{
  "workflow": {
    "id": 2784,
    "name": "Personal shopper chatbot for WooCommerce with RAG using Google Drive and openAI",
    "views": 13323,
    "recentViews": 1,
    "totalViews": 13323,
    "createdAt": "2025-01-23T14:55:58.691Z",
    "description": "This workflow combines **OpenAI**, **Retrieval-Augmented Generation (RAG)**, and **WooCommerce** to create an intelligent personal shopping assistant. It handles two scenarios:  \n1. **Product Search**: Extracts user intent (keywords, price ranges, SKUs) and fetches matching products from WooCommerce.  \n2. **General Inquiries**: Answers store-related questions (e.g., opening hours, policies) using RAG and documents stored in Google Drive.  \n\n---\n\n### **How It Works**  \n\n#### **1. Chat Interaction & Intent Detection**  \n- **Chat Trigger**:  \n  - Starts when a user sends a message (*\"When chat message received\"*).  \n- **Information Extractor**:  \n  - Uses OpenAI to analyze the message and determine if the user is **searching for a product** or asking a **general question**.  \n  - Extracts:  \n    - `search` (true/false).  \n    - `keyword`, `priceRange`, `SKU`, `category` (if product-related).  \n  - Example:  \n    ```json  \n    {  \n      \"search\": true,  \n      \"keyword\": \"red handbags\",  \n      \"priceRange\": { \"min\": 50, \"max\": 100 },  \n      \"SKU\": \"BAG123\",  \n      \"category\": \"women's accessories\"  \n    }  \n    ```  \n\n#### **2. Product Search (WooCommerce Integration)**  \n- **AI Agent**:  \n  - If `search: true`, routes the request to the `personal_shopper` tool.  \n  - **WooCommerce Node**:  \n    - Queries the WooCommerce store using extracted parameters (`keyword`, `priceRange`, `SKU`).  \n    - Filters products in stock (`stockStatus: \"instock\"`).  \n    - Returns matching products (e.g., \"red handbags under €100\").  \n\n#### **3. General Inquiries (RAG System)**  \n- **RAG Tool**:  \n  - If `search: false`, uses the **Qdrant Vector Store** to retrieve store information from documents.  \n  - **Google Drive Integration**:  \n    - Documents (e.g., store policies, FAQs) are stored in Google Drive.  \n    - Downloaded, split into chunks, and embedded into Qdrant for semantic search.  \n  - **OpenAI Chat Model**: Generates answers based on retrieved documents (e.g., \"Our store opens at 9 AM\").  \n\n### **Set Up Steps**  \n\n#### **1. Configure the RAG System**  \n- **Google Drive Setup**:  \n  - Upload store documents .  \n  - Update the **Google Drive2** node with your folder ID.  \n- **Qdrant Vector Database**:  \n  - Clean the collection (update **Qdrant Vector Store** node with your URL).  \n  - Use **Embeddings OpenAI** to convert documents into vectors.  \n\n#### **2. Configure OpenAI & WooCommerce**  \n- **OpenAI Credentials**:  \n  - Add your API key to all OpenAI nodes (`OpenAI Chat Model`, `Embeddings OpenAI`, etc.).  \n- **WooCommerce Integration**:  \n  - Connect your WooCommerce store (credentials in the `personal_shopper` node).  \n  - Ensure product data is synced and accessible.  \n\n#### **3. Customize the AI Agent**  \n- **Intent Detection**:  \n  - Modify the **Information Extractor**’s system prompt to align with your store’s terminology.  \n- **RAG Responses**:  \n  - Update the tool description to reflect your store’s documents.  \n\n---\n\n### **Notes**  \nThis template is ideal for e-commerce businesses needing a hybrid assistant for product discovery and customer support.\n\n\n### **Need help customizing?**  \n[Contact me](mailto:info@n3w.it) for consulting and support or add me on [Linkedin](https://www.linkedin.com/in/davideboizza/). ",
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      "name": "OpenAI Personal Shopper with RAG and WooCommerce",
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              "sku": "={{ $('Information Extractor').item.json.output.SKU }}",
              "search": "={{ $('Information Extractor').item.json.output.keyword }}",
              "maxPrice": "={{ $('Information Extractor').item.json.output.price_max }}",
              "minPrice": "={{ $('Information Extractor').item.json.output.price_min }}",
              "stockStatus": "instock"
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            "operation": "getAll"
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            "options": {
              "systemPromptTemplate": "You are an intelligent assistant for a shoe and accessories store (mainly bags). Your task is to analyze the input text coming from a chat and determine if the user is looking for a product. If the user is looking for a product, you need to extract the following information:\n1. The keyword (keyword) useful for the search.\n2. Any minimum or maximum prices specified.\n3. An SKU (product code) if mentioned.\n4. The name of the category to search in, if specified.\n\nInstructions:\n1. Identify the intent: Determine if the user is looking for a specific product.\n2. Extract the information:\n- If the user is looking for a product, identify:\n- Set the type \"search\" to true. Otherwise, set it to false\n- The keywords.\n- Any minimum or maximum prices (e.g. \"less than 50 euros\", \"between 30 and 60 euros\").\n- An SKU (e.g. \"ABC123 code\").\n- The category name (e.g. \"t-shirts\", \"jeans\", \"women\", \"men\").\n3. Output format: Return a JSON object with the given structure"
            },
            "schemaType": "manual",
            "inputSchema": "{\n       \"search_intent\": true,\n       \"search_params\": [\n         { \"type\": \"search\", \"value\": \"ture or false\" },\n         { \"type\": \"keyword\", \"value\": \"valore_keyword\" },\n         { \"type\": \"min_price\", \"value\": \"valore_min_price\" },\n         { \"type\": \"max_price\", \"value\": \"valore_max_price\" },\n         { \"type\": \"sku\", \"value\": \"valore_sku\" },\n         { \"type\": \"category\", \"value\": \"valore_categoria\" }\n       ]\n     }"
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          "typeVersion": 1.1
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                "mode": "list",
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                "cachedResultUrl": "https://drive.google.com/drive/folders/1lmnqpLFKS-gXmXT92C5VG0P1XlcoeFOb",
                "cachedResultName": "Scarperia Salò - RAG"
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            },
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            "resource": "fileFolder"
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          ],
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              "mode": "id",
              "value": "={{ $json.id }}"
            },
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              "googleFileConversion": {
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                  "docsToFormat": "text/plain"
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            "operation": "download"
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          ],
          "parameters": {
            "url": "https://YOUR_AWS_SECRET_KEY_HERE",
            "method": "POST",
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          "position": [
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          ],
          "parameters": {
            "width": 259.7740863787376,
            "height": 234.1528239202657,
            "content": "Replace the URL and Collection name with your own"
          },
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        },
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          "name": "Qdrant Vector Store1",
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          "position": [
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          ],
          "parameters": {
            "mode": "insert",
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              "__rl": true,
              "mode": "list",
              "value": "scarperia",
              "cachedResultName": "scarperia"
            }
          },
          "credentials": {
            "qdrantApi": {
              "id": "credential-id",
              "name": "qdrantApi Credential"
            }
          },
          "typeVersion": 1
        },
        {
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          "name": "Sticky Note1",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
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            -640
          ],
          "parameters": {
            "color": 3,
            "width": 1301.621262458471,
            "height": 105.6212624584717,
            "content": "## Step 1 \nCreate a collectiopn on your Qdrant instance. Then create a basic RAG system with documents uploaded to Google Drive and embedded in the Qdrant vector database"
          },
          "typeVersion": 1
        },
        {
          "id": "0ddbf6be-fa2d-4412-8e85-fe108cd6e84d",
          "name": "Sticky Note2",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
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          "parameters": {
            "color": 3,
            "width": 1301.621262458471,
            "height": 105.6212624584717,
            "content": "## Step 1 \nCreate a basic RAG system with documents uploaded to Google Drive and embedded in the Qdrant vector database"
          },
          "typeVersion": 1
        },
        {
          "id": "3782a22d-b3a7-44ea-ad36-fa4382c9fcfd",
          "name": "Sticky Note3",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
            -200,
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          "parameters": {
            "color": 3,
            "width": 1301.621262458471,
            "height": 105.6212624584717,
            "content": "## Step 2 \nThe Information Extractor tries to understand if the request is related to products and if so, it extracts the useful information to filter the products available on WooCommerce by calling the \"personal_shopper\". If it is a general question, the RAG system is called"
          },
          "typeVersion": 1
        },
        {
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          "name": "AI Agent",
          "type": "@n8n/n8n-nodes-langchain.agent",
          "position": [
            580,
            280
          ],
          "parameters": {
            "text": "={{ $('When chat message received').item.json.chatInput }}",
            "options": {
