Claude
17c7f5033f
Decision (Q15 resolved): start with text-only via TEI + bge-m3 in Phase 3.1, prepare data shapes so Phase 3.2 (visual collection via infinity + jina-clip-v2) is a pure additive operation — no rename, no schema migration, no n8n rewiring. Concretely: - Qdrant collection renamed nexa_knowledge → nexa_knowledge_text (1024-dim for bge-m3) with modality-aware payload (modality, source_type, media_uri, graph_iri, content_hash, context). Visual placeholder schema committed alongside (qdrant_schema_visual.json, 768-dim, jina-clip-v2). - Image attachments captured in 3.1 are recorded in GraphDB as nexa:Note with nexa:modality "image" + nexa:pendingVisualIndex true; the 3.2 backfill workflow picks them up and embeds. No data lost between phases — the queue is the GraphDB itself. - RDF schema (docs/08) gains nexa:modality, nexa:mediaUri, nexa:vectorCollection, nexa:pendingVisualIndex from day one. - docs/02 roadmap split: 3.1 = text RAG (Path A), 3.2 = visual collection (Path C), 3.4 = Ontotext GraphDB. - docs/09 grows a "Phase add-on: visual collection (Phase 3.2)" section with the TEI→infinity swap, second collection create, LiteLLM second model registration, and the SPARQL-driven backfill query. - New open questions: Q16 (queue ergonomics + does SAIA already proxy an embed model?), Q17 (reuse Immich's CLIP for photo-library queries?). - docs/03 + CLAUDE.md updated so future runs use the new collection names and don't re-decide the staging.
56 lines
5.2 KiB
Markdown
56 lines
5.2 KiB
Markdown
# 11 — Open Questions (user-info-required)
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Items that **block progress** and need a human decision before a workflow can be implemented or a service deployed. Tick them off as you decide.
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## Resolved
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- [x] **Q1 — Graph DB choice → Ontotext GraphDB (SPARQL).** Rationale: explore Nexa's memory through SPARQL is a stated goal. [08-graphrag-architecture](./08-graphrag-architecture.md) is rewritten accordingly.
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- [x] **Q2 — Vector store → reuse `qdrant_scientific`** with a `nexa_*` collection prefix. No dedicated container.
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- [x] **Q3 — Embeddings model → not OpenAI.** Self-host on the docker host via **TEI** (HF `text-embeddings-inference`) — Rust single-binary, OpenAI-compatible, ~500 MB image, no LLM runtime overhead. Speed analysis in §"Speed budget" below.
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- [x] **Q15 — Embeddings staging plan → A now, C prepared.**
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- **Phase 3.1 (now)**: TEI + `BAAI/bge-m3`, single collection `nexa_knowledge_text` (1024-dim). DE/EN multilingual, fits the corpus.
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- **Phase 3.2 (later)**: swap TEI → `infinity`, add `jinaai/jina-clip-v2` (768-dim), second collection `nexa_knowledge_visual`. Backfill from the queue (see Q16).
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- All schema fields needed for 3.2 (`modality`, `media_uri`, `graph_iri`, `nexa:pendingVisualIndex`) are introduced **now** so 3.2 is purely additive — no rename, no migration. See [`qdrant_schema.json`](../nexa-core/config/qdrant_schema.json) and [`qdrant_schema_visual.json`](../nexa-core/config/qdrant_schema_visual.json).
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## Architectural decisions
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- [ ] **Q4 — Obsidian sync mechanism.** `system_prime.txt` references Obsidian Context, but the current setup syncs via Nextcloud (`nc.nuclide.systems` → `Notizen` folder, ~200 MB). Should Nexa watch the **filesystem on LXC 105** (NC data dir) or **the Nextcloud WebDAV API**? FS is cheaper, WebDAV is portable.
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- [ ] **Q5 — Karakeep vs. Hoarder naming.** Zoraxy host is `hoarder.nuclide.systems` but containers are `karakeep-*` and Homepage labels it Karakeep. Same product (rename 2024). Pick one display name for docs and prompts.
