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User Profiles

MemSync automatically generates comprehensive user profiles from accumulated memories, providing rich context about users without manual curation. These profiles include biographical summaries, categorized insights, and derived personality traits.

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What are User Profiles?

User profiles in MemSync are auto-generated summaries that synthesize memories into meaningful insights about users. They’re designed to provide AI applications with deep user understanding while maintaining privacy and accuracy.

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Profile Components

User Bio

A concise biographical summary highlighting the most important aspects of the user

Category Profiles

Detailed profiles for each memory category (career, interests, relationships, etc.)

Insights

Extracted patterns, preferences, and behavioral insights from user interactions

Derived Traits

Personality traits and characteristics inferred from user behavior and preferences

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User Bio

The user bio is a high-level summary designed to give AI applications quick context about who the user is.

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Bio Characteristics

  • Concise: Typically 2-4 sentences
  • Information-dense: Combines related facts efficiently
  • Readable: Written in natural language, not bullet points
  • Dynamic: Updates automatically as new memories are added
  • Prioritized: Focuses on most important and stable information

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Example Bio

{
  "user_bio": "Software engineer at Google specializing in machine learning infrastructure with 8 years of experience. Passionate about hiking, photography, and sustainable living. Currently learning Spanish and training for a marathon while working on a side project in AI ethics."
}

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Bio Generation Process

1

Memory Analysis

MemSync analyzes all user memories to identify key themes and patterns
2

Information Prioritization

The system prioritizes information based on importance, frequency, and recency
3

Synthesis

Related information is combined into coherent, natural-sounding sentences
4

Validation

The bio is validated for accuracy and relevance before storage

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Category Profiles

MemSync generates detailed profiles for each memory category, providing deep insights into specific aspects of the user’s life.

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Available Category Profiles

Core personal information and self-identification
{
  "identity": "29-year-old software engineer living in San Francisco, originally from Chicago. Identifies as a lifelong learner and problem-solver. Values authenticity and continuous growth."
}
Professional background, skills, and career trajectory
{
  "career": "Senior Software Engineer at Google with 8+ years experience in ML infrastructure. Expert in Python, distributed systems, and model deployment. Mentors junior developers and leads cross-functional projects."
}
Hobbies, passions, and recreational activities
{
  "interests": "Avid hiker and photographer who enjoys exploring national parks. Practices sustainable living and urban gardening. Recently started learning guitar and cooking Mediterranean cuisine."
}
Social connections and relationship patterns
{
  "relationships": "Married to college sweetheart Sarah for 5 years. Maintains close friendships from university and work. Actively mentors junior colleagues and values collaborative teamwork."
}
Wellness practices and health-related information
{
  "health": "Maintains active lifestyle through running and hiking. Follows primarily plant-based diet for health and environmental reasons. Prioritizes mental health through meditation and work-life balance."
}

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Insights

MemSync extracts behavioral patterns and preferences from user interactions, providing deeper understanding of user motivations and tendencies.

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Insight Categories

Common Topics

Frequently discussed subjects and interests

Communication Tone

Preferred communication style and tone

Interests

Core interests and passionate subjects

Likes & Dislikes

Preferences and aversions across different areas

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Example Insights

{
  "insights": {
    "identity": {
      "insights": {
        "common_topics": ["technology", "career growth", "outdoor activities", "sustainability"],
        "communication_tone": "thoughtful and analytical, prefers detailed explanations",
        "interests": ["machine learning", "hiking", "photography", "environmental issues"],
        "likes": ["challenging problems", "collaborative work", "nature", "learning new skills"],
        "dislikes": ["micromanagement", "repetitive tasks", "waste", "rushed decisions"]
      },
      "derived_traits": ["analytical", "environmentally conscious", "growth-minded", "collaborative"]
    }
  }
}

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Derived Traits

MemSync infers personality traits and characteristics from user behavior patterns, providing insights into user psychology and preferences.

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Trait Categories

Core personality characteristics inferred from behavior
{
  "traits": ["analytical", "conscientious", "open-minded", "collaborative", "goal-oriented"]
}
Professional behavior and work preferences
{
  "work_style": ["detail-oriented", "systems-thinking", "mentorship-focused", "innovation-driven"]
}
How the user prefers to communicate and receive information
{
  "communication": ["direct", "thoughtful", "evidence-based", "collaborative"]
}
Core values and principles that guide behavior
{
  "values": ["continuous learning", "environmental sustainability", "authenticity", "teamwork"]
}

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Using Profiles in Applications

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Basic Profile Retrieval

import requests

# Get complete user profile
response = requests.get("https://api.memchat.io/v1/users/profile",
    headers={"Authorization": "Bearer YOUR_TOKEN"}
)

profile = response.json()
print(f"Bio: {profile['user_bio']}")
print(f"Career: {profile['profiles']['career']}")
print(f"Interests: {profile['profiles']['interests']}")

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Profile-Based Personalization

def personalize_content(user_profile):
    bio = user_profile['user_bio']
    insights = user_profile['insights']['identity']['insights']
    
    # Extract key information
    interests = insights['interests']
    communication_tone = insights['communication_tone']
    likes = insights['likes']
    
