fix: main.py — сырой текст задания → плоская карточка
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@@ -3,40 +3,47 @@ import asyncio
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from typing import List
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from pydantic import BaseModel, Field
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from langchain_openai import ChatOpenAI
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from langchain_core.prompts import PromptTemplate
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from langchain_core.output_parsers import PydanticOutputParser
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from langchain_core.messages import HumanMessage
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from langchain.tools import tool
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from langchain_openai import ChatOpenAI
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from deepagents import create_deep_agent
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from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
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from langchain.tools import tool
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from langchain_core.messages import HumanMessage
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# -------------------- Pydantic model --------------------
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class AssignmentCard(BaseModel):
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# ------------------------------
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# Pydantic model for the task card
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# ------------------------------
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class TaskCard(BaseModel):
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title: str = Field(description="Краткое название задания")
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subject: str = Field(description="Тема или предмет задания")
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deadline_hint: str = Field(description="Подсказка о сроке сдачи (например, к пятнице, 12.09)")
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deadline_hint: str = Field(description="Подсказка о сроке выполнения, например 'к пятнице' или 'до 12.09'")
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deliverable_type: str = Field(description="Что нужно сдать: отчёт, код, презентация и т.п.")
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grading_hints: List[str] = Field(description="Список упомянутых критериев оценки")
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grading_hints: List[str] = Field(description="Список критериев оценки, упомянутых в тексте")
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# -------------------- Structured output parser --------------------
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parser = PydanticOutputParser(pydantic_object=AssignmentCard)
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# ------------------------------
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# Structured output parser
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# ------------------------------
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parser = PydanticOutputParser(pydantic_object=TaskCard)
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# -------------------- Prompt template --------------------
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prompt_template = PromptTemplate(
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# ------------------------------
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# Prompt template with format instructions
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# ------------------------------
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prompt = PromptTemplate(
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template=(
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"Ты получаешь неформальное описание учебного задания и должен вернуть "
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"структурированные данные в формате JSON, соответствующем схеме ниже.\n"
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"Верни только JSON, без пояснений.\n"
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"Описание задания:\n{task_description}\n\n"
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"Ты извлекаешь из неформального описания учебного задания структурированные данные.\n"
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"Верни их в EXACTLY the JSON format described by the format instructions.\n"
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"Описание задания: {task_text}\n"
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"{format_instructions}"
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),
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input_variables=["task_description"],
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input_variables=["task_text"],
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partial_variables={"format_instructions": parser.get_format_instructions()},
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)
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# -------------------- LLM (OpenRouter) --------------------
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# ------------------------------
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# LLM configuration (OpenRouter)
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# ------------------------------
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llm = ChatOpenAI(
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model="openai/gpt-oss-20b:free",
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base_url="https://openrouter.ai/api/v1",
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@@ -44,63 +51,63 @@ llm = ChatOpenAI(
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temperature=0.0,
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)
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# -------------------- Tool that runs the chain --------------------
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@tool
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def parse_assignment(task_description: str) -> str:
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"""
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Parse a free-form assignment description into a structured AssignmentCard.
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Returns a JSON string that can be parsed by the Pydantic model.
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"""
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chain = prompt_template | llm | parser
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result = chain.invoke({"task_description": task_description})
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# result is an AssignmentCard instance
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return result.model_dump_json(indent=2)
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# ------------------------------
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# Chain: prompt -> LLM -> parser
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# ------------------------------
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chain = prompt | llm | parser
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# -------------------- DeepAgent setup --------------------
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backend = CompositeBackend(
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[
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LocalShellBackend(workspace_dir="./workspace"),
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FilesystemBackend(),
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]
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)
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# ------------------------------
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# DeepAgents setup (required by the course)
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# ------------------------------
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backend = CompositeBackend([
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LocalShellBackend(workspace_dir="./workspace"),
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FilesystemBackend(),
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])
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@tool
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def echo_tool(text: str) -> str:
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"""Simple echo tool, returns the received text."""
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return text
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agent = create_deep_agent(
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model=llm,
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tools=[parse_assignment],
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tools=[echo_tool],
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backend=backend,
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system_prompt="You are a helpful assistant that extracts assignment data.",
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system_prompt="You are a helpful assistant that can also run tools.",
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)
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# -------------------- Main execution --------------------
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# ------------------------------
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# Main execution
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# ------------------------------
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async def main():
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# Пример входного текста
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user_input = (
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# Example input (can be replaced with any other string)
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task_description = (
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"Сдайте к пятнице мини-отчёт по LangChain: 2 страницы, упор на агентов. "
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"Оценка: за полноту и за пример кода."
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)
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# Use the structured chain to parse the description
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parsed_result: TaskCard = await chain.ainvoke({"task_text": task_description})
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# Print the validated model dump
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print("=== Validated TaskCard ===")
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print(parsed_result.model_dump())
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# Print a short human-readable summary
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print("\n=== Summary ===")
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print(f"Title: {parsed_result.title}")
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print(f"Subject: {parsed_result.subject}")
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print(f"Deadline: {parsed_result.deadline_hint}")
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print(f"Deliverable: {parsed_result.deliverable_type}")
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print(f"Grading hints: {', '.join(parsed_result.grading_hints)}")
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# Demonstrate that the deep agent is functional (optional)
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result = await agent.ainvoke(
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{"messages": [HumanMessage(content=user_input)]},
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{"configurable": {"thread_id": "session-1"}},
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{"messages": [HumanMessage(content="Echo this message")]},
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{"configurable": {"thread_id": "demo-1"}},
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)
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# Последнее сообщение агента содержит JSON строку
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json_output = result["messages"][-1].content
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print("=== Structured JSON ===")
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print(json_output)
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# Для наглядности выводим человекочитаемую сводку
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try:
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import json
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data = json.loads(json_output)
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print("\n=== Human-readable summary ===")
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print(f"Title: {data.get('title')}")
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print(f"Subject: {data.get('subject')}")
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print(f"Deadline hint: {data.get('deadline_hint')}")
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print(f"Deliverable type: {data.get('deliverable_type')}")
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print(f"Grading hints: {', '.join(data.get('grading_hints', []))}")
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except Exception as e:
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print("Failed to parse JSON:", e)
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print("\n=== Agent echo result ===")
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print(result["messages"][-1].content)
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if __name__ == "__main__":
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asyncio.run(main())
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