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- [ ] **Q16 — Image-attachment queue ergonomics (Phase 3.2).** The agreed plan is: in 3.1 we record images as `nexa:Note` with `nexa:pendingVisualIndex true` but don't embed them. Open sub-questions for when 3.2 lands:
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- Where to store image bytes between capture and indexing? Three options: (a) leave them in their source (Memos attachments dir / Nextcloud / Obsidian), reference by `media_uri`; (b) copy to a staging area on the docker host; (c) push to `s3.nuclide.systems` immediately. **Recommendation: (a)** — zero copy, smallest blast radius.
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- Does SAIA already proxy any embedding model? If the SAIA backend offers e.g. `mistral-embed` we could simplify 3.1 by skipping TEI. Worth a 1-line check in the LiteLLM admin UI.
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- [ ] **Q17 — Reuse Immich's CLIP for photo-library queries?** Immich already runs CLIP server-side on the photo library. For images that live *in Immich*, querying its smart-search API is cheaper than re-embedding. Is the Immich API key OK to add to the n8n workflow, or do we treat Immich as out-of-band?
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## Identifiers needed (auto-discoverable, but list now if known)
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- [ ] **Q6 — Nextcloud Tasks list IDs** for: `Work_Tasks`, `Personal_Tasks`, `Shopping`, `Wishes`. Discovery via `#nexa:config` will fill these — confirm names match.
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- [ ] **Q7 — Nextcloud Calendar IDs** for: `Work_Calendar`, primary personal calendar.
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- [ ] **Q8 — IMAP credentials for the personal mail account.** Can n8n reuse a Nextcloud Mail account (preferred — no extra password) or must we add a dedicated IMAP entry?
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- [ ] **Q9 — ntfy topic name** for `nexa.system`. Is the topic public on `ntfy.nuclide.systems` or should it be authenticated?
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- [ ] **Q10 — Pocket-ID role.** `id.nuclide.systems` is running. Do we want SSO in front of the n8n / Memos UIs, or skip for now?
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## Hardware / capacity
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- [ ] **Q11 — RAM headroom on docker host.** 29.5 GiB free / ~31 GiB total, ~8.5 GB used. Phase-3 Qdrant indexing + TEI/`bge-m3` (~1.1 GB) + Ontotext GraphDB (~4 GB heap) ⇒ ~14 GB used worst case, still ample. Confirm acceptable.
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- [ ] **Q12 — S3 archive bucket.** `s3.nuclide.systems` is up. Bucket name + access key for Qdrant snapshots and GraphDB exports?
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## Process
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- [ ] **Q13 — Octoprint container is Exited** (Homepage). Out of scope for Nexa, but Phase-5 monitoring would alert on it. Suppress or is it intentional?
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- [ ] **Q14 — `Missing Widget Type: zoraxy`** on Homepage. Cosmetic, unrelated to Nexa.
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---
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## Speed budget (Q3 follow-up)
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Workload on the docker host (16 CPU, ~30 GB free RAM):
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| Task | Volume | Latency target | Achievable on CPU with `bge-m3` | Achievable with `nomic-embed-text` |
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|------|--------|----------------|---------------------------------|------------------------------------|
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| Real-time memo embed | 1 doc | <500 ms incl. n8n round-trip | ✅ ~50–100 ms | ✅ ~20 ms |
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| Daily ingest | ~70 docs | <60 s | ✅ ~5–10 s | ✅ ~2 s |
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| Obsidian backfill (one-shot) | ~2 000 docs | <15 min | ✅ ~2–4 min | ✅ <1 min |
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| RAG query embed (`#nexa:ask`) | 1 doc | <300 ms | ✅ ~50 ms | ✅ ~20 ms |
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Conclusion: CPU-only Ollama is sufficient — no GPU needed for current scope. Bottleneck is SAIA chat (already remote), not embeddings.
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