## Step 1: Define the symbolic representation of the problem

Let's denote the number of capsules A and B as $x_1$ and $x_2$ respectively. The objective is to minimize the cost, which is $2x_1 + 3x_2$. The constraints based on the minimum requirements are:

- Targeted medicine: $2x_1 + 3x_2 \geq 20$
- Pain reliever: $3x_1 + x_2 \geq 20$
- Filler: $x_1 + 3x_2 \geq 15$

## Step 2: Convert the problem into a symbolic representation

The symbolic representation can be written as:
```json
{
    'sym_variables': [('x1', 'capsule A'), ('x2', 'capsule B')],
    'objective_function': '2*x1 + 3*x2',
    'constraints': [
        '2*x1 + 3*x2 >= 20',
        '3*x1 + x2 >= 20',
        'x1 + 3*x2 >= 15',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Step 3: Write the Gurobi code to solve the optimization problem

```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(name="x1", lb=0, vtype=gurobi.GRB.CONTINUOUS)
    x2 = model.addVar(name="x2", lb=0, vtype=gurobi.GRB.CONTINUOUS)

    # Define the objective function
    model.setObjective(2*x1 + 3*x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(2*x1 + 3*x2 >= 20, name="targeted_medicine")
    model.addConstr(3*x1 + x2 >= 20, name="pain_reliever")
    model.addConstr(x1 + 3*x2 >= 15, name="filler")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Number of capsule A: {x1.varValue}")
        print(f"Number of capsule B: {x2.varValue}")
        print(f"Minimum cost: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```