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from crewai import Task
"""
Instructs the agent to perform a Vector Search and format the results.
KEY OBJECTIVE:
To convert unstructured PDF text into a structured JSON object.
It explicitly separates "Rules" (logic like if/else) from "Data Points" (hard numbers/rates),
making it easier for the Underwriter agent to apply these policies programmatically later.
"""
def create_policy_search_task(agent, query: str):
return Task(
description=(
f"**SEARCH REQUEST**: '{query}'\n\n"
"**YOUR JOB**: Fetch the policy rules. Do NOT analyze them. Do NOT format them into tables.\n"
"Just find the text and convert it into **Plain English Bullet Points** for the Supervisor.\n\n"
"**EXECUTION STEPS**:\n"
"1. Search for 'Overall Risk' and 'Interest Rates'.\n"
"2. **STOP** immediately after the first search.\n"
"3. **OUTPUT**: List the rules simply.\n\n"
"**REQUIRED OUTPUT FORMAT**:\n"
"Return a list like this:\n"
"- If Credit Score is [Range] and Account is [Status], then Risk is [Level].\n"
"- If Risk is [Level], then Interest Rate is [Value].\n"
"\n"
"(Include the specific numbers found in the search results)."
),
expected_output="A simple list of policy rules in plain text.",
agent=agent,
# 🛑 HARD STOP: Prevent the loop.
# The agent gets 1 try. If it finds anything, we take it.
max_iter=1
)
def create_policy_summary_task(agent, query: str):
"""
Returns a Task for explaining policy without making a decision.
This is for policy specific question like what is consider high risk
"""
return Task(
description=(
f"**QUERY**: '{query}'\n\n"
"**YOUR GOAL**: Explain the high-risk criteria or policy rules relevant to the query in plain text.\n"
"Do NOT make a decision or assign any verdict. Output only a descriptive summary.\n"
"Format:\n"
"- Topic / Section\n"
"- Rules Summary\n"
"- Data Points if available"
),
expected_output="Plain text summary of relevant policy rules.",
agent=agent
) |