This tool is like a magic lens that lets you see how QuadB64 keeps your data’s relationships intact, even after encoding. It shows you that similar things stay similar, and unrelated things don’t accidentally look alike, making your search and AI systems much smarter.

Similarity Visualizer

Interactive Similarity Preservation Demo

Imagine you’re a matchmaker for data, and this tool is your advanced compatibility scanner. It doesn’t just tell you if two data points are a good match; it shows you why they are, and how QuadB64 ensures no accidental, awkward pairings happen.

Imagine you’re a detective, and this tool is your forensic analysis kit for data relationships. It helps you uncover the true connections between pieces of information, filtering out the noise and false leads that traditional encoding methods often create.

This tool demonstrates how different QuadB64 variants preserve similarity relationships between data points. Visualize how Shq64 maintains semantic relationships while preventing substring pollution.

Data Input

Data Type: Text Documents Vector Embeddings Image Features Custom Data

QuadB64 Variant: Eq64 (Standard) Shq64 (Similarity Hash) Compare All

Sample Data Points

Similarity Visualization

Similarity Threshold: 70%

Similarity Analysis

Preserved Relationships

0

Similar pairs maintained

False Positives

0

Incorrect similarities detected

Similarity Accuracy

0%

Overall preservation quality

Position Safety

✓

Substring pollution prevented

Detailed Analysis

Click "Analyze Similarity" to see how QuadB64 preserves relationships while preventing false matches.

Features

This similarity visualizer demonstrates:

1. Multiple View Modes: Network graphs, similarity matrices, and encoding comparisons
2. Interactive Analysis: Adjust similarity thresholds and see real-time updates
3. QuadB64 Variants: Compare how different variants preserve relationships
4. Custom Data: Input your own text or vector data for analysis
5. Metrics Dashboard: Track preserved relationships and false positive rates

Understanding the Visualization

Network View

• Nodes: Represent your data points
• Edges: Show similarity relationships above the threshold
• Edge Thickness: Indicates similarity strength
• Labels: Display similarity percentages

Matrix View

• Color Coding: Green = high similarity, Red = low similarity
• Symmetric Matrix: Shows all pairwise similarities
• Interactive Cells: Hover for detailed similarity scores

Encoding View

• Original Text: Your input data
• Encoded Versions: How each variant encodes the data
• Position Context: See how position affects encoding

Key Insights

1. Position Safety: Each data point gets unique position-dependent encoding
2. Similarity Preservation: Related content maintains detectable relationships
3. False Positive Prevention: Accidental substring matches are eliminated
4. Semantic Relationships: True similarities remain while false ones are removed

Try different data types and variants to see how QuadB64 adapts to preserve meaningful relationships in your specific use case!