langchain/docs/modules/indexes/retrievers/examples/elastic_search_bm25.ipynb
2023-04-04 21:29:06 -07:00

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{
"cells": [
{
"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": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
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"nbformat": 4,
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}