final_version_search.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "30a9da7c-5276-4909-95d2-d2a4a7cac8d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: spacy in /opt/homebrew/lib/python3.12/site-packages (3.7.5)\n",
      "Requirement already satisfied: gensim in /opt/homebrew/lib/python3.12/site-packages (4.3.2)\n",
      "Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /opt/homebrew/lib/python3.12/site-packages (from spacy) (3.0.12)\n",
      "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /opt/homebrew/lib/python3.12/site-packages (from spacy) (1.0.5)\n",
      "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /opt/homebrew/lib/python3.12/site-packages (from spacy) (1.0.10)\n",
      "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /opt/homebrew/lib/python3.12/site-packages (from spacy) (2.0.8)\n",
      "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /opt/homebrew/lib/python3.12/site-packages (from spacy) (3.0.9)\n",
      "Requirement already satisfied: thinc<8.3.0,>=8.2.2 in /opt/homebrew/lib/python3.12/site-packages (from spacy) (8.2.5)\n",
      "Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /opt/homebrew/lib/python3.12/site-packages (from spacy) (1.1.3)\n",
      "Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /opt/homebrew/lib/python3.12/site-packages (from spacy) (2.4.8)\n",
      "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /opt/homebrew/lib/python3.12/site-packages (from spacy) (2.0.10)\n",
      "Requirement already satisfied: weasel<0.5.0,>=0.1.0 in /opt/homebrew/lib/python3.12/site-packages (from spacy) (0.4.1)\n",
      "Requirement already satisfied: typer<1.0.0,>=0.3.0 in /opt/homebrew/lib/python3.12/site-packages (from spacy) (0.12.3)\n",
      "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /opt/homebrew/lib/python3.12/site-packages (from spacy) (4.66.4)\n",
      "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /opt/homebrew/lib/python3.12/site-packages (from spacy) (2.31.0)\n",
      "Requirement already satisfied: pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4 in /opt/homebrew/lib/python3.12/site-packages (from spacy) (2.7.4)\n",
      "Requirement already satisfied: jinja2 in /opt/homebrew/lib/python3.12/site-packages (from spacy) (3.1.3)\n",
      "Requirement already satisfied: setuptools in /opt/homebrew/lib/python3.12/site-packages (from spacy) (69.0.3)\n",
      "Requirement already satisfied: packaging>=20.0 in /opt/homebrew/lib/python3.12/site-packages (from spacy) (23.2)\n",
      "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /opt/homebrew/lib/python3.12/site-packages (from spacy) (3.4.0)\n",
      "Requirement already satisfied: numpy>=1.19.0 in /opt/homebrew/lib/python3.12/site-packages (from spacy) (1.26.4)\n",
      "Requirement already satisfied: scipy>=1.7.0 in /opt/homebrew/lib/python3.12/site-packages (from gensim) (1.14.0)\n",
      "Requirement already satisfied: smart-open>=1.8.1 in /opt/homebrew/lib/python3.12/site-packages (from gensim) (7.0.4)\n",
      "Requirement already satisfied: language-data>=1.2 in /opt/homebrew/lib/python3.12/site-packages (from langcodes<4.0.0,>=3.2.0->spacy) (1.2.0)\n",
      "Requirement already satisfied: annotated-types>=0.4.0 in /opt/homebrew/lib/python3.12/site-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy) (0.7.0)\n",
      "Requirement already satisfied: pydantic-core==2.18.4 in /opt/homebrew/lib/python3.12/site-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy) (2.18.4)\n",
      "Requirement already satisfied: typing-extensions>=4.6.1 in /opt/homebrew/lib/python3.12/site-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy) (4.12.2)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/homebrew/lib/python3.12/site-packages (from requests<3.0.0,>=2.13.0->spacy) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/homebrew/lib/python3.12/site-packages (from requests<3.0.0,>=2.13.0->spacy) (3.6)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/homebrew/lib/python3.12/site-packages (from requests<3.0.0,>=2.13.0->spacy) (2.2.0)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/homebrew/lib/python3.12/site-packages (from requests<3.0.0,>=2.13.0->spacy) (2024.2.2)\n",
      "Requirement already satisfied: wrapt in /opt/homebrew/lib/python3.12/site-packages (from smart-open>=1.8.1->gensim) (1.16.0)\n",
      "Requirement already satisfied: blis<0.8.0,>=0.7.8 in /opt/homebrew/lib/python3.12/site-packages (from thinc<8.3.0,>=8.2.2->spacy) (0.7.11)\n",
      "Requirement already satisfied: confection<1.0.0,>=0.0.1 in /opt/homebrew/lib/python3.12/site-packages (from thinc<8.3.0,>=8.2.2->spacy) (0.1.5)\n",
      "Requirement already satisfied: click>=8.0.0 in /opt/homebrew/lib/python3.12/site-packages (from typer<1.0.0,>=0.3.0->spacy) (8.1.7)\n",
      "Requirement already satisfied: shellingham>=1.3.0 in /opt/homebrew/lib/python3.12/site-packages (from typer<1.0.0,>=0.3.0->spacy) (1.5.4)\n",
      "Requirement already satisfied: rich>=10.11.0 in /opt/homebrew/lib/python3.12/site-packages (from typer<1.0.0,>=0.3.0->spacy) (13.7.1)\n",
      "Requirement already satisfied: cloudpathlib<1.0.0,>=0.7.0 in /opt/homebrew/lib/python3.12/site-packages (from weasel<0.5.0,>=0.1.0->spacy) (0.18.1)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /opt/homebrew/lib/python3.12/site-packages (from jinja2->spacy) (2.1.4)\n",
      "Requirement already satisfied: marisa-trie>=0.7.7 in /opt/homebrew/lib/python3.12/site-packages (from language-data>=1.2->langcodes<4.0.0,>=3.2.0->spacy) (1.2.0)\n",
      "Requirement already satisfied: markdown-it-py>=2.2.0 in /opt/homebrew/lib/python3.12/site-packages (from rich>=10.11.0->typer<1.0.0,>=0.3.0->spacy) (3.0.0)\n",
      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /opt/homebrew/lib/python3.12/site-packages (from rich>=10.11.0->typer<1.0.0,>=0.3.0->spacy) (2.17.2)\n",
      "Requirement already satisfied: mdurl~=0.1 in /opt/homebrew/lib/python3.12/site-packages (from markdown-it-py>=2.2.0->rich>=10.11.0->typer<1.0.0,>=0.3.0->spacy) (0.1.2)\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Requirement already satisfied: scipy in /opt/homebrew/lib/python3.12/site-packages (1.14.0)\n",
      "Requirement already satisfied: gensim in /opt/homebrew/lib/python3.12/site-packages (4.3.2)\n",
      "Requirement already satisfied: numpy<2.3,>=1.23.5 in /opt/homebrew/lib/python3.12/site-packages (from scipy) (1.26.4)\n",
      "Requirement already satisfied: smart-open>=1.8.1 in /opt/homebrew/lib/python3.12/site-packages (from gensim) (7.0.4)\n",
