diff --git a/.github/workflows/_all_ci.yml b/.github/workflows/_all_ci.yml index 6fd789e4..abf425bf 100644 --- a/.github/workflows/_all_ci.yml +++ b/.github/workflows/_all_ci.yml @@ -41,13 +41,6 @@ jobs: working-directory: ${{ inputs.working-directory }} secrets: inherit - dependencies: - name: "-" - uses: ./.github/workflows/_dependencies.yml - with: - working-directory: ${{ inputs.working-directory }} - secrets: inherit - test: name: "-" uses: ./.github/workflows/_test.yml diff --git a/.github/workflows/_dependencies.yml b/.github/workflows/_dependencies.yml deleted file mode 100644 index aa6ad4b4..00000000 --- a/.github/workflows/_dependencies.yml +++ /dev/null @@ -1,103 +0,0 @@ -name: dependencies - -on: - workflow_call: - inputs: - working-directory: - required: true - type: string - description: "From which folder this pipeline executes" - -env: - POETRY_VERSION: "1.7.1" - -jobs: - build: - defaults: - run: - working-directory: ${{ inputs.working-directory }} - runs-on: ubuntu-latest - strategy: - matrix: - python-version: - - "3.8" - - "3.9" - - "3.10" - - "3.11" - name: dependency checks ${{ matrix.python-version }} - steps: - - uses: actions/checkout@v4 - - - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }} - uses: "./.github/actions/poetry_setup" - with: - python-version: ${{ matrix.python-version }} - poetry-version: ${{ env.POETRY_VERSION }} - working-directory: ${{ inputs.working-directory }} - cache-key: pydantic-cross-compat - - - name: Install dependencies - shell: bash - run: poetry install - - - name: Check imports with base dependencies - shell: bash - run: poetry run make check_imports - - - name: Install test dependencies - shell: bash - run: poetry install --with test - - - name: Install the opposite major version of pydantic - # If normal tests use pydantic v1, here we'll use v2, and vice versa. - shell: bash - run: | - # Determine the major part of pydantic version - REGULAR_VERSION=$(poetry run python -c "import pydantic; print(pydantic.__version__)" | cut -d. -f1) - - if [[ "$REGULAR_VERSION" == "1" ]]; then - PYDANTIC_DEP=">=2.1,<3" - TEST_WITH_VERSION="2" - elif [[ "$REGULAR_VERSION" == "2" ]]; then - PYDANTIC_DEP="<2" - TEST_WITH_VERSION="1" - else - echo "Unexpected pydantic major version '$REGULAR_VERSION', cannot determine which version to use for cross-compatibility test." - exit 1 - fi - - # Install via `pip` instead of `poetry add` to avoid changing lockfile, - # which would prevent caching from working: the cache would get saved - # to a different key than where it gets loaded from. - poetry run pip install "pydantic${PYDANTIC_DEP}" - - # Ensure that the correct pydantic is installed now. - echo "Checking pydantic version... Expecting ${TEST_WITH_VERSION}" - - # Determine the major part of pydantic version - CURRENT_VERSION=$(poetry run python -c "import pydantic; print(pydantic.__version__)" | cut -d. -f1) - - # Check that the major part of pydantic version is as expected, if not - # raise an error - if [[ "$CURRENT_VERSION" != "$TEST_WITH_VERSION" ]]; then - echo "Error: expected pydantic version ${CURRENT_VERSION} to have been installed, but found: ${TEST_WITH_VERSION}" - exit 1 - fi - echo "Found pydantic version ${CURRENT_VERSION}, as expected" - - - name: Run pydantic compatibility tests - # airbyte currently doesn't support pydantic v2 - shell: bash - run: make test - - - name: Ensure the tests did not create any additional files - shell: bash - run: | - set -eu - - STATUS="$(git status)" - echo "$STATUS" - - # grep will exit non-zero if the target message isn't found, - # and `set -e` above will cause the step to fail. - echo "$STATUS" | grep 'nothing to commit, working tree clean' diff --git a/libs/community/Makefile b/libs/community/Makefile index 5c7f091b..b4385e36 100644 --- a/libs/community/Makefile +++ b/libs/community/Makefile @@ -33,7 +33,6 @@ 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 . poetry run ruff format $(PYTHON_FILES) --diff diff --git a/libs/community/langchain_google_community/bq_storage_vectorstores/_base.py b/libs/community/langchain_google_community/bq_storage_vectorstores/_base.py index 31cb640f..d72bad29 100644 --- a/libs/community/langchain_google_community/bq_storage_vectorstores/_base.py +++ b/libs/community/langchain_google_community/bq_storage_vectorstores/_base.py @@ -13,8 +13,9 @@ from langchain_community.vectorstores.utils import maximal_marginal_relevance from langchain_core.documents import Document from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, ConfigDict, root_validator from langchain_core.vectorstores import VectorStore +from pydantic import BaseModel, ConfigDict, model_validator +from typing_extensions import Self from langchain_google_community._utils import get_client_info from langchain_google_community.bq_storage_vectorstores.utils import ( @@ -75,8 +76,9 @@ class BaseBigQueryVectorStore(VectorStore, BaseModel, ABC): _logger: Any = None _full_table_id: Optional[str] = None - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) @abstractmethod def sync_data(self) -> None: @@ -113,8 +115,8 @@ def _similarity_search_by_vectors_with_scores_and_embeddings( ) -> list[list[list[Any]]]: ... - @root_validator(pre=False, skip_on_failure=True) - def validate_vals(cls, values: dict) -> dict: + @model_validator(mode="after") + def validate_vals(self) -> Self: try: import pandas # noqa: F401 from google.cloud import bigquery # type: ignore[attr-defined] @@ -127,41 +129,37 @@ def validate_vals(cls, values: dict) -> dict: "Please, install feature store dependency group: " "`pip install langchain-google-community[featurestore]`" ) - values["_logger"] = base.Logger(__name__) - values["_bq_client"] = bigquery.Client( - project=values["project_id"], - location=values["location"], - credentials=values["credentials"], + self._logger = base.Logger(__name__) + self._bq_client = bigquery.Client( + project=self.project_id, + location=self.location, + credentials=self.credentials, client_info=get_client_info(module="bigquery-vector-search"), ) - if values["embedding_dimension"] is None: - values["embedding_dimension"] = len(values["embedding"].embed_query("test")) - full_table_id = ( - f"{values['project_id']}.{values['dataset_name']}.{values['table_name']}" - ) - values["_full_table_id"] = full_table_id - temp_dataset_id = f"{values['dataset_name']}_temp" + if self.embedding_dimension is None: + self.embedding_dimension = len(self.embedding.embed_query("test")) + full_table_id = f"{self.project_id}.{self.dataset_name}.{self.table_name}" + self._full_table_id = full_table_id + temp_dataset_id = f"{self.dataset_name}_temp" if not check_bq_dataset_exists( - client=values["_bq_client"], dataset_id=values["dataset_name"] + client=self._bq_client, dataset_id=self.dataset_name ): - values["_bq_client"].create_dataset( - dataset=values["dataset_name"], exists_ok=True - ) + self._bq_client.create_dataset(dataset=self.dataset_name, exists_ok=True) if not check_bq_dataset_exists( - client=values["_bq_client"], dataset_id=temp_dataset_id + client=self._bq_client, dataset_id=temp_dataset_id ): - values["_bq_client"].create_dataset(dataset=temp_dataset_id, exists_ok=True) + self._bq_client.create_dataset(dataset=temp_dataset_id, exists_ok=True) table_ref = bigquery.TableReference.from_string(full_table_id) - values["_bq_client"].create_table(table_ref, exists_ok=True) - values["_logger"].info( + self._bq_client.create_table(table_ref, exists_ok=True) + self._logger.info( f"BigQuery table {full_table_id} " f"initialized/validated as persistent storage. " f"Access via BigQuery console:\n " - f"https://console.cloud.google.com/bigquery?project={values['project_id']}" - f"&ws=!1m5!1m4!4m3!1s{values['project_id']}!2s{values['dataset_name']}!3s" - f"{values['table_name']}" + f"https://console.cloud.google.com/bigquery?project={self.project_id}" + f"&ws=!1m5!1m4!4m3!1s{self.project_id}!2s{self.dataset_name}!3s" + f"{self.table_name}" ) - return values + return self @property def embeddings(self) -> Optional[Embeddings]: diff --git a/libs/community/langchain_google_community/bq_storage_vectorstores/bigquery.py b/libs/community/langchain_google_community/bq_storage_vectorstores/bigquery.py index 2ba1667e..af2fa62f 100644 --- a/libs/community/langchain_google_community/bq_storage_vectorstores/bigquery.py +++ b/libs/community/langchain_google_community/bq_storage_vectorstores/bigquery.py @@ -6,11 +6,13 @@ from google.api_core.exceptions import ClientError from langchain_core.documents import Document from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import root_validator +from pydantic import model_validator if TYPE_CHECKING: from google.cloud.bigquery.table import Table +from typing_extensions import Self + from langchain_google_community.bq_storage_vectorstores._base import ( BaseBigQueryVectorStore, ) @@ -114,67 +116,67 @@ def get_documents( docs.append(doc) return docs - @root_validator(pre=False, skip_on_failure=True) - def initialize_bq_vector_index(cls, values: dict) -> dict: + @model_validator(mode="after") + def initialize_bq_vector_index(self) -> Self: """ A vector index in BigQuery table enables efficient approximate vector search. """ from google.cloud import bigquery # type: ignore[attr-defined] - values["_creating_index"] = values.get("_creating_index", False) - values["_have_index"] = values.get("_have_index", False) - values["_last_index_check"] = values.get("_last_index_check", datetime.min) + self._creating_index = self._creating_index + self._have_index = self._have_index + self._last_index_check = self._last_index_check - if values.get("_have_index") or values.get("_creating_index"): - return values + if self._have_index or self._creating_index: + return self - table = values["_bq_client"].get_table(values["_full_table_id"]) # type: ignore[union-attr] + table = self._bq_client.get_table(self._full_table_id) # type: ignore[union-attr] # Update existing table schema schema = table.schema.copy() if schema: ## Check if table has a schema - values["table_schema"] = {field.name: field.field_type for field in schema} + self.table_schema = {field.name: field.field_type for field in schema} if (table.num_rows or 0) < MIN_INDEX_ROWS: - values["_logger"].debug("Not enough rows to create a vector index.") - return values + self._logger.debug("Not enough rows to create a vector index.") + return self - if datetime.utcnow() - values["_last_index_check"] < INDEX_CHECK_INTERVAL: - return values + if datetime.utcnow() - self._last_index_check < INDEX_CHECK_INTERVAL: + return self with _vector_table_lock: - values["_last_index_check"] = datetime.utcnow() + self._last_index_check = datetime.utcnow() # Check if index exists, create if necessary check_query = ( - f"SELECT 1 FROM `{values['project_id']}." - f"{values['dataset_name']}" + f"SELECT 1 FROM `{self.project_id}." + f"{self.dataset_name}" ".INFORMATION_SCHEMA.VECTOR_INDEXES` WHERE" - f" table_name = '{values['table_name']}'" + f" table_name = '{self.table_name}'" ) - job = values["_bq_client"].query( # type: ignore[union-attr] + job = self._bq_client.query( # type: ignore[union-attr] check_query, api_method=bigquery.enums.QueryApiMethod.QUERY ) if job.result().total_rows == 0: # Need to create an index. Make it in a separate thread. - values["_logger"].debug("Trying to create a vector index.") + self._logger.debug("Trying to create a vector index.") Thread( target=_create_bq_index, kwargs={ - "bq_client": values["_bq_client"], - "table_name": values["table_name"], - "full_table_id": values["_full_table_id"], - "embedding_field": values["embedding_field"], - "distance_type": values["distance_type"], - "logger": values["_logger"], + "bq_client": self._bq_client, + "table_name": self.table_name, + "full_table_id": self._full_table_id, + "embedding_field": self.embedding_field, + "distance_type": self.distance_type, + "logger": self._logger, }, daemon=True, ).start() else: - values["_logger"].debug("Vector index already exists.") - values["_have_index"] = True - return values + self._logger.debug("Vector index already exists.") + self._have_index = True + return self def _similarity_search_by_vectors_with_scores_and_embeddings( self, @@ -565,7 +567,9 @@ def to_vertex_fs_vector_store(self, **kwargs: Any) -> Any: VertexFSVectorStore, ) - base_params = self.dict(include=BaseBigQueryVectorStore.