crew/tasks/context_tasks.py

from crewai import Task


"""
Create context analysis task will return output in JSON format
it helps to extract customer name, context, requires database, requires policy and summary briefing note
These data will help the manager decide which co-worker to delegate to
"""

def create_context_analysis_task(agent, query):
    return Task(
        description=(
            f"**INCOMING MEMO**: '{query}'\n\n"
            "**ROLE**: You are a JSON Classification Engine. You do not speak English. You output data.\n"
            "**OBJECTIVE**: Map the user's request to exactly ONE of the 4 intent categories below.\n\n"
            
            "**CLASSIFICATION TRUTH TABLE (STRICT)**:\n"
            "1. **General Chat**: Greetings, jokes, or vague comments (e.g., 'Hello', 'What can you do?').\n"
            "   -> requires_database: False | requires_policy: False\n"
            "2. **Policy Question**: Asking for rules, rates, limits, or eligibility WITHOUT naming a person.\n"
            "   -> requires_database: False | requires_policy: True\n"
            "3. **Customer Info**: Asking for static data about a person (ID, Address, Status) WITHOUT asking for rules/rates.\n"
            "   -> requires_database: True | requires_policy: False\n"
            "4. **Loan Request**: Asking if a specific person qualifies, or checking rates for a specific person.\n"
            "   -> requires_database: True | requires_policy: True\n\n"
            
            "**EXTRACTION RULES**:\n"
            "- **customer_name**: Extract the exact substring (e.g., 'Andy'). If no name, set to null.\n"
            "- **Context**: Look ONLY at the provided string. Do not infer previous conversations.\n\n"
            
            "**NEGATIVE CONSTRAINTS**:\n"
            "- DO NOT include 'Here is the JSON'.\n"
            "- DO NOT output markdown ticks (```json). Just the raw curly braces.\n\n"
            
            "**REQUIRED OUTPUT SCHEMA**:\n"
            "{\n"
            '  "thought_process": "A clear, 1-sentence summary of the strategy. (e.g. \'User asked for a loan, so I will trigger the Data and Policy agents.\')",\n'
            '  "intent": "General Chat" | "Policy Question" | "Customer Info" | "Loan Request",\n'
            '  "customer_name": "string or null",\n'
            '  "requires_database": boolean,\n'
            '  "requires_policy": boolean,\n'
            '  "briefing_note": "string"\n'
            "}"
        ),
        expected_output="A single valid JSON object containing the classification flags and intent.",
        agent=agent
    )