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chore: add pre-built dist folder for npx usage
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Romuald Członkowski
Romuald Członkowski
parent
a70d96a373
commit
5057481e70
252
dist/templates/metadata-generator.js
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252
dist/templates/metadata-generator.js
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"use strict";
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var __importDefault = (this && this.__importDefault) || function (mod) {
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return (mod && mod.__esModule) ? mod : { "default": mod };
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};
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Object.defineProperty(exports, "__esModule", { value: true });
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exports.MetadataGenerator = exports.TemplateMetadataSchema = void 0;
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const openai_1 = __importDefault(require("openai"));
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const zod_1 = require("zod");
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const logger_1 = require("../utils/logger");
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exports.TemplateMetadataSchema = zod_1.z.object({
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categories: zod_1.z.array(zod_1.z.string()).max(5).describe('Main categories (max 5)'),
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complexity: zod_1.z.enum(['simple', 'medium', 'complex']).describe('Implementation complexity'),
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use_cases: zod_1.z.array(zod_1.z.string()).max(5).describe('Primary use cases'),
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estimated_setup_minutes: zod_1.z.number().min(5).max(480).describe('Setup time in minutes'),
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required_services: zod_1.z.array(zod_1.z.string()).describe('External services needed'),
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key_features: zod_1.z.array(zod_1.z.string()).max(5).describe('Main capabilities'),
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target_audience: zod_1.z.array(zod_1.z.string()).max(3).describe('Target users')
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});
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class MetadataGenerator {
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constructor(apiKey, model = 'gpt-5-mini-2025-08-07') {
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this.client = new openai_1.default({ apiKey });
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this.model = model;
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}
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getJsonSchema() {
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return {
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name: 'template_metadata',
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strict: true,
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schema: {
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type: 'object',
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properties: {
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categories: {
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type: 'array',
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items: { type: 'string' },
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maxItems: 5,
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description: 'Main categories like automation, integration, data processing'
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},
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complexity: {
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type: 'string',
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enum: ['simple', 'medium', 'complex'],
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description: 'Implementation complexity level'
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},
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use_cases: {
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type: 'array',
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items: { type: 'string' },
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maxItems: 5,
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description: 'Primary use cases for this template'
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},
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estimated_setup_minutes: {
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type: 'number',
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minimum: 5,
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maximum: 480,
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description: 'Estimated setup time in minutes'
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},
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required_services: {
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type: 'array',
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items: { type: 'string' },
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description: 'External services or APIs required'
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},
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key_features: {
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type: 'array',
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items: { type: 'string' },
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maxItems: 5,
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description: 'Main capabilities or features'
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},
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target_audience: {
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type: 'array',
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items: { type: 'string' },
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maxItems: 3,
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description: 'Target users like developers, marketers, analysts'
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}
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},
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required: [
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'categories',
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'complexity',
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'use_cases',
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'estimated_setup_minutes',
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'required_services',
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'key_features',
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'target_audience'
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],
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additionalProperties: false
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}
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};
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}
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createBatchRequest(template) {
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const nodesSummary = this.summarizeNodes(template.nodes);
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const sanitizedName = this.sanitizeInput(template.name, Math.max(200, template.name.length));
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const sanitizedDescription = template.description ?
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this.sanitizeInput(template.description, 500) : '';
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const context = [
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`Template: ${sanitizedName}`,
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sanitizedDescription ? `Description: ${sanitizedDescription}` : '',
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`Nodes Used (${template.nodes.length}): ${nodesSummary}`,
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template.workflow ? `Workflow has ${template.workflow.nodes?.length || 0} nodes with ${Object.keys(template.workflow.connections || {}).length} connections` : ''
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].filter(Boolean).join('\n');
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return {
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custom_id: `template-${template.templateId}`,
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method: 'POST',
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url: '/v1/chat/completions',
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body: {
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model: this.model,
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max_completion_tokens: 3000,
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response_format: {
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type: 'json_schema',
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json_schema: this.getJsonSchema()
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},
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messages: [
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{
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role: 'system',
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content: `Analyze n8n workflow templates and extract metadata. Be concise.`
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},
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{
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role: 'user',
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content: context
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}
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]
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}
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};
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}
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sanitizeInput(input, maxLength) {
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let sanitized = input.slice(0, maxLength);
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sanitized = sanitized.replace(/[\x00-\x1F\x7F-\x9F]/g, '');
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sanitized = sanitized.replace(/\s+/g, ' ').trim();
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sanitized = sanitized.replace(/\b(system|assistant|user|human|ai):/gi, '');
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sanitized = sanitized.replace(/```[\s\S]*?```/g, '');
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sanitized = sanitized.replace(/\[INST\]|\[\/INST\]/g, '');
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return sanitized;
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}
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summarizeNodes(nodes) {
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const nodeGroups = {};
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for (const node of nodes) {
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const baseName = node.split('.').pop() || node;
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if (baseName.includes('webhook') || baseName.includes('http')) {
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nodeGroups['HTTP/Webhooks'] = (nodeGroups['HTTP/Webhooks'] || 0) + 1;
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}
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else if (baseName.includes('database') || baseName.includes('postgres') || baseName.includes('mysql')) {
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nodeGroups['Database'] = (nodeGroups['Database'] || 0) + 1;
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}
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else if (baseName.includes('slack') || baseName.includes('email') || baseName.includes('gmail')) {
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nodeGroups['Communication'] = (nodeGroups['Communication'] || 0) + 1;
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}
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else if (baseName.includes('ai') || baseName.includes('openai') || baseName.includes('langchain') ||
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baseName.toLowerCase().includes('openai') || baseName.includes('agent')) {
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nodeGroups['AI/ML'] = (nodeGroups['AI/ML'] || 0) + 1;
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}
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else if (baseName.includes('sheet') || baseName.includes('csv') || baseName.includes('excel') ||
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baseName.toLowerCase().includes('googlesheets')) {
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nodeGroups['Spreadsheets'] = (nodeGroups['Spreadsheets'] || 0) + 1;
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}
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else {
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let displayName;
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if (node.includes('.with.') && node.includes('@')) {
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displayName = node.split('/').pop() || baseName;
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}
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else {
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if (baseName.endsWith('Trigger') && baseName.length > 7) {
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displayName = baseName.slice(0, -7);
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}
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else if (baseName.endsWith('Node') && baseName.length > 4 && baseName !== 'unknownNode') {
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displayName = baseName.slice(0, -4);
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}
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else {
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displayName = baseName;
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}
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}
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nodeGroups[displayName] = (nodeGroups[displayName] || 0) + 1;
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}
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}
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const summary = Object.entries(nodeGroups)
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.sort((a, b) => b[1] - a[1])
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.slice(0, 10)
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.map(([name, count]) => count > 1 ? `${name} (${count})` : name)
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.join(', ');
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return summary;
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}
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parseResult(result) {
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try {
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if (result.error) {
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return {
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templateId: parseInt(result.custom_id.replace('template-', '')),
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metadata: this.getDefaultMetadata(),
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error: result.error.message
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};
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}
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const response = result.response;
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if (!response?.body?.choices?.[0]?.message?.content) {
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throw new Error('Invalid response structure');
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}
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const content = response.body.choices[0].message.content;
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const metadata = JSON.parse(content);
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const validated = exports.TemplateMetadataSchema.parse(metadata);
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return {
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templateId: parseInt(result.custom_id.replace('template-', '')),
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metadata: validated
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};
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}
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catch (error) {
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logger_1.logger.error(`Error parsing result for ${result.custom_id}:`, error);
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return {
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templateId: parseInt(result.custom_id.replace('template-', '')),
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metadata: this.getDefaultMetadata(),
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error: error instanceof Error ? error.message : 'Unknown error'
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};
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}
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}
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getDefaultMetadata() {
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return {
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categories: ['automation'],
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complexity: 'medium',
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use_cases: ['Process automation'],
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estimated_setup_minutes: 30,
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required_services: [],
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key_features: ['Workflow automation'],
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target_audience: ['developers']
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};
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}
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async generateSingle(template) {
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try {
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const completion = await this.client.chat.completions.create({
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model: this.model,
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max_completion_tokens: 3000,
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response_format: {
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type: 'json_schema',
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json_schema: this.getJsonSchema()
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},
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messages: [
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{
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role: 'system',
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content: `Analyze n8n workflow templates and extract metadata. Be concise.`
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},
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{
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role: 'user',
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content: `Template: ${template.name}\nNodes: ${template.nodes.slice(0, 10).join(', ')}`
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}
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]
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});
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const content = completion.choices[0].message.content;
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if (!content) {
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logger_1.logger.error('No content in OpenAI response');
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throw new Error('No content in response');
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}
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const metadata = JSON.parse(content);
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return exports.TemplateMetadataSchema.parse(metadata);
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}
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catch (error) {
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logger_1.logger.error('Error generating single metadata:', error);
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return this.getDefaultMetadata();
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}
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}
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}
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exports.MetadataGenerator = MetadataGenerator;
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//# sourceMappingURL=metadata-generator.js.map
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