26 +1,36 @@
-# Deterministic Matching - Zingg Enterprise Only Feature
+# Deterministic Matching - *Zingg Enterprise Feature*  
+  
+Zingg Enterprise allows the ability to plug rule based deterministic matching along with already Zingg AI's probabilistic matching. If the data contains sure identifiers like emails, SSNs, passport ids etc, we can use these attributes to resolve records. The deterministic matching flow is weaved into Zingg's flow to ensure that each record which has a match finds one, probabilistically, deterministcally or both. If the data has known identifiers, Zingg Enterprise's deterministic matching highly improves both matching accuracy and performance.  
+  
+## Example for configuring it in json:  
+```json  
+    "deterministicMatching":[  
+        {  
+           "matchCondition":[{"fieldName":"fname"},{"fieldName":"stNo"},{"fieldName":"add1"}]  
+        },  
+        {  
+           "matchCondition":[{"fieldName":"fname"},{"fieldName":"dob"},{"fieldName":"ssn"}]  
+        },   
+        {  
+           "matchCondition":[{"fieldName":"fname"},{"fieldName":"email"}]  
+        }  
+    ]  
+```  
+  
+### Python code example:  
+  
+detMatchNameAdd = DeterministicMatching('fname','stNo','add1')  
+detMatchNameDobSsn = DeterministicMatching('fname','dob','ssn')  
+detMatchNameEmail = DeterministicMatching('fname','email')  
+args.setDeterministicMatchingCondition(detMatchNameAdd,detMatchNameDobSsn,detMatchNameEmail)  
+  
+### How will it work:  
+  
+Above conditions would translate into following:  
+  
+1. Those rows which have exactly same fname, stNo and add1 => exact match with max score 1  
+OR  
+2. Those rows which have exactly same fname, dob and ssn => exact match with max score 1  
+OR  
+3. Those rows which have exactly same fname and email => exact match with max score 1  
 
-Zingg Enterprise allows the ability to plug rule based deterministic matching along with already Zingg AI's probabilistic matching. If the data contains sure identifiers like emails, SSNs, passport ids etc, we can use these attributes to resolve records. The deterministic matching flow is weaved into Zingg's flow to ensure that each record which has a match finds one, probabilistically, deterministcally or both. If the data has known identifiers, Zingg Enterprise's deterministic matching highly improves both matching accuracy and performance.
-
-Example for configuring it in json:
-
-    "deterministicMatching":[
-        {
-           "matchCondition":[{"fieldName":"fname"},{"fieldName":"stNo"},{"fieldName":"add1"}]
-        },
-        {
-           "matchCondition":[{"fieldName":"fname"},{"fieldName":"dob"},{"fieldName":"ssn"}]
-        }, 
-        {
-           "matchCondition":[{"fieldName":"fname"},{"fieldName":"email"}]
-        }
-    ]
-
-
-
-Python code example:
-
-detMatchNameAdd = DeterministicMatching('fname','stNo','add1')
-detMatchNameDobSsn = DeterministicMatching('fname','dob','ssn')
-detMatchNameEmail = DeterministicMatching('fname','email')
-args.setDeterministicMatchingCondition(detMatchNameAdd,detMatchNameDobSsn,detMatchNameEmail)

From c575b9de3b06e37c3a21e8b8d0c5d79561660261 Mon Sep 17 00:00:00 2001
From: Vikas Gupta <vikasgupta78@gmail.com>
Date: Tue, 2 Jul 2024 12:02:34 +0530
Subject: [PATCH 30/36] header formatting

---
 docs/runIncremental.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/runIncremental.md b/docs/runIncremental.md
index 05fce4946..2640ec168 100644
--- a/docs/runIncremental.md
+++ b/docs/runIncremental.md
@@ -1,4 +1,4 @@
-# Run Incremental Loads For New and Updated Records - Zingg Enterprise Feature  
+# Run Incremental Loads For New and Updated Records - *Zingg Enterprise Feature*  
   
 Rerunning matching on entire datasets is wasteful, and we lose the lineage of matched records against a persistent identifier. Using Zingg Enterprise, incremental loads can be run to match existing pre resolved entities. The new and updated records are matched to existing clusters, and new persistent ZINGG_IDs generated for records which do not find a match. If a record gets updated and Zingg Enterprise discovers that it is a more suitable match with another cluster, it will be reassigned. Cluster assignment, merge and unmerge happens automatically in the flow. Zingg Entperirse also takes care of human feedback on previously matched data to ensure that it doesnt override the approved records.   
   

From caaedc13ca4b7f02e17202070bee4a84a4102b24 Mon Sep 17 00:00:00 2001
From: Vikas Gupta <vikasgupta78@gmail.com>
Date: Tue, 2 Jul 2024 12:05:13 +0530
Subject: [PATCH 31/36] marked python example

---
 docs/deterministicMatching.md | 2 ++
 docs/runIncremental.md        | 2 ++
 2 files changed, 4 insertions(+)

diff --git a/docs/deterministicMatching.md b/docs/deterministicMatching.md
index 4cadb7eca..98871f125 100644
--- a/docs/deterministicMatching.md
+++ b/docs/deterministicMatching.md
@@ -19,10 +19,12 @@ Zingg Enterprise allows the ability to plug rule based deterministic matching al
   
 ### Python code example:  
   
+```{python}
 detMatchNameAdd = DeterministicMatching('fname','stNo','add1')  
 detMatchNameDobSsn = DeterministicMatching('fname','dob','ssn')  
 detMatchNameEmail = DeterministicMatching('fname','email')  
 args.setDeterministicMatchingCondition(detMatchNameAdd,detMatchNameDobSsn,detMatchNameEmail)  
+```
   
 ### How will it work:  
   
diff --git a/docs/runIncremental.md b/docs/runIncremental.md
index 2640ec168..56a85de48 100644
--- a/docs/runIncremental.md
+++ b/docs/runIncremental.md
@@ -29,6 +29,7 @@ Rerunning matching on entire datasets is wasteful, and we lose the lineage of ma
   
 ### Python code example:  
   
+```{python}
 #import the packages  
   
 from zingg.client import *  
@@ -94,3 +95,4 @@ incrArgs.setIncrementalData(incrPipe)
 options = ClientOptions([ClientOptions.PHASE,"runIncremental"])  
 zingg = EZingg(incrArgs, options)  
 zingg.initAndExecute()  
+```

From 6692bcaebcf6e2d262303af9ec734d833acfb22c Mon Sep 17 00:00:00 2001
From: Vikas Gupta <vikasgupta78@gmail.com>
Date: Tue, 2 Jul 2024 12:52:52 +0530
Subject: [PATCH 32/36] spelling corrected and added header2

---
 docs/deterministicMatching.md | 5 +++--
 docs/runIncremental.md        | 7 ++++---
 2 files changed, 7 insertions(+), 5 deletions(-)

diff --git a/docs/deterministicMatching.md b/docs/deterministicMatching.md
index 98871f125..ae77fe64f 100644
--- a/docs/deterministicMatching.md
+++ b/docs/deterministicMatching.md
@@ -1,6 +1,7 @@
-# Deterministic Matching - *Zingg Enterprise Feature*  
+
+## Deterministic Matching - *Zingg Enterprise Feature*  
   
-Zingg Enterprise allows the ability to plug rule based deterministic matching along with already Zingg AI's probabilistic matching. If the data contains sure identifiers like emails, SSNs, passport ids etc, we can use these attributes to resolve records. The deterministic matching flow is weaved into Zingg's flow to ensure that each record which has a match finds one, probabilistically, deterministcally or both. If the data has known identifiers, Zingg Enterprise's deterministic matching highly improves both matching accuracy and performance.  
+Zingg Enterprise allows the ability to plug rule based deterministic matching along with already Zingg AI's probabilistic matching. If the data contains sure identifiers like emails, SSNs, passport ids etc, we can use these attributes to resolve records. The deterministic matching flow is weaved into Zingg's flow to ensure that each record which has a match finds one, probabilistically, deterministically or both. If the data has known identifiers, Zingg Enterprise's deterministic matching highly improves both matching accuracy and performance.  
   
 ## Example for configuring it in json:  
 ```json  
diff --git a/docs/runIncremental.md b/docs/runIncremental.md
index 56a85de48..8986ef89e 100644
--- a/docs/runIncremental.md
+++ b/docs/runIncremental.md
@@ -1,6 +1,7 @@
-# Run Incremental Loads For New and Updated Records - *Zingg Enterprise Feature*  
+
+## Run Incremental Loads For New and Updated Records - *Zingg Enterprise Feature*  
   
-Rerunning matching on entire datasets is wasteful, and we lose the lineage of matched records against a persistent identifier. Using Zingg Enterprise, incremental loads can be run to match existing pre resolved entities. The new and updated records are matched to existing clusters, and new persistent ZINGG_IDs generated for records which do not find a match. If a record gets updated and Zingg Enterprise discovers that it is a more suitable match with another cluster, it will be reassigned. Cluster assignment, merge and unmerge happens automatically in the flow. Zingg Entperirse also takes care of human feedback on previously matched data to ensure that it doesnt override the approved records.   
+Rerunning matching on entire datasets is wasteful, and we lose the lineage of matched records against a persistent identifier. Using Zingg Enterprise, incremental loads can be run to match existing pre resolved entities. The new and updated records are matched to existing clusters, and new persistent ZINGG_IDs generated for records which do not find a match. If a record gets updated and Zingg Enterprise discovers that it is a more suitable match with another cluster, it will be reassigned. Cluster assignment, merge and unmerge happens automatically in the flow. Zingg Enterprise also takes care of human feedback on previously matched data to ensure that it does not override the approved records.   
   
 ## The incremental phase is run as follows:  
 `./scripts/zingg.sh --phase runIncremental --conf <location to incrementalConf.json>`  
@@ -24,7 +25,7 @@ Rerunning matching on entire datasets is wasteful, and we lose the lineage of ma
 }  
 ```
   
-## runIncremental can also be triggerred using python by invoking:  
+## runIncremental can also be triggered using python by invoking:  
 `./scripts/zingg.sh --run examples/FebrlExample.py`  
   
 ### Python code example:  

From 2522a94a0d79276cb22f327227dbd3afb5a6af3d Mon Sep 17 00:00:00 2001
From: Sonal Goyal <sonalgoyal4@gmail.com>
Date: Wed, 31 Jul 2024 05:25:08 +0000
Subject: [PATCH 33/36] GITBOOK-5: No subject


From b071e1c6c2f8103ab5fe2803eb2c76a27d113c60 Mon Sep 17 00:00:00 2001
From: surya <surya@zingg.ai>
Date: Wed, 31 Jul 2024 12:12:43 +0000
Subject: [PATCH 34/36] GITBOOK-25: Surya's Jul 31 changes

---
 docs/README.md                                |  4 +-
 docs/SUMMARY.md                               | 34 +++----
 docs/accuracy/definingOwn.md                  | 22 ++---
 docs/accuracy/stopWordsRemoval.md             | 11 +--
 docs/connectors/jdbc/mysql.md                 |  4 +-
 docs/connectors/jdbc/postgres.md              |  4 +-
 docs/dataSourcesAndSinks/bigquery.md          | 10 +-
 docs/dataSourcesAndSinks/connectors.md        |  7 +-
 docs/dataSourcesAndSinks/databricks.md        |  4 +-
 docs/dataSourcesAndSinks/exasol.md            | 38 ++++----
 docs/dataSourcesAndSinks/jdbc.md              |  7 +-
 docs/dataSourcesAndSinks/pipes.md             |  8 +-
 docs/dataSourcesAndSinks/snowflake.md         |  4 +-
 docs/deterministicMatching.md                 | 43 +++++----
 docs/generatingdocumentation.md               |  5 +-
 docs/python/markdown/index.md                 | 30 +++---
 docs/runIncremental.md                        | 33 ++++---
 docs/running/aws.md                           |  8 +-
 docs/scoring.md                               | 14 +--
 docs/settingUpZingg.md                        | 92 ++++++++++---------
 docs/setup/hardwareSizing.md                  | 10 +-
 docs/setup/installation.md                    |  5 +-
 docs/setup/link.md                            |  8 +-
 docs/setup/match.md                           | 12 +--
 docs/setup/train.md                           | 12 ++-
 docs/setup/training/addOwnTrainingData.md     | 12 +--
 docs/setup/training/createTrainingData.md     |  4 +-
 docs/setup/training/findAndLabel.md           |  4 +-
 docs/setup/training/findTrainingData.md       |  6 +-
 docs/setup/training/label.md                  |  6 +-
 docs/stepByStep.md                            | 10 +-
 docs/stepbystep/configuration/README.md       |  5 +-
 ...nfiguring-through-environment-variables.md |  4 +-
 .../configuration/field-definitions.md        | 24 ++---
 .../configuration/model-location.md           |  4 +-
 docs/stepbystep/configuration/telemetry.md    |  2 +-
 .../tuning-label-match-and-link-jobs.md       |  2 +-
 .../installation/compiling-from-source.md     |  8 +-
 docs/stepbystep/installation/docker/README.md |  4 +-
 ...copying-files-to-and-from-the-container.md |  7 +-
 .../docker/file-read-write-permissions.md     |  4 +-
 .../installation/docker/shared-locations.md   |  8 +-
 .../sharing-custom-data-and-config-files.md   |  4 +-
 .../installing-from-release/README.md         |  7 +-
 .../installing-zingg.md                       | 10 +-
 .../single-machine-setup.md                   | 14 +--
 .../spark-cluster-checklist.md                |  6 +-
 .../installing-from-release/verification.md   |  2 +-
 docs/stepbystep/zingg-command-line.md         |  4 +-
 docs/stepbystep/zingg-runtime-properties.md   |  6 +-
 docs/updatingLabels.md                        |  6 +-
 docs/working-with-python.md                   | 12 +--
 docs/zModels.md                               |  4 +-
 53 files changed, 307 insertions(+), 311 deletions(-)

diff --git a/docs/README.md b/docs/README.md
index f5e9c7371..2b8b0f879 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -2,7 +2,7 @@
 description: Hope you find us useful :-)
 ---
 
-# Welcome to Zingg
+# Welcome To Zingg
 
 ![](https://static.scarf.sh/a.png?x-pxid=d6dda06e-06c7-4e4a-99c9-ed9f6364dfeb)
 
@@ -24,4 +24,4 @@ Zingg is a quick and scalable way to build a single source of truth for core bus
 
 ## Book Office Hours
 
-If you want to schedule a 30-min call with our team to help you get set up, please select some time directly [here](https://calendly.com/sonalgoyal/30min).
+If you want to schedule a 30-min call with our team to help you get set up, please select a slot directly [here](https://calendly.com/sonalgoyal/30min).
diff --git a/docs/SUMMARY.md b/docs/SUMMARY.md
index f478af1d0..9aaefcb51 100644
--- a/docs/SUMMARY.md
+++ b/docs/SUMMARY.md
@@ -1,13 +1,13 @@
 # Table of contents
 
-* [Welcome to Zingg](README.md)
+* [Welcome To Zingg](README.md)
 * [Step-By-Step Guide](stepByStep.md)
   * [Installation](setup/installation.md)
     * [Docker](stepbystep/installation/docker/README.md)
-      * [Sharing custom data and config files](stepbystep/installation/docker/sharing-custom-data-and-config-files.md)
-      * [Shared locations](stepbystep/installation/docker/shared-locations.md)
-      * [File read/write permissions](stepbystep/installation/docker/file-read-write-permissions.md)
-      * [Copying Files To and From the Container](stepbystep/installation/docker/copying-files-to-and-from-the-container.md)
+      * [Sharing Custom Data And Config Files](stepbystep/installation/docker/sharing-custom-data-and-config-files.md)
+      * [Shared Locations](stepbystep/installation/docker/shared-locations.md)
+      * [File Read/Write Permissions](stepbystep/installation/docker/file-read-write-permissions.md)
+      * [Copying Files To And From The Container](stepbystep/installation/docker/copying-files-to-and-from-the-container.md)
     * [Installing From Release](stepbystep/installation/installing-from-release/README.md)
       * [Single Machine Setup](stepbystep/installation/installing-from-release/single-machine-setup.md)
       * [Spark Cluster Checklist](stepbystep/installation/installing-from-release/spark-cluster-checklist.md)
@@ -19,7 +19,7 @@
   * [Zingg Command Line](stepbystep/zingg-command-line.md)
   * [Configuration](stepbystep/configuration/README.md)
     * [Configuring Through Environment Variables](stepbystep/configuration/configuring-through-environment-variables.md)
-    * [Data Input and Output](stepbystep/configuration/data-input-and-output/README.md)
+    * [Data Input And Output](stepbystep/configuration/data-input-and-output/README.md)
       * [Input Data](stepbystep/configuration/data-input-and-output/data.md)
       * [Output](stepbystep/configuration/data-input-and-output/output.md)
     * [Field Definitions](stepbystep/configuration/field-definitions.md)
@@ -31,13 +31,13 @@
     * [Finding Records For Training Set Creation](setup/training/findTrainingData.md)
     * [Labeling Records](setup/training/label.md)
     * [Find And Label](setup/training/findAndLabel.md)
-    * [Using pre-existing training data](setup/training/addOwnTrainingData.md)
+    * [Using Pre-existing Training Data](setup/training/addOwnTrainingData.md)
     * [Updating Labeled Pairs](updatingLabels.md)
     * [Exporting Labeled Data](setup/training/exportLabeledData.md)
   * [Add Incremental Data](runIncremental.md)
-  * [Building and saving the model](setup/train.md)
-  * [Finding the matches](setup/match.md)
-  * [Linking across datasets](setup/link.md)
+  * [Building And Saving The Model](setup/train.md)
+  * [Finding The Matches](setup/match.md)
+  * [Linking Across Datasets](setup/link.md)
 * [Data Sources and Sinks](dataSourcesAndSinks/connectors.md)
   * [Zingg Pipes](dataSourcesAndSinks/pipes.md)
   * [Databricks](dataSourcesAndSinks/databricks.md)
@@ -54,19 +54,19 @@
   * [Exasol](dataSourcesAndSinks/exasol.md)
 * [Working With Python](working-with-python.md)
   * [Python API](python/markdown/index.md)
-* [Running Zingg on Cloud](running/running.md)
-  * [Running on AWS](running/aws.md)
-  * [Running on Azure](running/azure.md)
-  * [Running on Databricks](running/databricks.md)
+* [Running Zingg On Cloud](running/running.md)
+  * [Running On AWS](running/aws.md)
+  * [Running On Azure](running/azure.md)
+  * [Running On Databricks](running/databricks.md)
 * [Zingg Models](zModels.md)
-  * [Pre-trained models](pretrainedModels.md)
+  * [Pre-Trained Models](pretrainedModels.md)
 * [Improving Accuracy](improving-accuracy/README.md)
   * [Ignoring Commonly Occuring Words While Matching](accuracy/stopWordsRemoval.md)
   * [Defining Domain Specific Blocking And Similarity Functions](accuracy/definingOwn.md)
 * [Documenting The Model](generatingdocumentation.md)
 * [Interpreting Output Scores](scoring.md)
-* [Reporting bugs and contributing](contributing.md)
-  * [Setting Zingg Development Environment](settingUpZingg.md)
+* [Reporting Bugs And Contributing](contributing.md)
+  * [Setting Up Zingg Development Environment](settingUpZingg.md)
 * [Community](community.md)
 * [Frequently Asked Questions](faq.md)
 * [Reading Material](reading.md)
diff --git a/docs/accuracy/definingOwn.md b/docs/accuracy/definingOwn.md
index dccc195ef..9e5ad0dcd 100644
--- a/docs/accuracy/definingOwn.md
+++ b/docs/accuracy/definingOwn.md
@@ -3,27 +3,27 @@ nav_order: 6
 description: To add blocking functions and how they work
 ---
 
-# Defining Own Functions
+# Defining Domain Specific Blocking And Similarity Functions
 
 You can add your own [blocking functions](https://github.com/zinggAI/zingg/tree/main/common/core/src/main/java/zingg/common/core/hash) which will be evaluated by Zingg to build the [blocking tree.](../zModels.md)
 
-The blocking tree works on the matched records provided by the user as part of the training. At every node, it selects the hash function and the field on which it should be applied so that there is the least elimination of the matching pairs. Say we have data like this:
+The blocking tree works on the matched records provided by the user as part of the training. At every node, it selects the hash function and the field on which it should be applied so that there is the least elimination of the matching pairs. \
+\
+Say we have data like this:
 
 |  Pair 1  | firstname | lastname |
 | :------: | :-------: | :------: |
 | Record A |    john   |    doe   |
 | Record B |   johnh   |   d oe   |
 
-****
+***
 
 |   Pair 2  | firstname | lastname |
 | :-------: | :-------: | :------: |
 | Rrecord A |    mary   |    ann   |
 |  Record B |   marry   |          |
 
-
-
-Let us assume we have hash function first1char and we want to check if it is a good function to apply to firstname:
+Let us assume we have hash function **first1char** and we want to check if it is a good function to apply to **firstname**:
 
 | Pair |  Record  | Output |
 | :--: | :------: | ------ |
@@ -34,9 +34,7 @@ Let us assume we have hash function first1char and we want to check if it is a g
 
 There is no elimination in the pairs above, hence it is a good function.
 
-
-
-Now let us try last1char on firstname
+Now let us try **last1char** on **firstname:**
 
 | Pair |  Record  | Output |
 | :--: | :------: | ------ |
@@ -45,12 +43,12 @@ Now let us try last1char on firstname
 |   2  | Record A | y      |
 |   2  | Record B | y      |
 
-Pair 1 is getting eliminated above, hence last1char is not a good function.&#x20;
+Pair 1 is getting eliminated above, hence **last1char** is not a good function.
 
-So, first1char(firstname) will be chosen. This brings near similar records together - in a way, clusters them to break the cartesian join.
+So, **first1char**(**firstname**) will be chosen. This brings near similar records together - in a way, clusters them to break the cartesian join.
 
 These business-specific blocking functions go into [Hash Functions](https://github.com/zinggAI/zingg/tree/main/common/core/src/main/java/zingg/common/core/hash) and must be added to [HashFunctionRegistry](../../common/core/src/main/java/zingg/common/core/hash/HashFunctionRegistry.java) and [hash functions config](../../common/core/src/main/resources/hashFunctions.json).
 
-Also, for similarity, you can define your own measures. Each dataType has predefined features, for example, [String](../../common/core/src/main/java/zingg/common/core/feature/StringFeature.java) fuzzy type is configured for Affine and Jaro.
+Also, for similarity, you can define your own measures. Each **dataType** has predefined features, for example, [String](../../common/core/src/main/java/zingg/common/core/feature/StringFeature.java) fuzzy type is configured for Affine and Jaro.
 
 You can define your own [comparisons](https://github.com/zinggAI/zingg/tree/main/common/core/src/main/java/zingg/common/core/similarity/function) and use them.
diff --git a/docs/accuracy/stopWordsRemoval.md b/docs/accuracy/stopWordsRemoval.md
index 752f7bb22..5ca5ef32f 100644
--- a/docs/accuracy/stopWordsRemoval.md
+++ b/docs/accuracy/stopWordsRemoval.md
@@ -1,12 +1,10 @@
 # Ignoring Commonly Occuring Words While Matching
 
-Common words like Mr, Pvt, Av, St, Street etc do not add differential signal and confuse matching. These words are called stopwords and matching is more accurate when stopwrods are ignored.
+Common words like Mr, Pvt, Av, St, Street etc. do not add differential signals and confuse matching. These words are called **stopwords** and matching is more accurate when stopwords are ignored.
 
-In order to remove stopwords from a field, configure&#x20;
+The stopwords can be recommended by Zingg by invoking:
 
-The stopwords can be recommended by Zingg by invoking
-
-`./scripts/zingg.sh --phase recommend --conf <conf.json> --columns <list of columns to generate stop word recommendations>`&#x20;
+`./scripts/zingg.sh --phase recommend --conf <conf.json> --columns <list of columns to generate stop word recommendations>`
 
 By default, Zingg extracts 10% of the high-frequency unique words from a dataset. If the user wants a different selection, they should set up the following property in the config file:
 
@@ -14,7 +12,7 @@ By default, Zingg extracts 10% of the high-frequency unique words from a dataset
 stopWordsCutoff: <a value between 0 and 1>
 ```
 
-Once you have verified the above stop words, you can configure them in the JSON variable **stopWords** with the path to the CSV file containing them. Please ensure while editing the CSV or building it manually that it should contain one word per row.
+Once you have verified the above stop words, you can configure them in the JSON variable **stopWords** with the path to the CSV file containing them. Please ensure while editing the CSV or building it manually that it should contain _one word per row_.
 
 ```
 "fieldDefinition":[
@@ -26,4 +24,3 @@ Once you have verified the above stop words, you can configure them in the JSON
    		"stopWords": "models/100/stopWords/fname.csv"
    	},
 ```
-
diff --git a/docs/connectors/jdbc/mysql.md b/docs/connectors/jdbc/mysql.md
index c17ddd89c..e92de17db 100644
--- a/docs/connectors/jdbc/mysql.md
+++ b/docs/connectors/jdbc/mysql.md
@@ -1,6 +1,6 @@
 # MySQL
 
-## Reading from MySQL database:
+## Reading From MySQL Database:
 
 ```json
     "data" : [{
@@ -16,4 +16,4 @@
     }],
 ```
 
-Please replace \<db\_name> with the name of the database in addition to other props. For more details, refer to the [spark documentation](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html).
+Please replace `<db_name>` with the _name_ of the database in addition to other props. For more details, refer to the [Spark documentation](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html).
diff --git a/docs/connectors/jdbc/postgres.md b/docs/connectors/jdbc/postgres.md
index 52670b197..54f560e91 100644
--- a/docs/connectors/jdbc/postgres.md
+++ b/docs/connectors/jdbc/postgres.md
@@ -1,6 +1,6 @@
 # Postgres
 
-## JSON Settings for reading data from Postgres database:
+## JSON Settings For Reading Data From Postgres Database:
 
 ```json
     "data" : [{
@@ -16,4 +16,4 @@
     }],
 ```
 
-Please replace \<db\_name> with the name of the database in addition to other props. For more details, refer to the [spark documentation](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html).
+Replace `<db_name>` with the _name_ of the database in addition to other props. For more details, refer to the [Spark documentation](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html).
diff --git a/docs/dataSourcesAndSinks/bigquery.md b/docs/dataSourcesAndSinks/bigquery.md
index f14201d0e..b66230896 100644
--- a/docs/dataSourcesAndSinks/bigquery.md
+++ b/docs/dataSourcesAndSinks/bigquery.md
@@ -14,7 +14,7 @@ In addition, the following property needs to be set
 spark.hadoop.fs.gs.impl=com.google.cloud.hadoop.fs.gcs.GoogleHadoopFileSystem                                                      
 ```
 
-If Zingg is run from outside the Google cloud, it requires authentication, please set the following env variable to the location of the file containing the service account key. A service account key can be created and downloaded in JSON format from the [Google Cloud console](https://cloud.google.com/docs/authentication/getting-started).
+If Zingg is run from outside the Google cloud, it requires authentication, please set the following _environment variable_ to the location of the file containing the _service account key_. A service account key can be created and downloaded in JSON format from the [Google Cloud console](https://cloud.google.com/docs/authentication/getting-started).
 
 ```bash
 export GOOGLE_APPLICATION_CREDENTIALS=path to google service account key file
@@ -24,7 +24,7 @@ Connection properties for BigQuery as a data source and data sink are given belo
 
 ## Properties for reading data from BigQuery:
 
-The property **"credentialsFile"** should point to the google service account key file location. This is the same path that is used to set variable **GOOGLE\_APPLICATION\_CREDENTIALS**. The **"table"** property should point to a BigQuery table that contains source data. The property **"viewsEnabled"** must be set to true only.
+The property `credentialsFile` should point to the Google service account key file location. This is the same path that is used to set variable `GOOGLE_APPLICATION_CREDENTIALS`. The `table` property should point to a BigQuery table that contains source data. The property `viewsEnabled` must be set to **true** only.
 
