From 096e94361d4091c137e217313a49301ee4b89f58 Mon Sep 17 00:00:00 2001 From: Paula Date: Fri, 27 Jun 2025 12:54:11 +0200 Subject: [PATCH 1/3] 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 2/3] 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 >}} From cd410b46fb0b0464c8fa88f06026a4b847846565 Mon Sep 17 00:00:00 2001 From: Paula Date: Mon, 7 Jul 2025 13:20:44 +0200 Subject: [PATCH 3/3] notebook servers --- ...graph-notebooks.md => notebook-servers.md} | 32 ++++++++++--------- 1 file changed, 17 insertions(+), 15 deletions(-) rename site/content/3.13/data-science/{arangograph-notebooks.md => notebook-servers.md} (68%) diff --git a/site/content/3.13/data-science/arangograph-notebooks.md b/site/content/3.13/data-science/notebook-servers.md similarity index 68% rename from site/content/3.13/data-science/arangograph-notebooks.md rename to site/content/3.13/data-science/notebook-servers.md index 8ad08350eb..b40a2127d6 100644 --- a/site/content/3.13/data-science/arangograph-notebooks.md +++ b/site/content/3.13/data-science/notebook-servers.md @@ -1,21 +1,19 @@ --- -title: ArangoDB Notebooks -menuTitle: ArangoDB Notebooks +title: Notebook Servers +menuTitle: Notebook Servers weight: 130 description: >- Colocated Jupyter Notebooks within the ArangoDB Platform +aliases: + - data-science/arangograph-notebooks --- {{< 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 +workflows directly connected to ArangoDB databases. The notebook servers are +embedded in the ArangoGraph ecosystem and 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 GraphML and GenAI @@ -27,9 +25,16 @@ 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. -- **Integrations** : Use ArangoDB together with cuGraph, NetworkX, and other data science tools. +- **Integrations** : Use ArangoDB together with cuGraph, NetworkX, and other data science tools. + + - +The ArangoDB Notebooks also include the following services and tools: +- Automatically connect to ArangoDB databases and GenAI platform services +- [Magic commands](../arangograph/notebooks.md#arangograph-magic-commands) + that simplify database interactions +- [ArangoDB CLI Tools](../components/tools/) included in each Notebook server + to interact with ArangoDB from the command line ## Quickstart @@ -43,9 +48,6 @@ The notebooks are primarily focused on the following solutions: {{< tip >}} To get a better understanding of how to interact with ArangoDB, use the `GettingStarted.ipynb` template from the file browser. -{{< /tip >}} - - - - +{{< /tip >}} + \ No newline at end of file