From 1bd731446cab9714aec023c304c1794494ebcaa7 Mon Sep 17 00:00:00 2001
From: Bagatur <22008038+baskaryan@users.noreply.github.com>
Date: Tue, 25 Jun 2024 06:25:55 -0700
Subject: [PATCH] community[patch], infra: fix pydantic import and add import
 ci (#317)

---
 .github/workflows/_all_ci.yml                 |   9 +-
 .github/workflows/_dependencies.yml           | 103 +++
 libs/community/Makefile                       |   2 +
 .../bq_storage_vectorstores/_base.py          | 124 ++--
 .../bq_storage_vectorstores/bigquery.py       | 195 ++---
 .../bq_storage_vectorstores/featurestore.py   | 281 +++----
 .../langchain_google_community/gmail/base.py  |   2 +-
 .../gmail/get_message.py                      |   4 +-
 .../gmail/search.py                           |   6 +-
 .../langchain_google_community/places_api.py  |   2 +-
 libs/community/poetry.lock                    | 190 ++---
 libs/community/pyproject.toml                 |  19 +-
 libs/community/scripts/check_imports.py       |  17 +
 libs/community/scripts/check_pydantic.sh      |  27 +
 libs/community/scripts/lint_imports.sh        |  17 +
 .../test_feature_store_bq_vectorstore.py      |   3 +-
 .../test_feature_store_fs_vectorstore.py      |   4 +-
 .../test_bigquery_vector_search.py            |   2 +-
 .../test_googlesearch_api.py                  |   6 +-
 .../test_vertex_ai_search.py                  |   2 +-
 libs/genai/Makefile                           |   9 +-
 libs/genai/poetry.lock                        |  16 +-
 libs/genai/pyproject.toml                     |   2 +-
 libs/vertexai/Makefile                        |   2 +
 .../_anthropic_utils.py                       |   4 +-
 libs/vertexai/poetry.lock                     | 688 ++++++++++--------
 libs/vertexai/pyproject.toml                  |   9 +-
 .../integration_tests/test_embeddings.py      |   5 +-
 .../tests/integration_tests/test_standard.py  |   1 +
 .../tests/integration_tests/test_tools.py     | 241 +-----
 .../tests/unit_tests/test_embeddings.py       |   2 +-
 31 files changed, 1018 insertions(+), 976 deletions(-)
 create mode 100644 .github/workflows/_dependencies.yml
 create mode 100644 libs/community/scripts/check_imports.py
 create mode 100755 libs/community/scripts/check_pydantic.sh
 create mode 100755 libs/community/scripts/lint_imports.sh

diff --git a/.github/workflows/_all_ci.yml b/.github/workflows/_all_ci.yml
index 13af92fb..6fd789e4 100644
--- a/.github/workflows/_all_ci.yml
+++ b/.github/workflows/_all_ci.yml
@@ -41,6 +41,13 @@ 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
@@ -53,4 +60,4 @@ jobs:
     uses: ./.github/workflows/_compile_integration_test.yml
     with:
       working-directory: ${{ inputs.working-directory }}
-    secrets: inherit
\ No newline at end of file
+    secrets: inherit
diff --git a/.github/workflows/_dependencies.yml b/.github/workflows/_dependencies.yml
new file mode 100644
index 00000000..aa6ad4b4
--- /dev/null
+++ b/.github/workflows/_dependencies.yml
@@ -0,0 +1,103 @@
+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 e2925ccb..5c7f091b 100644
--- a/libs/community/Makefile
+++ b/libs/community/Makefile
@@ -33,6 +33,8 @@ 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
 	poetry run ruff --select I $(PYTHON_FILES)
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 ffb6d3f0..650f818c 100644
--- a/libs/community/langchain_google_community/bq_storage_vectorstores/_base.py
+++ b/libs/community/langchain_google_community/bq_storage_vectorstores/_base.py
@@ -7,15 +7,15 @@
 from functools import partial
 from importlib.util import find_spec
 from threading import Lock
-from typing import Any, Dict, List, Optional, Tuple, Type, Union
+from typing import Any, Dict, List, Optional, Tuple, Type, Union, cast
 
 import numpy as np
 from google.cloud.exceptions import NotFound
 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
 
 from langchain_google_community._utils import get_client_info
 from langchain_google_community.bq_storage_vectorstores.utils import (
@@ -44,8 +44,8 @@ class BaseBigQueryVectorStore(VectorStore, BaseModel, ABC):
         table_name: BigQuery table name.
         location: BigQuery region/location.
         content_field: Name of the column storing document content (default: "content").
-        text_embedding_field: Name of the column storing text embeddings (default:
-            "text_embedding").
+        embedding_field: Name of the column storing text embeddings (default:
+            "embedding").
         doc_id_field: Name of the column storing document IDs (default: "doc_id").
         credentials: Optional Google Cloud credentials object.
         embedding_dimension: Dimension of the embedding vectors (inferred if not
@@ -65,16 +65,22 @@ class BaseBigQueryVectorStore(VectorStore, BaseModel, ABC):
     table_name: str
     location: str
     content_field: str = "content"
-    text_embedding_field: str = "text_embedding"
+    embedding_field: str = "embedding"
     doc_id_field: str = "doc_id"
     credentials: Optional[Any] = None
     embedding_dimension: Optional[int] = None
-    _extra_fields: Union[Dict[str, str], None] = None
-    _table_schema: Any = None
+    extra_fields: Union[Dict[str, str], None] = None
+    table_schema: Any = None
+    _bq_client: Any = None
+    _logger: Any = None
+    _full_table_id: Optional[str] = None
+
+    class Config:
+        arbitrary_types_allowed = True
 
     @abstractmethod
     def sync_data(self) -> None:
-        raise NotImplementedError()
+        ...
 
     @abstractmethod
     def get_documents(
@@ -95,7 +101,7 @@ def get_documents(
         Returns:
             List of ids from adding the texts into the vectorstore.
         """
-        raise NotImplementedError
+        ...
 
     @abstractmethod
     def _similarity_search_by_vectors_with_scores_and_embeddings(
@@ -105,46 +111,53 @@ def _similarity_search_by_vectors_with_scores_and_embeddings(
         k: int = 5,
         batch_size: Union[int, None] = None,
     ) -> list[list[list[Any]]]:
-        raise NotImplementedError()
+        ...
 
-    def model_post_init(self, __context) -> None:  # type: ignore[no-untyped-def]
-        """Constructor for FeatureStore."""
+    @root_validator(pre=False, skip_on_failure=True)
+    def validate_vals(cls, values: dict) -> dict:
         try:
-            import pandas as pd  # type: ignore[import-untyped]
+            import pandas  # noqa: F401
             from google.cloud import bigquery  # type: ignore[attr-defined]
             from google.cloud.aiplatform import base
 
             find_spec("pyarrow")
             find_spec("db_types")
-            self._logger = base.Logger(__name__)
-            self._pd = pd
-            self._bigquery = bigquery
         except ModuleNotFoundError:
             raise ImportError(
                 "Please, install feature store dependency group: "
                 "`pip install langchain-google-community[featurestore]`"
             )
-        client_info = get_client_info(module="bigquery-vector-search")
-        self._bq_client = bigquery.Client(
-            project=self.project_id,
-            location=self.location,
-            credentials=self.credentials,
-            client_info=client_info,
+        values["_logger"] = base.Logger(__name__)
+        values["_bq_client"] = bigquery.Client(
+            project=values["project_id"],
+            location=values["location"],
+            credentials=values["credentials"],
+            client_info=get_client_info(module="bigquery-vector-search"),
         )
-        if self.embedding_dimension is None:
-            self.embedding_dimension = len(self.embedding.embed_query("test"))
-        self._full_table_id = (
-            f"{self.project_id}." f"{self.dataset_name}." f"{self.table_name}"
+        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']}"
         )
-        self._initialize_bq_table()
-        self._validate_bq_table()
-        self._logger.info(
-            f"BigQuery table {self._full_table_id} "
+        values["_full_table_id"] = full_table_id
+
+        values["_bq_client"].create_dataset(
+            dataset=values["dataset_name"], exists_ok=True
+        )
+        values["_bq_client"].create_dataset(
+            dataset=f"{values['dataset_name']}_temp", 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(
+            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={self.project_id}"
-            f"&ws=!1m5!1m4!4m3!1s{self.project_id}!2s{self.dataset_name}!3s{self.table_name}"
+            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']}"
         )
+        return values
 
     @property
     def embeddings(self) -> Optional[Embeddings]:
@@ -152,18 +165,19 @@ def embeddings(self) -> Optional[Embeddings]:
 
     @property
     def full_table_id(self) -> str:
-        return self._full_table_id
+        return cast(str, self._full_table_id)
 
     def _validate_bq_table(self) -> Any:
-        table_ref = self._bigquery.TableReference.from_string(self._full_table_id)
+        from google.cloud import bigquery  # type: ignore[attr-defined]
+
+        table_ref = bigquery.TableReference.from_string(self.full_table_id)
 
         try:
-            table = self._bq_client.get_table(
-                self._full_table_id
-            )  # Attempt to retrieve the table information
+            # Attempt to retrieve the table information
+            self._bq_client.get_table(self.full_table_id)
         except NotFound:
             self._logger.debug(
-                f"Couldn't find table {self._full_table_id}. "
+                f"Couldn't find table {self.full_table_id}. "
                 f"Table will be created once documents are added"
             )
             return
@@ -171,7 +185,7 @@ def _validate_bq_table(self) -> Any:
         table = self._bq_client.get_table(table_ref)
         schema = table.schema.copy()
         if schema:  ## Check if table has a schema
-            self._table_schema = {field.name: field.field_type for field in schema}
+            self.table_schema = {field.name: field.field_type for field in schema}
             columns = {c.name: c for c in schema}
             validate_column_in_bq_schema(
                 column_name=self.doc_id_field,
@@ -186,18 +200,18 @@ def _validate_bq_table(self) -> Any:
                 expected_modes=["NULLABLE", "REQUIRED"],
             )
             validate_column_in_bq_schema(
-                column_name=self.text_embedding_field,
+                column_name=self.embedding_field,
                 columns=columns,
                 expected_types=["FLOAT", "FLOAT64"],
                 expected_modes=["REPEATED"],
             )
-            if self._extra_fields is None:
+            if self.extra_fields is None:
                 extra_fields = {}
                 for column in schema:
                     if column.name not in [
                         self.doc_id_field,
                         self.content_field,
-                        self.text_embedding_field,
+                        self.embedding_field,
                     ]:
                         # Check for unsupported REPEATED mode
                         if column.mode == "REPEATED":
@@ -206,25 +220,27 @@ def _validate_bq_table(self) -> Any:
                                 f"REPEATED fields are not supported in this context."
                             )
                         extra_fields[column.name] = column.field_type
-                self._extra_fields = extra_fields
+                self.extra_fields = extra_fields
             else:
-                for field, type in self._extra_fields.items():
+                for field, type in self.extra_fields.items():
                     validate_column_in_bq_schema(
                         column_name=field,
                         columns=columns,
                         expected_types=[type],
                         expected_modes=["NULLABLE", "REQUIRED"],
                     )
-            self._logger.debug(f"Table {self._full_table_id} validated")
+            self._logger.debug(f"Table {self.full_table_id} validated")
         return table_ref
 
     def _initialize_bq_table(self) -> Any:
         """Validates or creates the BigQuery table."""
+        from google.cloud import bigquery  # type: ignore[attr-defined]
+
         self._bq_client.create_dataset(dataset=self.dataset_name, exists_ok=True)
         self._bq_client.create_dataset(
             dataset=f"{self.dataset_name}_temp", exists_ok=True
         )
-        table_ref = self._bigquery.TableReference.from_string(self._full_table_id)
+        table_ref = bigquery.TableReference.from_string(self.full_table_id)
         self._bq_client.create_table(table_ref, exists_ok=True)
         return table_ref
 
@@ -268,6 +284,8 @@ def add_texts_with_embeddings(
         Returns:
             List of ids from adding the texts into the vectorstore.
         """
+        import pandas as pd
+
         ids = [uuid.uuid4().hex for _ in texts]
         if metadatas is None:
             metadatas = [{} for _ in texts]
@@ -277,15 +295,15 @@ def add_texts_with_embeddings(
             record = {
                 self.doc_id_field: idx,
                 self.content_field: text,
-                self.text_embedding_field: emb,
+                self.embedding_field: emb,
             }
             record.update(metadata_dict)
             values_dict.append(record)  # type: ignore[arg-type]
 
         table = self._bq_client.get_table(
-            self._full_table_id
+            self.full_table_id
         )  # Attempt to retrieve the table information
-        df = self._pd.DataFrame(values_dict)
+        df = pd.DataFrame(values_dict)
         job = self._bq_client.load_table_from_dataframe(df, table)
         job.result()
         self._validate_bq_table()
@@ -304,15 +322,17 @@ def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[boo
             Optional[bool]: True if deletion is successful,
             False otherwise, None if not implemented.
         """
+        from google.cloud import bigquery  # type: ignore[attr-defined]
+
         if not ids or len(ids) == 0:
             return True
 
-        job_config = self._bigquery.QueryJobConfig(
-            query_parameters=[self._bigquery.ArrayQueryParameter("ids", "STRING", ids)],
+        job_config = bigquery.QueryJobConfig(
+            query_parameters=[bigquery.ArrayQueryParameter("ids", "STRING", ids)],
         )
         self._bq_client.query(
             f"""
-                    DELETE FROM `{self._full_table_id}` WHERE {self.doc_id_field}
+                    DELETE FROM `{self.full_table_id}` WHERE {self.doc_id_field}
                     IN UNNEST(@ids)
                     """,
             job_config=job_config,
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 86c1f6c1..5e7c5eb1 100644
--- a/libs/community/langchain_google_community/bq_storage_vectorstores/bigquery.py
+++ b/libs/community/langchain_google_community/bq_storage_vectorstores/bigquery.py
@@ -3,12 +3,12 @@
 from threading import Lock, Thread
 from typing import Any, Dict, List, Literal, Optional, Type, Union
 
