Update features/insight_and_tasks/agents/task_extraction_model.py

features/insight_and_tasks/agents/task_extraction_model.py

@@ -25,44 +25,69 @@ from features.insight_and_tasks.data_models.tasks import (
 )
 
 def create_example_structure():
-    """Creates an example structure to show the AI what the output should look like."""
+    """
+    Creates a valid example structure that conforms to the Pydantic models
+    to show the AI what the output should look like.
+    """
     return {
         "current_quarter_info": "Q2 2025, 24 days remaining",
         "okrs": [
             {
-                "objective_description": "Improve LinkedIn employer branding performance",
-                "objective_timeline": "short",
-                "objective_owner": "marketing",
+                "objective_description": "Significantly improve our LinkedIn employer branding performance to attract top-tier talent and establish our company as a thought leader in the tech industry.",
+                
+                "objective_timeline": "Short-term", 
+                "objective_owner": "Marketing Department",
                 "key_results": [
                     {
-                        "key_result_description": "Increase monthly follower growth by 50%",
-                        "target_metric": "+50 followers",
+                        # CORRECTION: Description expanded to satisfy the 'min_length=100' validation rule.
+                        "key_result_description": "Achieve a sustained 50% increase in the rate of monthly follower growth on our company LinkedIn page, demonstrating enhanced audience engagement and brand reach.",
+                        "target_metric": "Monthly Follower Growth Rate",
                         "target_value": "50% increase",
-                        "current_value": "22 followers/month",
-                        "key_result_type": "performance",
-                        "data_subject": "posts",
+                        
+                        # CORRECTION: Extra 'current_value' field removed as it's not in the Pydantic model.
+                        
+                        # CORRECTION: Value changed from "performance" to "PERFORMANCE" to match 'KeyResultType' enum.
+                        "key_result_type": "PERFORMANCE",
+                        
+                        # CORRECTION: Value changed from "posts" to "FOLLOWER_STATS" to match 'DataSubject' enum and better reflect the key result.
+                        "data_subject": "FOLLOWER_STATS",
                         "tasks": [
                             {
-                                "task_description": "Increase posting frequency",
-                                "objective_deliverable": "Post 2-3 times per week consistently",
-                                "task_category": "content_creation",
-                                "priority": "high",
-                                "effort": "medium",
-                                "timeline": "this_quarter",
-                                "data_subject": "linkedin_performance",
-                                "responsible_party": "marketing",
-                                "success_criteria_metrics": "post 3 times",
-                                "dependencies_prerequisites": "a linkedin account",
-                                "why_proposed": "because previous data show low post frequency and low follwoer gains"
+                                "task_description": "Increase posting frequency to a consistent, high-quality schedule.",
+                                "objective_deliverable": "Post a minimum of 3 high-quality, relevant articles or updates per week.",
+                                "task_category": "Content Creation",
+                                
+                                # CORRECTION: Added the missing required field 'task_type' with a valid 'TaskType' enum value.
+                                "task_type": "INITIATIVE",
+                                
+                                # CORRECTION: Value changed from "high" to "High" to match the 'PriorityLevel' enum.
+                                "priority": "High",
+
+                                # CORRECTION: Added the missing required field 'priority_justification'.
+                                "priority_justification": "Increasing post frequency is a primary driver for engagement and follower growth, directly impacting the key result.",
+
+                                # CORRECTION: Value changed from "medium" to "Medium" to match the 'EffortLevel' enum.
+                                "effort": "Medium",
+
+                                # CORRECTION: Value changed from "this_quarter" to "Short-term" to match the 'TimelineCategory' enum.
+                                "timeline": "Short-term",
+                                
+                                # CORRECTION: Value changed from "linkedin_performance" to "POSTS" to match the 'DataSubject' enum.
+                                "data_subject": "POSTS",
+                                "responsible_party": "Social Media Manager",
+                                "success_criteria_metrics": "A weekly average of 3 or more posts is maintained over the quarter.",
+                                "dependencies_prerequisites": "A finalized content calendar for the quarter.",
+                                "why_proposed": "Historical data analysis shows a direct correlation between low posting frequency and stagnant follower gains. This task addresses the root cause."
                             }
                         ]
                     }
                 ]
             }
         ],
-        "overall_strategic_focus": "increase followers"
+        "overall_strategic_focus": "Accelerate follower growth and enhance brand authority on LinkedIn."
     }
 
+
 # --- Helper Function for Date Calculations ---
 def get_quarter_info():
     """Calculates current quarter, year, and days remaining in the quarter."""
@@ -149,7 +174,9 @@ GENERATION RULES:
 2. For each OKR, create 1-3 KeyResult objects (MANDATORY - cannot be empty)
 3. For each KeyResult, create 1-3 Task objects (MANDATORY - cannot be empty)
 4. Make tasks specific, actionable, and directly related to the insights in the input text
-5. Ensure all required fields are populated with valid enum values
+5. No repetitive text allowed
+6. Complete JSON object with proper closing braces
+7. Maximum response length: 5000 characters
 
 Now, analyze the following text and generate the structured output:
 ---
@@ -166,6 +193,7 @@ TEXT TO ANALYZE:
                 'response_mime_type': 'application/json',
                 'response_schema': TaskExtractionOutput, # Pass the Pydantic model class
                 'temperature': 0.1, 
+                'top_p': 0.8,
             },
         )
     except Exception as e: