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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.html

#!pip install scikit-learn
#!pip install lark

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

import getpass
import os

os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
OpenAI API Key: ········
from langchain.embeddings import OpenAIEmbeddings
from langchain.retrievers import SVMRetriever

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={})]