47 lines
1.9 KiB
Python
47 lines
1.9 KiB
Python
from pydantic import BaseModel, Field
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from typing import List
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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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class TaskCard(BaseModel):
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title: str = Field(..., description="Title of the assignment")
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subject: str = Field(..., description="Subject or topic of the assignment")
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deadline_hint: str = Field(..., description="Free‑form deadline hint")
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deliverable_type: str = Field(..., description="What to submit: report, code, presentation, etc.")
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grading_hints: List[str] = Field(..., description="List of grading hints mentioned in the text")
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parser = PydanticOutputParser(pydantic_object=TaskCard)
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prompt_template = (
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"You are an assistant that extracts structured information from a short assignment description. "
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"Return the data in the following JSON format exactly as required by the parser. "
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"Do not add any extra keys or text.\n"
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"{format_instructions}\n"
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"Input: {input_text}\n"
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"Output:"
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)
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prompt = PromptTemplate(
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template=prompt_template,
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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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llm = ChatOpenAI(temperature=0)
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chain = prompt | llm | parser
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if __name__ == "__main__":
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# Example input
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input_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 = chain.invoke({"input_text": input_text})
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print("Parsed object:")
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print(result.model_dump())
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print("\nHuman readable summary:\n")
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print(f"Title: {result.title}")
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print(f"Subject: {result.subject}")
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print(f"Deadline: {result.deadline_hint}")
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print(f"Deliverable: {result.deliverable_type}")
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print(f"Grading hints: {', '.join(result.grading_hints)}")
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