> [Google Cloud SQL](https://cloud.google.com/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](https://github.com/googleapis/langchain-google-cloud-sql-mssql-python/).

[![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/googleapis/langchain-google-cloud-sql-mssql-python/blob/main/docs/chat_message_history.ipynb)

Open In Colab

## Before You Begin[​](#before-you-begin "Direct link to Before You Begin")

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

*   [Create a Google Cloud Project](https://developers.google.com/workspace/guides/create-project)
*   [Enable the Cloud SQL Admin API.](https://console.cloud.google.com/marketplace/product/google/sqladmin.googleapis.com)
*   [Create a Cloud SQL for SQL Server instance](https://cloud.google.com/sql/docs/sqlserver/create-instance)
*   [Create a Cloud SQL database](https://cloud.google.com/sql/docs/sqlserver/create-manage-databases)
*   [Create a database user](https://cloud.google.com/sql/docs/sqlserver/create-manage-users) (Optional if you choose to use the `sqlserver` user)

### 🦜🔗 Library Installation[​](#library-installation "Direct link to 🦜🔗 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[​](#authentication "Direct link to 🔐 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](https://github.com/GoogleCloudPlatform/generative-ai/tree/main/setup-env).

```
from google.colab import authauth.authenticate_user()
```

### ☁ Set Your Google Cloud Project[​](#set-your-google-cloud-project "Direct link to ☁ 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](https://support.google.com/googleapi/answer/7014113).

```
# @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[​](#api-enablement "Direct link to 💡 API Enablement")

The `langchain-google-cloud-sql-mssql` package requires that you [enable the Cloud SQL Admin API](https://console.cloud.google.com/flows/enableapi?apiid=sqladmin.googleapis.com) in your Google Cloud Project.

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

## Basic Usage[​](#basic-usage "Direct link to Basic Usage")

### Set Cloud SQL database values[​](#set-cloud-sql-database-values "Direct link to Set Cloud SQL database values")

Find your database values, in the [Cloud SQL Instances page](https://console.cloud.google.com/sql?_ga=2.223735448.2062268965.1707700487-2088871159.1707257687).

```
# @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[​](#mssqlengine-connection-pool "Direct link to 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](https://cloud.google.com/sql/docs/sqlserver/users) using a username and password to access the Cloud SQL database is used for database authentication.

```
from langchain_google_cloud_sql_mssql import MSSQLEngineengine = MSSQLEngine.from_instance(    project_id=PROJECT_ID,    region=REGION,    instance=INSTANCE,    database=DATABASE,    user=DB_USER,    password=DB_PASS,)
```

### Initialize a table[​](#initialize-a-table "Direct link to 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[​](#mssqlchatmessagehistory "Direct link to 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 MSSQLChatMessageHistoryhistory = MSSQLChatMessageHistory(    engine, session_id="test_session", table_name=TABLE_NAME)history.add_user_message("hi!")history.add_ai_message("whats up?")
```

```
[HumanMessage(content='hi!'), AIMessage(content='whats up?')]
```

#### Cleaning up[​](#cleaning-up "Direct link to 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.

## 🔗 Chaining[​](#chaining "Direct link to 🔗 Chaining")

We can easily combine this message history class with [LCEL Runnables](https://python.langchain.com/docs/expression_language/how_to/message_history/)

To do this we will use one of [Google’s Vertex AI chat models](https://python.langchain.com/docs/integrations/chat/google_vertex_ai_palm/) which requires that you [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com) in your Google Cloud Project.

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

```
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholderfrom langchain_core.runnables.history import RunnableWithMessageHistoryfrom 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 idconfig = {"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.')
```