pocketflow/docs/utility_function/llm.md

layout	title	parent	nav_order
default	LLM Wrapper	Utility Function	1

LLM Wrappers

We don't provide built-in LLM wrappers. Instead, please implement your own or check out libraries like litellm. Here, we provide some minimal example implementations:

1. OpenAI

   def call_llm(prompt):
       from openai import OpenAI
       client = OpenAI(api_key="YOUR_API_KEY_HERE")
       r = client.chat.completions.create(
           model="gpt-4o",
           messages=[{"role": "user", "content": prompt}]
       )
       return r.choices[0].message.content
   
   # Example usage
   call_llm("How are you?")
   

   Store the API key in an environment variable like OPENAI_API_KEY for security. {: .best-practice }

2. Claude (Anthropic)

   def call_llm(prompt):
       from anthropic import Anthropic
       client = Anthropic(api_key="YOUR_API_KEY_HERE")
       response = client.messages.create(
           model="claude-2",
           messages=[{"role": "user", "content": prompt}],
           max_tokens=100
       )
       return response.content
   

3. Google (Generative AI Studio / PaLM API)

   def call_llm(prompt):
       import google.generativeai as genai
       genai.configure(api_key="YOUR_API_KEY_HERE")
       response = genai.generate_text(
           model="models/text-bison-001",
           prompt=prompt
       )
       return response.result
   

4. Azure (Azure OpenAI)

   def call_llm(prompt):
       from openai import AzureOpenAI
       client = AzureOpenAI(
           azure_endpoint="https://<YOUR_RESOURCE_NAME>.openai.azure.com/",
           api_key="YOUR_API_KEY_HERE",
           api_version="2023-05-15"
       )
       r = client.chat.completions.create(
           model="<YOUR_DEPLOYMENT_NAME>",
           messages=[{"role": "user", "content": prompt}]
       )
       return r.choices[0].message.content
   

5. Ollama (Local LLM)

   def call_llm(prompt):
       from ollama import chat
       response = chat(
           model="llama2",
           messages=[{"role": "user", "content": prompt}]
       )
       return response.message.content
   

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-4o",
        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

⚠️ Caching conflicts with Node retries, as retries yield the same result.

To address this, you could use cached results only if not retried. {: .warning }

from functools import lru_cache

@lru_cache(maxsize=1000)
def cached_call(prompt):
    pass

def call_llm(prompt, use_cache):
    if use_cache:
        return cached_call(prompt)
    # Call the underlying function directly
    return cached_call.__wrapped__(prompt)

class SummarizeNode(Node):
    def exec(self, text):
        return call_llm(f"Summarize: {text}", self.cur_retry==0)

• Enable logging:

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

Why Not Provide Built-in LLM Wrappers?

I believe it is a bad practice to provide LLM-specific implementations in a general framework:

• LLM APIs change frequently. Hardcoding them makes maintenance a nightmare.
• You may need flexibility to switch vendors, use fine-tuned models, or deploy local LLMs.
• You may need optimizations like prompt caching, request batching, or response streaming.