0dayddl
31 agosto 2024, 01:45
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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
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
More Info (https://www.udemy.com/course/advanced-langchain-techniques-mastering-rag-applications/)
https://images2.imgbox.com/1c/5d/Z3vRUmLd_o.jpg
***Contenido oculto. Abra la versión completa del tema para visualizar los enlaces.***
***Contenido oculto. Abra la versión completa del tema para visualizar los enlaces.***
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
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
More Info (https://www.udemy.com/course/advanced-langchain-techniques-mastering-rag-applications/)
https://images2.imgbox.com/1c/5d/Z3vRUmLd_o.jpg
***Contenido oculto. Abra la versión completa del tema para visualizar los enlaces.***
***Contenido oculto. Abra la versión completa del tema para visualizar los enlaces.***