Claude
a1e14c64c3
- Q1 → Ontotext GraphDB (SPARQL). docs/08 fully rewritten with RDF schema, example SPARQL queries (transitive deps via property paths, time-windowed topic counts, cross-pillar joins via nexa:vectorId). - Q2 → reuse qdrant_scientific with nexa_* collection prefix; docs/09 step 2 now points there explicitly. - Q3 → no OpenAI embeddings. Self-host on the docker host. Use TEI (HuggingFace text-embeddings-inference) — single Rust binary, ~500 MB image, OpenAI-compatible — instead of Ollama, since we only need embeddings. - docs/09 Phase-3.4 add-on simplified to a single Ontotext compose snippet (Neo4j option dropped) plus repo creation curl. - docs/11 Q3 marked resolved; new Q15 picks the model (bge-m3 vs nomic-embed) and adds the open question of whether SAIA already proxies an embedding model that would let us skip TEI entirely. - docs/03 + CLAUDE.md updated with the new decisions so future runs don't re-litigate.
65 lines
4.0 KiB
Markdown
65 lines
4.0 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 → `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 existing `qdrant_scientific` with `nexa_*` collection prefix |
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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-graph-db-phase-34) |
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| **TEI** (HF text-embeddings-inference) | Self-hosted embeddings (`bge-m3` / TBD — see [11/Q15](./11-open-questions.md)) | docker host LXC 104, CPU only — single-binary embedding server |
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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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