fix: TEXT-to-VECTOR migration, table content normalization, ChatDocument DB fallback, Private-LLM log level

Co-authored-by: Cursor <cursoragent@cursor.com>

modules/aicore/aicorePluginAnthropic.py

@@ -30,6 +30,8 @@ def _supportsCustomTemperature(modelName: str) -> bool:
     if not modelName:
         return True
     name = modelName.lower()
+    if name.startswith("claude-opus-4-8"):
+        return False
     if name.startswith("claude-opus-4-7"):
         return False
     if name.startswith("claude-sonnet-4-7"):
@@ -78,6 +80,54 @@ class AiAnthropic(BaseConnectorAi):
     def getModels(self) -> List[AiModel]:
         # Get all available Anthropic models.
         return [
+            AiModel(
+                name="claude-opus-4-8",
+                displayName="Anthropic Claude Opus 4.8",
+                connectorType="anthropic",
+                apiUrl="https://api.anthropic.com/v1/messages",
+                temperature=0.2,
+                maxTokens=128000,
+                contextLength=1000000,
+                costPer1kTokensInput=0.005,  # $5/M tokens (Anthropic API, 2026-05)
+                costPer1kTokensOutput=0.025,  # $25/M tokens
+                speedRating=5,
+                qualityRating=10,
+                functionCall=self.callAiBasic,
+                functionCallStream=self.callAiBasicStream,
+                priority=PriorityEnum.QUALITY,
+                processingMode=ProcessingModeEnum.DETAILED,
+                operationTypes=createOperationTypeRatings(
+                    (OperationTypeEnum.PLAN, 10),
+                    (OperationTypeEnum.DATA_ANALYSE, 9),
+                    (OperationTypeEnum.DATA_GENERATE, 10),
+                    (OperationTypeEnum.DATA_EXTRACT, 9),
+                    (OperationTypeEnum.AGENT, 10),
+                    (OperationTypeEnum.DATA_QUERY, 3),
+                ),
+                version="claude-opus-4-8",
+                calculatepriceCHF=lambda processingTime, bytesSent, bytesReceived: (bytesSent / 4 / 1000) * 0.005 + (bytesReceived / 4 / 1000) * 0.025
+            ),
+            AiModel(
+                name="claude-opus-4-8",
+                displayName="Anthropic Claude Opus 4.8 Vision",
+                connectorType="anthropic",
+                apiUrl="https://api.anthropic.com/v1/messages",
+                temperature=0.2,
+                maxTokens=128000,
+                contextLength=1000000,
+                costPer1kTokensInput=0.005,
+                costPer1kTokensOutput=0.025,
+                speedRating=5,
+                qualityRating=10,
+                functionCall=self.callAiImage,
+                priority=PriorityEnum.QUALITY,
+                processingMode=ProcessingModeEnum.DETAILED,
+                operationTypes=createOperationTypeRatings(
+                    (OperationTypeEnum.IMAGE_ANALYSE, 10)
+                ),
+                version="claude-opus-4-8",
+                calculatepriceCHF=lambda processingTime, bytesSent, bytesReceived: (bytesSent / 4 / 1000) * 0.005 + (bytesReceived / 4 / 1000) * 0.025
+            ),
             AiModel(
                 name="claude-opus-4-7",
                 displayName="Anthropic Claude Opus 4.7",

modules/aicore/aicorePluginPrivateLlm.py

@@ -6,14 +6,21 @@ AI Connector for PowerOn Private-LLM Service.
 Connects to the private-llm service running on-premise with Ollama backend.
 Provides OCR and Vision capabilities via local AI models.
 
-Models:
-- poweron-text-general: Text (qwen2.5); NEUTRALIZATION_TEXT + data/plan ops
-- poweron-vision-general: Vision (qwen2.5vl); IMAGE_ANALYSE + NEUTRALIZATION_IMAGE
+Models (current — L4 24 GB):
+- poweron-text-general: Text (qwen2.5:7b); NEUTRALIZATION_TEXT + data/plan ops
+- poweron-vision-general: Vision (qwen2.5vl:7b); IMAGE_ANALYSE + NEUTRALIZATION_IMAGE
 - poweron-vision-deep: Vision (granite3.2); IMAGE_ANALYSE + NEUTRALIZATION_IMAGE
 
+Models (next-gen — RTX PRO 6000 96 GB, auto-activated when pulled in Ollama):
+- poweron-text-reasoning: Reasoning (deepseek-r1:70b); complex logic, math, planning
+- poweron-vision-general: Vision (llama4:scout); multimodal, long-context documents
+- poweron-embed: Embedding (nomic-embed-text); local RAG embedding
+
 Pricing (CHF per call):
 - Text models: CHF 0.010
 - Vision models: CHF 0.100
+- Reasoning models: CHF 0.050
+- Embedding: CHF 0.000 (flat rate)
 """
 
 import logging
@@ -39,6 +46,8 @@ logger = logging.getLogger(__name__)
 # Pricing constants (CHF)
 PRICE_TEXT_PER_CALL = 0.01      # CHF 0.010 per text model call
 PRICE_VISION_PER_CALL = 0.10   # CHF 0.100 per vision model call
+PRICE_REASONING_PER_CALL = 0.05  # CHF 0.050 per reasoning call (longer runtime)
+PRICE_EMBED_PER_CALL = 0.00    # CHF 0.000 flat rate (local embedding)
 
