vertex[major]: upgrade pydantic

libs/vertexai/Makefile

@@ -11,6 +11,9 @@ integration_test integration_tests: TEST_FILE = tests/integration_tests/
 test tests integration_test integration_tests:
 	poetry run pytest --release $(TEST_FILE)
 
+test_watch:
+	poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE)
+
 # Run unit tests and generate a coverage report.
 coverage:
 	poetry run pytest --cov \
@@ -33,7 +36,6 @@ lint_tests: PYTHON_FILES=tests
 lint_tests: MYPY_CACHE=.mypy_cache_test
 
 lint lint_diff lint_package lint_tests:
-	./scripts/check_pydantic.sh .
 	./scripts/lint_imports.sh
 	poetry run ruff check .
 	poetry run ruff format $(PYTHON_FILES) --diff

libs/vertexai/langchain_google_vertexai/_anthropic_parsers.py

@@ -4,16 +4,17 @@
 from langchain_core.messages.tool import tool_call
 from langchain_core.output_parsers import BaseGenerationOutputParser
 from langchain_core.outputs import ChatGeneration, Generation
-from langchain_core.pydantic_v1 import BaseModel
+from pydantic import BaseModel, ConfigDict
 
 
 class ToolsOutputParser(BaseGenerationOutputParser):
     first_tool_only: bool = False
     args_only: bool = False
     pydantic_schemas: Optional[List[Type[BaseModel]]] = None
 
-    class Config:
-        extra = "forbid"
+    model_config = ConfigDict(
+        extra="forbid",
+    )
 
     def parse_result(self, result: List[Generation], *, partial: bool = False) -> Any:
         """Parse a list of candidate model Generations into a specific format.

libs/vertexai/langchain_google_vertexai/_anthropic_utils.py

@@ -26,9 +26,9 @@
     ToolMessage,
 )
 from langchain_core.messages.ai import UsageMetadata
-from langchain_core.pydantic_v1 import BaseModel
 from langchain_core.tools import BaseTool
 from langchain_core.utils.function_calling import convert_to_openai_tool
+from pydantic import BaseModel
 
 if TYPE_CHECKING:
     from anthropic.types import RawMessageStreamEvent  # type: ignore

libs/vertexai/langchain_google_vertexai/_base.py

@@ -22,7 +22,8 @@
 from google.protobuf import json_format
 from google.protobuf.struct_pb2 import Value
 from langchain_core.outputs import Generation, LLMResult
-from langchain_core.pydantic_v1 import BaseModel, Field, root_validator
+from pydantic import BaseModel, ConfigDict, Field, model_validator
+from typing_extensions import Self
 from vertexai.generative_models._generative_models import (  # type: ignore
     SafetySettingsType,
 )
@@ -91,14 +92,15 @@ class _VertexAIBase(BaseModel):
     "when making API calls. If not provided, credentials will be ascertained from "
     "the environment."
 
-    class Config:
-        """Configuration for this pydantic object."""
+    model_config = ConfigDict(
+        populate_by_name=True,
+        arbitrary_types_allowed=True,
+        protected_namespaces=(),
+    )
 
-        allow_population_by_field_name = True
-        arbitrary_types_allowed = True
-
-    @root_validator(pre=True)
-    def validate_params_base(cls, values: dict) -> dict:
+    @model_validator(mode="before")
+    @classmethod
+    def validate_params_base(cls, values: dict) -> Any:
         if "model" in values and "model_name" not in values:
             values["model_name"] = values.pop("model")
         if values.get("project") is None:
@@ -108,7 +110,7 @@ def validate_params_base(cls, values: dict) -> dict:
         if values.get("api_endpoint"):
             api_endpoint = values["api_endpoint"]
         else:
-            location = values.get("location", cls.__fields__["location"].default)
+            location = values.get("location", cls.model_fields["location"].default)
             api_endpoint = f"{location}-{constants.PREDICTION_API_BASE_PATH}"
         client_options = ClientOptions(api_endpoint=api_endpoint)
         if values.get("client_cert_source"):
@@ -311,26 +313,26 @@ class _BaseVertexAIModelGarden(_VertexAIBase):
     single_example_per_request: bool = True
     "LLM endpoint currently serves only the first example in the request"
 
