617 lines
27 KiB
Python
617 lines
27 KiB
Python
import logging
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import asyncio
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import uuid
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import base64
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from typing import Dict, Any, List, Union, Tuple, Optional
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from dataclasses import dataclass
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import time
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logger = logging.getLogger(__name__)
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from modules.aicore.aicoreModelRegistry import modelRegistry
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from modules.aicore.aicoreModelSelector import modelSelector
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from modules.datamodels.datamodelAi import (
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AiModel,
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AiCallOptions,
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AiCallRequest,
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AiCallResponse,
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OperationTypeEnum,
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AiModelCall,
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AiModelResponse,
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)
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from modules.datamodels.datamodelExtraction import ContentPart
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# Dynamic model registry - models are now loaded from connectors via aicore system
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@dataclass(slots=True)
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class AiObjects:
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"""Centralized AI interface: dynamically discovers and uses AI models. Includes web functionality."""
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def __post_init__(self) -> None:
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# Auto-discover and register all available connectors
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self._discoverAndRegisterConnectors()
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def _discoverAndRegisterConnectors(self):
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"""Auto-discover and register all available AI connectors."""
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logger.info("Auto-discovering AI connectors...")
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# Use the model registry's built-in discovery mechanism
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discoveredConnectors = modelRegistry.discoverConnectors()
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# Register each discovered connector
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for connector in discoveredConnectors:
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modelRegistry.registerConnector(connector)
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logger.info(f"Registered connector: {connector.getConnectorType()}")
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logger.info(f"Total connectors registered: {len(discoveredConnectors)}")
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logger.info("All AI connectors registered with dynamic model registry")
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@classmethod
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async def create(cls) -> "AiObjects":
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"""Create AiObjects instance with auto-discovered connectors."""
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# No need to manually create connectors - they're auto-discovered
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return cls()
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def _selectModel(self, prompt: str, context: str, options: AiCallOptions) -> str:
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"""Select the best model using dynamic model selection system."""
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# Get available models from the dynamic registry
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availableModels = modelRegistry.getAvailableModels()
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if not availableModels:
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logger.error("No models available in the registry")
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raise ValueError("No AI models available")
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# Use the dynamic model selector
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selectedModel = modelSelector.selectModel(prompt, context, options, availableModels)
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if not selectedModel:
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logger.error("No suitable model found for the given criteria")
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raise ValueError("No suitable AI model found")
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logger.info(f"Selected model: {selectedModel.name} ({selectedModel.displayName})")
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return selectedModel.name
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# AI for Extraction and Text Generation
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async def call(self, request: AiCallRequest) -> AiCallResponse:
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"""Call AI model for text generation with model-aware chunking."""
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# Handle content parts (unified path)
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if hasattr(request, 'contentParts') and request.contentParts:
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return await self._callWithContentParts(request)
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# Handle traditional text/context calls
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return await self._callWithTextContext(request)
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async def _callWithTextContext(self, request: AiCallRequest) -> AiCallResponse:
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"""Call AI model for traditional text/context calls with fallback mechanism."""
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prompt = request.prompt
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context = request.context or ""
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options = request.options
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# Input bytes will be calculated inside _callWithModel
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# Compress optionally (prompt/context) - simple truncation fallback kept here
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def _maybeTruncate(text: str, limit: int) -> str:
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data = text.encode("utf-8")
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if len(data) <= limit:
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return text
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return data[:limit].decode("utf-8", errors="ignore") + "... [truncated]"
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if options.compressPrompt and len(prompt.encode("utf-8")) > 2000:
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prompt = _maybeTruncate(prompt, 2000)
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if options.compressContext and len(context.encode("utf-8")) > 70000:
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context = _maybeTruncate(context, 70000)
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# Generation parameters are handled inside _callWithModel
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# Get failover models for this operation type
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availableModels = modelRegistry.getAvailableModels()
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failoverModelList = modelSelector.getFailoverModelList(prompt, context, options, availableModels)
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if not failoverModelList:
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errorMsg = f"No suitable models found for operation {options.operationType}"
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logger.error(errorMsg)
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return AiCallResponse(
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content=errorMsg,
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modelName="error",
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priceUsd=0.0,
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processingTime=0.0,
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bytesSent=0,
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bytesReceived=0,
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errorCount=1
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)
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# Try each model in failover sequence
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lastError = None
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for attempt, model in enumerate(failoverModelList):
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try:
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logger.info(f"Attempting AI call with model: {model.name} (attempt {attempt + 1}/{len(failoverModelList)})")
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# Call the model
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response = await self._callWithModel(model, prompt, context, options)
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logger.info(f"✅ AI call successful with model: {model.name}")
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return response
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except Exception as e:
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lastError = e
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logger.warning(f"❌ AI call failed with model {model.name}: {str(e)}")
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# If this is not the last model, try the next one
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if attempt < len(failoverModelList) - 1:
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logger.info(f"🔄 Trying next failover model...")