              "systemMessage": "=You are an intelligent assistant for a clothing store. Your task is to analyze the input text from a chat and determine if the user is looking for a product.\n\nBehavior:\n- If the user is looking for a product the \"search\" field of the following JSON is set to true and you must pass the following JSON as input to the \"personal_shopper\" tool to extract:\n\n```json\n{{ JSON.stringify($json.output) }}\n```\n\n- If the user asks questions related to the store such as address or opening hours, you must use the \"RAG\" tool"
            },
            "promptType": "define"
          },
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        }
      ],
      "active": false,
      "pinData": {},
      "settings": {
        "executionOrder": "v1"
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      "versionId": "47513e11-8e9f-4b7c-b3de-e15cf00a1200",
      "connections": {
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              {
                "node": "AI Agent",
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                "index": 0
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            ]
          ]
        },
        "Calculator": {
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                "type": "ai_tool",
                "index": 0
              }
            ]
          ]
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              {
                "node": "Information Extractor",
                "type": "main",
                "index": 0
              }
            ]
          ]
        },
        "HTTP Request": {
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            [
              {
                "node": "Google Drive2",
                "type": "main",
                "index": 0
              }
            ]
          ]
        },
        "Google Drive1": {
          "main": [
            [
              {
                "node": "Qdrant Vector Store1",
                "type": "main",
                "index": 0
              }
            ]
          ]
        },
        "Google Drive2": {
          "main": [
            [
              {
                "node": "Google Drive1",
                "type": "main",
                "index": 0
              }
            ]
          ]
        },
        "Token Splitter1": {
          "ai_textSplitter": [
            [
              {
                "node": "Default Data Loader2",
                "type": "ai_textSplitter",
                "index": 0
              }
            ]
          ]
        },
        "personal_shopper": {
          "ai_tool": [
            [
              {
                "node": "AI Agent",
                "type": "ai_tool",
                "index": 0
              }
            ]
          ]
        },
        "Embeddings OpenAI": {
          "ai_embedding": [
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              {
                "node": "Qdrant Vector Store",
                "type": "ai_embedding",
                "index": 0
              }
            ]
          ]
        },
        "OpenAI Chat Model": {
          "ai_languageModel": [
            [
              {
                "node": "AI Agent",
                "type": "ai_languageModel",
                "index": 0
              }
            ]
          ]
        },
        "Embeddings OpenAI3": {
          "ai_embedding": [
            [
              {
                "node": "Qdrant Vector Store1",
                "type": "ai_embedding",
                "index": 0
              }
            ]
          ]
        },
        "OpenAI Chat Model1": {
          "ai_languageModel": [
            [
              {
                "node": "RAG",
                "type": "ai_languageModel",
                "index": 0
              }
            ]
          ]
        },
        "OpenAI Chat Model2": {
          "ai_languageModel": [
            [
              {
                "node": "Information Extractor",
                "type": "ai_languageModel",
                "index": 0
              }
            ]
          ]
        },
        "Qdrant Vector Store": {
          "ai_vectorStore": [
            [
              {
                "node": "RAG",
                "type": "ai_vectorStore",
                "index": 0
              }
            ]
          ]
        },
        "Default Data Loader2": {
          "ai_document": [
            [
              {
                "node": "Qdrant Vector Store1",
                "type": "ai_document",
                "index": 0
              }
            ]
          ]
        },
        "Window Buffer Memory": {
          "ai_memory": [
            [
              {
                "node": "AI Agent",
                "type": "ai_memory",
                "index": 0
              }
            ]
          ]
        },
        "Information Extractor": {
          "main": [
            [
              {
                "node": "AI Agent",
                "type": "main",
                "index": 0
              }
            ]
          ]
        },
        "When chat message received": {
          "main": [
            [
              {
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                "type": "main",
                "index": 0
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}