DuiZhang (对仗)

LLM Chinese Couplet Generation Evaluation Framework — Evaluate and compare large language models' Chinese language capabilities through traditional couplet (对联) generation tasks.

Features

• Rule-based Evaluation: 6 core metrics (POS matching, structure, rhythm, tone, content relevance, imagery correspondence) + 2 base metrics (length match, no duplicate)
• LLM Self-Evaluation: Model self-assessment across all 6 dimensions for meta-cognitive analysis
• PDF RAG Pipeline: Build knowledge bases from academic PDFs and generate literature reviews
• Multi-Model Comparison: Evaluate multiple Ollama models side by side with radar chart visualization
• CLI & Python API: Full command-line interface and programmatic access
• Offline Design: All processing runs locally via Ollama — no external API calls required

Requirements

• Python 3.10+
• Ollama running locally
• Chat model (e.g., qwen2.5:7b)
• Embedding model (e.g., nomic-embed-text)

Installation

# From PyPI
pip install duizhang

# From source
git clone https://github.com/cycleuser/DuiZhang.git
cd DuiZhang
pip install -e .

Quick Start

# Start Ollama
ollama serve
ollama pull qwen2.5:7b
ollama pull nomic-embed-text

# Evaluate a model
duizhang eval --model qwen2.5:7b --samples 10

# Quick generation
duizhang run --model qwen2.5:7b --input "春风送暖入屠苏"

# Process PDFs
duizhang pdf --force-rebuild

# List available models
duizhang models

# Show configuration
duizhang config show

Python API

from duizhang import evaluate_couplet, index_documents, ToolResult

# Evaluate a single couplet
result = evaluate_couplet(
    input_line="春风送暖",
    expected="冬雪飘香",
    generated="秋月寒江",
)
print(result.metrics)

# Index PDF documents
result = index_documents(["paper1.pdf", "paper2.pdf"])

Evaluation Metrics

Rule-based Metrics

Base Metrics

LLM Self-Evaluation

The model evaluates its own output across all 6 rule-based dimensions, enabling meta-cognitive analysis by comparing algorithm scores vs self-assessment scores.

Project Structure

DuiZhang/
├── duizhang/
│   ├── __init__.py          # Version & public API
│   ├── __main__.py          # python -m duizhang entry
│   ├── api.py               # Unified Python API
│   ├── cli.py               # Entry point routing
│   ├── cli_app.py           # CLI application
│   ├── tools.py             # OpenAI function-calling tools
│   ├── core/
│   │   ├── __init__.py      # Core module exports
│   │   ├── config.py        # Configuration dataclass
│   │   ├── constants.py     # Application constants
│   │   ├── errors.py        # Custom exception hierarchy
│   │   ├── ollama_client.py # Ollama API client
│   │   ├── pdf_processor.py # PDF text extraction
│   │   ├── kb_builder.py    # FAISS knowledge base builder
│   │   └── summarizer.py    # Document summarization
│   ├── evaluator/
│   │   ├── __init__.py
│   │   ├── metrics.py       # Rule-based metrics
│   │   ├── llm_metrics.py   # LLM self-evaluation
│   │   ├── data_loader.py   # Couplet data loading
│   │   ├── evaluator.py     # Main evaluation engine
│   │   └── visualizer.py    # Radar chart visualization
│   ├── pdf_rag/
│   │   ├── __init__.py
│   │   ├── pipeline.py      # PDF processing pipeline
│   │   └── report_generator.py
│   ├── templates/           # Web UI templates
│   └── static/              # CSS/JS assets
├── tests/                   # Test suite
├── data/                    # Runtime data (auto-created)
└── images/                  # Screenshots

Testing

pip install -e ".[dev]"
pytest -v
pytest -v --cov=duizhang

License

GPL-3.0-or-later. See LICENSE for details.