Chat RAG de base

Name: Chat RAG de base
Rating: 4.5 (10 reviews)
Author: JustinLee

Intermédiaire

Ceci est unSupport, Building Blocks, AIworkflow d'automatisation du domainecontenant 14 nœuds.Utilise principalement des nœuds comme ManualTrigger, ReadWriteFile, LmChatGroq, ChatTrigger, ChainRetrievalQa, combinant la technologie d'intelligence artificielle pour une automatisation intelligente. Chatbot RAG de base

Prérequis

•Clé API de service IA (comme OpenAI, Anthropic, etc.)

Nœuds utilisés (14)

DocumentDefaultDataLoader

TextSplitterRecursiveCharacterTextSplitter

Catégorie

Support

Blocs de construction

Intelligence Artificielle

Aperçu du workflow

Visualisation des connexions entre les nœuds, avec support du zoom et du déplacement

Recursive Character Text Splitter

Default Data Loader

Question and Answer Chaîne

Stockage vectoriel Retriever

Au clic 'Test Workflow' button

Au clic 'Chat' button below

Read/Write Files from Disk

In-Memory Stockage vectoriel1

In-Memory Stockage vectoriel

Incorporations Cohere

Groq Chat Model

Incorporations Cohere1

React Flow

Exporter le workflow

Copiez la configuration JSON suivante dans n8n pour importer et utiliser ce workflow

