Adds an opt-in cross-encoder rerank step between BM25 retrieval and LLM
synthesis. With BRAIN_RERANKER_URL set, brain_answer retrieves BM25
top-20, scores each excerpt against the query via Qwen3-Reranker on
Ollama, drops the "no" answers, and forwards up to 5 surviving sources
to the LLM. Unset, behaviour is unchanged (BM25 top-10 → LLM).
The reranker is a *filter*, not a re-ranker: Qwen3-Reranker emits a
binary yes/no token under its native chat template, and ties within the
"yes" set are broken by BM25 rank — what got retrieved first stays
ahead.
New package ingestion/internal/reranker:
- Client with URL, Model, HTTP fields.
- New(url, model) returns nil on empty url so callers can treat
"feature disabled" as a single nil check.
- Score(ctx, query, docs) issues one /api/generate call per doc using
the Qwen3-Reranker yes/no chat template (verbatim, because the model
was trained on this exact wording). Parses the first non-think token.
Wiring:
- mcp.Server gains a WithReranker fluent setter to keep NewServer
signature stable.
- brain_answer's BM25 limit jumps to 20 only when a reranker is wired,
to give the filter something to do.
- cmd/server/main.go reads BRAIN_RERANKER_URL (+ optional
BRAIN_RERANKER_MODEL, default dengcao/Qwen3-Reranker-0.6B:F16).
Tests cover: nil-on-empty-url, ordered yes/no scoring, request shape
(model, prompt contents, yes/no template), ambiguous response → 0,
empty doc slice, upstream-error propagation, plus an end-to-end
brain_answer integration that proves only the relevant note reaches the
LLM when noise.md is rejected.
Closes hyperguild#7.
Adds two new LLM-backed MCP tools to the ingestion service:
- brain_answer(query): BM25 retrieval + LLM synthesis → answer + sources
- brain_classify(text): classifies doc into type/title/tags via LLM
Adds llm.Router for primary→fallback routing (berget.ai → iguana).
Wired via BRAIN_LLM_PRIMARY_URL/BRAIN_LLM_FALLBACK_URL env vars;
no-op when unset so existing deployments are unaffected.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
