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hotfix workflows dynamic and automation after pr integrations

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This commit is contained in:
ValueOn AG 2026-01-11 22:42:02 +01:00
parent 5380e30f0d
commit cda0cb1093
10 changed files with 183 additions and 66 deletions
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3
modules/datamodels/datamodelChat.py
View file

@ -69,6 +69,9 @@ class ChatLog(BaseModel):
operationId: Optional[str] = Field(
None, description="Operation ID to group related log entries"
)
roundNumber: Optional[int] = Field(None, description="Round number in workflow")
taskNumber: Optional[int] = Field(None, description="Task number within round")
actionNumber: Optional[int] = Field(None, description="Action number within task")
registerModelLabels(

44
modules/interfaces/interfaceDbChatObjects.py
View file

@ -1411,26 +1411,28 @@ class ChatObjects:
# Create log in normalized table
createdLog = self.db.recordCreate(ChatLog, log_model)
# Emit log event for streaming (if event manager is available)
try:
from modules.features.chatbot.eventManager import get_event_manager
event_manager = get_event_manager()
log_timestamp = parseTimestamp(createdLog.get("timestamp"), default=getUtcTimestamp())
# Emit log event in exact chatData format: {type, createdAt, item}
asyncio.create_task(event_manager.emit_event(
workflowId,
"chatdata",
"New log",
"log",
{
"type": "log",
"createdAt": log_timestamp,
"item": ChatLog(**createdLog).dict()
}
))
except Exception as e:
# Event manager not available or error - continue without emitting
logger.debug(f"Could not emit log event: {e}")
# Emit log event for streaming (only for chatbot workflows)
# Only emit events for chatbot workflows, not for automation or dynamic workflows
if workflow.workflowMode == WorkflowModeEnum.WORKFLOW_CHATBOT:
try:
from modules.features.chatbot.eventManager import get_event_manager
event_manager = get_event_manager()
log_timestamp = parseTimestamp(createdLog.get("timestamp"), default=getUtcTimestamp())
# Emit log event in exact chatData format: {type, createdAt, item}
asyncio.create_task(event_manager.emit_event(
workflowId,
"chatdata",
"New log",
"log",
{
"type": "log",
"createdAt": log_timestamp,
"item": ChatLog(**createdLog).model_dump()
}
))
except Exception as e:
# Event manager not available or error - continue without emitting
logger.debug(f"Could not emit log event: {e}")
# Return validated ChatLog instance
return ChatLog(**createdLog)
@ -1541,7 +1543,7 @@ class ChatObjects:
"item": chatMessage
})
# Get logs
# Get logs - return all logs with roundNumber if available
logs = getRecordsetWithRBAC(self.db, ChatLog, self.currentUser, recordFilter={"workflowId": workflowId})
for log in logs:
# Apply timestamp filtering in Python

24
modules/services/serviceAi/subContentExtraction.py
View file

@ -138,6 +138,13 @@ class ContentExtractor:
logger.debug(f"Part {part.id}: reference={hasReferenceIntent}, render={hasRenderIntent}, extract={hasExtractIntent}, hasData={hasPartData}")
# SAFETY: For images with any intent, always ensure render is included
# This ensures the image object part is always available for later rendering
isImage = part.typeGroup == "image" or (part.mimeType and part.mimeType.startswith("image/"))
if isImage and hasPartData and not hasRenderIntent:
logger.info(f"🖼️ Auto-adding render intent for image {part.id} (original intents: {partIntent})")
hasRenderIntent = True
# Track ob der originale Part bereits hinzugefügt wurde
originalPartAdded = False
@ -332,7 +339,13 @@ class ContentExtractor:
# WICHTIG: "render" und "extract" können beide vorhanden sein!
# In diesem Fall erzeugen wir BEIDE ContentParts
if "render" in intent.intents:
# SAFETY: For images with any intent, always create object part for later rendering
isImageDocument = document.mimeType and document.mimeType.startswith("image/")
shouldAutoRender = isImageDocument and "render" not in intent.intents and ("extract" in intent.intents or "reference" in intent.intents)
if shouldAutoRender:
logger.info(f"🖼️ Auto-adding render for image document {document.id} (original intents: {intent.intents})")
if "render" in intent.intents or shouldAutoRender:
# Für Images/Binary: extrahiere als Object
if document.mimeType.startswith("image/") or self._isBinary(document.mimeType):
try:
@ -391,6 +404,9 @@ class ContentExtractor:
)
# Konvertiere extrahierte Ergebnisse zu ContentParts mit Metadaten
# Check if object part exists (either explicit render or auto-render for images)
hasObjectPart = "render" in intent.intents or shouldAutoRender
for extracted in extractedResults:
for part in extracted.parts:
# Markiere als extracted Format
@ -400,8 +416,8 @@ class ContentExtractor:
"extractionPrompt": extractionPrompt,
"intent": "extract",
"usageHint": f"Use extracted content from {document.fileName}",
# Verknüpfung zu object Part (falls vorhanden)
"relatedObjectPartId": f"obj_{document.id}" if "render" in intent.intents else None
# Verknüpfung zu object Part (falls vorhanden - including auto-render for images)
"relatedObjectPartId": f"obj_{document.id}" if hasObjectPart else None
})
# For images: Mark that Vision AI extraction is needed during section generation
@ -410,7 +426,7 @@ class ContentExtractor:
logger.info(f"📷 Image part {part.id} marked for Vision AI extraction during section generation")
# Stelle sicher, dass ID eindeutig ist (falls object Part existiert)
if "render" in intent.intents:
if hasObjectPart:
part.id = f"ext_{document.id}_{part.id}"
allContentParts.append(part)