-from google.api_core.exceptions import (
-    ClientError,
-)
+from google.api_core.exceptions import ClientError
+from google.cloud import bigquery  # type: ignore[attr-defined]
 from google.cloud.bigquery.table import Table
 from langchain_core.documents import Document
 from langchain_core.embeddings import Embeddings
+from langchain_core.pydantic_v1 import root_validator
 
 from langchain_google_community.bq_storage_vectorstores._base import (
     BaseBigQueryVectorStore,
@@ -41,8 +41,8 @@ class BigQueryVectorStore(BaseBigQueryVectorStore):
         table_name: BigQuery table name.
         location: BigQuery region/location.
         content_field: Name of the column storing document content (default: "content").
-        text_embedding_field: Name of the column storing text embeddings (default:
-            "text_embedding").
+        embedding_field: Name of the column storing text embeddings (default:
+            "embedding").
         doc_id_field: Name of the column storing document IDs (default: "doc_id").
         credentials: Optional Google Cloud credentials object.
         embedding_dimension: Dimension of the embedding vectors (inferred if not
@@ -56,19 +56,6 @@ class BigQueryVectorStore(BaseBigQueryVectorStore):
     _have_index: bool = False
     _last_index_check: datetime = datetime.min
 
-    def model_post_init(self, __context: Any) -> None:
-        # Initialize attributes after model creation
-        super().model_post_init(__context)
-        self._creating_index = False
-        self._have_index = False
-        self._last_index_check = datetime.min
-        self._initialize_bq_vector_index()
-        self._logger.info(
-            "BigQueryVectorStore initialized with BigQuery VectorSearch. \n"
-            "Optional online serving available via .to_vertex_fs_vector_store() "
-            "method."
-        )
-
     def sync_data(self) -> None:
         pass
 
@@ -92,9 +79,9 @@ def get_documents(
         """
 
         if ids and len(ids) > 0:
-            job_config = self._bigquery.QueryJobConfig(
+            job_config = bigquery.QueryJobConfig(
                 query_parameters=[
-                    self._bigquery.ArrayQueryParameter("ids", "STRING", ids),
+                    bigquery.ArrayQueryParameter("ids", "STRING", ids),
                 ]
             )
             id_expr = f"{self.doc_id_field} IN UNNEST(@ids)"
@@ -112,7 +99,7 @@ def get_documents(
 
         job = self._bq_client.query(  # type: ignore[union-attr]
             f"""
-                    SELECT * FROM `{self._full_table_id}` WHERE {id_expr}
+                    SELECT * FROM `{self.full_table_id}` WHERE {id_expr}
                     {where_filter_expr}
                     """,
             job_config=job_config,
@@ -122,7 +109,7 @@ def get_documents(
             metadata = {}
             for field in row.keys():
                 if field not in [
-                    self.text_embedding_field,
+                    self.embedding_field,
                     self.content_field,
                 ]:
                     metadata[field] = row[field]
@@ -131,75 +118,55 @@ def get_documents(
             docs.append(doc)
         return docs
 
-    def _initialize_bq_vector_index(self) -> Any:
+    @root_validator(pre=False, skip_on_failure=True)
+    def initialize_bq_vector_index(cls, values: dict) -> dict:
         """
         A vector index in BigQuery table enables efficient
         approximate vector search.
         """
-        if self._have_index or self._creating_index:
-            return
+        if values.get("_have_index") or values.get("_creating_index"):
+            return values
 
-        table = self._bq_client.get_table(self._full_table_id)  # type: ignore[union-attr]
+        table = values["_bq_client"].get_table(values["_full_table_id"])  # type: ignore[union-attr]
         if (table.num_rows or 0) < MIN_INDEX_ROWS:
-            self._logger.debug("Not enough rows to create a vector index.")
-            return
+            values["_logger"].debug("Not enough rows to create a vector index.")
+            return values
 
-        if datetime.utcnow() - self._last_index_check < INDEX_CHECK_INTERVAL:
-            return
+        if datetime.utcnow() - values["_last_index_check"] < INDEX_CHECK_INTERVAL:
+            return values
 
         with _vector_table_lock:
-            self._last_index_check = datetime.utcnow()
+            values["_last_index_check"] = datetime.utcnow()
             # Check if index exists, create if necessary
             check_query = (
-                f"SELECT 1 FROM `{self.project_id}."
-                f"{self.dataset_name}"
+                f"SELECT 1 FROM `{values['project_id']}."
+                f"{values['dataset_name']}"
                 ".INFORMATION_SCHEMA.VECTOR_INDEXES` WHERE"
-                f" table_name = '{self.table_name}'"
+                f" table_name = '{values['table_name']}'"
             )
-            job = self._bq_client.query(  # type: ignore[union-attr]
-                check_query, api_method=self._bigquery.enums.QueryApiMethod.QUERY
+            job = values["_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.
-                self._create_bq_index_in_background()
-            else:
-                self._logger.debug("Vector index already exists.")
-                self._have_index = True
-
-    def _create_bq_index_in_background(self) -> None:
-        if self._have_index or self._creating_index:
-            return
+                values["_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"],
+                    },
+                    daemon=True,
+                ).start()
 
-        self._creating_index = True
-        self._logger.debug("Trying to create a vector index.")
-        Thread(target=self._create_bq_index, daemon=True).start()
-
-    def _create_bq_index(self) -> None:
-        """
-        Create a BQ Vector Index if doesn't exists, if the number of rows is above
-        MIN_INDEX_ROWS constant
-        Returns:
-        None
-        """
-        table = self._bq_client.get_table(self._full_table_id)  # type: ignore[union-attr]
-        if (table.num_rows or 0) < MIN_INDEX_ROWS:
-            return
-
-        index_name = f"{self.table_name}_langchain_index"
-        try:
-            sql = f"""
-                CREATE VECTOR INDEX IF NOT EXISTS
-                `{index_name}`
-                ON `{self._full_table_id}`
-                ({self.text_embedding_field})
-                OPTIONS(distance_type="{self.distance_type}", index_type="IVF")
-            """
-            self._bq_client.query(sql).result()  # type: ignore[union-attr]
-            self._have_index = True
-        except ClientError as ex:
-            self._logger.debug("Vector index creation failed (%s).", ex.args[0])
-        finally:
-            self._creating_index = False
+            else:
+                values["_logger"].debug("Vector index already exists.")
+                values["_have_index"] = True
+            return values
 
     def _similarity_search_by_vectors_with_scores_and_embeddings(
         self,
@@ -262,7 +229,7 @@ def _create_search_query(
         if filter:
             filter_expressions = []
             for column, value in filter.items():
-                if self._table_schema[column] in ["INTEGER", "FLOAT"]:  # type: ignore[index]
+                if self.table_schema[column] in ["INTEGER", "FLOAT"]:  # type: ignore[index]
                     filter_expressions.append(f"base.{column} = {value}")
                 else:
                     filter_expressions.append(f"base.{column} = '{value}'")
@@ -273,7 +240,7 @@ def _create_search_query(
         if table_to_query is not None:
             embeddings_query = f"""
             with embeddings as (
-            SELECT {self.text_embedding_field}, ROW_NUMBER() OVER() as row_num
+            SELECT {self.embedding_field}, ROW_NUMBER() OVER() as row_num
             from `{table_to_query}`
             )"""
 
@@ -282,10 +249,10 @@ def _create_search_query(
 
             for i in range(num_embeddings):
                 embeddings_query += (
-                    f"SELECT {i} as row_num, @emb_{i} AS text_embedding"
+                    f"SELECT {i} as row_num, @emb_{i} AS {self.embedding_field}"
                     if i == 0
                     else f"\nUNION ALL\n"
-                    f"SELECT {i} as row_num, @emb_{i} AS text_embedding"
+                    f"SELECT {i} as row_num, @emb_{i} AS {self.embedding_field}"
                 )
             embeddings_query += "\n)\n"
 
@@ -305,9 +272,9 @@ def _create_search_query(
         full_query = f"""{embeddings_query}
         {select_clause}
         FROM VECTOR_SEARCH(
-            TABLE `{self._full_table_id}`,
-            "text_embedding",
-            (SELECT row_num, {self.text_embedding_field} from embeddings),
+            TABLE `{self.full_table_id}`,
+            "{self.embedding_field}",
+            (SELECT row_num, {self.embedding_field} from embeddings),
             distance_type => "{self.distance_type}",
             top_k => {k}
         )
@@ -326,19 +293,19 @@ def _search_embeddings(
             filter=filter, k=k, num_embeddings=len(embeddings)
         )
 
-        job_config = self._bigquery.QueryJobConfig(
+        job_config = bigquery.QueryJobConfig(
             query_parameters=[
-                self._bigquery.ArrayQueryParameter(f"emb_{i}", "FLOAT64", emb)
+                bigquery.ArrayQueryParameter(f"emb_{i}", "FLOAT64", emb)
                 for i, emb in enumerate(embeddings)
             ],
             use_query_cache=True,
-            priority=self._bigquery.QueryPriority.INTERACTIVE,
+            priority=bigquery.QueryPriority.INTERACTIVE,
         )
 
         results = self._bq_client.query(  # type: ignore[union-attr]
             full_query,
             job_config=job_config,
-            api_method=self._bigquery.enums.QueryApiMethod.QUERY,
+            api_method=bigquery.enums.QueryApiMethod.QUERY,
         )
         return list(results)
 
@@ -348,8 +315,11 @@ def _create_temp_bq_table(
         expire_hours_temp_table: int = 12,
     ) -> Table:
         """Create temporary table to store query embeddings prior to batch search"""
-        df = self._pd.DataFrame([])
-        df[self.text_embedding_field] = embeddings
+        import pandas as pd
+
+        df = pd.DataFrame([])
+
+        df[self.embedding_field] = embeddings
         table_id = (
             f"{self.project_id}."
             f"{self.dataset_name}_temp."
@@ -357,11 +327,9 @@ def _create_temp_bq_table(
         )
 
         schema = [
-            self._bigquery.SchemaField(
-                self.text_embedding_field, "FLOAT64", mode="REPEATED"
-            )
+            bigquery.SchemaField(self.embedding_field, "FLOAT64", mode="REPEATED")
         ]
-        table_ref = self._bigquery.Table(table_id, schema=schema)
+        table_ref = bigquery.Table(table_id, schema=schema)
         table = self._bq_client.create_table(table_ref)
         table.expires = datetime.now() + timedelta(hours=expire_hours_temp_table)
         table = self._bq_client.update_table(table, ["expires"])
@@ -383,7 +351,7 @@ def _create_langchain_documents(
         metadata_fields = [
             x
             for x in result_fields
-            if x not in [self.text_embedding_field, self.content_field, "row_num"]
+            if x not in [self.embedding_field, self.content_field, "row_num"]
         ]
         documents = []
         for result in search_results:
@@ -399,7 +367,7 @@ def _create_langchain_documents(
                 document_record = [
                     document,
                     metadata["score"],
-                    result[self.text_embedding_field],  # type: ignore
+                    result[self.embedding_field],  # type: ignore
                 ]
             else:
                 document_record = [document, metadata["score"]]
@@ -457,18 +425,18 @@ def batch_search(
             k=k,
             num_embeddings=len(embeddings),
             table_to_query=table_ref,
-            fields_to_exclude=[self.text_embedding_field],
+            fields_to_exclude=[self.embedding_field],
         )
 
-        job_config = self._bigquery.QueryJobConfig(
+        job_config = bigquery.QueryJobConfig(
             use_query_cache=True,
-            priority=self._bigquery.QueryPriority.INTERACTIVE,
+            priority=bigquery.QueryPriority.INTERACTIVE,
         )
 
         search_results = self._bq_client.query(  # type: ignore[union-attr]
             full_query,
             job_config=job_config,
-            api_method=self._bigquery.enums.QueryApiMethod.QUERY,
+            api_method=bigquery.enums.QueryApiMethod.QUERY,
         )
 
         return self._create_langchain_documents(
@@ -527,6 +495,7 @@ def to_vertex_fs_vector_store(self, **kwargs: Any) -> Any:
         )
 
         base_params = self.dict(include=BaseBigQueryVectorStore.__fields__.keys())
+        base_params["embedding"] = self.embedding
         all_params = {**base_params, **kwargs}
         fs_obj = VertexFSVectorStore(**all_params)
         return fs_obj
@@ -543,3 +512,35 @@ def job_stats(self, job_id: str) -> Dict:
             (https://cloud.google.com/bigquery/docs/reference/rest/v2/Job#JobStatistics2).
         """
         return self._bq_client.get_job(job_id)._properties["statistics"]
+
+
+def _create_bq_index(
+    bq_client: Any,
+    table_name: str,
+    full_table_id: str,
+    embedding_field: str,
+    distance_type: str,
+    logger: Any,
+) -> bool:
+    """
+    Create a BQ Vector Index if doesn't exist, if the number of rows is above
+    MIN_INDEX_ROWS constant
+    """
+    table = bq_client.get_table(full_table_id)  # type: ignore[union-attr]
+    if (table.num_rows or 0) < MIN_INDEX_ROWS:
+        return False
+
+    index_name = f"{table_name}_langchain_index"
+    try:
+        sql = f"""
+            CREATE VECTOR INDEX IF NOT EXISTS
+            `{index_name}`
+            ON `{full_table_id}`
+            ({embedding_field})
+            OPTIONS(distance_type="{distance_type}", index_type="IVF")
+        """
+        bq_client.query(sql).result()  # type: ignore[union-attr]
+        return True
+    except ClientError as ex:
+        logger.debug("Vector index creation failed (%s).", ex.args[0])
+        return False
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 a7338c72..63538532 100644
--- a/libs/community/langchain_google_community/bq_storage_vectorstores/featurestore.py
+++ b/libs/community/langchain_google_community/bq_storage_vectorstores/featurestore.py
@@ -3,7 +3,7 @@
 import time
 from datetime import timedelta
 from subprocess import TimeoutExpired
-from typing import Any, Dict, List, Literal, MutableSequence, Optional, Type, Union
+from typing import Any, Dict, List, MutableSequence, Optional, Type, Union
 
 import proto  # type: ignore[import-untyped]
 from google.api_core.exceptions import (
@@ -13,7 +13,7 @@
 )
 from langchain_core.documents import Document
 from langchain_core.embeddings import Embeddings
-from pydantic import ConfigDict
+from langchain_core.pydantic_v1 import root_validator
 
 from langchain_google_community._utils import get_client_info, get_user_agent
 from langchain_google_community.bq_storage_vectorstores._base import (
@@ -49,8 +49,8 @@ class VertexFSVectorStore(BaseBigQueryVectorStore):
         table_name: BigQuery table name.
         location: BigQuery region/location.
         content_field: Name of the column storing document content (default: "content").
-        text_embedding_field: Name of the column storing text embeddings (default:
-            "text_embedding").
+        embedding_field: Name of the column storing text embeddings (default:
+            "embedding").
         doc_id_field: Name of the column storing document IDs (default: "doc_id").
         credentials: Optional Google Cloud credentials object.
         embedding_dimension: Dimension of the embedding vectors (inferred if not
@@ -59,16 +59,8 @@ class VertexFSVectorStore(BaseBigQueryVectorStore):
             store. Defaults to the dataset name.
         online_store_location (str, optional): Location of the online store. Default
             to "location" parameter.
-        online_store_type (Literal["bigtable", "optimized"]): Type of online store.
-            Defaults to "optimized".
         view_name (str, optional): Name of the Feature View. Defaults to the table name.
         cron_schedule (str, optional): Cron schedule for data syncing.
-        min_node_count (int): Minimum node count for Bigtable online stores
-            (default: 1).
-        max_node_count (int): Maximum node count for Bigtable online stores
-            (default: 3).
-        cpu_utilization_target (int): CPU utilization target for Bigtable autoscaling
-            (default: 50).
         algorithm_config (Any, optional): Algorithm configuration for indexing.
         filter_columns (List[str], optional): Columns to use for filtering.
         crowding_column (str, optional): Column to use for crowding.
@@ -76,85 +68,89 @@ class VertexFSVectorStore(BaseBigQueryVectorStore):
             DOT_PRODUCT_DISTANCE).
     """
 
