fixes

modules/aicore/aicorePluginAnthropic.py

@@ -71,6 +71,7 @@ class AiAnthropic(BaseConnectorAi):
                     (OperationTypeEnum.DATA_GENERATE, 9),
                     (OperationTypeEnum.DATA_EXTRACT, 8),
                     (OperationTypeEnum.AGENT, 9),
+                    (OperationTypeEnum.DATA_QUERY, 9),
                 ),
                 version="claude-sonnet-4-5-20250929",
                 calculatepriceCHF=lambda processingTime, bytesSent, bytesReceived: (bytesSent / 4 / 1000) * 0.003 + (bytesReceived / 4 / 1000) * 0.015
@@ -97,6 +98,7 @@ class AiAnthropic(BaseConnectorAi):
                     (OperationTypeEnum.DATA_GENERATE, 8),
                     (OperationTypeEnum.DATA_EXTRACT, 7),
                     (OperationTypeEnum.AGENT, 7),
+                    (OperationTypeEnum.DATA_QUERY, 10),
                 ),
                 version="claude-haiku-4-5-20251001",
                 calculatepriceCHF=lambda processingTime, bytesSent, bytesReceived: (bytesSent / 4 / 1000) * 0.001 + (bytesReceived / 4 / 1000) * 0.005
@@ -123,6 +125,7 @@ class AiAnthropic(BaseConnectorAi):
                     (OperationTypeEnum.DATA_GENERATE, 10),
                     (OperationTypeEnum.DATA_EXTRACT, 9),
                     (OperationTypeEnum.AGENT, 10),
+                    (OperationTypeEnum.DATA_QUERY, 3),
                 ),
                 version="claude-opus-4-6",
                 calculatepriceCHF=lambda processingTime, bytesSent, bytesReceived: (bytesSent / 4 / 1000) * 0.005 + (bytesReceived / 4 / 1000) * 0.025

modules/aicore/aicorePluginMistral.py

@@ -67,6 +67,7 @@ class AiMistral(BaseConnectorAi):
                     (OperationTypeEnum.DATA_GENERATE, 9),
                     (OperationTypeEnum.DATA_EXTRACT, 8),
                     (OperationTypeEnum.AGENT, 8),
+                    (OperationTypeEnum.DATA_QUERY, 7),
                 ),
                 version="mistral-large-latest",
                 calculatepriceCHF=lambda processingTime, bytesSent, bytesReceived: (bytesSent / 4 / 1000) * 0.0005 + (bytesReceived / 4 / 1000) * 0.0015
@@ -93,6 +94,7 @@ class AiMistral(BaseConnectorAi):
                     (OperationTypeEnum.DATA_GENERATE, 8),
                     (OperationTypeEnum.DATA_EXTRACT, 7),
                     (OperationTypeEnum.AGENT, 6),
+                    (OperationTypeEnum.DATA_QUERY, 9),
                 ),
                 version="mistral-small-latest",
                 calculatepriceCHF=lambda processingTime, bytesSent, bytesReceived: (bytesSent / 4 / 1000) * 0.00006 + (bytesReceived / 4 / 1000) * 0.00018

modules/aicore/aicorePluginOpenai.py

@@ -68,6 +68,7 @@ class AiOpenai(BaseConnectorAi):
                     (OperationTypeEnum.DATA_GENERATE, 10),
                     (OperationTypeEnum.DATA_EXTRACT, 7),
                     (OperationTypeEnum.AGENT, 9),
+                    (OperationTypeEnum.DATA_QUERY, 8),
                 ),
                 version="gpt-4o",
                 calculatepriceCHF=lambda processingTime, bytesSent, bytesReceived: (bytesSent / 4 / 1000) * 0.0025 + (bytesReceived / 4 / 1000) * 0.01
@@ -95,6 +96,7 @@ class AiOpenai(BaseConnectorAi):
                     (OperationTypeEnum.DATA_GENERATE, 9),
                     (OperationTypeEnum.DATA_EXTRACT, 7),
                     (OperationTypeEnum.AGENT, 8),
+                    (OperationTypeEnum.DATA_QUERY, 10),
                 ),
                 version="gpt-4o-mini",
                 calculatepriceCHF=lambda processingTime, bytesSent, bytesReceived: (bytesSent / 4 / 1000) * 0.00015 + (bytesReceived / 4 / 1000) * 0.0006

modules/datamodels/datamodelAi.py

@@ -32,6 +32,7 @@ class OperationTypeEnum(str, Enum):
 
