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🧬 RAG & KnowledgeIntermediate🧠 AI-powered🧬 RAG

Imported Workflow

A 31-step rag & knowledge automation, triggered by manual, webhook, schedule, connecting OpenAI (LLM), OpenAI Embeddings, HTTP / REST API, Webhook, with built-in AI reasoning.

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💡

What this does for your business

In plain terms: this turns your documents into a smart Q&A brain. It reads and remembers your content, then answers questions accurately using OpenAI (LLM), OpenAI Embeddings — great for support and internal knowledge.

31
Nodes
4
Integrations
29
Connections
Manual, Webhook, Schedule
Trigger

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🏗️ Architecture diagram

The real node graph exported from n8n — triggers (green), AI nodes (violet), services and logic — laid out as they run.

▶When clicking ‘Test w…manualTrigger🧠AI Agentagent🧠OpenAI Chat ModellmChatOpenAi🧠Window Buffer MemorymemoryBufferWindow•Vector Store RetrieverretrieverVectorStore•In-Memory Vector StorevectorStoreInMemory🧬Embeddings OpenAIembeddingsOpenAi•Question and Answer C…chainRetrievalQa•Switchswitch•IFif•Dummy NodenoOp•Dummy Node (1)noOp•Loop Over ItemssplitInBatches•Dummy Node (2)noOp•Dummy Node (3)noOp•Dummy Node (4)noOp•Dummy Node (5)noOp•Dummy Node (6)noOp•Dummy Node (7)noOp•Dummy Node (8)noOp•Dummy Node (9)noOp🧠Dummy TooltoolHttpRequest🧠Dummy Tool (1)toolHttpRequest🧠OpenAI Chat Model (1)lmChatOpenAi•Update n8n Workflown8n🌐Magic Positioning IA2ShttpRequest🔗POST /workflow/magic/…webhook•Get n8n Workflown8n•Simple Webhook Respon…respondToWebhook▶Schedule TriggerscheduleTrigger🌐Magic PositioninghttpRequest

⚙️ How it works

  1. When clicking ‘Test workflow’ · manualTrigger
  2. OpenAI Chat Model · lmChatOpenAi
  3. Window Buffer Memory · memoryBufferWindow
  4. Embeddings OpenAI · embeddingsOpenAi
  5. Dummy Tool · toolHttpRequest
  6. Dummy Tool (1) · toolHttpRequest
  7. OpenAI Chat Model (1) · lmChatOpenAi
  8. POST /workflow/magic/position/id · webhook
  9. Schedule Trigger · scheduleTrigger
  10. Magic Positioning · httpRequest
  11. IF · if
  12. AI Agent · agent
  13. In-Memory Vector Store · vectorStoreInMemory
  14. Question and Answer Chain · chainRetrievalQa

💼 Business analysis

🎯

Business use case

Builds a retrieval-augmented knowledge system: content is embedded into a vector store and served back as grounded, citeable answers. Ideal for support deflection, internal knowledge bases, and AI assistants that must stay factual.

📈

Market need

Generic chatbots hallucinate; companies need answers grounded in their own documents. RAG is the proven pattern, and a working pipeline is scarce and valuable.

💰

Market value

RAG assistant implementations typically run $5,000–$20,000 bespoke. Packaged, it underpins support-deflection tools that sell for $99–$999/mo.

🧩 Integrations & stack

🧠OpenAI (LLM)🧬OpenAI Embeddings🌐HTTP / REST API🔗Webhook
Node typesmanualTrigger, agent, lmChatOpenAi, memoryBufferWindow, retrieverVectorStore, vectorStoreInMemory, embeddingsOpenAi, chainRetrievalQa, switch, if, noOp, splitInBatches, toolHttpRequest, n8n, httpRequest, webhook, respondToWebhook, scheduleTriggerWorkflow IDA5tffOa0C0P93PJz
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