mirror of
https://github.com/hwchase17/langchain
synced 2024-11-16 06:13:16 +00:00
ed58eeb9c5
Moved the following modules to new package langchain-community in a backwards compatible fashion: ``` mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community ``` Moved the following to core ``` mv langchain/langchain/utils/json_schema.py core/langchain_core/utils mv langchain/langchain/utils/html.py core/langchain_core/utils mv langchain/langchain/utils/strings.py core/langchain_core/utils cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py rm langchain/langchain/utils/env.py ``` See .scripts/community_split/script_integrations.sh for all changes
36 lines
1.1 KiB
Python
36 lines
1.1 KiB
Python
from __future__ import annotations
|
|
|
|
import tempfile
|
|
from typing import TYPE_CHECKING, List
|
|
|
|
from langchain_core.documents import Document
|
|
from langchain_core.pydantic_v1 import BaseModel, Field
|
|
|
|
from langchain_community.document_loaders.base import BaseLoader
|
|
from langchain_community.document_loaders.unstructured import UnstructuredFileLoader
|
|
|
|
if TYPE_CHECKING:
|
|
from O365.drive import File
|
|
|
|
CHUNK_SIZE = 1024 * 1024 * 5
|
|
|
|
|
|
class OneDriveFileLoader(BaseLoader, BaseModel):
|
|
"""Load a file from `Microsoft OneDrive`."""
|
|
|
|
file: File = Field(...)
|
|
"""The file to load."""
|
|
|
|
class Config:
|
|
arbitrary_types_allowed = True
|
|
"""Allow arbitrary types. This is needed for the File type. Default is True.
|
|
See https://pydantic-docs.helpmanual.io/usage/types/#arbitrary-types-allowed"""
|
|
|
|
def load(self) -> List[Document]:
|
|
"""Load Documents"""
|
|
with tempfile.TemporaryDirectory() as temp_dir:
|
|
file_path = f"{temp_dir}/{self.file.name}"
|
|
self.file.download(to_path=temp_dir, chunk_size=CHUNK_SIZE)
|
|
loader = UnstructuredFileLoader(file_path)
|
|
return loader.load()
|