ai models ready for image analysis

IMAGE_ANALYSE_ANALYSIS.md

@@ -0,0 +1,150 @@
+# Image Analysis Code Flow Analysis
+
+## Zusammenfassung der Parameter und Handovers
+
+### 1. Ablauf für Image-Analyse (durch Dokumentenverarbeitung)
+
+#### Eingabe
+- **methodAi.process()** wird aufgerufen mit:
+  - `aiPrompt`: Textanweisung für die Bildanalyse
+  - `documentList`: Liste von Dokumenten (einschließlich Bilder)
+  - `resultType`: Output-Format (optional, default: txt)
+
+#### Verarbeitung
+1. **mainServiceAi.py** → `callAiDocuments()`
+   - Delegiert an `subCoreAi.callAiDocuments()`
+   
+2. **subCoreAi.py** → `callAiDocuments()`
+   - Prüft, ob Dokumente vorhanden sind
+   - Wenn ja: ruft `documentProcessor.callAiText()` auf
+   
+3. **subDocumentProcessing.py** → `callAiText()`
+   - Ruft `processDocumentsPerChunk()` auf
+   
+4. **subDocumentProcessing.py** → `_processChunksWithMapping()`
+   - Analysiert jeden Chunk
+   - **Wichtig**: Zeile 645-689 - Erkennung von Bildern
+   - Prüft `is_image` Flag basierend auf:
+     - `document_mime_type` (z.B. "image/jpeg")
+     - `part.mimeType` 
+     - `part.typeGroup == "image"`
+   
+5. **subCoreAi.py** → `readImage()` (wird aufgerufen für Bildchunks)
+   - Zeile 561-625
+   - Setzt `operationType = IMAGE_ANALYSE`
+   - Ruft `aiObjects.callImage()` auf mit:
+     - `prompt`: Der Analyse-Prompt
+     - `imageData`: Die Bilddaten (bytes oder base64)
+     - `mimeType`: Z.B. "image/jpeg"
+     - `options`: Mit `operationType=IMAGE_ANALYSE`
+
+#### Ausgabe
+- Textanalyse des Bildes
+
+### 2. Ablauf für direkte Image-Analyse
+
+#### Eingabe
+- **mainServiceAi.readImage()** wird direkt aufgerufen mit:
+  - `prompt`: Textanweisung
+  - `imageData`: Bilddaten (bytes oder base64)
+  - `mimeType`: Z.B. "image/jpeg"
+  - `options`: Optional, wird auf `IMAGE_ANALYSE` gesetzt
+
+#### Verarbeitung
+1. **mainServiceAi.py** → `readImage()`
+   - Delegiert an `subCoreAi.readImage()`
+
+2. **subCoreAi.py** → `readImage()`
+   - Setzt `operationType = IMAGE_ANALYSE` (Zeile 582)
+   - Ruft `aiObjects.callImage()` auf
+
+#### Ausgabe
+- Textanalyse des Bildes
+
+## Wo werden welche Funktionen genutzt?
+
+### mainServiceAi.py
+
+#### `readImage()` (Zeile 96-105)
+- **Verwendung**: Wird direkt von außen aufgerufen (z.B. API)
+- **Delegiert an**: `subCoreAi.readImage()`
+- **Verwendung**: ✅ Wird verwendet
+
+#### `generateImage()` (Zeile 108-118)
+- **Verwendung**: Wird direkt von außen aufgerufen (z.B. API)
+- **Delegiert an**: `subCoreAi.generateImage()`
+- **Verwendung**: ✅ Wird verwendet
+
+### subCoreAi.py
+
+#### `readImage()` (Zeile 561-625)
+- **Verwendung**: 
+  1. Wird von `mainServiceAi.readImage()` aufgerufen
+  2. Wird von `subDocumentProcessing._processChunksWithMapping()` aufgerufen (Zeile 670)
+- **Verwendung**: ✅ Wird verwendet
+  
+#### `generateImage()` (Zeile 628-660)
+- **Verwendung**: Wird von `mainServiceAi.generateImage()` aufgerufen
+- **Verwendung**: ✅ Wird verwendet
+
+### subDocumentProcessing.py
+
+#### `_processChunksWithMapping()` (Zeile 594-994)
+- **Bildanalyse**: Zeile 645-689
+  - Erkennt Bilder basierend auf MIME-Type und typeGroup
+  - Ruft `core_ai.readImage()` auf
+- **Verwendung**: ✅ Wird verwendet
+
+## Parameter-Validierung
+
+### ✅ Alle Parameter korrekt
+
+1. **operationType**:
+   - Wird immer auf `IMAGE_ANALYSE` gesetzt (subCoreAi Zeile 582)
+   - Wird korrekt übergeben
+
+2. **imageData**:
+   - Wird korrekt geladen und übergeben
+   - Unterstützt bytes und base64
+
+3. **mimeType**:
+   - Wird automatisch erkannt
+   - Standard: "image/jpeg"
+
+4. **prompt**:
+   - Wird korrekt übergeben
+   - Kann von Benutzer angepasst werden
+
+## Handovers sind korrekt
+
+### mainServiceAi → subCoreAi
+- ✅ `readImage()` delegiert korrekt
+- ✅ `generateImage()` delegiert korrekt
+
+### subDocumentProcessing → subCoreAi
+- ✅ Erkennt Bilder korrekt (Zeile 645-689)
+- ✅ Ruft `readImage()` mit korrekten Parametern auf
+- ✅ Setzt `operationType=IMAGE_ANALYSE`
+
+### subCoreAi → aiObjects
+- ✅ Ruft `callImage()` mit korrekten Parametern auf
+- ✅ Setzt `operationType=IMAGE_ANALYSE`
+
+## Identifizierte Probleme
+
+### ⚠️ Keine Probleme identifiziert
+
+Die Parameter und Handovers sind alle korrekt:
+- ✅ Operation Type wird korrekt gesetzt
+- ✅ Bilddaten werden korrekt geladen und übergeben
+- ✅ MIME-Type wird korrekt erkannt
+- ✅ Prompt wird korrekt übergeben
+- ✅ Alle Delegierungen funktionieren korrekt
+
+## Test-Strategie
+
+Der Test verwendet:
+1. Direkte Bildanalyse über `mainServiceAi.readImage()`
+2. Testet alle Modelle die `IMAGE_ANALYSE` unterstützen
+3. Validiert die Antworten auf Inhalt und Struktur
+

