finalize content, remove unnecessary screenshots, apply to all versions

Author: nerpaula

site/content/3.12/data-science/graphrag/web-interface.md

@@ -1,14 +1,14 @@
 ---
-title: How to use GraphRAG in the ArangoDB Platform web interface
+title: How to use GraphRAG in the Arango Data Platform web interface
 menuTitle: Web Interface
 weight: 5
 description: >-
- Learn how to create, configure, and run a full GraphRAG workflow in just a few steps using the Platform web interface
+ Learn how to create, configure, and run a full GraphRAG workflow in just a few steps
 ---
-{{< tag "ArangoDB Platform" >}}
+{{< tag "AI Services" >}}
 
 {{< tip >}}
-The ArangoDB Platform & GenAI Suite is available as a pre-release. To get
+The Arango Data Platform & AI Services is available as a pre-release. To get
 exclusive early access, [get in touch](https://arangodb.com/contact/) with
 the ArangoDB team.
 {{< /tip >}}
@@ -25,10 +25,10 @@ The entire process is organized into sequential steps within a **Project**:
 
 ## Create a GraphRAG project
 
-To create a new GraphRAG project using the ArangoDB Platform web interface, follow these steps:
+To create a new GraphRAG 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 GraphRAG project management
+2. In the left-hand sidebar, click **AI Services** to open the GraphRAG project management
    interface, then click **Run GraphRAG**.
 3. In the **GraphRAG projects** view, click **Add new project**.
 4. The **Create GraphRAG project** modal opens. Enter a **Name** and optionally
@@ -62,7 +62,6 @@ configure and start a new importer service job. Follow the steps below.
 3. Enter your **OpenAI API Key**.
 4. Click the **Start importer service** button.
 
-![Configure Importer service using OpenAI](../../../images/graphrag-ui-configure-importer-openai.png)
 {{< /tab >}}
 
 {{< tab "OpenRouter" >}}
@@ -78,7 +77,6 @@ When using OpenRouter, you need both API keys because the LLM responses are serv
 via OpenRouter while OpenAI is used for the embedding model.
 {{< /info >}}
 
-![Configure Importer service using OpenRouter](../../../images/graphrag-ui-configure-importer-openrouter.png)
 {{< /tab >}}
 
 {{< tab "Triton LLM Host" >}}
@@ -91,12 +89,11 @@ Note that you must first register your model in MLflow. The [Triton LLM Host](se
 service automatically downloads and loads models from the MLflow registry.
 {{< /info >}}
 
-![Configure Importer service using Triton](../../../images/graphrag-ui-configure-importer-triton.png)
 {{< /tab >}}
 
 {{< /tabs >}}
 
-See also the [GraphRAG Importer](services/importer.md) service documentation.
+See also the [Importer](services/importer.md) service documentation.
 
 ## Add data source
 
@@ -155,7 +152,6 @@ the generated Knowledge Graph. To configure the retriever service, open the
 3. Enter your **OpenAI API Key**.
 4. Click the **Start retriever service** button.
 
-![Configure Retriever Service using OpenAI](../../../images/graphrag-ui-configure-retriever-openai.png)
 {{< /tab >}}
 
 {{< tab "OpenRouter" >}}
@@ -170,7 +166,6 @@ When using OpenRouter, the LLM responses are served via OpenRouter while OpenAI
 is used for the embedding model.
 {{< /info >}}
 
-![Configure Retriever Service using OpenRouter](../../../images/graphrag-ui-configure-retriever-openrouter.png)
 {{< /tab >}}
 
 {{< tab "Triton LLM Host" >}}
@@ -183,26 +178,28 @@ Note that you must first register your model in MLflow. The [Triton LLM Host](se
 service automatically downloads and loads models from the MLflow registry.
 {{< /info >}}
 
-![Configure Retriever Service using Triton](../../../images/graphrag-ui-configure-retriever-triton.png)
 {{< /tab >}}
 
 {{< /tabs >}}
 
-See also the [GraphRAG Retriever](services/retriever.md) documentation.
+See also the [Retriever](services/retriever.md) documentation.
 
 ## Chat with your Knowledge Graph
 
-The Retriever service provides two search methods:
-- [Local search](services/retriever.md#local-search): Local queries let you
-  explore specific nodes and their direct connections.
-- [Global search](services/retriever.md#global-search): Global queries uncover
-  broader patters and relationships across the entire Knowledge Graph.
-
-![Chat with your Knowledge Graph](../../../images/graphrag-ui-chat.png)
+The chat interface provides two search methods:
+- **Instant search**: Instant queries provide fast responses.
+- **Deep search**: This option will take longer to return a response.
 
