bump fastapi and add pydantic compatibility

RELEASE.md

@@ -10,6 +10,7 @@ Please follow the established format:
 ## Bug fixes and other changes
 
 - Fix for Kedro Viz Connection Error (#1507)
+- Add support for pydantic v2 and fastapi >= 0.100.0 (#1529)
 
 # Release 6.5.1
 

package/features/steps/lower_requirements.txt

@@ -1,6 +1,6 @@
 semver==3.0
 ipython==7.0.0
-fastapi==0.73.0
+fastapi==0.100.0
 fsspec==2021.4
 aiofiles==22.1.0
 uvicorn[standard]==0.22.0
@@ -13,3 +13,4 @@ strawberry-graphql==0.192.0
 networkx==2.5
 orjson==3.9
 secure==0.3.0
+pydantic<2

package/kedro_viz/api/rest/responses.py

@@ -4,8 +4,9 @@
 from typing import Any, Dict, List, Optional, Union
 
 import orjson
+from fastapi._compat import PYDANTIC_V2
 from fastapi.responses import ORJSONResponse
-from pydantic import BaseModel
+from pydantic import BaseModel, ConfigDict
 
 from kedro_viz.data_access import data_access_manager
 
@@ -15,8 +16,12 @@ class APIErrorMessage(BaseModel):
 
 
 class BaseAPIResponse(BaseModel, abc.ABC):
-    class Config:
-        orm_mode = True
+    if PYDANTIC_V2:
+        model_config = ConfigDict(from_attributes=True)
+    else:
+
+        class Config:
+            orm_mode = True
 
 
 class BaseGraphNodeAPIResponse(BaseAPIResponse):
@@ -27,58 +32,70 @@ class BaseGraphNodeAPIResponse(BaseAPIResponse):
     type: str
 
     # If a node is a ModularPipeline node, this value will be None, hence Optional.
-    modular_pipelines: Optional[List[str]]
+    modular_pipelines: Optional[List[str]] = None
+
+
+task_node_api_response_example = {
+    "example": {
+        "id": "6ab908b8",
+        "name": "split_data_node",
+        "tags": [],
+        "pipelines": ["__default__", "ds"],
+        "modular_pipelines": [],
+        "type": "task",
+        "parameters": {
+            "test_size": 0.2,
+            "random_state": 3,
+            "features": [
+                "engines",
+                "passenger_capacity",
+                "crew",
+                "d_check_complete",
+                "moon_clearance_complete",
+                "iata_approved",
+                "company_rating",
+                "review_scores_rating",
+            ],
+        },
+    }
+}
 
 
 class TaskNodeAPIResponse(BaseGraphNodeAPIResponse):
     parameters: Dict
+    if PYDANTIC_V2:
+        model_config = ConfigDict(json_schema_extra=task_node_api_response_example)  # type: ignore
+    else:
+
+        class Config:
+            schema_extra = task_node_api_response_example
 
-    class Config:
-        schema_extra = {
-            "example": {
-                "id": "6ab908b8",
-                "name": "split_data_node",
-                "tags": [],
-                "pipelines": ["__default__", "ds"],
-                "modular_pipelines": [],
-                "type": "task",
-                "parameters": {
-                    "test_size": 0.2,
-                    "random_state": 3,
-                    "features": [
-                        "engines",
-                        "passenger_capacity",
-                        "crew",
-                        "d_check_complete",
-                        "moon_clearance_complete",
-                        "iata_approved",
-                        "company_rating",
-                        "review_scores_rating",
-                    ],
-                },
-            }
-        }
+
+data_node_api_response_example = {
+    "example": {
+        "id": "d7b83b05",
+        "name": "master_table",
+        "tags": [],
+        "pipelines": ["__default__", "dp", "ds"],
+        "modular_pipelines": [],
+        "type": "data",
+        "layer": "primary",
+        "dataset_type": "kedro.extras.datasets.pandas.csv_dataset.CSVDataSet",
+        "stats": {"rows": 10, "columns": 2, "file_size": 2300},
+    }
+}
 
