Update tests for vector_store_code

dev_log.md

@@ -0,0 +1,29 @@
+# Development log for ReviewLlama
+
+## 06-12-2025
+
+- Fixed the tests for git functionality
+
+## 06-16-2025
+
+- Worked on the ollama integration module
+
+## 06-24-2025
+
+- Spent 5 minutes fixing some types for creating the Ollama chain
+
+## 06-25-2024
+
+- Spent time building out the llm client. I've added code up to the chat client. 
+
+TODOS:
+
+- [x] Add tests for the basic chat client
+- [x] Update ChatClient to use a LangChain Rag pipeline
+
+## 07-14-2025
+
+- Implement the Rag pipeline using langchain
+- Add tests for new functionality 
+
+

pyproject.toml

@@ -6,6 +6,7 @@ readme = "README.md"
 authors = [{ name = "Alex Selimov", email = "alex@alexselimov.com" }]
 requires-python = ">=3.13"
 dependencies = [
+    "faiss-cpu>=1.11.0",
     "gitpython>=3.1.44",
     "langchain>=0.3.25",
     "langchain-community>=0.3.25",

src/reviewllama/vector_store.py

@@ -7,7 +7,7 @@ from langchain_core.vectorstores import VectorStoreRetriever
 from langchain_ollama.embeddings import OllamaEmbeddings
 
 
-def documents_from_list(file_paths: list[Path | str]) -> list[Document]:
+def documents_from_path_list(file_paths: list[Path | str]) -> list[Document]:
     return [doc for file_path in file_paths for doc in TextLoader(file_path).load()]
 
 
@@ -15,7 +15,7 @@ def create_retriever(
     file_paths: list[Path | str], embedding_model: str
 ) -> VectorStoreRetriever:
     embeddings = OllamaEmbeddings(model=embedding_model)
-    vectorstore = FAISS.from_documents(documents_from_list(file_paths), embeddings)
+    vectorstore = FAISS.from_documents(documents_from_path_list(file_paths), embeddings)
     return vectorstore.as_retriever()
 
 

tests/conftest.py

@@ -0,0 +1,10 @@
+import pytest
+
+from reviewllama.configs import create_ollama_config
+
+
+@pytest.fixture
+def ollama_config():
+    return create_ollama_config(
+        "gemma3:4b", "localhost:11434", "You are a helpful assistant.", 0.0
+    )

tests/test_llm.py

@@ -4,18 +4,10 @@ Unit tests for llm chat client functionality
 
 import pytest
 
-from reviewllama.configs import create_ollama_config
 from reviewllama.llm import chat_with_client, create_chat_client
 from reviewllama.utilities import is_ollama_available
 
 
-@pytest.fixture
-def ollama_config():
-    return create_ollama_config(
-        "gemma3:4b", "localhost:11434", "You are a helpful assistant.", 0.0
-    )
-
-
 @pytest.fixture
 def chat_client(ollama_config):
     return create_chat_client(ollama_config)

