Suppose that $X$ and $Y$ are independent exponential rv's, with densities $ae^{-au}$ and $be^{-bu}$, $u > 0$, respectively. For any $0 < x < 1$ and $s > 0$, $ {\rm P}(\min (X,s) \le xs) = {\rm P}(X \le xs) = 1 - e^{ - axs} . $ For any $0 < x < 1$, by the law of total probability (conditioning on $Y$) $ {\rm P}\bigg(\frac{{\min (X,Y)}}{Y} \le x \bigg) = \int_0^\infty {{\rm P}\bigg(\frac{{\min (X,s)}}{s} \le x \bigg)be^{ - bs} \,ds} = \int_0^\infty {{\rm P}(\min (X,s) \le xs)be^{ - bs} \,ds} , $ hence $ {\rm P}\bigg(\frac{{\min (X,Y)}}{Y} \le x \bigg) = \int_0^\infty {(1 - e^{ - axs} )be^{ - bs} du} = 1 - b\int_0^\infty {e^{ - (ax + b)s} ds} = 1 - \frac{b}{{ax + b}}. $ The probability density function is obtained by differentiating the right-hand side.
EDIT (thanks to user11867's comment below): The ratio, call it $R$, has positive mass at $x=1$; hence, the density of $R$ on $(0,1)$ does not integrate to $1$. Specifically, the density $f_R$ of $R$ on $(0,1)$ is given by $ f_R (x) = \frac{d}{{dx}}\bigg(1 - \frac{b}{{ax + b}}\bigg) = \frac{{ab}}{{(ax + b)^2 }},\;\; 0 < x <1. $ It holds $ \int_0^1 {f_R (x)\,dx} = \bigg(1 - \frac{b}{{ax + b}}\bigg) \bigg|_0^1 = 1 - \frac{b}{{a + b}} , $ which is less than $1$. This implies that ${\rm P}(R=1)=b/(a+b)$. Indeed, $ {\rm P}(R=1)={\rm P}(\min (X,Y) = Y) = {\rm P}(Y \le X), $ and so, by the law of total probability (conditioning on $X$) $ {\rm P}(R=1) = \int_0^\infty {P(Y \le u)ae^{ - au}\, du} = \int_0^\infty {(1 - e^{ - bu} )ae^{ - au} \,du} = 1 - a\int_0^\infty {e^{ - (a + b)u} \,du} = 1 - \frac{a}{{a + b}} = \frac{b}{{a + b}}. $
To summarize: The ratio $R$ is a random variable supported on $[0,1]$. It has distribution function $F_R$ given by $ F_R (x) = 1 - \frac{b}{{ax + b}}, \;\; 0 \leq x < 1, $ and $ F_R (1) = 1. $ Thus $F_R$ has jump discontinuity at $x=1$: $ F_R (1) - \mathop {\lim }\limits_{x \to 1^ - } F_R (x) = 1 - \mathop {\lim }\limits_{x \to 1^ - } \bigg(1 - \frac{b}{{ax + b}}\bigg) = \frac{b}{{a + b}}, $ which is the probability ${\rm P}(R=1)$. In particular, the density function of $R$ exists only for $x < 1$; it is given by $ f_R (x) = \frac{{ab}}{{(ax + b)^2 }},\;\; 0 < x < 1. $
EDIT 2: The result $ F_R (x) = 1 - \frac{b}{{ax + b}}, \;\; 0 \leq x < 1, $ can be easily confirmed using Monte Carlo simulations, using the fact that an exponential random variable with density function $\lambda e^{-\lambda x}$, $x > 0$, can be generated as $-\ln(U)/\lambda$, where $U$ is a uniform$(0,1)$ random variable. (Indeed, it is straightforward to check that ${\rm P}(-\ln(U)/\lambda \leq x) = 1-e^{-\lambda x}$, for any $x > 0$.) Let $\hat F_R (x)$, for $0 < x < 1$ fixed, denote the Monte Carlo approximation for $F_R (x)$. The following results were obtained (using $N=10^7$ repetitions for each approximation; better approximations can be obtained by increasing $N$):
1) $a=0.8$, $b=1.34$, $x=0.53$. $\hat F_R (x) = 0.2401641$, $F_R (x) = 0.2403628...$;
2) $a=3.2$, $b=0.85$, $x=0.28$. $\hat F_R (x) = 0.5135828$, $F_R (x) = 0.5131729...$;
3) $a=2.4$, $b=5.18$, $x=0.21$. $\hat F_R (x) = 0.0885315$, $F_R (x) = 0.0886699...$;
4) $a=0.47$, $b=0.92$, $x=0.81$. $\hat F_R (x) = 0.2926322$, $F_R (x) = 0.2926885...$.