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kNN
 Contents 
Create New Retriever with Texts
Use Retriever
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.ipynb
from langchain.retrievers import KNNRetriever
from langchain.embeddings import OpenAIEmbeddings
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={})]
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Create New Retriever with Texts
Use Retriever
By Harrison Chase
    
      © Copyright 2023, Harrison Chase.
      
  Last updated on Jun 16, 2023.
rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/retrievers/examples/knn.html