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Centralized AI continuation agents

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This commit is contained in:
ValueOn AG 2025-10-20 01:33:48 +02:00
parent e368819b1b
commit 3b53889b7c
15 changed files with 262 additions and 232 deletions
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26
modules/services/serviceAi/mainServiceAi.py
View file

@ -69,6 +69,8 @@ class AiService:
def coreAi(self):
"""Lazy initialization of core AI service."""
if self._coreAi is None:
if self.aiObjects is None:
raise RuntimeError("AiService.aiObjects must be initialized before accessing coreAi. Use await AiService.create() or await service._ensureAiObjectsInitialized()")
logger.info("Lazy initializing SubCoreAi...")
self._coreAi = SubCoreAi(self.services, self.aiObjects)
return self._coreAi
@ -153,6 +155,30 @@ class AiService:
await self._ensureAiObjectsInitialized()
return await self.webResearchService.webResearch(request)
# Core AI Methods - Delegating to SubCoreAi
async def callAiPlanning(
self,
prompt: str,
placeholders: Optional[List[PromptPlaceholder]] = None,
options: Optional[AiCallOptions] = None,
loopInstruction: Optional[str] = None
) -> str:
"""Planning AI call for task planning, action planning, action selection, etc."""
await self._ensureAiObjectsInitialized()
return await self.coreAi.callAiPlanning(prompt, placeholders, options, loopInstruction)
async def callAiDocuments(
self,
prompt: str,
documents: Optional[List[ChatDocument]] = None,
options: Optional[AiCallOptions] = None,
outputFormat: Optional[str] = None,
title: Optional[str] = None
) -> Union[str, Dict[str, Any]]:
"""Document generation AI call for all non-planning calls."""
await self._ensureAiObjectsInitialized()
return await self.coreAi.callAiDocuments(prompt, documents, options, outputFormat, title)
def sanitizePromptContent(self, content: str, contentType: str = "text") -> str:
"""

