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Hello,
Your understanding is largely correct. Let's break it down:
- Function Output: This is indeed the direct output produced by the specific function executed within the agent framework. For example, in the MistralAI Cookbook (), the function
lyft_10k is called with the argument "investments". The function output is the detailed information about Lyft's investments in 2021.
- LLM Response: This is the final response generated by the Language Model (LLM) based on the function output. The LLM takes the function output and generates a response that is suitable for the end-user. The LLM can be any language model, such as GPT-4, as shown in the LLM Compiler Agent Cookbook ().
However, it's important to note that the LLM doesn't just "process" the function output. It uses the function output as part of its input when generating a response. The exact way the LLM uses the function output depends on how the LLM is set up and used within the agent framework.
In terms of the difference between the two, the function output is typically more structured and detailed, while the LLM response is more user-friendly and conversational. The LLM response is what the end-user typically sees and interacts with.
I hope this clarifies your understanding. Let me know if you have any more questions!
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Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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