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