This workflow automates the process of building a local knowledge base using files stored in Google Drive, combined with AI-powered chat capabilities. It begins with Google Drive triggers that detect new or updated files in specified folders. When triggered, the workflow downloads the file, extracts its text, and processes it into smaller chunks. Using Ollama’s language models and embeddings, the content is transformed into vector representations stored in Qdrant. This setup enables intelligent Q&A and retrieval of relevant information within a local environment. Practical uses include creating a searchable knowledge base for customer support, documentation, or internal project notes, ensuring quick access to information without relying on external services.
Local Knowledge Base Creation from Google Drive
Node Count | 11 – 20 Nodes |
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Nodes Used | @n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.chatTrigger, @n8n/n8n-nodes-langchain.documentDefaultDataLoader, @n8n/n8n-nodes-langchain.embeddingsOllama, @n8n/n8n-nodes-langchain.lmChatOllama, @n8n/n8n-nodes-langchain.lmOllama, @n8n/n8n-nodes-langchain.memoryPostgresChat, @n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter, @n8n/n8n-nodes-langchain.toolVectorStore, @n8n/n8n-nodes-langchain.vectorStoreQdrant, extractFromFile, googleDrive, googleDriveTrigger, set, stickyNote |
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