-    model_config = ConfigDict(arbitrary_types_allowed=True)
     online_store_name: Union[str, None] = None
     online_store_location: Union[str, None] = None
-    online_store_type: Literal["bigtable", "optimized"] = "optimized"
     view_name: Union[str, None] = None
     cron_schedule: Union[str, None] = None
-    min_node_count: int = 1
-    max_node_count: int = 3
-    cpu_utilization_target: int = 50
     algorithm_config: Optional[Any] = None
     filter_columns: Optional[List[str]] = None
     crowding_column: Optional[str] = None
     distance_measure_type: Optional[str] = None
     _user_agent: str = ""
+    feature_view: Any = None
+    _admin_client: Any = None
 
-    def model_post_init(self, __context: Any) -> None:
-        super().model_post_init(__context)
+    @root_validator(pre=False, skip_on_failure=True)
+    def _initialize_bq_vector_index(cls, values: dict) -> dict:
         import vertexai
-        from google.cloud import aiplatform
-        from google.cloud.aiplatform_v1beta1.types import (
-            NearestNeighborQuery,
-            feature_online_store_service,
-        )
-        from google.cloud.aiplatform_v1beta1.types import (
-            feature_online_store as feature_online_store_pb2,
-        )
-        from vertexai.resources.preview.feature_store import (  # type: ignore[import-untyped]
-            utils,
+        from google.cloud.aiplatform_v1beta1 import (
+            FeatureOnlineStoreAdminServiceClient,
+            FeatureOnlineStoreServiceClient,
         )
-
-        self._aiplatform = aiplatform
-        self._vertexai = vertexai
-        self._utils = utils
-        self._feature_online_store_pb2 = feature_online_store_pb2
-        self._feature_online_store_service = feature_online_store_service
-        self._NearestNeighborQuery = NearestNeighborQuery
-
-        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
-
-        vertexai.init(project=self.project_id, location=self.location)
-        _, self._user_agent = get_user_agent(
-            f"{USER_AGENT_PREFIX}-{type(self).__name__}"
+        from vertexai.resources.preview.feature_store import (
+            utils,  # type: ignore[import-untyped]
         )
-        self.online_store_name = self.online_store_name or self.dataset_name
-        self.view_name = self.view_name or self.table_name
-        self.location = self.location or self.location
-        from google.cloud.aiplatform_v1beta1 import FeatureOnlineStoreAdminServiceClient
 
-        api_endpoint = f"{self.location}-aiplatform.googleapis.com"
-        self._admin_client = FeatureOnlineStoreAdminServiceClient(
+        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(
             client_options={"api_endpoint": api_endpoint},
-            client_info=get_client_info(module=self._user_agent),
+            client_info=get_client_info(module=values["_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._init_store()
-        self._logger.info(
-            "VertexFSVectorStore initialized with Feature Store "
-            f"{self.online_store_type} Vector Search. \n"
+        gca_resource = values["online_store"].gca_resource
+        endpoint = gca_resource.dedicated_serving_endpoint.public_endpoint_domain_name
+        values["_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"]
+        )
+
+        values["_logger"].info(
+            "VertexFSVectorStore initialized with Feature Store Vector Search. \n"
             "Optional batch serving available via .to_bq_vector_store() method."
         )
+        return values
 
     def _init_store(self) -> None:
         from google.cloud.aiplatform_v1beta1 import FeatureOnlineStoreServiceClient
 
-        self._online_store = self._create_online_store()
-        gca_resource = self._online_store.gca_resource
+        self.online_store = self._create_online_store()
+        gca_resource = self.online_store.gca_resource
         endpoint = gca_resource.dedicated_serving_endpoint.public_endpoint_domain_name
         self._search_client = FeatureOnlineStoreServiceClient(
             client_options={"api_endpoint": endpoint},
             client_info=get_client_info(module=self._user_agent),
         )
-        self._feature_view = self._get_feature_view()
+        self.feature_view = self._get_feature_view()
 
     def _validate_bq_existing_source(
         self,
     ) -> None:
-        bq_uri = self._feature_view.gca_resource.big_query_source.uri  # type: ignore[union-attr]
+        bq_uri = self.feature_view.gca_resource.big_query_source.uri  # type: ignore[union-attr]
         bq_uri_split = bq_uri.split(".")
         project_id = bq_uri_split[0].replace("bq://", "")
         dataset = bq_uri_split[1]
@@ -200,7 +196,7 @@ def _wait_until_dummy_query_success(self, timeout_seconds: int = 6000) -> None:
 
     def sync_data(self) -> None:
         """Sync the data from the BigQuery source into the Executor source"""
-        self._feature_view = self._create_feature_view()
+        self.feature_view = self._create_feature_view()
         self._validate_bq_existing_source()
         sync_response = self._admin_client.sync_feature_view(
             feature_view=(
@@ -283,18 +279,20 @@ def get_documents(
         Returns:
             List of ids from adding the texts into the vectorstore.
         """
+        from google.cloud import aiplatform
+
         output = []
         if ids is None:
             raise ValueError(
                 "Feature Store executor doesn't support search by filter " "only"
             )
         for id in ids:
-            with self._aiplatform.telemetry.tool_context_manager(self._user_agent):
-                result = self._feature_view.read(key=[id])  # type: ignore[union-attr]
+            with aiplatform.telemetry.tool_context_manager(self._user_agent):
+                result = self.feature_view.read(key=[id])  # type: ignore[union-attr]
                 metadata, content = {}, None
                 for feature in result.to_dict()["features"]:
                     if feature["name"] not in [
-                        self.text_embedding_field,
+                        self.embedding_field,
                         self.content_field,
                     ]:
                         metadata[feature["name"]] = list(feature["value"].values())[0]
@@ -354,14 +352,14 @@ def _parse_proto_output(
 
             for feature in result.entity_key_values.key_values.features:
                 if feature.name not in [
-                    self.text_embedding_field,
+                    self.embedding_field,
                     self.content_field,
                 ]:
                     dict_values = proto.Message.to_dict(feature.value)
                     col_type, value = next(iter(dict_values.items()))
                     value = cast_proto_type(column=col_type, value=value)
                     metadata[feature.name] = value
-                if feature.name == self.text_embedding_field:
+                if feature.name == self.embedding_field:
                     embedding = feature.value.double_array_value.values
                 if feature.name == self.content_field:
                     dict_values = proto.Message.to_dict(feature.value)
@@ -384,7 +382,7 @@ def _parse_proto_output(
 
     def _search_embedding(
         self,
-        embedding: Optional[Any] = None,
+        embedding: Any = None,
         entity_id: Optional[str] = None,
         k: int = 5,
         string_filters: Optional[List[dict]] = None,
@@ -392,9 +390,15 @@ def _search_embedding(
         approximate_neighbor_candidates: Optional[int] = None,
         leaf_nodes_search_fraction: Optional[float] = None,
     ) -> MutableSequence[Any]:
+        from google.cloud import aiplatform
+        from google.cloud.aiplatform_v1beta1.types import (
+            NearestNeighborQuery,
+            feature_online_store_service,
+        )
+
         if embedding:
-            embedding = self._NearestNeighborQuery.Embedding(value=embedding)
-        query = self._NearestNeighborQuery(
+            embedding = NearestNeighborQuery.Embedding(value=embedding)
+        query = NearestNeighborQuery(
             entity_id=entity_id,
             embedding=embedding,
             neighbor_count=k,
@@ -405,11 +409,10 @@ def _search_embedding(
                 "leaf_nodes_search_fraction": leaf_nodes_search_fraction,
             },
         )
-        with self._aiplatform.telemetry.tool_context_manager(self._user_agent):
-            feature_online_store_service = self._feature_online_store_service
+        with aiplatform.telemetry.tool_context_manager(self._user_agent):
             result = self._search_client.search_nearest_entities(
                 request=feature_online_store_service.SearchNearestEntitiesRequest(
-                    feature_view=self._feature_view.gca_resource.name,  # type: ignore[union-attr]
+                    feature_view=self.feature_view.gca_resource.name,  # type: ignore[union-attr]
                     query=query,
                     return_full_entity=True,  # returning entities with metadata
                 )
@@ -418,80 +421,41 @@ def _search_embedding(
 
     def _create_online_store(self) -> Any:
         # Search for existing Online store
-        stores_list = self._vertexai.resources.preview.FeatureOnlineStore.list(
-            project=self.project_id, location=self.location
-        )
-        for store in stores_list:
-            if store.name == self.online_store_name:
-                return store
-
-        self._logger.info("Creating feature store online store")
-        # Create it otherwise
-        feature_online_store_pb2 = self._feature_online_store_pb2
-        if self.online_store_type == "bigtable":
-            online_store_config = feature_online_store_pb2.FeatureOnlineStore(
-                bigtable=feature_online_store_pb2.FeatureOnlineStore.Bigtable(
-                    auto_scaling=feature_online_store_pb2.FeatureOnlineStore.Bigtable.AutoScaling(
-                        min_node_count=self.min_node_count,
-                        max_node_count=self.max_node_count,
-                        cpu_utilization_target=self.cpu_utilization_target,
-                    )
-                ),
-                embedding_management=feature_online_store_pb2.FeatureOnlineStore.EmbeddingManagement(
-                    enabled=True
-                ),
-            )
-            create_store_lro = self._admin_client.create_feature_online_store(
-                parent=f"projects/{self.project_id}/locations/{self.location}",
-                feature_online_store_id=self.online_store_name,
-                feature_online_store=online_store_config,
-            )
-            self._logger.info(create_store_lro.result())
-        elif self.online_store_type == "optimized":
-            online_store_config = feature_online_store_pb2.FeatureOnlineStore(
-                optimized=feature_online_store_pb2.FeatureOnlineStore.Optimized()
-            )
-            create_store_lro = self._admin_client.create_feature_online_store(
-                parent=f"projects/{self.project_id}/locations/{self.location}",
-                feature_online_store_id=self.online_store_name,
-                feature_online_store=online_store_config,
+        if self.online_store_name:
+            return _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,
             )
-            self._logger.info(create_store_lro.result())
-            self._logger.info(create_store_lro.result())
 
-        else:
-            raise ValueError(
-                f"{self.online_store_type} not allowed. "
-                f"Accepted values are 'bigtable' or 'optimized'."
-            )
-        stores_list = self._vertexai.resources.preview.FeatureOnlineStore.list(
-            project=self.project_id, location=self.location
+    def _create_feature_view(self) -> Any:
+        import vertexai
+        from vertexai.resources.preview.feature_store import (
+            utils,  # type: ignore[import-untyped]
         )
-        for store in stores_list:
-            if store.name == self.online_store_name:
-                return store
 
-    def _create_feature_view(self) -> Any:
         fv = self._get_feature_view()
         if fv:
             return fv
         else:
             FeatureViewBigQuerySource = (
-                self._vertexai.resources.preview.FeatureViewBigQuerySource
+                vertexai.resources.preview.FeatureViewBigQuerySource
             )
             big_query_source = FeatureViewBigQuerySource(
-                uri=f"bq://{self._full_table_id}",
+                uri=f"bq://{self.full_table_id}",
                 entity_id_columns=[self.doc_id_field],
             )
-            index_config = self._utils.IndexConfig(
-                embedding_column=self.text_embedding_field,
+            index_config = utils.IndexConfig(
+                embedding_column=self.embedding_field,
                 crowding_column=self.crowding_column,
                 filter_columns=self.filter_columns,
                 dimensions=self.embedding_dimension,
                 distance_measure_type=self.distance_measure_type,
                 algorithm_config=self.algorithm_config,
             )
-            return self._online_store.create_feature_view(
+            return self.online_store.create_feature_view(
                 name=self.view_name,
                 source=big_query_source,
                 sync_config=self.cron_schedule,
@@ -502,13 +466,7 @@ def _create_feature_view(self) -> Any:
 
     def _get_feature_view(self) -> Any | None:
         # Search for existing Feature view
-        fv_list = self._vertexai.resources.preview.FeatureView.list(
-            feature_online_store_id=self._online_store.gca_resource.name
-        )
-        for fv in fv_list:
-            if fv.name == self.view_name:
-                return fv
-        return None
+        return _get_feature_view(self.online_store, self.view_name)
 
