usecases updated

georgia-tech-db · Aug 25, 2023 · a633d3a · a633d3a
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diff --git a/docs/_toc.yml b/docs/_toc.yml
@@ -4,23 +4,24 @@ parts:
   - caption: Overview
     chapters:
       - file: source/overview/getting-started
-      - file: source/overview/faq
       - file: source/overview/concepts
+      # - file: source/overview/faq
 
   - caption: Use Cases
     chapters:
       - file: source/usecases/image-classification.rst
         title: Image Classification
+      - file: source/usecases/similar-image-search.rst
+        title: Image Search [FAISS]
+      - file: source/usecases/qa-video.rst
+        title: Q&A from Videos [ChatGPT + HuggingFace]
       - file: source/usecases/02-object-detection.ipynb
         title: Object Detection
       - file: source/usecases/03-emotion-analysis.ipynb
         title: Emotion Analysis
       - file: source/usecases/07-object-segmentation-huggingface.ipynb
         title: Image Segmentation [HuggingFace]
-      - file: source/usecases/similar-image-search.rst
-        title: Image Search [FAISS]
-      - file: source/usecases/qa-video.rst
-        title: Q&A from Videos [ChatGPT + HuggingFace]
+
 
   - caption: User Reference
     chapters:

diff --git a/docs/source/usecases/image-classification.rst b/docs/source/usecases/image-classification.rst
@@ -1,4 +1,4 @@
-Implementing a Image Classification Pipeline using EvaDB on a Video
+Image Classification Pipeline using EvaDB
 ====
 
 Assume the database has loaded a video ``mnist_video``.
@@ -28,23 +28,25 @@ Create an image classification function from python source code.
 
 After the function is registered to EvaDB system, it can be directly called and used in SQL query.
 
-.. code-block:: python
-
-    query = cursor.table("mnist_video").select("MnistImageClassifier(data).label")
+.. tab-set::
+    
+    .. tab-item:: Python
 
-.. note::
+        .. code-block:: python
 
-    SQL statement
+            query = cursor.table("mnist_video").select("MnistImageClassifier(data).label")
+            
+            # Get results in a DataFrame.
+            query.df()
 
-.. code-block:: sql
 
-    SELECT MnistImageClassifier(data).label FROM mnist_video
+    .. tab-item:: SQL 
 
-Get results in a ``DataFrame``.
+        .. code-block:: sql
 
-.. code-block:: python
+            SELECT MnistImageClassifier(data).label FROM mnist_video;
 
-    query.df()
+    
 
 The result contains a projected ``label`` column, which indicates the digit of a particular frame.
 
@@ -69,26 +71,26 @@ The result contains a projected ``label`` column, which indicates the digit of a
 
 Like normal SQL, you can also specify conditions to filter out some frames of the video.
 
-.. code-block:: python
+.. tab-set::
+
+    .. tab-item:: Python
 
-    query = cursor.table("mnist_video") \
-                  .filter("id < 2") \
-                  .select("MnistImageClassifier(data).label")
+        .. code-block:: python
 
-.. note::
+            query = cursor.table("mnist_video") \
+                        .filter("id < 2") \
+                        .select("MnistImageClassifier(data).label")
+            
+            # Return results in a DataFrame.
+            query.df()
 
-    SQL statement
+    .. tab-item:: SQL
 
-.. code-block:: sql
+        .. code-block:: sql
 
-    SELECT MnistImageClassifier(data).label FROM mnist_video 
-        WHERE id < 2
-
-Return results in a ``DataFrame``.
-
-.. code-block:: python
+            SELECT MnistImageClassifier(data).label FROM mnist_video 
+                WHERE id < 2
 
-    query.df()
 
 Now, the ``DataFrame`` only contains 2 rows after filtering.
 

diff --git a/docs/source/usecases/qa-video.rst b/docs/source/usecases/qa-video.rst
@@ -12,8 +12,7 @@ Q&A Application on Videos
 2. Register Functions 
 ----
 
-Whisper
-****
+Register speech-to-text **whisper** model from `HuggingFace` 
 
 .. code-block:: python
 
@@ -28,8 +27,7 @@ Whisper
 
     EvaDB allows users to register any model in HuggingFace as a function.
 
-ChatGPT
-****
+Register **OpenAI** LLM model 
 
 .. code-block:: python
 
@@ -49,8 +47,7 @@ ChatGPT
 3. Summarize Video in Text
 ----
 
-Create a table with text summary of the video. 
-Text summarization is generated by running audio-to-text ``Whisper`` model from ``HuggingFace``.
+Create a table with text summary of the video. Text summarization is generated by running speech-to-text ``Whisper`` model from ``HuggingFace``.
 
 .. code-block:: python
 

diff --git a/docs/source/usecases/similar-image-search.rst b/docs/source/usecases/similar-image-search.rst
@@ -1,9 +1,7 @@
-Implementing a Similar Image Search Pipeline using EvaDB on Images
+Image Similarity Search Pipeline using EvaDB on Images
 ====
 
-In this use case, we want to search similar images based on an image provided by the user. 
-To implement this use case, we leverage EvaDB's capability of easily expressing feature extraction pipeline.
-Additionaly, we also leverage EvaDB's capability of building a similarity search index and searching the index to
+In this use case, we want to search similar images based on an image provided by the user. To implement this use case, we leverage EvaDB's capability of easily expressing feature extraction pipeline. Additionaly, we also leverage EvaDB's capability of building a similarity search index and searching the index to
 locate similar images through ``FAISS`` library.
 
 For this use case, we use a reddit image dataset that can be downloaded from `Here <https://www.dropbox.com/scl/fo/fcj6ojmii0gw92zg3jb2s/h\?dl\=1\&rlkey\=j3kj1ox4yn5fhonw06v0pn7r9>`_.