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Showing content from https://python.langchain.com/docs/integrations/document_loaders/google_cloud_sql_pg/ below:

Google Cloud SQL for PostgreSQL

Cloud SQL for PostgreSQL is a fully-managed database service that helps you set up, maintain, manage, and administer your PostgreSQL relational databases on Google Cloud Platform. Extend your database application to build AI-powered experiences leveraging Cloud SQL for PostgreSQL's Langchain integrations.

This notebook goes over how to use Cloud SQL for PostgreSQL to load Documents with the PostgresLoader class.

Learn more about the package on GitHub.

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

• Create a Google Cloud Project
• Enable the Cloud SQL Admin API.
• Create a Cloud SQL for PostgreSQL instance.
• Create a Cloud SQL for PostgreSQL database.
• Add a User to the database.

Install the integration library, langchain_google_cloud_sql_pg.

%pip install --upgrade --quiet  langchain_google_cloud_sql_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.

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()

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.

PROJECT_ID = "gcp_project_id"  


! gcloud config set project {PROJECT_ID}

Find your database variables, in the Cloud SQL Instances page.

REGION = "us-central1"  
INSTANCE = "my-primary"  
DATABASE = "my-database"  
TABLE_NAME = "vector_store"  

One of the requirements and arguments to establish PostgreSQL as a document loader is a PostgresEngine object. The PostgresEngine configures a connection pool to your Cloud SQL for PostgreSQL database, enabling successful connections from your application and following industry best practices.

To create a PostgresEngine using PostgresEngine.from_instance() you need to provide only 4 things:

1. project_id : Project ID of the Google Cloud Project where the Cloud SQL instance is located.
2. region : Region where the Cloud SQL instance is located.
3. instance : The name of the Cloud SQL instance.
4. database : The name of the database to connect to on the Cloud SQL 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 Cloud SQL database can also be used. Just provide the optional user and password arguments to PostgresEngine.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_cloud_sql_pg import PostgresEngine

engine = await PostgresEngine.afrom_instance(
    project_id=PROJECT_ID,
    region=REGION,
    instance=INSTANCE,
    database=DATABASE,
)

from langchain_google_cloud_sql_pg import PostgresLoader


loader = await PostgresLoader.create(engine, table_name=TABLE_NAME)

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. Please note that if you want your documents to have ids you will need to add them in.

from langchain_google_cloud_sql_pg import PostgresLoader


loader = await PostgresLoader.create(engine, table_name=TABLE_NAME)

docs = await loader.aload()
print(docs)

loader = await PostgresLoader.create(
    engine,
    table_name=TABLE_NAME,
    content_columns=["product_name"],  
    metadata_columns=["id"],  
)
docs = await loader.aload()
print(docs)

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 = await PostgresLoader.create(
    engine,
    table_name="products",
    content_columns=["product_name", "description"],
    format="YAML",
)
docs = await loader.aload()
print(docs)

• Document loader conceptual guide
• Document loader how-to guides

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