Master LangChain and build smarter AI solutions with large language model (LLM) integration! This course covers everything you need to know to build robust AI applications using LangChain. We’ll start by introducing you to key concepts like AI, large language models, and retrieval-augmented generation (RAG). From there, you’ll set up your environment and learn how to process data with document loaders and splitters, making sure your AI has the right data to work with.

Next, we’ll dive deep into embeddings and vector stores, essential for creating powerful AI search and retrieval systems. You’ll explore different vector store solutions such as FAISS, ChromaDB, and Pinecone, and learn how to select the best one for your needs. Our retriever modules will teach you how to make your AI smarter with multi-query and context-aware retrieval techniques.

In the second half of the course, we’ll focus on building AI chat models and composing effective prompts to get the best responses. You’ll also explore advanced workflow integration using the LangChain Component Execution Layer (LCEL), where you’ll learn to create dynamic, modular AI solutions. Finally, we’ll wrap up with essential debugging and tracing techniques to ensure your AI workflows are optimized and running efficiently.

What Will You Learn?

• How to set up LangChain and Ollama for local AI development

• Using document loaders and splitters to process text, PDFs, JSON, and other formats

• Creating embeddings for smarter AI search and retrieval

• Working with vector stores like FAISS, ChromaDB, Pinecone, and more

• Building interactive AI chat models and workflows using LangChain

• Optimizing and debugging AI workflows with tools like LangSmith and custom retriever tracing

Course Highlights

• Step-by-step guidance: Learn everything from setup to building advanced workflows

• Hands-on projects: Apply what you learn with real-world examples and exercises

• Reference code: All code is provided in a GitHub repository for easy access and practice

• Advanced techniques: Explore embedding caching, context-aware retrievers, and LangChain Component Execution Layer (LCEL)

What Will You Gain?

• Practical experience with LangChain, Ollama, and AI integrations

• A deep understanding of vector stores, embeddings, and document processing

• The ability to build scalable, efficient AI workflows

• Skills to debug and optimize AI solutions for real-world use cases

How Is This Course Taught?

• Clear, step-by-step explanations

• Hands-on demos and practical projects

• Reference code provided on GitHub for all exercises

• Real-world applications to reinforce learning

Join Me on This Exciting Journey!

• Build smarter AI solutions with LangChain and LLMs

• Stay ahead of the curve with cutting-edge AI integration techniques

• Gain practical skills that you can apply immediately in your projects

Let’s get started and unlock the full potential of LangChain together!

Who this course is for:

• Aspiring AI Developers: Ideal for developers with basic Python skills who want to master LangChain and integrate LLMs to build advanced, intelligent applications
• Data Scientists: Perfect for data professionals eager to enhance AI pipelines with efficient document loaders, embeddings, and vector databases for smarter data processing
• Machine Learning Enthusiasts: Designed for those familiar with AI/ML fundamentals who seek to expand their knowledge into cutting-edge LangChain architectures and workflows
• Software Engineers: Suited for engineers aiming to incorporate advanced prompt engineering, chain runnables, and agent integrations into robust AI solutions
• Generative AI Beginners: Great for learners new to generative models and LLMs, offering step-by-step guidance and accessible resources to build a strong foundation
• Tech Innovators & Integrators: Beneficial for professionals looking to integrate multiple AI tools—like Ollama and OpenAI—into scalable, production-ready systems