@cloudflare/codemode

5 Steps

1. 1

   Define Your Tool Function: Create a Python function that acts as an external tool or API wrapper. This function should accept parameters that an LLM can infer from a user's prompt. For example, a weather fetching tool:

2. 2

   Simulate LLM-Generated Code: Imagine an LLM has processed a user request (e.g., "What's the weather in London?") and generated Python code to call your tool. This code will specify the tool's name and its arguments.

3. 3

   Execute the Generated Code: Use Python's `exec()` function to run the LLM-generated code string. This executes the tool call within your application's context. Ensure `globals()` is passed to allow the generated code to find your defined tool function.

4. 4

   Handle Dynamic Tool Calls: Demonstrate how the LLM can adapt to different prompts (e.g., "Tell me the weather in New York in Fahrenheit") by generating different tool calls. This highlights the dynamic problem-solving capability.

5. 5

   Integrate with an Actual LLM: In a real-world scenario, you would integrate this pattern with an actual LLM. The LLM's output parser would extract the `tool_name` and `tool_args` from its response (or the LLM directly generates the executable Python code), which you then safely execute.