__fields__.keys()) + base_params = self.model_dump( + include=set(BaseBigQueryVectorStore.model_fields.keys()) + ) base_params["embedding"] = self.embedding all_params = {**base_params, **kwargs} fs_obj = VertexFSVectorStore(**all_params) diff --git a/libs/community/langchain_google_community/bq_storage_vectorstores/featurestore.py b/libs/community/langchain_google_community/bq_storage_vectorstores/featurestore.py index b0dd6cf9..3a9469b2 100644 --- a/libs/community/langchain_google_community/bq_storage_vectorstores/featurestore.py +++ b/libs/community/langchain_google_community/bq_storage_vectorstores/featurestore.py @@ -13,7 +13,8 @@ ) from langchain_core.documents import Document from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import root_validator +from pydantic import model_validator +from typing_extensions import Self from langchain_google_community._utils import get_client_info, get_user_agent from langchain_google_community.bq_storage_vectorstores._base import ( @@ -76,12 +77,13 @@ class VertexFSVectorStore(BaseBigQueryVectorStore): filter_columns: Optional[List[str]] = None crowding_column: Optional[str] = None distance_measure_type: Optional[str] = None + online_store: Any = None _user_agent: str = "" feature_view: Any = None _admin_client: Any = None - @root_validator(pre=False, skip_on_failure=True) - def _initialize_bq_vector_index(cls, values: dict) -> dict: + @model_validator(mode="after") + def _initialize_bq_vector_index(self) -> Self: import vertexai from google.cloud.aiplatform_v1beta1 import ( FeatureOnlineStoreAdminServiceClient, @@ -91,49 +93,43 @@ def _initialize_bq_vector_index(cls, values: dict) -> dict: utils, # type: ignore[import-untyped] ) - vertexai.init(project=values["project_id"], location=values["location"]) - values["_user_agent"] = get_user_agent( - f"{USER_AGENT_PREFIX}-VertexFSVectorStore" - )[1] - - if values["algorithm_config"] is None: - values["algorithm_config"] = utils.TreeAhConfig() - if values["distance_measure_type"] is None: - values[ - "distance_measure_type" - ] = utils.DistanceMeasureType.DOT_PRODUCT_DISTANCE - if values.get("online_store_name") is None: - values["online_store_name"] = values["dataset_name"] - if values.get("view_name") is None: - values["view_name"] = values["table_name"] - - api_endpoint = f"{values['location']}-aiplatform.googleapis.com" - values["_admin_client"] = FeatureOnlineStoreAdminServiceClient( + vertexai.init(project=self.project_id, location=self.location) + self._user_agent = get_user_agent(f"{USER_AGENT_PREFIX}-VertexFSVectorStore")[1] + + if self.algorithm_config is None: + self.algorithm_config = utils.TreeAhConfig() + if self.distance_measure_type is None: + self.distance_measure_type = utils.DistanceMeasureType.DOT_PRODUCT_DISTANCE + if self.online_store_name is None: + self.online_store_name = self.dataset_name + if self.view_name is None: + self.view_name = self.table_name + + api_endpoint = f"{self.location}-aiplatform.googleapis.com" + self._admin_client = FeatureOnlineStoreAdminServiceClient( client_options={"api_endpoint": api_endpoint}, - client_info=get_client_info(module=values["_user_agent"]), + client_info=get_client_info(module=self._user_agent), ) - values["online_store"] = _create_online_store( - project_id=values["project_id"], - location=values["location"], - online_store_name=values["online_store_name"], - _admin_client=values["_admin_client"], - _logger=values["_logger"], + self.online_store = _create_online_store( + project_id=self.project_id, + location=self.location, + online_store_name=self.online_store_name, + _admin_client=self._admin_client, + _logger=self._logger, ) - gca_resource = values["online_store"].gca_resource + gca_resource = self.online_store.gca_resource endpoint = gca_resource.dedicated_serving_endpoint.public_endpoint_domain_name - values["_search_client"] = FeatureOnlineStoreServiceClient( + self._search_client = FeatureOnlineStoreServiceClient( client_options={"api_endpoint": endpoint}, - client_info=get_client_info(module=values["_user_agent"]), - ) - values["feature_view"] = _get_feature_view( - values["online_store"], values["view_name"] + client_info=get_client_info(module=self._user_agent), ) + self.feature_view = _get_feature_view(self.online_store, self.view_name) - values["_logger"].info( + self._logger.info( "VertexFSVectorStore initialized with Feature Store Vector Search. \n" "Optional batch serving available via .to_bq_vector_store() method." ) - return values + return self def _init_store(self) -> None: from google.cloud.aiplatform_v1beta1 import FeatureOnlineStoreServiceClient @@ -518,7 +514,9 @@ def to_bq_vector_store(self, **kwargs: Any) -> Any: BigQueryVectorStore, ) - base_params = self.dict(include=BaseBigQueryVectorStore.__fields__.keys()) + base_params = self.model_dump( + include=set(BaseBigQueryVectorStore.model_fields.keys()) + ) base_params["embedding"] = self.embedding all_params = {**base_params, **kwargs} bq_obj = BigQueryVectorStore(**all_params) diff --git a/libs/community/langchain_google_community/documentai_warehouse.py b/libs/community/langchain_google_community/documentai_warehouse.py index 40553c22..bdf5672b 100644 --- a/libs/community/langchain_google_community/documentai_warehouse.py +++ b/libs/community/langchain_google_community/documentai_warehouse.py @@ -4,9 +4,9 @@ from langchain_core.callbacks import CallbackManagerForRetrieverRun from langchain_core.documents import Document -from langchain_core.pydantic_v1 import root_validator from langchain_core.retrievers import BaseRetriever from langchain_core.utils import get_from_dict_or_env +from pydantic import model_validator from langchain_google_community._utils import get_client_info @@ -42,8 +42,9 @@ class DocumentAIWarehouseRetriever(BaseRetriever): """The limit on the number of documents returned.""" client: "DocumentServiceClient" = None #: :meta private: - @root_validator(pre=False, skip_on_failure=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validates the environment.""" try: # noqa: F401 from google.cloud.contentwarehouse_v1 import DocumentServiceClient diff --git a/libs/community/langchain_google_community/drive.py b/libs/community/langchain_google_community/drive.py index 08b9ceba..8bdb93ff 100644 --- a/libs/community/langchain_google_community/drive.py +++ b/libs/community/langchain_google_community/drive.py @@ -13,7 +13,7 @@ from langchain_core.document_loaders import BaseLoader from langchain_core.documents import Document -from langchain_core.pydantic_v1 import BaseModel, root_validator, validator +from pydantic import BaseModel, field_validator, model_validator SCOPES = ["https://www.googleapis.com/auth/drive.file"] @@ -194,8 +194,9 @@ def _get_identity_metadata_from_id(self, id: str) -> List[str]: return authorized_identities - @root_validator - def validate_inputs(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def validate_inputs(cls, values: Dict[str, Any]) -> Any: """Validate that either folder_id or document_ids is set, but not both.""" if values.get("folder_id") and ( values.get("document_ids") or values.get("file_ids") @@ -241,7 +242,7 @@ def full_form(x: str) -> str: values["file_types"] = [full_form(file_type) for file_type in file_types] return values - @validator("credentials_path") + @field_validator("credentials_path") def validate_credentials_path(cls, v: Any, **kwargs: Any) -> Any: """Validate that credentials_path exists.""" if not v.exists(): diff --git a/libs/community/langchain_google_community/gmail/base.py b/libs/community/langchain_google_community/gmail/base.py index 98b0af08..e0d3ce2d 100644 --- a/libs/community/langchain_google_community/gmail/base.py +++ b/libs/community/langchain_google_community/gmail/base.py @@ -4,8 +4,8 @@ from typing import TYPE_CHECKING -from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool +from pydantic import Field from langchain_google_community.gmail.utils import build_resource_service diff --git a/libs/community/langchain_google_community/gmail/create_draft.py b/libs/community/langchain_google_community/gmail/create_draft.py index c0e7cefd..6e88ebf8 100644 --- a/libs/community/langchain_google_community/gmail/create_draft.py +++ b/libs/community/langchain_google_community/gmail/create_draft.py @@ -3,7 +3,7 @@ from typing import List, Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain_google_community.gmail.base import GmailBaseTool diff --git a/libs/community/langchain_google_community/gmail/get_message.py b/libs/community/langchain_google_community/gmail/get_message.py index 8cb14c2b..d424de8e 100644 --- a/libs/community/langchain_google_community/gmail/get_message.py +++ b/libs/community/langchain_google_community/gmail/get_message.py @@ -3,7 +3,7 @@ from typing import Dict, Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain_google_community.gmail.base import GmailBaseTool from langchain_google_community.gmail.utils import clean_email_body diff --git a/libs/community/langchain_google_community/gmail/get_thread.py b/libs/community/langchain_google_community/gmail/get_thread.py index ff18621f..5189ea08 100644 --- a/libs/community/langchain_google_community/gmail/get_thread.py +++ b/libs/community/langchain_google_community/gmail/get_thread.py @@ -1,7 +1,7 @@ from typing import Dict, Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain_google_community.gmail.base import GmailBaseTool diff --git a/libs/community/langchain_google_community/gmail/search.py b/libs/community/langchain_google_community/gmail/search.py index 1480116b..90b7a2be 100644 --- a/libs/community/langchain_google_community/gmail/search.py +++ b/libs/community/langchain_google_community/gmail/search.py @@ -4,7 +4,7 @@ from typing import Any, Dict, List, Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain_google_community.gmail.base import GmailBaseTool from langchain_google_community.gmail.utils import clean_email_body diff --git a/libs/community/langchain_google_community/gmail/send_message.py b/libs/community/langchain_google_community/gmail/send_message.py index af45c306..a777067f 100644 --- a/libs/community/langchain_google_community/gmail/send_message.py +++ b/libs/community/langchain_google_community/gmail/send_message.py @@ -6,7 +6,7 @@ from typing import Any, Dict, List, Optional, Type, Union from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain_google_community.gmail.base import GmailBaseTool diff --git a/libs/community/langchain_google_community/gmail/toolkit.py b/libs/community/langchain_google_community/gmail/toolkit.py index 7c2cb64f..6f68d652 100644 --- a/libs/community/langchain_google_community/gmail/toolkit.py +++ b/libs/community/langchain_google_community/gmail/toolkit.py @@ -3,8 +3,8 @@ from typing import TYPE_CHECKING, List from langchain_community.agent_toolkits.base import BaseToolkit -from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool +from pydantic import ConfigDict, Field from langchain_google_community.gmail.create_draft import GmailCreateDraft from langchain_google_community.gmail.get_message import GmailGetMessage @@ -42,10 +42,9 @@ class GmailToolkit(BaseToolkit): api_resource: Resource = Field(default_factory=build_resource_service) - class Config: - """Pydantic config.""" - - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def get_tools(self) -> List[BaseTool]: """Get the tools in the toolkit.""" diff --git a/libs/community/langchain_google_community/places_api.py b/libs/community/langchain_google_community/places_api.py index 3b457cf3..885128f0 100644 --- a/libs/community/langchain_google_community/places_api.py +++ b/libs/community/langchain_google_community/places_api.py @@ -4,9 +4,14 @@ from typing import Any, Dict, Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Extra, Field, root_validator from langchain_core.tools import BaseTool from langchain_core.utils import get_from_dict_or_env +from pydantic import ( + BaseModel, + ConfigDict, + Field, + model_validator, +) class GooglePlacesAPIWrapper(BaseModel): @@ -33,14 +38,14 @@ class GooglePlacesAPIWrapper(BaseModel): google_map_client: Any = None #: :meta private: top_k_results: Optional[int] = None - class Config: - """Configuration for this pydantic object.""" - - extra = Extra.forbid - arbitrary_types_allowed = True + model_config = ConfigDict( + extra="forbid", + arbitrary_types_allowed=True, + ) - @root_validator(pre=False, skip_on_failure=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key is in your environment variable.""" gplaces_api_key = get_from_dict_or_env( values, "gplaces_api_key", "GPLACES_API_KEY" diff --git a/libs/community/langchain_google_community/search.py b/libs/community/langchain_google_community/search.py index 2b68e0b8..0ffbaa2e 100644 --- a/libs/community/langchain_google_community/search.py +++ b/libs/community/langchain_google_community/search.py @@ -3,9 +3,9 @@ from typing import Any, Dict, List, Optional from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator from langchain_core.tools import BaseTool from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, model_validator class GoogleSearchAPIWrapper(BaseModel): @@ -53,10 +53,9 @@ class GoogleSearchAPIWrapper(BaseModel): k: int = 10 siterestrict: bool = False - class Config: - """Configuration for this pydantic object.""" - - extra = Extra.forbid + model_config = ConfigDict( + extra="forbid", + ) def _google_search_results(self, search_term: str, **kwargs: Any) -> List[dict]: cse = self.search_engine.cse() @@ -65,8 +64,9 @@ def _google_search_results(self, search_term: str, **kwargs: Any) -> List[dict]: res = cse.list(q=search_term, cx=self.google_cse_id, **kwargs).execute() return res.get("items", []) - @root_validator(pre=False, skip_on_failure=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and python package exists in environment.""" google_api_key = get_from_dict_or_env( values, "google_api_key", "GOOGLE_API_KEY" diff --git a/libs/community/langchain_google_community/vertex_ai_search.py b/libs/community/langchain_google_community/vertex_ai_search.py index e894a65f..db73e4fb 100644 --- a/libs/community/langchain_google_community/vertex_ai_search.py +++ b/libs/community/langchain_google_community/vertex_ai_search.py @@ -17,10 +17,10 @@ from langchain_core.callbacks import CallbackManagerForRetrieverRun from langchain_core.documents import Document from langchain_core.load import Serializable, load -from langchain_core.pydantic_v1 import Extra, Field, root_validator from langchain_core.retrievers import BaseRetriever from langchain_core.tools import BaseTool from langchain_core.utils import get_from_dict_or_env +from pydantic import ConfigDict, Field, PrivateAttr, model_validator from langchain_google_community._utils import get_client_info @@ -64,8 +64,9 @@ def is_lc_serializable(self) -> bool: def __reduce__(self) -> Any: return _load, (self.to_json(),) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validates the environment.""" try: from google.cloud import discoveryengine_v1beta # noqa: F401 @@ -255,15 +256,13 @@ class VertexAISearchRetriever(BaseRetriever, _BaseVertexAISearchRetriever): https://cloud.google.com/generative-ai-app-builder/docs/reference/rest/v1beta/BoostSpec """ - _client: SearchServiceClient - _serving_config: str - - class Config: - """Configuration for this pydantic object.""" + _client: SearchServiceClient = PrivateAttr() + _serving_config: str = PrivateAttr() - extra = Extra.forbid - arbitrary_types_allowed = True - underscore_attrs_are_private = True + model_config = ConfigDict( + extra="forbid", + arbitrary_types_allowed=True, + ) def __init__(self, **kwargs: Any) -> None: """Initializes private fields.""" @@ -418,15 +417,13 @@ class VertexAIMultiTurnSearchRetriever(BaseRetriever, _BaseVertexAISearchRetriev conversation_id: str = "-" """Vertex AI Search Conversation ID.""" - _client: ConversationalSearchServiceClient - _serving_config: str - - class Config: - """Configuration for this pydantic object.""" + _client: ConversationalSearchServiceClient = PrivateAttr() + _serving_config: str = PrivateAttr() - extra = Extra.ignore - arbitrary_types_allowed = True - underscore_attrs_are_private = True + model_config = ConfigDict( + extra="ignore", + arbitrary_types_allowed=True, + ) def __init__(self, **kwargs: Any): super().