{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1e770825",
   "metadata": {},
   "source": [
    "---\n",
    "# **Embeddings Notebook for scikit-learn/iris dataset**\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c8e5109",
   "metadata": {},
   "source": [
    "## 1. Setup necessary libraries and load the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c9cae3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install and import necessary libraries.\n",
    "!pip install pandas sentence-transformers faiss-cpu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53f2f84a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sentence_transformers import SentenceTransformer\n",
    "import faiss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "152710e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the dataset as a DataFrame\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"hf://datasets/scikit-learn/iris/Iris.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35085609",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify the column name that contains the text data to generate embeddings\n",
    "column_to_generate_embeddings = 'Species'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a774b21f",
   "metadata": {},
   "source": [
    "## 2. Loading embedding model and creating FAISS index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd3d6e6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove duplicate entries based on the specified column\n",
    "df = df.drop_duplicates(subset=column_to_generate_embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4bac4e77",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the column data to a list of text entries\n",
    "text_list = df[column_to_generate_embeddings].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5258d88e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify the embedding model you want to use\n",
    "model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f274463",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectors = model.encode(text_list)\n",
    "vector_dimension = vectors.shape[1]\n",
    "\n",
    "# Initialize the FAISS index with the appropriate dimension (384 for this model)\n",
    "index = faiss.IndexFlatL2(vector_dimension)\n",
    "\n",
    "# Encode the text list into embeddings and add them to the FAISS index\n",
    "index.add(vectors)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c2bebdd",
   "metadata": {},
   "source": [
    "## 3. Perform a text search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3966941",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify the text you want to search for in the list\n",
    "text_to_search = text_list[0]\n",
    "print(f\"Text to search: {text_to_search}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2322f493",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate the embedding for the search query\n",
    "query_embedding = model.encode([text_to_search])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f67cd14a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform the search to find the 'k' nearest neighbors (adjust 'k' as needed)\n",
    "D, I = index.search(query_embedding, k=10)\n",
    "\n",
    "# Print the similar documents\n",
    "print(f\"Similar documents: {[text_list[i] for i in I[0]]}\")"
   ]
  }
 ],
 "metadata": {},
 "nbformat": 4,
 "nbformat_minor": 5
}