This n8n workflow automates the process of uploading documents, transforming them into embeddings, and enabling AI-powered querying. Users upload a file (PDF or CSV) via a webhook, which triggers the workflow to load the data and process it into a vector store using OpenAI embeddings. The stored data can then be retrieved as context for an AI agent, allowing users to ask questions about their documents and receive accurate, context-aware responses. This setup is ideal for building intelligent document assistants, knowledge bases, or FAQ bots that leverage AI and vector search technology.
AI-Powered Document Q&A Workflow in n8n
| Node Count | 11 – 20 Nodes |
|---|---|
| Nodes Used | @n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.chatTrigger, @n8n/n8n-nodes-langchain.documentDefaultDataLoader, @n8n/n8n-nodes-langchain.embeddingsOpenAi, @n8n/n8n-nodes-langchain.lmChatOpenAi, @n8n/n8n-nodes-langchain.vectorStoreInMemory, formTrigger, stickyNote |
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