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langchain/docs/versioned_docs/version-0.2.x/integrations/retrievers/knn.ipynb
Jacob Lee aff771923a Jacob/new docs (#20570)
Use docusaurus versioning with a callout, merged master as well

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2024-04-18 11:10:55 -07:00

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{
"cells": [
{
"cell_type": "markdown",
"id": "ab66dd43",
"metadata": {},
"source": [
"# kNN\n",
"\n",
">In statistics, the [k-nearest neighbors 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 an kNN.\n",
"\n",
"Largely based on 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",
"metadata": {},
"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.get_relevant_documents(\"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"
]
}
],
"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": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}