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Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection.
This notebook goes over how to use a retriever that under the hood uses an SVM using scikit-learn package. Largely based on github.com/karpathy/randomfun/blob/master/knn_vs_svm.html 
pip install -qU  scikit-learn

pip install -qU  lark
We want to use OpenAIEmbeddings so we have to get the OpenAI API Key. 
import getpass
import os

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")

OpenAI API Key: ········

from langchain_community.retrievers import SVMRetriever
from langchain_openai import OpenAIEmbeddings

​
Create New Retriever with Texts

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

​
Use Retriever

We can now use the retriever! 
result = retriever.invoke("foo")

result

[Document(page_content='foo', metadata={}),
 Document(page_content='foo bar', metadata={}),
 Document(page_content='hello', metadata={}),
 Document(page_content='world', metadata={})]
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