Update installation prerequisites (#233)

* Update installation prerequisites

* Update with optional dependencies

* Change docs generation instructions

* Update README with optional dependencies

* Add PyG installation

---------

Co-authored-by: antepusic <ante.pusic@fer.hr>

Author: katarinasupe

README.md

@@ -14,33 +14,68 @@
 
 An Object Graph Mapper or OGM provides a developer-friendly workflow that allows for writing object-oriented notation to communicate with graph databases. Instead of writing Cypher queries, you will be able to write object-oriented code, which the OGM will automatically translate into Cypher queries.
 
+## Installation
+
 GQLAlchemy is built on top of Memgraph's low-level Python client `pymgclient`
 ([PyPI](https://pypi.org/project/pymgclient/) /
 [Documentation](https://memgraph.github.io/pymgclient/) /
-[GitHub](https://github.com/memgraph/pymgclient)).
+[GitHub](https://github.com/memgraph/pymgclient)). 
 
-## Installation
+To install GQLAlchemy, you first need to install `pymgclient` [build prerequisites](https://memgraph.github.io/pymgclient/introduction.html#build-prerequisites).
 
-Before you install `gqlalchemy`, make sure that you have `cmake` installed by running:
-```
-cmake --version
-```
-You can install `cmake` by following the [official instructions](https://cgold.readthedocs.io/en/latest/first-step/installation.html#).
+After you have installed the prerequisites, run the following command:
 
-To install `gqlalchemy`, simply run the following command:
-```
-pip install gqlalchemy
+`pip install gqlalchemy`
+
+With the above command, you get the basic GQLAlchemy capabilities. To add additional import/export capabilities, install GQLAlchemy with one of the following commands:
+
+- `pip install gqlalchemy[arrow]` # Support for the CSV, Parquet, ORC and IPC/Feather/Arrow formats
+- `pip install gqlalchemy[dgl]` # DGL support (includes PyTorch)
+
+- `pip install gqlalchemy[all]` # All of the above
+
+If you intend to use GQLAlchemy with PyTorch Geometric support, that library must be installed manually:
+
+```bash
+pip install gqlalchemy[torch_pyg] # prerequisite
+pip install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.13.0+cpu.html"
 ```
 
 If you are using [Conda](https://docs.conda.io/en/latest/) for Python environment management, you can install GQLAlchemy through pip.
 
 ## Build & Test
 
-The project uses [Poetry](https://python-poetry.org/) to build the GQLAlchemy Python library. To build and run tests, execute the following command:
-`poetry install`
+The project uses [Poetry](https://python-poetry.org/) to build the library. Clone or download the [GQLAlchemy source code](https://github.com/memgraph/gqlalchemy) locally and run the following command to build it from source with Poetry:
+
+```bash
+poetry install --all-extras
+```
 
-Before starting the tests, make sure you have an active Memgraph instance running. Execute the following command:
-`poetry run pytest .`
+The ``poetry install --all-extras`` command installs GQLAlchemy with all extras
+(optional dependencies). Alternatively, you can use the ``-E`` option to define
+what extras to install:
+
+```bash
+poetry install # No extras
+
+poetry install -E arrow # Support for the CSV, Parquet, ORC and IPC/Feather/Arrow formats
+poetry install -E dgl # DGL support (also includes torch)
+```
+
+To run the tests, make sure you have an [active Memgraph instance](/memgraph), and execute one of the following commands:
+
+```bash
+poetry run pytest . -k "not slow" # If all extras installed
+
+poetry run pytest . -k "not slow and not extras" # Otherwise
+```
+
+If you’ve installed only certain extras, it’s also possible to run their associated tests:
+
+```bash
+poetry run pytest . -k "arrow"
+poetry run pytest . -k "dgl"
+```
 
 ## GQLAlchemy capabilities
 
@@ -235,10 +270,11 @@ Since Memgraph is an in-memory graph database, the GQLAlchemy library provides a
 If you want to learn more about OGM, query builder, managing streams, importing data from different source, managing Memgraph instances, managing database triggers and using on-disk storage, check out the GQLAlchemy [how-to guides](https://memgraph.com/docs/gqlalchemy/how-to-guides).
 
 ## Development (how to build)
-```
+
+```bash
 poetry run flake8 .
 poetry run black .
-poetry run pytest . -k "not slow"
+poetry run pytest . -k "not slow and not extras"
 ```
 
 ## Documentation
@@ -247,8 +283,8 @@ The GQLAlchemy documentation is available on [memgraph.com/docs/gqlalchemy](http
 
 The documentation can be generated by executing:
 ```
-pip3 install python-markdown
-python-markdown
+pip3 install pydoc-markdown
+pydoc-markdown
 ```
 
 ## License