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docs: `providers` update 6 (#18610)

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Cleaned up the `Integrations/Components/Memory` navbar by shortening the
page titles. Updated page titles and file names to consistent formats.
pull/13988/merge
Leonid Ganeline 7 months ago committed by GitHub
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24
docs/docs/integrations/memory/google_alloydb.ipynb
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@ -6,9 +6,9 @@
"source": [
"# Google AlloyDB for PostgreSQL\n",
"\n",
"> [AlloyDB](https://cloud.google.com/alloydb) is a fully managed PostgreSQL compatible database service for your most demanding enterprise workloads. AlloyDB combines the best of Google with PostgreSQL, for superior performance, scale, and availability. Extend your database application to build AI-powered experiences leveraging AlloyDB Langchain integrations.\n",
"> [Google Cloud AlloyDB for PostgreSQL](https://cloud.google.com/alloydb) is a fully managed `PostgreSQL` compatible database service for your most demanding enterprise workloads. `AlloyDB` combines the best of `Google Cloud` with `PostgreSQL`, for superior performance, scale, and availability. Extend your database application to build AI-powered experiences leveraging `AlloyDB` Langchain integrations.\n",
"\n",
"This notebook goes over how to use `AlloyDB for PostgreSQL` to store chat message history with the `AlloyDBChatMessageHistory` class.\n",
"This notebook goes over how to use `Google Cloud AlloyDB for PostgreSQL` to store chat message history with the `AlloyDBChatMessageHistory` class.\n",
"\n",
"Learn more about the package on [GitHub](https://github.com/googleapis/langchain-google-alloydb-pg-python/).\n",
"\n",
@ -117,6 +117,24 @@
"!gcloud config set project {PROJECT_ID}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 💡 API Enablement\n",
"The `langchain-google-alloydb-pg` package requires that you [enable the AlloyDB Admin API](https://console.cloud.google.com/flows/enableapi?apiid=alloydb.googleapis.com) in your Google Cloud Project."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# enable AlloyDB API\n",
"!gcloud services enable alloydb.googleapis.com"
]
},
{
"cell_type": "markdown",
"metadata": {},
@ -374,7 +392,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
"version": "3.11.5"
}
},
"nbformat": 4,

6
docs/docs/integrations/memory/google_bigtable.ipynb
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"source": [
"# Google Bigtable\n",
"\n",
"> [Bigtable](https://cloud.google.com/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.\n",
"> [Google Cloud Bigtable](https://cloud.google.com/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.\n",
"\n",
"This notebook goes over how to use [Bigtable](https://cloud.google.com/bigtable) to store chat message history with the `BigtableChatMessageHistory` class.\n",
"This notebook goes over how to use [Google Cloud Bigtable](https://cloud.google.com/bigtable) to store chat message history with the `BigtableChatMessageHistory` class.\n",
"\n",
"Learn more about the package on [GitHub](https://github.com/googleapis/langchain-google-bigtable-python/).\n",
"\n",
@ -283,7 +283,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.5"
"version": "3.10.6"
}
},
"nbformat": 4,

