system test

README_document_test.md

@@ -0,0 +1,114 @@
+# Document Extraction Test
+
+This test procedure validates the DocumentManager's ability to extract content from files using AI-powered analysis.
+
+## Files Created
+
+- `test_document_extraction.py` - Main test script
+- `test_sample_document.txt` - Sample document for testing
+- `run_document_test.ps1` - PowerShell wrapper script
+- `test_document_extraction.log` - Generated log file (cleared on each run)
+
+## Usage
+
+### Method 1: Using PowerShell Script (Recommended)
+
+```powershell
+# Test with default sample file
+.\run_document_test.ps1
+
+# Test with custom file
+.\run_document_test.ps1 "path\to\your\document.pdf"
+```
+
+### Method 2: Direct Python Execution
+
+```bash
+# Test with default sample file
+python test_document_extraction.py test_sample_document.txt
+
+# Test with custom file
+python test_document_extraction.py "path/to/your/document.docx"
+```
+
+## Test Features
+
+1. **File Validation**: Checks if the specified file exists
+2. **MIME Type Detection**: Automatically detects file type based on extension
+3. **Content Extraction**: Uses the DocumentManager to extract content
+4. **AI Processing**: Applies the prompt "summarize the content and give list of the major topics"
+5. **Comprehensive Logging**: Logs all steps and results to `test_document_extraction.log`
+6. **Log Cleanup**: Clears the log file on each test run
+
+## Supported File Types
+
+- Text files (.txt, .md)
+- CSV files (.csv)
+- JSON files (.json)
+- XML files (.xml)
+- HTML files (.html, .htm)
+- Images (.jpg, .jpeg, .png, .gif, .svg)
+- PDF files (.pdf)
+- Office documents (.docx, .xlsx, .pptx)
+- And more (fallback to binary processing)
+
+## Test Output
+
+The test generates detailed logs including:
+
+- File information (path, size, MIME type)
+- Extraction process details
+- Extracted content summary
+- AI-processed results
+- Error details if any issues occur
+
+## Example Output
+
+```
+=== STARTING DOCUMENT EXTRACTION TEST ===
+File information: {
+  "file_path": "test_sample_document.txt",
+  "filename": "test_sample_document.txt",
+  "mime_type": "text/plain",
+  "file_size_bytes": 2048,
+  "file_size_mb": 0.0
+}
+Document extraction completed successfully: {
+  "extracted_content_id": "test-doc-1234567890",
+  "content_items_count": 1,
+  "object_type": "ExtractedContent"
+}
+COMPLETE EXTRACTED CONTENT: {
+  "total_length": 1500,
+  "content": "PowerOn System Architecture Overview... [AI processed summary]"
+}
+```
+
+## Error Handling
+
+The test includes comprehensive error handling for:
+
+- File not found errors
+- File reading errors
+- Document processing errors
+- AI processing errors
+- Import errors
+
+All errors are logged with detailed information for debugging.
+
+## Configuration
+
+The test uses the same configuration as other tests:
+
+- Environment variable: `POWERON_CONFIG_FILE = 'test_config.ini'`
+- Log file: `test_document_extraction.log`
+- Log level: DEBUG
+
+## Dependencies
+
+The test requires the same dependencies as the main PowerOn system:
+
+- Python 3.8+
+- Required Python packages (see requirements.txt)
+- Access to AI services (if AI processing is enabled)
+- Proper configuration in test_config.ini 

modules/connectors/connectorAiOpenai.py

@@ -98,7 +98,27 @@ class AiOpenai:
             The response from the OpenAI Vision API as text
         """
         try:
-            logger.debug(f"Starting image analysis for {mimeType} with query '{prompt}' for {mimeType} size {len(imageData)}B...")
+            logger.debug(f"Starting image analysis with query '{prompt}' for size {len(imageData)}B...")
+            
+            # Ensure imageData is a string (base64 encoded)
+            if not isinstance(imageData, str):
+                raise ValueError("imageData must be a string (base64 encoded)")
+            
+            # Fix base64 padding if needed
+            padding_needed = len(imageData) % 4
+            if padding_needed:
+                imageData += '=' * (4 - padding_needed)
+            
+            # Use default MIME type if not provided
+            if not mimeType:
+                mimeType = "image/jpeg"
+            
+            logger.debug(f"Using MIME type: {mimeType}")
+            logger.debug(f"Base64 data length: {len(imageData)} characters")
+            
+            # Create the data URL format as required by OpenAI Vision API
+            data_url = f"data:{mimeType};base64,{imageData}"
+            
             messages = [
                 {
                     "role": "user",
@@ -107,15 +127,40 @@ class AiOpenai:
                         {
                             "type": "image_url",
                             "image_url": {
-                                "url": f"data:{mimeType};base64,{imageData}"
+                                "url": data_url
                             }
                         }
                     ]
                 }
             ]
             
-            # Use the existing callApi function with the Vision model
+            # Use a vision-capable model for image analysis
-            response = await self.callApi(messages)
+            # Override the model for vision tasks
+            visionModel = "gpt-4o"  # or "gpt-4-vision-preview" depending on availability
+            
+            # Use parameters from configuration
+            temperature = self.config.get("temperature", 0.2)
+            maxTokens = self.config.get("maxTokens", 2000)
+            
+            payload = {
+                "model": visionModel,
+                "messages": messages,
+                "temperature": temperature,
+                "max_tokens": maxTokens
+            }
+            
+            response = await self.httpClient.post(
+                self.apiUrl,
+                json=payload
+            )
+            
+            if response.status_code != 200:
+                logger.error(f"OpenAI API error: {response.status_code} - {response.text}")
+                raise HTTPException(status_code=500, detail="Error communicating with OpenAI API")
+            
+            responseJson = response.json()
+            content = responseJson["choices"][0]["message"]["content"]
+            return content
             
             # Return content
             return response

modules/connectors/connectorDbJson.py

@@ -173,13 +173,31 @@ class DatabaseConnector:
             record["_modifiedAt"] = currentTime.isoformat()
             record["_modifiedBy"] = self.userId
             
-            # Save the record file
+            # Save the record file using atomic write
             recordPath = self._getRecordPath(table, recordId)
+            tempPath = recordPath + '.tmp'
+            
+            # Ensure directory exists
             os.makedirs(os.path.dirname(recordPath), exist_ok=True)
             
-            with open(recordPath, 'w', encoding='utf-8') as f:
+            # Write to temporary file first
+            with open(tempPath, 'w', encoding='utf-8') as f:
                 json.dump(record, f, indent=2, ensure_ascii=False)
             
+            # Verify the temporary file can be read back (validation)
+            try:
+                with open(tempPath, 'r', encoding='utf-8') as f:
+                    json.load(f)  # This will fail if file is corrupted
+            except Exception as e:
+                logger.error(f"Validation failed for record {recordId}: {e}")
+                # Clean up temp file
+                if os.path.exists(tempPath):
+                    os.remove(tempPath)
+                raise ValueError(f"Record validation failed: {e}")
+            
+            # Atomic move from temp to final location
+            os.replace(tempPath, recordPath)
+            
             # Update metadata
             metadata = self._loadTableMetadata(table)
             if recordId not in metadata["recordIds"]:
@@ -203,6 +221,13 @@ class DatabaseConnector:
             
         except Exception as e:
             logger.error(f"Error saving record {recordId} to table {table}: {e}")
+            # Clean up temp file if it exists
+            tempPath = self._getRecordPath(table, recordId) + '.tmp'
+            if os.path.exists(tempPath):
+                try:
+                    os.remove(tempPath)
+                except:
+                    pass
             return False
         
     def _loadTable(self, table: str) -> List[Dict[str, Any]]:

modules/interfaces/interfaceAiCalls.py

@@ -116,7 +116,7 @@ class AiCalls:
             The AI response as text
         """
         try:
-            return await self.openaiService.callAiImage(imageData, mimeType, prompt)
+            return await self.openaiService.callAiImage(prompt, imageData, mimeType)
         except Exception as e:
             logger.error(f"Error in OpenAI image call: {str(e)}")
             return f"Error: {str(e)}"

modules/interfaces/interfaceAppObjects.py

@@ -237,7 +237,6 @@ class AppObjects:
             # Find user by username
             for user_dict in users:
                 if user_dict.get("username") == username:
-                    logger.info(f"Found user with username {username}")
                     return User.from_dict(user_dict)
                     
             logger.info(f"No user found with username {username}")

modules/interfaces/interfaceChatObjects.py

@@ -760,7 +760,7 @@ class ChatObjects:
             else:
                 # Create new workflow
                 workflowData = {
-                    "name": userInput.name or "New Workflow",
+                    "name": "New Workflow",  # Default name since UserInputRequest doesn't have a name field
                     "status": "running",
                     "startedAt": currentTime,
                     "lastActivity": currentTime,

modules/interfaces/interfaceComponentObjects.py

@@ -690,34 +690,39 @@ class ComponentObjects:
                 return None
 
             # Process content based on file type
-            contentType = "binary"
+            isText = False
             content = ""
+            encoding = None
             
-            if file.get("mimeType", "").startswith("text/"):
+            # Use proper attribute access for FileItem object
+            if file.mimeType.startswith("text/"):
                 # For text files, return full content
                 try:
                     content = fileContent.decode('utf-8')
-                    contentType = "text"
+                    isText = True
+                    encoding = 'utf-8'
                 except UnicodeDecodeError:
                     content = fileContent.decode('latin-1')
-                    contentType = "text"
+                    isText = True
-            elif file.get("mimeType", "").startswith("image/"):
+                    encoding = 'latin-1'
+            elif file.mimeType.startswith("image/"):
                 # For images, return base64
-                contentType = "base64"
+                import base64
-                content = f"data:{file['mimeType']};base64,{fileContent.hex()}"
+                content = base64.b64encode(fileContent).decode('utf-8')
+                isText = False
             else:
                 # For other files, return as base64
-                contentType = "base64"
+                import base64
-                content = f"data:{file['mimeType']};base64,{fileContent.hex()}"
+                content = base64.b64encode(fileContent).decode('utf-8')
+                isText = False
 
             return FilePreview(
-                id=fileId,
-                name=file.get("name", "Unknown"),
-                mimeType=file.get("mimeType", "application/octet-stream"),
-                size=file.get("size", 0),
                 content=content,
-                contentType=contentType,
+                mimeType=file.mimeType,
-                metadata=file.get("metadata", {})
+                filename=file.filename,
+                isText=isText,
+                encoding=encoding,
+                size=file.fileSize
             )
 
         except Exception as e:

modules/methods/methodCoder.py

@@ -1,4 +1,4 @@
-from typing import Dict, Any, Optional
+from typing import Dict, Any, Optional, List
 import logging
 import uuid
 from datetime import datetime, UTC
@@ -11,10 +11,11 @@ class MethodCoder(MethodBase):
     """Coder method implementation for code operations"""
     
     def __init__(self, serviceContainer: Any):
+        """Initialize the coder method"""
         super().__init__(serviceContainer)
         self.name = "coder"
-        self.description = "Handle code operations like analysis and generation"
+        self.description = "Handle code operations like analysis, generation, and refactoring"
-    
+
     @action
     async def analyze(self, parameters: Dict[str, Any]) -> ActionResult:
         """
@@ -55,7 +56,7 @@ class MethodCoder(MethodBase):
                     error="No documents found for the provided reference"
                 )
             
