Building Advanced Assistants (Agentic RAGs) with Gemma 2

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Building Advanced Assistants (Agentic RAGs) with Gemma 2

Building Advanced Assistants (Agentic RAGs) with Gemma 2

General Python, Web/DevOps

Short Talk

Intermediate

By Ngesa Owiny

Engineer at Safaricom PLC

Abstract:

Session Title: Building Assistants (Agentic RAGs) with Gemma 2 Using Python

In this Python-focused session, developers will dive deep into the world of Retrieval Augmented Generation (RAG) using Gemma 2 and LlamaIndex, all implemented in Python. Attendees will learn how to leverage Python's powerful libraries and frameworks to create sophisticated agents capable of understanding and responding to complex queries based on documents.

Key topics:

Agentic RAG in Python: Discover how to build Python-based agents that can reason over documents and provide informative answers. We'll use Python's natural language processing libraries alongside Gemma 2 to create intelligent RAG systems.
Router Agents with Python: Learn to construct agents using Python that can handle various tasks, from Q&A to summarization, and effectively pass arguments. We'll explore Python's object-oriented features to create flexible and modular agent architectures.
Research Agents in Python: Explore techniques for building agents that can work with multiple documents using Python's file handling and data processing capabilities. We'll also cover different debugging methods specific to Python-based AI systems.

Throughout the session, we'll showcase Python code examples and best practices for integrating Gemma 2 and LlamaIndex into your projects.

By the end of this session, developers will have a solid understanding of how to leverage Python, Gemma 2, and LlamaIndex to create powerful and intelligent AI assistants.