feat: solution for unknown

solutions/unknown/solution.py

@@ -1,57 +1,75 @@
 from langchain_openai import ChatOpenAI
 from pydantic import SecretStr
-from langgraph.graph import StateGraph, START, END, interrupt
+import argparse
-from langgraph.checkpoint.memory import InMemorySaver
+import sys
-from typing import TypedDict
 
-# LLM placeholder
+def parse_args() -> argparse.Namespace:
-llm = ChatOpenAI(
+    """
+    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,
     )
 
-# State definition
+def main() -> None:
-class GraphState(TypedDict):
+    """
-    human_value: str | None
+    Main entry point of the orchestrator.
-
+    Parses arguments, validates input, initializes LLM, and prints the response.
-# Node that triggers an interrupt with a question and options
+    """
-def ask_node(state: GraphState) -> dict:
+    args = parse_args()
-    return interrupt(
-        {
-            "type": "question",
-            "question": "Выберите вариант:",
-            "options": ["Опция 1", "Опция 2", "Опция 3"],
-        }
-    )
-
-# Build the graph
-builder = StateGraph(GraphState)
-builder.add_node("ask", ask_node)
-builder.set_entry_point(START)
-builder.add_edge(START, "ask")
-builder.add_edge("ask", END)
-
-graph = builder.compile(checkpointer=InMemorySaver())
-
-# Main loop handling interrupts
-state: GraphState = {"human_value": None}
-while True:
-    result = graph.invoke(state)
-    if "__interrupt__" in result:
-        interrupt_data = result["__interrupt__"]
-        print(interrupt_data["question"])
-        for idx, opt in enumerate(interrupt_data["options"], 1):
-            print(f"{idx}. {opt}")
-        choice = input("Выберите номер: ").strip()
     try:
-            selected = interrupt_data["options"][int(choice) - 1]
+        validate_task_text(args.task_text)
-            state["human_value"] = selected
+    except ValueError as exc:
-        except (ValueError, IndexError):
+        print(f"Error: {exc}", file=sys.stderr)
-            print("Неверный выбор. Повторите.")
+        sys.exit(1)
-    else:
-        break
 
-print("\nИтоговое состояние:")
+    llm = init_llm()
-print(state)
+    # 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()