Dify: guides & tutorials

Dify and Flowise both let you build LLM-powered applications through a visual interface without writing a full backend. But they are aimed at different maturity stages: Flowise for rapid experimentation, Dify for production-grade deployments where teams need RAG knowledge bases, API publishing, and multi-user workspaces.

TL;DR

• One docker-compose.yml: Dify (API + worker + web + sandbox) + Qdrant + Ollama + PostgreSQL + Redis + Caddy
• Measured idle RAM: ~7.4 GB (Llama 3 8B loaded, CPU inference); minimum server: 16 GB VPS
• Replaces: OpenAI Assistants API ($0.03/1k tokens) + Pinecone Starter ($70/mo) with a fully local, zero-egress alternative
• Your documents never leave the server — not during upload, not during embedding, not during inference

Retrieval-Augmented Generation (RAG) lets you ask questions against a private document corpus and get answers grounded in the actual content. The dominant hosted pattern — OpenAI Assistants + Pinecone — works, but every document chunk and every query travels to OpenAI's servers. For legal contracts, internal knowledge bases, or any non-public data, that is a compliance liability.

This guide deploys a fully self-hosted RAG stack: Dify as the orchestration and document management layer, Qdrant as the vector database, and Ollama serving both the embedding model and the chat LLM — all on a single Liquid Web VPS.