Implementing automatic tool selection and use
To enable LLMs to automatically select and use tools, we can create a system that interprets the model’s output and executes the appropriate tools. Here’s an example:
- First, we define a function,
auto_tool_use
, that uses a pre-trained language model and tokenizer from Hugging Face’s Transformers library to decompose a task into executable steps using a prompt, parses the decomposition into steps, executes tools as needed using a toolkit, and collects the results:from transformers import AutoModelForCausalLM, AutoTokenizer def auto_tool_use(model, tokenizer, task, toolkit): # Generate task decomposition decomposition_prompt = task_decomposition_prompt( task, toolkit.tools.keys() ) inputs = tokenizer(decomposition_prompt, ...