Local DeepWiki MCP Server

# Test Configuration Documentation ## File Overview This file contains the test suite for the configuration system of the local_deepwiki application. It verifies that the `Config` class and related functions behave correctly with default values and specific configurations. ## Classes ### TestConfig Test suite for validating the `Config` class functionality. **Methods:** #### test_default_config Tests that the default configuration values are set correctly. ```python def test_default_config(self): """Test default configuration values.""" config = Config() assert config.embedding.provider == "local" assert config.llm.provider == "ollama" assert "python" in config.parsing.languages assert config.chunking.max_chunk_tokens == 512 ``` #### test_embedding_config Tests embedding-specific configuration values. ```python def test_embedding_config(self): """Test embedding configuration.""" config = Config() assert config.embedding.local.model == "all-MiniLM-L6-v2" assert config.embedding.openai.model == "text-embedding-3-small" ``` ## Functions ### get_config Retrieves the current configuration instance. ### set_config Sets the global configuration instance. ## Usage Examples ### Running Tests ```bash pytest tests/test_config.py ``` ### Basic Configuration Usage ```python from local_deepwiki.config import Config, get_config, set_config # Create a new config instance config = Config() # Access configuration values print(config.embedding.provider) # "local" print(config.llm.provider) # "ollama" # Get current config current_config = get_config() # Set new config new_config = Config() set_config(new_config) ``` ## Dependencies This file imports: - `pathlib.Path` - For path manipulation - `pytest` - Testing framework - `local_deepwiki.config.Config` - Main configuration class - `local_deepwiki.config.get_config` - Function to retrieve config - `local_deepwiki.config.set_config` - Function to set config The tests validate the default configuration values for: - Embedding provider defaults to "local" - LLM provider defaults to "ollama" - Python language support in parsing - Chunk size of 512 tokens - Specific embedding model names for local and OpenAI providers