679 lines
30 KiB
Python
679 lines
30 KiB
Python
import json
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import logging
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from typing import Dict, Any, List, Optional, Tuple, Union
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from modules.datamodels.datamodelChat import PromptPlaceholder, ChatDocument
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from modules.datamodels.datamodelAi import AiCallRequest, AiCallOptions, OperationTypeEnum, PriorityEnum, ProcessingModeEnum
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from modules.services.serviceAi.subSharedAiUtils import (
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buildPromptWithPlaceholders,
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extractTextFromContentParts,
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reduceText,
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determineCallType
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)
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from modules.shared.jsonUtils import (
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extractJsonString,
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repairBrokenJson,
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extractSectionsFromDocument,
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buildContinuationContext
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)
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logger = logging.getLogger(__name__)
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# Repair-based looping system - no longer needs LOOP_INSTRUCTION_TEXT
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# Sections are accumulated and repair mechanism handles broken JSON automatically
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# Rebuild the model to resolve forward references
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AiCallRequest.model_rebuild()
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class SubCoreAi:
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"""Core AI operations including image analysis, text generation, and planning calls."""
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def __init__(self, services, aiObjects):
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"""Initialize core AI operations.
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Args:
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services: Service center instance for accessing other services
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aiObjects: Initialized AiObjects instance
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"""
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self.services = services
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self.aiObjects = aiObjects
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async def _analyzePromptAndCreateOptions(self, prompt: str) -> AiCallOptions:
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"""Analyze prompt to determine appropriate AiCallOptions parameters."""
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try:
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# Get dynamic enum values from Pydantic models
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operation_types = [e.value for e in OperationTypeEnum]
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priorities = [e.value for e in PriorityEnum]
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processing_modes = [e.value for e in ProcessingModeEnum]
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# Create analysis prompt for AI to determine operation type and parameters
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analysisPrompt = f"""
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You are an AI operation analyzer. Analyze the following prompt and determine the most appropriate operation type and parameters.
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PROMPT TO ANALYZE:
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{self.services.ai.sanitizePromptContent(prompt, 'userinput')}
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Based on the prompt content, determine:
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1. operationType: Choose the most appropriate from: {', '.join(operation_types)}
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2. priority: Choose from: {', '.join(priorities)}
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3. processingMode: Choose from: {', '.join(processing_modes)}
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4. compressPrompt: true/false (true for story-like prompts, false for structured prompts with JSON/schemas)
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5. compressContext: true/false (true to summarize context, false to process fully)
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Respond with ONLY a JSON object in this exact format:
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{{
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"operationType": "dataAnalyse",
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"priority": "balanced",
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"processingMode": "basic",
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"compressPrompt": true,
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"compressContext": true
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}}
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"""
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# Use AI to analyze the prompt
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request = AiCallRequest(
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prompt=analysisPrompt,
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options=AiCallOptions(
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operationType=OperationTypeEnum.DATA_ANALYSE,
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priority=PriorityEnum.SPEED,
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processingMode=ProcessingModeEnum.BASIC,
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compressPrompt=True,
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compressContext=False
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)
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)
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response = await self.aiObjects.call(request)
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# Parse AI response
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try:
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import json
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json_start = response.content.find('{')
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json_end = response.content.rfind('}') + 1
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if json_start != -1 and json_end > json_start:
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analysis = json.loads(response.content[json_start:json_end])
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# Map string values to enums
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operation_type = OperationTypeEnum(analysis.get('operationType', 'dataAnalyse'))
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priority = PriorityEnum(analysis.get('priority', 'balanced'))
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processing_mode = ProcessingModeEnum(analysis.get('processingMode', 'basic'))
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return AiCallOptions(
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operationType=operation_type,
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priority=priority,
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processingMode=processing_mode,
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compressPrompt=analysis.get('compressPrompt', True),
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compressContext=analysis.get('compressContext', True)
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)
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except Exception as e:
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logger.warning(f"Failed to parse AI analysis response: {e}")
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except Exception as e:
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logger.warning(f"Prompt analysis failed: {e}")
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# Fallback to default options
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return AiCallOptions(
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operationType=OperationTypeEnum.DATA_ANALYSE,
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priority=PriorityEnum.BALANCED,
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processingMode=ProcessingModeEnum.BASIC
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)
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# Shared Core Function for AI Calls with Looping and Repair
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async def _callAiWithLooping(
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self,
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prompt: str,
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options: AiCallOptions,
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debugPrefix: str = "ai_call",
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promptBuilder: Optional[callable] = None,
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promptArgs: Optional[Dict[str, Any]] = None
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) -> str:
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"""
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Shared core function for AI calls with repair-based looping system.
