SVM

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 https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.ipynb

#!pip install scikit-learn

#!pip install lark

We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.

import os
import getpass

os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')

from langchain.retrievers import SVMRetriever
from langchain.embeddings 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.get_relevant_documents("foo")

result

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