llamaindex_nomic_embedding.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Jalam1001/rag-/blob/main/llamaindex_nomic_embedding.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uLgrvUbmCaWa"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/docs/examples/embeddings/nomic.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jR3T8tq-CaWc"
      },
      "source": [
        "# Nomic Embedding\n",
        "\n",
        "Nomic has released v1.5 🪆🪆🪆 is capable of variable sized embeddings with matryoshka learning and an 8192 context, embedding dimensions between 64 and 768.\n",
        "\n",
        "In this notebook, we will explore using Nomic v1.5 embedding at different dimensions."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dAKcQToYCaWc"
      },
      "source": [
        "### Installation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "lAk4VNzxCaWd",
        "outputId": "df87406b-7df2-4ca0-99dc-3df0ef81c02f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch<3.0.0,>=2.1.2->llama-index-embeddings-huggingface<0.2.0,>=0.1.3->llama-index-embeddings-nomic)\n",
            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
            "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch<3.0.0,>=2.1.2->llama-index-embeddings-huggingface<0.2.0,>=0.1.3->llama-index-embeddings-nomic)\n",
            "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
            "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch<3.0.0,>=2.1.2->llama-index-embeddings-huggingface<0.2.0,>=0.1.3->llama-index-embeddings-nomic)\n",
            "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch<3.0.0,>=2.1.2->llama-index-embeddings-huggingface<0.2.0,>=0.1.3->llama-index-embeddings-nomic)\n",
            "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
            "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch<3.0.0,>=2.1.2->llama-index-embeddings-huggingface<0.2.0,>=0.1.3->llama-index-embeddings-nomic)\n",
            "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
            "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch<3.0.0,>=2.1.2->llama-index-embeddings-huggingface<0.2.0,>=0.1.3->llama-index-embeddings-nomic)\n",
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            "Collecting nvidia-curand-cu12==10.3.2.106 (from torch<3.0.0,>=2.1.2->llama-index-embeddings-huggingface<0.2.0,>=0.1.3->llama-index-embeddings-nomic)\n",
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            "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch<3.0.0,>=2.1.2->llama-index-embeddings-huggingface<0.2.0,>=0.1.3->llama-index-embeddings-nomic)\n",
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            "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch<3.0.0,>=2.1.2->llama-index-embeddings-huggingface<0.2.0,>=0.1.3->llama-index-embeddings-nomic)\n",
            "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
            "Collecting nvidia-nccl-cu12==2.20.5 (from torch<3.0.0,>=2.1.2->llama-index-embeddings-huggingface<0.2.0,>=0.1.3->llama-index-embeddings-nomic)\n",
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            "Collecting nvidia-nvtx-cu12==12.1.105 (from torch<3.0.0,>=2.1.2->llama-index-embeddings-huggingface<0.2.0,>=0.1.3->llama-index-embeddings-nomic)\n",
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            "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch<3.0.0,>=2.1.2->llama-index-embeddings-huggingface<0.2.0,>=0.1.3->llama-index-embeddings-nomic)\n",
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            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich->nomic<4.0.0,>=3.0.29->llama-index-embeddings-nomic) (3.0.0)\n",
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            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio->httpx->llama-index-core<0.11.0,>=0.10.38->llama-index) (1.2.1)\n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->llama-index-core<0.11.0,>=0.10.38->llama-index) (1.16.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch<3.0.0,>=2.1.2->llama-index-embeddings-huggingface<0.2.0,>=0.1.3->llama-index-embeddings-nomic) (2.1.5)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch<3.0.0,>=2.1.2->llama-index-embeddings-huggingface<0.2.0,>=0.1.3->llama-index-embeddings-nomic) (1.3.0)\n",
            "Building wheels for collected packages: nomic\n",
            "  Building wheel for nomic (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for nomic: filename=nomic-3.0.29-py3-none-any.whl size=44793 sha256=ee0d57bf41f8163687de50a52b0d4b88b0efbc922c31b17e60fa7775d69b63e2\n",
