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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
What you'll learn
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[*]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
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Requirements
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[*]LangChain Basics
[*]Intermediate Python Skills (OOP, Datatypes, Functions, modules etc.)
[*]Basic Terminal and Docker knowledge
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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:
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[*]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
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Additional Resources
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[*]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.
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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
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https://filestore.me/kasq6xyaktif/Udemy_Advanced_LangChain_Techniques_Mastering_RAG_Applications.part1.rar
https://filestore.me/h3ju0baz0xas/Udemy_Advanced_LangChain_Techniques_Mastering_RAG_Applications.part2.rar
Видео