 ```json
     "data" : [{
@@ -38,9 +38,9 @@ The property **"credentialsFile"** should point to the google service account ke
     }],
 ```
 
-## Properties for writing data to BigQuery:
+## Properties For Writing Data To BigQuery:
 
-To write to BigQuery, a bucket needs to be created and assigned to the **"temporaryGcsBucket"** property.
+To write to BigQuery, a bucket needs to be created and assigned to the `temporaryGcsBucket` property.
 
 ```json
     "output" : [{
@@ -57,7 +57,7 @@ To write to BigQuery, a bucket needs to be created and assigned to the **"tempor
 ## Notes:
 
 * The library **"gcs-connector-hadoop2-latest.jar"** can be downloaded from [Google](https://storage.googleapis.com/hadoop-lib/gcs/gcs-connector-hadoop2-latest.jar) and the library **"spark-bigquery-with-dependencies\_2.12-0.24.2"** from the [maven repo](https://repo1.maven.org/maven2/com/google/cloud/spark/spark-bigquery-with-dependencies\_2.12/0.24.2/spark-bigquery-with-dependencies\_2.12-0.24.2.jar).
-* A typical service account key file looks like the below. The format of the file is JSON.
+* A typical service account key file looks like below (JSON).
 
 ```json
 {
diff --git a/docs/dataSourcesAndSinks/connectors.md b/docs/dataSourcesAndSinks/connectors.md
index 90ea98923..5de25d90a 100644
--- a/docs/dataSourcesAndSinks/connectors.md
+++ b/docs/dataSourcesAndSinks/connectors.md
@@ -6,9 +6,10 @@ has_children: true
 
 # Data Sources and Sinks
 
-Zingg connects, reads, and writes to most on-premise and cloud data sources.
-
-Zingg can read and write to Databricks, Snowflake, Cassandra, S3, Azure, Elastic, Exasol, major RDBMS, and any Spark-supported data sources. Zingg also works with all major file formats like Parquet, Avro, JSON, XLSX, CSV, TSV, etc. This is done through the Zingg [pipe](pipes.md) abstraction.
+Zingg _connects, reads,_ and _writes_ to most on-premise and cloud data sources.
 
+Zingg can read and write to **Databricks, Snowflake, Cassandra, S3, Azure, Elastic, Exasol**, major **RDBMS**, and any **Spark**-supported data sources. \
+\
+Zingg also works with all major file formats like Parquet, Avro, JSON, XLSX, CSV, TSV, etc. This is done through the Zingg [Pipe](pipes.md) abstraction.
 
 ![](../../assets/zinggOSS.png)
diff --git a/docs/dataSourcesAndSinks/databricks.md b/docs/dataSourcesAndSinks/databricks.md
index 2062ad842..d5d406d90 100644
--- a/docs/dataSourcesAndSinks/databricks.md
+++ b/docs/dataSourcesAndSinks/databricks.md
@@ -1,5 +1,5 @@
 # Databricks
 
-As a Spark based application, Zingg Open Source works seamlessly on Databricks. Zingg leverages Databricks Spark environment, and can access all the supported data sources like parquet and the delta file format. 
+As a Spark-based application, Zingg Open Source works seamlessly on Databricks. Zingg leverages Databricks' Spark environment, and can access all the supported data sources like parquet and the delta file format.
 
-Please check the various ways in which you can run Zingg On Databricks [here](../running/databricks.md) 
+Please check the various ways in which you can run Zingg on Databricks [here](../running/databricks.md)
diff --git a/docs/dataSourcesAndSinks/exasol.md b/docs/dataSourcesAndSinks/exasol.md
index f1d471ef4..215b540ff 100644
--- a/docs/dataSourcesAndSinks/exasol.md
+++ b/docs/dataSourcesAndSinks/exasol.md
@@ -26,7 +26,7 @@ For example:
 spark.jars=spark-connector_2.12-1.3.0-spark-3.3.2-assembly.jar
 ```
 
-If there are more than one jar files, please use comma as separator. Additionally, please change the version accordingly so that it matches your Zingg and Spark versions.
+If there are more than one jar files, please use _comma_ as separator. Additionally, please change the version accordingly so that it matches your Zingg and Spark versions.
 
 ## Connector Settings
 
@@ -52,28 +52,28 @@ For example:
  ...
 ```
 
- Similarly, for output:
+Similarly, for output:
 
- ```json
- ...
+```json
+...
 "output": [
-    {
-        "name": "output",
-        "format": "com.exasol.spark",
-        "props": {
-            "host": "10.11.0.2",
-            "port": "8563",
-            "username": "sys",
-            "password": "exasol",
-            "create_table": "true",
-            "table": "DB_SCHEMA.ENTITY_RESOLUTION",
-        },
-        "mode": "Append"
-    }
+   {
+       "name": "output",
+       "format": "com.exasol.spark",
+       "props": {
+           "host": "10.11.0.2",
+           "port": "8563",
+           "username": "sys",
+           "password": "exasol",
+           "create_table": "true",
+           "table": "DB_SCHEMA.ENTITY_RESOLUTION",
+       },
+       "mode": "Append"
+   }
 ],
 ...
 ```
 
-Please note that, the `host` parameter should be the first internal node's IPv4 address.
+Please note that, the `host` parameter should be the first internal node's **IPv4** **address**.
 
-As Zingg uses [Exasol Spark connector](https://github.com/exasol/spark-connector) underneath, please also check out the [user guide](https://github.com/exasol/spark-connector/blob/main/doc/user_guide/user_guide.md) and [configuration options](https://github.com/exasol/spark-connector/blob/main/doc/user_guide/user_guide.md#configuration-options) for more information.
+As Zingg uses [Exasol Spark connector](https://github.com/exasol/spark-connector) underneath, please also check out the [user guide](https://github.com/exasol/spark-connector/blob/main/doc/user\_guide/user\_guide.md) and [configuration options](https://github.com/exasol/spark-connector/blob/main/doc/user\_guide/user\_guide.md#configuration-options) for more information.
diff --git a/docs/dataSourcesAndSinks/jdbc.md b/docs/dataSourcesAndSinks/jdbc.md
index cbd25c220..d86f21449 100644
--- a/docs/dataSourcesAndSinks/jdbc.md
+++ b/docs/dataSourcesAndSinks/jdbc.md
@@ -1,6 +1,6 @@
-# Jdbc
+# JDBC
 
-Zingg can connect to various databases such as Mysql, DB2, MariaDB, MS SQL, Oracle, PostgreSQL, etc. using JDBC. One just needs to download the appropriate driver and made it accessible to the application.
+Zingg can connect to various databases such as MySQL, DB2, MariaDB, MS SQL, Oracle, PostgreSQL, etc. using JDBC. One just needs to download the appropriate driver and made it accessible to the application.
 
 To include the JDBC driver for your particular database on the Spark classpath, please add the property **spark.jars** in [Zingg's runtime properties.](../stepbystep/zingg-runtime-properties.md)
 
@@ -8,5 +8,4 @@ To include the JDBC driver for your particular database on the Spark classpath,
 spark.jars=<location of jdbc driver jar>
 ```
 
-Connection details are given in the following sections for a few common JDBC sources.&#x20;
-
+Connection details are given in the following sections for a few common JDBC sources.
diff --git a/docs/dataSourcesAndSinks/pipes.md b/docs/dataSourcesAndSinks/pipes.md
index c928c01f0..a786b639d 100644
--- a/docs/dataSourcesAndSinks/pipes.md
+++ b/docs/dataSourcesAndSinks/pipes.md
@@ -6,9 +6,9 @@ nav_order: 4
 
 # Zingg Pipes
 
-Zingg Pipes are an abstraction for a data source from which Zingg fetches data for matching or to which Zingg writes its output. This lets users connect to literally any datastore that has a Spark connector.
+Zingg Pipes are an _abstraction_ for a data source from which Zingg fetches data for matching or to which Zingg writes its output. This lets users connect to literally any datastore that has a Spark connector.
 
-The pipe is an easy way to specify properties and formats for the Spark connector of the relevant data source. Zingg pipes can be configured through the config [JSON](../stepbystep/configuration/) passed to the program by outlining the datastore connection properties.
+The pipe is an easy way to specify _properties_ and _formats_ for the Spark connector of the relevant data source. Zingg pipes can be configured through the config [JSON](../stepbystep/configuration/) passed to the program by outlining the datastore connection properties.
 
 Pipes can be configured for the data or the output attributes on the [JSON](../stepbystep/configuration/).
 
@@ -20,10 +20,10 @@ A unique name to identify the data store.
 
 ## format
 
-One of the Spark-supported connector formats. jdbc/avro/parquet etc.
+One of the Spark-supported connector formats - jdbc/avro/parquet etc.
 
 ## options
 
-Properties to be passed to spark.read and spark.write.
+Properties to be passed to **spark.read** and **spark.write.**
 
 Let us look at some common data sources and their configurations.
diff --git a/docs/dataSourcesAndSinks/snowflake.md b/docs/dataSourcesAndSinks/snowflake.md
index e5de99940..0bad2c0ac 100644
--- a/docs/dataSourcesAndSinks/snowflake.md
+++ b/docs/dataSourcesAndSinks/snowflake.md
@@ -28,7 +28,7 @@ The config value for the data and output attributes of the JSON is:
 		} ]
 ```
 
-One must include Snowflake JDBC driver and Spark dependency on the classpath. The jars can be downloaded from the maven repository ([1](https://mvnrepository.com/artifact/net.snowflake/snowflake-jdbc), [2](https://mvnrepository.com/artifact/net.snowflake/spark-snowflake)).
+One must include Snowflake JDBC driver and Spark dependency on the classpath. The jars can be downloaded from the **maven** repository ([1](https://mvnrepository.com/artifact/net.snowflake/snowflake-jdbc), [2](https://mvnrepository.com/artifact/net.snowflake/spark-snowflake)).
 
 ```
 spark.jars=snowflake-jdbc-3.13.18.jar,spark-snowflake_2.12-2.10.0-spark_3.1.jar
@@ -36,4 +36,4 @@ spark.jars=snowflake-jdbc-3.13.18.jar,spark-snowflake_2.12-2.10.0-spark_3.1.jar
 
 For Zingg to discover the Snowflake jars, please add the property **spark.jars** in [Zingg's runtime properties.](../stepbystep/zingg-runtime-properties.md)
 
-If you are looking for a native-run on Snowflake without using Spark, check [Zingg Enterprise Snowflake](https://www.zingg.ai/company/zingg-enterprise-snowflake).&#x20;
+If you are looking for a native-run on Snowflake without using Spark, check [Zingg Enterprise Snowflake](https://www.zingg.ai/company/zingg-enterprise-snowflake).
diff --git a/docs/deterministicMatching.md b/docs/deterministicMatching.md
index ae77fe64f..a0cba2906 100644
--- a/docs/deterministicMatching.md
+++ b/docs/deterministicMatching.md
@@ -1,10 +1,14 @@
+# Deterministic Matching
 
-## Deterministic Matching - *Zingg Enterprise Feature*  
-  
-Zingg Enterprise allows the ability to plug rule based deterministic matching along with already Zingg AI's probabilistic matching. If the data contains sure identifiers like emails, SSNs, passport ids etc, we can use these attributes to resolve records. The deterministic matching flow is weaved into Zingg's flow to ensure that each record which has a match finds one, probabilistically, deterministically or both. If the data has known identifiers, Zingg Enterprise's deterministic matching highly improves both matching accuracy and performance.  
-  
-## Example for configuring it in json:  
-```json  
+### Deterministic Matching - _Zingg Enterprise Feature_
+
+Zingg Enterprise allows the ability to plug rule-based deterministic matching along with already Zingg AI's probabilistic matching. If the data contains _sure_ identifiers like emails, SSNs, passport-ids etc, we can use these attributes to resolve records.\
+\
+The deterministic matching flow is weaved into Zingg's flow to ensure that each record which has a match finds one, probabilistically, deterministically or both. If the data has known identifiers, Zingg Enterprise's deterministic matching highly improves both matching accuracy and performance.
+
+### Example For Configuring In JSON:
+
+```json
     "deterministicMatching":[  
         {  
            "matchCondition":[{"fieldName":"fname"},{"fieldName":"stNo"},{"fieldName":"add1"}]  
@@ -16,24 +20,23 @@ Zingg Enterprise allows the ability to plug rule based deterministic matching al
            "matchCondition":[{"fieldName":"fname"},{"fieldName":"email"}]  
         }  
     ]  
-```  
-  
-### Python code example:  
-  
+```
+
+#### Python Code Example:
+
 ```{python}
 detMatchNameAdd = DeterministicMatching('fname','stNo','add1')  
 detMatchNameDobSsn = DeterministicMatching('fname','dob','ssn')  
 detMatchNameEmail = DeterministicMatching('fname','email')  
 args.setDeterministicMatchingCondition(detMatchNameAdd,detMatchNameDobSsn,detMatchNameEmail)  
 ```
-  
-### How will it work:  
-  
-Above conditions would translate into following:  
-  
-1. Those rows which have exactly same fname, stNo and add1 => exact match with max score 1  
-OR  
-2. Those rows which have exactly same fname, dob and ssn => exact match with max score 1  
-OR  
-3. Those rows which have exactly same fname and email => exact match with max score 1  
 
