Learning Assistant Example

This example demonstrates how to build an intelligent learning assistant using SmartMemory that can track learning progress, provide personalized recommendations, and adapt to individual learning patterns.

Overview

The Learning Assistant leverages SmartMemory's multiple memory types to create a comprehensive learning experience:

Semantic Memory: Store facts, concepts, and knowledge
Episodic Memory: Track learning sessions and experiences
Procedural Memory: Remember learning strategies and methods
Pending Memory: Maintain current learning context

Implementation

Basic Setup

from smartmemory import SmartMemory
from datetime import datetime, timedelta, UTC

class LearningAssistant:
    def __init__(self):
        self.memory = SmartMemory(
            config={
                "evolution": {
                    "consolidation": {"enabled": True},
                    "relationship_discovery": {"enabled": True}
                }
            }
        )
        self.current_session = None
    
    def start_learning_session(self, topic, learning_goals=None):
        """Start a new learning session"""
        session_data = {
            "topic": topic,
            "start_time": datetime.now(UTC).isoformat(),
            "goals": learning_goals or [],
            "session_id": f"session_{datetime.now(UTC).strftime('%Y%m%d_%H%M%S')}"
        }
        
        # Store session start in episodic memory
        self.memory.ingest({
            "content": f"Started learning session on {topic}",
            "memory_type": "episodic",
            "metadata": session_data
        })
        
        self.current_session = session_data
        return session_data["session_id"]

Knowledge Tracking

import smartmemory.utils


def learn_concept(self, concept, definition, examples=None):
    """Add a new concept to semantic memory"""
    concept_data = {
        "concept": concept,
        "definition": definition,
        "examples": examples or [],
        "learned_at": smartmemory.utils.now().isoformat(),
        "session_id": self.current_session["session_id"] if self.current_session else None
    }

    # Store in semantic memory
    memory_id = self.memory.ingest({
        "content": f"{concept}: {definition}",
        "memory_type": "semantic",
        "metadata": concept_data
    })

    return memory_id

Usage Example

# Initialize the learning assistant
assistant = LearningAssistant()

# Start a learning session
session_id = assistant.start_learning_session(
    topic="Machine Learning Fundamentals",
    learning_goals=["Understand supervised learning", "Learn about neural networks"]
)

# Learn new concepts
assistant.learn_concept(
    concept="Supervised Learning",
    definition="A type of machine learning where the algorithm learns from labeled training data",
    examples=["Classification", "Regression"]
)

# Get learning progress
progress = assistant.get_learning_progress(topic="Machine Learning", days=7)
print(f"Concepts learned: {progress['concepts_learned']}")

Features

Progress Tracking: Monitor learning over time
Knowledge Gaps: Identify areas needing attention
Personalized Recommendations: Suggest optimal learning strategies
Session Management: Track individual learning sessions
Concept Relationships: Discover connections between topics

This example shows how SmartMemory's multi-type memory system can create sophisticated learning applications that adapt to individual needs and learning patterns.