399 lines
No EOL
16 KiB
Python
399 lines
No EOL
16 KiB
Python
import logging
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import httpx
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from typing import List
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from fastapi import HTTPException
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from modules.shared.configuration import APP_CONFIG
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from modules.aicore.aicoreBase import BaseConnectorAi
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from modules.datamodels.datamodelAi import AiModel, PriorityEnum, ProcessingModeEnum, OperationTypeEnum, AiModelCall, AiModelResponse, createOperationTypeRatings
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# Configure logger
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logger = logging.getLogger(__name__)
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class ContextLengthExceededException(Exception):
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"""Exception raised when the context length exceeds the model's limit"""
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pass
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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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}
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class AiOpenai(BaseConnectorAi):
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"""Connector for communication with the OpenAI API."""
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def __init__(self):
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super().__init__()
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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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# 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("OpenAI Connector initialized")
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def getConnectorType(self) -> str:
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"""Get the connector type identifier."""
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return "openai"
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def getModels(self) -> List[AiModel]:
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"""Get all available OpenAI models."""
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return [
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AiModel(
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name="gpt-4o",
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displayName="OpenAI GPT-4o",
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connectorType="openai",
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apiUrl="https://api.openai.com/v1/chat/completions",
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temperature=0.2,
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maxTokens=16384,
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contextLength=128000,
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costPer1kTokensInput=0.03,
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costPer1kTokensOutput=0.06,
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speedRating=7, # Good speed for complex tasks
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qualityRating=9, # High quality
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# capabilities removed (not used in business logic)
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functionCall=self.callAiBasic,
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priority=PriorityEnum.BALANCED,
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processingMode=ProcessingModeEnum.ADVANCED,
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operationTypes=createOperationTypeRatings(
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(OperationTypeEnum.PLAN, 8),
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(OperationTypeEnum.DATA_ANALYSE, 9),
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(OperationTypeEnum.DATA_GENERATE, 9),
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(OperationTypeEnum.DATA_EXTRACT, 7)
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),
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version="gpt-4o",
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calculatePriceUsd=lambda processingTime, bytesSent, bytesReceived: (bytesSent / 4 / 1000) * 0.03 + (bytesReceived / 4 / 1000) * 0.06
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),
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AiModel(
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name="gpt-3.5-turbo",
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displayName="OpenAI GPT-3.5 Turbo",
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connectorType="openai",
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apiUrl="https://api.openai.com/v1/chat/completions",
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temperature=0.2,
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maxTokens=4096,
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contextLength=16000,
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costPer1kTokensInput=0.0015,
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costPer1kTokensOutput=0.002,
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speedRating=9, # Very fast
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qualityRating=7, # Good but not premium
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# capabilities removed (not used in business logic)
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functionCall=self.callAiBasic,
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priority=PriorityEnum.SPEED,
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processingMode=ProcessingModeEnum.BASIC,
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operationTypes=createOperationTypeRatings(
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(OperationTypeEnum.PLAN, 7),
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(OperationTypeEnum.DATA_ANALYSE, 8),
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(OperationTypeEnum.DATA_GENERATE, 8)
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),
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version="gpt-3.5-turbo",
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calculatePriceUsd=lambda processingTime, bytesSent, bytesReceived: (bytesSent / 4 / 1000) * 0.0015 + (bytesReceived / 4 / 1000) * 0.002
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),
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AiModel(
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name="gpt-4o-vision",
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displayName="OpenAI GPT-4o Vision",
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connectorType="openai",
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apiUrl="https://api.openai.com/v1/chat/completions",
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temperature=0.2,
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maxTokens=16384,
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contextLength=128000,
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costPer1kTokensInput=0.03,
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costPer1kTokensOutput=0.06,
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speedRating=6, # Slower for vision tasks
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qualityRating=9, # High quality vision
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# capabilities removed (not used in business logic)
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functionCall=self.callAiImage,
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priority=PriorityEnum.QUALITY,
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processingMode=ProcessingModeEnum.DETAILED,
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operationTypes=createOperationTypeRatings(
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(OperationTypeEnum.IMAGE_ANALYSE, 9)
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),
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version="gpt-4o",
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calculatePriceUsd=lambda processingTime, bytesSent, bytesReceived: (bytesSent / 4 / 1000) * 0.03 + (bytesReceived / 4 / 1000) * 0.06
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),
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AiModel(
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name="dall-e-3",
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displayName="OpenAI DALL-E 3",
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connectorType="openai",
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apiUrl="https://api.openai.com/v1/images/generations",
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temperature=0.0, # Image generation doesn't use temperature
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maxTokens=0, # Image generation doesn't use tokens
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contextLength=0,
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costPer1kTokensInput=0.04,
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costPer1kTokensOutput=0.0,
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speedRating=5, # Slow for image generation
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qualityRating=9, # High quality art generation
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# capabilities removed (not used in business logic)
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functionCall=self.generateImage,
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priority=PriorityEnum.QUALITY,
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processingMode=ProcessingModeEnum.DETAILED,
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operationTypes=createOperationTypeRatings(
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(OperationTypeEnum.IMAGE_GENERATE, 10)
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),
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version="dall-e-3",
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calculatePriceUsd=lambda processingTime, bytesSent, bytesReceived: (bytesSent / 4 / 1000) * 0.04
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)
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]
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async def callAiBasic(self, modelCall: AiModelCall) -> AiModelResponse:
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"""
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Calls the OpenAI API with the given messages using standardized pattern.
