How to Build an AI Agent With Semantic Router and LLM Tools

In the previous post, I introduced the semantic router: a pattern that enables AI agents to choose the right LLM for the right task, while also reducing their LLM dependency. In this follow-up tutorial, we will use the Semantic Router project to intelligently handle user queries by choosing the best way to retrieve information, such as whether to use a vector database and/or a tool-based retriever for real-time data.

Similar to previous tutorials, in our example we will track the flight status of planes in real-time using data from FlightAware’s AeroAPI.

First, note that the router dynamically routes queries based on intent, ensuring the retrieval of the most relevant context, making this approach unique. The semantic router takes OpenAI’s LLM and structured retrieval methods and combines them to make an adaptive, highly responsive assistant that can quickly handle both conversational queries and data-specific requests.

Read the entire article at?The New Stack

Janakiram MSV?is an analyst, advisor, and architect. Follow him on?Twitter,??Facebook?and?LinkedIn.