Developing Applications with LangChain | Master LLMs, RAG, and Agentic Workflows

Unlock the full potential of Large Language Models (LLMs) with LangChain 🔗 This full-course tutorial walks you through developing powerful AI applications, integrating Retrieval Augmented Generation (RAG) for enhanced data retrieval, and designing agentic workflows that automate complex tasks. Whether you’re new to LangChain or looking to master its advanced features, this comprehensive guide will equip you with the skills to build cutting-edge AI systems. 👩‍💻 Why Watch This LangChain Full Course? LangChain provides a unified framework for working with LLMs, allowing seamless integration of models, data sources, vector databases, and tools. In this tutorial, you’ll learn: - How to build LLM-powered applications using LangChain’s modular components. - How Retrieval Augmented Generation (RAG) extends LLM capabilities by integrating external data. - How to design LangChain agents that dynamically decide which tools to use. - The LangGraph framework for building multi-agent systems with memory and reasoning. 🧠 What You’ll Learn in This Course: - Developing LLM Applications: Use LangChain to create chatbots, optimize prompts, and connect with Hugging Face & OpenAI models. - Retrieval Augmented Generation (RAG): Load documents, create embeddings, store them in vector databases, and optimize retrieval chains. - Designing Agentic Systems: Implement LangChain agents that reason, take actions, and use tools dynamically. - LangGraph for AI Workflows: Structure workflows using nodes and edges, implement multi-turn conversations, and integrate external APIs. 📕 Video Highlights 00:00 Introduction to Developing Applications with LangChain 00:28 Overview of LangChain and Its Ecosystem 01:33 Core Components of LangChain 02:18 Using Hugging Face and OpenAI Models with LangChain 03:15 Unifying Different Models with LangChain 04:12 Implementing Prompting Strategies for Chatbots 05:11 Using Prompt Templates in LangChain 07:19 Chat Models and Chat Prompt Templates 08:19 Few-Shot Prompting in LangChain 10:01 Implementing Sequential Chains 12:16 Introduction to Agents in LangChain 13:06 Understanding React Agents 15:18 Creating a Math Solving Agent with LangGraph 17:33 Custom Tool Creation for Agents 18:04 Introduction to Retrieval-Augmented Generation (RAG) 19:30 Document Loading in LangChain 21:19 Document Splitting and Chunking 24:41 Using Vector Databases for Retrieval 26:40 Embedding and Storing Documents 27:55 Constructing a Retrieval Chain 32:17 Building Advanced RAG Architectures 36:47 Understanding Vector Stores and Embeddings 40:25 Improving Retrieval with Sparse and Dense Methods 44:38 Optimizing Document Splitting with Semantic Chunking 49:06 Introduction to Graph-Based RAG 52:14 Storing and Querying Graph Data with Neo4j 56:50 Generating Cypher Queries with LLMs 01:03:50 Implementing Graph RAG Chains 01:07:06 Enhancing Graph RAG with Validation and Filtering 01:10:04 Introduction to AI Agents and Tools 01:12:57 Building a Simple Math Agent 01:15:28 Developing Custom Chatbots with LangGraph 01:19:19 Adding Memory to Chatbots 01:23:58 Incorporating External APIs and Tools 01:27:08 Using Wikipedia API for Chatbot Responses 01:32:56 Enhancing Chatbots with Multiple Tools 01:40:51 Handling Follow-Up Questions in AI Agents 01:44:01 Course Recap and Next Steps ✅ Who Should Watch? This course is ideal for developers, data scientists, and AI enthusiasts looking to integrate LLMs into real-world applications. If you work in AI development, machine learning, or data engineering, this is the perfect course for you! 🖇️ Resources & Documentation Take this skill track on DataCamp: https://www.datacamp.com/tracks/developing-applications-with-langchain Developing LLM Applications with LangChain - https://www.datacamp.com/courses/developing-llm-applications-with-langchain Retrieval Augmented Generation (RAG) with LangChain - https://www.datacamp.com/courses/retrieval-augmented-generation-rag-with-langchain Designing Agentic Systems with LangChain - https://www.datacamp.com/courses/designing-agentic-systems-with-langchain Live Code Along: Chat with Your Documents Using GPT & LangChain - https://www.datacamp.com/code-along/chat-with-your-documents-using-gpt-and-lang-chain Tutorial: How to Build User Interfaces For AI Applications Using Streamlit And LangChain - https://www.datacamp.com/tutorial/how-to-build-user-interfaces-for-ai-applications-using-streamlit-and-langchain Tutorial: Building LangChain Agents to Automate Tasks in Python - https://www.datacamp.com/tutorial/building-langchain-agents-to-automate-tasks-in-python 📱 Follow Us on Social Facebook: https://www.facebook.com/datacampinc/ Twitter: https://twitter.com/datacamp LinkedIn: https://www.linkedin.com/school/datacampinc/ Instagram: https://www.instagram.com/datacamp/ #LangChain #RetrievalAugmentedGeneration #AIApplications #LLM #DataScience #MachineLearning #GraphRAG #LangGraph #Neo4j #AIChatbots #AgenticAI #HuggingFace #GPT4