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MCP Part 2: Build Complete RAG System with Business Automation | Advanced n8n Tutorial

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Take your MCP skills to the next level! In this comprehensive tutorial, learn how to build a production-ready Retrieval Augmented Generation (RAG) system that combines intelligent document processing, vector search, web scraping, and automated lead management - all powered by Claude AI and n8n workflows.
Part 1 MCP Web Scraper: https://www.youtube.com/watch?v=oWZCw54tV-I&t=1s

About Part 2:
Note: Make sure to watch complete video as i did some corrections in video.

🎯 What You'll Build:
✅ Smart Document Indexing System - Process PDFs, Excel, CSV, and Google Docs automatically
✅ Pinecone Vector Database Integration - Store and retrieve documents with semantic search
✅ Advanced MCP Server - Multi-tool integration with Firecrawl, Gmail, and vector search
✅ Intelligent Business Chatbot - AI agent that answers questions and captures leads
✅ Automated Lead Management - Email collection and notification system
✅ Real-time Web Scraping - Combine indexed data with live web content

🛠️ Advanced Technologies:
Google Drive API - Automated file processing and extraction
Pinecone Vector Database - Semantic search and document retrieval
OpenAI Embeddings - Text-to-vector conversion for similarity search
Claude Sonnet 3.7 - Advanced reasoning and conversation handling
Firecrawl API - Professional web scraping and crawling
Gmail Integration - Automated email notifications and lead management
Multi-format Processing - PDF, Excel, CSV, Google Docs support

🏢 Real Business Use Case:
See how to build a complete SEO agency chatbot that:

Answers technical questions using indexed documentation
Recommends services based on customer needs
Captures and processes lead information automatically
Combines company knowledge with real-time web data

📚 Essential API Keys & Resources:
Get Your API Keys:
🔑 Anthropic Claude API: https://docs.anthropic.com/en/api/admin-api/apikeys/get-api-key
🔑 OpenAI API Keys: https://platform.openai.com/api-keys
🔑 Firecrawl API (500 Free Credits): https://www.firecrawl.dev/app
🔑 Pinecone Vector Database (Free Tier): https://www.pinecone.io/
Documentation & Code Examples:
📖 Firecrawl Scraping Guide: https://docs.firecrawl.dev/features/scrape
💻 Complete Workflow Templates: https://github.com/Sarfaraz021/MCP-Model-with-Scrapping-Gmail-and-Pinecone-Retrieval

🎯 Perfect For:
AI developers building RAG applications
Business owners wanting intelligent automation
Data engineers processing multiple file formats
Agencies needing smart lead capture systems
Anyone building production-ready AI workflows

🌟 Key Features Covered:
Multi-File Processing - Handle any document type automatically
Semantic Search - Find relevant information using AI embeddings
Conversation Memory - Maintain context across interactions
Lead Qualification - Intelligent customer data collection
Error Handling - Robust workflow design patterns
Scalable Architecture - Production-ready system design

💡 Business Applications:
Customer support automation
Knowledge base chatbots
Lead generation systems
Document analysis workflows
Content recommendation engines

🎁 Free Templates Available!
Get all three complete n8n workflow templates from my GitHub repository - no cost, ready to import and customize for your business!
📞 Connect & Get Support:
🔗 LinkedIn: https://www.linkedin.com/in/sarfaraz-ahmed07/
📬 Email: safiibhai74@gmail.com
💻 GitHub: https://github.com/Sarfaraz021
🏢 My Agency: https://datalinkk.com/

Need help implementing this in your business? I offer custom automation solutions!

💬 Questions? Drop them in the comments below!
👍 Found this valuable? Like and subscribe for more advanced AI tutorials!
🔔 Stay Updated - Hit the notification bell for cutting-edge automation content!

#n8n #MCP #RAG #VectorDatabase #Pinecone #ClaudeAI #WebScraping #BusinessAutomation #LeadGeneration #DocumentProcessing #SemanticSearch #AI #NoCode #WorkflowAutomation #Firecrawl #OpenAI #ChatBot #CustomerSupport

Видео MCP Part 2: Build Complete RAG System with Business Automation | Advanced n8n Tutorial канала Ahmed AI
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