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Showing content from https://python.langchain.com/docs/integrations/document_loaders/google_datastore/ below:

Google Firestore in Datastore Mode

Firestore in Datastore Mode is a NoSQL document database built for automatic scaling, high performance and ease of application development. Extend your database application to build AI-powered experiences leveraging Datastore's Langchain integrations.

This notebook goes over how to use Firestore in Datastore Mode to save, load and delete langchain documents with DatastoreLoader and DatastoreSaver.

Learn more about the package on GitHub.

To run this notebook, you will need to do the following:

• Create a Google Cloud Project
• Enable the Datastore API
• Create a Firestore in Datastore Mode database

After confirmed access to database in the runtime environment of this notebook, filling the following values and run the cell before running example scripts.

The integration lives in its own langchain-google-datastore package, so we need to install it.

%pip install -upgrade --quiet langchain-google-datastore

Colab only: Uncomment the following cell to restart the kernel or use the button to restart the kernel. For Vertex AI Workbench you can restart the terminal using the button on top.

Set your Google Cloud project so that you can leverage Google Cloud resources within this notebook.

If you don't know your project ID, try the following:

• Run gcloud config list.
• Run gcloud projects list.
• See the support page: Locate the project ID.

PROJECT_ID = "my-project-id"  


!gcloud config set project {PROJECT_ID}

Authenticate to Google Cloud as the IAM user logged into this notebook in order to access your Google Cloud Project.

• If you are using Colab to run this notebook, use the cell below and continue.
• If you are using Vertex AI Workbench, check out the setup instructions here.

from google.colab import auth

auth.authenticate_user()

Save langchain documents with DatastoreSaver.upsert_documents(<documents>). By default it will try to extract the entity key from the key in the Document metadata.

from langchain_core.documents import Document
from langchain_google_datastore import DatastoreSaver

saver = DatastoreSaver()

data = [Document(page_content="Hello, World!")]
saver.upsert_documents(data)

If a kind is specified the documents will be stored with an auto generated id.

saver = DatastoreSaver("MyKind")

saver.upsert_documents(data)

Load langchain documents with DatastoreLoader.load() or DatastoreLoader.lazy_load(). lazy_load returns a generator that only queries database during the iteration. To initialize DatastoreLoader class you need to provide:

1. source - The source to load the documents. It can be an instance of Query or the name of the Datastore kind to read from.

from langchain_google_datastore import DatastoreLoader

loader = DatastoreLoader("MyKind")
data = loader.load()

Other than loading documents from kind, we can also choose to load documents from query. For example:

from google.cloud import datastore

client = datastore.Client(database="non-default-db", namespace="custom_namespace")
query_load = client.query(kind="MyKind")
query_load.add_filter("region", "=", "west_coast")

loader_document = DatastoreLoader(query_load)

data = loader_document.load()

Delete a list of langchain documents from Datastore with DatastoreSaver.delete_documents(<documents>).

saver = DatastoreSaver()

saver.delete_documents(data)

keys_to_delete = [
    ["Kind1", "identifier"],
    ["Kind2", 123],
    ["Kind3", "identifier", "NestedKind", 456],
]

saver.delete_documents(data, keys_to_delete)

The arguments of page_content_properties and metadata_properties will specify the Entity properties to be written into LangChain Document page_content and metadata.

loader = DatastoreLoader(
    source="MyKind",
    page_content_fields=["data_field"],
    metadata_fields=["metadata_field"],
)

data = loader.load()

When the page_content contains only one field the information will be the field value only. Otherwise the page_content will be in JSON format.

from google.auth import compute_engine
from google.cloud.firestore import Client

client = Client(database="non-default-db", creds=compute_engine.Credentials())
loader = DatastoreLoader(
    source="foo",
    client=client,
)

• Document loader conceptual guide
• Document loader how-to guides

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