            "  Stored in directory: /root/.cache/pip/wheels/3e/f4/11/cb0b148c56b412a1a3a08cf0c97aa87099d7147965ae44797f\n",
            "Successfully built nomic\n",
            "Installing collected packages: striprtf, dirtyjson, pypdf, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, mypy-extensions, minijinja, marshmallow, loguru, jsonlines, h11, deprecated, typing-inspect, tiktoken, nvidia-cusparse-cu12, nvidia-cudnn-cu12, httpcore, nvidia-cusolver-cu12, nomic, httpx, dataclasses-json, openai, llamaindex-py-client, llama-index-legacy, llama-index-core, llama-parse, llama-index-readers-file, llama-index-llms-openai, llama-index-indices-managed-llama-cloud, llama-index-embeddings-openai, llama-index-embeddings-huggingface, llama-index-readers-llama-parse, llama-index-multi-modal-llms-openai, llama-index-embeddings-nomic, llama-index-cli, llama-index-agent-openai, llama-index-program-openai, llama-index-question-gen-openai, llama-index\n",
            "Successfully installed dataclasses-json-0.6.6 deprecated-1.2.14 dirtyjson-1.0.8 h11-0.14.0 httpcore-1.0.5 httpx-0.27.0 jsonlines-4.0.0 llama-index-0.10.38 llama-index-agent-openai-0.2.5 llama-index-cli-0.1.12 llama-index-core-0.10.38.post2 llama-index-embeddings-huggingface-0.1.5 llama-index-embeddings-nomic-0.2.0 llama-index-embeddings-openai-0.1.10 llama-index-indices-managed-llama-cloud-0.1.6 llama-index-legacy-0.9.48 llama-index-llms-openai-0.1.20 llama-index-multi-modal-llms-openai-0.1.6 llama-index-program-openai-0.1.6 llama-index-question-gen-openai-0.1.3 llama-index-readers-file-0.1.23 llama-index-readers-llama-parse-0.1.4 llama-parse-0.4.3 llamaindex-py-client-0.1.19 loguru-0.7.2 marshmallow-3.21.2 minijinja-2.0.1 mypy-extensions-1.0.0 nomic-3.0.29 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.40 nvidia-nvtx-cu12-12.1.105 openai-1.30.2 pypdf-4.2.0 striprtf-0.0.26 tiktoken-0.7.0 typing-inspect-0.9.0\n"
          ]
        }
      ],
      "source": [
        "%pip install -U llama-index llama-index-embeddings-nomic"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9eXLyNNBCaWd"
      },
      "source": [
        "### Setup API Keys"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "bVWDq8P1CaWd"
      },
      "outputs": [],
      "source": [
        "from google.colab import userdata\n",
        "nomic_api_key = userdata.get('NOMIC_API_KEY')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "03803z_aCaWe"
      },
      "outputs": [],
      "source": [
        "import nest_asyncio\n",
        "\n",
        "nest_asyncio.apply()\n",
        "\n",
        "from llama_index.embeddings.nomic import NomicEmbedding"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zTy9OQcRCaWe"
      },
      "source": [
        "#### With dimension at 128"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "yEtouvnoCaWe"
      },
      "outputs": [],
      "source": [
        "embed_model = NomicEmbedding(\n",
        "    api_key=nomic_api_key,\n",
        "    dimensionality=128,\n",
        "    model_name=\"nomic-embed-text-v1.5\",\n",
        ")\n",
        "\n",
        "embedding = embed_model.get_text_embedding(\"Nomic Embeddings\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "LloYg5QXCaWe",
        "outputId": "1f2cc26b-4936-40e9-b344-6f2646ffcd9c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "128\n"
          ]
        }
      ],
      "source": [
        "print(len(embedding))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "FaEkv1xoCaWf",
        "outputId": "2004ada1-41cb-426f-b8ce-119e5cddcdd8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.05569458, 0.057922363, -0.30126953, -0.09832764, 0.05947876]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "embedding[:5]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gmgS3ZKnCaWf"
      },
      "source": [
        "#### With dimension at 256"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "5a5ynwOdCaWf"
      },
      "outputs": [],
      "source": [
        "embed_model = NomicEmbedding(\n",
        "    api_key=nomic_api_key,\n",
        "    dimensionality=256,\n",
        "    model_name=\"nomic-embed-text-v1.5\",\n",
        ")\n",
        "\n",
        "embedding = embed_model.get_text_embedding(\"Nomic Embeddings\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "lRtfS_F8CaWf",
        "outputId": "b714fb77-1208-4b14-cbeb-370f2631bff4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "256\n"
