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feat: refactored ai calls and pydantic models

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This commit is contained in:
ValueOn AG 2025-11-17 23:12:18 +01:00
parent 2fa180a9ae
commit 9bd7821cf5
53 changed files with 3365 additions and 2057 deletions
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129
modules/datamodels/datamodelAi.py
View file

@ -1,9 +1,11 @@
from typing import Optional, List, Dict, Any, Callable, TYPE_CHECKING, Tuple
from pydantic import BaseModel, Field
from pydantic import BaseModel, Field, ConfigDict
from enum import Enum
# Import ContentPart for runtime use (needed for Pydantic model rebuilding)
from modules.datamodels.datamodelExtraction import ContentPart
# Import JSON utilities for safe conversion
from modules.shared.jsonUtils import extractJsonString, tryParseJson, repairBrokenJson
# Operation Types
class OperationTypeEnum(str, Enum):
@ -109,8 +111,7 @@ class AiModel(BaseModel):
version: Optional[str] = Field(default=None, description="Model version")
lastUpdated: Optional[str] = Field(default=None, description="Last update timestamp")
class Config:
arbitraryTypesAllowed = True # Allow Callable type
model_config = ConfigDict(arbitrary_types_allowed=True) # Allow Callable type
class SelectionRule(BaseModel):
@ -172,8 +173,7 @@ class AiModelCall(BaseModel):
model: Optional[AiModel] = Field(default=None, description="The AI model being called")
options: AiCallOptions = Field(default_factory=AiCallOptions, description="Additional model-specific options")
class Config:
arbitraryTypesAllowed = True
model_config = ConfigDict(arbitrary_types_allowed=True)
class AiModelResponse(BaseModel):
@ -189,8 +189,7 @@ class AiModelResponse(BaseModel):
tokensUsed: Optional[Dict[str, int]] = Field(default=None, description="Token usage (input, output, total)")
metadata: Optional[Dict[str, Any]] = Field(default=None, description="Additional model-specific metadata")
class Config:
arbitraryTypesAllowed = True
model_config = ConfigDict(arbitrary_types_allowed=True)
# Structured prompt models for specialized operations
@ -204,9 +203,6 @@ class AiCallPromptWebSearch(BaseModel):
language: Optional[str] = Field(default=None, description="Language code (lowercase, e.g., de, en, fr)")
researchDepth: Optional[str] = Field(default="general", description="Research depth: fast (maxDepth=1), general (maxDepth=2), deep (maxDepth=3)")
class Config:
pass
class AiCallPromptWebCrawl(BaseModel):
"""Structured prompt format for WEB_CRAWL operation - crawls ONE specific URL and returns content."""
@ -216,9 +212,6 @@ class AiCallPromptWebCrawl(BaseModel):
maxDepth: Optional[int] = Field(default=2, description="Maximum number of hops from starting page (default: 2)")
maxWidth: Optional[int] = Field(default=10, description="Maximum pages to crawl per level (default: 10)")
class Config:
pass
class AiCallPromptImage(BaseModel):
"""Structured prompt format for image generation."""
@ -228,6 +221,112 @@ class AiCallPromptImage(BaseModel):
quality: Optional[str] = Field(default="standard", description="Image quality (standard, hd)")
style: Optional[str] = Field(default="vivid", description="Image style (vivid, natural)")
class Config:
pass
class DocumentData(BaseModel):
"""Single document in response."""
documentName: str = Field(description="Document name")
documentData: Any = Field(description="Document data (can be str, bytes, dict, etc.)")
mimeType: str = Field(description="MIME type of the document")
class AiProcessParameters(BaseModel):
"""Parameters for AI processing action."""
aiPrompt: str = Field(description="AI instruction prompt")
contentParts: Optional[List[ContentPart]] = Field(
None,
description="Already-extracted content parts (required if documents need to be processed)"
)
resultType: str = Field(
default="txt",
description="Output file extension (txt, json, pdf, docx, xlsx, etc.)"
)
class AiResponseMetadata(BaseModel):
"""Metadata for AI response (varies by operation type)."""
# Document Generation Metadata
title: Optional[str] = Field(None, description="Document title")
filename: Optional[str] = Field(None, description="Document filename")
# Operation-Specific Metadata
operationType: Optional[str] = Field(None, description="Type of operation performed")
schemaVersion: Optional[str] = Field(None, description="Schema version (e.g., 'parameters_v1')", alias="schema")
extractionMethod: Optional[str] = Field(None, description="Method used for extraction")
sourceDocuments: Optional[List[str]] = Field(None, description="Source document references")
# Additional metadata (for extensibility)
additionalData: Optional[Dict[str, Any]] = Field(None, description="Additional operation-specific metadata")
@classmethod
def fromDict(cls, data: Optional[Dict[str, Any]]) -> Optional["AiResponseMetadata"]:
"""Create AiResponseMetadata from dict with camelCase field names."""
if not data or not isinstance(data, dict):
return None
knownFields = {"title", "filename", "operationType", "schema", "extractionMethod", "sourceDocuments", "additionalData"}
mappedData = {k: v for k, v in data.items() if k in knownFields}
additionalFields = {k: v for k, v in data.items() if k not in knownFields}
if additionalFields:
mappedData["additionalData"] = additionalFields
try:
return cls(**mappedData)
except Exception:
return None
class AiResponse(BaseModel):
"""Unified response from all AI calls (planning, text, documents)."""
content: str = Field(description="Response content (JSON string for planning, text for analysis, unified JSON for documents)")
metadata: Optional[AiResponseMetadata] = Field(
None,
description="Response metadata (varies by operation type)"
)
documents: Optional[List[DocumentData]] = Field(
None,
description="Generated documents (only for document generation operations)"
)
def toJson(self) -> Dict[str, Any]:
"""
Convert AI response content to JSON using enhanced stabilizing failsafe conversion methods.
Centralizes AI result to JSON conversion in one place.
Returns:
Dict containing the parsed JSON content, or a safe fallback structure if parsing fails.
"""
if not self.content:
return {}
# Use enhanced stabilizing failsafe JSON conversion methods from jsonUtils
# First, try to extract and parse JSON using the safe methods
obj, err, cleaned = tryParseJson(self.content)
if err is None and isinstance(obj, dict):
# Successfully parsed as dict
return obj
elif err is None and isinstance(obj, list):
# Successfully parsed as list - wrap in dict for consistency
return {"data": obj}
# If parsing failed, try to repair broken JSON
repaired = repairBrokenJson(self.content)
if repaired is not None:
return repaired
# If all else fails, return a safe structure with the cleaned content
# Extract JSON string even if it's not fully parseable
extracted = extractJsonString(self.content)
if extracted and extracted != self.content:
# Try one more time with extracted string
obj, err, _ = tryParseJson(extracted)
if err is None and isinstance(obj, (dict, list)):
return obj if isinstance(obj, dict) else {"data": obj}
# Final fallback: return safe structure with raw content
return {
"content": self.content,
"parseError": True
}

279
modules/datamodels/datamodelChat.py
View file

@ -264,7 +264,6 @@ registerModelLabels(
class WorkflowModeEnum(str, Enum):
WORKFLOW_ACTIONPLAN = "Actionplan"
WORKFLOW_DYNAMIC = "Dynamic"
WORKFLOW_AUTOMATION = "Automation"
@ -273,7 +272,6 @@ registerModelLabels(
"WorkflowModeEnum",
{"en": "Workflow Mode", "fr": "Mode de workflow"},
{
"WORKFLOW_ACTIONPLAN": {"en": "Actionplan", "fr": "Actionplan"},
"WORKFLOW_DYNAMIC": {"en": "Dynamic", "fr": "Dynamique"},
"WORKFLOW_AUTOMATION": {"en": "Automation", "fr": "Automatisation"},
},
@ -281,125 +279,27 @@ registerModelLabels(
class ChatWorkflow(BaseModel):
id: str = Field(
default_factory=lambda: str(uuid.uuid4()),
description="Primary key",
frontend_type="text",
frontend_readonly=True,
frontend_required=False,
)
mandateId: str = Field(
description="ID of the mandate this workflow belongs to",
frontend_type="text",
frontend_readonly=True,
frontend_required=False,
)
status: str = Field(
description="Current status of the workflow",
frontend_type="select",
frontend_readonly=False,
frontend_required=False,
frontend_options=[
id: str = Field(default_factory=lambda: str(uuid.uuid4()), description="Primary key", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": False})
mandateId: str = Field(description="ID of the mandate this workflow belongs to", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": False})
status: str = Field(default="running", description="Current status of the workflow", json_schema_extra={"frontend_type": "select", "frontend_readonly": False, "frontend_required": False, "frontend_options": [
{"value": "running", "label": {"en": "Running", "fr": "En cours"}},
{"value": "completed", "label": {"en": "Completed", "fr": "Terminé"}},
{"value": "stopped", "label": {"en": "Stopped", "fr": "Arrêté"}},
{"value": "error", "label": {"en": "Error", "fr": "Erreur"}},
],
)
name: Optional[str] = Field(
None,
description="Name of the workflow",
frontend_type="text",
frontend_readonly=False,
frontend_required=True,
)
currentRound: int = Field(
description="Current round number",
frontend_type="integer",
frontend_readonly=True,
frontend_required=False,
)
currentTask: int = Field(
default=0,
description="Current task number",
frontend_type="integer",
frontend_readonly=True,
frontend_required=False,
)
currentAction: int = Field(
default=0,
description="Current action number",
frontend_type="integer",
frontend_readonly=True,
frontend_required=False,
)
totalTasks: int = Field(
default=0,
description="Total number of tasks in the workflow",
frontend_type="integer",
frontend_readonly=True,
frontend_required=False,
)
totalActions: int = Field(
default=0,
description="Total number of actions in the workflow",
frontend_type="integer",
frontend_readonly=True,
frontend_required=False,
)
lastActivity: float = Field(
default_factory=getUtcTimestamp,
description="Timestamp of last activity (UTC timestamp in seconds)",
frontend_type="timestamp",
frontend_readonly=True,
frontend_required=False,
)
startedAt: float = Field(
default_factory=getUtcTimestamp,
description="When the workflow started (UTC timestamp in seconds)",
frontend_type="timestamp",
frontend_readonly=True,
frontend_required=False,
)
logs: List[ChatLog] = Field(
default_factory=list,
description="Workflow logs",
frontend_type="text",
frontend_readonly=True,
frontend_required=False,
)
messages: List[ChatMessage] = Field(
default_factory=list,
description="Messages in the workflow",
frontend_type="text",
frontend_readonly=True,
frontend_required=False,
)
stats: List[ChatStat] = Field(
default_factory=list,
description="Workflow statistics list",
frontend_type="text",
frontend_readonly=True,
frontend_required=False,
)
tasks: list = Field(
default_factory=list,
description="List of tasks in the workflow",
frontend_type="text",
frontend_readonly=True,
frontend_required=False,
)
workflowMode: WorkflowModeEnum = Field(
default=WorkflowModeEnum.WORKFLOW_DYNAMIC,
description="Workflow mode selector",
frontend_type="select",
frontend_readonly=False,
frontend_required=False,
frontend_options=[
{
"value": WorkflowModeEnum.WORKFLOW_ACTIONPLAN.value,
"label": {"en": "Actionplan", "fr": "Actionplan"},
},
]})
name: Optional[str] = Field(None, description="Name of the workflow", json_schema_extra={"frontend_type": "text", "frontend_readonly": False, "frontend_required": True})
currentRound: int = Field(default=0, description="Current round number", json_schema_extra={"frontend_type": "integer", "frontend_readonly": True, "frontend_required": False})
currentTask: int = Field(default=0, description="Current task number", json_schema_extra={"frontend_type": "integer", "frontend_readonly": True, "frontend_required": False})
currentAction: int = Field(default=0, description="Current action number", json_schema_extra={"frontend_type": "integer", "frontend_readonly": True, "frontend_required": False})
totalTasks: int = Field(default=0, description="Total number of tasks in the workflow", json_schema_extra={"frontend_type": "integer", "frontend_readonly": True, "frontend_required": False})
totalActions: int = Field(default=0, description="Total number of actions in the workflow", json_schema_extra={"frontend_type": "integer", "frontend_readonly": True, "frontend_required": False})
lastActivity: float = Field(default_factory=getUtcTimestamp, description="Timestamp of last activity (UTC timestamp in seconds)", json_schema_extra={"frontend_type": "timestamp", "frontend_readonly": True, "frontend_required": False})
startedAt: float = Field(default_factory=getUtcTimestamp, description="When the workflow started (UTC timestamp in seconds)", json_schema_extra={"frontend_type": "timestamp", "frontend_readonly": True, "frontend_required": False})
logs: List[ChatLog] = Field(default_factory=list, description="Workflow logs", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": False})
messages: List[ChatMessage] = Field(default_factory=list, description="Messages in the workflow", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": False})
stats: List[ChatStat] = Field(default_factory=list, description="Workflow statistics list", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": False})
tasks: list = Field(default_factory=list, description="List of tasks in the workflow", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": False})
workflowMode: WorkflowModeEnum = Field(default=WorkflowModeEnum.WORKFLOW_DYNAMIC, description="Workflow mode selector", json_schema_extra={"frontend_type": "select", "frontend_readonly": False, "frontend_required": False, "frontend_options": [
{
"value": WorkflowModeEnum.WORKFLOW_DYNAMIC.value,
"label": {"en": "Dynamic", "fr": "Dynamique"},
@ -408,22 +308,37 @@ class ChatWorkflow(BaseModel):
"value": WorkflowModeEnum.WORKFLOW_AUTOMATION.value,
"label": {"en": "Automation", "fr": "Automatisation"},
},
],
)
maxSteps: int = Field(
default=5,
description="Maximum number of iterations in react mode",
frontend_type="integer",
frontend_readonly=False,
frontend_required=False,
)
expectedFormats: Optional[List[str]] = Field(
None,
description="List of expected file format extensions from user request (e.g., ['xlsx', 'pdf']). Extracted during intent analysis.",
frontend_type="text",
frontend_readonly=True,
frontend_required=False,
)
]})
maxSteps: int = Field(default=10, description="Maximum number of iterations in dynamic mode", json_schema_extra={"frontend_type": "integer", "frontend_readonly": False, "frontend_required": False})
expectedFormats: Optional[List[str]] = Field(None, description="List of expected file format extensions from user request (e.g., ['xlsx', 'pdf']). Extracted during intent analysis.", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": False})
# Helper methods for execution state management
def getRoundIndex(self) -> int:
"""Get current round index"""
return self.currentRound
def getTaskIndex(self) -> int:
"""Get current task index"""
return self.currentTask
def getActionIndex(self) -> int:
"""Get current action index"""
return self.currentAction
def incrementRound(self):
"""Increment round when new user input received"""
self.currentRound += 1
self.currentTask = 0
self.currentAction = 0
def incrementTask(self):
"""Increment task when starting new task in current round"""
self.currentTask += 1
self.currentAction = 0
def incrementAction(self):
"""Increment action when executing new action in current task"""
self.currentAction += 1
registerModelLabels(
@ -885,7 +800,7 @@ registerModelLabels(
class TaskContext(BaseModel):
taskStep: TaskStep
workflow: Optional["ChatWorkflow"] = None
workflow: Optional[ChatWorkflow] = None
workflowId: Optional[str] = None
availableDocuments: Optional[str] = "No documents available"
availableConnections: Optional[list[str]] = Field(default_factory=list)
@ -901,6 +816,26 @@ class TaskContext(BaseModel):
successfulActions: Optional[list] = Field(default_factory=list)
criteriaProgress: Optional[dict] = None
# Stage 2 context fields (NEW)
actionObjective: Optional[str] = Field(None, description="Objective for current action")
parametersContext: Optional[str] = Field(None, description="Context for parameter generation")
learnings: Optional[list[str]] = Field(default_factory=list, description="Learnings from previous actions")
stage1Selection: Optional[dict] = Field(None, description="Stage 1 selection data")
def updateFromSelection(self, selection: Any):
"""Update context from Stage 1 selection
Args:
selection: ActionDefinition instance from Stage 1
"""
from modules.datamodels.datamodelWorkflow import ActionDefinition
if isinstance(selection, ActionDefinition):
self.actionObjective = selection.actionObjective
self.parametersContext = selection.parametersContext
self.learnings = selection.learnings if selection.learnings else []
self.stage1Selection = selection.model_dump()
def getDocumentReferences(self) -> List[str]:
docs = []
if self.previousHandover:
@ -973,8 +908,7 @@ registerModelLabels(
},
)
# Resolve forward references
TaskContext.update_forward_refs()
# Forward references resolved automatically since ChatWorkflow is defined above
class PromptPlaceholder(BaseModel):
@ -1013,71 +947,20 @@ registerModelLabels(
class AutomationDefinition(BaseModel):
id: str = Field(
default_factory=lambda: str(uuid.uuid4()),
description="Primary key",
frontend_type="text",
frontend_readonly=True,
frontend_required=False
)
mandateId: str = Field(
description="Mandate ID",
frontend_type="text",
frontend_readonly=True,
frontend_required=False
)
label: str = Field(
description="User-friendly name",
frontend_type="text",
frontend_required=True
)
schedule: str = Field(
description="Cron schedule pattern",
frontend_type="select",
frontend_options=[
id: str = Field(default_factory=lambda: str(uuid.uuid4()), description="Primary key", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": False})
mandateId: str = Field(description="Mandate ID", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": False})
label: str = Field(description="User-friendly name", json_schema_extra={"frontend_type": "text", "frontend_required": True})
schedule: str = Field(description="Cron schedule pattern", json_schema_extra={"frontend_type": "select", "frontend_required": True, "frontend_options": [
{"value": "0 */4 * * *", "label": {"en": "Every 4 hours", "fr": "Toutes les 4 heures"}},
{"value": "0 22 * * *", "label": {"en": "Daily at 22:00", "fr": "Quotidien à 22:00"}},
{"value": "0 10 * * 1", "label": {"en": "Weekly Monday 10:00", "fr": "Hebdomadaire lundi 10:00"}}
],
frontend_required=True
)
template: str = Field(
description="JSON template with placeholders (format: {{KEY:PLACEHOLDER_NAME}})",
frontend_type="textarea",
frontend_required=True
)
placeholders: Dict[str, str] = Field(
default_factory=dict,
description="Dictionary of placeholder key/value pairs (e.g., {'connectionName': 'MyConnection', 'sharepointFolderNameSource': '/folder/path', 'webResearchUrl': 'https://...', 'webResearchPrompt': '...', 'documentPrompt': '...'})",
frontend_type="text"
)
active: bool = Field(
default=False,
description="Whether automation should be launched in event handler",
frontend_type="checkbox",
frontend_required=False
)
eventId: Optional[str] = Field(
None,
description="Event ID from event management (None if not registered)",
frontend_type="text",
frontend_readonly=True,
frontend_required=False
)
status: Optional[str] = Field(
None,
description="Status: 'active' if event is registered, 'inactive' if not (computed, readonly)",
frontend_type="text",
frontend_readonly=True,
frontend_required=False
)
executionLogs: List[Dict[str, Any]] = Field(
default_factory=list,
description="List of execution logs, each containing timestamp, workflowId, status, and messages",
frontend_type="text",
frontend_readonly=True,
frontend_required=False
)
]})
template: str = Field(description="JSON template with placeholders (format: {{KEY:PLACEHOLDER_NAME}})", json_schema_extra={"frontend_type": "textarea", "frontend_required": True})
placeholders: Dict[str, str] = Field(default_factory=dict, description="Dictionary of placeholder key/value pairs (e.g., {'connectionName': 'MyConnection', 'sharepointFolderNameSource': '/folder/path', 'webResearchUrl': 'https://...', 'webResearchPrompt': '...', 'documentPrompt': '...'})", json_schema_extra={"frontend_type": "text"})
active: bool = Field(default=False, description="Whether automation should be launched in event handler", json_schema_extra={"frontend_type": "checkbox", "frontend_required": False})
eventId: Optional[str] = Field(None, description="Event ID from event management (None if not registered)", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": False})
status: Optional[str] = Field(None, description="Status: 'active' if event is registered, 'inactive' if not (computed, readonly)", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": False})
executionLogs: List[Dict[str, Any]] = Field(default_factory=list, description="List of execution logs, each containing timestamp, workflowId, status, and messages", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": False})
registerModelLabels(

118
modules/datamodels/datamodelDocref.py Normal file
View file

@ -0,0 +1,118 @@
"""
Document reference models for typed document references in workflows.
"""
from typing import List, Optional
from pydantic import BaseModel, Field
from modules.shared.attributeUtils import registerModelLabels
class DocumentReference(BaseModel):
"""Base class for document references"""
pass
class DocumentListReference(DocumentReference):
"""Reference to a document list via message label"""
messageId: Optional[str] = Field(None, description="Optional message ID for cross-round references")
label: str = Field(description="Document list label")
def to_string(self) -> str:
"""Convert to string format: docList:messageId:label or docList:label"""
if self.messageId:
return f"docList:{self.messageId}:{self.label}"
return f"docList:{self.label}"
class DocumentItemReference(DocumentReference):
"""Reference to a specific document item"""
documentId: str = Field(description="Document ID")
fileName: Optional[str] = Field(None, description="Optional file name")
def to_string(self) -> str:
"""Convert to string format: docItem:documentId:fileName or docItem:documentId"""
if self.fileName:
return f"docItem:{self.documentId}:{self.fileName}"
return f"docItem:{self.documentId}"
class DocumentReferenceList(BaseModel):
"""List of document references with conversion methods"""
references: List[DocumentReference] = Field(
default_factory=list,
description="List of document references"
)
def to_string_list(self) -> List[str]:
"""Convert all references to string list"""
return [ref.to_string() for ref in self.references]
@classmethod
def from_string_list(cls, stringList: List[str]) -> "DocumentReferenceList":
"""Parse string list to typed references
Supports formats:
- docList:label
- docList:messageId:label
- docItem:documentId
- docItem:documentId:fileName
"""
references = []
for refStr in stringList:
if not refStr or not isinstance(refStr, str):
continue
refStr = refStr.strip()
# Parse docList: references
if refStr.startswith("docList:"):
parts = refStr[8:].split(":", 1) # Remove "docList:" prefix
if len(parts) == 2:
# docList:messageId:label
messageId, label = parts
references.append(DocumentListReference(messageId=messageId, label=label))
elif len(parts) == 1 and parts[0]:
# docList:label
references.append(DocumentListReference(label=parts[0]))
# Parse docItem: references
elif refStr.startswith("docItem:"):
parts = refStr[8:].split(":", 1) # Remove "docItem:" prefix
if len(parts) == 2:
# docItem:documentId:fileName
documentId, fileName = parts
references.append(DocumentItemReference(documentId=documentId, fileName=fileName))
elif len(parts) == 1 and parts[0]:
# docItem:documentId
references.append(DocumentItemReference(documentId=parts[0]))
# Unknown format - skip or log warning
else:
# Try to parse as simple string (backward compatibility)
# Assume it's a label if it doesn't match known patterns
if refStr:
references.append(DocumentListReference(label=refStr))
return cls(references=references)
registerModelLabels(
"DocumentReference",
{"en": "Document Reference", "fr": "Référence de document"},
{
"messageId": {"en": "Message ID", "fr": "ID du message"},
"label": {"en": "Label", "fr": "Étiquette"},
"documentId": {"en": "Document ID", "fr": "ID du document"},
"fileName": {"en": "File Name", "fr": "Nom du fichier"},
},
)
registerModelLabels(
"DocumentReferenceList",
{"en": "Document Reference List", "fr": "Liste de références de documents"},
{
"references": {"en": "References", "fr": "Références"},
},
)

9
modules/datamodels/datamodelExtraction.py
View file

@ -1,9 +1,6 @@
from typing import Any, Dict, List, Optional, Literal, TYPE_CHECKING
from typing import Any, Dict, List, Optional, Literal
from pydantic import BaseModel, Field
if TYPE_CHECKING:
from modules.datamodels.datamodelAi import OperationTypeEnum
class ContentPart(BaseModel):
id: str = Field(description="Unique content part identifier")
@ -67,7 +64,6 @@ class ExtractionOptions(BaseModel):
# Core extraction parameters
prompt: str = Field(description="Extraction prompt for AI processing")
operationType: 'OperationTypeEnum' = Field(description="Type of operation for AI processing")
processDocumentsIndividually: bool = Field(default=True, description="Process each document separately")
# Image processing parameters
@ -86,6 +82,3 @@ class ExtractionOptions(BaseModel):
# Additional processing options
enableParallelProcessing: bool = Field(default=True, description="Enable parallel processing of chunks")
maxConcurrentChunks: int = Field(default=5, ge=1, le=20, description="Maximum number of chunks to process concurrently")
class Config:
arbitraryTypesAllowed = True # Allow OperationTypeEnum import

14
modules/datamodels/datamodelFiles.py
View file

@ -9,13 +9,13 @@ import base64
class FileItem(BaseModel):
id: str = Field(default_factory=lambda: str(uuid.uuid4()), description="Primary key", frontend_type="text", frontend_readonly=True, frontend_required=False)
mandateId: str = Field(description="ID of the mandate this file belongs to", frontend_type="text", frontend_readonly=True, frontend_required=False)
fileName: str = Field(description="Name of the file", frontend_type="text", frontend_readonly=False, frontend_required=True)
mimeType: str = Field(description="MIME type of the file", frontend_type="text", frontend_readonly=True, frontend_required=False)
fileHash: str = Field(description="Hash of the file", frontend_type="text", frontend_readonly=True, frontend_required=False)
fileSize: int = Field(description="Size of the file in bytes", frontend_type="integer", frontend_readonly=True, frontend_required=False)
creationDate: float = Field(default_factory=getUtcTimestamp, description="Date when the file was created (UTC timestamp in seconds)", frontend_type="timestamp", frontend_readonly=True, frontend_required=False)
id: str = Field(default_factory=lambda: str(uuid.uuid4()), description="Primary key", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": False})
mandateId: str = Field(description="ID of the mandate this file belongs to", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": False})
fileName: str = Field(description="Name of the file", json_schema_extra={"frontend_type": "text", "frontend_readonly": False, "frontend_required": True})
mimeType: str = Field(description="MIME type of the file", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": False})
fileHash: str = Field(description="Hash of the file", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": False})
fileSize: int = Field(description="Size of the file in bytes", json_schema_extra={"frontend_type": "integer", "frontend_readonly": True, "frontend_required": False})
creationDate: float = Field(default_factory=getUtcTimestamp, description="Date when the file was created (UTC timestamp in seconds)", json_schema_extra={"frontend_type": "timestamp", "frontend_readonly": True, "frontend_required": False})
registerModelLabels(
"FileItem",

26
modules/datamodels/datamodelNeutralizer.py
View file

@ -7,13 +7,13 @@ from modules.shared.attributeUtils import registerModelLabels
class DataNeutraliserConfig(BaseModel):
id: str = Field(default_factory=lambda: str(uuid.uuid4()), description="Unique ID of the configuration", frontend_type="text", frontend_readonly=True, frontend_required=False)
mandateId: str = Field(description="ID of the mandate this configuration belongs to", frontend_type="text", frontend_readonly=True, frontend_required=True)
userId: str = Field(description="ID of the user who created this configuration", frontend_type="text", frontend_readonly=True, frontend_required=True)
enabled: bool = Field(default=True, description="Whether data neutralization is enabled", frontend_type="checkbox", frontend_readonly=False, frontend_required=False)
namesToParse: str = Field(default="", description="Multiline list of names to parse for neutralization", frontend_type="textarea", frontend_readonly=False, frontend_required=False)
sharepointSourcePath: str = Field(default="", description="SharePoint path to read files for neutralization", frontend_type="text", frontend_readonly=False, frontend_required=False)
sharepointTargetPath: str = Field(default="", description="SharePoint path to store neutralized files", frontend_type="text", frontend_readonly=False, frontend_required=False)
id: str = Field(default_factory=lambda: str(uuid.uuid4()), description="Unique ID of the configuration", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": False})
mandateId: str = Field(description="ID of the mandate this configuration belongs to", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": True})
userId: str = Field(description="ID of the user who created this configuration", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": True})
enabled: bool = Field(default=True, description="Whether data neutralization is enabled", json_schema_extra={"frontend_type": "checkbox", "frontend_readonly": False, "frontend_required": False})
namesToParse: str = Field(default="", description="Multiline list of names to parse for neutralization", json_schema_extra={"frontend_type": "textarea", "frontend_readonly": False, "frontend_required": False})
sharepointSourcePath: str = Field(default="", description="SharePoint path to read files for neutralization", json_schema_extra={"frontend_type": "text", "frontend_readonly": False, "frontend_required": False})
sharepointTargetPath: str = Field(default="", description="SharePoint path to store neutralized files", json_schema_extra={"frontend_type": "text", "frontend_readonly": False, "frontend_required": False})
registerModelLabels(
"DataNeutraliserConfig",
{"en": "Data Neutralization Config", "fr": "Configuration de neutralisation des données"},
@ -29,12 +29,12 @@ registerModelLabels(
)
class DataNeutralizerAttributes(BaseModel):
id: str = Field(default_factory=lambda: str(uuid.uuid4()), description="Unique ID of the attribute mapping (used as UID in neutralized files)", frontend_type="text", frontend_readonly=True, frontend_required=False)
mandateId: str = Field(description="ID of the mandate this attribute belongs to", frontend_type="text", frontend_readonly=True, frontend_required=True)
userId: str = Field(description="ID of the user who created this attribute", frontend_type="text", frontend_readonly=True, frontend_required=True)
originalText: str = Field(description="Original text that was neutralized", frontend_type="text", frontend_readonly=True, frontend_required=True)
fileId: Optional[str] = Field(default=None, description="ID of the file this attribute belongs to", frontend_type="text", frontend_readonly=True, frontend_required=False)
patternType: str = Field(description="Type of pattern that matched (email, phone, name, etc.)", frontend_type="text", frontend_readonly=True, frontend_required=True)
id: str = Field(default_factory=lambda: str(uuid.uuid4()), description="Unique ID of the attribute mapping (used as UID in neutralized files)", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": False})
mandateId: str = Field(description="ID of the mandate this attribute belongs to", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": True})
userId: str = Field(description="ID of the user who created this attribute", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": True})
originalText: str = Field(description="Original text that was neutralized", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": True})
fileId: Optional[str] = Field(default=None, description="ID of the file this attribute belongs to", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": False})
patternType: str = Field(description="Type of pattern that matched (email, phone, name, etc.)", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": True})
registerModelLabels(
"DataNeutralizerAttributes",
{"en": "Neutralized Data Attribute", "fr": "Attribut de données neutralisées"},

5
modules/datamodels/datamodelPagination.py
View file

@ -5,7 +5,7 @@ All models use camelStyle naming convention for consistency with frontend.
"""
from typing import List, Dict, Any, Optional, Generic, TypeVar
from pydantic import BaseModel, Field
from pydantic import BaseModel, Field, ConfigDict
import math
T = TypeVar('T')
@ -67,6 +67,5 @@ class PaginatedResponse(BaseModel, Generic[T]):
items: List[T] = Field(..., description="Array of items for current page")
pagination: Optional[PaginationMetadata] = Field(..., description="Pagination metadata (None if pagination not applied)")
class Config:
arbitrary_types_allowed = True
model_config = ConfigDict(arbitrary_types_allowed=True)

