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Agentic Ai Full‑stack Masterclass: Rag, Mcp & Ai Agents
Published 12/2025
Created by Nikhil Agarwal
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 70 Lectures ( 11h 28m ) | Size: 15.8 GB [/center]
Build Full-Stack Agentic AI: Angular, Node.js, RAG, Vector DBs, Custom MCP Servers, Gemini & OpenAI Tool Calling.
What you'll learn
Architect and build a complete Full-Stack Agentic AI application using Angular, Node.js, and Express.
Implement advanced Retrieval Augmented Generation (RAG) pipelines with embeddings, vector search, and context augmentation.
Master the Model Context Protocol (MCP) by building custom MCP Servers in Node.js to expose real-world tools to LLMs.
Build a production-ready Chat Interface in Angular that handles streaming responses, Markdown rendering, and tool outputs.
Set up and manage Vector Databases (ChromaDB and pgVector) to store high-dimensional embeddings for semantic search.
Create Static RAG Systems using JSON and math-based Cosine Similarity to understand the core algorithms of retrieval.
Implement Native Tool Calling with Gemini and OpenAI to turn natural language into executable code functions.
Connect your RAG Engine as an MCP Tool, creating a modular system where Agents can "choose" to search your database.
Requirements
Solid understanding of JavaScript & TypeScript: You must be comfortable with Async/Await, Promises, and ES6+ syntax.
Basic Node.js & Express: We build a backend API, so you should know how to set up a server and routes.
Frontend Fundamentals: Experience with Angular is required. We move fast on the UI (70% is made with Vibe Coding).
No AI Experience Needed: I will teach you RAG, Vector DBs, and MCP from the ground up.
Description
Stop building basic chatbots. It is time to build intelligent AI Agents that can actually do things.Welcome to the Agentic AI Full-Stack Masterclass, the only course you need to bridge the gap between simple LLM wrappers and complex, production-ready Agentic Systems.Most developers are stuck building "chat with PDF" apps. In this course, we go levels deeper. You will architect a Full-Stack Agentic Application from scratch using Angular and Node.js, integrating cutting-edge protocols like MCP (Model Context Protocol) and advanced RAG pipelines.Why this course? The industry is shifting from "Generative AI" to "Agentic AI." Companies don't just want text; they want Agents that can query databases, execute tools, and perform actions. This course puts you ahead of that curve.What you will build: We will build a professional-grade AI platform with a modern Angular UI and a robust Node.js/Express backend. You won't just learn syntax; you will learn the architecture behind autonomous systems.Key Technical Deep Dives:The Model Context Protocol (MCP): Be one of the first to master this standard. You will build Custom MCP Servers in Node.js to connect your AI to real-world data (like Weather APIs) and expose them as tools to Claude, Gemini, or OpenAI.Advanced RAG Pipelines: Move beyond basics. We implement Vector Search using ChromaDB and pgVector, handling embeddings, chunking, and ingestion manually to give you total control.Native Tool Calling: Learn how to make LLMs (Gemini & OpenAI) strictly structured JSON to trigger functions in your code-the backbone of Agentic AI.Math & Theory: We don't just import libraries. We cover the logic behind Cosine Similarity, Vector spaces, and Retrieval scoring so you understand why your retrieval works.The Tech Stack:Frontend: Angular (Latest), TailwindCSSBackend: Node.js, ExpressAI Models: Google Gemini, OpenAI (ChatGPT Versions)Vector Databases: ChromaDB, pgVector (PostgreSQL)Protocols: MCP (Model Context Protocol)If you are ready to stop playing with toys and start building intelligent, agentic systems, enroll now. Let's write some code.3. What will students learn in your course?