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continue
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else:
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# All models failed
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logger.error(f"💥 All {len(failoverModelList)} models failed for operation {options.operationType}")
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break
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# All failover attempts failed - return error response
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errorMsg = f"All AI models failed for operation {options.operationType}. Last error: {str(lastError)}"
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logger.error(errorMsg)
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return AiCallResponse(
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content=errorMsg,
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modelName="error",
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priceUsd=0.0,
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processingTime=0.0,
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bytesSent=0,
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bytesReceived=0,
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errorCount=1
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)
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async def _callWithContentParts(self, request: AiCallRequest) -> AiCallResponse:
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"""Process content parts with model-aware chunking (unified for single and multiple parts)."""
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prompt = request.prompt
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options = request.options
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contentParts = request.contentParts
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# Get failover models
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availableModels = modelRegistry.getAvailableModels()
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failoverModelList = modelSelector.getFailoverModelList(prompt, "", options, availableModels)
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if not failoverModelList:
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return self._createErrorResponse("No suitable models found", 0, 0)
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# Process each content part
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allResults = []
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for contentPart in contentParts:
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partResult = await self._processContentPartWithFallback(contentPart, prompt, options, failoverModelList)
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allResults.append(partResult)
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# Merge all results
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mergedContent = self._mergePartResults(allResults)
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return AiCallResponse(
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content=mergedContent,
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modelName="multiple",
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priceUsd=sum(r.priceUsd for r in allResults),
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processingTime=sum(r.processingTime for r in allResults),
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bytesSent=sum(r.bytesSent for r in allResults),
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bytesReceived=sum(r.bytesReceived for r in allResults),
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errorCount=sum(r.errorCount for r in allResults)
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)
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async def _processContentPartWithFallback(self, contentPart, prompt: str, options, failoverModelList) -> AiCallResponse:
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"""Process a single content part with model-aware chunking and fallback."""
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lastError = None
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# Check if this is an image - Vision models need special handling
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isImage = (contentPart.typeGroup == "image") or (contentPart.mimeType and contentPart.mimeType.startswith("image/"))
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for attempt, model in enumerate(failoverModelList):
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try:
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logger.info(f"Processing content part with model: {model.name} (attempt {attempt + 1}/{len(failoverModelList)})")
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# Special handling for images with Vision models
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if isImage and hasattr(model, 'functionCall'):
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# Call model's functionCall directly (for Vision models this is callAiImage)
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from modules.datamodels.datamodelAi import AiModelCall, AiCallOptions as AiCallOpts
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try:
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modelCall = AiModelCall(
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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:{contentPart.mimeType};base64,{contentPart.data}" if isinstance(contentPart.data, str) else
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f"data:{contentPart.mimeType};base64,{base64.b64encode(contentPart.data).decode('utf-8')}"
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}
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}
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]
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}
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],
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model=model,
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options=AiCallOpts(operationType=options.operationType)
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)
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modelResponse = await model.functionCall(modelCall)
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if not modelResponse.success:
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raise ValueError(f"Model call failed: {modelResponse.error}")
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logger.info(f"✅ Image content part processed successfully with model: {model.name}")
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# Convert to AiCallResponse format
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return AiCallResponse(
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content=modelResponse.content,
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modelName=model.name,
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priceUsd=modelResponse.priceUsd if hasattr(modelResponse, 'priceUsd') else 0.0,
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processingTime=modelResponse.processingTime if hasattr(modelResponse, 'processingTime') else 0.0,
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bytesSent=0, # Will be calculated elsewhere
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bytesReceived=0, # Will be calculated elsewhere
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errorCount=0
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)
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except Exception as e:
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# Image processing failed with this model
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lastError = e
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logger.warning(f"❌ Image processing failed with model {model.name}: {str(e)}")
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# If this is not the last model, try the next one
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if attempt < len(failoverModelList) - 1:
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logger.info(f"🔄 Trying next fallback model for image processing...")