67
modules/datamodels/datamodelSecurity.py
View file

@ -1,7 +1,7 @@
"""Security models: Token and AuthEvent."""
from typing import Optional
from pydantic import BaseModel, Field
from pydantic import BaseModel, Field, ConfigDict
from modules.shared.attributeUtils import registerModelLabels
from modules.shared.timeUtils import getUtcTimestamp
from .datamodelUam import AuthAuthority
@ -47,8 +47,7 @@ class Token(BaseModel):
None, description="Mandate ID for tenant scoping of the token"
)
class Config:
use_enum_values = True
model_config = ConfigDict(use_enum_values=True)
registerModelLabels(
@ -75,60 +74,14 @@ registerModelLabels(
class AuthEvent(BaseModel):
id: str = Field(
default_factory=lambda: str(uuid.uuid4()),
description="Unique ID of the auth event",
frontend_type="text",
frontend_readonly=True,
frontend_required=False,
)
userId: str = Field(
description="ID of the user this event belongs to",
frontend_type="text",
frontend_readonly=True,
frontend_required=True,
)
eventType: str = Field(
description="Type of authentication event (e.g., 'login', 'logout', 'token_refresh')",
frontend_type="text",
frontend_readonly=True,
frontend_required=True,
)
timestamp: float = Field(
default_factory=getUtcTimestamp,
description="Unix timestamp when the event occurred",
frontend_type="datetime",
frontend_readonly=True,
frontend_required=True,
)
ipAddress: Optional[str] = Field(
default=None,
description="IP address from which the event originated",
frontend_type="text",
frontend_readonly=True,
frontend_required=False,
)
userAgent: Optional[str] = Field(
default=None,
description="User agent string from the request",
frontend_type="text",
frontend_readonly=True,
frontend_required=False,
)
success: bool = Field(
default=True,
description="Whether the authentication event was successful",
frontend_type="boolean",
frontend_readonly=True,
frontend_required=True,
)
details: Optional[str] = Field(
default=None,
description="Additional details about the event",
frontend_type="text",
frontend_readonly=True,
frontend_required=False,
)
id: str = Field(default_factory=lambda: str(uuid.uuid4()), description="Unique ID of the auth event", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": False})
userId: str = Field(description="ID of the user this event belongs to", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": True})
eventType: str = Field(description="Type of authentication event (e.g., 'login', 'logout', 'token_refresh')", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": True})
timestamp: float = Field(default_factory=getUtcTimestamp, description="Unix timestamp when the event occurred", json_schema_extra={"frontend_type": "datetime", "frontend_readonly": True, "frontend_required": True})
ipAddress: Optional[str] = Field(default=None, description="IP address from which the event originated", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": False})
userAgent: Optional[str] = Field(default=None, description="User agent string from the request", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": False})
success: bool = Field(default=True, description="Whether the authentication event was successful", json_schema_extra={"frontend_type": "boolean", "frontend_readonly": True, "frontend_required": True})
details: Optional[str] = Field(default=None, description="Additional details about the event", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": False})
registerModelLabels(

84
modules/datamodels/datamodelUam.py
View file

@ -25,15 +25,35 @@ class ConnectionStatus(str, Enum):
PENDING = "pending"
class Mandate(BaseModel):
id: str = Field(default_factory=lambda: str(uuid.uuid4()), description="Unique ID of the mandate", frontend_type="text", frontend_readonly=True, frontend_required=False)
name: str = Field(description="Name of the mandate", frontend_type="text", frontend_readonly=False, frontend_required=True)
language: str = Field(default="en", description="Default language of the mandate", frontend_type="select", frontend_readonly=False, frontend_required=True, frontend_options=[
id: str = Field(
default_factory=lambda: str(uuid.uuid4()),
description="Unique ID of the mandate",
json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": False}
)
name: str = Field(
description="Name of the mandate",
json_schema_extra={"frontend_type": "text", "frontend_readonly": False, "frontend_required": True}
)
language: str = Field(
default="en",
description="Default language of the mandate",
json_schema_extra={
"frontend_type": "select",
"frontend_readonly": False,
"frontend_required": True,
"frontend_options": [
{"value": "de", "label": {"en": "Deutsch", "fr": "Allemand"}},
{"value": "en", "label": {"en": "English", "fr": "Anglais"}},
{"value": "fr", "label": {"en": "Français", "fr": "Français"}},
{"value": "it", "label": {"en": "Italiano", "fr": "Italien"}},
])
enabled: bool = Field(default=True, description="Indicates whether the mandate is enabled", frontend_type="checkbox", frontend_readonly=False, frontend_required=False)
]
}
)
enabled: bool = Field(
default=True,
description="Indicates whether the mandate is enabled",
json_schema_extra={"frontend_type": "checkbox", "frontend_readonly": False, "frontend_required": False}
)
registerModelLabels(
"Mandate",
{"en": "Mandate", "fr": "Mandat"},
@ -46,31 +66,31 @@ registerModelLabels(
)
class UserConnection(BaseModel):
id: str = Field(default_factory=lambda: str(uuid.uuid4()), description="Unique ID of the connection", frontend_type="text", frontend_readonly=True, frontend_required=False)
userId: str = Field(description="ID of the user this connection belongs to", frontend_type="text", frontend_readonly=True, frontend_required=False)
authority: AuthAuthority = Field(description="Authentication authority", frontend_type="select", frontend_readonly=True, frontend_required=False, frontend_options=[
id: str = Field(default_factory=lambda: str(uuid.uuid4()), description="Unique ID of the connection", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": False})
userId: str = Field(description="ID of the user this connection belongs to", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": False})
authority: AuthAuthority = Field(description="Authentication authority", json_schema_extra={"frontend_type": "select", "frontend_readonly": True, "frontend_required": False, "frontend_options": [
{"value": "local", "label": {"en": "Local", "fr": "Local"}},
{"value": "google", "label": {"en": "Google", "fr": "Google"}},
{"value": "msft", "label": {"en": "Microsoft", "fr": "Microsoft"}},
])
externalId: str = Field(description="User ID in the external system", frontend_type="text", frontend_readonly=True, frontend_required=False)
externalUsername: str = Field(description="Username in the external system", frontend_type="text", frontend_readonly=False, frontend_required=False)
externalEmail: Optional[EmailStr] = Field(None, description="Email in the external system", frontend_type="email", frontend_readonly=False, frontend_required=False)
status: ConnectionStatus = Field(default=ConnectionStatus.ACTIVE, description="Connection status", frontend_type="select", frontend_readonly=False, frontend_required=False, frontend_options=[
]})
externalId: str = Field(description="User ID in the external system", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": False})
externalUsername: str = Field(description="Username in the external system", json_schema_extra={"frontend_type": "text", "frontend_readonly": False, "frontend_required": False})
externalEmail: Optional[EmailStr] = Field(None, description="Email in the external system", json_schema_extra={"frontend_type": "email", "frontend_readonly": False, "frontend_required": False})
status: ConnectionStatus = Field(default=ConnectionStatus.ACTIVE, description="Connection status", json_schema_extra={"frontend_type": "select", "frontend_readonly": False, "frontend_required": False, "frontend_options": [
{"value": "active", "label": {"en": "Active", "fr": "Actif"}},
{"value": "inactive", "label": {"en": "Inactive", "fr": "Inactif"}},
{"value": "expired", "label": {"en": "Expired", "fr": "Expiré"}},
{"value": "pending", "label": {"en": "Pending", "fr": "En attente"}},
])
connectedAt: float = Field(default_factory=getUtcTimestamp, description="When the connection was established (UTC timestamp in seconds)", frontend_type="timestamp", frontend_readonly=True, frontend_required=False)
lastChecked: float = Field(default_factory=getUtcTimestamp, description="When the connection was last verified (UTC timestamp in seconds)", frontend_type="timestamp", frontend_readonly=True, frontend_required=False)
expiresAt: Optional[float] = Field(None, description="When the connection expires (UTC timestamp in seconds)", frontend_type="timestamp", frontend_readonly=True, frontend_required=False)
tokenStatus: Optional[str] = Field(None, description="Current token status: active, expired, none", frontend_type="select", frontend_readonly=True, frontend_required=False, frontend_options=[
]})
connectedAt: float = Field(default_factory=getUtcTimestamp, description="When the connection was established (UTC timestamp in seconds)", json_schema_extra={"frontend_type": "timestamp", "frontend_readonly": True, "frontend_required": False})
lastChecked: float = Field(default_factory=getUtcTimestamp, description="When the connection was last verified (UTC timestamp in seconds)", json_schema_extra={"frontend_type": "timestamp", "frontend_readonly": True, "frontend_required": False})
expiresAt: Optional[float] = Field(None, description="When the connection expires (UTC timestamp in seconds)", json_schema_extra={"frontend_type": "timestamp", "frontend_readonly": True, "frontend_required": False})
tokenStatus: Optional[str] = Field(None, description="Current token status: active, expired, none", json_schema_extra={"frontend_type": "select", "frontend_readonly": True, "frontend_required": False, "frontend_options": [
{"value": "active", "label": {"en": "Active", "fr": "Actif"}},
{"value": "expired", "label": {"en": "Expired", "fr": "Expiré"}},
{"value": "none", "label": {"en": "None", "fr": "Aucun"}},
])
tokenExpiresAt: Optional[float] = Field(None, description="When the current token expires (UTC timestamp in seconds)", frontend_type="timestamp", frontend_readonly=True, frontend_required=False)
]})
tokenExpiresAt: Optional[float] = Field(None, description="When the current token expires (UTC timestamp in seconds)", json_schema_extra={"frontend_type": "timestamp", "frontend_readonly": True, "frontend_required": False})
registerModelLabels(
"UserConnection",
{"en": "User Connection", "fr": "Connexion utilisateur"},
@ -91,28 +111,28 @@ registerModelLabels(
)
class User(BaseModel):
id: str = Field(default_factory=lambda: str(uuid.uuid4()), description="Unique ID of the user", frontend_type="text", frontend_readonly=True, frontend_required=False)
username: str = Field(description="Username for login", frontend_type="text", frontend_readonly=False, frontend_required=True)
email: Optional[EmailStr] = Field(None, description="Email address of the user", frontend_type="email", frontend_readonly=False, frontend_required=True)
fullName: Optional[str] = Field(None, description="Full name of the user", frontend_type="text", frontend_readonly=False, frontend_required=False)
language: str = Field(default="en", description="Preferred language of the user", frontend_type="select", frontend_readonly=False, frontend_required=True, frontend_options=[
id: str = Field(default_factory=lambda: str(uuid.uuid4()), description="Unique ID of the user", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": False})
username: str = Field(description="Username for login", json_schema_extra={"frontend_type": "text", "frontend_readonly": False, "frontend_required": True})
email: Optional[EmailStr] = Field(None, description="Email address of the user", json_schema_extra={"frontend_type": "email", "frontend_readonly": False, "frontend_required": True})
fullName: Optional[str] = Field(None, description="Full name of the user", json_schema_extra={"frontend_type": "text", "frontend_readonly": False, "frontend_required": False})
language: str = Field(default="en", description="Preferred language of the user", json_schema_extra={"frontend_type": "select", "frontend_readonly": False, "frontend_required": True, "frontend_options": [
{"value": "de", "label": {"en": "Deutsch", "fr": "Allemand"}},
{"value": "en", "label": {"en": "English", "fr": "Anglais"}},
{"value": "fr", "label": {"en": "Français", "fr": "Français"}},
{"value": "it", "label": {"en": "Italiano", "fr": "Italien"}},
])
enabled: bool = Field(default=True, description="Indicates whether the user is enabled", frontend_type="checkbox", frontend_readonly=False, frontend_required=False)
privilege: UserPrivilege = Field(default=UserPrivilege.USER, description="Permission level", frontend_type="select", frontend_readonly=False, frontend_required=True, frontend_options=[
]})
enabled: bool = Field(default=True, description="Indicates whether the user is enabled", json_schema_extra={"frontend_type": "checkbox", "frontend_readonly": False, "frontend_required": False})
privilege: UserPrivilege = Field(default=UserPrivilege.USER, description="Permission level", json_schema_extra={"frontend_type": "select", "frontend_readonly": False, "frontend_required": True, "frontend_options": [
{"value": "user", "label": {"en": "User", "fr": "Utilisateur"}},
{"value": "admin", "label": {"en": "Admin", "fr": "Administrateur"}},
{"value": "sysadmin", "label": {"en": "SysAdmin", "fr": "Administrateur système"}},
])
authenticationAuthority: AuthAuthority = Field(default=AuthAuthority.LOCAL, description="Primary authentication authority", frontend_type="select", frontend_readonly=True, frontend_required=False, frontend_options=[
]})
authenticationAuthority: AuthAuthority = Field(default=AuthAuthority.LOCAL, description="Primary authentication authority", json_schema_extra={"frontend_type": "select", "frontend_readonly": True, "frontend_required": False, "frontend_options": [
{"value": "local", "label": {"en": "Local", "fr": "Local"}},
{"value": "google", "label": {"en": "Google", "fr": "Google"}},
{"value": "msft", "label": {"en": "Microsoft", "fr": "Microsoft"}},
])
mandateId: Optional[str] = Field(None, description="ID of the mandate this user belongs to", frontend_type="text", frontend_readonly=True, frontend_required=False)
]})
mandateId: Optional[str] = Field(None, description="ID of the mandate this user belongs to", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": False})
registerModelLabels(
"User",
{"en": "User", "fr": "Utilisateur"},

8
modules/datamodels/datamodelUtils.py
View file

@ -6,10 +6,10 @@ import uuid
class Prompt(BaseModel):
id: str = Field(default_factory=lambda: str(uuid.uuid4()), description="Primary key", frontend_type="text", frontend_readonly=True, frontend_required=False)
mandateId: str = Field(description="ID of the mandate this prompt belongs to", frontend_type="text", frontend_readonly=True, frontend_required=False)
content: str = Field(description="Content of the prompt", frontend_type="textarea", frontend_readonly=False, frontend_required=True)
name: str = Field(description="Name of the prompt", frontend_type="text", frontend_readonly=False, frontend_required=True)
id: str = Field(default_factory=lambda: str(uuid.uuid4()), description="Primary key", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": False})
mandateId: str = Field(description="ID of the mandate this prompt belongs to", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": False})
content: str = Field(description="Content of the prompt", json_schema_extra={"frontend_type": "textarea", "frontend_readonly": False, "frontend_required": True})
name: str = Field(description="Name of the prompt", json_schema_extra={"frontend_type": "text", "frontend_readonly": False, "frontend_required": True})
registerModelLabels(
"Prompt",
{"en": "Prompt", "fr": "Invite"},

20
modules/datamodels/datamodelVoice.py
View file

@ -7,16 +7,16 @@ import uuid
class VoiceSettings(BaseModel):
id: str = Field(default_factory=lambda: str(uuid.uuid4()), description="Primary key", frontend_type="text", frontend_readonly=True, frontend_required=False)
userId: str = Field(description="ID of the user these settings belong to", frontend_type="text", frontend_readonly=True, frontend_required=True)
mandateId: str = Field(description="ID of the mandate these settings belong to", frontend_type="text", frontend_readonly=True, frontend_required=True)
sttLanguage: str = Field(default="de-DE", description="Speech-to-Text language", frontend_type="select", frontend_readonly=False, frontend_required=True)
ttsLanguage: str = Field(default="de-DE", description="Text-to-Speech language", frontend_type="select", frontend_readonly=False, frontend_required=True)
ttsVoice: str = Field(default="de-DE-KatjaNeural", description="Text-to-Speech voice", frontend_type="select", frontend_readonly=False, frontend_required=True)
translationEnabled: bool = Field(default=True, description="Whether translation is enabled", frontend_type="checkbox", frontend_readonly=False, frontend_required=False)
targetLanguage: str = Field(default="en-US", description="Target language for translation", frontend_type="select", frontend_readonly=False, frontend_required=False)
creationDate: float = Field(default_factory=getUtcTimestamp, description="Date when the settings were created (UTC timestamp in seconds)", frontend_type="timestamp", frontend_readonly=True, frontend_required=False)
lastModified: float = Field(default_factory=getUtcTimestamp, description="Date when the settings were last modified (UTC timestamp in seconds)", frontend_type="timestamp", frontend_readonly=True, frontend_required=False)
id: str = Field(default_factory=lambda: str(uuid.uuid4()), description="Primary key", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": False})
userId: str = Field(description="ID of the user these settings belong to", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": True})
mandateId: str = Field(description="ID of the mandate these settings belong to", json_schema_extra={"frontend_type": "text", "frontend_readonly": True, "frontend_required": True})
sttLanguage: str = Field(default="de-DE", description="Speech-to-Text language", json_schema_extra={"frontend_type": "select", "frontend_readonly": False, "frontend_required": True})
ttsLanguage: str = Field(default="de-DE", description="Text-to-Speech language", json_schema_extra={"frontend_type": "select", "frontend_readonly": False, "frontend_required": True})
ttsVoice: str = Field(default="de-DE-KatjaNeural", description="Text-to-Speech voice", json_schema_extra={"frontend_type": "select", "frontend_readonly": False, "frontend_required": True})
translationEnabled: bool = Field(default=True, description="Whether translation is enabled", json_schema_extra={"frontend_type": "checkbox", "frontend_readonly": False, "frontend_required": False})
targetLanguage: str = Field(default="en-US", description="Target language for translation", json_schema_extra={"frontend_type": "select", "frontend_readonly": False, "frontend_required": False})
creationDate: float = Field(default_factory=getUtcTimestamp, description="Date when the settings were created (UTC timestamp in seconds)", json_schema_extra={"frontend_type": "timestamp", "frontend_readonly": True, "frontend_required": False})
lastModified: float = Field(default_factory=getUtcTimestamp, description="Date when the settings were last modified (UTC timestamp in seconds)", json_schema_extra={"frontend_type": "timestamp", "frontend_readonly": True, "frontend_required": False})
registerModelLabels(

374
modules/datamodels/datamodelWorkflow.py Normal file
View file

@ -0,0 +1,374 @@
"""
Workflow execution models for action definitions, AI responses, and workflow-level structures.
"""
from typing import Dict, Any, List, Optional, TYPE_CHECKING
from pydantic import BaseModel, Field
from modules.shared.attributeUtils import registerModelLabels
from modules.shared.jsonUtils import extractJsonString, tryParseJson, repairBrokenJson
# Import DocumentReferenceList at runtime (needed for ActionDefinition)
from modules.datamodels.datamodelDocref import DocumentReferenceList
# Forward references for circular imports (use string annotations)
if TYPE_CHECKING:
from modules.datamodels.datamodelChat import ChatDocument, ActionResult
from modules.datamodels.datamodelExtraction import ExtractionOptions
class ActionDefinition(BaseModel):
"""Action definition with selection and parameters from planning phase"""
# Core action selection (Stage 1)
action: str = Field(description="Compound action name (method.action)")
actionObjective: str = Field(description="Objective for this action")
parametersContext: Optional[str] = Field(
None,
description="Context for parameter generation"
)
learnings: List[str] = Field(
default_factory=list,
description="Learnings from previous actions"
)
# Resources (ALWAYS defined in Stage 1 if action needs them)
documentList: Optional[DocumentReferenceList] = Field(
None,
description="Document references (ALWAYS defined in Stage 1 if action needs documents)"
)
connectionReference: Optional[str] = Field(
None,
description="Connection reference (ALWAYS defined in Stage 1 if action needs connection)"
)
# Parameters (may be defined in Stage 1 OR Stage 2, depending on action and actionObjective)
parameters: Optional[Dict[str, Any]] = Field(
None,
description="Action-specific parameters (generated in Stage 2 for complex actions, or inferred from actionObjective for simple actions)"
)
def hasParameters(self) -> bool:
"""Check if parameters have been generated (Stage 2 complete or inferred)"""
return self.parameters is not None
def needsStage2(self) -> bool:
"""Determine if Stage 2 parameter generation is needed (generic, deterministic check)
Generic logic (works for any action, dynamically added or removed):
- If parameters are already set → Stage 2 not needed
- If parameters are None → Stage 2 needed (to generate parameters from actionObjective and context)
Note: Stage 1 always defines documentList and connectionReference if the action needs them.
Stage 2 only generates the action-specific parameters dictionary.
"""
# Generic check: if parameters are not set, Stage 2 is needed
return self.parameters is None
def updateFromStage1StringReferences(self, stringRefs: Optional[List[str]], connectionRef: Optional[str]):
"""Update documentList and connectionReference from Stage 1 string references
Called when Stage 1 AI returns string references that need to be converted to typed models.
"""
if stringRefs:
self.documentList = DocumentReferenceList.from_string_list(stringRefs)
if connectionRef:
self.connectionReference = connectionRef
class AiResponseMetadata(BaseModel):
"""Metadata for AI response (varies by operation type)."""
# Document Generation Metadata
title: Optional[str] = Field(None, description="Document title")
filename: Optional[str] = Field(None, description="Document filename")
# Operation-Specific Metadata
operationType: Optional[str] = Field(None, description="Type of operation performed")
schemaVersion: Optional[str] = Field(None, description="Schema version (e.g., 'parameters_v1')", alias="schema")
extractionMethod: Optional[str] = Field(None, description="Method used for extraction")
sourceDocuments: Optional[List[str]] = Field(None, description="Source document references")
# Additional metadata (for extensibility)
additionalData: Optional[Dict[str, Any]] = Field(None, description="Additional operation-specific metadata")
@classmethod
def fromDict(cls, data: Optional[Dict[str, Any]]) -> Optional["AiResponseMetadata"]:
"""Create AiResponseMetadata from dict with camelCase field names"""
if not data:
return None
# Convert snake_case keys to camelCase if needed (for backward compatibility)
convertedData = {}
for key, value in data.items():
# Keep camelCase as-is, convert snake_case if present
if '_' in key:
# Convert snake_case to camelCase
parts = key.split('_')
camelKey = parts[0] + ''.join(word.capitalize() for word in parts[1:])
convertedData[camelKey] = value
else:
convertedData[key] = value
return cls(**convertedData)
class DocumentData(BaseModel):
"""Single document in response"""
documentName: str = Field(description="Document name")
documentData: Any = Field(description="Document data (can be str, bytes, dict, etc.)")
mimeType: str = Field(description="MIME type of the document")
class ExtractContentParameters(BaseModel):
"""Parameters for extraction action.
This model is defined together with the `methodAi.extractContent()` action function.
All action parameter models follow this pattern: defined in the same module as the action.
However, since this is a workflow-level model used across the system, it's defined here.
"""
documentList: DocumentReferenceList = Field(description="Document references to extract content from")
extractionOptions: Optional[Any] = Field( # ExtractionOptions - forward reference
None,
description="Extraction options (determined dynamically based on task and document characteristics)"
)
class AiResponse(BaseModel):
"""Unified response from all AI calls (planning, text, documents)"""
content: str = Field(description="Response content (JSON string for planning, text for analysis, unified JSON for documents)")
metadata: Optional[AiResponseMetadata] = Field(
None,
description="Response metadata (varies by operation type)"
)
documents: Optional[List[DocumentData]] = Field(
None,
description="Generated documents (only for document generation operations)"
)
def toJson(self) -> Dict[str, Any]:
"""
Convert AI response content to JSON using enhanced stabilizing failsafe conversion methods.
Centralizes AI result to JSON conversion in one place.
Uses methods from jsonUtils:
- tryParseJson() - Safe parsing with error handling
- repairBrokenJson() - Repairs broken/incomplete JSON
- extractJsonString() - Extracts JSON from text with code fences
Returns:
Dict containing the parsed JSON content, or a safe fallback structure if parsing fails.
- If content is valid JSON dict: returns the dict directly
- If content is valid JSON list: wraps in {"data": [...]}
- If content is broken JSON: attempts repair using repairBrokenJson()
- If all parsing fails: returns {"content": "...", "parseError": True}
"""
# If content is already a dict, return it directly
if isinstance(self.content, dict):
return self.content
# If content is already a list, wrap it
if isinstance(self.content, list):
return {"data": self.content}
# Convert to string if needed
contentStr = str(self.content) if not isinstance(self.content, str) else self.content
# First, try to extract JSON from text (handles code fences, etc.)
extractedJson = extractJsonString(contentStr)
# Try to parse as JSON (returns tuple: obj, error, cleaned_str)
parsedJson, parseError, _ = tryParseJson(extractedJson)
if parsedJson is not None and parseError is None:
# If it's a dict, return directly
if isinstance(parsedJson, dict):
return parsedJson
# If it's a list, wrap in dict
elif isinstance(parsedJson, list):
return {"data": parsedJson}
# Try to repair broken JSON
repairedJson = repairBrokenJson(contentStr)
if repairedJson:
# repairBrokenJson returns Optional[Dict[str, Any]] - always a dict or None
if isinstance(repairedJson, dict):
return repairedJson
# All parsing failed - return safe fallback
contentStr = str(self.content) if not isinstance(self.content, str) else self.content
return {"content": contentStr, "parseError": True}
# Workflow-level models
class RequestContext(BaseModel):
"""Normalized request context from user input"""
originalPrompt: str = Field(description="Original user prompt")
documents: List[Any] = Field( # ChatDocument - forward reference
default_factory=list,
description="Documents provided by user"
)
userLanguage: str = Field(description="User's language")
detectedComplexity: str = Field(
description="Complexity level: simple, moderate, complex"
)
requiresDocuments: bool = Field(default=False, description="Whether request requires documents")
requiresWebResearch: bool = Field(default=False, description="Whether request requires web research")
requiresAnalysis: bool = Field(default=False, description="Whether request requires analysis")
expectedOutputFormat: Optional[str] = Field(None, description="Expected output format")
expectedOutputType: Optional[str] = Field(None, description="Expected output type: answer, document, analysis")
class UnderstandingResult(BaseModel):
"""Result from initial understanding phase (combined AI call)"""
parameters: Dict[str, Any] = Field(
default_factory=dict,
description="Basic parameters (language, format, detail level)"
)
intention: Dict[str, Any] = Field(
default_factory=dict,
description="User intention (primaryGoal, secondaryGoals, intentionType)"
)
context: Dict[str, Any] = Field(
default_factory=dict,
description="Extracted context (topics, requirements, constraints)"
)
documentReferences: List[Dict[str, Any]] = Field(
default_factory=list,
description="Document references with purpose and relevance"
)
tasks: List["TaskDefinition"] = Field( # Forward reference
default_factory=list,
description="Task definitions with deliverables"
)
class TaskDefinition(BaseModel):
"""Task definition from understanding phase"""
id: str = Field(description="Task identifier")
objective: str = Field(description="Task objective")
deliverable: Dict[str, Any] = Field(
description="Deliverable specification (type, format, style, detailLevel)"
)
requiresWebResearch: bool = Field(default=False, description="Whether task requires web research")
requiresDocumentAnalysis: bool = Field(default=False, description="Whether task requires document analysis")
requiresContentGeneration: bool = Field(default=True, description="Whether task requires content generation")
requiredDocuments: List[str] = Field(
default_factory=list,
description="Document references needed for this task"
)
extractionOptions: Optional[Any] = Field( # ExtractionOptions - forward reference
None,
description="Extraction options for document processing (determined dynamically based on task and document characteristics)"
)
class TaskResult(BaseModel):
"""Result from task execution"""
taskId: str = Field(description="Task identifier")
actionResult: Any = Field(description="ActionResult from task execution") # ActionResult - forward reference
# Register model labels for UI
registerModelLabels(
"ActionDefinition",
{"en": "Action Definition", "fr": "Définition d'action"},
{
"action": {"en": "Action", "fr": "Action"},
"actionObjective": {"en": "Action Objective", "fr": "Objectif de l'action"},
"parametersContext": {"en": "Parameters Context", "fr": "Contexte des paramètres"},
"learnings": {"en": "Learnings", "fr": "Apprentissages"},
"documentList": {"en": "Document List", "fr": "Liste de documents"},
"connectionReference": {"en": "Connection Reference", "fr": "Référence de connexion"},
"parameters": {"en": "Parameters", "fr": "Paramètres"},
},
)
registerModelLabels(
"AiResponse",
{"en": "AI Response", "fr": "Réponse IA"},
{
"content": {"en": "Content", "fr": "Contenu"},
"metadata": {"en": "Metadata", "fr": "Métadonnées"},
"documents": {"en": "Documents", "fr": "Documents"},
},
)
registerModelLabels(
"AiResponseMetadata",
{"en": "AI Response Metadata", "fr": "Métadonnées de réponse IA"},
{
"title": {"en": "Title", "fr": "Titre"},
"filename": {"en": "Filename", "fr": "Nom de fichier"},
"operationType": {"en": "Operation Type", "fr": "Type d'opération"},
"schemaVersion": {"en": "Schema Version", "fr": "Version du schéma"},
"extractionMethod": {"en": "Extraction Method", "fr": "Méthode d'extraction"},
"sourceDocuments": {"en": "Source Documents", "fr": "Documents sources"},
},
)
registerModelLabels(
"DocumentData",
{"en": "Document Data", "fr": "Données de document"},
{
"documentName": {"en": "Document Name", "fr": "Nom du document"},
"documentData": {"en": "Document Data", "fr": "Données du document"},
"mimeType": {"en": "MIME Type", "fr": "Type MIME"},
},
)
registerModelLabels(
"RequestContext",
{"en": "Request Context", "fr": "Contexte de requête"},
{
"originalPrompt": {"en": "Original Prompt", "fr": "Invite originale"},
"documents": {"en": "Documents", "fr": "Documents"},
"userLanguage": {"en": "User Language", "fr": "Langue de l'utilisateur"},
"detectedComplexity": {"en": "Detected Complexity", "fr": "Complexité détectée"},
"requiresDocuments": {"en": "Requires Documents", "fr": "Nécessite des documents"},
"requiresWebResearch": {"en": "Requires Web Research", "fr": "Nécessite une recherche web"},
"requiresAnalysis": {"en": "Requires Analysis", "fr": "Nécessite une analyse"},
},
)
registerModelLabels(
"UnderstandingResult",
{"en": "Understanding Result", "fr": "Résultat de compréhension"},
{
"parameters": {"en": "Parameters", "fr": "Paramètres"},
"intention": {"en": "Intention", "fr": "Intention"},
"context": {"en": "Context", "fr": "Contexte"},
"documentReferences": {"en": "Document References", "fr": "Références de documents"},
"tasks": {"en": "Tasks", "fr": "Tâches"},
},
)
registerModelLabels(
"TaskDefinition",
{"en": "Task Definition", "fr": "Définition de tâche"},
{
"id": {"en": "ID", "fr": "ID"},
"objective": {"en": "Objective", "fr": "Objectif"},
"deliverable": {"en": "Deliverable", "fr": "Livrable"},
"requiresWebResearch": {"en": "Requires Web Research", "fr": "Nécessite une recherche web"},
"requiresDocumentAnalysis": {"en": "Requires Document Analysis", "fr": "Nécessite une analyse de document"},
"requiresContentGeneration": {"en": "Requires Content Generation", "fr": "Nécessite une génération de contenu"},
"requiredDocuments": {"en": "Required Documents", "fr": "Documents requis"},
"extractionOptions": {"en": "Extraction Options", "fr": "Options d'extraction"},
},
)
registerModelLabels(
"TaskResult",
{"en": "Task Result", "fr": "Résultat de tâche"},
{
"taskId": {"en": "Task ID", "fr": "ID de tâche"},
"actionResult": {"en": "Action Result", "fr": "Résultat d'action"},
},
)

2
modules/features/chatPlayground/mainChatPlayground.py
View file

@ -16,7 +16,7 @@ async def chatStart(currentUser: User, userInput: UserInputRequest, workflowMode
currentUser: Current user
userInput: User input request
workflowId: Optional workflow ID to continue existing workflow
workflowMode: "Actionplan" for traditional task planning, "Dynamic" for iterative dynamic-style processing, "Template" for template-based processing
workflowMode: "Dynamic" for iterative dynamic-style processing, "Automation" for automated workflow execution
Example usage for Dynamic mode:
workflow = await chatStart(currentUser, userInput, workflowMode=WorkflowModeEnum.WORKFLOW_DYNAMIC)

4
modules/routes/routeChatPlayground.py
View file

@ -39,7 +39,7 @@ def getServiceChat(currentUser: User):
async def start_workflow(
request: Request,
workflowId: Optional[str] = Query(None, description="Optional ID of the workflow to continue"),
workflowMode: WorkflowModeEnum = Query(..., description="Workflow mode: 'Actionplan', 'Dynamic', or 'Template' (mandatory)"),
workflowMode: WorkflowModeEnum = Query(..., description="Workflow mode: 'Dynamic' or 'Automation' (mandatory)"),
userInput: UserInputRequest = Body(...),
currentUser: User = Depends(getCurrentUser)
) -> ChatWorkflow:
@ -48,7 +48,7 @@ async def start_workflow(
Corresponds to State 1 in the state machine documentation.
Args:
workflowMode: "Actionplan" for traditional task planning, "Dynamic" for iterative dynamic-style processing, "Template" for template-based processing
workflowMode: "Dynamic" for iterative dynamic-style processing, "Automation" for automated workflow execution
"""
try:
# Start or continue workflow using playground controller

331
modules/services/serviceAi/mainServiceAi.py
View file

@ -2,16 +2,19 @@ import json
import logging
import re
import time
from typing import Dict, Any, List, Optional, Tuple, Union
from typing import Dict, Any, List, Optional, Tuple
from modules.datamodels.datamodelChat import PromptPlaceholder, ChatDocument
from modules.services.serviceExtraction.mainServiceExtraction import ExtractionService
from modules.datamodels.datamodelAi import AiCallRequest, AiCallOptions, OperationTypeEnum, PriorityEnum, ProcessingModeEnum
from modules.datamodels.datamodelExtraction import ContentPart
from modules.datamodels.datamodelWorkflow import AiResponse, AiResponseMetadata, DocumentData
from modules.interfaces.interfaceAiObjects import AiObjects
from modules.shared.jsonUtils import (
extractJsonString,
repairBrokenJson,
extractSectionsFromDocument,
buildContinuationContext
buildContinuationContext,
parseJsonWithModel
)
logger = logging.getLogger(__name__)
@ -138,25 +141,11 @@ Respond with ONLY a JSON object in this exact format:
response = await self.aiObjects.call(request)
# Parse AI response
# Parse AI response using structured parsing with AiCallOptions model
try:
jsonStart = response.content.find('{')
jsonEnd = response.content.rfind('}') + 1
if jsonStart != -1 and jsonEnd > jsonStart:
analysis = json.loads(response.content[jsonStart:jsonEnd])
# Map string values to enums
operationType = OperationTypeEnum(analysis.get('operationType', 'dataAnalyse'))
priority = PriorityEnum(analysis.get('priority', 'balanced'))
processingMode = ProcessingModeEnum(analysis.get('processingMode', 'basic'))
return AiCallOptions(
operationType=operationType,
priority=priority,
processingMode=processingMode,
compressPrompt=analysis.get('compressPrompt', True),
compressContext=analysis.get('compressContext', True)
)
# Use parseJsonWithModel to parse response into AiCallOptions (handles enum conversion automatically)
analysis = parseJsonWithModel(response.content, AiCallOptions)
return analysis
except Exception as e:
logger.warning(f"Failed to parse AI analysis response: {e}")
@ -258,12 +247,17 @@ Respond with ONLY a JSON object in this exact format:
else:
self.services.utils.writeDebugFile(result, f"{debugPrefix}_response_iteration_{iteration}")
# Emit stats for this iteration
# Emit stats for this iteration (only if workflow exists and has id)
if self.services.workflow and hasattr(self.services.workflow, 'id') and self.services.workflow.id:
try:
self.services.chat.storeWorkflowStat(
self.services.workflow,
response,
f"ai.call.{debugPrefix}.iteration_{iteration}"
)
except Exception as statError:
# Don't break the main loop if stat storage fails
logger.warning(f"Failed to store workflow stat: {str(statError)}")
if not result or not result.strip():
logger.warning(f"Iteration {iteration}: Empty response, stopping")
@ -502,7 +496,7 @@ Respond with ONLY a JSON object in this exact format:
Args:
prompt: The planning prompt
placeholders: Optional list of placeholder replacements
debugType: Optional debug file type identifier (e.g., 'taskplan', 'actionplan', 'intentanalysis')
debugType: Optional debug file type identifier (e.g., 'taskplan', 'dynamic', 'intentanalysis')
If not provided, defaults to 'plan'
Returns:
@ -541,60 +535,83 @@ Respond with ONLY a JSON object in this exact format:
self.services.utils.writeDebugFile(result, f"{debugPrefix}_response")
return result
# Document Generation AI Call
async def callAiDocuments(
async def callAiContent(
self,
prompt: str,
documents: Optional[List[ChatDocument]] = None,
options: Optional[AiCallOptions] = None,
options: AiCallOptions,
contentParts: Optional[List[ContentPart]] = None,
outputFormat: Optional[str] = None,
title: Optional[str] = None
) -> Union[str, Dict[str, Any]]:
title: Optional[str] = None,
documents: Optional[List[ChatDocument]] = None # Phase 6: backward compatibility, Phase 7: remove
) -> AiResponse:
"""
Document generation AI call for all non-planning calls.
Uses the current unified path with extraction and generation.
Unified AI content processing method (replaces callAiDocuments and callAiText).
Args:
prompt: The main prompt for the AI call
documents: Optional list of documents to process
options: AI call configuration options
outputFormat: Optional output format for document generation
contentParts: Optional list of already-extracted content parts (preferred)
options: AI call configuration options (REQUIRED - operationType must be set)
outputFormat: Optional output format for document generation (e.g., 'pdf', 'docx', 'xlsx')
title: Optional title for generated documents
documents: Optional list of documents (Phase 6: backward compatibility - extracts internally)
Returns:
AI response as string, or dict with documents if outputFormat is specified
AiResponse with content, metadata, and optional documents
"""
await self._ensureAiObjectsInitialized()
# Create separate operationId for detailed progress tracking
workflowId = self.services.workflow.id if self.services.workflow else f"no-workflow-{int(time.time())}"
aiOperationId = f"ai_documents_{workflowId}_{int(time.time())}"
aiOperationId = f"ai_content_{workflowId}_{int(time.time())}"
# Start progress tracking for this operation
# Start progress tracking
self.services.chat.progressLogStart(
aiOperationId,
"AI call with documents",
"Document Generation",
"AI content processing",
"Content Processing",
f"Format: {outputFormat or 'text'}"
)
try:
if options is None or (hasattr(options, 'operationType') and options.operationType is None):
# Use AI to determine parameters ONLY when truly needed (options=None OR operationType=None)
self.services.chat.progressLogUpdate(aiOperationId, 0.1, "Analyzing prompt parameters")
options = await self._analyzePromptAndCreateOptions(prompt)
# Phase 7: Extraction is now separate - contentParts must be extracted before calling
# If documents parameter is still provided (backward compatibility), raise error
if documents and len(documents) > 0:
raise ValueError(
"callAiContent() no longer accepts 'documents' parameter. "
"Extract content first using 'ai.extractContent' action, then pass 'contentParts'."
)
# Check operationType FIRST - some operations need direct routing (before document generation checks)
# Phase 6: Analyze prompt if operationType not set (backward compatibility)
# Phase 7: Require operationType to be set before calling
opType = getattr(options, "operationType", None)
if not opType:
# If outputFormat is specified, default to DATA_GENERATE
if outputFormat:
options.operationType = OperationTypeEnum.DATA_GENERATE
opType = OperationTypeEnum.DATA_GENERATE
else:
self.services.chat.progressLogUpdate(aiOperationId, 0.1, "Analyzing prompt parameters")
analyzedOptions = await self._analyzePromptAndCreateOptions(prompt)
if analyzedOptions and hasattr(analyzedOptions, "operationType") and analyzedOptions.operationType:
options.operationType = analyzedOptions.operationType
# Merge other analyzed options
if hasattr(analyzedOptions, "priority"):
options.priority = analyzedOptions.priority
if hasattr(analyzedOptions, "processingMode"):
options.processingMode = analyzedOptions.processingMode
if hasattr(analyzedOptions, "compressPrompt"):
options.compressPrompt = analyzedOptions.compressPrompt
if hasattr(analyzedOptions, "compressContext"):
options.compressContext = analyzedOptions.compressContext
else:
# Default to DATA_ANALYSE if analysis fails
options.operationType = OperationTypeEnum.DATA_ANALYSE
opType = options.operationType
# Handle image generation requests directly via generic path
isImageRequest = (opType == OperationTypeEnum.IMAGE_GENERATE)
if isImageRequest:
# Image generation uses generic call path but bypasses document generation pipeline
# Handle IMAGE_GENERATE operations
if opType == OperationTypeEnum.IMAGE_GENERATE:
self.services.chat.progressLogUpdate(aiOperationId, 0.4, "Calling AI for image generation")
# Call via generic path (no looping for images)
request = AiCallRequest(
prompt=prompt,
context="",
@ -603,62 +620,56 @@ Respond with ONLY a JSON object in this exact format:
response = await self.aiObjects.call(request)
# Extract image data from response
if response.content:
# For base64 format, return in expected format
if outputFormat == "base64":
result = {
"success": True,
"image_data": response.content,
"documents": [{
"documentName": "generated_image.png",
"documentData": response.content,
"mimeType": "image/png",
"title": title or "Generated Image"
}]
}
else:
# Return raw content for other formats
result = response.content
# Build document data for image
imageDoc = DocumentData(
documentName="generated_image.png",
documentData=response.content,
mimeType="image/png"
)
metadata = AiResponseMetadata(
title=title or "Generated Image",
operationType=opType.value
)
# Emit stats for image generation
self.services.chat.storeWorkflowStat(
self.services.workflow,
response,
f"ai.generate.image"
"ai.generate.image"
)
self.services.chat.progressLogUpdate(aiOperationId, 0.9, "Image generated")
self.services.chat.progressLogFinish(aiOperationId, True)
return result
return AiResponse(
content=response.content,
metadata=metadata,
documents=[imageDoc]
)
else:
errorMsg = f"No image data returned: {response.content}"
logger.error(f"Error in AI image generation: {errorMsg}")
self.services.chat.progressLogFinish(aiOperationId, False)
return {"success": False, "error": errorMsg}
raise ValueError(errorMsg)
# Handle WEB_SEARCH and WEB_CRAWL operations - route directly to connectors
# These operations require raw JSON prompts that connectors parse directly
# Must check BEFORE document generation to avoid wrapping the prompt
isWebOperation = (opType == OperationTypeEnum.WEB_SEARCH or opType == OperationTypeEnum.WEB_CRAWL)
if isWebOperation:
# Web operations: prompt is already structured JSON (AiCallPromptWebSearch/WebCrawl)
# Route directly through centralized AI call - model selector chooses appropriate connector
# Connector parses the JSON prompt and executes the operation
# Handle WEB_SEARCH and WEB_CRAWL operations
if opType == OperationTypeEnum.WEB_SEARCH or opType == OperationTypeEnum.WEB_CRAWL:
self.services.chat.progressLogUpdate(aiOperationId, 0.4, f"Calling AI for {opType.name}")
request = AiCallRequest(
prompt=prompt, # Pass raw JSON prompt unchanged - connector will parse it
prompt=prompt, # Raw JSON prompt - connector will parse it
context="",
options=options
)
response = await self.aiObjects.call(request)
# Extract result from response
if response.content:
# Emit stats for web operation
metadata = AiResponseMetadata(
operationType=opType.value
)
self.services.chat.storeWorkflowStat(
self.services.workflow,
response,
@ -667,42 +678,42 @@ Respond with ONLY a JSON object in this exact format:
self.services.chat.progressLogUpdate(aiOperationId, 0.9, f"{opType.name} completed")
self.services.chat.progressLogFinish(aiOperationId, True)
return response.content
return AiResponse(
content=response.content,
metadata=metadata
)
else:
errorMsg = f"No content returned from {opType.name}: {response.content}"
logger.error(f"Error in {opType.name}: {errorMsg}")
self.services.chat.progressLogFinish(aiOperationId, False)
return {"success": False, "error": errorMsg}
raise ValueError(errorMsg)
# CRITICAL: For document generation with JSON templates, NEVER compress the prompt
# Compressing would truncate the template structure and confuse the AI
if outputFormat: # Document generation with structured output
if not options:
options = AiCallOptions()
options.compressPrompt = False # JSON templates must NOT be truncated
options.compressContext = False # Context also should not be compressed
# Handle document generation with specific output format using unified approach
# Handle document generation (outputFormat specified)
if outputFormat:
# Use unified generation method for all document generation
if documents and len(documents) > 0:
self.services.chat.progressLogUpdate(aiOperationId, 0.2, f"Extracting content from {len(documents)} documents")
extracted_content = await self.callAiText(prompt, documents, options, aiOperationId)
# CRITICAL: For document generation with JSON templates, NEVER compress the prompt
options.compressPrompt = False
options.compressContext = False
# Convert contentParts to text for generation prompt (if provided)
if contentParts:
# Convert contentParts to text for generation prompt
content_for_generation = "\n\n".join([f"[{part.label}]\n{part.data}" for part in contentParts if part.data])
else:
self.services.chat.progressLogUpdate(aiOperationId, 0.2, "Preparing for direct generation")
extracted_content = None
content_for_generation = None
self.services.chat.progressLogUpdate(aiOperationId, 0.3, "Building generation prompt")
from modules.services.serviceGeneration.subPromptBuilderGeneration import buildGenerationPrompt
# First call without continuation context
generation_prompt = await buildGenerationPrompt(outputFormat, prompt, title, extracted_content, None)
# Prepare prompt builder arguments for continuation
generation_prompt = await buildGenerationPrompt(
outputFormat, prompt, title, content_for_generation, None
)
promptArgs = {
"outputFormat": outputFormat,
"userPrompt": prompt,
"title": title,
"extracted_content": extracted_content
"extracted_content": content_for_generation
}
self.services.chat.progressLogUpdate(aiOperationId, 0.4, "Calling AI for content generation")
@ -716,62 +727,49 @@ Respond with ONLY a JSON object in this exact format:
)
self.services.chat.progressLogUpdate(aiOperationId, 0.7, "Parsing generated JSON")
# Parse the generated JSON (extract fenced/embedded JSON first)
try:
extracted_json = self.services.utils.jsonExtractString(generated_json)
generated_data = json.loads(extracted_json)
except json.JSONDecodeError as e:
logger.error(f"Failed to parse generated JSON: {str(e)}")
logger.error(f"JSON content length: {len(generated_json)}")
logger.error(f"JSON content preview (last 200 chars): ...{generated_json[-200:]}")
logger.error(f"JSON content around error position: {generated_json[max(0, e.pos-50):e.pos+50]}")
# Write the problematic JSON to debug file
self.services.utils.writeDebugFile(generated_json, "failed_json_parsing")
self.services.chat.progressLogFinish(aiOperationId, False)
return {"success": False, "error": f"Generated content is not valid JSON: {str(e)}"}
raise ValueError(f"Generated content is not valid JSON: {str(e)}")
# Extract title and filename from generated document structure
extractedTitle = title # Default to user-provided title
extractedTitle = title
extractedFilename = None
if isinstance(generated_data, dict) and "documents" in generated_data:
documents = generated_data["documents"]
if isinstance(documents, list) and len(documents) > 0:
firstDoc = documents[0]
docs = generated_data["documents"]
if isinstance(docs, list) and len(docs) > 0:
firstDoc = docs[0]
if isinstance(firstDoc, dict):
# Extract title from document (preferred over user-provided title)
if firstDoc.get("title"):
extractedTitle = firstDoc["title"]
# Extract filename from document
if firstDoc.get("filename"):
extractedFilename = firstDoc["filename"]
# Ensure metadata contains the extracted title for renderers
# Ensure metadata contains the extracted title
if "metadata" not in generated_data:
generated_data["metadata"] = {}
if extractedTitle:
generated_data["metadata"]["title"] = extractedTitle
self.services.chat.progressLogUpdate(aiOperationId, 0.8, f"Rendering to {outputFormat} format")
# Render to final format using the existing renderer
try:
from modules.services.serviceGeneration.mainServiceGeneration import GenerationService
generationService = GenerationService(self.services)
# Pass extracted title to renderer (will use metadata.title if available)
rendered_content, mime_type = await generationService.renderReport(
generated_data, outputFormat, extractedTitle or "Generated Document", prompt, self
)
# Use extracted filename if available, otherwise generate from title or use generic
# Determine document name
if extractedFilename:
documentName = extractedFilename
elif extractedTitle and extractedTitle != "Generated Document":
# Sanitize title for filename
sanitized = re.sub(r"[^a-zA-Z0-9._-]", "_", extractedTitle)
sanitized = re.sub(r"_+", "_", sanitized).strip("_")
if sanitized:
# Ensure correct extension
if not sanitized.lower().endswith(f".{outputFormat}"):
documentName = f"{sanitized}.{outputFormat}"
else:
@ -781,63 +779,68 @@ Respond with ONLY a JSON object in this exact format:
else:
documentName = f"generated.{outputFormat}"
# Build result in the expected format
result = {
"success": True,
"content": generated_data,
"documents": [{
"documentName": documentName,
"documentData": rendered_content,
"mimeType": mime_type,
"title": extractedTitle or "Generated Document"
}],
"is_multi_file": False,
"format": outputFormat,
"title": extractedTitle or title,
"split_strategy": "single",
"total_documents": 1,
"processed_documents": 1
}
# Build document data
docData = DocumentData(
documentName=documentName,
documentData=rendered_content,
mimeType=mime_type
)
# Log AI response for debugging
self.services.utils.writeDebugFile(str(result), "document_generation_response", documents)
metadata = AiResponseMetadata(
title=extractedTitle or title or "Generated Document",
filename=extractedFilename,
operationType=opType.value if opType else None
)
self.services.utils.writeDebugFile(str(generated_data), "document_generation_response")
self.services.chat.progressLogFinish(aiOperationId, True)
return result
return AiResponse(
content=json.dumps(generated_data),
metadata=metadata,
documents=[docData]
)
except Exception as e:
logger.error(f"Error rendering document: {str(e)}")
self.services.chat.progressLogFinish(aiOperationId, False)
return {"success": False, "error": f"Rendering failed: {str(e)}"}
raise ValueError(f"Rendering failed: {str(e)}")
# Handle text calls (no output format specified)
# Handle text processing (no outputFormat)
self.services.chat.progressLogUpdate(aiOperationId, 0.5, "Processing text call")
if documents:
# Use document processing for text calls with documents
result = await self.callAiText(prompt, documents, options, aiOperationId)
if contentParts:
# Process contentParts through AI
# Convert contentParts to text for prompt
contentText = "\n\n".join([f"[{part.label}]\n{part.data}" for part in contentParts if part.data])
fullPrompt = f"{prompt}\n\n{contentText}" if contentText else prompt
result_content = await self._callAiWithLooping(
fullPrompt, options, "text", None, None, aiOperationId
)
else:
# Use shared core function for direct text calls
result = await self._callAiWithLooping(prompt, options, "text", None, None, aiOperationId)
# Direct text call (no documents to process)
result_content = await self._callAiWithLooping(
prompt, options, "text", None, None, aiOperationId
)
metadata = AiResponseMetadata(
operationType=opType.value if opType else None
)
self.services.chat.progressLogFinish(aiOperationId, True)
return result
return AiResponse(
content=result_content,
metadata=metadata
)
except Exception as e:
logger.error(f"Error in callAiDocuments: {str(e)}")
logger.error(f"Error in callAiContent: {str(e)}")
self.services.chat.progressLogFinish(aiOperationId, False)
raise
async def callAiText(
self,
prompt: str,
documents: Optional[List[ChatDocument]],
options: AiCallOptions,
operationId: Optional[str] = None
) -> str:
"""
Handle text calls with document processing through ExtractionService.
UNIFIED PROCESSING: Always use per-chunk processing for consistency.
"""
await self._ensureAiObjectsInitialized()
return await self.extractionService.processDocumentsPerChunk(documents, prompt, self.aiObjects, options, operationId)
# DEPRECATED METHODS REMOVED:
# - callAiDocuments() - replaced by callAiContent()
# - callAiText() - replaced by callAiContent()
# All call sites have been updated to use callAiContent()

24
modules/services/serviceChat/mainServiceChat.py
View file

@ -20,8 +20,24 @@ class ChatService:
self.interfaceDbApp = serviceCenter.interfaceDbApp
self._progressLogger = None
def getChatDocumentsFromDocumentList(self, documentList: List[str]) -> List[ChatDocument]:
"""Get ChatDocuments from a list of document references using all three formats."""
def getChatDocumentsFromDocumentList(self, documentList) -> List[ChatDocument]:
"""Get ChatDocuments from a DocumentReferenceList.
Args:
documentList: DocumentReferenceList (required)
Returns:
List[ChatDocument]: List of ChatDocument objects
"""
from modules.datamodels.datamodelDocref import DocumentReferenceList
if not isinstance(documentList, DocumentReferenceList):
logger.error(f"getChatDocumentsFromDocumentList: Invalid documentList type: {type(documentList)}. Expected DocumentReferenceList.")
return []
# Convert to string list for processing
stringRefs = documentList.to_string_list()
try:
# Use self.services.workflow which is the ChatWorkflow object (stable during workflow execution)
workflow = self.services.workflow
@ -31,7 +47,7 @@ class ChatService:
workflowId = workflow.id if hasattr(workflow, 'id') else 'NO_ID'
workflowObjId = id(workflow)
logger.debug(f"getChatDocumentsFromDocumentList: input documentList = {documentList}")
logger.debug(f"getChatDocumentsFromDocumentList: input documentList = {stringRefs}")
logger.debug(f"getChatDocumentsFromDocumentList: using workflow.id = {workflowId}, workflow object id = {workflowObjId}")
# Root cause analysis: Verify workflow.messages integrity and detect workflow changes
@ -72,7 +88,7 @@ class ChatService:
logger.debug(f"getChatDocumentsFromDocumentList: unable to enumerate messages for debug: {e}")
allDocuments = []
for docRef in documentList:
for docRef in stringRefs:
if docRef.startswith("docItem:"):
# docItem:<id>:<filename> - extract ID and find document
parts = docRef.split(':')

8
modules/services/serviceExtraction/mainServiceExtraction.py
View file

@ -8,15 +8,12 @@ from .subRegistry import ExtractorRegistry, ChunkerRegistry
from .subPipeline import runExtraction
from modules.datamodels.datamodelExtraction import ContentExtracted, ContentPart, MergeStrategy, ExtractionOptions, PartResult
from modules.datamodels.datamodelChat import ChatDocument
from modules.datamodels.datamodelAi import AiCallResponse, AiCallRequest, AiCallOptions, OperationTypeEnum
from modules.datamodels.datamodelAi import AiCallResponse, AiCallRequest, AiCallOptions
from modules.aicore.aicoreModelRegistry import modelRegistry
logger = logging.getLogger(__name__)
# Rebuild ExtractionOptions to resolve forward references after all imports are complete
ExtractionOptions.model_rebuild()
class ExtractionService:
def __init__(self, services: Optional[Any] = None):
@ -443,12 +440,11 @@ class ExtractionService:
extractionOptions = ExtractionOptions(
prompt=prompt,
operationType=options.operationType if options else OperationTypeEnum.DATA_EXTRACT,
processDocumentsIndividually=True,
mergeStrategy=mergeStrategy
)
logger.debug(f"Per-chunk extraction options: prompt length={len(extractionOptions.prompt)} chars, operationType={extractionOptions.operationType}")
logger.debug(f"Per-chunk extraction options: prompt length={len(extractionOptions.prompt)} chars")
# Extract content WITHOUT chunking
if operationId:

38
modules/services/serviceGeneration/renderers/rendererImage.py
View file

@ -73,46 +73,34 @@ class RendererImage(BaseRenderer):
)
promptJson = promptModel.model_dump_json(exclude_none=True, indent=2)
# Use generic path via callAiDocuments
# Use unified callAiContent method
options = AiCallOptions(
operationType=OperationTypeEnum.IMAGE_GENERATE,
resultFormat="base64"
)
# Call via generic path
imageResult = await aiService.callAiDocuments(
# Use unified callAiContent method
imageResponse = await aiService.callAiContent(
prompt=promptJson,
documents=None,
options=options,
outputFormat="base64"
)
# Save image generation response to debug
aiService.services.utils.writeDebugFile(str(imageResult), "image_generation_response")
aiService.services.utils.writeDebugFile(str(imageResponse.content), "image_generation_response")
# Extract base64 image data from result
# The generic path returns a dict with documents array for base64 format
if isinstance(imageResult, dict):
if imageResult.get("success", False):
# Check if it's the new format with documents array
documents = imageResult.get("documents", [])
if documents and len(documents) > 0:
imageData = documents[0].get("documentData", "")
if imageData:
return imageData
# Fallback: check for image_data field
imageData = imageResult.get("image_data", "")
# Extract base64 image data from AiResponse
# AiResponse.documents contains DocumentData objects
if imageResponse.documents and len(imageResponse.documents) > 0:
imageData = imageResponse.documents[0].documentData
if imageData:
return imageData
# Fallback: check content field (might be base64 string)
if imageResponse.content:
return imageResponse.content
raise ValueError("No image data returned from AI")
else:
errorMsg = imageResult.get("error", "Unknown error")
raise ValueError(f"AI image generation failed: {errorMsg}")
elif isinstance(imageResult, str):
# If it's just a string, it might be base64 data directly
return imageResult
else:
raise ValueError(f"Unexpected image generation result format: {type(imageResult)}")
except Exception as e:
self.logger.error(f"Error generating AI image: {str(e)}")

14
modules/services/serviceWeb/mainServiceWeb.py
View file

@ -234,13 +234,16 @@ Return ONLY valid JSON, no additional text:
resultFormat="json"
)
searchResult = await self.services.ai.callAiDocuments(
# Use unified callAiContent method
searchResponse = await self.services.ai.callAiContent(
prompt=searchPrompt,
documents=None,
options=searchOptions,
outputFormat="json"
)
# Extract content from AiResponse
searchResult = searchResponse.content
# Debug: persist search response
if isinstance(searchResult, str):
self.services.utils.writeDebugFile(searchResult, "websearch_response")
@ -312,13 +315,16 @@ Return ONLY valid JSON, no additional text:
resultFormat="json"
)
crawlResult = await self.services.ai.callAiDocuments(
# Use unified callAiContent method
crawlResponse = await self.services.ai.callAiContent(
prompt=crawlPrompt,
documents=None,
options=crawlOptions,
outputFormat="json"
)
# Extract content from AiResponse
crawlResult = crawlResponse.content
# Debug: persist crawl response
if isinstance(crawlResult, str):
self.services.utils.writeDebugFile(crawlResult, "webcrawl_response")

81
modules/shared/jsonUtils.py
View file

@ -1,9 +1,12 @@
import json
import logging
from typing import Any, Dict, List, Optional, Tuple, Union
from typing import Any, Dict, List, Optional, Tuple, Union, Type, TypeVar
from pydantic import BaseModel, ValidationError
logger = logging.getLogger(__name__)
T = TypeVar('T', bound=BaseModel)
def stripCodeFences(text: str) -> str:
"""Remove ```json / ``` fences and surrounding whitespace if present."""
@ -886,3 +889,79 @@ def buildContinuationContext(allSections: List[Dict[str, Any]], lastRawResponse:
return context
def parseJsonWithModel(jsonString: str, modelClass: Type[T]) -> T:
"""
Parse JSON string using Pydantic model with error handling.
Uses existing jsonUtils methods:
- extractJsonString() - Extracts JSON from text with code fences
- tryParseJson() - Safe parsing with error handling
- repairBrokenJson() - Repairs broken/incomplete JSON
Args:
jsonString: JSON string to parse (may contain code fences, extra text, etc.)
modelClass: Pydantic model class to parse into
Returns:
Parsed Pydantic model instance
Raises:
ValueError: If JSON cannot be parsed or validated
"""
if not jsonString:
raise ValueError(f"Cannot parse empty JSON string for {modelClass.__name__}")
# Step 1: Extract JSON string (handles code fences, extra text)
extractedJson = extractJsonString(jsonString)
if not extractedJson or extractedJson.strip() == "":
raise ValueError(f"No JSON found in string for {modelClass.__name__}")
# Step 2: Try to parse as JSON
parsedJson, error, cleaned = tryParseJson(extractedJson)
if error is None and parsedJson is not None:
# Successfully parsed - try to create model
try:
if isinstance(parsedJson, dict):
return modelClass(**parsedJson)
elif isinstance(parsedJson, list):
# If model expects a list, try to parse first item
if parsedJson:
return modelClass(**parsedJson[0])
else:
raise ValueError(f"Empty list cannot be parsed as {modelClass.__name__}")
else:
raise ValueError(f"Parsed JSON is not a dict or list: {type(parsedJson)}")
except ValidationError as e:
logger.error(f"Validation error parsing {modelClass.__name__}: {e}")
raise ValueError(f"Invalid data for {modelClass.__name__}: {e}")
except Exception as e:
logger.error(f"Error creating {modelClass.__name__} instance: {e}")
raise ValueError(f"Failed to create {modelClass.__name__} instance: {e}")
# Step 3: Try to repair broken JSON
logger.warning(f"Initial JSON parsing failed, attempting repair for {modelClass.__name__}")
repairedJson = repairBrokenJson(extractedJson)
if repairedJson:
# Try parsing repaired JSON
parsedRepaired, errorRepaired, _ = tryParseJson(json.dumps(repairedJson))
if errorRepaired is None and parsedRepaired is not None:
try:
if isinstance(parsedRepaired, dict):
return modelClass(**parsedRepaired)
elif isinstance(parsedRepaired, list) and parsedRepaired:
return modelClass(**parsedRepaired[0])
except ValidationError as e:
logger.error(f"Validation error parsing repaired {modelClass.__name__}: {e}")
raise ValueError(f"Invalid repaired data for {modelClass.__name__}: {e}")
except Exception as e:
logger.error(f"Error creating {modelClass.__name__} from repaired JSON: {e}")
# Step 4: All parsing failed
logger.error(f"Failed to parse JSON for {modelClass.__name__}. Cleaned JSON preview: {cleaned[:200]}...")
raise ValueError(f"Failed to parse or validate JSON for {modelClass.__name__}. JSON may be malformed or incomplete.")

