docs/examples/data_connectors/GoogleMapsTextSearchReaderDemo.ipynb
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This notebook demonstrates how to use the GoogleMapsTextSearchReader from the llama_index library to load and query data from the Google Maps Places API.
If you're opening this Notebook on colab, you will need to install the llama-index library.
!pip install llama-index llama-index-readers-google
We will import the necessary libraries including the GoogleMapsTextSearchReader from llama_index and other utility libraries.
import logging
import sys
from llama_index.readers.google import GoogleMapsTextSearchReader
from llama_index.core import VectorStoreIndex
from IPython.display import Markdown, display
import os
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
Make sure you have your Google Maps API key ready. You can set it directly in the code or store it in an environment variable named GOOGLE_MAPS_API_KEY.
# Set your API key here if not using environment variable
os.environ["GOOGLE_MAPS_API_KEY"] = api_key
Using the GoogleMapsTextSearchReader, we will load data for a search query. In this example, we search for quality Turkish food in Istanbul.
loader = GoogleMapsTextSearchReader()
documents = loader.load_data(
text="I want to eat quality Turkish food in Istanbul",
number_of_results=160,
)
# Displaying the first document to understand its structure
print(documents[0])
We will now create a VectorStoreIndex from the loaded documents. This index will allow us to perform efficient queries on the data.
index = VectorStoreIndex.from_documents(documents)
Finally, we will query the index to find the Turkish restaurant with the best reviews.
response = index.query("Which Turkish restaurant has the best reviews?")
display(Markdown(f"<b>{response}</b>"))
In this notebook, we demonstrated how to use the GoogleMapsTextSearchReader to load data from Google Maps, index it using the VectorStoreIndex, and perform a query to find the best-reviewed Turkish restaurant in Istanbul.