Description
Master LangChain: Build LLM Apps & RAG Pipelines with Python is a course on using LangChain to build powerful AI applications published by Udemy Online Academy. This course teaches you how to build powerful AI applications using LangChain, guiding you from the basics to advanced, production-ready systems. You will learn how large language models interact with tools, memory, and external data, and how to design modular AI workflows using LangChain’s core components. The course explains Retrieval Augmentative Generation (RAG) in a practical, step-by-step way—including document loading, text splitting, embeddings, vector databases, and retrieval optimization—then shows you how to build complete RAG pipelines for real-world use.
This course covers LangChain basics, LLM integration, notification formats, tools and agents, memory systems, embeddings, text segmentation, vector stores, retrieval methods, RAG pipeline construction, optimization techniques, evaluation, deployment, and hands-on Python projects. With hands-on Python projects, you will build LLM-based chatbots, data assistants, automation tools, and custom applications, gaining the skills needed to efficiently develop, evaluate, and deploy modern AI systems.
What you will learn in Master LangChain: Build LLM Apps & RAG Pipelines with Python:
- Build production-ready LLM applications using LangChain, from basic chatbots to advanced RAG pipelines with vector databases like FAISS and Pinecone
- Master document processing, text segmentation, embeddings, and vector storage to create intelligent retrieval systems for productive AI applications
- Implement a RAG (Retrieval-Augmented Generation) architecture end-to-end, including document loading, retrieval, and generation steps with LangChain
- Create custom LangChain tools and deploy a full web summarization project using Streamlit, Groq API, and LangChain document loaders
- Work with ChatPrompt templates, output parsers (JSON, Pydantic), and chaining multiple components to build advanced LLM-based workflows
- Set up professional development environments with virtual environments, API key management, and best practices for building scalable AI applications
- Understand and implement HuggingFace and Ollama embeds for search Semantics and building real-world applications with multiple vector database solutions
- and …
Course specifications
Publisher: Udemy
Instructors: Pratham Chandratre
Language: English
Level: Introductory
Number of Lessons: 40
Duration: 8 hours and 18 minutes
Course topics
Master LangChain: Build LLM Apps & RAG Pipelines with Python Prerequisites
No prior experience with LangChain, RAG, or LLMs required – course starts from absolute basics and builds to advanced projects
A computer with internet connection and ability to install Python packages – all tools used in the course are free and open-source
Willingness to learn and experiment with generative AI technologies – course includes hands-on projects and practical implementations
No expensive API subscriptions needed – course covers free-tier options and alternatives for all services including OpenAI and Groq
Pictures
Master LangChain: Build LLM Apps & RAG Pipelines with Python introduction video
Installation guide
After Extract, watch with your favorite Player.
Subtitle: None
Quality: 1080p
Downloadly link
Rapidgator link
File password (s): www.downloadly.ir
Size
10 GB
