mirror of
https://github.com/langchain-ai/langgraph.git
synced 2026-08-26 17:42:24 +02:00
Only throw if in union
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@@ -62,18 +62,22 @@ class SchemaCoercionMapper:
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processed = {}
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if self._field_coercers is None:
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self._field_coercers = {
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n: self._build_coercer(t) for n, t in self._fields.items()
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n: self._build_coercer(t, depth - 1) for n, t in self._fields.items()
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}
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for k, v in input_data.items():
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fn = self._field_coercers.get(k)
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processed[k] = fn(v, depth - 1) if fn else v
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return self._construct(**processed)
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def _build_coercer(self, field_type: Any) -> Callable[[Any, Any], Any]:
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def _build_coercer(
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self, field_type: Any, depth: int, throw: bool = False
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) -> Callable[[Any, Any], Any]:
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if depth == 0:
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return self._passthrough
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origin = get_origin(field_type)
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if origin is Annotated:
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real_type, *_ = get_args(field_type)
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sub = self._build_coercer(real_type)
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sub = self._build_coercer(real_type, depth - 1)
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return lambda v, d: sub(v, d)
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if isclass(field_type):
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is_class_ = True
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@@ -84,16 +88,16 @@ class SchemaCoercionMapper:
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is_base_model = False
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if is_base_model:
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mapper = SchemaCoercionMapper(field_type, self.max_depth)
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mapper = SchemaCoercionMapper(field_type, depth - 1)
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return lambda v, d: mapper.coerce(v, d) if isinstance(v, dict) else v
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if is_class_ and issubclass(field_type, BaseModelV1):
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mapper = SchemaCoercionMapper(field_type, self.max_depth)
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mapper = SchemaCoercionMapper(field_type, depth - 1)
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return lambda v, d: mapper.coerce(v, d) if isinstance(v, dict) else v
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if origin is list or field_type is list:
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args = get_args(field_type)
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if len(args) != 1:
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return lambda v, d: v
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sub = self._build_coercer(args[0])
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sub = self._build_coercer(args[0], depth - 1)
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def list_coercer(v: Any, d: Any) -> Any:
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if not isinstance(v, (list, tuple)):
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@@ -104,9 +108,17 @@ class SchemaCoercionMapper:
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if origin is dict or field_type is dict:
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args = get_args(field_type)
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if len(args) != 2:
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return self._passthrough
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k_sub = self._build_coercer(args[0])
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v_sub = self._build_coercer(args[1])
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if not throw:
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return self._passthrough
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def dict_coercer(v: Any, d: Any) -> Any:
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if not isinstance(v, dict):
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raise TypeError("Expected dict, got %s" % type(v))
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return {k_sub(k, d - 1): v_sub(val, d - 1) for k, val in v.items()}
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return dict_coercer
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k_sub = self._build_coercer(args[0], depth - 1)
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v_sub = self._build_coercer(args[1], depth - 1)
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def dict_coercer(v: Any, d: Any) -> Any:
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if not isinstance(v, dict):
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@@ -119,7 +131,7 @@ class SchemaCoercionMapper:
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targs = get_args(field_type)
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if not targs:
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return lambda v, d: v
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subs = [self._build_coercer(a) for a in targs]
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subs = [self._build_coercer(a, depth - 1) for a in targs]
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def tuple_coercer(v: Any, d: Any) -> Any:
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if not isinstance(v, (list, tuple)):
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@@ -133,11 +145,13 @@ class SchemaCoercionMapper:
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if origin is Union:
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uargs = get_args(field_type)
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subs, none_in_union = [], False
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for arg in uargs:
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for ix, arg in enumerate(uargs):
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if arg is type(None):
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none_in_union = True
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else:
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subs.append(self._build_coercer(arg))
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subs.append(
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self._build_coercer(arg, depth - 1, throw=ix < len(uargs) - 1)
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)
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def union_coercer(v: Any, d: Any) -> Any:
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if v is None and none_in_union:
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@@ -27,11 +27,7 @@ from uuid import UUID
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import httpx
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import pytest
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from langchain_core.language_models import GenericFakeChatModel
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from langchain_core.runnables import (
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RunnableConfig,
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RunnableLambda,
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RunnablePassthrough,
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)
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from langchain_core.runnables import RunnableConfig, RunnableLambda, RunnablePassthrough
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from langchain_core.utils.aiter import aclosing
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from pytest_mock import MockerFixture
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from syrupy import SnapshotAssertion
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@@ -4524,6 +4520,7 @@ async def test_nested_pydantic_models(version: str) -> None:
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class NestedModel(BaseModel):
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value: int
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name: str
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something: Optional[str] = None
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# Forward reference model
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class RecursiveModel(BaseModel):
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@@ -4545,18 +4542,27 @@ async def test_nested_pydantic_models(version: str) -> None:
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name: str
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friends: list[str] = Field(default_factory=list) # IDs of friends
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class MyTypedDict(TypedDict):
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x: int
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class State(BaseModel):
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# Basic nested model tests
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top_level: str
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nested: NestedModel
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optional_nested: Optional[NestedModel] = None
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dict_nested: dict[str, NestedModel]
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my_set: set[int]
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list_nested: Annotated[
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Union[dict, list[dict[str, NestedModel]]], lambda x, y: (x or []) + [y]
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]
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list_nested_reversed: Annotated[
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Union[list[dict[str, NestedModel]], NestedModel, dict, list],
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lambda x, y: (x or []) + [y],
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]
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tuple_nested: tuple[str, NestedModel]
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tuple_list_nested: list[tuple[int, NestedModel]]
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complex_tuple: tuple[str, dict[str, tuple[int, NestedModel]]]
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my_typed_dict: MyTypedDict
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# Forward reference test
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recursive: RecursiveModel
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@@ -4572,8 +4578,11 @@ async def test_nested_pydantic_models(version: str) -> None:
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"top_level": "initial",
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"nested": {"value": 42, "name": "test"},
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"optional_nested": {"value": 10, "name": "optional"},
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"my_set": [1, 2, 4.5],
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"my_typed_dict": {"x": 1},
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"dict_nested": {"a": {"value": 5, "name": "a"}},
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"list_nested": [{"a": {"value": 6, "name": "b"}}],
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"list_nested_reversed": ["foo", "bar"],
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"tuple_nested": ["tuple-key", {"value": 7, "name": "tuple-value"}],
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"tuple_list_nested": [[1, {"value": 8, "name": "tuple-in-list"}]],
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"complex_tuple": [
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@@ -5882,10 +5891,12 @@ async def test_store_injected_async(checkpointer_name: str, store_name: str) ->
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):
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assert isinstance(store, BaseStore)
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await store.aput(
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namespace
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if self.i is not None
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and config["configurable"]["thread_id"] in (thread_1, thread_2)
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else (f"foo_{self.i}", "bar"),
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(
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namespace
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if self.i is not None
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and config["configurable"]["thread_id"] in (thread_1, thread_2)
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else (f"foo_{self.i}", "bar")
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),
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doc_id,
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{
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**doc,
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