A 26-step rag & knowledge automation, triggered by chat, connecting Google Drive, OpenAI Embeddings, HTTP / REST API, OpenAI (LLM), with built-in AI reasoning.
In plain terms: this turns your documents into a smart Q&A brain. It reads and remembers your content, then answers questions accurately using Google Drive, OpenAI Embeddings, OpenAI (LLM) — great for support and internal knowledge.
26
Nodes
4
Integrations
24
Connections
Chat
Trigger
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The real node graph exported from n8n — triggers (green), AI nodes (violet), services and logic — laid out as they run.
⚙️ How it works
File Created· googleDriveTrigger
File Updated· googleDriveTrigger
Embeddings OpenAI· embeddingsOpenAi
Qdrant – Create Collection· httpRequest
When chat message received· chatTrigger
Create the - Qdrant Index· httpRequest
Embeddings OpenAI2· embeddingsOpenAi
OpenAI Chat Model· lmChatOpenAi
Simple Memory· memoryBufferWindow
Recursive Character Text Splitter· textSplitterRecursiveCharacterTextSplitter
Think· toolThink
Loop Over Items· splitInBatches
Qdrant Vector Store· vectorStoreQdrant
AI Agent· agent
💼 Business analysis
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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.
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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.
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Market value
RAG assistant implementations typically run $5,000–$20,000 bespoke. Packaged, it underpins support-deflection tools that sell for $99–$999/mo.