     @classmethod
     def from_texts(
@@ -558,6 +516,61 @@ def to_bq_vector_store(self, **kwargs: Any) -> Any:
         )
 
         base_params = self.dict(include=BaseBigQueryVectorStore.__fields__.keys())
+        base_params["embedding"] = self.embedding
         all_params = {**base_params, **kwargs}
         bq_obj = BigQueryVectorStore(**all_params)
         return bq_obj
+
+
+def _create_online_store(
+    project_id: str,
+    location: str,
+    online_store_name: str,
+    _logger: Any,
+    _admin_client: Any,
+) -> Any:
+    # Search for existing Online store
+    import vertexai
+    from google.cloud.aiplatform_v1beta1.types import (
+        feature_online_store as feature_online_store_pb2,
+    )
+
+    stores_list = vertexai.resources.preview.FeatureOnlineStore.list(
+        project=project_id, location=location
+    )
+    for store in stores_list:
+        if store.name == online_store_name:
+            return store
+
+    _logger.info("Creating feature store online store")
+    # Create it otherwise
+
+    online_store_config = feature_online_store_pb2.FeatureOnlineStore(
+        optimized=feature_online_store_pb2.FeatureOnlineStore.Optimized()
+    )
+    create_store_lro = _admin_client.create_feature_online_store(
+        parent=f"projects/{project_id}/locations/{location}",
+        feature_online_store_id=online_store_name,
+        feature_online_store=online_store_config,
+    )
+    _logger.info(create_store_lro.result())
+    _logger.info(create_store_lro.result())
+    stores_list = vertexai.resources.preview.FeatureOnlineStore.list(
+        project=project_id, location=location
+    )
+    for store in stores_list:
+        if store.name == online_store_name:
+            return store
+
+
+def _get_feature_view(online_store: Any, view_name: Optional[str]) -> Any:
+    # Search for existing Feature view
+    import vertexai
+
+    fv_list = vertexai.resources.preview.FeatureView.list(
+        feature_online_store_id=online_store.gca_resource.name
+    )
+    for fv in fv_list:
+        if fv.name == view_name:
+            return fv
+    return None
diff --git a/libs/community/langchain_google_community/gmail/base.py b/libs/community/langchain_google_community/gmail/base.py
index 8678d4f5..98b0af08 100644
--- a/libs/community/langchain_google_community/gmail/base.py
+++ b/libs/community/langchain_google_community/gmail/base.py
@@ -35,4 +35,4 @@ def from_api_resource(cls, api_resource: Resource) -> "GmailBaseTool":
         Returns:
             A tool.
         """
-        return cls(service=api_resource)
+        return cls(service=api_resource)  # type: ignore[call-arg]
diff --git a/libs/community/langchain_google_community/gmail/get_message.py b/libs/community/langchain_google_community/gmail/get_message.py
index 3bd62d39..8cb14c2b 100644
--- a/libs/community/langchain_google_community/gmail/get_message.py
+++ b/libs/community/langchain_google_community/gmail/get_message.py
@@ -53,10 +53,10 @@ def _run(
                 ctype = part.get_content_type()
                 cdispo = str(part.get("Content-Disposition"))
                 if ctype == "text/plain" and "attachment" not in cdispo:
-                    message_body = part.get_payload(decode=True).decode("utf-8")
+                    message_body = part.get_payload(decode=True).decode("utf-8")  # type: ignore[union-attr]
                     break
         else:
-            message_body = email_msg.get_payload(decode=True).decode("utf-8")
+            message_body = email_msg.get_payload(decode=True).decode("utf-8")  # type: ignore[union-attr]
 
         body = clean_email_body(message_body)
 
diff --git a/libs/community/langchain_google_community/gmail/search.py b/libs/community/langchain_google_community/gmail/search.py
index e4ececdb..1480116b 100644
--- a/libs/community/langchain_google_community/gmail/search.py
+++ b/libs/community/langchain_google_community/gmail/search.py
@@ -99,14 +99,14 @@ def _parse_messages(self, messages: List[Dict[str, Any]]) -> List[Dict[str, Any]
                     cdispo = str(part.get("Content-Disposition"))
                     if ctype == "text/plain" and "attachment" not in cdispo:
                         try:
-                            message_body = part.get_payload(decode=True).decode("utf-8")
+                            message_body = part.get_payload(decode=True).decode("utf-8")  # type: ignore[union-attr]
                         except UnicodeDecodeError:
-                            message_body = part.get_payload(decode=True).decode(
+                            message_body = part.get_payload(decode=True).decode(  # type: ignore[union-attr]
                                 "latin-1"
                             )
                         break
             else:
-                message_body = email_msg.get_payload(decode=True).decode("utf-8")
+                message_body = email_msg.get_payload(decode=True).decode("utf-8")  # type: ignore[union-attr]
 
             body = clean_email_body(message_body)
 
diff --git a/libs/community/langchain_google_community/places_api.py b/libs/community/langchain_google_community/places_api.py
index 331a5baf..bca0cede 100644
--- a/libs/community/langchain_google_community/places_api.py
+++ b/libs/community/langchain_google_community/places_api.py
@@ -132,7 +132,7 @@ class GooglePlacesTool(BaseTool):
         "discover addressed from ambiguous text. "
         "Input should be a search query."
     )
-    api_wrapper: GooglePlacesAPIWrapper = Field(default_factory=GooglePlacesAPIWrapper)
+    api_wrapper: GooglePlacesAPIWrapper = Field(default_factory=GooglePlacesAPIWrapper)  # type: ignore[arg-type]
     args_schema: Type[BaseModel] = GooglePlacesSchema
 
     def _run(
diff --git a/libs/community/poetry.lock b/libs/community/poetry.lock
index 003d7764..8d9ad973 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"
@@ -682,12 +682,12 @@ files = [
 google-auth = ">=2.14.1,<3.0.dev0"
 googleapis-common-protos = ">=1.56.2,<2.0.dev0"
 grpcio = [
-    {version = ">=1.33.2,<2.0dev", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
     {version = ">=1.49.1,<2.0dev", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
+    {version = ">=1.33.2,<2.0dev", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
 ]
 grpcio-status = [
-    {version = ">=1.33.2,<2.0.dev0", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
     {version = ">=1.49.1,<2.0.dev0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
+    {version = ">=1.33.2,<2.0.dev0", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
 ]
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0.dev0"
@@ -865,8 +865,8 @@ files = [
 google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
 google-auth = ">=2.14.1,<3.0.0dev"
 proto-plus = [
-    {version = ">=1.22.0,<2.0.0dev", markers = "python_version < \"3.11\""},
     {version = ">=1.22.2,<2.0.0dev", markers = "python_version >= \"3.11\""},
+    {version = ">=1.22.0,<2.0.0dev", markers = "python_version < \"3.11\""},
 ]
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
@@ -1512,30 +1512,6 @@ files = [
     {file = "jsonpointer-3.0.0.tar.gz", hash = "sha256:2b2d729f2091522d61c3b31f82e11870f60b68f43fbc705cb76bf4b832af59ef"},
 ]
 
-[[package]]
-name = "langchain"
-version = "0.2.3"
-description = "Building applications with LLMs through composability"
-optional = false
-python-versions = "<4.0,>=3.8.1"
-files = [
-    {file = "langchain-0.2.3-py3-none-any.whl", hash = "sha256:5dc33cd9c8008693d328b7cb698df69073acecc89ad9c2a95f243b3314f8d834"},
-    {file = "langchain-0.2.3.tar.gz", hash = "sha256:81962cc72cce6515f7bd71e01542727870789bf8b666c6913d85559080c1a201"},
-]
-
-[package.dependencies]
-aiohttp = ">=3.8.3,<4.0.0"
-async-timeout = {version = ">=4.0.0,<5.0.0", markers = "python_version < \"3.11\""}
-langchain-core = ">=0.2.0,<0.3.0"
-langchain-text-splitters = ">=0.2.0,<0.3.0"
-langsmith = ">=0.1.17,<0.2.0"
-numpy = ">=1,<2"
-pydantic = ">=1,<3"
-PyYAML = ">=5.3"
-requests = ">=2,<3"
-SQLAlchemy = ">=1.4,<3"
-tenacity = ">=8.1.0,<9.0.0"
-
 [[package]]
 name = "langchain"
 version = "0.2.5"
@@ -1553,31 +1529,11 @@ async-timeout = {version = ">=4.0.0,<5.0.0", markers = "python_version < \"3.11\
 langchain-core = ">=0.2.7,<0.3.0"
 langchain-text-splitters = ">=0.2.0,<0.3.0"
 langsmith = ">=0.1.17,<0.2.0"
-numpy = {version = ">=1,<2", markers = "python_version < \"3.12\""}
-pydantic = ">=1,<3"
-PyYAML = ">=5.3"
-requests = ">=2,<3"
-SQLAlchemy = ">=1.4,<3"
-tenacity = ">=8.1.0,<9.0.0"
-
-[[package]]
-name = "langchain-community"
-version = "0.2.4"
-description = "Community contributed LangChain integrations."
-optional = false
-python-versions = "<4.0,>=3.8.1"
-files = [
-    {file = "langchain_community-0.2.4-py3-none-any.whl", hash = "sha256:8582e9800f4837660dc297cccd2ee1ddc1d8c440d0fe8b64edb07620f0373b0e"},
-    {file = "langchain_community-0.2.4.tar.gz", hash = "sha256:2bb6a1a36b8500a564d25d76469c02457b1a7c3afea6d4a609a47c06b993e3e4"},
+numpy = [
+    {version = ">=1,<2", markers = "python_version < \"3.12\""},
+    {version = ">=1.26.0,<2.0.0", markers = "python_version >= \"3.12\""},
 ]
-
-[package.dependencies]
-aiohttp = ">=3.8.3,<4.0.0"
-dataclasses-json = ">=0.5.7,<0.7"
-langchain = ">=0.2.0,<0.3.0"
-langchain-core = ">=0.2.0,<0.3.0"
-langsmith = ">=0.1.0,<0.2.0"
-numpy = ">=1,<2"
+pydantic = ">=1,<3"
 PyYAML = ">=5.3"
 requests = ">=2,<3"
 SQLAlchemy = ">=1.4,<3"
@@ -1600,7 +1556,10 @@ dataclasses-json = ">=0.5.7,<0.7"
 langchain = ">=0.2.5,<0.3.0"
 langchain-core = ">=0.2.7,<0.3.0"
 langsmith = ">=0.1.0,<0.2.0"
-numpy = {version = ">=1,<2", markers = "python_version < \"3.12\""}
+numpy = [
+    {version = ">=1,<2", markers = "python_version < \"3.12\""},
+    {version = ">=1.26.0,<2.0.0", markers = "python_version >= \"3.12\""},
+]
 PyYAML = ">=5.3"
 requests = ">=2,<3"
 SQLAlchemy = ">=1.4,<3"
@@ -2038,52 +1997,49 @@ files = [
 
 [[package]]
 name = "mypy"
-version = "0.991"
+version = "1.10.0"
 description = "Optional static typing for Python"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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+    {file = "mypy-1.10.0-py3-none-any.whl", hash = "sha256:f8c083976eb530019175aabadb60921e73b4f45736760826aa1689dda8208aee"},
+    {file = "mypy-1.10.0.tar.gz", hash = "sha256:3d087fcbec056c4ee34974da493a826ce316947485cef3901f511848e687c131"},
+]
+
+[package.dependencies]
+mypy-extensions = ">=1.0.0"
 tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
-typing-extensions = ">=3.10"
+typing-extensions = ">=4.1.0"
 
 [package.extras]
 dmypy = ["psutil (>=4.0)"]
 install-types = ["pip"]
-python2 = ["typed-ast (>=1.4.0,<2)"]
+mypyc = ["setuptools (>=50)"]
 reports = ["lxml"]
 
 [[package]]
@@ -2211,6 +2167,51 @@ files = [
     {file = "numpy-1.24.4.tar.gz", hash = "sha256:80f5e3a4e498641401868df4208b74581206afbee7cf7b8329daae82676d9463"},
 ]
 
