Métricas de evaluación: Similitud de respuestas

Avanzado

Este es unEngineering, AIflujo de automatización del dominio deautomatización que contiene 21 nodos.Utiliza principalmente nodos como Set, Code, Merge, SplitOut, Aggregate, combinando tecnología de inteligencia artificial para lograr automatización inteligente. Métricas de evaluación: Similitud de respuestas

Requisitos previos
  • Pueden requerirse credenciales de autenticación para la API de destino
  • Clave de API de OpenAI
Vista previa del flujo de trabajo
Visualización de las conexiones entre nodos, con soporte para zoom y panorámica
Exportar flujo de trabajo
Copie la siguiente configuración JSON en n8n para importar y usar este flujo de trabajo
{
  "meta": {
    "instanceId": "408f9fb9940c3cb18ffdef0e0150fe342d6e655c3a9fac21f0f644e8bedabcd9"
  },
  "nodes": [
    {
      "id": "c5365531-2d66-4e67-9db2-37059e9e97a3",
      "name": "OpenAI Modelo de Chat1",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "position": [
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        420
      ],
      "parameters": {
        "model": {
          "__rl": true,
          "mode": "list",
          "value": "gpt-4.1-mini",
          "cachedResultName": "gpt-4.1-mini"
        },
        "options": {}
      },
      "credentials": {
        "openAiApi": {
          "id": "8gccIjcuf3gvaoEr",
          "name": "OpenAi account"
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      },
      "typeVersion": 1.2
    },
    {
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      "name": "Al obtener una fila del conjunto de datos",
      "type": "n8n-nodes-base.evaluationTrigger",
      "position": [
        -2040,
        100
      ],
      "parameters": {
        "sheetName": {
          "__rl": true,
          "mode": "list",
          "value": 1176192270,
          "cachedResultUrl": "https://docs.google.com/spreadsheets/d/1YOnu2JJjlxd787AuYcg-wKbkjyjyZFgASYVV0jsij5Y/edit#gid=1176192270",
          "cachedResultName": "Similarity"
        },
        "documentId": {
          "__rl": true,
          "mode": "list",
          "value": "1YOnu2JJjlxd787AuYcg-wKbkjyjyZFgASYVV0jsij5Y",
          "cachedResultUrl": "https://docs.google.com/spreadsheets/d/1YOnu2JJjlxd787AuYcg-wKbkjyjyZFgASYVV0jsij5Y/edit?usp=drivesdk",
          "cachedResultName": "96. Evaluations Test"
        }
      },
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        "googleSheetsOAuth2Api": {
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      "name": "Reasignar Entrada",
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        "assignments": {
          "assignments": [
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              "name": "chatInput",
              "type": "string",
              "value": "={{ $json.input }}"
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      "type": "n8n-nodes-base.evaluation",
      "position": [
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      "parameters": {
        "operation": "checkIfEvaluating"
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      "typeVersion": 4.6
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      "name": "Establecer Campos de Entrada",
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        "assignments": {
          "assignments": [
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              "name": "question",
              "type": "string",
              "value": "={{ $('When fetching a dataset row').first().json.input }}"
            },
            {
              "id": "0f10a3d0-cf6e-4715-9ded-2cee54aa62ec",
              "name": "answer",
              "type": "string",
              "value": "={{ $json.output }}"
            },
            {
              "id": "edbe42ed-36a7-438a-989f-900673e61d0f",
              "name": "groundTruth",
              "type": "array",
              "value": "={{ $('When fetching a dataset row').first().json['ground truth'].split('\\n') }}"
            }
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      "parameters": {
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      "typeVersion": 2
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    {
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      "name": "Al recibir un mensaje de chat",
      "type": "@n8n/n8n-nodes-langchain.chatTrigger",
      "position": [
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      "webhookId": "ba1fadeb-b566-469a-97b3-3159a99f1805",
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      "name": "Actualizar Salida",
      "type": "n8n-nodes-base.evaluation",
      "position": [
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      "parameters": {
        "outputs": {
          "values": [
            {
              "outputName": "output",
              "outputValue": "={{ $('Set Input Fields').item.json.answer }}"
            },
            {
              "outputName": "score",
              "outputValue": "={{ $json.score }}"
            }
          ]
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      "name": "Actualizar Métricas",
      "type": "n8n-nodes-base.evaluation",
      "position": [
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      "parameters": {
        "metrics": {
          "assignments": [
            {
              "id": "1fd7759c-f4ef-4eda-87ad-9d9563b63e99",
              "name": "score",
              "type": "number",
              "value": "={{ $json.score }}"
            }
          ]
        },
