Hermes Agent
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feat: implement Reader-Guided Reranking — bridge R@5 vs E2E gap (#666)
Resolves #666. RIDER reranks retrieved passages by how well the LLM
can actually answer from them, bridging the gap between high retrieval
recall (98.4% R@5) and low end-to-end accuracy (17%).
agent/rider.py (256 lines):
- RIDER class with rerank(passages, query) method
- Batch LLM prediction from each passage individually
- Confidence-based scoring: specificity, grounding, hedge detection,
query relevance, refusal penalty
- Async scoring with configurable batch size
- Convenience functions: rerank_passages(), is_rider_available()
tools/session_search_tool.py:
- Wired RIDER into session search pipeline after FTS5 results
- Reranks sessions by LLM answerability before summarization
- Graceful fallback if RIDER unavailable
tests/test_reader_guided_reranking.py (10 tests):
- Empty passages, few passages, disabled mode
- Confidence scoring: short answers, hedging, grounding, refusal
- Convenience function, availability check
Config via env vars: RIDER_ENABLED, RIDER_TOP_K, RIDER_TOP_N,
RIDER_MAX_TOKENS, RIDER_BATCH_SIZE.
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