diff --git a/libs/community/langchain_google_community/__init__.py b/libs/community/langchain_google_community/__init__.py
index e5196e236..a769a571b 100644
--- a/libs/community/langchain_google_community/__init__.py
+++ b/libs/community/langchain_google_community/__init__.py
@@ -1,7 +1,12 @@
 from langchain_google_community.bigquery_vector_search import BigQueryVectorSearch
+from langchain_google_community.docai import DocAIParser, DocAIParsingResults
 from langchain_google_community.documentai_warehouse import DocumentAIWarehouseRetriever
+from langchain_google_community.gcs_directory import GCSDirectoryLoader
+from langchain_google_community.gcs_file import GCSFileLoader
 from langchain_google_community.gmail.loader import GMailLoader
 from langchain_google_community.gmail.toolkit import GmailToolkit
+from langchain_google_community.google_speech_to_text import GoogleSpeechToTextLoader
+from langchain_google_community.googledrive import GoogleDriveLoader
 from langchain_google_community.vertex_ai_search import (
     VertexAIMultiTurnSearchRetriever,
     VertexAISearchRetriever,
@@ -9,9 +14,15 @@
 
 __all__ = [
     "BigQueryVectorSearch",
+    "DocAIParser",
+    "DocAIParsingResults",
     "DocumentAIWarehouseRetriever",
+    "GCSDirectoryLoader",
+    "GCSFileLoader",
     "GMailLoader",
     "GmailToolkit",
+    "GoogleDriveLoader",
+    "GoogleSpeechToTextLoader",
     "VertexAIMultiTurnSearchRetriever",
     "VertexAISearchRetriever",
 ]
diff --git a/libs/community/langchain_google_community/_utils.py b/libs/community/langchain_google_community/_utils.py
new file mode 100644
index 000000000..ac7f6e3ef
--- /dev/null
+++ b/libs/community/langchain_google_community/_utils.py
@@ -0,0 +1,41 @@
+"""Utilities to init Vertex AI."""
+
+from importlib import metadata
+from typing import Optional, Tuple
+
+from google.api_core.gapic_v1.client_info import ClientInfo
+
+
+def get_user_agent(module: Optional[str] = None) -> Tuple[str, str]:
+    r"""Returns a custom user agent header.
+
+    Args:
+        module (Optional[str]):
+            Optional. The module for a custom user agent header.
+    Returns:
+        Tuple[str, str]
+    """
+    try:
+        langchain_version = metadata.version("langchain")
+    except metadata.PackageNotFoundError:
+        langchain_version = "0.0.0"
+    client_library_version = (
+        f"{langchain_version}-{module}" if module else langchain_version
+    )
+    return client_library_version, f"langchain/{client_library_version}"
+
+
+def get_client_info(module: Optional[str] = None) -> "ClientInfo":
+    r"""Returns a client info object with a custom user agent header.
+
+    Args:
+        module (Optional[str]):
+            Optional. The module for a custom user agent header.
+    Returns:
+        google.api_core.gapic_v1.client_info.ClientInfo
+    """
+    client_library_version, user_agent = get_user_agent(module)
+    return ClientInfo(
+        client_library_version=client_library_version,
+        user_agent=user_agent,
+    )
diff --git a/libs/community/langchain_google_community/bigquery_vector_search.py b/libs/community/langchain_google_community/bigquery_vector_search.py
index 1312f5df8..c610d5c88 100644
--- a/libs/community/langchain_google_community/bigquery_vector_search.py
+++ b/libs/community/langchain_google_community/bigquery_vector_search.py
@@ -13,7 +13,6 @@
 
 import numpy as np
 from google.api_core.exceptions import ClientError
-from langchain_community.utils.google import get_client_info
 from langchain_community.vectorstores.utils import (
     DistanceStrategy,
     maximal_marginal_relevance,
@@ -22,6 +21,8 @@
 from langchain_core.embeddings import Embeddings
 from langchain_core.vectorstores import VectorStore
 
