Pydantic usage

This package uses Pydantic to serialize/deserialize the JSON from Meilisearch into Python objects wherever possible, and in the process uses CamelBase from camel-converter to convert the camelCaseNames from JSON into more Pythonic snake_case_names.

In some instances it is not possible to return the data as an object because the structure will be dependant on your particular dataset and can't be known ahead of time. In these instances you can either work with the data in the dictionary that is returned, or because you will know the structure you can generate your own Pydantic models.

As an example, if you want to get a movie from the small movies example you could put the results into an object with the following:

from datetime import datetime
from typing import Optional

from camel_converter.pydantic_base import CamelBase
from meilisearch_python_sdk import Client


# Inheriting from CamelBase will allow your class to automatically convert
# variables returned from the server in camelCase into snake_case. It will
# also make it a Pydantic Model.
class Movie(CamelBase):
    id: int
    title: str
    poster: str
    overview: str
    release_date: datetime
    genre: Optional[str] = None


async with Client("http://127.0.0.1:7700", "masterKey") as client:
    index = client.index("movies")
    movie_dict = await index.get_document(287947)
    movie = Movie(**movie_dict)

And then the movie variable would contain the movie object with the following information

Movie(
    id = 287947,
    title = "Shazam!",
    poster = "https://image.tmdb.org/t/p/w1280/xnopI5Xtky18MPhK40cZAGAOVeV.jpg",
    overview = "A boy is given the ability to become an adult superhero in times of need with a single magic word.",
    release_date = datetime.datetime(2019, 3, 23, 0, 0, tzinfo=datetime.timezone.utc),
    genre = "action",
)

By inheriting from CamelBase, or any of the other provided models you will be inheriting Pydantic models and therefore have access to the funcitonality Pydantic provides such as validators and Fields. Pydantic will also automatically deserialized the data into the correct data type based on the type hint provided.

For SearchResults, the hits field is generic so you can specify a model that matches your data. If no type is specified it will default to JsonDict (dict[str, Any]).

from datetime import datetime
from typing import Optional

from camel_converter.pydantic_base import CamelBase
from meilisearch_python_sdk import Client


# Inheriting from CamelBase will allow your class to automatically convert
# variables returned from the server in camelCase into snake_case. It will
# also make it a Pydantic Model.
class Movie(CamelBase):
    id: int
    title: str
    poster: str
    overview: str
    release_date: datetime
    genre: Optional[str] = None


async with Client("http://127.0.0.1:7700", "masterKey") as client:
    index = client.index("movies", hits_type=Movie)
    movies = await index.search("Spiderman")

movies.hits will now have items of type Movie instead of JsonDict.