n8n-MCP

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