SVM retriever (#2947) (#2949)

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>

docs/modules/indexes/retrievers/examples/svm_retriever.ipynb

@@ -0,0 +1,128 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ab66dd43",
+   "metadata": {},
+   "source": [
+    "# SVM Retriever\n",
+    "\n",
+    "This notebook goes over how to use a retriever that under the hood uses an SVM using scikit-learn.\n",
+    "\n",
+    "Largely based on https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.ipynb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "393ac030",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.retrievers import SVMRetriever\n",
+    "from langchain.embeddings import OpenAIEmbeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a801b57c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip install scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aaf80e7f",
+   "metadata": {},
+   "source": [
+    "## Create New Retriever with Texts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "98b1c017",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "retriever = SVMRetriever.from_texts([\"foo\", \"bar\", \"world\", \"hello\", \"foo bar\"], OpenAIEmbeddings())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08437fa2",
+   "metadata": {},
+   "source": [
+    "## Use Retriever\n",
+    "\n",
+    "We can now use the retriever!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c0455218",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = retriever.get_relevant_documents(\"foo\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7dfa5c29",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(page_content='foo', metadata={}),\n",
+       " Document(page_content='foo bar', metadata={}),\n",
+       " Document(page_content='hello', metadata={}),\n",
+       " Document(page_content='world', metadata={})]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "74bd9256",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

langchain/retrievers/__init__.py

@@ -4,6 +4,7 @@ from langchain.retrievers.elastic_search_bm25 import ElasticSearchBM25Retriever
 from langchain.retrievers.metal import MetalRetriever
 from langchain.retrievers.pinecone_hybrid_search import PineconeHybridSearchRetriever
 from langchain.retrievers.remote_retriever import RemoteLangChainRetriever
+from langchain.retrievers.svm import SVMRetriever
 from langchain.retrievers.tfidf import TFIDFRetriever
 from langchain.retrievers.weaviate_hybrid_search import WeaviateHybridSearchRetriever
 
@@ -16,4 +17,5 @@ __all__ = [
     "TFIDFRetriever",
     "WeaviateHybridSearchRetriever",
     "DataberryRetriever",
+    "SVMRetriever",
 ]

langchain/retrievers/svm.py

@@ -0,0 +1,61 @@
+"""SMV Retriever.
+Largely based on
+https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.ipynb"""
+
+from __future__ import annotations
+
+from typing import Any, List
+
+import numpy as np
+from pydantic import BaseModel
+
+from langchain.embeddings.base import Embeddings
+from langchain.schema import BaseRetriever, Document
+
+
+def create_index(contexts: List[str], embeddings: Embeddings) -> np.ndarray:
+    return np.array([embeddings.embed_query(split) for split in contexts])
+
+
+class SVMRetriever(BaseRetriever, BaseModel):
+    embeddings: Embeddings
+    index: Any
+    texts: List[str]
+    k: int = 4
+
+    class Config:
+
+        """Configuration for this pydantic object."""
+
+        arbitrary_types_allowed = True
+
+    @classmethod
+    def from_texts(
+        cls, texts: List[str], embeddings: Embeddings, **kwargs: Any
+    ) -> SVMRetriever:
+        index = create_index(texts, embeddings)
+        return cls(embeddings=embeddings, index=index, texts=texts, **kwargs)
+
+    def get_relevant_documents(self, query: str) -> List[Document]:
+        from sklearn import svm
+
+        query_embeds = np.array(self.embeddings.embed_query(query))
+        x = np.concatenate([query_embeds[None, ...], self.index])
+        y = np.zeros(x.shape[0])
+        y[0] = 1
+
+        clf = svm.LinearSVC(
+            class_weight="balanced", verbose=False, max_iter=10000, tol=1e-6, C=0.1
+        )
+        clf.fit(x, y)
+
+        similarities = clf.decision_function(x)
+        sorted_ix = np.argsort(-similarities)
+
+        top_k_results = []
+        for row in sorted_ix[1 : self.k + 1]:
+            top_k_results.append(Document(page_content=self.texts[row - 1]))
+        return top_k_results
+
+    async def aget_relevant_documents(self, query: str) -> List[Document]:
+        raise NotImplementedError