Exemple de métrique d'évaluation : Pertinence documentaire RAG

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Ceci est unEngineering, AIworkflow d'automatisation du domainecontenant 26 nœuds.Utilise principalement des nœuds comme Set, Evaluation, GoogleSheets, ManualTrigger, Agent, combinant la technologie d'intelligence artificielle pour une automatisation intelligente. Exemple d'indicateur d'évaluation : Pertinence documentaire RAG

Prérequis
  • Informations d'identification Google Sheets API
  • Clé API OpenAI
Aperçu du workflow
Visualisation des connexions entre les nœuds, avec support du zoom et du déplacement
Exporter le workflow
Copiez la configuration JSON suivante dans n8n pour importer et utiliser ce workflow
{
  "meta": {
    "instanceId": "bf40384a063e00f3b983f4f9bada22b57a8231a04c0fb48d363e26d7b0f2b7e7",
    "templateCredsSetupCompleted": true
  },
  "nodes": [
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      "id": "86d6a658-f056-4434-9720-dfd3ca514cb3",
      "name": "Note adhésive1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        420,
        20
      ],
      "parameters": {
        "color": 7,
        "width": 300,
        "height": 180,
        "content": "Check whether the documents returned a relevant to the question"
      },
      "typeVersion": 1
    },
    {
      "id": "90aac144-164f-4961-89f8-4b8402177202",
      "name": "Note adhésive3",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -1220,
        -40
      ],
      "parameters": {
        "width": 200,
        "height": 500,
        "content": "## How it works\nThis template shows how to calculate a workflow evaluation metric: **retrieved document relevance** (i.e. whether the information retrieved from the vector store is relevant to the question).\n\nThe workflow takes a question and checks whether the information retrieved to answer it is relevant.\n\nYou can find more information on workflow evaluation [here](https://docs.n8n.io/advanced-ai/evaluations/overview), and other metric examples [here](https://docs.n8n.io/advanced-ai/evaluations/metric-based-evaluations/#2-calculate-metrics)."
      },
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      "name": "Note adhésive4",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -960,
        280
      ],
      "parameters": {
        "color": 7,
        "width": 220,
        "height": 220,
        "content": "Read in [this test dataset](https://docs.google.com/spreadsheets/d/1uuPS5cHtSNZ6HNLOi75A2m8nVWZrdBZ_Ivf58osDAS8/edit?gid=630157309#gid=630157309) of questions"
      },
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    {
      "id": "677b90c8-aa6f-4cc5-88df-d8e635b0bde2",
      "name": "Lors de la récupération d'une ligne de jeu de données",
      "type": "n8n-nodes-base.evaluationTrigger",
      "position": [
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        340
      ],
      "parameters": {
        "sheetName": {
          "__rl": true,
          "mode": "url",
          "value": "https://docs.google.com/spreadsheets/d/1uuPS5cHtSNZ6HNLOi75A2m8nVWZrdBZ_Ivf58osDAS8/edit?gid=630157309#gid=630157309"
        },
        "documentId": {
          "__rl": true,
          "mode": "url",
          "value": "https://docs.google.com/spreadsheets/d/1uuPS5cHtSNZ6HNLOi75A2m8nVWZrdBZ_Ivf58osDAS8/edit?gid=630157309#gid=630157309"
        }
      },
      "credentials": {
        "googleSheetsOAuth2Api": {
          "id": "bpr2LoSELMlxpwnN",
          "name": "Google Sheets account David"
        }
      },
      "typeVersion": 4.6
    },
    {
      "id": "c381a3c3-12b6-44fe-a2ed-6adf6f997534",
      "name": "Évaluation ?",
      "type": "n8n-nodes-base.evaluation",
      "position": [
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      ],
      "parameters": {
        "operation": "checkIfEvaluating"
      },
      "typeVersion": 4.6
    },
    {
      "id": "e1cf0122-c668-41d2-9316-a173d425d897",
      "name": "Lors de la réception d'un message de chat",
      "type": "@n8n/n8n-nodes-langchain.chatTrigger",
      "position": [
        -900,
        100
      ],
      "webhookId": "d53bc846-db11-446c-8436-9d25dc265663",
      "parameters": {
        "options": {}
      },
      "typeVersion": 1.1
    },
    {
      "id": "b521aa40-c8da-4485-b834-50a129c27d3c",
