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In this chapter, we covered the core steps to build conversational applications. We started with a plain vanilla chatbot to then add more complext components, such as memory, non-parametric knowledge and external tools. All of this was made straightforward with the pre-built components of LangChain, as well as Streamlit for UI rendering.Even though conversational applications are often seen as the “confort zone” of generative AI and LLMs. Nevertheless, those models embrace a wider spectrum of applications. In this chapter, we are going to cover how LLMs can enhance recommendion systems, using both embeddings and generative models.Throughout this chapter we will cover the following topics:
- Definition and evolutions of recommendation systems
- How generative AI is impacting this field of research
- Building recommendation systems with LangChain
By the end of this chapter, you will be able to create your...