     # Agent Operations
     AGENT = "agent"                 # Agent loop: reasoning + tool use
+    DATA_QUERY = "dataQuery"        # Data query sub-agent: fast model, schema-aware
 
     # Embedding Operations
     EMBEDDING = "embedding"         # Text → vector conversion for semantic search

modules/features/trustee/accounting/accountingDataSync.py

@@ -5,10 +5,13 @@
 Flow: load config → resolve connector → fetch data → clear old records → write new records → compute balances.
 """
 
+import json as _json
 import logging
+import os
 import time
 from collections import defaultdict
-from typing import Dict, Any, Optional
+from pathlib import Path
+from typing import Dict, Any, List, Optional
 
 from .accountingConnectorBase import BaseAccountingConnector
 from .accountingRegistry import _getAccountingRegistry
@@ -16,6 +19,45 @@ from .accountingRegistry import _getAccountingRegistry
 logger = logging.getLogger(__name__)
 
 
+_DEBUG_SYNC_DIR = Path("D:/Athi/Local/Web/poweron/local/debug/sync")
+
+
+def _debugSyncDir() -> Path:
+    _DEBUG_SYNC_DIR.mkdir(parents=True, exist_ok=True)
+    return _DEBUG_SYNC_DIR
+
+
+def _isDebugEnabled() -> bool:
+    try:
+        from modules.shared.configuration import APP_CONFIG
+        return APP_CONFIG.get("APP_LOGGING_FILE_ENABLED", False) is True or str(APP_CONFIG.get("APP_LOGGING_FILE_ENABLED", "")).lower() == "true"
+    except Exception:
+        return False
+
+
+def _dumpSyncData(tag: str, rows: list):
+    """Write raw connector data to a timestamped JSON file in local/debug/sync/."""
+    if not _isDebugEnabled():
+        return
+    try:
+        d = _debugSyncDir()
+        ts = time.strftime("%Y%m%d-%H%M%S")
+        path = d / f"{ts}_{tag}.json"
+        serializable = []
+        for r in rows:
+            if isinstance(r, dict):
+                serializable.append(r)
+            elif hasattr(r, "__dict__"):
+                serializable.append({k: v for k, v in r.__dict__.items() if not k.startswith("_")})
+            else:
+                serializable.append(str(r))
+        with open(path, "w", encoding="utf-8") as f:
+            _json.dump({"count": len(serializable), "rows": serializable}, f, ensure_ascii=False, indent=2, default=str)
+        logger.info(f"Debug sync dump: {path.name} ({len(serializable)} rows)")
+    except Exception as e:
+        logger.warning(f"Failed to write debug sync dump for {tag}: {e}")
+
+
 class AccountingDataSync:
     """Imports accounting data (read-only) from an external system into local TrusteeData* tables."""
 
