search.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/avii09/semantic-search-engine/blob/main/search.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U sentence-transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2UnZ6pRhS24U",
        "outputId": "d2d55b23-d900-475c-af10-d1eba128c1c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: sentence-transformers in /usr/local/lib/python3.10/dist-packages (3.0.1)\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install streamlit\n",
        "!npm install -g localtunnel"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LgQwC38BMOB4",
        "outputId": "a97452dc-1181-4bb7-bb05-5813c566d92a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: watchdog, smmap, pydeck, gitdb, gitpython, streamlit\n",
            "Successfully installed gitdb-4.0.11 gitpython-3.1.43 pydeck-0.9.1 smmap-5.0.1 streamlit-1.36.0 watchdog-4.0.1\n",
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            "added 22 packages from 22 contributors in 2.184s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile app.py\n",
        "import streamlit as st\n",
        "import json\n",
        "from sentence_transformers import SentenceTransformer, CrossEncoder, util\n",
        "import time\n",
        "import gzip\n",
        "import os\n",
        "import torch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VVybh-sKlbVS",
        "outputId": "537a7a95-575b-4ab2-e1c1-8cf8fa748098"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overwriting app.py\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_name ='nq-distilbert-base-v1'\n",
        "model = SentenceTransformer(model_name)\n",
        "#this is the model used for generating vectors (encoding)(biencoder)"
      ],
      "metadata": {
        "id": "2CDjY-cBUNaJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "top_k = 5\n",
        "#the no. of passages we want"
      ],
      "metadata": {
        "id": "-8RMuYvwUNm9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text = r\"/content/simplewiki-2020-11-01.jsonl\"\n",
        "# this is the corpus data which will be put into the biencoder to generate the corpus embeddings.\n",
        "\n",
        "\n",
        "#here we are splitting the dataset as paragraphs and encode them.\n",
        "corpus = []\n",
        "with open(text, \"rt\", encoding=\"utf8\") as fIn:\n",
        "    for line in fIn:\n",
        "        data = json.loads(line.strip())\n",
        "        for paragraph in data[\"paragraphs\"]:\n",
        "            corpus.append([data[\"title\"], paragraph])     #we split them as [title, paragraphs]\n",
        "\n",
        "print(\"corpus:\", len(corpus))\n",
        "\n",
        "\n",
        "#here we are generating the embeddings for the split corpus dataset. This is our corpus dataset.\n",
        "if model_name == 'nq-distilbert-base-v1':\n",
        "    embeddings_filepath = 'simplewiki-2020-11-01-nq-distilbert-base-v1.pt'\n",
        "    if not os.path.exists(embeddings_filepath):\n",
        "        util.http_get('http://sbert.net/datasets/simplewiki-2020-11-01-nq-distilbert-base-v1.pt', embeddings_filepath)\n",
        "\n",
        "    corpus_embeddings = torch.load(embeddings_filepath, map_location=torch.device('cpu'))\n",
        "    corpus_embeddings = corpus_embeddings.float()  # Convert embedding file to float\n",
        "    if torch.cuda.is_available():\n",
        "        corpus_embeddings = corpus_embeddings.to('cuda')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "du7eoJ7VbV4N",
        "outputId": "cef63d84-e0b9-4bf9-c814-6c51a4a1a80d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "corpus: 509663\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile app.py\n",
        "#now we ask for the query and embed the query.\n",
        "def search(query):\n",
        "    # Encode the query using the bi-encoder and find potentially relevant passages\n",
        "    start_time = time.time()\n",
        "    query_embedding = model.encode(query, convert_to_tensor=True)\n",
        "    hits = util.semantic_search(query_embedding, corpus_embeddings, top_k=top_k)\n",
        "    hits = hits[0]  # Get the hits for the first query\n",
        "    end_time = time.time()\n",
        "\n",
        "    sorted_hits = sorted(hits, key=lambda x: x['score'])\n",
        "\n",
        "    # Output of top-k hits\n",
        "    results = []\n",
        "    for hit in sorted_hits:\n",
        "      title, paragraph = corpus[hit['corpus_id']]\n",
        "      results.append((title, paragraph))\n",
        "    return results\n",
        "    print(\"Results (after {:.3f} seconds):\".format(end_time - start_time))\n",
        "\n",
        "\n",
        "\n",
        "import streamlit as st\n",
        "st.title('Semantic Search Engine')\n",
        "query = st.text_input('Enter your query:')\n",
        "if query:\n",
        "    results = search(query)\n",
        "    st.write(f\"Input question: {query}\")\n",
        "    st.write(\"Results:\")\n",
        "    for title, paragraph in results:\n",
        "        st.write(f\"**Title:** {title}\")\n",
        "        st.write(f\"**Paragraph:** {paragraph}\")\n",
        "        st.write(\"\\n\")\n",
        "\n",
        "# Streamlit UI"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LeqpiuKUbV-V",
        "outputId": "ab89980d-49ca-4b74-cb8e-66465a3ce919"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overwriting app.py\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile app.py\n",
        "import streamlit as st\n",
        "st.title('Semantic Search Engine')\n",
        "query = st.text_input('Enter your query:')\n",
        "if query:\n",
        "    results = search(query)\n",
        "    st.write(f\"Input question: {query}\")\n",
        "    st.write(\"Results:\")\n",
        "    for title, paragraph in results:\n",
        "        st.write(f\"**Title:** {title}\")\n",
        "        st.write(f\"**Paragraph:** {paragraph}\")\n",
        "        st.write(\"\\n\")\n",
        "\n",
        "# Streamlit UI\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T37iyxhBNaBM",
        "outputId": "330a523c-4c04-4c87-89a6-9cbc3c034c19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overwriting app.py\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!streamlit run app.py & npx localtunnel --port 8501"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bvixkZzqNjwx",
        "outputId": "77c27ae0-e25b-4b70-ca26-30f80c1cba25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Collecting usage statistics. To deactivate, set browser.gatherUsageStats to false.\n",
            "\u001b[0m\n",
            "\u001b[0m\n",
            "\u001b[34m\u001b[1m  You can now view your Streamlit app in your browser.\u001b[0m\n",
            "\u001b[0m\n",
            "\u001b[34m  Local URL: \u001b[0m\u001b[1mhttp://localhost:8501\u001b[0m\n",
            "\u001b[34m  Network URL: \u001b[0m\u001b[1mhttp://172.28.0.12:8501\u001b[0m\n",
            "\u001b[34m  External URL: \u001b[0m\u001b[1mhttp://34.125.96.151:8501\u001b[0m\n",
            "\u001b[0m\n",
            "\u001b[K\u001b[?25hnpx: installed 22 in 2.793s\n",
            "your url is: https://calm-files-go.loca.lt\n",
            "2024-07-03 11:34:03.633 Uncaught app exception\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/streamlit/runtime/scriptrunner/script_runner.py\", line 589, in _run_script\n",
            "    exec(code, module.__dict__)\n",
            "  File \"/content/app.py\", line 26, in <module>\n",
            "    results = search(query)\n",
            "  File \"/content/app.py\", line 4, in search\n",
            "    start_time = time.time()\n",
            "NameError: name 'time' is not defined\n",
            "\u001b[34m  Stopping...\u001b[0m\n",
            "^C\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import urllib\n",
        "print(\"Password/Enpoint IP for localtunnel is:\",urllib.request.urlopen('https://ipv4.icanhazip.com').read().decode('utf8').strip(\"\\n\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nAfQ0IHARGK5",
        "outputId": "63f3407b-a468-4d68-e816-9e1df3e04df4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Password/Enpoint IP for localtunnel is: 34.80.79.204\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "search(query = \"What is the capital of the France?\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_p3T6zx-bWAb",
        "outputId": "2ecbe117-3113-40f1-ee3c-df81d3841d3b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Input question: What is the capital of the France?\n",
