Workflow Library
← LibraryPricingLog in
🧬 RAG & KnowledgeIntermediate🧠 AI-powered🧬 RAG

Personalized AI Tech Newsletter Using RSS, OpenAI and Gmail

A 17-step rag & knowledge automation, triggered by schedule, connecting OpenAI Embeddings, OpenAI (LLM), Gmail, with built-in AI reasoning.

⬇ Download workflow JSON
💡

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 Embeddings, OpenAI (LLM), Gmail — great for support and internal knowledge.

17
Nodes
3
Integrations
15
Connections
Schedule
Trigger

🚀 Deploy this workflow

Deploy this automation in minutes

Log in and we'll walk you through every key step by step, then deploy it for you.

Get started free Log in

🏗️ Architecture diagram

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

•Split OutsplitOut•Normalize Fieldsset🧬Embeddings OpenAIembeddingsOpenAi•Default Data LoaderdocumentDefaultData…•Recursive Character T…textSplitterRecursi…🧠OpenAI Chat ModellmChatOpenAi▶Get Articles DailyscheduleTrigger▶Send Weekly SummaryscheduleTrigger🧠News reader AIagent📧Send Newslettergmail•Convert Response to a…markdown•Your topics of intere…set•Store News ArticlesvectorStoreInMemory•Set Tech News RSS Fee…set•Read RSS News FeedsrssFeedRead•Get News ArticlesvectorStoreInMemory🧬Embeddings OpenAI2embeddingsOpenAi

⚙️ How it works

  1. Embeddings OpenAI · embeddingsOpenAi
  2. Recursive Character Text Splitter · textSplitterRecursiveCharacterTextSplitter
  3. OpenAI Chat Model · lmChatOpenAi
  4. Get Articles Daily · scheduleTrigger
  5. Send Weekly Summary · scheduleTrigger
  6. Embeddings OpenAI2 · embeddingsOpenAi
  7. Store News Articles · vectorStoreInMemory
  8. Default Data Loader · documentDefaultDataLoader
  9. News reader AI · agent
  10. Set Tech News RSS Feeds · set
  11. Your topics of interest · set
  12. Get News Articles · vectorStoreInMemory
  13. Convert Response to an Email-Friendly Format · markdown
  14. Split Out · splitOut

💼 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 Embeddings🧠OpenAI (LLM)📧Gmail
Node typessplitOut, set, embeddingsOpenAi, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, lmChatOpenAi, scheduleTrigger, agent, gmail, markdown, vectorStoreInMemory, rssFeedReadWorkflow IDWZ3UJk9uczejj4rL
⬇ Download workflow JSON ← Back to library