Advanced LangChain Techniques: Mastering RAG Applications
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 3h 29m | 1.98 GB
Instructor: Markus Lang

Elevate Your RAG Applications to the Next Level

What you'll learn

• Learn LangChain Expression Language (LCEL)
  
• Master advanced RAG techniques using the LangChain framework
  
• Evaluate RAG pipelines using the RAGAS framework
  
• Apply NeMo Guardrails for safe and reliable AI interactions
  

Requirements

• LangChain Basics
  
• Intermediate Python Skills (OOP, Datatypes, Functions, modules etc.)
  
• Basic Terminal and Docker knowledge
  

Description

What to Expect from This Course

Welcome to our course on Advanced Retrieval-Augmented Generation (RAG) with the LangChain Framework!

In this course, we dive into advanced techniques for Retrieval-Augmented Generation, leveraging the powerful LangChain framework to enhance your AI-powered language tasks. LangChain is an open-source tool that connects large language models (LLMs) with other components, making it an essential resource for developers and data scientists working with AI.

Course Highlights

Focus on RAG Techniques: This course provides a deep understanding of Retrieval-Augmented Generation, guiding you through the intricacies of the LangChain framework. We cover a range of topics from basic concepts to advanced implementations, ensuring you gain comprehensive knowledge.

Comprehensive Content: The course is designed for developers, software engineers, and data scientists with some experience in the world of LLMs and LangChain. Throughout the course, you'll explore:

• LCEL Deepdive and Runnables
  
• Chat with History
  
• Indexing API
  
• RAG Evaluation Tools
  
• Advanced Chunking Techniques
  
• Other Embedding Models
  
• Query Formulation and Retrieval
  
• Cross-Encoder Reranking
  
• Routing
  
• Agents
  
• Tool Calling
  
• NeMo Guardrails
  
• Langfuse Integration
  

Additional Resources

• Helper Scripts: Scripts for data ingestion, inspection, and cleanup to streamline your workflow.
  
• Full-Stack App and Docker: A comprehensive chatbot application with a React frontend and FastAPI backend, complete with Docker support for easy setup and deployment.
  
• Additional resources are available to support your learning.
  

Happy Learning! :-)

Who this course is for:

Software Engineers and Data Scientists with Experience in Langchain who want to bring RAG applications to the next level

More Info