Dataset-aware agent – understands the structure of the dataset (instruction, category, intent, response).; Function-calling with ReAct – the OpenAI model can invoke Python helpers such as count_intent ...

ReACT agent is more flexible than function calling agent, as it doesn’t rely on pre-defined functions. The LLM can choose its own actions based on its reasoning. ReACT agents are often used in ...

Here is the step-by-step breakdown of the tool planning and function calling for above torch example: Tool Planning. Identify the functions needed: torch.zeros, torch.ones, and torch.dot. Plan the ...

Step 2: Bind the Function to the LLM. Once you have defined your Python function, the next step is to bind it to the LLM. This binding process allows the LLM to call the function and execute it ...

It could lead to advancements in large-scale LLM-based software development. It is recommended to investigate the achievable speedup with LLMCompiler compared to ReAct while considering both planning ...