+#### How Will It Work:
+
+The above conditions would translate into the following:
+
+1. Those rows which have **exactly** same `fname`, `stNo` and `add1` => exact match with max score 1\
+   _OR_
+2. Those rows which have **exactly** same `fname`, `dob` and `ssn` => exact match with max score 1\
+   _OR_
+3. Those rows which have **exactly** same `fname` and `email` => exact match with max score 1
diff --git a/docs/generatingdocumentation.md b/docs/generatingdocumentation.md
index 11fb09921..c01307313 100644
--- a/docs/generatingdocumentation.md
+++ b/docs/generatingdocumentation.md
@@ -1,12 +1,11 @@
 # Documenting The Model
 
-Zingg generates readable documentation about the training data, including those marked as matches, as well as non-matches. The documentation is written to the zinggDir/modelId folder and can be built using the following
+Zingg generates readable documentation about the training data, including those marked as matches, as well as non-matches. The documentation is written to the **zinggDir/modelId** folder and can be built using the following
 
 ```
 ./scripts/zingg.sh --phase generateDocs --conf <location to conf.json>
 ```
 
-The generated documentation file can be viewed in a browser and looks like as below.
+The generated documentation file can be viewed in a browser and looks like as below:
 
 ![Training Data](../assets/documentation1.png)
-
diff --git a/docs/python/markdown/index.md b/docs/python/markdown/index.md
index d8b455d19..9f14d1aaa 100644
--- a/docs/python/markdown/index.md
+++ b/docs/python/markdown/index.md
@@ -1,16 +1,12 @@
-<!-- zingg documentation master file, created by
-sphinx-quickstart on Thu Jul  7 12:24:41 2022.
-You can adapt this file completely to your liking, but it should at least
-contain the root `toctree` directive. -->
+# Python API
 
-# Zingg Entity Resolution Python Package
+## Zingg Entity Resolution Python Package
 
-Zingg Python APIs for entity resolution, identity resolution, record linkage, data mastering and deduplication using ML
-([https://www.zingg.ai](https://www.zingg.ai))
+Zingg Python APIs for entity resolution, identity resolution, record linkage, data mastering and deduplication using ML ([https://www.zingg.ai](https://www.zingg.ai))
 
-#### NOTE
-Requires python 3.6+; spark 3.5.0
-Otherwise, [`zingg.client.Zingg()`](zingg.md#zingg.client.Zingg) cannot be executed
+**NOTE**
+
+Requires **python 3.6+**; **spark 3.5.0.** Otherwise, [`zingg.client.Zingg()`](zingg.md#zingg.client.Zingg) cannot be executed
 
 * [Zingg Entity Resolution Package](zingg.md)
   * [zingg.client](zingg.md#zingg-client)
@@ -44,10 +40,10 @@ Otherwise, [`zingg.client.Zingg()`](zingg.md#zingg.client.Zingg) cannot be execu
     * [`ClientOptions.EMAIL`](zingg.md#zingg.client.ClientOptions.EMAIL)
     * [`ClientOptions.LICENSE`](zingg.md#zingg.client.ClientOptions.LICENSE)
     * [`ClientOptions.LOCATION`](zingg.md#zingg.client.ClientOptions.LOCATION)
-    * [`ClientOptions.MODEL_ID`](zingg.md#zingg.client.ClientOptions.MODEL_ID)
+    * [`ClientOptions.MODEL_ID`](zingg.md#zingg.client.ClientOptions.MODEL\_ID)
     * [`ClientOptions.PHASE`](zingg.md#zingg.client.ClientOptions.PHASE)
     * [`ClientOptions.REMOTE`](zingg.md#zingg.client.ClientOptions.REMOTE)
-    * [`ClientOptions.ZINGG_DIR`](zingg.md#zingg.client.ClientOptions.ZINGG_DIR)
+    * [`ClientOptions.ZINGG_DIR`](zingg.md#zingg.client.ClientOptions.ZINGG\_DIR)
     * [`ClientOptions.getClientOptions()`](zingg.md#zingg.client.ClientOptions.getClientOptions)
     * [`ClientOptions.getConf()`](zingg.md#zingg.client.ClientOptions.getConf)
     * [`ClientOptions.getLocation()`](zingg.md#zingg.client.ClientOptions.getLocation)
@@ -94,10 +90,10 @@ Otherwise, [`zingg.client.Zingg()`](zingg.md#zingg.client.Zingg) cannot be execu
   * [`parseArguments()`](zingg.md#zingg.client.parseArguments)
   * [zingg.pipes](zingg.md#zingg-pipes)
   * [`BigQueryPipe`](zingg.md#zingg.pipes.BigQueryPipe)
-    * [`BigQueryPipe.CREDENTIAL_FILE`](zingg.md#zingg.pipes.BigQueryPipe.CREDENTIAL_FILE)
+    * [`BigQueryPipe.CREDENTIAL_FILE`](zingg.md#zingg.pipes.BigQueryPipe.CREDENTIAL\_FILE)
     * [`BigQueryPipe.TABLE`](zingg.md#zingg.pipes.BigQueryPipe.TABLE)
-    * [`BigQueryPipe.TEMP_GCS_BUCKET`](zingg.md#zingg.pipes.BigQueryPipe.TEMP_GCS_BUCKET)
-    * [`BigQueryPipe.VIEWS_ENABLED`](zingg.md#zingg.pipes.BigQueryPipe.VIEWS_ENABLED)
+    * [`BigQueryPipe.TEMP_GCS_BUCKET`](zingg.md#zingg.pipes.BigQueryPipe.TEMP\_GCS\_BUCKET)
+    * [`BigQueryPipe.VIEWS_ENABLED`](zingg.md#zingg.pipes.BigQueryPipe.VIEWS\_ENABLED)
     * [`BigQueryPipe.setCredentialFile()`](zingg.md#zingg.pipes.BigQueryPipe.setCredentialFile)
     * [`BigQueryPipe.setTable()`](zingg.md#zingg.pipes.BigQueryPipe.setTable)
     * [`BigQueryPipe.setTemporaryGcsBucket()`](zingg.md#zingg.pipes.BigQueryPipe.setTemporaryGcsBucket)
@@ -130,13 +126,13 @@ Otherwise, [`zingg.client.Zingg()`](zingg.md#zingg.client.Zingg) cannot be execu
     * [`SnowflakePipe.setUser()`](zingg.md#zingg.pipes.SnowflakePipe.setUser)
     * [`SnowflakePipe.setWarehouse()`](zingg.md#zingg.pipes.SnowflakePipe.setWarehouse)
 
-# API Reference
+## API Reference
 
 * [Module Index](py-modindex.md)
 * [Index](genindex.md)
 * [Search Page](search.md)
 
-# Example API Usage
+## Example API Usage
 
 ```python
 from zingg.client import *
diff --git a/docs/runIncremental.md b/docs/runIncremental.md
index 8986ef89e..2a1597fab 100644
--- a/docs/runIncremental.md
+++ b/docs/runIncremental.md
@@ -1,14 +1,16 @@
+# Add Incremental Data
 
-## Run Incremental Loads For New and Updated Records - *Zingg Enterprise Feature*  
-  
-Rerunning matching on entire datasets is wasteful, and we lose the lineage of matched records against a persistent identifier. Using Zingg Enterprise, incremental loads can be run to match existing pre resolved entities. The new and updated records are matched to existing clusters, and new persistent ZINGG_IDs generated for records which do not find a match. If a record gets updated and Zingg Enterprise discovers that it is a more suitable match with another cluster, it will be reassigned. Cluster assignment, merge and unmerge happens automatically in the flow. Zingg Enterprise also takes care of human feedback on previously matched data to ensure that it does not override the approved records.   
-  
-## The incremental phase is run as follows:  
-`./scripts/zingg.sh --phase runIncremental --conf <location to incrementalConf.json>`  
-  
-## Example incrementalConf.json:  
+### Run Incremental Loads For New and Updated Records - _Zingg Enterprise Feature_
+
+Rerunning matching on entire datasets is wasteful, and we lose the lineage of matched records against a persistent identifier. Using Zingg Enterprise, incremental loads can be run to match existing pre-resolved entities. The new and updated records are matched to existing clusters, and new persistent **ZINGG\_IDs** are generated for records that do not find a match. If a record gets updated and Zingg Enterprise discovers that it is a more suitable match with another cluster, it will be reassigned. Cluster assignment, merge, and unmerge happens automatically in the flow. Zingg Enterprise also takes care of human feedback on previously matched data to ensure that it does not override the approved records.
+
+### The incremental phase is run as follows:
+
+`./scripts/zingg.sh --phase runIncremental --conf <location to incrementalConf.json>`
+
+### Example incrementalConf.json:
 
-```json  
+```json
 {      
     "config" : "config.json",  
     "incrementalData": [{  
@@ -24,12 +26,13 @@ Rerunning matching on entire datasets is wasteful, and we lose the lineage of ma
     ]   
 }  
 ```
-  
-## runIncremental can also be triggered using python by invoking:  
-`./scripts/zingg.sh --run examples/FebrlExample.py`  
-  
-### Python code example:  
-  
+
+### runIncremental can also be triggered using Python by invoking:
+
+`./scripts/zingg.sh --run examples/FebrlExample.py`
+
+#### Python Code Example:
+
 ```{python}
 #import the packages  
   
diff --git a/docs/running/aws.md b/docs/running/aws.md
index 4965bc3df..358a53574 100644
--- a/docs/running/aws.md
+++ b/docs/running/aws.md
@@ -4,9 +4,9 @@ parent: Running Zingg on Cloud
 nav_order: 5
 ---
 
-# Running on AWS
+# Running On AWS
 
-One option is to use the spark-submit option with the Zingg config and phase. Please note that the config.json should be available locally at the driver for Zingg to use it.&#x20;
+One option is to use the `spark-submit` option with the Zingg config and phase. Please note that the **config.json** should be available locally at the driver for Zingg to use it.
 
