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Examples

These examples demonstrate how to use Intent Kit's DAG approach to build intelligent applications.

Getting Started

Advanced Examples

Example Categories

Basic Patterns

The Basic Examples guide covers essential patterns:

  • Simple Greeting Bot - Intent classification and parameter extraction
  • Calculator with Multiple Operations - Multiple intents and operations
  • Weather Information Bot - Complex parameter extraction and external APIs
  • Task Management System - Context management and state persistence
  • Customer Support Router - Complex routing and error handling
  • Data Query System - Complex data processing and multiple parameter types

Context Management

Examples showing different ways to use context:

  • Simple State - Track basic information like user names
  • Complex Memory - Maintain conversation history and preferences
  • Multi-User Support - Handle multiple users with separate contexts

Parameter Extraction

Examples demonstrate parameter extraction for:

  • Simple Parameters - Names, numbers, locations
  • Complex Parameters - Calculations, preferences, settings
  • Contextual Parameters - Information that depends on previous interactions

Configuration

Examples show different configuration approaches:

  • JSON Configuration - Declarative DAG definitions
  • Programmatic Building - Code-based DAG construction
  • Mixed Approaches - Combining JSON and code

Running Examples

All examples can be run with:

# Set your API key
export OPENAI_API_KEY="your-api-key-here"

# Run an example
python examples/example_name.py

Key Concepts Demonstrated

  • DAG Architecture - Flexible workflow design with nodes and edges
  • Context Persistence - Maintaining state across multiple interactions
  • Natural Language Processing - Understanding user intent and extracting parameters
  • Error Handling - Graceful handling of unclear or invalid requests
  • Multi-Intent Support - Handling different types of user requests in a single DAG
  • JSON Configuration - Declarative workflow definitions
  • Node Reuse - Sharing nodes across different execution paths

Example Structure

Most examples follow this pattern:

  1. Classifier Node - Determines user intent
  2. Extractor Node - Extracts parameters from natural language
  3. Action Node - Executes the desired action
  4. Clarification Node - Handles unclear requests

Best Practices from Examples

1. Start Simple

Begin with basic workflows and gradually add complexity. Each example builds on previous concepts.

2. Use Descriptive Names

Choose clear, descriptive names for your nodes and actions: 

# Good
builder.add_node("extract_user_name", "extractor", ...)
builder.add_node("send_greeting", "action", ...)

# Avoid
builder.add_node("extract", "extractor", ...)
builder.add_node("action1", "action", ...)

3. Handle Edge Cases

Always consider what happens when: - Required parameters are missing - Invalid data is provided - External services are unavailable

4. Test Thoroughly

Test your workflows with various inputs: 

test_cases = [
    "Normal case",
    "Edge case",
    "Error case",
    "Empty input",
    "Very long input"
]

5. Use Context Effectively

Leverage context to maintain state across interactions: 

# Store user preferences
context.set("user_preferences", {"language": "en", "timezone": "UTC"})

# Retrieve in later interactions
prefs = context.get("user_preferences", {})

Next Steps

After exploring these examples: