Clean up tests and add base_url to embedding model

src/reviewllama/llm.py

@@ -1,5 +1,4 @@
 from dataclasses import dataclass
-from pathlib import Path
 from typing import Any
 
 from langchain.memory import ConversationBufferMemory

src/reviewllama/logger.py

@@ -39,7 +39,7 @@ def log_paths(paths: List[Path]) -> None:
     console.print()
 
 def log_info(info: str) -> None:
-    """Log message with colored output."""
+    """Log error message with colored output."""
     console = create_console()
     console.print(f"{info}")
 

src/reviewllama/reviewllama.py

@@ -17,7 +17,7 @@ def run_reviewllama(config: ReviewConfig):
         chat_client = create_and_log_chat_client(config.ollama)
         analysis = create_and_log_git_diff_analysis(path, config.base_branch)
         retriever = create_and_log_vector_store_retriever(
-            analysis.repo, config.ollama.embedding_model
+            analysis.repo, config.ollama
         )
 
         for diff in analysis.diffs:
@@ -37,12 +37,12 @@ def create_and_log_git_diff_analysis(path: Path, base_branch: str) -> GitAnalysi
 
 
 def create_and_log_vector_store_retriever(
-    repo: Repo, embedding_model: str
+        repo: Repo, config: OllamaConfig 
 ) -> VectorStoreRetriever:
     log_info("Creating vector_store...")
     retriever = create_retriever(
         get_tracked_files(repo),
-        embedding_model,
+        config
     )
     log_info("Done creating vector store")
     return retriever

src/reviewllama/vector_store.py

@@ -6,21 +6,25 @@ from langchain_core.documents.base import Document
 from langchain_core.vectorstores import VectorStoreRetriever
 from langchain_ollama.embeddings import OllamaEmbeddings
 
+from .configs import OllamaConfig
+
 
 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()]
 
 
 def create_retriever(
-    file_paths: list[Path | str], embedding_model: str
+    file_paths: list[Path | str], config: OllamaConfig
 ) -> VectorStoreRetriever:
-    embeddings = OllamaEmbeddings(model=embedding_model)
+    embeddings = OllamaEmbeddings(
+        model=config.embedding_model, base_url=config.base_url
+    )
     vectorstore = FAISS.from_documents(documents_from_path_list(file_paths), embeddings)
     return vectorstore.as_retriever()
 
 
 def get_context_from_store(message: str, retriever: VectorStoreRetriever):
-    docs = retriever.get_relevant_documents(message)
+    docs = retriever.invoke(message)
     return "\n\n".join([doc.page_content for doc in docs])
 
 

tests/conftest.py

@@ -6,5 +6,5 @@ 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
+        "gemma3:4b", "localhost:11434", "You are a helpful assistant.", 0.0, "nomic-embed-text"
     )

tests/test_vector_store.py

@@ -1,16 +1,13 @@
 import os
 import tempfile
 from pathlib import Path
-from unittest.mock import MagicMock, Mock, patch
+from unittest.mock import 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
-from reviewllama.vector_store import (create_retriever,
-                                      documents_from_path_list,
-                                      get_context_from_store)
 
 
 @pytest.fixture
@@ -52,7 +49,9 @@ def test_load_documents(temp_files):
 @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):
+def test_create_retriever(
+    mock_docs_from_list, mock_faiss, mock_embeddings, ollama_config
+):
     """Test successful retriever creation"""
     # Setup mocks
     mock_docs = [Document(page_content="test", metadata={"source": "test.txt"})]
@@ -67,83 +66,15 @@ def test_create_retriever(mock_docs_from_list, mock_faiss, mock_embeddings):
     mock_faiss.from_documents.return_value = mock_vectorstore
 
     # Test
-    result = create_retriever(["test.txt"], "test-embedding-model")
+    result = create_retriever(["test.txt"], ollama_config)
 
     # Assertions
     assert result == mock_retriever
-    mock_embeddings.assert_called_once_with(model="test-embedding-model")
+    mock_embeddings.assert_called_once_with(
+        model=ollama_config.embedding_model, base_url=ollama_config.base_url
+    )
     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}")