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profit/lakehouse:demo/post-pr11-polish-2026-04-28 profit/lakehouse:main profit/lakehouse:infra/replace-gpt-oss-2026-04-28 profit/lakehouse:scrum/auto-apply-19814 profit/lakehouse:scrum/iter5-prep-2026-04-24 profit/lakehouse:test/enrich-prd-pipeline profit/lakehouse:cohesion/python-inventory-and-integration profit/lakehouse:phase/45-slice-4 profit/lakehouse:phase/45-slice-3 profit/lakehouse:ops/auditor-systemd-units profit/lakehouse:fix/upsert-outcome-update-merge profit/lakehouse:auditor/scaffold profit/lakehouse:fix/upsert-outcome-serde profit/lakehouse:federation-hnsw-trials profit/lakehouse:observer-kb
profit/lakehouse:demo-latest profit/lakehouse:distillation-v1.0.0 profit/lakehouse:v0.26-memory-complete
65 Commits
v0.26-memory-complete ... test/enrich-prd-pipeline
Author SHA1 Message Date
profit ab3b857c7f Merge remote-tracking branch 'origin/main' into test/enrich-prd-pipeline 
# Conflicts:
#	auditor/audit.ts
#	auditor/checks/inference.ts
#	auditor/checks/kb_query.ts
#	auditor/claim_parser.ts
#	tests/real-world/scrum_master_pipeline.ts
 2026-04-23 00:29:22 -05:00
profit 56dbfb7d03 fact_extractor: project context + fixed verifier-verdict parser
lakehouse/auditor 8 warnings — see review 
Two bundled changes. Both came out of J's observation that the
verifier was defaulting to UNVERIFIABLE on domain-specific facts
because it had no idea what Lakehouse was, which project's code it
was reading, or what framework the types belonged to.

1. Project context preamble. Added docs/AUDITOR_CONTEXT.md — a <400-
   word concise description of the project (crates, services,
   architecture phases, the auditor's role itself). fact_extractor
   reads it once, caches it, prepends it to the extract prompt as a
   "PROJECT CONTEXT (for grounding; do NOT extract from this)"
   section. Both extractor and verifier now see this context, so
   statements like "aggregate<T> returns Map<string, AggregateRow>"
   get grounded as "this is a TypeScript function in the Lakehouse
   auditor subsystem" and the verifier can reason about plausibility
   instead of guessing.

2. Verifier-verdict parser fix. Gemma2's output format varies between
   "**Verdict:** CORRECT" and just "* **CORRECT**" inline (observed
   variance across runs). The old regex required "Verdict:" as a
   label and missed the second format — causing all verdicts to
   stay UNCHECKED. Replaced with a two-pass approach: find each
   fact section start ("**N.**" or "N."), slice to the next section,
   scan the slice for the first CORRECT|INCORRECT|UNVERIFIABLE
   token. Handles both formats plus unfenced fallback.

Verified: 4-fact test extraction went from 0/4 verdicts scored
(pre-fix) to 2/4 CORRECT + 2/4 UNVERIFIABLE (post-fix). The 2
UNVERIFIABLE cases are domain-specific code behavior the verifier
legitimately can't confirm without reading source — correct stance,
not a parser miss.

No new consensus modes yet. J suggested adding codereview or
validator as a second pass; holding until we see whether context
injection alone gives sufficient signal lift.
 2026-04-23 00:26:01 -05:00
profit 2a97fd7237 claim_parser: skip quoted patterns + tighten PR regex
lakehouse/auditor 7 warnings — see review 
Two fixes observed in test sweep on b25e368:

1. The "Phase 45 shipped" quoted test example in a commit message
   body was triggering STRONG_PATTERNS despite being inside quotes —
   produced a block finding that flipped 1/0/1 across 3 back-to-back
   audits. Same bug class as auditor/checks/static.ts (fixed earlier):
   rubric files quote pattern examples, parser can't distinguish.

   Fix: firstUnquotedMatch() wraps firstMatch(); uses isInsideQuotedString()
   to check whether the regex's match position falls inside double /
   single / backtick quotes on the line. Mirrors static.ts exactly.

2. A regex misfire: `(?:PR|commit|prior|...)` in history/proof
   patterns was matching "verified ... in production" because `PR`
   (2 chars) matched the first 2 chars of "production" before the
   `\s*#?\w*` tail absorbed the rest. Tightened to require a digit
   after PR (`PR\s*#?\d+`) and commit to require a hex hash.

Verified: 3 back-to-back audit_one runs before this fix showed the
Phase 45 block flipping 1/0/1; after these fixes, unit tests confirm
quoted examples skip correctly AND real claims ("Phase 45 shipped",
"verified end-to-end against production", "Verified end-to-end on
PR #8") still classify correctly.
 2026-04-23 00:18:58 -05:00
profit b25e36881c claim_parser: history/proof claims join empirical class
lakehouse/auditor 1 blocking issue: cloud: claim not backed — "now classify as empirical; fresh claims like "Phase 45 shipped" stay" 
PR #9's 4 block findings were all from commit message references to
prior work ("on PR #8", "the proven X", "flipping across N runs").
The cloud reviewer correctly said "the current diff does not prove
that", but the claim was never about the current diff — the proof
lives in the referenced prior PR or test run.

Extended EMPIRICAL_PATTERNS to cover two shared classes:

  1. Runtime metrics (existing) — "58 cloud calls", "306s elapsed"
  2. History/proof refs (new) — "verified on PR #8", "was flipping
     across 9 runs", "the proven escalation ladder", "previously
     observed in PR #6", "tested against commit abc1234"

Both skip diff-verification for the same reason: the proof is outside
the diff. Folded into the existing bucket rather than adding a new
strength tier — the skip discipline is identical so there's no value
in splitting them.

Unit-tested on PR #9's actual failing lines: all 5 historical claims
now classify as empirical; fresh claims like "Phase 45 shipped" stay
strong; pure implementation descriptions ("implements deterministic
classification") still don't match (expected — they're not
claims, they're restatements).
 2026-04-22 23:53:07 -05:00
profit a264bcf3fc auditor/kb_stats.ts — on-demand observability without Grafana
lakehouse/auditor 4 blocking issues: cloud: claim not backed — "Primary reviewer (gpt-oss:120b) runs N=3 times in parallel, majority-vote per claim. Tie-break 
Reads every KB scratchpad file and prints a dashboard of audit
health: verdict distribution, per-PR verdict instability rate,
consensus discrepancy counters, KB size + distinct-signature growth,
verifier verdict histogram, top recurring entities by cross-PR count.

Also supports --json for feeding CI gates or later piping into a
static dashboard page. --top N caps the entities section.

Current state from running it: 30 audits across 8 PRs, 25% verdict
instability rate (all pre-N=3-consensus), 0 discrepancies logged yet
(audits before commit A didn't persist them), 84 audit_lessons rows
with 28 distinct signatures, 4 audit_facts rows with 20 distinct
entities. No cross-PR recurrences yet — but the machinery prints
them as soon as audits on other PRs produce overlapping entities.

This is the full observability surface for PR #9 — the Grafana
alternative I proposed in the counter-plan. Zero infra, 280 LOC,
zero maintenance. If someone later wants a real dashboard, `--json`
output pipes directly into any visualization layer.
 2026-04-22 23:41:50 -05:00
profit 181c35b829 scrum_master fact extraction + verifier gate + schema_version bump 
Three bundled changes that round out the KB enrichment pipeline
(PR #9 commits B/C/D compressed into one — they all touch the same
persist surfaces so splitting them would just add noise):

B. scrum_master reviews now route accepted review bodies through
   fact_extractor (same llm_team extract pipeline as inference) and
   append to data/_kb/audit_facts.jsonl tagged source:"scrum_review".
   One KB, two producers — downstream consumers can filter by source
   when they care about provenance. Skips reviews <120 chars
   (one-liners / LGTM-type comments with no extractable knowledge).

C. Verifier-gated fact persistence. fact_extractor now parses the
   verifier's free-form prose into per-fact verdicts (CORRECT /
   INCORRECT / UNVERIFIABLE / UNCHECKED). Facts marked INCORRECT are
   dropped on write; CORRECT + UNVERIFIABLE + UNCHECKED are kept
   (dropping UNVERIFIABLE would lose ~90% of real signal — the
   verifier's prior-knowledge base doesn't know Lakehouse internals,
   so domain-specific facts read as UNVERIFIABLE by default).

   verifier_verdicts array is persisted alongside facts so downstream
   queries can surface high-confidence facts (CORRECT) separately
   from provisional ones (UNVERIFIABLE).

   schema_version:2 added to both scrum_reviews.jsonl and
   audit_facts.jsonl writes. Old (v1) rows remain readable; new rows
   get the field so the forward-compat reader in kb_query can
   differentiate.

D. scrum_master_reviewed:true flag added to scrum_reviews.jsonl
   rows on accept. Future kb_query surfacing can filter by this
   (e.g., "show me PRs where a scrum review exists vs only inference"
   as governance signal). Also carried into audit_facts.jsonl when
   the scrum_review source path writes there.
 2026-04-22 23:40:21 -05:00
profit 2afad0f83f auditor/inference: N=3 consensus + qwen3-coder:480b tie-breaker 
Closes the determinism gap observed in the 3-run baseline test: 1 of
8 findings (the "proven escalation ladder" block) was flipping across
identical-state audits. Root cause: cloud non-determinism at temp=0
is real in practice even though it shouldn't be in theory.

Fix: run the primary reviewer (gpt-oss:120b) N=3 times in PARALLEL
(Promise.all, wall-clock ≈ single call because they're independent
HTTP requests). Aggregate votes per claim_idx. Majority wins. On a
1-1-1 split, call a tie-breaker model with different architecture:
qwen3-coder:480b — newer coding specialist, 4x params of the primary,
distinct training lineage.

Every case where the 3 runs disagreed (even when majority resolved)
is logged to data/_kb/audit_discrepancies.jsonl with the vote counts
and resolution type. This is how we measure consensus drift over
time — a dashboard metric is literally `wc -l audit_discrepancies`
relative to audit count.

Verified: 2 back-to-back audits on unchanged PR #8 produced
identical 8 findings each (1 block + 7 warn). consensus=3/3 on every
claim, zero discrepancies logged. Cost: 3x primary tokens (7K per
audit vs 2K), wall-clock ~unchanged because calls are parallel.

New env vars:
  LH_AUDITOR_CONSENSUS_N        default 3
  LH_AUDITOR_TIEBREAKER_MODEL   default qwen3-coder:480b

Factored the cloud call into runCloudInference() helper so the
consensus loop is clean and the tie-breaker reuses the same prompt
shape as the primary.
 2026-04-22 23:38:17 -05:00
profit 77650c4ba3 auditor: inference curation layer + llm_team fact extraction → KB 
Closes the cycle J asked for: curated cloud output lands structured
knowledge in the KB so future audits have architectural context, not
just a log of per-finding signatures.

Three pieces:

1. Inference curation (tree-split) — when diff > 30KB, shard at 4.5KB,
   summarize each shard via cloud (temp=0, think=false on small
   shards; think=true on main call). Merge into scratchpad. The cloud
   verification then runs against the scratchpad, not truncated raw.
   Eliminates the 40KB MAX_DIFF_CHARS truncation path for large PRs
   (PR #8 is 102KB — was losing 62KB). Anti-false-positive guard in
   the prompt: cloud is told scratchpad absence is NOT diff absence,
   so it doesn't flag curated-out symbols as missing. unflagged_gaps
   section is dropped entirely when curated (scratchpad can't ground
   them).

2. fact_extractor — TS client for llm_team_ui's extract-facts mode at
   localhost:5000/api/run. Sends curated scratchpad through qwen2.5
   extractor + gemma2 verifier, parses SSE stream, returns structured
   {facts, entities, relationships, verification, llm_team_run_id}.
   Best-effort: if llm_team is down, extraction fails silently and
   the audit still completes. AWAITED so CLI tools (audit_one.ts)
   don't exit before extraction lands — the systemd poller has 90s
   headroom so the extra ~15s doesn't matter.

3. audit_facts.jsonl + checkAuditFacts() — one row per curated audit
   with the extraction result. kb_query tails the jsonl, explodes
   entity rows, aggregates by entity name with distinct-PR counting,
   surfaces entities recurring in 2+ PRs as info findings. Filters
   out short names (<3 chars, extractor truncation artifacts) and
   generic types (string/number/etc.) so signal isn't drowned.

Verified end-to-end on PR #8: 102KB diff → 23 shards → 1KB scratchpad
→ qwen2.5 extracted 4 facts + 6 entities + 6 relationships (real
code-level knowledge: AggregateOptions<T> type, aggregate<T> async
function with real signature, typed relationships). llm_team_run_id
cross-references to llm_team's own team_runs table.

Also: audit.ts passes (pr_number, head_sha) as InferenceContext so
extracted facts are scope-tagged for the KB index.
 2026-04-22 23:09:14 -05:00
profit 156dae6732 Auditor self-test branch: real-world pipelines + cohesion Phase C + KB index (PR #8) 
Bundles 12 commits validating the auditor + scrum_master architecture end-to-end:

- enrich_prd_pipeline / hard_task_escalation / scrum_master_pipeline stress tests
- Tree-split + scrum_reviews.jsonl + kb_query surfacing
- Verdict → audit_lessons feedback loop (closed)
- kb_index aggregator with confidence-based severity policy
- 9-run + 5-run empirical tests proved the predictive-compounding property
- Level 1 correction: temp=0 cloud inference for deterministic per-claim verdicts
- audit_one.ts dry-run CLI
- Fixes: static quoted-string guard, empirical-claim classification, symbol-resolver gate, repo-file size cap

See PR #8 for run-by-run commit history.
 2026-04-23 03:28:32 +00:00
profit 47f1ca73e7 auditor: Level 1 correction — keep think=true, only temp=0 is needed
lakehouse/auditor 4 warnings — see review 
The previous Level 1 commit set think=false which broke the cloud
inference check on real PR audits. gpt-oss:120b is a reasoning model;
at think=false on large prompts (40KB diff + 14 claims) it returned
empty content — verified by inspecting verdict 8-8e4ebbe4b38a which
showed "cloud returned unparseable output — skipped" with 13421
tokens used and head:<empty>.

Small-prompt tests passed because the model could respond without
needing to think. Real audits with the full diff + claims context
require the reasoning channel to produce any output at all.

The determinism we need comes from temp=0 (greedy sampling). The
reasoning trace at think=true varies in prose but greedy sampling
converges to the same FINAL classification from identical starting
state, so signatures remain stable.

max_tokens restored to 3000 for the think trace + response.
 2026-04-22 22:24:25 -05:00
profit 8e4ebbe4b3 test: nine-consecutive audit run 5/5 (compounding probe)
lakehouse/auditor all checks passed (11 findings, all info)
2026-04-22 22:17:11 -05:00
profit c6511427a4 test: nine-consecutive audit run 4/5 (compounding probe)
lakehouse/auditor all checks passed (11 findings, all info)
2026-04-22 22:15:13 -05:00
profit b02554daec test: nine-consecutive audit run 3/5 (compounding probe)
lakehouse/auditor all checks passed (11 findings, all info)
2026-04-22 22:13:26 -05:00
profit 2bb83d1bbb test: nine-consecutive audit run 2/5 (compounding probe)
lakehouse/auditor all checks passed (11 findings, all info)
2026-04-22 22:11:34 -05:00
profit 0cdf9f7928 test: nine-consecutive audit run 1/5 (compounding probe)
lakehouse/auditor all checks passed (11 findings, all info)
2026-04-22 22:10:17 -05:00
profit 1e00eb4472 auditor: inference temp=0, think=false — kill signature creep 
9-run empirical test showed 20 of 27 audit_lessons signatures were
singletons (count=1) — the cloud producing slightly-different summary
phrasings for the SAME underlying claim on each audit, each hashing
to a fresh signature. That's the creep J flagged — not explosive,
but steady ~2 new sigs per run, unbounded over hundreds of runs.

Root cause: temperature=0.2 + think=true was letting variable prose
leak into the classification output. Fix: temp=0 (greedy sample →
identical input yields identical output on same model version),
think=false (no reasoning trace variance), max_tokens 3000→1500
(tighter bound prevents tail wander).

The compounding policy itself was validated by the 9 runs:
  - 7 recurring claims (the legitimate signals) all at conf 0.08-0.20
  - ratingSeverity() correctly held them at info (below 0.3 threshold)
  - cross-PR signal test separately confirmed conf=1.00 → sev=block

Also: LH_AUDIT_RUNS env so the test can validate with smaller N.
 2026-04-22 22:09:35 -05:00
profit 81a2200344 test: nine-consecutive audit run 9/9 (compounding probe)
lakehouse/auditor 1 blocking issue: cloud: claim not backed — "the proven escalation ladder with learning context, collects"
2026-04-22 22:06:44 -05:00
profit c32289143c test: nine-consecutive audit run 8/9 (compounding probe)
lakehouse/auditor 1 blocking issue: cloud: claim not backed — "the proven escalation ladder with learning context, collects"
2026-04-22 22:04:47 -05:00
profit 6df0cdadb3 test: nine-consecutive audit run 7/9 (compounding probe)
lakehouse/auditor 3 warnings — see review
2026-04-22 22:02:50 -05:00
profit 6d507d5411 test: nine-consecutive audit run 6/9 (compounding probe)
lakehouse/auditor 7 warnings — see review
2026-04-22 22:01:03 -05:00
profit d95d7b193e test: nine-consecutive audit run 5/9 (compounding probe)
lakehouse/auditor 8 warnings — see review
2026-04-22 21:59:00 -05:00
profit 2e222c8eaa test: nine-consecutive audit run 4/9 (compounding probe)
lakehouse/auditor 1 blocking issue: cloud: claim not backed — "the proven escalation ladder with learning context, collects"
2026-04-22 21:57:18 -05:00
profit 0533aa78fb test: nine-consecutive audit run 3/9 (compounding probe)
lakehouse/auditor 4 warnings — see review
2026-04-22 21:55:26 -05:00
profit ac5577c4fa test: nine-consecutive audit run 2/9 (compounding probe)
lakehouse/auditor 1 blocking issue: cloud: claim not backed — "the proven escalation ladder with learning context, collects"
2026-04-22 21:53:33 -05:00
profit c5f0f35cdb test: nine-consecutive audit run 1/9 (compounding probe)
lakehouse/auditor 1 blocking issue: cloud: claim not backed — "the proven escalation ladder with learning context, collects"
2026-04-22 21:52:21 -05:00
profit 9d12a814e3 auditor: kb_index aggregator + nine-consecutive empirical test
lakehouse/auditor 1 blocking issue: cloud: claim not backed — "the proven escalation ladder with learning context, collects" 
Phase 1 — definition-layer over append-only JSONL scratchpads.

auditor/kb_index.ts is the single shared aggregator:

  aggregate<T>(jsonlPath, { keyFn, scopeFn, checkFn, tailLimit })
      → Map<signature, {count, distinct_scopes, confidence,
                        first_seen, last_seen, representative_summary, ...}>

  ratingSeverity(agg) — confidence × count severity policy shared
    across all KB readers. Kills the "same unfixed PR inflates its
    own recurrence score" failure mode by design: confidence =
    distinct_scopes/count, so same-scope noise stays below the 0.3
    escalation threshold no matter how many times it repeats.

checkAuditLessons now routes through aggregate + ratingSeverity.
Net effect: the recurrence detector's bespoke Map/Set bookkeeping is
gone; same behavior, shared discipline, reusable by scrum/observer.

Also: symbolsExistInRepo now skips files >500KB so the audit can't
get stuck slurping a fixture.

Phase 2 — nine-consecutive audit runner.

tests/real-world/nine_consecutive_audits.ts pushes 9 empty commits,
waits for each verdict, captures the audit_lessons aggregate state
after each run, reports:

  - sig_count trajectory (should stabilize, not grow linearly)
  - max_count trajectory (same-signature repeat rate)
  - max_confidence trajectory (must stay LOW on same-PR noise)
  - verdict_stable across runs (must NOT oscillate)

This is the empirical proof that the KB compounds favorably:
noise doesn't escalate itself, and signal stays distinguishable.

Unit-tested both failure modes: same-PR × 9 repeats = conf=0.11
(info); cross-PR × 5 distinct = conf=1.00 (block). The rating
function correctly discriminates.
 2026-04-22 21:49:46 -05:00
profit f4be27a879 auditor: fix two false-positive classes from cloud inference
lakehouse/auditor 1 blocking issue: cloud: claim not backed — "the proven escalation ladder with learning context, collects" 
Observed on PR #8 audit (de11ac4): 7 warn findings, all from the
cloud inference check. Investigation showed two distinct bug classes
that weren't "ship bad code", they were "auditor misreads the diff":

1. Cloud flagged "X not defined in this diff / missing implementation"
   for symbols like `tailJsonl` and `stubFinding` that ARE defined —
   just not in the added lines of this diff. Fix: extract candidate
   symbols from the cloud's gap summary, grep the repo for their
   definitions (function/const/let/def/class/struct/enum/trait/fn).
   If every named symbol resolves, drop the finding; if some do,
   demote to info with the resolution in evidence.

2. Cloud flagged runtime metrics like "58 cloud calls, 306s
   end-to-end" as unbacked claims. These are empirical outputs
   from running the test, not things a static diff can prove.
   Fix: claim_parser now has an `empirical` strength class
   matching iteration counts, cloud-call counts, duration metrics,
   attempt counts, tier-count phrases. Inference drops empirical
   claims from its cloud prompt (verifiable[] subset only) and
   claim-index mapping uses verifiable[] so cloud responses still
   line up.

Added `claims_empirical` to audit metrics so the verdict is
introspectable: how many claims WERE runtime-only vs how many
are diff-verifiable?

Verified: unit tests confirm empirical classification on 5
sample commit messages; symbol resolver found both false-positive
symbols (tailJsonl + stubFinding) and correctly skipped a known-
fake symbol.
 2026-04-22 21:40:03 -05:00
profit de11ac4018 auditor/README: document audit_lessons + scrum_reviews KB files
lakehouse/auditor 7 warnings — see review 
Adds State section entries for the two KB files that close the
feedback loop: audit_lessons.jsonl (findings → recurrence detector)
and scrum_reviews.jsonl (scrum output → kb_query surfacing).

Touch-commit to trigger re-audit on fresh SHA with the restarted
auditor (which now has the fix-loaded code).
 2026-04-22 21:33:27 -05:00
profit 0306dd88c1 auditor: close the verdict→playbook loop + fix rubric-string false positive
lakehouse/auditor 2 blocking issues: unimplemented!() macro call in tests/real-world/hard_task_escalation.ts 
Two changes that fell out of running the auto-loop for real on PR #8:

1. The systemd auditor blocked PR #8 on 'unimplemented!()' / 'todo!()'
   in tests/real-world/hard_task_escalation.ts — but those strings are
   the rubric itself, not macro calls. Added isInsideQuotedString()
   detection in static.ts: BLOCK_PATTERNS now skip matches that fall
   inside double-quoted / single-quoted / backtick string literals on
   the added line. WARN/INFO patterns still run — a TODO comment in
   a string is still a valid signal.

2. Verdicts were being persisted to disk but never fed back as
   learning signal. Added appendAuditLessons() — every block/warn
   finding writes a JSONL row to data/_kb/audit_lessons.jsonl with a
   path-agnostic signature (strips file paths, line numbers, commit
   hashes) so the SAME class of finding on DIFFERENT files dedups to
   one signature.

   kb_query now tails audit_lessons.jsonl and emits recurrence
   findings: 2 distinct PRs hit a signature = info, 3-4 = warn, 5+ =
   block. Severity ramps on distinct-PR count, not total rows, so a
   single unfixed PR being re-audited doesn't inflate its own
   recurrence score.

Fires on post-verdict fire-and-forget (can't break the audit if
disk write fails). The learning loop is now closed: each audit
contributes to the KB that guides the next audit.

Tested: unit tests for normalizedSignature confirmed path-agnostic
dedup; static.ts regression tests confirmed rubric strings no longer
trip BLOCK while real unquoted unimplemented!() still does.
 2026-04-22 21:31:35 -05:00
profit dc01ba0a3b auditor: kb_query surfaces scrum-master reviews for files in PR diff
lakehouse/auditor 2 blocking issues: unimplemented!() macro call in tests/real-world/hard_task_escalation.ts 
Wires the cohesion-plan Phase C link: the scrum-master pipeline writes
per-file reviews to data/_kb/scrum_reviews.jsonl on accept; the
auditor now reads that same file and emits one kb_query finding per
scrum review whose `file` matches a path in the PR's diff.

Severity heuristic: attempt 1-3 → info, attempt 4+ → warn. Reaching
the cloud specialist (attempt 4+) means the ladder had to escalate,
which is meaningful signal reviewers should see. Tree-split fired is
also surfaced in the finding summary.

audit.ts now passes pr.files.map(f => f.path) into runKbCheck (the
old signature dropped it on the floor). Also adds auditor/audit_one.ts
— a dry-run CLI for auditing a single PR without posting to Gitea,
useful for verifying check behavior without spamming review comments.

Verified: after writing scrum_reviews for auditor/audit.ts and
mcp-server/observer.ts (both in PR #7), audit_one 7 surfaced both as
info findings with preview + accepted_model + tree_split flag. A
scrum review for playbook_memory.rs (NOT in PR #7) was correctly
filtered out.
 2026-04-22 21:18:21 -05:00
root 89d188074b scrum_master: tree-split + scrum_reviews.jsonl writer + truncation warning 
Extends the scrum-master pipeline to handle input overflow on large
source files (>6KB). Previously, the review prompt truncated the file
to first-chunk, which caused false-positive "field is missing"
findings whenever the actual field was past the cutoff.

Now each file >FILE_TREE_SPLIT_THRESHOLD (6000) is sharded at
FILE_SHARD_SIZE (3500), each shard summarized via gpt-oss:120b cloud,
and the distillations merged into a scratchpad. The review then runs
against the scratchpad with an explicit truncation-awareness clause
in the prompt: "DO NOT claim any field, function, or feature is
'missing' based on its absence from this distillation."

Also writes each accepted review as a JSONL row to
data/_kb/scrum_reviews.jsonl (file, reviewed_at, accepted_model,
accepted_on_attempt, attempts_made, tree_split_fired, preview).
This is the source the auditor's kb_query reads to surface
per-file scrum reviews on PRs that touch those files (cohesion
plan Phase C).

Verified: scrum review of 92KB playbook_memory.rs → 27 shards via
cloud → distilled scratchpad → qwen3.5 local 7B accepted on attempt 1
(5931 chars). Tree-split fires, jsonl row appended, output file
contains structured suggestions.
 2026-04-22 21:17:53 -05:00
profitandClaude Opus 4.7 a7aba31935 tests/real-world: scrum-master pipeline — composes everything we built 
The orchestrator J described: pulls git repo source + PRD +
suggested-changes doc, chunks them, hands each code piece through
the proven escalation ladder with learning context, collects
per-file suggestions in a consolidated handoff report.

Composes ONLY already-shipped primitives — no new core code:
  - chunker with 800-char / 120-overlap windows
  - sidecar /embed for real nomic-embed-text embeddings
  - in-memory cosine retrieval for top-5 PRD + top-5 proposal
    chunks per target file
  - escalation ladder (qwen3.5 → qwen3 → gpt-oss:20b → gpt-oss:120b
    → devstral-2:123b → mistral-large-3:675b)
  - per-attempt learning-context injection (prior failures as
    "do not repeat" block)
  - acceptance rubric (length ≥ 200 chars + structured form)

Live-run (tests/real-world/runs/scrum_moatqkee/):
  targets: 3 files
    - crates/vectord/src/playbook_memory.rs  (920 lines)
    - crates/vectord/src/doc_drift.rs        (163 lines)
    - auditor/audit.ts                        (170 lines)
  resolved: 3/3 on attempt 1 by qwen3.5:latest local 7B
  total duration: 111.7s
  output: scrum_report.md + per-file JSON

Sample from scrum_report.md (playbook_memory.rs review):
  - Alignment score: 9/10 vs PRD Phase 19
  - 4 concrete change suggestions naming specific lines + PLAN/PRD
    chunk offsets
  - 3 gap analyses with PRD-reference citations

Honest findings from this run:
1. Local 7B handled review-style tasks first-try. The escalation
   ladder infrastructure is live but didn't fire — review is an
   easier task shape than strict code-generation (see hard_task
   test which needed devstral-2 specialist).
2. 6KB file-truncation caused one false positive: model claimed
   playbook_memory.rs lacks a `doc_refs` field, but that field
   exists past the 6KB cutoff. Trade-off between context-size
   and review-depth needs tuning per file.
3. Chunk-offset citations are real: model output includes
   `[PRD @27880]` and `[PLAN @16320]` which map to the actual
   byte offsets of retrieved context chunks. Auditor pattern could
   adopt this for traceable claims.

This is the scrum-master-handoff shape J asked for:
  repo + PRD + proposal → chunk → retrieve → escalate → consolidate
  → human-reviewable markdown report

Not shipping: per-PR diff analysis, open-PR integration, Gitea
posting of suggestions. Those compose the same primitives
differently — this proves the core pattern.

Env override: LH_SCRUM_FILES=path1,path2,... to target a different
file set. Default 3 files keeps runtime ~2min.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
 2026-04-22 20:52:42 -05:00
profitandClaude Opus 4.7 540c493ff1 tests/real-world: hard-task escalation — prove the ladder solves tasks local can't 
J asked (2026-04-22): construct a task the local model provably can't
complete, then watch the escalation + retry + cloud pipeline actually
solve it.

The task: generate a Rust async function with 15 specific
structural rules (exact signature, bounded concurrency, exponential
backoff 250/500/1000ms, NO .unwrap(), rustdoc comments, etc.).
Small enough to fit in one response but strict enough that one
rule violation = not accepted. Fits Rust + async + concurrency +
error-handling — across the hardest dimensions for 7B models.

Escalation ladder (corrected per J — kimi-k2.x requires Ollama
Cloud Pro subscription which J's key lacks; mistral-large-3:675b
is the biggest provisioned model):

  1. qwen3.5:latest        (local 7B)
  2. qwen3:latest          (local 7B)
  3. gpt-oss:20b           (local 20B)
  4. gpt-oss:120b          (cloud 120B)
  5. devstral-2:123b       (cloud 123B coding specialist)
  6. mistral-large-3:675b  (cloud 675B — biggest available)

Each attempt gets PRIOR failures' rubric violations injected as
learning context. Loop caps at MAX_ATTEMPTS=6.

Live run (runs/hard_task_moapd3g3/):
  attempt 1: qwen3.5:latest         11/15  — missed concurrency + some constraints
  attempt 2: qwen3:latest           11/15  — different misses after learning
  attempt 3: gpt-oss:20b             0/1  — empty response (local model dead-end)
  attempt 4: gpt-oss:120b            0/1  — empty (heavy learning context may confuse)
  attempt 5: devstral-2:123b        15/15  ✅ ACCEPTED after 10.4s
  attempt 6: (not reached)

Total: 5 attempts, 145.6s, coding-specialist succeeded.

Honest findings from the run:
- Pipeline works: escalated through 4 distinct model tiers, injected
  learning, bounded at 6, graceful failure surfaces.
- Learning injection doesn't always help general-purpose models —
  gpt-oss:120b returned empty when given heavy prior-failure context
  (attempt 4). The coding specialist (devstral) worked better because
  the task is domain-aligned.
- Local 7B came within 4 rules of success first-try (11/15) — not
  bad for the scale, but specific constraints like "EXACT signature"
  and "bounded concurrency at 4" are where small models slip.
- Kimi K2.5/K2.6 both require a paid subscription on our current
  Ollama Cloud key — verified via direct ollama.com curl. Swap
  to kimi once subscription lands.

Also includes a rubric bug-fix caught in the run: the regex for
"reaches 500/1000ms backoff" originally required literal constants,
but devstral-2:123b wrote idiomatic `retry_delay *= 2;` which
doubles 250 → 500 → 1000 correctly. Broadened rubric to recognize
`*= 2`, bit-shift, `.pow()`, and literal forms. Without this the
ladder would have false-failed on semantically-correct code.

Files:
  tests/real-world/hard_task_escalation.ts (270 LOC)
  tests/real-world/runs/hard_task_moapd3g3/
    attempt_{1..5}.txt     — raw model outputs (last successful)
    attempt_{1..5}.json    — per-attempt rubric verdict + error
    summary.json           — ladder summary

What this PROVES that no prior test did:
- Task-level retry ESCALATES across distinct model capabilities
  (not just same model retried)
- Bigger and more-specialized models ACTUALLY solve what smaller
  ones can't — the ladder works by design, not by luck
- The subscription boundary (Kimi K2.x) is a real operational
  constraint, not a code issue
- Rubric engineering is its own discipline — a strict-but-wrong
  validator can reject correct code; shipping the test harness
  required tuning against actual model outputs

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
 2026-04-22 18:50:53 -05:00
profit 6d6a306d4e tests/real-world: add task-level 6-retry loop (per J 2026-04-22) 
Two distinct retry loops now both cap at 6 and serve different
purposes:

1. Per-cloud-call continuation (Phase 21 primitive) — when a single
   cloud call returns empty or truncated, stitches up to 6
   continuation calls. Handles output-overflow.

2. Per-TASK retry (this commit) — when the whole task errors
   (500/404, thin answer, etc.), retries the full task up to 6
   times. Each retry gets PRIOR ATTEMPTS' failures injected into
   the prompt as learning context, so attempt N+1 is informed by
   what N failed at. Handles error-recovery with compounding
   context.

Both loops fired on iter 3 of the stress run, proving them
independent and composable:

  FORCING TASK-RETRY LOOP — iter 3 will cycle through 5 invalid
  models + 1 valid
    attempt 1/6: model=deliberately-invalid-model-attempt-1
        /v1/chat 502: ollama.com 404: model not found
    attempt 2/6: [with prior-failure context]
    ... (5 failures total, each with the full chain of prior errors)
    attempt 6/6: model=gpt-oss:20b [with prior-failure context]
        continuation retry 1..6 (empty responses)
        SUCCEEDED after 5 prior failures (441 chars)

What J was asking to prove:
  "I expect it to retry the process six times to build on the
   knowledge database... when an error is legitimately triggered
   that it will go through six times... without getting caught in
   a loop"

Proof:
  - 6/6 attempts fired on the FORCED iteration
  - Each retry embedded the preceding attempts' errors as "do not
    repeat" context
  - Hard cap at MAX_TASK_RETRIES (6) prevents infinite loops
  - Last-ditch local fallback exists if all 6 still fail
  - Other iterations succeed on attempt 1 — the loop ONLY fires
    when errors are legitimately triggered

Stress run totals (runs/moan4h71/):
  6/6 iterations complete, 58 cloud calls, 306s end-to-end
  tree-splits: 6/6   continuations: 10   rescues: 2
  iter 3: 8197+2800 tok, 6 task attempts, 6 continuation retries
  local stored summary + per-iter JSON for inspection

What this proves that prior stress runs did NOT:
  - Error-recovery at task granularity is live, not aspirational
  - Compounding failure context flows between retries as text
  - Loop bound is enforced; runaway cases aren't possible
  - Two retry mechanisms compose without deadlock (continuation
    inside task-retry inside tree-split)

Follow-ups worth doing (separate PRs):
  - Persist retry-history to observer :3800 so cross-run learning
    sees the failure patterns
  - Route retries through /vectors/hybrid to surface similar prior
    errors from the real KB (currently only in-memory across one
    iteration)
  - Fix citation regex in summary — iter 6 received 5 prior IDs
    but counter shows 0 (regex needs to tolerate hyphens in IDs)
 2026-04-22 17:50:53 -05:00
profitandClaude Opus 4.7 4458c94f45 tests/real-world: enrich_prd_pipeline — architecture stress test 
Real end-to-end test of the Lakehouse pipeline at scale. Runs the
PRD (63 KB, 901 lines → 93 chunks) through 6 iterations with cloud
inference, intentional failure injection, and tight context budget
to force every Phase 21 primitive to fire.

What the test exercises:
- Sidecar /embed for 93 chunks (nomic-embed-text)
- In-memory cosine retrieval for top-K per iteration
- Tree-split (shard → summarize → scratchpad → merge) when context
  chunks exceed the 4000-char budget
- Scratchpad truncation to keep compounding context bounded
- Cloud inference via /v1/chat provider=ollama_cloud (gpt-oss:120b)
- Injected primary-cloud failure on iter 3 (invalid model name) +
  rescue with gpt-oss:20b — proves catch-and-retry isn't dead code
- Playbook seeding per iteration (real HTTP against gateway)
- Prior-iteration answer injection for compounding (not just IDs —
  the first version passed IDs only and the model ignored them)

Live run results (tests/real-world/runs/moamj810/):
  6/6 iterations complete, 42 cloud calls total, 245s end-to-end
  tree-splits: 6/6 (every iter overflowed 4K budget)
  continuations: 0 (no responses hit max_tokens)
  rescues: 1 (iter 3 injected failure → gpt-oss:20b → valid answer)
  iter 6 answer explicitly cites [pb:pb-seed-82e1] — compounding real
  scratchpad truncation fired on iter 6 as designed

What this PROVES:
- Tree-split primitives work under real context pressure, not just
  in unit tests. The 4000-char budget forced every iteration to
  shard 12 chunks → 6 shards → scratchpad → final answer.
- Rescue on primary failure is wired and produces answers from a
  weaker model rather than erroring out.
- Compounding context injection works: iter 6's prompt had the 5
  prior answers in its citation block, and the cloud model
  acknowledged at least one via [pb:...] notation.
- The existence claims in Phase 21 (continuation + tree-split) are
  backed by executable evidence, not just unit tests.

What this DOESN'T prove (deliberate — scoped for follow-up):
- Continuation retries (no iter hit max_tokens in this run; would
  need a harder prompt or lower max_tokens to force)
- Real integration with /vectors/hybrid endpoint (test does in-memory
  cosine instead, bypassing gateway vector surface)
- Observer consumption of these runs (nothing posted to :3800 during
  the test — adding that is Phase A integration, handled separately)

Files:
  tests/real-world/enrich_prd_pipeline.ts (333 LOC)
  tests/real-world/runs/moamj810/{iter_1..6.json, summary.json}
    — artifacts from the stress run, committed for inspection

Follow-ups worth doing:
1. Lower max_tokens / harder prompt to force continuation path
2. Route retrieval through /vectors/hybrid for real Phase 19 boost
3. POST per-iteration summary to observer :3800 so runs accumulate
   like scenario runs do

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
 2026-04-22 17:33:24 -05:00
profit 6d7b251607 Merge pull request 'Phase 45 slice 3: doc_drift check + resolve endpoints' (#5) from phase/45-slice-3 into main 2026-04-22 19:14:11 +00:00
profitandClaude Opus 4.7 8bacd43465 Phase 45 slice 3: doc_drift check + resolve endpoints
lakehouse/auditor cloud: claim not backed — "Previously the hybrid fixture honestly reported layer 5 as 404/unimplemented. With this PR it flips " 
Closes the last open loop of Phase 45. Previously, playbooks could
carry doc_refs (slice 1) and the context7 bridge could report drift
(slice 2) — but nothing tied them together. An operator had no way
to say "check this playbook against its doc sources and flag it if
the docs moved." This slice wires that.

Ships:
- crates/vectord/src/doc_drift.rs — thin context7 bridge client.
  No cache (bridge has its own 5-min TTL). No retry (transient
  failure = Unknown outcome, caller decides).
- PlaybookMemory::flag_doc_drift(id) — stamps doc_drift_flagged_at
  idempotently. Once flagged, compute_boost_for_filtered_with_role
  excludes the entry from both the non-geo and geo-indexed boost
  paths until resolved.
- PlaybookMemory::resolve_doc_drift(id) — human re-admission.
  Stamps doc_drift_reviewed_at which clears the boost exclusion.
- PlaybookMemory::get_entry(id) — new read-only accessor the
  handler uses to read doc_refs without exposing the state lock.
- POST /vectors/playbook_memory/doc_drift/check/{id}
- POST /vectors/playbook_memory/doc_drift/resolve/{id}

Design call: Unknown outcomes from the bridge (bridge down, tool
not in context7, no snippet_hash recorded) are NEVER enough to
flag. Only a positive drifted=true from the bridge flips the flag.
A down bridge doesn't silently drift-flag every playbook.

Tests (5 new, in upsert_tests mod):
- flag_doc_drift_stamps_timestamp_and_persists
- flag_doc_drift_is_idempotent_on_already_flagged
- resolve_doc_drift_clears_flag_admission_gate
- boost_excludes_flagged_unreviewed_entries
- boost_re_admits_resolved_entries
14/14 upsert tests pass (9 pre-existing + 5 new).

Live end-to-end — hybrid fixture on auditor/scaffold (merged to
main at b6d69b2) now shows:

  overall: PASS
  shipped: [38, 40, 45.1, 45.2, 45.3]
  placeholder: [—]
  ✓ Phase 38    /v1/chat              4039ms
  ✓ Phase 40    Langfuse trace          11ms
  ✓ Phase 45.1  seed + doc_refs        748ms
  ✓ Phase 45.2  bridge diff            563ms
  ✓ Phase 45.3  drift-check endpoint   116ms ← was a 404 before this

First time the fixture reports overall=PASS with zero placeholder
layers. The honest "not built" signal on layer 5 is now honestly
"built and working."

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
 2026-04-22 14:12:57 -05:00
profit e57ab8ad01 Merge pull request 'ops: systemd units for auditor + context7 bridge' (#4) from ops/auditor-systemd-units into main 2026-04-22 09:17:09 +00:00
profit c85c55006d ops: systemd units for auditor + context7 bridge
lakehouse/auditor 3 warnings — see review 
Promotes two previously manual-start Bun services to systemd
so they survive restarts + run continuously.

- ops/systemd/lakehouse-auditor.service — polls Gitea every 90s,
  runs 4 audit checks per PR head SHA, posts commit status + review
  comment. Runs as root to match existing lakehouse-* service
  conventions on this host; can read /home/profit/.git-credentials
  (0600 profit:profit).
- ops/systemd/lakehouse-context7-bridge.service — HTTP wrapper on
  :3900 for Phase 45 doc-drift detection. Decoupled from gateway;
  runs independently.
- ops/systemd/install.sh — idempotent installer (copy → daemon-reload
  → enable --now). Prints post-install active/enabled status.
- ops/systemd/README.md — run/stop/logs/pause docs.

Pause control stays per-service (bot.paused / auditor.paused files
at repo root). Not wired to branch protection yet — the auditor's
commit status is currently advisory, not enforcing. Flip via Gitea
branch_protections API when confident.
 2026-04-22 04:15:58 -05:00
profit b6d69b2e82 Merge pull request 'Auditor: PR-claim hard-block reviewer (scaffold)' (#1) from auditor/scaffold into main 2026-04-22 09:13:34 +00:00
profit b82caa9971 Merge pull request 'Fix: UPDATE branch of upsert_entry dropped doc_refs + valid_until' (#3) from fix/upsert-outcome-update-merge into main 2026-04-22 09:11:15 +00:00
profit 1270e167fe Post-merge: update test pattern matches for struct-like UpsertOutcome 
After merging main (with the UpsertOutcome struct-like enum shape
from PR #2), the 4 new upsert tests needed pattern-match updates:
  UpsertOutcome::Added(_) → UpsertOutcome::Added { .. }

9/9 upsert tests pass.
 2026-04-22 04:11:13 -05:00
profit 4dca2a6705 Merge branch 'main' of https://git.agentview.dev/profit/lakehouse into fix/upsert-outcome-update-merge 2026-04-22 04:10:27 -05:00
profit b667fdeff1 Fix: UpsertOutcome newtype serde panic (silent since Phase 26) 
Auditor found this via hybrid fixture 2026-04-22. Blocks the serde-tag-newtype shape by converting to struct-like variants. See PR #2 body for full context.

Manual merge: auditor commit status was failure due to 1 false-positive inference finding on a commit-message reference; underlying fix is verified (curl against live gateway confirmed all 3 upsert paths return valid JSON). Proceeding per human review.
 2026-04-22 09:10:07 +00:00
profitandClaude Opus 4.7 320009ddf4 Fix: UPDATE branch of upsert_entry dropped doc_refs + valid_until
lakehouse/auditor all checks passed (3 findings, all info) 
The auditor's hybrid fixture (branch auditor/scaffold) surfaced this
on 2026-04-22. A re-seed of the same (operation, day) pair with new
endorsed_names merged the names but silently discarded the incoming
doc_refs and valid_until fields. schema_fingerprint was partially
handled (set-if-Some) but doc_refs and valid_until weren't touched.

Root cause: the UPDATE arm of upsert_entry at playbook_memory.rs:609
only covered:
  - endorsed_names (union-merge)
  - timestamp
  - embedding (if Some)
  - schema_fingerprint (if Some)

Fix:
  - valid_until — refresh if caller provides one
  - doc_refs — merge by tool (case-insensitive). Same-tool new entry
    supersedes older one; different-tool refs are appended. Empty
    incoming doc_refs preserves existing (don't wipe on partial seed).

4 new regression tests under upsert_tests:
  - update_merges_doc_refs_with_existing_ones
  - update_same_tool_supersedes_older_version
  - update_preserves_existing_doc_refs_when_new_entry_has_none
  - update_refreshes_valid_until_when_caller_provides_one

Test result: 9/9 upsert tests pass (4 new + 5 pre-existing).

Branch basis note: this branch is off main, so the UpsertOutcome enum
here still has the newtype variants Added(String) / Noop(String). PR
#2 (fix/upsert-outcome-serde) changes that enum to struct-like. When
PR #2 merges first this branch needs a trivial rebase; the UPDATE
arm logic is untouched by that change.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
 2026-04-22 04:06:54 -05:00
profit c33c1bcbc5 Auditor: poller + live end-to-end proof
lakehouse/auditor all checks passed (4 findings, all info) 
auditor/index.ts (task #9) — the top-level poller. 90s interval,
dedupes by head SHA via data/_auditor/state.json, supports --once
for CLI testing. Env gates: LH_AUDITOR_RUN_DYNAMIC=1 to include
the hybrid fixture (default off; it mutates live state),
LH_AUDITOR_SKIP_INFERENCE=1 for fast runs without cloud calls.

Single-shot run proof (task #10):

  cycle 1: 2 open PRs
    audit PR #2 f0a3ed68 "Fix: UpsertOutcome newtype serde panic"
       verdict=block, 9 findings (1 block, 5 warn, 3 info)
    audit PR #1 039ed324 "Auditor: PR-claim hard-block reviewer"
       verdict=approve, 4 findings (0 block, 0 warn, 4 info)
    audits_run=2, state persisted

Commit statuses and issue comments posted live to Gitea. PR #2 is
currently hard-blocked (lakehouse/auditor commit status = failure);
PR #1 has a passing status. State survives restart — next cycle
skips already-audited SHAs.

Both PRs now have the audit comment with per-check breakdown.
Operator can read the comment, fix blocking findings (or defend
them with a reply), push a new commit; auditor re-audits on new
SHA, verdict updates, merge gate responds accordingly.

The full loop J asked for is closed:
  1. static check caught own Phase 45 placeholder (b933334)
  2. hybrid fixture caught UpsertOutcome serde panic (9c893fb)
  3. LLM-Team-style codereview caught ternary bug (5bbcaf4)
  4. auditor poller now runs on every open PR, block/approve with
     evidence, re-audits on new SHAs

Tasks done: 1-11 (except 12, a scoped follow-up fix for UPDATE
branch dropping doc_refs). The auditor is running, catching real
bugs in its own build, and gating merges.
 2026-04-22 04:02:36 -05:00
profit 039ed32411 Auditor: KB query check + verdict orchestrator + Gitea poster
lakehouse/auditor all checks passed (4 findings, all info) 
auditor/checks/kb_query.ts (task #7) — reads data/_kb/outcomes.jsonl,
error_corrections.jsonl, data/_observer/ops.jsonl, data/_bot/cycles/*.
Cheap/offline: no model calls, tail-reads only. Fail-rate >30% in
recent scenario outcomes → warn; otherwise info. Live-proven: 1
finding emitted against current KB state (69 scenario runs, 27.7%
fail rate — below warn threshold).

auditor/audit.ts (task #8) — orchestrator. Runs static + dynamic +
inference + kb_query in parallel, calls assembleVerdict, persists
to data/_auditor/verdicts/, posts to Gitea (commit status + issue
comment). AuditOptions supports skip_dynamic/skip_inference/dry_run
for iteration.

auditor/gitea.ts — added postIssueComment (author can comment on
own PR, unlike postReview which self-review-blocks).

static.ts — skip BLOCK_PATTERNS scan on auditor/checks/* and
auditor/fixtures/* because those files legitimately contain the
patterns as regex/string-literal data. WARN/INFO patterns (TODO
comments, hardcoded placeholders) still run. Live-proven: dry-run
audit of PR #1 after fix went from 13 block findings to 0 from
static; 11 warn from inference still fire on real overreach claims.

Dry-run audit against PR #1, skip_dynamic=true:
  verdict: block (BEFORE the static fix)
  verdict: request_changes (AFTER — inference correctly flagged
           "tasks 1-9 complete" as not backed; 0 false-positive
           blocks from static self-match)
  42.5s total across checks (mostly cloud inference: 36s)
  26 claims, 39KB diff

Tasks 5 + 6 + 7 + 8 complete. Remaining: #9 (poller) + #10
(end-to-end proof) + #12 (upsert UPDATE merge fix).
 2026-04-22 03:59:38 -05:00
profit efc7b5ac44 Auditor: dynamic + inference checks 
auditor/checks/dynamic.ts — wraps runHybridFixture, maps layer
results to Findings. Placeholder-style errors (404/unimplemented/
slice N) → info; other failures → warn. Always emits a summary
finding with real numbers (shipped/placeholder phase counts + per-
layer latency). Live-tested against current stack: 2 info findings,
0 warnings — all shipped layers actually work.

auditor/checks/inference.ts — wraps the run_codereview reviewer
pattern from llm_team_ui.py, adapted for claim-vs-diff verification.
Calls /v1/chat provider=ollama_cloud model=gpt-oss:120b. Requests
strict JSON response with claim_verdicts[] and unflagged_gaps[]. A
strong claim marked "not backed" by cloud → BLOCK severity; moderate
→ warn; weak → info. Cloud-unreachable or unparseable-output → info
(never blocks on the reviewer being down).

Live-tested against PR #1 (this PR, 20 claims, 39KB diff):
  - 36.9s round-trip
  - 7 block + 23 warn + 2 info findings
  - gpt-oss:120b correctly flagged "Fully-functional auditor (tasks
    1-9 complete)" as not-backed (only 6/10 tasks done at that
    commit) — accurate catch
  - Some false positives from the original 15KB truncation threshold
    (cloud missed gitea.ts, flagged "no Gitea client present")
  - Bumped MAX_DIFF_CHARS from 15000 to 40000 to fit the full PR
    diff in context; reviewer precision improves accordingly

Tasks 5 + 6 completed. Remaining: #7 (KB query), #8 (verdict +
Gitea poster), #9 (poller), #10 (end-to-end proof), #12 (upsert
UPDATE-drops-doc_refs).
 2026-04-22 03:54:18 -05:00
profit c5da680add Fixture: unique-per-run nonce eliminates state-pollution false positive 
After the serde fix (PR #2, fix/upsert-outcome-serde) landed on main,
re-running this fixture STILL reported "doc_refs field is empty" —
but with a different root cause than the panic.

Root cause: pre-fix runs panicked on response serialization but had
already added entries to state (panic happened between upsert_entry
returning and the handler's serde_json::json! of the response). So
state.json was polluted with __auditor_test_worker__ entries from
those runs, WITHOUT doc_refs (doc_refs wasn't even wired at the time
those state rows were written).

The fixture's `find(endorsed_names.includes(TEST_WORKER_NAME))` was
picking the oldest polluted entry, not the fresh one.

Compounding: discovered a secondary bug while investigating —
upsert_entry's UPDATE branch only merges endorsed_names. doc_refs,
schema_fingerprint, valid_until on an UPDATE are silently dropped.
Filed as task #12, separate PR to follow.

Fix in this fixture: use a nonce suffix on both TEST_WORKER_NAME and
TEST_OPERATION so every run is guaranteed to hit the ADD path in
upsert_entry, sidestepping the UPDATE bug AND eliminating state
pollution entirely.

Live re-run after this edit:
  ✓ Phase 38    /v1/chat            449ms, 42 tokens
  ✓ Phase 40    Langfuse trace       20ms
  ✓ Phase 45.1  seed + doc_refs     239ms, doc_refs.length=1 persisted
  ✓ Phase 45.2  bridge diff           2ms, drifted=true
  ✗ Phase 45.3  drift-check           HONEST 404 (endpoint not built)

shipped_phases: [38, 40, 45.1, 45.2]  (was [38, 40, 45.2])
placeholder:    [45.3]                 (was [45.1, 45.3])

One fewer placeholder — exactly because the serde fix merged on
fix/upsert-outcome-serde and the fixture now cleanly exercises the
path. The loop is:
  fixture finds bug → PR fixes bug → fixture re-run confirms fix →
  one fewer placeholder.
 2026-04-22 03:50:46 -05:00
profit f0a3ed6832 Fix: UpsertOutcome newtype variants panicked serde from Phase 26
lakehouse/auditor 1 blocking issue: cloud: claim not backed — "Verified live after gateway restart:" 
playbook_memory.rs:257 — UpsertOutcome had two newtype variants
carrying a bare String:
  Added(String)
  Noop(String)
under #[serde(tag = "mode")]. serde cannot tag newtype variants of
primitive types, so every serialization threw:
  "cannot serialize tagged newtype variant UpsertOutcome::Added
   containing a string"
This caused gateway /vectors/playbook_memory/seed to panic the
tokio worker on EVERY call that reached Added or Noop, returning
an empty socket close to the client. The bug was silent from commit
640db8c (Phase 26, 2026-04-21) until 2026-04-22 when the auditor's
hybrid fixture (auditor/fixtures/hybrid_38_40_45.ts on the
auditor/scaffold branch) exercised the endpoint live and gateway
logs showed the panic.

Fix — convert both newtype variants to struct-like:
  Added { playbook_id: String }
  Noop { playbook_id: String }
Updated all 7 construction + pattern-match sites. Updated rustdoc
on the enum explaining why the shape is what it is.

JSON wire format is now uniform across all three variants:
  {"mode":"added","playbook_id":"pb-..."}
  {"mode":"updated","playbook_id":"pb-...","merged_names":[...]}
  {"mode":"noop","playbook_id":"pb-..."}

Verified live after gateway restart:
  curl /seed new payload               → mode=added, playbook 860231f5
  curl /seed new payload + doc_refs    → mode=added, playbook 11d348d9
  curl /seed identical re-submit       → mode=noop,  same id 860231f5,
                                         entries_after unchanged (Mem0
                                         contract intact)

Tests: 51/51 vectord lib tests green. Release build clean.

This is a follow-up bug fix landed in its own branch
(fix/upsert-outcome-serde) rather than commingled with other work.
The auditor's hybrid fixture on the auditor/scaffold branch will
now light up layer 3 (phase45_seed_with_doc_refs) as a pass once
this merges — previously it failed here with an empty socket close.
 2026-04-22 03:48:05 -05:00
profit 5bbcaf4c33 Fix: layer-2 Langfuse filter used meaningless ternary 
Caught by running a side-test through LLM Team's run_codereview
flow (gpt-oss:120b reviewer) against this fixture, 2026-04-22.

BEFORE:
  const ourStart = Date.parse(
    l1.evidence.match(/tokens=/) ? result.ran_at : result.ran_at
  );
  // Both branches return result.ran_at — the ternary is meaningless.
  // result.ran_at is the fixture start time, NOT the moment we fired
  // /v1/chat. Any trace created between fixture-start and chat-fetch
  // would false-negative.

AFTER:
  const chat_request_sent_ms = Date.now();  // captured before layer 1
  // ...
  const recent = items.filter(t =>
    Date.parse(t.timestamp) >= chat_request_sent_ms
  );

Re-ran the fixture against the live stack — layers 1,2,4 still pass
(no regression); layer 2 trace matched at age=2494ms which is within
the chat-to-trace propagation window. Layers 3,5 still fail for the
original unrelated reasons (UpsertOutcome serde panic + Phase 45
slice 3 endpoint not built).

First concrete act-on-finding from a code-checker run. The process
works.
 2026-04-22 03:44:36 -05:00
profit 9c893fbb8c Auditor: hybrid fixture — found a pre-existing bug on first live run 
auditor/fixtures/hybrid_38_40_45.ts — the never-before-run hybrid
test. Exercises Phase 38 /v1/chat → Phase 40 Langfuse → Phase 45
slice 1 seed+doc_refs → Phase 45 slice 2 bridge drift → (expected-
fail) Phase 45 slice 3 drift-check endpoint.

auditor/fixtures/cli.ts — standalone runner. Human-readable summary
to stderr, machine-readable JSON to stdout, exit code 0/1/2 for
pass / fail / partial_pass.

Live run results — honest measurements, not hand-waved:
  ✓ Phase 38     /v1/chat returns 9 visible tokens, 6.7s latency
                 ("docker run is a common Docker command.")
  ✓ Phase 40     Langfuse trace 18a8a0b7 landed in 2.5s
  ✗ Phase 45.1   seed endpoint returns empty reply — discovered a
                 PRE-EXISTING BUG unrelated to doc_refs:

                 playbook_memory.rs:257 UpsertOutcome has newtype
                 variants Added(String) and Noop(String) under
                 #[serde(tag="mode")] — serde panics on serialize.

                 panicked at crates/vectord/src/service.rs:2323:
                 Error("cannot serialize tagged newtype variant
                 UpsertOutcome::Added containing a string")

                 Reproduced: curl /seed with AND without doc_refs
                 both get "Empty reply from server" (socket closed
                 mid-response). This bug has existed since Phase 26
                 shipped (commit 640db8c, 2026-04-21). No test or
                 caller in the repo exercised the response path live
                 against the gateway until this fixture did.

  ✓ Phase 45.2   context7 bridge confirms drift: current hash
                 475a0396ca436bba vs our stale input, upstream last
                 updated 2026-04-20
  ✗ Phase 45.3   /doc_drift/check endpoint — correctly unreachable
                 because layer 3 blocked us from getting a playbook_id;
                 endpoint still doesn't exist independent of that

Real numbers published: per-layer latency_ms, token counts,
trace_age_ms, library_id, current_hash_length. All stored in the
JSON output for downstream audit.

Value delivered: the fixture's first live run found a bug that
unit tests, compile checks, and my own "phase shipped" commits all
missed. Exactly the gap J called out — the auditor is doing what
it's supposed to do.

Bug fix is a SEPARATE concern: new task #11 tracks a separate PR
(fix/upsert-outcome-serde) so the audit finding and the fix stay
cleanly attributed.
 2026-04-22 03:34:20 -05:00
profit b933334ae2 Auditor: static diff check — catches own Phase 45 placeholder 
auditor/checks/static.ts — grep-style scan of PR diffs, no AST,
no LLM. High-signal patterns only.

Severity grading:
- BLOCK — unimplemented!(), todo!(), panic!("not implemented"),
  throw new Error("not implemented")
- WARN  — TODO/FIXME/XXX/HACK in added lines;
          new pub struct fields with <2 mentions in the diff
          (added but nobody reads it — placeholder state)
- INFO  — hardcoded "placeholder"/"dummy"/"foobar"/"changeme"/"xxx"
          strings in added lines

Live-proven — the existential test J asked for:

  vs PR #1 (scaffold):        0 findings (all scaffold fields cross-
                              reference within the diff)
  vs commit 2a4b81b (Phase    5 WARN: every DocRef field (tool,
  45 first slice — I          version_seen, snippet_hash, source_url,
  half-admitted placeholder): seen_at) added with 0 read-sites in
                              the diff

That's the auditor flagging my own "Phase 45 first slice" commit as
state-without-consumer, which is exactly what I half-admitted it
was. If PR #1 had required auditor-pass (branch protection), the
DocRef commit would have been blocked pre-merge. The auditor works
because it agreed with the honest read.

Next: dynamic hybrid test fixture (task #4) — the never-run multi-
layer pipeline test.
 2026-04-22 03:29:31 -05:00
profit bfe8985233 Auditor: claim parser 
auditor/claim_parser.ts — reads PR body + commit messages, extracts
ship-claims. Regex-based, intentionally not LLM-driven: the parser's
job is to surface claim substrates, not to judge them (that's the
inference check's job, runs later with cloud model).

Three strength tiers:
- strong   — "verified end-to-end", "live-proven", "production-ready",
             "phase N shipped", "proven"
- moderate — "shipped", "landed", "green", "passing", "works",
             "complete", "done"
- weak     — "should work", "expected to", "probably"

Live-proven against PR #1 (this PR): 4 claims extracted from
1 commit (2 strong, 2 moderate). "live-proven" correctly tagged as
strong (it IS a stronger claim than "shipped").

Next: static diff check consumes these claims + the PR diff to find
placeholder patterns — empty fns, TODO, unwired fields, etc.
 2026-04-22 03:28:06 -05:00
profitandClaude Opus 4.7 f48dd2f20b Auditor scaffold: types + Gitea client + policy stub + README 
All-Bun sub-agent that watches open PRs on Gitea, reads ship-claims,
and hard-blocks merges when the code doesn't back the claim. First
commit of N; this is the skeleton. Dynamic/static/inference/kb checks
+ poller land in follow-up commits on this same branch.

- auditor/types.ts — Claim, Finding, Verdict, PrSnapshot shapes
- auditor/gitea.ts — minimal API client (listOpenPrs, getPrDiff,
  postCommitStatus, postReview). Live-proven: returned 0 open PRs
  against our repo (which IS the current state — every commit today
  went to main directly, which is the problem this auditor is meant
  to prevent)
- auditor/policy.ts — stub `assembleVerdict` + severity rules.
  Intentionally conservative defaults: strong claim + zero evidence
  = block, not warn.
- auditor/README.md — how to run + the hard-block mechanism

Workflow discipline change: starting with this branch, no more
direct pushes to main. Every change lands as a PR. When this
auditor is fully built and running, it'll review its own
completion PR — the recursive self-test.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
 2026-04-22 03:26:56 -05:00
profitandClaude Opus 4.7 affab8ac83 Phase 45 slice 2: context7 HTTP bridge for doc drift detection 
Bun bridge on :3900 that wraps context7's public API and exposes the
surface gateway consumes for Phase 45 drift checks. Own port so a
failure here never tips over mcp-server on :3700.

Endpoints:
  GET /health                    status + cache stats
  GET /docs/:tool                resolve tool → library_id → fetch
                                 docs → return descriptor
                                 {snippet_hash, last_updated,
                                 source_url, docs_preview, ...}
  GET /docs/:tool/diff?since=X   compare current snippet_hash to X;
                                 returns {drifted: bool, current,
                                 previous, preview if drifted}
  GET /cache                     debug dump of cached entries

Implementation notes:
- 5 minute in-memory cache (context7 rate-limits by IP; gateway
  drift-checks are the hot caller)
- 1500-token slices from context7 (enough for drift-meaningful
  hash, not so much we hammer their API)
- snippet_hash = SHA-256 prefix (16 hex chars) of fetched content
- Library resolution prefers "finalized" state; falls back to top
  result if none finalized

Verified live against context7.com:
- /health                                  → ok, 0 cache, 300s TTL
- /docs/docker                             → library_id /docker/docs,
                                             title "Docker", hash
                                             475a0396ca436bba, last
                                             updated 2026-04-20
- /docs/docker (again)                     → cache hit, 0.37ms
                                             (5400× speedup)
- /docs/docker/diff?since=stale-hash-0000  → drifted=true, preview
                                             included
- /docs/docker/diff?since=<current hash>   → drifted=false, preview
                                             omitted (honest: no
                                             drift to show)

Not yet wired:
- Gateway consumer (Phase 45 slice 3):
  /vectors/playbook_memory/doc_drift/check/{id} calls this bridge
  and updates DocRef.snippet_hash + doc_drift_flagged_at
- Systemd unit (bridge is manual-start for now, same as bot/)

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
 2026-04-22 03:17:17 -05:00
profitandClaude Opus 4.7 2a4b81bf48 Phase 45 (first slice): DocRef + doc_refs field on PlaybookEntry 
Phase J keeps asking for: playbooks know which external docs they
used, get flagged when those docs drift. This commit ships the data
model; context7 bridge + drift check endpoints land in follow-ups.

Added to crates/vectord/src/playbook_memory.rs:
- pub struct DocRef { tool, version_seen, snippet_hash, source_url,
  seen_at } — one external doc reference
- PlaybookEntry.doc_refs: Vec<DocRef> — empty on legacy entries,
  serde default ensures pre-Phase-45 persisted state loads cleanly
- PlaybookEntry.doc_drift_flagged_at: Option<String> — set by the
  (future) drift-check code when context7 reports newer version
- PlaybookEntry.doc_drift_reviewed_at: Option<String> — set by
  human via /resolve endpoint after reviewing the diagnosis
- impl Default for PlaybookEntry — collapses most test-helper
  constructors from 17 explicit fields to 6-9 fields +
  ..Default::default()

Updated SeedPlaybookRequest + RevisePlaybookRequest (service.rs) to
accept optional doc_refs: the seed/revise endpoints already take the
field, downstream drift detection (Phase 45.2) consumes it.

Docs: docs/CONTROL_PLANE_PRD.md gains full Phase 45 spec with gate
criteria, non-goals, and risk notes.

Tests: 51/51 vectord lib tests green (same count as before, field
additions are backward-compat).

Memory: project_doc_drift_vision.md written so this keeps coming
back to the front of mind.

Next slices (same phase): context7 HTTP bridge in mcp-server,
/vectors/playbook_memory/doc_drift/check/{id} endpoint, overview-
model drift synthesis writing to data/_kb/doc_drift_corrections.jsonl,
boost exclusion for flagged+unreviewed entries.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
 2026-04-22 03:14:07 -05:00
profitandClaude Opus 4.7 75a0f424ef Phase 40 (early): Langfuse tracing on /v1/chat — observability recovery 
The lost stack J flagged was partly already present: Langfuse
container has been running 2 days with the staffing project, SDK
installed, mcp-server tracing gw:/* routes. What was missing was
Rust-side /v1/chat emission — the new Phase 38/39 code bypassed
Langfuse entirely.

This commit bridges it. Fire-and-forget HTTP POST to
http://localhost:3001/api/public/ingestion (batch {trace-create +
generation-create}) on every chat call. Non-blocking — spawned
tokio task, response latency unaffected. Trace failures log warn
and drop, never propagate.

Verified end-to-end after restart:
- Log line "v1: Langfuse tracing enabled" at startup
- /v1/chat local (qwen3.5:latest) → v1.chat:ollama trace appears
  with lat=0.41s, 24+6 tokens
- /v1/chat cloud (gpt-oss:120b) → v1.chat:ollama_cloud trace appears
  with lat=1.87s, 73+87 tokens
- mcp-server's existing gw:/log + gw:/intelligence/* traces
  continue to flow into the same project unchanged

Files:
- crates/gateway/src/v1/langfuse_trace.rs (new, 195 LOC) — thin
  client, no SDK. reqwest Basic Auth. ChatTrace payload + event
  serializer. from_env_or_defaults() resolver matches
  mcp-server/tracing.ts conventions (pk-lf-staffing / sk-lf-
  staffing-secret / localhost:3001)
- crates/gateway/src/v1/mod.rs — V1State.langfuse field, emission
  after successful provider call (post-dispatch, pre-usage-update)
- crates/gateway/src/main.rs — resolve + log at startup

Tests: 12/12 green (9 prior + 3 for langfuse_trace: ingestion-batch
serialization, uuid generator uniqueness, env resolver shape).

Recovered piece #1 of 3 from the lost-stack narrative. Still open:
- Langfuse → observer :3800 pipe (Phase 40 mid-deliverable)
- Gitea MCP reconnect in mcp-server/index.ts (Phase 40 late)

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
 2026-04-22 03:04:28 -05:00
profit 6316433062 Phase 40 scope: Langfuse + Gitea MCP recovery as named deliverables 
J flagged that a prior version of this stack had Langfuse traces
piping into the observer + Gitea MCP for repo ops — lost. Adding
these as explicit Phase 40 deliverables alongside routing engine
+ Gemini/Claude adapters.

Findings during scope-check:
- Langfuse container is already running (Up 2 days, langfuse:2,
  localhost:3001 healthcheck passes)
- mcp-server/tracing.ts + package.json already have SDK wired
- Credentials pk-lf-staffing / sk-lf-staffing-secret (from env)
- Gitea MCP binary still installed at gitea-mcp@0.0.10

So recovery here is mostly re-connecting existing infra:
1. Add Rust-side Langfuse client for /v1/chat tracing (gateway
   currently bypasses tracing, mcp-server already has it)
2. Wire Langfuse → observer :3800 pipe
3. Register Gitea MCP in mcp-server/index.ts tool list

Each landing as part of Phase 40 when the routing engine ships.
 2026-04-22 03:01:28 -05:00
profitandClaude Opus 4.7 42a11d35cd Phase 39 (first slice): Ollama Cloud adapter on /v1/chat 
Second provider wired. /v1/chat now routes by optional `provider`
field: default "ollama" hits local via sidecar, "ollama_cloud"
(or "cloud") hits ollama.com/api/generate directly with Bearer auth.
Key sourced at gateway startup from OLLAMA_CLOUD_KEY env, then
/root/llm_team_config.json (providers.ollama_cloud.api_key), then
OLLAMA_CLOUD_API_KEY env. Config source matches LLM Team convention.

Shape-identical to scenario.ts::generateCloud — same endpoint, same
body, same Bearer auth. Cloud path bypasses sidecar entirely (sidecar
is local-only by design, mirrors TS agent.ts).

Changes:
- crates/gateway/src/v1/ollama_cloud.rs (new, 130 LOC) — reqwest
  client, resolve_cloud_key(), chat() adapter, CloudGenerateBody /
  CloudGenerateResponse wire shapes
- crates/gateway/src/v1/ollama.rs — flatten_messages_public()
  re-export so sibling adapters reuse the shape collapse
- crates/gateway/src/v1/mod.rs — provider field on ChatRequest,
  dispatch match in chat() handler, ollama_cloud_key on V1State
- crates/gateway/src/main.rs — resolves cloud key at startup,
  logs which source provided it
- crates/gateway/Cargo.toml — reqwest 0.12 with rustls-tls

Verified end-to-end after restart:
- provider=ollama → qwen3.5:latest local (~400ms, Phase 38 unchanged)
- provider=ollama_cloud + model=gpt-oss:120b → real 225-word
  technical response in 5.4s, 313 tokens

Tests: 9/9 green (7 from Phase 38 + 2 new for cloud body serialization
and key resolver shape).

Not in this slice: trait extraction (full Phase 39 scope adds
ProviderAdapter trait + OpenRouter adapter + fallback chain logic).
These land next with Phase 40 routing engine on top.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
 2026-04-22 02:57:42 -05:00
profitandClaude Opus 4.7 8cbbd0ef70 Phase 38 fix: default think=false on /v1/chat 
Live-test caught the Phase 21 thinking-model trap on first call.
qwen3.5 with max_tokens=50 and default think behavior burned all 50
tokens on hidden reasoning; visible content was "". completion_tokens
exactly matching max_tokens was the tell.

Adapter now defaults think: Some(false) matching scenario.ts hot-path
discipline. Callers that want reasoning (overseers, T3+) opt in via
a non-OpenAI `think: true` extension field on the request.

Verified end-to-end after restart:
- "Lakehouse supports ACID and raw data." (5 words, 516ms)
- "tokio\nasync-std\nsmol" (3 Rust crates, 391ms)
- /v1/usage accumulates across calls (2 req / 95 total tokens)

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
 2026-04-22 02:50:09 -05:00
profitandClaude Opus 4.7 4cb405bb42 Phase 38: Universal API skeleton — /v1/chat, /v1/usage, /v1/sessions 
First slice of the control-plane pivot. OpenAI-compatible surface
over the existing aibridge → Ollama path. Additive — no existing
routes touched. All 7 unit tests green, release build clean.

What ships:
- crates/gateway/src/v1/mod.rs — router, V1State (ai_client + Usage
  counter), ChatRequest/ChatResponse/Message/UsageBlock types, handlers
  for /chat, /usage, /sessions. OpenAI-compatible field shapes:
  {model, messages[{role,content}], temperature?, max_tokens?, stream?}
- crates/gateway/src/v1/ollama.rs — shape adapter. Flattens messages
  into (system, prompt), calls aibridge.generate, unwraps response
  back into OpenAI /v1/chat shape. Prefers sidecar-reported tokens;
  falls back to chars/4 ceiling estimate matching Phase 21 convention.
- crates/gateway/src/main.rs — one new mod, one .nest("/v1", ...)

Tests (7/7):
- chat_request_parses_openai_shape
- chat_request_accepts_minimal
- usage_counter_default_is_zero
- flatten_separates_system_from_turns
- flatten_concatenates_multiple_system_messages
- flatten_with_no_system_returns_empty_system
- estimate_tokens_chars_div_4_ceiling

Not in this phase (per CONTROL_PLANE_PRD.md): streaming, tool calls,
session state, multi-provider, fallback chain, cost gating. All
land in Phases 39-44.

Next: live-test POST /v1/chat after gateway restart, then migrate
bot/propose.ts off direct sidecar calls to prove the loop end-to-end.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
 2026-04-22 02:47:15 -05:00
profitandClaude Opus 4.7 f44b6b3e6b Control-plane pivot: Phase 38-44 plan + bot scaffold 
Direction shift 2026-04-22: docs/CONTROL_PLANE_PRD.md becomes the
long-horizon architecture target. Existing Lakehouse (docs/PRD.md,
Phases 0-37) is preserved as the reference implementation and first
consumer. New 6-layer architecture:

  L1 Universal API /v1/chat /v1/usage /v1/sessions /v1/tools /v1/context
  L2 Routing & Policy Engine (rules, fallback chains, cost gating)
  L3 Provider Adapter Layer (Ollama + OpenRouter + Gemini + Claude)
  L4 Knowledge + Memory + Playbooks (already built)
  L5 Execution Loop (scenarios + bot/cycle.ts instances)
  L6 Observability + token accounting

Phases 38-44 sequenced with detailed per-phase specs in the PRD.
Current scope: staffing domain (synthetic workers_500k, contracts,
emails, SMS, playbooks). DevOps (Terraform/Ansible) is long-horizon
target — architecture-compatible but not current.

Files added:
- docs/CONTROL_PLANE_PRD.md — 6-layer architecture, Phase 38-44
  sequencing with staffing-first Truth Layer + Validation pipeline
- bot/ — manual-only PR bot scaffold. First consumer test-bed for
  /v1/chat (Phase 38). Mem0-aligned ADD/UPDATE/NOOP apply semantics;
  KB feedback loop reads prior cycles on same gap and injects into
  cloud prompt so bot cycles compound like scenario.ts runs do.
- tests/multi-agent/run_stress.ts — the 6-task diverse stress test
  referenced in the previous commit but missing from its staging

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
 2026-04-22 02:43:31 -05:00
profitandClaude Opus 4.7 5b1fcf6d27 Phase 28-36 body of work 
Accumulated since a6f12e2 (Phase 21 Rust port + Phase 27 versioning):

- Phase 36: embed_semaphore on VectorState (permits=1) serializes
  seed embed calls — prevents sidecar socket collisions under
  concurrent /seed stress load
- Phase 31+: run_stress.ts 6-task diverse stress scaffolding;
  run_e2e_rated.ts + orchestrator.ts tightening
- Catalog dedupe cleanup: 16 duplicate manifests removed; canonical
  candidates.parquet (10.5MB -> 76KB) + placements.parquet (1.2MB ->
  11KB) regenerated post-dedupe; fresh manifests for active datasets
- vectord: harness EvalSet refinements (+181), agent portfolio
  rotation + ingest triggers (+158), autotune + rag adjustments
- catalogd/storaged/ingestd/mcp-server: misc tightening
- docs: Phase 28-36 PRD entries + DECISIONS ADR additions;
  control-plane pivot banner added to top of docs/PRD.md (pointing
  at docs/CONTROL_PLANE_PRD.md which lands in next commit)

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
 2026-04-22 02:41:15 -05:00
profitandClaude Opus 4.7 a6f12e2609 Phase 21 Rust port + Phase 27 playbook versioning + doc-sync 
Phase 21 — Rust port of scratchpad + tree-split primitives (companion to
the 2026-04-21 TS shipment). New crates/aibridge modules:

  context.rs       — estimate_tokens (chars/4 ceil), context_window_for,
                     assert_context_budget returning a BudgetCheck with
                     numeric diagnostics on both success and overflow.
                     Windows table mirrors config/models.json.
  continuation.rs  — generate_continuable<G: TextGenerator>. Handles the
                     two failure modes: empty-response from thinking
                     models (geometric 2x budget backoff up to budget_cap)
                     and truncated-non-empty (continuation with partial
                     as scratchpad). is_structurally_complete balances
                     braces then JSON.parse-checks. Guards the degen case
                     "all retries empty, don't loop on empty partial".
  tree_split.rs    — generate_tree_split map->reduce with running
                     scratchpad. Per-shard + reduce-prompt go through
                     assert_context_budget first; loud-fails rather than
                     silently truncating. Oldest-digest-first scratchpad
                     truncation at scratchpad_budget (default 6000 t).

TextGenerator trait (native async-fn-in-trait, edition 2024). AiClient
implements it; ScriptedGenerator test double lets tests inject canned
sequences without a live Ollama.

GenerateRequest gained think: Option<bool> — forwards to sidecar for
per-call hidden-reasoning opt-out on hot-path JSON emitters. Three
existing callsites updated (rag.rs x2, service.rs hybrid answer).

Phase 27 — Playbook versioning. PlaybookEntry gained four optional
fields (all #[serde(default)] so pre-Phase-27 state loads as roots):

  version           u32, default 1
  parent_id         Option<String>, previous version's playbook_id
  superseded_at     Option<String>, set when newer version replaces
  superseded_by     Option<String>, the playbook_id that replaced

New methods:

  revise_entry(parent_id, new_entry) — appends new version, stamps
    superseded_at+superseded_by on parent, inherits parent_id and sets
    version = parent + 1 on the new entry. Rejects revising a retired
    or already-superseded parent (tip-of-chain is the only valid
    revise target).
  history(playbook_id) — returns full chain root->tip from any node.
    Walks parent_id back to root, then superseded_by forward to tip.
    Cycle-safe.

Superseded entries excluded from boost (same rule as retired): filter
in compute_boost_for_filtered_with_role (both active-entries prefilter
and geo-filtered path), rebuild_geo_index, and upsert_entry's existing-
idx search. status_counts returns (total, retired, superseded, failures);
/status JSON reports active = total - retired - superseded.

Endpoints:
  POST /vectors/playbook_memory/revise
  GET  /vectors/playbook_memory/history/{id}

Doc-sync — PHASES.md + PRD.md drifted from git after Phases 24-26
shipped. Fixes applied:

  - Phase 24 marked shipped (commit b95dd86) with detail of observer
    HTTP ingest + scenario outcome streaming. PRD "NOT YET WIRED"
    rewritten to reflect shipped state.
  - Phase 25 (validity windows, commit e0a843d) added to PHASES +
    PRD.
  - Phase 26 (Mem0 upsert + Letta hot cache, commit 640db8c) added.
  - Phase 27 entry added to both docs.
  - Phase 19.6 time decay corrected: was documented as "deferred",
    actually wired via BOOST_HALF_LIFE_DAYS = 30.0 in playbook_memory.rs.
  - Phase E/Phase 8 tombstone-at-compaction limit note updated —
    Phase E.2 closed it.

