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 to to store vectors and query them using theFirestoreVectorStore class.

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.
COLLECTION_NAME="test"# @param {type:"CollectionReference"|"string"}

🦜🔗 Library Installation​

The integration lives in its ownlangchain-google-firestore package, so we need to install it. For this notebook, we will also installlangchain-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)

☁ 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="extensions-testing"# @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

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_firestoreimport FirestoreVectorStore
from langchain_google_vertexaiimport 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 thefrom_texts andfrom_documents method.

vector_store= FirestoreVectorStore.from_texts(
    collection="fruits",
    texts=fruits_texts,
    embedding=embedding,
)

from langchain_core.documentsimport Document

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

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

Delete Vectors​

You can delete documents with vectors from the database using thedelete 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)

Update Vectors​

Updating vectors is similar to adding them. You can use theadd 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])

Similarity Search​

You can use theFirestoreVectorStore 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 thefilters parameter. This is useful for filtering by a specific field or value.

from google.cloud.firestore_v1.base_queryimport FieldFilter

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

Customize Connection & Authentication​

from google.api_core.client_optionsimport ClientOptions
from google.cloudimport firestore
from langchain_google_firestoreimport FirestoreVectorStore

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

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

Related​

• Vector storeconceptual guide
• Vector storehow-to guides