558
docs/docs/integrations/memory/google_cloud_sql_pg.ipynb
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@ -1,558 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "f22eab3f84cbeb37",
"metadata": {
"id": "f22eab3f84cbeb37"
},
"source": [
"# Google Cloud SQL for PostgreSQL\n",
"\n",
"> [Cloud SQL](https://cloud.google.com/sql) is a fully managed relational database service that offers high performance, seamless integration, and impressive scalability. It offers MySQL, PostgreSQL, and SQL Server database engines. Extend your database application to build AI-powered experiences leveraging Cloud SQL's Langchain integrations.\n",
"\n",
"This notebook goes over how to use `Cloud SQL for PostgreSQL` to store chat message history with the `PostgresChatMessageHistory` class.\n",
"\n",
"Learn more about the package on [GitHub](https://github.com/googleapis/langchain-google-cloud-sql-pg-python/).\n",
"\n",
"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/googleapis/langchain-google-cloud-sql-pg-python/blob/main/docs/chat_message_history.ipynb)"
]
},
{
"cell_type": "markdown",
"id": "da400c79-a360-43e2-be60-401fd02b2819",
"metadata": {
"id": "da400c79-a360-43e2-be60-401fd02b2819"
},
"source": [
"## Before You Begin\n",
"\n",
"To run this notebook, you will need to do the following:\n",
"\n",
" * [Create a Google Cloud Project](https://developers.google.com/workspace/guides/create-project)\n",
" * [Enable the Cloud SQL Admin API.](https://console.cloud.google.com/marketplace/product/google/sqladmin.googleapis.com)\n",
" * [Create a Cloud SQL for PostgreSQL instance](https://cloud.google.com/sql/docs/postgres/create-instance)\n",
" * [Create a Cloud SQL database](https://cloud.google.com/sql/docs/mysql/create-manage-databases)\n",
" * [Add an IAM database user to the database](https://cloud.google.com/sql/docs/postgres/add-manage-iam-users#creating-a-database-user) (Optional)"
]
},
{
"cell_type": "markdown",
"id": "Mm7-fG_LltD7",
"metadata": {
"id": "Mm7-fG_LltD7"
},
"source": [
"### 🦜🔗 Library Installation\n",
"The integration lives in its own `langchain-google-cloud-sql-pg` package, so we need to install it."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1VELXvcj8AId",
"metadata": {
"id": "1VELXvcj8AId"
},
"outputs": [],
"source": [
"%pip install --upgrade --quiet langchain-google-cloud-sql-pg langchain-google-vertexai"
]
},
{
"cell_type": "markdown",
"id": "98TVoM3MNDHu",
"metadata": {
"id": "98TVoM3MNDHu"
},
"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",
"execution_count": null,
"id": "v6jBDnYnNM08",
"metadata": {
"id": "v6jBDnYnNM08"
},
"outputs": [],
"source": [
"# # Automatically restart kernel after installs so that your environment can access the new packages\n",
"# import IPython\n",
"\n",
"# app = IPython.Application.instance()\n",
"# app.kernel.do_shutdown(True)"
]
},
{
"cell_type": "markdown",
"id": "yygMe6rPWxHS",
"metadata": {
"id": "yygMe6rPWxHS"
},
"source": [
"### 🔐 Authentication\n",
"Authenticate to Google Cloud as the IAM user logged into this notebook in order to access your Google Cloud Project.\n",
"\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)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "PTXN1_DSXj2b",
"metadata": {
"id": "PTXN1_DSXj2b"
},
"outputs": [],
"source": [
"from google.colab import auth\n",
"\n",
"auth.authenticate_user()"
]
},
{
"cell_type": "markdown",
"id": "NEvB9BoLEulY",
"metadata": {
"id": "NEvB9BoLEulY"
},
"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,
"id": "gfkS3yVRE4_W",
"metadata": {
"cellView": "form",
"id": "gfkS3yVRE4_W"
},
"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",
"id": "rEWWNoNnKOgq",
"metadata": {
"id": "rEWWNoNnKOgq"
},
"source": [
"### 💡 API Enablement\n",
"The `langchain-google-cloud-sql-pg` package requires that you [enable the Cloud SQL Admin API](https://console.cloud.google.com/flows/enableapi?apiid=sqladmin.googleapis.com) in your Google Cloud Project."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "5utKIdq7KYi5",
"metadata": {
"id": "5utKIdq7KYi5"
},
"outputs": [],
"source": [
"# enable Cloud SQL Admin API\n",
"!gcloud services enable sqladmin.googleapis.com"
]
},
{
"cell_type": "markdown",
"id": "f8f2830ee9ca1e01",
"metadata": {
"id": "f8f2830ee9ca1e01"
},
"source": [
"## Basic Usage"
]
},
{
"cell_type": "markdown",
"id": "OMvzMWRrR6n7",
"metadata": {
"id": "OMvzMWRrR6n7"
},
"source": [
"### Set Cloud SQL database values\n",
"Find your database values, in the [Cloud SQL Instances page](https://console.cloud.google.com/sql?_ga=2.223735448.2062268965.1707700487-2088871159.1707257687)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "irl7eMFnSPZr",
"metadata": {
"id": "irl7eMFnSPZr"
},
"outputs": [],
"source": [
"# @title Set Your Values Here { display-mode: \"form\" }\n",
"REGION = \"us-central1\" # @param {type: \"string\"}\n",
"INSTANCE = \"my-postgresql-instance\" # @param {type: \"string\"}\n",
"DATABASE = \"my-database\" # @param {type: \"string\"}\n",
"TABLE_NAME = \"message_store\" # @param {type: \"string\"}"
]
},
{
"cell_type": "markdown",
"id": "QuQigs4UoFQ2",
"metadata": {
"id": "QuQigs4UoFQ2"
},
"source": [
"### PostgresEngine Connection Pool\n",
"\n",
"One of the requirements and arguments to establish Cloud SQL as a ChatMessageHistory memory store is a `PostgresEngine` object. The `PostgresEngine` configures a connection pool to your Cloud SQL database, enabling successful connections from your application and following industry best practices.\n",
"\n",
"To create a `PostgresEngine` using `PostgresEngine.from_instance()` you need to provide only 4 things:\n",
"\n",
"1. `project_id` : Project ID of the Google Cloud Project where the Cloud SQL instance is located.\n",
"1. `region` : Region where the Cloud SQL instance is located.\n",
"1. `instance` : The name of the Cloud SQL instance.\n",
"1. `database` : The name of the database to connect to on the Cloud SQL instance.\n",
"\n",
"By default, [IAM database authentication](https://cloud.google.com/sql/docs/postgres/iam-authentication#iam-db-auth) will be used as the method of database authentication. This library uses the IAM principal belonging to the [Application Default Credentials (ADC)](https://cloud.google.com/docs/authentication/application-default-credentials) sourced from the envionment.\n",
"\n",
"For more informatin on IAM database authentication please see:\n",
"* [Configure an instance for IAM database authentication](https://cloud.google.com/sql/docs/postgres/create-edit-iam-instances)\n",
"* [Manage users with IAM database authentication](https://cloud.google.com/sql/docs/postgres/add-manage-iam-users)\n",
"\n",
"Optionally, [built-in database authentication](https://cloud.google.com/sql/docs/postgres/built-in-authentication) using a username and password to access the Cloud SQL database can also be used. Just provide the optional `user` and `password` arguments to `PostgresEngine.from_instance()`:\n",
"\n",
"* `user` : Database user to use for built-in database authentication and login\n",
"* `password` : Database password to use for built-in database authentication and login.\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "4576e914a866fb40",
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-28T10:04:38.077748Z",
"start_time": "2023-08-28T10:04:36.105894Z"
},
"id": "4576e914a866fb40",
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"from langchain_google_cloud_sql_pg import PostgresEngine\n",
"\n",
"engine = PostgresEngine.from_instance(\n",
" project_id=PROJECT_ID, region=REGION, instance=INSTANCE, database=DATABASE\n",
")"
]
},
{
"cell_type": "markdown",
"id": "qPV8WfWr7O54",
"metadata": {
"id": "qPV8WfWr7O54"
},
"source": [
"### Initialize a table\n",
"The `PostgresChatMessageHistory` class requires a database table with a specific schema in order to store the chat message history.\n",
"\n",
"The `PostgresEngine` engine has a helper method `init_chat_history_table()` that can be used to create a table with the proper schema for you."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "TEu4VHArRttE",
"metadata": {
"id": "TEu4VHArRttE"
},
"outputs": [],
"source": [
"engine.init_chat_history_table(table_name=TABLE_NAME)"
]
},
{
"cell_type": "markdown",
"id": "zSYQTYf3UfOi",
"metadata": {
"id": "zSYQTYf3UfOi"
},
"source": [
"### PostgresChatMessageHistory\n",
"\n",
"To initialize the `PostgresChatMessageHistory` class you need to provide only 3 things:\n",
"\n",