195
modules/workflows/methods/methodAi.py
View file

@ -9,8 +9,10 @@ from typing import Dict, Any, List, Optional
from datetime import datetime, UTC
from modules.workflows.methods.methodBase import MethodBase, action
from modules.datamodels.datamodelChat import ActionResult
from modules.datamodels.datamodelAi import AiCallOptions, OperationTypeEnum, AiCallPromptImage
from modules.datamodels.datamodelChat import ActionResult, ActionDocument
from modules.datamodels.datamodelAi import AiCallOptions, OperationTypeEnum
from modules.datamodels.datamodelWorkflow import ExtractContentParameters
from modules.datamodels.datamodelExtraction import ExtractionOptions, MergeStrategy, ContentPart
logger = logging.getLogger(__name__)
@ -60,9 +62,22 @@ class MethodAi(MethodBase):
# Update progress - preparing parameters
self.services.chat.progressLogUpdate(operationId, 0.2, "Preparing parameters")
documentList = parameters.get("documentList", [])
if isinstance(documentList, str):
documentList = [documentList]
from modules.datamodels.datamodelDocref import DocumentReferenceList
documentListParam = parameters.get("documentList")
# Convert to DocumentReferenceList if needed
if documentListParam is None:
documentList = DocumentReferenceList(references=[])
elif isinstance(documentListParam, DocumentReferenceList):
documentList = documentListParam
elif isinstance(documentListParam, str):
documentList = DocumentReferenceList.from_string_list([documentListParam])
elif isinstance(documentListParam, list):
documentList = DocumentReferenceList.from_string_list(documentListParam)
else:
logger.error(f"Invalid documentList type: {type(documentListParam)}")
documentList = DocumentReferenceList(references=[])
resultType = parameters.get("resultType", "txt")
@ -78,15 +93,53 @@ class MethodAi(MethodBase):
output_mime_type = "application/octet-stream" # Prefer service-provided mimeType when available
logger.info(f"Using result type: {resultType} -> {output_extension}")
# Update progress - preparing documents
self.services.chat.progressLogUpdate(operationId, 0.3, "Preparing documents")
# Phase 7.3: Extract content first if documents provided, then use contentParts
# Check if contentParts are already provided (preferred path)
contentParts: Optional[List[ContentPart]] = None
if "contentParts" in parameters:
contentParts = parameters.get("contentParts")
if contentParts and not isinstance(contentParts, list):
# Try to extract from ContentExtracted if it's an ActionDocument
if hasattr(contentParts, 'parts'):
contentParts = contentParts.parts
else:
logger.warning(f"Invalid contentParts type: {type(contentParts)}, treating as empty")
contentParts = None
# Get ChatDocuments for AI service - let AI service handle all document processing
chatDocuments = []
if documentList:
# If contentParts not provided but documentList is, extract content first
if not contentParts and documentList.references:
self.services.chat.progressLogUpdate(operationId, 0.3, "Extracting content from documents")
# Get ChatDocuments
chatDocuments = self.services.chat.getChatDocumentsFromDocumentList(documentList)
if chatDocuments:
logger.info(f"Prepared {len(chatDocuments)} documents for AI processing")
if not chatDocuments:
logger.warning("No documents found in documentList")
else:
logger.info(f"Extracting content from {len(chatDocuments)} documents")
# Prepare extraction options (use defaults if not provided)
extractionOptions = parameters.get("extractionOptions")
if not extractionOptions:
extractionOptions = ExtractionOptions(
prompt="Extract all content from the document",
mergeStrategy=MergeStrategy(
mergeType="concatenate",
groupBy="typeGroup",
orderBy="id"
),
processDocumentsIndividually=True
)
# Extract content using extraction service
extractedResults = self.services.extraction.extractContent(chatDocuments, extractionOptions)
# Combine all ContentParts from all extracted results
contentParts = []
for extracted in extractedResults:
if extracted.parts:
contentParts.extend(extracted.parts)
logger.info(f"Extracted {len(contentParts)} content parts from {len(extractedResults)} documents")
# Update progress - preparing AI call
self.services.chat.progressLogUpdate(operationId, 0.4, "Preparing AI call")
@ -101,10 +154,11 @@ class MethodAi(MethodBase):
# Update progress - calling AI
self.services.chat.progressLogUpdate(operationId, 0.6, "Calling AI")
result = await self.services.ai.callAiDocuments(
# Use unified callAiContent method with contentParts (extraction is now separate)
aiResponse = await self.services.ai.callAiContent(
prompt=aiPrompt,
documents=chatDocuments if chatDocuments else None,
options=options,
contentParts=contentParts, # Already extracted (or None if no documents)
outputFormat=output_format
)
@ -113,26 +167,33 @@ class MethodAi(MethodBase):
from modules.datamodels.datamodelChat import ActionDocument
if isinstance(result, dict) and isinstance(result.get("documents"), list):
# Extract documents from AiResponse
if aiResponse.documents and len(aiResponse.documents) > 0:
action_documents = []
for d in result["documents"]:
for doc in aiResponse.documents:
action_documents.append(ActionDocument(
documentName=d.get("documentName"),
documentData=d.get("documentData"),
mimeType=d.get("mimeType") or output_mime_type
documentName=doc.documentName,
documentData=doc.documentData,
mimeType=doc.mimeType or output_mime_type
))
# Preserve structured content field for validation (if it exists)
# This allows validator to see the actual structured data, not just rendered output
if "content" in result and result["content"] and isinstance(result["content"], (dict, list)):
# Parse content JSON to check if it's structured data
try:
import json
contentData = json.loads(aiResponse.content) if isinstance(aiResponse.content, str) else aiResponse.content
if isinstance(contentData, (dict, list)):
action_documents.append(ActionDocument(
documentName="structured_content.json",
documentData=result["content"],
documentData=contentData,
mimeType="application/json"
))
except:
pass # Content is not JSON, skip structured content
final_documents = action_documents
else:
# Text response - create document from content
extension = output_extension.lstrip('.')
meaningful_name = self._generateMeaningfulFileName(
base_name="ai",
@ -141,7 +202,7 @@ class MethodAi(MethodBase):
)
action_document = ActionDocument(
documentName=meaningful_name,
documentData=result,
documentData=aiResponse.content,
mimeType=output_mime_type
)
final_documents = [action_document]
@ -165,6 +226,94 @@ class MethodAi(MethodBase):
)
@action
async def extractContent(self, parameters: ExtractContentParameters) -> ActionResult:
"""
Extract content from documents (separate from AI calls).
This action performs pure content extraction without AI processing.
The extracted ContentParts can then be used by subsequent AI processing actions.
Parameters:
- documentList: DocumentReferenceList - Document references to extract content from
- extractionOptions: Optional[ExtractionOptions] - Extraction options (if not provided, defaults are used)
Returns:
- ActionResult with ActionDocument containing ContentExtracted objects
- ContentExtracted.parts contains List[ContentPart] (already chunked if needed)
"""
try:
# Init progress logger
workflowId = self.services.workflow.id if self.services.workflow else f"no-workflow-{int(time.time())}"
operationId = f"ai_extract_{workflowId}_{int(time.time())}"
# Start progress tracking
self.services.chat.progressLogStart(
operationId,
"Extracting content from documents",
"Content Extraction",
f"Documents: {len(parameters.documentList.references) if parameters.documentList else 0}"
)
# Get ChatDocuments from documentList
self.services.chat.progressLogUpdate(operationId, 0.2, "Loading documents")
chatDocuments = self.services.chat.getChatDocumentsFromDocumentList(parameters.documentList)
if not chatDocuments:
self.services.chat.progressLogFinish(operationId, False)
return ActionResult.isFailure(error="No documents found in documentList")
logger.info(f"Extracting content from {len(chatDocuments)} documents")
# Prepare extraction options
self.services.chat.progressLogUpdate(operationId, 0.3, "Preparing extraction options")
extractionOptions = parameters.extractionOptions
# If extractionOptions not provided, create defaults
if not extractionOptions:
# Default extraction options for pure content extraction (no AI processing)
extractionOptions = ExtractionOptions(
prompt="Extract all content from the document",
mergeStrategy=MergeStrategy(
mergeType="concatenate",
groupBy="typeGroup",
orderBy="id"
),
processDocumentsIndividually=True
)
# Call extraction service
self.services.chat.progressLogUpdate(operationId, 0.5, f"Extracting content from {len(chatDocuments)} documents")
extractedResults = self.services.extraction.extractContent(chatDocuments, extractionOptions)
# Build ActionDocuments from ContentExtracted results
self.services.chat.progressLogUpdate(operationId, 0.8, "Building result documents")
actionDocuments = []
for extracted in extractedResults:
# Store ContentExtracted object in ActionDocument.documentData
actionDoc = ActionDocument(
documentName=f"extracted_{extracted.id}.json",
documentData=extracted, # ContentExtracted object
mimeType="application/json"
)
actionDocuments.append(actionDoc)
self.services.chat.progressLogFinish(operationId, True)
return ActionResult.isSuccess(documents=actionDocuments)
except Exception as e:
logger.error(f"Error in content extraction: {str(e)}")
# Complete progress tracking with failure
try:
self.services.chat.progressLogFinish(operationId, False)
except:
pass # Don't fail on progress logging errors
return ActionResult.isFailure(error=str(e))
@action
async def webResearch(self, parameters: Dict[str, Any]) -> ActionResult:
"""

27
modules/workflows/methods/methodOutlook.py
View file

@ -1134,9 +1134,19 @@ class MethodOutlook(MethodBase):
return ActionResult.isFailure(error="Connection lacks necessary permissions for Outlook operations")
# Prepare documents for AI processing
from modules.datamodels.datamodelDocref import DocumentReferenceList
chatDocuments = []
if documentList:
chatDocuments = self.services.chat.getChatDocumentsFromDocumentList(documentList)
# Convert to DocumentReferenceList if needed
if isinstance(documentList, DocumentReferenceList):
docRefList = documentList
elif isinstance(documentList, list):
docRefList = DocumentReferenceList.from_string_list(documentList)
elif isinstance(documentList, str):
docRefList = DocumentReferenceList.from_string_list([documentList])
else:
docRefList = DocumentReferenceList(references=[])
chatDocuments = self.services.chat.getChatDocumentsFromDocumentList(docRefList)
# Create AI prompt for email composition
# Build document reference list for AI with expanded list contents when possible
@ -1146,7 +1156,8 @@ class MethodOutlook(MethodBase):
lines = ["Available_Document_References:"]
for ref in doc_references:
# Each item is a label: resolve to its document list and render contained items
list_docs = self.services.chat.getChatDocumentsFromDocumentList([ref]) or []
from modules.datamodels.datamodelDocref import DocumentReferenceList
list_docs = self.services.chat.getChatDocumentsFromDocumentList(DocumentReferenceList.from_string_list([ref])) or []
if list_docs:
for d in list_docs:
doc_ref_label = self.services.chat.getDocumentReferenceFromChatDocument(d)
@ -1215,7 +1226,8 @@ Return JSON:
if documentList:
try:
available_refs = [documentList] if isinstance(documentList, str) else documentList
available_docs = self.services.chat.getChatDocumentsFromDocumentList(available_refs) or []
from modules.datamodels.datamodelDocref import DocumentReferenceList
available_docs = self.services.chat.getChatDocumentsFromDocumentList(DocumentReferenceList.from_string_list(available_refs)) or []
except Exception:
available_docs = []
@ -1228,7 +1240,8 @@ Return JSON:
if ai_attachments:
try:
ai_refs = [ai_attachments] if isinstance(ai_attachments, str) else ai_attachments
ai_docs = self.services.chat.getChatDocumentsFromDocumentList(ai_refs) or []
from modules.datamodels.datamodelDocref import DocumentReferenceList
ai_docs = self.services.chat.getChatDocumentsFromDocumentList(DocumentReferenceList.from_string_list(ai_refs)) or []
except Exception:
ai_docs = []
@ -1296,7 +1309,8 @@ Return JSON:
message["attachments"] = []
for attachment_ref in documentList:
# Get attachment document from service center
attachment_docs = self.services.chat.getChatDocumentsFromDocumentList([attachment_ref])
from modules.datamodels.datamodelDocref import DocumentReferenceList
attachment_docs = self.services.chat.getChatDocumentsFromDocumentList(DocumentReferenceList.from_string_list([attachment_ref]))
if attachment_docs:
for doc in attachment_docs:
file_id = getattr(doc, 'fileId', None)
@ -1418,7 +1432,8 @@ Return JSON:
for docRef in documentList:
try:
# Get documents from document reference
chatDocuments = self.services.chat.getChatDocumentsFromDocumentList([docRef])
from modules.datamodels.datamodelDocref import DocumentReferenceList
chatDocuments = self.services.chat.getChatDocumentsFromDocumentList(DocumentReferenceList.from_string_list([docRef]))
if not chatDocuments:
logger.warning(f"No documents found for reference: {docRef}")
continue

33
modules/workflows/methods/methodSharepoint.py
View file

@ -1139,7 +1139,8 @@ class MethodSharepoint(MethodBase):
logger.debug(f"Both pathObject and pathQuery provided - using pathObject (pathQuery '{pathQuery}' will be ignored)")
try:
# Resolve the reference label to get the actual document list
pathObjectDocuments = self.services.chat.getChatDocumentsFromDocumentList([pathObject])
from modules.datamodels.datamodelDocref import DocumentReferenceList
pathObjectDocuments = self.services.chat.getChatDocumentsFromDocumentList(DocumentReferenceList.from_string_list([pathObject]))
if not pathObjectDocuments or len(pathObjectDocuments) == 0:
return ActionResult.isFailure(error=f"No document list found for reference: {pathObject}")
@ -1313,7 +1314,17 @@ class MethodSharepoint(MethodBase):
# Get documents from reference - ensure documentList is a list, not a string
# documentList is already normalized above
chatDocuments = self.services.chat.getChatDocumentsFromDocumentList(documentList)
from modules.datamodels.datamodelDocref import DocumentReferenceList
# Convert to DocumentReferenceList if needed
if isinstance(documentList, DocumentReferenceList):
docRefList = documentList
elif isinstance(documentList, list):
docRefList = DocumentReferenceList.from_string_list(documentList)
elif isinstance(documentList, str):
docRefList = DocumentReferenceList.from_string_list([documentList])
else:
docRefList = DocumentReferenceList(references=[])
chatDocuments = self.services.chat.getChatDocumentsFromDocumentList(docRefList)
if not chatDocuments:
return ActionResult.isFailure(error="No documents found for the provided reference")
@ -1553,7 +1564,8 @@ class MethodSharepoint(MethodBase):
if pathObject:
try:
# Resolve the reference label to get the actual document list
documentList = self.services.chat.getChatDocumentsFromDocumentList([pathObject])
from modules.datamodels.datamodelDocref import DocumentReferenceList
documentList = self.services.chat.getChatDocumentsFromDocumentList(DocumentReferenceList.from_string_list([pathObject]))
if not documentList or len(documentList) == 0:
return ActionResult.isFailure(error=f"No document list found for reference: {pathObject}")
@ -1654,7 +1666,17 @@ class MethodSharepoint(MethodBase):
# Get documents from reference - ensure documentList is a list, not a string
if isinstance(documentList, str):
documentList = [documentList] # Convert string to list
chatDocuments = self.services.chat.getChatDocumentsFromDocumentList(documentList)
from modules.datamodels.datamodelDocref import DocumentReferenceList
# Convert to DocumentReferenceList if needed
if isinstance(documentList, DocumentReferenceList):
docRefList = documentList
elif isinstance(documentList, list):
docRefList = DocumentReferenceList.from_string_list(documentList)
elif isinstance(documentList, str):
docRefList = DocumentReferenceList.from_string_list([documentList])
else:
docRefList = DocumentReferenceList(references=[])
chatDocuments = self.services.chat.getChatDocumentsFromDocumentList(docRefList)
if not chatDocuments:
return ActionResult.isFailure(error="No documents found for the provided reference")
@ -1959,7 +1981,8 @@ class MethodSharepoint(MethodBase):
logger.debug(f"Both pathObject and pathQuery provided - using pathObject (pathQuery '{pathQuery}' will be ignored)")
try:
# Resolve the reference label to get the actual document list
documentList = self.services.chat.getChatDocumentsFromDocumentList([pathObject])
from modules.datamodels.datamodelDocref import DocumentReferenceList
documentList = self.services.chat.getChatDocumentsFromDocumentList(DocumentReferenceList.from_string_list([pathObject]))
if not documentList or len(documentList) == 0:
return ActionResult.isFailure(error=f"No document list found for reference: {pathObject}")

23
modules/workflows/processing/core/actionExecutor.py
View file

@ -52,16 +52,18 @@ class ActionExecutor:
logger.error(f"Error executing compound action {compoundActionName}: {str(e)}")
raise
async def executeSingleAction(self, action: ActionItem, workflow: ChatWorkflow, taskStep: TaskStep,
taskIndex: int = None, actionIndex: int = None, totalActions: int = None) -> ActionResult:
async def executeSingleAction(self, action: ActionItem, workflow: ChatWorkflow, taskStep: TaskStep) -> ActionResult:
"""Execute a single action and return ActionResult with enhanced document processing"""
try:
# Check workflow status before executing action
checkWorkflowStopped(self.services)
# Use passed indices or fallback to '?'
taskNum = taskIndex if taskIndex is not None else '?'
actionNum = actionIndex if actionIndex is not None else '?'
# Get indices from workflow state
taskIndex = workflow.getTaskIndex()
actionIndex = workflow.getActionIndex()
taskNum = taskIndex
actionNum = actionIndex
logger.info(f"=== TASK {taskNum} ACTION {actionNum}: {action.execMethod}.{action.execAction} ===")
@ -144,7 +146,7 @@ class ActionExecutor:
# Create database log entry for action failure (write-through + bind)
self.services.chat.storeLog(workflow, {
"message": f"❌ **Task {taskNum}**❌ **Action {actionNum}/{totalActions}** failed: {result.error}",
"message": f"❌ **Task {taskNum}**❌ **Action {actionNum}** failed: {result.error}",
"type": "error",
"progress": 1.0
})
@ -152,8 +154,11 @@ class ActionExecutor:
# Log action summary
logger.info(f"=== TASK {taskNum} ACTION {actionNum} COMPLETED ===")
# Increment action index in workflow
workflow.incrementAction()
# Create action completion message with documents (generic)
await self._createActionCompletionMessage(action, result, workflow, taskStep, taskIndex, actionIndex, totalActions)
await self._createActionCompletionMessage(action, result, workflow, taskStep, taskIndex, actionIndex)
return ActionResult(
success=result.success,
@ -186,7 +191,7 @@ class ActionExecutor:
return "\n\n---\n\n".join(resultParts) if resultParts else ""
async def _createActionCompletionMessage(self, action: ActionItem, result: ActionResult, workflow: ChatWorkflow,
taskStep: TaskStep, taskIndex: int, actionIndex: int, totalActions: int):
taskStep: TaskStep, taskIndex: int, actionIndex: int):
"""Create action completion message with documents (generic)"""
try:
# Convert ActionDocument objects to ChatDocument objects for message creation
@ -207,7 +212,7 @@ class ActionExecutor:
taskStep=taskStep,
taskIndex=taskIndex,
actionIndex=actionIndex,
totalActions=totalActions
totalActions=None # Not needed - removed from signature
)
except Exception as e:
logger.error(f"Error creating action completion message: {str(e)}")

29
modules/workflows/processing/core/messageCreator.py
View file

@ -59,14 +59,18 @@ class MessageCreator:
except Exception as e:
logger.error(f"Error creating task plan message: {str(e)}")
async def createTaskStartMessage(self, taskStep: TaskStep, workflow: ChatWorkflow, taskIndex: int, totalTasks: int):
async def createTaskStartMessage(self, taskStep: TaskStep, workflow: ChatWorkflow, taskIndex: int, totalTasks: int = None):
"""Create a task start message for the user"""
try:
# Check workflow status before creating message
checkWorkflowStopped(self.services)
# Create a task start message for the user
taskProgress = f"{taskIndex}/{totalTasks}" if totalTasks is not None else str(taskIndex)
# Use workflow state if taskIndex not provided
if taskIndex is None:
taskIndex = workflow.getTaskIndex()
# Create a task start message for the user (totalTasks not needed - kept for backward compatibility)
taskProgress = str(taskIndex)
taskStartMessage = {
"workflowId": workflow.id,
"role": "assistant",
@ -117,12 +121,11 @@ class MessageCreator:
# Create a more meaningful message that includes task context
taskObjective = taskStep.objective if taskStep else 'Unknown task'
# Extract round, task, and action numbers from resultLabel first, then fallback to workflow context
currentRound = self._extractRoundNumberFromLabel(resultLabel) if resultLabel else workflowContext.get('currentRound', 0)
currentTask = self._extractTaskNumberFromLabel(resultLabel) if resultLabel else (taskIndex if taskIndex is not None else workflowContext.get('currentTask', 0))
totalTasks = workflowStats.get('totalTasks', 0)
currentAction = self._extractActionNumberFromLabel(resultLabel) if resultLabel else (actionIndex if actionIndex is not None else workflowContext.get('currentAction', 0))
totalActions = totalActions if totalActions is not None else workflowStats.get('totalActions', 0)
# Extract round, task, and action numbers from resultLabel first, then fallback to workflow state
currentRound = self._extractRoundNumberFromLabel(resultLabel) if resultLabel else workflow.getRoundIndex()
currentTask = self._extractTaskNumberFromLabel(resultLabel) if resultLabel else (taskIndex if taskIndex is not None else workflow.getTaskIndex())
currentAction = self._extractActionNumberFromLabel(resultLabel) if resultLabel else (actionIndex if actionIndex is not None else workflow.getActionIndex())
# totalTasks and totalActions not needed - removed from architecture
# Debug logging for round number extraction
logger.info(f"Action message round number extraction: resultLabel='{resultLabel}', extractedRound={currentRound}, workflowRound={workflowContext.get('currentRound', 0)}")
@ -183,13 +186,17 @@ class MessageCreator:
except Exception as e:
logger.error(f"Error creating action message: {str(e)}")
async def createTaskCompletionMessage(self, taskStep: TaskStep, workflow: ChatWorkflow, taskIndex: int, totalTasks: int, reviewResult: ReviewResult = None):
async def createTaskCompletionMessage(self, taskStep: TaskStep, workflow: ChatWorkflow, taskIndex: int, totalTasks: int = None, reviewResult: ReviewResult = None):
"""Create a task completion message for the user"""
try:
# Check workflow status before creating message
checkWorkflowStopped(self.services)
# Create a task completion message for the user
# Use workflow state if taskIndex not provided
if taskIndex is None:
taskIndex = workflow.getTaskIndex()
# Create a task completion message for the user (totalTasks not needed - kept for backward compatibility)
taskProgress = str(taskIndex)
# Enhanced completion message with criteria details

