src/reviewllama/cli.py

@@ -3,10 +3,15 @@ import sys
 from pathlib import Path
 from typing import List, Optional
 
-from reviewllama.reviewllama import run_reviewllama
+from reviewllama.git_diff import analyze_git_repository
 
-from .configs import ReviewConfig, namespace_to_config
-from .logger import log_paths, log_review_start
+from .configs import ReviewConfig, create_config_from_vars
+from .logger import (
+    log_git_analysis_result,
+    log_git_analysis_start,
+    log_paths,
+    log_review_start,
+)
 
 
 def normalize_server_url(url: str) -> str:
@@ -25,7 +30,7 @@ def create_argument_parser() -> argparse.ArgumentParser:
         epilog="""
 Examples:
   reviewllama . --model gemma3:27b --server localhost:11434
-  reviewllama src/ tests/ --model gemma3:4b
+  reviewllama src/ tests/ --model llama3.2:7b 
         """,
     )
 
@@ -38,7 +43,7 @@ Examples:
 
     parser.add_argument(
         "--model",
-        default="gemma3:4b",
+        default="llama3.2:3b",
         help="Ollama model to use for code review (default: %(default)s)",
     )
 
@@ -56,26 +61,6 @@ Examples:
         help="Base branch to compare against (default: %(default)s)",
     )
 
-    parser.add_argument(
-        "--embedding_model",
-        dest="embedding_model",
-        default="nomic-embed-text",
-        help="Base branch to compare against (default: %(default)s)",
-    )
-
-    parser.add_argument(
-        "--system_prompt",
-        dest="system_prompt",
-        default=(
-            "You are a PR review assistant in charge of softare quality control. "
-            "You analyze code changes in the context of the full code base to verify style, "
-            "syntax, and functionality. Each suggestion should consist of the original code, "
-            "suggested changes if relevant, and a short description of why the change is suggested "
-            "Examples\nInput:\n```\nvarialbe=1+1\n```\nOutput:\nOriginal:\n```\nvarialbe=1+1\n"
-            "Suggestion:\n```variable=1+1\n```\nReason: `varialbe` is likely a typo."
-        ),
-        help="Base branch to compare against (default: %(default)s)",
-    )
     return parser
 
 
@@ -85,20 +70,39 @@ def parse_raw_arguments(args: Optional[List[str]] = None) -> argparse.Namespace:
     return parser.parse_args(args)
 
 
+def transform_namespace_to_config(namespace: argparse.Namespace) -> ReviewConfig:
+    """Transform argparse namespace into ReviewConfig."""
+    paths = [Path(path_str) for path_str in namespace.paths]
+
+    return create_config_from_vars(
+        paths=paths,
+        model=namespace.model,
+        server_url=normalize_server_url(namespace.server_url),
+        # TODO: Update this system prompt. Either allow the user to provide it or engineer our own for this.
+        system_prompt="You are a helpful AI assistant",
+        base_branch=namespace.base_branch,
+    )
+
+
 def parse_arguments(args: Optional[List[str]] = None) -> ReviewConfig:
     """Parse command line arguments and return validated configuration."""
     raw_namespace = parse_raw_arguments(args)
-    return namespace_to_config(raw_namespace)
+    return transform_namespace_to_config(raw_namespace)
 
 
 def cli() -> None:
     """Main entry point for the CLI."""
     try:
         config = parse_arguments()
+        # TODO: Pass config to review engine
         log_review_start(config)
         log_paths(config.paths)
 
-        run_reviewllama(config)
+        for path in config.paths:
+            analysis = analyze_git_repository(path, config.base_branch)
+            log_git_analysis_start(path, config.base_branch)
+            log_git_analysis_result(analysis)
+            print(analysis.diffs)
 
     except SystemExit:
         # argparse calls sys.exit on error, let it propagate

src/reviewllama/configs.py

@@ -1,4 +1,3 @@
-import argparse
 from dataclasses import dataclass, field
 from pathlib import Path
 from typing import List
@@ -49,13 +48,15 @@ def create_review_config(
     return ReviewConfig(paths=paths, ollama=ollama_config, base_branch=base_branch)
 
 
-def namespace_to_config(
-        namespace: argparse.Namespace
+def create_config_from_vars(
+    paths: List[Path],
+    model: str,
+    server_url: str,
+    system_prompt: str,
+    base_branch: str,
 ):
-    """Transform argparse namespace into ReviewConfig."""
-    paths = [Path(path_str) for path_str in namespace.paths]
     ollama_config = OllamaConfig(
-        chat_model=namespace.model, base_url=namespace.server_url, system_prompt=namespace.system_prompt, embedding_model=namespace.embedding_model
+        chat_model=model, base_url=server_url, system_prompt=system_prompt
     )
 
-    return create_review_config(paths, ollama_config, namespace.base_branch)
+    return create_review_config(paths, ollama_config, base_branch)

src/reviewllama/git_diff.py

@@ -62,9 +62,9 @@ def branch_exists(repo: Repo, branch_name: str) -> bool:
         return False
 
 
-def get_tracked_files(repo: Repo) -> list[Path]:
+def get_tracked_files(repo: Repo):
     return [
-        Path(entry.abspath)
+        entry.abspath
         for entry in repo.commit().tree.traverse()
         if Path(entry.abspath).is_file()
     ]

src/reviewllama/llm.py

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

src/reviewllama/logger.py

@@ -38,10 +38,6 @@ def log_paths(paths: List[Path]) -> None:
         console.print(f"  • {path}")
     console.print()
 
-def log_info(info: str) -> None:
-    """Log error message with colored output."""
-    console = create_console()
-    console.print(f"{info}")
 
 def log_error(error: str) -> None:
     """Log error message with colored output."""

src/reviewllama/reviewllama.py

@@ -1,63 +0,0 @@
-from pathlib import Path
-
-from git import Repo
-from langchain_core.vectorstores import VectorStoreRetriever
-
-from reviewllama.configs import OllamaConfig, ReviewConfig
-from reviewllama.git_diff import (GitAnalysis, GitDiff, analyze_git_repository,
-                                  get_tracked_files)
-from reviewllama.llm import ChatClient, chat_with_client, create_chat_client
-from reviewllama.vector_store import create_retriever
-
-from .logger import log_git_analysis_result, log_git_analysis_start, log_info
-
-
-def run_reviewllama(config: ReviewConfig):
-    for path in config.paths:
-        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)
-
-        for diff in analysis.diffs:
-            chat_client = get_suggestions(diff, retriever, chat_client)
-
-
-def create_and_log_chat_client(config: OllamaConfig) -> ChatClient:
-    log_info("Initializing LLM chat client")
-    return create_chat_client(config)
-
-
-def create_and_log_git_diff_analysis(path: Path, base_branch: str) -> GitAnalysis:
-    log_git_analysis_start(path, base_branch)
-    analysis = analyze_git_repository(path, base_branch)
-    log_git_analysis_result(analysis)
-    return analysis
-
-
-def create_and_log_vector_store_retriever(
-    repo: Repo, config: OllamaConfig
-) -> VectorStoreRetriever:
-    log_info("Creating vector_store...")
-    retriever = create_retriever(get_tracked_files(repo), config)
-    log_info("Done creating vector store")
-    return retriever
-
-
-def get_suggestions(
-    diff: GitDiff, retriever: VectorStoreRetriever, chat_client: ChatClient
-) -> ChatClient:
-    new_client = chat_with_client(chat_client, craft_message(diff), retriever)
-    log_info(str(new_client.get_last_response_or_none().content))
-    return new_client
-
-
-def craft_message(diff) -> str:
-    return (
-        "Review the following code changes and make up to three suggestions on "
-        "how to improve it. If the code is sufficiently simple or accurate then say "
-        "no suggestions can be found. Important issues you should consider are consistent "
-        "style, introduction of syntax errors, and potentially breaking changes in "
-        "interfaces/APIs that aren't properly handled.\n\n"
-        f"The original code:\n```\n{diff.old_content}\n```\n"
-        f"The new code:\n```\n{diff.new_content}```"
-    )

src/reviewllama/vector_store.py

@@ -6,25 +6,21 @@ 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], config: OllamaConfig
+    file_paths: list[Path | str], embedding_model: str
 ) -> VectorStoreRetriever:
-    embeddings = OllamaEmbeddings(
-        model=config.embedding_model, base_url=config.base_url
-    )
+    embeddings = OllamaEmbeddings(model=embedding_model)
     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.invoke(message)
+    docs = retriever.get_relevant_documents(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, "nomic-embed-text"
+        "gemma3:4b", "localhost:11434", "You are a helpful assistant.", 0.0
     )

tests/test_vector_store.py

@@ -1,13 +1,16 @@
 import os
 import tempfile
 from pathlib import Path
-from unittest.mock import Mock, patch
+from unittest.mock import MagicMock, Mock, patch
 
 import pytest
 from langchain_core.documents.base import Document
 from langchain_core.vectorstores import VectorStoreRetriever
 
-from reviewllama.vector_store import create_retriever, documents_from_path_list
+from reviewllama.utilities import is_ollama_available
+from reviewllama.vector_store import (create_retriever,
+                                      documents_from_path_list,
+                                      get_context_from_store)
 
 
 @pytest.fixture
@@ -49,9 +52,7 @@ 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, ollama_config
-):
+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"})]
@@ -66,15 +67,83 @@ def test_create_retriever(
     mock_faiss.from_documents.return_value = mock_vectorstore
 
     # Test
-    result = create_retriever(["test.txt"], ollama_config)
+    result = create_retriever(["test.txt"], "test-embedding-model")
 
     # Assertions
     assert result == mock_retriever
-    mock_embeddings.assert_called_once_with(
-        model=ollama_config.embedding_model, base_url=ollama_config.base_url
-    )
+    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}")