@@ -86,6 +128,7 @@ class AccountingDataSync:
         # 1) Chart of accounts
         try:
             charts = await connector.getChartOfAccounts(connConfig)
+            _dumpSyncData("accounts", charts)
             fetchedAccountNumbers = [acc.accountNumber for acc in charts if acc.accountNumber]
             self._clearTable(TrusteeDataAccount, featureInstanceId)
             for acc in charts:
@@ -105,6 +148,7 @@ class AccountingDataSync:
         # 2) Journal entries + lines (pass already-fetched chart to avoid redundant API call)
         try:
             rawEntries = await connector.getJournalEntries(connConfig, dateFrom=dateFrom, dateTo=dateTo, accountNumbers=fetchedAccountNumbers or None)
+            _dumpSyncData("journalEntries", rawEntries)
             self._clearTable(TrusteeDataJournalEntry, featureInstanceId)
             self._clearTable(TrusteeDataJournalLine, featureInstanceId)
             lineCount = 0
@@ -146,11 +190,13 @@ class AccountingDataSync:
             contactCount = 0
 
             customers = await connector.getCustomers(connConfig)
+            _dumpSyncData("customers", customers)
             for c in customers:
                 self._if.db.recordCreate(TrusteeDataContact, self._mapContact(c, "customer", scope))
                 contactCount += 1
 
             vendors = await connector.getVendors(connConfig)
+            _dumpSyncData("vendors", vendors)
             for v in vendors:
                 self._if.db.recordCreate(TrusteeDataContact, self._mapContact(v, "vendor", scope))
                 contactCount += 1

modules/features/workspace/routeFeatureWorkspace.py

@@ -235,6 +235,8 @@ def _buildFeatureDataSourceContext(featureDataSourceIds: List[str]) -> str:
     parts = [
         "The user has attached data from the following feature instances.",
         "Use queryFeatureInstance(featureInstanceId, question) to query this data.",
+        "IMPORTANT: Formulate ONE comprehensive question per call that covers everything you need.",
+        "The sub-agent can browse, filter, and aggregate -- ask precisely and avoid repeated calls.",
         "",
     ]
     found = False

modules/serviceCenter/services/serviceAgent/agentLoop.py

@@ -169,7 +169,7 @@ async def runAgentLoop(
         aiRequest = AiCallRequest(
             prompt="",
             options=AiCallOptions(
-                operationType=OperationTypeEnum.AGENT,
+                operationType=config.operationType or OperationTypeEnum.AGENT,
                 temperature=config.temperature
             ),
             messages=conversation.messages,

modules/serviceCenter/services/serviceAgent/coreTools/_featureSubAgentTools.py

@@ -185,8 +185,12 @@ def _registerFeatureSubAgentTools(registry: ToolRegistry, services):
         description=(
             "Query data from a feature instance (e.g. Trustee, CommCoach). "
             "Delegates to a specialized sub-agent that knows the feature's data schema "
-            "and can browse/query its tables. Use this when the user has attached "
-            "feature data sources or asks about feature-specific data."
+            "and can browse, filter, and aggregate its tables. Use this when the user "
+            "has attached feature data sources or asks about feature-specific data.\n\n"
+            "GUIDELINES:\n"
+            "- Ask a precise, self-contained question (include all context the sub-agent needs).\n"
+            "- Combine related data needs into ONE call instead of multiple small ones.\n"
+            "- Avoid calling this tool repeatedly with slight variations of the same question."
         ),
         parameters={
             "type": "object",

modules/serviceCenter/services/serviceAgent/datamodelAgent.py

@@ -6,6 +6,7 @@ from typing import List, Dict, Any, Optional
 from enum import Enum
 from pydantic import BaseModel, Field
 from modules.shared.timeUtils import getUtcTimestamp
+from modules.datamodels.datamodelAi import OperationTypeEnum
 import uuid
 
 
@@ -91,6 +92,7 @@ class AgentConfig(BaseModel):
     initialToolboxes: List[str] = Field(default_factory=lambda: ["core"])
     availableToolboxes: List[str] = Field(default_factory=list)
     temperature: Optional[float] = Field(default=None, ge=0.0, le=2.0)
+    operationType: Optional[OperationTypeEnum] = Field(default=None, description="Override the default AGENT operationType for model selection")
 
 
 class AgentState(BaseModel):

modules/serviceCenter/services/serviceAgent/featureDataAgent.py

@@ -24,8 +24,8 @@ from modules.serviceCenter.services.serviceAgent.featureDataProvider import Feat
 
 logger = logging.getLogger(__name__)
 
