Build a WordPress RAG chatbot with OpenAI, Qdrant or MongoDB

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 — WordPress content is retrieved, cleaned, chunked, embedded, and stored in the selected vector database. 2. Intent routing — Incoming questions are classified so conversational requests, content searches, and optional profile/skills queries can follow different paths. 3. Semantic retrieval — Relevant chunks are retrieved by meaning rather than exact keyword matching. 4. Reranking — Cohere can rerank retrieved documents before response generation to improve relevance. 5. Response generation — OpenAI generates an answer using the retrieved context and configured assistant behavior. 6. WordPress delivery — The supplied WordPress integration exposes the chatbot through a responsive frontend and shortcode. 7. Security and logging — The architecture includes webhook authentication, input handling, server-side secrets, and privacy-oriented logging controls. Setup 1. Purchase the complete package — Obtain the workflows, WordPress plugin, test frontend, supporting scripts, and complete documentation. 2. Choose a vector backend — Configure either the Qdrant path or the MongoDB Atlas path according to the included deployment guide. 3. Configure AI credentials — Connect OpenAI and any optional reranking services used by your selected setup. 4. Import and configure workflows — Configure the indexing workflow first, then the real-time chatbot workflow and their required credentials. 5. Index WordPress content — Run the indexing process and verify that embeddings and document metadata are available in the vector store. 6. Deploy the frontend — Install/configure the WordPress integration, set the webhook and authentication values, and place the chatbot shortcode where required. 7. Test retrieval and security — 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 — Extend indexing beyond WordPress posts or add separate collections for specialized data. - Retrieval — Adjust chunking, top-K retrieval, metadata filters, vector backend, and reranking behavior. - Assistant behavior — Modify intent classification, prompts, response style, language handling, and fallback behavior. - Frontend — Customize WordPress styling, labels, placement, responsive behavior, and shortcode integration. - Security — Adapt authentication, request validation, rate limiting, logging, and privacy controls to your deployment. - Models and providers — 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.