𝗦𝗽𝗿𝗶𝗻𝗴 𝗔𝗜 𝗧𝗼𝗼𝗹 𝗖𝗮𝗹𝗹𝗶𝗻𝗴 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱
Ask ChatGPT for a stock price. It fails. LLMs have a data cutoff. They do not know your private data. They do not check the weather.
Tool calling fixes this. The LLM identifies a need. It asks your app for data. Your app runs the function. Your app returns the result. The LLM writes the final answer.
Spring AI makes this easy. Use the @Tool annotation on your Java methods.
Follow these rules for success:
- Write precise descriptions. The LLM reads these to choose the tool.
- Treat descriptions as prompts. Vague text leads to errors.
- Use @ToolParam to explain inputs.
Security is a priority. The LLM does not touch your database. It does not make HTTP requests. Your app handles the execution. You control authentication and rate limits.
Keep latency in mind. Each tool call adds time. Only use tools for data the LLM lacks. Do not use tools for general knowledge.
Avoid circular chains. Ensure tools have narrow roles. This stops infinite loops.
Update your code. Stop using FunctionCallback. Move to @Tool annotations. Use defaultTools in your ChatClient configuration.
Optional learning community: https://t.me/GyaanSetuAi