94 lines
3.8 KiB
Python
94 lines
3.8 KiB
Python
import os
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import asyncio
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from dotenv import load_dotenv
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from langchain_openai import ChatOpenAI
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from langchain_core.messages import HumanMessage
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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 pydantic import BaseModel, Field
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from langchain.tools import tool
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from deepagents import create_deep_agent
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from deepagents.backends import CompositeBackend, LocalShellBackend, FilesystemBackend
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# Load environment variables (OPENAI_API_KEY)
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load_dotenv()
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# 1. Define the Pydantic model for the task card
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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="Предмет или тема задания")
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deadline_hint: str = Field(description="Краткая подсказка о сроке выполнения, например 'к пятнице'")
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deliverable_type: str = Field(description="Тип сдачи: отчёт, код, презентация и т.п.")
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grading_hints: list[str] = Field(description="Список критериев оценки, упомянутых в тексте")
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# 2. Create the parser that will enforce the structure
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parser = PydanticOutputParser(pydantic_object=TaskCard)
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# 3. Prompt template that asks the model to output the data in the required format
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prompt = PromptTemplate(
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template="""
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Ниже приведено описание задания. Ваша задача — извлечь из него следующую информацию и вернуть в формате JSON, соответствующем модели TaskCard:
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{input_text}
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{format_instructions}
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""",
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input_variables=["input_text"],
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partial_variables={"format_instructions": parser.get_format_instructions()},
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)
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# 4. LLM configuration (OpenRouter)
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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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api_key=os.getenv("OPENAI_API_KEY"),
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temperature=0.0,
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)
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# 5. Define a tool that performs the parsing chain
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@tool
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def parse_task(input_text: str) -> str:
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"""Parse a raw task description into a structured JSON string."""
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chain = prompt | llm | parser
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result = chain.invoke({"input_text": input_text})
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# parser returns a dict; convert to JSON string for the agent to return
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return result.model_dump_json()
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# 6. Backend for the agent (filesystem + local shell, though not used here)
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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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# 7. Create the deep agent with the parsing tool
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agent = create_deep_agent(
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model=llm,
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tools=[parse_task],
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backend=backend,
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system_prompt="You are a task‑card extractor. Use the provided tool to parse the input and return the JSON string.",
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)
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# 8. Main entry point
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async def main():
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# Example input – replace with any user text
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user_text = "Сдайте к пятнице мини‑отчёт по LangChain: 2 страницы, упор на агентов. Оценка: за полноту и за пример кода."
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result = await agent.ainvoke(
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{"messages": [HumanMessage(content=user_text)]},
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{"configurable": {"thread_id": "session-1"}},
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)
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# The agent returns the tool output as the last message
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output = result["messages"][-1].content
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print("Parsed JSON:")
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print(output)
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# Pretty‑print the parsed object
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card = TaskCard.parse_raw(output)
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print("\nHuman‑readable summary:")
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print(f"Title: {card.title}")
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print(f"Subject: {card.subject}")
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print(f"Deadline hint: {card.deadline_hint}")
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print(f"Deliverable type: {card.deliverable_type}")
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print(f"Grading hints: {', '.join(card.grading_hints)}")
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if __name__ == "__main__":
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asyncio.run(main())
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