Skip to main content

kNN

In statistics, the k-nearest neighbors algorithm (k-NN) 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.

This notebook goes over how to use a retriever that under the hood uses an kNN.

Largely based on https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.html

from langchain.embeddings import OpenAIEmbeddings
from langchain.retrievers import KNNRetriever

Create New Retriever with Texts​

retriever = KNNRetriever.from_texts(
    ["foo", "bar", "world", "hello", "foo bar"], OpenAIEmbeddings()
)

Use Retriever​

We can now use the retriever!

result = retriever.get_relevant_documents("foo")
result
[Document(page_content='foo', metadata={}),
 Document(page_content='foo bar', metadata={}),
 Document(page_content='hello', metadata={}),
 Document(page_content='bar', metadata={})]