3D Memory Field

Semantic navigation through the Nosos vault


What Is This?

The 3D Memory Field is a spatial interface to the Nosos vault. Every note, transcript, and session exists as a point in 3D space β€” positioned not by folder hierarchy, but by semantic meaning.

β€œNavigate memories in three dimensions.”


How It Works

1. Vector Embeddings

Every file in the vault is converted to a high-dimensional vector using sentence-transformers (all-MiniLM-L6-v2). Similar content = similar vectors.

2. Dimensionality Reduction

PCA reduces embeddings to 3D coordinates (X, Y, Z). Files that are semantically related cluster together in space.

3. LanceDB Storage

Vectors and coordinates are stored in ~/.openclaw/workspace/memory_vectors_3d/ for fast semantic search.


The Index

440+ chunks indexed
β”œβ”€β”€ 🧬 Core/          Identity, soul, system (priority 9-10)
β”œβ”€β”€ 🧠 Cognition/     Fallacies, frameworks, models (priority 7)
β”œβ”€β”€ πŸ”§ Projects/      Active work, KBIRD, tools (priority 6)
β”œβ”€β”€ πŸ•ΈοΈ Graph/         Navigation MOCs (priority 5)
β”œβ”€β”€ πŸ“… Time/          Daily logs, sessions (priority 4)
└── πŸŽ™οΈ Transcripts/   Podcasts, recordings (priority 4)

Search command:

python3 ~/.openclaw/workspace/scripts/memory-search.py "your query" 5

3D Visualization

The 3D coordinate data lives in:

  • JSON: ~/.openclaw/workspace/memory_3d.json
  • Database: ~/.openclaw/workspace/memory_vectors_3d/
  • Prototype: ~/.openclaw/workspace/memory_3d_prototype.py

To Regenerate

cd ~/.openclaw/workspace
python3 scripts/memory_3d_prototype.py

Spatial Relationships

In the 3D field:

  • Distance = semantic dissimilarity
  • Clusters = topic neighborhoods
  • Time = one axis (usually)
  • Depth = abstraction level

Future: Interactive 3D View

Planned: A web-based 3D scatter plot where you can:

  • Fly through the memory space
  • Click points to open files
  • Search and highlight related content
  • See temporal drift over time


The vault is not a hierarchy. It’s a field. 🌐