Google AlloyDB for PostgreSQL
Google Cloud AlloyDB for PostgreSQL is a fully managed
PostgreSQL
compatible database service for your most demanding enterprise workloads.AlloyDB
combines the best ofGoogle Cloud
withPostgreSQL
, for superior performance, scale, and availability. Extend your database application to build AI-powered experiences leveragingAlloyDB
Langchain integrations.
This notebook goes over how to use Google Cloud AlloyDB for PostgreSQL
to store chat message history with the AlloyDBChatMessageHistory
class.
Learn more about the package on GitHub.
Before You Beginโ
To run this notebook, you will need to do the following:
- Create a Google Cloud Project
- Enable the AlloyDB API
- Create a AlloyDB instance
- Create a AlloyDB database
- Add an IAM database user to the database (Optional)
๐ฆ๐ Library Installationโ
The integration lives in its own langchain-google-alloydb-pg
package, so we need to install it.
%pip install --upgrade --quiet langchain-google-alloydb-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.
# # 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)
๐ Authenticationโ
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()
โ Set Your Google Cloud Projectโ
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}
๐ก API Enablementโ
The langchain-google-alloydb-pg
package requires that you enable the AlloyDB Admin API in your Google Cloud Project.
# enable AlloyDB API
!gcloud services enable alloydb.googleapis.com
Basic Usageโ
Set AlloyDB database valuesโ
Find your database values, in the AlloyDB cluster page.
# @title Set Your Values Here { display-mode: "form" }
REGION = "us-central1" # @param {type: "string"}
CLUSTER = "my-alloydb-cluster" # @param {type: "string"}
INSTANCE = "my-alloydb-instance" # @param {type: "string"}
DATABASE = "my-database" # @param {type: "string"}
TABLE_NAME = "message_store" # @param {type: "string"}
AlloyDBEngine Connection Poolโ
One of the requirements and arguments to establish AlloyDB as a ChatMessageHistory memory store is a AlloyDBEngine
object. The AlloyDBEngine
configures a connection pool to your AlloyDB database, enabling successful connections from your application and following industry best practices.
To create a AlloyDBEngine
using AlloyDBEngine.from_instance()
you need to provide only 5 things:
project_id
: Project ID of the Google Cloud Project where the AlloyDB instance is located.region
: Region where the AlloyDB instance is located.cluster
: The name of the AlloyDB cluster.instance
: The name of the AlloyDB instance.database
: The name of the database to connect to on the AlloyDB 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.
Optionally, built-in database authentication using a username and password to access the AlloyDB database can also be used. Just provide the optional user
and password
arguments to AlloyDBEngine.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.
from langchain_google_alloydb_pg import AlloyDBEngine
engine = AlloyDBEngine.from_instance(
project_id=PROJECT_ID,
region=REGION,
cluster=CLUSTER,
instance=INSTANCE,
database=DATABASE,
)
Initialize a tableโ
The AlloyDBChatMessageHistory
class requires a database table with a specific schema in order to store the chat message history.
The AlloyDBEngine
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)
AlloyDBChatMessageHistoryโ
To initialize the AlloyDBChatMessageHistory
class you need to provide only 3 things:
engine
- An instance of aAlloyDBEngine
engine.session_id
- A unique identifier string that specifies an id for the session.table_name
: The name of the table within the AlloyDB database to store the chat message history.
from langchain_google_alloydb_pg import AlloyDBChatMessageHistory
history = AlloyDBChatMessageHistory.create_sync(
engine, session_id="test_session", table_name=TABLE_NAME
)
history.add_user_message("hi!")
history.add_ai_message("whats up?")
history.messages
Cleaning upโ
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 AlloyDB and is gone forever.
history.clear()
๐ Chainingโ
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: AlloyDBChatMessageHistory.create_sync(
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)
chain_with_history.invoke({"question": "Whats my name"}, config=config)