{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ab66dd43",
   "metadata": {},
   "source": [
    "# SVM Retriever\n",
    "\n",
    "This notebook goes over how to use a retriever that under the hood uses an SVM using scikit-learn.\n",
    "\n",
    "Largely based on https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "393ac030",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.retrievers import SVMRetriever\n",
    "from langchain.embeddings import OpenAIEmbeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a801b57c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install scikit-learn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaf80e7f",
   "metadata": {},
   "source": [
    "## Create New Retriever with Texts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "98b1c017",
   "metadata": {},
   "outputs": [],
   "source": [
    "retriever = SVMRetriever.from_texts([\"foo\", \"bar\", \"world\", \"hello\", \"foo bar\"], OpenAIEmbeddings())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08437fa2",
   "metadata": {},
   "source": [
    "## Use Retriever\n",
    "\n",
    "We can now use the retriever!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c0455218",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = retriever.get_relevant_documents(\"foo\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7dfa5c29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Document(page_content='foo', metadata={}),\n",
       " Document(page_content='foo bar', metadata={}),\n",
       " Document(page_content='hello', metadata={}),\n",
       " Document(page_content='world', metadata={})]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74bd9256",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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