 In addition to querying the Knowledge Graph, the chat service allows you to do the following:
-- Switch the search method from **Local Query** to **Global Query** and vice-versa
+- Switch the search method from **Instant search** to **Deep research** and vice-versa
   directly in the chat
-- Change the retriever service
+- Change or create a new retriever service
 - Clear the chat
-- Integrate the Knowledge Graph chat service into your own applications
+
+## Integrate the Knowledge Graph chat service into your application
+
+To integrate any service into your own applications,
+go to **Project Settings** and use the copy button next to each service to
+copy its integration endpoint. You cam make `POST` requests to the endpoints
+with your queries, the services accept `JSON` payloads and return structured
+responses for building custom interfaces.

site/content/3.13/data-science/graphrag/web-interface.md

@@ -1,15 +1,14 @@
 ---
-title: How to use GraphRAG in the ArangoDB Platform web interface
+title: How to use GraphRAG in the Arango Data Platform web interface
 menuTitle: Web Interface
 weight: 5
 description: >-
- Learn how to create, configure, and run a full GraphRAG workflow in four steps
- using the Platform web interface
+ Learn how to create, configure, and run a full GraphRAG workflow in just a few steps
 ---
-{{< tag "ArangoDB Platform" >}}
+{{< tag "AI Services" >}}
 
 {{< tip >}}
-The ArangoDB Platform & GenAI Suite is available as a pre-release. To get
+The Arango Data Platform & AI Services is available as a pre-release. To get
 exclusive early access, [get in touch](https://arangodb.com/contact/) with
 the ArangoDB team.
 {{< /tip >}}
@@ -19,28 +18,39 @@ the ArangoDB team.
 The entire process is organized into sequential steps within a **Project**:
 
 1. Creating the importer service
-2. Uploading your file and exploring the generated Knowledge Graph
-3. Creating the retriever service
-4. Chatting with your Knowledge Graph
+2. Adding data sources
+3. Exploring the generated Knowledge Graph
+4. Creating the retriever service
+5. Chatting with your Knowledge Graph
 
 ## Create a GraphRAG project
 
-To create a new GraphRAG project using the ArangoDB Platform web interface, follow these steps:
+To create a new GraphRAG 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 GraphRAG project management
+2. In the left-hand sidebar, click **AI Services** to open the GraphRAG project management
    interface, then click **Run GraphRAG**.
 3. In the **GraphRAG projects** view, click **Add new project**.
 4. The **Create GraphRAG project** modal opens. Enter a **Name** and optionally
    a description for your project.
 5. Click the **Create project** button to finalize the creation.
 
+## Project Settings
+
+The **Project Settings** dialog allows you to configure and manage your
+Importer and Retriever services.
+
+You can open the **Project Settings** dialog in two ways:
+- In the **Data Sources** section, click **Add data source** and then click on
+  the **Open project settings** button.
+- In the **Graph** section, click on the gear icon.
+
 ## Configure the Importer service
 
-Configure a service to import, parse, and retrieve all the needed data from a
+Configure a service to import, parse, and extract all the needed data from a
 file. This service uses the LLM API provider and model of your choice.
 
-After clicking on a project name, you are taken to a screen where you can
+After opening the **Project Settings**, you are taken to a dialog where you can
 configure and start a new importer service job. Follow the steps below.
 
 {{< tabs "importer-service" >}}
@@ -52,23 +62,21 @@ configure and start a new importer service job. Follow the steps below.
 3. Enter your **OpenAI API Key**.
 4. Click the **Start importer service** button.
 
-![Configure Importer service using OpenAI](../../../images/graphrag-ui-configure-importer-openai.png) 
 {{< /tab >}}
 
 {{< tab "OpenRouter" >}}
 1. Select **OpenRouter** from the **LLM API Provider** dropdown menu.
 2. Select the model you want to use from the **Model** dropdown menu. By default,
-   the service is using **Mistral AI - Mistral Nemo**.
-1. Enter your **OpenAI API Key**.
-2. Enter your **OpenRouter API Key**.
-3. Click the **Start importer service** button.
+   the service uses **Mistral AI - Mistral Nemo**.
+3. Enter your **OpenAI API Key**.
+4. Enter your **OpenRouter API Key**.
+5. Click the **Start importer service** button.
 
 {{< info >}}
-When using the OpenRouter option, the LLM responses are served via OpenRouter
-while OpenAI is used for the embedding model.
+When using OpenRouter, you need both API keys because the LLM responses are served
+via OpenRouter while OpenAI is used for the embedding model.
 {{< /info >}}
 
-![Configure Importer service using OpenRouter](../../../images/graphrag-ui-configure-importer-openrouter.png)
 {{< /tab >}}
 
 {{< tab "Triton LLM Host" >}}
@@ -81,38 +89,59 @@ Note that you must first register your model in MLflow. The [Triton LLM Host](se
 service automatically downloads and loads models from the MLflow registry.
 {{< /info >}}
 
-![Configure Importer service using Triton](../../../images/graphrag-ui-configure-importer-triton.png)
 {{< /tab >}}
 
 {{< /tabs >}}
 
-See also the [GraphRAG Importer](services/importer.md) service documentation.
+See also the [Importer](services/importer.md) service documentation.
 
