Building RAG Agents with LLMs (BRAL)

 

Course Overview

The evolution and adoption of large language models (LLMs) have been nothing short of revolutionary, with retrieval-based systems at the forefront of this technological leap. These models are not just tools for automation; they are partners in enhancing productivity, capable of holding informed conversations by interacting with a vast array of tools and documents. This course is designed for those eager to explore the potential of these systems, focusing on practical deployment and the efficient implementation required to manage the considerable demands of both users and deep learning models. As we delve into the intricacies of LLMs, participants will gain insights into advanced orchestration techniques that include internal reasoning, dialog management, and effective tooling strategies.

Participants will embark on a learning journey that encompasses the composition of LLM systems, fostering predictable interactions through a blend of internal and external reasoning components. The course emphasizes the creation of robust dialog management and document reasoning systems that not only maintain state but also structure information in easily digestible formats. A key component of our exploration will be the use of embedding models, which are essential for executing efficient similarity queries, enhancing content retrieval, and establishing dialog guardrails. Furthermore, we will tackle the implementation and modularization of retrieval-augmented generation (RAG) agents, which are adept at navigating research papers to provide answers without the need for fine-tuning.

Course Content

The workshop includes topics such as LLM Inference Interfaces, Pipeline Design with LangChain, Gradio, and LangServe, Dialog Management with Running States, Working with Documents, Embeddings for Semantic Similarity and Guardrailing, and Vector Stores for RAG Agents. Each of these sections is designed to equip participants with the knowledge and skills necessary to develop and deploy advanced LLM systems effectively.

Prerequisites

  • Introductory deep learning knowledge, with comfort with PyTorch and transfer learning preferred.
  • Intermediate Python experience, including object-oriented programming and libraries.

Course Objectives

Our journey begins with an introduction to the workshop, setting the stage for a deep dive into the world of LLM inference interfaces and the strategic use of microservices. We will explore the design of LLM pipelines, leveraging tools such as LangChain, Gradio, and LangServe to create dynamic and efficient systems. The course will guide participants through managing dialog states, integrating knowledge extraction techniques, and employing strategies for handling long-form documents. The exploration continues with an examination of embeddings for semantic similarity and guardrailing, culminating in the implementation of vector stores for document retrieval. The final phase of the course focuses on the evaluation, assessment, and certification of participants, ensuring a comprehensive understanding of RAG agents and the development of LLM applications.

  • Compose an LLM system that can interact predictably with a user by leveraging internal and external reasoning components.
  • Design a dialog management and document reasoning system that maintains state and coerces information into structured formats.
  • Leverage embedding models for efficient similarity queries for content retrieval and dialog guardrailing.
  • Implement, modularize, and evaluate a RAG agent that can answer questions about the research papers in its dataset without any fine-tuning.

Outline: Building RAG Agents with LLMs (BRAL)

  • Introduction to the workshop and setting up the environment.
  • Exploration of LLM inference interfaces and microservices.
  • Designing LLM pipelines using LangChain, Gradio, and LangServe.
  • Managing dialog states and integrating knowledge extraction.
  • Strategies for working with long-form documents.
  • Utilizing embeddings for semantic similarity and guardrailing.
  • Implementing vector stores for efficient document retrieval.
  • Evaluation, assessment, and certification.

Prices & Delivery methods

Online Training

Duration
1 day

Price
  • US$ 500
Classroom Training

Duration
1 day

Price
  • United States: US$ 500

Click on town name or "Online Training" to book Schedule

Instructor-led Online Training:   This is an Instructor-Led Online (ILO) course. These sessions are conducted via WebEx in a VoIP environment and require an Internet Connection and headset with microphone connected to your computer or laptop.

United States

Online Training 09:00 US/Eastern Enroll

Canada

Online Training 09:00 Canada/Eastern Enroll