Cloud SQL is a fully managed relational database service that offers high performance, seamless integration, and impressive scalability. It offers PostgreSQL, MySQL, 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 use Cloud SQL for MySQL to store vector embeddings with the MySQLVectorStore class. Learn more about the package on GitHub. Open In Colab

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Before you begin

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

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🦜🔗 Library Installation

Install the integration library, langchain-google-cloud-sql-mysql, and the library for the embedding service, langchain-google-vertexai. 
%pip install --upgrade --quiet langchain-google-cloud-sql-mysql 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)

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🔐 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()

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☁ 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: 
# @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}

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Basic Usage

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Set Cloud SQL database values

Find your database values, in the Cloud SQL Instances page. Note: MySQL vector support is only available on MySQL instances with version >= 8.0.36. For existing instances, you may need to perform a self-service maintenance update to update your maintenance version to MYSQL_8_0_36.R20240401.03_00 or greater. Once updated, configure your database flags to have the new cloudsql_vector flag to “On”. 
# @title Set Your Values Here { display-mode: "form" }
REGION = "us-central1"  # @param {type: "string"}
INSTANCE = "my-mysql-instance"  # @param {type: "string"}
DATABASE = "my-database"  # @param {type: "string"}
TABLE_NAME = "vector_store"  # @param {type: "string"}

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MySQLEngine Connection Pool

One of the requirements and arguments to establish Cloud SQL as a vector store is a MySQLEngine object. The MySQLEngine configures a connection pool to your Cloud SQL database, enabling successful connections from your application and following industry best practices. To create a MySQLEngine using MySQLEngine.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 MySQLEngine.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.
from langchain_google_cloud_sql_mysql import MySQLEngine

engine = MySQLEngine.from_instance(
    project_id=PROJECT_ID, region=REGION, instance=INSTANCE, database=DATABASE
)

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Initialize a table

The MySQLVectorStore class requires a database table. The MySQLEngine class has a helper method init_vectorstore_table() that can be used to create a table with the proper schema for you. 
engine.init_vectorstore_table(
    table_name=TABLE_NAME,
    vector_size=768,  # Vector size for VertexAI model(textembedding-gecko@latest)
)

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Create an embedding class instance

You can use any LangChain embeddings model. You may need to enable the Vertex AI API to use VertexAIEmbeddings. We recommend pinning the embedding model’s version for production, learn more about the Text embeddings models. 
# enable Vertex AI API
!gcloud services enable aiplatform.googleapis.com

from langchain_google_vertexai import VertexAIEmbeddings

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

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Initialize a default MySQLVectorStore

To initialize a MySQLVectorStore class you need to provide only 3 things:
  1. engine - An instance of a MySQLEngine engine.
  2. embedding_service - An instance of a LangChain embedding model.
  3. table_name : The name of the table within the Cloud SQL database to use as the vector store.
from langchain_google_cloud_sql_mysql import MySQLVectorStore

store = MySQLVectorStore(
    engine=engine,
    embedding_service=embedding,
    table_name=TABLE_NAME,
)

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Add texts

import uuid

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

store.add_texts(all_texts, metadatas=metadatas, ids=ids)

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Delete texts

Delete vectors from the vector store by ID. 
store.delete([ids[1]])

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Search for documents

query = "I'd like a fruit."
docs = store.similarity_search(query)
print(docs[0].page_content)

Pineapple

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Search for documents by vector

It is also possible to do a search for documents similar to a given embedding vector using similarity_search_by_vector which accepts an embedding vector as a parameter instead of a string. 
query_vector = embedding.embed_query(query)
docs = store.similarity_search_by_vector(query_vector, k=2)
print(docs)

[Document(page_content='Pineapple', metadata={'len': 9}), Document(page_content='Banana', metadata={'len': 6})]

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Add an index

Speed up vector search queries by applying a vector index. Learn more about MySQL vector indexes. Note: For IAM database authentication (default usage), the IAM database user will need to be granted the following permissions by a privileged database user for full control of vector indexes. 
GRANT EXECUTE ON PROCEDURE mysql.create_vector_index TO '<IAM_DB_USER>'@'%';
GRANT EXECUTE ON PROCEDURE mysql.alter_vector_index TO '<IAM_DB_USER>'@'%';
GRANT EXECUTE ON PROCEDURE mysql.drop_vector_index TO '<IAM_DB_USER>'@'%';
GRANT SELECT ON mysql.vector_indexes TO '<IAM_DB_USER>'@'%';

from langchain_google_cloud_sql_mysql import VectorIndex

store.apply_vector_index(VectorIndex())

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Remove an index

store.drop_vector_index()

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Advanced Usage

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Create a MySQLVectorStore with custom metadata

A vector store can take advantage of relational data to filter similarity searches. Create a table and MySQLVectorStore instance with custom metadata columns. 
from langchain_google_cloud_sql_mysql import Column

# set table name
CUSTOM_TABLE_NAME = "vector_store_custom"

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


# initialize MySQLVectorStore with custom metadata columns
custom_store = MySQLVectorStore(
    engine=engine,
    embedding_service=embedding,
    table_name=CUSTOM_TABLE_NAME,
    metadata_columns=["len"],
    # connect to an existing VectorStore by customizing the table schema:
    # id_column="uuid",
    # content_column="documents",
    # embedding_column="vectors",
)

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Search for documents with metadata filter

It can be helpful to narrow down the documents before working with them. For example, documents can be filtered on metadata using the filter argument. 
import uuid

# add texts to the vector store
all_texts = ["Apples and oranges", "Cars and airplanes", "Pineapple", "Train", "Banana"]
metadatas = [{"len": len(t)} for t in all_texts]
ids = [str(uuid.uuid4()) for _ in all_texts]
custom_store.add_texts(all_texts, metadatas=metadatas, ids=ids)

# use filter on search
query_vector = embedding.embed_query("I'd like a fruit.")
docs = custom_store.similarity_search_by_vector(query_vector, filter="len >= 6")

print(docs)

[Document(page_content='Pineapple', metadata={'len': 9}), Document(page_content='Banana', metadata={'len': 6}), Document(page_content='Apples and oranges', metadata={'len': 18}), Document(page_content='Cars and airplanes', metadata={'len': 18})]