docs/document_loader.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E_RJy7C1bpCT"
      },
      "source": [
        "# Google Cloud SQL for PostgreSQL\n",
        "\n",
        "> [Cloud SQL for PostgreSQL](https://cloud.google.com/sql/docs/postgres) is a fully-managed database service that helps you set up, maintain, manage, and administer your PostgreSQL relational databases on Google Cloud Platform. Extend your database application to build AI-powered experiences leveraging Cloud SQL for PostgreSQL's Langchain integrations.\n",
        "\n",
        "This notebook goes over how to use `Cloud SQL for PostgreSQL` to load Documents with the `PostgresLoader` 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/document_loader.ipynb)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xjcxaw6--Xyy"
      },
      "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 for PostgreSQL database.](https://cloud.google.com/sql/docs/postgres/create-manage-databases)\n",
        " * [Add a User to the database.](https://cloud.google.com/sql/docs/postgres/create-manage-users)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IR54BmgvdHT_"
      },
      "source": [
        "### 🦜🔗 Library Installation\n",
        "Install the integration library, `langchain_google_cloud_sql_pg`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "0ZITIDE160OD",
        "outputId": "90e0636e-ff34-4e1e-ad37-d2a6db4a317e"
      },
      "outputs": [],
      "source": [
        "%pip install --upgrade --quiet  langchain_google_cloud_sql_pg"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v40bB_GMcr9f"
      },
      "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": {
        "id": "6o0iGVIdDD6K"
      },
      "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": {
        "id": "cTXTbj4UltKf"
      },
      "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": {
        "id": "Uj02bMRAc9_c"
      },
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wnp1R1PYc9_c",
        "outputId": "6502c721-a2fd-451f-b946-9f7b850d5966"
      },
      "outputs": [],
      "source": [
        "# @title Project { display-mode: \"form\" }\n",
        "PROJECT_ID = \"gcp_project_id\"  # @param {type:\"string\"}\n",
        "\n",
        "# Set the project id\n",
        "! gcloud config set project {PROJECT_ID}"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f8f2830ee9ca1e01",
      "metadata": {
        "id": "f8f2830ee9ca1e01"
      },
      "source": [
        "## Create PostgresLoader"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "OMvzMWRrR6n7",
      "metadata": {
        "id": "OMvzMWRrR6n7"
      },
      "source": [
        "### Set Cloud SQL database values\n",
        "Find your database variables, in the [Cloud SQL Instances page](https://console.cloud.google.com/sql/instances)."
      ]
    },
    {
      "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-primary\"  # @param {type: \"string\"}\n",
        "DATABASE = \"my-database\"  # @param {type: \"string\"}\n",
        "TABLE_NAME = \"vector_store\"  # @param {type: \"string\"}"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "QuQigs4UoFQ2",
      "metadata": {
        "id": "QuQigs4UoFQ2"
      },
      "source": [
        "### Cloud SQL Engine Connection Pool\n",
        "\n",
        "One of the requirements and arguments to establish PostgreSQL as a document loader is a `PostgresEngine` object. The `PostgresEngine`  configures a connection pool to your Cloud SQL for PostgreSQL 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) 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 environment.\n",
        "\n",
        "Optionally, [built-in database authentication](https://cloud.google.com/sql/docs/postgres/users) 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",
        "\n",
        "\n",
        "To connect to your Cloud SQL instance from this notebook, you will need to enable public IP on your instance. Alternatively, you can follow [these instructions](https://cloud.google.com/sql/docs/postgres/connect-to-instance-from-outside-vpc) to connect to an Cloud SQL for PostgreSQL instance with Private IP from outside your VPC. Learn more about [specifying IP types](https://github.com/GoogleCloudPlatform/cloud-sql-python-connector?tab=readme-ov-file#specifying-ip-address-type)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Note**: This tutorial demonstrates the async interface. All async methods have corresponding sync methods."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from langchain_google_cloud_sql_pg import PostgresEngine\n",
        "\n",
        "engine = await PostgresEngine.afrom_instance(\n",
        "    project_id=PROJECT_ID,\n",
        "    region=REGION,\n",
        "    instance=INSTANCE,\n",
        "    database=DATABASE,\n",
        "    ip_type=\"public\",\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e1tl0aNx7SWy"
      },
      "source": [
        "### Create PostgresLoader"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "When creating an `PostgresLoader` for fetching data from Cloud SQL PG, you have two main options to specify the data you want to load:\n",
        "* using the table_name argument - When you specify the table_name argument, you're telling the loader to fetch all the data from the given table.\n",
        "* using the query argument - When you specify the query argument, you can provide a custom SQL query to fetch the data. This allows you to have full control over the SQL query, including selecting specific columns, applying filters, sorting, joining tables, etc.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Load Documents using the `table_name` argument"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PeOMpftjc9_e"
      },
      "source": [
        "#### Load Documents via default table\n",
        "The loader returns a list of Documents from the table using the first column as page_content and all other columns as metadata. The default table will have the first column as\n",
        "page_content and the second column as metadata (JSON). Each row becomes a document. \n",
        "\n",
        "Please note that if you want your documents to have ids you will need to add them in.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cwvi_O5Wc9_e"
      },
      "outputs": [],
      "source": [
        "from langchain_google_cloud_sql_pg import PostgresLoader\n",
        "\n",
        "# Creating a basic PostgresLoader object\n",
        "loader = await PostgresLoader.create(\n",
        "    engine,\n",
        "    table_name=TABLE_NAME,\n",
