mala/_openai.py

from openai import OpenAI, RateLimitError
from dotenv import load_dotenv
import os
from _llm import LLM as LLM_ollama
from print_color import *
from time import sleep
load_dotenv()

class LLM_OpenAI:
    def __init__(
        self,
        system_prompt='Svara alltid på svenska. Svara bara på det som efterfrågas. Om du inte kan svara, skriv "Jag vet inte".',
        chat=False,
        model="gpt-3.5-turbo-0125",
        max_tokens=24000,
        sleep_time=0
    ):  
        self.chat = chat
        self.model = model
        self.temperature=0
        self.max_tokens = max_tokens
        self.system_message = {"role": "system", "content": system_prompt}
        self.messages =[self.system_message]
        self.client = OpenAI(
            # This is the default and can be omitted
            api_key=os.getenv("OPEN_AI"),
        )
        self.llm_ollama = LLM_ollama(chat=False, stream=True) # For backup
        self.sleep_time = sleep_time

    def build_message(self, message):
        # Add the new message to the list
        self.messages.append({"role": "user", "content": message})

        # Calculate the total token length of the messages
        total_tokens = sum([len((msg["content"])) for msg in self.messages])

        # While the total token length exceeds the limit, remove the oldest messages
        while total_tokens > self.max_tokens:
            removed_message = self.messages.pop(
                1
            )  # Remove the oldest message (not the system message)
            total_tokens -= len((removed_message["content"]))

    def generate(self, prompt, stream=False, local=False):
        sleep(self.sleep_time)
        if self.chat:
            self.build_message(prompt)
            messages = self.messages
        else:
            messages = [self.system_message, {"role": "user", "content": prompt}]
            print(sum([len((msg["content"])) for msg in messages]))
        
        if local:
            response = self.llm_ollama.generate_stream(prompt)
        
        else:
            try:
                response = self.client.chat.completions.create(
                    messages=messages,
                    model=self.model,
                    stream=stream
                )
            except RateLimitError as e:
                print_red(e)
                response = self.llm_ollama.generate_stream(prompt)

        if stream:
            return response
        else:
            answer = response.choices[0].message.content
            if self.chat:
                self.messages.append({"role": "assistant", "content": answer})
            return answer