Hands-on LangChain
Master LangChain for building production-ready RAG systems. Learn document loaders, embeddings, vector stores, chains, and agents through 10+ hands-on projects.
Created by Baljeet Dogra
Course Overview
A comprehensive 6-week hands-on course on LangChain. Learn to build production-ready RAG systems from scratch, covering all components from document loading to agentic systems.
Production-Ready RAG
Build complete RAG systems from document loading to answer generation. Learn best practices for production deployment.
Comprehensive Coverage
Master document loaders, text splitters, embeddings, vector stores, retrievers, chains, and agents. Everything you need for RAG.
Hands-On Projects
Build 10+ real-world projects including document Q&A systems, multi-source RAG, web scraping RAG, and agentic assistants.
Real-World Applications
Learn to build document Q&A systems, knowledge bases, chatbots, and agentic AI assistants using LangChain.
Multiple Vector Stores
Work with FAISS, Chroma, Pinecone, Weaviate, and more. Learn when to use each and how to optimize performance.
Advanced Techniques
Master advanced RAG techniques including MMR retrieval, multi-query retrieval, re-ranking, and chain types (stuff, map_reduce, refine).
6-Week Curriculum
A structured 6-week program covering all aspects of LangChain for RAG systems. Each week includes hands-on projects and practical exercises.
Week 1: LangChain Foundations & Document Loading
Module 1: Introduction to LangChain
LangChain architecture, core concepts, installation, and setup. Understanding chains, components, and the LangChain ecosystem.
Project:
Setup LangChain environment and build your first simple chain
Module 2: Document Loaders
PyPDFLoader, TextLoader, CSVLoader, DirectoryLoader, WebBaseLoader. Loading documents from various sources.
Project:
Build a multi-format document loader system
Module 3: Text Splitting Strategies
RecursiveCharacterTextSplitter, chunk size, overlap, separators. Best practices for document chunking.
Project:
Implement optimal chunking strategy for different document types
Week 2: Embeddings & Vector Stores
Module 4: Embeddings Deep Dive
OpenAIEmbeddings, HuggingFaceEmbeddings, embedding models, batch processing, cost optimization.
Project:
Compare different embedding models and optimize for your use case
Module 5: Vector Stores - FAISS & Chroma
FAISS for local storage, Chroma for persistent storage, indexing strategies, similarity search.
Project:
Build a document search system with FAISS and Chroma
Module 6: Cloud Vector Stores
Pinecone, Weaviate, Qdrant. Managed vector stores, scaling, production deployment considerations.
Project:
Deploy a scalable RAG system using Pinecone
Week 3: Retrievers & RAG Chains
Module 7: Retrievers & Search Strategies
Similarity search, MMR (Maximal Marginal Relevance), search parameters, k selection, fetch_k optimization.
Project:
Build a retriever with MMR for diverse document retrieval
Module 8: RetrievalQA Chain
Building your first RAG chain, RetrievalQA.from_chain_type, prompt customization, source documents.
Project:
Build a complete document Q&A system
Module 9: Chain Types - Stuff, Map_Reduce, Refine
Understanding different chain types, when to use each, handling long documents, cost considerations.
Project:
Compare chain types and optimize for your document length
Week 4: Advanced RAG Techniques
Module 10: Multi-Query Retrieval
Query expansion, generating multiple queries, combining results, improving retrieval quality.
Project:
Implement multi-query retrieval for better answer quality
Module 11: Re-ranking & Hybrid Search
Re-ranking retrieved documents, hybrid search (semantic + keyword), improving answer relevance.
Project:
Build a hybrid search system with re-ranking
Module 12: Custom Prompts & Memory
Custom prompt templates, conversation memory, chat history, context window management.
Project:
Build a conversational RAG system with memory
Week 5: LangChain Agents
Module 13: Introduction to Agents
Agent architecture, tools, ReAct pattern, agent types, decision-making process.
Project:
Build your first LangChain agent with tools
Module 14: Building Custom Tools
Creating custom tools, tool descriptions, tool selection, error handling in agents.
Project:
Create custom tools and build a specialized agent
Module 15: RAG Agents
Combining RAG with agents, agentic RAG workflows, multi-step reasoning, tool-augmented RAG.
Project:
Build an agentic RAG system with multiple tools
Week 6: Production Deployment & Capstone
Module 16: Performance Optimization
Optimizing retrieval speed, batch processing, caching strategies, cost optimization, monitoring.
Project:
Optimize a RAG system for production performance
Module 17: Production Deployment
Deploying RAG systems, API design, error handling, logging, monitoring, scaling strategies.
Project:
Deploy a RAG system as a production API
Module 18: Capstone Project
Build a complete production-ready RAG system from scratch. Multi-source documents, advanced retrieval, agentic capabilities.
Capstone:
Enterprise Knowledge Base with RAG and Agentic Capabilities
Technical Requirements
Everything you need to get started with LangChain.
Prerequisites
- Python 3.8 or higher
- Basic understanding of Python programming
- Familiarity with LLMs and embeddings (helpful but not required)
- API keys for OpenAI or other LLM providers
Required Libraries
pip install langchain
pip install langchain-openai
pip install langchain-community
pip install faiss-cpu
pip install chromadb
Additional libraries covered in course
Ready to Master LangChain?
Join the Hands on LangChain course and build production-ready RAG systems. Master all components from document loading to agentic systems.
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