Renamed GenAI Suite to AI Services across all files, including endpoints; renamed Services folder to Reference; fix all broken links

Author: nerpaula

README.md

@@ -367,7 +367,7 @@ Inner shortcode
 Tags let you display badges, usually below a headline.
 
 This is mainly used for pointing out if a feature is only available in the
-GenAI Suite, the Data Platform, the Arango Managed Platform (AMP), or multiple
+AI Services, the Data Platform, the Arango Managed Platform (AMP), or multiple
 of them. See [Environment remarks](#environment-remarks) for details.
 
 It is also used for [Edition remarks](#edition-remarks) in content before
@@ -677,15 +677,15 @@ Pages and sections about features that are only available in certain environment
 such as in ArangoDB Shell should indicate where they are available using the
 `tag` shortcode.
 
-Features exclusive to the Data Platform, GenAI Data Platform,
+Features exclusive to the Data Platform, AI Services Data Platform,
 Arango Managed Platform (AMP), and ArangoDB generally don't need to be tagged
 because they are in dedicated parts of the documentation. However, if there are
 subsections with different procedures, each can be tagged accordingly.
 
-In the GenAI Data Platform only:
+In the AI Services Data Platform only:
 
 ```markdown
-{{< tag "GenAI Data Platform" >}}
+{{< tag "AI Services Data Platform" >}}
 ```
 
 In the Arango Managed Platform only:

site/content/gen-ai/_index.md renamed to site/content/ai-services/_index.md

@@ -1,14 +1,14 @@
 ---
-title: GenAI Suite
-menuTitle: GenAI Suite
+title: AI Services
+menuTitle: AI Services
 weight: 2
 description: >-
   A comprehensive AI solution that transforms your data into intelligent knowledge graphs with GraphRAG capabilities, applies advanced machine learning with GraphML, and provides enterprise-grade tools for analytics, natural language querying, and AI-powered insights, all through an intuitive web interface
 ---
 
 ## What's included
 
-The GenAI Suite is comprised of two major components:
+AI Services are comprised of two major components:
 
 - [**GraphRAG**](./graphrag/_index.md): A complete solution for extracting entities
   from text files to create a knowledge graph that you can then query with a
@@ -27,10 +27,10 @@ Alongside these components, you also get the following additional features:
   interactive notebooks for experimentation and development of applications
   that use ArangoDB as their backend.
 - **Public and private LLM support**: Use public LLMs such as OpenAI
-  or private LLMs with [Triton Inference Server](services/triton-inference-server.md).  
-- [**MLflow integration**](services/mlflow.md): Use the popular MLflow as a model registry for private LLMs
+  or private LLMs with [Triton Inference Server](reference/triton-inference-server.md).  
+- [**MLflow integration**](reference/mlflow.md): Use the popular MLflow as a model registry for private LLMs
   or to run machine learning experiments as part of the Arango Data Platform.
 - **Application Programming Interfaces**: Use the underlying APIs of the
-  GenAI Suite services and build your own integrations. See the
+  AI Services and build your own integrations. See the
   [API reference](https://arangoml.github.io/platform-dss-api/GenAI-Service/proto/index.html) documentation
   for more details.

site/content/gen-ai/graph-analytics.md renamed to site/content/ai-services/graph-analytics.md

@@ -44,7 +44,7 @@ How to perform the steps is detailed in the subsequent sections.
 1. Determine the approximate size of the data that you will load into the GAE
    and ensure the machine to run the engine on has sufficient memory. The data as well as the
    temporarily needed space for computations and results needs to fit in memory.
-2. [Start a `graphanalytics` service](#start-a-graphanalytics-service) via the GenAI service
+2. [Start a `graphanalytics` service](#start-a-graphanalytics-service) via the AI service
    that manages various Platform components for graph intelligence and machine learning.
    It only takes a few seconds until the engine service can be used. The engine
    runs adjacent to the pods of the ArangoDB Core.
@@ -90,7 +90,7 @@ Single server deployments using ArangoDB version 3.11 are not supported.
 
 {{< tab "Arango Data Platform" >}}
 You can use any of the available authentication methods the Arango Data Platform
-supports to start and stop `graphanalytics` services via the GenAI service as
+supports to start and stop `graphanalytics` services via the AI service as
 well as to authenticate requests to the [Engine API](#engine-api).
 
