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Author SHA1 Message Date
root
dbe00d018f Federation foundation + HNSW trial system + Postgres streaming + PRD reframe 
Four shipped features and a PRD realignment, all measured end-to-end:

HNSW trial system (Phase 15 horizon item → complete)
- vectord: EmbeddingCache, harness (eval sets + brute-force ground truth),
  TrialJournal, parameterized HnswConfig on build_index_with_config
- /vectors/hnsw/trial, /hnsw/trials/{idx}, /hnsw/trials/{idx}/best,
  /hnsw/evals/{name}/autogen, /hnsw/cache/stats
- Measured on resumes_100k_v2 (100K × 768d): brute-force 44ms -> HNSW 873us
  at 100% recall@10. ec=80 es=30 locked as HnswConfig::default()
- Lower ec values trade recall for build time: 20/30 = 0.96 recall in 8s,
  80/30 = 1.00 recall in 230s

Catalog manifest repair
- catalogd: resync_from_parquet reads parquet footers to restore row_count
  and columns on drifted manifests
- POST /catalog/datasets/{name}/resync + POST /catalog/resync-missing
- All 7 staffing tables recovered to PRD-matching 2,469,278 rows

Federation foundation (ADR-017)
- shared::secrets: SecretsProvider trait + FileSecretsProvider (reads
  /etc/lakehouse/secrets.toml, enforces 0600 perms)
- storaged::registry::BucketRegistry — multi-bucket resolution with
  rescue_bucket read fallback and reachability probing
- storaged::error_journal — bucket op failures visible in one HTTP call
- storaged::append_log — write-once batched append pattern (fixes the RMW
  anti-pattern llms3.com calls out; errors and trial journals both use it)
- /storage/buckets, /storage/errors, /storage/bucket-health,
  /storage/errors/{flush,compact}
- Bucket-aware I/O at /storage/buckets/{bucket}/objects/{*key} with
  X-Lakehouse-Rescue-Used observability headers on fallback

Postgres streaming ingest
- ingestd::pg_stream: DSN parser, batched ORDER BY + LIMIT/OFFSET pagination
  into ArrowWriter, lineage redacts password
- POST /ingest/db — verified against live knowledge_base.team_runs
  (586 rows × 13 cols, 6 batches, 196ms end-to-end)

PRD realignment (2026-04-16)
- Dual use case: staffing analytics + local LLM knowledge substrate
- Removed "multi-tenancy (single-owner system)" from non-goals
- Added invariants 8-11: indexes hot-swappable, per-reader profiles,
  trials-as-data, operational failures findable in one HTTP call
- New phases 16 (hot-swap generations), 17 (model profiles + dataset
  bindings), 18 (Lance vs Parquet+sidecar evaluation)
- Known ceilings table documents the 5M vector wall and escape hatches
- ADR-017 (federation), ADR-018 (append-log pattern) added
- EXECUTION_PLAN.md sequences phases B-E with success gates and
  decision rules

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-16 01:50:05 -05:00
root
04770c97eb HNSW vector index: 100K search in 27ms (58x faster than brute-force) 
- instant-distance HNSW implementation for approximate nearest neighbors
- HnswStore: build from stored embeddings, in-memory index, thread-safe
- POST /vectors/hnsw/build — build index from Parquet (100K in 35s release)
- POST /vectors/hnsw/search — fast ANN search
- GET /vectors/hnsw/list — list loaded indexes

Benchmark (100K × 768d, release build):
  Brute-force: 1,567ms
  HNSW:           31ms (50x)
  HNSW warm:      27ms (58x)

Build cost: 35s one-time for 100K vectors (release mode)
ef_construction=40, ef_search=50 — good recall/speed balance

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-03-27 20:00:50 -05:00
root
6cd1daeb51 Phase 11: Embedding versioning — model-proof vector layer 
- IndexRegistry: tracks all vector indexes with model metadata
  (model_name, model_version, dimensions, build stats)
- Index metadata persisted as JSON in vectors/meta/
- Rebuilt on startup for crash recovery
- GET /vectors/indexes — list all indexes (filter by source/model)
- GET /vectors/indexes/{name} — get index metadata
- Background jobs auto-register metadata on completion
- Multi-version support: same data, different models, coexist
- Per ADR-014: enables incremental re-embed on model upgrade

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-03-27 09:27:10 -05:00
root
3b695cd592 Dual-pipeline supervisor for embedding ingestion 
- 4 parallel pipelines (tuned for i9 + A4000)
- Range-based work splitting (2500 chunks per range)
- Round-robin retry on failure (3 attempts before dead-letter)
- Checkpointing to disk every 1000 chunks (crash recovery)
- On restart, loads checkpoint and skips completed ranges
- Dead-letter queue for permanently failed ranges
- Vectors assembled in order after all pipelines finish
- Batch size 64 for GPU throughput

Architecture:
  Supervisor → splits 100K chunks into 40 ranges
    ├── Pipeline 0: grabs range, embeds, reports progress
    ├── Pipeline 1: grabs range, embeds, reports progress
    ├── Pipeline 2: grabs range, embeds, reports progress
    └── Pipeline 3: grabs range, embeds, reports progress
    Failed range → back to queue → next available pipeline retries

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-03-27 09:06:28 -05:00
root
6a532cb248 Background job system for embedding — fixes 100K timeout 
- JobTracker: create/update/complete/fail jobs with progress tracking
- POST /vectors/index now returns immediately with job_id (HTTP 202)
- Embedding runs in tokio::spawn background task
- GET /vectors/jobs/{id} returns live progress (chunks embedded, rate, ETA)
- GET /vectors/jobs lists all jobs
- Progress logged every 100 batches with chunks/sec and ETA
- 100K embedding job running successfully at 44 chunks/sec
- System stays responsive during embedding (queries in 23ms)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-03-27 09:03:07 -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