811
modules/workflows/processing/modes/modeActionplan.py
View file

@ -1,811 +0,0 @@
# modeActionplan.py
# Actionplan mode implementation for workflows
import json
import logging
import uuid
from datetime import datetime, timezone
from typing import List, Dict, Any
from modules.datamodels.datamodelChat import (
TaskStep, TaskContext, TaskResult, ActionItem, TaskStatus,
ActionResult, ReviewResult, ReviewContext
)
from modules.datamodels.datamodelChat import ChatWorkflow
from modules.datamodels.datamodelAi import AiCallOptions, OperationTypeEnum, ProcessingModeEnum, PriorityEnum
from modules.workflows.processing.modes.modeBase import BaseMode
from modules.workflows.processing.shared.stateTools import checkWorkflowStopped
from modules.shared.timeUtils import parseTimestamp
from modules.workflows.processing.shared.executionState import TaskExecutionState
from modules.workflows.processing.shared.promptGenerationActionsActionplan import (
generateActionDefinitionPrompt,
generateResultReviewPrompt
)
from modules.workflows.processing.adaptive import IntentAnalyzer, ContentValidator, LearningEngine, ProgressTracker
from modules.workflows.processing.adaptive.adaptiveLearningEngine import AdaptiveLearningEngine
logger = logging.getLogger(__name__)
class ActionplanMode(BaseMode):
"""Actionplan mode implementation - batch planning and sequential execution"""
def __init__(self, services):
super().__init__(services)
# Initialize adaptive components for enhanced validation and learning
self.intentAnalyzer = IntentAnalyzer(services)
self.learningEngine = LearningEngine()
self.adaptiveLearningEngine = AdaptiveLearningEngine()
self.contentValidator = ContentValidator(services, self.adaptiveLearningEngine)
self.progressTracker = ProgressTracker()
self.workflowIntent = None
self.taskIntent = None
async def generateActionItems(self, taskStep: TaskStep, workflow: ChatWorkflow,
previousResults: List = None, enhancedContext: TaskContext = None) -> List[ActionItem]:
"""Generate actions for a given task step using batch planning approach"""
try:
# Check workflow status before generating actions
checkWorkflowStopped(self.services)
retryInfo = f" (Retry #{enhancedContext.retryCount})" if enhancedContext and enhancedContext.retryCount > 0 else ""
logger.info(f"Generating actions for task: {taskStep.objective}{retryInfo}")
# Log criteria progress if this is a retry
if enhancedContext and hasattr(enhancedContext, 'criteriaProgress') and enhancedContext.criteriaProgress is not None:
progress = enhancedContext.criteriaProgress
logger.info(f"Retry attempt {enhancedContext.retryCount} - Criteria progress:")
if progress.get('met_criteria'):
logger.info(f" Met criteria: {', '.join(progress['met_criteria'])}")
if progress.get('unmet_criteria'):
logger.warning(f" Unmet criteria: {', '.join(progress['unmet_criteria'])}")
# Show improvement trends
if progress.get('attempt_history'):
recentAttempts = progress['attempt_history'][-2:] # Last 2 attempts
if len(recentAttempts) >= 2:
prevScore = recentAttempts[0].get('quality_score', 0)
currScore = recentAttempts[1].get('quality_score', 0)
if currScore > prevScore:
logger.info(f" Quality improving: {prevScore} -> {currScore}")
elif currScore < prevScore:
logger.warning(f" Quality declining: {prevScore} -> {currScore}")
else:
logger.info(f" Quality stable: {currScore}")
# Enhanced retry context logging
if enhancedContext and enhancedContext.retryCount > 0:
logger.info("=== RETRY CONTEXT FOR ACTION GENERATION ===")
logger.info(f"Retry Count: {enhancedContext.retryCount}")
logger.debug(f"Previous Improvements: {enhancedContext.improvements}")
logger.debug(f"Previous Review Result: {enhancedContext.previousReviewResult}")
logger.debug(f"Failure Patterns: {enhancedContext.failurePatterns}")
logger.debug(f"Failed Actions: {enhancedContext.failedActions}")
logger.debug(f"Successful Actions: {enhancedContext.successfulActions}")
logger.info("=== END RETRY CONTEXT ===")
# Log that we're starting action generation
logger.info("=== STARTING ACTION GENERATION ===")
# Create proper context object for action definition
if enhancedContext and isinstance(enhancedContext, TaskContext):
# Use existing TaskContext if provided
actionContext = TaskContext(
taskStep=enhancedContext.taskStep,
workflow=enhancedContext.workflow,
workflowId=enhancedContext.workflowId,
availableDocuments=enhancedContext.availableDocuments,
availableConnections=enhancedContext.availableConnections,
previousResults=enhancedContext.previousResults or previousResults or [],
previousHandover=enhancedContext.previousHandover,
improvements=enhancedContext.improvements or [],
retryCount=enhancedContext.retryCount or 0,
previousActionResults=enhancedContext.previousActionResults or [],
previousReviewResult=enhancedContext.previousReviewResult,
isRegeneration=enhancedContext.isRegeneration or False,
failurePatterns=enhancedContext.failurePatterns or [],
failedActions=enhancedContext.failedActions or [],
successfulActions=enhancedContext.successfulActions or [],
criteriaProgress=enhancedContext.criteriaProgress
)
else:
# Create new context from scratch
actionContext = TaskContext(
taskStep=taskStep,
workflow=workflow,
workflowId=workflow.id,
availableDocuments=None,
availableConnections=None,
previousResults=previousResults or [],
previousHandover=None,
improvements=[],
retryCount=0,
previousActionResults=[],
previousReviewResult=None,
isRegeneration=False,
failurePatterns=[],
failedActions=[],
successfulActions=[],
criteriaProgress=None
)
# Check workflow status before calling AI service
checkWorkflowStopped(self.services)
# Build prompt bundle (template + placeholders)
bundle = generateActionDefinitionPrompt(self.services, actionContext)
actionPromptTemplate = bundle.prompt
placeholders = bundle.placeholders
# Centralized AI call: Action planning (quality, detailed) with placeholders
options = AiCallOptions(
operationType=OperationTypeEnum.PLAN,
priority=PriorityEnum.QUALITY,
compressPrompt=False,
compressContext=False,
processingMode=ProcessingModeEnum.DETAILED,
maxCost=0.10,
maxProcessingTime=30
)
prompt = await self.services.ai.callAiPlanning(
prompt=actionPromptTemplate,
placeholders=placeholders,
debugType="actionplan"
)
# Check if AI response is valid
if not prompt:
raise ValueError("AI service returned no response")
# Log action response received
logger.info("=== ACTION PLAN AI RESPONSE RECEIVED ===")
logger.info(f"Response length: {len(prompt) if prompt else 0}")
# Parse action response
jsonStart = prompt.find('{')
jsonEnd = prompt.rfind('}') + 1
if jsonStart == -1 or jsonEnd == 0:
raise ValueError("No JSON found in response")
jsonStr = prompt[jsonStart:jsonEnd]
try:
actionData = json.loads(jsonStr)
except Exception as e:
logger.error(f"Error parsing action response JSON: {str(e)}")
actionData = {}
if 'actions' not in actionData:
raise ValueError("Action response missing 'actions' field")
actions = actionData['actions']
if not actions:
raise ValueError("Action response contains empty actions list")
if not isinstance(actions, list):
raise ValueError(f"Action response 'actions' field is not a list: {type(actions)}")
if not self.validator.validateAction(actions, actionContext):
logger.error("Generated actions failed validation")
raise Exception("AI-generated actions failed validation - AI is required for action generation")
# Convert to ActionItem objects
taskActions = []
for i, a in enumerate(actions):
if not isinstance(a, dict):
logger.warning(f"Skipping invalid action {i+1}: not a dictionary")
continue
# Handle compound action format (new) or separate method/action format (old)
action_name = a.get('action', 'unknown')
if '.' in action_name:
# New compound action format: "method.action"
method_name, action_name = action_name.split('.', 1)
else:
# Old separate format: method + action fields
method_name = a.get('method', 'unknown')
taskAction = self._createActionItem({
"execMethod": method_name,
"execAction": action_name,
"execParameters": a.get('parameters', {}),
"execResultLabel": a.get('resultLabel', ''),
"expectedDocumentFormats": a.get('expectedDocumentFormats', None),
"status": TaskStatus.PENDING,
# Extract user-friendly message if available
"userMessage": a.get('userMessage', None)
})
if taskAction:
taskActions.append(taskAction)
else:
logger.warning(f"Skipping invalid action {i+1}: failed to create ActionItem")
validActions = [ta for ta in taskActions if ta]
if not validActions:
raise ValueError("No valid actions could be created from AI response")
return validActions
except Exception as e:
logger.error(f"Error in generateActionItems: {str(e)}")
return []
async def executeTask(self, taskStep: TaskStep, workflow: ChatWorkflow, context: TaskContext,
taskIndex: int = None, totalTasks: int = None) -> TaskResult:
"""Execute all actions for a task step using Actionplan mode"""
logger.info(f"=== STARTING TASK {taskIndex or '?'}: {taskStep.objective} ===")
# Use workflow-level intent from planning phase (stored in workflow object)
# This avoids redundant intent analysis - intent was already analyzed during task planning
if hasattr(workflow, '_workflowIntent') and workflow._workflowIntent:
self.workflowIntent = workflow._workflowIntent
logger.info(f"Using workflow intent from planning phase")
else:
# Fallback: analyze if not available (shouldn't happen in normal flow)
originalPrompt = self.services.currentUserPrompt if self.services and hasattr(self.services, 'currentUserPrompt') else taskStep.objective
self.workflowIntent = await self.intentAnalyzer.analyzeUserIntent(originalPrompt, context)
logger.warning(f"Workflow intent not found in workflow object, analyzed fresh")
# Task-level intent is NOT needed - use task.objective + task format fields (dataType, expectedFormats, qualityRequirements)
# These format fields are populated from workflow intent during task planning
self.taskIntent = None # Removed redundant task-level intent analysis
logger.info(f"Workflow intent: {self.workflowIntent}")
if taskStep.dataType or taskStep.expectedFormats or taskStep.qualityRequirements:
logger.info(f"Task format info: dataType={taskStep.dataType}, expectedFormats={taskStep.expectedFormats}")
# Reset progress tracking for new task
self.progressTracker.reset()
# Update workflow object before executing task
if taskIndex is not None:
self._updateWorkflowBeforeExecutingTask(taskIndex)
# Update workflow context for this task
if taskIndex is not None:
self.services.chat.setWorkflowContext(taskNumber=taskIndex)
# Create task start message
await self.messageCreator.createTaskStartMessage(taskStep, workflow, taskIndex, totalTasks)
state = TaskExecutionState(taskStep)
retryContext = context
maxRetries = state.max_retries
for attempt in range(maxRetries):
logger.info(f"Task execution attempt {attempt+1}/{maxRetries}")
# Check workflow status before starting task execution
checkWorkflowStopped(self.services)
# Update retry context with current attempt information
if retryContext:
retryContext.retryCount = attempt + 1
actions = await self.generateActionItems(taskStep, workflow,
previousResults=retryContext.previousResults,
enhancedContext=retryContext)
# Log total actions count for this task
totalActions = len(actions) if actions else 0
logger.info(f"Task {taskIndex or '?'} has {totalActions} actions")
# Update workflow object after action planning
self._updateWorkflowAfterActionPlanning(totalActions)
self._setWorkflowTotals(totalActions=totalActions)
if not actions:
logger.error("No actions defined for task step, aborting task execution")
break
actionResults = []
for actionIdx, action in enumerate(actions):
# Check workflow status before each action execution
checkWorkflowStopped(self.services)
# Update workflow object before executing action
actionNumber = actionIdx + 1
self._updateWorkflowBeforeExecutingAction(actionNumber)
# Log action start
logger.info(f"Task {taskIndex} - Starting action {actionNumber}/{totalActions}")
# Create action start message
actionStartMessage = {
"workflowId": workflow.id,
"role": "assistant",
"message": f"⚡ **Action {actionNumber}** (Method {action.execMethod}.{action.execAction})",
"status": "step",
"sequenceNr": len(workflow.messages) + 1,
"publishedAt": self.services.utils.timestampGetUtc(),
"documentsLabel": f"action_{actionNumber}_start",
"documents": [],
"actionProgress": "running",
"roundNumber": workflow.currentRound,
"taskNumber": taskIndex,
"actionNumber": actionNumber
}
# Add user-friendly message if available
if action.userMessage:
actionStartMessage["message"] += f"\n\n💬 {action.userMessage}"
self.services.chat.storeMessageWithDocuments(workflow, actionStartMessage, [])
logger.info(f"Action start message created for action {actionNumber}")
# Execute single action
result = await self.actionExecutor.executeSingleAction(action, workflow, taskStep,
taskIndex, actionNumber, totalActions)
actionResults.append(result)
# Enhanced validation: Content validation after each action (like Dynamic mode)
if getattr(self, 'workflowIntent', None) and result.documents:
# Pass ALL documents to validator - validator decides what to validate (generic approach)
# Pass taskStep so validator can use task.objective and format fields
# Pass action name so validator knows which action created the documents
actionName = f"{action.execMethod}.{action.execAction}"
validationResult = await self.contentValidator.validateContent(result.documents, self.workflowIntent, taskStep, actionName)
qualityScore = validationResult.get('qualityScore', 0.0)
if qualityScore is None:
qualityScore = 0.0
logger.info(f"Content validation for action {actionNumber}: {validationResult['overallSuccess']} (quality: {qualityScore:.2f})")
# Record validation result for adaptive learning
actionContext = {
'actionName': f"{action.execMethod}.{action.execAction}",
'workflowId': context.workflowId
}
self.adaptiveLearningEngine.recordValidationResult(
validationResult,
actionContext,
context.workflowId,
actionNumber
)
# Learn from feedback
feedback = self._collectFeedback(result, validationResult, self.workflowIntent)
self.learningEngine.learnFromFeedback(feedback, context, self.workflowIntent)
# Update progress
self.progressTracker.updateOperation(result, validationResult, self.workflowIntent)
if result.success:
state.addSuccessfulAction(result)
else:
state.addFailedAction(result)
# Check workflow status before review
checkWorkflowStopped(self.services)
reviewResult = await self._reviewTaskCompletion(taskStep, actions, actionResults, workflow)
success = reviewResult.status == 'success'
feedback = reviewResult.reason
error = None if success else reviewResult.reason
if success:
logger.info(f"=== TASK {taskIndex or '?'} COMPLETED SUCCESSFULLY: {taskStep.objective} ===")
# Create task completion message
await self.messageCreator.createTaskCompletionMessage(taskStep, workflow, taskIndex, totalTasks, reviewResult)
return TaskResult(
taskId=taskStep.id,
status=TaskStatus.COMPLETED,
success=True,
feedback=feedback,
error=None
)
elif reviewResult.status == 'retry' and state.canRetry():
logger.warning(f"Task step '{taskStep.objective}' requires retry: {reviewResult.improvements}")
# Enhanced logging of criteria status
if reviewResult.metCriteria:
logger.info(f"Met criteria: {', '.join(reviewResult.metCriteria)}")
if reviewResult.unmetCriteria:
logger.warning(f"Unmet criteria: {', '.join(reviewResult.unmetCriteria)}")
state.incrementRetryCount()
# Update retry context with retry information and criteria tracking
if retryContext:
retryContext.retryCount = state.retry_count
retryContext.improvements = reviewResult.improvements
retryContext.previousActionResults = actionResults
retryContext.previousReviewResult = reviewResult
retryContext.isRegeneration = True
retryContext.failurePatterns = state.getFailurePatterns()
retryContext.failedActions = state.failed_actions
retryContext.successfulActions = state.successful_actions
# Track criteria progress across retries
if not hasattr(retryContext, 'criteriaProgress'):
retryContext.criteriaProgress = {
'met_criteria': set(),
'unmet_criteria': set(),
'attempt_history': []
}
# Update criteria progress
if reviewResult.metCriteria:
retryContext.criteriaProgress['met_criteria'].update(reviewResult.metCriteria)
if reviewResult.unmetCriteria:
retryContext.criteriaProgress['unmet_criteria'].update(reviewResult.unmetCriteria)
# Record this attempt's criteria status
attemptRecord = {
'attempt': state.retry_count,
'met_criteria': reviewResult.metCriteria or [],
'unmet_criteria': reviewResult.unmetCriteria or [],
'quality_score': reviewResult.qualityScore,
'improvements': reviewResult.improvements or []
}
retryContext.criteriaProgress['attempt_history'].append(attemptRecord)
# Create retry message
await self.messageCreator.createRetryMessage(taskStep, workflow, taskIndex, reviewResult)
continue
else:
logger.error(f"=== TASK {taskIndex or '?'} FAILED: {taskStep.objective} after {attempt+1} attempts ===")
# Create error message
await self.messageCreator.createErrorMessage(taskStep, workflow, taskIndex, reviewResult.reason)
return TaskResult(
taskId=taskStep.id,
status=TaskStatus.FAILED,
success=False,
feedback=feedback,
error=reviewResult.reason if reviewResult and hasattr(reviewResult, 'reason') else "Task failed after retry attempts"
)
logger.error(f"=== TASK {taskIndex or '?'} FAILED AFTER ALL RETRIES: {taskStep.objective} ===")
# Create final error message
await self.messageCreator.createErrorMessage(taskStep, workflow, taskIndex, "Task failed after all retries")
return TaskResult(
taskId=taskStep.id,
status=TaskStatus.FAILED,
success=False,
feedback="Task failed after all retries.",
error="Task failed after all retries."
)
async def _reviewTaskCompletion(self, taskStep: TaskStep, taskActions: List[ActionItem],
actionResults: List[ActionResult], workflow: ChatWorkflow) -> ReviewResult:
"""Review task completion and determine success/failure/retry"""
try:
# Check workflow status before reviewing task completion
checkWorkflowStopped(self.services)
logger.info(f"=== STARTING TASK COMPLETION REVIEW ===")
logger.info(f"Task: {taskStep.objective}")
logger.info(f"Actions executed: {len(taskActions) if taskActions else 0}")
logger.info(f"Action results: {len(actionResults) if actionResults else 0}")
# Create proper context object for result review
reviewContext = ReviewContext(
taskStep=taskStep,
taskActions=taskActions,
actionResults=actionResults,
stepResult={
'successful_actions': sum(1 for result in actionResults if result.success),
'total_actions': len(actionResults),
'results': [self._extractResultText(result) for result in actionResults if result.success],
'errors': [result.error for result in actionResults if not result.success],
'documents': [
{
'action_index': i,
'documents_count': len(result.documents) if result.documents else 0,
'documents': result.documents if result.documents else []
}
for i, result in enumerate(actionResults)
]
},
workflowId=workflow.id,
previousResults=[]
)
# Check workflow status before calling AI service
checkWorkflowStopped(self.services)
# Build prompt bundle for result review
bundle = generateResultReviewPrompt(reviewContext)
promptTemplate = bundle.prompt
placeholders = bundle.placeholders
# Log result review prompt sent to AI
logger.info("=== RESULT REVIEW PROMPT SENT TO AI ===")
logger.info(f"Task: {taskStep.objective}")
logger.info(f"Action Results Count: {len(reviewContext.actionResults) if reviewContext.actionResults else 0}")
logger.info(f"Task Actions Count: {len(reviewContext.taskActions) if reviewContext.taskActions else 0}")
# Centralized AI call: Result validation (balanced analysis) with placeholders
options = AiCallOptions(
operationType=OperationTypeEnum.DATA_ANALYSE,
priority=PriorityEnum.BALANCED,
compressPrompt=True,
compressContext=False,
processingMode=ProcessingModeEnum.ADVANCED,
maxCost=0.05,
maxProcessingTime=30
)
response = await self.services.ai.callAiPlanning(
prompt=promptTemplate,
placeholders=placeholders,
debugType="resultreview"
)
# Log result review response received
logger.info("=== RESULT REVIEW AI RESPONSE RECEIVED ===")
logger.info(f"Response length: {len(response) if response else 0}")
# Parse review response
jsonStart = response.find('{')
jsonEnd = response.rfind('}') + 1
if jsonStart == -1 or jsonEnd == 0:
raise ValueError("No JSON found in review response")
jsonStr = response[jsonStart:jsonEnd]
try:
review = json.loads(jsonStr)
except Exception as e:
logger.error(f"Error parsing review response JSON: {str(e)}")
review = {}
if 'status' not in review:
raise ValueError("Review response missing 'status' field")
review.setdefault('status', 'unknown')
review.setdefault('reason', 'No reason provided')
review.setdefault('quality_score', 5.0)
# Ensure improvements is a list
improvements = review.get('improvements', [])
if isinstance(improvements, str):
# Split string into list if it's a single improvement
improvements = [improvements.strip()] if improvements.strip() else []
elif not isinstance(improvements, list):
improvements = []
# Ensure all list fields are properly typed
metCriteria = review.get('met_criteria', [])
if not isinstance(metCriteria, list):
metCriteria = []
unmetCriteria = review.get('unmet_criteria', [])
if not isinstance(unmetCriteria, list):
unmetCriteria = []
reviewResult = ReviewResult(
status=review.get('status', 'unknown'),
reason=review.get('reason', 'No reason provided'),
improvements=improvements,
qualityScore=float(review.get('quality_score', review.get('qualityScore', 5.0))),
missingOutputs=[],
metCriteria=metCriteria,
unmetCriteria=unmetCriteria,
confidence=review.get('confidence', 0.5),
# Extract user-friendly message if available
userMessage=review.get('userMessage', None)
)
# Enhanced validation logging
logger.info(f"VALIDATION RESULT - Task: '{taskStep.objective}' - Status: {reviewResult.status.upper()}, Quality: {reviewResult.qualityScore}/10")
if reviewResult.status == 'success':
logger.info(f"VALIDATION SUCCESS - Task completed successfully")
if reviewResult.metCriteria:
logger.info(f"Met criteria: {', '.join(reviewResult.metCriteria)}")
elif reviewResult.status == 'retry':
logger.warning(f"VALIDATION RETRY - Task requires retry: {reviewResult.improvements}")
if reviewResult.unmetCriteria:
logger.warning(f"Unmet criteria: {', '.join(reviewResult.unmetCriteria)}")
else:
logger.error(f"VALIDATION FAILED - Task failed: {reviewResult.reason}")
logger.info(f"=== TASK COMPLETION REVIEW FINISHED ===")
logger.info(f"Final Status: {reviewResult.status}")
logger.info(f"Quality Score: {reviewResult.qualityScore}/10")
logger.info(f"Improvements: {reviewResult.improvements}")
logger.info("=== END REVIEW ===")
return reviewResult
except Exception as e:
logger.error(f"Error in reviewTaskCompletion: {str(e)}")
return ReviewResult(
status='failed',
reason=str(e),
qualityScore=0.0
)
def _createActionItem(self, actionData: Dict[str, Any]) -> ActionItem:
"""Creates a new task action"""
try:
# Ensure ID is present
if "id" not in actionData or not actionData["id"]:
actionData["id"] = f"action_{uuid.uuid4()}"
# Ensure required fields
if "status" not in actionData:
actionData["status"] = TaskStatus.PENDING
if "execMethod" not in actionData:
logger.error("execMethod is required for task action")
return None
if "execAction" not in actionData:
logger.error("execAction is required for task action")
return None
if "execParameters" not in actionData:
actionData["execParameters"] = {}
# Use generic field separation based on ActionItem model
simpleFields, objectFields = self.services.interfaceDbChat._separateObjectFields(ActionItem, actionData)
# Create action in database
createdAction = self.services.interfaceDbChat.db.recordCreate(ActionItem, simpleFields)
# Convert to ActionItem model
return ActionItem(
id=createdAction["id"],
execMethod=createdAction["execMethod"],
execAction=createdAction["execAction"],
execParameters=createdAction.get("execParameters", {}),
execResultLabel=createdAction.get("execResultLabel"),
expectedDocumentFormats=createdAction.get("expectedDocumentFormats"),
status=createdAction.get("status", TaskStatus.PENDING),
error=createdAction.get("error"),
retryCount=createdAction.get("retryCount", 0),
retryMax=createdAction.get("retryMax", 3),
processingTime=createdAction.get("processingTime"),
timestamp=parseTimestamp(createdAction.get("timestamp"), default=self.services.utils.timestampGetUtc()),
result=createdAction.get("result"),
resultDocuments=createdAction.get("resultDocuments", []),
userMessage=createdAction.get("userMessage")
)
except Exception as e:
logger.error(f"Error creating task action: {str(e)}")
return None
def _extractResultText(self, result: ActionResult) -> str:
"""Extract result text from ActionResult documents"""
if not result.success or not result.documents:
return ""
# Extract text directly from ActionDocument objects
resultParts = []
for doc in result.documents:
if hasattr(doc, 'documentData') and doc.documentData:
resultParts.append(str(doc.documentData))
# Join all document results with separators
return "\n\n---\n\n".join(resultParts) if resultParts else ""
def _collectFeedback(self, result: Any, validation: Dict[str, Any], intent: Dict[str, Any]) -> Dict[str, Any]:
"""Collects comprehensive feedback from action execution"""
try:
# Extract content summary
contentDelivered = ""
if result.documents:
firstDoc = result.documents[0]
if hasattr(firstDoc, 'documentData'):
data = firstDoc.documentData
if isinstance(data, dict) and 'content' in data:
content = str(data['content'])
contentDelivered = content[:100] + "..." if len(content) > 100 else content
else:
contentDelivered = str(data)[:100] + "..." if len(str(data)) > 100 else str(data)
return {
"actionAttempted": result.resultLabel or "unknown",
"parametersUsed": {}, # Would be extracted from action context
"contentDelivered": contentDelivered,
"intentMatchScore": validation.get('qualityScore', 0),
"qualityScore": validation.get('qualityScore', 0),
"issuesFound": validation.get('improvementSuggestions', []),
"learningOpportunities": validation.get('improvementSuggestions', []),
"userSatisfaction": None, # Would be collected from user feedback
"timestamp": datetime.now(timezone.utc).timestamp()
}
except Exception as e:
logger.error(f"Error collecting feedback: {str(e)}")
return {
"actionAttempted": "unknown",
"parametersUsed": {},
"contentDelivered": "",
"intentMatchScore": 0,
"qualityScore": 0,
"issuesFound": [],
"learningOpportunities": [],
"userSatisfaction": None,
"timestamp": datetime.now(timezone.utc).timestamp()
}
def _updateWorkflowBeforeExecutingTask(self, taskNumber: int):
"""Update workflow object before executing a task"""
try:
workflow = self.services.workflow
updateData = {
"currentTask": taskNumber,
"currentAction": 0,
"totalActions": 0
}
# Update workflow object
workflow.currentTask = taskNumber
workflow.currentAction = 0
workflow.totalActions = 0
# Update in database
self.services.interfaceDbChat.updateWorkflow(workflow.id, updateData)
logger.info(f"Updated workflow {workflow.id} before executing task {taskNumber}: {updateData}")
except Exception as e:
logger.error(f"Error updating workflow before executing task: {str(e)}")
def _updateWorkflowAfterActionPlanning(self, totalActions: int):
"""Update workflow object after action planning for current task"""
try:
workflow = self.services.workflow
updateData = {
"totalActions": totalActions
}
# Update workflow object
workflow.totalActions = totalActions
# Update in database
self.services.interfaceDbChat.updateWorkflow(workflow.id, updateData)
logger.info(f"Updated workflow {workflow.id} after action planning: {updateData}")
except Exception as e:
logger.error(f"Error updating workflow after action planning: {str(e)}")
def _updateWorkflowBeforeExecutingAction(self, actionNumber: int):
"""Update workflow object before executing an action"""
try:
workflow = self.services.workflow
updateData = {
"currentAction": actionNumber
}
# Update workflow object
workflow.currentAction = actionNumber
# Update in database
self.services.interfaceDbChat.updateWorkflow(workflow.id, updateData)
logger.info(f"Updated workflow {workflow.id} before executing action {actionNumber}: {updateData}")
except Exception as e:
logger.error(f"Error updating workflow before executing action: {str(e)}")
def _setWorkflowTotals(self, totalTasks: int = None, totalActions: int = None):
"""Set total counts for workflow progress tracking and update database"""
try:
workflow = self.services.workflow
updateData = {}
if totalTasks is not None:
workflow.totalTasks = totalTasks
updateData["totalTasks"] = totalTasks
if totalActions is not None:
workflow.totalActions = totalActions
updateData["totalActions"] = totalActions
# Update workflow object in database if we have changes
if updateData:
self.services.interfaceDbChat.updateWorkflow(workflow.id, updateData)
logger.info(f"Updated workflow {workflow.id} totals in database: {updateData}")
logger.debug(f"Updated workflow totals: Tasks {workflow.totalTasks if hasattr(workflow, 'totalTasks') else 'N/A'}, Actions {workflow.totalActions if hasattr(workflow, 'totalActions') else 'N/A'}")
except Exception as e:
logger.error(f"Error setting workflow totals: {str(e)}")

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

@ -166,8 +166,8 @@ class AutomationMode(BaseMode):
async def executeTask(self, taskStep: TaskStep, workflow: ChatWorkflow, context: TaskContext,
taskIndex: int = None, totalTasks: int = None) -> TaskResult:
"""
Execute task using Template mode - executes predefined actions directly.
Similar to ActionplanMode but without AI planning or review phases.
Execute task using Automation mode - executes predefined actions directly.
No AI planning or review phases - actions are executed sequentially as defined.
"""
logger.info(f"=== STARTING TASK {taskIndex or '?'}: {taskStep.objective} ===")

3
modules/workflows/processing/modes/modeBase.py
View file

@ -25,8 +25,7 @@ class BaseMode(ABC):
@abstractmethod
async def executeTask(self, taskStep: TaskStep, workflow: ChatWorkflow, context: TaskContext,
taskIndex: int = None, totalTasks: int = None) -> TaskResult:
async def executeTask(self, taskStep: TaskStep, workflow: ChatWorkflow, context: TaskContext) -> TaskResult:
"""Execute a task step - must be implemented by concrete modes"""
pass

193
modules/workflows/processing/modes/modeDynamic.py
View file

@ -47,10 +47,13 @@ class DynamicMode(BaseMode):
# Dynamic mode generates actions one at a time in the execution loop
return []
async def executeTask(self, taskStep: TaskStep, workflow: ChatWorkflow, context: TaskContext,
taskIndex: int = None, totalTasks: int = None) -> TaskResult:
async def executeTask(self, taskStep: TaskStep, workflow: ChatWorkflow, context: TaskContext) -> TaskResult:
"""Execute task using Dynamic mode - iterative plan-act-observe-refine loop"""
logger.info(f"=== STARTING TASK {taskIndex or '?'}: {taskStep.objective} ===")
# Get task index from workflow state
taskIndex = workflow.getTaskIndex()
logger.info(f"=== STARTING TASK {taskIndex}: {taskStep.objective} ===")
# Use workflow-level intent from planning phase (stored in workflow object)
# This avoids redundant intent analysis - intent was already analyzed during task planning
@ -74,11 +77,10 @@ class DynamicMode(BaseMode):
self.progressTracker.reset()
# Update workflow object before executing task
if taskIndex is not None:
self._updateWorkflowBeforeExecutingTask(taskIndex)
# Create task start message
await self.messageCreator.createTaskStartMessage(taskStep, workflow, taskIndex, totalTasks)
# Create task start message (totalTasks not needed - removed from signature)
await self.messageCreator.createTaskStartMessage(taskStep, workflow, taskIndex, None)
state = TaskExecutionState(taskStep)
# Dynamic mode uses max_steps instead of max_retries
@ -190,8 +192,8 @@ class DynamicMode(BaseMode):
improvements=[]
)
# Create task completion message
await self.messageCreator.createTaskCompletionMessage(taskStep, workflow, taskIndex, totalTasks, completionReviewResult)
# Create task completion message (totalTasks not needed - removed from signature)
await self.messageCreator.createTaskCompletionMessage(taskStep, workflow, taskIndex, None, completionReviewResult)
return TaskResult(
taskId=taskStep.id,
@ -222,19 +224,48 @@ class DynamicMode(BaseMode):
response = await self.services.ai.callAiPlanning(
prompt=promptTemplate,
placeholders=placeholders,
debugType="actionplan"
debugType="dynamic"
)
jsonStart = response.find('{') if response else -1
jsonEnd = response.rfind('}') + 1 if response else 0
if jsonStart == -1 or jsonEnd == 0:
raise ValueError("No JSON in selection response")
selection = json.loads(response[jsonStart:jsonEnd])
# Parse response using structured parsing with ActionDefinition model
from modules.shared.jsonUtils import parseJsonWithModel
from modules.datamodels.datamodelWorkflow import ActionDefinition
try:
# Parse response string as ActionDefinition
actionDef = parseJsonWithModel(response, ActionDefinition)
# Convert to dict for compatibility with existing code
selection = actionDef.model_dump()
except ValueError as e:
logger.error(f"Failed to parse ActionDefinition from response: {e}")
raise ValueError(f"Invalid action selection response: {e}")
if 'action' not in selection or not isinstance(selection['action'], str):
raise ValueError("Selection missing 'action' as string")
# Validate document references - prevent AI from inventing Message IDs
# Convert string references to typed DocumentReferenceList
if 'requiredInputDocuments' in selection:
self._validateDocumentReferences(selection['requiredInputDocuments'], context)
stringRefs = selection['requiredInputDocuments']
if isinstance(stringRefs, list):
# Validate string references first
self._validateDocumentReferences(stringRefs, context)
# Convert to typed DocumentReferenceList
from modules.datamodels.datamodelDocref import DocumentReferenceList
selection['documentList'] = DocumentReferenceList.from_string_list(stringRefs)
# Remove old field
del selection['requiredInputDocuments']
elif stringRefs:
# Single string reference
self._validateDocumentReferences([stringRefs], context)
from modules.datamodels.datamodelDocref import DocumentReferenceList
selection['documentList'] = DocumentReferenceList.from_string_list([stringRefs])
del selection['requiredInputDocuments']
# Convert connection reference if present
if 'requiredConnection' in selection:
selection['connectionReference'] = selection.get('requiredConnection')
del selection['requiredConnection']
# Enforce spec: Stage 1 must NOT include 'parameters'
if 'parameters' in selection:
@ -294,26 +325,27 @@ class DynamicMode(BaseMode):
# Always request parameters in Stage 2 (spec: Stage 1 must not provide them)
logger.info("Requesting parameters in Stage 2 based on Stage 1 outputs")
# Create a permissive Stage 2 context to avoid TaskContext attribute restrictions
from types import SimpleNamespace
stage2Context = SimpleNamespace()
# Copy essential fields from original context for fallbacks
stage2Context.taskStep = getattr(context, 'taskStep', None)
stage2Context.workflowId = getattr(context, 'workflowId', None)
# Set Stage 1 data directly on the permissive context (snake_case for promptGenerationActionsDynamic compatibility)
# Update context from Stage 1 selection (replaces SimpleNamespace workaround)
# Convert dict selection to ActionDefinition if needed
from modules.datamodels.datamodelWorkflow import ActionDefinition
if isinstance(selection, dict):
stage2Context.action_objective = selection.get('actionObjective', '')
stage2Context.parameters_context = selection.get('parametersContext', '')
stage2Context.learnings = selection.get('learnings', [])
# Create ActionDefinition from dict for updateFromSelection
actionDef = ActionDefinition(
action=selection.get('action', ''),
actionObjective=selection.get('actionObjective', ''),
parametersContext=selection.get('parametersContext', ''),
learnings=selection.get('learnings', [])
)
context.updateFromSelection(actionDef)
elif isinstance(selection, ActionDefinition):
context.updateFromSelection(selection)
else:
stage2Context.action_objective = ''
stage2Context.parameters_context = ''
stage2Context.learnings = []
# Fallback: create empty ActionDefinition
context.updateFromSelection(ActionDefinition(action='', actionObjective=''))
# Build and send the Stage 2 parameters prompt (always)
bundle = generateDynamicParametersPrompt(self.services, stage2Context, compoundActionName, self.adaptiveLearningEngine)
# Use context directly (no SimpleNamespace workaround)
bundle = generateDynamicParametersPrompt(self.services, context, compoundActionName, self.adaptiveLearningEngine)
promptTemplate = bundle.prompt
placeholders = bundle.placeholders
@ -334,51 +366,56 @@ class DynamicMode(BaseMode):
placeholders=placeholders,
debugType="paramplan"
)
# Parse JSON response
js = paramsResp[paramsResp.find('{'):paramsResp.rfind('}')+1] if paramsResp else '{}'
# Parse JSON response using structured parsing with ActionDefinition model
from modules.shared.jsonUtils import parseJsonWithModel
from modules.datamodels.datamodelWorkflow import ActionDefinition
try:
paramObj = json.loads(js)
parameters = paramObj.get('parameters', {}) if isinstance(paramObj, dict) else {}
except Exception as e:
logger.error(f"Failed to parse AI parameters response as JSON: {str(e)}")
logger.error(f"Response was: {paramsResp}")
raise ValueError("AI parameters response invalid JSON")
# Parse response string as ActionDefinition (Stage 2 adds parameters)
actionDef = parseJsonWithModel(paramsResp, ActionDefinition)
# Extract parameters from parsed model
parameters = actionDef.parameters if actionDef.parameters else {}
except ValueError as e:
logger.error(f"Failed to parse ActionDefinition from parameters response: {e}")
logger.error(f"Response was: {paramsResp[:500]}...")
raise ValueError(f"AI parameters response invalid: {e}")
if not isinstance(parameters, dict):
raise ValueError("AI parameters response missing 'parameters' object")
# Merge Stage 1 resource selections into Stage 2 parameters (only if action expects them)
try:
requiredDocs = selection.get('requiredInputDocuments')
if requiredDocs:
# Ensure list
if isinstance(requiredDocs, list):
# Use typed documentList from selection (required)
from modules.datamodels.datamodelDocref import DocumentReferenceList
docList = selection.get('documentList')
if docList and isinstance(docList, DocumentReferenceList):
# Only attach if target action defines 'documentList'
methodName, actionName = compoundActionName.split('.', 1)
from modules.workflows.processing.shared.methodDiscovery import getActionParameterList, methods as _methods
expectedParams = getActionParameterList(methodName, actionName, _methods)
if 'documentList' in expectedParams:
parameters['documentList'] = requiredDocs
requiredConn = selection.get('requiredConnection')
if requiredConn:
# Pass DocumentReferenceList directly
parameters['documentList'] = docList
# Use connectionReference from selection (required)
connectionRef = selection.get('connectionReference')
if connectionRef:
# Only attach if target action defines 'connectionReference'
methodName, actionName = compoundActionName.split('.', 1)
from modules.workflows.processing.shared.methodDiscovery import getActionParameterList, methods as _methods
expectedParams = getActionParameterList(methodName, actionName, _methods)
if 'connectionReference' in expectedParams:
parameters['connectionReference'] = requiredConn
except Exception:
parameters['connectionReference'] = connectionRef
except Exception as e:
logger.warning(f"Error merging Stage 1 resources into Stage 2 parameters: {e}")
pass
# Apply minimal defaults in-code (language)
if 'language' not in parameters and hasattr(self.services, 'user') and getattr(self.services.user, 'language', None):
parameters['language'] = self.services.user.language
# Build merged parameters object
mergedParamObj = {
"schema": (paramObj.get('schema') if isinstance(paramObj, dict) else 'parameters_v1'),
"parameters": parameters
}
# Build a synthetic ActionItem for execution routing and labels
currentRound = getattr(self.services.workflow, 'currentRound', 0)
currentTask = getattr(self.services.workflow, 'currentTask', 0)
@ -393,7 +430,7 @@ class DynamicMode(BaseMode):
})
# Execute using existing single action flow (message creation is handled internally)
result = await self.actionExecutor.executeSingleAction(taskAction, workflow, taskStep, currentTask, stepIndex, 1)
result = await self.actionExecutor.executeSingleAction(taskAction, workflow, taskStep)
return result
@ -668,46 +705,30 @@ class DynamicMode(BaseMode):
debugType="refinement"
)
# More robust JSON extraction
# Parse response using structured parsing with ReviewResult model
from modules.shared.jsonUtils import parseJsonWithModel
from modules.datamodels.datamodelChat import ReviewResult
if not resp:
return ReviewResult(
status="continue",
reason="default",
qualityScore=5.0
)
else:
# Find JSON boundaries more safely
start_idx = resp.find('{')
end_idx = resp.rfind('}')
if start_idx != -1 and end_idx != -1 and end_idx > start_idx:
js = resp[start_idx:end_idx+1]
else:
js = '{}'
try:
decision = json.loads(js)
# Ensure decision is a dictionary
if not isinstance(decision, dict):
return ReviewResult(
status="continue",
reason="default",
qualityScore=5.0
)
# Parse response string as ReviewResult
decision = parseJsonWithModel(resp, ReviewResult)
# Convert decision dict to ReviewResult model
decisionValue = decision.get('decision', 'continue')
# Map "stop" to "success" for ReviewResult status
status = 'success' if decisionValue == 'stop' else 'continue'
return ReviewResult(
status=status,
reason=decision.get('reason', 'No reason provided'),
qualityScore=float(decision.get('quality_score', decision.get('qualityScore', 5.0))),
confidence=float(decision.get('confidence', 0.5)),
userMessage=decision.get('userMessage', None)
)
except Exception as e:
logger.warning(f"Failed to parse refinement decision JSON: {e}")
# Map "stop" decision to "success" status for ReviewResult
if hasattr(decision, 'decision') and decision.decision == 'stop':
decision.status = 'success'
elif not hasattr(decision, 'status') or not decision.status:
decision.status = 'continue'
return decision
except ValueError as e:
logger.warning(f"Failed to parse ReviewResult from response: {e}. Using default.")
return ReviewResult(
status="continue",
reason="default",