11
modules/services/serviceChat/mainServiceChat.py
View file

@ -647,6 +647,17 @@ class ChatService:
"""Persist ChatLog and map it into the in-memory workflow logs list."""
logData = dict(logData or {})
logData["workflowId"] = workflow.id
# Auto-populate roundNumber from workflow's currentRound if not explicitly set
if "roundNumber" not in logData or logData["roundNumber"] is None:
currentRound = getattr(workflow, 'currentRound', None)
# Default to 1 if workflow doesn't have currentRound set
if currentRound is None:
currentRound = 1
logger.warning(f"storeLog: workflow.currentRound is None, defaulting to 1")
logData["roundNumber"] = currentRound
logger.debug(f"storeLog: Set roundNumber={currentRound} for log: {logData.get('message', '')[:50]}")
chatInterface = self.interfaceDbChat
chatLog = chatInterface.createLog(logData)
if not chatLog:

22
modules/services/serviceSharepoint/mainServiceSharepoint.py
View file

@ -731,7 +731,7 @@ class SharepointService:
async def getSiteByStandardPath(self, sitePath: str, allSites: Optional[List[Dict[str, Any]]] = None) -> Optional[Dict[str, Any]]:
"""
Get SharePoint site directly by Microsoft-standard path (/sites/SiteName)
without loading all sites. Uses hostname from first available site.
without loading all sites. Uses hostname from root site (single API call).
Parameters:
sitePath (str): Site path like 'company-share' (without /sites/ prefix)
@ -741,18 +741,28 @@ class SharepointService:
Optional[Dict[str, Any]]: Site information if found, None otherwise
"""
try:
# Get hostname from first available site (minimal load - only 1 site)
from urllib.parse import urlparse
hostname = None
# Get hostname - priority order: allSites > root site API > full discovery (fallback)
if allSites and len(allSites) > 0:
from urllib.parse import urlparse
webUrl = allSites[0].get("webUrl", "")
hostname = urlparse(webUrl).hostname if webUrl else None
else:
# Discover minimal sites to get hostname
if not hostname:
# Get hostname from root site (single efficient API call instead of discovering all sites)
rootSite = await self._makeGraphApiCall("sites/root")
if rootSite and "webUrl" in rootSite and "error" not in rootSite:
hostname = urlparse(rootSite.get("webUrl", "")).hostname
logger.debug(f"Got hostname '{hostname}' from root site (efficient)")
if not hostname:
# Fallback: discover all sites (expensive, avoid if possible)
logger.warning("Could not get hostname from root site, falling back to full site discovery")
minimalSites = await self.discoverSites()
if not minimalSites:
logger.warning("No sites available to extract hostname")
return None
from urllib.parse import urlparse
hostname = urlparse(minimalSites[0].get("webUrl", "")).hostname
if not hostname:

11
modules/shared/progressLogger.py
View file

@ -149,6 +149,13 @@ class ProgressLogger:
# Parent operation never existed - log warning
logger.debug(f"WARNING: Parent operation '{parentOperationId}' not found in activeOperations when creating log for '{operationId}'. Available operations: {list(self.activeOperations.keys())}. Child operation may appear at root level.")
# Get round number from workflow - include in operationId for unique per-round operations
roundNumber = getattr(workflow, 'currentRound', None) or 1
# Create round-specific operationId and parentId for the log
roundOperationId = f"{operationId}_r{roundNumber}"
roundParentId = f"{parentOperationId}_r{roundNumber}" if parentOperationId else None
# parentId in ChatLog should be the operationId of the parent operation, not the log entry ID
logData = {
"workflowId": workflow.id,
@ -156,8 +163,8 @@ class ProgressLogger:
"type": "info",
"status": status,
"progress": progress,
"operationId": operationId,
"parentId": parentOperationId # Set to parent's operationId, not log entry ID
"operationId": roundOperationId,
"parentId": roundParentId # Set to parent's operationId, not log entry ID
}
try:

8
modules/workflows/methods/methodAi/actions/process.py
View file

@ -192,8 +192,14 @@ async def process(self, parameters: Dict[str, Any]) -> ActionResult:
# automatically in _extractAndPrepareContent() (Phase 5B)
logger.info(f"ai.process: Calling callAiContent with {len(documentList.references)} document references")
if documentList.references:
from modules.datamodels.datamodelDocref import DocumentListReference, DocumentItemReference
for idx, ref in enumerate(documentList.references):
logger.info(f" Passing reference {idx + 1}: documentId={ref.documentId}")
if isinstance(ref, DocumentItemReference):
logger.info(f" Passing reference {idx + 1}: documentId={ref.documentId}")
elif isinstance(ref, DocumentListReference):
logger.info(f" Passing reference {idx + 1}: label={ref.label}")
else:
logger.info(f" Passing reference {idx + 1}: {ref}")
aiResponse = await self.services.ai.callAiContent(
prompt=aiPrompt,

13
modules/workflows/methods/methodSharepoint/actions/uploadDocument.py
View file

@ -142,8 +142,17 @@ async def uploadDocument(self, parameters: Dict[str, Any]) -> ActionResult:
logger.info(f"Using folder ID upload endpoint: {uploadEndpoint}")
else:
# It's a regular path - use the root-based upload endpoint
uploadPath = uploadPath.rstrip('/') + '/' + fileName
uploadPathClean = uploadPath.lstrip('/')
# Strip /sites/{siteName}/ prefix if present (Graph API path is relative to site's drive)
uploadPathForApi = uploadPath
if uploadPathForApi.startswith('/sites/'):
# Extract path after /sites/{siteName}/
parts = uploadPathForApi.split('/', 3) # ['', 'sites', 'sitename', 'rest/of/path']
if len(parts) >= 4:
uploadPathForApi = '/' + parts[3] # Keep the rest after /sites/sitename/
logger.info(f"Stripped site prefix from upload path: '{uploadPath}' -> '{uploadPathForApi}'")
uploadPathForApi = uploadPathForApi.rstrip('/') + '/' + fileName
uploadPathClean = uploadPathForApi.lstrip('/')
uploadEndpoint = f"sites/{siteId}/drive/root:/{uploadPathClean}:/content"
logger.info(f"Using path-based upload endpoint: {uploadEndpoint}")

93
modules/workflows/methods/methodSharepoint/helpers/documentParsing.py
View file