121
modules/services/serviceAi/subCoreAi.py
View file

@ -2,7 +2,7 @@ import logging
from typing import Dict, Any, List, Optional, Tuple, Union
from modules.datamodels.datamodelChat import PromptPlaceholder, ChatDocument
from modules.datamodels.datamodelAi import AiCallRequest, AiCallOptions, ModelCapabilities, OperationType, Priority
from modules.interfaces.interfaceAiObjects import AiObjects
from modules.shared.debugLogger import writeDebugFile
logger = logging.getLogger(__name__)
@ -25,7 +25,8 @@ class SubCoreAi:
self,
prompt: str,
options: AiCallOptions,
debug_prefix: str = "ai_call"
debugPrefix: str = "ai_call",
loopInstruction: str = None
) -> str:
"""
Shared core function for AI calls with looping system.
@ -35,68 +36,85 @@ class SubCoreAi:
Args:
prompt: The prompt to send to AI
options: AI call configuration options
debug_prefix: Prefix for debug file names
debugPrefix: Prefix for debug file names
loopInstruction: If provided, replaces LOOP_INSTRUCTION placeholder and includes in continuation prompts
Returns:
Complete AI response after all iterations
"""
max_iterations = 10 # Prevent infinite loops
max_iterations = 100 # Prevent infinite loops
iteration = 0
accumulated_content = []
accumulatedContent = []
logger.info(f"Starting AI call with looping (debug prefix: {debug_prefix})")
logger.debug(f"Starting AI call with looping (debug prefix: {debugPrefix}, loopInstruction: {loopInstruction is not None})")
# Write initial prompt to debug file
from modules.shared.debugLogger import writeDebugFile
writeDebugFile(prompt, f"{debug_prefix}_prompt", None)
# Import debug logger for use in iterations
# Store original prompt to preserve LOOP_INSTRUCTION placeholder
originalPrompt = prompt
# Handle LOOP_INSTRUCTION placeholder replacement for first iteration
if loopInstruction and iteration == 0:
if "LOOP_INSTRUCTION" not in prompt:
raise ValueError("LOOP_INSTRUCTION placeholder not found in prompt when loopInstruction provided")
prompt = prompt.replace("LOOP_INSTRUCTION", loopInstruction)
logger.debug("Replaced LOOP_INSTRUCTION placeholder with provided instruction")
while iteration < max_iterations:
iteration += 1
logger.info(f"AI call iteration {iteration}/{max_iterations}")
logger.debug(f"AI call iteration {iteration}/{max_iterations}")
# Build iteration prompt
if iteration == 1:
iteration_prompt = prompt
iterationPrompt = prompt
elif loopInstruction and iteration > 1:
# Only use continuation logic if loopInstruction is provided
iterationPrompt = self._buildContinuationPrompt(originalPrompt, accumulatedContent, iteration, loopInstruction)
else:
iteration_prompt = self._buildContinuationPrompt(prompt, accumulated_content, iteration)
# No looping - use original prompt
iterationPrompt = prompt
# Make AI call
try:
from modules.datamodels.datamodelAi import AiCallRequest
request = AiCallRequest(
prompt=iteration_prompt,
prompt=iterationPrompt,
context="",
options=options
)
# Write the ACTUAL prompt sent to AI (including continuation context)
writeDebugFile(iterationPrompt, f"{debugPrefix}_prompt_iteration_{iteration}", None)
response = await self.aiObjects.call(request)
result = response.content
# Write raw AI response to debug file
writeDebugFile(result, f"{debug_prefix}_response_iteration_{iteration}", None)
writeDebugFile(result, f"{debugPrefix}_response_iteration_{iteration}", None)
# Emit stats for this iteration
self.services.workflow.storeWorkflowStat(
self.services.currentWorkflow,
response,
f"ai.call.{debug_prefix}.iteration_{iteration}"
f"ai.call.{debugPrefix}.iteration_{iteration}"
)
if not result or not result.strip():
logger.warning(f"Iteration {iteration}: Empty response, stopping")
break
# Check if this is a continuation response
if "[CONTINUE:" in result:
# Check if this is a continuation response (only if loopInstruction is provided)
if loopInstruction and "[CONTINUE:" in result:
# Extract the content before the continuation marker
content_part = result.split("[CONTINUE:")[0].strip()
if content_part:
accumulated_content.append(content_part)
logger.info(f"Iteration {iteration}: Continuation detected, continuing...")
contentPart = result.split("[CONTINUE:")[0].strip()
if contentPart:
accumulatedContent.append(contentPart)
logger.debug(f"Iteration {iteration}: Continuation detected, continuing...")
continue
else:
# This is the final response
accumulated_content.append(result)
logger.info(f"Iteration {iteration}: Final response received")
accumulatedContent.append(result)
logger.debug(f"Iteration {iteration}: Final response received")
break
except Exception as e:
@ -107,19 +125,20 @@ class SubCoreAi:
logger.warning(f"AI call stopped after maximum iterations ({max_iterations})")
# Combine all accumulated content
final_result = "\n\n".join(accumulated_content) if accumulated_content else ""
final_result = "\n\n".join(accumulatedContent) if accumulatedContent else ""
# Write final result to debug file
writeDebugFile(final_result, f"{debug_prefix}_final_result", None)
writeDebugFile(final_result, f"{debugPrefix}_final_result", None)
logger.info(f"AI call completed: {len(accumulated_content)} parts from {iteration} iterations")
logger.info(f"AI call completed: {len(accumulatedContent)} parts from {iteration} iterations")
return final_result
def _buildContinuationPrompt(
