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Advanced LangChain Techniques: Mastering RAG Applications - Printable Version

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Advanced LangChain Techniques: Mastering RAG Applications - AD-TEAM - 08-31-2024

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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

[b]What you'll learn[/b]
  • 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

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

[b]Description

What to Expect from This Course[/b]

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

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