+from langchain_google_community._utils import get_client_info
+
 DEFAULT_DISTANCE_STRATEGY = DistanceStrategy.EUCLIDEAN_DISTANCE
 DEFAULT_DOC_ID_COLUMN_NAME = "doc_id"  # document id
 DEFAULT_TEXT_EMBEDDING_COLUMN_NAME = "text_embedding"  # embeddings vectors
diff --git a/libs/community/langchain_google_community/docai.py b/libs/community/langchain_google_community/docai.py
new file mode 100644
index 000000000..9ce9870b5
--- /dev/null
+++ b/libs/community/langchain_google_community/docai.py
@@ -0,0 +1,396 @@
+"""Module contains a PDF parser based on Document AI from Google Cloud.
+
+You need to install two libraries to use this parser:
+pip install google-cloud-documentai
+pip install google-cloud-documentai-toolbox
+"""
+import logging
+import re
+import time
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Iterator, List, Optional, Sequence
+
+from langchain_core.document_loaders import BaseBlobParser
+from langchain_core.document_loaders.blob_loaders import Blob
+from langchain_core.documents import Document
+from langchain_core.utils.iter import batch_iterate
+
+from langchain_google_community._utils import get_client_info
+
+if TYPE_CHECKING:
+    from google.api_core.operation import Operation  # type: ignore[import]
+    from google.cloud.documentai import (  # type: ignore[import]
+        DocumentProcessorServiceClient,
+    )
+
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class DocAIParsingResults:
+    """A dataclass to store Document AI parsing results."""
+
+    source_path: str
+    parsed_path: str
+
+
+class DocAIParser(BaseBlobParser):
+    """`Google Cloud Document AI` parser.
+
+    For a detailed explanation of Document AI, refer to the product documentation.
+    https://cloud.google.com/document-ai/docs/overview
+    """
+
+    def __init__(
+        self,
+        *,
+        client: Optional["DocumentProcessorServiceClient"] = None,
+        location: Optional[str] = None,
+        gcs_output_path: Optional[str] = None,
+        processor_name: Optional[str] = None,
+    ):
+        """Initializes the parser.
+
+        Args:
+            client: a DocumentProcessorServiceClient to use
+            location: a Google Cloud location where a Document AI processor is located
+            gcs_output_path: a path on Google Cloud Storage to store parsing results
+            processor_name: full resource name of a Document AI processor or processor
+                version
+
+        You should provide either a client or location (and then a client
+            would be instantiated).
+        """
+
+        if bool(client) == bool(location):
+            raise ValueError(
+                "You must specify either a client or a location to instantiate "
+                "a client."
+            )
+
+        pattern = r"projects\/[0-9]+\/locations\/[a-z\-0-9]+\/processors\/[a-z0-9]+"
+        if processor_name and not re.fullmatch(pattern, processor_name):
+            raise ValueError(
+                f"Processor name {processor_name} has the wrong format. If your "
+                "prediction endpoint looks like https://us-documentai.googleapis.com"
+                "/v1/projects/PROJECT_ID/locations/us/processors/PROCESSOR_ID:process,"
+                " use only projects/PROJECT_ID/locations/us/processors/PROCESSOR_ID "
+                "part."
+            )
+
+        self._gcs_output_path = gcs_output_path
+        self._processor_name = processor_name
+        if client:
+            self._client = client
+        else:
+            try:
+                from google.api_core.client_options import ClientOptions
+                from google.cloud.documentai import DocumentProcessorServiceClient
+            except ImportError as exc:
+                raise ImportError(
+                    "documentai package not found, please install it with"
+                    " `pip install google-cloud-documentai`"
+                ) from exc
+            options = ClientOptions(
+                api_endpoint=f"{location}-documentai.googleapis.com"
+            )
+            self._client = DocumentProcessorServiceClient(
+                client_options=options,
+                client_info=get_client_info(module="document-ai"),
+            )
+
+    def lazy_parse(self, blob: Blob) -> Iterator[Document]:
+        """Parses a blob lazily.
+
+        Args:
+            blobs: a Blob to parse
+
+        This is a long-running operation. A recommended way is to batch
+            documents together and use the `batch_parse()` method.
+        """
+        yield from self.batch_parse([blob], gcs_output_path=self._gcs_output_path)
+
+    def online_process(
+        self,
+        blob: Blob,
+        enable_native_pdf_parsing: bool = True,
+        field_mask: Optional[str] = None,
+        page_range: Optional[List[int]] = None,
+    ) -> Iterator[Document]:
+        """Parses a blob lazily using online processing.
+
+        Args:
+            blob: a blob to parse.
+            enable_native_pdf_parsing: enable pdf embedded text extraction
+            field_mask: a comma-separated list of which fields to include in the
+                Document AI response.
+                suggested: "text,pages.pageNumber,pages.layout"
+            page_range: list of page numbers to parse. If `None`,
+                entire document will be parsed.
+        """
+        try:
+            from google.cloud import documentai
+            from google.cloud.documentai_v1.types import (  # type: ignore[import]
+                IndividualPageSelector,
+                OcrConfig,
+                ProcessOptions,
+            )
+        except ImportError as exc:
+            raise ImportError(
+                "documentai package not found, please install it with"
+                " `pip install google-cloud-documentai`"
+            ) from exc
+        try:
+            from google.cloud.documentai_toolbox.wrappers.page import (
+                _text_from_layout,  # type: ignore[import]
+            )
+        except ImportError as exc:
+            raise ImportError(
+                "documentai_toolbox package not found, please install it with"
+                " `pip install google-cloud-documentai-toolbox`"
+            ) from exc
+        ocr_config = (
+            OcrConfig(enable_native_pdf_parsing=enable_native_pdf_parsing)
+            if enable_native_pdf_parsing
+            else None
+        )
+        individual_page_selector = (
+            IndividualPageSelector(pages=page_range) if page_range else None
+        )
+
+        response = self._client.process_document(
+            documentai.ProcessRequest(
+                name=self._processor_name,
+                gcs_document=documentai.GcsDocument(
+                    gcs_uri=blob.path,
+                    mime_type=blob.mimetype or "application/pdf",
+                ),
+                process_options=ProcessOptions(
+                    ocr_config=ocr_config,
+                    individual_page_selector=individual_page_selector,
+                ),
+                skip_human_review=True,
+                field_mask=field_mask,
+            )
+        )
+        yield from (
+            Document(
+                page_content=_text_from_layout(page.layout, response.document.text),
+                metadata={
+                    "page": page.page_number,
+                    "source": blob.path,
+                },
+            )
+            for page in response.document.pages
+        )
+
+    def batch_parse(
+        self,
+        blobs: Sequence[Blob],
+        gcs_output_path: Optional[str] = None,
+        timeout_sec: int = 3600,
+        check_in_interval_sec: int = 60,
+    ) -> Iterator[Document]:
+        """Parses a list of blobs lazily.
+
+        Args:
+            blobs: a list of blobs to parse.
+            gcs_output_path: a path on Google Cloud Storage to store parsing results.
+            timeout_sec: a timeout to wait for Document AI to complete, in seconds.
+            check_in_interval_sec: an interval to wait until next check
+                whether parsing operations have been completed, in seconds
+        This is a long-running operation. A recommended way is to decouple
+            parsing from creating LangChain Documents:
+            >>> operations = parser.docai_parse(blobs, gcs_path)
+            >>> parser.is_running(operations)
+            You can get operations names and save them:
+            >>> names = [op.operation.name for op in operations]
+            And when all operations are finished, you can use their results:
+            >>> operations = parser.operations_from_names(operation_names)
+            >>> results = parser.get_results(operations)
+            >>> docs = parser.parse_from_results(results)
+        """
+        output_path = gcs_output_path or self._gcs_output_path
+        if not output_path:
+            raise ValueError(
+                "An output path on Google Cloud Storage should be provided."
+            )
+        operations = self.docai_parse(blobs, gcs_output_path=output_path)
+        operation_names = [op.operation.name for op in operations]
+        logger.debug(
+            "Started parsing with Document AI, submitted operations %s", operation_names
+        )
+        time_elapsed = 0
+        while self.is_running(operations):
+            time.sleep(check_in_interval_sec)
+            time_elapsed += check_in_interval_sec
+            if time_elapsed > timeout_sec:
+                raise TimeoutError(
+                    "Timeout exceeded! Check operations " f"{operation_names} later!"
+                )
+            logger.debug(".")
+
+        results = self.get_results(operations=operations)
+        yield from self.parse_from_results(results)
+
+    def parse_from_results(
+        self, results: List[DocAIParsingResults]
+    ) -> Iterator[Document]:
+        try:
+            from google.cloud.documentai_toolbox.utilities.gcs_utilities import (  # type: ignore[import]
+                split_gcs_uri,
+            )
+            from google.cloud.documentai_toolbox.wrappers.document import (  # type: ignore[import]
+                _get_shards,
+            )
+            from google.cloud.documentai_toolbox.wrappers.page import _text_from_layout
+        except ImportError as exc:
+            raise ImportError(
+                "documentai_toolbox package not found, please install it with"
+                " `pip install google-cloud-documentai-toolbox`"
+            ) from exc
+        for result in results:
+            gcs_bucket_name, gcs_prefix = split_gcs_uri(result.parsed_path)
+            shards = _get_shards(gcs_bucket_name, gcs_prefix)
+            yield from (
+                Document(
+                    page_content=_text_from_layout(page.layout, shard.text),
+                    metadata={"page": page.page_number, "source": result.source_path},
+                )
+                for shard in shards
+                for page in shard.pages
+            )
+
+    def operations_from_names(self, operation_names: List[str]) -> List["Operation"]:
+        """Initializes Long-Running Operations from their names."""
+        try:
+            from google.longrunning.operations_pb2 import (  # type: ignore[import]
+                GetOperationRequest,
+            )
+        except ImportError as exc:
+            raise ImportError(
+                "long running operations package not found, please install it with"
+                " `pip install gapic-google-longrunning`"
+            ) from exc
+
+        return [
+            self._client.get_operation(request=GetOperationRequest(name=name))
+            for name in operation_names
+        ]
+
+    def is_running(self, operations: List["Operation"]) -> bool:
+        return any(not op.done() for op in operations)
+
+    def docai_parse(
+        self,
+        blobs: Sequence[Blob],
+        *,
+        gcs_output_path: Optional[str] = None,
+        processor_name: Optional[str] = None,
+        batch_size: int = 1000,
+        enable_native_pdf_parsing: bool = True,
+        field_mask: Optional[str] = None,
+    ) -> List["Operation"]:
+        """Runs Google Document AI PDF Batch Processing on a list of blobs.
+
+        Args:
+            blobs: a list of blobs to be parsed
+            gcs_output_path: a path (folder) on GCS to store results
+            processor_name: name of a Document AI processor.
+            batch_size: amount of documents per batch
+            enable_native_pdf_parsing: a config option for the parser
+            field_mask: a comma-separated list of which fields to include in the
+                Document AI response.
+                suggested: "text,pages.pageNumber,pages.layout"
+
+        Document AI has a 1000 file limit per batch, so batches larger than that need
+        to be split into multiple requests.
+        Batch processing is an async long-running operation
+        and results are stored in a output GCS bucket.
+        """
+        try:
+            from google.cloud import documentai
+            from google.cloud.documentai_v1.types import OcrConfig, ProcessOptions
+        except ImportError as exc:
+            raise ImportError(
+                "documentai package not found, please install it with"
+                " `pip install google-cloud-documentai`"
+            ) from exc
+
+        output_path = gcs_output_path or self._gcs_output_path
+        if output_path is None:
+            raise ValueError(
+                "An output path on Google Cloud Storage should be provided."
+            )
+        processor_name = processor_name or self._processor_name
+        if processor_name is None:
+            raise ValueError("A Document AI processor name should be provided.")
+
+        operations = []
+        for batch in batch_iterate(size=batch_size, iterable=blobs):
+            input_config = documentai.BatchDocumentsInputConfig(
+                gcs_documents=documentai.GcsDocuments(
+                    documents=[
+                        documentai.GcsDocument(
+                            gcs_uri=blob.path,
+                            mime_type=blob.mimetype or "application/pdf",
+                        )
+                        for blob in batch
+                    ]
+                )
+            )
+
+            output_config = documentai.DocumentOutputConfig(
+                gcs_output_config=documentai.DocumentOutputConfig.GcsOutputConfig(
+                    gcs_uri=output_path, field_mask=field_mask
+                )
+            )
+
+            process_options = (
+                ProcessOptions(
+                    ocr_config=OcrConfig(
+                        enable_native_pdf_parsing=enable_native_pdf_parsing
+                    )
+                )
+                if enable_native_pdf_parsing
+                else None
+            )
+            operations.append(
+                self._client.batch_process_documents(
+                    documentai.BatchProcessRequest(
+                        name=processor_name,
+                        input_documents=input_config,
+                        document_output_config=output_config,
+                        process_options=process_options,
+                        skip_human_review=True,
+                    )
+                )
+            )
+        return operations
+
+    def get_results(self, operations: List["Operation"]) -> List[DocAIParsingResults]:
+        try:
+            from google.cloud.documentai_v1 import (  # type: ignore[import]
+                BatchProcessMetadata,
+            )
+        except ImportError as exc:
+            raise ImportError(
+                "documentai package not found, please install it with"
+                " `pip install google-cloud-documentai`"
+            ) from exc
+
+        return [
+            DocAIParsingResults(
+                source_path=status.input_gcs_source,
+                parsed_path=status.output_gcs_destination,
+            )
+            for op in operations
+            for status in (
+                op.metadata.individual_process_statuses
+                if isinstance(op.metadata, BatchProcessMetadata)
+                else BatchProcessMetadata.deserialize(
+                    op.metadata.value
+                ).individual_process_statuses
+            )
+        ]
diff --git a/libs/community/langchain_google_community/documentai_warehouse.py b/libs/community/langchain_google_community/documentai_warehouse.py
index 8367c9f08..71958b45c 100644
--- a/libs/community/langchain_google_community/documentai_warehouse.py
+++ b/libs/community/langchain_google_community/documentai_warehouse.py
@@ -1,13 +1,14 @@
 """Retriever wrapper for Google Cloud Document AI Warehouse."""
 from typing import TYPE_CHECKING, Any, Dict, List, Optional
 
