Summary
In the previous chapter, we discussed tool-augmented LLMs, which involve the utilization of external tools or knowledge resources such as document corpora. In this chapter, we focused on retrieving relevant data from sources through vector search and injecting it into the context. This retrieved data serves as additional information to augment the prompts given to LLMs. I also introduced retrieval and vector mechanisms, and we discussed implementing a chatbot, the importance of memory mechanisms, and the importance of appropriate responses.
The chapter started with an overview of chatbots, their evolution, and the current state of chatbots, highlighting the practical implications and enhancements of the capabilities of the current technology. We discussed the importance of proactive communication. We explored retrieval mechanisms, including vector storage, with the goal of improving the accuracy of chatbot responses. We went into detail on methods for loading documents...