 `````
 aws emr create-cluster --name "Add Spark Step Cluster" --release-label emr-6.2.0 --applications Name=Zingg \
@@ -14,8 +14,6 @@ aws emr create-cluster --name "Add Spark Step Cluster" --release-label emr-6.2.0
 --steps Type=Spark,Name="Zingg",ActionOnFailure=CONTINUE,Args=[--class,zingg.spark.client.SparkClient,<s3 location of zingg.jar>,--phase,<name of phase - findTrainingData,match etc>,--conf,<local location of config.json>] --use-default-roles````
 `````
 
-A step by step s provided at the following [link](https://blog.infostrux.com/identity-resolution-with-zingg-ai-snowflake-and-aws-emr-for-the-canadian-football-league-22cf0850ab53). The guide mentions training locally using Zingg docker, but the findTrainingData and label phases can be executed on EMR directly.&#x20;
-
-
+A step-by-step is provided [here](https://blog.infostrux.com/identity-resolution-with-zingg-ai-snowflake-and-aws-emr-for-the-canadian-football-league-22cf0850ab53). The guide mentions training locally using Zingg Docker, but the **findTrainingData** and **label** phases can be executed on EMR directly.
 
 A second option is to run Zingg Python code in [AWS EMR Notebooks](https://aws.amazon.com/emr/features/notebooks/)
diff --git a/docs/scoring.md b/docs/scoring.md
index aa52a8764..b3bc8790a 100644
--- a/docs/scoring.md
+++ b/docs/scoring.md
@@ -2,13 +2,13 @@
 nav_order: 6
 ---
 
-# Output Scores
+# Interpreting Output Scores
 
-For each field(FNAME, NAME, EMAIL..), Zingg computes multiple features and feeds them to a classifier. These features are typically different ways to compare strings. We consider string lengths and their differences, character differences, and which characters actually differed.
+For each field(**FNAME, NAME, EMAIL** ..), Zingg computes multiple features and feeds them to a classifier. These features are typically different ways to compare strings. We consider string lengths and their differences, character differences, and which characters _actually_ differed.
 
-* The shorter string pair ABCD and ABCE will be less similar than ABCDEF and ABCEEF.
+* The shorter string pair ABCD and ABCE will be _less similar_ than ABCDEF and ABCEEF.
 * Common typos, for e.g. m instead of n, are penalized less severely and will be scored higher than a word with a replaced by non-obvious character b.
-* The more changes you need to make to the strings to make them match, the less similar they will be.
+* The _more changes_ you need to make to the strings to make them match, the less similar they will be.
 * Differences in the middle are penalized more than prefixes and suffixes.
 
 No individual feature is perfect, but the whole is greater than the sum of its parts. That's where the strength of Zingg matching and the accuracy comes from.
@@ -19,6 +19,6 @@ The threshold is automatically chosen so that you can pick up most of the result
 
 A few things to keep in mind while interpreting the scores:
 
-* Matching is transitive, so if record A matches Record B and Record B matches C, we put records A, B, and C in the same cluster. That is how different records in a cluster get matched at various confidence levels to the rest and all these records showed up together.
-* Our recommendation is to keep a threshold below x% of the max score as suspect records - manual review/ to be visited later/flow through but will less confidence. x will depend on how accurate you find the results and how much you want to control the outcome.
-* Keep cluster size above 4 or 5 for inspection. You could keep it irrespective of the score, or look at only those clusters whose z\_minScore is 0. This will depend on what the results look like to you.
+* Matching is _transitive_, so if record A matches Record B and Record B matches C, we put records A, B, and C in the same cluster. That is how different records in a cluster get matched at various confidence levels to the rest and all these records showed up together.
+* Our recommendation is to keep a threshold below **x**% of the max score as suspect records - manual review/to be visited later/flow through but will lessen confidence. **x** will depend on how accurate you find the results and how much you want to control the outcome.
+* Keep cluster size above 4 or 5 for inspection. You could keep it irrespective of the score, or look at only those clusters whose **z\_minScore** is 0. This will depend on what the results look like to you.
diff --git a/docs/settingUpZingg.md b/docs/settingUpZingg.md
index 9f1f3982a..a16ca1897 100644
--- a/docs/settingUpZingg.md
+++ b/docs/settingUpZingg.md
@@ -1,59 +1,65 @@
-# Setting Zingg Development Environment
+# Setting Up Zingg Development Environment
 
-The following steps will help you set up the Zingg Development Environment. While the steps remain the same across different OS, we have provided detailed instructions for Ubuntu OS. Below examples have been created using Ubuntu 22.04.2 LTS
+The following steps will help you set up the Zingg Development Environment. While the steps remain the same across different OS, we have provided detailed instructions for Ubuntu OS. \
+\
+The below steps have been created using Ubuntu 22.04.2 LTS
 
-Make sure to update your ubutu installation
+Make sure to update your Ubuntu installation:
 
-sudo apt update
+`sudo apt update`
+
+***
 
-****
+**Step 0: Install Ubuntu on WSL2 on Windows**
 
-_**Step 0 :  Install Ubuntu on WSL2 on Windows**_
+* Install **wsl**: Type the following command in **Windows PowerShell**.
 
-* Install wsl: Type the following command in **Windows PowerShell**.
 ```
 wsl --install
 ```
+
 * Download Ubuntu from **Microsoft Store**, **Ubuntu 20.04 LTS**
 * Configure Ubuntu with a **username** and **password**
 * Open **Ubuntu 20.04 LTS** and start working
+
 ```
 sudo apt update
 ```
+
 * Follow this [tutorial](https://ubuntu.com/tutorials/install-ubuntu-on-wsl2-on-windows-10#1-overview) for more information.
 
-****
+***
 
-_**Step 1 :  Clone the Zingg Repository**_
+**Step 1: Clone The Zingg Repository**
 
 * Install and SetUp Git: **sudo apt install git**
 * Verify : **git --version**
 * Set up Git by following the [tutorial](https://www.digitalocean.com/community/tutorials/how-to-install-git-on-ubuntu-20-04).
 * Clone the Zingg Repository: **git clone https://github.com/zinggAI/zingg.git**
 
-_**Note :-**_ It is suggested to fork the repository to your account and then clone the repository.
-
-****
+**Note:** It is suggested to fork the repository to your account and then clone the repository.
 
-_**Step 2 :  Install JDK 1.8 (Java Development Kit)**_
+***
 
-* Follow this [tutorial](https://linuxize.com/post/install-java-on-ubuntu-20-04/) to install Java8 JDK1.8 in Ubuntu.&#x20;
+**Step 2: Install JDK 1.8 (Java Development Kit)**
 
+* Follow this [tutorial](https://linuxize.com/post/install-java-on-ubuntu-20-04/) to install Java8 JDK1.8 in Ubuntu.
 * For example:
+
 ```
 sudo apt install openjdk-8-jdk openjdk-8-jre
 javac -version
 java -version
 ```
 
-****
+***
 
-_**Step 3 :  Install Apache Spark -**_
+**Step 3: Install Apache Spark**
 
 * Download Apache Spark - from the [Apache Spark Official Website](https://spark.apache.org/downloads.html).
 * Install downloaded Apache Spark - on your Ubuntu by following [this tutorial](https://computingforgeeks.com/how-to-install-apache-spark-on-ubuntu-debian/).
-
 * For example for 3.5.0:
+
 ```
 wget https://www.apache.org/dyn/closer.lua/spark/spark-3.5.0/spark-3.5.0-bin-hadoop3.tgz
 tar -xvf spark-3.5.0-bin-hadoop3.tgz
@@ -61,17 +67,17 @@ rm -rf spark-3.5.0-bin-hadoop3.tgz
 sudo mv spark-3.5.0-bin-hadoop3 /opt/spark
 ```
 
-Make sure that spark version you have installed is compatible with java you have installed, and Zingg is supporting those versions.
-
-_**Note :-**_ Zingg supports Spark 3.5 and the corresponding Java version.
+Make sure that Spark version you have installed is compatible with Java you have installed, and Zingg is supporting those versions.
 
-****
+**Note**: Zingg supports Spark 3.5 and the corresponding Java version.
 
-_**Step 4 :  Install Apache Maven**_
+***
 
-* Install the latest maven package.
+**Step 4: Install Apache Maven**
 
+* Install the latest **maven** package.
 * For example for 3.8.8:
+
 ```
 wget https://dlcdn.apache.org/maven/maven-3/3.8.8/binaries/apache-maven-3.8.8-bin.tar.gz
 tar -xvf apache-maven-3.8.8-bin.tar.gz 
@@ -81,11 +87,12 @@ cd bin
 ./mvn --version
 ```
 
-****
+***
+
+**Step 5: Update Environment Variables**
 
-_**Step 5 :  Update Env Variables**_
+Open `.bashrc` and add env variables at the end of the file.
 
-Open .bashrc and add env variables at end of file
 ```
 vim ~/.bashrc
 