-    @root_validator(pre=False, skip_on_failure=True)
-    def validate_environment(cls, values: Dict) -> Dict:
+    @model_validator(mode="after")
+    def validate_environment(self) -> Self:
         """Validate that the python package exists in environment."""
 
-        if not values["project"]:
+        if not self.project:
             raise ValueError(
                 "A GCP project should be provided to run inference on Model Garden!"
             )
 
         client_options = ClientOptions(
-            api_endpoint=f"{values['location']}-aiplatform.googleapis.com"
+            api_endpoint=f"{self.location}-aiplatform.googleapis.com"
         )
         client_info = get_client_info(module="vertex-ai-model-garden")
-        values["client"] = PredictionServiceClient(
+        self.client = PredictionServiceClient(
             client_options=client_options, client_info=client_info
         )
-        values["async_client"] = PredictionServiceAsyncClient(
+        self.async_client = PredictionServiceAsyncClient(
             client_options=client_options, client_info=client_info
         )
-        return values
+        return self
 
     @property
     def endpoint_path(self) -> str:

libs/vertexai/langchain_google_vertexai/chains.py

@@ -13,8 +13,8 @@
     StrOutputParser,
 )
 from langchain_core.prompts import BasePromptTemplate, ChatPromptTemplate
-from langchain_core.pydantic_v1 import BaseModel
 from langchain_core.runnables import Runnable
+from pydantic import BaseModel
 
 from langchain_google_vertexai.functions_utils import PydanticFunctionsOutputParser
 
@@ -51,7 +51,12 @@ def _create_structured_runnable_extra_step(
     *,
     prompt: Optional[BasePromptTemplate] = None,
 ) -> Runnable:
-    names = [schema.schema()["title"] for schema in functions]
+    names = [
+        schema.model_json_schema()["title"]
+        if hasattr(schema, "model_json_schema")
+        else schema.schema()["title"]
+        for schema in functions
+    ]
     if hasattr(llm, "is_gemini_advanced") and llm._is_gemini_advanced:  # type: ignore
         llm_with_functions = llm.bind(
             functions=functions,
@@ -111,7 +116,7 @@ def create_structured_runnable(
 
                 from langchain_google_vertexai import ChatVertexAI, create_structured_runnable
                 from langchain_core.prompts import ChatPromptTemplate
-                from langchain_core.pydantic_v1 import BaseModel, Field
+                from pydantic import BaseModel, Field
 
 
                 class RecordPerson(BaseModel):

libs/vertexai/langchain_google_vertexai/chat_models.py

@@ -62,7 +62,7 @@
 )
 from langchain_core.output_parsers.openai_tools import parse_tool_calls
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
-from langchain_core.pydantic_v1 import BaseModel, root_validator, Field
+from pydantic import BaseModel, Field, model_validator
 from langchain_core.runnables import Runnable, RunnablePassthrough, RunnableGenerator
 from langchain_core.utils.function_calling import convert_to_openai_tool
 from langchain_core.utils.pydantic import is_basemodel_subclass
@@ -124,6 +124,9 @@
     _format_to_gapic_tool,
     _ToolType,
 )
+from pydantic import ConfigDict
+from typing_extensions import Self
+
 
 logger = logging.getLogger(__name__)
 
@@ -762,7 +765,7 @@ class ChatVertexAI(_VertexAICommon, BaseChatModel):
     Tool calling:
         .. code-block:: python
 