self,
base_prompt: str,
accumulated_content: List[str],
iteration: int
accumulatedContent: List[str],
iteration: int,
loopInstruction: str = None
) -> str:
"""
Build a prompt for continuation iterations.
@ -132,11 +151,11 @@ You are continuing from a previous response. Please continue generating content
IMPORTANT:
- Continue from the exact point where you stopped
- Maintain the same format and structure
- If you cannot complete the full response, end with: [CONTINUE: brief description of what still needs to be generated]
- {loopInstruction if loopInstruction else "If you cannot complete the full response, end with: [CONTINUE: brief description of what still needs to be generated]"}
- Only stop when the response is completely generated
Previous content generated:
{chr(10).join(accumulated_content[-1:]) if accumulated_content else "None"}
{chr(10).join(accumulatedContent[-1:]) if accumulatedContent else "None"}
Continue generating content now:
"""
@ -194,7 +213,8 @@ Continue generating content now:
self,
prompt: str,
placeholders: Optional[List[PromptPlaceholder]] = None,
options: Optional[AiCallOptions] = None
options: Optional[AiCallOptions] = None,
loopInstruction: Optional[str] = None
) -> str:
"""
Planning AI call for task planning, action planning, action selection, etc.
@ -212,13 +232,13 @@ Continue generating content now:
# Build full prompt with placeholders
if placeholders:
placeholders_dict = {p.key: p.value for p in placeholders}
placeholders_dict = {p.label: p.content for p in placeholders}
full_prompt = self._buildPromptWithPlaceholders(prompt, placeholders_dict)
else:
full_prompt = prompt
# Use shared core function with planning-specific debug prefix
return await self._callAiWithLooping(full_prompt, options, "planning")
return await self._callAiWithLooping(full_prompt, options, "planning", loopInstruction=loopInstruction)
# Document Generation AI Call
async def callAiDocuments(
@ -227,9 +247,7 @@ Continue generating content now:
documents: Optional[List[ChatDocument]] = None,
options: Optional[AiCallOptions] = None,
outputFormat: Optional[str] = None,
title: Optional[str] = None,
documentProcessor=None,
documentGenerator=None
title: Optional[str] = None
) -> Union[str, Dict[str, Any]]:
"""
Document generation AI call for all non-planning calls.
@ -241,8 +259,6 @@ Continue generating content now:
options: AI call configuration options
outputFormat: Optional output format for document generation
title: Optional title for generated documents
documentProcessor: Document processing service instance
documentGenerator: Document generation service instance
Returns:
AI response as string, or dict with documents if outputFormat is specified
@ -251,24 +267,16 @@ Continue generating content now:
options = AiCallOptions()
# Handle document generation with specific output format using unified approach
if outputFormat and documentGenerator:
if outputFormat:
# Use unified generation method for all document generation
if documents and len(documents) > 0:
# Extract content from documents first
logger.info(f"Extracting content from {len(documents)} documents")
extracted_content = await documentProcessor.callAiText(prompt, documents, options)
# Generate with extracted content using shared core function
generation_prompt = await self._buildGenerationPrompt(prompt, extracted_content, outputFormat, title)
generated_json = await self._callAiWithLooping(generation_prompt, options, "document_generation")
extracted_content = await self.services.ai.documentProcessor.callAiText(prompt, documents, options)
else:
# Direct generation without documents
logger.info("No documents provided - using direct generation")
generation_prompt = await self._buildGenerationPrompt(prompt, None, outputFormat, title)
generated_json = await self._callAiWithLooping(generation_prompt, options, "document_generation")
# Write the generated JSON to debug file
from modules.shared.debugLogger import writeDebugFile
writeDebugFile(generated_json, "unified_generation_response", documents)
extracted_content = None
generation_prompt = await self._buildGenerationPrompt(prompt, extracted_content, outputFormat, title)
generated_json = await self._callAiWithLooping(generation_prompt, options, "document_generation", loopInstruction="If you cannot complete the full response, end with: [CONTINUE: brief description of what still needs to be generated]")
# Parse the generated JSON
try:
@ -313,7 +321,6 @@ Continue generating content now:
# Log AI response for debugging
try:
from modules.shared.debugLogger import writeDebugFile
writeDebugFile(str(result), "documentGenerationResponse", documents)
except Exception:
pass
@ -325,14 +332,14 @@ Continue generating content now:
return {"success": False, "error": f"Rendering failed: {str(e)}"}
# Handle text calls (no output format specified)
if documents and documentProcessor:
if documents:
# Use document processing for text calls with documents
result = await documentProcessor.callAiText(prompt, documents, options)
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")
result = await self._callAiWithLooping(prompt, options, "text", loopInstruction=None)
return result
return result
# AI Image Analysis
@ -448,7 +455,7 @@ Continue generating content now:
# TO CHECK FUNCTIONS TODO