__init__(**kwargs) @@ -502,10 +499,10 @@ class VertexAISearchSummaryTool(BaseTool, VertexAISearchRetriever): summary_spec_kwargs: Dict[str, Any] = Field(default_factory=dict) """ Additional kwargs for `SearchRequest.ContentSearchSpec.SummarySpec`""" - class Config(VertexAISearchRetriever.Config): - """Redefinition to specify that inherits config from `VertexAISearchRetriever` - not BaseTool - """ + model_config = ConfigDict( + extra="forbid", + arbitrary_types_allowed=True, + ) def _get_content_spec_kwargs(self) -> Optional[Dict[str, Any]]: """Adds additional summary_spec parameters to the configuration of the search. diff --git a/libs/community/langchain_google_community/vertex_check_grounding.py b/libs/community/langchain_google_community/vertex_check_grounding.py index fe253a06..1efc4789 100644 --- a/libs/community/langchain_google_community/vertex_check_grounding.py +++ b/libs/community/langchain_google_community/vertex_check_grounding.py @@ -3,8 +3,8 @@ from google.api_core import exceptions as core_exceptions # type: ignore from google.auth.credentials import Credentials # type: ignore from langchain_core.documents import Document -from langchain_core.pydantic_v1 import BaseModel, Extra, Field from langchain_core.runnables import RunnableConfig, RunnableSerializable +from pydantic import BaseModel, ConfigDict, Field from langchain_google_community._utils import get_client_info @@ -45,7 +45,7 @@ class VertexAICheckGroundingWrapper( location_id: str = Field(default="global") grounding_config: str = Field(default="default_grounding_config") citation_threshold: Optional[float] = Field(default=0.6) - client: Any + client: Any = None credentials: Optional[Credentials] = Field(default=None) credentials_path: Optional[str] = Field(default=None) @@ -239,6 +239,7 @@ def get_lc_namespace(cls) -> List[str]: def is_lc_serializable(cls) -> bool: return False - class Config: - extra = Extra.ignore - arbitrary_types_allowed = True + model_config = ConfigDict( + extra="ignore", + arbitrary_types_allowed=True, + ) diff --git a/libs/community/langchain_google_community/vertex_rank.py b/libs/community/langchain_google_community/vertex_rank.py index 2af66966..02e68e35 100644 --- a/libs/community/langchain_google_community/vertex_rank.py +++ b/libs/community/langchain_google_community/vertex_rank.py @@ -6,7 +6,7 @@ from langchain_core.callbacks import Callbacks from langchain_core.documents import Document from langchain_core.documents.compressor import BaseDocumentCompressor -from langchain_core.pydantic_v1 import Extra, Field +from pydantic import ConfigDict, Field from langchain_google_community._utils import get_client_info @@ -64,7 +64,7 @@ class VertexAIRank(BaseDocumentCompressor): title_field: Optional[str] = Field(default=None) credentials: Optional[Credentials] = Field(default=None) credentials_path: Optional[str] = Field(default=None) - client: Any + client: Any = None def __init__(self, **kwargs: Any): """ @@ -190,6 +190,7 @@ def compress_documents( """ return self._rerank_documents(query, documents) - class Config: - extra = Extra.ignore - arbitrary_types_allowed = True + model_config = ConfigDict( + extra="ignore", + arbitrary_types_allowed=True, + ) diff --git a/libs/community/poetry.lock b/libs/community/poetry.lock index ea81f586..b7c5d55f 100644 --- a/libs/community/poetry.lock +++ b/libs/community/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. [[package]] name = "aiohttp" @@ -121,8 +121,27 @@ files = [ {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"}, ] +[[package]] +name = "anyio" +version = "4.4.0" +description = "High level compatibility layer for multiple asynchronous event loop implementations" +optional = false +python-versions = ">=3.8" +files = [ + {file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"}, + {file = "anyio-4.4.0.tar.gz", hash = "sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94"}, +] + [package.dependencies] -typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""} +exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""} +idna = ">=2.8" +sniffio = ">=1.1" +typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""} + +[package.extras] +doc = ["Sphinx (>=7)", "packaging", 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--git a/libs/community/pyproject.toml b/libs/community/pyproject.toml index 451a2e10..4ad0cc05 100644 --- a/libs/community/pyproject.toml +++ b/libs/community/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langchain-google-community" -version = "1.0.8" +version = "2.0.0" description = "An integration package connecting miscellaneous Google's products and LangChain" authors = [] readme = "README.md" @@ -11,11 +11,12 @@ license = "MIT" "Source Code" = "https://github.com/langchain-ai/langchain-google/tree/main/libs/community" [tool.poetry.dependencies] -python = ">=3.8.1,<4.0" -langchain-core = ">=0.2.9,<0.3" -langchain-community = "^0.2.1" +python = ">=3.9,<4.0" +langchain-core = ">=0.3.0,<0.4" +langchain-community = "^0.3.0" google-api-core = "^2.17.1" google-api-python-client = "^2.122.0" +google-cloud-core = "^2.4.1" grpcio = "^1.62.0" tenacity = "~8.3.0" google-cloud-bigquery = { version = "^3.21.0", optional = true } diff --git a/libs/community/scripts/check_pydantic.sh b/libs/community/scripts/check_pydantic.sh deleted file mode 100755 index 06b5bb81..00000000 --- a/libs/community/scripts/check_pydantic.sh +++ /dev/null @@ -1,27 +0,0 @@ -#!/bin/bash -# -# This script searches for lines starting with "import pydantic" or "from pydantic" -# in tracked files within a Git repository. -# -# Usage: ./scripts/check_pydantic.sh /path/to/repository - -# Check if a path argument is provided -if [ $# -ne 1 ]; then - echo "Usage: $0 /path/to/repository" - exit 1 -fi - -repository_path="$1" - -# Search for lines matching the pattern within the specified repository -result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic') - -# Check if any matching lines were found -if [ -n "$result" ]; then - echo "ERROR: The following lines need to be updated:" - echo "$result" - echo "Please replace the code with an import from langchain_core.pydantic_v1." - echo "For example, replace 'from pydantic import BaseModel'" - echo "with 'from langchain_core.pydantic_v1 import BaseModel'" - exit 1 -fi diff --git a/libs/community/tests/integration_tests/fake.py b/libs/community/tests/integration_tests/fake.py index 7961bd04..be5aac30 100644 --- a/libs/community/tests/integration_tests/fake.py +++ b/libs/community/tests/integration_tests/fake.py @@ -2,7 +2,7 @@ import numpy as np from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel class FakeEmbeddings(Embeddings, BaseModel): diff --git a/libs/community/tests/integration_tests/test_rank.py b/libs/community/tests/integration_tests/test_rank.py index c6676a06..1f818290 100644 --- a/libs/community/tests/integration_tests/test_rank.py +++ b/libs/community/tests/integration_tests/test_rank.py @@ -6,9 +6,9 @@ from google.cloud import discoveryengine_v1alpha # type: ignore from langchain_core.callbacks import CallbackManagerForRetrieverRun from langchain_core.documents import Document -from langchain_core.pydantic_v1 import Field from langchain_core.retrievers import BaseRetriever from langchain_core.vectorstores import VectorStore, VectorStoreRetriever +from pydantic import Field from langchain_google_community.vertex_rank import VertexAIRank diff --git a/libs/community/tests/integration_tests/test_vertex_ai_search.py b/libs/community/tests/integration_tests/test_vertex_ai_search.py index a125d572..94c34dc7 100644 --- a/libs/community/tests/integration_tests/test_vertex_ai_search.py +++ b/libs/community/tests/integration_tests/test_vertex_ai_search.py @@ -117,7 +117,7 @@ def test_native_serialization(spec: Optional[Dict]) -> None: retriever_loaded = load( json.loads(serialized), valid_namespaces=["langchain_google_community"] ) - assert retriever == retriever_loaded + assert retriever.model_dump() == retriever_loaded.model_dump() @pytest.mark.extended @@ -128,4 +128,4 @@ def test_cloudpickle(spec: Optional[Dict]) -> None: ) serialized = cloudpickle.dumps(retriever) retriever_loaded = pickle.loads(serialized) - assert retriever == retriever_loaded + assert retriever.model_dump() == retriever_loaded.model_dump() diff --git a/libs/genai/Makefile b/libs/genai/Makefile index 288310ec..7c4a6e58 100644 --- a/libs/genai/Makefile +++ b/libs/genai/Makefile @@ -14,6 +14,9 @@ test tests integration_test integration_tests: check_imports: $(shell find langchain_google_genai -name '*.py') poetry run python ./scripts/check_imports.py $^ +test_watch: + poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE) + # Run unit tests and generate a coverage report. coverage: poetry run pytest --cov \ @@ -36,7 +39,6 @@ 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 diff --git a/libs/genai/langchain_google_genai/_function_utils.py b/libs/genai/langchain_google_genai/_function_utils.py index 3b0a9259..5ac6c52c 100644 --- a/libs/genai/langchain_google_genai/_function_utils.py +++ b/libs/genai/langchain_google_genai/_function_utils.py @@ -22,7 +22,6 @@ import google.ai.generativelanguage_v1beta.types as gapic import proto # type: ignore[import] from google.generativeai.types.content_types import ToolDict # type: ignore[import] -from langchain_core.pydantic_v1 import BaseModel from langchain_core.tools import BaseTool from langchain_core.tools import tool as callable_as_lc_tool from langchain_core.utils.function_calling import ( @@ -30,6 +29,8 @@ convert_to_openai_tool, ) from langchain_core.utils.json_schema import dereference_refs +from pydantic import BaseModel +from pydantic.v1 import BaseModel as BaseModelV1 logger = logging.getLogger(__name__) @@ -205,7 +206,7 @@ def _format_to_gapic_function_declaration( function = cast(dict, tool) function["parameters"] = {} else: - if "parameters" in tool and tool["parameters"].get("properties"): + if "parameters" in tool and tool["parameters"].get("properties"): # type: ignore[index] function = convert_to_openai_tool(cast(dict, tool))["function"] else: function = cast(dict, tool) @@ -232,7 +233,14 @@ def _format_base_tool_to_function_declaration( ), ) - schema = tool.args_schema.schema() + if issubclass(tool.args_schema, BaseModel): + schema = tool.args_schema.model_json_schema() + elif issubclass(tool.args_schema, BaseModelV1): + schema = tool.args_schema.schema() + else: + raise NotImplementedError( + f"args_schema must be a Pydantic BaseModel, got {tool.args_schema}." + ) parameters = _dict_to_gapic_schema(schema) return gapic.FunctionDeclaration( @@ -247,7 +255,15 @@ def _convert_pydantic_to_genai_function( tool_name: Optional[str] = None, tool_description: Optional[str] = None, ) -> gapic.FunctionDeclaration: - schema = dereference_refs(pydantic_model.schema()) + if issubclass(pydantic_model, BaseModel): + schema = pydantic_model.model_json_schema() + elif issubclass(pydantic_model, BaseModelV1): + schema = pydantic_model.schema() + else: + raise NotImplementedError( + f"pydantic_model must be a Pydantic BaseModel, got {pydantic_model}" + ) + schema = dereference_refs(schema) schema.pop("definitions", None) function_declaration = gapic.FunctionDeclaration( name=tool_name if tool_name else schema.get("title"), diff --git a/libs/genai/langchain_google_genai/chat_models.py b/libs/genai/langchain_google_genai/chat_models.py index 41a5b7d4..80204c72 100644 --- a/libs/genai/langchain_google_genai/chat_models.py +++ b/libs/genai/langchain_google_genai/chat_models.py @@ -75,10 +75,16 @@ parse_tool_calls, ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator from langchain_core.runnables import Runnable, RunnablePassthrough from langchain_core.utils import secret_from_env from langchain_core.utils.pydantic import is_basemodel_subclass +from pydantic import ( + BaseModel, + ConfigDict, + Field, + SecretStr, + model_validator, +) from tenacity import ( before_sleep_log, retry, @@ -86,6 +92,7 @@ stop_after_attempt, wait_exponential, ) +from typing_extensions import Self from langchain_google_genai._common import ( GoogleGenerativeAIError, @@ -696,7 +703,7 @@ class ChatGoogleGenerativeAI(_BaseGoogleGenerativeAI, BaseChatModel): Tool calling: .. code-block:: python - from langchain_core.pydantic_v1 import BaseModel, Field + from pydantic import BaseModel, Field class GetWeather(BaseModel): @@ -741,7 +748,7 @@ class GetPopulation(BaseModel): from typing import Optional - from langchain_core.pydantic_v1 import BaseModel, Field + from pydantic import BaseModel, Field class Joke(BaseModel): @@ -832,8 +839,9 @@ class Joke(BaseModel): Gemini does not support system messages; any unsupported messages will raise an error.""" - class Config: - allow_population_by_field_name = True + model_config = ConfigDict( + populate_by_name=True, + ) @property def lc_secrets(self) -> Dict[str, str]: @@ -847,38 +855,36 @@ def _llm_type(self) -> str: def is_lc_serializable(self) -> bool: return True - @root_validator(pre=False, skip_on_failure=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_environment(self) -> Self: """Validates params and passes them to google-generativeai package.""" - if ( - values.get("temperature") is not None - and not 0 <= values["temperature"] <= 1 - ): + if self.temperature is not None and not 0 <= self.temperature <= 1: raise ValueError("temperature must be in the range [0.0, 1.0]") - if values.get("top_p") is not None and not 0 <= values["top_p"] <= 1: + if self.top_p is not None and not 0 <= self.top_p <= 1: raise ValueError("top_p must be in the range [0.0, 1.0]") - if values.get("top_k") is not None and values["top_k"] <= 0: + if self.top_k is not None and self.top_k <= 0: raise ValueError("top_k must be positive") - if not values["model"].startswith("models/"): - values["model"] = f"models/{values['model']}" + if not self.model.startswith("models/"): + self.model = f"models/{self.model}" - additional_headers = values.get("additional_headers") or {} - values["default_metadata"] = tuple(additional_headers.items()) + additional_headers = self.additional_headers or {} + self.default_metadata = tuple(additional_headers.items()) client_info = get_client_info("ChatGoogleGenerativeAI") google_api_key = None - if not values.get("credentials"): - google_api_key = values.get("google_api_key") - if isinstance(google_api_key, SecretStr): - google_api_key = google_api_key.get_secret_value() - transport: Optional[str] = values.get("transport") - values["client"] = genaix.build_generative_service( - credentials=values.get("credentials"), + if not self.credentials: + if isinstance(self.google_api_key, SecretStr): + google_api_key = self.google_api_key.get_secret_value() + else: + google_api_key = self.google_api_key + transport: Optional[str] = self.transport + self.client = genaix.build_generative_service( + credentials=self.credentials, api_key=google_api_key, client_info=client_info, - client_options=values.get("client_options"), + client_options=self.client_options, transport=transport, ) @@ -888,17 +894,17 @@ def validate_environment(cls, values: Dict) -> Dict: # this check ensures that async client is only initialized # within an asyncio event loop to avoid the error if _is_event_loop_running(): - values["async_client"] = genaix.build_generative_async_service( - credentials=values.get("credentials"), + self.async_client = genaix.build_generative_async_service( + credentials=self.credentials, api_key=google_api_key, client_info=client_info, - client_options=values.get("client_options"), + client_options=self.client_options, transport=transport, ) else: - values["async_client"] = None + self.async_client = None - return values + return self @property def _identifying_params(self) -> Dict[str, Any]: diff --git a/libs/genai/langchain_google_genai/embeddings.py b/libs/genai/langchain_google_genai/embeddings.py index 1dc2fc57..a920c7ff 100644 --- a/libs/genai/langchain_google_genai/embeddings.py +++ b/libs/genai/langchain_google_genai/embeddings.py @@ -8,8 +8,9 @@ EmbedContentRequest, ) from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator from langchain_core.utils import secret_from_env +from pydantic import BaseModel, Field, SecretStr, model_validator +from typing_extensions import Self from langchain_google_genai._common import ( GoogleGenerativeAIError, @@ -82,21 +83,22 @@ class GoogleGenerativeAIEmbeddings(BaseModel, Embeddings): "Example: `{'timeout': 10}`", ) - @root_validator(pre=False, skip_on_failure=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_environment(self) -> Self: """Validates params and passes them to google-generativeai package.""" - google_api_key = values.get("google_api_key") - if isinstance(google_api_key, SecretStr): - google_api_key = google_api_key.get_secret_value() + if isinstance(self.google_api_key, SecretStr): + google_api_key: Optional[str] = self.google_api_key.get_secret_value() + else: + google_api_key = self.google_api_key client_info = get_client_info("GoogleGenerativeAIEmbeddings") - values["client"] = build_generative_service( - credentials=values.get("credentials"), + self.client = build_generative_service( + credentials=self.credentials, api_key=google_api_key, client_info=client_info, - client_options=values.get("client_options"), + client_options=self.client_options, ) - return values + return self @staticmethod def _split_by_punctuation(text: str) -> List[str]: diff --git a/libs/genai/langchain_google_genai/genai_aqa.py b/libs/genai/langchain_google_genai/genai_aqa.py index c339f4d0..9402f803 100644 --- a/libs/genai/langchain_google_genai/genai_aqa.py +++ b/libs/genai/langchain_google_genai/genai_aqa.py @@ -9,9 +9,9 @@ from typing import Any, List, Optional import google.ai.generativelanguage as genai -from langchain_core.pydantic_v1 import BaseModel, PrivateAttr from langchain_core.runnables import RunnableSerializable from langchain_core.runnables.config import RunnableConfig +from pydantic import BaseModel, PrivateAttr from . import _genai_extension as genaix diff --git a/libs/genai/langchain_google_genai/google_vector_store.py b/libs/genai/langchain_google_genai/google_vector_store.py index 79c75d15..ce584a88 100644 --- a/libs/genai/langchain_google_genai/google_vector_store.py +++ b/libs/genai/langchain_google_genai/google_vector_store.py @@ -21,9 +21,9 @@ import google.ai.generativelanguage as genai from langchain_core.documents import Document from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, PrivateAttr from langchain_core.runnables import Runnable, RunnableLambda, RunnablePassthrough from langchain_core.vectorstores import VectorStore +from pydantic import BaseModel, PrivateAttr from . import _genai_extension as genaix from .genai_aqa import ( @@ -467,7 +467,7 @@ def as_aqa(self, **kwargs: Any) -> Runnable[str, AqaOutput]: return ( RunnablePassthrough[str]() | { - "prompt": RunnablePassthrough(), + "prompt": RunnablePassthrough(), # type: ignore[dict-item] "passages": self.as_retriever(), } | RunnableLambda(_toAqaInput) diff --git a/libs/genai/langchain_google_genai/llms.py b/libs/genai/langchain_google_genai/llms.py index ffad41ca..18e208ff 100644 --- a/libs/genai/langchain_google_genai/llms.py +++ b/libs/genai/langchain_google_genai/llms.py @@ -12,8 +12,9 @@ from langchain_core.language_models import LangSmithParams, LanguageModelInput from langchain_core.language_models.llms import BaseLLM, create_base_retry_decorator from langchain_core.outputs import Generation, GenerationChunk, LLMResult -from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator from langchain_core.utils import secret_from_env +from pydantic import BaseModel, Field, SecretStr, model_validator +from typing_extensions import Self from langchain_google_genai._enums import ( HarmBlockThreshold, @@ -216,28 +217,29 @@ class GoogleGenerativeAI(_BaseGoogleGenerativeAI, BaseLLM): client: Any = None #: :meta private: - @root_validator(pre=False, skip_on_failure=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_environment(self) -> Self: """Validates params and passes them to google-generativeai package.""" - if values.get("credentials"): + if self.credentials: genai.configure( - credentials=values.get("credentials"), - transport=values.get("transport"), - client_options=values.get("client_options"), + credentials=self.credentials, + transport=self.transport, + client_options=self.client_options, ) else: - google_api_key = values.get("google_api_key") - if isinstance(google_api_key, SecretStr): - google_api_key = google_api_key.get_secret_value() + if isinstance(self.google_api_key, SecretStr): + google_api_key: Optional[str] = self.google_api_key.get_secret_value() + else: + google_api_key = self.google_api_key genai.configure( api_key=google_api_key, - transport=values.get("transport"), - client_options=values.get("client_options"), + transport=self.transport, + client_options=self.client_options, ) - model_name = values["model"] + model_name = self.model - safety_settings = values["safety_settings"] + safety_settings = self.safety_settings if safety_settings and ( not GoogleModelFamily(model_name) == GoogleModelFamily.GEMINI @@ -245,28 +247,28 @@ def validate_environment(cls, values: Dict) -> Dict: raise ValueError("Safety settings are only supported for Gemini models") if GoogleModelFamily(model_name) == GoogleModelFamily.GEMINI: - values["client"] = genai.GenerativeModel( + self.client = genai.GenerativeModel( model_name=model_name, safety_settings=safety_settings ) else: - values["client"] = genai + self.client = genai - if values["temperature"] is not None and not 0 <= values["temperature"] <= 1: + if self.temperature is not None and not 0 <= self.temperature <= 1: raise ValueError("temperature must be in the range [0.0, 1.0]") - if values["top_p"] is not None and not 0 <= values["top_p"] <= 1: + if self.top_p is not None and not 0 <= self.top_p <= 1: raise ValueError("top_p must be in the range [0.0, 1.0]") - if values["top_k"] is not None and values["top_k"] <= 0: + if self.top_k is not None and self.top_k <= 0: raise ValueError("top_k must be positive") - if values["max_output_tokens"] is not None and values["max_output_tokens"] <= 0: + if self.max_output_tokens is not None and self.max_output_tokens <= 0: raise ValueError("max_output_tokens must be greater than zero") - if values["timeout"] is not None and values["timeout"] <= 0: + if self.timeout is not None and self.timeout <= 0: raise ValueError("timeout must be greater than zero") - return values + return self def _get_ls_params( self, stop: Optional[List[str]] = None, **kwargs: Any diff --git a/libs/genai/poetry.lock b/libs/genai/poetry.lock index 24b56466..55cc5221 100644 --- a/libs/genai/poetry.lock +++ b/libs/genai/poetry.lock @@ -547,19 +547,19 @@ files = [ [[package]] name = "langchain-core" -version = "0.2.33" +version = "0.3.0" description = "Building applications with LLMs through composability" optional = false -python-versions = ">=3.8.1,<4.0" +python-versions = ">=3.9,<4.0" files = [] develop = false [package.dependencies] jsonpatch = "^1.33" -langsmith = "^0.1.75" +langsmith = "^0.1.117" packaging = ">=23.2,<25" pydantic = [ - {version = ">=1,<3", markers = "python_full_version < \"3.12.4\""}, + {version = ">=2.5.2,<3.0.0", markers = "python_full_version < \"3.12.4\""}, {version = ">=2.7.4,<3.0.0", markers = "python_full_version >= \"3.12.4\""}, ] PyYAML = ">=5.3" @@ -570,7 +570,7 @@ typing-extensions = ">=4.7" type = "git" url = "https://github.com/langchain-ai/langchain.git" reference = "HEAD" -resolved_reference = "c1bd4e05bcd180a36ba7b5d89ca2b5ce4f1e5d4a" +resolved_reference = "d1462badaf6f4eb0353a640e00ed499ec23ec784" subdirectory = "libs/core" [[package]] @@ -578,34 +578,36 @@ name = "langchain-standard-tests" version = "0.1.1" description = "Standard tests for LangChain implementations" optional = false -python-versions = ">=3.8.1,<4.0" +python-versions = ">=3.9,<4.0" files = [] develop = false [package.dependencies] httpx = "^0.27.0" -langchain-core = ">=0.1.40,<0.3" +langchain-core = ">=0.3.0.dev1" pytest = ">=7,<9" +syrupy = "^4" [package.source] type = "git" url = "https://github.com/langchain-ai/langchain.git" reference = "HEAD" -resolved_reference = "b83f1eb0d56dbf99605a1fce93953ee09d77aabf" +resolved_reference = "d1462badaf6f4eb0353a640e00ed499ec23ec784" subdirectory = "libs/standard-tests" [[package]] name = "langsmith" 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(>=1.4.0,<2)"] +mypyc = ["setuptools (>=50)"] reports = ["lxml"] [[package]] @@ -1537,4 +1536,4 @@ images = ["pillow"] [metadata] lock-version = "2.0" python-versions = ">=3.9,<4.0" -content-hash = "6cdb00c9e430e592cea5d0bd158053ceb9e7e64734345fbb75cac364a8ee30c3" +content-hash = "a48fe92c234e30ff78e298a8f09acd6d586f1c14e266f64104fbc8539fccb1ba" diff --git a/libs/genai/pyproject.toml b/libs/genai/pyproject.toml index 300662dc..bffe660d 100644 --- a/libs/genai/pyproject.toml +++ b/libs/genai/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langchain-google-genai" -version = "1.0.10" +version = "2.0.0" description = "An integration package connecting Google's genai package and LangChain" authors = [] readme = "README.md" @@ -12,9 +12,10 @@ license = "MIT" [tool.poetry.dependencies] python = ">=3.9,<4.0" -langchain-core = ">=0.2.33,<0.3" +langchain-core = ">=0.3.0,<0.4" google-generativeai = "^0.7.0" pillow = { version = "^10.1.0", optional = true } +pydantic = ">=2,<3" [tool.poetry.extras] images = ["pillow"] @@ -56,7 +57,7 @@ optional = true ruff = "^0.1.5" [tool.poetry.group.typing.dependencies] -mypy = "^0.991" +mypy = "^1.10" types-requests = "^2.28.11.5" types-google-cloud-ndb = "^2.2.0.1" types-pillow = "^10.1.0.2" diff --git a/libs/genai/scripts/check_pydantic.sh b/libs/genai/scripts/check_pydantic.sh deleted file mode 100755 index 06b5bb81..00000000 --- a/libs/genai/scripts/check_pydantic.sh +++ /dev/null @@ -1,27 +0,0 @@ -#!