Building RAG Agents with LLMs (BRAL)

Kursinhalt

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.

Voraussetzungen

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

Detaillierter Kursinhalt

• 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.