Google Cloud SQL for PostgreSQL - PostgresVectorStore¶

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 vector embeddings with the PostgresVectorStore 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 Cloud SQL Admin API.
• Create a Cloud SQL instance.
• Create a Cloud SQL database.
• Add a User to the database.

🦙 Library Installation¶

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

In [ ]:

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

%pip install --upgrade --quiet llama-index-cloud-sql-pg llama-index-embeddings-vertex 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.

In [ ]:

# # 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)

# # 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.

In [ ]:

from google.colab import auth

auth.authenticate_user()

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.

In [ ]:

# @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}

# @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.

In [ ]:

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

# @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 = "vector_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:

• Configure an instance for IAM database authentication
• Manage users with IAM database authentication

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.

In [ ]:

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,
)

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

In [ ]:

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

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

Optional Tip: 💡¶

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

In [ ]:

SCHEMA_NAME = "my_schema"

await engine.ainit_vector_store_table(
    table_name=TABLE_NAME,
    schema_name=SCHEMA_NAME,
    vector_size=768,
)

SCHEMA_NAME = "my_schema" await engine.ainit_vector_store_table( table_name=TABLE_NAME, schema_name=SCHEMA_NAME, vector_size=768, )

Create an embedding class instance¶

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

In [ ]:

# enable Vertex AI API
!gcloud services enable aiplatform.googleapis.com

# enable Vertex AI API !gcloud services enable aiplatform.googleapis.com

In [ ]:

from llama_index.core import Settings
from llama_index.embeddings.vertex import VertexTextEmbedding
from llama_index.llms.vertex import Vertex
import google.auth

credentials, project_id = google.auth.default()
Settings.embed_model = VertexTextEmbedding(
    model_name="textembedding-gecko@003",
    project=PROJECT_ID,
    credentials=credentials,
)

Settings.llm = Vertex(model="gemini-1.5-flash-002", project=PROJECT_ID)

from llama_index.core import Settings from llama_index.embeddings.vertex import VertexTextEmbedding from llama_index.llms.vertex import Vertex import google.auth credentials, project_id = google.auth.default() Settings.embed_model = VertexTextEmbedding( model_name="textembedding-gecko@003", project=PROJECT_ID, credentials=credentials, ) Settings.llm = Vertex(model="gemini-1.5-flash-002", project=PROJECT_ID)

Initialize a default PostgresVectorStore¶

In [ ]:

from llama_index_cloud_sql_pg import PostgresVectorStore

vector_store = await PostgresVectorStore.create(
    engine=engine,
    table_name=TABLE_NAME,
    # schema_name=SCHEMA_NAME
)

from llama_index_cloud_sql_pg import PostgresVectorStore vector_store = await PostgresVectorStore.create( engine=engine, table_name=TABLE_NAME, # schema_name=SCHEMA_NAME )

Download data¶

In [ ]:

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

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

Load documents¶

In [ ]:

from llama_index.core import SimpleDirectoryReader

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

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

Use with VectorStoreIndex¶

Create an index from the vector store by using VectorStoreIndex.

Initialize Vector Store with documents¶

The simplest way to use a Vector Store is to load a set of documents and build an index from them using from_documents.

In [ ]:

from llama_index.core import StorageContext, VectorStoreIndex

storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context, show_progress=True
)

from llama_index.core import StorageContext, VectorStoreIndex storage_context = StorageContext.from_defaults(vector_store=vector_store) index = VectorStoreIndex.from_documents( documents, storage_context=storage_context, show_progress=True )

Query the index¶

In [ ]:

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

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

Create a custom Vector Store¶

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

Create a new table with custom metadata columns. You can also re-use an existing table which already has custom columns for a Document's id, content, embedding, and/or metadata.

In [ ]:

from llama_index_cloud_sql_pg import Column

# Set table name
TABLE_NAME = "vectorstore_custom"
# SCHEMA_NAME = "my_schema"

await engine.ainit_vector_store_table(
    table_name=TABLE_NAME,
    # schema_name=SCHEMA_NAME,
    vector_size=768,  # VertexAI model: textembedding-gecko@003
    metadata_columns=[Column("len", "INTEGER")],
)


# Initialize PostgresVectorStore
custom_store = await PostgresVectorStore.create(
    engine=engine,
    table_name=TABLE_NAME,
    # schema_name=SCHEMA_NAME,
    metadata_columns=["len"],
)

from llama_index_cloud_sql_pg import Column # Set table name TABLE_NAME = "vectorstore_custom" # SCHEMA_NAME = "my_schema" await engine.ainit_vector_store_table( table_name=TABLE_NAME, # schema_name=SCHEMA_NAME, vector_size=768, # VertexAI model: textembedding-gecko@003 metadata_columns=[Column("len", "INTEGER")], ) # Initialize PostgresVectorStore custom_store = await PostgresVectorStore.create( engine=engine, table_name=TABLE_NAME, # schema_name=SCHEMA_NAME, metadata_columns=["len"], )

Add documents with metadata¶

Document metadata can provide the LLM and retrieval process with more information. Learn more about different approaches for extracting and adding metadata.

In [ ]:

from llama_index.core import Document

fruits = ["apple", "pear", "orange", "strawberry", "banana", "kiwi"]
documents = [
    Document(text=fruit, metadata={"len": len(fruit)}) for fruit in fruits
]

storage_context = StorageContext.from_defaults(vector_store=custom_store)
custom_doc_index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context, show_progress=True
)

from llama_index.core import Document fruits = ["apple", "pear", "orange", "strawberry", "banana", "kiwi"] documents = [ Document(text=fruit, metadata={"len": len(fruit)}) for fruit in fruits ] storage_context = StorageContext.from_defaults(vector_store=custom_store) custom_doc_index = VectorStoreIndex.from_documents( documents, storage_context=storage_context, show_progress=True )

Search for documents with metadata filter¶

You can apply pre-filtering to the search results by specifying a filters argument

In [ ]:

from llama_index.core.vector_stores.types import (
    MetadataFilter,
    MetadataFilters,
    FilterOperator,
)

filters = MetadataFilters(
    filters=[
        MetadataFilter(key="len", operator=FilterOperator.GT, value="5"),
    ],
)

query_engine = custom_doc_index.as_query_engine(filters=filters)
res = query_engine.query("List some fruits")
print(str(res.source_nodes[0].text))

from llama_index.core.vector_stores.types import ( MetadataFilter, MetadataFilters, FilterOperator, ) filters = MetadataFilters( filters=[ MetadataFilter(key="len", operator=FilterOperator.GT, value="5"), ], ) query_engine = custom_doc_index.as_query_engine(filters=filters) res = query_engine.query("List some fruits") print(str(res.source_nodes[0].text))

Add a Index¶

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

In [ ]:

from llama_index_cloud_sql_pg.indexes import IVFFlatIndex

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

from llama_index_cloud_sql_pg.indexes import IVFFlatIndex index = IVFFlatIndex() await vector_store.aapply_vector_index(index)

Re-index¶

In [ ]:

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

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

Remove an index¶

In [ ]:

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

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