fixed condition and added intergration test

rag_app/vector_store_handler/vectorstores.py

@@ -167,7 +167,7 @@ class FAISSVectorStore(BaseVectorStore):
         Raises:
             ValueError: If the vector store is not initialized.
         """
-        if self.vectorstore is not None:
+        if self.vectorstore is None:
             raise ValueError("Vector store not initialized. Nothing to save.")
         self.vectorstore.save_local(self.persist_directory)
 

tests/integration/test_vector_store_integration.py

@@ -0,0 +1,89 @@
+import pytest
+from langchain.schema import Document
+from rag_app.vector_store_handler.vectorstores import ChromaVectorStore, FAISSVectorStore
+# from rag_app.database.init_db import db
+from config import EMBEDDING_MODEL, VECTOR_DATABASE_LOCATION
+from langchain.embeddings import HuggingFaceEmbeddings  # Or whatever embedding you're using
+
+@pytest.fixture(scope="module")
+def embedding_model():
+    return HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL)
+
+@pytest.fixture(params=[ChromaVectorStore, FAISSVectorStore])
+def vector_store(request, embedding_model, tmp_path):
+    store = request.param(embedding_model, persist_directory=str(tmp_path))
+    yield store
+    # Clean up (if necessary)
+    if hasattr(store, 'vectorstore'):
+        store.vectorstore.delete_collection()
+
+@pytest.fixture
+def sample_documents():
+    return [
+        Document(page_content="This is a test document about AI."),
+        Document(page_content="Another document discussing machine learning."),
+        Document(page_content="A third document about natural language processing.")
+    ]
+
+def test_create_and_search(vector_store, sample_documents):
+    # Create vector store
+    vector_store.create_vectorstore(sample_documents)
+
+    # Perform a search
+    results = vector_store.similarity_search("AI and machine learning")
+
+    assert len(results) > 0
+    assert any("AI" in doc.page_content for doc in results)
+    assert any("machine learning" in doc.page_content for doc in results)
+
+def test_save_and_load(vector_store, sample_documents, tmp_path):
+    # Create and save vector store
+    vector_store.create_vectorstore(sample_documents)
+    vector_store.save()
+
+    # Load the vector store
+    loaded_store = type(vector_store)(vector_store.embeddings, persist_directory=str(tmp_path))
+    loaded_store.load_existing_vectorstore()
+
+    # Perform a search on the loaded store
+    results = loaded_store.similarity_search("natural language processing")
+
+    assert len(results) > 0
+    assert any("natural language processing" in doc.page_content for doc in results)
+
+def test_update_vectorstore(vector_store, sample_documents):
+    # Create initial vector store
+    vector_store.create_vectorstore(sample_documents)
+
+    # Add a new document
+    new_doc = Document(page_content="A new document about deep learning.")
+    vector_store.vectorstore.add_documents([new_doc])
+
+    # Search for the new content
+    results = vector_store.similarity_search("deep learning")
+
+    assert len(results) > 0
+    assert any("deep learning" in doc.page_content for doc in results)
+
+@pytest.mark.parametrize("query,expected_content", [
+    ("AI", "AI"),
+    ("machine learning", "machine learning"),
+    ("natural language processing", "natural language processing")
+])
+def test_search_accuracy(vector_store, sample_documents, query, expected_content):
+    vector_store.create_vectorstore(sample_documents)
+    results = vector_store.similarity_search(query)
+    assert any(expected_content in doc.page_content for doc in results)
+
+# def test_database_integration(vector_store, sample_documents):
+#     # This test assumes your vector store interacts with the database in some way
+#     # You may need to adjust this based on your actual implementation
+#     vector_store.create_vectorstore(sample_documents)
+
+#     # Here, you might add some assertions about how the vector store interacts with the database
+#     # For example, if you're storing metadata about the documents in the database:
+#     for doc in sample_documents:
+#         result = db.session.query(YourDocumentModel).filter_by(content=doc.page_content).first()
+#         assert result is not None
+
+# Add more integration tests as needed