run-llama--llama_index/docs/examples/vector_stores/AwadbDemo.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "68da6d2b",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/vector_stores/AwadbDemo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "307804a3-c02b-4a57-ac0d-172c30ddc851",
   "metadata": {},
   "source": [
    "# Awadb Vector Store"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "295deb84",
   "metadata": {},
   "source": [
    "If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65d6e7e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install llama-index-embeddings-huggingface\n",
    "%pip install llama-index-vector-stores-awadb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f9014eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install llama-index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4334feda",
   "metadata": {},
   "source": [
    "## Creating an Awadb index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1b5e530",
   "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": "markdown",
   "id": "8ee4473a-094f-4d0a-a825-e1213db07240",
   "metadata": {},
   "source": [
    "#### Load documents, build the 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 (\n",
    "    SimpleDirectoryReader,\n",
    "    VectorStoreIndex,\n",
    "    StorageContext,\n",
    ")\n",
    "from IPython.display import Markdown, display\n",
    "import openai\n",
    "\n",
    "openai.api_key = \"\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "bfa5ac36",
   "metadata": {},
   "source": [
    "#### Download Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ae97358",
   "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": "f5060ac6",
   "metadata": {},
   "source": [
    "#### Load Data"
   ]
  },
  {
   "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": [
    "from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n",
    "from llama_index.vector_stores.awadb import AwaDBVectorStore\n",
    "\n",
    "embed_model = HuggingFaceEmbedding(model_name=\"BAAI/bge-small-en-v1.5\")\n",
    "\n",
    "vector_store = AwaDBVectorStore()\n",
    "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
    "\n",
    "index = VectorStoreIndex.from_documents(\n",
    "    documents, storage_context=storage_context, embed_model=embed_model\n",
    ")"
   ]
  },
  {
   "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>\n",
       "Growing up, the author wrote short stories, experimented with programming on an IBM 1401, nagged his father to buy a TRS-80 computer, wrote simple games, a program to predict how high his model rockets would fly, and a word processor. He also studied philosophy in college, switched to AI, and worked on building the infrastructure of the web. He wrote essays and published them online, had dinners for a group of friends every Thursday night, painted, and bought a building in Cambridge.</b>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Markdown(f\"<b>{response}</b>\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99212d33",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set Logging to DEBUG for more detailed outputs\n",
    "query_engine = index.as_query_engine()\n",
    "response = query_engine.query(\n",
    "    \"What did the author do after his time at Y Combinator?\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a720ad6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<b>\n",
       "After his time at Y Combinator, the author wrote essays, worked on Lisp, and painted. He also visited his mother in Oregon and helped her get out of a nursing home.</b>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Markdown(f\"<b>{response}</b>\"))"
   ]
  }
 ],
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