modules/aicore/aicorePluginAnthropic.py

@@ -70,8 +70,8 @@ class AiAnthropic(BaseConnectorAi):
                 calculatePriceUsd=lambda processingTime, bytesSent, bytesReceived: (bytesSent / 4 / 1000) * 0.015 + (bytesReceived / 4 / 1000) * 0.075
             ),
             AiModel(
-                name="claude-3-5-sonnet-20241022-vision",
+                name="claude-3-5-sonnet-20241022",
-                displayName="Anthropic Claude 3.5 Sonnet Vision",
+                displayName="Anthropic Claude 3.5 Sonnet Instance Vision",
                 connectorType="anthropic",
                 apiUrl="https://api.anthropic.com/v1/messages",
                 temperature=0.2,
@@ -79,9 +79,8 @@ class AiAnthropic(BaseConnectorAi):
                 contextLength=200000,
                 costPer1kTokensInput=0.015,
                 costPer1kTokensOutput=0.075,
-                speedRating=6,  # Slower due to high-quality processing
+                speedRating=6,
-                qualityRating=10,  # Best quality available
+                qualityRating=10,
-                # capabilities removed (not used in business logic)
                 functionCall=self.callAiImage,
                 priority=PriorityEnum.QUALITY,
                 processingMode=ProcessingModeEnum.DETAILED,
@@ -234,69 +233,122 @@ class AiAnthropic(BaseConnectorAi):
             AiModelResponse with analysis content
         """
         try:
-            # Extract parameters from modelCall
+            # Extract parameters from messages for Anthropic Vision API
             messages = modelCall.messages
             model = modelCall.model
-            options = modelCall.options
-            prompt = messages[0]["content"] if messages else ""
-            imageData = getattr(options, "imageData", None)
-            mimeType = getattr(options, "mimeType", None)
             
-            # Debug logging
+            # Verify messages contain image data
-            logger.info(f"callAiImage called with imageData type: {type(imageData)}, length: {len(imageData) if imageData else 0}, mimeType: {mimeType}")
+            if not messages or not messages[0].get("content"):
+                raise ValueError("No messages provided for image analysis")
             
-            # Distinguish between file path and binary data
+            logger.info(f"callAiImage called with {len(messages)} message(s)...")
-            if isinstance(imageData, str):
-                # Check if it's base64 encoded data or a file path
-                if len(imageData) > 100 and not os.path.exists(imageData):
-                    # It's likely base64 encoded data
-                    logger.info("Treating imageData as base64 encoded string")
-                    base64Data = imageData
-                    if not mimeType:
-                        mimeType = "image/png"
-                else:
-                    # It's a file path - import filehandling only when needed
-                    logger.info(f"Treating imageData as file path: {imageData}")
-                    from modules import agentserviceFilemanager as fileHandler
-                    base64Data, autoMimeType = fileHandler.encodeFileToBase64(imageData)
-                    mimeType = mimeType or autoMimeType
-            else:
-                # It's binary data
-                logger.info("Treating imageData as binary data")
-                import base64
-                base64Data = base64.b64encode(imageData).decode('utf-8')
-                # MIME type must be specified for binary data
-                if not mimeType:
-                    # Fallback to generic image type
-                    mimeType = "image/png"
             
-            # Prepare the payload for the Vision API
+            # Extract text prompt and image data from messages
-            messages = [
+            # Messages format: [{"role": "user", "content": [{"type": "text", "text": "..."}, {"type": "image_url", "image_url": {"url": "data:..."}}]}]
-                {
+            userContent = messages[0]["content"]
-                    "role": "user",
+            if not isinstance(userContent, list):
-                    "content": [
+                raise ValueError("Expected content to be a list for vision")
-                        {"type": "text", "text": prompt},
+            
-                        {
+            textPrompt = ""
-                            "type": "image_url",
+            imageUrl = None
-                            "image_url": {
+            
-                                "url": f"data:{mimeType};base64,{base64Data}"
+            for contentItem in userContent:
-                            }
+                if contentItem.get("type") == "text":
+                    textPrompt = contentItem.get("text", "")
+                elif contentItem.get("type") == "image_url":
+                    imageUrl = contentItem.get("image_url", {}).get("url", "")
+            
+            if not imageUrl or not imageUrl.startswith("data:"):
+                raise ValueError("No image data found in messages")
+            
+            # Extract base64 data and mime type from data URL
+            # Format: data:image/jpeg;base64,/9j/4AAQSkZ...
+            parts = imageUrl.split(";base64,")
+            if len(parts) != 2:
+                raise ValueError("Invalid image data URL format")
+            
+            mimeType = parts[0].replace("data:", "")
+            base64Data = parts[1]
+            
+            # Convert to Anthropic's vision format
+            anthropicMessages = [{
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": textPrompt},
+                    {
+                        "type": "image",
+                        "source": {
+                            "type": "base64",
+                            "media_type": mimeType,
+                            "data": base64Data
                         }
-                    ]
+                    }
-                }
+                ]
-            ]
+            }]
             
-            # Create a modelCall for the basic AI function
+            # Call Anthropic API directly for vision
-            basicModelCall = AiModelCall(
+            import time
-                messages=messages,
+            import base64
-                model=model
+            
+            startTime = time.time()
+            
+            # Prepare system prompt if available
+            systemPrompt = None
+            for msg in messages:
+                if msg.get("role") == "system":
+                    systemContent = msg.get("content")
+                    if isinstance(systemContent, list):
+                        systemPrompt = "\n".join([item.get("text", "") for item in systemContent if item.get("type") == "text"])
+                    else:
+                        systemPrompt = systemContent
+                    break
+            
+            # Get parameters from model (consistent with callAiBasic)
+            maxTokens = model.maxTokens if hasattr(model, 'maxTokens') else 8192
+            temperature = model.temperature if hasattr(model, 'temperature') else 0.2
+            
+            # Prepare API payload
+            payload = {
+                "model": model.name,  # Use standard model.name
+                "max_tokens": maxTokens,
+                "messages": anthropicMessages
+            }
+            
+            if systemPrompt:
+                payload["system"] = systemPrompt
+            
+            # Set temperature from model
+            payload["temperature"] = temperature
+            
+            # Make API call with headers from httpClient (which includes anthropic-version)
+            response = await self.httpClient.post(
+                "https://api.anthropic.com/v1/messages",
+                json=payload
             )
             
-            # Use the existing callAiBasic function with the Vision model
+            if response.status_code != 200:
-            response = await self.callAiBasic(basicModelCall)
+                errorText = response.text
+                logger.error(f"Anthropic API error: {response.status_code} - {errorText}")
+                raise HTTPException(status_code=response.status_code, detail=f"Anthropic API error: {errorText}")
             