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Automatically repairs broken JSON and continues generation seamlessly.
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Args:
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prompt: The prompt to send to AI
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options: AI call configuration options
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debugPrefix: Prefix for debug file names
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Returns:
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Complete AI response after all iterations
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"""
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max_iterations = 50 # Prevent infinite loops
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iteration = 0
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allSections = [] # Accumulate all sections across iterations
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lastRawResponse = None # Store last raw JSON response for continuation
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logger.debug(f"Starting AI call with repair-based looping (debug prefix: {debugPrefix})")
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while iteration < max_iterations:
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iteration += 1
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logger.debug(f"AI call iteration {iteration}/{max_iterations}")
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# Build iteration prompt
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if len(allSections) > 0 and promptBuilder and promptArgs:
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# This is a continuation - build continuation context with raw JSON and rebuild prompt
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continuationContext = buildContinuationContext(allSections, lastRawResponse)
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logger.info(f"Continuation context: {continuationContext.get('section_count')} sections")
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if lastRawResponse:
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logger.debug(f"Iteration {iteration}: Including previous response in continuation context ({len(lastRawResponse)} chars)")
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else:
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logger.warning(f"Iteration {iteration}: No previous response available for continuation!")
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# Rebuild prompt with continuation context using the provided prompt builder
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iterationPrompt = await promptBuilder(**promptArgs, continuationContext=continuationContext)
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logger.debug(f"Rebuilt prompt with continuation context for iteration {iteration}")
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else:
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# First iteration - use original prompt
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iterationPrompt = prompt
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# Make AI call
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try:
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from modules.datamodels.datamodelAi import AiCallRequest
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request = AiCallRequest(
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prompt=iterationPrompt,
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context="",
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options=options
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)
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# Write the ACTUAL prompt sent to AI
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if iteration == 1:
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self.services.utils.writeDebugFile(iterationPrompt, f"{debugPrefix}_prompt")
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else:
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self.services.utils.writeDebugFile(iterationPrompt, f"{debugPrefix}_prompt_iteration_{iteration}")
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response = await self.aiObjects.call(request)
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result = response.content
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# Debug: Check response immediately from API
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if iteration == 1 and result:
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first_chars = result[:200].replace('\n', '\\n').replace('\r', '\\r')
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logger.debug(f"Iteration 1: Raw API response starts with (first 200 chars): '{first_chars}'")
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if result.strip().startswith('},') or result.strip().startswith('],'):
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logger.error(f"Iteration 1: API returned fragment! Full start: '{result[:200]}'")
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# Write raw AI response to debug file
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if iteration == 1:
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self.services.utils.writeDebugFile(result, f"{debugPrefix}_response")
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else:
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self.services.utils.writeDebugFile(result, f"{debugPrefix}_response_iteration_{iteration}")
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# Emit stats for this iteration
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self.services.workflow.storeWorkflowStat(
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self.services.currentWorkflow,
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response,
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f"ai.call.{debugPrefix}.iteration_{iteration}"
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)
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if not result or not result.strip():
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logger.warning(f"Iteration {iteration}: Empty response, stopping")
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break
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# Store raw response for continuation (even if broken)
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lastRawResponse = result
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# Check for complete_response flag in raw response (before parsing)
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import re
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if re.search(r'"complete_response"\s*:\s*true', result, re.IGNORECASE):
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logger.info(f"Iteration {iteration}: Detected complete_response flag in raw response")
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# Extract sections from response (handles both valid and broken JSON)
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extractedSections, wasJsonComplete = self._extractSectionsFromResponse(result, iteration, debugPrefix)
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if not extractedSections:
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# If we're in continuation mode and JSON was incomplete, don't stop - continue to allow retry
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if iteration > 1 and not wasJsonComplete:
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logger.warning(f"Iteration {iteration}: No sections extracted from continuation fragment, continuing for another attempt")
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continue
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# Otherwise, stop if no sections
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logger.warning(f"Iteration {iteration}: No sections extracted, stopping")
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break
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# Add new sections to accumulator