-from langchain_community.utilities.vertexai import get_client_info
 from langchain_core.callbacks import CallbackManagerForRetrieverRun
 from langchain_core.documents import Document
 from langchain_core.pydantic_v1 import root_validator
 from langchain_core.retrievers import BaseRetriever
 from langchain_core.utils import get_from_dict_or_env
 
+from langchain_google_community._utils import get_client_info
+
 if TYPE_CHECKING:
     from google.cloud.contentwarehouse_v1 import (  # type: ignore[import]
         DocumentServiceClient,
diff --git a/libs/community/langchain_google_community/gcs_directory.py b/libs/community/langchain_google_community/gcs_directory.py
new file mode 100644
index 000000000..496d0f87a
--- /dev/null
+++ b/libs/community/langchain_google_community/gcs_directory.py
@@ -0,0 +1,58 @@
+from typing import Callable, List, Optional
+
+from langchain_core.document_loaders import BaseLoader
+from langchain_core.documents import Document
+
+from langchain_google_community._utils import get_client_info
+from langchain_google_community.gcs_file import GCSFileLoader
+
+
+class GCSDirectoryLoader(BaseLoader):
+    """Load from GCS directory."""
+
+    def __init__(
+        self,
+        project_name: str,
+        bucket: str,
+        prefix: str = "",
+        loader_func: Optional[Callable[[str], BaseLoader]] = None,
+    ):
+        """Initialize with bucket and key name.
+
+        Args:
+            project_name: The ID of the project for the GCS bucket.
+            bucket: The name of the GCS bucket.
+            prefix: The prefix of the GCS bucket.
+            loader_func: A loader function that instantiates a loader based on a
+                file_path argument. If nothing is provided, the  GCSFileLoader
+                would use its default loader.
+        """
+        self.project_name = project_name
+        self.bucket = bucket
+        self.prefix = prefix
+        self._loader_func = loader_func
+
+    def load(self) -> List[Document]:
+        """Load documents."""
+        try:
+            from google.cloud import storage  # type: ignore[attr-defined]
+        except ImportError:
+            raise ImportError(
+                "Could not import google-cloud-storage python package. "
+                "Please install it with `pip install google-cloud-storage`."
+            )
+        client = storage.Client(
+            project=self.project_name,
+            client_info=get_client_info(module="google-cloud-storage"),
+        )
+        docs = []
+        for blob in client.list_blobs(self.bucket, prefix=self.prefix):
+            # we shall just skip directories since GCSFileLoader creates
+            # intermediate directories on the fly
+            if blob.name.endswith("/"):
+                continue
+            loader = GCSFileLoader(
+                self.project_name, self.bucket, blob.name, loader_func=self._loader_func
+            )
+            docs.extend(loader.load())
+        return docs
diff --git a/libs/community/langchain_google_community/gcs_file.py b/libs/community/langchain_google_community/gcs_file.py
new file mode 100644
index 000000000..098ffb651
--- /dev/null
+++ b/libs/community/langchain_google_community/gcs_file.py
@@ -0,0 +1,93 @@
+import os
+import tempfile
+from typing import Callable, List, Optional
+
+from langchain_core.document_loaders import BaseLoader
+from langchain_core.documents import Document
+
+from langchain_google_community._utils import get_client_info
+
+
+class GCSFileLoader(BaseLoader):
+    """Load from GCS file."""
+
+    def __init__(
+        self,
+        project_name: str,
+        bucket: str,
+        blob: str,
+        loader_func: Optional[Callable[[str], BaseLoader]] = None,
+    ):
+        """Initialize with bucket and key name.
+
+        Args:
+            project_name: The name of the project to load
+            bucket: The name of the GCS bucket.
+            blob: The name of the GCS blob to load.
+            loader_func: A loader function that instantiates a loader based on a
+                file_path argument. If nothing is provided, the
+                UnstructuredFileLoader is used.
+
+        Examples:
+            To use an alternative PDF loader:
+            >> from from langchain_community.document_loaders import PyPDFLoader
+            >> loader = GCSFileLoader(..., loader_func=PyPDFLoader)
+
+            To use UnstructuredFileLoader with additional arguments:
+            >> loader = GCSFileLoader(...,
+            >>      loader_func=lambda x: UnstructuredFileLoader(x, mode="elements"))
+
+        """
+        self.bucket = bucket
+        self.blob = blob
+        self.project_name = project_name
+
+        def default_loader_func(file_path: str) -> BaseLoader:
+            try:
+                from langchain_community.document_loaders.unstructured import (
+                    UnstructuredFileLoader,
+                )
+            except ImportError:
+                message = (
+                    "UnstructuredFileLoader loader not found! Either provide a "
+                    "custom loader with loader_func argument, or install "
+                    " `pip install langchain_community`"
+                )
+                print(message)
+            return UnstructuredFileLoader(file_path)
+
+        self._loader_func = loader_func if loader_func else default_loader_func
+
+    def load(self) -> List[Document]:
+        """Load documents."""
+        try:
+            from google.cloud import storage  # type: ignore[attr-defined]
+        except ImportError:
+            raise ImportError(
+                "Could not import google-cloud-storage python package. "
+                "Please install it with `pip install google-cloud-storage`."
+            )
+
+        # initialize a client
+        storage_client = storage.Client(
+            self.project_name, client_info=get_client_info("google-cloud-storage")
+        )
+        # Create a bucket object for our bucket
+        bucket = storage_client.get_bucket(self.bucket)
+        # Create a blob object from the filepath
+        blob = bucket.blob(self.blob)
+        # retrieve custom metadata associated with the blob
+        metadata = bucket.get_blob(self.blob).metadata
+        with tempfile.TemporaryDirectory() as temp_dir:
+            file_path = f"{temp_dir}/{self.blob}"
+            os.makedirs(os.path.dirname(file_path), exist_ok=True)
+            # Download the file to a destination
+            blob.download_to_filename(file_path)
+            loader = self._loader_func(file_path)
+            docs = loader.load()
+            for doc in docs:
+                if "source" in doc.metadata:
+                    doc.metadata["source"] = f"gs://{self.bucket}/{self.blob}"
+                if metadata:
+                    doc.metadata.update(metadata)
+            return docs
diff --git a/libs/community/langchain_google_community/google_speech_to_text.py b/libs/community/langchain_google_community/google_speech_to_text.py
new file mode 100644
index 000000000..bcfb817ed
--- /dev/null
+++ b/libs/community/langchain_google_community/google_speech_to_text.py
@@ -0,0 +1,137 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, List, Optional
+
+from langchain_core.document_loaders import BaseLoader
+from langchain_core.documents import Document
+
+from langchain_google_community._utils import get_client_info
+
+if TYPE_CHECKING:
+    from google.cloud.speech_v2 import RecognitionConfig  # type: ignore[import]
+    from google.protobuf.field_mask_pb2 import FieldMask
+
+
+class GoogleSpeechToTextLoader(BaseLoader):
+    """
+    Loader for Google Cloud Speech-to-Text audio transcripts.
+
+    It uses the Google Cloud Speech-to-Text API to transcribe audio files
+    and loads the transcribed text into one or more Documents,
+    depending on the specified format.
+
+    To use, you should have the ``google-cloud-speech`` python package installed.
+
+    Audio files can be specified via a Google Cloud Storage uri or a local file path.
+
+    For a detailed explanation of Google Cloud Speech-to-Text, refer to the product
+    documentation.
+    https://cloud.google.com/speech-to-text
+    """
+
+    def __init__(
+        self,
+        project_id: str,
+        file_path: str,
+        location: str = "us-central1",
+        recognizer_id: str = "_",
+        config: Optional[RecognitionConfig] = None,
+        config_mask: Optional[FieldMask] = None,
+    ):
+        """
+        Initializes the GoogleSpeechToTextLoader.
+
+        Args:
+            project_id: Google Cloud Project ID.
+            file_path: A Google Cloud Storage URI or a local file path.
+            location: Speech-to-Text recognizer location.
+            recognizer_id: Speech-to-Text recognizer id.
+            config: Recognition options and features.
+                For more information:
+                https://cloud.google.com/python/docs/reference/speech/latest/google.cloud.speech_v2.types.RecognitionConfig
+            config_mask: The list of fields in config that override the values in the
+                ``default_recognition_config`` of the recognizer during this
+                recognition request.
+                For more information:
+                https://cloud.google.com/python/docs/reference/speech/latest/google.cloud.speech_v2.types.RecognizeRequest
+        """
+        try:
