langchain/docs/extras/modules/data_connection/retrievers/integrations/bm25.ipynb

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   "source": [
    "# BM25\n",
    "\n",
    "[BM25](https://en.wikipedia.org/wiki/Okapi_BM25) also known as the Okapi BM25, is a ranking function used in information retrieval systems to estimate the relevance of documents to a given search query.\n",
    "\n",
    "This notebook goes over how to use a retriever that under the hood uses BM25 using [`rank_bm25`](https://github.com/dorianbrown/rank_bm25) package.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a801b57c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install rank_bm25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "393ac030",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/workspaces/langchain/.venv/lib/python3.10/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.10) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from langchain.retrievers import BM25Retriever"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaf80e7f",
   "metadata": {},
   "source": [
    "## Create New Retriever with Texts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "98b1c017",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "retriever = BM25Retriever.from_texts([\"foo\", \"bar\", \"world\", \"hello\", \"foo bar\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c016b266",
   "metadata": {},
   "source": [
    "## Create a New Retriever with Documents\n",
    "\n",
    "You can now create a new retriever with the documents you created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "53af4f00",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.schema import Document\n",
    "\n",
    "retriever = BM25Retriever.from_documents(\n",
    "    [\n",
    "        Document(page_content=\"foo\"),\n",
    "        Document(page_content=\"bar\"),\n",
    "        Document(page_content=\"world\"),\n",
    "        Document(page_content=\"hello\"),\n",
    "        Document(page_content=\"foo bar\"),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "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": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "result = retriever.get_relevant_documents(\"foo\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "997aaa8d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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