Build AI Email Search That Finds Anything in 10K Emails Instantly (n8n)
Transform how you interact with your email! Learn how to create an AI-powered search capability that makes finding important information in your emails effortless and intelligent.
Template Download ⬇️: https://gettingautomated.com/build-ai...
Github Repo 🖥️: https://github.com/Getting-Automated/...
🔥 What You'll Learn:
• How to build a RAG-based AI email search system
• Setting up vector databases for email storage
• Creating embeddings for intelligent search
• Building both no-code (n8n) and code-based solutions
• Deploying a user-friendly chatbot interface
🛠️ Core Components:
• Email processing and extraction
• Vector database setup (PG Vector, Pinecone, or Supabase)
• Embedding generation with OpenAI
• Streamlit for the chatbot interface
• RAG (Retrieval Augmented Generation) implementation
💻 Hands-On Tutorial:
• Setting up email extraction workflows
• Configuring your vector database
• Creating and storing embeddings
• Building the search interface
• Testing and querying your email archive
💰 Cost Breakdown:
• Vector Database: $15-25/month
• OpenAI API: $12-20/month for 100k emails
• Total estimated cost: $33-65/month
• Budget option available for less than $20/month
🎯 Perfect for:
• Business professionals drowning in emails
• Teams needing better email knowledge management
• Anyone wanting to leverage AI for email organization
• Developers interested in RAG implementations
💼 Want to implement this in your business? Book a call: https://calendly.com/workflowsy/30-mi...
🔗 Resources:
n8n Cloud (affiliate link): https://n8n.partnerlinks.io/getting-a...
AI Workflow Tools: https://tools.gettingautomated.com
Explore our automation consulting: https://workflowsy.io/
🙏 Like, subscribe, and share your thoughts in the comments!
#AISearch #EmailAutomation #VectorDatabases #RAG #Streamlit #n8n #Python
Timestamps:
0:00 - Introduction and overview
0:32 - Chatbot Preview
1:16 - What To Expect
2:11 - About Getting Automated
2:51 - The email search challenge
4:08 - The Mission
4:42 - Anatomy of the AI Search Chatbot
9:38 - Core Components of Setup
13:03 - Putting It All Together
14:40 - What about No / Low Code?
16:50 - The Workflows + Repo
15:20 - Building the chatbot interface
17:55 - Live demo of the search capability (n8n & Code)
26:25 - Real-world use cases
28:06 - Cost breakdown
30:54 - Implementation options
32:03 - Getting Started
33:04 - Closing Thoughts
36:44 - Connect With Me + Thank You For Watching