This chapter provided an in-depth exploration of building chatbots and agents with LlamaIndex. We covered ChatEngine for conversation tracking and different built-in chat modes, such as simple, context, condense question, and condense plus context.

Then, we explored different agent architectures and strategies using OpenAIAgent, ReActAgent, and the more advanced LLMCompiler agent. Key concepts such as tools, tool orchestration, reasoning loops, and parallel execution were explained.

We concluded this chapter with a hands-on implementation of conversation tracking for the PITS tutoring application.

Overall, you should now have a comprehensive understanding of leveraging LlamaIndex capabilities to create useful and engaging conversational interfaces.

Throughout the next chapter, we’ll discover how to customize our RAG pipeline and provide a straightforward guide to deploying it with Streamlit. We’ll also explore advanced tracing methods for seamless...