Construcción de una API de preguntas y respuestas de documentos con PDF vectoriales y Webhooks
Este es unInternal Wiki, AI RAG, Multimodal AIflujo de automatización del dominio deautomatización que contiene 11 nodos.Utiliza principalmente nodos como If, Code, Webhook, PdfVector, RespondToWebhook. Construir un API de preguntas y respuestas de documentos usando PDF vectors y Webhooks
- •Punto final de HTTP Webhook (n8n generará automáticamente)
Nodos utilizados (11)
Categoría
{
"meta": {
"instanceId": "placeholder"
},
"nodes": [
{
"id": "overview-note",
"name": "API Resumen General",
"type": "n8n-nodes-base.stickyNote",
"position": [
50,
50
],
"parameters": {
"color": 5,
"width": 350,
"height": 160,
"content": "## 🤖 Document Q&A API\n\nRESTful service for document intelligence:\n• **Webhook** endpoint accepts documents\n• **AI processes** questions in context\n• **Returns** JSON with answers & citations\n• **Sub-second** response times"
},
"typeVersion": 1
},
{
"id": "request-note",
"name": "Formato de Solicitud",
"type": "n8n-nodes-base.stickyNote",
"position": [
450,
450
],
"parameters": {
"width": 280,
"height": 180,
"content": "## 📥 API Request\n\n**POST** to `/document-qa`\n\nBody:\n```json\n{\n \"question\": \"Your question\",\n \"maxTokens\": 500,\n \"file\": <binary>\n}\n```"
},
"typeVersion": 1
},
{
"id": "process-note",
"name": "Procesamiento de Preguntas y Respuestas",
"type": "n8n-nodes-base.stickyNote",
"position": [
850,
450
],
"parameters": {
"width": 260,
"height": 160,
"content": "## 🔍 AI Processing\n\nPDF Vector:\n• Parses document\n• Finds relevant sections\n• Generates answer\n• Includes citations\n\n💡 GPT-4 powered"
},
"typeVersion": 1
},
{
"id": "response-note",
"name": "Formato de Respuesta",
"type": "n8n-nodes-base.stickyNote",
"position": [
1150,
450
],
"parameters": {
"color": 6,
"width": 260,
"height": 180,
"content": "## 📤 API Response\n\n```json\n{\n \"success\": true,\n \"answer\": \"...\",\n \"sources\": [...],\n \"confidence\": 0.95\n}\n```\n\n✅ Production ready!"
},
"typeVersion": 1
},
{
"id": "webhook-trigger",
"name": "Webhook",
"type": "n8n-nodes-base.webhook",
"notes": "API endpoint for document Q&A",
"position": [
250,
300
],
"webhookId": "doc-qa-webhook",
"parameters": {
"path": "doc-qa",
"httpMethod": "POST"
},
"typeVersion": 1
},
{
"id": "validate-request",
"name": "Validar Solicitud",
"type": "n8n-nodes-base.code",
"notes": "Validate and prepare request",
"position": [
450,
300
],
"parameters": {
"jsCode": "// Validate incoming request\nconst body = $input.first().json.body;\nconst errors = [];\n\nif (!body.documentUrl && !body.documentId) {\n errors.push('Either documentUrl or documentId is required');\n}\nif (!body.question) {\n errors.push('Question is required');\n}\n\n// Generate session ID if not provided\nconst sessionId = body.sessionId || `session-${Date.now()}`;\n\nreturn [{\n json: {\n ...body,\n sessionId,\n valid: errors.length === 0,\n errors,\n timestamp: new Date().toISOString()\n }\n}];"
},
"typeVersion": 2
},
{
"id": "check-valid",
"name": "¿Solicitud Válida?",
"type": "n8n-nodes-base.if",
"position": [
650,
300
],
"parameters": {
"conditions": {
"boolean": [
{
"value1": "={{ $json.valid }}",
"value2": true
}
]
}
},
"typeVersion": 1
},
{
"id": "pdfvector-ask",
"name": "PDF Vector - Hacer Pregunta",
"type": "n8n-nodes-pdfvector.pdfVector",
"notes": "Get answer from document",
"position": [
850,
250
],
"parameters": {
"url": "={{ $json.documentUrl }}",
"prompt": "Answer the following question about this document or image: {{ $json.question }}",
"resource": "document",
"inputType": "url",
"operation": "ask"
},
"typeVersion": 1
},
{
"id": "format-success",
"name": "Formatear Respuesta Exitosa",
"type": "n8n-nodes-base.code",
"notes": "Prepare successful response",
"position": [
1050,
250
],
"parameters": {
"jsCode": "// Prepare successful response\nconst answer = $json.answer;\nconst request = $node['Validate Request'].json;\n\n// Calculate confidence score based on answer length and keywords\nlet confidence = 0.8; // Base confidence\nif (answer.length > 100) confidence += 0.1;\nif (answer.toLowerCase().includes('specifically') || answer.toLowerCase().includes('according to')) confidence += 0.1;\nconfidence = Math.min(confidence, 1.0);\n\nreturn [{\n json: {\n success: true,\n data: {\n answer,\n confidence,\n sessionId: request.sessionId,\n documentUrl: request.documentUrl,\n question: request.question\n },\n metadata: {\n processedAt: new Date().toISOString(),\n responseTime: Date.now() - new Date(request.timestamp).getTime(),\n creditsUsed: 1\n }\n }\n}];"
},
"typeVersion": 2
},
{
"id": "format-error",
"name": "Formatear Respuesta de Error",
"type": "n8n-nodes-base.code",
"notes": "Prepare error response",
"position": [
850,
350
],
"parameters": {
"jsCode": "// Prepare error response\nconst errors = $json.errors || ['An error occurred processing your request'];\n\nreturn [{\n json: {\n success: false,\n errors,\n message: 'Invalid request',\n timestamp: new Date().toISOString()\n }\n}];"
},
"typeVersion": 2
},
{
"id": "webhook-response",
"name": "Enviar Respuesta",
"type": "n8n-nodes-base.respondToWebhook",
"notes": "Send API response",
"position": [
1250,
300
],
"parameters": {
"respondWith": "json",
"responseBody": "={{ JSON.stringify($json) }}",
"responseHeaders": {
"entries": [
{
"name": "Content-Type",
"value": "application/json"
}
]
}
},
"typeVersion": 1
}
],
"connections": {
"webhook-trigger": {
"main": [
[
{
"node": "validate-request",
"type": "main",
"index": 0
}
]
]
},
"check-valid": {
"main": [
[
{
"node": "pdfvector-ask",
"type": "main",
"index": 0
}
],
[
{
"node": "format-error",
"type": "main",
"index": 0
}
]
]
},
"validate-request": {
"main": [
[
{
"node": "check-valid",
"type": "main",
"index": 0
}
]
]
},
"format-error": {
"main": [
[
{
"node": "webhook-response",
"type": "main",
"index": 0
}
]
]
},
"format-success": {
"main": [
[
{
"node": "webhook-response",
"type": "main",
"index": 0
}
]
]
},
"pdfvector-ask": {
"main": [
[
{
"node": "format-success",
"type": "main",
"index": 0
}
]
]
}
}
}¿Cómo usar este flujo de trabajo?
Copie el código de configuración JSON de arriba, cree un nuevo flujo de trabajo en su instancia de n8n y seleccione "Importar desde JSON", pegue la configuración y luego modifique la configuración de credenciales según sea necesario.
¿En qué escenarios es adecuado este flujo de trabajo?
Intermedio - Wiki interno, RAG de IA, IA Multimodal
¿Es de pago?
Este flujo de trabajo es completamente gratuito, puede importarlo y usarlo directamente. Sin embargo, tenga en cuenta que los servicios de terceros utilizados en el flujo de trabajo (como la API de OpenAI) pueden requerir un pago por su cuenta.
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