+[[package]]
+name = "numpy"
+version = "1.26.4"
+description = "Fundamental package for array computing in Python"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "numpy-1.26.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:9ff0f4f29c51e2803569d7a51c2304de5554655a60c5d776e35b4a41413830d0"},
+    {file = "numpy-1.26.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:2e4ee3380d6de9c9ec04745830fd9e2eccb3e6cf790d39d7b98ffd19b0dd754a"},
+    {file = "numpy-1.26.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d209d8969599b27ad20994c8e41936ee0964e6da07478d6c35016bc386b66ad4"},
+    {file = "numpy-1.26.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ffa75af20b44f8dba823498024771d5ac50620e6915abac414251bd971b4529f"},
+    {file = "numpy-1.26.4-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:62b8e4b1e28009ef2846b4c7852046736bab361f7aeadeb6a5b89ebec3c7055a"},
+    {file = "numpy-1.26.4-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:a4abb4f9001ad2858e7ac189089c42178fcce737e4169dc61321660f1a96c7d2"},
+    {file = "numpy-1.26.4-cp310-cp310-win32.whl", hash = "sha256:bfe25acf8b437eb2a8b2d49d443800a5f18508cd811fea3181723922a8a82b07"},
+    {file = "numpy-1.26.4-cp310-cp310-win_amd64.whl", hash = "sha256:b97fe8060236edf3662adfc2c633f56a08ae30560c56310562cb4f95500022d5"},
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+    {file = "numpy-1.26.4-cp312-cp312-win_amd64.whl", hash = "sha256:08beddf13648eb95f8d867350f6a018a4be2e5ad54c8d8caed89ebca558b2818"},
+    {file = "numpy-1.26.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:7349ab0fa0c429c82442a27a9673fc802ffdb7c7775fad780226cb234965e53c"},
+    {file = "numpy-1.26.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:52b8b60467cd7dd1e9ed082188b4e6bb35aa5cdd01777621a1658910745b90be"},
+    {file = "numpy-1.26.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d5241e0a80d808d70546c697135da2c613f30e28251ff8307eb72ba696945764"},
+    {file = "numpy-1.26.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f870204a840a60da0b12273ef34f7051e98c3b5961b61b0c2c1be6dfd64fbcd3"},
+    {file = "numpy-1.26.4-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:679b0076f67ecc0138fd2ede3a8fd196dddc2ad3254069bcb9faf9a79b1cebcd"},
+    {file = "numpy-1.26.4-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:47711010ad8555514b434df65f7d7b076bb8261df1ca9bb78f53d3b2db02e95c"},
+    {file = "numpy-1.26.4-cp39-cp39-win32.whl", hash = "sha256:a354325ee03388678242a4d7ebcd08b5c727033fcff3b2f536aea978e15ee9e6"},
+    {file = "numpy-1.26.4-cp39-cp39-win_amd64.whl", hash = "sha256:3373d5d70a5fe74a2c1bb6d2cfd9609ecf686d47a2d7b1d37a8f3b6bf6003aea"},
+    {file = "numpy-1.26.4-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:afedb719a9dcfc7eaf2287b839d8198e06dcd4cb5d276a3df279231138e83d30"},
+    {file = "numpy-1.26.4-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:95a7476c59002f2f6c590b9b7b998306fba6a5aa646b1e22ddfeaf8f78c3a29c"},
+    {file = "numpy-1.26.4-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:7e50d0a0cc3189f9cb0aeb3a6a6af18c16f59f004b866cd2be1c14b36134a4a0"},
+    {file = "numpy-1.26.4.tar.gz", hash = "sha256:2a02aba9ed12e4ac4eb3ea9421c420301a0c6460d9830d74a9df87efa4912010"},
+]
+
 [[package]]
 name = "oauth2client"
 version = "3.0.0"
@@ -2350,9 +2351,9 @@ gcsfs = {version = ">=2021.07.0", optional = true, markers = "extra == \"gcp\""}
 numba = {version = ">=0.53.1", optional = true, markers = "extra == \"performance\""}
 numexpr = {version = ">=2.7.1", optional = true, markers = "extra == \"performance\""}
 numpy = [
+    {version = ">=1.23.2", markers = "python_version >= \"3.11\""},
     {version = ">=1.20.3", markers = "python_version < \"3.10\""},
     {version = ">=1.21.0", markers = "python_version >= \"3.10\" and python_version < \"3.11\""},
-    {version = ">=1.23.2", markers = "python_version >= \"3.11\""},
 ]
 pandas-gbq = {version = ">=0.15.0", optional = true, markers = "extra == \"gcp\""}
 python-dateutil = ">=2.8.2"
@@ -2952,6 +2953,7 @@ files = [
     {file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"},
     {file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"},
     {file = "PyYAML-6.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"},
+    {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a08c6f0fe150303c1c6b71ebcd7213c2858041a7e01975da3a99aed1e7a378ef"},
     {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"},
     {file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"},
     {file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"},
@@ -3693,7 +3695,7 @@ test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools",
 bigquery = ["google-cloud-bigquery"]
 docai = ["gapic-google-longrunning", "google-cloud-contentwarehouse", "google-cloud-documentai", "google-cloud-documentai-toolbox"]
 drive = ["google-auth-httplib2", "google-auth-oauthlib"]
-featurestore = ["db-dtypes", "google-cloud-aiplatform", "google-cloud-bigquery-storage", "pandas", "pyarrow", "pydantic"]
+featurestore = ["db-dtypes", "google-cloud-aiplatform", "google-cloud-bigquery-storage", "pandas", "pandas", "pyarrow", "pydantic"]
 gcs = ["google-cloud-storage"]
 gmail = ["beautifulsoup4", "google-auth-httplib2", "google-auth-oauthlib"]
 places = ["googlemaps"]
@@ -3706,4 +3708,4 @@ vision = ["google-cloud-vision"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0"
-content-hash = "b55f53a241ae77625e4d0970f56ade100781d8044cadedea243d7780e904b048"
+content-hash = "0050159f79ba808e5300b12e6237c486e5ffd83dcfeefe3e110fc06e862f7f5e"
diff --git a/libs/community/pyproject.toml b/libs/community/pyproject.toml
index c5239ce4..6b76d9e0 100644
--- a/libs/community/pyproject.toml
+++ b/libs/community/pyproject.toml
@@ -17,6 +17,7 @@ langchain-community = "^0.2.1"
 google-api-core = "^2.17.1"
 google-api-python-client = "^2.122.0"
 grpcio = "^1.62.0"
+tenacity = "~8.3.0"
 google-cloud-bigquery = { version = "^3.21.0", optional = true }
 google-cloud-documentai = { version = "^2.26.0", optional = true }
 google-cloud-contentwarehouse = { version = "^0.7.7", optional = true }
@@ -32,13 +33,15 @@ google-cloud-translate = { version = "^3.15.3", optional = true }
 google-cloud-discoveryengine = { version = "^0.11.11", optional = true }
 google-cloud-vision = { version = "^3.7.2", optional = true }
 beautifulsoup4 = { version = "^4.12.3", optional = true }
-pandas = { version = ">= 1.0.0", optional = true }
+pandas = [
+  { version = ">=1.0.0", python = ">=3.8.1,<3.12", optional = true },
+  { version = ">=2.0.0,<3.0", python = ">=3.12,<4.0", optional = true },
+]
 google-cloud-bigquery-storage = { version = ">=2.6.0,<3",  optional = true }
 pyarrow = { version = ">= 6.0.1", optional = true }
 db-dtypes = { version = "^1.2.0", optional = true }
 google-cloud-aiplatform = { version = "^1.47.0", optional = true }
-pydantic = { version = "^2.7.2", optional = true }
-tenacity = "~8.3.0"
+pydantic = { version = "^2.7.4", optional = true }
 
 [tool.poetry.extras]
 bigquery = ["google-cloud-bigquery"]
@@ -84,14 +87,15 @@ ignore-words-list = "rouge"
 [tool.poetry.group.codespell.dependencies]
 codespell = "^2.2.0"
 
-
-
 [tool.poetry.group.test_integration]
 optional = true
 
 [tool.poetry.group.test_integration.dependencies]
 pillow = "^10.1.0"
-
+numpy = [
+  { version = "^1", python = "<3.12" },
+  { version = "^1.26.0", python = ">=3.12" },
+]
 
 
 [tool.poetry.group.lint]
@@ -101,9 +105,8 @@ optional = true
 ruff = "^0.1.5"
 
 
-
 [tool.poetry.group.typing.dependencies]
-mypy = "^0.991"
+mypy = "^1"
 types-requests = "^2.28.11.5"
 types-google-cloud-ndb = "^2.2.0.1"
 types-pillow = "^10.1.0.2"
diff --git a/libs/community/scripts/check_imports.py b/libs/community/scripts/check_imports.py
new file mode 100644
index 00000000..fd21a497
--- /dev/null
+++ b/libs/community/scripts/check_imports.py
@@ -0,0 +1,17 @@
+import sys
+import traceback
+from importlib.machinery import SourceFileLoader
+
+if __name__ == "__main__":
+    files = sys.argv[1:]
+    has_failure = False
+    for file in files:
+        try:
+            SourceFileLoader("x", file).load_module()
+        except Exception:
+            has_faillure = True
+            print(file)
+            traceback.print_exc()
+            print()
+
+    sys.exit(1 if has_failure else 0)
diff --git a/libs/community/scripts/check_pydantic.sh b/libs/community/scripts/check_pydantic.sh
new file mode 100755
index 00000000..06b5bb81
--- /dev/null
+++ b/libs/community/scripts/check_pydantic.sh
@@ -0,0 +1,27 @@
+#!/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 "$result"
+  echo "Please replace the code with an import from langchain_core.pydantic_v1."
+  echo "For example, replace 'from pydantic import BaseModel'"
+  echo "with 'from langchain_core.pydantic_v1 import BaseModel'"
+  exit 1
+fi
diff --git a/libs/community/scripts/lint_imports.sh b/libs/community/scripts/lint_imports.sh
new file mode 100755
index 00000000..695613c7
--- /dev/null
+++ b/libs/community/scripts/lint_imports.sh
@@ -0,0 +1,17 @@
+#!/bin/bash
+
+set -eu
+
+# Initialize a variable to keep track of errors
+errors=0
+
+# make sure not importing from langchain or langchain_experimental
+git --no-pager grep '^from langchain\.' . && errors=$((errors+1))
+git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1))
+
+# Decide on an exit status based on the errors
+if [ "$errors" -gt 0 ]; then
+    exit 1
+else
+    exit 0
+fi
diff --git a/libs/community/tests/integration_tests/feature_store/test_feature_store_bq_vectorstore.py b/libs/community/tests/integration_tests/feature_store/test_feature_store_bq_vectorstore.py
index b1f6c505..f860f35e 100644
--- a/libs/community/tests/integration_tests/feature_store/test_feature_store_bq_vectorstore.py
+++ b/libs/community/tests/integration_tests/feature_store/test_feature_store_bq_vectorstore.py
@@ -3,6 +3,7 @@
 """
 
 import os
+import random
 
 import pytest
 
@@ -12,7 +13,7 @@
 from tests.integration_tests.fake import FakeEmbeddings
 
 TEST_DATASET = "langchain_test_dataset"
-TEST_TABLE_NAME = "langchain_test_table"
+TEST_TABLE_NAME = f"langchain_test_table{str(random.randint(1,100000))}"
 TEST_FOS_NAME = "langchain_test_fos"
 EMBEDDING_SIZE = 768
 
diff --git a/libs/community/tests/integration_tests/feature_store/test_feature_store_fs_vectorstore.py b/libs/community/tests/integration_tests/feature_store/test_feature_store_fs_vectorstore.py
index 7f7a609f..b85b6957 100644
--- a/libs/community/tests/integration_tests/feature_store/test_feature_store_fs_vectorstore.py
+++ b/libs/community/tests/integration_tests/feature_store/test_feature_store_fs_vectorstore.py
@@ -54,7 +54,7 @@ def teardown() -> None:
             not_found_ok=True,
         )
         fs_vs = TestVertexFSVectorStore_fs_vectorstore.store_fs_vectorstore
-        fs_vs._feature_view.delete()  # type: ignore[union-attr]
+        fs_vs.feature_view.delete()  # type: ignore[union-attr]
 
     request.addfinalizer(teardown)
     return TestVertexFSVectorStore_fs_vectorstore.store_fs_vectorstore
@@ -104,7 +104,7 @@ def test_add_texts_with_embeddings_with_error(
             embedding=store_fs_vectorstore.embedding,
             location="us-central1",
             dataset_name=store_fs_vectorstore.dataset_name,
-            table_name="error_table",
+            table_name=f"error_table{str(random.randint(1,100000))}",
             online_store_name=store_fs_vectorstore.online_store_name,
             view_name=store_fs_vectorstore.view_name,
         )
diff --git a/libs/community/tests/integration_tests/test_bigquery_vector_search.py b/libs/community/tests/integration_tests/test_bigquery_vector_search.py
index 2ee55d9c..67b0e1ab 100644
--- a/libs/community/tests/integration_tests/test_bigquery_vector_search.py
+++ b/libs/community/tests/integration_tests/test_bigquery_vector_search.py
@@ -33,7 +33,7 @@ def store(request: pytest.FixtureRequest) -> BigQueryVectorSearch:
     )
     TestBigQueryVectorStore.store = BigQueryVectorSearch(
         project_id=os.environ.get("PROJECT_ID", None),  # type: ignore[arg-type]
-        embedding=FakeEmbeddings(),
+        embedding=FakeEmbeddings(),  # type: ignore[call-arg]
         dataset_name=TestBigQueryVectorStore.dataset_name,
         table_name=TEST_TABLE_NAME,
     )
diff --git a/libs/community/tests/integration_tests/test_googlesearch_api.py b/libs/community/tests/integration_tests/test_googlesearch_api.py
index c3c4ad88..8414fc0a 100644
--- a/libs/community/tests/integration_tests/test_googlesearch_api.py
+++ b/libs/community/tests/integration_tests/test_googlesearch_api.py
@@ -12,7 +12,7 @@ def test_call() -> None:
     """Test that call gives the correct answer."""
     google_api_key = os.environ["GOOGLE_API_KEY"]
     google_cse_id = os.environ["GOOGLE_CSE_ID"]
-    search = GoogleSearchAPIWrapper(
+    search = GoogleSearchAPIWrapper(  # type: ignore[call-arg]
         google_api_key=google_api_key, google_cse_id=google_cse_id
     )
     output = search.run("What was Obama's first name?")
@@ -24,7 +24,7 @@ def test_no_result_call() -> None:
     """Test that call gives no result."""
     google_api_key = os.environ["GOOGLE_API_KEY"]
     google_cse_id = os.environ["GOOGLE_CSE_ID"]
-    search = GoogleSearchAPIWrapper(
+    search = GoogleSearchAPIWrapper(  # type: ignore[call-arg]
         google_api_key=google_api_key, google_cse_id=google_cse_id
     )
     output = search.run(
@@ -38,7 +38,7 @@ def test_result_with_params_call() -> None:
     """Test that call gives the correct answer with extra params."""
     google_api_key = os.environ["GOOGLE_API_KEY"]
     google_cse_id = os.environ["GOOGLE_CSE_ID"]
-    search = GoogleSearchAPIWrapper(
+    search = GoogleSearchAPIWrapper(  # type: ignore[call-arg]
         google_api_key=google_api_key, google_cse_id=google_cse_id
     )
     output = search.results(
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 9ea8f6c5..65c572ed 100644
--- a/libs/community/tests/integration_tests/test_vertex_ai_search.py
+++ b/libs/community/tests/integration_tests/test_vertex_ai_search.py
@@ -58,7 +58,7 @@ def test_google_vertex_ai_multiturnsearch_get_relevant_documents() -> None:
 @pytest.mark.extended
 def test_vertex_search_tool() -> None:
     data_store_id = os.environ["DATA_STORE_ID"]
-    tool = VertexAISearchSummaryTool(
+    tool = VertexAISearchSummaryTool(  # type: ignore[call-arg, call-arg, call-arg]
         name="vertex-search",
         description="Vertex Search Tool",
         data_store_id=data_store_id,
diff --git a/libs/genai/Makefile b/libs/genai/Makefile
index c221ca70..c334af71 100644
--- a/libs/genai/Makefile
+++ b/libs/genai/Makefile
@@ -39,8 +39,9 @@ lint lint_diff lint_package lint_tests:
 	./scripts/check_pydantic.sh .
 	./scripts/lint_imports.sh
 	poetry run ruff .
-	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff
-	[ "$(PYTHON_FILES)" = "" ] || poetry run mypy $(PYTHON_FILES)
+	poetry run ruff format $(PYTHON_FILES) --diff
+	poetry run ruff --select I $(PYTHON_FILES)
+	mkdir $(MYPY_CACHE); poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
 
 format format_diff:
 	poetry run ruff format $(PYTHON_FILES)
@@ -52,6 +53,10 @@ spell_check:
 spell_fix:
 	poetry run codespell --toml pyproject.toml -w
 
+
+check_imports: $(shell find langchain_google_genai -name '*.py')
+	poetry run python ./scripts/check_imports.py $^
+
 ######################
 # HELP
 ######################
diff --git a/libs/genai/poetry.lock b/libs/genai/poetry.lock
index c44ebe41..ca47c6a5 100644
--- a/libs/genai/poetry.lock
+++ b/libs/genai/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 = "annotated-types"
@@ -469,7 +469,7 @@ files = [
 
