langchain/docs/modules/indexes/document_loaders/examples/stripe.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Stripe\n",
"\n",
"This notebook covers how to load data from the Stripe REST API into a format that can be ingested into LangChain, along with example usage for vectorization."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"\n",
"from langchain.document_loaders import StripeLoader\n",
"from langchain.indexes import VectorstoreIndexCreator"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Stripe API requires an access token, which can be found inside of the Stripe 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",
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"`balance_transations` [Documentation](https://stripe.com/docs/api/balance_transactions/list)\n",
"\n",
"`charges` [Documentation](https://stripe.com/docs/api/charges/list)\n",
"\n",
"`customers` [Documentation](https://stripe.com/docs/api/customers/list)\n",
"\n",
"`events` [Documentation](https://stripe.com/docs/api/events/list)\n",
"\n",
"`refunds` [Documentation](https://stripe.com/docs/api/refunds/list)\n",
"\n",
"`disputes` [Documentation](https://stripe.com/docs/api/disputes/list)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"stripe_loader = StripeLoader(os.environ[\"STRIPE_ACCESS_TOKEN\"], \"charges\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Create a vectorstore retriver from the loader\n",
"# see https://python.langchain.com/en/latest/modules/indexes/getting_started.html for more details\n",
"\n",
"index = VectorstoreIndexCreator().from_loaders([stripe_loader])\n",
"stripe_doc_retriever = index.vectorstore.as_retriever()"
]
}
],
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