{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "600e8429",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/vector_stores/DashvectorIndexDemo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "307804a3-c02b-4a57-ac0d-172c30ddc851",
   "metadata": {},
   "source": [
    "# DashVector Vector Store"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "47bbdd33",
   "metadata": {},
   "source": [
    "If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3bd0b321",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install llama-index-vector-stores-dashvector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f6b3761",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install llama-index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d48af8e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import sys\n",
    "import os\n",
    "\n",
    "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
    "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f7010b1d-d1bb-4f08-9309-a328bb4ea396",
   "metadata": {},
   "source": [
    "#### Creating a DashVector Collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ce3143d-198c-4dd2-8e5a-c5cdf94f017a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import dashvector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ad14111-0bbb-4c62-906d-6d6253e0cdee",
   "metadata": {},
   "outputs": [],
   "source": [
    "api_key = os.environ[\"DASHVECTOR_API_KEY\"]\n",
    "client = dashvector.Client(api_key=api_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2c90087-bdd9-4ca4-b06b-2af883559f88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{\"code\": 0, \"message\": \"\", \"requests_id\": \"82b969d2-2568-4e18-b0dc-aa159b503c84\"}"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dimensions are for text-embedding-ada-002\n",
    "client.create(\"llama-demo\", dimension=1536)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "667f3cb3-ce18-48d5-b9aa-bfc1a1f0f0f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "dashvector_collection = client.get(\"quickstart\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4b00ea0d",
   "metadata": {},
   "source": [
    "#### Download Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d21ebdc",
   "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'"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8ee4473a-094f-4d0a-a825-e1213db07240",
   "metadata": {},
   "source": [
    "#### Load documents, build the DashVectorStore and VectorStoreIndex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a2bcc07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:numexpr.utils:Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
      "Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
      "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n",
      "NumExpr defaulting to 8 threads.\n"
     ]
    }
   ],
   "source": [
    "from llama_index.core import VectorStoreIndex, SimpleDirectoryReader\n",
    "from llama_index.vector_stores.dashvector import DashVectorStore\n",
    "from IPython.display import Markdown, display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68cbd239-880e-41a3-98d8-dbb3fab55431",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load documents\n",
    "documents = SimpleDirectoryReader(\"./data/paul_graham\").load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba1558b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize without metadata filter\n",
    "from llama_index.core import StorageContext\n",
    "\n",
    "vector_store = DashVectorStore(dashvector_collection)\n",
    "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
    "index = VectorStoreIndex.from_documents(\n",
    "    documents, storage_context=storage_context\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "04304299-fc3e-40a0-8600-f50c3292767e",
   "metadata": {},
   "source": [
    "#### Query Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35369eda",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set Logging to DEBUG for more detailed outputs\n",
    "query_engine = index.as_query_engine()\n",
    "response = query_engine.query(\"What did the author do growing up?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bedbb693-725f-478f-be26-fa7180ea38b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<b>The author worked on writing and programming outside of school. They wrote short stories and tried writing programs on the IBM 1401 computer. They also built a microcomputer and started programming on it, writing simple games and a word processor.</b>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Markdown(f\"<b>{response}</b>\"))"
   ]
  }
 ],
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