@@ -105,15 +112,16 @@ echo $PATH
 mvn --version
 ```
 
-where \<path\_to\_zingg> will be a directory where you clone the repository of the Zingg. Similarly, if you have installed spark on a different directory you can set **SPARK\_HOME** accordingly.
+where `<path_to_zingg>` will be a directory where you clone the repository of the Zingg. Similarly, if you have installed spark on a different directory you can set **SPARK\_HOME** accordingly.
+
+**Note:** If you have already set up **JAVA\_HOME** and **SPARK\_HOME** in the steps before you don't need to do this again.
 
-_**Note :-**_  If you have already set up **JAVA\_HOME** and **SPARK\_HOME** in the steps before you don't need to do this again.
+***
 
-****
+**Step 6: Compile The Zingg Repository**
 
-_**Step 6 :  Compile the Zingg Repository**_
+* Run the following to compile the Zingg Repository -
 
-* Run the following to Compile the Zingg Repository -
 ```
 git branch
 (Ensure you are on main branch)
@@ -122,23 +130,25 @@ mvn initialize
 * **mvn clean compile package -Dspark=sparkVer**
 ```
 
-_**Note :-**_	Replace the **sparkVer** with the version of spark you installed, For example, **-Dspark=3.5** and if still facing error, include **-Dmaven.test.skip=true** with the above command.
+**Note:** Replace the `sparkVer` with the version of Spark you installed. \
+\
+For example, **-Dspark=3.5** you still face an error, include **-Dmaven.test.skip=true** with the above command.
 
+**Note:** Substitute 3.3 with the **profile** of the Spark version you have installed. This is based on profiles specified in **pom.xml**
 
-_**Note :-**_ substitute 3.3 with profile of the spark version you have installed. This is based on profiles specified in pom.xml
-****
+***
 
-_**Step 7 :  If had any issue with 'SPARK\_LOCAL\_IP'**_
+**Step 7: If you have any issue with 'SPARK\_LOCAL\_IP'**
 
 * Install **net-tools** using **sudo apt-get install -y net-tools**
-* Run command in the terminal **ifconfig**, find the **IP address** and paste the same in **/opt/hosts** IP address of your Pc-Name
+* Run `ifconfig` in the terminal, find the **IP address** and paste the same in **/opt/hosts** IP address of your Pc-Name
 
-****
+***
 
-_**Step 8 :  Run Zingg to Find Training Data**_
+**Step 8: Run Zingg To Find Training Data**
 
-* Run this Script in terminal opened in zingg clones directory - **./scripts/zingg.sh --phase findTrainingData --conf examples/febrl/config.json**
+* Run this script in the terminal opened in Zingg clones directory `./scripts/zingg.sh --phase findTrainingData --conf examples/febrl/config.json`
 
-****
+***
 
-**If everything is right, it should show Zingg Icon.**
+**If everything is right, it should show Zingg icon.**
diff --git a/docs/setup/hardwareSizing.md b/docs/setup/hardwareSizing.md
index 3e69e6244..d13df4d86 100644
--- a/docs/setup/hardwareSizing.md
+++ b/docs/setup/hardwareSizing.md
@@ -9,15 +9,15 @@ description: Hardware required for different sizes of data
 
 Zingg has been built to scale. Performance is dependent on:
 
-* The number of records to be matched.
-* The number of fields to be compared against each other.
-* The actual number of duplicates.
+* The **number of records** to be matched.
+* The **number of fields** to be compared against each other.
+* The **actual number** of duplicates.
 
 Here are some performance numbers you can use to determine the appropriate hardware for your data.
 
-* 120k records of examples/febrl120k/test.csv take 5 minutes to run on a 4 core, 10 GB RAM local Spark cluster.
+* 120k records of **examples/febrl120k/test.csv** take 5 minutes to run on a 4 core, 10 GB RAM local Spark cluster.
 * 5m records of [North Carolina Voters](https://github.com/zinggAI/zingg/tree/main/examples/ncVoters5M) take \~4 hours on a 4 core, 10 GB RAM local Spark cluster.
 * 9m records with 3 fields - first name, last name, email take 45 minutes to run on AWS m5.24xlarge instance with 96 cores, 384 GB RAM
-* 80m records with 8-10 fields took less than 2 hours on 1 driver(128 GB RAM, 32 cores), 8 workers(224 GB RAM, 64 cores). This is a user-reported stat without any optimization. 
+* 80m records with 8-10 fields took less than 2 hours on 1 driver (128 GB RAM, 32 cores), 8 workers (224 GB RAM, 64 cores). This is a user-reported stat without any optimization.
 
 If you have up to a few million records, it may be easier to run Zingg on a single machine in Spark local mode.
diff --git a/docs/setup/installation.md b/docs/setup/installation.md
index ce5976b77..6fbcd35cc 100644
--- a/docs/setup/installation.md
+++ b/docs/setup/installation.md
@@ -7,7 +7,6 @@ description: From the pre-built release hosted on GitHub
 
 # Installation
 
-Zingg runs on [Spark](https://spark.apache.org) and can be used on all major Spark distributions. Zingg can run on all major Linux flavors.&#x20;
-
-Zingg can be installed from the [release on github](../stepbystep/installation/installing-from-release/), [built from sources](../stepbystep/installation/compiling-from-source.md) or used [directly through Docker](../stepbystep/installation/docker/). The next sections describe each option in more detail.&#x20;
+Zingg runs on [Spark](https://spark.apache.org) and can be used on all major Spark distributions. Zingg can run on all major Linux flavors.
 
+Zingg can be installed from the [release on GitHub](../stepbystep/installation/installing-from-release/), [built from sources](../stepbystep/installation/compiling-from-source.md) or used [directly through Docker](../stepbystep/installation/docker/). The next sections describe each option in more detail.
diff --git a/docs/setup/link.md b/docs/setup/link.md
index 88140984a..760ca30f6 100644
--- a/docs/setup/link.md
+++ b/docs/setup/link.md
@@ -1,11 +1,11 @@
-# Linking across datasets
+# Linking Across Datasets
 
-In many cases like reference data mastering, enrichment, etc, two individual datasets are duplicates free but they need to be matched against each other. The link phase is used for such scenarios.
+In many cases like reference data mastering, enrichment, etc, two individual datasets are duplicate-free but they need to be matched against each other. The link phase is used for such scenarios.
 
 `./zingg.sh --phase link --conf config.json`
 
 Sample configuration file [configLink.json](../../examples/febrl/configLink.json) is defined at [examples/febrl](https://github.com/zinggAI/zingg/tree/main/examples/febrl). In this option, each record from the first source is matched with all the records from the remaining sources.
 
-The sample output is given in the image below. The linked records are given the same z\_cluster id. The last column (z\_source) in the output tells the source dataset of that record.
+The sample output is given in the image below. The linked records are given the same **z\_cluster** id. The last column (**z\_source**) in the output tells the source dataset of that record.
 
-![Link results](../../assets/link.png)
+![Link Results](../../assets/link.png)
diff --git a/docs/setup/match.md b/docs/setup/match.md
index 050d95fa4..c4fdc1f93 100644
--- a/docs/setup/match.md
+++ b/docs/setup/match.md
@@ -1,17 +1,17 @@
 ---
-layout: default
 title: Find the matches
 parent: Step By Step Guide
 nav_order: 8
 ---
 
-### match
-Finds the records which match with each other. 
+# Finding The Matches
+
+## Finds the records that match with each other.
 
 `./zingg.sh --phase match --conf config.json`
 
-As can be seen in the image below, matching records are given the same z_cluster id. Each record also gets a z_minScore and z_maxScore which shows the least/greatest it matched with other records in the same cluster. 
+As can be seen in the image below, matching records are given the same **z\_cluster** id. Each record also gets a **z\_minScore** and **z\_maxScore** which shows the _least/greatest_ it matched with other records in the same cluster.
 
-![Match results](/assets/match.gif)
+![Match results](../../assets/match.gif)
 
-If records across multiple sources have to be matched, the [link phase](./link.md) should be used.
\ No newline at end of file
+If records across multiple sources have to be matched, the [link phase](link.md) should be used.
diff --git a/docs/setup/train.md b/docs/setup/train.md
index 413e50aae..f40cc508b 100644
--- a/docs/setup/train.md
+++ b/docs/setup/train.md
@@ -1,10 +1,14 @@
 ---
-layout: default
 title: Build and save the model
 parent: Step By Step Guide
 nav_order: 7
 ---
-### train - training and saving the models
-Builds up the Zingg models using the training data from the above phases and writes them to the folder zinggDir/modelId as specified in the config.
 
-    ./zingg.sh --phase train --conf config.json
+# Building And Saving The Model
+
+Builds up the Zingg models using the training data from the above phases and writes them to the folder **zinggDir/modelId** as specified in the config.
+
+```
+./zingg.sh --phase train --conf config.json
+```
+
diff --git a/docs/setup/training/addOwnTrainingData.md b/docs/setup/training/addOwnTrainingData.md
index e05fec60e..9ad57e86f 100644
--- a/docs/setup/training/addOwnTrainingData.md
+++ b/docs/setup/training/addOwnTrainingData.md
@@ -5,18 +5,18 @@ title: Using preexisting training data
 grand_parent: Step By Step Guide
 ---
 
-# Using pre-existing training data
+# Using Pre-existing Training Data
 
-## Supplementing Zingg with existing training data
+## Supplementing Zingg With Existing Training Data
 
-If you already have some training data that you want to start with, you can use that as well with Zingg. Add an attribute trainingSamples to the config and define the training pairs.
+If you already have some training data that you want to start with, you can use that as well with Zingg. Add an attribute **trainingSamples** to the config and define the training pairs.
 
-The training data supplied to Zingg should have a z\_cluster column that groups the records together. The z\_cluster uniquely identies the group. We also need to add the z\_isMatch column which is 1 if the pairs match or 0 if they do not match. The z\_isMatch value has to be same for all the records in the z\_cluster group. They either match with each other or they dont.  
+The training data supplied to Zingg should have a **z\_cluster** column that groups the records together. The **z\_cluster** uniquely identifies the group. We also need to add the **z\_isMatch** column which is **1** if the pairs _match_ or **0** if they do _not_ match. The **z\_isMatch** value has to be the same for all the records in the **z\_cluster** group. They either match with each other or they don't.
 
-An example is provided in [Github training data](../../../examples/febrl/training.csv).
+An example is provided in [GitHub training data](../../../examples/febrl/training.csv).
 
 The above training data can be specified using [trainingSamples attribute in the configuration.](../../../examples/febrl/configWithTrainingSamples.json)
 
 In addition, labeled data of one model can also be exported and used as training data for another model. For details, check out [exporting labeled data](exportLabeledData.md).
 
-Please note: It is advisable to still run [findTrainingData](findTrainingData.md) and [label](label.md) a few rounds to tune Zingg with the supplied training data as well as patterns it needs to learn independently.
+**Note**: It is advisable to still run [findTrainingData](findTrainingData.md) and [label](label.md) a few rounds to tune Zingg with the supplied training data as well as patterns it needs to learn independently.
diff --git a/docs/setup/training/createTrainingData.md b/docs/setup/training/createTrainingData.md
index 0246ae013..b016c466f 100644
--- a/docs/setup/training/createTrainingData.md
+++ b/docs/setup/training/createTrainingData.md
@@ -5,6 +5,6 @@ title: Creating training data
 has_children: true
 ---
 
-# Training data
+# Working With Training Data
 
-Zingg builds models to predict similarity. Training data is needed to build these models. The next sections describe how you can use the Zingg Interactive Labeler to create the training data.
+Zingg builds models to predict _similarity_. Training data is needed to build these models. The next sections describe how you can use the **Zingg Interactive Labeler** to create the training data.
diff --git a/docs/setup/training/findAndLabel.md b/docs/setup/training/findAndLabel.md
index 152a6b66e..152d78fc3 100644
--- a/docs/setup/training/findAndLabel.md
+++ b/docs/setup/training/findAndLabel.md
@@ -7,8 +7,8 @@ nav_order: 2
 
 # Find And Label
 
-This phase is composed of two phases namely [findTrainingData](findTrainingData.md) and [label](label.md). This will help experienced users to quicken the process of creating Training data.
+This phase is composed of two phases namely [findTrainingData](findTrainingData.md) and [label](label.md). This will help experienced users to quicken the process of creating training data.
 
 `./zingg.sh --phase findAndLabel --conf config.json`
 
-As this is phase runs findTrainingData and label together, it should be run only for small datasets where findTrainingData takes a short time to run, else the the user will have to wait long for the console for labeling.&#x20;
+As this is phase runs **findTrainingData** and **label** together, it should be run only for small datasets where **findTrainingData** takes a short time to run, else the the user will have to wait long for the console to label.
diff --git a/docs/setup/training/findTrainingData.md b/docs/setup/training/findTrainingData.md
index 5430b50b9..e3e04437f 100644
--- a/docs/setup/training/findTrainingData.md
+++ b/docs/setup/training/findTrainingData.md
@@ -7,10 +7,10 @@ description: pairs of records that could be similar to train Zingg
 
 # Finding Records For Training Set Creation
 
-The findTrainingData phase prompts Zingg to search for edge cases in the data which can be labeled by the user and used for learning. During this phase, Zingg combs through the data samples and judiciously selects limited representative pairs which can be marked by the user.  Zingg is very frugal about the training so that user effort is minimized and models can be built and deployed quickly.
+The **findTrainingData** phase prompts Zingg to search for edge cases in the data which can be labeled by the user and used for learning. During this phase, Zingg combs through the data samples and judiciously selects limited representative pairs that can be marked by the user. Zingg is very frugal about the training so that user effort is minimized and models can be built and deployed quickly.
 
-This findTrainingData job writes the edge cases to the folder configured through zinggDir/modelId in the config.
+This **findTrainingData** job writes the edge cases to the folder configured through `zinggDir/modelId` in the config:
 
 `./zingg.sh --phase findTrainingData --conf config.json`
 
-The findTrainingData phase is run first and then the label phase is run and this cycle is repeated so that the Zingg models get smarter from user feedback.
+The **findTrainingData** phase is run first and then the label phase is run and this cycle is repeated so that the Zingg models get smarter from user feedback.
diff --git a/docs/setup/training/label.md b/docs/setup/training/label.md
index 4081f08b5..77dbf1123 100644
--- a/docs/setup/training/label.md
+++ b/docs/setup/training/label.md
@@ -7,12 +7,12 @@ description: Providing user feedback on the training pairs
 
 # Labeling Records
 
-The label phase opens an interactive learner where the user can mark the pairs found by findTrainingData phase as matches or non-matches. The findTrainingData phase generates edge cases for labeling and the label phase helps the user mark them.
+The label phase opens an _interactive_ learner where the user can mark the pairs found by **findTrainingData** phase as matches or non-matches. The **findTrainingData** phase generates edge cases for labeling and the label phase helps the user mark them.
 
 `./zingg.sh --phase label --conf config.json <optional --showConcise=true|false>`
 
 ![Shows records and asks user to mark yes, no, can't say on the cli.](../../../assets/label.gif)
 
-Proceed running findTrainingData followed by label phases till you have at least 30-40 positives, or when you see the predictions by Zingg converging with the output you want. At each stage, the user will get different variations of attributes across the records. Zingg performs pretty well with even a small number of training, as the samples to be labeled are chosen by the algorithm itself.
+Proceed running **findTrainingData** followed by label phases till you have at least 30-40 positives, or when you see the predictions by Zingg converging with the output you want. At each stage, the user will get different variations of attributes across the records. Zingg performs pretty well with even a small number of training, as the samples to be labeled are chosen by the algorithm itself.
 
-The showConcise flag when passed to the Zingg command line only shows fields which are NOT DONT\_USE
+The **showConcise** flag when passed to the Zingg command line only shows fields which are **NOT DONT\_USE**
diff --git a/docs/stepByStep.md b/docs/stepByStep.md
index f64352dc2..d11f06d04 100644
--- a/docs/stepByStep.md
+++ b/docs/stepByStep.md
@@ -8,23 +8,23 @@ description: Instructions on how to install and use Zingg
 
 Installation instructions for docker, as well as GitHub release, are [here](setup/installation.md). If you need to build from the sources or compile for a different flavor of Spark, check [compiling](setup/installation.md#compiling-from-sources).
 
-## Step 2: Plan for Hardware
+## Step 2: Plan For Hardware
 
 Decide your hardware based on the [performance numbers](setup/hardwareSizing.md).
 
-## Step 3: Build the config for your data
+## Step 3: Build The Config For Your Data
 
 Zingg needs a configuration file that defines the data and what kind of matching is needed. You can create the configuration file by following the instructions [here](stepbystep/configuration/).
 
-## Step 4: Create the training data
+## Step 4: Create Training Data
 
 Zingg builds a new set of models(blocking and similarity) for every new schema definition(columns and match types). This means running the _findTrainingData_ and _label_ phases multiple times to build the training dataset from which Zingg will learn. You can read more [here](setup/training/createTrainingData.md).
 
-## Step 5: Build and save the model
+## Step 5: Build & Save The Model
 
 The training data in Step 4 above is used to train Zingg and build and save the models. This is done by running the _train_ phase. Read more [here](setup/train.md).
 
-## Step 6: Voila, let's match!
+## Step 6: Voila, Let's Match!
 
 It's now time to apply the model to our data. This is done by running the _match_ or the _link_ phases depending on whether you are matching within a single source or linking multiple sources respectively. You can read more about [matching](setup/match.md) and [linking](setup/link.md).
 
diff --git a/docs/stepbystep/configuration/README.md b/docs/stepbystep/configuration/README.md
index 23417002c..28a1ee262 100644
--- a/docs/stepbystep/configuration/README.md
+++ b/docs/stepbystep/configuration/README.md
@@ -4,11 +4,8 @@ description: JSON configuration file required to work with Zingg
 
 # Configuration
 
-Zingg comes with a command-line script that invokes spark-submit. This script needs a JSON configuration file to define the input data and match types, location of training data, models, and output.
+Zingg comes with a command-line script that invokes `spark-submit`. This script needs a **JSON configuration** file to define the input data and match types, location of training data, models, and output.
 
 Sample configuration files are defined at [examples/febrl](https://github.com/zinggAI/zingg/tree/main/examples/febrl) and [examples/febrl120k](https://github.com/zinggAI/zingg/tree/main/examples/febrl120k).
 
 The following sections describe the different JSON attributes of the configuration to get Zingg to work with your data.
-
-
-
diff --git a/docs/stepbystep/configuration/configuring-through-environment-variables.md b/docs/stepbystep/configuration/configuring-through-environment-variables.md
index 540f15385..444806dc8 100644
--- a/docs/stepbystep/configuration/configuring-through-environment-variables.md
+++ b/docs/stepbystep/configuration/configuring-through-environment-variables.md
@@ -4,7 +4,7 @@ description: Passing Configuration value through the system environment variable
 
 # Configuring Through Environment Variables
 
-If a user does not want to pass the value of any JSON parameter through the config file for security reasons or otherwise, they can configure that value through the system environment variable. The system variable name needs to be put in the config file in place of its JSON value. At runtime, the config file gets updated with the value of the environment variable.
+If a user does not want to pass the value of any JSON parameter through the config file for security reasons or otherwise, they can configure that value through the _system environment variable_. The system variable name needs to be put in the config file in place of its JSON value. At runtime, the config file gets updated with the value of the environment variable.
 
 Below is the config file snippet that references a few environment variables.
 
@@ -27,4 +27,4 @@ Below is the config file snippet that references a few environment variables.
 "collectMetrics": $collectMetrics$
 ```
 
-Environment variables must be enclosed within dollar signs **$var$** to take effect. Also, the config file name must be suffixed with \***.env**. As usual, String variables need to be put within quotes **"$var$"**, Boolean and Numeric values should be put without quotes **$var$**.
+Environment variables must be enclosed within dollar signs **$var$** to take effect. Also, the config file name must be _suffixed_ with \***.env**. As usual, `String`variables need to be put _within_ quotes **"$var$"**, `Boolean` and `Numeric` values should be put _without_ quotes **$var$**.
diff --git a/docs/stepbystep/configuration/field-definitions.md b/docs/stepbystep/configuration/field-definitions.md
index d2bd5c291..f5058006c 100644
--- a/docs/stepbystep/configuration/field-definitions.md
+++ b/docs/stepbystep/configuration/field-definitions.md
@@ -10,38 +10,38 @@ description: >-
 
 This is a JSON array representing the fields from the source data to be used for matching, and the kind of matching they need.
 
-Each field denotes a column from the input. Fields have the following JSON attributes:
+Each field denotes a **column** from the input. Fields have the following JSON attributes:
 
 **fieldName**
 
-The name of the field from the input data schema
+The **name** of the field from the input data schema
 
 **fields**
 
-To be defined later. For now, please keep this as the fieldName
+To be defined later. For now, please keep this as the `fieldName`
 
 **dataType**
 
-Type of the column - string, integer, double, etc.
+Type of the column - `string, integer, double`, etc.
 