123
modules/services/serviceGeneration/subPromptBuilder.py
View file

@ -195,74 +195,69 @@ Consider the user's intent and the most logical way to organize the extracted co
except Exception as e:
services.utils.debugLogToFile(f"Generic prompt analysis failed: {str(e)}", "PROMPT_BUILDER")
# Fallback to single-file prompt
example_data = {
"metadata": {
"title": "Example Document",
"author": "AI Assistant",
"source_documents": ["document_001"],
"extraction_method": "ai_extraction"
},
"sections": [
{
"id": "section_001",
"content_type": "heading",
"elements": [
{
"level": 1,
"text": "1. SECTION TITLE"
}
],
"order": 1,
"metadata": {}
}
],
"summary": "",
"tags": []
}
return f"""
{userPrompt}
# Always use the proper generation prompt template with LOOP_INSTRUCTION
result = f"""You are an AI assistant that generates structured JSON content for document creation.
You are a document processing assistant that extracts and structures content from documents. Your task is to analyze the provided document content and create a structured JSON output.
USER REQUEST: "{userPrompt}"
DOCUMENT TITLE: "{title}"
TARGET FORMAT: {outputFormat}
TASK: Extract the actual content from the document and organize it into structured sections.
TASK: Generate JSON content that fulfills the user's request.
REQUIREMENTS:
1. Analyze the document content provided in the context below
2. Extract all content and organize it into logical sections
3. Create structured JSON with sections containing the extracted content
4. Preserve the original structure and data
CRITICAL: You MUST return ONLY valid JSON in this exact structure:
{{
"metadata": {{
"title": "{title}",
"splitStrategy": "single_document",
"source_documents": [],
"extraction_method": "ai_generation"
}},
"documents": [
{{
"id": "doc_1",
"title": "{title}",
"filename": "document.{outputFormat}",
"sections": [
{{
"id": "section_1",
"content_type": "heading",
"elements": [
{{
"level": 1,
"text": "1. SECTION TITLE"
}}
],
"order": 1
}},
{{
"id": "section_2",
"content_type": "paragraph",
"elements": [
{{
"text": "This is the actual content that should be generated."
}}
],
"order": 2
}}
]
}}
]
}}
OUTPUT FORMAT: Return only valid JSON in this exact structure:
{json.dumps(example_data, indent=2)}
Requirements:
- Preserve all original data - do not summarize or interpret
- Use the exact JSON format shown above
- Maintain data integrity and structure
Content Types to Extract:
1. Tables: Extract all rows and columns with proper headers
2. Lists: Extract all items with proper nesting
3. Headings: Extract with appropriate levels
4. Paragraphs: Extract as structured text
5. Code: Extract code blocks with language identification
6. Images: Analyze images and describe all visible content including text, tables, logos, graphics, layout, and visual elements
Image Analysis Requirements:
- If you cannot analyze an image for any reason, explain why in the JSON response
- Describe everything you see in the image
- Include all text content, tables, logos, graphics, layout, and visual elements
- If the image is too small, corrupted, or unclear, explain this
- Always provide feedback - never return empty responses
Return only the JSON structure with actual data from the documents. Do not include any text before or after the JSON.
Extract the ACTUAL CONTENT from the source documents. Do not use placeholder text like "Section 1", "Section 2", etc. Extract the real headings, paragraphs, and content from the documents.
DO NOT return a schema description - return actual extracted content in the JSON format shown above.
IMPORTANT:
- Return ONLY the JSON structure above
- Do NOT include any text before or after the JSON
- Fill in the actual content based on the user request: {userPrompt}
- If the content is too large, you can split it into multiple sections
- Each section should have a unique id and appropriate content_type
- LOOP_INSTRUCTION
"""
# Debug output
if services:
services.utils.debugLogToFile(f"GENERATION PROMPT: Generated successfully", "PROMPT_BUILDER")
return result.strip()
async def buildExtractionPrompt(
outputFormat: str,
@ -499,6 +494,8 @@ IMPORTANT:
- Fill in the actual content based on the user request: {safeUserPrompt}
- If the content is too large, you can split it into multiple sections
- Each section should have a unique id and appropriate content_type
LOOP_INSTRUCTION
"""
# Debug output

