langchain/docs/extras/integrations/retrievers/svm.ipynb

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    "# SVM\n",
    "\n",
    ">[Support vector machines (SVMs)](https://scikit-learn.org/stable/modules/svm.html#support-vector-machines) are a set of supervised learning methods used for classification, regression and outliers detection.\n",
    "\n",
    "This notebook goes over how to use a retriever that under the hood uses an `SVM` using `scikit-learn` package.\n",
    "\n",
    "Largely based on https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.html"
   ]
  },
  {
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   "execution_count": null,
   "id": "a801b57c",
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   "source": [
    "#!pip install scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05b33419-fd3e-49c6-bae3-f20195d09c0c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#!pip install lark"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc5e2d59-9510-40b2-a810-74af28e5a5e8",
   "metadata": {
    "tags": []
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   "source": [
    "We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f9936d67-0471-4a82-954b-033c46ddb303",
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   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "OpenAI API Key: ········\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import getpass\n",
    "\n",
    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "393ac030",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.retrievers import SVMRetriever\n",
    "from langchain.embeddings import OpenAIEmbeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaf80e7f",
   "metadata": {},
   "source": [
    "## Create New Retriever with Texts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "98b1c017",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "retriever = SVMRetriever.from_texts(\n",
    "    [\"foo\", \"bar\", \"world\", \"hello\", \"foo bar\"], OpenAIEmbeddings()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08437fa2",
   "metadata": {},
   "source": [
    "## Use Retriever\n",
    "\n",
    "We can now use the retriever!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c0455218",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "result = retriever.get_relevant_documents(\"foo\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7dfa5c29",
   "metadata": {
    "tags": []
   },
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74bd9256",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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