 [[package]]
 name = "langchain-core"
-version = "0.2.7"
+version = "0.2.9"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.8.1,<4.0"
@@ -480,15 +480,18 @@ develop = false
 jsonpatch = "^1.33"
 langsmith = "^0.1.75"
 packaging = ">=23.2,<25"
-pydantic = ">=1,<3"
+pydantic = [
+    {version = ">=1,<3", markers = "python_full_version < \"3.12.4\""},
+    {version = ">=2.7.4,<3.0.0", markers = "python_full_version >= \"3.12.4\""},
+]
 PyYAML = ">=5.3"
-tenacity = "^8.1.0"
+tenacity = "^8.1.0,!=8.4.0"
 
 [package.source]
 type = "git"
 url = "https://github.com/langchain-ai/langchain.git"
 reference = "HEAD"
-resolved_reference = "892bd4c29be34c0cc095ed178be6d60c6858e2ec"
+resolved_reference = "1fcf875fe3292f801e68b16aa995facd8f3f1d7a"
 subdirectory = "libs/core"
 
 [[package]]
@@ -1077,6 +1080,7 @@ files = [
     {file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"},
     {file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"},
     {file = "PyYAML-6.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"},
+    {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a08c6f0fe150303c1c6b71ebcd7213c2858041a7e01975da3a99aed1e7a378ef"},
     {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"},
     {file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"},
     {file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"},
@@ -1379,4 +1383,4 @@ images = ["pillow"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4.0"
-content-hash = "17a2d39244dcd4aa110687b3acc6bdc69630fa9ddfa6f4598e8a15cec54776d3"
+content-hash = "c14466fccfffc2d80ba13a9fdc7cfdf59546fae89a5244024764fad231ffed1b"
diff --git a/libs/genai/pyproject.toml b/libs/genai/pyproject.toml
index 22cac8ef..b41ced6b 100644
--- a/libs/genai/pyproject.toml
+++ b/libs/genai/pyproject.toml
@@ -12,7 +12,7 @@ license = "MIT"
 
 [tool.poetry.dependencies]
 python = ">=3.9,<4.0"
-langchain-core = ">=0.2.2,<0.3"
+langchain-core = ">=0.2.9,<0.3"
 google-generativeai = "^0.5.2"
 pillow = { version = "^10.1.0", optional = true }
 
diff --git a/libs/vertexai/Makefile b/libs/vertexai/Makefile
index 9b126f97..f5a56349 100644
--- a/libs/vertexai/Makefile
+++ b/libs/vertexai/Makefile
@@ -33,6 +33,8 @@ 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
 	poetry run ruff --select I $(PYTHON_FILES)
diff --git a/libs/vertexai/langchain_google_vertexai/_anthropic_utils.py b/libs/vertexai/langchain_google_vertexai/_anthropic_utils.py
index 345d8953..38e092d3 100644
--- a/libs/vertexai/langchain_google_vertexai/_anthropic_utils.py
+++ b/libs/vertexai/langchain_google_vertexai/_anthropic_utils.py
@@ -190,9 +190,9 @@ def _merge_messages(
                 isinstance(block, dict) and block.get("type") == "tool_result"
                 for block in curr.content
             ):
-                curr = HumanMessage(curr.content)  # type: ignore[misc]
+                curr = HumanMessage(curr.content)
             else:
-                curr = HumanMessage(  # type: ignore[misc]
+                curr = HumanMessage(
                     [
                         {
                             "type": "tool_result",
diff --git a/libs/vertexai/poetry.lock b/libs/vertexai/poetry.lock
index 89b2aaad..b77cd263 100644
--- a/libs/vertexai/poetry.lock
+++ b/libs/vertexai/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
 
 [[package]]
 name = "aiohttp"
@@ -153,7 +153,7 @@ vertex = ["google-auth (>=2,<3)"]
 name = "anyio"
 version = "4.4.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
-optional = true
+optional = false
 python-versions = ">=3.8"
 files = [
     {file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"},
@@ -388,18 +388,18 @@ test = ["pytest (>=6)"]
 
 [[package]]
 name = "filelock"
-version = "3.14.0"
+version = "3.15.4"
 description = "A platform independent file lock."
 optional = true
 python-versions = ">=3.8"
 files = [
-    {file = "filelock-3.14.0-py3-none-any.whl", hash = "sha256:43339835842f110ca7ae60f1e1c160714c5a6afd15a2873419ab185334975c0f"},
-    {file = "filelock-3.14.0.tar.gz", hash = "sha256:6ea72da3be9b8c82afd3edcf99f2fffbb5076335a5ae4d03248bb5b6c3eae78a"},
+    {file = "filelock-3.15.4-py3-none-any.whl", hash = "sha256:6ca1fffae96225dab4c6eaf1c4f4f28cd2568d3ec2a44e15a08520504de468e7"},
+    {file = "filelock-3.15.4.tar.gz", hash = "sha256:2207938cbc1844345cb01a5a95524dae30f0ce089eba5b00378295a17e3e90cb"},
 ]
 
 [package.extras]
 docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.25.2)"]
-testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8.0.1)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)"]
+testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8.0.1)", "pytest (>=7.4.3)", "pytest-asyncio (>=0.21)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)", "virtualenv (>=20.26.2)"]
 typing = ["typing-extensions (>=4.8)"]
 
 [[package]]
@@ -543,13 +543,13 @@ tqdm = ["tqdm"]
 
 [[package]]
 name = "google-api-core"
-version = "2.19.0"
+version = "2.19.1"
 description = "Google API client core library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-api-core-2.19.0.tar.gz", hash = "sha256:cf1b7c2694047886d2af1128a03ae99e391108a08804f87cfd35970e49c9cd10"},
-    {file = "google_api_core-2.19.0-py3-none-any.whl", hash = "sha256:8661eec4078c35428fd3f69a2c7ee29e342896b70f01d1a1cbcb334372dd6251"},
+    {file = "google-api-core-2.19.1.tar.gz", hash = "sha256:f4695f1e3650b316a795108a76a1c416e6afb036199d1c1f1f110916df479ffd"},
+    {file = "google_api_core-2.19.1-py3-none-any.whl", hash = "sha256:f12a9b8309b5e21d92483bbd47ce2c445861ec7d269ef6784ecc0ea8c1fa6125"},
 ]
 
 [package.dependencies]
@@ -564,7 +564,7 @@ grpcio-status = [
     {version = ">=1.33.2,<2.0.dev0", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
 ]
 proto-plus = ">=1.22.3,<2.0.0dev"
-protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0.dev0"
+protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<6.0.0.dev0"
 requests = ">=2.18.0,<3.0.0.dev0"
 
 [package.extras]
@@ -574,13 +574,13 @@ grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"]
 
 [[package]]
 name = "google-api-python-client"
-version = "2.132.0"
+version = "2.134.0"
 description = "Google API Client Library for Python"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-api-python-client-2.132.0.tar.gz", hash = "sha256:d6340dc83b72d72333cee5d50f7dcfecbff66a8783164090e945f985ec4c374d"},
-    {file = "google_api_python_client-2.132.0-py2.py3-none-any.whl", hash = "sha256:cde87700bd4d37f39f5e940292c1c6cd0910990b5b01f50b1332a8cea38e8595"},
+    {file = "google-api-python-client-2.134.0.tar.gz", hash = "sha256:4a8f0bea651a212997cc83c0f271fc86f80ef93d1cee9d84de7dfaeef2a858b6"},
+    {file = "google_api_python_client-2.134.0-py2.py3-none-any.whl", hash = "sha256:ba05d60f6239990b7994f6328f17bb154c602d31860fb553016dc9f8ce886945"},
 ]
 
 [package.dependencies]
@@ -630,13 +630,13 @@ httplib2 = ">=0.19.0"
 
 [[package]]
 name = "google-cloud-aiplatform"
-version = "1.54.1"
+version = "1.56.0"
 description = "Vertex AI API client library"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "google-cloud-aiplatform-1.54.1.tar.gz", hash = "sha256:01c231961cc1a1a3b049ea3ef71fb11e77b2d56d632d020ce09e419b27ff77f2"},
-    {file = "google_cloud_aiplatform-1.54.1-py2.py3-none-any.whl", hash = "sha256:43f70fcd572f15317d769e5a0e04cfb7c0e259ead3fe581d2fba4f203ace5617"},
+    {file = "google-cloud-aiplatform-1.56.0.tar.gz", hash = "sha256:d4cfb085427dac01142915f523949ac2955d6c7f148d95017d3286a77caf5d5e"},
+    {file = "google_cloud_aiplatform-1.56.0-py2.py3-none-any.whl", hash = "sha256:ee1ab3bd115c3caebf8ddfd3e47eeb8396a3ec2fc5f5baf1a5c295c8d64333ab"},
 ]
 
 [package.dependencies]
@@ -657,9 +657,9 @@ autologging = ["mlflow (>=1.27.0,<=2.1.1)"]
 cloud-profiler = ["tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "werkzeug (>=2.0.0,<2.1.0dev)"]
 datasets = ["pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)"]
 endpoint = ["requests (>=2.28.1)"]
-full = ["cloudpickle (<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<=0.109.1)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (>=0.1.6)", "httpx (>=0.23.0,<0.25.0)", "immutabledict", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "nest-asyncio (>=1.0.0,<1.6.0)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyyaml (>=5.3.1,<7)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "requests (>=2.28.1)", "setuptools (<70.0.0)", "starlette (>=0.17.1)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)"]
-langchain = ["langchain (>=0.1.16,<0.2)", "langchain-core (<0.2)", "langchain-google-vertexai (<2)"]
-langchain-testing = ["absl-py", "cloudpickle (>=2.2.1,<4.0)", "langchain (>=0.1.16,<0.2)", "langchain-core (<0.2)", "langchain-google-vertexai (<2)", "pydantic (>=2.6.3,<3)", "pytest-xdist"]
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+langchain = ["langchain (>=0.1.16,<0.3)", "langchain-core (<0.2)", "langchain-google-vertexai (<2)", "openinference-instrumentation-langchain (>=0.1.19,<0.2)", "tenacity (<=8.3)"]
+langchain-testing = ["absl-py", "cloudpickle (>=3.0,<4.0)", "langchain (>=0.1.16,<0.3)", "langchain-core (<0.2)", "langchain-google-vertexai (<2)", "openinference-instrumentation-langchain (>=0.1.19,<0.2)", "opentelemetry-exporter-gcp-trace (<2)", "opentelemetry-sdk (<2)", "pydantic (>=2.6.3,<3)", "pytest-xdist", "tenacity (<=8.3)"]
 lit = ["explainable-ai-sdk (>=1.0.0)", "lit-nlp (==0.4.0)", "pandas (>=1.0.0)", "tensorflow (>=2.3.0,<3.0.0dev)"]
 metadata = ["numpy (>=1.15.0)", "pandas (>=1.0.0)"]
 pipelines = ["pyyaml (>=5.3.1,<7)"]
@@ -669,21 +669,21 @@ private-endpoints = ["requests (>=2.28.1)", "urllib3 (>=1.21.1,<1.27)"]
 rapid-evaluation = ["nest-asyncio (>=1.0.0,<1.6.0)", "pandas (>=1.0.0,<2.2.0)"]
 ray = ["google-cloud-bigquery", "google-cloud-bigquery-storage", "immutabledict", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=6.0.1)", "pydantic (<2)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "setuptools (<70.0.0)"]
 ray-testing = ["google-cloud-bigquery", "google-cloud-bigquery-storage", "immutabledict", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pytest-xdist", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "ray[train] (==2.9.3)", "scikit-learn", "setuptools (<70.0.0)", "tensorflow", "torch (>=2.0.0,<2.1.0)", "xgboost", "xgboost-ray"]
-reasoningengine = ["cloudpickle (>=2.2.1,<4.0)", "pydantic (>=2.6.3,<3)"]
-tensorboard = ["tensorflow (>=2.3.0,<3.0.0dev)"]
+reasoningengine = ["cloudpickle (>=3.0,<4.0)", "opentelemetry-exporter-gcp-trace (<2)", "opentelemetry-sdk (<2)", "pydantic (>=2.6.3,<3)"]
+tensorboard = ["tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "werkzeug (>=2.0.0,<2.1.0dev)"]
 testing = ["bigframes", "cloudpickle (<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<=0.109.1)", "google-api-core (>=2.11,<3.0.0)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (>=0.1.6)", "grpcio-testing", "httpx (>=0.23.0,<0.25.0)", "immutabledict", "ipython", "kfp (>=2.6.0,<3.0.0)", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "nest-asyncio (>=1.0.0,<1.6.0)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyfakefs", "pytest-asyncio", "pytest-xdist", "pyyaml (>=5.3.1,<7)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "requests (>=2.28.1)", "requests-toolbelt (<1.0.0)", "scikit-learn", "setuptools (<70.0.0)", "starlette (>=0.17.1)", "tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (==2.13.0)", "tensorflow (==2.16.1)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "torch (>=2.0.0,<2.1.0)", "torch (>=2.2.0)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)", "werkzeug (>=2.0.0,<2.1.0dev)", "xgboost"]
 vizier = ["google-vizier (>=0.1.6)"]
 xai = ["tensorflow (>=2.3.0,<3.0.0dev)"]
 
 [[package]]
 name = "google-cloud-bigquery"
-version = "3.24.0"
+version = "3.25.0"
 description = "Google BigQuery API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-bigquery-3.24.0.tar.gz", hash = "sha256:e95e6f6e0aa32e6c453d44e2b3298931fdd7947c309ea329a31b6ff1f939e17e"},
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+    {file = "google-cloud-bigquery-3.25.0.tar.gz", hash = "sha256:5b2aff3205a854481117436836ae1403f11f2594e6810a98886afd57eda28509"},
+    {file = "google_cloud_bigquery-3.25.0-py2.py3-none-any.whl", hash = "sha256:7f0c371bc74d2a7fb74dacbc00ac0f90c8c2bec2289b51dd6685a275873b1ce9"},
 ]
 
 [package.dependencies]
@@ -767,13 +767,13 @@ protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4
 
