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Description
Problem
RAGService._emit_retrieval_feedback() (#1516) writes retrieval events to Redis streams rag:feedback:{YYYY-MM-DD} with a 7-day TTL. Nothing in the codebase reads these streams. The data expires after 7 days without ever being consumed.
Evidence
Grep for rag:feedback shows only writes — no consumer exists:
- Writer:
autobot-backend/services/rag_service.py:346—xaddtorag:feedback:{date_key} - Readers: None.
events/stream_manager.pyusesxrangebut only for task event streams, not RAG feedback. - No background task, Celery worker, or consumer group reads
rag:feedback:*streams.
Impact
- Wasted work: Every RAG search performs a Redis
xaddthat serves no purpose - Lost learning data: Retrieval outcomes (which chunks were retrieved, how they ranked) are valuable for Neural Mesh Hebbian reinforcement (feat: EdgeLearner — Hebbian reinforcement from retrieval feedback (Neural Mesh RAG Phase 3) #2056) and retrieval pattern learning (Enhancement: Retrieval pattern distillation — close the RAG feedback loop #2095) — but the data expires before those features can use it
- False observability: The system appears to have a feedback loop but it's write-only
Discovered During
Internal research audit of AutoBot's RAG feedback architecture
Options
- Remove the dead write — stop calling
_emit_retrieval_feedback()until a consumer exists (eliminates wasted Redis ops) - Extend TTL — increase from 7 days to 30+ days so the data survives until Neural Mesh Phase 3 (feat: EdgeLearner — Hebbian reinforcement from retrieval feedback (Neural Mesh RAG Phase 3) #2056) ships a consumer
- Build consumer now — addressed by Enhancement: Retrieval pattern distillation — close the RAG feedback loop #2095 (retrieval pattern distillation), but that's a larger effort
Recommendation
Option 2 (extend TTL to 30 days) as an immediate fix. The data is small per entry and valuable for Phase 3. Option 1 if storage is a concern.
Files Affected
autobot-backend/services/rag_service.py— the_emit_retrieval_feedback()method
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