ragtrace-lite 1.0.7

Lightweight RAG evaluation framework with Korean language support, BGE-M3 embeddings, and HCX-005/Gemini LLMs

RAGTrace Lite

A lightweight RAG (Retrieval-Augmented Generation) evaluation framework with Korean language support

한국어 버전: README_KO.md

Overview

RAGTrace Lite is a lightweight framework for evaluating RAG system performance. Built on the RAGAS framework and optimized for Korean language environments.

Key Features:

• Intelligent Metric Selection: Automatically selects 5 or 4 metrics based on ground truth data availability
• Local BGE-M3 Embeddings: Offline embedding support for air-gapped environments
• Multi-LLM Support: HCX-005 (Naver CLOVA Studio) and Gemini (Google)
• Offline Deployment: Complete air-gapped deployment for closed networks
• Korean Language Optimized: Native Korean language support

Quick Start

Installation from PyPI (Recommended)

# Basic installation
pip install ragtrace-lite

# Full installation (LLM + Embeddings + Enhanced features)
pip install "ragtrace-lite[all]"

# Optional installations
pip install "ragtrace-lite[llm]"        # LLM support only
pip install "ragtrace-lite[embeddings]" # Local embeddings only

Development Installation

# Clone repository and install in development mode
git clone https://github.com/ntts9990/ragtrace-lite.git
cd ragtrace-lite

# Using uv (recommended)
uv sync

# Or using pip
pip install -e .[all]

API Key Configuration

Create a .env file and add your API keys:

CLOVA_STUDIO_API_KEY=nv-your-hcx-api-key
GEMINI_API_KEY=your-gemini-api-key

Run Sample Evaluation

# Run evaluation with BGE-M3 + HCX
ragtrace-lite evaluate data/sample_data.json --llm hcx

# Generate web dashboard
ragtrace-lite dashboard --open

Platform Support

• Windows 10+ (PowerShell/CMD)
• macOS 10.15+ (Intel/Apple Silicon)
• Linux Ubuntu 18.04+
• Python 3.9, 3.10, 3.11, 3.12

GPU Support: CUDA (Linux), MPS (Apple Silicon), CPU (All platforms)

Detailed Setup Guide: SETUP.md

Offline Deployment

RAGTrace Lite supports complete offline execution in air-gapped environments.

Quick Offline Deployment

# 1. Create deployment package (internet-connected environment)
python scripts/prepare_offline_deployment.py

# 2. Copy generated ZIP file to air-gapped PC
# dist/ragtrace-lite-offline-YYYYMMDD-HHMMSS.zip

# 3. Extract and install in air-gapped environment
scripts/install.bat

# 4. Run evaluation
scripts/run_evaluation.bat

Offline Support Features

• Python 3.11 Auto-Install: Windows installer included
• BGE-M3 Local Model: 2.3GB embedding model pre-downloaded
• All Dependencies Included: Complete offline installation with wheel files
• Automated Install Scripts: One-click installation with Windows batch files
• Complete Manual Guide: Manual installation support when scripts fail

Air-gapped Requirements

• OS: Windows 10+ (64bit)
• CPU: x86_64 architecture
• Memory: Minimum 4GB RAM (for BGE-M3 loading)
• Storage: Minimum 5GB (Python + model + dependencies)
• LLM: HCX-005 API (internal network host)

Offline Deployment Guide: OFFLINE_DEPLOYMENT.md
Manual Installation Guide: MANUAL_INSTALLATION_GUIDE.md

Key Features

• Fast Installation & Execution: Quick start with minimal dependencies
• Multi-LLM Support: HCX-005 (Naver CLOVA Studio) & Gemini (Google)
• Local Embeddings: Offline embedding support via BGE-M3
• Intelligent Metric Selection: Automatically selects 5 or 4 metrics based on ground truth availability
• Complete Offline Support: Full air-gapped execution for closed networks
• Data Storage: SQLite-based evaluation result storage and history management
• Enhanced Reports: JSON, CSV, Markdown, Elasticsearch NDJSON format support
• Security: Environment variable-based API key management

License

This project is provided under Apache License 2.0:

See the LICENSE file for details.

Usage

CLI Commands

# Run evaluation
ragtrace-lite evaluate data.json --llm hcx

# List available datasets
ragtrace-lite list-datasets

# Generate web dashboard
ragtrace-lite dashboard --open

# Check version
ragtrace-lite version

Python API

from ragtrace_lite import RAGTraceLite
from ragtrace_lite.config_loader import load_config

# Load configuration
config = load_config()

# Initialize RAGTraceLite
rag_trace = RAGTraceLite(config)

# Run evaluation
results = rag_trace.evaluate("your_data.json")

Environment Configuration

Create a .env file and set your API keys:

# HCX-005 (Naver CLOVA Studio)
CLOVA_STUDIO_API_KEY=nv-your-hcx-api-key

# Gemini (Google)
GEMINI_API_KEY=your-gemini-api-key

Supported Metrics

With Ground Truth Data (5 Metrics)

• Context Recall: Recall of retrieved contexts
• Context Precision: Precision of retrieved contexts
• Answer Correctness: Correctness of the answer
• Answer Relevancy: Relevance of the answer to the question
• Answer Similarity: Similarity between generated and ground truth answers

Without Ground Truth Data (4 Metrics)

• Context Relevancy: Relevance of context to the question
• Answer Relevancy: Relevance of answer to the question
• Faithfulness: How faithful the answer is to the context
• Coherence: Logical coherence of the answer

Project Structure

ragtrace-lite/
├── src/ragtrace_lite/          # Source code
│   ├── __init__.py
│   ├── cli.py                  # CLI interface
│   ├── config_loader.py        # Configuration management
│   ├── evaluator.py            # Evaluation engine
│   ├── llm_factory.py          # LLM integration
│   ├── db_manager.py           # Database management
│   └── report_generator.py     # Report generation
├── tests/                      # Tests
├── scripts/                    # Utility scripts
├── data/                       # Sample data
├── config.yaml                 # Default configuration
└── pyproject.toml             # Project configuration

Contributing

Contributions are welcome! See CONTRIBUTING.md for details.

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

Support

Having issues or questions?

• Issue Tracker: GitHub Issues
• Documentation: Full Documentation
• Email: [e-mail]

Acknowledgments

This project is based on the following open-source projects:

• RAGAS - RAG evaluation framework
• BGE-M3 - Multilingual embedding model
• LangChain - LLM application framework

Changelog

See CHANGELOG.md for a full history of changes.

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Made with ❤️ by ntts9990