95 lines
2.5 KiB
Markdown
95 lines
2.5 KiB
Markdown
---
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layout: default
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title: "LLM Wrapper"
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parent: "Utility Function"
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nav_order: 1
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---
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# LLM Wrappers
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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:
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```python
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def call_llm(prompt):
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from openai import OpenAI
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client = OpenAI(api_key="YOUR_API_KEY_HERE")
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r = client.chat.completions.create(
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model="gpt-4o",
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messages=[{"role": "user", "content": prompt}]
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)
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return r.choices[0].message.content
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# Example usage
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call_llm("How are you?")
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```
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> Store the API key in an environment variable like OPENAI_API_KEY for security.
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{: .note }
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## Improvements
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Feel free to enhance your `call_llm` function as needed. Here are examples:
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- Handle chat history:
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```python
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def call_llm(messages):
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from openai import OpenAI
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client = OpenAI(api_key="YOUR_API_KEY_HERE")
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r = client.chat.completions.create(
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model="gpt-4o",
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messages=messages
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)
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return r.choices[0].message.content
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```
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- Add in-memory caching
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```python
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from functools import lru_cache
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@lru_cache(maxsize=1000)
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def call_llm(prompt):
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# Your implementation here
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pass
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```
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> ⚠️ Caching conflicts with Node retries, as retries yield the same result.
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>
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> To address this, you could use cached results only if not retried.
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{: .warning }
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```python
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from functools import lru_cache
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@lru_cache(maxsize=1000)
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def cached_call(prompt):
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pass
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def call_llm(prompt, use_cache):
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if use_cache:
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return cached_call(prompt)
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# Call the underlying function directly
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return cached_call.__wrapped__(prompt)
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class SummarizeNode(Node):
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def exec(self, text):
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return call_llm(f"Summarize: {text}", self.cur_retry==0)
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```
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- Enable logging:
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```python
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def call_llm(prompt):
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import logging
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logging.info(f"Prompt: {prompt}")
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response = ... # Your implementation here
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logging.info(f"Response: {response}")
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return response
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```
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## Why Not Provide Built-in LLM Wrappers?
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I believe it is a **bad practice** to provide LLM-specific implementations in a general framework:
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- **LLM APIs change frequently**. Hardcoding them makes maintenance a nightmare.
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- You may need **flexibility** to switch vendors, use fine-tuned models, or deploy local LLMs.
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- You may need **optimizations** like prompt caching, request batching, or response streaming. |