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https://github.com/Memo-2023/mana-monorepo.git
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56065c8537
Adding an app to a workbench scene threw DataCloneError. scenesState is a $state array, so current.openApps was a Svelte 5 proxy and spreading it into a new array left proxy entries inside; IndexedDB's structured clone refuses to serialise those. Snapshot before handing the array to patchScene / createScene so Dexie sees plain objects. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
82 lines
2.9 KiB
TypeScript
82 lines
2.9 KiB
TypeScript
/**
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* The LlmTask contract — the unit of work modules describe to the
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* orchestrator. Tasks bundle:
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*
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* 1. The LLM-side implementation (used for browser/server/cloud tiers)
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* 2. An optional rules-tier fallback (used when the LLM tier is
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* unavailable, fails, or the user has opted out of all LLM tiers)
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* 3. Routing metadata (minimum tier, content class, capability needs)
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*
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* Tasks live next to the modules that use them — there is intentionally
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* no central task registry. The convention is:
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*
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* apps/mana/apps/web/src/lib/llm-tasks/ ← cross-module helpers
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* apps/mana/apps/web/src/lib/modules/notes/llm-tasks/ ← notes-specific
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*
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* The orchestrator never imports tasks directly — modules import tasks
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* AND the orchestrator and call `orchestrator.run(task, input)`.
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*/
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import type { LlmTier } from './tiers';
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import type { ContentClass, CapabilityRequirements, LlmBackend, LlmTaskRequest } from './types';
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export interface LlmTask<TInput, TOutput> {
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/**
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* Stable identifier for this task. Used for telemetry, per-task
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* tier overrides in user settings, and debug logs. Convention is
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* `{module}.{action}` — e.g. `notes.extractTags`, `todo.parseQuickAdd`.
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*/
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readonly name: string;
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/** Lowest tier this task can produce a useful result on. */
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readonly minTier: LlmTier;
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/** Privacy class of inputs this task handles. */
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readonly contentClass: ContentClass;
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/** Capability requirements that exclude tiers/backends that can't satisfy them. */
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readonly requires?: CapabilityRequirements;
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/**
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* User-facing label, shown when telling the user "this task needs
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* AI" or "this result was computed via tier X".
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*/
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readonly displayLabel: string;
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/**
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* The LLM-based implementation. Builds an LlmTaskRequest from the
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* task input and asks the backend to run it, then maps the
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* generated text back into the typed TOutput shape (e.g. parses
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* JSON, validates a date, looks up a tag).
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*/
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runLlm(input: TInput, backend: LlmBackend): Promise<TOutput>;
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/**
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* Optional deterministic fallback — runs when no LLM tier is
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* available, or when the LLM tier failed and
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* `fallbackToRulesOnError` is enabled in user settings.
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*
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* Returning the typed TOutput indicates success. Throwing means
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* the rules implementation also can't handle this input — the
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* orchestrator will then surface a NoTierAvailableError so the
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* UI can ask the user for direct input.
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*/
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runRules?(input: TInput): Promise<TOutput>;
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}
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/**
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* Helper for tasks that need to construct an LlmTaskRequest from their
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* own input. Centralizes the boilerplate so individual tasks don't have
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* to redeclare taskName / contentClass / requires every time.
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*/
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export function buildTaskRequest<TInput, TOutput>(
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task: LlmTask<TInput, TOutput>,
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overrides: Omit<LlmTaskRequest, 'taskName' | 'contentClass' | 'requires'>
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): LlmTaskRequest {
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return {
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...overrides,
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taskName: task.name,
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contentClass: task.contentClass,
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requires: task.requires,
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};
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}
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