"1. `engine` - An instance of a `PostgresEngine` engine.\n",
"1. `session_id` - A unique identifier string that specifies an id for the session.\n",
"1. `table_name` : The name of the table within the Cloud SQL database to store the chat message history."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "Kq7RLtfOq0wi",
"metadata": {
"id": "Kq7RLtfOq0wi"
},
"outputs": [],
"source": [
"from langchain_google_cloud_sql_pg import PostgresChatMessageHistory\n",
"\n",
"history = PostgresChatMessageHistory.create_sync(\n",
" 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": 11,
"id": "b476688cbb32ba90",
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-28T10:04:38.929396Z",
"start_time": "2023-08-28T10:04:38.915727Z"
},
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "b476688cbb32ba90",
"jupyter": {
"outputs_hidden": false
},
"outputId": "a19e5cd8-4225-476a-d28d-e870c6b838bb"
},
"outputs": [
{
"data": {
"text/plain": [
"[HumanMessage(content='hi!'), AIMessage(content='whats up?')]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"history.messages"
]
},
{
"cell_type": "markdown",
"id": "ss6CbqcTTedr",
"metadata": {
"id": "ss6CbqcTTedr"
},
"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 Cloud SQL and is gone forever."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "3khxzFxYO7x6",
"metadata": {
"id": "3khxzFxYO7x6"
},
"outputs": [],
"source": [
"history.clear()"
]
},
{
"cell_type": "markdown",
"id": "2e5337719d5614fd",
"metadata": {
"id": "2e5337719d5614fd"
},
"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": 13,
"id": "hYtHM3-TOMCe",
"metadata": {
"id": "hYtHM3-TOMCe"
},
"outputs": [],
"source": [
"# enable Vertex AI API\n",
"!gcloud services enable aiplatform.googleapis.com"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "6558418b-0ece-4d01-9661-56d562d78f7a",
"metadata": {
"id": "6558418b-0ece-4d01-9661-56d562d78f7a"
},
"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": 15,
"id": "82149122-61d3-490d-9bdb-bb98606e8ba1",
"metadata": {
"id": "82149122-61d3-490d-9bdb-bb98606e8ba1"
},
"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": 16,
"id": "2df90853-b67c-490f-b7f8-b69d69270b9c",
"metadata": {
"id": "2df90853-b67c-490f-b7f8-b69d69270b9c"
},
"outputs": [],
"source": [
"chain_with_history = RunnableWithMessageHistory(\n",
" chain,\n",
" lambda session_id: PostgresChatMessageHistory.create_sync(\n",
" 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": 17,
"id": "0ce596b8-3b78-48fd-9f92-46dccbbfd58b",
"metadata": {
"id": "0ce596b8-3b78-48fd-9f92-46dccbbfd58b"
},
"outputs": [],
"source": [
"# This is where we configure the session id\n",
"config = {\"configurable\": {\"session_id\": \"test_session\"}}"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "38e1423b-ba86-4496-9151-25932fab1a8b",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "38e1423b-ba86-4496-9151-25932fab1a8b",
"outputId": "d5c93570-4b0b-4fe8-d19c-4b361fe74291"
},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=' Hello Bob, how can I help you today?')"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain_with_history.invoke({\"question\": \"Hi! I'm bob\"}, config=config)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "2ee4ee62-a216-4fb1-bf33-57476a84cf16",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "2ee4ee62-a216-4fb1-bf33-57476a84cf16",
"outputId": "288fe388-3f60-41b8-8edb-37cfbec18981"
},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=' Your name is Bob.')"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain_with_history.invoke({\"question\": \"Whats my name\"}, config=config)"
]
}
],
"metadata": {
"colab": {
"provenance": [],
"toc_visible": true
},
"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": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Google Firestore in Datastore Mode\n",
"\n",
"> [Firestore in Datastore Mode](https://cloud.google.com/datastore) is a NoSQL document database build for automatic scaling, high performance and ease of application development. Extend your database application to build AI-powered experiences leveraging Datastore's Langchain integrations.\n",
"\n",
"This notebook goes over how to use [Firestore in Datastore Mode](https://cloud.google.com/datastore) to save chat messages into `Firestore` in Datastore Mode.\n",
"\n",
"Learn more about the package on [GitHub](https://github.com/googleapis/langchain-google-datastore-python/).\n",
"\n",
"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/googleapis/langchain-google-datastore-python/blob/main/docs/chat_message_history.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Before You Begin\n",
"\n",
"To run this notebook, you will need to do the following:\n",
"\n",
"* [Create a Google Cloud Project](https://developers.google.com/workspace/guides/create-project)\n",
"* [Enable the Datastore API](https://console.cloud.google.com/flows/enableapi?apiid=datastore.googleapis.com)\n",
"* [Create a Firestore in Datastore Mode database](https://cloud.google.com/datastore/docs/manage-databases)\n",
"\n",
"After confirmed access to database in the runtime environment of this notebook, filling the following values and run the cell before running example scripts."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 🦜🔗 Library Installation\n",
"\n",
"The integration lives in its own `langchain-google-datastore` package, so we need to install it."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"%pip install -upgrade --quiet langchain-google-datastore"
]
},
{
"cell_type": "markdown",
"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",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# # Automatically restart kernel after installs so that your environment can access the new packages\n",
"# import IPython\n",
"\n",
"# app = IPython.Application.instance()\n",
"# app.kernel.do_shutdown(True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"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": [
"### 🔐 Authentication\n",
"\n",
"Authenticate to Google Cloud as the IAM user logged into this notebook in order to access your Google Cloud Project.\n",
"\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)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from google.colab import auth\n",
"\n",
"auth.authenticate_user()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Basic Usage"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### DatastoreChatMessageHistory\n",
"\n",
"To initialize the `DatastoreChatMessageHistory` class you need to provide only 2 things:\n",
"\n",
"1. `session_id` - A unique identifier string that specifies an id for the session.\n",
"1. `kind` : The name of the Datastore kind to write into. This is an optional value and by default it will use `ChatHistory` as the kind."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain_google_datastore import DatastoreChatMessageHistory\n",
"\n",
"chat_history = DatastoreChatMessageHistory(\n",
" session_id=\"user-session-id\", kind=\"HistoryMessages\"\n",
")\n",
"\n",
"chat_history.add_user_message(\"Hi!\")\n",
"chat_history.add_ai_message(\"How can I help you?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"chat_history.messages"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Cleanup\n",
"\n",
"When the history of a specific session is obsolete and can be deleted from the database and memory, it can be done the following way.\n",
"\n",
"**Note:** Once deleted, the data is no longer stored in Datastore and is gone forever."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"chat_history.clear()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Custom Client\n",
"\n",
"The client is created by default using the available environment variables. A [custom client](https://cloud.google.com/python/docs/reference/datastore/latest/client) can be passed to the constructor."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from google.auth import compute_engine\n",
"from google.cloud import datastore\n",
"\n",
"client = datastore.Client(\n",
" project=\"project-custom\",\n",
" database=\"non-default-database\",\n",
" credentials=compute_engine.Credentials(),\n",
")\n",
"\n",
"history = DatastoreChatMessageHistory(\n",
" session_id=\"session-id\", kind=\"History\", client=client\n",
")\n",
"\n",
"history.add_user_message(\"New message\")\n",
"\n",
"history.messages\n",
"\n",
"history.clear()"
]
}
],
"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": "3.10.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

