Google Spanner
Google Cloud Spanner is a highly scalable database that combines unlimited scalability with relational semantics, such as secondary indexes, strong consistency, schemas, and SQL providing 99.999% availability in one easy solution.
This notebook goes over how to use Spanner
to store chat message history with the SpannerChatMessageHistory
class.
Learn more about the package on GitHub.
Before You Beginβ
To run this notebook, you will need to do the following:
- Create a Google Cloud Project
- Enable the Cloud Spanner API
- Create a Spanner instance
- Create a Spanner database
π¦π Library Installationβ
The integration lives in its own langchain-google-spanner
package, so we need to install it.
%pip install --upgrade --quiet langchain-google-spanner
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)
π Authenticationβ
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()
β Set Your Google Cloud Projectβ
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.
# @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}
π‘ API Enablementβ
The langchain-google-spanner
package requires that you enable the Spanner API in your Google Cloud Project.
# enable Spanner API
!gcloud services enable spanner.googleapis.com
Basic Usageβ
Set Spanner database valuesβ
Find your database values, in the Spanner Instances page.
# @title Set Your Values Here { display-mode: "form" }
INSTANCE = "my-instance" # @param {type: "string"}
DATABASE = "my-database" # @param {type: "string"}
TABLE_NAME = "message_store" # @param {type: "string"}
Initialize a tableβ
The SpannerChatMessageHistory
class requires a database table with a specific schema in order to store the chat message history.
The helper method init_chat_history_table()
that can be used to create a table with the proper schema for you.
from langchain_google_spanner import (
SpannerChatMessageHistory,
)
SpannerChatMessageHistory.init_chat_history_table(table_name=TABLE_NAME)
SpannerChatMessageHistoryβ
To initialize the SpannerChatMessageHistory
class you need to provide only 3 things:
instance_id
- The name of the Spanner instancedatabase_id
- The name of the Spanner databasesession_id
- A unique identifier string that specifies an id for the session.table_name
- The name of the table within the database to store the chat message history.
message_history = SpannerChatMessageHistory(
instance_id=INSTANCE,
database_id=DATABASE,
table_name=TABLE_NAME,
session_id="user-session-id",
)
message_history.add_user_message("hi!")
message_history.add_ai_message("whats up?")
message_history.messages
Custom clientβ
The client created by default is the default client. To use a non-default, a custom client can be passed to the constructor.
from google.cloud import spanner
custom_client_message_history = SpannerChatMessageHistory(
instance_id="my-instance",
database_id="my-database",
client=spanner.Client(...),
)
Cleaning upβ
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 Spanner and is gone forever.
message_history = SpannerChatMessageHistory(
instance_id=INSTANCE,
database_id=DATABASE,
table_name=TABLE_NAME,
session_id="user-session-id",
)
message_history.clear()