{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Iugu\n",
    "\n",
    ">[Iugu](https://www.iugu.com/) is a Brazilian services and software as a service (SaaS) company. It offers payment-processing software and application programming interfaces for e-commerce websites and mobile applications.\n",
    "\n",
    "This notebook covers how to load data from the `Iugu REST API` into a format that can be ingested into LangChain, along with example usage for vectorization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "\n",
    "from langchain.document_loaders import IuguLoader\n",
    "from langchain.indexes import VectorstoreIndexCreator"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Iugu API requires an access token, which can be found inside of the Iugu dashboard.\n",
    "\n",
    "This document loader also requires a `resource` option which defines what data you want to load.\n",
    "\n",
    "Following resources are available:\n",
    "\n",
    "`Documentation` [Documentation](https://dev.iugu.com/reference/metadados)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iugu_loader = IuguLoader(\"charges\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a vectorstore retriever from the loader\n",
    "# see https://python.langchain.com/en/latest/modules/data_connection/getting_started.html for more details\n",
    "\n",
    "index = VectorstoreIndexCreator().from_loaders([iugu_loader])\n",
    "iugu_doc_retriever = index.vectorstore.as_retriever()"
   ]
  }
 ],
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