+            from google.api_core.client_options import ClientOptions
+            from google.cloud.speech_v2 import (
+                AutoDetectDecodingConfig,
+                RecognitionConfig,
+                RecognitionFeatures,
+                SpeechClient,
+            )
+        except ImportError as exc:
+            raise ImportError(
+                "Could not import google-cloud-speech python package. "
+                "Please install it with `pip install google-cloud-speech`."
+            ) from exc
+
+        self.project_id = project_id
+        self.file_path = file_path
+        self.location = location
+        self.recognizer_id = recognizer_id
+        # Config must be set in speech recognition request.
+        self.config = config or RecognitionConfig(
+            auto_decoding_config=AutoDetectDecodingConfig(),
+            language_codes=["en-US"],
+            model="chirp",
+            features=RecognitionFeatures(
+                # Automatic punctuation could be useful for language applications
+                enable_automatic_punctuation=True,
+            ),
+        )
+        self.config_mask = config_mask
+
+        self._client = SpeechClient(
+            client_info=get_client_info(module="speech-to-text"),
+            client_options=(
+                ClientOptions(api_endpoint=f"{location}-speech.googleapis.com")
+                if location != "global"
+                else None
+            ),
+        )
+        self._recognizer_path = self._client.recognizer_path(
+            project_id, location, recognizer_id
+        )
+
+    def load(self) -> List[Document]:
+        """Transcribes the audio file and loads the transcript into documents.
+
+        It uses the Google Cloud Speech-to-Text API to transcribe the audio file
+        and blocks until the transcription is finished.
+        """
+        try:
+            from google.cloud.speech_v2 import RecognizeRequest
+        except ImportError as exc:
+            raise ImportError(
+                "Could not import google-cloud-speech python package. "
+                "Please install it with `pip install google-cloud-speech`."
+            ) from exc
+
+        request = RecognizeRequest(
+            recognizer=self._recognizer_path,
+            config=self.config,
+            config_mask=self.config_mask,
+        )
+
+        if "gs://" in self.file_path:
+            request.uri = self.file_path
+        else:
+            with open(self.file_path, "rb") as f:
+                request.content = f.read()
+
+        response = self._client.recognize(request=request)
+
+        return [
+            Document(
+                page_content=result.alternatives[0].transcript,
+                metadata={
+                    "language_code": result.language_code,
+                    "result_end_offset": result.result_end_offset,
+                },
+            )
+            for result in response.results
+        ]
diff --git a/libs/community/langchain_google_community/googledrive.py b/libs/community/langchain_google_community/googledrive.py
new file mode 100644
index 000000000..2848608d2
--- /dev/null
+++ b/libs/community/langchain_google_community/googledrive.py
@@ -0,0 +1,361 @@
+# Prerequisites:
+# 1. Create a Google Cloud project
+# 2. Enable the Google Drive API:
+#   https://console.cloud.google.com/flows/enableapi?apiid=drive.googleapis.com
+# 3. Authorize credentials for desktop app:
+#   https://developers.google.com/drive/api/quickstart/python#authorize_credentials_for_a_desktop_application # noqa: E501
+# 4. For service accounts visit
+#   https://cloud.google.com/iam/docs/service-accounts-create
+
+import os
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Sequence, Union
+
+from langchain_core.document_loaders import BaseLoader
+from langchain_core.documents import Document
+from langchain_core.pydantic_v1 import BaseModel, root_validator, validator
+
+SCOPES = ["https://www.googleapis.com/auth/drive.readonly"]
+
+
+class GoogleDriveLoader(BaseLoader, BaseModel):
+    """Load Google Docs from `Google Drive`."""
+
+    service_account_key: Path = Path.home() / ".credentials" / "keys.json"
+    """Path to the service account key file."""
+    credentials_path: Path = Path.home() / ".credentials" / "credentials.json"
+    """Path to the credentials file."""
+    token_path: Path = Path.home() / ".credentials" / "token.json"
+    """Path to the token file."""
+    folder_id: Optional[str] = None
+    """The folder id to load from."""
+    document_ids: Optional[List[str]] = None
+    """The document ids to load from."""
+    file_ids: Optional[List[str]] = None
+    """The file ids to load from."""
+    recursive: bool = False
+    """Whether to load recursively. Only applies when folder_id is given."""
+    file_types: Optional[Sequence[str]] = None
+    """The file types to load. Only applies when folder_id is given."""
+    load_trashed_files: bool = False
+    """Whether to load trashed files. Only applies when folder_id is given."""
+    # NOTE(MthwRobinson) - changing the file_loader_cls to type here currently
+    # results in pydantic validation errors
+    file_loader_cls: Any = None
+    """The file loader class to use."""
+    file_loader_kwargs: Dict["str", Any] = {}
+    """The file loader kwargs to use."""
+
+    @root_validator
+    def validate_inputs(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+        """Validate that either folder_id or document_ids is set, but not both."""
+        if values.get("folder_id") and (
+            values.get("document_ids") or values.get("file_ids")
+        ):
+            raise ValueError(
+                "Cannot specify both folder_id and document_ids nor "
+                "folder_id and file_ids"
+            )
+        if (
+            not values.get("folder_id")
+            and not values.get("document_ids")
+            and not values.get("file_ids")
+        ):
+            raise ValueError("Must specify either folder_id, document_ids, or file_ids")
+
+        file_types = values.get("file_types")
+        if file_types:
+            if values.get("document_ids") or values.get("file_ids"):
+                raise ValueError(
+                    "file_types can only be given when folder_id is given,"
+                    " (not when document_ids or file_ids are given)."
+                )
+            type_mapping = {
+                "document": "application/vnd.google-apps.document",
+                "sheet": "application/vnd.google-apps.spreadsheet",
+                "pdf": "application/pdf",
+            }
+            allowed_types = list(type_mapping.keys()) + list(type_mapping.values())
+            short_names = ", ".join([f"'{x}'" for x in type_mapping.keys()])
+            full_names = ", ".join([f"'{x}'" for x in type_mapping.values()])
+            for file_type in file_types:
+                if file_type not in allowed_types:
+                    raise ValueError(
+                        f"Given file type {file_type} is not supported. "
+                        f"Supported values are: {short_names}; and "
+                        f"their full-form names: {full_names}"
+                    )
+
+            # replace short-form file types by full-form file types
+            def full_form(x: str) -> str:
+                return type_mapping[x] if x in type_mapping else x
+
+            values["file_types"] = [full_form(file_type) for file_type in file_types]
+        return values
+
+    @validator("credentials_path")
+    def validate_credentials_path(cls, v: Any, **kwargs: Any) -> Any:
+        """Validate that credentials_path exists."""
+        if not v.exists():
+            raise ValueError(f"credentials_path {v} does not exist")
+        return v
+
+    def _load_credentials(self) -> Any:
+        """Load credentials."""
+        # Adapted from https://developers.google.com/drive/api/v3/quickstart/python
+        try:
+            from google.auth import default  # type: ignore[import]
+            from google.auth.transport.requests import Request  # type: ignore[import]
+            from google.oauth2 import service_account  # type: ignore[import]
+            from google.oauth2.credentials import Credentials  # type: ignore[import]
+            from google_auth_oauthlib.flow import (  # type: ignore[import]
+                InstalledAppFlow,
+            )
+        except ImportError:
+            raise ImportError(
+                "You must run "
+                "`pip install --upgrade "
+                "google-api-python-client google-auth-httplib2 "
+                "google-auth-oauthlib` "
+                "to use the Google Drive loader."
+            )
+
+        creds = None
+        if self.service_account_key.exists():
+            return service_account.Credentials.from_service_account_file(
+                str(self.service_account_key), scopes=SCOPES
+            )
+
+        if self.token_path.exists():
+            creds = Credentials.from_authorized_user_file(str(self.token_path), SCOPES)
+
+        if not creds or not creds.valid:
+            if creds and creds.expired and creds.refresh_token:
+                creds.refresh(Request())
+            elif "GOOGLE_APPLICATION_CREDENTIALS" not in os.environ:
+                creds, project = default()
+                creds = creds.with_scopes(SCOPES)
+                # no need to write to file
+                if creds:
+                    return creds
+            else:
+                flow = InstalledAppFlow.from_client_secrets_file(
+                    str(self.credentials_path), SCOPES
+                )
+                creds = flow.run_local_server(port=0)
+            with open(self.token_path, "w") as token:
+                token.write(creds.to_json())
+
+        return creds
+
+    def _load_sheet_from_id(self, id: str) -> List[Document]:
+        """Load a sheet and all tabs from an ID."""
+
+        from googleapiclient.discovery import build  # type: ignore[import]
+
+        creds = self._load_credentials()
+        sheets_service = build("sheets", "v4", credentials=creds)
+        spreadsheet = sheets_service.spreadsheets().get(spreadsheetId=id).execute()
+        sheets = spreadsheet.get("sheets", [])
+
+        documents = []
+        for sheet in sheets:
+            sheet_name = sheet["properties"]["title"]
+            result = (
+                sheets_service.spreadsheets()
+                .values()
+                .get(spreadsheetId=id, range=sheet_name)
+                .execute()
+            )
+            values = result.get("values", [])
+            if not values:
+                continue  # empty sheet
+
+            header = values[0]
+            for i, row in enumerate(values[1:], start=1):
+                metadata = {
+                    "source": (
+                        f"https://docs.google.com/spreadsheets/d/{id}/"
+                        f"edit?gid={sheet['properties']['sheetId']}"
+                    ),
+                    "title": f"{spreadsheet['properties']['title']} - {sheet_name}",
+                    "row": i,
+                }
+                content = []
+                for j, v in enumerate(row):
+                    title = header[j].strip() if len(header) > j else ""
+                    content.append(f"{title}: {v.strip()}")
+
+                page_content = "\n".join(content)
+                documents.append(Document(page_content=page_content, metadata=metadata))
+
+        return documents
+
+    def _load_document_from_id(self, id: str) -> Document:
+        """Load a document from an ID."""
+        from io import BytesIO
+
+        from googleapiclient.discovery import build
+        from googleapiclient.errors import HttpError  # type: ignore[import]
+        from googleapiclient.http import MediaIoBaseDownload  # type: ignore[import]
+
+        creds = self._load_credentials()
+        service = build("drive", "v3", credentials=creds)
+
+        file = (
+            service.files()
+            .get(fileId=id, supportsAllDrives=True, fields="modifiedTime,name")
+            .execute()
+        )
+        request = service.files().export_media(fileId=id, mimeType="text/plain")
+        fh = BytesIO()
+        downloader = MediaIoBaseDownload(fh, request)
+        done = False
+        try:
+            while done is False:
+                status, done = downloader.next_chunk()
+
+        except HttpError as e:
+            if e.resp.status == 404:
+                print("File not found: {}".format(id))  # noqa: T201
+            else:
+                print("An error occurred: {}".format(e))  # noqa: T201
+
+        text = fh.getvalue().decode("utf-8")
+        metadata = {
+            "source": f"https://docs.google.com/document/d/{id}/edit",
+            "title": f"{file.get('name')}",
+            "when": f"{file.get('modifiedTime')}",
+        }
+        return Document(page_content=text, metadata=metadata)
+
+    def _load_documents_from_folder(
+        self, folder_id: str, *, file_types: Optional[Sequence[str]] = None
+    ) -> List[Document]:
+        """Load documents from a folder."""
+        from googleapiclient.discovery import build
+
+        creds = self._load_credentials()
+        service = build("drive", "v3", credentials=creds)
+        files = self._fetch_files_recursive(service, folder_id)
+        # If file types filter is provided, we'll filter by the file type.
+        if file_types:
+            _files = [f for f in files if f["mimeType"] in file_types]  # type: ignore
+        else:
+            _files = files
+
+        returns = []
+        for file in _files:
+            if file["trashed"] and not self.load_trashed_files:
+                continue
+            elif file["mimeType"] == "application/vnd.google-apps.document":
+                returns.append(self._load_document_from_id(file["id"]))  # type: ignore
+            elif file["mimeType"] == "application/vnd.google-apps.spreadsheet":
+                returns.extend(self._load_sheet_from_id(file["id"]))  # type: ignore
+            elif (
+                file["mimeType"] == "application/pdf"
+                or self.file_loader_cls is not None
+            ):
+                returns.extend(self._load_file_from_id(file["id"]))  # type: ignore
+            else:
+                pass
+        return returns
+
+    def _fetch_files_recursive(
+        self, service: Any, folder_id: str
+    ) -> List[Dict[str, Union[str, List[str]]]]:
+        """Fetch all files and subfolders recursively."""
+        results = (
+            service.files()
+            .list(
+                q=f"'{folder_id}' in parents",
+                pageSize=1000,
+                includeItemsFromAllDrives=True,
+                supportsAllDrives=True,
+                fields="nextPageToken, files(id, name, mimeType, parents, trashed)",
+            )
+            .execute()
+        )
+        files = results.get("files", [])
+        returns = []
+        for file in files:
+            if file["mimeType"] == "application/vnd.google-apps.folder":
+                if self.recursive:
+                    returns.extend(self._fetch_files_recursive(service, file["id"]))
+            else:
+                returns.append(file)
+
+        return returns
+
+    def _load_documents_from_ids(self) -> List[Document]:
+        """Load documents from a list of IDs."""
+        if not self.document_ids:
+            raise ValueError("document_ids must be set")
+
+        return [self._load_document_from_id(doc_id) for doc_id in self.document_ids]
+
+    def _load_file_from_id(self, id: str) -> List[Document]:
+        """Load a file from an ID."""
+        from io import BytesIO
+
+        from googleapiclient.discovery import build
+        from googleapiclient.http import MediaIoBaseDownload
+
+        creds = self._load_credentials()
+        service = build("drive", "v3", credentials=creds)
+
+        file = service.files().get(fileId=id, supportsAllDrives=True).execute()
+        request = service.files().get_media(fileId=id)
+        fh = BytesIO()
+        downloader = MediaIoBaseDownload(fh, request)
+        done = False
+        while done is False:
+            status, done = downloader.next_chunk()
+
+        if self.file_loader_cls is not None:
+            fh.seek(0)
+            loader = self.file_loader_cls(file=fh, **self.file_loader_kwargs)
+            docs = loader.load()
+            for doc in docs:
+                doc.metadata["source"] = f"https://drive.google.com/file/d/{id}/view"
+                if "title" not in doc.metadata:
+                    doc.metadata["title"] = f"{file.get('name')}"
+            return docs
+
+        else:
+            from PyPDF2 import PdfReader  # type: ignore[import]
+
+            content = fh.getvalue()
+            pdf_reader = PdfReader(BytesIO(content))
+
+            return [
+                Document(
+                    page_content=page.extract_text(),
+                    metadata={
+                        "source": f"https://drive.google.com/file/d/{id}/view",
+                        "title": f"{file.get('name')}",
+                        "page": i,
+                    },
+                )
+                for i, page in enumerate(pdf_reader.pages)
+            ]
+
+    def _load_file_from_ids(self) -> List[Document]:
+        """Load files from a list of IDs."""
+        if not self.file_ids:
+            raise ValueError("file_ids must be set")
+        docs = []
+        for file_id in self.file_ids:
+            docs.extend(self._load_file_from_id(file_id))
+        return docs
+
+    def load(self) -> List[Document]:
+        """Load documents."""
+        if self.folder_id:
+            return self._load_documents_from_folder(
+                self.folder_id, file_types=self.file_types
+            )
+        elif self.document_ids:
+            return self._load_documents_from_ids()
+        else:
+            return self._load_file_from_ids()
diff --git a/libs/community/langchain_google_community/vertex_ai_search.py b/libs/community/langchain_google_community/vertex_ai_search.py
index d65bf2c53..7997304db 100644
--- a/libs/community/langchain_google_community/vertex_ai_search.py
+++ b/libs/community/langchain_google_community/vertex_ai_search.py
@@ -14,13 +14,14 @@
 from google.api_core.client_options import ClientOptions
 from google.api_core.exceptions import InvalidArgument
 from google.protobuf.json_format import MessageToDict
-from langchain_community.utilities.vertexai import get_client_info
 from langchain_core.callbacks import CallbackManagerForRetrieverRun
 from langchain_core.documents import Document
 from langchain_core.pydantic_v1 import BaseModel, Extra, Field, root_validator
 from langchain_core.retrievers import BaseRetriever
 from langchain_core.utils import get_from_dict_or_env
 
