Natan Mish: Build a RAG to Brag About @ PyCon Ireland 2024 скачать в хорошем качестве

Natan Mish: Build a RAG to Brag About @ PyCon Ireland 2024

Build a RAG to Brag About - Natan Mish PyCon Ireland 2024 Room 2 Session 02 Abstract: In this talk, we will explore advanced techniques for enhancing Retrieval-Augmented Generation (RAG) applications. Designed for an audience with prior knowledge in Python, this session will delve into innovative methods to improve the efficiency, accuracy, and explainability of RAG systems. We will demonstrate using an example use-case and use open source tools such as ChromaDB vector database, the LangChain package and popular industry LLM providers such as OpenAI. Attendees will gain insights into cutting-edge practices such as Context Enrichment Techniques, Intelligent Reranking, Dynamic Chunk Sizing, Semantic Chunking, Explainable Retrieval, Ensemble Retrieval, and Knowledge Graph Integration. Learning Objectives: Understand advanced techniques to improve RAG applications Learn practical implementation strategies for each technique Gain insights into the benefits and challenges of these methods Target Audience: This talk is aimed at developers, data scientists, and researchers with a background in Python who are looking to dive deeper into the world of RAGs and LLMs for common use cases. Description: With the rapid adoption of LLMs across industries, the RAG framework has emerged as a powerful tool for building applications like customer support chatbots, virtual assistants, and search engines. By augmenting LLMs with external knowledge, RAG can significantly enhance performance and accuracy. However, developing a robust, reliable, and secure RAG application is challenging due to hurdles like unmet requirements, subpar performance, and cost constraints. This talk will equip you with the essential skills to build a RAG application you can be proud of. We'll delve into various aspects of the RAG application lifecycle to optimize performance and reliability. From dynamically and semantically splitting long documents into smaller chunks to employing intelligent reranking and multi-source document retrieval, we'll explore practical techniques to improve search efficiency. To enhance user trust and understanding, we'll incorporate explainable AI methods. Finally, we'll demonstrate how to enrich prompt context with external information and knowledge graphs for more comprehensive and informative responses. Outline: Introduction (5 minutes) Context Enrichment Techniques (4 minutes) Intelligent Reranking (4 minutes) Dynamic Chunk Sizing (3 minutes) Semantic Chunking (3 minutes) Explainable Retrieval (3 minutes) Ensemble Retrieval (2 minutes) Knowledge Graph Integration (2 minutes) Conclusion (2 minutes) Q&A (5 minutes)