LLMs are remarkable — they will memorize vast amounts of knowledge, answer general knowledge questions, write code, generate stories, and even fix your grammar. Nevertheless, they will not be without limitations. They hallucinate, have a knowledge cut-off which will range from just a few months to several years, and are confined to generating text, unable to interact with the actual world. This restricts their utility for tasks requiring real-time data, source citations, or functionalities beyond text generation. That is the fundamental issue that Agents and Tools are trying to unravel: they bridge this gap by augmenting LLMs with additional capabilities. These improvements allow LLMs to access up-to-date information, interact with APIs, Search, and even influence the physical world, like adjusting a wise home’s temperature.
On this tutorial, we’re going to construct a straightforward LLM agent that is supplied with 4 tools that it may use to reply a user’s query. This Agent could have the next specifications:
- Can answer general Knowledge questions with up-to-date verifiable information.
- Can use 4 forms of tools: DuckDuckGo…
