{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from llama_index.llms.azure_openai import AzureOpenAI\n",
    "from llama_index.embeddings.azure_openai import AzureOpenAIEmbedding\n",
    "from llama_index.core import SimpleDirectoryReader, Settings, VectorStoreIndex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "api_key = os.getenv(\"AZURE_OPENAI_API_KEY\")\n",
    "api_version = \"2024-05-01-preview\"\n",
    "azure_endpoint = os.getenv(\"AZURE_OPENAI_ENDPOINT\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "llm = AzureOpenAI(\n",
    "    model=\"gpt-4o\",\n",
    "    deployment_name=\"gpt-4o\",\n",
    "    api_key=api_key,\n",
    "    azure_endpoint=azure_endpoint,\n",
    "    api_version=api_version,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You need to deploy your own embedding model as well as your own chat completion model\n",
    "embed_model = AzureOpenAIEmbedding(\n",
    "    model=\"text-embedding-3-small\",\n",
    "    deployment_name=\"text-embedding-3-small\",\n",
    "    api_key=api_key,\n",
    "    azure_endpoint=azure_endpoint,\n",
    "    api_version=api_version,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "Settings.llm = llm\n",
    "Settings.embed_model = embed_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Source -> Documents化を行うStep\n",
    "documents = SimpleDirectoryReader(\n",
    "    input_dir=\"./data/text\"\n",
    ").load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = VectorStoreIndex.from_documents(documents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "index.storage_context.persist(persist_dir=\"../index\")"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
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