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 CourseWelcome 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 HighlightsFocus 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 RunnablesChat with HistoryIndexing APIRAG Evaluation ToolsAdvanced Chunking TechniquesOther Embedding ModelsQuery Formulation and RetrievalCross-Encoder RerankingRoutingAgentsTool CallingNeMo GuardrailsLangfuse IntegrationAdditional ResourcesHelper 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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