llm.py

import os
import re
from openai import OpenAI
from llama_index.llms.groq import Groq
from mistralai.client import MistralClient
from mistralai.models.chat_completion import ChatMessage
import prompt_tracing
import comet_llm
from openai import AzureOpenAI

from dotenv import load_dotenv

load_dotenv()

def check_claim_validity(s):
    pattern = r'claim validity:\s*[,.!\s]*true'
    if re.search(pattern, s, re.IGNORECASE | re.DOTALL):
        return True
    else:
        return False
    
def check_response_validity(s):
    pattern = r'response validity:\s*[,.!\s]*true'
    if re.search(pattern, s, re.IGNORECASE | re.DOTALL):
        return True
    else:
        return False
    
def check_response_given(s):
    pattern = r'response given:\s*[,.!\s]*true'
    if re.search(pattern, s, re.IGNORECASE | re.DOTALL):
        return True
    else:
        return False
    
def check_prima_facie_rebutted(s):
    # pattern = r'Prima Facie refuted:\s*[,.!\s]*true'
    pattern = r'Prima Facie refuted:\s*[,.!\s]*(?:true|yes)'
    if re.search(pattern, s, re.IGNORECASE | re.DOTALL):
        return True
    else:
        return False
    
def go_to_stage_4_1(s):
    pattern = r'Defendant provided new evidence:\s*[,.!\s]*true'
    if re.search(pattern, s, re.IGNORECASE | re.DOTALL):
        return True
    else:
        return False

def llm_function_call(prompt, model):
    if "gpt" in model:
        client = AzureOpenAI(
            api_key=os.getenv("AZURE_OPENAI_API_KEY"),  
            api_version="2024-02-01",
            azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
            )
        chat_completion = client.chat.completions.create(
                    model="gpt-4",
                    temperature=0, 
                    messages = [
                        {
                                "role":"user", 
                                "content":prompt
                        }])

        if prompt_tracing.tracking:
            comet_llm.log_prompt(
                prompt=prompt_tracing.user + ":\n" + prompt,
                output=chat_completion.choices[0].message.content,
            )

        return chat_completion.choices[0].message.content

    elif model == "llama3-70b-8192":
        llm = Groq(model="llama3-70b-8192", api_key=os.environ.get("GROQ_API_KEY"), temperature=0)
        response = llm.complete(prompt)
        return response.text
    
    elif model == "mistral-large-azure":
        client = MistralClient(
            endpoint=os.environ.get("AZURE_AI_MISTRAL_LARGE_ENDPOINT"), api_key=os.environ.get("AZURE_AI_MISTRAL_LARGE_KEY")
        )

        response = client.chat(
            model="azureai",
            messages=[
                ChatMessage(
                    role="user",
                    content=prompt,
                )
            ],
            max_tokens=4096,
            temperature=0,
        )
        print(response.usage)
        return response.choices[0].message.content

    else:
        return "no model available"