{
  "nodes": [
    {
      "id": "3bc2f88b-c14e-4ee5-84ce-dc16a54aa12b",
      "name": "Recursive Character Text Splitter",
      "type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
      "position": [
        -580,
        320
      ],
      "parameters": {
        "options": {
          "splitCode": "markdown"
        },
        "chunkOverlap": 50
      },
      "typeVersion": 1
    },
    {
      "id": "6bd91468-17db-4918-a232-87fb295a30c2",
      "name": "Note adhésive",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -1240,
        -140
      ],
      "parameters": {
        "color": 7,
        "width": 978.0454109366399,
        "height": 806.6556079800943,
        "content": "### Load data into database\nFetch file from Google Drive, split it into chunks and insert into Pinecone index"
      },
      "typeVersion": 1
    },
    {
      "id": "3af4e8e9-0503-470e-b449-4551191fb405",
      "name": "Note adhésive1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        0,
        -160
      ],
      "parameters": {
        "color": 7,
        "width": 795.4336844920119,
        "height": 849.4411596574598,
        "content": "### Chat with database\nEmbed the incoming chat message and use it retrieve relevant chunks from the vector store. These are passed to the model to formulate an answer "
      },
      "typeVersion": 1
    },
    {
      "id": "6f94ec58-4fca-40ee-a1a0-012998093589",
      "name": "Default Data Loader",
      "type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
      "position": [
        -580,
        200
      ],
      "parameters": {
        "options": {},
        "dataType": "binary"
      },
      "typeVersion": 1
    },
    {
      "id": "3e145342-458d-4222-a707-9fee78e91c4d",
      "name": "Question and Answer Chaîne",
      "type": "@n8n/n8n-nodes-langchain.chainRetrievalQa",
      "position": [
        60,
        -20
      ],
      "parameters": {
        "options": {}
      },
      "typeVersion": 1.2
    },
    {
      "id": "7f2b288a-a002-4cd3-93c0-b2a0e491699c",
      "name": "Stockage vectoriel Retriever",
      "type": "@n8n/n8n-nodes-langchain.retrieverVectorStore",
      "position": [
        240,
        200
      ],
      "parameters": {},
      "typeVersion": 1
    },
    {
      "id": "ca930ba7-b45d-47d8-9f36-9db3a25ee77a",
      "name": "Au clic 'Test Workflow' button",
      "type": "n8n-nodes-base.manualTrigger",
      "position": [
        -1420,
        -20
      ],
      "parameters": {},
      "typeVersion": 1
    },
    {
      "id": "90782052-5df2-4f1e-84fc-c47095a81852",
      "name": "Au clic 'Chat' button below",
      "type": "@n8n/n8n-nodes-langchain.chatTrigger",
      "position": [
        -140,
        -20
      ],
      "webhookId": "066b342b-f2b6-401e-b560-12f5d23b6103",
      "parameters": {},
      "typeVersion": 1
    },
    {
      "id": "712dc9d3-af2d-4436-9315-78f66f748b91",
      "name": "Read/Write Files from Disk",
      "type": "n8n-nodes-base.readWriteFile",
      "position": [
        -1200,
        -20
      ],
      "parameters": {
        "options": {},
        "fileSelector": "/tmp/external_data/news.txt"
      },
      "typeVersion": 1,
      "alwaysOutputData": true
    },
    {
      "id": "1cd768c1-fcc0-480a-8b33-fbe714788b32",
      "name": "In-Memory Stockage vectoriel1",
      "type": "@n8n/n8n-nodes-langchain.vectorStoreInMemory",
      "position": [
        240,
        380
      ],
      "parameters": {},
      "typeVersion": 1
    },
    {
      "id": "2393e667-7e4f-4392-9a7e-b2b4d74d46e8",
      "name": "In-Memory Stockage vectoriel",
      "type": "@n8n/n8n-nodes-langchain.vectorStoreInMemory",
      "position": [
        -980,
        -20
      ],
      "parameters": {
        "mode": "insert",
        "clearStore": true
      },
      "typeVersion": 1
    },
    {
      "id": "e53f51f3-04f3-46ef-aebd-e0b32b415101",
      "name": "Incorporations Cohere",
      "type": "@n8n/n8n-nodes-langchain.embeddingsCohere",
      "position": [
        -940,
        300
      ],
      "parameters": {
        "modelName": "embed-multilingual-v3.0"
      },
      "credentials": {
        "cohereApi": {
          "id": "rXh87ikYuJfDKuCk",
          "name": "CohereApi account"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "cf1333b6-b69b-4ff1-bfc3-d3d579585efb",
      "name": "Groq Chat Model",
      "type": "@n8n/n8n-nodes-langchain.lmChatGroq",
      "notes": "使用繁體中文",
      "position": [
        100,
        220
      ],
      "parameters": {
        "model": "llama-3.3-70b-versatile",
        "options": {}
      },
      "credentials": {
        "groqApi": {
          "id": "dznjL979E8j0L4Zc",
          "name": "Groq account"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "e49cfb2e-5eca-4b43-973d-4bf7285b5d94",
      "name": "Incorporations Cohere1",
      "type": "@n8n/n8n-nodes-langchain.embeddingsCohere",
      "position": [
        340,
        560
      ],
      "parameters": {
        "modelName": "embed-multilingual-v3.0"
      },
      "credentials": {
        "cohereApi": {
          "id": "rXh87ikYuJfDKuCk",
          "name": "CohereApi account"
        }
      },
      "typeVersion": 1
    }
  ],
  "connections": {
    "cf1333b6-b69b-4ff1-bfc3-d3d579585efb": {
      "ai_languageModel": [
        [
          {
            "node": "Question and Answer Chain",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Embeddings Cohere": {
      "ai_embedding": [
        [
          {
            "node": "In-Memory Vector Store",
            "type": "ai_embedding",
            "index": 0
          }
        ]
      ]
    },
    "Embeddings Cohere1": {
      "ai_embedding": [
        [
          {
            "node": "In-Memory Vector Store1",
            "type": "ai_embedding",
            "index": 0
          }
        ]
      ]
    },
    "6f94ec58-4fca-40ee-a1a0-012998093589": {
      "ai_document": [
        [
          {
            "node": "In-Memory Vector Store",
            "type": "ai_document",
            "index": 0
          }
        ]
      ]
    },
    "Vector Store Retriever": {
      "ai_retriever": [
        [
          {
            "node": "Question and Answer Chain",
            "type": "ai_retriever",
            "index": 0
          }
        ]
      ]
    },
    "In-Memory Vector Store1": {
      "ai_vectorStore": [
        [
          {
            "node": "Vector Store Retriever",
            "type": "ai_vectorStore",
            "index": 0
          }
        ]
      ]
    },
    "712dc9d3-af2d-4436-9315-78f66f748b91": {
      "main": [
        [
          {
            "node": "In-Memory Vector Store",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "3bc2f88b-c14e-4ee5-84ce-dc16a54aa12b": {
      "ai_textSplitter": [
        [
          {
            "node": "6f94ec58-4fca-40ee-a1a0-012998093589",
            "type": "ai_textSplitter",
            "index": 0
          }
        ]
      ]
    },
    "When clicking 'Chat' button below": {
      "main": [
        [
          {
            "node": "Question and Answer Chain",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "When clicking 'Test Workflow' button": {
      "main": [
        [
          {
            "node": "712dc9d3-af2d-4436-9315-78f66f748b91",
            "type": "main",
            "index": 0
          }
        ]
      ]
    }
  }
}

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 ?

Intermédiaire - Support, Blocs de construction, Intelligence Artificielle

Est-ce payant ?

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.