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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 load Documents with the AlloyDBLoader 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:

🦜🔗 Library Installation

Install the integration library, langchain-google-alloydb-pg.

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

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)

🔐 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:

# @title Project { display-mode: "form" }
PROJECT_ID = "gcp_project_id" # @param {type:"string"}

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

Basic Usage

Set AlloyDB database variables

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(

Create AlloyDBLoader

from langchain_google_alloydb_pg import AlloyDBLoader

# Creating a basic AlloyDBLoader object
loader = await AlloyDBLoader.create(engine, table_name=TABLE_NAME)

Load Documents via default table

The loader returns a list of Documents from the table using the first column as page_content and all other columns as metadata. The default table will have the first column as page_content and the second column as metadata (JSON). Each row becomes a document.

docs = await loader.aload()

Load documents via custom table/metadata or custom page content columns

loader = await AlloyDBLoader.create(
content_columns=["product_name"], # Optional
metadata_columns=["id"], # Optional
docs = await loader.aload()

Set page content format

The loader returns a list of Documents, with one document per row, with page content in specified string format, i.e. text (space separated concatenation), JSON, YAML, CSV, etc. JSON and YAML formats include headers, while text and CSV do not include field headers.

loader = AlloyDBLoader.create(
content_columns=["product_name", "description"],
docs = await loader.aload()

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