 [[package]]
 name = "google-cloud-storage"
-version = "2.16.0"
+version = "2.17.0"
 description = "Google Cloud Storage API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-storage-2.16.0.tar.gz", hash = "sha256:dda485fa503710a828d01246bd16ce9db0823dc51bbca742ce96a6817d58669f"},
-    {file = "google_cloud_storage-2.16.0-py2.py3-none-any.whl", hash = "sha256:91a06b96fb79cf9cdfb4e759f178ce11ea885c79938f89590344d079305f5852"},
+    {file = "google-cloud-storage-2.17.0.tar.gz", hash = "sha256:49378abff54ef656b52dca5ef0f2eba9aa83dc2b2c72c78714b03a1a95fe9388"},
+    {file = "google_cloud_storage-2.17.0-py2.py3-none-any.whl", hash = "sha256:5b393bc766b7a3bc6f5407b9e665b2450d36282614b7945e570b3480a456d1e1"},
 ]
 
 [package.dependencies]
@@ -869,13 +869,13 @@ testing = ["pytest"]
 
 [[package]]
 name = "google-resumable-media"
-version = "2.7.0"
+version = "2.7.1"
 description = "Utilities for Google Media Downloads and Resumable Uploads"
 optional = false
-python-versions = ">= 3.7"
+python-versions = ">=3.7"
 files = [
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-    {file = "google_resumable_media-2.7.0-py2.py3-none-any.whl", hash = "sha256:79543cfe433b63fd81c0844b7803aba1bb8950b47bedf7d980c38fa123937e08"},
+    {file = "google-resumable-media-2.7.1.tar.gz", hash = "sha256:eae451a7b2e2cdbaaa0fd2eb00cc8a1ee5e95e16b55597359cbc3d27d7d90e33"},
+    {file = "google_resumable_media-2.7.1-py2.py3-none-any.whl", hash = "sha256:103ebc4ba331ab1bfdac0250f8033627a2cd7cde09e7ccff9181e31ba4315b2c"},
 ]
 
 [package.dependencies]
@@ -887,18 +887,18 @@ requests = ["requests (>=2.18.0,<3.0.0dev)"]
 
 [[package]]
 name = "googleapis-common-protos"
-version = "1.63.1"
+version = "1.63.2"
 description = "Common protobufs used in Google APIs"
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "googleapis-common-protos-1.63.2.tar.gz", hash = "sha256:27c5abdffc4911f28101e635de1533fb4cfd2c37fbaa9174587c799fac90aa87"},
+    {file = "googleapis_common_protos-1.63.2-py2.py3-none-any.whl", hash = "sha256:27a2499c7e8aff199665b22741997e485eccc8645aa9176c7c988e6fae507945"},
 ]
 
 [package.dependencies]
 grpcio = {version = ">=1.44.0,<2.0.0.dev0", optional = true, markers = "extra == \"grpc\""}
-protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<6.0.0.dev0"
+protobuf = ">=3.20.2,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<6.0.0.dev0"
 
 [package.extras]
 grpc = ["grpcio (>=1.44.0,<2.0.0.dev0)"]
@@ -976,19 +976,19 @@ test = ["objgraph", "psutil"]
 
 [[package]]
 name = "grpc-google-iam-v1"
-version = "0.13.0"
+version = "0.13.1"
 description = "IAM API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "grpc-google-iam-v1-0.13.1.tar.gz", hash = "sha256:3ff4b2fd9d990965e410965253c0da6f66205d5a8291c4c31c6ebecca18a9001"},
+    {file = "grpc_google_iam_v1-0.13.1-py2.py3-none-any.whl", hash = "sha256:c3e86151a981811f30d5e7330f271cee53e73bb87755e88cc3b6f0c7b5fe374e"},
 ]
 
 [package.dependencies]
 googleapis-common-protos = {version = ">=1.56.0,<2.0.0dev", extras = ["grpc"]}
 grpcio = ">=1.44.0,<2.0.0dev"
-protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
+protobuf = ">=3.20.2,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<6.0.0dev"
 
 [[package]]
 name = "grpcio"
@@ -1068,7 +1068,7 @@ protobuf = ">=4.21.6"
 name = "h11"
 version = "0.14.0"
 description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
-optional = true
+optional = false
 python-versions = ">=3.7"
 files = [
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@@ -1079,7 +1079,7 @@ files = [
 name = "httpcore"
 version = "1.0.5"
 description = "A minimal low-level HTTP client."
-optional = true
+optional = false
 python-versions = ">=3.8"
 files = [
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@@ -1114,7 +1114,7 @@ pyparsing = {version = ">=2.4.2,<3.0.0 || >3.0.0,<3.0.1 || >3.0.1,<3.0.2 || >3.0
 name = "httpx"
 version = "0.27.0"
 description = "The next generation HTTP client."
-optional = true
+optional = false
 python-versions = ">=3.8"
 files = [
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@@ -1136,13 +1136,13 @@ socks = ["socksio (==1.*)"]
 
 [[package]]
 name = "huggingface-hub"
-version = "0.23.3"
+version = "0.23.4"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = true
 python-versions = ">=3.8.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -1192,72 +1192,72 @@ files = [
 
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 name = "jiter"
-version = "0.4.1"
+version = "0.5.0"
 description = "Fast iterable JSON parser."
 optional = true
 python-versions = ">=3.8"
 files = [
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+    {file = "jiter-0.5.0.tar.gz", hash = "sha256:1d916ba875bcab5c5f7d927df998c4cb694d27dceddf3392e58beaf10563368a"},
 ]
 
 [[package]]
@@ -1276,18 +1276,18 @@ jsonpointer = ">=1.9"
 
 [[package]]
 name = "jsonpointer"
-version = "2.4"
+version = "3.0.0"
 description = "Identify specific nodes in a JSON document (RFC 6901)"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
+python-versions = ">=3.7"
 files = [
-    {file = "jsonpointer-2.4-py2.py3-none-any.whl", hash = "sha256:15d51bba20eea3165644553647711d150376234112651b4f1811022aecad7d7a"},
-    {file = "jsonpointer-2.4.tar.gz", hash = "sha256:585cee82b70211fa9e6043b7bb89db6e1aa49524340dde8ad6b63206ea689d88"},
+    {file = "jsonpointer-3.0.0-py2.py3-none-any.whl", hash = "sha256:13e088adc14fca8b6aa8177c044e12701e6ad4b28ff10e65f2267a90109c9942"},
+    {file = "jsonpointer-3.0.0.tar.gz", hash = "sha256:2b2d729f2091522d61c3b31f82e11870f60b68f43fbc705cb76bf4b832af59ef"},
 ]
 
 [[package]]
 name = "langchain"
-version = "0.2.3"
+version = "0.2.5"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.8.1,<4.0"
@@ -1297,26 +1297,29 @@ develop = false
 [package.dependencies]
 aiohttp = "^3.8.3"
 async-timeout = {version = "^4.0.0", markers = "python_version < \"3.11\""}
-langchain-core = "^0.2.0"
+langchain-core = "^0.2.7"
 langchain-text-splitters = "^0.2.0"
 langsmith = "^0.1.17"
-numpy = "^1"
+numpy = [
+    {version = ">=1,<2", markers = "python_version < \"3.12\""},
+    {version = ">=1.26.0,<2.0.0", markers = "python_version >= \"3.12\""},
+]
 pydantic = ">=1,<3"
 PyYAML = ">=5.3"
 requests = "^2"
 SQLAlchemy = ">=1.4,<3"
-tenacity = "^8.1.0"
+tenacity = "^8.1.0,!=8.4.0"
 
 [package.source]
 type = "git"
 url = "https://github.com/langchain-ai/langchain.git"
 reference = "HEAD"
-resolved_reference = "058a64c563571615e4a022433266fb14c1666490"
+resolved_reference = "ad50702934b07810305572cca7919dd3c5ff7949"
 subdirectory = "libs/langchain"
 
 [[package]]
 name = "langchain-core"
-version = "0.2.5"
+version = "0.2.9"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.8.1,<4.0"
@@ -1326,16 +1329,19 @@ develop = false
 [package.dependencies]
 jsonpatch = "^1.33"
 langsmith = "^0.1.75"
-packaging = "^23.2"
-pydantic = ">=1,<3"
+packaging = ">=23.2,<25"
+pydantic = [
+    {version = ">=1,<3", markers = "python_full_version < \"3.12.4\""},
+    {version = ">=2.7.4,<3.0.0", markers = "python_full_version >= \"3.12.4\""},
+]
 PyYAML = ">=5.3"
-tenacity = "^8.1.0"
+tenacity = "^8.1.0,!=8.4.0"
 
 [package.source]
 type = "git"
 url = "https://github.com/langchain-ai/langchain.git"
 reference = "HEAD"
-resolved_reference = "058a64c563571615e4a022433266fb14c1666490"
+resolved_reference = "ad50702934b07810305572cca7919dd3c5ff7949"
 subdirectory = "libs/core"
 
 [[package]]
@@ -1348,6 +1354,7 @@ files = []
 develop = false
 
 [package.dependencies]
+httpx = "^0.27.0"
 langchain-core = ">=0.1.40,<0.3"
 pytest = ">=7,<9"
 
@@ -1355,7 +1362,7 @@ pytest = ">=7,<9"
 type = "git"
 url = "https://github.com/langchain-ai/langchain.git"
 reference = "HEAD"
-resolved_reference = "6605ae22f6001d7428eca57c55d9f22c521abe6f"
+resolved_reference = "ad50702934b07810305572cca7919dd3c5ff7949"
 subdirectory = "libs/standard-tests"
 
 [[package]]
@@ -1363,34 +1370,35 @@ name = "langchain-text-splitters"
 version = "0.2.1"
 description = "LangChain text splitting utilities"
 optional = false
-python-versions = ">=3.8.1,<4.0"
-files = []
-develop = false
+python-versions = "<4.0,>=3.8.1"
+files = [
+    {file = "langchain_text_splitters-0.2.1-py3-none-any.whl", hash = "sha256:c2774a85f17189eaca50339629d2316d13130d4a8d9f1a1a96f3a03670c4a138"},
+    {file = "langchain_text_splitters-0.2.1.tar.gz", hash = "sha256:06853d17d7241ecf5c97c7b6ef01f600f9b0fb953dd997838142a527a4f32ea4"},
+]
 
 [package.dependencies]
-langchain-core = "^0.2.0"
+langchain-core = ">=0.2.0,<0.3.0"
 
-[package.source]
-type = "git"
-url = "https://github.com/langchain-ai/langchain.git"
-reference = "HEAD"
-resolved_reference = "058a64c563571615e4a022433266fb14c1666490"
-subdirectory = "libs/text-splitters"
+[package.extras]
+extended-testing = ["beautifulsoup4 (>=4.12.3,<5.0.0)", "lxml (>=4.9.3,<6.0)"]
 
 [[package]]
 name = "langsmith"
-version = "0.1.75"
+version = "0.1.82"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
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-    {file = "langsmith-0.1.75.tar.gz", hash = "sha256:61274e144ea94c297dd78ce03e6dfae18459fe9bd8ab5094d61a0c4816561279"},
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 ]
 
 [package.dependencies]
 orjson = ">=3.9.14,<4.0.0"
-pydantic = ">=1,<3"
+pydantic = [
+    {version = ">=1,<3", markers = "python_full_version < \"3.12.4\""},
+    {version = ">=2.7.4,<3.0.0", markers = "python_full_version >= \"3.12.4\""},
+]
 requests = ">=2,<3"
 
 [[package]]
@@ -1494,38 +1502,38 @@ files = [
 
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+version = "1.10.1"
 description = "Optional static typing for Python"
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 python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
@@ -1594,44 +1602,44 @@ numpy = ">=1.13.3"
 
 [[package]]
 name = "numexpr"
-version = "2.10.0"
+version = "2.10.1"
 description = "Fast numerical expression evaluator for NumPy"
 optional = false
 python-versions = ">=3.9"
 files = [
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+    {file = "SQLAlchemy-2.0.31-cp312-cp312-win_amd64.whl", hash = "sha256:fa4b1af3e619b5b0b435e333f3967612db06351217c58bfb50cee5f003db2a5a"},
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+    {file = "SQLAlchemy-2.0.31-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8a41514c1a779e2aa9a19f67aaadeb5cbddf0b2b508843fcd7bafdf4c6864005"},
+    {file = "SQLAlchemy-2.0.31-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:afb6dde6c11ea4525318e279cd93c8734b795ac8bb5dda0eedd9ebaca7fa23f1"},
+    {file = "SQLAlchemy-2.0.31-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:3f9faef422cfbb8fd53716cd14ba95e2ef655400235c3dfad1b5f467ba179c8c"},
+    {file = "SQLAlchemy-2.0.31-cp39-cp39-win32.whl", hash = "sha256:fc6b14e8602f59c6ba893980bea96571dd0ed83d8ebb9c4479d9ed5425d562e9"},
+    {file = "SQLAlchemy-2.0.31-cp39-cp39-win_amd64.whl", hash = "sha256:3cb8a66b167b033ec72c3812ffc8441d4e9f5f78f5e31e54dcd4c90a4ca5bebc"},
+    {file = "SQLAlchemy-2.0.31-py3-none-any.whl", hash = "sha256:69f3e3c08867a8e4856e92d7afb618b95cdee18e0bc1647b77599722c9a28911"},
+    {file = "SQLAlchemy-2.0.31.tar.gz", hash = "sha256:b607489dd4a54de56984a0c7656247504bd5523d9d0ba799aef59d4add009484"},
 ]
 
 [package.dependencies]
-greenlet = {version = "!=0.4.17", markers = "platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\""}
+greenlet = {version = "!=0.4.17", markers = "python_version < \"3.13\" and (platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\")"}
 typing-extensions = ">=4.6.0"
 
 [package.extras]
@@ -2326,13 +2378,13 @@ pytest = ">=7.0.0,<9.0.0"
 
 [[package]]
 name = "tenacity"
-version = "8.3.0"
+version = "8.4.2"
 description = "Retry code until it succeeds"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "tenacity-8.3.0-py3-none-any.whl", hash = "sha256:3649f6443dbc0d9b01b9d8020a9c4ec7a1ff5f6f3c6c8a036ef371f573fe9185"},
-    {file = "tenacity-8.3.0.tar.gz", hash = "sha256:953d4e6ad24357bceffbc9707bc74349aca9d245f68eb65419cf0c249a1949a2"},
+    {file = "tenacity-8.4.2-py3-none-any.whl", hash = "sha256:9e6f7cf7da729125c7437222f8a522279751cdfbe6b67bfe64f75d3a348661b2"},
+    {file = "tenacity-8.4.2.tar.gz", hash = "sha256:cd80a53a79336edba8489e767f729e4f391c896956b57140b5d7511a64bbd3ef"},
 ]
 
