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README.md

Mellea Examples

This directory contains comprehensive examples demonstrating Mellea's features and capabilities. Examples are organized by topic and complexity level.

🚀 Getting Started

New to Mellea? Start here:

  1. tutorial/simple_email.py - Your first Mellea program
  2. instruct_validate_repair/ - Core paradigm
  3. generative_slots/ - Type-safe LLM functions
  4. notebooks/ - Interactive tutorials

📚 Example Categories

Core Concepts

instruct_validate_repair/ Learn Mellea's core instruct-validate-repair paradigm for reliable LLM outputs.

  • Basic instruction without requirements
  • Adding validation constraints
  • Automatic repair on validation failure
  • Custom validation functions

generative_slots/ Type-safe, composable LLM functions using the @generative decorator.

  • Sentiment classification
  • Text summarization
  • Function composition
  • Type-constrained outputs

context/ Understanding and working with Mellea's context system.

  • Context inspection
  • Sampling with contexts
  • Context trees and navigation

sessions/ Creating and customizing Mellea sessions.

  • Session configuration
  • Custom session types
  • Backend selection

Advanced Features

aLora/ Adaptive Low-Rank Adaptation for fast constraint checking.

  • Training custom aLoRA adapters
  • Performance optimization
  • Constraint validation speedup

intrinsics/ Specialized model capabilities through adapters.

  • Answer relevance checking
  • Hallucination detection
  • Citation validation
  • Context relevance assessment

sofai/ Two-tier sampling with fast and slow models.

  • Cost optimization
  • Iterative refinement with fast models
  • Escalation to slow models
  • Constraint satisfaction problems

Data & Documents

information_extraction/ Extracting structured information from unstructured text.

  • Named entity recognition
  • Type-safe extraction
  • Structured output generation

mobject/ Working with structured data types (tables, documents).

  • Table queries and transformations
  • Document processing
  • Structured data operations

mify/ Making custom Python objects work with LLMs.

  • Object integration with @mify
  • Custom string representations
  • Template integration
  • Tool generation from methods

rag/ Retrieval-Augmented Generation pipelines.

  • Vector search with FAISS
  • Relevance filtering
  • Grounded answer generation
  • Multi-stage RAG pipelines

Agents & Tools

agents/ Implementing agent patterns (ReACT).

  • Reasoning and acting loops
  • Tool selection and execution
  • Multi-turn agent workflows

tools/ Tool calling and code execution.

  • Code interpreter integration
  • Custom tool definition
  • Tool argument validation
  • Safe code execution

Safety & Validation

safety/ Content safety with Granite Guardian models.

  • Harm detection
  • Jailbreak prevention
  • Bias checking
  • Groundedness validation
  • Function call hallucination detection

Integration & Deployment

m_serve/ Deploying Mellea programs as REST APIs.

  • API service creation
  • Production deployment patterns
  • Client integration

library_interop/ Integrating with other LLM libraries.

  • LangChain message conversion
  • OpenAI format compatibility
  • Cross-library workflows

mcp/ Model Context Protocol integration.

  • MCP tool creation
  • Claude Desktop integration
  • Langflow integration

Multimodal

image_text_models/ Working with vision-language models.

  • Image understanding
  • Multimodal prompting
  • Vision model backends

Complete Applications

mini_researcher/ Full-featured research assistant with RAG and validation.

  • Multi-model architecture
  • Document retrieval
  • Safety checks
  • Custom validation pipeline

Interactive Learning

notebooks/ Jupyter notebooks for interactive exploration.

  • Step-by-step tutorials
  • Immediate feedback
  • Visualization of results

tutorial/ Python script versions of tutorials.

  • Non-interactive examples
  • Easy to run and modify
  • Version control friendly

Experimental

melp/ ⚠️ Experimental lazy evaluation system.

  • Lazy computation
  • Thunks and deferred execution
  • Advanced control flow

Utilities

helper/ Utility functions used across examples.

  • Text formatting helpers
  • Common utilities

🎯 Examples by Use Case

Text Generation

Data Processing

Agent Systems

Production Deployment

Performance Optimization

📖 Documentation

🏃 Running Examples

# Run any Python example
python docs/examples/tutorial/simple_email.py

# Or with uv
uv run docs/examples/tutorial/simple_email.py

# Run notebooks
jupyter notebook docs/examples/notebooks/

# Run tests
uv run pytest test/

💡 Tips

  • Start with tutorial/ for basics
  • Check notebooks/ for interactive learning
  • See mini_researcher/ for complete application patterns
  • Refer to individual README.md files in each directory for details

🤝 Contributing

Found a bug or have an improvement? See ../../AGENTS.md for contribution guidelines.