By Alex Johnson, Mia Roberts, and Daniel Lee
On 08 AUG 2024
In Amazon Q, Artificial Intelligence, Generative AI, Intermediate (200), Technical How-to
In today’s competitive landscape, accessing content and insights effortlessly is essential for providing outstanding customer experiences and achieving successful business outcomes. Box, a prominent cloud content management platform, acts as a central hub for various digital assets and documents across numerous organizations. An enterprise Box account usually houses a treasure trove of materials, such as documents, presentations, knowledge articles, and more. Nevertheless, distilling meaningful information from the extensive data within Box can be daunting without suitable tools and capabilities. Team members in roles like customer support, project management, and product management must easily query Box content, unveil pertinent insights, and make informed decisions that effectively meet customer demands.
Developing a generative artificial intelligence (AI)-driven conversational application that integrates seamlessly with your enterprise’s data sources requires significant investment in time, resources, and personnel. Initially, you must create connectors for these data sources. Following this, you need to index this data to enable a Retrieval Augmented Generation (RAG) approach, where relevant excerpts are delivered with high accuracy to a large language model (LLM). This involves selecting an index capable of supporting semantic and vector search, constructing the infrastructure for retrieving and ranking answers, and developing a feature-rich web application. Additionally, staffing a sizable team to build, maintain, and manage such a system is necessary.
Amazon Q Business is a fully managed generative AI-powered assistant designed to provide answers, summaries, content generation, and secure task completion based on data from your enterprise systems. With Amazon Q Business, you can obtain quick, relevant answers to urgent inquiries, address challenges, generate content, and take action using the knowledge stored in your organization’s information repositories, including Box and others. Amazon Q offers native data source connectors that can index content into an integrated retriever, utilizing an LLM to deliver accurate and well-crafted responses. A data source connector is a component of Amazon Q that facilitates the integration and synchronization of data from multiple repositories into one index.
Amazon Q Business features numerous prebuilt connectors for a wide array of data sources, including Box Content Cloud, Atlassian Confluence, Amazon Simple Storage Service (Amazon S3), Microsoft SharePoint, Salesforce, and many more, helping you to establish your generative AI solution with minimal configuration. For a complete list of Amazon Q Business supported data source connectors, see Amazon Q Business connectors.
Find Precise Answers from Box Documents Using Amazon Q Business
Once you connect Amazon Q Business with Box, you can pose questions based on the documents stored in your Box account. For instance:
- Natural language search – You can inquire about information within documents located in any folder using conversational language, streamlining the process of retrieving desired data without needing to remember specific keywords or filters.
- Summarization – You can request Amazon Q Business to summarize the contents of documents to fit your needs. This functionality allows you to quickly grasp the key points and locate relevant information in your documents without manually sifting through individual document descriptions.
Overview of the Box Connector for Amazon Q Business
To crawl and index contents in Box, you can configure the Amazon Q Business Box connector as a data source in your Amazon Q Business application. When you link Amazon Q Business to a data source and initiate the sync process, Amazon Q Business crawls and indexes documents from the data source into its index.
Types of Documents
Let’s examine what constitutes documents in the context of the Amazon Q Business Box connector. A document is an aggregation of information comprising a title, content (or body), metadata (data about the document), and access control list (ACL) information to ensure answers are drawn from documents accessible to the user.
The Amazon Q Business Box connector supports crawling the following entities in Box:
- Files – Each file is treated as a distinct document.
- Comments – Each comment is regarded as a single document.
- Tasks – Each task is counted as a separate document.
- Web links – Each web link is classified as an individual document.
Moreover, Box users can create custom objects and custom metadata fields. Amazon Q accommodates the crawling and indexing of these custom objects and metadata.
The Amazon Q Business Box connector also indexes a rich array of metadata from various entities in Box. It offers the capability to map these source metadata fields to Amazon Q index fields for efficient indexing. These field mappings allow you to align Box field names with Amazon Q index field names. There are two types of metadata fields supported by Amazon Q connectors:
- Reserved or default fields – These are obligatory for each document, such as title, creation date, or author.
- Custom metadata fields – These are additional fields created in the data source beyond the existing provisions.
For further details, refer to Box data source connector field mappings.
Authentication
Before indexing content from Box, it is essential to establish a secure connection between the Amazon Q Business connector for Box and your Box cloud instance. To do this, authentication with the data source is required. Let’s review the supported authentication methods for the Box connector.
The Amazon Q Box connector supports tokens with JWT authentication by Box as the authentication mechanism. This method necessitates the configuration of several parameters, including Box client ID, client secret, public key ID, private key, and passphrase. By implementing this token-based JWT authentication, the Amazon Q Business assistant can securely connect to and engage with data stored within the Box platform on behalf of your organization. For more information on setting up and managing JWT tokens in Box, refer to the JWT Auth in the Box Developer documentation.
Supported Box Subscriptions
To connect Amazon Q Business with Box using the Box connector, access to Box Enterprise or Box Enterprise Plus plans is required. Both plans provide the necessary functionalities to create a custom application, download a JWT token as an administrator, and configure the connector to ingest relevant data from Box.
Secure Querying with ACL Crawling, Identity Crawling, and User Store
The effectiveness of Amazon Q Business applications relies on two critical aspects: ensuring that end-users only receive responses generated from documents they are authorized to view and safeguarding the privacy and security of each user’s conversation history. Amazon Q Business accomplishes this by validating user identity every time they access the application, restricting tasks and answers to documents the user is authorized to access. This is achieved through the integration of AWS IAM. For additional insights on this topic, you can check out this excellent resource.
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