fix mkdocs documentation links

.github/workflows/deploy-docs.yml

@@ -13,12 +13,9 @@ jobs:
       - uses: actions/setup-python@v2
         with:
           python-version: 3.x
-      - run: pip install mkdocs-material mkdocstrings[python] mkdocs-badges
+      - run: pip install mkdocs-material mkdocstrings[python] mkdocs-badges mkdocs-same-dir
       - run: |
            # remove badges 
-           cat README.md |sed  '/img\.shields\.io/d' > ./data-processing-lib/doc/index.md 
-           # copy repo docs to mkdocs `docs_dir`
-           cp doc/* ./data-processing-lib/doc/
-           # copy kfp tutorials to mkdocs `docs_dir`
-           cp kfp/doc/* ./data-processing-lib/doc/
-           cd data-processing-lib && mkdocs gh-deploy --force
+           cat README.md |sed  '/img\.shields\.io/d' > README_.md
+           mv README_.md README.md  
+           mkdocs gh-deploy --force

README.md

@@ -39,7 +39,7 @@ Features of the toolkit:
 - Collection of [scalable transformations](transforms) to expedite user onboarding
 - [Data processing library](data-processing-lib) designed to facilitate effortless addition and deployment of new scalable transformations
 - Operate efficiently and seamlessly from laptop-scale to cluster-scale supporting data processing at any data size
-- [Kube Flow Pipelines](https://www.kubeflow.org/docs/components/pipelines/v1/introduction/)-based [workflow automation](kfp) of transforms.
+- [Kube Flow Pipelines](https://www.kubeflow.org/docs/components/pipelines/v1/introduction/)-based [workflow automation](kfp/transform_workflows/Readme.md) of transforms.
 
 Data modalities supported: 
 
@@ -64,7 +64,7 @@ A transform can follow one of the two patterns: filter or annotator pattern.
 In the annotator design pattern, a transform adds information during the processing by adding one more column to the parquet file.
 The annotator design also allows a user to verify the results of the processing before actual filtering of the data.
 When a transform acts as a filter, it processes the data and outputs the transformed data (example exact deduplication).
-A general purpose [SQL-based filter transform](transforms/filter) enables a powerful mechanism for identifying 
+A general purpose [SQL-based filter transform](transforms/universal/filter) enables a powerful mechanism for identifying 
 columns and rows of interest for downstream processing.
 For a new module to be added, a user can pick the right design based on the processing to be applied. More details [here](transforms). 
 

mkdocs.yml

@@ -0,0 +1,27 @@
+site_name: "Data Prep LAB"
+docs_dir: .
+site_dir: ../site
+nav:
+  - Home: README.md
+  - Overview: data-processing-lib/doc/overview.md
+  - Tutorials:
+      - data-processing-lib/doc/transform-tutorials.md
+      - Simple: data-processing-lib/doc/simplest-transform-tutorial.md
+      - Advanced: data-processing-lib/doc/advanced-transform-tutorial.md
+      - KFP Pipeline: kfp/doc/simple_transform_pipeline.md
+theme:
+  name: 'material'
+  favicon: 'data-processing-lib/doc/favicon.ico'
+  logo: 'data-processing-lib/doc/logo-ibm.png'
+  palette:
+    primary: black
+#  palette:
+#    primary: 'blue grey'
+  features:
+    - navigation.tabs
+plugins:
+- search
+- mkdocstrings
+- badges
+- same-dir
+