105 lines
3.4 KiB
Python
105 lines
3.4 KiB
Python
"""Task 69dd4221f309a98be0006b2e – Structured task card parser.
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The script accepts a single natural‑language description of a course task and
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returns a validated Pydantic model instance. It demonstrates a one‑shot
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prompting pattern with a structured output parser.
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Usage:
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python main.py "<task description>"
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The script prints the raw model dump and a short human‑readable summary.
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"""
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import sys
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from typing import List
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from pydantic import BaseModel, Field
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from langchain_core.prompts import PromptTemplate
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from langchain_openai import ChatOpenAI
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from langchain_core.output_parsers import PydanticOutputParser
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# ---------------------------------------------------------------------------
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# 1. Pydantic model
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# ---------------------------------------------------------------------------
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class TaskCard(BaseModel):
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"""Compact representation of a course task.
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All fields are optional because the model may not be able to infer every
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piece of information from a very short description. The parser will
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still return a valid instance – missing values will be ``None``.
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"""
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title: str | None = Field(None, description="Short title of the task")
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subject: str | None = Field(None, description="Subject or topic of the task")
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deadline_hint: str | None = Field(
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None, description="Free‑form hint about the deadline (e.g. 'к пятнице')"
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)
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deliverable_type: str | None = Field(
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None, description="What is expected to be submitted (report, code, etc.)"
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)
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grading_hints: List[str] | None = Field(
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None, description="List of hints mentioned about grading"
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)
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# ---------------------------------------------------------------------------
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# 2. Prompt + chain
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# ---------------------------------------------------------------------------
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parser = PydanticOutputParser(pydantic_object=TaskCard)
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prompt_template = """You are a helper that extracts structured information from a
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course task description. Return a JSON object that matches the following
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schema:
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{schema}
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The input description is:
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{description}
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Respond ONLY with the JSON object. Do not add any extra text.
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"""
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prompt = PromptTemplate(
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template=prompt_template,
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input_variables=["description"],
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partial_variables={"schema": parser.get_format_instructions()},
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)
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# LLM – use the default OpenAI OSS endpoint via environment variables
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llm = ChatOpenAI(model="openai/gpt-oss-20b:free", temperature=0.2)
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chain = prompt | llm | parser
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# ---------------------------------------------------------------------------
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# 3. Main entry point
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# ---------------------------------------------------------------------------
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def main() -> None:
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if len(sys.argv) < 2:
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print("Usage: python main.py '<task description>'")
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sys.exit(1)
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description = sys.argv[1]
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result: TaskCard = chain.invoke({"description": description})
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# Print raw model dump
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print("\n--- Parsed model ---")
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print(result.model_dump(indent=2))
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# Human‑readable summary
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print("\n--- Summary ---")
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print(f"Title: {result.title or 'N/A'}")
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print(f"Subject: {result.subject or 'N/A'}")
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print(f"Deadline hint: {result.deadline_hint or 'N/A'}")
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print(f"Deliverable: {result.deliverable_type or 'N/A'}")
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if result.grading_hints:
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print("Grading hints:")
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for hint in result.grading_hints:
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print(f"- {hint}")
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else:
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print("Grading hints: N/A")
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if __name__ == "__main__":
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main()
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