3 Commits

Author SHA1 Message Date
profit
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
root
eae51977ab Scale test: 2.47M rows + 10K vector index benchmarked
Benchmarks on 128GB RAM server:
- 100K candidate filter (skills+city+status): 257ms
- 1M timesheet aggregation (revenue by client): 942ms
- 800K call log cross-reference (cold leads): 642ms
- Triple JOIN recruiter performance: 487ms
- 500K email open rate aggregation: 259ms
- COUNT all 2.47M rows: 84ms
- 10K vector search (cosine similarity): ~450ms
- Embedding throughput: 49 chunks/sec via Ollama
- RAG correctly refuses to hallucinate when no match exists

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-27 08:31:37 -05:00
root
26fc98c885 Phase 7: Vector index + RAG pipeline
- vectord crate: chunk → embed → store → search → RAG
- chunker: configurable chunk size + overlap, sentence-boundary aware splitting
- store: embeddings as Parquet (binary blob f32 vectors), portable format
- search: brute-force cosine similarity (works up to ~100K vectors)
- rag: full pipeline — embed question → search index → retrieve context → LLM answer
- Endpoints: POST /vectors/index, /vectors/search, /vectors/rag
- Gateway wired with vectord service
- Tested: 200 candidate resumes indexed in 5.4s, semantic search + RAG working
- 20 unit tests passing (chunker, search, ingestd, shared)
- AI gives honest "no match found" when context doesn't support an answer

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-27 08:12:28 -05:00