This notebook covers how to retrieve documents from Google Drive.

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Prerequisites

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

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Retrieve the Google Docs

By default, the GoogleDriveRetriever expects the credentials.json file to be ~/.credentials/credentials.json, but this is configurable using the GOOGLE_ACCOUNT_FILE environment variable. The location of token.json uses the same directory (or use the parameter token_path). Note that token.json will be created automatically the first time you use the retriever. GoogleDriveRetriever can retrieve a selection of files with some requests. By default, If you use a folder_id, all the files inside this folder can be retrieved to Document. You can obtain your folder and document id from the URL: The special value root is for your personal home. 
from langchain_googledrive.retrievers import GoogleDriveRetriever

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

retriever = GoogleDriveRetriever(
    num_results=2,
)
By default, all files with these MIME types 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 GoogleDriveRetriever. But, the corresponding packages must be installed. 
%pip install --upgrade --quiet  unstructured

retriever.invoke("machine learning")
You can customize the criteria to select the files. A set of predefined filter are proposed:
TemplateDescription
gdrive-all-in-folderReturn all compatible files from a folder_id
gdrive-querySearch query in all drives
gdrive-by-nameSearch file with name query
gdrive-query-in-folderSearch query in folder_id (and sub-folders in _recursive=true)
gdrive-mime-typeSearch a specific mime_type
gdrive-mime-type-in-folderSearch a specific mime_type in folder_id
gdrive-query-with-mime-typeSearch query with a specific mime_type
gdrive-query-with-mime-type-and-folderSearch query with a specific mime_type and in folder_id
retriever = GoogleDriveRetriever(
    template="gdrive-query",  # Search everywhere
    num_results=2,  # But take only 2 documents
)
for doc in retriever.invoke("machine learning"):
    print("---")
    print(doc.page_content.strip()[:60] + "...")
Else, you can customize the prompt with a specialized PromptTemplate 
from langchain_core.prompts import PromptTemplate

retriever = GoogleDriveRetriever(
    template=PromptTemplate(
        input_variables=["query"],
        # See https://developers.google.com/drive/api/guides/search-files
        template="(fullText contains '{query}') "
        "and mimeType='application/vnd.google-apps.document' "
        "and modifiedTime > '2000-01-01T00:00:00' "
        "and trashed=false",
    ),
    num_results=2,
    # See https://developers.google.com/drive/api/v3/reference/files/list
    includeItemsFromAllDrives=False,
    supportsAllDrives=False,
)
for doc in retriever.invoke("machine learning"):
    print(f"{doc.metadata['name']}:")
    print("---")
    print(doc.page_content.strip()[:60] + "...")

​
Use Google Drive ‘description’ metadata

Each Google Drive has a description field in metadata (see the details of a file). Use the snippets mode to return the description of selected files. 
retriever = GoogleDriveRetriever(
    template="gdrive-mime-type-in-folder",
    folder_id=folder_id,
    mime_type="application/vnd.google-apps.document",  # Only Google Docs
    num_results=2,
    mode="snippets",
    includeItemsFromAllDrives=False,
    supportsAllDrives=False,
)
retriever.invoke("machine learning")