          ]
        }
      ],
      "source": [
        "print(len(embedding))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "l_mmZNtdCaWf",
        "outputId": "b6b43327-54f5-4e86-bf87-172b6ba7b5ca",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.044708252, 0.04650879, -0.24182129, -0.07897949, 0.04776001]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "embedding[:5]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Efmxox4bCaWg"
      },
      "source": [
        "#### With dimension at 768"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "i4qK4bf1CaWg"
      },
      "outputs": [],
      "source": [
        "embed_model = NomicEmbedding(\n",
        "    api_key=nomic_api_key,\n",
        "    dimensionality=768,\n",
        "    model_name=\"nomic-embed-text-v1.5\",\n",
        ")\n",
        "\n",
        "embedding = embed_model.get_text_embedding(\"Nomic Embeddings\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "Mld0XNkcCaWg",
        "outputId": "24476fe1-1dd7-46a9-fb0b-83dd3f176fbf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "768\n"
          ]
        }
      ],
      "source": [
        "print(len(embedding))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "hU1LjAnpCaWg",
        "outputId": "2c2b51e0-fe2e-4114-e720-7f2bf077af14",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.027374268, 0.028427124, -0.14770508, -0.04827881, 0.029144287]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "embedding[:5]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YUpKX_VlCaWg"
      },
      "source": [
        "#### You can still use v1 Nomic Embeddings\n",
        "\n",
        "It has 768 fixed embedding dimensions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "__hByhRZCaWg"
      },
      "outputs": [],
      "source": [
        "embed_model = NomicEmbedding(\n",
        "    api_key=nomic_api_key, model_name=\"nomic-embed-text-v1\"\n",
        ")\n",
        "\n",
        "embedding = embed_model.get_text_embedding(\"Nomic Embeddings\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "cuJxncegCaWg",
        "outputId": "f3a975f3-005d-4ae5-fd9d-2941411994dd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "768\n"
          ]
        }
      ],
      "source": [
        "print(len(embedding))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CFOi8yy6CaWh",
        "outputId": "0d85bbe9-b710-4d4a-d3cb-381a12d54907",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.0059013367, 0.03744507, 0.0035305023, -0.047180176, 0.0154418945]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "embedding[:5]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IQP6mz91CaWh"
      },
      "source": [
        "### Let's Build end to end RAG pipeline with Nomic v1.5 Embedding.\n",
        "\n",
        "We will use OpenAI for Generation step."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FSmRSHoYCaWh"
      },
      "source": [
        "#### Set Embedding model and llm."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "r5AkPSt9CaWh"
      },
      "outputs": [],
      "source": [
        "from llama_index.core import settings\n",
        "from llama_index.core import VectorStoreIndex, SimpleDirectoryReader\n",
        "from llama_index.llms.openai import OpenAI\n",
        "\n",
        "import os\n",
        "\n",
        "os.environ[\"OPENAI_API_KEY\"] = userdata.get(\"OPENAI_API_KEY\")\n",
        "\n",
        "embed_model = NomicEmbedding(\n",
        "    api_key=nomic_api_key,\n",
        "    dimensionality=128,\n",
        "    model_name=\"nomic-embed-text-v1.5\",\n",
        ")\n",
        "\n",
        "llm = OpenAI(model=\"gpt-3.5-turbo\")\n",
        "\n",
        "settings.llm = llm\n",
        "settings.embed_model = embed_model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c7wSz1aTCaWh"
      },
      "source": [
        "#### Download Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "op6b4T5OCaWh",
        "outputId": "3210aa6a-fcfa-4930-881a-a1f832e35cea",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-05-23 20:35:37--  https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 75042 (73K) [text/plain]\n",
            "Saving to: ‘data/paul_graham/paul_graham_essay.txt’\n",
            "\n",
            "\r          data/paul   0%[                    ]       0  --.-KB/s               \rdata/paul_graham/pa 100%[===================>]  73.28K  --.-KB/s    in 0.02s   \n",
            "\n",
