Semantic_Search_Vector_db.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mekhiya/vector-database-ai-apps/blob/main/Semantic_Search_Vector_db.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WkPAvZQZGHiQ",
        "outputId": "4505b81b-b391-44a6-ca37-979fe0bd5a24"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting python-dotenv\n",
            "  Downloading python_dotenv-1.0.1-py3-none-any.whl (19 kB)\n",
            "Installing collected packages: python-dotenv\n",
            "Successfully installed python-dotenv-1.0.1\n"
          ]
        }
      ],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "!pip install python-dotenv"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile requirements.txt\n",
        "# requirements file\n",
        "# note which revision of python, for example 3.9.6\n",
        "# in this file, insert all the pip install needs, include revision\n",
        "\n",
        "#for example:\n",
        "#torch==2.0.1\n",
        "#matplotlib==3.7.2\n",
        "\n",
        "python-dotenv==1.0.0\n",
        "\n",
        "numpy==1.25.2\n",
        "pandas==2.1.3\n",
        "scikit-learn==1.3.2\n",
        "sentence-transformers==2.2.2\n",
        "matplotlib==3.8.2\n",
        "torch==2.1.1\n",
        "\n",
        "langchain==0.0.346\n",
        "openai==0.28.1 ## From the notebooks\n",
        "\n",
        "pinecone-client==3.0.0dev4\n",
        "pinecone-datasets==0.5.0rc11\n",
        "pinecone-text==0.7.1\n",
        "\n",
        "tiktoken==0.5.2\n",
        "tqdm==4.66.1\n",
        "\n",
        "datasets==2.15.0\n",
        "deepface==0.0.79"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DvMJbTkDJzgp",
        "outputId": "3f1b5987-e253-47e5-93ca-d05de0fd625e"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing requirements.txt\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install -r requirements.txt"
      ],
      "metadata": {
        "id": "n11aF6VyIbzR"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile DLAIUtils.py\n",
        "import os\n",
        "import sys\n",
        "from dotenv import load_dotenv, find_dotenv\n",
        "\n",
        "class Utils:\n",
        "  def __init__(self):\n",
        "    pass\n",
        "\n",
        "  def create_dlai_index_name(self, index_name):\n",
        "    openai_key = ''\n",
        "    if self.is_colab(): # google colab\n",
        "      from google.colab import userdata\n",
        "      openai_key = userdata.get(\"OPENAI_API_KEY\")\n",
        "    else: # jupyter notebook\n",
        "      openai_key = os.getenv(\"OPENAI_API_KEY\")\n",
        "    return f'{index_name}-{openai_key[-36:].lower().replace(\"_\", \"-\")}'\n",
        "\n",
        "  def is_colab(self):\n",
        "    return 'google.colab' in sys.modules\n",
        "\n",
        "  def get_openai_api_key(self):\n",
        "    _ = load_dotenv(find_dotenv())\n",
        "    return os.getenv(\"OPENAI_API_KEY\")\n",
        "\n",
        "  def get_pinecone_api_key(self):\n",
        "    _ = load_dotenv(find_dotenv())\n",
        "    return os.getenv(\"PINECONE_API_KEY\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lS-kl5Ui9E0S",
        "outputId": "8e32f877-cc7b-4cae-8e42-8f18920c0b3c"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing DLAIUtils.py\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python -m pip install python-dotenv\n",
        "!python DLAIUtils.py"
      ],
      "metadata": {
        "id": "qRMkJLWPVJSw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5d5e28f0-9e1d-4488-96c4-45344777642c"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: python-dotenv in /usr/local/lib/python3.10/dist-packages (1.0.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "add .env file with keys for\n",
        "\n",
        "OPENAI_API_KEY = ''\n",
        "PINECONE_API_KEY = ''\n",
        "'''"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "bwh2UjUwWLcm",
        "outputId": "0a5f193b-9acf-44c3-cfb0-c823a67fdec2"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"\\nadd .env file with keys for\\n\\nOPENAI_API_KEY = ''\\nPINECONE_API_KEY = ''\\n\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install datasets\n",
        "# !pip install -U sentence-transformers\n",
        "# !pip install pinecone-client"
      ],
      "metadata": {
        "id": "J6XYmD2NBkd2"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "from sentence_transformers import SentenceTransformer\n",
        "from pinecone import Pinecone, ServerlessSpec\n",
        "from DLAIUtils import Utils\n",
        "import DLAIUtils\n",
        "\n",
        "import os\n",
        "import time\n",
        "import torch"
      ],
      "metadata": {
        "id": "PCL8YSshMi6r"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm.auto import tqdm"
      ],
      "metadata": {
        "id": "Li15-IgRGZKA"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = load_dataset('quora', split='train[240000:290000]')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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        "id": "Agy_cCvHGZGy",
        "outputId": "0bdf0e20-a45c-4d94-d72c-3766cc3071f1"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/35.9M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "73b1482a99914172b2267403485cdcc8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/404290 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cb17a1c327934fbe973d1f0575700fc7"
