mirror of https://github.com/hwchase17/langchain
docs: Update Google El Carro for Oracle Workload Documentation. (#18394)
In this commit we update the documentation for Google El Carro for Oracle Workloads. We amend the documentation in the Google Providers page to use the correct name which is El Carro for Oracle Workloads. We also add changes to the document_loaders and memory pages to reflect changes we made in our repo.pull/18463/head
Tabby
4 months ago
committed by
GitHub
parent
e192f6b6eb
commit
dd6f85caf1
@ -1,368 +1,393 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Google El Carro Oracle Operator\n",
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"\n",
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"> Google [El Carro Oracle Operator](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 database application to build AI-powered experiences leveraging Oracle Langchain integrations.\n",
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"\n",
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"This guide goes over how to use the El Carro Langchain integration to store chat message history with the `ElCarroChatMessageHistory` class.\n",
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"\n",
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"[](https://colab.research.google.com/github/googleapis/langchain-google-el-carro-python/blob/main/docs/chat_message_history.ipynb)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Before You Begin\n",
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"\n",
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"To run this notebook, you will need to do the following:\n",
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" * Complete the [Getting Started](https://github.com/googleapis/langchain-google-el-carro-python/tree/main/README.md#getting-started) section"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 🦜🔗 Library Installation\n",
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"The integration lives in its own `langchain-google-el-carro` package, so we need to install it."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install --upgrade --quiet langchain-google-el-carro langchain-google-vertexai langchain"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# # Automatically restart kernel after installs so that your environment can access the new packages\n",
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"# import IPython\n",
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"\n",
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"# app = IPython.Application.instance()\n",
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"# app.kernel.do_shutdown(True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 🔐 Authentication\n",
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"Authenticate to Google Cloud as the IAM user logged into this notebook in order to access your Google Cloud Project.\n",
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"\n",
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"* If you are using Colab to run this notebook, use the cell below and continue.\n",
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"* If you are using Vertex AI Workbench, check out the setup instructions [here](https://github.com/GoogleCloudPlatform/generative-ai/tree/main/setup-env)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from google.colab import auth\n",
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"\n",
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"auth.authenticate_user()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### ☁ Set Your Google Cloud Project\n",
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"Set your Google Cloud project so that you can leverage Google Cloud resources within this notebook.\n",
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"\n",
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"If you don't know your project ID, try the following:\n",
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"\n",
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"* Run `gcloud config list`.\n",
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"* Run `gcloud projects list`.\n",
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"* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# @markdown Please fill in the value below with your Google Cloud project ID and then run the cell.\n",
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"\n",
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"PROJECT_ID = \"my-project-id\" # @param {type:\"string\"}\n",
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"\n",
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"# Set the project id\n",
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"!gcloud config set project {PROJECT_ID}"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Basic Usage"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Set Up Oracle Database Connection\n",
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"You can find the hostname and port values in the status of the El Carro\n",
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"Kubernetes instance. Use the user password you created for your PDB."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# @title Set Your Values Here { display-mode: \"form\" }\n",
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"HOST = \"127.0.0.1\" # @param {type: \"string\"}\n",
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"PORT = 3307 # @param {type: \"integer\"}\n",
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"DATABASE = \"my-database\" # @param {type: \"string\"}\n",
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"TABLE_NAME = \"message_store\" # @param {type: \"string\"}\n",
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"USER = \"my-user\" # @param {type: \"string\"}\n",
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"PASSWORD = input(\"Please provide a password to be used for the database user: \")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### ElCarroEngine Connection Pool\n",
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"\n",
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"`ElCarroEngine` configures a connection pool to your Oracle database, enabling successful connections from your application and following industry best practices."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_google_el_carro import ElCarroEngine\n",
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"\n",
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"elcarro_engine = ElCarroEngine.from_instance(\n",
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" db_host=HOST,\n",
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" db_port=PORT,\n",
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" db_name=DATABASE,\n",
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" db_user=USER,\n",
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" db_password=PASSWORD,\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Initialize a table\n",
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"The `ElCarroChatMessageHistory` class requires a database table with a specific\n",
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"schema in order to store the chat message history.\n",
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"\n",
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"The `ElCarroEngine` class has a\n",
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"method `init_chat_history_table()` that can be used to create a table with the\n",
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"proper schema for you."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"elcarro_engine.init_chat_history_table(table_name=TABLE_NAME)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### ElCarroChatMessageHistory\n",
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"\n",
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"To initialize the `ElCarroChatMessageHistory` class you need to provide only 3\n",
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"things:\n",
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"\n",
