docarray · jupyterjazz · Mar 30, 2023
diff --git a/docarray/array/abstract_array.py b/docarray/array/abstract_array.py
@@ -59,6 +59,8 @@ def __class_getitem__(cls, item: Union[Type[BaseDoc], TypeVar, str]):
 
             class _DocArrayTyped(cls):  # type: ignore
                 document_type: Type[BaseDoc] = cast(Type[BaseDoc], item)
+                __origin__ = cls
+                __args__ = [item]
 
             for field in _DocArrayTyped.document_type.__fields__.keys():
 

diff --git a/docarray/array/array/array.py b/docarray/array/array/array.py
@@ -28,9 +28,6 @@
 from docarray.typing import NdArray
 
 if TYPE_CHECKING:
-    from pydantic import BaseConfig
-    from pydantic.fields import ModelField
-
     from docarray.array.stacked.array_stacked import DocArrayStacked
     from docarray.proto import DocumentArrayProto
     from docarray.typing import TorchTensor
@@ -127,6 +124,7 @@ def __init__(
         self,
         docs: Optional[Iterable[T_doc]] = None,
     ):
+        super().__init__()
         self._data: List[T_doc] = list(self._validate_docs(docs)) if docs else []
 
     @classmethod
@@ -259,22 +257,6 @@ def stack(
             self, tensor_type=tensor_type
         )
 
-    @classmethod
-    def validate(
-        cls: Type[T],
-        value: Union[T, Iterable[BaseDoc]],
-        field: 'ModelField',
-        config: 'BaseConfig',
-    ):
-        from docarray.array.stacked.array_stacked import DocArrayStacked
-
-        if isinstance(value, (cls, DocArrayStacked)):
-            return value
-        elif isinstance(value, Iterable):
-            return cls(value)
-        else:
-            raise TypeError(f'Expecting an Iterable of {cls.document_type}')
-
     def traverse_flat(
         self: 'DocArray',
         access_path: str,

diff --git a/docarray/array/array/io.py b/docarray/array/array/io.py
@@ -93,7 +93,6 @@ def __getitem__(self, item: slice):
 
 
 class IOMixinArray(Iterable[BaseDoc]):
-
     document_type: Type[BaseDoc]
 
     @abstractmethod
@@ -250,7 +249,7 @@ def to_bytes(
         :return: the binary serialization in bytes or None if file_ctx is passed where to store
         """
 
-        with (file_ctx or io.BytesIO()) as bf:
+        with file_ctx or io.BytesIO() as bf:
             self._write_bytes(
                 bf=bf,
                 protocol=protocol,
@@ -318,13 +317,14 @@ def from_json(
         :return: the deserialized DocArray
         """
         json_docs = json.loads(file)
-        return cls([cls.document_type.parse_raw(v) for v in json_docs])
+        return cls([cls.document_type(**v) for v in json_docs])
 
     def to_json(self) -> str:
         """Convert the object into a JSON string. Can be loaded via :meth:`.from_json`.
         :return: JSON serialization of DocArray
         """
-        return json.dumps([doc.json() for doc in self])
+        doc_jsons = ', '.join([doc.json() for doc in self])
+        return f'[{doc_jsons}]'
 
     @classmethod
     def from_csv(

diff --git a/docarray/array/array/sequence_indexing_mixin.py b/docarray/array/array/sequence_indexing_mixin.py
@@ -10,6 +10,7 @@
     cast,
     no_type_check,
     overload,
+    List,
 )
 
 import numpy as np
@@ -33,9 +34,9 @@ def _is_np_int(item: Any) -> bool:
     return False  # this is unreachable, but mypy wants it
 
 
-class IndexingSequenceMixin(Iterable[T_item]):
+class IndexingSequenceMixin(List[T_item]):
     """
-    This mixin allow sto extend a list into an object that can be indexed
+    This mixin allows to extend a list into an object that can be indexed
     a la numpy/pytorch.
 