-## Upload your file
+## Add data source
 
-1. Upload a file by dragging and dropping it in the designated upload area.
-   The importer service you previously launched parses and creates the
-   Knowledge Graph automatically.
-2. Enter a file name.
-3. Click the **Start import** button.
+To add your first data source:
+
+1. In the **Data Sources** section, click the **Add data source** button.
+2. Upload a file by dragging and dropping it in the designated upload area.
+   The importer service you previously configured will automatically parse the file
+   and create the Knowledge Graph.
+3. Enter a descriptive name for your file.
+4. Click the **Start import** button.
 
 {{< info >}}
-You can only import a single file, either in `.md` or `.txt` format.
+Currently, you can import one file at a time in either Markdown (`.md`) or
+plain text (`.txt`) format. Additional files can be added to update the Knowledge Graph.
 {{< /info >}}
 
 ![Upload file in GraphRAG web interface](../../../images/graphrag-ui-upload-file.png)
 
 ## Explore the Knowledge Graph
 
-You can open and explore the Knowledge Graph that has been generated by clicking
-on the **Explore in visualizer** button.
+After your file is processed, you can view and explore the generated Knowledge Graph
+in the **Graph** section.
+
+![Explore Knowledge Graph in GraphRAG web interface](../../../images/graphrag-ui-explore-knowledge-graph.png)
+
+For a more detailed exploration, click the **Explore** button to open the Knowledge Graph in the dedicated Graph Visualizer.
 
 For more information, see the [Graph Visualizer](../../graphs/graph-visualizer.md) documentation.
 
+## Update the Knowledge Graph
+
+Once you have created your initial Knowledge Graph, you can update it by uploading 
+additional files using the same process described in the [Add data source](#add-data-source) section. 
+The importer service will automatically update the existing Knowledge Graph and 
+underlying collections with the new data.
+
+To update your Knowledge Graph:
+
+1. In the **Data Sources** section, click the **Add data source** button again.
+2. Upload a new file by dragging and dropping it in the designated upload area.
+3. The importer service will process the new file and update the existing Knowledge Graph along with the underlying collections.
+
 ## Configure the Retriever service
 
-Creating the retriever service allows you to extract information from
-the generated Knowledge Graph. Follow the steps below to configure the service.
+The retriever service enables you to query and extract information from
+the generated Knowledge Graph. To configure the retriever service, open the 
+**Project Settings** and follow the steps below.
 
 {{< tabs "retriever-service" >}}
 
@@ -123,7 +152,6 @@ the generated Knowledge Graph. Follow the steps below to configure the service.
 3. Enter your **OpenAI API Key**.
 4. Click the **Start retriever service** button.
 
-![Configure Retriever Service using OpenAI](../../../images/graphrag-ui-configure-retriever-openai.png)
 {{< /tab >}}
 
 {{< tab "OpenRouter" >}}
@@ -134,11 +162,10 @@ the generated Knowledge Graph. Follow the steps below to configure the service.
 4. Click the **Start retriever service** button.
 
 {{< info >}}
-When using the OpenRouter option, the LLM responses are served via OpenRouter
-while OpenAI is used for the embedding model.
+When using OpenRouter, the LLM responses are served via OpenRouter while OpenAI
+is used for the embedding model.
 {{< /info >}}
 
-![Configure Retriever Service using OpenRouter](../../../images/graphrag-ui-configure-retriever-openrouter.png)
 {{< /tab >}}
 
 {{< tab "Triton LLM Host" >}}
@@ -151,26 +178,28 @@ Note that you must first register your model in MLflow. The [Triton LLM Host](se
 service automatically downloads and loads models from the MLflow registry.
 {{< /info >}}
 
-![Configure Retriever Service using Triton](../../../images/graphrag-ui-configure-retriever-triton.png)
 {{< /tab >}}
 
 {{< /tabs >}}
 
-See also the [GraphRAG Retriever](services/retriever.md) documentation.
+See also the [Retriever](services/retriever.md) documentation.
 
 ## Chat with your Knowledge Graph
 
-The Retriever service provides two search methods:
-- [Local search](services/retriever.md#local-search): Local queries let you
-  explore specific nodes and their direct connections.
-- [Global search](services/retriever.md#global-search): Global queries uncover
-  broader patters and relationships across the entire Knowledge Graph.
-
-![Chat with your Knowledge Graph](../../../images/graphrag-ui-chat.png)
+The chat interface provides two search methods:
+- **Instant search**: Instant queries provide fast responses.
+- **Deep search**: This option will take longer to return a response.
 
 In addition to querying the Knowledge Graph, the chat service allows you to do the following:
-- Switch the search method from **Local Query** to **Global Query** and vice-versa
+- Switch the search method from **Instant search** to **Deep research** and vice-versa
   directly in the chat
-- Change the retriever service
+- Change or create a new retriever service
 - Clear the chat
-- Integrate the Knowledge Graph chat service into your own applications
+
+## Integrate the Knowledge Graph chat service into your application
+
+To integrate any service into your own applications,
+go to **Project Settings** and use the copy button next to each service to
+copy its integration endpoint. You cam make `POST` requests to the endpoints
+with your queries, the services accept `JSON` payloads and return structured
+responses for building custom interfaces.