From 096e94361d4091c137e217313a49301ee4b89f58 Mon Sep 17 00:00:00 2001 From: Paula Date: Fri, 27 Jun 2025 12:54:11 +0200 Subject: [PATCH 1/2] initial draft --- .../data-science/arangograph-notebooks.md | 55 +++++++++++++------ 1 file changed, 37 insertions(+), 18 deletions(-) diff --git a/site/content/3.13/data-science/arangograph-notebooks.md b/site/content/3.13/data-science/arangograph-notebooks.md index 34ca9529be..49c3d54471 100644 --- a/site/content/3.13/data-science/arangograph-notebooks.md +++ b/site/content/3.13/data-science/arangograph-notebooks.md @@ -1,22 +1,41 @@ --- -title: ArangoGraph Notebooks -menuTitle: ArangoGraph Notebooks +title: ArangoDB Notebooks +menuTitle: ArangoDB Notebooks weight: 130 description: >- - Colocated Jupyter Notebooks within the ArangoGraph Insights Platform + Colocated Jupyter Notebooks within the ArangoDB Platform --- -{{< tip >}} -ArangoGraph Notebooks don't include the ArangoGraphML services. -To enable the ArangoGraphML services, -[get in touch](https://www.arangodb.com/contact/) -with the ArangoDB team. -{{< /tip >}} - -The ArangoGraph Notebook is a JupyterLab notebook embedded in the -[ArangoGraph Insights Platform](https://dashboard.arangodb.cloud/home?utm_source=docs&utm_medium=cluster_pages&utm_campaign=docs_traffic). -The notebook integrates seamlessly with the platform, -automatically connecting to ArangoGraph services and ArangoDB. -This makes it much easier to leverage these resources without having -to download any data locally or to remember user IDs, passwords, and endpoint URLs. - -For more information, see the [Notebooks](../arangograph/notebooks.md) documentation. + +{{< tag "ArangoDB Platform" >}} + +ArangoDB Notebooks provide a Jupyter-based environment for interactive data science +and GenAI, GraphRAG, graph analytics, and exploration of ArangoDB datasets. +The notebooks enable seamless integration of ArangoDB’s multi-model capabilities +with data science tools and libraries in Python. + +ArangoDB Notebooks provide a Python-based, Jupyter-compatible interface for building +and experimenting with graph-powered data, GenAI, and graph machine learning +workflows directly connected to ArangoDB databases. The notebooks offer a +pre-configured environment where everything, including all the necessary services +and configurations, comes preloaded. You don't need to set up or configure the +infrastructure, and can immediately start using the data science and GenAI +functionalities. + +The notebooks are primarily focused on the following solutions: +- **GraphRAG**: A complete solution for extracting entities + from text files to create a knowledge graph that you can then query with a + natural language interface. +- **GraphML**: Apply machine learning to graphs for link prediction, + classification, and similar tasks. +- **Adapters** : Use ArangoDB together with cuGraph, NetworkX, and other tools. + + + +## Quickstart + + + + + + + From 4d02dc779845f0d5fedb038dc341b3df72e551a8 Mon Sep 17 00:00:00 2001 From: Paula Date: Thu, 3 Jul 2025 12:31:43 +0200 Subject: [PATCH 2/2] add quickstart --- .../3.13/data-science/arangograph-notebooks.md | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/site/content/3.13/data-science/arangograph-notebooks.md b/site/content/3.13/data-science/arangograph-notebooks.md index 49c3d54471..8ad08350eb 100644 --- a/site/content/3.13/data-science/arangograph-notebooks.md +++ b/site/content/3.13/data-science/arangograph-notebooks.md @@ -18,7 +18,7 @@ and experimenting with graph-powered data, GenAI, and graph machine learning workflows directly connected to ArangoDB databases. The notebooks offer a pre-configured environment where everything, including all the necessary services and configurations, comes preloaded. You don't need to set up or configure the -infrastructure, and can immediately start using the data science and GenAI +infrastructure, and can immediately start using the GraphML and GenAI functionalities. The notebooks are primarily focused on the following solutions: @@ -27,13 +27,23 @@ The notebooks are primarily focused on the following solutions: natural language interface. - **GraphML**: Apply machine learning to graphs for link prediction, classification, and similar tasks. -- **Adapters** : Use ArangoDB together with cuGraph, NetworkX, and other tools. +- **Integrations** : Use ArangoDB together with cuGraph, NetworkX, and other data science tools. ## Quickstart - +1. In the ArangoDB Platform web interface, select a database. +2. Under **GenAI Tools**, click **Notebook servers**. +3. The **Notebook servers** page displays an overview of the notebook services. Click + **New notebook server** to create a new one. +4. After your notebook service is launched, you can start interacting with the + Jupyter interface. + +{{< tip >}} +To get a better understanding of how to interact with ArangoDB, use +the `GettingStarted.ipynb` template from the file browser. +{{< /tip >}}