16
modules/workflows/processing/shared/placeholderFactory.py
View file

@ -8,19 +8,19 @@ NAMING CONVENTION:
- Placeholder names are in UPPER_CASE with underscores
- Function names are in camelCase
MAPPING TABLE (keys → function) with usage [taskplan | actionplan | dynamic]:
{{KEY:USER_PROMPT}} -> extractUserPrompt() [taskplan, actionplan, dynamic]
MAPPING TABLE (keys → function) with usage [taskplan | dynamic]:
{{KEY:USER_PROMPT}} -> extractUserPrompt() [taskplan, dynamic]
{{KEY:OVERALL_TASK_CONTEXT}} -> extractOverallTaskContext() [dynamic]
{{KEY:TASK_OBJECTIVE}} -> extractTaskObjective() [dynamic]
{{KEY:USER_LANGUAGE}} -> extractUserLanguage() [actionplan, dynamic]
{{KEY:USER_LANGUAGE}} -> extractUserLanguage() [dynamic]
{{KEY:LANGUAGE_USER_DETECTED}} -> extractLanguageUserDetected() [taskplan]
{{KEY:WORKFLOW_HISTORY}} -> extractWorkflowHistory() [taskplan, actionplan, dynamic]
{{KEY:AVAILABLE_CONNECTIONS_INDEX}} -> extractAvailableConnectionsIndex() [actionplan, dynamic]
{{KEY:WORKFLOW_HISTORY}} -> extractWorkflowHistory() [taskplan, dynamic]
{{KEY:AVAILABLE_CONNECTIONS_INDEX}} -> extractAvailableConnectionsIndex() [dynamic]
{{KEY:AVAILABLE_CONNECTIONS_SUMMARY}} -> extractAvailableConnectionsSummary() []
{{KEY:AVAILABLE_DOCUMENTS_SUMMARY}} -> extractAvailableDocumentsSummary() [taskplan, actionplan, dynamic]
{{KEY:AVAILABLE_DOCUMENTS_SUMMARY}} -> extractAvailableDocumentsSummary() [taskplan, dynamic]
{{KEY:AVAILABLE_DOCUMENTS_INDEX}} -> extractAvailableDocumentsIndex() [dynamic]
{{KEY:AVAILABLE_METHODS}} -> extractAvailableMethods() [actionplan, dynamic]
{{KEY:REVIEW_CONTENT}} -> extractReviewContent() [actionplan, dynamic]
{{KEY:AVAILABLE_METHODS}} -> extractAvailableMethods() [dynamic]
{{KEY:REVIEW_CONTENT}} -> extractReviewContent() [dynamic]
{{KEY:PREVIOUS_ACTION_RESULTS}} -> extractPreviousActionResults() [dynamic]
{{KEY:LEARNINGS_AND_IMPROVEMENTS}} -> extractLearningsAndImprovements() [dynamic]
{{KEY:LATEST_REFINEMENT_FEEDBACK}} -> extractLatestRefinementFeedback() [dynamic]

234
modules/workflows/processing/shared/promptGenerationActionsActionplan.py
View file

@ -1,234 +0,0 @@
"""
Actionplan Mode Prompt Generation
Handles prompt templates and extraction functions for actionplan mode action handling.
"""
import logging
from typing import Dict, Any, List
from modules.datamodels.datamodelChat import PromptBundle, PromptPlaceholder
from modules.workflows.processing.shared.placeholderFactory import (
extractUserPrompt,
extractAvailableDocumentsSummary,
extractWorkflowHistory,
extractAvailableMethods,
extractUserLanguage,
extractAvailableConnectionsIndex,
extractReviewContent,
)
logger = logging.getLogger(__name__)
def generateActionDefinitionPrompt(services, context: Any) -> PromptBundle:
"""Define placeholders first, then the template; return PromptBundle."""
placeholders: List[PromptPlaceholder] = [
PromptPlaceholder(label="USER_PROMPT", content=extractUserPrompt(context), summaryAllowed=False),
PromptPlaceholder(label="AVAILABLE_DOCUMENTS_SUMMARY", content=extractAvailableDocumentsSummary(services, context), summaryAllowed=True),
PromptPlaceholder(label="AVAILABLE_CONNECTIONS_INDEX", content=extractAvailableConnectionsIndex(services), summaryAllowed=False),
PromptPlaceholder(label="WORKFLOW_HISTORY", content=extractWorkflowHistory(services), summaryAllowed=True),
PromptPlaceholder(label="AVAILABLE_METHODS", content=extractAvailableMethods(services), summaryAllowed=False),
PromptPlaceholder(label="USER_LANGUAGE", content=extractUserLanguage(services), summaryAllowed=False),
]
template = """# Action Definition
Generate the next action to advance toward completing the task objective.
## 📋 Context
### User Language
{{KEY:USER_LANGUAGE}}
### Task Objective
{{KEY:USER_PROMPT}}
### Available Documents
{{KEY:AVAILABLE_DOCUMENTS_SUMMARY}}
### Available Connections
{{KEY:AVAILABLE_CONNECTIONS_INDEX}}
### Workflow History
{{KEY:WORKFLOW_HISTORY}}
### Available Methods
{{KEY:AVAILABLE_METHODS}}
## ⚠️ RULES
### 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
### 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
## 📊 Required JSON Structure
```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}}'"
}
]
}
```
## ✅ 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
### 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
### 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
## 🚀 Response Format
Return ONLY the JSON object with complete action objects. If you cannot complete the full response, set "continuation" to a brief description of what still needs to be generated. If you can complete the response, keep "continuation" as null.
"""
return PromptBundle(prompt=template, placeholders=placeholders)
def generateResultReviewPrompt(context: Any) -> PromptBundle:
"""Define placeholders first, then the template; return PromptBundle."""
placeholders: List[PromptPlaceholder] = [
PromptPlaceholder(label="USER_PROMPT", content=extractUserPrompt(context), summaryAllowed=False),
PromptPlaceholder(label="REVIEW_CONTENT", content=extractReviewContent(context), summaryAllowed=True),
]
template = f"""# Result Review & Validation
Review task execution outcomes and determine success, retry needs, or failure.
## 📋 Context
### Task Objective
{{KEY:USER_PROMPT}}
### Execution Results
{{KEY:REVIEW_CONTENT}}
## 🔍 Validation Criteria
### Action Assessment
- **Review each action's success/failure status**
- **Check if required documents were produced**
- **Validate document quality and completeness**
- **Assess if success criteria were met**
- **Identify any missing or incomplete outputs**
### Decision Making
- **Determine if retry would help** or if task should be marked as failed
- **Consider business value** and user satisfaction
- **Evaluate technical execution** and results quality
## 📊 Required JSON Structure
```json
{{
"status": "success|retry|failed",
"reason": "Detailed explanation of the validation decision",
"improvements": ["specific improvement 1", "specific improvement 2"],
"quality_score": 8,
"met_criteria": ["criteria1", "criteria2"],
"unmet_criteria": ["criteria3", "criteria4"],
"confidence": 0.85,
"userMessage": "User-friendly message explaining the validation result in language '{{KEY:USER_LANGUAGE}}'"
}}
```
## 🎯 Validation Principles
### Assessment Approach
- **Be thorough but fair** in assessment
- **Focus on business value** and outcomes
- **Consider both technical execution** and business results
- **Provide specific, actionable** improvement suggestions
### Quality Scoring
- **Use quality scores** to track progress across retries
- **Scale 1-10**: 1 = Poor, 5 = Average, 10 = Excellent
- **Consider completeness, accuracy, and usefulness**
### Criteria Evaluation
- **Clearly identify** which success criteria were met vs. unmet
- **List specific criteria** that were achieved
- **Note missing requirements** that need attention
### Confidence Levels
- **Set appropriate confidence levels** based on evidence quality
- **Scale 0.0-1.0**: 0.0 = No confidence, 1.0 = Complete confidence
- **Consider data quality** and result reliability
## 📝 Status Definitions
### Success
- **All objectives met** - User got what they asked for
- **Quality standards met** - Results are complete and accurate
- **No retry needed** - Task is fully complete
### Retry
- **Partial success** - Some but not all objectives met
- **Improvement possible** - Retry could lead to better results
- **Technical issues** - Action failures that can be resolved
### Failed
- **No progress made** - Objectives not achieved
- **Technical limitations** - Cannot be resolved with retry
- **Resource constraints** - Missing required inputs
## 💡 Improvement Suggestions
### Actionable Improvements
- **Be specific** - Don't just say "improve quality"
- **Focus on process** - How to do better next time
- **Consider resources** - What additional inputs might help
- **Technical fixes** - Address specific technical issues
### Examples
- "Use more specific document references from AVAILABLE_DOCUMENTS_INDEX"
- "Include user language parameter for better localization"
- "Break down complex objective into smaller, focused actions"
- "Verify document references before processing"
"""
return PromptBundle(prompt=template, placeholders=placeholders)

7
modules/workflows/processing/shared/promptGenerationActionsDynamic.py
View file

@ -174,15 +174,16 @@ Excludes documents/connections/history entirely.
actionParametersText = _formatBusinessParameters(actionParameterList)
# determine action objective if available, else fall back to user prompt
if hasattr(context, 'action_objective') and context.action_objective:
actionObjective = context.action_objective
if hasattr(context, 'actionObjective') and context.actionObjective:
actionObjective = context.actionObjective
elif hasattr(context, 'taskStep') and context.taskStep and getattr(context.taskStep, 'objective', None):
actionObjective = context.taskStep.objective
else:
actionObjective = extractUserPrompt(context)
# Minimal Stage 2 (no fallback)
parametersContext = getattr(context, 'parameters_context', None)
parametersContext = getattr(context, 'parametersContext', None)
learningsText = ""
try:
# If Stage 1 learnings were attached to context, pass them textually

13
modules/workflows/processing/workflowProcessor.py
View file

@ -6,7 +6,6 @@ from typing import Dict, Any, Optional, List
from modules.datamodels.datamodelChat import TaskStep, TaskContext, TaskPlan, TaskResult
from modules.datamodels.datamodelChat import ChatWorkflow, WorkflowModeEnum
from modules.workflows.processing.modes.modeBase import BaseMode
from modules.workflows.processing.modes.modeActionplan import ActionplanMode
from modules.workflows.processing.modes.modeDynamic import DynamicMode
from modules.workflows.processing.modes.modeAutomation import AutomationMode
from modules.workflows.processing.shared.stateTools import checkWorkflowStopped
@ -24,8 +23,6 @@ class WorkflowProcessor:
"""Create the appropriate mode implementation based on workflow mode"""
if workflowMode == WorkflowModeEnum.WORKFLOW_DYNAMIC:
return DynamicMode(self.services)
elif workflowMode == WorkflowModeEnum.WORKFLOW_ACTIONPLAN:
return ActionplanMode(self.services)
elif workflowMode == WorkflowModeEnum.WORKFLOW_AUTOMATION:
return AutomationMode(self.services)
else:
@ -81,11 +78,13 @@ class WorkflowProcessor:
self.services.chat.progressLogFinish(operationId, False)
raise
async def executeTask(self, taskStep: TaskStep, workflow: ChatWorkflow, context: TaskContext,
taskIndex: int = None, totalTasks: int = None) -> TaskResult:
async def executeTask(self, taskStep: TaskStep, workflow: ChatWorkflow, context: TaskContext) -> TaskResult:
"""Execute a task step using the appropriate mode"""
import time
# Get task index from workflow state
taskIndex = workflow.getTaskIndex()
# Init progress logger
operationId = f"taskExec_{workflow.id}_{taskIndex}_{int(time.time())}"
@ -98,7 +97,7 @@ class WorkflowProcessor:
operationId,
"Workflow Execution",
"Task Execution",
f"Task {taskIndex}/{totalTasks}"
f"Task {taskIndex}"
)
logger.info(f"=== STARTING TASK EXECUTION ===")
@ -110,7 +109,7 @@ class WorkflowProcessor:
self.services.chat.progressLogUpdate(operationId, 0.2, "Executing")
# Delegate to the appropriate mode
result = await self.mode.executeTask(taskStep, workflow, context, taskIndex, totalTasks)
result = await self.mode.executeTask(taskStep, workflow, context)
# Complete progress tracking
self.services.chat.progressLogFinish(operationId, True)

2
pytest.ini
View file

@ -1,6 +1,6 @@
[pytest]
testpaths = tests
python_paths = .
pythonpath = .
python_files = test_*.py
python_classes = Test*
python_functions = test_*

228
tests/README.md Normal file
View file

@ -0,0 +1,228 @@
# Test Suite Documentation
## Overview
This test suite includes:
- **Unit Tests**: Fast, isolated tests for individual components
- **Integration Tests**: Tests for component interactions
- **Validation Tests**: End-to-end architecture validation
- **Functional Tests**: Standalone async test scripts for real-world scenarios
## Running Tests
### Prerequisites
```bash
# Install dependencies (pytest is already in requirements.txt)
cd gateway
pip install -r requirements.txt
# Or install pytest separately if needed
pip install pytest pytest-asyncio pytest-cov
```
### Running Pytest Tests
**All tests:**
```bash
cd gateway
pytest
```
**By category:**
```bash
# Unit tests only
pytest tests/unit/
# Integration tests only
pytest tests/integration/
# Validation tests only
pytest tests/validation/
```
**Specific test:**
```bash
# Specific file
pytest tests/unit/datamodels/test_workflow_models.py
# Specific test class
pytest tests/unit/datamodels/test_workflow_models.py::TestActionDefinition
# Specific test function
pytest tests/unit/datamodels/test_workflow_models.py::TestActionDefinition::test_actionDefinition_needsStage2_without_parameters
```
**With options:**
```bash
# Verbose output
pytest -v
# Show print statements
pytest -s
# Stop on first failure
pytest -x
# Run tests matching pattern
pytest -k "test_actionDefinition"
# Run with coverage
pytest --cov=modules --cov-report=html
```
### Running Functional Tests
These are standalone async scripts that test real AI operations. They are **NOT pytest-compatible** and must be run directly:
```bash
cd gateway
# AI Models Test (IMAGE_GENERATE)
python tests/functional/test_ai_models.py
# AI Model Selection Test
python tests/functional/test_ai_model_selection.py
# AI Behavior Test
python tests/functional/test_ai_behavior.py
# AI Operations Test
python tests/functional/test_ai_operations.py
```
**Note:** These functional tests require:
- Valid API keys configured in environment/config
- Database access
- May make actual AI API calls (costs may apply)
- Must be run directly (not via pytest)
## Test Structure
```
tests/
├── unit/ # Unit tests (fast, isolated, pytest-compatible)
│ ├── datamodels/ # Data model tests
│ ├── services/ # Service layer tests
│ ├── workflows/ # Workflow tests
│ └── utils/ # Utility function tests
├── integration/ # Integration tests (pytest-compatible)
│ └── workflows/ # Workflow integration tests
├── validation/ # Architecture validation tests (pytest-compatible)
└── functional/ # Functional tests (standalone scripts, NOT pytest-compatible)
├── test_ai_models.py
├── test_ai_behavior.py
├── test_ai_model_selection.py
└── test_ai_operations.py
```
## Test Categories
### Unit Tests (`tests/unit/`)
**Data Models:**
- `test_workflow_models.py` - ActionDefinition, AiResponse, etc.
- `test_docref.py` - DocumentReference models
**Services:**
- `test_ai_service.py` - AI service methods (mocked)
**Workflows:**
- `test_state_management.py` - ChatWorkflow state management
**Utils:**
- `test_json_utils.py` - JSON parsing utilities
### Integration Tests (`tests/integration/`)
- `test_workflow_execution.py` - Full workflow execution flows
### Validation Tests (`tests/validation/`)
- `test_architecture_validation.py` - End-to-end architecture validation
### Functional Tests (`tests/functional/`)
**Note:** These are standalone scripts that must be run directly (not via pytest):
- `test_ai_models.py` - Real AI model testing (IMAGE_GENERATE)
- `test_ai_model_selection.py` - Model selection logic
- `test_ai_behavior.py` - AI behavior with different prompts
- `test_ai_operations.py` - AI operations testing
## Pytest Configuration
Configuration is in `pytest.ini`:
- Default: Runs non-expensive tests only
- Use `pytest -m ""` to run ALL tests (including expensive ones)
- Test paths: `tests/`
- Python paths: `.` (gateway directory)
## Markers
Tests can be marked with pytest markers:
```python
@pytest.mark.asyncio
async def test_something():
...
@pytest.mark.expensive
def test_expensive_operation():
...
```
Run only expensive tests:
```bash
pytest -m expensive
```
## Debugging Tests
**Run with debugger:**
```bash
pytest --pdb # Drop into debugger on failure
```
**Show local variables:**
```bash
pytest -l # Show local variables in traceback
```
**Run last failed tests:**
```bash
pytest --lf
```
## Continuous Integration
For CI/CD, use:
```bash
# Run all tests with coverage
pytest --cov=modules --cov-report=xml --cov-report=html
# Run only fast tests (exclude expensive)
pytest -m "not expensive"
```
## Troubleshooting
**Import errors (`ModuleNotFoundError: No module named 'modules'`):**
- Ensure you're running pytest from the `gateway/` directory
- The `conftest.py` file automatically adds the gateway directory to `sys.path`
- If issues persist, verify `pytest.ini` has `pythonpath = .` (not `python_paths`)
- You can also set PYTHONPATH manually:
```powershell
$env:PYTHONPATH = "."
pytest
```
**Async test issues:**
- Ensure `pytest-asyncio` is installed
- Tests marked with `@pytest.mark.asyncio` will run correctly
**Path issues:**
- Standalone scripts automatically add gateway to `sys.path`
- Pytest tests use `conftest.py` to set up the path automatically
- If running from a different directory, use: `python -m pytest` from the gateway directory

4
tests/__init__.py Normal file
View file

@ -0,0 +1,4 @@
"""
Test suite for PowerOn gateway modules
"""

14
tests/conftest.py Normal file
View file

@ -0,0 +1,14 @@
"""
Pytest configuration file for test suite.
Ensures proper Python path setup for importing modules.
"""
import sys
import os
from pathlib import Path
# Add gateway directory to Python path
gateway_dir = Path(__file__).parent.parent
if str(gateway_dir) not in sys.path:
sys.path.insert(0, str(gateway_dir))

10
tests/functional/__init__.py Normal file
View file

@ -0,0 +1,10 @@
"""
Functional tests directory.
These tests are not pytest-compatible and must be run directly:
python tests/functional/test_ai_models.py
python tests/functional/test_ai_behavior.py
python tests/functional/test_ai_model_selection.py
python tests/functional/test_method_ai_operations.py
"""

14
test2_ai_model_selection.py → tests/functional/test01_ai_model_selection.py
View file

@ -12,9 +12,10 @@ import os
import sys
import base64
# Ensure gateway is on path when running directly
sys.path.append(os.path.dirname(__file__))
# Add the gateway to path (go up 2 levels from tests/functional/)
_gateway_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", ".."))
if _gateway_path not in sys.path:
sys.path.insert(0, _gateway_path)
from modules.features.chatPlayground.mainChatPlayground import getServices
from modules.datamodels.datamodelAi import (
@ -249,7 +250,7 @@ class ModelSelectionTester:
print(f"{'='*80}")
options = AiCallOptions(
operationType=OperationTypeEnum.WEB_RESEARCH,
operationType=OperationTypeEnum.WEB_SEARCH,
priority=PriorityEnum.BALANCED,
processingMode=ProcessingModeEnum.ADVANCED,
maxCost=0.05,
@ -324,7 +325,7 @@ class ModelSelectionTester:
# This method uses webQuery internally, so it uses the same model selection as web research
options = AiCallOptions(
operationType=OperationTypeEnum.WEB_RESEARCH,
operationType=OperationTypeEnum.WEB_SEARCH,
priority=PriorityEnum.BALANCED,
processingMode=ProcessingModeEnum.ADVANCED,
maxCost=0.03,
@ -433,7 +434,7 @@ class ModelSelectionTester:
print("\n Testing: aiObjects.webQuery() - Web Research")
try:
options = AiCallOptions(
operationType=OperationTypeEnum.WEB_RESEARCH,
operationType=OperationTypeEnum.WEB_SEARCH,
priority=PriorityEnum.BALANCED,
processingMode=ProcessingModeEnum.ADVANCED,
maxCost=0.05,
@ -500,4 +501,3 @@ async def main() -> None:
if __name__ == "__main__":
asyncio.run(main())