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[:1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ibjycbIlGZDx",
        "outputId": "f8a0164c-1044-4951-8085-722d80c07444"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'questions': [{'id': [207550, 351729],\n",
              "   'text': ['What is the truth of life?', \"What's the evil truth of life?\"]}],\n",
              " 'is_duplicate': [False]}"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "questions = []\n",
        "for record in dataset['questions']:\n",
        "  questions.extend(record['text'])\n",
        "question = list(set(questions))\n",
        "print('\\n'.join(questions[:10]))\n",
        "print('-'*50)\n",
        "print(f'Number of questions: {len(questions)}')"
      ],
      "metadata": {
        "id": "X7Qo0H2bGZA1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd27ce13-a8c0-4719-ed3f-0f43a0da0aa8"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "What is the truth of life?\n",
            "What's the evil truth of life?\n",
            "Which is the best smartphone under 20K in India?\n",
            "Which is the best smartphone with in 20k in India?\n",
            "Steps taken by Canadian government to improve literacy rate?\n",
            "Can I send homemade herbal hair oil from India to US via postal or private courier services?\n",
            "What is a good way to lose 30 pounds in 2 months?\n",
            "What can I do to lose 30 pounds in 2 months?\n",
            "Which of the following most accurately describes the translation of the graph y = (x+3)^2 -2 to the graph of y = (x -2)^2 +2?\n",
            "How do you graph x + 2y = -2?\n",
            "--------------------------------------------------\n",
            "Number of questions: 100000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "model = SentenceTransformer('all-MiniLM-L6-v2')"
      ],
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        "id": "zqiq2NI4GY96",
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        "outputId": "163af980-e6ec-49f0-b4fc-0f1c18a8297a"
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      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
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            "text/plain": [
              "modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query = 'which city is the costliest in the world?'\n",
        "xq = model.encode(query)\n",
        "xq.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Js648xJPQZ7t",
        "outputId": "babbcd4a-e86e-4b63-e42f-94c7e25e9663"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(384,)"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "utils = Utils()\n",
        "PINCECODE_API_KEY = utils.get_pinecone_api_key()\n",
        "OPENAI_API_KEY = utils.get_openai_api_key()"
      ],
      "metadata": {
        "id": "ILFOSGbuQZ4G"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(PINCECODE_API_KEY)\n",
        "print(OPENAI_API_KEY)\n",
        "OPENAI_API_KEY[-36:].lower().replace(\"_\", \"-\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "E7sabOzyQZ1s",
        "outputId": "d42ac6e2-75dc-4f0f-da46-7da55b3f7cc0"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ee8a2b59-62cb-4d6f-a8f2-26870d88a0f5\n",
            "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\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'4moiewzrt3blbkfjjsgo0r70hk3mut42mfj4'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "INDEX_NAME = 'dl-ai' + OPENAI_API_KEY[-36:].lower().replace(\"_\", \"-\")\n",
        "INDEX_NAME"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "1O9RRlybejDF",
        "outputId": "01a20140-66f5-497b-c31d-e05b9f8b52db"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'dl-ai4moiewzrt3blbkfjjsgo0r70hk3mut42mfj4'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pinecone = Pinecone(api_key=PINCECODE_API_KEY)\n",
        "#INDEX_NAME = utils.create_dlai_index_name('dl-ai')\n",
        "\n",
        "if INDEX_NAME in [index.name for index in pinecone.list_indexes()]:\n",
        "  pinecone.delete_index(INDEX_NAME)\n",
        "  print(f'deleting index : {INDEX_NAME}')\n",
        "print(INDEX_NAME)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pvre6eoffIVY",
        "outputId": "4995d4b1-1440-4040-fcd2-bf4a760aaf25"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "deleting index : dl-ai4moiewzrt3blbkfjjsgo0r70hk3mut42mfj4\n",
            "dl-ai4moiewzrt3blbkfjjsgo0r70hk3mut42mfj4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pinecone.create_index(name=INDEX_NAME,\n",
        "                      dimension=model.get_sentence_embedding_dimension(),\n",
        "                      metric='cosine',\n",
        "                      spec=ServerlessSpec(cloud='aws', region='us-west-2'))\n",
        "\n",
        "index = pinecone.Index(INDEX_NAME)\n",
        "print(index)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lHgp8wVfQZtl",
        "outputId": "e5a847dd-b53c-494d-db60-03383f49124f"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<pinecone.data.index.Index object at 0x7e5a21310af0>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# i = 0\n",
        "# i_end = 200\n",
        "# ids = [str(x) for x in range(i, i_end)]\n",
