Experimental/brain-of-reese
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fc0d9a2d5cababc59f24ac8b2c82de7097b7bfa5
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5 Commits
Author SHA1 Message Date
ducoterra fc0d9a2d5c feat(rag): steering notes — tune how Brain answers, stored in Postgres and injected into every system prompt 2026-08-22 16:44:42 -04:00
ducoterra 0da5275eeb fix(rag): lift chat output cap to 32768 tokens — long answers no longer cut off 2026-08-22 11:30:19 -04:00
ducoterra 7e8d14702e feat(rag): hybrid FTS+vector retrieval and multi-format ingestion — name-your-tool questions find the right document 2026-08-22 01:27:02 -04:00
ducoterra 2364e1ee7d feat(ui): onboarding suggestion chips with one-tap submit, keyboard access, and mobile scroll row 2026-08-21 18:18:47 -04:00
ducoterra 022da8e2bc feat: scaffold Brain of Reese — FastAPI RAG chat over Postgres 17 + pgvector 
Foundation (phase 01, verified):
- FastAPI app: /api/health, /api/suggestions, /api/chat (placeholder),
  static frontend served locally (no CDN)
- Postgres 17 + pgvector via db/Containerfile + compose.yaml
  (podman compose up -d db), Alembic initial migration (documents,
  chunks with vector(768), query_log)
- LLM client targeting https://aipi.reeseapps.com/v1 (turbo/embed);
  scripts/llm_probe.py verified models + 768-dim embeddings live
- Conditional debugpy: imported only when DEBUGPY=1 (attach on demand,
  :5678); logging config for clean single-line logs
- Frontend shell: mobile-first chat + Sources pages, tokens, a11y baselines
- Tests: 24 unit+integration (99% coverage on app/), ruff + pyright clean,
  Playwright smoke E2E (3 tests) against a deterministic mock LLM
- Planning: .agent/PLAN.md (architecture + LOCKED decisions), AGENTS.md,
  6 user stories, 7 phase files (one story / one phase / one Playwright
  suite each)
 2026-08-21 13:42:21 -04:00