2
modules/services/serviceNormalization/mainServiceNormalization.py
View file

@ -90,7 +90,7 @@ class NormalizationService:
" \"Date\": {\"formats\": [\"DD.MM.YYYY\",\"YYYY-MM-DD\"]}\n }\n}\n"
)
response = await self.services.ai.coreAi.callAiPlanning(prompt=prompt, placeholders=None, options=None)
response = await self.services.ai.callAiPlanning(prompt=prompt, placeholders=None, options=None)
if not response:
return {"mapping": {}, "normalizationPolicy": {}}

33
modules/services/serviceWorkflow/mainServiceWorkflow.py
View file

@ -7,7 +7,6 @@ from modules.datamodels.datamodelChat import ChatContentExtracted
from modules.services.serviceExtraction.mainServiceExtraction import ExtractionService
from modules.services.serviceGeneration.subDocumentUtility import getFileExtension, getMimeTypeFromExtension, detectContentTypeFromData
from modules.shared.timezoneUtils import get_utc_timestamp
from modules.services.serviceAi.mainServiceAi import AiService
from modules.security.tokenManager import TokenManager
from modules.shared.progressLogger import ProgressLogger
@ -43,23 +42,25 @@ class WorkflowService:
break
# Create prompt for AI
prompt = f"""You are an AI assistant providing a summary of a chat conversation.
Please respond in '{self.user.language}' language.
prompt = f"""
You are an AI assistant providing a summary of a chat conversation.
Please respond in '{self.user.language}' language.
Chat History:
{chr(10).join(f"- {msg.message}" for msg in reversed(relevantMessages))}
Chat History:
{chr(10).join(f"- {msg.message}" for msg in reversed(relevantMessages))}
Instructions:
1. Summarize the conversation's key points and outcomes
2. Be concise but informative
3. Use a professional but friendly tone
4. Focus on important decisions and next steps if any
Instructions:
1. Summarize the conversation's key points and outcomes
2. Be concise but informative
3. Use a professional but friendly tone
4. Focus on important decisions and next steps if any
Please provide a comprehensive summary of this conversation."""
LOOP_INSTRUCTION
Please provide a comprehensive summary of this conversation."""
# Get summary using AI service directly (avoiding circular dependency)
ai_service = AiService(self)
return await ai_service.coreAi.callAiDocuments(
# Get summary using AI service through proper main service interface
return await self.services.ai.callAiDocuments(
prompt=prompt,
documents=None,
options={
@ -69,9 +70,7 @@ class WorkflowService:
"compress_prompt": True,
"compress_documents": False,
"max_cost": 0.01
},
documentProcessor=ai_service.documentProcessor,
documentGenerator=ai_service.documentGenerator
}
)
except Exception as e:

6
modules/workflows/methods/methodAi.py
View file

@ -127,13 +127,11 @@ class MethodAi(MethodBase):
# Update progress - calling AI
progressLogger.updateProgress(operationId, 0.6, "Calling AI")
result = await self.services.ai.coreAi.callAiDocuments(
result = await self.services.ai.callAiDocuments(
prompt=aiPrompt, # Use original prompt, let unified generation handle prompt building
documents=chatDocuments if chatDocuments else None,
options=options,
outputFormat=output_format,
documentProcessor=self.services.ai.documentProcessor,
documentGenerator=self.services.ai.documentGenerator
outputFormat=output_format
)
# Update progress - processing result

10
modules/workflows/methods/methodOutlook.py
View file

@ -1182,11 +1182,13 @@ Return JSON:
"subject": "subject line",
"body": "email body (HTML allowed)",
"attachments": ["doc_ref1", "doc_ref2"]
}}"""
}}
LOOP_INSTRUCTION"""
# Call AI service to generate email content
try:
ai_response = await self.services.ai.coreAi.callAiDocuments(
ai_response = await self.services.ai.callAiDocuments(
prompt=ai_prompt,
documents=chatDocuments,
options=AiCallOptions(
@ -1199,9 +1201,7 @@ Return JSON:
resultFormat="json",
maxCost=0.50,
maxProcessingTime=30
),
documentProcessor=self.services.ai.documentProcessor,
documentGenerator=self.services.ai.documentGenerator
)
)
# Parse AI response

2
modules/workflows/processing/adaptive/contentValidator.py
View file

@ -120,7 +120,7 @@ DELIVERED CONTENT TO CHECK:
request_options = AiCallOptions()
request_options.operationType = OperationType.GENERAL
response = await self.services.ai.coreAi.callAiPlanning(
response = await self.services.ai.callAiPlanning(
prompt=validationPrompt,
placeholders=None,
options=request_options

2
modules/workflows/processing/adaptive/intentAnalyzer.py
View file

@ -63,7 +63,7 @@ CRITICAL: Respond with ONLY the JSON object below. Do not include any explanator
request_options = AiCallOptions()
request_options.operationType = OperationType.GENERAL
response = await self.services.ai.coreAi.callAiPlanning(
response = await self.services.ai.callAiPlanning(
prompt=analysisPrompt,
placeholders=None,
options=request_options

2
modules/workflows/processing/core/taskPlanner.py
View file

@ -105,7 +105,7 @@ class TaskPlanner:
maxProcessingTime=30
)
prompt = await self.services.ai.coreAi.callAiPlanning(
prompt = await self.services.ai.callAiPlanning(
prompt=taskPlanningPromptTemplate,
placeholders=placeholders,
options=options

4
modules/workflows/processing/modes/modeActionplan.py
View file

@ -137,7 +137,7 @@ class ActionplanMode(BaseMode):
maxProcessingTime=30
)
prompt = await self.services.ai.coreAi.callAiPlanning(prompt=actionPromptTemplate, placeholders=placeholders, options=options)
prompt = await self.services.ai.callAiPlanning(prompt=actionPromptTemplate, placeholders=placeholders, options=options)
# Check if AI response is valid
if not prompt:
@ -476,7 +476,7 @@ class ActionplanMode(BaseMode):
maxProcessingTime=30
)
response = await self.services.ai.coreAi.callAiPlanning(prompt=promptTemplate, placeholders=placeholders, options=options)
response = await self.services.ai.callAiPlanning(prompt=promptTemplate, placeholders=placeholders, options=options)
# Log result review response received
logger.info("=== RESULT REVIEW AI RESPONSE RECEIVED ===")

