feat(summarizer): resilient endpoint chain with local→cloud fallback (ADR-022)

The first friendly-pilot live run produced zero summaries: koala/phi4-mini hit
three silent failure modes — 8k context overflow on long transcripts (HTTP 400),
intermittent malformed JSON (highlights as a bare string), and no fallback wired
at all (summarizer.New(primary, nil)).

Keep phi4-mini as the fast primary and add resilience around it:

- Ordered endpoint chain (summarizer.NewChain): phi4-mini → koala/phi4-14b
  (local) → berget/mistral-small (worst-case external). All reached through the
  one LiteLLM gateway by alias.
- A parse failure now advances the chain like a transport error — the old
  Primary→Fallback shape returned the parse error without trying anyone else.
- Tolerant parse: highlights/takeaways coerce string→[]string, absorbing the
  common small-model quirk without spending a fallback round-trip.
- Transcript truncation (TAPIR_MAX_TRANSCRIPT_CHARS=18000) prevents the overflow
  rather than recovering from it; validated to fit phi4-mini's 8k window.
- Bounded completion budget (TAPIR_SUMMARY_MAX_TOKENS=1500) — the old 8192 budget
  itself contributed to the overflow.

Local-first guarantee preserved by ordering: external endpoint is tried only
after every local one fails. TAPIR_CLOUD_FALLBACK_MODEL="" disables it entirely
for client/NDA deployments.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

docs/homelab-integration.md

@@ -27,6 +27,18 @@ it** — endpoints and aliases drift, and this file is a snapshot (2026-06-06),
   `iguana/deepseek-r1-14b`) is preferred for summary quality if its latency/output is acceptable.
   The `max_tokens` fix below means thinking models no longer return empty content, so they are now
   viable choices, not blocked ones. Do not assume a coder alias is right for prose.
+- **Summarizer fallback chain (ADR-022).** The primary alias is the *first* of an ordered chain;
+  on failure or unparseable output the summarizer advances to the next model. All reached through
+  the same gateway by alias.
+  - `TAPIR_FALLBACK_MODEL` — local fallback. **Default `koala/phi4-14b`.** Empty disables it.
+  - `TAPIR_CLOUD_FALLBACK_MODEL` — worst-case EXTERNAL fallback. **Default `berget/mistral-small`.**
+    **Set this empty (`""`) for any client/NDA deployment** so content never leaves the local
+    stack — the chain then contains only local endpoints.
+  - `TAPIR_SUMMARY_MAX_TOKENS` — per-summary completion budget. **Default `1500`.** Small on
+    purpose: with the old 8192 budget, prompt + completion overflowed `phi4-mini`'s 8k window.
+  - `TAPIR_MAX_TRANSCRIPT_CHARS` — transcript truncation budget sent to the model. **Default
+    `18000`** (~fits an 8k-context model). `0` disables truncation. Prevents the context-overflow
+    HTTP 400 a long transcript caused on `phi4-mini`.
 - **Thinking models need an explicit `max_tokens`.** qwen3 / deepseek-r1 spend the budget on
   reasoning and return **empty content** if `max_tokens` is too low (or unset). The summarizer's
   parser treats an empty summary as an error for exactly this reason. **Done (2026-06-02, Worker F):**

docs/use-cases/ai_routing.feature

@@ -31,5 +31,17 @@ Feature: Local-first AI with optional BYO fallback
     When any transcript is summarized
     Then my content is only ever sent to the local AI stack
 
-  # "Reliably" is operationalized as: Primary returned without error within timeout.
+  Scenario: A model returns unparseable output and the next endpoint succeeds
+    Given the local AI stack is available
+    But the primary model returns output that cannot be parsed into a summary
+    And a fallback model is configured
+    When a transcript is summarized
+    Then Tapir falls back to the next model in the chain
+    And the summary records fallback_used as true
+
+  # "Reliably" is operationalized as: an endpoint returned a PARSEABLE summary
+  # within timeout. A 200 with malformed JSON (or highlights emitted as a bare
+  # string) counts as a failure and advances the chain (ADR-022). Endpoints are
+  # tried in order, locals first, so the external worst-case model only ever sees
+  # content after every local endpoint has failed.
   # Quality scoring may be added later without changing these scenarios.