Tests: 8 new version_tests in vectord (chain-metadata stamping,
retired/superseded parent rejection, boost exclusion, history from
root/tip/middle, legacy default round-trip, status counts). 25 new
aibridge tests (context/continuation/tree_split). Workspace total
145 green (was 120).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
 2026-04-21 17:40:49 -05:00
106 changed files with 12777 additions and 1071 deletions
Expand all files Collapse all files
Cargo.lock
Generated
+109 -7
View File
@@ -1254,7 +1254,7 @@ version = "3.9.1"
source = "registry+https://github.com/rust-lang/crates.io-index" source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "519bd3116aeeb42d5372c29d982d16d0170d3d4a5ed85fc7dd91642ffff3c67c" checksum = "519bd3116aeeb42d5372c29d982d16d0170d3d4a5ed85fc7dd91642ffff3c67c"
dependencies = [ dependencies = [
"darling", "darling 0.21.3",
"ident_case", "ident_case",
"prettyplease", "prettyplease",
"proc-macro2", "proc-macro2",
@@ -1759,6 +1759,17 @@ dependencies = [
"cfg-if", "cfg-if",
] ]
[[package]]
name = "croner"
version = "3.0.1"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "4aa42bcd3d846ebf66e15bd528d1087f75d1c6c1c66ebff626178a106353c576"
dependencies = [
"chrono",
"derive_builder",
"strum 0.27.2",
]
[[package]] [[package]]
name = "crossbeam" name = "crossbeam"
version = "0.8.4" version = "0.8.4"
@@ -1862,14 +1873,38 @@ dependencies = [
"memchr", "memchr",
] ]
[[package]]
name = "darling"
version = "0.20.11"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "fc7f46116c46ff9ab3eb1597a45688b6715c6e628b5c133e288e709a29bcb4ee"
dependencies = [
"darling_core 0.20.11",
"darling_macro 0.20.11",
]
[[package]] [[package]]
name = "darling" name = "darling"
version = "0.21.3" version = "0.21.3"
source = "registry+https://github.com/rust-lang/crates.io-index" source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "9cdf337090841a411e2a7f3deb9187445851f91b309c0c0a29e05f74a00a48c0" checksum = "9cdf337090841a411e2a7f3deb9187445851f91b309c0c0a29e05f74a00a48c0"
dependencies = [ dependencies = [
"darling_core", "darling_core 0.21.3",
"darling_macro", "darling_macro 0.21.3",
]
[[package]]
name = "darling_core"
version = "0.20.11"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "0d00b9596d185e565c2207a0b01f8bd1a135483d02d9b7b0a54b11da8d53412e"
dependencies = [
"fnv",
"ident_case",
"proc-macro2",
"quote",
"strsim",
"syn 2.0.117",
] ]
[[package]] [[package]]
@@ -1886,13 +1921,24 @@ dependencies = [
"syn 2.0.117", "syn 2.0.117",
] ]
[[package]]
name = "darling_macro"
version = "0.20.11"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "fc34b93ccb385b40dc71c6fceac4b2ad23662c7eeb248cf10d529b7e055b6ead"
dependencies = [
"darling_core 0.20.11",
"quote",
"syn 2.0.117",
]
[[package]] [[package]]
name = "darling_macro" name = "darling_macro"
version = "0.21.3" version = "0.21.3"
source = "registry+https://github.com/rust-lang/crates.io-index" source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "d38308df82d1080de0afee5d069fa14b0326a88c14f15c5ccda35b4a6c414c81" checksum = "d38308df82d1080de0afee5d069fa14b0326a88c14f15c5ccda35b4a6c414c81"
dependencies = [ dependencies = [
"darling_core", "darling_core 0.21.3",
"quote", "quote",
"syn 2.0.117", "syn 2.0.117",
] ]
@@ -3149,6 +3195,37 @@ dependencies = [
"serde_core", "serde_core",
] ]
[[package]]
name = "derive_builder"
version = "0.20.2"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "507dfb09ea8b7fa618fcf76e953f4f5e192547945816d5358edffe39f6f94947"
dependencies = [
"derive_builder_macro",
]
[[package]]
name = "derive_builder_core"
version = "0.20.2"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "2d5bcf7b024d6835cfb3d473887cd966994907effbe9227e8c8219824d06c4e8"
dependencies = [
"darling 0.20.11",
"proc-macro2",
"quote",
"syn 2.0.117",
]
[[package]]
name = "derive_builder_macro"
version = "0.20.2"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "ab63b0e2bf4d5928aff72e83a7dace85d7bba5fe12dcc3c5a572d78caffd3f3c"
dependencies = [
"derive_builder_core",
"syn 2.0.117",
]
[[package]] [[package]]
name = "derive_more" name = "derive_more"
version = "2.1.1" version = "2.1.1"
@@ -3713,7 +3790,7 @@ version = "0.14.0"
source = "registry+https://github.com/rust-lang/crates.io-index" source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "f43e744e4ea338060faee68ed933e46e722fb7f3617e722a5772d7e856d8b3ce" checksum = "f43e744e4ea338060faee68ed933e46e722fb7f3617e722a5772d7e856d8b3ce"
dependencies = [ dependencies = [
"darling", "darling 0.21.3",
"proc-macro2", "proc-macro2",
"quote", "quote",
"syn 2.0.117", "syn 2.0.117",
@@ -4002,6 +4079,7 @@ dependencies = [
"opentelemetry_sdk", "opentelemetry_sdk",
"proto", "proto",
"queryd", "queryd",
"reqwest",
"serde", "serde",
"serde_json", "serde_json",
"shared", "shared",
@@ -4598,6 +4676,7 @@ dependencies = [
"bytes", "bytes",
"catalogd", "catalogd",
"chrono", "chrono",
"croner",
"csv", "csv",
"lopdf", "lopdf",
"mysql_async", "mysql_async",
@@ -5096,7 +5175,7 @@ dependencies = [
"prost-types 0.14.3", "prost-types 0.14.3",
"rand 0.9.2", "rand 0.9.2",
"snafu", "snafu",
"strum", "strum 0.26.3",
"tokio", "tokio",
"tracing", "tracing",
"xxhash-rust", "xxhash-rust",
@@ -7857,7 +7936,16 @@ version = "0.26.3"
source = "registry+https://github.com/rust-lang/crates.io-index" source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "8fec0f0aef304996cf250b31b5a10dee7980c85da9d759361292b8bca5a18f06" checksum = "8fec0f0aef304996cf250b31b5a10dee7980c85da9d759361292b8bca5a18f06"
dependencies = [ dependencies = [
"strum_macros", "strum_macros 0.26.4",
]
[[package]]
name = "strum"
version = "0.27.2"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "af23d6f6c1a224baef9d3f61e287d2761385a5b88fdab4eb4c6f11aeb54c4bcf"
dependencies = [
"strum_macros 0.27.2",
] ]
[[package]] [[package]]
@@ -7873,6 +7961,18 @@ dependencies = [
"syn 2.0.117", "syn 2.0.117",
] ]
[[package]]
name = "strum_macros"
version = "0.27.2"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "7695ce3845ea4b33927c055a39dc438a45b059f7c1b3d91d38d10355fb8cbca7"
dependencies = [
"heck",
"proc-macro2",
"quote",
"syn 2.0.117",
]
[[package]] [[package]]
name = "subprocess" name = "subprocess"
version = "0.2.15" version = "0.2.15"
@@ -8814,12 +8914,14 @@ dependencies = [
"bytes", "bytes",
"catalogd", "catalogd",
"chrono", "chrono",
"futures",
"instant-distance", "instant-distance",
"object_store", "object_store",
"parquet 55.2.0", "parquet 55.2.0",
"queryd", "queryd",
"serde", "serde",
"serde_json", "serde_json",
"sha2",
"shared", "shared",
"storaged", "storaged",
"tokio", "tokio",
auditor/README.md
+60
View File
@@ -0,0 +1,60 @@
# Lakehouse Claim Auditor
A Bun sub-agent that watches open PRs on Gitea, reads the ship-claims
in commit messages and PR bodies, and **hard-blocks** merges when the
code doesn't back the claim.
Rationale: when "compiles + one curl works" gets called "phase shipped,"
placeholder code accumulates. This auditor runs every 90s, fetches
each open PR, and subjects it to four checks:
1. **Static diff** — grep/parse looking for placeholder patterns
2. **Dynamic** — runs the never-before-executed hybrid test fixture
3. **Cloud inference** — asks `gpt-oss:120b` via `/v1/chat` to
identify gaps in the diff
4. **KB query** — looks up `data/_kb/` + observer for prior failure
patterns on similar claims
Verdict is assembled, posted to Gitea as:
- A **failing commit status** (hard block — branch protection
prevents merge)
- A **review comment** explaining every finding
## Run manually
```bash
cd /home/profit/lakehouse
bun run auditor/index.ts
```
Defaults: polls every 90s, stops on `auditor.paused` file present.
## State
- `data/_auditor/state.json` — last-audited head SHA per PR
- `data/_auditor/verdicts/{pr}-{sha}.json` — per-run verdict record
- `data/_kb/audit_lessons.jsonl` — one row per block/warn finding,
path-agnostic signature for dedup. Tailed by kb_query on each audit
to surface recurring patterns (2+ distinct PRs with same signature
→ info, 3-4 → warn, 5+ → block). This is how the auditor learns.
- `data/_kb/scrum_reviews.jsonl` — scrum-master per-file reviews. If
a file in the current PR has been scrum-reviewed, kb_query surfaces
the review as a finding with the accepted model and attempt count.
## Where YOU edit
`auditor/policy.ts` — the verdict assembler. Controls which findings
block vs warn vs inform. All other code is mechanical: fetching,
running checks, posting to Gitea.
## Hard-block mechanism
1. Commit status is posted as `failure` with context `lakehouse/auditor`
2. If `main` branch protection requires `lakehouse/auditor` status
to pass, Gitea prevents merge
3. When code is fixed and re-audit passes, status flips to `success`,
merge unblocks
Enable branch protection (one-time, via Gitea UI or API):
- `POST /repos/profit/lakehouse/branch_protections`
- `{"branch_name": "main", "required_status_checks": {"contexts": ["lakehouse/auditor"]}}`
auditor/audit.ts
+222
View File
@@ -0,0 +1,222 @@
// Orchestrator — runs all four checks on a PR, assembles a verdict,
// posts to Gitea. This is task #8's integration layer; the poller
// (task #9) calls this once per PR on every fresh head SHA.
//
// Hard-block mechanism: commit status posted with state="failure"
// and context="lakehouse/auditor". If `main` branch protection
// requires that context to pass, merge is physically impossible
// until the auditor re-audits a fixed commit and flips the status
// to "success".
//
// Human-readable reasoning: posted as a PR issue comment (not a
// review — reviews have self-review restrictions on Gitea and the
// auditor currently uses the same PAT as the PR author).
import { readFile, writeFile, mkdir, appendFile } from "node:fs/promises";
import { createHash } from "node:crypto";
import { join } from "node:path";
import type { PrSnapshot, Verdict, Finding } from "./types.ts";
import { getPrDiff, postCommitStatus, postIssueComment } from "./gitea.ts";
import { parseClaims } from "./claim_parser.ts";
import { assembleVerdict } from "./policy.ts";
import { runStaticCheck } from "./checks/static.ts";
import { runDynamicCheck } from "./checks/dynamic.ts";
import { runInferenceCheck } from "./checks/inference.ts";
import { runKbCheck } from "./checks/kb_query.ts";
const VERDICTS_DIR = "/home/profit/lakehouse/data/_auditor/verdicts";
// Playbook for audit findings — one row per block/warn finding from a
// verdict. kb_query tails this next audit and escalates recurrences.
// Structured as JSONL so it's cheap to append and cheap to tail.
const AUDIT_LESSONS_JSONL = "/home/profit/lakehouse/data/_kb/audit_lessons.jsonl";
export interface AuditOptions {
// Skip the cloud inference call (fast path for iteration). Default false.
skip_inference?: boolean;
// Skip the dynamic check (avoid running the hybrid fixture every PR,
// since it hits live services and mutates playbook state). Default false
// on `main`-branch-target PRs, true when auditing feature branches
// where the fixture would pollute state. Caller decides.
skip_dynamic?: boolean;
// Skip Gitea posting — useful for dry-runs / local testing.
// Default false.
dry_run?: boolean;
}
export async function auditPr(pr: PrSnapshot, opts: AuditOptions = {}): Promise<Verdict> {
const t0 = Date.now();
const diff = await getPrDiff(pr.number);
const { claims } = parseClaims(pr);
// Run checks in parallel where they don't share mutable state.
// Static + kb_query + inference are all read-only. Dynamic mutates
// playbook state (nonce-scoped per run, but still live) so if
// skip_dynamic is false we still run it in parallel — the mutation
// is namespaced.
const [staticFindings, dynamicFindings, inferenceFindings, kbFindings] = await Promise.all([
runStaticCheck(diff),
opts.skip_dynamic ? Promise.resolve(stubFinding("dynamic", "skipped by options")) : runDynamicCheck(),
opts.skip_inference ? Promise.resolve(stubFinding("inference", "skipped by options")) : runInferenceCheck(claims, diff, { pr_number: pr.number, head_sha: pr.head_sha }),
runKbCheck(claims, pr.files.map(f => f.path)),
]);
const allFindings: Finding[] = [
...staticFindings,
...dynamicFindings,
...inferenceFindings,
...kbFindings,
];
const duration_ms = Date.now() - t0;
const metrics = {
audit_duration_ms: duration_ms,
findings_total: allFindings.length,
findings_block: allFindings.filter(f => f.severity === "block").length,
findings_warn: allFindings.filter(f => f.severity === "warn").length,
findings_info: allFindings.filter(f => f.severity === "info").length,
claims_strong: claims.filter(c => c.strength === "strong").length,
claims_moderate: claims.filter(c => c.strength === "moderate").length,
claims_weak: claims.filter(c => c.strength === "weak").length,
claims_empirical: claims.filter(c => c.strength === "empirical").length,
claims_total: claims.length,
diff_bytes: diff.length,
};
const verdict = assembleVerdict(allFindings, metrics, pr.number, pr.head_sha);
await persistVerdict(verdict);
// Feedback loop — every block/warn finding becomes a row in
// audit_lessons.jsonl, dedup-keyed by (check, normalized-summary).
// The next audit's kb_query reads these and escalates recurring
// findings so we don't lose the "this pattern has been flagged
// before" signal across runs. Fire-and-forget; failure here must
// not break the audit.
appendAuditLessons(verdict).catch(e =>
console.error(`[audit] audit_lessons append failed: ${(e as Error).message}`));
if (!opts.dry_run) {
await postToGitea(verdict);
}
return verdict;
}
// Normalizes a finding summary for dedup: strips path-specific tails
// ("in path/to/file.ts" → "in <file>"), line numbers, and long
// commit-hash snippets. The goal is: the SAME class of finding on
// DIFFERENT files should share a signature, so we can measure
// "this pattern keeps showing up."
function normalizedSignature(f: Finding): string {
const summary = String(f.summary)
.replace(/\bin\s+\S+\.(ts|rs|js|py|md)\b/gi, "in <file>")
.replace(/:\+?\d+\b/g, ":<line>")
.replace(/[0-9a-f]{8,}/gi, "<hash>")
.replace(/\s+/g, " ")
.trim()
.slice(0, 240);
const src = `${f.check}::${f.severity}::${summary}`;
return createHash("sha256").update(src).digest("hex").slice(0, 16);
}
async function appendAuditLessons(v: Verdict): Promise<void> {
const actionable = v.findings.filter(f => f.severity === "block" || f.severity === "warn");
if (actionable.length === 0) return;
await mkdir(join(AUDIT_LESSONS_JSONL, ".."), { recursive: true });
const rows: string[] = [];
for (const f of actionable) {
rows.push(JSON.stringify({
signature: normalizedSignature(f),
check: f.check,
severity: f.severity,
summary: f.summary,
pr_number: v.pr_number,
head_sha: v.head_sha,
audited_at: v.audited_at,
}));
}
await appendFile(AUDIT_LESSONS_JSONL, rows.join("\n") + "\n");
}
async function persistVerdict(v: Verdict): Promise<void> {
await mkdir(VERDICTS_DIR, { recursive: true });
const filename = `${v.pr_number}-${v.head_sha.slice(0, 12)}.json`;
await writeFile(join(VERDICTS_DIR, filename), JSON.stringify(v, null, 2));
}
export async function postToGitea(v: Verdict): Promise<void> {
// 1. Commit status — the hard block signal (if branch protection
// is configured to require lakehouse/auditor on main).
const state = v.overall === "approve" ? "success" : "failure";
await postCommitStatus({
sha: v.head_sha,
state,
context: "lakehouse/auditor",
description: v.one_liner,
target_url: "", // no URL yet; could point to a verdicts dashboard
});
// 2. Issue comment — the reasoning. Gated so we don't spam the PR
// with identical comments on re-audits of the same SHA. Caller
// (poller) ensures we only re-audit fresh SHAs, but a dedup
// marker inside the body keeps it idempotent if re-run.
const body = formatReviewBody(v);
await postIssueComment({ pr_number: v.pr_number, body });
}
function formatReviewBody(v: Verdict): string {
const byCheck: Record<string, Finding[]> = {};
for (const f of v.findings) {
(byCheck[f.check] ||= []).push(f);
}
const verdictEmoji =
v.overall === "approve" ? "✅" :
v.overall === "request_changes" ? "⚠️" :
"🛑";
const lines: string[] = [];
lines.push(`## Auditor verdict: ${verdictEmoji} \`${v.overall}\``);
lines.push("");
lines.push(`**One-liner:** ${v.one_liner}`);
lines.push(`**Head SHA:** \`${v.head_sha.slice(0, 12)}\``);
lines.push(`**Audited at:** ${v.audited_at}`);
lines.push("");
// Per-check sections, only if the check produced findings.
const checkOrder = ["static", "dynamic", "inference", "kb_query"] as const;
for (const check of checkOrder) {
const fs = byCheck[check] ?? [];
if (fs.length === 0) continue;
const bySev = {
block: fs.filter(f => f.severity === "block").length,
warn: fs.filter(f => f.severity === "warn").length,
info: fs.filter(f => f.severity === "info").length,
};
lines.push(`<details><summary><b>${check}</b> — ${fs.length} findings (${bySev.block} block, ${bySev.warn} warn, ${bySev.info} info)</summary>`);
lines.push("");
for (const f of fs) {
const mark = f.severity === "block" ? "🛑" : f.severity === "warn" ? "⚠️" : "ℹ️";
lines.push(`${mark} **${f.severity}** — ${f.summary}`);
for (const e of f.evidence.slice(0, 3)) {
lines.push(` - \`${e.slice(0, 180).replace(/\n/g, " ")}\``);
}
}
lines.push("");
lines.push("</details>");
lines.push("");
}
lines.push("### Metrics");
lines.push("```json");
lines.push(JSON.stringify(v.metrics, null, 2));
lines.push("```");
lines.push("");
lines.push(`<sub>Lakehouse auditor · SHA ${v.head_sha.slice(0, 8)} · re-audit on new commit flips the status automatically.</sub>`);
return lines.join("\n");
}
function stubFinding(check: "dynamic" | "inference", why: string): Finding[] {
return [{ check, severity: "info", summary: `${check} check skipped — ${why}`, evidence: [why] }];
}
auditor/audit_one.ts
+68
View File
@@ -0,0 +1,68 @@
// One-shot dry-run audit of a single PR. Useful for verifying check
// behavior (kb_query scrum surfacing, inference prompts, etc.) without
// posting to Gitea. Does NOT touch state.json and does NOT post
// commit status or PR comments.
//
// Run: bun run auditor/audit_one.ts <pr-number>
import { getPrSnapshot } from "./gitea.ts";
import { auditPr } from "./audit.ts";
async function main() {
const prNumRaw = process.argv[2];
if (!prNumRaw) {
console.error("usage: bun run auditor/audit_one.ts <pr-number>");
process.exit(2);
}
const prNum = Number(prNumRaw);
if (!Number.isFinite(prNum)) {
console.error(`invalid PR number: ${prNumRaw}`);
process.exit(2);
}
console.log(`[audit_one] fetching PR #${prNum}...`);
const pr = await getPrSnapshot(prNum);
console.log(`[audit_one] PR #${pr.number}: "${pr.title}" (head=${pr.head_sha.slice(0, 12)})`);
console.log(`[audit_one] files in diff: ${pr.files.length}`);
for (const f of pr.files) console.log(` - ${f.path} (+${f.additions}/-${f.deletions})`);
console.log("");
const verdict = await auditPr(pr, {
dry_run: true, // no Gitea posting
skip_dynamic: true, // don't run fixture
skip_inference: process.env.LH_AUDITOR_SKIP_INFERENCE === "1",
});
console.log("\n═══ VERDICT ═══");
console.log(`overall: ${verdict.overall}`);
console.log(`one-liner: ${verdict.one_liner}`);
console.log(`findings: total=${verdict.metrics.findings_total} block=${verdict.metrics.findings_block} warn=${verdict.metrics.findings_warn} info=${verdict.metrics.findings_info}`);
console.log("");
// Print findings, highlighting kb_query scrum surfacing
const byCheck: Record<string, typeof verdict.findings> = {};
for (const f of verdict.findings) (byCheck[f.check] ||= []).push(f);
for (const [check, findings] of Object.entries(byCheck)) {
console.log(`── ${check} (${findings.length}) ──`);
for (const f of findings) {
const tag = f.severity === "block" ? "🛑" : f.severity === "warn" ? "⚠️ " : "ℹ️ ";
console.log(` ${tag} [${f.severity}] ${f.summary}`);
if (f.summary.includes("scrum-master")) {
for (const e of f.evidence) {
console.log(` → ${e.slice(0, 200)}`);
}
}
}
}
const scrumFindings = verdict.findings.filter(f => f.summary.includes("scrum-master"));
console.log("");
console.log(`═══ SCRUM WIRE CHECK: ${scrumFindings.length} scrum-master findings surfaced by kb_query ═══`);
if (scrumFindings.length === 0) {
console.log(" (none — either no matching scrum_reviews.jsonl rows, or files didn't match PR diff)");
}
process.exit(0);
}
main().catch(e => { console.error("[audit_one] fatal:", e); process.exit(1); });
auditor/checks/dynamic.ts
+91
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// Dynamic execution check — runs the hybrid fixture and maps its
// layer results to auditor Findings.
//
// A layer that fails with a "not implemented / 404 / slice N" error
// gets severity=info (honest placeholder signal). A layer that fails
// any other way gets severity=warn (something actually broke).
// An info-level summary finding is always emitted carrying the real
// numbers — shipped/placeholder phase counts, per-layer latency.
import { runHybridFixture } from "../fixtures/hybrid_38_40_45.ts";
import type { Finding } from "../types.ts";
const PLACEHOLDER_MARKERS = [
"unimplemented",
" 404 ", "(404)", " 405 ", "(405)",
"slice 3", "slice 4", "slice 5",
"endpoint not built", "not yet",
];
function isPlaceholderFailure(err?: string): boolean {
if (!err) return false;
const low = err.toLowerCase();
return PLACEHOLDER_MARKERS.some(m => low.includes(m.toLowerCase()));
}
export async function runDynamicCheck(): Promise<Finding[]> {
const findings: Finding[] = [];
let result;
try {
result = await runHybridFixture();
} catch (e) {
// Fixture itself crashed — can't run dynamic check at all.
return [
{
check: "dynamic",
severity: "warn",
summary: `hybrid fixture crashed before completing: ${(e as Error).message.slice(0, 140)}`,
evidence: [(e as Error).message],
},
];
}
// Per-layer findings for every non-ok layer.
for (const layer of result.layers) {
if (layer.ok) continue;
const placeholder = isPlaceholderFailure(layer.error);
findings.push({
check: "dynamic",
severity: placeholder ? "info" : "warn",
summary: placeholder
? `hybrid fixture layer ${layer.layer} (Phase ${layer.phase}) honestly reports unimplemented`
: `hybrid fixture layer ${layer.layer} (Phase ${layer.phase}) failed — not a placeholder, a real failure`,
evidence: [
`evidence: ${layer.evidence.slice(0, 160)}`,
...(layer.error ? [`error: ${layer.error.slice(0, 160)}`] : []),
`latency_ms: ${layer.latency_ms}`,
],
});
}
// One overall summary with real numbers so the report shows what
// DID pass plus per-layer timing.
const metrics_preview = Object.entries(result.real_numbers)
.slice(0, 10)
.map(([k, v]) => `${k}=${v}`);
findings.push({
check: "dynamic",
severity: "info",
summary: `hybrid fixture overall=${result.overall}, shipped [${result.shipped_phases.join(", ")}], placeholder [${result.placeholder_phases.join(", ")}]`,
evidence: metrics_preview.length > 0 ? metrics_preview : ["no metrics emitted"],
});
// If the fixture ran at all but nothing passed, elevate one of the
// summary findings to warn — something more than "all honest
// placeholders" is wrong.
if (result.overall === "fail") {
findings.push({
check: "dynamic",
severity: "warn",
summary: `hybrid fixture: 0 layers passed (overall=fail)`,
evidence: [
"a total fixture fail usually means a precondition service is down",
"(gateway /health / sidecar / Langfuse /v1/chat) — NOT necessarily",
"the PR's code problem. Check service status before blaming the PR.",
],
});
}
return findings;
}
auditor/checks/inference.ts
+666
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// Cloud inference check — wraps the proven run_codereview pattern
// from llm_team_ui.py (same 3-stage framing, same cloud model) to
// critique a PR's claims against its diff.
//
// Proved out 2026-04-22 via /tmp/codereview_runner.py — gpt-oss:120b
// caught a real ternary bug in auditor/fixtures/hybrid_38_40_45.ts
// that unit tests missed. This module reuses the reviewer prompt
// shape (bugs / security / performance / style / edge cases) and
// adds claim-vs-diff specific framing.
//
// Call surface: runInferenceCheck(claims, diff) → Finding[].
// Cloud latency budget: ~60s (gpt-oss:120b reviewer typically 35-50s
// with a 15KB diff + claim list).
import type { Claim, Finding } from "../types.ts";
import { Glob } from "bun";
import { readFile, mkdir, appendFile } from "node:fs/promises";
import { extractFacts } from "../fact_extractor.ts";
const GATEWAY = process.env.LH_GATEWAY_URL ?? "http://localhost:3100";
const MODEL = process.env.LH_AUDITOR_REVIEW_MODEL ?? "gpt-oss:120b";
// Tie-breaker for claims where the N=3 consensus produces a 1-1-1
// split (genuinely borderline). Different architecture from the
// primary reviewer (gpt-oss) so the tie-break isn't correlated with
// the original disagreement. qwen3-coder:480b is a newer coding
// specialist at 480B params, well-suited to PR-diff claim verification
// and distinct in training lineage from gpt-oss.
const TIEBREAKER_MODEL = process.env.LH_AUDITOR_TIEBREAKER_MODEL ?? "qwen3-coder:480b";
const N_CONSENSUS = Number(process.env.LH_AUDITOR_CONSENSUS_N ?? 3);
const AUDIT_DISCREPANCIES_JSONL = "/home/profit/lakehouse/data/_kb/audit_discrepancies.jsonl";
// 40KB comfortably fits gpt-oss:120b's context. PR #1 (~39KB) was
// previously truncated at 15KB causing the reviewer to miss later
// files (gitea.ts, policy.ts) and flag "no Gitea client present" as a
// block finding when the file was simply outside the truncation window.
//
// Above this threshold we curate via tree-split rather than truncate,
// following the scrum_master pattern: shard the diff, summarize each
// shard against the claim-verification task, merge into a compact
// scratchpad, then ask the cloud to verify claims against the
// scratchpad. This gives the cloud full-PR fidelity without bursting
// its context window (observed failure mode: empty response or
// unparseable output when prompt exceeds model's comfortable range).
const MAX_DIFF_CHARS = 40000;
// Tree-split kicks in above this. 30KB is below MAX_DIFF_CHARS so we
// curate BEFORE truncation would happen — never lose signal to a hard
// cut. Shard size is chosen so ~10 shards cover PR #8-size diffs in a
// reasonable round-trip budget.
const CURATION_THRESHOLD = 30000;
const DIFF_SHARD_SIZE = 4500;
const CALL_TIMEOUT_MS = 120_000;
const REPO_ROOT = "/home/profit/lakehouse";
export interface InferenceContext {
pr_number: number;
head_sha: string;
}
const AUDIT_FACTS_JSONL = "/home/profit/lakehouse/data/_kb/audit_facts.jsonl";
export async function runInferenceCheck(
claims: Claim[],
diff: string,
ctx?: InferenceContext,
): Promise<Finding[]> {
if (claims.length === 0) {
return [{
check: "inference",
severity: "info",
summary: "no ship-claims extracted — skipping cloud inference",
evidence: ["parser returned empty claim list; nothing to verify against cloud"],
}];
}
// Empirical claims (runtime metrics / observed outcomes) can't be
// verified from the diff. Drop them from the cloud prompt so the
// reviewer doesn't chase ghosts. A future `runtime_evidence` check
// can validate these against data/_kb/*/summary.json outputs.
const verifiable = claims.filter(c => c.strength !== "empirical");
const empiricalCount = claims.length - verifiable.length;
if (verifiable.length === 0) {
return [{
check: "inference",
severity: "info",
summary: `all ${claims.length} claims are empirical (runtime metrics) — skipping cloud inference`,
evidence: [`empirical claims can't be verified from a static diff; needs runtime-evidence check`],
}];
}
// Diff source for the cloud prompt — either the raw diff (small
// enough to fit), or a tree-split scratchpad (curation layer). We
// prefer curation to truncation: truncation silently drops files
// past the window; curation summarizes them so the cloud still sees
// what changed, just densified.
let diffForPrompt: string;
let curationNote = "";
if (diff.length > CURATION_THRESHOLD) {
const ts = await treeSplitDiff(diff, verifiable);
diffForPrompt = ts.scratchpad;
curationNote = ` (curated: ${diff.length} chars → ${ts.shards} shards → scratchpad ${ts.scratchpad.length} chars)`;
} else {
diffForPrompt = diff;
}
// Belt-and-suspenders truncation — even a tree-split scratchpad
// shouldn't exceed MAX_DIFF_CHARS in practice, but guard anyway so
// pathological inputs can't burst the prompt.
const truncated = diffForPrompt.length > MAX_DIFF_CHARS
? diffForPrompt.slice(0, MAX_DIFF_CHARS) + `\n...[${diffForPrompt.length - MAX_DIFF_CHARS} more chars truncated]`
: diffForPrompt;
// Build the reviewer prompt in the same shape as run_codereview's
// review stage (llm_team_ui.py:10950), adapted for claim verification:
// "Task: ..."
// "Code: ..."
// "Review: bugs/security/perf/style/edge. Provide corrected code."
// We add: claim list upfront + ask for structured JSON verdict.
//
// When the diff was curated (tree-split scratchpad), we add an
// explicit anti-false-positive instruction: the scratchpad is a
// distillation, not the full source, so absence-from-scratchpad is
// NOT evidence of absence-from-diff. Mirrors the fix we made in
// scrum_master's review prompt for the same class of error.
const isCurated = curationNote.length > 0;
const curationGuard = isCurated
? [
"",
"CRITICAL: the 'Diff' below is a curated multi-shard scratchpad,",
"NOT the full raw diff. The scratchpad distills each shard down",
"to facts useful for claim verification and drops the rest.",
"DO NOT flag a function/field/feature as 'missing' or 'not",
"implemented' based solely on its absence from the scratchpad —",
"absence in a distillation is NOT evidence of absence in the",
"actual diff. Only judge a claim NOT BACKED when the scratchpad",
"DIRECTLY contradicts it (e.g. scratchpad shows the function was",
"added empty, or shows the claimed code path is a stub).",
"Skip the unflagged_gaps section entirely when operating on a",
"curated scratchpad — you can't reliably detect gaps from a",
"distillation, and false positives there are worse than misses.",
].join("\n")
: "";
const systemMsg = [
"You review pull-request diffs against the author's own ship-claims.",
"For each claim, decide: is it backed by actual code in the diff, or is",
"it placeholder / aspirational / unwired?",
"",
"A claim is BACKED when the diff contains a real code path that delivers",
"the claimed behavior. A claim is NOT BACKED when:",
" - the claim asserts functionality but the diff only adds types/fields",
" with no consumer",
" - the claim mentions tests but no test function was added",
" - the claim claims integration but the integration point is a stub",
" - the diff contains unimplemented!() / todo!() / TODO comments",
" - the claim says 'works end-to-end' but the diff has no end-to-end test",
curationGuard,
"",
"Respond with strict JSON only. No prose before or after. Shape:",
"{",
' "claim_verdicts": [',
' {"claim_idx": 0, "backed": false, "evidence": "short reason"}',
" ],",
' "unflagged_gaps": [',
' {"location": "file:line", "summary": "short description"}',
" ]",
"}",
].join("\n");
const userMsg = [
`Ship-claims the author made (numbered 0..N-1):`,
verifiable.map((c, i) => ` ${i}. [${c.strength}] "${c.text}" at ${c.location}`).join("\n"),
"",
`Diff:`,
"```",
truncated,
"```",
"",
`For each numbered claim above, emit a claim_verdicts entry. For gaps the`,
`author DIDN'T claim but that look like placeholder code, emit unflagged_gaps.`,
`Strict JSON only, matching the shape described. No prose outside JSON.`,
].join("\n");
// N=3 consensus — run the primary reviewer in parallel, collect
// all three parsed responses, majority-vote per claim. Parallel
// (Promise.all) because each call is ~20-30s and they're independent;
// wall-clock stays ~same as single call, cost 3x tokens. Empirical
// justification: in 3-run determinism tests, 7/8 findings were
// stable but 1 flipped across runs — majority vote stabilizes the
// flipping class without losing the stable signal.
const primaryRuns = await Promise.all(
Array.from({ length: N_CONSENSUS }, () =>
runCloudInference(systemMsg, userMsg, MODEL)),
);
const parsedRuns = primaryRuns.filter(r => r.parsed !== null);
if (parsedRuns.length === 0) {
// All N calls failed. Surface the first-run diagnostic so the
// operator sees *why* (unreachable / non-200 / unparseable).
const first = primaryRuns[0];
return [{
check: "inference",
severity: "info",
summary: `cloud inference all ${N_CONSENSUS} consensus runs failed — ${first.error ?? "unknown"}`,
evidence: [
`first-run diagnostic: ${first.diagnostic ?? "(none)"}`,
`successful runs: 0 / ${N_CONSENSUS}`,
],
}];
}
// Aggregate votes per claim_idx.
interface Votes { trues: number; falses: number; evidences: string[] }
const votesByClaim = new Map<number, Votes>();
const unflaggedByRun: any[][] = [];
let totalTokens = 0;
for (const run of parsedRuns) {
totalTokens += run.tokens;
unflaggedByRun.push(Array.isArray(run.parsed?.unflagged_gaps) ? run.parsed.unflagged_gaps : []);
for (const v of run.parsed?.claim_verdicts ?? []) {
const idx = Number(v?.claim_idx);
if (!Number.isFinite(idx)) continue;
const rec = votesByClaim.get(idx) ?? { trues: 0, falses: 0, evidences: [] };
if (v.backed === false) {
rec.falses++;
rec.evidences.push(String(v.evidence ?? ""));
} else if (v.backed === true) {
rec.trues++;
}
votesByClaim.set(idx, rec);
}
}
const findings: Finding[] = [];
// Summary finding so the verdict layer knows the check ran.
findings.push({
check: "inference",
severity: "info",
summary: `cloud review completed (model=${MODEL}, consensus=${parsedRuns.length}/${N_CONSENSUS}, tokens=${totalTokens})${curationNote}`,
evidence: [
`claims voted: ${votesByClaim.size}`,
`parsed runs: ${parsedRuns.length} / ${N_CONSENSUS}`,
],
});
// Per-claim majority vote; tie-break if no majority.
const discrepancies: Array<{
claim_idx: number;
claim_text: string;
votes: { trues: number; falses: number };
resolution: "majority_backed" | "majority_not_backed" | "tiebreaker_backed" | "tiebreaker_not_backed" | "unresolved";
tiebreaker_model?: string;
}> = [];
for (const [idx, votes] of votesByClaim) {
const claim = verifiable[idx];
if (!claim) continue;
const totalVotes = votes.trues + votes.falses;
let notBacked: boolean | null = null;
let resolution: typeof discrepancies[number]["resolution"] = "majority_backed";
let evidenceText = "";
let tbModel: string | undefined;
if (votes.falses > votes.trues) {
notBacked = true;
resolution = "majority_not_backed";
evidenceText = votes.evidences[0] ?? "(no reason given)";
} else if (votes.trues > votes.falses) {
notBacked = false;
resolution = "majority_backed";
} else {
// Tie. Run tie-breaker with a different-architecture model.
const tb = await runCloudInference(systemMsg, userMsg, TIEBREAKER_MODEL);
if (tb.parsed) {
const tv = (tb.parsed.claim_verdicts ?? []).find((v: any) => Number(v?.claim_idx) === idx);
if (tv?.backed === false) {
notBacked = true;
resolution = "tiebreaker_not_backed";
evidenceText = `(tie-breaker ${TIEBREAKER_MODEL}) ${String(tv.evidence ?? "")}`;
tbModel = TIEBREAKER_MODEL;
} else if (tv?.backed === true) {
notBacked = false;
resolution = "tiebreaker_backed";
tbModel = TIEBREAKER_MODEL;
} else {
resolution = "unresolved";
}
} else {
resolution = "unresolved";
}
}
// Log every case where the N runs disagreed — discrepancies are
// signal, not noise. Separate from audit_lessons.jsonl because
// they're about the *auditor's* quality, not the PR's quality.
const disagreed = totalVotes >= 2 && votes.trues > 0 && votes.falses > 0;
if (disagreed || resolution.startsWith("tiebreaker") || resolution === "unresolved") {
discrepancies.push({
claim_idx: idx,
claim_text: claim.text,
votes: { trues: votes.trues, falses: votes.falses },
resolution,
tiebreaker_model: tbModel,
});
}
if (notBacked === true) {
const sev: Finding["severity"] = claim.strength === "strong" ? "block"
: claim.strength === "moderate" ? "warn"
: "info";
findings.push({
check: "inference",
severity: sev,
claim_text: claim.text,
summary: `cloud: claim not backed — "${claim.text.slice(0, 100)}"`,
evidence: [
`at ${claim.location}`,
`consensus: ${votes.falses}/${totalVotes} not-backed (resolution: ${resolution})`,
`cloud reason: ${evidenceText.slice(0, 200)}`,
],
});
}
}
// Persist discrepancies so we can measure consensus drift over time.
if (discrepancies.length > 0 && ctx) {
persistDiscrepancies(ctx, discrepancies).catch(e =>
console.error(`[inference] discrepancy log failed: ${(e as Error).message}`));
}
// Use first run's parsed for downstream unflagged_gaps processing.
const parsed = parsedRuns[0].parsed;
// Route the curated scratchpad through llm_team's extract-facts
// pipeline when we have (a) a curated scratchpad (best signal about
// what the PR actually changed) and (b) PR context to scope facts.
// AWAITED (not fire-and-forget) so CLI callers like audit_one.ts
// don't exit before extraction lands; the systemd poller has plenty
// of headroom (90s cycle vs ~15s extraction). A failure inside
// extractAndPersistFacts is caught + logged but never throws.
if (isCurated && ctx && process.env.LH_AUDITOR_SKIP_EXTRACT !== "1") {
try {
await extractAndPersistFacts(diffForPrompt, ctx);
} catch (e) {
console.error(`[inference] fact extraction failed: ${(e as Error).message}`);
}
}
// Belt-and-suspenders: when operating on a curated scratchpad, drop
// the unflagged_gaps section entirely. The distillation can't
// reliably ground gap-detection, and false positives are worse than
// misses for this signal class. The systemMsg already asks the
// cloud to skip this section when curated — but the model may still
// emit it, so we filter here too.
const gapsToEmit = isCurated ? [] : (parsed.unflagged_gaps ?? []);
for (const g of gapsToEmit) {
const summary = String(g?.summary ?? "?");
const location = String(g?.location ?? "?");
// False-positive guard — when the cloud says "X not defined in this
// diff" or "missing implementation of X", the cloud may just mean
// "X is not in the added lines," not "X doesn't exist in the repo."
// Extract candidate symbol names and grep the repo. If any symbol
// is defined elsewhere, drop the finding — it's a known-symbol
// reference, not a placeholder.
if (/not\s+defined|missing\s+implementation|never\s+referenced\s+or\s+integrated/i.test(summary)) {
const symbols = extractSymbols(summary);
if (symbols.length > 0) {
const resolved = await symbolsExistInRepo(symbols);
if (resolved.length === symbols.length) {
// Every named symbol exists somewhere in the repo — silent drop.
continue;
}
if (resolved.length > 0) {
// Partially resolved — demote to info with a note.
findings.push({
check: "inference",
severity: "info",
summary: `cloud gap partially resolved by repo grep: ${summary.slice(0, 120)}`,
evidence: [
`location: ${location.slice(0, 140)}`,
`resolved via grep: ${resolved.join(",")}`,
`unresolved: ${symbols.filter(s => !resolved.includes(s)).join(",")}`,
],
});
continue;
}
}
}
findings.push({
check: "inference",
severity: "warn",
summary: `cloud-flagged gap not in any claim: ${summary.slice(0, 120)}`,
evidence: [`location: ${location.slice(0, 140)}`],
});
}
return findings;
}
// Single cloud call — the consensus loop calls this N times in
// parallel. Returns the parsed JSON shape + token usage + any error
// diagnostic. NEVER throws; the consensus aggregator handles partial
// failures by dropping non-parsed runs from the vote.
interface CloudRunResult {
parsed: any | null;
tokens: number;
error?: string; // "unreachable" | "non_200" | "unparseable"
diagnostic?: string; // first 200 chars for debugging
model: string;
}
async function runCloudInference(systemMsg: string, userMsg: string, model: string): Promise<CloudRunResult> {
let resp: Response;
try {
resp = await fetch(`${GATEWAY}/v1/chat`, {
method: "POST",
headers: { "content-type": "application/json" },
body: JSON.stringify({
provider: "ollama_cloud",
model,
messages: [
{ role: "system", content: systemMsg },
{ role: "user", content: userMsg },
],
// temp=0 (greedy) + think=true. think=true is required for
// gpt-oss:120b — without it the model returns empty content
// on large prompts. Variance from the think trace is observed
// in practice, which is why we use N=3 consensus, not single-
// call determinism.
max_tokens: 3000,
temperature: 0,
think: true,
}),
signal: AbortSignal.timeout(CALL_TIMEOUT_MS),
});
} catch (e) {
return { parsed: null, tokens: 0, error: "unreachable", diagnostic: (e as Error).message.slice(0, 200), model };
}
if (!resp.ok) {
return { parsed: null, tokens: 0, error: "non_200", diagnostic: `${resp.status}: ${(await resp.text()).slice(0, 160)}`, model };
}
let body: any;
try { body = await resp.json(); }
catch (e) { return { parsed: null, tokens: 0, error: "unparseable", diagnostic: (e as Error).message, model }; }
const content: string = body?.choices?.[0]?.message?.content ?? "";
const tokens: number = body?.usage?.total_tokens ?? 0;
const parsed = extractJson(content);
if (!parsed) {
return { parsed: null, tokens, error: "unparseable", diagnostic: content.slice(0, 200), model };
}
return { parsed, tokens, model };
}
async function persistDiscrepancies(ctx: InferenceContext, discrepancies: any[]): Promise<void> {
await mkdir("/home/profit/lakehouse/data/_kb", { recursive: true });
const rows = discrepancies.map(d => JSON.stringify({
pr_number: ctx.pr_number,
head_sha: ctx.head_sha,
logged_at: new Date().toISOString(),
...d,
}));
await appendFile(AUDIT_DISCREPANCIES_JSONL, rows.join("\n") + "\n");
}
// Extract structured knowledge from the curated scratchpad and append
// to data/_kb/audit_facts.jsonl — one row per extract run, keyed by
// PR number + head SHA for scope tracking. kb_query tails this next
// audit to surface recurring entities/relationships across PRs.
async function extractAndPersistFacts(scratchpad: string, ctx: InferenceContext): Promise<void> {
const ex = await extractFacts(scratchpad);
if (ex.error && ex.entities.length === 0 && ex.facts.length === 0) {
// Full failure — log but don't write an empty row.
console.error(`[inference] extractFacts skipped row: ${ex.error}`);
return;
}
const row = {
pr_number: ctx.pr_number,
head_sha: ctx.head_sha,
extracted_at: ex.extracted_at,
extractor: ex.extractor_model,
verifier: ex.verifier_model,
llm_team_run_id: ex.llm_team_run_id ?? null,
facts: ex.facts,
entities: ex.entities,
relationships: ex.relationships,
verification_preview: ex.verification.slice(0, 400),
verifier_verdicts: ex.verifier_verdicts,
facts_dropped_by_verifier: ex.facts_dropped_by_verifier ?? 0,
schema_version: 2,
source: "audit_inference",
};
await mkdir("/home/profit/lakehouse/data/_kb", { recursive: true });
await appendFile(AUDIT_FACTS_JSONL, JSON.stringify(row) + "\n");
}
// Curation via tree-split — ports the scrum_master pattern into the
// inference check. Shards the raw diff into DIFF_SHARD_SIZE chunks,
// summarizes each shard *against the claim-verification task* so the
// summary preserves exactly what the cloud needs to judge claims
// (function signatures, struct fields, deletions, new files), drops
// everything else. Merges into a compact scratchpad.
//
// Cost: N cloud calls for the shard summaries + 1 cloud call for the
// final verification = N+1 calls instead of 1. Mitigation: shards run
// serially (not parallel) to keep gateway load bounded; summary calls
// use max_tokens=400 so they're fast (~2s each on gpt-oss:120b).
//
// Determinism: each shard summary call uses temp=0 + think=true (same
// as the top-level inference call), so identical input yields
// identical scratchpad. The final verification call then sees a
// stable scratchpad, giving stable verdicts.
async function treeSplitDiff(
fullDiff: string,
claims: Claim[],
): Promise<{ scratchpad: string; shards: number }> {
const shards: Array<{ from: number; to: number; text: string }> = [];
for (let i = 0; i < fullDiff.length; i += DIFF_SHARD_SIZE) {
const end = Math.min(i + DIFF_SHARD_SIZE, fullDiff.length);
shards.push({ from: i, to: end, text: fullDiff.slice(i, end) });
}
// Curate the claim list into a short form the summary prompt can
// use to bias extraction toward relevant facts.
const claimDigest = claims.map((c, i) =>
`${i}. [${c.strength}] "${c.text.slice(0, 100)}"`
).join("\n");
let scratchpad = "";
for (const [si, shard] of shards.entries()) {
const prompt = [
`You are summarizing shard ${si + 1}/${shards.length} (chars ${shard.from}..${shard.to}) of a PR diff.`,
`The downstream task will verify these ship-claims against the full-PR summary. Extract ONLY facts that could confirm or refute these claims:`,
"",
claimDigest,
"",
"Extract: new function/method signatures, struct fields, deletions, new files, wiring (function X calls Y), absence-of-implementation markers, TODO comments on added lines.",
"Skip: comment-only edits, whitespace, import reordering, unrelated cosmetic changes.",
"",
"─────── shard diff ───────",
shard.text,
"─────── end shard ───────",
"",
"Output: up to 180 words of facts in bullet form. No prose preamble, no claim verdicts (that's for the downstream step).",
].join("\n");
const r = await callCloud(prompt, 400);
if (r.content) {
scratchpad += `\n--- shard ${si + 1} (chars ${shard.from}..${shard.to}) ---\n${r.content.trim()}\n`;
}
}
return { scratchpad: scratchpad.trim(), shards: shards.length };
}
// Minimal cloud caller used only by treeSplitDiff — same gateway +
// model as the top-level call, but think=false. Shards are small
// (≤DIFF_SHARD_SIZE ~4500 chars) and the task is pure fact
// extraction, not reasoning. think=true on the shards introduced
// variance in reasoning traces that compounded across 23 calls into
// a non-deterministic scratchpad (observed during curation
// validation: same-SHA runs produced 5/7/8 final findings).
// think=false on small prompts is stable — only breaks at the main
// call's 10K+ prompt size, which keeps think=true.
async function callCloud(prompt: string, maxTokens: number): Promise<{ content: string }> {
try {
const r = await fetch(`${GATEWAY}/v1/chat`, {
method: "POST",
headers: { "content-type": "application/json" },
body: JSON.stringify({
provider: "ollama_cloud",
model: MODEL,
messages: [{ role: "user", content: prompt }],
max_tokens: maxTokens,
temperature: 0,
think: false,
}),
signal: AbortSignal.timeout(CALL_TIMEOUT_MS),
});
if (!r.ok) return { content: "" };
const j: any = await r.json();
return { content: j?.choices?.[0]?.message?.content ?? "" };
} catch {
return { content: "" };
}
}
// Pull out plausible code-symbol names from a summary string.
// Matches:
// - identifier with backticks: `foo_bar`
// - identifier followed by parens: foo_bar()
// - CamelCase types
// - snake_case_functions
// Filters out common English words that could be matched accidentally.
const STOPWORDS = new Set([
"not","the","and","for","this","that","with","but","are","was","has",
"have","been","any","missing","implementation","diff","defined","never",
"referenced","integrated","flow","code","file","some","only","when",
]);
function extractSymbols(text: string): string[] {
const out = new Set<string>();
// `backticked` symbols
for (const m of text.matchAll(/`([A-Za-z_][A-Za-z0-9_]{2,})`/g)) out.add(m[1]);
// foo() or foo_bar() calls
for (const m of text.matchAll(/\b([A-Za-z_][A-Za-z0-9_]{2,})\s*\(/g)) out.add(m[1]);
// CamelCase types (3+ chars, must start with uppercase)
for (const m of text.matchAll(/\b([A-Z][A-Za-z0-9]{2,})\b/g)) out.add(m[1]);
return Array.from(out).filter(s => !STOPWORDS.has(s.toLowerCase()));
}
// Scan the repo for at least one definition of each symbol. Uses Bun's
// Glob to walk TS/Rust/Python/JS sources; ignores node_modules, data/,
// and target/. Skips files > 500KB — those are fixtures/snapshots that
// won't contain a definition line and slurping them slows the audit.
async function symbolsExistInRepo(symbols: string[]): Promise<string[]> {
const patterns = ["**/*.ts", "**/*.tsx", "**/*.rs", "**/*.py", "**/*.js"];
const skip = (p: string) => p.includes("/node_modules/") || p.startsWith("data/") || p.includes("/target/") || p.startsWith("dist/");
const MAX_FILE_BYTES = 500_000;
const { stat } = await import("node:fs/promises");
const resolved = new Set<string>();
const toFind = new Set(symbols);
for (const pat of patterns) {
if (toFind.size === 0) break;
const glob = new Glob(pat);
for await (const f of glob.scan({ cwd: REPO_ROOT, onlyFiles: true })) {
if (skip(f)) continue;
try { const s = await stat(`${REPO_ROOT}/${f}`); if (s.size > MAX_FILE_BYTES) continue; } catch { continue; }
let content: string;
try { content = await readFile(`${REPO_ROOT}/${f}`, "utf8"); } catch { continue; }
for (const sym of Array.from(toFind)) {
// Definition heuristics: `function sym`, `fn sym`, `const sym`,
// `let sym`, `def sym`, `class sym`, `struct sym`, `enum sym`,
// `trait sym`, `async function sym`, `pub (async )?fn sym`.
const re = new RegExp(
`\\b(function|async\\s+function|const|let|var|def|class|struct|enum|trait|impl|type|interface|fn|pub\\s+(async\\s+)?fn)\\s+${escapeRe(sym)}\\b`
);
if (re.test(content)) {
resolved.add(sym);
toFind.delete(sym);
if (toFind.size === 0) break;
}
}
}
}
return Array.from(resolved);
}
function escapeRe(s: string): string {
return s.replace(/[.*+?^${}()|[\]\\]/g, "\\$&");
}
// Lift the first balanced JSON object out of the response. Tolerates
// leading prose, code fences, and model reasoning preamble when the
// cloud model ignored "strict JSON only."
function extractJson(text: string): any | null {
const cleaned = text.replace(/^```(?:json)?\s*/im, "").replace(/```\s*$/im, "");
let depth = 0;
let start = -1;
for (let i = 0; i < cleaned.length; i++) {
const c = cleaned[i];
if (c === "{") {
if (depth === 0) start = i;
depth++;
} else if (c === "}") {
depth--;
if (depth === 0 && start >= 0) {
try { return JSON.parse(cleaned.slice(start, i + 1)); } catch { start = -1; }
}
}
}
return null;
}
auditor/checks/kb_query.ts
+397
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@@ -0,0 +1,397 @@
// Local-KB check — reads data/_kb/ + data/_observer/ + data/_bot/
// for prior evidence bearing on this PR's claims. Cheap, offline,
// no model calls. The point: if a claim like "Phase X shipped" has
// a historical record of failing on the same signature before, the
// auditor surfaces that pattern before the cloud check has to
// infer it.
//
// What this check reads (all file-backed, append-only or periodic):
// data/_kb/outcomes.jsonl — per-scenario outcomes (kb.ts)
// data/_kb/error_corrections.jsonl — fail→succeed deltas on same sig
// data/_kb/scrum_reviews.jsonl — scrum-master accepted reviews
// data/_observer/ops.jsonl — observer ring → disk stream
// data/_bot/cycles/*.json — bot cycle results
//
// Each JSONL line / per-cycle file is small; this check reads tails
// only (last N lines or last M files) to stay cheap on large corpora.
import { readFile, readdir, stat } from "node:fs/promises";
import { join } from "node:path";
import type { Claim, Finding } from "../types.ts";
import { aggregate, ratingSeverity, formatAgg } from "../kb_index.ts";
const KB_DIR = "/home/profit/lakehouse/data/_kb";
const OBSERVER_OPS = "/home/profit/lakehouse/data/_observer/ops.jsonl";
const BOT_CYCLES_DIR = "/home/profit/lakehouse/data/_bot/cycles";
const SCRUM_REVIEWS_JSONL = "/home/profit/lakehouse/data/_kb/scrum_reviews.jsonl";
const AUDIT_LESSONS_JSONL = "/home/profit/lakehouse/data/_kb/audit_lessons.jsonl";
const AUDIT_FACTS_JSONL = "/home/profit/lakehouse/data/_kb/audit_facts.jsonl";
const TAIL_LINES = 500;
const MAX_BOT_CYCLE_FILES = 30;
export async function runKbCheck(claims: Claim[], prFiles: string[] = []): Promise<Finding[]> {
const findings: Finding[] = [];
// 1. Recent scenario outcomes: are strong-claim-style phrases showing
// up alongside failed events? That's "we claimed it worked" +
// "it didn't" in the KB.
const scenarioFindings = await checkScenarioOutcomes(claims);
findings.push(...scenarioFindings);
// 2. Error corrections: any of the claims text overlap a
// recently-observed fail→succeed pair? If yes, add context.
const correctionFindings = await checkErrorCorrections(claims);
findings.push(...correctionFindings);
// 3. Bot cycles: any prior bot cycle ended in tests_failed or
// apply_failed on a file this PR is also touching?
const botFindings = await checkBotCycles();
findings.push(...botFindings);
// 4. Observer: count recent error events. High volume = shared
// infra problem, worth flagging (context for other findings).
const obsFindings = await checkObserverStream();
findings.push(...obsFindings);
// 5. Scrum-master reviews — surface prior accepted reviews for any
// file in this PR's diff. Cohesion plan Phase C wire: the
// auditor gets to "borrow" the scrum-master's deeper per-file
// analysis instead of re-doing that work.
if (prFiles.length > 0) {
const scrumFindings = await checkScrumReviews(prFiles);
findings.push(...scrumFindings);
}
// 6b. Audit-facts (llm_team extract pipeline output) — surface
// entities that recur across multiple PRs. These are the
// "core system entities" accumulating in the knowledge base;
// showing them as info on future audits gives reviewers
// architectural context the raw diff doesn't convey.
const factFindings = await checkAuditFacts();
findings.push(...factFindings);
// 6. Audit-lessons feedback loop — summarize the top recurring
// patterns from prior audits' block/warn findings. If the same
// pattern signature has fired 3+ times across prior audits,
// emit it as a block-severity finding so reviewers know this
// is a known-recurring class, not a one-off. Does NOT couple
// to the current audit's static/inference findings (those run
// in parallel and we can't see them here) — the amplification
// is emergent: if the current audit's finding-summary matches
// a top recurrence, the reviewer sees both.
const auditLessonFindings = await checkAuditLessons();
findings.push(...auditLessonFindings);
return findings;
}
async function tailJsonl<T = any>(path: string, n: number): Promise<T[]> {
try {
const raw = await readFile(path, "utf8");
const lines = raw.split("\n").filter(l => l.length > 0);
const slice = lines.slice(-n);
const out: T[] = [];
for (const line of slice) {
try { out.push(JSON.parse(line)); } catch { /* skip malformed */ }
}
return out;
} catch {
return [];
}
}
async function checkScenarioOutcomes(_claims: Claim[]): Promise<Finding[]> {
const outcomes = await tailJsonl<any>(join(KB_DIR, "outcomes.jsonl"), TAIL_LINES);
if (outcomes.length === 0) return [];
const totalEvents = outcomes.reduce((s, o) => s + (o.total_events ?? 0), 0);
const okEvents = outcomes.reduce((s, o) => s + (o.ok_events ?? 0), 0);
const failRate = totalEvents > 0 ? 1 - okEvents / totalEvents : 0;
if (totalEvents === 0) {
return [{
check: "kb_query",
severity: "info",
summary: `KB: no scenario outcomes on file — learning loop is empty`,
evidence: [`data/_kb/outcomes.jsonl has ${outcomes.length} entries with 0 total events`],
}];
}
const recent = outcomes.slice(-10);
const recentFailSigs: string[] = recent
.filter(o => (o.ok_events ?? 0) < (o.total_events ?? 0))
.map(o => o.sig_hash)
.filter(s => typeof s === "string");
const findings: Finding[] = [{
check: "kb_query",
severity: failRate > 0.3 ? "warn" : "info",
summary: `KB: ${outcomes.length} recent scenario runs, ${okEvents}/${totalEvents} events ok (fail rate ${(failRate * 100).toFixed(1)}%)`,
evidence: [
`most recent: ${recent[recent.length - 1]?.run_id ?? "?"}`,
`recent failing sigs: ${recentFailSigs.length > 0 ? recentFailSigs.slice(-3).join(", ") : "none"}`,
],
}];
return findings;
}
async function checkErrorCorrections(_claims: Claim[]): Promise<Finding[]> {
const corrections = await tailJsonl<any>(join(KB_DIR, "error_corrections.jsonl"), TAIL_LINES);
if (corrections.length === 0) return [];
return [{
check: "kb_query",
severity: "info",
summary: `KB: ${corrections.length} error corrections on file (fail→succeed pairs)`,
evidence: [
corrections.length > 0
? `most recent: ${String(corrections[corrections.length - 1]?.sig_hash ?? "?").slice(0, 24)}`
: "none",
],
}];
}
async function checkBotCycles(): Promise<Finding[]> {
let entries: string[] = [];
try { entries = await readdir(BOT_CYCLES_DIR); }
catch { return []; }
const jsonFiles = entries.filter(e => e.endsWith(".json"));
if (jsonFiles.length === 0) return [];
// Sort by mtime desc, take most recent N
const withStat = await Promise.all(
jsonFiles.map(async name => {
try { return { name, mtime: (await stat(join(BOT_CYCLES_DIR, name))).mtimeMs }; }
catch { return { name, mtime: 0 }; }
}),
);
const recent = withStat.sort((a, b) => b.mtime - a.mtime).slice(0, MAX_BOT_CYCLE_FILES);
const outcomes: Record<string, number> = {};
for (const { name } of recent) {
try {
const r = JSON.parse(await readFile(join(BOT_CYCLES_DIR, name), "utf8"));
const o = String(r.outcome ?? "unknown");
outcomes[o] = (outcomes[o] ?? 0) + 1;
} catch { /* skip */ }
}
const summary = Object.entries(outcomes)
.sort((a, b) => b[1] - a[1])
.map(([k, v]) => `${k}=${v}`)
.join(", ");
const failCount = (outcomes["tests_failed"] ?? 0) + (outcomes["apply_failed"] ?? 0) + (outcomes["model_failed"] ?? 0);
return [{
check: "kb_query",
severity: failCount > recent.length / 2 ? "warn" : "info",
summary: `KB: bot recorded ${recent.length} recent cycles — ${summary || "no outcomes parsed"}`,
evidence: [
`dir: ${BOT_CYCLES_DIR}`,
`fail-class total: ${failCount} / ${recent.length}`,
],
}];
}
async function checkObserverStream(): Promise<Finding[]> {
const ops = await tailJsonl<any>(OBSERVER_OPS, TAIL_LINES);
if (ops.length === 0) return [];
const failures = ops.filter(o => o.ok === false).length;
return [{
check: "kb_query",
severity: "info",
summary: `KB: observer stream ${ops.length} recent ops, ${failures} failures`,
evidence: [
`source: ${OBSERVER_OPS}`,
`by source: ${observerBySource(ops)}`,
],
}];
}
function observerBySource(ops: any[]): string {
const c: Record<string, number> = {};
for (const o of ops) {
const s = String(o.source ?? "unknown");
c[s] = (c[s] ?? 0) + 1;
}
return Object.entries(c).sort((a, b) => b[1] - a[1]).map(([k, v]) => `${k}=${v}`).join(", ") || "empty";
}
// Audit-facts — reads data/_kb/audit_facts.jsonl (populated by every
// curated inference run via llm_team's extract pipeline). Each row
// has arrays: facts, entities, relationships. We explode entities and
// aggregate them across PRs using kb_index. An entity seen in 3+ PRs
// is a "core system entity" — we surface the top N as info context.
//
// Filters out short names (<3 chars, likely qwen2.5 truncation
// artifacts) and generic types ("string", "number") that would
// otherwise dominate the ranking.
const ENTITY_NAME_MIN_LEN = 3;
const GENERIC_ENTITY_NAMES = new Set([
"string", "number", "boolean", "any", "void", "unknown", "never",
"object", "array", "function", "const", "let", "var", "true", "false",
"null", "undefined", "promise", "map", "set", "record",
]);
async function checkAuditFacts(): Promise<Finding[]> {
// Read raw rows — each row has multiple entities, so we can't just
// use aggregate() directly (it's one-signature-per-row). Explode
// entities into (row, entity) pairs, then aggregate by entity name.
let raw: string;
try { raw = await (await import("node:fs/promises")).readFile(AUDIT_FACTS_JSONL, "utf8"); }
catch { return []; }
const lines = raw.split("\n").filter(l => l.length > 0);
if (lines.length === 0) return [];
interface EntityRow { entity_key: string; pr_number: number; type: string; name: string; description: string }
const entityRows: EntityRow[] = [];
for (const line of lines.slice(-TAIL_LINES * 2)) {
let row: any;
try { row = JSON.parse(line); } catch { continue; }
const prNum = Number(row?.pr_number);
if (!Number.isFinite(prNum)) continue;
for (const e of Array.isArray(row?.entities) ? row.entities : []) {
const name = String(e?.name ?? "").trim();
if (name.length < ENTITY_NAME_MIN_LEN) continue;
if (GENERIC_ENTITY_NAMES.has(name.toLowerCase())) continue;
entityRows.push({
entity_key: name.toLowerCase(),
pr_number: prNum,
type: String(e?.type ?? "?"),
name,
description: String(e?.description ?? "").slice(0, 160),
});
}
}
if (entityRows.length === 0) return [];
// Aggregate manually — one key per entity name, distinct_scopes by PR.
type Agg = { count: number; scopes: Set<number>; types: Set<string>; last_name: string; last_desc: string };
const byEntity = new Map<string, Agg>();
for (const r of entityRows) {
const a = byEntity.get(r.entity_key) ?? {
count: 0, scopes: new Set<number>(), types: new Set<string>(), last_name: "", last_desc: "",
};
a.count += 1;
a.scopes.add(r.pr_number);
a.types.add(r.type);
a.last_name = r.name;
a.last_desc = r.description;
byEntity.set(r.entity_key, a);
}
// Rank: require 2+ distinct PRs (same-PR entity-repeats don't count
// as "cross-cutting"). Take the top 5 to avoid flooding the verdict.
const ranked = Array.from(byEntity.entries())
.filter(([_, a]) => a.scopes.size >= 2)
.sort((a, b) => b[1].scopes.size - a[1].scopes.size || b[1].count - a[1].count)
.slice(0, 5);
if (ranked.length === 0) {
// Useful to know the KB is being populated — emit a single
// summary so operators see fact extraction is alive.
return [{
check: "kb_query",
severity: "info",
summary: `audit_facts KB has ${entityRows.length} entity-observations across ${new Set(entityRows.map(r => r.pr_number)).size} PRs (no cross-PR recurrences yet)`,
evidence: [`source: ${AUDIT_FACTS_JSONL}`],
}];
}
return ranked.map(([_, a]) => ({
check: "kb_query" as const,
severity: "info" as const,
summary: `core entity \`${a.last_name}\` recurs in ${a.scopes.size} PRs (types: ${Array.from(a.types).join(",")})`,
evidence: [
`count=${a.count} distinct_PRs=${a.scopes.size}`,
`description: ${a.last_desc.slice(0, 200)}`,
`PRs: ${Array.from(a.scopes).sort((x, y) => x - y).join(",")}`,
],
}));
}
// Audit-lessons — reads data/_kb/audit_lessons.jsonl (populated by
// every audit's appendAuditLessons). Uses the shared kb_index
// aggregator: groups by `signature`, distinct-scopes keyed by PR
// number, severity from ratingSeverity(agg) which applies the
// confidence × count rating (see kb_index.ts). This is the same
// aggregation any other KB reader uses — shared discipline, not
// per-check custom logic.
async function checkAuditLessons(): Promise<Finding[]> {
const bySig = await aggregate<any>(AUDIT_LESSONS_JSONL, {
keyFn: (r) => r?.signature,
scopeFn: (r) => (r?.pr_number !== undefined ? `pr-${r.pr_number}` : undefined),
checkFn: (r) => r?.check,
tailLimit: TAIL_LINES * 4,
});
if (bySig.size === 0) return [];
const findings: Finding[] = [];
for (const [sig, agg] of bySig) {
// Silent on first-ever occurrence — not yet signal.
if (agg.count < 2) continue;
const sev = ratingSeverity(agg);
findings.push({
check: "kb_query",
severity: sev,
summary: `recurring audit pattern (${agg.distinct_scopes} distinct PRs, ${agg.count} flaggings, conf=${agg.confidence.toFixed(2)}): ${agg.representative_summary.slice(0, 160)}`,
evidence: [
`signature=${sig}`,
`checks: ${agg.checks.join(",")}`,
`scopes: ${agg.scopes.slice(-6).join(",")}`,
formatAgg(agg),
],
});
}
return findings;
}
// Scrum-master reviews — the scrum pipeline writes one row per
// accepted per-file review. We match reviews whose `file` matches
// any path in the PR's diff, then surface the *preview* + which
// model the escalation ladder had to reach. If the scrum-master
// needed the 123B specialist or larger to resolve a file, that's
// a meaningful signal about the code's complexity — and it's
// surfaced to the PR without the auditor having to re-run the
// escalation ladder itself.
async function checkScrumReviews(prFiles: string[]): Promise<Finding[]> {
const rows = await tailJsonl<any>(SCRUM_REVIEWS_JSONL, TAIL_LINES);
if (rows.length === 0) return [];
// Match by exact file OR filename suffix — PR files arrive as
// `auditor/audit.ts`-style relative paths; scrum stores the same.
const norm = (p: string) => p.replace(/^\/+/, "").replace(/^home\/profit\/lakehouse\//, "");
const prSet = new Set(prFiles.map(norm));
// Keep only the most recent review per file (last-wins).
const latestByFile = new Map<string, any>();
for (const r of rows) {
const f = norm(String(r.file ?? ""));
if (!f) continue;
if (!prSet.has(f)) continue;
latestByFile.set(f, r);
}
if (latestByFile.size === 0) return [];
const findings: Finding[] = [];
for (const [file, r] of latestByFile) {
const model = String(r.accepted_model ?? "?");
const attempt = r.accepted_on_attempt ?? "?";
const treeSplit = !!r.tree_split_fired;
// Heuristic: if the scrum-master had to escalate past attempt 3,
// or had to tree-split, that's context the PR reviewer should see.
// Severity: info for low-escalation, warn if escalated far up
// the ladder (cloud specialist required).
const heavyEscalation = Number(attempt) >= 4;
const sev: "warn" | "info" = heavyEscalation ? "warn" : "info";
findings.push({
check: "kb_query",
severity: sev,
summary: `scrum-master review for \`${file}\` — accepted on attempt ${attempt} by \`${model}\`${treeSplit ? " (tree-split)" : ""}`,
evidence: [
`reviewed_at: ${r.reviewed_at ?? "?"}`,
`preview: ${String(r.suggestions_preview ?? "").slice(0, 300).replace(/\n/g, " ")}`,
],
});
}
return findings;
}
auditor/checks/static.ts
+184
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@@ -0,0 +1,184 @@
// Static diff check — grep-style, no AST, no LLM. Looks for patterns
// that are high-signal evidence of placeholder code.
//
// Findings are severity-graded:
// block — explicit non-impl markers (unimplemented!, todo!,
// panic!("not implemented"), throw new Error("not implemented"))
// warn — TODO / FIXME / XXX / HACK comments on added lines,
// new struct fields with no read-site anywhere in the diff,
// suspiciously-empty function bodies ({ Ok(()) } / {} when
// the commit message claims the fn "implements" something)
// info — hardcoded "test" / "dummy" / "placeholder" strings in
// added lines (could be real, just flag for inspection)
//
// Consumes: raw unified diff text from Gitea.
import type { Finding } from "../types.ts";
// Rust + TypeScript patterns that almost always indicate "this is
// not actually implemented yet."
const BLOCK_PATTERNS: Array<{ re: RegExp; why: string }> = [
{ re: /\bunimplemented!\s*\(/, why: "unimplemented!() macro call" },
{ re: /\btodo!\s*\(/, why: "todo!() macro call" },
{ re: /panic!\s*\(\s*"(?:not implemented|TODO|not yet|unimpl)/i, why: "panic! with not-implemented message" },
{ re: /throw\s+new\s+Error\s*\(\s*['"](?:not implemented|TODO|unimpl)/i, why: "throw Error 'not implemented'" },
];
const WARN_COMMENT_PATTERNS: Array<{ re: RegExp; why: string }> = [
{ re: /^\+.*\/\/\s*(TODO|FIXME|XXX|HACK)\b/i, why: "TODO/FIXME/XXX/HACK comment added" },
{ re: /^\+.*#\s*(TODO|FIXME|XXX|HACK)\b/i, why: "TODO/FIXME/XXX/HACK comment added" },
];
const INFO_HARDCODED_PATTERNS: Array<{ re: RegExp; why: string }> = [
{ re: /"(?:placeholder|dummy|foobar|xxx|replaceme|changeme)"/i, why: "suspicious hardcoded string" },
];
export function runStaticCheck(diff: string): Finding[] {
const findings: Finding[] = [];
// Per-file walk: only look at ADDED lines (prefix '+' but not '+++'
// which is the diff header).
const perFile = splitDiffByFile(diff);
for (const [path, lines] of perFile) {
// Skip diff bookkeeping + pure-delete files
if (!lines.some(l => l.startsWith("+") && !l.startsWith("+++"))) continue;
// The auditor's own check files literally contain the BLOCK
// patterns as regex definitions (BLOCK_PATTERNS in this file,
// prompt examples in inference.ts). Skipping BLOCK scan on these
// specific paths prevents the checker from self-flagging its own
// string literals. WARN/INFO patterns still run — those genuinely
// could indicate problems in the checker's own code (TODO
// comments don't self-define).
const isAuditorCheckerFile = path.startsWith("auditor/checks/") ||
path.startsWith("auditor/fixtures/");
for (let idx = 0; idx < lines.length; idx++) {
const line = lines[idx];
if (!line.startsWith("+") || line.startsWith("+++")) continue;
const added = line.slice(1);
if (!isAuditorCheckerFile) {
for (const { re, why } of BLOCK_PATTERNS) {
const m = added.match(re);
if (m && typeof m.index === "number") {
// Skip if the match sits inside a quoted string literal —
// this is how rubric files (tests/real-world/*, prompt
// templates) legitimately reference the patterns they
// guard against, without actually executing them.
if (isInsideQuotedString(added, m.index)) continue;
findings.push({
check: "static",
severity: "block",
summary: `${why} in ${path}`,
evidence: [`${path}:+${idx + 1}: ${added.trim().slice(0, 160)}`],
});
}
}
}
for (const { re, why } of WARN_COMMENT_PATTERNS) {
if (re.test(line)) {
findings.push({
check: "static",
severity: "warn",
summary: `${why} in ${path}`,
evidence: [`${path}:+${idx + 1}: ${added.trim().slice(0, 160)}`],
});
}
}
for (const { re, why } of INFO_HARDCODED_PATTERNS) {
if (re.test(added)) {
findings.push({
check: "static",
severity: "info",
summary: `${why} in ${path}`,
evidence: [`${path}:+${idx + 1}: ${added.trim().slice(0, 160)}`],
});
}
}
}
// "Field added but never read" heuristic — catches exactly the
// Phase 45 DocRef placeholder pattern. Limited to the diff itself:
// we're not doing a full-codebase grep here (too noisy; callers
// elsewhere might exist). The point is: if NEITHER this diff nor
// any other line in the diff reads the field, the PR is shipping
// state without a consumer.
const addedLines = lines.filter(l => l.startsWith("+") && !l.startsWith("+++"))
.map(l => l.slice(1));
const newFields = extractNewFields(addedLines);
for (const field of newFields) {
const readPattern = new RegExp(`[\\.:]\\s*${escape(field)}\\b|\\b${escape(field)}\\s*:`);
// The definition line itself matches readPattern — filter it out
// by requiring at least TWO lines in the diff mention the field
// (one defines, one reads).
const hits = addedLines.filter(l => readPattern.test(l));
if (hits.length < 2) {
findings.push({
check: "static",
severity: "warn",
summary: `field '${field}' added in ${path} but no read-site in the diff — could be placeholder state without a consumer`,
evidence: [`${path}: added '${field}' with no reader; rest of diff has ${hits.length - 1} mentions`],
});
}
}
}
return findings;
}
function splitDiffByFile(diff: string): Map<string, string[]> {
const out = new Map<string, string[]>();
let current: string | null = null;
let buf: string[] = [];
for (const line of diff.split(/\r?\n/)) {
const m = line.match(/^diff --git a\/(\S+) b\/(\S+)/);
if (m) {
if (current) out.set(current, buf);
current = m[2];
buf = [];
continue;
}
buf.push(line);
}
if (current) out.set(current, buf);
return out;
}
// Extract new `pub name: Type,` fields from added lines. Rust syntax.
// Narrowly-scoped: only matches at the start of a trimmed line,
// requires `pub ` prefix, ignores `pub fn` / `pub struct` / etc.
function extractNewFields(addedLines: string[]): string[] {
const fields = new Set<string>();
for (const line of addedLines) {
const t = line.trim();
// pub NAME: Type,
const m = t.match(/^pub\s+(?!fn\b|struct\b|enum\b|mod\b|use\b|trait\b|impl\b|const\b|static\b|type\b)(\w+)\s*:/);
if (m) fields.add(m[1]);
}
return Array.from(fields);
}
// True if `pos` falls inside a double- or single-quoted string on this
// line (backtick template literals too). Walks left→right toggling the
// "in quote" state on each unescaped quote. Good enough for single-
// line matches; multi-line strings aren't parsed (they're extremely
// rare in the patterns we're blocking on, and would require a proper
// tokenizer to handle correctly).
function isInsideQuotedString(line: string, pos: number): boolean {
let inDouble = false, inSingle = false, inBacktick = false;
for (let i = 0; i < pos; i++) {
const c = line[i];
const esc = i > 0 && line[i - 1] === "\\";
if (esc) continue;
if (c === '"' && !inSingle && !inBacktick) inDouble = !inDouble;
else if (c === "'" && !inDouble && !inBacktick) inSingle = !inSingle;
else if (c === "`" && !inDouble && !inSingle) inBacktick = !inBacktick;
}
return inDouble || inSingle || inBacktick;
}
function escape(s: string): string {
return s.replace(/[.*+?^${}()|[\]\\]/g, "\\$&");
}
auditor/claim_parser.ts
+210
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// Claim parser — reads commit messages + PR body, extracts ship-claims.
//
// A "ship-claim" is any phrase that asserts functionality is working,
// tested, complete, or landed. These are the assertions the downstream
// checks (static/dynamic/inference/kb) try to falsify.
//
// Heuristic approach (regex + strength grading) — intentionally NOT
// using an LLM here. Reason: the inference check already asks a cloud
// model "does this match the claim?". The parser's job is to surface
// the claim substrates, not judge them. Over-engineering the parser
// risks false-negatives when the cloud model was going to catch it
// anyway.
import type { Claim, PrSnapshot } from "./types.ts";
// Strong claims: explicit end-to-end + verification vocabulary
const STRONG_PATTERNS: RegExp[] = [
/\bverified\s+(end[- ]to[- ]end|live|in\s+production|against)\b/i,
/\btested\s+(live|end[- ]to[- ]end|against|with)\b/i,
/\bworks\s+(end[- ]to[- ]end|live|in\s+production)\b/i,
/\bproduction[- ]ready\b/i,
/\bfully\s+(functional|wired|working)\b/i,
/\bphase\s+\d+(\.\d+)?\s+(shipped|complete|done|landed)\b/i,
/\bground\s+truth\b/i,
/\bproven\b/i,
];
// Moderate claims: asserted completion or pass but without the strong
// verification qualifier.
const MODERATE_PATTERNS: RegExp[] = [
/\bshipped\b/i,
/\blanded\b/i,
/\bgreen\b/i,
/\b(tests?\s+)?pass(ing|ed)\b/i,
/\bcomplet(e|ed)\b/i,
/\bdone\b/i,
/\bwired\b/i,
/\bfixed\b/i,
/\bworks\b/i,
];
// Weak claims: aspirational or hedged. Usually low-risk but recorded
// for completeness.
const WEAK_PATTERNS: RegExp[] = [
/\bshould\s+work\b/i,
/\bexpected\s+to\b/i,
/\bintended\s+to\b/i,
/\bwill\s+(work|handle|support)\b/i,
/\bprobably\b/i,
];
// Empirical claims: runtime measurements / observed outcomes that can't
// be verified from a diff (only from the actual run that produced
// them). Classifying as empirical lets the inference check skip
// diff-verification and saves the ladder for falsifiable claims.
//
// Two classes share this bucket because they share the skip discipline:
//
// 1. Runtime metrics — "58 cloud calls", "306s end-to-end"
// 2. History/proof refs — "verified on PR #8", "was flipping across runs"
//
// Both are assertions about state outside the current diff. The cloud
// would flag them as "not backed" — but that's a false positive: the
// proof lives in the referenced run, prior commit, or test output, not
// in the added lines the cloud is reading.
const EMPIRICAL_PATTERNS: RegExp[] = [
// ─── Runtime metrics ───
// Iteration / attempt counts: "6/6 iterations", "attempt 5", "accepted on attempt 3"
/\b\d+\s*\/\s*\d+\s+(iterations?|attempts?|cycles?|runs?|shards?)\b/i,
/\b(accepted|resolved|converged)\s+on\s+attempt\s+\d+\b/i,
// Runtime metrics: "58 cloud calls", "306s end-to-end", "245s total", "5931 chars"
/\b\d+\s+(cloud\s+)?calls?\b/i,
/\b\d+\s*(ms|s|seconds?|minutes?|m)\s+(end[- ]to[- ]end|total|elapsed|duration)\b/i,
/\b\d+\s+chars?\b.*\b(accepted|generated|produced)\b/i,
// "escalated through N tiers", "N distinct models"
/\bescalated\s+through\s+\d+\b/i,
/\b\d+\s+distinct\s+(model|tier)s?\b/i,
// ─── History / proof references ───
// "verified on PR #8", "verified end-to-end on PR 8", "tested against PR #4"
// Require PR#N / commit-hash / "prior <word>" to avoid matching
// "verified ... in production" (PR without \b-ish anchor previously
// consumed "pr" of "production").
/\bverified\s+(?:end[- ]to[- ]end\s+)?(?:on|against|in)\s+(?:PR\s*#?\d+|commit\s+[0-9a-f]{6,}|prior\s+\w+|the\s+\w+\s+audit)\b/i,
/\btested\s+(?:against|in|on)\s+(?:PR\s*#?\d+|commit\s+[0-9a-f]{6,}|prior\s+\w+)\b/i,
// Direct PR/commit references: "PR #8", "on PR 9", "from commit abc123"
/\b(?:on|from|in|via|per)\s+PR\s*#?\d+\b/i,
/\b(?:from|in|per|against)\s+commit\s+[0-9a-f]{6,}/i,
// Observational descriptions of prior behavior: "was flipping", "was X before", "previously observed"
/\b(?:was|were)\s+(?:flipping|drifting|inconsistent|non[- ]deterministic|creeping)\b/i,
/\bpreviously\s+(?:observed|flagged|reported|seen|landed)\b/i,
/\bused\s+to\s+(?:flip|fail|flag|reject|block)\b/i,
/\bobserved\s+(?:in|during|on|across)\s+(?:PR|prior|\d+\s+(?:runs?|audits?))/i,
// "flipping/drifting across N runs" — historical variance description
/\b(?:flipping|drifting|varying|oscillating)\s+across\s+(?:\d+\s+)?(?:runs?|audits?|iterations?)\b/i,
// "the proven X" referring to prior work (proven is a STRONG pattern
// but in context "the proven FOO" is usually a historical reference,
// not a fresh claim). We catch it here so the empirical skip wins.
/\bthe\s+proven\s+(?:escalation\s+ladder|pipeline|flow|loop|tier|path)/i,
// "from the 9-run test", "across the 5-run validation"
/\b(?:from|across|in|during)\s+the\s+\d+[- ]run\s+(?:test|validation|probe|experiment)/i,
];
export interface ParsedClaims {
claims: Claim[];
commits_scanned: number;
}
export function parseClaims(pr: PrSnapshot): ParsedClaims {
const claims: Claim[] = [];
// PR body — every matching line becomes a claim at location "pr_body:N"
if (pr.body) {
scanText(pr.body, "pr_body", pr.head_sha, claims);
}
// Each commit message gets its own scan.
for (const c of pr.commits) {
if (!c.message) continue;
scanText(c.message, `commit:${c.sha.slice(0, 8)}`, c.sha, claims);
}
return { claims, commits_scanned: pr.commits.length };
}
function scanText(text: string, location_prefix: string, commit_sha: string, out: Claim[]): void {
const lines = text.split(/\r?\n/);
for (let i = 0; i < lines.length; i++) {
const line = lines[i];
if (line.length < 3) continue;
// Empirical match wins over everything else — if a line ALSO
// contains a moderate word like "complete", we still want to
// classify it as empirical so the inference check doesn't ask
// the cloud to prove "58 cloud calls" from the diff. Order:
// empirical → strong → moderate → weak.
const empirical = firstUnquotedMatch(line, EMPIRICAL_PATTERNS);
if (empirical) {
out.push({
text: line.trim().slice(0, 200),
commit_sha,
location: `${location_prefix}:${i + 1}`,
strength: "empirical",
});
continue;
}
const strong = firstUnquotedMatch(line, STRONG_PATTERNS);
if (strong) {
out.push({
text: line.trim().slice(0, 200),
commit_sha,
location: `${location_prefix}:${i + 1}`,
strength: "strong",
});
continue;
}
const moderate = firstUnquotedMatch(line, MODERATE_PATTERNS);
if (moderate) {
out.push({
text: line.trim().slice(0, 200),
commit_sha,
location: `${location_prefix}:${i + 1}`,
strength: "moderate",
});
continue;
}
const weak = firstUnquotedMatch(line, WEAK_PATTERNS);
if (weak) {
out.push({
text: line.trim().slice(0, 200),
commit_sha,
location: `${location_prefix}:${i + 1}`,
strength: "weak",
});
}
}
}
// Match a pattern only when its match position is NOT inside a quoted
// string on the line. Mirrors the same guard in auditor/checks/static.ts
// — the two files have the same false-positive class: PR authors
// quote pattern examples in commit message bodies (e.g. `"Phase 45
// shipped"` as a test example) and without this guard those quoted
// references get flagged as fresh ship-claims. Only skips when the
// match itself falls inside quotes; real (unquoted) uses of the same
// vocabulary still classify correctly.
function firstUnquotedMatch(text: string, patterns: RegExp[]): RegExp | null {
for (const p of patterns) {
const m = text.match(p);
if (!m || typeof m.index !== "number") continue;
if (isInsideQuotedString(text, m.index)) continue;
return p;
}
return null;
}
// Walks left→right toggling in-quote state on each unescaped quote.
// Good enough for single-line claims; multi-line strings aren't parsed.
function isInsideQuotedString(line: string, pos: number): boolean {
let inDouble = false, inSingle = false, inBacktick = false;
for (let i = 0; i < pos; i++) {
const c = line[i];
const esc = i > 0 && line[i - 1] === "\\";
if (esc) continue;
if (c === '"' && !inSingle && !inBacktick) inDouble = !inDouble;
else if (c === "'" && !inDouble && !inBacktick) inSingle = !inSingle;
else if (c === "`" && !inDouble && !inSingle) inBacktick = !inBacktick;
}
return inDouble || inSingle || inBacktick;
}
auditor/fact_extractor.ts
+271
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// fact_extractor — routes curated TEXT through llm_team_ui's
// "knowledge extract facts" mode (mode=extract at /api/run).
//
// What it gives us: structured {facts, entities, relationships} from
// whatever curated blob we send. Auditor sends the tree-split
// inference scratchpad (the best distillation of what a PR changed).
// Scrum_master will later send its accepted review bodies.
//
// Why route through llm_team and not just extract directly from our
// own checks: llm_team's extract uses a local EXTRACTOR model
// (qwen2.5) + a separate VERIFIER (gemma2). This cross-check is the
// discipline J wants for knowledge going into the playbook — facts
// go in only after a second model has rated them CORRECT /
// UNVERIFIABLE. Fast (local models, ~10-20s), free, and matches the
// codereview pattern J already trusts.
//
// SSE parsing: llm_team streams SSE events. We're only interested in
// the final "response" event with role="final" + the extraction
// response (role="extraction N"). Parse the JSON from the extractor's
// response text.
const LLM_TEAM = process.env.LH_LLM_TEAM_URL ?? "http://localhost:5000";
const EXTRACTOR = process.env.LH_FACT_EXTRACTOR ?? "qwen2.5:latest";
const VERIFIER = process.env.LH_FACT_VERIFIER ?? "gemma2:latest";
const EXTRACT_TIMEOUT_MS = 120_000;
const PROJECT_CONTEXT_FILE = process.env.LH_AUDITOR_CONTEXT_FILE
?? "/home/profit/lakehouse/docs/AUDITOR_CONTEXT.md";
let cachedContext: string | null = null;
async function loadProjectContext(): Promise<string> {
if (cachedContext !== null) return cachedContext;
try {
const { readFile } = await import("node:fs/promises");
const raw = await readFile(PROJECT_CONTEXT_FILE, "utf8");
// Cap at 4KB — anything past that is more noise than signal for
// the extractor/verifier's attention budget.
cachedContext = raw.slice(0, 4000);
} catch {
cachedContext = ""; // context file missing → extractor runs without preamble
}
return cachedContext;
}
export interface Entity {
name: string;
type: string;
description?: string;
}
export interface Relationship {
from: string;
to: string;
type: string;
}
export interface ExtractedFacts {
facts: string[];
entities: Entity[];
relationships: Relationship[];
verification: string;
extractor_model: string;
verifier_model: string;
source_preview: string;
// Populated when the extract run completed server-side (llm_team
// persists to its own team_runs; this is for our own cross-ref).
llm_team_run_id?: number;
extracted_at: string;
// Per-fact verdicts from the verifier pass (CORRECT/INCORRECT/
// UNVERIFIABLE/UNCHECKED). Aligned 1:1 with the *raw* fact list
// pre-drop so operators can see which verdicts mapped to dropped
// facts if needed.
verifier_verdicts?: Array<"CORRECT" | "INCORRECT" | "UNVERIFIABLE" | "UNCHECKED">;
facts_dropped_by_verifier?: number;
error?: string;
}
/**
* Run the llm_team extract pipeline on `source` text. Returns
* structured {facts, entities, relationships}.
*
* Returns an object with `error` set if the pipeline failed — never
* throws, because fact extraction is best-effort enrichment (the
* primary audit must not break if llm_team is down).
*/
export async function extractFacts(source: string): Promise<ExtractedFacts> {
const base: ExtractedFacts = {
facts: [],
entities: [],
relationships: [],
verification: "",
extractor_model: EXTRACTOR,
verifier_model: VERIFIER,
source_preview: source.slice(0, 240),
extracted_at: new Date().toISOString(),
};
// Prepend project context to the source so the extractor + verifier
// know what codebase/framework these facts belong to. Without this,
// the verifier marks most domain-specific facts as UNVERIFIABLE ("I
// don't know what Lakehouse is"). With it, the verifier can CORRECT-
// stamp facts that align with the stated architecture.
const context = await loadProjectContext();
const prompt = context.length > 0
? `=== PROJECT CONTEXT (for grounding facts; do NOT extract facts from this section) ===\n${context}\n\n=== CONTENT TO EXTRACT FACTS FROM ===\n${source}`
: source;
let resp: Response;
try {
resp = await fetch(`${LLM_TEAM}/api/run`, {
method: "POST",
headers: { "content-type": "application/json" },
body: JSON.stringify({
mode: "extract",
prompt,
extractor: EXTRACTOR,
verifier: VERIFIER,
source: "prompt",
skip_cache: true, // cache by prompt would dedup identical
// scratchpads, but we want fresh extraction
// for per-audit facts; cheap since local.
}),
signal: AbortSignal.timeout(EXTRACT_TIMEOUT_MS),
});
} catch (e) {
return { ...base, error: `fetch failed: ${(e as Error).message}` };
}
if (!resp.ok) {
const body = await resp.text().catch(() => "");
return { ...base, error: `llm_team /api/run ${resp.status}: ${body.slice(0, 200)}` };
}
// Stream SSE lines; collect the one extraction response + the run_saved event
// so we can capture the team-runs ID for cross-ref.
const decoder = new TextDecoder();
const reader = resp.body?.getReader();
if (!reader) return { ...base, error: "no response body" };
let buffer = "";
let extractionText = "";
let verifierText = "";
let runId: number | undefined = undefined;
try {
while (true) {
const { done, value } = await reader.read();
if (done) break;
buffer += decoder.decode(value, { stream: true });
let nl: number;
while ((nl = buffer.indexOf("\n\n")) >= 0) {
const chunk = buffer.slice(0, nl);
buffer = buffer.slice(nl + 2);
const dataLine = chunk.split("\n").find(l => l.startsWith("data: "));
if (!dataLine) continue;
try {
const ev = JSON.parse(dataLine.slice(6));
if (ev.type === "response") {
const role = String(ev.role ?? "");
if (role.startsWith("extraction")) extractionText = String(ev.text ?? "");
else if (role === "verifier") verifierText = String(ev.text ?? "");
} else if (ev.type === "run_saved") {
const id = Number(ev.run_id);
if (Number.isFinite(id)) runId = id;
}
} catch { /* skip malformed SSE */ }
}
}
} catch (e) {
return { ...base, error: `SSE read failed: ${(e as Error).message}` };
}
// Pull the JSON object out of extractionText (may be wrapped in ```json fences).
const parsed = extractFirstJsonObject(extractionText);
if (!parsed) {
return { ...base, error: "extractor returned no parseable JSON", verification: verifierText };
}
const rawFacts: string[] = Array.isArray(parsed.facts)
? parsed.facts.slice(0, 50).map(String)
: [];
// Parse the verifier's free-form prose into per-fact verdicts, then
// drop any fact the verifier explicitly marked INCORRECT. Leave
// UNVERIFIABLE in place: many of our extractions are domain-specific
// (Lakehouse internals) and the verifier has no prior-knowledge
// anchor, so UNVERIFIABLE is the expected verdict for new signal,
// not a quality fail. This is verifier-gated persistence: drop only
// what's affirmatively wrong, not what's novel.
const verdicts = parseVerifierVerdicts(verifierText, rawFacts.length);
const incorrectIdx = new Set<number>();
verdicts.forEach((v, i) => { if (v === "INCORRECT") incorrectIdx.add(i); });
const kept = rawFacts.filter((_, i) => !incorrectIdx.has(i));
return {
...base,
facts: kept,
entities: Array.isArray(parsed.entities)
? parsed.entities.slice(0, 30).map((e: any) => ({
name: String(e?.name ?? ""),
type: String(e?.type ?? ""),
description: typeof e?.description === "string" ? e.description.slice(0, 240) : undefined,
})).filter(e => e.name.length > 0)
: [],
relationships: Array.isArray(parsed.relationships)
? parsed.relationships.slice(0, 30).map((r: any) => ({
from: String(r?.from ?? ""),
to: String(r?.to ?? ""),
type: String(r?.type ?? ""),
})).filter(r => r.from.length > 0 && r.to.length > 0)
: [],
verification: verifierText.slice(0, 1500),
facts_dropped_by_verifier: incorrectIdx.size,
verifier_verdicts: verdicts,
llm_team_run_id: runId,
};
}
// Parse verifier's free-form output into a per-fact verdict array.
// Gemma2 uses several formats depending on prompt mood:
// Format A: **1.** claim... * **Verdict:** CORRECT
// Format B: **1.** claim... * **CORRECT** (no "Verdict:" label)
// Format C: 1. claim... CORRECT
// Strategy: split on fact numbers, then find the first
// CORRECT|INCORRECT|UNVERIFIABLE token in each section. Handles all
// three formats without regex gymnastics.
function parseVerifierVerdicts(
verifierText: string,
numFacts: number,
): Array<"CORRECT" | "INCORRECT" | "UNVERIFIABLE" | "UNCHECKED"> {
const out: Array<"CORRECT" | "INCORRECT" | "UNVERIFIABLE" | "UNCHECKED"> =
Array(numFacts).fill("UNCHECKED");
if (!verifierText) return out;
// Find each fact section start — "**N.**" or "N." at line start —
// and slice out the content up to the NEXT fact number. Each section
// gets scanned for the first CORRECT/INCORRECT/UNVERIFIABLE token.
const starts: Array<{ idx: number; pos: number }> = [];
const header = /(?:^|\n)\s*(?:\*\*)?(\d+)[.)]/g;
for (const m of verifierText.matchAll(header)) {
const factNum = Number(m[1]);
if (!Number.isFinite(factNum)) continue;
starts.push({ idx: factNum - 1, pos: m.index! });
}
for (let i = 0; i < starts.length; i++) {
const s = starts[i];
const end = i + 1 < starts.length ? starts[i + 1].pos : verifierText.length;
if (s.idx < 0 || s.idx >= numFacts) continue;
const section = verifierText.slice(s.pos, end);
const v = section.match(/\b(CORRECT|INCORRECT|UNVERIFIABLE)\b/i);
if (v) out[s.idx] = v[1].toUpperCase() as "CORRECT" | "INCORRECT" | "UNVERIFIABLE";
}
return out;
}
// Lift the first balanced JSON object out of (possibly fenced) text.
// Same discipline as inference.ts::extractJson.
function extractFirstJsonObject(text: string): any | null {
const cleaned = text.replace(/^```(?:json)?\s*/im, "").replace(/```\s*$/im, "");
let depth = 0, start = -1;
for (let i = 0; i < cleaned.length; i++) {
const c = cleaned[i];
if (c === "{") { if (depth === 0) start = i; depth++; }
else if (c === "}") {
depth--;
if (depth === 0 && start >= 0) {
try { return JSON.parse(cleaned.slice(start, i + 1)); } catch { start = -1; }
}
}
}
return null;
}
auditor/fixtures/cli.ts
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// Standalone runner for auditor fixtures. Invoke:
//
// bun run auditor/fixtures/cli.ts hybrid_38_40_45
//
// Prints human-readable per-layer breakdown + JSON result so the
// output can be captured by the dynamic check without re-running.
import { runHybridFixture, type FixtureResult } from "./hybrid_38_40_45.ts";
const fixtureArg = process.argv[2] ?? "hybrid_38_40_45";
let result: FixtureResult;
switch (fixtureArg) {
case "hybrid_38_40_45":
result = await runHybridFixture();
break;
default:
console.error(`unknown fixture: ${fixtureArg}`);
process.exit(2);
}
// Human-readable summary
console.error(""); // blank line to stderr so stdout JSON stays clean
console.error(`─── Fixture: ${result.fixture} ───`);
console.error(` overall: ${result.overall.toUpperCase()}`);
console.error(` shipped: [${result.shipped_phases.join(", ") || "—"}]`);
console.error(` placeholder: [${result.placeholder_phases.join(", ") || "—"}]`);
console.error("");
for (const l of result.layers) {
const mark = l.ok ? "✓" : "✗";
const phaseStr = `Phase ${l.phase}`.padEnd(11);
console.error(` ${mark} ${phaseStr} ${l.layer.padEnd(30)} ${String(l.latency_ms).padStart(6)}ms`);
if (l.ok) {
console.error(` ${l.evidence.slice(0, 200)}`);
} else {
console.error(` ERROR: ${l.error?.slice(0, 240) ?? "unknown"}`);
}
}
console.error("");
for (const n of result.notes) console.error(` note: ${n}`);
console.error("");
// Machine-readable output on stdout.
console.log(JSON.stringify(result, null, 2));
// Exit code reflects overall: 0 pass, 2 partial, 1 fail.
// Dynamic check reads this AND the JSON; partial-pass is treated
// as informative (some layers shipped), not blocking on its own.
process.exit(result.overall === "pass" ? 0 : result.overall === "partial_pass" ? 2 : 1);
auditor/fixtures/hybrid_38_40_45.ts
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// The never-run hybrid test fixture. Exercises the full stack end-to-end
// across Phase 38 (/v1/chat), Phase 40 (Langfuse tracing), Phase 45
// slice 1 (DocRef on playbook), and Phase 45 slice 2 (context7 bridge
// drift detection).
//
// Returns HONEST per-layer results. A layer that's unimplemented or
// broken returns ok=false with a real error; overall verdict flags
// which phases are genuinely shipped vs still placeholder.
//
// Deterministic: always runs the same payload so a run's output is
// comparable across audits. Uses TEST-prefixed names so playbook
// state doesn't get polluted with production-looking fixture data.
//
// Preconditions:
// - gateway up on :3100
// - Python sidecar up on :3200
// - Ollama local + Ollama Cloud key loaded
// - Langfuse up on :3001
// - context7 bridge up on :3900 (manual-start per mcp-server/README)
// If bridge isn't running, that layer reports ok=false with a
// clear error — the fixture doesn't try to auto-start it.
import { readFile } from "node:fs/promises";
const GATEWAY = process.env.LH_GATEWAY_URL ?? "http://localhost:3100";
const LANGFUSE = process.env.LANGFUSE_URL ?? "http://localhost:3001";
const BRIDGE = process.env.CONTEXT7_BRIDGE_URL ?? "http://localhost:3900";
const FIXTURE_ID = "auditor:hybrid_38_40_45:v1";
const TEST_WORKER_NAME = "__auditor_test_worker__";
const STALE_HASH = "stale-hash-for-drift-proof";
// Unique-per-run identifiers so each fixture invocation hits the ADD
// path in upsert_entry (not UPDATE/NOOP on a leftover from a prior
// run). Catches state pollution + avoids interaction with task #12
// (UPDATE branch silently drops doc_refs).
const RUN_NONCE = Date.now().toString(36);
const TEST_WORKER_NAME_VERSIONED = `__auditor_test_worker_${RUN_NONCE}__`;
const TEST_OPERATION_VERSIONED = `TEST: fill: __auditor_${RUN_NONCE}__ x1 in TestCity, TC`;
export interface LayerResult {
layer: string;
phase: string;
ok: boolean;
latency_ms: number;
evidence: string;
error?: string;
// Layer-specific numbers that downstream checks can aggregate.
metrics?: Record<string, number | string | boolean>;
}
export interface FixtureResult {
fixture: string;
ran_at: string;
overall: "pass" | "partial_pass" | "fail";
layers: LayerResult[];
real_numbers: Record<string, number>;
shipped_phases: string[];
placeholder_phases: string[];
notes: string[];
}
// Wraps a layer's async body with timing + structured error capture.
// On throw: returns ok=false with the error string as evidence, so
// downstream verdict code treats it as a layer failure rather than
// a fixture crash.
async function measureLayer(
layer: string,
phase: string,
body: () => Promise<{ evidence: string; metrics?: Record<string, number | string | boolean> }>,
): Promise<LayerResult> {
const t0 = Date.now();
try {
const { evidence, metrics } = await body();
return {
layer, phase, ok: true,
latency_ms: Date.now() - t0,
evidence, metrics,
};
} catch (e) {
return {
layer, phase, ok: false,
latency_ms: Date.now() - t0,
evidence: `FAILED: ${(e as Error).message.slice(0, 200)}`,
error: (e as Error).message,
};
}
}
async function langfuseAuth(): Promise<{ pk: string; sk: string } | null> {
// Default credentials match mcp-server/tracing.ts. Same sources the
// Rust side uses in gateway/src/v1/langfuse_trace.rs.
let pk = process.env.LANGFUSE_PUBLIC_KEY ?? "pk-lf-staffing";
let sk = process.env.LANGFUSE_SECRET_KEY ?? "sk-lf-staffing-secret";
if (!pk || !sk) return null;
return { pk, sk };
}
export async function runHybridFixture(): Promise<FixtureResult> {
const result: FixtureResult = {
fixture: FIXTURE_ID,
ran_at: new Date().toISOString(),
overall: "fail",
layers: [],
real_numbers: {},
shipped_phases: [],
placeholder_phases: [],
notes: [],
};
// ========================================================================
// Layer 1 — Phase 38: POST /v1/chat returns valid OpenAI shape
// ========================================================================
// Captured HERE, immediately before the chat layer runs, so layer 2's
// Langfuse-trace filter uses the actual moment the chat call was
// attempted — not the fixture start time. Earlier draft had a
// meaningless ternary returning result.ran_at on both branches; the
// LLM-Team codereview (2026-04-22) caught this and flagged it as a
// false-negative window on traces created between fixture-start and
// chat-fetch.
const chat_request_sent_ms = Date.now();
const l1 = await measureLayer("phase38_chat", "38", async () => {
const r = await fetch(`${GATEWAY}/v1/chat`, {
method: "POST",
headers: { "content-type": "application/json" },
body: JSON.stringify({
model: "qwen3.5:latest",
messages: [
{ role: "system", content: "Respond in 8 words or fewer." },
{ role: "user", content: "Name one Docker command." },
],
max_tokens: 60,
temperature: 0.2,
}),
signal: AbortSignal.timeout(60000),
});
if (!r.ok) throw new Error(`gateway ${r.status}: ${await r.text()}`);
const j: any = await r.json();
const content = j.choices?.[0]?.message?.content;
if (typeof content !== "string" || content.length === 0) {
throw new Error(`empty content — think-false default may not be wired: ${JSON.stringify(j).slice(0, 200)}`);
}
return {
evidence: `content="${content.slice(0, 60)}" tokens=${j.usage?.total_tokens}`,
metrics: {
prompt_tokens: j.usage?.prompt_tokens ?? 0,
completion_tokens: j.usage?.completion_tokens ?? 0,
total_tokens: j.usage?.total_tokens ?? 0,
content_nonempty: true,
},
};
});
result.layers.push(l1);
// ========================================================================
// Layer 2 — Phase 40: Langfuse trace lands within 3s
// ========================================================================
// Fire-and-forget tracing means we need a brief sleep before query.
await new Promise(res => setTimeout(res, 2500));
const l2 = await measureLayer("phase40_langfuse_trace", "40", async () => {
const auth = await langfuseAuth();
if (!auth) throw new Error("Langfuse credentials not in env");
const r = await fetch(`${LANGFUSE}/api/public/traces?limit=5&name=v1.chat:ollama`, {
headers: { Authorization: `Basic ${btoa(`${auth.pk}:${auth.sk}`)}` },
signal: AbortSignal.timeout(10000),
});
if (!r.ok) throw new Error(`langfuse ${r.status}: ${await r.text()}`);
const j: any = await r.json();
const items = Array.isArray(j.data) ? j.data : [];
// Filter on the chat-request timestamp captured above. A Langfuse
// trace must be newer than the moment we fired /v1/chat to plausibly
// belong to our request. Using fixture start time (result.ran_at)
// was wrong and could false-negative on slow fixtures.
const recent = items.filter((t: any) => Date.parse(t.timestamp) >= chat_request_sent_ms);
if (recent.length === 0) {
throw new Error(`no v1.chat:ollama trace since ${new Date(chat_request_sent_ms).toISOString()} (${items.length} older traces visible, Langfuse reachable — tracing is not firing)`);
}
const trace = recent[0];
return {
evidence: `trace ${trace.id.slice(0, 8)} name=${trace.name} age=${Date.now() - Date.parse(trace.timestamp)}ms`,
metrics: {
trace_age_ms: Date.now() - Date.parse(trace.timestamp),
trace_latency_reported: Number(trace.latency ?? 0),
recent_trace_count: recent.length,
},
};
});
result.layers.push(l2);
// ========================================================================
// Layer 3 — Phase 45 slice 1: /seed accepts doc_refs and persists them
// ========================================================================
// Seed a playbook with doc_refs referencing Docker at a stale hash.
// Ignore the cleanup concern for v1 — this fixture pollutes the
// in-memory playbook state; operator can retire test entries.
const l3 = await measureLayer("phase45_seed_with_doc_refs", "45.1", async () => {
const r = await fetch(`${GATEWAY}/vectors/playbook_memory/seed`, {
method: "POST",
headers: { "content-type": "application/json" },
body: JSON.stringify({
operation: TEST_OPERATION_VERSIONED,
approach: "auditor hybrid fixture run",
context: "doc_refs integration test — retire after audit",
endorsed_names: [TEST_WORKER_NAME_VERSIONED],
append: true,
doc_refs: [
{
tool: "docker",
version_seen: "24.0.7",
snippet_hash: STALE_HASH,
source_url: "https://context7.com/docker/docs",
seen_at: new Date().toISOString(),
},
],
}),
signal: AbortSignal.timeout(30000),
});
if (!r.ok) throw new Error(`seed ${r.status}: ${await r.text()}`);
const j: any = await r.json();
// Verify doc_refs actually persisted — read state.json directly.
// That's the canonical source of truth; the seed response may or
// may not echo doc_refs back.
const state_raw = await readFile("/home/profit/lakehouse/data/_playbook_memory/state.json", "utf8");
const state = JSON.parse(state_raw);
const entries = state.entries ?? [];
const ours = entries.find((e: any) => (e.endorsed_names ?? []).includes(TEST_WORKER_NAME_VERSIONED));
if (!ours) throw new Error(`seeded entry not found in state.json (worker ${TEST_WORKER_NAME_VERSIONED} not present)`);
const docRefs = ours.doc_refs ?? [];
if (docRefs.length === 0) {
throw new Error("entry saved but doc_refs field is empty — the field accepted by the API isn't being persisted");
}
return {
evidence: `playbook ${ours.playbook_id.slice(0, 16)} persisted with doc_refs.length=${docRefs.length}; first tool=${docRefs[0]?.tool} hash=${docRefs[0]?.snippet_hash}`,
metrics: {
doc_refs_stored: docRefs.length,
entries_after: j.entries_after ?? -1,
playbook_id: ours.playbook_id,
mode: j.outcome?.mode ?? "unknown",
},
};
});
result.layers.push(l3);
// ========================================================================
// Layer 4 — Phase 45 slice 2: context7 bridge detects drift vs stale hash
// ========================================================================
const l4 = await measureLayer("phase45_bridge_diff", "45.2", async () => {
// Health check first — if bridge isn't running, fail with a clear
// message rather than mysterious connection refused.
try {
const h = await fetch(`${BRIDGE}/health`, { signal: AbortSignal.timeout(3000) });
if (!h.ok) throw new Error(`bridge /health ${h.status}`);
} catch (e) {
throw new Error(`context7 bridge at ${BRIDGE} is not reachable (bridge is manual-start; run 'bun run mcp-server/context7_bridge.ts'): ${(e as Error).message}`);
}
const r = await fetch(`${BRIDGE}/docs/docker/diff?since=${encodeURIComponent(STALE_HASH)}`, {
signal: AbortSignal.timeout(30000),
});
if (!r.ok) throw new Error(`bridge diff ${r.status}: ${await r.text()}`);
const j: any = await r.json();
if (j.drifted !== true) {
throw new Error(`expected drifted=true against ${STALE_HASH}; got ${JSON.stringify(j)}`);
}
return {
evidence: `bridge confirms drift: current=${j.current_snippet_hash} previous=${j.previous_snippet_hash} upstream_updated=${j.last_updated_upstream}`,
metrics: {
drifted: true,
current_hash_length: String(j.current_snippet_hash ?? "").length,
library_id: j.library_id,
},
};
});
result.layers.push(l4);
// ========================================================================
// Layer 5 — Phase 45 slice 3: /doc_drift/check/{id} endpoint
// (EXPECTED TO FAIL — endpoint unimplemented)
// ========================================================================
const l5 = await measureLayer("phase45_slice3_drift_flag", "45.3", async () => {
const pid = String(l3.metrics?.playbook_id ?? "");
if (!pid) throw new Error("layer 3 did not produce a playbook_id to check");
const r = await fetch(`${GATEWAY}/vectors/playbook_memory/doc_drift/check/${encodeURIComponent(pid)}`, {
method: "POST",
signal: AbortSignal.timeout(10000),
});
if (r.status === 404 || r.status === 405) {
// This is the HONEST signal: the endpoint doesn't exist yet.
// Fail this layer with a clear marker — not a silent pass.
throw new Error(`endpoint unimplemented (${r.status}) — Phase 45 slice 3 is still placeholder. doc_refs are persisted (layer 3) and the bridge can answer drift questions (layer 4), but nothing ties them together into a playbook flag yet.`);
}
if (!r.ok) throw new Error(`drift/check ${r.status}: ${await r.text()}`);
const j: any = await r.json();
// If the endpoint DID exist, assert the flag got set.
if (!j.flagged) {
throw new Error(`endpoint responded but flag not set: ${JSON.stringify(j).slice(0, 200)}`);
}
return {
evidence: `playbook flagged: ${JSON.stringify(j).slice(0, 160)}`,
metrics: { flagged: true },
};
});
result.layers.push(l5);
// ========================================================================
// Verdict assembly
// ========================================================================
for (const l of result.layers) {
if (l.ok) result.shipped_phases.push(l.phase);
else result.placeholder_phases.push(l.phase);
result.real_numbers[`${l.layer}_latency_ms`] = l.latency_ms;
if (l.metrics) {
for (const [k, v] of Object.entries(l.metrics)) {
if (typeof v === "number") result.real_numbers[`${l.layer}.${k}`] = v;
}
}
}
const anyFail = result.placeholder_phases.length > 0;
const allPass = result.layers.every(l => l.ok);
result.overall = allPass ? "pass" : (result.shipped_phases.length > 0 ? "partial_pass" : "fail");
if (!l5.ok) {
result.notes.push(
"Phase 45 slice 3 (doc_drift/check endpoint) is the expected-fail layer. " +
"Layer 5 failing with a 404/405 is the CORRECT honest signal — don't mask this " +
"to get a green result. When slice 3 ships, layer 5 flips to ok=true automatically."
);
}
if (anyFail) {
result.notes.push(
`${result.placeholder_phases.length} layer(s) failed. Check evidence per layer for specifics.`
);
}
return result;
}
auditor/gitea.ts
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// Gitea API client. Minimal surface — only what the auditor needs:
// list open PRs, get commits + files for a PR, fetch a diff, post a
// commit status, post a review.
//
// Auth: reads PAT from ~/.git-credentials (set up by the credential
// helper flow in 2026-04-22 session). Gitea's "token" auth scheme
// matches what `git fetch` is already using.
import { readFile } from "node:fs/promises";
import type { PrSnapshot } from "./types.ts";
const HOST = process.env.GITEA_HOST ?? "https://git.agentview.dev";
const OWNER = "profit";
const REPO = "lakehouse";
const CRED_FILE = "/home/profit/.git-credentials";
let cachedPat: string | null = null;
async function getPat(): Promise<string> {
if (cachedPat) return cachedPat;
const raw = await readFile(CRED_FILE, "utf8");
for (const line of raw.split("\n")) {
const m = line.match(/^https:\/\/[^:]+:([^@]+)@git\.agentview\.dev/);
if (m) { cachedPat = m[1]; return m[1]; }
}
throw new Error(`no Gitea PAT in ${CRED_FILE}`);
}
async function giteaFetch(path: string, init: RequestInit = {}): Promise<Response> {
const pat = await getPat();
const url = `${HOST}/api/v1${path}`;
const headers = new Headers(init.headers);
headers.set("Authorization", `token ${pat}`);
if (init.body && !headers.has("content-type")) {
headers.set("content-type", "application/json");
}
return fetch(url, { ...init, headers, signal: AbortSignal.timeout(20000) });
}
export async function listOpenPrs(): Promise<PrSnapshot[]> {
const r = await giteaFetch(`/repos/${OWNER}/${REPO}/pulls?state=open&page=1&limit=50`);
if (!r.ok) throw new Error(`listOpenPrs ${r.status}: ${await r.text()}`);
const rows = (await r.json()) as any[];
return Promise.all(rows.map(row => snapshotFromPr(row)));
}
export async function getPrSnapshot(num: number): Promise<PrSnapshot> {
const r = await giteaFetch(`/repos/${OWNER}/${REPO}/pulls/${num}`);
if (!r.ok) throw new Error(`getPr ${num} ${r.status}: ${await r.text()}`);
return snapshotFromPr((await r.json()) as any);
}
async function snapshotFromPr(row: any): Promise<PrSnapshot> {
const num = row.number;
const commitsResp = await giteaFetch(`/repos/${OWNER}/${REPO}/pulls/${num}/commits`);
const commits = commitsResp.ok ? ((await commitsResp.json()) as any[]) : [];
const filesResp = await giteaFetch(`/repos/${OWNER}/${REPO}/pulls/${num}/files`);
const files = filesResp.ok ? ((await filesResp.json()) as any[]) : [];
return {
number: num,
head_sha: row.head?.sha ?? "",
base_sha: row.base?.sha ?? "",
title: row.title ?? "",
body: row.body ?? "",
state: row.state === "open" ? "open" : (row.merged ? "merged" : "closed"),
author: row.user?.login ?? "",
commits: commits.map(c => ({
sha: (c.sha ?? "").slice(0, 12),
message: c.commit?.message ?? "",
author: c.commit?.author?.name ?? "",
})),
files: files.map(f => ({
path: f.filename ?? "",
additions: f.additions ?? 0,
deletions: f.deletions ?? 0,
})),
};
}
/// Returns the unified diff text of the PR. Used by static checks.
export async function getPrDiff(num: number): Promise<string> {
const r = await giteaFetch(`/repos/${OWNER}/${REPO}/pulls/${num}.diff`);
if (!r.ok) throw new Error(`getDiff ${num} ${r.status}: ${await r.text()}`);
return await r.text();
}
/// Hard-block mechanism: post a failing commit status on the PR head
/// SHA. Branch protection (if enabled on `main`) treats this as a
/// required-check fail and prevents merge. The description is shown
/// in the Gitea UI next to the red X.
export async function postCommitStatus(args: {
sha: string;
state: "success" | "pending" | "failure" | "error";
context: string;
description: string;
target_url?: string;
}): Promise<void> {
const r = await giteaFetch(`/repos/${OWNER}/${REPO}/statuses/${args.sha}`, {
method: "POST",
body: JSON.stringify({
state: args.state,
context: args.context,
description: args.description.slice(0, 140),
target_url: args.target_url ?? "",
}),
});
if (!r.ok) throw new Error(`postCommitStatus ${r.status}: ${await r.text()}`);
}
/// Post a review comment. Gitea typically blocks self-review
/// (author posting a review on their own PR). Prefer
/// `postIssueComment` when running with the author's PAT.
export async function postReview(args: {
pr_number: number;
commit_id: string;
body: string;
event: "APPROVE" | "REQUEST_CHANGES" | "COMMENT";
}): Promise<void> {
const r = await giteaFetch(`/repos/${OWNER}/${REPO}/pulls/${args.pr_number}/reviews`, {
method: "POST",
body: JSON.stringify({
commit_id: args.commit_id,
body: args.body,
event: args.event,
}),
});
if (!r.ok) throw new Error(`postReview ${r.status}: ${await r.text()}`);
}
/// Plain issue comment. Works for the auditor's own PAT because
/// Gitea allows authors to comment on their own PRs (just not
/// review them). Auditor uses this for the reasoning body; the
/// actual block signal is the commit status.
export async function postIssueComment(args: {
pr_number: number;
body: string;
}): Promise<{ id: number; html_url: string }> {
const r = await giteaFetch(`/repos/${OWNER}/${REPO}/issues/${args.pr_number}/comments`, {
method: "POST",
body: JSON.stringify({ body: args.body }),
});
if (!r.ok) throw new Error(`postIssueComment ${r.status}: ${await r.text()}`);
const j = await r.json() as any;
return { id: j.id, html_url: j.html_url };
}
auditor/index.ts
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// Auditor poller — the top-level entry. Polls Gitea for open PRs on
// a fixed interval, dedupes by head SHA, runs audit + posts verdict
// for each new (pr, sha) pair.
//
// Run manually:
// bun run auditor/index.ts
//
// Stop:
// touch auditor.paused (skips next cycle)
// pkill -f auditor/index.ts (kills in-flight)
//
// State:
// data/_auditor/state.json — last-audited SHA per PR
// data/_auditor/verdicts/{id}.json — per-run verdict records
//
// This entry runs forever. A systemd unit would wrap it once the
// workflow is trusted (same pattern as mcp-server, observer).
import { readFile, writeFile, mkdir, access } from "node:fs/promises";
import { listOpenPrs } from "./gitea.ts";
import { auditPr } from "./audit.ts";
const POLL_INTERVAL_MS = 90_000; // 90s — enough budget for audit runs to complete
const PAUSE_FILE = "/home/profit/lakehouse/auditor.paused";
const STATE_FILE = "/home/profit/lakehouse/data/_auditor/state.json";
interface State {
// Map: PR number → last-audited head SHA. Lets us dedupe audits
// across restarts (poller can crash/restart without re-auditing
// all open PRs from scratch).
last_audited: Record<string, string>;
started_at: string;
cycles_total: number;
cycles_skipped_paused: number;
audits_run: number;
last_cycle_at?: string;
}
async function fileExists(path: string): Promise<boolean> {
try { await access(path); return true; } catch { return false; }
}
async function loadState(): Promise<State> {
try {
const raw = await readFile(STATE_FILE, "utf8");
const s = JSON.parse(raw);
return {
last_audited: s.last_audited ?? {},
started_at: s.started_at ?? new Date().toISOString(),
cycles_total: s.cycles_total ?? 0,
cycles_skipped_paused: s.cycles_skipped_paused ?? 0,
audits_run: s.audits_run ?? 0,
last_cycle_at: s.last_cycle_at,
};
} catch {
return {
last_audited: {},
started_at: new Date().toISOString(),
cycles_total: 0,
cycles_skipped_paused: 0,
audits_run: 0,
};
}
}
async function saveState(s: State): Promise<void> {
await mkdir("/home/profit/lakehouse/data/_auditor", { recursive: true });
await writeFile(STATE_FILE, JSON.stringify(s, null, 2));
}
async function runCycle(state: State): Promise<State> {
state.cycles_total += 1;
state.last_cycle_at = new Date().toISOString();
if (await fileExists(PAUSE_FILE)) {
state.cycles_skipped_paused += 1;
console.log(`[auditor] cycle ${state.cycles_total}: paused (touch ${PAUSE_FILE} exists)`);
return state;
}
let prs;
try {
prs = await listOpenPrs();
} catch (e) {
console.error(`[auditor] listOpenPrs failed: ${(e as Error).message}`);
return state;
}
console.log(`[auditor] cycle ${state.cycles_total}: ${prs.length} open PR(s)`);
for (const pr of prs) {
const last = state.last_audited[String(pr.number)];
if (last === pr.head_sha) {
console.log(`[auditor] skip PR #${pr.number} (SHA ${pr.head_sha.slice(0, 8)} already audited)`);
continue;
}
console.log(`[auditor] audit PR #${pr.number} (${pr.head_sha.slice(0, 8)}) — ${pr.title.slice(0, 60)}`);
try {
// Skip dynamic by default: it mutates live playbook state and
// re-runs on every PR update would pollute quickly. Operator
// can run dynamic via `bun run auditor/fixtures/cli.ts` manually
// OR set LH_AUDITOR_RUN_DYNAMIC=1 to opt in.
const run_dynamic = process.env.LH_AUDITOR_RUN_DYNAMIC === "1";
const verdict = await auditPr(pr, {
skip_dynamic: !run_dynamic,
skip_inference: process.env.LH_AUDITOR_SKIP_INFERENCE === "1",
});
console.log(`[auditor] verdict=${verdict.overall} findings=${verdict.metrics.findings_total} (block=${verdict.metrics.findings_block} warn=${verdict.metrics.findings_warn})`);
state.last_audited[String(pr.number)] = pr.head_sha;
state.audits_run += 1;
} catch (e) {
console.error(`[auditor] audit failed: ${(e as Error).message}`);
}
}
return state;
}
async function main(): Promise<void> {
console.log(`[auditor] starting poller — interval ${POLL_INTERVAL_MS / 1000}s`);
console.log(`[auditor] pause file: ${PAUSE_FILE}`);
console.log(`[auditor] state file: ${STATE_FILE}`);
let state = await loadState();
console.log(`[auditor] loaded state: ${Object.keys(state.last_audited).length} PRs previously audited, ${state.cycles_total} cycles so far`);
// Single-shot mode for CLI testing: `bun run auditor/index.ts --once`
const once = process.argv.includes("--once");
if (once) {
state = await runCycle(state);
await saveState(state);
console.log(`[auditor] single-shot complete. total audits: ${state.audits_run}`);
return;
}
// Loop.
while (true) {
state = await runCycle(state);
await saveState(state);
await new Promise(res => setTimeout(res, POLL_INTERVAL_MS));
}
}
main().catch(e => {
console.error("[auditor] fatal:", e);
process.exit(1);
});
auditor/kb_index.ts
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// kb_index — generic on-the-fly aggregation over append-only JSONL
// scratchpads (audit_lessons, scrum_reviews, outcomes, observer ops).
//
// The mem0 insight: raw rows are CHEAP and tell the full story, but
// downstream prompts need a DEFINITION, not a log. A definition is
// the aggregate: "this signature has fired N times across M distinct
// scopes, first_seen=X, last_seen=Y, confidence=M/N."
//
// This library is the single shared aggregator. Every KB writer keeps
// appending raw rows; every KB reader uses aggregate() instead of
// tailing the raw stream. No second file to sync, no ADD/UPDATE/NOOP
// routing — the stats roll up from the raw rows every time.
//
// Why this works past hundreds of runs:
// - aggregate() is bounded by distinct_signatures, not total_rows.
// - confidence = distinct_scopes / count — low for same-scope noise,
// high for cross-scope patterns. Downstream severity ramps on
// confidence × count, not raw count, so one unfixed PR can't
// inflate its own recurrence score (the classic mem0 failure).
// - rotation (later) moves old raw to archive files; aggregate()
// can still read both to compute lifetime counts when needed.
import { readFile } from "node:fs/promises";
export interface AggregateRow {
signature: string;
count: number;
distinct_scopes: number;
first_seen: string;
last_seen: string;
confidence: number; // distinct_scopes / count — capped at 1.0
representative_summary: string; // most-recent summary for this signature
scopes: string[]; // up to 20 most-recent scopes for debugging
checks: string[]; // distinct `check` values (audit_lessons-specific)
}
export interface AggregateOptions<T> {
/** How to extract the dedup key from a row. */
keyFn: (row: T) => string | undefined;
/** How to extract the "scope" — distinct scopes count gives confidence. */
scopeFn: (row: T) => string | undefined;
/** How to extract the timestamp (defaults to row.audited_at / row.reviewed_at / row.timestamp). */
timeFn?: (row: T) => string | undefined;
/** How to extract a representative summary (defaults to row.summary). */
summaryFn?: (row: T) => string | undefined;
/** Max rows to read from the JSONL tail; 0 = read all. */
tailLimit?: number;
/** Include per-row check field (for multi-check aggregates). */
checkFn?: (row: T) => string | undefined;
}
/**
* Read a JSONL file and produce the aggregate map keyed by signature.
* Safe on missing or malformed files — returns empty map.
*/
export async function aggregate<T = any>(
jsonlPath: string,
opts: AggregateOptions<T>,
): Promise<Map<string, AggregateRow>> {
const out = new Map<string, AggregateRow>();
let raw: string;
try { raw = await readFile(jsonlPath, "utf8"); } catch { return out; }
const lines = raw.split("\n").filter(l => l.length > 0);
const sliceFrom = opts.tailLimit && opts.tailLimit > 0 ? Math.max(0, lines.length - opts.tailLimit) : 0;
const timeFn = opts.timeFn ?? ((r: any) => r?.audited_at ?? r?.reviewed_at ?? r?.timestamp ?? r?.ran_at);
const summaryFn = opts.summaryFn ?? ((r: any) => r?.summary ?? r?.representative_summary);
// Per-signature scope tracking — need counts by scope to compute
// distinct_scopes without double-counting a scope that appears 50
// times. Using a Set<scope> per signature.
const scopeSets = new Map<string, Set<string>>();
const checkSets = new Map<string, Set<string>>();
for (let i = sliceFrom; i < lines.length; i++) {
let row: T;
try { row = JSON.parse(lines[i]) as T; } catch { continue; }
const sig = opts.keyFn(row);
if (!sig) continue;
let agg = out.get(sig);
if (!agg) {
agg = {
signature: sig,
count: 0,
distinct_scopes: 0,
first_seen: "",
last_seen: "",
confidence: 0,
representative_summary: "",
scopes: [],
checks: [],
};
out.set(sig, agg);
scopeSets.set(sig, new Set<string>());
checkSets.set(sig, new Set<string>());
}
agg.count += 1;
const scope = opts.scopeFn(row);
if (scope !== undefined && scope !== null && scope !== "") {
scopeSets.get(sig)!.add(String(scope));
// Keep scopes array ordered by recency (newest wins — shift
// oldest when at cap).
const arr = agg.scopes;
const s = String(scope);
const existing = arr.indexOf(s);
if (existing >= 0) arr.splice(existing, 1);
arr.push(s);
if (arr.length > 20) arr.shift();
}
if (opts.checkFn) {
const c = opts.checkFn(row);
if (c) checkSets.get(sig)!.add(String(c));
}
const t = timeFn(row);
if (t) {
if (!agg.first_seen || t < agg.first_seen) agg.first_seen = t;
if (!agg.last_seen || t > agg.last_seen) agg.last_seen = t;
}
const s = summaryFn(row);
if (s) agg.representative_summary = String(s);
}
// Finalize derived fields.
for (const [sig, agg] of out) {
const scopes = scopeSets.get(sig) ?? new Set<string>();
agg.distinct_scopes = scopes.size;
agg.confidence = agg.count > 0 ? Math.min(1, agg.distinct_scopes / agg.count) : 0;
const checks = checkSets.get(sig);
if (checks) agg.checks = Array.from(checks).sort();
}
return out;
}
/**
* Severity policy derived from aggregate stats. The rating lives here
* (not in each check) so all KB readers ramp severity consistently.
*
* - confidence × count product is the real signal.
* - Low confidence (< 0.3) = same-scope noise → info regardless of count.
* - Mid confidence (0.3-0.6) = mixed signal → warn at count ≥ 3.
* - High confidence (> 0.6) with count ≥ 5 = block-worthy cross-cutting pattern.
*
* Callers can override by reading agg directly; this is the default
* policy that matches the "don't escalate one unfixed PR" discipline.
*/
export function ratingSeverity(agg: AggregateRow): "info" | "warn" | "block" {
if (agg.confidence >= 0.6 && agg.count >= 5) return "block";
if (agg.confidence >= 0.3 && agg.count >= 3) return "warn";
return "info";
}
/** Human-friendly one-line summary of an aggregate row for finding evidence. */
export function formatAgg(agg: AggregateRow): string {
return `count=${agg.count} distinct_scopes=${agg.distinct_scopes} confidence=${agg.confidence.toFixed(2)} seen=[${agg.first_seen.slice(0, 10)}..${agg.last_seen.slice(0, 10)}]`;
}
auditor/kb_stats.ts
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// kb_stats — on-demand dashboard numbers from the KB scratchpad
// files. Reads data/_auditor/verdicts/*, data/_kb/audit_lessons.jsonl,
// data/_kb/audit_facts.jsonl, data/_kb/audit_discrepancies.jsonl,
// data/_kb/scrum_reviews.jsonl and prints:
//
// - verdict flip-flop rate (same SHA re-audited, verdict changed?)
// - consensus discrepancy rate (N runs disagreed on a claim)
// - confidence distribution from kb_index aggregator
// - top N recurring entities from audit_facts
// - fact growth over time
// - scrum vs inference KB split
//
// Run: bun run auditor/kb_stats.ts
// bun run auditor/kb_stats.ts --top 15 # show top 15 entities
// bun run auditor/kb_stats.ts --json # machine-readable
//
// This is the "dashboard" without running Grafana. If someone really
// wants a dashboard, wire this output into a static HTML page + cron.
import { readFile, readdir } from "node:fs/promises";
import { join } from "node:path";
import { aggregate } from "./kb_index.ts";
const REPO = "/home/profit/lakehouse";
const VERDICTS_DIR = `${REPO}/data/_auditor/verdicts`;
const AUDIT_LESSONS = `${REPO}/data/_kb/audit_lessons.jsonl`;
const AUDIT_FACTS = `${REPO}/data/_kb/audit_facts.jsonl`;
const AUDIT_DISCREPANCIES = `${REPO}/data/_kb/audit_discrepancies.jsonl`;
const SCRUM_REVIEWS = `${REPO}/data/_kb/scrum_reviews.jsonl`;
interface Args {
top: number;
json: boolean;
}
function parseArgs(argv: string[]): Args {
const a: Args = { top: 10, json: false };
for (let i = 2; i < argv.length; i++) {
if (argv[i] === "--top") a.top = Number(argv[++i] ?? 10);
else if (argv[i] === "--json") a.json = true;
}
return a;
}
async function readJsonl<T = any>(path: string): Promise<T[]> {
try {
const raw = await readFile(path, "utf8");
return raw.split("\n").filter(l => l.length > 0).map(l => {
try { return JSON.parse(l) as T; } catch { return null as any; }
}).filter(r => r !== null);
} catch { return []; }
}
async function loadVerdicts(): Promise<Array<{ pr: number; sha: string; overall: string; findings_total: number; findings_block: number; findings_warn: number }>> {
let files: string[] = [];
try { files = await readdir(VERDICTS_DIR); } catch { return []; }
const out = [];
for (const f of files) {
if (!f.endsWith(".json")) continue;
const m = f.match(/^(\d+)-([0-9a-f]+)\.json$/);
if (!m) continue;
try {
const v = JSON.parse(await readFile(join(VERDICTS_DIR, f), "utf8"));
out.push({
pr: Number(m[1]),
sha: m[2],
overall: String(v.overall),
findings_total: Number(v.metrics?.findings_total ?? 0),
findings_block: Number(v.metrics?.findings_block ?? 0),
findings_warn: Number(v.metrics?.findings_warn ?? 0),
});
} catch { /* skip corrupt */ }
}
return out;
}
interface Stats {
audit_count: number;
verdict_distribution: Record<string, number>;
// Same PR with multiple SHAs — if verdicts differ, that's drift across
// the PR's commit history. Not a flip-flop in the classical sense,
// but worth surfacing (e.g. "PR #8 was block block req req block").
per_pr_verdict_sequences: Record<number, string[]>;
// For each PR with ≥ 2 audits, how many distinct verdicts did it
// produce? 1 = stable; 2+ = some flipping.
verdict_instability: { pr_count: number; pr_with_multiple_verdicts: number; pr_with_3plus: number };
consensus: { discrepancy_count: number; tiebreaker_used: number; unresolved: number };
kb: {
audit_lessons_rows: number;
audit_facts_rows: number;
scrum_reviews_rows: number;
distinct_finding_signatures: number;
distinct_entities_across_prs: number;
entities_in_2plus_prs: number;
entities_in_5plus_prs: number;
};
fact_quality: {
verifier_verdict_distribution: Record<string, number>;
facts_dropped_by_verifier_total: number;
extraction_success_rate: number;
};
top_entities: Array<{ name: string; distinct_prs: number; count: number; types: string[] }>;
kb_by_source: Record<string, number>;
}
async function collect(args: Args): Promise<Stats> {
const verdicts = await loadVerdicts();
const lessons = await readJsonl<any>(AUDIT_LESSONS);
const facts = await readJsonl<any>(AUDIT_FACTS);
const disc = await readJsonl<any>(AUDIT_DISCREPANCIES);
const reviews = await readJsonl<any>(SCRUM_REVIEWS);
// Verdict stability
const byPr: Record<number, string[]> = {};
const verdictDist: Record<string, number> = {};
for (const v of verdicts) {
(byPr[v.pr] ??= []).push(v.overall);
verdictDist[v.overall] = (verdictDist[v.overall] ?? 0) + 1;
}
let multi = 0, tri = 0;
for (const [_, seq] of Object.entries(byPr)) {
const distinct = new Set(seq);
if (distinct.size >= 2) multi++;
if (distinct.size >= 3) tri++;
}
// Consensus drift
const consensus = {
discrepancy_count: disc.length,
tiebreaker_used: disc.filter(d => String(d.resolution).startsWith("tiebreaker")).length,
unresolved: disc.filter(d => d.resolution === "unresolved").length,
};
// Lesson signatures
const lessonAgg = await aggregate<any>(AUDIT_LESSONS, {
keyFn: r => r?.signature,
scopeFn: r => (r?.pr_number !== undefined ? `pr-${r.pr_number}` : undefined),
});
// Entity aggregation across audit_facts rows
interface EntAgg { distinct_prs: Set<number>; count: number; types: Set<string>; name: string; sources: Set<string> }
const entAgg = new Map<string, EntAgg>();
const sourceCount: Record<string, number> = {};
let totalVerdictDist: Record<string, number> = { CORRECT: 0, INCORRECT: 0, UNVERIFIABLE: 0, UNCHECKED: 0 };
let factsDroppedTotal = 0;
let extractionsWithFacts = 0;
for (const row of facts) {
const src = String(row.source ?? "unknown");
sourceCount[src] = (sourceCount[src] ?? 0) + 1;
const pr = Number(row.pr_number);
if (Array.isArray(row.verifier_verdicts)) {
for (const v of row.verifier_verdicts) {
totalVerdictDist[v] = (totalVerdictDist[v] ?? 0) + 1;
}
}
factsDroppedTotal += Number(row.facts_dropped_by_verifier ?? 0);
if ((Array.isArray(row.facts) && row.facts.length > 0) || (Array.isArray(row.entities) && row.entities.length > 0)) {
extractionsWithFacts++;
}
for (const e of Array.isArray(row.entities) ? row.entities : []) {
const name = String(e?.name ?? "").trim();
if (name.length < 3) continue;
const key = name.toLowerCase();
const agg = entAgg.get(key) ?? { distinct_prs: new Set(), count: 0, types: new Set(), name, sources: new Set() };
agg.count++;
if (Number.isFinite(pr) && pr > 0) agg.distinct_prs.add(pr);
if (e?.type) agg.types.add(String(e.type));
agg.sources.add(src);
entAgg.set(key, agg);
}
}
const entitiesIn2Plus = Array.from(entAgg.values()).filter(a => a.distinct_prs.size >= 2).length;
const entitiesIn5Plus = Array.from(entAgg.values()).filter(a => a.distinct_prs.size >= 5).length;
const topEntities = Array.from(entAgg.values())
.sort((a, b) => b.distinct_prs.size - a.distinct_prs.size || b.count - a.count)
.slice(0, args.top)
.map(a => ({
name: a.name,
distinct_prs: a.distinct_prs.size,
count: a.count,
types: Array.from(a.types),
}));
const stats: Stats = {
audit_count: verdicts.length,
verdict_distribution: verdictDist,
per_pr_verdict_sequences: byPr,
verdict_instability: {
pr_count: Object.keys(byPr).length,
pr_with_multiple_verdicts: multi,
pr_with_3plus: tri,
},
consensus,
kb: {
audit_lessons_rows: lessons.length,
audit_facts_rows: facts.length,
scrum_reviews_rows: reviews.length,
distinct_finding_signatures: lessonAgg.size,
distinct_entities_across_prs: entAgg.size,
entities_in_2plus_prs: entitiesIn2Plus,
entities_in_5plus_prs: entitiesIn5Plus,
},
fact_quality: {
verifier_verdict_distribution: totalVerdictDist,
facts_dropped_by_verifier_total: factsDroppedTotal,
extraction_success_rate: facts.length > 0 ? extractionsWithFacts / facts.length : 0,
},
top_entities: topEntities,
kb_by_source: sourceCount,
};
return stats;
}
function renderHuman(s: Stats): string {
const lines: string[] = [];
lines.push("═══ KB STATS ═══");
lines.push("");
lines.push(`Audits: ${s.audit_count} total across ${s.verdict_instability.pr_count} distinct PRs`);
lines.push(`Verdicts: ${Object.entries(s.verdict_distribution).map(([k, v]) => `${k}=${v}`).join(" ")}`);
const multiplePct = s.verdict_instability.pr_count > 0
? Math.round(100 * s.verdict_instability.pr_with_multiple_verdicts / s.verdict_instability.pr_count)
: 0;
lines.push(`Verdict instability: ${s.verdict_instability.pr_with_multiple_verdicts}/${s.verdict_instability.pr_count} PRs had 2+ distinct verdicts (${multiplePct}%) — 3+ distinct: ${s.verdict_instability.pr_with_3plus}`);
lines.push("");
lines.push("─── Consensus ───");
lines.push(` discrepancies logged: ${s.consensus.discrepancy_count}`);
lines.push(` tiebreaker used: ${s.consensus.tiebreaker_used}`);
lines.push(` unresolved: ${s.consensus.unresolved}`);
const dRate = s.audit_count > 0 ? (100 * s.consensus.discrepancy_count / s.audit_count).toFixed(1) : "0";
lines.push(` discrepancy rate: ${dRate}% of audits`);
lines.push("");
lines.push("─── KB size ───");
lines.push(` audit_lessons.jsonl: ${s.kb.audit_lessons_rows} rows, ${s.kb.distinct_finding_signatures} distinct signatures`);
lines.push(` audit_facts.jsonl: ${s.kb.audit_facts_rows} rows, ${s.kb.distinct_entities_across_prs} distinct entities`);
lines.push(` scrum_reviews.jsonl: ${s.kb.scrum_reviews_rows} rows`);
lines.push(` entities in 2+ PRs: ${s.kb.entities_in_2plus_prs}`);
lines.push(` entities in 5+ PRs: ${s.kb.entities_in_5plus_prs} ← strong cross-cutting signal`);
lines.push("");
lines.push("─── Fact quality ───");
const v = s.fact_quality.verifier_verdict_distribution;
lines.push(` verifier verdicts: CORRECT=${v.CORRECT ?? 0} UNVERIFIABLE=${v.UNVERIFIABLE ?? 0} UNCHECKED=${v.UNCHECKED ?? 0} INCORRECT=${v.INCORRECT ?? 0}`);
lines.push(` facts dropped by verifier: ${s.fact_quality.facts_dropped_by_verifier_total}`);
lines.push(` extraction success rate: ${(s.fact_quality.extraction_success_rate * 100).toFixed(1)}%`);
lines.push("");
lines.push("─── KB sources ───");
for (const [src, n] of Object.entries(s.kb_by_source)) {
lines.push(` ${src}: ${n}`);
}
lines.push("");
lines.push(`─── Top ${s.top_entities.length} recurring entities ───`);
for (const e of s.top_entities) {
lines.push(` [${e.distinct_prs} PRs × ${e.count} obs] ${e.name} (${e.types.join(",")})`);
}
return lines.join("\n");
}
async function main() {
const args = parseArgs(process.argv);
const stats = await collect(args);
if (args.json) {
console.log(JSON.stringify(stats, (_, v) => v instanceof Set ? Array.from(v) : v, 2));
} else {
console.log(renderHuman(stats));
}
}
main().catch(e => { console.error("[kb_stats] fatal:", e); process.exit(1); });
auditor/policy.ts
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// ═══════════════════════════════════════════════════════════════════
// YOU WRITE THIS FILE. Policy decides what blocks vs what's a comment.
// Defaults are opinionated on the "stop clicking past placeholder"
// side — easier to loosen than to tighten when you're watching the
// auditor behave in live PRs.
// ═══════════════════════════════════════════════════════════════════
import type { Finding, Verdict } from "./types.ts";
/// Translate the four-check output into a single verdict. This is the
/// single pane of glass the auditor operates on — tune thresholds here.
export function assembleVerdict(
findings: Finding[],
metrics: Record<string, number>,
pr_number: number,
head_sha: string,
): Verdict {
const blocking = findings.filter(f => f.severity === "block");
const warning = findings.filter(f => f.severity === "warn");
let overall: Verdict["overall"];
let one_liner: string;
if (blocking.length > 0) {
overall = "block";
one_liner = `${blocking.length} blocking issue${blocking.length > 1 ? "s" : ""}: ${blocking[0].summary}`;
} else if (warning.length >= 3) {
// Three or more warnings is a block — death by a thousand cuts.
overall = "request_changes";
one_liner = `${warning.length} warnings — see review`;
} else if (warning.length > 0) {
overall = "request_changes";
one_liner = warning[0].summary;
} else {
overall = "approve";
one_liner = `all checks passed (${findings.length} findings, all info)`;
}
return {
pr_number,
head_sha,
audited_at: new Date().toISOString(),
overall,
findings,
metrics,
one_liner,
};
}
/// Which strength-of-claim warrants which severity when evidence is
/// weak? A "Phase X shipped" claim with zero integration tests is a
/// blocker. A "should work" claim with no test is a warn.
export function severityFromClaimEvidence(
claim_strength: "weak" | "moderate" | "strong",
evidence_grade: "none" | "partial" | "full",
): "info" | "warn" | "block" {
if (evidence_grade === "full") return "info";
if (claim_strength === "strong" && evidence_grade === "none") return "block";
if (claim_strength === "strong" && evidence_grade === "partial") return "warn";
if (claim_strength === "moderate" && evidence_grade === "none") return "warn";
return "info";
}
auditor/types.ts
+72
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// Shared types for the claim-auditor. Every field exists for a reason;
// if something can't be verified from a check, it goes into `evidence`
// so the verdict is inspectable, not a black box.
export type CheckKind = "static" | "dynamic" | "inference" | "kb_query";
export type Severity = "info" | "warn" | "block";
export interface Claim {
// Verbatim phrase that raised the claim — e.g. "Phase 38 shipped",
// "verified end-to-end", "works after restart". Used as the "what
// does the author assert" input to downstream checks.
text: string;
// Where it came from. `commit_sha` is the short hash; `location`
// is a file:line for in-diff claims, or "pr_body" / "commit_message".
commit_sha: string;
location: string;
// Heuristic rating of how strong the claim is. "green+tested"
// is strong; "should work" is weak. Drives sensitivity — stronger
// claims get harder-blocked on weak evidence.
//
// "empirical" is a separate class: runtime measurements like
// "N cloud calls" / "306s end-to-end" / "accepted on attempt N".
// These cannot be verified from a static diff — only from the test
// output that produced them. Inference skips diff-verification for
// empirical claims; they become info-level context unless a future
// runtime_evidence check contradicts them.
strength: "weak" | "moderate" | "strong" | "empirical";
}
export interface Finding {
check: CheckKind;
severity: Severity;
claim_text?: string;
// Free-form short description: "field added but never read", "no
// test covers this code path", "cloud model says placeholder".
summary: string;
// Concrete evidence: file paths, line numbers, log excerpts, test
// output, cloud-model verdict. No handwaving.
evidence: string[];
}
export interface Verdict {
pr_number: number;
head_sha: string;
audited_at: string;
overall: "approve" | "request_changes" | "block";
findings: Finding[];
// Real numbers that downstream policy can gate on. e.g. if the
// hybrid test produced latency numbers or token counts, they
// surface here so /auditor/history is queryable.
metrics: Record<string, number>;
// Short one-line justification for the `overall` verdict. What
// gets posted as the commit-status description in Gitea (max 140
// chars) must fit here.
one_liner: string;
}
export interface PrSnapshot {
number: number;
head_sha: string;
base_sha: string;
title: string;
body: string;
state: "open" | "closed" | "merged";
author: string;
// Array of commit messages in the PR (not diffs — those are
// fetched on-demand per-check).
commits: Array<{ sha: string; message: string; author: string }>;
// File paths touched by the PR, with lines-added / lines-removed.
files: Array<{ path: string; additions: number; deletions: number }>;
}
bot/README.md
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# Lakehouse PR-Bot
A local sub-agent that reads the PRD, asks a cloud model for a small change
proposal, applies it, runs tests, and opens a draft PR on Gitea. Manual-only
right now — no systemd, no cron. Run one cycle at a time and watch.
## Run one cycle
```bash
cd /home/profit/lakehouse
bun run bot/cycle.ts
```
Prerequisites:
- Working tree must be clean (`git status` shows nothing). The bot refuses on a dirty tree so its changes don't mix with in-flight work.
- Sidecar running on :3200, gateway on :3100, observer on :3800.
- At least one PRD line tagged `[bot-eligible]` (see below).
- Gitea PAT configured in `~/.git-credentials` (already set up).
## Tag a gap as bot-eligible
Add `[bot-eligible]` to any PRD line the bot is allowed to work on:
```md
- [ ] Add a unit test for parse_city_state covering "South Bend, IN" edge case [bot-eligible]
```
The bot scans `docs/PRD.md` for these tags. Each tagged line becomes a candidate.
Start small — one tag at a time — until you trust the loop.
## Stop it mid-cycle
Create the pause file:
```bash
touch /home/profit/lakehouse/bot.paused
```
The next `bot/cycle.ts` invocation exits immediately with `skipped_pause`.
(It does NOT kill a cycle already in-flight — use `Ctrl-C` or `pkill -f bot/cycle.ts` for that.)
## Budget
- **20 cloud calls/day, 160k tokens/day** (hard ceiling, see `bot/cost.ts`).
- Tracked in `data/_bot/cost-YYYY-MM-DD.json`.
- Resets at UTC midnight.
## Cycle outcomes
Every run writes a result to `data/_bot/cycles/{cycle_id}.json` and POSTs an event to the observer on :3800.
Possible outcomes:
| Outcome | Meaning |
|---|---|
| `ok` | PR opened |
| `cycle_noop` | proposal applied, every file was **identical to current content** (Mem0 NOOP); no test run, no PR |
| `skipped_pause` | pause file present |
| `skipped_cost` | daily budget exhausted |
| `skipped_policy` | `policy.shouldRunCycle` said no |
| `skipped_dirty_tree` | uncommitted changes present |
| `skipped_no_gap` | no `[bot-eligible]` tags in PRD |
| `model_failed` | sidecar/cloud model errored or returned unparseable JSON |
| `proposal_rejected` | `policy.scoreProposal` rejected it (size, path, etc.) |
| `apply_failed` | file write errored |
| `tests_failed` | cargo or bun test red |
| `pr_skipped_by_policy` | green tests, but `policy.shouldOpenPR` said no |
| `pr_failed` | Gitea API or git push errored |
## Mem0-aligned apply semantics
`apply.ts` categorizes every proposed file into one of three modes:
| Mode | Trigger | Action |
|---|---|---|
| **ADD** | `is_new: true` and file doesn't exist | Create the file |
| **UPDATE** | `is_new: false`, file exists, content **differs** from current | Overwrite |
| **NOOP** | `is_new: false`, file exists, content **matches** current exactly | Skip (no write, no diff) |
If every file in the proposal is NOOP, the cycle short-circuits to `cycle_noop` **before** running tests or opening a PR. Mismatched shapes (`is_new:true` but file exists, or `is_new:false` but file missing) become `apply_failed` — model state confusion is surfaced, not papered over.
PR bodies report the three counts separately so reviewers see what actually changed vs. what was confirmed identical.
## How the bot compounds over cycles
Every finished cycle persists a `CycleResult` to `data/_bot/cycles/{cycle_id}.json`. At the **start** of the next cycle, `bot/kb.ts::loadHistory(gap_id)` scans that directory, filters to prior cycles on the same gap, and returns the five most recent outcomes (`ok`, `tests_failed`, `proposal_rejected`, `cycle_noop`, etc.) with their reasons and touched files.
Those outcomes are summarized into a compact block and **injected into the cloud prompt** before asking for a new proposal. The model sees things like:
```
Prior attempts on this gap (3 most recent):
- 2026-04-22 03:15 UTC — tests_failed
reason: cargo test -p vectord::lance failed on field_type_coerce
- 2026-04-22 02:48 UTC — proposal_rejected
reason: touched forbidden path (docs/ADR-): docs/ADR-019-update.md
- 2026-04-22 01:23 UTC — ok PR: https://git.agentview.dev/profit/lakehouse/pulls/142
reason: PR #142 opened
Learn from these: build on what worked, avoid paths that failed.
```
This is the same compounding pattern `scenario.ts` uses via `kb.loadRecommendation` — the bot's cycles are the bot's memory. No embedding, no separate jsonl, no cross-run orchestration required. First cycle on a new gap skips the block cleanly.
The observer events POSTed on every cycle carry the same data, so `GET :3800/stats?source=bot` aggregates "% of bot cycles on similar gaps that landed a PR" without extra plumbing.
## Where YOU edit
`bot/policy.ts` — four small functions that define what the bot does. The rest
(`propose.ts`, `apply.ts`, `test.ts`, `pr.ts`, `cycle.ts`, `kb.ts`) is mechanical
orchestration — you shouldn't need to touch it unless a pipeline changes.
One policy upgrade the history opens up: in `shouldRunCycle`, you can now bail
out if the same gap has failed N times in a row. Example addition:
```ts
import { loadHistory, statsFor } from "./kb.ts";
// inside shouldRunCycle — but you'd need the gap, which is picked later.
// A more natural place is scoreProposal: if prior N failures on this
// gap's path+summary pattern, reject before testing.
```
## Hard-coded guardrails (not in policy — can't be disabled)
- Bot **never deletes files** (`apply.ts` has no delete path).
- Bot **never touches** `.git/`, `secrets`, `lakehouse.toml`, `docs/ADR-*`, `docs/DECISIONS.md`, `docs/PRD.md`, `/etc/`, `/root/`, `Cargo.lock`.
- All paths validated for traversal (`path/../`) and repo-escape.
- PRs always open as **draft** — never auto-merge.
- Budget check is before the policy function runs — no way to override the daily cap from `policy.ts`.
bot/apply.ts
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// Apply a proposal to disk with Mem0-aligned ADD / UPDATE / NOOP
// semantics. Whole-file writes only — never deletes. All paths
// validated repo-relative + path-traversal-free.
//
// Three outcomes per file:
// ADD — file didn't exist, new content written (is_new must be true)
// UPDATE — file existed, content differs from current, overwritten
// NOOP — file existed, content identical to current, nothing written
//
// The NOOP case is important: a cloud proposal that "re-derives" an
// existing file shouldn't churn the repo or burn test cycles.
import { writeFile, readFile, mkdir, access } from "node:fs/promises";
import { dirname, resolve, relative } from "node:path";
import type { ApplyOutcome, Proposal } from "./types.ts";
const REPO_ROOT = "/home/profit/lakehouse";
export async function applyProposal(p: Proposal): Promise<ApplyOutcome> {
const added: string[] = [];
const updated: string[] = [];
const noop: string[] = [];
const errors: string[] = [];
for (const f of p.files) {
const err = validatePath(f.path);
if (err) {
errors.push(`${f.path}: ${err}`);
continue;
}
const abs = resolve(REPO_ROOT, f.path);
try {
const exists = await fileExists(abs);
if (f.is_new && exists) {
errors.push(`${f.path}: is_new=true but file exists`);
continue;
}
if (!f.is_new && !exists) {
// Proposal claims to update a file that doesn't exist.
// Refuse rather than silently ADD — model is confused about
// repo state, and we want that surfaced, not papered over.
errors.push(`${f.path}: is_new=false but file does not exist`);
continue;
}
if (!f.is_new) {
// UPDATE candidate — compare to detect NOOP.
const current = await readFile(abs, "utf8");
if (current === f.content) {
noop.push(f.path);
continue;
}
await writeFile(abs, f.content);
updated.push(f.path);
} else {
// ADD — file doesn't exist yet.
await mkdir(dirname(abs), { recursive: true });
await writeFile(abs, f.content);
added.push(f.path);
}
} catch (e) {
errors.push(`${f.path}: ${(e as Error).message}`);
}
}
return { added, updated, noop, errors };
}
function validatePath(p: string): string | null {
if (!p) return "empty path";
if (p.startsWith("/")) return "must be repo-relative";
if (p.startsWith("..") || p.includes("/../")) return "path traversal";
const abs = resolve(REPO_ROOT, p);
const rel = relative(REPO_ROOT, abs);
if (rel.startsWith("..")) return "resolves outside repo";
return null;
}
async function fileExists(abs: string): Promise<boolean> {
try {
await access(abs);
return true;
} catch {
return false;
}
}
bot/cost.ts
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// Daily cost tracker. Resets at UTC midnight by keying the file by date.
// Hard ceilings are the policy surface — the bot refuses to call the
// cloud once the budget is exhausted for the day.
import { readFile, writeFile, mkdir } from "node:fs/promises";
import { join } from "node:path";
import type { CostState } from "./types.ts";
const COST_DIR = "/home/profit/lakehouse/data/_bot";
export const DAILY_CALLS_BUDGET = 20;
export const DAILY_TOKENS_BUDGET = 8000 * DAILY_CALLS_BUDGET; // 160k tokens/day ceiling
function todayUtc(): string {
return new Date().toISOString().slice(0, 10);
}
function costFile(date: string): string {
return join(COST_DIR, `cost-${date}.json`);
}
export async function readCost(): Promise<CostState> {
const date = todayUtc();
try {
const raw = await readFile(costFile(date), "utf8");
return JSON.parse(raw) as CostState;
} catch {
return { date, calls: 0, tokens: 0 };
}
}
export async function recordCost(calls: number, tokens: number): Promise<CostState> {
await mkdir(COST_DIR, { recursive: true });
const current = await readCost();
const updated: CostState = {
date: current.date,
calls: current.calls + calls,
tokens: current.tokens + tokens,
};
await writeFile(costFile(updated.date), JSON.stringify(updated, null, 2));
return updated;
}
export function budgetCheck(c: CostState): { ok: boolean; reason: string } {
if (c.calls >= DAILY_CALLS_BUDGET) {
return { ok: false, reason: `daily call budget exhausted (${c.calls}/${DAILY_CALLS_BUDGET})` };
}
if (c.tokens >= DAILY_TOKENS_BUDGET) {
return { ok: false, reason: `daily token budget exhausted (${c.tokens}/${DAILY_TOKENS_BUDGET})` };
}
return { ok: true, reason: `${c.calls}/${DAILY_CALLS_BUDGET} calls, ${c.tokens}/${DAILY_TOKENS_BUDGET} tokens used today` };
}
bot/cycle.ts
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// Single-cycle orchestrator. This is the manual entry point:
// bun run bot/cycle.ts
//
// Steps (each step can short-circuit with a clear outcome):
// 1. Pause-file check → skipped_pause
// 2. Cost budget check → skipped_cost
// 3. Gather CycleContext (dirty tree, last cycle, autotune status)
// 4. policy.shouldRunCycle → skipped_policy
// 5. findGaps + policy.pickGap → skipped_no_gap
// 6. generateProposal → model_failed
// 7. policy.scoreProposal → proposal_rejected
// 8. git checkout new branch
// 9. applyProposal → apply_failed
// 10. runTests → tests_failed
// 11. policy.shouldOpenPR → pr_skipped_by_policy
// 12. git commit + push
// 13. openPr → pr_failed / ok
// 14. recordCost + postEvent (every outcome)
// 15. write cycle result to data/_bot/cycles/{cycle_id}.json
import { readFile, writeFile, mkdir, access } from "node:fs/promises";
import { createHash, randomUUID } from "node:crypto";
import { spawnSync } from "node:child_process";
import { join } from "node:path";
import type { CycleContext, CycleResult, CycleOutcome, Proposal } from "./types.ts";
import * as policy from "./policy.ts";
import { readCost, recordCost, budgetCheck, DAILY_CALLS_BUDGET, DAILY_TOKENS_BUDGET } from "./cost.ts";
import { findGaps, generateProposal } from "./propose.ts";
import { applyProposal } from "./apply.ts";
import { runTests } from "./test.ts";
import { openPr } from "./pr.ts";
import { postEvent } from "./observer.ts";
import { loadHistory, summarizeHistory, statsFor } from "./kb.ts";
const REPO_ROOT = "/home/profit/lakehouse";
const PAUSE_FILE = `${REPO_ROOT}/bot.paused`;
const CYCLES_DIR = `${REPO_ROOT}/data/_bot/cycles`;
const GATEWAY_URL = process.env.LH_GATEWAY_URL ?? "http://localhost:3100";
function log(msg: string) { console.log(`[bot] ${msg}`); }
async function fileExists(p: string): Promise<boolean> {
try { await access(p); return true; } catch { return false; }
}
function git(args: string[]): { code: number; stdout: string; stderr: string } {
const r = spawnSync("git", args, { cwd: REPO_ROOT, encoding: "utf8" });
return { code: r.status ?? -1, stdout: r.stdout ?? "", stderr: r.stderr ?? "" };
}
async function gatherContext(cost: { calls: number; tokens: number }): Promise<CycleContext> {
// Last cycle: newest file in CYCLES_DIR.
let lastCycleAt: string | null = null;
let lastCycleGapId: string | null = null;
try {
const { readdir, stat } = await import("node:fs/promises");
const entries = await readdir(CYCLES_DIR).catch(() => [] as string[]);
let newestTs = 0;
let newestPath: string | null = null;
for (const e of entries) {
if (!e.endsWith(".json")) continue;
const p = join(CYCLES_DIR, e);
const s = await stat(p);
if (s.mtimeMs > newestTs) { newestTs = s.mtimeMs; newestPath = p; }
}
if (newestPath) {
const r = JSON.parse(await readFile(newestPath, "utf8")) as CycleResult;
lastCycleAt = r.started_at;
lastCycleGapId = r.gap?.id ?? null;
}
} catch {}
// Autotune status — if the agent is busy, skip this cycle to avoid
// contention on shared Ollama GPU memory.
let autotuneBusy = false;
try {
const r = await fetch(`${GATEWAY_URL}/vectors/agent/status`, { signal: AbortSignal.timeout(2000) });
if (r.ok) {
const j = await r.json() as any;
autotuneBusy = Boolean(j.running) && (j.queue_depth ?? 0) > 0;
}
} catch {}
// Dirty tree check.
const status = git(["status", "--porcelain"]);
const workingTreeDirty = status.code === 0 && status.stdout.trim().length > 0;
return {
startedAt: new Date().toISOString(),
dailyCallsUsed: cost.calls,
dailyCallsBudget: DAILY_CALLS_BUDGET,
dailyTokensUsed: cost.tokens,
dailyTokensBudget: DAILY_TOKENS_BUDGET,
lastCycleAt,
lastCycleGapId,
autotuneBusy,
workingTreeDirty,
};
}
async function persistResult(res: CycleResult): Promise<void> {
await mkdir(CYCLES_DIR, { recursive: true });
await writeFile(join(CYCLES_DIR, `${res.cycle_id}.json`), JSON.stringify(res, null, 2));
}
function sigHash(gap_id: string | null, proposalSummary: string): string {
const h = createHash("sha256");
h.update(gap_id ?? "no-gap");
h.update("|");
h.update(proposalSummary);
return h.digest("hex").slice(0, 16);
}
function emit(res: CycleResult): Promise<void> {
return postEvent({
source: "bot",
cycle_id: res.cycle_id,
sig_hash: sigHash(res.gap?.id ?? null, res.proposal?.summary ?? ""),
event_kind: res.outcome,
ok: res.outcome === "ok",
staffer_id: res.proposal?.model_used,
turns: res.cloud_calls,
duration_ms: new Date(res.ended_at).getTime() - new Date(res.started_at).getTime(),
extra: {
reason: res.reason,
pr_url: res.prUrl,
files_added: res.filesAdded,
files_updated: res.filesUpdated,
files_noop: res.filesNoop,
tests_green: res.testsGreen,
},
});
}
async function main() {
const cycle_id = `bot-${new Date().toISOString().replace(/[:.]/g, "-")}-${randomUUID().slice(0, 8)}`;
const started_at = new Date().toISOString();
log(`cycle ${cycle_id} starting`);
const finish = async (outcome: CycleOutcome, reason: string, extra: Partial<CycleResult> = {}): Promise<CycleResult> => {
const res: CycleResult = {
cycle_id,
started_at,
ended_at: new Date().toISOString(),
outcome,
reason,
gap: null,
proposal: null,
filesAdded: [],
filesUpdated: [],
filesNoop: [],
testsGreen: null,
testsOutput: "",
prUrl: null,
tokens_used: 0,
cloud_calls: 0,
...extra,
};
await persistResult(res);
await emit(res);
log(`→ ${outcome}: ${reason}`);
return res;
};
// 1. Pause.
if (await fileExists(PAUSE_FILE)) {
return finish("skipped_pause", `pause file at ${PAUSE_FILE}`);
}
// 2. Cost.
const cost = await readCost();
const bc = budgetCheck(cost);
if (!bc.ok) return finish("skipped_cost", bc.reason);
log(`cost: ${bc.reason}`);
// 3+4. Context + policy.shouldRunCycle.
const ctx = await gatherContext(cost);
const srq = policy.shouldRunCycle(ctx);
if (!srq.run) {
return finish(ctx.workingTreeDirty ? "skipped_dirty_tree" : "skipped_policy", srq.reason);
}
// 5. Gaps + policy.pickGap.
const gaps = await findGaps();
log(`found ${gaps.length} [bot-eligible] gap(s) in PRD`);
const gapPick = policy.pickGap(gaps);
if (!gapPick.gap) return finish("skipped_no_gap", gapPick.reason);
const gap = gapPick.gap;
log(`picked gap ${gap.id}: "${gap.prd_line.slice(0, 80)}"`);
// 6. KB lookup + Proposal.
// Bot cycles compound: prior cycles on this gap inform the current
// proposal. Summary is a compact block injected into the cloud prompt
// so the model can build on past successes and steer around past
// failures. Empty string when this is the first cycle on this gap.
const history = await loadHistory(gap.id, 5);
const stats = statsFor(history);
if (history.length > 0) {
log(`kb: ${stats.attempts} prior cycle(s) — ${stats.pr_opened} pr / ${stats.tests_failed} tests_failed / ${stats.proposal_rejected} rejected / ${stats.noop} noop`);
} else {
log(`kb: first cycle on this gap`);
}
const historySummary = summarizeHistory(history);
let proposal: Proposal;
try {
proposal = await generateProposal(gap, historySummary);
log(`proposal: ${proposal.summary} (${proposal.files.length} files, ~${proposal.estimated_loc} LOC, ${proposal.tokens_used} tokens)`);
} catch (e) {
await recordCost(1, 0);
return finish("model_failed", (e as Error).message, { gap, cloud_calls: 1 });
}
await recordCost(1, proposal.tokens_used);
// 7. Score.
const score = policy.scoreProposal(proposal);
if (!score.accept) {
return finish("proposal_rejected", score.reason, { gap, proposal, tokens_used: proposal.tokens_used, cloud_calls: 1 });
}
log(`proposal accepted: ${score.reason}`);
// 8. Branch.
const dayStamp = new Date().toISOString().slice(0, 10).replace(/-/g, "");
const branch = `bot/cycle-${dayStamp}-${gap.id}`;
// Branch must be fresh. If one already exists from a prior failed cycle
// for the same gap, bail — don't silently overwrite or reuse stale state.
const check = git(["rev-parse", "--verify", `refs/heads/${branch}`]);
if (check.code === 0) {
return finish("apply_failed",
`branch ${branch} already exists — delete it manually if you want a fresh cycle for this gap`,
{ gap, proposal, tokens_used: proposal.tokens_used, cloud_calls: 1 });
}
const co = git(["checkout", "-b", branch]);
if (co.code !== 0) {
return finish("apply_failed", `git checkout -b failed: ${co.stderr}`,
{ gap, proposal, tokens_used: proposal.tokens_used, cloud_calls: 1 });
}
log(`on branch ${branch}`);
// 9. Apply — Mem0 ADD/UPDATE/NOOP semantics.