        "    # schema_name=SCHEMA_NAME,\n",
        ")\n",
        "\n",
        "docs = await loader.aload()\n",
        "print(docs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kSkL9l1Hc9_e"
      },
      "source": [
        "#### Load documents via custom table/metadata or custom page content columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "loader = await PostgresLoader.create(\n",
        "    engine,\n",
        "    table_name=TABLE_NAME,\n",
        "    # schema_name=SCHEMA_NAME,\n",
        "    content_columns=[\"product_name\"],  # Optional\n",
        "    metadata_columns=[\"id\"],  # Optional\n",
        ")\n",
        "docs = await loader.aload()\n",
        "print(docs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Load Documents using a SQL query\n",
        "The query parameter allows users to specify a custom SQL query which can include filters to load specific documents from a database."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "table_name = \"products\"\n",
        "content_columns = [\"product_name\", \"description\"]\n",
        "metadata_columns = [\"id\", \"content\"]\n",
        "\n",
        "loader = PostgresLoader.create(\n",
        "    engine=engine,\n",
        "    query=f\"SELECT * FROM {table_name};\",\n",
        "    content_columns=content_columns,\n",
        "    metadata_columns=metadata_columns,\n",
        ")\n",
        "\n",
        "docs = await loader.aload()\n",
        "print(docs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Note**: If the `content_columns` and `metadata_columns` are not specified, the loader will automatically treat the first returned column as the document’s `page_content` and all subsequent columns as `metadata`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5R6h0_Cvc9_f"
      },
      "source": [
        "### Set page content format\n",
        "The loader returns a list of Documents, with one document per row, with page content in specified string format, i.e. text (space separated concatenation), JSON, YAML, CSV, etc. JSON and YAML formats include headers, while text and CSV do not include field headers.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NGNdS7cqc9_f"
      },
      "outputs": [],
      "source": [
        "loader = await PostgresLoader.create(\n",
        "    engine,\n",
        "    table_name=TABLE_NAME,\n",
        "    # schema_name=SCHEMA_NAME,\n",
        "    content_columns=[\"product_name\", \"description\"],\n",
        "    format=\"YAML\",\n",
        ")\n",
        "docs = await loader.aload()\n",
        "print(docs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create PostgresSaver\n",
        "The `PostgresSaver` allows for saving of pre-processed documents to the table using the first column as page_content and all other columns as metadata. This table can easily be loaded via a Document Loader or updated to be a VectorStore. The default table will have the first column as page_content and the second column as metadata (JSON)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create a table (if not already exists)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from langchain_google_cloud_sql_pg import Column\n",
        "\n",
        "await engine.ainit_document_table(\n",
        "    table_name=TABLE_NAME,\n",
        "    content_column=\"product_name\",\n",
        "    metadata_columns=[\n",
        "        Column(\"id\", \"SERIAL\", nullable=False),\n",
        "        Column(\"content\", \"VARCHAR\", nullable=False),\n",
        "        Column(\"description\", \"VARCHAR\", nullable=False),\n",
        "    ],\n",
        "    metadata_json_column=\"metadata\",\n",
        "    store_metadata=True,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Optional Tip: 💡\n",
        "You can also specify a schema name by passing `schema_name` wherever you pass `table_name`. Eg:\n",
        "\n",
        "```python\n",
        "SCHEMA_NAME=\"my_schema\"\n",
        "\n",
        "await engine.ainit_document_table(\n",
        "    table_name=TABLE_NAME,\n",
        "    schema_name=SCHEMA_NAME     # Default: \"public\"\n",
        "    \n",
        "    ...\n",
        ")\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create PostgresSaver"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from langchain_google_cloud_sql_pg import PostgresDocumentSaver\n",
        "\n",
        "# Creating a basic PostgresDocumentSaver object\n",
        "saver = await PostgresDocumentSaver.create(\n",
        "    engine,\n",
        "    table_name=TABLE_NAME,\n",
        "    # schema_name=SCHEMA_NAME,\n",
        "    content_column=\"product_name\",\n",
        "    metadata_columns=[\"description\", \"content\"],\n",
        "    metadata_json_column=\"metadata\",\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Save Documents to default table\n",
        "Each document becomes a row in the table."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from langchain_core.documents import Document\n",
        "\n",
        "test_docs = [\n",
        "    Document(\n",
        "        page_content=\"Red Apple\",\n",
        "        metadata={\"description\": \"red\", \"content\": \"1\", \"category\": \"fruit\"},\n",
        "    ),\n",
        "    Document(\n",
        "        page_content=\"Banana Cavendish\",\n",
        "        metadata={\"description\": \"yellow\", \"content\": \"2\", \"category\": \"fruit\"},\n",
        "    ),\n",
        "    Document(\n",
        "        page_content=\"Orange Navel\",\n",
        "        metadata={\"description\": \"orange\", \"content\": \"3\", \"category\": \"fruit\"},\n",
        "    ),\n",
        "]\n",
        "await saver.aadd_documents(test_docs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Load the documents with PostgresLoader\n",
        "PostgresLoader can be used with `TABLE_NAME` (and optionally `SCHEMA_NAME`) to query and load the whole table."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "loader = await PostgresLoader.create(\n",
        "    engine,\n",
        "    table_name=TABLE_NAME,\n",
        "    # schema_name=SCHEMA_NAME,\n",
        ")\n",
        "docs = await loader.aload()\n",
        "\n",
        "print(docs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Delete Documents from default table\n",
        "The saver deletes a list of Documents, one document at a time internally."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "await saver.adelete(test_docs)"
      ]
    }
  ],
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