2 Commits

Author	SHA1	Message	Date
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