-_MAX_ROUNDS = 5
-_MAX_COST_CHF = 0.10
+_MAX_ROUNDS = 8
+_MAX_COST_CHF = 0.15
 
 
 async def runFeatureDataAgent(
@@ -69,25 +69,37 @@ async def runFeatureDataAgent(
             if realCols:
                 meta["fields"] = realCols
 
-    schemaContext = _buildSchemaContext(featureCode, instanceLabel, selectedTables)
-    prompt = f"{schemaContext}\n\nUser question:\n{question}"
+    systemPrompt = _buildSchemaContext(featureCode, instanceLabel, selectedTables)
 
-    config = AgentConfig(maxRounds=_MAX_ROUNDS, maxCostCHF=_MAX_COST_CHF)
+    config = AgentConfig(
+        maxRounds=_MAX_ROUNDS,
+        maxCostCHF=_MAX_COST_CHF,
+        operationType=OperationTypeEnum.DATA_QUERY,
+    )
+
+    costAccumulator = 0.0
+
+    async def _trackingAiCallFn(req):
+        nonlocal costAccumulator
+        resp = await aiCallFn(req)
+        costAccumulator += resp.priceCHF
+        return resp
 
     async def _getWorkflowCost() -> float:
-        return 0.0
+        return costAccumulator
 
     result = ""
     async for event in runAgentLoop(
-        prompt=prompt,
+        prompt=question,
         toolRegistry=registry,
         config=config,
-        aiCallFn=aiCallFn,
+        aiCallFn=_trackingAiCallFn,
         getWorkflowCostFn=_getWorkflowCost,
         workflowId=f"fda-{featureInstanceId[:8]}",
         userId=userId,
         featureInstanceId=featureInstanceId,
         mandateId=mandateId,
+        systemPromptOverride=systemPrompt,
     ):
         if event.type == AgentEventTypeEnum.FINAL and event.content:
             result = event.content
@@ -282,7 +294,7 @@ def _buildSchemaContext(
     instanceLabel: str,
     selectedTables: List[Dict[str, Any]],
 ) -> str:
-    """Build a system-level context block describing available tables."""
+    """Build a system prompt describing available tables and query strategy."""
     tableNames = []
     tableBlocks = []
 
@@ -298,34 +310,28 @@ def _buildSchemaContext(
             block += f"\n    Fields: {', '.join(fields)}"
         tableBlocks.append(block)
 
-    parts = [
-        f"You are a data query assistant for the '{featureCode}' feature",
-    ]
+    header = f"You are a data query assistant for the '{featureCode}' feature"
     if instanceLabel:
-        parts[0] += f' (instance: "{instanceLabel}")'
-    parts[0] += "."
+        header += f' (instance: "{instanceLabel}")'
+    header += "."
 
-    parts.append("")
-    parts.append("AVAILABLE TABLES (use EXACTLY these names as tableName parameter):")
-    parts.extend(tableBlocks)
-    parts.append("")
-    parts.append(
-        "IMPORTANT RULES:\n"
-        f"- The ONLY valid tableName values are: {tableNames}\n"
-        "- Do NOT invent table names, do NOT use UUIDs or IDs as table names.\n"
-        "- Field names are plain column names (e.g. 'accountNumber', 'periodYear').\n"
-        "  Do NOT prefix field names with UUIDs, table names, or dots.\n"
-        "- If unsure about column names, call browseTable with only tableName (no fields)\n"
-        "  to see actual columns first."
-    )
-    parts.append("")
-    parts.append(
-        "Tools: browseTable (list rows), queryTable (filter/search), "
-        "aggregateTable (SUM/COUNT/AVG/MIN/MAX with optional GROUP BY)."
-    )
-    parts.append("")
-    parts.append(
-        "Answer the user's question using the data from these tables. "
-        "Be precise, cite row counts, and format data clearly."
-    )
+    parts = [
+        header,
+        "",
+        "AVAILABLE TABLES (use EXACTLY these names as tableName parameter):",
+        *tableBlocks,
+        "",
+        f"Valid tableName values: {tableNames}",
+        "Field names are plain column names (e.g. 'accountNumber', 'periodYear').",
+        "",
+        "QUERY STRATEGY:",
+        "1. If unsure about columns, call browseTable(tableName) first to inspect the schema.",
+        "2. Use queryTable with filters for targeted lookups.",
+        "3. Use aggregateTable for SUM/COUNT/AVG/MIN/MAX with optional GROUP BY.",
+        "4. Combine what you need into as few tool calls as possible.",
+        "",
+        "RULES:",
+        "- Do NOT invent table or field names. Do NOT prefix fields with UUIDs or dots.",
+        "- Answer concisely. Cite row counts and key values.",
+    ]
     return "\n".join(parts)

