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7c1745611a
Bundles PR #9's work for the audit pipeline: - N=3 consensus on cloud inference (gpt-oss:120b parallel) with qwen3-coder:480b tie-breaker - audit_discrepancies.jsonl logs N-run disagreements - scrum_master reviews route through llm_team fact extraction; source="scrum_review" - Verifier-gated persistence: drops INCORRECT, keeps UNVERIFIABLE/UNCHECKED; schema_version:2 - scrum_master_reviewed flag on accepted reviews - auditor/kb_stats.ts: on-demand observability script - claim_parser history/proof pattern class (verified-on-PR, was-flipping, the-proven-X) - claim_parser quoted-string guard (mirrors static.ts fix) - fact_extractor project context injection via docs/AUDITOR_CONTEXT.md - Fixed verifier-verdict parser to handle multiple gemma2 output formats Empirical: 3-run determinism test on unchanged PR #9 SHA showed 7/7 warn findings stable; block count oscillation eliminated; llm_team quality scores 8-9 on context-injected extract runs. See PR #9 for full run-by-run commit history.
70 lines
3.3 KiB
Markdown
70 lines
3.3 KiB
Markdown
# Auditor Context — project preamble for fact extraction
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This file is read by `auditor/fact_extractor.ts` and prepended to the
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extract-facts prompt sent to llm_team. The goal: give the extractor +
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verifier enough grounding to ground domain-specific facts instead of
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marking them UNVERIFIABLE by default.
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Keep this short (< 400 words). Verifier only reads the first ~4KB of
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the prompt alongside the facts. Longer = noise, not signal.
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Update when: a new Phase lands, a crate is added/removed, the project's
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primary domain shifts (e.g. staffing → DevOps).
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---
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## What Lakehouse is
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Lakehouse is a Rust-first data platform over S3-compatible object
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storage. Primary use: a staffing company ingesting legacy CRM data for
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AI-powered worker matching, contract fulfillment, and playbook-driven
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coordination.
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Architecture: 13 Rust crates + a Python sidecar (Ollama) + TypeScript
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sub-agents (auditor, scrum_master, bot). Runs on a single server
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(Nvidia A4000, 128GB RAM). All services on localhost: gateway :3100,
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sidecar :3200, UI :3300, MCP :3700, observer :3800, MinIO :9000.
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## Key crates (each maps to a responsibility)
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- **shared** — types, Arrow helpers, PII utilities, SecretsProvider
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- **proto** — gRPC definitions
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- **storaged** — S3/MinIO I/O, AppendLog, ErrorJournal
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- **catalogd** — metadata authority (manifests, views, tombstones)
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- **queryd** — DataFusion SQL, MemTable cache, compaction
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- **ingestd** — CSV/JSON/PDF/Postgres/MySQL ingest
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- **vectord** — embeddings, HNSW index, **playbook_memory meta-index** (Phase 19+)
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- **vectord-lance** — Lance 4.0 firewall crate (separate Arrow version)
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- **journald** — append-only mutation event log
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- **aibridge** — Rust↔Python sidecar bridge, context budget + continuation
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- **gateway** — Axum HTTP :3100 + gRPC :3101 (Phase 38+ adds /v1/chat)
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- **ui** — Dioxus WASM (stale, pre-Phase-9)
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- **lance-bench** — standalone benchmark
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## Current architectural direction (Phase 38-44)
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Universal AI Control Plane: a `/v1/chat` OpenAI-compatible API that
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routes all LLM traffic through one layer for token accounting + provider
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fallback. Truth Layer + Validation Pipeline enforce staffing-domain
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invariants (worker eligibility, PII, contract rules). The Auditor
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(Phase A of cohesion plan) hard-blocks PR merges on placeholder code.
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## Auditor sub-agent role
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`auditor/` (TypeScript, Bun runtime) polls Gitea every 90s for open PRs.
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For each fresh head SHA it runs 4 checks in parallel: static (grep-style
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placeholder detection), dynamic (runs the hybrid fixture), inference
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(gpt-oss:120b cloud review with N=3 consensus + qwen3-coder:480b
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tie-breaker), and kb_query (reads `data/_kb/*.jsonl` for prior evidence).
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Verdicts post to Gitea as commit status + review comment. Findings
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append to `data/_kb/audit_lessons.jsonl` (path-agnostic signatures for
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dedup). Curated scratchpads from tree-split get routed through this
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extract-facts pipeline to populate `audit_facts.jsonl` — which is what
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you (the extractor) are currently producing.
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## Things that are NOT the auditor
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- The LLM Team UI at `/root/llm_team_ui.py` (devop.live:5000) — a separate product for human-facing multi-model experimentation
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- The scrum_master pipeline at `tests/real-world/scrum_master_pipeline.ts` — reviews files, not claims
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- The bot at `bot/` — will apply fixes, doesn't audit
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