Templates / Create a private document Q&A system with Llama3, Postgres, Qdrant and Google Drive

Create a private document Q&A system with Llama3, Postgres, Qdrant and Google Drive

Ready for 3.0

by David Olusola ✓ · 4,033 views · created 2025-07-01 · Internal Wiki, AI RAG

Ready for 3.0. This template uses no nodes that n8n 3.0 removes. No removed nodes, versions or helpers found. Static check, last run 2026-10-19.

What this workflow does

This AI workflow has 17 nodes and runs when a chat message arrives (plus 2 other triggers). Along the way it connects Ollama Chat Model, Postgres Chat Memory, Qdrant Vector Store and Ollama Model and 3 more.

How it runs

Trigger: When a chat message arrives (plus 2 other triggers).

  1. AI Agent: runs an AI agent, with model: Ollama Chat Model; 1 tool (Answer questions with a vector store); memory: Postgres Chat Memory
  2. Set File ID (Edit Fields (Set)): sets 2 fields: file_id, folder_id
  3. Download File (Google Drive): download
  4. Extract Document Text (Extract from File): extracts data from a file (text)
  5. Qdrant Vector Store: stores documents in the Qdrant vector store, with embeddings: Embeddings Ollama; document loader: Default Data Loader

Services it uses

Ollama Chat ModelPostgres Chat MemoryQdrant Vector StoreOllama ModelEmbeddings OllamaVector Store Question AnswerGoogle Drive

Before you upgrade to n8n 3.0

Nothing in this workflow is removed in n8n 3.0, so it should import and run after the upgrade.

Nodes used (14)

From the author

⚙️ How It Works: LocalRAG.AI ⚠️ Note: This system only works for self-hosted n8n instances. It will not function on n8n.cloud or other remote setups. LocalRAG.AI is a private, on-prem AI assistant that uses your own documents to answer questions…

Excerpt from David Olusola's description on n8n.io.

View the original on n8n.io → Check your own copy

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