> For the complete documentation index, see [llms.txt](https://support.meetuma.ai/uma-knowledgebase/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://support.meetuma.ai/uma-knowledgebase/release-notes/web/2024/v3.0.2-19th-february-2024.md). # v3.0.2 - 19th February 2024 ### Neighbourhoods Office neighbourhoods refer to the various sections or areas within a workplace that are often organised based on departments, teams or functions. Think of them as mini-communities within the larger office space, where groups of colleagues with similar roles or responsibilities work in proximity to one another. Within UMA you can now create a neighbourhood by grouping together a collective of resources on the same floor.

The neighbourhood can then be set up to allow bookings for different groups of users on different days of the week. You can be as flexible as you want to accomplish an efficient hybrid working environment. For more information on neighbourhoods click [here](/uma-knowledgebase/getting-started/neighbourhoods.md). ### Teams In order to create a [neighbourhood](/uma-knowledgebase/getting-started/neighbourhoods.md), you first have to create a [team](/uma-knowledgebase/getting-started/teams.md). A team in UMA is a group of work colleagues who are affiliated with a particular department or specialised group within an organisation. Teams typically collaborate on projects, tasks or initiatives related to their shared objectives. In the context of resource management, such as booking meeting rooms or other office resources, teams are granted specific privileges or access based on their group affiliation, enabling them to efficiently coordinate and utilise these resources for their collective work purposes. Within UMA you can now create a team by grouping users together.

Teams can then be assigned to neighbourhoods to establish booking policies against resources in your office environment. For more information on teams, click [here](/uma-knowledgebase/getting-started/teams.md). --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://support.meetuma.ai/uma-knowledgebase/release-notes/web/2024/v3.0.2-19th-february-2024.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.