/bin/bash -# -# This script searches for lines starting with "import pydantic" or "from pydantic" -# in tracked files within a Git repository. -# -# Usage: ./scripts/check_pydantic.sh /path/to/repository - -# Check if a path argument is provided -if [ $# -ne 1 ]; then - echo "Usage: $0 /path/to/repository" - exit 1 -fi - -repository_path="$1" - -# Search for lines matching the pattern within the specified repository -result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic') - -# Check if any matching lines were found -if [ -n "$result" ]; then - echo "ERROR: The following lines need to be updated:" - echo "$result" - echo "Please replace the code with an import from langchain_core.pydantic_v1." - echo "For example, replace 'from pydantic import BaseModel'" - echo "with 'from langchain_core.pydantic_v1 import BaseModel'" - exit 1 -fi diff --git a/libs/genai/tests/integration_tests/test_chat_models.py b/libs/genai/tests/integration_tests/test_chat_models.py index 06077c3e..dc62d09a 100644 --- a/libs/genai/tests/integration_tests/test_chat_models.py +++ b/libs/genai/tests/integration_tests/test_chat_models.py @@ -2,7 +2,7 @@ import asyncio import json -from typing import Generator, List, Optional +from typing import Dict, Generator, List, Optional import pytest from langchain_core.messages import ( @@ -14,8 +14,8 @@ SystemMessage, ToolMessage, ) -from langchain_core.pydantic_v1 import BaseModel from langchain_core.tools import tool +from pydantic import BaseModel from langchain_google_genai import ( ChatGoogleGenerativeAI, @@ -244,8 +244,8 @@ def test_generativeai_get_num_tokens_gemini() -> None: def test_safety_settings_gemini() -> None: - safety_settings = { - HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE, + safety_settings: Dict[HarmCategory, HarmBlockThreshold] = { + HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE # type: ignore[dict-item] } # test with safety filters on bind llm = ChatGoogleGenerativeAI(temperature=0, model="gemini-pro").bind( @@ -286,8 +286,8 @@ def search( tools = [search] - safety = { - HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_ONLY_HIGH + safety: Dict[HarmCategory, HarmBlockThreshold] = { + HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_ONLY_HIGH # type: ignore[dict-item] } llm = ChatGoogleGenerativeAI( model="models/gemini-1.5-pro-latest", safety_settings=safety @@ -358,8 +358,8 @@ class MyModel(BaseModel): name: str age: int - safety = { - HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_ONLY_HIGH + safety: Dict[HarmCategory, HarmBlockThreshold] = { + HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_ONLY_HIGH # type: ignore[dict-item] } # Test .bind_tools with BaseModel message = HumanMessage(content="My name is Erick and I am 27 years old") @@ -418,8 +418,8 @@ class MyModel(BaseModel): name: str age: int - safety = { - HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_ONLY_HIGH + safety: Dict[HarmCategory, HarmBlockThreshold] = { + HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_ONLY_HIGH # type: ignore[dict-item] } llm = ChatGoogleGenerativeAI(model=model_name, safety_settings=safety) model = llm.with_structured_output(MyModel) @@ -430,7 +430,11 @@ class MyModel(BaseModel): assert response == MyModel(name="Erick", age=27) model = llm.with_structured_output( - {"name": "MyModel", "description": "MyModel", "parameters": MyModel.schema()} + { + "name": "MyModel", + "description": "MyModel", + "parameters": MyModel.model_json_schema(), + } ) response = model.invoke([message]) expected = [ diff --git a/libs/genai/tests/integration_tests/test_embeddings.py b/libs/genai/tests/integration_tests/test_embeddings.py index 1412ab18..07fe2195 100644 --- a/libs/genai/tests/integration_tests/test_embeddings.py +++ b/libs/genai/tests/integration_tests/test_embeddings.py @@ -1,6 +1,6 @@ import numpy as np import pytest -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from langchain_google_genai._common import GoogleGenerativeAIError from langchain_google_genai.embeddings import GoogleGenerativeAIEmbeddings diff --git a/libs/genai/tests/integration_tests/test_function_call.py b/libs/genai/tests/integration_tests/test_function_call.py index 57d0005a..27dbb283 100644 --- a/libs/genai/tests/integration_tests/test_function_call.py +++ b/libs/genai/tests/integration_tests/test_function_call.py @@ -3,8 +3,8 @@ import json from langchain_core.messages import AIMessage -from langchain_core.pydantic_v1 import BaseModel from langchain_core.tools import tool +from pydantic import BaseModel from langchain_google_genai.chat_models import ( ChatGoogleGenerativeAI, diff --git a/libs/genai/tests/integration_tests/test_llms.py b/libs/genai/tests/integration_tests/test_llms.py index deb90b80..b816c35c 100644 --- a/libs/genai/tests/integration_tests/test_llms.py +++ b/libs/genai/tests/integration_tests/test_llms.py @@ -4,7 +4,7 @@ valid API key. """ -from typing import Generator +from typing import Dict, Generator import pytest from langchain_core.outputs import LLMResult @@ -23,7 +23,7 @@ def test_google_generativeai_call(model_name: str) -> None: if model_name: llm = GoogleGenerativeAI(max_output_tokens=10, model=model_name) else: - llm = GoogleGenerativeAI(max_output_tokens=10) + llm = GoogleGenerativeAI(max_output_tokens=10) # type: ignore[call-arg] output = llm("Say foo:") assert isinstance(output, str) assert llm._llm_type == "google_palm" @@ -69,8 +69,8 @@ def test_safety_settings_gemini() -> None: assert len(output.generations[0]) > 0 # safety filters - safety_settings = { - HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE, + safety_settings: Dict[HarmCategory, HarmBlockThreshold] = { + HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE, # type: ignore[dict-item] } # test with safety filters directly to generate diff --git a/libs/genai/tests/unit_tests/__snapshots__/test_standard.ambr b/libs/genai/tests/unit_tests/__snapshots__/test_standard.ambr new file mode 100644 index 00000000..9a6f4aad --- /dev/null +++ b/libs/genai/tests/unit_tests/__snapshots__/test_standard.ambr @@ -0,0 +1,57 @@ +# serializer version: 1 +# name: TestGeminiAIStandard.test_serdes[serialized] + dict({ + 'id': list([ + 'langchain_google_genai', + 'chat_models', + 'ChatGoogleGenerativeAI', + ]), + 'kwargs': dict({ + 'default_metadata': list([ + ]), + 'google_api_key': dict({ + 'id': list([ + 'GOOGLE_API_KEY', + ]), + 'lc': 1, + 'type': 'secret', + }), + 'max_retries': 2, + 'model': 'models/gemini-1.0-pro-001', + 'n': 1, + 'temperature': 0.0, + 'timeout': 60.0, + }), + 'lc': 1, + 'name': 'ChatGoogleGenerativeAI', + 'type': 'constructor', + }) +# --- +# name: TestGemini_15_AIStandard.test_serdes[serialized] + dict({ + 'id': list([ + 'langchain_google_genai', + 'chat_models', + 'ChatGoogleGenerativeAI', + ]), + 'kwargs': dict({ + 'default_metadata': list([ + ]), + 'google_api_key': dict({ + 'id': list([ + 'GOOGLE_API_KEY', + ]), + 'lc': 1, + 'type': 'secret', + }), + 'max_retries': 2, + 'model': 'models/gemini-1.5-pro-001', + 'n': 1, + 'temperature': 0.0, + 'timeout': 60.0, + }), + 'lc': 1, + 'name': 'ChatGoogleGenerativeAI', + 'type': 'constructor', + }) +# --- diff --git a/libs/genai/tests/unit_tests/test_chat_models.py b/libs/genai/tests/unit_tests/test_chat_models.py index 36dd13e5..d23bfc31 100644 --- a/libs/genai/tests/unit_tests/test_chat_models.py +++ b/libs/genai/tests/unit_tests/test_chat_models.py @@ -23,7 +23,7 @@ ToolMessage, ) from langchain_core.messages.tool import tool_call as create_tool_call -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import CaptureFixture from langchain_google_genai.chat_models import ( @@ -37,7 +37,7 @@ def test_integration_initialization() -> None: """Test chat model initialization.""" llm = ChatGoogleGenerativeAI( model="gemini-nano", - google_api_key=SecretStr("..."), + google_api_key=SecretStr("..."), # type: ignore[call-arg] top_k=2, top_p=1, temperature=0.7, @@ -53,7 +53,7 @@ def test_integration_initialization() -> None: llm = ChatGoogleGenerativeAI( model="gemini-nano", - google_api_key=SecretStr("..."), + google_api_key=SecretStr("..."), # type: ignore[call-arg] max_output_tokens=10, ) ls_params = llm._get_ls_params() @@ -67,7 +67,7 @@ def test_integration_initialization() -> None: ChatGoogleGenerativeAI( model="gemini-nano", - google_api_key=SecretStr("..."), + api_key=SecretStr("..."), top_k=2, top_p=1, temperature=0.7, @@ -81,13 +81,14 @@ def test_initialization_inside_threadpool() -> None: executor.submit( ChatGoogleGenerativeAI, model="gemini-nano", - google_api_key=SecretStr("secret-api-key"), + google_api_key=SecretStr("secret-api-key"), # type: ignore[call-arg] ).result() def test_initalization_without_async() -> None: chat = ChatGoogleGenerativeAI( - model="gemini-nano", google_api_key=SecretStr("secret-api-key") + model="gemini-nano", + google_api_key=SecretStr("secret-api-key"), # type: ignore[call-arg] ) assert chat.async_client is None @@ -95,7 +96,8 @@ def test_initalization_without_async() -> None: def test_initialization_with_async() -> None: async def initialize_chat_with_async_client() -> ChatGoogleGenerativeAI: return ChatGoogleGenerativeAI( - model="gemini-nano", google_api_key=SecretStr("secret-api-key") + model="gemini-nano", + google_api_key=SecretStr("secret-api-key"), # type: ignore[call-arg] ) loop = asyncio.get_event_loop() @@ -105,14 +107,16 @@ async def initialize_chat_with_async_client() -> ChatGoogleGenerativeAI: def test_api_key_is_string() -> None: chat = ChatGoogleGenerativeAI( - model="gemini-nano", google_api_key=SecretStr("secret-api-key") + model="gemini-nano", + google_api_key=SecretStr("secret-api-key"), # type: ignore[call-arg] ) assert isinstance(chat.google_api_key, SecretStr) def test_api_key_masked_when_passed_via_constructor(capsys: CaptureFixture) -> None: chat = ChatGoogleGenerativeAI( - model="gemini-nano", google_api_key=SecretStr("secret-api-key") + model="gemini-nano", + google_api_key=SecretStr("secret-api-key"), # type: ignore[call-arg] ) print(chat.google_api_key, end="") # noqa: T201 captured = capsys.readouterr() @@ -265,7 +269,7 @@ def test_additional_headers_support(headers: Optional[Dict[str, str]]) -> None: ): chat = ChatGoogleGenerativeAI( model="gemini-pro", - google_api_key=param_secret_api_key, + google_api_key=param_secret_api_key, # type: ignore[call-arg] client_options=param_client_options, transport=param_transport, additional_headers=headers, @@ -541,11 +545,15 @@ def test_parse_response_candidate(raw_candidate: Dict, expected: AIMessage) -> N def test_serialize() -> None: - llm = ChatGoogleGenerativeAI(model="gemini-pro-1.5", google_api_key="test-key") + llm = ChatGoogleGenerativeAI(model="gemini-pro-1.5", google_api_key="test-key") # type: ignore[call-arg] serialized = dumps(llm) llm_loaded = loads( serialized, secrets_map={"GOOGLE_API_KEY": "test-key"}, valid_namespaces=["langchain_google_genai"], ) + # Pydantic 2 equality will fail on complex attributes like clients with + # different IDs + llm.client = None + llm_loaded.client = None assert llm == llm_loaded diff --git a/libs/genai/tests/unit_tests/test_embeddings.py b/libs/genai/tests/unit_tests/test_embeddings.py index ef901bd9..1b0ebb6e 100644 --- a/libs/genai/tests/unit_tests/test_embeddings.py +++ b/libs/genai/tests/unit_tests/test_embeddings.py @@ -8,7 +8,7 @@ ContentEmbedding, EmbedContentRequest, ) -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import CaptureFixture from langchain_google_genai.embeddings import GoogleGenerativeAIEmbeddings diff --git a/libs/genai/tests/unit_tests/test_function_utils.py b/libs/genai/tests/unit_tests/test_function_utils.py index 786ad9f5..3e8a5e88 100644 --- a/libs/genai/tests/unit_tests/test_function_utils.py +++ b/libs/genai/tests/unit_tests/test_function_utils.py @@ -2,8 +2,8 @@ import google.ai.generativelanguage as glm import pytest -from langchain_core.pydantic_v1 import BaseModel from langchain_core.tools import tool +from pydantic import BaseModel from langchain_google_genai._function_utils import ( _tool_choice_to_tool_config, diff --git a/libs/genai/tests/unit_tests/test_llms.py b/libs/genai/tests/unit_tests/test_llms.py index c4e4327f..29a68980 100644 --- a/libs/genai/tests/unit_tests/test_llms.py +++ b/libs/genai/tests/unit_tests/test_llms.py @@ -10,7 +10,7 @@ def test_model_family() -> None: def test_tracing_params() -> None: # Test standard tracing params - llm = GoogleGenerativeAI(model="gemini-pro", google_api_key="foo") + llm = GoogleGenerativeAI(model="gemini-pro", google_api_key="foo") # type: ignore[call-arg] ls_params = llm._get_ls_params() assert ls_params == { "ls_provider": "google_genai", @@ -20,7 +20,10 @@ def test_tracing_params() -> None: } llm = GoogleGenerativeAI( - model="gemini-pro", temperature=0.1, max_output_tokens=10, google_api_key="foo" + model="gemini-pro", + temperature=0.1, + max_output_tokens=10, + google_api_key="foo", # type: ignore[call-arg] ) ls_params = llm._get_ls_params() assert ls_params == { diff --git a/libs/genai/tests/unit_tests/test_standard.py b/libs/genai/tests/unit_tests/test_standard.py index fd8b79cc..098bf053 100644 --- a/libs/genai/tests/unit_tests/test_standard.py +++ b/libs/genai/tests/unit_tests/test_standard.py @@ -1,4 +1,4 @@ -from typing import Type +from typing import Tuple, Type from langchain_core.language_models import BaseChatModel from langchain_standard_tests.unit_tests import ChatModelUnitTests @@ -15,6 +15,14 @@ def chat_model_class(self) -> Type[BaseChatModel]: def chat_model_params(self) -> dict: return {"model": "models/gemini-1.0-pro-001"} + @property + def init_from_env_params(self) -> Tuple[dict, dict, dict]: + return ( + {"GOOGLE_API_KEY": "api_key"}, + self.chat_model_params, + {"google_api_key": "api_key"}, + ) + class TestGemini_15_AIStandard(ChatModelUnitTests): @property @@ -24,3 +32,11 @@ def chat_model_class(self) -> Type[BaseChatModel]: @property def chat_model_params(self) -> dict: return {"model": "models/gemini-1.5-pro-001"} + + @property + def init_from_env_params(self) -> Tuple[dict, dict, dict]: + return ( + {"GOOGLE_API_KEY": "api_key"}, + self.chat_model_params, + {"google_api_key": "api_key"}, + ) diff --git a/libs/vertexai/Makefile b/libs/vertexai/Makefile index d86dffd2..e68f0a70 100644 --- a/libs/vertexai/Makefile +++ b/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 diff --git a/libs/vertexai/langchain_google_vertexai/_anthropic_parsers.py b/libs/vertexai/langchain_google_vertexai/_anthropic_parsers.py index d401f78f..a31759bf 100644 --- a/libs/vertexai/langchain_google_vertexai/_anthropic_parsers.py +++ b/libs/vertexai/langchain_google_vertexai/_anthropic_parsers.py @@ -4,7 +4,7 @@ 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): @@ -12,8 +12,9 @@ class ToolsOutputParser(BaseGenerationOutputParser): 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. diff --git a/libs/vertexai/langchain_google_vertexai/_anthropic_utils.py b/libs/vertexai/langchain_google_vertexai/_anthropic_utils.py index e6205d79..976398fa 100644 --- a/libs/vertexai/langchain_google_vertexai/_anthropic_utils.py +++ b/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 diff --git a/libs/vertexai/langchain_google_vertexai/_base.py b/libs/vertexai/langchain_google_vertexai/_base.py index 9bcd09a1..d5ac6bfe 100644 --- a/libs/vertexai/langchain_google_vertexai/_base.py +++ b/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: diff --git a/libs/vertexai/langchain_google_vertexai/chains.py b/libs/vertexai/langchain_google_vertexai/chains.py index a6f63455..6f3c2680 100644 --- a/libs/vertexai/langchain_google_vertexai/chains.py +++ b/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): diff --git a/libs/vertexai/langchain_google_vertexai/chat_models.py b/libs/vertexai/langchain_google_vertexai/chat_models.py index ad60efe2..fba782ce 100644 --- a/libs/vertexai/langchain_google_vertexai/chat_models.py +++ b/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 diff --git a/libs/vertexai/langchain_google_vertexai/embeddings.py b/libs/vertexai/langchain_google_vertexai/embeddings.py index ce3f03df..e2fb6e3e 100644 --- a/libs/vertexai/langchain_google_vertexai/embeddings.py +++ b/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, diff --git a/libs/vertexai/langchain_google_vertexai/functions_utils.py b/libs/vertexai/langchain_google_vertexai/functions_utils.py index 455d298c..abdf1a56 100644 --- a/libs/vertexai/langchain_google_vertexai/functions_utils.py +++ b/libs/vertexai/langchain_google_vertexai/functions_utils.py @@ -21,7 +21,6 @@ from langchain_core.exceptions import OutputParserException from langchain_core.output_parsers import BaseOutputParser from langchain_core.outputs import ChatGeneration, Generation -from langchain_core.pydantic_v1 import BaseModel from langchain_core.tools import BaseTool from langchain_core.tools import tool as callable_as_lc_tool from langchain_core.utils.function_calling import ( @@ -29,6 +28,7 @@ convert_to_openai_tool, ) from langchain_core.utils.json_schema import dereference_refs +from pydantic import BaseModel logger = logging.getLogger(__name__) @@ -69,18 +69,19 @@ class _ToolDictLike(TypedDict): _ALLOWED_SCHEMA_FIELDS_SET = set(_ALLOWED_SCHEMA_FIELDS) -def _format_json_schema_to_gapic(schema: Dict[str, Any]) -> Dict[str, Any]: +def _format_json_schema_to_gapic_v1(schema: Dict[str, Any]) -> Dict[str, Any]: + """Format a JSON schema from a Pydantic V1 BaseModel to gapic.""" converted_schema: Dict[str, Any] = {} for key, value in schema.items(): if key == "definitions": continue elif key == "items": - converted_schema["items"] = _format_json_schema_to_gapic(value) + converted_schema["items"] = _format_json_schema_to_gapic_v1(value) elif key == "properties": if "properties" not in converted_schema: converted_schema["properties"] = {} for pkey, pvalue in value.items(): - converted_schema["properties"][pkey] = _format_json_schema_to_gapic( + converted_schema["properties"][pkey] = _format_json_schema_to_gapic_v1( pvalue ) continue @@ -92,7 +93,57 @@ def _format_json_schema_to_gapic(schema: Dict[str, Any]) -> Dict[str, Any]: "Only first value for 'allOf' key is supported. " f"Got {len(value)}, ignoring other than first value!" ) - return _format_json_schema_to_gapic(value[0]) + return _format_json_schema_to_gapic_v1(value[0]) + elif key not in _ALLOWED_SCHEMA_FIELDS_SET: + logger.warning(f"Key '{key}' is not supported in schema, ignoring") + else: + converted_schema[key] = value + return converted_schema + + +def _format_json_schema_to_gapic( + schema: Dict[str, Any], + parent_key: Optional[str] = None, + required_fields: Optional[list] = None, +) -> Dict[str, Any]: + """Format a JSON schema from a Pydantic V2 BaseModel to gapic.""" + converted_schema: Dict[str, Any] = {} + for key, value in schema.items(): + if key == "definitions": + continue + elif key == "items": + converted_schema["items"] = _format_json_schema_to_gapic( + value, parent_key, required_fields + ) + elif key == "properties": + if "properties" not in converted_schema: + converted_schema["properties"] = {} + for pkey, pvalue in value.items(): + converted_schema["properties"][pkey] = _format_json_schema_to_gapic( + pvalue, pkey, schema.get("required", []) + ) + continue + elif key in ["type", "_type"]: + converted_schema["type"] = str(value).upper() + elif key == "allOf": + if len(value) > 1: + logger.warning( + "Only first value for 'allOf' key is supported. " + f"Got {len(value)}, ignoring other than first value!" + ) + return _format_json_schema_to_gapic(value[0], parent_key, required_fields) + elif key == "anyOf": + if len(value) == 2 and any(v.get("type") == "null" for v in value): + non_null_type = next(v for v in value if v.get("type") != "null") + converted_schema.update( + _format_json_schema_to_gapic( + non_null_type, parent_key, required_fields + ) + ) + # Remove the field from required if it exists + if required_fields and parent_key in required_fields: + required_fields.remove(parent_key) + continue elif key not in _ALLOWED_SCHEMA_FIELDS_SET: logger.warning(f"Key '{key}' is not supported in schema, ignoring") else: @@ -100,9 +151,14 @@ def _format_json_schema_to_gapic(schema: Dict[str, Any]) -> Dict[str, Any]: return converted_schema -def _dict_to_gapic_schema(schema: Dict[str, Any]) -> gapic.Schema: +def _dict_to_gapic_schema( + schema: Dict[str, Any], pydantic_version: str = "v1" +) -> gapic.Schema: dereferenced_schema = dereference_refs(schema) - formatted_schema = _format_json_schema_to_gapic(dereferenced_schema) + if pydantic_version == "v1": + formatted_schema = _format_json_schema_to_gapic_v1(dereferenced_schema) + else: + formatted_schema = _format_json_schema_to_gapic(dereferenced_schema) json_schema = json.dumps(formatted_schema) return gapic.Schema.from_json(json_schema) @@ -124,8 +180,14 @@ def _format_base_tool_to_function_declaration( ), ) - schema = tool.args_schema.schema() - parameters = _dict_to_gapic_schema(schema) + if hasattr(tool.args_schema, "model_json_schema"): + schema = tool.args_schema.model_json_schema() + pydantic_version = "v2" + else: + schema = tool.args_schema.schema() + pydantic_version = "v1" + + parameters = _dict_to_gapic_schema(schema, pydantic_version=pydantic_version) return gapic.FunctionDeclaration( name=tool.name or schema.get("title"), @@ -137,22 +199,41 @@ def _format_base_tool_to_function_declaration( def _format_pydantic_to_function_declaration( pydantic_model: Type[BaseModel], ) -> gapic.FunctionDeclaration: - schema = pydantic_model.schema() + if hasattr(pydantic_model, "model_json_schema"): + schema = pydantic_model.model_json_schema() + pydantic_version = "v2" + else: + schema = pydantic_model.schema() + pydantic_version = "v1" return gapic.FunctionDeclaration( name=schema["title"], description=schema.get("description", ""), - parameters=_dict_to_gapic_schema(schema), + parameters=_dict_to_gapic_schema(schema, pydantic_version=pydantic_version), ) def _format_dict_to_function_declaration( tool: Union[FunctionDescription, Dict[str, Any]], ) -> gapic.FunctionDeclaration: + # Ensure we send "anyOf" parameters through pydantic v2 schema parsing + pydantic_version = None + if isinstance(tool, dict): + properties = tool.get("parameters", {}).get("properties", {}).values() + for property in properties: + if "anyOf" in property: + pydantic_version = "v2" + if pydantic_version: + parameters = _dict_to_gapic_schema( + tool.get("parameters", {}), pydantic_version=pydantic_version + ) + else: + parameters = _dict_to_gapic_schema(tool.get("parameters", {})) + return gapic.FunctionDeclaration( name=tool.get("name"), description=tool.get("description"), - parameters=_dict_to_gapic_schema(tool.get("parameters", {})), + parameters=parameters, ) diff --git a/libs/vertexai/langchain_google_vertexai/gemma.py b/libs/vertexai/langchain_google_vertexai/gemma.py index 55e945e8..a854af05 100644 --- a/libs/vertexai/langchain_google_vertexai/gemma.py +++ b/libs/vertexai/langchain_google_vertexai/gemma.py @@ -21,7 +21,8 @@ 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 langchain_google_vertexai._base import _BaseVertexAIModelGarden from langchain_google_vertexai._utils import enforce_stop_tokens @@ -76,6 +77,8 @@ class _GemmaBase(BaseModel): top_k: Optional[int] = None """The top-k value to use for sampling.""" + model_config = ConfigDict(protected_namespaces=()) + @property def _default_params(self) -> Dict[str, Any]: """Get the default parameters for calling gemma.""" @@ -127,10 +130,10 @@ 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 + model_config = ConfigDict( + populate_by_name=True, + protected_namespaces=(), + ) @property def _llm_type(self) -> str: @@ -200,10 +203,9 @@ class _GemmaLocalKaggleBase(_GemmaBase): model_name: str = Field(default="gemma_2b_en", alias="model") """Gemma model name.""" - class Config: - """Configuration for this pydantic object.""" - - allow_population_by_field_name = True + model_config = ConfigDict( + populate_by_name=True, + ) def __init__(self, *, model_name: Optional[str] = None, **kwargs: Any) -> None: """Needed for mypy typing to recognize model_name as a valid arg.""" @@ -211,11 +213,11 @@ def __init__(self, *, model_name: Optional[str] = None, **kwargs: Any) -> None: kwargs["model_name"] = model_name super().