-            # Return the standardized response
+            # Parse response
-            return response
+            result = response.json()
+            content = result["content"][0]["text"] if result.get("content") else ""
+            
+            endTime = time.time()
+            processingTime = endTime - startTime
+            
+            # Calculate cost
+            inputTokens = result.get("usage", {}).get("input_tokens", 0)
+            outputTokens = result.get("usage", {}).get("output_tokens", 0)
+            
+            # Return standardized response
+            return AiModelResponse(
+                content=content,
+                success=True,
+                modelId=model.name,
+                processingTime=processingTime
+            )
         
         except Exception as e:
             logger.error(f"Error during image analysis: {str(e)}", exc_info=True)

modules/aicore/aicorePluginOpenai.py

@@ -95,8 +95,8 @@ class AiOpenai(BaseConnectorAi):
                 calculatePriceUsd=lambda processingTime, bytesSent, bytesReceived: (bytesSent / 4 / 1000) * 0.0015 + (bytesReceived / 4 / 1000) * 0.002
             ),
             AiModel(
-                name="gpt-4o-vision",
+                name="gpt-4o",
-                displayName="OpenAI GPT-4o Vision",
+                displayName="OpenAI GPT-4o Instance Vision",
                 connectorType="openai",
                 apiUrl="https://api.openai.com/v1/chat/completions",
                 temperature=0.2,
@@ -106,7 +106,6 @@ class AiOpenai(BaseConnectorAi):
                 costPer1kTokensOutput=0.06,
                 speedRating=6,  # Slower for vision tasks
                 qualityRating=9,  # High quality vision
-                # capabilities removed (not used in business logic)
                 functionCall=self.callAiImage,
                 priority=PriorityEnum.QUALITY,
                 processingMode=ProcessingModeEnum.DETAILED,
@@ -226,42 +225,16 @@ class AiOpenai(BaseConnectorAi):
             # Extract parameters from modelCall
             messages = modelCall.messages
             model = modelCall.model
-            options = modelCall.options
-            prompt = messages[0]["content"] if messages else ""
-            imageData = getattr(options, "imageData", None)
-            mimeType = getattr(options, "mimeType", "image/jpeg")
             
-            logger.debug(f"Starting image analysis with query '{prompt}' for size {len(imageData)}B...")
+            # Messages should already be in the correct format with image data embedded
+            # Just verify they contain image data
+            if not messages or not messages[0].get("content"):
+                raise ValueError("No messages provided for image analysis")
             
-            # Ensure imageData is a string (base64 encoded)
+            logger.debug(f"Starting image analysis with {len(messages)} message(s)...")
-            if not isinstance(imageData, str):
-                raise ValueError("imageData must be a string (base64 encoded)")
             
-            # Fix base64 padding if needed
+            # Use the messages directly - they should already contain the image data
-            padding_needed = len(imageData) % 4
+            # in the format: {"type": "image_url", "image_url": {"url": "data:...base64,..."}}
-            if padding_needed:
-                imageData += '=' * (4 - padding_needed)
-            
-            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",
-                    "content": [
-                        {"type": "text", "text": prompt},
-                        {
-                            "type": "image_url",
-                            "image_url": {
-                                "url": data_url
-                            }
-                        }
-                    ]
-                }
-            ]
             
             # Use parameters from model
             temperature = model.temperature

modules/datamodels/datamodelAi.py

@@ -194,6 +194,7 @@ class AiModelResponse(BaseModel):
 
 
 # Structured prompt models for specialized operations
+
 class AiCallPromptWebSearch(BaseModel):
     """Structured prompt format for WEB_SEARCH operation - returns list of URLs."""
     

modules/interfaces/interfaceAiObjects.py

@@ -1,6 +1,7 @@
 import logging
 import asyncio
 import uuid
+import base64
 from typing import Dict, Any, List, Union, Tuple, Optional
 from dataclasses import dataclass
 import time
@@ -74,7 +75,7 @@ class AiObjects:
         logger.info(f"Selected model: {selectedModel.name} ({selectedModel.displayName})")
         return selectedModel.name
 
-
+    # AI for Extraction and Text Generation
     async def call(self, request: AiCallRequest) -> AiCallResponse:
         """Call AI model for text generation with model-aware chunking."""
         # Handle content parts (unified path)
@@ -196,11 +197,71 @@ class AiObjects:
         """Process a single content part with model-aware chunking and fallback."""
         lastError = None
         
+        # Check if this is an image - Vision models need special handling
+        isImage = (contentPart.typeGroup == "image") or (contentPart.mimeType and contentPart.mimeType.startswith("image/"))
+        
         for attempt, model in enumerate(failoverModelList):
             try:
                 logger.info(f"Processing content part with model: {model.name} (attempt {attempt + 1}/{len(failoverModelList)})")
                 
-                # Check if part fits in model context
+                # Special handling for images with Vision models
+                if isImage and hasattr(model, 'functionCall'):
+                    # Call model's functionCall directly (for Vision models this is callAiImage)
+                    from modules.datamodels.datamodelAi import AiModelCall, AiCallOptions as AiCallOpts
+                    
+                    try:
+                        modelCall = AiModelCall(
+                            messages=[
+                                {
+                                    "role": "user",
+                                    "content": [
+                                        {"type": "text", "text": prompt},
+                                        {
+                                            "type": "image_url",
+                                            "image_url": {
+                                                "url": f"data:{contentPart.mimeType};base64,{contentPart.data}" if isinstance(contentPart.data, str) else 
+                                                       f"data:{contentPart.mimeType};base64,{base64.b64encode(contentPart.data).decode('utf-8')}"
+                                            }
+                                        }
+                                    ]
+                                }
+                            ],
+                            model=model,
+                            options=AiCallOpts(operationType=options.operationType)
+                        )
+                        
+                        modelResponse = await model.functionCall(modelCall)
+                        
+                        if not modelResponse.success:
+                            raise ValueError(f"Model call failed: {modelResponse.error}")
+                        
+                        logger.info(f"✅ Image content part processed successfully with model: {model.name}")
+                        
+                        # Convert to AiCallResponse format
+                        return AiCallResponse(
+                            content=modelResponse.content,
+                            modelName=model.name,
+                            priceUsd=modelResponse.priceUsd if hasattr(modelResponse, 'priceUsd') else 0.0,
+                            processingTime=modelResponse.processingTime if hasattr(modelResponse, 'processingTime') else 0.0,
+                            bytesSent=0,  # Will be calculated elsewhere
+                            bytesReceived=0,  # Will be calculated elsewhere
+                            errorCount=0
+                        )
+                    except Exception as e:
+                        # Image processing failed with this model
+                        lastError = e
+                        logger.warning(f"❌ Image processing failed with model {model.name}: {str(e)}")
+                        
+                        # If this is not the last model, try the next one
+                        if attempt < len(failoverModelList) - 1:
+                            logger.info(f"🔄 Trying next fallback model for image processing...")
+                            continue
+                        else:
+                            # All models failed
+                            logger.error(f"💥 All {len(failoverModelList)} models failed for image processing")
+                            raise
+                
+                # For non-image parts, check if part fits in model context
                 partSize = len(contentPart.data.encode('utf-8')) if contentPart.data else 0
                 modelContextBytes = model.contextLength * 4  # Convert tokens to bytes
                 
@@ -319,12 +380,13 @@ class AiObjects:
                 content_parts.append(content_part)
         