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continue
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else:
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# All models failed
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logger.error(f"💥 All {len(failoverModelList)} models failed for image processing")
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raise
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# For non-image parts, check if part fits in model context
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partSize = len(contentPart.data.encode('utf-8')) if contentPart.data else 0
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modelContextBytes = model.contextLength * 4 # Convert tokens to bytes
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if partSize <= modelContextBytes:
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# Part fits - call AI directly
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response = await self._callWithModel(model, prompt, contentPart.data, options)
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logger.info(f"✅ Content part processed successfully with model: {model.name}")
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return response
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else:
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# Part too large - chunk it
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chunks = await self._chunkContentPart(contentPart, model, options)
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if not chunks:
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raise ValueError(f"Failed to chunk content part for model {model.name}")
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# Process each chunk
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chunkResults = []
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for chunk in chunks:
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chunkResponse = await self._callWithModel(model, prompt, chunk['data'], options)
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chunkResults.append(chunkResponse)
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# Merge chunk results
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mergedContent = self._mergeChunkResults(chunkResults)
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totalPrice = sum(r.priceUsd for r in chunkResults)
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totalTime = sum(r.processingTime for r in chunkResults)
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totalBytesSent = sum(r.bytesSent for r in chunkResults)
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totalBytesReceived = sum(r.bytesReceived for r in chunkResults)
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totalErrors = sum(r.errorCount for r in chunkResults)
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logger.info(f"✅ Content part chunked and processed with model: {model.name} ({len(chunks)} chunks)")
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return AiCallResponse(
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content=mergedContent,
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modelName=model.name,
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priceUsd=totalPrice,
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processingTime=totalTime,
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bytesSent=totalBytesSent,
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bytesReceived=totalBytesReceived,
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errorCount=totalErrors
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)
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except Exception as e:
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lastError = e
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logger.warning(f"❌ Model {model.name} failed for content part: {str(e)}")
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if attempt < len(failoverModelList) - 1:
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logger.info(f"🔄 Trying next failover model...")
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continue
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else:
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logger.error(f"💥 All {len(failoverModelList)} models failed for content part")
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break
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# All models failed
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return self._createErrorResponse(f"All models failed: {str(lastError)}", 0, 0)
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async def _chunkContentPart(self, contentPart, model, options) -> List[Dict[str, Any]]:
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"""Chunk a content part based on model capabilities."""
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# Calculate model-specific chunk sizes
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modelContextBytes = model.contextLength * 4 # Convert tokens to bytes
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maxContextBytes = int(modelContextBytes * 0.9) # 90% of context length
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textChunkSize = int(maxContextBytes * 0.7) # 70% of max context for text chunks
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imageChunkSize = int(maxContextBytes * 0.8) # 80% of max context for image chunks
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# Build chunking options
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chunkingOptions = {
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"textChunkSize": textChunkSize,
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"imageChunkSize": imageChunkSize,
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"maxSize": maxContextBytes,
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"chunkAllowed": True
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}
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# Get appropriate chunker
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from modules.services.serviceExtraction.subRegistry import ChunkerRegistry
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chunkerRegistry = ChunkerRegistry()
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chunker = chunkerRegistry.resolve(contentPart.typeGroup)
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if not chunker:
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logger.warning(f"No chunker found for typeGroup: {contentPart.typeGroup}")
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return []
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# Chunk the content part
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try:
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chunks = chunker.chunk(contentPart, chunkingOptions)
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logger.debug(f"Created {len(chunks)} chunks for {contentPart.typeGroup} part")
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return chunks
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except Exception as e:
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logger.error(f"Chunking failed for {contentPart.typeGroup}: {str(e)}")
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return []
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def _mergePartResults(self, partResults: List[AiCallResponse]) -> str:
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"""Merge part results using the existing sophisticated merging system."""
