> ## Documentation Index > Fetch the complete documentation index at: https://developer.box.com/llms.txt > Use this file to discover all available pages before exploring further. # Box and Weaviate > Build an end-to-end RAG workflow by embedding Box content into Weaviate and querying it with the Weaviate Query Agent. export const RelatedLinks = ({title, items = []}) => { const getBadgeClass = badge => { if (!badge) return "badge-default"; const badgeType = badge.toLowerCase().replace(/\s+/g, "-"); return `badge-${badge === "ガイド" ? "guide" : badgeType}`; }; if (!items || items.length === 0) { return null; } return
{}

{title}

{}
{items.map((item, index) => {} {item.badge} {} {item.label} )}
; }; export const SignupCTA = ({children}) => { return
{children}
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; }; This tutorial walks you through building a retrieval-augmented generation (RAG) workflow by embedding Box content into a [Weaviate](https://weaviate.io/) vector database and using Weaviate's [Query Agent](https://weaviate.io/blog/weaviate-agents) to answer questions about your data. The complete recipe is available as a Jupyter Notebook in the [Weaviate recipes repository](https://github.com/weaviate/recipes/tree/main/integrations/data-platforms/box). A free developer account gives you access to the Box API and everything you need to build AI-powered workflows with Weaviate. ## Overview ### What is Weaviate? [Weaviate](https://weaviate.io/) is an open-source vector database built for speed, scale, and AI-driven search. It stores data as objects and vectors, letting you combine semantic search via embeddings with structured filtering. Weaviate is cloud native, fault tolerant, and integrates directly with large language models (LLMs). ### How Box and Weaviate create an end-to-end RAG solution RAG is a technique that pairs vector search (retrieval) with a language model (generation) to answer questions using your own data. The flow works as follows: * **Content storage**: Box holds your files (PDFs, docs, text reports, and other supported formats). * **Embedding creation**: Text is extracted from your Box files, chunked, and converted into vector embeddings using [Weaviate Embeddings](https://weaviate.io/developers/wcs/embeddings). * **Querying**: Weaviate's Query Agent takes a natural language question, generates the necessary search and aggregation queries, and returns a single answer — all using agentic RAG. ## Prerequisites Before you begin, make sure you have the following: * A Box developer account. If you don't already have one, [sign up for a free developer account](https://account.box.com/signup/developer#ty9l3). * A Jupyter Notebook environment such as [Visual Studio Code](https://code.visualstudio.com/docs/datascience/jupyter-notebooks) with the Jupyter extension, or a local Jupyter installation. * A Weaviate Cloud account. [Sign up for a free sandbox tier](https://console.weaviate.cloud/). ## Get a Box developer token 1. Click **New App** in the top right corner. 2. Enter an app name and select **OAuth 2.0** as the authentication method. 3. Click **Create App**. 4. Under **Application Scopes**, add read/write scopes for files if not already enabled, then click **Save Changes**. 5. From the **Configuration** tab, copy and save the developer token. You will need it for the notebook. Developer tokens are valid for 60 minutes. If your session takes longer, you will need to generate a new token. ## Create a Weaviate cluster 1. Log in to [Weaviate Cloud](https://console.weaviate.cloud/). 2. Create a new cluster from the dashboard. You can name it whatever you like. 3. Once the cluster is ready, go to the **Details** tab and note the **cluster URL** and **API key**. ## Run the recipe ### Clone the repository Clone or download the [Weaviate recipes repository](https://github.com/weaviate/recipes): ```bash theme={null} git clone https://github.com/weaviate/recipes.git ``` Navigate to the Box integration folder: ```bash theme={null} cd recipes/integrations/data-platforms/box ``` Open the Jupyter Notebook (`weaviate_box.ipynb`) in your development environment. Weaviate recipes repository structure ### Configure authentication The notebook includes a step to set authentication variables. Update the code block in **step 3** with: * Your **Box developer token** * Your **Weaviate cluster URL** and **API key** Authentication variables in the notebook ### Run the notebook Execute each cell in the notebook sequentially. The notebook: 1. Uploads demo files to Box (or uses files you provide). 2. Extracts text content from the Box files. 3. Chunks the text and creates vector embeddings in Weaviate. 4. Uses the Weaviate Query Agent to answer questions about the content. The repository includes a `demo_files` folder with four 10-K financial reports for testing. You can replace these with your own files if you prefer to work with different content. The final cell demonstrates querying your data. You can modify the query in **step 7** to ask different questions based on your content. Final answer from the Weaviate Query Agent ## Next steps Upload additional files to Box such as annual reports, articles, or any documents you want to search, and rerun the notebook to embed them in Weaviate. Adjust the `system_prompt` parameter to change the agent's behavior. For example, you can request more detailed analysis or a specific response format. Weaviate offers additional agent types. The [Transformation Agent](https://docs.weaviate.io/agents#transformation-agent) can preprocess your data, and the [Personalization Agent](https://docs.weaviate.io/agents#personalization-agent) can tailor responses to individual users. ## Resources The complete Jupyter Notebook in the Weaviate recipes repository. Learn about Weaviate's agentic RAG capabilities. Documentation for Weaviate's embedding service. Share feedback and get support from other Box developers.