feat: graph-aware retrieval planner (opt-in capability)

[Programmer7129]

Summary

Adds a one-LLM-call up-front retrieval planner that produces a structured RetrievalPlan (expected answer type, priority predicates, optional hop sequence) from a compressed view of the relevant fact-graph slice. The plan biases — never replaces — the existing PPR + beam + triple-ANN + Cohere Rerank pipeline.

This is the first capability in the OSS RAG starter space that pairs HippoRAG-2-style PPR with an explicit pre-retrieval plan. Existing public KG-RAG systems (HippoRAG 2, MS GraphRAG, LightRAG, DAVIS) either skip planning entirely or do reactive agentic loops (latency cliffs).

What's in

New modules:

• src/engram/core/graph_view.py — CompressedGraphView, EntityNeighborhood + build_query_graph_view. Pure logic over backend.fact_graph.
• src/engram/dialogue/prompts/retrieval_plan.py — RetrievalPlan / HopStep schemas + prompt with 4 worked examples + confidence calibration.
• src/engram/dialogue/retrieval_planner.py — async plan_retrieval with confidence-floor abstention (0.5), graceful no-op on empty view or LLM error.
• benchmarks/failure_tagger.py — Phase 0 LLM classifier; tagged 120 n=200 failures, 22.5% planner-addressable.

Plumbing:

• kg_hybrid_neighbors gains plan kwarg → drives predicate_boost in beam_search, post-fusion fact-type filter (capped at 30% removal), plan-aware Cohere Rerank query suffix.
• beam_search_facts gains predicate_boost + multiplier (default 1.5x).
• answer_one caches plan per question across IRCoT rounds.
• --retrieval-planner flag in benchmarks/musique.py, default OFF.

Pre-existing main hardening (ported):

• _LMDB_MAX_KEY_BYTES (480) guards in entity/alias/fact upsert paths — prevents cold-path BadValsizeError on LLM-extracted runaway names.
• exc_info=True on the swallowed background-task warning so cold-path failures surface tracebacks.

n=100 ablation (kg-hybrid + IRCoT + synth OFF)

Run	EM	F1	Plan fire rate
No-planner baseline	0.40	0.5475	n/a
Planner-on (refined prompt)	0.39	0.5389	8% confident plans

Flat metrics within run-to-run variance (±0.04 EM at n=100). Shipping as opt-in capability, not as a metric-lift feature. The planner is correct in isolation; the lift is bottlenecked by the upstream entity extractor producing 30-40% query-slot noise and by n=100 sample variance exceeding the +0.02 gate.

Positioning: Engram becomes the only OSS RAG starter with explicit graph-aware planning + structured retrieval traces — capability differentiation, not benchmark dominance.

Test plan

• 366/366 unit + integration tests pass
• 9 new graph_view tests, 8 planner-dialogue tests, 3 plan-biased-retrieval integration tests
• ruff check + ruff format --check clean
• Default --retrieval-planner OFF — runs without the flag are byte-identical to main
• LMDB guards verified end-to-end on n=100 store build
• README mention of the new opt-in capability (follow-up commit on main)

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