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Google Cloud SQL for PostgreSQL - `PostgresDocumentStore` & `PostgresIndexStore`

docs/examples/docstore/CloudSQLPgDocstoreDemo.ipynb

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Google Cloud SQL for PostgreSQL - PostgresDocumentStore & PostgresIndexStore

Cloud SQL is a fully managed relational database service that offers high performance, seamless integration, and impressive scalability. It offers MySQL, PostgreSQL, and SQL Server database engines. Extend your database application to build AI-powered experiences leveraging Cloud SQL's LlamaIndex integrations.

This notebook goes over how to use Cloud SQL for PostgreSQL to store documents and indexes with the PostgresDocumentStore and PostgresIndexStore classes.

Learn more about the package on GitHub.

Before you begin

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

🦙 Library Installation

Install the integration library, llama-index-cloud-sql-pg, and the library for the embedding service, llama-index-embeddings-vertex.

python
%pip install --upgrade --quiet llama-index-cloud-sql-pg llama-index-llms-vertex llama-index

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.

python
# # 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.
python
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:

python
# @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 Cloud SQL database values

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

python
# @title Set Your Values Here { display-mode: "form" }
REGION = "us-central1"  # @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"}

PostgresEngine Connection Pool

One of the requirements and arguments to establish Cloud SQL as a vector store is a PostgresEngine object. The PostgresEngine configures a connection pool to your Cloud SQL 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 envionment.

For more informatin on IAM database authentication please see:

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.

python
from llama_index_cloud_sql_pg import PostgresEngine

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

Initialize a table

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

python
await engine.ainit_doc_store_table(
    table_name=TABLE_NAME,
)

Optional Tip: 💡

You can also specify a schema name by passing schema_name wherever you pass table_name.

python
SCHEMA_NAME = "my_schema"

await engine.ainit_doc_store_table(
    table_name=TABLE_NAME,
    schema_name=SCHEMA_NAME,
)

Initialize a default PostgresDocumentStore

python
from llama_index_cloud_sql_pg import PostgresDocumentStore

doc_store = await PostgresDocumentStore.create(
    engine=engine,
    table_name=TABLE_NAME,
    # schema_name=SCHEMA_NAME
)

Download data

python
!mkdir -p 'data/paul_graham/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'

Load documents

python
from llama_index.core import SimpleDirectoryReader

documents = SimpleDirectoryReader("./data/paul_graham").load_data()
print("Document ID:", documents[0].doc_id)

Parse into nodes

python
from llama_index.core.node_parser import SentenceSplitter

nodes = SentenceSplitter().get_nodes_from_documents(documents)

Set up an IndexStore

python
from llama_index_cloud_sql_pg import PostgresIndexStore


INDEX_TABLE_NAME = "index_store"
await engine.ainit_index_store_table(
    table_name=INDEX_TABLE_NAME,
)

index_store = await PostgresIndexStore.create(
    engine=engine,
    table_name=INDEX_TABLE_NAME,
    # schema_name=SCHEMA_NAME
)

Add to Docstore

python
from llama_index.core import StorageContext

storage_context = StorageContext.from_defaults(
    docstore=doc_store, index_store=index_store
)

storage_context.docstore.add_documents(nodes)

Use with Indexes

The Document Store can be used with multiple indexes. Each index uses the same underlying nodes.

python
from llama_index.core import Settings, SimpleKeywordTableIndex, SummaryIndex
from llama_index.llms.vertex import Vertex

Settings.llm = Vertex(model="gemini-1.5-flash", project=PROJECT_ID)
summary_index = SummaryIndex(nodes, storage_context=storage_context)
keyword_table_index = SimpleKeywordTableIndex(
    nodes, storage_context=storage_context
)

Query the index

python
query_engine = summary_index.as_query_engine()
response = query_engine.query("What did the author do?")
print(response)

Load existing indexes

The Document Store can be used with multiple indexes. Each index uses the same underlying nodes.

python
# note down index IDs
list_id = summary_index.index_id
keyword_id = keyword_table_index.index_id
python
from llama_index.core import load_index_from_storage

# re-create storage context
storage_context = StorageContext.from_defaults(
    docstore=doc_store, index_store=index_store
)

# load indices
summary_index = load_index_from_storage(
    storage_context=storage_context, index_id=list_id
)
keyword_table_index = load_index_from_storage(
    storage_context=storage_context, index_id=keyword_id
)