 - HTTP Basic Authentication
@@ -129,30 +129,30 @@ setting in ArangoGraph:
 
 The interface for managing the engines depends on the environment you use:
 
-- **Arango Data Platform**: [GenAI service](#genai-service)
+- **Arango Data Platform**: [AI service](#ai-service)
 - **ArangoGraph**: [Management API](#management-api)
 
-### GenAI service
+### AI service
 
-{{< tag "GenAI Data Platform" >}}
+{{< tag "AI Services Data Platform" >}}
 
-GAEs are deployed and deleted via the [GenAI service](services/gen-ai.md)
+GAEs are deployed and deleted via the [AI service](reference/gen-ai.md)
 in the Arango Data Platform.
 
 If you use cURL, you need to use the `-k` / `--insecure` option for requests
 if the Platform deployment uses a self-signed certificate (default).
 
 #### Start a `graphanalytics` service
 
-`POST <ENGINE_URL>/gen-ai/v1/graphanalytics`
+`POST <ENGINE_URL>/ai/v1/graphanalytics`
 
-Start a GAE via the GenAI service with an empty request body:
+Start a GAE via the AI service with an empty request body:
 
 ```sh
 # Example with a JWT session token
 ADB_TOKEN=$(curl -sSk -d '{"username":"root", "password": ""}' -X POST https://127.0.0.1:8529/_open/auth | jq -r .jwt)
 
-Service=$(curl -sSk -H "Authorization: bearer $ADB_TOKEN" -X POST https://127.0.0.1:8529/gen-ai/v1/graphanalytics)
+Service=$(curl -sSk -H "Authorization: bearer $ADB_TOKEN" -X POST https://127.0.0.1:8529/ai/v1/graphanalytics)
 ServiceID=$(echo "$Service" | jq -r ".serviceInfo.serviceId")
 if [[ "$ServiceID" == "null" ]]; then 
   echo "Error starting gral engine"
@@ -164,21 +164,21 @@ echo "$Service" | jq
 
 #### List the services
 
-`POST <ENGINE_URL>/gen-ai/v1/list_services`
+`POST <ENGINE_URL>/ai/v1/list_services`
 
-You can list all running services managed by the GenAI service, including the
+You can list all running services managed by the AI service, including the
 `graphanalytics` services:
 
 ```sh
-curl -sSk -H "Authorization: bearer $ADB_TOKEN" -X POST https://127.0.0.1:8529/gen-ai/v1/list_services | jq
+curl -sSk -H "Authorization: bearer $ADB_TOKEN" -X POST https://127.0.0.1:8529/ai/v1/list_services | jq
 ```
 
 #### Stop a `graphanalytics` service
 
-Delete the desired engine via the GenAI service using the service ID:
+Delete the desired engine via the AI service using the service ID:
 