-            from langchain_core.pydantic_v1 import BaseModel, Field
+            from pydantic import BaseModel, Field
 
             class GetWeather(BaseModel):
                 '''Get the current weather in a given location'''
@@ -800,7 +803,7 @@ class GetPopulation(BaseModel):
 
             from typing import Optional
 
-            from langchain_core.pydantic_v1 import BaseModel, Field
+            from pydantic import BaseModel, Field
 
             class Joke(BaseModel):
                 '''Joke to tell user.'''
@@ -1024,11 +1027,10 @@ def __init__(self, *, model_name: Optional[str] = None, **kwargs: Any) -> None:
             kwargs["model_name"] = model_name
         super().__init__(**kwargs)
 
-    class Config:
-        """Configuration for this pydantic object."""
-
-        allow_population_by_field_name = True
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(
+        populate_by_name=True,
+        arbitrary_types_allowed=True,
+    )
 
     @classmethod
     def is_lc_serializable(self) -> bool:
@@ -1039,57 +1041,65 @@ def get_lc_namespace(cls) -> List[str]:
         """Get the namespace of the langchain object."""
         return ["langchain", "chat_models", "vertexai"]
 
-    @root_validator(pre=False, skip_on_failure=True)
-    def validate_environment(cls, values: Dict) -> Dict:
+    @model_validator(mode="after")
+    def validate_environment(self) -> Self:
         """Validate that the python package exists in environment."""
-        safety_settings = values.get("safety_settings")
-        tuned_model_name = values.get("tuned_model_name")
-        values["model_family"] = GoogleModelFamily(values["model_name"])
+        safety_settings = self.safety_settings
+        tuned_model_name = self.tuned_model_name
+        self.model_family = GoogleModelFamily(self.model_name)
 
-        if values["model_name"] == "chat-bison-default":
+        if self.model_name == "chat-bison-default":
             logger.warning(
                 "Model_name will become a required arg for VertexAIEmbeddings "
                 "starting from Sep-01-2024. Currently the default is set to "
                 "chat-bison"
             )
-            values["model_name"] = "chat-bison"
+            self.model_name = "chat-bison"
 
-        if values.get("full_model_name") is not None:
+        if self.full_model_name is not None:
             pass
-        elif values.get("tuned_model_name") is not None:
-            values["full_model_name"] = _format_model_name(
-                values["tuned_model_name"],
-                location=values["location"],
-                project=values["project"],
+        elif self.tuned_model_name is not None:
+            self.full_model_name = _format_model_name(
+                self.tuned_model_name,
+                location=self.location,
+                project=cast(str, self.project),
             )
         else:
-            values["full_model_name"] = _format_model_name(
-                values["model_name"],
-                location=values["location"],
-                project=values["project"],
+            self.full_model_name = _format_model_name(
+                self.model_name,
+                location=self.location,
+                project=cast(str, self.project),
             )
 
-        if safety_settings and not is_gemini_model(values["model_family"]):
+        if safety_settings and not is_gemini_model(self.model_family):
             raise ValueError("Safety settings are only supported for Gemini models")
 
         if tuned_model_name:
-            generative_model_name = values["tuned_model_name"]
+            generative_model_name = self.tuned_model_name
         else:
-            generative_model_name = values["model_name"]
-
-        if not is_gemini_model(values["model_family"]):
-            cls._init_vertexai(values)
-            if values["model_family"] == GoogleModelFamily.CODEY:
+            generative_model_name = self.model_name
+
+        if not is_gemini_model(self.model_family):
+            values = {
+                "project": self.project,
+                "location": self.location,
+                "credentials": self.credentials,
+                "api_transport": self.api_transport,
+                "api_endpoint": self.api_endpoint,
+                "default_metadata": self.default_metadata,
+            }
+            self._init_vertexai(values)
+            if self.model_family == GoogleModelFamily.CODEY:
                 model_cls = CodeChatModel
                 model_cls_preview = PreviewCodeChatModel
             else:
                 model_cls = ChatModel
                 model_cls_preview = PreviewChatModel
-            values["client"] = model_cls.from_pretrained(generative_model_name)
-            values["client_preview"] = model_cls_preview.from_pretrained(
+            self.client = model_cls.from_pretrained(generative_model_name)
+            self.client_preview = model_cls_preview.from_pretrained(
                 generative_model_name
             )
-        return values
+        return self
 
