Google Drive

Google Drive is a file storage and synchronization service developed by Google.

This notebook covers how to load documents from Google Drive. Currently, only Google Docs are supported.

Prerequisites

Create a Google Cloud project or use an existing project
Enable the Google Drive API
Authorize credentials for desktop app
pip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlib

🧑 Instructions for ingesting your Google Docs data

By default, the GoogleDriveLoader expects the credentials.json file to be ~/.credentials/credentials.json, but this is configurable using the credentials_path keyword argument. Same thing with token.json - token_path. Note that token.json will be created automatically the first time you use the loader.

The first time you use GoogleDriveLoader, you will be displayed with the consent screen in your browser. If this doesn't happen and you get a RefreshError, do not use credentials_path in your GoogleDriveLoader constructor call. Instead, put that path in a GOOGLE_APPLICATION_CREDENTIALS environmental variable.

GoogleDriveLoader can load from a list of Google Docs document ids or a folder id. You can obtain your folder and document id from the URL:

Folder: https://drive.google.com/drive/u/0/folders/1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5 -> folder id is "1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5"
Document: https://docs.google.com/document/d/1bfaMQ18_i56204VaQDVeAFpqEijJTgvurupdEDiaUQw/edit -> document id is "1bfaMQ18_i56204VaQDVeAFpqEijJTgvurupdEDiaUQw"

pip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlib

from langchain.document_loaders import GoogleDriveLoader

loader = GoogleDriveLoader(
    folder_id="1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5",
    token_path="/path/where/you/want/token/to/be/created/google_token.json",
    # Optional: configure whether to recursively fetch files from subfolders. Defaults to False.
    recursive=False,
)

docs = loader.load()

When you pass a folder_id by default all files of type document, sheet and pdf are loaded. You can modify this behaviour by passing a file_types argument

loader = GoogleDriveLoader(
    folder_id="1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5",
    file_types=["document", "sheet"],
    recursive=False,
)

Passing in Optional File Loaders

When processing files other than Google Docs and Google Sheets, it can be helpful to pass an optional file loader to GoogleDriveLoader. If you pass in a file loader, that file loader will be used on documents that do not have a Google Docs or Google Sheets MIME type. Here is an example of how to load an Excel document from Google Drive using a file loader.

from langchain.document_loaders import GoogleDriveLoader, UnstructuredFileIOLoader

file_id = "1x9WBtFPWMEAdjcJzPScRsjpjQvpSo_kz"
loader = GoogleDriveLoader(
    file_ids=[file_id],
    file_loader_cls=UnstructuredFileIOLoader,
    file_loader_kwargs={"mode": "elements"},
)

docs = loader.load()

docs[0]

You can also process a folder with a mix of files and Google Docs/Sheets using the following pattern:

folder_id = "1asMOHY1BqBS84JcRbOag5LOJac74gpmD"
loader = GoogleDriveLoader(
    folder_id=folder_id,
    file_loader_cls=UnstructuredFileIOLoader,
    file_loader_kwargs={"mode": "elements"},
)

docs = loader.load()

docs[0]

Extended usage

An external component can manage the complexity of Google Drive : langchain-googledrive It's compatible with the ̀langchain.document_loaders.GoogleDriveLoader and can be used in its place.

To be compatible with containers, the authentication uses an environment variable ̀GOOGLE_ACCOUNT_FILE` to credential file (for user or service).

pip install langchain-googledrive

folder_id = "root"
# folder_id='1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5'

# Use the advanced version.
from langchain_googledrive.document_loaders import GoogleDriveLoader

loader = GoogleDriveLoader(
    folder_id=folder_id,
    recursive=False,
    num_results=2,  # Maximum number of file to load
)

By default, all files with these mime-type can be converted to Document.

text/text
text/plain
text/html
text/csv
text/markdown
image/png
image/jpeg
application/epub+zip
application/pdf
application/rtf
application/vnd.google-apps.document (GDoc)
application/vnd.google-apps.presentation (GSlide)
application/vnd.google-apps.spreadsheet (GSheet)
application/vnd.google.colaboratory (Notebook colab)
application/vnd.openxmlformats-officedocument.presentationml.presentation (PPTX)
application/vnd.openxmlformats-officedocument.wordprocessingml.document (DOCX)

It's possible to update or customize this. See the documentation of GDriveLoader.

