basic

Basic Examples

This directory contains simple, foundational pipeline examples that demonstrate core orchestrator concepts. These examples are perfect for learning the fundamentals and testing your installation.

Examples Overview

🌟 hello_world.yaml

Perfect Starting Point

• The simplest possible pipeline
• Single step text generation
• Basic parameter usage
• Great for testing your setup

# Run with default greeting
python scripts/execution/run_pipeline.py examples/basic/hello_world.yaml

# Custom greeting
python scripts/execution/run_pipeline.py examples/basic/hello_world.yaml -i name="Alice"

📝 text_analysis.yaml

Text Processing Fundamentals

• Analyze text for sentiment and themes
• Structured JSON output
• Multiple analysis types
• Word counting

# Analyze text sentiment
python scripts/execution/run_pipeline.py examples/basic/text_analysis.yaml \
  -i text="I love using this orchestrator system!" \
  -i analysis_type="sentiment"

# Comprehensive analysis
python scripts/execution/run_pipeline.py examples/basic/text_analysis.yaml \
  -i text="The future of AI is bright with many exciting developments ahead." \
  -i analysis_type="comprehensive"

🔍 simple_research.yaml

Multi-Step Workflows

• Web search integration
• Step dependencies
• File output generation
• Template usage

# Research any topic
python scripts/execution/run_pipeline.py examples/basic/simple_research.yaml \
  -i topic="quantum computing" \
  -i max_sources=5

# Quick research with fewer sources
python scripts/execution/run_pipeline.py examples/basic/simple_research.yaml \
  -i topic="machine learning trends" \
  -i max_sources=3

🔄 data_transformation.yaml

Data Processing Basics

• Structured data manipulation
• Data validation and cleaning
• Multiple transformation types
• JSON output formatting

# Clean and standardize data
python scripts/execution/run_pipeline.py examples/basic/data_transformation.yaml \
  -i transformation_type="clean"

# Generate data summaries
python scripts/execution/run_pipeline.py examples/basic/data_transformation.yaml \
  -i transformation_type="summarize"

# Categorize records
python scripts/execution/run_pipeline.py examples/basic/data_transformation.yaml \
  -i transformation_type="categorize"

🔀 conditional_logic.yaml

Dynamic Workflows

• Conditional step execution
• User-specific content generation
• Dynamic parameter usage
• Multi-path processing

# Admin user with analytics
python scripts/execution/run_pipeline.py examples/basic/conditional_logic.yaml \
  -i user_type="admin" \
  -i include_analytics=true \
  -i content_length="long"

# Guest user, short content
python scripts/execution/run_pipeline.py examples/basic/conditional_logic.yaml \
  -i user_type="guest" \
  -i include_analytics=false \
  -i content_length="short"

# Standard user, medium content
python scripts/execution/run_pipeline.py examples/basic/conditional_logic.yaml \
  -i user_type="standard" \
  -i content_length="medium"

Learning Path

Recommended order for new users:

1. hello_world.yaml - Test your setup and learn basic syntax
2. text_analysis.yaml - Understand parameters and output handling
3. simple_research.yaml - Learn multi-step workflows and dependencies
4. data_transformation.yaml - Explore structured data processing
5. conditional_logic.yaml - Master dynamic and conditional workflows

Key Concepts Demonstrated

📋 Basic Pipeline Structure

• Pipeline metadata (id, name, description)
• Parameter definitions with types and defaults
• Step definitions with actions and parameters
• Output specifications
• Dependencies between steps

🔧 Core Features

• AUTO tags - Intelligent model selection
• Template syntax - Jinja2 templating with {{ }}
• Dependencies - Controlling step execution order
• Conditions - Conditional step execution
• Tool integration - Web search, filesystem operations
• Structured output - JSON formatting and validation

💡 Best Practices

• Clear, descriptive step IDs
• Meaningful parameter names and descriptions
• Proper dependency chains
• Error-resistant template usage
• Helpful metadata for documentation

Requirements

All basic examples require:

• Text Generation Model - Any model capable of text generation
• Web Search Tool - For research examples (web-search)
• Filesystem Tool - For file operations (filesystem)

See the main models configuration for setup details.

Troubleshooting

Common Issues

Pipeline fails to start:

• Check that models are properly initialized: init_models()
• Verify required tools are available
• Ensure parameter types match expected formats

Template errors:

• Check for typos in variable names: {{ variabel }} vs {{ variable }}
• Verify step IDs match dependency references
• Ensure proper JSON formatting for structured data

Model selection issues:

• Confirm at least one text generation model is available
• Check API keys for cloud models
• Consider using local models (Ollama) for testing

Getting Help

• Check the troubleshooting guide
• Review pipeline debugging tips
• See configuration documentation

Next Steps

Once comfortable with basic examples, explore:

• Advanced Examples - Complex workflows and patterns
• Integration Examples - External service integrations
• Migration Examples - Upgrading from older versions
• Platform Examples - Cross-platform considerations