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Advanced LangChain Techniques: Mastering RAG Applications

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Advanced LangChain Techniques: Mastering RAG Applications

Advanced LangChain Techniques: Mastering RAG Applications, Elevate Your RAG Applications to the Next Level

Preview this Course

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

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

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