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langchain/libs/community/langchain_community/document_loaders/gcs_file.py

90 lines
3.2 KiB
Python

import os
import tempfile
from typing import Callable, List, Optional
from langchain_core._api.deprecation import deprecated
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
from langchain_community.document_loaders.unstructured import UnstructuredFileLoader
from langchain_community.utilities.vertexai import get_client_info
@deprecated(
since="0.0.32",
removal="0.3.0",
alternative_import="langchain_google_community.GCSFileLoader",
)
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:
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
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