modules/serviceCenter/services/serviceAgent/featureDataProvider.py

@@ -10,10 +10,50 @@ and mandateId so data isolation is guaranteed.
 
 import logging
 import json
+import os
+import time
+from pathlib import Path
 from typing import Any, Dict, List, Optional
 
 logger = logging.getLogger(__name__)
 
+_DEBUG_DIR = Path("D:/Athi/Local/Web/poweron/local/debug")
+
+
+def _isDebugEnabled() -> bool:
+    try:
+        from modules.shared.configuration import APP_CONFIG
+        val = APP_CONFIG.get("APP_LOGGING_FILE_ENABLED", False)
+        return val is True or str(val).lower() == "true"
+    except Exception:
+        return False
+
+
+def _debugQueryLog(method: str, tableName: str, params: dict, result: dict, elapsed: float):
+    """Append query + result to local/debug/debug_queryTable.log."""
+    if not _isDebugEnabled():
+        return
+    debugDir = _DEBUG_DIR
+    try:
+        debugDir.mkdir(parents=True, exist_ok=True)
+        logPath = debugDir / "debug_queryTable.log"
+        ts = time.strftime("%Y-%m-%d %H:%M:%S")
+        rows = result.get("rows", [])
+        total = result.get("total", len(rows))
+        err = result.get("error")
+        header = f"[{ts}] {method}({tableName}) — {len(rows)} rows returned, total={total}, elapsed={elapsed:.0f}ms"
+        if err:
+            header += f", ERROR={err}"
+        lines = [header]
+        lines.append(f"  params: {json.dumps(params, ensure_ascii=False, default=str)}")
+        for i, row in enumerate(rows):
+            lines.append(f"  [{i}] {json.dumps(row, ensure_ascii=False, default=str)}")
+        lines.append("")
+        with open(logPath, "a", encoding="utf-8") as f:
+            f.write("\n".join(lines) + "\n")
+    except Exception:
+        pass
+
 _ALLOWED_OPERATORS = {"=", "!=", ">", "<", ">=", "<=", "LIKE", "ILIKE", "IS NULL", "IS NOT NULL"}
 _ALLOWED_AGGREGATES = {"SUM", "COUNT", "AVG", "MIN", "MAX"}
 
@@ -96,6 +136,7 @@ class FeatureDataProvider:
             fullWhere += " AND " + extraWhere
             allParams.extend(extraParams)
 
+        t0 = time.time()
         try:
             with conn.cursor() as cur:
                 countSql = f'SELECT COUNT(*) FROM "{tableName}" WHERE {fullWhere}'
@@ -111,14 +152,23 @@ class FeatureDataProvider:
                 cur.execute(dataSql, allParams + [limit, offset])
                 rows = [_serializeRow(dict(r)) for r in cur.fetchall()]
 