797
docs/docs/integrations/memory/google_el_carro.ipynb
Unescape Escape View File

@ -1,400 +1,405 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Google El Carro for Oracle Workloads\n",
"\n",
"> 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",
"\n",
"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",
"\n",
"Learn more about the package on [GitHub](https://github.com/googleapis/langchain-google-el-carro-python/).\n",
"\n",
"[![Open In Colab](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)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Before You Begin\n",
"\n",
"To run this notebook, you will need to do the following:\n",
"\n",
" * Complete the [Getting Started](https://github.com/googleapis/langchain-google-el-carro-python/tree/main/README.md#getting-started) section"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 🦜🔗 Library Installation\n",
"The integration lives in its own `langchain-google-el-carro` package, so we need to install it."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%pip install --upgrade --quiet langchain-google-el-carro langchain-google-vertexai langchain"
]
},
{
"cell_type": "markdown",
"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",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# # Automatically restart kernel after installs so that your environment can access the new packages\n",
"# import IPython\n",
"\n",
"# app = IPython.Application.instance()\n",
"# app.kernel.do_shutdown(True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 🔐 Authentication\n",
"Authenticate to Google Cloud as the IAM user logged into this notebook in order to access your Google Cloud Project.\n",
"\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)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# from google.colab import auth\n",
"\n",
"# auth.authenticate_user()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"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": {
"id": "azV0k45WWSVI"
},
"source": [
"## Basic Usage\n",
"\n",
"### Set Up Oracle Database Connection\n",
"Fill out the following variable with your Oracle database connections details."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"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: \")"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"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 Output"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"```\n",
"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",
"\n",
"mydb Oracle 18c Express mydb-svc.db 34.71.69.25:6021 ['pdbname'] TRUE CreateComplete True CreateComplete\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"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": {
"id": "xG1mYFkEWbkp"
},
"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 the 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)"
]
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Google El Carro Oracle\n",
"\n",
"> [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.\n",
"\n",
"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",
"\n",
"Learn more about the package on [GitHub](https://github.com/googleapis/langchain-google-el-carro-python/).\n",
"\n",
"[![Open In Colab](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)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Before You Begin\n",
"\n",
"To run this notebook, you will need to do the following:\n",
"\n",
" * 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."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 🦜🔗 Library Installation\n",
"The integration lives in its own `langchain-google-el-carro` package, so we need to install it."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%pip install --upgrade --quiet langchain-google-el-carro langchain-google-vertexai langchain"
]
},
{
"cell_type": "markdown",
"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",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# # Automatically restart kernel after installs so that your environment can access the new packages\n",
"# import IPython\n",
"\n",
"# app = IPython.Application.instance()\n",
"# app.kernel.do_shutdown(True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 🔐 Authentication\n",
"Authenticate to Google Cloud as the IAM user logged into this notebook in order to access your Google Cloud Project.\n",
"\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)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# from google.colab import auth\n",
"\n",
"# auth.authenticate_user()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"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,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
],
"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"
},
"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: \")"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"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"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"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"
]
},
{
"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"
},
"nbformat": 4,
"nbformat_minor": 4
}
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

6
docs/docs/integrations/memory/google_firestore.ipynb
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@ -6,9 +6,9 @@
"source": [
"# Google Firestore (Native Mode)\n",
"\n",
"> [Firestore](https://cloud.google.com/firestore) is a serverless document-oriented database that scales to meet any demand. Extend your database application to build AI-powered experiences leveraging Firestore's Langchain integrations.\n",
"> [Google Cloud Firestore](https://cloud.google.com/firestore) is a serverless document-oriented database that scales to meet any demand. Extend your database application to build AI-powered experiences leveraging `Firestore's` Langchain integrations.\n",
"\n",
"This notebook goes over how to use [Firestore](https://cloud.google.com/firestore) to to store chat message history with the `FirestoreChatMessageHistory` class.\n",
"This notebook goes over how to use [Google Cloud Firestore](https://cloud.google.com/firestore) to store chat message history with the `FirestoreChatMessageHistory` class.\n",
"\n",
"Learn more about the package on [GitHub](https://github.com/googleapis/langchain-google-firestore-python/).\n",
"\n",
@ -236,7 +236,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.6"
"version": "3.10.12"
}
},
"nbformat": 4,

263
docs/docs/integrations/memory/google_firestore_datastore.ipynb
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@ -0,0 +1,263 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Google Firestore (Datastore Mode)\n",
"\n",
"> [Google Cloud Firestore in Datastore](https://cloud.google.com/datastore) is a serverless document-oriented database that scales to meet any demand. Extend your database application to build AI-powered experiences leveraging `Datastore's` Langchain integrations.\n",
"\n",
"This notebook goes over how to use [Google Cloud Firestore in Datastore](https://cloud.google.com/datastore) to store chat message history with the `DatastoreChatMessageHistory` class.\n",
"\n",
"Learn more about the package on [GitHub](https://github.com/googleapis/langchain-google-datastore-python/).\n",
"\n",
"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/googleapis/langchain-google-datastore-python/blob/main/docs/chat_message_history.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Before You Begin\n",
"\n",
"To run this notebook, you will need to do the following:\n",
"\n",
"* [Create a Google Cloud Project](https://developers.google.com/workspace/guides/create-project)\n",
"* [Enable the Datastore API](https://console.cloud.google.com/flows/enableapi?apiid=datastore.googleapis.com)\n",
"* [Create a Datastore database](https://cloud.google.com/datastore/docs/manage-databases)\n",
"\n",
"After confirming access to the database in the runtime environment of this notebook, filling the following values and run the cell before running example scripts."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 🦜🔗 Library Installation\n",
"\n",
"The integration lives in its own `langchain-google-datastore` package, so we need to install it."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"%pip install -upgrade --quiet langchain-google-datastore"
]
},
{
"cell_type": "markdown",
"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",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# # Automatically restart kernel after installs so that your environment can access the new packages\n",
"# import IPython\n",
"\n",
"# app = IPython.Application.instance()\n",
"# app.kernel.do_shutdown(True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"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": [
"### 🔐 Authentication\n",
"\n",
"Authenticate to Google Cloud as the IAM user logged into this notebook in order to access your Google Cloud Project.\n",
"\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)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from google.colab import auth\n",
"\n",
"auth.authenticate_user()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### API Enablement\n",
"The `langchain-google-datastore` package requires that you [enable the Datastore API](https://console.cloud.google.com/flows/enableapi?apiid=datastore.googleapis.com) in your Google Cloud Project."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# enable Datastore API\n",
"!gcloud services enable datastore.googleapis.com"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Basic Usage"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### DatastoreChatMessageHistory\n",
"\n",
"To initialize the `DatastoreChatMessageHistory` class you need to provide only 3 things:\n",
"\n",
"1. `session_id` - A unique identifier string that specifies an id for the session.\n",
"1. `kind` - The name of the Datastore kind to write into. This is an optional value and by default, it will use `ChatHistory` as the kind.\n",
"1. `collection` - The single `/`-delimited path to a Datastore collection."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain_google_datastore import DatastoreChatMessageHistory\n",
"\n",
"chat_history = DatastoreChatMessageHistory(\n",
" session_id=\"user-session-id\", collection=\"HistoryMessages\"\n",
")\n",
"\n",
"chat_history.add_user_message(\"Hi!\")\n",
"chat_history.add_ai_message(\"How can I help you?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"chat_history.messages"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Cleaning up\n",
"When the history of a specific session is obsolete and can be deleted from the database and memory, it can be done the following way.\n",
"\n",
"**Note:** Once deleted, the data is no longer stored in Datastore and is gone forever."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"chat_history.clear()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Custom Client\n",
"\n",
"The client is created by default using the available environment variables. A [custom client](https://cloud.google.com/python/docs/reference/datastore/latest/client) can be passed to the constructor."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from google.auth import compute_engine\n",
"from google.cloud import datastore\n",
"\n",
"client = datastore.Client(\n",
" project=\"project-custom\",\n",
" database=\"non-default-database\",\n",
" credentials=compute_engine.Credentials(),\n",
")\n",
"\n",
"history = DatastoreChatMessageHistory(\n",
" session_id=\"session-id\", collection=\"History\", client=client\n",
")\n",
"\n",
"history.add_user_message(\"New message\")\n",
"\n",
"history.messages\n",
"\n",
"history.clear()"
]
}
],
"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": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

8
docs/docs/integrations/memory/google_memorystore_redis.ipynb
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@ -8,9 +8,9 @@
"source": [
"# Google Memorystore for Redis\n",
"\n",
"> [Google Memorystore for Redis](https://cloud.google.com/memorystore/docs/redis/memorystore-for-redis-overview) is a fully-managed service that is powered by the Redis in-memory data store to build application caches that provide sub-millisecond data access. Extend your database application to build AI-powered experiences leveraging Memorystore for Redis's Langchain integrations.\n",
"> [Google Cloud Memorystore for Redis](https://cloud.google.com/memorystore/docs/redis/memorystore-for-redis-overview) is a fully-managed service that is powered by the Redis in-memory data store to build application caches that provide sub-millisecond data access. Extend your database application to build AI-powered experiences leveraging Memorystore for Redis's Langchain integrations.\n",
"\n",
"This notebook goes over how to use [Memorystore for Redis](https://cloud.google.com/memorystore/docs/redis/memorystore-for-redis-overview) to store chat message history with the `MemorystoreChatMessageHistory` class.\n",
"This notebook goes over how to use [Google Cloud Memorystore for Redis](https://cloud.google.com/memorystore/docs/redis/memorystore-for-redis-overview) to store chat message history with the `MemorystoreChatMessageHistory` class.\n",
"\n",
"Learn more about the package on [GitHub](https://github.com/googleapis/langchain-google-memorystore-redis-python/).\n",
"\n",
@ -228,9 +228,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.6"
"version": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
"nbformat_minor": 4
}

29
docs/docs/integrations/memory/google_spanner.ipynb
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@ -3,14 +3,16 @@
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"# Google Spanner\n",
"> [Spanner](https://cloud.google.com/spanner) is a highly scalable database that combines unlimited scalability with relational semantics, such as secondary indexes, strong consistency, schemas, and SQL providing 99.999% availability in one easy solution.\n",
"> [Google Cloud Spanner](https://cloud.google.com/spanner) is a highly scalable database that combines unlimited scalability with relational semantics, such as secondary indexes, strong consistency, schemas, and SQL providing 99.999% availability in one easy solution.\n",
"\n",
"This notebook goes over how to use `Spanner` to store chat message history with the `SpannerChatMessageHistory` class.\n",
"\n",
"Learn more about the package on [GitHub](https://github.com/googleapis/langchain-google-spanner-python/).\n",
"\n",
"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/googleapis/langchain-google-spanner-python/blob/main/samples/chat_message_history.ipynb)"
@ -19,7 +21,10 @@
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"## Before You Begin\n",
@ -71,7 +76,6 @@
},
{
"cell_type": "markdown",
"id": "yygMe6rPWxHS",
"metadata": {
"id": "yygMe6rPWxHS"
},
@ -86,7 +90,6 @@
{
"cell_type": "code",
"execution_count": 1,
"id": "PTXN1_DSXj2b",
"metadata": {
"id": "PTXN1_DSXj2b"
},
@ -99,7 +102,6 @@
},
{
"cell_type": "markdown",
"id": "NEvB9BoLEulY",
"metadata": {
"id": "NEvB9BoLEulY"
},
@ -117,7 +119,6 @@
{
"cell_type": "code",
"execution_count": null,
"id": "gfkS3yVRE4_W",
"metadata": {
"cellView": "form",
"id": "gfkS3yVRE4_W"
@ -134,7 +135,6 @@
},
{
"cell_type": "markdown",
"id": "rEWWNoNnKOgq",
"metadata": {
"id": "rEWWNoNnKOgq"
},
@ -146,7 +146,6 @@
{
"cell_type": "code",
"execution_count": 3,
"id": "5utKIdq7KYi5",
"metadata": {
"id": "5utKIdq7KYi5"
},
@ -158,7 +157,6 @@
},
{
"cell_type": "markdown",
"id": "f8f2830ee9ca1e01",
"metadata": {
"id": "f8f2830ee9ca1e01"
},
@ -168,7 +166,6 @@
},
{
"cell_type": "markdown",
"id": "OMvzMWRrR6n7",
"metadata": {
"id": "OMvzMWRrR6n7"
},
@ -180,7 +177,6 @@
{
"cell_type": "code",
"execution_count": 4,
"id": "irl7eMFnSPZr",
"metadata": {
"id": "irl7eMFnSPZr"
},
@ -194,7 +190,6 @@
},
{
"cell_type": "markdown",
"id": "qPV8WfWr7O54",
"metadata": {
"id": "qPV8WfWr7O54"
},
@ -208,7 +203,6 @@
{
"cell_type": "code",
"execution_count": null,
"id": "TEu4VHArRttE",
"metadata": {
"id": "TEu4VHArRttE"
},
@ -239,7 +233,10 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [

34
docs/docs/integrations/memory/google_cloud_sql_mssql.ipynb → docs/docs/integrations/memory/google_sql_mssql.ipynb
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@ -7,11 +7,11 @@
"id": "f22eab3f84cbeb37"
},
"source": [
"# Google Cloud SQL for SQL Server\n",
"# Google SQL for SQL Server\n",
"\n",
"> [Cloud SQL](https://cloud.google.com/sql) is a fully managed relational database service that offers high performance, seamless integration, and impressive scalability. It offers MySQL, PostgreSQL, and SQL Server database engines. Extend your database application to build AI-powered experiences leveraging Cloud SQL's Langchain integrations.\n",
"> [Google Cloud SQL](https://cloud.google.com/sql) is a fully managed relational database service that offers high performance, seamless integration, and impressive scalability. It offers `MySQL`, `PostgreSQL`, and `SQL Server` database engines. Extend your database application to build AI-powered experiences leveraging Cloud SQL's Langchain integrations.\n",
"\n",
"This notebook goes over how to use `Cloud SQL for SQL Server` to store chat message history with the `MSSQLChatMessageHistory` class.\n",
"This notebook goes over how to use `Google Cloud SQL for SQL Server` to store chat message history with the `MSSQLChatMessageHistory` class.\n",
"\n",
"Learn more about the package on [GitHub](https://github.com/googleapis/langchain-google-cloud-sql-mssql-python/).\n",
"\n",
@ -148,6 +148,30 @@
"!gcloud config set project {PROJECT_ID}"
]
},
{
"cell_type": "markdown",
"id": "rEWWNoNnKOgq",
"metadata": {
"id": "rEWWNoNnKOgq"
},
"source": [
"### 💡 API Enablement\n",
"The `langchain-google-cloud-sql-mssql` package requires that you [enable the Cloud SQL Admin API](https://console.cloud.google.com/flows/enableapi?apiid=sqladmin.googleapis.com) in your Google Cloud Project."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "5utKIdq7KYi5",
"metadata": {
"id": "5utKIdq7KYi5"
},
"outputs": [],
"source": [
"# enable Cloud SQL Admin API\n",
"!gcloud services enable sqladmin.googleapis.com"
]
},
{
"cell_type": "markdown",
"id": "f8f2830ee9ca1e01",
@ -219,6 +243,7 @@
"end_time": "2023-08-28T10:04:38.077748Z",
"start_time": "2023-08-28T10:04:36.105894Z"
},
"collapsed": false,
"id": "4576e914a866fb40",
"jupyter": {
"outputs_hidden": false
@ -309,6 +334,7 @@
"colab": {
"base_uri": "https://localhost:8080/"
},
"collapsed": false,
"id": "b476688cbb32ba90",
"jupyter": {
"outputs_hidden": false
@ -526,7 +552,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
"version": "3.10.12"
}
},
"nbformat": 4,

35
docs/docs/integrations/memory/google_cloud_sql_mysql.ipynb → docs/docs/integrations/memory/google_sql_mysql.ipynb
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@ -7,11 +7,11 @@
"id": "f22eab3f84cbeb37"
},
"source": [
"# Google Cloud SQL for MySQL\n",
"# Google SQL for MySQL\n",
"\n",
"> [Cloud SQL](https://cloud.google.com/sql) is a fully managed relational database service that offers high performance, seamless integration, and impressive scalability. It offers MySQL, PostgreSQL, and SQL Server database engines. Extend your database application to build AI-powered experiences leveraging Cloud SQL's Langchain integrations.\n",
"> [Cloud Cloud SQL](https://cloud.google.com/sql) is a fully managed relational database service that offers high performance, seamless integration, and impressive scalability. It offers `MySQL`, `PostgreSQL`, and `SQL Server` database engines. Extend your database application to build AI-powered experiences leveraging Cloud SQL's Langchain integrations.\n",
"\n",
"This notebook goes over how to use `Cloud SQL for MySQL` to store chat message history with the `MySQLChatMessageHistory` class.\n",
"This notebook goes over how to use `Google Cloud SQL for MySQL` to store chat message history with the `MySQLChatMessageHistory` class.\n",
"\n",
"Learn more about the package on [GitHub](https://github.com/googleapis/langchain-google-cloud-sql-mysql-python/).\n",
"\n",
@ -148,6 +148,30 @@
"!gcloud config set project {PROJECT_ID}"
]
},
{
"cell_type": "markdown",
"id": "rEWWNoNnKOgq",
"metadata": {
"id": "rEWWNoNnKOgq"
},
"source": [
"### 💡 API Enablement\n",
"The `langchain-google-cloud-sql-mysql` package requires that you [enable the Cloud SQL Admin API](https://console.cloud.google.com/flows/enableapi?apiid=sqladmin.googleapis.com) in your Google Cloud Project."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "5utKIdq7KYi5",
"metadata": {
"id": "5utKIdq7KYi5"
},
"outputs": [],
"source": [
"# enable Cloud SQL Admin API\n",
"!gcloud services enable sqladmin.googleapis.com"
]
},
{
"cell_type": "markdown",
"id": "f8f2830ee9ca1e01",
@ -206,6 +230,7 @@
"By default, [IAM database authentication](https://cloud.google.com/sql/docs/mysql/iam-authentication#iam-db-auth) will be used as the method of database authentication. This library uses the IAM principal belonging to the [Application Default Credentials (ADC)](https://cloud.google.com/docs/authentication/application-default-credentials) sourced from the envionment.\n",
"\n",
"For more informatin on IAM database authentication please see:\n",
"\n",
"* [Configure an instance for IAM database authentication](https://cloud.google.com/sql/docs/mysql/create-edit-iam-instances)\n",
"* [Manage users with IAM database authentication](https://cloud.google.com/sql/docs/mysql/add-manage-iam-users)\n",
"\n",
@ -224,6 +249,7 @@
"end_time": "2023-08-28T10:04:38.077748Z",
"start_time": "2023-08-28T10:04:36.105894Z"
},
"collapsed": false,
"id": "4576e914a866fb40",
"jupyter": {
"outputs_hidden": false
@ -309,6 +335,7 @@
"colab": {
"base_uri": "https://localhost:8080/"
},
"collapsed": false,
"id": "b476688cbb32ba90",
"jupyter": {
"outputs_hidden": false
@ -526,7 +553,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.5"
"version": "3.9.1"
}
},
"nbformat": 4,