 **matchType**
 
-&#x20;The way to match the given field. Multiple match types, separated by commas, can also be used. For example FUZZY,NUMERIC. Here are the different types supported.&#x20;
+The way to match the given field. Multiple match types, separated by commas, can also be used. For example **FUZZY**, **NUMERIC**. Here are the different types supported:
 
 #### showConcise
 
-| Match Type             | Description                                                                                                                                                                                                                                           | Can be applied to             |
+| Match Type             | Description                                                                                                                                                                                                                                           | Applicable To                 |
 | ---------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------- |
 | FUZZY                  | Broad matches with typos, abbreviations, and other variations.                                                                                                                                                                                        | string, integer, double, date |
 | EXACT                  | No tolerance with variations, Preferable for country codes, pin codes, and other categorical variables where you expect no variations.                                                                                                                | string                        |
 | DONT\_USE              | Appears in the output but no computation is done on these. Helpful for fields like ids that are required in the output. DONT\_USE fields are not shown to the user while labeling, if [showConcise](field-definitions.md#showconcise) is set to true. | any                           |
 | EMAIL                  | Matches only the id part of the email before the @ character                                                                                                                                                                                          | any                           |
 | PINCODE                | Matches pin codes like xxxxx-xxxx with xxxxx                                                                                                                                                                                                          | string                        |
-| NULL\_OR\_BLANK      | By default Zingg treats nulls as matches, but if we add this to a field which has other match type like FUZZY, Zingg will build a feature for null values and learn                                                                                                                                                                                                           | string                        |
-| TEXT                   | Compares words overlap between two strings. Good for descriptive fields without much typos                                                                                                                               | string                        |
-| NUMERIC                | extracts numbers from strings and compares how many of them are same across both strings, for example apartment numbers.                                                                                                                                                              | string                        | 
-| NUMERIC\_WITH\_UNITS   | extracts product codes or numbers with units, for example 16gb from strings and compares how many are same across both strings                                                                                                                        | string                        | 
-| ONLY\_ALPHABETS\_EXACT | only looks at the alphabetical characters and compares if they are exactly the same. when the numbers inside strings do not matter, for example if you are looking at buildings but want to ignore flat numbers                                                                                                                                                                  | string                        | 
-| ONLY\_ALPHABETS\_FUZZY | ignores any numbers in the strings and then does a fuzzy comparison, useful for fields like addresses with typos where you want to look at street number separately using NUMERIC                                                                                                                                                                                 | string                        | 
+| NULL\_OR\_BLANK        | By default Zingg treats nulls as matches, but if we add this to a field which has other match type like FUZZY, Zingg will build a feature for null values and learn                                                                                   | string                        |
+| TEXT                   | Compares words overlap between two strings. Good for descriptive fields without much typos                                                                                                                                                            | string                        |
+| NUMERIC                | Extracts numbers from strings and compares how many of them are same across both strings, for example apartment numbers.                                                                                                                              | string                        |
+| NUMERIC\_WITH\_UNITS   | Extracts product codes or numbers with units, for example 16gb from strings and compares how many are same across both strings                                                                                                                        | string                        |
+| ONLY\_ALPHABETS\_EXACT | Only looks at the alphabetical characters and compares if they are exactly the same. When the numbers inside strings do not matter, for example if you are looking at buildings but want to ignore flat numbers                                       | string                        |
+| ONLY\_ALPHABETS\_FUZZY | Ignores any numbers in the strings and then does a fuzzy comparison, useful for fields like addresses with typos where you want to look at street number separately using NUMERIC                                                                     | string                        |
 
 ####
diff --git a/docs/stepbystep/configuration/model-location.md b/docs/stepbystep/configuration/model-location.md
index b5a5ae0cf..7aa86e685 100644
--- a/docs/stepbystep/configuration/model-location.md
+++ b/docs/stepbystep/configuration/model-location.md
@@ -2,8 +2,8 @@
 
 #### zinggDir
 
-The location where trained models will be saved. Defaults to /tmp/zingg
+The **location** where trained models will be saved. Defaults to `/tmp/zingg`
 
 #### modelId
 
-An identifier for the model. You can train multiple models - say, one for customers matching names, age, and other personal details and one for households matching addresses. Each model gets saved under zinggDir/modelId
+An **identifier** for the model. You can train multiple models - say, one for **customers** matching _names_, _age_, and other personal details and one for **households** matching _addresses_. Each model gets saved under `zinggDir/modelId`
diff --git a/docs/stepbystep/configuration/telemetry.md b/docs/stepbystep/configuration/telemetry.md
index 486714f5a..71d3ac25a 100644
--- a/docs/stepbystep/configuration/telemetry.md
+++ b/docs/stepbystep/configuration/telemetry.md
@@ -10,4 +10,4 @@ Application captures a few measurements for runtime metrics such as _no. of data
 
 **Zingg does not capture any user data or input data and will never do so.**
 
-This feature may be disabled by setting this flag to false. The default value is true. For details, refer to [Security And Privacy](../../security.md).
+This feature may be disabled by setting this flag to **false**. The default value is **true**. For details, refer to [Security And Privacy](../../security.md).
diff --git a/docs/stepbystep/configuration/tuning-label-match-and-link-jobs.md b/docs/stepbystep/configuration/tuning-label-match-and-link-jobs.md
index e4553f543..4ca6ea578 100644
--- a/docs/stepbystep/configuration/tuning-label-match-and-link-jobs.md
+++ b/docs/stepbystep/configuration/tuning-label-match-and-link-jobs.md
@@ -6,4 +6,4 @@ The number of Spark partitions over which the input data is distributed. Keep it
 
 #### labelDataSampleSize
 
-Fraction of the data to be used for training the models. Adjust it between 0.0001 and 0.1 to keep the sample size small enough so that it finds enough edge cases fast. If the size is bigger, the findTrainingData job will spend more time combing through samples. If the size is too small, Zingg may not find the right edge cases.
+Fraction of the data to be used for training the models. Adjust it between 0.0001 and 0.1 to keep the sample size small enough so that it finds enough edge cases fast. If the size is bigger, the `findTrainingData` job will spend more time combing through samples. If the size is too small, Zingg may not find the right edge cases.
diff --git a/docs/stepbystep/installation/compiling-from-source.md b/docs/stepbystep/installation/compiling-from-source.md
index 1de4f32fa..8181bddfb 100644
--- a/docs/stepbystep/installation/compiling-from-source.md
+++ b/docs/stepbystep/installation/compiling-from-source.md
@@ -6,9 +6,7 @@ description: For a different Spark version or compiling latest code
 
 If you need to compile the latest code or build for a different Spark version, you can clone this repo and
 
-* Install maven
-* Install JDK 1.8
-* Set JAVA\_HOME to JDK base directory
+* Install **maven**
+* Install **JDK 1.8**
+* Set `JAVA_HOME` to JDK base directory
 * Run the following: `mvn initialize` and then `mvn clean compile package`
-
-
diff --git a/docs/stepbystep/installation/docker/README.md b/docs/stepbystep/installation/docker/README.md
index 13a9f1b30..d1282f112 100644
--- a/docs/stepbystep/installation/docker/README.md
+++ b/docs/stepbystep/installation/docker/README.md
@@ -4,7 +4,7 @@ description: From pre-built Docker image with all dependencies included
 
 # Docker
 
-## Running from Docker image from Docker hub
+## Running From Docker Image From Docker Hub
 
 The easiest way to get started is to pull the Docker image with the last release of Zingg.
 
@@ -13,6 +13,6 @@ docker pull zingg/zingg:0.4.0
 docker run -it zingg/zingg:0.4.0 bash
 ```
 
-To know more about Docker, please refer to the official [docker documentation](https://docs.docker.com/).
+To know more about Docker, please refer to the official [Docker documentation](https://docs.docker.com/).
 
 ##
diff --git a/docs/stepbystep/installation/docker/copying-files-to-and-from-the-container.md b/docs/stepbystep/installation/docker/copying-files-to-and-from-the-container.md
index 57d5cd355..60f70e0a2 100644
--- a/docs/stepbystep/installation/docker/copying-files-to-and-from-the-container.md
+++ b/docs/stepbystep/installation/docker/copying-files-to-and-from-the-container.md
@@ -2,9 +2,9 @@
 description: Alternative to volume/bind mount
 ---
 
-# Copying Files To and From the Container
+# Copying Files To And From The Container
 
-A quick alternative to **Volume/bind Mount** is to just copy necessary files to and forth between the host and the container.
+A quick alternative to **Volume/Bind Mount** is to just copy necessary files to and forth between the host and the container.
 
 One specific file/directory can be copied TO and FROM the container. e.g.
 
@@ -13,9 +13,8 @@ $ docker cp foo.txt <container_id>:/foo.txt
 $ docker cp <container_id>:/foo.txt foo.txt
 ```
 
-The container id of the running instance can be found using the below command.
+The _container id_ of the running instance can be found using the below command:
 
 ```
 $ docker ps
 ```
-
diff --git a/docs/stepbystep/installation/docker/file-read-write-permissions.md b/docs/stepbystep/installation/docker/file-read-write-permissions.md
index 0c5ec43f0..1026469c1 100644
--- a/docs/stepbystep/installation/docker/file-read-write-permissions.md
+++ b/docs/stepbystep/installation/docker/file-read-write-permissions.md
@@ -2,9 +2,9 @@
 description: To enable user to have create/read/write files in shared location
 ---
 
-# File read/write permissions
+# File Read/Write Permissions
 
-A docker image is preferred to run with a non-root user. By default, the Zingg container runs with uid '1001'. A valid 'uid' can be passed through the command line in order to run the container with that user id. This will enable the user to have requisite permissions to create/read/write files in the shared location.
+A docker image is preferred to run with a non-root user. By default, the Zingg container runs with **uid '1001'**. A valid '_uid_' can be passed through the command line in order to run the container with that user id. This will enable the user to have requisite permissions to create/read/write files in the shared location.
 
 ```
 $ id 
diff --git a/docs/stepbystep/installation/docker/shared-locations.md b/docs/stepbystep/installation/docker/shared-locations.md
index e580b84de..ecf9f7c4b 100644
--- a/docs/stepbystep/installation/docker/shared-locations.md
+++ b/docs/stepbystep/installation/docker/shared-locations.md
@@ -2,14 +2,14 @@
 description: Shared location used to store Zingg configurations
 ---
 
-# Shared locations
+# Shared Locations
 
-## Zingg configurations using shared location
+## Zingg Configurations Using Shared Location
 
-The **zinggDir** location where model information is stored may use a shared location. In fact, any oft-editable file such as config.json should be kept in this location only.
+The **zinggDir** location where model information is stored may use a shared location. In fact, any _oft-editable_ file such as **config.json** should be kept in this location only.
 
 ```
 zingg.sh --phase label --conf config.json --zinggDir /location
 ```
 
-Similarly, the output and data dir [configurations](../../../stepbystep/configuration) inside config.json can be made using a shared location. Please ensure that the running user has access permissions for this location.
+Similarly, the output and data dir [configurations](../../configuration/) inside **config.json** can be made using a shared location. Please ensure that the running user has access permissions for this location.
diff --git a/docs/stepbystep/installation/docker/sharing-custom-data-and-config-files.md b/docs/stepbystep/installation/docker/sharing-custom-data-and-config-files.md
index c85a615a7..edeb11450 100644
--- a/docs/stepbystep/installation/docker/sharing-custom-data-and-config-files.md
+++ b/docs/stepbystep/installation/docker/sharing-custom-data-and-config-files.md
@@ -2,9 +2,9 @@
 description: Using custom data to save data files on host machine
 ---
 
-# Sharing custom data and config files
+# Sharing Custom Data And Config Files
 
-However, note that once the docker container is stopped, all the work done in that session is lost. If we want to use custom data or persist the generated model or data files, we have to use **Volumes** or **Bind mount** to share files between the two.
+However, note that once the docker container is stopped, all the work done in that session is lost. If we want to use custom data or persist the generated model or data files, we have to use **Volumes** or **Bind Mount** to share files between the two.
 
 ```
 docker run -v <local-location>:<container-location> -it zingg/zingg:0.4.0 bash
diff --git a/docs/stepbystep/installation/installing-from-release/README.md b/docs/stepbystep/installation/installing-from-release/README.md
index 66180845a..e246576f5 100644
--- a/docs/stepbystep/installation/installing-from-release/README.md
+++ b/docs/stepbystep/installation/installing-from-release/README.md
@@ -4,7 +4,7 @@ description: From the pre-built release hosted on GitHub
 
 # Installing From Release
 
-Zingg is prebuilt for common Spark versions so that you can use those directly. The following document assumes that we are installing Zingg 0.3 on Spark 3.1.2, but you can follow the same process for other versions too.
+Zingg is prebuilt for common Spark versions so that you can use those directly. The following document assumes that we are installing **Zingg 0.3** on **Spark 3.1.2**, but you can follow the same process for other versions too.
 
 ## Prerequisites
 
@@ -12,9 +12,6 @@ A) Java JDK - version "1.8.0\_131"
 
 B) Apache Spark - version spark-3.5.0-bin-hadoop3
 
-****
-
-
+***
 
 ####
-
diff --git a/docs/stepbystep/installation/installing-from-release/installing-zingg.md b/docs/stepbystep/installation/installing-from-release/installing-zingg.md
index 79bf1fc70..e39f05e70 100644
--- a/docs/stepbystep/installation/installing-from-release/installing-zingg.md
+++ b/docs/stepbystep/installation/installing-from-release/installing-zingg.md
@@ -6,14 +6,14 @@ description: Downloading and setting things up
 
 Download the tar zingg-version.tar.gz from the [Zingg releases page](https://github.com/zinggAI/zingg/releases) to a folder of your choice and run the following:
 
-> gzip -d zingg-0.4.0.tar.gz ; tar xvf zingg-0.4.0.tar
+> `gzip -d zingg-0.4.0.tar.gz ; tar xvf zingg-0.4.0.tar`
 
-This will create a folder zingg-0.4.0 under the chosen folder.
+This will create a folder _zingg-0.4.0_ under the chosen folder.
 
 Move the above folder to zingg.
 
