Build a WordPress RAG chatbot with OpenAI, Qdrant or MongoDB Atlas

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        "displayName": "Respond to Webhook",
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            "id": 7,
            "name": "Utility"
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            "id": 9,
            "name": "Core Nodes"
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      {
        "id": 565,
        "icon": "fa:sticky-note",
        "name": "n8n-nodes-base.stickyNote",
        "codex": {
          "data": {
            "alias": [
              "Comments",
              "Notes",
              "Sticky"
            ],
            "categories": [
              "Core Nodes"
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            "nodeVersion": "1.0",
            "codexVersion": "1.0",
            "subcategories": {
              "Core Nodes": [
                "Helpers"
              ]
            }
          }
        },
        "group": "[\"input\"]",
        "defaults": {
          "name": "Sticky Note",
          "color": "#FFD233"
        },
        "iconData": {
          "icon": "sticky-note",
          "type": "icon"
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        "displayName": "Sticky Note",
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            "name": "Core Nodes"
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        "icon": "file:googleCloudStorage.svg",
        "name": "n8n-nodes-base.googleCloudStorage",
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                  "url": "https://n8n.io/blog/automate-google-apps-for-productivity/",
                  "icon": "\ud83d\udca1",
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                }
              ],
              "primaryDocumentation": [
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                  "url": "https://docs.n8n.io/integrations/builtin/app-nodes/n8n-nodes-base.googlecloudstorage/"
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              ],
              "credentialDocumentation": [
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                  "url": "https://docs.n8n.io/integrations/builtin/credentials/google/oauth-single-service/"
                }
              ]
            },
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              "Data & Storage"
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            "nodeVersion": "1.0",
            "codexVersion": "1.0"
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        },
        "group": "[\"transform\"]",
        "defaults": {
          "name": "Google Cloud Storage"
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        "iconData": {
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          "fileBuffer": "data:image/svg+xml;base64,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"
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              "Javascript",
              "JS",
              "Python",
              "Script",
              "Custom Code",
              "Function"
            ],
            "details": "The Code node allows you to execute JavaScript in your workflow.",
            "resources": {
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                  "url": "https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.code/"
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              ]
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            "subcategories": {
              "Core Nodes": [
                "Helpers",
                "Data Transformation"
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          }
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        "group": "[\"transform\"]",
        "defaults": {
          "name": "Code"
        },
        "iconData": {
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            "name": "Development"
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            "name": "Core Nodes"
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        "id": 838,
        "icon": "fa:mouse-pointer",
        "name": "n8n-nodes-base.manualTrigger",
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            "subcategories": {
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                "Root Nodes"
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          "color": "#404040"
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            "name": "AI"
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            "id": 26,
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        "icon": "file:openAiLight.svg",
        "name": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
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            "subcategories": {
              "AI": [
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          }
        },
        "group": "[\"transform\"]",
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        "displayName": "Embeddings OpenAI",
        "typeVersion": 1,
        "nodeCategories": [
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            "id": 25,
            "name": "AI"
          },
          {
            "id": 26,
            "name": "Langchain"
          }
        ]
      },
      {
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              "Langchain"
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                "Language Models",
                "Root Nodes"
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              "Language Models": [
                "Chat Models (Recommended)"
              ]
            }
          }
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        "displayName": "OpenAI Chat Model",
        "typeVersion": 1,
        "nodeCategories": [
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            "id": 25,
            "name": "AI"
          },
          {
            "id": 26,
            "name": "Langchain"
          }
        ]
      },
      {
        "id": 1179,
        "icon": "fa:code",
        "name": "@n8n/n8n-nodes-langchain.outputParserStructured",
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            "alias": [
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              "zod"
            ],
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                }
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            },
            "categories": [
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              "Langchain"
            ],
            "subcategories": {
              "AI": [
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              ]
            }
          }
        },
        "group": "[\"transform\"]",
        "defaults": {
          "name": "Structured Output Parser"
        },
        "iconData": {
          "icon": "code",
          "type": "icon"
        },
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        "typeVersion": 1,
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            "name": "AI"
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            "id": 26,
            "name": "Langchain"
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      },
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        "icon": "fa:grip-lines-vertical",
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              ]
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              "Langchain"
            ],
            "subcategories": {
              "AI": [
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              ]
            }
          }
        },
        "group": "[\"transform\"]",
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          "name": "Recursive Character Text Splitter"
        },
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          "type": "icon"
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            "name": "Langchain"
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        ]
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        "icon": "file:binary.svg",
        "name": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
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                  "url": "https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.documentdefaultdataloader/"
                }
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            "categories": [
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              "Langchain"
            ],
            "subcategories": {
              "AI": [
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              ]
            }
          }
        },
        "group": "[\"transform\"]",
        "defaults": {
          "name": "Default Data Loader"
        },
        "iconData": {
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        "typeVersion": 1,
        "nodeCategories": [
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            "id": 25,
            "name": "AI"
          },
          {
            "id": 26,
            "name": "Langchain"
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        "icon": "file:qdrant.svg",
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        "codex": {
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                }
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            "categories": [
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              "Langchain"
            ],
            "subcategories": {
              "AI": [
                "Vector Stores",
                "Tools",
                "Root Nodes"
              ],
              "Tools": [
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              ],
              "Vector Stores": [
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            }
          }
        },
        "group": "[\"transform\"]",
        "defaults": {
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            "id": 25,
            "name": "AI"
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            "id": 26,
            "name": "Langchain"
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        ]
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        "id": 1250,
        "icon": "file:openAi.svg",
        "name": "@n8n/n8n-nodes-langchain.openAi",
        "codex": {
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              "Sora",
              "DallE",
              "whisper",
              "audio",
              "transcribe",
              "tts",
              "assistant"
            ],
            "resources": {
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                  "url": "https://docs.n8n.io/integrations/builtin/app-nodes/n8n-nodes-langchain.openai/"
                }
              ]
            },
            "categories": [
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              "Langchain"
            ],
            "subcategories": {
              "AI": [
                "Agents",
                "Miscellaneous",
                "Root Nodes"
              ]
            }
          }
        },
        "group": "[\"transform\"]",
        "defaults": {
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        },
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        },
        "displayName": "OpenAI",
        "typeVersion": 2,
        "nodeCategories": [
          {
            "id": 25,
            "name": "AI"
          },
          {
            "id": 26,
            "name": "Langchain"
          }
        ]
      },
      {
        "id": 1290,
        "icon": "file:mongodb.svg",
        "name": "@n8n/n8n-nodes-langchain.vectorStoreMongoDBAtlas",
        "codex": {
          "data": {
            "resources": {
              "primaryDocumentation": [
                {
                  "url": "https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstoremongodbatlas/"
                }
              ]
            },
            "categories": [
              "AI",
              "Langchain"
            ],
            "subcategories": {
              "AI": [
                "Vector Stores",
                "Tools",
                "Root Nodes"
              ],
              "Tools": [
                "Other Tools"
              ],
              "Vector Stores": [
                "Other Vector Stores"
              ]
            }
          }
        },
        "group": "[\"transform\"]",
        "defaults": {
          "name": "MongoDB Atlas Vector Store"
        },
        "iconData": {
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        },
        "displayName": "MongoDB Atlas Vector Store",
        "typeVersion": 1,
        "nodeCategories": [
          {
            "id": 25,
            "name": "AI"
          },
          {
            "id": 26,
            "name": "Langchain"
          }
        ]
      },
      {
        "id": 1305,
        "icon": "file:cohere.svg",
        "name": "@n8n/n8n-nodes-langchain.rerankerCohere",
        "codex": {
          "data": {
            "resources": {
              "primaryDocumentation": [
                {
                  "url": "https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.rerankercohere/"
                }
              ]
            },
            "categories": [
              "AI",
              "Langchain"
            ],
            "subcategories": {
              "AI": [
                "Rerankers"
              ]
            }
          }
        },
        "group": "[\"transform\"]",
        "defaults": {
          "name": "Reranker Cohere"
        },
        "iconData": {
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        },
        "displayName": "Reranker Cohere",
        "typeVersion": 1,
        "nodeCategories": [
          {
            "id": 25,
            "name": "AI"
          },
          {
            "id": 26,
            "name": "Langchain"
          }
        ]
      }
    ]
  },
  "detail": {
    "description": "Quick overview Build a WordPress chatbot that answers questions from your site content using Retrieval-Augmented Generation. n8n handles indexing and chat orchestration, while Qdrant or MongoDB Atlas provides vector search and OpenAI generates context-grounded responses. How it works 1. Content indexing \u2014 WordPress content is retrieved, cleaned, chunked, embedded, and stored in the selected vector database. 2. Intent routing \u2014 Incoming questions are classified so conversational requests, content searches, and optional profile/skills queries can follow different paths. 3. Semantic retrieval \u2014 Relevant chunks are retrieved by meaning rather than exact keyword matching. 4. Reranking \u2014 Cohere can rerank retrieved documents before response generation to improve relevance. 5. Response generation \u2014 OpenAI generates an answer using the retrieved context and configured assistant behavior. 6. WordPress delivery \u2014 The supplied WordPress integration exposes the chatbot through a responsive frontend and shortcode. 7. Security and logging \u2014 The architecture includes webhook authentication, input handling, server-side secrets, and privacy-oriented logging controls. Setup 1. Purchase the complete package \u2014 Obtain the workflows, WordPress plugin, test frontend, supporting scripts, and complete documentation. 2. Choose a vector backend \u2014 Configure either the Qdrant path or the MongoDB Atlas path according to the included deployment guide. 3. Configure AI credentials \u2014 Connect OpenAI and any optional reranking services used by your selected setup. 4. Import and configure workflows \u2014 Configure the indexing workflow first, then the real-time chatbot workflow and their required credentials. 5. Index WordPress content \u2014 Run the indexing process and verify that embeddings and document metadata are available in the vector store. 6. Deploy the frontend \u2014 Install/configure the WordPress integration, set the webhook and authentication values, and place the chatbot shortcode where required. 7. Test retrieval and security \u2014 Validate normal conversation, RAG queries, unavailable answers, authentication, and frontend behavior before production deployment. 8. Cohere API credentials for reranking (optional) 9. MongoDB-based profile/skills knowledge path (optional) 10. Standalone test micro-website (optional) 11. Additional content sources or vector collections (optional) Requirements - WordPress site with REST API access - n8n instance - OpenAI API credentials - Qdrant or MongoDB Atlas vector database - HTTPS for production deployments - Ability to install/configure the supplied WordPress integration Customization - Knowledge sources \u2014 Extend indexing beyond WordPress posts or add separate collections for specialized data. - Retrieval \u2014 Adjust chunking, top-K retrieval, metadata filters, vector backend, and reranking behavior. - Assistant behavior \u2014 Modify intent classification, prompts, response style, language handling, and fallback behavior. - Frontend \u2014 Customize WordPress styling, labels, placement, responsive behavior, and shortcode integration. - Security \u2014 Adapt authentication, request validation, rate limiting, logging, and privacy controls to your deployment. - Models and providers \u2014 Change supported LLM, embedding, or reranking components where the workflow architecture allows it. Additional info The complete purchased package includes two n8n workflows, WordPress integration, test frontend, setup/customization documentation, and supporting tools. The public repository preserves promotional images and frontend screenshots under assets/; the paid workflow package itself is distributed through the purchase channels."
  }
}