chapter ten
10 RAG and agentic apps with LangGraph and Streamlit
This chapter covers
- Developing a chatbot frontend with Streamlit’s chat elements
- Streamlining an advanced AI app with LangGraph and LangChain
- How embeddings and vector databases work
- Augmenting an LLM’s pre-trained knowledge with retrieval-augmented generation (RAG)
- Enabling an LLM to access and execute real-world actions
Creating a fun and engaging experience, like the trivia game we built in chapter 9, is exciting, but the true power of AI lies in its ability to drive real business value. AI isn’t just about answering questions or generating text; it’s about transforming industries, streamlining operations, and enabling entirely new business models.
But building AI applications that deliver economic value requires more than calling a pre-trained model. To be useful in real-world scenarios, AI must understand the context in which it operates, connect to external data sources, and take meaningful actions. Companies need AI to understand and respond to domain-specific queries, interact with business systems, and provide personalized assistance.