-            # Extract content from all documents
+            # Process each document individually
             all_code_content = []
             
             for chatDocument in chatDocuments:
@@ -85,15 +86,18 @@ class MethodCoder(MethodBase):
                     error="No code content could be extracted from any documents"
                 )
             
-            # Combine all code content for analysis
+            # Extract text content from ExtractedContent objects
-            combined_code = "\n\n--- CODE SEPARATOR ---\n\n".join(all_code_content)
+            text_contents = self.service.extractTextFromContentObjects(all_code_content)
+            
+            # Combine all extracted text content for analysis
+            combined_content = "\n\n--- CODE SEPARATOR ---\n\n".join(text_contents)
             
             # Create analysis prompt
             analysis_prompt = f"""
             Analyze this {language} code for quality, structure, and potential issues.
             
             Code to analyze:
-            {combined_code}
+            {combined_content}
             
             Please check for:
             {', '.join(checks)}

modules/methods/methodDocument.py

@@ -26,18 +26,16 @@ class MethodDocument(MethodBase):
     @action
     async def extract(self, parameters: Dict[str, Any]) -> ActionResult:
         """
-        Extract content from document
+        Extract specific content from document with ai prompt and return it as a json file
         
         Parameters:
             documentList (str): Reference to the document list to extract content from
             aiPrompt (str): AI prompt for content extraction
-            format (str, optional): Output format (default: "text")
             includeMetadata (bool, optional): Whether to include metadata (default: True)
         """
         try:
             documentList = parameters.get("documentList")
             aiPrompt = parameters.get("aiPrompt")
-            format = parameters.get("format", "text")
             includeMetadata = parameters.get("includeMetadata", True)
             
             if not documentList:
@@ -95,12 +93,14 @@ class MethodDocument(MethodBase):
                     error="No content could be extracted from any documents"
                 )
             
-            # Combine all extracted content
+            # Extract text content from ExtractedContent objects
-            combined_content = "\n\n--- DOCUMENT SEPARATOR ---\n\n".join(all_extracted_content)
+            text_contents = self.service.extractTextFromContentObjects(all_extracted_content)
+            
+            # Combine all extracted text content
+            combined_content = "\n\n--- DOCUMENT SEPARATOR ---\n\n".join(text_contents)
             
             result_data = {
                 "documentCount": len(chatDocuments),
-                "format": format,
                 "content": combined_content,
                 "fileInfos": file_infos if includeMetadata else None,
                 "timestamp": datetime.now(UTC).isoformat()
@@ -124,236 +124,3 @@ class MethodDocument(MethodBase):
                 data={},
                 error=str(e)
             )
-
-    @action
-    async def analyze(self, parameters: Dict[str, Any]) -> ActionResult:
-        """
-        Analyze document content
-        
-        Parameters:
-            documentList (str): Reference to the document list to analyze
-            aiPrompt (str): AI prompt for content analysis
-            analysis (List[str], optional): Types of analysis to perform (default: ["entities", "topics", "sentiment"])
-        """
-        try:
-            documentList = parameters.get("documentList")
-            aiPrompt = parameters.get("aiPrompt")
-            analysis = parameters.get("analysis", ["entities", "topics", "sentiment"])
-            
-            if not documentList:
-                return self._createResult(
-                    success=False,
-                    data={},
-                    error="Document list reference is required"
-                )
-            
-            if not aiPrompt:
-                return self._createResult(
-                    success=False,
-                    data={},
-                    error="AI prompt is required"
-                )
-            
-            chatDocuments = self.service.getChatDocumentsFromDocumentList(documentList)
-            if not chatDocuments:
-                return self._createResult(
-                    success=False,
-                    data={},
-                    error="No documents found for the provided reference"
-                )
-            
-            # Extract content from all documents
-            all_extracted_content = []
-            
-            for chatDocument in chatDocuments:
-                fileId = chatDocument.fileId
-                file_data = self.service.getFileData(fileId)
-                file_info = self.service.getFileInfo(fileId)
-                
-                if not file_data:
-                    logger.warning(f"File not found or empty for fileId: {fileId}")
-                    continue
-                
-                extracted_content = await self.service.extractContentFromFileData(
-                    prompt=aiPrompt,
-                    fileData=file_data,
-                    filename=file_info.get('name', 'document'),
-                    mimeType=file_info.get('mimeType', 'application/octet-stream'),
-                    base64Encoded=False,
-                    documentId=chatDocument.id
-                )
-                
-                all_extracted_content.append(extracted_content)
-            
-            if not all_extracted_content:
-                return self._createResult(
-                    success=False,
-                    data={},
-                    error="No content could be extracted from any documents"
-                )
-            
-            # Combine all extracted content for analysis
-            combined_content = "\n\n--- DOCUMENT SEPARATOR ---\n\n".join(all_extracted_content)
-            
-            analysis_prompt = f"""
-            Analyze this document content for the following aspects:
-            {', '.join(analysis)}
-            
-            Document content:
-            {combined_content[:8000]}  # Limit content length
-            
-            Please provide a detailed analysis including:
-            1. Key entities (people, organizations, locations, dates)
-            2. Main topics and themes
-            3. Sentiment analysis (positive, negative, neutral)
-            4. Key insights and patterns
-            5. Important relationships between entities
-            6. Document structure and organization
-            """
-            
-            analysis_result = await self.service.interfaceAiCalls.callAiTextAdvanced(analysis_prompt)
-            
-            result_data = {
-                "documentCount": len(chatDocuments),
-                "analysis": analysis,
-                "results": analysis_result,
-                "content": combined_content,
-                "timestamp": datetime.now(UTC).isoformat()
-            }
-            
-            return self._createResult(
-                success=True,
-                data={
-                    "documents": [
-                        {
-                            "documentName": f"document_analysis_{datetime.now(UTC).strftime('%Y%m%d_%H%M%S')}.json",
-                            "documentData": result_data
-                        }
-                    ]
-                }
-            )
-        except Exception as e:
-            logger.error(f"Error analyzing content: {str(e)}")
-            return self._createResult(
-                success=False,
-                data={},
-                error=str(e)
-            )
-
-    @action
-    async def summarize(self, parameters: Dict[str, Any]) -> ActionResult:
-        """
-        Summarize document content
-        
-        Parameters:
-            documentList (str): Reference to the document list to summarize
-            aiPrompt (str): AI prompt for content extraction
-            maxLength (int, optional): Maximum length of summary in words (default: 200)
-            format (str, optional): Output format (default: "text")
-        """
-        try:
-            documentList = parameters.get("documentList")
-            aiPrompt = parameters.get("aiPrompt")
-            maxLength = parameters.get("maxLength", 200)
-            format = parameters.get("format", "text")
-            
-            if not documentList:
-                return self._createResult(
-                    success=False,
-                    data={},
-                    error="Document list reference is required"
-                )
-            
-            if not aiPrompt:
-                return self._createResult(
-                    success=False,
-                    data={},
-                    error="AI prompt is required"
-                )
-            
-            chatDocuments = self.service.getChatDocumentsFromDocumentList(documentList)
-            if not chatDocuments:
-                return self._createResult(
-                    success=False,
-                    data={},
-                    error="No documents found for the provided reference"
-                )
-            
-            # Extract content from all documents
-            all_extracted_content = []
-            
-            for chatDocument in chatDocuments:
-                fileId = chatDocument.fileId
-                file_data = self.service.getFileData(fileId)
-                file_info = self.service.getFileInfo(fileId)
-                
-                if not file_data:
-                    logger.warning(f"File not found or empty for fileId: {fileId}")
-                    continue
-                
-                extracted_content = await self.service.extractContentFromFileData(
-                    prompt=aiPrompt,
-                    fileData=file_data,
-                    filename=file_info.get('name', 'document'),
-                    mimeType=file_info.get('mimeType', 'application/octet-stream'),
-                    base64Encoded=False,
-                    documentId=chatDocument.id
-                )
-                
-                all_extracted_content.append(extracted_content)
-            
-            if not all_extracted_content:
-                return self._createResult(
-                    success=False,
-                    data={},
-                    error="No content could be extracted from any documents"
-                )
-            
-            # Combine all extracted content for summarization
-            combined_content = "\n\n--- DOCUMENT SEPARATOR ---\n\n".join(all_extracted_content)
-            
-            summary_prompt = f"""
-            Create a comprehensive summary of this document content.
-            
-            Document content:
-            {combined_content[:8000]}  # Limit content length
-            
-            Requirements:
-            - Maximum length: {maxLength} words
-            - Format: {format}
-            - Include key points and main ideas
-            - Maintain accuracy and completeness
-            - Use clear, professional language
-            - Highlight important insights and conclusions
-            """
-            
-            summary = await self.service.interfaceAiCalls.callAiTextAdvanced(summary_prompt)
-            
-            result_data = {
-                "documentCount": len(chatDocuments),
-                "maxLength": maxLength,
-                "format": format,
-                "summary": summary,
-                "wordCount": len(summary.split()),
-                "originalContent": combined_content,
-                "timestamp": datetime.now(UTC).isoformat()
-            }
-            
-            return self._createResult(
-                success=True,
-                data={
-                    "documents": [
-                        {
-                            "documentName": f"document_summary_{datetime.now(UTC).strftime('%Y%m%d_%H%M%S')}.txt",
-                            "documentData": result_data
-                        }
-                    ]
-                }
-            )
-        except Exception as e:
-            logger.error(f"Error summarizing content: {str(e)}")
-            return self._createResult(
-                success=False,
-                data={},
-                error=str(e)
-            )

modules/routes/routeDataFiles.py

@@ -133,7 +133,7 @@ async def get_file(
                 detail=f"File with ID {fileId} not found"
             )
             
-        return FileItem(**fileData)
+        return fileData
             
     except interfaceComponentObjects.FileNotFoundError as e:
         logger.warning(f"File not found: {str(e)}")
@@ -180,8 +180,8 @@ async def update_file(
                 detail=f"File with ID {fileId} not found"
             )
             
-        # Check if user has access to the file
+        # Check if user has access to the file using the interface's permission system
-        if file.get("userId", 0) != currentUser.get("id", 0):
+        if not managementInterface._canModify("files", fileId):
             raise HTTPException(
                 status_code=status.HTTP_403_FORBIDDEN,
                 detail="Not authorized to update this file"
@@ -195,9 +195,9 @@ async def update_file(
                 detail="Failed to update file"
             )
             
-        # Get updated file and convert to FileItem
+        # Get updated file
         updatedFile = managementInterface.getFile(fileId)
-        return FileItem(**updatedFile)
+        return updatedFile
         
     except HTTPException as he:
         raise he
@@ -328,15 +328,15 @@ async def preview_file(
     try:
         managementInterface = interfaceComponentObjects.getInterface(currentUser)
         
-        # Get file preview
+        # Get file preview using the correct method
-        preview = managementInterface.getFilePreview(fileId)
+        preview = managementInterface.getFileContent(fileId)
         if not preview:
             raise HTTPException(
                 status_code=status.HTTP_404_NOT_FOUND,
                 detail=f"File with ID {fileId} not found or no content available"
             )
         
-        return FilePreview(**preview)
+        return preview
     except HTTPException:
         raise
     except Exception as e:

modules/routes/routeDataPrompts.py

@@ -54,7 +54,7 @@ async def create_prompt(
     # Create prompt
     newPrompt = managementInterface.createPrompt(prompt_data)
     
-    return Prompt.from_dict(newPrompt)
+    return Prompt(**newPrompt)
 
 @router.get("/{promptId}", response_model=Prompt)
 @limiter.limit("30/minute")
@@ -74,7 +74,7 @@ async def get_prompt(
             detail=f"Prompt with ID {promptId} not found"
         )
     
-    return Prompt.from_dict(prompt)
+    return prompt
 
 @router.put("/{promptId}", response_model=Prompt)
 @limiter.limit("10/minute")
@@ -107,7 +107,7 @@ async def update_prompt(
             detail="Error updating the prompt"
         )
     
-    return Prompt.from_dict(updatedPrompt)
+    return Prompt(**updatedPrompt)
 
 @router.delete("/{promptId}", response_model=Dict[str, Any])
 @limiter.limit("10/minute")

modules/routes/routeWorkflows.py

@@ -48,7 +48,7 @@ def getServiceChat(currentUser: User):
 
 # Consolidated endpoint for getting all workflows
 @router.get("/", response_model=List[ChatWorkflow])
-@limiter.limit("30/minute")
+@limiter.limit("120/minute")
 async def get_workflows(
     request: Request,
     currentUser: User = Depends(getCurrentUser)
@@ -56,7 +56,31 @@ async def get_workflows(
     """Get all workflows for the current user."""
     try:
         appInterface = getInterface(currentUser)
-        return appInterface.getAllWorkflows()
+        workflows_data = appInterface.getAllWorkflows()
+        
+        # Convert raw dictionaries to ChatWorkflow objects
+        workflows = []
+        for workflow_data in workflows_data:
+            try:
+                workflow = ChatWorkflow(
+                    id=workflow_data["id"],
+                    status=workflow_data.get("status", "running"),
+                    name=workflow_data.get("name"),
+                    currentRound=workflow_data.get("currentRound", 1),
+                    lastActivity=workflow_data.get("lastActivity", appInterface._getCurrentTimestamp()),
+                    startedAt=workflow_data.get("startedAt", appInterface._getCurrentTimestamp()),
+                    logs=[ChatLog(**log) for log in workflow_data.get("logs", [])],
+                    messages=[ChatMessage(**msg) for msg in workflow_data.get("messages", [])],
+                    stats=ChatStat(**workflow_data.get("dataStats", {})) if workflow_data.get("dataStats") else None,
+                    mandateId=workflow_data.get("mandateId", currentUser.mandateId or "")
+                )
+                workflows.append(workflow)
+            except Exception as e:
+                logger.warning(f"Error converting workflow data to ChatWorkflow object: {str(e)}")
+                # Skip invalid workflows instead of failing the entire request
+                continue
+        
+        return workflows
     except Exception as e:
         logger.error(f"Error getting workflows: {str(e)}")
         raise HTTPException(
@@ -65,7 +89,7 @@ async def get_workflows(
         )
 
 @router.get("/{workflowId}", response_model=ChatWorkflow)
-@limiter.limit("30/minute")
+@limiter.limit("120/minute")
 async def get_workflow(
     request: Request,
     workflowId: str = Path(..., description="ID of the workflow"),
@@ -93,9 +117,58 @@ async def get_workflow(
             detail=f"Failed to get workflow: {str(e)}"
         )
 
+@router.put("/{workflowId}", response_model=ChatWorkflow)
+@limiter.limit("120/minute")
+async def update_workflow(
+    request: Request,
+    workflowId: str = Path(..., description="ID of the workflow to update"),
+    workflowData: Dict[str, Any] = Body(...),
+    currentUser: User = Depends(getCurrentUser)
+) -> ChatWorkflow:
+    """Update workflow by ID"""
+    try:
+        # Get workflow interface with current user context
+        workflowInterface = getInterface(currentUser)
+        
+        # Get raw workflow data from database to check permissions
+        workflows = workflowInterface.db.getRecordset("workflows", recordFilter={"id": workflowId})
+        if not workflows:
+            raise HTTPException(
+                status_code=status.HTTP_404_NOT_FOUND,
+                detail="Workflow not found"
+            )
+        
+        workflow_data = workflows[0]
+        
+        # Check if user has permission to update using the interface's permission system
+        if not workflowInterface._canModify("workflows", workflowId):
+            raise HTTPException(
+                status_code=status.HTTP_403_FORBIDDEN,
+                detail="You don't have permission to update this workflow"
+            )
+        
+        # Update workflow
+        updatedWorkflow = workflowInterface.updateWorkflow(workflowId, workflowData)
+        if not updatedWorkflow:
+            raise HTTPException(
+                status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+                detail="Failed to update workflow"
+            )
+            
+        return updatedWorkflow
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"Error updating workflow: {str(e)}")
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail=f"Failed to update workflow: {str(e)}"
+        )
+
 # API Endpoint for workflow status
 @router.get("/{workflowId}/status", response_model=ChatWorkflow)
-@limiter.limit("30/minute")
+@limiter.limit("120/minute")
 async def get_workflow_status(
     request: Request,
     workflowId: str = Path(..., description="ID of the workflow"),
@@ -114,7 +187,7 @@ async def get_workflow_status(
                 detail=f"Workflow with ID {workflowId} not found"
             )
         
-        return ChatWorkflow(**workflow)
+        return workflow
     except HTTPException:
         raise
     except Exception as e:
@@ -126,7 +199,7 @@ async def get_workflow_status(
 
 # API Endpoint for workflow logs with selective data transfer
 @router.get("/{workflowId}/logs", response_model=List[ChatLog])
-@limiter.limit("30/minute")
+@limiter.limit("120/minute")
 async def get_workflow_logs(
     request: Request,
     workflowId: str = Path(..., description="ID of the workflow"),
@@ -152,12 +225,12 @@ async def get_workflow_logs(
         # Apply selective data transfer if logId is provided
         if logId:
             # Find the index of the log with the given ID
-            logIndex = next((i for i, log in enumerate(allLogs) if log.get("id") == logId), -1)
+            logIndex = next((i for i, log in enumerate(allLogs) if log.id == logId), -1)
             if logIndex >= 0:
                 # Return only logs after the specified log
-                return [ChatLog(**log) for log in allLogs[logIndex + 1:]]
+                return allLogs[logIndex + 1:]
         
-        return [ChatLog(**log) for log in allLogs]
+        return allLogs
     except HTTPException:
         raise
     except Exception as e:
@@ -169,7 +242,7 @@ async def get_workflow_logs(
 
 # API Endpoint for workflow messages with selective data transfer
 @router.get("/{workflowId}/messages", response_model=List[ChatMessage])
-@limiter.limit("30/minute")
+@limiter.limit("120/minute")
 async def get_workflow_messages(
     request: Request,
     workflowId: str = Path(..., description="ID of the workflow"),
@@ -195,12 +268,12 @@ async def get_workflow_messages(
         # Apply selective data transfer if messageId is provided
         if messageId:
             # Find the index of the message with the given ID
-            messageIndex = next((i for i, msg in enumerate(allMessages) if msg.get("id") == messageId), -1)
+            messageIndex = next((i for i, msg in enumerate(allMessages) if msg.id == messageId), -1)
             if messageIndex >= 0:
                 # Return only messages after the specified message
-                return [ChatMessage(**msg) for msg in allMessages[messageIndex + 1:]]
+                return allMessages[messageIndex + 1:]
         
-        return [ChatMessage(**msg) for msg in allMessages]
+        return allMessages
     except HTTPException:
         raise
     except Exception as e:
@@ -212,7 +285,7 @@ async def get_workflow_messages(
 
 # State 1: Workflow Initialization endpoint
 @router.post("/start", response_model=ChatWorkflow)
-@limiter.limit("10/minute")
+@limiter.limit("120/minute")
 async def start_workflow(
     request: Request,
     workflowId: Optional[str] = Query(None, description="Optional ID of the workflow to continue"),
@@ -230,7 +303,7 @@ async def start_workflow(
         # Start or continue workflow using ChatObjects
         workflow = await interfaceChat.workflowStart(currentUser, userInput, workflowId)
         
-        return ChatWorkflow(**workflow)
+        return workflow
         
     except Exception as e:
         logger.error(f"Error in start_workflow: {str(e)}")
@@ -241,7 +314,7 @@ async def start_workflow(
 
 # State 8: Workflow Stopped endpoint
 @router.post("/{workflowId}/stop", response_model=ChatWorkflow)
-@limiter.limit("10/minute")
+@limiter.limit("120/minute")
 async def stop_workflow(
     request: Request,
     workflowId: str = Path(..., description="ID of the workflow to stop"),
@@ -255,7 +328,7 @@ async def stop_workflow(
         # Stop workflow using ChatObjects
         workflow = await interfaceChat.workflowStop(workflowId)
         
-        return ChatWorkflow(**workflow)
+        return workflow
         
     except Exception as e:
         logger.error(f"Error in stop_workflow: {str(e)}")
@@ -266,7 +339,7 @@ async def stop_workflow(
 
 # State 11: Workflow Reset/Deletion endpoint
 @router.delete("/{workflowId}", response_model=Dict[str, Any])
-@limiter.limit("10/minute")
+@limiter.limit("120/minute")
 async def delete_workflow(
     request: Request,
     workflowId: str = Path(..., description="ID of the workflow to delete"),
@@ -277,16 +350,18 @@ async def delete_workflow(
         # Get service container
         interfaceChat = getServiceChat(currentUser)
         
-        # Verify workflow exists
+        # Get raw workflow data from database to check permissions
-        workflow = interfaceChat.getWorkflow(workflowId)
+        workflows = interfaceChat.db.getRecordset("workflows", recordFilter={"id": workflowId})
-        if not workflow:
+        if not workflows:
             raise HTTPException(
                 status_code=status.HTTP_404_NOT_FOUND,
                 detail=f"Workflow with ID {workflowId} not found"
             )
         
-        # Check if user has permission to delete
+        workflow_data = workflows[0]
-        if workflow.get("_userId") != currentUser["id"]:
+        
+        # Check if user has permission to delete using the interface's permission system
+        if not interfaceChat._canModify("workflows", workflowId):
             raise HTTPException(
                 status_code=status.HTTP_403_FORBIDDEN,
                 detail="You don't have permission to delete this workflow"
@@ -318,7 +393,7 @@ async def delete_workflow(
 # Document Management Endpoints
 
 @router.delete("/{workflowId}/messages/{messageId}", response_model=Dict[str, Any])
-@limiter.limit("10/minute")
+@limiter.limit("120/minute")
 async def delete_workflow_message(
     request: Request,
     workflowId: str = Path(..., description="ID of the workflow"),
@@ -368,7 +443,7 @@ async def delete_workflow_message(
         )
 
 @router.delete("/{workflowId}/messages/{messageId}/files/{fileId}", response_model=Dict[str, Any])
-@limiter.limit("10/minute")
+@limiter.limit("120/minute")
 async def delete_file_from_message(
     request: Request,
     workflowId: str = Path(..., description="ID of the workflow"),

modules/workflow/managerDocument.py

@@ -17,8 +17,8 @@ class DocumentManager:
     
     def __init__(self, serviceContainer):
         self.service = serviceContainer
-        # Create processor without any dependencies
+        # Create processor with service container for AI calls
-        self._processor = DocumentProcessor()
+        self._processor = DocumentProcessor(serviceContainer)
     
     async def extractContentFromDocument(self, prompt: str, document: ChatDocument) -> ExtractedContent:
         """Extract content from ChatDocument using prompt"""

modules/workflow/managerWorkflow.py

@@ -52,8 +52,56 @@ class WorkflowManager:
             
         except WorkflowStoppedException:
             logger.info("Workflow stopped by user")
+            # Update workflow status to stopped
+            workflow.status = "stopped"
+            workflow.lastActivity = datetime.now(UTC).isoformat()
+            self.chatInterface.updateWorkflow(workflow.id, {
+                "status": "stopped",
+                "lastActivity": workflow.lastActivity
+            })
+            
+            # Add log entry
+            self.chatInterface.createWorkflowLog({
+                "workflowId": workflow.id,
+                "message": "Workflow stopped by user",
+                "type": "warning",
+                "status": "stopped",
+                "progress": 100
+            })
+            
         except Exception as e:
             logger.error(f"Workflow processing error: {str(e)}")
+            
+            # Update workflow status to failed
+            workflow.status = "failed"
+            workflow.lastActivity = datetime.now(UTC).isoformat()
+            self.chatInterface.updateWorkflow(workflow.id, {
+                "status": "failed",
+                "lastActivity": workflow.lastActivity
+            })
+            
+            # Create error message
+            error_message = {
+                "workflowId": workflow.id,
+                "role": "assistant",
+                "message": f"Workflow processing failed: {str(e)}",
+                "status": "last",
+                "sequenceNr": len(workflow.messages) + 1,
+                "publishedAt": datetime.now(UTC).isoformat()
+            }
+            message = self.chatInterface.createWorkflowMessage(error_message)
+            if message:
+                workflow.messages.append(message)
+            
+            # Add error log entry
+            self.chatInterface.createWorkflowLog({
+                "workflowId": workflow.id,
+                "message": f"Workflow failed: {str(e)}",
+                "type": "error",
+                "status": "failed",
+                "progress": 100
+            })
+            
             raise
             
     async def _sendFirstMessage(self, userInput: UserInputRequest, workflow: ChatWorkflow) -> ChatMessage:
@@ -108,6 +156,25 @@ class WorkflowManager:
             if message:
                 workflow.messages.append(message)
             
+            # Update workflow status to completed
+            workflow.status = "completed"
+            workflow.lastActivity = datetime.now(UTC).isoformat()
+            
+            # Update workflow in database
+            self.chatInterface.updateWorkflow(workflow.id, {
+                "status": "completed",
+                "lastActivity": workflow.lastActivity
+            })
+            
+            # Add completion log entry
+            self.chatInterface.createWorkflowLog({
+                "workflowId": workflow.id,
+                "message": "Workflow completed successfully",
+                "type": "success",
+                "status": "completed",
+                "progress": 100
+            })
+            
         except Exception as e:
             logger.error(f"Error sending last message: {str(e)}")
             raise
@@ -128,6 +195,14 @@ class WorkflowManager:
                 message = self.chatInterface.createWorkflowMessage(error_message)
                 if message:
                     workflow.messages.append(message)
+                
+                # Update workflow status to failed
+                workflow.status = "failed"
+                workflow.lastActivity = datetime.now(UTC).isoformat()
+                self.chatInterface.updateWorkflow(workflow.id, {
+                    "status": "failed",
+                    "lastActivity": workflow.lastActivity
+                })
                 return
             
             # Process successful workflow results
@@ -174,6 +249,14 @@ class WorkflowManager:
             if message:
                 workflow.messages.append(message)
             
+            # Update workflow status to completed for successful workflows
+            workflow.status = "completed"
+            workflow.lastActivity = datetime.now(UTC).isoformat()
+            self.chatInterface.updateWorkflow(workflow.id, {
+                "status": "completed",
+                "lastActivity": workflow.lastActivity
+            })
+            
         except Exception as e:
             logger.error(f"Error processing workflow results: {str(e)}")
             # Create error message
@@ -188,4 +271,12 @@ class WorkflowManager:
             message = self.chatInterface.createWorkflowMessage(error_message)
             if message:
                 workflow.messages.append(message)
+            
+            # Update workflow status to failed
+            workflow.status = "failed"
+            workflow.lastActivity = datetime.now(UTC).isoformat()
+            self.chatInterface.updateWorkflow(workflow.id, {
+                "status": "failed",
+                "lastActivity": workflow.lastActivity
+            })
 

modules/workflow/processorDocument.py

@@ -32,9 +32,10 @@ class FileProcessingError(Exception):
 class DocumentProcessor:
     """Processor for handling document operations and content extraction."""
     
-    def __init__(self):
+    def __init__(self, serviceContainer=None):
         """Initialize the document processor."""
         self._neutralizer = DataAnonymizer() if APP_CONFIG.get("ENABLE_CONTENT_NEUTRALIZATION", False) else None
+        self._serviceContainer = serviceContainer
 
         self.supportedTypes: Dict[str, Callable[[bytes, str, str], Awaitable[List[ContentItem]]]] = {
             'text/plain': self._processText,
@@ -108,7 +109,9 @@ class DocumentProcessor:
                 logger.info("Image processing libraries successfully loaded")
             except ImportError as e:
                 logger.warning(f"Image processing libraries could not be loaded: {e}")
-    
+
+
+
     async def processFileData(self, fileData: bytes, filename: str, mimeType: str, base64Encoded: bool = False, prompt: str = None, documentId: str = None) -> ExtractedContent:
         """
         Process file data directly and extract its contents with AI processing.
@@ -133,7 +136,7 @@ class DocumentProcessor:
             
             # Detect content type if needed
             if mimeType == "application/octet-stream":
-                mimeType = self._detectContentTypeFromData(fileData, filename)
+                mimeType = self._serviceContainer.detectContentTypeFromData(fileData, filename)
             
