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Google AlloyDB for PostgreSQL

AlloyDB is a fully managed relational database service that offers high performance, seamless integration, and impressive scalability. AlloyDB is 100% compatible with PostgreSQL. Extend your database application to build AI-powered experiences leveraging AlloyDB’s Langchain integrations.

This notebook goes over how to use AlloyDB for PostgreSQL to store vector embeddings with the AlloyDBVectorStore class.

Learn more about the package on GitHub.

Open In Colab

Before you begin​

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

πŸ¦œπŸ”— Library Installation​

Install the integration library, langchain-google-alloydb-pg, and the library for the embedding service, langchain-google-vertexai.

%pip install --upgrade --quiet  langchain-google-alloydb-pg 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)

πŸ” 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


☁ 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 = "my-project-id" # @param {type:"string"}

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

Basic Usage​

Set AlloyDB database values​

Find your database values, in the AlloyDB Instances page.

# @title Set Your Values Here { display-mode: "form" }
REGION = "us-central1" # @param {type: "string"}
CLUSTER = "my-cluster" # @param {type: "string"}
INSTANCE = "my-primary" # @param {type: "string"}
DATABASE = "my-database" # @param {type: "string"}
TABLE_NAME = "vector_store" # @param {type: "string"}

AlloyDBEngine Connection Pool​

One of the requirements and arguments to establish AlloyDB as a vector store is a AlloyDBEngine object. The AlloyDBEngine configures a connection pool to your AlloyDB database, enabling successful connections from your application and following industry best practices.

To create a AlloyDBEngine using AlloyDBEngine.from_instance() you need to provide only 5 things:

  1. project_id : Project ID of the Google Cloud Project where the AlloyDB instance is located.
  2. region : Region where the AlloyDB instance is located.
  3. cluster: The name of the AlloyDB cluster.
  4. instance : The name of the AlloyDB instance.
  5. database : The name of the database to connect to on the AlloyDB instance.

By default, IAM database authentication will be used as the method of database authentication. This library uses the IAM principal belonging to the Application Default Credentials (ADC) sourced from the environment.

Optionally, built-in database authentication using a username and password to access the AlloyDB database can also be used. Just provide the optional user and password arguments to AlloyDBEngine.from_instance():

  • user : Database user to use for built-in database authentication and login
  • password : Database password to use for built-in database authentication and login.

Note: This tutorial demonstrates the async interface. All async methods have corresponding sync methods.

from langchain_google_alloydb_pg import AlloyDBEngine

engine = await AlloyDBEngine.afrom_instance(

Initialize a table​

The AlloyDBVectorStore class requires a database table. The AlloyDBEngine engine has a helper method init_vectorstore_table() that can be used to create a table with the proper schema for you.

await engine.ainit_vectorstore_table(
vector_size=768, # Vector size for VertexAI model(textembedding-gecko@latest)

Create an embedding class instance​

You can use any LangChain embeddings model. You may need to enable Vertex AI API to use VertexAIEmbeddings. We recommend setting the embedding model’s version for production, learn more about the Text embeddings models.

# enable Vertex AI API
!gcloud services enable
from langchain_google_vertexai import VertexAIEmbeddings

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

Initialize a default AlloyDBVectorStore​

from langchain_google_alloydb_pg import AlloyDBVectorStore

store = await AlloyDBVectorStore.create(

Add texts​

import uuid

all_texts = ["Apples and oranges", "Cars and airplanes", "Pineapple", "Train", "Banana"]
metadatas = [{"len": len(t)} for t in all_texts]
ids = [str(uuid.uuid4()) for _ in all_texts]

await store.aadd_texts(all_texts, metadatas=metadatas, ids=ids)

Delete texts​

await store.adelete([ids[1]])

Search for documents​

query = "I'd like a fruit."
docs = await store.asimilarity_search(query)

Search for documents by vector​

query_vector = embedding.embed_query(query)
docs = await store.asimilarity_search_by_vector(query_vector, k=2)

Add a Index​

Speed up vector search queries by applying a vector index. Learn more about vector indexes.

from langchain_google_alloydb_pg.indexes import IVFFlatIndex

index = IVFFlatIndex()
await store.aapply_vector_index(index)


await store.areindex()  # Re-index using default index name

Remove an index​

await store.adrop_vector_index()  # Delete index using default name

Create a custom Vector Store​

A Vector Store can take advantage of relational data to filter similarity searches.

Create a table with custom metadata columns.

from langchain_google_alloydb_pg import Column

# Set table name
TABLE_NAME = "vectorstore_custom"

await engine.ainit_vectorstore_table(
vector_size=768, # VertexAI model: textembedding-gecko@latest
metadata_columns=[Column("len", "INTEGER")],

# Initialize AlloyDBVectorStore
custom_store = await AlloyDBVectorStore.create(
# Connect to a existing VectorStore by customizing the table schema:
# id_column="uuid",
# content_column="documents",
# embedding_column="vectors",

Search for documents with metadata filter​

import uuid

# Add texts to the Vector Store
all_texts = ["Apples and oranges", "Cars and airplanes", "Pineapple", "Train", "Banana"]
metadatas = [{"len": len(t)} for t in all_texts]
ids = [str(uuid.uuid4()) for _ in all_texts]
await store.aadd_texts(all_texts, metadatas=metadatas, ids=ids)

# Use filter on search
docs = await custom_store.asimilarity_search_by_vector(query_vector, filter="len >= 6")


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