run-llama--llama_index/docs/examples/docstore/FirestoreDemo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Firestore Demo\n",
    "\n",
    "This guide shows you how to directly use our `DocumentStore` abstraction backed by Google Firestore. By putting nodes in the docstore, this allows you to define multiple indices over the same underlying docstore, instead of duplicating data across indices.\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/docstore/FirestoreDemo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install llama-index-storage-docstore-firestore\n",
    "%pip install llama-index-storage-kvstore-firestore\n",
    "%pip install llama-index-storage-index-store-firestore\n",
    "%pip install llama-index-llms-openai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install llama-index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nest_asyncio\n",
    "\n",
    "nest_asyncio.apply()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import sys\n",
    "\n",
    "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
    "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.core import SimpleDirectoryReader, StorageContext\n",
    "from llama_index.core import VectorStoreIndex, SimpleKeywordTableIndex\n",
    "from llama_index.core import SummaryIndex\n",
    "from llama_index.core import ComposableGraph\n",
    "from llama_index.llms.openai import OpenAI\n",
    "from llama_index.core.response.notebook_utils import display_response\n",
    "from llama_index.core import Settings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Download Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!mkdir -p 'data/paul_graham/'\n",
    "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load Documents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "reader = SimpleDirectoryReader(\"./data/paul_graham/\")\n",
    "documents = reader.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Parse into Nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.core.node_parser import SentenceSplitter\n",
    "\n",
    "nodes = SentenceSplitter().get_nodes_from_documents(documents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Add to Docstore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.storage.kvstore.firestore import FirestoreKVStore\n",
    "from llama_index.storage.docstore.firestore import FirestoreDocumentStore\n",
    "from llama_index.storage.index_store.firestore import FirestoreIndexStore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "kvstore = FirestoreKVStore()\n",
    "\n",
    "storage_context = StorageContext.from_defaults(\n",
    "    docstore=FirestoreDocumentStore(kvstore),\n",
    "    index_store=FirestoreIndexStore(kvstore),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "storage_context.docstore.add_documents(nodes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define Multiple Indexes\n",
    "\n",
    "Each index uses the same underlying Node."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "summary_index = SummaryIndex(nodes, storage_context=storage_context)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vector_index = VectorStoreIndex(nodes, storage_context=storage_context)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "keyword_table_index = SimpleKeywordTableIndex(\n",
    "    nodes, storage_context=storage_context\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NOTE: the docstore still has the same nodes\n",
    "len(storage_context.docstore.docs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test out saving and loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NOTE: docstore and index_store is persisted in Firestore by default\n",
    "# NOTE: here only need to persist simple vector store to disk\n",
    "storage_context.persist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# note down index IDs\n",
    "list_id = summary_index.index_id\n",
    "vector_id = vector_index.index_id\n",
    "keyword_id = keyword_table_index.index_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.core import load_index_from_storage\n",
    "\n",
    "kvstore = FirestoreKVStore()\n",
    "\n",
    "# re-create storage context\n",
    "storage_context = StorageContext.from_defaults(\n",
    "    docstore=FirestoreDocumentStore(kvstore),\n",
    "    index_store=FirestoreIndexStore(kvstore),\n",
    ")\n",
    "\n",
    "# load indices\n",
    "summary_index = load_index_from_storage(\n",
    "    storage_context=storage_context, index_id=list_id\n",
    ")\n",
    "vector_index = load_index_from_storage(\n",
    "    storage_context=storage_context, vector_id=vector_id\n",
    ")\n",
    "keyword_table_index = load_index_from_storage(\n",
    "    storage_context=storage_context, keyword_id=keyword_id\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test out some Queries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chatgpt = OpenAI(temperature=0, model=\"gpt-3.5-turbo\")\n",
    "Settings.llm = chatgpt\n",
    "Settings.chunk_size = 1024"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "query_engine = summary_index.as_query_engine()\n",
    "list_response = query_engine.query(\"What is a summary of this document?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "display_response(list_response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "query_engine = vector_index.as_query_engine()\n",
    "vector_response = query_engine.query(\"What did the author do growing up?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "display_response(vector_response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "query_engine = keyword_table_index.as_query_engine()\n",
    "keyword_response = query_engine.query(\n",
    "    \"What did the author do after his time at YC?\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "display_response(keyword_response)"
   ]
  }
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