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