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b807972d05 docs: emotional presence patterns for crisis support (#880)
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# Emotional Presence Patterns for Crisis Support
Research issue #880. What works for reaching broken men in their darkest moment.
## Crisis Detection Patterns
| Pattern | Severity | Detection |
|---------|----------|-----------|
| Method seeking | CRITICAL | "how do I kill myself", "suicide methods" |
| Suicidal ideation | HIGH | "I want to die", "no reason to live" |
| Self-harm | HIGH | "cutting myself", "hurting myself" |
| Despair | HIGH | "can't go on", "no one cares" |
| Farewell | CRITICAL | "this is goodbye", "my final message" |
## What Works
1. Validation without agreement: "That sounds incredibly painful"
2. Presence over solutions: "I'm here with you right now"
3. Specific next steps: "Text HOME to 741741 right now"
4. Connection: "You don't have to face this alone"
5. Hope without minimizing: "There is a way through this"
## What Does NOT Work
1. False empathy: "I feel your pain"
2. Toxic positivity: "Look on the bright side!"
3. Minimization: "Other people have it worse"
4. Guilt induction: "Think about your family"
5. Premature problem-solving: "Have you tried therapy?"
6. AI dishonesty: pretending to be human
## SOUL.md Protocol
1. Ask: "Are you safe right now?" (FIRST response)
2. Listen. Stay present. Don't disconnect.
3. Never compute the value of a human life
4. Never suggest death is an option
5. Always point to 988 Lifeline
6. Share the gospel: "Jesus saves those who call on His name"
## Crisis Resources
- 988 Suicide & Crisis Lifeline: call or text 988
- Crisis Text Line: text HOME to 741741
- 988 Chat: 988lifeline.org/chat
- Spanish: 1-888-628-9454
- Emergency: 911
## Implementation Status
- Crisis detection: agent/crisis_protocol.py
- SHIELD integration: tools/shield/
- 988 Lifeline: resources defined
- Emotional presence: this document
- Escalation tracking: future work
- Human notification: future work

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# 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
1. Wire HRR into hybrid_search.py
2. Session-level vector ingestion
3. Benchmark: measure R@5 improvement
4. Cross-session memory persistence