Enhancing Vector Embeddings with Context-Aware Memory

Vector embeddings have revolutionized how we store and retrieve information in AI systems, providing powerful semantic search capabilities. This article explores how we can build upon this foundation by adding context-aware memory capabilities to create even more effective AI applications.

Building on Vector Embeddings

1. Adding Context

Vector embeddings excel at capturing semantic similarity. We can enhance this with additional context:

# Example of context enhancement
text1 = "The bank is closed on Sundays"  # Financial context
text2 = "The river bank is beautiful"    # Geographic context

# Enhance embeddings with context
embedding1 = get_embedding(text1, context="financial")
embedding2 = get_embedding(text2, context="geographic")

2. Temporal Awareness

We can augment embeddings with time-based information:

# Adding temporal information
historical = "The company was founded in 1990"
recent = "The company was acquired in 2020"

# Store with temporal context
store_with_time(historical, timestamp="1990")
store_with_time(recent, timestamp="2020")

The Need for Structured Memory

1. Hierarchical Organization

# Structured memory example
structured_memory = {
    "hierarchy": {
        "parent": "AI Systems",
        "children": {
            "memory": {
                "types": ["short-term", "long-term"],
                "mechanisms": ["retrieval", "storage"]
            },
            "learning": {
                "methods": ["supervised", "unsupervised"],
                "applications": ["classification", "clustering"]
            }
        }
    }
}

2. Relationship Mapping

# Relationship mapping example
relationships = {
    "concepts": {
        "memory": {
            "related_to": ["learning", "retrieval"],
            "depends_on": ["storage", "indexing"],
            "influences": ["performance", "accuracy"]
        }
    },
    "temporal": {
        "before": ["initialization"],
        "after": ["retrieval"],
        "during": ["processing"]
    }
}

3. Context Preservation

# Context preservation example
context = {
    "source": "research paper",
    "date": "2024-04-13",
    "authors": ["Researcher A", "Researcher B"],
    "domain": "AI Memory Systems",
    "importance": 0.8,
    "reliability": 0.9
}

Advanced Memory Features

1. Salience-Based Retrieval

# Salience scoring example
async def score_salience(content, context):
    return {
        "relevance": calculate_relevance(content, context),
        "importance": assess_importance(content),
        "recency": evaluate_recency(content),
        "usage": track_usage_patterns(content)
    }

2. Dynamic Adaptation

# Dynamic adaptation example
async def adapt_memory(content, feedback):
    # Update importance based on usage
    await update_importance(content, feedback)
    
    # Adjust relationships based on patterns
    await adjust_relationships(content)
    
    # Optimize storage based on access patterns
    await optimize_storage(content)

3. Self-Healing Capabilities

# Self-healing example
async def heal_memory(content):
    # Detect and resolve inconsistencies
    inconsistencies = await detect_inconsistencies(content)
    await resolve_inconsistencies(inconsistencies)
    
    # Update relationships
    await update_relationships(content)
    
    # Optimize structure
    await optimize_structure(content)

Real-World Examples

1. Customer Support Systems

# Customer support memory example
support_memory = {
    "customer": {
        "history": "previous interactions",
        "preferences": "communication style",
        "issues": "reported problems"
    },
    "context": {
        "current_issue": "problem description",
        "related_issues": "similar cases",
        "solutions": "proposed fixes"
    }
}

2. Research Assistants

# Research assistant memory example
research_memory = {
    "topics": {
        "main": "primary research area",
        "related": "connected fields",
        "methods": "research techniques"
    },
    "findings": {
        "results": "research outcomes",
        "implications": "significance",
        "limitations": "study constraints"
    }
}

3. Personal Assistants

# Personal assistant memory example
assistant_memory = {
    "user": {
        "preferences": "user settings",
        "habits": "behavior patterns",
        "goals": "user objectives"
    },
    "context": {
        "current_task": "active work",
        "schedule": "time management",
        "resources": "available tools"
    }
}

Best Practices

1. Memory Organization

   • Implement hierarchical structures
   • Maintain relationship maps
   • Preserve context information

2. Retrieval Optimization

   • Use salience-based scoring
   • Implement efficient indexing
   • Balance speed and accuracy

3. System Maintenance

   • Regular consistency checks
   • Automated optimization
   • Performance monitoring

Next Steps

Ready to move beyond simple embeddings? Check out our documentation or try our advanced memory guide.