georgia-tech-db · gaurav274 · Aug 25, 2023 · Aug 15, 2023 · Aug 17, 2023 · Aug 17, 2023
diff --git a/docs/_static/js/top-navigation.js b/docs/_static/js/top-navigation.js
@@ -126,7 +126,7 @@ topNavContent.append(topNavContentLeft);
 
 topNavContentRight = document.createElement("div");
 topNavContentRight.setAttribute("class", "image-header");
-topNavContentRight.innerHTML = "<a href='https://github.com/georgia-tech-db/evadb'><img class='icon-hover' src='_static/icons/github.png' width='25px' height='25px'></a> <a href='https://join.slack.com/t/eva-db/shared_invite/zt-1i10zyddy-PlJ4iawLdurDv~aIAq90Dg'><img class='icon-hover' src='_static/icons/slack.png' width='25px' height='25px'> </a><a href='https://twitter.com/evadb_ai'> <img class='icon-hover' src='_static/icons/twitter.png' width='25px' height='25px'> </a>"
+topNavContentRight.innerHTML = "<a href='https://github.com/georgia-tech-db/evadb'><img class='icon-hover' src='https://raw.githubusercontent.com/georgia-tech-db/evadb/master/docs/_static/icons/github.png' width='25px' height='25px'></a> <a href='https://join.slack.com/t/eva-db/shared_invite/zt-1i10zyddy-PlJ4iawLdurDv~aIAq90Dg'><img class='icon-hover' src='https://raw.githubusercontent.com/georgia-tech-db/evadb/master/docs/_static/icons/slack.png' width='25px' height='25px'> </a><a href='https://twitter.com/evadb_ai'> <img class='icon-hover' src='https://raw.githubusercontent.com/georgia-tech-db/evadb/master/docs/_static/icons/twitter.png' width='25px' height='25px'> </a>"
 
 topNavContent.append(topNavContentRight)
 

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/index.rst b/docs/index.rst
@@ -45,7 +45,7 @@ Getting Started
 .. raw:: html
 
     <div class="grid-container">
-    <a class="no-underline" href="source/overview/getting-started" target="_blank"> <div class="info-box" >
+    <a class="no-underline" href="source/overview/getting-started.html" target="_blank"> <div class="info-box" >
             <div class="image-header" style="padding:0px;">
                 <img src="_static/icons/code.png" width="44px" height="44px" />
                 <h3 style="font-size:20px;">Learn basics</h3>
@@ -54,7 +54,7 @@ Getting Started
             <p class="only-dark" style="color:#FFFFFF;">Understand how to use EvaDB to build AI apps.</p>    
             <p style="font-weight:600;">Learn more > </p>  
     </div> </a>  
-    <a class="no-underline" href="source/overview/concepts" target="_blank"> 
+    <a class="no-underline" href="source/overview/concepts.html" target="_blank"> 
         <div class="info-box" >
             <div class="image-header" style="padding:0px;">
                 <img src="_static/icons/download.png" width="44px" height="44px" />
@@ -74,7 +74,7 @@ Getting Started
             <p class="only-light" style="color:#000000;">Have a question? Join our Slack community.</p> 
             <p class="only-dark" style="color:#FFFFFF;">Have a question? Join our Slack community.</p>   
             <p style="color:#515151;"></p>
-            <p style="font-weight:600;">Open the notebook></p> 
+            <p style="font-weight:600;">Support > </p> 
     </div></a>
     </div>
 

diff --git a/docs/source/overview/concepts.rst b/docs/source/overview/concepts.rst
@@ -2,13 +2,100 @@
 Concepts
 =========
 