But, the corresponding packages must be installed.

pip install unstructured

for doc in loader.load():
    print("---")
    print(doc.page_content.strip()[:60] + "...")

Customize the search pattern

All parameter compatible with Google list() API can be set.

To specify the new pattern of the Google request, you can use a PromptTemplate(). The variables for the prompt can be set with kwargs in the constructor. Some pre-formated request are proposed (use {query}, {folder_id} and/or {mime_type}):

You can customize the criteria to select the files. A set of predefined filter are proposed: | template | description | | -------------------------------------- | --------------------------------------------------------------------- | | gdrive-all-in-folder | Return all compatible files from a folder_id | | gdrive-query | Search query in all drives | | gdrive-by-name | Search file with name query | | gdrive-query-in-folder | Search query in folder_id (and sub-folders if recursive=true) | | gdrive-mime-type | Search a specific mime_type | | gdrive-mime-type-in-folder | Search a specific mime_type in folder_id | | gdrive-query-with-mime-type | Search query with a specific mime_type | | gdrive-query-with-mime-type-and-folder | Search query with a specific mime_type and in folder_id |

loader = GoogleDriveLoader(
    folder_id=folder_id,
    recursive=False,
    template="gdrive-query",  # Default template to use
    query="machine learning",
    num_results=2,  # Maximum number of file to load
    supportsAllDrives=False,  # GDrive `list()` parameter
)
for doc in loader.load():
    print("---")
    print(doc.page_content.strip()[:60] + "...")

You can customize your pattern.

from langchain.prompts.prompt import PromptTemplate

loader = GoogleDriveLoader(
    folder_id=folder_id,
    recursive=False,
    template=PromptTemplate(
        input_variables=["query", "query_name"],
        template="fullText contains '{query}' and name contains '{query_name}' and trashed=false",
    ),  # Default template to use
    query="machine learning",
    query_name="ML",
    num_results=2,  # Maximum number of file to load
)
for doc in loader.load():
    print("---")
    print(doc.page_content.strip()[:60] + "...")

Modes for GSlide and GSheet

The parameter mode accepts different values:

"document": return the body of each document
"snippets": return the description of each file (set in metadata of Google Drive files).

The conversion can manage in Markdown format:

bullet
link
table
titles

The parameter gslide_mode accepts different values:

"single" : one document with <PAGE BREAK>
"slide" : one document by slide
"elements" : one document for each elements.

loader = GoogleDriveLoader(
    template="gdrive-mime-type",
    mime_type="application/vnd.google-apps.presentation",  # Only GSlide files
    gslide_mode="slide",
    num_results=2,  # Maximum number of file to load
)
for doc in loader.load():
    print("---")
    print(doc.page_content.strip()[:60] + "...")

The parameter gsheet_mode accepts different values:

"single": Generate one document by line
"elements" : one document with markdown array and <PAGE BREAK> tags.

loader = GoogleDriveLoader(
    template="gdrive-mime-type",
    mime_type="application/vnd.google-apps.spreadsheet",  # Only GSheet files
    gsheet_mode="elements",
    num_results=2,  # Maximum number of file to load
)
for doc in loader.load():
    print("---")
    print(doc.page_content.strip()[:60] + "...")

Advanced usage

All Google File have a 'description' in the metadata. This field can be used to memorize a summary of the document or others indexed tags (See method lazy_update_description_with_summary()).

If you use the mode="snippet", only the description will be used for the body. Else, the metadata['summary'] has the field.

Sometime, a specific filter can be used to extract some information from the filename, to select some files with specific criteria. You can use a filter.

Sometimes, many documents are returned. It's not necessary to have all documents in memory at the same time. You can use the lazy versions of methods, to get one document at a time. It's better to use a complex query in place of a recursive search. For each folder, a query must be applied if you activate recursive=True.

import os

loader = GoogleDriveLoader(
    gdrive_api_file=os.environ["GOOGLE_ACCOUNT_FILE"],
    num_results=2,
    template="gdrive-query",
    filter=lambda search, file: "#test" not in file.get("description", ""),
    query="machine learning",
    supportsAllDrives=False,
)
for doc in loader.load():
    print("---")
    print(doc.page_content.strip()[:60] + "...")

Google Drive

Prerequisites​

🧑 Instructions for ingesting your Google Docs data​

Passing in Optional File Loaders​

Extended usage​

Customize the search pattern​

Modes for GSlide and GSheet​

Advanced usage​