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if not partResults:
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return ""
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# Convert AiCallResponse results to ContentParts for merging
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from modules.datamodels.datamodelExtraction import ContentPart
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from modules.services.serviceExtraction.subUtils import makeId
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content_parts = []
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for i, result in enumerate(partResults):
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if result.content:
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content_part = ContentPart(
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id=str(uuid.uuid4()),
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parentId=None,
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label=f"ai_result_{i}",
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typeGroup="text", # Default to text for AI results
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mimeType="text/plain",
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data=result.content,
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metadata={
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"aiResult": True,
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"modelName": result.modelName,
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"priceUsd": result.priceUsd,
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"processingTime": result.processingTime,
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"bytesSent": result.bytesSent,
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"bytesReceived": result.bytesReceived
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}
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)
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content_parts.append(content_part)
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# Use existing merging system
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from modules.datamodels.datamodelExtraction import MergeStrategy
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merge_strategy = MergeStrategy(
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useIntelligentMerging=True,
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groupBy="typeGroup",
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orderBy="id",
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mergeType="concatenate"
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)
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from modules.services.serviceExtraction.subPipeline import _applyMerging
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merged_parts = _applyMerging(content_parts, merge_strategy)
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# Convert merged parts back to final string
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final_content = "\n\n".join([part.data for part in merged_parts])
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logger.info(f"Merged {len(partResults)} AI results using existing merging system")
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return final_content.strip()
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def _mergeChunkResults(self, chunkResults: List[AiCallResponse]) -> str:
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"""Merge chunk results using the existing sophisticated merging system."""
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if not chunkResults:
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return ""
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# Convert AiCallResponse results to ContentParts for merging
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content_parts = []
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for i, result in enumerate(chunkResults):
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if result.content:
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content_part = ContentPart(
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id=str(uuid.uuid4()),
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parentId=None,
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label=f"chunk_result_{i}",
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typeGroup="text", # Default to text for AI results
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mimeType="text/plain",
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data=result.content,
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metadata={
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"aiResult": True,
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"chunk": True,
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"modelName": result.modelName,
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"priceUsd": result.priceUsd,
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"processingTime": result.processingTime,
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"bytesSent": result.bytesSent,
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"bytesReceived": result.bytesReceived
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}
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)
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content_parts.append(content_part)
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# Use existing merging system
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from modules.datamodels.datamodelExtraction import MergeStrategy
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merge_strategy = MergeStrategy(
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useIntelligentMerging=True,
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groupBy="typeGroup",
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orderBy="id",
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mergeType="concatenate"
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)
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from modules.services.serviceExtraction.subPipeline import _applyMerging
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merged_parts = _applyMerging(content_parts, merge_strategy)
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# Convert merged parts back to final string
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final_content = "\n\n".join([part.data for part in merged_parts])
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logger.info(f"Merged {len(chunkResults)} chunk results using existing merging system")
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return final_content.strip()
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def _createErrorResponse(self, errorMsg: str, inputBytes: int, outputBytes: int) -> AiCallResponse:
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"""Create an error response."""
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return AiCallResponse(
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content=errorMsg,
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modelName="error",
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priceUsd=0.0,
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processingTime=0.0,
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bytesSent=inputBytes,
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bytesReceived=outputBytes,
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errorCount=1
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)
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async def _callWithModel(self, model: AiModel, prompt: str, context: str, options: AiCallOptions = None) -> AiCallResponse:
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"""Call a specific model and return the response."""
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# Calculate input bytes from prompt and context
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inputBytes = len((prompt + context).encode('utf-8'))
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# Replace <TOKEN_LIMIT> placeholder with model's maxTokens value
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if "<TOKEN_LIMIT>" in prompt:
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if model.maxTokens > 0:
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tokenLimit = str(model.maxTokens)
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modelPrompt = prompt.replace("<TOKEN_LIMIT>", tokenLimit)
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logger.debug(f"Replaced <TOKEN_LIMIT> with {tokenLimit} for model {model.name}")
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else:
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raise ValueError(f"Model {model.name} has invalid maxTokens ({model.maxTokens}). Cannot set token limit.")
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else:
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modelPrompt = prompt
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# Update messages array with replaced content
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messages = []
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if context:
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messages.append({"role": "system", "content": f"Context from documents:\n{context}"})
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messages.append({"role": "user", "content": modelPrompt})
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# Start timing
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startTime = time.time()
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# Call the model's function directly - completely generic
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if model.functionCall:
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# Create standardized call object
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modelCall = AiModelCall(
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messages=messages,
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model=model,
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options=options or {}
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)
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# Call the model with standardized interface
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modelResponse = await model.functionCall(modelCall)
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# Extract content from standardized response
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if not modelResponse.success:
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raise ValueError(f"Model call failed: {modelResponse.error}")
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content = modelResponse.content
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else:
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raise ValueError(f"Model {model.name} has no function call defined")
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# Calculate timing and output bytes
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endTime = time.time()
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processingTime = endTime - startTime
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outputBytes = len(content.encode("utf-8"))
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# Calculate price using model's own price calculation method
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priceUsd = model.calculatePriceUsd(processingTime, inputBytes, outputBytes)
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return AiCallResponse(
|
|
content=content,
|
|
modelName=model.name,
|
|
priceUsd=priceUsd,
|
|
processingTime=processingTime,
|
|
bytesSent=inputBytes,
|
|
bytesReceived=outputBytes,
|
|
errorCount=0
|
|
)
|
|
|
|
|
|
# AI for Image Generation
|
|
async def generateImage(self, prompt: str, size: str = "1024x1024", quality: str = "standard", style: str = "vivid", options: AiCallOptions = None) -> AiCallResponse:
|
|
"""Generate an image using AI."""
|
|
|
|
if options is None:
|
|
options = AiCallOptions(operationType=OperationTypeEnum.IMAGE_GENERATE)
|
|
|
|
# Calculate input bytes
|
|
inputBytes = len(prompt.encode("utf-8"))
|
|
|
|
try:
|
|
# Select the best model for image generation
|
|
modelName = self._selectModel(prompt, "", options)
|
|
selectedModel = modelRegistry.getModel(modelName)
|
|
|
|
if not selectedModel:
|
|
raise ValueError(f"Selected model {modelName} not found in registry")
|
|
|
|
# Get the connector for this model
|
|
connector = modelRegistry.getConnectorForModel(modelName)
|
|
if not connector:
|
|
raise ValueError(f"No connector found for model {modelName}")
|
|
|
|
# Start timing
|
|
startTime = time.time()
|
|
|
|
# Create standardized call object for image generation
|
|
modelCall = AiModelCall(
|
|
messages=[{"role": "user", "content": prompt}],
|
|
model=selectedModel,
|
|
options=AiCallOptions(size=size, quality=quality, style=style)
|
|
)
|
|
|
|
# Call the model with standardized interface
|
|
if selectedModel.functionCall:
|
|
modelResponse = await selectedModel.functionCall(modelCall)
|
|
|
|
# Extract content from standardized response
|
|
if not modelResponse.success:
|
|
raise ValueError(f"Model call failed: {modelResponse.error}")
|
|
content = modelResponse.content
|
|
else:
|
|
raise ValueError(f"Model {modelName} has no function call defined")
|
|
|
|
# Calculate timing and output bytes
|
|
endTime = time.time()
|
|
processingTime = endTime - startTime
|
|
outputBytes = len(content.encode("utf-8"))
|
|
|
|
# Calculate price using model's own price calculation method
|
|
priceUsd = selectedModel.calculatePriceUsd(processingTime, inputBytes, outputBytes)
|
|
|
|
logger.info(f"✅ Image generation successful with model: {modelName}")
|
|
return AiCallResponse(
|
|
success=True,
|
|
content=content,
|
|
modelName=modelName,
|
|
processingTime=processingTime,
|
|
priceUsd=priceUsd,
|
|
bytesSent=inputBytes,
|
|
bytesReceived=outputBytes,
|
|
errorCount=0
|
|
)
|
|
|
|
except Exception as e:
|
|
logger.error(f"❌ Image generation failed with model {modelName}: {str(e)}")
|
|
return AiCallResponse(
|
|
content=f"Image generation failed: {str(e)}",
|
|
modelName=modelName,
|
|
priceUsd=0.0,
|
|
processingTime=0.0,
|
|
bytesSent=inputBytes,
|
|
bytesReceived=0,
|
|
errorCount=1
|
|
)
|
|
|
|
# Utility methods
|
|
async def listAvailableModels(self, connectorType: str = None) -> List[Dict[str, Any]]:
|
|
"""List available models, optionally filtered by connector type."""
|
|
models = modelRegistry.getAvailableModels()
|
|
if connectorType:
|
|
return [model.model_dump() for model in models if model.connectorType == connectorType]
|
|
return [model.model_dump() for model in models]
|
|
|
|
async def getModelInfo(self, modelName: str) -> Dict[str, Any]:
|
|
"""Get information about a specific model."""
|
|
model = modelRegistry.getModel(modelName)
|
|
if not model:
|
|
raise ValueError(f"Model {modelName} not found")
|
|
return model.model_dump()
|
|
|
|
async def getModelsByTag(self, tag: str) -> List[str]:
|
|
"""Get model names that have a specific tag."""
|
|
models = modelRegistry.getModelsByTag(tag)
|
|
return [model.name for model in models]
|
|
|