Google AlloyDB for PostgreSQL - AlloyDBReader

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 LlamaIndex integrations.

This notebook goes over how to use AlloyDB for PostgreSQL to retrieve data as documents with the AlloyDBReader class.

Learn more about the package on GitHub.

Before you begin

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

• Create a Google Cloud Project
• Enable the AlloyDB API
• Create a AlloyDB cluster and instance.
• Create a AlloyDB database.
• Add a User to the database.

🦙 Library Installation

Install the integration library, llama-index-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

auth.authenticate_user()

☁ 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 = "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 = "document_store"  # @param {type: "string"}
USER = "postgres"  # @param {type: "string"}
PASSWORD = "my-password"  # @param {type: "string"}

AlloyDBEngine Connection Pool

One of the requirements and arguments to establish AlloyDB Reader 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 llama_index_alloydb_pg import AlloyDBEngine

engine = await AlloyDBEngine.afrom_instance(
    project_id=PROJECT_ID,
    region=REGION,
    cluster=CLUSTER,
    instance=INSTANCE,
    database=DATABASE,
    user=USER,
    password=PASSWORD,
)

Create AlloyDBReader

When creating an AlloyDBReader for fetching data from AlloyDB, you have two main options to specify the data you want to load:

• using the table_name argument - When you specify the table_name argument, you're telling the reader to fetch all the data from the given table.
• using the query argument - When you specify the query argument, you can provide a custom SQL query to fetch the data. This allows you to have full control over the SQL query, including selecting specific columns, applying filters, sorting, joining tables, etc.

Load Documents using the table_name argument

Load Documents via default table

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

from llama_index_alloydb_pg import AlloyDBReader

# Creating a basic AlloyDBReader object
reader = await AlloyDBReader.create(
    engine,
    table_name=TABLE_NAME,
    # schema_name=SCHEMA_NAME,
)

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

reader = await AlloyDBReader.create(
    engine,
    table_name=TABLE_NAME,
    # schema_name=SCHEMA_NAME,
    content_columns=["product_name"],  # Optional
    metadata_columns=["id"],  # Optional
)

Load Documents using a SQL query

The query parameter allows users to specify a custom SQL query which can include filters to load specific documents from a database.

table_name = "products"
content_columns = ["product_name", "description"]
metadata_columns = ["id", "content"]

reader = AlloyDBReader.create(
    engine=engine,
    query=f"SELECT * FROM {table_name};",
    content_columns=content_columns,
    metadata_columns=metadata_columns,
)

Note: If the content_columns and metadata_columns are not specified, the reader will automatically treat the first returned column as the document’s text and all subsequent columns as metadata.

Set page content format

The reader 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.

reader = await AlloyDBReader.create(
    engine,
    table_name=TABLE_NAME,
    # schema_name=SCHEMA_NAME,
    content_columns=["product_name", "description"],
    format="YAML",
)

Load the documents

You can choose to load the documents in two ways:

• Load all the data at once
• Lazy load data

Load data all at once

docs = await reader.aload_data()

print(docs)

Lazy Load the data

docs_iterable = reader.alazy_load_data()

docs = []
async for doc in docs_iterable:
    docs.append(doc)

print(docs)