diff --git a/langchain/retrievers/svm.py b/langchain/retrievers/svm.py
index d69abc4b..2060d3b0 100644
--- a/langchain/retrievers/svm.py
+++ b/langchain/retrievers/svm.py
@@ -4,7 +4,7 @@ https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.ipynb"""
 
 from __future__ import annotations
 
-from typing import Any, List
+from typing import Any, List, Optional
 
 import numpy as np
 from pydantic import BaseModel
@@ -22,6 +22,7 @@ class SVMRetriever(BaseRetriever, BaseModel):
     index: Any
     texts: List[str]
     k: int = 4
+    relevancy_threshold: Optional[float] = None
 
     class Config:
 
@@ -52,9 +53,25 @@ class SVMRetriever(BaseRetriever, BaseModel):
         similarities = clf.decision_function(x)
         sorted_ix = np.argsort(-similarities)
 
+        # svm.LinearSVC in scikit-learn is non-deterministic.
+        # if a text is the same as a query, there is no guarantee
+        # the query will be in the first index.
+        # this performs a simple swap, this works because anything
+        # left of the 0 should be equivalent.
+        zero_index = np.where(sorted_ix == 0)[0][0]
+        if zero_index != 0:
+            sorted_ix[0], sorted_ix[zero_index] = sorted_ix[zero_index], sorted_ix[0]
+
+        denominator = np.max(similarities) - np.min(similarities) + 1e-6
+        normalized_similarities = (similarities - np.min(similarities)) / denominator
+
         top_k_results = []
         for row in sorted_ix[1 : self.k + 1]:
-            top_k_results.append(Document(page_content=self.texts[row - 1]))
+            if (
+                self.relevancy_threshold is None
+                or normalized_similarities[row] >= self.relevancy_threshold
+            ):
+                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]: