docs: updates

docs/_toc.yml

@@ -9,14 +9,11 @@ parts:
             title: Installation Options
       - file: source/overview/concepts
         title: Concepts
-      - file: source/overview/model-inference
-        title: AI Models
-        sections:
-          - file: source/overview/concepts/data-sources
-            title: Data Sources
-      - file: source/overview/connect-to-database
+      - file: source/overview/ai-queries
+        title: AI Queries
+      - file: source/overview/connect-to-data-sources
         title: Connect to Data Sources
-        #- file: source/overview/faq
+      - file: source/overview/faq
 
   - caption: Use Cases
     chapters:
@@ -98,8 +95,9 @@ parts:
             title: OpenAI 
           - file: source/reference/ai/yolo
             title: YOLO 
-          - file: source/reference/ai/custom
-            title: Custom Model
+
+      - file: source/reference/ai/custom-ai-function
+        title: Bring Your Own AI Function
 
       - file: source/reference/optimizations
         title: Optimizations

docs/source/overview/ai-queries.rst

@@ -0,0 +1,136 @@
+.. _ai-queries:
+
+AI Queries
+==========
+
+In EvaDB, AI models are simple function calls similar to traditional SQL functions like ``MAX``. 
+
+This page details how you can use AI models in different ways to construct AI queries in EvaDB. EvaDB automatically optimizes AI queries to save money and time, as detailed in the :ref:`optimizations<optimizations>` page.
+
+.. note::
+
+   EvaDB ships with a wide range of built-in functions listed in the :ref:`models` page. If your desired AI model is not available, you can also bring your own AI function by referrring to the :ref:`custom_ai_function` page.
+
+SELECT Clause
+-------------
+
+AI queries often contain the AI function(s) in the ``SELECT`` clause (projection list).
+
+For example, the following query calls the `MnistImageClassifier <https://github.com/georgia-tech-db/evadb/blob/staging/evadb/functions/mnist_image_classifier.py>`_ function to identify digits in a collection of frames in the `mnist_video`.
+
+.. code-block:: sql
+
+   SELECT MnistImageClassifier(data).label FROM mnist_video;
+
+WHERE Clause
+------------
+
+Another common position in the AI query with model inference is the ``WHERE`` clause (selection). 
+
+For example, the following query uses the ``TextSummarizer`` and ``TextClassifier`` functions from the :ref:`HuggingFace<hf>` AI engine to summarize the sentiment of food reviews and identify those expressing a `negative` sentiment in the ``SELECT`` and ``WHERE`` clauses, respectively.
+
+.. code-block:: sql
+
+   SELECT TextSummarizer(data)
+   FROM food_reviews
+   WHERE TextClassifier(data).label = 'NEGATIVE';
+
+
+ARRAY Operators
+---------------
+
+EvaDB supports specialized array operators. 
+
+For example, the following query applies the ``CONTAIN`` operator (``@>``) on the output of an object detection function:
+
+.. code-block:: sql
+
+   SELECT id 
+   FROM camera_videos 
+   WHERE ObjectDetector(data).labels @> ['person', 'car'];
+
+
+Here is another query with the ``UNNEST`` function that flattens the output of an `one-input-to-many-outputs` AI function.
+
+.. code-block:: sql
+
+   SELECT UNNEST(FaceDetector(data)) AS Face(bbox, conf)
+   FROM movie;
+
+The `face detector <https://github.com/georgia-tech-db/evadb/blob/staging/evadb/functions/face_detector.py>`_ model returns multiple outputs (e.g., bounding box and confidence score) as an array. The ``UNNEST`` function unrolls elements from the array into multiple rows.
+
+LATERAL JOIN
+------------
+
+For more challenging AI apps, EvaDB supports lateral joins.
+
+The following AI query uses both a ``LATERAL JOIN`` and an ``UNNEST`` function to detect emotions from faces in a movie, where a single scene may contain multiple faces. The output of the `object detector <https://github.com/georgia-tech-db/evadb/blob/staging/evadb/functions/fastrcnn_object_detector.py>`_ is used to crop the bounding box from the image, and the cropped image is then sent to an `emotion detector <https://github.com/georgia-tech-db/evadb/blob/staging/evadb/functions/emotion_detector.py>`_ to detect the emotion of the face inside the bounding box.
+
+.. code-block:: sql
+   
+   SELECT EmotionDetector(Crop(data, Face.bbox))
+   FROM movie
+   LATERAL JOIN UNNEST(FaceDetector(data)) AS Face(bbox, conf);
+
+Ordering 
+--------
+
+AI models may also be used in the ``ORDER BY`` clause to enable usecases like similarity search.
+
+For example, in the following query, the output of the `SentenceFeatureExtractor <https://github.com/georgia-tech-db/evadb/blob/staging/evadb/functions/sentence_feature_extractor.py>`_ 
+is used to find relevant context for answering the user's question (`When was the NATO created`) from a collection of PDFs.
+
+.. code-block:: sql
+
+   SELECT data FROM MyPDFs
+   ORDER BY Similarity(
+       SentenceFeatureExtractor('When was the NATO created?'),
+       SentenceFeatureExtractor(data)
+   );
+
+Similarity search maps to ordering based on the distance computed by the `Similarity` function, between the features extracted from the query and those extracted from the paragraphs loaded from the documents. EvaDB automatically accelerates such queries using vector databases.
+
+.. note::
+   Go over the `PrivateGPT <https://github.com/georgia-tech-db/evadb/blob/staging/tutorials/13-privategpt.ipynb>`_ notebook for more details.
+
+Given a queried image, we can use a different feature extractor (`SiftFeatureExtractor <https://github.com/georgia-tech-db/evadb/blob/staging/evadb/functions/sift_feature_extractor.py>`_ function) to find the most similar image from an existing collection of images (`reddit_dataset`).
+
+.. code-block:: sql
+
+   SELECT name 
+   FROM reddit_dataset
+   ORDER BY Similarity(
+       SiftFeatureExtractor(Open('reddit-images/cat.jpg')),
+       SiftFeatureExtractor(data)
+   );
+
+.. note::
+   Go over the :ref:`Image Search <image-search>` page for more details.
+
+
+Aggregate Functions
+-------------------
+
+AI models can be applied on a sequence of tuples using the ``GROUP BY`` and ``SEGMENT`` clauses. 
+
+The following query concatenates consecutive frames in a movie into a single segment and applies an action recognition model on the segment:
+
+.. code-block:: sql
+
+   SELECT ASLActionRecognition(SEGMENT(data)) 
+   FROM ASL_ACTIONS 
+   SAMPLE 5 
+   GROUP BY '16 frames';
+
+Here is another illustrative query that groups together paragraphs from a PDF document:
+
+.. code-block:: sql
+
+   SELECT SEGMENT(data) 
+   FROM MyPDFs 
+   GROUP BY '10 paragraphs';
+
+
+.. note::
+
+   The :ref:`use cases <sentiment-analysis>` illustrate more ways of utilizing AI queries for building AI apps.

