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https://github.com/memgraph/gqlalchemy

GQLAlchemy is a library developed with the purpose of assisting in writing and running queries on Memgraph. GQLAlchemy supports high-level connection to Memgraph as well as modular query builder.
https://github.com/memgraph/gqlalchemy

graph-database graphs memgraph neo4j neo4j-client networkx object-graph-mapper ogm python query-builder schema-validation

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GQLAlchemy is a library developed with the purpose of assisting in writing and running queries on Memgraph. GQLAlchemy supports high-level connection to Memgraph as well as modular query builder.

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README 

        
# GQLAlchemy





    

    

    

    Code style: black

    

    




**GQLAlchemy** is a fully open-source Python library and **Object Graph Mapper** (OGM) - a link between graph database objects and Python objects.



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



### Prerequisites



- **Python 3.8 - 3.11**

- [`pymgclient`](https://github.com/memgraph/pymgclient):



  - Install `pymgclient` [build prerequisites](https://memgraph.github.io/pymgclient/introduction.html#build-prerequisites)

  - Install `pymgclient` via pip:



  ```bash

  pip install --user pymgclient

  ```



> [!WARNING]  

> Python 3.11 users: On Windows, GQLAlchemy is not yet compatible with this Python version. Linux users can install GQLAlchemy **without** the DGL extra (due to its dependencies not supporting Python 3.11 yet). If this is currently a blocker for you, please let us know by [opening an issue](https://github.com/memgraph/gqlalchemy/issues).



### Install GQLAlchemy



After you’ve installed the [prerequisites](#prerequisites), run the following command to install

GQLAlchemy:



```bash

pip install gqlalchemy

```



With the above command, you get the default GQLAlchemy installation which

doesn’t include import/export support for certain formats (see below). To get

additional import/export capabilities, use one of the following install options:



```bash

pip install gqlalchemy[arrow] # Support for the CSV, Parquet, ORC and IPC/Feather/Arrow formats

pip install gqlalchemy[dgl] # DGL support (also includes torch)

pip install gqlalchemy[docker] # Docker support



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 the zsh terminal, surround `gqlalchemy[$extras]` with quotes:



```bash

pip install 'gqlalchemy[arrow]'

```



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 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

```



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)

poetry install -E docker # Docker support

```



To run the tests, make sure you have an [active Memgraph instance](https://memgraph.com/docs/getting-started), 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"

poetry run pytest . -k "docker"

```



## Development (how to build)



```bash

poetry run flake8 .

poetry run black .

poetry run pytest . -k "not slow and not extras"

```



## Documentation



The GQLAlchemy documentation is available on [GitHub](https://memgraph.github.io/gqlalchemy/).



The reference guide can be generated from the code by executing:



```

pip3 install pydoc-markdown

pydoc-markdown

```



Other parts of the documentation are written and located at docs directory. To test the documentation locally execute:



```

pip3 install mkdocs

pip3 install mkdocs-material

pip3 install pymdown-extensions

mkdocs serve

```



## License



Copyright (c) 2016-2023 [Memgraph Ltd.](https://memgraph.com)



Licensed under the Apache License, Version 2.0 (the "License"); you may not use

this file except in compliance with the License. You may obtain a copy of the

License at



     http://www.apache.org/licenses/LICENSE-2.0



Unless required by applicable law or agreed to in writing, software distributed

under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR

CONDITIONS OF ANY KIND, either express or implied. See the License for the

specific language governing permissions and limitations under the License.