🏠 Genesis Home Layer 1: Cognitive Layer 2: Memory Layer 3: Carter Intelligence

🧠 Layer 2: Living Memory

Session Persistence • Semantic Understanding • Truth Verification

✅ COMPLETE & ACTIVE
Neo4j Memory
33.8 GB
of 95 GB allocated (35.6% used)
Weaviate Memory
619 MB
of 50 GB allocated (1.2% used)
Redis Cache
185 MB
of 3 GB allocated (6.2% used)
Total Embeddings
128K+
Documents indexed in knowledge graph
Session Closeouts
382
Complete session histories captured
Truth Confidence
85%+
Average verification confidence

🏗️ Memory Architecture

Layer 2 implements a three-tier memory system that mimics human cognition: short-term (Redis), working (Weaviate), and long-term (Neo4j) memory stores.

⚡ Short-Term Memory
Redis Cache
Ultra-fast in-memory cache for immediate context. Stores current session state, recent queries, and active conversations. Data expires after session or time limit.
Storage
185 MB
Access Time
<10ms
Retention
Hours
Use Case
Session State
🔍 Working Memory
Weaviate Vector DB
Semantic search and embedding storage. Enables finding similar concepts, ideas, and patterns across all captured knowledge. Perfect for "what did we discuss about X?"
Storage
619 MB
Access Time
<100ms
Collections
65
Use Case
Semantic Search
💾 Long-Term Memory
Neo4j Knowledge Graph
Permanent storage of relationships, philosophies, decisions, and insights. The knowledge graph connects everything: ideas to sessions, sessions to decisions, decisions to outcomes. Nothing is ever lost.
Storage
33.8 GB
Access Time
<500ms
Nodes
128K+
Use Case
Knowledge Graph

🔄 Layer 2 Processing Pipeline

How events flow through Layer 2 from raw input to verified semantic understanding.

1. Event Received
Raw event from Layer 0 backbone
2. Extract Meaning
Semantic concepts extracted from event data
3. Verify Truth
Truth verification engine validates meaning
4. Enrich & Score
Add confidence scores and metadata
5. Embed & Index
Generate embeddings, store in vector DB
6. Emit to Layer 3
Send verified meaning to relationship layer
✅ TRUTH VERIFIED

Truth-Aware Semantic Processing

Every extracted meaning is verified through the Living Truth verification engine before being stored or propagated. This ensures that only factually accurate information flows through the system.

HIGH
0.8 - 1.0
Verified through multiple pathways
MEDIUM
0.5 - 0.8
Partially verified, uncertain
LOW
0.0 - 0.5
Questionable, needs review
Average Verification Confidence
85%

⚡ Key Capabilities

🔄
Session Persistence
Complete session state saved to Neo4j. Next session restores in <100ms with full context from previous conversations.
🧠
Semantic Understanding
Extract meaning from events, not just keywords. Understand concepts, relationships, and implications.
✅
Truth Verification
Every extracted meaning verified against knowledge graph, consensus validation, and temporal consistency checks.
📊
Batch Processing
Efficient batch verification of multiple meanings simultaneously. Process hundreds of concepts in parallel.
🎯
Zero Amnesia
IGNITION system ensures perfect memory restoration. Session closeouts capture everything - no context ever lost.
⚡
Fast Restore
Redis + Neo4j caching enables <100ms session restore. Start working immediately with full context.
🔍
Semantic Search
Find anything by meaning, not just keywords. "What did we discuss about X?" searches across all captured knowledge.
📈
Statistics Tracking
Track verification rates, confidence distributions, and failure patterns. Continuously improve verification accuracy.

📋 Implementation Status

Core Memory System ✅ COMPLETE
100%
Truth Integration ✅ COMPLETE
100%
IGNITION Fast Restore ✅ COMPLETE
100%
Session Persistence ✅ COMPLETE
100%
Semantic Embeddings 🟡 ACTIVE
95%

📁 Key Files

  • api/layers/layer2_meaning/truth_integration.py - Truth-aware semantic processor (230 LOC)
  • api/layers/layer2_meaning/test_truth_integration.py - Test suite (230 LOC, 6/6 passing)
  • api/lib/ignition/orchestrator.py - Session restore orchestrator
  • api/lib/ignition/neo4j_session_state.py - Session persistence to Neo4j
  • api/lib/ignition/redis_ignition_cache.py - Fast cache (<10ms reads)
  • api/layers/layer0_backbone/event_schemas.py - MeaningExtractionEvent schema