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 use Firestore to store vectors and query them using the FirestoreVectorStore class. Open In Colab

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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. 
# @markdown Please specify a source for demo purpose.
COLLECTION_NAME = "test"  # @param {type:"CollectionReference"|"string"}

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🦜🔗 Library Installation

The integration lives in its own langchain-google-firestore package, so we need to install it. For this notebook, we will also install langchain-google-genai to use Google Generative AI embeddings. 
%pip install -upgrade --quiet langchain-google-firestore langchain-google-vertexai
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)

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☁ 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: 
# @markdown Please fill in the value below with your Google Cloud project ID and then run the cell.

PROJECT_ID = "extensions-testing"  # @param {type:"string"}

# Set the project id
!gcloud config set project {PROJECT_ID}

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🔐 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()

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Basic Usage

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Initialize FirestoreVectorStore

FirestoreVectorStore allows you to store new vectors in a Firestore database. You can use it to store embeddings from any model, including those from Google Generative AI. 
from langchain_google_firestore import FirestoreVectorStore
from langchain_google_vertexai import VertexAIEmbeddings

embedding = VertexAIEmbeddings(
    model_name="textembedding-gecko@latest",
    project=PROJECT_ID,
)

# Sample data
ids = ["apple", "banana", "orange"]
fruits_texts = ['{"name": "apple"}', '{"name": "banana"}', '{"name": "orange"}']

# Create a vector store
vector_store = FirestoreVectorStore(
    collection="fruits",
    embedding=embedding,
)

# Add the fruits to the vector store
vector_store.add_texts(fruits_texts, ids=ids)
As a shorthand, you can initilize and add vectors in a single step using the from_texts and from_documents method. 
vector_store = FirestoreVectorStore.from_texts(
    collection="fruits",
    texts=fruits_texts,
    embedding=embedding,
)

from langchain_core.documents import Document

fruits_docs = [Document(page_content=fruit) for fruit in fruits_texts]

vector_store = FirestoreVectorStore.from_documents(
    collection="fruits",
    documents=fruits_docs,
    embedding=embedding,
)

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Delete Vectors

You can delete documents with vectors from the database using the delete method. You’ll need to provide the document ID of the vector you want to delete. This will remove the whole document from the database, including any other fields it may have. 
vector_store.delete(ids)

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Update Vectors

Updating vectors is similar to adding them. You can use the add method to update the vector of a document by providing the document ID and the new vector. 
fruit_to_update = ['{"name": "apple","price": 12}']
apple_id = "apple"

vector_store.add_texts(fruit_to_update, ids=[apple_id])
You can use the FirestoreVectorStore to perform similarity searches on the vectors you have stored. This is useful for finding similar documents or text. 
vector_store.similarity_search("I like fuji apples", k=3)

vector_store.max_marginal_relevance_search("fuji", 5)
You can add a pre-filter to the search by using the filters parameter. This is useful for filtering by a specific field or value. 
from google.cloud.firestore_v1.base_query import FieldFilter

vector_store.max_marginal_relevance_search(
    "fuji", 5, filters=FieldFilter("content", "==", "apple")
)

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Customize Connection & Authentication

from google.api_core.client_options import ClientOptions
from google.cloud import firestore
from langchain_google_firestore import FirestoreVectorStore

client_options = ClientOptions()
client = firestore.Client(client_options=client_options)

# Create a vector store
vector_store = FirestoreVectorStore(
    collection="fruits",
    embedding=embedding,
    client=client,
)