     @property
     def _is_gemini_advanced(self) -> bool:
@@ -1647,7 +1657,7 @@ def with_structured_output(
         Example: Pydantic schema, exclude raw:
             .. code-block:: python
 
-                from langchain_core.pydantic_v1 import BaseModel
+                from pydantic import BaseModel
                 from langchain_google_vertexai import ChatVertexAI
 
                 class AnswerWithJustification(BaseModel):
@@ -1666,7 +1676,7 @@ class AnswerWithJustification(BaseModel):
         Example: Pydantic schema, include raw:
             .. code-block:: python
 
-                from langchain_core.pydantic_v1 import BaseModel
+                from pydantic import BaseModel
                 from langchain_google_vertexai import ChatVertexAI
 
                 class AnswerWithJustification(BaseModel):
@@ -1687,7 +1697,7 @@ class AnswerWithJustification(BaseModel):
         Example: Dict schema, exclude raw:
             .. code-block:: python
 
-                from langchain_core.pydantic_v1 import BaseModel
+                from pydantic import BaseModel
                 from langchain_core.utils.function_calling import convert_to_openai_function
                 from langchain_google_vertexai import ChatVertexAI
 

libs/vertexai/langchain_google_vertexai/embeddings.py

@@ -17,7 +17,8 @@
 from google.cloud.aiplatform import telemetry
 from langchain_core.embeddings import Embeddings
 from langchain_core.language_models.llms import create_base_retry_decorator
-from langchain_core.pydantic_v1 import root_validator
+from pydantic import ConfigDict, model_validator
+from typing_extensions import Self
 from vertexai.language_models import (  # type: ignore
     TextEmbeddingInput,
     TextEmbeddingModel,
@@ -100,24 +101,33 @@ class VertexAIEmbeddings(_VertexAICommon, Embeddings):
     # Instance context
     instance: Dict[str, Any] = {}  #: :meta private:
 
-    @root_validator(pre=False, skip_on_failure=True)
-    def validate_environment(cls, values: Dict) -> Dict:
+    model_config = ConfigDict(
+        extra="forbid",
+        protected_namespaces=(),
+    )
+
+    @model_validator(mode="after")
+    def validate_environment(self) -> Self:
         """Validates that the python package exists in environment."""
-        cls._init_vertexai(values)
-        _, user_agent = get_user_agent(f"{cls.__name__}_{values['model_name']}")  # type: ignore
+        values = {
+            "project": self.project,
+            "location": self.location,
+            "credentials": self.credentials,
+            "api_transport": self.api_transport,
+            "api_endpoint": self.api_endpoint,
+            "default_metadata": self.default_metadata,
+        }
+        self._init_vertexai(values)
+        _, user_agent = get_user_agent(f"{self.__class__.__name__}_{self.model_name}")
         with telemetry.tool_context_manager(user_agent):
             if (
-                GoogleEmbeddingModelType(values["model_name"])
+                GoogleEmbeddingModelType(self.model_name)
                 == GoogleEmbeddingModelType.MULTIMODAL
             ):
-                values["client"] = MultiModalEmbeddingModel.from_pretrained(
-                    values["model_name"]
-                )
+                self.client = MultiModalEmbeddingModel.from_pretrained(self.model_name)
             else:
-                values["client"] = TextEmbeddingModel.from_pretrained(
-                    values["model_name"]
-                )
-        return values
+                self.client = TextEmbeddingModel.from_pretrained(self.model_name)
+        return self
 
     def __init__(
         self,