Firestore is a serverless document-oriented database that scales to meet any demand. Extend your database application to build AI-powered experiences leveraging Firestore's Langchain integrations.

This notebook goes over how to useFirestore tosave, load and delete langchain documents withFirestoreLoader andFirestoreSaver.

Learn more about the package onGitHub.

Before You Begin​

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

• Create a Google Cloud Project
• Enable the Firestore API
• Create a Firestore 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.

# @markdown Please specify a source for demo purpose.
SOURCE="test"# @param {type:"Query"|"CollectionGroup"|"DocumentReference"|"string"}

🦜🔗 Library Installation​

The integration lives in its ownlangchain-google-firestore package, so we need to install it.

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

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:

• Rungcloud config list.
• Rungcloud 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 configset 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 instructionshere.

from google.colabimport auth

auth.authenticate_user()

Basic Usage​

Save documents​

FirestoreSaver can store Documents into Firestore. By default it will try to extract the Document reference from the metadata

Save langchain documents withFirestoreSaver.upsert_documents(<documents>).

from langchain_core.documentsimport Document
from langchain_google_firestoreimport FirestoreSaver

saver= FirestoreSaver()

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

saver.upsert_documents(data)

Save documents without reference​

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

saver= FirestoreSaver("Collection")

saver.upsert_documents(data)

Save documents with other references​

doc_ids=["AnotherCollection/doc_id","foo/bar"]
saver= FirestoreSaver()

saver.upsert_documents(documents=data, document_ids=doc_ids)

Load from Collection or SubCollection​

Load langchain documents withFirestoreLoader.load() orFirestore.lazy_load().lazy_load returns a generator that only queries database during the iteration. To initializeFirestoreLoader class you need to provide:

1. source - An instance of a Query, CollectionGroup, DocumentReference or the single\-delimited path to a Firestore collection.

from langchain_google_firestoreimport FirestoreLoader

loader_collection= FirestoreLoader("Collection")
loader_subcollection= FirestoreLoader("Collection/doc/SubCollection")


data_collection= loader_collection.load()
data_subcollection= loader_subcollection.load()

Load a single Document​

from google.cloudimport firestore

client= firestore.Client()
doc_ref= client.collection("foo").document("bar")

loader_document= FirestoreLoader(doc_ref)

data= loader_document.load()

Load from CollectionGroup or Query​

from google.cloud.firestoreimport CollectionGroup, FieldFilter, Query

col_ref= client.collection("col_group")
collection_group= CollectionGroup(col_ref)

loader_group= FirestoreLoader(collection_group)

col_ref= client.collection("collection")
query= col_ref.where(filter=FieldFilter("region","==","west_coast"))

loader_query= FirestoreLoader(query)

Delete documents​

Delete a list of langchain documents from Firestore collection withFirestoreSaver.delete_documents(<documents>).

If document ids is provided, the Documents will be ignored.

saver= FirestoreSaver()

saver.delete_documents(data)

# The Documents will be ignored and only the document ids will be used.
saver.delete_documents(data, doc_ids)

Advanced Usage​

Load documents with customize document page content & metadata​

The arguments ofpage_content_fields andmetadata_fields will specify the Firestore Document fields to be written into LangChain Documentpage_content andmetadata.

loader= FirestoreLoader(
    source="foo/bar/subcol",
    page_content_fields=["data_field"],
    metadata_fields=["metadata_field"],
)

data= loader.load()

Customize Page Content Format​

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

Customize Connection & Authentication​

from google.authimport compute_engine
from google.cloud.firestoreimport Client

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

Related​

• Document loaderconceptual guide
• Document loaderhow-to guides