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allSections.extend(extractedSections)
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logger.info(f"Iteration {iteration}: Extracted {len(extractedSections)} sections (total: {len(allSections)})")
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# Check if we should continue (completion detection)
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if self._shouldContinueGeneration(allSections, iteration, wasJsonComplete, result):
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logger.debug(f"Iteration {iteration}: Continuing generation")
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continue
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else:
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# Done - build final result
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logger.info(f"Iteration {iteration}: Generation complete")
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break
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except Exception as e:
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logger.error(f"Error in AI call iteration {iteration}: {str(e)}")
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break
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if iteration >= max_iterations:
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logger.warning(f"AI call stopped after maximum iterations ({max_iterations})")
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# Build final result from accumulated sections
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final_result = self._buildFinalResultFromSections(allSections)
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# Write final result to debug file
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self.services.utils.writeDebugFile(final_result, f"{debugPrefix}_final_result")
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logger.info(f"AI call completed: {len(allSections)} total sections from {iteration} iterations")
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return final_result
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def _extractSectionsFromResponse(
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self,
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result: str,
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iteration: int,
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debugPrefix: str
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) -> Tuple[List[Dict[str, Any]], bool]:
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"""
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Extract sections from AI response, handling both valid and broken JSON.
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Uses repair mechanism for broken JSON.
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Checks for "complete_response": true flag to determine completion.
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Returns (sections, wasJsonComplete)
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"""
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# First, try to parse as valid JSON
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try:
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extracted = extractJsonString(result)
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parsed_result = json.loads(extracted)
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# Check if AI marked response as complete
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isComplete = parsed_result.get("complete_response", False) == True
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if isComplete:
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logger.info(f"Iteration {iteration}: AI marked response as complete (complete_response: true)")
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# Extract sections from parsed JSON
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sections = extractSectionsFromDocument(parsed_result)
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logger.debug(f"Iteration {iteration}: Valid JSON - extracted {len(sections)} sections")
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# If AI marked as complete, always return as complete
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if isComplete:
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return sections, True
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# If in continuation mode (iteration > 1), continuation responses are expected to be fragments
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# A fragment with 0 extractable sections means JSON is incomplete - need another iteration
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# Don't use repair mechanism - just mark as incomplete so loop continues
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if len(sections) == 0 and iteration > 1:
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logger.info(f"Iteration {iteration}: Continuation fragment with 0 extractable sections - JSON incomplete, continuing")
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return sections, False # Mark as incomplete so loop continues
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# First iteration with 0 sections means empty response - stop
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if len(sections) == 0:
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return sections, True # Complete but empty
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return sections, True # JSON was complete with sections
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except json.JSONDecodeError as e:
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# Broken JSON - try repair mechanism (normal in iterative generation)
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logger.info(f"Iteration {iteration}: JSON incomplete/broken, attempting repair: {str(e)}")
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self.services.utils.writeDebugFile(result, f"{debugPrefix}_broken_json_iteration_{iteration}")
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# Try to repair
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repaired_json = repairBrokenJson(result)
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if repaired_json:
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# Extract sections from repaired JSON
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sections = extractSectionsFromDocument(repaired_json)
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logger.info(f"Iteration {iteration}: Repaired JSON - extracted {len(sections)} sections")
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return sections, False # JSON was broken but repaired
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else:
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# Repair failed - log error
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logger.error(f"Iteration {iteration}: All repair strategies failed")
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return [], False
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except Exception as e:
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logger.error(f"Iteration {iteration}: Unexpected error during parsing: {str(e)}")
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return [], False
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def _shouldContinueGeneration(
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self,
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allSections: List[Dict[str, Any]],
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iteration: int,
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wasJsonComplete: bool,
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rawResponse: str = None
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) -> bool:
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"""
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Determine if generation should continue based on JSON completeness and complete_response flag.
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Returns True if we should continue, False if done.