            "2024-05-23 20:35:37 (3.47 MB/s) - ‘data/paul_graham/paul_graham_essay.txt’ saved [75042/75042]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!mkdir -p 'data/paul_graham/'\n",
        "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dk-r8JDECaWh"
      },
      "source": [
        "#### Load data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "vqtiKshpCaWi"
      },
      "outputs": [],
      "source": [
        "documents = SimpleDirectoryReader(\"./data/paul_graham\").load_data()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dm0CwUA2CaWi"
      },
      "source": [
        "#### Index creation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "rqYwBN_yCaWi"
      },
      "outputs": [],
      "source": [
        "index = VectorStoreIndex.from_documents(documents)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XTZK3mqMCaWi"
      },
      "source": [
        "#### Query Engine"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "TT6j3aQWCaWi"
      },
      "outputs": [],
      "source": [
        "query_engine = index.as_query_engine()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vev43YdmCaWi",
        "outputId": "b88182fd-0041-41bd-e6ee-d7c50e1188f7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The document discusses the author's experiences and reflections on various aspects of his life, including his time spent writing essays, working at Interleaf, learning valuable lessons, and attending art school. It touches on topics such as choosing what to work on, the evolution of technology companies, the importance of being the \"entry level\" option, and the concept of a signature style in art. The author shares insights gained from his experiences, such as the impact of product-oriented leadership in technology companies, the pitfalls of editing code by multiple individuals, and the significance of living cheaply to save money.\n"
          ]
        }
      ],
      "source": [
        "response = query_engine.query(\"summarize the document?\")\n",
        "print(response)"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "P8_gg0QlHaZC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "outputId": "ab92d105-451a-489c-d681-ab1928efb421",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EUbBOkbMHbCE"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ask a questionsummarize the document\n",
            "The document discusses the author's experiences and reflections on various aspects of his life, including his time spent writing essays, working at Interleaf, learning valuable lessons, and attending art school. It touches on topics such as choosing what to work on, the evolution of computers, the intersection of art and money, the importance of being the \"entry level\" option in technology companies, and the concept of a signature style in art. The author shares insights gained from his experiences, such as the impact of product-oriented leadership in technology companies and the significance of avoiding prestige as a danger sign.\n"
          ]
        }
      ],
      "source": [
        "response = query_engine.query(input(\"ask a question\"))\n",
        "print(response)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import Markdown\n",
        "response_str = str(response)\n",
        "Markdown(response_str)"
      ],
      "metadata": {
        "id": "dzDUD8aIEofU",
        "outputId": "f50e379f-9746-4ab0-f81e-63213c43436e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98
        }
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "The document discusses the author's experiences and reflections on various aspects of his life, including his time spent writing essays, working at Interleaf, learning valuable lessons, and attending art school. It touches on topics such as choosing what to work on, the evolution of computers, the intersection of art and money, the importance of being the \"entry level\" option in technology companies, and the concept of a signature style in art. The author shares insights gained from his experiences, such as the impact of product-oriented leadership in technology companies and the significance of avoiding prestige as a danger sign."
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "llama",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3"
    },
    "vscode": {
      "interpreter": {
        "hash": "b1d2a638b53f4d7129cb7686d8e3b97ae1d80a593a1618479f60cef5591ea888"
      }
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}