llama-index-readers-google 0.2.9

llama-index readers google integration

LlamaIndex Integration: Google Readers

Effortlessly incorporate Google-based data loaders into your Python workflow using LlamaIndex. Unlock the potential of various readers to enhance your data loading capabilities, including:

• Google Calendar
• Google Chat
• Google Docs
• Google Drive
• Gmail
• Google Keep
• Google Maps
• Google Sheets

Installation

pip install llama-index-readers-google

Authentication

You will need a credentials.json file from Google Cloud to interact with Google Services. To get this file, follow these steps:

• Create a new project in the Google Cloud Console
• Go to APIs & Services -> Library and search for the API you want, e.g. Gmail
• Go to APIs & Services -> Credentials and create a new OAuth client ID
  • Application type: Web application
  • Authorized redirect URIs: http://localhost:8080/ (the last slash seems important)
• Go to APIs & Services -> OAuth consent screen and make the app external, which allows you to connect your personal Google data once you explicitly add yourself as an allowed test user
• Download the credentials JSON file from this screen and save it as credentials.json in the root of your project

See this example for a sample of code that successfully authenticates with Gmail once you have the credentials.json file.

Examples

Google Docs Reader

from llama_index.readers.google import GoogleDocsReader

# Specify the document IDs you want to load
document_ids = ["<document_id>"]

# Load data from Google Docs
documents = GoogleDocsReader().load_data(document_ids=document_ids)

Google Sheets Reader (Documents and Dataframes)

from llama_index.readers.google import GoogleSheetsReader

# Specify the list of sheet IDs you want to load
list_of_sheets = ["spreadsheet_id"]

# Create a Google Sheets Reader instance
sheets_reader = GoogleSheetsReader()

# Load data into Pandas in Data Classes of choice (Documents or Dataframes)
documents = sheets.load_data(list_of_sheets)
dataframes = sheets_reader.load_data_in_pandas(list_of_sheets)

Integrate these readers seamlessly to efficiently manage and process your data within your Python environment, providing a robust foundation for your data-driven workflows with LlamaIndex.

Google Maps Text Search Reader

from llama_index.readers.google import GoogleMapsTextSearchReader
from llama_index.core import VectorStoreIndex

loader = GoogleMapsTextSearchReader()
documents = loader.load_data(
    text="I want to eat quality Turkish food in Istanbul",
    number_of_results=160,
)


index = VectorStoreIndex.from_documents(documents)
index.query("Which Turkish restaurant has the best reviews?")

Google Chat Reader

from llama_index.readers.google import GoogleChatReader
from llama_index.core import VectorStoreIndex

space_names = ["<CHAT_ID>"]
chatReader = GoogleChatReader()
docs = chatReader.load_data(space_names=space_names)
index = VectorStoreIndex.from_documents(docs)
query_eng = index.as_query_engine()
print(query_eng.query("What was this conversation about?"))