      "Requirement already satisfied: wrapt in /opt/homebrew/lib/python3.12/site-packages (from smart-open>=1.8.1->gensim) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Requirement already satisfied: annoy in /opt/homebrew/lib/python3.12/site-packages (1.17.3)\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Collecting qdrant_client\n",
      "  Downloading qdrant_client-1.10.1-py3-none-any.whl.metadata (10 kB)\n",
      "Requirement already satisfied: grpcio>=1.41.0 in /opt/homebrew/lib/python3.12/site-packages (from qdrant_client) (1.64.1)\n",
      "Collecting grpcio-tools>=1.41.0 (from qdrant_client)\n",
      "  Downloading grpcio_tools-1.64.1-cp312-cp312-macosx_10_9_universal2.whl.metadata (5.3 kB)\n",
      "Requirement already satisfied: httpx>=0.20.0 in /opt/homebrew/lib/python3.12/site-packages (from httpx[http2]>=0.20.0->qdrant_client) (0.27.0)\n",
      "Requirement already satisfied: numpy>=1.26 in /opt/homebrew/lib/python3.12/site-packages (from qdrant_client) (1.26.4)\n",
      "Collecting portalocker<3.0.0,>=2.7.0 (from qdrant_client)\n",
      "  Downloading portalocker-2.10.1-py3-none-any.whl.metadata (8.5 kB)\n",
      "Requirement already satisfied: pydantic>=1.10.8 in /opt/homebrew/lib/python3.12/site-packages (from qdrant_client) (2.7.4)\n",
      "Requirement already satisfied: urllib3<3,>=1.26.14 in /opt/homebrew/lib/python3.12/site-packages (from qdrant_client) (2.2.0)\n",
      "Collecting protobuf<6.0dev,>=5.26.1 (from grpcio-tools>=1.41.0->qdrant_client)\n",
      "  Downloading protobuf-5.27.2-cp38-abi3-macosx_10_9_universal2.whl.metadata (592 bytes)\n",
      "Requirement already satisfied: setuptools in /opt/homebrew/lib/python3.12/site-packages (from grpcio-tools>=1.41.0->qdrant_client) (69.0.3)\n",
      "Requirement already satisfied: anyio in /opt/homebrew/lib/python3.12/site-packages (from httpx>=0.20.0->httpx[http2]>=0.20.0->qdrant_client) (4.3.0)\n",
      "Requirement already satisfied: certifi in /opt/homebrew/lib/python3.12/site-packages (from httpx>=0.20.0->httpx[http2]>=0.20.0->qdrant_client) (2024.2.2)\n",
      "Requirement already satisfied: httpcore==1.* in /opt/homebrew/lib/python3.12/site-packages (from httpx>=0.20.0->httpx[http2]>=0.20.0->qdrant_client) (1.0.5)\n",
      "Requirement already satisfied: idna in /opt/homebrew/lib/python3.12/site-packages (from httpx>=0.20.0->httpx[http2]>=0.20.0->qdrant_client) (3.6)\n",
      "Requirement already satisfied: sniffio in /opt/homebrew/lib/python3.12/site-packages (from httpx>=0.20.0->httpx[http2]>=0.20.0->qdrant_client) (1.3.1)\n",
      "Requirement already satisfied: h11<0.15,>=0.13 in /opt/homebrew/lib/python3.12/site-packages (from httpcore==1.*->httpx>=0.20.0->httpx[http2]>=0.20.0->qdrant_client) (0.14.0)\n",
      "Collecting h2<5,>=3 (from httpx[http2]>=0.20.0->qdrant_client)\n",
      "  Downloading h2-4.1.0-py3-none-any.whl.metadata (3.6 kB)\n",
      "Requirement already satisfied: annotated-types>=0.4.0 in /opt/homebrew/lib/python3.12/site-packages (from pydantic>=1.10.8->qdrant_client) (0.7.0)\n",
      "Requirement already satisfied: pydantic-core==2.18.4 in /opt/homebrew/lib/python3.12/site-packages (from pydantic>=1.10.8->qdrant_client) (2.18.4)\n",
      "Requirement already satisfied: typing-extensions>=4.6.1 in /opt/homebrew/lib/python3.12/site-packages (from pydantic>=1.10.8->qdrant_client) (4.12.2)\n",
      "Collecting hyperframe<7,>=6.0 (from h2<5,>=3->httpx[http2]>=0.20.0->qdrant_client)\n",
      "  Downloading hyperframe-6.0.1-py3-none-any.whl.metadata (2.7 kB)\n",
      "Collecting hpack<5,>=4.0 (from h2<5,>=3->httpx[http2]>=0.20.0->qdrant_client)\n",
      "  Downloading hpack-4.0.0-py3-none-any.whl.metadata (2.5 kB)\n",
      "Downloading qdrant_client-1.10.1-py3-none-any.whl (254 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m254.1/254.1 kB\u001b[0m \u001b[31m631.5 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hDownloading grpcio_tools-1.64.1-cp312-cp312-macosx_10_9_universal2.whl (5.3 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.3/5.3 MB\u001b[0m \u001b[31m942.3 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25hDownloading portalocker-2.10.1-py3-none-any.whl (18 kB)\n",
      "Downloading h2-4.1.0-py3-none-any.whl (57 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.5/57.5 kB\u001b[0m \u001b[31m411.0 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hDownloading protobuf-5.27.2-cp38-abi3-macosx_10_9_universal2.whl (412 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m412.2/412.2 kB\u001b[0m \u001b[31m987.4 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hDownloading hpack-4.0.0-py3-none-any.whl (32 kB)\n",
      "Downloading hyperframe-6.0.1-py3-none-any.whl (12 kB)\n",
      "Installing collected packages: protobuf, portalocker, hyperframe, hpack, h2, grpcio-tools, qdrant_client\n",
      "  Attempting uninstall: protobuf\n",
      "    Found existing installation: protobuf 4.25.3\n",
      "    Uninstalling protobuf-4.25.3:\n",
      "      Successfully uninstalled protobuf-4.25.3\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "opentelemetry-proto 1.25.0 requires protobuf<5.0,>=3.19, but you have protobuf 5.27.2 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed grpcio-tools-1.64.1 h2-4.1.0 hpack-4.0.0 hyperframe-6.0.1 portalocker-2.10.1 protobuf-5.27.2 qdrant_client-1.10.1\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Collecting en-core-web-sm==3.7.1\n",
      "  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1-py3-none-any.whl (12.8 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.8/12.8 MB\u001b[0m \u001b[31m24.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: spacy<3.8.0,>=3.7.2 in /opt/homebrew/lib/python3.12/site-packages (from en-core-web-sm==3.7.1) (3.7.5)\n",
      "Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /opt/homebrew/lib/python3.12/site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.0.12)\n",
      "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /opt/homebrew/lib/python3.12/site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.0.5)\n",
      "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /opt/homebrew/lib/python3.12/site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.0.10)\n",
      "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /opt/homebrew/lib/python3.12/site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.0.8)\n",
      "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /opt/homebrew/lib/python3.12/site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.0.9)\n",
      "Requirement already satisfied: thinc<8.3.0,>=8.2.2 in /opt/homebrew/lib/python3.12/site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (8.2.5)\n",
      "Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /opt/homebrew/lib/python3.12/site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.1.3)\n",
