10 RAG and Agentic apps with LangGraph and Streamlit
This chapter covers
- Developing a chatbot frontend with Streamlit's chat elements
- Using LangGraph and LangChain to streamline an advanced AI app
- How embeddings and vector databases work
- Augmenting an LLM's pre-trained knowledge using 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.
However, building AI applications that deliver economic value requires more than just calling a pre-trained model. For AI to be useful in real-world scenarios, it needs to be aware of 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.