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 --upgrade --quiet scikit-learn
%pip install --upgrade --quiet 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_community.retrievers import SVMRetriever
from langchain_openai import OpenAIEmbeddings
API Reference:SVMRetriever | 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={})]