Add installation and usage content

docs/getstarted.rst

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 Getting Started
 ===============
 
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+Installation and usage instructions are detailed in the following sections.
 
 Installation
 ------------
 
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-proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
+Installing flowcept can be accomplished by cloning the GitHub repository and installing with pip using the following terminal commands:
+
+.. code-block:: text
+
+   git clone https://github.com/ORNL/flowcept.git
+   cd flowcept
+   pip install .
+
+Or it can be installed directly from `PyPI <https://pypi.org/project/flowcept/>`_ with:
+
+.. code-block:: text
+
+   pip install flowcept
+
+Use ``pip install flowcept[all]`` to install all dependencies for all the adapters. Alternatively, dependencies for a particular adapter can be installed; for example, ``pip install flowcept[dask]`` will install only the dependencies for the Dask adapter. The optional dependencies currently available are:
+
+.. code-block:: text
+
+   pip install flowcept[mlflow]        # To install mlflow's adapter
+   pip install flowcept[dask]          # To install dask's adapter
+   pip install flowcept[tensorboard]   # To install tensorboaard's adapter
+   pip install flowcept[kafka]         # To utilize Kafka as the MQ, instead of Redis
+   pip install flowcept[nvidia]        # To capture NVIDIA GPU runtime information
+   pip install flowcept[analytics]     # For extra analytics features
+   pip install flowcept[dev]           # To install dev dependencies
+
+You do not need to install any optional dependencies to run FlowCept without an adapter; for example, if you want to use simple instrumentation. In this case, you need to remove the adapter part from the settings.yaml file.
 
 Usage
 -----
 
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-proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
+To use FlowCept, one needs to start a database and a MQ system. FlowCept currently supports MongoDB as its database and it supports both Redis and Kafka as the MQ system. For convenience, the default needed services can be started using the Docker compose deployment file from the GitHub repository:
+
+.. code-block:: text
+
+   git clone https://github.com/ORNL/flowcept.git
+   cd flowcept
+   docker compose -f deployment/compose.yml up -d
+
+A simple example of using FlowCept without any adapters is given here:
+
+.. code-block:: python
+
+   from flowcept import Flowcept, flowcept_task
+
+   @flowcept_task
+   def sum_one(n):
+       return n + 1
+
+
+   @flowcept_task
+   def mult_two(n):
+       return n * 2
+
+
+   with Flowcept(workflow_name='test_workflow'):
+       n = 3
+       o1 = sum_one(n)
+       o2 = mult_two(o1)
+       print(o2)
+
+   print(Flowcept.db.query(filter={"workflow_id": Flowcept.current_workflow_id}))

docs/index.rst

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-.. FlowCept documentation master file, created by
-   sphinx-quickstart on Thu Oct 24 13:41:54 2024.
-   You can adapt this file completely to your liking, but it should at least
-   contain the root `toctree` directive.
-
 FlowCept
 ========
 
-Add your content using ``reStructuredText`` syntax. See the
-`reStructuredText <https://www.sphinx-doc.org/en/master/usage/restructuredtext/index.html>`_
-documentation for details.
-
+FlowCept is a runtime data integration system that empowers any data processing system to capture and query workflow provenance data using data observability, requiring minimal or no changes in the target system code. It seamlessly integrates data from multiple workflows, enabling users to comprehend complex, heterogeneous, and large-scale data from various sources in federated environments.
 
 .. toctree::
    :maxdepth: 2