 ```sh
-curl -sSk -H "Authorization: bearer $ADB_TOKEN" -X DELETE https://127.0.0.1:8529/gen-ai/v1/service/$ServiceID | jq
+curl -sSk -H "Authorization: bearer $ADB_TOKEN" -X DELETE https://127.0.0.1:8529/ai/v1/service/$ServiceID | jq
 ```
 
 ### Management API
@@ -314,7 +314,7 @@ To determine the base URL of the engine API, use the base URL of the Platform
 deployment and append `/gral/<SERVICE_ID>`, e.g.
 `https://127.0.0.1:8529/gral/arangodb-gral-tqcge`.
 
-The service ID is returned by the call to the GenAI service for
+The service ID is returned by the call to the AI service for
 [starting the `graphanalytics` service](#start-a-graphanalytics-service).
 You can also list the service IDs like so:
 

site/content/gen-ai/graph-to-ai.md renamed to site/content/ai-services/graph-to-ai.md

@@ -4,13 +4,13 @@ menuTitle: From Graph to AI
 weight: 25
 description: >-
   ArangoDB's set of tools and technologies enables analytics, machine learning,
-  and GenAI applications powered by graph data
+  and AI applications powered by graph data
 aliases:
   - data-science/overview
 ---
 
 {{< tip >}}
-The Arango Data Platform & GenAI Suite is available as a pre-release. To get
+The Arango Data Platform & AI Services are available as a pre-release. To get
 exclusive early access, [get in touch](https://arangodb.com/contact/) with
 the ArangoDB team.
 {{< /tip >}}
@@ -20,7 +20,7 @@ data science applications. The core database system includes multi-model storage
 of information with scalable graph and information retrieval capabilities that
 you can directly use for your research and product development.
 
-ArangoDB also offers a dedicated GenAI Suite, using the database core
+ArangoDB also offers dedicated AI Services, using the database core
 as the foundation for higher-level features. Whether you want to turbocharge
 generative AI applications with a GraphRAG solution or apply analytics and
 machine learning to graph data at scale, ArangoDB covers these needs.

site/content/gen-ai/graphml/_index.md renamed to site/content/ai-services/graphml/_index.md

@@ -8,7 +8,7 @@ aliases:
   - arangographml
 ---
 {{< tip >}}
-The Arango Data Platform & GenAI Suite is available as a pre-release. To get
+The Arango Data Platform & AI Services are available as a pre-release. To get
 exclusive early access, [get in touch](https://arangodb.com/contact/) with
 the ArangoDB team.
 {{< /tip >}}

site/content/gen-ai/graphml/notebooks-api.md renamed to site/content/ai-services/graphml/notebooks-api.md

@@ -22,7 +22,7 @@ giving you a clear path from data to prediction:
 To create a new GraphML project using the Arango Data Platform web interface, follow these steps:
 
 1. From the left-hand sidebar, select the database where you want to create the project.
-2. In the left-hand sidebar, click **GenAI Suite** to open the GraphML project management interface, then click **Run GraphML**.
+2. In the left-hand sidebar, click **AI Services** to open the GraphML project management interface, then click **Run GraphML**.
    ![Create GraphML Project](../../images/create-graphml-project-ui.png)  
 3. In the **GraphML projects** view, click **Add new project**.
 4. The **Create ML project** modal opens. Enter a **Name** for your machine learning project.

site/content/gen-ai/graphml/quickstart.md renamed to site/content/ai-services/graphml/quickstart.md

@@ -4,12 +4,12 @@ menuTitle: GraphRAG
 weight: 5
 description: >-
   ArangoDB's GraphRAG solution combines graph-based retrieval-augmented generation
-  with Large Language Models (LLMs) for turbocharged GenAI solutions
+  with Large Language Models (LLMs) for turbocharged AI solutions
 aliases:
   llm-knowledge-graphs
 ---
 {{< tip >}}
-The Arango Data Platform & GenAI Suite is available as a pre-release. To get
+The Arango Data Platform & AI Services are available as a pre-release. To get
 exclusive early access, [get in touch](https://arangodb.com/contact/) with
 the ArangoDB team.
 {{< /tip >}}
@@ -56,5 +56,5 @@ relationships and connections becoming more sophisticated over time.
 - **[GraphRAG Tutorial using integrated Notebook servers](tutorial-notebook.md)**: Follow hands-on examples and implementation guidance via Jupyter Notebooks.
 
 For deeper implementation details, explore the individual services:
-- **[Importer Service](../services/importer.md)**: Transform documents into knowledge graphs.
-- **[Retriever Service](../services/retriever.md)**: Query and extract insights from your knowledge graphs.
+- **[Importer Service](../reference/importer.md)**: Transform documents into knowledge graphs.
+- **[Retriever Service](../reference/retriever.md)**: Query and extract insights from your knowledge graphs.

site/content/gen-ai/graphml/ui.md renamed to site/content/ai-services/graphml/ui.md

@@ -9,7 +9,7 @@ description: >-
 {{< tag "ArangoDB Platform" >}}
 
 {{< tip >}}
-The ArangoDB Platform & GenAI Suite is available as a pre-release. To get
+The ArangoDB Platform & AI Services are available as a pre-release. To get
 exclusive early access, [get in touch](https://arangodb.com/contact/) with
 the ArangoDB team.
 {{< /tip >}}
@@ -34,7 +34,7 @@ ArangoDB's unique capabilities and flexible integration of knowledge graphs and
 LLMs provide a powerful and efficient solution for anyone seeking to extract
 valuable insights from diverse datasets.
 
-The GraphRAG component of the GenAI Suite brings all the capabilities
+The GraphRAG component of AI Services brings all the capabilities
 together with an easy-to-use interface, so you can make the knowledge accessible
 to your organization.
 
@@ -76,7 +76,7 @@ information in a structured graph format, allowing efficient querying and retrie
 3. Store the generated Knowledge Graph in the database for retrieval and reasoning.
 
 For detailed information about the service, see the
-[Importer](../services/importer.md) service documentation.
+[Importer](../reference/importer.md) service documentation.
 
 ### Extract information from the Knowledge Graph
 
@@ -87,7 +87,7 @@ You can extract information from Knowledge Graphs using two distinct methods:
 - Local retrieval
 
 For detailed information about the service, see the
-[Retriever](../services/retriever.md) service documentation.
+[Retriever](../reference/retriever.md) service documentation.
 
 #### Global retrieval
 
@@ -134,7 +134,7 @@ collection, and then it expands that subgraph over related entities, relations
 
 If you're working in an air-gapped environment or need to keep your data
 private, you can use the private LLM mode with 
-[Triton Inference Server](../services/triton-inference-server.md).
+[Triton Inference Server](../reference/triton-inference-server.md).
 
 This option allows you to run the service completely within your own
 infrastructure. The Triton Inference Server is a crucial component when