-> mv zingg-0.4.0 \~/zingg
+> `mv zingg-0.4.0 ~/zingg`
 
-> export ZINGG\_HOME=path to zingg
+> `export ZINGG_HOME=path to zingg`
 
-> export PATH=$PATH:$JAVA\_HOME/bin:$SPARK\_HOME/bin:$SPARK\_HOME/sbin:ZINGG\_HOME/scripts
+> `export PATH=$PATH:$JAVA_HOME/bin:$SPARK_HOME/bin:$SPARK_HOME/sbin:ZINGG_HOME/scripts`
diff --git a/docs/stepbystep/installation/installing-from-release/single-machine-setup.md b/docs/stepbystep/installation/installing-from-release/single-machine-setup.md
index 629dfbfb1..d0503db6b 100644
--- a/docs/stepbystep/installation/installing-from-release/single-machine-setup.md
+++ b/docs/stepbystep/installation/installing-from-release/single-machine-setup.md
@@ -8,18 +8,18 @@ Zingg can be easily run on a single machine to process up to a few million recor
 
 To prepare your machine, please do the following steps:
 
-A) Install the specified JDK version
+A) Install the specified **JDK** version
 
 B) Apache Spark - Download the specified version from spark.apache.org and unzip it in a folder under home
 
-Please add the following entries to \~/.bash\_aliases
+Please add the following entries to `~/.bash_aliases`
 
-> export JAVA\_HOME=path to jdk
+> `export JAVA_HOME=path to jdk`
 
-> export SPARK\_HOME=path to location of Apache Spark
+> `export SPARK_HOME=path to location of Apache Spark`
 
-> export SPARK\_MASTER=local\[\*]
+> `export SPARK_MASTER=local[*]`
 
-C) Correct entry of host under /etc/hosts
+C) Correct entry of host under `/etc/hosts`
 
-Run ifconfig to find the IP of the machine and make sure it is added to the /etc/hosts for localhost.
+Run `ifconfig` to find the IP of the machine and make sure it is added to the `/etc/hosts` for localhost.
diff --git a/docs/stepbystep/installation/installing-from-release/spark-cluster-checklist.md b/docs/stepbystep/installation/installing-from-release/spark-cluster-checklist.md
index 555a6f44c..af2e8b3d9 100644
--- a/docs/stepbystep/installation/installing-from-release/spark-cluster-checklist.md
+++ b/docs/stepbystep/installation/installing-from-release/spark-cluster-checklist.md
@@ -6,8 +6,8 @@ description: To configure Zingg in a Spark cluster
 
 If you have a ready Spark cluster, you can run Zingg by configuring the following environment on your driver machine:
 
-> export JAVA\_HOME=path to jdk
+> `export JAVA_HOME=path to jdk`
 
-> export SPARK\_HOME=path to Apache Spark
+> `export SPARK_HOME=path to Apache Spark`
 
-> export SPARK\_MASTER=spark://master-host:master-port
+> `export SPARK_MASTER=spark://master-host:master-port`
diff --git a/docs/stepbystep/installation/installing-from-release/verification.md b/docs/stepbystep/installation/installing-from-release/verification.md
index 8b94c9ac1..5b1adc941 100644
--- a/docs/stepbystep/installation/installing-from-release/verification.md
+++ b/docs/stepbystep/installation/installing-from-release/verification.md
@@ -22,6 +22,6 @@ Let us now run a sample program to ensure that our installation is correct.
 
 > `./scripts/zingg.sh --phase trainMatch --conf examples/febrl/config.json`
 
-The above will build Zingg models and use that to find duplicates in the examples/febl/test.csv file. You will see Zingg logs on the console and once the job finishes, you will see some files under /tmp/zinggOutput with matching records sharing the same cluster id.
+The above will build Zingg models and use that to find duplicates in the **examples/febl/test.csv** file. You will see Zingg logs on the console and once the job finishes, you will see some files under **/tmp/zinggOutput** with matching records sharing the same _cluster id_.
 
 Congratulations, Zingg has been installed!
diff --git a/docs/stepbystep/zingg-command-line.md b/docs/stepbystep/zingg-command-line.md
index 59dfc70d3..54bdd9c61 100644
--- a/docs/stepbystep/zingg-command-line.md
+++ b/docs/stepbystep/zingg-command-line.md
@@ -4,12 +4,12 @@ description: Using Zingg command line to interact with Zingg
 
 # Zingg Command Line
 
-The Zingg command line interface is the way to interact with Zingg to fire labeling, training, and matching jobs and perform other Zingg functions.
+The Zingg command line interface is the way to interact with Zingg to fire _labeling, training,_ and _matching_ jobs and perform other Zingg functions.
 
 #### To invoke the command line and pass a JSON configuration :
 
 `./scripts/zingg.sh <optional --properties-file path to zingg.conf> --phase <phase> --conf <path to json>`
 
-#### To invoke the command line and run python programs :
+#### To invoke the command line and run Python programs :
 
 `./scripts/zingg.sh <optional --properties-file path-to-zingg.conf> --run <path-to-python-program>`
diff --git a/docs/stepbystep/zingg-runtime-properties.md b/docs/stepbystep/zingg-runtime-properties.md
index 20b04a062..f686fb35d 100644
--- a/docs/stepbystep/zingg-runtime-properties.md
+++ b/docs/stepbystep/zingg-runtime-properties.md
@@ -4,8 +4,8 @@ description: Memory, external jars and other runtime properties
 
 # Zingg Runtime Properties
 
-Zingg jobs can be passed JVM and other settings through a properties file. A sample file exists at [config/zingg.conf](https://github.com/zinggAI/zingg/blob/main/config/zingg.conf). The properties can be passed by invoking
+Zingg jobs can be passed JVM and other settings through a _properties_ file. A sample file exists at [config/zingg.conf](https://github.com/zinggAI/zingg/blob/main/config/zingg.conf). The properties can be passed by invoking
 
-`./scripts/zingg.sh --properties-file <location to file> --conf conf.json`&#x20;
+`./scripts/zingg.sh --properties-file <location to file> --conf conf.json`
 
-To include jars for Snowflake/BigQuery/MySQL etc, please download them and add them to the spark.jars property.
+To include jars for Snowflake/BigQuery/MySQL etc, please download them and add them to the `spark.jars` property.
diff --git a/docs/updatingLabels.md b/docs/updatingLabels.md
index af81222dd..0bb1a8695 100644
--- a/docs/updatingLabels.md
+++ b/docs/updatingLabels.md
@@ -1,12 +1,12 @@
 # Updating Labeled Pairs
 
-**Please note: This is an experimental feature. Please keep a backup copy of your model folder in a separate place before running this**
+N**ote: This is an experimental feature. Please keep a backup copy of your model folder in a separate place before running this**
 
 As our understanding of our data changes, we may need to revisit the previously marked pairs and update them. To do this, please [generate the documentation of the model.](generatingdocumentation.md)
 
 You can then invoke the updater by invoking\
 `./scripts/zingg.sh --phase updateLabel --conf <location to conf.json>`
 
-This brings up the console labeler which accepts the cluster id of the pairs you want to update.
+This brings up the console labeler which accepts the **cluster id** of the pairs you want to update.
 
-![Shows records and asks user to update yes, no, cant say on the cli.](../assets/update.gif)
+![Shows records and asks user to update yes, no, can't say on the CLI.](../assets/update.gif)
diff --git a/docs/working-with-python.md b/docs/working-with-python.md
index 5c6f6e2fc..b19857fbe 100644
--- a/docs/working-with-python.md
+++ b/docs/working-with-python.md
@@ -4,17 +4,15 @@ description: A whole new way to work with Zingg!
 
 # Working With Python
 
-Instead of configuring Zingg using the JSON, we can now use Python to build and run Zingg entity and identity resolution programs. This is handy when you want to run Zingg on an existing Spark cluster. To run on local machine, please do the installation of the release before running Zingg python programs.
+Instead of configuring Zingg using JSON, we can now use Python to build and run Zingg entity and identity resolution programs. This is handy when you want to run Zingg on an existing Spark cluster. To run on a local machine, please install from the release before running Zingg Python programs.
 
-The Zingg Python package can be installed by invoking
+The Zingg Python package can be installed by invoking:
 
 `python -m pip install zingg`
 
-Detailed documentation of the python api is available at [https://readthedocs.org/projects/zingg/](https://readthedocs.org/projects/zingg/)
+Detailed documentation of the Python API is available at:[https://readthedocs.org/projects/zingg/](https://readthedocs.org/projects/zingg/)
 
-Example programs for python exist under examples. Please check examples/febrl/FebrlExample.py to get started.
+Example programs for Python exist under [examples](https://github.com/zinggAI/zingg/tree/main/examples/febrl). Please check [examples/febrl/FebrlExample.py](https://github.com/zinggAI/zingg/blob/main/examples/febrl/FebrlExample.py) to get started.
 
-Please refer to the [command line guide](stepbystep/zingg-command-line.md) for running python programs. Please note that Zingg Python programs are PySpark programs and hence need the Zingg cli to execute.
-
-``
+Please refer to the [command line guide](stepbystep/zingg-command-line.md) for running Python programs. Please note that Zingg Python programs are PySpark programs and hence need the Zingg CLI to execute.
 
diff --git a/docs/zModels.md b/docs/zModels.md
index f377583d0..5f0d66764 100644
--- a/docs/zModels.md
+++ b/docs/zModels.md
@@ -10,11 +10,11 @@ Zingg learns two models from the data.
 
 ## 1. Blocking Model
 
-One fundamental problem with scaling data mastering is that the number of comparisons increases quadratically as the number of input records increases.
+One fundamental problem with scaling data mastering is that the number of comparisons increases **quadratically** as the number of input records increases.
 
 ![Data Mastering At Scale](../assets/fuzzymatchingcomparisons.jpg)
 
-Zingg learns a clustering/blocking model which indexes near similar records. This means that Zingg does not compare every record with every other record. Typical Zingg comparisons are 0.05-1% of the possible problem space.
+Zingg learns a clustering/blocking model which indexes near similar records. This means that Zingg does not compare every record with every other record. Typical Zingg comparisons are **0.05-1%** of the possible problem space.
 
 ## 2. Similarity Model
 

From 00fcc321b88a5a73953b5129a835942be31d0790 Mon Sep 17 00:00:00 2001
From: Sonal <sonalgoyal4@gmail.com>
Date: Thu, 22 Aug 2024 14:55:36 +0530
Subject: [PATCH 35/36] Update SUMMARY.md

---
 docs/SUMMARY.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/SUMMARY.md b/docs/SUMMARY.md
index 9aaefcb51..30f01b9c4 100644
--- a/docs/SUMMARY.md
+++ b/docs/SUMMARY.md
@@ -34,9 +34,9 @@
     * [Using Pre-existing Training Data](setup/training/addOwnTrainingData.md)
     * [Updating Labeled Pairs](updatingLabels.md)
     * [Exporting Labeled Data](setup/training/exportLabeledData.md)
-  * [Add Incremental Data](runIncremental.md)
   * [Building And Saving The Model](setup/train.md)
   * [Finding The Matches](setup/match.md)
+  * [Add Incremental Data](runIncremental.md)
   * [Linking Across Datasets](setup/link.md)
 * [Data Sources and Sinks](dataSourcesAndSinks/connectors.md)
   * [Zingg Pipes](dataSourcesAndSinks/pipes.md)

From 3be49c428fca63d0855265fa44f947eac938651a Mon Sep 17 00:00:00 2001
From: Sonal Goyal <sonalgoyal4@gmail.com>
Date: Wed, 28 Aug 2024 05:01:27 +0000
Subject: [PATCH 36/36] GITBOOK-26: No subject

---
 docs/SUMMARY.md        |  2 +-
 docs/runIncremental.md | 14 +++++++++++---
 2 files changed, 12 insertions(+), 4 deletions(-)

diff --git a/docs/SUMMARY.md b/docs/SUMMARY.md
index 30f01b9c4..6608c8212 100644
--- a/docs/SUMMARY.md
+++ b/docs/SUMMARY.md
@@ -36,7 +36,7 @@
     * [Exporting Labeled Data](setup/training/exportLabeledData.md)
   * [Building And Saving The Model](setup/train.md)
   * [Finding The Matches](setup/match.md)
-  * [Add Incremental Data](runIncremental.md)
+  * [Adding Incremental Data](runIncremental.md)
   * [Linking Across Datasets](setup/link.md)
 * [Data Sources and Sinks](dataSourcesAndSinks/connectors.md)
   * [Zingg Pipes](dataSourcesAndSinks/pipes.md)
diff --git a/docs/runIncremental.md b/docs/runIncremental.md
index 2a1597fab..cc7391d0b 100644
--- a/docs/runIncremental.md
+++ b/docs/runIncremental.md
@@ -1,8 +1,14 @@
-# Add Incremental Data
+---
+description: >-
+  Building a continuosly updated identity graph with new, updated and deleted
+  records
+---
 
-### Run Incremental Loads For New and Updated Records - _Zingg Enterprise Feature_
+# Adding Incremental Data
 
-Rerunning matching on entire datasets is wasteful, and we lose the lineage of matched records against a persistent identifier. Using Zingg Enterprise, incremental loads can be run to match existing pre-resolved entities. The new and updated records are matched to existing clusters, and new persistent **ZINGG\_IDs** are generated for records that do not find a match. If a record gets updated and Zingg Enterprise discovers that it is a more suitable match with another cluster, it will be reassigned. Cluster assignment, merge, and unmerge happens automatically in the flow. Zingg Enterprise also takes care of human feedback on previously matched data to ensure that it does not override the approved records.
+[Zingg Enterprise Feature](#user-content-fn-1)[^1]
+
+Rerunning matching on entire datasets is wasteful, and we lose the lineage of matched records against a persistent identifier. Using the[ incremental flow](https://www.learningfromdata.zingg.ai/p/zingg-incremental-flow) feature in [Zingg Enterprise](https://www.zingg.ai/company/zingg-enterprise), incremental loads can be run to match existing pre-resolved entities. The new and updated records are matched to existing clusters, and new persistent [**ZINGG\_IDs**](https://www.learningfromdata.zingg.ai/p/hello-zingg-id) are generated for records that do not find a match. If a record gets updated and Zingg Enterprise discovers that it is a more suitable match with another cluster, it will be reassigned. Cluster assignment, merge, and unmerge happens automatically in the flow. Zingg Enterprise also takes care of human feedback on previously matched data to ensure that it does not override the approved records.
 
 ### The incremental phase is run as follows:
 
@@ -100,3 +106,5 @@ options = ClientOptions([ClientOptions.PHASE,"runIncremental"])
 zingg = EZingg(incrArgs, options)  
 zingg.initAndExecute()  
 ```
+
+[^1]: Zingg Enterprise is an advance version of Zingg Community with production grade features