naive-knowledge-base 0.1.6

A dependency graph analyzer using smolagents

Naive Knowledge Base

A dependency graph analyzer using smolagents for building and analyzing code dependencies.

Features

• 🔍 Dependency Graph Generation: Automatically generate dependency graphs from source code
• 🤖 AI-Powered Analysis: Uses smolagents for intelligent code analysis
• 📊 Multiple Language Support: Support for Java and other languages
• 🌳 Directory Tree Visualization: Generate visual representations of project structure
• 📝 File Operations: Read, write, and manage files programmatically

Installation

From Source

git clone https://github.com/yourusername/naive-knowledge-base.git
cd naive-knowledge-base
pip install -e .

From PyPI (when published)

pip install naive-knowledge-base

Requirements

• Python 3.8+
• OpenAI API key (or compatible API)

Configuration

Create a .env file in your project directory with the following:

OPENAI_API_KEY=your_api_key_here
# Or configure your API endpoint
API_BASE_URL=your_api_base_url

Usage

Command Line Interface

After installation, you can use the naive-kb command:

# Basic usage
naive-kb /path/to/source/directory

# Specify file extensions
naive-kb /path/to/source/directory java

# Specify directories to ignore
naive-kb /path/to/source/directory java "target,.git,test,node_modules"

Python API

from naive_knowledge_base import run_analysis

# Run dependency graph analysis
result = run_analysis(
    source_directory="/path/to/source",
    file_extensions="java",
    ignore_dirs="target,.git,test"
)

Advanced Usage

from smolagents import CodeAgent, ToolCallingAgent
from naive_knowledge_base.api_models import FlowApiModel
from naive_knowledge_base.tools import (
    save_content_to_file,
    read_file_content,
    delete_folder_or_file,
    generate_folder_tree
)
from naive_knowledge_base.agents.dependency_graph import generate_dependency_graph

# Create custom agents
model = FlowApiModel(model_id="gpt-4.1", temperature=0.5)

dependency_agent = ToolCallingAgent(
    tools=[generate_dependency_graph],
    model=model,
    max_steps=7,
    name="dependency_graph_agent"
)

manager_agent = CodeAgent(
    managed_agents=[dependency_agent],
    model=model,
    tools=[read_file_content, save_content_to_file],
    max_steps=30,
    name="tech_lead_agent"
)

# Run analysis
result = manager_agent.run("Analyze dependencies in /path/to/source")

Package Structure

naive_knowledge_base/
├── agents/
│   ├── common/          # Common agent utilities
│   │   ├── base.py
│   │   ├── exceptions.py
│   │   ├── logging.py
│   │   └── utils.py
│   └── dependency_graph/  # Dependency graph analysis
│       ├── agent.py
│       └── model.py
├── api_models/          # API model integrations
│   └── flow_api_model.py
└── tools/              # Agent tools
    ├── io.py           # File I/O operations
    └── tree.py         # Directory tree generation

Development

Setup Development Environment

# Clone the repository
git clone https://github.com/yourusername/naive-knowledge-base.git
cd naive-knowledge-base

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install in editable mode with dev dependencies
pip install -e ".[dev]"

Running Tests

pytest tests/

Code Formatting

# Format code
black .
isort .

# Check code quality
pylint naive_knowledge_base/

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Built with smolagents
• Uses OpenAI API for AI-powered analysis

Support

For issues and questions, please file an issue on the GitHub repository.