(These are corrected and punchy. I fixed your "context au" and "fundar" errors.)Architect and build a complete Full-Stack Agentic AI application using Angular, Node.js, and Express.Implement advanced Retrieval Augmented Generation (RAG) pipelines with embeddings, vector search, and context augmentation.Master the Model Context Protocol (MCP) by building custom MCP Servers in Node.js to expose real-world tools to LLMs.Build a production-ready Chat Interface in Angular that handles streaming responses, Markdown rendering, and tool outputs.Set up and manage Vector Databases (ChromaDB and pgVector) to store high-dimensional embeddings for semantic search.Create Static RAG Systems using JSON and math-based Cosine Similarity to understand the core algorithms of retrieval.Implement Native Tool Calling with Gemini and OpenAI to turn natural language into executable code functions.Connect your RAG Engine as an MCP Tool, creating a modular system where Agents can "choose" to search your database.4. Requirements (Prerequisites)(I tightened this. Do not welcome absolute beginners; they will ruin your rating.)Solid understanding of JavaScript & TypeScript: You must be comfortable with Async/Await, Promises, and ES6+ syntax.Basic Node.js & Express: We build a backend API, so you should know how to set up a server and routes.Frontend Fundamentals: Experience with Angular (or React/Vue) is required. We move fast on the UI.No AI Experience Needed: I will teach you RAG, Vector DBs, and MCP from the ground up.5. Who is this course for? (Intended Learners)Full-Stack Developers who want to transition from traditional web apps to building AI-powered Agentic systems.AI Enthusiasts looking to master the Model Context Protocol (MCP) and modern Tool Calling standards.Software Engineers who need to implement RAG and Vector Search without relying on "black box" frameworks like LangChain.Freelancers & Consultants who want to offer high-value "Custom AI Agent" services to clients.
Who this course is for
Full-Stack Developers who want to transition from traditional web apps to building AI-powered Agentic systems.
AI Enthusiasts looking to master the Model Context Protocol (MCP) and modern Tool Calling standards.
Software Engineers who need to implement RAG and Vector Search without relying on "black box" frameworks like LangChain.
Freelancers & Consultants who want to offer high-value "Custom AI Agent" services to clients.
Agentic Ai Full‑stack Masterclass: Rag, Mcp & Ai Agents
Published 12/2025
Created by Nikhil Agarwal
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 70 Lectures ( 11h 28m ) | Size: 15.8 GB [/center]
Build Full-Stack Agentic AI: Angular, Node.js, RAG, Vector DBs, Custom MCP Servers, Gemini & OpenAI Tool Calling.
What you'll learn
Architect and build a complete Full-Stack Agentic AI application using Angular, Node.js, and Express.
Implement advanced Retrieval Augmented Generation (RAG) pipelines with embeddings, vector search, and context augmentation.
Master the Model Context Protocol (MCP) by building custom MCP Servers in Node.js to expose real-world tools to LLMs.
Build a production-ready Chat Interface in Angular that handles streaming responses, Markdown rendering, and tool outputs.
Set up and manage Vector Databases (ChromaDB and pgVector) to store high-dimensional embeddings for semantic search.
Create Static RAG Systems using JSON and math-based Cosine Similarity to understand the core algorithms of retrieval.
Implement Native Tool Calling with Gemini and OpenAI to turn natural language into executable code functions.
Connect your RAG Engine as an MCP Tool, creating a modular system where Agents can "choose" to search your database.
Requirements
Solid understanding of JavaScript & TypeScript: You must be comfortable with Async/Await, Promises, and ES6+ syntax.
Basic Node.js & Express: We build a backend API, so you should know how to set up a server and routes.
Frontend Fundamentals: Experience with Angular is required. We move fast on the UI (70% is made with Vibe Coding).
No AI Experience Needed: I will teach you RAG, Vector DBs, and MCP from the ground up.
