Google Cloud El Carro Oracle offers a way to run Oracle databases in Kubernetes as a portable, open source, community-driven, no vendor lock-in container orchestration system. El Carro provides a powerful declarative API for comprehensive and consistent configuration and deployment as well as for real-time operations and monitoring. Extend your Oracle database’s capabilities to build AI-powered experiences by leveraging the El Carro LangChain integration.
This guide goes over how to use the El Carro LangChain integration to store chat message history with the ElCarroChatMessageHistory class. This integration works for any Oracle database, regardless of where it is running. Learn more about the package on GitHub. Open In Colab

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Before You Begin

To run this notebook, you will need to do the following:
  • Complete the Getting Started section if you would like to run your Oracle database with El Carro.

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🦜🔗 Library Installation

The integration lives in its own langchain-google-el-carro package, so we need to install it. 
%pip install -qU langchain-google-el-carro langchain-google-vertexai langchain
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)

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🔐 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()

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☁ 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}

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Basic Usage

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Set Up Oracle Database Connection

Fill out the following variable with your Oracle database connections details. 
# @title Set Your Values Here { display-mode: "form" }
HOST = "127.0.0.1"  # @param {type: "string"}
PORT = 3307  # @param {type: "integer"}
DATABASE = "my-database"  # @param {type: "string"}
TABLE_NAME = "message_store"  # @param {type: "string"}
USER = "my-user"  # @param {type: "string"}
PASSWORD = input("Please provide a password to be used for the database user: ")
If you are using El Carro, you can find the hostname and port values in the status of the El Carro Kubernetes instance. Use the user password you created for your PDB. Example kubectl get -w instances.oracle.db.anthosapis.com -n db NAME DB ENGINE VERSION EDITION ENDPOINT URL DB NAMES BACKUP ID READYSTATUS READYREASON DBREADYSTATUS DBREADYREASON mydb Oracle 18c Express mydb-svc.db 34.71.69.25:6021 False CreateInProgress

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ElCarroEngine Connection Pool

ElCarroEngine configures a connection pool to your Oracle database, enabling successful connections from your application and following industry best practices. 
from langchain_google_el_carro import ElCarroEngine

elcarro_engine = ElCarroEngine.from_instance(
    db_host=HOST,
    db_port=PORT,
    db_name=DATABASE,
    db_user=USER,
    db_password=PASSWORD,
)

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Initialize a table

The ElCarroChatMessageHistory class requires a database table with a specific schema in order to store the chat message history. The ElCarroEngine class has a method init_chat_history_table() that can be used to create a table with the proper schema for you. 
elcarro_engine.init_chat_history_table(table_name=TABLE_NAME)

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ElCarroChatMessageHistory

To initialize the ElCarroChatMessageHistory class you need to provide only 3 things:
  1. elcarro_engine - An instance of an ElCarroEngine 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 Oracle database to store the chat message history.
from langchain_google_el_carro import ElCarroChatMessageHistory

history = ElCarroChatMessageHistory(
    elcarro_engine=elcarro_engine, session_id="test_session", table_name=TABLE_NAME
)
history.add_user_message("hi!")
history.add_ai_message("whats up?")

history.messages

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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 your database and is gone forever. 
history.clear()

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🔗 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: ElCarroChatMessageHistory(
        elcarro_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)