llmhandler.py

import httpx
import json
from Configurator import get_config

class LLMHandler:
    def __init__(self):
        # Load configuration from keys.json
        config = get_config()
        
        # Azure OpenAI configuration from config
        self.api_key = config.azure_api_key
        self.deployment_name = config.llm.model
        self.api_version = config.azure_api_version
        self.azure_endpoint = config.azure_endpoint
        self.url = f"{self.azure_endpoint}/openai/deployments/{self.deployment_name}/chat/completions?api-version={self.api_version}"
        
        # LLM parameters from config
        self.max_tokens = config.llm.max_tokens
        self.temperature = config.llm.temperature
        
        # Headers for the request
        self.headers = {
            "Content-Type": "application/json",
            "api-key": self.api_key
        }
    
    async def process_request(self, user_input: str) -> str:
        """
        Process user input through OpenAI GPT-4o and return the response.
        
        Args:
            user_input (str): The input text from the user
            
        Returns:
            str: The response from OpenAI GPT-4o
        """
        try:
            # Prepare the request payload
            payload = {
                "messages": [
                    {"role": "system", "content": "You are a helpful AI assistant."},
                    {"role": "user", "content": user_input}
                ],
                "max_tokens": self.max_tokens,
                "temperature": self.temperature
            }
            
            # Make the HTTP request
            async with httpx.AsyncClient() as client:
                response = await client.post(
                    self.url,
                    headers=self.headers,
                    json=payload,
                    timeout=30.0
                )
                
                if response.status_code == 200:
                    result = response.json()
                    return result["choices"][0]["message"]["content"]
                else:
                    return f"Error: HTTP {response.status_code} - {response.text}"
            
        except Exception as e:
            return f"Error processing request: {str(e)}"
    

# Create a global instance
llm_handler = LLMHandler()