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34 行
2.7 KiB
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34 行
2.7 KiB
Markdown
# AI primitives
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This spec describes the AI primitives in Joplin. They are not features themselves; they are the foundation features and plugins are built on. Detailed specs for each primitive live in their own documents — links below.
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## Target use cases
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The primitives are designed against five concrete features. None of them are core Joplin features by themselves; they exist as the bar the primitives have to clear.
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- **Chat with your note** — a sidebar that can summarise, rewrite, or answer questions about the current note.
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- **Chat with your note collection** — ask a question across all notes and get a cited answer.
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- **AI-based note categorisation and tagging** — background analysis that suggests tags and notebook structures based on note content.
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- **AI-generated note graphs** — surfacing semantically related but unlinked notes.
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- **Fuzzy semantic search** — finding notes by meaning rather than exact terms (e.g. "the note about pet sitters for my dog").
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Each primitive serves at least two of these. Features build on top of these primitives, never alongside them.
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## The primitives
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1. **Provider abstraction** — pluggable layer for LLM and embedding models, so users can mix cloud, self-hosted, and on-device providers. See [ai_chat.md](ai_chat.md).
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2. **Local embeddings index** — background-indexed semantic store, local-only, not synced. See [ai_embeddings.md](ai_embeddings.md).
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3. **Retrieval helpers** — the shared query surface plugins call (`joplin.ai.search()`). Covered in [ai_embeddings.md](ai_embeddings.md).
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4. **Privacy & cost guardrails** — enforced at the provider layer so every feature inherits them. Covered in [ai_chat.md](ai_chat.md).
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5. **MCP server** — exposes Joplin notes to external AI tools. See [ai_mcp.md](ai_mcp.md).
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## Design principles
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A few rules carried through every primitive:
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- **Off by default.** No AI capability is reachable without an explicit user opt-in, and remote providers need a second opt-in on top.
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- **Plugins don't choose the provider.** Plugins write against `joplin.ai.chat()` and `joplin.ai.search()`; the user picks the active provider in settings. A plugin written against one provider works against all of them.
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- **Local first.** The bundled embedding model is local; remote chat providers are optional. Users on small profiles (CLI, mobile) get clean degradation rather than broken features.
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- **No conversation state in core.** Chat history, system prompts, retrieval orchestration, and UI all live in the feature (plugin or external app), not in the primitive. The primitives are stateless functions.
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- **MCP is a peer, not a client.** The MCP server exposes Joplin to external AI apps as a tool surface. Joplin itself is not an MCP client and does not host conversations.
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