 
 class DataNodeAPIResponse(BaseGraphNodeAPIResponse):
-    layer: Optional[str]
-    dataset_type: Optional[str]
-    stats: Optional[Dict]
-
-    class Config:
-        schema_extra = {
-            "example": {
-                "id": "d7b83b05",
-                "name": "master_table",
-                "tags": [],
-                "pipelines": ["__default__", "dp", "ds"],
-                "modular_pipelines": [],
-                "type": "data",
-                "layer": "primary",
-                "dataset_type": "kedro.extras.datasets.pandas.csv_dataset.CSVDataSet",
-                "stats": {"rows": 10, "columns": 2, "file_size": 2300},
-            }
-        }
+    layer: Optional[str] = None
+    dataset_type: Optional[str] = None
+    stats: Optional[Dict] = None
+    if PYDANTIC_V2:
+        model_config = ConfigDict(json_schema_extra=data_node_api_response_example)  # type: ignore
+    else:
+
+        class Config:
+            schema_extra = data_node_api_response_example
 
 
 NodeAPIResponse = Union[
@@ -87,77 +104,97 @@ class Config:
 ]
 
 
+task_node_metadata_api_example = {
+    "example": {
+        "code": "def split_data(data: pd.DataFrame, parameters: Dict) -> Tuple:",
+        "filepath": "proj/src/new_kedro_project/pipelines/data_science/nodes.py",
+        "parameters": {"test_size": 0.2},
+        "inputs": ["params:input1", "input2"],
+        "outputs": ["output1"],
+        "run_command": "kedro run --to-nodes=split_data",
+    }
+}
+
+
 class TaskNodeMetadataAPIResponse(BaseAPIResponse):
-    code: Optional[str]
-    filepath: Optional[str]
-    parameters: Optional[Dict]
+    code: Optional[str] = None
+    filepath: Optional[str] = None
+    parameters: Optional[Dict] = None
     inputs: List[str]
     outputs: List[str]
-    run_command: Optional[str]
-
-    class Config:
-        schema_extra = {
-            "example": {
-                "code": "def split_data(data: pd.DataFrame, parameters: Dict) -> Tuple:",
-                "filepath": "proj/src/new_kedro_project/pipelines/data_science/nodes.py",
-                "parameters": {"test_size": 0.2},
-                "inputs": ["params:input1", "input2"],
-                "outputs": ["output1"],
-                "run_command": "kedro run --to-nodes=split_data",
-            }
-        }
+    run_command: Optional[str] = None
+    if PYDANTIC_V2:
+        model_config = ConfigDict(json_schema_extra=task_node_metadata_api_example)  # type: ignore
+    else:
+
+        class Config:
+            schema_extra = task_node_metadata_api_example
+
+
+data_node_metadata_api_example = {
+    "example": {
+        "filepath": "/my-kedro-project/data/03_primary/master_table.csv",
+        "type": "pandas.csv_dataset.CSVDataSet",
+        "run_command": "kedro run --to-outputs=master_table",
+    }
+}
 
 
 class DataNodeMetadataAPIResponse(BaseAPIResponse):
-    filepath: Optional[str]
+    filepath: Optional[str] = None
     type: str
-    plot: Optional[Dict]
-    image: Optional[str]
-    tracking_data: Optional[Dict]
-    run_command: Optional[str]
-    preview: Optional[Dict]
-    stats: Optional[Dict]
-
-    class Config:
-        schema_extra = {
-            "example": {
-                "filepath": "/my-kedro-project/data/03_primary/master_table.csv",
-                "type": "pandas.csv_dataset.CSVDataSet",
-                "run_command": "kedro run --to-outputs=master_table",
-            }
-        }
+    plot: Optional[Dict] = None
+    image: Optional[str] = None
+    tracking_data: Optional[Dict] = None
+    run_command: Optional[str] = None
+    preview: Optional[Dict] = None
+    stats: Optional[Dict] = None
+    if PYDANTIC_V2:
+        model_config = ConfigDict(json_schema_extra=data_node_metadata_api_example)  # type: ignore
+    else:
+
+        class Config:
+            schema_extra = data_node_metadata_api_example
 
 
 class TranscodedDataNodeMetadataAPIReponse(BaseAPIResponse):
     filepath: str
     original_type: str
     transcoded_types: List[str]
-    run_command: Optional[str]
-    stats: Optional[Dict]
+    run_command: Optional[str] = None
+    stats: Optional[Dict] = None
+
+
+parameters_node_metaxata_api_example = {
+    "example": {
+        "parameters": {
+            "test_size": 0.2,
+            "random_state": 3,
+            "features": [
+                "engines",
+                "passenger_capacity",
+                "crew",
+                "d_check_complete",
+                "moon_clearance_complete",
+                "iata_approved",
+                "company_rating",
+                "review_scores_rating",
+            ],
+        }
+    }
+}
 
 
 class ParametersNodeMetadataAPIResponse(BaseAPIResponse):
     parameters: Dict
+    if PYDANTIC_V2:
+        model_config = ConfigDict(
+            json_schema_extra=parameters_node_metaxata_api_example  # type: ignore
+        )
+    else:
 
-    class Config:
-        schema_extra = {
-            "example": {
-                "parameters": {
-                    "test_size": 0.2,
-                    "random_state": 3,
-                    "features": [
-                        "engines",
-                        "passenger_capacity",
-                        "crew",
-                        "d_check_complete",
-                        "moon_clearance_complete",
-                        "iata_approved",
-                        "company_rating",
-                        "review_scores_rating",
-                    ],
-                }
-            }
-        }
+        class Config:
+            schema_extra = parameters_node_metaxata_api_example
 
 
 NodeMetadataAPIResponse = Union[
@@ -179,7 +216,7 @@ class NamedEntityAPIResponse(BaseAPIResponse):
     """
 
     id: str
-    name: Optional[str]
+    name: Optional[str] = None
 
 
 class ModularPipelineChildAPIResponse(BaseAPIResponse):

package/kedro_viz/models/experiment_tracking.py

@@ -6,7 +6,9 @@
 from enum import Enum
 from typing import TYPE_CHECKING, Any, Dict
 
+from fastapi._compat import PYDANTIC_V2
 from kedro.io import Version
+from pydantic import ConfigDict
 from sqlalchemy import Column
 from sqlalchemy.orm import declarative_base  # type: ignore
 from sqlalchemy.sql.schema import ForeignKey
@@ -33,9 +35,12 @@ class RunModel(Base):  # type: ignore
 
     id = Column(String, primary_key=True, index=True)
     blob = Column(JSON)
+    if PYDANTIC_V2:
+        model_config = ConfigDict(from_attributes=True)
+    else:
 
-    class Config:
-        orm_mode = True
+        class Config:
+            orm_mode = True
 
 
 class UserRunDetailsModel(Base):  # type: ignore
@@ -48,9 +53,12 @@ class UserRunDetailsModel(Base):  # type: ignore
     bookmark = Column(Boolean, default=False)
     title = Column(String)
     notes = Column(String)
+    if PYDANTIC_V2:
+        model_config = ConfigDict(from_attributes=True)
+    else:
 
-    class Config:
-        orm_mode = True
+        class Config:
+            orm_mode = True
 
 
 class TrackingDatasetGroup(str, Enum):

package/requirements.txt

@@ -1,8 +1,8 @@
 semver>=2.10  # Needs to be at least 2.10.0 to make use of `VersionInfo.match`.
 kedro>=0.17.5
 ipython>=7.0.0, <9.0
-fastapi>=0.73.0, <0.96.0
-fsspec>=2021.4, <2024.1 
+fastapi>=0.73.0, <0.104.0
+fsspec>=2021.4, <2024.1
 aiofiles==22.1.0
 uvicorn[standard]~=0.22.0
 watchgod~=0.8.2

package/test_requirements.txt

@@ -8,11 +8,11 @@ behave~=1.2
 black~=23.3
 boto3~=1.26
 flake8~=5.0
-fastapi[all]>=0.73.0, <0.96.0
+fastapi[all]>=0.73.0, <0.104.0
 isort~=5.11
 matplotlib~=3.5
 mypy~=1.0
-moto~=4.1.14 
+moto~=4.1.14
 psutil==5.9.5  # same as Kedro for now
 pylint~=2.17
 pytest~=7.4