tests/test_vector_store.py

@@ -0,0 +1,149 @@
+import os
+import tempfile
+from pathlib import Path
+from unittest.mock import MagicMock, Mock, patch
+
+import pytest
+from langchain_core.documents.base import Document
+from langchain_core.vectorstores import VectorStoreRetriever
+
+from reviewllama.utilities import is_ollama_available
+from reviewllama.vector_store import (create_retriever,
+                                      documents_from_path_list,
+                                      get_context_from_store)
+
+
+@pytest.fixture
+def temp_files():
+    """Create temporary test files"""
+    temp_dir = tempfile.mkdtemp()
+    files = []
+
+    # Create test files with different content
+    file_contents = [
+        "This is the first test file with Python code examples.",
+        "The second file contains JavaScript functions and classes.",
+        "File three has documentation about testing best practices.",
+        "Final file includes configuration settings and deployment info.",
+    ]
+
+    for i, content in enumerate(file_contents):
+        file_path = Path(temp_dir) / f"test_file_{i}.txt"
+        file_path.write_text(content)
+        files.append(file_path)
+
+    yield files
+
+    # Cleanup
+    for file_path in files:
+        file_path.unlink()
+    os.rmdir(temp_dir)
+
+
+def test_load_documents(temp_files):
+    docs = documents_from_path_list(temp_files)
+
+    assert len(docs) == 4
+    assert all(isinstance(doc, Document) for doc in docs)
+    assert all(doc.page_content for doc in docs)
+    assert all(doc.metadata.get("source") for doc in docs)
+
+
+@patch("reviewllama.vector_store.OllamaEmbeddings")
+@patch("reviewllama.vector_store.FAISS")
+@patch("reviewllama.vector_store.documents_from_path_list")
+def test_create_retriever(mock_docs_from_list, mock_faiss, mock_embeddings):
+    """Test successful retriever creation"""
+    # Setup mocks
+    mock_docs = [Document(page_content="test", metadata={"source": "test.txt"})]
+    mock_docs_from_list.return_value = mock_docs
+
+    mock_embedding_instance = Mock()
+    mock_embeddings.return_value = mock_embedding_instance
+
+    mock_vectorstore = Mock()
+    mock_retriever = Mock(spec=VectorStoreRetriever)
+    mock_vectorstore.as_retriever.return_value = mock_retriever
+    mock_faiss.from_documents.return_value = mock_vectorstore
+
+    # Test
+    result = create_retriever(["test.txt"], "test-embedding-model")
+
+    # Assertions
+    assert result == mock_retriever
+    mock_embeddings.assert_called_once_with(model="test-embedding-model")
+    mock_docs_from_list.assert_called_once_with(["test.txt"])
+    mock_faiss.from_documents.assert_called_once_with(
+        mock_docs, mock_embedding_instance
+    )
+    mock_vectorstore.as_retriever.assert_called_once()
+
+def test_get_context_from_store_success():
+    """Test successful context retrieval"""
+    mock_retriever = Mock(spec=VectorStoreRetriever)
+    mock_docs = [
+        Document(page_content="First relevant document", metadata={}),
+        Document(page_content="Second relevant document", metadata={}),
+        Document(page_content="Third relevant document", metadata={}),
+    ]
+    mock_retriever.get_relevant_documents.return_value = mock_docs
+    
+    result = get_context_from_store("test query", mock_retriever)
+    
+    expected = "First relevant document\n\nSecond relevant document\n\nThird relevant document"
+    assert result == expected
+    mock_retriever.get_relevant_documents.assert_called_once_with("test query")
+
+@patch('reviewllama.vector_store.OllamaEmbeddings')
+@patch('reviewllama.vector_store.FAISS')
+def test_full_pipeline_mock(mock_faiss, mock_embeddings, temp_files):
+    """Test the full pipeline with mocked external dependencies"""
+    # Setup mocks
+    mock_embedding_instance = Mock()
+    mock_embeddings.return_value = mock_embedding_instance
+    
+    mock_vectorstore = Mock()
+    mock_retriever = Mock(spec=VectorStoreRetriever)
+    mock_retriever.get_relevant_documents.return_value = [
+        Document(page_content="Relevant test content", metadata={})
+    ]
+    mock_vectorstore.as_retriever.return_value = mock_retriever
+    mock_faiss.from_documents.return_value = mock_vectorstore
+    
+    # Test full pipeline
+    retriever = create_retriever(temp_files[:2], "test-model")
+    context = get_context_from_store("test query", retriever)
+    
+    assert context == "Relevant test content"
+    mock_embeddings.assert_called_once_with(model="test-model")
+    mock_retriever.get_relevant_documents.assert_called_once_with("test query")
+
+def test_documents_from_list_content_verification(temp_files):
+    """Test that documents contain expected content"""
+    docs = documents_from_path_list(temp_files)
+    
+    contents = [doc.page_content for doc in docs]
+    
+    # Check that we have the expected content
+    assert any("Python code examples" in content for content in contents)
+    assert any("JavaScript functions" in content for content in contents)
+    assert any("testing best practices" in content for content in contents)
+    assert any("deployment info" in content for content in contents)
+
+# Optional: Integration test that requires actual Ollama server
+def test_create_retriever_with_real_ollama(temp_files, ollama_config):
+    """Integration test with real Ollama (requires server running)"""
+    if not is_ollama_available(ollama_config):
+        pytest.skip("Local Ollama server is not available")
+    try:
+        # This test would use a real embedding model
+        # Skip by default unless explicitly testing integration
+        retriever = create_retriever(temp_files[:2], "nomic-embed-text")
+        assert retriever is not None
+        
+        # Test actual retrieval
+        context = get_context_from_store("Python code", retriever)
+        assert isinstance(context, str)
+        
+    except Exception as e:
+        pytest.skip(f"Ollama server not available or model not found: {e}")

uv.lock

@@ -163,6 +163,23 @@ wheels = [
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 ]
 
+[[package]]
+name = "faiss-cpu"
+version = "1.11.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+    { name = "packaging" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/e7/9a/e33fc563f007924dd4ec3c5101fe5320298d6c13c158a24a9ed849058569/faiss_cpu-1.11.0.tar.gz", hash = "sha256:44877b896a2b30a61e35ea4970d008e8822545cb340eca4eff223ac7f40a1db9", size = 70218, upload-time = "2025-04-28T07:48:30.459Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/92/90/d2329ce56423cc61f4c20ae6b4db001c6f88f28bf5a7ef7f8bbc246fd485/faiss_cpu-1.11.0-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:0c98e5feff83b87348e44eac4d578d6f201780dae6f27f08a11d55536a20b3a8", size = 3313807, upload-time = "2025-04-28T07:48:06.486Z" },
+    { url = "https://files.pythonhosted.org/packages/24/14/8af8f996d54e6097a86e6048b1a2c958c52dc985eb4f935027615079939e/faiss_cpu-1.11.0-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:796e90389427b1c1fb06abdb0427bb343b6350f80112a2e6090ac8f176ff7416", size = 7913539, upload-time = "2025-04-28T07:48:08.338Z" },
+    { url = "https://files.pythonhosted.org/packages/b2/2b/437c2f36c3aa3cffe041479fced1c76420d3e92e1f434f1da3be3e6f32b1/faiss_cpu-1.11.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:2b6e355dda72b3050991bc32031b558b8f83a2b3537a2b9e905a84f28585b47e", size = 3785181, upload-time = "2025-04-28T07:48:10.594Z" },
+    { url = "https://files.pythonhosted.org/packages/66/75/955527414371843f558234df66fa0b62c6e86e71e4022b1be9333ac6004c/faiss_cpu-1.11.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:6c482d07194638c169b4422774366e7472877d09181ea86835e782e6304d4185", size = 31287635, upload-time = "2025-04-28T07:48:12.93Z" },
+    { url = "https://files.pythonhosted.org/packages/50/51/35b7a3f47f7859363a367c344ae5d415ea9eda65db0a7d497c7ea2c0b576/faiss_cpu-1.11.0-cp313-cp313-win_amd64.whl", hash = "sha256:13eac45299532b10e911bff1abbb19d1bf5211aa9e72afeade653c3f1e50e042", size = 15005455, upload-time = "2025-04-28T07:48:16.173Z" },
+]
+
 [[package]]
 name = "frozenlist"
 version = "1.7.0"
@@ -802,6 +819,7 @@ name = "reviewllama"
 version = "0.1.0"
 source = { editable = "." }
 dependencies = [
+    { name = "faiss-cpu" },
     { name = "gitpython" },
     { name = "langchain" },
     { name = "langchain-community" },
@@ -812,6 +830,7 @@ dependencies = [
 
 [package.metadata]
 requires-dist = [
+    { name = "faiss-cpu", specifier = ">=1.11.0" },
     { name = "gitpython", specifier = ">=3.1.44" },
     { name = "langchain", specifier = ">=0.3.25" },
     { name = "langchain-community", specifier = ">=0.3.25" },