fix: Correct indentation in _compress_tool_result method

src/orchestrator.py

@@ -1095,118 +1095,118 @@ You are a DOER. Complete workflows based on user intent."""
         return compressed
     
     def _compress_tool_result(self, tool_name: str, result: Dict[str, Any]) -> Dict[str, Any]:
-    """
-    Compress tool results for small context models (production-grade approach).
-    
-    Keep only:
-    - Status (success/failure)
-    - Key metrics (5-10 most important numbers)
-    - File paths created  
-    - Next action hints
-    
-    Full results stored in workflow_history and session memory.
-    LLM doesn't need verbose output - only decision-making info.
-    
-    Args:
-        tool_name: Name of the tool executed
-        result: Full tool result dict
+        """
+        Compress tool results for small context models (production-grade approach).
         
-    Returns:
-        Compressed result dict (typically 100-500 tokens vs 5K-10K)
-    """
-    if not result.get("success", True):
-        # Keep full error info (critical for debugging)
-        return result
-    
-    compressed = {
-        "success": True,
-        "tool": tool_name
-    }
-    
-    # Tool-specific compression rules
-    if tool_name == "profile_dataset":
-        # Original: ~5K tokens with full stats
-        # Compressed: ~200 tokens with key metrics
-        r = result.get("result", {})
-        compressed["summary"] = {
-            "rows": r.get("num_rows"),
-            "cols": r.get("num_columns"),
-            "missing_pct": r.get("missing_percentage"),
-            "numeric_cols": len(r.get("numeric_columns", [])),
-            "categorical_cols": len(r.get("categorical_columns", [])),
-            "file_size_mb": round(r.get("memory_usage_mb", 0), 1),
-            "key_columns": list(r.get("columns", {}).keys())[:5]  # First 5 columns only
-        }
-        compressed["next_steps"] = ["clean_missing_values", "detect_data_quality_issues"]
-        
-    elif tool_name == "detect_data_quality_issues":
-        r = result.get("result", {})
-        compressed["summary"] = {
-            "total_issues": r.get("total_issues", 0),
-            "critical_issues": r.get("critical_issues", 0),
-            "missing_data": r.get("has_missing"),
-            "outliers": r.get("has_outliers"),
-            "duplicates": r.get("has_duplicates")
-        }
-        compressed["next_steps"] = ["clean_missing_values", "handle_outliers"]
-        
-    elif tool_name in ["clean_missing_values", "handle_outliers", "encode_categorical"]:
-        r = result.get("result", {})
-        compressed["summary"] = {
-            "output_file": r.get("output_file", r.get("output_path")),
-            "rows_processed": r.get("rows_after", r.get("num_rows")),
-            "changes_made": bool(r.get("changes", {}) or r.get("imputed_columns"))
+        Keep only:
+        - Status (success/failure)
+        - Key metrics (5-10 most important numbers)
+        - File paths created  
+        - Next action hints
+        
+        Full results stored in workflow_history and session memory.
+        LLM doesn't need verbose output - only decision-making info.
+        
+        Args:
+            tool_name: Name of the tool executed
+            result: Full tool result dict
+            
+        Returns:
+            Compressed result dict (typically 100-500 tokens vs 5K-10K)
+        """
+        if not result.get("success", True):
+            # Keep full error info (critical for debugging)
+            return result
+        
+        compressed = {
+            "success": True,
+            "tool": tool_name
         }
-        compressed["next_steps"] = ["Use this file for next step"]
         
-    elif tool_name == "train_baseline_models":
-        r = result.get("result", {})
-        models = r.get("models", [])
-        if models:
-            best = max(models, key=lambda m: m.get("test_score", 0))
+        # Tool-specific compression rules
+        if tool_name == "profile_dataset":
+            # Original: ~5K tokens with full stats
+            # Compressed: ~200 tokens with key metrics
+            r = result.get("result", {})
             compressed["summary"] = {
-                "best_model": best.get("model"),
-                "test_score": round(best.get("test_score", 0), 4),
-                "train_score": round(best.get("train_score", 0), 4),
-                "task_type": r.get("task_type"),
-                "models_trained": len(models)
+                "rows": r.get("num_rows"),
+                "cols": r.get("num_columns"),
+                "missing_pct": r.get("missing_percentage"),
+                "numeric_cols": len(r.get("numeric_columns", [])),
+                "categorical_cols": len(r.get("categorical_columns", [])),
+                "file_size_mb": round(r.get("memory_usage_mb", 0), 1),
+                "key_columns": list(r.get("columns", {}).keys())[:5]  # First 5 columns only
             }
-        compressed["next_steps"] = ["hyperparameter_tuning", "generate_combined_eda_report"]
-        
-    elif tool_name in ["generate_plotly_dashboard", "generate_ydata_profiling_report", "generate_combined_eda_report"]:
-        r = result.get("result", {})
-        compressed["summary"] = {
-            "report_path": r.get("report_path", r.get("output_path")),
-            "report_type": tool_name,
-            "success": True
-        }
-        compressed["next_steps"] = ["Report ready for viewing"]
-        
-    elif tool_name == "hyperparameter_tuning":
-        r = result.get("result", {})
-        compressed["summary"] = {
-            "best_params": r.get("best_params", {}),
-            "best_score": round(r.get("best_score", 0), 4),
-            "model_type": r.get("model_type"),
-            "trials_completed": r.get("n_trials")
-        }
-        compressed["next_steps"] = ["perform_cross_validation", "generate_model_performance_plots"]
-        
-    else:
-        # Generic compression: Keep only key fields
-        r = result.get("result", {})
-        if isinstance(r, dict):
-            # Extract key fields (common patterns)
-            key_fields = {}
-            for key in ["output_path", "output_file", "status", "message", "success"]:
-                if key in r:
-                    key_fields[key] = r[key]
-            compressed["summary"] = key_fields or {"result": "completed"}
+            compressed["next_steps"] = ["clean_missing_values", "detect_data_quality_issues"]
+            
+        elif tool_name == "detect_data_quality_issues":
+            r = result.get("result", {})
+            compressed["summary"] = {
+                "total_issues": r.get("total_issues", 0),
+                "critical_issues": r.get("critical_issues", 0),
+                "missing_data": r.get("has_missing"),
+                "outliers": r.get("has_outliers"),
+                "duplicates": r.get("has_duplicates")
+            }
+            compressed["next_steps"] = ["clean_missing_values", "handle_outliers"]
+            
+        elif tool_name in ["clean_missing_values", "handle_outliers", "encode_categorical"]:
+            r = result.get("result", {})
+            compressed["summary"] = {
+                "output_file": r.get("output_file", r.get("output_path")),
+                "rows_processed": r.get("rows_after", r.get("num_rows")),
+                "changes_made": bool(r.get("changes", {}) or r.get("imputed_columns"))
+            }
+            compressed["next_steps"] = ["Use this file for next step"]
+            
+        elif tool_name == "train_baseline_models":
+            r = result.get("result", {})
+            models = r.get("models", [])
+            if models:
+                best = max(models, key=lambda m: m.get("test_score", 0))
+                compressed["summary"] = {
+                    "best_model": best.get("model"),
+                    "test_score": round(best.get("test_score", 0), 4),
+                    "train_score": round(best.get("train_score", 0), 4),
+                    "task_type": r.get("task_type"),
+                    "models_trained": len(models)
+                }
+            compressed["next_steps"] = ["hyperparameter_tuning", "generate_combined_eda_report"]
+            
+        elif tool_name in ["generate_plotly_dashboard", "generate_ydata_profiling_report", "generate_combined_eda_report"]:
+            r = result.get("result", {})
+            compressed["summary"] = {
+                "report_path": r.get("report_path", r.get("output_path")),
+                "report_type": tool_name,
+                "success": True
+            }
+            compressed["next_steps"] = ["Report ready for viewing"]
+            
+        elif tool_name == "hyperparameter_tuning":
+            r = result.get("result", {})
+            compressed["summary"] = {
+                "best_params": r.get("best_params", {}),
+                "best_score": round(r.get("best_score", 0), 4),
+                "model_type": r.get("model_type"),
+                "trials_completed": r.get("n_trials")
+            }
+            compressed["next_steps"] = ["perform_cross_validation", "generate_model_performance_plots"]
+            
         else:
-            compressed["summary"] = {"result": str(r)[:200] if r else "completed"}
-        compressed["next_steps"] = ["Continue workflow"]
-    
-    return compressed
+            # Generic compression: Keep only key fields
+            r = result.get("result", {})
+            if isinstance(r, dict):
+                # Extract key fields (common patterns)
+                key_fields = {}
+                for key in ["output_path", "output_file", "status", "message", "success"]:
+                    if key in r:
+                        key_fields[key] = r[key]
+                compressed["summary"] = key_fields or {"result": "completed"}
+            else:
+                compressed["summary"] = {"result": str(r)[:200] if r else "completed"}
+            compressed["next_steps"] = ["Continue workflow"]
+        
+        return compressed
 
 
     def _parse_text_tool_calls(self, text_response: str) -> List[Dict[str, Any]]: