This n8n workflow creates a comprehensive AI-powered personal assistant integrated with WhatsApp, leveraging GPT-4, memory management, scheduling, and document processing. The workflow connects multiple AI models, data storage, and workflow automation to provide personalized responses, schedule management, and knowledge handling.

The process begins with webhooks for user input, then uses OpenAI GPT models for generating responses, classifying intent, and summarizing data. It manages messages via Redis and PostgreSQL databases, extracts text from PDFs, and creates embeddings for semantic search. Additionally, the workflow includes a memory system using Postgres, a vector store with Supabase, and tools for knowledge addition and exclusion.

This setup is suitable for building intelligent chatbots, customer support automation, or virtual assistants that can understand, remember, and respond intelligently based on stored knowledge and user prompts. It can handle document uploads, extract relevant information, maintain conversation state, and trigger scheduled tasks or workflows seamlessly.

Overall, this workflow demonstrates a robust integration of AI models, data management, and automation to deliver a personalized, context-aware assistant on WhatsApp or similar platforms.

Node Count

>20 Nodes

Nodes Used

@n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.chainLlm, @n8n/n8n-nodes-langchain.documentDefaultDataLoader, @n8n/n8n-nodes-langchain.embeddingsOpenAi, @n8n/n8n-nodes-langchain.lmChatOpenAi, @n8n/n8n-nodes-langchain.memoryPostgresChat, @n8n/n8n-nodes-langchain.openAi, @n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter, @n8n/n8n-nodes-langchain.toolVectorStore, @n8n/n8n-nodes-langchain.toolWorkflow, @n8n/n8n-nodes-langchain.vectorStoreSupabase, aggregate, convertToFile, executeWorkflow, extractFromFile, if, merge, postgres, redis, set, stickyNote, supabase, switch, wait, webhook