@ -62,6 +62,18 @@ class DocumentParsingHelper:
try:
resultData = json.loads(fileData)
# Handle nested structure: documentData.data may contain the actual JSON as a string
if "documentData" in resultData and isinstance(resultData.get("documentData"), dict):
innerData = resultData["documentData"].get("data")
if innerData and isinstance(innerData, str):
try:
# Parse the inner JSON string
resultData = json.loads(innerData)
logger.debug(f"Parsed nested documentData.data structure")
except json.JSONDecodeError:
logger.debug(f"documentData.data is not valid JSON, using as-is")
foundDocuments = resultData.get("foundDocuments", [])
# If no foundDocuments, check if it's a listDocuments result (has listResults)
@ -202,40 +214,67 @@ class DocumentParsingHelper:
if fileData:
try:
resultData = json.loads(fileData)
foundDocuments = resultData.get("foundDocuments", [])
# Check if fileData is binary (not text/JSON)
# Binary files (xlsx, pdf, etc.) can't be parsed as JSON
isBinaryFile = False
if isinstance(fileData, bytes):
try:
fileData = fileData.decode('utf-8')
except UnicodeDecodeError:
# Binary file - cannot parse as JSON
isBinaryFile = True
logger.debug(f"File is binary (not UTF-8 decodable), treating as regular file to upload")
if foundDocuments:
# Extract folder path from first found document
firstDoc = foundDocuments[0]
parentPath = firstDoc.get("parentPath", "")
if parentPath:
folderPath = parentPath
if isBinaryFile:
# Binary file - treat as regular file to upload
filesToUpload = chatDocuments
else:
resultData = json.loads(fileData)
# Extract site information
siteName = firstDoc.get("siteName")
siteId = firstDoc.get("siteId")
# Handle nested structure: documentData.data may contain the actual JSON as a string
if "documentData" in resultData and isinstance(resultData.get("documentData"), dict):
innerData = resultData["documentData"].get("data")
if innerData and isinstance(innerData, str):
try:
# Parse the inner JSON string
resultData = json.loads(innerData)
logger.debug(f"Parsed nested documentData.data structure for folder parsing")
except json.JSONDecodeError:
logger.debug(f"documentData.data is not valid JSON, using as-is")
if siteName and siteId:
sites = [{
"id": siteId,
"displayName": siteName,
"webUrl": firstDoc.get("webUrl", "")
}]
elif siteName:
# Discover sites to find siteId
allSites = await self.method.siteDiscovery.discoverSharePointSites()
matchingSites = self.method.siteDiscovery.filterSitesByHint(allSites, siteName)
if matchingSites:
foundDocuments = resultData.get("foundDocuments", [])
if foundDocuments:
# Extract folder path from first found document
firstDoc = foundDocuments[0]
parentPath = firstDoc.get("parentPath", "")
if parentPath:
folderPath = parentPath
# Extract site information
siteName = firstDoc.get("siteName")
siteId = firstDoc.get("siteId")
if siteName and siteId:
sites = [{
"id": matchingSites[0].get("id"),
"id": siteId,
"displayName": siteName,
"webUrl": matchingSites[0].get("webUrl", "")
"webUrl": firstDoc.get("webUrl", "")
}]
elif siteName:
# Discover sites to find siteId
allSites = await self.method.siteDiscovery.discoverSharePointSites()
matchingSites = self.method.siteDiscovery.filterSitesByHint(allSites, siteName)
if matchingSites:
sites = [{
"id": matchingSites[0].get("id"),
"displayName": siteName,
"webUrl": matchingSites[0].get("webUrl", "")
}]
# For uploadDocument: filesToUpload are the chatDocuments themselves
# (they contain the files to upload)
filesToUpload = chatDocuments
# For uploadDocument: filesToUpload are the chatDocuments themselves
# (they contain the files to upload)
filesToUpload = chatDocuments
except json.JSONDecodeError:
# Not a findDocumentPath result - treat as regular files to upload

18
modules/workflows/workflowManager.py
View file

@ -209,6 +209,17 @@ class WorkflowManager:
complexity = analysisResult.get('complexity', 'moderate')
needsWorkflowHistory = analysisResult.get('needsWorkflowHistory', False)
fastTrack = analysisResult.get('fastTrack', False)
workflowName = analysisResult.get('workflowName')
# Update workflow name if provided by analysis
if workflowName and workflowName.strip():
try:
workflow = self.services.workflow
if workflow:
self.services.chat.updateWorkflow(workflow.id, {"name": workflowName.strip()})
logger.debug(f"Updated workflow {workflow.id} name to: {workflowName.strip()}")
except Exception as e:
logger.warning(f"Failed to update workflow name: {e}")
# Extract intent analysis fields and store as workflowIntent
workflowIntent = {
@ -324,6 +335,7 @@ class WorkflowManager:
9. expectedFormats: What file format(s) they expect - provide matching file format extensions list (e.g., ["xlsx", "pdf"]). If format is unclear or not specified, use empty list []
10. qualityRequirements: Quality requirements they have (accuracy, completeness) as {{accuracyThreshold: 0.0-1.0, completenessThreshold: 0.0-1.0}}
11. successCriteria: Specific success criteria that define completion (array of strings)
12. workflowName: Create a concise, descriptive name for this workflow in the detected language. The name should summarize the main task or goal (e.g., "Service Report January 2026", "Email Analysis", "Document Generation"). Keep it short (max 60 characters) and meaningful.
Rules:
- If total content (intent + data) is < 10% of model max tokens, do not extract; return empty contextItems and keep intent compact and self-contained
@ -357,7 +369,8 @@ Return ONLY JSON (no markdown) with this exact structure:
"accuracyThreshold": 0.0-1.0,
"completenessThreshold": 0.0-1.0
}},
"successCriteria": ["specific criterion 1", "specific criterion 2"]
"successCriteria": ["specific criterion 1", "specific criterion 2"],
"workflowName": "Concise workflow name in detected language (max 40 characters)"
}}
## User Message
@ -406,7 +419,8 @@ The following is the user's original input message. Analyze intent, normalize th
"accuracyThreshold": 0.8,
"completenessThreshold": 0.8
},
"successCriteria": []
"successCriteria": [],
"workflowName": "New Workflow"
}
async def _executeFastPath(self, userInput: UserInputRequest, documents: List[ChatDocument]) -> None: