langchain/docs/docs/integrations/retrievers/knn.ipynb

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   "source": [
    "# kNN\n",
    "\n",
    ">In statistics, the [k-nearest neighbours algorithm (k-NN)](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm) is a non-parametric supervised learning method first developed by `Evelyn Fix` and `Joseph Hodges` in 1951, and later expanded by `Thomas Cover`. It is used for classification and regression.\n",
    "\n",
    "This notebook goes over how to use a retriever that under the hood uses a kNN.\n",
    "\n",
    "Largely based on the code of [Andrej Karpathy](https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "393ac030",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.retrievers import KNNRetriever\n",
    "from langchain_openai import OpenAIEmbeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaf80e7f",
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   "source": [
    "## Create New Retriever with Texts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "98b1c017",
   "metadata": {},
   "outputs": [],
   "source": [
    "retriever = KNNRetriever.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": 3,
   "id": "c0455218",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = retriever.invoke(\"foo\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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='bar', metadata={})]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  }
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