        "# print(f'ids is {ids}')\n",
        "# s = [str(n) for n in range(0,10)]\n",
        "# print(s)"
      ],
      "metadata": {
        "id": "47C6G0BhIXmw"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Creating embeddings & upsert pinecone\n",
        "batch_size = 200\n",
        "vector_limit = 10000\n",
        "print(f'len(questions) is {len(questions)}')\n",
        "print(f'batch_size is {batch_size}')\n",
        "questions = questions[:vector_limit]\n",
        "# len(questions)\n",
        "\n",
        "for i in tqdm(range(0,len(questions), batch_size)):\n",
        "  i_end = min(i+batch_size, len(questions))\n",
        "  ids = [str(x) for x in range(i, i_end)]\n",
        "  metadatas = [{'text' : text } for text in questions[i:i_end]]\n",
        "  xc = model.encode(questions[i:i_end])\n",
        "\n",
        "  # print(f'len(questions) is {len(questions)}')\n",
        "  # print(f'i is {i}')\n",
        "  # print(f'i_end is {i_end}')\n",
        "  # print(f'ids is {ids}')\n",
        "  # print(f'metadatas is {metadatas}')\n",
        "  # print(f'xc.shape is {xc.shape}')\n",
        "  ## print(f'xc[1,:] is {xc[1,:]}')\n",
        "  ##break\n",
        "\n",
        "  records = zip(ids, xc, metadatas)\n",
        "  index.upsert(records)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
            "f548d92a820149a2b576451d3eabb8f5",
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            "0a7f0f4110c04998b22ee6c8c74cb438",
            "325b275450ce4010aa806ed7880dee9a",
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            "4e6d9719a549493581730dc117d8a43b",
            "9200a5c9831a40e1b3a397d4e79ebd28",
            "dc3ae7de1d044630bbe81019b5e38f75"
          ]
        },
        "id": "kUwP9TPkQZqu",
        "outputId": "e24c7945-b250-4f73-8e78-b6efd4a23091"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "len(questions) is 100000\n",
            "batch_size is 200\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/50 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f548d92a820149a2b576451d3eabb8f5"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Run Query\n",
        "def run_query(query):\n",
        "  embedding = model.encode(query).tolist()\n",
        "  results = index.query(top_k=10, vector=embedding, include_metadata=True, include_value=False)\n",
        "  for result in results['matches']:\n",
        "    print(f\"{round(result['score'],2)} : {result['metadata']['text']}\")\n"
      ],
      "metadata": {
        "id": "QXvAWvu9-hJH"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "query = 'what breakfast should I have after run?'\n",
        "run_query(query)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bATKgSg5-hFg",
        "outputId": "43b2f40a-7721-419a-dbbe-cfccb21dd1d2"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.59 : What are the breakfast items that can be cooked in 5 minutes?\n",
            "0.55 : How soon should I eat after a workout?\n",
            "0.54 : What are some vegetarian breakfast items which could be prepared in 5 minutes?\n",
            "0.51 : What is the best food to eat on an empty stomach?\n",
            "0.47 : What should i eat after workout for reducing body fat and gaining muscle ?\n",
            "0.46 : What is the first you do in the morning?\n",
            "0.43 : Do I have to eat salad?\n",
            "0.43 : What should a single guy do for the healthy meals?\n",
            "0.41 : What is the correct time to take green tea for weight loss?\n",
            "0.41 : Which is the best time to workout, morning or evening?\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query = 'which city is coldest?'\n",
        "run_query(query)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SEF7hp3M-g5T",
        "outputId": "7763385e-75af-4fc8-c969-f76ac5936f70"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.76 : Which is the coldest country in the world?\n",
            "0.71 : What are the coldest countries in the world?\n",
            "0.63 : How cold is Scotland?\n",
            "0.6 : Where is the most beautiful city in the world?\n",
            "0.58 : Which is the most beautiful city in world?\n",
            "0.52 : How hot does Austin, TX get?\n",
            "0.51 : Which is the best city to reside in India?\n",
            "0.48 : Where is the best place to travel?\n",
            "0.48 : What is the weather like in port Townsend, WA compared to Tacoma?\n",
            "0.48 : Why do most people hate winters?\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4RK2ymla-g2E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "d0AirN_Y-gzQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KbhKzsp2-gwW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HhVAtlr0-gti"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AL80ycp1-gqc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "78BQHJjp-gnX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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      "metadata": {
        "id": "dVg3Fchc-gkZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Fg3-WQwb-ghQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pqRHkMfR-gVA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3avGkzU6-f5q"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}