9e3a7206b9
- Add @bytelyst/llm dependency (file: ref) + llm.ts singleton wrapper - Add LLM env vars to config (LLM_PROVIDER, LLM_DEFAULT_MODEL, LLM_VISION_MODEL, LLM_EMBEDDING_MODEL) - Create note-prompts module: types, repository, runner, routes, seed (20 built-in templates) - Built-in templates: 8 transform, 3 extract, 3 generate, 2 analyze, 2 vision, 2 export - Prompt runner supports text, image, and text+image inputs via @bytelyst/llm vision - Upgrade copilot-transform.ts to use @bytelyst/llm directly (with local heuristic fallback) - Add reading-time endpoint (GET /api/notes/:id/reading-time) - Extend agent-action types with smart_action and auto_enrich - Add note_prompts Cosmos container to cosmos-init - Register notePromptRoutes in server.ts - 15 new tests (CRUD, run, slug resolution, seed validation, reading-time)
76 lines
2.1 KiB
TypeScript
76 lines
2.1 KiB
TypeScript
/**
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* Prompt runner — executes a PromptTemplate against note content via @bytelyst/llm.
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*/
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import { llm } from '../../lib/llm.js';
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import { config } from '../../lib/config.js';
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import {
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buildVisionMessage,
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hasVisionContent,
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type ChatMessage,
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} from '@bytelyst/llm';
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import type { PromptTemplateDoc, RunPromptInput, RunPromptOutput } from './types.js';
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/**
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* Interpolate {{variable}} placeholders in a template string.
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*/
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function interpolate(template: string, vars: Record<string, string>): string {
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return template.replace(/\{\{(\w+)\}\}/g, (_, key) => vars[key] ?? `{{${key}}}`);
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}
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/**
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* Run a prompt template against provided input.
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* Handles text-only, image-only, and text+image inputs.
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*/
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export async function executePrompt(
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template: PromptTemplateDoc,
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input: RunPromptInput,
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noteBody: string,
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): Promise<RunPromptOutput> {
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const provider = llm();
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// Build variables map
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const vars: Record<string, string> = {
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...input.variables,
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noteBody,
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noteId: input.noteId,
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workspaceId: input.workspaceId,
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};
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if (input.inputText) vars.inputText = input.inputText;
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const userPrompt = interpolate(template.userPromptTemplate, vars);
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// Build messages
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const messages: ChatMessage[] = [
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{ role: 'system', content: template.systemPrompt },
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];
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if (input.imageUrl && (template.inputType === 'image' || template.inputType === 'text+image')) {
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messages.push(buildVisionMessage(userPrompt, input.imageUrl));
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} else {
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messages.push({ role: 'user', content: userPrompt });
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}
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// Select model: vision model for image content, custom model, or default
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const req = { messages };
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let model = template.model || config.LLM_DEFAULT_MODEL;
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if (hasVisionContent(req)) {
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model = config.LLM_VISION_MODEL;
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}
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const result = await provider.chatCompletion({
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messages,
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model,
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temperature: template.temperature ?? 0.7,
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maxTokens: template.maxTokens ?? 4096,
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});
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return {
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content: result.content,
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model: result.model,
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usage: result.usage,
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templateSlug: template.slug,
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outputType: template.outputType,
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};
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}
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