Adds an empirical surface for evaluating model swaps. One row per
assistant turn captures: model, think_enabled, tools_available,
tools_attempted, tools_succeeded, tools_failed (with error details
as JSONB). Without this, judging whether a new model "actually fires
record_moment when it should" relies on anecdote across user-reported
sessions. With it, the data is queryable directly.
Pieces:
- Migration 0046: generation_tool_log table with user_created and
per-conversation indexes.
- Model: SQLAlchemy GenerationToolLog with to_dict() for plain-dict
consumption outside session scope.
- Service: log_tool_outcomes() normalizes the in-app tool-call shape
(function/result/status) into the split buckets and persists. It
catches its own exceptions — telemetry failure must NEVER affect
the user-facing generation flow. recent_logs() helper for read.
- Integration in run_generation: called once per turn right after
log_generation, fire-and-forget.
- Tests: pure-normalization unit tests using a stub session — no DB
needed in CI. Cover the success/error split, the empty-tool-calls
case, the exception-swallowing contract, and the success=False
edge case where status incorrectly says "success".
No UI for the telemetry yet — internal infrastructure (the operator
is the consumer, not the journal user), which the FabledRulebook
"no UI no ship" explicitly excepts. Query via psql or extend the
Fable MCP later if direct shell access gets tiresome.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
bvandeusen
bf7a29e8a0