     You can index into, delete from, and set items in a IndexingSequenceMixin like a numpy array or torch tensor:

diff --git a/docarray/base_doc/doc.py b/docarray/base_doc/doc.py
@@ -9,6 +9,7 @@
 from docarray.base_doc.io.json import orjson_dumps, orjson_dumps_and_decode
 from docarray.base_doc.mixins import IOMixin, UpdateMixin
 from docarray.typing import ID
+from docarray.typing.tensor.abstract_tensor import AbstractTensor
 
 if TYPE_CHECKING:
     from docarray.array.stacked.column_storage import ColumnStorageView
@@ -28,7 +29,7 @@ class BaseDoc(BaseModel, IOMixin, UpdateMixin, BaseNode):
     class Config:
         json_loads = orjson.loads
         json_dumps = orjson_dumps_and_decode
-        json_encoders = {dict: orjson_dumps}
+        json_encoders = {AbstractTensor: orjson_dumps}
 
         validate_assignment = True
 

diff --git a/docarray/typing/abstract_type.py b/docarray/typing/abstract_type.py
@@ -1,41 +1,32 @@
-from abc import abstractmethod
+from abc import ABC, abstractmethod
 from typing import TYPE_CHECKING, Any, Optional, Type, TypeVar
 
 from pydantic import BaseConfig
 from pydantic.fields import ModelField
 
-from docarray.base_doc.base_node import BaseNode
 
 if TYPE_CHECKING:
     from docarray.proto import NodeProto
 
 T = TypeVar('T')
 
 
-class AbstractType(BaseNode):
+class AbstractType(ABC):
     _proto_type_name: Optional[str] = None
 
-    @classmethod
-    def __get_validators__(cls):
-        yield cls.validate
-
-    @classmethod
-    @abstractmethod
-    def validate(
-        cls: Type[T],
-        value: Any,
-        field: 'ModelField',
-        config: 'BaseConfig',
-    ) -> T:
-        ...
-
     @classmethod
     @abstractmethod
     def from_protobuf(cls: Type[T], pb_msg: T) -> T:
         ...
 
     @abstractmethod
     def _to_node_protobuf(self: T) -> 'NodeProto':
+        """Convert itself into a NodeProto message. This function should
+        be called when the self is nested into another Document that need to be
+        converted into a protobuf
+
+        :return: the nested item protobuf message
+        """
         ...
 
     def _docarray_to_json_compatible(self):
@@ -44,3 +35,19 @@ def _docarray_to_json_compatible(self):
         :return: a representation of the tensor compatible with orjson
         """
         return self
+
+
+class AbstractValidator(ABC):
+    @classmethod
+    def __get_validators__(cls):
+        yield cls.validate
+
+    @classmethod
+    @abstractmethod
+    def validate(
+        cls: Type[T],
+        value: Any,
+        field: 'ModelField',
+        config: 'BaseConfig',
+    ) -> T:
+        ...
diff --git a/tests/integrations/externals/test_fastapi.py b/tests/integrations/externals/test_fastapi.py
@@ -1,9 +1,11 @@
+from typing import List
+
 import numpy as np
 import pytest
 from fastapi import FastAPI
 from httpx import AsyncClient
 
-from docarray import BaseDoc
+from docarray import BaseDoc, DocArray
 from docarray.base_doc import DocResponse
 from docarray.documents import ImageDoc, TextDoc
 from docarray.typing import NdArray
@@ -107,3 +109,25 @@ async def create_item(doc: InputDoc) -> OutputDoc:
     assert isinstance(doc, OutputDoc)
     assert doc.embedding_clip.shape == (100, 1)
     assert doc.embedding_bert.shape == (100, 1)
+
+
+@pytest.mark.asyncio
+async def test_docarray():
+    doc = TextDoc(text='some txt')
+    docs = DocArray[TextDoc]([doc])
+
+    app = FastAPI()
+
+    @app.post("/doc/")
+    async def func(fastapi_docs: List[TextDoc]) -> DocArray[TextDoc]:
+        fastapi_docs = DocArray[TextDoc].construct(fastapi_docs)
+        return fastapi_docs
+
+    async with AsyncClient(app=app, base_url="http://test") as ac:
+        response = await ac.post("/doc/", data=docs.to_json())
+
+    assert response.status_code == 200
+
+    docs = DocArray[TextDoc].from_json(response.content.decode())
+    assert docs and len(docs) == 1
+    assert docs[0].text == 'some txt'