docs/document_loader.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Google Cloud 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](https://cloud.google.com/sql/mysql), [PostgreSQL](https://cloud.google.com/sql/postgresql), and [SQL Server](https://cloud.google.com/sql/sqlserver) 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](https://cloud.google.com/sql/mysql) to [save, load and delete langchain documents](https://python.langchain.com/docs/modules/data_connection/document_loaders/) with `MySQLLoader` and `MySQLDocumentSaver`.\n",
    "\n",
    "Learn more about the package on [GitHub](https://github.com/googleapis/langchain-google-cloud-sql-mysql-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-mysql-python/blob/main/docs/document_loader.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 Cloud SQL Admin API.](https://console.cloud.google.com/marketplace/product/google/sqladmin.googleapis.com)\n",
    "* [Create a Cloud SQL for MySQL instance](https://cloud.google.com/sql/docs/mysql/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/mysql/add-manage-iam-users#creating-a-database-user) (Optional)\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": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# @markdown Please fill in the both the Google Cloud region and name of your Cloud SQL instance.\n",
    "REGION = \"us-central1\"  # @param {type:\"string\"}\n",
    "INSTANCE = \"test-instance\"  # @param {type:\"string\"}\n",
    "\n",
    "# @markdown Please specify a database and a table for demo purpose.\n",
    "DATABASE = \"test\"  # @param {type:\"string\"}\n",
    "TABLE_NAME = \"test-default\"  # @param {type:\"string\"}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 🦜🔗 Library Installation\n",
    "\n",
    "The integration lives in its own `langchain-google-cloud-sql-mysql` package, so we need to install it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%pip install -upgrade --quiet langchain-google-cloud-sql-mysql"
   ]
  },
  {
   "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": [
    "### MySQLEngine Connection Pool\n",
    "\n",
    "Before saving or loading documents from MySQL table, we need first configures a connection pool to Cloud SQL database. The `MySQLEngine` 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 `MySQLEngine` using `MySQLEngine.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",
    "2. `region` : Region where the Cloud SQL instance is located.\n",
    "3. `instance` : The name of the Cloud SQL instance.\n",
    "4. `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/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",
    "Optionally, [built-in database authentication](https://cloud.google.com/sql/docs/mysql/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 `MySQLEngine.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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_google_cloud_sql_mysql import MySQLEngine\n",
    "\n",
    "engine = MySQLEngine.from_instance(\n",
    "    project_id=PROJECT_ID, region=REGION, instance=INSTANCE, database=DATABASE\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialize a table\n",
    "\n",
    "Initialize a table of default schema via `MySQLEngine.init_document_table(<table_name>)`. Table Columns:\n",
    "\n",
    "- page_content (type: text)\n",
    "- langchain_metadata (type: JSON)\n",
    "\n",
    "`overwrite_existing=True` flag means the newly initialized table will replace any existing table of the same name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine.init_document_table(TABLE_NAME, overwrite_existing=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save documents\n",
    "\n",
    "Save langchain documents with `MySQLDocumentSaver.add_documents(<documents>)`. To initialize `MySQLDocumentSaver` class you need to provide 2 things:\n",
    "\n",
    "1. `engine` - An instance of a `MySQLEngine` engine.\n",
    "2. `table_name` - The name of the table within the Cloud SQL database to store langchain documents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain_core.documents import Document\n",
    "from langchain_google_cloud_sql_mysql import MySQLDocumentSaver\n",
    "\n",
    "test_docs = [\n",
    "    Document(\n",
    "        page_content=\"Apple Granny Smith 150 0.99 1\",\n",
    "        metadata={\"fruit_id\": 1},\n",
    "    ),\n",
    "    Document(\n",
    "        page_content=\"Banana Cavendish 200 0.59 0\",\n",
    "        metadata={\"fruit_id\": 2},\n",
    "    ),\n",
    "    Document(\n",
    "        page_content=\"Orange Navel 80 1.29 1\",\n",
    "        metadata={\"fruit_id\": 3},\n",
    "    ),\n",
    "]\n",
    "saver = MySQLDocumentSaver(engine=engine, table_name=TABLE_NAME)\n",
    "saver.add_documents(test_docs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load documents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load langchain documents with `MySQLLoader.load()` or `MySQLLoader.lazy_load()`. `lazy_load` returns a generator that only queries database during the iteration. To initialize `MySQLLoader` class you need to provide:\n",