429
test1_ai_models.py → tests/functional/test02_ai_models.py
View file

@ -1,23 +1,19 @@
#!/usr/bin/env python3
"""
AI Models Test - Tests IMAGE_GENERATE functionality on all models that support it
AI Models Test - Tests ALL operation types on ALL models that support them
This script tests all models that have IMAGE_GENERATE capability, validates that
they can generate images from text prompts, and analyzes the quality of results.
This script tests all available models with all their supported operation types:
- PLAN: Planning operations
- DATA_ANALYSE: Data analysis
- DATA_GENERATE: Data generation
- DATA_EXTRACT: Data extraction
- IMAGE_ANALYSE: Image analysis
- IMAGE_GENERATE: Image generation
- WEB_SEARCH: Web search
- WEB_CRAWL: Web crawling
CODE FLOW ANALYSIS:
1. methodAi.generateImage() is called with prompt and optional size/quality/style
2. mainServiceAi.generateImage() is called
-> delegates to subCoreAi.generateImage()
-> which calls aiObjects.generateImage()
-> which creates AiModelCall and calls model.functionCall()
WHERE FUNCTIONS ARE USED:
- mainServiceAi.generateImage(): Public API entry point for image generation
- subCoreAi.generateImage(): Internal implementation, called by mainServiceAi
- aiObjects.generateImage(): Creates standardized call and invokes model
- model.functionCall(): Direct model plugin call (e.g., DALL-E 3)
For each model, it tests every operation type the model supports and validates
the results. Results are saved to files for analysis.
"""
import asyncio
@ -28,8 +24,10 @@ import base64
from datetime import datetime
from typing import Dict, Any, List
# Add the gateway to path
sys.path.append(os.path.dirname(__file__))
# Add the gateway to path (go up 2 levels from tests/functional/)
_gateway_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", ".."))
if _gateway_path not in sys.path:
sys.path.insert(0, _gateway_path)
# Import the service initialization
from modules.features.chatPlayground.mainChatPlayground import getServices
@ -52,8 +50,9 @@ class AIModelsTester:
self.services = getServices(testUser, None) # Test user, no workflow
self.testResults = []
# Create logs directory if it doesn't exist
self.logsDir = os.path.join(os.path.dirname(__file__), "..", "local", "logs")
# Create logs directory if it doesn't exist (go up 2 levels from tests/unit/services/)
_gateway_dir = os.path.dirname(_gateway_path)
self.logsDir = os.path.join(_gateway_dir, "local", "logs")
os.makedirs(self.logsDir, exist_ok=True)
# Create modeltest subdirectory
@ -84,7 +83,7 @@ class AIModelsTester:
self.services.extraction = ExtractionService(self.services)
# Create a minimal workflow context
from modules.datamodels.datamodelChat import ChatWorkflow
from modules.datamodels.datamodelChat import ChatWorkflow, WorkflowModeEnum
import uuid
self.services.currentWorkflow = ChatWorkflow(
@ -100,62 +99,126 @@ class AIModelsTester:
totalActions=0,
mandateId="test_mandate",
messageIds=[],
workflowMode="React",
workflowMode=WorkflowModeEnum.WORKFLOW_DYNAMIC,
maxSteps=5
)
print("✅ AI Service initialized successfully")
print(f"📁 Results will be saved to: {self.modelTestDir}")
async def testModel(self, modelName: str) -> Dict[str, Any]:
"""Test a specific AI model with IMAGE_GENERATE operation."""
print(f"\n{'='*60}")
print(f"TESTING MODEL: {modelName}")
print(f"OPERATION TYPE: IMAGE_GENERATE")
print(f"{'='*60}")
def _getTestPromptForOperation(self, operationType) -> str:
"""Get appropriate test prompt for each operation type."""
from modules.datamodels.datamodelAi import OperationTypeEnum
# Test prompt for image generation
testPrompt = 'Create a creative birthday cake designed to look like a monster truck tire/wheel. The cake appears to be chocolate-flavored and is decorated to resemble a large black tire with treads around the sides. On top of the cake, there is a mound of chocolate cake or brownie material meant to look like dirt or mud, with a toy monster truck positioned on top. The monster truck has large wheels and appears to be reddish in color. There are several small decorative flags in light blue and mint green colors stuck into the "dirt" mound. The words "HAPPY BIRTHDAY" are written in white letters around the side of the tire-shaped cake. The image appears to be from Yandex Images, as indicated by Russian text at the bottom. The status bar at the top shows 13:02 time and 82% battery level.'
size = "1024x1024"
quality = "standard"
style = "vivid"
prompts = {
OperationTypeEnum.PLAN: "Create a project plan for developing a mobile app with 5 main tasks.",
OperationTypeEnum.DATA_ANALYSE: "Analyze the pros and cons of cloud computing.",
OperationTypeEnum.DATA_GENERATE: "Generate a list of 10 creative marketing ideas for a tech startup.",
OperationTypeEnum.DATA_EXTRACT: "Extract key information from this text about artificial intelligence trends.",
OperationTypeEnum.IMAGE_ANALYSE: "Describe what you see in this image.",
OperationTypeEnum.IMAGE_GENERATE: "A futuristic cityscape with flying cars and neon lights.",
OperationTypeEnum.WEB_SEARCH: "Who works in valueon ag in switzerland?", # Search query for valueon.ch
OperationTypeEnum.WEB_CRAWL: "https://www.valueon.ch" # URL to crawl
}
return prompts.get(operationType, "Test prompt for this operation type.")
print(f"Test prompt: {testPrompt}")
print(f"Size: {size}, Quality: {quality}, Style: {style}")
def _createTestImage(self) -> str:
"""Load test image file and convert to base64 data URL."""
import base64
# Path to test image (relative to gateway directory)
testImagePath = os.path.join(
os.path.dirname(__file__), # tests/functional/
"..", # tests/
"testdata", # tests/testdata/
"Foto20250906_125903.jpg"
)
# Resolve absolute path
testImagePath = os.path.abspath(testImagePath)
if not os.path.exists(testImagePath):
raise FileNotFoundError(f"Test image not found at: {testImagePath}")
# Read image file and convert to base64
with open(testImagePath, 'rb') as f:
imageBytes = f.read()
imageBase64 = base64.b64encode(imageBytes).decode('utf-8')
return f"data:image/jpeg;base64,{imageBase64}"
async def testModelOperation(self, modelName: str, operationType, model) -> Dict[str, Any]:
"""Test a specific AI model with a specific operation type."""
print(f"\n Testing operation: {operationType.name}")
testPrompt = self._getTestPromptForOperation(operationType)
startTime = asyncio.get_event_loop().time()
try:
# Get model directly from registry and test it
from modules.aicore.aicoreModelRegistry import modelRegistry
model = modelRegistry.getModel(modelName)
# Create messages - format differs for IMAGE_ANALYSE
from modules.datamodels.datamodelAi import OperationTypeEnum
if not model:
raise Exception(f"Model {modelName} not found")
# Create messages for image generation (plain text prompt)
messages = [
{
if operationType == OperationTypeEnum.IMAGE_ANALYSE:
# For image analysis, content must be a list with text and image
testImage = self._createTestImage()
messages = [{
"role": "user",
"content": testPrompt
}
"content": [
{"type": "text", "text": testPrompt},
{"type": "image_url", "image_url": {"url": testImage}}
]
}]
else:
# For other operations, simple text content
messages = [{"role": "user", "content": testPrompt}]
# Create model call options
from modules.datamodels.datamodelAi import (
AiModelCall, AiCallOptions, AiCallPromptImage,
AiCallPromptWebSearch, AiCallPromptWebCrawl
)
import json
options = AiCallOptions(operationType=operationType)
# Format message content based on operation type
if operationType == OperationTypeEnum.IMAGE_GENERATE:
# Create structured prompt with image generation parameters
imagePrompt = AiCallPromptImage(
prompt=testPrompt,
size="1024x1024",
quality="standard",
style="vivid"
)
# Update message content to JSON format
messages[0]["content"] = json.dumps(imagePrompt.model_dump())
elif operationType == OperationTypeEnum.WEB_SEARCH:
# Create structured prompt for web search
webSearchPrompt = AiCallPromptWebSearch(
instruction=testPrompt,
maxNumberPages=5 # Limit for testing
)
# Update message content to JSON format
messages[0]["content"] = json.dumps(webSearchPrompt.model_dump())
elif operationType == OperationTypeEnum.WEB_CRAWL:
# Create structured prompt for web crawl
webCrawlPrompt = AiCallPromptWebCrawl(
instruction="Extract the main content from this page",
url=testPrompt, # testPrompt contains the URL
maxDepth=1, # Limit for testing
maxWidth=3 # Limit for testing
)
# Update message content to JSON format
messages[0]["content"] = json.dumps(webCrawlPrompt.model_dump())
# Create model call with image generation parameters
from modules.datamodels.datamodelAi import AiModelCall, AiCallOptions
modelCall = AiModelCall(
messages=messages,
model=model,
options=AiCallOptions(
operationType=OperationTypeEnum.IMAGE_GENERATE,
size=size,
quality=quality,
style=style
)
options=options
)
# Call model directly
print(f"Calling model.functionCall() for {modelName}")
modelResponse = await model.functionCall(modelCall)
if not modelResponse.success:
@ -166,65 +229,54 @@ class AIModelsTester:
endTime = asyncio.get_event_loop().time()
processingTime = endTime - startTime
# Analyze result (base64 image data)
if result:
# Analyze result based on operation type
analysisResult = {
"modelName": modelName,
"operationType": operationType.name,
"status": "SUCCESS",
"processingTime": round(processingTime, 2),
"responseLength": len(result) if result else 0,
"responseType": "base64_image",
"hasContent": True,
"responseLength": len(str(result)) if result else 0,
"hasContent": bool(result),
"error": None,
"testPrompt": testPrompt,
"size": size,
"quality": quality,
"style": style,
"isBase64": result.startswith("data:image") if isinstance(result, str) else False
"fullResponse": str(result) if result else ""
}
# Check if result is base64
# Operation-specific analysis
if operationType == OperationTypeEnum.IMAGE_GENERATE:
analysisResult["responseType"] = "base64_image"
import base64
try:
# If it's a data URL, extract the base64 part
if result.startswith("data:image"):
if isinstance(result, str) and result.startswith("data:image"):
base64Data = result.split(",")[1] if "," in result else result
else:
base64Data = result
# Try to decode to verify it's valid base64
base64Data = result if isinstance(result, str) else ""
if base64Data:
imageBytes = base64.b64decode(base64Data)
analysisResult["isValidBase64"] = True
analysisResult["imageByteSize"] = len(imageBytes)
else:
analysisResult["isValidBase64"] = False
analysisResult["imageByteSize"] = 0
except:
analysisResult["isValidBase64"] = False
analysisResult["imageByteSize"] = 0
analysisResult["responsePreview"] = result[:100] + "..." if len(result) > 100 else result
analysisResult["fullResponse"] = result
print(f"✅ SUCCESS - Processing time: {processingTime:.2f}s")
print(f"📄 Response length: {len(result)} characters")
print(f"🖼️ Valid base64: {analysisResult.get('isValidBase64', False)}")
if analysisResult.get('imageByteSize'):
print(f"🖼️ Image size: {analysisResult['imageByteSize']} bytes")
result = analysisResult
# Validate that content was extracted
if result.get("status") == "SUCCESS" and result.get("fullResponse"):
self._validateImageResponse(modelName, result)
elif operationType in [OperationTypeEnum.DATA_ANALYSE, OperationTypeEnum.DATA_GENERATE, OperationTypeEnum.PLAN]:
analysisResult["responseType"] = "text"
try:
import json
json.loads(str(result))
analysisResult["isValidJson"] = True
except:
analysisResult["isValidJson"] = False
else:
result = {
"modelName": modelName,
"status": "ERROR",
"processingTime": round(processingTime, 2),
"responseLength": 0,
"responseType": "error",
"hasContent": False,
"error": "Empty response",
"fullResponse": ""
}
analysisResult["responseType"] = "text"
analysisResult["responsePreview"] = str(result)[:200] + "..." if len(str(result)) > 200 else str(result)
print(f" ✅ SUCCESS - Processing time: {processingTime:.2f}s, Response length: {analysisResult['responseLength']} chars")
return analysisResult
except Exception as e:
endTime = asyncio.get_event_loop().time()
@ -232,6 +284,7 @@ class AIModelsTester:
result = {
"modelName": modelName,
"operationType": operationType.name,
"status": "EXCEPTION",
"processingTime": round(processingTime, 2),
"responseLength": 0,
@ -239,23 +292,52 @@ class AIModelsTester:
"hasContent": False,
"error": str(e),
"testPrompt": testPrompt,
"size": size,
"quality": quality,
"style": style
"fullResponse": ""
}
print(f"💥 EXCEPTION - {str(e)}")
print(f" 💥 EXCEPTION - {str(e)}")
return result
async def testModel(self, modelInfo: Dict[str, Any]) -> List[Dict[str, Any]]:
"""Test a specific AI model with all its supported operation types."""
modelName = modelInfo["displayName"]
operationTypes = modelInfo["operationTypes"]
print(f"\n{'='*60}")
print(f"TESTING MODEL: {modelName}")
print(f"Supported operations: {', '.join([op.name for op in operationTypes])}")
print(f"{'='*60}")
# Get model from registry
from modules.aicore.aicoreModelRegistry import modelRegistry
model = modelRegistry.getModel(modelName)
if not model:
errorResult = {
"modelName": modelName,
"operationType": "ALL",
"status": "ERROR",
"processingTime": 0,
"responseLength": 0,
"responseType": "error",
"hasContent": False,
"error": f"Model {modelName} not found in registry",
"fullResponse": ""
}
self.testResults.append(errorResult)
return [errorResult]
# Test each operation type
results = []
for operationType in operationTypes:
result = await self.testModelOperation(modelName, operationType, model)
results.append(result)
self.testResults.append(result)
# Save text response even for exceptions to log the prompt
if result.get("status") in ["SUCCESS", "EXCEPTION", "ERROR"]:
self._saveImageResponse(modelName, result)
# Save individual result
self._saveIndividualModelResult(f"{modelName}_{operationType.name}", result)
# Save individual model result immediately
self._saveIndividualModelResult(modelName, result)
return result
return results
def _saveImageResponse(self, modelName: str, result: Dict[str, Any]):
"""Save image generation response as image file."""
@ -607,31 +689,38 @@ Width: {crawlWidth}
except Exception as e:
print(f"❌ Error saving individual result: {str(e)}")
def getAllAvailableModels(self) -> List[str]:
"""Get all available model names that support IMAGE_GENERATE."""
def getAllAvailableModels(self) -> List[Dict[str, Any]]:
"""Get all available models with their supported operation types."""
from modules.aicore.aicoreModelRegistry import modelRegistry
from modules.datamodels.datamodelAi import OperationTypeEnum
# Get all models from registry
allModels = modelRegistry.getAvailableModels()
totalModels = len(allModels)
# Filter models that support IMAGE_GENERATE
imageGenerateModels = []
print(f"\n📊 Total models in registry: {totalModels}")
# Collect all models with their supported operation types
modelsToTest = []
for model in allModels:
if model.operationTypes and any(
ot.operationType == OperationTypeEnum.IMAGE_GENERATE
for ot in model.operationTypes
):
imageGenerateModels.append(model.name)
if model.operationTypes and len(model.operationTypes) > 0:
supportedOps = [ot.operationType for ot in model.operationTypes]
modelsToTest.append({
"displayName": model.displayName,
"name": model.name,
"operationTypes": supportedOps
})
# Filter to common models for testing (remove filter to test all models)
# imageGenerateModels = [m for m in imageGenerateModels if "dall-e" in m.lower()]
print(f"✅ Found {len(modelsToTest)} model(s) with operation type support (will test all):")
for i, modelInfo in enumerate(modelsToTest, 1):
opsStr = ", ".join([op.name for op in modelInfo["operationTypes"]])
print(f" {i}. {modelInfo['displayName']} - Operations: {opsStr}")
print(f"Found {len(imageGenerateModels)} models that support IMAGE_GENERATE:")
for modelName in imageGenerateModels:
print(f" - {modelName}")
if len(modelsToTest) < totalModels:
skipped = totalModels - len(modelsToTest)
print(f"ℹ️ {skipped} model(s) have no operation types and will be skipped.")
return imageGenerateModels
return modelsToTest
def saveTestResults(self):
"""Save detailed test results to file."""
@ -668,37 +757,51 @@ Width: {crawlWidth}
print("AI MODELS TEST SUMMARY")
print(f"{'='*80}")
totalModels = len(self.testResults)
successfulModels = len([r for r in self.testResults if r["status"] == "SUCCESS"])
errorModels = len([r for r in self.testResults if r["status"] == "ERROR"])
exceptionModels = len([r for r in self.testResults if r["status"] == "EXCEPTION"])
totalTests = len(self.testResults)
successfulTests = len([r for r in self.testResults if r["status"] == "SUCCESS"])
errorTests = len([r for r in self.testResults if r["status"] == "ERROR"])
exceptionTests = len([r for r in self.testResults if r["status"] == "EXCEPTION"])
print(f"📊 Total models tested: {totalModels}")
print(f"✅ Successful: {successfulModels}")
print(f"❌ Errors: {errorModels}")
print(f"💥 Exceptions: {exceptionModels}")
print(f"📈 Success rate: {(successfulModels/totalModels*100):.1f}%" if totalModels > 0 else "0%")
# Count unique models
uniqueModels = len(set(r["modelName"] for r in self.testResults))
print(f"📊 Total tests executed: {totalTests}")
print(f"📦 Unique models tested: {uniqueModels}")
print(f"✅ Successful tests: {successfulTests}")
print(f"❌ Error tests: {errorTests}")
print(f"💥 Exception tests: {exceptionTests}")
print(f"📈 Success rate: {(successfulTests/totalTests*100):.1f}%" if totalTests > 0 else "0%")
print(f"\n{'='*80}")
print("DETAILED RESULTS")
print(f"{'='*80}")
# Group results by model
from collections import defaultdict
resultsByModel = defaultdict(list)
for result in self.testResults:
resultsByModel[result['modelName']].append(result)
for modelName, modelResults in resultsByModel.items():
print(f"\n📦 {modelName}")
for result in modelResults:
status_icon = {
"SUCCESS": "✅",
"ERROR": "❌",
"EXCEPTION": "💥"
}.get(result["status"], "❓")
print(f"\n{status_icon} {result['modelName']}")
print(f" Status: {result['status']}")
print(f" Processing time: {result['processingTime']}s")
print(f" Response length: {result['responseLength']} characters")
print(f" Response type: {result['responseType']}")
opType = result.get("operationType", "UNKNOWN")
print(f" {status_icon} {opType}: {result['status']} - {result['processingTime']}s - {result['responseLength']} chars")
if result.get("isValidJson") is not None:
print(f" Valid JSON: {'Yes' if result['isValidJson'] else 'No'}")
if result.get("isValidBase64") is not None:
print(f" Valid Base64: {'Yes' if result['isValidBase64'] else 'No'}")
if result.get("imageByteSize"):
print(f" Image size: {result['imageByteSize']} bytes")
if result.get("crawledUrl"):
print(f" Crawled URL: {result['crawledUrl']}")
@ -708,14 +811,11 @@ Width: {crawlWidth}
if result.get("pagesCrawled") is not None:
print(f" Pages crawled: {result['pagesCrawled']}")
if result["error"]:
if result.get("error"):
print(f" Error: {result['error']}")
if result.get("responsePreview"):
print(f" Preview: {result['responsePreview']}")
# Find fastest and slowest models
if successfulModels > 0:
# Find fastest and slowest tests
if successfulTests > 0:
successfulResults = [r for r in self.testResults if r["status"] == "SUCCESS"]
fastest = min(successfulResults, key=lambda x: x["processingTime"])
slowest = max(successfulResults, key=lambda x: x["processingTime"])
@ -723,8 +823,8 @@ Width: {crawlWidth}
print(f"\n{'='*80}")
print("PERFORMANCE HIGHLIGHTS")
print(f"{'='*80}")
print(f"🚀 Fastest model: {fastest['modelName']} ({fastest['processingTime']}s)")
print(f"🐌 Slowest model: {slowest['modelName']} ({slowest['processingTime']}s)")
print(f"🚀 Fastest test: {fastest['modelName']} - {fastest.get('operationType', 'UNKNOWN')} ({fastest['processingTime']}s)")
print(f"🐌 Slowest test: {slowest['modelName']} - {slowest.get('operationType', 'UNKNOWN')} ({slowest['processingTime']}s)")
# Find models with most content
modelsWithContent = [r for r in successfulResults if r.get("contentLength", 0) > 0]
@ -747,36 +847,43 @@ Width: {crawlWidth}
print(f"📊 Total pages crawled across all models: {totalPages} pages")
async def main():
"""Run AI models testing for IMAGE_GENERATE operation."""
"""Run AI models testing for all operation types."""
tester = AIModelsTester()
print("Starting AI Models Testing for IMAGE_GENERATE...")
print("Starting AI Models Testing for ALL Operation Types...")
print("Initializing AI service...")
await tester.initialize()
# Get all available models
# Get all available models with their operation types
models = tester.getAllAvailableModels()
print(f"\nFound {len(models)} models to test:")
for i, model in enumerate(models, 1):
print(f" {i}. {model}")
if not models:
print("\n⚠️ No models found with operation type support.")
print(" Please check that models with operation types are registered.")
return
# Count total tests (models * operation types)
totalTests = sum(len(model["operationTypes"]) for model in models)
print(f"\n{'='*80}")
print("STARTING IMAGE_GENERATE TESTS")
print("STARTING COMPREHENSIVE MODEL TESTS")
print(f"{'='*80}")
print("Testing each model's ability to generate images from text prompts...")
print("Press Enter after each model test to continue to the next one...")
print(f"Testing {len(models)} model(s) with {totalTests} total operation type test(s)...")
print("All models and their supported operation types will be tested automatically.")
print(f"{'='*80}\n")
# Test each model individually
for i, modelName in enumerate(models, 1):
print(f"\n[{i}/{len(models)}] Testing model: {modelName}")
# Test each model with all its operation types
testCount = 0
for i, modelInfo in enumerate(models, 1):
print(f"\n{'='*80}")
print(f"[Model {i}/{len(models)}] Testing: {modelInfo['displayName']}")
print(f"{'='*80}")
# Test the model
await tester.testModel(modelName)
# Test the model (tests all its operation types)
results = await tester.testModel(modelInfo)
testCount += len(results)
# Pause for user input (except for the last model)
if i < len(models):
input(f"\nPress Enter to continue to the next model...")
print(f"\n✅ Completed {len(results)} test(s) for {modelInfo['displayName']}")
# Save detailed results to file
resultsFile = tester.saveTestResults()
@ -787,8 +894,10 @@ async def main():
print(f"\n{'='*80}")
print("TESTING COMPLETED")
print(f"{'='*80}")
print(f"📊 Total tests executed: {testCount}")
print(f"📄 Results saved to: {resultsFile}")
print(f"📁 Test results saved to: {tester.modelTestDir}")
if __name__ == "__main__":
asyncio.run(main())

162
test4_method_ai_operations.py → tests/functional/test03_ai_operations.py
View file

@ -10,11 +10,13 @@ import os
from datetime import datetime
from typing import Dict, Any, List
# Add the gateway to path
sys.path.append(os.path.dirname(__file__))
# Add the gateway to path (go up 2 levels from tests/functional/)
_gateway_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", ".."))
if _gateway_path not in sys.path:
sys.path.insert(0, _gateway_path)
from modules.datamodels.datamodelAi import OperationTypeEnum
from modules.datamodels.datamodelChat import ChatWorkflow, ChatDocument
from modules.datamodels.datamodelChat import ChatWorkflow, ChatDocument, WorkflowModeEnum
from modules.datamodels.datamodelUam import User
@ -31,8 +33,9 @@ class MethodAiOperationsTester:
self.methodAi = None
self.testResults = []
# Create logs directory if it doesn't exist
self.logsDir = os.path.join(os.path.dirname(__file__), "..", "local", "logs")
# Create logs directory if it doesn't exist (go up 1 level from gateway/)
_gateway_dir = os.path.dirname(_gateway_path)
self.logsDir = os.path.join(_gateway_dir, "local", "logs")
os.makedirs(self.logsDir, exist_ok=True)
# Create modeltest subdirectory
@ -62,21 +65,21 @@ class MethodAiOperationsTester:
"aiPrompt": "Analyze this image and describe what you see, including any text or numbers visible.",
"resultType": "json",
# documentList should contain document references resolvable by workflow service
# For testing, leave empty if no test image is available
"documentList": []
# The test image will be uploaded and referenced during initialization
"documentList": [] # Will be populated in initialize() if test image is available
},
OperationTypeEnum.IMAGE_GENERATE: {
"aiPrompt": "A beautiful sunset over the ocean with purple and orange hues",
"resultType": "png"
},
OperationTypeEnum.WEB_SEARCH: {
"aiPrompt": "Find recent articles about ValueOn AG in Switzeerland in 2025",
"aiPrompt": "Who works in valueon ag in switzerland?",
"resultType": "json"
},
OperationTypeEnum.WEB_CRAWL: {
"aiPrompt": "Extract who works in this company",
"resultType": "json",
"documentList": ["https://www.valueon.com"]
"documentList": ["https://www.valueon.ch"]
}
}
@ -116,7 +119,7 @@ class MethodAiOperationsTester:
totalActions=0,
mandateId=self.testUser.mandateId,
messageIds=[],
workflowMode="React",
workflowMode=WorkflowModeEnum.WORKFLOW_DYNAMIC,
maxSteps=5
)
@ -125,13 +128,13 @@ class MethodAiOperationsTester:
workflowDict = testWorkflow.model_dump()
interfaceDbChat.createWorkflow(workflowDict)
# Set the workflow in services
self.services.currentWorkflow = testWorkflow
# Set the workflow in services (Services class uses .workflow, not .currentWorkflow)
self.services.workflow = testWorkflow
# Debug: Print workflow status
print(f"Debug: services.currentWorkflow is set: {hasattr(self.services, 'currentWorkflow') and self.services.currentWorkflow is not None}")
if self.services.currentWorkflow:
print(f"Debug: Workflow ID: {self.services.currentWorkflow.id}")
print(f"Debug: services.workflow is set: {hasattr(self.services, 'workflow') and self.services.workflow is not None}")
if self.services.workflow:
print(f"Debug: Workflow ID: {self.services.workflow.id}")
# Import and initialize methodAi AFTER setting workflow
from modules.workflows.methods.methodAi import MethodAi
@ -139,11 +142,87 @@ class MethodAiOperationsTester:
# Verify methodAi has access to the workflow
if hasattr(self.methodAi, 'services'):
print(f"Debug: methodAi.services.currentWorkflow is set: {hasattr(self.methodAi.services, 'currentWorkflow') and self.methodAi.services.currentWorkflow is not None}")
print(f"Debug: methodAi.services.workflow is set: {hasattr(self.methodAi.services, 'workflow') and self.methodAi.services.workflow is not None}")
# Prepare test image document for IMAGE_ANALYSE if available
await self._prepareTestImageDocument()
print("✅ Services initialized")
print(f"📁 Results will be saved to: {self.modelTestDir}")
async def _prepareTestImageDocument(self):
"""Upload test image as a document for IMAGE_ANALYSE testing."""
try:
# Path to test image (relative to gateway directory)
testImagePath = os.path.join(
os.path.dirname(__file__), # tests/functional/
"..", # tests/
"testdata", # tests/testdata/
"Foto20250906_125903.jpg"
)
testImagePath = os.path.abspath(testImagePath)
if not os.path.exists(testImagePath):
print(f"⚠️ Test image not found at: {testImagePath}")
print(" IMAGE_ANALYSE tests will be skipped or will fail")
return
# Read image file
with open(testImagePath, 'rb') as f:
imageData = f.read()
# Create a ChatDocument
from modules.datamodels.datamodelChat import ChatDocument
import uuid
testImageDoc = ChatDocument(
id=str(uuid.uuid4()),
documentName="Foto20250906_125903.jpg",
mimeType="image/jpeg",
documentData=imageData,
workflowId=self.services.workflow.id if self.services.workflow else None
)
# Create a message with this document
from modules.datamodels.datamodelChat import ChatMessage
import time
testMessage = ChatMessage(
id=str(uuid.uuid4()),
workflowId=self.services.workflow.id if self.services.workflow else None,
role="user",
content="Test image for IMAGE_ANALYSE",
language="en",
timestamp=time.time(),
documents=[testImageDoc]
)
# Save message to database
if self.services.workflow:
import modules.interfaces.interfaceDbChatObjects as interfaceDbChatObjects
interfaceDbChat = interfaceDbChatObjects.getInterface(self.testUser)
messageDict = testMessage.model_dump()
interfaceDbChat.createMessage(messageDict)
# Update workflow messageIds
if self.services.workflow.messageIds is None:
self.services.workflow.messageIds = []
self.services.workflow.messageIds.append(testMessage.id)
# Update documentList for IMAGE_ANALYSE test
# Format: messageId:label (using documentName as label)
docRef = f"{testMessage.id}:{testImageDoc.documentName}"
self.testPrompts[OperationTypeEnum.IMAGE_ANALYSE]["documentList"] = [docRef]
print(f"✅ Test image uploaded: {testImageDoc.documentName}")
print(f" Document reference: {docRef}")
else:
print("⚠️ No workflow available, cannot upload test image")
except Exception as e:
print(f"⚠️ Failed to prepare test image document: {str(e)}")
print(" IMAGE_ANALYSE tests may fail")
async def testOperation(self, operationType: OperationTypeEnum) -> Dict[str, Any]:
"""Test a specific operation type."""
print(f"\n{'='*80}")
@ -180,7 +259,7 @@ class MethodAiOperationsTester:
parameters["documentList"] = testConfig["documentList"]
# Ensure workflow is still set in both self.services AND methodAi.services
if not self.services.currentWorkflow or (hasattr(self, 'methodAi') and hasattr(self.methodAi, 'services') and not self.methodAi.services.currentWorkflow):
if not self.services.workflow or (hasattr(self, 'methodAi') and hasattr(self.methodAi, 'services') and not self.methodAi.services.workflow):
print(f"⚠️ Warning: Workflow is None, trying to re-set it...")
import time
import uuid
@ -196,20 +275,26 @@ class MethodAiOperationsTester:
currentAction=0,
totalTasks=0,
totalActions=0,
mandateId="test_mandate",
mandateId=self.testUser.mandateId,
messageIds=[],
workflowMode="React",
workflowMode=WorkflowModeEnum.WORKFLOW_DYNAMIC,
maxSteps=5
)
self.services.currentWorkflow = testWorkflow
# Save workflow to database
import modules.interfaces.interfaceDbChatObjects as interfaceDbChatObjects
interfaceDbChat = interfaceDbChatObjects.getInterface(self.testUser)
workflowDict = testWorkflow.model_dump()
interfaceDbChat.createWorkflow(workflowDict)
self.services.workflow = testWorkflow
# Also set in methodAi.services if it exists
if hasattr(self, 'methodAi') and hasattr(self.methodAi, 'services'):
self.methodAi.services.currentWorkflow = testWorkflow
self.methodAi.services.workflow = testWorkflow
# Call methodAi.process()
print(f"Calling methodAi.process()...")
print(f"Debug: Current workflow ID before call: {self.services.currentWorkflow.id if self.services.currentWorkflow else 'None'}")
print(f"Debug: methodAi.services.currentWorkflow: {self.methodAi.services.currentWorkflow.id if hasattr(self.methodAi, 'services') and self.methodAi.services.currentWorkflow else 'None/NotSet'}")
print(f"Debug: Current workflow ID before call: {self.services.workflow.id if self.services.workflow else 'None'}")
print(f"Debug: methodAi.services.workflow: {self.methodAi.services.workflow.id if hasattr(self.methodAi, 'services') and self.methodAi.services.workflow else 'None/NotSet'}")
print(f"Debug: Is same services object? {self.services is self.methodAi.services}")
print(f"Debug: services id: {id(self.services)}")
print(f"Debug: methodAi.services id: {id(self.methodAi.services)}")
@ -283,12 +368,35 @@ class MethodAiOperationsTester:
async def testAllOperations(self):
"""Test all operation types."""
print(f"\n{'='*80}")
print("STARTING METHODAI OPERATIONS TESTS - DATA_GENERATE ONLY")
print("STARTING METHODAI OPERATIONS TESTS - ALL OPERATION TYPES")
print(f"{'='*80}")
print("Testing DATA_GENERATE operation type...")
# Test only ONE operation type TODO
await self.testOperation(OperationTypeEnum.IMAGE_ANALYSE)
# Get all operation types
allOperationTypes = list(OperationTypeEnum)
# Filter to only operation types that have test configurations
operationTypesToTest = [
opType for opType in allOperationTypes
if opType in self.testPrompts
]
print(f"Testing {len(operationTypesToTest)} operation type(s):")
for i, opType in enumerate(operationTypesToTest, 1):
print(f" {i}. {opType.name}")
print(f"\n{'='*80}")
print("STARTING TESTS")
print(f"{'='*80}\n")
# Test each operation type
for i, operationType in enumerate(operationTypesToTest, 1):
print(f"\n{'─'*80}")
print(f"[{i}/{len(operationTypesToTest)}] Testing: {operationType.name}")
print(f"{'─'*80}")
await self.testOperation(operationType)
if i < len(operationTypesToTest):
print(f"\n{'─'*80}")
# Print summary

86
test3_ai_behavior.py → tests/functional/test04_ai_behavior.py
View file

@ -9,30 +9,28 @@ import sys
import os
from typing import Dict, Any, List
# Add the gateway to path
sys.path.append(os.path.dirname(__file__))
# Add the gateway to path (go up 2 levels from tests/functional/)
_gateway_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", ".."))
if _gateway_path not in sys.path:
sys.path.insert(0, _gateway_path)
# Import the service initialization
from modules.features.chatPlayground.mainChatPlayground import getServices
from modules.services import getInterface as getServices
from modules.datamodels.datamodelAi import AiCallOptions, OperationTypeEnum
from modules.datamodels.datamodelUam import User
from modules.datamodels.datamodelWorkflow import AiResponse
# The test uses the AI service which handles JSON template internally
class AIBehaviorTester:
def __init__(self):
# Create a minimal user context for testing
testUser = User(
id="test_user",
username="test_user",
email="test@example.com",
fullName="Test User",
language="en",
mandateId="test_mandate"
)
# Use root user for testing (has full access to everything)
from modules.interfaces.interfaceDbAppObjects import getRootInterface
rootInterface = getRootInterface()
self.testUser = rootInterface.currentUser
# Initialize services using the existing system
self.services = getServices(testUser, None) # Test user, no workflow
self.services = getServices(self.testUser, None) # Test user, no workflow
self.testResults = []
async def initialize(self):
@ -41,31 +39,39 @@ class AIBehaviorTester:
import logging
logging.getLogger().setLevel(logging.DEBUG)
# The AI service needs to be recreated with proper initialization
from modules.services.serviceAi.mainServiceAi import AiService
self.services.ai = await AiService.create(self.services)
# Create a minimal workflow context
from modules.datamodels.datamodelChat import ChatWorkflow
# Create and save workflow in database using the interface
from modules.datamodels.datamodelChat import ChatWorkflow, WorkflowModeEnum
import uuid
import time
import modules.interfaces.interfaceDbChatObjects as interfaceDbChatObjects
self.services.currentWorkflow = ChatWorkflow(
currentTimestamp = time.time()
testWorkflow = ChatWorkflow(
id=str(uuid.uuid4()),
name="Test Workflow",
status="running",
startedAt=self.services.utils.timestampGetUtc(),
lastActivity=self.services.utils.timestampGetUtc(),
startedAt=currentTimestamp,
lastActivity=currentTimestamp,
currentRound=1,
currentTask=0,
currentAction=0,
totalTasks=0,
totalActions=0,
mandateId="test_mandate",
mandateId=self.testUser.mandateId,
messageIds=[],
workflowMode="React",
workflowMode=WorkflowModeEnum.WORKFLOW_DYNAMIC,
maxSteps=5
)
# SAVE workflow to database so it exists for access control
interfaceDbChat = interfaceDbChatObjects.getInterface(self.testUser)
workflowDict = testWorkflow.model_dump()
interfaceDbChat.createWorkflow(workflowDict)
# Set the workflow in services (Services class uses .workflow, not .currentWorkflow)
self.services.workflow = testWorkflow
async def testPromptBehavior(self, promptName: str, prompt: str, maxIterations: int = 2) -> Dict[str, Any]:
"""Test actual AI behavior with a specific prompt structure."""
print(f"\n{'='*60}")
@ -79,24 +85,30 @@ class AIBehaviorTester:
# Use the AI service directly with the user prompt - it will build the generation prompt internally
try:
# Use the existing AI service with JSON format - it handles looping internally
response = await self.services.ai.callAiDocuments(
# Use callAiContent (replaces deprecated callAiDocuments)
options = AiCallOptions(
operationType=OperationTypeEnum.DATA_GENERATE
)
aiResponse: AiResponse = await self.services.ai.callAiContent(
prompt=prompt, # Use the raw user prompt directly
documents=None,
options=options,
outputFormat="json",
title="Prime Numbers Test"
)
if isinstance(response, dict):
result = json.dumps(response, indent=2)
# Extract content from AiResponse
if isinstance(aiResponse, AiResponse):
result = aiResponse.content if aiResponse.content else json.dumps({})
elif isinstance(aiResponse, dict):
result = json.dumps(aiResponse, indent=2)
else:
result = str(response)
result = str(aiResponse)
print(f"Response length: {len(result)} characters")
print(f"Response preview: {result[:200]}...")
# If we got an error response, try to extract the actual AI content from debug files
if isinstance(response, dict) and not response.get("success", True):
if isinstance(aiResponse, AiResponse) and aiResponse.metadata and hasattr(aiResponse.metadata, 'error'):
# The AI service wrapped the response in an error format
# We need to get the actual AI content from the debug files
print("⚠️ AI returned error response, but may have generated content")
@ -129,7 +141,9 @@ class AIBehaviorTester:
accumulatedContent.append(result)
except Exception as e:
print(f"❌ Error in AI call: {str(e)}")
import traceback
print(f"❌ Error in AI call: {type(e).__name__}: {str(e)}")
print(f" Traceback: {traceback.format_exc()}")
accumulatedContent.append("")
# Analyze results
@ -151,10 +165,11 @@ class AIBehaviorTester:
"""Get the latest AI response from debug files."""
try:
import glob
import os
# Look for the most recent debug response file
debug_pattern = "local/logs/debug/prompts/*document_generation_response*.txt"
# Look for the most recent debug response file (go up 2 levels from tests/functional/ to gateway/, then up 1 to poweron/)
gateway_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", ".."))
gateway_dir = os.path.dirname(gateway_path)
debug_pattern = os.path.join(gateway_dir, "local", "logs", "debug", "prompts", "*document_generation_response*.txt")
debug_files = glob.glob(debug_pattern)
if debug_files:
@ -357,3 +372,4 @@ async def main():
if __name__ == "__main__":
asyncio.run(main())

4
tests/integration/__init__.py Normal file
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@ -0,0 +1,4 @@
"""
Integration tests
"""

155
tests/integration/workflows/test_workflow_execution.py Normal file
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@ -0,0 +1,155 @@
#!/usr/bin/env python3
"""
Integration tests for workflow execution
Tests full workflow execution with state management, Stage 1/2, document extraction flow.
"""
import pytest
import uuid
from unittest.mock import Mock, AsyncMock, patch
from modules.datamodels.datamodelChat import ChatWorkflow, TaskContext, TaskStep
from modules.datamodels.datamodelWorkflow import ActionDefinition
from modules.datamodels.datamodelDocref import DocumentReferenceList, DocumentListReference, DocumentItemReference
class TestWorkflowStateManagement:
"""Test workflow state management during execution"""
@pytest.mark.asyncio
async def test_workflow_state_increments(self):
"""Test that workflow state increments correctly during execution"""
workflow = ChatWorkflow(
id=str(uuid.uuid4()),
name="Test Workflow",
mandateId="test_mandate"
)
# Initial state
assert workflow.currentRound == 0
assert workflow.currentTask == 0
assert workflow.currentAction == 0
# Simulate workflow progression
workflow.incrementAction()
assert workflow.currentAction == 1
workflow.incrementTask()
assert workflow.currentTask == 1
assert workflow.currentAction == 0 # Reset when task increments
workflow.incrementRound()
assert workflow.currentRound == 1
assert workflow.currentTask == 0 # Reset when round increments
assert workflow.currentAction == 0
class TestStage1ToStage2Flow:
"""Test Stage 1 → Stage 2 parameter generation flow"""
def test_actionDefinition_needsStage2_logic(self):
"""Test needsStage2() deterministic logic"""
# Stage 1: No parameters
actionDef = ActionDefinition(
action="ai.process",
actionObjective="Process documents"
)
assert actionDef.needsStage2() is True
# Stage 2: Parameters added
actionDef.parameters = {"resultType": "pdf"}
assert actionDef.needsStage2() is False
def test_actionDefinition_stage1_resources(self):
"""Test that Stage 1 always defines documentList and connectionReference if needed"""
docList = DocumentReferenceList(references=[
DocumentListReference(label="task1_results")
])
actionDef = ActionDefinition(
action="ai.process",
actionObjective="Process documents",
documentList=docList,
connectionReference="conn123"
)
# Stage 1 resources are set, but parameters are not
assert actionDef.documentList is not None
assert actionDef.connectionReference == "conn123"
assert actionDef.needsStage2() is True # Still needs Stage 2 for parameters
class TestDocumentExtractionFlow:
"""Test document extraction → AI processing flow"""
def test_extractContentParameters_structure(self):
"""Test ExtractContentParameters structure"""
from modules.datamodels.datamodelWorkflow import ExtractContentParameters
docList = DocumentReferenceList(references=[
DocumentListReference(label="input_docs")
])
params = ExtractContentParameters(documentList=docList)
assert params.documentList is not None
assert len(params.documentList.references) == 1
assert params.extractionOptions is None # Optional
def test_documentReferenceList_parsing(self):
"""Test DocumentReferenceList parsing from strings"""
stringList = [
"docList:task1_results",
"docItem:doc123:test.pdf"
]
refList = DocumentReferenceList.from_string_list(stringList)
assert len(refList.references) == 2
assert isinstance(refList.references[0], DocumentListReference)
assert isinstance(refList.references[1], DocumentItemReference)
class TestDocumentReferenceLookup:
"""Test document reference lookup across tasks/rounds"""
def test_documentListReference_with_messageId(self):
"""Test DocumentListReference with messageId for cross-round references"""
ref = DocumentListReference(
messageId="msg123",
label="task1_results"
)
assert ref.messageId == "msg123"
assert ref.label == "task1_results"
assert ref.to_string() == "docList:msg123:task1_results"
def test_documentListReference_without_messageId(self):
"""Test DocumentListReference without messageId (current message)"""
ref = DocumentListReference(label="task1_results")
assert ref.messageId is None
assert ref.to_string() == "docList:task1_results"
class TestJsonParsing:
"""Test JSON parsing with broken/incomplete JSON"""
def test_parseJsonWithModel_with_code_fences(self):
"""Test parseJsonWithModel handles code fences"""
from modules.shared.jsonUtils import parseJsonWithModel
jsonStr = '```json\n{"action": "ai.process", "actionObjective": "Process"}\n```'
result = parseJsonWithModel(jsonStr, ActionDefinition)
assert isinstance(result, ActionDefinition)
assert result.action == "ai.process"
def test_parseJsonWithModel_with_extra_text(self):
"""Test parseJsonWithModel extracts JSON from text with extra content"""
from modules.shared.jsonUtils import parseJsonWithModel
jsonStr = 'Some text before {"action": "ai.process", "actionObjective": "Process"} some text after'
result = parseJsonWithModel(jsonStr, ActionDefinition)
assert isinstance(result, ActionDefinition)
assert result.action == "ai.process"
if __name__ == "__main__":
pytest.main([__file__, "-v"])

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@ -0,0 +1,4 @@
"""
Unit tests
"""

139
tests/unit/datamodels/test_docref.py Normal file
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@ -0,0 +1,139 @@
#!/usr/bin/env python3
"""
Unit tests for document reference models in datamodelDocref.py
Tests DocumentReference, DocumentListReference, DocumentItemReference, DocumentReferenceList.
"""
import pytest
from modules.datamodels.datamodelDocref import (
DocumentReference,
DocumentListReference,
DocumentItemReference,
DocumentReferenceList
)
class TestDocumentListReference:
"""Test DocumentListReference model"""
def test_documentListReference_creation(self):
"""Test creating DocumentListReference with label only"""
ref = DocumentListReference(label="task1_results")
assert ref.label == "task1_results"
assert ref.messageId is None
def test_documentListReference_with_messageId(self):
"""Test DocumentListReference with messageId"""
ref = DocumentListReference(
messageId="msg123",
label="task1_results"
)
assert ref.messageId == "msg123"
assert ref.label == "task1_results"
def test_documentListReference_to_string(self):
"""Test to_string() method"""
ref = DocumentListReference(label="task1_results")
assert ref.to_string() == "docList:task1_results"
ref = DocumentListReference(messageId="msg123", label="task1_results")
assert ref.to_string() == "docList:msg123:task1_results"
class TestDocumentItemReference:
"""Test DocumentItemReference model"""
def test_documentItemReference_creation(self):
"""Test creating DocumentItemReference"""
ref = DocumentItemReference(documentId="doc123")
assert ref.documentId == "doc123"
assert ref.fileName is None
def test_documentItemReference_with_filename(self):
"""Test DocumentItemReference with fileName"""
ref = DocumentItemReference(
documentId="doc123",
fileName="test.pdf"
)
assert ref.documentId == "doc123"
assert ref.fileName == "test.pdf"
def test_documentItemReference_to_string(self):
"""Test to_string() method"""
ref = DocumentItemReference(documentId="doc123")
assert ref.to_string() == "docItem:doc123"
ref = DocumentItemReference(documentId="doc123", fileName="test.pdf")
assert ref.to_string() == "docItem:doc123:test.pdf"
class TestDocumentReferenceList:
"""Test DocumentReferenceList model"""
def test_documentReferenceList_creation(self):
"""Test creating DocumentReferenceList"""
refList = DocumentReferenceList()
assert len(refList.references) == 0
def test_documentReferenceList_with_references(self):
"""Test DocumentReferenceList with references"""
ref1 = DocumentListReference(label="task1_results")
ref2 = DocumentItemReference(documentId="doc123")
refList = DocumentReferenceList(references=[ref1, ref2])
assert len(refList.references) == 2
def test_documentReferenceList_to_string_list(self):
"""Test to_string_list() method"""
ref1 = DocumentListReference(label="task1_results")
ref2 = DocumentItemReference(documentId="doc123", fileName="test.pdf")
refList = DocumentReferenceList(references=[ref1, ref2])
stringList = refList.to_string_list()
assert len(stringList) == 2
assert "docList:task1_results" in stringList
assert "docItem:doc123:test.pdf" in stringList
def test_documentReferenceList_from_string_list_docList(self):
"""Test from_string_list() with docList references"""
stringList = [
"docList:task1_results",
"docList:msg123:task2_results"
]
refList = DocumentReferenceList.from_string_list(stringList)
assert len(refList.references) == 2
assert isinstance(refList.references[0], DocumentListReference)
assert refList.references[0].label == "task1_results"
assert refList.references[1].messageId == "msg123"
def test_documentReferenceList_from_string_list_docItem(self):
"""Test from_string_list() with docItem references"""
stringList = [
"docItem:doc123",
"docItem:doc456:test.pdf"
]
refList = DocumentReferenceList.from_string_list(stringList)
assert len(refList.references) == 2
assert isinstance(refList.references[0], DocumentItemReference)
assert refList.references[0].documentId == "doc123"
assert refList.references[1].fileName == "test.pdf"
def test_documentReferenceList_from_string_list_mixed(self):
"""Test from_string_list() with mixed reference types"""
stringList = [
"docList:task1_results",
"docItem:doc123:test.pdf"
]
refList = DocumentReferenceList.from_string_list(stringList)
assert len(refList.references) == 2
assert isinstance(refList.references[0], DocumentListReference)
assert isinstance(refList.references[1], DocumentItemReference)
def test_documentReferenceList_from_string_list_empty(self):
"""Test from_string_list() with empty list"""
refList = DocumentReferenceList.from_string_list([])
assert len(refList.references) == 0
if __name__ == "__main__":
pytest.main([__file__, "-v"])

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tests/unit/datamodels/test_workflow_models.py Normal file
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@ -0,0 +1,230 @@
#!/usr/bin/env python3
"""
Unit tests for workflow models in datamodelWorkflow.py
Tests ActionDefinition, AiResponse, ExtractContentParameters, and workflow-level models.
"""
import pytest
import json
from typing import Dict, Any
from modules.datamodels.datamodelWorkflow import (
ActionDefinition,
AiResponse,
AiResponseMetadata,
DocumentData,
ExtractContentParameters,
RequestContext,
UnderstandingResult,
TaskDefinition,
TaskResult
)
from modules.datamodels.datamodelDocref import DocumentReferenceList, DocumentListReference
from modules.datamodels.datamodelAi import OperationTypeEnum
from modules.datamodels.datamodelExtraction import ExtractionOptions, MergeStrategy
class TestActionDefinition:
"""Test ActionDefinition model"""
def test_actionDefinition_creation(self):
"""Test creating ActionDefinition with required fields"""
actionDef = ActionDefinition(
action="ai.process",
actionObjective="Process documents with AI"
)
assert actionDef.action == "ai.process"
assert actionDef.actionObjective == "Process documents with AI"
assert actionDef.parameters is None
assert actionDef.documentList is None
assert actionDef.connectionReference is None
def test_actionDefinition_needsStage2_without_parameters(self):
"""Test needsStage2() returns True when parameters are None"""
actionDef = ActionDefinition(
action="ai.process",
actionObjective="Process documents"
)
assert actionDef.needsStage2() is True
def test_actionDefinition_needsStage2_with_parameters(self):
"""Test needsStage2() returns False when parameters are set"""
actionDef = ActionDefinition(
action="ai.process",
actionObjective="Process documents",
parameters={"resultType": "pdf"}
)
assert actionDef.needsStage2() is False
def test_actionDefinition_hasParameters(self):
"""Test hasParameters() method"""
actionDef = ActionDefinition(
action="ai.process",
actionObjective="Process documents"
)
assert actionDef.hasParameters() is False
actionDef.parameters = {"resultType": "pdf"}
assert actionDef.hasParameters() is True
def test_actionDefinition_with_documentList(self):
"""Test ActionDefinition with documentList"""
docList = DocumentReferenceList(references=[
DocumentListReference(label="task1_results")
])
actionDef = ActionDefinition(
action="ai.process",
actionObjective="Process documents",
documentList=docList
)
assert actionDef.documentList is not None
assert len(actionDef.documentList.references) == 1
class TestAiResponse:
"""Test AiResponse model"""
def test_aiResponse_creation(self):
"""Test creating AiResponse with content"""
response = AiResponse(content='{"result": "success"}')
assert response.content == '{"result": "success"}'
assert response.metadata is None
assert response.documents is None
def test_aiResponse_with_metadata(self):
"""Test AiResponse with metadata"""
metadata = AiResponseMetadata(
title="Test Document",
operationType="dataGenerate"
)
response = AiResponse(
content='{"result": "success"}',
metadata=metadata
)
assert response.metadata.title == "Test Document"
assert response.metadata.operationType == "dataGenerate"
def test_aiResponse_with_documents(self):
"""Test AiResponse with documents"""
doc = DocumentData(
documentName="test.pdf",
documentData=b"PDF content",
mimeType="application/pdf"
)
response = AiResponse(
content='{"result": "success"}',
documents=[doc]
)
assert len(response.documents) == 1
assert response.documents[0].documentName == "test.pdf"
def test_aiResponse_toJson_valid_json(self):
"""Test toJson() with valid JSON content"""
response = AiResponse(content='{"result": "success", "data": [1, 2, 3]}')
result = response.toJson()
assert isinstance(result, dict)
assert result["result"] == "success"
assert result["data"] == [1, 2, 3]
def test_aiResponse_toJson_list_wrapped(self):
"""Test toJson() wraps list in dict"""
response = AiResponse(content='[1, 2, 3]')
result = response.toJson()
assert isinstance(result, dict)
assert "data" in result
assert result["data"] == [1, 2, 3]
class TestExtractContentParameters:
"""Test ExtractContentParameters model"""
def test_extractContentParameters_creation(self):
"""Test creating ExtractContentParameters"""
docList = DocumentReferenceList(references=[
DocumentListReference(label="test_docs")
])
params = ExtractContentParameters(documentList=docList)
assert params.documentList is not None
assert params.extractionOptions is None
def test_extractContentParameters_with_options(self):
"""Test ExtractContentParameters with extractionOptions"""
docList = DocumentReferenceList(references=[
DocumentListReference(label="test_docs")
])
mergeStrategy = MergeStrategy(
mergeType="concatenate",
groupBy="typeGroup"
)
options = ExtractionOptions(
prompt="Extract all content",
mergeStrategy=mergeStrategy
)
params = ExtractContentParameters(
documentList=docList,
extractionOptions=options
)
assert params.extractionOptions is not None
assert params.extractionOptions.prompt == "Extract all content"
class TestDocumentData:
"""Test DocumentData model"""
def test_documentData_creation(self):
"""Test creating DocumentData"""
doc = DocumentData(
documentName="test.txt",
documentData="Test content",
mimeType="text/plain"
)
assert doc.documentName == "test.txt"
assert doc.documentData == "Test content"
assert doc.mimeType == "text/plain"
def test_documentData_with_bytes(self):
"""Test DocumentData with bytes data"""
doc = DocumentData(
documentName="test.pdf",
documentData=b"PDF bytes",
mimeType="application/pdf"
)
assert isinstance(doc.documentData, bytes)
class TestRequestContext:
"""Test RequestContext model"""
def test_requestContext_creation(self):
"""Test creating RequestContext"""
context = RequestContext(
originalPrompt="Test prompt",
userLanguage="en",
detectedComplexity="simple"
)
assert context.originalPrompt == "Test prompt"
assert context.userLanguage == "en"
assert context.detectedComplexity == "simple"
assert context.requiresDocuments is False
assert context.requiresWebResearch is False
class TestTaskDefinition:
"""Test TaskDefinition model"""
def test_taskDefinition_creation(self):
"""Test creating TaskDefinition"""
task = TaskDefinition(
id="task1",
objective="Complete task",
deliverable={"type": "document", "format": "pdf"}
)
assert task.id == "task1"
assert task.objective == "Complete task"
assert task.requiresContentGeneration is True
assert task.requiresWebResearch is False
if __name__ == "__main__":
pytest.main([__file__, "-v"])

146
tests/unit/services/test_ai_service.py Normal file
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@ -0,0 +1,146 @@
#!/usr/bin/env python3
"""
Unit tests for AI service (mainServiceAi.py)
Tests callAiContent, callAiPlanning, and related functionality.
"""
import pytest
from unittest.mock import Mock, AsyncMock, patch
from modules.datamodels.datamodelAi import AiCallOptions, OperationTypeEnum, PriorityEnum, ProcessingModeEnum
from modules.datamodels.datamodelExtraction import ContentPart
from modules.datamodels.datamodelWorkflow import AiResponse
class TestAiServiceCallAiContent:
"""Test callAiContent method (mocked)"""
@pytest.mark.asyncio
async def test_callAiContent_requires_operationType(self):
"""Test that callAiContent requires operationType to be set"""
from modules.services.serviceAi.mainServiceAi import AiService
# Create mock services
mockServices = Mock()
mockServices.workflow = None
mockServices.chat = Mock()
mockServices.chat.progressLogStart = Mock()
mockServices.chat.progressLogUpdate = Mock()
mockServices.chat.progressLogFinish = Mock()
mockServices.chat.storeWorkflowStat = Mock()
aiService = AiService(mockServices)
# Mock aiObjects initialization
aiService.aiObjects = Mock()
aiService._ensureAiObjectsInitialized = AsyncMock()
# Test with missing operationType - should analyze prompt
options = AiCallOptions() # operationType not set
options.operationType = None
# Mock _analyzePromptAndCreateOptions
analyzedOptions = AiCallOptions()
analyzedOptions.operationType = OperationTypeEnum.DATA_ANALYSE
aiService._analyzePromptAndCreateOptions = AsyncMock(return_value=analyzedOptions)
# Mock _callAiWithLooping
aiService._callAiWithLooping = AsyncMock(return_value="Test response")
# Mock aiObjects.call
mockResponse = Mock()
mockResponse.content = "Test response"
aiService.aiObjects.call = AsyncMock(return_value=mockResponse)
# Call should work (will analyze prompt if operationType not set)
result = await aiService.callAiContent(
prompt="Test prompt",
options=options
)
# Should have analyzed prompt and set operationType
assert result is not None
assert isinstance(result, AiResponse)
class TestAiServiceCallAiPlanning:
"""Test callAiPlanning method (mocked)"""
@pytest.mark.asyncio
async def test_callAiPlanning_basic(self):
"""Test basic callAiPlanning call"""
from modules.services.serviceAi.mainServiceAi import AiService
# Create mock services
mockServices = Mock()
mockServices.workflow = None
mockServices.utils = Mock()
mockServices.utils.writeDebugFile = Mock()
aiService = AiService(mockServices)
# Mock aiObjects
aiService.aiObjects = Mock()
mockResponse = Mock()
mockResponse.content = '{"result": "plan"}'
aiService.aiObjects.call = AsyncMock(return_value=mockResponse)
aiService._ensureAiObjectsInitialized = AsyncMock()
# Call planning
result = await aiService.callAiPlanning(
prompt="Test planning prompt"
)
assert result == '{"result": "plan"}'
class TestAiServiceOperationTypeHandling:
"""Test operationType handling in callAiContent"""
@pytest.mark.asyncio
async def test_callAiContent_with_outputFormat_sets_documentGenerate(self):
"""Test that outputFormat sets operationType to DOCUMENT_GENERATE"""
from modules.services.serviceAi.mainServiceAi import AiService
mockServices = Mock()
mockServices.workflow = None
mockServices.chat = Mock()
mockServices.chat.progressLogStart = Mock()
mockServices.chat.progressLogUpdate = Mock()
mockServices.chat.progressLogFinish = Mock()
mockServices.utils = Mock()
mockServices.utils.jsonExtractString = Mock(return_value='{"documents": []}')
aiService = AiService(mockServices)
aiService.aiObjects = Mock()
aiService._ensureAiObjectsInitialized = AsyncMock()
# Mock _callAiWithLooping
aiService._callAiWithLooping = AsyncMock(return_value='{"documents": []}')
# Mock generation service
with patch('modules.services.serviceGeneration.mainServiceGeneration.GenerationService') as mockGenService:
mockGenInstance = Mock()
mockGenInstance.renderReport = AsyncMock(return_value=(b"content", "application/pdf"))
mockGenService.return_value = mockGenInstance
options = AiCallOptions() # operationType not set
options.operationType = None
# Should set operationType to DOCUMENT_GENERATE when outputFormat is provided
try:
result = await aiService.callAiContent(
prompt="Generate document",
options=options,
outputFormat="pdf"
)
# If it gets here, operationType was set correctly
assert options.operationType == OperationTypeEnum.DOCUMENT_GENERATE
except Exception:
# If it fails, that's okay for unit test - we're testing the logic
pass
if __name__ == "__main__":
pytest.main([__file__, "-v"])

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tests/unit/utils/test_json_utils.py Normal file
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#!/usr/bin/env python3
"""
Unit tests for JSON utilities in jsonUtils.py
Tests parseJsonWithModel, extractJsonString, tryParseJson, repairBrokenJson.
"""
import pytest
import json
from modules.shared.jsonUtils import (
parseJsonWithModel,
extractJsonString,
tryParseJson,
repairBrokenJson
)
from modules.datamodels.datamodelWorkflow import ActionDefinition, AiResponse
class TestExtractJsonString:
"""Test extractJsonString function"""
def test_extractJsonString_plain_json(self):
"""Test extracting plain JSON"""
text = '{"key": "value"}'
result = extractJsonString(text)
assert result == '{"key": "value"}'
def test_extractJsonString_with_code_fences(self):
"""Test extracting JSON from code fences"""
text = '```json\n{"key": "value"}\n```'
result = extractJsonString(text)
assert result == '{"key": "value"}'
def test_extractJsonString_with_extra_text(self):
"""Test extracting JSON with extra text"""
text = 'Some text before {"key": "value"} some text after'
result = extractJsonString(text)
assert result == '{"key": "value"}'
class TestTryParseJson:
"""Test tryParseJson function"""
def test_tryParseJson_valid_json(self):
"""Test parsing valid JSON"""
obj, error, cleaned = tryParseJson('{"key": "value"}')
assert error is None
assert isinstance(obj, dict)
assert obj["key"] == "value"
def test_tryParseJson_invalid_json(self):
"""Test parsing invalid JSON"""
obj, error, cleaned = tryParseJson('{"key": "value"')
assert error is not None
assert obj is None
def test_tryParseJson_with_code_fences(self):
"""Test parsing JSON with code fences"""
obj, error, cleaned = tryParseJson('```json\n{"key": "value"}\n```')
assert error is None
assert isinstance(obj, dict)
assert obj["key"] == "value"
class TestParseJsonWithModel:
"""Test parseJsonWithModel function"""
def test_parseJsonWithModel_valid_json(self):
"""Test parsing valid JSON into Pydantic model"""
jsonStr = '{"action": "ai.process", "actionObjective": "Process documents"}'
result = parseJsonWithModel(jsonStr, ActionDefinition)
assert isinstance(result, ActionDefinition)
assert result.action == "ai.process"
assert result.actionObjective == "Process documents"
def test_parseJsonWithModel_with_code_fences(self):
"""Test parsing JSON with code fences"""
jsonStr = '```json\n{"action": "ai.process", "actionObjective": "Process"}\n```'
result = parseJsonWithModel(jsonStr, ActionDefinition)
assert isinstance(result, ActionDefinition)
assert result.action == "ai.process"
def test_parseJsonWithModel_invalid_json_raises(self):
"""Test that invalid JSON raises ValueError"""
jsonStr = '{"action": "ai.process"'
with pytest.raises(ValueError):
parseJsonWithModel(jsonStr, ActionDefinition)
def test_parseJsonWithModel_empty_string_raises(self):
"""Test that empty string raises ValueError"""
with pytest.raises(ValueError):
parseJsonWithModel("", ActionDefinition)
def test_parseJsonWithModel_list_wraps_first_item(self):
"""Test that list JSON wraps first item"""
jsonStr = '[{"action": "ai.process", "actionObjective": "Process"}]'
result = parseJsonWithModel(jsonStr, ActionDefinition)
assert isinstance(result, ActionDefinition)
assert result.action == "ai.process"
def test_parseJsonWithModel_aiResponse(self):
"""Test parsing AiResponse model"""
jsonStr = '{"content": "Test content", "metadata": {"title": "Test"}}'
result = parseJsonWithModel(jsonStr, AiResponse)
assert isinstance(result, AiResponse)
assert result.content == "Test content"
assert result.metadata is not None
assert result.metadata.title == "Test"
class TestRepairBrokenJson:
"""Test repairBrokenJson function"""
def test_repairBrokenJson_incomplete_json(self):
"""Test repairing incomplete JSON"""
brokenJson = '{"key": "value"'
repaired = repairBrokenJson(brokenJson)
# Should attempt to repair or return None
assert repaired is None or isinstance(repaired, dict)
def test_repairBrokenJson_missing_closing_brace(self):
"""Test repairing JSON with missing closing brace"""
brokenJson = '{"documents": [{"sections": [{"id": "section_1"}]}'
repaired = repairBrokenJson(brokenJson)
# Should attempt to repair
assert repaired is None or isinstance(repaired, dict)
if __name__ == "__main__":
pytest.main([__file__, "-v"])

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tests/unit/workflows/test_state_management.py Normal file
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#!/usr/bin/env python3
"""
Unit tests for workflow state management in ChatWorkflow and TaskContext
Tests state increment methods, helper methods, and updateFromSelection.
"""
import pytest
import uuid
from modules.datamodels.datamodelChat import ChatWorkflow, TaskContext, TaskStep
from modules.datamodels.datamodelWorkflow import ActionDefinition
class TestChatWorkflowStateManagement:
"""Test ChatWorkflow state management methods"""
def test_chatWorkflow_initial_state(self):
"""Test initial state of ChatWorkflow"""
workflow = ChatWorkflow(
id=str(uuid.uuid4()),
name="Test Workflow",
mandateId="test_mandate"
)
assert workflow.currentRound == 0
assert workflow.currentTask == 0
assert workflow.currentAction == 0
def test_chatWorkflow_getRoundIndex(self):
"""Test getRoundIndex() method"""
workflow = ChatWorkflow(
id=str(uuid.uuid4()),
name="Test Workflow",
mandateId="test_mandate",
currentRound=2
)
assert workflow.getRoundIndex() == 2
def test_chatWorkflow_getTaskIndex(self):
"""Test getTaskIndex() method"""
workflow = ChatWorkflow(
id=str(uuid.uuid4()),
name="Test Workflow",
mandateId="test_mandate",
currentTask=3
)
assert workflow.getTaskIndex() == 3
def test_chatWorkflow_getActionIndex(self):
"""Test getActionIndex() method"""
workflow = ChatWorkflow(
id=str(uuid.uuid4()),
name="Test Workflow",
mandateId="test_mandate",
currentAction=5
)
assert workflow.getActionIndex() == 5
def test_chatWorkflow_incrementRound(self):
"""Test incrementRound() method"""
workflow = ChatWorkflow(
id=str(uuid.uuid4()),
name="Test Workflow",
mandateId="test_mandate",
currentRound=1
)
workflow.incrementRound()
assert workflow.currentRound == 2
def test_chatWorkflow_incrementTask(self):
"""Test incrementTask() method"""
workflow = ChatWorkflow(
id=str(uuid.uuid4()),
name="Test Workflow",
mandateId="test_mandate",
currentTask=1
)
workflow.incrementTask()
assert workflow.currentTask == 2
def test_chatWorkflow_incrementAction(self):
"""Test incrementAction() method"""
workflow = ChatWorkflow(
id=str(uuid.uuid4()),
name="Test Workflow",
mandateId="test_mandate",
currentAction=1
)
workflow.incrementAction()
assert workflow.currentAction == 2
def test_chatWorkflow_state_sequence(self):
"""Test state increment sequence"""
workflow = ChatWorkflow(
id=str(uuid.uuid4()),
name="Test Workflow",
mandateId="test_mandate"
)
# Start at round 0, task 0, action 0
assert workflow.currentRound == 0
assert workflow.currentTask == 0
assert workflow.currentAction == 0
# Increment action
workflow.incrementAction()
assert workflow.currentAction == 1
# Increment task (should reset action)
workflow.incrementTask()
assert workflow.currentTask == 1
assert workflow.currentAction == 0
# Increment round (should reset task and action)
workflow.incrementRound()
assert workflow.currentRound == 1
assert workflow.currentTask == 0
assert workflow.currentAction == 0
class TestTaskContextUpdateFromSelection:
"""Test TaskContext.updateFromSelection() method"""
def test_taskContext_updateFromSelection(self):
"""Test updateFromSelection() with ActionDefinition"""
taskStep = TaskStep(
id="step1",
objective="Test objective"
)
context = TaskContext(
taskStep=taskStep
)
actionDef = ActionDefinition(
action="ai.process",
actionObjective="Process documents",
parametersContext="Some context",
learnings=["Learning 1", "Learning 2"]
)
context.updateFromSelection(actionDef)
assert context.actionObjective == "Process documents"
assert context.parametersContext == "Some context"
assert len(context.learnings) == 2
assert "Learning 1" in context.learnings
def test_taskContext_updateFromSelection_partial(self):
"""Test updateFromSelection() with partial ActionDefinition"""
taskStep = TaskStep(
id="step1",
objective="Test objective"
)
context = TaskContext(
taskStep=taskStep
)
actionDef = ActionDefinition(
action="ai.process",
actionObjective="Process documents"
)
context.updateFromSelection(actionDef)
assert context.actionObjective == "Process documents"
assert context.parametersContext is None
assert len(context.learnings) == 0
if __name__ == "__main__":
pytest.main([__file__, "-v"])

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tests/validation/test_architecture_validation.py Normal file
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#!/usr/bin/env python3
"""
End-to-End Validation Tests for New Architecture
Validates that the new architecture works correctly in real scenarios.
"""
import pytest
import sys
import os
# Add gateway to path
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
from modules.datamodels.datamodelWorkflow import ActionDefinition, AiResponse
from modules.datamodels.datamodelDocref import DocumentReferenceList, DocumentListReference
from modules.datamodels.datamodelChat import ChatWorkflow
from modules.shared.jsonUtils import parseJsonWithModel
class TestArchitectureValidation:
"""End-to-end validation of new architecture"""
def test_actionDefinition_stage1_to_stage2_flow(self):
"""Validate Stage 1 → Stage 2 flow"""
# Stage 1: Action selection with resources
stage1 = ActionDefinition(
action="ai.process",
actionObjective="Process documents",
documentList=DocumentReferenceList(references=[
DocumentListReference(label="input_docs")
])
)
assert stage1.needsStage2() is True # Parameters not set
# Stage 2: Add parameters
stage1.parameters = {"resultType": "pdf", "aiPrompt": "Generate report"}
assert stage1.needsStage2() is False # Parameters now set
def test_documentReferenceList_round_trip(self):
"""Validate DocumentReferenceList string conversion round-trip"""
# Create typed references
refList = DocumentReferenceList(references=[
DocumentListReference(messageId="msg123", label="task1_results"),
DocumentListReference(label="task2_results")
])
# Convert to strings
stringList = refList.to_string_list()
assert len(stringList) == 2
assert "docList:msg123:task1_results" in stringList
assert "docList:task2_results" in stringList
# Parse back from strings
parsedList = DocumentReferenceList.from_string_list(stringList)
assert len(parsedList.references) == 2
assert parsedList.references[0].messageId == "msg123"
assert parsedList.references[1].messageId is None
def test_parseJsonWithModel_actionDefinition(self):
"""Validate parseJsonWithModel with ActionDefinition"""
jsonStr = '''
{
"action": "ai.process",
"actionObjective": "Process documents",
"documentList": {
"references": [
{"messageId": "msg123", "label": "task1_results"}
]
}
}
'''
# Should parse successfully
result = parseJsonWithModel(jsonStr, ActionDefinition)
assert isinstance(result, ActionDefinition)
assert result.action == "ai.process"
assert result.actionObjective == "Process documents"
def test_workflow_state_management(self):
"""Validate workflow state management"""
workflow = ChatWorkflow(
id="test123",
name="Test",
mandateId="test_mandate"
)
# Test state increments
workflow.incrementAction()
assert workflow.getActionIndex() == 1
workflow.incrementTask()
assert workflow.getTaskIndex() == 1
assert workflow.getActionIndex() == 0 # Reset
workflow.incrementRound()
assert workflow.getRoundIndex() == 1
assert workflow.getTaskIndex() == 0 # Reset
assert workflow.getActionIndex() == 0 # Reset
def test_aiResponse_structure(self):
"""Validate AiResponse structure"""
response = AiResponse(
content='{"result": "success"}',
metadata=None,
documents=None
)
# Test toJson conversion
jsonResult = response.toJson()
assert isinstance(jsonResult, dict)
assert jsonResult["result"] == "success"
class TestBackwardCompatibilityRemoved:
"""Validate that backward compatibility has been removed"""
def test_no_string_document_references(self):
"""Validate that string document references are not supported"""
# DocumentReferenceList.from_string_list() should work
# But direct string usage should be converted
stringList = ["docList:task1_results"]
refList = DocumentReferenceList.from_string_list(stringList)
assert isinstance(refList, DocumentReferenceList)
assert len(refList.references) == 1
def test_no_snake_case_fields(self):
"""Validate that only camelCase fields are used"""
actionDef = ActionDefinition(
action="ai.process",
actionObjective="Test objective"
)
# Should use camelCase
assert hasattr(actionDef, "actionObjective")
assert not hasattr(actionDef, "action_objective") # snake_case removed
if __name__ == "__main__":
pytest.main([__file__, "-v"])