            "Results (after 4.335 seconds):\n",
            "\t0.745\t['Arrondissement of Confolens', 'The arrondissement of Confolens is an arrondissement of France, in the Charente department, Nouvelle-Aquitaine region. Its capital is the city of Confolens.']\n",
            "\t0.746\t[\"Arrondissement of Saint-Jean-d'Angély\", \"The arrondissement of Saint-Jean-d'Angély is an arrondissement of France, in the Charente-Maritime department, Nouvelle-Aquitaine region. Its capital is the city of Saint-Jean-d'Angély.\"]\n",
            "\t0.752\t['Arrondissement of Figeac', 'The arrondissement of Figeac is an arrondissement of France. It is part of the Lot \"département\" in the Occitanie region. Its capital is the city of Figeac.']\n",
            "\t0.753\t['Arrondissement of Sarlat-la-Canéda', 'The arrondissement of Sarlat-la-Canéda is an arrondissement of France. It is part of the Dordogne \"département\" in the Nouvelle-Aquitaine region. Its capital is the city of Sarlat-la-Canéda.']\n",
            "\t0.826\t['Capital of France', 'The capital of France is Paris. In the course of history, the national capital has been in many locations other than Paris.']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "search(query=\"who turned out to be the mother on how i met your mother\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OcP_IbnBbWDz",
        "outputId": "ab661958-fa88-4ac1-d2be-6433637e6b36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Input question: who turned out to be the mother on how i met your mother\n",
            "Results (after 1.946 seconds):\n",
            "\t0.513\t['Josh Radnor', 'Joshua Thomas Radnor (born July 29, 1974) is an American actor, director, producer, and screenwriter. He is best known for playing Ted Mosby on the CBS sitcom \"How I Met Your Mother\".']\n",
            "\t0.583\t['Jason Segel', 'Jason Jordan Segel (born January 18, 1980) is an American movie, television, and voice actor who is known for his role as Marshall Eriksen in \"How I Met Your Mother\". He has also appeared in \"The Muppets\" (2011). He was born in Los Angeles, California. He was partners with actress Michelle Williams from 2012 through early 2013.']\n",
            "\t0.597\t['How I Met Your Mother', 'The story is set in New York City. It is about a man named Ted Mosby who tells his son and daughter how he met their mother. Ted is played by Josh Radnor and the story is narrated by Bob Saget who plays Ted Mosby in the future year 2030. Other characters and friends of Ted are Marshall Eriksen (Jason Segel), Robin Scherbatsky (Cobie Smulders), Barney Stinson (Neil Patrick Harris), and Lily Aldrin (Alyson Hannigan). \"How I Met Your Mother\" is also said to be the inheritor of \"Friends\" -- another famous sitcom from 1994 to 2004.']\n",
            "\t0.674\t['How I Met Your Mother', \"A retelling of Ted Mosby's (Josh Radnor) life before he met his wife (Cristin Milioti), whom is telling his kids in the year 2030, hence the title, 'How I Met Your Mother'. The story focuses on Ted's everyday life and relationships between his four best friends, Marshall Eriksen (Jason Segel), Lily Auldren (Alyson Hannigan), Barney Stinson (Neil Patrick Harris) and Robin Scherbatsky (Cobie Smulders). The show is mainly a comedy, along with heart-warming scenes and tearful moments. The show was loosely based on the lives of the show's producers; Carter Bays and Craig Thomas.\"]\n",
            "\t0.730\t['Cristin Milioti', 'Cristin Milioti (born August 16, 1985) is an American actress and singer. She is best known for playing the Mother on the sitcom \"How I Met Your Mother\" from 2013 to 2014. She has also played Teresa Petrillo Belfort in the 2013 movie \"The Wolf of Wall Street\", and Betsy Solverson in the second season of \"Fargo\" (2015).']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import urllib\n",
        "print(\"Password/Enpoint IP for localtunnel is:\",urllib.request.urlopen('https://ipv4.icanhazip.com').read().decode('utf8').strip(\"\\n\"))"
      ],
      "metadata": {
        "id": "QlfHfzWxLrc2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "625335d7-e280-4306-bb1a-98d001d4cf09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Password/Enpoint IP for localtunnel is: 34.80.79.204\n"
          ]
        }
      ]
    }
  ]
}