This n8n workflow automates the process of converting unstructured text data into structured, searchable ‘IdeaBlocks’ using AI and API integrations. It starts by manually triggering the workflow, then downloads and extracts text from a Google Drive file. The text is chunked into manageable pieces, which are sent to the Blockify Ingest API to generate structured IdeaBlocks. These blocks are stored in-memory vector databases with embedding for efficient retrieval. Additionally, the workflow incorporates a RAG (Retrieval-Augmented Generation) chatbot to interact with users and fetch relevant information from the structured data. This setup is highly useful for enterprises handling large unstructured data sources, such as transcripts, reports, or lengthy documents, aiming to improve data searchability and accuracy of AI responses.
AI-Powered Data Structuring and Retrieval Workflow
| Node Count | >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.textSplitterCharacterTextSplitter, @n8n/n8n-nodes-langchain.vectorStoreInMemory, code, extractFromFile, googleDrive, httpRequest, manualTrigger, set, splitInBatches, stickyNote |
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