Nvidia Building RAG Agents with LLMs (BRAL)

Ziele der Schulung

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.

Zielgruppe Seminar

This course is intended for AI developers, data scientists, and machine learning engineers interested in working with large language models (LLMs), particularly in the context of inference interfaces, microservices, and pipeline design. It is ideal for professionals in NLP, conversational AI, and enterprise-level AI solutions who want to explore efficient ways of handling long-form documents, managing dialog states, and implementing knowledge extraction. Participants should have a basic understanding of LLMs, embeddings, and vector databases, with an interest in applying these concepts using frameworks like LangChain, Gradio, and LangServe.

Voraussetzungen

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

Seminarinhalt

• 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.

Hinweise

Partner

Dieses Seminar bieten wir in Kooperation mit unserem Nvidia Learning Partner Fast Lane Institute for Knowledge Transfer GmbH an.

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