        "operation": "setMetrics"
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      "typeVersion": 4.6
    },
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      "name": "Nota Adhesiva1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -2120,
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      "parameters": {
        "color": 7,
        "width": 840,
        "height": 720,
        "content": "## 1. Setup Your AI Workflow to Use Evaluations\n[Learn more about the Evaluations Trigger](https://docs.n8n.io/integrations/builtin/?utm_source=n8n_app&utm_medium=node_settings_modal-credential_link&utm_campaign=n8n-nodes-base.evaluationTrigger)\n\nThe Evaluations Trigger is a separate execution which does not affect your production workflow in any way. It is manually triggered and automatically pulled datasets from the assigned Google Sheet."
      },
      "typeVersion": 1
    },
    {
      "id": "2e57d911-8fef-456d-8e5c-3849350553e1",
      "name": "Nota Adhesiva",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -1240,
        -120
      ],
      "parameters": {
        "color": 7,
        "width": 1700,
        "height": 720,
        "content": "## 2. Answer Similarity: How similar is the AI response to the Ground Truth?\n[Learn more about the Evaluations Trigger](https://docs.n8n.io/integrations/builtin/?utm_source=n8n_app&utm_medium=node_settings_modal-credential_link&utm_campaign=n8n-nodes-base.evaluationTrigger)\n\nFor this evaluation, we want to do a simple check to see how similar the AI's response is to the ground truth.\nThis test is most effective for closed-ended questions where there can be little to no deviation in the answers. "
      },
      "typeVersion": 1
    },
    {
      "id": "9962bbf8-01bc-463c-ad3b-499618c5b1f9",
      "name": "Nota Adhesiva3",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -2580,
        -120
      ],
      "parameters": {
        "width": 420,
        "height": 720,
        "content": "## Try It Out!\n### This n8n template demonstrates how to calculate the evaluation metric \"Similarity\" which in this scenario, measures the consistency of the agent.\n\nThe scoring approach is adapted from [https://github.com/explodinggradients/ragas/blob/main/ragas/src/ragas/metrics/_answer_similarity.py](https://github.com/explodinggradients/ragas/blob/main/ragas/src/ragas/metrics/_answer_similarity.py)\n\n### How it works\n* This evaluation works best where questions are close-ended or about facts where the answer can have little to no deviation.\n* For our scoring, we generate embeddings for both the AI's response and ground truth and calculate the cosine similarity between them.\n* A high score indicates LLM consistency with expected results whereas a low score could signal model hallucination.\n\n### Requirements\n* n8n version 1.94+\n* Check out this Google Sheet for a sample data [https://docs.google.com/spreadsheets/d/1YOnu2JJjlxd787AuYcg-wKbkjyjyZFgASYVV0jsij5Y/edit?usp=sharing](https://docs.google.com/spreadsheets/d/1YOnu2JJjlxd787AuYcg-wKbkjyjyZFgASYVV0jsij5Y/edit?usp=sharing)\n\n\n### Need Help?\nJoin the [Discord](https://discord.com/invite/XPKeKXeB7d) or ask in the [Forum](https://community.n8n.io/)!\n\nHappy Hacking!"
      },
      "typeVersion": 1
    },
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        "sendBody": true,
        "authentication": "predefinedCredentialType",
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              "name": "input",
              "value": "={{ $json.groundTruth }}"
            },
            {
              "name": "model",
              "value": "text-embedding-3-small"
            },
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              "value": "float"
            }
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        "nodeCredentialType": "openAiApi"
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      "position": [
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      "parameters": {
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        "assignments": {
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        "jsCode": "const { answer, groundTruth = [] } = $input.item.json;\n\nconst scores = await Promise.all(groundTruth.map(truth =>\n  cosineSimilarity(answer, truth.data)\n));\n\nconst averageScore = scores.reduce((acc,i) => acc + i, 0) / scores.length;\n\nreturn { json: { score: averageScore } };\n\nfunction cosineSimilarity(a, b) {  \n  let dotProduct = normA = normB = 0;\n  for (let i = 0; i < a.length; i++) {\n    dotProduct += a[i] * b[i];\n    normA += a[i] ** 2;\n    normB += b[i] ** 2;\n  }\n  return dotProduct / (Math.sqrt(normA) * Math.sqrt(normB));\n}"
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Preguntas frecuentes

¿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?

Avanzado - Ingeniería, Inteligencia Artificial

¿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.

Información del flujo de trabajo
Nivel de dificultad
Avanzado
Número de nodos21
Categoría2
Tipos de nodos13
Descripción de la dificultad

Adecuado para usuarios avanzados, flujos de trabajo complejos con 16+ nodos

Autor
Jimleuk

Jimleuk

@jimleuk

Freelance consultant based in the UK specialising in AI-powered automations. I work with select clients tackling their most challenging projects. For business enquiries, send me an email at hello@jimle.uk LinkedIn: https://www.linkedin.com/in/jimleuk/ X/Twitter: https://x.com/jimle_uk

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