DOC-768 | ArangoDB Platform Notebooks

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. 
+
+<!-- TODO: Add links to corressponding pages -->
+
+## Quickstart
+
+<!-- TODO: Describe how to create and manage Notebooks in the ArangoDB Platform UI -->
+
+<!-- TODO: Describe underlying services? -->
+
+<!-- TODO: Add links to interactive tutorials? -->
+