## Google El Carro Oracle

> [Google Cloud El Carro Oracle](https://github.com/GoogleCloudPlatform/elcarro-oracle-operator) 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](https://github.com/googleapis/langchain-google-el-carro-python/).

[![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/googleapis/langchain-google-el-carro-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:

*   Complete the [Getting Started](https://github.com/googleapis/langchain-google-el-carro-python/tree/main/README.md#getting-started) section if you would like to run your Oracle database with El Carro.

### 🦜🔗 Library Installation[​](#library-installation "Direct link to 🦜🔗 Library Installation")

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

```
%pip install --upgrade --quiet 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)
```

### 🔐 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 auth# auth.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}
```

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

### Set Up Oracle Database Connection[​](#set-up-oracle-database-connection "Direct link to 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

### ElCarroEngine Connection Pool[​](#elcarroengine-connection-pool "Direct link to 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 ElCarroEngineelcarro_engine = ElCarroEngine.from_instance(    db_host=HOST,    db_port=PORT,    db_name=DATABASE,    db_user=USER,    db_password=PASSWORD,)
```

### Initialize a table[​](#initialize-a-table "Direct link to 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)
```

### ElCarroChatMessageHistory[​](#elcarrochatmessagehistory "Direct link to 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 ElCarroChatMessageHistoryhistory = ElCarroChatMessageHistory(    elcarro_engine=elcarro_engine, session_id="test_session", table_name=TABLE_NAME)history.add_user_message("hi!")history.add_ai_message("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 your database 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: 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 idconfig = {"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)
```