Langfuse v2.95.11 running on :3001 (Docker + Postgres).
Login: j@lakehouse.local / lakehouse2026
tracing.ts: startTrace → logGeneration/logRetrieval/logSpan → scoreTrace → flush.
Every hybrid search, SQL generation, RAG pipeline, and co-pilot
briefing gets a full trace: model, prompt, output, latency, tokens.
The observer can now score traces based on verification results —
Langfuse aggregates accuracy over time so we can see which models
and approaches actually work in production, not just in tests.
Services: lakehouse(:3100) + sidecar(:3200) + agent(:3700) +
observer + langfuse(:3001) + minio(:9000) + mariadb(:3306)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Three new systemd services:
- lakehouse-agent (:3700) — REST gateway wrapping all lakehouse tools.
Clean JSON in/out, no protocol complexity. 9 endpoints: /search,
/sql, /match, /worker/:id, /ask, /log, /playbooks, /profile/:id, /vram
- lakehouse-observer — watches operations, logs to lakehouse, asks
local model to diagnose failure patterns, consolidates successful
patterns into playbooks every 5 cycles
- Stdio MCP transport preserved for Claude Code integration
AGENT_INSTRUCTIONS.md: complete operating manual for sub-agents.
Rules: never hallucinate, SQL first for structured questions, hybrid
for matching, log every success, check playbooks before complex tasks.
Observer loop:
observed() wrapper timestamps + persists every gateway call →
error analyzer reads failures + asks LLM for diagnosis →
playbook consolidator groups successes by endpoint pattern
All three designed for zero human intervention — agents operate,
observer watches, playbooks accumulate, iteration happens internally.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
MCP server at mcp-server/index.ts — 9 tools exposing the full
lakehouse to any MCP-compatible model:
search_workers (hybrid SQL+vector), query_sql, match_contract,
get_worker, rag_question, log_success, get_playbooks,
swap_profile, vram_status
The "successful playbooks" pattern: log_success writes outcomes
back to the lakehouse as a queryable dataset. Small models call
get_playbooks to learn what approaches worked for similar tasks —
no retraining needed, just data.
generate_workers.py scales to 100K+ with realistic distributions:
- 20 roles weighted by staffing industry frequency
- 44 real Midwest/South cities across 12 states
- Per-role skill pools (warehouse/production/machine/maintenance)
- 13 certification types with realistic probability
- 8 behavioral archetypes with score distributions
- SMS communication templates (20 patterns)
100K worker dataset ingested: 70MB CSV → Parquet in 1.1s. Verified:
11K forklift ops, 27K in IL, archetype distribution matches weights.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