561
docs/docs/integrations/memory/google_sql_pg.ipynb
Unescape Escape View File

@ -0,0 +1,561 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "f22eab3f84cbeb37",
"metadata": {
"id": "f22eab3f84cbeb37"
},
"source": [
"# Google SQL for PostgreSQL\n",
"\n",
"> [Google Cloud SQL](https://cloud.google.com/sql) is a fully managed relational database service that offers high performance, seamless integration, and impressive scalability. It offers `MySQL`, `PostgreSQL`, and `SQL Server` database engines. Extend your database application to build AI-powered experiences leveraging Cloud SQL's Langchain integrations.\n",
"\n",
"This notebook goes over how to use `Google Cloud SQL for PostgreSQL` to store chat message history with the `PostgresChatMessageHistory` class.\n",
"\n",
"Learn more about the package on [GitHub](https://github.com/googleapis/langchain-google-cloud-sql-pg-python/).\n",
"\n",
"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/googleapis/langchain-google-cloud-sql-pg-python/blob/main/docs/chat_message_history.ipynb)"
]
},
{
"cell_type": "markdown",
"id": "da400c79-a360-43e2-be60-401fd02b2819",
"metadata": {
"id": "da400c79-a360-43e2-be60-401fd02b2819"
},
"source": [
"## Before You Begin\n",
"\n",
"To run this notebook, you will need to do the following:\n",
"\n",
" * [Create a Google Cloud Project](https://developers.google.com/workspace/guides/create-project)\n",
" * [Enable the Cloud SQL Admin API.](https://console.cloud.google.com/marketplace/product/google/sqladmin.googleapis.com)\n",
" * [Create a Cloud SQL for PostgreSQL instance](https://cloud.google.com/sql/docs/postgres/create-instance)\n",
" * [Create a Cloud SQL database](https://cloud.google.com/sql/docs/mysql/create-manage-databases)\n",
" * [Add an IAM database user to the database](https://cloud.google.com/sql/docs/postgres/add-manage-iam-users#creating-a-database-user) (Optional)"
]
},
{
"cell_type": "markdown",
"id": "Mm7-fG_LltD7",
"metadata": {
"id": "Mm7-fG_LltD7"
},
"source": [
"### 🦜🔗 Library Installation\n",
"The integration lives in its own `langchain-google-cloud-sql-pg` package, so we need to install it."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1VELXvcj8AId",
"metadata": {
"id": "1VELXvcj8AId"
},
"outputs": [],
"source": [
"%pip install --upgrade --quiet langchain-google-cloud-sql-pg langchain-google-vertexai"
]
},
{
"cell_type": "markdown",
"id": "98TVoM3MNDHu",
"metadata": {
"id": "98TVoM3MNDHu"
},
"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",
"execution_count": null,
"id": "v6jBDnYnNM08",
"metadata": {
"id": "v6jBDnYnNM08"
},
"outputs": [],
"source": [
"# # Automatically restart kernel after installs so that your environment can access the new packages\n",
"# import IPython\n",
"\n",
"# app = IPython.Application.instance()\n",
"# app.kernel.do_shutdown(True)"
]
},
{
"cell_type": "markdown",
"id": "yygMe6rPWxHS",
"metadata": {
"id": "yygMe6rPWxHS"
},
"source": [
"### 🔐 Authentication\n",
"Authenticate to Google Cloud as the IAM user logged into this notebook in order to access your Google Cloud Project.\n",
"\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)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "PTXN1_DSXj2b",
"metadata": {
"id": "PTXN1_DSXj2b"
},
"outputs": [],
"source": [
"from google.colab import auth\n",
"\n",
"auth.authenticate_user()"
]
},
{
"cell_type": "markdown",
"id": "NEvB9BoLEulY",
"metadata": {
"id": "NEvB9BoLEulY"
},
"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,
"id": "gfkS3yVRE4_W",
"metadata": {
"cellView": "form",
"id": "gfkS3yVRE4_W"
},
"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",
"id": "rEWWNoNnKOgq",
"metadata": {
"id": "rEWWNoNnKOgq"
},
"source": [
"### 💡 API Enablement\n",
"The `langchain-google-cloud-sql-pg` package requires that you [enable the Cloud SQL Admin API](https://console.cloud.google.com/flows/enableapi?apiid=sqladmin.googleapis.com) in your Google Cloud Project."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "5utKIdq7KYi5",
"metadata": {
"id": "5utKIdq7KYi5"
},
"outputs": [],
"source": [
"# enable Cloud SQL Admin API\n",
"!gcloud services enable sqladmin.googleapis.com"
]
},
{
"cell_type": "markdown",
"id": "f8f2830ee9ca1e01",
"metadata": {
"id": "f8f2830ee9ca1e01"
},
"source": [
"## Basic Usage"
]
},
{
"cell_type": "markdown",
"id": "OMvzMWRrR6n7",
"metadata": {
"id": "OMvzMWRrR6n7"
},
"source": [
"### Set Cloud SQL database values\n",
"Find your database values, in the [Cloud SQL Instances page](https://console.cloud.google.com/sql?_ga=2.223735448.2062268965.1707700487-2088871159.1707257687)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "irl7eMFnSPZr",
"metadata": {
"id": "irl7eMFnSPZr"
},
"outputs": [],
"source": [
"# @title Set Your Values Here { display-mode: \"form\" }\n",
"REGION = \"us-central1\" # @param {type: \"string\"}\n",
"INSTANCE = \"my-postgresql-instance\" # @param {type: \"string\"}\n",
"DATABASE = \"my-database\" # @param {type: \"string\"}\n",
"TABLE_NAME = \"message_store\" # @param {type: \"string\"}"
]
},
{
"cell_type": "markdown",
"id": "QuQigs4UoFQ2",
"metadata": {
"id": "QuQigs4UoFQ2"
},
"source": [
"### PostgresEngine Connection Pool\n",
"\n",
"One of the requirements and arguments to establish Cloud SQL as a ChatMessageHistory memory store is a `PostgresEngine` object. The `PostgresEngine` configures a connection pool to your Cloud SQL database, enabling successful connections from your application and following industry best practices.\n",
"\n",
"To create a `PostgresEngine` using `PostgresEngine.from_instance()` you need to provide only 4 things:\n",
"\n",
"1. `project_id` : Project ID of the Google Cloud Project where the Cloud SQL instance is located.\n",
"1. `region` : Region where the Cloud SQL instance is located.\n",
"1. `instance` : The name of the Cloud SQL instance.\n",
"1. `database` : The name of the database to connect to on the Cloud SQL instance.\n",
"\n",
"By default, [IAM database authentication](https://cloud.google.com/sql/docs/postgres/iam-authentication#iam-db-auth) will be used as the method of database authentication. This library uses the IAM principal belonging to the [Application Default Credentials (ADC)](https://cloud.google.com/docs/authentication/application-default-credentials) sourced from the envionment.\n",
"\n",
"For more informatin on IAM database authentication please see:\n",
"\n",
"* [Configure an instance for IAM database authentication](https://cloud.google.com/sql/docs/postgres/create-edit-iam-instances)\n",
"* [Manage users with IAM database authentication](https://cloud.google.com/sql/docs/postgres/add-manage-iam-users)\n",
