Update features/insight_and_tasks/agents/task_extraction_model.py

features/insight_and_tasks/agents/task_extraction_model.py

@@ -24,6 +24,36 @@ from features.insight_and_tasks.data_models.tasks import (
     DataSubject # Ensure all are imported
 )
 
+def create_example_structure():
+    """Creates an example structure to show the AI what the output should look like."""
+    return {
+        "current_quarter_info": "Q2 2025, 24 days remaining",
+        "okrs": [
+            {
+                "objective": "Improve LinkedIn employer branding performance",
+                "data_subject": "linkedin_performance",
+                "key_results": [
+                    {
+                        "description": "Increase monthly follower growth by 50%",
+                        "target_value": "50% increase",
+                        "current_value": "22 followers/month",
+                        "tasks": [
+                            {
+                                "title": "Increase posting frequency",
+                                "description": "Post 2-3 times per week consistently",
+                                "task_type": "content_creation",
+                                "priority": "high",
+                                "effort_level": "medium",
+                                "timeline": "this_quarter",
+                                "data_subject": "linkedin_performance"
+                            }
+                        ]
+                    }
+                ]
+            }
+        ]
+    }
+
 # --- Helper Function for Date Calculations ---
 def get_quarter_info():
     """Calculates current quarter, year, and days remaining in the quarter."""
@@ -93,6 +123,8 @@ def extract_tasks_from_text(user_text_input: str, api_key: str) -> TaskExtractio
     prompt = f"""You are a Time-Aware Task Extraction Specialist, an AI expert in meticulously analyzing strategic insights (e.g., from LinkedIn analytics) and transforming them into a structured set of actionable tasks, organized within an Objectives and KeyResults (OKRs) framework.
 
 Your output MUST be a valid JSON object that strictly conforms to the 'TaskExtractionOutput' schema provided.
+EXAMPLE STRUCTURE (follow this pattern exactly):
+{json.dumps(example_structure, indent=2)}
 
 CURRENT CONTEXTUAL INFORMATION (CRITICAL - Use these exact values in your output where specified):
 - Current Quarter: Q{quarter}
@@ -102,11 +134,12 @@ CURRENT CONTEXTUAL INFORMATION (CRITICAL - Use these exact values in your output
 
 When populating the 'current_quarter_info' field in the TaskExtractionOutput, use the format: 'Q{quarter} {year}, {days_remaining} days remaining'.
 
-GUIDELINES FOR TASK GENERATION:
-1.  For EACH 'OKR' object, you MUST generate a 'key_results' array containing 1 to 3 'KeyResult' objects.
-2.  For EACH 'KeyResult' object, you MUST generate a 'tasks' array containing 1 to 3 'Task' objects.
-3.  It is CRITICAL that you populate the 'key_results' list for every OKR, and the 'tasks' list for every KeyResult.
-4.  Ensure all fields in the schema are appropriately filled based on the input text. If information for an optional field is not present, omit it or use its default.
+GENERATION RULES:
+1. Create 1-3 OKR objects based on the input text
+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
 
 Now, analyze the following text and generate the structured output:
 ---
@@ -122,6 +155,7 @@ TEXT TO ANALYZE:
             config={
                 'response_mime_type': 'application/json',
                 'response_schema': TaskExtractionOutput, # Pass the Pydantic model class
+                'temperature': 0.1, 
             },
         )
     except Exception as e: