This workflow creates an integrated system for handling WhatsApp customer inquiries using AI, Qdrant vector database, Google Drive, and OpenAI models. Starting with a webhook trigger, the workflow verifies incoming messages from WhatsApp, responds with a basic acknowledgment, and then processes the message to fetch relevant knowledge-based data. It utilizes Langchain’s AI agents and vector store to provide accurate, context-aware responses. The setup includes creating and managing Qdrant collections, indexing documents from Google Drive, and applying advanced language models for chat interactions. This automation is ideal for businesses aiming to enhance customer support via WhatsApp by providing instant, intelligent responses backed by stored knowledge. The workflow can also support dynamic document retrieval and AI-powered troubleshooting, significantly improving customer experience and operational efficiency.
Automated WhatsApp AI Customer Support & Knowledge Retrieval
Node Count | >20 Nodes |
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Nodes Used | @n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.documentDefaultDataLoader, @n8n/n8n-nodes-langchain.embeddingsOpenAi, @n8n/n8n-nodes-langchain.lmChatOpenAi, @n8n/n8n-nodes-langchain.memoryBufferWindow, @n8n/n8n-nodes-langchain.textSplitterTokenSplitter, @n8n/n8n-nodes-langchain.toolVectorStore, @n8n/n8n-nodes-langchain.vectorStoreQdrant, googleDrive, httpRequest, if, manualTrigger, respondToWebhook, stickyNote, webhook, whatsApp |
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