Knowledge Fusion + Context Fabric - Multi-Tier Vector Storage

🧠 Knowledge Fusion + Context Fabric

Purpose: Build the "brain" layer that lets agents share embeddings and reasoning traces.

Key Features

1. Multi-Tier Vector Storage

Combine multiple vector databases:

• Qdrant - Primary vector store
• PgVector - Persistent relational context
• LanceDB - Zero-dependency local caches

2. Semantic Federation

• Shared memory indexes across all agents
• Route embeddings by domain/topic
• Cross-agent knowledge sharing
• Contextual routing strategies

3. Trace & Metrics Correlation

Integrate observability:

• OpenLLMetry - LLM metrics
• Langfuse - Trace correlation
• Semantic metrics aggregation
• Performance tracking per agent

4. In-Context Caching

• Portkey + Redis for caching
• Re-inject successful reasoning chains
• Cache hit optimization
• Context reuse patterns

Expected Result

K-agent can recall from the collective memory of all previous deployments.

Architecture

Agent A ─┐
         ├─→ Semantic Router → [Qdrant | PgVector | LanceDB]
Agent B ─┤                              ↓
Agent C ─┘                        Shared Memory
                                        ↓
                                  Context Fabric

Implementation Tasks

• Set up multi-tier storage architecture
• Implement semantic routing
• Integrate OpenLLMetry + Langfuse
• Build caching layer with Portkey + Redis
• Create memory compaction jobs
• Add trace indexing to Qdrant

Priority

High - Core memory infrastructure

Phase

Phase 3 - Memory optimization