diff --git a/tests/system/knowledge_base/test_knowledge_base.py b/tests/system/knowledge_base/test_knowledge_base.py
index 3ede466a..0f995ef5 100644
--- a/tests/system/knowledge_base/test_knowledge_base.py
+++ b/tests/system/knowledge_base/test_knowledge_base.py
@@ -59,7 +59,7 @@ def chunker():
 @pytest.fixture(scope="module")
 def encoder():
     return StubRecordEncoder(
-        StubDenseEncoder(dimension=3))
+        StubDenseEncoder())
 
 
 @pytest.fixture(scope="module", autouse=True)
diff --git a/tests/unit/stubs/stub_dense_encoder.py b/tests/unit/stubs/stub_dense_encoder.py
index d0e02ff2..9d55bb7f 100644
--- a/tests/unit/stubs/stub_dense_encoder.py
+++ b/tests/unit/stubs/stub_dense_encoder.py
@@ -1,15 +1,47 @@
-import hashlib
+import mmh3
 import numpy as np
+from collections import defaultdict
 from typing import Union, List
 
 from pinecone_text.dense.base_dense_ecoder import BaseDenseEncoder
 
 
 class StubDenseEncoder(BaseDenseEncoder):
-
-    def __init__(self, dimension: int = 3):
+    """
+    Bag-of-words encoder that uses a random projection matrix to
+    project sparse vectors to dense vectors.
+    uses Johnson–Lindenstrauss lemma to project BOW sparse vectors to dense vectors.
+    """
+
+    def __init__(self,
+                 dimension: int = 8,
+                 vocab_size: int = 2 ** 12):
+        self.input_dim = vocab_size
         self.dimension = dimension
 
+    def _text_to_word_counts(self, text: str) -> defaultdict:
+        words = text.split()
+        word_counts = defaultdict(int)
+        for word in words:
+            hashed_word = mmh3.hash(word) % self.input_dim
+            word_counts[hashed_word] += 1
+        return word_counts
+
+    def _encode_text(self, text: str) -> List[float]:
+        word_counts = self._text_to_word_counts(text)
+
+        # This will hold the result of word_counts * random_matrix
+        projected_embedding = np.zeros(self.dimension, dtype=np.float32)
+
+        for hashed_word, count in word_counts.items():
+            rng = np.random.default_rng(hashed_word)
+            # Seed the RNG with the hashed word index for consistency
+            random_vector = rng.standard_normal(self.dimension)
+            projected_embedding += count * random_vector
+
+        projected_embedding = projected_embedding.astype(np.float32)
+        return list(projected_embedding / np.linalg.norm(projected_embedding))
+
     def encode_documents(self,
                          texts: Union[str, List[str]]
                          ) -> Union[List[float], List[List[float]]]:
@@ -20,23 +52,10 @@ def encode_queries(self,
                        ) -> Union[List[float], List[List[float]]]:
         return self._encode(texts)
 
-    def consistent_embedding(self, text: str) -> List[float]:
-        # consistent embedding function that project each text to a unique angle
-        embedding = []
-        for i in range(self.dimension):
-            sha256_hash = hashlib.sha256(f"{text} {i}".encode()).hexdigest()
-            int_value = int(sha256_hash, 16)
-            embedding.append(int_value / float(1 << 256))
-
-        l2_norm = np.linalg.norm(embedding)
-        normalized_embedding = [float(value / l2_norm) for value in embedding]
-
-        return normalized_embedding
-
     def _encode(self,
                 texts: Union[str, List[str]]
                 ) -> Union[List[float], List[List[float]]]:
         if isinstance(texts, str):
-            return self.consistent_embedding(texts)
+            return self._encode_text(texts)
         else:
-            return [self.consistent_embedding(text) for text in texts]
+            return [self._encode_text(text) for text in texts]