-            return {"rows": rows, "total": total, "limit": limit, "offset": offset}
+            result = {"rows": rows, "total": total, "limit": limit, "offset": offset}
+            _debugQueryLog("browseTable", tableName, {
+                "fields": fields, "limit": limit, "offset": offset,
+            }, result, (time.time() - t0) * 1000)
+            return result
         except Exception as e:
             logger.error(f"browseTable({tableName}) failed: {e}")
+            elapsed = (time.time() - t0) * 1000
+            errResult = {"rows": [], "total": 0, "limit": limit, "offset": offset, "error": str(e)}
+            _debugQueryLog("browseTable", tableName, {
+                "fields": fields, "limit": limit, "offset": offset,
+            }, errResult, elapsed)
             try:
                 conn.rollback()
             except Exception:
                 pass
-            return {"rows": [], "total": 0, "limit": limit, "offset": offset, "error": str(e)}
+            return errResult
 
     def aggregateTable(
         self,
@@ -153,6 +203,7 @@ class FeatureDataProvider:
             fullWhere += " AND " + extraWhere
             allParams.extend(extraParams)
 
+        t0 = time.time()
         try:
             with conn.cursor() as cur:
                 if groupBy:
@@ -169,19 +220,28 @@ class FeatureDataProvider:
                 cur.execute(sql, allParams)
                 rows = [_serializeRow(dict(r)) for r in cur.fetchall()]
 
-            return {
+            result = {
                 "rows": rows,
                 "aggregate": aggregate,
                 "field": field,
                 "groupBy": groupBy,
             }
+            _debugQueryLog("aggregateTable", tableName, {
+                "aggregate": aggregate, "field": field, "groupBy": groupBy,
+            }, result, (time.time() - t0) * 1000)
+            return result
         except Exception as e:
             logger.error(f"aggregateTable({tableName}, {aggregate}({field})) failed: {e}")
+            elapsed = (time.time() - t0) * 1000
+            errResult = {"rows": [], "error": str(e), "aggregate": aggregate, "field": field, "groupBy": groupBy}
+            _debugQueryLog("aggregateTable", tableName, {
+                "aggregate": aggregate, "field": field, "groupBy": groupBy,
+            }, errResult, elapsed)
             try:
                 conn.rollback()
             except Exception:
                 pass
-            return {"rows": [], "error": str(e), "aggregate": aggregate, "field": field, "groupBy": groupBy}
+            return errResult
 
     def queryTable(
         self,
@@ -222,6 +282,7 @@ class FeatureDataProvider:
             fullWhere += " AND " + extraWhere
             allParams.extend(extraParams)
 
+        t0 = time.time()
         try:
             with conn.cursor() as cur:
                 countSql = f'SELECT COUNT(*) FROM "{tableName}" WHERE {fullWhere}'
@@ -237,14 +298,25 @@ class FeatureDataProvider:
                 cur.execute(dataSql, allParams + [limit, offset])
                 rows = [_serializeRow(dict(r)) for r in cur.fetchall()]
 
-            return {"rows": rows, "total": total, "limit": limit, "offset": offset}
+            result = {"rows": rows, "total": total, "limit": limit, "offset": offset}
+            _debugQueryLog("queryTable", tableName, {
+                "filters": filters, "fields": fields, "orderBy": orderBy,
+                "limit": limit, "offset": offset,
+            }, result, (time.time() - t0) * 1000)
+            return result
         except Exception as e:
             logger.error(f"queryTable({tableName}) failed: {e}")
+            elapsed = (time.time() - t0) * 1000
+            errResult = {"rows": [], "total": 0, "limit": limit, "offset": offset, "error": str(e)}
+            _debugQueryLog("queryTable", tableName, {
+                "filters": filters, "fields": fields, "orderBy": orderBy,
+                "limit": limit, "offset": offset,
+            }, errResult, elapsed)
             try:
                 conn.rollback()
             except Exception:
                 pass
-            return {"rows": [], "total": 0, "limit": limit, "offset": offset, "error": str(e)}
+            return errResult
 
 
 # ------------------------------------------------------------------