langchain/docs/modules/indexes/retrievers/examples/elastic_search_bm25.ipynb

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  {
   "cell_type": "markdown",
   "id": "ab66dd43",
   "metadata": {},
   "source": [
    "# ElasticSearch BM25\n",
    "\n",
    "This notebook goes over how to use a retriever that under the hood uses ElasticSearcha and BM25.\n",
    "\n",
    "For more information on the details of BM25 see [this blog post](https://www.elastic.co/blog/practical-bm25-part-2-the-bm25-algorithm-and-its-variables)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "393ac030",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.retrievers import ElasticSearchBM25Retriever"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaf80e7f",
   "metadata": {},
   "source": [
    "## Create New Retriever"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bcb3c8c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "elasticsearch_url=\"http://localhost:9200\"\n",
    "retriever = ElasticSearchBM25Retriever.create(elasticsearch_url, \"langchain-index-3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b605284d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Alternatively, you can load an existing index\n",
    "# import elasticsearch\n",
    "# elasticsearch_url=\"http://localhost:9200\"\n",
    "# retriever = ElasticSearchBM25Retriever(elasticsearch.Elasticsearch(elasticsearch_url), \"langchain-index\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c518c42",
   "metadata": {},
   "source": [
    "## Add texts (if necessary)\n",
    "\n",
    "We can optionally add texts to the retriever (if they aren't already in there)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "98b1c017",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['386c76c9-4355-4c12-aaeb-7b80054caf93',\n",
       " 'fffd279c-a0c9-4158-a904-6e242c517c99',\n",
       " '7f5528a3-18d0-43b0-894d-f6770a002219',\n",
       " 'e2ef5e32-d5bd-44e2-b045-cfc5a8e0a0a1',\n",
       " 'cce8ba48-e473-4235-bca2-2c8d65e73ccf']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retriever.add_texts([\"foo\", \"bar\", \"world\", \"hello\", \"foo bar\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08437fa2",
   "metadata": {},
   "source": [
    "## Use Retriever\n",
    "\n",
    "We can now use the retriever!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c0455218",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = retriever.get_relevant_documents(\"foo\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7dfa5c29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Document(page_content='foo', metadata={}),\n",
       " Document(page_content='foo bar', metadata={})]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74bd9256",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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