Function Calling + ReAct— From Generation to Agent

Enhancing Q&A Assistants with Tools

In the previous article, I introduced how to build an intelligent Q&A assistant based on the RAG (Retrieval-Augmented Generation) architecture. While this approach helps reduce hallucinations and grounds responses in domain-specific knowledge, it has limitations. For instance, consider questions like: 

“When should I visit the theme park to avoid peak seasons?” 

“I plan to visit the park next Sunday. What will the weather be like?” 

In such cases, the assistant may query the vector database but still respond: 

“I’m happy to help, but I don’t have information on the visit flow or weather forecast.” 

Clearly, more capabilities are needed. This article will introduce function calling and the ReAct pattern, demonstrating how to empower the assistant to reason, act, and utilize external tools effectively. We will explore technologies like OpenAI, LangChain, Text-to-SQL, and Qwen-Agent.

Please read the full article in my Medium stories. Here is the link: 👉 Function Calling + ReAct— From Generation to Agent