diff --git a/site/content/ai-suite/graphrag/web-interface.md b/site/content/ai-suite/graphrag/web-interface.md index 7438127d6f..927da744a2 100644 --- a/site/content/ai-suite/graphrag/web-interface.md +++ b/site/content/ai-suite/graphrag/web-interface.md @@ -3,8 +3,7 @@ title: How to use GraphRAG in the Arango Data Platform web interface menuTitle: Web Interface weight: 20 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 --- {{< tip >}} The Arango Data Platform & AI Suite are available as a pre-release. To get @@ -17,9 +16,10 @@ the Arango 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 @@ -33,12 +33,22 @@ To create a new GraphRAG project using the Arango Data Platform web interface, f 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" >}} @@ -49,24 +59,20 @@ configure and start a new importer service job. Follow the steps below. the service is using **O4 Mini**. 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" >}} @@ -78,39 +84,59 @@ while OpenAI is used for the embedding model. Note that you must first register your model in MLflow. The [Triton LLM Host](../reference/triton-inference-server.md) 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](../reference/importer.md) service documentation. +See also the [Importer](../reference/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](../../data-platform/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" >}} @@ -120,8 +146,6 @@ the generated Knowledge Graph. Follow the steps below to configure the service. the service uses **O4 Mini**. 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" >}} @@ -132,11 +156,9 @@ 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" >}} @@ -148,27 +170,28 @@ while OpenAI is used for the embedding model. Note that you must first register your model in MLflow. The [Triton LLM Host](../reference/triton-inference-server.md) 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](../reference/retriever.md) documentation. +See also the [Retriever](../reference/retriever.md) documentation. ## Chat with your Knowledge Graph -The Retriever service provides two search methods: -- [Local search](../reference/retriever.md#local-search): Local queries let you - explore specific nodes and their direct connections. -- [Global search](../reference/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 search** 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. 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