Customizing and Deploying Our LlamaIndex Project
Customizing Retrieval-Augmented Generation (RAG) components and optimizing performance is critical to building robust, production-ready applications with LlamaIndex. This chapter explores methods for leveraging open source models, intelligent routing across large language models (LLMs), and using community-built modules to increase flexibility and cost-effectiveness. Advanced tracing, evaluation methods, and deployment options are explored to gain deep insight, ensure reliable operation, and streamline the development life cycle.
Throughout this chapter, we’re going to cover the following main topics:
- Customizing our RAG components
- Using advanced tracing and evaluation techniques
- Introduction to deployment with Streamlit
- Hands-on – a step-by-step deployment guide
