synthlang 0.1.3

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: A sophisticated system that uses mathematical symbols to represent different aspects of prompt engineering:

  • ↹ (Input): Represents data ingestion and parameter definition
  • ⊕ (Process): Denotes transformations and computational operations
  • Σ (Output): Specifies result formats and expectations
  • These symbols create a standardized way to express prompt logic

• Compositional Design: A modular approach to prompt engineering that:

  • Breaks down complex prompts into smaller, manageable components
  • Enables reuse of common prompt patterns
  • Facilitates testing and optimization of individual components
  • Allows for easy composition of complex workflows

• Measurable Quality: A comprehensive metrics system that:

  • Evaluates prompt effectiveness using quantitative measures
  • Tracks performance across multiple dimensions
  • Provides actionable feedback for improvement
  • Enables data-driven optimization

• Evolutionary Optimization: An advanced genetic algorithm system that:

  • Automatically improves prompts through successive generations
  • Uses mutation and crossover operations
  • Selects the best-performing variants
  • Adapts to specific use cases and requirements

• Framework Translation: A sophisticated translation system that:

  • Converts between different prompt engineering approaches
  • Maintains semantic equivalence
  • Optimizes for target framework characteristics
  • Preserves intent and functionality

How It Works

SynthLang transforms natural language prompts into a structured format through a multi-step process:

1. Input (↹):

   • Defines data sources and their characteristics
   • Specifies input parameters and their types
   • Establishes validation rules
   • Sets up preprocessing requirements

2. Process (⊕):

   • Details transformation steps
   • Defines computational operations
   • Specifies conditional logic
   • Establishes processing pipelines

3. Output (Σ):

   • Describes expected result formats
   • Specifies success criteria
   • Defines error handling
   • Sets quality thresholds

4. Operators:

   • Mathematical symbols for relationship definition:
     • (+) Addition/Combination
     • (>) Greater than/Progression
     • (<) Less than/Regression
     • (^) Power/Amplification

5. Joins (•):

   • Connect related concepts
   • Establish hierarchical relationships
   • Define concept groupings
   • Create logical bridges

Metrics & Performance

SynthLang evaluates prompts across multiple dimensions with sophisticated scoring systems:

Clarity Score (0-1)

Measures the structural quality of prompts:

• Symbol Usage Correctness: Evaluates proper application of symbolic notation
• Structure Adherence: Assesses compliance with SynthLang patterns
• Concept Separation: Measures clear delineation between components
• Line Length Optimization: Analyzes prompt formatting efficiency

Specificity Score (0-1)

Evaluates the precision of prompt instructions:

• Operator Precision: Measures accuracy of mathematical operators
• Join Relationships: Assesses the quality of concept connections
• Transformation Clarity: Evaluates process step definitions
• Parameter Definition: Analyzes input/output specifications

Consistency Score (0-1)

Measures prompt reliability and standardization:

• Symbol Alignment: Evaluates symbolic notation consistency
• Format Compliance: Assesses adherence to style guidelines
• Terminology Usage: Measures vocabulary standardization
• Pattern Adherence: Analyzes structural consistency

Task Completion Score (0-1)

Evaluates practical effectiveness:

• Test Case Success: Measures performance on standard tests
• Output Matching: Evaluates result accuracy
• Error Handling: Assesses robustness
• Edge Case Coverage: Analyzes comprehensive handling

Features

• 🔄 Framework Translation:

  • Converts natural language to SynthLang format
  • Preserves semantic meaning
  • Optimizes structure
  • Maintains context
  • Supports multiple frameworks
  • Provides bidirectional translation

• ⚡ Prompt Optimization:

  • Enhances prompts using DSPy techniques
  • Improves efficiency
  • Reduces token usage
  • Maintains accuracy
  • Implements best practices
  • Provides optimization suggestions

• 🧬 Evolutionary Algorithms:

  • Evolves prompts through genetic algorithms
  • Implements mutation strategies
  • Uses crossover techniques
  • Selects optimal variants
  • Adapts to requirements
  • Tracks evolutionary history

• 📊 Performance Metrics:

  • Tracks clarity scores
  • Measures specificity
  • Evaluates consistency
  • Analyzes effectiveness
  • Provides detailed reports
  • Suggests improvements

• 🎯 Task-Based Testing:

  • Evaluates against test cases
  • Measures success rates
  • Tracks performance
  • Identifies weaknesses
  • Suggests improvements
  • Maintains test history

• 🔍 Smart Classification:

  • Categorizes prompts
  • Analyzes patterns
  • Identifies similarities
  • Groups related prompts
  • Suggests optimizations
  • Maintains taxonomies

• 🛠️ Extensible Architecture:

  • Supports custom modules
  • Enables pipeline creation
  • Allows framework extension
  • Facilitates integration
  • Provides plugin system
  • Enables customization

Benefits

• Improved Efficiency:

  • Streamline prompt engineering workflow
  • Reduce development time
  • Optimize resource usage
  • Automate repetitive tasks
  • Enhance productivity

• Better Results:

  • Generate more effective prompts
  • Improve consistency
  • Enhance reliability
  • Reduce errors
  • Optimize performance

• Rapid Iteration:

  • Quick experimentation
  • Fast optimization
  • Efficient testing
  • Easy refinement
  • Continuous improvement

• Quality Metrics:

  • Quantitative feedback
  • Performance tracking
  • Quality assurance
  • Progress monitoring
  • Improvement suggestions

• Framework Integration:

  • Seamless tool integration
  • Easy adoption
  • Flexible deployment
  • Extensible design
  • Broad compatibility

• DSPy Powered:

  • Advanced language model techniques
  • State-of-the-art optimization
  • Efficient processing
  • Robust performance
  • Continuous updates

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.