fix(test): better test + cov

.github/workflows/pytest.yml

@@ -21,7 +21,7 @@ jobs:
           if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
       - name: Test with pytest and generate coverage report
         run: |
-          python -m pytest --cov=application --cov=scripts --cov=extensions --cov-report=xml
+          python -m pytest --cov=application --cov-report=xml
       - name: Upload coverage reports to Codecov
         if: github.event_name == 'pull_request' && matrix.python-version == '3.11'
         uses: codecov/codecov-action@v3

tests/test_vector_store.py

@@ -9,11 +9,33 @@ from application.core.settings import settings
 
 def test_init_local_faiss_store_huggingface():
     """
-    Test that asserts that trying to initialize a FaissStore with
+    Test that asserts that initializing a FaissStore with
     the huggingface sentence transformer below together with the
     index.faiss file in the application/ folder results in a
     dimension mismatch error.
     """
-    settings.EMBEDDINGS_NAME = "openai_text-embedding-ada-002"
-    with pytest.raises(ValueError):
-        FaissStore("application/", "", None)
+    import os
+    from langchain.embeddings import HuggingFaceEmbeddings
+    from langchain.docstore.document import Document
+    from langchain_community.vectorstores import FAISS
+
+    # Ensure application directory exists
+    index_path = os.path.join("application")
+    os.makedirs(index_path, exist_ok=True)
+
+    # Create an index.faiss with a different embeddings dimension
+    # Use a different embedding model with a smaller dimension
+    other_embedding_model = "sentence-transformers/all-MiniLM-L6-v2"  # Dimension 384
+    other_embeddings = HuggingFaceEmbeddings(model_name=other_embedding_model)
+    # Create some dummy documents
+    docs = [Document(page_content="Test document")]
+    # Create index using the other embeddings
+    other_docsearch = FAISS.from_documents(docs, other_embeddings)
+    # Save index to application/
+    other_docsearch.save_local(index_path)
+
+    # Now set the EMBEDDINGS_NAME to the one with a different dimension
+    settings.EMBEDDINGS_NAME = "huggingface_sentence-transformers/all-mpnet-base-v2"  # Dimension 768
+    with pytest.raises(ValueError) as exc_info:
+        FaissStore("", None)
+    assert "Embedding dimension mismatch" in str(exc_info.value)