    "\n",
    "1. `engine` - An instance of a `MySQLEngine` engine.\n",
    "2. `table_name` - The name of the table within the Cloud SQL database to store langchain documents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_google_cloud_sql_mysql import MySQLLoader\n",
    "\n",
    "loader = MySQLLoader(engine=engine, table_name=TABLE_NAME)\n",
    "docs = loader.lazy_load()\n",
    "for doc in docs:\n",
    "    print(\"Loaded documents:\", doc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load documents via query"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Other than loading documents from a table, we can also choose to load documents from a view generated from a SQL query. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_google_cloud_sql_mysql import MySQLLoader\n",
    "\n",
    "loader = MySQLLoader(\n",
    "    engine=engine,\n",
    "    query=f\"select * from `{TABLE_NAME}` where JSON_EXTRACT(langchain_metadata, '$.fruit_id') = 1;\",\n",
    ")\n",
    "onedoc = loader.load()\n",
    "onedoc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The view generated from SQL query can have different schema than default table. In such cases, the behavior of MySQLLoader is the same as loading from table with non-default schema. Please refer to section [Load documents with customized document page content & metadata](#Load-documents-with-customized-document-page-content-&-metadata)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Delete documents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Delete a list of langchain documents from MySQL table with `MySQLDocumentSaver.delete(<documents>)`.\n",
    "\n",
    "For table with default schema (page_content, langchain_metadata), the deletion criteria is:\n",
    "\n",
    "A `row` should be deleted if there exists a `document` in the list, such that\n",
    "\n",
    "- `document.page_content` equals `row[page_content]`\n",
    "- `document.metadata` equals `row[langchain_metadata]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_google_cloud_sql_mysql import MySQLLoader\n",
    "\n",
    "loader = MySQLLoader(engine=engine, table_name=TABLE_NAME)\n",
    "docs = loader.load()\n",
    "print(\"Documents before delete:\", docs)\n",
    "saver.delete(onedoc)\n",
    "print(\"Documents after delete:\", loader.load())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Advanced Usage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load documents with customized document page content & metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we prepare an example table with non-default schema, and populate it with some arbitary data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlalchemy\n",
    "\n",
    "with engine.connect() as conn:\n",
    "    conn.execute(sqlalchemy.text(f\"DROP TABLE IF EXISTS `{TABLE_NAME}`\"))\n",
    "    conn.commit()\n",
    "    conn.execute(\n",
    "        sqlalchemy.text(\n",
    "            f\"\"\"\n",
    "            CREATE TABLE IF NOT EXISTS `{TABLE_NAME}`(\n",
    "                fruit_id INT AUTO_INCREMENT PRIMARY KEY,\n",
    "                fruit_name VARCHAR(100) NOT NULL,\n",
    "                variety VARCHAR(50),\n",
    "                quantity_in_stock INT NOT NULL,\n",
    "                price_per_unit DECIMAL(6,2) NOT NULL,\n",
    "                organic TINYINT(1) NOT NULL\n",
    "            )\n",
    "            \"\"\"\n",
    "        )\n",
    "    )\n",
    "    conn.execute(\n",
    "        sqlalchemy.text(\n",
    "            f\"\"\"\n",
    "            INSERT INTO `{TABLE_NAME}` (fruit_name, variety, quantity_in_stock, price_per_unit, organic)\n",
    "            VALUES\n",
    "                ('Apple', 'Granny Smith', 150, 0.99, 1),\n",
    "                ('Banana', 'Cavendish', 200, 0.59, 0),\n",
    "                ('Orange', 'Navel', 80, 1.29, 1);\n",
    "            \"\"\"\n",
    "        )\n",
    "    )\n",
    "    conn.commit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we still load langchain documents with default parameters of `MySQLLoader` from this example table, the `page_content` of loaded documents will be the first column of the table, and `metadata` will be consisting of key-value pairs of all the other columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "loader = MySQLLoader(\n",
    "    engine=engine,\n",
    "    table_name=TABLE_NAME,\n",
    ")\n",
    "loader.load()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can specify the content and metadata we want to load by setting the `content_columns` and `metadata_columns` when initializing the `MySQLLoader`.\n",
    "\n",
    "1. `content_columns`: The columns to write into the `page_content` of the document.\n",
    "2. `metadata_columns`: The columns to write into the `metadata` of the document.\n",
    "\n",
    "For example here, the values of columns in `content_columns` will be joined together into a space-separated string, as `page_content` of loaded documents, and `metadata` of loaded documents will only contain key-value pairs of columns specified in `metadata_columns`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "loader = MySQLLoader(\n",
    "    engine=engine,\n",
    "    table_name=TABLE_NAME,\n",
    "    content_columns=[\n",
    "        \"variety\",\n",
    "        \"quantity_in_stock\",\n",
    "        \"price_per_unit\",\n",
    "        \"organic\",\n",
    "    ],\n",
    "    metadata_columns=[\"fruit_id\", \"fruit_name\"],\n",
    ")\n",
    "loader.load()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save document with customized page content & metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to save langchain document into table with customized metadata fields. We need first create such a table via `MySQLEngine.init_document_table()`, and specify the list of `metadata_columns` we want it to have. In this example, the created table will have table columns:\n",
    "\n",
    "- description (type: text): for storing fruit description.\n",
    "- fruit_name (type text): for storing fruit name.\n",
    "- organic (type tinyint(1)): to tell if the fruit is organic.\n",
    "- other_metadata (type: JSON): for storing other metadata information of the fruit.\n",
    "\n",
    "We can use the following parameters with `MySQLEngine.init_document_table()` to create the table:\n",
    "\n",
    "1. `table_name`: The name of the table within the Cloud SQL database to store langchain documents.\n",
    "2. `metadata_columns`: A list of `sqlalchemy.Column` indicating the list of metadata columns we need.\n",
    "3. `content_column`: The name of column to store `page_content` of langchain document. Default: `page_content`.\n",
    "4. `metadata_json_column`: The name of JSON column to store extra `metadata` of langchain document. Default: `langchain_metadata`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine.init_document_table(\n",
    "    TABLE_NAME,\n",
    "    metadata_columns=[\n",
    "        sqlalchemy.Column(\n",
    "            \"fruit_name\",\n",
    "            sqlalchemy.UnicodeText,\n",
    "            primary_key=False,\n",
    "            nullable=True,\n",
    "        ),\n",
    "        sqlalchemy.Column(\n",
    "            \"organic\",\n",
    "            sqlalchemy.Boolean,\n",
    "            primary_key=False,\n",
    "            nullable=True,\n",
    "        ),\n",
    "    ],\n",
    "    content_column=\"description\",\n",
    "    metadata_json_column=\"other_metadata\",\n",
    "    overwrite_existing=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save documents with `MySQLDocumentSaver.add_documents(<documents>)`. As you can see in this example, \n",
    "\n",
    "- `document.page_content` will be saved into `description` column.\n",
    "- `document.metadata.fruit_name` will be saved into `fruit_name` column.\n",
    "- `document.metadata.organic` will be saved into `organic` column.\n",
    "- `document.metadata.fruit_id` will be saved into `other_metadata` column in JSON format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_docs = [\n",
    "    Document(\n",
    "        page_content=\"Granny Smith 150 0.99\",\n",
    "        metadata={\"fruit_id\": 1, \"fruit_name\": \"Apple\", \"organic\": 1},\n",
    "    ),\n",
    "]\n",
    "saver = MySQLDocumentSaver(\n",
    "    engine=engine,\n",
    "    table_name=TABLE_NAME,\n",
    "    content_column=\"description\",\n",
    "    metadata_json_column=\"other_metadata\",\n",
    ")\n",
    "saver.add_documents(test_docs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with engine.connect() as conn:\n",
    "    result = conn.execute(sqlalchemy.text(f\"select * from `{TABLE_NAME}`;\"))\n",
    "    print(result.keys())\n",
    "    print(result.fetchall())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Delete documents with customized page content & metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also delete documents from table with customized metadata columns via `MySQLDocumentSaver.delete(<documents>)`. The deletion criteria is:\n",
    "\n",
    "A `row` should be deleted if there exists a `document` in the list, such that\n",
    "\n",
    "- `document.page_content` equals `row[page_content]`\n",
    "- For every metadata field `k` in `document.metadata`\n",
    "    - `document.metadata[k]` equals `row[k]` or `document.metadata[k]` equals `row[langchain_metadata][k]`\n",
    "- There no extra metadata field presents in `row` but not in `document.metadata`.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "loader = MySQLLoader(engine=engine, table_name=TABLE_NAME)\n",
    "docs = loader.load()\n",
    "print(\"Documents before delete:\", docs)\n",
    "saver.delete(docs)\n",
    "print(\"Documents after delete:\", loader.load())"
   ]
  }
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