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165 lines
3.6 KiB
Plaintext
165 lines
3.6 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "ab66dd43",
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"metadata": {},
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"source": [
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"# ElasticSearch BM25\n",
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"\n",
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"This notebook goes over how to use a retriever that under the hood uses ElasticSearcha and BM25.\n",
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"\n",
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"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)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "393ac030",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.retrievers import ElasticSearchBM25Retriever"
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]
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},
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{
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"cell_type": "markdown",
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"id": "aaf80e7f",
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"metadata": {},
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"source": [
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"## Create New Retriever"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "bcb3c8c2",
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"metadata": {},
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"outputs": [],
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"source": [
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"elasticsearch_url=\"http://localhost:9200\"\n",
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"retriever = ElasticSearchBM25Retriever.create(elasticsearch_url, \"langchain-index-3\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "b605284d",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Alternatively, you can load an existing index\n",
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"# import elasticsearch\n",
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"# elasticsearch_url=\"http://localhost:9200\"\n",
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"# retriever = ElasticSearchBM25Retriever(elasticsearch.Elasticsearch(elasticsearch_url), \"langchain-index\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1c518c42",
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"metadata": {},
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"source": [
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"## Add texts (if necessary)\n",
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"\n",
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"We can optionally add texts to the retriever (if they aren't already in there)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "98b1c017",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['386c76c9-4355-4c12-aaeb-7b80054caf93',\n",
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" 'fffd279c-a0c9-4158-a904-6e242c517c99',\n",
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" '7f5528a3-18d0-43b0-894d-f6770a002219',\n",
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" 'e2ef5e32-d5bd-44e2-b045-cfc5a8e0a0a1',\n",
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" 'cce8ba48-e473-4235-bca2-2c8d65e73ccf']"
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]
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},
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"retriever.add_texts([\"foo\", \"bar\", \"world\", \"hello\", \"foo bar\"])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "08437fa2",
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"metadata": {},
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"source": [
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"## Use Retriever\n",
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"\n",
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"We can now use the retriever!"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "c0455218",
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"metadata": {},
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"outputs": [],
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"source": [
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"result = retriever.get_relevant_documents(\"foo\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "7dfa5c29",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[Document(page_content='foo', metadata={}),\n",
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" Document(page_content='foo bar', metadata={})]"
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]
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},
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"execution_count": 16,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"result"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "74bd9256",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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