10
modules/workflows/processing/modes/modeReact.py
View file

@ -201,7 +201,7 @@ class ReactMode(BaseMode):
maxProcessingTime=30
)
response = await self.services.ai.coreAi.callAiPlanning(
response = await self.services.ai.callAiPlanning(
prompt=promptTemplate,
placeholders=placeholders,
options=options
@ -313,7 +313,7 @@ class ReactMode(BaseMode):
resultFormat="json" # Explicitly request JSON format
)
paramsResp = await self.services.ai.coreAi.callAiPlanning(
paramsResp = await self.services.ai.callAiPlanning(
prompt=promptTemplate,
placeholders=placeholders,
options=options
@ -625,7 +625,7 @@ class ReactMode(BaseMode):
maxProcessingTime=30
)
resp = await self.services.ai.coreAi.callAiPlanning(
resp = await self.services.ai.callAiPlanning(
prompt=promptTemplate,
placeholders=placeholders,
options=options
@ -719,7 +719,7 @@ User language: {userLanguage}
Return only the user-friendly message, no technical details."""
# Call AI to generate user-friendly message
response = await self.services.ai.coreAi.callAiPlanning(
response = await self.services.ai.callAiPlanning(
prompt=prompt,
placeholders=None,
options=AiCallOptions(
@ -760,7 +760,7 @@ Result context: {resultContext}
Return only the user-friendly message, no technical details."""
# Call AI to generate user-friendly result message
response = await self.services.ai.coreAi.callAiPlanning(
response = await self.services.ai.callAiPlanning(
prompt=prompt,
placeholders=None,
options=AiCallOptions(

146
modules/workflows/processing/shared/promptGenerationActionsActionplan.py
View file

