gateway/modules/connectors/connectorAiOpenai.py

import logging
import base64
import httpx
from typing import Dict, Any, List, Union
from fastapi import HTTPException
from modules.shared.configuration import APP_CONFIG

# Configure logger
logger = logging.getLogger(__name__)

def loadConfigData():
    """Load configuration data for OpenAI connector"""
    return {
        "apiKey": APP_CONFIG.get('Connector_AiOpenai_API_SECRET'),
        "apiUrl": APP_CONFIG.get('Connector_AiOpenai_API_URL'),
        "modelName": APP_CONFIG.get('Connector_AiOpenai_MODEL_NAME'),
        "temperature": float(APP_CONFIG.get('Connector_AiOpenai_TEMPERATURE')),
        "maxTokens": int(APP_CONFIG.get('Connector_AiOpenai_MAX_TOKENS'))
    }

class AiOpenai:
    """Connector for communication with the OpenAI API."""
    
    def __init__(self):
        # Load configuration
        self.config = loadConfigData()
        self.apiKey = self.config["apiKey"]
        self.apiUrl = self.config["apiUrl"]
        self.modelName = self.config["modelName"]
        
        # HttpClient for API calls
        self.httpClient = httpx.AsyncClient(
            timeout=120.0,  # Longer timeout for complex requests
            headers={
                "Authorization": f"Bearer {self.apiKey}",
                "Content-Type": "application/json"
            }
        )
        logger.info(f"OpenAI Connector initialized with model: {self.modelName}")
    
    async def callAiBasic(self, messages: List[Dict[str, Any]], temperature: float = None, maxTokens: int = None) -> str:
        """
        Calls the OpenAI API with the given messages.
        
        Args:
            messages: List of messages in OpenAI format (role, content)
            temperature: Temperature for response generation (0.0-1.0)
            maxTokens: Maximum number of tokens in the response
            
        Returns:
            The response from the OpenAI API
            
        Raises:
            HTTPException: For errors in API communication
        """
        try:
            # Use parameters from configuration if none were overridden
            if temperature is None:
                temperature = self.config.get("temperature", 0.2)
                
            if maxTokens is None:
                maxTokens = self.config.get("maxTokens", 2000)
            
            payload = {
                "model": self.modelName,
                "messages": messages,
                "temperature": temperature,
                "max_tokens": maxTokens
            }
            
            response = await self.httpClient.post(
                self.apiUrl,
                json=payload
            )
            
            if response.status_code != 200:
                logger.error(f"OpenAI API error: {response.status_code} - {response.text}")
                raise HTTPException(status_code=500, detail="Error communicating with OpenAI API")
            
            responseJson = response.json()
            content = responseJson["choices"][0]["message"]["content"]
            return content
            
        except Exception as e:
            logger.error(f"Error calling OpenAI API: {str(e)}")
            raise HTTPException(status_code=500, detail=f"Error calling OpenAI API: {str(e)}")
    
    async def callAiImage(self, prompt: str, imageData: Union[str, bytes], mimeType: str = None) -> str:
        """
        Analyzes an image with the OpenAI Vision API.
        
        Args:
            imageData: base64encoded data
            mimeType: The MIME type of the image (optional, only for binary data)
            prompt: The prompt for analysis
            
        Returns:
            The response from the OpenAI Vision API as text
        """
        try:
            logger.debug(f"Starting image analysis with query '{prompt}' for size {len(imageData)}B...")
            
            # Ensure imageData is a string (base64 encoded)
            if not isinstance(imageData, str):
                raise ValueError("imageData must be a string (base64 encoded)")
            
            # Fix base64 padding if needed
            padding_needed = len(imageData) % 4
            if padding_needed:
                imageData += '=' * (4 - padding_needed)
            
            # Use default MIME type if not provided
            if not mimeType:
                mimeType = "image/jpeg"
            
            logger.debug(f"Using MIME type: {mimeType}")
            logger.debug(f"Base64 data length: {len(imageData)} characters")
            
            # Create the data URL format as required by OpenAI Vision API
            data_url = f"data:{mimeType};base64,{imageData}"
            
            messages = [
                {
                    "role": "user",
                    "content": [
                        {"type": "text", "text": prompt},
                        {
                            "type": "image_url",
                            "image_url": {
                                "url": data_url
                            }
                        }
                    ]
                }
            ]
            
            # Use a vision-capable model for image analysis
            # Override the model for vision tasks
            visionModel = "gpt-4o"  # or "gpt-4-vision-preview" depending on availability
            
            # Use parameters from configuration
            temperature = self.config.get("temperature", 0.2)
            maxTokens = self.config.get("maxTokens", 2000)
            
            payload = {
                "model": visionModel,
                "messages": messages,
                "temperature": temperature,
                "max_tokens": maxTokens
            }
            
            response = await self.httpClient.post(
                self.apiUrl,
                json=payload
            )
            
            if response.status_code != 200:
                logger.error(f"OpenAI API error: {response.status_code} - {response.text}")
                raise HTTPException(status_code=500, detail="Error communicating with OpenAI API")
            
            responseJson = response.json()
            content = responseJson["choices"][0]["message"]["content"]
            return content
            
            # Return content
            return response
        
        except Exception as e:
            logger.error(f"Error during image analysis: {str(e)}", exc_info=True)
            return f"[Error during image analysis: {str(e)}]"