Learn RAG with LLMWare (2024)

Learn RAG with LLMWare (2024), available at Free, has an average rating of 4.7, with 7 lectures, based on 21 reviews, and has 877 subscribers.

Free Enroll Now

You will learn about Fast Start to RAG (Retrieval Augmented Generation) with LLMWare teaches the basic components of connecting your documents and data to LLMs Learn all the basic components of RAG: parsing, text chunking, embedding, prompting models, text query, semantic query and multi-step query Provides the foundation for many business process automation using AI Launching pad for progressing to more complex AI use cases to automate workflow This course is ideal for individuals who are Python developers looking to develop generative AI skills, particularly in business or the enterprise It is particularly useful for Python developers looking to develop generative AI skills, particularly in business or the enterprise.

Enroll now: Learn RAG with LLMWare (2024)

Summary

Title: Learn RAG with LLMWare (2024)

Price: Free

Average Rating: 4.7

Number of Lectures: 7

Number of Published Lectures: 7

Number of Curriculum Items: 7

Number of Published Curriculum Objects: 7

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

Fast Start to RAG (Retrieval Augmented Generation) with LLMWare teaches the basic components of connecting your documents and data to LLMs

Learn all the basic components of RAG: parsing, text chunking, embedding, prompting models, text query, semantic query and multi-step query

Provides the foundation for many business process automation using AI

Launching pad for progressing to more complex AI use cases to automate workflow

Who Should Attend

Python developers looking to develop generative AI skills, particularly in business or the enterprise

Target Audiences

Python developers looking to develop generative AI skills, particularly in business or the enterprise

Interested in learning more about Generative AI? Getting started with Generative AI is fast and simple with LLMWare!

To use Generative AI skillfully in most work environments, you need to be able to link a large language model (LLM) to a knowledge base. Retrieval Augmented Generation (RAG) is the method of linking such a knowledge base (usually some form of documents such as PDFs, PowerPoint, Word or Excel) to query to a model.

In this step-by-step course, learn all the key components of RAG such as knowledge ingestion, parsing, indexing, embedding and prompting.  This is a foundational course in working with Generative AI so that you can start to build effective workflows and chatbots.

The course has 7 short videos:

1. Intro Video:

Learn the basics of Retrieval Augmented Generation in this Step-by-Step series that will teach you all the fundamentals of how to get started with Generative AI.

Following our in-depth tutorial series of 7 short videos, you will learn the most important components of learning to work with an LLM-based framework using LLMWare’s easy, integrated open source library.

In this intro, you will learn how to pip install LLMWare, the basic requirements to get started, and get an overview of the different components of RAG. Learn how to create a state of the art data pipeline using the latest LLMs, all on your laptop.

2. Parsing, Text Chunking and Indexing

Create your first library for Retrieval Augmented Generation and get started by learning the first basic steps of parsing, text chunking and indexing documents in this easy-to-follow tutorial.

3. Build Embeddings

What are embeddings, embedding models, vectors and vector databases? In this Example 2 tutorial, learn the fundamental concepts behind embeddings, embedding models, vectors and vector databases.

With our easy-to-follow example, you will also build your first embeddings with embedding models from Hugging Face and store to a database, and use these embeddings to run your semantic queries.

4. Prompt Models

Learn how to prompt models. In this example, you will learn how to load and start prompting models from Hugging Face and OpenAI.

Start inferencing models using this example and see how you can check if the model is providing the right answer using the context that was passed with LLMWare’s similarity score. Learn also how to capture model usage data such as token consumption, the output, the total processing time, etc. in downloadable files for future auditability.

5. RAG with Text Query

Start searching! In this example, we will be taking some form of knowledge in a library with embeddings and bring the pieces together with a model. Learn how to put together the right RAG strategy with a thoughtful retrieval and querying strategy combined with the right model to do the job.

We will use LLMWare’s smallest (but mighty), laptop friendly BLING 1B parameter model from Hugging Face to run contract analysis. Learn how we automatically extract names of parties from contracts and other key information with text query.

6. RAG with Semantic Query

Start semantic searching! Also known as natural language querying, this is where we reap the benefits of embeddings and vector databases. You will be able to query your knowledge base using natural language to ask questions like “what is the executive’s base salary?” to derive answers from complex employment contracts.

Learn the fundamentals of semantic searches in this easy-to-follow example.

7. RAG with Multi-Step Query

It’s Graduation Day if you have been following along with our other examples! We will quickly recap through all that you have learned in the previous videos and learn how to use a quantized DRAGON-Yi-6b-GGUF model from Hugging Face on a laptop.

Perform multi-step hybrid queries to get the responses you need.

Learn also how to perform evidence comparison and how to save all the output as JSON or CSV files for future datasets or auditing.

After following this series, you will be ready to leap to the next launching pad for creating the most sophisticated enterprise-ready AI workflows with LLMWare.

Get started on your AI journey today!

Course Curriculum

Chapter 1: Fast Start to Retrieval Augmented Generation (RAG) using LLMWare

Lecture 1: Introduction: Fast Start to RAG (Retrieval Augmented Generation) using LLMWare

Lecture 2: Parsing, Text Chunking and Indexing using LLMWare

Lecture 3: Building Embeddings using LLMWare

Lecture 4: Prompting Models

Lecture 5: RAG with Text Query

Lecture 6: RAG with Semantic Query

Lecture 7: RAG with Multi-Step Query

Instructors

Darren Oberst
Innovating the most cutting-edge technology in AI

Rating Distribution

1 stars: 0 votes

2 stars: 0 votes

3 stars: 1 votes

4 stars: 7 votes

5 stars: 13 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!