 [package.extras]
@@ -2511,13 +2563,13 @@ files = [
 
 [[package]]
 name = "types-requests"
-version = "2.32.0.20240602"
+version = "2.32.0.20240622"
 description = "Typing stubs for requests"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "types-requests-2.32.0.20240602.tar.gz", hash = "sha256:3f98d7bbd0dd94ebd10ff43a7fbe20c3b8528acace6d8efafef0b6a184793f06"},
-    {file = "types_requests-2.32.0.20240602-py3-none-any.whl", hash = "sha256:ed3946063ea9fbc6b5fc0c44fa279188bae42d582cb63760be6cb4b9d06c3de8"},
+    {file = "types-requests-2.32.0.20240622.tar.gz", hash = "sha256:ed5e8a412fcc39159d6319385c009d642845f250c63902718f605cd90faade31"},
+    {file = "types_requests-2.32.0.20240622-py3-none-any.whl", hash = "sha256:97bac6b54b5bd4cf91d407e62f0932a74821bc2211f22116d9ee1dd643826caf"},
 ]
 
 [package.dependencies]
@@ -2547,13 +2599,13 @@ files = [
 
 [[package]]
 name = "urllib3"
-version = "2.2.1"
+version = "2.2.2"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "urllib3-2.2.1-py3-none-any.whl", hash = "sha256:450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d"},
-    {file = "urllib3-2.2.1.tar.gz", hash = "sha256:d0570876c61ab9e520d776c38acbbb5b05a776d3f9ff98a5c8fd5162a444cf19"},
+    {file = "urllib3-2.2.2-py3-none-any.whl", hash = "sha256:a448b2f64d686155468037e1ace9f2d2199776e17f0a46610480d311f73e3472"},
+    {file = "urllib3-2.2.2.tar.gz", hash = "sha256:dd505485549a7a552833da5e6063639d0d177c04f23bc3864e41e5dc5f612168"},
 ]
 
 [package.extras]
@@ -2715,4 +2767,4 @@ anthropic = ["anthropic"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0"
-content-hash = "1c567dc1662634573ef716cc39707de9095f47ebd8a8fdab28db45ac2a207039"
+content-hash = "7787455b7180eafe80b00d4629cd11e405623ad63f8ec5c444600221fe36f0b2"
diff --git a/libs/vertexai/pyproject.toml b/libs/vertexai/pyproject.toml
index acc8a25a..7215ead7 100644
--- a/libs/vertexai/pyproject.toml
+++ b/libs/vertexai/pyproject.toml
@@ -12,7 +12,7 @@ license = "MIT"
 
 [tool.poetry.dependencies]
 python = ">=3.8.1,<4.0"
-langchain-core = ">=0.2.5,<0.3"
+langchain-core = ">=0.2.9,<0.3"
 google-cloud-aiplatform = "^1.54.1"
 google-cloud-storage = "^2.16.0"
 # optional dependencies
@@ -28,11 +28,13 @@ pytest-mock = "^3.10.0"
 syrupy = "^4.0.2"
 pytest-watcher = "^0.3.4"
 pytest-asyncio = "^0.21.1"
-langchain = { git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/langchain" }
-langchain-text-splitters = { git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/text-splitters" }
 types-requests = "^2.31.0.20231231"
 types-protobuf = "^4.24.0.4"
 numexpr = "^2.8.6"
+numpy = [
+  { version = "^1", python = "<3.12" },
+  { version = "^1.26.0", python = ">=3.12" },
+]
 google-api-python-client = "^2.117.0"
 langchain-core = { git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/core" }
 langchain-standard-tests = {git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/standard-tests"}
@@ -54,6 +56,7 @@ optional = true
 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" }
 
 [tool.poetry.group.lint]
 optional = true
diff --git a/libs/vertexai/tests/integration_tests/test_embeddings.py b/libs/vertexai/tests/integration_tests/test_embeddings.py
index aaae0548..254a34c8 100644
--- a/libs/vertexai/tests/integration_tests/test_embeddings.py
+++ b/libs/vertexai/tests/integration_tests/test_embeddings.py
@@ -62,7 +62,8 @@ def test_langchain_google_vertexai_embedding_query(model_name, embeddings_dim) -
 
 @pytest.mark.release
 def test_langchain_google_vertexai_large_batches() -> None:
-    documents = ["foo bar" for _ in range(0, 251)]
+    batch_size = 32
+    documents = ["foo bar" for _ in range(batch_size)]
     model_uscentral1 = VertexAIEmbeddings(
         model_name="textembedding-gecko@001", location="us-central1"
     )
@@ -71,7 +72,7 @@ def test_langchain_google_vertexai_large_batches() -> None:
     # )
     model_uscentral1.embed_documents(documents)
     # model_asianortheast1.embed_documents(documents)
-    assert model_uscentral1.instance["batch_size"] >= 250
+    assert model_uscentral1.instance["batch_size"] >= batch_size
     # assert model_asianortheast1.instance["batch_size"] < 50
 
 
diff --git a/libs/vertexai/tests/integration_tests/test_standard.py b/libs/vertexai/tests/integration_tests/test_standard.py
index 5cd22912..af8ed3b8 100644
--- a/libs/vertexai/tests/integration_tests/test_standard.py
+++ b/libs/vertexai/tests/integration_tests/test_standard.py
@@ -1,4 +1,5 @@
 """Standard LangChain interface tests"""
+
 import json
 from typing import Type
 
diff --git a/libs/vertexai/tests/integration_tests/test_tools.py b/libs/vertexai/tests/integration_tests/test_tools.py
index 7f0f349f..1a674931 100644
--- a/libs/vertexai/tests/integration_tests/test_tools.py
+++ b/libs/vertexai/tests/integration_tests/test_tools.py
@@ -1,19 +1,8 @@
-import json
-import os
-import re
-from typing import Any, List, Union
+from typing import List, Union
 
-import pytest
 from langchain_core.agents import AgentAction, AgentActionMessageLog, AgentFinish
-from langchain_core.messages import AIMessageChunk
 from langchain_core.output_parsers import BaseOutputParser
 from langchain_core.outputs import ChatGeneration, Generation
-from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-from langchain_core.pydantic_v1 import BaseModel, Field
-from langchain_core.tools import Tool
-
-from langchain_google_vertexai.chat_models import ChatVertexAI
-from tests.integration_tests.conftest import _DEFAULT_MODEL_NAME
 
 
 class _TestOutputParser(BaseOutputParser):
@@ -45,231 +34,3 @@ def parse_result(
 
     def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
         raise ValueError("Can only parse messages")
-
-
-@pytest.mark.xfail(reason="investigating")
-@pytest.mark.extended
-def test_tools() -> None:
-    from langchain.agents import AgentExecutor
-    from langchain.agents.format_scratchpad import (
-        format_to_openai_function_messages,
-    )
-    from langchain.chains import LLMMathChain
-
-    llm = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME)
-    math_chain = LLMMathChain.from_llm(llm=llm)
-    tools = [
-        Tool(
-            name="Calculator",
-            func=math_chain.run,
-            description="useful for when you need to answer questions about math",
-        )
-    ]
-    prompt = ChatPromptTemplate.from_messages(
-        [
-            ("user", "{input}"),
-            MessagesPlaceholder(variable_name="agent_scratchpad"),
-        ]
-    )
-    llm_with_tools = llm.bind(functions=tools)
-
-    agent: Any = (
-        {
-            "input": lambda x: x["input"],
-            "agent_scratchpad": lambda x: format_to_openai_function_messages(
-                x["intermediate_steps"]
-            ),
-        }
-        | prompt
-        | llm_with_tools
-        | _TestOutputParser()
-    )
-    agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
-
-    response = agent_executor.invoke({"input": "What is 6 raised to the 0.43 power?"})
-    assert isinstance(response, dict)
-    assert response["input"] == "What is 6 raised to the 0.43 power?"
-
-    # convert string " The result is 2.160752567226312" to just numbers/periods
-    # use regex to find \d+\.\d+
-    just_numbers = re.findall(r"\d+\.\d+", response["output"])[0]
-
-    assert round(float(just_numbers), 2) == 2.16
-
-
-@pytest.mark.xfail(reason="investigating")
-@pytest.mark.extended
-def test_custom_tool() -> None:
-    from langchain.agents import AgentExecutor, tool
-    from langchain.agents.format_scratchpad import (
-        format_to_openai_function_messages,
-    )
-
-    @tool("search", return_direct=True)
-    def search(query: str) -> str:
-        """Look up things online."""
-        return "LangChain"
-
-    tools = [search]
-
-    llm = ChatVertexAI(
-        model_name=_DEFAULT_MODEL_NAME,
-        temperature=0.0,
-        convert_system_message_to_human=True,
-    )
-    prompt = ChatPromptTemplate.from_messages(
-        [
-            ("system", "You are a helpful assistant"),
-            MessagesPlaceholder("chat_history", optional=True),
-            ("human", "{input}"),
-            MessagesPlaceholder("agent_scratchpad"),
-        ]
-    )
-    llm_with_tools = llm.bind(functions=tools)
-
-    agent: Any = (
-        {
-            "input": lambda x: x["input"],
-            "agent_scratchpad": lambda x: format_to_openai_function_messages(
-                x["intermediate_steps"]
-            ),
-        }
-        | prompt
-        | llm_with_tools
-        | _TestOutputParser()
-    )
-    agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
-
-    response = agent_executor.invoke({"input": "What is LangChain?"})
-    assert isinstance(response, dict)
-    assert response["input"] == "What is LangChain?"
-
-    assert "LangChain" in response["output"]
-
-
-@pytest.mark.extended
-def test_tool_nested_properties() -> None:
-    from langchain.agents import tool
-
-    class Movie(BaseModel):
-        actor: str = Field(description="Actor in the film")
-        director: str = Field(description="Director of the film")
-
-    class Input(BaseModel):
-        movie: Movie
-
-    @tool("movie_search", return_direct=True)
-    def movie_search(input: Input) -> str:
-        """Return last movie title by actor and director."""
-        return "Pulp Fiction"
-
-    tools = [movie_search]
-
-    llm = ChatVertexAI(
-        model_name=_DEFAULT_MODEL_NAME,
-        temperature=0.0,
-        convert_system_message_to_human=True,
-    )
-    llm_with_tools = llm.bind(functions=tools)
-
-    response = llm_with_tools.invoke(
-        "What was the last movie directed by Quentin Tarantino with Bruce Willis?"
-    )
-
-    assert "function_call" in response.additional_kwargs
-    function_call = response.additional_kwargs["function_call"]
-
-    assert function_call["name"] == "movie_search"
-
-    assert "arguments" in function_call
-
-    arguments = json.loads(function_call["arguments"])
-    assert "input" in arguments
-    assert "movie" in arguments["input"]
-    assert "actor" in arguments["input"]["movie"]
-    assert "director" in arguments["input"]["movie"]
-
-
-@pytest.mark.xfail(reason="investigating")
-@pytest.mark.extended
-def test_stream() -> None:
-    from langchain.chains import LLMMathChain
-
-    llm = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME)
-    math_chain = LLMMathChain.from_llm(llm=llm)
-    tools = [
-        Tool(
-            name="Calculator",
-            func=math_chain.run,
-            description="useful for when you need to answer questions about math",
-        )
-    ]
-    response = list(llm.stream("What is 6 raised to the 0.43 power?", functions=tools))
-    assert len(response) == 1
-    assert isinstance(response[0], AIMessageChunk)
-    assert "function_call" in response[0].additional_kwargs
-
-
-@pytest.mark.xfail(reason="investigating")
-@pytest.mark.extended
-def test_multiple_tools() -> None:
-    from langchain.agents import AgentExecutor
-    from langchain.agents.format_scratchpad import format_to_openai_function_messages
-    from langchain.chains import LLMMathChain
-    from langchain.utilities import (
-        GoogleSearchAPIWrapper,
-    )
-
-    llm = ChatVertexAI(model_name=_DEFAULT_MODEL_NAME, max_output_tokens=1024)
-    math_chain = LLMMathChain.from_llm(llm=llm)
-    google_search_api_key = os.environ["GOOGLE_SEARCH_API_KEY"]
-    google_cse_id = os.environ["GOOGLE_CSE_ID"]
-    search = GoogleSearchAPIWrapper(
-        k=10, google_api_key=google_search_api_key, google_cse_id=google_cse_id
-    )
-    tools = [
-        Tool(
-            name="Calculator",
-            func=math_chain.run,
-            description="useful for when you need to answer questions about math",
-        ),
-        Tool(
-            name="Search",
-            func=search.run,
-            description=(
-                "useful for when you need to answer questions about current events. "
-                "You should ask targeted questions"
-            ),
-        ),
-    ]
-    prompt = ChatPromptTemplate.from_messages(
-        [
-            ("user", "{input}"),
-            MessagesPlaceholder(variable_name="agent_scratchpad"),
-        ]
-    )
-    llm_with_tools = llm.bind(functions=tools)
-
-    agent: Any = (
-        {
-            "input": lambda x: x["input"],
-            "agent_scratchpad": lambda x: format_to_openai_function_messages(
-                x["intermediate_steps"]
-            ),
-        }
-        | prompt
-        | llm_with_tools
-        | _TestOutputParser()
-    )
-    agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
-
-    question = (
-        "Who is Leo DiCaprio's girlfriend? What is her "
-        "current age raised to the 0.43 power?"
-    )
-    response = agent_executor.invoke({"input": question})
-    assert isinstance(response, dict)
-    assert response["input"] == question
-
-    # xfail: not getting age in search result most of time
-    # assert "3.850" in response["output"]
diff --git a/libs/vertexai/tests/unit_tests/test_embeddings.py b/libs/vertexai/tests/unit_tests/test_embeddings.py
index 4a11e587..efba09d1 100644
--- a/libs/vertexai/tests/unit_tests/test_embeddings.py
+++ b/libs/vertexai/tests/unit_tests/test_embeddings.py
@@ -2,7 +2,7 @@
 from unittest.mock import MagicMock
 
 import pytest
-from pydantic.v1 import root_validator
+from langchain_core.pydantic_v1 import root_validator
 
 from langchain_google_vertexai import VertexAIEmbeddings
 from langchain_google_vertexai.embeddings import GoogleEmbeddingModelType