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Before diving deeper into the Dependency Injection system, let's upgrade the previous example.
A dict from the previous example
In the previous example, we where returning a dict from our dependency ("dependable"):
{!./tutorial/src/dependencies/tutorial001.py!}
But then we get a dict in the parameter commons of the path operation function.
And we know that dicts can't provide a lot of editor support because they can't know their keys and value types.
Create a Pydantic model
But we are already using Pydantic models in other places and we have already seen all the benefits.
Let's use them here too.
Create a model for the common parameters (and don't pay attention to the rest, for now):
{!./tutorial/src/dependencies/tutorial002.py!}
Return a Pydantic model
Now we can return a Pydantic model from the dependency ("dependable") with the same data as the dict before:
{!./tutorial/src/dependencies/tutorial002.py!}
Declare the Pydantic model
We can now come back to the path operation function and declare the type of the commons parameter to be that Pydantic model:
commons: CommonQueryParams = Depends(common_parameters)
It won't be interpreted as a JSON request Body because we are using Depends:
{!./tutorial/src/dependencies/tutorial002.py!}
!!! info
In the case of dependencies with Depends, the type of the parameter is only to get editor support.
Your dependencies won't be enforced to return a specific type of data.
Use the Pydantic model
And now we can use that model in our code, with all the lovable editor support:
{!./tutorial/src/dependencies/tutorial002.py!}
Trees of hierarchical dependencies
With the Dependency Injection system you can build arbitrarily deep trees of hierarchical dependencies (also known as dependency graphs) by having dependencies that also have dependencies themselves.
You will see examples of these dependency trees in the next chapters about security.
Recap
By using Pydantic models in your dependencies too you can keep all the editor support that FastAPI is designed to support.