-These are some high-level concepts related to EvaDB.
+These are some high-level concepts related to EvaDB. If you still have questions after reading this documents,  ping us on `our Slack <https://join.slack.com/t/eva-db/shared_invite/zt-1i10zyddy-PlJ4iawLdurDv~aIAq90Dg>`__!
+
+
+Quickly build AI-Powered Apps
+---------------------------------
+
+EvaDB supports a simple SQL-like query language designed to make it easier for users to leverage AI models. It is easy to chain multiple models in a single query to accomplish complicated tasks with minimal programming.
+
+Here is an illustrative EvaDB app for ChatGPT-based question answering on videos. The app loads a collection of news videos into EvaDB and runs a query for extracting audio transcripts from the videos using a HuggingFace model, followed by question answering using ChatGPT.
+
+.. code-block:: python
+
+    # pip install evadb and import it
+    import evadb
+
+    # Grab a evadb cursor to load data and run queries    
+    cursor = evadb.connect().cursor()
+
+    # Load a collection of news videos into the 'news_videos' table
+    # This command returns a Pandas Dataframe with the query's output
+    # In this case, the output indicates the number of loaded videos
+    cursor.load(
+        file_regex="news_videos/*.mp4", 
+        format="VIDEO", 
+        table_name="news_videos"
+    ).df()
+
+    # Define a function that wraps around a speech-to-text (Whisper) model 
+    # After creating the function, we can use the function in any future query
+    cursor.create_function(
+        udf_name="SpeechRecognizer",
+        type="HuggingFace",
+        task='automatic-speech-recognition',
+        model='openai/whisper-base'
+    ).df()
+
+    # EvaDB automatically extract the audio from the video
+    # We only need to run the SpeechRecognizer UDF on the 'audio' column 
+    # to get the transcript and persist it in a table called 'transcripts'
+
+
+    cursor.query(
+        """CREATE TABLE transcripts AS 
+            SELECT SpeechRecognizer(audio) from news_videos;"""
+    ).df()
+
+    # We next incrementally construct the ChatGPT query using EvaDB's Python API
+    # The query is based on the 'transcripts' table 
+    # This table has a column called 'text' with the transcript text
+    query = cursor.table('transcripts')
+
+    # Since ChatGPT is a built-in function, we don't have to define it
+    # We can just directly use it in the query
+    # We need to set the OPENAI_KEY as an environment variable
+    os.environ["OPENAI_KEY"] = OPENAI_KEY 
+    query = query.select("ChatGPT('Is this video summary related to LLM', text)")
+
+    # Finally, we run the query to get the results as a dataframe
+    response = query.df()
+
+
+The same AI query can also be written directly in SQL and run on EvaDB.
+
+.. code-block:: sql
+
+    --- Query for asking question using ChatGPT
+    SELECT ChatGPT('Is this video summary related to LLM', 
+                SpeechRecognizer(audio)) FROM news_videos;
+
+EvaDB's declarative query language reduces the complexity of the app, leading to more maintainable code that allows users to build on top of each other's queries.
+
+EvaDB comes with a wide range of models for analyzing unstructured data including image classification, object detection, OCR, face detection, etc. It is fully implemented in Python, and `licensed under the Apache license <https://github.com/georgia-tech-db/evadb>`__. It already contains integrations with widely-used AI pipelines based on Hugging Face, PyTorch, and Open AI.
+
+The high-level SQL API allows even beginners to use EvaDB in a few lines of code. Advanced users can define custom user-defined functions that wrap around any AI model or Python library.
+
+Save time and money
+----------------------
+
+EvaDB automatically optimizes the queries to save inference cost and query execution time using its Cascades-style extensible query optimizer. EvaDB's optimizer is tailored for AI pipelines. The Cascades query optimization framework has worked well in SQL database systems for several decades. Query optimization in EvaDB is the bridge that connects the declarative query language to efficient execution.
+
+EvaDB accelerates AI pipelines using a collection of optimizations inspired by SQL database systems including function caching, sampling, and cost-based operator reordering.
+
+EvaDB supports an AI-oriented query language for analyzing both structured and unstructured data. Here are some illustrative apps:
+
+
+The `Getting Started <source/overview/installation.html>`__ page shows how you can use EvaDB for different AI tasks and how you can easily extend EvaDB to support your custom deep learning model through user-defined functions.
+
+The `User Guides <source/tutorials/index.html>`__ section contains Jupyter Notebooks that demonstrate how to use various features of EvaDB. Each notebook includes a link to Google Colab, where you can run the code yourself.
+
+
 
-If you still have questions after reading this documents,  ping us on
-`our Slack <https://join.slack.com/t/eva-db/shared_invite/zt-1i10zyddy-PlJ4iawLdurDv~aIAq90Dg>`__!
 
 User-Defined Function (UDF) or Function
-======================================
+------------------------------------------
 
 User-defined functions are thin wrappers around deep learning models. They 
 allow us to use deep learning models in AI queries.

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>`_.