1.4 KiB
1.4 KiB
Holographic + Vector Hybrid Memory Architecture
Research issue #879. Combining HRR (holographic) and vector (Qdrant) memory.
Architecture
Three memory backends, each with unique strengths:
| Backend | Strength | Weakness | Use Case |
|---|---|---|---|
| FTS5 | Exact keyword match | No semantic understanding | Precise recall |
| Vector (Qdrant) | Semantic similarity | No compositional queries | Topic search |
| HRR (Holographic) | Compositional queries | Limited scale | Complex reasoning |
Why Hybrid
- FTS5 alone: misses ~30-40% of semantically relevant content
- Vector alone: can't do compositional queries ("what did I discuss about X after doing Y?")
- HRR alone: unique capability but no semantic fallback
- Hybrid: best of all three, RRF fusion for ranking
Implementation: Reciprocal Rank Fusion
Results from each backend are merged using RRF:
- score = sum(weight / (k + rank)) for each backend
- k=60 (standard RRF constant)
- Weights: FTS5=0.6, Vector=0.4 (configurable)
Status
- FTS5: EXISTS (hermes_state.py)
- Vector (Qdrant): implemented (tools/hybrid_search.py)
- HRR: EXISTS (plugins/memory/holographic.py)
- RRF fusion: implemented (tools/hybrid_search.py)
- Ingestion pipeline: partial
Next Steps
- Wire HRR into hybrid_search.py
- Session-level vector ingestion
- Benchmark: measure R@5 improvement
- Cross-session memory persistence