40 LPA Series Day 58 | Advanced RAG Tutorial | LangChain, ChromaDB & Vector Database Explained

CodeWithPrashant · Beginner ·🔍 RAG & Vector Search ·3w ago

Skills: Advanced RAG90%Vector Stores80%

Key Takeaways

Explains advanced RAG tutorial using LangChain, ChromaDB, and vector databases

Original Description

Welcome to Day 58of the 40 LPA Series! 🚀 In this video, we dive deep into Advanced RAG (Retrieval-Augmented Generation) and learn how modern AI applications retrieve relevant information using Vector Databases, Embeddings, LangChain, ChromaDB, and LLMs. What you'll learn: Advanced RAG Architecture Vector Search Explained Embeddings & Semantic Search LangChain Integration ChromaDB Setup & Usage Knowledge Retrieval Pipeline Production AI Application Design Best Practices for RAG Systems Whether you're preparing for AI Engineer roles, building GenAI products, or targeting high-paying software jobs, this video will help you understand the core concepts behind modern AI assistants and chatbots. 🔥 Part of the 40 LPA Roadmap Series designed to help developers become industry-ready. ⏱️ Chapters 00:00 Introduction 01:20 What is Advanced RAG? 05:10 Embeddings Explained 10:45 Vector Database Architecture 18:30 LangChain Integration 27:50 ChromaDB Implementation 35:20 End-to-End RAG Pipeline 45:00 Best Practices & Optimization 🏷️ Hashtags #AdvancedRAG #RAG #GenAI #AIEngineer #LangChain #ChromaDB #VectorDatabase #LLM #OpenAI #MachineLearning #ArtificialIntelligence #Python #FastAPI #MERNStack #SoftwareEngineer #TechCareer #CodingTutorial #40LPASeries #AIProjects #FullStackDeveloper 🔍 Tags Advanced RAG, RAG Tutorial, LangChain Tutorial, ChromaDB Tutorial, Vector Database, Embeddings, Semantic Search, LLM, Generative AI, AI Engineer Roadmap, AI Projects, ChatGPT, OpenAI API, Python AI Project, FastAPI, Machine Learning, Gen AI Course, Retrieval Augmented Generation, AI Developer, 40 LPA Series, Prashant Pal, CodeWithPrashant30, AI Interview Preparation, Production RAG, Knowledge Retrieval System, AI Application Development, Vector Search, RAG Architecture, AI Tutorial Hindi, LangChain ChromaDB Project

Watch on YouTube ↗ (saves to browser)

Sign in to unlock AI tutor explanation · ⚡30

More on: Advanced RAG

View skill →

Advanced RAG Patterns

Advanced RAG Patterns

Advanced RAG 06 - RAG Fusion

Advanced RAG 06 - RAG Fusion

Advanced RAG 01: Small-to-Big Retrieval with LlamaIndex

Advanced RAG 01: Small-to-Big Retrieval with LlamaIndex

Sophia Yang (AI)

Toy Augmented Generation Project to a Production-Ready AI System

Toy Augmented Generation Project to a Production-Ready AI System

LLMOPS 02: RAG Analysis & Evaluation Strategy Part-2 | Advanced RAG Pipeline in LLMOPS

LLMOPS 02: RAG Analysis & Evaluation Strategy Part-2 | Advanced RAG Pipeline in LLMOPS

Advanced RAG Starts Today 🚀 | Day 54/180 AI Engineering Challenge | RAG Series Day 13

Advanced RAG Starts Today 🚀 | Day 54/180 AI Engineering Challenge | RAG Series Day 13

CodeWithPrashant

Related Reads

Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40%

Learn how to optimize RAG at scale using chunking, retrieval, and Bayesian search to reduce latency by 40% and achieve 95% recall@10

Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40%

Learn how to optimize RAG at scale using chunking, retrieval, and Bayesian search to reduce latency by 40%

Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40%

Optimize RAG at scale using chunking, retrieval, and Bayesian search to reduce latency by 40% and achieve 95% recall@10

Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40%

Learn how to optimize RAG at scale using chunking, retrieval, and Bayesian search to reduce latency by 40%

Chapters (8)

Introduction

1:20 What is Advanced RAG?

5:10 Embeddings Explained

10:45 Vector Database Architecture

18:30 LangChain Integration

27:50 ChromaDB Implementation

35:20 End-to-End RAG Pipeline

45:00 Best Practices & Optimization

Build a Chatbot with RAG in 10 minutes | Python, LangChain, OpenAI