      "name": "Correspondre au format de chat",
      "type": "n8n-nodes-base.set",
      "position": [
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      ],
      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "93f89095-7918-45ad-aa74-a0bbcf0d5788",
              "name": "chatInput",
              "type": "string",
              "value": "={{ $json.question }}"
            }
          ]
        }
      },
      "typeVersion": 3.4
    },
    {
      "id": "d0170cbd-ba01-45db-a405-aec943bf1608",
      "name": "Note adhésive",
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      "position": [
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      ],
      "parameters": {
        "color": 7,
        "width": 150,
        "height": 260,
        "content": "Only calculate metrics if we're evaluating, to reduce costs"
      },
      "typeVersion": 1
    },
    {
      "id": "a21f98f0-25eb-44cc-a04a-33181d7e5288",
      "name": "Renvoi de la réponse du chat",
      "type": "n8n-nodes-base.noOp",
      "position": [
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        340
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      "parameters": {},
      "typeVersion": 1
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    {
      "id": "fd8628a1-9f31-4f42-88df-b87eda7216ee",
      "name": "Définir les métriques",
      "type": "n8n-nodes-base.evaluation",
      "position": [
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      "parameters": {
        "metrics": {
          "assignments": [
            {
              "id": "230589eb-34c8-4d10-9296-4a78d673077a",
              "name": "similarity",
              "type": "number",
              "value": "={{ $json.message.content.score }}"
            }
          ]
        },
        "operation": "setMetrics"
      },
      "typeVersion": 4.6
    },
    {
      "id": "eefe3a5a-722a-4cce-b84b-65c0520a225f",
      "name": "Obtenir le jeu de données",
      "type": "n8n-nodes-base.googleSheets",
      "position": [
        -680,
        -620
      ],
      "parameters": {
        "options": {},
        "sheetName": {
          "__rl": true,
          "mode": "url",
          "value": "https://docs.google.com/spreadsheets/d/1uuPS5cHtSNZ6HNLOi75A2m8nVWZrdBZ_Ivf58osDAS8/edit?gid=630157309#gid=630157309"
        },
        "documentId": {
          "__rl": true,
          "mode": "url",
          "value": "https://docs.google.com/spreadsheets/d/1uuPS5cHtSNZ6HNLOi75A2m8nVWZrdBZ_Ivf58osDAS8/edit?gid=630157309#gid=630157309"
        }
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      "credentials": {
        "googleSheetsOAuth2Api": {
          "id": "bpr2LoSELMlxpwnN",
          "name": "Google Sheets account David"
        }
      },
      "typeVersion": 4.5
    },
    {
      "id": "ea83d68c-f499-4864-918c-5ccaccf555a5",
      "name": "Supprimer les doublons",
      "type": "n8n-nodes-base.removeDuplicates",
      "position": [
        -460,
        -620
      ],
      "parameters": {
        "compare": "selectedFields",
        "options": {},
        "fieldsToCompare": "document_id"
      },
      "typeVersion": 2
    },
    {
      "id": "6bf59dc8-fe59-49cf-971f-e22dac5d0500",
      "name": "Simple Vector Store",
      "type": "@n8n/n8n-nodes-langchain.vectorStoreInMemory",
      "position": [
        -240,
        -620
      ],
      "parameters": {
        "mode": "insert",
        "memoryKey": "rag_evaluation_demo",
        "clearStore": true
      },
      "typeVersion": 1.1
    },
    {
      "id": "66dd59b0-b2e4-43c9-b1f5-7f5a65568f5e",
      "name": "Embeddings OpenAI",
      "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
      "position": [
        -300,
        -400
      ],
      "parameters": {
        "options": {}
      },
      "credentials": {
        "openAiApi": {
          "id": "Ag9qPAsY7lpIGkvC",
          "name": "JPs n8n openAI key"
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "213b230d-0f40-4e71-b0bf-4783274e2cdb",
      "name": "Default Data Loader",
      "type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
      "position": [
        -100,
        -380
      ],
      "parameters": {
        "options": {},
        "jsonData": "={{ $json.document_text }}",
        "jsonMode": "expressionData"
      },
      "typeVersion": 1
    },
    {
      "id": "64ec7e8c-63f9-4198-9e8d-61345c564c13",
      "name": "Recursive Character Text Splitter",
      "type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
      "position": [
        -20,
        -220
      ],
      "parameters": {
        "options": {}
      },
      "typeVersion": 1
    },
    {
      "id": "c561f2ce-ef05-4f42-9b6e-6fabd12d6d23",
      "name": "Note adhésive2",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -980,
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      ],
      "parameters": {
        "color": 7,
        "width": 1180,
        "height": 620,
        "content": "### Setup: Populate vector DB\nRun this once before running the main workflow. It inserts the documents from the dataset in the vector store, so they can be queried by the agent below"
      },
      "typeVersion": 1
    },
    {
      "id": "4a7de6bd-17ac-40df-9b27-2d8f6fdbeb07",
      "name": "Lors du clic sur 'Exécuter le workflow'",
      "type": "n8n-nodes-base.manualTrigger",
      "position": [
        -900,
        -620
      ],
      "parameters": {},
      "typeVersion": 1
    },
    {
      "id": "a0f6211f-6202-462c-8fa3-52e10ad89b5d",
      "name": "Calculer la métrique de pertinence des documents",
      "type": "@n8n/n8n-nodes-langchain.openAi",
      "position": [
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      "parameters": {
        "modelId": {
          "__rl": true,
          "mode": "list",
          "value": "gpt-4o-mini",
          "cachedResultName": "GPT-4O-MINI"
        },
        "options": {},
        "messages": {
          "values": [
            {
              "role": "system",
              "content": "=You are a teacher evaluating the relevance of student responses to questions.\n\nYou will be given a QUESTION and one or more FACTS provided by a student. Your task is to determine whether these FACTS are relevant to answering the QUESTION.\n\nGrading criteria:\n(1) Evaluate whether the provided FACTS contain information that is relevant to answering the QUESTION\n(2) A FACT is relevant if it contains keywords or concepts semantically related to the QUESTION\n(3) A FACT is relevant if it provides context, examples, or explanations that address any aspect of the QUESTION\n(4) A FACT may contain some unrelated information and still be considered relevant if it also contains information that satisfies criteria (2) or (3)\n\nScoring:\n- Score 1: At least one of the provided FACTS is relevant to the QUESTION\n- Score 0: All provided FACTS are completely unrelated to the QUESTION\n\nEvaluation process:\n1. Identify the key concepts and requirements in the QUESTION\n2. Examine each provided FACT for keywords or concepts related to these key elements\n3. Consider semantic relationships, not just exact keyword matches\n4. Determine if any FACT contributes useful information toward answering the QUESTION\n5. Provide your reasoning in a detailed, step-by-step manner first\n6. Then create a one-sentence summary of your reasoning\n7. Avoid stating your conclusion at the beginning of your extended reasoning\n\nOutput format:\n{\n  \"extended_reasoning\": \"<detailed step-by-step analysis of how you evaluated the relevance>\",\n  \"reasoning_summary\": \"<one sentence summary of your conclusion>\",\n  \"score\": <number: either 0 or 1>\n}\n\nExamples:\n\nQUESTION: \"What factors contributed to the fall of the Roman Empire?\"\nFACTS: \"The Roman Empire fell due to economic problems, military failures, and political corruption. Germanic tribes invaded from the north while the empire was weakened.\"\nEVALUATION: This would receive a score of 1 because the facts directly address causes of the Roman Empire's fall.\n\nQUESTION: \"What factors contributed to the fall of the Roman Empire?\"\nFACTS: \"The Pythagorean theorem states that a² + b² = c² in a right triangle. Jupiter is the largest planet in our solar system.\"\nEVALUATION: This would receive a score of 0 because neither fact relates to the Roman Empire or its fall."
            },
            {
              "content": "=QUESTION: {{ $('When fetching a dataset row').item.json.question }}\n\nFACTS: \n{{ $json.documents }}"
            }
          ]
        },
        "jsonOutput": true
      },
      "credentials": {
        "openAiApi": {
          "id": "Ag9qPAsY7lpIGkvC",
          "name": "JPs n8n openAI key"
        }
      },
      "typeVersion": 1.8
    },
    {
      "id": "b01d3427-4907-4715-8c15-669500012f89",
      "name": "AI Agent",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "position": [
        -420,
        200
      ],
      "parameters": {
        "options": {
          "systemMessage": "You are a helpful assistant. Answer the user's questions using information from your vector knowledge base only.",
          "returnIntermediateSteps": true
        }
      },
      "typeVersion": 1.9
    },
    {
      "id": "97e2db38-0114-449f-bb41-0ab490fc7327",
      "name": "OpenAI Chat Model",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "position": [
        -480,
        440
      ],
      "parameters": {
        "model": {
          "__rl": true,
          "mode": "list",
          "value": "gpt-4o-mini"
        },
        "options": {}
      },
      "credentials": {
        "openAiApi": {
          "id": "Ag9qPAsY7lpIGkvC",
          "name": "JPs n8n openAI key"
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "5b3679ab-b0a6-4461-9276-89eddb16f699",
      "name": "Extraire les documents",
      "type": "n8n-nodes-base.set",
      "position": [
        220,
        80
      ],
      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "2d771c52-6637-4af0-9ff7-c87368041bfd",
              "name": "documents",
              "type": "string",
              "value": "={{ $json.intermediateSteps.filter(x => x.action.tool == 'vector_knowledge_base')[0].observation }}"
            }
          ]
        }
      },
      "typeVersion": 3.4
    },
    {
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      "name": "Simple Vector Store1",
      "type": "@n8n/n8n-nodes-langchain.vectorStoreInMemory",
      "position": [
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      ],
      "parameters": {
        "mode": "retrieve-as-tool",
        "toolName": "vector_knowledge_base",
        "memoryKey": "rag_evaluation_demo",
        "toolDescription": "Source of all knowledge to work with"
      },
      "typeVersion": 1.1
    },
    {
      "id": "edde6c75-dea5-46cc-937e-68e4a925559b",
      "name": "Embeddings OpenAI1",
      "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
      "position": [
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      ],
      "parameters": {
        "options": {}
      },
      "credentials": {
        "openAiApi": {
          "id": "Ag9qPAsY7lpIGkvC",
          "name": "JPs n8n openAI key"
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "e23289e7-a769-4aae-82e6-b7f9e6d903f0",
      "name": "Note adhésive6",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
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      ],
      "parameters": {
        "color": 7,
        "width": 2000,
        "height": 820,
        "content": "### Main workflow"
      },
      "typeVersion": 1
    },
    {
      "id": "c90baefc-22bd-4782-a8eb-236bfb1d2751",
      "name": "Note adhésive5",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -440,
        120
      ],
      "parameters": {
        "color": 7,
        "width": 300,
        "height": 200,
        "content": "Make sure to enable 'Return intermediate steps' in the agent, to get the list of executed tools"
      },
      "typeVersion": 1
    }
  ],
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}
Foire aux questions

Comment utiliser ce workflow ?

Copiez le code de configuration JSON ci-dessus, créez un nouveau workflow dans votre instance n8n et sélectionnez "Importer depuis le JSON", collez la configuration et modifiez les paramètres d'authentification selon vos besoins.

Dans quelles scénarios ce workflow est-il adapté ?

Avancé - Ingénierie, Intelligence Artificielle

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Ce workflow est entièrement gratuit et peut être utilisé directement. Veuillez noter que les services tiers utilisés dans le workflow (comme l'API OpenAI) peuvent nécessiter un paiement de votre part.

Informations sur le workflow
Niveau de difficulté
Avancé
Nombre de nœuds26
Catégorie2
Types de nœuds16
Description de la difficulté

Adapté aux utilisateurs avancés, avec des workflows complexes contenant 16+ nœuds

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