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"1. `elcarro_engine` - An instance of an `ElCarroEngine` engine.\n",
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"1. `session_id` - A unique identifier string that specifies an id for the\n",
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" session.\n",
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"1. `table_name` : The name of the table within the Oracle database to store the\n",
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" chat message history."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_google_el_carro import ElCarroChatMessageHistory\n",
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"\n",
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"history = ElCarroChatMessageHistory(\n",
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" elcarro_engine=elcarro_engine, session_id=\"test_session\", table_name=TABLE_NAME\n",
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")\n",
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"history.add_user_message(\"hi!\")\n",
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"history.add_ai_message(\"whats up?\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"history.messages"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Cleaning up\n",
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"When the history of a specific session is obsolete and can be deleted, it can be done the following way.\n",
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"\n",
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"**Note:** Once deleted, the data is no longer stored in AlloyDB and is gone forever."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"history.clear()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 🔗 Chaining\n",
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"\n",
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"We can easily combine this message history class with [LCEL Runnables](/docs/expression_language/how_to/message_history)\n",
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"\n",
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"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.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# enable Vertex AI API\n",
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"!gcloud services enable aiplatform.googleapis.com"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
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"from langchain_core.runnables.history import RunnableWithMessageHistory\n",
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"from langchain_google_vertexai import ChatVertexAI"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"prompt = ChatPromptTemplate.from_messages(\n",
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" [\n",
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" (\"system\", \"You are a helpful assistant.\"),\n",
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" MessagesPlaceholder(variable_name=\"history\"),\n",
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" (\"human\", \"{question}\"),\n",
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" ]\n",
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")\n",
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"\n",
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"chain = prompt | ChatVertexAI(project=PROJECT_ID)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"chain_with_history = RunnableWithMessageHistory(\n",
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" chain,\n",
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" lambda session_id: ElCarroChatMessageHistory(\n",
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" elcarro_engine,\n",
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" session_id=session_id,\n",
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" table_name=TABLE_NAME,\n",
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" ),\n",
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" input_messages_key=\"question\",\n",
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" history_messages_key=\"history\",\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# This is where we configure the session id\n",
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"config = {\"configurable\": {\"session_id\": \"test_session\"}}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"chain_with_history.invoke({\"question\": \"Hi! I'm bob\"}, config=config)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"chain_with_history.invoke({\"question\": \"Whats my name\"}, config=config)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Google El Carro Oracle Operator\n",
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"\n",
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"> Google [El Carro Oracle Operator](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.\n",
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"\n",
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"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.\n",
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"\n",
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"[](https://colab.research.google.com/github/googleapis/langchain-google-el-carro-python/blob/main/docs/chat_message_history.ipynb)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Before You Begin\n",
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"\n",
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"To run this notebook, you will need to do the following:\n",
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" * 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."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
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"### 🦜🔗 Library Installation\n",
|
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"The integration lives in its own `langchain-google-el-carro` package, so we need to install it."
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||||
]
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||||
},
|
||||
{
|
||||
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
|
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"%pip install --upgrade --quiet langchain-google-el-carro langchain-google-vertexai langchain"
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]
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},
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{
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"cell_type": "markdown",
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||||
"metadata": {},
|
||||
"source": [
|
||||
"**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."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
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||||
"execution_count": null,
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||||
"metadata": {},
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||||
"outputs": [],
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||||
"source": [
|
||||
"# # Automatically restart kernel after installs so that your environment can access the new packages\n",
|
||||
"# import IPython\n",
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"\n",
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||||
"# app = IPython.Application.instance()\n",
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"# app.kernel.do_shutdown(True)"
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]
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},
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||||
{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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||||
"### 🔐 Authentication\n",
|
||||
"Authenticate to Google Cloud as the IAM user logged into this notebook in order to access your Google Cloud Project.\n",
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||||
"\n",
|
||||
"* If you are using Colab to run this notebook, use the cell below and continue.\n",
|
||||
"* If you are using Vertex AI Workbench, check out the setup instructions [here](https://github.com/GoogleCloudPlatform/generative-ai/tree/main/setup-env)."
