You can translate and multiply to get results in any range. For example, if the range of values you want is (L, U), you can sample
\theta \sim \text{Dirichlet}(\alpha)
and set
\phi = (U - L) \cdot \theta + L.
You can look at the marginals in a Dirichlet, which are beta distributions, but they’re not uniform. There’s also correlation because of the sum-to-one constraint on the simplex \theta.
If you want to get more detailed here, you can parameterize on an unconstrained scale with an isometric log ratio (ILR) transform—I think @aseyboldt was discussing the ILR here, but I can’t find the post. It’s also under discussion for inclusion directly: Implement `Ordered` distribution factory · Issue #7297 · pymc-devs/pymc · GitHub