Description
Stop building basic chatbots. It is time to build intelligent AI Agents that can actually do things.Welcome to the Agentic AI Full-Stack Masterclass, the only course you need to bridge the gap between simple LLM wrappers and complex, production-ready Agentic Systems.Most developers are stuck building "chat with PDF" apps. In this course, we go levels deeper. You will architect a Full-Stack Agentic Application from scratch using Angular and Node.js, integrating cutting-edge protocols like MCP (Model Context Protocol) and advanced RAG pipelines.Why this course? The industry is shifting from "Generative AI" to "Agentic AI." Companies don't just want text; they want Agents that can query databases, execute tools, and perform actions. This course puts you ahead of that curve.What you will build: We will build a professional-grade AI platform with a modern Angular UI and a robust Node.js/Express backend. You won't just learn syntax; you will learn the architecture behind autonomous systems.Key Technical Deep Dives:The Model Context Protocol (MCP): Be one of the first to master this standard. You will build Custom MCP Servers in Node.js to connect your AI to real-world data (like Weather APIs) and expose them as tools to Claude, Gemini, or OpenAI.Advanced RAG Pipelines: Move beyond basics. We implement Vector Search using ChromaDB and pgVector, handling embeddings, chunking, and ingestion manually to give you total control.Native Tool Calling: Learn how to make LLMs (Gemini & OpenAI) strictly structured JSON to trigger functions in your code-the backbone of Agentic AI.Math & Theory: We don't just import libraries. We cover the logic behind Cosine Similarity, Vector spaces, and Retrieval scoring so you understand why your retrieval works.The Tech Stack:Frontend: Angular (Latest), TailwindCSSBackend: Node.js, ExpressAI Models: Google Gemini, OpenAI (ChatGPT Versions)Vector Databases: ChromaDB, pgVector (PostgreSQL)Protocols: MCP (Model Context Protocol)If you are ready to stop playing with toys and start building intelligent, agentic systems, enroll now. Let's write some code.3. What will students learn in your course?(These are corrected and punchy. I fixed your "context au" and "fundar" errors.)Architect and build a complete Full-Stack Agentic AI application using Angular, Node.js, and Express.Implement advanced Retrieval Augmented Generation (RAG) pipelines with embeddings, vector search, and context augmentation.Master the Model Context Protocol (MCP) by building custom MCP Servers in Node.js to expose real-world tools to LLMs.Build a production-ready Chat Interface in Angular that handles streaming responses, Markdown rendering, and tool outputs.Set up and manage Vector Databases (ChromaDB and pgVector) to store high-dimensional embeddings for semantic search.Create Static RAG Systems using JSON and math-based Cosine Similarity to understand the core algorithms of retrieval.Implement Native Tool Calling with Gemini and OpenAI to turn natural language into executable code functions.Connect your RAG Engine as an MCP Tool, creating a modular system where Agents can "choose" to search your database.4. Requirements (Prerequisites)(I tightened this. Do not welcome absolute beginners; they will ruin your rating.)Solid understanding of JavaScript & TypeScript: You must be comfortable with Async/Await, Promises, and ES6+ syntax.Basic Node.js & Express: We build a backend API, so you should know how to set up a server and routes.Frontend Fundamentals: Experience with Angular (or React/Vue) is required. We move fast on the UI.No AI Experience Needed: I will teach you RAG, Vector DBs, and MCP from the ground up.5. Who is this course for? (Intended Learners)Full-Stack Developers who want to transition from traditional web apps to building AI-powered Agentic systems.AI Enthusiasts looking to master the Model Context Protocol (MCP) and modern Tool Calling standards.Software Engineers who need to implement RAG and Vector Search without relying on "black box" frameworks like LangChain.Freelancers & Consultants who want to offer high-value "Custom AI Agent" services to clients.
Who this course is for
Full-Stack Developers who want to transition from traditional web apps to building AI-powered Agentic systems.
AI Enthusiasts looking to master the Model Context Protocol (MCP) and modern Tool Calling standards.
Software Engineers who need to implement RAG and Vector Search without relying on "black box" frameworks like LangChain.
Freelancers & Consultants who want to offer high-value "Custom AI Agent" services to clients.
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