Document Q&A with PDF Ingestion, RAG AI, Google Drive, Cohere & Milvus

This workflow automates document-based question answering using an RAG AI agent, replacing manual knowledge base searches. It ingests PDFs from Google Drive, processes them with Cohere embeddings, stores vectors in Milvus, and answers queries via a chat interface. Key nodes include Google Drive Trigger for file monitoring, Extract From File for PDF processing, Milvus for vector storage, and Chat Trigger for user queries. It saves 12-15 hours weekly for knowledge workers (5-30 members) handling 100+ queries monthly, ideal for enterprises or research teams needing fast, accurate document insights. Saves 12-15 hours weekly on 100+ monthly queries. Suits enterprises or research teams (10-100 employees). Requires Google Drive (free), Cohere (~$10/month), Milvus (~$50/month), OpenAI (~$20/month), n8n (~$20/month). Scalable to 1,000 documents/month, limited by API quotas. Install n8n via cloud.n8n.io. Set up Google Drive OAuth2 via Google Cloud Console (APIs & Services > Credentials). Obtain Cohere API key from cohere.ai (Dashboard > API Keys). Get Milvus credentials from zilliz.com (Cloud > Credentials). Acquire OpenAI API key from platform.openai.com (API Keys). Configure Chat Trigger webhook (e.g., https://your-n8n-instance/webhook/a4257301-3fb9-4b9d-a965-1fa66f314696) and Google Drive Trigger with folder ID. Test with a sample PDF (<50MB) and query (e.g., 'What is in this document?'). Handle errors: invalid API keys—regenerate in platforms; large files—enforce size limits. Activate workflow in n8n. Monitor Milvus for vector updates. Optimize by adjusting chunk size or topK settings.", "businessValue": "Saves 12-15 hours/week on 100+ monthly queries", "setupTime": "60-75 minutes", "difficulty": "Advanced", "requirements": [ "Google Drive (free), Cohere (~$10/month), Milvus (~$50/month), OpenAI (~$20/month), n8n (~$20/month)", "Google Drive OAuth2, Cohere, Milvus, OpenAI API keys", "Knowledge of vector databases and RAG systems" ], "useCase": "Automating document-based question answering"