+from langchain_google_community._utils import get_client_info
+
 if TYPE_CHECKING:
     from google.cloud.discoveryengine_v1beta import (  # type: ignore[import]
         ConversationalSearchServiceClient,
diff --git a/libs/community/poetry.lock b/libs/community/poetry.lock
index 353243916..edac23ab4 100644
--- a/libs/community/poetry.lock
+++ b/libs/community/poetry.lock
@@ -176,6 +176,64 @@ tests = ["attrs[tests-no-zope]", "zope-interface"]
 tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"]
 tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"]
 
+[[package]]
+name = "bottleneck"
+version = "1.3.8"
+description = "Fast NumPy array functions written in C"
+optional = false
+python-versions = "*"
+files = [
+    {file = "Bottleneck-1.3.8-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:865c8ed5b798c0198b0b80553e09cc0d890c4f5feb3d81d31661517ca7819fa3"},
+    {file = "Bottleneck-1.3.8-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d073a31e259d40b25e29dbba80f73abf38afe98fd730c79dad7edd9a0ad6cff5"},
+    {file = "Bottleneck-1.3.8-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b806b277ab47495032822f55f43b8d336e4b7e73f8506ed34d3ea3da6d644abc"},
+    {file = "Bottleneck-1.3.8-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:770b517609916adeb39d3b1a386a29bc316da03dd61e7ee6e8a38325b80cc327"},
+    {file = "Bottleneck-1.3.8-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:2948502b0394ee419945b55b092585222a505c61d41a874c741be49f2cac056f"},
+    {file = "Bottleneck-1.3.8-cp310-cp310-win32.whl", hash = "sha256:271b6333522beb8aee32e640ba49a2064491d2c10317baa58a5996be3dd443e4"},
+    {file = "Bottleneck-1.3.8-cp310-cp310-win_amd64.whl", hash = "sha256:d41000ea7ca196b5fd39d6fccd34bf0704c8831731cedd2da2dcae3c6ac49c42"},
+    {file = "Bottleneck-1.3.8-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:d0a7f454394cd3642498b6e077e70f4a6b9fd46a8eb908c83ac737fdc9f9a98c"},
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+
+[package.dependencies]
+numpy = "*"
+
+[package.extras]
+doc = ["gitpython", "numpydoc", "sphinx"]
+
 [[package]]
 name = "cachetools"
 version = "5.3.3"
@@ -340,6 +398,51 @@ files = [
 marshmallow = ">=3.18.0,<4.0.0"
 typing-inspect = ">=0.4.0,<1"
 
+[[package]]
+name = "db-dtypes"
+version = "1.2.0"
+description = "Pandas Data Types for SQL systems (BigQuery, Spanner)"
+optional = false
+python-versions = ">=3.7"
+files = [
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+packaging = ">=17.0"
+pandas = ">=0.24.2"
+pyarrow = ">=3.0.0"
+
+[[package]]
+name = "decorator"
+version = "5.1.1"
+description = "Decorators for Humans"
+optional = false
+python-versions = ">=3.5"
+files = [
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+    {file = "decorator-5.1.1.tar.gz", hash = "sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330"},
+]
+
+[[package]]
+name = "deprecated"
+version = "1.2.14"
+description = "Python @deprecated decorator to deprecate old python classes, functions or methods."
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+files = [
+    {file = "Deprecated-1.2.14-py2.py3-none-any.whl", hash = "sha256:6fac8b097794a90302bdbb17b9b815e732d3c4720583ff1b198499d78470466c"},
+    {file = "Deprecated-1.2.14.tar.gz", hash = "sha256:e5323eb936458dccc2582dc6f9c322c852a775a27065ff2b0c4970b9d53d01b3"},
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+
+[package.extras]
+dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "sphinx (<2)", "tox"]
+
 [[package]]
 name = "exceptiongroup"
 version = "1.2.0"
@@ -454,20 +557,88 @@ files = [
     {file = "frozenlist-1.4.1.tar.gz", hash = "sha256:c037a86e8513059a2613aaba4d817bb90b9d9b6b69aace3ce9c877e8c8ed402b"},
 ]
 
