Hands-On Rag With Langchain: Build Real-World Projects

Master Retrieval-Augmented Generation by building practical, production-ready applications with LangChain

What you'll learn

Set up PostgreSQL with the pgvector extension to enable efficient vector search

Build an end to end RAG pipeline connecting Large Language Models LLMs with PostgreSQL

Implement a RAG pipeline step-by-step: retrieval, context injection, and grounded LLM responses.

Explore vector indexes (HNSW, IVFFlat) and learn how they improve retrieval speed and accuracy.

Apply semantic caching to reduce cost and latency while improving response times.

Evaluate RAG systems using RAGAS metrics like faithfulness, context recall, and precision.

Enhance retrieval quality with re-ranking and metadata filtering.

Deploy APIs for ingestion and RAG Q&A to make your projects production-ready and testable.

Requirements

Basic Python knowledge (functions, imports, virtual environments).

Familiarity with APIs and JSON is helpful but not mandatory.

An OpenAI API key

Docker basics are a plus, but we’ll cover the commands you need.

Curiosity to build real-world AI apps using vector databases and LLMs.

Visual Studio Code,Docker,PGAdmin

Description

Retrieval-Augmented Generation (RAG) is one of the most powerful ways to make Large Language Models (LLMs) smarter, more reliable, and production-ready. Instead of depending only on what the model “knows,” RAG allows us to fetch relevant knowledge from external sources and provide precise, up-to-date answers.In this hands-on course, you’ll go beyond the basics and actually build RAG pipelines step by step using LangChain, the leading framework for LLM applications. Whether you are a developer, data scientist, or AI enthusiast, this course will give you the practical skills to design, implement, and optimize real-world RAG projects.What You’ll LearnReal-World Project: Build two end-to-end RAG Projects on Company Data and E-Commerce Semantic Search.Caching Strategies: Use embedding and response caching to reduce cost, latency, and improve efficiency.Indexing: Explore Flat, IVF Flat, HNSW, and disk-based indexes; learn which one to use for your dataset.Reranking: Improve answer precision using similarity scores, cross-encoders, and LLM-based reranking.Evaluations (Evals & Ragas): Measure faithfulness, relevance, and retrieval quality with Ragas metrics.Metadata: Use metadata filters to make retrieval precise, context-aware, and production-ready.Why Take This Course?It’s hands-on — you won’t just learn theory; you’ll build working RAG pipelines.You’ll learn best practices for scaling from demo to production.Content is designed for real-world applications in enterprise, startups, and research.You’ll walk away with code, skills, and confidence to build your own RAG-powered apps.Who This Course Is ForDevelopers and data scientists interested in LangChain and LLM applications.AI/ML engineers who want to deploy production-ready RAG systems.Professionals curious about vector databases, embeddings, and retrieval systems.Anyone who wants to go beyond ChatGPT and build AI that leverages their own data.By the end of this course, you’ll have the knowledge and hands-on experience to design and implement efficient RAG pipelines with LangChain — and the skills to apply them to your own projects or business use cases.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Software Setup

Lecture 3 Download Slides

Lecture 4 Download Projects

Lecture 5 Why PostgreSQL for Vector Search?

Section 2: Real-World RAG Implementation

Lecture 6 Usecase and Project Walkthrough

Lecture 7 Install Requirements Locally

Lecture 8 Use PGAdmin

Lecture 9 Connect to vector db

Lecture 10 Load Docs

Lecture 11 Chunk

Lecture 12 Ingest

Lecture 13 Implement Ingest API

Lecture 14 Test Ingest

Lecture 15 E-Commerce Semantic Search Assignment Walkthrough

Lecture 16 Implement RAG Pipeline - Build Chain

Lecture 17 Implement RAG Pipeline - Answer

Lecture 18 Implement RAG API

Lecture 19 Test RAG

Lecture 20 Langsmith Dashboard

Section 3: Indexes

Lecture 21 Introduction

Lecture 22 Code Walkthrough

Lecture 23 Create Index

Section 4: Caching

Lecture 24 Introduction

Lecture 25 Semantic Cache In Action

Lecture 26 Test Cache

Section 5: Evals

Lecture 27 Introduction

Lecture 28 Code Walkthrough

Lecture 29 Return Chunks

Lecture 30 Implement Evals

Lecture 31 Trace with langsmith

Section 6: Re-Ranking

Lecture 32 Introduction

Lecture 33 Re-Ranking Implementation

Section 7: Using Metadata

Lecture 34 Introduction

Lecture 35 Update Schema

Lecture 36 Ingest Metadata

Lecture 37 Use Metadata Filtering

Lecture 38 Add API Support

Lecture 39 Test

Section 8: Wrap Up

Lecture 40 Bonus Lecture

Students who have completed my LangChain course and now want to dive deeper into advanced RAG use cases.,Python developers who want to integrate LLMs and vector search into real-world apps.,Data engineers & ML practitioners curious about Retrieval-Augmented Generation.,Backend developers exploring LangChain, RAG pipelines, and vector databases.,Anyone with basics of RAG and wants hands-on projects and not just theory.