Claude
12ed4426e0
- LXC 104 (docker) confirmed: 16 CPU, 31.25 GiB RAM (7.86 used / 25%), 200 GiB boot disk at 47.7% — disk is the real near-term constraint, not RAM. Numbers propagated into Q11 (docs/11), docs/03 architecture table, and CLAUDE.md infra block. - New optimization #26 in docs/12: fix the Intel Arc iGPU passthrough flagged as broken in the LXC notes. Concrete /etc/pve/lxc/104.conf snippet provided. Once working, TEI / infinity / Immich CLIP can run GPU-accelerated for ~5–10x throughput. - New optimization #27: disk-pressure mitigation playbook (move Qdrant to unas if it grows, prune docker layers monthly, cap n8n executions). - New optimization #28: shrink the LXC's 8 GiB swap to 2 GiB.
65 lines
4.1 KiB
Markdown
65 lines
4.1 KiB
Markdown
# 03 — Architecture Overview
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A one-page mental model. For details follow the cross-links.
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## Topology
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```
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┌────────────────┐
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voice / typing ──────▶│ Memos │◀──── Nexa replies as comments
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│ (interface) │
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└───────┬────────┘
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│ webhook (- [ ] / #nexa:*)
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▼
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┌────────────────┐
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IMAP / RSS / NC ────▶│ n8n │◀──── workflows live in
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Karakeep / Bluesky │ (logic) │ ./nexa-core/n8n-workflows
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└───┬────────┬───┘
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classify ▲ │ │ ▲ retrieve
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│ ▼ ▼ │
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┌──────────┐ ┌──────────┐
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│ SAIA │ │ Qdrant │
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│ LiteLLM │ │ vector │
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└──────────┘ └────┬─────┘
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│
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▼
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┌──────────┐
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│ GraphDB │ Ontotext, SPARQL
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│ (RDF) │ Phase 3.4
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└──────────┘
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│
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▼ writes
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┌──────────────────────────────┐
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│ Nextcloud (Tasks, Calendar, │
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│ Mail, Files / Obsidian) │
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└──────────────────────────────┘
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```
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## Components
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| Component | Role | Where it runs (today) |
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|-----------|------|-----------------------|
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| **Memos** | Interface, voice input, webhook source | docker host LXC 104 (unprivileged, 16 CPU / 31 GiB / 200 GiB) → `memos.nuclide.systems` |
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| **n8n** | Workflow / logic engine | docker host LXC 104 → `n8n.nuclide.systems` |
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| **SAIA / LiteLLM** | Model gateway, embeddings, classification | docker host LXC 104 → `ai.nuclide.systems` (LiteLLM internal :4000) |
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| **Qdrant** | Vector memory (semantic recall) | docker host LXC 104 — reuse `qdrant_scientific`. Collections: `nexa_knowledge_text` (Phase 3.1, 1024-dim) and `nexa_knowledge_visual` (Phase 3.2, 768-dim) |
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| **Ontotext GraphDB** | Structural memory via SPARQL (Phase 3.4) | not yet deployed; see [09-deployment](./09-deployment.md#phase-add-on-ontotext-graphdb-phase-34) |
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| **TEI** (HF text-embeddings-inference) | Self-hosted text embeddings, `BAAI/bge-m3`, Phase 3.1 | docker host LXC 104, CPU only — swapped for `infinity` in Phase 3.2 to add `jina-clip-v2` |
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| **Nextcloud** | Tasks, calendar, mail, files | dedicated LXC 105 → `nc.nuclide.systems` |
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| **ntfy** | Push channel for system alerts | docker host → `ntfy.nuclide.systems` |
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| **Backrest** | Backup orchestration | LXC 103 |
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| **Zoraxy** | Reverse proxy + TLS | LXC 108 (`192.168.1.4:8000`) |
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| **AdGuard DNS** | Internal name resolution | LXC 102 |
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| **Home Assistant** | Voice + house automation | VM 100 (HAOS) |
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## Two-pillar memory
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- **Qdrant** answers *"what is similar / relevant?"* (cosine search over embeddings).
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- **Ontotext GraphDB** answers *"who, what depends on what, how is it structured?"* (SPARQL over RDF).
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Both pillars are queried in parallel for `#nexa:ask` and merged before SAIA generates the final answer. See [08 — GraphRAG architecture](./08-graphrag-architecture.md).
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## Dual-context routing
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Every input is classified `work` or `personal` before any side effect (task creation, calendar write). See [04 — Integration matrix](./04-integration-matrix.md) and [06 — Classification logic](./06-classification-logic.md).
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