docs/source/overview/faq.rst

@@ -1,53 +1,36 @@
-:orphan:
+Frequently Asked Questions
+==========================
 
-===
-FAQ
-===
+.. _faq:
 
-These are some Frequently Asked Questions that we've seen pop up for EvaDB.
+Here are some frequently asked questions that we have seen pop up for EvaDB.
 
-If you still have questions after reading this FAQ,  ping us on
-`our Slack <https://join.slack.com/t/eva-db/shared_invite/zt-1i10zyddy-PlJ4iawLdurDv~aIAq90Dg>`__!
+.. note::
 
-Why am I not able to install EvaDB in my Python environment?
-============================================================
+    Have another question or want to give feedback? Ask us on `Slack <https://evadb.ai/community>`__!
 
-Ensure that the local Python version is >= 3.8 and <= 3.10. EvaDB cannot support 3.11 due to its `dependency on Ray <https://github.com/autogluon/autogluon/issues/2687>`__.
+Why am I not able to install EvaDB?
+-----------------------------------
 
-Where does EvaDB store all the data?
-====================================
-
-By default, EvaDB stores all the data in a local folder named ``evadb_data``. Deleting this folder will reset the system's state and lead to data loss.
-
-Why does the EvaDB server not start?
-====================================
-
-Check if another process is already running on the target port where EvaDB server is being launched (default port of EvaDB is ``8803``) using these commands:
+Ensure that the Python interpreter's version is >= `3.9`. 
 
-.. code-block:: bash
+.. note::
 
-    sudo lsof -i :<port_number>
-    kill -9 <process_id>
+    If you are using the `evadb[ray]` installation option, ensure that the Python  version is <= `3.10` due to a `Ray issue <https://github.com/autogluon/autogluon/issues/2687>`_. Follow `these instructions <https://github.com/ray-project/ray/issues/33039>`_ to install `ray`.
 
-You can either kill that process or launch EvaDB server on another free port in this way:
 
-.. code-block:: bash
+Where does EvaDB store all the data?
+------------------------------------
 
-    evadb_server -p 9330
+By default, EvaDB connects to **existing** data sources like SQL database systems. It stores all the meta-data (i.e., data about data sources) in a local folder named ``evadb_data``. Deleting this folder will reset EvaDB's state and lead to data loss.
 
 Why do I see no output from the server?
-=======================================
+---------------------------------------
 
-If a query runs a complex vision task (such as object detection) on a long video, the query is expected to take a non-trivial amount of time to finish.
-You can check the status of the server by running ``top`` or ``pgrep``:
+If a query runs a complex AI task (e.g., sentiment analysis) on a large table, the query is expected to take a non-trivial amount of time to finish. You can check the status of the server by running ``top`` or ``pgrep``:
 
 .. code-block:: bash
 
     top
     pgrep evadb_server
 
-pip install ray fails because of grpcio
-=======================================
-
-Follow these instructions to install ``ray``:
-https://github.com/ray-project/ray/issues/33039

docs/source/overview/getting-started.rst

@@ -35,7 +35,7 @@ Now, activate the virtual environment:
 
 .. code-block:: bash
 
-   pip install evadb --upgrade
+   pip install --upgrade evadb
 
 .. note::
 

docs/source/reference/ai/custom.rst → docs/source/reference/ai/custom-ai-function.rst

@@ -1,7 +1,7 @@
-.. _udf:
+.. _custom_ai_function:
 
-Functions
-======================
+Bring Your Own AI Function
+==========================
 
 This section provides an overview of how you can create and use a custom function in your queries. For example, you could write an function that wraps around your custom PyTorch model.
 

docs/source/usecases/question-answering.rst

@@ -61,7 +61,7 @@ EvaDB has built-in support for ``ChatGPT`` function from ``OpenAI``. You will ne
 
 .. note::
 
-    EvaDB has built-in support for a wide range of :ref:`OpenAI<openai>` models. You can also switch to another large language models that runs locally by defining a :ref:`Custom function<udf>`.
+    EvaDB has built-in support for a wide range of :ref:`OpenAI<openai>` models. You can also switch to another large language models that runs locally by defining a :ref:`custom AI function<custom_ai_function>`.
 
 
     ChatGPT function is a wrapper around OpenAI API call. You can also switch to other LLM models that can run locally.