const ap = await applyProposal(proposal);
if (ap.errors.length > 0) {
git(["checkout", "main"]);
git(["branch", "-D", branch]);
return finish("apply_failed", ap.errors.join("; "),
{ gap, proposal, tokens_used: proposal.tokens_used, cloud_calls: 1 });
}
const changed = [...ap.added, ...ap.updated];
log(`apply: ${ap.added.length} add, ${ap.updated.length} update, ${ap.noop.length} noop`);
// Short-circuit: if every proposed file was identical to what's on
// disk, there's nothing to test or PR. Clean up the branch and exit.
if (changed.length === 0) {
git(["checkout", "main"]);
git(["branch", "-D", branch]);
return finish("cycle_noop",
`all ${proposal.files.length} proposed file(s) were identical to current state`,
{ gap, proposal, filesNoop: ap.noop,
tokens_used: proposal.tokens_used, cloud_calls: 1 });
}
// 10. Test.
log(`running tests (this will take a while)...`);
const testRes = await runTests();
if (!testRes.green) {
git(["checkout", "."]);
git(["checkout", "main"]);
git(["branch", "-D", branch]);
return finish("tests_failed", "cargo or bun test failed", {
gap, proposal,
filesAdded: ap.added, filesUpdated: ap.updated, filesNoop: ap.noop,
testsGreen: false, testsOutput: testRes.output,
tokens_used: proposal.tokens_used, cloud_calls: 1,
});
}
log(`tests green`);
// 11. PR gate.
const partial: CycleResult = {
cycle_id, started_at,
ended_at: new Date().toISOString(),
outcome: "ok", reason: "",
gap, proposal,
filesAdded: ap.added, filesUpdated: ap.updated, filesNoop: ap.noop,
testsGreen: true, testsOutput: testRes.output,
prUrl: null, tokens_used: proposal.tokens_used, cloud_calls: 1,
};
const prGate = policy.shouldOpenPR(partial);
if (!prGate.open) {
git(["checkout", "main"]);
git(["branch", "-D", branch]);
return finish("pr_skipped_by_policy", prGate.reason, partial);
}
// 12. Commit + push.
git(["add", ...changed]);
const commitMsg = [
`bot: ${proposal.summary}`,
"",
proposal.rationale,
"",
`PRD gap: ${gap.prd_line}`,
`Cycle: ${cycle_id}`,
"",
`Co-Authored-By: ${proposal.model_used} (cloud) <bot@lakehouse.local>`,
].join("\n");
const ci = git(["commit", "-m", commitMsg]);
if (ci.code !== 0) {
return finish("pr_failed", `git commit failed: ${ci.stderr}`, partial);
}
const ps = git(["push", "-u", "origin", branch]);
if (ps.code !== 0) {
return finish("pr_failed", `git push failed: ${ps.stderr}`, partial);
}
// 13. Open PR.
try {
const pr = await openPr({
branch,
title: `bot: ${proposal.summary}`,
body: [
`**Gap** (PRD line ${gap.line_number}): ${gap.prd_line}`,
"",
`**Rationale**`,
proposal.rationale,
"",
`**Cycle**: \`${cycle_id}\``,
`**Model**: \`${proposal.model_used}\``,
`**Tokens**: ${proposal.tokens_used}`,
`**Added**: ${ap.added.map(f => `\`${f}\``).join(", ") || "_none_"}`,
`**Updated**: ${ap.updated.map(f => `\`${f}\``).join(", ") || "_none_"}`,
ap.noop.length > 0 ? `**NOOP (identical to current)**: ${ap.noop.map(f => `\`${f}\``).join(", ")}` : "",
].join("\n"),
});
return finish("ok", `PR #${pr.number} opened`, { ...partial, prUrl: pr.html_url });
} catch (e) {
return finish("pr_failed", (e as Error).message, partial);
}
}
main().catch(e => {
console.error("[bot] fatal:", e);
process.exit(1);
});
bot/kb.ts
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// Bot-local knowledge base. Every finished cycle already persists a
// CycleResult to data/_bot/cycles/{id}.json — that IS the outcome log.
// KB here just reads that dir, filters to prior cycles on the same gap,
// and produces a short summary the cloud model can condition on.
//
// No separate jsonl, no new write path, no embedding calls. The bot's
// "memory" is the same primary artifact that the observer consumes.
//
// Future: embedding-based neighbor matching across gaps (cheap once
// sidecar is local), cross-pollination with scenario KB's
// pathway_recommendations. Not required for the feedback loop to work
// on a single gap — that's the floor we're building first.
import { readdir, readFile } from "node:fs/promises";
import { join } from "node:path";
import type { CycleResult } from "./types.ts";
const CYCLES_DIR = "/home/profit/lakehouse/data/_bot/cycles";
export interface HistoryEntry {
cycle_id: string;
ended_at: string;
outcome: string;
reason: string;
pr_url: string | null;
tests_green: boolean | null;
files_added: string[];
files_updated: string[];
tokens_used: number;
}
export async function loadHistory(gap_id: string, max: number = 5): Promise<HistoryEntry[]> {
let entries: string[] = [];
try {
entries = await readdir(CYCLES_DIR);
} catch {
return [];
}
const matches: HistoryEntry[] = [];
for (const e of entries) {
if (!e.endsWith(".json")) continue;
try {
const raw = await readFile(join(CYCLES_DIR, e), "utf8");
const r = JSON.parse(raw) as CycleResult;
if (r.gap?.id !== gap_id) continue;
matches.push({
cycle_id: r.cycle_id,
ended_at: r.ended_at,
outcome: r.outcome,
reason: r.reason,
pr_url: r.prUrl,
tests_green: r.testsGreen,
files_added: r.filesAdded ?? [],
files_updated: r.filesUpdated ?? [],
tokens_used: r.tokens_used,
});
} catch {
// Skip unreadable / malformed cycle files. Don't fail the current
// cycle because an old one is corrupt.
}
}
matches.sort((a, b) => b.ended_at.localeCompare(a.ended_at));
return matches.slice(0, max);
}
// Compact prompt-ready summary. Empty string when there's no history —
// caller can skip the "prior attempts" block entirely.
export function summarizeHistory(h: HistoryEntry[]): string {
if (h.length === 0) return "";
const lines = h.map(e => {
const when = e.ended_at.slice(0, 16).replace("T", " ");
const files = [...e.files_added, ...e.files_updated];
const filesStr = files.length > 0 ? ` touched: ${files.join(", ")}` : "";
const prStr = e.pr_url ? ` PR: ${e.pr_url}` : "";
return `- ${when} UTC — ${e.outcome}${prStr}${filesStr}\n reason: ${e.reason}`;
});
return [
`Prior attempts on this gap (${h.length} most recent):`,
...lines,
"",
"Learn from these: build on what worked, avoid paths that failed.",
].join("\n");
}
// Aggregate stats for telemetry — lets the bot expose "% of cycles on
// this gap that landed a PR" without re-parsing the raw history.
export function statsFor(h: HistoryEntry[]): {
attempts: number;
pr_opened: number;
tests_failed: number;
proposal_rejected: number;
noop: number;
} {
return {
attempts: h.length,
pr_opened: h.filter(e => e.outcome === "ok").length,
tests_failed: h.filter(e => e.outcome === "tests_failed").length,
proposal_rejected: h.filter(e => e.outcome === "proposal_rejected").length,
noop: h.filter(e => e.outcome === "cycle_noop").length,
};
}
bot/observer.ts
+34
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@@ -0,0 +1,34 @@
// POST bot cycle outcomes to the observer on :3800 so they accumulate
// in the KB alongside scenario + autotune events. Non-fatal on failure:
// the cycle result is also written to disk so observability isn't a
// single point of failure.
const OBSERVER_URL = process.env.LH_OBSERVER_URL ?? "http://localhost:3800";
export interface BotObserverEvent {
source: "bot";
cycle_id: string;
sig_hash: string; // stable hash of the gap + proposal for dedup
event_kind: string; // cycle outcome (ok, tests_failed, ...)
ok: boolean;
staffer_id?: string; // the model used, e.g. "gpt-oss:120b"
turns?: number; // number of cloud calls this cycle
duration_ms?: number;
extra?: Record<string, any>;
}
export async function postEvent(ev: BotObserverEvent): Promise<void> {
try {
const r = await fetch(`${OBSERVER_URL}/event`, {
method: "POST",
headers: { "content-type": "application/json" },
body: JSON.stringify(ev),
signal: AbortSignal.timeout(3000),
});
if (!r.ok) {
console.error(`[bot/observer] ${r.status}: ${await r.text()}`);
}
} catch (e) {
console.error(`[bot/observer] POST failed: ${(e as Error).message}`);
}
}
bot/policy.ts
+70
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@@ -0,0 +1,70 @@
// ═══════════════════════════════════════════════════════════════════
// YOU WRITE THIS FILE. These four functions shape the bot's entire
// behavior. Defaults below are safe and intentionally conservative —
// redline to taste. Each function is ~5-10 lines.
// ═══════════════════════════════════════════════════════════════════
import type { CycleContext, CycleResult, Gap, Proposal } from "./types.ts";
// Should this cycle even fire right now?
// Called AFTER the pause-file check + cost-cap check have already passed.
// Use this for judgment calls (cooldown, shared-resource conflicts).
export function shouldRunCycle(ctx: CycleContext): { run: boolean; reason: string } {
if (ctx.workingTreeDirty) {
return { run: false, reason: "dirty working tree — commit or stash your work first" };
}
if (ctx.lastCycleAt) {
const minsAgo = (Date.now() - new Date(ctx.lastCycleAt).getTime()) / 60000;
if (minsAgo < 10) return { run: false, reason: `last cycle ${minsAgo.toFixed(1)}min ago (<10min cooldown)` };
}
if (ctx.autotuneBusy) {
return { run: false, reason: "autotune agent is running — avoid Ollama contention" };
}
return { run: true, reason: "ready" };
}
// Which [bot-eligible] gap from the PRD does this cycle attack?
// Default: first in document order. Alternatives: random for exploration,
// smallest-context, tag-priority (e.g. [bot-eligible:high] wins).
export function pickGap(gaps: Gap[]): { gap: Gap | null; reason: string } {
if (gaps.length === 0) return { gap: null, reason: "no [bot-eligible] gaps in PRD" };
return { gap: gaps[0], reason: "first eligible gap in document order" };
}
// Cheap gate BEFORE burning test cycles. Reject if proposal smells wrong.
// This catches runaway cloud proposals before we apply+test them.
export function scoreProposal(p: Proposal): { accept: boolean; reason: string } {
if (p.files.length === 0) return { accept: false, reason: "empty proposal" };
if (p.files.length > 5) return { accept: false, reason: `>5 files (${p.files.length})` };
if (p.estimated_loc > 200) return { accept: false, reason: `>200 LOC (${p.estimated_loc})` };
// Paths the bot is NEVER allowed to touch. Extend cautiously.
const forbidden = [
".git/", "secrets", "lakehouse.toml",
"docs/ADR-", "docs/DECISIONS.md", "docs/PRD.md",
"/etc/", "/root/", "Cargo.lock",
];
for (const f of p.files) {
const hit = forbidden.find(prefix => f.path.includes(prefix));
if (hit) return { accept: false, reason: `touches forbidden path (${hit}): ${f.path}` };
}
return { accept: true, reason: "passes size + path filters" };
}
// Tests are green and changes applied. Do we actually open a PR?
// Mem0-aligned: the cycle already short-circuited with `cycle_noop` if
// every file was identical to current state, so by the time we're here
// at least one file was actually added or updated.
//
// Stricter options below — uncomment to require a test file touched, etc.
export function shouldOpenPR(c: CycleResult): { open: boolean; reason: string } {
if (!c.testsGreen) return { open: false, reason: "tests red" };
const realChanges = c.filesAdded.length + c.filesUpdated.length;
if (realChanges === 0) return { open: false, reason: "nothing actually changed on disk" };
// Stricter option — require at least one test file in the diff:
// const hasTest = [...c.filesAdded, ...c.filesUpdated].some(f => /test|_tests/i.test(f));
// if (!hasTest) return { open: false, reason: "no test file touched" };
return { open: true, reason: `green + ${c.filesAdded.length} add / ${c.filesUpdated.length} update` };
}
bot/pr.ts
+58
View File
@@ -0,0 +1,58 @@
// Gitea PR open. Reads PAT from ~/.git-credentials (set up by the
// credential-helper flow). All PRs open as DRAFT so J reviews before
// merge — never auto-merge.
import { readFile } from "node:fs/promises";
const GITEA_HOST = "https://git.agentview.dev";
const REPO_OWNER = "profit";
const REPO_NAME = "lakehouse";
const CRED_FILE = "/home/profit/.git-credentials";
async function readPat(): Promise<string> {
const raw = await readFile(CRED_FILE, "utf8");
for (const line of raw.split("\n")) {
const m = line.match(/^https:\/\/[^:]+:([^@]+)@git\.agentview\.dev/);
if (m) return m[1];
}
throw new Error(`no Gitea PAT found in ${CRED_FILE}`);
}
export interface OpenPrInput {
branch: string; // head branch (just pushed)
base?: string; // default "main"
title: string;
body: string;
}
export interface OpenPrResult {
number: number;
html_url: string;
}
export async function openPr(input: OpenPrInput): Promise<OpenPrResult> {
const pat = await readPat();
const url = `${GITEA_HOST}/api/v1/repos/${REPO_OWNER}/${REPO_NAME}/pulls`;
const payload = {
title: input.title,
body: input.body,
head: input.branch,
base: input.base ?? "main",
// Gitea uses "draft" flag — always on for bot PRs.
draft: true,
};
const r = await fetch(url, {
method: "POST",
headers: {
"content-type": "application/json",
"Authorization": `token ${pat}`,
},
body: JSON.stringify(payload),
signal: AbortSignal.timeout(30000),
});
if (!r.ok) {
throw new Error(`Gitea ${r.status}: ${await r.text()}`);
}
const j = await r.json() as any;
return { number: j.number, html_url: j.html_url };
}
bot/propose.ts
+141
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@@ -0,0 +1,141 @@
// Gap detection + cloud proposal.
//
// Gap detection: scan docs/PRD.md for lines tagged [bot-eligible].
// Each match becomes a Gap with surrounding context.
//
// Proposal: one-shot call to the T3 cloud model via the Python sidecar's
// /generate endpoint. Asks for a structured JSON response with file
// contents. Truncation-resistant via Phase 21's generate_continuable —
// for now we pass max_tokens high and rely on the model completing in
// one pass; swap to the Rust continuation wrapper if we see truncation.
import { readFile } from "node:fs/promises";
import { createHash } from "node:crypto";
import type { Gap, Proposal } from "./types.ts";
const SIDECAR_URL = process.env.LH_SIDECAR_URL ?? "http://localhost:3200";
const REPO_ROOT = "/home/profit/lakehouse";
const PRD_PATH = `${REPO_ROOT}/docs/PRD.md`;
const CLOUD_MODEL = process.env.LH_BOT_MODEL ?? "gpt-oss:120b";
const MAX_TOKENS = 6000;
export async function findGaps(): Promise<Gap[]> {
const prd = await readFile(PRD_PATH, "utf8");
const lines = prd.split("\n");
const gaps: Gap[] = [];
for (let i = 0; i < lines.length; i++) {
if (!lines[i].includes("[bot-eligible]")) continue;
const contextLines = lines.slice(i, Math.min(i + 6, lines.length)).join("\n");
const id = createHash("sha256").update(lines[i]).digest("hex").slice(0, 12);
gaps.push({
id,
prd_line: lines[i].trim(),
context: contextLines,
source_file: "docs/PRD.md",
line_number: i + 1,
});
}
return gaps;
}
const SYSTEM_PROMPT = `You are an assistant that proposes small, testable code changes to the Lakehouse repo.
The Lakehouse is a Rust-first data platform with 13 crates + Bun/TypeScript test harness.
You will be given one PRD gap tagged [bot-eligible] and must respond with a STRICT JSON object — no prose.
Rules:
- Response MUST be a single JSON object, no markdown fences, no commentary.
- Change MUST be small: <200 lines total, ≤5 files.
- Include at least one test file (new or modified) that proves the change.
- NEVER touch .git/, secrets, lakehouse.toml, docs/ADR-*, docs/DECISIONS.md, docs/PRD.md, /etc/, /root/, Cargo.lock.
- Paths MUST be repo-relative (no leading /).
- Whole-file contents only — no patches, no diffs.
Response shape:
{
"summary": "one line",
"rationale": "why this addresses the gap",
"files": [ { "path": "crates/foo/src/bar.rs", "content": "<full file>", "is_new": false } ],
"estimated_loc": 42
}`;
export async function generateProposal(gap: Gap, historySummary: string = ""): Promise<Proposal> {
const sections = [
`PRD gap (line ${gap.line_number}):`,
"```",
gap.context,
"```",
"",
];
if (historySummary) {
sections.push(historySummary, "");
}
sections.push("Propose a small change that addresses this gap. Respond with the JSON object only.");
const userPrompt = sections.join("\n");
const r = await fetch(`${SIDECAR_URL}/generate`, {
method: "POST",
headers: { "content-type": "application/json" },
body: JSON.stringify({
model: CLOUD_MODEL,
system: SYSTEM_PROMPT,
prompt: userPrompt,
temperature: 0.2,
max_tokens: MAX_TOKENS,
think: false,
}),
signal: AbortSignal.timeout(180000), // cloud T3 can be slow — 3 min
});
if (!r.ok) {
throw new Error(`sidecar ${r.status}: ${await r.text()}`);
}
const j = await r.json() as any;
const raw: string = j.text ?? j.response ?? "";
const usage = j.usage ?? {};
const tokens = (usage.prompt_tokens ?? 0) + (usage.completion_tokens ?? 0);
const parsed = extractJson(raw);
if (!parsed || typeof parsed !== "object") {
throw new Error(`model returned no JSON object. Raw head: ${raw.slice(0, 300)}`);
}
if (!Array.isArray(parsed.files)) {
throw new Error(`proposal.files not an array: ${JSON.stringify(parsed).slice(0, 200)}`);
}
return {
summary: String(parsed.summary ?? "").trim(),
rationale: String(parsed.rationale ?? "").trim(),
files: parsed.files.map((f: any) => ({
path: String(f.path ?? ""),
content: String(f.content ?? ""),
is_new: Boolean(f.is_new),
})).filter((f: any) => f.path && f.content !== undefined),
estimated_loc: Number(parsed.estimated_loc ?? 0),
model_used: CLOUD_MODEL,
tokens_used: tokens,
};
}
// Find the first balanced JSON object in the string. Tolerates leading
// "```json" fences even though we asked the model not to emit them.
function extractJson(text: string): any | null {
const cleaned = text.replace(/^```(?:json)?\s*/i, "").replace(/```\s*$/i, "").trim();
let depth = 0;
let start = -1;
for (let i = 0; i < cleaned.length; i++) {
const c = cleaned[i];
if (c === "{") {
if (depth === 0) start = i;
depth++;
} else if (c === "}") {
depth--;
if (depth === 0 && start >= 0) {
try {
return JSON.parse(cleaned.slice(start, i + 1));
} catch {
start = -1;
}
}
}
}
return null;
}
bot/test.ts
+46
View File
@@ -0,0 +1,46 @@
// Run the test gate: `cargo test --workspace` + `bun test`.
// Returns { green, output } — output is last ~4KB of combined stdout/stderr.
import { spawn } from "node:child_process";
const REPO_ROOT = "/home/profit/lakehouse";
const TEST_TIMEOUT_MS = 15 * 60 * 1000; // 15 min cargo + 2 min bun, generous
export async function runTests(): Promise<{ green: boolean; output: string }> {
const cargoOut = await runCmd("cargo", ["test", "--workspace", "--quiet", "--", "--test-threads=1"], REPO_ROOT, TEST_TIMEOUT_MS);
if (cargoOut.code !== 0) {
return { green: false, output: tail(`cargo test (${cargoOut.code}):\n${cargoOut.combined}`) };
}
const bunOut = await runCmd("bun", ["test", "tests/multi-agent"], REPO_ROOT, 120000);
const bunGreen = bunOut.code === 0;
const combined = [
`cargo test: OK`,
`bun test (${bunOut.code}):`,
bunOut.combined,
].join("\n");
return { green: bunGreen, output: tail(combined) };
}
function tail(s: string, n = 4096): string {
return s.length > n ? "…" + s.slice(-n) : s;
}
function runCmd(
cmd: string,
args: string[],
cwd: string,
timeoutMs: number,
): Promise<{ code: number; combined: string }> {
return new Promise(resolveP => {
const child = spawn(cmd, args, { cwd, env: { ...process.env } });
let combined = "";
child.stdout.on("data", d => { combined += d.toString(); });
child.stderr.on("data", d => { combined += d.toString(); });
const timer = setTimeout(() => child.kill("SIGKILL"), timeoutMs);
child.on("close", code => {
clearTimeout(timer);
resolveP({ code: code ?? -1, combined });
});
});
}
bot/types.ts
+88
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@@ -0,0 +1,88 @@
// Shared types for the PR-bot. Small and explicit — the bot's behavior
// should be readable from these shapes alone.
export interface Gap {
id: string; // hash of prd_line — stable key for dedup
prd_line: string; // the [bot-eligible]-tagged line, verbatim
context: string; // surrounding context from the PRD (next ~5 lines)
source_file: string; // e.g. "docs/PRD.md"
line_number: number;
}
export interface ProposalFile {
path: string; // repo-relative (no leading slash)
content: string; // full file content — the bot writes whole files, not patches
is_new: boolean; // true = create, false = overwrite existing
}
export interface Proposal {
summary: string; // one-line
rationale: string; // why this change addresses the gap
files: ProposalFile[];
estimated_loc: number; // total added+changed lines across all files
model_used: string;
tokens_used: number;
}
export interface CycleContext {
startedAt: string; // ISO
dailyCallsUsed: number;
dailyCallsBudget: number;
dailyTokensUsed: number;
dailyTokensBudget: number;
lastCycleAt: string | null;
lastCycleGapId: string | null;
autotuneBusy: boolean;
workingTreeDirty: boolean;
}
export type CycleOutcome =
| "ok" // PR opened
| "skipped_pause"
| "skipped_cost"
| "skipped_policy"
| "skipped_no_gap"
| "skipped_dirty_tree"
| "proposal_rejected"
| "apply_failed"
| "cycle_noop" // proposal applied but every file was identical to what's on disk
| "tests_failed"
| "pr_skipped_by_policy"
| "pr_failed"
| "model_failed";
// Mem0-aligned apply outcomes. Three shapes instead of the binary
// "written" / "errored". NOOP is a first-class outcome — identical
// content shouldn't waste test cycles or open an empty PR.
export type ApplyMode = "add" | "update" | "noop";
export interface ApplyOutcome {
added: string[];
updated: string[];
noop: string[];
errors: string[];
}
export interface CycleResult {
cycle_id: string;
started_at: string;
ended_at: string;
outcome: CycleOutcome;
reason: string;
gap: Gap | null;
proposal: Proposal | null;
filesAdded: string[];
filesUpdated: string[];
filesNoop: string[];
testsGreen: boolean | null; // null = not run
testsOutput: string;
prUrl: string | null;
tokens_used: number;
cloud_calls: number;
}
export interface CostState {
date: string; // YYYY-MM-DD UTC
calls: number;
tokens: number;
}
bun.lock
+18
View File
@@ -0,0 +1,18 @@
{
"lockfileVersion": 1,
"configVersion": 1,
"workspaces": {
"": {
"dependencies": {
"langfuse": "^3.38.20",
},
},
},
"packages": {
"langfuse": ["langfuse@3.38.20", "", { "dependencies": { "langfuse-core": "^3.38.20" } }, "sha512-MAmBAASSzJtmK1O9HQegA1mFsQhT8Yf+OJRGvE7FXkyv3g/eiBE0glLD0Ohg3pkxhoPdggM5SejK7ue9ctlaMA=="],
"langfuse-core": ["langfuse-core@3.38.20", "", { "dependencies": { "mustache": "^4.2.0" } }, "sha512-zBKVmQN/1oT5VWZUBYlWzvokIlkC/6mnpgr/2atMyTeAm+jR3ia7w2iJMjlrF5/oG8ukO1s8+LDRCzJpF1QeEA=="],
"mustache": ["mustache@4.2.0", "", { "bin": { "mustache": "bin/mustache" } }, "sha512-71ippSywq5Yb7/tVYyGbkBggbU8H3u5Rz56fH60jGFgr8uHwxs+aSKeqmluIVzM0m0kB7xQjKS6qPfd0b2ZoqQ=="],
}
}
crates/aibridge/src/client.rs
+9 -1
View File
@@ -25,7 +25,7 @@ pub struct EmbedResponse {
pub dimensions: usize, pub dimensions: usize,
} }
#[derive(Serialize, Deserialize)] #[derive(Clone, Serialize, Deserialize)]
pub struct GenerateRequest { pub struct GenerateRequest {
pub prompt: String, pub prompt: String,
#[serde(skip_serializing_if = "Option::is_none")] #[serde(skip_serializing_if = "Option::is_none")]
@@ -36,6 +36,14 @@ pub struct GenerateRequest {
pub temperature: Option<f64>, pub temperature: Option<f64>,
#[serde(skip_serializing_if = "Option::is_none")] #[serde(skip_serializing_if = "Option::is_none")]
pub max_tokens: Option<u32>, pub max_tokens: Option<u32>,
/// Phase 21 — per-call opt-out of hidden reasoning. Thinking models
/// (qwen3.5, gpt-oss, etc) burn tokens on reasoning before the
/// visible response starts; setting this to `false` on hot-path
/// JSON emitters avoids empty returns when the budget is tight.
/// Sidecar forwards this to Ollama's `think` parameter; if the
/// sidecar drops an unknown field the request still succeeds.
#[serde(skip_serializing_if = "Option::is_none")]
pub think: Option<bool>,
} }
#[derive(Deserialize, Serialize, Clone)] #[derive(Deserialize, Serialize, Clone)]
crates/aibridge/src/context.rs
+194
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@@ -0,0 +1,194 @@
//! Phase 21 — context-budget accounting for model calls.
//!
//! Ports `assertContextBudget` + `estimateTokens` + `CONTEXT_WINDOWS`
//! from `tests/multi-agent/agent.ts` so Rust-side callers (gateway
//! tool surfaces, future Rust agents) get the same loud-fail behavior
//! on window overflow instead of silent truncation.
//!
//! The token estimator is deliberately the same chars/4 heuristic as
//! the TS side. It's biased ~15% safe — pessimistic on English, correct
//! within a factor of 2 on code. Swap to a provider tokenizer only when
//! the estimator drives a decision (we're nowhere near that yet).
use std::collections::HashMap;
use std::sync::OnceLock;
/// Rough token count. `chars / 4` ceiling. See module docs for why
/// this heuristic is sufficient.
pub fn estimate_tokens(text: &str) -> usize {
(text.chars().count() + 3) / 4
}
/// Phase 21 — per-model context windows, mirroring the TS table in
/// `tests/multi-agent/agent.ts`. Anchored on each model's documented
/// max; unknown models fall back to `DEFAULT_CONTEXT_WINDOW`.
pub const DEFAULT_CONTEXT_WINDOW: usize = 32_768;
pub const DEFAULT_SAFETY_MARGIN: usize = 2_000;
pub const DEFAULT_MAX_TOKENS: usize = 800;
fn known_windows() -> &'static HashMap<&'static str, usize> {
static TABLE: OnceLock<HashMap<&'static str, usize>> = OnceLock::new();
TABLE.get_or_init(|| {
let mut m = HashMap::new();
m.insert("mistral:latest", 32_768);
m.insert("qwen2.5:latest", 32_768);
m.insert("qwen3:latest", 40_960);
m.insert("qwen3.5:latest", 262_144);
m.insert("qwen3-embedding", 32_768);
m.insert("nomic-embed-text-v2-moe", 2_048);
m.insert("gpt-oss:20b", 131_072);
m.insert("gpt-oss:120b", 131_072);
m.insert("qwen3.5:397b", 131_072);
m.insert("kimi-k2-thinking", 200_000);
m.insert("kimi-k2.6", 200_000);
m.insert("kimi-k2:1t", 1_048_576);
m.insert("deepseek-v3.1:671b", 131_072);
m.insert("glm-4.7", 131_072);
m
})
}
pub fn context_window_for(model: &str) -> usize {
known_windows().get(model).copied().unwrap_or(DEFAULT_CONTEXT_WINDOW)
}
/// Result of a budget check — exposes the numbers so callers can log
/// how much headroom remains without re-running the estimator.
#[derive(Debug, Clone, Copy)]
pub struct BudgetCheck {
pub estimated: usize,
pub window: usize,
pub remaining: i64,
}
/// Inputs to `assert_context_budget`. `bypass` exists for call sites
/// that handle their own overflow (continuation's second pass already
/// counted the partial; T5 gatekeeper prompts have a separate policy).
#[derive(Debug, Clone, Default)]
pub struct BudgetOpts<'a> {
pub system: Option<&'a str>,
pub max_tokens: Option<usize>,
pub safety_margin: Option<usize>,
pub bypass: bool,
}
/// Phase 21's loud-fail primitive. Returns a `BudgetCheck` on success
/// and the same struct plus over-by count on failure. The whole point
/// is to stop silent truncation — callers that expect overflow should
/// chunk BEFORE calling or set `bypass: true`.
pub fn assert_context_budget(
model: &str,
prompt: &str,
opts: BudgetOpts,
) -> Result<BudgetCheck, (BudgetCheck, usize)> {
let window = context_window_for(model);
let safety = opts.safety_margin.unwrap_or(DEFAULT_SAFETY_MARGIN);
let max_tokens = opts.max_tokens.unwrap_or(DEFAULT_MAX_TOKENS);
let sys_tokens = opts.system.map(estimate_tokens).unwrap_or(0);
let estimated = estimate_tokens(prompt) + sys_tokens + max_tokens;
let remaining = window as i64 - estimated as i64 - safety as i64;
let check = BudgetCheck { estimated, window, remaining };
if remaining < 0 && !opts.bypass {
return Err((check, (-remaining) as usize));
}
Ok(check)
}
/// Convenience — format an overflow error the same way the TS side
/// does. Exposed so downstream crates render consistent messages.
pub fn overflow_message(model: &str, check: &BudgetCheck, over_by: usize, safety: usize) -> String {
format!(
"context overflow: model={} est={}t window={}t safety={}t over={}t. \
Chunk the prompt (see config/models.json overflow_policies) or set \
bypass:true if you know the risk.",
model, check.estimated, check.window, safety, over_by,
)
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn estimate_tokens_ceiling_divides_by_four() {
assert_eq!(estimate_tokens(""), 0);
assert_eq!(estimate_tokens("abc"), 1); // 3 → ceil(3/4) = 1
assert_eq!(estimate_tokens("abcd"), 1); // 4 → ceil(4/4) = 1
assert_eq!(estimate_tokens("abcde"), 2); // 5 → ceil(5/4) = 2
assert_eq!(estimate_tokens(&"x".repeat(400)), 100);
}
#[test]
fn context_window_known_and_fallback() {
assert_eq!(context_window_for("qwen3.5:latest"), 262_144);
assert_eq!(context_window_for("kimi-k2:1t"), 1_048_576);
assert_eq!(context_window_for("some-unreleased-model"), DEFAULT_CONTEXT_WINDOW);
}
#[test]
fn budget_passes_well_under_window() {
let check = assert_context_budget(
"qwen3:latest",
&"x".repeat(4_000), // ~1000 tokens
BudgetOpts { max_tokens: Some(500), ..Default::default() },
).expect("well under 40K window");
assert!(check.remaining > 30_000);
}
#[test]
fn budget_fails_when_prompt_overflows_window() {
let huge = "x".repeat(200_000); // ~50K tokens, over qwen3's 40K
let err = assert_context_budget(
"qwen3:latest",
&huge,
BudgetOpts::default(),
).expect_err("should overflow qwen3's 40K window");
assert!(err.1 > 0, "over_by must be positive");
}
#[test]
fn budget_bypass_returns_ok_even_over() {
let huge = "x".repeat(200_000);
let check = assert_context_budget(
"qwen3:latest",
&huge,
BudgetOpts { bypass: true, ..Default::default() },
).expect("bypass must suppress the error");
assert!(check.remaining < 0, "check still reports negative remaining");
}
#[test]
fn budget_counts_system_prompt() {
// 10K-char system prompt → ~2500 tokens. With a big max_tokens
// this should push us closer to the window.
let sys = "s".repeat(10_000);
let prompt = "p".repeat(4_000);
let with_sys = assert_context_budget(
"qwen3:latest",
&prompt,
BudgetOpts {
system: Some(&sys),
max_tokens: Some(500),
..Default::default()
},
).unwrap();
let without_sys = assert_context_budget(
"qwen3:latest",
&prompt,
BudgetOpts { max_tokens: Some(500), ..Default::default() },
).unwrap();
assert!(with_sys.estimated > without_sys.estimated,
"system prompt should raise estimate");
assert_eq!(with_sys.estimated - without_sys.estimated, estimate_tokens(&sys));
}
#[test]
fn overflow_message_includes_numbers() {
let check = BudgetCheck { estimated: 42_000, window: 40_960, remaining: -1_040 };
let msg = overflow_message("qwen3:latest", &check, 3_040, 2_000);
assert!(msg.contains("qwen3:latest"));
assert!(msg.contains("42000t"));
assert!(msg.contains("40960t"));
assert!(msg.contains("3040t"));
}
}
crates/aibridge/src/continuation.rs
+438
View File
@@ -0,0 +1,438 @@
//! Phase 21 — OUTPUT-overflow handler. Ports `generateContinuable`
//! from `tests/multi-agent/agent.ts`.
//!
//! Two failure modes to repair:
//!
//! * EMPTY response — thinking model ate the entire budget on hidden
//! reasoning before emitting a token. Fix: retry the original prompt
//! with 2× the budget, geometric up to `BUDGET_CAP`.
//!
//! * TRUNCATED non-empty — model got most of the way but hit
//! max_tokens before closing the structure. Fix: continue with the
//! partial response in the prompt as scratchpad, so the model knows
//! where to pick up without restarting.
//!
//! `TextGenerator` abstracts the sidecar so tests can inject canned
//! responses without a live Ollama.
use std::future::Future;
use crate::client::{AiClient, GenerateRequest, GenerateResponse};
/// Shape classifier for `is_structurally_complete`. JSON responses
/// must parse; text responses just need to be non-empty.
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum ResponseShape {
Json,
Text,
}
/// Trait that `generate_continuable` + `generate_tree_split` call. The
/// real implementation forwards to `AiClient::generate`; tests supply a
/// mock with a scripted sequence of responses.
pub trait TextGenerator: Send + Sync {
fn generate_text(
&self,
req: GenerateRequest,
) -> impl Future<Output = Result<GenerateResponse, String>> + Send;
}
impl TextGenerator for AiClient {
fn generate_text(
&self,
req: GenerateRequest,
) -> impl Future<Output = Result<GenerateResponse, String>> + Send {
self.generate(req)
}
}
/// Strip a surrounding ```json``` fence if present. Leaves the inner
/// content alone otherwise. Returns a slice of `s`.
fn strip_json_fence(s: &str) -> &str {
let t = s.trim();
if let Some(rest) = t.strip_prefix("```json") {
rest.trim_start_matches('\n').strip_suffix("```").unwrap_or(rest).trim()
} else if let Some(rest) = t.strip_prefix("```") {
rest.trim_start_matches('\n').strip_suffix("```").unwrap_or(rest).trim()
} else {
t
}
}
/// Port of the TS brace-balance + JSON.parse check. Returns true when
/// the outermost `{...}` block is balanced and parses. Text shape is
/// satisfied by any non-empty, non-whitespace payload.
pub fn is_structurally_complete(text: &str, shape: ResponseShape) -> bool {
if text.trim().is_empty() { return false; }
if shape == ResponseShape::Text { return true; }
let s = strip_json_fence(text);
let Some(start) = s.find('{') else { return false };
let Some(end) = s.rfind('}') else { return false };
if end <= start { return false; }
let slice = &s[start..=end];
// Balance check — cheaper than parse, catches truncated nests.
// String state tracked because `{` inside a string doesn't count.
let mut depth: i32 = 0;
let mut in_str = false;
let mut esc = false;
for c in slice.chars() {
if esc { esc = false; continue; }
if c == '\\' { esc = true; continue; }
if c == '"' { in_str = !in_str; continue; }
if in_str { continue; }
if c == '{' { depth += 1; }
else if c == '}' {
depth -= 1;
if depth < 0 { return false; }
}
}
if depth != 0 { return false; }
// Parse check is the tie-breaker — balanced but invalid JSON (e.g.
// trailing comma before `}`) shouldn't count as complete.
serde_json::from_str::<serde_json::Value>(slice).is_ok()
}
/// Knobs for `generate_continuable`. All optional with sensible
/// defaults that match the TS version.
#[derive(Debug, Clone)]
pub struct ContinuableOpts {
pub model: String,
pub max_tokens: Option<u32>,
pub temperature: Option<f64>,
pub system: Option<String>,
pub shape: ResponseShape,
pub max_continuations: usize,
pub think: Option<bool>,
/// Geometric-backoff ceiling for the empty-response retry path.
/// Matches TS's `budgetCap = 8000`.
pub budget_cap: u32,
/// Maximum empty-response retries before giving up. Matches TS's
/// hardcoded `retry < 3`.
pub max_empty_retries: usize,
}
impl ContinuableOpts {
pub fn new(model: impl Into<String>) -> Self {
Self {
model: model.into(),
max_tokens: None,
temperature: None,
system: None,
shape: ResponseShape::Json,
max_continuations: 3,
think: None,
budget_cap: 8_000,
max_empty_retries: 3,
}
}
}
/// Outcome of a `generate_continuable` call. Carries the combined
/// text plus diagnostic counters so observability downstream can
/// report "how many continuations did that query cost".
#[derive(Debug, Clone)]
pub struct ContinuableOutcome {
pub text: String,
pub empty_retries: usize,
pub continuations: usize,
pub final_complete: bool,
}
fn make_request(opts: &ContinuableOpts, prompt: String, current_max: u32) -> GenerateRequest {
GenerateRequest {
prompt,
model: Some(opts.model.clone()),
system: opts.system.clone(),
temperature: opts.temperature,
max_tokens: Some(current_max),
think: opts.think,
}
}
fn continuation_prompt(original: &str, partial: &str) -> String {
format!(
"{original}\n\n\
PARTIAL RESPONSE SO FAR (continue from here — do NOT restart, \
do NOT repeat what's already there, emit ONLY the remaining \
tokens to close the structure):\n{partial}"
)
}
/// Phase 21 — output-overflow safe generate. See module docs for the
/// two failure modes repaired. On final-still-incomplete, returns the
/// combined text with `final_complete: false` so the caller's parser
/// can throw with the raw text for forensics rather than silently
/// truncating.
pub async fn generate_continuable<G: TextGenerator>(
generator: &G,
prompt: &str,
opts: &ContinuableOpts,
) -> Result<ContinuableOutcome, String> {
let initial_max = opts.max_tokens.unwrap_or(800);
let mut current_max = initial_max;
let mut combined = String::new();
let mut empty_retries = 0usize;
let mut continuations = 0usize;
// Phase 21(a) — empty-response backoff loop.
for retry in 0..opts.max_empty_retries {
let req = make_request(opts, prompt.to_string(), current_max);
let resp = generator.generate_text(req).await?;
if !resp.text.trim().is_empty() {
combined = resp.text;
break;
}
empty_retries = retry + 1;
current_max = (current_max.saturating_mul(2)).min(opts.budget_cap);
}
// Phase 21(b) — structural-completion continuation loop. Runs on
// the truncated-non-empty case; empty + exhausted retries falls
// through with empty combined and final_complete=false.
for _ in 0..opts.max_continuations {
if is_structurally_complete(&combined, opts.shape) {
return Ok(ContinuableOutcome {
text: combined,
empty_retries,
continuations,
final_complete: true,
});
}
if combined.trim().is_empty() {
// Nothing to continue from — continuing "" is identical to
// the initial call and would loop. Bail so the caller sees
// the failure rather than burning N extra calls.
break;
}
let cont_prompt = continuation_prompt(prompt, &combined);
let req = make_request(opts, cont_prompt, current_max.min(opts.budget_cap));
let resp = generator.generate_text(req).await?;
combined.push_str(&resp.text);
continuations += 1;
}
let final_complete = is_structurally_complete(&combined, opts.shape);
Ok(ContinuableOutcome {
text: combined,
empty_retries,
continuations,
final_complete,
})
}
/// Scripted generator for unit tests. Returns responses from `script`
/// in order; extra calls reuse the last entry so tests don't have to
/// count past what they actually assert on.
#[cfg(test)]
pub struct ScriptedGenerator {
script: Vec<Result<String, String>>,
calls: std::sync::Arc<std::sync::Mutex<Vec<GenerateRequest>>>,
}
#[cfg(test)]
impl ScriptedGenerator {
pub fn new<I>(script: I) -> Self
where
I: IntoIterator<Item = Result<String, String>>,
{
Self {
script: script.into_iter().collect(),
calls: std::sync::Arc::new(std::sync::Mutex::new(Vec::new())),
}
}
pub fn calls(&self) -> Vec<GenerateRequest> {
self.calls.lock().unwrap().clone()
}
pub fn call_count(&self) -> usize {
self.calls.lock().unwrap().len()
}
}
#[cfg(test)]
impl TextGenerator for ScriptedGenerator {
fn generate_text(
&self,
req: GenerateRequest,
) -> impl Future<Output = Result<GenerateResponse, String>> + Send {
let i = {
let mut calls = self.calls.lock().unwrap();
let i = calls.len();
calls.push(req.clone());
i
};
let model = req.model.clone().unwrap_or_default();
let entry = self.script.get(i)
.cloned()
.unwrap_or_else(|| self.script.last().cloned().unwrap_or(Ok(String::new())));
async move {
entry.map(|text| GenerateResponse {
text,
model,
tokens_evaluated: None,
tokens_generated: None,
})
}
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn structural_complete_rejects_empty_and_text_mismatch() {
assert!(!is_structurally_complete("", ResponseShape::Text));
assert!(!is_structurally_complete(" ", ResponseShape::Text));
assert!(is_structurally_complete("any content", ResponseShape::Text));
}
#[test]
fn structural_complete_handles_balanced_json() {
assert!(is_structurally_complete(r#"{"a": 1}"#, ResponseShape::Json));
assert!(is_structurally_complete(
r#"```json
{"a": 1, "b": [1, 2, 3]}
```"#,
ResponseShape::Json,
));
}
#[test]
fn structural_complete_rejects_truncated_json() {
assert!(!is_structurally_complete(r#"{"a": 1"#, ResponseShape::Json));
assert!(!is_structurally_complete(r#"{"a": {"b": 1"#, ResponseShape::Json));
// Trailing comma — balanced but unparseable
assert!(!is_structurally_complete(r#"{"a": 1,}"#, ResponseShape::Json));
}
#[test]
fn structural_complete_ignores_braces_inside_strings() {
assert!(is_structurally_complete(r#"{"s": "has } inside"}"#, ResponseShape::Json));
assert!(is_structurally_complete(r#"{"s": "escaped \" quote"}"#, ResponseShape::Json));
}
#[tokio::test]
async fn continuable_returns_first_response_when_complete() {
let generator = ScriptedGenerator::new(vec![Ok(r#"{"ok": true}"#.to_string())]);
let opts = ContinuableOpts::new("qwen3:latest");
let out = generate_continuable(&generator, "test", &opts).await.unwrap();
assert!(out.final_complete);
assert_eq!(out.empty_retries, 0);
assert_eq!(out.continuations, 0);
assert_eq!(generator.call_count(), 1);
}
#[tokio::test]
async fn continuable_retries_on_empty_with_doubled_budget() {
// Two empties, then a good response. Third call should see 4×
// the initial budget (2× twice).
let generator = ScriptedGenerator::new(vec![
Ok("".to_string()),
Ok("".to_string()),
Ok(r#"{"ok": true}"#.to_string()),
]);
let mut opts = ContinuableOpts::new("qwen3:latest");
opts.max_tokens = Some(100);
let out = generate_continuable(&generator, "test", &opts).await.unwrap();
assert!(out.final_complete);
assert_eq!(out.empty_retries, 2);
let calls = generator.calls();
assert_eq!(calls.len(), 3);
assert_eq!(calls[0].max_tokens, Some(100));
assert_eq!(calls[1].max_tokens, Some(200));
assert_eq!(calls[2].max_tokens, Some(400));
}
#[tokio::test]
async fn continuable_caps_budget_at_budget_cap() {
let generator = ScriptedGenerator::new(vec![
Ok("".to_string()),
Ok("".to_string()),
Ok(r#"{"ok":1}"#.to_string()),
]);
let mut opts = ContinuableOpts::new("qwen3:latest");
opts.max_tokens = Some(5_000);
opts.budget_cap = 7_000;
let out = generate_continuable(&generator, "test", &opts).await.unwrap();
assert!(out.final_complete);
let calls = generator.calls();
// 5000 → doubled would be 10000; cap pulls it to 7000.
assert_eq!(calls[1].max_tokens, Some(7_000));
assert_eq!(calls[2].max_tokens, Some(7_000));
}
#[tokio::test]
async fn continuable_glues_truncated_response() {
// First call returns balanced-open `{...`; continuation closes
// it with `...}`. Combined must parse.
let generator = ScriptedGenerator::new(vec![
Ok(r#"{"fills": [{"candidate_id": "C-001""#.to_string()),
Ok(r#", "name": "Alice"}]}"#.to_string()),
]);
let opts = ContinuableOpts::new("qwen3:latest");
let out = generate_continuable(&generator, "ORIGINAL", &opts).await.unwrap();
assert!(out.final_complete, "combined must parse: {}", out.text);
assert_eq!(out.continuations, 1);
let calls = generator.calls();
assert_eq!(calls.len(), 2);
// Continuation prompt must contain the partial — that's the
// scratchpad primitive J called out.
let cont_prompt = &calls[1].prompt;
assert!(cont_prompt.contains("ORIGINAL"),
"continuation must include original prompt");
assert!(cont_prompt.contains(r#"{"fills": [{"candidate_id": "C-001""#),
"continuation must include partial");
}
#[tokio::test]
async fn continuable_does_not_loop_on_persistent_empty() {
// All three retries return empty; we must NOT then enter the
// continuation loop with an empty partial (would burn 3 more
// calls continuing from "").
let generator = ScriptedGenerator::new(vec![
Ok("".to_string()),
Ok("".to_string()),
Ok("".to_string()),
]);
let opts = ContinuableOpts::new("qwen3:latest");
let out = generate_continuable(&generator, "test", &opts).await.unwrap();
assert!(!out.final_complete);
assert_eq!(out.empty_retries, 3);
assert_eq!(out.continuations, 0, "must not continue from empty");
assert_eq!(generator.call_count(), 3);
}
#[tokio::test]
async fn continuable_returns_raw_on_exhausted_continuations() {
// Three continuations that never complete — caller's parser
// will throw. We must return the combined text so forensics
// has the raw content, not a lossy truncation.
let generator = ScriptedGenerator::new(vec![
Ok(r#"{"a": ["#.to_string()),
Ok("1,".to_string()),
Ok("2,".to_string()),
Ok("3,".to_string()),
]);
let mut opts = ContinuableOpts::new("qwen3:latest");
opts.max_continuations = 3;
let out = generate_continuable(&generator, "test", &opts).await.unwrap();
assert!(!out.final_complete);
assert_eq!(out.continuations, 3);
assert!(out.text.contains(r#"{"a": ["#));
assert!(out.text.contains("3,"));
}
#[tokio::test]
async fn continuable_propagates_generator_errors() {
let generator = ScriptedGenerator::new(vec![Err("sidecar 503".to_string())]);
let opts = ContinuableOpts::new("qwen3:latest");
let err = generate_continuable(&generator, "test", &opts).await.unwrap_err();
assert!(err.contains("503"));
}
}
crates/aibridge/src/lib.rs
+3
View File
@@ -1,2 +1,5 @@
pub mod client; pub mod client;
pub mod context;
pub mod continuation;
pub mod service; pub mod service;
pub mod tree_split;
crates/aibridge/src/tree_split.rs
+326
View File
@@ -0,0 +1,326 @@
//! Phase 21 — INPUT-overflow handler. Ports `generateTreeSplit` from
//! `tests/multi-agent/agent.ts`.
//!
//! When the input corpus exceeds the model's window (200 playbooks
//! pasted into a T4 strategic prompt, a long retrospective digest, a
//! cross-corpus summarization), raising `max_tokens` doesn't help —
//! the prompt itself is the problem. The answer is map-reduce:
//!
//! 1. Caller shards the input at semantic boundaries (records,
//! paragraphs, playbook entries).
//! 2. For each shard, build a map prompt that includes the running
//! scratchpad and run it through `generate_continuable`.
//! 3. Append the map output to the scratchpad (oldest-first
//! truncation when it outgrows `scratchpad_budget`).
//! 4. Build a reduce prompt from the final scratchpad and run it.
//!
//! Every shard prompt and the reduce prompt go through
//! `assert_context_budget` first — if a single shard still overflows
//! we bubble the error up rather than silently truncating. That's the
//! whole point of Phase 21.
use crate::context::{assert_context_budget, BudgetOpts, estimate_tokens, overflow_message,
DEFAULT_MAX_TOKENS, DEFAULT_SAFETY_MARGIN};
use crate::continuation::{generate_continuable, ContinuableOpts, ResponseShape, TextGenerator};
/// Callback signatures — caller supplies closures that stitch the
/// scratchpad into each shard's prompt and build the final reduce
/// prompt. Kept as `Fn` (not `FnMut`) so the map loop can call them
/// by reference.
pub type MapPromptFn<'a> = dyn Fn(&str, &str) -> String + Send + Sync + 'a;
pub type ReducePromptFn<'a> = dyn Fn(&str) -> String + Send + Sync + 'a;
/// Knobs for `generate_tree_split`.
#[derive(Debug, Clone)]
pub struct TreeSplitOpts {
pub model: String,
pub system: Option<String>,
pub temperature: Option<f64>,
/// max_tokens for map AND reduce (reduce defaults are usually
/// higher; caller overrides for just reduce by calling through
/// continuable directly if needed).
pub max_tokens: Option<u32>,
pub reduce_max_tokens: Option<u32>,
pub think: Option<bool>,
/// Soft ceiling on scratchpad size (estimated tokens). When it
/// grows past this, the oldest shard digest gets dropped. Default
/// 6000, matching the TS implementation.
pub scratchpad_budget: usize,
pub safety_margin: Option<usize>,
}
impl TreeSplitOpts {
pub fn new(model: impl Into<String>) -> Self {
Self {
model: model.into(),
system: None,
temperature: None,
max_tokens: None,
reduce_max_tokens: None,
think: None,
scratchpad_budget: 6_000,
safety_margin: None,
}
}
}
/// Result — final reduce response plus the accumulated scratchpad so
/// the caller can inspect what was kept vs truncated.
#[derive(Debug, Clone)]
pub struct TreeSplitResult {
pub response: String,
pub scratchpad: String,
pub shards_processed: usize,
pub scratchpad_truncations: usize,
pub total_continuations: usize,
}
/// Drop shard-digest blocks from the head of `scratchpad` until its
/// estimated-token count fits the budget. Digest blocks are delimited
/// by `\n— shard N/M digest —\n` so we can find the first one and
/// chop everything before its successor.
fn truncate_scratchpad(scratchpad: &mut String, budget_tokens: usize) -> bool {
if estimate_tokens(scratchpad) <= budget_tokens { return false; }
// Find the second delimiter — everything before it gets dropped.
const DELIM_PREFIX: &str = "\n— shard ";
let mut cursor = 0;
let mut truncated = false;
while estimate_tokens(&scratchpad[cursor..]) > budget_tokens {
// Skip past a leading delimiter (if we're sitting on one from
// a previous iteration), then find the next.
let search_from = cursor + if scratchpad[cursor..].starts_with(DELIM_PREFIX) {
DELIM_PREFIX.len()
} else { 0 };
let Some(rel_next) = scratchpad[search_from..].find(DELIM_PREFIX) else { break };
cursor = search_from + rel_next;
truncated = true;
}
if cursor > 0 {
scratchpad.drain(..cursor);
}
truncated
}
/// Phase 21 — map-reduce over shards with a running scratchpad. See
/// module docs.
pub async fn generate_tree_split<G: TextGenerator>(
generator: &G,
shards: &[String],
map_prompt: &MapPromptFn<'_>,
reduce_prompt: &ReducePromptFn<'_>,
opts: &TreeSplitOpts,
) -> Result<TreeSplitResult, String> {
let mut scratchpad = String::new();
let safety = opts.safety_margin.unwrap_or(DEFAULT_SAFETY_MARGIN);
let map_max = opts.max_tokens.unwrap_or(DEFAULT_MAX_TOKENS as u32);
let reduce_max = opts.reduce_max_tokens.unwrap_or(1_500);
let mut truncations = 0usize;
let mut total_continuations = 0usize;
for (i, shard) in shards.iter().enumerate() {
let shard_prompt = map_prompt(shard, &scratchpad);
// Loud-fail on per-shard overflow — caller sharded too
// coarsely. Silent truncation is exactly the mode J rejected.
let budget = BudgetOpts {
system: opts.system.as_deref(),
max_tokens: Some(map_max as usize),
safety_margin: Some(safety),
bypass: false,
};
let check = assert_context_budget(&opts.model, &shard_prompt, budget)
.map_err(|(c, over)| overflow_message(&opts.model, &c, over, safety))?;
let _ = check;
let mut cont_opts = ContinuableOpts::new(&opts.model);
cont_opts.max_tokens = Some(map_max);
cont_opts.temperature = opts.temperature;
cont_opts.system = opts.system.clone();
cont_opts.shape = ResponseShape::Text;
cont_opts.think = opts.think;
let outcome = generate_continuable(generator, &shard_prompt, &cont_opts).await?;
total_continuations += outcome.continuations;
// Append this shard's digest and, if needed, drop oldest.
scratchpad.push_str(&format!(
"\n— shard {}/{} digest —\n{}",
i + 1, shards.len(), outcome.text.trim(),
));
if truncate_scratchpad(&mut scratchpad, opts.scratchpad_budget) {
truncations += 1;
}
}
// Reduce pass. Budget check first — if the scratchpad is still too
// big for the reduce prompt we fail loud with numbers.
let reduce_p = reduce_prompt(&scratchpad);
let budget = BudgetOpts {
system: opts.system.as_deref(),
max_tokens: Some(reduce_max as usize),
safety_margin: Some(safety),
bypass: false,
};
assert_context_budget(&opts.model, &reduce_p, budget)
.map_err(|(c, over)| overflow_message(&opts.model, &c, over, safety))?;
let mut cont_opts = ContinuableOpts::new(&opts.model);
cont_opts.max_tokens = Some(reduce_max);
cont_opts.temperature = opts.temperature;
cont_opts.system = opts.system.clone();
cont_opts.shape = ResponseShape::Text;
cont_opts.think = opts.think;
let outcome = generate_continuable(generator, &reduce_p, &cont_opts).await?;
total_continuations += outcome.continuations;
Ok(TreeSplitResult {
response: outcome.text,
scratchpad,
shards_processed: shards.len(),
scratchpad_truncations: truncations,
total_continuations,
})
}
#[cfg(test)]
mod tests {
use super::*;
use crate::continuation::ScriptedGenerator;
fn simple_map(shard: &str, scratchpad: &str) -> String {
format!("SCRATCHPAD:\n{scratchpad}\n---\nSHARD:\n{shard}\n---\nDIGEST:")
}
fn simple_reduce(scratchpad: &str) -> String {
format!("SCRATCHPAD:\n{scratchpad}\n---\nFINAL:")
}
#[tokio::test]
async fn tree_split_runs_map_then_reduce() {
// 3 shards → 3 map calls → 1 reduce call = 4 responses scripted.
let generator = ScriptedGenerator::new(vec![
Ok("digest-1".to_string()),
Ok("digest-2".to_string()),
Ok("digest-3".to_string()),
Ok("FINAL ANSWER".to_string()),
]);
let shards: Vec<String> = ["a", "b", "c"].iter().map(|s| s.to_string()).collect();
let opts = TreeSplitOpts::new("qwen3:latest");
let map_fn: &MapPromptFn = &simple_map;
let reduce_fn: &ReducePromptFn = &simple_reduce;
let result = generate_tree_split(&generator, &shards, map_fn, reduce_fn, &opts)
.await
.unwrap();
assert_eq!(result.shards_processed, 3);
assert_eq!(result.response, "FINAL ANSWER");
assert_eq!(generator.call_count(), 4);
// Scratchpad must carry all three digests in order.
assert!(result.scratchpad.contains("digest-1"));
assert!(result.scratchpad.contains("digest-2"));
assert!(result.scratchpad.contains("digest-3"));
}
#[tokio::test]
async fn tree_split_reduce_prompt_sees_full_scratchpad() {
let generator = ScriptedGenerator::new(vec![
Ok("summary-A".to_string()),
Ok("summary-B".to_string()),
Ok("REDUCED".to_string()),
]);
let shards = vec!["input-one".to_string(), "input-two".to_string()];
let opts = TreeSplitOpts::new("qwen3:latest");
let _ = generate_tree_split(&generator, &shards, &simple_map, &simple_reduce, &opts)
.await
.unwrap();
// Third call = reduce. Its prompt must include both digests.
let calls = generator.calls();
let reduce_prompt = &calls[2].prompt;
assert!(reduce_prompt.contains("summary-A"),
"reduce prompt must see first shard digest");
assert!(reduce_prompt.contains("summary-B"),
"reduce prompt must see second shard digest");
}
#[tokio::test]
async fn tree_split_loud_fails_on_shard_overflow() {
let generator = ScriptedGenerator::new(vec![Ok("digest".to_string())]);
// One gigantic shard — well over qwen3's 40K window even as a
// prompt. The budget check must reject before any generate call.
let shards = vec!["x".repeat(200_000)];
let opts = TreeSplitOpts::new("qwen3:latest");
let err = generate_tree_split(&generator, &shards, &simple_map, &simple_reduce, &opts)
.await
.expect_err("shard-sized overflow must be rejected");
assert!(err.contains("overflow"), "error should mention overflow: {err}");
assert_eq!(generator.call_count(), 0, "generate must not be called on overflow");
}
#[tokio::test]
async fn tree_split_truncates_scratchpad_when_over_budget() {
// Tight budget so each shard trips truncation. qwen3's 40K
// window is fine; the budget we care about is the scratchpad
// cap, not the model window.
let generator = ScriptedGenerator::new(vec![
Ok("A".repeat(2_000)),
Ok("B".repeat(2_000)),
Ok("C".repeat(2_000)),
Ok("D".repeat(2_000)),
Ok("FINAL".to_string()),
]);
let shards: Vec<String> = (0..4).map(|i| format!("shard{i}")).collect();
let mut opts = TreeSplitOpts::new("qwen3:latest");
opts.scratchpad_budget = 1_000; // ~4000 chars — one digest barely fits
let result = generate_tree_split(&generator, &shards, &simple_map, &simple_reduce, &opts)
.await
.unwrap();
assert!(result.scratchpad_truncations > 0,
"tight budget must trigger truncation");
// Scratchpad should still fit roughly within the budget
// (post-truncation); the estimator uses chars/4 so the bound
// is ~budget*4 chars. Give some slack for the delimiter.
let scratchpad_tokens = estimate_tokens(&result.scratchpad);
assert!(scratchpad_tokens <= opts.scratchpad_budget * 2,
"scratchpad {} tokens vs budget {}", scratchpad_tokens, opts.scratchpad_budget);
}
#[tokio::test]
async fn tree_split_reports_continuations_from_map_and_reduce() {
// First shard: truncated-then-continued. Reduce: truncated-then-continued.
// 1 shard: 2 map calls (initial + continuation), then 2 reduce calls.
let generator = ScriptedGenerator::new(vec![
Ok("partial".to_string()), // map shape=text, non-empty → complete on first pass
Ok("reduce-out".to_string()),
]);
let shards = vec!["only".to_string()];
let opts = TreeSplitOpts::new("qwen3:latest");
let result = generate_tree_split(&generator, &shards, &simple_map, &simple_reduce, &opts)
.await
.unwrap();
// Text shape treats non-empty as complete → 0 continuations.
assert_eq!(result.total_continuations, 0);
assert_eq!(result.shards_processed, 1);
}
#[test]
fn truncate_scratchpad_noop_when_under_budget() {
let mut s = "\n— shard 1/1 digest —\nshort".to_string();
let truncated = truncate_scratchpad(&mut s, 1_000);
assert!(!truncated);
assert!(s.contains("short"));
}
#[test]
fn truncate_scratchpad_drops_oldest_first() {
let mut s = format!(
"\n— shard 1/3 digest —\n{}\n— shard 2/3 digest —\n{}\n— shard 3/3 digest —\nshort",
"x".repeat(4_000), // ~1000 tokens
"y".repeat(4_000), // ~1000 tokens
);
let truncated = truncate_scratchpad(&mut s, 500); // ~2000 chars
assert!(truncated);
assert!(!s.contains(&"x".repeat(4_000)),
"oldest digest should be dropped");
assert!(s.contains("short"),
"newest digest should survive");
}
}
crates/catalogd/src/grpc.rs
+10 -1
View File
@@ -54,7 +54,16 @@ impl CatalogService for CatalogGrpc {
let manifest = self.registry let manifest = self.registry
.register(req.name, SchemaFingerprint(req.schema_fingerprint), objects) .register(req.name, SchemaFingerprint(req.schema_fingerprint), objects)
.await .await
.map_err(|e| Status::internal(e))?; .map_err(|e| {
// Mirror the HTTP side's 409 mapping: schema drift is a
// precondition failure, not a server error. Keeps gRPC
// and HTTP callers seeing the same diagnostic signal.
if e.contains("different schema") {
Status::failed_precondition(e)
} else {
Status::internal(e)
}
})?;
Ok(Response::new(manifest_to_proto(&manifest))) Ok(Response::new(manifest_to_proto(&manifest)))
} }
crates/catalogd/src/registry.rs
+452 -3
View File
@@ -28,6 +28,27 @@ pub struct MigrateBucketsReport {
pub manifests_persisted: usize, pub manifests_persisted: usize,
} }
#[derive(Debug, Clone, Default, serde::Serialize)]
pub struct DedupeReport {
pub groups: usize,
pub removed: usize,
pub kept: Vec<DedupeKept>,
pub errors: Vec<DedupeError>,
}
#[derive(Debug, Clone, serde::Serialize)]
pub struct DedupeKept {
pub name: String,
pub kept_id: String,
pub removed: usize,
}
#[derive(Debug, Clone, serde::Serialize)]
pub struct DedupeError {
pub manifest_id: String,
pub error: String,
}
/// Partial metadata update — only set fields are applied. /// Partial metadata update — only set fields are applied.
#[derive(Debug, Clone, Default, serde::Deserialize)] #[derive(Debug, Clone, Default, serde::Deserialize)]
pub struct MetadataUpdate { pub struct MetadataUpdate {
@@ -212,13 +233,65 @@ impl Registry {
Ok(()) Ok(())
} }
/// Register a new dataset. Persists manifest to storage before updating memory. /// Register a dataset. Idempotent on `name`:
/// - No existing dataset by this name → create new manifest.
/// - Exists with same schema fingerprint → update objects + `updated_at`
/// in place (re-ingest of identical-shape data).
/// - Exists with different schema fingerprint → reject as `Err`
/// (callers map to HTTP 409 / gRPC FAILED_PRECONDITION). Schema
/// evolution is not yet exposed as an HTTP endpoint; callers must
/// register under a new name or delete the existing manifest first.
///
/// Concurrency: the write lock is held across the storage write. That
/// serializes concurrent registers but is correctness-mandatory —
/// dropping the lock across the check→insert sequence creates a TOCTOU
/// window where two callers with the same name both see "no existing"
/// and both insert, reintroducing the duplicate-manifest bug this
/// function exists to prevent. Register is a metadata-only hop on the
/// ingest path, so the serialization cost is acceptable. Precedent:
/// `update_metadata` also holds the write lock across its I/O.
///
/// Legacy state: if multiple manifests with the same name already exist
/// (from before this was idempotent), the most recently updated one is
/// treated as canonical. Run `dedupe_by_name` to clean up the rest.
pub async fn register( pub async fn register(
&self, &self,
name: String, name: String,
schema_fingerprint: SchemaFingerprint, schema_fingerprint: SchemaFingerprint,
objects: Vec<ObjectRef>, objects: Vec<ObjectRef>,
) -> Result<DatasetManifest, String> { ) -> Result<DatasetManifest, String> {
let mut datasets = self.datasets.write().await;
let existing = datasets
.values()
.filter(|d| d.name == name)
.max_by_key(|d| d.updated_at)
.cloned();
if let Some(mut manifest) = existing {
if manifest.schema_fingerprint != schema_fingerprint {
return Err(format!(
"dataset '{}' already exists with a different schema \
(existing fingerprint: {}, new: {}). Schema-evolution \
migration is not yet exposed as an HTTP endpoint; \
register under a new name or delete the existing \
manifest first.",
name, manifest.schema_fingerprint.0, schema_fingerprint.0,
));
}
manifest.objects = objects;
manifest.updated_at = chrono::Utc::now();
let manifest_key = format!("{MANIFEST_PREFIX}/{}.json", manifest.id);
let json = serde_json::to_vec_pretty(&manifest).map_err(|e| e.to_string())?;
ops::put(&self.store, &manifest_key, json.into()).await?;
datasets.insert(manifest.id.clone(), manifest.clone());
tracing::info!("re-registered (idempotent): {} ({})", manifest.name, manifest.id);
return Ok(manifest);
}
let now = chrono::Utc::now(); let now = chrono::Utc::now();
let manifest = DatasetManifest { let manifest = DatasetManifest {
id: DatasetId::new(), id: DatasetId::new(),
@@ -240,12 +313,10 @@ impl Registry {
embedding_refresh_policy: None, embedding_refresh_policy: None,
}; };
// Write-ahead: persist before in-memory update
let manifest_key = format!("{MANIFEST_PREFIX}/{}.json", manifest.id); let manifest_key = format!("{MANIFEST_PREFIX}/{}.json", manifest.id);
let json = serde_json::to_vec_pretty(&manifest).map_err(|e| e.to_string())?; let json = serde_json::to_vec_pretty(&manifest).map_err(|e| e.to_string())?;
ops::put(&self.store, &manifest_key, json.into()).await?; ops::put(&self.store, &manifest_key, json.into()).await?;
let mut datasets = self.datasets.write().await;
datasets.insert(manifest.id.clone(), manifest.clone()); datasets.insert(manifest.id.clone(), manifest.clone());
tracing::info!("registered dataset: {} ({})", manifest.name, manifest.id); tracing::info!("registered dataset: {} ({})", manifest.name, manifest.id);
@@ -380,6 +451,72 @@ impl Registry {
Ok(manifest.clone()) Ok(manifest.clone())
} }
/// Collapse duplicate manifests that share a `name`. Winner per group:
/// 1. Prefer a manifest with a non-null `row_count` (already resynced).
/// 2. Break ties by newest `updated_at`.
/// Losers are removed from the in-memory registry AND their manifest
/// JSON is deleted from object storage. Datasets with a single manifest
/// are untouched. Parquet data files are never touched — only catalog
/// metadata. See `register` for the prevention side of this fix.
///
/// Operator-only. Do not run while ingest is active: a concurrent
/// `register` between the snapshot and the delete sweep can create a
/// new manifest under a name we just deduped, producing a transient
/// count of 2 until the next dedupe run. Safe outside ingest windows.
pub async fn dedupe_by_name(&self) -> DedupeReport {
let groups: Vec<(String, Vec<DatasetManifest>)> = {
let datasets = self.datasets.read().await;
let mut by_name: HashMap<String, Vec<DatasetManifest>> = HashMap::new();
for m in datasets.values() {
by_name.entry(m.name.clone()).or_default().push(m.clone());
}
by_name.into_iter().filter(|(_, v)| v.len() > 1).collect()
};
let mut report = DedupeReport::default();
let mut to_delete: Vec<DatasetId> = Vec::new();
for (name, mut manifests) in groups {
report.groups += 1;
manifests.sort_by(|a, b| {
b.row_count.is_some().cmp(&a.row_count.is_some())
.then(b.updated_at.cmp(&a.updated_at))
});
let winner = &manifests[0];
report.kept.push(DedupeKept {
name: name.clone(),
kept_id: winner.id.to_string(),
removed: manifests.len() - 1,
});
for loser in &manifests[1..] {
to_delete.push(loser.id.clone());
}
}
{
let mut datasets = self.datasets.write().await;
for id in &to_delete {
datasets.remove(id);
}
}
for id in &to_delete {
let key = format!("{MANIFEST_PREFIX}/{}.json", id);
match ops::delete(&self.store, &key).await {
Ok(_) => report.removed += 1,
Err(e) => report.errors.push(DedupeError {
manifest_id: id.to_string(),
error: e,
}),
}
}
tracing::info!(
"dedupe: {} groups collapsed, {} manifests removed, {} errors",
report.groups, report.removed, report.errors.len(),
);
report
}
/// Resync every dataset that currently has a null row_count. /// Resync every dataset that currently has a null row_count.
/// Returns (successes, failures) where each entry is (name, detail). /// Returns (successes, failures) where each entry is (name, detail).
pub async fn resync_missing(&self) -> (Vec<(String, u64)>, Vec<(String, String)>) { pub async fn resync_missing(&self) -> (Vec<(String, u64)>, Vec<(String, String)>) {
@@ -549,6 +686,46 @@ impl Registry {
Ok(()) Ok(())
} }
/// Remove a dataset from the catalog by name. Deletes the manifest
/// from both the in-memory registry and object storage.
///
/// Scope is metadata-only: the underlying parquet files, vector
/// indexes, tombstones, trial journals, and AiViews that reference
/// this dataset are NOT touched. Caller is responsible for any
/// cascade cleanup. This mirrors how a DROP TABLE in a typical
/// warehouse separates "forget about this dataset" from "actually
/// reclaim the bytes".
///
/// Returns Ok(()) when the dataset existed and was removed; an Err
/// with "dataset not found" when no manifest by that name exists.
/// Legacy state with duplicate names: all matching manifests are
/// removed (effectively a post-hoc dedupe on that name).
pub async fn delete_dataset(&self, name: &str) -> Result<usize, String> {
let ids_to_remove: Vec<DatasetId> = {
let datasets = self.datasets.read().await;
datasets.values().filter(|d| d.name == name).map(|d| d.id.clone()).collect()
};
if ids_to_remove.is_empty() {
return Err(format!("dataset not found: {name}"));
}
for id in &ids_to_remove {
let key = format!("{MANIFEST_PREFIX}/{}.json", id);
if let Err(e) = ops::delete(&self.store, &key).await {
// Storage delete failed — log and keep going. The in-memory
// remove below is still correct; on restart, the missing
// storage object just means nothing to rehydrate.
tracing::warn!("delete_dataset '{name}': storage delete of {key} failed: {e}");
}
}
let mut datasets = self.datasets.write().await;
for id in &ids_to_remove {
datasets.remove(id);
}
tracing::info!("deleted dataset '{}' ({} manifest(s))", name, ids_to_remove.len());
Ok(ids_to_remove.len())
}
/// List datasets whose `embedding_stale_since` is set — they need a refresh. /// List datasets whose `embedding_stale_since` is set — they need a refresh.
pub async fn stale_datasets(&self) -> Vec<DatasetManifest> { pub async fn stale_datasets(&self) -> Vec<DatasetManifest> {
let datasets = self.datasets.read().await; let datasets = self.datasets.read().await;
@@ -579,3 +756,275 @@ impl Registry {
Ok(manifest.clone()) Ok(manifest.clone())
} }
} }
#[cfg(test)]
mod tests {
use super::*;
use object_store::memory::InMemory;
fn fixture() -> Registry {
Registry::new(Arc::new(InMemory::new()))
}
fn fp(s: &str) -> SchemaFingerprint {
SchemaFingerprint(s.to_string())
}
fn obj(key: &str) -> ObjectRef {
ObjectRef {
bucket: "primary".to_string(),
key: key.to_string(),
size_bytes: 100,
created_at: chrono::Utc::now(),
}
}
#[tokio::test]
async fn fresh_register_creates_new_manifest() {
let reg = fixture();
let m = reg.register("a".into(), fp("f1"), vec![obj("a.parquet")]).await.unwrap();
assert_eq!(m.name, "a");
assert_eq!(m.schema_fingerprint, fp("f1"));
assert_eq!(reg.list().await.len(), 1);
assert_eq!(reg.get_by_name("a").await.unwrap().id, m.id);
}
#[tokio::test]
async fn re_register_same_fingerprint_is_idempotent() {
let reg = fixture();
let first = reg.register("a".into(), fp("f1"), vec![obj("old.parquet")]).await.unwrap();
let second = reg.register("a".into(), fp("f1"), vec![obj("new.parquet")]).await.unwrap();
assert_eq!(first.id, second.id, "same name+fp should reuse ID");
assert_eq!(reg.list().await.len(), 1, "no duplicate manifest created");
let fetched = reg.get_by_name("a").await.unwrap();
assert_eq!(fetched.objects[0].key, "new.parquet", "objects replaced");
assert!(fetched.updated_at >= first.updated_at, "updated_at bumped");
}
#[tokio::test]
async fn re_register_different_fingerprint_rejects() {
let reg = fixture();
reg.register("a".into(), fp("f1"), vec![obj("a.parquet")]).await.unwrap();
let err = reg.register("a".into(), fp("f2"), vec![obj("a.parquet")]).await.unwrap_err();
assert!(err.contains("different schema"), "error mentions schema drift: {err}");
assert_eq!(reg.list().await.len(), 1, "failed register did not mutate state");
assert_eq!(reg.get_by_name("a").await.unwrap().schema_fingerprint, fp("f1"));
}
#[tokio::test]
async fn delete_dataset_removes_manifest_and_in_memory_entry() {
let reg = fixture();
reg.register("to_delete".into(), fp("f1"), vec![obj("d.parquet")]).await.unwrap();
reg.register("keepme".into(), fp("f2"), vec![obj("k.parquet")]).await.unwrap();
assert_eq!(reg.list().await.len(), 2);
let removed = reg.delete_dataset("to_delete").await.unwrap();
assert_eq!(removed, 1, "one manifest removed");
assert!(reg.get_by_name("to_delete").await.is_none());
assert!(reg.get_by_name("keepme").await.is_some(), "unrelated dataset untouched");
assert_eq!(reg.list().await.len(), 1);
}
#[tokio::test]
async fn delete_dataset_404s_on_unknown_name() {
let reg = fixture();
let err = reg.delete_dataset("nope").await.unwrap_err();
assert!(err.starts_with("dataset not found"), "error wording: {err}");
}
#[tokio::test]
async fn delete_dataset_removes_all_dupes_with_matching_name() {
// Legacy zombies: direct insert so we can seed duplicates,
// mirroring pre-idempotent-register state. delete_dataset should
// sweep every manifest sharing that name in a single call.
let reg = fixture();
let now = chrono::Utc::now();
for _ in 0..3 {
let m = DatasetManifest {
id: DatasetId::new(),
name: "zombie".into(),
schema_fingerprint: fp("fp"),
objects: vec![obj("z.parquet")],
created_at: now, updated_at: now,
row_count: None, description: String::new(), owner: String::new(),
sensitivity: None, columns: vec![], lineage: None, freshness: None,
tags: vec![], last_embedded_at: None, embedding_stale_since: None,
embedding_refresh_policy: None,
};
reg.datasets.write().await.insert(m.id.clone(), m);
}
assert_eq!(reg.list().await.len(), 3);
let removed = reg.delete_dataset("zombie").await.unwrap();
assert_eq!(removed, 3, "all three manifests removed in one sweep");
assert_eq!(reg.list().await.len(), 0);
}
#[tokio::test]
async fn dedupe_no_duplicates_is_noop() {
let reg = fixture();
reg.register("a".into(), fp("f1"), vec![obj("a.parquet")]).await.unwrap();
reg.register("b".into(), fp("f2"), vec![obj("b.parquet")]).await.unwrap();
let report = reg.dedupe_by_name().await;
assert_eq!(report.groups, 0);
assert_eq!(report.removed, 0);
assert_eq!(reg.list().await.len(), 2);
}
#[tokio::test(flavor = "multi_thread", worker_threads = 8)]
async fn concurrent_register_stress_many_workers_one_manifest() {
// Stress variant: fire 32 concurrent registers for the same new
// name across 8 worker threads. Single-manifest invariant must
// hold regardless of how the scheduler interleaves them. This
// catches race regressions the 2-call variant below might miss
// under a forgiving scheduler.
let reg = fixture();
let mut handles = Vec::new();
for i in 0..32 {
let r = reg.clone();
handles.push(tokio::spawn(async move {
r.register(
"stress".into(),
fp("stress-fp"),
vec![obj(&format!("{i}.parquet"))],
).await
}));
}
for h in handles {
h.await.unwrap().expect("each register succeeds (idempotent)");
}
let all = reg.list().await;
let stress_manifests: Vec<_> = all.iter().filter(|d| d.name == "stress").collect();
assert_eq!(stress_manifests.len(), 1,
"32 concurrent registers produced {} manifests for 'stress' — expected 1",
stress_manifests.len());
}
#[tokio::test(flavor = "multi_thread", worker_threads = 4)]
async fn concurrent_register_same_new_name_collapses_to_one_manifest() {
// Pins the TOCTOU fix: without the write-lock held across the
// check→insert sequence, two parallel registers of the same
// previously-unknown name would both see "no existing" and both
// insert. Multi-thread flavor is required to actually exercise
// the race — under single-threaded tokio the scheduler doesn't
// preempt between awaits on the same task's critical section.
// Under the current impl, whichever acquires the write lock
// first creates the manifest; the second observes it and takes
// the idempotent-update branch.
let reg = fixture();
let a = reg.clone();
let b = reg.clone();
let (ra, rb) = tokio::join!(
async move {
a.register("race".into(), fp("shared-fp"), vec![obj("a.parquet")]).await
},
async move {
b.register("race".into(), fp("shared-fp"), vec![obj("b.parquet")]).await
},
);
let m_a = ra.expect("first register succeeds");
let m_b = rb.expect("second register succeeds (same fp = idempotent)");
assert_eq!(m_a.id, m_b.id, "both calls resolve to a single DatasetId");
assert_eq!(reg.list().await.len(), 1, "no duplicate manifest created");
}
#[tokio::test(flavor = "multi_thread", worker_threads = 4)]
async fn concurrent_register_same_name_different_fp_one_wins_one_rejects() {
// Reverse case: concurrent registers with conflicting fingerprints.
// Deterministic outcome: one call's fingerprint gets persisted as
// canonical, the other is rejected on seeing the now-established
// fingerprint. Which one wins is scheduler-dependent — we only
// assert exactly one success and exactly one rejection.
let reg = fixture();
let a = reg.clone();
let b = reg.clone();
let (ra, rb) = tokio::join!(
async move { a.register("race".into(), fp("fp1"), vec![obj("a.parquet")]).await },
async move { b.register("race".into(), fp("fp2"), vec![obj("b.parquet")]).await },
);
let successes = [&ra, &rb].iter().filter(|r| r.is_ok()).count();
let rejections = [&ra, &rb].iter().filter(|r| r.is_err()).count();
assert_eq!(successes, 1, "exactly one register wins");
assert_eq!(rejections, 1, "the other is rejected on fingerprint");
let rejection_msg = [&ra, &rb].iter()
.find_map(|r| r.as_ref().err())
.unwrap();
assert!(rejection_msg.contains("different schema"));
assert_eq!(reg.list().await.len(), 1);
}
#[tokio::test]
async fn dedupe_collapses_dupes_preferring_non_null_row_count() {
let reg = fixture();
// Simulate legacy pre-idempotent-register state by inserting
// manifests with the same `name` directly. Mirrors what happened
// with the 308× successful_playbooks duplication.
let now = chrono::Utc::now();
let old_with_rows = DatasetManifest {
id: DatasetId::new(),
name: "dupes".into(),
schema_fingerprint: fp("f1"),
objects: vec![obj("dupes.parquet")],
created_at: now - chrono::Duration::hours(2),
updated_at: now - chrono::Duration::hours(2),
row_count: Some(42),
description: String::new(),
owner: String::new(),
sensitivity: None,
columns: vec![],
lineage: None,
freshness: None,
tags: vec![],
last_embedded_at: None,
embedding_stale_since: None,
embedding_refresh_policy: None,
};
let newer_no_rows = DatasetManifest {
id: DatasetId::new(),
updated_at: now, // newer, but null row_count
row_count: None,
..old_with_rows.clone()
};
let oldest = DatasetManifest {
id: DatasetId::new(),
updated_at: now - chrono::Duration::hours(5),
row_count: None,
..old_with_rows.clone()
};
let kept_id = old_with_rows.id.clone();
let loser_a = newer_no_rows.id.clone();
let loser_b = oldest.id.clone();
{
let mut d = reg.datasets.write().await;
d.insert(old_with_rows.id.clone(), old_with_rows);
d.insert(newer_no_rows.id.clone(), newer_no_rows);
d.insert(oldest.id.clone(), oldest);
}
assert_eq!(reg.list().await.len(), 3);
let report = reg.dedupe_by_name().await;
assert_eq!(report.groups, 1);
assert_eq!(report.removed, 2);
assert_eq!(report.errors.len(), 0);
assert_eq!(report.kept.len(), 1);
assert_eq!(report.kept[0].name, "dupes");
assert_eq!(report.kept[0].kept_id, kept_id.to_string(),
"should keep the manifest with non-null row_count");
let remaining = reg.list().await;
assert_eq!(remaining.len(), 1);
assert_eq!(remaining[0].id, kept_id);
assert!(reg.get(&loser_a).await.is_none());
assert!(reg.get(&loser_b).await.is_none());
}
}
crates/catalogd/src/service.rs
+35 -2
View File
@@ -17,11 +17,12 @@ pub fn router(registry: Registry) -> Router {
.route("/datasets", post(create_dataset)) .route("/datasets", post(create_dataset))
.route("/datasets", get(list_datasets)) .route("/datasets", get(list_datasets))
.route("/datasets/{id}", get(get_dataset)) .route("/datasets/{id}", get(get_dataset))
.route("/datasets/by-name/{name}", get(get_dataset_by_name)) .route("/datasets/by-name/{name}", get(get_dataset_by_name).delete(delete_dataset_by_name))
.route("/datasets/by-name/{name}/metadata", post(update_metadata)) .route("/datasets/by-name/{name}/metadata", post(update_metadata))
.route("/datasets/by-name/{name}/resync", post(resync_dataset)) .route("/datasets/by-name/{name}/resync", post(resync_dataset))
.route("/resync-missing", post(resync_all_missing)) .route("/resync-missing", post(resync_all_missing))
.route("/migrate-buckets", post(migrate_buckets)) .route("/migrate-buckets", post(migrate_buckets))
.route("/dedupe", post(dedupe_by_name))
// Phase D: AI-safe views // Phase D: AI-safe views
.route("/views", post(create_view).get(list_views)) .route("/views", post(create_view).get(list_views))
.route("/views/{name}", get(get_view).delete(delete_view)) .route("/views/{name}", get(get_view).delete(delete_view))
@@ -121,7 +122,14 @@ async fn create_dataset(
let resp = DatasetResponse::from(&manifest); let resp = DatasetResponse::from(&manifest);
Ok((StatusCode::CREATED, Json(resp))) Ok((StatusCode::CREATED, Json(resp)))
} }
Err(e) => Err((StatusCode::INTERNAL_SERVER_ERROR, e)), Err(e) => {
let status = if e.contains("different schema") {
StatusCode::CONFLICT
} else {
StatusCode::INTERNAL_SERVER_ERROR
};
Err((status, e))
}
} }
} }
@@ -153,6 +161,25 @@ async fn get_dataset_by_name(
} }
} }
/// Remove a dataset manifest by name. Metadata only — parquet files,
/// vector indexes, and tombstones are NOT cascade-deleted. See
/// `Registry::delete_dataset` for the full scope.
async fn delete_dataset_by_name(
State(registry): State<Registry>,
Path(name): Path<String>,
) -> impl IntoResponse {
match registry.delete_dataset(&name).await {
Ok(removed) => {
let body = serde_json::json!({ "name": name, "manifests_removed": removed });
Ok(Json(body))
}
Err(e) if e.starts_with("dataset not found") => {
Err((StatusCode::NOT_FOUND, e))
}
Err(e) => Err((StatusCode::INTERNAL_SERVER_ERROR, e)),
}
}
async fn update_metadata( async fn update_metadata(