@ -32,98 +32,100 @@ def generateActionDefinitionPrompt(services, context: Any) -> PromptBundle:
template = """# Action Definition
Generate the next action to advance toward completing the task objective.
Generate the next action to advance toward completing the task objective.
## 📋 Context
## 📋 Context
### User Language
{{KEY:USER_LANGUAGE}}
### User Language
{{KEY:USER_LANGUAGE}}
### Task Objective
{{KEY:USER_PROMPT}}
### Task Objective
{{KEY:USER_PROMPT}}
### Available Documents
{{KEY:AVAILABLE_DOCUMENTS_SUMMARY}}
### Available Documents
{{KEY:AVAILABLE_DOCUMENTS_SUMMARY}}
### Available Connections
{{KEY:AVAILABLE_CONNECTIONS_INDEX}}
### Workflow History
{{KEY:WORKFLOW_HISTORY}}
### Available Connections
{{KEY:AVAILABLE_CONNECTIONS_INDEX}}
### Available Methods
{{KEY:AVAILABLE_METHODS}}
### Workflow History
{{KEY:WORKFLOW_HISTORY}}
## ⚠️ RULES
### Available Methods
{{KEY:AVAILABLE_METHODS}}
### Action Names
- **Use EXACT compound action names** from AVAILABLE_METHODS (e.g., "ai.process", "document.extract", "web.search")
- **DO NOT create** new action names - only use those listed in AVAILABLE_METHODS
- **DO NOT separate** method and action names - use the full compound name
## ⚠️ RULES
### Parameter Guidelines
- **Use exact document references** from AVAILABLE_DOCUMENTS_INDEX
- **Use exact connection references** from AVAILABLE_CONNECTIONS_INDEX
- **Include user language** if relevant
- **Avoid unnecessary fields** - host applies defaults
### Action Names
- **Use EXACT compound action names** from AVAILABLE_METHODS (e.g., "ai.process", "document.extract", "web.search")
- **DO NOT create** new action names - only use those listed in AVAILABLE_METHODS
- **DO NOT separate** method and action names - use the full compound name
## 📊 Required JSON Structure
### Parameter Guidelines
- **Use exact document references** from AVAILABLE_DOCUMENTS_INDEX
- **Use exact connection references** from AVAILABLE_CONNECTIONS_INDEX
- **Include user language** if relevant
- **Avoid unnecessary fields** - host applies defaults
```json
{
"actions": [
{
"action": "method.action_name",
"parameters": {},
"resultLabel": "round{current_round}_task{current_task}_action{action_number}_{descriptive_label}",
"description": "What this action accomplishes",
"userMessage": "User-friendly message in language '{{KEY:USER_LANGUAGE}}'"
}
]
}
```
## 📊 Required JSON Structure
## ✅ Correct Example
```json
{
"actions": [
{
"action": "method.action_name",
"parameters": {},
"resultLabel": "round{current_round}_task{current_task}_action{action_number}_{descriptive_label}",
"description": "What this action accomplishes",
"userMessage": "User-friendly message in language '{{KEY:USER_LANGUAGE}}'"
}
]
}
```
```json
{
"actions": [
{
"action": "document.extract",
"parameters": {"documentList": ["docList:msg_123:results"]},
"resultLabel": "round1_task1_action1_extract_results",
"description": "Extract data from documents",
"userMessage": "Extracting data from documents"
}
]
}
```
## ✅ Correct Example
```json
{
"actions": [
{
"action": "document.extract",
"parameters": {"documentList": ["docList:msg_123:results"]},
"resultLabel": "round1_task1_action1_extract_results",
"description": "Extract data from documents",
"userMessage": "Extracting data from documents"
}
]
}
```
## 🎯 Action Planning Guidelines
## 🎯 Action Planning Guidelines
### Method Selection
- **Choose appropriate method** based on task requirements
- **Consider available resources** (documents, connections)
- **Match method capabilities** to task objectives
### Method Selection
- **Choose appropriate method** based on task requirements
- **Consider available resources** (documents, connections)
- **Match method capabilities** to task objectives
### Parameter Design
- **Use ACTION SIGNATURE** to understand required parameters
- **Convert objective** into appropriate parameter values
- **Include all required parameters** for the action
### Parameter Design
- **Use ACTION SIGNATURE** to understand required parameters
- **Convert objective** into appropriate parameter values
- **Include all required parameters** for the action
### Result Labeling
- **Use descriptive labels** that explain what the action produces
- **Follow naming convention**: `round{round}_task{task}_action{action}_{label}`
- **Make labels meaningful** for future reference
### Result Labeling
- **Use descriptive labels** that explain what the action produces
- **Follow naming convention**: `round{round}_task{task}_action{action}_{label}`
- **Make labels meaningful** for future reference
### User Messages
- **Write in user language:** '{{KEY:USER_LANGUAGE}}'
- **Explain what's happening** in user-friendly terms
- **Keep messages concise** but informative
### User Messages
- **Write in user language:** '{{KEY:USER_LANGUAGE}}'
- **Explain what's happening** in user-friendly terms
- **Keep messages concise** but informative
## 🚀 Response Format
Return ONLY the JSON object."""
## 🚀 Response Format
Return ONLY the JSON object with complete action objects. If you cannot complete the full response, ensure each action object is complete and valid.
LOOP_INSTRUCTION
"""
return PromptBundle(prompt=template, placeholders=placeholders)

5
modules/workflows/processing/shared/promptGenerationTaskplan.py
View file

@ -129,6 +129,7 @@ Break down user requests into logical, executable task steps.
- **High**: Complex strategic tasks (6+ actions)
## 🚀 Response Format
Return ONLY the JSON object."""
Return ONLY the JSON object with complete task objects. If you cannot complete the full response, ensure each task object is complete and valid.
LOOP_INSTRUCTION
"""
return PromptBundle(prompt=template, placeholders=placeholders)

2
modules/workflows/workflowManager.py
View file

@ -220,7 +220,7 @@ class WorkflowManager:
)
# Call AI analyzer
aiResponse = await self.services.ai.coreAi.callAiPlanning(prompt=analyzerPrompt, placeholders=None, options=None)
aiResponse = await self.services.ai.callAiPlanning(prompt=analyzerPrompt, placeholders=None, options=None)
detectedLanguage = None
normalizedRequest = None