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"""
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if len(allSections) == 0:
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return True # No sections yet, continue
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# Check for complete_response flag in raw response
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if rawResponse:
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import re
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# Look for complete_response: true pattern (allowing for whitespace variations)
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if re.search(r'"complete_response"\s*:\s*true', rawResponse, re.IGNORECASE):
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logger.info("AI marked response as complete (complete_response: true) - stopping generation")
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return False
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# If JSON was complete (and no complete_response flag), we're done
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# If JSON was broken and repaired, continue to get more content
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if wasJsonComplete:
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logger.info("JSON was complete - stopping generation")
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return False
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else:
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logger.info("JSON was broken/repaired - continuing generation")
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return True
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def _buildFinalResultFromSections(
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self,
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allSections: List[Dict[str, Any]]
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) -> str:
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"""
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Build final JSON result from accumulated sections.
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"""
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if not allSections:
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return ""
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# Build documents structure
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# Assuming single document for now
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documents = [{
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"id": "doc_1",
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"title": "Generated Document", # This should come from prompt
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"filename": "document.json",
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"sections": allSections
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}]
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result = {
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"metadata": {
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"split_strategy": "single_document",
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"source_documents": [],
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"extraction_method": "ai_generation"
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},
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"documents": documents
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}
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return json.dumps(result, indent=2)
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# Old _buildContinuationPrompt and _mergeJsonContent methods removed
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# Now handled by repair mechanism in jsonUtils.py and section accumulation
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# Planning AI Call
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async def callAiPlanning(
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self,
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prompt: str,
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placeholders: Optional[List[PromptPlaceholder]] = None
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) -> str:
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"""
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Planning AI call for task planning, action planning, action selection, etc.
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Always uses static parameters optimized for planning tasks.
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Args:
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prompt: The planning prompt
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placeholders: Optional list of placeholder replacements
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Returns:
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Planning JSON response
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"""
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# Planning calls always use static parameters
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logger.debug("Using static parameters for planning call")
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options = AiCallOptions(
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operationType=OperationTypeEnum.PLAN,
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priority=PriorityEnum.QUALITY,
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processingMode=ProcessingModeEnum.DETAILED,
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compressPrompt=False,
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compressContext=False
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)
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# Build full prompt with placeholders
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if placeholders:
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placeholders_dict = {p.label: p.content for p in placeholders}
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full_prompt = buildPromptWithPlaceholders(prompt, placeholders_dict)
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else:
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full_prompt = prompt
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# Use shared core function with planning-specific debug prefix
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return await self._callAiWithLooping(full_prompt, options, "plan")
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# Document Generation AI Call
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async def callAiDocuments(
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self,
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prompt: str,
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documents: Optional[List[ChatDocument]] = None,
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options: Optional[AiCallOptions] = None,
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outputFormat: Optional[str] = None,
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title: Optional[str] = None
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) -> Union[str, Dict[str, Any]]:
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"""
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Document generation AI call for all non-planning calls.
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Uses the current unified path with extraction and generation.
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Args:
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prompt: The main prompt for the AI call
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documents: Optional list of documents to process
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options: AI call configuration options
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outputFormat: Optional output format for document generation
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title: Optional title for generated documents
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Returns:
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AI response as string, or dict with documents if outputFormat is specified
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"""
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if options is None or (hasattr(options, 'operationType') and options.operationType is None):
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# Use AI to determine parameters ONLY when truly needed (options=None OR operationType=None)
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logger.debug("Analyzing prompt to determine optimal parameters")
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options = await self._analyzePromptAndCreateOptions(prompt)
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else:
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logger.debug(f"Using provided options: operationType={options.operationType}, priority={options.priority}")
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# CRITICAL: For document generation with JSON templates, NEVER compress the prompt
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# Compressing would truncate the template structure and confuse the AI
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if outputFormat: # Document generation with structured output
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if not options:
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options = AiCallOptions()
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options.compressPrompt = False # JSON templates must NOT be truncated
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options.compressContext = False # Context also should not be compressed
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logger.debug("Document generation detected - disabled prompt/context compression")
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# Handle document generation with specific output format using unified approach
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if outputFormat:
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# Use unified generation method for all document generation
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if documents and len(documents) > 0:
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logger.debug(f"Extracting content from {len(documents)} documents")
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extracted_content = await self.services.ai.documentProcessor.callAiText(prompt, documents, options)
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else:
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logger.debug("No documents provided - using direct generation")
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extracted_content = None
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logger.debug(f"[DEBUG] title value: {title}, type: {type(title)}")
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from modules.services.serviceGeneration.subPromptBuilderGeneration import buildGenerationPrompt
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# First call without continuation context
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generation_prompt = await buildGenerationPrompt(outputFormat, prompt, title, extracted_content, None)
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# Prepare prompt builder arguments for continuation
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promptArgs = {
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"outputFormat": outputFormat,
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"userPrompt": prompt,
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"title": title,
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"extracted_content": extracted_content
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}
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generated_json = await self._callAiWithLooping(
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generation_prompt,
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options,
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"document_generation",
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buildGenerationPrompt,
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promptArgs
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)
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# Parse the generated JSON (extract fenced/embedded JSON first)
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try:
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extracted_json = self.services.utils.jsonExtractString(generated_json)
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generated_data = json.loads(extracted_json)
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except json.JSONDecodeError as e:
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logger.error(f"Failed to parse generated JSON: {str(e)}")
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logger.error(f"JSON content length: {len(generated_json)}")
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logger.error(f"JSON content preview (last 200 chars): ...{generated_json[-200:]}")
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logger.error(f"JSON content around error position: {generated_json[max(0, e.pos-50):e.pos+50]}")
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# Write the problematic JSON to debug file
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self.services.utils.writeDebugFile(generated_json, "failed_json_parsing")
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return {"success": False, "error": f"Generated content is not valid JSON: {str(e)}"}
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# Render to final format using the existing renderer
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|
try:
|
|
from modules.services.serviceGeneration.mainServiceGeneration import GenerationService
|
|
generationService = GenerationService(self.services)
|
|
rendered_content, mime_type = await generationService.renderReport(
|
|
generated_data, outputFormat, title or "Generated Document", prompt, self
|
|
)
|
|
|
|
# Build result in the expected format
|
|
result = {
|
|
"success": True,
|
|
"content": generated_data,
|
|
"documents": [{
|
|
"documentName": f"generated.{outputFormat}",
|
|
"documentData": rendered_content,
|
|
"mimeType": mime_type,
|
|
"title": title or "Generated Document"
|
|
}],
|
|
"is_multi_file": False,
|
|
"format": outputFormat,
|
|
"title": title,
|
|
"split_strategy": "single",
|
|
"total_documents": 1,
|
|
"processed_documents": 1
|
|
}
|
|
|
|
# Log AI response for debugging
|
|
self.services.utils.writeDebugFile(str(result), "document_generation_response", documents)
|
|
return result
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error rendering document: {str(e)}")
|
|
return {"success": False, "error": f"Rendering failed: {str(e)}"}
|
|
|
|
# Handle text calls (no output format specified)
|
|
if documents:
|
|
# Use document processing for text calls with documents
|
|
result = await self.services.ai.documentProcessor.callAiText(prompt, documents, options)
|
|
else:
|
|
# Use shared core function for direct text calls
|
|
result = await self._callAiWithLooping(prompt, options, "text")
|
|
|
|
return result
|
|
|
|
|
|
# AI Image Analysis
|
|
async def readImage(
|
|
self,
|
|
prompt: str,
|
|
imageData: Union[str, bytes],
|
|
mimeType: str = None,
|
|
options: Optional[AiCallOptions] = None,
|
|
) -> str:
|
|
"""Call AI for image analysis using interface.call() with contentParts."""