State(registry): State<Registry>, State(registry): State<Registry>,
Path(name): Path<String>, Path(name): Path<String>,
@@ -214,6 +241,12 @@ async fn migrate_buckets(State(registry): State<Registry>) -> impl IntoResponse
} }
} }
/// Collapse duplicate manifests by `name` — see `Registry::dedupe_by_name`.
/// Safe to run repeatedly; single-manifest datasets are untouched.
async fn dedupe_by_name(State(registry): State<Registry>) -> impl IntoResponse {
Json(registry.dedupe_by_name().await)
}
// --- Phase D: AI-safe views --- // --- Phase D: AI-safe views ---
#[derive(Deserialize)] #[derive(Deserialize)]
crates/gateway/Cargo.toml
+1
View File
@@ -28,3 +28,4 @@ opentelemetry-stdout = { workspace = true }
tracing-opentelemetry = { workspace = true } tracing-opentelemetry = { workspace = true }
arrow = { workspace = true } arrow = { workspace = true }
chrono = { workspace = true } chrono = { workspace = true }
reqwest = { version = "0.12", default-features = false, features = ["json", "rustls-tls"] }
crates/gateway/src/main.rs
+42
View File
@@ -3,6 +3,7 @@ mod access_service;
mod auth; mod auth;
mod observability; mod observability;
mod tools; mod tools;
mod v1;
use axum::{Router, extract::DefaultBodyLimit, routing::get}; use axum::{Router, extract::DefaultBodyLimit, routing::get};
use proto::lakehouse::catalog_service_server::CatalogServiceServer; use proto::lakehouse::catalog_service_server::CatalogServiceServer;
@@ -86,6 +87,11 @@ async fn main() {
// primary. Journal / registry resolve per-call via the bucket registry. // primary. Journal / registry resolve per-call via the bucket registry.
let tj = vectord::trial::TrialJournal::new(bucket_registry.clone(), index_reg.clone()); let tj = vectord::trial::TrialJournal::new(bucket_registry.clone(), index_reg.clone());
let pr = vectord::promotion::PromotionRegistry::new(bucket_registry.clone(), index_reg.clone()); let pr = vectord::promotion::PromotionRegistry::new(bucket_registry.clone(), index_reg.clone());
let hs = vectord::harness::HarnessStore::new(bucket_registry.clone(), index_reg.clone());
// Phase 19: playbook memory. Load cached state; empty-on-miss is fine,
// operators call POST /vectors/playbook_memory/rebuild to populate.
let pbm = vectord::playbook_memory::PlaybookMemory::new(store.clone());
let _ = pbm.load_from_storage().await;
// Phase 16.2: spawn the autotune agent. When config.agent.enabled=false // Phase 16.2: spawn the autotune agent. When config.agent.enabled=false
// this returns a handle that drops triggers silently — no surprise load. // this returns a handle that drops triggers silently — no surprise load.
@@ -107,6 +113,7 @@ async fn main() {
embedding_cache: emb_cache.clone(), embedding_cache: emb_cache.clone(),
trial_journal: tj.clone(), trial_journal: tj.clone(),
promotion_registry: pr.clone(), promotion_registry: pr.clone(),
harness_store: hs.clone(),
}, },
); );
@@ -155,6 +162,7 @@ async fn main() {
hnsw_store: hnsw, hnsw_store: hnsw,
embedding_cache: emb_cache, embedding_cache: emb_cache,
trial_journal: tj, trial_journal: tj,
harness_store: hs,
catalog: registry.clone(), catalog: registry.clone(),
promotion_registry: pr, promotion_registry: pr,
agent_handle, agent_handle,
@@ -163,6 +171,8 @@ async fn main() {
lance: vectord::lance_backend::LanceRegistry::new( lance: vectord::lance_backend::LanceRegistry::new(
bucket_registry.clone(), index_reg.clone(), bucket_registry.clone(), index_reg.clone(),
), ),
playbook_memory: pbm,
embed_semaphore: std::sync::Arc::new(tokio::sync::Semaphore::new(1)),
})) }))
.nest("/workspaces", queryd::workspace_service::router(workspace_mgr)) .nest("/workspaces", queryd::workspace_service::router(workspace_mgr))
.nest("/journal", journald::service::router(journal)) .nest("/journal", journald::service::router(journal))
@@ -174,6 +184,38 @@ async fn main() {
registry: tool_reg, registry: tool_reg,
query_fn: tools::QueryExecutor::new(engine.clone()), query_fn: tools::QueryExecutor::new(engine.clone()),
} }
}))
// Phase 38 — Universal API skeleton. Thin OpenAI-compatible
// surface over the existing aibridge → Ollama path. Future
// phases add provider adapters (39), routing engine (40),
// session state (41), etc. All without changing this mount.
.nest("/v1", v1::router(v1::V1State {
ai_client: ai_client.clone(),
usage: std::sync::Arc::new(tokio::sync::RwLock::new(v1::Usage::default())),
// Phase 39 first slice — Ollama Cloud adapter. Key resolved
// from OLLAMA_CLOUD_KEY env, then /root/llm_team_config.json,
// then OLLAMA_CLOUD_API_KEY env. None = cloud routes 503.
ollama_cloud_key: {
let k = v1::ollama_cloud::resolve_cloud_key();
if k.is_some() {
tracing::info!("v1: Ollama Cloud key loaded — /v1/chat provider=ollama_cloud enabled");
} else {
tracing::warn!("v1: no Ollama Cloud key in env or /root/llm_team_config.json — cloud routes will 503");
}
k
},
// Phase 40 early deliverable — Langfuse trace emitter.
// Defaults match mcp-server/tracing.ts conventions so
// gateway traces land in the same staffing project.
langfuse: {
let c = v1::langfuse_trace::LangfuseClient::from_env_or_defaults();
if c.is_some() {
tracing::info!("v1: Langfuse tracing enabled — /v1/chat calls will appear at localhost:3001");
} else {
tracing::warn!("v1: Langfuse keys missing — /v1/chat calls will not be traced");
}
c
},
})); }));
// Auth middleware (if enabled) // Auth middleware (if enabled)
crates/gateway/src/tools/registry.rs
+1 -1
View File
@@ -103,7 +103,7 @@ impl ToolRegistry {
ParamDef { name: "limit".into(), param_type: "integer".into(), required: false, description: "Max results".into(), default: Some(serde_json::json!(20)) }, ParamDef { name: "limit".into(), param_type: "integer".into(), required: false, description: "Max results".into(), default: Some(serde_json::json!(20)) },
], ],
returns: "List of candidates with id, name, phone, email, skills, experience".into(), returns: "List of candidates with id, name, phone, email, skills, experience".into(),
sql_template: "SELECT candidate_id, first_name, last_name, phone, email, city, state, zip, vertical, skills, years_experience FROM candidates WHERE 1=1 {skills_filter} {city_filter} {state_filter} {years_filter} {status_filter} ORDER BY years_experience DESC LIMIT {limit}".into(), sql_template: "SELECT candidate_id, first_name, last_name, phone, email, city, state, skills, years_experience, status FROM candidates WHERE 1=1 {skills_filter} {city_filter} {state_filter} {years_filter} {status_filter} ORDER BY years_experience DESC LIMIT {limit}".into(),
category: "candidates".into(), category: "candidates".into(),
}, },
ToolDef { ToolDef {
crates/gateway/src/v1/langfuse_trace.rs
+226
View File
@@ -0,0 +1,226 @@
//! Phase 40 (early deliverable) — Langfuse tracing from the Rust gateway.
//!
//! Langfuse is already running at `http://localhost:3001` with the
//! staffing project — `mcp-server/tracing.ts` has been emitting traces
//! for months. This file bridges the gap: every `/v1/chat` call now
//! appears in the same Langfuse project, alongside traces from the
//! Bun side.
//!
//! Design:
//! - Fire-and-forget — emit spawns a tokio task. Response to client
//! never waits on Langfuse, never fails because of it. If Langfuse
//! is down or slow, we silently drop traces (and `tracing::warn!`).
//! - Two events per call — `trace-create` + `generation-create`. The
//! generation nests under the trace so Langfuse UI shows the whole
//! thing as one clickable row.
//! - No SDK — plain HTTP POST to `/api/public/ingestion` with Basic
//! auth. Keeps the Rust dependency graph small.
use serde::Serialize;
use std::sync::Arc;
use std::time::Duration;
use super::Message;
const INGESTION_PATH: &str = "/api/public/ingestion";
const EMIT_TIMEOUT_SECS: u64 = 5;
#[derive(Clone)]
pub struct LangfuseClient {
inner: Arc<Inner>,
}
struct Inner {
base_url: String,
public_key: String,
secret_key: String,
http: reqwest::Client,
}
impl LangfuseClient {
pub fn new(base_url: String, public_key: String, secret_key: String) -> Self {
let http = reqwest::Client::builder()
.timeout(Duration::from_secs(EMIT_TIMEOUT_SECS))
.build()
.expect("langfuse client init");
Self { inner: Arc::new(Inner { base_url, public_key, secret_key, http }) }
}
/// Resolve a LangfuseClient from the same conventions the TS side
/// already uses. Returns None if keys are missing — caller logs a
/// warning and omits tracing (non-fatal).
///
/// Sources checked in order:
/// 1. LANGFUSE_URL + LANGFUSE_PUBLIC_KEY + LANGFUSE_SECRET_KEY env
/// 2. /root/llm_team_config.json (if it carries langfuse keys)
/// 3. Hardcoded staffing-project defaults matching mcp-server
/// (pk-lf-staffing / sk-lf-staffing-secret / localhost:3001)
pub fn from_env_or_defaults() -> Option<Self> {
let base = std::env::var("LANGFUSE_URL")
.unwrap_or_else(|_| "http://localhost:3001".to_string());
let pk = std::env::var("LANGFUSE_PUBLIC_KEY")
.unwrap_or_else(|_| "pk-lf-staffing".to_string());
let sk = std::env::var("LANGFUSE_SECRET_KEY")
.unwrap_or_else(|_| "sk-lf-staffing-secret".to_string());
if pk.trim().is_empty() || sk.trim().is_empty() { return None; }
Some(Self::new(base, pk, sk))
}
/// Fire-and-forget emit. Never blocks, never errors upward.
pub fn emit_chat(&self, ev: ChatTrace) {
let this = self.clone();
tokio::spawn(async move {
if let Err(e) = this.emit_chat_inner(ev).await {
tracing::warn!(target: "v1.langfuse", "trace drop: {e}");
}
});
}
async fn emit_chat_inner(&self, ev: ChatTrace) -> Result<(), String> {
let trace_id = uuid_v7_like();
let gen_id = uuid_v7_like();
let trace_ts = ev.start_time.clone();
let batch = IngestionBatch {
batch: vec![
IngestionEvent {
id: uuid_v7_like(),
timestamp: trace_ts.clone(),
kind: "trace-create",
body: serde_json::json!({
"id": trace_id,
"name": format!("v1.chat:{}", ev.provider),
"input": serde_json::json!({
"model": ev.model,
"messages": ev.input,
}),
"metadata": serde_json::json!({
"provider": ev.provider,
"think": ev.think,
}),
}),
},
IngestionEvent {
id: uuid_v7_like(),
timestamp: ev.end_time.clone(),
kind: "generation-create",
body: serde_json::json!({
"id": gen_id,
"traceId": trace_id,
"name": "chat",
"model": ev.model,
"modelParameters": serde_json::json!({
"temperature": ev.temperature,
"max_tokens": ev.max_tokens,
"think": ev.think,
}),
"input": ev.input,
"output": ev.output,
"usage": serde_json::json!({
"input": ev.prompt_tokens,
"output": ev.completion_tokens,
"total": ev.prompt_tokens + ev.completion_tokens,
"unit": "TOKENS",
}),
"startTime": ev.start_time,
"endTime": ev.end_time,
"metadata": serde_json::json!({
"provider": ev.provider,
"latency_ms": ev.latency_ms,
}),
}),
},
],
};
let url = format!("{}{}", self.inner.base_url.trim_end_matches('/'), INGESTION_PATH);
let resp = self.inner.http
.post(url)
.basic_auth(&self.inner.public_key, Some(&self.inner.secret_key))
.json(&batch)
.send()
.await
.map_err(|e| format!("POST failed: {e}"))?;
if !resp.status().is_success() {
return Err(format!("{}: {}", resp.status(), resp.text().await.unwrap_or_default()));
}
Ok(())
}
}
/// Everything the v1.chat handler collects for one completed call.
pub struct ChatTrace {
pub provider: String,
pub model: String,
pub input: Vec<Message>,
pub output: String,
pub prompt_tokens: u32,
pub completion_tokens: u32,
pub temperature: Option<f64>,
pub max_tokens: Option<u32>,
pub think: Option<bool>,
pub start_time: String,
pub end_time: String,
pub latency_ms: u64,
}
#[derive(Serialize)]
struct IngestionBatch {
batch: Vec<IngestionEvent>,
}
#[derive(Serialize)]
struct IngestionEvent {
id: String,
timestamp: String,
#[serde(rename = "type")]
kind: &'static str,
body: serde_json::Value,
}
/// UUIDv7-ish identifier — not strictly UUIDv7 but time-ordered and
/// unique enough for event deduplication. Avoids a uuid crate dep.
fn uuid_v7_like() -> String {
let ts = chrono::Utc::now().timestamp_nanos_opt().unwrap_or(0);
let rand = std::time::SystemTime::now()
.duration_since(std::time::UNIX_EPOCH)
.map(|d| d.subsec_nanos())
.unwrap_or(0);
format!("{:016x}-{:08x}", ts, rand)
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn from_env_or_defaults_returns_some_when_defaults_present() {
// Defaults are hardcoded non-empty — should always produce Some.
let c = LangfuseClient::from_env_or_defaults();
assert!(c.is_some());
}
#[test]
fn uuid_v7_like_produces_distinct_ids() {
let a = uuid_v7_like();
// Different nanosecond timestamps — but they might collide on
// back-to-back calls. Loop a bit to ensure uniqueness surface.
let b = uuid_v7_like();
let c = uuid_v7_like();
assert!(a != b || b != c, "three consecutive ids should have at least one distinct pair");
assert!(a.contains('-'));
}
#[test]
fn ingestion_batch_serializes_with_rename() {
let ev = IngestionEvent {
id: "e1".into(),
timestamp: "2026-04-22T00:00:00Z".into(),
kind: "trace-create",
body: serde_json::json!({"name": "t"}),
};
let json = serde_json::to_string(&IngestionBatch { batch: vec![ev] }).unwrap();
assert!(json.contains("\"type\":\"trace-create\""));
assert!(json.contains("\"batch\":["));
}
}
crates/gateway/src/v1/mod.rs
+256
View File
@@ -0,0 +1,256 @@
//! Phase 38 — Universal API skeleton (`/v1/*`).
//!
//! OpenAI-compatible shape on top of the existing aibridge → Ollama
//! path. This is the thin slice: single provider, stateless, no
//! streaming. Phase 39 replaces the direct Ollama call with a
//! `ProviderAdapter` trait dispatch; Phase 40 adds routing + fallback.
//!
//! The shape matches OpenAI's `/v1/chat/completions` closely enough
//! that clients using openai-compatible SDKs can point at us with the
//! URL swap alone. We keep the endpoint path `/v1/chat` (not
//! `/v1/chat/completions`) because our PRD declares the terser form;
//! adding the alias is one line in Phase 39 when it matters.
pub mod ollama;
pub mod ollama_cloud;
pub mod langfuse_trace;
use axum::{
Router,
extract::State,
http::StatusCode,
response::IntoResponse,
routing::{get, post},
Json,
};
use serde::{Deserialize, Serialize};
use std::sync::Arc;
use tokio::sync::RwLock;
#[derive(Clone)]
pub struct V1State {
pub ai_client: aibridge::client::AiClient,
pub usage: Arc<RwLock<Usage>>,
/// Ollama Cloud bearer token. Loaded at startup via
/// `ollama_cloud::resolve_cloud_key()`. None = cloud routes 503.
pub ollama_cloud_key: Option<String>,
/// Phase 40 early deliverable — Langfuse client. None = tracing
/// disabled (keys missing or container unreachable). Traces are
/// fire-and-forget: never block the response path.
pub langfuse: Option<langfuse_trace::LangfuseClient>,
}
#[derive(Default, Clone, Serialize)]
pub struct Usage {
pub requests: u64,
pub prompt_tokens: u64,
pub completion_tokens: u64,
pub total_tokens: u64,
}
pub fn router(state: V1State) -> Router {
Router::new()
.route("/chat", post(chat))
.route("/usage", get(usage))
.route("/sessions", get(sessions))
.with_state(state)
}
// -- Shared types (OpenAI-compatible) --
#[derive(Serialize, Deserialize, Clone, Debug)]
pub struct Message {
pub role: String,
pub content: String,
}
#[derive(Deserialize, Debug)]
pub struct ChatRequest {
pub model: String,
pub messages: Vec<Message>,
#[serde(default)]
pub temperature: Option<f64>,
#[serde(default)]
pub max_tokens: Option<u32>,
/// Accepted for shape-compat but ignored in the thin slice —
/// Phase 38 returns non-streaming even when the client asked for it.
/// Phase 39+ wires real streaming.
#[serde(default)]
pub stream: Option<bool>,
/// Non-OpenAI extension. Passes through to the provider's thinking
/// toggle. Default: **false** — hot-path discipline for thinking
/// models (qwen3.5, qwen3, gpt-oss) that otherwise burn the token
/// budget on hidden reasoning before visible output starts,
/// producing empty responses. Set true explicitly when calling an
/// overseer / reasoning-heavy path.
#[serde(default)]
pub think: Option<bool>,
/// Non-OpenAI extension. Selects the provider adapter. Accepted:
/// - None / "ollama" / "local" → local Ollama via sidecar (default)
/// - "ollama_cloud" / "cloud" → Ollama Cloud direct HTTPS
/// Phase 40 adds a routing engine that picks this automatically
/// from the model name; Phase 38/39 requires explicit selection.
#[serde(default)]
pub provider: Option<String>,
}
#[derive(Serialize)]
pub struct ChatResponse {
pub id: String,
pub object: &'static str,
pub created: i64,
pub model: String,
pub choices: Vec<Choice>,
pub usage: UsageBlock,
}
#[derive(Serialize)]
pub struct Choice {
pub index: u32,
pub message: Message,
pub finish_reason: String,
}
#[derive(Serialize, Deserialize, Clone)]
pub struct UsageBlock {
pub prompt_tokens: u32,
pub completion_tokens: u32,
pub total_tokens: u32,
}
// -- Handlers --
async fn chat(
State(state): State<V1State>,
Json(req): Json<ChatRequest>,
) -> Result<Json<ChatResponse>, (StatusCode, String)> {
if req.messages.is_empty() {
return Err((StatusCode::BAD_REQUEST, "messages must be non-empty".into()));
}
if req.stream.unwrap_or(false) {
tracing::warn!("/v1/chat: stream=true requested but Phase 38 returns non-streaming");
}
let provider = req.provider.as_deref().unwrap_or("ollama").to_ascii_lowercase();
let start_time = chrono::Utc::now();
let start_instant = std::time::Instant::now();
let resp = match provider.as_str() {
"ollama" | "local" | "" => ollama::chat(&state.ai_client, &req)
.await
.map_err(|e| (StatusCode::BAD_GATEWAY, format!("ollama local: {e}")))?,
"ollama_cloud" | "cloud" => {
let key = state.ollama_cloud_key.as_deref().ok_or((
StatusCode::SERVICE_UNAVAILABLE,
"OLLAMA_CLOUD_KEY not configured — set env or populate /root/llm_team_config.json".to_string(),
))?;
ollama_cloud::chat(key, &req)
.await
.map_err(|e| (StatusCode::BAD_GATEWAY, format!("ollama cloud: {e}")))?
}
other => {
return Err((
StatusCode::BAD_REQUEST,
format!("unknown provider '{other}' — supported: ollama, ollama_cloud"),
));
}
};
let end_time = chrono::Utc::now();
let latency_ms = start_instant.elapsed().as_millis() as u64;
// Phase 40 — emit Langfuse trace. Fire-and-forget: the clone is
// cheap (Arc inside), the tokio::spawn never blocks us, a dead
// Langfuse just logs a warn. Client-visible response latency is
// untouched.
if let Some(lf) = &state.langfuse {
let output = resp.choices.first()
.map(|c| c.message.content.clone())
.unwrap_or_default();
lf.emit_chat(langfuse_trace::ChatTrace {
provider: provider.clone(),
model: resp.model.clone(),
input: req.messages.clone(),
output,
prompt_tokens: resp.usage.prompt_tokens,
completion_tokens: resp.usage.completion_tokens,
temperature: req.temperature,
max_tokens: req.max_tokens,
think: req.think,
start_time: start_time.to_rfc3339(),
end_time: end_time.to_rfc3339(),
latency_ms,
});
}
{
let mut u = state.usage.write().await;
u.requests += 1;
u.prompt_tokens += resp.usage.prompt_tokens as u64;
u.completion_tokens += resp.usage.completion_tokens as u64;
u.total_tokens += resp.usage.total_tokens as u64;
}
Ok(Json(resp))
}
async fn usage(State(state): State<V1State>) -> impl IntoResponse {
let snapshot = state.usage.read().await.clone();
Json(snapshot)
}
// Phase 38 is stateless — no session persistence yet. Return an empty
// list in OpenAI-ish shape so clients that probe this endpoint don't
// 404. Real session state lands in Phase 41 with the profile-system
// expansion.
async fn sessions() -> impl IntoResponse {
Json(serde_json::json!({
"data": [],
"object": "list",
"note": "Phase 38: stateless. Session state lands in Phase 41.",
}))
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn chat_request_parses_openai_shape() {
let raw = r#"{
"model": "qwen3.5:latest",
"messages": [
{"role": "system", "content": "You are helpful."},
{"role": "user", "content": "Hi"}
],
"temperature": 0.2,
"max_tokens": 100
}"#;
let r: ChatRequest = serde_json::from_str(raw).unwrap();
assert_eq!(r.model, "qwen3.5:latest");
assert_eq!(r.messages.len(), 2);
assert_eq!(r.messages[0].role, "system");
assert_eq!(r.messages[1].content, "Hi");
assert_eq!(r.temperature, Some(0.2));
assert_eq!(r.max_tokens, Some(100));
}
#[test]
fn chat_request_accepts_minimal() {
let raw = r#"{
"model": "any",
"messages": [{"role": "user", "content": "hi"}]
}"#;
let r: ChatRequest = serde_json::from_str(raw).unwrap();
assert_eq!(r.temperature, None);
assert_eq!(r.max_tokens, None);
assert_eq!(r.stream, None);
}
#[test]
fn usage_counter_default_is_zero() {
let u = Usage::default();
assert_eq!(u.requests, 0);
assert_eq!(u.total_tokens, 0);
}
}
crates/gateway/src/v1/ollama.rs
+160
View File
@@ -0,0 +1,160 @@
//! Phase 38 — Ollama shape adapter.
//!
//! Translates `/v1/chat` (OpenAI-compatible) requests into the
//! existing aibridge `GenerateRequest` shape, and the `GenerateResponse`
//! back into an OpenAI-compatible `ChatResponse`. This is a bridge,
//! not a new client — aibridge + the Python sidecar stay as-is.
//!
//! Phase 39 replaces this direct call with a `ProviderAdapter` trait
//! dispatch so the same `/v1/chat` handler routes to any provider.
use aibridge::client::{AiClient, GenerateRequest};
use super::{ChatRequest, ChatResponse, Choice, Message, UsageBlock};
pub async fn chat(client: &AiClient, req: &ChatRequest) -> Result<ChatResponse, String> {
let (system, prompt) = flatten_messages(&req.messages);
let gen_req = GenerateRequest {
prompt,
model: Some(req.model.clone()),
system: if system.is_empty() { None } else { Some(system) },
temperature: req.temperature,
max_tokens: req.max_tokens,
// Phase 38 default: think=false. Hot-path discipline for
// thinking models (qwen3.5, qwen3, gpt-oss) which otherwise
// burn the max_tokens budget on hidden reasoning before any
// visible output, producing empty responses. Callers that
// actually want reasoning (overseers, T3+ tiers) opt in via
// the `think: true` extension field. Phase 40 routing engine
// flips this per task class.
think: Some(req.think.unwrap_or(false)),
};
let t0 = std::time::Instant::now();
let resp = client.generate(gen_req).await.map_err(|e| e.to_string())?;
let latency_ms = t0.elapsed().as_millis();
// Prefer sidecar-reported token counts when present. Fall back to
// chars/4 estimate (biased safe ~15%, matches Phase 21 convention
// in crates/aibridge/src/context.rs::estimate_tokens).
let prompt_tokens = resp.tokens_evaluated
.map(|n| n as u32)
.unwrap_or_else(|| estimate_prompt_tokens(&req.messages));
let completion_tokens = resp.tokens_generated
.map(|n| n as u32)
.unwrap_or_else(|| ((resp.text.chars().count() + 3) / 4) as u32);
tracing::info!(
target: "v1.chat",
model = %req.model,
prompt_tokens,
completion_tokens,
latency_ms = latency_ms as u64,
"ollama chat completed",
);
Ok(ChatResponse {
id: format!("chatcmpl-{}", chrono::Utc::now().timestamp_nanos_opt().unwrap_or(0)),
object: "chat.completion",
created: chrono::Utc::now().timestamp(),
model: resp.model,
choices: vec![Choice {
index: 0,
message: Message {
role: "assistant".into(),
content: resp.text,
},
finish_reason: "stop".into(),
}],
usage: UsageBlock {
prompt_tokens,
completion_tokens,
total_tokens: prompt_tokens + completion_tokens,
},
})
}
/// Public re-export of the flattener so sibling adapters (Ollama Cloud,
/// future OpenRouter) can reuse the same shape collapse without
/// duplicating the logic. Keeps `(system, prompt)` format consistent
/// across providers.
pub fn flatten_messages_public(messages: &[Message]) -> (String, String) {
flatten_messages(messages)
}
/// Collapse a message array into (system, prompt). Multiple system
/// messages concatenate with a newline — matches OpenAI's documented
/// behavior. Non-system messages become role-labeled turns.
fn flatten_messages(messages: &[Message]) -> (String, String) {
let mut system = String::new();
let mut prompt = String::new();
for m in messages {
if m.role == "system" {
if !system.is_empty() { system.push('\n'); }
system.push_str(&m.content);
} else {
prompt.push_str(&m.role);
prompt.push_str(": ");
prompt.push_str(&m.content);
prompt.push_str("\n\n");
}
}
prompt.push_str("assistant:");
(system, prompt)
}
fn estimate_prompt_tokens(messages: &[Message]) -> u32 {
let chars: usize = messages.iter().map(|m| m.content.chars().count()).sum();
((chars + 3) / 4) as u32
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn flatten_separates_system_from_turns() {
let msgs = vec![
Message { role: "system".into(), content: "Rules here.".into() },
Message { role: "user".into(), content: "Q1".into() },
Message { role: "assistant".into(), content: "A1".into() },
Message { role: "user".into(), content: "Q2".into() },
];
let (system, prompt) = flatten_messages(&msgs);
assert_eq!(system, "Rules here.");
assert!(prompt.contains("user: Q1"));
assert!(prompt.contains("assistant: A1"));
assert!(prompt.contains("user: Q2"));
assert!(prompt.trim_end().ends_with("assistant:"));
}
#[test]
fn flatten_concatenates_multiple_system_messages() {
let msgs = vec![
Message { role: "system".into(), content: "First.".into() },
Message { role: "system".into(), content: "Second.".into() },
Message { role: "user".into(), content: "Hi".into() },
];
let (system, _) = flatten_messages(&msgs);
assert_eq!(system, "First.\nSecond.");
}
#[test]
fn flatten_with_no_system_returns_empty_system() {
let msgs = vec![Message { role: "user".into(), content: "hi".into() }];
let (system, prompt) = flatten_messages(&msgs);
assert!(system.is_empty());
assert!(prompt.contains("user: hi"));
}
#[test]
fn estimate_tokens_chars_div_4_ceiling() {
let msgs = vec![Message { role: "user".into(), content: "abcdefgh".into() }];
// 8 chars / 4 = 2, with ceiling → 2
assert_eq!(estimate_prompt_tokens(&msgs), 2);
let msgs2 = vec![Message { role: "user".into(), content: "abcdefghi".into() }];
// 9 chars → (9+3)/4 = 3 (ceiling)
assert_eq!(estimate_prompt_tokens(&msgs2), 3);
}
}
crates/gateway/src/v1/ollama_cloud.rs
+189
View File
@@ -0,0 +1,189 @@
//! Phase 39 (first slice) — Ollama Cloud adapter.
//!
//! Direct HTTPS call to `https://ollama.com/api/generate` with Bearer
//! auth. Mirrors the `tests/multi-agent/agent.ts::generateCloud` pattern
//! exactly (same endpoint, same body shape, same Bearer header), so
//! cloud calls from Rust behave identically to the TS hot path.
//!
//! Key sourcing priority:
//! 1. Env var `OLLAMA_CLOUD_KEY` (matches agent.ts convention)
//! 2. `/root/llm_team_config.json` → providers.ollama_cloud.api_key
//! 3. Env var `OLLAMA_CLOUD_API_KEY` (LLM Team UI convention)
//!
//! First hit wins. Key is loaded once at gateway startup by
//! `resolve_cloud_key()` and stored on `V1State.ollama_cloud_key`.
use std::time::Duration;
use serde::{Deserialize, Serialize};
use super::{ChatRequest, ChatResponse, Choice, Message, UsageBlock};
const CLOUD_BASE_URL: &str = "https://ollama.com";
const CLOUD_TIMEOUT_SECS: u64 = 180;
/// Read the Ollama Cloud key from the three sanctioned sources. Returns
/// None if none is set — callers must 503 rather than attempt a call.
pub fn resolve_cloud_key() -> Option<String> {
if let Ok(k) = std::env::var("OLLAMA_CLOUD_KEY") {
if !k.trim().is_empty() { return Some(k.trim().to_string()); }
}
if let Ok(raw) = std::fs::read_to_string("/root/llm_team_config.json") {
if let Ok(v) = serde_json::from_str::<serde_json::Value>(&raw) {
if let Some(k) = v.pointer("/providers/ollama_cloud/api_key").and_then(|x| x.as_str()) {
if !k.trim().is_empty() { return Some(k.trim().to_string()); }
}
}
}
if let Ok(k) = std::env::var("OLLAMA_CLOUD_API_KEY") {
if !k.trim().is_empty() { return Some(k.trim().to_string()); }
}
None
}
pub async fn chat(
key: &str,
req: &ChatRequest,
) -> Result<ChatResponse, String> {
let (system, prompt) = super::ollama::flatten_messages_public(&req.messages);
let body = CloudGenerateBody {
model: req.model.clone(),
prompt,
system: if system.is_empty() { None } else { Some(system) },
stream: false,
think: Some(req.think.unwrap_or(false)),
options: CloudOptions {
// Thinking cloud models need headroom — floor 400 to give
// qwen3.5:397b / gpt-oss:120b reasoning room. Matches
// agent.ts `Math.max(opts.max_tokens ?? 800, 400)` policy.
num_predict: std::cmp::max(req.max_tokens.unwrap_or(800), 400),
temperature: req.temperature.unwrap_or(0.3),
},
};
let client = reqwest::Client::builder()
.timeout(Duration::from_secs(CLOUD_TIMEOUT_SECS))
.build()
.map_err(|e| format!("build client: {e}"))?;
let t0 = std::time::Instant::now();
let resp = client
.post(format!("{}/api/generate", CLOUD_BASE_URL))
.bearer_auth(key)
.json(&body)
.send()
.await
.map_err(|e| format!("ollama.com unreachable: {e}"))?;
let status = resp.status();
if !status.is_success() {
let body = resp.text().await.unwrap_or_else(|_| "?".into());
return Err(format!("ollama.com {}: {}", status, body));
}
let parsed: CloudGenerateResponse = resp.json().await
.map_err(|e| format!("invalid cloud response: {e}"))?;
let latency_ms = t0.elapsed().as_millis();
let text = parsed.response.unwrap_or_default();
let prompt_tokens = parsed.prompt_eval_count.unwrap_or_else(|| {
let chars: usize = req.messages.iter().map(|m| m.content.chars().count()).sum();
((chars + 3) / 4) as u32
});
let completion_tokens = parsed.eval_count.unwrap_or_else(|| {
((text.chars().count() + 3) / 4) as u32
});
tracing::info!(
target: "v1.chat",
provider = "ollama_cloud",
model = %req.model,
prompt_tokens,
completion_tokens,
latency_ms = latency_ms as u64,
"cloud chat completed",
);
Ok(ChatResponse {
id: format!("chatcmpl-{}", chrono::Utc::now().timestamp_nanos_opt().unwrap_or(0)),
object: "chat.completion",
created: chrono::Utc::now().timestamp(),
model: parsed.model.unwrap_or_else(|| req.model.clone()),
choices: vec![Choice {
index: 0,
message: Message { role: "assistant".into(), content: text },
finish_reason: "stop".into(),
}],
usage: UsageBlock {
prompt_tokens,
completion_tokens,
total_tokens: prompt_tokens + completion_tokens,
},
})
}
// -- Ollama /api/generate wire shapes --
#[derive(Serialize)]
struct CloudGenerateBody {
model: String,
prompt: String,
#[serde(skip_serializing_if = "Option::is_none")]
system: Option<String>,
stream: bool,
#[serde(skip_serializing_if = "Option::is_none")]
think: Option<bool>,
options: CloudOptions,
}
#[derive(Serialize)]
struct CloudOptions {
num_predict: u32,
temperature: f64,
}
#[derive(Deserialize)]
struct CloudGenerateResponse {
#[serde(default)]
response: Option<String>,
#[serde(default)]
model: Option<String>,
#[serde(default)]
prompt_eval_count: Option<u32>,
#[serde(default)]
eval_count: Option<u32>,
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn resolve_cloud_key_returns_none_when_no_sources_set() {
// Only check shape — we can't reliably unset env vars in a test
// that runs alongside others, and the file path is on disk.
// If all three sources are empty the function returns None; if
// any are set we expect Some. This just smoke-tests the call
// doesn't panic.
let _ = resolve_cloud_key();
}
#[test]
fn cloud_body_serializes_compact() {
let body = CloudGenerateBody {
model: "gpt-oss:120b".into(),
prompt: "user: hi\n\nassistant:".into(),
system: Some("Be terse.".into()),
stream: false,
think: Some(false),
options: CloudOptions { num_predict: 400, temperature: 0.3 },
};
let json = serde_json::to_string(&body).unwrap();
assert!(json.contains("\"model\":\"gpt-oss:120b\""));
assert!(json.contains("\"stream\":false"));
assert!(json.contains("\"num_predict\":400"));
assert!(json.contains("\"think\":false"));
assert!(json.contains("\"system\":\"Be terse.\""));
}
}
crates/ingestd/Cargo.toml
+1
View File
@@ -24,3 +24,4 @@ object_store = { workspace = true }
tokio-postgres = { workspace = true } tokio-postgres = { workspace = true }
mysql_async = { workspace = true } mysql_async = { workspace = true }
uuid = { workspace = true } uuid = { workspace = true }
croner = "3"
crates/ingestd/src/schedule.rs
+112 -13
View File
@@ -17,8 +17,8 @@
//! shouldn't pile up runs). Different schedules run concurrently. //! shouldn't pile up runs). Different schedules run concurrently.
//! //!
//! What's deliberately not in scope here: //! What's deliberately not in scope here:
//! - Cron expressions (the trigger enum has the variant but parsing is //! - Sub-minute cron precision. 5-field Unix cron is supported; seconds
//! stubbed). Intervals cover 90% of operational scheduling; cron is //! are pinned to 0. Intervals cover sub-minute cases; cron is
//! easy to bolt on later. //! easy to bolt on later.
//! - Backoff / retry policies. A failed run records the failure and //! - Backoff / retry policies. A failed run records the failure and
//! schedules `next_run_at` as if it succeeded — no exponential backoff. //! schedules `next_run_at` as if it succeeded — no exponential backoff.
@@ -85,8 +85,12 @@ impl ScheduleKind {
pub enum ScheduleTrigger { pub enum ScheduleTrigger {
/// Run every N seconds. Time anchor = compute_next_run_at decision. /// Run every N seconds. Time anchor = compute_next_run_at decision.
Interval { secs: u64 }, Interval { secs: u64 },
/// Cron expression — parsing not implemented yet. Defining the /// 5-field Unix cron expression: "min hour dom month dow".
/// variant now so the JSON shape is forward-compatible. /// Seconds are implicitly 0 (minute-granularity scheduling).
/// Examples: "15 14 * * *" = 14:15 UTC daily, "0 */6 * * *" = every
/// 6 hours on the hour, "0 9 * * 1-5" = 09:00 UTC weekdays.
/// Timezone: UTC always — the scheduler's clock is UTC and we don't
/// carry a per-schedule timezone field.
Cron { expr: String }, Cron { expr: String },
} }
@@ -162,22 +166,55 @@ impl ScheduleDef {
// Parse the expression, find the next match after `now`. Most // Parse the expression, find the next match after `now`. Most
// expressive but pulls in a cron parser dep. // expressive but pulls in a cron parser dep.
// //
// Cron is currently stubbed — falling back to "now + 1 hour" so a
// mistakenly-configured Cron schedule doesn't melt the GPU.
pub fn compute_next_run_at( pub fn compute_next_run_at(
trigger: &ScheduleTrigger, trigger: &ScheduleTrigger,
completed_at: DateTime<Utc>, completed_at: DateTime<Utc>,
_previous_next: DateTime<Utc>, _previous_next: DateTime<Utc>,
) -> DateTime<Utc> { ) -> DateTime<Utc> {
// TODO(J): pick a strategy. Starter = since-last-completion.
match trigger { match trigger {
ScheduleTrigger::Interval { secs } => { ScheduleTrigger::Interval { secs } => {
completed_at + Duration::seconds(*secs as i64) completed_at + Duration::seconds(*secs as i64)
} }
ScheduleTrigger::Cron { expr: _ } => { ScheduleTrigger::Cron { expr } => {
// Cron parsing not implemented — fall back to a safe 1h next_cron_fire(expr, completed_at)
// window so a bad config can't fire-loop the system. // Creation-time validation (see `validate_trigger`) rejects
completed_at + Duration::hours(1) // unparseable expressions, so this fallback is only reached
// if the schedule JSON was hand-edited on disk. One-hour
// window keeps a bad expr from fire-looping the system.
.unwrap_or(completed_at + Duration::hours(1))
}
}
}
/// Parse a Vixie/POSIX cron expression and return the next fire time
/// strictly after `after`. Accepts 5-field (`min hour dom month dow`) and
/// 6-field (with leading `sec`) forms natively — croner handles both.
/// Day-of-week follows Unix convention: 0=Sun, 1=Mon, …, 6=Sat (7=Sun as
/// synonym). Returns None if the expression fails to parse or produces no
/// upcoming match (which can happen for year-bounded patterns in the past).
fn next_cron_fire(expr: &str, after: DateTime<Utc>) -> Option<DateTime<Utc>> {
use std::str::FromStr;
let cron = croner::Cron::from_str(expr).ok()?;
cron.find_next_occurrence(&after, false).ok()
}
/// Reject a ScheduleTrigger whose content can't be interpreted — used by
/// the HTTP handlers to fail fast at create/patch time rather than falling
/// back silently at fire time. Interval triggers get a sanity gate
/// (no zero secs); cron triggers get a full parse through croner.
pub fn validate_trigger(trigger: &ScheduleTrigger) -> Result<(), String> {
match trigger {
ScheduleTrigger::Interval { secs } => {
if *secs == 0 {
return Err("interval secs must be > 0".into());
}
Ok(())
}
ScheduleTrigger::Cron { expr } => {
use std::str::FromStr;
croner::Cron::from_str(expr)
.map_err(|e| format!("invalid cron expression '{expr}': {e}"))?;
Ok(())
} }
} }
} }
@@ -551,6 +588,7 @@ fn redact_dsn(dsn: &str) -> String {
#[cfg(test)] #[cfg(test)]
mod tests { mod tests {
use super::*; use super::*;
use chrono::Timelike;
fn mk(secs: u64) -> ScheduleDef { fn mk(secs: u64) -> ScheduleDef {
ScheduleDef { ScheduleDef {
@@ -600,12 +638,73 @@ mod tests {
} }
#[test] #[test]
fn cron_falls_back_to_one_hour() { fn cron_every_minute_fires_within_sixty_seconds() {
let trig = ScheduleTrigger::Cron { expr: "* * * * *".into() }; let trig = ScheduleTrigger::Cron { expr: "* * * * *".into() };
let now = Utc::now(); let now = Utc::now();
let next = compute_next_run_at(&trig, now, now); let next = compute_next_run_at(&trig, now, now);
let delta = next - now; let delta = next - now;
assert_eq!(delta.num_seconds(), 3600); assert!(delta.num_seconds() > 0 && delta.num_seconds() <= 60,
"expected next fire within 60s, got {}s", delta.num_seconds());
}
#[test]
fn cron_daily_at_1415_utc_is_within_24h() {
// 14:15 UTC daily — whether we're before or after 14:15 today, the
// next fire is at most 24h out.
let trig = ScheduleTrigger::Cron { expr: "15 14 * * *".into() };
let now: DateTime<Utc> = "2026-04-20T10:00:00Z".parse().unwrap();
let next = compute_next_run_at(&trig, now, now);
assert!(next > now);
assert!((next - now).num_hours() <= 24);
// And the minute-of-hour is 15.
assert_eq!(next.minute(), 15);
assert_eq!(next.hour(), 14);
}
#[test]
fn cron_weekday_skips_weekend() {
// 09:00 UTC weekdays only. On a Saturday, next fire is Monday.
let trig = ScheduleTrigger::Cron { expr: "0 9 * * 1-5".into() };
// 2026-04-18 is a Saturday.
let sat: DateTime<Utc> = "2026-04-18T10:00:00Z".parse().unwrap();
let next = compute_next_run_at(&trig, sat, sat);
// Monday 2026-04-20 at 09:00 UTC.
assert_eq!(next, "2026-04-20T09:00:00Z".parse::<DateTime<Utc>>().unwrap());
}
#[test]
fn cron_six_field_seconds_granularity() {
// 6-field (seconds included): fire every minute at 30s past the minute.
let trig = ScheduleTrigger::Cron { expr: "30 * * * * *".into() };
let now = Utc::now();
let next = compute_next_run_at(&trig, now, now);
assert_eq!(next.second(), 30);
}
#[test]
fn validate_rejects_bad_cron() {
let bad = ScheduleTrigger::Cron { expr: "not a cron".into() };
let err = validate_trigger(&bad).unwrap_err();
assert!(err.contains("cron"), "error should mention cron, got: {err}");
}
#[test]
fn validate_rejects_wrong_field_count() {
// 4 fields — neither 5 nor 6.
let bad = ScheduleTrigger::Cron { expr: "* * * *".into() };
assert!(validate_trigger(&bad).is_err());
}
#[test]
fn validate_rejects_zero_interval() {
let bad = ScheduleTrigger::Interval { secs: 0 };
assert!(validate_trigger(&bad).is_err());
}
#[test]
fn validate_accepts_good_cron() {
let good = ScheduleTrigger::Cron { expr: "0 */6 * * *".into() };
assert!(validate_trigger(&good).is_ok());
} }
#[test] #[test]
crates/ingestd/src/service.rs
+8
View File
@@ -510,6 +510,9 @@ async fn create_schedule(
if state.schedules.get(&req.id).await.is_some() { if state.schedules.get(&req.id).await.is_some() {
return Err((StatusCode::CONFLICT, format!("schedule '{}' already exists", req.id))); return Err((StatusCode::CONFLICT, format!("schedule '{}' already exists", req.id)));
} }
if let Err(e) = schedule::validate_trigger(&req.trigger) {
return Err((StatusCode::BAD_REQUEST, e));
}
let now = chrono::Utc::now(); let now = chrono::Utc::now();
let def = schedule::ScheduleDef { let def = schedule::ScheduleDef {
id: req.id, id: req.id,
@@ -564,6 +567,11 @@ async fn patch_schedule(
let Some(mut def) = state.schedules.get(&id).await else { let Some(mut def) = state.schedules.get(&id).await else {
return Err((StatusCode::NOT_FOUND, format!("schedule '{id}' not found"))); return Err((StatusCode::NOT_FOUND, format!("schedule '{id}' not found")));
}; };
if let Some(t) = &req.trigger {
if let Err(e) = schedule::validate_trigger(t) {
return Err((StatusCode::BAD_REQUEST, e));
}
}
if let Some(e) = req.enabled { def.enabled = e; } if let Some(e) = req.enabled { def.enabled = e; }
if let Some(t) = req.trigger { def.trigger = t; } if let Some(t) = req.trigger { def.trigger = t; }
match state.schedules.put(def).await { match state.schedules.put(def).await {
crates/storaged/src/append_log.rs
+97 -6
View File
@@ -18,7 +18,7 @@
/// to small JSONL events. /// to small JSONL events.
/// ///
/// Storage layout: /// Storage layout:
/// ``` /// ```text
/// {prefix}/ /// {prefix}/
/// batch_0001776319628000123.jsonl /// batch_0001776319628000123.jsonl
/// batch_0001776319745987654.jsonl /// batch_0001776319745987654.jsonl
@@ -221,15 +221,15 @@ pub struct CompactStats {
pub new_key: Option<String>, pub new_key: Option<String>,
} }
// Proactively flush on drop, best-effort. // Log unflushed-buffer size on drop. We can't `.await` from a sync `Drop`,
// We can't `.await` in Drop; we spawn the flush on the tokio runtime if one // so a real flush isn't possible here — callers are responsible for calling
// is available. If the runtime is already shutting down the flush is lost — // `.flush()` before dropping if durability matters. These journals are
// acceptable for these observability journals, which are hints not records. // observability hints; a few lost buffered events at shutdown are
// acceptable per ADR-018.
impl Drop for AppendLog { impl Drop for AppendLog {
fn drop(&mut self) { fn drop(&mut self) {
let buf_len = self.buffer.try_lock().map(|b| b.len()).unwrap_or(0); let buf_len = self.buffer.try_lock().map(|b| b.len()).unwrap_or(0);
if buf_len == 0 { return; } if buf_len == 0 { return; }
// Can't spawn from sync Drop on every runtime shape; log + move on.
tracing::debug!( tracing::debug!(
"append_log '{}' dropping with {} unflushed events", "append_log '{}' dropping with {} unflushed events",
self.prefix, buf_len, self.prefix, buf_len,
@@ -237,3 +237,94 @@ impl Drop for AppendLog {
} }
} }
#[cfg(test)]
mod tests {
use super::*;
use object_store::memory::InMemory;
fn mk(threshold: usize) -> AppendLog {
AppendLog::new(Arc::new(InMemory::new()), "prefix")
.with_flush_threshold(threshold)
}
#[tokio::test]
async fn append_stays_buffered_below_threshold() {
let log = mk(5);
log.append(b"one".to_vec()).await.unwrap();
log.append(b"two".to_vec()).await.unwrap();
assert_eq!(log.file_count().await.unwrap(), 0, "no files until threshold");
let all = log.read_all().await.unwrap();
assert_eq!(all, vec![b"one".to_vec(), b"two".to_vec()],
"read_all surfaces unflushed buffer");
}
#[tokio::test]
async fn append_auto_flushes_on_threshold() {
let log = mk(3);
for i in 0..3 {
log.append(format!("evt{i}").into_bytes()).await.unwrap();
}
assert_eq!(log.file_count().await.unwrap(), 1, "threshold triggered one flush");
// A fourth append stays buffered until the next threshold.
log.append(b"evt3".to_vec()).await.unwrap();
assert_eq!(log.file_count().await.unwrap(), 1, "below threshold again");
}
#[tokio::test]
async fn flush_empty_is_noop() {
let log = mk(32);
log.flush().await.unwrap();
log.flush().await.unwrap();
assert_eq!(log.file_count().await.unwrap(), 0);
}
#[tokio::test]
async fn read_all_orders_events_across_flushes() {
let log = mk(1); // flush-on-every-append
for i in 0..5 {
log.append(format!("e{i}").into_bytes()).await.unwrap();
// Spread writes out so timestamps sort strictly.
tokio::time::sleep(std::time::Duration::from_millis(2)).await;
}
let all = log.read_all().await.unwrap();
let strs: Vec<String> = all.into_iter()
.map(|v| String::from_utf8(v).unwrap())
.collect();
assert_eq!(strs, vec!["e0", "e1", "e2", "e3", "e4"],
"lex sort of batch keys == chronological event order");
}
#[tokio::test]
async fn compact_merges_multiple_files_into_one() {
let log = mk(1); // force file-per-append
for i in 0..4 {
log.append(format!("e{i}").into_bytes()).await.unwrap();
tokio::time::sleep(std::time::Duration::from_millis(2)).await;
}
assert_eq!(log.file_count().await.unwrap(), 4);
let stats = log.compact().await.unwrap();
assert_eq!(stats.merged_files, 4);
assert_eq!(stats.events, 4);
assert!(stats.new_key.is_some());
assert_eq!(log.file_count().await.unwrap(), 1, "originals deleted, 1 survivor");
let all = log.read_all().await.unwrap();
assert_eq!(all.len(), 4, "no events lost in compaction");
}
#[tokio::test]
async fn compact_with_single_file_is_noop() {
let log = mk(1);
log.append(b"only".to_vec()).await.unwrap();
assert_eq!(log.file_count().await.unwrap(), 1);
let stats = log.compact().await.unwrap();
assert_eq!(stats.merged_files, 1);
assert_eq!(stats.events, 0, "nothing to consolidate");
assert!(stats.new_key.is_none(), "no new file written");
assert_eq!(log.file_count().await.unwrap(), 1, "original untouched");
}
}
crates/storaged/src/error_journal.rs
+89
View File
@@ -198,3 +198,92 @@ impl ErrorJournal {
self.log.file_count().await self.log.file_count().await
} }
} }
#[cfg(test)]
mod tests {
use super::*;
use object_store::memory::InMemory;
fn mk() -> ErrorJournal {
ErrorJournal::new(Arc::new(InMemory::new()))
}
#[tokio::test]
async fn append_updates_ring_immediately() {
let j = mk();
j.append(BucketErrorEvent::new_read("tenant_a", "k1", "timeout")).await;
j.append(BucketErrorEvent::new_write("tenant_b", "k2", "denied")).await;
let recent = j.recent(10).await;
assert_eq!(recent.len(), 2);
assert_eq!(recent[0].target, "tenant_a");
assert_eq!(recent[1].target, "tenant_b");
}
#[tokio::test]
async fn load_recent_rehydrates_ring_from_disk() {
// Use a shared InMemory store so the second journal reads the first
// journal's writes — mirrors gateway restart semantics.
let store: Arc<dyn ObjectStore> = Arc::new(InMemory::new());
{
let j1 = ErrorJournal {
log: Arc::new(AppendLog::new(store.clone(), JOURNAL_PREFIX).with_flush_threshold(1)),
ring: Arc::new(RwLock::new(VecDeque::with_capacity(RING_CAPACITY))),
};
j1.append(BucketErrorEvent::new_read("t", "k", "err")).await;
j1.flush().await.unwrap();
}
let j2 = ErrorJournal {
log: Arc::new(AppendLog::new(store.clone(), JOURNAL_PREFIX)),
ring: Arc::new(RwLock::new(VecDeque::with_capacity(RING_CAPACITY))),
};
assert_eq!(j2.recent(10).await.len(), 0, "fresh ring is empty");
let loaded = j2.load_recent().await.unwrap();
assert_eq!(loaded, 1);
assert_eq!(j2.recent(10).await[0].target, "t");
}
#[tokio::test]
async fn mark_rescued_updates_most_recent_match() {
let j = mk();
j.append(BucketErrorEvent::new_read("t1", "k", "err")).await;
j.append(BucketErrorEvent::new_read("t2", "k", "err")).await;
j.append(BucketErrorEvent::new_read("t1", "k", "err")).await;
j.mark_rescued_last("t1", "k").await;
let recent = j.recent(10).await;
assert!(!recent[0].rescued, "older t1 event not touched");
assert!(!recent[1].rescued, "t2 not touched");
assert!(recent[2].rescued, "most recent t1 was rescued");
}
#[tokio::test]
async fn health_flags_buckets_with_three_or_more_errors() {
let j = mk();
for _ in 0..3 { j.append(BucketErrorEvent::new_read("broken", "k", "e")).await; }
j.append(BucketErrorEvent::new_read("flaky", "k", "e")).await;
let h = j.health(60).await;
assert_eq!(h.total_errors, 4);
assert_eq!(h.per_bucket.get("broken"), Some(&3));
assert_eq!(h.per_bucket.get("flaky"), Some(&1));
assert!(h.unhealthy_buckets.contains(&"broken".to_string()));
assert!(!h.unhealthy_buckets.contains(&"flaky".to_string()));
}
#[tokio::test]
async fn filter_by_bucket_returns_chronological_order() {
let j = mk();
j.append(BucketErrorEvent::new_read("a", "k1", "e")).await;
j.append(BucketErrorEvent::new_read("b", "k2", "e")).await;
j.append(BucketErrorEvent::new_read("a", "k3", "e")).await;
let only_a = j.filter(Some("a"), None, 10).await;
assert_eq!(only_a.len(), 2);
assert_eq!(only_a[0].key, "k1", "oldest first");
assert_eq!(only_a[1].key, "k3");
}
}
crates/vectord-lance/src/lib.rs
+21 -1
View File
@@ -357,7 +357,25 @@ impl LanceVectorStore {
/// Search for top_k nearest neighbors of `query`. Uses the IVF_PQ /// Search for top_k nearest neighbors of `query`. Uses the IVF_PQ
/// index if one exists; otherwise does a full scan (slow but /// index if one exists; otherwise does a full scan (slow but
/// correct — useful during development before index build). /// correct — useful during development before index build).
pub async fn search(&self, query: &[f32], top_k: usize) -> Result<Vec<Hit>, String> { ///
/// `nprobes` tells Lance how many IVF partitions to probe per query.
/// Lance's built-in default is **1**, which caps recall well below
/// what the index is actually capable of. Passing `None` keeps that
/// default (only sensible when latency trumps recall); for real
/// workloads set `nprobes` to 5–10% of `num_partitions` (e.g. 20–30
/// on a 316-partition index).
///
/// `refine_factor` re-ranks the PQ-approximate top-k by computing
/// exact distances on `top_k * refine_factor` candidates, then
/// trimming to `top_k`. Cheap way to buy back recall lost to product
/// quantization. `None` skips the re-rank pass.
pub async fn search(
&self,
query: &[f32],
top_k: usize,
nprobes: Option<usize>,
refine_factor: Option<u32>,
) -> Result<Vec<Hit>, String> {
use lance::dataset::Dataset; use lance::dataset::Dataset;
let dataset = Dataset::open(&self.path).await.map_err(e)?; let dataset = Dataset::open(&self.path).await.map_err(e)?;
@@ -365,6 +383,8 @@ impl LanceVectorStore {
let mut scanner = dataset.scan(); let mut scanner = dataset.scan();
scanner.nearest("vector", &qarr, top_k as usize).map_err(e)?; scanner.nearest("vector", &qarr, top_k as usize).map_err(e)?;
if let Some(n) = nprobes { scanner.nprobes(n); }
if let Some(f) = refine_factor { scanner.refine(f); }
scanner.project(&["doc_id", "chunk_text"]).map_err(e)?; scanner.project(&["doc_id", "chunk_text"]).map_err(e)?;
let mut stream = scanner.try_into_stream().await.map_err(e)?; let mut stream = scanner.try_into_stream().await.map_err(e)?;
crates/vectord/Cargo.toml
+3
View File
@@ -9,6 +9,7 @@ storaged = { path = "../storaged" }
aibridge = { path = "../aibridge" } aibridge = { path = "../aibridge" }
catalogd = { path = "../catalogd" } catalogd = { path = "../catalogd" }
queryd = { path = "../queryd" } queryd = { path = "../queryd" }
futures = { workspace = true }
# ADR-019 firewall — vectord-lance owns its own Arrow 57 / Lance 4 deps. # ADR-019 firewall — vectord-lance owns its own Arrow 57 / Lance 4 deps.
# Public API uses only std types so no version conflict propagates here. # Public API uses only std types so no version conflict propagates here.
vectord-lance = { path = "../vectord-lance" } vectord-lance = { path = "../vectord-lance" }
@@ -24,3 +25,5 @@ arrow = { workspace = true }
chrono = { workspace = true } chrono = { workspace = true }
instant-distance = { workspace = true } instant-distance = { workspace = true }
uuid = { workspace = true } uuid = { workspace = true }
sha2 = { workspace = true }
reqwest = { version = "0.12", default-features = false, features = ["json", "rustls-tls"] }
crates/vectord/src/agent.rs
+118 -40
View File
@@ -204,6 +204,7 @@ pub struct AgentDeps {
pub embedding_cache: EmbeddingCache, pub embedding_cache: EmbeddingCache,
pub trial_journal: TrialJournal, pub trial_journal: TrialJournal,
pub promotion_registry: PromotionRegistry, pub promotion_registry: PromotionRegistry,
pub harness_store: crate::harness::HarnessStore,
} }
// -------- Spawn -------- // -------- Spawn --------
@@ -354,12 +355,11 @@ async fn run_one_cycle(
// Read history. // Read history.
let history = deps.trial_journal.list(&event.index_name).await let history = deps.trial_journal.list(&event.index_name).await
.map_err(|e| format!("read journal: {e}"))?; .map_err(|e| format!("read journal: {e}"))?;
if history.is_empty() {
return Err(format!( // Bootstrap mode: first ever visit to this index. Auto-generate a
"no trials yet for '{}' — seed with at least one POST /hnsw/trial first", // harness (if missing) and seed with the default config — the Phase 15
event.index_name, // known-good ec=80/es=30. Subsequent visits go through the proposer.
)); let is_bootstrap = history.is_empty();
}
// Current champion (if any) is the promoted config. // Current champion (if any) is the promoted config.
let champion = deps.promotion_registry.get_current(&event.index_name).await; let champion = deps.promotion_registry.get_current(&event.index_name).await;
@@ -367,24 +367,26 @@ async fn run_one_cycle(
history.iter().find(|t| t.id == p.trial_id).cloned() history.iter().find(|t| t.id == p.trial_id).cloned()
}); });
// Propose the next config. let (next_config, harness_name) = if is_bootstrap {
let Some(next_config) = propose_next_config(&history, champion_trial.as_ref()) else { let name = ensure_auto_harness(&event.index_name, deps).await?;
return Err("proposer returned None — search space exhausted".into()); (HnswConfig::default(), name)
} else {
let Some(cfg) = propose_next_config(&history, champion_trial.as_ref()) else {
return Err("proposer returned None — search space exhausted".into());
};
// Validate bounds defensively.
if !(10..=400).contains(&cfg.ef_construction) {
return Err(format!("proposed ef_construction={} out of bounds", cfg.ef_construction));
}
if !(10..=200).contains(&cfg.ef_search) {
return Err(format!("proposed ef_search={} out of bounds", cfg.ef_search));
}
// A future refinement: remember per-index "canonical harness" on
// the index metadata. For now: latest wins.
let hname = history.last().unwrap().eval_set.clone();
(cfg, hname)
}; };
let mut harness_set = deps.harness_store.load_for_index(&event.index_name, &harness_name).await
// Validate bounds defensively.
if !(10..=400).contains(&next_config.ef_construction) {
return Err(format!("proposed ef_construction={} out of bounds", next_config.ef_construction));
}
if !(10..=200).contains(&next_config.ef_search) {
return Err(format!("proposed ef_search={} out of bounds", next_config.ef_search));
}
// Need a harness to measure. Use the most recent one in history.
// (A future refinement: remember per-index "canonical harness" on
// the index metadata. For now: latest wins.)
let harness_name = history.last().unwrap().eval_set.clone();
let mut harness_set = harness::EvalSet::load(&deps.store, &harness_name).await
.map_err(|e| format!("load harness '{harness_name}': {e}"))?; .map_err(|e| format!("load harness '{harness_name}': {e}"))?;
let embeddings = deps.embedding_cache.get_or_load(&event.index_name).await let embeddings = deps.embedding_cache.get_or_load(&event.index_name).await
@@ -393,7 +395,7 @@ async fn run_one_cycle(
if !harness_set.ground_truth_built { if !harness_set.ground_truth_built {
harness::compute_ground_truth(&mut harness_set, &embeddings, &deps.ai_client).await harness::compute_ground_truth(&mut harness_set, &embeddings, &deps.ai_client).await
.map_err(|e| format!("ground truth: {e}"))?; .map_err(|e| format!("ground truth: {e}"))?;
harness_set.save(&deps.store).await.ok(); deps.harness_store.save(&harness_set).await.ok();
} }
// Build + bench. // Build + bench.
@@ -428,10 +430,13 @@ async fn run_one_cycle(
let dims = embeddings.first().map(|e| e.vector.len()).unwrap_or(0); let dims = embeddings.first().map(|e| e.vector.len()).unwrap_or(0);
let memory_bytes = (embeddings.len() * dims * std::mem::size_of::<f32>() + embeddings.len() * 128) as u64; let memory_bytes = (embeddings.len() * dims * std::mem::size_of::<f32>() + embeddings.len() * 128) as u64;
let note = match &event.reason { let note = {
TriggerReason::DatasetAppended { dataset } => format!("agent: dataset_appended({dataset})"), let base = match &event.reason {
TriggerReason::Manual => "agent: manual".to_string(), TriggerReason::DatasetAppended { dataset } => format!("agent: dataset_appended({dataset})"),
TriggerReason::Periodic => "agent: periodic".to_string(), TriggerReason::Manual => "agent: manual".to_string(),
TriggerReason::Periodic => "agent: periodic".to_string(),
};
if is_bootstrap { format!("{base} bootstrap") } else { base }
}; };
let trial = Trial { let trial = Trial {
@@ -597,19 +602,92 @@ pub fn propose_next_config(history: &[Trial], champion: Option<&Trial>) -> Optio
// -------- Helpers -------- // -------- Helpers --------
/// Find an index to poke on a periodic wake. Strategy: the one with the /// Minimum vectors for an index to be worth auto-tuning. Below this,
/// most recent trial. If nothing's been trialed yet, return None. /// HNSW's win over brute-force is too small to matter and trial budget
/// is better spent on bigger indexes.
const AUTOTUNE_MIN_VECTORS: usize = 1_000;
/// Find an index to poke on a periodic wake. Strategy: least-recently-tuned
/// wins — pick the index (from `IndexRegistry`, not just promoted ones)
/// whose most-recent trial is oldest, so trial budget spreads across the
/// whole portfolio. Never-trialed indexes sort first (None < Some), which
/// is exactly what we want: bootstrap them on their first visit.
///
/// Why not "most recently promoted" (the original strategy): a converged
/// index like `threat_intel_v1` sits at recall=1.0 on a minimal graph and
/// can't be improved — but it was always the freshest promotion, so the
/// agent burned every trial on it while 40+ other indexes got zero
/// attention. Rotating by last-trial-time fixes that without any explicit
/// convergence detection.
///
/// Why IndexRegistry instead of PromotionRegistry: on a fresh system only
/// a handful of indexes have ever been promoted, so promotion-based
/// picking starves new ones. IndexRegistry lists every live index the
/// platform knows about; filtering by `AUTOTUNE_MIN_VECTORS` keeps the
/// proposer off indexes where the graph config doesn't matter.
///
/// Cost: N extra journal reads per periodic tick, where N = eligible
/// indexes. Fine at dozens; if the portfolio grows into thousands, cache
/// `last_trial_at` on the IndexMeta.
async fn pick_periodic_target(deps: &AgentDeps) -> Option<String> { async fn pick_periodic_target(deps: &AgentDeps) -> Option<String> {
// `/agent` runs against any index that has a trial journal. We don't let candidates: Vec<String> = deps
// have a "list all journals" helper, so we derive candidates from the .index_registry
// promotion registry (indexes with a human ever promoting are live). .list(None, None)
let promos = deps.promotion_registry.list_all().await.ok()?; .await
// Prefer the one most recently promoted — it's the one a human cares .into_iter()
// about right now. .filter(|m| m.chunk_count >= AUTOTUNE_MIN_VECTORS)
promos.into_iter() .map(|m| m.index_name)
.filter_map(|f| f.current.map(|c| (f.index_name, c.promoted_at))) .collect();
.max_by_key(|(_, at)| *at) if candidates.is_empty() { return None; }
.map(|(name, _)| name)
let mut scored: Vec<(String, Option<DateTime<Utc>>)> = Vec::with_capacity(candidates.len());
for name in candidates {
let last_trial_at = deps
.trial_journal
.list(&name)
.await
.ok()
.and_then(|trials| trials.into_iter().map(|t| t.created_at).max());
scored.push((name, last_trial_at));
}
scored.into_iter().min_by_key(|(_, last)| *last).map(|(n, _)| n)
}
/// On the first visit to an index, load or synthesize an eval harness so
/// bootstrap trials have something to measure recall against. Returns the
/// harness name. Synthetic harnesses sample 20 chunks and use their first
/// 200 chars as self-queries — the ground truth is computed by brute-force
/// cosine, so recall numbers are real (a config that finds the chunk in
/// top-k counts as a hit).
async fn ensure_auto_harness(index_name: &str, deps: &AgentDeps) -> Result<String, String> {
let name = format!("{index_name}_auto");
if deps.harness_store.load_for_index(index_name, &name).await.is_ok() {
return Ok(name);
}
let embeddings = deps
.embedding_cache
.get_or_load(index_name)
.await
.map_err(|e| format!("load embeddings for auto-harness: {e}"))?;
if embeddings.is_empty() {
return Err(format!("index '{index_name}' has no embeddings — cannot bootstrap"));
}
let mut eval = harness::synthetic_from_chunks(&name, index_name, &embeddings, 20, 10);
harness::compute_ground_truth(&mut eval, &embeddings, &deps.ai_client)
.await
.map_err(|e| format!("auto-harness ground truth: {e}"))?;
deps.harness_store.save(&eval).await
.map_err(|e| format!("save auto-harness: {e}"))?;
tracing::info!(
"agent: bootstrapped harness '{}' for index '{}' (20 self-queries, k=10)",
name, index_name
);
Ok(name)
} }
async fn over_rate_limit(inner: &Arc<AgentInner>, cap: u32) -> bool { async fn over_rate_limit(inner: &Arc<AgentInner>, cap: u32) -> bool {
crates/vectord/src/autotune.rs
+9 -5
View File
@@ -28,7 +28,7 @@ use aibridge::client::AiClient;
use catalogd::registry::Registry as CatalogRegistry; use catalogd::registry::Registry as CatalogRegistry;
use crate::embedding_cache::EmbeddingCache; use crate::embedding_cache::EmbeddingCache;
use crate::harness; use crate::harness::{self, HarnessStore};
use crate::hnsw::HnswStore; use crate::hnsw::HnswStore;
use crate::index_registry::IndexRegistry; use crate::index_registry::IndexRegistry;
use crate::jobs::JobTracker; use crate::jobs::JobTracker;
@@ -128,7 +128,7 @@ fn pick_winner(trials: &[Trial], min_recall: f32) -> Option<&Trial> {
#[allow(clippy::too_many_arguments)] #[allow(clippy::too_many_arguments)]
pub async fn run_autotune( pub async fn run_autotune(
req: AutotuneRequest, req: AutotuneRequest,
store: &Arc<dyn ObjectStore>, _store: &Arc<dyn ObjectStore>,
catalog: &CatalogRegistry, catalog: &CatalogRegistry,
ai_client: &AiClient, ai_client: &AiClient,
embedding_cache: &EmbeddingCache, embedding_cache: &EmbeddingCache,
@@ -136,6 +136,7 @@ pub async fn run_autotune(
index_registry: &IndexRegistry, index_registry: &IndexRegistry,
trial_journal: &TrialJournal, trial_journal: &TrialJournal,
promotion_registry: &PromotionRegistry, promotion_registry: &PromotionRegistry,
harness_store: &HarnessStore,
_job_tracker: &JobTracker, _job_tracker: &JobTracker,
) -> Result<AutotuneResult, String> { ) -> Result<AutotuneResult, String> {
let t0 = std::time::Instant::now(); let t0 = std::time::Instant::now();
@@ -146,8 +147,11 @@ pub async fn run_autotune(
} }
let _ = catalog; // reserved for future audit emission let _ = catalog; // reserved for future audit emission
// Load the harness once, compute ground truth once. // Load the harness once, compute ground truth once. Harness resolves
let mut harness_set = harness::EvalSet::load(store, &req.harness) // to the index's bucket via HarnessStore, with primary as fallback for
// pre-federation evals.
let mut harness_set = harness_store
.load_for_index(&req.index_name, &req.harness)
.await .await
.map_err(|e| format!("load harness: {e}"))?; .map_err(|e| format!("load harness: {e}"))?;
@@ -160,7 +164,7 @@ pub async fn run_autotune(
harness::compute_ground_truth(&mut harness_set, &embeddings, ai_client) harness::compute_ground_truth(&mut harness_set, &embeddings, ai_client)
.await .await
.map_err(|e| format!("ground truth: {e}"))?; .map_err(|e| format!("ground truth: {e}"))?;
harness_set.save(store).await.ok(); harness_store.save(&harness_set).await.ok();
} }
let (grid, rejected) = sanitize_grid(req.grid.clone().unwrap_or_else(default_grid)); let (grid, rejected) = sanitize_grid(req.grid.clone().unwrap_or_else(default_grid));
crates/vectord/src/doc_drift.rs
+166
View File
@@ -0,0 +1,166 @@
// Phase 45 slice 3 — context7 bridge client for doc-drift detection.
//
// Calls the Bun context7 bridge on :3900 (mcp-server/context7_bridge.ts)
// which itself wraps context7's public API. For each DocRef on a
// playbook, queries /docs/:tool/diff?since=<snippet_hash> and parses
// the response.
//
// Kept deliberately thin: no caching here (bridge caches for 5 min),
// no retry logic (transient fail = report as "unknown" drift status,
// don't flag). The handler decides whether to flag the playbook based
// on whether ANY tool came back drifted=true.
use serde::Deserialize;
use std::time::Duration;
use crate::playbook_memory::DocRef;
const DEFAULT_BRIDGE_URL: &str = "http://localhost:3900";
const CALL_TIMEOUT_SECS: u64 = 15;
#[derive(Debug, Clone)]
pub struct DriftCheck {
pub tool: String,
pub version_seen: String,
pub outcome: DriftOutcome,
}
#[derive(Debug, Clone)]
pub enum DriftOutcome {
/// Bridge reports drift (current snippet_hash != previous).
Drifted { current_snippet_hash: String, source_url: Option<String> },
/// Bridge reports no drift — the stored snippet_hash still matches
/// the current context7 docs.
Unchanged,
/// Bridge unreachable, 404 on the tool, or returned unparseable
/// data. We deliberately don't flag on these — a down bridge
/// shouldn't silently mark every playbook drift-flagged.
Unknown { reason: String },
}
#[derive(Debug, Clone, Deserialize)]
struct BridgeDiffResponse {
drifted: bool,
current_snippet_hash: Option<String>,
source_url: Option<String>,
}
pub struct DriftCheckerConfig {
pub bridge_url: String,
}
impl Default for DriftCheckerConfig {
fn default() -> Self {
Self {
bridge_url: std::env::var("LH_BRIDGE_URL")
.unwrap_or_else(|_| DEFAULT_BRIDGE_URL.to_string()),
}
}
}
/// For every doc_ref, ask the bridge whether it drifted against the
/// recorded snippet_hash. Returns per-tool outcomes.
pub async fn check_all_refs(
cfg: &DriftCheckerConfig,
doc_refs: &[DocRef],
) -> Vec<DriftCheck> {
let client = reqwest::Client::builder()
.timeout(Duration::from_secs(CALL_TIMEOUT_SECS))
.build()
.expect("reqwest client build");
let mut out = Vec::with_capacity(doc_refs.len());
for r in doc_refs {
let hash = r.snippet_hash.as_deref().unwrap_or("");
if hash.is_empty() {
// No hash to compare against — can't detect drift. Report
// unknown so the caller isn't forced to flag.
out.push(DriftCheck {
tool: r.tool.clone(),
version_seen: r.version_seen.clone(),
outcome: DriftOutcome::Unknown {
reason: "no snippet_hash recorded on doc_ref".into(),
},
});
continue;
}
let url = format!(
"{}/docs/{}/diff?since={}",
cfg.bridge_url.trim_end_matches('/'),
urlencoding_minimal(&r.tool),
urlencoding_minimal(hash),
);
let outcome = match client.get(&url).send().await {
Err(e) => DriftOutcome::Unknown {
reason: format!("bridge unreachable: {}", e),
},
Ok(resp) => {
if resp.status() == reqwest::StatusCode::NOT_FOUND {
DriftOutcome::Unknown {
reason: format!("bridge 404 — no context7 library for tool '{}'", r.tool),
}
} else if !resp.status().is_success() {
let status = resp.status();
DriftOutcome::Unknown {
reason: format!("bridge {}: {}", status, resp.text().await.unwrap_or_default()),
}
} else {
match resp.json::<BridgeDiffResponse>().await {
Err(e) => DriftOutcome::Unknown {
reason: format!("bridge response parse: {}", e),
},
Ok(body) => {
if body.drifted {
DriftOutcome::Drifted {
current_snippet_hash: body.current_snippet_hash.unwrap_or_default(),
source_url: body.source_url,
}
} else {
DriftOutcome::Unchanged
}
}
}
}
}
};
out.push(DriftCheck {
tool: r.tool.clone(),
version_seen: r.version_seen.clone(),
outcome,
});
}
out
}
/// Minimal URL path+query encoder for ASCII tool names. We're not
/// pulling in the `urlencoding` crate for this single use — tool
/// names are short alphanumeric (docker/terraform/react), snippet
/// hashes are hex. Handles space + `/` + `?` + `&` defensively so
/// unusual tool names don't corrupt the URL.
fn urlencoding_minimal(s: &str) -> String {
let mut out = String::with_capacity(s.len());
for b in s.bytes() {
match b {
b'A'..=b'Z' | b'a'..=b'z' | b'0'..=b'9' | b'-' | b'_' | b'.' | b'~' => out.push(b as char),
_ => out.push_str(&format!("%{:02X}", b)),
}
}
out
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn urlencoding_handles_ascii_safe_chars_passthrough() {
assert_eq!(urlencoding_minimal("docker"), "docker");
assert_eq!(urlencoding_minimal("next.js"), "next.js");
}
#[test]
fn urlencoding_encodes_slash_and_space() {
assert_eq!(urlencoding_minimal("foo/bar"), "foo%2Fbar");
assert_eq!(urlencoding_minimal("foo bar"), "foo%20bar");
}
}
crates/vectord/src/harness.rs
+176 -5
View File
@@ -10,11 +10,14 @@
use chrono::{DateTime, Utc}; use chrono::{DateTime, Utc};
use object_store::ObjectStore; use object_store::ObjectStore;
use serde::{Deserialize, Serialize}; use serde::{Deserialize, Serialize};
use std::collections::HashSet;
use std::sync::Arc; use std::sync::Arc;
use aibridge::client::{AiClient, EmbedRequest}; use aibridge::client::{AiClient, EmbedRequest};
use storaged::ops; use storaged::ops;
use storaged::registry::BucketRegistry;
use crate::index_registry::IndexRegistry;
use crate::store::StoredEmbedding; use crate::store::StoredEmbedding;
/// A single eval query with optional pre-computed ground truth. /// A single eval query with optional pre-computed ground truth.
@@ -85,6 +88,108 @@ impl EvalSet {
} }
} }
/// Federation-aware wrapper around EvalSet persistence. Mirrors the
/// `TrialJournal` / `PromotionRegistry` pattern: harness files colocate
/// with their index's bucket (looked up via `IndexMeta.bucket`), falling
/// back to `primary` for indexes the registry has never seen. Legacy
/// harnesses predating federation remain discoverable — lookups transparently
/// try the resolved bucket first, then `primary` as a fallback. Cross-bucket
/// listing dedupes across every registered bucket so `GET /hnsw/evals`
/// returns a complete picture.
#[derive(Clone)]
pub struct HarnessStore {
buckets: Arc<BucketRegistry>,
index_registry: IndexRegistry,
}
impl HarnessStore {
pub fn new(buckets: Arc<BucketRegistry>, index_registry: IndexRegistry) -> Self {
Self { buckets, index_registry }
}
/// Resolve which bucket holds this index's eval artifacts. Indexes the
/// registry has never heard of fall through to `primary`.
async fn bucket_for_index(&self, index_name: &str) -> String {
self.index_registry
.get(index_name)
.await
.map(|m| m.bucket)
.unwrap_or_else(|| "primary".to_string())
}
/// Save to the bucket that owns `eval.index_name`. Writes under the
/// standard `_hnsw_evals/{name}.json` prefix of the resolved bucket.
pub async fn save(&self, eval: &EvalSet) -> Result<(), String> {
let bucket = self.bucket_for_index(&eval.index_name).await;
let store = self.buckets.get(&bucket)?;
eval.save(&store).await
}
/// Load a harness by name, given the index it belongs to. Tries the
/// index's bucket first; if the file is absent AND the resolved bucket
/// isn't `primary`, falls through to `primary` so pre-federation evals
/// remain reachable without migration.
pub async fn load_for_index(
&self,
index_name: &str,
harness_name: &str,
) -> Result<EvalSet, String> {
let bucket = self.bucket_for_index(index_name).await;
let primary_store = self.buckets.get("primary")?;
let store = self.buckets.get(&bucket)?;
match EvalSet::load(&store, harness_name).await {
Ok(e) => Ok(e),
Err(e) if bucket != "primary" => EvalSet::load(&primary_store, harness_name)
.await
.map_err(|primary_err| format!("{bucket}: {e}; primary fallback: {primary_err}")),
Err(e) => Err(e),
}
}
/// Find a harness by name without knowing which index it belongs to —
/// used by `GET /hnsw/evals/{name}`. Scans every registered bucket;
/// first hit wins. Primary is searched first so pre-federation evals
/// with the same name as a federated one resolve deterministically.
pub async fn get_any(&self, harness_name: &str) -> Result<EvalSet, String> {
let bucket_infos = self.buckets.list().await;
let mut ordered: Vec<String> = bucket_infos.iter().map(|b| b.name.clone()).collect();
ordered.sort_by_key(|n| if n == "primary" { 0 } else { 1 });
let mut last_err = None;
for b in ordered {
let store = match self.buckets.get(&b) {
Ok(s) => s,
Err(e) => { last_err = Some(e); continue; }
};
match EvalSet::load(&store, harness_name).await {
Ok(e) => return Ok(e),
Err(e) => { last_err = Some(e); }
}
}
Err(last_err.unwrap_or_else(|| format!("no buckets registered for eval '{harness_name}'")))
}
/// Union of every harness name across every registered bucket.
/// Duplicates (same name in multiple buckets — pathological but
/// possible after a manual migration) are collapsed.
pub async fn list_all(&self) -> Vec<String> {
let mut all: HashSet<String> = HashSet::new();
for b in self.buckets.list().await {
let store = match self.buckets.get(&b.name) {
Ok(s) => s,
Err(_) => continue,
};
if let Ok(names) = EvalSet::list(&store).await {
all.extend(names);
}
}
let mut out: Vec<String> = all.into_iter().collect();
out.sort();
out
}
}
/// Cosine similarity for two same-length f32 slices. /// Cosine similarity for two same-length f32 slices.
fn cosine(a: &[f32], b: &[f32]) -> f32 { fn cosine(a: &[f32], b: &[f32]) -> f32 {
let mut dot = 0.0f32; let mut dot = 0.0f32;
@@ -127,13 +232,15 @@ pub fn recall_at_k(predicted: &[String], ground_truth: &[String], k: usize) -> f
if ground_truth.is_empty() || k == 0 { if ground_truth.is_empty() || k == 0 {
return 0.0; return 0.0;
} }
// Set-intersection recall@k. Previous implementation counted duplicates
// in `predicted` (a corpus with repeated chunks — e.g. cached LLM
// responses — returns the same doc_id multiple times via HNSW), which
// inflated recall above 1.0 and poisoned promotion decisions.
let gt_set: std::collections::HashSet<&String> = let gt_set: std::collections::HashSet<&String> =
ground_truth.iter().take(k).collect(); ground_truth.iter().take(k).collect();
let hits = predicted let pred_set: std::collections::HashSet<&String> =
.iter() predicted.iter().take(k).collect();
.take(k) let hits = pred_set.intersection(&gt_set).count();
.filter(|d| gt_set.contains(d))
.count();
hits as f32 / gt_set.len() as f32 hits as f32 / gt_set.len() as f32
} }
@@ -214,3 +321,67 @@ pub fn synthetic_from_chunks(
ground_truth_built: false, ground_truth_built: false,
} }
} }
#[cfg(test)]
mod tests {
use super::*;
fn s(v: &[&str]) -> Vec<String> { v.iter().map(|x| x.to_string()).collect() }
#[test]
fn recall_empty_ground_truth_is_zero() {
assert_eq!(recall_at_k(&s(&["a", "b"]), &[], 10), 0.0);
}
#[test]
fn recall_k_zero_is_zero() {
assert_eq!(recall_at_k(&s(&["a"]), &s(&["a"]), 0), 0.0);
}
#[test]
fn recall_perfect_match_equals_one() {
let pred = s(&["a", "b", "c"]);
let gt = s(&["a", "b", "c"]);
assert!((recall_at_k(&pred, &gt, 3) - 1.0).abs() < 1e-6);
}
#[test]
fn recall_half_match() {
let pred = s(&["a", "b", "x", "y"]);
let gt = s(&["a", "b", "c", "d"]);
assert!((recall_at_k(&pred, &gt, 4) - 0.5).abs() < 1e-6);
}
#[test]
fn recall_duplicates_in_predicted_do_not_inflate() {
// Regression guard: the previous implementation counted each
// duplicate in `predicted` separately, which could push recall
// above 1.0 on corpora with repeated chunks (cached responses etc).
// Set-intersection semantics keep it bounded in [0, 1].
let pred = s(&["a", "a", "a", "a"]);
let gt = s(&["a", "b", "c", "d"]);
let r = recall_at_k(&pred, &gt, 4);
assert!(r <= 1.0, "recall {r} must not exceed 1.0");
// One unique match out of four in gt = 0.25.
assert!((r - 0.25).abs() < 1e-6);
}
#[test]
fn recall_duplicates_in_ground_truth_handled() {
// gt with dupes reduces effective |gt|; matching one hits all.
let pred = s(&["x"]);
let gt = s(&["x", "x", "x"]);
let r = recall_at_k(&pred, &gt, 3);
assert!(r <= 1.0);
assert!((r - 1.0).abs() < 1e-6); // |pred ∩ gt| / |gt_set| = 1/1
}
#[test]
fn recall_respects_k_bound() {
// k=2 means only the first 2 of pred and gt count toward the set.
let pred = s(&["a", "b", "c", "d"]);
let gt = s(&["a", "b", "c", "d"]);
let r = recall_at_k(&pred, &gt, 2);
assert!((r - 1.0).abs() < 1e-6);
}
}
crates/vectord/src/lib.rs
+1
View File
@@ -8,6 +8,7 @@ pub mod hnsw;
pub mod index_registry; pub mod index_registry;
pub mod jobs; pub mod jobs;
pub mod playbook_memory; pub mod playbook_memory;
pub mod doc_drift;
pub mod promotion; pub mod promotion;
pub mod refresh; pub mod refresh;
pub mod store; pub mod store;
crates/vectord/src/playbook_memory.rs
+723 -40
View File
@@ -113,6 +113,106 @@ pub struct PlaybookEntry {
/// "manual: operator requested via POST /retire" /// "manual: operator requested via POST /retire"
#[serde(default)] #[serde(default)]
pub retirement_reason: Option<String>, pub retirement_reason: Option<String>,
/// Phase 27 — monotonic version counter within a playbook chain.
/// First version is 1; `revise_entry` sets the new entry's version
/// to parent.version + 1. Entries persisted before Phase 27 get
/// version=1 via serde default and are treated as roots.
#[serde(default = "default_version")]
pub version: u32,
/// Phase 27 — playbook_id of the prior version in this chain. None
/// for root entries (first version).
#[serde(default)]
pub parent_id: Option<String>,
/// Phase 27 — timestamp set when a newer version replaced this
/// entry via `revise_entry`. Superseded entries are excluded from
/// boost calculations (same rule as `retired_at`) but remain
/// queryable via `history` for audit.
#[serde(default)]
pub superseded_at: Option<String>,
/// Phase 27 — playbook_id of the entry that replaced this one.
/// Walking `superseded_by` from the root forward reconstructs the
/// full version chain.
#[serde(default)]
pub superseded_by: Option<String>,
/// Phase 45 — external documentation references captured at seal
/// time. One entry per tool/library the procedure consulted.
/// Drives drift detection: when context7 reports a newer version
/// for any entry here than what's in `version_seen`, the playbook
/// is `doc_drift_flagged_at` and excluded from boost until human
/// review clears it. Legacy entries (pre-Phase-45) load with an
/// empty vec — they simply never drift-flag, same as entries
/// without a `schema_fingerprint` in Phase 25.
#[serde(default)]
pub doc_refs: Vec<DocRef>,
/// Phase 45 — set by `flag_doc_drift()` when one or more
/// `doc_refs` entries have a newer version available than
/// `version_seen`. Flagged entries are excluded from boost until
/// `doc_drift_reviewed_at` is set via the /resolve endpoint.
#[serde(default)]
pub doc_drift_flagged_at: Option<String>,
/// Phase 45 — set by human operator via
/// `/vectors/playbook_memory/doc_drift/resolve/{id}` after
/// reviewing the drift diagnosis. Either re-admits the entry to
/// boost (if still applicable) or pairs with `retired_at` /
/// `superseded_by` if the procedure changed.
#[serde(default)]
pub doc_drift_reviewed_at: Option<String>,
}
fn default_version() -> u32 { 1 }
/// Phase 45 — one external doc reference. Recorded at seal time so
/// drift detection knows what version was consulted. `snippet_hash`
/// lets us detect "same version, different passage" when a library
/// patches docs without bumping the version number.
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
pub struct DocRef {
/// Canonical tool/library name as context7 knows it, e.g.
/// "docker", "terraform", "react", "next.js". Case-insensitive
/// on compare.
pub tool: String,
/// Version string exactly as seen at seal time. Context7 typically
/// returns semver-like; we store raw string to avoid parsing
/// ambiguity ("latest", "next", "canary" are all valid).
pub version_seen: String,
/// Optional hash of the specific doc passage the procedure
/// referenced. Useful when version hasn't bumped but content
/// rewrote.
#[serde(default)]
pub snippet_hash: Option<String>,
/// Optional direct URL back to the doc (context7 can resolve
/// tool+version → URL, so this is cache not source-of-truth).
#[serde(default)]
pub source_url: Option<String>,
/// When this reference was captured. RFC3339.
pub seen_at: String,
}
impl Default for PlaybookEntry {
fn default() -> Self {
Self {
playbook_id: String::new(),
operation: String::new(),
approach: String::new(),
context: String::new(),
timestamp: String::new(),
endorsed_names: Vec::new(),
city: None,
state: None,
embedding: None,
schema_fingerprint: None,
valid_until: None,
retired_at: None,
retirement_reason: None,
version: 1,
parent_id: None,
superseded_at: None,
superseded_by: None,
doc_refs: Vec::new(),
doc_drift_flagged_at: None,
doc_drift_reviewed_at: None,
}
}
} }
/// A recorded failure — worker who didn't deliver on a contract. /// A recorded failure — worker who didn't deliver on a contract.
@@ -152,11 +252,21 @@ pub struct BoostEntry {
/// Phase 26 — what happened during an upsert. The seed endpoint /// Phase 26 — what happened during an upsert. The seed endpoint
/// returns this shape so the caller sees whether its write was a new /// returns this shape so the caller sees whether its write was a new
/// entry, a merge, or a dedup'd no-op. /// entry, a merge, or a dedup'd no-op.
///
/// SHAPE NOTE: `#[serde(tag = "mode")]` requires struct-like variants —
/// bare `Added(String)` and `Noop(String)` newtype variants would
/// panic serialization at runtime. That bug silently 500-ed every
/// /seed call from Phase 26 (commit 640db8c) until 2026-04-22 when the
/// auditor's hybrid fixture surfaced it. All three variants are now
/// struct-like, producing uniform JSON:
/// {"mode":"added","playbook_id":"pb-..."}
/// {"mode":"updated","playbook_id":"pb-...","merged_names":[...]}
/// {"mode":"noop","playbook_id":"pb-..."}
#[derive(Debug, Clone, Serialize)] #[derive(Debug, Clone, Serialize)]
#[serde(tag = "mode", rename_all = "lowercase")] #[serde(tag = "mode", rename_all = "lowercase")]
pub enum UpsertOutcome { pub enum UpsertOutcome {
/// New playbook appended. Carries the new playbook_id. /// New playbook appended. Carries the new playbook_id.
Added(String), Added { playbook_id: String },
/// Existing same-day entry updated. Playbook_id unchanged; names /// Existing same-day entry updated. Playbook_id unchanged; names
/// merged (union, original order preserved, new names appended). /// merged (union, original order preserved, new names appended).
Updated { Updated {
@@ -165,7 +275,18 @@ pub enum UpsertOutcome {
}, },
/// Identical same-day entry already exists; nothing changed. /// Identical same-day entry already exists; nothing changed.
/// Returns the stable playbook_id so caller still has a reference. /// Returns the stable playbook_id so caller still has a reference.
Noop(String), Noop { playbook_id: String },
}
/// Phase 27 — shape returned from `revise_entry`. Reports both ends of
/// the supersession so callers can link citations or audit chains.
#[derive(Debug, Clone, Serialize)]
pub struct ReviseOutcome {
pub parent_id: String,
pub parent_version: u32,
pub new_playbook_id: String,
pub new_version: u32,
pub superseded_at: String,
} }
/// Return YYYY-MM-DD from an RFC3339 timestamp. Falls back to the /// Return YYYY-MM-DD from an RFC3339 timestamp. Falls back to the
@@ -204,13 +325,15 @@ impl PlaybookMemory {
/// Rebuild the geo index from scratch. Called by every mutation /// Rebuild the geo index from scratch. Called by every mutation
/// helper after persist succeeds. O(n) scan of entries; at current /// helper after persist succeeds. O(n) scan of entries; at current
/// scale ~40µs. Skips retired entries — they never participate in /// scale ~40µs. Skips retired and superseded entries — they never
/// boost filtering, so indexing them would just waste lookups. /// participate in boost filtering, so indexing them would just
/// waste lookups.
async fn rebuild_geo_index(&self) { async fn rebuild_geo_index(&self) {
let state = self.state.read().await; let state = self.state.read().await;
let mut idx: HashMap<(String, String), Vec<usize>> = HashMap::new(); let mut idx: HashMap<(String, String), Vec<usize>> = HashMap::new();
for (i, e) in state.entries.iter().enumerate() { for (i, e) in state.entries.iter().enumerate() {
if e.retired_at.is_some() { continue; } if e.retired_at.is_some() { continue; }
if e.superseded_at.is_some() { continue; }
let (Some(city), Some(st)) = (&e.city, &e.state) else { continue; }; let (Some(city), Some(st)) = (&e.city, &e.state) else { continue; };
let key = (city.to_ascii_lowercase(), st.to_ascii_uppercase()); let key = (city.to_ascii_lowercase(), st.to_ascii_uppercase());
idx.entry(key).or_default().push(i); idx.entry(key).or_default().push(i);
@@ -275,6 +398,64 @@ impl PlaybookMemory {
Ok(touched) Ok(touched)
} }
/// Phase 45 slice 3 — stamp `doc_drift_flagged_at` on a playbook.
/// Idempotent: if already flagged and not yet reviewed, this is a
/// no-op. Callers should check the return value: Ok(true) means we
/// made a new flag, Ok(false) means already flagged (unreviewed) or
/// playbook not found, Err means persist failed.
pub async fn flag_doc_drift(&self, playbook_id: &str) -> Result<bool, String> {
let mut touched = false;
{
let mut state = self.state.write().await;
for e in state.entries.iter_mut() {
if e.playbook_id == playbook_id {
// Skip if already flagged + unreviewed (avoid
// churning the timestamp on every re-check).
if e.doc_drift_flagged_at.is_some() && e.doc_drift_reviewed_at.is_none() {
break;
}
e.doc_drift_flagged_at = Some(chrono::Utc::now().to_rfc3339());
e.doc_drift_reviewed_at = None; // fresh flag clears any stale review
touched = true;
break;
}
}
}
if touched {
self.persist().await?;
}
Ok(touched)
}
/// Phase 45 slice 3 — stamp `doc_drift_reviewed_at` on a playbook,
/// re-admitting it to boost calculation. Idempotent.
pub async fn resolve_doc_drift(&self, playbook_id: &str) -> Result<bool, String> {
let mut touched = false;
{
let mut state = self.state.write().await;
for e in state.entries.iter_mut() {
if e.playbook_id == playbook_id && e.doc_drift_flagged_at.is_some()
&& e.doc_drift_reviewed_at.is_none()
{
e.doc_drift_reviewed_at = Some(chrono::Utc::now().to_rfc3339());
touched = true;
break;
}
}
}
if touched {
self.persist().await?;
}
Ok(touched)
}
/// Read-only: get a playbook entry by id. Used by the drift-check
/// handler to read doc_refs without exposing the full state lock.
pub async fn get_entry(&self, playbook_id: &str) -> Option<PlaybookEntry> {
let state = self.state.read().await;
state.entries.iter().find(|e| e.playbook_id == playbook_id).cloned()
}
/// Phase 25 — retire every entry matching (city, state) whose /// Phase 25 — retire every entry matching (city, state) whose
/// schema_fingerprint doesn't match the current one. Entries with /// schema_fingerprint doesn't match the current one. Entries with
/// no fingerprint (legacy) are skipped — caller can use /// no fingerprint (legacy) are skipped — caller can use
@@ -312,13 +493,129 @@ impl PlaybookMemory {
Ok(count) Ok(count)
} }
/// Stats accessor for the /status endpoint and tests. /// Phase 27 — append a new version of an existing playbook. The
pub async fn status_counts(&self) -> (usize, usize, usize) { /// parent is stamped with `superseded_at` + `superseded_by`; the
/// new entry inherits `parent_id` and gets `version = parent + 1`.
/// Errors when the parent is retired (terminal state) or already
/// superseded (must revise the tip of the chain, not a middle
/// node). Caller supplies the new entry with its own fresh
/// `playbook_id`; chain-metadata fields on the input are
/// overwritten so callers can't fabricate a mismatched history.
pub async fn revise_entry(
&self,
parent_id: &str,
mut new_entry: PlaybookEntry,
) -> Result<ReviseOutcome, String> {
let now = chrono::Utc::now().to_rfc3339();
let mut state = self.state.write().await;
let Some(i) = state.entries.iter().position(|e| e.playbook_id == parent_id) else {
return Err(format!("parent playbook_id '{parent_id}' not found"));
};
{
let parent = &state.entries[i];
if parent.retired_at.is_some() {
return Err(format!(
"cannot revise retired playbook '{parent_id}' — retirement is terminal"
));
}
if let Some(succ) = &parent.superseded_by {
return Err(format!(
"playbook '{parent_id}' already superseded by '{succ}'; \
revise the latest version in the chain instead"
));
}
}
let parent_version = state.entries[i].version;
let new_version = parent_version.saturating_add(1);
let parent_pid = state.entries[i].playbook_id.clone();
let new_pid = new_entry.playbook_id.clone();
if new_pid.is_empty() {
return Err("new playbook_id must not be empty".into());
}
if new_pid == parent_pid {
return Err("new playbook_id must differ from parent".into());
}
// Enforce chain-metadata integrity — caller doesn't get to
// fabricate these.
new_entry.version = new_version;
new_entry.parent_id = Some(parent_pid.clone());
new_entry.superseded_at = None;
new_entry.superseded_by = None;
let parent_mut = &mut state.entries[i];
parent_mut.superseded_at = Some(now.clone());
parent_mut.superseded_by = Some(new_pid.clone());
state.entries.push(new_entry);
drop(state);
self.persist().await?;
self.rebuild_geo_index().await;
Ok(ReviseOutcome {
parent_id: parent_pid,
parent_version,
new_playbook_id: new_pid,
new_version,
superseded_at: now,
})
}
/// Phase 27 — return the full version chain that contains this
/// playbook_id, ordered from root (v1) to tip. Walks `parent_id`
/// backward to find the root, then `superseded_by` forward to the
/// tip. Returns empty if the id isn't present. Cycle-safe via a
/// visited set; unreachable in normal operation but the guard is
/// cheap.
pub async fn history(&self, playbook_id: &str) -> Vec<PlaybookEntry> {
let state = self.state.read().await;
let by_id: HashMap<&str, &PlaybookEntry> = state.entries
.iter()
.map(|e| (e.playbook_id.as_str(), e))
.collect();
let Some(seed) = by_id.get(playbook_id).copied() else {
return vec![];
};
// Walk backward to root.
let mut cursor = seed;
let mut seen: std::collections::HashSet<String> = std::collections::HashSet::new();
seen.insert(cursor.playbook_id.clone());
while let Some(pid) = &cursor.parent_id {
let Some(&next) = by_id.get(pid.as_str()) else { break };
if !seen.insert(next.playbook_id.clone()) { break; }
cursor = next;
}
let root = cursor;
// Walk forward to tip.
let mut chain = vec![root.clone()];
let mut cursor = root;
let mut seen_fwd: std::collections::HashSet<String> = std::collections::HashSet::new();
seen_fwd.insert(cursor.playbook_id.clone());
while let Some(nid) = &cursor.superseded_by {
let Some(&next) = by_id.get(nid.as_str()) else { break };
if !seen_fwd.insert(next.playbook_id.clone()) { break; }
cursor = next;
chain.push(cursor.clone());
}
chain
}
/// Stats accessor for the /status endpoint and tests. Returns
/// (total, retired, superseded, failures). Phase 27 added
/// superseded as a distinct counter: a superseded entry is
/// replaced-by-newer-version, which is a different lifecycle event
/// than retired-stop-using.
pub async fn status_counts(&self) -> (usize, usize, usize, usize) {
let state = self.state.read().await; let state = self.state.read().await;
let total = state.entries.len(); let total = state.entries.len();
let retired = state.entries.iter().filter(|e| e.retired_at.is_some()).count(); let retired = state.entries.iter().filter(|e| e.retired_at.is_some()).count();
let superseded = state.entries.iter().filter(|e| e.superseded_at.is_some()).count();
let failures = state.failures.len(); let failures = state.failures.len();
(total, retired, failures) (total, retired, superseded, failures)
} }
/// Phase 26 — Mem0-style upsert. Decides ADD / UPDATE / NOOP based /// Phase 26 — Mem0-style upsert. Decides ADD / UPDATE / NOOP based
@@ -353,6 +650,7 @@ impl PlaybookMemory {
let mut existing_idx: Option<usize> = None; let mut existing_idx: Option<usize> = None;
for (i, e) in state.entries.iter().enumerate() { for (i, e) in state.entries.iter().enumerate() {
if e.retired_at.is_some() { continue; } if e.retired_at.is_some() { continue; }
if e.superseded_at.is_some() { continue; }
if e.operation != new_entry.operation { continue; } if e.operation != new_entry.operation { continue; }
if day_key(&e.timestamp) != new_day { continue; } if day_key(&e.timestamp) != new_day { continue; }
if e.city != new_entry.city || e.state != new_entry.state { continue; } if e.city != new_entry.city || e.state != new_entry.state { continue; }
@@ -367,7 +665,7 @@ impl PlaybookMemory {
drop(state); drop(state);
self.persist().await?; self.persist().await?;
self.rebuild_geo_index().await; self.rebuild_geo_index().await;
Ok(UpsertOutcome::Added(pid)) Ok(UpsertOutcome::Added { playbook_id: pid })
} }
Some(i) => { Some(i) => {
let mut existing_names_sorted = state.entries[i].endorsed_names.clone(); let mut existing_names_sorted = state.entries[i].endorsed_names.clone();
@@ -375,7 +673,7 @@ impl PlaybookMemory {
if existing_names_sorted == new_names_sorted { if existing_names_sorted == new_names_sorted {
// NOOP — identical data, just report the existing id // NOOP — identical data, just report the existing id
let pid = state.entries[i].playbook_id.clone(); let pid = state.entries[i].playbook_id.clone();
Ok(UpsertOutcome::Noop(pid)) Ok(UpsertOutcome::Noop { playbook_id: pid })
} else { } else {
// UPDATE — merge names (union, stable order). // UPDATE — merge names (union, stable order).
let existing = state.entries.get_mut(i).ok_or("index invalidated")?; let existing = state.entries.get_mut(i).ok_or("index invalidated")?;
@@ -385,15 +683,45 @@ impl PlaybookMemory {
} }
existing.endorsed_names = merged.clone(); existing.endorsed_names = merged.clone();
existing.timestamp = new_entry.timestamp.clone(); existing.timestamp = new_entry.timestamp.clone();
// Keep original playbook_id + embedding + schema fingerprint. // Keep original playbook_id. Refresh embedding only
// Refresh embedding only if the caller passed a non-None // if the caller passed a non-None one (indicates
// one (indicates the text shape changed). // the text shape changed).
if new_entry.embedding.is_some() { if new_entry.embedding.is_some() {
existing.embedding = new_entry.embedding.clone(); existing.embedding = new_entry.embedding.clone();
} }
// Lifecycle / drift-signal fields — refresh only
// when the caller provides a new value. Each was
// previously either ignored entirely (valid_until,
// doc_refs) or only partially handled. Caught by
// the auditor's hybrid fixture on 2026-04-22 when
// re-seeding with fresh doc_refs silently dropped
// them on the UPDATE path.
if new_entry.schema_fingerprint.is_some() { if new_entry.schema_fingerprint.is_some() {
existing.schema_fingerprint = new_entry.schema_fingerprint.clone(); existing.schema_fingerprint = new_entry.schema_fingerprint.clone();
} }
if new_entry.valid_until.is_some() {
existing.valid_until = new_entry.valid_until.clone();
}
if !new_entry.doc_refs.is_empty() {
// Merge by tool (case-insensitive). A new ref
// for the same tool supersedes the old one —
// caller is asserting "this is what I used
// now." Refs for tools the existing entry
// didn't track are appended. This keeps the
// cumulative tool history without letting a
// single re-seed erase it.
let mut merged_refs = existing.doc_refs.clone();
for new_ref in &new_entry.doc_refs {
let hit = merged_refs.iter().position(|r| {
r.tool.eq_ignore_ascii_case(&new_ref.tool)
});
match hit {
Some(idx) => merged_refs[idx] = new_ref.clone(),
None => merged_refs.push(new_ref.clone()),
}
}
existing.doc_refs = merged_refs;
}
let pid = existing.playbook_id.clone(); let pid = existing.playbook_id.clone();
drop(state); drop(state);
self.persist().await?; self.persist().await?;
@@ -514,6 +842,14 @@ impl PlaybookMemory {
.iter() .iter()
.filter(|e| { .filter(|e| {
if e.retired_at.is_some() { return false; } if e.retired_at.is_some() { return false; }
if e.superseded_at.is_some() { return false; }
// Phase 45 slice 3 — entries flagged for doc drift and
// not yet human-reviewed are excluded from boost. The
// flag is the same strength of exclusion as retirement
// for this purpose; reviewing (via /resolve) re-admits.
if e.doc_drift_flagged_at.is_some() && e.doc_drift_reviewed_at.is_none() {
return false;
}
if let Some(vu) = &e.valid_until { if let Some(vu) = &e.valid_until {
if let Ok(parsed) = chrono::DateTime::parse_from_rfc3339(vu) { if let Ok(parsed) = chrono::DateTime::parse_from_rfc3339(vu) {
if now > parsed.with_timezone(&chrono::Utc) { return false; } if now > parsed.with_timezone(&chrono::Utc) { return false; }
@@ -543,6 +879,14 @@ impl PlaybookMemory {
.filter_map(|i| state.entries.get(i)) .filter_map(|i| state.entries.get(i))
.filter(|e| { .filter(|e| {
if e.retired_at.is_some() { return false; } if e.retired_at.is_some() { return false; }
if e.superseded_at.is_some() { return false; }
// Phase 45 slice 3 — same drift exclusion as the
// non-geo path above. Keeps the two filter paths
// consistent so a flagged entry is invisible to
// BOTH geo-targeted and global boost queries.
if e.doc_drift_flagged_at.is_some() && e.doc_drift_reviewed_at.is_none() {
return false;
}
if let Some(vu) = &e.valid_until { if let Some(vu) = &e.valid_until {
if let Ok(parsed) = chrono::DateTime::parse_from_rfc3339(vu) { if let Ok(parsed) = chrono::DateTime::parse_from_rfc3339(vu) {
if now > parsed.with_timezone(&chrono::Utc) { return false; } if now > parsed.with_timezone(&chrono::Utc) { return false; }
@@ -1046,15 +1390,10 @@ pub async fn rebuild(
endorsed_names: names, endorsed_names: names,
city, city,
state, state,
embedding: None, // Rebuild doesn't know fingerprints or doc_refs;
// Rebuild doesn't know fingerprints; historical entries // historical entries get no drift signal until a seed
// get no retirement signal until a seed with a // supersedes them or /retire is called manually.
// fingerprint supersedes them or the operator calls ..Default::default()
// /retire manually.
schema_fingerprint: None,
valid_until: None,
retired_at: None,
retirement_reason: None,
} }
}) })
.collect(); .collect();
@@ -1227,16 +1566,12 @@ mod tests {
playbook_id: format!("pb-{i}"), playbook_id: format!("pb-{i}"),
operation: "fill: Welder x1 in Toledo, OH".into(), operation: "fill: Welder x1 in Toledo, OH".into(),
approach: "transfer".into(), approach: "transfer".into(),
context: "".into(),
timestamp: "2026-04-20".into(), timestamp: "2026-04-20".into(),
endorsed_names: vec!["Deborah Powell".into()], endorsed_names: vec!["Deborah Powell".into()],
city: Some("Toledo".into()), city: Some("Toledo".into()),
state: Some("OH".into()), state: Some("OH".into()),
embedding: Some(vec![1.0, 0.0, 0.0]), embedding: Some(vec![1.0, 0.0, 0.0]),
schema_fingerprint: None, ..Default::default()
valid_until: None,
retired_at: None,
retirement_reason: None,
}) })
.collect(); .collect();
tokio::runtime::Runtime::new().unwrap().block_on(async { tokio::runtime::Runtime::new().unwrap().block_on(async {
@@ -1268,8 +1603,7 @@ mod validity_window_tests {
embedding: Some(vec![1.0, 0.0, 0.0]), embedding: Some(vec![1.0, 0.0, 0.0]),
schema_fingerprint: fingerprint, schema_fingerprint: fingerprint,
valid_until, valid_until,
retired_at: None, ..Default::default()
retirement_reason: None,
} }
} }
@@ -1279,7 +1613,7 @@ mod validity_window_tests {
pm.set_entries(vec![mkentry("pb-1", "Nashville", "TN", None, None)]).await.unwrap(); pm.set_entries(vec![mkentry("pb-1", "Nashville", "TN", None, None)]).await.unwrap();
let touched = pm.retire_one("pb-1", "manual test").await.unwrap(); let touched = pm.retire_one("pb-1", "manual test").await.unwrap();
assert!(touched); assert!(touched);
let (total, retired, _) = pm.status_counts().await; let (total, retired, _, _) = pm.status_counts().await;
assert_eq!(total, 1); assert_eq!(total, 1);
assert_eq!(retired, 1); assert_eq!(retired, 1);
// Second retirement is a no-op // Second retirement is a no-op
@@ -1335,7 +1669,7 @@ mod validity_window_tests {
// Only pb-old-schema should be retired — pb-new-schema matches, // Only pb-old-schema should be retired — pb-new-schema matches,
// pb-no-fp has no fingerprint so it's legacy-safe. // pb-no-fp has no fingerprint so it's legacy-safe.
assert_eq!(retired, 1); assert_eq!(retired, 1);
let (_, total_retired, _) = pm.status_counts().await; let (_, total_retired, _, _) = pm.status_counts().await;
assert_eq!(total_retired, 1); assert_eq!(total_retired, 1);
} }
@@ -1348,7 +1682,7 @@ mod validity_window_tests {
// Nashville migration shouldn't touch Chicago // Nashville migration shouldn't touch Chicago
let retired = pm.retire_on_schema_drift("Nashville", "TN", "fp-v2", "test").await.unwrap(); let retired = pm.retire_on_schema_drift("Nashville", "TN", "fp-v2", "test").await.unwrap();
assert_eq!(retired, 1); assert_eq!(retired, 1);
let (_, r, _) = pm.status_counts().await; let (_, r, _, _) = pm.status_counts().await;
assert_eq!(r, 1); assert_eq!(r, 1);
} }
} }
@@ -1369,10 +1703,7 @@ mod upsert_tests {
city: Some("Nashville".into()), city: Some("Nashville".into()),
state: Some("TN".into()), state: Some("TN".into()),
embedding: Some(vec![1.0, 0.0, 0.0]), embedding: Some(vec![1.0, 0.0, 0.0]),
schema_fingerprint: None, ..Default::default()
valid_until: None,
retired_at: None,
retirement_reason: None,
} }
} }
@@ -1381,7 +1712,7 @@ mod upsert_tests {
let pm = PlaybookMemory::new(Arc::new(InMemory::new())); let pm = PlaybookMemory::new(Arc::new(InMemory::new()));
let e = mk("fill: Welder x2 in Nashville, TN", "2026-04-21", &["Alice Smith"]); let e = mk("fill: Welder x2 in Nashville, TN", "2026-04-21", &["Alice Smith"]);
match pm.upsert_entry(e).await.unwrap() { match pm.upsert_entry(e).await.unwrap() {
UpsertOutcome::Added(_) => {} UpsertOutcome::Added { .. } => {}
other => panic!("expected Added, got {:?}", other), other => panic!("expected Added, got {:?}", other),
} }
assert_eq!(pm.entry_count().await, 1); assert_eq!(pm.entry_count().await, 1);
@@ -1394,7 +1725,7 @@ mod upsert_tests {
let e2 = mk("fill: Welder x2 in Nashville, TN", "2026-04-21", &["Alice Smith", "Bob Jones"]); let e2 = mk("fill: Welder x2 in Nashville, TN", "2026-04-21", &["Alice Smith", "Bob Jones"]);
pm.upsert_entry(e1).await.unwrap(); pm.upsert_entry(e1).await.unwrap();
let outcome = pm.upsert_entry(e2).await.unwrap(); let outcome = pm.upsert_entry(e2).await.unwrap();
assert!(matches!(outcome, UpsertOutcome::Noop(_))); assert!(matches!(outcome, UpsertOutcome::Noop { .. }));
// Still exactly one entry, no duplicate from the re-seed. // Still exactly one entry, no duplicate from the re-seed.
assert_eq!(pm.entry_count().await, 1); assert_eq!(pm.entry_count().await, 1);
} }
@@ -1406,7 +1737,7 @@ mod upsert_tests {
let e2 = mk("fill: Welder x2 in Nashville, TN", "2026-04-21", &["Alice Smith", "Bob Jones"]); let e2 = mk("fill: Welder x2 in Nashville, TN", "2026-04-21", &["Alice Smith", "Bob Jones"]);
let o1 = pm.upsert_entry(e1).await.unwrap(); let o1 = pm.upsert_entry(e1).await.unwrap();
let pid = match o1 { let pid = match o1 {
UpsertOutcome::Added(p) => p, UpsertOutcome::Added { playbook_id: p } => p,
other => panic!("expected Added, got {:?}", other), other => panic!("expected Added, got {:?}", other),
}; };
let o2 = pm.upsert_entry(e2).await.unwrap(); let o2 = pm.upsert_entry(e2).await.unwrap();
@@ -1427,7 +1758,7 @@ mod upsert_tests {
let e2 = mk("fill: Welder x2 in Nashville, TN", "2026-04-22", &["Alice Smith"]); let e2 = mk("fill: Welder x2 in Nashville, TN", "2026-04-22", &["Alice Smith"]);
pm.upsert_entry(e1).await.unwrap(); pm.upsert_entry(e1).await.unwrap();
let o2 = pm.upsert_entry(e2).await.unwrap(); let o2 = pm.upsert_entry(e2).await.unwrap();
assert!(matches!(o2, UpsertOutcome::Added(_)), "different day → fresh ADD"); assert!(matches!(o2, UpsertOutcome::Added { .. }), "different day → fresh ADD");
assert_eq!(pm.entry_count().await, 2); assert_eq!(pm.entry_count().await, 2);
} }
@@ -1440,7 +1771,359 @@ mod upsert_tests {
// A new seed on same day should ADD, not merge into the retired one. // A new seed on same day should ADD, not merge into the retired one.
let e2 = mk("fill: Welder x2 in Nashville, TN", "2026-04-21", &["Carol Davis"]); let e2 = mk("fill: Welder x2 in Nashville, TN", "2026-04-21", &["Carol Davis"]);
let o = pm.upsert_entry(e2).await.unwrap(); let o = pm.upsert_entry(e2).await.unwrap();
assert!(matches!(o, UpsertOutcome::Added(_))); assert!(matches!(o, UpsertOutcome::Added { .. }));
assert_eq!(pm.entry_count().await, 2); assert_eq!(pm.entry_count().await, 2);
} }
// Regression test for the auditor-discovered bug: UPDATE branch
// silently dropped doc_refs, valid_until, and (partially)
// schema_fingerprint from the incoming entry.
//
// Before the fix: a re-seed with different endorsed_names AND new
// doc_refs would merge names but discard the new doc_refs. After:
// both sets are unioned (doc_refs merged by tool, same-tool
// supersedes).
fn docref(tool: &str, version: &str) -> DocRef {
DocRef {
tool: tool.into(),
version_seen: version.into(),
snippet_hash: Some(format!("hash-{tool}-{version}")),
source_url: None,
seen_at: "2026-04-22T00:00:00Z".into(),
}
}
fn mk_with_docs(op: &str, day: &str, names: &[&str], docs: Vec<DocRef>) -> PlaybookEntry {
let mut e = mk(op, day, names);
e.doc_refs = docs;
e
}
#[tokio::test]
async fn update_merges_doc_refs_with_existing_ones() {
let pm = PlaybookMemory::new(Arc::new(InMemory::new()));
let day = "2026-04-22";
let op = "fill: Welder x2 in Nashville, TN";
// First seed: Alice + Docker doc ref
let e1 = mk_with_docs(op, day, &["Alice"], vec![docref("docker", "24.0.7")]);
let o1 = pm.upsert_entry(e1).await.unwrap();
assert!(matches!(o1, UpsertOutcome::Added { .. }));
// Second seed on same (op, day) but new names AND new tool ref
let e2 = mk_with_docs(op, day, &["Bob"], vec![docref("terraform", "1.5.0")]);
let o2 = pm.upsert_entry(e2).await.unwrap();
assert!(matches!(o2, UpsertOutcome::Updated { .. }));
assert_eq!(pm.entry_count().await, 1); // still one entry, merged
let snap = pm.snapshot().await;
assert_eq!(snap.len(), 1);
let merged = &snap[0];
// Names unioned
assert!(merged.endorsed_names.contains(&"Alice".to_string()));
assert!(merged.endorsed_names.contains(&"Bob".to_string()));
// Both doc_refs preserved (different tools → union)
assert_eq!(merged.doc_refs.len(), 2);
let tools: Vec<_> = merged.doc_refs.iter().map(|r| r.tool.as_str()).collect();
assert!(tools.contains(&"docker"));
assert!(tools.contains(&"terraform"));
}
#[tokio::test]
async fn update_same_tool_supersedes_older_version() {
let pm = PlaybookMemory::new(Arc::new(InMemory::new()));
let day = "2026-04-22";
let op = "fill: Welder x2 in Nashville, TN";
let e1 = mk_with_docs(op, day, &["Alice"], vec![docref("docker", "24.0.7")]);
pm.upsert_entry(e1).await.unwrap();
// Same day, different name, SAME tool newer version
let e2 = mk_with_docs(op, day, &["Bob"], vec![docref("docker", "25.0.1")]);
let o2 = pm.upsert_entry(e2).await.unwrap();
assert!(matches!(o2, UpsertOutcome::Updated { .. }));
let snap = pm.snapshot().await;
assert_eq!(snap[0].doc_refs.len(), 1, "same tool should supersede, not duplicate");
assert_eq!(snap[0].doc_refs[0].version_seen, "25.0.1");
}
#[tokio::test]
async fn update_preserves_existing_doc_refs_when_new_entry_has_none() {
let pm = PlaybookMemory::new(Arc::new(InMemory::new()));
let day = "2026-04-22";
let op = "fill: Welder x2 in Nashville, TN";
let e1 = mk_with_docs(op, day, &["Alice"], vec![docref("docker", "24.0.7")]);
pm.upsert_entry(e1).await.unwrap();
// Second seed doesn't carry doc_refs — shouldn't wipe the existing
let e2 = mk(op, day, &["Bob"]); // no doc_refs
pm.upsert_entry(e2).await.unwrap();
let snap = pm.snapshot().await;
assert_eq!(snap[0].doc_refs.len(), 1, "empty new doc_refs shouldn't wipe existing");
assert_eq!(snap[0].doc_refs[0].tool, "docker");
}
// ─── Phase 45 slice 3 — doc_drift flag / resolve / boost exclusion ───
#[tokio::test]
async fn flag_doc_drift_stamps_timestamp_and_persists() {
let pm = PlaybookMemory::new(Arc::new(InMemory::new()));
let e = mk("fill: Welder x1 in Nashville, TN", "2026-04-22", &["Alice"]);
pm.set_entries(vec![e.clone()]).await.unwrap();
let touched = pm.flag_doc_drift(&e.playbook_id).await.unwrap();
assert!(touched, "first flag should return true");
let fetched = pm.get_entry(&e.playbook_id).await.unwrap();
assert!(fetched.doc_drift_flagged_at.is_some(), "flag_doc_drift should stamp the timestamp");
assert!(fetched.doc_drift_reviewed_at.is_none(), "reviewed_at starts None on fresh flag");
}
#[tokio::test]
async fn flag_doc_drift_is_idempotent_on_already_flagged() {
let pm = PlaybookMemory::new(Arc::new(InMemory::new()));
let mut e = mk("fill: Welder x1 in Nashville, TN", "2026-04-22", &["Alice"]);
e.doc_drift_flagged_at = Some("2026-04-01T00:00:00Z".into());
pm.set_entries(vec![e.clone()]).await.unwrap();
let touched = pm.flag_doc_drift(&e.playbook_id).await.unwrap();
assert!(!touched, "already-flagged, unreviewed entry shouldn't re-stamp");
}
#[tokio::test]
async fn resolve_doc_drift_clears_flag_admission_gate() {
let pm = PlaybookMemory::new(Arc::new(InMemory::new()));
let mut e = mk("fill: Welder x1 in Nashville, TN", "2026-04-22", &["Alice"]);
e.doc_drift_flagged_at = Some("2026-04-21T00:00:00Z".into());
pm.set_entries(vec![e.clone()]).await.unwrap();
let resolved = pm.resolve_doc_drift(&e.playbook_id).await.unwrap();
assert!(resolved);
let fetched = pm.get_entry(&e.playbook_id).await.unwrap();
assert!(fetched.doc_drift_reviewed_at.is_some());
// Second resolve is a no-op
let again = pm.resolve_doc_drift(&e.playbook_id).await.unwrap();
assert!(!again);
}
#[tokio::test]
async fn boost_excludes_flagged_unreviewed_entries() {
let pm = PlaybookMemory::new(Arc::new(InMemory::new()));
let e_clean = mk("fill: Welder x1 in Nashville, TN", "2026-04-22", &["Alice"]);
let mut e_flagged = mk("fill: Welder x1 in Nashville, TN", "2026-04-21", &["Bob"]);
e_flagged.doc_drift_flagged_at = Some("2026-04-22T00:00:00Z".into());
e_flagged.doc_drift_reviewed_at = None;
pm.set_entries(vec![e_clean, e_flagged]).await.unwrap();
let boosts = pm
.compute_boost_for_filtered_with_role(
&[1.0, 0.0, 0.0], 10, 0.5,
Some(("Nashville", "TN")), None,
).await;
let keys: Vec<_> = boosts.keys().map(|(_, _, n)| n.clone()).collect();
assert!(keys.contains(&"Alice".to_string()));
assert!(!keys.contains(&"Bob".to_string()), "flagged+unreviewed entry leaked into boost");
}
#[tokio::test]
async fn boost_re_admits_resolved_entries() {
let pm = PlaybookMemory::new(Arc::new(InMemory::new()));
let mut e = mk("fill: Welder x1 in Nashville, TN", "2026-04-22", &["Alice"]);
e.doc_drift_flagged_at = Some("2026-04-22T00:00:00Z".into());
e.doc_drift_reviewed_at = Some("2026-04-22T00:01:00Z".into()); // human reviewed
pm.set_entries(vec![e]).await.unwrap();
let boosts = pm
.compute_boost_for_filtered_with_role(
&[1.0, 0.0, 0.0], 10, 0.5,
Some(("Nashville", "TN")), None,
).await;
let keys: Vec<_> = boosts.keys().map(|(_, _, n)| n.clone()).collect();
assert!(keys.contains(&"Alice".to_string()), "resolved entry should re-enter boost pool");
}
#[tokio::test]
async fn update_refreshes_valid_until_when_caller_provides_one() {
let pm = PlaybookMemory::new(Arc::new(InMemory::new()));
let day = "2026-04-22";
let op = "fill: Welder x2 in Nashville, TN";
// First seed with no valid_until
let e1 = mk(op, day, &["Alice"]);
pm.upsert_entry(e1).await.unwrap();
// Second seed with a valid_until deadline
let mut e2 = mk(op, day, &["Bob"]);
e2.valid_until = Some("2026-05-01T00:00:00Z".into());
pm.upsert_entry(e2).await.unwrap();
let snap = pm.snapshot().await;
assert_eq!(snap[0].valid_until.as_deref(), Some("2026-05-01T00:00:00Z"));
}
}
#[cfg(test)]
mod version_tests {
use super::*;
use object_store::memory::InMemory;
fn mk(id: &str, city: &str, state: &str) -> PlaybookEntry {
PlaybookEntry {
playbook_id: id.into(),
operation: format!("fill: Welder x1 in {city}, {state}"),
approach: "hybrid".into(),
context: "test".into(),
timestamp: chrono::Utc::now().to_rfc3339(),
endorsed_names: vec!["Alice Smith".into()],
city: Some(city.into()),
state: Some(state.into()),
embedding: Some(vec![1.0, 0.0, 0.0]),
..Default::default()
}
}
#[tokio::test]
async fn revise_stamps_chain_metadata_on_both_ends() {
let pm = PlaybookMemory::new(Arc::new(InMemory::new()));
pm.set_entries(vec![mk("pb-v1", "Nashville", "TN")]).await.unwrap();
let outcome = pm.revise_entry("pb-v1", mk("pb-v2", "Nashville", "TN"))
.await
.expect("revise should succeed against active root");
assert_eq!(outcome.parent_id, "pb-v1");
assert_eq!(outcome.parent_version, 1);
assert_eq!(outcome.new_playbook_id, "pb-v2");
assert_eq!(outcome.new_version, 2);
assert!(!outcome.superseded_at.is_empty());
let snap = pm.snapshot().await;
let v1 = snap.iter().find(|e| e.playbook_id == "pb-v1").unwrap();
let v2 = snap.iter().find(|e| e.playbook_id == "pb-v2").unwrap();
assert_eq!(v1.superseded_by.as_deref(), Some("pb-v2"));
assert!(v1.superseded_at.is_some());
assert_eq!(v2.parent_id.as_deref(), Some("pb-v1"));
assert_eq!(v2.version, 2);
assert!(v2.superseded_at.is_none());
}
#[tokio::test]
async fn revise_rejects_retired_parent() {
let pm = PlaybookMemory::new(Arc::new(InMemory::new()));
let mut e1 = mk("pb-v1", "Nashville", "TN");
e1.retired_at = Some(chrono::Utc::now().to_rfc3339());
pm.set_entries(vec![e1]).await.unwrap();
let err = pm.revise_entry("pb-v1", mk("pb-v2", "Nashville", "TN")).await
.expect_err("revise on retired parent must error");
assert!(err.contains("retired"), "error should mention retirement: {err}");
}
#[tokio::test]
async fn revise_rejects_already_superseded_parent() {
let pm = PlaybookMemory::new(Arc::new(InMemory::new()));
pm.set_entries(vec![mk("pb-v1", "Nashville", "TN")]).await.unwrap();
pm.revise_entry("pb-v1", mk("pb-v2", "Nashville", "TN")).await.unwrap();
// pb-v1 is now superseded; revising it again must fail — caller
// should revise pb-v2 (the tip) instead.
let err = pm.revise_entry("pb-v1", mk("pb-v3-fake", "Nashville", "TN")).await
.expect_err("revise on superseded parent must error");
assert!(err.contains("superseded"), "error should mention supersession: {err}");
}
#[tokio::test]
async fn superseded_entries_excluded_from_boost() {
let pm = PlaybookMemory::new(Arc::new(InMemory::new()));
pm.set_entries(vec![mk("pb-v1", "Nashville", "TN")]).await.unwrap();
let mut v2 = mk("pb-v2", "Nashville", "TN");
v2.endorsed_names = vec!["Carol Davis".into()];
pm.revise_entry("pb-v1", v2).await.unwrap();
let boosts = pm.compute_boost_for_filtered_with_role(
&[1.0, 0.0, 0.0], 100, 0.5,
Some(("Nashville", "TN")), Some("Welder"),
).await;
// v1's endorsement (Alice Smith) should be absent — it was
// superseded. v2's endorsement (Carol Davis) should be present.
assert!(
!boosts.contains_key(&("Nashville".into(), "TN".into(), "Alice Smith".into())),
"superseded entry's endorsement must not boost"
);
let carol = boosts.get(&("Nashville".into(), "TN".into(), "Carol Davis".into()));
assert!(carol.is_some(), "tip version's endorsement must still boost");
assert!(carol.unwrap().citations.contains(&"pb-v2".to_string()));
}
#[tokio::test]
async fn history_walks_root_to_tip_from_any_node() {
let pm = PlaybookMemory::new(Arc::new(InMemory::new()));
pm.set_entries(vec![mk("pb-v1", "Nashville", "TN")]).await.unwrap();
pm.revise_entry("pb-v1", mk("pb-v2", "Nashville", "TN")).await.unwrap();
pm.revise_entry("pb-v2", mk("pb-v3", "Nashville", "TN")).await.unwrap();
// Starting from the root — same chain.
let chain_from_root = pm.history("pb-v1").await;
assert_eq!(chain_from_root.len(), 3);
assert_eq!(chain_from_root[0].playbook_id, "pb-v1");
assert_eq!(chain_from_root[1].playbook_id, "pb-v2");
assert_eq!(chain_from_root[2].playbook_id, "pb-v3");
// Starting from the tip — same chain, same order.
let chain_from_tip = pm.history("pb-v3").await;
assert_eq!(chain_from_tip.len(), 3);
assert_eq!(chain_from_tip[0].playbook_id, "pb-v1");
assert_eq!(chain_from_tip[2].playbook_id, "pb-v3");
// Starting from the middle — same chain.
let chain_from_mid = pm.history("pb-v2").await;
assert_eq!(chain_from_mid.len(), 3);
assert_eq!(chain_from_mid[0].playbook_id, "pb-v1");
assert_eq!(chain_from_mid[2].playbook_id, "pb-v3");
}
#[tokio::test]
async fn history_empty_for_unknown_id() {
let pm = PlaybookMemory::new(Arc::new(InMemory::new()));
pm.set_entries(vec![mk("pb-v1", "Nashville", "TN")]).await.unwrap();
assert!(pm.history("pb-nonexistent").await.is_empty());
}
#[tokio::test]
async fn status_counts_reports_superseded_separately() {
let pm = PlaybookMemory::new(Arc::new(InMemory::new()));
pm.set_entries(vec![mk("pb-v1", "Nashville", "TN")]).await.unwrap();
pm.revise_entry("pb-v1", mk("pb-v2", "Nashville", "TN")).await.unwrap();
let (total, retired, superseded, _) = pm.status_counts().await;
assert_eq!(total, 2);
assert_eq!(retired, 0);
assert_eq!(superseded, 1);
}
#[tokio::test]
async fn legacy_entries_without_version_default_to_v1() {
// Simulate state persisted before Phase 27 — no version field.
// Serde default kicks in; entries should be treated as roots.
let json = r#"{
"entries": [{
"playbook_id": "pb-legacy",
"operation": "fill: Welder x1 in Nashville, TN",
"approach": "hybrid",
"context": "",
"timestamp": "2026-04-21T00:00:00Z",
"endorsed_names": ["Alice"],
"city": "Nashville",
"state": "TN"
}],
"last_rebuilt_at": 0,
"failures": []
}"#;
let state: PlaybookMemoryState = serde_json::from_str(json).unwrap();
let legacy = &state.entries[0];
assert_eq!(legacy.version, 1);
assert!(legacy.parent_id.is_none());
assert!(legacy.superseded_at.is_none());
assert!(legacy.superseded_by.is_none());
}
} }
crates/vectord/src/rag.rs
+26 -10
View File
@@ -10,6 +10,7 @@ use object_store::ObjectStore;
use std::sync::Arc; use std::sync::Arc;
use aibridge::client::{AiClient, EmbedRequest, GenerateRequest}; use aibridge::client::{AiClient, EmbedRequest, GenerateRequest};
use aibridge::continuation::{generate_continuable, ContinuableOpts, ResponseShape};
use crate::search::{self, SearchResult}; use crate::search::{self, SearchResult};
use crate::store; use crate::store;
@@ -42,6 +43,10 @@ async fn rerank(
system: None, system: None,
temperature: Some(0.0), temperature: Some(0.0),
max_tokens: Some(50), max_tokens: Some(50),
// Reranker returns a comma-separated int list — pure structured
// output, zero benefit from hidden reasoning. Opt out to avoid
// the empty-response failure mode Phase 21 catalogued.
think: Some(false),
}).await; }).await;
match resp { match resp {
@@ -150,18 +155,29 @@ pub async fn query(
Answer:" Answer:"
); );
let gen_resp = ai_client.generate(GenerateRequest { // Route the answer call through Phase 21's generate_continuable so
prompt, // a thinking-model empty-response or a mid-JSON truncation self-
model: None, // repairs instead of silently returning half an answer. Shape is
system: None, // Text (the answer is prose, not JSON), think is Some(false) to
temperature: Some(0.2), // opt out of hidden reasoning on the hot path. This is the first
max_tokens: Some(512), // production caller of the Phase 21 primitives — see audit finding
}).await?; // "Phase 21 Rust primitives are wired but not CALLED by any
// production surface" from 2026-04-21.
let mut cont_opts = ContinuableOpts::new("qwen2.5:latest");
cont_opts.max_tokens = Some(512);
cont_opts.temperature = Some(0.2);
cont_opts.shape = ResponseShape::Text;
cont_opts.think = Some(false);
let outcome = generate_continuable(ai_client, &prompt, &cont_opts).await?;
Ok(RagResponse { Ok(RagResponse {
answer: gen_resp.text.trim().to_string(), answer: outcome.text.trim().to_string(),
model: gen_resp.model, // generate_continuable doesn't surface the model name (sidecar
// echoes whatever Ollama loaded). Use the configured tier model
// for now; if RAG needs to report the actual resolved model,
// the runner can add a post-call ps probe later.
model: "qwen2.5:latest".to_string(),
sources: results, sources: results,
tokens_generated: gen_resp.tokens_generated, tokens_generated: None,
}) })
} }
crates/vectord/src/service.rs
+278 -12
View File
@@ -13,6 +13,7 @@ use aibridge::client::{AiClient, EmbedRequest, GenerateRequest};
use catalogd::registry::Registry as CatalogRegistry; use catalogd::registry::Registry as CatalogRegistry;
use storaged::registry::BucketRegistry; use storaged::registry::BucketRegistry;
use crate::{agent, autotune, chunker, embedding_cache, harness, hnsw, index_registry, jobs, lance_backend, playbook_memory, promotion, rag, refresh, search, store, supervisor, trial}; use crate::{agent, autotune, chunker, embedding_cache, harness, hnsw, index_registry, jobs, lance_backend, playbook_memory, promotion, rag, refresh, search, store, supervisor, trial};
use tokio::sync::Semaphore;
#[derive(Clone)] #[derive(Clone)]
pub struct VectorState { pub struct VectorState {
@@ -53,6 +54,9 @@ pub struct VectorState {
/// and, when `use_playbook_memory` is set on /vectors/hybrid, boosts /// and, when `use_playbook_memory` is set on /vectors/hybrid, boosts
/// workers that were actually filled in semantically-similar past ops. /// workers that were actually filled in semantically-similar past ops.
pub playbook_memory: playbook_memory::PlaybookMemory, pub playbook_memory: playbook_memory::PlaybookMemory,
/// Serializes embed calls from seed_playbook_memory to avoid
/// concurrent socket collisions with the Python sidecar.
pub embed_semaphore: Arc<Semaphore>,
} }
/// What the active-profile singleton records. Narrow — we don't need the /// What the active-profile singleton records. Narrow — we don't need the
@@ -126,7 +130,12 @@ pub fn router(state: VectorState) -> Router {
.route("/playbook_memory/patterns", post(discover_playbook_patterns)) .route("/playbook_memory/patterns", post(discover_playbook_patterns))
.route("/playbook_memory/mark_failed", post(mark_playbook_failed)) .route("/playbook_memory/mark_failed", post(mark_playbook_failed))
.route("/playbook_memory/retire", post(retire_playbook_memory)) .route("/playbook_memory/retire", post(retire_playbook_memory))
.route("/playbook_memory/revise", post(revise_playbook_memory))
.route("/playbook_memory/history/{id}", get(playbook_memory_history))
.route("/playbook_memory/status", get(playbook_memory_status)) .route("/playbook_memory/status", get(playbook_memory_status))
// Phase 45 slice 3 — doc drift detection + human re-admission.
.route("/playbook_memory/doc_drift/check/{id}", post(check_doc_drift))
.route("/playbook_memory/doc_drift/resolve/{id}", post(resolve_doc_drift))
.with_state(state) .with_state(state)
} }
@@ -891,6 +900,9 @@ async fn hybrid_search(
system: None, system: None,
temperature: Some(0.2), temperature: Some(0.2),
max_tokens: Some(512), max_tokens: Some(512),
// Hybrid's answer step — prose output over retrieved records,
// no reasoning needed on the hot path.
think: Some(false),
}).await; }).await;
gen_resp.ok().map(|r| r.text.trim().to_string()) gen_resp.ok().map(|r| r.text.trim().to_string())
@@ -1580,7 +1592,7 @@ async fn activate_profile(
}); });
} }
Ok(Json(ActivateReport { let report = ActivateReport {
profile_id: profile.id, profile_id: profile.id,
ollama_name: profile.ollama_name, ollama_name: profile.ollama_name,
indexes_warmed: warmed, indexes_warmed: warmed,
@@ -1589,7 +1601,9 @@ async fn activate_profile(
duration_secs: t0.elapsed().as_secs_f32(), duration_secs: t0.elapsed().as_secs_f32(),
model_preloaded, model_preloaded,
previous_profile: previous_slot.map(|s| s.profile_id), previous_profile: previous_slot.map(|s| s.profile_id),
})) };
Ok(Json(report))
} }
/// Unload this profile's model and clear the active slot. No-op if the /// Unload this profile's model and clear the active slot. No-op if the
@@ -2200,6 +2214,12 @@ struct SeedPlaybookRequest {
/// retired, just inactive). Useful for seasonal/temp contracts. /// retired, just inactive). Useful for seasonal/temp contracts.
#[serde(default)] #[serde(default)]
valid_until: Option<String>, valid_until: Option<String>,
/// Phase 45 — optional external doc references captured at seal
/// time. Each entry names a tool + version_seen; context7-driven
/// drift check compares against current versions later. None or
/// empty = no drift signal (never flagged).
#[serde(default)]
doc_refs: Option<Vec<playbook_memory::DocRef>>,
} }
/// Bootstrap / test-only: inject a playbook entry directly into /// Bootstrap / test-only: inject a playbook entry directly into
@@ -2214,20 +2234,19 @@ async fn seed_playbook_memory(
State(state): State<VectorState>, State(state): State<VectorState>,
Json(req): Json<SeedPlaybookRequest>, Json(req): Json<SeedPlaybookRequest>,
) -> impl IntoResponse { ) -> impl IntoResponse {
// Serialize embed calls to avoid concurrent socket collisions.
let _permit = state.embed_semaphore.acquire().await.map_err(|e|
(StatusCode::INTERNAL_SERVER_ERROR, format!("semaphore error: {e}")))?;
// Embed the entry through the same text shape `rebuild` uses so // Embed the entry through the same text shape `rebuild` uses so
// similarity math is comparable across seed + real entries. // similarity math is comparable across seed + real entries.
let tmp_entry = playbook_memory::PlaybookEntry { let tmp_entry = playbook_memory::PlaybookEntry {
playbook_id: String::new(),
operation: req.operation.clone(), operation: req.operation.clone(),
approach: req.approach.clone(), approach: req.approach.clone(),
context: req.context.clone(), context: req.context.clone(),
timestamp: chrono::Utc::now().to_rfc3339(), timestamp: chrono::Utc::now().to_rfc3339(),
endorsed_names: req.endorsed_names.clone(), endorsed_names: req.endorsed_names.clone(),
city: None, state: None, embedding: None, ..Default::default()
schema_fingerprint: None,
valid_until: None,
retired_at: None,
retirement_reason: None,
}; };
let text = format!( let text = format!(
"{} | {} | {} | fills: {}", "{} | {} | {} | fills: {}",
@@ -2285,8 +2304,11 @@ async fn seed_playbook_memory(
// works). valid_until + retired_at start None. // works). valid_until + retired_at start None.
schema_fingerprint: req.schema_fingerprint.clone(), schema_fingerprint: req.schema_fingerprint.clone(),
valid_until: req.valid_until.clone(), valid_until: req.valid_until.clone(),
retired_at: None, // Phase 45 — seed request may also carry doc_refs; defaults
retirement_reason: None, // empty so pre-Phase-45 callers still work and the entry
// degrades to "no drift signal" (never flagged).
doc_refs: req.doc_refs.clone().unwrap_or_default(),
..Default::default()
}; };
// Phase 26 — when append=true (default), route through upsert so // Phase 26 — when append=true (default), route through upsert so
@@ -2481,16 +2503,260 @@ async fn retire_playbook_memory(
"supply either {playbook_id, reason} or {city, state, current_schema_fingerprint, reason}".into())) "supply either {playbook_id, reason} or {city, state, current_schema_fingerprint, reason}".into()))
} }
/// Phase 45 slice 3 — POST /playbook_memory/doc_drift/check/{id}
///
/// Iterates the playbook's `doc_refs`, asks the context7 bridge whether
/// each one drifted against the recorded snippet_hash. If any tool
/// returned `drifted: true`, stamps `doc_drift_flagged_at` on the
/// entry — which excludes it from boost (via the filter in
/// `compute_boost_for_filtered_with_role`) until a human reviews and
/// resolves.
///
/// Unknown outcomes (bridge down, tool not in context7, no snippet
/// hash) are explicitly NOT enough to flag. Only a positive drifted=true
/// from the bridge flips the flag.
async fn check_doc_drift(
State(state): State<VectorState>,
axum::extract::Path(id): axum::extract::Path<String>,
) -> Result<Json<serde_json::Value>, (StatusCode, String)> {
use crate::doc_drift::{check_all_refs, DriftCheckerConfig, DriftOutcome};
let entry = state.playbook_memory.get_entry(&id).await
.ok_or((StatusCode::NOT_FOUND, format!("playbook not found: {id}")))?;
if entry.doc_refs.is_empty() {
return Ok(Json(serde_json::json!({
"playbook_id": id,
"checked_tools": [],
"drifted": false,
"flagged": false,
"reason": "entry has no doc_refs — nothing to check",
})));
}
let cfg = DriftCheckerConfig::default();
let results = check_all_refs(&cfg, &entry.doc_refs).await;
let per_tool: Vec<serde_json::Value> = results.iter().map(|r| {
let (drifted, current, src, reason) = match &r.outcome {
DriftOutcome::Drifted { current_snippet_hash, source_url } =>
(true, Some(current_snippet_hash.clone()), source_url.clone(), None),
DriftOutcome::Unchanged =>
(false, None, None, None),
DriftOutcome::Unknown { reason } =>
(false, None, None, Some(reason.clone())),
};
serde_json::json!({
"tool": r.tool,
"version_seen": r.version_seen,
"drifted": drifted,
"current_snippet_hash": current,
"source_url": src,
"unknown_reason": reason,
})
}).collect();
let any_drifted = results.iter().any(|r| matches!(r.outcome, DriftOutcome::Drifted { .. }));
let flagged = if any_drifted {
state.playbook_memory.flag_doc_drift(&id).await
.map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, format!("flag: {e}")))?
} else {
false
};
Ok(Json(serde_json::json!({
"playbook_id": id,
"checked_tools": results.iter().map(|r| &r.tool).collect::<Vec<_>>(),
"drifted": any_drifted,
"flagged": flagged,
"per_tool": per_tool,
})))
}
/// Phase 45 slice 3 — POST /playbook_memory/doc_drift/resolve/{id}
///
/// Human-in-the-loop re-admission. Stamps `doc_drift_reviewed_at`.
/// Idempotent: returns `resolved: false` if nothing changed (entry
/// wasn't flagged, already reviewed, or doesn't exist).
async fn resolve_doc_drift(
State(state): State<VectorState>,
axum::extract::Path(id): axum::extract::Path<String>,
) -> Result<Json<serde_json::Value>, (StatusCode, String)> {
let resolved = state.playbook_memory.resolve_doc_drift(&id).await
.map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, format!("resolve: {e}")))?;
Ok(Json(serde_json::json!({
"playbook_id": id,
"resolved": resolved,
})))
}
/// Phase 27 — request body for `POST /playbook_memory/revise`. Same
/// shape as a seed request minus `append` (revise is always
/// append-semantics for a specific parent) plus `parent_id`. The new
/// version's `playbook_id` is derived deterministically so callers get
/// the same id back from repeated revises with identical content —
/// useful for idempotent retry paths.
#[derive(Deserialize)]
struct RevisePlaybookRequest {
parent_id: String,
operation: String,
approach: String,
context: String,
endorsed_names: Vec<String>,
#[serde(default)]
schema_fingerprint: Option<String>,
#[serde(default)]
valid_until: Option<String>,
/// Phase 45 — updated doc references. Typically a revise happens
/// BECAUSE docs drifted; pass the new versions seen so the revised
/// entry starts with fresh drift signal.
#[serde(default)]
doc_refs: Option<Vec<playbook_memory::DocRef>>,
}
/// Phase 27 — create a new version of an existing playbook. The parent
/// is marked superseded; the new entry inherits the chain via
/// `parent_id` and carries `version = parent.version + 1`. Errors with
/// 400 on a retired or already-superseded parent (must revise the tip
/// of the chain). Embeds the new text through the same shape as
/// `/seed` so cosine similarity stays comparable across rebuild + seed
/// + revise entries.
async fn revise_playbook_memory(
State(state): State<VectorState>,
Json(req): Json<RevisePlaybookRequest>,
) -> Result<Json<serde_json::Value>, (StatusCode, String)> {
let text = format!(
"{} | {} | {} | fills: {}",
req.operation, req.approach, req.context,
req.endorsed_names.join(", "),
);
let resp = state.ai_client.embed(EmbedRequest { texts: vec![text], model: None })
.await
.map_err(|e| (StatusCode::BAD_GATEWAY, format!("embed revise: {e}")))?;
if resp.embeddings.is_empty() {
return Err((StatusCode::BAD_GATEWAY, "embed returned nothing".into()));
}
let emb: Vec<f32> = resp.embeddings[0].iter().map(|&x| x as f32).collect();
let (city, state_) = {
let after_in = req.operation.split(" in ").nth(1).unwrap_or("");
let mut parts = after_in.splitn(2, ',');
let city = parts.next().map(|s| s.trim().to_string()).filter(|s| !s.is_empty());
let state = parts.next()
.map(|s| s.trim().chars().take_while(|c| c.is_ascii_alphabetic()).collect::<String>())
.filter(|s| !s.is_empty());
(city, state)
};
if city.is_none() || state_.is_none() {
return Err((StatusCode::BAD_REQUEST,
"operation must match 'fill: Role xN in City, ST' shape".into()));
}
// Phase 27 — deterministic pid derived ONLY from content-shaped
// inputs (parent_id + operation + approach + context + sorted
// endorsed_names). Excluding wall-clock ts means two revise calls
// with identical payloads produce the same pid, which is the
// contract the docstring promises. On retry the caller gets back
// the same id and the pre-flight idempotency check below short-
// circuits to the existing entry instead of re-appending.
let mut names_sorted = req.endorsed_names.clone();
names_sorted.sort();
use sha2::{Digest, Sha256};
let mut h = Sha256::new();
h.update(req.parent_id.as_bytes()); h.update(b"|");
h.update(req.operation.as_bytes()); h.update(b"|");
h.update(req.approach.as_bytes()); h.update(b"|");
h.update(req.context.as_bytes()); h.update(b"|");
h.update(names_sorted.join(",").as_bytes());
let bytes = h.finalize();
let pid = format!("pb-rev-{}", bytes.iter().take(8).map(|b| format!("{b:02x}")).collect::<String>());
// Idempotency short-circuit — if this exact pid already exists in
// memory (from a prior successful revise with the same content),
// return it directly rather than re-appending or 400ing on the
// superseded-parent rejection. Walks the parent's chain via
// history() because the parent may itself have been superseded by
// our prior successful call.
let chain = state.playbook_memory.history(&req.parent_id).await;
if let Some(existing) = chain.iter().find(|e| e.playbook_id == pid) {
return Ok(Json(serde_json::json!({
"outcome": {
"parent_id": req.parent_id,
"parent_version": existing.version.saturating_sub(1),
"new_playbook_id": existing.playbook_id,
"new_version": existing.version,
"superseded_at": existing.superseded_at.clone().unwrap_or_default(),
"idempotent_return": true,
},
"entries_after": state.playbook_memory.entry_count().await,
})));
}
let ts = chrono::Utc::now().to_rfc3339();
let new_entry = playbook_memory::PlaybookEntry {
playbook_id: pid.clone(),
operation: req.operation,
approach: req.approach,
context: req.context,
timestamp: ts,
endorsed_names: req.endorsed_names,
city, state: state_,
embedding: Some(emb),
schema_fingerprint: req.schema_fingerprint,
valid_until: req.valid_until,
// Phase 45 — doc_refs may be provided on revise too.
doc_refs: req.doc_refs.clone().unwrap_or_default(),
// revise_entry overwrites version / parent_id / supersession
// from the parent; other fields keep defaults.
..Default::default()
};
let outcome = state.playbook_memory.revise_entry(&req.parent_id, new_entry)
.await
.map_err(|e| (StatusCode::BAD_REQUEST, e))?;
Ok(Json(serde_json::json!({
"outcome": outcome,
"entries_after": state.playbook_memory.entry_count().await,
})))
}
/// Phase 27 — return the full version chain containing `playbook_id`,
/// ordered root → tip. 404 if the id isn't present. The walker is
/// cycle-safe by construction (visited set per direction).
async fn playbook_memory_history(
State(state): State<VectorState>,
Path(playbook_id): Path<String>,
) -> Result<Json<serde_json::Value>, (StatusCode, String)> {
let chain = state.playbook_memory.history(&playbook_id).await;
if chain.is_empty() {
return Err((StatusCode::NOT_FOUND, format!("no playbook with id '{playbook_id}'")));
}
Ok(Json(serde_json::json!({
"playbook_id": playbook_id,
"versions": chain.len(),
"chain": chain,
})))
}
/// Phase 25 status endpoint — reports retirement counts so dashboards /// Phase 25 status endpoint — reports retirement counts so dashboards
/// can show "N playbooks retired (12 from 2026-05 schema migration)". /// can show "N playbooks retired (12 from 2026-05 schema migration)".
/// Phase 27 added `superseded` as a distinct counter.
async fn playbook_memory_status( async fn playbook_memory_status(
State(state): State<VectorState>, State(state): State<VectorState>,
) -> impl IntoResponse { ) -> impl IntoResponse {
let (total, retired, failures) = state.playbook_memory.status_counts().await; let (total, retired, superseded, failures) = state.playbook_memory.status_counts().await;
// `active` = entries eligible for boost. Retired and superseded are
// distinct exclusion reasons; subtract both. An entry can in principle
// be both retired AND superseded (e.g. revised then retired) so
// saturating_sub guards against underflow if that pathological case
// ever lands.
let inactive = retired + superseded;
Json(serde_json::json!({ Json(serde_json::json!({
"total": total, "total": total,
"retired": retired, "retired": retired,
"active": total.saturating_sub(retired), "superseded": superseded,
"active": total.saturating_sub(inactive),
"failures": failures, "failures": failures,
})) }))
} }
data/_catalog/manifests/07c119c3-2e59-463c-89a8-7980a849b6d6.json
+159
View File
@@ -0,0 +1,159 @@
{
"id": "07c119c3-2e59-463c-89a8-7980a849b6d6",
"name": "workers_500k",
"schema_fingerprint": "71686e497f5ee2fc77d50b13c39a46d89ca3d7673e9a0879e67c2db476764b25",
"objects": [
{
"bucket": "primary",
"key": "datasets/workers_500k.parquet",
"size_bytes": 323794818,
"created_at": "2026-04-17T05:57:41.972944716Z"
}
],
"created_at": "2026-04-17T05:57:41.972945960Z",
"updated_at": "2026-04-17T05:57:41.973093967Z",
"description": "",
"owner": "",
"sensitivity": "pii",
"columns": [
{
"name": "worker_id",
"data_type": "Int64",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "name",
"data_type": "Utf8",
"sensitivity": "pii",
"description": "",
"is_pii": true
},
{
"name": "role",
"data_type": "Utf8",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "email",
"data_type": "Utf8",
"sensitivity": "pii",
"description": "",
"is_pii": true
},
{
"name": "phone",
"data_type": "Int64",
"sensitivity": "pii",
"description": "",
"is_pii": true
},
{
"name": "city",
"data_type": "Utf8",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "state",
"data_type": "Utf8",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "zip",
"data_type": "Int64",
"sensitivity": "pii",
"description": "",
"is_pii": true
},
{
"name": "skills",
"data_type": "Utf8",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "certifications",
"data_type": "Utf8",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "archetype",
"data_type": "Utf8",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "reliability",
"data_type": "Float64",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "responsiveness",
"data_type": "Float64",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "engagement",
"data_type": "Float64",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "compliance",
"data_type": "Float64",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "availability",
"data_type": "Float64",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "communications",
"data_type": "Utf8",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "resume_text",
"data_type": "Utf8",
"sensitivity": null,
"description": "",
"is_pii": false
}
],
"lineage": {
"source_system": "csv",
"source_file": "workers_500k.csv",
"ingest_job": "ingest-1776405461972",
"ingest_timestamp": "2026-04-17T05:57:41.972944716Z",
"parent_datasets": []
},
"freshness": null,
"tags": [],
"row_count": 500000,
"last_embedded_at": null,
"embedding_stale_since": null,
"embedding_refresh_policy": null
}
data/_catalog/manifests/443d63f1-b0ed-4d4b-8e5a-ce59c097b97b.json → data/_catalog/manifests/2b8e8b01-a44e-49c5-a103-a1b55898bee1.json
+6 -6
View File
@@ -1,5 +1,5 @@
{ {
"id": "443d63f1-b0ed-4d4b-8e5a-ce59c097b97b", "id": "2b8e8b01-a44e-49c5-a103-a1b55898bee1",
"name": "kb_memory_entries", "name": "kb_memory_entries",
"schema_fingerprint": "15dbebd0abb906577e11cbb73083abeb8961b8c603a2472cafa8c46100c3fb1c", "schema_fingerprint": "15dbebd0abb906577e11cbb73083abeb8961b8c603a2472cafa8c46100c3fb1c",
"objects": [ "objects": [
@@ -7,11 +7,11 @@
"bucket": "primary", "bucket": "primary",
"key": "datasets/kb_memory_entries.parquet", "key": "datasets/kb_memory_entries.parquet",
"size_bytes": 8795, "size_bytes": 8795,
"created_at": "2026-04-17T02:54:43.330897823Z" "created_at": "2026-04-17T05:22:06.356227657Z"
} }
], ],
"created_at": "2026-04-17T02:54:43.330898330Z", "created_at": "2026-04-17T05:22:06.356228933Z",
"updated_at": "2026-04-17T02:54:43.330972517Z", "updated_at": "2026-04-17T05:22:06.356776285Z",
"description": "", "description": "",
"owner": "", "owner": "",
"sensitivity": null, "sensitivity": null,
@@ -97,8 +97,8 @@
"lineage": { "lineage": {
"source_system": "postgresql", "source_system": "postgresql",
"source_file": "dsn: postgresql://kbuser@localhost:5432/knowledge_base", "source_file": "dsn: postgresql://kbuser@localhost:5432/knowledge_base",
"ingest_job": "pg-stream-1776394483330", "ingest_job": "pg-stream-1776403326356",
"ingest_timestamp": "2026-04-17T02:54:43.330897823Z", "ingest_timestamp": "2026-04-17T05:22:06.356227657Z",
"parent_datasets": [] "parent_datasets": []
}, },
"freshness": null, "freshness": null,
data/_catalog/manifests/2db2d92f-1171-4f2f-a707-ad387b6cd464.json
+117
View File
@@ -0,0 +1,117 @@
{
"id": "2db2d92f-1171-4f2f-a707-ad387b6cd464",
"name": "onboard_live_1776729919",
"schema_fingerprint": "9cdf2fc924bad875e63bd12a82dfb1cd080c5cbba71f671b4a2d41e746df187d",
"objects": [
{
"bucket": "primary",
"key": "datasets/onboard_live_1776729919.parquet",
"size_bytes": 31444,
"created_at": "2026-04-21T00:05:19.717239967Z"
}
],
"created_at": "2026-04-21T00:05:19.717241621Z",
"updated_at": "2026-04-21T00:05:19.717370050Z",
"description": "",
"owner": "",
"sensitivity": "pii",
"columns": [
{
"name": "worker_id",
"data_type": "Utf8",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "name",
"data_type": "Utf8",
"sensitivity": "pii",
"description": "",
"is_pii": true
},
{
"name": "role",
"data_type": "Utf8",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "city",
"data_type": "Utf8",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "state",
"data_type": "Utf8",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "email",
"data_type": "Utf8",
"sensitivity": "pii",
"description": "",
"is_pii": true
},
{
"name": "phone",
"data_type": "Utf8",
"sensitivity": "pii",
"description": "",
"is_pii": true
},
{
"name": "skills",
"data_type": "Utf8",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "certifications",
"data_type": "Utf8",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "availability",
"data_type": "Float64",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "reliability",
"data_type": "Float64",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "archetype",
"data_type": "Utf8",
"sensitivity": null,
"description": "",
"is_pii": false
}
],
"lineage": {
"source_system": "csv",
"source_file": "sample_1776729919.csv",
"ingest_job": "ingest-1776729919717",
"ingest_timestamp": "2026-04-21T00:05:19.717239967Z",
"parent_datasets": []
},
"freshness": null,
"tags": [],
"row_count": 173,
"last_embedded_at": null,
"embedding_stale_since": null,
"embedding_refresh_policy": null
}
data/_catalog/manifests/ab2b610a-cee7-40e1-9dab-c709e2292709.json → data/_catalog/manifests/3178f0da-5985-4731-b343-162dd415f3be.json
+6 -6
View File
@@ -1,5 +1,5 @@
{ {
"id": "ab2b610a-cee7-40e1-9dab-c709e2292709", "id": "3178f0da-5985-4731-b343-162dd415f3be",
"name": "kb_self_reports", "name": "kb_self_reports",
"schema_fingerprint": "76382b028b92bb0b361306dced3d773af0ee8de6aa160cedfc4d649f2860167a", "schema_fingerprint": "76382b028b92bb0b361306dced3d773af0ee8de6aa160cedfc4d649f2860167a",
"objects": [ "objects": [
@@ -7,11 +7,11 @@
"bucket": "primary", "bucket": "primary",
"key": "datasets/kb_self_reports.parquet", "key": "datasets/kb_self_reports.parquet",
"size_bytes": 51134, "size_bytes": 51134,
"created_at": "2026-04-17T02:54:43.335457986Z" "created_at": "2026-04-17T04:14:34.355419647Z"
} }
], ],
"created_at": "2026-04-17T02:54:43.335458749Z", "created_at": "2026-04-17T04:14:34.355420701Z",
"updated_at": "2026-04-17T02:54:43.335536869Z", "updated_at": "2026-04-17T06:06:21.358604060Z",
"description": "", "description": "",
"owner": "", "owner": "",
"sensitivity": null, "sensitivity": null,
@@ -62,8 +62,8 @@
"lineage": { "lineage": {
"source_system": "postgresql", "source_system": "postgresql",
"source_file": "dsn: postgresql://kbuser@localhost:5432/knowledge_base", "source_file": "dsn: postgresql://kbuser@localhost:5432/knowledge_base",
"ingest_job": "pg-stream-1776394483335", "ingest_job": "pg-stream-1776399274355",
"ingest_timestamp": "2026-04-17T02:54:43.335457986Z", "ingest_timestamp": "2026-04-17T04:14:34.355419647Z",
"parent_datasets": [] "parent_datasets": []
}, },
"freshness": null, "freshness": null,
data/_catalog/manifests/32ee74a0-59b4-4e5b-8edb-70c9347a4bf3.json
+26
View File
@@ -0,0 +1,26 @@
{
"id": "32ee74a0-59b4-4e5b-8edb-70c9347a4bf3",
"name": "successful_playbooks_live",
"schema_fingerprint": "4e28e94650d9f5689e952674aebfd09db70edfcba617713081c0a83bf59121ce",
"objects": [
{
"bucket": "primary",
"key": "datasets/successful_playbooks_live.parquet",
"size_bytes": 284341,
"created_at": "2026-04-22T03:28:28.343840136Z"
}
],
"created_at": "2026-04-20T11:07:57.308050648Z",
"updated_at": "2026-04-22T03:28:28.343843823Z",
"description": "",
"owner": "",
"sensitivity": null,
"columns": [],
"lineage": null,
"freshness": null,
"tags": [],
"row_count": null,
"last_embedded_at": null,
"embedding_stale_since": null,
"embedding_refresh_policy": null
}
data/_catalog/manifests/33544879-fcfa-4458-b782-a4ca8dafed08.json
+159
View File
@@ -0,0 +1,159 @@
{
"id": "33544879-fcfa-4458-b782-a4ca8dafed08",
"name": "workers_100k",
"schema_fingerprint": "1cc2a0caf174aa9bf67b1e2cb7858a312a0c94d604d906f807eb5610864ff70e",
"objects": [
{
"bucket": "primary",
"key": "datasets/workers_100k.parquet",
"size_bytes": 65225188,
"created_at": "2026-04-17T04:54:03.306737596Z"
}
],
"created_at": "2026-04-17T04:54:03.306740279Z",
"updated_at": "2026-04-17T04:54:03.306927852Z",
"description": "",
"owner": "",
"sensitivity": "pii",
"columns": [
{
"name": "worker_id",
"data_type": "Int64",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "name",
"data_type": "Utf8",
"sensitivity": "pii",
"description": "",
"is_pii": true
},
{
"name": "role",
"data_type": "Utf8",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "email",
"data_type": "Utf8",
"sensitivity": "pii",
"description": "",
"is_pii": true
},
{
"name": "phone",
"data_type": "Int64",
"sensitivity": "pii",
"description": "",
"is_pii": true
},
{
"name": "city",
"data_type": "Utf8",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "state",
"data_type": "Utf8",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "zip",
"data_type": "Int64",
"sensitivity": "pii",
"description": "",
"is_pii": true
},
{
"name": "skills",
"data_type": "Utf8",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "certifications",
"data_type": "Utf8",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "archetype",
"data_type": "Utf8",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "reliability",
"data_type": "Float64",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "responsiveness",
"data_type": "Float64",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "engagement",
"data_type": "Float64",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "compliance",
"data_type": "Float64",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "availability",
"data_type": "Float64",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "communications",
"data_type": "Utf8",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "resume_text",
"data_type": "Utf8",
"sensitivity": null,
"description": "",
"is_pii": false
}
],
"lineage": {
"source_system": "csv",
"source_file": "workers_100k.csv",
"ingest_job": "ingest-1776401643306",
"ingest_timestamp": "2026-04-17T04:54:03.306737596Z",
"parent_datasets": []
},
"freshness": null,
"tags": [],
"row_count": 100000,
"last_embedded_at": null,
"embedding_stale_since": null,
"embedding_refresh_policy": null
}
data/_catalog/manifests/ce3fe55b-61cd-4ac4-95de-b91f9186d6e3.json → data/_catalog/manifests/35f36c38-5cd3-438e-b93d-7ff7720f0346.json
+5 -11
View File
@@ -1,5 +1,5 @@
{ {
"id": "ce3fe55b-61cd-4ac4-95de-b91f9186d6e3", "id": "35f36c38-5cd3-438e-b93d-7ff7720f0346",
"name": "kb_meta_runs", "name": "kb_meta_runs",
"schema_fingerprint": "68f2c0d7a3ceb0aaa3c17c64900704519c72d213161bc9e5179c42ee53f6d0df", "schema_fingerprint": "68f2c0d7a3ceb0aaa3c17c64900704519c72d213161bc9e5179c42ee53f6d0df",
"objects": [ "objects": [
@@ -7,11 +7,11 @@
"bucket": "primary", "bucket": "primary",
"key": "datasets/kb_meta_runs.parquet", "key": "datasets/kb_meta_runs.parquet",
"size_bytes": 886387, "size_bytes": 886387,
"created_at": "2026-04-17T02:54:43.299467359Z" "created_at": "2026-04-17T05:22:06.314413387Z"
} }
], ],
"created_at": "2026-04-17T02:54:43.299468094Z", "created_at": "2026-04-17T05:22:06.314414515Z",
"updated_at": "2026-04-17T04:23:16.134134852Z", "updated_at": "2026-04-17T06:06:21.300610797Z",
"description": "", "description": "",
"owner": "", "owner": "",
"sensitivity": null, "sensitivity": null,
@@ -80,13 +80,7 @@
"is_pii": false "is_pii": false
} }
], ],
"lineage": { "lineage": null,
"source_system": "postgresql",
"source_file": "dsn: postgresql://kbuser@localhost:5432/knowledge_base",
"ingest_job": "pg-stream-1776399796133",
"ingest_timestamp": "2026-04-17T04:23:16.133868872Z",
"parent_datasets": []
},
"freshness": null, "freshness": null,
"tags": [], "tags": [],
"row_count": 48, "row_count": 48,
data/_catalog/manifests/36b0f141-93a1-4776-beef-b0b8d17a12a9.json
-96
View File
@@ -1,96 +0,0 @@
{
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data/_catalog/manifests/a5858f94-267f-4382-ba32-0934e1b984f7.json → data/_catalog/manifests/c2f0acdb-df65-4c21-9633-08af4248c85b.json
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data/_catalog/manifests/03c62234-f9f8-40e9-a27e-d5e09ab2713d.json → data/_catalog/manifests/d668a036-7b50-4845-8194-6a03ef370c8d.json
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data/_catalog/manifests/765985e6-ea16-46b5-a6c0-ddaff346827f.json → data/_catalog/manifests/eeb0d288-e227-478d-9407-78a43b623c5c.json
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"name": "quality_score",
"data_type": "Float64",
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"ingest_job": "pg-stream-1776403326071",
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# Auditor Context — project preamble for fact extraction
This file is read by `auditor/fact_extractor.ts` and prepended to the
extract-facts prompt sent to llm_team. The goal: give the extractor +
verifier enough grounding to ground domain-specific facts instead of
marking them UNVERIFIABLE by default.
Keep this short (< 400 words). Verifier only reads the first ~4KB of
the prompt alongside the facts. Longer = noise, not signal.
Update when: a new Phase lands, a crate is added/removed, the project's
primary domain shifts (e.g. staffing → DevOps).
---
## What Lakehouse is
Lakehouse is a Rust-first data platform over S3-compatible object
storage. Primary use: a staffing company ingesting legacy CRM data for
AI-powered worker matching, contract fulfillment, and playbook-driven
coordination.
Architecture: 13 Rust crates + a Python sidecar (Ollama) + TypeScript
sub-agents (auditor, scrum_master, bot). Runs on a single server
(Nvidia A4000, 128GB RAM). All services on localhost: gateway :3100,
sidecar :3200, UI :3300, MCP :3700, observer :3800, MinIO :9000.
## Key crates (each maps to a responsibility)
- **shared** — types, Arrow helpers, PII utilities, SecretsProvider
- **proto** — gRPC definitions
- **storaged** — S3/MinIO I/O, AppendLog, ErrorJournal
- **catalogd** — metadata authority (manifests, views, tombstones)
- **queryd** — DataFusion SQL, MemTable cache, compaction
- **ingestd** — CSV/JSON/PDF/Postgres/MySQL ingest
- **vectord** — embeddings, HNSW index, **playbook_memory meta-index** (Phase 19+)
- **vectord-lance** — Lance 4.0 firewall crate (separate Arrow version)
- **journald** — append-only mutation event log
- **aibridge** — Rust↔Python sidecar bridge, context budget + continuation
- **gateway** — Axum HTTP :3100 + gRPC :3101 (Phase 38+ adds /v1/chat)
- **ui** — Dioxus WASM (stale, pre-Phase-9)
- **lance-bench** — standalone benchmark
## Current architectural direction (Phase 38-44)
Universal AI Control Plane: a `/v1/chat` OpenAI-compatible API that
routes all LLM traffic through one layer for token accounting + provider
fallback. Truth Layer + Validation Pipeline enforce staffing-domain
invariants (worker eligibility, PII, contract rules). The Auditor
(Phase A of cohesion plan) hard-blocks PR merges on placeholder code.
## Auditor sub-agent role
`auditor/` (TypeScript, Bun runtime) polls Gitea every 90s for open PRs.
For each fresh head SHA it runs 4 checks in parallel: static (grep-style
placeholder detection), dynamic (runs the hybrid fixture), inference
(gpt-oss:120b cloud review with N=3 consensus + qwen3-coder:480b
tie-breaker), and kb_query (reads `data/_kb/*.jsonl` for prior evidence).
Verdicts post to Gitea as commit status + review comment. Findings
append to `data/_kb/audit_lessons.jsonl` (path-agnostic signatures for
dedup). Curated scratchpads from tree-split get routed through this
extract-facts pipeline to populate `audit_facts.jsonl` — which is what
you (the extractor) are currently producing.
## Things that are NOT the auditor
- The LLM Team UI at `/root/llm_team_ui.py` (devop.live:5000) — a separate product for human-facing multi-model experimentation
- The scrum_master pipeline at `tests/real-world/scrum_master_pipeline.ts` — reviews files, not claims
- The bot at `bot/` — will apply fixes, doesn't audit
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# PRD — Universal AI Control Plane
**Status:** Long-horizon architecture target as of 2026-04-22. Lakehouse Phases 0-37 (`docs/PRD.md`) are preserved as the reference implementation and first domain-specific consumer. Phases 38+ (control-plane layers) are sequenced below.
**Current domain: staffing.** The immediate proving ground is the staffing substrate already built — synthetic workers_500k, contracts, emails, SMS drafts, playbook memory. Everything Phase 38-44 ships is validated first against that domain. The DevOps / Terraform / Ansible framing from the original PRD draft stays as a **long-horizon target** — architecture-compatible but not in current scope. See §Long-horizon domains at the bottom.
**Owner:** J
**Cross-read:** `docs/PRD.md` for what's shipped (staffing + AI substrate, 13 crates, ~3M rows). This doc for the layered architecture those pieces now fit into.
---
## Phase Sequencing (Phases 38-44)
Ship each phase before starting the next. Each ends with green tests + docs update.
| Phase | Layer | What ships | Est. LOC | Risk |
|---|---|---|---|---|
| 38 | Layer 1 skeleton | `/v1/chat`, `/v1/usage`, `/v1/sessions` routes forwarding to existing `aibridge` → Ollama. Bot migrates as first consumer. | ~400 | Low — additive, no existing routes touched |
| 39 | Layer 3 adapters | `aibridge::ProviderAdapter` trait; Ollama + one new (OpenRouter). `/v1/chat` routes by config. | ~500 | Low-medium |
| 40 | Layer 2 engine | Rules-based routing (`config/routing.toml`), fallback chains, cost gating. Add Gemini + Claude adapters. | ~600 | Medium |
| 41 | Profile split | Separate Retrieval / Memory / Execution / Observer profiles; Phase 17 backward-compat. Absorbs Phase 37 hot-swap-async. | ~300 | Medium |
| 42 | Truth Layer | New `crates/truth`; Terraform/Ansible schemas; `/v1/context` serves rules to router + observer. | ~700 | Medium |
| 43 | Validation pipeline | Syntax/lint/dry-run/policy gates per output type. Plugs into Layer 5 execution loop. | ~400 | Medium |
| 44 | Caller migration | All internal callers route through `/v1/chat`. Direct sidecar access deprecated. | ~200 | Low |
**Total ≈3100 LOC.** Phase 37 (hot-swap async) folds into Phase 41 — it's an Execution-Profile activation concern.
---
## Phase 38 — Universal API Skeleton
**Goal:** OpenAI-compatible `/v1/*` surface exists and forwards to existing aibridge → Ollama. Nothing about multi-provider yet — just the SHAPE, so every downstream piece (adapters, routing, usage accounting) has a surface to plug into.
**Ships:**
- `crates/gateway/src/v1/mod.rs` — router + `/v1/chat`, `/v1/usage`, `/v1/sessions`
- `crates/gateway/src/v1/ollama.rs` — shape adapter (OpenAI chat ↔ existing aibridge `GenerateRequest`)
- One-line `nest("/v1", ...)` in `crates/gateway/src/main.rs`
- Unit test: `POST /v1/chat` roundtrips through mocked provider
**Gate:**
- `curl -X POST localhost:3100/v1/chat -d '{"model":"qwen3.5:latest","messages":[{"role":"user","content":"hi"}]}'` returns valid OpenAI-shape response.
- `GET localhost:3100/v1/usage` returns `{requests, prompt_tokens, completion_tokens, total_tokens}`.
- `GET localhost:3100/v1/sessions` returns `{data:[]}` (stub; real impl Phase 41).
- `cargo test -p gateway` green.
**Non-goals (explicit):** streaming, tool calls, function calling, session state, multi-provider, fallback, cost gating.
**Risk:** Low — additive, doesn't touch existing routes. Worst case: `/v1/*` returns 502 and we fix the adapter. No existing caller affected.
---
## Phase 39 — Provider Adapter Refactor
**Goal:** `aibridge` grows a `ProviderAdapter` trait. Ollama implementation wraps existing sidecar code. One new provider lands as proof: **OpenRouter** (simplest — it's OpenAI-compatible, so adapter is mostly passthrough).
**Ships:**
- `crates/aibridge/src/provider.rs` — `ProviderAdapter` trait with `chat()` + `embed()` + `unload()` methods
- `crates/aibridge/src/providers/ollama.rs` — existing sidecar code moved behind the trait
- `crates/aibridge/src/providers/openrouter.rs` — new, HTTP client to `openrouter.ai/api/v1/chat/completions`
- `config/providers.toml` — provider registry (name, base_url, auth, default_models)
- `/v1/chat` routes by `model` field: prefix match (e.g. `openrouter/anthropic/claude-3.5-sonnet` → OpenRouter; bare names → Ollama)
**Gate:**
- `/v1/chat` with `model: "qwen3.5:latest"` hits Ollama → green
- `/v1/chat` with `model: "openrouter/openai/gpt-4o-mini"` hits OpenRouter (key from secrets.toml) → green
- Neither call leaks provider-specific fields upward. Response is always the `/v1/chat` shape.
**Non-goals:** Fallback chain (Phase 40), cost gating (Phase 40), Gemini/Claude adapters (Phase 40).
**Risk:** Low-medium. The trait extraction is mostly a rearrange; OpenRouter is thin. Biggest risk is secret-loading conventions — `SecretsProvider` is already in place, so reuse that path.
---
## Phase 40 — Routing & Policy Engine + Observability Recovery
**Goal:** Replace hardcoded T1-T5 routing with a rules engine. Add Gemini + Claude adapters. Cost gating enforced at router level. **Reinstate Langfuse + Gitea MCP** — recovery of the observability + repo-ops stack J built previously (see `project_lost_stack` memory).
**Ships — routing:**
- `crates/aibridge/src/routing.rs` — rules engine (match on: task type, token budget, previous attempt failures, profile ID)
- `config/routing.toml` — rules in TOML (human-editable, hot-reloadable)
- `crates/aibridge/src/providers/gemini.rs` — `generativelanguage.googleapis.com` adapter
- `crates/aibridge/src/providers/claude.rs` — `api.anthropic.com` adapter
- Fallback chain support: if primary returns 5xx or times out, try next in chain
- Cost gate: per-request budget + daily budget per-provider
**Ships — observability (was lost, now restored):**
- **Langfuse** self-hosted via Docker Compose. Single source of truth for every LLM call trace: prompt / response / tokens / cost / latency / provider / fallback chain / profile used. UI at `localhost:3000`. Keys in `/etc/lakehouse/secrets.toml`.
- `crates/aibridge/src/langfuse.rs` — thin fire-and-forget trace emitter. Every `/v1/chat` call spawns a background task that POSTs to `langfuse/api/public/ingestion`. Non-blocking: trace failures never affect response.
- **Langfuse → observer pipe** — `mcp-server/langfuse_bridge.ts` or similar. Polls Langfuse's trace API at interval, forwards completed traces to observer `:3800/event` with `source: "langfuse"`. KB now sees cost/latency deltas per model, not just outcome deltas.
- **Gitea MCP reconnect** — the MCP server binary still installed at `/home/profit/.bun/install/cache/gitea-mcp@0.0.10/` gets wired into `mcp-server/index.ts` tool registry. Agents can open PRs, comment on issues, list commits via named tools. Closes Phase 28's repo-ops gap.
**Gate:**
- Rule like "local models for simple JSON emitters, cloud for reasoning" fires correctly by task type
- Primary fails → fallback provider hits, response still matches `/v1/chat` shape
- Daily budget hit → subsequent requests return 429 with clear retry-at header
- `/v1/usage` reports per-provider breakdown
- **Every `/v1/chat` call appears in Langfuse UI** with correct prompt, response, latency, token count within 2 seconds of the request completing
- **Langfuse → observer pipe** delivers trace deltas to KB: `GET :3800/stats?source=langfuse` shows non-zero count after a few scenarios run
- **Gitea MCP tools callable** — `list_prs`, `open_pr`, `comment_on_issue` exposed in `mcp-server/index.ts`, verifiable via a quick agent scenario
**Non-goals:** Retrieval Profile split (Phase 41), Truth Layer (Phase 42). Langfuse self-hosted UI customization / SSO.
**Risk:** Medium. Multi-provider auth + cost tracking is cross-cutting; Langfuse adds 4-5 Docker containers (PostgreSQL, ClickHouse, Redis, web, worker). Mitigation: every provider call wrapped in a single `dispatch()` function so observability flows through one point; Langfuse Docker Compose is their supported deployment path, well-tested.
---
## Phase 41 — Profile System Expansion (+ Phase 37 hot-swap async folded in)
**Goal:** The existing `ModelProfile` (Phase 17) becomes **ExecutionProfile**. Three new profile types land alongside. Profile activation is async — returns job_id, work runs in background (Phase 37 deliverable).
**Ships:**
- `crates/shared/src/profiles/` — `ExecutionProfile`, `RetrievalProfile`, `MemoryProfile`, `ObserverProfile`
- `crates/catalogd` gains per-profile-type CRUD endpoints (`/catalog/profiles/retrieval`, etc.)
- `crates/vectord/src/activation.rs` — `ActivationTracker` with background-job pattern (Phase 37 content)
- `POST /vectors/profile/{id}/activate` returns 202 + job_id, polling at `GET /vectors/profile/jobs/{id}`
- Single-flight guard: refuse new activation if one is pending/running
- Backward compat: `ModelProfile` still loads, aliased to ExecutionProfile
**Gate:**
- Activate a profile → returns 202 in <100ms → job completes in background → `/vectors/profile/jobs/{id}` shows progress + final report
- `tests/multi-agent/run_stress.ts` Phase 3 (hot-swap stress) passes (was SKIPPED)
- Retrieval + Memory + Observer profiles can be created independently of Execution profile
**Non-goals:** Truth Layer (Phase 42), validation (Phase 43), caller migration (Phase 44).
**Risk:** Medium. Schema change + async refactor. Mitigation: `#[serde(default)]` on all new fields; existing profiles load unchanged.
---
## Phase 42 — Truth Layer (staffing rules first)
**Goal:** New `crates/truth` crate holds immutable task-class constraints. Served via `/v1/context` to router and observer. No layer can override truth. **Staffing rules ship first**; Terraform/Ansible rule shapes are scaffolded but unpopulated until the long-horizon phase.
**Ships:**
- `crates/truth/src/lib.rs` — `TruthStore` with schema loading (TOML/YAML rules)
- `crates/truth/src/staffing.rs` — staffing rule shapes:
- Worker eligibility (active status, not blacklisted for client, geo match, role match, availability window)
- Contract invariants (deadline present, role/count/city/state populated, budget_per_hour_max ≥ 0)
- PII handling (redaction rules on fields tagged `PII` before any cloud call — covers existing Phase 10 sensitivity tags)
- Client blacklist enforcement (auto-applied before any fill proposal)
- Fill requirements (endorsed_names count matches target_count, no duplicate worker_ids within a single fill)
- `crates/truth/src/devops.rs` — **scaffold only**: empty rule struct for Terraform/Ansible, populated in the long-horizon phase. Keeps the dispatcher signature stable so no refactor needed later.
- `truth/` dir at repo root — rule files, versioned in git
- `/v1/context` endpoint — returns applicable rules for a task class (`staffing.fill`, `staffing.rescue`, `staffing.sms_draft`, etc.)
- Router consults truth before dispatching: if task violates a rule, hard-fail with structured error + rule citation (matches existing Phase 13 access-control pattern)
**Gate:**
- Submit a fill proposal where a worker is client-blacklisted — router returns 422 + rule citation, no cloud tokens burned
- Submit a fill with `endorsed_names.length != target_count` — 422 before dispatch
- Observer cannot promote a correction that violates truth (rejected at router gate)
- PII redaction verified: SSN / salary fields stripped from prompts before cloud calls
- Truth reload is explicit (no file-watch hot reload in this phase)
**Non-goals:** Validation execution (Phase 43), policy learning / evolution (deferred), actual Terraform/Ansible rules (long-horizon phase).
**Risk:** Medium. Domain-specific rule enumeration takes discovery — start with a minimal rule set (5-10 staffing rules, derived from existing Phase 10-13 work) and grow organically as real fills surface edge cases.
---
## Phase 43 — Validation Pipeline (staffing outputs first)
**Goal:** Staffing outputs run through schema / completeness / consistency / policy gates. Plug into Layer 5 execution loop — failure triggers observer-correction iteration. This is where the **0→85% pattern reproduces on real staffing tasks** — the iteration loop with validation in place is what made small models successful.
**Ships:**
- `crates/validator/src/lib.rs` — `Validator` trait: `validate(artifact) -> Result<Report, ValidationError>` + `Artifact` enum over output types
- `crates/validator/src/staffing/fill.rs` — fill-proposal validator:
- Schema compliance (propose_done shape matches `{fills: [{candidate_id, name}]}`)
- Completeness (endorsed count == target_count)
- Worker existence (every candidate_id present in workers_500k via SQL lookup)
- Status check (every worker has status=active, not_on_client_blacklist)
- Geo/role match (worker city/state/role matches contract)
- `crates/validator/src/staffing/email.rs` — generated email/SMS drafts:
- Schema (TO/BODY fields present)
- Length (SMS ≤ 160 chars; email subject ≤ 78 chars)
- PII absence (no SSN / salary leaked into outgoing text)
- Worker-name consistency (name in message matches worker record)
- `crates/validator/src/staffing/playbook.rs` — sealed playbook:
- Operation format (`fill: Role xN in City, ST`)
- endorsed_names non-empty, ≤ target_count × 2
- fingerprint populated (Phase 25 validity window requirement)
- `crates/validator/src/devops.rs` — **scaffold only**: stubbed Terraform/Ansible validators (`terraform validate`, `ansible-lint`) for the long-horizon phase
- Task execution loop in gateway: generate → validate → if fail, observer correction + retry (bounded by `max_iterations=3`)
- Validation results logged to observer (`data/_observer/ops.jsonl`) + KB (`data/_kb/outcomes.jsonl`)
**Gate:**
- Generate a fill proposal → validator catches a phantom worker_id → observer + cloud rescue propose correction → retry → green. This reproduces the 0→85% pattern on the live staffing pipeline.
- `/v1/usage` shows iteration count per task, provider fallback chain, and tokens-per-iteration. Cost attribution per task class visible.
- Reproduces 14× citation-lift finding from Phase 19 refinement on similar geos after validation gates.
**Non-goals:** Caller migration (Phase 44), Terraform/Ansible wired validation (long-horizon).
**Risk:** Medium. Validation shapes have to match actual executor outputs; mitigation is using real scenario runs as test fixtures (we have ~100 of them in `tests/multi-agent/playbooks/`).
---
## Phase 44 — Caller Migration + Direct-Provider Deprecation
**Goal:** Every internal LLM caller routes through `/v1/chat`. Direct sidecar / direct Ollama / direct OpenAI calls are removed or explicitly deprecated with a warning.
**Ships:**
- `aibridge::AiClient` becomes a thin `/v1/chat` client (was direct-to-sidecar)
- `crates/vectord::agent` (autotune): routes through `/v1`
- `crates/vectord::autotune`: routes through `/v1`
- `tests/multi-agent/agent.ts::generate()`: routes through `/v1`
- `bot/propose.ts`: routes through `/v1` (already proposed as Phase 38's test consumer, formalized here)
- Lint rule / grep pre-commit hook: no `fetch.*:3200/generate` outside the provider adapters
**Gate:**
- `grep -r "localhost:3200/generate\|/api/generate"` returns only adapter files + deprecation shims
- `/v1/usage` accounts for every LLM call in the system within a 1-minute window after hitting a fresh scenario
- Full scenario passes end-to-end without any caller bypassing `/v1/*`
**Non-goals:** New features. This phase is purely mechanical migration.
**Risk:** Low. Mechanical. Tests catch regressions.
---
## Phase 45 — Doc-drift detection + context7 integration
**Goal:** Playbooks know which external docs they were written against. When those docs change (Docker adds a feature, npm lib goes major, Terraform renames a resource), the playbook is automatically flagged. Small models never run confidently-outdated procedures — the drift signal reaches them before the next execution does.
**Why this phase exists at all:** The 0→85% thesis depends on the hyperfocus lane staying valid. External doc drift invalidates the lane silently — popular playbooks can compound the wrong way, accumulating boost while growing more wrong. Phase 25 already retires playbooks on *internal* schema drift; Phase 45 is the same mechanism against *external* doc drift. This is the completion of the learning loop, not an optional add-on.
**Ships:**
- `shared::types::DocRef` — `{ tool: String, version_seen: String, snippet_hash: Option<String>, source_url: Option<String>, seen_at: DateTime<Utc> }`
- `PlaybookEntry.doc_refs: Vec<DocRef>` — `#[serde(default)]` so pre-Phase-45 entries load as empty vec
- `/vectors/playbook_memory/seed` + `/revise` accept `doc_refs` in the request body
- `/vectors/playbook_memory/doc_drift/check/{id}` — manual drift check: looks up each `doc_refs[]` entry via the context7 bridge, returns per-tool `{version_seen, version_current, drifted: bool}` plus overall verdict
- `/vectors/playbook_memory/doc_drift/scan` — batch scan across all active playbooks (scheduled path for Phase 45.2)
- `mcp-server/context7_bridge.ts` — Bun HTTP bridge. Exposes `GET /docs/:tool/version` + `GET /docs/:tool/:version/diff?since=X` against the installed context7 MCP plugin. Gateway calls this over localhost.
- `PlaybookMemory::compute_boost_for_filtered_with_role` — excludes entries where `doc_drift_flagged_at.is_some() && doc_drift_review.is_none()` (same rule as retired + superseded)
- Overview model synthesis writes `data/_kb/doc_drift_corrections.jsonl` per detected drift: `{playbook_id, tool, version_seen, version_current, diff_summary, recommended_action, generated_at}`
- Human-in-the-loop re-seal path: `/vectors/playbook_memory/doc_drift/resolve/{id}` — marks reviewed, optionally triggers `revise_entry` if procedure changed
**Gate:**
- Seal a playbook referencing Docker 24.x → doc_refs captured. Bump Docker version behind the scenes → `/doc_drift/check/{id}` returns `drifted: true, from: 24.0.7, to: 25.0.1, summary: "..."`. The boosted playbook count on next `/vectors/hybrid` query drops by 1 (drift-flagged skipped).
- `doc_drift_corrections.jsonl` contains the overview model's synthesis for the drift with at least: summary of change, recommended action, cost/impact estimate.
- Human calls `/doc_drift/resolve/{id}` after reviewing → playbook returns to active boost pool (or supersedes via Phase 27 if procedure materially changed).
- Unit tests: DocRef serde default (legacy entries load as empty), drift check against mocked context7 bridge, boost exclusion when drifted+unreviewed.
**Non-goals (explicit):**
- Automatic re-seal without human review. Drift-detection → flag, not silent rewrite.
- Cross-playbook propagation of one drift diagnosis. Each playbook reviewed individually (aggregation later if warranted).
- Generating the updated procedure. T3 *suggests*; human or separate bot (see `bot/`) *writes*.
**Risk:** Medium. The context7 bridge is new infrastructure (Bun ↔ context7 MCP plugin ↔ HTTP shape for gateway consumption). Mitigation: context7 plugin is already installed; its MCP tools return structured JSON; the bridge is thin adapter code. Start with single-tool drift check (Docker) before broadening.
---
## Long-horizon domains (not in current phase sequence)
The architecture was drafted with DevOps execution (Terraform, Ansible) as the eventual target. **That remains aspirational, not current scope** — we don't start wiring `terraform validate` / `ansible-lint` until the staffing domain proves the six-layer architecture at scale.
What "proves at scale" means concretely:
- Phases 38-44 all shipped against staffing, green tests
- Live staffing pipeline handles **multiple concurrent contracts** with emails + SMS + indexed playbooks via `/v1/*`
- Observed **iteration success lift** (the 0→85% pattern) reproduced on varied staffing scenarios, not just the original proof-of-concept
- Token + cost accounting stable across provider fallback chains under real load
- Truth Layer rules prevent real fill errors before cloud burn (not just theoretical)
When staffing hits that bar, the DevOps domain lights up by:
- Populating `crates/truth/src/devops.rs` with real Terraform/Ansible rule shapes
- Populating `crates/validator/src/devops.rs` with `terraform validate` / `ansible-lint` shell-out
- Adding DevOps task classes to `/v1/context` rule lookup
- No architectural changes needed — the dispatcher, router, and execution loop stay identical.
Other candidate long-horizon domains (same pattern):
- Code generation tasks (validation via `cargo check` / `bun test`)
- SQL query generation (validation via EXPLAIN + schema compliance)
- Data pipeline definitions (validation via lineage check + schema compliance)
None of these are in the current roadmap. **Staffing first, production-proven, then expand.**
---
## 1. Purpose
Design and implement a universal AI control-plane API that enables:
- **deterministic high-stakes task execution** — the immediate domain is staffing fills (contracts, workers, emails, SMS) at scale; the same architecture extends later to DevOps (Terraform, Ansible) without redesign
- iterative capability amplification via observer loops
- hybrid local + cloud model orchestration
- structured knowledge + memory + playbook reuse
- controlled improvement over time through validated iteration
The system prioritizes **validated pipeline success over raw model intelligence**.
### Current scope — staffing at scale
The architecture must make the already-built staffing substrate reliably answer millions of inputs: pull real data, graph it across contracts, handle multiple concurrent contracts, index emails + SMS + playbooks via the hybrid SQL+vector method, and get **faster and better each iteration** via the feedback loops (Phase 19 playbook boost, Phase 22 KB pathway recommender, Phase 24 observer, Phase 26 Mem0 upsert).
DevOps is an eventual domain — see §Long-horizon domains.
## 2. Core Objectives
### 2.1 Functional Goals
- Provide a single universal API for all AI interactions
- Support multi-provider routing (local, flat-rate, token-based)
- Enable iterative execution loops with observer correction
- Store and reuse successful execution playbooks
- Integrate: S3-based knowledge storage, LanceDB retrieval/indexing, Mem0 memory layer, MCP tool ecosystem
### 2.2 Non-Functional Goals
- Deterministic behavior under constrained execution
- Full observability and cost accounting
- Safe DevOps execution (no uncontrolled mutation)
- Profile-driven routing and execution
- Reproducibility of successful runs
## 3. System Architecture
### 3.1 Layer Overview
**Layer 1 — Universal API**
Single entry point for all applications. Endpoints:
- `/v1/chat`
- `/v1/respond`
- `/v1/tools`
- `/v1/context`
- `/v1/usage`
- `/v1/sessions`
All programs must use this layer. No direct provider calls allowed.
**Layer 2 — Routing & Policy Engine**
Responsibilities: provider selection, fallback logic, cost gating, premium access control, profile enforcement.
Routing based on: task type, constraints, execution profile, system health.
**Layer 3 — Provider Adapter Layer**
Normalizes all providers: Ollama (local), OpenRouter, Gemini (direct), Claude (direct or routed), future providers.
Guarantee: no provider-specific logic leaks upward.
**Layer 4 — Knowledge & Memory Plane**
- Knowledge (S3 + LanceDB): raw documents, processed chunks, embeddings, index profiles
- Memory (Mem0): extracted facts, entity-linked memory, session-aware retrieval
- Playbooks: successful execution traces, reusable patterns, correction strategies
**Layer 5 — Execution Loop**
Each task runs through: Retrieval → Planning → Generation → Validation → Observer feedback → Iteration (if needed).
**Layer 6 — Observability & Accounting**
Every request logs: tokens (input/output), cost, latency, provider, fallback chain, profile used, iteration delta.
## 4. Execution Model
### 4.1 Iterative Loop
Each task follows: **Attempt → Validate → Observe → Adjust → Retry**
Constraints:
- max iterations (default: 3)
- minimum improvement threshold
- cost ceiling per task
### 4.2 Observer Role
Observer can: analyze failure, suggest corrections, recommend profile changes.
Observer cannot: modify truth layer, auto-promote changes, override constraints.
### 4.3 Cloud Escalation
Cloud models (Gemini, Claude) are used for: structural correction, reasoning gaps, complex decomposition.
They are not used for: brute-force retries, bulk execution.
## 5. Profile System
### 5.1 Profile Types
- **Retrieval Profile** — chunking strategy, embedding method, reranking rules
- **Memory Profile** — memory weighting, context injection rules
- **Execution Profile** — allowed providers, tool access, risk level
- **Observer Profile** — mutation aggressiveness, iteration strategy
### 5.2 Profile Constraints
- only one major profile change per iteration
- profiles must produce measurable deltas
- promotion requires repeated success
## 6. Truth Layer (Critical)
Defines non-negotiable constraints:
- Terraform rules
- Ansible structure requirements
- security policies
- organization standards
Rules:
- immutable at runtime
- referenced by all layers
- cannot be overridden by observer
## 7. Playbook System
### 7.1 Playbook Definition
Each successful run produces: task class, context used, steps executed, tools used, output artifacts, validation results, cost/latency, success score.
### 7.2 Playbook Lifecycle
- created on success
- reused for similar tasks
- decayed over time
- pruned if ineffective
## 8. Validation System
All DevOps outputs must pass: syntax validation, linting, dry-run, policy compliance.
Failure → iteration continues or task fails.
## 9. MCP Integration
MCP servers provide: tools, external data, execution capabilities.
All MCP outputs must be: normalized, validated, schema-compliant.
No direct MCP output reaches the model.
## 10. Token Accounting & Budget Control
Each request tracks: input tokens, output tokens, retries, fallback cost.
Policies: premium providers gated, cost ceilings enforced, per-task budget limits.
## 11. Failure Handling
**Recoverable failures:** bad decomposition, missing steps, weak retrieval → observer + iteration.
**Hard failures:** missing truth data, invalid task classification, unsafe execution → termination + error report.
## 12. Success Criteria
A task is successful only if:
- output is valid
- all validators pass
- no policy violations
- result is reproducible
- cost within limits
## 13. Key Risks & Mitigations
- **Observer drift** → bounded authority, confidence tracking
- **Memory poisoning** → validation layer, memory weighting
- **Cost explosion** → token accounting, iteration caps
- **Retrieval errors** → post-retrieval validation, profile tuning
docs/DECISIONS.md
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@@ -94,3 +94,8 @@
**Date:** 2026-04-16 **Date:** 2026-04-16
**Decision:** Keep Parquet + binary-blob vectors + in-RAM HNSW as the primary vector backend. Add Lance as a second backend available per-profile for workloads where Lance wins architecturally. Per-profile `vector_backend: Parquet | Lance` field becomes part of Phase 17 model profiles. Implementation kicks off via the standalone `crates/lance-bench` crate and is promoted into `vectord::lance_store` when the API stabilizes. **Decision:** Keep Parquet + binary-blob vectors + in-RAM HNSW as the primary vector backend. Add Lance as a second backend available per-profile for workloads where Lance wins architecturally. Per-profile `vector_backend: Parquet | Lance` field becomes part of Phase 17 model profiles. Implementation kicks off via the standalone `crates/lance-bench` crate and is promoted into `vectord::lance_store` when the API stabilizes.
**Rationale:** Head-to-head benchmark on the 100K × 768d `resumes_100k_v2` index (see `docs/ADR-019-vector-storage.md` for the full scorecard). Parquet+HNSW wins current-scale search latency by 2.55× (873us vs 2229us p50). Lance wins index build time by 14× (16s vs 230s), random row access by 112× (311us vs ~35ms full-file scan), and append speed structurally (0.08s vs full Parquet rewrite). Neither strictly dominates — the dual-use PRD framing (staffing + LLM brain) means both workloads exist in the same system. Keeps ADR-008's "Parquet is the format" principle intact for dataset tables; adds Lance as a purpose-built vector-tier option without discarding the tuned HNSW stack. **Rationale:** Head-to-head benchmark on the 100K × 768d `resumes_100k_v2` index (see `docs/ADR-019-vector-storage.md` for the full scorecard). Parquet+HNSW wins current-scale search latency by 2.55× (873us vs 2229us p50). Lance wins index build time by 14× (16s vs 230s), random row access by 112× (311us vs ~35ms full-file scan), and append speed structurally (0.08s vs full Parquet rewrite). Neither strictly dominates — the dual-use PRD framing (staffing + LLM brain) means both workloads exist in the same system. Keeps ADR-008's "Parquet is the format" principle intact for dataset tables; adds Lance as a purpose-built vector-tier option without discarding the tuned HNSW stack.
## ADR-020: `register()` is idempotent by name with a schema-fingerprint gate
**Date:** 2026-04-19
**Decision:** `catalogd::Registry::register(name, fingerprint, objects)` is idempotent on `name`. If no manifest for `name` exists, create one. If one exists with the same `schema_fingerprint`, reuse its `DatasetId`, replace `objects`, bump `updated_at`, and write through. If one exists with a different `schema_fingerprint`, reject with `409 Conflict` (HTTP) / `FAILED_PRECONDITION` (gRPC). A one-shot operator endpoint `POST /catalog/dedupe` collapses any pre-existing duplicates (preferring the manifest with a non-null `row_count`, then the most recently updated).
**Rationale:** Registry was keyed by surrogate `DatasetId` with no uniqueness constraint on `name`, so every caller that re-registered (re-ingest, external cron, gRPC retry) silently created a parallel manifest pointing at the same parquet — accumulating 308× `successful_playbooks` in live state before detection. The fingerprint gate turns re-ingest into an explicit no-op (matching PRD invariant #5 "ingestd is idempotent — re-ingesting the same file is a no-op") while forcing schema drift to be visible instead of silently clobbering. 409 status separates policy rejections from server errors, which matters for the Phase 12 tool-consumer ecosystem. Concurrency: the write lock is held across the storage write to close the check→insert TOCTOU window; serializing registers is acceptable because registers-per-second is low on the ingest path. Audit: idempotent-register events are visible as bumps to the stored manifest's `updated_at` field and in `catalogd` tracing output (tracing is non-durable, operator view only); `DedupeReport` is the return-value audit for cleanup runs. No event-journal entries are emitted — ADR-012 scopes the journal to row-level mutations, not catalog-manifest operations.
docs/PHASES.md
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@@ -188,7 +188,7 @@
- Endpoint: `POST /catalog/datasets/by-name/{name}/tombstone` accepting `{row_key_column, row_key_values[], actor, reason}`; companion `GET` lists active tombstones - Endpoint: `POST /catalog/datasets/by-name/{name}/tombstone` accepting `{row_key_column, row_key_values[], actor, reason}`; companion `GET` lists active tombstones
- `queryd::context::build_context` wraps tombstoned tables: raw goes to `__raw__{name}`, public name becomes a DataFusion view with `WHERE CAST(col AS VARCHAR) NOT IN (...)` filter - `queryd::context::build_context` wraps tombstoned tables: raw goes to `__raw__{name}`, public name becomes a DataFusion view with `WHERE CAST(col AS VARCHAR) NOT IN (...)` filter
- End-to-end on candidates: tombstone 3 IDs, COUNT drops 100,000 → 99,997, specific WHERE returns empty, AiView candidates_safe transitively excludes them too, restart preserves all tombstones - End-to-end on candidates: tombstone 3 IDs, COUNT drops 100,000 → 99,997, specific WHERE returns empty, AiView candidates_safe transitively excludes them too, restart preserves all tombstones
- Limits / not in MVP: physical compaction (Phase 8 doesn't yet read tombstones during merge); journal integration (tombstones don't yet emit Phase 9 mutation events — covered by audit fields on the tombstone itself) - Limits / not in MVP: journal integration (tombstones don't yet emit Phase 9 mutation events — covered by audit fields on the tombstone itself). Physical compaction integration was closed by Phase E.2 below.
- [x] Phase D: AI-safe views — 2026-04-16 - [x] Phase D: AI-safe views — 2026-04-16
- `shared::types::AiView` — name, base_dataset, columns whitelist, optional row_filter, column_redactions - `shared::types::AiView` — name, base_dataset, columns whitelist, optional row_filter, column_redactions
- `shared::types::Redaction` — Null | Hash | Mask { keep_prefix, keep_suffix } - `shared::types::Redaction` — Null | Hash | Mask { keep_prefix, keep_suffix }
@@ -273,7 +273,7 @@
- [x] 19.3 — Endorsed names parsed out of `result` column, keyed by `(city, state, name)` tuple so shared names across cities don't cross-pollinate. Parsing via `parse_names` + `parse_city_state` helpers (7 unit tests) - [x] 19.3 — Endorsed names parsed out of `result` column, keyed by `(city, state, name)` tuple so shared names across cities don't cross-pollinate. Parsing via `parse_names` + `parse_city_state` helpers (7 unit tests)
- [x] 19.4 — `/vectors/hybrid?use_playbook_memory=true`: fetches `top_k * 5` candidates so endorsed workers outside the vanilla top-K can still climb. Boost is additive on vector score, each hit carries `playbook_boost` + `playbook_citations` in the response for explainability - [x] 19.4 — `/vectors/hybrid?use_playbook_memory=true`: fetches `top_k * 5` candidates so endorsed workers outside the vanilla top-K can still climb. Boost is additive on vector score, each hit carries `playbook_boost` + `playbook_citations` in the response for explainability
- [x] 19.5 — Multi-agent orchestrator (`tests/multi-agent/orchestrator.ts`) auto-seeds `POST /vectors/playbook_memory/seed` on consensus_done, so the next query sees the new endorsement without a full `/rebuild`. Closes the feedback loop: two agents reach consensus → playbook sealed → next query re-ranks - [x] 19.5 — Multi-agent orchestrator (`tests/multi-agent/orchestrator.ts`) auto-seeds `POST /vectors/playbook_memory/seed` on consensus_done, so the next query sees the new endorsement without a full `/rebuild`. Closes the feedback loop: two agents reach consensus → playbook sealed → next query re-ranks
- [x] 19.6 — `MAX_BOOST_PER_WORKER = 0.25` enforced in `compute_boost_for`; verified with unit test (100 identical playbooks → boost capped at 0.25) and live test (5 identical seeds → exactly 0.25). Time decay deferred as optional - [x] 19.6 — `MAX_BOOST_PER_WORKER = 0.25` enforced in `compute_boost_for`; verified with unit test (100 identical playbooks → boost capped at 0.25) and live test (5 identical seeds → exactly 0.25). Time decay also wired: `BOOST_HALF_LIFE_DAYS = 30.0` — 30-day-old playbooks contribute half, 60-day a quarter, via `exp(-age_days / 30)` in the boost loop
- Real finding surfaced during build: the 32 bootstrap rows in `successful_playbooks` reference phantom worker names — 80 of 82 don't correspond to actual rows in `workers_500k`. `/seed` endpoint bypasses `successful_playbooks` so operators can prime memory with real fixtures; production path is the orchestrator write-through - Real finding surfaced during build: the 32 bootstrap rows in `successful_playbooks` reference phantom worker names — 80 of 82 don't correspond to actual rows in `workers_500k`. `/seed` endpoint bypasses `successful_playbooks` so operators can prime memory with real fixtures; production path is the orchestrator write-through
- [x] **Phase 19 refinement — geo + role prefilter on boost** (2026-04-21) - [x] **Phase 19 refinement — geo + role prefilter on boost** (2026-04-21)
- Added `compute_boost_for_filtered` and `compute_boost_for_filtered_with_role` to `playbook_memory.rs`. SQL filter's `(city, state, role)` parsed in `service.rs`; exact role-matches in target geo skip cosine and earn similarity=1.0. Restored the feedback loop: matched=0 → matched=11 per query on the same Nashville test. Citation density on Riverfront Steel: 2 → 28 per run (14×). - Added `compute_boost_for_filtered` and `compute_boost_for_filtered_with_role` to `playbook_memory.rs`. SQL filter's `(city, state, role)` parsed in `service.rs`; exact role-matches in target geo skip cosine and earn similarity=1.0. Restored the feedback loop: matched=0 → matched=11 per query on the same Nashville test. Citation density on Riverfront Steel: 2 → 28 per run (14×).
@@ -285,7 +285,13 @@
- T3 cloud: gpt-oss:120b via Ollama Cloud — verified 4-8s latency, strict JSON-shape output for remediation. - T3 cloud: gpt-oss:120b via Ollama Cloud — verified 4-8s latency, strict JSON-shape output for remediation.
- [x] **Phase 21: Scratchpad + Tree-Split Continuation** (2026-04-21) - [x] **Phase 21: Scratchpad + Tree-Split Continuation** (2026-04-21)
- `tests/multi-agent/agent.ts`: `estimateTokens()`, `assertContextBudget()`, `generateContinuable()`, `generateTreeSplit()`. `think` flag plumbed through sidecar's `/generate`. Empty-response backoff + truncation-continuation, no max_tokens tourniquet. - `tests/multi-agent/agent.ts`: `estimateTokens()`, `assertContextBudget()`, `generateContinuable()`, `generateTreeSplit()`. `think` flag plumbed through sidecar's `/generate`. Empty-response backoff + truncation-continuation, no max_tokens tourniquet.
- Rust port queued: `crates/aibridge/src/continuation.rs`, `tree_split.rs`. - Rust port shipped (2026-04-21, companion to Phase 27):
- `crates/aibridge/src/context.rs` — `estimate_tokens` (chars/4 ceil, matches TS), `context_window_for`, `assert_context_budget` returning `Result<BudgetCheck, (BudgetCheck, usize over_by)>` so callers get the numbers back on both success and overflow. Windows table mirrors `config/models.json`.
- `crates/aibridge/src/continuation.rs` — `generate_continuable<G: TextGenerator>` handles the two failure modes from TS: (a) empty thinking-model response → geometric-backoff retry with 2× budget up to `budget_cap`; (b) truncated non-empty → continuation with partial-as-scratchpad. `is_structurally_complete` balances braces then JSON.parse-check for the JSON shape; text shape is "non-empty". Guards the degen case "all retries empty → bail, don't loop on empty partial" — the TS impl has this implicit, Rust makes it explicit.
- `crates/aibridge/src/tree_split.rs` — `generate_tree_split` map→reduce with running scratchpad. Per-shard + reduce-prompt budget checked through `assert_context_budget`; loud-fails with the overflow message rather than silently truncating. Scratchpad oldest-digest-first truncation once it exceeds `scratchpad_budget` (default 6000 tokens, matches TS).
- `TextGenerator` trait (native async-fn-in-trait, edition 2024) so `ScriptedGenerator` test double can inject canned sequences without a live Ollama. `AiClient` implements `TextGenerator`.
- `GenerateRequest` gained `think: Option<bool>` field — forwards to sidecar for per-call hidden-reasoning opt-out on hot-path JSON emitters.
- 25 aibridge unit tests (8 context + 10 continuation + 7 tree_split) — all green, no network required.
- [x] **Phase 22: Internal Knowledge Library** (2026-04-21) - [x] **Phase 22: Internal Knowledge Library** (2026-04-21)
- `data/_kb/` — signatures.jsonl, outcomes.jsonl, pathway_recommendations.jsonl, error_corrections.jsonl, config_snapshots.jsonl. Event-driven cycle: indexRun → recommendFor → loadRecommendation. - `data/_kb/` — signatures.jsonl, outcomes.jsonl, pathway_recommendations.jsonl, error_corrections.jsonl, config_snapshots.jsonl. Event-driven cycle: indexRun → recommendFor → loadRecommendation.
- Item B cloud rescue: failed event → cloud remediation JSON → retry with pivot. Verified 1/3 rescues succeeded on stress_01 (Gary IN → South Bend IN pivot). - Item B cloud rescue: failed event → cloud remediation JSON → retry with pivot. Verified 1/3 rescues succeeded on stress_01 (Gary IN → South Bend IN pivot).
@@ -296,13 +302,32 @@
- `data/_kb/staffers.jsonl` — competence_score = 0.45·fill + 0.20·turn_eff + 0.20·cite + 0.15·rescue. Recomputed per run. - `data/_kb/staffers.jsonl` — competence_score = 0.45·fill + 0.20·turn_eff + 0.20·cite + 0.15·rescue. Recomputed per run.
- `findNeighbors` now returns `weighted_score = cosine × max_staffer_competence`. `scripts/kb_staffer_report.py` — leaderboard + cross-staffer worker overlap (Rachel D. Lewis 12× across 4 staffers → auto-discovered high-value label). - `findNeighbors` now returns `weighted_score = cosine × max_staffer_competence`. `scripts/kb_staffer_report.py` — leaderboard + cross-staffer worker overlap (Rachel D. Lewis 12× across 4 staffers → auto-discovered high-value label).
- `gen_staffer_demo.ts` + `run_staffer_demo.sh` — 4 personas × 3 contracts = 12 runs. - `gen_staffer_demo.ts` + `run_staffer_demo.sh` — 4 personas × 3 contracts = 12 runs.
- [ ] **Phase 24: Observer / Autotune integration** (GAP, not wired) - [x] **Phase 27: Playbook versioning** (2026-04-21)
- `lakehouse-observer.service` watches MCP :3700; scenario.ts hits gateway :3100 directly. Observer idle at 0 ops across 3600+ cycles. Autotune runs on its own schedule, never sees scenario outcomes. - `PlaybookEntry` gained `version: u32` (default 1), `parent_id`, `superseded_at`, `superseded_by` fields. All `#[serde(default)]` so entries persisted before Phase 27 load as roots with version=1.
- Next-sprint: scenario emits per-event outcome summaries to observer's ingest path; observer ERROR_ANALYZER + PLAYBOOK_BUILDER loops consume them; autotune subscribes to the metric stream. - `PlaybookMemory::revise_entry(parent_id, new_entry)` appends a new version, stamps `superseded_at`+`superseded_by` on the parent, inherits `parent_id` and sets `version = parent + 1` on the new entry. Rejects revising a retired or already-superseded parent with a clear error — the tip of the chain is the only valid revise target.
- `PlaybookMemory::history(playbook_id)` returns the full chain root→tip, walking `parent_id` backward then `superseded_by` forward. Cycle-safe. Works from any node in the chain.
- Superseded entries excluded from boost (same rule as retired): `compute_boost_for_filtered_with_role`, the active-entries prefilter, the geo-index rebuild, and the upsert existing-entry search all skip `superseded_at.is_some()`.
- Endpoints: `POST /vectors/playbook_memory/revise` + `GET /vectors/playbook_memory/history/{id}`.
- `status_counts` now returns `(total, retired, superseded, failures)`. `/status` JSON reports `superseded` as a distinct counter; `active = total - retired - superseded`.
- 8 unit tests under `mod version_tests` covering: chain-metadata stamping, retired-parent rejection, already-superseded-parent rejection, superseded endorsement exclusion from boost, history traversal from root/tip/middle, empty-for-unknown-id, superseded-status-count, legacy-entry-default-version round-trip. 26/26 playbook_memory tests pass.
- [x] **Phase 24: Observer / Autotune integration** (2026-04-20, commit b95dd86)
- Closed the gap where `lakehouse-observer.service` wrapped MCP :3700 while `tests/multi-agent/scenario.ts` hit gateway :3100 directly — observer sat idle at 0 ops across 3600+ cycles.
- `observer.ts` gained a Bun HTTP listener on `OBSERVER_PORT` (default 3800) with `GET /health`, `GET /stats` (totals + by_source + recent scenario digest), and `POST /event` for scenario outcomes. Body shapes into `ObservedOp` with `source="scenario"` + `staffer_id` + `sig_hash` + `event_kind` + geo + rescue flags.
- `recordExternalOp()` shared ring-buffer insert — ERROR_ANALYZER and PLAYBOOK_BUILDER loops now see both MCP-wrapped and scenario-posted ops through the same path.
- `persistOp()` swap: old path wrote via `/ingest/file?name=observed_operations` which has REPLACE semantics (wiped prior ops); now uses append-friendly Parquet write-through.
- [x] **Phase 25: Validity windows + playbook retirement** (2026-04-21, commit e0a843d)
- `PlaybookEntry` gained four optional fields (`#[serde(default)]` so legacy entries load as never-expiring): `schema_fingerprint` (SHA-256 over target dataset columns at seed time), `valid_until` (RFC3339 hard expiry), `retired_at` (set by retire calls), `retirement_reason` (human string).
- `compute_boost_for_filtered_with_role` now skips retired + expired entries before geo/cosine — no silent boosting from stale playbooks. Unit-tested on expired `valid_until` + retired + schema-drift retirement.
- Two retirement paths: `retire_one(playbook_id, reason)` for manual, `retire_on_schema_drift(city, state, current_fingerprint, reason)` for batch schema-migration sweep. Legacy entries without a fingerprint skip drift retirement (safe).
- Endpoint: `POST /vectors/playbook_memory/retire` — accepts either `{playbook_id, reason}` or `{city, state, current_schema_fingerprint, reason}`.
- [x] **Phase 26: Mem0 upsert + Letta geo hot cache** (2026-04-21, commit 640db8c)
- Mem0-style upsert: `/seed` with `append=true` (default) routes through `upsert_entry`, which decides ADD / UPDATE / NOOP on (operation, day, city, state). Same-day re-seed merges names (union, stable order) instead of duplicating the row. Identical re-seed is a no-op. Different-day same-operation is a fresh ADD. Playbook_id stays stable on UPDATE so prior citations remain valid.
- Letta-style hot cache: `PlaybookMemory` now holds a `geo_index: HashMap<(city_lower, state_upper), Vec<entry_idx>>` rebuilt on every mutation. Geo-filtered boost queries skip the full scan and hit the O(1) key lookup. At 1.9K entries the full scan was sub-ms; the index scales the same path to 100K+ without code changes.
- `UpsertOutcome` enum reported back to callers — `{mode: added|updated|noop, playbook_id, merged_names?}` + `entries_after`.
- [ ] Fine-tuned domain models (Phase 25+) - [ ] Fine-tuned domain models (Phase 25+)
- [ ] Multi-node query distribution (only if ceilings bite) - [ ] Multi-node query distribution (only if ceilings bite)
--- ---
**102 unit tests | 13 crates | 20 ADRs | 2.47M rows | 100K vectors | Hybrid Parquet+HNSW ⊕ Lance | Phase 19 refined + 20-23 shipped** **145 unit tests | 13 crates | 21 ADRs | 2.47M rows | 100K vectors | Hybrid Parquet+HNSW ⊕ Lance | Phases 0-27 shipped**
**Latest: 2026-04-21 — Phases 20-23 shipped. Geo+role prefilter lifted playbook citation density 14×. Cloud rescue converts zero-supply failures into successful pivots. Staffer competence weighting differentiates full-tool senior from minimal-tool trainee by 46.4pt fill rate on same contracts. Phase 24 observer integration flagged as honest gap.** **Latest: 2026-04-21 — Phase 27 (playbook versioning: `version` + `parent_id` + `superseded_at` + `superseded_by` on `PlaybookEntry`, `/revise` + `/history` endpoints, 8 new tests). Doc-sync pass: Phase 24 observer + Phase 25 validity windows + Phase 26 Mem0/Letta now reflected in phase tracker. Phase 19.6 time decay noted as wired (was misdocumented as deferred). Phase E.2 tombstone-at-compaction noted as closed in Phase 8 MVP limits.**
docs/PRD.md
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@@ -1,8 +1,28 @@
# PRD: Lakehouse — Rust-First Substrate for Versioned Knowledge Stores # PRD: Lakehouse — Rust-First Substrate for Versioned Knowledge Stores
**Status:** Active — Phases 0-18 shipped; hybrid SQL+Vector search operational; IVF_PQ recall tuned to 1.000 at p50 ≈ 7.4ms via `nprobes`+`refine`; autonomous agent rotates across full index portfolio; cron-scheduled ingest; eval federation complete > ## ▶ Direction pivot — 2026-04-22
>
> **`docs/CONTROL_PLANE_PRD.md` is now the long-horizon architecture target.**
> Lakehouse (this document) is being refactored to conform to the Universal AI
> Control Plane architecture — six layers, `/v1/*` universal API, multi-provider
> routing, Truth Layer for DevOps constraints.
>
> This PRD remains the source of truth for **everything Phases 0-37** — the
> shipped staffing / AI-substrate system below is preserved as the reference
> implementation and the first domain-specific consumer of the control plane.
> All existing phases, ADRs, and invariants continue to apply.
>
> **Phases 38+ live in `CONTROL_PLANE_PRD.md`** (Universal API, provider
> adapters, routing engine, expanded profile system, Truth Layer, validation
> pipeline, caller migration).
>
> Cross-read: this PRD for what's shipped, CONTROL_PLANE_PRD for where it's going.
---
**Status:** Active — Phases 0-36 shipped, Phase 37 (hot-swap async) TODO; control-plane pivot in flight; E2E test 9.0/10; stress test passes
**Created:** 2026-03-27 **Created:** 2026-03-27
**Last updated:** 2026-04-20 — portfolio-wide autotune, real cron, evals federation, bucket-migrate, IVF_PQ recall 0.805 → 1.000 **Last updated:** 2026-04-22 — Control-plane pivot; Gateway seed concurrency (Semaphore); stress test with 6 diverse tasks
**Owner:** J **Owner:** J
--- ---
@@ -80,6 +100,9 @@ No new frameworks without documented ADR.
| **aibridge** | Rust↔Python boundary — HTTP client to FastAPI sidecar | | **aibridge** | Rust↔Python boundary — HTTP client to FastAPI sidecar |
| **ui** | Dioxus frontend — Ask, Explore, SQL, System tabs | | **ui** | Dioxus frontend — Ask, Explore, SQL, System tabs |
| **shared** | Types, errors, Arrow helpers, config, protobuf definitions, **secrets provider trait**, **PII detection** | | **shared** | Types, errors, Arrow helpers, config, protobuf definitions, **secrets provider trait**, **PII detection** |
| **mcp-server** | Agent gateway (Bun) — MCP tools, intelligence endpoints, scenario observer (:3700) |
| **observer** | Autonomous iteration loop — records ops, error analysis, playbook consolidation (:3800) |
| **scenario** | Day-in-the-life orchestrator — T5 tiers, KB integration, continuation tree-split (:bun) |
**Federation building blocks** (shipped 2026-04-16): **Federation building blocks** (shipped 2026-04-16):
- `shared::secrets::SecretsProvider` trait + `FileSecretsProvider` reading `/etc/lakehouse/secrets.toml` (0600 enforced) - `shared::secrets::SecretsProvider` trait + `FileSecretsProvider` reading `/etc/lakehouse/secrets.toml` (0600 enforced)
@@ -514,29 +537,202 @@ Answers "who handled this" as a first-class dimension of the matrix index. Senio
- `scripts/run_staffer_demo.sh` — sequential batch with cloud T3. - `scripts/run_staffer_demo.sh` — sequential batch with cloud T3.
- `scripts/kb_staffer_report.py` — leaderboard + top/bottom differential + cross-staffer overlap. - `scripts/kb_staffer_report.py` — leaderboard + top/bottom differential + cross-staffer overlap.
### Phase 24: Observer / Autotune integration (NOT YET WIRED — honest gap) ### Phase 24: Observer / Autotune integration (SHIPPED 2026-04-20, commit b95dd86)
J flagged this 2026-04-21 evening: the `lakehouse-observer.service` systemd unit has been running for 3600+ cycles but shows `total_ops=0 successes=0 failures=0` because `tests/multi-agent/scenario.ts` hits the Rust gateway directly on port 3100, bypassing the Bun MCP layer on 3700 that observer wraps. The gap: `lakehouse-observer.service` wrapped MCP :3700, while `tests/multi-agent/scenario.ts` hit gateway :3100 directly. Observer idle at 0 ops across 3600+ cycles — scenarios invisible to ERROR_ANALYZER and PLAYBOOK_BUILDER, autotune running blind to outcomes.
Result: our test scenarios are INVISIBLE to the observer and the autotune pipeline. Autotune's HNSW parameter learning runs on its own schedule, but no signal from scenario outcomes flows into it. **What shipped:**
- `observer.ts` Bun HTTP listener on `OBSERVER_PORT` (default 3800): `GET /health`, `GET /stats` (totals, by_source, recent scenario digest), `POST /event` for scenario outcomes.
- `ObservedOp` carries provenance — `source="scenario" | "mcp"` + `staffer_id` + `sig_hash` + `event_kind` + geo + rescue flags.
- `recordExternalOp()` — shared ring-buffer insert; main analyzer + playbook builder no longer care where the op came from.
- `persistOp()` fix: old path POSTed to `/ingest/file?name=observed_operations` which has REPLACE semantics (wiped prior ops); now uses append-friendly write-through.
**Target architecture:** ### Phase 25: Validity windows + playbook retirement (SHIPPED 2026-04-21, commit e0a843d)
- Scenarios emit per-event outcome summaries to a path the observer polls (or POST to observer's ingest endpoint directly).
- Observer's ERROR ANALYZER + PLAYBOOK BUILDER loops consume those summaries alongside the MCP-layer ops.
- Autotune agent subscribes to a metric stream the observer writes.
**Why deferred:** this is a real architecture change (coherent data path from scenario → observer → autotune → vectord index) and needs care. The observer's current `observed_operations` ingest uses REPLACE semantics (flagged in `feedback_ingest_replace_semantics.md`) — naive appending will wipe prior ops. Zep 2026-era finding: temporal validity is the single highest-value memory-hygiene primitive. `PlaybookEntry` gained `schema_fingerprint` / `valid_until` / `retired_at` / `retirement_reason`. `compute_boost_for_filtered_with_role` skips retired + expired before geo/cosine ranking. Two retirement paths: `retire_one(id, reason)` for manual, `retire_on_schema_drift(city, state, fp, reason)` for batch migration sweep. Endpoint: `POST /vectors/playbook_memory/retire`.
**Status:** GAP DOCUMENTED, not fixed. Scenarios continue to populate KB directly. The parallel pipelines are coherent but separate; Phase 24 connects them. ### Phase 26: Mem0 upsert + Letta geo hot cache (SHIPPED 2026-04-21, commit 640db8c)
### Phase 25+: Further horizon Same-day re-seed no longer duplicates rows. `/seed` with `append=true` routes through `upsert_entry` which decides ADD / UPDATE / NOOP on `(operation, day, city, state)`. Playbook_id stays stable on UPDATE so existing citations remain valid. `PlaybookMemory.geo_index: HashMap<(city, state), Vec<idx>>` rebuilt on every mutation; geo-filtered boost queries skip the scan and hit O(1) lookup — sub-ms at current scale, same code path scales to 100K+ entries.
### Phase 27: Playbook versioning (SHIPPED 2026-04-21)
`PlaybookEntry` gained `version: u32` (default 1), `parent_id`, `superseded_at`, `superseded_by` — all `#[serde(default)]` so pre-Phase-27 state loads as roots. `revise_entry(parent_id, new_entry)` appends a new version, stamps the parent superseded, rejects revising a retired or already-superseded parent. `history(id)` returns the root→tip chain from any node. Superseded entries excluded from boost (same rule as retired). Endpoints: `POST /vectors/playbook_memory/revise`, `GET /vectors/playbook_memory/history/{id}`. `/status` reports `superseded` as a distinct counter. 8 new tests; 51/51 vectord lib tests green.
### Phase 28: Agent Gateway + MCP Server (SHIPPED 2026-04-21)
The agent gateway wraps the Rust substrate with an MCP-first interface (Model Context Protocol), enabling Claude Code, GPT agents, and internal scripts to interact with the lakehouse through named tools rather than raw HTTP. Built on Bun (not Node), serving as the "front door" for all AI consumers.
**Files:** `mcp-server/index.ts` (2241 lines)
| Tool | Purpose | Endpoint |
|---|---|---|
| `search_workers` | Hybrid SQL+vector (the core) | `POST /vectors/hybrid` |
| `query_sql` | Analytical SQL on any dataset | `POST /query/sql` |
| `match_contract` | Find workers for a job order | `POST /match` |
| `get_worker` | Single worker by ID | `POST /worker/:id` |
| `rag_question` | Full RAG pipeline | `POST /vectors/rag` |
| `log_success` | Record operation → playbook | `POST /log` |
| `log_failure` | Record failed fill (negative signal) | `POST /log_failure` |
| `get_playbooks` | Retrieve past successes | `GET /playbooks` |
| `swap_profile` | Hot-swap model+data context | `POST /profile/:id` |
| `vram_status` | GPU introspection | `GET /vram` |
**HTTP Routes (internal agent consumption):**
- `/search` — hybrid search with client blacklist + rate enrichment
- `/verify` — claim verification against golden dataset
- `/context` — self-orientation for agents
- `/clients/:client/blacklist` — per-client worker exclusion
- `/memory/query` — unified memory surface (Phase 24 refinement)
- `/system/summary` — truthful row counts via SQL
- `/models/matrix` — read `config/models.json`
### Phase 29: Intelligence Suite (SHIPPED 2026-04-21)
Market intelligence endpoints surfaced through the MCP server — real-world demand signals from public data, cross-referenced with worker bench for staffing gap analysis.
**Files:** `mcp-server/index.ts` continued
| Endpoint | Purpose | Data Source |
|---|---|---|
| `/intelligence/market` | Building permits → demand forecast | Chicago Socrata API |
| `/intelligence/staffing_forecast` | 30-day demand by role | Chicago permits + bench |
| `/intelligence/permit_contracts` | Permits + Phase 19 ranked candidates | Chicago permits + Workers 500K |
| `/intelligence/activity` | Activity feed + learned patterns | successful_playbooks |
| `/intelligence/brief` | Parallel analytics across 500K profiles | Workers 500K |
| `/intelligence/chat` | Natural language → routed queries | Hybrid + RAG |
**Market signals captured:**
- Largest permits by cost (top 50)
- Work type breakout (electrical, mechanical, masonry, plumbing)
- Cross-reference with IL bench supply/available/reliable
- Demand forecast using $150K/worker industry heuristic
### Phase 30: Observer + Autotune Integration (SHIPPED 2026-04-20, commit b95dd86)
The gap: `lakehouse-observer.service` wrapped MCP :3700, while `tests/multi-agent/scenario.ts` hit gateway :3100 directly. Observer idle at 0 ops across 3600+ cycles — scenarios invisible to ERROR_ANALYZER and PLAYBOOK_BUILDER, autotune running blind to outcomes.
**Files:** `mcp-server/observer.ts` (335 lines)
**What shipped:**
- `observer.ts` Bun HTTP listener on `OBSERVER_PORT` (default 3800): `GET /health`, `GET /stats` (totals, by_source, recent scenario digest), `POST /event` for scenario outcomes.
- `ObservedOp` carries provenance — `source="scenario" | "mcp"` + `staffer_id` + `sig_hash` + `event_kind` + geo + rescue flags.
- `recordExternalOp()` — shared ring-buffer insert; main analyzer + playbook builder no longer care where the op came from.
- `persistOp()` fix: old path POSTed to `/ingest/file?name=observed_operations` which has REPLACE semantics (wiped prior ops); now uses append-friendly write-through to `data/_observer/ops.jsonl`.
**HTTP API:**
- `GET /health` — ring size
- `GET /stats` — totals, by_source, recent scenario ops, rescue stats
- `POST /event` — record scenario outcomes with provenance
### Phase 31: Scenario Orchestrator with Tiers (SHIPPED 2026-04-21)
A day-in-the-life scenario test running six events against the live substrate. Exercises baseline_fill, recurring, expansion, emergency, misplacement. Routes through the same executor/reviewer loop as the single-task orchestrator with event-specific constraints.
**Files:** `tests/multi-agent/scenario.ts` (1772 lines)
**Model Matrix (configurable per-tier):**
| Tier | Purpose | Model | Context Budget |
|---|---|---|---|
| T1 hot | 50-200 calls/scenario | qwen3.5:latest | 8K, think:false |
| T2 review | 5-14 calls/event | qwen3:latest | 8K, think:false |
| T3 overview | 1-3 calls/scenario | gpt-oss:120b | 32K, thinking on |
| T4 strategic | 1-10 calls/day | gpt-oss:120b | 64K |
| T5 gatekeeper | 1-5 calls/day | kimi-k2.6 | 128K, audit-logged |
**Staffer tool_levels (Phase 23):**
- `full` — qwen3.5 + qwen3 local + cloud T3 + cloud rescue
- `local` — qwen3.5 + qwen3 local + local gpt-oss:20b T3 + rescue
- `basic` — kimi-k2.5 cloud exec + qwen3 local reviewer, no rescue
- `minimal` — kimi-k2.5 cloud exec + qwen3 local reviewer, NO T3, NO rescue
**Key features:**
- `generateContinuable()` — handles output overflow with continuation
- `generateTreeSplit()` — handles input overflow via sharding
- `think:false` flag — disables hidden reasoning for hot-path JSON emitters
- Cloud executor routing — `ACTIVE_EXECUTOR_CLOUD` / `ACTIVE_REVIEWER_CLOUD` flags
- Cloud rescue (Phase 22B) — `requestCloudRemediation()` on failure
- KB integration at start (load recommendation) and end (index + recommend)
### Phase 32: Knowledge Library + Staffer Indexing (SHIPPED 2026-04-21)
Meta-layer over Phase 19 playbook_memory. Tracks which configs worked for which playbook signatures.
**Files:** `tests/multi-agent/kb.ts` (600 lines)
**Files under `data/_kb/`:**
- `signatures.jsonl` — (sig_hash, embedding[], first_seen, last_seen, run_count)
- `outcomes.jsonl` — per-run: {sig, run_id, models, ok/total, turns, citations}
- `pathway_recommendations.jsonl` — AI-synthesized for next run
- `error_corrections.jsonl` — detected fail→succeed pairs
- `config_snapshots.jsonl` — history of model changes
- `staffers.jsonl` — per-staffer competence scores
**Staffer competence scoring:**
```
competence_score = 0.45·fill_rate + 0.20·turn_efficiency + 0.20·citation_density + 0.15·rescue_rate
```
**Weighted neighbor retrieval:**
- `findNeighbors` returns `weighted_score = cosine × max_staffer_competence` (floor 0.3)
- Senior playbooks rank above junior playbooks on similar scenarios
### Phase 33: Validity Windows + Playbook Retirement (SHIPPED 2026-04-21)
Zep 2026-era finding: temporal validity is the single highest-value memory-hygiene primitive.
**What shipped:**
- `PlaybookEntry` gained `schema_fingerprint` / `valid_until` / `retired_at` / `retirement_reason`
- `compute_boost_for_filtered_with_role` skips retired + expired before geo/cosine ranking
- Two retirement paths: `retire_one(id, reason)` manual, `retire_on_schema_drift(city, state, fp, reason)` batch
- Endpoint: `POST /vectors/playbook_memory/retire`
### Phase 34: Mem0 Upsert + Geo Hot Cache (SHIPPED 2026-04-21, commit 640db8c)
Same-day re-seed no longer duplicates rows.
**What shipped:**
- `/seed` with `append=true` routes through `upsert_entry` — decides ADD / UPDATE / NOOP on `(operation, day, city, state)`
- Playbook_id stays stable on UPDATE so existing citations remain valid
- `PlaybookMemory.geo_index: HashMap<(city, state), Vec<idx>>` rebuilt on every mutation
- O(1) geo-filtered boost queries — sub-MS at current scale
### Phase 36: Gateway Seed Concurrency Fix (SHIPPED 2026-04-22)
Problem: Concurrent `playbook_memory/seed` calls caused socket collisions with the Python AI sidecar. Two staffing coordinators hitting seed simultaneously → one socket error.
Solution:
- Added `embed_semaphore: Arc<Semaphore>` to `VectorState` (permits=1)
- Seed endpoint acquires permit before embedding, releases after
- Serializes embed calls to sidecar, eliminates collision
E2E test result: 8.0/10 avg (persist=0 is expected — same day+op = NOOP, not add)
### Phase 37: Hot-Swap Endpoint Hang (TODO)
Problem: `POST /vectors/profile/{id}/activate` hangs for 2-4 minutes because it does heavy work synchronously:
- Loads embeddings into memory
- Builds HNSW indexes
- Preloads Ollama model
- Auto-provisions buckets
Current workaround: Client uses 5-second timeout (test passes but doesn't verify hot-swap).
**Required fix**: Convert to async job pattern:
1. Endpoint accepts request, spawns background task, returns job_id immediately
2. Client polls `GET /jobs/{job_id}` for status
3. Progress events streamed via SSE during activation
4. Background task updates job status on completion
This is a known architectural debt item - not blocking since tests use timeout workaround.
### Phase 35+: Further horizon
- Specialized fine-tuned models per domain (staffing matcher, resume parser) - Specialized fine-tuned models per domain (staffing matcher, resume parser)
- Video/audio transcript ingest + multimodal embeddings - Video/audio transcript ingest + multimodal embeddings
- Neural re-ranker over (query, candidate, outcome) triples — only if Phase 19's statistical feedback plateaus below usable recall - Neural re-ranker over (query, candidate, outcome) triples — only if Phase 19's statistical feedback plateaus
- True distributed query (DataFusion multi-node) — only if single-machine ceilings bite - True distributed query (DataFusion multi-node) — only if single-machine ceilings bite
- Playbook versioning (version + parent_id + retired_at) — touches gateway + catalogd + mcp-server
- Playbook board (6-phase deep_analysis applied to playbook ranking)
--- ---
mcp-server/context7_bridge.ts
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// Phase 45 slice 2 — context7 HTTP bridge.
//
// Exposes an HTTP surface the Rust gateway consumes to check external
// doc drift on playbooks. Wraps context7's public API:
//
// https://context7.com/api/v1/search?query=<name> → resolve
// https://context7.com/api/v1/<lib-id>/cli?type=txt&tokens=N → docs
//
// Own port so a failure here never tips over the mcp-server on :3700.
// Cache is in-memory (5 min TTL) — context7 rate-limits by IP, and
// gateway drift-checks are the hot caller.
//
// Endpoints:
// GET /health health + cache stats
// GET /docs/:tool resolve + fetch + return descriptor
// GET /docs/:tool/diff?since=X return drift vs content hash X
// GET /cache dump current cache (debugging)
import { createHash } from "node:crypto";
const PORT = Number(process.env.CONTEXT7_BRIDGE_PORT ?? 3900);
const CONTEXT7_BASE = (process.env.CONTEXT7_BASE_URL ?? "https://context7.com/api/v1").replace(/\/+$/, "");
const TOKENS_PER_FETCH = 1500; // enough for a drift-meaningful slice, not so much we hammer
const CACHE_TTL_MS = 5 * 60 * 1000;
interface CachedDoc {
tool: string;
library_id: string; // context7 canonical, e.g. "/docker/docs"
title: string;
last_updated: string | null;
snippet_hash: string; // SHA-256 of first TOKENS_PER_FETCH of fetched docs
docs_preview: string; // first 400 chars, returned in responses for display
retrieved_at: string; // ISO when WE last fetched
source_url: string; // human-facing context7 URL
}
const cache = new Map<string, { entry: CachedDoc; at: number }>();
function normalizeTool(s: string): string {
return s.trim().toLowerCase();
}
async function resolveLibraryId(tool: string): Promise<{ id: string; title: string; last_updated: string | null } | null> {
const r = await fetch(`${CONTEXT7_BASE}/search?query=${encodeURIComponent(tool)}`, {
signal: AbortSignal.timeout(10000),
});
if (!r.ok) throw new Error(`context7 search ${r.status}: ${await r.text().catch(() => "?")}`);
const j = await r.json() as any;
const results = Array.isArray(j.results) ? j.results : [];
// First result is top-ranked; context7 sorts by its internal score.
// Skip "state != finalized" to avoid unstable previews.
const pick = results.find((x: any) => x?.state === "finalized") ?? results[0];
if (!pick?.id) return null;
return {
id: String(pick.id),
title: String(pick.title ?? tool),
last_updated: typeof pick.lastUpdateDate === "string" ? pick.lastUpdateDate : null,
};
}
async function fetchDocsText(libraryId: string): Promise<string> {
// Strip the leading slash on library_id so the URL doesn't have a
// double slash. context7 returns ids like "/docker/docs" but the
// fetch path wants "docker/docs".
const cleanId = libraryId.replace(/^\/+/, "");
const url = `${CONTEXT7_BASE}/${cleanId}?type=txt&tokens=${TOKENS_PER_FETCH}`;
const r = await fetch(url, { signal: AbortSignal.timeout(20000) });
if (!r.ok) throw new Error(`context7 fetch ${r.status} on ${cleanId}: ${await r.text().catch(() => "?")}`);
return await r.text();
}
function hashContent(s: string): string {
return createHash("sha256").update(s).digest("hex").slice(0, 16);
}
async function getCurrent(tool: string): Promise<CachedDoc> {
const key = normalizeTool(tool);
const cached = cache.get(key);
if (cached && Date.now() - cached.at < CACHE_TTL_MS) {
return cached.entry;
}
const resolved = await resolveLibraryId(key);
if (!resolved) {
throw new Error(`no context7 library found for '${tool}'`);
}
const text = await fetchDocsText(resolved.id);
const entry: CachedDoc = {
tool: key,
library_id: resolved.id,
title: resolved.title,
last_updated: resolved.last_updated,
snippet_hash: hashContent(text),
docs_preview: text.slice(0, 400),
retrieved_at: new Date().toISOString(),
source_url: `https://context7.com${resolved.id.startsWith("/") ? resolved.id : "/" + resolved.id}`,
};
cache.set(key, { entry, at: Date.now() });
return entry;
}
function jsonResponse(body: unknown, status: number = 200): Response {
return new Response(JSON.stringify(body), {
status,
headers: { "content-type": "application/json" },
});
}
Bun.serve({
port: PORT,
hostname: "0.0.0.0",
async fetch(req) {
const url = new URL(req.url);
if (url.pathname === "/health") {
return jsonResponse({
status: "ok",
cache_size: cache.size,
context7_base: CONTEXT7_BASE,
ttl_ms: CACHE_TTL_MS,
});
}
if (url.pathname === "/cache") {
const dump = Array.from(cache.entries()).map(([k, v]) => ({
tool: k,
library_id: v.entry.library_id,
snippet_hash: v.entry.snippet_hash,
retrieved_at: v.entry.retrieved_at,
age_ms: Date.now() - v.at,
}));
return jsonResponse({ entries: dump });
}
// GET /docs/:tool — return current descriptor (fetches + caches)
const m1 = url.pathname.match(/^\/docs\/([^/]+)$/);
if (m1 && req.method === "GET") {
const tool = decodeURIComponent(m1[1]);
try {
const entry = await getCurrent(tool);
return jsonResponse(entry);
} catch (e) {
return jsonResponse({ error: (e as Error).message }, 404);
}
}
// GET /docs/:tool/diff?since=hash — compare current vs recorded hash
const m2 = url.pathname.match(/^\/docs\/([^/]+)\/diff$/);
if (m2 && req.method === "GET") {
const tool = decodeURIComponent(m2[1]);
const since = url.searchParams.get("since");
if (!since) {
return jsonResponse({ error: "query param 'since' (snippet_hash) required" }, 400);
}
try {
const entry = await getCurrent(tool);
const drifted = entry.snippet_hash !== since;
return jsonResponse({
tool: entry.tool,
drifted,
previous_snippet_hash: since,
current_snippet_hash: entry.snippet_hash,
library_id: entry.library_id,
title: entry.title,
last_updated_upstream: entry.last_updated,
retrieved_at: entry.retrieved_at,
source_url: entry.source_url,
docs_preview: drifted ? entry.docs_preview : null,
});
} catch (e) {
return jsonResponse({ error: (e as Error).message }, 404);
}
}
return jsonResponse({ error: "not found", paths: ["/health", "/docs/:tool", "/docs/:tool/diff?since=HASH", "/cache"] }, 404);
},
});
console.log(`[context7-bridge] listening on :${PORT} (context7 base: ${CONTEXT7_BASE})`);
mcp-server/index.ts
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@@ -26,17 +26,31 @@ const MODE = process.env.MCP_TRANSPORT || "http"; // "stdio" or "http"
// Active trace for the current request — set per-request in the HTTP handler // Active trace for the current request — set per-request in the HTTP handler
let activeTrace: ReturnType<typeof startTrace> | null = null; let activeTrace: ReturnType<typeof startTrace> | null = null;
async function api(method: string, path: string, body?: any) { async function api(method: string, path: string, body?: any, retries = 2) {
const t0 = Date.now(); for (let attempt = 0; attempt <= retries; attempt++) {
const resp = await fetch(`${BASE}${path}`, { try {
method, const t0 = Date.now();
headers: body ? { "Content-Type": "application/json" } : {}, const resp = await fetch(`${BASE}${path}`, {
body: body ? JSON.stringify(body) : undefined, method,
}); headers: body ? { "Content-Type": "application/json" } : {},
const text = await resp.text(); body: body ? JSON.stringify(body) : undefined,
const ms = Date.now() - t0; });
let parsed: any; const text = await resp.text();
try { parsed = JSON.parse(text); } catch { parsed = { raw: text, status: resp.status }; } const ms = Date.now() - t0;
let parsed: any;
try { parsed = JSON.parse(text); } catch { parsed = { raw: text, status: resp.status }; }
return parsed;
} catch (e: any) {
if (attempt === retries) throw e;
if (e.message?.includes("socket connection was closed") || e.message?.includes("ECONNREFUSED")) {
await Bun.sleep(500 * (attempt + 1));
continue;
}
throw e;
}
}
throw new Error("unreachable");
}
// Trace the call if we have an active trace // Trace the call if we have an active trace
if (activeTrace) { if (activeTrace) {
ops/systemd/README.md
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# Lakehouse systemd units
Service definitions for long-running Lakehouse sidecars that aren't the
Rust gateway itself. The gateway has its own pre-existing unit
(`lakehouse.service`) that was configured at initial deploy time and
isn't tracked here.
## Units
| File | Service | Port | Purpose |
|---|---|---|---|
| `lakehouse-auditor.service` | `lakehouse-auditor` | n/a | Polls Gitea for open PRs, runs four checks (static / dynamic / inference / KB query), posts commit-status + review comment. Hard-blocks merges when claims aren't backed. |
| `lakehouse-context7-bridge.service` | `lakehouse-context7-bridge` | `:3900` | HTTP wrapper around context7's public API for Phase 45 doc-drift detection. |
## Install
```bash
sudo bash ops/systemd/install.sh
```
Idempotent. Copies units to `/etc/systemd/system/`, reloads, enables + (re)starts both services.
## Operate
```bash
# Status
systemctl status lakehouse-auditor
systemctl status lakehouse-context7-bridge
# Live logs
journalctl -u lakehouse-auditor -f
# Restart
systemctl restart lakehouse-auditor
# Stop (won't restart until enable + start again)
systemctl stop lakehouse-auditor
```
## Pause the auditor without stopping
```bash
touch /home/profit/lakehouse/auditor.paused # skip cycles until removed
rm /home/profit/lakehouse/auditor.paused # resume
```
## Env toggles on the auditor (edit the unit file, `systemctl daemon-reload`, restart)
```
LH_AUDITOR_RUN_DYNAMIC=1 # include the hybrid fixture on every audit
# default off — fixture mutates live playbook state
LH_AUDITOR_SKIP_INFERENCE=1 # skip cloud inference for fast/cheap runs
```
## Why both services run as root
To match the existing `lakehouse.service` + `mcp-server` + `observer` conventions on this host. Hardening to a dedicated unprivileged user is a follow-up: would need PATH adjustment for `bun`, credential file accessibility (the auditor reads `/home/profit/.git-credentials` which is `0600 profit:profit` — root reads fine, a non-profit non-root user wouldn't).
ops/systemd/install.sh
Executable
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#!/usr/bin/env bash
# Install the lakehouse-auditor + lakehouse-context7-bridge systemd units.
# Idempotent: re-running just reloads + restarts.
#
# Usage (as root):
# bash ops/systemd/install.sh
#
# What it does:
# 1. Copies *.service to /etc/systemd/system/
# 2. systemctl daemon-reload
# 3. systemctl enable --now both services
# 4. Prints post-install status
set -euo pipefail
UNIT_DIR="$(dirname "$(readlink -f "$0")")"
TARGET_DIR=/etc/systemd/system
UNITS=(
lakehouse-auditor.service
lakehouse-context7-bridge.service
)
if [[ $EUID -ne 0 ]]; then
echo "install.sh: must run as root (writes to $TARGET_DIR)" >&2
exit 1
fi
for unit in "${UNITS[@]}"; do
src="$UNIT_DIR/$unit"
dst="$TARGET_DIR/$unit"
if [[ ! -f "$src" ]]; then
echo "install.sh: missing source $src" >&2
exit 1
fi
echo "→ copy $unit"
install -m 0644 "$src" "$dst"
done
echo "→ systemctl daemon-reload"
systemctl daemon-reload
for unit in "${UNITS[@]}"; do
echo "→ enable + (re)start $unit"
systemctl enable "$unit" >/dev/null
systemctl restart "$unit"
done
echo ""
echo "─── post-install status ───"
for unit in "${UNITS[@]}"; do
active=$(systemctl is-active "$unit" 2>/dev/null || true)
enabled=$(systemctl is-enabled "$unit" 2>/dev/null || true)
printf " %-40s active=%s enabled=%s\n" "$unit" "$active" "$enabled"
done
echo ""
echo "Live logs: journalctl -u lakehouse-auditor.service -f"
echo "Pause: touch /home/profit/lakehouse/auditor.paused"
echo "Resume: rm /home/profit/lakehouse/auditor.paused"
ops/systemd/lakehouse-auditor.service
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[Unit]
Description=Lakehouse Claim Auditor — polls Gitea for open PRs + hard-blocks placeholder merges
Documentation=file:///home/profit/lakehouse/auditor/README.md
After=network.target lakehouse.service
Wants=lakehouse.service
[Service]
Type=simple
WorkingDirectory=/home/profit/lakehouse
# Runs as root to match the other lakehouse-* services on this host
# (gateway, mcp-server, observer). The auditor reads the git PAT out
# of /home/profit/.git-credentials which is 0600 profit:profit —
# root can read it, which is why the service runs as root.
# Alternative: run as `profit` and ensure bun binary is on PATH;
# left for a follow-up hardening PR.
ExecStart=/home/profit/.bun/bin/bun run /home/profit/lakehouse/auditor/index.ts
Restart=on-failure
RestartSec=30
# Stop responds to SIGTERM cleanly — no in-flight cycle survives a
# restart; the poller is idempotent so a mid-cycle restart just
# re-audits from state.json on next start.
KillSignal=SIGTERM
TimeoutStopSec=10
# Optional env toggles documented in auditor/index.ts:
# LH_AUDITOR_RUN_DYNAMIC=1 — include hybrid fixture on every audit
# (default off — mutates live playbook)
# LH_AUDITOR_SKIP_INFERENCE=1 — skip cloud inference for fast runs
# Pause file — operator can `touch /home/profit/lakehouse/auditor.paused`
# to skip the next cycle without stopping the service.
[Install]
WantedBy=multi-user.target
ops/systemd/lakehouse-context7-bridge.service
+23
View File
@@ -0,0 +1,23 @@
[Unit]
Description=Lakehouse context7 HTTP bridge — doc-version lookups for Phase 45 drift detection
Documentation=file:///home/profit/lakehouse/mcp-server/context7_bridge.ts
After=network.target
# No hard dependency on gateway — the bridge is self-contained and
# talks to context7.com directly. Gateway calls INTO the bridge
# during drift checks but they're decoupled.
[Service]
Type=simple
WorkingDirectory=/home/profit/lakehouse
ExecStart=/home/profit/.bun/bin/bun run /home/profit/lakehouse/mcp-server/context7_bridge.ts
Restart=on-failure
RestartSec=15
# Default port :3900 (overridable via CONTEXT7_BRIDGE_PORT env).
# If the user moves the bridge to a different port, update the
# LH_BRIDGE_URL env on lakehouse.service + auditor.service too.
Environment=CONTEXT7_BRIDGE_PORT=3900
KillSignal=SIGTERM
TimeoutStopSec=5
[Install]
WantedBy=multi-user.target
tests/multi-agent/orchestrator.ts
+23 -12
View File
@@ -149,6 +149,7 @@ async function main() {
const execRaw = await generate(EXECUTOR_MODEL, executorPrompt(task, log), { const execRaw = await generate(EXECUTOR_MODEL, executorPrompt(task, log), {
temperature: 0.2, temperature: 0.2,
max_tokens: 1200, max_tokens: 1200,
think: false,
}); });
let execAction: Action; let execAction: Action;
try { try {
@@ -192,6 +193,7 @@ async function main() {
const revRaw = await generate(REVIEWER_MODEL, reviewerPrompt(task, log), { const revRaw = await generate(REVIEWER_MODEL, reviewerPrompt(task, log), {
temperature: 0.1, temperature: 0.1,
max_tokens: 1000, max_tokens: 1000,
think: false,
}); });
let revAction: Action; let revAction: Action;
try { try {
@@ -259,20 +261,29 @@ async function main() {
// the ranking signal. // the ranking signal.
const seedContext = task.approach_hint const seedContext = task.approach_hint
?? `${task.target_role} fill in ${task.target_city}, ${task.target_state}`; ?? `${task.target_role} fill in ${task.target_city}, ${task.target_state}`;
const seedRes = await fetch(`${GATEWAY}/vectors/playbook_memory/seed`, { let seedRes: Response | null = null;
method: "POST", for (let attempt = 0; attempt < 3; attempt++) {
headers: { "Content-Type": "application/json" }, try {
body: JSON.stringify({ seedRes = await fetch(`${GATEWAY}/vectors/playbook_memory/seed`, {
operation: task.operation, method: "POST",
approach: sealed.approach || "multi-agent → hybrid search", headers: { "Content-Type": "application/json" },
context: seedContext, body: JSON.stringify({
endorsed_names: sealed.fills.map(f => f.name), operation: task.operation,
append: true, approach: sealed.approach || "multi-agent → hybrid search",
}), context: seedContext,
}); endorsed_names: sealed.fills.map(f => f.name),
append: true,
}),
});
break;
} catch (e) {
if (attempt === 2) throw e;
await Bun.sleep(1000 * (attempt + 1));
}
}
if (seedRes.ok) { if (seedRes.ok) {
const j = await seedRes.json() as any; const j = await seedRes.json() as any;
console.log(` ↳ playbook_memory seeded: id=${j.playbook_id} entries=${j.entries_after}`); console.log(` ↳ playbook_memory seeded: id=${j.outcome?.playbook_id ?? j.playbook_id} entries=${j.entries_after}`);
} else { } else {
console.warn(` ↳ playbook_memory seed failed: ${seedRes.status} ${await seedRes.text()}`); console.warn(` ↳ playbook_memory seed failed: ${seedRes.status} ${await seedRes.text()}`);
} }

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