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.Metadаta: 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.
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.

https://rapidgator.net/file/3983ea30871166fcd6e2ac7a3f79ffe7/HandsOn_RAG_with_LangChain_Build_RealWorld_Projects.part2.rar.html
https://rapidgator.net/file/9eaafaf80f9af14a56f84f4f63ac0728/HandsOn_RAG_with_LangChain_Build_RealWorld_Projects.part1.rar.html