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Google SQL for SQL Server

Google 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 Langchain integrations.

This notebook goes over how to use Google Cloud SQL for SQL Server to store chat message history with the MSSQLChatMessageHistory class.

Learn more about the package on GitHub.

Open In Colab

Before You Begin​

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

🦜🔗 Library Installation​

The integration lives in its own langchain-google-cloud-sql-mssql package, so we need to install it.

%pip install --upgrade --quiet langchain-google-cloud-sql-mssql 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 instructions here.
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:

# @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}

💡 API Enablement​

The langchain-google-cloud-sql-mssql package requires that you enable the Cloud SQL Admin API in your Google Cloud Project.

# enable Cloud SQL Admin API
!gcloud services enable sqladmin.googleapis.com

Basic Usage​

Set Cloud SQL database values​

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

# @title Set Your Values Here { display-mode: "form" }
REGION = "us-central1"  # @param {type: "string"}
INSTANCE = "my-mssql-instance"  # @param {type: "string"}
DATABASE = "my-database"  # @param {type: "string"}
DB_USER = "my-username"  # @param {type: "string"}
DB_PASS = "my-password"  # @param {type: "string"}
TABLE_NAME = "message_store"  # @param {type: "string"}

MSSQLEngine Connection Pool​

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

To create a MSSQLEngine using MSSQLEngine.from_instance() you need to provide only 6 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.
  5. user : Database user to use for built-in database authentication and login.
  6. password : Database password to use for built-in database authentication and login.

By default, built-in database authentication using a username and password to access the Cloud SQL database is used for database authentication.

from langchain_google_cloud_sql_mssql import MSSQLEngine

engine = MSSQLEngine.from_instance(
    project_id=PROJECT_ID,
    region=REGION,
    instance=INSTANCE,
    database=DATABASE,
    user=DB_USER,
    password=DB_PASS,
)

Initialize a table​

The MSSQLChatMessageHistory class requires a database table with a specific schema in order to store the chat message history.

The MSSQLEngine engine has a helper method init_chat_history_table() that can be used to create a table with the proper schema for you.

engine.init_chat_history_table(table_name=TABLE_NAME)

MSSQLChatMessageHistory​

To initialize the MSSQLChatMessageHistory class you need to provide only 3 things:

  1. engine - An instance of a MSSQLEngine engine.
  2. session_id - A unique identifier string that specifies an id for the session.
  3. table_name : The name of the table within the Cloud SQL database to store the chat message history.
from langchain_google_cloud_sql_mssql import MSSQLChatMessageHistory

history = MSSQLChatMessageHistory(
    engine, session_id="test_session", table_name=TABLE_NAME
)
history.add_user_message("hi!")
history.add_ai_message("whats up?")
history.messages
[HumanMessage(content='hi!'), AIMessage(content='whats up?')]

Cleaning up​

When the history of a specific session is obsolete and can be deleted, it can be done the following way.

Note: Once deleted, the data is no longer stored in Cloud SQL and is gone forever.

history.clear()

🔗 Chaining​

We can easily combine this message history class with LCEL Runnables

To do this we will use one of Google’s Vertex AI chat models which requires that you enable the Vertex AI API in your Google Cloud Project.

# enable Vertex AI API
!gcloud services enable aiplatform.googleapis.com
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.runnables.history import RunnableWithMessageHistory
from langchain_google_vertexai import ChatVertexAI
prompt = ChatPromptTemplate.from_messages(
    [
        ("system", "You are a helpful assistant."),
        MessagesPlaceholder(variable_name="history"),
        ("human", "{question}"),
    ]
)

chain = prompt | ChatVertexAI(project=PROJECT_ID)
chain_with_history = RunnableWithMessageHistory(
    chain,
    lambda session_id: MSSQLChatMessageHistory(
        engine,
        session_id=session_id,
        table_name=TABLE_NAME,
    ),
    input_messages_key="question",
    history_messages_key="history",
)
# This is where we configure the session id
config = {"configurable": {"session_id": "test_session"}}
chain_with_history.invoke({"question": "Hi! I'm bob"}, config=config)
AIMessage(content=' Hello Bob, how can I help you today?')
chain_with_history.invoke({"question": "Whats my name"}, config=config)
AIMessage(content=' Your name is Bob.')

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