 
 # Private-LLM Service URL (fix, nicht via env konfigurierbar)
@@ -306,6 +315,88 @@ class AiPrivateLlm(BaseConnectorAi):
                 ),
                 "ollamaModel": "granite3.2-vision"
             },
+            # --- Next-gen models (auto-activated when available in Ollama) ---
+            # Reasoning Model (deepseek-r1:70b — chain-of-thought, math, logic)
+            {
+                "model": AiModel(
+                    name="poweron-text-reasoning",
+                    displayName="PowerOn Reasoning",
+                    connectorType="privatellm",
+                    apiUrl=f"{self.baseUrl}/api/analyze",
+                    temperature=0.1,
+                    maxTokens=8192,
+                    contextLength=65536,
+                    costPer1kTokensInput=0.0,
+                    costPer1kTokensOutput=0.0,
+                    speedRating=5,
+                    qualityRating=10,
+                    functionCall=self.callAiText,
+                    priority=PriorityEnum.QUALITY,
+                    processingMode=ProcessingModeEnum.DETAILED,
+                    operationTypes=createOperationTypeRatings(
+                        (OperationTypeEnum.PLAN, 10),
+                        (OperationTypeEnum.DATA_ANALYSE, 10),
+                        (OperationTypeEnum.DATA_GENERATE, 9),
+                        (OperationTypeEnum.DATA_EXTRACT, 9),
+                        (OperationTypeEnum.NEUTRALIZATION_TEXT, 10),
+                        (OperationTypeEnum.AGENT, 9),
+                    ),
+                    version="deepseek-r1:70b",
+                    calculatepriceCHF=lambda processingTime, bytesSent, bytesReceived: PRICE_REASONING_PER_CALL
+                ),
+                "ollamaModel": "deepseek-r1:70b"
+            },
+            # Vision Multimodal (llama4:scout — native vision, 10M context)
+            {
+                "model": AiModel(
+                    name="poweron-vision-multimodal",
+                    displayName="PowerOn Vision Multimodal",
+                    connectorType="privatellm",
+                    apiUrl=f"{self.baseUrl}/api/analyze",
+                    temperature=0.2,
+                    maxTokens=4096,
+                    contextLength=131072,
+                    costPer1kTokensInput=0.0,
+                    costPer1kTokensOutput=0.0,
+                    speedRating=7,
+                    qualityRating=10,
+                    functionCall=self.callAiVision,
+                    priority=PriorityEnum.QUALITY,
+                    processingMode=ProcessingModeEnum.DETAILED,
+                    operationTypes=createOperationTypeRatings(
+                        (OperationTypeEnum.IMAGE_ANALYSE, 10),
+                        (OperationTypeEnum.NEUTRALIZATION_IMAGE, 10),
+                    ),
+                    version="llama4:scout",
+                    calculatepriceCHF=lambda processingTime, bytesSent, bytesReceived: PRICE_VISION_PER_CALL
+                ),
+                "ollamaModel": "llama4:scout"
+            },
+            # Local Embedding (nomic-embed-text — replaces OpenAI text-embedding-3-small)
+            {
+                "model": AiModel(
+                    name="poweron-embed",
+                    displayName="PowerOn Embedding",
+                    connectorType="privatellm",
+                    apiUrl=f"{self.baseUrl}/v1/embeddings",
+                    temperature=0.0,
+                    maxTokens=0,
+                    contextLength=8192,
+                    costPer1kTokensInput=0.0,
+                    costPer1kTokensOutput=0.0,
+                    speedRating=10,
+                    qualityRating=8,
+                    functionCall=self.callAiText,
+                    priority=PriorityEnum.COST,
+                    processingMode=ProcessingModeEnum.BASIC,
+                    operationTypes=createOperationTypeRatings(
+                        (OperationTypeEnum.EMBEDDING, 9),
+                    ),
+                    version="nomic-embed-text",
+                    calculatepriceCHF=lambda processingTime, bytesSent, bytesReceived: PRICE_EMBED_PER_CALL
+                ),
+                "ollamaModel": "nomic-embed-text"
+            },
         ]
         
         # Filter models by Ollama availability
@@ -320,7 +411,7 @@ class AiPrivateLlm(BaseConnectorAi):
                 unavailableModels.append(modelDef["model"].name)
         
         if unavailableModels:
-            logger.warning(
+            logger.info(
                 f"Private-LLM: {len(unavailableModels)} models not available in Ollama: {', '.join(unavailableModels)}. "
                 f"Install with: ollama pull <model-name>"
             )

modules/connectors/connectorDbPostgre.py

@@ -868,6 +868,8 @@ class DatabaseConnector:
                                 desired_sql = (model_fields.get(col) or "").upper()
                                 currentType = existing_column_types.get(col, "")
                                 migration = _SAFE_TYPE_CHANGES.get((currentType, desired_sql))
+                                if not migration and desired_sql.startswith("VECTOR") and currentType == "text":
+                                    migration = f'{desired_sql} USING CASE WHEN "{col}" IS NULL OR "{col}" = \'\' THEN NULL ELSE "{col}"::vector END'
                                 if migration:
                                     castExpr = migration.replace("{col}", col)
                                     try:

modules/serviceCenter/services/serviceAi/subStructureFilling.py

@@ -2662,6 +2662,17 @@ CRITICAL:
                 if tableCount > 0:
                     logger.info(f"Section {sectionId}: JSON block {i+1}: {tableCount} table(s) with {rowCount} total rows")
         
+        # Validate table elements: AI sometimes returns content as string instead of {headers, rows}
+        if contentType == "table":
+            for idx, elem in enumerate(allElements):
+                if not isinstance(elem, dict) or elem.get("type") != "table":
+                    continue
+                content = elem.get("content")
+                if isinstance(content, str):
+                    normalized = self._normalizeTableContentString(content, sectionId, idx)
+                    elem["content"] = normalized
+                    logger.info(f"Section {sectionId}: Normalized string table content in element {idx}")
+
         # Merge elements based on contentType
         if contentType == "table" and len(allElements) > 1:
             # Find all table elements
@@ -2682,6 +2693,45 @@ CRITICAL:
         
         return allElements
     
+    def _normalizeTableContentString(self, text: str, sectionId: str, elemIndex: int) -> Dict[str, Any]:
+        """Convert a string table content (CSV, markdown, pipe-delimited) into {headers, rows}."""
+        import csv
+        import io
+
+        lines = [l for l in text.strip().splitlines() if l.strip()]
+        if not lines:
+            return {"headers": [], "rows": []}
+
+        # Detect markdown pipe table (| col1 | col2 |)
+        if "|" in lines[0]:
+            rows = []
+            for line in lines:
+                stripped = line.strip().strip("|")
+                if stripped and not all(c in "-| " for c in stripped):
+                    cells = [c.strip() for c in stripped.split("|")]
+                    rows.append(cells)
+            if rows:
+                return {"headers": rows[0], "rows": rows[1:]}
+
+        # Detect CSV/TSV
+        try:
+            dialect = csv.Sniffer().sniff(lines[0])
+            reader = csv.reader(io.StringIO(text.strip()), dialect)
+            allRows = list(reader)
+            if allRows:
+                return {"headers": allRows[0], "rows": allRows[1:]}
+        except csv.Error:
+            pass
+
+        # Tab-separated fallback
+        if "\t" in lines[0]:
+            rows = [line.split("\t") for line in lines]
+            return {"headers": rows[0], "rows": rows[1:]}
+
+        # Last resort: single-column table from lines
+        logger.warning(f"Section {sectionId}: element {elemIndex}: could not parse table string format, wrapping as single column")
+        return {"headers": ["Value"], "rows": [[line] for line in lines]}
+
     def _canMergeTables(self, tableElements: List[Dict[str, Any]]) -> bool:
         """Check if tables can be safely merged (same column counts)."""
         if len(tableElements) <= 1:

modules/serviceCenter/services/serviceChat/mainServiceChat.py

@@ -36,6 +36,16 @@ class ChatService:
         """Workflow from context (stable during workflow execution)."""
         return self._context.workflow
 
+    def _chatDocumentFromDb(self, docId: str) -> Optional[ChatDocument]:
+        """Lookup a ChatDocument directly in the DB by its primary key."""
+        try:
+            results = self.interfaceDbChat.db.getRecordset(ChatDocument, recordFilter={"id": docId})
+            if results:
+                return results[0]
+        except Exception as e:
+            logger.debug("_chatDocumentFromDb(%s) failed: %s", docId, e)
+        return None
+
     def _chat_document_from_management_file(self, file_id: str) -> Optional[ChatDocument]:
         """Build a ChatDocument when docItem references a management FileItem (e.g. automation uploads) without a chat message."""
         try:
@@ -157,6 +167,16 @@ class ChatService:
                                 if docFound:
                                     break
                         
+                        if not docFound:
+                            dbDoc = self._chatDocumentFromDb(docId)
+                            if dbDoc is not None:
+                                allDocuments.append(dbDoc)
+                                docFound = True
+                                logger.info(
+                                    "Resolved document reference %r via DB ChatDocument lookup",
+                                    docRef,
+                                )
+
                         if not docFound:
                             synth = self._chat_document_from_management_file(docId)
                             if synth is not None: