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Troubleshooting

This section will describe tools for troubleshooting and address common errors.

Logging

General logging capabilities for Workers also apply to embedded function calling.

Function invocations

The invocations of tools can be logged as in any Worker using console.log():

Logging tool invocations
export default {
  async fetch(request, env, ctx) {
    const sum = (args: { a: number; b: number }): Promise<string> => {
      const { a, b } = args;
      // Logging from within embedded function invocations
      console.log(`The sum function has been invoked with the arguments a: ${a} and b: ${b}`)
      return Promise.resolve((a + b).toString());
    };
    ...
  }
}

Logging within runWithTools

The runWithTools function has a verbose mode that emits helpful logs for debugging of function calls as well input and output statistics.

Enabled verbose mode
const response = await runWithTools(
  env.AI,
  '@hf/nousresearch/hermes-2-pro-mistral-7b',
  {
    messages: [
      ...
    ],
    tools: [
      ...
    ],
  },
  // Enable verbose mode
  { verbose: true }
);

Performance

To respond to a LLM prompt with embedded function, potentially multiple AI inference requests and function invocations are needed, which can have an impact on user experience.

Consider the following to improve performance:

  • Shorten prompts (to reduce time for input processing)
  • Reduce number of tools provided
  • Stream the final response to the end user (to minimize the time to interaction). See example below:
Streamed response example
async fetch(request, env, ctx) {
  const response = (await runWithTools(
    env.AI,
    '@hf/nousresearch/hermes-2-pro-mistral-7b',
    {
      messages: [
        ...
      ],
      tools: [
        ...
      ],
    },
    {
      // Enable response streaming
      streamFinalResponse: true,
    }
  )) as ReadableStream;


  // Set response headers for streaming
  return new Response(response, {
    headers: {
      'content-type': 'text/event-stream',
    },
  });
}

Common Errors

If you are getting a BadInput error, your inputs may exceed our current context window for our models. Try reducing input tokens to resolve this error.

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