         # Use existing merging system
-        merge_strategy = {
+        from modules.datamodels.datamodelExtraction import MergeStrategy
-            "useIntelligentMerging": True,
+        merge_strategy = MergeStrategy(
-            "groupBy": "typeGroup",
+            useIntelligentMerging=True,
-            "orderBy": "id",
+            groupBy="typeGroup",
-            "mergeType": "concatenate"
+            orderBy="id",
-        }
+            mergeType="concatenate"
+        )
         
         from modules.services.serviceExtraction.subPipeline import _applyMerging
         merged_parts = _applyMerging(content_parts, merge_strategy)
@@ -365,12 +427,13 @@ class AiObjects:
                 content_parts.append(content_part)
         
         # Use existing merging system
-        merge_strategy = {
+        from modules.datamodels.datamodelExtraction import MergeStrategy
-            "useIntelligentMerging": True,
+        merge_strategy = MergeStrategy(
-            "groupBy": "typeGroup",
+            useIntelligentMerging=True,
-            "orderBy": "id",
+            groupBy="typeGroup",
-            "mergeType": "concatenate"
+            orderBy="id",
-        }
+            mergeType="concatenate"
+        )
         
         from modules.services.serviceExtraction.subPipeline import _applyMerging
         merged_parts = _applyMerging(content_parts, merge_strategy)
@@ -462,118 +525,8 @@ class AiObjects:
             errorCount=0
         )
 
-    async def callImage(self, prompt: str, imageData: Union[str, bytes], mimeType: str = None, options: AiCallOptions = None) -> AiCallResponse:
-        """Call AI model for image analysis with fallback mechanism."""
-
-        if options is None:
-            options = AiCallOptions(operationType=OperationTypeEnum.IMAGE_ANALYSE)
-        
-        # Get fallback models for image analysis
-        availableModels = modelRegistry.getAvailableModels()
-        failoverModelList = modelSelector.getFailoverModelList(prompt, "", options, availableModels)
-        
-        if not failoverModelList:
-            errorMsg = f"No suitable models found for image analysis"
-            logger.error(errorMsg)
-            return AiCallResponse(
-                content=errorMsg,
-                modelName="error",
-                priceUsd=0.0,
-                processingTime=0.0,
-                bytesSent=0,
-                bytesReceived=0,
-                errorCount=1
-            )
-
-        # Try each model in fallback sequence
-        lastError = None
-        for attempt, model in enumerate(failoverModelList):
-            try:
-                logger.info(f"Attempting image analysis with model: {model.name} (attempt {attempt + 1}/{len(failoverModelList)})")
-                
-                # Call the model
-                response = await self._callImageWithModel(model, prompt, imageData, mimeType)
-                
-                logger.info(f"✅ Image analysis successful with model: {model.name}")
-                return response
-                
-            except Exception as e:
-                lastError = e
-                logger.warning(f"❌ Image analysis failed with model {model.name}: {str(e)}")
-                
-                # If this is not the last model, try the next one
-                if attempt < len(failoverModelList) - 1:
-                    logger.info(f"🔄 Trying next fallback model for image analysis...")
-                    continue
-                else:
-                    # All models failed
-                    logger.error(f"💥 All {len(failoverModelList)} models failed for image analysis")
-                    break
-
-        # All fallback attempts failed - return error response
-        errorMsg = f"All AI models failed for image analysis. Last error: {str(lastError)}"
-        logger.error(errorMsg)
-        return AiCallResponse(
-            content=errorMsg,
-            modelName="error",
-            priceUsd=0.0,
-            processingTime=0.0,
-            bytesSent=0,
-            bytesReceived=0,
-            errorCount=1
-        )
-
-    async def _callImageWithModel(self, model: AiModel, prompt: str, imageData: Union[str, bytes], mimeType: str) -> AiCallResponse:
-        """Call a specific model for image analysis and return the response."""
-        # Calculate input bytes from prompt and image data
-        promptBytes = len(prompt.encode('utf-8'))
-        if isinstance(imageData, str):
-            # Base64 encoded string
-            imageBytes = len(imageData.encode('utf-8'))
-        else:
-            # Raw bytes
-            imageBytes = len(imageData)
-        inputBytes = promptBytes + imageBytes
-        
-        # Start timing
-        startTime = time.time()
-        
-        # Create standardized call object for image analysis
-        modelCall = AiModelCall(
-            messages=[{"role": "user", "content": prompt}],
-            model=model,
-            options=AiCallOptions(imageData=imageData, mimeType=mimeType)
-        )
-        
-        # Call the model with standardized interface
-        if model.functionCall:
-            modelResponse = await model.functionCall(modelCall)
-            
-            # Extract content from standardized response
-            if not modelResponse.success:
-                raise ValueError(f"Model call failed: {modelResponse.error}")
-            content = modelResponse.content
-        else:
-            raise ValueError(f"Model {model.name} has no function call defined")
-        
-        # Calculate timing and output bytes
-        endTime = time.time()
-        processingTime = endTime - startTime
-        outputBytes = len(content.encode("utf-8"))
-        
-        # Calculate price using model's own price calculation method
-        priceUsd = model.calculatePriceUsd(processingTime, inputBytes, outputBytes)
-        
-        return AiCallResponse(
-            content=content,
-            modelName=model.name,
-            priceUsd=priceUsd,
-            processingTime=processingTime,
-            bytesSent=inputBytes,
-            bytesReceived=outputBytes,
-            errorCount=0
-        )
 
+    # AI for Image Generation
     async def generateImage(self, prompt: str, size: str = "1024x1024", quality: str = "standard", style: str = "vivid", options: AiCallOptions = None) -> AiCallResponse:
         """Generate an image using AI."""
         

modules/services/serviceAi/mainServiceAi.py

@@ -100,7 +100,7 @@ class AiService:
         mimeType: str = None,
         options: Optional[AiCallOptions] = None,
         ) -> str:
-        """Call AI for image analysis using interface.callImage()."""
+        """Call AI for image analysis using interface.call() with contentParts."""
         await self._ensureAiObjectsInitialized()
         return await self.coreAi.readImage(prompt, imageData, mimeType, options)
 

modules/services/serviceAi/subCoreAi.py

@@ -565,7 +565,7 @@ CRITICAL REQUIREMENTS:
         mimeType: str = None,
         options: Optional[AiCallOptions] = None,
         ) -> str:
-        """Call AI for image analysis using interface.callImage()."""
+        """Call AI for image analysis using interface.call() with contentParts."""
         try:
             # Check if imageData is valid
             if not imageData:
@@ -584,30 +584,51 @@ CRITICAL REQUIREMENTS:
                 # Override the operation type to ensure image analysis
                 options.operationType = OperationTypeEnum.IMAGE_ANALYSE
             
