Dynamically build a lambda function in python

Supose that I want to generate a function to be later incorporated in a set of equations to be solved with scipy nsolve function. I want to create a function like this:

xi + xi+1 + xi+3 = 1

in which the number of variables will be dependent on the number of components. For example, if I have 2 components:

 f = lambda x: x[0] + x[1] - 1

for 3:

 f = lambda x: x[0] + x[1] + x[2] - 1

I specify the components as an array within the arguments of the function to be called:

 def my_func(components):
        for component in components:
        .....
        .....
        return f

I can't just find a way of doing this. I've to be able to make it this way as this function and other functions need to be solved together with nsolve:

 x0 = scipy.optimize.fsolve(f, [0, 0, 0, 0 ....])

Any help would be appreciated

Thanks!

Since I'm not sure which is the best way of doing this I will fully explain what I'm trying to do:

-I'm trying to generate this two functions to be later nsolved:

So I want to create a function teste([list of components]) that can return me this two equations (Psat(T) is a function I can call depending on the component and P is a constant(value = 760)).

Example:

  teste(['Benzene','Toluene'])

would return:

xBenzene + xToluene = 1

xBenzenePsat('Benzene') + xToluenePsat('Toluene') = 760

in the case of calling:

   teste(['Benzene','Toluene','Cumene'])

it would return:

xBenzene + xToluene + xCumene = 1

xBenzenePsat('Benzene') + xToluenePsat('Toluene') + xCumene*Psat('Cumene