synthlang 0.1.2

Command-line interface for the SynthLang framework using DSPy

SynthLang CLI

A powerful command-line interface for the SynthLang framework, providing advanced prompt engineering, framework translation, and optimization capabilities using DSPy.

What is SynthLang?

Reduce AI costs by up to 95% with SynthLang's efficient prompt optimization. Experience up to 1100% faster processing while maintaining effectiveness.

Transform your AI interactions with mathematically-structured prompts. Symbolic Scribe brings academic rigor to prompt engineering, helping you create more precise, reliable, and powerful AI interactions. SynthLang is a revolutionary framework for prompt engineering and language model optimization. It introduces a structured, mathematical approach to prompt design that makes prompts more consistent, measurable, and effective. The framework uses a unique symbolic notation system that bridges natural language and computational thinking.

Core Concepts

• Symbolic Notation: Uses mathematical symbols (↹, ⊕, Σ) to represent input, process, and output
• Compositional Design: Break complex prompts into atomic operations
• Measurable Quality: Quantitative metrics for prompt effectiveness
• Evolutionary Optimization: Systematic improvement through genetic algorithms
• Framework Translation: Convert between different prompt engineering approaches

How It Works

SynthLang transforms natural language prompts into a structured format:

1. Input (↹): Define data sources and parameters
2. Process (⊕): Specify transformations and operations
3. Output (Σ): Describe expected results and formats
4. Operators: Use mathematical symbols (+, >, <, ^) for relationships
5. Joins (•): Connect related concepts

Metrics & Performance

SynthLang evaluates prompts across multiple dimensions:

Clarity Score (0-1)

• Symbol usage correctness
• Structure adherence
• Concept separation
• Line length optimization

Specificity Score (0-1)

• Operator precision
• Join relationships
• Transformation clarity
• Parameter definition

Consistency Score (0-1)

• Symbol alignment
• Format compliance
• Terminology usage
• Pattern adherence

Task Completion Score (0-1)

• Test case success
• Output matching
• Error handling
• Edge case coverage

Features

• 🔄 Framework Translation: Convert natural language to SynthLang format
• ⚡ Prompt Optimization: Enhance prompts using DSPy techniques
• 🧬 Evolutionary Algorithms: Evolve prompts through genetic algorithms
• 📊 Performance Metrics: Track clarity, specificity, and consistency scores
• 🎯 Task-Based Testing: Evaluate prompts against specific test cases
• 🔍 Smart Classification: Categorize and analyze prompts
• 🛠️ Extensible Architecture: Build custom modules and pipelines

Benefits

• Improved Efficiency: Streamline prompt engineering workflow
• Better Results: Generate more effective and consistent prompts
• Rapid Iteration: Quick experimentation and optimization
• Quality Metrics: Quantitative feedback on prompt quality
• Framework Integration: Seamless integration with existing tools
• DSPy Powered: Leverage advanced language model techniques

Installation

pip install synthlang

Basic Usage

1. Translate Natural Language to SynthLang

synthlang translate "Analyze customer feedback and generate sentiment insights"

2. Optimize a Prompt

synthlang optimize "path/to/prompt.txt" --iterations 5

3. Evolve Prompts

synthlang evolve "initial_prompt" --generations 10 --population 5

4. Classify Prompts

synthlang classify "prompt_text" --labels "task,query,instruction"

Advanced Usage

Custom Evolution Parameters

synthlang evolve "prompt" \
  --generations 20 \
  --population 10 \
  --mutation-rate 0.3 \
  --tournament-size 3 \
  --fitness-type hybrid

Test-Driven Optimization

synthlang optimize "prompt" \
  --test-cases tests.json \
  --target-score 0.95 \
  --max-iterations 50

Batch Processing

synthlang batch-translate prompts.txt \
  --output translated/ \
  --format json \
  --parallel 4

Environment Configuration

Create a .env file:

OPENAI_API_KEY=your_key_here
SYNTHLANG_MODEL=gpt-4o-mini
SYNTHLANG_TEMPERATURE=0.7

Examples

Framework Translation

# Input
synthlang translate "Get news articles about AI and analyze their sentiment"

# Output
↹ news•ai
⊕ fetch => articles
⊕ analyze => sentiment
Σ results + metrics

Prompt Evolution

# Start with basic prompt
synthlang evolve "Summarize text" \
  --test-cases summary_tests.json \
  --generations 5

# Evolution produces optimized versions:
# Generation 1: "Extract key points and create concise summary"
# Generation 2: "Identify main themes and synthesize core message"
# Generation 3: "Analyze content, extract insights, generate summary"

Classification Pipeline

# Classify multiple prompts
synthlang classify-batch prompts.txt \
  --labels "query,task,instruction,conversation" \
  --output classifications.json

Metrics Analysis

# Get detailed metrics for a prompt
synthlang analyze "prompt.txt" --detailed

# Output
{
  "clarity_score": 0.95,
  "specificity_score": 0.87,
  "consistency_score": 0.92,
  "task_score": 0.89,
  "overall_quality": 0.91
}

Development

1. Clone the repository:

git clone https://github.com/ruvnet/SynthLang.git
cd SynthLang/cli

2. Install development dependencies:

pip install -e ".[dev]"

3. Run tests:

pytest

Credits

SynthLang CLI is part of the SynthLang Framework created by @ruvnet.

• Framework: SynthLang
• Creator: @ruvnet
• Documentation: synthlang.org
• License: MIT

Contributing

Contributions are welcome! Please check out our Contributing Guidelines for details on how to submit pull requests, report issues, and contribute to the project.

License

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