    # Customize content based on profile
    if 'analytical' in communication_tone:
        response_style = "detailed and data-driven"
    else:
        response_style = "concise and practical"
    
    return {
        'content_topics': interests,
        'response_style': response_style,
        'engagement_triggers': likes
    }

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Dynamic Profile Updates

def check_profile_freshness(user_id):
    # Get current profile
    profile = get_user_profile(user_id)
    
    # Get recent memories
    recent_memories = get_recent_memories(user_id, days=7)
    
    # Check if profile should be updated
    if should_update_profile(profile, recent_memories):
        # Trigger profile regeneration
        trigger_profile_update(user_id)

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Profile Quality and Accuracy

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Quality Indicators

Profiles are more accurate with diverse memory coverage across categories
def assess_profile_coverage(memories):
    categories = set(m.category for m in memories)
    coverage_score = len(categories) / 10  # 10 total categories
    return coverage_score
Recent memories ensure profiles reflect current user state
def assess_profile_freshness(memories):
    recent_memories = [m for m in memories if days_since(m.created_at) < 30]
    freshness_score = len(recent_memories) / len(memories)
    return freshness_score
Consistent information across memories improves profile reliability
def assess_profile_consistency(memories):
    # Check for conflicting information
    conflicts = detect_memory_conflicts(memories)
    consistency_score = 1 - (len(conflicts) / len(memories))
    return consistency_score

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Improving Profile Quality

1

Encourage Diverse Conversations

Ask users about different aspects of their lives to build comprehensive profiles
2

Regular Updates

Ensure conversations happen regularly to keep profiles current
3

Depth Over Breadth

Sometimes, encourage deeper discussions about specific topics
4

Validate Information

Occasionally confirm important details to ensure accuracy

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Privacy and Control

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Profile Visibility

Users have control over their profile information:
  • Bio Editing: Users can manually edit their auto-generated bio
  • Category Control: Users can choose which categories to include in profiles
  • Privacy Settings: Sensitive information can be marked as private
  • Data Export: Users can export their complete profile data

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Manual Bio Override

# Allow users to override auto-generated bio
def update_user_bio(user_id, custom_bio):
    response = requests.put("https://api.memchat.io/v1/users/bio",
        headers={"Authorization": "Bearer YOUR_TOKEN"},
        json={"user_bio": custom_bio}
    )
    return response.json()

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Profile Evolution

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Tracking Changes Over Time

def track_profile_evolution(user_id):
    # Get historical profile snapshots
    profiles = get_profile_history(user_id)
    
    # Analyze changes
    changes = []
    for i in range(1, len(profiles)):
        prev_profile = profiles[i-1]
        curr_profile = profiles[i]
        
        # Detect significant changes
        if bio_changed_significantly(prev_profile, curr_profile):
            changes.append({
                'date': curr_profile['updated_at'],
                'type': 'bio_update',
                'description': 'Major life change detected'
            })
    
    return changes

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Adaptation Patterns

Common profile evolution patterns:
  • Career Transitions: Changes in professional profiles during job changes
  • Life Events: Updates following major life events (marriage, moving, etc.)
  • Skill Development: Learning profiles evolving as users acquire new skills
  • Interest Shifts: Interest profiles adapting to new hobbies and passions

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Best Practices

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For Developers

Integrate profiles naturally into your application flow
def generate_response(user_message, user_id):
    # Get user profile for context
    profile = get_user_profile(user_id)
    
    # Use profile to inform response
    context = f"User background: {profile['user_bio']}"
    interests = profile['insights']['identity']['insights']['interests']
    
    # Generate personalized response
    return create_contextual_response(user_message, context, interests)
Cache profiles for better performance
from functools import lru_cache
from datetime import datetime, timedelta

@lru_cache(maxsize=1000)
def get_cached_profile(user_id, cache_key):
    return get_user_profile(user_id)

def get_profile_with_cache(user_id):
    # Create cache key that expires daily
    cache_key = datetime.now().strftime("%Y-%m-%d")
    return get_cached_profile(user_id, cache_key)
Handle cases where profiles are incomplete or unavailable
def get_user_context(user_id):
    try:
        profile = get_user_profile(user_id)
        return profile['user_bio']
    except Exception:
        # Fallback to basic memory search
        memories = search_memories("user overview", user_id, limit=5)
        return summarize_memories(memories)

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For Applications

Use profiles to provide contextual assistance
def provide_recommendations(user_id, request_type):
    profile = get_user_profile(user_id)
    interests = profile['insights']['identity']['insights']['interests']
    
    if request_type == "learning":
        return recommend_courses(interests)
    elif request_type == "content":
        return recommend_articles(interests)
Tailor user experience based on profile insights
def customize_interface(user_id):
    profile = get_user_profile(user_id)
    traits = profile['insights']['identity']['derived_traits']
    
    if 'analytical' in traits:
        return show_detailed_interface()
    else:
        return show_simple_interface()

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Next Steps

Memory Types

Learn how different memory types contribute to profile generation

Search

Use search to find specific aspects of user profiles

Integrations

Import external data to enrich user profiles

Profile API

Explore the complete profile API documentation
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