__init__(**kwargs) - @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 llama-cpp-python library is installed.""" try: - os.environ["KERAS_BACKEND"] = values["keras_backend"] + os.environ["KERAS_BACKEND"] = self.keras_backend from keras_nlp.models import GemmaCausalLM # type: ignore except ImportError: raise ImportError( @@ -224,8 +226,8 @@ def validate_environment(cls, values: Dict) -> Dict: "use this model: pip install keras-nlp keras>=3 kaggle" ) - values["client"] = GemmaCausalLM.from_preset(values["model_name"]) - return values + self.client = GemmaCausalLM.from_preset(self.model_name) + return self @property def _default_params(self) -> Dict[str, Any]: @@ -239,7 +241,7 @@ def _get_params(self, **kwargs) -> Dict[str, Any]: return {**self._default_params, **params} -class GemmaLocalKaggle(_GemmaLocalKaggleBase, BaseLLM): # type: ignore +class GemmaLocalKaggle(_GemmaLocalKaggleBase, BaseLLM): """Local gemma chat model loaded from Kaggle.""" def __init__(self, *, model_name: Optional[str] = None, **kwargs: Any) -> None: @@ -269,7 +271,7 @@ def _llm_type(self) -> str: return "gemma_local_kaggle" -class GemmaChatLocalKaggle(_GemmaLocalKaggleBase, BaseChatModel): # type: ignore +class GemmaChatLocalKaggle(_GemmaLocalKaggleBase, BaseChatModel): parse_response: bool = False """Whether to post-process the chat response and clean repeations """ """or multi-turn statements.""" @@ -313,13 +315,12 @@ class _GemmaLocalHFBase(_GemmaBase): model_name: str = Field(default="google/gemma-2b", alias="model") """Gemma model name.""" - class Config: - """Configuration for this pydantic object.""" - - allow_population_by_field_name = True + model_config = ConfigDict( + populate_by_name=True, + ) - @root_validator() - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_environment(self) -> Self: """Validate that llama-cpp-python library is installed.""" try: from transformers import AutoTokenizer, GemmaForCausalLM # type: ignore @@ -330,15 +331,15 @@ def validate_environment(cls, values: Dict) -> Dict: "use this model: pip install transformers>=4.38.1" ) - values["tokenizer"] = AutoTokenizer.from_pretrained( - values["model_name"], token=values["hf_access_token"] + self.tokenizer = AutoTokenizer.from_pretrained( + self.model_name, token=self.hf_access_token ) - values["client"] = GemmaForCausalLM.from_pretrained( - values["model_name"], - token=values["hf_access_token"], - cache_dir=values["cache_dir"], + self.client = GemmaForCausalLM.from_pretrained( + self.model_name, + token=self.hf_access_token, + cache_dir=self.cache_dir, ) - return values + return self @property def _default_params(self) -> Dict[str, Any]: @@ -360,7 +361,7 @@ def _run(self, prompt: str, **kwargs: Any) -> str: )[0] -class GemmaLocalHF(_GemmaLocalHFBase, BaseLLM): # type: ignore +class GemmaLocalHF(_GemmaLocalHFBase, BaseLLM): """Local gemma model loaded from HuggingFace.""" def _generate( @@ -382,7 +383,7 @@ def _llm_type(self) -> str: return "gemma_local_hf" -class GemmaChatLocalHF(_GemmaLocalHFBase, BaseChatModel): # type: ignore +class GemmaChatLocalHF(_GemmaLocalHFBase, BaseChatModel): parse_response: bool = False """Whether to post-process the chat response and clean repeations """ """or multi-turn statements.""" diff --git a/libs/vertexai/langchain_google_vertexai/llms.py b/libs/vertexai/langchain_google_vertexai/llms.py index b9dc76db..303169fb 100644 --- a/libs/vertexai/langchain_google_vertexai/llms.py +++ b/libs/vertexai/langchain_google_vertexai/llms.py @@ -9,7 +9,8 @@ ) from langchain_core.language_models.llms import BaseLLM, LangSmithParams from langchain_core.outputs import Generation, GenerationChunk, LLMResult -from langchain_core.pydantic_v1 import Field, root_validator +from pydantic import ConfigDict, Field, model_validator +from typing_extensions import Self from vertexai.generative_models import ( # type: ignore[import-untyped] Candidate, GenerativeModel, @@ -120,10 +121,9 @@ 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 + model_config = ConfigDict( + populate_by_name=True, + ) @classmethod def is_lc_serializable(self) -> bool: @@ -134,19 +134,27 @@ def get_lc_namespace(cls) -> List[str]: """Get the namespace of the langchain object.""" return ["langchain", "llms", "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.""" - tuned_model_name = values.get("tuned_model_name") - safety_settings = values["safety_settings"] - values["model_family"] = GoogleModelFamily(values["model_name"]) - is_gemini = is_gemini_model(values["model_family"]) - cls._init_vertexai(values) + tuned_model_name = self.tuned_model_name or None + safety_settings = self.safety_settings + self.model_family = GoogleModelFamily(self.model_name) + is_gemini = 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 safety_settings and (not is_gemini or tuned_model_name): raise ValueError("Safety settings are only supported for Gemini models") - if values["model_family"] == GoogleModelFamily.CODEY: + if self.model_family == GoogleModelFamily.CODEY: model_cls = CodeGenerationModel preview_model_cls = PreviewCodeGenerationModel elif is_gemini: @@ -157,31 +165,31 @@ def validate_environment(cls, values: Dict) -> Dict: preview_model_cls = PreviewTextGenerationModel 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"] + generative_model_name = self.model_name if is_gemini: - values["client"] = model_cls( + self.client = model_cls( model_name=generative_model_name, safety_settings=safety_settings ) - values["client_preview"] = preview_model_cls( + self.client_preview = preview_model_cls( model_name=generative_model_name, safety_settings=safety_settings ) else: if tuned_model_name: - values["client"] = model_cls.get_tuned_model(generative_model_name) - values["client_preview"] = preview_model_cls.get_tuned_model( + self.client = model_cls.get_tuned_model(generative_model_name) + self.client_preview = preview_model_cls.get_tuned_model( generative_model_name ) else: - values["client"] = model_cls.from_pretrained(generative_model_name) - values["client_preview"] = preview_model_cls.from_pretrained( + self.client = model_cls.from_pretrained(generative_model_name) + self.client_preview = preview_model_cls.from_pretrained( generative_model_name ) - if values["streaming"] and values["n"] > 1: + if self.streaming and self.n > 1: raise ValueError("Only one candidate can be generated with streaming!") - return values + return self def _get_ls_params( self, stop: Optional[List[str]] = None, **kwargs: Any diff --git a/libs/vertexai/langchain_google_vertexai/model_garden.py b/libs/vertexai/langchain_google_vertexai/model_garden.py index 86d6f00e..4fee0562 100644 --- a/libs/vertexai/langchain_google_vertexai/model_garden.py +++ b/libs/vertexai/langchain_google_vertexai/model_garden.py @@ -39,13 +39,14 @@ Generation, LLMResult, ) -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator from langchain_core.runnables import ( Runnable, RunnableMap, RunnablePassthrough, ) from langchain_core.tools import BaseTool +from pydantic import BaseModel, ConfigDict, Field, model_validator +from typing_extensions import Self from langchain_google_vertexai._anthropic_parsers import ( ToolsOutputParser, @@ -63,10 +64,10 @@ class VertexAIModelGarden(_BaseVertexAIModelGarden, BaseLLM): """Large language models served from Vertex AI Model Garden.""" - class Config: - """Configuration for this pydantic object.""" - - allow_population_by_field_name = True + model_config = ConfigDict( + populate_by_name=True, + protected_namespaces=(), + ) # Needed so that mypy doesn't flag missing aliased init args. def __init__(self, **kwargs: Any) -> None: @@ -137,37 +138,36 @@ class ChatAnthropicVertex(_VertexAICommon, BaseChatModel): stream_usage: bool = True # Whether to include usage metadata in streaming output credentials: Optional[Credentials] = None - class Config: - """Configuration for this pydantic object.""" - - allow_population_by_field_name = True + model_config = ConfigDict( + populate_by_name=True, + ) # Needed so that mypy doesn't flag missing aliased init args. def __init__(self, **kwargs: Any) -> None: super().__init__(**kwargs) - @root_validator(pre=False, skip_on_failure=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_environment(self) -> Self: from anthropic import ( # type: ignore AnthropicVertex, AsyncAnthropicVertex, ) - values["client"] = AnthropicVertex( - project_id=values["project"], - region=values["location"], - max_retries=values["max_retries"], - access_token=values["access_token"], - credentials=values["credentials"], + self.client = AnthropicVertex( + project_id=self.project, + region=self.location, + max_retries=self.max_retries, + access_token=self.access_token, + credentials=self.credentials, ) - values["async_client"] = AsyncAnthropicVertex( - project_id=values["project"], - region=values["location"], - max_retries=values["max_retries"], - access_token=values["access_token"], - credentials=values["credentials"], + self.async_client = AsyncAnthropicVertex( + project_id=self.project, + region=self.location, + max_retries=self.max_retries, + access_token=self.access_token, + credentials=self.credentials, ) - return values + return self @property def _default_params(self): diff --git a/libs/vertexai/langchain_google_vertexai/model_garden_maas/_base.py b/libs/vertexai/langchain_google_vertexai/model_garden_maas/_base.py index 030b1c69..9129f2af 100644 --- a/libs/vertexai/langchain_google_vertexai/model_garden_maas/_base.py +++ b/libs/vertexai/langchain_google_vertexai/model_garden_maas/_base.py @@ -24,7 +24,8 @@ CallbackManagerForLLMRun, ) 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 langchain_google_vertexai._base import _VertexAIBase @@ -101,11 +102,10 @@ class _BaseVertexMaasModelGarden(_VertexAIBase): model_family: Optional[VertexMaaSModelFamily] = None timeout: int = 120 - 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, + ) def __init__(self, **kwargs): super().__init__(**kwargs) @@ -129,16 +129,16 @@ def __init__(self, **kwargs): timeout=self.timeout, ) - @root_validator(pre=True) - def validate_environment_model_garden(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_environment_model_garden(self) -> Self: """Validate that the python package exists in environment.""" - family = VertexMaaSModelFamily(values["model_name"]) - values["model_family"] = family + family = VertexMaaSModelFamily(self.model_name) + self.model_family = family if family == VertexMaaSModelFamily.MISTRAL: - model = values["model_name"].split("@")[0] - values["full_model_name"] = values["model_name"] - values["model_name"] = model - return values + model = self.model_name.split("@")[0] if self.model_name else None + self.full_model_name = self.model_name + self.model_name = model + return self def _enrich_params(self, params: Dict[str, Any]) -> Dict[str, Any]: """Fix params to be compliant with Vertex AI.""" diff --git a/libs/vertexai/langchain_google_vertexai/model_garden_maas/llama.py b/libs/vertexai/langchain_google_vertexai/model_garden_maas/llama.py index d80c4dd7..ebfdeaaa 100644 --- a/libs/vertexai/langchain_google_vertexai/model_garden_maas/llama.py +++ b/libs/vertexai/langchain_google_vertexai/model_garden_maas/llama.py @@ -114,7 +114,7 @@ def _parse_response_candidate_llama( ) -class VertexModelGardenLlama(_BaseVertexMaasModelGarden, BaseChatModel): # type: ignore[misc] +class VertexModelGardenLlama(_BaseVertexMaasModelGarden, BaseChatModel): """Integration for Llama 3.1 on Google Cloud Vertex AI Model-as-a-Service. For more information, see: diff --git a/libs/vertexai/langchain_google_vertexai/vision_models.py b/libs/vertexai/langchain_google_vertexai/vision_models.py index 499f5e58..8d5d0e8f 100644 --- a/libs/vertexai/langchain_google_vertexai/vision_models.py +++ b/libs/vertexai/langchain_google_vertexai/vision_models.py @@ -9,7 +9,7 @@ from langchain_core.outputs import ChatResult, LLMResult from langchain_core.outputs.chat_generation import ChatGeneration from langchain_core.outputs.generation import Generation -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, ConfigDict, Field from vertexai.preview.vision_models import ( # type: ignore[import-untyped] GeneratedImage, ImageGenerationModel, @@ -39,6 +39,8 @@ class _BaseImageTextModel(BaseModel): project: Union[str, None] = Field(default=None) """Google cloud project""" + model_config = ConfigDict(protected_namespaces=()) + @property def client(self) -> ImageTextModel: if self.cached_client is None: @@ -334,6 +336,8 @@ class _BaseVertexAIImageGenerator(BaseModel): project: Union[str, None] = Field(default=None) """Google cloud project id""" + model_config = ConfigDict(protected_namespaces=()) + @property def client(self) -> ImageGenerationModel: if not self.cached_client: diff --git a/libs/vertexai/poetry.lock b/libs/vertexai/poetry.lock index 5a63e1ec..f723777d 100644 --- a/libs/vertexai/poetry.lock +++ b/libs/vertexai/poetry.lock @@ -1,99 +1,114 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. 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b/libs/vertexai/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langchain-google-vertexai" -version = "1.0.10" +version = "2.0.0" description = "An integration package connecting Google VertexAI and LangChain" authors = [] readme = "README.md" @@ -11,15 +11,16 @@ license = "MIT" "Source Code" = "https://github.com/langchain-ai/langchain-google/tree/main/libs/vertexai" [tool.poetry.dependencies] -python = ">=3.8.1,<4.0" -langchain-core = ">=0.2.33,<0.3" +python = ">=3.9,<4.0" +langchain-core = ">=0.3.0,<0.4" google-cloud-aiplatform = "^1.56.0" google-cloud-storage = "^2.17.0" # optional dependencies anthropic = { extras = ["vertexai"], version = ">=0.30.0,<1", optional = true } -langchain-mistralai = { version = ">=0.1.12,<1", optional = true } +langchain-mistralai = { version = ">=0.2.0,<1", optional = true } httpx = "^0.27.0" httpx-sse = "^0.4.0" +pydantic = ">=2,<3" [tool.poetry.group.test] optional = true @@ -69,7 +70,7 @@ numexpr = { version = "^2.8.8", python = ">=3.9,<4.0" } google-api-python-client = "^2.114.0" google-cloud-datastore = "^2.19.0" langchain = { git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/langchain" } -langchain-mistralai = "^0.1.12" +langchain-mistralai = { git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/partners/mistralai"} [tool.poetry.group.lint] optional = true diff --git a/libs/vertexai/scripts/check_pydantic.sh b/libs/vertexai/scripts/check_pydantic.sh deleted file mode 100755 index 06b5bb81..00000000 --- a/libs/vertexai/scripts/check_pydantic.sh +++ /dev/null @@ -1,27 +0,0 @@ -#!/bin/bash -# -# This script searches for lines starting with "import pydantic" or "from pydantic" -# in tracked files within a Git repository. -# -# Usage: ./scripts/check_pydantic.sh /path/to/repository - -# Check if a path argument is provided -if [ $# -ne 1 ]; then - echo "Usage: $0 /path/to/repository" - exit 1 -fi - -repository_path="$1" - -# Search for lines matching the pattern within the specified repository -result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic') - -# Check if any matching lines were found -if [ -n "$result" ]; then - echo "ERROR: The following lines need to be updated:" - echo "$result" - echo "Please replace the code with an import from langchain_core.pydantic_v1." - echo "For example, replace 'from pydantic import BaseModel'" - echo "with 'from langchain_core.pydantic_v1 import BaseModel'" - exit 1 -fi diff --git a/libs/vertexai/tests/integration_tests/test_chains.py b/libs/vertexai/tests/integration_tests/test_chains.py index e2e3560f..295219f2 100644 --- a/libs/vertexai/tests/integration_tests/test_chains.py +++ b/libs/vertexai/tests/integration_tests/test_chains.py @@ -5,7 +5,7 @@ AIMessage, ) from langchain_core.prompts import ChatPromptTemplate -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain_google_vertexai import ChatVertexAI, create_structured_runnable from tests.integration_tests.conftest import _DEFAULT_MODEL_NAME diff --git a/libs/vertexai/tests/integration_tests/test_chat_models.py b/libs/vertexai/tests/integration_tests/test_chat_models.py index e58a3223..7965a5b7 100644 --- a/libs/vertexai/tests/integration_tests/test_chat_models.py +++ b/libs/vertexai/tests/integration_tests/test_chat_models.py @@ -22,9 +22,9 @@ ) from langchain_core.outputs import ChatGeneration, LLMResult from langchain_core.prompts import ChatPromptTemplate -from langchain_core.pydantic_v1 import BaseModel from langchain_core.rate_limiters import InMemoryRateLimiter from langchain_core.tools import tool +from pydantic import BaseModel from langchain_google_vertexai import ( ChatVertexAI, @@ -302,7 +302,7 @@ def get_climate_info(query: str): prompt=prompt_template, ) agent_executor = agents.AgentExecutor( # type: ignore[call-arg] - agent=agent, # type: ignore[arg-type] + agent=agent, tools=tools, # type: ignore[arg-type] verbose=False, stream_runnable=False, @@ -616,7 +616,11 @@ class MyModel(BaseModel): assert response == MyModel(name="Erick", age=27) model = llm.with_structured_output( - {"name": "MyModel", "description": "MyModel", "parameters": MyModel.schema()} + { + "name": "MyModel", + "description": "MyModel", + "parameters": MyModel.model_json_schema(), + } ) response = model.invoke([message]) assert response == { diff --git a/libs/vertexai/tests/integration_tests/test_model_garden.py b/libs/vertexai/tests/integration_tests/test_model_garden.py index d0a2a781..a0946a6c 100644 --- a/libs/vertexai/tests/integration_tests/test_model_garden.py +++ b/libs/vertexai/tests/integration_tests/test_model_garden.py @@ -11,8 +11,8 @@ SystemMessage, ) from langchain_core.outputs import LLMResult -from langchain_core.pydantic_v1 import BaseModel from langchain_core.tools import tool +from pydantic import BaseModel from langchain_google_vertexai.model_garden import ( ChatAnthropicVertex, diff --git a/libs/vertexai/tests/unit_tests/__snapshots__/test_standard.ambr b/libs/vertexai/tests/unit_tests/__snapshots__/test_standard.ambr new file mode 100644 index 00000000..2906991c --- /dev/null +++ b/libs/vertexai/tests/unit_tests/__snapshots__/test_standard.ambr @@ -0,0 +1,59 @@ +# serializer version: 1 +# name: TestGeminiAIStandard.test_serdes[serialized] + dict({ + 'id': list([ + 'langchain', + 'chat_models', + 'vertexai', + 'ChatVertexAI', + ]), + 'kwargs': dict({ + 'default_metadata': list([ + ]), + 'full_model_name': 'projects/test-project/locations/us-central1/publishers/google/models/gemini-1.0-pro-001', + 'location': 'us-central1', + 'max_output_tokens': 100, + 'max_retries': 2, + 'model_family': '1', + 'model_name': 'gemini-1.0-pro-001', + 'n': 1, + 'project': 'test-project', + 'request_parallelism': 5, + 'stop': list([ + ]), + 'temperature': 0.0, + }), + 'lc': 1, + 'name': 'ChatVertexAI', + 'type': 'constructor', + }) +# --- +# name: TestGemini_15_AIStandard.test_serdes[serialized] + dict({ + 'id': list([ + 'langchain', + 'chat_models', + 'vertexai', + 'ChatVertexAI', + ]), + 'kwargs': dict({ + 'default_metadata': list([ + ]), + 'full_model_name': 'projects/test-project/locations/us-central1/publishers/google/models/gemini-1.5-pro-001', + 'location': 'us-central1', + 'max_output_tokens': 100, + 'max_retries': 2, + 'model_family': '2', + 'model_name': 'gemini-1.5-pro-001', + 'n': 1, + 'project': 'test-project', + 'request_parallelism': 5, + 'stop': list([ + ]), + 'temperature': 0.0, + }), + 'lc': 1, + 'name': 'ChatVertexAI', + 'type': 'constructor', + }) +# --- diff --git a/libs/vertexai/tests/unit_tests/test_chat_models.py b/libs/vertexai/tests/unit_tests/test_chat_models.py index 27727acb..5df10ad5 100644 --- a/libs/vertexai/tests/unit_tests/test_chat_models.py +++ b/libs/vertexai/tests/unit_tests/test_chat_models.py @@ -28,7 +28,7 @@ from langchain_core.output_parsers.openai_tools import ( PydanticToolsParser, ) -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel from vertexai.language_models import ( # type: ignore ChatMessage, InputOutputTextPair, diff --git a/libs/vertexai/tests/unit_tests/test_embeddings.py b/libs/vertexai/tests/unit_tests/test_embeddings.py index b47d8b5b..e1b3ceb8 100644 --- a/libs/vertexai/tests/unit_tests/test_embeddings.py +++ b/libs/vertexai/tests/unit_tests/test_embeddings.py @@ -2,7 +2,8 @@ from unittest.mock import MagicMock import pytest -from langchain_core.pydantic_v1 import root_validator +from pydantic import model_validator +from typing_extensions import Self from langchain_google_vertexai import VertexAIEmbeddings from langchain_google_vertexai.embeddings import GoogleEmbeddingModelType @@ -41,7 +42,7 @@ def __init__(self, model_name, **kwargs: Any) -> None: def _init_vertexai(cls, values: Dict) -> None: pass - @root_validator(pre=False, skip_on_failure=True) - def validate_environment(cls, values: Dict) -> Dict: - values["client"] = MagicMock() - return values + @model_validator(mode="after") + def validate_environment(self) -> Self: + self.client = MagicMock() + return self diff --git a/libs/vertexai/tests/unit_tests/test_function_utils.py b/libs/vertexai/tests/unit_tests/test_function_utils.py index d9cab148..326c14e3 100644 --- a/libs/vertexai/tests/unit_tests/test_function_utils.py +++ b/libs/vertexai/tests/unit_tests/test_function_utils.py @@ -1,19 +1,26 @@ import json from enum import Enum -from typing import Any, Dict, List, Optional, Sequence, Tuple, cast +from typing import Any, Dict, List, Optional, Sequence, Tuple, Union, cast from unittest.mock import Mock, patch import google.cloud.aiplatform_v1beta1.types as gapic import pytest import vertexai.generative_models as vertexai # type: ignore -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool, tool from langchain_core.utils.json_schema import dereference_refs +from pydantic import BaseModel, Field +from pydantic.v1 import ( + BaseModel as BaseModelV1, +) +from pydantic.v1 import ( + Field as FieldV1, +) from langchain_google_vertexai.functions_utils import ( _format_base_tool_to_function_declaration, _format_dict_to_function_declaration, _format_json_schema_to_gapic, + _format_json_schema_to_gapic_v1, _format_pydantic_to_function_declaration, _format_to_gapic_function_declaration, _format_to_gapic_tool, @@ -27,6 +34,28 @@ def test_format_json_schema_to_gapic(): + # Simple case + class RecordPerson(BaseModel): + """Record some identifying information about a person.""" + + name: str + age: Optional[int] + + schema = RecordPerson.model_json_schema() + result = _format_json_schema_to_gapic(schema) + expected = { + "title": "RecordPerson", + "type": "OBJECT", + "description": "Record some identifying information about a person.", + "properties": { + "name": {"title": "Name", "type": "STRING"}, + "age": {"type": "INTEGER", "title": "Age"}, + }, + "required": ["name"], + } + assert result == expected + + # Nested case class StringEnum(str, Enum): pear = "pear" banana = "banana" @@ -39,14 +68,120 @@ class A(BaseModel): class B(BaseModel): object_field: Optional[A] = Field(description="Class A") array_field: Sequence[A] - int_field: int = Field(description="int field", minimum=1, maximum=10) + int_field: int = Field(description="int field", ge=1, le=10) str_field: str = Field( + min_length=1, + max_length=10, + pattern="^[A-Z]{1,10}$", + json_schema_extra={"example": "ABCD"}, + ) + str_enum_field: StringEnum + + schema = B.model_json_schema() + result = _format_json_schema_to_gapic(dereference_refs(schema)) + + expected = { + "properties": { + "object_field": { + "description": "Class A", + "properties": {"int_field": {"type": "INTEGER", "title": "Int Field"}}, + "required": [], + "title": "A", + "type": "OBJECT", + }, + "array_field": { + "items": { + "description": "Class A", + "properties": { + "int_field": {"type": "INTEGER", "title": "Int Field"} + }, + "required": [], + "title": "A", + "type": "OBJECT", + }, + "type": "ARRAY", + "title": "Array Field", + }, + "int_field": { + "description": "int field", + "maximum": 10, + "minimum": 1, + "title": "Int Field", + "type": "INTEGER", + }, + "str_field": { + "example": "ABCD", + "maxLength": 10, + "minLength": 1, + "pattern": "^[A-Z]{1,10}$", + "title": "Str Field", + "type": "STRING", + }, + "str_enum_field": { + "enum": ["pear", "banana"], + "title": "StringEnum", + "type": "STRING", + }, + }, + "type": "OBJECT", + "title": "B", + "required": ["array_field", "int_field", "str_field", "str_enum_field"], + } + assert result == expected + + gapic_schema = cast(gapic.Schema, gapic.Schema.from_json(json.dumps(result))) + assert gapic_schema.type_ == gapic.Type.OBJECT + assert gapic_schema.title == expected["title"] + assert gapic_schema.required == expected["required"] + assert ( + gapic_schema.properties["str_field"].example + == expected["properties"]["str_field"]["example"] # type: ignore + ) + + +def test_format_json_schema_to_gapic_v1(): + # Simple case + class RecordPerson(BaseModelV1): + """Record some identifying information about a person.""" + + name: str + age: Optional[int] + + schema = RecordPerson.schema() + result = _format_json_schema_to_gapic_v1(schema) + expected = { + "title": "RecordPerson", + "type": "OBJECT", + "description": "Record some identifying information about a person.", + "properties": { + "name": {"title": "Name", "type": "STRING"}, + "age": {"type": "INTEGER", "title": "Age"}, + }, + "required": ["name"], + } + assert result == expected + + # Nested case + class StringEnum(str, Enum): + pear = "pear" + banana = "banana" + + class A(BaseModelV1): + """Class A""" + + int_field: Optional[int] + + class B(BaseModelV1): + object_field: Optional[A] = FieldV1(description="Class A") + array_field: Sequence[A] + int_field: int = FieldV1(description="int field", minimum=1, maximum=10) + str_field: str = FieldV1( min_length=1, max_length=10, pattern="^[A-Z]{1,10}$", example="ABCD" ) str_enum_field: StringEnum schema = B.schema() - result = _format_json_schema_to_gapic(dereference_refs(schema)) + result = _format_json_schema_to_gapic_v1(dereference_refs(schema)) expected = { "properties": { @@ -106,6 +241,27 @@ class B(BaseModel): ) +def test_format_json_schema_to_gapic_union_types() -> None: + """Test that union types are consistent between v1 and v2.""" + + class RecordPerson_v1(BaseModelV1): + name: str + age: Union[int, str] + + class RecordPerson(BaseModel): + name: str + age: Union[int, str] + + schema_v1 = RecordPerson_v1.schema() + schema_v2 = RecordPerson.model_json_schema() + + result_v1 = _format_json_schema_to_gapic_v1(schema_v1) + result_v2 = _format_json_schema_to_gapic(schema_v2) + result_v1["title"] = "RecordPerson" + + assert result_v1 == result_v2 + + # reusable test inputs def search(question: str) -> str: """Search tool""" @@ -119,7 +275,7 @@ def search(question: str) -> str: parameters=gapic.Schema( type=gapic.Type.OBJECT, description="Search tool", - title="searchSchema", + title="search", properties={"question": gapic.Schema(type=gapic.Type.STRING, title="Question")}, required=["question"], ), @@ -164,7 +320,7 @@ class SearchModel(BaseModel): question: str -search_model_schema = SearchModel.schema() +search_model_schema = SearchModel.model_json_schema() search_model_dict = { "name": search_model_schema["title"], "description": search_model_schema["description"], diff --git a/libs/vertexai/tests/unit_tests/test_llm.py b/libs/vertexai/tests/unit_tests/test_llm.py index e9271b41..ac6afdba 100644 --- a/libs/vertexai/tests/unit_tests/test_llm.py +++ b/libs/vertexai/tests/unit_tests/test_llm.py @@ -2,7 +2,8 @@ from unittest import TestCase from unittest.mock import MagicMock, patch -from langchain_core.pydantic_v1 import root_validator +from pydantic import model_validator +from typing_extensions import Self from langchain_google_vertexai._base import _BaseVertexAIModelGarden from langchain_google_vertexai.llms import VertexAI @@ -80,9 +81,9 @@ def test_vertexai_args_passed() -> None: def test_extract_response() -> None: class FakeModelGarden(_BaseVertexAIModelGarden): - @root_validator(pre=False, skip_on_failure=True) - def validate_environment(cls, values: Dict) -> Dict: - return values + @model_validator(mode="after") + def validate_environment(self) -> Self: + return self prompts_results = [ ("a prediction", "a prediction"),