IBM
Fundamentals of AI Agents Using RAG and LangChain
IBM

Fundamentals of AI Agents Using RAG and LangChain

This course is part of multiple programs.

Joseph Santarcangelo
Kang Wang
Sina Nazeri

Instructors: Joseph Santarcangelo

Sponsored by EdgePoint Software

2,851 already enrolled

Gain insight into a topic and learn the fundamentals.
4.6

(21 reviews)

Intermediate level

Recommended experience

6 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
4.6

(21 reviews)

Intermediate level

Recommended experience

6 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • In-demand job-ready skills businesses need for building AI agents using RAG and LangChain in just 8 hours.

  • How to apply the fundamentals of in-context learning and advanced methods of prompt engineering to enhance prompt design.

  • Key LangChain concepts, tools, components, chat models, chains, and agents.

  • How to apply RAG, PyTorch, Hugging Face, LLMs, and LangChain technologies to different applications.

Details to know

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Assessments

4 assignments

Taught in English
Recently updated!

September 2024

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There are 2 modules in this course

In this module, you will learn how RAG is used to generate responses for different applications such as chatbots. You’ll then learn about the RAG process, the Dense Passage Retrieval (DPR) context encoder and question encoder with their tokenizers, and the Faiss library developed by Facebook AI Research for searching high-dimensional vectors. In hands-on labs, you will use RAG with PyTorch to evaluate content appropriateness and with Hugging Face to retrieve information from the dataset.

What's included

3 videos3 readings2 assignments2 app items1 plugin

In this module, you will learn about in-context learning and advanced methods of prompt engineering to design and refine the prompts for generating relevant and accurate responses from AI. You’ll then be introduced to the LangChain framework, which is an open-source interface for simplifying the application development process using LLM. You’ll learn about its tools, components, and chat models. The module also includes concepts such as prompt templates, example selectors, and output parsers. You’ll then explore the LangChain document loader and retriever, LangChain chains and agents for building applications. In hands-on labs, you will enhance LLM applications and develop an agent that uses integrated LLM, LangChain, and RAG technologies for interactive and efficient document retrieval.

What's included

6 videos4 readings2 assignments3 app items2 plugins

Instructors

Joseph Santarcangelo
IBM
33 Courses1,706,314 learners

Offered by

IBM

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