Answering questions with tools
LLMs are trained on general corpus data and may not be as effective for tasks that require domain-specific knowledge. On their own, LLMs can’t interact with the environment and access external data sources; however, LangChain provides a platform for creating tools that access real-time information and perform tasks such as weather forecasting, making reservations, suggesting recipes, and managing tasks. Tools within the framework of agents and chains allow for the development of applications powered by LLMs that are data-aware and agentic and open up a wide range of approaches to solving problems with LLMs, expanding their use cases, and making them more versatile and powerful.
One important aspect of tools is their capability to work within specific domains or process specific inputs. For example, an LLM lacks inherent mathematical capabilities. However, a mathematical tool like a calculator can accept mathematical expressions or equations...