"\n",
"Optionally, [built-in database authentication](https://cloud.google.com/sql/docs/postgres/built-in-authentication) using a username and password to access the Cloud SQL database can also be used. Just provide the optional `user` and `password` arguments to `PostgresEngine.from_instance()`:\n",
"\n",
"* `user` : Database user to use for built-in database authentication and login\n",
"* `password` : Database password to use for built-in database authentication and login.\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "4576e914a866fb40",
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-28T10:04:38.077748Z",
"start_time": "2023-08-28T10:04:36.105894Z"
},
"collapsed": false,
"id": "4576e914a866fb40",
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"from langchain_google_cloud_sql_pg import PostgresEngine\n",
"\n",
"engine = PostgresEngine.from_instance(\n",
" project_id=PROJECT_ID, region=REGION, instance=INSTANCE, database=DATABASE\n",
")"
]
},
{
"cell_type": "markdown",
"id": "qPV8WfWr7O54",
"metadata": {
"id": "qPV8WfWr7O54"
},
"source": [
"### Initialize a table\n",
"The `PostgresChatMessageHistory` class requires a database table with a specific schema in order to store the chat message history.\n",
"\n",
"The `PostgresEngine` engine has a helper method `init_chat_history_table()` that can be used to create a table with the proper schema for you."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "TEu4VHArRttE",
"metadata": {
"id": "TEu4VHArRttE"
},
"outputs": [],
"source": [
"engine.init_chat_history_table(table_name=TABLE_NAME)"
]
},
{
"cell_type": "markdown",
"id": "zSYQTYf3UfOi",
"metadata": {
"id": "zSYQTYf3UfOi"
},
"source": [
"### PostgresChatMessageHistory\n",
"\n",
"To initialize the `PostgresChatMessageHistory` class you need to provide only 3 things:\n",
"\n",
"1. `engine` - An instance of a `PostgresEngine` engine.\n",
"1. `session_id` - A unique identifier string that specifies an id for the session.\n",
"1. `table_name` : The name of the table within the Cloud SQL database to store the chat message history."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "Kq7RLtfOq0wi",
"metadata": {
"id": "Kq7RLtfOq0wi"
},
"outputs": [],
"source": [
"from langchain_google_cloud_sql_pg import PostgresChatMessageHistory\n",
"\n",
"history = PostgresChatMessageHistory.create_sync(\n",
" 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": 11,
"id": "b476688cbb32ba90",
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-28T10:04:38.929396Z",
"start_time": "2023-08-28T10:04:38.915727Z"
},
"colab": {
"base_uri": "https://localhost:8080/"
},
"collapsed": false,
"id": "b476688cbb32ba90",
"jupyter": {
"outputs_hidden": false
},
"outputId": "a19e5cd8-4225-476a-d28d-e870c6b838bb"
},
"outputs": [
{
"data": {
"text/plain": [
"[HumanMessage(content='hi!'), AIMessage(content='whats up?')]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"history.messages"
]
},
{
"cell_type": "markdown",
"id": "ss6CbqcTTedr",
"metadata": {
"id": "ss6CbqcTTedr"
},
"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 Cloud SQL and is gone forever."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "3khxzFxYO7x6",
"metadata": {
"id": "3khxzFxYO7x6"
},
"outputs": [],
"source": [
"history.clear()"
]
},
{
"cell_type": "markdown",
"id": "2e5337719d5614fd",
"metadata": {
"id": "2e5337719d5614fd"
},
"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": 13,
"id": "hYtHM3-TOMCe",
"metadata": {
"id": "hYtHM3-TOMCe"
},
"outputs": [],
"source": [
"# enable Vertex AI API\n",
"!gcloud services enable aiplatform.googleapis.com"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "6558418b-0ece-4d01-9661-56d562d78f7a",
"metadata": {
"id": "6558418b-0ece-4d01-9661-56d562d78f7a"
},
"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": 15,
"id": "82149122-61d3-490d-9bdb-bb98606e8ba1",
"metadata": {
"id": "82149122-61d3-490d-9bdb-bb98606e8ba1"
},
"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": 16,
"id": "2df90853-b67c-490f-b7f8-b69d69270b9c",
"metadata": {
"id": "2df90853-b67c-490f-b7f8-b69d69270b9c"
},
"outputs": [],
"source": [
"chain_with_history = RunnableWithMessageHistory(\n",
" chain,\n",
" lambda session_id: PostgresChatMessageHistory.create_sync(\n",
" 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": 17,
"id": "0ce596b8-3b78-48fd-9f92-46dccbbfd58b",
"metadata": {
"id": "0ce596b8-3b78-48fd-9f92-46dccbbfd58b"
},
"outputs": [],
"source": [
"# This is where we configure the session id\n",
"config = {\"configurable\": {\"session_id\": \"test_session\"}}"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "38e1423b-ba86-4496-9151-25932fab1a8b",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "38e1423b-ba86-4496-9151-25932fab1a8b",
"outputId": "d5c93570-4b0b-4fe8-d19c-4b361fe74291"
},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=' Hello Bob, how can I help you today?')"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain_with_history.invoke({\"question\": \"Hi! I'm bob\"}, config=config)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "2ee4ee62-a216-4fb1-bf33-57476a84cf16",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "2ee4ee62-a216-4fb1-bf33-57476a84cf16",
"outputId": "288fe388-3f60-41b8-8edb-37cfbec18981"
},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=' Your name is Bob.')"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain_with_history.invoke({\"question\": \"Whats my name\"}, config=config)"
]
}
],
"metadata": {
"colab": {
"provenance": [],
"toc_visible": true
},
"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": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

16
docs/vercel.json
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@ -56,6 +56,22 @@
"source": "/docs/use_cases/graph/neptune_sparql_qa",
"destination": "/docs/use_cases/graph/integrations/neptune_sparql_qa"
},
{
"source": "/docs/integrations/memory/google_cloud_sql_mssql",
"destination": "/docs/integrations/memory/google_sql_mssql"
},
{
"source": "/docs/integrations/memory/google_cloud_sql_mysql",
"destination": "/docs/integrations/memory/google_sql_mysql"
},
{
"source": "/docs/integrations/memory/google_cloud_sql_pg",
"destination": "/docs/integrations/memory/google_sql_pg"
},
{
"source": "/docs/integrations/memory/google_datastore",
"destination": "/docs/integrations/memory/google_firestore_datastore"
},
{
"source": "/docs/integrations/llms/huggingface_textgen_inference",
"destination": "/docs/integrations/llms/huggingface_endpoint"

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