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 use Cloud SQL for PostgreSQL to store vector embeddings with the PostgresVectorStore class.
Learn more about the package on GitHub.
Before you beginTo run this notebook, you will need to do the following:
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.
🔐 AuthenticationAuthenticate to Google Cloud as the IAM user logged into this notebook in order to access your Google Cloud Project.
from google.colab import auth
auth.authenticate_user()
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:
gcloud config list.gcloud projects list.
PROJECT_ID = "my-project-id"
!gcloud config set project {PROJECT_ID}
Find your database values, in the Cloud SQL Instances page.
REGION = "us-central1"
INSTANCE = "my-pg-instance"
DATABASE = "my-database"
TABLE_NAME = "vector_store"
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:
project_id : Project ID of the Google Cloud Project where the Cloud SQL instance is located.region : Region where the Cloud SQL instance is located.instance : The name of the Cloud SQL instance.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 loginpassword : 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_pg import PostgresEngine
engine = await PostgresEngine.afrom_instance(
project_id=PROJECT_ID, region=REGION, instance=INSTANCE, database=DATABASE
)
The PostgresVectorStore class requires a database table. The PostgresEngine engine has a helper method init_vectorstore_table() that can be used to create a table with the proper schema for you.
from langchain_google_cloud_sql_pg import PostgresEngine
await engine.ainit_vectorstore_table(
table_name=TABLE_NAME,
vector_size=768,
)
You can use any LangChain embeddings model. You may need to enable Vertex AI API to use VertexAIEmbeddings. We recommend setting the embedding model's version for production, learn more about the Text embeddings models.
!gcloud services enable aiplatform.googleapis.com
from langchain_google_vertexai import VertexAIEmbeddings
embedding = VertexAIEmbeddings(
model_name="textembedding-gecko@latest", project=PROJECT_ID
)
from langchain_google_cloud_sql_pg import PostgresVectorStore
store = await PostgresVectorStore.create(
engine=engine,
table_name=TABLE_NAME,
embedding_service=embedding,
)
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]
await store.aadd_texts(all_texts, metadatas=metadatas, ids=ids)
await store.adelete([ids[1]])
query = "I'd like a fruit."
docs = await store.asimilarity_search(query)
print(docs)
query_vector = embedding.embed_query(query)
docs = await store.asimilarity_search_by_vector(query_vector, k=2)
print(docs)
Speed up vector search queries by applying a vector index. Learn more about vector indexes.
from langchain_google_cloud_sql_pg.indexes import IVFFlatIndex
index = IVFFlatIndex()
await store.aapply_vector_index(index)
await store.aadrop_vector_index()
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_pg import Column
TABLE_NAME = "vectorstore_custom"
await engine.ainit_vectorstore_table(
table_name=TABLE_NAME,
vector_size=768,
metadata_columns=[Column("len", "INTEGER")],
)
custom_store = await PostgresVectorStore.create(
engine=engine,
table_name=TABLE_NAME,
embedding_service=embedding,
metadata_columns=["len"],
)
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]
await store.aadd_texts(all_texts, metadatas=metadatas, ids=ids)
docs = await custom_store.asimilarity_search_by_vector(query_vector, filter="len >= 6")
print(docs)
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