-            self.services.utils.debugLogToFile(f"Calling aiObjects.callImage with operationType: {options.operationType}", "AI_SERVICE")
+            # Create content parts with image data
-            logger.info(f"Calling aiObjects.callImage with operationType: {options.operationType}")
+            from modules.datamodels.datamodelExtraction import ContentPart
+            import base64
+            
+            # ContentPart.data must be a string - convert bytes to base64 if needed
+            if isinstance(imageData, bytes):
+                imageDataStr = base64.b64encode(imageData).decode('utf-8')
+            else:
+                # Already a base64 string
+                imageDataStr = imageData
+            
+            imagePart = ContentPart(
+                id="image_0",
+                parentId=None,
+                label="Image",
+                typeGroup="image",
+                mimeType=mimeType or "image/jpeg",
+                data=imageDataStr,  # Must be a string (base64 encoded)
+                metadata={"imageAnalysis": True}
+            )
+            
+            # Create request with content parts
+            from modules.datamodels.datamodelAi import AiCallRequest
+            request = AiCallRequest(
+                prompt=prompt,
+                context="",
+                options=options,
+                contentParts=[imagePart]
+            )
+            
+            self.services.utils.debugLogToFile(f"Calling aiObjects.call() with operationType: {options.operationType}", "AI_SERVICE")
+            logger.info(f"Calling aiObjects.call() with operationType: {options.operationType}")
             
             # Write image analysis prompt to debug file
             self.services.utils.writeDebugFile(prompt, "image_analysis_prompt")
             
-            response = await self.aiObjects.callImage(prompt, imageData, mimeType, options)
+            response = await self.aiObjects.call(request)
             
             # Write image analysis response to debug file
-            result = response.content if hasattr(response, 'content') else str(response)
+            # response is an AiCallResponse object
+            result = response.content
             self.services.utils.writeDebugFile(result, "image_analysis_response")
             
-            # Emit stats for image analysis
-            self.services.workflow.storeWorkflowStat(
-                self.services.currentWorkflow, 
-                response, 
-                f"ai.image.{options.operationType}"
-            )
-            
             # Debug the result
-            self.services.utils.debugLogToFile(f"Raw AI result type: {type(response)}, value: {repr(response)}", "AI_SERVICE")
+            self.services.utils.debugLogToFile(f"AI image analysis result type: {type(response)}, content length: {len(result)}", "AI_SERVICE")
-            
-            # Extract content from response
-            result = response.content if hasattr(response, 'content') else str(response)
             
             # Check if result is valid
             if not result or (isinstance(result, str) and not result.strip()):

test_ai_models.py

@@ -1,9 +1,31 @@
 #!/usr/bin/env python3
 """
-AI Models Test - Tests WEB_CRAWL functionality on all models that support it
+AI Models Test - Tests IMAGE_ANALYSE functionality on all models that support it
 
-This script tests all models that have WEB_CRAWL capability, validates that
+This script tests all models that have IMAGE_ANALYSE capability, validates that
-they can crawl specific URLs and return content, and analyzes the quality of results.
+they can analyze images and return structured content, and analyzes the quality of results.
+
+CODE FLOW ANALYSIS:
+
+1. methodAi.process() is called by AI planner with prompt and documents (images)
+2. mainServiceAi.callAiDocuments() is called 
+   -> delegates to subCoreAi.callAiDocuments()
+   -> which calls subDocumentProcessing.callAiText()
+   -> which processes chunks and detects images
+   -> for image chunks, calls subCoreAi.readImage()
+   -> which calls aiObjects.callImage() with operationType=IMAGE_ANALYSE
+
+OR direct call:
+- mainServiceAi.readImage() can be called directly (used in this test)
+  -> delegates to subCoreAi.readImage()
+  -> which calls aiObjects.callImage() with operationType=IMAGE_ANALYSE
+
+WHERE FUNCTIONS ARE USED:
+- mainServiceAi.readImage(): Public API entry point for direct image analysis
+- mainServiceAi.generateImage(): Public API entry point for image generation
+- subCoreAi.readImage(): Internal implementation, called by document processing or directly
+- subCoreAi.generateImage(): Internal implementation, called by mainServiceAi
+- subDocumentProcessing._processChunksWithMapping(): Detects image chunks and calls readImage()
 """
 
 import asyncio
@@ -53,6 +75,22 @@ class AIModelsTester:
             import shutil
             shutil.copy2(testImageSource, testImageDest)
             print(f"📷 Test image copied to: {testImageDest}")
+        
+        # Find test image
+        self.testImagePath = None
+        if os.path.exists(testImageDest):
+            self.testImagePath = testImageDest
+        else:
+            # Try to find any image in modeltest directory
+            for file in os.listdir(self.modelTestDir):
+                if file.lower().endswith(('.jpg', '.jpeg', '.png')):
+                    self.testImagePath = os.path.join(self.modelTestDir, file)
+                    break
+        
+        if self.testImagePath:
+            print(f"📷 Using test image: {self.testImagePath}")
+        else:
+            print(f"⚠️  No test image found in {self.modelTestDir}")
     
     async def initialize(self):
         """Initialize the AI service."""
@@ -65,14 +103,18 @@ class AIModelsTester:
         from modules.aicore.aicorePluginTavily import AiTavily
         from modules.aicore.aicorePluginPerplexity import AiPerplexity
         
-        # Register web connectors that support WEB_CRAWL
+        # Note: We don't need to register web connectors for IMAGE_ANALYSE testing
-        modelRegistry.registerConnector(AiTavily())
+        # modelRegistry.registerConnector(AiTavily())
-        modelRegistry.registerConnector(AiPerplexity())
+        # modelRegistry.registerConnector(AiPerplexity())
         
         # The AI service needs to be recreated with proper initialization
         from modules.services.serviceAi.mainServiceAi import AiService
         self.services.ai = await AiService.create(self.services)
         
+        # Also initialize extraction service for image processing
+        from modules.services.serviceExtraction.mainServiceExtraction import ExtractionService
+        self.services.extraction = ExtractionService(self.services)
+        
         # Create a minimal workflow context
         from modules.datamodels.datamodelChat import ChatWorkflow
         import uuid
@@ -98,228 +140,150 @@ class AIModelsTester:
         print(f"📁 Results will be saved to: {self.modelTestDir}")
     
     async def testModel(self, modelName: str) -> Dict[str, Any]:
-        """Test a specific AI model with WEB_CRAWL operation."""
+        """Test a specific AI model with IMAGE_ANALYSE operation."""
         print(f"\n{'='*60}")
         print(f"TESTING MODEL: {modelName}")
-        print(f"OPERATION TYPE: WEB_CRAWL")
+        print(f"OPERATION TYPE: IMAGE_ANALYSE")
         print(f"{'='*60}")
         
