diff --git a/sweepai/core/vector_db_test.py b/sweepai/core/vector_db_test.py
new file mode 100644
index 0000000000..b638aa233d
--- /dev/null
+++ b/sweepai/core/vector_db_test.py
@@ -0,0 +1,114 @@
+import pytest
+import numpy as np
+from unittest.mock import patch, MagicMock
+
+from sweepai.core.vector_db import (
+    embed_text_array,
+    openai_call_embedding,
+    cosine_similarity,
+    multi_get_query_texts_similarity,
+    normalize_l2,
+)
+from sweepai.utils.hash import hash_sha25
+
+# Mock the Cache class
+@pytest.fixture
+def mock_cache():
+    with patch('sweepai.core.vector_db.Cache') as mock:
+        yield mock.return_value
+
+# Mock the openai client
+@pytest.fixture
+def mock_openai_client():
+    with patch('sweepai.core.vector_db.get_embeddings_client') as mock:
+        yield mock.return_value
+
+def test_embed_text_array(mock_cache, mock_openai_client):
+    texts = ["Hello, world!", "This is a test."]
+    mock_embeddings = np.random.rand(2, 512)
+    mock_openai_client.embeddings.create.return_value.data = [
+        MagicMock(embedding=mock_embeddings[i]) for i in range(2)
+    ]
+
+    result = embed_text_array(texts)
+
+    assert len(result) == 1
+    assert result[0].shape == (2, 512)
+    np.testing.assert_array_almost_equal(result[0], normalize_l2(mock_embeddings[:, :512]))
+
+def test_openai_call_embedding(mock_openai_client):
+    batch = ["Test sentence 1", "Test sentence 2"]
+    mock_embeddings = np.random.rand(2, 1536)
+    mock_openai_client.embeddings.create.return_value.data = [
+        MagicMock(embedding=mock_embeddings[i]) for i in range(2)
+    ]
+
+    result = openai_call_embedding(batch)
+
+    assert result.shape == (2, 512)
+    np.testing.assert_array_almost_equal(result, normalize_l2(mock_embeddings[:, :512]))
+
+def test_cosine_similarity():
+    a = np.array([[1, 0, 0]])
+    B = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
+    result = cosine_similarity(a, B)
+    expected = np.array([[1, 0, 0]])
+    np.testing.assert_array_almost_equal(result, expected)
+
+def test_multi_get_query_texts_similarity(mock_openai_client):
+    queries = ["Query 1", "Query 2"]
+    documents = ["Document 1", "Document 2", "Document 3"]
+    
+    mock_query_embeddings = np.random.rand(2, 512)
+    mock_doc_embeddings = np.random.rand(3, 512)
+    
+    mock_openai_client.embeddings.create.side_effect = [
+        MagicMock(data=[MagicMock(embedding=e) for e in mock_query_embeddings]),
+        MagicMock(data=[MagicMock(embedding=e) for e in mock_doc_embeddings])
+    ]
+
+    result = multi_get_query_texts_similarity(queries, documents)
+
+    assert len(result) == 2
+    assert len(result[0]) == 3
+    assert all(0 <= sim <= 1 for sim in result[0])
+    assert all(0 <= sim <= 1 for sim in result[1])
+
+def test_caching(mock_cache, mock_openai_client):
+    texts = ["Cached text", "New text"]
+    cache_key = hash_sha256(texts[0]) + "v2.1.1"
+    mock_cache_embedding = np.random.rand(512)
+    mock_new_embedding = np.random.rand(512)
+
+    mock_cache.get.side_effect = [mock_cache_embedding, None]
+    mock_openai_client.embeddings.create.return_value.data = [
+        MagicMock(embedding=mock_new_embedding)
+    ]
+
+    result = embed_text_array(texts)
+
+    assert len(result) == 1
+    assert result[0].shape == (2, 512)
+    np.testing.assert_array_almost_equal(result[0][0], normalize_l2(mock_cache_embedding))
+    np.testing.assert_array_almost_equal(result[0][1], normalize_l2(mock_new_embedding))
+
+    mock_cache.set.assert_called_once_with(hash_sha256(texts[1]) + "v2.1.1", normalize_l2(mock_new_embedding))
+
+def test_openai_call_embedding_error_handling(mock_openai_client):
+    batch = ["Test sentence 1", "Test sentence 2"]
+    mock_openai_client.embeddings.create.side_effect = [
+        Exception("API Error"),
+        MagicMock(data=[MagicMock(embedding=np.random.rand(512)) for _ in range(2)])
+    ]
+
+    with pytest.raises(Exception, match="API Error"):
+        openai_call_embedding(batch)
+
+    # Test truncation on BadRequestError
+    mock_openai_client.embeddings.create.side_effect = [
+        openai.BadRequestError("maximum context length"),
+        MagicMock(data=[MagicMock(embedding=np.random.rand(512)) for _ in range(2)])
+    ]
+
+    result = openai_call_embedding(batch)
+    assert result.shape == (2, 512)
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