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Args:
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modelCall: AiModelCall with messages and options
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Returns:
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AiModelResponse with content and metadata
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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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# Extract parameters from modelCall
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messages = modelCall.messages
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model = modelCall.model
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options = modelCall.options
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temperature = getattr(options, "temperature", None)
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if temperature is None:
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temperature = model.temperature
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maxTokens = model.maxTokens
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payload = {
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"model": model.name,
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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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model.apiUrl,
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json=payload
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)
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if response.status_code != 200:
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error_message = f"OpenAI API error: {response.status_code} - {response.text}"
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logger.error(error_message)
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# Check for context length exceeded error
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if response.status_code == 400:
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try:
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error_data = response.json()
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if (error_data.get("error", {}).get("code") == "context_length_exceeded" or
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"context length" in error_data.get("error", {}).get("message", "").lower()):
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# Raise a specific exception for context length issues
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raise ContextLengthExceededException(
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f"Context length exceeded: {error_data.get('error', {}).get('message', 'Unknown error')}"
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)
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except (ValueError, KeyError):
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pass # If we can't parse the error, fall through to generic error
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# Include the actual error details in the exception
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raise HTTPException(status_code=500, detail=error_message)
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responseJson = response.json()
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content = responseJson["choices"][0]["message"]["content"]
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return AiModelResponse(
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content=content,
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success=True,
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modelId=model.name,
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metadata={"response_id": responseJson.get("id", "")}
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)
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except ContextLengthExceededException:
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# Re-raise context length exceptions without wrapping
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raise
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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 callAiImage(self, modelCall: AiModelCall) -> AiModelResponse:
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"""
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Analyzes an image with the OpenAI Vision API using standardized pattern.
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Args:
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modelCall: AiModelCall with messages and image data in options
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Returns:
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AiModelResponse with analysis content
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"""
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try:
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# Extract parameters from modelCall
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messages = modelCall.messages
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model = modelCall.model
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options = modelCall.options
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prompt = messages[0]["content"] if messages else ""
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imageData = getattr(options, "imageData", None)
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mimeType = getattr(options, "mimeType", "image/jpeg")
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logger.debug(f"Starting image analysis with query '{prompt}' for size {len(imageData)}B...")
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# Ensure imageData is a string (base64 encoded)
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if not isinstance(imageData, str):
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raise ValueError("imageData must be a string (base64 encoded)")
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# Fix base64 padding if needed
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padding_needed = len(imageData) % 4
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if padding_needed:
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imageData += '=' * (4 - padding_needed)
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logger.debug(f"Using MIME type: {mimeType}")
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logger.debug(f"Base64 data length: {len(imageData)} characters")
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# Create the data URL format as required by OpenAI Vision API
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data_url = f"data:{mimeType};base64,{imageData}"
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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": data_url
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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 parameters from model
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temperature = model.temperature
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# Don't set maxTokens - let the model use its full context length
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payload = {
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"model": model.name,
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"messages": messages,
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"temperature": temperature
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}
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response = await self.httpClient.post(
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model.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 AiModelResponse(
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content=content,
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success=True,
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modelId=model.name,
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metadata={"response_id": responseJson.get("id", "")}
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)
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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 AiModelResponse(
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content="",
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success=False,
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error=f"Error during image analysis: {str(e)}"
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)
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async def generateImage(self, modelCall: AiModelCall) -> AiModelResponse:
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"""
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Generate an image using DALL-E 3 using standardized pattern.
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Args:
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modelCall: AiModelCall with messages and generation options
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Returns:
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AiModelResponse with generated image data
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"""
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try:
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# Extract parameters from modelCall
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messages = modelCall.messages
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model = modelCall.model
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options = modelCall.options
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# Parse unified prompt JSON format
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promptContent = messages[0]["content"] if messages else ""
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import json
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promptData = json.loads(promptContent)
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# Extract parameters from unified prompt JSON
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prompt = promptData.get("prompt", promptContent)
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size = promptData.get("size", "1024x1024")
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quality = promptData.get("quality", "standard")
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style = promptData.get("style", "vivid")
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logger.debug(f"Starting image generation with prompt: '{prompt[:100]}...'")
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# DALL-E 3 API endpoint
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dalle_url = "https://api.openai.com/v1/images/generations"
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payload = {
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"model": "dall-e-3",
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"prompt": prompt,
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"size": size,
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"quality": quality,
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"style": style,
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"n": 1,
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"response_format": "b64_json" # Get base64 data directly instead of URLs
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}
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# Create a separate client for DALL-E API calls
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dalle_client = httpx.AsyncClient(
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timeout=120.0,
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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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response = await dalle_client.post(
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dalle_url,
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json=payload
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)
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await dalle_client.aclose()
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if response.status_code != 200:
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logger.error(f"DALL-E API error: {response.status_code} - {response.text}")
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return {
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"success": False,
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"error": f"DALL-E API error: {response.status_code} - {response.text}"
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}
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responseJson = response.json()
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if "data" in responseJson and len(responseJson["data"]) > 0:
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image_data = responseJson["data"][0]["b64_json"]
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logger.info(f"Successfully generated image: {len(image_data)} characters")
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return AiModelResponse(
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content=image_data,
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success=True,
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modelId="dall-e-3",
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metadata={
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"size": size,
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"quality": quality,
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"style": style,
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"response_id": responseJson.get("id", "")
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}
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)
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else:
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logger.error("No image data in DALL-E response")
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return AiModelResponse(
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content="",
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success=False,
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error="No image data in DALL-E response"
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)
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except Exception as e:
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logger.error(f"Error during image generation: {str(e)}", exc_info=True)
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return AiModelResponse(
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content="",
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success=False,
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error=f"Error during image generation: {str(e)}"
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) |