# Component Architecture

# Component Architecture Documentation

## 1. Component Breakdown

The system is composed of the following key components:

### setup
This module handles the package setup and installation process, leveraging the `setuptools` library.

### `anthropic_provider`, `ollama_provider`, `openai_provider`
These modules encapsulate the integration with different language model providers, abstracting away the provider-specific details behind a common interface defined in the `base` module.

### `factory`
The `factory` module is responsible for creating instances of the appropriate language model provider based on the user's configuration.

### `base`
The `base` module defines the common interface and base classes for language model providers, ensuring a consistent API across different providers.

### `models`
The `models` module defines the core data structures used throughout the system, such as prompts, responses, and configuration settings.

### `cli`
The `cli` module provides the command-line interface, allowing users to interact with the system through the terminal.

### `loader`
The `loader` module is responsible for loading configuration settings from YAML files.

### `analyzer`
The `analyzer` module analyzes the user's code and extracts relevant information, such as function signatures and docstrings.

### `diagram`
The `diagram` module generates visual diagrams, such as class diagrams and sequence diagrams, to help users understand the system's architecture and behavior.

### `feature_generator`
The `feature_generator` module generates new features based on the user's code and configuration.

### `generator`
The `generator` module is responsible for orchestrating the overall feature generation process, coordinating the various components.

### `file_utils`, `logger`, `parser`, `handler`, `watcher`
These modules provide supporting functionality, such as file management, logging, parsing, and file system monitoring.

### `test_config`
The `test_config` module contains utility functions for testing the system's configuration and data models.

## 2. Communication Patterns

The key communication patterns in the system are as follows:

### Prompt Generation Workflow
```mermaid
sequenceDiagram
    participant User
    participant CLI
    participant Generator
    participant FeatureGenerator
    participant Analyzer
    participant Provider

    User->>CLI: Initiate prompt generation
    CLI->>Generator: Request prompt generation
    Generator->>FeatureGenerator: Generate features
    FeatureGenerator->>Analyzer: Analyze user code
    Analyzer->>FeatureGenerator: Return analysis results
    FeatureGenerator->>Generator: Return generated features
    Generator->>Provider: Request prompt completion
    Provider->>Generator: Return completed prompt
    Generator->>CLI: Return completed prompt
    CLI->>User: Display completed prompt
```

### Configuration Loading Workflow
```mermaid
sequenceDiagram
    participant User
    participant CLI
    participant Loader
    participant Models

    User->>CLI: Request configuration load
    CLI->>Loader: Load configuration
    Loader->>Models: Validate and parse configuration
    Models->>Loader: Return configuration objects
    Loader->>CLI: Return configuration
    CLI->>User: Display configuration
```

## 3. Data Models

The system's core data models are defined in the `models` module:

```mermaid
classDiagram
    class Prompt {
        +text: str
        +max_tokens: int
        +temperature: float
        +top_p: float
        +n: int
        +stop: list[str]
        +presence_penalty: float
        +frequency_penalty: float
    }

    class Response {
        +text: str
        +tokens: list[str]
        +token_count: int
    }

    class ProviderConfig {
        +name: str
        +api_key: str
        +model: str
    }

    class GeneratorConfig {
        +prompt_template: str
        +max_features: int
        +feature_quality_threshold: float
    }

    class AnalyzerConfig {
        +include_patterns: list[str]
        +exclude_patterns: list[str]
    }

    Prompt "1" -- "1" Response
    ProviderConfig "1" -- "1" Provider
    GeneratorConfig "1" -- "1" Generator
    AnalyzerConfig "1" -- "1" Analyzer
```

## 4. Design Patterns

The system employs the following design patterns:

1. **Factory Pattern**: The `factory` module uses the Factory pattern to create instances of the appropriate language model provider based on the user's configuration.
2. **Strategy Pattern**: The `base`, `anthropic_provider`, `ollama_provider`, and `openai_provider` modules use the Strategy pattern to provide a common interface for interacting with different language model providers.
3. **Observer Pattern**: The `watcher` module uses the Observer pattern to monitor file system changes and notify the `handler` module.
4. **Singleton Pattern**: The `logger` module uses the Singleton pattern to ensure a single, global logger instance.

These patterns help to improve the system's modularity, flexibility, and maintainability.

## 5. Extension Points

The system provides the following extension points:

1. **Language Model Providers**: New language model providers can be added by implementing the `LanguageModelProvider` interface defined in the `base` module and registering them with the `factory` module.
2. **Configuration Loaders**: New configuration loaders can be added by implementing the `ConfigLoader` interface defined in the `loader` module.
3. **Feature Generators**: New feature generators can be added by implementing the `FeatureGenerator` interface defined in the `feature_generator` module.
4. **Diagram Generators**: New diagram generators can be added by implementing the `DiagramGenerator` interface defined in the `diagram` module.

These extension points allow the system to be easily expanded and customized to meet new requirements or integrate with additional language models and tools.

## System Architecture Diagram

```mermaid
graph TD
    title[System Architecture]
    style title fill:#f9f,stroke:#333,stroke-width:2px

    M0[setup]
    M1[__init__]
    M2[anthropic_provider]
    M3[base]
    M4[factory]
    M5[ollama_provider]
    M6[openai_provider]
    M7[cli]
    M8[loader]
    M9[models]
    M10[analyzer]
    M11[diagram]
    M12[feature_generator]
    M13[generator]
    M14[file_utils]
    M15[logger]
    M16[parser]
    M17[handler]
    M18[watcher]
    M19[test_config]
    M20[test_diagram]
    M21[test_exclusions]
    M22[test_file_utils]
    M23[test_parser]

    M2 --> M3
    M4 --> M2
    M4 --> M9
    M4 --> M6
    M4 --> M3
    M4 --> M5
    M5 --> M3
    M6 --> M3
    M7 --> M13
    M7 --> M18
    M7 --> M9
    M7 --> M15
    M7 --> M8
    M8 --> M9
    M10 --> M9
    M10 --> M16
    M10 --> M15
    M10 --> M14
    M12 --> M3
    M12 --> M14
    M13 --> M9
    M13 --> M4
    M13 --> M3
    M13 --> M14
    M13 --> M15
    M13 --> M10
    M13 --> M11
    M13 --> M12
    M17 --> M15
    M18 --> M17
    M18 --> M9
    M18 --> M15
    M19 --> M9
    M19 --> M8
    M20 --> M11
    M21 --> M9
    M21 --> M14
    M23 --> M16
```