             # Process document based on type
             if mimeType not in self.supportedTypes:
@@ -161,61 +164,8 @@ class DocumentProcessor:
         except Exception as e:
             logger.error(f"Error processing file data: {str(e)}")
             raise FileProcessingError(f"Failed to process file data: {str(e)}")
-    
+   
-    def _detectContentTypeFromData(self, fileData: bytes, filename: str) -> str:
+
-        """Detect content type from file data and filename"""
-        try:
-            # Check file extension first
-            ext = os.path.splitext(filename)[1].lower()
-            if ext:
-                # Map common extensions to MIME types
-                extToMime = {
-                    '.txt': 'text/plain',
-                    '.md': 'text/markdown',
-                    '.csv': 'text/csv',
-                    '.json': 'application/json',
-                    '.xml': 'application/xml',
-                    '.js': 'application/javascript',
-                    '.py': 'application/x-python',
-                    '.svg': 'image/svg+xml',
-                    '.jpg': 'image/jpeg',
-                    '.png': 'image/png',
-                    '.gif': 'image/gif',
-                    '.pdf': 'application/pdf',
-                    '.docx': 'application/vnd.openxmlformats-officedocument.wordprocessingml.document',
-                    '.doc': 'application/msword',
-                    '.xlsx': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet',
-                    '.xls': 'application/vnd.ms-excel',
-                    '.pptx': 'application/vnd.openxmlformats-officedocument.presentationml.presentation',
-                    '.ppt': 'application/vnd.ms-powerpoint'
-                }
-                if ext in extToMime:
-                    return extToMime[ext]
-            
-            # Try to detect from content
-            if fileData.startswith(b'%PDF'):
-                return 'application/pdf'
-            elif fileData.startswith(b'PK\x03\x04'):
-                # ZIP-based formats (docx, xlsx, pptx)
-                return 'application/zip'
-            elif fileData.startswith(b'<'):
-                # XML-based formats
-                try:
-                    text = fileData.decode('utf-8', errors='ignore')
-                    if '<svg' in text.lower():
-                        return 'image/svg+xml'
-                    elif '<html' in text.lower():
-                        return 'text/html'
-                    else:
-                        return 'application/xml'
-                except:
-                    pass
-            
-            return 'application/octet-stream'
-            
-        except Exception as e:
-            logger.error(f"Error detecting content type from data: {str(e)}")
-            return 'application/octet-stream'
     
     async def _processText(self, fileData: bytes, filename: str, mimeType: str) -> List[ContentItem]:
         """Process text document"""
@@ -546,14 +496,22 @@ class DocumentProcessor:
             try:
                 # Get content type from metadata
                 mimeType = item.metadata.mimeType if hasattr(item.metadata, 'mimeType') else "text/plain"
+                logger.debug(f"Processing content item with MIME type: {mimeType}, label: {item.label}")
                 
                 # Chunk content based on type
                 if mimeType.startswith('text/'):
                     chunks = self._chunkText(item.data, mimeType)
                 elif mimeType.startswith('image/'):
-                    chunks = self._chunkImage(item.data)
+                    # Images should not be chunked - process as single unit
-                elif mimeType.startswith('video/'):
+                    chunks = [item.data]
-                    chunks = self._chunkVideo(item.data)
+                elif mimeType == "application/pdf":
+                    chunks = self._chunkPdf(item.data)
+                elif mimeType == "application/vnd.openxmlformats-officedocument.wordprocessingml.document":
+                    chunks = self._chunkDocx(item.data)
+                elif mimeType == "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet":
+                    chunks = self._chunkXlsx(item.data)
+                elif mimeType.startswith('application/vnd.openxmlformats-officedocument.presentationml.presentation'):
+                    chunks = self._chunkPptx(item.data)
                 else:
                     # Binary data - no chunking
                     chunks = [item.data]
@@ -561,26 +519,42 @@ class DocumentProcessor:
                 # Process each chunk
                 chunkResults = []
                 for chunk in chunks:
-                    # Neutralize content if neutralizer is enabled
+                    # Process with AI based on content type
-                    contentToProcess = chunk
+                    try:
-                    if self._neutralizer and contentToProcess:
+                        logger.debug(f"AI processing chunk with MIME type: {mimeType}")
-                        contentToProcess = self._neutralizer.neutralize(contentToProcess)
+                        if mimeType.startswith('image/'):
-                    
+                            # For images, use image AI service with base64 data
-                    # Create AI prompt for this chunk
+                            # chunk is already base64 encoded string from _processImage
-                    aiPrompt = f"""
+                            # Use the original prompt directly for images (no content embedding)
-                    Extract relevant information from this content based on the following prompt:
+                            logger.debug(f"Calling image AI service for MIME type: {mimeType}")
-                    
+                            processedContent = await self._serviceContainer.callAiImageBasic(prompt, chunk, mimeType)
-                    PROMPT: {prompt}
+                        else:
-                    
+                            # For text content, use text AI service
-                    CONTENT:
+                            # Neutralize content if neutralizer is enabled (only for text)
-                    {contentToProcess}
+                            contentToProcess = chunk
-                    
+                            if self._neutralizer and contentToProcess:
-                    Return ONLY the extracted information in a clear, concise format.
+                                contentToProcess = self._neutralizer.neutralize(contentToProcess)
-                    """
+                            
-                    
+                            # Create AI prompt for text content
-                    # Note: This would need to be implemented with actual AI service
+                            aiPrompt = f"""
-                    # For now, just return the original content
+                            Extract relevant information from this content based on the following prompt:
-                    chunkResults.append(contentToProcess)
+                            
+                            PROMPT: {prompt}
+                            
+                            CONTENT:
+                            {contentToProcess}
+                            
+                            Return ONLY the extracted information in a clear, concise format.
+                            """
+                            
+                            logger.debug(f"Calling text AI service for MIME type: {mimeType}")
+                            processedContent = await self._serviceContainer.callAiTextBasic(aiPrompt, contentToProcess)
+                        
+                        chunkResults.append(processedContent)
+                    except Exception as aiError:
+                        logger.error(f"AI processing failed for chunk: {str(aiError)}")
+                        # Fallback to original content
+                        chunkResults.append(chunk)
                 
                 # Combine chunk results
                 combinedResult = "\n".join(chunkResults)
@@ -604,6 +578,8 @@ class DocumentProcessor:
         
         return processedItems
 
+
+
     def _chunkText(self, content: str, mimeType: str) -> List[str]:
         """Chunk text content based on mime type"""
         if mimeType == "text/plain":
@@ -765,36 +741,6 @@ class DocumentProcessor:
         except Exception:
             return [content]
 
-    def _chunkImage(self, content: str) -> List[str]:
-        """Chunk image content"""
-        try:
-            imageData = base64.b64decode(content)
-            chunks = []
-            chunkSize = self.chunkSizes["image"]
-            
-            for i in range(0, len(imageData), chunkSize):
-                chunk = imageData[i:i + chunkSize]
-                chunks.append(base64.b64encode(chunk).decode('utf-8'))
-            
-            return chunks
-        except Exception:
-            return [content]
-
-    def _chunkVideo(self, content: str) -> List[str]:
-        """Chunk video content"""
-        try:
-            videoData = base64.b64decode(content)
-            chunks = []
-            chunkSize = self.chunkSizes["video"]
-            
-            for i in range(0, len(videoData), chunkSize):
-                chunk = videoData[i:i + chunkSize]
-                chunks.append(base64.b64encode(chunk).decode('utf-8'))
-            
-            return chunks
-        except Exception:
-            return [content]
-
     def _chunkBinary(self, content: str) -> List[str]:
         """Chunk binary content"""
         try:
@@ -810,4 +756,87 @@ class DocumentProcessor:
         except Exception:
             return [content]
 
+    async def _chunkPdf(self, content: str) -> List[str]:
+        """Chunk PDF content"""
+        try:
+            pdfData = base64.b64decode(content)
+            chunks = []
+            chunkSize = self.chunkSizes["pdf"]
+            
+            with io.BytesIO(pdfData) as pdfStream:
+                pdfReader = PyPDF2.PdfReader(pdfStream)
+                for pageNum in range(len(pdfReader.pages)):
+                    page = pdfReader.pages[pageNum]
+                    pageText = page.extract_text()
+                    if pageText:
+                        chunks.append(pageText)
+            
+            return chunks
+        except Exception:
+            return [content]
+
+    async def _chunkDocx(self, content: str) -> List[str]:
+        """Chunk Word document content"""
+        try:
+            docxData = base64.b64decode(content)
+            chunks = []
+            chunkSize = self.chunkSizes["docx"]
+            
+            with io.BytesIO(docxData) as docxStream:
+                doc = docx.Document(docxStream)
+                for para in doc.paragraphs:
+                    chunks.append(para.text)
+                for table in doc.tables:
+                    for row in table.rows:
+                        rowText = []
+                        for cell in row.cells:
+                            rowText.append(cell.text)
+                        chunks.append(" | ".join(rowText))
+            
+            return chunks
+        except Exception:
+            return [content]
+
+    async def _chunkXlsx(self, content: str) -> List[str]:
+        """Chunk Excel document content"""
+        try:
+            xlsxData = base64.b64decode(content)
+            chunks = []
+            chunkSize = self.chunkSizes["xlsx"]
+            
+            with io.BytesIO(xlsxData) as xlsxStream:
+                workbook = openpyxl.load_workbook(xlsxStream, data_only=True)
+                for sheetName in workbook.sheetnames:
+                    sheet = workbook[sheetName]
+                    for row in sheet.iter_rows():
+                        rowText = []
+                        for cell in row:
+                            value = cell.value
+                            if value is None:
+                                rowText.append("")
+                            else:
+                                rowText.append(str(value).replace('"', '""'))
+                        chunks.append(','.join(f'"{cell}"' for cell in rowText))
+            
+            return chunks
+        except Exception:
+            return [content]
+
+    async def _chunkPptx(self, content: str) -> List[str]:
+        """Chunk PowerPoint document content"""
+        try:
+            pptxData = base64.b64decode(content)
+            chunks = []
+            chunkSize = self.chunkSizes["pptx"]
+            
+            with io.BytesIO(pptxData) as pptxStream:
+                # openpyxl is not suitable for PowerPoint, so we'll just read text
+                # This is a placeholder and would require a different library for full pptx processing
+                # For now, we'll just return the base64 encoded content as a single chunk
+                chunks.append(content)
+            
+            return chunks
+        except Exception:
+            return [content]
+
         

modules/workflow/serviceContainer.py

@@ -2,6 +2,7 @@ import logging
 import importlib
 import pkgutil
 import inspect
+import os
 from typing import Dict, Any, List, Optional
 from modules.interfaces.interfaceAppModel import User, UserConnection
 from modules.interfaces.interfaceChatModel import (
@@ -111,6 +112,155 @@ class ServiceContainer:
         except Exception as e:
             logger.error(f"Error discovering methods: {str(e)}")
 