+[[package]]
+name = "fsspec"
+version = "2024.3.1"
+description = "File-system specification"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "fsspec-2024.3.1-py3-none-any.whl", hash = "sha256:918d18d41bf73f0e2b261824baeb1b124bcf771767e3a26425cd7dec3332f512"},
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+]
+
+[package.extras]
+abfs = ["adlfs"]
+adl = ["adlfs"]
+arrow = ["pyarrow (>=1)"]
+dask = ["dask", "distributed"]
+devel = ["pytest", "pytest-cov"]
+dropbox = ["dropbox", "dropboxdrivefs", "requests"]
+full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
+fuse = ["fusepy"]
+gcs = ["gcsfs"]
+git = ["pygit2"]
+github = ["requests"]
+gs = ["gcsfs"]
+gui = ["panel"]
+hdfs = ["pyarrow (>=1)"]
+http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)"]
+libarchive = ["libarchive-c"]
+oci = ["ocifs"]
+s3 = ["s3fs"]
+sftp = ["paramiko"]
+smb = ["smbprotocol"]
+ssh = ["paramiko"]
+tqdm = ["tqdm"]
+
+[[package]]
+name = "gcsfs"
+version = "2024.3.1"
+description = "Convenient Filesystem interface over GCS"
+optional = false
+python-versions = ">=3.8"
+files = [
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+[package.dependencies]
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+decorator = ">4.1.2"
+fsspec = "2024.3.1"
+google-auth = ">=1.2"
+google-auth-oauthlib = "*"
+google-cloud-storage = "*"
+requests = "*"
+
+[package.extras]
+crc = ["crcmod"]
+gcsfuse = ["fusepy"]
+
 [[package]]
 name = "google-api-core"
-version = "2.17.1"
+version = "2.18.0"
 description = "Google API client core library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-api-core-2.17.1.tar.gz", hash = "sha256:9df18a1f87ee0df0bc4eea2770ebc4228392d8cc4066655b320e2cfccb15db95"},
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+    {file = "google-api-core-2.18.0.tar.gz", hash = "sha256:62d97417bfc674d6cef251e5c4d639a9655e00c45528c4364fbfebb478ce72a9"},
+    {file = "google_api_core-2.18.0-py3-none-any.whl", hash = "sha256:5a63aa102e0049abe85b5b88cb9409234c1f70afcda21ce1e40b285b9629c1d6"},
 ]
 
 [package.dependencies]
 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\""},
+]
+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\""},
+]
+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"
 requests = ">=2.18.0,<3.0.0.dev0"
 
@@ -478,13 +649,13 @@ grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"]
 
 [[package]]
 name = "google-api-python-client"
-version = "2.122.0"
+version = "2.123.0"
 description = "Google API Client Library for Python"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-api-python-client-2.122.0.tar.gz", hash = "sha256:77447bf2d6b6ea9e686fd66fc2f12ee7a63e3889b7427676429ebf09fcb5dcf9"},
-    {file = "google_api_python_client-2.122.0-py2.py3-none-any.whl", hash = "sha256:a5953e60394b77b98bcc7ff7c4971ed784b3b693e9a569c176eaccb1549330f2"},
+    {file = "google-api-python-client-2.123.0.tar.gz", hash = "sha256:a17226b02f71de581afe045437b441844110a9cd91580b73549d41108cf1b9f0"},
+    {file = "google_api_python_client-2.123.0-py2.py3-none-any.whl", hash = "sha256:1c2bcaa846acf5bac4d6f244d8373d4de9de73d64eb6e77b56767ab4cf681419"},
 ]
 
 [package.dependencies]
@@ -496,13 +667,13 @@ uritemplate = ">=3.0.1,<5"
 
 [[package]]
 name = "google-auth"
-version = "2.28.2"
+version = "2.29.0"
 description = "Google Authentication Library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-auth-2.28.2.tar.gz", hash = "sha256:80b8b4969aa9ed5938c7828308f20f035bc79f9d8fb8120bf9dc8db20b41ba30"},
-    {file = "google_auth-2.28.2-py2.py3-none-any.whl", hash = "sha256:9fd67bbcd40f16d9d42f950228e9cf02a2ded4ae49198b27432d0cded5a74c38"},
+    {file = "google-auth-2.29.0.tar.gz", hash = "sha256:672dff332d073227550ffc7457868ac4218d6c500b155fe6cc17d2b13602c360"},
+    {file = "google_auth-2.29.0-py2.py3-none-any.whl", hash = "sha256:d452ad095688cd52bae0ad6fafe027f6a6d6f560e810fec20914e17a09526415"},
 ]
 
 [package.dependencies]
@@ -532,6 +703,256 @@ files = [
 google-auth = "*"
 httplib2 = ">=0.19.0"
 
+[[package]]
+name = "google-auth-oauthlib"
+version = "1.2.0"
+description = "Google Authentication Library"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "google-auth-oauthlib-1.2.0.tar.gz", hash = "sha256:292d2d3783349f2b0734a0a0207b1e1e322ac193c2c09d8f7c613fb7cc501ea8"},
+    {file = "google_auth_oauthlib-1.2.0-py2.py3-none-any.whl", hash = "sha256:297c1ce4cb13a99b5834c74a1fe03252e1e499716718b190f56bcb9c4abc4faf"},
+]
+
+[package.dependencies]
+google-auth = ">=2.15.0"
+requests-oauthlib = ">=0.7.0"
+
+[package.extras]
+tool = ["click (>=6.0.0)"]
+
+[[package]]
+name = "google-cloud-bigquery"
+version = "3.19.0"
+description = "Google BigQuery API client library"
+optional = false
+python-versions = ">=3.7"
+files = [
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+    {file = "google_cloud_bigquery-3.19.0-py2.py3-none-any.whl", hash = "sha256:c6b8850247a4b132066e49f6e45f850c22824482838688d744a4398eea1120ed"},
+]
+
+[package.dependencies]
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<3.0.0dev"
+google-cloud-core = ">=1.6.0,<3.0.0dev"
+google-resumable-media = ">=0.6.0,<3.0dev"
+packaging = ">=20.0.0"
+python-dateutil = ">=2.7.2,<3.0dev"
+requests = ">=2.21.0,<3.0.0dev"
+
+[package.extras]
+all = ["Shapely (>=1.8.4,<3.0.0dev)", "db-dtypes (>=0.3.0,<2.0.0dev)", "geopandas (>=0.9.0,<1.0dev)", "google-cloud-bigquery-storage (>=2.6.0,<3.0.0dev)", "grpcio (>=1.47.0,<2.0dev)", "grpcio (>=1.49.1,<2.0dev)", "importlib-metadata (>=1.0.0)", "ipykernel (>=6.0.0)", "ipython (>=7.23.1,!=8.1.0)", "ipywidgets (>=7.7.0)", "opentelemetry-api (>=1.1.0)", "opentelemetry-instrumentation (>=0.20b0)", "opentelemetry-sdk (>=1.1.0)", "pandas (>=1.1.0)", "proto-plus (>=1.15.0,<2.0.0dev)", "protobuf (>=3.19.5,!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev)", "pyarrow (>=3.0.0)", "tqdm (>=4.7.4,<5.0.0dev)"]
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+[package.extras]
+testing = ["pytest"]
+
+[[package]]
+name = "google-resumable-media"
+version = "2.7.0"
+description = "Utilities for Google Media Downloads and Resumable Uploads"
+optional = false
+python-versions = ">= 3.7"
+files = [
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+[package.dependencies]
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+
+[package.extras]
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+requests = ["requests (>=2.18.0,<3.0.0dev)"]
+
 [[package]]
 name = "googleapis-common-protos"
 version = "1.63.0"
@@ -544,6 +965,7 @@ files = [
 ]
 
 [package.dependencies]
+grpcio = {version = ">=1.44.0,<2.0.0.dev0", optional = true, markers = "extra == \"grpc\""}
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 [package.extras]
@@ -620,6 +1042,104 @@ files = [
 docs = ["Sphinx", "furo"]
 test = ["objgraph", "psutil"]
 
+[[package]]
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+description = "IAM API client library"
+optional = false
+python-versions = ">=3.7"
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+
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+
+[package.extras]
+protobuf = ["grpcio-tools (>=1.62.1)"]
+
+[[package]]
+name = "grpcio-status"
+version = "1.62.1"
+description = "Status proto mapping for gRPC"
+optional = false
+python-versions = ">=3.6"
+files = [
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+]
+
+[package.dependencies]
+googleapis-common-protos = ">=1.5.5"
+grpcio = ">=1.62.1"
+protobuf = ">=4.21.6"
+
 [[package]]
 name = "httplib2"
 version = "0.22.0"
@@ -645,6 +1165,36 @@ files = [
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 ]
 
+[[package]]
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+description = "Immutable wrapper around dictionaries (a fork of frozendict)"
+optional = false
+python-versions = ">=3.8,<4.0"
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+]
+
+[[package]]
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+version = "7.1.0"
+description = "Read metadata from Python packages"
+optional = false
+python-versions = ">=3.8"
+files = [
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+
+[package.extras]
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+perf = ["ipython"]
+testing = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
+
 [[package]]
 name = "iniconfig"
 version = "2.0.0"
@@ -656,6 +1206,36 @@ files = [
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 ]
 
+[[package]]
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+version = "3.1.0"
+description = "Editable interval tree data structure for Python 2 and 3"
+optional = false
+python-versions = "*"
+files = [
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+[package.dependencies]
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+
+[[package]]
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+version = "3.1.3"
+description = "A very fast and expressive template engine."
+optional = false
+python-versions = ">=3.7"
+files = [
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+
+[package.extras]
+i18n = ["Babel (>=2.7)"]
+
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 version = "1.33"
@@ -709,13 +1289,13 @@ extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.
 
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 requests = ">=2,<3"
 
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+
+[package.dependencies]
+numpy = ">=1.16.6,<2"
+
 [[package]]
 name = "pyasn1"
 version = "0.5.1"
@@ -1288,6 +2488,22 @@ files = [
 [package.dependencies]
 typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
 
+[[package]]
+name = "pydata-google-auth"
+version = "1.8.2"
+description = "PyData helpers for authenticating to Google APIs"
+optional = false
+python-versions = "*"
+files = [
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+google-auth-oauthlib = {version = ">=0.4.0", markers = "python_version >= \"3.6\""}
+setuptools = "*"
+
 [[package]]
 name = "pyparsing"
 version = "3.1.2"
@@ -1344,17 +2560,17 @@ testing = ["coverage (>=6.2)", "flaky (>=3.5.0)", "hypothesis (>=5.7.1)", "mypy
 
 [[package]]
 name = "pytest-mock"
-version = "3.12.0"
+version = "3.14.0"
 description = "Thin-wrapper around the mock package for easier use with pytest"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
-pytest = ">=5.0"
+pytest = ">=6.2.5"
 
 [package.extras]
 dev = ["pre-commit", "pytest-asyncio", "tox"]
@@ -1388,6 +2604,17 @@ files = [
 [package.dependencies]
 six = ">=1.5"
 
+[[package]]
+name = "pytz"
+version = "2024.1"
+description = "World timezone definitions, modern and historical"
+optional = false
+python-versions = "*"
+files = [
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+
 [[package]]
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 version = "6.0.1"
@@ -1468,6 +2695,24 @@ urllib3 = ">=1.21.1,<3"
 socks = ["PySocks (>=1.5.6,!=1.5.7)"]
 use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
+[[package]]
+name = "requests-oauthlib"
+version = "2.0.0"
+description = "OAuthlib authentication support for Requests."
+optional = false
+python-versions = ">=3.4"
+files = [
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+requests = ">=2.0.0"
+
+[package.extras]
+rsa = ["oauthlib[signedtoken] (>=3.0.0)"]
+
 [[package]]
 name = "rsa"
 version = "4.9"
@@ -1508,6 +2753,22 @@ files = [
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 ]
 
+[[package]]
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+version = "69.2.0"
+description = "Easily download, build, install, upgrade, and uninstall Python packages"
+optional = false
+python-versions = ">=3.8"
+files = [
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+
 [[package]]
 name = "six"
 version = "1.16.0"
@@ -1530,62 +2791,73 @@ files = [
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+description = "Sorted Containers -- Sorted List, Sorted Dict, Sorted Set"
+optional = false
+python-versions = "*"
+files = [
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+
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 description = "Database Abstraction Library"
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 python-versions = ">=3.7"
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+    {file = "wrapt-1.16.0-py3-none-any.whl", hash = "sha256:6906c4100a8fcbf2fa735f6059214bb13b97f75b1a61777fcf6432121ef12ef1"},
+    {file = "wrapt-1.16.0.tar.gz", hash = "sha256:5f370f952971e7d17c7d1ead40e49f32345a7f7a5373571ef44d800d06b1899d"},
+]
+
 [[package]]
 name = "yarl"
 version = "1.9.4"
@@ -1901,7 +3277,22 @@ files = [
 idna = ">=2.0"
 multidict = ">=4.0"
 
+[[package]]
+name = "zipp"
+version = "3.18.1"
+description = "Backport of pathlib-compatible object wrapper for zip files"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "zipp-3.18.1-py3-none-any.whl", hash = "sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b"},
+    {file = "zipp-3.18.1.tar.gz", hash = "sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"},
+]
+
+[package.extras]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
+
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0"
-content-hash = "d56c577b8e44f956bc8ed1bfd07b54af9ddda214e20257a817413f0cc53f02ab"
+content-hash = "5e339be1a11064883f6b1f42169906cae8148cd644a0bc1ae83e7b435eadabe8"
diff --git a/libs/community/pyproject.toml b/libs/community/pyproject.toml
index 334232d15..9d9a3674a 100644
--- a/libs/community/pyproject.toml
+++ b/libs/community/pyproject.toml
@@ -27,6 +27,8 @@ pytest-mock = "^3.10.0"
 syrupy = "^4.0.2"
 pytest-watcher = "^0.3.4"
 pytest-asyncio = "^0.21.1"
+google-cloud-documentai = "^2.24.2"
+google-cloud-documentai-toolbox = "^0.13.3a0"
 
 [tool.poetry.group.codespell]
 optional = true
diff --git a/libs/community/tests/unit_tests/test_docai.py b/libs/community/tests/unit_tests/test_docai.py
new file mode 100644
index 000000000..b6c422a04
--- /dev/null
+++ b/libs/community/tests/unit_tests/test_docai.py
@@ -0,0 +1,24 @@
+"""Tests for the Google Cloud DocAI parser."""
+from unittest.mock import ANY, patch
+
+import pytest
+
+from langchain_google_community.docai import DocAIParser
+
+
+def test_docai_parser_valid_processor_name() -> None:
+    processor_name = "projects/123456/locations/us-central1/processors/ab123dfg"
+    with patch("google.cloud.documentai.DocumentProcessorServiceClient") as test_client:
+        parser = DocAIParser(processor_name=processor_name, location="us")
+        test_client.assert_called_once_with(client_options=ANY, client_info=ANY)
+        assert parser._processor_name == processor_name
+
+
+@pytest.mark.parametrize(
+    "processor_name",
+    ["projects/123456/locations/us-central1/processors/ab123dfg:publish", "ab123dfg"],
+)
+def test_docai_parser_invalid_processor_name(processor_name: str) -> None:
+    with patch("google.cloud.documentai.DocumentProcessorServiceClient"):
+        with pytest.raises(ValueError):
+            _ = DocAIParser(processor_name=processor_name, location="us")