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Library extending Jupyter notebooks to integrate with Apache TinkerPop and RDF SPARQL.

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Graph Notebook: easily query and visualize graphs

The graph notebook provides an easy way to interact with graph databases using Jupyter notebooks. Using this open-source Python package, you can connect to any graph database that supports the Apache TinkerPop or the RDF SPARQL graph model. These databases could be running locally on your desktop or in the cloud. Graph databases can be used to explore a variety of use cases including knowledge graphs and identity graphs.

Visualizing Gremlin queries:

Gremlin query and graph

Visualizing SPARQL queries:

SPARL query and graph

Instructions for connecting to the following graph databases:

| Endpoint | Graph model | Query language | | :-----------------------------: | :---------------------: | :-----------------: | |Gremlin Server| property graph | Gremlin | | Blazegraph | RDF | SPARQL | |Amazon Neptune| property graph or RDF | Gremlin or SPARQL |

We encourage others to contribute configurations they find useful. There is an

folder where more information can be found.


Notebook cell 'magic' extensions in the IPython 3 kernel

- Executes a SPARQL query against your configured database endpoint.

- Executes a Gremlin query against your database using web sockets. The results are similar to what the Gremlin console would return.

- Sets the executing notebook's database configuration to the JSON payload provided in the cell body.

- Sets the executing notebook's vis.js options to the JSON payload provided in the cell body.

- Set of commands to integrate with NeptuneML functionality. Documentation

TIP :point_right: There is syntax highlighting for both

queries to help you structure your queries more easily.

Notebook line 'magic' extensions in the IPython 3 kernel

- Obtain the status of Gremlin queries. Documentation

- Obtain the status of SPARQL queries. Documentation

- Generate a form to submit a bulk loader job. Documentation

- Get ids of bulk load jobs. Documentation

- Get the status of a provided
. Documentation

- Set of commands to integrate with NeptuneML functionality. You can find a set of tutorial notebooks here. Documentation

- Check the Health Status of the configured host endpoint. Documentation

- Provides a form to add data to your graph without the use of a bulk loader. both SPARQL and Gremlin have an airport routes dataset.

- Returns a JSON payload that contains connection information for your host.

- Set the host endpoint to send queries to.

- Print the version of the

- Print the Vis.js options being used for rendered graphs

TIP :point_right: You can list all the magics installed in the Python 3 kernel using the



You will need:


# pin specific versions of Jupyter and Tornado dependency
pip install notebook==5.7.10
pip install tornado==4.5.3

install the package

pip install graph-notebook

install and enable the visualization widget

jupyter nbextension install --py --sys-prefix graph_notebook.widgets jupyter nbextension enable --py --sys-prefix graph_notebook.widgets

copy static html resources

python -m graph_notebook.static_resources.install python -m graph_notebook.nbextensions.install

copy premade starter notebooks

python -m graph_notebook.notebooks.install --destination ~/notebook/destination/dir

start jupyter

jupyter notebook ~/notebook/destination/dir

Connecting to a graph database

Gremlin Server

In a new cell in the Jupyter notebook, change the configuration using

and modify the fields for
, and
. For a local Gremlin server (HTTP or WebSockets), you can use the following command:
  "host": "localhost",
  "port": 8182,
  "auth_mode": "DEFAULT",
  "iam_credentials_provider_type": "ROLE",
  "load_from_s3_arn": "",
  "ssl": false,
  "aws_region": "us-east-1"

To setup a new local Gremlin Server for use with the graph notebook, check out

additional-databases/gremlin server


Change the configuration using

and modify the fields for
, and
. For a local Blazegraph database, you can use the following command:
  "host": "localhost",
  "port": 9999,
  "auth_mode": "DEFAULT",
  "iam_credentials_provider_type": "ROLE",
  "load_from_s3_arn": "",
  "ssl": false,
  "aws_region": "us-east-1"

To setup a new local Blazegraph database for use with the graph notebook, check out the Quick Start from Blazegraph.

Amazon Neptune

Change the configuration using

and modify the defaults as they apply to your Neptune cluster:
  "host": "your-neptune-endpoint",
  "port": 8182,
  "auth_mode": "DEFAULT",
  "iam_credentials_provider_type": "ENV",
  "load_from_s3_arn": "",
  "ssl": true,
  "aws_region": "your-neptune-region"

To setup a new Amazon Neptune cluster, check out the AWS documentation.

When connecting the graph notebook to Neptune, make sure you have a network setup to communicate to the VPC that Neptune runs on. If not, you can follow this guide.

Authentication (Amazon Neptune)

If you are running a SigV4 authenticated endpoint, ensure that the config field

is set to
and that you have set the following environment variables:
  • AWSSESSIONTOKEN (OPTIONAL. Use if you are using temporary credentials)

Contributing Guidelines

See CONTRIBUTING for more information.


This project is licensed under the Apache-2.0 License.

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