|
|
try:
|
|
# Check if imageData is valid
|
|
if not imageData:
|
|
error_msg = "No image data provided"
|
|
self.services.utils.debugLogToFile(f"Error in AI image analysis: {error_msg}", "AI_SERVICE")
|
|
logger.error(f"Error in AI image analysis: {error_msg}")
|
|
return f"Error: {error_msg}"
|
|
|
|
self.services.utils.debugLogToFile(f"readImage called with prompt, imageData type: {type(imageData)}, length: {len(imageData) if imageData else 0}, mimeType: {mimeType}", "AI_SERVICE")
|
|
logger.info(f"readImage called with prompt, imageData type: {type(imageData)}, length: {len(imageData) if imageData else 0}, mimeType: {mimeType}")
|
|
|
|
# Always use IMAGE_ANALYSE operation type for image processing
|
|
if options is None:
|
|
options = AiCallOptions(operationType=OperationTypeEnum.IMAGE_ANALYSE)
|
|
else:
|
|
# Override the operation type to ensure image analysis
|
|
options.operationType = OperationTypeEnum.IMAGE_ANALYSE
|
|
|
|
# Create content parts with image data
|
|
from modules.datamodels.datamodelExtraction import ContentPart
|
|
import base64
|
|
|
|
# ContentPart.data must be a string - convert bytes to base64 if needed
|
|
if isinstance(imageData, bytes):
|
|
imageDataStr = base64.b64encode(imageData).decode('utf-8')
|
|
else:
|
|
# Already a base64 string
|
|
imageDataStr = imageData
|
|
|
|
imagePart = ContentPart(
|
|
id="image_0",
|
|
parentId=None,
|
|
label="Image",
|
|
typeGroup="image",
|
|
mimeType=mimeType or "image/jpeg",
|
|
data=imageDataStr, # Must be a string (base64 encoded)
|
|
metadata={"imageAnalysis": True}
|
|
)
|
|
|
|
# Create request with content parts
|
|
from modules.datamodels.datamodelAi import AiCallRequest
|
|
request = AiCallRequest(
|
|
prompt=prompt,
|
|
context="",
|
|
options=options,
|
|
contentParts=[imagePart]
|
|
)
|
|
|
|
self.services.utils.debugLogToFile(f"Calling aiObjects.call() with operationType: {options.operationType}", "AI_SERVICE")
|
|
logger.info(f"Calling aiObjects.call() with operationType: {options.operationType}")
|
|
|
|
# Write image analysis prompt to debug file
|
|
self.services.utils.writeDebugFile(prompt, "image_analysis_prompt")
|
|
|
|
response = await self.aiObjects.call(request)
|
|
|
|
# Write image analysis response to debug file
|
|
# response is an AiCallResponse object
|
|
result = response.content
|
|
self.services.utils.writeDebugFile(result, "image_analysis_response")
|
|
|
|
# Debug the result
|
|
self.services.utils.debugLogToFile(f"AI image analysis result type: {type(response)}, content length: {len(result)}", "AI_SERVICE")
|
|
|
|
# Check if result is valid
|
|
if not result or (isinstance(result, str) and not result.strip()):
|
|
error_msg = f"No response from AI image analysis (result: {repr(result)})"
|
|
self.services.utils.debugLogToFile(f"Error in AI image analysis: {error_msg}", "AI_SERVICE")
|
|
logger.error(f"Error in AI image analysis: {error_msg}")
|
|
return f"Error: {error_msg}"
|
|
|
|
self.services.utils.debugLogToFile(f"callImage returned: {result[:200]}..." if len(result) > 200 else result, "AI_SERVICE")
|
|
logger.info(f"callImage returned: {result[:200]}..." if len(result) > 200 else result)
|
|
return result
|
|
except Exception as e:
|
|
self.services.utils.debugLogToFile(f"Error in AI image analysis: {str(e)}", "AI_SERVICE")
|
|
logger.error(f"Error in AI image analysis: {str(e)}")
|
|
return f"Error: {str(e)}"
|
|
|
|
# AI Image Generation
|
|
async def generateImage(
|
|
self,
|
|
prompt: str,
|
|
size: str = "1024x1024",
|
|
quality: str = "standard",
|
|
style: str = "vivid",
|
|
options: Optional[AiCallOptions] = None,
|
|
) -> Dict[str, Any]:
|
|
"""Generate an image using AI using interface.generateImage()."""
|
|
try:
|
|
response = await self.aiObjects.generateImage(prompt, size, quality, style, options)
|
|
|
|
# Emit stats for image generation
|
|
self.services.workflow.storeWorkflowStat(
|
|
self.services.currentWorkflow,
|
|
response,
|
|
f"ai.generate.image"
|
|
)
|
|
|
|
# Convert response to dict format for backward compatibility
|
|
if hasattr(response, 'content'):
|
|
return {
|
|
"success": True,
|
|
"content": response.content,
|
|
"modelName": response.modelName,
|
|
"priceUsd": response.priceUsd,
|
|
"processingTime": response.processingTime
|
|
}
|
|
else:
|
|
return response
|
|
except Exception as e:
|
|
logger.error(f"Error in AI image generation: {str(e)}")
|
|
return {"success": False, "error": str(e)}
|