Google Bigtable

Google Cloud Bigtable is a key-value and wide-column store, ideal for fast access to structured, semi-structured, or unstructured data. Extend your database application to build AI-powered experiences leveraging Bigtable’s Langchain integrations.

This notebook goes over how to use Google Cloud Bigtable to store chat message history with the BigtableChatMessageHistory 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:

• Create a Google Cloud Project
• Enable the Bigtable API
• Create a Bigtable instance
• Create a Bigtable table
• Create Bigtable access credentials

🦜🔗 Library Installation

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

%pip install -upgrade --quiet langchain-google-bigtable

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)

☁ 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.

# @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}

🔐 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()

Basic Usage

Initialize Bigtable schema

The schema for BigtableChatMessageHistory requires the instance and table to exist, and have a column family called langchain.

# @markdown Please specify an instance and a table for demo purpose.
INSTANCE_ID = "my_instance"  # @param {type:"string"}
TABLE_ID = "my_table"  # @param {type:"string"}

If the table or the column family do not exist, you can use the following function to create them:

from google.cloud import bigtable
from langchain_google_bigtable import create_chat_history_table

create_chat_history_table(
    instance_id=INSTANCE_ID,
    table_id=TABLE_ID,
)

BigtableChatMessageHistory

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

1. instance_id - The Bigtable instance to use for chat message history.
2. table_id : The Bigtable table to store the chat message history.
3. session_id - A unique identifier string that specifies an id for the session.

from langchain_google_bigtable import BigtableChatMessageHistory

message_history = BigtableChatMessageHistory(
    instance_id=INSTANCE_ID,
    table_id=TABLE_ID,
    session_id="user-session-id",
)

message_history.add_user_message("hi!")
message_history.add_ai_message("whats up?")

message_history.messages

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 Bigtable and is gone forever.

message_history.clear()

Advanced Usage

Custom client

The client created by default is the default client, using only admin=True option. To use a non-default, a custom client can be passed to the constructor.

from google.cloud import bigtable

client = (bigtable.Client(...),)

create_chat_history_table(
    instance_id="my-instance",
    table_id="my-table",
    client=client,
)

custom_client_message_history = BigtableChatMessageHistory(
    instance_id="my-instance",
    table_id="my-table",
    client=client,
)