Understanding Tool Calling in LLMs – Step-by-Step with REST and Spring AI
10 months ago
- #Tool Calling
- #LLM
- #Spring AI
- LLM tool calling enables models to interact with external functions, APIs, or services to fetch live data.
- Tool calling involves steps: sending a prompt with tool definitions, receiving a tool call request, executing the function, sending back the result, and getting the final answer.
- Multi-tool calls allow parallel execution of multiple functions, while sequential reasoning enables step-by-step operations like dynamic SQL generation.
- Manual implementation of tool calling is complex due to JSON schema writing, argument parsing, and conversation history management.
- Spring AI simplifies tool calling with annotations like @Tool and @ToolParam, handling schema generation, argument binding, and orchestration automatically.
- Spring AI supports interoperability via the Model Context Protocol (MCP), making tools available across different clients without extra code.
- The blog is based on Chapter 5 of 'Spring AI for Your Organization — GCP Vertex Edition', which includes full REST and Spring AI examples.