144 lines
No EOL
5.5 KiB
Python
144 lines
No EOL
5.5 KiB
Python
import logging
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import httpx
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from typing import Dict, Any, List, Union
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from fastapi import HTTPException
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from modules.configuration import APP_CONFIG
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# Configure logger
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logger = logging.getLogger(__name__)
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def loadConfigData():
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"""Load configuration data for OpenAI connector"""
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return {
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"apiKey": APP_CONFIG.get('Connector_AiOpenai_API_SECRET'),
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"apiUrl": APP_CONFIG.get('Connector_AiOpenai_API_URL'),
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"modelName": APP_CONFIG.get('Connector_AiOpenai_MODEL_NAME'),
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"temperature": float(APP_CONFIG.get('Connector_AiOpenai_TEMPERATURE')),
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"maxTokens": int(APP_CONFIG.get('Connector_AiOpenai_MAX_TOKENS'))
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}
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class ChatService:
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"""Connector for communication with the OpenAI API."""
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def __init__(self):
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# Load configuration
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self.config = loadConfigData()
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self.apiKey = self.config["apiKey"]
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self.apiUrl = self.config["apiUrl"]
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self.modelName = self.config["modelName"]
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# HttpClient for API calls
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self.httpClient = httpx.AsyncClient(
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timeout=120.0, # Longer timeout for complex requests
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headers={
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"Authorization": f"Bearer {self.apiKey}",
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"Content-Type": "application/json"
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}
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)
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logger.info(f"OpenAI Connector initialized with model: {self.modelName}")
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async def callApi(self, messages: List[Dict[str, Any]], temperature: float = None, maxTokens: int = None) -> str:
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"""
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Calls the OpenAI API with the given messages.
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Args:
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messages: List of messages in OpenAI format (role, content)
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temperature: Temperature for response generation (0.0-1.0)
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maxTokens: Maximum number of tokens in the response
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Returns:
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The response from the OpenAI API
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Raises:
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HTTPException: For errors in API communication
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"""
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try:
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# Use parameters from configuration if none were overridden
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if temperature is None:
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temperature = self.config.get("temperature", 0.2)
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if maxTokens is None:
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maxTokens = self.config.get("maxTokens", 2000)
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payload = {
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"model": self.modelName,
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"messages": messages,
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"temperature": temperature,
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"max_tokens": maxTokens
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}
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response = await self.httpClient.post(
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self.apiUrl,
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json=payload
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)
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if response.status_code != 200:
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logger.error(f"OpenAI API error: {response.status_code} - {response.text}")
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raise HTTPException(status_code=500, detail="Error communicating with OpenAI API")
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responseJson = response.json()
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content = responseJson["choices"][0]["message"]["content"]
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return content
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except Exception as e:
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logger.error(f"Error calling OpenAI API: {str(e)}")
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raise HTTPException(status_code=500, detail=f"Error calling OpenAI API: {str(e)}")
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async def close(self):
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"""Closes the HTTP client when the application exits"""
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await self.httpClient.aclose()
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async def analyzeImage(self, imageData: Union[str, bytes], mimeType: str = None, prompt: str = "Describe this image") -> str:
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"""
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Analyzes an image with the OpenAI Vision API.
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Args:
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imageData: Either a file path (str) or image data (bytes)
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mimeType: The MIME type of the image (optional, only for binary data)
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prompt: The prompt for analysis
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Returns:
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The response from the OpenAI Vision API as text
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"""
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try:
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logger.debug("Starting image analysis...")
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# Distinguish between file path and binary data
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if isinstance(imageData, str):
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# It's a file path - import filehandling only when needed
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from modules import agentserviceFilemanager as fileHandler
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base64Data, autoMimeType = fileHandler.encodeFileToBase64(imageData)
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mimeType = mimeType or autoMimeType
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else:
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# It's binary data
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import base64
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base64Data = base64.b64encode(imageData).decode('utf-8')
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# MIME type must be specified for binary data
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if not mimeType:
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# Fallback to generic image type
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mimeType = "image/png"
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# Prepare the payload for the Vision API
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "text", "text": prompt},
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{
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"type": "image_url",
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"image_url": {
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"url": f"data:{mimeType};base64,{base64Data}"
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}
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}
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]
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}
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]
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# Use the existing callApi function with the Vision model
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response = await self.callApi(messages)
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# Return content
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return response
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except Exception as e:
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logger.error(f"Error during image analysis: {str(e)}", exc_info=True)
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return f"[Error during image analysis: {str(e)}]" |