Master LangChain for building production-ready RAG systems. Learn document loaders, embeddings, vector stores, chains, and agents through 10+ hands-on projects.
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.
Build complete RAG systems from document loading to answer generation. Learn best practices for production deployment.
Master document loaders, text splitters, embeddings, vector stores, retrievers, chains, and agents. Everything you need for RAG.
Build 10+ real-world projects including document Q&A systems, multi-source RAG, web scraping RAG, and agentic assistants.
Learn to build document Q&A systems, knowledge bases, chatbots, and agentic AI assistants using LangChain.
Work with FAISS, Chroma, Pinecone, Weaviate, and more. Learn when to use each and how to optimize performance.
Master advanced RAG techniques including MMR retrieval, multi-query retrieval, re-ranking, and chain types (stuff, map_reduce, refine).
A structured 6-week program covering all aspects of LangChain for RAG systems. Each week includes hands-on projects and practical exercises.
LangChain architecture, core concepts, installation, and setup. Understanding chains, components, and the LangChain ecosystem.
Project:
Setup LangChain environment and build your first simple chain
PyPDFLoader, TextLoader, CSVLoader, DirectoryLoader, WebBaseLoader. Loading documents from various sources.
Project:
Build a multi-format document loader system
RecursiveCharacterTextSplitter, chunk size, overlap, separators. Best practices for document chunking.
Project:
Implement optimal chunking strategy for different document types
OpenAIEmbeddings, HuggingFaceEmbeddings, embedding models, batch processing, cost optimization.
Project:
Compare different embedding models and optimize for your use case
FAISS for local storage, Chroma for persistent storage, indexing strategies, similarity search.
Project:
Build a document search system with FAISS and Chroma
Pinecone, Weaviate, Qdrant. Managed vector stores, scaling, production deployment considerations.
Project:
Deploy a scalable RAG system using Pinecone
Similarity search, MMR (Maximal Marginal Relevance), search parameters, k selection, fetch_k optimization.
Project:
Build a retriever with MMR for diverse document retrieval
Building your first RAG chain, RetrievalQA.from_chain_type, prompt customization, source documents.
Project:
Build a complete document Q&A system
Understanding different chain types, when to use each, handling long documents, cost considerations.
Project:
Compare chain types and optimize for your document length
Query expansion, generating multiple queries, combining results, improving retrieval quality.
Project:
Implement multi-query retrieval for better answer quality
Re-ranking retrieved documents, hybrid search (semantic + keyword), improving answer relevance.
Project:
Build a hybrid search system with re-ranking
Custom prompt templates, conversation memory, chat history, context window management.
Project:
Build a conversational RAG system with memory
Agent architecture, tools, ReAct pattern, agent types, decision-making process.
Project:
Build your first LangChain agent with tools
Creating custom tools, tool descriptions, tool selection, error handling in agents.
Project:
Create custom tools and build a specialized agent
Combining RAG with agents, agentic RAG workflows, multi-step reasoning, tool-augmented RAG.
Project:
Build an agentic RAG system with multiple tools
Optimizing retrieval speed, batch processing, caching strategies, cost optimization, monitoring.
Project:
Optimize a RAG system for production performance
Deploying RAG systems, API design, error handling, logging, monitoring, scaling strategies.
Project:
Deploy a RAG system as a production API
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
Everything you need to get started with LangChain.
pip install langchain
pip install langchain-openai
pip install langchain-community
pip install faiss-cpu
pip install chromadb
Additional libraries covered in course
Join the Hands on LangChain course and build production-ready RAG systems. Master all components from document loading to agentic systems.
Enroll Now