forked from Archives/langchain
274b25c010
Add SVM retriever class, based on https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.ipynb. Testing still WIP, but the logic is correct (I have a local implementation outside of Langchain working). --------- Co-authored-by: Lance Martin <122662504+PineappleExpress808@users.noreply.github.com> Co-authored-by: rlm <31treehaus@31s-MacBook-Pro.local>
129 lines
2.6 KiB
Plaintext
129 lines
2.6 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "ab66dd43",
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"metadata": {},
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"source": [
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"# SVM Retriever\n",
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"\n",
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"This notebook goes over how to use a retriever that under the hood uses an SVM using scikit-learn.\n",
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"\n",
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"Largely based on https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.ipynb"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "393ac030",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.retrievers import SVMRetriever\n",
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"from langchain.embeddings import OpenAIEmbeddings"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "a801b57c",
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"metadata": {},
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"outputs": [],
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"source": [
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"# !pip install scikit-learn"
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]
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},
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{
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"cell_type": "markdown",
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"id": "aaf80e7f",
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"metadata": {},
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"source": [
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"## Create New Retriever with Texts"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "98b1c017",
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"metadata": {},
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"outputs": [],
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"source": [
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"retriever = SVMRetriever.from_texts([\"foo\", \"bar\", \"world\", \"hello\", \"foo bar\"], OpenAIEmbeddings())"
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]
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},
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{
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"cell_type": "markdown",
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"id": "08437fa2",
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"metadata": {},
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"source": [
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"## Use Retriever\n",
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"\n",
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"We can now use the retriever!"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "c0455218",
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"metadata": {},
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"outputs": [],
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"source": [
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"result = retriever.get_relevant_documents(\"foo\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "7dfa5c29",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[Document(page_content='foo', metadata={}),\n",
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" Document(page_content='foo bar', metadata={}),\n",
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" Document(page_content='hello', metadata={}),\n",
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" Document(page_content='world', metadata={})]"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"result"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "74bd9256",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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