Summary
In this chapter, we explored the key aspects of RAG, including vector storage, document processing, retrieval strategies, and implementation. Following this, we built a comprehensive RAG chatbot that leverages LangChain for LLM interactions and LangGraph for state management and workflow orchestration. This is a prime example of how you can design modular, maintainable, and user-friendly LLM applications that not only generate creative outputs but also incorporate dynamic feedback loops.
This foundation opens the door to more advanced RAG systems, whether you’re retrieving documents, enhancing context, or tailoring outputs to meet specific user needs. As you continue to develop production-ready LLM applications, consider how these patterns can be adapted and extended to suit your requirements. In Chapter 8, we’ll be discussing how to benchmark and quantify the performance of RAG systems to ensure performance is up to requirements.
In the next chapter, we...
