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.
Before You Begin
To run this notebook, you will need to do the following:
After confirmed access to database in the runtime environment of this notebook, filling the following values and run the cell before running example scripts.
🦜🔗 Library Installation
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.
# # Automatically restart kernel after installs so that your environment can access the new packages
# import IPython
# app = IPython.Application.instance()
# app.kernel.do_shutdown(True)
☁ Set Your Google Cloud Project
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.
# @markdown Please fill in the value below with your Google Cloud project ID and then run the cell.
PROJECT_ID = "my-project-id" # @param {type:"string"}
# Set the project id
!gcloud config set project {PROJECT_ID}
🔐 Authentication
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()
Basic Usage
Save documents
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)
API Reference:
Save documents without key
If a kind
is specified the documents will be stored with an auto generated id.
saver = DatastoreSaver("MyKind")
saver.upsert_documents(data)
Load documents via Kind
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:
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()
Load documents via query
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 documents
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],
]
# The Documents will be ignored and only the document ids will be used.
saver.delete_documents(data, keys_to_delete)
Advanced Usage
Load documents with customized document page content & metadata
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()
Customize Page Content Format
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.
Customize Connection & Authentication
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,
)