Google Firestore (Native Mode)
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 to store vectors and query them using the FirestoreVectorStore
class.
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"}
🦜🔗 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)
☁ 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 = "extensions-testing" # @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
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,
)
API Reference:
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)
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])
Similarity Search
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")
)
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,
)