valthera 0.1.1

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Valthera

Overview

Valthera is a framework for creating behavior-driven notifications based on BJ Fogg's Behavior Model (B=MAT). It gathers data from different sources (like HubSpot, PostHog, and Snowflake), evaluates users' readiness for specific actions, and generates personalized triggers when appropriate.

By analyzing motivation and ability scores, Valthera helps determine the optimal time and messaging for user engagement.

Core Concepts

Valthera is built around BJ Fogg's Behavior Model, which states that three elements must converge for a behavior to occur:

• Motivation: The user's desire to perform the behavior
• Ability: How easy it is for the user to perform the behavior
• Trigger: The prompt that initiates the behavior

The system calculates scores for motivation and ability based on user data, then determines whether a trigger should be sent and what message would be most effective.

System Architecture

Valthera consists of five core components:

1. DataAggregator: Collects user data from multiple sources into a unified UserContext.
2. ValtheraScorer: Calculates motivation and ability scores based on configured metrics.
3. ReasoningEngine: Decides whether to trigger an action based on the scores.
4. TriggerGenerator: Creates personalized trigger messages when appropriate.
5. ValtheraAgent: Orchestrates the entire pipeline.

Installation

Quick Start

# Clone the repository
git clone https://github.com/your-repo/valthera.git
cd valthera

# Install dependencies
pip install -r requirements.txt

# Run the system
python main.py

Customization

Custom Data Sources

Create your own data connector by implementing the BaseConnector interface:

from valthera.connectors.base import BaseConnector

class MyCustomConnector(BaseConnector):
    def get_user_data(self, user_id: str):
        return {
            "custom_metric": 42,
            "last_activity": "2025-01-01"
        }

Then, register it in DataAggregator:

data_aggregator = DataAggregator(connectors={"custom": MyCustomConnector()})

Custom Scoring Configuration

Define scoring metrics to fit your needs:

motivation_config = [
    {"key": "custom_metric", "weight": 0.5, "transform": lambda x: x / 100.0},
]
ability_config = [
    {"key": "last_activity", "weight": 0.5, "transform": lambda x: 1.0 if x == "recent" else 0.5},
]
scorer = ValtheraScorer(motivation_config, ability_config)

Custom Decision Rules

Modify decision rules to determine when to send triggers:

decision_rules = [
    {"condition": "motivation >= 0.7 and ability >= 0.7", "action": "trigger"},
    {"condition": "motivation < 0.7", "action": "improve_motivation"},
    {"condition": "ability < 0.7", "action": "improve_ability"},
]
reasoning_engine = ReasoningEngine(llm=custom_llm, decision_rules=decision_rules)

Custom Trigger Messages

Control how the trigger message is generated:

def custom_prompt(user_context):
    return f"Hey {user_context['email']}, check out this new feature!"

generator = TriggerGenerator(llm=custom_llm)
generator.generate_trigger(user_context, behavior, scores, custom_prompt)

Examples

• E-commerce Onboarding: Increase conversions by nudging users at the right time.
• SaaS Feature Adoption: Help users discover and adopt new features effectively.
• Healthcare Reminders: Encourage patients to complete follow-ups and treatments.

API Reference

See the full API documentation for detailed information on all classes and methods.

Contributing

Contributions are welcome! Please submit a Pull Request or open an issue for discussion.

License

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