      "Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /opt/homebrew/lib/python3.12/site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.4.8)\n",
      "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /opt/homebrew/lib/python3.12/site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.0.10)\n",
      "Requirement already satisfied: weasel<0.5.0,>=0.1.0 in /opt/homebrew/lib/python3.12/site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.4.1)\n",
      "Requirement already satisfied: typer<1.0.0,>=0.3.0 in /opt/homebrew/lib/python3.12/site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.12.3)\n",
      "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /opt/homebrew/lib/python3.12/site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (4.66.4)\n",
      "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /opt/homebrew/lib/python3.12/site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.31.0)\n",
      "Requirement already satisfied: pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4 in /opt/homebrew/lib/python3.12/site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.7.4)\n",
      "Requirement already satisfied: jinja2 in /opt/homebrew/lib/python3.12/site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.1.3)\n",
      "Requirement already satisfied: setuptools in /opt/homebrew/lib/python3.12/site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (69.0.3)\n",
      "Requirement already satisfied: packaging>=20.0 in /opt/homebrew/lib/python3.12/site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (23.2)\n",
      "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /opt/homebrew/lib/python3.12/site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.4.0)\n",
      "Requirement already satisfied: numpy>=1.19.0 in /opt/homebrew/lib/python3.12/site-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.26.4)\n",
      "Requirement already satisfied: language-data>=1.2 in /opt/homebrew/lib/python3.12/site-packages (from langcodes<4.0.0,>=3.2.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.2.0)\n",
      "Requirement already satisfied: annotated-types>=0.4.0 in /opt/homebrew/lib/python3.12/site-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.7.0)\n",
      "Requirement already satisfied: pydantic-core==2.18.4 in /opt/homebrew/lib/python3.12/site-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.18.4)\n",
      "Requirement already satisfied: typing-extensions>=4.6.1 in /opt/homebrew/lib/python3.12/site-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (4.12.2)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/homebrew/lib/python3.12/site-packages (from requests<3.0.0,>=2.13.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/homebrew/lib/python3.12/site-packages (from requests<3.0.0,>=2.13.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.6)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/homebrew/lib/python3.12/site-packages (from requests<3.0.0,>=2.13.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.2.0)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/homebrew/lib/python3.12/site-packages (from requests<3.0.0,>=2.13.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2024.2.2)\n",
      "Requirement already satisfied: blis<0.8.0,>=0.7.8 in /opt/homebrew/lib/python3.12/site-packages (from thinc<8.3.0,>=8.2.2->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.7.11)\n",
      "Requirement already satisfied: confection<1.0.0,>=0.0.1 in /opt/homebrew/lib/python3.12/site-packages (from thinc<8.3.0,>=8.2.2->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.1.5)\n",
      "Requirement already satisfied: click>=8.0.0 in /opt/homebrew/lib/python3.12/site-packages (from typer<1.0.0,>=0.3.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (8.1.7)\n",
      "Requirement already satisfied: shellingham>=1.3.0 in /opt/homebrew/lib/python3.12/site-packages (from typer<1.0.0,>=0.3.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.5.4)\n",
      "Requirement already satisfied: rich>=10.11.0 in /opt/homebrew/lib/python3.12/site-packages (from typer<1.0.0,>=0.3.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (13.7.1)\n",
      "Requirement already satisfied: cloudpathlib<1.0.0,>=0.7.0 in /opt/homebrew/lib/python3.12/site-packages (from weasel<0.5.0,>=0.1.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.18.1)\n",
      "Requirement already satisfied: smart-open<8.0.0,>=5.2.1 in /opt/homebrew/lib/python3.12/site-packages (from weasel<0.5.0,>=0.1.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (7.0.4)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /opt/homebrew/lib/python3.12/site-packages (from jinja2->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.1.4)\n",
      "Requirement already satisfied: marisa-trie>=0.7.7 in /opt/homebrew/lib/python3.12/site-packages (from language-data>=1.2->langcodes<4.0.0,>=3.2.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.2.0)\n",
      "Requirement already satisfied: markdown-it-py>=2.2.0 in /opt/homebrew/lib/python3.12/site-packages (from rich>=10.11.0->typer<1.0.0,>=0.3.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.0.0)\n",
      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /opt/homebrew/lib/python3.12/site-packages (from rich>=10.11.0->typer<1.0.0,>=0.3.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.17.2)\n",
      "Requirement already satisfied: wrapt in /opt/homebrew/lib/python3.12/site-packages (from smart-open<8.0.0,>=5.2.1->weasel<0.5.0,>=0.1.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.16.0)\n",
      "Requirement already satisfied: mdurl~=0.1 in /opt/homebrew/lib/python3.12/site-packages (from markdown-it-py>=2.2.0->rich>=10.11.0->typer<1.0.0,>=0.3.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.1.2)\n",
      "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
      "You can now load the package via spacy.load('en_core_web_sm')\n"
     ]
    }
   ],
   "source": [
    "%pip install spacy gensim --break-system-packages\n",
    "%pip install --upgrade scipy gensim --break-system-packages\n",
    "%pip install annoy --break-system-packages\n",
    "%pip install qdrant_client --break-system-packages\n",
    "!python3 -m spacy download en_core_web_sm --break-system-packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "67b1a17a-e7d2-4daa-9f32-e0601794ab78",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import spacy\n",
    "import faiss\n",
    "import numpy as np\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "from annoy import AnnoyIndex\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c032eecb-33dc-4708-80e7-b85700bc6d52",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/1d/hb016sss4n7f0rs9qdktfc_00000gn/T/ipykernel_66047/1527728918.py:21: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  sampled_df = df.groupby(['title_length_bin', 'ingredient_count_bin', 'direction_length_bin'], group_keys=False).apply(lambda x: x.sample(frac=50000/len(df)))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Unnamed: 0                  title  \\\n",
      "0           0    No-Bake Nut Cookies   \n",
      "1           1  Jewell Ball'S Chicken   \n",
      "2           2            Creamy Corn   \n",
      "3           3          Chicken Funny   \n",
      "4           4   Reeses Cups(Candy)     \n",
      "\n",
      "                                         ingredients  \\\n",
      "0  [\"1 c. firmly packed brown sugar\", \"1/2 c. eva...   \n",
      "1  [\"1 small jar chipped beef, cut up\", \"4 boned ...   \n",
      "2  [\"2 (16 oz.) pkg. frozen corn\", \"1 (8 oz.) pkg...   \n",
      "3  [\"1 large whole chicken\", \"2 (10 1/2 oz.) cans...   \n",
      "4  [\"1 c. peanut butter\", \"3/4 c. graham cracker ...   \n",
      "\n",
      "                                          directions  \\\n",
      "0  [\"In a heavy 2-quart saucepan, mix brown sugar...   \n",
      "1  [\"Place chipped beef on bottom of baking dish....   \n",
      "2  [\"In a slow cooker, combine all ingredients. C...   \n",
      "3  [\"Boil and debone chicken.\", \"Put bite size pi...   \n",
      "4  [\"Combine first four ingredients and press in ...   \n",
      "\n",
      "                                              link    source  \\\n",
      "0   www.cookbooks.com/Recipe-Details.aspx?id=44874  Gathered   \n",
      "1  www.cookbooks.com/Recipe-Details.aspx?id=699419  Gathered   \n",
      "2   www.cookbooks.com/Recipe-Details.aspx?id=10570  Gathered   \n",
      "3  www.cookbooks.com/Recipe-Details.aspx?id=897570  Gathered   \n",
      "4  www.cookbooks.com/Recipe-Details.aspx?id=659239  Gathered   \n",
      "\n",
      "                                                 NER  title_length  \\\n",
      "0  [\"brown sugar\", \"milk\", \"vanilla\", \"nuts\", \"bu...            19   \n",
      "1  [\"beef\", \"chicken breasts\", \"cream of mushroom...            21   \n",
      "2  [\"frozen corn\", \"cream cheese\", \"butter\", \"gar...            11   \n",
      "3  [\"chicken\", \"chicken gravy\", \"cream of mushroo...            13   \n",
      "4  [\"peanut butter\", \"graham cracker crumbs\", \"bu...            20   \n",
      "\n",
      "   ingredient_count  direction_length title_length_bin ingredient_count_bin  \\\n",
      "0                 6               357     (17.0, 19.0]           (5.0, 6.0]   \n",
      "1                 5               175     (19.0, 21.0]         (0.999, 5.0]   \n",
      "2                 8               171   (-0.001, 12.0]           (7.0, 8.0]   \n",
      "3                 5               394     (12.0, 15.0]         (0.999, 5.0]   \n",
      "4                 5               229     (19.0, 21.0]         (0.999, 5.0]   \n",
      "\n",
      "  direction_length_bin  \n",
      "0       (304.0, 371.0]  \n",
      "1       (134.0, 194.0]  \n",
      "2       (134.0, 194.0]  \n",
      "3       (371.0, 454.0]  \n",
      "4       (194.0, 248.0]  \n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2231142 entries, 0 to 2231141\n",
      "Data columns (total 13 columns):\n",
      " #   Column                Dtype   \n",
      "---  ------                -----   \n",
      " 0   Unnamed: 0            int64   \n",
      " 1   title                 object  \n",
      " 2   ingredients           object  \n",
      " 3   directions            object  \n",
      " 4   link                  object  \n",
      " 5   source                object  \n",
      " 6   NER                   object  \n",
      " 7   title_length          int64   \n",
      " 8   ingredient_count      int64   \n",
      " 9   direction_length      int64   \n",
      " 10  title_length_bin      category\n",
      " 11  ingredient_count_bin  category\n",
      " 12  direction_length_bin  category\n",
      "dtypes: category(3), int64(4), object(6)\n",
      "memory usage: 176.6+ MB\n",
      "None\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/1d/hb016sss4n7f0rs9qdktfc_00000gn/T/ipykernel_66047/1527728918.py:21: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  sampled_df = df.groupby(['title_length_bin', 'ingredient_count_bin', 'direction_length_bin'], group_keys=False).apply(lambda x: x.sample(frac=50000/len(df)))\n"
     ]
    }
   ],
   "source": [
    "file_path = 'dataset/full_dataset.csv'\n",
    "df = pd.read_csv(file_path)\n",
    "\n",
    "# Fill missing values with empty strings for text fields\n",
    "df['title'] = df['title'].fillna('')\n",
    "df['ingredients'] = df['ingredients'].fillna('')\n",
    "df['directions'] = df['directions'].fillna('')\n",
    "\n",
    "# Add columns for key characteristics\n",
    "df['title_length'] = df['title'].apply(len)\n",
    "df['ingredient_count'] = df['ingredients'].apply(lambda x: len(x.split(',')))\n",
    "df['direction_length'] = df['directions'].apply(len)\n",
    "\n",
    "# Stratified sampling\n",
    "# First, we'll need to bin some continuous variables for stratification\n",
    "df['title_length_bin'] = pd.qcut(df['title_length'], q=10, duplicates='drop')\n",
    "df['ingredient_count_bin'] = pd.qcut(df['ingredient_count'], q=10, duplicates='drop')\n",
    "df['direction_length_bin'] = pd.qcut(df['direction_length'], q=10, duplicates='drop')\n",
    "\n",
    "# Perform stratified sampling\n",
    "sampled_df = df.groupby(['title_length_bin', 'ingredient_count_bin', 'direction_length_bin'], group_keys=False).apply(lambda x: x.sample(frac=50000/len(df)))\n",
    "\n",
    "# Reset index after sampling\n",
    "sampled_df = sampled_df.reset_index(drop=True)\n",
    "\n",
    "print(df.head())\n",
    "print(df.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8190077f-9cfe-4a9b-baae-5390864a21eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Function to compare distributions side by side\n",
    "def plot_distribution_comparison_side_by_side(original, sampled, column, title):\n",
    "    fig, axes = plt.subplots(1, 2, figsize=(20, 6))\n",
    "    \n",
    "    original[column].value_counts(normalize=True).sort_index().plot(kind='bar', alpha=0.5, color='blue', ax=axes[0])\n",
    "    axes[0].set_title(f'Original {title}')\n",
    "    axes[0].set_xlabel(column)\n",
    "    axes[0].set_ylabel('Proportion')\n",
    "    \n",
    "    sampled[column].value_counts(normalize=True).sort_index().plot(kind='bar', alpha=0.5, color='red', ax=axes[1])\n",
    "    axes[1].set_title(f'Sampled {title}')\n",
    "    axes[1].set_xlabel(column)\n",
    "    axes[1].set_ylabel('Proportion')\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "# Compare distributions side by side\n",
    "plot_distribution_comparison_side_by_side(df, sampled_df, 'title_length_bin', 'Title Length Distribution')\n",
    "plot_distribution_comparison_side_by_side(df, sampled_df, 'ingredient_count_bin', 'Ingredient Count Distribution')\n",
    "plot_distribution_comparison_side_by_side(df, sampled_df, 'direction_length_bin', 'Direction Length Distribution')\n",
    "\n",
    "# Drop the temporary bin columns before saving\n",
    "sampled_df = sampled_df.drop(columns=['title_length_bin', 'ingredient_count_bin', 'direction_length_bin'])\n",
    "\n",
    "# Save the sampled dataset for further use\n",
    "sampled_df.to_csv('sampled_recipes.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3c270ce0-16bd-46d2-9370-a47ed8f14381",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = 'sampled_recipes.csv'\n",
    "sampled_df = pd.read_csv(file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "05ea5c36-f42d-4a3c-8a61-bec0e30819ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "recipes = {}\n",
    "for _, row in sampled_df.iterrows():\n",
    "    recipes[row['title']] = {\n",
    "        'directions': row['directions'],\n",
    "        'ingredients': row['ingredients'],\n",
    "        'NER': row['NER']\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "86c3f74a-ded1-400c-b31e-ea89d6817a98",
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = spacy.load('en_core_web_sm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f6ef0744-70b4-4ed9-b5ed-ffd37890a349",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top recipes for query: chicken, olive oil, garlic \n",
      "Title: Whole Roasted Garlic, \n",
      "Ingredients: [\"1 head garlic\", \"1 tablespoon olive oil\"], \n",
      "Directions: [\"Preheat the oven to 375 degrees F.\", \"Cut the top third of the garlic head off so that the tops of the cloves are exposed.\", \"Place the garlic head, unpeeled, into a small ovenproof dish and drizzle with the oil.\", \"Cover with foil and bake for 30 minutes.\", \"Uncover and bake for 30 to 40 minutes more, until garlic is soft and golden brown.\", \"Serve with toasted bread.\"]\n",
      "\n",
      "Top recipes for query: chicken, olive oil, garlic \n",
      "Title: Baked Spinach Chips, \n",
      "Ingredients: [\"2 cups Baby Spinach Leaves\", \"1 Tablespoon Olive Oil\", \"1/4 teaspoons Garlic Powder\", \"Salt And Pepper, to taste\"], \n",
      "Directions: [\"Preheat the oven to 325 F.\", \"Combine the spinach and olive oil in a bowl and gently toss until all the leaves are coated.\", \"Lay them in a single row on a rimmed baking sheet that youve lined with parchment paper.\", \"Dont overlap the leaves.\", \"Use more than one baking sheet, if you have them, or work in batches.\", \"Sprinkle with salt, pepper and garlic powder.\", \"Bake for 12 minutes then turn off the oven but leave the pan in there for an additional 5 minutes.\", \"Remove the pan from the oven and let the spinach sit in the pan for 5 minutes more to crisp up.\", \"Makes 2 to 4 servings.\"]\n",
      "\n",
      "Top recipes for query: chicken, olive oil, garlic \n",
      "Title: Alioli, \n",
      "Ingredients: [\"1 head garlic\", \"1 cup olive oil\", \"salt, to taste\"], \n",
      "Directions: [\"Peel the garlic. Add salt.\", \"Crush together in a mortar until a pulp forms. Or, use a little plate with raised nubs that you rub the garlic over to form a paste, then add the salt.\", \"Beat oil into garlic slowly, until you get a consistency like mayonnaise.\"]\n",
      "\n",
      "Top recipes for query: chicken, olive oil, garlic \n",
      "Title: Simple Roasted Butternut Squash, \n",
      "Ingredients: [\"1 butternut squash - peeled, seeded, and cut into 1-inch cubes\", \"2 tablespoons olive oil\", \"2 cloves garlic, minced\", \"salt and ground black pepper to taste\"], \n",
      "Directions: [\"Preheat oven to 400 degrees F (200 degrees C).\", \"Toss butternut squash with olive oil and garlic in a large bowl. Season with salt and black pepper. Arrange coated squash on a baking sheet.\", \"Roast in the preheated oven until squash is tender and lightly browned, 25 to 30 minutes.\"]\n",
      "\n",
      "Top recipes for query: chicken, olive oil, garlic \n",
      "Title: Roasted Garlic Baked Chicken, \n",
      "Ingredients: [\"1 head garlic\", \"1 teaspoon olive oil or any vegetable oil\", \"4 tablespoons butter softened\", \"4 each chicken breasts with part wing bone, no skin\", \"1 x salt and black pepper to taste\"], \n",
      "Directions: [\"Preheat the oven to 180C (355F).\", \"Cut off the top of the garlic and drizzle with the olive oil or vegetable oil.\", \"Wrap the garlic in foil, arrange on a baking sheet and bake for 30 to 35 minutes or until the garlic is soft.\", \"Let cool slightly, then squeeze the garlic cloves from the skin.\", \"Mix together the garlic and butter.\", \"Make a small hole between the skin and the flesh of the chicken and spoon the garlic butter under the skin of each chicken breast.\", \"Use your fingers to spread the butter all over the flesh.\", \"Sprinkle the chicken with the salt and pepper.\", \"Heat a nonstick skillet over high heat.\", \"Add the chicken, skin-side down, and cook for 2 to 3 minutes or until golden.\", \"Turn and cook for another 1 minute or until golden.\", \"Set the chicken on a baking sheet coated with cooking spray or lined with parchment papper and bake for about 9 minutes or until completely cooked.\", \"Serve with salsa verde and roasted potatoes.\"]\n",
      "\n",
      "Top recipes for query: chicken, olive oil, garlic \n",
      "Title: Roasted Parmesan Asparagus, \n",
      "Ingredients: [\"1 lb. (450 g) fresh asparagus spears, trimmed\", \"1 tsp. olive oil\", \"2 Tbsp. Kraft Extra Virgin Olive Oil Aged Balsamic Vinaigrette Dressing\", \"1/3 cup Kraft 100% Parmesan Shaved Cheese\"], \n",
      "Directions: [\"Heat oven to 400 degrees F.\", \"Place asparagus on parchment-covered rimmed baking sheet.\", \"Drizzle with oil; toss to evenly coat.\", \"Spread to form even layer.\", \"Bake 15 min.\", \"or until asparagus is crisp-tender.\", \"Drizzle with dressing; sprinkle with cheese.\"]\n",
      "\n",
      "Top recipes for query: chicken, olive oil, garlic \n",
      "Title: Marinated Mandarin Orange Chicken, \n",
      "Ingredients: [\"4 Tablespoons Frozen Orange Juice Concentrate\", \"3 Tablespoons Low Sodium Soy Sauce\", \"1 teaspoon Olive Oil\", \"1- 1/4 teaspoon Chili Garlic Sauce\", \"3 cloves Garlic\", \"11 ounces, weight Mandarin Oranges In Light Syrup\", \"5 pieces Boneless Skinless Chicken Breast\"], \n",
      "Directions: [\"In a large resealable plastic bag, mix the juice, soy sauce, oil and chili garlic sauce.\", \"Mince the garlic and add to the bag.\", \"Shake to mix.\", \"Then pour the oranges with the liquid into the bag.\", \"Gently add the chicken and seal the zip bag.\", \"Let this marinate in the fridge for at least 6 hours.\", \"Preheat the oven to 375F.\", \"Put chicken in a GLASS baking dish sprayed with non stick cooking spray.\", \"Pour the marinade on top of the chicken.\", \"Cover with foil and bake for 25-30 minutes or until the chicken is no longer pink inside.\", \"Then uncover the chicken and broil for 5-7 minutes to brown the top of the chicken.\", \"Chili Garlic Sauce is on the international food isle in the grocery store.\"]\n",
      "\n",
      "Top recipes for query: chicken, olive oil, garlic \n",
      "Title: Grilled Lemon Rosemary Chicken, \n",
      "Ingredients: [\"1 tablespoon dried rosemary\", \"3 tablespoons olive oil\", \"1 teaspoon minced garlic\", \"4 chicken breast halves\", \"14 cup lemon juice\"], \n",
      "Directions: [\"In a small saucepan, combine rosemary with 1/3 c water.\", \"Boil, then let stand for 5 minutes, covered.\", \"Blend rosemary water, oil and garlic.\", \"Let cool completely.\", \"Marinate chicken in mixture for 8-12 hours.\", \"Heat grill to medium.\", \"Cook chicken 6-7 minutes on each side, or until a thermometer reads 165 degrees .\"]\n",
      "\n",
      "Top recipes for query: chicken, olive oil, garlic \n",
      "Title: Grilled Chicken Bruschetta, \n",
      "Ingredients: [\"4 pieces Boneless Skinless Chicken Breast\", \"1 dash Salt\", \"1 dash Pepper\", \"1 dash Garlic Powder\", \"1 dash Onion Powder\", \"2 Tablespoons Olive Oil\", \"1/2 cups Fresh Shredded Parmesan Cheese\", \"2 cups Bruschetta Spread\"], \n",
      "Directions: [\"First light your grill and cover one side with aluminum foil, close grill and allow it to heat up.\", \"Second butterfly or pound chicken breasts thin and place on a platter to bring to the grill.\", \"Drizzle the olive oil over the chicken, turn to coat well.\", \"Then sprinkle the salt, pepper, garlic and onion powder all over.\", \"Once grill is hot place the chicken breast on the exposed size of the grill and cook a couple minutes or until the chicken is half cooked.\", \"Now here is the trick.\", \"Flip the chicken onto the tin foil.\", \"Sprinkle the breasts with the Parmesan cheese and spoon on the bruschetta into a good layer.\", \"Now close the lid and let it finish cooking.\", \"Once the chicken is finished slide all of it (aluminum foil and all) onto a platter using the aluminum foil to keep the tomato juice contained.\"]\n",
      "\n",
      "Top recipes for query: chicken, olive oil, garlic \n",
      "Title: Roasted Kale, \n",
      "Ingredients: [\"4 cups Kale (firmly Packed)\", \"1 Tablespoon Extra Virgin Olive Oil\", \"1 teaspoon Sea Salt\"], \n",
      "Directions: [\"Preheat oven to 375 degrees F. Wash and trim the kale: peel off the tough stems by folding the kale leaves in half like a book and stripping the stems off.\", \"You can toss the kale with extra virgin olive oil or I prefer to put the oil on my hands and coat the front and back of the leaves.\", \"Roast for five minutes.\", \"Turn kale over.\", \"Roast another 7 to 10 minutes until kale turns brown and becomes paper thin and brittle.\", \"Remove from the oven and sprinkle with sea salt.\", \"Serve immediately.\"]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Function to create vector embeddings using TF-IDF\n",
    "def embedding_function_tfidf(texts):\n",
    "    vectorizer = TfidfVectorizer()\n",
    "    vectors = vectorizer.fit_transform(texts).toarray()\n",
    "    return vectors, vectorizer\n",
    "\n",
    "\n",
    "# Prepare document embeddings using TF-IDF\n",
    "corpus = [f\"{data['NER']}\" for data in recipes.values()]\n",
    "document_vectors, vectorizer = embedding_function_tfidf(corpus)\n",
    "titles = list(recipes.keys())\n",
    "\n",
    "# Function to search the vector corpus\n",
    "def search(query, k=10):\n",
    "    # Create embedding for the query\n",
    "    query_vec = vectorizer.transform([query]).toarray()\n",
    "    \n",
    "    # Calculate cosine similarity between query and document vectors\n",
    "    similarities = cosine_similarity(query_vec, document_vectors)\n",
    "    \n",
    "    # Get top k results\n",
    "    top_k_indices = similarities[0].argsort()[-k:][::-1]\n",
    "    top_k_titles = [titles[i] for i in top_k_indices]\n",
    "    \n",
    "    return top_k_titles\n",
    "\n",
    "# Example query\n",
    "query = \"chicken, olive oil, garlic\"\n",
    "results = search(query)\n",
    "\n",
    "for r in results:\n",
    "    print(f\"Top recipes for query: {query} \\nTitle: {r}, \\nIngredients: {recipes[r]['ingredients']}, \\nDirections: {recipes[r]['directions']}\\n\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f2d60d8f-3336-4155-9990-8896f4b9877e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query: olive oil, cilantro, tomatoes\n",
      "Results: [\"3 bunches cilantro\", \"1 bunch parsley\", \"1 cup olive oil\", \"1 teaspoon salt\"][\"1 (14.5 oz.) can diced tomatoes (undrained)\", \"1 (10 oz.) can diced tomatoes and green chilies (undrained)\", \"1 tsp. olive oil\", \"1/2 c. chopped onion\", \"2 garlic cloves, minced\", \"1 (8 oz.) block fat-free cream cheese, softened\", \"1 tsp. chili powder\", \"6 oz. light processed cheese, cubed (such as Velveeta Light)\", \"cilantro sprigs (optional)\"][\"3 cups cherry tomatoes\", \"1 small yellow onion, chunked\", \"1 jalapeno, seeded, topped, and minced\", \"2 tablespoons cilantro, roughly chopped\", \"2 tablespoons olive oil\", \"1 tablespoon lime juice\", \"2 garlic cloves, skinned and mashed\", \"salt\"][\"1 (15 ounce) can black beans, drained\", \"1 (10 ounce) can corn, drained\", \"1 tomatoes, chopped\", \"1/4 cup fresh cilantro, chopped\", \"1/8 cup red onion, chopped\", \"3 tablespoons lemon juice\", \"2 tablespoons olive oil\", \"salt\", \"pepper\"][\"4-5 medium tomatoes, chopped\", \"1 medium white onion, chopped\", \"1/2 cup fresh parsley, chopped\", \"1/2 cup fresh cilantro, chopped\", \"3 tablespoons lime juice (adjust to taste)\", \"3 tablespoons olive oil\", \"1/4-1/2 of a jalapeno, chopped (optional)\", \"salt + pepper\"][\"1 lb. (450 g) fresh asparagus spears, trimmed\", \"1 tsp. olive oil\", \"2 Tbsp. Kraft Extra Virgin Olive Oil Aged Balsamic Vinaigrette Dressing\", \"1/3 cup Kraft 100% Parmesan Shaved Cheese\"][\"1 ripe avocado, diced\", \"1 large tomatoes, diced\", \"14 red onion, diced\", \"1 jalapeno pepper, seeded and diced\", \"12 cup cilantro, chopped\", \"1 12 tablespoons olive oil\", \"1 12 tablespoons lime juice, to taste\", \"1 (14 ounce) can black beans, rinsed\"][\"2 Tablespoons Olive Oil\", \"12 ounces, weight Frozen Corn, Slightly Thawed\", \"Salt To Taste\", \"2 Tablespoons Mayonnaise\", \"1/2 cups Heaping, Cotija Cheese (basically Mexican Powdered Parmesan Cheese)\", \"1/2 cups Snipped Cilantro\", \"1/2 whole Lime, Juiced\", \"1 pinch Chili Powder\", \"Garlic Powder, To Taste\"][\"2 large tomatoes\", \"2 tablespoons olive oil\", \"2 cloves garlic, crushed\", \"1/2 teaspoon salt\", \"4 large basil leaves, chopped\"][\"2 Serano Peppers\", \"1/2 bunch cilantro\", \"2 1/2 large Tomatoes\", \"1 large onion\", \"1/2 lime\", \"1 dash salt\"]\n",
      "Ground Truth: 1 tbsp Olive oil, 3 tbsp Fresh Cilantro, Chopped, 1 pound Cherry Tomatoes\n",
      "Precision@5: 0.60\n",
      "Recall@5: 0.12\n",
      "\n",
      "Query: mayonnaise, pie crust, garlic\n",
      "Results: [\"1 Pie crust\", \"1 Granulated sugar\"][\"6 Hershey with almonds candy bars\", \"1 Cool Whip\", \"1 chocolate pie crust\"][\"1 (9-inch) pie crust, unbaked\", \"1 1/2 c. smoked salmon, flaked\", \"1 1/2 c. Tillamook medium Cheddar, cubed\", \"1/2 c. mayonnaise\", \"1/2 c. milk\", \"2 eggs\", \"1 Tbsp. cornstarch\"][\"1 quart Strawberries, Chopped, Divided\", \"1 whole Baked Pie Crust\", \"1 cup Sugar\", \"3 Tablespoons Cornstarch\"][\"1 whole Store-Bought Pie Crust\", \"4 whole Tomatoes, Peeled, Cored, Seeds Removed\", \"8 leaves Fresh Basil (8-10 Leaves)\", \"1/3 cups Chopped Green Onion\", \"1 pinch Salt To Taste\", \"1 pinch Pepper To Taste\", \"1 cup Grated Part-skim Mozzarella Cheese\", \"1 cup Grated Cheddar Cheese\", \"1 cup Mayonnaise\"][\"1 (9-inch) pie crust (unbaked)\", \"1 c. peanut butter\", \"1 tsp. vanilla\", \"1 1/2 c. sugar\", \"1/2 tsp. salt\", \"2 eggs, well beaten\", \"1 1/2 c. milk\"][\"3 small Sweet potatoes\", \"1/3 cup Firmly packed brown sugar\", \"5 oz Evaporated milk\", \"1 Egg\", \"1 tbsp Melted butter\", \"1/2 tsp Ground pumpkin pie spice\", \"1 Pie crust\"][\"1 (9-inch) frozen pie crust shell\", \"1/2 lb. bulk hot pork sausage\"][\"1 pkg. sugar-free chocolate pudding\", \"1 pkg. sugar-free white chocolate pudding\", \"8 oz. container fat-free Cool Whip, thawed\", \"1 1/2 c. milk\", \"9-inch graham cracker pie crust\"][\"1 (9 inch) graham cracker pie crust\", \"1 (16 ounce) can lemon pie filling\", \"1 (10 ounce) jarof stemless maraschino cherries\"]\n",
      "Ground Truth: 1/2 cup mayonnaise, 1 baked 9 inch pie crust, 2 garlic cloves, crushed\n",
      "Precision@5: 0.40\n",
      "Recall@5: 0.08\n",
      "\n",
      "Query: sugar, soya sauce, oil\n",
      "Results: [\"3 cups soya oil\", \"2 lbs fresh cod fish fillets\", \"1 13 cups self rising flour\", \"1 cup water\", \"1 egg\", \"2 teaspoons granulated sugar\", \"2 teaspoons salt\"][\"2 Tbsp. cornstarch or 2 Tbsp. sifted flour, mixed thoroughly with 1/4 c. cold water\", \"3/4 c. cold water\", \"3/4 c. brown sugar\", \"1/4 c. vinegar\", \"1/8 tsp. black pepper\", \"1 large green pepper, cut into thin rings or strips\", \"1 Tbsp. soya sauce\", \"1/2 c. canned pineapple chunks or 3/4 c. fresh pineapple chunks lightly cooked with 1 Tbsp. brown sugar\"][\"1 lb sliced mushrooms\", \"2 heads broccoli\", \"1 whole onion, sliced\", \"1 fresh garlic clove\", \"1/4 cup chicken broth\", \"2 chicken breasts, cut into pieces\", \"2 tablespoons soya sauce\", \"salt\", \"pepper\", \"2 tablespoons vegetable oil\"][\"1 (16 ounce) can pineapple chunks\", \"1 lb sirloin beef, cut in slices\", \"1/3 cup soya sauce\", \"1 garlic clove, minced\", \"12 olives (specifies stuffed)\", \"1/4 teaspoon ground ginger\"][\"1 cup soya sauce\", \"1/2 cup maple syrup (or regular pancake syrup)\", \"3 teaspoons minced garlic\", \"1/2 teaspoon ginger\", \"1/2 teaspoon ground black pepper\", \"1/2 teaspoon crushed red pepper flakes\", \"1/2 teaspoon cayenne pepper\"][\"2 kg boneless chicken, cut into small cube-sized pieces\", \"3 eggs, beaten\", \"3 tablespoons soya sauce\", \"12 tablespoon baking soda\", \"3 tablespoons cornflour\", \"600 g low-fat plain yogurt\", \"3 tablespoons ginger-garlic paste\", \"1 tablespoon salt\", \"1 tablespoon red chili powder\", \"1 tablespoon lemon juice\", \"5 -6 green chilies\", \"5 -6 fresh curry leaves\", \"1 tablespoon oil\", \"oil, to deep fry\"][\"1 lb round steak (1/4 inch thick)\", \"1/2 lb carrot, sliced diagonally in 1 inch pieces\", \"1 medium onion, sliced\", \"1 clove garlic, sliced\", \"1 dash ground ginger\", \"2 cups broccoli (1 inch pieces)\", \"2 cups chopped celery (one inch pieces)\", \"2 cups chopped green peppers (1 inch pieces)\", \"2 tablespoons vegetable oil\", \"1 (8 ounce) can cream of onion soup or (8 ounce) can cream of mushroom soup\", \"1/4 cup water\", \"1 tablespoon soya sauce\"][\"7/8 pound ground beef\", \"2 slices bread made into crumbs\", \"1 garlic glove, chopped finely\", \"2 spring onion chopped finely\", \"1 teaspoon ginger grated\", \"1 tablespoon chopped parsley\", \"2 tablespoons soya sauce to your taste\", \"3 tablespoons peanut butter best crunchy type\", \"non stick spray\"][\"12 white radish, diced\", \"2 small potatoes, diced\", \"12 carrot, diced\", \"pork\", \"2 tablespoons soya sauce\", \"1 tablespoon light sesame oil\", \"2 tablespoons miso\", \"water\"][\"10 oz (280 grm). chicken or capon legs, deboned\", \"2 tbsp (30 ml) sugar\", \"2 cups (475 ml) soya oil\", \"2 tbsp (30 ml) soy sauce\", \"1 tbsp (15 ml) ginger root, minced\", \"1-1/2 tsp (7 ml) vinegar\", \"2 ea. scallions, chopped\", \"2 tbsp (30 ml) cornstarch\", \"1 tbsp (15 ml) garlic, minced\", \"1/4 cup (60 ml) chicken stock\", \"2 tbsp (30 ml) dry chili pepper\", \"1 tbsp (15 ml) sesame oil\", \"cooked rice\", \"1 egg white\", \"1 tbsp (15 ml) soy sauce\", \"1 tbsp (15 ml) cornstarch\"]\n",
      "Ground Truth: 1 1/2 tbsp Sugar, 2 tsp soya sauce, Frying oil\n",
      "Precision@5: 0.40\n",
      "Recall@5: 0.09\n",
      "\n",
      "Query: pork chops, peas, water\n",
      "Results: [\"4 pork chops (1/2-inch thick)\", \"1/2 tsp. salt\", \"dash of pepper\", \"1/4 c. water\"][\"meat (pork chops, chicken or steak), fried\", \"2 Tbsp. of grease with meat drippings\", \"1 small onion, cut up\", \"about 2 Tbsp. flour\", \"water\", \"salt and pepper to taste\"][\"as many pork chops as needed\", \"Shake 'n Bake for pork\", \"seasoned salt\", \"pepper\"][\"pork chops\", \"potatoes\"][\"8 pork chops\", \"2 cans chicken and rice soup, undiluted\", \"flour for battering pork chops\"][\"2 tbsp. vegetable oil\", \"6 (1/2 inch thick) pork chops\", \"3/4 teaspoon salt\", \"1/2 teaspoon pepper\", \"6 sm. potatoes (unpeeled)\", \"10 ounce. pkg. frzn English peas (thawed)\", \"5 med. carrots (sliced)\", \"4 sm. onions (quartered)\", \"1/2 c. water\", \"1/2 teaspoon salt\", \"1/4 teaspoon pepper\"][\"6 pork chops\", \"1 c. uncooked rice\", \"2 c. water\", \"1 envelope onion soup mix\"][\"1 can Campbells chicken and rice soup\", \"4 pork chops\", \"2 c. Minute Rice\"][\"1/2 kg Pork Chops\"][\"1 stick butter\", \"cheese or cheese crackers\", \"4 to 6 pork chops\"]\n",
      "Ground Truth: 2 lbs pork chops, 1/4 c. warm water, 1 pkg. frozen green peas, cooked\n",
      "Precision@5: 0.60\n",
      "Recall@5: 0.12\n",
      "\n",
      "Query: egg noodles, rice, olive oil\n",
      "Results: [\"1 packet Frozen udon noodles\"][\"Egg noodles\", \"Butter\", \"Salt to taste\", \"Pepper to taste\"][\"4 1/2 lbs cabbage, cored and shredded (about 1 large head of cabbage, this is also pretty if using red and green together)\", \"6 tablespoons butter\", \"6 tablespoons oil (olive works well)\", \"3/4 lb egg noodles, cooked according to package directions and drained\", \"salt and pepper\"][\"1 whole chicken, thawed and washed\", \"1 cup water\", \"1 teaspoon salt\", \"1/2 teaspoon pepper\", \"1 (16 ounce) package wide egg noodles\"][\"1 cup of rice\", \"1 cup of water\", \"1 spoon olive oil\", \"salt\", \"pepper\"][\"2/3 cup noodles (dry pasta, small ones, the kind you use for soup)\", \"1 teaspoon butter\", \"1 tablespoon parmesan cheese\", \"1 egg\", \"salt\"][\"16 oz rice noodles cooked to directions\", \"1/4 cup rice wine vinegar\", \"1/4 veg oil, I like canola/olive blend\", \"1tsp hot sauce\", \"1 tbsp sesame oil\", \"Veggies of choice, steamed or raw\", \"Salt to taste\"][\"1 cup flour\", \"1 egg\", \"1 tbsp olive oil\", \"1/2 tsp salt\"][\"2 c. boiled water\", \"1 bag noodles\", \"a half of cheese\"][\"1 (2 1/3 to 3 lb.) chicken, cut up\", \"1 (16 oz.) pkg. egg noodles\", \"2 c. mayonnaise\", \"1/2 c. flour\", \"1 1/2 c. water\", \"salt and pepper to taste\"]\n",
      "Ground Truth: 6 cups egg noodles, cooked, 5 cups hot cooked rice, 1 tbsp olive oil\n",
      "Precision@5: 0.60\n",
      "Recall@5: 0.14\n",
      "\n",
      "Average Precision@5: 0.52\n",
      "Average Recall@5: 0.11\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import ndcg_score\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "import numpy as np\n",
    "\n",
    "# Assuming `recipes` is a dictionary containing recipe data\n",
    "# Sample recipes data (keys are titles, values are dictionaries with 'NER', 'ingredients', and 'directions')\n",
    "recipes_example = {\n",
    "    \"Recipe1\": {\"NER\": \"olive oil, cilantro, tomatoes\", \"ingredients\": \"1 tbsp Olive oil, 3 tbsp Fresh Cilantro, Chopped, 1 pound Cherry Tomatoes\", \"directions\": \"Mix all ingredients.\"},\n",
    "    \"Recipe2\": {\"NER\": \"mayonnaise, pie crust, garlic\", \"ingredients\": \"1/2 cup mayonnaise, 1 baked 9 inch pie crust, 2 garlic cloves, crushed\", \"directions\": \"Combine and serve.\"},\n",
    "    \"Recipe3\": {\"NER\": \"sugar, soya sauce, oil\", \"ingredients\": \"1 1/2 tbsp Sugar, 2 tsp soya sauce, Frying oil\", \"directions\": \"Mix and fry.\"},\n",
    "    \"Recipe4\": {\"NER\": \"pork chops, peas, water\", \"ingredients\": \"2 lbs pork chops, 1/4 c. warm water, 1 pkg. frozen green peas, cooked\", \"directions\": \"Cook pork chops and peas.\"},\n",
    "    \"Recipe5\": {\"NER\": \"egg noodles, rice, olive oil\", \"ingredients\": \"6 cups egg noodles, cooked, 5 cups hot cooked rice, 1 tbsp olive oil\", \"directions\": \"Combine and serve.\"}\n",
    "}\n",
    "\n",
    "# Example queries and their relevant ground truth recipes\n",
    "example_queries = [\n",
    "    \"olive oil, cilantro, tomatoes\",\n",
    "    \"mayonnaise, pie crust, garlic\",\n",
    "    \"sugar, soya sauce, oil\",\n",
    "    \"pork chops, peas, water\",\n",
    "    \"egg noodles, rice, olive oil\"\n",
    "]\n",
    "\n",
    "# Ground truth relevant recipes (titles) for each query\n",
    "ground_truths = {\n",
    "    example_queries[0]: recipes_example[\"Recipe1\"][\"ingredients\"],\n",
    "    example_queries[1]: recipes_example[\"Recipe2\"][\"ingredients\"],\n",
    "    example_queries[2]: recipes_example[\"Recipe3\"][\"ingredients\"],\n",
    "    example_queries[3]: recipes_example[\"Recipe4\"][\"ingredients\"],\n",
    "    example_queries[4]: recipes_example[\"Recipe5\"][\"ingredients\"]\n",
    "}\n",
    "\n",
    "# Function to calculate precision@k\n",
    "def precision_at_k(results, ground_truth, k):\n",
    "    relevant = set(ground_truth)\n",
    "    retrieved = results[:k]\n",
    "    relevant_retrieved = [doc for doc in retrieved if doc in relevant]\n",
    "    return len(relevant_retrieved) / k\n",
    "\n",
    "# Function to calculate recall@k\n",
    "def recall_at_k(results, ground_truth, k):\n",
    "    relevant = set(ground_truth)\n",
    "    retrieved = results[:k]\n",
    "    relevant_retrieved = [doc for doc in retrieved if doc in relevant]\n",
    "    return len(relevant_retrieved) / len(relevant)\n",
    "\n",
    "\n",
    "# Evaluate search results\n",
    "def evaluate_search(example_queries, ground_truths, k=5):\n",
    "    precisions = []\n",
    "    recalls = []\n",
    "    all_results = []\n",
    "    all_similarities = []\n",
    "    for query in example_queries:\n",
    "        results = \"\"\n",
    "        results_before = search(query)\n",
    "        for r in results_before:\n",
    "            results += recipes[r]['ingredients']\n",
    "        all_results.append(results)\n",
    "        ground_truth = ground_truths[query]\n",
    "        \n",
    "        precision = precision_at_k(results, ground_truth, k)\n",
    "        recall = recall_at_k(results, ground_truth, k)\n",
    "        \n",
    "        precisions.append(precision)\n",
    "        recalls.append(recall)\n",
    "        \n",
    "        print(f\"Query: {query}\")\n",
    "        print(f\"Results: {results}\")\n",
    "        print(f\"Ground Truth: {ground_truth}\")\n",
    "        print(f\"Precision@{k}: {precision:.2f}\")\n",
    "        print(f\"Recall@{k}: {recall:.2f}\")\n",
    "        print()\n",
    "    \n",
    "    avg_precision = np.mean(precisions)\n",
    "    avg_recall = np.mean(recalls)\n",
    "    \n",
    "    print(f\"Average Precision@{k}: {avg_precision:.2f}\")\n",
    "    print(f\"Average Recall@{k}: {avg_recall:.2f}\")\n",
    "\n",
    "# Evaluate search function\n",
    "evaluate_search(example_queries, ground_truths)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "234312c4-0273-4b88-a7a4-6528e1c9ca8f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}