The main goal of this paper is to describe an architecture for solving large general hybrid Bayesian networks (BNs) with deterministic variables. In the presence of deterministic variables, we have to deal with non-existence of joint densities. We represent deterministic conditional distributions using Dirac delta functions. Using the properties of Dirac delta functions, we can deal with a large class of deterministic functions. The architecture we develop is an extension of the Shenoy-Shafer architecture for discrete BNs. We illustrate the architecture with some small illustrative examples.