pocketflow/docs/llm.md

layout	title	parent	nav_order
default	LLM Wrapper	Preparation	1

LLM Wrappers

We don't provide built-in LLM wrappers. Instead, please implement your own, for example by asking an assistant like ChatGPT or Claude. If you ask ChatGPT to "implement a call_llm function that takes a prompt and returns the LLM response," you shall get something like:

def call_llm(prompt):
    from openai import OpenAI
    # Set the OpenAI API key (use environment variables, etc.)
    client = OpenAI(api_key="YOUR_API_KEY_HERE")
    r = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}]
    )
    return r.choices[0].message.content

# Example usage
call_llm("How are you?")

Improvements

Feel free to enhance your call_llm function as needed. Here are examples:

• Handle chat history:

def call_llm(messages):
    from openai import OpenAI
    client = OpenAI(api_key="YOUR_API_KEY_HERE")
    r = client.chat.completions.create(
        model="gpt-4",
        messages=messages
    )
    return r.choices[0].message.content

• Add in-memory caching:

from functools import lru_cache

@lru_cache(maxsize=1000)
def call_llm(prompt):
    # Your implementation here
    pass

• Enable logging:

def call_llm(prompt):
    import logging
    logging.info(f"Prompt: {prompt}")
    response = ... # Your implementation here
    logging.info(f"Response: {response}")
    return response