+    def detectContentTypeFromData(self, fileData: bytes, filename: str) -> str:
+        """
+        Detect content type from file data and filename.
+        This method makes the MIME type detection function accessible through the service container.
+        
+        Args:
+            fileData: Raw file data as bytes
+            filename: Name of the file
+            
+        Returns:
+            str: Detected MIME type
+        """
+        try:
+            # Check file extension first
+            ext = os.path.splitext(filename)[1].lower()
+            if ext:
+                # Map common extensions to MIME types
+                extToMime = {
+                    '.txt': 'text/plain',
+                    '.md': 'text/markdown',
+                    '.csv': 'text/csv',
+                    '.json': 'application/json',
+                    '.xml': 'application/xml',
+                    '.js': 'application/javascript',
+                    '.py': 'application/x-python',
+                    '.svg': 'image/svg+xml',
+                    '.jpg': 'image/jpeg',
+                    '.jpeg': 'image/jpeg',
+                    '.png': 'image/png',
+                    '.gif': 'image/gif',
+                    '.bmp': 'image/bmp',
+                    '.webp': 'image/webp',
+                    '.pdf': 'application/pdf',
+                    '.docx': 'application/vnd.openxmlformats-officedocument.wordprocessingml.document',
+                    '.doc': 'application/msword',
+                    '.xlsx': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet',
+                    '.xls': 'application/vnd.ms-excel',
+                    '.pptx': 'application/vnd.openxmlformats-officedocument.presentationml.presentation',
+                    '.ppt': 'application/vnd.ms-powerpoint',
+                    '.html': 'text/html',
+                    '.htm': 'text/html',
+                    '.css': 'text/css',
+                    '.zip': 'application/zip',
+                    '.rar': 'application/x-rar-compressed',
+                    '.7z': 'application/x-7z-compressed',
+                    '.tar': 'application/x-tar',
+                    '.gz': 'application/gzip'
+                }
+                if ext in extToMime:
+                    return extToMime[ext]
+            
+            # Try to detect from content
+            if fileData.startswith(b'%PDF'):
+                return 'application/pdf'
+            elif fileData.startswith(b'PK\x03\x04'):
+                # ZIP-based formats (docx, xlsx, pptx)
+                return 'application/zip'
+            elif fileData.startswith(b'<'):
+                # XML-based formats
+                try:
+                    text = fileData.decode('utf-8', errors='ignore')
+                    if '<svg' in text.lower():
+                        return 'image/svg+xml'
+                    elif '<html' in text.lower():
+                        return 'text/html'
+                    else:
+                        return 'application/xml'
+                except:
+                    pass
+            elif fileData.startswith(b'\x89PNG\r\n\x1a\n'):
+                return 'image/png'
+            elif fileData.startswith(b'\xff\xd8\xff'):
+                return 'image/jpeg'
+            elif fileData.startswith(b'GIF87a') or fileData.startswith(b'GIF89a'):
+                return 'image/gif'
+            elif fileData.startswith(b'BM'):
+                return 'image/bmp'
+            elif fileData.startswith(b'RIFF') and fileData[8:12] == b'WEBP':
+                return 'image/webp'
+            
+            return 'application/octet-stream'
+            
+        except Exception as e:
+            logger.error(f"Error detecting content type from data: {str(e)}")
+            return 'application/octet-stream'
+
+    def getMimeTypeFromExtension(self, extension: str) -> str:
+        """
+        Get MIME type based on file extension.
+        This method consolidates MIME type detection from extension.
+        
+        Args:
+            extension: File extension (with or without dot)
+            
+        Returns:
+            str: MIME type for the extension
+        """
+        # Normalize extension (remove dot if present)
+        if extension.startswith('.'):
+            extension = extension[1:]
+        
+        # Map extensions to MIME types
+        mime_types = {
+            'txt': 'text/plain',
+            'json': 'application/json',
+            'xml': 'application/xml',
+            'csv': 'text/csv',
+            'html': 'text/html',
+            'htm': 'text/html',
+            'md': 'text/markdown',
+            'py': 'text/x-python',
+            'js': 'application/javascript',
+            'css': 'text/css',
+            'pdf': 'application/pdf',
+            'doc': 'application/msword',
+            'docx': 'application/vnd.openxmlformats-officedocument.wordprocessingml.document',
+            'xls': 'application/vnd.ms-excel',
+            'xlsx': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet',
+            'ppt': 'application/vnd.ms-powerpoint',
+            'pptx': 'application/vnd.openxmlformats-officedocument.presentationml.presentation',
+            'svg': 'image/svg+xml',
+            'jpg': 'image/jpeg',
+            'jpeg': 'image/jpeg',
+            'png': 'image/png',
+            'gif': 'image/gif',
+            'bmp': 'image/bmp',
+            'webp': 'image/webp',
+            'zip': 'application/zip',
+            'rar': 'application/x-rar-compressed',
+            '7z': 'application/x-7z-compressed',
+            'tar': 'application/x-tar',
+            'gz': 'application/gzip'
+        }
+        return mime_types.get(extension.lower(), 'application/octet-stream')
+
+    def getFileExtension(self, filename: str) -> str:
+        """
+        Extract file extension from filename.
+        
+        Args:
+            filename: Name of the file
+            
+        Returns:
+            str: File extension (without dot)
+        """
+        if '.' in filename:
+            return filename.split('.')[-1].lower()
+        return "txt"  # Default to text
+
     # ===== Functions =====
     
     def extractContent(self, prompt: str, document: ChatDocument) -> str:
@@ -399,11 +549,11 @@ Please provide a clear summary of this message."""
         """Advanced text processing using Anthropic"""
         return self.interfaceAiCalls.callAiTextAdvanced(prompt, context)
 
-    def callAiImageBasic(self, prompt: str, imageData: bytes, mimeType: str) -> str:
+    def callAiImageBasic(self, prompt: str, imageData: str, mimeType: str) -> str:
         """Basic image processing using OpenAI"""
         return self.interfaceAiCalls.callAiImageBasic(prompt, imageData, mimeType)
 
-    def callAiImageAdvanced(self, prompt: str, imageData: bytes, mimeType: str) -> str:
+    def callAiImageAdvanced(self, prompt: str, imageData: str, mimeType: str) -> str:
         """Advanced image processing using Anthropic"""
         return self.interfaceAiCalls.callAiImageAdvanced(prompt, imageData, mimeType)
 
@@ -463,6 +613,30 @@ Please provide a clear summary of this message."""
             mimeType=mimeType
         )
 
+    def extractTextFromContentObjects(self, content_objects: List[Any]) -> List[str]:
+        """
+        Extract text content from ExtractedContent objects or other content objects.
+        
+        Args:
+            content_objects: List of ExtractedContent objects or other content objects
+            
+        Returns:
+            List of extracted text strings
+        """
+        text_contents = []
+        for content_obj in content_objects:
+            if hasattr(content_obj, 'contents') and content_obj.contents:
+                # Extract text from ContentItem objects
+                for content_item in content_obj.contents:
+                    if hasattr(content_item, 'data') and content_item.data:
+                        text_contents.append(content_item.data)
+            elif isinstance(content_obj, str):
+                text_contents.append(content_obj)
+            else:
+                # Fallback: convert to string representation
+                text_contents.append(str(content_obj))
+        return text_contents
+
     async def executeAction(self, methodName: str, actionName: str, parameters: Dict[str, Any]) -> ActionResult:
         """Execute a method action"""
         try:

run_document_test.ps1

@@ -0,0 +1,31 @@
+# PowerShell script to run document extraction test
+# Usage: .\run_document_test.ps1 [file_path]
+
+param(
+    [string]$FilePath = "test_sample_document.txt"
+)
+
+Write-Host "=== PowerOn Document Extraction Test ===" -ForegroundColor Green
+Write-Host ""
+
+# Check if file exists
+if (-not (Test-Path $FilePath)) {
+    Write-Host "Error: File not found: $FilePath" -ForegroundColor Red
+    Write-Host "Please provide a valid file path as parameter or ensure test_sample_document.txt exists." -ForegroundColor Yellow
+    exit 1
+}
+
+Write-Host "Testing document extraction for file: $FilePath" -ForegroundColor Cyan
+Write-Host "Log file will be: test_document_extraction.log" -ForegroundColor Cyan
+Write-Host ""
+
+# Run the Python test
+try {
+    python test_document_extraction.py $FilePath
+    Write-Host ""
+    Write-Host "Test completed successfully!" -ForegroundColor Green
+    Write-Host "Check test_document_extraction.log for detailed results." -ForegroundColor Cyan
+} catch {
+    Write-Host "Test failed with error: $($_.Exception.Message)" -ForegroundColor Red
+    exit 1
+} 

test_document_extraction.py

@@ -0,0 +1,288 @@
+#!/usr/bin/env python3
+"""
+Test procedure for DocumentManager document extraction functionality.
+"""
+
+import asyncio
+import sys
+import os
+import json
+import argparse
+from datetime import datetime, UTC
+from pathlib import Path
+import logging
+
+print("Starting test_document_extraction.py...")
+
+# Configure logging FIRST, before any other imports
+import logging
+
+# Clear any existing handlers to avoid duplicate logs
+for handler in logging.root.handlers[:]:
+    logging.root.removeHandler(handler)
+
+logging.basicConfig(
+    level=logging.DEBUG,
+    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
+    handlers=[
+        logging.StreamHandler(sys.stdout),
+        logging.FileHandler('test_document_extraction.log', mode='w', encoding='utf-8')  # 'w' mode clears the file
+    ],
+    force=True  # Force reconfiguration even if already configured
+)
+
+# Filter out httpcore messages
+logging.getLogger('httpcore').setLevel(logging.WARNING)
+logging.getLogger('httpx').setLevel(logging.WARNING)
+
+logger = logging.getLogger(__name__)
+
+# Set up test configuration
+os.environ['POWERON_CONFIG_FILE'] = 'test_config.ini'
+print("Set POWERON_CONFIG_FILE environment variable")
+
+try:
+    # Import required modules
+    from modules.interfaces.interfaceAppObjects import User, UserConnection
+    from modules.interfaces.interfaceChatModel import ChatWorkflow
+    from modules.workflow.managerDocument import DocumentManager
+    from modules.workflow.serviceContainer import ServiceContainer
+    print("All imports successful")
+except Exception as e:
+    print(f"Import error: {e}")
+    import traceback
+    traceback.print_exc()
+    sys.exit(1)
+
+def log_extraction_debug(message: str, data: dict = None):
+    """Log extraction debug data with JSON dumps"""
+    timestamp = datetime.now(UTC).isoformat()
+    if data:
+        logger.debug(f"[{timestamp}] {message}\n{json.dumps(data, indent=2, ensure_ascii=False)}")
+    else:
+        logger.debug(f"[{timestamp}] {message}")
+
+def create_test_user() -> User:
+    """Create a test user for the document extraction"""
+    return User(
+        id="test-user-doc-001",
+        mandateId="test-mandate-doc-001",
+        username="testuser_doc",
+        email="test_doc@example.com",
+        fullName="Test Document User",
+        enabled=True,
+        language="en",
+        privilege="user",
+        authenticationAuthority="local"
+    )
+
+def create_test_workflow() -> ChatWorkflow:
+    """Create a test workflow for document extraction"""
+    return ChatWorkflow(
+        id="test-workflow-doc-001",
+        mandateId="test-mandate-doc-001",
+        status="running",
+        name="Document Extraction Test Workflow",
+        currentRound=1,
+        lastActivity=datetime.now(UTC).isoformat(),
+        startedAt=datetime.now(UTC).isoformat(),
+        logs=[],
+        messages=[],
+        stats=None,
+        tasks=[]
+    )
+
+def detect_mime_type(file_path: str) -> str:
+    """Detect MIME type based on file extension"""
+    ext = Path(file_path).suffix.lower()
+    mime_types = {
+        '.txt': 'text/plain',
+        '.md': 'text/markdown',
+        '.csv': 'text/csv',
+        '.json': 'application/json',
+        '.xml': 'application/xml',
+        '.js': 'application/javascript',
+        '.py': 'application/x-python',
+        '.svg': 'image/svg+xml',
+        '.jpg': 'image/jpeg',
+        '.jpeg': 'image/jpeg',
+        '.png': 'image/png',
+        '.gif': 'image/gif',
+        '.pdf': 'application/pdf',
+        '.docx': 'application/vnd.openxmlformats-officedocument.wordprocessingml.document',
+        '.doc': 'application/msword',
+        '.xlsx': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet',
+        '.xls': 'application/vnd.ms-excel',
+        '.pptx': 'application/vnd.openxmlformats-officedocument.presentationml.presentation',
+        '.ppt': 'application/vnd.ms-powerpoint',
+        '.html': 'text/html',
+        '.htm': 'text/html'
+    }
+    return mime_types.get(ext, 'application/octet-stream')
+
+async def test_document_extraction(file_path: str):
+    """Test document extraction from a file path"""
+    try:
+        # Clear the log file before each run
+        log_file_path = "test_document_extraction.log"
+        if os.path.exists(log_file_path):
+            with open(log_file_path, 'w') as f:
+                f.write("")  # Clear the file
+            logger.info(f"Cleared log file: {log_file_path}")
+        
+        logger.info("=== STARTING DOCUMENT EXTRACTION TEST ===")
+        
+        # Validate file path
+        if not os.path.exists(file_path):
+            raise FileNotFoundError(f"File not found: {file_path}")
+        
+        # Get file info
+        file_path_obj = Path(file_path)
+        filename = file_path_obj.name
+        mime_type = detect_mime_type(file_path)
+        file_size = file_path_obj.stat().st_size
+        
+        log_extraction_debug("File information", {
+            "file_path": file_path,
+            "filename": filename,
+            "mime_type": mime_type,
+            "file_size_bytes": file_size,
+            "file_size_mb": round(file_size / (1024 * 1024), 2)
+        })
+        
+        # Read file data
+        try:
+            with open(file_path, 'rb') as f:
+                file_data = f.read()
+            log_extraction_debug("File read successfully", {
+                "bytes_read": len(file_data),
+                "file_encoding": "binary"
+            })
+        except Exception as e:
+            logger.error(f"Error reading file: {str(e)}")
+            raise
+        
+        # Create test user and workflow
+        test_user = create_test_user()
+        test_workflow = create_test_workflow()
+        
+        # Create service container
+        service_container = ServiceContainer(test_user, test_workflow)
+        log_extraction_debug("Service container created", {
+            "user_id": test_user.id,
+            "workflow_id": test_workflow.id
+        })
+        
+        # Create document manager
+        document_manager = DocumentManager(service_container)
+        log_extraction_debug("Document manager created")
+        
+        # Define extraction prompt
+        extraction_prompt = "extract the table and convert it to a csv table"
+        
+        log_extraction_debug("Starting document extraction", {
+            "prompt": extraction_prompt,
+            "filename": filename,
+            "mime_type": mime_type
+        })
+        
+        # Extract content from file data
+        try:
+            extracted_content = await document_manager.extractContentFromFileData(
+                prompt=extraction_prompt,
+                fileData=file_data,
+                filename=filename,
+                mimeType=mime_type,
+                base64Encoded=False,
+                documentId=f"test-doc-{datetime.now(UTC).timestamp()}"
+            )
+            
+            # Log extraction results
+            extraction_result = {
+                "extracted_content_id": extracted_content.id,
+                "content_items_count": len(extracted_content.contents)
+            }
+            
+            # Add objectId and objectType if they exist (set by DocumentManager)
+            if hasattr(extracted_content, 'objectId'):
+                extraction_result["object_id"] = extracted_content.objectId
+            if hasattr(extracted_content, 'objectType'):
+                extraction_result["object_type"] = extracted_content.objectType
+                
+            log_extraction_debug("Document extraction completed successfully", extraction_result)
+            
+            # Log detailed content information
+            for i, content_item in enumerate(extracted_content.contents):
+                content_info = {
+                    "label": content_item.label,
+                    "data_length": len(content_item.data) if content_item.data else 0,
+                    "data_preview": content_item.data[:500] + "..." if content_item.data and len(content_item.data) > 500 else content_item.data
+                }
+                
+                # Add metadata if available
+                if content_item.metadata:
+                    content_info["metadata"] = {
+                        "size": content_item.metadata.size,
+                        "mime_type": content_item.metadata.mimeType,
+                        "base64_encoded": content_item.metadata.base64Encoded,
+                        "pages": content_item.metadata.pages
+                    }
+                
+                log_extraction_debug(f"CONTENT ITEM {i+1}:", content_info)
+            
+            # Log summary of all extracted content
+            all_content = "\n\n".join([item.data for item in extracted_content.contents if item.data])
+            log_extraction_debug("COMPLETE EXTRACTED CONTENT:", {
+                "total_length": len(all_content),
+                "content": all_content
+            })
+            
+            return extracted_content
+            
+        except Exception as e:
+            log_extraction_debug("DOCUMENT EXTRACTION EXCEPTION:", {
+                "error_type": type(e).__name__,
+                "error_message": str(e),
+                "error_args": e.args if hasattr(e, 'args') else None
+            })
+            raise
+        
+        logger.info("=== DOCUMENT EXTRACTION TEST COMPLETED ===")
+        return extracted_content
+        
+    except Exception as e:
+        logger.error(f"❌ Document extraction test failed with error: {str(e)}")
+        log_extraction_debug("Full error details", {
+            "error_type": type(e).__name__,
+            "error_message": str(e)
+        })
+        raise
+
+async def main():
+    """Main function to run the document extraction test"""
+    print("Inside main()")
+    logger.info("=" * 50)
+    logger.info("DOCUMENT EXTRACTION TEST")
+    logger.info("=" * 50)
+    
+    # Parse command line arguments
+    parser = argparse.ArgumentParser(description='Test document extraction functionality')
+    parser.add_argument('file_path', help='Path to the file to extract content from')
+    args = parser.parse_args()
+    
+    try:
+        extracted_content = await test_document_extraction(args.file_path)
+        logger.info("=" * 50)
+        logger.info("TEST COMPLETED SUCCESSFULLY")
+        logger.info("=" * 50)
+        return extracted_content
+    except Exception as e:
+        logger.error("=" * 50)
+        logger.error("TEST FAILED")
+        logger.error("=" * 50)
+        raise
+
+if __name__ == "__main__":
+    print("About to run main()")
+    asyncio.run(main())
+    print("main() finished") 

test_retry_enhancement.py

@@ -0,0 +1,289 @@
+#!/usr/bin/env python3
+"""
+Test script for retry enhancement in managerChat.py
+Tests that previous action results and review feedback are properly passed to retry prompts.
+"""
+
+import asyncio
+import logging
+import sys
+import os
+
+# Add the gateway directory to the Python path
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'gateway'))
+
+from modules.workflow.managerChat import ChatManager
+from modules.interfaces.interfaceAppModel import User
+from modules.interfaces.interfaceChatModel import ChatWorkflow, ChatMessage
+from modules.interfaces.interfaceChatObjects import ChatObjects
+
+# Configure logging
+logging.basicConfig(level=logging.DEBUG)
+logger = logging.getLogger(__name__)
+
+class MockChatObjects(ChatObjects):
+    """Mock implementation of ChatObjects for testing"""
+    
+    def createTaskAction(self, action_data):
+        """Mock task action creation"""
+        class MockTaskAction:
+            def __init__(self, data):
+                self.id = "test_action_id"
+                self.execMethod = data.get("execMethod", "unknown")
+                self.execAction = data.get("execAction", "unknown")
+                self.execParameters = data.get("execParameters", {})
+                self.execResultLabel = data.get("execResultLabel", "")
+                self.status = data.get("status", "PENDING")
+                self.result = ""
+                self.error = ""
+            
+            def setSuccess(self):
+                self.status = "COMPLETED"
+            
+            def setError(self, error):
+                self.status = "FAILED"
+                self.error = error
+            
+            def isSuccessful(self):
+                return self.status == "COMPLETED"
+        
+        return MockTaskAction(action_data)
+    
+    def createChatDocument(self, document_data):
+        """Mock document creation"""
+        class MockChatDocument:
+            def __init__(self, data):
+                self.fileId = data.get("fileId", "")
+                self.filename = data.get("filename", "unknown")
+                self.fileSize = data.get("fileSize", 0)
+                self.mimeType = data.get("mimeType", "application/octet-stream")
+                self.content = ""
+        
+        return MockChatDocument(document_data)
+    
+    def createWorkflowMessage(self, message_data):
+        """Mock message creation"""
+        class MockWorkflowMessage:
+            def __init__(self, data):
+                self.workflowId = data.get("workflowId", "")
+                self.role = data.get("role", "assistant")
+                self.message = data.get("message", "")
+                self.status = data.get("status", "step")
+                self.sequenceNr = data.get("sequenceNr", 1)
+                self.publishedAt = data.get("publishedAt", "")
+                self.actionId = data.get("actionId", "")
+                self.actionMethod = data.get("actionMethod", "")
+                self.actionName = data.get("actionName", "")
+                self.documentsLabel = data.get("documentsLabel", "")
+                self.documents = data.get("documents", [])
+        
+        return MockWorkflowMessage(message_data)
+
+class MockServiceContainer:
+    """Mock service container for testing"""
+    
+    def __init__(self, user, workflow):
+        self.user = user
+        self.workflow = workflow
+    
+    def getMethodsList(self):
+        """Mock methods list"""
+        return ["document.extract(documentList, aiPrompt)", "document.analyze(documentList, aiPrompt)"]
+    
+    async def summarizeChat(self, messages):
+        """Mock chat summarization"""
+        return "Mock chat history summary"
+    
+    def getDocumentReferenceList(self):
+        """Mock document references"""
+        return {
+            'chat': [],
+            'history': []
+        }
+    
+    def getConnectionReferenceList(self):
+        """Mock connection references"""
+        return ["connection1", "connection2"]
+    
+    def getFileInfo(self, fileId):
+        """Mock file info"""
+        return {
+            "filename": f"test_file_{fileId}.txt",
+            "size": 1024,
+            "mimeType": "text/plain"
+        }
+    
+    def createFile(self, fileName, mimeType, content, base64encoded=False):
+        """Mock file creation"""
+        return f"file_id_{fileName}"
+    
+    def createDocument(self, fileName, mimeType, content, base64encoded=False):
+        """Mock document creation"""
+        class MockDocument:
+            def __init__(self, name, mime, cont):
+                self.filename = name
+                self.mimeType = mime
+                self.content = cont
+                self.fileSize = len(cont)
+        
+        return MockDocument(fileName, mimeType, content)
+    
+    def getFileExtension(self, filename):
+        """Mock file extension extraction"""
+        return filename.split('.')[-1] if '.' in filename else 'txt'
+    
+    def getMimeTypeFromExtension(self, extension):
+        """Mock MIME type detection"""
+        mime_types = {
+            'txt': 'text/plain',
+            'pdf': 'application/pdf',
+            'doc': 'application/msword',
+            'json': 'application/json'
+        }
+        return mime_types.get(extension, 'application/octet-stream')
+    
+    def detectContentTypeFromData(self, file_bytes, filename):
+        """Mock content type detection"""
+        if filename.endswith('.txt'):
+            return 'text/plain'
+        elif filename.endswith('.pdf'):
+            return 'application/pdf'
+        elif filename.endswith('.json'):
+            return 'application/json'
+        return 'application/octet-stream'
+    
+    async def callAiTextBasic(self, prompt):
+        """Mock AI call"""
+        return '{"actions": [{"method": "document", "action": "extract", "parameters": {"documentList": ["test"], "aiPrompt": "Test prompt"}, "resultLabel": "task1_action1_test", "description": "Test action"}]}'
+    
+    async def callAiTextAdvanced(self, prompt):
+        """Mock advanced AI call"""
+        return '{"overview": "Test plan", "tasks": [{"id": "task_1", "description": "Test task", "dependencies": [], "expected_outputs": ["output1"], "success_criteria": ["criteria1"], "required_documents": [], "estimated_complexity": "low", "ai_prompt": "Test prompt"}]}'
+    
+    async def executeAction(self, methodName, actionName, parameters):
+        """Mock action execution"""
+        class MockResult:
+            def __init__(self):
+                self.success = True
+                self.data = {
+                    "result": "Mock execution result",
+                    "documents": []
+                }
+                self.error = None
+        
+        return MockResult()
+
+async def test_retry_enhancement():
+    """Test the retry enhancement functionality"""
+    logger.info("Testing retry enhancement in managerChat.py")
+    
+    # Create mock objects
+    mock_user = User(id="test_user", username="testuser", email="test@example.com", mandateId="test_mandate")
+    mock_chat_objects = MockChatObjects()
+    mock_workflow = ChatWorkflow(
+        id="test_workflow",
+        userId="test_user",
+        status="active",
+        messages=[],
+        createdAt="2024-01-01T00:00:00Z",
+        updatedAt="2024-01-01T00:00:00Z",
+        mandateId="test_mandate",
+        currentRound=1,
+        lastActivity="2024-01-01T00:00:00Z",
+        startedAt="2024-01-01T00:00:00Z"
+    )
+    
+    # Create chat manager
+    chat_manager = ChatManager(mock_user, mock_chat_objects)
+    
+    # Mock the service container directly instead of initializing
+    chat_manager.service = MockServiceContainer(mock_user, mock_workflow)
+    chat_manager.workflow = mock_workflow
+    
+    # Test 1: Basic action definition without retry
+    logger.info("Test 1: Basic action definition")
+    task_step = {
+        "id": "task_1",
+        "description": "Test task",
+        "expected_outputs": ["output1"],
+        "success_criteria": ["criteria1"],
+        "ai_prompt": "Test AI prompt"
+    }
+    
+    actions = await chat_manager.defineTaskActions(task_step, mock_workflow, [])
+    logger.info(f"Generated {len(actions)} actions without retry context")
+    
+    # Test 2: Action definition with retry context
+    logger.info("Test 2: Action definition with retry context")
+    enhanced_context = {
+        'task_step': task_step,
+        'workflow': mock_workflow,
+        'workflow_id': mock_workflow.id,
+        'available_documents': ["test_doc.txt"],
+        'previous_results': ["task0_action1_results"],
+        'improvements': "Previous attempt failed - ensure comprehensive extraction",
+        'retry_count': 1,
+        'previous_action_results': [
+            {
+                'actionMethod': 'document',
+                'actionName': 'extract',
+                'status': 'failed',
+                'error': 'Empty result returned',
+                'result': 'No content extracted',
+                'resultLabel': 'task1_action1_failed'
+            }
+        ],
+        'previous_review_result': {
+            'status': 'retry',
+            'reason': 'Incomplete extraction',
+            'quality_score': 3,
+            'missing_outputs': ['detailed_analysis'],
+            'unmet_criteria': ['comprehensive_coverage']
+        }
+    }
+    
+    retry_actions = await chat_manager.defineTaskActions(task_step, mock_workflow, [], enhanced_context)
+    logger.info(f"Generated {len(retry_actions)} actions with retry context")
+    
+    # Test 3: Verify retry context is properly handled
+    logger.info("Test 3: Verifying retry context handling")
+    
+    # Create a test prompt to see if retry context is included
+    test_prompt = await chat_manager._createActionDefinitionPrompt(enhanced_context)
+    
+    # Check if retry context is in the prompt
+    if "RETRY CONTEXT" in test_prompt:
+        logger.info("✓ Retry context properly included in prompt")
+    else:
+        logger.error("✗ Retry context not found in prompt")
+    
+    if "Previous action results that failed" in test_prompt:
+        logger.info("✓ Previous action results included in prompt")
+    else:
+        logger.error("✗ Previous action results not found in prompt")
+    
+    if "Previous review feedback" in test_prompt:
+        logger.info("✓ Previous review feedback included in prompt")
+    else:
+        logger.error("✗ Previous review feedback not found in prompt")
+    
+    if "Previous attempt failed" in test_prompt:
+        logger.info("✓ Improvements needed included in prompt")
+    else:
+        logger.error("✗ Improvements needed not found in prompt")
+    
+    # Test 4: Verify fallback actions with retry context
+    logger.info("Test 4: Testing fallback actions with retry context")
+    fallback_actions = chat_manager._createFallbackActions(task_step, enhanced_context)
+    logger.info(f"Generated {len(fallback_actions)} fallback actions with retry context")
+    
+    # Check if fallback actions include retry information
+    if any("retry" in action.get("resultLabel", "") for action in fallback_actions):
+        logger.info("✓ Fallback actions include retry information")
+    else:
+        logger.error("✗ Fallback actions missing retry information")
+    
+    logger.info("Retry enhancement test completed successfully!")
+
+if __name__ == "__main__":
+    asyncio.run(test_retry_enhancement()) 

test_sample_document.txt

@@ -0,0 +1,47 @@
+PowerOn System Architecture Overview
+
+This document provides a comprehensive overview of the PowerOn system architecture, including its key components, data flow, and technical specifications.
+
+MAJOR TOPICS:
+
+1. System Architecture
+   - Frontend Agents: Web-based user interface components
+   - Gateway: Central API and workflow management system
+   - Database: JSON-based data storage with component interfaces
+   - AI Integration: Anthropic and OpenAI connectors for intelligent processing
+
+2. Core Components
+   - Document Manager: Handles file processing and content extraction
+   - Workflow Manager: Orchestrates complex business processes
+   - Service Container: Provides unified access to all system services
+   - Neutralizer: Data anonymization and privacy protection
+
+3. Data Flow Architecture
+   - User authentication and authorization
+   - Document upload and processing pipeline
+   - AI-powered content analysis and extraction
+   - Workflow execution and task management
+   - Result generation and storage
+
+4. Technical Specifications
+   - Python-based backend with async/await support
+   - RESTful API design with JSON data exchange
+   - Modular component architecture
+   - Extensible method system for business logic
+   - Comprehensive logging and monitoring
+
+5. Security Features
+   - Multi-authentication authority support (Local, Microsoft, Google)
+   - Token-based session management
+   - Data encryption and anonymization
+   - Role-based access control
+   - Audit trail and compliance features
+
+6. Integration Capabilities
+   - SharePoint document management
+   - Email system integration (Outlook)
+   - Web crawling and data collection
+   - AI service integration (Anthropic, OpenAI)
+   - Custom method development framework
+
+The PowerOn system is designed to provide a comprehensive platform for intelligent document processing, workflow automation, and AI-powered business process management. It combines modern web technologies with advanced AI capabilities to deliver a robust and scalable solution for enterprise document management and workflow automation.