From 18ec8befb50dfc78c02bd1462046e89560964996 Mon Sep 17 00:00:00 2001
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Date: Thu, 2 Jul 2026 01:43:01 +0000
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---
 main.py | 106 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 106 insertions(+)
 create mode 100644 main.py

diff --git a/main.py b/main.py
new file mode 100644
index 0000000..a3c2bec
--- /dev/null
+++ b/main.py
@@ -0,0 +1,106 @@
+import os
+import asyncio
+from typing import List
+
+from pydantic import BaseModel, Field
+from langchain_openai import ChatOpenAI
+from langchain_core.prompts import PromptTemplate
+from langchain_core.output_parsers import PydanticOutputParser
+from langchain_core.messages import HumanMessage
+from langchain.tools import tool
+
+from deepagents import create_deep_agent
+from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
+
+# -------------------- Pydantic model --------------------
+class AssignmentCard(BaseModel):
+    title: str = Field(description="Краткое название задания")
+    subject: str = Field(description="Тема или предмет задания")
+    deadline_hint: str = Field(description="Подсказка о сроке сдачи (например, к пятнице, 12.09)")
+    deliverable_type: str = Field(description="Что нужно сдать: отчёт, код, презентация и т.п.")
+    grading_hints: List[str] = Field(description="Список упомянутых критериев оценки")
+
+# -------------------- Structured output parser --------------------
+parser = PydanticOutputParser(pydantic_object=AssignmentCard)
+
+# -------------------- Prompt template --------------------
+prompt_template = PromptTemplate(
+    template=(
+        "Ты получаешь неформальное описание учебного задания и должен вернуть "
+        "структурированные данные в формате JSON, соответствующем схеме ниже.\n"
+        "Верни только JSON, без пояснений.\n"
+        "Описание задания:\n{task_description}\n\n"
+        "{format_instructions}"
+    ),
+    input_variables=["task_description"],
+    partial_variables={"format_instructions": parser.get_format_instructions()},
+)
+
+# -------------------- LLM (OpenRouter) --------------------
+llm = ChatOpenAI(
+    model="openai/gpt-oss-20b:free",
+    base_url="https://openrouter.ai/api/v1",
+    api_key=os.getenv("OPENAI_API_KEY"),
+    temperature=0.0,
+)
+
+# -------------------- Tool that runs the chain --------------------
+@tool
+def parse_assignment(task_description: str) -> str:
+    """
+    Parse a free-form assignment description into a structured AssignmentCard.
+    Returns a JSON string that can be parsed by the Pydantic model.
+    """
+    chain = prompt_template | llm | parser
+    result = chain.invoke({"task_description": task_description})
+    # result is an AssignmentCard instance
+    return result.model_dump_json(indent=2)
+
+# -------------------- DeepAgent setup --------------------
+backend = CompositeBackend(
+    [
+        LocalShellBackend(workspace_dir="./workspace"),
+        FilesystemBackend(),
+    ]
+)
+
+agent = create_deep_agent(
+    model=llm,
+    tools=[parse_assignment],
+    backend=backend,
+    system_prompt="You are a helpful assistant that extracts assignment data.",
+)
+
+# -------------------- Main execution --------------------
+async def main():
+    # Пример входного текста
+    user_input = (
+        "Сдайте к пятнице мини-отчёт по LangChain: 2 страницы, упор на агентов. "
+        "Оценка: за полноту и за пример кода."
+    )
+    result = await agent.ainvoke(
+        {"messages": [HumanMessage(content=user_input)]},
+        {"configurable": {"thread_id": "session-1"}},
+    )
+    # Последнее сообщение агента содержит JSON строку
+    json_output = result["messages"][-1].content
+    print("=== Structured JSON ===")
+    print(json_output)
+
+    # Для наглядности выводим человекочитаемую сводку
+    try:
+        import json
+
+        data = json.loads(json_output)
+        print("\n=== Human-readable summary ===")
+        print(f"Title: {data.get('title')}")
+        print(f"Subject: {data.get('subject')}")
+        print(f"Deadline hint: {data.get('deadline_hint')}")
+        print(f"Deliverable type: {data.get('deliverable_type')}")
+        print(f"Grading hints: {', '.join(data.get('grading_hints', []))}")
+    except Exception as e:
+        print("Failed to parse JSON:", e)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
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