Contradiction Detection

Reeflect includes sophisticated contradiction detection capabilities to identify when new information conflicts with existing memories. This helps maintain consistency and accuracy in the memory system.

How Contradiction Detection Works

When new information is added to the memory system, it can automatically check for potential contradictions with existing memories:

1. New memory content is compared against semantically similar existing memories
2. The system analyzes whether the new content contradicts or conflicts with existing memories
3. Detected contradictions are scored by confidence level
4. The system can either flag contradictions for review or automatically resolve them

Detecting Contradictions

# Check if new information contradicts existing memories
contradictions = memory.detect_contradictions(
    new_memory="The user prefers light mode for all applications.",
    namespace="user_preferences"
)

if contradictions:
    print("Contradictions detected:")
    for contradiction in contradictions:
        print(f"- Conflicts with memory {contradiction['memory_id']}: {contradiction['reason']}")
        print(f"  Confidence: {contradiction['confidence']}")

# You can also check an existing Memory object
existing_memory = memory.retrieve("memory-id-123")
new_memory = Memory(
    content="The user is allergic to dairy.",
    namespace="user_health",
    importance=0.9
)

contradictions = memory.detect_contradictions(
    new_memory=new_memory,
    namespace="user_health"
)

Automatic Contradiction Resolution

For applications that need automatic handling of contradictions, you can use the contradiction resolver:

from reeflect.intelligence.contradictions import ContradictionResolver

# Create a contradiction resolver
resolver = ContradictionResolver(memory_system)

# Resolve contradictions with a new memory
resolution = resolver.resolve(
    new_memory="The user now prefers dark mode for all applications.",
    namespace="user_preferences",
    resolution_strategy="newest_wins",  # Options: newest_wins, oldest_wins, highest_confidence, manual
    add_relationship=True  # Add CONTRADICTS relationship between memories
)

print(f"Resolution action: {resolution['action']}")
print(f"Affected memories: {resolution['affected_memories']}")
print(f"Resolution explanation: {resolution['explanation']}")

Resolution Strategies

Strategy	Description	Use Cases
newest_wins	Prioritize the most recent information	User preference changes, updated facts
oldest_wins	Preserve original information over new data	Core preferences, fundamental facts
highest_confidence	Choose the memory with highest confidence score	Factual information, verified data
manual	Flag for manual review without automatic resolution	Critical information, sensitive data

Best Practice

For user preference data, newest_wins is usually the best strategy as preferences change over time. For health information or critical facts, consider using manual resolution to ensure accuracy.

Next Steps

Explore Memory Analytics to learn how to gain insights into your memory system's performance and usage patterns.