Files
cucumbers-solutions/solutions/unknown/solution.py
T

75 lines
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Python

from langchain_openai import ChatOpenAI
from pydantic import SecretStr
import argparse
import sys
def parse_args() -> argparse.Namespace:
"""
Parse command line arguments.
Returns:
Namespace: Parsed arguments containing the task text.
"""
parser = argparse.ArgumentParser(
description="Run an LLM-based task orchestrator."
)
parser.add_argument(
"--task-text",
required=True,
help="Text of the task to be processed by the LLM.",
)
return parser.parse_args()
def validate_task_text(text: str) -> None:
"""
Validate that the provided task text is non-empty.
Raises:
ValueError: If the text is empty or consists only of whitespace.
"""
if not text.strip():
raise ValueError("Task text must be a non-empty string.")
def init_llm() -> ChatOpenAI:
"""
Initialize the LLM client with placeholder configuration.
Returns:
ChatOpenAI: Configured LLM instance.
"""
return ChatOpenAI(
model="openai/gpt-oss-20b",
base_url='https://platform.brojs.ru/jrnl-bh/api/inference/v1',
api_key=SecretStr("jrnl_30283ab953615cbb6846ff9940a1eedce0b76d7b2f59a2394f29e74643e6a90d"),
temperature=0.7,
)
def main() -> None:
"""
Main entry point of the orchestrator.
Parses arguments, validates input, initializes LLM, and prints the response.
"""
args = parse_args()
try:
validate_task_text(args.task_text)
except ValueError as exc:
print(f"Error: {exc}", file=sys.stderr)
sys.exit(1)
llm = init_llm()
# Invoke the LLM with the task text
response = llm.invoke(
{"messages": [{"role": "human", "content": args.task_text}]}
)
# The result contains a list of messages; we print the content of the first AI message.
ai_message = next(
(msg for msg in response["messages"] if getattr(msg, "type", None) == "ai"),
None,
)
if ai_message:
print(ai_message.content)
else:
print("No AI response received.", file=sys.stderr)
if __name__ == "__main__":
main()