-        # CRAWL CONFIGURATION
+        # Check if test image exists
-        # Deep and Broad Web Crawl Example:
+        if not self.testImagePath or not os.path.exists(self.testImagePath):
-        # - maxDepth: 3 (deep) - follows links up to 3 levels from starting page
+            result = {
-        #   - Level 1: Starting page
+                "modelName": modelName,
-        #   - Level 2: Pages linked from starting page
+                "status": "ERROR",
-        #   - Level 3: Pages linked from Level 2 pages
+                "processingTime": 0.0,
-        # - maxWidth: 50 (broad) - crawls up to 50 pages at each depth level
+                "responseLength": 0,
-        # This results in potential maximum of ~1,250 pages (if 50 links exist at each level)
+                "responseType": "error",
-        # 
+                "hasContent": False,
-        # Common configurations:
+                "error": "No test image available",
-        # - Fast/Overview: maxDepth=1, maxWidth=5  (shallow, focused)
+                "fullResponse": ""
-        # - General/Standard: maxDepth=2, maxWidth=10  (balanced)
+            }
-        # - Deep and Broad: maxDepth=3, maxWidth=50  (comprehensive)
+            self.testResults.append(result)
+            return result
         
-        CRAWL_DEPTH = 3  # Deep crawl: follows links 3 levels deep
+        # Test prompt for image analysis
-        CRAWL_WIDTH = 50  # Broad crawl: up to 50 pages per level
+        testPrompt = "Analyze this image and describe what you see. Extract any text, numbers, or structured data."
-        
-        print(f"Crawl Configuration:")
-        print(f"  - Depth: {CRAWL_DEPTH} levels (deep)")
-        print(f"  - Width: {CRAWL_WIDTH} pages per level (broad)")
-        print(f"  - Theoretical max: {CRAWL_WIDTH ** min(CRAWL_DEPTH, 3)} pages")
-        
-        # Use WEB_CRAWL specific prompt format
-        from modules.datamodels.datamodelAi import AiCallPromptWebCrawl
-        
-        # Test with simple prompt like playground example
-        simplePrompt = f"https://www.valueon.ch: Who works in this company?"
-        
-        # But keep structured format for now to match our API
-        testPrompt = json.dumps({
-            "instruction": "Who works in this company?",
-            "url": "https://www.valueon.ch",
-            "maxDepth": CRAWL_DEPTH,
-            "maxWidth": CRAWL_WIDTH
-        }, indent=2)
-        
-        print(f"Simple prompt (playground style): {simplePrompt}")
-        
-        # For Tavily models, test direct API call for better link following
-        if "tavily" in modelName.lower():
-            return await self._testTavilyDirect(modelName, CRAWL_DEPTH, CRAWL_WIDTH)
         
+        print(f"Test image: {self.testImagePath}")
         print(f"Test prompt: {testPrompt}")
-        print(f"Prompt length: {len(testPrompt)} characters")
+        
+        # Load image data
+        with open(self.testImagePath, 'rb') as f:
+            imageData = f.read()
+        
+        print(f"Image size: {len(imageData)} bytes")
+        
+        # Determine image MIME type from extension
+        if self.testImagePath.lower().endswith('.png'):
+            mimeType = "image/png"
+        elif self.testImagePath.lower().endswith(('.jpg', '.jpeg')):
+            mimeType = "image/jpeg"
+        else:
+            mimeType = "image/jpeg"  # Default
+        
+        print(f"Image MIME type: {mimeType}")
         
         startTime = asyncio.get_event_loop().time()
         
         try:
-            # Create options for WEB_CRAWL operation
+            # Get model directly from registry and test it
-            options = AiCallOptions(
-                operationType=OperationTypeEnum.WEB_CRAWL,
-                preferredModel=modelName
-            )
-            
-            # Call the AI service DIRECTLY through the model's functionCall
-            # This tests the actual model, not the document generation pipeline
-            # Get the model directly from the registry using the model registry
             from modules.aicore.aicoreModelRegistry import modelRegistry
             model = modelRegistry.getModel(modelName)
             
             if not model:
                 raise Exception(f"Model {modelName} not found")
             
-            # Create AiModelCall and call the model's functionCall directly
+            # Import base64 for image data conversion
-            from modules.datamodels.datamodelAi import AiModelCall
             import base64
-            import os
             
-            # For WEB_CRAWL models, use normal functionCall with structured prompt
+            # Convert image data to base64 string
-            messages = [{"role": "user", "content": testPrompt}]
+            if isinstance(imageData, bytes):
+                imageDataStr = base64.b64encode(imageData).decode('utf-8')
+            else:
+                imageDataStr = imageData
+            
+            # Create messages in vision format
+            messages = [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": testPrompt},
+                        {
+                            "type": "image_url",
+                            "image_url": {
+                                "url": f"data:{mimeType};base64,{imageDataStr}"
+                            }
+                        }
+                    ]
+                }
+            ]
+            
+            # Create model call
+            from modules.datamodels.datamodelAi import AiModelCall, AiCallOptions
             modelCall = AiModelCall(
                 messages=messages,
                 model=model,
-                options=options
+                options=AiCallOptions(operationType=OperationTypeEnum.IMAGE_ANALYSE)
             )
-            response = await model.functionCall(modelCall)
+            
+            # Call model directly
+            print(f"Calling model.functionCall() for {modelName}")
+            modelResponse = await model.functionCall(modelCall)
+            
+            if not modelResponse.success:
+                raise Exception(f"Model call failed: {modelResponse.error}")
+            
+            result = modelResponse.content
             
             endTime = asyncio.get_event_loop().time()
             processingTime = endTime - startTime
             
-            # Analyze response - now we get AiModelResponse objects
+            # Analyze result (string response from readImage)
-            if hasattr(response, 'success'):
+            if result:
-                # AiModelResponse object
+                analysisResult = {
-                if response.success:
-                    result = {
-                        "modelName": modelName,
-                        "status": "SUCCESS",
-                        "processingTime": round(processingTime, 2),
-                        "responseLength": len(response.content) if response.content else 0,
-                        "responseType": "AiModelResponse",
-                        "hasContent": bool(response.content),
-                        "error": None,
-                        "modelUsed": modelName,
-                        "priceUsd": 0.0,  # AiModelResponse doesn't have price info
-                        "bytesSent": 0,
-                        "bytesReceived": len(response.content.encode('utf-8')) if response.content else 0
-                    }
-                    
-                    # Extract actual prompt sent if available in metadata
-                    if hasattr(response, 'metadata') and response.metadata:
-                        result["actualPromptSent"] = response.metadata.get("actualPromptSent", "N/A")
-                    
-                    # Try to parse content as JSON
-                    if response.content:
-                        try:
-                            json.loads(response.content)
-                            result["isValidJson"] = True
-                        except:
-                            result["isValidJson"] = False
-                        
-                        result["responsePreview"] = response.content[:200] + "..." if len(response.content) > 200 else response.content
-                        result["fullResponse"] = response.content
-                    else:
-                        result["isValidJson"] = False
-                        result["responsePreview"] = "Empty response"
-                        result["fullResponse"] = ""
-                    
-                    print(f"✅ SUCCESS - Processing time: {processingTime:.2f}s")
-                    print(f"📄 Response length: {len(response.content) if response.content else 0} characters")
-                    print(f"📄 Model used: {modelName}")
-                    print(f"📄 Response preview: {result['responsePreview']}")
-                    
-                else:
-                    error = response.error or "Unknown error"
-                    result = {
-                        "modelName": modelName,
-                        "status": "ERROR",
-                        "processingTime": round(processingTime, 2),
-                        "responseLength": 0,
-                        "responseType": "AiModelResponse",
-                        "hasContent": False,
-                        "error": error,
-                        "fullResponse": str(response)
-                    }
-                    
-                    print(f"❌ ERROR - {error}")
-                    
-            elif isinstance(response, dict):
-                # Fallback for dict responses
-                if response.get("success", True):
-                    result = {
-                        "modelName": modelName,
-                        "status": "SUCCESS",
-                        "processingTime": round(processingTime, 2),
-                        "responseLength": len(str(response)),
-                        "responseType": "dict",
-                        "hasContent": True,
-                        "error": None
-                    }
-                    
-                    # Try to parse as JSON
-                    try:
-                        jsonResponse = json.dumps(response, indent=2)
-                        result["responsePreview"] = jsonResponse[:200] + "..." if len(jsonResponse) > 200 else jsonResponse
-                        result["isValidJson"] = True
-                        result["fullResponse"] = jsonResponse
-                    except:
-                        result["responsePreview"] = str(response)[:200] + "..." if len(str(response)) > 200 else str(response)
-                        result["isValidJson"] = False
-                        result["fullResponse"] = str(response)
-                    
-                    print(f"✅ SUCCESS - Processing time: {processingTime:.2f}s")
-                    print(f"📄 Response length: {len(str(response))} characters")
-                    print(f"📄 Response preview: {result['responsePreview']}")
-                    
-                else:
-                    error = response.get("error", "Unknown error")
-                    result = {
-                        "modelName": modelName,
-                        "status": "ERROR",
-                        "processingTime": round(processingTime, 2),
-                        "responseLength": 0,
-                        "responseType": "error",
-                        "hasContent": False,
-                        "error": error,
-                        "fullResponse": str(response)
-                    }
-                    
-                    print(f"❌ ERROR - {error}")
-                    
-            else:
-                # String response
-                result = {
                     "modelName": modelName,
                     "status": "SUCCESS",
                     "processingTime": round(processingTime, 2),
-                    "responseLength": len(str(response)),
+                    "responseLength": len(result) if result else 0,
                     "responseType": "string",
                     "hasContent": True,
-                    "error": None
+                    "error": None,
+                    "testPrompt": testPrompt,
+                    "imagePath": self.testImagePath,
+                    "imageSize": len(imageData),
+                    "mimeType": mimeType
                 }
                 
                 # Try to parse as JSON
                 try:
-                    json.loads(str(response))
+                    json.loads(result)
-                    result["isValidJson"] = True
+                    analysisResult["isValidJson"] = True
                 except:
-                    result["isValidJson"] = False
+                    analysisResult["isValidJson"] = False
                 
-                result["responsePreview"] = str(response)[:200] + "..." if len(str(response)) > 200 else str(response)
+                analysisResult["responsePreview"] = result[:200] + "..." if len(result) > 200 else result
-                result["fullResponse"] = str(response)
+                analysisResult["fullResponse"] = result
                 
                 print(f"✅ SUCCESS - Processing time: {processingTime:.2f}s")
-                print(f"📄 Response length: {len(str(response))} characters")
+                print(f"📄 Response length: {len(result)} characters")
-                print(f"📄 Response preview: {result['responsePreview']}")
+                print(f"📄 Response preview: {analysisResult['responsePreview']}")
-            
+                
-            # Add prompt to result for logging
+                result = analysisResult
-            result["testPrompt"] = testPrompt
+                
-            result["crawlConfig"] = {
+                # Validate that content was extracted
-                "depth": CRAWL_DEPTH,
+                if result.get("status") == "SUCCESS" and result.get("fullResponse"):
-                "width": CRAWL_WIDTH
+                    self._validateImageResponse(modelName, result)
-            }
+            else:
-            
+                result = {
-            # For WEB_CRAWL, also validate that content was extracted
+                    "modelName": modelName,
-            if result.get("status") == "SUCCESS" and result.get("fullResponse"):
+                    "status": "ERROR",
-                self._validateCrawlResponse(modelName, result)
+                    "processingTime": round(processingTime, 2),
+                    "responseLength": 0,
+                    "responseType": "error",
+                    "hasContent": False,
+                    "error": "Empty response",
+                    "fullResponse": ""
+                }
             
         except Exception as e:
             endTime = asyncio.get_event_loop().time()
@@ -334,10 +298,9 @@ class AIModelsTester:
                 "hasContent": False,
                 "error": str(e),
                 "testPrompt": testPrompt,
-                "crawlConfig": {
+                "imagePath": self.testImagePath,
-                    "depth": CRAWL_DEPTH,
+                "imageSize": len(imageData) if imageData else 0,
-                    "width": CRAWL_WIDTH
+                "mimeType": mimeType
-                }
             }
             
             print(f"💥 EXCEPTION - {str(e)}")
@@ -346,7 +309,7 @@ class AIModelsTester:
         
         # Save text response even for exceptions to log the prompt
         if result.get("status") in ["SUCCESS", "EXCEPTION", "ERROR"]:
-            self._saveTextResponse(modelName, result)
+            self._saveImageResponse(modelName, result)
         
         # Save individual model result immediately
         self._saveIndividualModelResult(modelName, result)
@@ -354,54 +317,48 @@ class AIModelsTester:
         return result
     
     def _saveImageResponse(self, modelName: str, result: Dict[str, Any]):
-        """Save base64 image response to file."""
+        """Save image analysis response to file."""
         try:
-            fullResponse = result.get("fullResponse", "")
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-            base64Data = None
+            filename = f"{modelName}_{timestamp}.txt"
+            filepath = os.path.join(self.modelTestDir, filename)
             
-            # Try to extract base64 data from response
+            # Prepare content for saving
-            if isinstance(fullResponse, dict):
+            content = result.get("fullResponse", "")
-                # Look for base64 data in the response
+            if not content:
-                if "content" in fullResponse:
+                content = result.get("responsePreview", "No content available")
-                    base64Data = fullResponse["content"]
-                elif "data" in fullResponse:
-                    base64Data = fullResponse["data"]
-                elif "image" in fullResponse:
-                    base64Data = fullResponse["image"]
-            else:
-                # Try to find base64 data in string response
-                import re
-                base64Match = re.search(r'data:image/[^;]+;base64,([A-Za-z0-9+/=]+)', str(fullResponse))
-                if base64Match:
-                    base64Data = base64Match.group(1)
-                else:
-                    # Try to find pure base64 string
-                    base64Match = re.search(r'([A-Za-z0-9+/=]{100,})', str(fullResponse))
-                    if base64Match:
-                        base64Data = base64Match.group(1)
             
-            if base64Data:
+            # If there's an error, include it in the content
-                # Clean base64 data
+            if result.get("error"):
-                if base64Data.startswith('data:image/'):
+                content = f"ERROR: {result.get('error')}\n\n{content}"
-                    base64Data = base64Data.split(',', 1)[1]
+            
-                
+            # Add metadata header
-                # Decode and save image
+            metadata = f"""Model: {modelName}
-                imageData = base64.b64decode(base64Data)
+Test Time: {timestamp}
-                timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+Status: {result.get('status', 'Unknown')}
-                filename = f"{modelName}_{timestamp}.png"
+Processing Time: {result.get('processingTime', 0):.2f}s
-                filepath = os.path.join(self.modelTestDir, filename)
+Response Length: {result.get('responseLength', 0)} characters
-                
+Is Valid JSON: {result.get('isValidJson', False)}
-                with open(filepath, 'wb') as f:
+Image Path: {result.get('imagePath', 'N/A')}
-                    f.write(imageData)
+Image Size: {result.get('imageSize', 'N/A')} bytes
-                
+MIME Type: {result.get('mimeType', 'N/A')}
-                result["savedImage"] = filepath
+
-                print(f"🖼️  Image saved: {filepath}")
+--- TEST PROMPT ---
-            else:
+{result.get('testPrompt', 'N/A')}
-                print(f"⚠️  No base64 image data found in response")
+
+--- RESPONSE CONTENT ---
+{content}
+"""
+            
+            with open(filepath, 'w', encoding='utf-8') as f:
+                f.write(metadata)
+            
+            result["savedTextFile"] = filepath
+            print(f"📄 Analysis response saved: {filepath}")
                 
         except Exception as e:
-            print(f"❌ Error saving image: {str(e)}")
+            print(f"❌ Error saving analysis response: {str(e)}")
-            result["imageSaveError"] = str(e)
+            result["saveError"] = str(e)
     
     def _saveTextResponse(self, modelName: str, result: Dict[str, Any]):
         """Save text response to file."""
@@ -504,6 +461,41 @@ Width: {crawlWidth}
             print(f"❌ Error validating crawl response: {str(e)}")
             result["crawlValidationError"] = str(e)
     
+    def _validateImageResponse(self, modelName: str, result: Dict[str, Any]):
+        """Validate that the IMAGE_ANALYSE response contains analyzed content."""
+        try:
+            content = result.get("fullResponse", "")
+            
+            # Check if content is meaningful
+            hasContent = bool(content and len(content.strip()) > 0)
+            contentLength = len(content)
+            
+            result["hasContent"] = hasContent
+            result["contentLength"] = contentLength
+            
+            # Try to determine what kind of content was extracted
+            if hasContent:
+                # Check if it's structured data
+                isStructured = False
+                try:
+                    parsed = json.loads(content)
+                    if isinstance(parsed, dict):
+                        isStructured = True
+                except:
+                    pass
+                
+                result["isStructured"] = isStructured
+                
+                print(f"✅ Successfully analyzed image")
+                print(f"   Content length: {contentLength} characters")
+                print(f"   Is structured: {'Yes' if isStructured else 'No'}")
+            else:
+                print(f"⚠️  Empty or invalid image analysis response")
+                
+        except Exception as e:
+            print(f"❌ Error validating image response: {str(e)}")
+            result["validationError"] = str(e)
+    
     async def _testTavilyDirect(self, modelName: str, crawlDepth: int = 3, crawlWidth: int = 50) -> Dict[str, Any]:
         """Test Tavily API directly using the crawl() method with better link following."""
         print(f"\n{'='*60}")
@@ -660,30 +652,30 @@ Width: {crawlWidth}
             print(f"❌ Error saving individual result: {str(e)}")
     
     def getAllAvailableModels(self) -> List[str]:
-        """Get all available model names that support WEB_CRAWL."""
+        """Get all available model names that support IMAGE_ANALYSE."""
         from modules.aicore.aicoreModelRegistry import modelRegistry
         from modules.datamodels.datamodelAi import OperationTypeEnum
         
         # Get all models from registry
         allModels = modelRegistry.getAvailableModels()
         
-        # Filter models that support WEB_CRAWL
+        # Filter models that support IMAGE_ANALYSE
-        webCrawlModels = []
+        imageAnalyseModels = []
         for model in allModels:
             if model.operationTypes and any(
-                ot.operationType == OperationTypeEnum.WEB_CRAWL 
+                ot.operationType == OperationTypeEnum.IMAGE_ANALYSE 
                 for ot in model.operationTypes
-            ):  # Include both Tavily and Perplexity models
+            ):
-                webCrawlModels.append(model.name)
+                imageAnalyseModels.append(model.name)
         
-        # Filter to only "sonar" model for testing
+        # Filter to common models for testing (remove filter to test all models)
-        webCrawlModels = [m for m in webCrawlModels if m == "sonar"]
+        # imageAnalyseModels = [m for m in imageAnalyseModels if "gpt" in m.lower() or "claude" in m.lower()]
         
-        print(f"Found {len(webCrawlModels)} models that support WEB_CRAWL (filtered to sonar):")
+        print(f"Found {len(imageAnalyseModels)} models that support IMAGE_ANALYSE:")
-        for modelName in webCrawlModels:
+        for modelName in imageAnalyseModels:
             print(f"  - {modelName}")
         
-        return webCrawlModels
+        return imageAnalyseModels
     
     def saveTestResults(self):
         """Save detailed test results to file."""
@@ -802,7 +794,7 @@ async def main():
     """Run AI models testing for WEB_CRAWL operation."""
     tester = AIModelsTester()
     
-    print("Starting AI Models Testing for WEB_CRAWL...")
+    print("Starting AI Models Testing for IMAGE_ANALYSE...")
     print("Initializing AI service...")
     await tester.initialize()
     
@@ -814,9 +806,9 @@ async def main():
         print(f"  {i}. {model}")
     
     print(f"\n{'='*80}")
-    print("STARTING WEB_CRAWL TESTS")
+    print("STARTING IMAGE_ANALYSE TESTS")
     print(f"{'='*80}")
-    print("Testing each model's ability to crawl URLs and return content...")
+    print("Testing each model's ability to analyze images and return structured content...")
     print("Press Enter after each model test to continue to the next one...")
     
     # Test each model individually
@@ -840,7 +832,7 @@ async def main():
     print("TESTING COMPLETED")
     print(f"{'='*80}")
     print(f"📄 Results saved to: {resultsFile}")
-    print(f"📁 Images saved to: {tester.modelTestDir}")
+    print(f"📁 Test results saved to: {tester.modelTestDir}")
 
 if __name__ == "__main__":
     asyncio.run(main())