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||||
]
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||||
},
|
||||
{
|
||||
"cell_type": "code",
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||||
"execution_count": null,
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||||
"metadata": {},
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||||
"outputs": [],
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||||
"source": [
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"from google.colab import auth\n",
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"\n",
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||||
"auth.authenticate_user()"
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]
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||||
},
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||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
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||||
"source": [
|
||||
"### ☁ Set Your Google Cloud Project\n",
|
||||
"Set your Google Cloud project so that you can leverage Google Cloud resources within this notebook.\n",
|
||||
"\n",
|
||||
"If you don't know your project ID, try the following:\n",
|
||||
"\n",
|
||||
"* Run `gcloud config list`.\n",
|
||||
"* Run `gcloud projects list`.\n",
|
||||
"* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# @markdown Please fill in the value below with your Google Cloud project ID and then run the cell.\n",
|
||||
"\n",
|
||||
"PROJECT_ID = \"my-project-id\" # @param {type:\"string\"}\n",
|
||||
"\n",
|
||||
"# Set the project id\n",
|
||||
"!gcloud config set project {PROJECT_ID}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Basic Usage"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Set Up Oracle Database Connection\n",
|
||||
"Fill out the following variable with your Oracle database connections details."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# @title Set Your Values Here { display-mode: \"form\" }\n",
|
||||
"HOST = \"127.0.0.1\" # @param {type: \"string\"}\n",
|
||||
"PORT = 3307 # @param {type: \"integer\"}\n",
|
||||
"DATABASE = \"my-database\" # @param {type: \"string\"}\n",
|
||||
"TABLE_NAME = \"message_store\" # @param {type: \"string\"}\n",
|
||||
"USER = \"my-user\" # @param {type: \"string\"}\n",
|
||||
"PASSWORD = input(\"Please provide a password to be used for the database user: \")"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"\n",
|
||||
"If you are using `El Carro`, you can find the hostname and port values in the\n",
|
||||
"status of the `El Carro` Kubernetes instance.\n",
|
||||
"Use the user password you created for your PDB.\n",
|
||||
"Example"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"kubectl get -w instances.oracle.db.anthosapis.com -n db\n",
|
||||
"NAME DB ENGINE VERSION EDITION ENDPOINT URL DB NAMES BACKUP ID READYSTATUS READYREASON DBREADYSTATUS DBREADYREASON\n",
|
||||
"mydb Oracle 18c Express mydb-svc.db 34.71.69.25:6021 False CreateInProgress"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### ElCarroEngine Connection Pool\n",
|
||||
"\n",
|
||||
"`ElCarroEngine` configures a connection pool to your Oracle database, enabling successful connections from your application and following industry best practices."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_google_el_carro import ElCarroEngine\n",
|
||||
"\n",
|
||||
"elcarro_engine = ElCarroEngine.from_instance(\n",
|
||||
" db_host=HOST,\n",
|
||||
" db_port=PORT,\n",
|
||||
" db_name=DATABASE,\n",
|
||||
" db_user=USER,\n",
|
||||
" db_password=PASSWORD,\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Initialize a table\n",
|
||||
"The `ElCarroChatMessageHistory` class requires a database table with a specific\n",
|
||||
"schema in order to store the chat message history.\n",
|
||||
"\n",
|
||||
"The `ElCarroEngine` class has a\n",
|
||||
"method `init_chat_history_table()` that can be used to create a table with the\n",
|
||||
"proper schema for you."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"elcarro_engine.init_chat_history_table(table_name=TABLE_NAME)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### ElCarroChatMessageHistory\n",
|
||||
"\n",
|
||||
"To initialize the `ElCarroChatMessageHistory` class you need to provide only 3\n",
|
||||
"things:\n",
|
||||
"\n",
|
||||
"1. `elcarro_engine` - An instance of an `ElCarroEngine` engine.\n",
|
||||
"1. `session_id` - A unique identifier string that specifies an id for the\n",
|
||||
" session.\n",
|
||||
"1. `table_name` : The name of the table within the Oracle database to store the\n",
|
||||
" chat message history."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_google_el_carro import ElCarroChatMessageHistory\n",
|
||||
"\n",
|
||||
"history = ElCarroChatMessageHistory(\n",
|
||||
" elcarro_engine=elcarro_engine, session_id=\"test_session\", table_name=TABLE_NAME\n",
|
||||
")\n",
|
||||
"history.add_user_message(\"hi!\")\n",
|
||||
"history.add_ai_message(\"whats up?\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"history.messages"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Cleaning up\n",
|
||||
"When the history of a specific session is obsolete and can be deleted, it can be done the following way.\n",
|
||||
"\n",
|
||||
"**Note:** Once deleted, the data is no longer stored in your database and is gone forever."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"history.clear()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 🔗 Chaining\n",
|
||||
"\n",
|
||||
"We can easily combine this message history class with [LCEL Runnables](/docs/expression_language/how_to/message_history)\n",
|
||||
"\n",
|
||||
"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.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# enable Vertex AI API\n",
|
||||
"!gcloud services enable aiplatform.googleapis.com"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
|
||||
"from langchain_core.runnables.history import RunnableWithMessageHistory\n",
|
||||
"from langchain_google_vertexai import ChatVertexAI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"prompt = ChatPromptTemplate.from_messages(\n",
|
||||
" [\n",
|
||||
" (\"system\", \"You are a helpful assistant.\"),\n",
|
||||
" MessagesPlaceholder(variable_name=\"history\"),\n",
|
||||
" (\"human\", \"{question}\"),\n",
|
||||
" ]\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"chain = prompt | ChatVertexAI(project=PROJECT_ID)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"chain_with_history = RunnableWithMessageHistory(\n",
|
||||
" chain,\n",
|
||||
" lambda session_id: ElCarroChatMessageHistory(\n",
|
||||
" elcarro_engine,\n",
|
||||
" session_id=session_id,\n",
|
||||
" table_name=TABLE_NAME,\n",
|
||||
" ),\n",
|
||||
" input_messages_key=\"question\",\n",
|
||||
" history_messages_key=\"history\",\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# This is where we configure the session id\n",
|
||||
"config = {\"configurable\": {\"session_id\": \"test_session\"}}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"chain_with_history.invoke({\"question\": \"Hi! I'm bob\"}, config=config)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"chain_with_history.invoke({\"question\": \"Whats my name\"}, config=config)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue