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

This notebook goes over how to useCloud SQL for PostgreSQL to store vector embeddings with thePostgresVectorStore class.

Learn more about the package onGitHub.

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,langchain-google-cloud-sql-pg, and the library for the embedding service,langchain-google-vertexai.

%pip install--upgrade--quiet  langchain-google-cloud-sql-pg langchain-google-vertexai

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 instructionshere.

from google.colabimport 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:

• Rungcloud config list.
• Rungcloud 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 configset project{PROJECT_ID}

Basic Usage​

Set Cloud SQL database values​

Find your database values, in theCloud SQL Instances page.

# @title Set Your Values Here { display-mode: "form" }
REGION="us-central1"# @param {type: "string"}
INSTANCE="my-pg-instance"# @param {type: "string"}
DATABASE="my-database"# @param {type: "string"}
TABLE_NAME="vector_store"# @param {type: "string"}

PostgresEngine Connection Pool​

One of the requirements and arguments to establish Cloud SQL as a vector store is aPostgresEngine object. ThePostgresEngine configures a connection pool to your Cloud SQL database, enabling successful connections from your application and following industry best practices.

To create aPostgresEngine usingPostgresEngine.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 theApplication 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 optionaluser andpassword arguments toPostgresEngine.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_pgimport PostgresEngine

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

Initialize a table​

ThePostgresVectorStore class requires a database table. ThePostgresEngine engine has a helper methodinit_vectorstore_table() that can be used to create a table with the proper schema for you.

from langchain_google_cloud_sql_pgimport PostgresEngine

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

Create an embedding class instance​

You can use anyLangChain embeddings model.You may need to enable Vertex AI API to useVertexAIEmbeddings. We recommend setting the embedding model's version for production, learn more about theText embeddings models.

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

from langchain_google_vertexaiimport VertexAIEmbeddings

embedding= VertexAIEmbeddings(
    model_name="textembedding-gecko@latest", project=PROJECT_ID
)

Initialize a default PostgresVectorStore​

from langchain_google_cloud_sql_pgimport PostgresVectorStore

store=await PostgresVectorStore.create(# Use .create() to initialize an async vector store
    engine=engine,
    table_name=TABLE_NAME,
    embedding_service=embedding,
)

Add texts​

import uuid

all_texts=["Apples and oranges","Cars and airplanes","Pineapple","Train","Banana"]
metadatas=[{"len":len(t)}for tin all_texts]
ids=[str(uuid.uuid4())for _in all_texts]

await store.aadd_texts(all_texts, metadatas=metadatas, ids=ids)

Delete texts​

await store.adelete([ids[1]])

Search for documents​

query="I'd like a fruit."
docs=await store.asimilarity_search(query)
print(docs)

Search for documents by vector​

query_vector= embedding.embed_query(query)
docs=await store.asimilarity_search_by_vector(query_vector, k=2)
print(docs)

Add a Index​

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

from langchain_google_cloud_sql_pg.indexesimport IVFFlatIndex

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

Re-index​

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

Remove an index​

await store.aadrop_vector_index()# Delete index using default name

Create a custom Vector Store​

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

Create a table with custom metadata columns.

from langchain_google_cloud_sql_pgimport Column

# Set table name
TABLE_NAME="vectorstore_custom"

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


# Initialize PostgresVectorStore
custom_store=await PostgresVectorStore.create(
    engine=engine,
    table_name=TABLE_NAME,
    embedding_service=embedding,
    metadata_columns=["len"],
# Connect to a existing VectorStore by customizing the table schema:
# id_column="uuid",
# content_column="documents",
# embedding_column="vectors",
)

Search for documents with metadata filter​

import uuid

# Add texts to the Vector Store
all_texts=["Apples and oranges","Cars and airplanes","Pineapple","Train","Banana"]
metadatas=[{"len":len(t)}for tin all_texts]
ids=[str(uuid.uuid4())for _in all_texts]
await store.aadd_texts(all_texts, metadatas=metadatas, ids=ids)

# Use filter on search
docs=await custom_store.asimilarity_search_by_vector(query_vector,filter="len >= 6")

print(docs)

Related​

• Vector storeconceptual guide
• Vector storehow-to guides