Sub-dependencies

You can create dependencies that have sub-dependencies.

They can be as deep as you need them to be.

FastAPI will take care of solving them.

You could create a first dependency ("dependable") like:

from typing import Annotated

from fastapi import Cookie, Depends, FastAPI

app = FastAPI()


def query_extractor(q: str | None = None):
    return q


def query_or_cookie_extractor(
    q: Annotated[str, Depends(query_extractor)],
    last_query: Annotated[str | None, Cookie()] = None,
):
    if not q:
        return last_query
    return q


@app.get("/items/")
async def read_query(
    query_or_default: Annotated[str, Depends(query_or_cookie_extractor)],
):
    return {"q_or_cookie": query_or_default}

It declares an optional query parameter q as a str, and then it just returns it.

This is quite simple (not very useful), but will help us focus on how the sub-dependencies work.

Then you can create another dependency function (a "dependable") that at the same time declares a dependency of its own (so it is a "dependant" too):

from typing import Annotated

from fastapi import Cookie, Depends, FastAPI

app = FastAPI()


def query_extractor(q: str | None = None):
    return q


def query_or_cookie_extractor(
    q: Annotated[str, Depends(query_extractor)],
    last_query: Annotated[str | None, Cookie()] = None,
):
    if not q:
        return last_query
    return q


@app.get("/items/")
async def read_query(
    query_or_default: Annotated[str, Depends(query_or_cookie_extractor)],
):
    return {"q_or_cookie": query_or_default}

Let's focus on the parameters declared:

• Even though this function is a dependency ("dependable") itself, it also declares another dependency (it "depends" on something else).
  • It depends on the query_extractor, and assigns the value returned by it to the parameter q.
• It also declares an optional last_query cookie, as a str.
  • If the user didn't provide any query q, we use the last query used, which we saved to a cookie before.

Then we can use the dependency with:

from typing import Annotated

from fastapi import Cookie, Depends, FastAPI

app = FastAPI()


def query_extractor(q: str | None = None):
    return q


def query_or_cookie_extractor(
    q: Annotated[str, Depends(query_extractor)],
    last_query: Annotated[str | None, Cookie()] = None,
):
    if not q:
        return last_query
    return q


@app.get("/items/")
async def read_query(
    query_or_default: Annotated[str, Depends(query_or_cookie_extractor)],
):
    return {"q_or_cookie": query_or_default}

graph TB

query_extractor(["query_extractor"])
query_or_cookie_extractor(["query_or_cookie_extractor"])

read_query["/items/"]

query_extractor --> query_or_cookie_extractor --> read_query

If one of your dependencies is declared multiple times for the same path operation, for example, multiple dependencies have a common sub-dependency, FastAPI will know to call that sub-dependency only once per request.

And it will save the returned value in a "cache" and pass it to all the "dependants" that need it in that specific request, instead of calling the dependency multiple times for the same request.

In an advanced scenario where you know you need the dependency to be called at every step (possibly multiple times) in the same request instead of using the "cached" value, you can set the parameter use_cache=False when using Depends:

async def needy_dependency(fresh_value: Annotated[str, Depends(get_value, use_cache=False)]):    return {"fresh_value": fresh_value}

async def needy_dependency(fresh_value: str = Depends(get_value, use_cache=False)):    return {"fresh_value": fresh_value}

Apart from all the fancy words used here, the Dependency Injection system is quite simple.

Just functions that look the same as the path operation functions.

But still, it is very powerful, and allows you to declare arbitrarily deeply nested dependency "graphs" (trees).