LLM Development Environment

A comprehensive environment for testing and comparing different LLM providers and tools.

Features

• Support for multiple LLM providers (OpenAI, Anthropic, etc.)
• Vector store integration (Chroma, Pinecone, etc.)
• GUI interface for easy testing
• Comprehensive testing suite
• Docker support
• Automated setup and configuration

Quick Start

1. Clone the repository:

git clone https://github.com/yourusername/llm-dev-env.git
cd llm-dev-env

2. Install the environment:

make install

3. Copy .env.template to .env and add your API keys:

cp .env.template .env
# Edit .env with your API keys

4. Set up the environment:

make setup

5. Start the GUI:

make gui

Available Commands

• make install: Install the environment
• make setup: Initialize the environment
• make test: Run tests
• make docker: Build and start Docker container
• make clean: Clean up temporary files
• make format: Format code
• make lint: Run linters
• make jupyter: Start Jupyter Lab
• make gui: Start Streamlit GUI

Directory Structure

llm-dev-env/
├── config/           # Configuration files
├── data/            # Data storage
├── output/          # Output files
├── logs/            # Log files
├── prompts/         # Saved prompts
├── src/             # Source code
├── tests/           # Test files
├── .env             # Environment variables
├── Dockerfile       # Docker configuration
├── docker-compose.yml
├── Makefile
└── README.md

Contributing

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

MIT License

5. Finally, add a `.gitignore`:

```gitignore
# Python
__pycache__/
*.py[cod]
*$py.class
*.so
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
*.egg-info/
.installed.cfg
*.egg

# Virtual Environment
venv/
ENV/

# IDE
.idea/
.vscode/
*.swp
*.swo

# Project specific
.env
logs/
output/
data/
.cache/

# Jupyter
.ipynb_checkpoints
*.ipynb

# Testing
.coverage
htmlcov/
.pytest_cache/
.mypy_cache/

# Docker
.docker/

To use everything:

1. Clone/download all files to your project directory
2. Run:

make install
# Edit .env with your API keys
make setup
make gui  # For the Streamlit interface

The Streamlit GUI provides an easy way to:

• Test different LLM providers
• Save and load prompts
• Adjust parameters
• Compare responses
• View and export results